diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2015-11-09 09:05:15 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2015-11-09 09:05:15 +0100 |
| commit | 3151647526f299686b68420a83ae38cd7f3d08f5 (patch) | |
| tree | c061b5a23ac6cc5644c6851012105b185715ef5e /inst | |
| parent | 415ca2bea5d5c3815bd9f8fa1566cec5bb3fc775 (diff) | |
Rebuild vignettes and static documentation
Diffstat (limited to 'inst')
61 files changed, 16319 insertions, 0 deletions
diff --git a/inst/staticdocs/index.r b/inst/staticdocs/index.r new file mode 100644 index 00000000..2a976f70 --- /dev/null +++ b/inst/staticdocs/index.r @@ -0,0 +1,63 @@ +sd_section( + "Main functions", + "Essential functionality", + c("mkinmod", "mkinfit", "mmkin") +) +sd_section( + "Show results", + "Functions working on mkinfit objects", + c("plot.mkinfit", + "summary.mkinfit", + "mkinresplot", + "mkinparplot", + "endpoints", + "mkinerrmin") +) +sd_section( + "Work with mmkin objects", + "Functions working with aggregated results", + c("[.mmkin", + "plot.mmkin") +) +sd_section( + "Datasets and known results", + "", + c("FOCUS_2006_datasets", + "FOCUS_2006_SFO_ref_A_to_F", + "FOCUS_2006_FOMC_ref_A_to_F", + "FOCUS_2006_HS_ref_A_to_F", + "FOCUS_2006_DFOP_ref_A_to_B", + "mccall81_245T", + "schaefer07_complex_case", + "synthetic_data_for_UBA_2014" + ) +) +sd_section( + "Helper functions", + "", + c("mkin_wide_to_long", + "mkin_long_to_wide", + "mkinsub", + "mkinds", "print.mkinds", + "print.mkinmod", + "mkinpredict", + "transform_odeparms", + "ilr", + "geometric_mean") +) +sd_section( + "Analytical solutions", + "Parent only model solutions", + c("SFO.solution", + "FOMC.solution", + "DFOP.solution", + "SFORB.solution", + "HS.solution", + "IORE.solution" + ) +) +sd_section( + "Deprecated functions", + "Functions that have been superseeded", + c("mkinplot") +) diff --git a/inst/web/DFOP.solution-2.png b/inst/web/DFOP.solution-2.png Binary files differnew file mode 100644 index 00000000..30e6db46 --- /dev/null +++ b/inst/web/DFOP.solution-2.png diff --git a/inst/web/DFOP.solution.html b/inst/web/DFOP.solution.html new file mode 100644 index 00000000..87f3ea21 --- /dev/null +++ b/inst/web/DFOP.solution.html @@ -0,0 +1,126 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>DFOP.solution. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> +Double First-Order in Parallel kinetics +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>DFOP.solution(t, parent.0, k1, k2, g)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>t</dt> + <dd> Time. </dd> + <dt>parent.0</dt> + <dd> Starting value for the response variable at time zero. </dd> + <dt>k1</dt> + <dd> First kinetic constant. </dd> + <dt>k2</dt> + <dd> Second kinetic constant. </dd> + <dt>g</dt> + <dd> Fraction of the starting value declining according to the + first kinetic constant. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Function describing decline from a defined starting value using the sum + of two exponential decline functions.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The value of the response variable at time <code>t</code>. +</dl></p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim=c(0,100)) +</div> +<p><img src='DFOP.solution-2.png' alt='' width='540' height='400' /></p></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>DFOP.solution</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/Extract.mmkin.html b/inst/web/Extract.mmkin.html new file mode 100644 index 00000000..257445e1 --- /dev/null +++ b/inst/web/Extract.mmkin.html @@ -0,0 +1,1195 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>[.mmkin. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1>Subsetting method for mmkin objects</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>"["(x, i, j, ..., drop = FALSE)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>x</dt> + <dd>An <code><a href='mmkin.html'>mmkin</a> object</code></dd> + <dt>i</dt> + <dd>Row index selecting the fits for specific models</dd> + <dt>j</dt> + <dd>Column index selecting the fits to specific datasets</dd> + <dt>...</dt> + <dd>Not used, only there to satisfy the generic method definition</dd> + <dt>drop</dt> + <dd>If FALSE, the method always returns an mmkin object, otherwise either + a list of mkinfit objects or a single mkinfit object.</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Subsetting method for mmkin objects.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + An object of class <code><a href='mmkin.html'>mmkin</a></code>. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> # Only use one core, to pass R CMD check --as-cran + fits <- mmkin(c("SFO", "FOMC"), list(B = FOCUS_2006_B, C = FOCUS_2006_C), + cores = 1, quiet = TRUE) + fits["FOMC", ] +</div> +<div class='output'> dataset +model B C + FOMC List,42 List,42 +attr(,"class") +[1] "mmkin" +</div> +<div class='input'> fits[, "B"] +</div> +<div class='output'> dataset +model B + SFO List,42 + FOMC List,42 +attr(,"class") +[1] "mmkin" +</div> +<div class='input'> fits[, "B", drop = TRUE]$FOMC +</div> +<div class='output'>$par + parent_0 log_alpha log_beta +99.666193 2.549849 5.050586 + +$ssr +[1] 28.58291 + +$convergence +[1] 0 + +$iterations +[1] 21 + +$evaluations +function gradient + 25 78 + +$counts +[1] "both X-convergence and relative convergence (5)" + +$hessian + parent_0 log_alpha log_beta +parent_0 4.123033 -95.69983 93.17699 +log_alpha -95.699832 6618.85833 -6352.46648 +log_beta 93.176993 -6352.46648 6101.23483 + +$residuals + parent parent parent parent parent parent parent parent + 1.046192647 -3.322396479 3.655156669 -1.705316770 0.406306255 -0.123734689 -0.036886982 -0.006240458 + +$ms +[1] 3.572863 + +$var_ms + parent +3.572863 + +$var_ms_unscaled + parent +3.572863 + +$var_ms_unweighted + parent +3.572863 + +$rank +[1] 3 + +$df.residual +[1] 5 + +$solution_type +[1] "analytical" + +$transform_rates +[1] TRUE + +$transform_fractions +[1] TRUE + +$method.modFit +[1] "Port" + +$maxit.modFit +[1] "auto" + +$calls +[1] 111 + +$time + user system elapsed + 0.264 0.000 0.264 + +$mkinmod +<mkinmod> model generated with +Use of formation fractions $use_of_ff: min +Specification $spec: +$parent +$type: FOMC + +$observed + name time value +1 parent 0 98.62 +2 parent 3 81.43 +3 parent 7 53.18 +4 parent 14 34.89 +5 parent 30 10.09 +6 parent 62 1.50 +7 parent 90 0.33 +8 parent 118 0.08 + +$obs_vars +[1] "parent" + +$predicted + name time value +1 parent 0.000000 99.66619265 +2 parent 1.191919 90.41690342 +3 parent 2.383838 82.08630014 +4 parent 3.000000 78.10760352 +5 parent 3.575758 74.57722848 +6 parent 4.767677 67.80342415 +7 parent 5.959596 61.68822425 +8 parent 7.000000 56.83515667 +9 parent 7.151515 56.16343898 +10 parent 8.343434 51.16836285 +11 parent 9.535354 46.64890734 +12 parent 10.727273 42.55683931 +13 parent 11.919192 38.84911158 +14 parent 13.111111 35.48727414 +15 parent 14.000000 33.18468323 +16 parent 14.303030 32.43695565 +17 parent 15.494949 29.66740651 +18 parent 16.686869 27.15109578 +19 parent 17.878788 24.86335532 +20 parent 19.070707 22.78206538 +21 parent 20.262626 20.88737647 +22 parent 21.454545 19.16146324 +23 parent 22.646465 17.58830644 +24 parent 23.838384 16.15349953 +25 parent 25.030303 14.84407724 +26 parent 26.222222 13.64836315 +27 parent 27.414141 12.55583436 +28 parent 28.606061 11.55700107 +29 parent 29.797980 10.64329940 +30 parent 30.000000 10.49630626 +31 parent 30.989899 9.80699593 +32 parent 32.181818 9.04110261 +33 parent 33.373737 8.33930082 +34 parent 34.565657 7.69587362 +35 parent 35.757576 7.10564515 +36 parent 36.949495 6.56392657 +37 parent 38.141414 6.06646759 +38 parent 39.333333 5.60941311 +39 parent 40.525253 5.18926438 +40 parent 41.717172 4.80284421 +41 parent 42.909091 4.44726569 +42 parent 44.101010 4.11990420 +43 parent 45.292929 3.81837216 +44 parent 46.484848 3.54049644 +45 parent 47.676768 3.28429799 +46 parent 48.868687 3.04797350 +47 parent 50.060606 2.82987892 +48 parent 51.252525 2.62851456 +49 parent 52.444444 2.44251172 +50 parent 53.636364 2.27062056 +51 parent 54.828283 2.11169922 +52 parent 56.020202 1.96470393 +53 parent 57.212121 1.82868009 +54 parent 58.404040 1.70275424 +55 parent 59.595960 1.58612677 +56 parent 60.787879 1.47806529 +57 parent 61.979798 1.37789865 +58 parent 62.000000 1.37626531 +59 parent 63.171717 1.28501157 +60 parent 64.363636 1.19883967 +61 parent 65.555556 1.11886504 +62 parent 66.747475 1.04461220 +63 parent 67.939394 0.97564441 +64 parent 69.131313 0.91156031 +65 parent 70.323232 0.85199096 +66 parent 71.515152 0.79659697 +67 parent 72.707071 0.74506609 +68 parent 73.898990 0.69711084 +69 parent 75.090909 0.65246649 +70 parent 76.282828 0.61088912 +71 parent 77.474747 0.57215389 +72 parent 78.666667 0.53605348 +73 parent 79.858586 0.50239663 +74 parent 81.050505 0.47100683 +75 parent 82.242424 0.44172111 +76 parent 83.434343 0.41438896 +77 parent 84.626263 0.38887128 +78 parent 85.818182 0.36503953 +79 parent 87.010101 0.34277481 +80 parent 88.202020 0.32196716 +81 parent 89.393939 0.30251479 +82 parent 90.000000 0.29311302 +83 parent 90.585859 0.28432347 +84 parent 91.777778 0.26730596 +85 parent 92.969697 0.25138141 +86 parent 94.161616 0.23647487 +87 parent 95.353535 0.22251689 +88 parent 96.545455 0.20944302 +89 parent 97.737374 0.19719349 +90 parent 98.929293 0.18571281 +91 parent 100.121212 0.17494947 +92 parent 101.313131 0.16485560 +93 parent 102.505051 0.15538676 +94 parent 103.696970 0.14650163 +95 parent 104.888889 0.13816179 +96 parent 106.080808 0.13033150 +97 parent 107.272727 0.12297753 +98 parent 108.464646 0.11606895 +99 parent 109.656566 0.10957695 +100 parent 110.848485 0.10347470 +101 parent 112.040404 0.09773723 +102 parent 113.232323 0.09234125 +103 parent 114.424242 0.08726506 +104 parent 115.616162 0.08248842 +105 parent 116.808081 0.07799245 +106 parent 118.000000 0.07375954 + +$cost +function (P) +{ + assign("calls", calls + 1, inherits = TRUE) + if (trace_parms) + cat(P, "\n") + if (length(state.ini.optim) > 0) { + odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) + names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) + } + else { + odeini <- state.ini.fixed + names(odeini) <- state.ini.fixed.boxnames + } + odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], + transparms.fixed) + parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, + transform_fractions = transform_fractions) + out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, + use_compiled = use_compiled, method.ode = method.ode, + atol = atol, rtol = rtol, ...) + assign("out_predicted", out, inherits = TRUE) + mC <- modCost(out, observed, y = "value", err = err, weight = weight, + scaleVar = scaleVar) + if (mC$model < cost.old) { + if (!quiet) + cat("Model cost at call ", calls, ": ", mC$model, + "\n") + if (plot) { + outtimes_plot = seq(min(observed$time), max(observed$time), + length.out = 100) + out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, + solution_type = solution_type, use_compiled = use_compiled, + method.ode = method.ode, atol = atol, rtol = rtol, + ...) + plot(0, type = "n", xlim = range(observed$time), + ylim = c(0, max(observed$value, na.rm = TRUE)), + xlab = "Time", ylab = "Observed") + col_obs <- pch_obs <- 1:length(obs_vars) + lty_obs <- rep(1, length(obs_vars)) + names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars + for (obs_var in obs_vars) { + points(subset(observed, name == obs_var, c(time, + value)), pch = pch_obs[obs_var], col = col_obs[obs_var]) + } + matlines(out_plot$time, out_plot[-1], col = col_obs, + lty = lty_obs) + legend("topright", inset = c(0.05, 0.05), legend = obs_vars, + col = col_obs, pch = pch_obs, lty = 1:length(pch_obs)) + } + assign("cost.old", mC$model, inherits = TRUE) + } + return(mC) +} +<environment: 0x3b41780> + +$cost_notrans +function (P) +{ + if (length(state.ini.optim) > 0) { + odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) + names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) + } + else { + odeini <- state.ini.fixed + names(odeini) <- state.ini.fixed.boxnames + } + odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], + parms.fixed) + out <- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, + use_compiled = use_compiled, method.ode = method.ode, + atol = atol, rtol = rtol, ...) + mC <- modCost(out, observed, y = "value", err = err, weight = weight, + scaleVar = scaleVar) + return(mC) +} +<environment: 0x3b41780> + +$hessian_notrans + parent_0 alpha beta +parent_0 4.1230329 -7.473531 0.5968527 +alpha -7.4735307 40.365690 -3.1777189 +beta 0.5968527 -3.177719 0.2503425 + +$start + value type +parent_0 98.62 state +alpha 1.00 deparm +beta 10.00 deparm + +$start_transformed + value lower upper +parent_0 98.620000 -Inf Inf +log_alpha 0.000000 -Inf Inf +log_beta 2.302585 -Inf Inf + +$fixed +[1] value type +<0 rows> (or 0-length row.names) + +$data + time variable observed predicted residual +1 0 parent 98.62 99.66619265 -1.046192647 +2 3 parent 81.43 78.10760352 3.322396479 +3 7 parent 53.18 56.83515667 -3.655156669 +4 14 parent 34.89 33.18468323 1.705316770 +5 30 parent 10.09 10.49630626 -0.406306255 +6 62 parent 1.50 1.37626531 0.123734689 +7 90 parent 0.33 0.29311302 0.036886982 +8 118 parent 0.08 0.07375954 0.006240458 + +$atol +[1] 1e-08 + +$rtol +[1] 1e-10 + +$weight.ini +[1] "none" + +$reweight.tol +[1] 1e-08 + +$reweight.max.iter +[1] 10 + +$bparms.optim + parent_0 alpha beta + 99.66619 12.80517 156.11390 + +$bparms.fixed +numeric(0) + +$bparms.ode + alpha beta + 12.80517 156.11390 + +$bparms.state + parent +99.66619 + +$date +[1] "Mon Nov 9 08:25:11 2015" + +attr(,"class") +[1] "mkinfit" "modFit" +</div> +<div class='input'> fits["SFO", "B"] +</div> +<div class='output'> dataset +model B + SFO List,42 +attr(,"class") +[1] "mmkin" +</div> +<div class='input'> fits[["SFO", "B"]] # This is equivalent to +</div> +<div class='output'>$par + parent_0 log_k_parent_sink + 99.174072 -2.549028 + +$ssr +[1] 30.65564 + +$convergence +[1] 0 + +$iterations +[1] 5 + +$evaluations +function gradient + 8 15 + +$counts +[1] "relative convergence (4)" + +$hessian + parent_0 log_k_parent_sink +parent_0 4.163631 -94.09343 +log_k_parent_sink -94.093431 6311.34610 + +$residuals + parent parent parent parent parent parent parent parent + 0.55407218 -2.98452128 4.20445742 -1.68599939 -0.58185357 -0.72033730 -0.24260405 -0.07020339 + +$ms +[1] 3.831956 + +$var_ms + parent +3.831956 + +$var_ms_unscaled + parent +3.831956 + +$var_ms_unweighted + parent +3.831956 + +$rank +[1] 2 + +$df.residual +[1] 6 + +$solution_type +[1] "analytical" + +$transform_rates +[1] TRUE + +$transform_fractions +[1] TRUE + +$method.modFit +[1] "Port" + +$maxit.modFit +[1] "auto" + +$calls +[1] 29 + +$time + user system elapsed + 0.100 0.000 0.101 + +$mkinmod +<mkinmod> model generated with +Use of formation fractions $use_of_ff: min +Specification $spec: +$parent +$type: SFO +Coefficient matrix $coefmat available + +$observed + name time value +1 parent 0 98.62 +2 parent 3 81.43 +3 parent 7 53.18 +4 parent 14 34.89 +5 parent 30 10.09 +6 parent 62 1.50 +7 parent 90 0.33 +8 parent 118 0.08 + +$obs_vars +[1] "parent" + +$predicted + name time value +1 parent 0.000000 99.17407218 +2 parent 1.191919 90.35253561 +3 parent 2.383838 82.31567498 +4 parent 3.000000 78.44547872 +5 parent 3.575758 74.99369333 +6 parent 4.767677 68.32300215 +7 parent 5.959596 62.24566915 +8 parent 7.000000 57.38445742 +9 parent 7.151515 56.70891509 +10 parent 8.343434 51.66465547 +11 parent 9.535354 47.06908288 +12 parent 10.727273 42.88228661 +13 parent 11.919192 39.06790599 +14 parent 13.111111 35.59281463 +15 parent 14.000000 33.20400061 +16 parent 14.303030 32.42683275 +17 parent 15.494949 29.54246504 +18 parent 16.686869 26.91466193 +19 parent 17.878788 24.52060198 +20 parent 19.070707 22.33949373 +21 parent 20.262626 20.35239512 +22 parent 21.454545 18.54204899 +23 parent 22.646465 16.89273320 +24 parent 23.838384 15.39012410 +25 parent 25.030303 14.02117212 +26 parent 26.222222 12.77398846 +27 parent 27.414141 11.63774182 +28 parent 28.606061 10.60256435 +29 parent 29.797980 9.65946594 +30 parent 30.000000 9.50814643 +31 parent 30.989899 8.80025617 +32 parent 32.181818 8.01747313 +33 parent 33.373737 7.30431867 +34 parent 34.565657 6.65459931 +35 parent 35.757576 6.06267251 +36 parent 36.949495 5.52339762 +37 parent 38.141414 5.03209124 +38 parent 39.333333 4.58448658 +39 parent 40.525253 4.17669637 +40 parent 41.717172 3.80517911 +41 parent 42.909091 3.46670832 +42 parent 44.101010 3.15834451 +43 parent 45.292929 2.87740968 +44 parent 46.484848 2.62146400 +45 parent 47.676768 2.38828471 +46 parent 48.868687 2.17584671 +47 parent 50.060606 1.98230508 +48 parent 51.252525 1.80597899 +49 parent 52.444444 1.64533711 +50 parent 53.636364 1.49898432 +51 parent 54.828283 1.36564963 +52 parent 56.020202 1.24417505 +53 parent 57.212121 1.13350565 +54 parent 58.404040 1.03268029 +55 parent 59.595960 0.94082335 +56 parent 60.787879 0.85713708 +57 parent 61.979798 0.78089471 +58 parent 62.000000 0.77966270 +59 parent 63.171717 0.71143411 +60 parent 64.363636 0.64815202 +61 parent 65.555556 0.59049888 +62 parent 66.747475 0.53797399 +63 parent 67.939394 0.49012119 +64 parent 69.131313 0.44652489 +65 parent 70.323232 0.40680649 +66 parent 71.515152 0.37062104 +67 parent 72.707071 0.33765429 +68 parent 73.898990 0.30761993 +69 parent 75.090909 0.28025713 +70 parent 76.282828 0.25532825 +71 parent 77.474747 0.23261679 +72 parent 78.666667 0.21192552 +73 parent 79.858586 0.19307474 +74 parent 81.050505 0.17590074 +75 parent 82.242424 0.16025436 +76 parent 83.434343 0.14599973 +77 parent 84.626263 0.13301305 +78 parent 85.818182 0.12118154 +79 parent 87.010101 0.11040244 +80 parent 88.202020 0.10058214 +81 parent 89.393939 0.09163535 +82 parent 90.000000 0.08739595 +83 parent 90.585859 0.08348439 +84 parent 91.777778 0.07605845 +85 parent 92.969697 0.06929305 +86 parent 94.161616 0.06312943 +87 parent 95.353535 0.05751406 +88 parent 96.545455 0.05239819 +89 parent 97.737374 0.04773737 +90 parent 98.929293 0.04349113 +91 parent 100.121212 0.03962259 +92 parent 101.313131 0.03609816 +93 parent 102.505051 0.03288723 +94 parent 103.696970 0.02996191 +95 parent 104.888889 0.02729679 +96 parent 106.080808 0.02486874 +97 parent 107.272727 0.02265667 +98 parent 108.464646 0.02064136 +99 parent 109.656566 0.01880531 +100 parent 110.848485 0.01713257 +101 parent 112.040404 0.01560863 +102 parent 113.232323 0.01422024 +103 parent 114.424242 0.01295535 +104 parent 115.616162 0.01180297 +105 parent 116.808081 0.01075310 +106 parent 118.000000 0.00979661 + +$cost +function (P) +{ + assign("calls", calls + 1, inherits = TRUE) + if (trace_parms) + cat(P, "\n") + if (length(state.ini.optim) > 0) { + odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) + names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) + } + else { + odeini <- state.ini.fixed + names(odeini) <- state.ini.fixed.boxnames + } + odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], + transparms.fixed) + parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, + transform_fractions = transform_fractions) + out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, + use_compiled = use_compiled, method.ode = method.ode, + atol = atol, rtol = rtol, ...) + assign("out_predicted", out, inherits = TRUE) + mC <- modCost(out, observed, y = "value", err = err, weight = weight, + scaleVar = scaleVar) + if (mC$model < cost.old) { + if (!quiet) + cat("Model cost at call ", calls, ": ", mC$model, + "\n") + if (plot) { + outtimes_plot = seq(min(observed$time), max(observed$time), + length.out = 100) + out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, + solution_type = solution_type, use_compiled = use_compiled, + method.ode = method.ode, atol = atol, rtol = rtol, + ...) + plot(0, type = "n", xlim = range(observed$time), + ylim = c(0, max(observed$value, na.rm = TRUE)), + xlab = "Time", ylab = "Observed") + col_obs <- pch_obs <- 1:length(obs_vars) + lty_obs <- rep(1, length(obs_vars)) + names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars + for (obs_var in obs_vars) { + points(subset(observed, name == obs_var, c(time, + value)), pch = pch_obs[obs_var], col = col_obs[obs_var]) + } + matlines(out_plot$time, out_plot[-1], col = col_obs, + lty = lty_obs) + legend("topright", inset = c(0.05, 0.05), legend = obs_vars, + col = col_obs, pch = pch_obs, lty = 1:length(pch_obs)) + } + assign("cost.old", mC$model, inherits = TRUE) + } + return(mC) +} +<environment: 0x3d4fdb8> + +$cost_notrans +function (P) +{ + if (length(state.ini.optim) > 0) { + odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) + names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) + } + else { + odeini <- state.ini.fixed + names(odeini) <- state.ini.fixed.boxnames + } + odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], + parms.fixed) + out <- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, + use_compiled = use_compiled, method.ode = method.ode, + atol = atol, rtol = rtol, ...) + mC <- modCost(out, observed, y = "value", err = err, weight = weight, + scaleVar = scaleVar) + return(mC) +} +<environment: 0x3d4fdb8> + +$hessian_notrans + parent_0 k_parent_sink +parent_0 4.163631 -1203.894 +k_parent_sink -1203.893702 1033188.753 + +$start + value type +parent_0 98.62 state +k_parent_sink 0.10 deparm + +$start_transformed + value lower upper +parent_0 98.620000 -Inf Inf +log_k_parent_sink -2.302585 -Inf Inf + +$fixed +[1] value type +<0 rows> (or 0-length row.names) + +$data + time variable observed predicted residual +1 0 parent 98.62 99.17407218 -0.55407218 +2 3 parent 81.43 78.44547872 2.98452128 +3 7 parent 53.18 57.38445742 -4.20445742 +4 14 parent 34.89 33.20400061 1.68599939 +5 30 parent 10.09 9.50814643 0.58185357 +6 62 parent 1.50 0.77966270 0.72033730 +7 90 parent 0.33 0.08739595 0.24260405 +8 118 parent 0.08 0.00979661 0.07020339 + +$atol +[1] 1e-08 + +$rtol +[1] 1e-10 + +$weight.ini +[1] "none" + +$reweight.tol +[1] 1e-08 + +$reweight.max.iter +[1] 10 + +$bparms.optim + parent_0 k_parent_sink + 99.17407218 0.07815759 + +$bparms.fixed +numeric(0) + +$bparms.ode +k_parent_sink + 0.07815759 + +$bparms.state + parent +99.17407 + +$date +[1] "Mon Nov 9 08:25:11 2015" + +attr(,"class") +[1] "mkinfit" "modFit" +</div> +<div class='input'> fits["SFO", "B", drop = TRUE] +</div> +<div class='output'>[[1]] +$par + parent_0 log_k_parent_sink + 99.174072 -2.549028 + +$ssr +[1] 30.65564 + +$convergence +[1] 0 + +$iterations +[1] 5 + +$evaluations +function gradient + 8 15 + +$counts +[1] "relative convergence (4)" + +$hessian + parent_0 log_k_parent_sink +parent_0 4.163631 -94.09343 +log_k_parent_sink -94.093431 6311.34610 + +$residuals + parent parent parent parent parent parent parent parent + 0.55407218 -2.98452128 4.20445742 -1.68599939 -0.58185357 -0.72033730 -0.24260405 -0.07020339 + +$ms +[1] 3.831956 + +$var_ms + parent +3.831956 + +$var_ms_unscaled + parent +3.831956 + +$var_ms_unweighted + parent +3.831956 + +$rank +[1] 2 + +$df.residual +[1] 6 + +$solution_type +[1] "analytical" + +$transform_rates +[1] TRUE + +$transform_fractions +[1] TRUE + +$method.modFit +[1] "Port" + +$maxit.modFit +[1] "auto" + +$calls +[1] 29 + +$time + user system elapsed + 0.100 0.000 0.101 + +$mkinmod +<mkinmod> model generated with +Use of formation fractions $use_of_ff: min +Specification $spec: +$parent +$type: SFO +Coefficient matrix $coefmat available + +$observed + name time value +1 parent 0 98.62 +2 parent 3 81.43 +3 parent 7 53.18 +4 parent 14 34.89 +5 parent 30 10.09 +6 parent 62 1.50 +7 parent 90 0.33 +8 parent 118 0.08 + +$obs_vars +[1] "parent" + +$predicted + name time value +1 parent 0.000000 99.17407218 +2 parent 1.191919 90.35253561 +3 parent 2.383838 82.31567498 +4 parent 3.000000 78.44547872 +5 parent 3.575758 74.99369333 +6 parent 4.767677 68.32300215 +7 parent 5.959596 62.24566915 +8 parent 7.000000 57.38445742 +9 parent 7.151515 56.70891509 +10 parent 8.343434 51.66465547 +11 parent 9.535354 47.06908288 +12 parent 10.727273 42.88228661 +13 parent 11.919192 39.06790599 +14 parent 13.111111 35.59281463 +15 parent 14.000000 33.20400061 +16 parent 14.303030 32.42683275 +17 parent 15.494949 29.54246504 +18 parent 16.686869 26.91466193 +19 parent 17.878788 24.52060198 +20 parent 19.070707 22.33949373 +21 parent 20.262626 20.35239512 +22 parent 21.454545 18.54204899 +23 parent 22.646465 16.89273320 +24 parent 23.838384 15.39012410 +25 parent 25.030303 14.02117212 +26 parent 26.222222 12.77398846 +27 parent 27.414141 11.63774182 +28 parent 28.606061 10.60256435 +29 parent 29.797980 9.65946594 +30 parent 30.000000 9.50814643 +31 parent 30.989899 8.80025617 +32 parent 32.181818 8.01747313 +33 parent 33.373737 7.30431867 +34 parent 34.565657 6.65459931 +35 parent 35.757576 6.06267251 +36 parent 36.949495 5.52339762 +37 parent 38.141414 5.03209124 +38 parent 39.333333 4.58448658 +39 parent 40.525253 4.17669637 +40 parent 41.717172 3.80517911 +41 parent 42.909091 3.46670832 +42 parent 44.101010 3.15834451 +43 parent 45.292929 2.87740968 +44 parent 46.484848 2.62146400 +45 parent 47.676768 2.38828471 +46 parent 48.868687 2.17584671 +47 parent 50.060606 1.98230508 +48 parent 51.252525 1.80597899 +49 parent 52.444444 1.64533711 +50 parent 53.636364 1.49898432 +51 parent 54.828283 1.36564963 +52 parent 56.020202 1.24417505 +53 parent 57.212121 1.13350565 +54 parent 58.404040 1.03268029 +55 parent 59.595960 0.94082335 +56 parent 60.787879 0.85713708 +57 parent 61.979798 0.78089471 +58 parent 62.000000 0.77966270 +59 parent 63.171717 0.71143411 +60 parent 64.363636 0.64815202 +61 parent 65.555556 0.59049888 +62 parent 66.747475 0.53797399 +63 parent 67.939394 0.49012119 +64 parent 69.131313 0.44652489 +65 parent 70.323232 0.40680649 +66 parent 71.515152 0.37062104 +67 parent 72.707071 0.33765429 +68 parent 73.898990 0.30761993 +69 parent 75.090909 0.28025713 +70 parent 76.282828 0.25532825 +71 parent 77.474747 0.23261679 +72 parent 78.666667 0.21192552 +73 parent 79.858586 0.19307474 +74 parent 81.050505 0.17590074 +75 parent 82.242424 0.16025436 +76 parent 83.434343 0.14599973 +77 parent 84.626263 0.13301305 +78 parent 85.818182 0.12118154 +79 parent 87.010101 0.11040244 +80 parent 88.202020 0.10058214 +81 parent 89.393939 0.09163535 +82 parent 90.000000 0.08739595 +83 parent 90.585859 0.08348439 +84 parent 91.777778 0.07605845 +85 parent 92.969697 0.06929305 +86 parent 94.161616 0.06312943 +87 parent 95.353535 0.05751406 +88 parent 96.545455 0.05239819 +89 parent 97.737374 0.04773737 +90 parent 98.929293 0.04349113 +91 parent 100.121212 0.03962259 +92 parent 101.313131 0.03609816 +93 parent 102.505051 0.03288723 +94 parent 103.696970 0.02996191 +95 parent 104.888889 0.02729679 +96 parent 106.080808 0.02486874 +97 parent 107.272727 0.02265667 +98 parent 108.464646 0.02064136 +99 parent 109.656566 0.01880531 +100 parent 110.848485 0.01713257 +101 parent 112.040404 0.01560863 +102 parent 113.232323 0.01422024 +103 parent 114.424242 0.01295535 +104 parent 115.616162 0.01180297 +105 parent 116.808081 0.01075310 +106 parent 118.000000 0.00979661 + +$cost +function (P) +{ + assign("calls", calls + 1, inherits = TRUE) + if (trace_parms) + cat(P, "\n") + if (length(state.ini.optim) > 0) { + odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) + names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) + } + else { + odeini <- state.ini.fixed + names(odeini) <- state.ini.fixed.boxnames + } + odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], + transparms.fixed) + parms <- backtransform_odeparms(odeparms, mkinmod, transform_rates = transform_rates, + transform_fractions = transform_fractions) + out <- mkinpredict(mkinmod, parms, odeini, outtimes, solution_type = solution_type, + use_compiled = use_compiled, method.ode = method.ode, + atol = atol, rtol = rtol, ...) + assign("out_predicted", out, inherits = TRUE) + mC <- modCost(out, observed, y = "value", err = err, weight = weight, + scaleVar = scaleVar) + if (mC$model < cost.old) { + if (!quiet) + cat("Model cost at call ", calls, ": ", mC$model, + "\n") + if (plot) { + outtimes_plot = seq(min(observed$time), max(observed$time), + length.out = 100) + out_plot <- mkinpredict(mkinmod, parms, odeini, outtimes_plot, + solution_type = solution_type, use_compiled = use_compiled, + method.ode = method.ode, atol = atol, rtol = rtol, + ...) + plot(0, type = "n", xlim = range(observed$time), + ylim = c(0, max(observed$value, na.rm = TRUE)), + xlab = "Time", ylab = "Observed") + col_obs <- pch_obs <- 1:length(obs_vars) + lty_obs <- rep(1, length(obs_vars)) + names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars + for (obs_var in obs_vars) { + points(subset(observed, name == obs_var, c(time, + value)), pch = pch_obs[obs_var], col = col_obs[obs_var]) + } + matlines(out_plot$time, out_plot[-1], col = col_obs, + lty = lty_obs) + legend("topright", inset = c(0.05, 0.05), legend = obs_vars, + col = col_obs, pch = pch_obs, lty = 1:length(pch_obs)) + } + assign("cost.old", mC$model, inherits = TRUE) + } + return(mC) +} +<environment: 0x3d4fdb8> + +$cost_notrans +function (P) +{ + if (length(state.ini.optim) > 0) { + odeini <- c(P[1:length(state.ini.optim)], state.ini.fixed) + names(odeini) <- c(state.ini.optim.boxnames, state.ini.fixed.boxnames) + } + else { + odeini <- state.ini.fixed + names(odeini) <- state.ini.fixed.boxnames + } + odeparms <- c(P[(length(state.ini.optim) + 1):length(P)], + parms.fixed) + out <- mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = solution_type, + use_compiled = use_compiled, method.ode = method.ode, + atol = atol, rtol = rtol, ...) + mC <- modCost(out, observed, y = "value", err = err, weight = weight, + scaleVar = scaleVar) + return(mC) +} +<environment: 0x3d4fdb8> + +$hessian_notrans + parent_0 k_parent_sink +parent_0 4.163631 -1203.894 +k_parent_sink -1203.893702 1033188.753 + +$start + value type +parent_0 98.62 state +k_parent_sink 0.10 deparm + +$start_transformed + value lower upper +parent_0 98.620000 -Inf Inf +log_k_parent_sink -2.302585 -Inf Inf + +$fixed +[1] value type +<0 rows> (or 0-length row.names) + +$data + time variable observed predicted residual +1 0 parent 98.62 99.17407218 -0.55407218 +2 3 parent 81.43 78.44547872 2.98452128 +3 7 parent 53.18 57.38445742 -4.20445742 +4 14 parent 34.89 33.20400061 1.68599939 +5 30 parent 10.09 9.50814643 0.58185357 +6 62 parent 1.50 0.77966270 0.72033730 +7 90 parent 0.33 0.08739595 0.24260405 +8 118 parent 0.08 0.00979661 0.07020339 + +$atol +[1] 1e-08 + +$rtol +[1] 1e-10 + +$weight.ini +[1] "none" + +$reweight.tol +[1] 1e-08 + +$reweight.max.iter +[1] 10 + +$bparms.optim + parent_0 k_parent_sink + 99.17407218 0.07815759 + +$bparms.fixed +numeric(0) + +$bparms.ode +k_parent_sink + 0.07815759 + +$bparms.state + parent +99.17407 + +$date +[1] "Mon Nov 9 08:25:11 2015" + +attr(,"class") +[1] "mkinfit" "modFit" + +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>[.mmkin</li> + </ul> + <ul> + + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/FOCUS_2006_DFOP_ref_A_to_B.html b/inst/web/FOCUS_2006_DFOP_ref_A_to_B.html new file mode 100644 index 00000000..19ec4c4b --- /dev/null +++ b/inst/web/FOCUS_2006_DFOP_ref_A_to_B.html @@ -0,0 +1,130 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>FOCUS_2006_DFOP_ref_A_to_B. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> +Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>data(FOCUS_2006_DFOP_ref_A_to_B)</div></pre> + + <div class="Description"> + <h2>Description</h2> + + <p>A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit.</p> + + </div> + + <div class="Format"> + <h2>Format</h2> + + <p>A data frame containing the following variables. + <dl> + <dt><code>package</code></dt><dd>a factor giving the name of the software package</dd></p> + + <p><dt><code>M0</code></dt><dd>The fitted initial concentration of the parent compound</dd></p> + + <p><dt><code>f</code></dt><dd>The fitted f parameter</dd></p> + + <p><dt><code>k1</code></dt><dd>The fitted k1 parameter</dd></p> + + <p><dt><code>k2</code></dt><dd>The fitted k2 parameter</dd></p> + + <p><dt><code>DT50</code></dt><dd>The resulting half-life of the parent compound</dd></p> + + <p><dt><code>DT90</code></dt><dd>The resulting DT90 of the parent compound</dd></p> + + <p><dt><code>dataset</code></dt><dd>The FOCUS dataset that was used</dd></p> + + <p></dl></p> + + <p></p> + + </div> + + <div class="Source"> + <h2>Source</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>data(FOCUS_2006_DFOP_ref_A_to_B) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>FOCUS_2006_DFOP_ref_A_to_B</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/FOCUS_2006_FOMC_ref_A_to_F.html b/inst/web/FOCUS_2006_FOMC_ref_A_to_F.html new file mode 100644 index 00000000..ff7883ec --- /dev/null +++ b/inst/web/FOCUS_2006_FOMC_ref_A_to_F.html @@ -0,0 +1,128 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>FOCUS_2006_FOMC_ref_A_to_F. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> +Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>data(FOCUS_2006_FOMC_ref_A_to_F)</div></pre> + + <div class="Description"> + <h2>Description</h2> + + <p>A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit.</p> + + </div> + + <div class="Format"> + <h2>Format</h2> + + <p>A data frame containing the following variables. + <dl> + <dt><code>package</code></dt><dd>a factor giving the name of the software package</dd></p> + + <p><dt><code>M0</code></dt><dd>The fitted initial concentration of the parent compound</dd></p> + + <p><dt><code>alpha</code></dt><dd>The fitted alpha parameter</dd></p> + + <p><dt><code>beta</code></dt><dd>The fitted beta parameter</dd></p> + + <p><dt><code>DT50</code></dt><dd>The resulting half-life of the parent compound</dd></p> + + <p><dt><code>DT90</code></dt><dd>The resulting DT90 of the parent compound</dd></p> + + <p><dt><code>dataset</code></dt><dd>The FOCUS dataset that was used</dd></p> + + <p></dl></p> + + <p></p> + + </div> + + <div class="Source"> + <h2>Source</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>data(FOCUS_2006_FOMC_ref_A_to_F) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>FOCUS_2006_FOMC_ref_A_to_F</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/FOCUS_2006_HS_ref_A_to_F.html b/inst/web/FOCUS_2006_HS_ref_A_to_F.html new file mode 100644 index 00000000..5786889a --- /dev/null +++ b/inst/web/FOCUS_2006_HS_ref_A_to_F.html @@ -0,0 +1,130 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>FOCUS_2006_HS_ref_A_to_F. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> +Results of fitting the HS model to Datasets A to F of FOCUS (2006) +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>data(FOCUS_2006_HS_ref_A_to_F)</div></pre> + + <div class="Description"> + <h2>Description</h2> + + <p>A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit.</p> + + </div> + + <div class="Format"> + <h2>Format</h2> + + <p>A data frame containing the following variables. + <dl> + <dt><code>package</code></dt><dd>a factor giving the name of the software package</dd></p> + + <p><dt><code>M0</code></dt><dd>The fitted initial concentration of the parent compound</dd></p> + + <p><dt><code>tb</code></dt><dd>The fitted tb parameter</dd></p> + + <p><dt><code>k1</code></dt><dd>The fitted k1 parameter</dd></p> + + <p><dt><code>k2</code></dt><dd>The fitted k2 parameter</dd></p> + + <p><dt><code>DT50</code></dt><dd>The resulting half-life of the parent compound</dd></p> + + <p><dt><code>DT90</code></dt><dd>The resulting DT90 of the parent compound</dd></p> + + <p><dt><code>dataset</code></dt><dd>The FOCUS dataset that was used</dd></p> + + <p></dl></p> + + <p></p> + + </div> + + <div class="Source"> + <h2>Source</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>data(FOCUS_2006_HS_ref_A_to_F) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>FOCUS_2006_HS_ref_A_to_F</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/FOCUS_2006_SFO_ref_A_to_F.html b/inst/web/FOCUS_2006_SFO_ref_A_to_F.html new file mode 100644 index 00000000..7663cb53 --- /dev/null +++ b/inst/web/FOCUS_2006_SFO_ref_A_to_F.html @@ -0,0 +1,126 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>FOCUS_2006_SFO_ref_A_to_F. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> +Results of fitting the SFO model to Datasets A to F of FOCUS (2006) +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>data(FOCUS_2006_SFO_ref_A_to_F)</div></pre> + + <div class="Description"> + <h2>Description</h2> + + <p>A table with the fitted parameters and the resulting DT50 and DT90 values +generated with different software packages. Taken directly from FOCUS (2006). +The results from fitting the data with the Topfit software was removed, as +the initial concentration of the parent compound was fixed to a value of 100 +in this fit.</p> + + </div> + + <div class="Format"> + <h2>Format</h2> + + <p>A data frame containing the following variables. + <dl> + <dt><code>package</code></dt><dd>a factor giving the name of the software package</dd></p> + + <p><dt><code>M0</code></dt><dd>The fitted initial concentration of the parent compound</dd></p> + + <p><dt><code>k</code></dt><dd>The fitted first-order degradation rate constant</dd></p> + + <p><dt><code>DT50</code></dt><dd>The resulting half-life of the parent compound</dd></p> + + <p><dt><code>DT90</code></dt><dd>The resulting DT90 of the parent compound</dd></p> + + <p><dt><code>dataset</code></dt><dd>The FOCUS dataset that was used</dd></p> + + <p></dl></p> + + <p></p> + + </div> + + <div class="Source"> + <h2>Source</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>data(FOCUS_2006_SFO_ref_A_to_F) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>FOCUS_2006_SFO_ref_A_to_F</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/FOCUS_2006_datasets.html b/inst/web/FOCUS_2006_datasets.html new file mode 100644 index 00000000..92e069b5 --- /dev/null +++ b/inst/web/FOCUS_2006_datasets.html @@ -0,0 +1,127 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>FOCUS_2006_datasets. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> +Datasets A to F from the FOCUS Kinetics report from 2006 +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>FOCUS_2006_datasets</div></pre> + + <div class="Description"> + <h2>Description</h2> + + <p>Data taken from FOCUS (2006), p. 258.</p> + + </div> + + <div class="Format"> + <h2>Format</h2> + + <p>6 datasets with observations on the following variables. + <dl> + <dt><code>name</code></dt><dd>a factor containing the name of the observed variable</dd></p> + + <p><dt><code>time</code></dt><dd>a numeric vector containing time points</dd></p> + + <p><dt><code>value</code></dt><dd>a numeric vector containing concentrations in percent of applied radioactivity</dd></p> + + <p></dl></p> + + <p></p> + + </div> + + <div class="Source"> + <h2>Source</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>FOCUS_2006_C +</div> +<div class='output'> name time value +1 parent 0 85.1 +2 parent 1 57.9 +3 parent 3 29.9 +4 parent 7 14.6 +5 parent 14 9.7 +6 parent 28 6.6 +7 parent 63 4.0 +8 parent 91 3.9 +9 parent 119 0.6 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>FOCUS_2006_A</li><li>FOCUS_2006_B</li><li>FOCUS_2006_C</li><li>FOCUS_2006_D</li><li>FOCUS_2006_E</li><li>FOCUS_2006_F</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/FOMC.solution-2.png b/inst/web/FOMC.solution-2.png Binary files differnew file mode 100644 index 00000000..7e592cea --- /dev/null +++ b/inst/web/FOMC.solution-2.png diff --git a/inst/web/FOMC.solution.html b/inst/web/FOMC.solution.html new file mode 100644 index 00000000..0f3f265b --- /dev/null +++ b/inst/web/FOMC.solution.html @@ -0,0 +1,142 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>FOMC.solution. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> First-Order Multi-Compartment kinetics </h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>FOMC.solution(t, parent.0, alpha, beta)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>t</dt> + <dd> Time. </dd> + <dt>parent.0</dt> + <dd> Starting value for the response variable at time zero. </dd> + <dt>alpha</dt> + <dd> + Shape parameter determined by coefficient of variation of rate constant + values. </dd> + <dt>beta</dt> + <dd> + Location parameter. +</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Function describing exponential decline from a defined starting value, with + a decreasing rate constant.</p> + + <p>The form given here differs slightly from the original reference by Gustafson + and Holden (1990). The parameter <code>beta</code> corresponds to 1/beta in the + original equation.</p> + + </div> + + <div class="Note"> + <h2>Note</h2> + + <p>The solution of the FOMC kinetic model reduces to the + <code><a href='SFO.solution.html'>SFO.solution</a></code> for large values of <code>alpha</code> and + <code>beta</code> with + $k = beta/alpha$.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The value of the response variable at time <code>t</code>. +</dl></p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + <p>Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil: A + new model based on spatial variability. <em>Environmental Science and + Technology</em> <b>24</b>, 1032-1038</p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100)) +</div> +<p><img src='FOMC.solution-2.png' alt='' width='540' height='400' /></p></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>FOMC.solution</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/HS.solution-2.png b/inst/web/HS.solution-2.png Binary files differnew file mode 100644 index 00000000..cbd1ff3d --- /dev/null +++ b/inst/web/HS.solution-2.png diff --git a/inst/web/HS.solution.html b/inst/web/HS.solution.html new file mode 100644 index 00000000..80cc543b --- /dev/null +++ b/inst/web/HS.solution.html @@ -0,0 +1,124 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>HS.solution. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> Hockey-Stick kinetics </h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>HS.solution(t, parent.0, k1, k2, tb)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>t</dt> + <dd> Time. </dd> + <dt>parent.0</dt> + <dd> Starting value for the response variable at time zero. </dd> + <dt>k1</dt> + <dd> First kinetic constant. </dd> + <dt>k2</dt> + <dd> Second kinetic constant. </dd> + <dt>tb</dt> + <dd> Break point. Before this time, exponential decline according + to <code>k1</code> is calculated, after this time, exponential decline proceeds + according to <code>k2</code>. </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Function describing two exponential decline functions with a break point + between them.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The value of the response variable at time <code>t</code>. +</dl></p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100)) +</div> +<p><img src='HS.solution-2.png' alt='' width='540' height='400' /></p></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>HS.solution</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/IORE.solution-2.png b/inst/web/IORE.solution-2.png Binary files differnew file mode 100644 index 00000000..6f7d4411 --- /dev/null +++ b/inst/web/IORE.solution-2.png diff --git a/inst/web/IORE.solution.html b/inst/web/IORE.solution.html new file mode 100644 index 00000000..d90724eb --- /dev/null +++ b/inst/web/IORE.solution.html @@ -0,0 +1,149 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>IORE.solution. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> Indeterminate order rate equation kinetics </h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>IORE.solution(t, parent.0, k__iore, N)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>t</dt> + <dd> Time. </dd> + <dt>parent.0</dt> + <dd> Starting value for the response variable at time zero. </dd> + <dt>k__iore</dt> + <dd> Rate constant. Note that this depends on the concentration units used. </dd> + <dt>N</dt> + <dd> Exponent describing the nonlinearity of the rate equation </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Function describing exponential decline from a defined starting value, with + a concentration dependent rate constant.</p> + + </div> + + <div class="Note"> + <h2>Note</h2> + + <p>The solution of the IORE kinetic model reduces to the + <code><a href='SFO.solution.html'>SFO.solution</a></code> if N = 1. + The parameters of the IORE model can be transformed to equivalent parameters + of the FOMC mode - see the NAFTA guidance for details.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The value of the response variable at time <code>t</code>. +</dl></p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>NAFTA Technical Working Group on Pesticides (not dated) Guidance for + Evaluating and Calculating Degradation Kinetics in Environmental + Media</p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2, + ylim = c(0, 100)) +</div> +<p><img src='IORE.solution-2.png' alt='' width='540' height='400' /></p> +<div class='input'> fit.fomc <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE) + fit.iore <- mkinfit("IORE", FOCUS_2006_C, quiet = TRUE) + fit.iore.deS <- mkinfit("IORE", FOCUS_2006_C, solution_type = "deSolve", quiet = TRUE) + + print(data.frame(coef(fit.fomc), coef(fit.iore), coef(fit.iore.deS), + row.names = paste("model par", 1:3))) +</div> +<div class='output'> coef.fit.fomc. coef.fit.iore. coef.fit.iore.deS. +model par 1 85.87489063 85.874891 85.874890 +model par 2 0.05192238 -4.826631 -4.826631 +model par 3 0.65096665 1.949403 1.949403 +</div> +<div class='input'> print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes, + iore.deS = endpoints(fit.iore)$distimes)) +</div> +<div class='output'> DT50 DT90 DT50back +fomc 1.785233 15.1479 4.559973 +iore 1.785233 15.1479 4.559973 +iore.deS 1.785233 15.1479 4.559973 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>IORE.solution</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/SFO.solution.html b/inst/web/SFO.solution.html new file mode 100644 index 00000000..b7d81cb7 --- /dev/null +++ b/inst/web/SFO.solution.html @@ -0,0 +1,116 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>SFO.solution. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> Single First-Order kinetics </h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>SFO.solution(t, parent.0, k)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>t</dt> + <dd> Time. </dd> + <dt>parent.0</dt> + <dd> Starting value for the response variable at time zero. </dd> + <dt>k</dt> + <dd> Kinetic constant. </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Function describing exponential decline from a defined starting value.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The value of the response variable at time <code>t</code>. +</dl></p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> ## Not run: plot(function(x) SFO.solution(x, 100, 3), 0, 2) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>SFO.solution</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
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500px; +} + +.offset5 { + margin-left: 420px; +} + +.offset4 { + margin-left: 340px; +} + +.offset3 { + margin-left: 260px; +} + +.offset2 { + margin-left: 180px; +} + +.offset1 { + margin-left: 100px; +} + +.row-fluid { + width: 100%; + *zoom: 1; +} + +.row-fluid:before, +.row-fluid:after { + display: table; + content: ""; +} + +.row-fluid:after { + clear: both; +} + +.row-fluid [class*="span"] { + display: block; + float: left; + width: 100%; + min-height: 28px; + margin-left: 2.127659574%; + *margin-left: 2.0744680846382977%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + -ms-box-sizing: border-box; + box-sizing: border-box; +} + +.row-fluid [class*="span"]:first-child { + margin-left: 0; +} + +.row-fluid .span12 { + width: 99.99999998999999%; + *width: 99.94680850063828%; +} + +.row-fluid .span11 { + width: 91.489361693%; + *width: 91.4361702036383%; +} + +.row-fluid .span10 { + width: 82.97872339599999%; + *width: 82.92553190663828%; +} + +.row-fluid .span9 { + width: 74.468085099%; + *width: 74.4148936096383%; +} + +.row-fluid .span8 { + width: 65.95744680199999%; + *width: 65.90425531263828%; +} + +.row-fluid .span7 { + width: 57.446808505%; + *width: 57.3936170156383%; +} + +.row-fluid .span6 { + width: 48.93617020799999%; + *width: 48.88297871863829%; +} + +.row-fluid .span5 { + width: 40.425531911%; + *width: 40.3723404216383%; +} + +.row-fluid .span4 { + width: 31.914893614%; + *width: 31.8617021246383%; +} + +.row-fluid .span3 { + width: 23.404255317%; + *width: 23.3510638276383%; +} + +.row-fluid .span2 { + width: 14.89361702%; + *width: 14.8404255306383%; +} + +.row-fluid .span1 { + width: 6.382978723%; + *width: 6.329787233638298%; +} + +.container { + margin-right: auto; + margin-left: auto; + *zoom: 1; +} + +.container:before, +.container:after { + display: table; + content: ""; +} + +.container:after { + clear: both; +} + +.container-fluid { + padding-right: 20px; + padding-left: 20px; + *zoom: 1; +} + +.container-fluid:before, +.container-fluid:after { + display: table; + content: ""; +} + +.container-fluid:after { + clear: both; +} + +p { + margin: 0 0 9px; +} + +p small { + font-size: 11px; + color: #999999; +} + +.lead { + margin-bottom: 18px; + font-size: 20px; + font-weight: 200; + line-height: 27px; +} + +h1, +h2, +h3, +h4, +h5, +h6 { + margin: 0; + font-family: inherit; + font-weight: bold; + color: inherit; + text-rendering: optimizelegibility; +} + +h1 small, +h2 small, +h3 small, +h4 small, +h5 small, +h6 small { + font-weight: normal; + color: #999999; +} + +h1 { + font-size: 30px; + line-height: 36px; +} + +h1 small { + font-size: 18px; +} + +h2 { + font-size: 24px; + line-height: 36px; +} + +h2 small { + font-size: 18px; +} + +h3 { + font-size: 18px; + line-height: 27px; +} + +h3 small { + font-size: 14px; +} + +h4, +h5, +h6 { + line-height: 18px; +} + +h4 { + font-size: 14px; +} + +h4 small { + font-size: 12px; +} + +h5 { + font-size: 12px; +} + +h6 { + font-size: 11px; + color: #999999; + text-transform: uppercase; +} + +.page-header { + padding-bottom: 17px; + margin: 18px 0; + border-bottom: 1px solid #eeeeee; +} + +.page-header h1 { + line-height: 1; +} + +ul, +ol { + padding: 0; + margin: 0 0 9px 25px; +} + +ul ul, +ul ol, +ol ol, +ol ul { + margin-bottom: 0; +} + +ul { + list-style: disc; +} + +ol { + list-style: decimal; +} + +li { + line-height: 18px; +} + +ul.unstyled, +ol.unstyled { + margin-left: 0; + list-style: none; +} + +dl { + margin-bottom: 18px; +} + +dt, +dd { + line-height: 18px; +} + +dt { + font-weight: bold; + line-height: 17px; +} + +dd { + margin-left: 9px; +} + +.dl-horizontal dt { + float: left; + width: 120px; + overflow: hidden; + clear: left; + text-align: right; + text-overflow: ellipsis; + white-space: nowrap; +} + +.dl-horizontal dd { + margin-left: 130px; +} + +hr { + margin: 18px 0; + border: 0; + border-top: 1px solid #eeeeee; + border-bottom: 1px solid #ffffff; +} + +strong { + font-weight: bold; +} + +em { + font-style: italic; +} + +.muted { + color: #999999; +} + +abbr[title] { + cursor: help; + border-bottom: 1px dotted #999999; +} + +abbr.initialism { + font-size: 90%; + text-transform: uppercase; +} + +blockquote { + padding: 0 0 0 15px; + margin: 0 0 18px; + border-left: 5px solid #eeeeee; +} + +blockquote p { + margin-bottom: 0; + font-size: 16px; + font-weight: 300; + line-height: 22.5px; +} + +blockquote small { + display: block; + line-height: 18px; + color: #999999; +} + +blockquote small:before { + content: '\2014 \00A0'; +} + +blockquote.pull-right { + float: right; + padding-right: 15px; + padding-left: 0; + border-right: 5px solid #eeeeee; + border-left: 0; +} + +blockquote.pull-right p, +blockquote.pull-right small { + text-align: right; +} + +q:before, +q:after, +blockquote:before, +blockquote:after { + content: ""; +} + +address { + display: block; + margin-bottom: 18px; + font-style: normal; + line-height: 18px; +} + +small { + font-size: 100%; +} + +cite { + font-style: normal; +} + +code, +pre { + padding: 0 3px 2px; + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; + font-size: 12px; + color: #333333; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +code { + padding: 2px 4px; + color: #d14; + background-color: #f7f7f9; + border: 1px solid #e1e1e8; +} + +pre { + display: block; + padding: 8.5px; + margin: 0 0 9px; + font-size: 12.025px; + line-height: 18px; + word-break: break-all; + word-wrap: break-word; + white-space: pre; + white-space: pre-wrap; + background-color: #f5f5f5; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.15); + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +pre.prettyprint { + margin-bottom: 18px; +} + +pre code { + padding: 0; + color: inherit; + background-color: transparent; + border: 0; +} + +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} + +form { + margin: 0 0 18px; +} + +fieldset { + padding: 0; + margin: 0; + border: 0; +} + +legend { + display: block; + width: 100%; + padding: 0; + margin-bottom: 27px; + font-size: 19.5px; + line-height: 36px; + color: #333333; + border: 0; + border-bottom: 1px solid #e5e5e5; +} + +legend small { + font-size: 13.5px; + color: #999999; +} + +label, +input, +button, +select, +textarea { + font-size: 13px; + font-weight: normal; + line-height: 18px; +} + +input, +button, +select, +textarea { + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; +} + +label { + display: block; + margin-bottom: 5px; +} + +select, +textarea, +input[type="text"], +input[type="password"], +input[type="datetime"], +input[type="datetime-local"], +input[type="date"], +input[type="month"], +input[type="time"], +input[type="week"], +input[type="number"], +input[type="email"], +input[type="url"], +input[type="search"], +input[type="tel"], +input[type="color"], +.uneditable-input { + display: inline-block; + height: 18px; + padding: 4px; + margin-bottom: 9px; + font-size: 13px; + line-height: 18px; + color: #555555; +} + +input, +textarea { + width: 210px; +} + +textarea { + height: auto; +} + +textarea, +input[type="text"], +input[type="password"], +input[type="datetime"], +input[type="datetime-local"], +input[type="date"], +input[type="month"], +input[type="time"], +input[type="week"], +input[type="number"], +input[type="email"], +input[type="url"], +input[type="search"], +input[type="tel"], +input[type="color"], +.uneditable-input { + background-color: #ffffff; + border: 1px solid #cccccc; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border linear 0.2s, box-shadow linear 0.2s; + -moz-transition: border linear 0.2s, box-shadow linear 0.2s; + -ms-transition: border linear 0.2s, box-shadow linear 0.2s; + -o-transition: border linear 0.2s, box-shadow linear 0.2s; + transition: border linear 0.2s, box-shadow linear 0.2s; +} + +textarea:focus, +input[type="text"]:focus, +input[type="password"]:focus, +input[type="datetime"]:focus, +input[type="datetime-local"]:focus, +input[type="date"]:focus, +input[type="month"]:focus, +input[type="time"]:focus, +input[type="week"]:focus, +input[type="number"]:focus, +input[type="email"]:focus, +input[type="url"]:focus, +input[type="search"]:focus, +input[type="tel"]:focus, +input[type="color"]:focus, +.uneditable-input:focus { + border-color: rgba(82, 168, 236, 0.8); + outline: 0; + outline: thin dotted \9; + /* IE6-9 */ + + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(82, 168, 236, 0.6); +} + +input[type="radio"], +input[type="checkbox"] { + margin: 3px 0; + *margin-top: 0; + /* IE7 */ + + line-height: normal; + cursor: pointer; +} + +input[type="submit"], +input[type="reset"], +input[type="button"], +input[type="radio"], +input[type="checkbox"] { + width: auto; +} + +.uneditable-textarea { + width: auto; + height: auto; +} + +select, +input[type="file"] { + height: 28px; + /* In IE7, the height of the select element cannot be changed by height, only font-size */ + + *margin-top: 4px; + /* For IE7, add top margin to align select with labels */ + + line-height: 28px; +} + +select { + width: 220px; + border: 1px solid #bbb; +} + +select[multiple], +select[size] { + height: auto; +} + +select:focus, +input[type="file"]:focus, +input[type="radio"]:focus, +input[type="checkbox"]:focus { + outline: thin dotted #333; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +.radio, +.checkbox { + min-height: 18px; + padding-left: 18px; +} + +.radio input[type="radio"], +.checkbox input[type="checkbox"] { + float: left; + margin-left: -18px; +} + +.controls > .radio:first-child, +.controls > .checkbox:first-child { + padding-top: 5px; +} + +.radio.inline, +.checkbox.inline { + display: inline-block; + padding-top: 5px; + margin-bottom: 0; + vertical-align: middle; +} + +.radio.inline + .radio.inline, +.checkbox.inline + .checkbox.inline { + margin-left: 10px; +} + +.input-mini { + width: 60px; +} + +.input-small { + width: 90px; +} + +.input-medium { + width: 150px; +} + +.input-large { + width: 210px; +} + +.input-xlarge { + width: 270px; +} + +.input-xxlarge { + width: 530px; +} + +input[class*="span"], +select[class*="span"], +textarea[class*="span"], +.uneditable-input[class*="span"], +.row-fluid input[class*="span"], +.row-fluid select[class*="span"], +.row-fluid textarea[class*="span"], +.row-fluid .uneditable-input[class*="span"] { + float: none; + margin-left: 0; +} + +.input-append input[class*="span"], +.input-append .uneditable-input[class*="span"], +.input-prepend input[class*="span"], +.input-prepend .uneditable-input[class*="span"], +.row-fluid .input-prepend [class*="span"], +.row-fluid .input-append [class*="span"] { + display: inline-block; +} + +input, +textarea, +.uneditable-input { + margin-left: 0; +} + +input.span12, +textarea.span12, +.uneditable-input.span12 { + width: 930px; +} + +input.span11, +textarea.span11, +.uneditable-input.span11 { + width: 850px; +} + +input.span10, +textarea.span10, +.uneditable-input.span10 { + width: 770px; +} + +input.span9, +textarea.span9, +.uneditable-input.span9 { + width: 690px; +} + +input.span8, +textarea.span8, +.uneditable-input.span8 { + width: 610px; +} + +input.span7, +textarea.span7, +.uneditable-input.span7 { + width: 530px; +} + +input.span6, +textarea.span6, +.uneditable-input.span6 { + width: 450px; +} + +input.span5, +textarea.span5, +.uneditable-input.span5 { + width: 370px; +} + +input.span4, +textarea.span4, +.uneditable-input.span4 { + width: 290px; +} + +input.span3, +textarea.span3, +.uneditable-input.span3 { + width: 210px; +} + +input.span2, +textarea.span2, +.uneditable-input.span2 { + width: 130px; +} + +input.span1, +textarea.span1, +.uneditable-input.span1 { + width: 50px; +} + +input[disabled], +select[disabled], +textarea[disabled], +input[readonly], +select[readonly], +textarea[readonly] { + cursor: not-allowed; + background-color: #eeeeee; + border-color: #ddd; +} + +input[type="radio"][disabled], +input[type="checkbox"][disabled], +input[type="radio"][readonly], +input[type="checkbox"][readonly] { + background-color: transparent; +} + +.control-group.warning > label, +.control-group.warning .help-block, +.control-group.warning .help-inline { + color: #c09853; +} + +.control-group.warning .checkbox, +.control-group.warning .radio, +.control-group.warning input, +.control-group.warning select, +.control-group.warning textarea { + color: #c09853; + border-color: #c09853; +} + +.control-group.warning .checkbox:focus, +.control-group.warning .radio:focus, +.control-group.warning input:focus, +.control-group.warning select:focus, +.control-group.warning textarea:focus { + border-color: #a47e3c; + -webkit-box-shadow: 0 0 6px #dbc59e; + -moz-box-shadow: 0 0 6px #dbc59e; + box-shadow: 0 0 6px #dbc59e; +} + +.control-group.warning .input-prepend .add-on, +.control-group.warning .input-append .add-on { + color: #c09853; + background-color: #fcf8e3; + border-color: #c09853; +} + +.control-group.error > label, +.control-group.error .help-block, +.control-group.error .help-inline { + color: #b94a48; +} + +.control-group.error .checkbox, +.control-group.error .radio, +.control-group.error input, +.control-group.error select, +.control-group.error textarea { + color: #b94a48; + border-color: #b94a48; +} + +.control-group.error .checkbox:focus, +.control-group.error .radio:focus, +.control-group.error input:focus, +.control-group.error select:focus, +.control-group.error textarea:focus { + border-color: #953b39; + -webkit-box-shadow: 0 0 6px #d59392; + -moz-box-shadow: 0 0 6px #d59392; + box-shadow: 0 0 6px #d59392; +} + +.control-group.error .input-prepend .add-on, +.control-group.error .input-append .add-on { + color: #b94a48; + background-color: #f2dede; + border-color: #b94a48; +} + +.control-group.success > label, +.control-group.success .help-block, +.control-group.success .help-inline { + color: #468847; +} + +.control-group.success .checkbox, +.control-group.success .radio, +.control-group.success input, +.control-group.success select, +.control-group.success textarea { + color: #468847; + border-color: #468847; +} + +.control-group.success .checkbox:focus, +.control-group.success .radio:focus, +.control-group.success input:focus, +.control-group.success select:focus, +.control-group.success textarea:focus { + border-color: #356635; + -webkit-box-shadow: 0 0 6px #7aba7b; + -moz-box-shadow: 0 0 6px #7aba7b; + box-shadow: 0 0 6px #7aba7b; +} + +.control-group.success .input-prepend .add-on, +.control-group.success .input-append .add-on { + color: #468847; + background-color: #dff0d8; + border-color: #468847; +} + +input:focus:required:invalid, +textarea:focus:required:invalid, +select:focus:required:invalid { + color: #b94a48; + border-color: #ee5f5b; +} + +input:focus:required:invalid:focus, +textarea:focus:required:invalid:focus, +select:focus:required:invalid:focus { + border-color: #e9322d; + -webkit-box-shadow: 0 0 6px #f8b9b7; + -moz-box-shadow: 0 0 6px #f8b9b7; + box-shadow: 0 0 6px #f8b9b7; +} + +.form-actions { + padding: 17px 20px 18px; + margin-top: 18px; + margin-bottom: 18px; + background-color: #f5f5f5; + border-top: 1px solid #e5e5e5; + *zoom: 1; +} + +.form-actions:before, +.form-actions:after { + display: table; + content: ""; +} + +.form-actions:after { + clear: both; +} + +.uneditable-input { + overflow: hidden; + white-space: nowrap; + cursor: not-allowed; + background-color: #ffffff; + border-color: #eee; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025); +} + +:-moz-placeholder { + color: #999999; +} + +:-ms-input-placeholder { + color: #999999; +} + +::-webkit-input-placeholder { + color: #999999; +} + +.help-block, +.help-inline { + color: #555555; +} + +.help-block { + display: block; + margin-bottom: 9px; +} + +.help-inline { + display: inline-block; + *display: inline; + padding-left: 5px; + vertical-align: middle; + *zoom: 1; +} + +.input-prepend, +.input-append { + margin-bottom: 5px; +} + +.input-prepend input, +.input-append input, +.input-prepend select, +.input-append select, +.input-prepend .uneditable-input, +.input-append .uneditable-input { + position: relative; + margin-bottom: 0; + *margin-left: 0; + vertical-align: middle; + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.input-prepend input:focus, +.input-append input:focus, +.input-prepend select:focus, +.input-append select:focus, +.input-prepend .uneditable-input:focus, +.input-append .uneditable-input:focus { + z-index: 2; +} + +.input-prepend .uneditable-input, +.input-append .uneditable-input { + border-left-color: #ccc; +} + +.input-prepend .add-on, +.input-append .add-on { + display: inline-block; + width: auto; + height: 18px; + min-width: 16px; + padding: 4px 5px; + font-weight: normal; + line-height: 18px; + text-align: center; + text-shadow: 0 1px 0 #ffffff; + vertical-align: middle; + background-color: #eeeeee; + border: 1px solid #ccc; +} + +.input-prepend .add-on, +.input-append .add-on, +.input-prepend .btn, +.input-append .btn { + margin-left: -1px; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.input-prepend .active, +.input-append .active { + background-color: #a9dba9; + border-color: #46a546; +} + +.input-prepend .add-on, +.input-prepend .btn { + margin-right: -1px; +} + +.input-prepend .add-on:first-child, +.input-prepend .btn:first-child { + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-append input, +.input-append select, +.input-append .uneditable-input { + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-append .uneditable-input { + border-right-color: #ccc; + border-left-color: #eee; +} + +.input-append .add-on:last-child, +.input-append .btn:last-child { + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.input-prepend.input-append input, +.input-prepend.input-append select, +.input-prepend.input-append .uneditable-input { + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.input-prepend.input-append .add-on:first-child, +.input-prepend.input-append .btn:first-child { + margin-right: -1px; + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.input-prepend.input-append .add-on:last-child, +.input-prepend.input-append .btn:last-child { + margin-left: -1px; + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.search-query { + padding-right: 14px; + padding-right: 4px \9; + padding-left: 14px; + padding-left: 4px \9; + /* IE7-8 doesn't have border-radius, so don't indent the padding */ + + margin-bottom: 0; + -webkit-border-radius: 14px; + -moz-border-radius: 14px; + border-radius: 14px; +} + +.form-search input, +.form-inline input, +.form-horizontal input, +.form-search textarea, +.form-inline textarea, +.form-horizontal textarea, +.form-search select, +.form-inline select, +.form-horizontal select, +.form-search .help-inline, +.form-inline .help-inline, +.form-horizontal .help-inline, +.form-search .uneditable-input, +.form-inline .uneditable-input, +.form-horizontal .uneditable-input, +.form-search .input-prepend, +.form-inline .input-prepend, +.form-horizontal .input-prepend, +.form-search .input-append, +.form-inline .input-append, +.form-horizontal .input-append { + display: inline-block; + *display: inline; + margin-bottom: 0; + *zoom: 1; +} + +.form-search .hide, +.form-inline .hide, +.form-horizontal .hide { + display: none; +} + +.form-search label, +.form-inline label { + display: inline-block; +} + +.form-search .input-append, +.form-inline .input-append, +.form-search .input-prepend, +.form-inline .input-prepend { + margin-bottom: 0; +} + +.form-search .radio, +.form-search .checkbox, +.form-inline .radio, +.form-inline .checkbox { + padding-left: 0; + margin-bottom: 0; + vertical-align: middle; +} + +.form-search .radio input[type="radio"], +.form-search .checkbox input[type="checkbox"], +.form-inline .radio input[type="radio"], +.form-inline .checkbox input[type="checkbox"] { + float: left; + margin-right: 3px; + margin-left: 0; +} + +.control-group { + margin-bottom: 9px; +} + +legend + .control-group { + margin-top: 18px; + -webkit-margin-top-collapse: separate; +} + +.form-horizontal .control-group { + margin-bottom: 18px; + *zoom: 1; +} + +.form-horizontal .control-group:before, +.form-horizontal .control-group:after { + display: table; + content: ""; +} + +.form-horizontal .control-group:after { + clear: both; +} + +.form-horizontal .control-label { + float: left; + width: 140px; + padding-top: 5px; + text-align: right; +} + +.form-horizontal .controls { + *display: inline-block; + *padding-left: 20px; + margin-left: 160px; + *margin-left: 0; +} + +.form-horizontal .controls:first-child { + *padding-left: 160px; +} + +.form-horizontal .help-block { + margin-top: 9px; + margin-bottom: 0; +} + +.form-horizontal .form-actions { + padding-left: 160px; +} + +table { + max-width: 100%; + background-color: transparent; + border-collapse: collapse; + border-spacing: 0; +} + +.table { + width: 100%; + margin-bottom: 18px; +} + +.table th, +.table td { + padding: 8px; + line-height: 18px; + text-align: left; + vertical-align: top; + border-top: 1px solid #dddddd; +} + +.table th { + font-weight: bold; +} + +.table thead th { + vertical-align: bottom; +} + +.table caption + thead tr:first-child th, +.table caption + thead tr:first-child td, +.table colgroup + thead tr:first-child th, +.table colgroup + thead tr:first-child td, +.table thead:first-child tr:first-child th, +.table thead:first-child tr:first-child td { + border-top: 0; +} + +.table tbody + tbody { + border-top: 2px solid #dddddd; +} + +.table-condensed th, +.table-condensed td { + padding: 4px 5px; +} + +.table-bordered { + border: 1px solid #dddddd; + border-collapse: separate; + *border-collapse: collapsed; + border-left: 0; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.table-bordered th, +.table-bordered td { + border-left: 1px solid #dddddd; +} + +.table-bordered caption + thead tr:first-child th, +.table-bordered caption + tbody tr:first-child th, +.table-bordered caption + tbody tr:first-child td, +.table-bordered colgroup + thead tr:first-child th, +.table-bordered colgroup + tbody tr:first-child th, +.table-bordered colgroup + tbody tr:first-child td, +.table-bordered thead:first-child tr:first-child th, +.table-bordered tbody:first-child tr:first-child th, +.table-bordered tbody:first-child tr:first-child td { + border-top: 0; +} + +.table-bordered thead:first-child tr:first-child th:first-child, +.table-bordered tbody:first-child tr:first-child td:first-child { + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-topleft: 4px; +} + +.table-bordered thead:first-child tr:first-child th:last-child, +.table-bordered tbody:first-child tr:first-child td:last-child { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -moz-border-radius-topright: 4px; +} + +.table-bordered thead:last-child tr:last-child th:first-child, +.table-bordered tbody:last-child tr:last-child td:first-child { + -webkit-border-radius: 0 0 0 4px; + -moz-border-radius: 0 0 0 4px; + border-radius: 0 0 0 4px; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; +} + +.table-bordered thead:last-child tr:last-child th:last-child, +.table-bordered tbody:last-child tr:last-child td:last-child { + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-bottomright: 4px; +} + +.table-striped tbody tr:nth-child(odd) td, +.table-striped tbody tr:nth-child(odd) th { + background-color: #f9f9f9; +} + +.table tbody tr:hover td, +.table tbody tr:hover th { + background-color: #f5f5f5; +} + +table .span1 { + float: none; + width: 44px; + margin-left: 0; +} + +table .span2 { + float: none; + width: 124px; + margin-left: 0; +} + +table .span3 { + float: none; + width: 204px; + margin-left: 0; +} + +table .span4 { + float: none; + width: 284px; + margin-left: 0; +} + +table .span5 { + float: none; + width: 364px; + margin-left: 0; +} + +table .span6 { + float: none; + width: 444px; + margin-left: 0; +} + +table .span7 { + float: none; + width: 524px; + margin-left: 0; +} + +table .span8 { + float: none; + width: 604px; + margin-left: 0; +} + +table .span9 { + float: none; + width: 684px; + margin-left: 0; +} + +table .span10 { + float: none; + width: 764px; + margin-left: 0; +} + +table .span11 { + float: none; + width: 844px; + margin-left: 0; +} + +table .span12 { + float: none; + width: 924px; + margin-left: 0; +} + +table .span13 { + float: none; + width: 1004px; + margin-left: 0; +} + +table .span14 { + float: none; + width: 1084px; + margin-left: 0; +} + +table .span15 { + float: none; + width: 1164px; + margin-left: 0; +} + +table .span16 { + float: none; + width: 1244px; + margin-left: 0; +} + +table .span17 { + float: none; + width: 1324px; + margin-left: 0; +} + +table .span18 { + float: none; + width: 1404px; + margin-left: 0; +} + +table .span19 { + float: none; + width: 1484px; + margin-left: 0; +} + +table .span20 { + float: none; + width: 1564px; + margin-left: 0; +} + +table .span21 { + float: none; + width: 1644px; + margin-left: 0; +} + +table .span22 { + float: none; + width: 1724px; + margin-left: 0; +} + +table .span23 { + float: none; + width: 1804px; + margin-left: 0; +} + +table .span24 { + float: none; + width: 1884px; + margin-left: 0; +} + +[class^="icon-"], +[class*=" icon-"] { + display: inline-block; + width: 14px; + height: 14px; + *margin-right: .3em; + line-height: 14px; + vertical-align: text-top; + background-image: url("../img/glyphicons-halflings.png"); + background-position: 14px 14px; + background-repeat: no-repeat; +} + +[class^="icon-"]:last-child, +[class*=" icon-"]:last-child { + *margin-left: 0; +} + +.icon-white { + background-image: url("../img/glyphicons-halflings-white.png"); +} + +.icon-glass { + background-position: 0 0; +} + +.icon-music { + background-position: -24px 0; +} + +.icon-search { + background-position: -48px 0; +} + +.icon-envelope { + background-position: -72px 0; +} + +.icon-heart { + background-position: -96px 0; +} + +.icon-star { + background-position: -120px 0; +} + +.icon-star-empty { + background-position: -144px 0; +} + +.icon-user { + background-position: -168px 0; +} + +.icon-film { + background-position: -192px 0; +} + +.icon-th-large { + background-position: -216px 0; +} + +.icon-th { + background-position: -240px 0; +} + +.icon-th-list { + background-position: -264px 0; +} + +.icon-ok { + background-position: -288px 0; +} + +.icon-remove { + background-position: -312px 0; +} + +.icon-zoom-in { + background-position: -336px 0; +} + +.icon-zoom-out { + background-position: -360px 0; +} + +.icon-off { + background-position: -384px 0; +} + +.icon-signal { + background-position: -408px 0; +} + +.icon-cog { + background-position: -432px 0; +} + +.icon-trash { + background-position: -456px 0; +} + +.icon-home { + background-position: 0 -24px; +} + +.icon-file { + background-position: -24px -24px; +} + +.icon-time { + background-position: -48px -24px; +} + +.icon-road { + background-position: -72px -24px; +} + +.icon-download-alt { + background-position: -96px -24px; +} + +.icon-download { + background-position: -120px -24px; +} + +.icon-upload { + background-position: -144px -24px; +} + +.icon-inbox { + background-position: -168px -24px; +} + +.icon-play-circle { + background-position: -192px -24px; +} + +.icon-repeat { + background-position: -216px -24px; +} + +.icon-refresh { + background-position: -240px -24px; +} + +.icon-list-alt { + background-position: -264px -24px; +} + +.icon-lock { + background-position: -287px -24px; +} + +.icon-flag { + background-position: -312px -24px; +} + +.icon-headphones { + background-position: -336px -24px; +} + +.icon-volume-off { + background-position: -360px -24px; +} + +.icon-volume-down { + background-position: -384px -24px; +} + +.icon-volume-up { + background-position: -408px -24px; +} + +.icon-qrcode { + background-position: -432px -24px; +} + +.icon-barcode { + background-position: -456px -24px; +} + +.icon-tag { + background-position: 0 -48px; +} + +.icon-tags { + background-position: -25px -48px; +} + +.icon-book { + background-position: -48px -48px; +} + +.icon-bookmark { + background-position: -72px -48px; +} + +.icon-print { + background-position: -96px -48px; +} + +.icon-camera { + background-position: -120px -48px; +} + +.icon-font { + background-position: -144px -48px; +} + +.icon-bold { + background-position: -167px -48px; +} + +.icon-italic { + background-position: -192px -48px; +} + +.icon-text-height { + background-position: -216px -48px; +} + +.icon-text-width { + background-position: -240px -48px; +} + +.icon-align-left { + background-position: -264px -48px; +} + +.icon-align-center { + background-position: -288px -48px; +} + +.icon-align-right { + background-position: -312px -48px; +} + +.icon-align-justify { + background-position: -336px -48px; +} + +.icon-list { + background-position: -360px -48px; +} + +.icon-indent-left { + background-position: -384px -48px; +} + +.icon-indent-right { + background-position: -408px -48px; +} + +.icon-facetime-video { + background-position: -432px -48px; +} + +.icon-picture { + background-position: -456px -48px; +} + +.icon-pencil { + background-position: 0 -72px; +} + +.icon-map-marker { + background-position: -24px -72px; +} + +.icon-adjust { + background-position: -48px -72px; +} + +.icon-tint { + background-position: -72px -72px; +} + +.icon-edit { + background-position: -96px -72px; +} + +.icon-share { + background-position: -120px -72px; +} + +.icon-check { + background-position: -144px -72px; +} + +.icon-move { + background-position: -168px -72px; +} + +.icon-step-backward { + background-position: -192px -72px; +} + +.icon-fast-backward { + background-position: -216px -72px; +} + +.icon-backward { + background-position: -240px -72px; +} + +.icon-play { + background-position: -264px -72px; +} + +.icon-pause { + background-position: -288px -72px; +} + +.icon-stop { + background-position: -312px -72px; +} + +.icon-forward { + background-position: -336px -72px; +} + +.icon-fast-forward { + background-position: -360px -72px; +} + +.icon-step-forward { + background-position: -384px -72px; +} + +.icon-eject { + background-position: -408px -72px; +} + +.icon-chevron-left { + background-position: -432px -72px; +} + +.icon-chevron-right { + background-position: -456px -72px; +} + +.icon-plus-sign { + background-position: 0 -96px; +} + +.icon-minus-sign { + background-position: -24px -96px; +} + +.icon-remove-sign { + background-position: -48px -96px; +} + +.icon-ok-sign { + background-position: -72px -96px; +} + +.icon-question-sign { + background-position: -96px -96px; +} + +.icon-info-sign { + background-position: -120px -96px; +} + +.icon-screenshot { + background-position: -144px -96px; +} + +.icon-remove-circle { + background-position: -168px -96px; +} + +.icon-ok-circle { + background-position: -192px -96px; +} + +.icon-ban-circle { + background-position: -216px -96px; +} + +.icon-arrow-left { + background-position: -240px -96px; +} + +.icon-arrow-right { + background-position: -264px -96px; +} + +.icon-arrow-up { + background-position: -289px -96px; +} + +.icon-arrow-down { + background-position: -312px -96px; +} + +.icon-share-alt { + background-position: -336px -96px; +} + +.icon-resize-full { + background-position: -360px -96px; +} + +.icon-resize-small { + background-position: -384px -96px; +} + +.icon-plus { + background-position: -408px -96px; +} + +.icon-minus { + background-position: -433px -96px; +} + +.icon-asterisk { + background-position: -456px -96px; +} + +.icon-exclamation-sign { + background-position: 0 -120px; +} + +.icon-gift { + background-position: -24px -120px; +} + +.icon-leaf { + background-position: -48px -120px; +} + +.icon-fire { + background-position: -72px -120px; +} + +.icon-eye-open { + background-position: -96px -120px; +} + +.icon-eye-close { + background-position: -120px -120px; +} + +.icon-warning-sign { + background-position: -144px -120px; +} + +.icon-plane { + background-position: -168px -120px; +} + +.icon-calendar { + background-position: -192px -120px; +} + +.icon-random { + background-position: -216px -120px; +} + +.icon-comment { + background-position: -240px -120px; +} + +.icon-magnet { + background-position: -264px -120px; +} + +.icon-chevron-up { + background-position: -288px -120px; +} + +.icon-chevron-down { + background-position: -313px -119px; +} + +.icon-retweet { + background-position: -336px -120px; +} + +.icon-shopping-cart { + background-position: -360px -120px; +} + +.icon-folder-close { + background-position: -384px -120px; +} + +.icon-folder-open { + background-position: -408px -120px; +} + +.icon-resize-vertical { + background-position: -432px -119px; +} + +.icon-resize-horizontal { + background-position: -456px -118px; +} + +.icon-hdd { + background-position: 0 -144px; +} + +.icon-bullhorn { + background-position: -24px -144px; +} + +.icon-bell { + background-position: -48px -144px; +} + +.icon-certificate { + background-position: -72px -144px; +} + +.icon-thumbs-up { + background-position: -96px -144px; +} + +.icon-thumbs-down { + background-position: -120px -144px; +} + +.icon-hand-right { + background-position: -144px -144px; +} + +.icon-hand-left { + background-position: -168px -144px; +} + +.icon-hand-up { + background-position: -192px -144px; +} + +.icon-hand-down { + background-position: -216px -144px; +} + +.icon-circle-arrow-right { + background-position: -240px -144px; +} + +.icon-circle-arrow-left { + background-position: -264px -144px; +} + +.icon-circle-arrow-up { + background-position: -288px -144px; +} + +.icon-circle-arrow-down { + background-position: -312px -144px; +} + +.icon-globe { + background-position: -336px -144px; +} + +.icon-wrench { + background-position: -360px -144px; +} + +.icon-tasks { + background-position: -384px -144px; +} + +.icon-filter { + background-position: -408px -144px; +} + +.icon-briefcase { + background-position: -432px -144px; +} + +.icon-fullscreen { + background-position: -456px -144px; +} + +.dropup, +.dropdown { + position: relative; +} + +.dropdown-toggle { + *margin-bottom: -3px; +} + +.dropdown-toggle:active, +.open .dropdown-toggle { + outline: 0; +} + +.caret { + display: inline-block; + width: 0; + height: 0; + vertical-align: top; + border-top: 4px solid #000000; + border-right: 4px solid transparent; + border-left: 4px solid transparent; + content: ""; + opacity: 0.3; + filter: alpha(opacity=30); +} + +.dropdown .caret { + margin-top: 8px; + margin-left: 2px; +} + +.dropdown:hover .caret, +.open .caret { + opacity: 1; + filter: alpha(opacity=100); +} + +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 4px 0; + margin: 1px 0 0; + list-style: none; + background-color: #ffffff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.2); + *border-right-width: 2px; + *border-bottom-width: 2px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + -webkit-background-clip: padding-box; + -moz-background-clip: padding; + background-clip: padding-box; +} + +.dropdown-menu.pull-right { + right: 0; + left: auto; +} + +.dropdown-menu .divider { + *width: 100%; + height: 1px; + margin: 8px 1px; + *margin: -5px 0 5px; + overflow: hidden; + background-color: #e5e5e5; + border-bottom: 1px solid #ffffff; +} + +.dropdown-menu a { + display: block; + padding: 3px 15px; + clear: both; + font-weight: normal; + line-height: 18px; + color: #333333; + white-space: nowrap; +} + +.dropdown-menu li > a:hover, +.dropdown-menu .active > a, +.dropdown-menu .active > a:hover { + color: #ffffff; + text-decoration: none; + background-color: #0088cc; +} + +.open { + *z-index: 1000; +} + +.open > .dropdown-menu { + display: block; +} + +.pull-right > .dropdown-menu { + right: 0; + left: auto; +} + +.dropup .caret, +.navbar-fixed-bottom .dropdown .caret { + border-top: 0; + border-bottom: 4px solid #000000; + content: "\2191"; +} + +.dropup .dropdown-menu, +.navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 1px; +} + +.typeahead { + margin-top: 2px; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #f5f5f5; + border: 1px solid #eee; + border: 1px solid rgba(0, 0, 0, 0.05); + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); +} + +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, 0.15); +} + +.well-large { + padding: 24px; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.well-small { + padding: 9px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.fade { + opacity: 0; + -webkit-transition: opacity 0.15s linear; + -moz-transition: opacity 0.15s linear; + -ms-transition: opacity 0.15s linear; + -o-transition: opacity 0.15s linear; + transition: opacity 0.15s linear; +} + +.fade.in { + opacity: 1; +} + +.collapse { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition: height 0.35s ease; + -moz-transition: height 0.35s ease; + -ms-transition: height 0.35s ease; + -o-transition: height 0.35s ease; + transition: height 0.35s ease; +} + +.collapse.in { + height: auto; +} + +.close { + float: right; + font-size: 20px; + font-weight: bold; + line-height: 18px; + color: #000000; + text-shadow: 0 1px 0 #ffffff; + opacity: 0.2; + filter: alpha(opacity=20); +} + +.close:hover { + color: #000000; + text-decoration: none; + cursor: pointer; + opacity: 0.4; + filter: alpha(opacity=40); +} + +button.close { + padding: 0; + cursor: pointer; + background: transparent; + border: 0; + -webkit-appearance: none; +} + +.btn { + display: inline-block; + *display: inline; + padding: 4px 10px 4px; + margin-bottom: 0; + *margin-left: .3em; + font-size: 13px; + line-height: 18px; + *line-height: 20px; + color: #333333; + text-align: center; + text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); + vertical-align: middle; + cursor: pointer; + background-color: #f5f5f5; + *background-color: #e6e6e6; + background-image: -ms-linear-gradient(top, #ffffff, #e6e6e6); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); + background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); + background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); + background-image: linear-gradient(top, #ffffff, #e6e6e6); + background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); + background-repeat: repeat-x; + border: 1px solid #cccccc; + *border: 0; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + border-color: #e6e6e6 #e6e6e6 #bfbfbf; + border-bottom-color: #b3b3b3; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffff', endColorstr='#e6e6e6', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); + *zoom: 1; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn:hover, +.btn:active, +.btn.active, +.btn.disabled, +.btn[disabled] { + background-color: #e6e6e6; + *background-color: #d9d9d9; +} + +.btn:active, +.btn.active { + background-color: #cccccc \9; +} + +.btn:first-child { + *margin-left: 0; +} + +.btn:hover { + color: #333333; + text-decoration: none; + background-color: #e6e6e6; + *background-color: #d9d9d9; + /* Buttons in IE7 don't get borders, so darken on hover */ + + background-position: 0 -15px; + -webkit-transition: background-position 0.1s linear; + -moz-transition: background-position 0.1s linear; + -ms-transition: background-position 0.1s linear; + -o-transition: background-position 0.1s linear; + transition: background-position 0.1s linear; +} + +.btn:focus { + outline: thin dotted #333; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +.btn.active, +.btn:active { + background-color: #e6e6e6; + background-color: #d9d9d9 \9; + background-image: none; + outline: 0; + -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn.disabled, +.btn[disabled] { + cursor: default; + background-color: #e6e6e6; + background-image: none; + opacity: 0.65; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + -moz-box-shadow: none; + box-shadow: none; +} + +.btn-large { + padding: 9px 14px; + font-size: 15px; + line-height: normal; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} + +.btn-large [class^="icon-"] { + margin-top: 1px; +} + +.btn-small { + padding: 5px 9px; + font-size: 11px; + line-height: 16px; +} + +.btn-small [class^="icon-"] { + margin-top: -1px; +} + +.btn-mini { + padding: 2px 6px; + font-size: 11px; + line-height: 14px; +} + +.btn-primary, +.btn-primary:hover, +.btn-warning, +.btn-warning:hover, +.btn-danger, +.btn-danger:hover, +.btn-success, +.btn-success:hover, +.btn-info, +.btn-info:hover, +.btn-inverse, +.btn-inverse:hover { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); +} + +.btn-primary.active, +.btn-warning.active, +.btn-danger.active, +.btn-success.active, +.btn-info.active, +.btn-inverse.active { + color: rgba(255, 255, 255, 0.75); +} + +.btn { + border-color: #ccc; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); +} + +.btn-primary { + background-color: #0074cc; + *background-color: #0055cc; + background-image: -ms-linear-gradient(top, #0088cc, #0055cc); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0055cc)); + background-image: -webkit-linear-gradient(top, #0088cc, #0055cc); + background-image: -o-linear-gradient(top, #0088cc, #0055cc); + background-image: -moz-linear-gradient(top, #0088cc, #0055cc); + background-image: linear-gradient(top, #0088cc, #0055cc); + background-repeat: repeat-x; + border-color: #0055cc #0055cc #003580; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#0088cc', endColorstr='#0055cc', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-primary:hover, +.btn-primary:active, +.btn-primary.active, +.btn-primary.disabled, +.btn-primary[disabled] { + background-color: #0055cc; + *background-color: #004ab3; +} + +.btn-primary:active, +.btn-primary.active { + background-color: #004099 \9; +} + +.btn-warning { + background-color: #faa732; + *background-color: #f89406; + background-image: -ms-linear-gradient(top, #fbb450, #f89406); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); + background-image: -webkit-linear-gradient(top, #fbb450, #f89406); + background-image: -o-linear-gradient(top, #fbb450, #f89406); + background-image: -moz-linear-gradient(top, #fbb450, #f89406); + background-image: linear-gradient(top, #fbb450, #f89406); + background-repeat: repeat-x; + border-color: #f89406 #f89406 #ad6704; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#fbb450', endColorstr='#f89406', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-warning:hover, +.btn-warning:active, +.btn-warning.active, +.btn-warning.disabled, +.btn-warning[disabled] { + background-color: #f89406; + *background-color: #df8505; +} + +.btn-warning:active, +.btn-warning.active { + background-color: #c67605 \9; +} + +.btn-danger { + background-color: #da4f49; + *background-color: #bd362f; + background-image: -ms-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f)); + background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -o-linear-gradient(top, #ee5f5b, #bd362f); + background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f); + background-image: linear-gradient(top, #ee5f5b, #bd362f); + background-repeat: repeat-x; + border-color: #bd362f #bd362f #802420; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ee5f5b', endColorstr='#bd362f', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-danger:hover, +.btn-danger:active, +.btn-danger.active, +.btn-danger.disabled, +.btn-danger[disabled] { + background-color: #bd362f; + *background-color: #a9302a; +} + +.btn-danger:active, +.btn-danger.active { + background-color: #942a25 \9; +} + +.btn-success { + background-color: #5bb75b; + *background-color: #51a351; + background-image: -ms-linear-gradient(top, #62c462, #51a351); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351)); + background-image: -webkit-linear-gradient(top, #62c462, #51a351); + background-image: -o-linear-gradient(top, #62c462, #51a351); + background-image: -moz-linear-gradient(top, #62c462, #51a351); + background-image: linear-gradient(top, #62c462, #51a351); + background-repeat: repeat-x; + border-color: #51a351 #51a351 #387038; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#62c462', endColorstr='#51a351', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-success:hover, +.btn-success:active, +.btn-success.active, +.btn-success.disabled, +.btn-success[disabled] { + background-color: #51a351; + *background-color: #499249; +} + +.btn-success:active, +.btn-success.active { + background-color: #408140 \9; +} + +.btn-info { + background-color: #49afcd; + *background-color: #2f96b4; + background-image: -ms-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4)); + background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -o-linear-gradient(top, #5bc0de, #2f96b4); + background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4); + background-image: linear-gradient(top, #5bc0de, #2f96b4); + background-repeat: repeat-x; + border-color: #2f96b4 #2f96b4 #1f6377; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#5bc0de', endColorstr='#2f96b4', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-info:hover, +.btn-info:active, +.btn-info.active, +.btn-info.disabled, +.btn-info[disabled] { + background-color: #2f96b4; + *background-color: #2a85a0; +} + +.btn-info:active, +.btn-info.active { + background-color: #24748c \9; +} + +.btn-inverse { + background-color: #414141; + *background-color: #222222; + background-image: -ms-linear-gradient(top, #555555, #222222); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#555555), to(#222222)); + background-image: -webkit-linear-gradient(top, #555555, #222222); + background-image: -o-linear-gradient(top, #555555, #222222); + background-image: -moz-linear-gradient(top, #555555, #222222); + background-image: linear-gradient(top, #555555, #222222); + background-repeat: repeat-x; + border-color: #222222 #222222 #000000; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#555555', endColorstr='#222222', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); +} + +.btn-inverse:hover, +.btn-inverse:active, +.btn-inverse.active, +.btn-inverse.disabled, +.btn-inverse[disabled] { + background-color: #222222; + *background-color: #151515; +} + +.btn-inverse:active, +.btn-inverse.active { + background-color: #080808 \9; +} + +button.btn, +input[type="submit"].btn { + *padding-top: 2px; + *padding-bottom: 2px; +} + +button.btn::-moz-focus-inner, +input[type="submit"].btn::-moz-focus-inner { + padding: 0; + border: 0; +} + +button.btn.btn-large, +input[type="submit"].btn.btn-large { + *padding-top: 7px; + *padding-bottom: 7px; +} + +button.btn.btn-small, +input[type="submit"].btn.btn-small { + *padding-top: 3px; + *padding-bottom: 3px; +} + +button.btn.btn-mini, +input[type="submit"].btn.btn-mini { + *padding-top: 1px; + *padding-bottom: 1px; +} + +.btn-group { + position: relative; + *margin-left: .3em; + *zoom: 1; +} + +.btn-group:before, +.btn-group:after { + display: table; + content: ""; +} + +.btn-group:after { + clear: both; +} + +.btn-group:first-child { + *margin-left: 0; +} + +.btn-group + .btn-group { + margin-left: 5px; +} + +.btn-toolbar { + margin-top: 9px; + margin-bottom: 9px; +} + +.btn-toolbar .btn-group { + display: inline-block; + *display: inline; + /* IE7 inline-block hack */ + + *zoom: 1; +} + +.btn-group > .btn { + position: relative; + float: left; + margin-left: -1px; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.btn-group > .btn:first-child { + margin-left: 0; + -webkit-border-bottom-left-radius: 4px; + border-bottom-left-radius: 4px; + -webkit-border-top-left-radius: 4px; + border-top-left-radius: 4px; + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-topleft: 4px; +} + +.btn-group > .btn:last-child, +.btn-group > .dropdown-toggle { + -webkit-border-top-right-radius: 4px; + border-top-right-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + border-bottom-right-radius: 4px; + -moz-border-radius-topright: 4px; + -moz-border-radius-bottomright: 4px; +} + +.btn-group > .btn.large:first-child { + margin-left: 0; + -webkit-border-bottom-left-radius: 6px; + border-bottom-left-radius: 6px; + -webkit-border-top-left-radius: 6px; + border-top-left-radius: 6px; + -moz-border-radius-bottomleft: 6px; + -moz-border-radius-topleft: 6px; +} + +.btn-group > .btn.large:last-child, +.btn-group > .large.dropdown-toggle { + -webkit-border-top-right-radius: 6px; + border-top-right-radius: 6px; + -webkit-border-bottom-right-radius: 6px; + border-bottom-right-radius: 6px; + -moz-border-radius-topright: 6px; + -moz-border-radius-bottomright: 6px; +} + +.btn-group > .btn:hover, +.btn-group > .btn:focus, +.btn-group > .btn:active, +.btn-group > .btn.active { + z-index: 2; +} + +.btn-group .dropdown-toggle:active, +.btn-group.open .dropdown-toggle { + outline: 0; +} + +.btn-group > .dropdown-toggle { + *padding-top: 4px; + padding-right: 8px; + *padding-bottom: 4px; + padding-left: 8px; + -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn-group > .btn-mini.dropdown-toggle { + padding-right: 5px; + padding-left: 5px; +} + +.btn-group > .btn-small.dropdown-toggle { + *padding-top: 4px; + *padding-bottom: 4px; +} + +.btn-group > .btn-large.dropdown-toggle { + padding-right: 12px; + padding-left: 12px; +} + +.btn-group.open .dropdown-toggle { + background-image: none; + -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.btn-group.open .btn.dropdown-toggle { + background-color: #e6e6e6; +} + +.btn-group.open .btn-primary.dropdown-toggle { + background-color: #0055cc; +} + +.btn-group.open .btn-warning.dropdown-toggle { + background-color: #f89406; +} + +.btn-group.open .btn-danger.dropdown-toggle { + background-color: #bd362f; +} + +.btn-group.open .btn-success.dropdown-toggle { + background-color: #51a351; +} + +.btn-group.open .btn-info.dropdown-toggle { + background-color: #2f96b4; +} + +.btn-group.open .btn-inverse.dropdown-toggle { + background-color: #222222; +} + +.btn .caret { + margin-top: 7px; + margin-left: 0; +} + +.btn:hover .caret, +.open.btn-group .caret { + opacity: 1; + filter: alpha(opacity=100); +} + +.btn-mini .caret { + margin-top: 5px; +} + +.btn-small .caret { + margin-top: 6px; +} + +.btn-large .caret { + margin-top: 6px; + border-top-width: 5px; + border-right-width: 5px; + border-left-width: 5px; +} + +.dropup .btn-large .caret { + border-top: 0; + border-bottom: 5px solid #000000; +} + +.btn-primary .caret, +.btn-warning .caret, +.btn-danger .caret, +.btn-info .caret, +.btn-success .caret, +.btn-inverse .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; + opacity: 0.75; + filter: alpha(opacity=75); +} + +.alert { + padding: 8px 35px 8px 14px; + margin-bottom: 18px; + color: #c09853; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); + background-color: #fcf8e3; + border: 1px solid #fbeed5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.alert-heading { + color: inherit; +} + +.alert .close { + position: relative; + top: -2px; + right: -21px; + line-height: 18px; +} + +.alert-success { + color: #468847; + background-color: #dff0d8; + border-color: #d6e9c6; +} + +.alert-danger, +.alert-error { + color: #b94a48; + background-color: #f2dede; + border-color: #eed3d7; +} + +.alert-info { + color: #3a87ad; + background-color: #d9edf7; + border-color: #bce8f1; +} + +.alert-block { + padding-top: 14px; + padding-bottom: 14px; +} + +.alert-block > p, +.alert-block > ul { + margin-bottom: 0; +} + +.alert-block p + p { + margin-top: 5px; +} + +.nav { + margin-bottom: 18px; + margin-left: 0; + list-style: none; +} + +.nav > li > a { + display: block; +} + +.nav > li > a:hover { + text-decoration: none; + background-color: #eeeeee; +} + +.nav > .pull-right { + float: right; +} + +.nav .nav-header { + display: block; + padding: 3px 15px; + font-size: 11px; + font-weight: bold; + line-height: 18px; + color: #999999; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); + text-transform: uppercase; +} + +.nav li + .nav-header { + margin-top: 9px; +} + +.nav-list { + padding-right: 15px; + padding-left: 15px; + margin-bottom: 0; +} + +.nav-list > li > a, +.nav-list .nav-header { + margin-right: -15px; + margin-left: -15px; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5); +} + +.nav-list > li > a { + padding: 3px 15px; +} + +.nav-list > .active > a, +.nav-list > .active > a:hover { + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2); + background-color: #0088cc; +} + +.nav-list [class^="icon-"] { + margin-right: 2px; +} + +.nav-list .divider { + *width: 100%; + height: 1px; + margin: 8px 1px; + *margin: -5px 0 5px; + overflow: hidden; + background-color: #e5e5e5; + border-bottom: 1px solid #ffffff; +} + +.nav-tabs, +.nav-pills { + *zoom: 1; +} + +.nav-tabs:before, +.nav-pills:before, +.nav-tabs:after, +.nav-pills:after { + display: table; + content: ""; +} + +.nav-tabs:after, +.nav-pills:after { + clear: both; +} + +.nav-tabs > li, +.nav-pills > li { + float: left; +} + +.nav-tabs > li > a, +.nav-pills > li > a { + padding-right: 12px; + padding-left: 12px; + margin-right: 2px; + line-height: 14px; +} + +.nav-tabs { + border-bottom: 1px solid #ddd; +} + +.nav-tabs > li { + margin-bottom: -1px; +} + +.nav-tabs > li > a { + padding-top: 8px; + padding-bottom: 8px; + line-height: 18px; + border: 1px solid transparent; + -webkit-border-radius: 4px 4px 0 0; + -moz-border-radius: 4px 4px 0 0; + border-radius: 4px 4px 0 0; +} + +.nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee #dddddd; +} + +.nav-tabs > .active > a, +.nav-tabs > .active > a:hover { + color: #555555; + cursor: default; + background-color: #ffffff; + border: 1px solid #ddd; + border-bottom-color: transparent; +} + +.nav-pills > li > a { + padding-top: 8px; + padding-bottom: 8px; + margin-top: 2px; + margin-bottom: 2px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} + +.nav-pills > .active > a, +.nav-pills > .active > a:hover { + color: #ffffff; + background-color: #0088cc; +} + +.nav-stacked > li { + float: none; +} + +.nav-stacked > li > a { + margin-right: 0; +} + +.nav-tabs.nav-stacked { + border-bottom: 0; +} + +.nav-tabs.nav-stacked > li > a { + border: 1px solid #ddd; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.nav-tabs.nav-stacked > li:first-child > a { + -webkit-border-radius: 4px 4px 0 0; + -moz-border-radius: 4px 4px 0 0; + border-radius: 4px 4px 0 0; +} + +.nav-tabs.nav-stacked > li:last-child > a { + -webkit-border-radius: 0 0 4px 4px; + -moz-border-radius: 0 0 4px 4px; + border-radius: 0 0 4px 4px; +} + +.nav-tabs.nav-stacked > li > a:hover { + z-index: 2; + border-color: #ddd; +} + +.nav-pills.nav-stacked > li > a { + margin-bottom: 3px; +} + +.nav-pills.nav-stacked > li:last-child > a { + margin-bottom: 1px; +} + +.nav-tabs .dropdown-menu { + -webkit-border-radius: 0 0 5px 5px; + -moz-border-radius: 0 0 5px 5px; + border-radius: 0 0 5px 5px; +} + +.nav-pills .dropdown-menu { + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.nav-tabs .dropdown-toggle .caret, +.nav-pills .dropdown-toggle .caret { + margin-top: 6px; + border-top-color: #0088cc; + border-bottom-color: #0088cc; +} + +.nav-tabs .dropdown-toggle:hover .caret, +.nav-pills .dropdown-toggle:hover .caret { + border-top-color: #005580; + border-bottom-color: #005580; +} + +.nav-tabs .active .dropdown-toggle .caret, +.nav-pills .active .dropdown-toggle .caret { + border-top-color: #333333; + border-bottom-color: #333333; +} + +.nav > .dropdown.active > a:hover { + color: #000000; + cursor: pointer; +} + +.nav-tabs .open .dropdown-toggle, +.nav-pills .open .dropdown-toggle, +.nav > li.dropdown.open.active > a:hover { + color: #ffffff; + background-color: #999999; + border-color: #999999; +} + +.nav li.dropdown.open .caret, +.nav li.dropdown.open.active .caret, +.nav li.dropdown.open a:hover .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; + opacity: 1; + filter: alpha(opacity=100); +} + +.tabs-stacked .open > a:hover { + border-color: #999999; +} + +.tabbable { + *zoom: 1; +} + +.tabbable:before, +.tabbable:after { + display: table; + content: ""; +} + +.tabbable:after { + clear: both; +} + +.tab-content { + overflow: auto; +} + +.tabs-below > .nav-tabs, +.tabs-right > .nav-tabs, +.tabs-left > .nav-tabs { + border-bottom: 0; +} + +.tab-content > .tab-pane, +.pill-content > .pill-pane { + display: none; +} + +.tab-content > .active, +.pill-content > .active { + display: block; +} + +.tabs-below > .nav-tabs { + border-top: 1px solid #ddd; +} + +.tabs-below > .nav-tabs > li { + margin-top: -1px; + margin-bottom: 0; +} + +.tabs-below > .nav-tabs > li > a { + -webkit-border-radius: 0 0 4px 4px; + -moz-border-radius: 0 0 4px 4px; + border-radius: 0 0 4px 4px; +} + +.tabs-below > .nav-tabs > li > a:hover { + border-top-color: #ddd; + border-bottom-color: transparent; +} + +.tabs-below > .nav-tabs > .active > a, +.tabs-below > .nav-tabs > .active > a:hover { + border-color: transparent #ddd #ddd #ddd; +} + +.tabs-left > .nav-tabs > li, +.tabs-right > .nav-tabs > li { + float: none; +} + +.tabs-left > .nav-tabs > li > a, +.tabs-right > .nav-tabs > li > a { + min-width: 74px; + margin-right: 0; + margin-bottom: 3px; +} + +.tabs-left > .nav-tabs { + float: left; + margin-right: 19px; + border-right: 1px solid #ddd; +} + +.tabs-left > .nav-tabs > li > a { + margin-right: -1px; + -webkit-border-radius: 4px 0 0 4px; + -moz-border-radius: 4px 0 0 4px; + border-radius: 4px 0 0 4px; +} + +.tabs-left > .nav-tabs > li > a:hover { + border-color: #eeeeee #dddddd #eeeeee #eeeeee; +} + +.tabs-left > .nav-tabs .active > a, +.tabs-left > .nav-tabs .active > a:hover { + border-color: #ddd transparent #ddd #ddd; + *border-right-color: #ffffff; +} + +.tabs-right > .nav-tabs { + float: right; + margin-left: 19px; + border-left: 1px solid #ddd; +} + +.tabs-right > .nav-tabs > li > a { + margin-left: -1px; + -webkit-border-radius: 0 4px 4px 0; + -moz-border-radius: 0 4px 4px 0; + border-radius: 0 4px 4px 0; +} + +.tabs-right > .nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee #eeeeee #dddddd; +} + +.tabs-right > .nav-tabs .active > a, +.tabs-right > .nav-tabs .active > a:hover { + border-color: #ddd #ddd #ddd transparent; + *border-left-color: #ffffff; +} + +.navbar { + *position: relative; + *z-index: 2; + margin-bottom: 18px; + overflow: visible; +} + +.navbar-inner { + min-height: 40px; + padding-right: 20px; + padding-left: 20px; + background-color: #2c2c2c; + background-image: -moz-linear-gradient(top, #333333, #222222); + background-image: -ms-linear-gradient(top, #333333, #222222); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#333333), to(#222222)); + background-image: -webkit-linear-gradient(top, #333333, #222222); + background-image: -o-linear-gradient(top, #333333, #222222); + background-image: linear-gradient(top, #333333, #222222); + background-repeat: repeat-x; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#333333', endColorstr='#222222', GradientType=0); + -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.25), inset 0 -1px 0 rgba(0, 0, 0, 0.1); + -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.25), inset 0 -1px 0 rgba(0, 0, 0, 0.1); + box-shadow: 0 1px 3px rgba(0, 0, 0, 0.25), inset 0 -1px 0 rgba(0, 0, 0, 0.1); +} + +.navbar .container { + width: auto; +} + +.nav-collapse.collapse { + height: auto; +} + +.navbar { + color: #999999; +} + +.navbar .brand:hover { + text-decoration: none; +} + +.navbar .brand { + display: block; + float: left; + padding: 8px 20px 12px; + margin-left: -20px; + font-size: 20px; + font-weight: 200; + line-height: 1; + color: #999999; +} + +.navbar .navbar-text { + margin-bottom: 0; + line-height: 40px; +} + +.navbar .navbar-link { + color: #999999; +} + +.navbar .navbar-link:hover { + color: #ffffff; +} + +.navbar .btn, +.navbar .btn-group { + margin-top: 5px; +} + +.navbar .btn-group .btn { + margin: 0; +} + +.navbar-form { + margin-bottom: 0; + *zoom: 1; +} + +.navbar-form:before, +.navbar-form:after { + display: table; + content: ""; +} + +.navbar-form:after { + clear: both; +} + +.navbar-form input, +.navbar-form select, +.navbar-form .radio, +.navbar-form .checkbox { + margin-top: 5px; +} + +.navbar-form input, +.navbar-form select { + display: inline-block; + margin-bottom: 0; +} + +.navbar-form input[type="image"], +.navbar-form input[type="checkbox"], +.navbar-form input[type="radio"] { + margin-top: 3px; +} + +.navbar-form .input-append, +.navbar-form .input-prepend { + margin-top: 6px; + white-space: nowrap; +} + +.navbar-form .input-append input, +.navbar-form .input-prepend input { + margin-top: 0; +} + +.navbar-search { + position: relative; + float: left; + margin-top: 6px; + margin-bottom: 0; +} + +.navbar-search .search-query { + padding: 4px 9px; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 13px; + font-weight: normal; + line-height: 1; + color: #ffffff; + background-color: #626262; + border: 1px solid #151515; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); + -webkit-transition: none; + -moz-transition: none; + -ms-transition: none; + -o-transition: none; + transition: none; +} + +.navbar-search .search-query:-moz-placeholder { + color: #cccccc; +} + +.navbar-search .search-query:-ms-input-placeholder { + color: #cccccc; +} + +.navbar-search .search-query::-webkit-input-placeholder { + color: #cccccc; +} + +.navbar-search .search-query:focus, +.navbar-search .search-query.focused { + padding: 5px 10px; + color: #333333; + text-shadow: 0 1px 0 #ffffff; + background-color: #ffffff; + border: 0; + outline: 0; + -webkit-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); + -moz-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); + box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); +} + +.navbar-fixed-top, +.navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; + margin-bottom: 0; +} + +.navbar-fixed-top .navbar-inner, +.navbar-fixed-bottom .navbar-inner { + padding-right: 0; + padding-left: 0; + -webkit-border-radius: 0; + -moz-border-radius: 0; + border-radius: 0; +} + +.navbar-fixed-top .container, +.navbar-fixed-bottom .container { + width: 940px; +} + +.navbar-fixed-top { + top: 0; +} + +.navbar-fixed-bottom { + bottom: 0; +} + +.navbar .nav { + position: relative; + left: 0; + display: block; + float: left; + margin: 0 10px 0 0; +} + +.navbar .nav.pull-right { + float: right; +} + +.navbar .nav > li { + display: block; + float: left; +} + +.navbar .nav > li > a { + float: none; + padding: 9px 10px 11px; + line-height: 19px; + color: #999999; + text-decoration: none; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); +} + +.navbar .btn { + display: inline-block; + padding: 4px 10px 4px; + margin: 5px 5px 6px; + line-height: 18px; +} + +.navbar .btn-group { + padding: 5px 5px 6px; + margin: 0; +} + +.navbar .nav > li > a:hover { + color: #ffffff; + text-decoration: none; + background-color: transparent; +} + +.navbar .nav .active > a, +.navbar .nav .active > a:hover { + color: #ffffff; + text-decoration: none; + background-color: #222222; +} + +.navbar .divider-vertical { + width: 1px; + height: 40px; + margin: 0 9px; + overflow: hidden; + background-color: #222222; + border-right: 1px solid #333333; +} + +.navbar .nav.pull-right { + margin-right: 0; + margin-left: 10px; +} + +.navbar .btn-navbar { + display: none; + float: right; + padding: 7px 10px; + margin-right: 5px; + margin-left: 5px; + background-color: #2c2c2c; + *background-color: #222222; + background-image: -ms-linear-gradient(top, #333333, #222222); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#333333), to(#222222)); + background-image: -webkit-linear-gradient(top, #333333, #222222); + background-image: -o-linear-gradient(top, #333333, #222222); + background-image: linear-gradient(top, #333333, #222222); + background-image: -moz-linear-gradient(top, #333333, #222222); + background-repeat: repeat-x; + border-color: #222222 #222222 #000000; + border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#333333', endColorstr='#222222', GradientType=0); + filter: progid:dximagetransform.microsoft.gradient(enabled=false); + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); + -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075); +} + +.navbar .btn-navbar:hover, +.navbar .btn-navbar:active, +.navbar .btn-navbar.active, +.navbar .btn-navbar.disabled, +.navbar .btn-navbar[disabled] { + background-color: #222222; + *background-color: #151515; +} + +.navbar .btn-navbar:active, +.navbar .btn-navbar.active { + background-color: #080808 \9; +} + +.navbar .btn-navbar .icon-bar { + display: block; + width: 18px; + height: 2px; + background-color: #f5f5f5; + -webkit-border-radius: 1px; + -moz-border-radius: 1px; + border-radius: 1px; + -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); + -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); + box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); +} + +.btn-navbar .icon-bar + .icon-bar { + margin-top: 3px; +} + +.navbar .dropdown-menu:before { + position: absolute; + top: -7px; + left: 9px; + display: inline-block; + border-right: 7px solid transparent; + border-bottom: 7px solid #ccc; + border-left: 7px solid transparent; + border-bottom-color: rgba(0, 0, 0, 0.2); + content: ''; +} + +.navbar .dropdown-menu:after { + position: absolute; + top: -6px; + left: 10px; + display: inline-block; + border-right: 6px solid transparent; + border-bottom: 6px solid #ffffff; + border-left: 6px solid transparent; + content: ''; +} + +.navbar-fixed-bottom .dropdown-menu:before { + top: auto; + bottom: -7px; + border-top: 7px solid #ccc; + border-bottom: 0; + border-top-color: rgba(0, 0, 0, 0.2); +} + +.navbar-fixed-bottom .dropdown-menu:after { + top: auto; + bottom: -6px; + border-top: 6px solid #ffffff; + border-bottom: 0; +} + +.navbar .nav li.dropdown .dropdown-toggle .caret, +.navbar .nav li.dropdown.open .caret { + border-top-color: #ffffff; + border-bottom-color: #ffffff; +} + +.navbar .nav li.dropdown.active .caret { + opacity: 1; + filter: alpha(opacity=100); +} + +.navbar .nav li.dropdown.open > .dropdown-toggle, +.navbar .nav li.dropdown.active > .dropdown-toggle, +.navbar .nav li.dropdown.open.active > .dropdown-toggle { + background-color: transparent; +} + +.navbar .nav li.dropdown.active > .dropdown-toggle:hover { + color: #ffffff; +} + +.navbar .pull-right .dropdown-menu, +.navbar .dropdown-menu.pull-right { + right: 0; + left: auto; +} + +.navbar .pull-right .dropdown-menu:before, +.navbar .dropdown-menu.pull-right:before { + right: 12px; + left: auto; +} + +.navbar .pull-right .dropdown-menu:after, +.navbar .dropdown-menu.pull-right:after { + right: 13px; + left: auto; +} + +.breadcrumb { + padding: 7px 14px; + margin: 0 0 18px; + list-style: none; + background-color: #fbfbfb; + background-image: -moz-linear-gradient(top, #ffffff, #f5f5f5); + background-image: -ms-linear-gradient(top, #ffffff, #f5f5f5); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f5f5f5)); + background-image: -webkit-linear-gradient(top, #ffffff, #f5f5f5); + background-image: -o-linear-gradient(top, #ffffff, #f5f5f5); + background-image: linear-gradient(top, #ffffff, #f5f5f5); + background-repeat: repeat-x; + border: 1px solid #ddd; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ffffff', endColorstr='#f5f5f5', GradientType=0); + -webkit-box-shadow: inset 0 1px 0 #ffffff; + -moz-box-shadow: inset 0 1px 0 #ffffff; + box-shadow: inset 0 1px 0 #ffffff; +} + +.breadcrumb li { + display: inline-block; + *display: inline; + text-shadow: 0 1px 0 #ffffff; + *zoom: 1; +} + +.breadcrumb .divider { + padding: 0 5px; + color: #999999; +} + +.breadcrumb .active a { + color: #333333; +} + +.pagination { + height: 36px; + margin: 18px 0; +} + +.pagination ul { + display: inline-block; + *display: inline; + margin-bottom: 0; + margin-left: 0; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; + *zoom: 1; + -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); +} + +.pagination li { + display: inline; +} + +.pagination a { + float: left; + padding: 0 14px; + line-height: 34px; + text-decoration: none; + border: 1px solid #ddd; + border-left-width: 0; +} + +.pagination a:hover, +.pagination .active a { + background-color: #f5f5f5; +} + +.pagination .active a { + color: #999999; + cursor: default; +} + +.pagination .disabled span, +.pagination .disabled a, +.pagination .disabled a:hover { + color: #999999; + cursor: default; + background-color: transparent; +} + +.pagination li:first-child a { + border-left-width: 1px; + -webkit-border-radius: 3px 0 0 3px; + -moz-border-radius: 3px 0 0 3px; + border-radius: 3px 0 0 3px; +} + +.pagination li:last-child a { + -webkit-border-radius: 0 3px 3px 0; + -moz-border-radius: 0 3px 3px 0; + border-radius: 0 3px 3px 0; +} + +.pagination-centered { + text-align: center; +} + +.pagination-right { + text-align: right; +} + +.pager { + margin-bottom: 18px; + margin-left: 0; + text-align: center; + list-style: none; + *zoom: 1; +} + +.pager:before, +.pager:after { + display: table; + content: ""; +} + +.pager:after { + clear: both; +} + +.pager li { + display: inline; +} + +.pager a { + display: inline-block; + padding: 5px 14px; + background-color: #fff; + border: 1px solid #ddd; + -webkit-border-radius: 15px; + -moz-border-radius: 15px; + border-radius: 15px; +} + +.pager a:hover { + text-decoration: none; + background-color: #f5f5f5; +} + +.pager .next a { + float: right; +} + +.pager .previous a { + float: left; +} + +.pager .disabled a, +.pager .disabled a:hover { + color: #999999; + cursor: default; + background-color: #fff; +} + +.modal-open .dropdown-menu { + z-index: 2050; +} + +.modal-open .dropdown.open { + *z-index: 2050; +} + +.modal-open .popover { + z-index: 2060; +} + +.modal-open .tooltip { + z-index: 2070; +} + +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000000; +} + +.modal-backdrop.fade { + opacity: 0; +} + +.modal-backdrop, +.modal-backdrop.fade.in { + opacity: 0.8; + filter: alpha(opacity=80); +} + +.modal { + position: fixed; + top: 50%; + left: 50%; + z-index: 1050; + width: 560px; + margin: -250px 0 0 -280px; + overflow: auto; + background-color: #ffffff; + border: 1px solid #999; + border: 1px solid rgba(0, 0, 0, 0.3); + *border: 1px solid #999; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -webkit-background-clip: padding-box; + -moz-background-clip: padding-box; + background-clip: padding-box; +} + +.modal.fade { + top: -25%; + -webkit-transition: opacity 0.3s linear, top 0.3s ease-out; + -moz-transition: opacity 0.3s linear, top 0.3s ease-out; + -ms-transition: opacity 0.3s linear, top 0.3s ease-out; + -o-transition: opacity 0.3s linear, top 0.3s ease-out; + transition: opacity 0.3s linear, top 0.3s ease-out; +} + +.modal.fade.in { + top: 50%; +} + +.modal-header { + padding: 9px 15px; + border-bottom: 1px solid #eee; +} + +.modal-header .close { + margin-top: 2px; +} + +.modal-body { + max-height: 400px; + padding: 15px; + overflow-y: auto; +} + +.modal-form { + margin-bottom: 0; +} + +.modal-footer { + padding: 14px 15px 15px; + margin-bottom: 0; + text-align: right; + background-color: #f5f5f5; + border-top: 1px solid #ddd; + -webkit-border-radius: 0 0 6px 6px; + -moz-border-radius: 0 0 6px 6px; + border-radius: 0 0 6px 6px; + *zoom: 1; + -webkit-box-shadow: inset 0 1px 0 #ffffff; + -moz-box-shadow: inset 0 1px 0 #ffffff; + box-shadow: inset 0 1px 0 #ffffff; +} + +.modal-footer:before, +.modal-footer:after { + display: table; + content: ""; +} + +.modal-footer:after { + clear: both; +} + +.modal-footer .btn + .btn { + margin-bottom: 0; + margin-left: 5px; +} + +.modal-footer .btn-group .btn + .btn { + margin-left: -1px; +} + +.tooltip { + position: absolute; + z-index: 1020; + display: block; + padding: 5px; + font-size: 11px; + opacity: 0; + filter: alpha(opacity=0); + visibility: visible; +} + +.tooltip.in { + opacity: 0.8; + filter: alpha(opacity=80); +} + +.tooltip.top { + margin-top: -2px; +} + +.tooltip.right { + margin-left: 2px; +} + +.tooltip.bottom { + margin-top: 2px; +} + +.tooltip.left { + margin-left: -2px; +} + +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-top: 5px solid #000000; + border-right: 5px solid transparent; + border-left: 5px solid transparent; +} + +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-top: 5px solid transparent; + border-bottom: 5px solid transparent; + border-left: 5px solid #000000; +} + +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-right: 5px solid transparent; + border-bottom: 5px solid #000000; + border-left: 5px solid transparent; +} + +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-top: 5px solid transparent; + border-right: 5px solid #000000; + border-bottom: 5px solid transparent; +} + +.tooltip-inner { + max-width: 200px; + padding: 3px 8px; + color: #ffffff; + text-align: center; + text-decoration: none; + background-color: #000000; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; +} + +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1010; + display: none; + padding: 5px; +} + +.popover.top { + margin-top: -5px; +} + +.popover.right { + margin-left: 5px; +} + +.popover.bottom { + margin-top: 5px; +} + +.popover.left { + margin-left: -5px; +} + +.popover.top .arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-top: 5px solid #000000; + border-right: 5px solid transparent; + border-left: 5px solid transparent; +} + +.popover.right .arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-top: 5px solid transparent; + border-right: 5px solid #000000; + border-bottom: 5px solid transparent; +} + +.popover.bottom .arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-right: 5px solid transparent; + border-bottom: 5px solid #000000; + border-left: 5px solid transparent; +} + +.popover.left .arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-top: 5px solid transparent; + border-bottom: 5px solid transparent; + border-left: 5px solid #000000; +} + +.popover .arrow { + position: absolute; + width: 0; + height: 0; +} + +.popover-inner { + width: 280px; + padding: 3px; + overflow: hidden; + background: #000000; + background: rgba(0, 0, 0, 0.8); + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; + -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); + box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3); +} + +.popover-title { + padding: 9px 15px; + line-height: 1; + background-color: #f5f5f5; + border-bottom: 1px solid #eee; + -webkit-border-radius: 3px 3px 0 0; + -moz-border-radius: 3px 3px 0 0; + border-radius: 3px 3px 0 0; +} + +.popover-content { + padding: 14px; + background-color: #ffffff; + -webkit-border-radius: 0 0 3px 3px; + -moz-border-radius: 0 0 3px 3px; + border-radius: 0 0 3px 3px; + -webkit-background-clip: padding-box; + -moz-background-clip: padding-box; + background-clip: padding-box; +} + +.popover-content p, +.popover-content ul, +.popover-content ol { + margin-bottom: 0; +} + +.thumbnails { + margin-left: -20px; + list-style: none; + *zoom: 1; +} + +.thumbnails:before, +.thumbnails:after { + display: table; + content: ""; +} + +.thumbnails:after { + clear: both; +} + +.row-fluid .thumbnails { + margin-left: 0; +} + +.thumbnails > li { + float: left; + margin-bottom: 18px; + margin-left: 20px; +} + +.thumbnail { + display: block; + padding: 4px; + line-height: 1; + border: 1px solid #ddd; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075); + -moz-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: 0 1px 1px rgba(0, 0, 0, 0.075); +} + +a.thumbnail:hover { + border-color: #0088cc; + -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); + -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); + box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25); +} + +.thumbnail > img { + display: block; + max-width: 100%; + margin-right: auto; + margin-left: auto; +} + +.thumbnail .caption { + padding: 9px; +} + +.label, +.badge { + font-size: 10.998px; + font-weight: bold; + line-height: 14px; + color: #ffffff; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + white-space: nowrap; + vertical-align: baseline; + background-color: #999999; +} + +.label { + padding: 1px 4px 2px; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} + +.badge { + padding: 1px 9px 2px; + -webkit-border-radius: 9px; + -moz-border-radius: 9px; + border-radius: 9px; +} + +a.label:hover, +a.badge:hover { + color: #ffffff; + text-decoration: none; + cursor: pointer; +} + +.label-important, +.badge-important { + background-color: #b94a48; +} + +.label-important[href], +.badge-important[href] { + background-color: #953b39; +} + +.label-warning, +.badge-warning { + background-color: #f89406; +} + +.label-warning[href], +.badge-warning[href] { + background-color: #c67605; +} + +.label-success, +.badge-success { + background-color: #468847; +} + +.label-success[href], +.badge-success[href] { + background-color: #356635; +} + +.label-info, +.badge-info { + background-color: #3a87ad; +} + +.label-info[href], +.badge-info[href] { + background-color: #2d6987; +} + +.label-inverse, +.badge-inverse { + background-color: #333333; +} + +.label-inverse[href], +.badge-inverse[href] { + background-color: #1a1a1a; +} + +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-moz-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-ms-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +@-o-keyframes progress-bar-stripes { + from { + background-position: 0 0; + } + to { + background-position: 40px 0; + } +} + +@keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} + +.progress { + height: 18px; + margin-bottom: 18px; + overflow: hidden; + background-color: #f7f7f7; + background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -ms-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9)); + background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9); + background-image: linear-gradient(top, #f5f5f5, #f9f9f9); + background-repeat: repeat-x; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#f5f5f5', endColorstr='#f9f9f9', GradientType=0); + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} + +.progress .bar { + width: 0; + height: 18px; + font-size: 12px; + color: #ffffff; + text-align: center; + text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); + background-color: #0e90d2; + background-image: -moz-linear-gradient(top, #149bdf, #0480be); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be)); + background-image: -webkit-linear-gradient(top, #149bdf, #0480be); + background-image: -o-linear-gradient(top, #149bdf, #0480be); + background-image: linear-gradient(top, #149bdf, #0480be); + background-image: -ms-linear-gradient(top, #149bdf, #0480be); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#149bdf', endColorstr='#0480be', GradientType=0); + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + -ms-box-sizing: border-box; + box-sizing: border-box; + -webkit-transition: width 0.6s ease; + -moz-transition: width 0.6s ease; + -ms-transition: width 0.6s ease; + -o-transition: width 0.6s ease; + transition: width 0.6s ease; +} + +.progress-striped .bar { + background-color: #149bdf; + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + -webkit-background-size: 40px 40px; + -moz-background-size: 40px 40px; + -o-background-size: 40px 40px; + background-size: 40px 40px; +} + +.progress.active .bar { + -webkit-animation: progress-bar-stripes 2s linear infinite; + -moz-animation: progress-bar-stripes 2s linear infinite; + -ms-animation: progress-bar-stripes 2s linear infinite; + -o-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} + +.progress-danger .bar { + background-color: #dd514c; + background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -ms-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35)); + background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35); + background-image: -o-linear-gradient(top, #ee5f5b, #c43c35); + background-image: linear-gradient(top, #ee5f5b, #c43c35); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#ee5f5b', endColorstr='#c43c35', GradientType=0); +} + +.progress-danger.progress-striped .bar { + background-color: #ee5f5b; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-success .bar { + background-color: #5eb95e; + background-image: -moz-linear-gradient(top, #62c462, #57a957); + background-image: -ms-linear-gradient(top, #62c462, #57a957); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957)); + background-image: -webkit-linear-gradient(top, #62c462, #57a957); + background-image: -o-linear-gradient(top, #62c462, #57a957); + background-image: linear-gradient(top, #62c462, #57a957); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#62c462', endColorstr='#57a957', GradientType=0); +} + +.progress-success.progress-striped .bar { + background-color: #62c462; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-info .bar { + background-color: #4bb1cf; + background-image: -moz-linear-gradient(top, #5bc0de, #339bb9); + background-image: -ms-linear-gradient(top, #5bc0de, #339bb9); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9)); + background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9); + background-image: -o-linear-gradient(top, #5bc0de, #339bb9); + background-image: linear-gradient(top, #5bc0de, #339bb9); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#5bc0de', endColorstr='#339bb9', GradientType=0); +} + +.progress-info.progress-striped .bar { + background-color: #5bc0de; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.progress-warning .bar { + background-color: #faa732; + background-image: -moz-linear-gradient(top, #fbb450, #f89406); + background-image: -ms-linear-gradient(top, #fbb450, #f89406); + background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406)); + background-image: -webkit-linear-gradient(top, #fbb450, #f89406); + background-image: -o-linear-gradient(top, #fbb450, #f89406); + background-image: linear-gradient(top, #fbb450, #f89406); + background-repeat: repeat-x; + filter: progid:dximagetransform.microsoft.gradient(startColorstr='#fbb450', endColorstr='#f89406', GradientType=0); +} + +.progress-warning.progress-striped .bar { + background-color: #fbb450; + background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent)); + background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} + +.accordion { + margin-bottom: 18px; +} + +.accordion-group { + margin-bottom: 2px; + border: 1px solid #e5e5e5; + -webkit-border-radius: 4px; + -moz-border-radius: 4px; + border-radius: 4px; +} + +.accordion-heading { + border-bottom: 0; +} + +.accordion-heading .accordion-toggle { + display: block; + padding: 8px 15px; +} + +.accordion-toggle { + cursor: pointer; +} + +.accordion-inner { + padding: 9px 15px; + border-top: 1px solid #e5e5e5; +} + +.carousel { + position: relative; + margin-bottom: 18px; + line-height: 1; +} + +.carousel-inner { + position: relative; + width: 100%; + overflow: hidden; +} + +.carousel .item { + position: relative; + display: none; + -webkit-transition: 0.6s ease-in-out left; + -moz-transition: 0.6s ease-in-out left; + -ms-transition: 0.6s ease-in-out left; + -o-transition: 0.6s ease-in-out left; + transition: 0.6s ease-in-out left; +} + +.carousel .item > img { + display: block; + line-height: 1; +} + +.carousel .active, +.carousel .next, +.carousel .prev { + display: block; +} + +.carousel .active { + left: 0; +} + +.carousel .next, +.carousel .prev { + position: absolute; + top: 0; + width: 100%; +} + +.carousel .next { + left: 100%; +} + +.carousel .prev { + left: -100%; +} + +.carousel .next.left, +.carousel .prev.right { + left: 0; +} + +.carousel .active.left { + left: -100%; +} + +.carousel .active.right { + left: 100%; +} + +.carousel-control { + position: absolute; + top: 40%; + left: 15px; + width: 40px; + height: 40px; + margin-top: -20px; + font-size: 60px; + font-weight: 100; + line-height: 30px; + color: #ffffff; + text-align: center; + background: #222222; + border: 3px solid #ffffff; + -webkit-border-radius: 23px; + -moz-border-radius: 23px; + border-radius: 23px; + opacity: 0.5; + filter: alpha(opacity=50); +} + +.carousel-control.right { + right: 15px; + left: auto; +} + +.carousel-control:hover { + color: #ffffff; + text-decoration: none; + opacity: 0.9; + filter: alpha(opacity=90); +} + +.carousel-caption { + position: absolute; + right: 0; + bottom: 0; + left: 0; + padding: 10px 15px 5px; + background: #333333; + background: rgba(0, 0, 0, 0.75); +} + +.carousel-caption h4, +.carousel-caption p { + color: #ffffff; +} + +.hero-unit { + padding: 60px; + margin-bottom: 30px; + background-color: #eeeeee; + -webkit-border-radius: 6px; + -moz-border-radius: 6px; + border-radius: 6px; +} + +.hero-unit h1 { + margin-bottom: 0; + font-size: 60px; + line-height: 1; + letter-spacing: -1px; + color: inherit; +} + +.hero-unit p { + font-size: 18px; + font-weight: 200; + line-height: 27px; + color: inherit; +} + +.pull-right { + float: right; +} + +.pull-left { + float: left; +} + +.hide { + display: none; +} + +.show { + display: block; +} + +.invisible { + visibility: hidden; +} diff --git a/inst/web/css/bootstrap.min.css b/inst/web/css/bootstrap.min.css new file mode 100644 index 00000000..b74b4546 --- /dev/null +++ b/inst/web/css/bootstrap.min.css @@ -0,0 +1,9 @@ +/*! + * Bootstrap v2.0.4 + * + * Copyright 2012 Twitter, Inc + * Licensed under the Apache License v2.0 + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Designed and built with all the love in the world @twitter by @mdo and @fat. + 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+ul.index li {margin-bottom: 0.5em; clear: both;} + +footer { + margin-top: 45px; + padding: 35px 0 36px; + border-top: 1px solid #e5e5e5; +} +footer p { + margin-bottom: 0; + color: #555; +} diff --git a/inst/web/endpoints.html b/inst/web/endpoints.html new file mode 100644 index 00000000..458d359c --- /dev/null +++ b/inst/web/endpoints.html @@ -0,0 +1,118 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>endpoints. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> +Function to calculate endpoints for further use from kinetic models fitted with mkinfit +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>endpoints(fit)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>fit</dt> + <dd> + An object of class <code><a href='mkinfit.html'>mkinfit</a></code>. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function calculates DT50 and DT90 values as well as formation fractions from kinetic models +fitted with mkinfit. If the SFORB model was specified for one of the parents or metabolites, +the Eigenvalues are returned. These are equivalent to the rate constantes of the DFOP model, but +with the advantage that the SFORB model can also be used for metabolites.</p> + + </div> + + <div class="Note"> + <h2>Note</h2> + + <p>The function is used internally by <code><a href='summary.mkinfit.html'>summary.mkinfit</a></code>.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + A list with the components mentioned above. +</dl></p> + + </div> + </div> + <div class="span4"> + <!-- <ul> + <li>endpoints</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/geometric_mean.html b/inst/web/geometric_mean.html new file mode 100644 index 00000000..93962dca --- /dev/null +++ b/inst/web/geometric_mean.html @@ -0,0 +1,113 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>geometric_mean. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> Calculate the geometric mean </h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>geometric_mean(x, na.rm = FALSE)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>x</dt> + <dd> A numeric vector </dd> + <dt>na.rm</dt> + <dd> Should NA values be ignored </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Function calculating the geometric mean of numeric vectors</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The geometric mean. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> geometric_mean(c(1,3, 9)) +</div> +<div class='output'>[1] 3 +</div> +<div class='input'> geometric_mean(c(1,3, NA)) +</div> +<div class='output'>[1] NA +</div> +<div class='input'> geometric_mean(c(1,3, NA), na.rm = TRUE) +</div> +<div class='output'>[1] 1.732051 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>geometric_mean</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/ilr.html b/inst/web/ilr.html new file mode 100644 index 00000000..07076e73 --- /dev/null +++ b/inst/web/ilr.html @@ -0,0 +1,176 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>ilr. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + René Lehmann and Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Function to perform isometric log-ratio transformation +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>ilr(x) invilr(x)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>x</dt> + <dd> + A numeric vector. Naturally, the forward transformation is only sensible for + vectors with all elements being greater than zero. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This implementation is a special case of the class of isometric log-ratio transformations.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The result of the forward or backward transformation. The returned components always + sum to 1 for the case of the inverse log-ratio transformation. +</dl></p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>Peter Filzmoser, Karel Hron (2008) Outlier Detection for Compositional Data Using Robust Methods. Math Geosci 40 233-248</p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'># Order matters +ilr(c(0.1, 1, 10)) +</div> +<div class='output'>[1] -1.628174 -2.820079 +</div> +<div class='input'>ilr(c(10, 1, 0.1)) +</div> +<div class='output'>[1] 1.628174 2.820079 +</div> +<div class='input'># Equal entries give ilr transformations with zeros as elements +ilr(c(3, 3, 3)) +</div> +<div class='output'>[1] 0 0 +</div> +<div class='input'># Almost equal entries give small numbers +ilr(c(0.3, 0.4, 0.3)) +</div> +<div class='output'>[1] -0.2034219 0.1174457 +</div> +<div class='input'># Only the ratio between the numbers counts, not their sum +invilr(ilr(c(0.7, 0.29, 0.01))) +</div> +<div class='output'>[1] 0.70 0.29 0.01 +</div> +<div class='input'>invilr(ilr(2.1 * c(0.7, 0.29, 0.01))) +</div> +<div class='output'>[1] 0.70 0.29 0.01 +</div> +<div class='input'># Inverse transformation of larger numbers gives unequal elements +invilr(-10) +</div> +<div class='output'>[1] 7.213536e-07 9.999993e-01 +</div> +<div class='input'>invilr(c(-10, 0)) +</div> +<div class='output'>[1] 7.207415e-07 9.991507e-01 8.486044e-04 +</div> +<div class='input'># The sum of the elements of the inverse ilr is 1 +sum(invilr(c(-10, 0))) +</div> +<div class='output'>[1] 1 +</div> +<div class='input'># This is why we do not need all elements of the inverse transformation to go back: +a <- c(0.1, 0.3, 0.5) +b <- invilr(a) +length(b) # Four elements +</div> +<div class='output'>[1] 4 +</div> +<div class='input'>ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5) +</div> +<div class='output'>[1] 0.1 0.3 0.5 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>ilr</li><li>invilr</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + <h2>See also</h2> + + Another implementation can be found in R package <code>robCompositions</code>. + + + <h2>Author</h2> + + René Lehmann and Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/img/glyphicons-halflings-white.png b/inst/web/img/glyphicons-halflings-white.png Binary files differnew file mode 100644 index 00000000..3bf6484a --- /dev/null +++ b/inst/web/img/glyphicons-halflings-white.png diff --git a/inst/web/img/glyphicons-halflings.png b/inst/web/img/glyphicons-halflings.png Binary files differnew file mode 100644 index 00000000..79bc568c --- /dev/null +++ b/inst/web/img/glyphicons-halflings.png diff --git a/inst/web/index.html b/inst/web/index.html new file mode 100644 index 00000000..a08af34f --- /dev/null +++ b/inst/web/index.html @@ -0,0 +1,452 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>Index. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <div class="row"> + <div class="span8"> + <h1>mkin</h1> + +<p><a href="http://cran.rstudio.com/web/packages/mkin/index.html"><img src="http://www.r-pkg.org/badges/version/mkin" alt=""/></a></p> + +<p>The R package <strong>mkin</strong> provides calculation routines for the analysis of +chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as +needed for modelling the formation and decline of transformation products, or +if several compartments are involved.</p> + +<h2>Installation</h2> + +<p>You can install the latest released version from +<a href="http://cran.r-project.org/package=mkin">CRAN</a> from within R:</p> + +<pre><code class="r">install.packages("mkin") +</code></pre> + +<p>If looking for the latest features, you can install directly from +<a href="http://github.com/jranke/mkin">github</a>, e.g. using the <code>devtools</code> package. +Using <code>quick = TRUE</code> skips docs, multiple-architecture builds, demos, and +vignettes, to make installation as fast and painless as possible.</p> + +<pre><code class="r">require(devtools) +install_github("jranke/mkin", quick = TRUE) +</code></pre> + +<h2>Background</h2> + +<p>In the regulatory evaluation of chemical substances like plant protection +products (pesticides), biocides and other chemicals, degradation data play an +important role. For the evaluation of pesticide degradation experiments, +detailed guidance and helpful tools have been developed as detailed in +'Credits and historical remarks' below.</p> + +<h2>Usage</h2> + +<p>For a start, have a look a the code examples provided for +<a href="http://kinfit.r-forge.r-project.org/mkin_static/plot.mkinfit.html"><code>plot.mkinfit</code></a> +and +<a href="http://kinfit.r-forge.r-project.org/mkin_static/plot.mmkin.html"><code>plot.mmkin</code></a>, and +at the package vignettes +<a href="http://kinfit.r-forge.r-project.org/mkin_static/vignettes/FOCUS_L.html"><code>FOCUS L</code></a> and +<a href="http://kinfit.r-forge.r-project.org/mkin_static/vignettes/FOCUS_D.html"><code>FOCUS D</code></a>.</p> + +<h2>Documentation</h2> + +<p>The <a href="http://kinfit.r-forge.r-project.org/mkin_static">HTML documentation</a> is +maintained at the R-Forge project site.</p> + +<h2>Features</h2> + +<ul> +<li>Highly flexible model specification using +<a href="http://kinfit.r-forge.r-project.org/mkin_static/mkinmod.html"><code>mkinmod</code></a>, +including equilibrium reactions and using the single first-order +reversible binding (SFORB) model, which will automatically create +two latent state variables for the observed variable.</li> +<li>As of version 0.9-39, fitting of several models to several datasets, optionally in +parallel, is supported, see for example +<a href="http://kinfit.r-forge.r-project.org/mkin_static/plot.mmkin.html"><code>plot.mmkin</code></a> </li> +<li>Model solution (forward modelling) in the function +<a href="http://kinfit.r-forge.r-project.org/mkin_static/mkinpredict.html"><code>mkinpredict</code></a> +is performed either using the analytical solution for the case of +parent only degradation, an eigenvalue based solution if only simple +first-order (SFO) or SFORB kinetics are used in the model, or +using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> +<li>If a C compiler is installed, the kinetic models are compiled from automatically +generated C code, see<br/> +<a href="http://kinfit.r-forge.r-project.org/mkin_static/vignettes/compiled_models.html">vignette <code>compiled_models</code></a>. +The autogeneration of C code was +inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks +to Karline Soetaert for her work on that.</li> +<li>Model optimisation with +<a href="http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html"><code>mkinfit</code></a> +internally using the <code>modFit</code> function from the <code>FME</code> package, +but using the Port routine <code>nlminb</code> per default.</li> +<li>By default, kinetic rate constants and kinetic formation fractions are +transformed internally using +<a href="http://kinfit.r-forge.r-project.org/mkin_static/transform_odeparms.html"><code>transform_odeparms</code></a> +so their estimators can more reasonably be expected to follow +a normal distribution. This has the side effect that no constraints +are needed in the optimisation. Thanks to René Lehmann for the nice +cooperation on this, especially the isometric logration transformation +that is now used for the formation fractions.</li> +<li>A side effect of this is that when parameter estimates are backtransformed +to match the model definition, confidence intervals calculated from +standard errors are also backtransformed to the correct scale, and will +not include meaningless values like negative rate constants or +formation fractions adding up to more than 1, which can not occur in +a single experiment with a single defined radiolabel position.</li> +<li>The usual one-sided t-test for significant difference from zero is nevertheless +shown based on estimators for the untransformed parameters.</li> +<li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in +fact a full report that should give enough information to be able to +approximately reproduce the fit with other tools.</li> +<li>The chi-squared error level as defined in the FOCUS kinetics guidance +(see below) is calculated for each observed variable.</li> +<li>Iteratively reweighted least squares fitting is implemented in a similar way +as in KinGUII and CAKE (see below). Simply add the argument +<code>reweight = "obs"</code> to your call to <code>mkinfit</code> and a separate variance +componenent for each of the observed variables will be optimised +in a second stage after the primary optimisation algorithm has converged.</li> +<li>When a metabolite decline phase is not described well by SFO kinetics, +either IORE kinetics (often producing failures of the integration algorithm) +or SFORB kinetics (working nicely) can be used for the metabolite, adding one +respectively two parameters to the system.</li> +</ul> + +<h2>GUI</h2> + +<p>There is a graphical user interface that I consider useful for real work. Please +refer to its <a href="http://kinfit.r-forge.r-project.org/gmkin_static">documentation page</a> +for installation instructions and a manual.</p> + +<h2>News</h2> + +<p>Yes, there is a ChangeLog, for the latest <a href="http://cran.r-project.org/web/packages/mkin/news.html">CRAN release</a> +and one for the <a href="https://github.com/jranke/mkin/blob/master/NEWS.md">github master branch</a>.</p> + +<h2>Credits and historical remarks</h2> + +<p><code>mkin</code> would not be possible without the underlying software stack consisting +of R and the packages <a href="http://cran.r-project.org/package=deSolve">deSolve</a>, +<a href="http://cran.r-project.org/package=minpack.lm">minpack.lm</a> and +<a href="http://cran.r-project.org/package=FME">FME</a>, to say the least.</p> + +<p>It could not have been written without me being introduced to regulatory fate +modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories +Ltd (formerly RCC Ltd). <code>mkin</code> greatly profits from and largely follows +the work done by the +<a href="http://focus.jrc.ec.europa.eu/dk">FOCUS Degradation Kinetics Workgroup</a>, +as detailed in their guidance document from 2006, slightly updated in 2011 and +2014.</p> + +<p>Also, it was inspired by the first version of KinGUI developed by +BayerCropScience, which is based on the MatLab runtime environment.</p> + +<p>The companion package +<a href="http://kinfit.r-forge.r-project.org/kinfit_static/index.html">kinfit</a> was +<a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=2">started in 2008</a> and +<a href="http://cran.r-project.org/src/contrib/Archive/kinfit/">first published</a> on +CRAN on 01 May 2010.</p> + +<p>The first <code>mkin</code> code was +<a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8">published on 11 May 2010</a> and the +<a href="http://cran.r-project.org/src/contrib/Archive/mkin">first CRAN version</a> +on 18 May 2010.</p> + +<p>In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named +KinGUII whose R code is based on <code>mkin</code>, but which added, amongst other +refinements, a closed source graphical user interface (GUI), iteratively +reweighted least squares (IRLS) optimisation of the variance for each of the +observed variables, and Markov Chain Monte Carlo (MCMC) simulation +functionality, similar to what is available e.g. in the <code>FME</code> package.</p> + +<p>Somewhat in parallel, Syngenta has sponsored the development of an <code>mkin</code> and +KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is +more limited in the model formulation, but puts more weight on usability. +CAKE is available for download from the <a href="http://projects.tessella.com/cake">CAKE +website</a>, where you can also +find a zip archive of the R scripts derived from <code>mkin</code>, published under the GPL +license.</p> + +<p>Finally, there is +<a href="http://github.com/zhenglei-gao/KineticEval">KineticEval</a>, which contains +a further development of the scripts used for KinGUII, so the different tools +will hopefully be able to learn from each other in the future as well.</p> + +<h2>Development</h2> + +<p>Contributions are welcome! Your +<a href="https://help.github.com/articles/fork-a-repo">mkin fork</a> is just a mouse click +away… The master branch on github should always be in good shape, I implement +new features in separate branches now. If you prefer subversion, project +members for the +<a href="http://r-forge.r-project.org/R/?group_id=615">r-forge project</a> are welcome as well. +Generally, the source code of the latest CRAN version should be available there. +You can also browse the source code at <a href="http://cgit.jrwb.de/mkin">cgit.jrwb.de/mkin</a>.</p> + + + <h2>Help topics</h2> + + <h3>Main functions</h3> + <p>Essential functionality</p> + + + <ul class="index"> + + <li> + <code><a href="mkinmod.html">mkinmod</a></code><br /> Function to set up a kinetic model with one or more state variables +</li> + + <li> + <code><a href="mkinfit.html">mkinfit</a></code><br /> Fit a kinetic model to data with one or more state variables +</li> + + <li> + <code><a href="mmkin.html">mmkin</a></code><br /> Fit one or more kinetic models with one or more state variables to one or more datasets +</li> + + </ul> + <h3>Show results</h3> + <p>Functions working on mkinfit objects</p> + + + <ul class="index"> + + <li> + <code><a href="plot.mkinfit.html">plot.mkinfit</a></code><br /> Plot the observed data and the fitted model of an mkinfit object +</li> + + <li> + <code><a href="summary.mkinfit.html">summary.mkinfit</a></code>(print.summary.mkinfit)<br /> Summary method for class "mkinfit" +</li> + + <li> + <code><a href="mkinresplot.html">mkinresplot</a></code><br /> Function to plot residuals stored in an mkin object +</li> + + <li> + <code><a href="mkinparplot.html">mkinparplot</a></code><br /> Function to plot the confidence intervals obtained using +</li> + + <li> + <code><a href="endpoints.html">endpoints</a></code><br />Function to calculate endpoints for further use from kinetic models fitted with mkinfit +</li> + + <li> + <code><a href="mkinerrmin.html">mkinerrmin</a></code><br />Calculate the minimum error to assume in order to pass the variance test +</li> + + </ul> + <h3>Work with mmkin objects</h3> + <p>Functions working with aggregated results</p> + + + <ul class="index"> + + <li> + <code><a href="Extract.mmkin.html">[.mmkin</a></code><br />Subsetting method for mmkin objects</li> + + <li> + <code><a href="plot.mmkin.html">plot.mmkin</a></code><br /> Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object +</li> + + </ul> + <h3>Datasets and known results</h3> + + + <ul class="index"> + + <li> + <code><a href="FOCUS_2006_datasets.html">FOCUS_2006_datasets</a></code>(FOCUS_2006_A, FOCUS_2006_B, FOCUS_2006_C, FOCUS_2006_D, FOCUS_2006_E, FOCUS_2006_F)<br />Datasets A to F from the FOCUS Kinetics report from 2006 +</li> + + <li> + <code><a href="FOCUS_2006_SFO_ref_A_to_F.html">FOCUS_2006_SFO_ref_A_to_F</a></code><br />Results of fitting the SFO model to Datasets A to F of FOCUS (2006) +</li> + + <li> + <code><a href="FOCUS_2006_FOMC_ref_A_to_F.html">FOCUS_2006_FOMC_ref_A_to_F</a></code><br />Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) +</li> + + <li> + <code><a href="FOCUS_2006_HS_ref_A_to_F.html">FOCUS_2006_HS_ref_A_to_F</a></code><br />Results of fitting the HS model to Datasets A to F of FOCUS (2006) +</li> + + <li> + <code><a href="FOCUS_2006_DFOP_ref_A_to_B.html">FOCUS_2006_DFOP_ref_A_to_B</a></code><br />Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) +</li> + + <li> + <code><a href="mccall81_245T.html">mccall81_245T</a></code><br /> Datasets on aerobic soil metabolism of 2,4,5-T in six soils +</li> + + <li> + <code><a href="schaefer07_complex_case.html">schaefer07_complex_case</a></code>(schaefer07_complex_results)<br /> Metabolism data set used for checking the software quality of KinGUI +</li> + + <li> + <code><a href="synthetic_data_for_UBA.html">synthetic_data_for_UBA_2014</a></code><br /> Synthetic datasets for one parent compound with two metabolites +</li> + + </ul> + <h3>Helper functions</h3> + + + <ul class="index"> + + <li> + <code><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></code><br /> Convert a dataframe with observations over time into long format +</li> + + <li> + <code><a href="mkin_long_to_wide.html">mkin_long_to_wide</a></code><br /> Convert a dataframe from long to wide format +</li> + + <li> + <code><a href="mkinsub.html">mkinsub</a></code><br /> Function to set up a kinetic submodel for one state variable +</li> + + <li> + <code><a href="mkinds.html">mkinds</a></code><br />A dataset class for mkin</li> + + <li> + <code><a href="print.mkinds.html">print.mkinds</a></code><br /> Print mkinds objects +</li> + + <li> + <code><a href="print.mkinmod.html">print.mkinmod</a></code><br /> Print mkinmod objects +</li> + + <li> + <code><a href="mkinpredict.html">mkinpredict</a></code><br /> Produce predictions from a kinetic model using specifc parameters +</li> + + <li> + <code><a href="transform_odeparms.html">transform_odeparms</a></code>(backtransform_odeparms)<br /> Functions to transform and backtransform kinetic parameters for fitting +</li> + + <li> + <code><a href="ilr.html">ilr</a></code>(invilr)<br /> Function to perform isometric log-ratio transformation +</li> + + <li> + <code><a href="geometric_mean.html">geometric_mean</a></code><br /> Calculate the geometric mean </li> + + </ul> + <h3>Analytical solutions</h3> + <p>Parent only model solutions</p> + + + <ul class="index"> + + <li> + <code><a href="SFO.solution.html">SFO.solution</a></code><br /> Single First-Order kinetics </li> + + <li> + <code><a href="FOMC.solution.html">FOMC.solution</a></code><br /> First-Order Multi-Compartment kinetics </li> + + <li> + <code><a href="DFOP.solution.html">DFOP.solution</a></code><br />Double First-Order in Parallel kinetics +</li> + + <li> + <code><a href="SFORB.solution.html">SFORB.solution</a></code><br /> Single First-Order Reversible Binding kinetics </li> + + <li> + <code><a href="HS.solution.html">HS.solution</a></code><br /> Hockey-Stick kinetics </li> + + <li> + <code><a href="IORE.solution.html">IORE.solution</a></code><br /> Indeterminate order rate equation kinetics </li> + + </ul> + <h3>Deprecated functions</h3> + <p>Functions that have been superseeded</p> + + + <ul class="index"> + + <li> + <code><a href="mkinplot.html">mkinplot</a></code><br /> Plot the observed data and the fitted model of an mkinfit object +</li> + + </ul> + </div> + + <div class="span3 offset1"> + <h2>Vignettes</h2> + <ul> + <li><a href="vignettes/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></li> + <li><a href="vignettes/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></li> + <li><a href="vignettes/FOCUS_Z.pdf">Example evaluation of FOCUS dataset Z</a></li> + <li><a href="vignettes/mkin.pdf">Routines for fitting kinetic models with one or more state variables to chemical degradation data</a></li> + </ul> + + <h2>Dependencies</h2> + <ul> + <li><strong>Depends</strong>: minpack.lm, rootSolve, inline, parallel</li> + <li><strong>Imports</strong>: stats, graphics, methods, FME, deSolve, R6</li> + <li><strong>Suggests</strong>: knitr, testthat, microbenchmark</li> + + </ul> + <h2>Authors</h2> + <ul> + <li><a href="mailto:jranke@uni-bremen.de">Johannes Ranke</a> [aut, cre, cph]</li> + <li>Katrin Lindenberger [ctb]</li> + <li>René Lehmann [ctb]</li> + <li>Eurofins Regulatory AG [cph]</li> + </ul> + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
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'width' : 'height' + } + + , show: function () { + var dimension + , scroll + , actives + , hasData + + if (this.transitioning) return + + dimension = this.dimension() + scroll = $.camelCase(['scroll', dimension].join('-')) + actives = this.$parent && this.$parent.find('> .accordion-group > .in') + + if (actives && actives.length) { + hasData = actives.data('collapse') + if (hasData && hasData.transitioning) return + actives.collapse('hide') + hasData || actives.data('collapse', null) + } + + this.$element[dimension](0) + this.transition('addClass', $.Event('show'), 'shown') + this.$element[dimension](this.$element[0][scroll]) + } + + , hide: function () { + var dimension + if (this.transitioning) return + dimension = this.dimension() + this.reset(this.$element[dimension]()) + this.transition('removeClass', $.Event('hide'), 'hidden') + this.$element[dimension](0) + } + + , reset: function (size) { + var dimension = this.dimension() + + this.$element + .removeClass('collapse') + [dimension](size || 'auto') + [0].offsetWidth + + this.$element[size !== null ? 'addClass' : 'removeClass']('collapse') + + return this + } + + , transition: function (method, startEvent, completeEvent) { + var that = this + , complete = function () { + if (startEvent.type == 'show') that.reset() + that.transitioning = 0 + that.$element.trigger(completeEvent) + } + + this.$element.trigger(startEvent) + + if (startEvent.isDefaultPrevented()) return + + this.transitioning = 1 + + this.$element[method]('in') + + $.support.transition && this.$element.hasClass('collapse') ? + this.$element.one($.support.transition.end, complete) : + complete() + } + + , toggle: function () { + this[this.$element.hasClass('in') ? 'hide' : 'show']() + } + + } + + + /* COLLAPSIBLE PLUGIN DEFINITION + * ============================== */ + + $.fn.collapse = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('collapse') + , options = typeof option == 'object' && option + if (!data) $this.data('collapse', (data = new Collapse(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.collapse.defaults = { + toggle: true + } + + $.fn.collapse.Constructor = Collapse + + + /* COLLAPSIBLE DATA-API + * ==================== */ + + $(function () { + $('body').on('click.collapse.data-api', '[data-toggle=collapse]', function ( e ) { + var $this = $(this), href + , target = $this.attr('data-target') + || e.preventDefault() + || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7 + , option = $(target).data('collapse') ? 'toggle' : $this.data() + $(target).collapse(option) + }) + }) + +}(window.jQuery);/* ============================================================ + * bootstrap-dropdown.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#dropdowns + * ============================================================ + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* DROPDOWN CLASS DEFINITION + * ========================= */ + + var toggle = '[data-toggle="dropdown"]' + , Dropdown = function (element) { + var $el = $(element).on('click.dropdown.data-api', this.toggle) + $('html').on('click.dropdown.data-api', function () { + $el.parent().removeClass('open') + }) + } + + Dropdown.prototype = { + + constructor: Dropdown + + , toggle: function (e) { + var $this = $(this) + , $parent + , selector + , isActive + + if ($this.is('.disabled, :disabled')) return + + selector = $this.attr('data-target') + + if (!selector) { + selector = $this.attr('href') + selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 + } + + $parent = $(selector) + $parent.length || ($parent = $this.parent()) + + isActive = $parent.hasClass('open') + + clearMenus() + + if (!isActive) $parent.toggleClass('open') + + return false + } + + } + + function clearMenus() { + $(toggle).parent().removeClass('open') + } + + + /* DROPDOWN PLUGIN DEFINITION + * ========================== */ + + $.fn.dropdown = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('dropdown') + if (!data) $this.data('dropdown', (data = new Dropdown(this))) + if (typeof option == 'string') data[option].call($this) + }) + } + + $.fn.dropdown.Constructor = Dropdown + + + /* APPLY TO STANDARD DROPDOWN ELEMENTS + * =================================== */ + + $(function () { + $('html').on('click.dropdown.data-api', clearMenus) + $('body') + .on('click.dropdown', '.dropdown form', function (e) { e.stopPropagation() }) + .on('click.dropdown.data-api', toggle, Dropdown.prototype.toggle) + }) + +}(window.jQuery);/* ========================================================= + * bootstrap-modal.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#modals + * ========================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================= */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* MODAL CLASS DEFINITION + * ====================== */ + + var Modal = function (content, options) { + this.options = options + this.$element = $(content) + .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this)) + } + + Modal.prototype = { + + constructor: Modal + + , toggle: function () { + return this[!this.isShown ? 'show' : 'hide']() + } + + , show: function () { + var that = this + , e = $.Event('show') + + this.$element.trigger(e) + + if (this.isShown || e.isDefaultPrevented()) return + + $('body').addClass('modal-open') + + this.isShown = true + + escape.call(this) + backdrop.call(this, function () { + var transition = $.support.transition && that.$element.hasClass('fade') + + if (!that.$element.parent().length) { + that.$element.appendTo(document.body) //don't move modals dom position + } + + that.$element + .show() + + if (transition) { + that.$element[0].offsetWidth // force reflow + } + + that.$element.addClass('in') + + transition ? + that.$element.one($.support.transition.end, function () { that.$element.trigger('shown') }) : + that.$element.trigger('shown') + + }) + } + + , hide: function (e) { + e && e.preventDefault() + + var that = this + + e = $.Event('hide') + + this.$element.trigger(e) + + if (!this.isShown || e.isDefaultPrevented()) return + + this.isShown = false + + $('body').removeClass('modal-open') + + escape.call(this) + + this.$element.removeClass('in') + + $.support.transition && this.$element.hasClass('fade') ? + hideWithTransition.call(this) : + hideModal.call(this) + } + + } + + + /* MODAL PRIVATE METHODS + * ===================== */ + + function hideWithTransition() { + var that = this + , timeout = setTimeout(function () { + that.$element.off($.support.transition.end) + hideModal.call(that) + }, 500) + + this.$element.one($.support.transition.end, function () { + clearTimeout(timeout) + hideModal.call(that) + }) + } + + function hideModal(that) { + this.$element + .hide() + .trigger('hidden') + + backdrop.call(this) + } + + function backdrop(callback) { + var that = this + , animate = this.$element.hasClass('fade') ? 'fade' : '' + + if (this.isShown && this.options.backdrop) { + var doAnimate = $.support.transition && animate + + this.$backdrop = $('<div class="modal-backdrop ' + animate + '" />') + .appendTo(document.body) + + if (this.options.backdrop != 'static') { + this.$backdrop.click($.proxy(this.hide, this)) + } + + if (doAnimate) this.$backdrop[0].offsetWidth // force reflow + + this.$backdrop.addClass('in') + + doAnimate ? + this.$backdrop.one($.support.transition.end, callback) : + callback() + + } else if (!this.isShown && this.$backdrop) { + this.$backdrop.removeClass('in') + + $.support.transition && this.$element.hasClass('fade')? + this.$backdrop.one($.support.transition.end, $.proxy(removeBackdrop, this)) : + removeBackdrop.call(this) + + } else if (callback) { + callback() + } + } + + function removeBackdrop() { + this.$backdrop.remove() + this.$backdrop = null + } + + function escape() { + var that = this + if (this.isShown && this.options.keyboard) { + $(document).on('keyup.dismiss.modal', function ( e ) { + e.which == 27 && that.hide() + }) + } else if (!this.isShown) { + $(document).off('keyup.dismiss.modal') + } + } + + + /* MODAL PLUGIN DEFINITION + * ======================= */ + + $.fn.modal = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('modal') + , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option) + if (!data) $this.data('modal', (data = new Modal(this, options))) + if (typeof option == 'string') data[option]() + else if (options.show) data.show() + }) + } + + $.fn.modal.defaults = { + backdrop: true + , keyboard: true + , show: true + } + + $.fn.modal.Constructor = Modal + + + /* MODAL DATA-API + * ============== */ + + $(function () { + $('body').on('click.modal.data-api', '[data-toggle="modal"]', function ( e ) { + var $this = $(this), href + , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 + , option = $target.data('modal') ? 'toggle' : $.extend({}, $target.data(), $this.data()) + + e.preventDefault() + $target.modal(option) + }) + }) + +}(window.jQuery);/* =========================================================== + * bootstrap-tooltip.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#tooltips + * Inspired by the original jQuery.tipsy by Jason Frame + * =========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* TOOLTIP PUBLIC CLASS DEFINITION + * =============================== */ + + var Tooltip = function (element, options) { + this.init('tooltip', element, options) + } + + Tooltip.prototype = { + + constructor: Tooltip + + , init: function (type, element, options) { + var eventIn + , eventOut + + this.type = type + this.$element = $(element) + this.options = this.getOptions(options) + this.enabled = true + + if (this.options.trigger != 'manual') { + eventIn = this.options.trigger == 'hover' ? 'mouseenter' : 'focus' + eventOut = this.options.trigger == 'hover' ? 'mouseleave' : 'blur' + this.$element.on(eventIn, this.options.selector, $.proxy(this.enter, this)) + this.$element.on(eventOut, this.options.selector, $.proxy(this.leave, this)) + } + + this.options.selector ? + (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) : + this.fixTitle() + } + + , getOptions: function (options) { + options = $.extend({}, $.fn[this.type].defaults, options, this.$element.data()) + + if (options.delay && typeof options.delay == 'number') { + options.delay = { + show: options.delay + , hide: options.delay + } + } + + return options + } + + , enter: function (e) { + var self = $(e.currentTarget)[this.type](this._options).data(this.type) + + if (!self.options.delay || !self.options.delay.show) return self.show() + + clearTimeout(this.timeout) + self.hoverState = 'in' + this.timeout = setTimeout(function() { + if (self.hoverState == 'in') self.show() + }, self.options.delay.show) + } + + , leave: function (e) { + var self = $(e.currentTarget)[this.type](this._options).data(this.type) + + if (this.timeout) clearTimeout(this.timeout) + if (!self.options.delay || !self.options.delay.hide) return self.hide() + + self.hoverState = 'out' + this.timeout = setTimeout(function() { + if (self.hoverState == 'out') self.hide() + }, self.options.delay.hide) + } + + , show: function () { + var $tip + , inside + , pos + , actualWidth + , actualHeight + , placement + , tp + + if (this.hasContent() && this.enabled) { + $tip = this.tip() + this.setContent() + + if (this.options.animation) { + $tip.addClass('fade') + } + + placement = typeof this.options.placement == 'function' ? + this.options.placement.call(this, $tip[0], this.$element[0]) : + this.options.placement + + inside = /in/.test(placement) + + $tip + .remove() + .css({ top: 0, left: 0, display: 'block' }) + .appendTo(inside ? this.$element : document.body) + + pos = this.getPosition(inside) + + actualWidth = $tip[0].offsetWidth + actualHeight = $tip[0].offsetHeight + + switch (inside ? placement.split(' ')[1] : placement) { + case 'bottom': + tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2} + break + case 'top': + tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2} + break + case 'left': + tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth} + break + case 'right': + tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width} + break + } + + $tip + .css(tp) + .addClass(placement) + .addClass('in') + } + } + + , isHTML: function(text) { + // html string detection logic adapted from jQuery + return typeof text != 'string' + || ( text.charAt(0) === "<" + && text.charAt( text.length - 1 ) === ">" + && text.length >= 3 + ) || /^(?:[^<]*<[\w\W]+>[^>]*$)/.exec(text) + } + + , setContent: function () { + var $tip = this.tip() + , title = this.getTitle() + + $tip.find('.tooltip-inner')[this.isHTML(title) ? 'html' : 'text'](title) + $tip.removeClass('fade in top bottom left right') + } + + , hide: function () { + var that = this + , $tip = this.tip() + + $tip.removeClass('in') + + function removeWithAnimation() { + var timeout = setTimeout(function () { + $tip.off($.support.transition.end).remove() + }, 500) + + $tip.one($.support.transition.end, function () { + clearTimeout(timeout) + $tip.remove() + }) + } + + $.support.transition && this.$tip.hasClass('fade') ? + removeWithAnimation() : + $tip.remove() + } + + , fixTitle: function () { + var $e = this.$element + if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') { + $e.attr('data-original-title', $e.attr('title') || '').removeAttr('title') + } + } + + , hasContent: function () { + return this.getTitle() + } + + , getPosition: function (inside) { + return $.extend({}, (inside ? {top: 0, left: 0} : this.$element.offset()), { + width: this.$element[0].offsetWidth + , height: this.$element[0].offsetHeight + }) + } + + , getTitle: function () { + var title + , $e = this.$element + , o = this.options + + title = $e.attr('data-original-title') + || (typeof o.title == 'function' ? o.title.call($e[0]) : o.title) + + return title + } + + , tip: function () { + return this.$tip = this.$tip || $(this.options.template) + } + + , validate: function () { + if (!this.$element[0].parentNode) { + this.hide() + this.$element = null + this.options = null + } + } + + , enable: function () { + this.enabled = true + } + + , disable: function () { + this.enabled = false + } + + , toggleEnabled: function () { + this.enabled = !this.enabled + } + + , toggle: function () { + this[this.tip().hasClass('in') ? 'hide' : 'show']() + } + + } + + + /* TOOLTIP PLUGIN DEFINITION + * ========================= */ + + $.fn.tooltip = function ( option ) { + return this.each(function () { + var $this = $(this) + , data = $this.data('tooltip') + , options = typeof option == 'object' && option + if (!data) $this.data('tooltip', (data = new Tooltip(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.tooltip.Constructor = Tooltip + + $.fn.tooltip.defaults = { + animation: true + , placement: 'top' + , selector: false + , template: '<div class="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>' + , trigger: 'hover' + , title: '' + , delay: 0 + } + +}(window.jQuery); +/* =========================================================== + * bootstrap-popover.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#popovers + * =========================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * =========================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* POPOVER PUBLIC CLASS DEFINITION + * =============================== */ + + var Popover = function ( element, options ) { + this.init('popover', element, options) + } + + + /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js + ========================================== */ + + Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, { + + constructor: Popover + + , setContent: function () { + var $tip = this.tip() + , title = this.getTitle() + , content = this.getContent() + + $tip.find('.popover-title')[this.isHTML(title) ? 'html' : 'text'](title) + $tip.find('.popover-content > *')[this.isHTML(content) ? 'html' : 'text'](content) + + $tip.removeClass('fade top bottom left right in') + } + + , hasContent: function () { + return this.getTitle() || this.getContent() + } + + , getContent: function () { + var content + , $e = this.$element + , o = this.options + + content = $e.attr('data-content') + || (typeof o.content == 'function' ? o.content.call($e[0]) : o.content) + + return content + } + + , tip: function () { + if (!this.$tip) { + this.$tip = $(this.options.template) + } + return this.$tip + } + + }) + + + /* POPOVER PLUGIN DEFINITION + * ======================= */ + + $.fn.popover = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('popover') + , options = typeof option == 'object' && option + if (!data) $this.data('popover', (data = new Popover(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.popover.Constructor = Popover + + $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, { + placement: 'right' + , content: '' + , template: '<div class="popover"><div class="arrow"></div><div class="popover-inner"><h3 class="popover-title"></h3><div class="popover-content"><p></p></div></div></div>' + }) + +}(window.jQuery);/* ============================================================= + * bootstrap-scrollspy.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#scrollspy + * ============================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* SCROLLSPY CLASS DEFINITION + * ========================== */ + + function ScrollSpy( element, options) { + var process = $.proxy(this.process, this) + , $element = $(element).is('body') ? $(window) : $(element) + , href + this.options = $.extend({}, $.fn.scrollspy.defaults, options) + this.$scrollElement = $element.on('scroll.scroll.data-api', process) + this.selector = (this.options.target + || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 + || '') + ' .nav li > a' + this.$body = $('body') + this.refresh() + this.process() + } + + ScrollSpy.prototype = { + + constructor: ScrollSpy + + , refresh: function () { + var self = this + , $targets + + this.offsets = $([]) + this.targets = $([]) + + $targets = this.$body + .find(this.selector) + .map(function () { + var $el = $(this) + , href = $el.data('target') || $el.attr('href') + , $href = /^#\w/.test(href) && $(href) + return ( $href + && href.length + && [[ $href.position().top, href ]] ) || null + }) + .sort(function (a, b) { return a[0] - b[0] }) + .each(function () { + self.offsets.push(this[0]) + self.targets.push(this[1]) + }) + } + + , process: function () { + var scrollTop = this.$scrollElement.scrollTop() + this.options.offset + , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight + , maxScroll = scrollHeight - this.$scrollElement.height() + , offsets = this.offsets + , targets = this.targets + , activeTarget = this.activeTarget + , i + + if (scrollTop >= maxScroll) { + return activeTarget != (i = targets.last()[0]) + && this.activate ( i ) + } + + for (i = offsets.length; i--;) { + activeTarget != targets[i] + && scrollTop >= offsets[i] + && (!offsets[i + 1] || scrollTop <= offsets[i + 1]) + && this.activate( targets[i] ) + } + } + + , activate: function (target) { + var active + , selector + + this.activeTarget = target + + $(this.selector) + .parent('.active') + .removeClass('active') + + selector = this.selector + + '[data-target="' + target + '"],' + + this.selector + '[href="' + target + '"]' + + active = $(selector) + .parent('li') + .addClass('active') + + if (active.parent('.dropdown-menu')) { + active = active.closest('li.dropdown').addClass('active') + } + + active.trigger('activate') + } + + } + + + /* SCROLLSPY PLUGIN DEFINITION + * =========================== */ + + $.fn.scrollspy = function ( option ) { + return this.each(function () { + var $this = $(this) + , data = $this.data('scrollspy') + , options = typeof option == 'object' && option + if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.scrollspy.Constructor = ScrollSpy + + $.fn.scrollspy.defaults = { + offset: 10 + } + + + /* SCROLLSPY DATA-API + * ================== */ + + $(function () { + $('[data-spy="scroll"]').each(function () { + var $spy = $(this) + $spy.scrollspy($spy.data()) + }) + }) + +}(window.jQuery);/* ======================================================== + * bootstrap-tab.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#tabs + * ======================================================== + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ======================================================== */ + + +!function ($) { + + "use strict"; // jshint ;_; + + + /* TAB CLASS DEFINITION + * ==================== */ + + var Tab = function ( element ) { + this.element = $(element) + } + + Tab.prototype = { + + constructor: Tab + + , show: function () { + var $this = this.element + , $ul = $this.closest('ul:not(.dropdown-menu)') + , selector = $this.attr('data-target') + , previous + , $target + , e + + if (!selector) { + selector = $this.attr('href') + selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 + } + + if ( $this.parent('li').hasClass('active') ) return + + previous = $ul.find('.active a').last()[0] + + e = $.Event('show', { + relatedTarget: previous + }) + + $this.trigger(e) + + if (e.isDefaultPrevented()) return + + $target = $(selector) + + this.activate($this.parent('li'), $ul) + this.activate($target, $target.parent(), function () { + $this.trigger({ + type: 'shown' + , relatedTarget: previous + }) + }) + } + + , activate: function ( element, container, callback) { + var $active = container.find('> .active') + , transition = callback + && $.support.transition + && $active.hasClass('fade') + + function next() { + $active + .removeClass('active') + .find('> .dropdown-menu > .active') + .removeClass('active') + + element.addClass('active') + + if (transition) { + element[0].offsetWidth // reflow for transition + element.addClass('in') + } else { + element.removeClass('fade') + } + + if ( element.parent('.dropdown-menu') ) { + element.closest('li.dropdown').addClass('active') + } + + callback && callback() + } + + transition ? + $active.one($.support.transition.end, next) : + next() + + $active.removeClass('in') + } + } + + + /* TAB PLUGIN DEFINITION + * ===================== */ + + $.fn.tab = function ( option ) { + return this.each(function () { + var $this = $(this) + , data = $this.data('tab') + if (!data) $this.data('tab', (data = new Tab(this))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.tab.Constructor = Tab + + + /* TAB DATA-API + * ============ */ + + $(function () { + $('body').on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) { + e.preventDefault() + $(this).tab('show') + }) + }) + +}(window.jQuery);/* ============================================================= + * bootstrap-typeahead.js v2.0.4 + * http://twitter.github.com/bootstrap/javascript.html#typeahead + * ============================================================= + * Copyright 2012 Twitter, Inc. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================ */ + + +!function($){ + + "use strict"; // jshint ;_; + + + /* TYPEAHEAD PUBLIC CLASS DEFINITION + * ================================= */ + + var Typeahead = function (element, options) { + this.$element = $(element) + this.options = $.extend({}, $.fn.typeahead.defaults, options) + this.matcher = this.options.matcher || this.matcher + this.sorter = this.options.sorter || this.sorter + this.highlighter = this.options.highlighter || this.highlighter + this.updater = this.options.updater || this.updater + this.$menu = $(this.options.menu).appendTo('body') + this.source = this.options.source + this.shown = false + this.listen() + } + + Typeahead.prototype = { + + constructor: Typeahead + + , select: function () { + var val = this.$menu.find('.active').attr('data-value') + this.$element + .val(this.updater(val)) + .change() + return this.hide() + } + + , updater: function (item) { + return item + } + + , show: function () { + var pos = $.extend({}, this.$element.offset(), { + height: this.$element[0].offsetHeight + }) + + this.$menu.css({ + top: pos.top + pos.height + , left: pos.left + }) + + this.$menu.show() + this.shown = true + return this + } + + , hide: function () { + this.$menu.hide() + this.shown = false + return this + } + + , lookup: function (event) { + var that = this + , items + , q + + this.query = this.$element.val() + + if (!this.query) { + return this.shown ? this.hide() : this + } + + items = $.grep(this.source, function (item) { + return that.matcher(item) + }) + + items = this.sorter(items) + + if (!items.length) { + return this.shown ? this.hide() : this + } + + return this.render(items.slice(0, this.options.items)).show() + } + + , matcher: function (item) { + return ~item.toLowerCase().indexOf(this.query.toLowerCase()) + } + + , sorter: function (items) { + var beginswith = [] + , caseSensitive = [] + , caseInsensitive = [] + , item + + while (item = items.shift()) { + if (!item.toLowerCase().indexOf(this.query.toLowerCase())) beginswith.push(item) + else if (~item.indexOf(this.query)) caseSensitive.push(item) + else caseInsensitive.push(item) + } + + return beginswith.concat(caseSensitive, caseInsensitive) + } + + , highlighter: function (item) { + var query = this.query.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g, '\\$&') + return item.replace(new RegExp('(' + query + ')', 'ig'), function ($1, match) { + return '<strong>' + match + '</strong>' + }) + } + + , render: function (items) { + var that = this + + items = $(items).map(function (i, item) { + i = $(that.options.item).attr('data-value', item) + i.find('a').html(that.highlighter(item)) + return i[0] + }) + + items.first().addClass('active') + this.$menu.html(items) + return this + } + + , next: function (event) { + var active = this.$menu.find('.active').removeClass('active') + , next = active.next() + + if (!next.length) { + next = $(this.$menu.find('li')[0]) + } + + next.addClass('active') + } + + , prev: function (event) { + var active = this.$menu.find('.active').removeClass('active') + , prev = active.prev() + + if (!prev.length) { + prev = this.$menu.find('li').last() + } + + prev.addClass('active') + } + + , listen: function () { + this.$element + .on('blur', $.proxy(this.blur, this)) + .on('keypress', $.proxy(this.keypress, this)) + .on('keyup', $.proxy(this.keyup, this)) + + if ($.browser.webkit || $.browser.msie) { + this.$element.on('keydown', $.proxy(this.keypress, this)) + } + + this.$menu + .on('click', $.proxy(this.click, this)) + .on('mouseenter', 'li', $.proxy(this.mouseenter, this)) + } + + , keyup: function (e) { + switch(e.keyCode) { + case 40: // down arrow + case 38: // up arrow + break + + case 9: // tab + case 13: // enter + if (!this.shown) return + this.select() + break + + case 27: // escape + if (!this.shown) return + this.hide() + break + + default: + this.lookup() + } + + e.stopPropagation() + e.preventDefault() + } + + , keypress: function (e) { + if (!this.shown) return + + switch(e.keyCode) { + case 9: // tab + case 13: // enter + case 27: // escape + e.preventDefault() + break + + case 38: // up arrow + if (e.type != 'keydown') break + e.preventDefault() + this.prev() + break + + case 40: // down arrow + if (e.type != 'keydown') break + e.preventDefault() + this.next() + break + } + + e.stopPropagation() + } + + , blur: function (e) { + var that = this + setTimeout(function () { that.hide() }, 150) + } + + , click: function (e) { + e.stopPropagation() + e.preventDefault() + this.select() + } + + , mouseenter: function (e) { + this.$menu.find('.active').removeClass('active') + $(e.currentTarget).addClass('active') + } + + } + + + /* TYPEAHEAD PLUGIN DEFINITION + * =========================== */ + + $.fn.typeahead = function (option) { + return this.each(function () { + var $this = $(this) + , data = $this.data('typeahead') + , options = typeof option == 'object' && option + if (!data) $this.data('typeahead', (data = new Typeahead(this, options))) + if (typeof option == 'string') data[option]() + }) + } + + $.fn.typeahead.defaults = { + source: [] + , items: 8 + , menu: '<ul class="typeahead dropdown-menu"></ul>' + , item: '<li><a href="#"></a></li>' + } + + $.fn.typeahead.Constructor = Typeahead + + + /* TYPEAHEAD DATA-API + * ================== */ + + $(function () { + $('body').on('focus.typeahead.data-api', '[data-provide="typeahead"]', function (e) { + var $this = $(this) + if ($this.data('typeahead')) return + e.preventDefault() + $this.typeahead($this.data()) + }) + }) + +}(window.jQuery);
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\ No newline at end of file diff --git a/inst/web/mccall81_245T.html b/inst/web/mccall81_245T.html new file mode 100644 index 00000000..a24673c2 --- /dev/null +++ b/inst/web/mccall81_245T.html @@ -0,0 +1,222 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mccall81_245T. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Datasets on aerobic soil metabolism of 2,4,5-T in six soils +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mccall81_245T</div></pre> + + <div class="Description"> + <h2>Description</h2> + + <p>Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding + 2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether + extracts.</p> + + </div> + + <div class="Format"> + <h2>Format</h2> + + <p>A dataframe containing the following variables. + <dl> + <dt><code>name</code></dt><dd>the name of the compound observed. Note that T245 is used as + an acronym for 2,4,5-T. T245 is a legitimate object name + in R, which is necessary for specifying models using + <code><a href='mkinmod.html'>mkinmod</a></code>.</dd></p> + + <p><dt><code>time</code></dt><dd>a numeric vector containing sampling times in days after + treatment</dd></p> + + <p><dt><code>value</code></dt><dd>a numeric vector containing concentrations in percent of applied radioactivity</dd></p> + + <p><dt><code>soil</code></dt><dd>a factor containing the name of the soil</dd></p> + + <p></dl></p> + + <p></p> + + </div> + + <div class="Source"> + <h2>Source</h2> + + <p>McCall P, Vrona SA, Kelley SS (1981) Fate of uniformly carbon-14 ring labeled 2,4,5-Trichlorophenoxyacetic acid and 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107 + <a href = 'http://dx.doi.org/10.1021/jf00103a026'>http://dx.doi.org/10.1021/jf00103a026</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> SFO_SFO_SFO <- mkinmod(T245 = list(type = "SFO", to = "phenol"), + phenol = list(type = "SFO", to = "anisole"), + anisole = list(type = "SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'> ## Not run: +# fit.1 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce")) +# summary(fit.1, data = FALSE) +# ## End(Not run) + # No covariance matrix and k_phenol_sink is really small, therefore fix it to zero + fit.2 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"), + parms.ini = c(k_phenol_sink = 0), + fixed_parms = "k_phenol_sink", quiet = TRUE) + summary(fit.2, data = FALSE) +</div> +<div class='output'>mkin version: 0.9.41 +R version: 3.2.2 +Date of fit: Mon Nov 9 08:25:18 2015 +Date of summary: Mon Nov 9 08:25:18 2015 + +Equations: +d_T245 = - k_T245_sink * T245 - k_T245_phenol * T245 +d_phenol = + k_T245_phenol * T245 - k_phenol_sink * phenol - k_phenol_anisole * phenol +d_anisole = + k_phenol_anisole * phenol - k_anisole_sink * anisole + +Model predictions using solution type deSolve + +Fitted with method Port using 246 model solutions performed in 1.369 s + +Weighting: none + +Starting values for parameters to be optimised: + value type +T245_0 100.9000 state +k_T245_sink 0.1000 deparm +k_T245_phenol 0.1001 deparm +k_phenol_anisole 0.1002 deparm +k_anisole_sink 0.1003 deparm + +Starting values for the transformed parameters actually optimised: + value lower upper +T245_0 100.900000 -Inf Inf +log_k_T245_sink -2.302585 -Inf Inf +log_k_T245_phenol -2.301586 -Inf Inf +log_k_phenol_anisole -2.300587 -Inf Inf +log_k_anisole_sink -2.299590 -Inf Inf + +Fixed parameter values: + value type +phenol_0 0 state +anisole_0 0 state +k_phenol_sink 0 deparm + +Optimised, transformed parameters with symmetric confidence intervals: + Estimate Std. Error Lower Upper +T245_0 103.9000 2.35200 98.930 108.8000 +log_k_T245_sink -4.1130 0.13250 -4.390 -3.8350 +log_k_T245_phenol -3.6120 0.05002 -3.716 -3.5070 +log_k_phenol_anisole -0.9037 0.30580 -1.544 -0.2637 +log_k_anisole_sink -5.0090 0.11180 -5.243 -4.7750 + +Parameter correlation: + T245_0 log_k_T245_sink log_k_T245_phenol log_k_phenol_anisole log_k_anisole_sink +T245_0 1.00000 0.63761 -0.1742 -0.05948 -0.1621 +log_k_T245_sink 0.63761 1.00000 -0.3831 0.08745 -0.6047 +log_k_T245_phenol -0.17416 -0.38313 1.0000 -0.30470 0.5227 +log_k_phenol_anisole -0.05948 0.08745 -0.3047 1.00000 -0.1774 +log_k_anisole_sink -0.16208 -0.60469 0.5227 -0.17744 1.0000 + +Residual standard error: 2.706 on 19 degrees of freedom + +Backtransformed parameters: +Confidence intervals for internally transformed parameters are asymmetric. +t-test (unrealistically) based on the assumption of normal distribution +for estimators of untransformed parameters. + Estimate t value Pr(>t) Lower Upper +T245_0 1.039e+02 44.160 6.462e-21 98.930000 108.80000 +k_T245_sink 1.636e-02 7.545 1.978e-07 0.012400 0.02159 +k_T245_phenol 2.701e-02 19.990 1.607e-14 0.024320 0.02999 +k_phenol_anisole 4.051e-01 3.270 2.014e-03 0.213600 0.76820 +k_anisole_sink 6.679e-03 8.942 1.544e-08 0.005285 0.00844 + +Chi2 error levels in percent: + err.min n.optim df +All data 9.831 5 17 +T245 7.908 3 5 +phenol 99.808 1 6 +anisole 5.379 1 6 + +Resulting formation fractions: + ff +T245_sink 0.3772 +T245_phenol 0.6228 +phenol_anisole 1.0000 +phenol_sink 0.0000 +anisole_sink 1.0000 + +Estimated disappearance times: + DT50 DT90 +T245 15.982 53.091 +phenol 1.711 5.685 +anisole 103.784 344.763 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mccall81_245T</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. 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\ No newline at end of file diff --git a/inst/web/mkin_long_to_wide.html b/inst/web/mkin_long_to_wide.html new file mode 100644 index 00000000..7c459da7 --- /dev/null +++ b/inst/web/mkin_long_to_wide.html @@ -0,0 +1,148 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkin_long_to_wide. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Convert a dataframe from long to wide format +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkin_long_to_wide(long_data, time = "time", outtime = "time")</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>long_data</dt> + <dd> + The dataframe must contain one variable called "time" with the time values specified by the + <code>time</code> argument, one column called "name" with the grouping of the observed values, and + finally one column of observed values called "value". +</dd> + <dt>time</dt> + <dd> + The name of the time variable in the long input data. +</dd> + <dt>outtime</dt> + <dd> + The name of the time variable in the wide output data. +</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function takes a dataframe in the long form as required by <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modCost'>modCost</a></code> + and converts it into a dataframe with one independent variable and several + dependent variables as columns.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + Dataframe in wide format. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>mkin_long_to_wide(FOCUS_2006_D) +</div> +<div class='output'> time parent m1 +1 0 99.46 0.00 +2 0 102.04 0.00 +3 1 93.50 4.84 +4 1 92.50 5.64 +5 3 63.23 12.91 +6 3 68.99 12.96 +7 7 52.32 22.97 +8 7 55.13 24.47 +9 14 27.27 41.69 +10 14 26.64 33.21 +11 21 11.50 44.37 +12 21 11.64 46.44 +13 35 2.85 41.22 +14 35 2.91 37.95 +15 50 0.69 41.19 +16 50 0.63 40.01 +17 75 0.05 40.09 +18 75 0.06 33.85 +19 100 NA 31.04 +20 100 NA 33.13 +21 120 NA 25.15 +22 120 NA 33.31 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkin_long_to_wide</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkin_wide_to_long.html b/inst/web/mkin_wide_to_long.html new file mode 100644 index 00000000..57e3e8ef --- /dev/null +++ b/inst/web/mkin_wide_to_long.html @@ -0,0 +1,127 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkin_wide_to_long. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Convert a dataframe with observations over time into long format +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkin_wide_to_long(wide_data, time = "t")</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>wide_data</dt> + <dd> + The dataframe must contain one variable with the time values specified by the + <code>time</code> argument and usually more than one column of observed values. +</dd> + <dt>time</dt> + <dd> + The name of the time variable. +</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function simply takes a dataframe with one independent variable and several + dependent variable and converts it into the long form as required by <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modCost'>modCost</a></code>.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + Dataframe in long format as needed for <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modCost'>modCost</a></code>. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>wide <- data.frame(t = c(1,2,3), x = c(1,4,7), y = c(3,4,5)) +mkin_wide_to_long(wide) +</div> +<div class='output'> name time value +1 x 1 1 +2 x 2 4 +3 x 3 7 +4 y 1 3 +5 y 2 4 +6 y 3 5 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkin_wide_to_long</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinds.html b/inst/web/mkinds.html new file mode 100644 index 00000000..5ff5e40c --- /dev/null +++ b/inst/web/mkinds.html @@ -0,0 +1,118 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinds. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1>A dataset class for mkin</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinds</div></pre> + + <div class="Format"> + <h2>Format</h2> + + <p>An <code><a href='http://www.inside-r.org/packages/cran/R6/docs/R6Class'>R6Class</a></code> generator object.</p> + + </div> + + <div class="Description"> + <h2>Description</h2> + + <p>A dataset class for mkin</p> + + </div> + + <div class="Fields"> + <h2>Fields</h2> + + <p></p> + + <p><dl> +<dt><code>title</code></dt><dd>A full title for the dataset</dd></p> + + <p><dt><code>sampling</code></dt><dd>times The sampling times</dd></p> + + <p><dt><code>time_unit</code></dt><dd>The time unit</dd></p> + + <p><dt><code>observed</code></dt><dd>Names of the observed compounds</dd></p> + + <p><dt><code>unit</code></dt><dd>The unit of the observations</dd></p> + + <p><dt><code>replicates</code></dt><dd>The number of replicates</dd></p> + + <p><dt><code>data</code></dt><dd>A dataframe with at least the columns name, time and value +in order to be compatible with mkinfit</dd></p> + + <p></dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>mds <- mkinds$new("FOCUS A", FOCUS_2006_A) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinds</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinerrmin.html b/inst/web/mkinerrmin.html new file mode 100644 index 00000000..d8a90977 --- /dev/null +++ b/inst/web/mkinerrmin.html @@ -0,0 +1,160 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinerrmin. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> +Calculate the minimum error to assume in order to pass the variance test +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinerrmin(fit, alpha = 0.05)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>fit</dt> + <dd> + an object of class <code><a href='mkinfit.html'>mkinfit</a></code>. + </dd> + <dt>alpha</dt> + <dd> + The confidence level chosen for the chi-squared test. +</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function uses <code><a href='http://www.inside-r.org/r-doc/stats/optimize'>optimize</a></code> in order to iteratively find the +smallest relative error still resulting in passing the chi-squared test +as defined in the FOCUS kinetics report from 2006.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + A dataframe with the following components: + <dt>err.min</dt><dd>The relative error, expressed as a fraction.</dd></p> + + <p><dt>n.optim</dt><dd>The number of optimised parameters attributed to the data series.</dd></p> + + <p><dt>df</dt><dd>The number of remaining degrees of freedom for the chi2 error level + calculations. Note that mean values are used for the chi2 statistic and + therefore every time point with observed values in the series only counts + one time.</dd></p> + + <p>The dataframe has one row for the total dataset and one further row for + each observed state variable in the model. +</dl></p> + + </div> + + <div class="Details"> + <h2>Details</h2> + + <p>This function is used internally by <code><a href='summary.mkinfit.html'>summary.mkinfit</a></code>.</p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"), + m1 = list(type = "SFO"), + use_of_ff = "max") +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'> +fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) +round(mkinerrmin(fit_FOCUS_D), 4) +</div> +<div class='output'> err.min n.optim df +All data 0.0640 4 15 +parent 0.0646 2 7 +m1 0.0469 2 8 +</div> +<div class='input'>fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE) +round(mkinerrmin(fit_FOCUS_E), 4) +</div> +<div class='output'> err.min n.optim df +All data 0.1544 4 13 +parent 0.1659 2 7 +m1 0.1095 2 6 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinerrmin</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinfit.html b/inst/web/mkinfit.html new file mode 100644 index 00000000..8536925c --- /dev/null +++ b/inst/web/mkinfit.html @@ -0,0 +1,517 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinfit. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Fit a kinetic model to data with one or more state variables +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinfit(mkinmod, observed, parms.ini = "auto", state.ini = "auto", fixed_parms = NULL, fixed_initials = names(mkinmod$diffs)[-1], solution_type = c("auto", "analytical", "eigen", "deSolve"), method.ode = "lsoda", use_compiled = "auto", method.modFit = c("Port", "Marq", "SANN", "Nelder-Mead", "BFGS", "CG", "L-BFGS-B"), maxit.modFit = "auto", control.modFit = list(), transform_rates = TRUE, transform_fractions = TRUE, plot = FALSE, quiet = FALSE, err = NULL, weight = "none", scaleVar = FALSE, atol = 1e-8, rtol = 1e-10, n.outtimes = 100, reweight.method = NULL, reweight.tol = 1e-8, reweight.max.iter = 10, trace_parms = FALSE, ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>mkinmod</dt> + <dd> + A list of class <code><a href='mkinmod.html'>mkinmod</a></code>, containing the kinetic model to be + fitted to the data, or one of the shorthand names ("SFO", "FOMC", "DFOP", + "HS", "SFORB"). If a shorthand name is given, a parent only degradation + model is generated for the variable with the highest value in + <code>observed</code>. + </dd> + <dt>observed</dt> + <dd> + The observed data. It has to be in the long format as described in + <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modFit'>modFit</a></code>, i.e. the first column called "name" must contain the + name of the observed variable for each data point. The second column must + contain the times of observation, named "time". The third column must be + named "value" and contain the observed values. Optionally, a further column + can contain weights for each data point. If it is not named "err", its name + must be passed as a further argument named <code>err</code> which is then passed + on to <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modFit'>modFit</a></code>. + </dd> + <dt>parms.ini</dt> + <dd> + A named vector of initial values for the parameters, including parameters + to be optimised and potentially also fixed parameters as indicated by + <code>fixed_parms</code>. If set to "auto", initial values for rate constants + are set to default values. Using parameter names that are not in the model + gives an error. + + It is possible to only specify a subset of the parameters that the model + needs. You can use the parameter lists "bparms.ode" from a previously + fitted model, which contains the differential equation parameters from this + model. This works nicely if the models are nested. An example is given + below. + </dd> + <dt>state.ini</dt> + <dd> + A named vector of initial values for the state variables of the model. In + case the observed variables are represented by more than one model + variable, the names will differ from the names of the observed variables + (see <code>map</code> component of <code><a href='mkinmod.html'>mkinmod</a></code>). The default is to set + the initial value of the first model variable to the mean of the time zero + values for the variable with the maximum observed value, and all others to 0. + If this variable has no time zero observations, its initial value is set to 100. + </dd> + <dt>fixed_parms</dt> + <dd> + The names of parameters that should not be optimised but rather kept at the + values specified in <code>parms.ini</code>. + </dd> + <dt>fixed_initials</dt> + <dd> + The names of model variables for which the initial state at time 0 should + be excluded from the optimisation. Defaults to all state variables except + for the first one. + </dd> + <dt>solution_type</dt> + <dd> + If set to "eigen", the solution of the system of differential equations is + based on the spectral decomposition of the coefficient matrix in cases that + this is possible. If set to "deSolve", a numerical ode solver from package + <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/deSolve'>deSolve</a></code> is used. If set to "analytical", an analytical + solution of the model is used. This is only implemented for simple + degradation experiments with only one state variable, i.e. with no + metabolites. The default is "auto", which uses "analytical" if possible, + otherwise "eigen" if the model can be expressed using eigenvalues and + eigenvectors, and finally "deSolve" for the remaining models (time + dependence of degradation rates and metabolites). This argument is passed + on to the helper function <code><a href='mkinpredict.html'>mkinpredict</a></code>. + </dd> + <dt>method.ode</dt> + <dd> + The solution method passed via <code><a href='mkinpredict.html'>mkinpredict</a></code> to + <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code> in case the solution type is "deSolve". The default + "lsoda" is performant, but sometimes fails to converge. + </dd> + <dt>use_compiled</dt> + <dd> + If set to <code>FALSE</code>, no compiled version of the <code><a href='mkinmod.html'>mkinmod</a></code> + model is used, in the calls to <code><a href='mkinpredict.html'>mkinpredict</a></code> even if + a compiled verion is present. + </dd> + <dt>method.modFit</dt> + <dd> + The optimisation method passed to <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modFit'>modFit</a></code>. + + In order to optimally deal with problems where local minima occur, the + "Port" algorithm is now used per default as it is less prone to get trapped + in local minima and depends less on starting values for parameters than + the Levenberg Marquardt variant selected by "Marq". However, "Port" needs + more iterations. + + The former default "Marq" is the Levenberg Marquardt algorithm + <code><a href='http://www.inside-r.org/packages/cran/minpack.lm/docs/nls.lm'>nls.lm</a></code> from the package <code>minpack.lm</code> and usually needs + the least number of iterations. + + The "Pseudo" algorithm is not included because it needs finite parameter bounds + which are currently not supported. + + The "Newton" algorithm is not included because its number of iterations + can not be controlled by <code>control.modFit</code> and it does not appear + to provide advantages over the other algorithms. + </dd> + <dt>maxit.modFit</dt> + <dd> + Maximum number of iterations in the optimisation. If not "auto", this will + be passed to the method called by <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modFit'>modFit</a></code>, overriding + what may be specified in the next argument <code>control.modFit</code>. + </dd> + <dt>control.modFit</dt> + <dd> + Additional arguments passed to the optimisation method used by + <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modFit'>modFit</a></code>. + </dd> + <dt>transform_rates</dt> + <dd> + Boolean specifying if kinetic rate constants should be transformed in the + model specification used in the fitting for better compliance with the + assumption of normal distribution of the estimator. If TRUE, also + alpha and beta parameters of the FOMC model are log-transformed, as well + as k1 and k2 rate constants for the DFOP and HS models and the break point + tb of the HS model. + If FALSE, zero is used as a lower bound for the rates in the optimisation. + </dd> + <dt>transform_fractions</dt> + <dd> + Boolean specifying if formation fractions constants should be transformed in the + model specification used in the fitting for better compliance with the + assumption of normal distribution of the estimator. The default (TRUE) is + to do transformations. If TRUE, the g parameter of the DFOP and HS + models are also transformed, as they can also be seen as compositional + data. The transformation used for these transformations is the + <code><a href='ilr.html'>ilr</a></code> transformation. + </dd> + <dt>plot</dt> + <dd> + Should the observed values and the numerical solutions be plotted at each + stage of the optimisation? + </dd> + <dt>quiet</dt> + <dd> + Suppress printing out the current model cost after each improvement? + </dd> + <dt>err </dt> + <dd>either <code>NULL</code>, or the name of the column with the + <em>error</em> estimates, used to weigh the residuals (see details of + <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modCost'>modCost</a></code>); if <code>NULL</code>, then the residuals are not weighed. + </dd> + <dt>weight</dt> + <dd> + only if <code>err</code>=<code>NULL</code>: how to weight the residuals, one of "none", + "std", "mean", see details of <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modCost'>modCost</a></code>. + </dd> + <dt>scaleVar</dt> + <dd> + Will be passed to <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modCost'>modCost</a></code>. Default is not to scale Variables + according to the number of observations. + </dd> + <dt>atol</dt> + <dd> + Absolute error tolerance, passed to <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code>. Default is 1e-8, + lower than in <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/lsoda'>lsoda</a></code>. + </dd> + <dt>rtol</dt> + <dd> + Absolute error tolerance, passed to <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code>. Default is 1e-10, + much lower than in <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/lsoda'>lsoda</a></code>. + </dd> + <dt>n.outtimes</dt> + <dd> + The length of the dataseries that is produced by the model prediction + function <code><a href='mkinpredict.html'>mkinpredict</a></code>. This impacts the accuracy of + the numerical solver if that is used (see <code>solution_type</code> argument. + The default value is 100. + </dd> + <dt>reweight.method</dt> + <dd> + The method used for iteratively reweighting residuals, also known + as iteratively reweighted least squares (IRLS). Default is NULL, + the other method implemented is called "obs", meaning that each + observed variable is assumed to have its own variance, this is + estimated from the fit and used for weighting the residuals + in each iteration until convergence of this estimate up to + <code>reweight.tol</code> or up to the maximum number of iterations + specified by <code>reweight.max.iter</code>. + </dd> + <dt>reweight.tol</dt> + <dd> + Tolerance for convergence criterion for the variance components + in IRLS fits. + </dd> + <dt>reweight.max.iter</dt> + <dd> + Maximum iterations in IRLS fits. + </dd> + <dt>trace_parms</dt> + <dd> + Should a trace of the parameter values be listed? + </dd> + <dt>...</dt> + <dd> + Further arguments that will be passed to <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modFit'>modFit</a></code>. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function uses the Flexible Modelling Environment package + <code><a href='http://www.inside-r.org/packages/cran/FME/docs/FME'>FME</a></code> to create a function calculating the model cost, i.e. the + deviation between the kinetic model and the observed data. This model cost is + then minimised using the Port algorithm <code><a href='http://www.inside-r.org/r-doc/stats/nlminb'>nlminb</a></code>, + using the specified initial or fixed parameters and starting values. + Per default, parameters in the kinetic models are internally transformed in order + to better satisfy the assumption of a normal distribution of their estimators. + In each step of the optimsation, the kinetic model is solved using the + function <code><a href='mkinpredict.html'>mkinpredict</a></code>. The variance of the residuals for each + observed variable can optionally be iteratively reweighted until convergence + using the argument <code>reweight.method = "obs"</code>.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + A list with "mkinfit" and "modFit" in the class attribute. + A summary can be obtained by <code><a href='summary.mkinfit.html'>summary.mkinfit</a></code>. +</dl></p> + + </div> + + <div class="Note"> + <h2>Note</h2> + + <p>The implementation of iteratively reweighted least squares is inspired by the + work of the KinGUII team at Bayer Crop Science (Walter Schmitt and Zhenglei + Gao). A similar implemention can also be found in CAKE 2.0, which is the + other GUI derivative of mkin, sponsored by Syngenta.</p> + + </div> + + <div class="Note"> + <h2>Note</h2> + + <p>When using the "IORE" submodel for metabolites, fitting with + "transform_rates = TRUE" (the default) often leads to failures of the + numerical ODE solver. In this situation it may help to switch off the + internal rate transformation.</p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'># Use shorthand notation for parent only degradation +fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE) +summary(fit) +</div> +<div class='output'>mkin version: 0.9.41 +R version: 3.2.2 +Date of fit: Mon Nov 9 08:25:21 2015 +Date of summary: Mon Nov 9 08:25:21 2015 + +Equations: +d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent + +Model predictions using solution type analytical + +Fitted with method Port using 64 model solutions performed in 0.18 s + +Weighting: none + +Starting values for parameters to be optimised: + value type +parent_0 85.1 state +alpha 1.0 deparm +beta 10.0 deparm + +Starting values for the transformed parameters actually optimised: + value lower upper +parent_0 85.100000 -Inf Inf +log_alpha 0.000000 -Inf Inf +log_beta 2.302585 -Inf Inf + +Fixed parameter values: +None + +Optimised, transformed parameters with symmetric confidence intervals: + Estimate Std. Error Lower Upper +parent_0 85.87000 2.2460 80.38000 91.3700 +log_alpha 0.05192 0.1605 -0.34080 0.4446 +log_beta 0.65100 0.2801 -0.03452 1.3360 + +Parameter correlation: + parent_0 log_alpha log_beta +parent_0 1.0000 -0.2033 -0.3624 +log_alpha -0.2033 1.0000 0.9547 +log_beta -0.3624 0.9547 1.0000 + +Residual standard error: 2.275 on 6 degrees of freedom + +Backtransformed parameters: +Confidence intervals for internally transformed parameters are asymmetric. +t-test (unrealistically) based on the assumption of normal distribution +for estimators of untransformed parameters. + Estimate t value Pr(>t) Lower Upper +parent_0 85.870 38.230 1.069e-08 80.3800 91.370 +alpha 1.053 6.231 3.953e-04 0.7112 1.560 +beta 1.917 3.570 5.895e-03 0.9661 3.806 + +Chi2 error levels in percent: + err.min n.optim df +All data 6.657 3 6 +parent 6.657 3 6 + +Estimated disappearance times: + DT50 DT90 DT50back +parent 1.785 15.15 4.56 + +Data: + time variable observed predicted residual + 0 parent 85.1 85.875 -0.7749 + 1 parent 57.9 55.191 2.7091 + 3 parent 29.9 31.845 -1.9452 + 7 parent 14.6 17.012 -2.4124 + 14 parent 9.7 9.241 0.4590 + 28 parent 6.6 4.754 1.8460 + 63 parent 4.0 2.102 1.8977 + 91 parent 3.9 1.441 2.4590 + 119 parent 0.6 1.092 -0.4919 +</div> +<div class='input'> +# One parent compound, one metabolite, both single first order. +# Use mkinsub for convenience in model formulation. Pathway to sink included per default. +SFO_SFO <- mkinmod( + parent = mkinsub("SFO", "m1"), + m1 = mkinsub("SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'># Fit the model to the FOCUS example dataset D using defaults +print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D, + solution_type = "eigen", quiet = TRUE))) +</div> +<div class='output'> user system elapsed + 1.176 1.204 0.894 +</div> +<div class='input'>coef(fit) +</div> +<div class='output'> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink + 99.59848 -3.03822 -2.98030 -5.24750 +</div> +<div class='input'>endpoints(fit) +</div> +<div class='output'>$ff +parent_sink parent_m1 m1_sink + 0.485524 0.514476 1.000000 + +$SFORB +logical(0) + +$distimes + DT50 DT90 +parent 7.022929 23.32967 +m1 131.760712 437.69961 + +</div> +<div class='input'>## Not run: +# # deSolve is slower when no C compiler (gcc) was available during model generation +# print(system.time(fit.deSolve <- mkinfit(SFO_SFO, FOCUS_2006_D, +# solution_type = "deSolve"))) +# coef(fit.deSolve) +# endpoints(fit.deSolve) +# ## End(Not run) + +# Use stepwise fitting, using optimised parameters from parent only fit, FOMC +## Not run: +# FOMC_SFO <- mkinmod( +# parent = mkinsub("FOMC", "m1"), +# m1 = mkinsub("SFO")) +# # Fit the model to the FOCUS example dataset D using defaults +# fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D) +# # Use starting parameters from parent only FOMC fit +# fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, plot=TRUE) +# fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, +# parms.ini = fit.FOMC$bparms.ode, plot=TRUE) +# +# # Use stepwise fitting, using optimised parameters from parent only fit, SFORB +# SFORB_SFO <- mkinmod( +# parent = list(type = "SFORB", to = "m1", sink = TRUE), +# m1 = list(type = "SFO")) +# # Fit the model to the FOCUS example dataset D using defaults +# fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D) +# fit.SFORB_SFO.deSolve <- mkinfit(SFORB_SFO, FOCUS_2006_D, solution_type = "deSolve") +# # Use starting parameters from parent only SFORB fit (not really needed in this case) +# fit.SFORB = mkinfit("SFORB", FOCUS_2006_D) +# fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode) +# ## End(Not run) + +## Not run: +# # Weighted fits, including IRLS +# SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"), +# m1 = mkinsub("SFO"), use_of_ff = "max") +# f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D) +# summary(f.noweight) +# f.irls <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, reweight.method = "obs") +# summary(f.irls) +# f.w.mean <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, weight = "mean") +# summary(f.w.mean) +# f.w.mean.irls <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, weight = "mean", +# reweight.method = "obs") +# summary(f.w.mean.irls) +# ## End(Not run) + +## Not run: +# # Manual weighting +# dw <- FOCUS_2006_D +# errors <- c(parent = 2, m1 = 1) +# dw$err.man <- errors[FOCUS_2006_D$name] +# f.w.man <- mkinfit(SFO_SFO.ff, dw, err = "err.man") +# summary(f.w.man) +# f.w.man.irls <- mkinfit(SFO_SFO.ff, dw, err = "err.man", +# reweight.method = "obs") +# summary(f.w.man.irls) +# ## End(Not run) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinfit</li> + </ul> + <ul> + <li> optimize </li> + </ul> --> + + <h2>See also</h2> + + Plotting methods <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code> and + <code><a href='mkinparplot.html'>mkinparplot</a></code>. + + Fitting of several models to several datasets in a single call to + <code><a href='mmkin.html'>mmkin</a></code>. + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinmod.html b/inst/web/mkinmod.html new file mode 100644 index 00000000..8d162cd8 --- /dev/null +++ b/inst/web/mkinmod.html @@ -0,0 +1,247 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinmod. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Function to set up a kinetic model with one or more state variables +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinmod(..., use_of_ff = "min", speclist = NULL, quiet = FALSE, verbose = FALSE)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>...</dt> + <dd> + For each observed variable, a list has to be specified as an argument, containing + at least a component <code>type</code>, specifying the type of kinetics to use + for the variable. Currently, single first order kinetics "SFO", + indeterminate order rate equation kinetics "IORE", or + single first order with reversible binding "SFORB" are implemented for all + variables, while + "FOMC", "DFOP" and "HS" can additionally be chosen for the first + variable which is assumed to be the source compartment. + Additionally, each component of the list can include a character vector <code>to</code>, + specifying names of variables to which a transfer is to be assumed in the + model. + If the argument <code>use_of_ff</code> is set to "min" (default) and the model for + the compartment is "SFO" or "SFORB", an additional component of the list + can be "sink=FALSE" effectively fixing the flux to sink to zero. + </dd> + <dt>use_of_ff</dt> + <dd> + Specification of the use of formation fractions in the model equations and, if + applicable, the coefficient matrix. If "min", a minimum use of formation + fractions is made in order to avoid fitting the product of formation fractions + and rate constants. If "max", formation fractions are always used. + </dd> + <dt>speclist</dt> + <dd> + The specification of the observed variables and their submodel types and + pathways can be given as a single list using this argument. Default is NULL. + </dd> + <dt>quiet</dt> + <dd> + Should messages be suppressed? + </dd> + <dt>verbose</dt> + <dd> + If <code>TRUE</code>, passed to <code><a href='http://www.inside-r.org/packages/cran/inline/docs/cfunction'>cfunction</a></code> if applicable to give + detailed information about the C function being built. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>The function usually takes several expressions, each assigning a compound name to + a list, specifying the kinetic model type and reaction or transfer to other + observed compartments. Instead of specifying several expressions, a list + of lists can be given in the speclist argument.</p> + + <p>For the definition of model types and their parameters, the equations given + in the FOCUS and NAFTA guidance documents are used.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + A list of class <code>mkinmod</code> for use with <code><a href='mkinfit.html'>mkinfit</a></code>, containing + <dt>diffs</dt><dd> A vector of string representations of differential equations, + one for each modelling variable. </dd></p> + + <p><dt>parms</dt><dd> A vector of parameter names occurring in the differential equations. </dd></p> + + <p><dt>map</dt><dd> A list containing named character vectors for each observed variable, specifying + the modelling variables by which it is represented. </dd></p> + + <p><dt>use_of_ff</dt><dd> The content of <code>use_of_ff</code> is passed on in this list component. </dd></p> + + <p><dt>coefmat</dt><dd> The coefficient matrix, if the system of differential equations can be + represented by one. </dd></p> + + <p></dl></p> + + </div> + + <div class="Note"> + <h2>Note</h2> + + <p>The IORE submodel is not well tested (yet). When using this model for metabolites, + you may want to read the second note in the help page to + <code><a href='mkinfit.html'>mkinfit</a></code>.</p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + <p>NAFTA Technical Working Group on Pesticides (not dated) Guidance for + Evaluating and Calculating Degradation Kinetics in Environmental + Media</p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...) +SFO <- mkinmod(parent = list(type = "SFO")) + +# One parent compound, one metabolite, both single first order +SFO_SFO <- mkinmod( + parent = mkinsub("SFO", "m1"), + m1 = mkinsub("SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'> +# The above model used to be specified like this, before the advent of mkinsub() +SFO_SFO <- mkinmod( + parent = list(type = "SFO", to = "m1"), + m1 = list(type = "SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'> +# Show details of creating the C function +SFO_SFO <- mkinmod( + parent = mkinsub("SFO", "m1"), + m1 = mkinsub("SFO"), verbose = TRUE) +</div> +<div class='output'>Compilation argument: + /usr/lib/R/bin/R CMD SHLIB file5a355b71ca9.c 2> file5a355b71ca9.c.err.txt +Program source: + 1: #include <R.h> + 2: + 3: + 4: static double parms [3]; + 5: #define k_parent_sink parms[0] + 6: #define k_parent_m1 parms[1] + 7: #define k_m1_sink parms[2] + 8: + 9: void initpar(void (* odeparms)(int *, double *)) { + 10: int N = 3; + 11: odeparms(&N, parms); + 12: } + 13: + 14: + 15: void func ( int * n, double * t, double * y, double * f, double * rpar, int * ipar ) { + 16: + 17: f[0] = - k_parent_sink * y[0] - k_parent_m1 * y[0]; + 18: f[1] = + k_parent_m1 * y[0] - k_m1_sink * y[1]; + 19: } +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'> +# If we have several parallel metabolites +# (compare tests/testthat/test_synthetic_data_for_UBA_2014.R) +m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")), + M1 = mkinsub("SFO"), + M2 = mkinsub("SFO"), + use_of_ff = "max", quiet = TRUE) + +## Not run: +# fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par, +# synthetic_data_for_UBA_2014[[12]]$data, +# quiet = TRUE) +# ## End(Not run) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinmod</li> + </ul> + <ul> + <li> models </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinparplot-4.png b/inst/web/mkinparplot-4.png Binary files differnew file mode 100644 index 00000000..3055a0cd --- /dev/null +++ b/inst/web/mkinparplot-4.png diff --git a/inst/web/mkinparplot.html b/inst/web/mkinparplot.html new file mode 100644 index 00000000..396e1428 --- /dev/null +++ b/inst/web/mkinparplot.html @@ -0,0 +1,122 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinparplot. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Function to plot the confidence intervals obtained using + <code><a href='mkinfit.html'>mkinfit</a></code> +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinparplot(object)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>object</dt> + <dd> + A fit represented in an <code><a href='mkinfit.html'>mkinfit</a></code> object. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function plots the confidence intervals for the parameters + fitted using <code><a href='mkinfit.html'>mkinfit</a></code>.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + Nothing is returned by this function, as it is called for its side effect, namely to produce a plot. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>model <- mkinmod( + T245 = list(type = "SFO", to = c("phenol"), sink = FALSE), + phenol = list(type = "SFO", to = c("anisole")), + anisole = list(type = "SFO"), use_of_ff = "max") +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'>fit <- mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE) +mkinparplot(fit) +</div> +<p><img src='mkinparplot-4.png' alt='' width='540' height='400' /></p></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinparplot</li> + </ul> + <ul> + <li> hplot </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinplot.html b/inst/web/mkinplot.html new file mode 100644 index 00000000..74c8c29f --- /dev/null +++ b/inst/web/mkinplot.html @@ -0,0 +1,112 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinplot. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Plot the observed data and the fitted model of an mkinfit object +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinplot(fit, ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>fit</dt> + <dd> + an object of class <code><a href='mkinfit.html'>mkinfit</a></code>. + </dd> + <dt>...</dt> + <dd> + further arguments passed to <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code>. +</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Deprecated function. It now only calls the plot method <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code>.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The function is called for its side effect. +</dl></p> + + </div> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinplot</li> + </ul> + <ul> + + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinpredict.html b/inst/web/mkinpredict.html new file mode 100644 index 00000000..9f229fd5 --- /dev/null +++ b/inst/web/mkinpredict.html @@ -0,0 +1,355 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinpredict. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Produce predictions from a kinetic model using specifc parameters +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = "deSolve", + use_compiled = "auto", method.ode = "lsoda", atol = 1e-08, rtol = 1e-10, map_output = TRUE, ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>mkinmod</dt> + <dd> + A kinetic model as produced by <code><a href='mkinmod.html'>mkinmod</a></code>. + </dd> + <dt>odeparms</dt> + <dd> + A numeric vector specifying the parameters used in the kinetic model, which + is generally defined as a set of ordinary differential equations. + </dd> + <dt>odeini</dt> + <dd> + A numeric vectory containing the initial values of the state variables of + the model. Note that the state variables can differ from the observed + variables, for example in the case of the SFORB model. + </dd> + <dt>outtimes</dt> + <dd> + A numeric vector specifying the time points for which model predictions + should be generated. + </dd> + <dt>solution_type</dt> + <dd> + The method that should be used for producing the predictions. This should + generally be "analytical" if there is only one observed variable, and + usually "deSolve" in the case of several observed variables. The third + possibility "eigen" is faster but not applicable to some models e.g. + using FOMC for the parent compound. + </dd> + <dt>method.ode</dt> + <dd> + The solution method passed via <code><a href='mkinpredict.html'>mkinpredict</a></code> to + <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code> in case the solution type is "deSolve". The default + "lsoda" is performant, but sometimes fails to converge. + </dd> + <dt>use_compiled</dt> + <dd> + If set to <code>FALSE</code>, no compiled version of the <code><a href='mkinmod.html'>mkinmod</a></code> + model is used, even if is present. + </dd> + <dt>atol</dt> + <dd> + Absolute error tolerance, passed to <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code>. Default is 1e-8, + lower than in <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/lsoda'>lsoda</a></code>. + </dd> + <dt>rtol</dt> + <dd> + Absolute error tolerance, passed to <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code>. Default is 1e-10, + much lower than in <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/lsoda'>lsoda</a></code>. + </dd> + <dt>map_output</dt> + <dd> + Boolean to specify if the output should list values for the observed + variables (default) or for all state variables (if set to FALSE). + </dd> + <dt>...</dt> + <dd> + Further arguments passed to the ode solver in case such a solver is used. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function produces a time series for all the observed variables in a + kinetic model as specified by <code><a href='mkinmod.html'>mkinmod</a></code>, using a specific set of + kinetic parameters and initial values for the state variables.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + A matrix in the same format as the output of <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code>. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> SFO <- mkinmod(degradinol = list(type = "SFO")) + # Compare solution types + mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, + solution_type = "analytical") +</div> +<div class='output'> time degradinol +1 0 100.0000000 +2 1 74.0818221 +3 2 54.8811636 +4 3 40.6569660 +5 4 30.1194212 +6 5 22.3130160 +7 6 16.5298888 +8 7 12.2456428 +9 8 9.0717953 +10 9 6.7205513 +11 10 4.9787068 +12 11 3.6883167 +13 12 2.7323722 +14 13 2.0241911 +15 14 1.4995577 +16 15 1.1108997 +17 16 0.8229747 +18 17 0.6096747 +19 18 0.4516581 +20 19 0.3345965 +21 20 0.2478752 +</div> +<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, + solution_type = "deSolve") +</div> +<div class='output'> time degradinol +1 0 100.0000000 +2 1 74.0818221 +3 2 54.8811636 +4 3 40.6569660 +5 4 30.1194212 +6 5 22.3130160 +7 6 16.5298888 +8 7 12.2456428 +9 8 9.0717953 +10 9 6.7205513 +11 10 4.9787068 +12 11 3.6883167 +13 12 2.7323722 +14 13 2.0241911 +15 14 1.4995577 +16 15 1.1108996 +17 16 0.8229747 +18 17 0.6096747 +19 18 0.4516581 +20 19 0.3345965 +21 20 0.2478752 +</div> +<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, + solution_type = "deSolve", use_compiled = FALSE) +</div> +<div class='output'> time degradinol +1 0 100.0000000 +2 1 74.0818221 +3 2 54.8811636 +4 3 40.6569660 +5 4 30.1194212 +6 5 22.3130160 +7 6 16.5298888 +8 7 12.2456428 +9 8 9.0717953 +10 9 6.7205513 +11 10 4.9787068 +12 11 3.6883167 +13 12 2.7323722 +14 13 2.0241911 +15 14 1.4995577 +16 15 1.1108996 +17 16 0.8229747 +18 17 0.6096747 +19 18 0.4516581 +20 19 0.3345965 +21 20 0.2478752 +</div> +<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, + solution_type = "eigen") +</div> +<div class='output'> time degradinol +1 0 100.0000000 +2 1 74.0818221 +3 2 54.8811636 +4 3 40.6569660 +5 4 30.1194212 +6 5 22.3130160 +7 6 16.5298888 +8 7 12.2456428 +9 8 9.0717953 +10 9 6.7205513 +11 10 4.9787068 +12 11 3.6883167 +13 12 2.7323722 +14 13 2.0241911 +15 14 1.4995577 +16 15 1.1108997 +17 16 0.8229747 +18 17 0.6096747 +19 18 0.4516581 +20 19 0.3345965 +21 20 0.2478752 +</div> +<div class='input'> + + # Compare integration methods to analytical solution + mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, + solution_type = "analytical")[21,] +</div> +<div class='output'> time degradinol +21 20 0.2478752 +</div> +<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, + method = "lsoda")[21,] +</div> +<div class='output'> time degradinol +21 20 0.2478752 +</div> +<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, + method = "ode45")[21,] +</div> +<div class='output'> time degradinol +21 20 0.2478752 +</div> +<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20, + method = "rk4")[21,] +</div> +<div class='output'> time degradinol +21 20 0.2480043 +</div> +<div class='input'> # rk4 is not as precise here + + # The number of output times used to make a lot of difference until the + # default for atol was adjusted + mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), + seq(0, 20, by = 0.1))[201,] +</div> +<div class='output'> time degradinol +201 20 0.2478752 +</div> +<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), + seq(0, 20, by = 0.01))[2001,] +</div> +<div class='output'> time degradinol +2001 20 0.2478752 +</div> +<div class='input'> + # Check compiled model versions - they are faster than the eigenvalue based solutions! + SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"), + m1 = list(type = "SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'> system.time( + print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), + c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), + solution_type = "eigen")[201,])) +</div> +<div class='output'> time parent m1 +201 20 4.978707 27.46227 +</div> +<div class='output'> user system elapsed + 0.000 0.028 0.004 +</div> +<div class='input'> system.time( + print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), + c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), + solution_type = "deSolve")[201,])) +</div> +<div class='output'> time parent m1 +201 20 4.978707 27.46227 +</div> +<div class='output'> user system elapsed + 0.000 0.020 0.003 +</div> +<div class='input'> system.time( + print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), + c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), + solution_type = "deSolve", use_compiled = FALSE)[201,])) +</div> +<div class='output'> time parent m1 +201 20 4.978707 27.46227 +</div> +<div class='output'> user system elapsed + 0.052 0.000 0.054 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinpredict</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinresplot-4.png b/inst/web/mkinresplot-4.png Binary files differnew file mode 100644 index 00000000..ef8c1dd7 --- /dev/null +++ b/inst/web/mkinresplot-4.png diff --git a/inst/web/mkinresplot.html b/inst/web/mkinresplot.html new file mode 100644 index 00000000..579c047e --- /dev/null +++ b/inst/web/mkinresplot.html @@ -0,0 +1,158 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinresplot. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Function to plot residuals stored in an mkin object +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinresplot(object, obs_vars = names(object$mkinmod$map), xlim = c(0, 1.1 * max(object$data$time)), xlab = "Time", ylab = "Residual", maxabs = "auto", legend = TRUE, lpos = "topright", ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>object</dt> + <dd> + A fit represented in an <code><a href='mkinfit.html'>mkinfit</a></code> object. +</dd> + <dt>obs_vars</dt> + <dd> + A character vector of names of the observed variables for which residuals + should be plotted. Defaults to all observed variables in the model +</dd> + <dt>xlim</dt> + <dd> + plot range in x direction. + </dd> + <dt>xlab</dt> + <dd> + Label for the x axis. Defaults to "Time [days]". +</dd> + <dt>ylab</dt> + <dd> + Label for the y axis. Defaults to "Residual [% of applied radioactivity]". +</dd> + <dt>maxabs</dt> + <dd> + Maximum absolute value of the residuals. This is used for the scaling of + the y axis and defaults to "auto". +</dd> + <dt>legend</dt> + <dd> + Should a legend be plotted? Defaults to "TRUE". +</dd> + <dt>lpos</dt> + <dd> + Where should the legend be placed? Default is "topright". Will be passed on to + <code><a href='http://www.inside-r.org/r-doc/graphics/legend'>legend</a></code>. </dd> + <dt>...</dt> + <dd> + further arguments passed to <code><a href='http://www.inside-r.org/r-doc/graphics/plot'>plot</a></code>. +</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function plots the residuals for the specified subset of the + observed variables from an mkinfit object. A combined plot of the fitted + model and the residuals can be obtained using <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code> + using the argument <code>show_residuals = TRUE</code>.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + Nothing is returned by this function, as it is called for its side effect, namely to produce a plot. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'>fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE) +mkinresplot(fit, "m1") +</div> +<p><img src='mkinresplot-4.png' alt='' width='540' height='400' /></p></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinresplot</li> + </ul> + <ul> + <li> hplot </li> + </ul> --> + + <h2>See also</h2> + + <code><a href='mkinplot.html'>mkinplot</a></code>, for a way to plot the data and the fitted lines of the + mkinfit object. + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mkinsub.html b/inst/web/mkinsub.html new file mode 100644 index 00000000..94e650ff --- /dev/null +++ b/inst/web/mkinsub.html @@ -0,0 +1,149 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mkinsub. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Function to set up a kinetic submodel for one state variable +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>submodel</dt> + <dd> + Character vector of length one to specify the submodel type. See + <code><a href='mkinmod.html'>mkinmod</a></code> for the list of allowed submodel names. + </dd> + <dt>to</dt> + <dd> + Vector of the names of the state variable to which a transformation + shall be included in the model. + </dd> + <dt>sink</dt> + <dd> + Should a pathway to sink be included in the model in addition to the + pathways to other state variables? + </dd> + <dt>full_name</dt> + <dd> + An optional name to be used e.g. for plotting fits performed with the model. + You can use non-ASCII characters here, but then your R code will not be + portable, <em>i.e.</em> may produce unintended plot results on other + operating systems or system configurations. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This is a convenience function to set up the lists used as arguments for + <code><a href='mkinmod.html'>mkinmod</a></code>.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + A list for use with <code><a href='mkinmod.html'>mkinmod</a></code>. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'># One parent compound, one metabolite, both single first order. +SFO_SFO <- mkinmod( + parent = list(type = "SFO", to = "m1"), + m1 = list(type = "SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'> +# The same model using mkinsub +SFO_SFO.2 <- mkinmod( + parent = mkinsub("SFO", "m1"), + m1 = mkinsub("SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'> +# Now supplying full names +SFO_SFO.2 <- mkinmod( + parent = mkinsub("SFO", "m1", full_name = "Test compound"), + m1 = mkinsub("SFO", full_name = "Metabolite M1")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mkinsub</li> + </ul> + <ul> + + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/mmkin.html b/inst/web/mmkin.html new file mode 100644 index 00000000..e09e6859 --- /dev/null +++ b/inst/web/mmkin.html @@ -0,0 +1,158 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>mmkin. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Fit one or more kinetic models with one or more state variables to one or more datasets +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>mmkin(models, datasets, cores = round(detectCores()/2), cluster = NULL, ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>models</dt> + <dd> + Either a character vector of shorthand names ("SFO", "FOMC", "DFOP", + "HS", "SFORB"), or an optionally named list of <code><a href='mkinmod.html'>mkinmod</a></code> + objects. + </dd> + <dt>datasets</dt> + <dd> + An optionally named list of datasets suitable as observed data for + <code><a href='mkinfit.html'>mkinfit</a></code>. + </dd> + <dt>cores</dt> + <dd> + The number of cores to be used for multicore processing. This is only + used when the <code>cluster</code> argument is <code>NULL</code>. + </dd> + <dt>cluster</dt> + <dd> + A cluster as returned by <code><a href='http://www.inside-r.org/r-doc/parallel/makeCluster'>makeCluster</a></code> to be used for parallel + execution. + </dd> + <dt>...</dt> + <dd> + Further arguments that will be passed to <code><a href='mkinfit.html'>mkinfit</a></code>. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>This function calls <code><a href='mkinfit.html'>mkinfit</a></code> on all combinations of models and datasets + specified in its first two arguments.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + A matrix of <code><a href='mkinfit.html'>mkinfit</a></code> objects that can be indexed using the model + and dataset names as row and column indices. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>## Not run: +# m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"), +# M1 = list(type = "SFO", to = "M2"), +# M2 = list(type = "SFO"), use_of_ff = "max") +# +# m_synth_FOMC_lin <- mkinmod(parent = list(type = "FOMC", to = "M1"), +# M1 = list(type = "SFO", to = "M2"), +# M2 = list(type = "SFO"), use_of_ff = "max") +# +# models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin) +# datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data) +# +# time_default <- system.time(fits <- mmkin(models, datasets)) +# time_1 <- system.time(fits.1 <- mmkin(models, datasets, cores = 1)) +# +# time_default +# time_1 +# +# endpoints(fits[["SFO_lin", 2]]) +# ## End(Not run) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>mmkin</li> + </ul> + <ul> + <li> optimize </li> + </ul> --> + + <h2>See also</h2> + + <code><a href='Extract.mmkin.html'>[.mmkin</a></code> for subsetting, <code><a href='plot.mmkin.html'>plot.mmkin</a></code> for plotting. + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/plot.mkinfit-4.png b/inst/web/plot.mkinfit-4.png Binary files differnew file mode 100644 index 00000000..55852399 --- /dev/null +++ b/inst/web/plot.mkinfit-4.png diff --git a/inst/web/plot.mkinfit.html b/inst/web/plot.mkinfit.html new file mode 100644 index 00000000..5308ecec --- /dev/null +++ b/inst/web/plot.mkinfit.html @@ -0,0 +1,189 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>plot.mkinfit. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Plot the observed data and the fitted model of an mkinfit object +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>"plot"(x, fit = x, obs_vars = names(fit$mkinmod$map), xlab = "Time", ylab = "Observed", xlim = range(fit$data$time), ylim = "default", col_obs = 1:length(fit$mkinmod$map), pch_obs = col_obs, lty_obs = rep(1, length(fit$mkinmod$map)), add = FALSE, legend = !add, show_residuals = FALSE, maxabs = "auto", lpos = "topright", inset = c(0.05, 0.05), ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>x</dt> + <dd> + Alias for fit introduced for compatibility with the generic S3 method. + </dd> + <dt>fit</dt> + <dd> + an object of class <code><a href='mkinfit.html'>mkinfit</a></code>. + </dd> + <dt>obs_vars</dt> + <dd> + A character vector of names of the observed variables for which the + data and the model should be plotted. Defauls to all observed variables + in the model. + </dd> + <dt>xlab</dt> + <dd> + label for the x axis. + </dd> + <dt>ylab</dt> + <dd> + label for the y axis. + </dd> + <dt>xlim</dt> + <dd> + plot range in x direction. + </dd> + <dt>ylim</dt> + <dd> + plot range in y direction. + </dd> + <dt>col_obs</dt> + <dd> + colors used for plotting the observed data and the corresponding model prediction lines. + </dd> + <dt>pch_obs</dt> + <dd> + symbols to be used for plotting the data. + </dd> + <dt>lty_obs</dt> + <dd> + line types to be used for the model predictions. + </dd> + <dt>add</dt> + <dd> + should the plot be added to an existing plot? + </dd> + <dt>legend</dt> + <dd> + should a legend be added to the plot? + </dd> + <dt>show_residuals</dt> + <dd> + should residuals be shown in the lower third of the plot? + </dd> + <dt>maxabs</dt> + <dd> + Maximum absolute value of the residuals. This is used for the scaling of + the y axis and defaults to "auto". + </dd> + <dt>lpos</dt> + <dd> + position of the legend. Passed to <code><a href='http://www.inside-r.org/r-doc/graphics/legend'>legend</a></code> as the first argument. + </dd> + <dt>inset</dt> + <dd> + Passed to <code><a href='http://www.inside-r.org/r-doc/graphics/legend'>legend</a></code> if applicable. + </dd> + <dt>...</dt> + <dd> + further arguments passed to <code><a href='http://www.inside-r.org/r-doc/graphics/plot'>plot</a></code>. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Solves the differential equations with the optimised and fixed parameters + from a previous successful call to <code><a href='mkinfit.html'>mkinfit</a></code> and plots + the observed data together with the solution of the fitted model.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The function is called for its side effect. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'># One parent compound, one metabolite, both single first order, path from +# parent to sink included +SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"), + m1 = mkinsub("SFO", full = "Metabolite M1" )) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'>fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) +plot(fit) +</div> +<p><img src='plot.mkinfit-4.png' alt='' width='540' height='400' /></p></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>plot.mkinfit</li> + </ul> + <ul> + <li> hplot </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/plot.mmkin-2.png b/inst/web/plot.mmkin-2.png Binary files differnew file mode 100644 index 00000000..049caa73 --- /dev/null +++ b/inst/web/plot.mmkin-2.png diff --git a/inst/web/plot.mmkin-4.png b/inst/web/plot.mmkin-4.png Binary files differnew file mode 100644 index 00000000..78bd5cd7 --- /dev/null +++ b/inst/web/plot.mmkin-4.png diff --git a/inst/web/plot.mmkin.html b/inst/web/plot.mmkin.html new file mode 100644 index 00000000..49b5d3af --- /dev/null +++ b/inst/web/plot.mmkin.html @@ -0,0 +1,149 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>plot.mmkin. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>"plot"(x, main = "auto", legends = 1, errmin_var = "All data", errmin_digits = 2, cex = 0.7, rel.height.middle = 0.9, ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>x</dt> + <dd> + An object of class <code><a href='mmkin.html'>mmkin</a></code>, with either one row or one column. +</dd> + <dt>main</dt> + <dd> + The main title placed on the outer margin of the plot. +</dd> + <dt>legends</dt> + <dd> + An index for the fits for which legends should be shown. +</dd> + <dt>errmin_var</dt> + <dd> + The variable for which the FOCUS chi2 error value should be shown. +</dd> + <dt>errmin_digits</dt> + <dd> + The number of digits for rounding the FOCUS chi2 error percentage. +</dd> + <dt>cex</dt> + <dd> + Passed to the plot functions and <code><a href='http://www.inside-r.org/r-doc/graphics/mtext'>mtext</a></code>. +</dd> + <dt>rel.height.middle</dt> + <dd> + The relative height of the middle plot. +</dd> + <dt>...</dt> + <dd> + Further arguments passed to <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code> and <code><a href='mkinresplot.html'>mkinresplot</a></code>. +</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>When x is a row selected from an mmkin object (<code><a href='Extract.mmkin.html'>[.mmkin</a></code>), the same model + fitted for at least one dataset is shown. When it is a column, the fit of at least one model + to the same dataset is shown.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The function is called for its side effect. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> # Only use one core not to offend CRAN checks + fits <- mmkin(c("FOMC", "HS"), list("FOCUS B" = FOCUS_2006_B, "FOCUS C" = FOCUS_2006_C), + cores = 1, quiet = TRUE) + plot(fits[, "FOCUS C"]) +</div> +<p><img src='plot.mmkin-2.png' alt='' width='540' height='400' /></p> +<div class='input'> plot(fits["FOMC", ]) +</div> +<p><img src='plot.mmkin-4.png' alt='' width='540' height='400' /></p></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>plot.mmkin</li> + </ul> + <ul> + + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/print.mkinds.html b/inst/web/print.mkinds.html new file mode 100644 index 00000000..d2f5f806 --- /dev/null +++ b/inst/web/print.mkinds.html @@ -0,0 +1,96 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>print.mkinds. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Print mkinds objects +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>"print"(x, ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>x</dt> + <dd> + An <code><a href='mkinds.html'>mkinds</a></code> object. + </dd> + <dt>...</dt> + <dd> + Not used. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Print mkinds objects.</p> + + </div> + </div> + <div class="span4"> + <!-- <ul> + <li>print.mkinds</li> + </ul> + <ul> + + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/print.mkinmod.html b/inst/web/print.mkinmod.html new file mode 100644 index 00000000..dd94db34 --- /dev/null +++ b/inst/web/print.mkinmod.html @@ -0,0 +1,96 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>print.mkinmod. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Print mkinmod objects +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>"print"(x, ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>x</dt> + <dd> + An <code><a href='mkinmod.html'>mkinmod</a></code> object. + </dd> + <dt>...</dt> + <dd> + Not used. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Print mkinmod objects in a way that the user finds his way to get to its components.</p> + + </div> + </div> + <div class="span4"> + <!-- <ul> + <li>print.mkinmod</li> + </ul> + <ul> + + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/schaefer07_complex_case.html b/inst/web/schaefer07_complex_case.html new file mode 100644 index 00000000..403e7229 --- /dev/null +++ b/inst/web/schaefer07_complex_case.html @@ -0,0 +1,132 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>schaefer07_complex_case. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Metabolism data set used for checking the software quality of KinGUI +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>data(schaefer07_complex_case)</div></pre> + + <div class="Description"> + <h2>Description</h2> + + <p>This dataset was used for a comparison of KinGUI and ModelMaker to check the + software quality of KinGUI in the original publication (Schäfer et al., 2007). + The results from the fitting are also included.</p> + + </div> + + <div class="Format"> + <h2>Format</h2> + + <p>The data set is a data frame with 8 observations on the following 6 variables. + <dl> + <dt><code>time</code></dt><dd>a numeric vector</dd></p> + + <p><dt><code>parent</code></dt><dd>a numeric vector</dd></p> + + <p><dt><code>A1</code></dt><dd>a numeric vector</dd></p> + + <p><dt><code>B1</code></dt><dd>a numeric vector</dd></p> + + <p><dt><code>C1</code></dt><dd>a numeric vector</dd></p> + + <p><dt><code>A2</code></dt><dd>a numeric vector</dd></p> + + <p></dl></p> + + <p>The results are a data frame with 14 results for different parameter values</p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>Schäfer D, Mikolasch B, Rainbird P and Harvey B (2007). KinGUI: a new kinetic + software tool for evaluations according to FOCUS degradation kinetics. In: Del + Re AAM, Capri E, Fragoulis G and Trevisan M (Eds.). Proceedings of the XIII + Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.</p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>data <- mkin_wide_to_long(schaefer07_complex_case, time = "time") +model <- mkinmod( + parent = list(type = "SFO", to = c("A1", "B1", "C1"), sink = FALSE), + A1 = list(type = "SFO", to = "A2"), + B1 = list(type = "SFO"), + C1 = list(type = "SFO"), + A2 = list(type = "SFO"), use_of_ff = "max") +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'>## Not run: mkinfit(model, data, plot=TRUE) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>schaefer07_complex_case</li><li>schaefer07_complex_results</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/summary.mkinfit.html b/inst/web/summary.mkinfit.html new file mode 100644 index 00000000..fe129c49 --- /dev/null +++ b/inst/web/summary.mkinfit.html @@ -0,0 +1,255 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>summary.mkinfit. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Summary method for class "mkinfit" +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>"summary"(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...) +"print"(x, digits = max(3, getOption("digits") - 3), ...)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>object</dt> + <dd> + an object of class <code><a href='mkinfit.html'>mkinfit</a></code>. +</dd> + <dt>x</dt> + <dd> + an object of class <code>summary.mkinfit</code>. +</dd> + <dt>data</dt> + <dd> + logical, indicating whether the data should be included in the summary. +</dd> + <dt>distimes</dt> + <dd> + logical, indicating whether DT50 and DT90 values should be included. +</dd> + <dt>alpha</dt> + <dd> + error level for confidence interval estimation from t distribution +</dd> + <dt>digits</dt> + <dd> + Number of digits to use for printing +</dd> + <dt>...</dt> + <dd> + optional arguments passed to methods like <code>print</code>. +</dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>Lists model equations, the summary as returned by <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modFit'>summary.modFit</a></code>, + the chi2 error levels calculated according to FOCUS guidance (2006) as far + as defined therein, and optionally the data, consisting of observed, predicted + and residual values.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + The summary function returns a list derived from + <code><a href='http://www.inside-r.org/packages/cran/FME/docs/modFit'>summary.modFit</a></code>, with components, among others + <dt>version, Rversion</dt><dd>The mkin and R versions used</dd></p> + + <p><dt>date.fit, date.summary</dt><dd>The dates where the fit and the summary were produced</dd></p> + + <p><dt>use_of_ff</dt><dd>Was maximum or minimum use made of formation fractions</dd></p> + + <p><dt>residuals, residualVariance, sigma, modVariance, df</dt><dd>As in summary.modFit</dd></p> + + <p><dt>cov.unscaled, cov.scaled, info, niter, stopmess, par</dt><dd>As in summary.modFit</dd></p> + + <p><dt>bpar</dt><dd>Optimised and backtransformed parameters</dd></p> + + <p><dt>diffs </dt><dd>The differential equations used in the model</dd></p> + + <p><dt>data </dt><dd>The data (see Description above).</dd></p> + + <p><dt>start </dt><dd>The starting values and bounds, if applicable, for optimised parameters.</dd></p> + + <p><dt>fixed </dt><dd>The values of fixed parameters.</dd></p> + + <p><dt>errmin </dt><dd>The chi2 error levels for each observed variable.</dd></p> + + <p><dt>bparms.ode </dt><dd>All backtransformed ODE parameters, for use as starting parameters for + related models.</dd></p> + + <p><dt>ff </dt><dd>The estimated formation fractions derived from the fitted model.</dd></p> + + <p><dt>distimes </dt><dd>The DT50 and DT90 values for each observed variable.</dd></p> + + <p><dt>SFORB</dt><dd>If applicable, eigenvalues of SFORB components of the model.</dd></p> + + <p>The print method is called for its side effect, i.e. printing the summary. +</dl></p> + + </div> + + <div class="References"> + <h2>References</h2> + + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + Degradation Kinetics from Environmental Fate Studies on Pesticides in EU + Registration” Report of the FOCUS Work Group on Degradation Kinetics, + EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, + <a href = 'http://focus.jrc.ec.europa.eu/dk'>http://focus.jrc.ec.europa.eu/dk</a></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'> summary(mkinfit(mkinmod(parent = list(type = "SFO")), FOCUS_2006_A, quiet = TRUE)) +</div> +<div class='output'>mkin version: 0.9.41 +R version: 3.2.2 +Date of fit: Mon Nov 9 08:25:31 2015 +Date of summary: Mon Nov 9 08:25:31 2015 + +Equations: +d_parent = - k_parent_sink * parent + +Model predictions using solution type analytical + +Fitted with method Port using 35 model solutions performed in 0.104 s + +Weighting: none + +Starting values for parameters to be optimised: + value type +parent_0 101.24 state +k_parent_sink 0.10 deparm + +Starting values for the transformed parameters actually optimised: + value lower upper +parent_0 101.240000 -Inf Inf +log_k_parent_sink -2.302585 -Inf Inf + +Fixed parameter values: +None + +Optimised, transformed parameters with symmetric confidence intervals: + Estimate Std. Error Lower Upper +parent_0 109.200 4.3910 98.410 119.900 +log_k_parent_sink -3.291 0.1152 -3.573 -3.009 + +Parameter correlation: + parent_0 log_k_parent_sink +parent_0 1.000 0.575 +log_k_parent_sink 0.575 1.000 + +Residual standard error: 6.08 on 6 degrees of freedom + +Backtransformed parameters: +Confidence intervals for internally transformed parameters are asymmetric. +t-test (unrealistically) based on the assumption of normal distribution +for estimators of untransformed parameters. + Estimate t value Pr(>t) Lower Upper +parent_0 109.20000 24.860 1.394e-07 98.41000 119.90000 +k_parent_sink 0.03722 8.679 6.457e-05 0.02807 0.04934 + +Chi2 error levels in percent: + err.min n.optim df +All data 8.385 2 6 +parent 8.385 2 6 + +Resulting formation fractions: + ff +parent_sink 1 + +Estimated disappearance times: + DT50 DT90 +parent 18.62 61.87 + +Data: + time variable observed predicted residual + 0 parent 101.24 109.153 -7.9132 + 3 parent 99.27 97.622 1.6484 + 7 parent 90.11 84.119 5.9913 + 14 parent 72.19 64.826 7.3641 + 30 parent 29.71 35.738 -6.0283 + 62 parent 5.98 10.862 -4.8818 + 90 parent 1.54 3.831 -2.2911 + 118 parent 0.39 1.351 -0.9613 +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>summary.mkinfit</li><li>print.summary.mkinfit</li> + </ul> + <ul> + <li> utilities </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. Styled with <a href="http://twitter.github.com/bootstrap">bootstrap</a>.</p> + </footer> + </div> + </body> +</html>
\ No newline at end of file diff --git a/inst/web/synthetic_data_for_UBA.html b/inst/web/synthetic_data_for_UBA.html new file mode 100644 index 00000000..3694bcb9 --- /dev/null +++ b/inst/web/synthetic_data_for_UBA.html @@ -0,0 +1,153 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>synthetic_data_for_UBA_2014. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=""> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Synthetic datasets for one parent compound with two metabolites +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>synthetic_data_for_UBA_2014</div></pre> + + <div class="Description"> + <h2>Description</h2> + + <p>The 12 datasets were generated using four different models and three different + variance components. The four models are either the SFO or the DFOP model with either + two sequential or two parallel metabolites.</p> + + <p>Variance component 'a' is based on a normal distribution with standard deviation of 3, + Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7. + Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the + minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07 + for the increase of the standard deviation with y.</p> + + <p>Initial concentrations for metabolites and all values where adding the variance component resulted + in a value below the assumed limit of detection of 0.1 were set to <code>NA</code>.</p> + + <p>As an example, the first dataset has the title <code>SFO_lin_a</code> and is based on the SFO model + with two sequential metabolites (linear pathway), with added variance component 'a'.</p> + + <p>Compare also the code in the example section to see the degradation models.</p> + + </div> + + <div class="Format"> + <h2>Format</h2> + + <p>A list containing datasets in the form internally used by the 'gmkin' package. + The list has twelfe components. Each of the components is one dataset that has, + among others, the following components + <dl> + <dt><code>title</code></dt><dd>The name of the dataset, e.g. <code>SFO_lin_a</code></dd></p> + + <p><dt><code>data</code></dt><dd>A data frame with the data in the form expected by <code><a href='mkinfit.html'>mkinfit</a></code></dd></p> + + <p></dl></p> + + <p></p> + + </div> + + <div class="Source"> + <h2>Source</h2> + + <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative + zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p> + + <p>Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for + measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>## Not run: +# m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"), +# M1 = list(type = "SFO", to = "M2"), +# M2 = list(type = "SFO"), use_of_ff = "max") +# +# +# m_synth_SFO_par <- mkinmod(parent = list(type = "SFO", to = c("M1", "M2"), +# sink = FALSE), +# M1 = list(type = "SFO"), +# M2 = list(type = "SFO"), use_of_ff = "max") +# +# m_synth_DFOP_lin <- mkinmod(parent = list(type = "DFOP", to = "M1"), +# M1 = list(type = "SFO", to = "M2"), +# M2 = list(type = "SFO"), use_of_ff = "max") +# +# m_synth_DFOP_par <- mkinmod(parent = list(type = "DFOP", to = c("M1", "M2"), +# sink = FALSE), +# M1 = list(type = "SFO"), +# M2 = list(type = "SFO"), use_of_ff = "max") +# +# mkinfit(m_synth_SFO_lin, synthetic_data_for_UBA_2014[[1]]$data) +# ## End(Not run) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>synthetic_data_for_UBA_2014</li> + </ul> + <ul> + <li>datasets</li> + </ul> --> + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. 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\ No newline at end of file diff --git a/inst/web/transform_odeparms.html b/inst/web/transform_odeparms.html new file mode 100644 index 00000000..2690f4c3 --- /dev/null +++ b/inst/web/transform_odeparms.html @@ -0,0 +1,281 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<title>transform_odeparms. mkin 0.9-41</title> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<meta name="author" content=" + Johannes Ranke +"> + +<link href="css/bootstrap.css" rel="stylesheet"> +<link href="css/bootstrap-responsive.css" rel="stylesheet"> +<link href="css/highlight.css" rel="stylesheet"> +<link href="css/staticdocs.css" rel="stylesheet"> + +<!--[if lt IE 9]> + <script src="http://html5shim.googlecode.com/svn/trunk/html5.js"></script> +<![endif]--> + +<script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + processEscapes: true + } + }); +</script> +<script type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> +</script> + </head> + + <body> + <div class="navbar"> + <div class="navbar-inner"> + <div class="container"> + <a class="brand" href="#">mkin 0.9-41</a> + <div class="nav"> + <ul class="nav"> + <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> + </ul> + </div> + </div> + </div> +</div> + + <div class="container"> + <header> + + </header> + + <h1> + Functions to transform and backtransform kinetic parameters for fitting +</h1> + +<div class="row"> + <div class="span8"> + <h2>Usage</h2> + <pre><div>transform_odeparms(parms, mkinmod, transform_rates = TRUE, transform_fractions = TRUE) +backtransform_odeparms(transparms, mkinmod, transform_rates = TRUE, transform_fractions = TRUE)</div></pre> + + <h2>Arguments</h2> + <dl> + <dt>parms</dt> + <dd> + Parameters of kinetic models as used in the differential equations. + </dd> + <dt>transparms</dt> + <dd> + Transformed parameters of kinetic models as used in the fitting procedure. + </dd> + <dt>mkinmod</dt> + <dd> + The kinetic model of class <code><a href='mkinmod.html'>mkinmod</a></code>, containing the names of + the model variables that are needed for grouping the formation fractions + before <code><a href='ilr.html'>ilr</a></code> transformation, the parameter names and + the information if the pathway to sink is included in the model. + </dd> + <dt>transform_rates</dt> + <dd> + Boolean specifying if kinetic rate constants should be transformed in the + model specification used in the fitting for better compliance with the + assumption of normal distribution of the estimator. If TRUE, also + alpha and beta parameters of the FOMC model are log-transformed, as well + as k1 and k2 rate constants for the DFOP and HS models and the break point tb + of the HS model. + </dd> + <dt>transform_fractions</dt> + <dd> + Boolean specifying if formation fractions constants should be transformed in the + model specification used in the fitting for better compliance with the + assumption of normal distribution of the estimator. The default (TRUE) is + to do transformations. The g parameter of the DFOP and HS models are also + transformed, as they can also be seen as compositional data. The + transformation used for these transformations is the <code><a href='ilr.html'>ilr</a></code> + transformation. + </dd> + </dl> + + <div class="Description"> + <h2>Description</h2> + + <p>The transformations are intended to map parameters that should only take + on restricted values to the full scale of real numbers. For kinetic rate + constants and other paramters that can only take on positive values, a + simple log transformation is used. For compositional parameters, such as + the formations fractions that should always sum up to 1 and can not be + negative, the <code><a href='ilr.html'>ilr</a></code> transformation is used.</p> + + <p>The transformation of sets of formation fractions is fragile, as it supposes + the same ordering of the components in forward and backward transformation. + This is no problem for the internal use in <code><a href='mkinfit.html'>mkinfit</a></code>.</p> + + </div> + + <div class="Value"> + <h2>Value</h2> + + <p><dl> + A vector of transformed or backtransformed parameters with the same names + as the original parameters. +</dl></p> + + </div> + + <h2 id="examples">Examples</h2> + <pre class="examples"><div class='input'>SFO_SFO <- mkinmod( + parent = list(type = "SFO", to = "m1", sink = TRUE), + m1 = list(type = "SFO")) +</div> +<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong> +<div class='input'># Fit the model to the FOCUS example dataset D using defaults +fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) +summary(fit, data=FALSE) # See transformed and backtransformed parameters +</div> +<div class='output'>mkin version: 0.9.41 +R version: 3.2.2 +Date of fit: Mon Nov 9 08:25:33 2015 +Date of summary: Mon Nov 9 08:25:33 2015 + +Equations: +d_parent = - k_parent_sink * parent - k_parent_m1 * parent +d_m1 = + k_parent_m1 * parent - k_m1_sink * m1 + +Model predictions using solution type deSolve + +Fitted with method Port using 153 model solutions performed in 0.626 s + +Weighting: none + +Starting values for parameters to be optimised: + value type +parent_0 100.7500 state +k_parent_sink 0.1000 deparm +k_parent_m1 0.1001 deparm +k_m1_sink 0.1002 deparm + +Starting values for the transformed parameters actually optimised: + value lower upper +parent_0 100.750000 -Inf Inf +log_k_parent_sink -2.302585 -Inf Inf +log_k_parent_m1 -2.301586 -Inf Inf +log_k_m1_sink -2.300587 -Inf Inf + +Fixed parameter values: + value type +m1_0 0 state + +Optimised, transformed parameters with symmetric confidence intervals: + Estimate Std. Error Lower Upper +parent_0 99.600 1.61400 96.330 102.900 +log_k_parent_sink -3.038 0.07826 -3.197 -2.879 +log_k_parent_m1 -2.980 0.04124 -3.064 -2.897 +log_k_m1_sink -5.248 0.13610 -5.523 -4.972 + +Parameter correlation: + parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink +parent_0 1.00000 0.6075 -0.06625 -0.1701 +log_k_parent_sink 0.60752 1.0000 -0.08740 -0.6253 +log_k_parent_m1 -0.06625 -0.0874 1.00000 0.4716 +log_k_m1_sink -0.17006 -0.6253 0.47163 1.0000 + +Residual standard error: 3.211 on 36 degrees of freedom + +Backtransformed parameters: +Confidence intervals for internally transformed parameters are asymmetric. +t-test (unrealistically) based on the assumption of normal distribution +for estimators of untransformed parameters. + Estimate t value Pr(>t) Lower Upper +parent_0 99.600000 61.720 2.024e-38 96.330000 1.029e+02 +k_parent_sink 0.047920 12.780 3.050e-15 0.040890 5.616e-02 +k_parent_m1 0.050780 24.250 3.407e-24 0.046700 5.521e-02 +k_m1_sink 0.005261 7.349 5.758e-09 0.003992 6.933e-03 + +Chi2 error levels in percent: + err.min n.optim df +All data 6.398 4 15 +parent 6.827 3 6 +m1 4.490 1 9 + +Resulting formation fractions: + ff +parent_sink 0.4855 +parent_m1 0.5145 +m1_sink 1.0000 + +Estimated disappearance times: + DT50 DT90 +parent 7.023 23.33 +m1 131.761 437.70 +</div> +<div class='input'> +## Not run: +# fit.2 <- mkinfit(SFO_SFO, FOCUS_2006_D, transform_rates = FALSE) +# summary(fit.2, data=FALSE) +# ## End(Not run) + +initials <- fit$start$value +names(initials) <- rownames(fit$start) +transformed <- fit$start_transformed$value +names(transformed) <- rownames(fit$start_transformed) +transform_odeparms(initials, SFO_SFO) +</div> +<div class='output'> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink + 100.750000 -2.302585 -2.301586 -2.300587 +</div> +<div class='input'>backtransform_odeparms(transformed, SFO_SFO) +</div> +<div class='output'> parent_0 k_parent_sink k_parent_m1 k_m1_sink + 100.7500 0.1000 0.1001 0.1002 +</div> +<div class='input'> +## Not run: +# # The case of formation fractions +# SFO_SFO.ff <- mkinmod( +# parent = list(type = "SFO", to = "m1", sink = TRUE), +# m1 = list(type = "SFO"), +# use_of_ff = "max") +# +# fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_2006_D) +# summary(fit.ff, data = FALSE) +# initials <- c("f_parent_to_m1" = 0.5) +# transformed <- transform_odeparms(initials, SFO_SFO.ff) +# backtransform_odeparms(transformed, SFO_SFO.ff) +# +# # And without sink +# SFO_SFO.ff.2 <- mkinmod( +# parent = list(type = "SFO", to = "m1", sink = FALSE), +# m1 = list(type = "SFO"), +# use_of_ff = "max") +# +# +# fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_2006_D) +# summary(fit.ff.2, data = FALSE) +# ## End(Not run) +</div></pre> + </div> + <div class="span4"> + <!-- <ul> + <li>transform_odeparms</li><li>backtransform_odeparms</li> + </ul> + <ul> + <li> manip </li> + </ul> --> + + + <h2>Author</h2> + + Johannes Ranke + + + </div> +</div> + + <footer> + <p class="pull-right"><a href="#">Back to top</a></p> +<p>Built by <a href="https://github.com/hadley/staticdocs">staticdocs</a>. 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\ No newline at end of file diff --git a/inst/web/vignettes/FOCUS_D.html b/inst/web/vignettes/FOCUS_D.html new file mode 100644 index 00000000..557ceb03 --- /dev/null +++ b/inst/web/vignettes/FOCUS_D.html @@ -0,0 +1,275 @@ +<!DOCTYPE html> + +<html xmlns="http://www.w3.org/1999/xhtml"> + +<head> + +<meta charset="utf-8"> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<meta name="generator" content="pandoc" /> + +<meta name="author" content="Johannes Ranke" /> + +<meta name="date" content="2015-11-09" /> + +<title>Example evaluation of FOCUS Example Dataset D</title> + +<script 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Lj%2F8LYMDgBMACAbuP%2FwtQ4ATRuPCbj%2F8EATDgBNCVcREA4ATRGPAxAOAE0DWAA%2FK%2B0rPyvtKwEQ3isrKyvtENYrKysr7TEwACsrKysBKysrKysrKysTNDYzMhYVFAYjIiYlNC4CIyIOAhUUHgIzMj4CMnttbXt7bW17AW4NHzQmJjQfDQ0fNCYmNB8NAVuuvLyurry8rjlmTC0tTGY5OWZMLS1MZgAAAAABAF0AAAFxArUADABQQDUDBIsHHwABAA8GHwYCCAYGDg2NB50HAl8Hbwd%2FBwMHAI4QDCAMMAxQDGAMcAwGDAwDBVUDVgA%2FPxI5L13tM11dARESOS9eXc5dM%2B05MTATPgE3MxEjEQ4DB105bC1CXQwjKS0VAiYWRTT9SwI0CxgWFAcAAAABADgAAAHqAsUALADlQC8DBxMXAxcTiwcDFAcDEwcXBxcQGowMAEAQAE0AQAgJAEwALiQlawp7CgIKjBAtJLj%2F8LMSAE0kuP%2FgsxEATSS4%2F8hAGBAATSQfCBEATR8QDxAATB%2BPByAODwBMF7j%2F6LQREgBMF7j%2F6LYPAE0HFwwluP%2FgtBASAEwluP%2F4tAkATSUquP%2FwtBESAEwquP%2FosxAATSq4%2F%2Fi0CQsATCq4%2F%2FBACQgATSpYCo4MVQA%2F7T8rKysrMysrEjk5Kysr7SsrMisrKwEQ3u1xxjIQ1isrxO0SOTkvLwDBhwUrEADBhwV9EMQxMAEUDgIHDgMVIRUhJjQ1ND4CNz4DNTQuAiMiDgIHJz4DMzIWAdMdMDweES0oGwE%2F%2FlwBHzI%2BHhktIxUTICsYHTAmGgcuCSQyPiRtbQIEJENBPx4RLjIuEU8GDAUvUUhAHhgvLzIcHiocDRAVFwZCCh0aEmUAAAABADf%2F8QHpAsUAOADDQB6nIQGWIQGHIQEOCA8ATQUYDwBNNhAPAE00CA8ATQK4%2F%2FBAYg8ATVkQaRACEIyPI58jryMDFyMBIyt7JgFdJm0mAgomCiY1agMBSQNZAwIDjIcrAR8rAQ4rAQgrOhgZNjU5JiYKjggIHjYAjzUQDwBNNTAIEBIATDBXGBWPGR4IDQ4ATB5YAD8rM%2B0yPyszK%2B0yEjkv7Tk9LwEYEN4yxjIQ1l5dXV3tXV0SOTkvL11dENRdXe1dMTAAKysrASsrXV1dNzI2NTQuAisBNTMyPgI1NC4CIyIGByc%2BAzMyHgIVFAYHHgMVFA4CIyIuAic3HgHiWU0gNEUkGCEZNSwcFCErGDBDFyQMJS41HTdQNBo%2BLBsyJRcfQWJDGjcwJAgSEU9ERjsmNB8NTAoaLSIcKBoLHA9HCBMQCxsxQyc2TRQIHi48JS1NOCAIDAwEUQgaAAAAAgAoAAACCgK1AA4AFQBrQD8oAgEIEQEOFSsFASgFOAUCCQUBDwUFDYsHCgkXDxQBO18UbxR%2FFAMUiwAADhYAFI4NDQeOCQkFC1UPjgQEBVYAPzMQ7T8SOS%2FtMy%2FtMgEQzjIQ7XJeXRDW3TLtMi9eXV1xMjEwAF5dAV03PgM3MxEzFSMVIzUhAQ4DBzMoE0JTXzBZUlJa%2FsoBNh49OjMU3PAsdXx4MP5ITLGxAZYgT1ZaKwAAAQBB%2F%2FEB6wK1ACcApkAQGSAPAE0BIA8ATRggDwBNFLj%2F8EA7DwBNDhAPAE0iahYBFowPAx8DAggDKQAjI4sgGxQgGxsgDg0oIyKOIACOGxAREgBMGwgQAE0bEA4ATRu4%2F%2BBAHg0ATRsQDABNGxsIIFYOEY8NIA8ATQ0ICBASAEwIVwA%2FKzMr7TI%2FEjkvKysrKyvtEO0yARDeMtY5hyt9EMQBGBDWXl3tXcQxMAArKysBKysTHgEVFA4CIyIuAic3HgEzMj4CNTQuAiM%2BAzchFSMOA8eXjR8%2FYEIaNi4kCBIRTTgtPSYRGkJuVAYHBgQCAU79AQQEBAGqBnhnLk44IAgMDARRCBoUIi0ZJjsoFTFWUlEtTw8zNzQAAAACAD%2F%2F8QIBArgAGgAtAJi1LQgPAE0kuP%2Fwsw8ATRq4%2F%2FizDwBNCbj%2F%2BEA5EgBNAxgPAE1XAwEFMAYBDwYBBgYAWSsBK4wHE3cTAhMvHoYLlgsCCyGMRwBXAAIALgsOjx4bGwUmuP%2FwQAsODwBMJo8YVwaPBbj%2F8LQOAE0FWAA%2FK%2B0%2F7SsSOS8z7TIBEN5d7TJdMhDWXe1dEjkvXV0zMTAAXQErKysrKxM0PgI3Fw4DBz4BMzIeAhUUDgIjIiY3IgYHDgEVFB4CMzI%2BAjU0Jj84Z5NcBzthTDQNGkEjO1I0Fxo1Ujhyd%2BAjPB4BAQwfNCghLh4NQQEWZZtqNwFOARg0Uz0MECQ7TSomUEIqnNANDQsXCyxOOyMcLDMYREgAAAABADwAAAH1ArUADgAlQBIOiwAABwWLCBAHDw5VCQWOB1YAP%2B0yPwEQzhDW7RI5L%2B0xMDM%2BAzchNSEVDgMHowYtQk8m%2Fq8BuSFQRzMGULGnjy1RTSaFpbtdAAAAAAMAN%2F%2FxAf0CxQAhADMARQEOuQAy%2F%2BizDwBNMrj%2F8EAJDgBNLhAPAE0NuP%2Fwsw8ATQO4%2F%2Fi0DxAATEW4%2F%2FCzDgBNQLj%2F8LUPAE00jBu4%2F%2FhAIRESAEwbAENBIEExLw8vEA8ATS8PQSAvIEGLDy8UDy9BD7j%2F4EA3DwBNUA8BICAPAE1fIAEPIA8gCiyMdgABDwABAEc%2BjBEIERIATBEijAoQEABNCkYPDyAgBTmPFrj%2F8LYICQBMFlgnuP%2FwtQ8ATSePBbj%2F%2BEALDgBNBRAICQBMBVcAPysr7Ss%2FK%2B0SORkvMy8BGBDeK%2B3UK%2B0Q1l1d7RI5OS8vXStdK8GHBCsQAcGHBH0QxAArhw7EEIcOxAEYENQr7TEwACsrASsrKysrJRQOAiMiLgI1ND4CNyY1ND4CMzIeAhUUDgIHFgUUHgIzMj4CNTQuAicOARM0LgIjIg4CFRQeAhc%2BAQH9HTlWOEFXNBYTHykXYh43TjA4UDMXER0lFHb%2Bmg0gMiYiMyAQGC0%2FKC0x%2Bg0cLCAgLRwNECI3JioytCpHNR0lOEMeHTUuJQ44bSZFNB8iNUAeHTMsIg04fxInIRYUHygVIDAkGQkZSgEwDyQfFRQfJRIWLCYeCRlDAAIAM%2F%2F%2BAfUCxQAaAC0AzkALJCAPAE0YIA8ATQK4%2F%2BCzDwBNArj%2F8LMOAE0CuP%2F4swgATQG4%2F%2FCzDwBNAbj%2F6LMOAE0BuP%2F4QCkJAE0eCSEIDQ4ATCGMAASbA6sDAncDAU8DAQMDEWgAAQ8AHwACCAAvK7j%2F8LMPAE0ruP%2F4QBEOAE0rjBEIEABNES4eG48JDLj%2FwEAYCgBNDAwDJggOAE0mjxZYBI8DCA4ATQNXAD8r7T%2FtKxI5Lysz7TIBEN4r7SsrEMZeXV0ROS9dXV0zEO0rMjIxMCsrKysrKysrARQGBycyPgI3DgEjIi4CNTQ%2BAjMyHgIHMjY3NjQ1NC4CIyIOAhUUFgH1zcoDP2VMMwwaQiM7UjQXGjVSODlXOx7gIz4dAQwfNCghLh4NQQGgztMBThg1VD0MDyM7TSolUEIqKExtiQ0NCxULK087IxwrMxhER%2F%2F%2FADj%2F8wC%2BAggCJgARAAABBwARAAABjwAQQAsBABEMBQBQAQgOTysrNAAA%2F%2F8AJv9hAL4CCAImAA8AAAEHABEAAAGPABBACwEOEw4HAVABABBPKys0AAAAAQA1AEwB%2FwIKAAYAYEAmBAUGAAQABq8FBBQFBAYFAwIBAAMAAa8CAxQCAwECAAUQDQBNBQK4%2F%2FBACg0ATQJQAIAAAgAAGS9dzSvNKwEYLy%2FBhwQrEAHBhwR9EMQBGC%2FBhwQrEAHBhwR9EMQxMBMFByU1JRecAWMa%2FlABsBoBK5JNt1C3TQAAAAIANQCZAf8BvQADAAcALkAaAwcHAgUFCQgCrz8ATwCvAAMABK8wBkAGAgYAL13t3l3tARESOS8zMy8zMTA3IRUhESEVITUByv42Acr%2BNuhPASRPAAEANQBMAf8CCgAGAGRAIgMEBQYDBgWvBAMUBAMFBAIBAAYCBgCvAQIUAQIAAQYEAQS4%2F%2FBAEA0ATQQBEA0ATQFQBoAGAgYAGS9dzSvNKwEYLy8vEMGHBCsQAcGHBH0QxBABwYcEGCsQAcGHBH0QxDEwEzcFFQUnJTUaAbD%2BUBoBYwG9TbdQt02SAAAAAgAT%2F%2FMBeQLFACEALQCSuQAJ%2F%2BCzCQBNCLj%2F4LMKAE0EuP%2F4sxEATQO4%2F%2FhANhESAEwigihAGzhIKCgSfwAYAQgYGANYH2gfeB8DH38HCycLNwsDCy8vLwECAy4VICsBKyVjArj%2F8LYPAE0CAIgDuP%2Fwtg8QAEwDBmgAPzMr7TIrP91xzgEQzjJdENZd7V0SOS9eXe0zLyvtMTAAKysBKysTIgcnPgEzMh4CFRQOBBUcARcjJjU0PgQ1NCYTFAYjIiY1NDYzMhauQj8aIlUwOUksERomLiYaAU4EFyQoJBc2CyQfHyQkHx8kAnciSBIWHzA7GyI1LiotNSEFDAUUFiA0LScoLBoqNf2%2FGygoGxsoKAAAAgBB%2F2cDdQLFAEUAVQD3QK2mRAGJQ5lDAmc%2FAVU%2FAVg1AZYpAZcjpyMCihkBaRkBCQIBqAMBA0lNfxMuLy%2FvEwGWE6YTAogTAVYTAROZHKkcAngcAVkcARx%2FkEGgQQKHQQEPQR9BAghBV1N%2F0AvgC%2FALA3kLiQuZCwMLdiaGJpYmAycmNyZXJgMmf%2BA3AZ83ATdWTVCHEDyHIRMQYEkXRocGLzKHLitpA3kDiQMDSwNbAwI6AwEDIAgLSAMABgAvMzMrXV1d3jLtMhDtMjI%2FM97tEO0yARDeXXH9XV3eXXHtENZeXV1d%2FV1dXc5dXV1xMi8zEO0yOV0xMABdAV1dXV1dXV1dXSUiJicOASMiLgI1ND4CMzIWFxEUFjMyPgI1NC4CIyIOAhUUHgIzMjY3Fw4BIyIuAjU0PgIzMh4CFRQOAicyNjcuAT0BLgEjIgYVFBYCsiAqDhY%2BHzBPOiAeO1U2M1UXFhIaJxkMLFJ1SUl6WDE0XX9MO00LChFZM1medkVEc5dUUpJuQB80SPAVKxEDAgYiFUdGRCsSDwwRHjtWOTBVPyQQCP7OIBokOkkmP29UMDFbhFRYhVktDgRKBg4zaaNwbKJrNjVlj1pEaEckUQkJDRoM6wIEWENHVAAAAgAIAAACjwK1ABEAGADcuQAP%2F%2FhANA0ATRAPDg0MEQwHCAkKCwYLBBgXFhUFFQUgDBIBTAsFFQVzBgsUBgsFBgECAxITFBUAFQC4%2F%2FBAQwwBTQwAFQBzEQwUEQwAERUVBhEaABoBQBpwGqAa0BoEDxoBDwYfBgIIBhkDBHkYXxVvFX8VAxIVEhUMEUIMQQtBBkIAPz8%2FPxI5OS8vXTPtMgEQxl5dXV1xEMYRORkvEADBhwUYK4d9xAErEIcOxMQFxMQOxMQQAMGHBRgrh33EASsQhw7ExAXExBCHDsTExMQQhw7ExMTEMTABKyEuASchByM%2BAzczHgMXAy4BJw4BBwIlER4Q%2FsY%2FZShGQ0IkWSRCQ0YoxSA%2FIiM%2FHy1XLbFuu6iZS0uZqLtuAQFXo0tLo1cAAAADAFb%2F%2BgJQArwAGAApADgAh7VXFQEwdhO4%2F%2FC0DgBNExC4%2F%2FC0DhIATBC4%2F%2FBADQkATRAkdhZACAkBTBa4%2F%2FhAEQ4QAEwWQAkATRYwCABNFjo6uP%2FAQBwJAU0ZOHMFqDk4NXwLEyp5KSkABgtFGh98BQBGAD8y7TI%2FMxI5L%2B05EO0yARD27TIrEN4rKysr7dQrKzIr7TEwXQUiLgInET4DMzIeAhUUBgceARUUBgEVFhceATMyPgI1NC4CIyczMj4CNTQuAiMiBgcBERUxMTAUFDAxMBU8aU0sNi09SJn%2B%2FwsPDSMXK006IxoxRCp9ZiRALxscMUIlJSoRBgIEBgUCoAUGBAISK0YzOUkSEk5HaGkBSfQBAQEBCxwyJiIvHAxOChoqIB4pGQwCAgABADr%2F8QJTAsUAJwDmQCMpQAwSSGgDAR4IEgBNhxuXGwICCBIATQIIDwBNEA8PIiMpGrj%2F8LMKAE0auP%2F4QBAJAE0adgUIEgBNBQgPAE0FuP%2F4tg4ATQWpKBC4%2F%2BhAFA0PAEwQFQgODwBMFQgJCgBMFXwPuP%2F4tBESAEwPuP%2FoQBkNDwBMDwoIEQBNCggOAE0KRSIYDQ8ATCIfuP%2F4tA4PAEwfuP%2F4QBMJCgBMH3wjGA0PAEwjAAgQAE0AuP%2F4tA4ATQBGAD8rKzIr7SsrMis%2FKyszKyvtKysyKwEQ%2FisrK%2B0rKxDGMjIvMzEwACsrXSsBXSsFIi4CNTQ%2BAjMyHgIXBy4DIyIOAhUUHgIzMjY3Fw4DAYBJeFYvNVp4RCtFMyAGHAkiKzMaN1g%2BIR06VzpDWBYZByQ2SA8wXIdXV4dcMAwQEARQBg4OCSZIZ0A%2BZkkoHAtQBQ8OCgAAAAACAFb%2F%2BgKPArwAEAAdAIJAHRQQDg8ATBQQCgBNFAgJAE0UEAgATRR2AEAJAU0AuP%2F4sxIATQC4%2F%2FBAGw4PAEwAQAkATQCpHxpzCKgeGhcICgBNF3wJDLj%2F%2BLYOAE0MRRsRuP%2F4QA0KAE0RfAgFCA4ATQVGAD8rM%2B0rMj8rM%2B0rMgEQ9u0Q9isrKyvtKysrKzEwARQOAiMiJicRPgEzMh4CATI2NTQmIyIGBxEeAQKPOGaOVSplKSllKlWOZjj%2Bh4qIiIopLQkJLQFbWoVXKwcKAqAKBytXhf6ajX9%2FjQIB%2Fe4BAgABAFYAAAIWArUACwA1QB0HAw8KHwoCCAoNBAhzAKgMBXkHBwEJeQtCA3kBQQA%2F7T%2FtEjkv7QEQ%2Fu0yEN5eXcbOMTAzESEVIRUhFSEVIRVWAaf%2BugEi%2Ft4BXwK1U85R8FMAAAAAAQBWAAAB%2BAK1AAkAKEAUBwMLBAhzAKgKBXkHBwEJQgN5AUEAP%2B0%2FEjkv7QEQ%2Fu0yENbOMTAzESEVIRUhFSERVgGi%2Fr8BHf7jArVT0VL%2BwQABADr%2F8gJeAsUAJwDRtQoQEABND7j%2F%2BEAsDwBNCRgPAE0XFidzAUAIAU0BQAgATQ8BjwECAagpKUAOAE0pQAsATQApAR%2B4%2F%2FizDgBNH7j%2F8EAQCgBNH3YMQAgBTQwIDwBNDLj%2F%2BEAMDgBNDEAIAE0MqSgXuP%2FwQBAOAE0XGhAKAE0afBEBAQcWuP%2FoQA4NDwBMFhEIDgBNEUUnJLj%2F%2BLMOAE0kuP%2FwtwoATSR8AgdGAD8z7SsrMj8rMysSOS8Q7SsyKwEQ9isrKyvtKytdKysQ9l0rK%2B3GMjEwACsrASsBMxEOAyMiLgI1ND4CMzIeAhcHLgEjIg4CFRQeAjMyNjcB%2FWEMLTtGJEl4Vi81WnlFL0k0IQYfHWAyNVc9Ih07VzsqOw0BVf68BAoKBzBchldXh1wwDBAQBFETGSZIZ0A%2BZkkoCQQAAAAAAQBWAAACawK1AAsAN0AfAwtzDwIfAgIIAqgNBQlzB6gMCnkEBAYIQQZCAkIAQQA%2FPz8%2FEjkv7QEQ%2Fu0yEP5eXf3AMTABMxEjESERIxEzESECCmFh%2Fq1hYQFTArX9SwFA%2FsACtf7fAAABAFYAAAC3ArUAAwAkuQAF%2F8BAEQgBTQAFAQWoAXMDqAQCQgBBAD8%2FARD2%2FeZdKzEwEzMRI1ZhYQK1%2FUsAAAAAAQAI%2F%2FEBowK1ABMAebkAA%2F%2FwQBQJAE1nEAERcxNACQFNDxMBE6gVFbj%2FwEAYCQFNCwpACQFNCkAJAE0PCgEIChQSQQsOuP%2F4QB0PAE0OfAoQDA4ATAoFCBAATQUQDwBNBQgIAE0FRgA%2FKysrMyvtKzI%2FARDGXl0rKzIrEPZdK%2B0xMABdASslFA4CIyIuAic3HgEzMjY1ETMBoxU1WUMiOS4iCiYVQzJIQmHfMlZBJQsREwlNDyFJWQHNAAABAFYAAAJvArUAGgDbQGAaCA8ATVkOeQ4CDRAUAE2IDQHYDAHYCAGnBgELGBMATQsgCxJICAsYCygLAwgLcwkJChYZGhUaBAMCAQAFABoIEwBNwBoByhoBGgUABXMVGhQVFRopADkAAggAGAACAAq4%2F%2BhALxQATTAKUApgCgMKHBURFHMSqBsaQcAQARB7wAUBBc8VAQ8VARUVERNBEUIKQgBBAD8%2FPz8SOS9dcTlx7XE%2FARD%2B7TIyENZdK8ZdXYcQK4d9xAFdcSsQhw7ExMTEEIcOxMQBETMYEO1eXSsrMTBdXV1dK10rAQ4DBx4DFyMuAycRIxEzET4DNwJXH05VVCUrYFxRG3IeTldZKmFhI1RSSRkCtSJTV1QkHlhkaC8vX1dKGv63ArX%2BziFTVE0dAAAAAAEAVgAAAfcCtQAFABtADQAHBHMCqAYDQQV5AUIAP%2B0%2FARD27RDGMTAlFSERMxEB9%2F5fYVRUArX9nwAAAAEAQwAAAyUCtQApAUq5ACX%2F8LMRAE0kuP%2FwsxEATSG4%2F%2FhAEhUZSGYhpiEC9yABpx0BZh0BGbj%2F6LYRAE1XGQEYuP%2FwQDkRAE0JFQGIDvgOAmkOAfcJAQgIGAgC%2BQgB2AjoCAKZCKkIAogIAWkIAVcDAQeWEaYRAocRARFzECu4%2F8C1CQFNK6kfuP%2FwtgwSAUwfcx64%2F8BAJAoNSB4imRsBG3OmHAGFHJUcAk8cARwpdk8AAQCJEJkQqRADELj%2FwEAmCg1IEAoQDBIBTApzDwsBCAupKhZAGh5IFnwpKRwfQhxBG0ERQSK4%2F%2BC0DQBNIge4%2F8C3FRkBTLAHAQe4%2F8BADBoeSKQHAQd8EEEKQgA%2FP%2B1dK10rMis%2FPz8%2FEjkv7SsBEPZeXe0r1Std1F391F1dXe1dMtUr7SvmKxDtXV0yMTBdXV1dXV1xXV1dXStdK11dXV0rKyslLgUnBgIHIz4DNzMeAxc%2BAzczEhMjJgInDgUHAY0KISYrJiEKCw4FXwQKDQ8JVRs%2BPjoYGDo%2BPhtVIhFfBQ4LCiEmKyYhCmAYSlZbUUARdv7tjFq3sahLLHiDgzY2g4N4LP6x%2FpqMARN2EUBRW1ZKGAAAAAABAFYAAAKCArUAEwCWQCCHDgEgAwF3CwEDCwAHAHMQCxQQCxBzEkAICQFMEqgVFbj%2FwEBDCQFNBBWEFQIIB3MJqBQTQosHmwcCegcBawcBWgcBSQcBhBCUEAJiEHIQAgJAEFAQAhQQNBACBxAIEUELQQpBCEIAQgA%2FPz8%2FPxI5OV1dX11dXV1dXV0%2FARD27V5dKxD2K%2F2HK4d9xAFfXTEwAF1dIS4FJxEjETMeAxcRMxECMRY5QkdGQhxfTS9raV0gXyVcY2ZfVCD94wK1MoWNiDUCAf1LAAACADr%2F8QLQAsUAEwAnAQe1EiAQAE0MuP%2FgsxAATQi4%2F%2BBAHRAATQIgEABNEQgSAE0RCA8ATQ0IEgBNDAgPAE0HuP%2F4sxIATQe4%2F%2FizDwBNA7j%2F%2BLMSAE0DuP%2F4QBUPAE0eCA4ATR4QCgBNHnYKQAkBTQq4%2F%2FizEgBNCrj%2F%2BEAPDwBNCkAJAE0KqSnwKQEUuP%2F4sw4ATRS4%2F%2FBAHQoATRR2AEAIAU0ACBIATQAIDwBNAEAIAE0AqSgZuP%2F4sw4ATRm4%2F%2FizCgBNGbj%2F8EAaCQBNGXwPRiMIDgBNIwgKAE0jEAkATSN8BUUAP%2B0rKys%2F7SsrKwEQ9isrKyvtKytdEPYrKysr7SsrMTAAKysrKysrKysBKysrKxM0PgIzMh4CFRQOAiMiLgI3FB4CMzI%2BAjU0LgIjIg4COjVbeURDd1o1NVp3Q0R5WzVmIDtVNTVUOyAgO1Q1NVU7IAFbWYhbLi5biFlZiFsuLluIWT9nSCcnSGc%2FP2dIJydIZwAAAAIAVgAAAj0CvAAOABwAUrkABf%2Fwsw0ATQS4%2F%2BizDQBNArj%2F8LUNAE0YdgO4%2F%2FhAGQ4PAEwDHgkRcwuoHRJ5CAgACkIRD3wMAEUAPzLtMj8SOS%2FtARD27TIQ1ivtMTArKysBMhYVFA4CKwERIxE%2BARciBxEzMj4CNTQuAgEOkp0rUXRJTWEpZTI%2BIUkyUDceHzVGArxvbjxVNRj%2B%2FwKrCgdVA%2F7wDR82Kig0HwwAAAAAAgA6%2F0cC0ALFAB4AMgELuQAN%2F%2BizEABNCLj%2F4EAREABNAiAQAE14HAENCA8ATQe4%2F%2FizEgBNB7j%2F%2BLMPAE0DuP%2F4sxIATQO4%2F%2FhAHQ8ATQ92GhoAKRAOAE0pEAoATSl2ChUVCkAJAU0KuP%2F4sxIATQq4%2F%2FhADA8ATQpACQBNCqk0H7j%2F8LMOAE0fuP%2FwQB0KAE0fdgBACAFNAAgSAE0ACA8ATQBACABNAKkzJLj%2F8LcJCgBMJHwPGrj%2F%2BLcOAE0aRhR8Fbj%2F8LQREgBMFbj%2FyEATCABNFS4QCQoATC58BQgOAE0FRQA%2FK%2B0rLysr7T8rM%2B0rARD2KysrK%2B0rKxDmKysrKzIvEO0rKxI5L%2B0xMAArKysrK10BKysrEzQ%2BAjMyHgIVFA4CBx4DFwcuAycuAzcUHgIzMj4CNTQuAiMiDgI6NVt5REN3WjUoR182ASc8SiUXNF9NNAk%2FcFIwZiA7VTU1VDsgIDtUNTVVOyABW1mIWy4uW4hZTnpZNwoXIxgQBEwHFyY7LAQzW4JVP2dIJydIZz8%2FZ0gnJ0hnAAIAVgAAAmMCvAAaACgAhUATCAgSAE2YCAGJCAFXAgELJHYAGLj%2F%2BLMPAE0YuP%2FwQAkOAE0YEA0ATRi4%2F8BALAoLAEwYBQZzZwSHBAIEBAUqYCoBDx1zEagpHRt8FR55CwAODhASFUUQQgVCAD8%2FPzMSOS8zM%2B0Q7TIBEPbtMl0QxjIvXe0Q1isrKysy7TIxMF1dXSsBHgMXIy4DJwYiKwERIxE%2BATMyFhUUBgMiBxEzMj4CNTQuAgGpEDE0MxJqFC8wLxMNGw5XYSllKpKZSdo%2BIUUyUDceHjNFASIUQU9WKCZORz4XAf7xAqsKB25tRWIBLQP%2B%2BwocMyonNB4MAAABACH%2F8QH1AsUAMAEfQDkFJwEIDgEIDQEKJgGXIgEFHwETqBIBmRIBiBIBEiUAIQGJCAEhHggFIQUIcx4hFB4hCB4eBRsCdiW4%2F%2FC0EBIATCW4%2F8C0Cw4ATCW4%2F%2FBAYQkATXAlASUyG3YMEBIATQwYEQBNDBAQAE0MEAkATQwuLUAIDkgtMZgFAXkFAT0FTQUCCwUBCHQeAUIeAQUeBR4PRy4BLmcAAQB8LQgPAE1YLQEtKEZ3E5cTAlYTARMWfBK4%2F%2FBAEQ8ATYYSlhICdxIBVhIBEg9FAD8zXV1dK%2B0yXV0%2FM10r7V0yXRI5OS8vXV1eXV1dXQEQ1isy1isrKyvtENZdKysr7RE5ORDBhwQrEAHBhwR9EMQAXV0BGBDEXV1dMjEwAF1dXQFdXV03MjU0LgInLgM1NDYzMhYXBy4BIyIOAhUUHgIXHgMVFAYjIi4CJzceAfeYGy47ISZEMh18bT9nGB8VUzccMCQVFig1IC1LNx6FeSlFNicLHRdeRWggLSEYDA0hLT4sW2YbEE8NGQwYJRkcJh0YDBIkMkUxW2ILDxEGUA0gAAAAAAEAEAAAAiUCtQAHADpAJAl%2FAY8BAgABAQEDc38EjwQCAAQBBA8GHwYCCAYIA0IBBnkAQQA%2F%2FcA%2FARDWXl3WXV391l1dxjEwARUjESMRIzUCJdph2gK1VP2fAmFUAAAAAAEAUf%2FxAl8CtQAXAFlALhYIDQBNFQgNAE0DCA0ATQIIDQBNEHMPEh8SAggSqBmfGQEHcwWoGBFBBkELfAC4%2F%2FizDABNALj%2F8LQLAE0ARgA%2FKyvtPz8BEPbtXRD2Xl3tMTAAKysrKwUiLgI1ETMRFBYzMj4CNREzERQOAgFYRGRAH2FcSiU9LBhhH0FjDypJZDoBs%2F5YaF4XMEs0Aaj%2BTTpkSSoAAQAKAAAChgK1ABIAx0AqiBEBeBABZw4BNw1HDYcNA3cMAbgKAYcJAXgJATgBSAEC1Q%2FlDwLGDwESuP%2FwQDAMEgFMDxIPDBJzAAUUAAAFDBAMEgFMDwwLBg8GDHMLBhQLCwYUzwABAAUgExsBTAW4%2F8C1ExZIBXMGuP%2FgQBwTGwFMzwYBBg8LHwsCCAsTEkEMQQtBBkIFQgBBAD8%2FPz8%2FPwEQ1l5d1V0r%2FSsr1V3GhxArEADBhwV9EMQBK4cYECsIfRDEASsAXV0xMAFdXV1dXV1dXV0BDgMHIy4DJzMWEhc2EjcChiZFQkEiWyJBQ0YlazZoNzdoNQK1bruomUtLmai7bp7%2B3IB%2FASWeAAEAHAAAA4UCtQAgAV5AJDggAeggAR8IEgBNOB8BeBgBNxIB5xIBdwkBhwIBhwEBmwABHbj%2F8EAKEQBNdB0BtB0BGbj%2F8EBVDBIBTB0ZFBlzGh0UGh2gIgEiGhQ3HgELHhseAjYRAYkRARQRAR4RFBFzAB4UAB4gFAHQFAEUAHsEAbsEAZkEAQgQDBIBTAQIDQhzBwQUBwcElgMBELj%2F8EBgEQBNNhABmhABKRBJEFkQAxQQAQMQDRBzAAMUAAMAcA0B4A0BDaAHAQ8HHwcCCAchHkIdQhpBFA0AIBEATZkAAXgAAWYAAZcNAWANAUQNVA0CAA0EEBEZQQhBB0EEQgNCAD8%2FPz8%2F1sAROTldXV1dXV0rETM%2FPz8BENZeXV0Z1V1xGNWHK4d9xAFdXV1xK12HGBArh33EAStdXXEYEMVdcYcrh33EAV1dcV1dGRDVGMZdhyuHfcQBK11xKzEwAF0BXV1dXXFdcStdcQEOAQcjJgInMx4DFz4BNzMeARc%2BAzczBgIHIy4BAdEqVithNVEjZg4eICARKFonVCdcKBAgHx4OYyRRNGEsVAHWhuRsmQFUyE6ck4Y5afuGhvtpOIaTnU7I%2FqyZbOQAAAABABcAAAJjArUAFwDjs6AZARm4%2F8BALAwQSIoUAQMIEQBNhhEBFBMKFxAREgsXEA4SCwAPCRMKBRMKAA8KEAwSAUwKuP%2F4tBESAEwSuP%2FwQBQMEgFMChILEnMTChQTChMXAQAPALj%2F8EBIDBIBTAAIEgBNEBAMEgFMABAXEHMPABQPDwBAF1AXYBeAF6AXBRcZkBkBDxkvGQIPDwsfCwIICxgXQhNBEkEQQQ9BC0IKQgBCAD8%2FPz8%2FPz8%2FARDWXl3GXV0Qxl2HECuHfcQBKysrhw7EARgQ1ocrh33EASsrKw%2BHDsQPDw8xMABdAStdK10hLgMnDgMHIz4BNwMzGwEzAx4BFwH2DyoyNhkZNjErD2std0PecKupb9pEeC4eSU9PIyNPT0keWLZgAUf%2B%2FgEC%2Fr1huFkAAAABAAYAAAJQArUAEACLuQAL%2F%2FBAUgwSAUwICwgFC3MMDxQMDw8QEm8MnwyvDAMMEHMABRAMEgFMCAUEAQgBBXMEARQEAQFvAJ8ArwADAA8EHwQCCAQRAQEPDwwQQgxBC0EFQQRBAEIAPz8%2FPz8%2FEjkvMy8BENZeXcZdMocrEADBhwV9EMQBKxgQ%2FdZdxhEzhysIfRDEASsxMDMRLgEnMx4BFz4BNzMOAQcR%2B0V6NnImXjIxXidsNnpEARZr0GRSqU5OqVJkz2r%2B6AAAAAABACEAAAIYArUAFQBNQCoMERILEgEABwcSCxJzAAcUAAcADwgfCAIICBcTCxYSABN5FEELBwh5CUIAP%2F0yxT%2F9xTMBEN7GEN5eXdaHK4d9xIcOxBCHDsTEMTABDgUHIRUhNT4FNyE1IQIOFz1ESEU%2BGQGG%2FgkVOUFGRUEb%2FpwB2wJoGklZY2VkLVNBKGBkZl5SH1MAAQBg%2F0cBQQMPAAcAHUANAgUJA38HCASHBgKHAAAv7S%2FtARDe7RDWwDEwEzMVIxEzFSNg4YqK4QMPSfzKSQAAAAH%2F6P9HAZgDDwADAClAFgEDAAOuAgEUAgIBAAQCA2ICYgFkAGQAPz8%2FPwEvEMaHECuHfcQxMAMzASMYXQFTXwMP%2FDgAAQAI%2F0cA6QMPAAcAHUANA38HCQUBCASHBgKHAAAv7S%2FtARDWwBDe7TEwFyM1MxEjNTPp4YqK4blJAzZJAAAAAAEALwE7AgUCtQAGAGxAQokFmQUCOAVoBXgFA6sEuwQCpAK0AgKGAZYBAjcBZwF3AQNKA1oDAgQEBQMCAgEDBgYDAAADAwcCBH8DjwOfAwMDBgAvxV3NMgERMxkvMxgvETMvGRDFMhgvGRDFMhgvMTAAXQFdXV1dXV0BEwcLAScTAUPCSqGhSsICtf6sJgEa%2FuYmAVQAAAAAAf%2F8%2F0cB8P%2BWAAMAGEAKAQEFAAAEAIcCSwA%2F7QERMy8RMy8xMAchFSEEAfT%2BDGpPAAAAAAEAPQJEAPkDCAADADeyAgEAuP%2FAQAkREgBMAAMEAwC4%2F8CzEgBNALj%2FwLUNAE0AAQIAL83dKyvNARDeGcUrGN0ZxTEwExcHJ3mAL40DCJoqiQAAAAIAK%2F%2F1AcACFQAOADUA2EALIQgKAE0sCBEATSK4%2F%2BCzCABNErj%2F4EAXCABNCggRAE0DKQgSAE0pfxRACAkBTBS4%2F%2FhAEQoATRQ4CAkATBSqNx83ATIzuP%2FAQA4KDABMMwyCH0AICQFMH7j%2F6LMSAE0fuP%2FwQBoRAE0fUAkKAEwfQAgATR82KSSIBAdAEgFNB7j%2FwEAKCAFNB0AREgBMB7j%2FwEASCABNBwcPAwCIFRpRMi%2BIMw9QAD8z7TI%2FM%2B0yEjkvKysrKzPtMgEQ1isrKysr7cQrMl0Q9isrK%2F0rwDEwACsrKysBKyUyNjc1LgEjIg4CFRQWEzIeAhURDgMjIi4CNTQ%2BAjMyHgIXNTQuAiMiBgcnPgEBACEzEQotIBUvJxpALzhNLxQMKzY%2FHyxKNh4jPFAsDh4bFAQKGi0kLkURCxJUQwMEmwUHBhMhGzInAdIdNUks%2FrsCBwYFEidALSs%2BJhIDBQQBGhctIxUNB00IDwAAAAIAUP%2F0AhoDCAASACEAiLUbIA0ATRi4%2F%2BBAKw0ATRkIDgBNGYIIEBIATQgIDgBNCBALDQBMCKsjEgAhfxCqIhJOExaIEA24%2F%2Fi0ERIATA24%2F%2FC1DgBNDVEhuP%2FgtxESAEwhHogAuP%2FgQAwREgBMAAMIDgBNA1AAPyszK%2B0yKz8rKzPtMj8BEPbtMjIQ9isrK%2B0rMTAAKysTPgEzMh4CFRQOAiMiJicRNxEeATMyNjU0LgIjIgYHrRFFLThXPB8lRGA8QWQgXQ4zJktaECQ5KCZAEQHxCxgoSGQ8P2VGJhIJAukQ%2FUkEB2NbKEYzHRoOAAEAM%2F%2F0Ab0CFQAhALe5ABf%2F8LMNAE0SuP%2Fwsw0ATQe4%2F%2FCzDQBNA7j%2F8EAKDQBNAggQAE0NHbj%2FwEAQDBZIHSM%2FI08jAiNADxZIFLj%2F%2BEAMDQBNFIIFQAgJAUwFuP%2FwQCMOAE0FQAgJAEwFqyIOEQgNAE0REAwATRGIDQoIDgBNClAcGbj%2F%2BLMNAE0ZuP%2FwtgwATRmIHQC4%2F%2Fi0DgBNAFEAPysy7SsrMj8rM%2B0rKzIBEPYrKyvtKytdENYrxjEwKysrKysFIi4CNTQ%2BAjMyFhcHLgEjIgYVFB4CMzI2NxcOAwEyP19BICNAXTkjRiAVFTcfTlMTKUAuJTwRDQgeJisMKEhjPDxkSSkMDU8KDGJeKkYyGw4ITgUJBwUAAgAz%2F%2FQB%2FQMIAA4AIQCtuQAX%2F%2FCzDQBNCrj%2F2LMNAE0KuP%2F4tAsMAEwFuP%2FgQBYNAE0OIX8QQAgJAUwQQAgJAEwQqiMIuP%2FwQAwOAE0IghlACAkBTBm4%2F%2FC0ERIATBm4%2F%2FBADQ4ATRlACAkATBmrIgC4%2F%2BC3ERIATAADiCG4%2F%2BBACRESAEwhHlAOC7j%2F8EAKEgBNC4gRFFEQTgA%2FPzPtKzI%2FMyvtMisBEPYrKysr7SsQ9isr%2FcAxMCsrKysBLgEjIg4CFRQWMzI2NxE3EQ4BIyIuAjU0PgIzMhYXAaARQCYoOSQQWksmMw5dIGRBPGBEJR88VzgtRREBmg4aHTNGKFtjBwQCpxD9BwkSJkZlPzxkSCgYCwAAAAIAM%2F%2F0AfkCFQAaACMA7LkAIf%2Fwsw0ATRm4%2F%2FCzDQBNGLj%2F4LMNAE0OuP%2FYsw0ATQe4%2F%2FBACQgATRgQDwBNB7j%2FwLQPEABMBrj%2F%2BEAVDwBNEl8TbxMCExuCCKslTyVvJQIjuP%2F4tQ4ATSOCDLj%2F%2BEAMDgBNDIIAQAgJAUwAuP%2F4sw4ATQC4%2F%2FBADgwATQBACAkATACrJBIPuP%2FwQAkNAE0PiBYjhwu4%2F8BACRMUAEwLCwUTFrj%2F%2BEATDgBNFlEeCA4ATR4QDQBNHogFUAA%2F7SsrPyszEjkvK%2B0Q7SsyARD2KysrK%2B0r7StdEPbtxF0yMTAAKysrASsrKysrEzQ%2BAjMyFhUcAQchHgEzMjY3Fw4BIyIuAiU2JiMiDgIHMyhCVi1pcAH%2BnAZSVzFDEQ0RVTZEY0AfAWUBQTkgMSITAwEDRWdEIoOGBhMIUVQRCE4JFClIY24%2FURkoNBsAAAEAUAAAAYsDCAATAERAIQQTAQgHEgEMAxUKDn8QqhQPSg2HC0kEBwgPEABMB4gDALj%2F%2BLQRAE0ATwA%2FKzLtKzI%2F7T8BEPbtMhDWxjEwAF1eXQEyFhcHLgEjIgYdATMVIxEjETQ2ARspOwwRDCwgQTTIyF1iAwgMBVAGC0c8LU7%2BRgI3ZG0AAAACADP%2FRAHyAhMAHgAtAOq5ACj%2F4LMNAE0nuP%2Fwsw0ATSO4%2F9izDQBNErj%2F8LMQAE0SuP%2FoswgATQu4%2F%2FCzDQBNCrj%2F8EAZDQBNAB4tfxBACAkBTBBACAkATBCqLxgXJbj%2F%2BLMOAE0luP%2FwswwATSW4%2F%2FhADAsATSWCCEAICQFMCLj%2F8LQREgBMCLj%2F6EAVDgBNCEAJAE0ISAgATQirLhgbiBcUuP%2FwQCEODwBMFFIfIogQDVAtIBESAEwtKogAIBESAEwADwMBCAMAL15dMyvtMis%2FM%2B0yPysz7TIBEPYrKysrK%2B0rKyvEMhD2Kyv9xDIxMCsrKysrKyslDgEjIi4CNTQ%2BAjMyFhcRFAYjIiYnNx4BMzI2NQMuASMiBhUUHgIzMjY3AZYMRS4vUz4kIkFeO0FhIXx%2BMVcgERxRLFNJAQ4vKEtRFyczGyU%2BEjQIGR4%2FXkE5X0UnEwj%2BMHhsEAtRCxFCSAGYBAdiUS1AKhQVDgAAAAABAFAAAAH0AwgAGQBuuQAJ%2F%2FC2CABNSxABCbj%2F%2BLQPEABMCLj%2F%2BEAsEABNDX8LQAgJAUwLQAgJAEwLqht%2FG%2B8bAk8bvxsCAhh%2FAKoaGUoMShgTiAa4%2F%2Fi2DwBNBlACTgA%2FPyvtMj8%2FARD27TJdcRD2KyvtMTAAKytdASszETcRPgEzMh4CFREjETQuAiMiDgIHEVBdGjsdPlIxFF0NHjIlDyAdFwUC%2BBD%2B9goLIz9YNf7cARAwRCoTBAUGAv5QAAACAEIAAAC6AuAAAwAPADNAEAAREBECCBGqDQN%2FBwGqEAS4%2F8BACwwSSASJClMCSQBKAD8%2FP%2B0rARD2Mv0y5l5dMTAzIxEzJyImNTQ2MzIWFRQGrV1dLxkjIxkZIyMCCF4hHBwhIRwcIQAAAAAC%2F7z%2FRQC6AuAADwAbAE%2B5AA7%2F6LQJCgBMDrj%2F2EAUCABNAB0QHQIIHaoZDH8TAwqqHBC4%2F8BADwwSSBCJFlMLSQQHiAMAUgA%2FMu0yPz%2FtKwEQ9sQz%2FTLmXl0xMCsrByImJzceATMyNjURMxEUBhMiJjU0NjMyFhUUBgUMJg0MChoLMCldWywZIyMZGSMjuwYFTAMEOzkB%2F%2F4CZGEDISEcHCEhHBwhAAABAFAAAAH8AwgAGgD%2FQBUVCBUATQkYEABNBggYAE0LEA8ATQu4%2F%2FBAGAwATQsQCwBNCggLAE0JEBgATQgIDwBNArj%2F8LMZAE0GuP%2FgtBQYAUwGuP%2FgtAgBTQYVuP%2FgsxsBTRW4%2F%2FBAGxQVAUwVCA4ATQAgGwFNACAWGAFMABAUFQFMALj%2F4EAbDwBNFQAWAH8QFRQQFQAQFhAIAU0WBRAIAU0FuP%2FgsxIATQW4%2F8BAIg0QSAUcEAx%2FDqobFkkQEAsMAEwQEAYVSQ9ODUoMSgZKBUoAPz8%2FPz8%2FEjkvKz8BEPb9wBDWKysrxisQAMGHBSuHfcQBKysrKysrKzIrKzEwACsrKysrKysBKysrAR4DFyMuAycVIxE3ET4DNzMOAwEMHkNBOhRtFTc8PhxdXRg6ODISbRc2OjsBHhdET1EjI0lDOBP6AvgQ%2FiYYOjw3FRk8PT0AAQBO%2F%2FYA%2BQMIAAsAPUATASAMAE0AEAgSAUwBIAgLAEwNC7j%2FwEAQCAxIC4AFfwOqDAVOC4gAUQA%2F7T8BEPb9Gt4rxjEwACsBKysXLgE1ETcRFB4CF%2BxWSF0IEh4WCgJGSgJwEP2PFx4SCQMAAQBQAAADEwITAC0BIUARIyANAE0aEBESAEwVIA0ATQ64%2F%2FC0ERIATA64%2F%2BCzEABNDrj%2F0LMPAE0NuP%2FwtBESAEwNuP%2FosxAATQ24%2F9izDwBNDbj%2F4LMIAE0MuP%2FwQBQQAE0JEBIATQkIEQBNCBAQEgBMBrj%2F%2BLMPAE0FuP%2FgtBESAEwFuP%2FosxAATQW4%2F9CzDwBNBLj%2F6LMQAE0vuP%2FAtAgJAUwvuP%2FAQDcJAE0vqhJ%2FEEAcAE0PEAEQBiB%2FG08eAQ8eAQgeK38tqi4sSh9KEUorJiYbEBAATRsYiAsAAwMGuP%2FwtAgATQYLuP%2F4tQkKAEwLUAA%2FKzMrMy8zEO0yKzIvMz8%2FPwEQ9u3cXl1xMv053F0r7eYrKzEwACsrKysrKysrKysrKysrKysrKysTPgEzMhYXPgMzMh4CFREjETQuAiMiBgceARURIxE0LgIjIg4CBxEjUCBpRTJEFwceLDYfPEwrD10JGSsiLz0LCAhdChkrIQ4gHRgEXQH5CBIbGgUSEQ0jQFg0%2FtwBEC5CLBUZCho%2BIv7cARAuQiwVAgMEAf5JAAAAAAEAUAAAAfQCEwAXAG65AAb%2F8EAOCABNDggNAE0NIA0ATQa4%2F%2FCzEABNBrj%2F%2BLMPAE0FuP%2FoQB8QAE0Kfw8IHwgCCAiqGb8ZARV%2FF6oYFkoJShUQiAADuP%2F4tA8ATQNQAD8rM%2B0yPz8BEPbtXRD2Xl3tMTAAKysrKysBKxM%2BATMyHgIVESMRNC4CIyIOAgcRI1AgakU%2BUjEUXQ0eMiUPIB0XBV0B%2BQgSIz9YNf7cARAwRCoTAgMEAf5JAAAAAgAz%2F%2FMCGwIVABMAHwCBuQAf%2F9izDQBNG7j%2F2EAbDQBNGSgNAE0VKA0ATRQQDgBNFIIAqyF%2FIQEauP%2FwQCMOAE0aggpACAkBTApACAkATAqrIBcIEgBNFwgOAE0XiA9QHbj%2F%2BLMSAE0duP%2F4tg4ATR2IBVEAP%2B0rKz%2FtKysBEPYrK%2B0rXRD27SsxMAArKysrARQOAiMiLgI1ND4CMzIeAgc0JiMiBhUUFjMyNgIbJEFaNTVaQSQkQVo1NVpBJGFPRERPT0RETwEEPmRIJydIZD49ZUgnJ0hlPVhnZ1hYZ2cAAAAAAgBQ%2F0cCGgITAA4AIQB6QFgNEA0ATQwgDQBNAhgSAE0CMA0ATQAQEQBNABgOAE0Afw8IEwBNDxASAE0PCA4ATQ8QCwwATA%2BrIwYXfxmqIgYDiBodUBhLByAREgBMBwqIFyAREgBMFxRRAD8zK%2B0yKz8%2FM%2B0yARD27TIQ9isrKyvtKysxMCsrKysBNCYjIgYHER4BMzI%2BAjcUDgIjIiYnFSMRPgEzMh4CAblaSyovDhFAJig5JBBhHzxXOC1FEV0hY0E8YEQlAQNbYgYE%2FrgOGh0zRSg7ZEgoGAvQArEIEyZGZQAAAgAz%2F0cB%2FQITAA4AIQC5uQAR%2F%2FCzDQBNDbj%2F8LMSAE0NuP%2Fgsw0ATQO4%2F%2FBAFw0ATRkZCH8XQAgJAUwXQAgJAEwXqiMAuP%2F4QAwOAE0Agg9ACAkBTA%2B4%2F%2BizEgBND7j%2F8EAUDgBND0AICQBMD6siCCAREgBMCAW4%2F%2FBAJg0ATQWIGiAREgBMGh1RGEsJDBARAE0MCA4ATQwQCw0ATAyIFxRQAD8z7SsrKzI%2FPzMr7SsyKwEQ9isrKyvtKxD2KyvtMi8xMCsrKysTFB4CMzI2NxEuASMiBgc0PgIzMhYXESM1DgEjIi4ClBAkOSgmQBEOLypLWmElRGA8QWMhXRFFLThXPB8BAyhFMx0aDgFIBAZiWz9lRiYTCP1P0AsYKEhkAAAAAAEAUAAAAXgCEwARACVAEgYFEwx%2FDqoSDUoMBgmIDwUAUAA%2FMjLtMjI%2FARD27RDGMjEwATIeAhcHLgEjIgYHESMRPgEBEAwfHhkGEAszKBozCF0hYgITAwUFAlEECwsC%2FksB8gwVAAAAAQAo%2F%2FQBlwIVAC0BOrkAJf%2FYswkATSW4%2F8hAEwgATQwwCQBNDEAIAE0JGBIATSu4%2F%2FBADxMATSYYCgwATCUIDgBNIrj%2F%2BLMSAE0iuP%2FgsxEATSK4%2F%2FhACQ8ATSEIFABNIbj%2F8LMSAE0huP%2FwswoATQ24%2F%2Bi0CwwATA24%2F%2BBADgoATQkQEQBNCBgKAE0GuP%2F4QEITAE0GCBIATQYIDxAATAUIDwBNBRALDABMCAUhHggeIX8FCBQFCCEFHgUaA3%2BfEwETJKsvPy8BGn8LKi4eCBUATR64%2F%2Fi2EABNBR4OK7j%2F8LMVAE0ruP%2F4QBQUAE0rAIgqJ1EUF4gTDggTAE0OUAA%2FKzPtMj8z7TIrKxI5OSsrARDe1u1dEPbEXe0ROTkQwYcEKxABwYcEfRDEMTAAKysrKysrKysrKysrKysrKysrASsrKysrNzI2NTQmJy4DNTQ2MzIeAhcHLgEjIgYVFB4CFx4DFRQGIyImJzceAck5NzY%2BHjcoGGZYFiwmHQgRD0AtJzoNGygcJTopFmtjRU4OERBGQx4hIigZDBkiMCNFUQUHCANQCBEfIREaFRIKDhskMyVIShcGUAYYAAAAAAEASv%2F1AYACqQAXAF5ADx8ZLxk%2FGQMTIAgATQwCDbj%2FwEAQDRhIDRlPGQEDF38VqhgMCbj%2F%2BEAZERIATAmIDRAICgsATBAQCQBNEFEXAocASQA%2F7c0%2FKysz7SsyARD27TJdENYrxjMxMCtdEzMVIxUUHgIzMjY3Fw4BIyIuAjURN6fFxQwYJBgqMwoSDkYtNUUqEV0CCE7wJzMdDBMETQcVGzZPNAHQEAAAAAABAEr%2F9QHuAggAFQBQQCEGGAgATVQMARN%2FDxUfFQIIFaoXvxcBCn8IqhYUSQlJEw64%2F%2FBADw0OAEwOiAADEA8QAEwDUQA%2FKzPtKzI%2FPwEQ9u1dEPZeXe0xMABdASslDgEjIi4CNREzERQWMzI%2BAjcRMwHuIGlFPFIyFl08Rw8gHBcFXQ8IEiNAVzUBJP7wX1ICAwMCAbcAAAEAEAAAAeYCCAASALu2DwgPEABMC7j%2F%2BEAPDxAATAYICgBNBhAJAE0BuP%2F4twkKAEyQDQENuP%2FAszxCSA24%2F8CzJi1IDbj%2F4EBKExZIDRINCBJ%2FAAMUAAADDQgHBA0ECH8HBBQHBwQUnwCvAAIAABAAAgADf58ErwQCAAQQBAIEDwcfBwIIBxMSSQhJB0kESgNKAEkAPz8%2FPz8%2FARDWXl3VXV391V1dxocQKxAAwYcFfRDEhxgQKwh9EMQAKysrcjEwASsrKysrAQYCByMmAiczHgMXPgM3AeYnaTFUMWknZAwhJSYRESYlIQwCCJL%2B9GpqAQySMW9tZigoZm1vMQABABIAAAL3AggAJAPKQAlAJgEdCBAATRa4%2F%2FCzEAFNFbj%2F%2BLMSAE0LuP%2F4sxcATQW4%2F%2FizEgBNBbj%2F8EATEQBNBSgIAE0BCBEATSMYDwBNGbj%2F8EAJEABNCBgPAE0kuP%2FwswkBTSS4%2F%2FC0ExUATCS4%2F%2Bi0DQ4ATCS4%2F%2BhACgkATUAkATQkASS4%2F8CzJipIJLj%2F4EAOICVIciQBQCRQJGAkAyS4%2F%2BBADxMXSCQgGyB%2FISQUISQmIbj%2FwLMOAU0huP%2FAtQ4ATSEbALj%2F6LMOAU0AuP%2FwQEoNAU0AIAoBTQAQExUATAAgDwBNABAKAE1bAAEAICgrSP0AAesAAdkAAbsAAZoAAYsAAX0AAV8AbwACSgABuwDLANsAAxYQEgFNFrj%2F8EAODwFNFigOAU0WGA0BTRa4%2F%2FBADwoBTRYgFhcATBYQFQBNFrj%2F%2BLMUAE0WuP%2FwQAkTAE0WGBIATRa4%2F%2FBADg8ATRYgDgBNFhgNAE0WuP%2FwQIYKAE02FgEEFgH2FgHkFgGXFrcWAnYWAWQWAVIWAUYWAfkWAbYWAQAWGxZ%2FAwAUAwA%2FG08bAn8b7xsCGwMHCA4BTQcYCQFNBxATFQBMBxAOAE0HGA0ATQcYCQBNTwcBOwcBB0AmKkgHICAlSH0HAU8HXwdvBwMHIBMXSAcLEAt%2FCgcUCgoHBrj%2F6LMOAU0GuP%2F4QAkJAU0GGBkATQa4%2F%2BC0FRYATAa4%2F%2FC0EhQATAa4%2F%2BCzDgBNBrj%2F6LYNAE1UBgEGuP%2FgQBInK0jWBgGEBgF2BgFEBmQGAga4%2F%2BBAExMXSBUQEgFNFRAOAU0VCA0BTRW4%2F%2FCzCgFNFbj%2F4ECmGQBNFRgWAE0VIBUATRUQExQATBUgDgBNFSAJAE02FQELFQH5FQG0FcQVApYVAWcVAVYVAUoVAfkVAcsV2xXrFQO5FQEGFRAVfwMGFAMGPwNPA18DvwPPAwV%2FA48D7wMDA0AQ0BACYBDwEAIQQAoBDwofCgIICiUkSiFJIEkWSRsQJBABZBB0EAIrAwGcAwGNAwFrA3sDAhADBhVJC0kKSQdKBkoASgA%2FPz8%2FPz8SOTldXV1xXXERMz8%2FPz8BENZeXXEZ1V1xGNVdcYcrh33EAV1dXXFxcXFxcXJyKysrKysrKysrKytxcXFxK3IrKysrKysrhxgQK4d9xAErcXErK3JyKysrKysrGBDFXXGHK4d9xAFdXXFxcXFxcXFycisrKysrKysrKysrKysrXXFxcXFxcXFxcStyKysrKysrGRDVKysYxocrh33EAStxcSsrcnIrKysrMTAAKysrASsrKysrKysrcSEuAScOAQcjJgInMx4DFz4DNzMeAxc%2BAzczBgIHAfkcPxoaPhxRLlgnZAocHyIPESEeGwpNCRoeIREPISAcCmAnWC5IuWRkuUhnAQiZL25uZiYvaWtnLS1na2kvJmZubi%2BZ%2FvhnAAAAAAEAEAAAAe0CCAAXAKlAJEYUARQTChAXERILEBdJDgEOEgsPAAkTCgUTCg8AChAMEgFMErj%2F8EAUDBIBTAoSCxJ%2FEwoUEwoTFwEPAAC4%2F%2FBAMAwSAUwQEAwSAUwAEBcQfw8AFA8PABcZDw8LHwsCCAsYF0oTSRJJEEkPSQtKCkoASgA%2FPz8%2FPz8%2FPwEQ1l5dxhDGhxArh33EASsrhw7EARgQ1ocrh33EASsrD4cOxA8BcQ8PcTEwIS4DJw4DByM%2BATcnMxc3MwceARcBhwsgJigTEyglIAtgI2EwrGl8fWOoMGUjFjY5OBkZOTk2FUSNQPe2tvFBkUUAAQAC%2F0QB3wIIACAA9bkAGf%2F4sw8ATRm4%2F%2Bi2DgBNBBgBFbj%2F%2BECIDQBNBw8BBwgQAE0HEA8ATQkHAQUIDQBNACAJFhUUExcTEBIXExIFFwUSfxMXFBMXIhNAERIATBNADQBNEwgMAE0TQAkATRMMDQ8KDwgHBgkGICYBTQYgJypIBgoPCn8JBhQJBgUPDwkfCQIICSHQD%2BAP8A8DDwYFAxIAA4ggG1ITSRJJCkkJSQA%2FPz8%2FPzPtMhIXOXEBENZeXRnFMocYK4d9xAErK4cOxMQQhw7ExAEZ1SsrKysYxocrEADBhwV9EMQQDsQQhw7ExMQBGBDGMjEwK10rK10rXSsrFx4BMzI2NyYCJzMeAxc%2BATczBgIHDgMjIi4CJxMLIxE3Phk%2FbSRkCx8mLBgmOBlgJFgzFCs0QSkLGBcTBGEFCTE3eAENii1obm4zac5tk%2F7xdi1BKhQDBQYCAAAAAAEAJAAAAbECCAARAEFAJQUOCQ5%2FAAUUAAUADwYfBgIGEwATAQgPCRIOAA%2BHEEkJBQaHB0oAP%2F3BxT%2F9xcEBEN7GXl0Q3l3WhyuHfcQxMAEOAwchFSE1PgM3ITUhAakVRU9PHgEe%2FnMYRk1JG%2F7%2FAXcBwhdUZnAzTj4samldIE4AAQAo%2F0cBRQMPACgAWkALCSABCQgBCA0bERe4%2F%2FBAKAgNSBd%2FiSiZKAJiKHIoAigGoCIBIhAIDUgiKSiIAAAUFAsbiB0NiAsAL%2B0v7RI5GS8zGC%2FtARDeK10yzl1d%2FSsyzjIxMF5dXRMyPgI9ATQ%2BAjsBFSIGHQEUBgceAR0BFBYzFSMiLgI9ATQuAiMoGCIXCxIsSTYEOTEjICAjMTkENkksEgsXIhgBTxEbIxK0KUArF0kpOppCSBQUSEKaOilJFixAKbQSIxsRAAAAAQBg%2F0cAtwMPAAMAFLcBrgMDBQQCAAAvLwEREjkv7TEwEzMRI2BXVwMP%2FDgAAAAAAQAI%2F0cBJQMPACgAXkAkZyABBiABBggBCBsNFxAQCA1IEH8ihgCWAAJtAH0AAgCvBQEFuP%2FwQBIIDUgFKgCIKCgUFAsbiB0NiAsAL%2B0v7RI5GS8zGC%2FtARDeK13OXV0z%2FSsyzjIxMF5dXV0BIg4CHQEUDgIrATUyNj0BNDY3LgE9ATQmIzUzMh4CHQEUHgIzASUYIhcLEixJNgQ5MSMgICMxOQQ2SSwSCxciGAEHERsjErQpQCwWSSk6mkJIFBRIQpo6KUkXK0AptBIjGxEAAAEAKgDZAggBfgAhAFhAOqYYAQkUAXkIiQgCqQcBBAMBCCGvUABwAJAAAwQAFAAkAAMAEK8JERkRKREDEREiACEWrwsREByvBQsAL9ztxDIQ%2FcQyAREzL13t1F1d7TEwXl1dXV1dAQ4DIyImJy4BIyIOAgcnPgMzMhYXHgEzMj4CNwIIBBEgMSMeMxcbMhwQFhAKBEAEEh8xIx4zFxsyHBAWEAoEAWUUMCsdGA4QHw8XGw0SEzErHRgOEB8PFxsNAAEALf%2FzAicCwgAzAMVAeZknAVgmARoZERAZEBkQLgcGISI1MypHGwEbGBIPpxUBFX8xKygApi4BmC4BBi4WLkYuhi4ELjSbIashAiEeiCUAAA8BCA%2BFETExERsoKCsYhRoRGhEaA4simyICIpclASVRhAeUBwIHCoiTBgGEBgEGmAMBDwMBA1QAP11dM11d7TJdP10zXRI5OS8vEO0yMy8zETMvEO1eXTIQ7TJdARDcXV1dMjIyMu1dMjIyMl3OMhDWMsYyEjk5Ly8RMxEzMTBdXRM%2BATMyFhcHLgEjIg4CBzMHIwYUFRwBFzMHIx4BMzI2NxcOASMiJicjNTMmNDU8ATcjNYkXiGosOh0UGDkeJjkpGgfpDuUBAdEOuxFZQyo9GBQRVDN1gBNaUgEBUgHYdXULCVAJCxYoOSNIDRoODRgLSFlGEAtPCRR%2FcUgLGA0OGg1IAAAAAQAh%2F4IArwByAA0ANLkABv%2FgQBoIC0gGAAOCDQcKCg4PPw1PDQINBxAGIAYCBgAvXTMvXQEREjkvzjPtMjIrMTA3HAEHDgEHJz4BNTQmJ68BBCYbSBUVAQFyCxUKNGYsFyxaJwoXC%2F%2F%2FACH%2FggFlAHIAJgBjAAAABwBjALYAAAABADQCRgFEAwEABQAuQBhQBQEFBV8BAQEBAwMGfwOPA58DAwMAAgQALzPdzV0BETMZLzIYL10yL10xMBMXBycHJ7yIJ2FhJwMBjyxYWCwAAAEAHgA%2FAPsB%2BQAFACu0BAIFAQO4%2F8BADA4SSAMABgQFAgEAAwAZLzPNMs0yARgQ1sUr1TLFMjEwEzcXBxcHHpxBb29BARzdI7q6IwACADr%2F%2BQO8ArwAEgAtAG9ASCMncwoKGCYiKS8HABcAAgB2eBiYGKgYA2YYARguCih5KiR5JiYgKkILInkgQQgOGA5YDmgOBA58HUUHBRcFVwVnBQQIBXwTRgA%2F7V5dP%2B1dP%2B0yPxI5L%2B0Q7TIBEN5dXe1dENbGxhI5L%2B0yMTATFB4CMzI2NzY3ES4BIyIOAgEiLgI1ND4CMzIWFyEVIRUhFSEVIRUhDgGhJkdmPxchCwwJFiwWP2ZHJgESUYplOTplilEXOBkBh%2F7JARP%2B7QFQ%2FmAZOQFbQGRFJQEBAQICEQMCJUVk%2Fl8sWIVZWYRYLAQDUdFP81EDBAAAAAABADwCCADKAvgADQBLuQAI%2F%2FCzEgBNCLj%2F%2BEAmEQBNBwY6BgELBhsGKwYDCAYAgg0KD0ANAQ0HkAYBLwY%2FBm8GAwYAL11dMy9dARDeMu0yXl1dETMxMCsrEzwBNz4BNxcOARUUFhc8AQQmG0gVFQEBAggLFQo0ZiwXLFonChcLAAEAIQH%2BAK8C7gANAClAFwYHDQCCCg6QDQEvDT8Nbw0DDQdABgEGAC9dMy9dXQEQ1O0zzjIxMBMcAQcOAQcnPgE1NCYnrwEEJhtIFRUBAQLuCxUKNGUtFy1ZJwoXCwAA%2F%2F8APAIIAYAC%2BAAmAGgAAAAHAGgAtgAA%2F%2F8AIQH%2BAWUC7gAmAGkAAAAHAGkAtgAAAAEANADiATQB4AATABO2CgAAFRQPBQAvzQEREjkvzTEwARQOAiMiLgI1ND4CMzIeAgE0EiEvHh4vIRISIS8eHi8hEgFhGi4iFRUiLhoZLyIVFSIvAAH%2F%2FgEEAfIBUwADABG1AQUDBAACAC%2FNARDGEMQxMAMhFSECAfT%2BDAFTTwAAAf%2F%2BAQQD5gFTAAMAE7UBBQMEAAIAL80BEMYQxDEwMTADIRUhAgPo%2FBgBU08AAAAAAQAXAmcBYALfAB0AVUA3aQd5B4kHAx0PAB8ALwADCAAODx4AHR2gC7ALwAsDCzQURBQCFK8avxrPGgMaBQ8ODjkFSQUCBQAvXTMvMxDdXdxdzV0zLzMBENYyzF5dMjEwAF0BDgMjIiYnLgEjIgYHJz4DMzIWFx4BMzI2NwFgBREaIxUUIxARGAsRHQgwBBIaIxUUIxARGAsRHQgCxwwgGxMOCAgIHBAYDCAbEw4ICAgcEAAAAQAoAD8BBQH5AAUAKEASAwEEAAJADhJIAgUHAwQBAAUCABkvM80yzTIBGBDezSvdMs0yMTA3JzcnNxdpQW9vQZw%2FI7q6I90AAwAz%2F%2FMDfQIVACcAMwA8AKBAaHcXhxcCJCUDE6c8ATxUHmQeAkYeAR6CKH8lAVkoaSgCJSglKAtpNAE0gncZhxkCGas%2BVi5mLgIugg8LAQgLqz08hR0dBhNnFgEWUDdJK1krAiuIEFAkISFGMVYxAjGIBlElA2gAAQBRAD9dMjI%2F7V0yETM%2F7V0yP10zEjkv7QEQ%2Fl5d7V0Q%2Fl3tXRI5OS8vXV0Q7V1dMl05OREzMTBdBSImJw4BIyIuAjU0PgIzMhYXPgEzMhYVFAYdASEeATMyNjcXDgEBNCYjIgYVFBYzMjYlNiYjIg4CBwK9T2kdIGI%2FNVpBJCRBWjU%2FYx8hZTZocQH%2BnAZTVjFDEQ0RVf7HT0RET09ERE8BYgFBOSAxIRQDDDozNjgnSGQ%2BPWVIJzszODaEhQgLBQlQVREITgkUARBYZ2dYWGdnij9RGSg0GwAAAAIAR%2F9HAM0CCAAJABUAI0AQEAQHfwoDCQkWFwMTDVAISwA%2FP93OARESOS8zxP0yxDEwNzQ2NzMeAR0BIwM0NjMyFhUUBiMiJlgMCToJDGQRJB8fJCQfHyQCVIlERIlUuwJ%2BGygoGxsoKAAAAAEAVv%2BTAeACtQAkAGxAQwcdfwYfQA8SSB8fAAsKGa8aARomkCYBVhEBEYIAJRlGFlYWAhaIIB4aCR0ZHQIdSgtJDlkOAg6ICgcFBggWCAIICFAAP15dM8Uz7V0yP10zxTPtXTIBEN7tXV0Q1l0yxjISOS8rM%2B0yMTATND4CNzUzFRYXBy4BIyIGFRQeAjMyNjcXDgEHFSM1LgNWGzFILVM6NBUVNx9OUxMpQC4lPBENDkQkUzBIMBkBIzFWRC4JkIsDFE0JDF5aKEMwGg4HTAgPAouQCS1DVgAAAAABADwAAAIHAsIAIgCVuQAf%2F%2BizDwBNH7j%2F8EBUDgBNFRAQAE0UCBAATRQICQBNCwgPAE0HFQEbGggBAREIJAcDInMNCggQEgBMChEwDwEPDwEPIxsdfBcQAHkCDg4AAhACAggCAgkaZxcBF0UHeQlCAD%2FtP10zEjkvXl0zLxDtMhDtMgEQ1l1d3cwrwP0yxRDGETkvEMYyMTAAXQErKysrKysTMxUjFRQGByEVIT4BPQEjNTM1ND4CMzIWFwcmIyIOAhX2vb0ICAEh%2FnYLEV1dHzlQMCw8HBc1PBssHxEBdUsIMm4yUEeNRw9LU0phORYQC1AZECZAMAAAAAACACwAbQIIAkkAHgAyAJJAYA80LzRPNAMIEgyHDAFlDHUMAgwTC6AfAR8PHAMdAogCmAICagJ6AgICrykBKQAzGhWIFZgVAmoVehUCFRsUFwQKCQWHBZcFAmUFdQUCBaAksCTAJAMkB68uvy7PLgMuFwAvzV3czV0zXV0RM84yEM4yM11dETMBEN7NXTNdXREzzjLczV3OMjNdXREzMTBeXRM0Nyc3FzYzMhc3FwceARUUBgcXBycGIyImJwcnNyYlNC4CIyIOAhUUHgIzMj4CWRxJOkovOzsuSzpKDg8PDko6SzA5HTYXSjpJHAE3EyArGBgrIBMTICsYGCsgEwFbPS1KOksdHUs6ShY1HyA0Fko6ShwODko6SS0%2BHC4hEREhLhwdLSERESEtAAABABEAAAIkArUAHgC1QG4AGBAYAhgcHg8SDwwScxMWFBMWBxYXFgIWHiBvE58TrxMDExkecwAPDAsIDwgMcwsIFAsLCAgIGAgCCA8GHwYCBgIEbwCfAK8AAwAPCx8LAggLHwMaeQIcHgYYeRYICBYWEx5CE0ESQQxBC0EAQgA%2FPz8%2FPz8SOS8zLxDtMhDeMu0yARDWXl3UXTLOMl0zXYcQKxAAwYcFfRDEARgQ%2FTLUXcYRM12HKwh9EMQBGBDOMl0xMDM1IzUzNSM1My4BJzMeARc%2BATczDgEHMxUjFTMVIxXtoqKiiDZhK2ckVC0sUSVlLGE2iaKiompHZkdYrVJOnEhInE5TrVdHZkdqAAAAAAIAYP9HALcDDwADAAcAHUANAQWuAwcHCQAIBFoCWQA%2FPxDGARI5L8DtMjEwNzMRIxEzESNgV1dXV8b%2BgQPI%2FoEAAAAAAgAr%2F5MBwgLFADsAUQELQLMlNgE3K6crAigXAagQAVdFARo3AQs3AaQ0AYYzAagyAagvAWUrARUYAQQYAakMAYgMAUpNgi0GgjUdHDgLATYqAQsqDqQyATcyATI3LQEtUz9CggA7DiOCFqoTARM4DgEOUh0dIIh3EwEGEwETP5gyAQkyAQgySpQqAXYqhioCayoBVioBiD8Bej8BbT8Bl0oBmQsBeguKCwJpCwFaCwELSj8qBJYcARwZU5k7ATs4iAADUQA%2FM%2B0yXT8zXRc5XV1dXV1dXV1dXV1dETNeXV0RM11d7TIvARDeXTJd1O0QxDLtMhDWXTJdXRE5OV1dxDLU7RDtMjEwAF1dXV1dXV1dXV1dXQFdXV1dFx4BMzI2NTQmJy4DNTQ%2BAjcuATU0NjMyFhcHLgEjIgYVFB4CFx4DFRQOAgceARUUBiMiJicTLgEnDgEVFB4CFx4BFz4BNTQuAkQeSDg4OTZAJEExHBEZIA8ZHWFYM1gaFRhFNCc5DBooHCRBMRwQGB8PHSRqYkNXGq8HDQcdJBcpOCEFCQUdJBcoNAENEiEkIycWDBwoOCgaLSUbCRQ0IkVRFAhNCRQhIRIZFRIJDBwoOCgZLCQdCRU4JUtNFQ0B4wIFAxMzHhojGxULAgMCEzIfGiMaFAAAAAACACMCaAFVAt4ACwAXACm5ABX%2FwEASDRJIFQ8DQA0SSAMJCRgSDAYAAC%2FNL80BETMvzSvezSsxMBMiJjU0NjMyFhUUBjMiJjU0NjMyFhUUBl0XIyMXFyMjpxcjIxcXIyMCaCEaGiEhGhohIRoaISEaGiEAAAMAQf%2FxAu0CxQATACcASQDbQJ43oEUBRbAUwBTQFAOZFAFoFAEUd1gAaAACAygAAQwAHAACAgBLvz7PPt8%2BA6Y%2BAT53jy0BLb8ezx7fHgOWHgFnHgEed1cKZwoCBgoWCiYKAwgKSsg4ATg7fTdwMgFhMgEyoBmwGcAZA4kZARl9Zw8BSA9YDwIZDwEPRURBfUV%2FKAFuKAEoryO%2FI88jA4YjASN9aAUBRwVXBQIWBQEFRgA%2FXV1d%2FV1d1l1dMu0yP11dXf1dXdZdXTLtMl0BEN5eXV39XV1d3l3tXV0Q1l9dXV9d%2FV1dXcZdMjEwARQOAiMiLgI1ND4CMzIeAgc0LgIjIg4CFRQeAjMyPgIFIi4CNTQ%2BAjMyHgIXBy4BIyIGFRQWMzI2NxcOAwLtOF58RER8Xjg4XnxERHxeOEkoR2M7O2NHKChHYzs7Y0co%2Fv8rRjMcHzRGJxorIBUEFww1IzNEPjwnMw4TBRciKwFbVYddMTFdh1VVh10xMV2HVUJtTioqTm1CQm1OKipObY0cNE0yMUw1HAgLCwM8BxNHRD5PEQc8AwsJBwACACYBSQFQAsIADgAxAJZAKkgfWB8CSBsBVxIBExIBBBIBA7AlwCXQJQMlk1cUZxQCJhQ2FEYUAxQzL7j%2FwEAVDRBIL78MzwzfDAMMlEkdWR1pHQMduP%2FAQCQIC0gdMi4rmg8lACIQIgIIIpoQByAH0AfgBwQHBw8AmhgvD2gAPzPU7RI5L13tXl0yEO0yARDeK13tXcQrENZdXf1dwDEwXV1dXV0TMjY3NS4BIyIOAhUUFhMyHgIdAQ4BIyIuAjU0PgIzMhYXNTQuAiMiBgcnPgHEFiwFCyIQESMcEi8iKTgjDxhSKSE3KBcaLDogDiIVBhIgGhc3FAoTPQGHAgJkAgIEDBUQHhkBOxQkMyDgBQkNHCwgHywbDAIDAw8eFw8GBz8FCgAA%2F%2F8AHgA%2FAcsB%2BQAmAGYAAAAHAGYA0AAAAAEANQA1Af8BigAFABxADACvAgQCBAcGBa8DAQAvL%2B0BERI5OS8v7TEwAREjESE1Af9Q%2FoYBiv6rAQZP%2F%2F8AGgEAAREBVwIWABAAAAAEAEH%2F8QLtAsUAEwAnAD0ASAFFQO5WNwE1N0U3AjU0RTRVNAPHDAHHBwHIAwF3NAEDKCi0QsRC1EIDQnc12Sv5KwIrODg9PZc1AWo1ASc1VzUCNbQUxBTUFAOYFAEUdygAWABoAAMMABwAAgIASiy%2FSM9I30gDSHXALgFZLmkumS4DQC4BKS4BLr8ezx7fHgOWHgEedwYKFgomClYKZgoFCApJ0D7gPgI%2BezggKzArAisrLUV9hjIBYTIBVTIBFjJGMgIyoBmwGcAZA4kZARl9Zw8BGQ9JD1kPAw9FPYktAW4tARktSS1ZLQMtryO%2FI88jA4YjASN9aAUBFgVGBVYFAwVGAD9dXf1dXc5dXV0yP11d%2FV1d3l1dXV3tEjkvXTPtXQEQ3l5d%2FV1d3l1dXV3tXTIQ1l9dXf1dXc5dXV0yfS8yETNdGBDtXTJ9LzEwAF9dAV1dXV1dXQEUDgIjIi4CNTQ%2BAjMyHgIHNC4CIyIOAhUUHgIzMj4CBy4BJyMVIxE%2BATMyFhUUBgceAxcnMzI2NTQmIyIGBwLtOF58RER8Xjg4XnxERHxeOEkoR2M7O2NHKChHYzs7Y0coqhc5FUNHGzwZVlonJAoaHBwL9SYtPDkoCxgLAVtVh10xMV2HVVWHXTExXYdVQm1OKipObUJCbU4qKk5thS1RGJYBhAYGQEElNw8MJCwwGNAZJyUdAQEAAAAAAQAuAn8BSgLHAAMAHkAPAQEAAASvAL8AAgAwAgECAC9dzV0BETMvMi8xMBMhFSEuARz%2B5ALHSAACABoB8gEyAwgAEwAfAGlASL8AzwDfAAMAlBS%2FGs8a3xoDGpQKwBQBDwoBCApACxBIFAoUCiEgzw8BD5rAFwFGFwE9FwEGFxYXJhcDF2jPHQEdmgUQCQ1IBQAvK%2B1dP11dXV3tXQEREjk5Ly8rXl1dEO1dEO1dMTABFA4CIyIuAjU0PgIzMh4CBzQmIyIGFRQWMzI2ATIXJjMcHDMmFxcmMxwcMyYXQyseHisrHh4rAn0gNCQTEyQ0ICA0JBMTJDQgIykpIyMpKQAAAAACADUAAAH%2FAmwACwAPAGBAOg0FAwevAQmvCwELD4AFkAUCHwUvBQJQDwEFCQ8PCQUDERAMrw5cBK8GBgpAAgECAK9PCAEIrwoBCl8AP13FXf3GXREzL%2B0%2F7QEREhc5Ly8vXV1dETNdEMDtMhEzMTATMzUzFTMVIxUjNSMRIRUhNb1Qvb1QvQHK%2FjYBo8nJT8nJ%2FvtPAAAAAAEAHgE7ATgCxQAhAF9AOxMgIyAzIAMTsmcAdwACVgABAAAJCLILCxscCRwJHCMiGxazHwizSQoBCrFmHHYchhwDVRwBHEYfAR9xAD9dM11d9F3tEO0yARESOTkvLxEzMy%2FtETMvXV3tMTAAXQEUDgIHDgEHMxUhJj4CNz4BNTQmIyIOAgcnPgEzMhYBKBMgKhgRIQO6%2FvYDDx0mFSIrIR0RHhgTBSQRRS1FQgJWFSgnJxUOIQ0%2FITMqIxIdMRgaGAkNDQQxEiM6AAAAAAEAIAEzATYCxQAtAGdAGhUkAQQkAQg3HGccAh8QsnYdAR0iCQkqA7IiuP%2FwQB8ID0giLxYXK2AqASouHwqzCAgaKwCzKiexFhOzFxpxAD8z7TL0Mu0yEjkv7TkBENZdMsYyENYr7RI5LxDUXe0zMTBdXl1dEzI2NTQuAisBNTMyPgI1NCYjIgYHJz4BMzIWFRQHHgEVFA4CIyImJzceAZA1KBIfKBUTGQ4fGRAlHRsuERkQQyJJPTcjKRIoPywfQBIPFjEBcSEaERgPBzkFDBUPGRUTCDcLFzsuMR8LMyMaKyESDgk8CA0AAAAAAQB%2FAkQBOwMIAAMAOEAOAQIDQBESAEwDAAUAQAO4%2F8CzEgBNA7j%2FwLYNAE0DgAIBAC%2FNGt0rKxrNARDeGcUrGN0ZxTEwAQcnNwE7jS%2BAAs2JKpoAAQBQ%2F0cB9AIIABgAOEAfC38JEhV%2FAH8XCRcJFxoZGEkWSxIwDRJIEgSID1EKSQA%2FP%2B0zKz8%2FARESOTkvLxDt7TIQ7TEwExEUFjMyPgI3ETMRDgEjIiYnHgEdASMRrT5FDyAcFwVdIGU8N0EUAwJcAgj%2B6l9MAgMDAgG3%2FgcIEhcWHDkfZwLBAAEAK%2F9HAiECvAAWADFAGxF1EwN1BRMFCQkFEwMYFxJDvwDPAAIADkUEQwA%2FP81dPwEREhc5Ly8vEO0Q7TEwASIGBxEjES4BNTQ%2BAjMyFhcRIxEuAQGCCBAGTm98LVR4SipgKU4OKgJvAQH82gG%2FC2hmN1I4HAcK%2FJwDIwIDAAD%2F%2FwA4AOMAvgFpAhcAEQAAAPAAAAABAEX%2FQwD%2BABQAHQAyQBkAGwOiERuhFQsLHhUVHx4ADKYPpggaaQhqAD8%2FEO3tMgEREjkvETkvEO0Q7REzMTAXHgEVFA4CIyImJzceATMyNTQmLwE%2BAzczDgHHHRoKGCgdGisNCwwbESQWFAcDDAwLA0AGEiUOISALGhYOCAU5BAYXCw8IAwobGhUFCyIAAAABADIBOwDuAr0ACgAuQBkAfwoBCgSyBgYMC4oHAXsHAQcKswAFsQNwAD%2Ft3u0yXV0BERI5L%2B3OXTIxMBM%2BATczESMRDgEHMiZKGzFGFjkUAm0NKRr%2BfgEmDhYGAAIAKwFIAZMCxAATAB8AWkA%2BvwDPAN8AAwCUFL8azxrfGgOWGqYaAhqUCrAUwBTQFAOZFKkUAgYUFhQCCQoZCgIIFAoUCiEgF5oPaB2aBWcAP%2B0%2F7QEREjk5Ly9eXV1dXRDtXV0Q7V0xMAEUDgIjIi4CNTQ%2BAjMyHgIHNCYjIgYVFBYzMjYBkxswQicnQjAbGzBCJydCMBtKNzMyODczMzcCBitGMhsbMkYrK0cxGxsxRys4Q0M4N0REAAD%2F%2FwAoAD8B1QH5ACYAcAAAAAcAcADQAAD%2F%2FwAyAAADUgK9ACYAiwAAACcA1QFrAAAABwDXAgP%2ByP%2F%2FADIAAAM7Ar0AJgCLAAAAJwDVAVYAAAAHAIQCA%2F7I%2F%2F8AIAAAA1ICxQAmAIUAAAAnANUBegAAAAcA1wID%2FsgAAgAc%2F0QBgQIIACEALQBhQD6HEAFmEAEKDgEIIoIoKBh%2FGxsOBXAGgAaQBgMGLwB%2FDkAJD0gOLhgrJVAFA4h5BokGmQYDBgkJGQkpCQMJUgA%2FXTNd7TI%2F3c4BENYr7RDGXTIROS%2FtMy%2FtXl0xMABdXRcUFjMyNxcOASMiLgI1ND4ENTwBJzMWFRQOBBM0NjMyFhUUBiMiJn43MUI%2FGiJVMDZILRMZJismGQFOBBchKCEXJSQfHyQkHx8kFyYxIkgSFhwsNxshNC8qLzQhBQkFFBMgNC0oKCoBxBsoKBsbKCgAAAD%2F%2FwAIAAACjwOkAiYAJAAAAQcAQwCWAJwAE7kAAv%2FnQAkcGgYRUAILG08rKzQAAAD%2F%2FwAIAAACjwOkAiYAJAAAAQcAhgCOAJwAEEALAh4bGQYRUAILGk8rKzQAAP%2F%2FAAgAAAKPA50CJgAkAAABBwBlAI8AnAAQQAsCAB4aBhFQAgsbTysrNAAA%2F%2F8ACAAAAo8DewImACQAAAEHAG8AkACcABBACwIAKBkGEVACCydPKys0AAD%2F%2FwAIAAACjwN6AiYAJAAAAQcAegCPAJwAF0AQAwIAGy0GEVADCyVPAgsZTysrKzQ0AAAAAAMACAAAAo8DRwAgACcAMwEiuQAa%2F%2FizDQBNCrj%2FwEATEx1ICi5AFBdILi4xFEATHUgUKLj%2FwEBSExdIKCgxMR4fICEiIyQdJBsaGRgXHBcdJB1zHBcUHBcdHAMEBQYHAgcAJyYlJAcBJAFzAgcUAgcBAiQkAhw1QDVwNaA1Aw81AQ8CHwICCAI0D7j%2F8LYPEkhXDwEPuP%2FoQC0IDUgPKzEgAHknQCGAIZAhA5IkAYMkAXokAVkkaSQCISQhJBwHBxcxQRxCAkIAPz8%2FxTIvETk5Ly9dXV1dXTPtMhDezStdKwEQxl5dXV0QxhE5GS8QAMGHBRgrh33EEA7ExAXExBCHDsTExMQQAMGHBRgrh33Ehw7ExMTEEIcOxMQFxMQOxMQBMxEzGC8rzSsRMy8rzSsxMCs3ByM%2BAzcuATU0PgIzMh4CFRQGBx4DFyMuAS8BLgEnDgEHEzQmIyIGFRQWMzI2rD9lJUI%2FPSETFhEcJRUVJRwRFhIhPj9CJmoRHhAcID8iIz8ftx8WFh8fFhYfsrJmsZ%2BQRw4qHBglGw4OGyUYGykOR5GfsWctWC1PV6NLS6JYAeAaHR0aGh0dAAIABgAAA4ICtQAVABoAg0BGEBESExQPFA0LGRYOFhQYCBJIFA4WDnMPFBQPDxQJFnMCBgYPFQQHHA8bAnkEGhl5CgsKBAoECggWAXkUQQ8PCA4OBnkIQgA%2F7TMvETMvP%2B0yEjk5Ly8RMxDtMhDtARDGENbGxhI5LzPtMocQK4d9xAErEIfExA7EEIcOxMTExDEwASEVIRUhFSEVITUhDgEHIz4DNyEFDgEHMwNp%2FtMBCf73AUb%2BWv7vGC8WaDltaGUyAb7%2BczVyPeQCZNFP81GyLFktbL%2BqmUdMSLZqAAAAAQA6%2F0MCUwLFAEABGEBMQkAMEkhkLgFTLgFCLgEjLjMuAhIuAQMuAVUqASMqMypDKgMSKgEDKgEIaAMBKQI5AgJqL3ovii8Dmi4BcyqDKpMqA2IqAUUqVSoCG7j%2F%2BEB%2BDxJIKj46PgI%2BNCw6OgUQDw8iI0IWGgEHGgEadmgFmAUCVwUBBalBMTg%2BKUoiWiJqIgMiBx8XH1cfZx8EH3xKI1ojaiMDIwBoJngmmCYDVyYBJkZFEFUQZRADEAgVGBVYFWgVBBV8hw%2BXDwJFD1UPZQ8DD4gKAWcKAVgKAQpFAD9dXV0zXV3tXTJdP11dMzNd7V0yXdbd3s0BEP5dXe1dXRDGMjIvMxE5L83Mzl0xMAArXV1dXV0BXV1eXV1dXV1dXV1dXSsFLgM1ND4CMzIeAhcHLgMjIg4CFRQeAjMyNjcXDgEHDgEHHgEVFA4CIyImJzceATMyNTQmLwE%2BAQFKPmRHJzVaeEQrRTMgBhwJIiszGjdYPiEdOlc6Q1gWGQ5rUgMFAh0aChgoHRorDQsMGxEkFhQHBA0LCDdbfU9Xh1wwDBAQBFAGDg4JJkhnQD5mSSgcC1ALHwIGCwUOISALGhYOCAU5BAYXCw8IAwwgAP%2F%2FAFYAAAIWA6QCJgAoAAABBwBDAIsAnAATuQAB%2F%2FJACQ8NAApQAQEOTysrNAAAAP%2F%2FAFYAAAIWA6QCJgAoAAABBwCGAH4AnAAQQAsBIw4MAApQAQENTysrNAAA%2F%2F8AVgAAAhYDnQImACgAAAEHAGUAbQCcABO5AAH%2F80AJEQ0AClABAQ5PKys0AAAA%2F%2F8AVgAAAhYDegImACgAAAEHAHoAbQCcABqxAgG4%2F%2FNADQ4gAApQAgEYTwEBDE8rKys0NP%2F%2FAA8AAADLA6QCJgAsAAABBwBD%2F9IAnAATuQAB%2F%2BhACQcFAAFQAQAGTysrNAAAAP%2F%2FAEQAAAEAA6QCJgAsAAABBwCG%2F8UAnAAQQAsBGgYEAAFQAQAFTysrNAAA%2F%2F%2F%2F%2FwAAAQ8DnQImACwAAAEHAGX%2FywCcABBACwEACQUAAVABAAZPKys0AAD%2F%2F%2F%2FuAAABIAN6AiYALAAAAQcAev%2FLAJwAF0AQAgEABhgAAVACABBPAQAETysrKzQ0AAAAAAIAE%2F%2F6ApwCvAAUACUAd0BNKRhpGHkYAxgYAQkYAQgYdpcAAVYAZgACDwABAKknACdwJ7AnAyMgHnMNCgioJgsfeQohIQUeGBsBG3wNVxABEEUjFxUBFXwIWAUBBUYAP10z7V0yP10z7V0yEjkvM%2B0yARD2zjP9zjNdEPZdXV3tXl1dXTEwARQOAiMiJicRIzUzET4BMzIeAgEyNjU0JiMiBgcVMxUjFR4BApw4Zo5VKmUpUFApZSpVjmY4%2FoeKiIiKKS0JgoIJLQFbWoVXKwcKATlLARwKBytXhf6ajX9%2FjQIB1UvyAQIAAAD%2F%2FwBWAAACggN7AiYAMQAAAQcAbwCxAJwAEEALAQAjFAkSUAEKIk8rKzQAAP%2F%2FADr%2F8QLQA6QCJgAyAAABBwBDANAAnAATuQAC%2F%2BhACSspAAlQAgQqTysrNAAAAP%2F%2FADr%2F8QLQA6QCJgAyAAABBwCGAMMAnAAQQAsCGSooAAlQAgQpTysrNAAA%2F%2F8AOv%2FxAtADnQImADIAAAEHAGUAyQCcABBACwIALSkACVACBCpPKys0AAD%2F%2FwA6%2F%2FEC0AN7AiYAMgAAAQcAbwDKAJwAEEALAgA3KAAJUAIENk8rKzQAAP%2F%2FADr%2F8QLQA3oCJgAyAAABBwB6AMkAnAAXQBADAgAqPAAJUAMENE8CBChPKysrNDQAAAAAAQBQAGEB5AH1AAsAiUBYC2AJsAkClgkBCQkBBZkDAQMDyQoBtgrGCgKWCgEKxgQBuQTJBAKZBAEEAQEMDQawCAGWCAEICAoCmQABAADJBwG2B8YHApYHAQcExgEBuQHJAQKZAQEBCgAZLzNdcXIzM11xcjMvXTIRMy9dcjMBERI5LzNdcXIzXXFyMy9dMxEzL11yMzEwJScHJzcnNxc3FwcXAaySkjiSkjiSkjiSkmGSkjiSkjiSkjiSkgADADr%2F3ALQAtoAGgAkAC8CbEAmRS5lLgI2LWYtAoksAXgeAWgdATkdASgdAUoYAWQXAWQWAWYVAQ%2B4%2F%2BC2EABNWQsBCrj%2F%2BEBUEABNRgqGCgJqCQFsCAEDIBAATXkumS4CWi0BWiwBZikBVSgBSCMBlh4BVh0BRR0BgxwBVBoBVxkBlhgBZRYBahUBWRQBmgoBagkBdwMBKRg5GAIHuP%2FwtAwNAEwHuP%2FoQBoLAE0YHR4HCBcIFxASAE0XGBEATRcQEABNF7j%2F4EAUDQBNFxYgBSUSFRAMAE0VGAsATS24%2F%2BizDQBNCrj%2F8EAKDABNFS4tCgkWCbj%2F8EAJEBIATAkFEggWuP%2FgQBoNAE0WCBYIACoQEQBNKggOAE0qEAoATSp2Dbj%2F%2BLMSAE0NuP%2F4QA8PAE0NQAkATQ2pMfAxARu4%2F%2FizDgBNG7j%2F8EALCgBNG3YACBIATQC4%2F%2FhAEREATQAIDwBNAEAIAE0AqTAduP%2Fwtw8QAEwdLi4luP%2F4sw4ATSW4%2F%2FizCgBNJbj%2F8LUJAE0lfBe4%2F%2FBAPRASAEwXFhgQCwBNGBUVEggSAE0SCA8ATRJGLRAPEABMLR4eIAgOAE0gCAoATSAQCQBNIHwJEBASAEwJCAq4%2F%2Bi2CwBNCgcHBbj%2F%2BLMSAE0FuP%2F4tA8ATQVFAD8rKzMRMyvGMivtKysrMhEzKz8rKzMRMyvGMivtKysrMhEzKwEQ9isrKyvtKytdEPYrKyvtKysrEjk5GS8vKxI5OTIrfYfExMTEASsrKysRMxEzETMrKysrEIfExMTEASsrXTEwAF1dXV1dXV1dXV1dXV1dXV1dXV0BK11dXStdK11dXV1dXV1dXV1dEzQ%2BAjMyFzcXBx4BFRQOAiMiJicHJzcuATcUFwEmIyIOAhMyPgI1NCYnARY6NVt5RGpSOz5AJy01WndDNV8pOj4%2BKC5mLAE%2FN081VTsg5TVUOyAWFP7CNQFbWYhbLjhNLlMtf1JZiFsuGxtLLlAtgFRtRgGdKydIZ%2F6sJ0hnPzVXI%2F5lKQD%2F%2FwBR%2F%2FECXwOkAiYAOAAAAQcAQwCjAJwAE7kAAf%2FoQAkbGQQSUAEGGk8rKzQAAAD%2F%2FwBR%2F%2FECXwOkAiYAOAAAAQcAhgCWAJwAEEALARkaGAQSUAEGGU8rKzQAAP%2F%2FAFH%2F8QJfA50CJgA4AAABBwBlAJwAnAAQQAsBAB0ZBBJQAQYaTysrNAAA%2F%2F8AUf%2FxAl8DegImADgAAAEHAHoAnACcABdAEAIBABosBBJQAgYkTwEGGE8rKys0NAAAAP%2F%2FAAYAAAJQA6QCJgA8AAABBwCGAGoAnAAQQAsBGhMRBAxQAQQSTysrNAAAAAIAVgAAAj0CtQAOAB4AUrkAF%2F%2Fosw4ATRS4%2F%2BhAIw4ATRV2Bg4PDxAbcx0GHQYdIB8QE3wLAHkaCxoLGhweQRxCAD8%2FEjk5Ly8Q7RDtMgEREjk5Ly8Q7TIyETMQ7TEwKys3MzI%2BAjU0LgIjKgEHNRU%2BATMgFRQOAisBFSMRt0kyUDceHzhOLxInExMpEwE3K1F0SU1h3g0gNiooNR8MAsRxAgHdPFM1GI4CtQABAFD%2F9AJHAwgARQDUQJKmRAEnRAEVRAGpMAGpLwGpLgErKAEcKAEGEwEGDwEHQwF4OAF3MAE5FAGFEAEGEhYSJhIDEoIjAIIzCIKoKwErGjl%2FO5gjAZgzAYAzAQ8zASMzOzszIwNHRngmmCaoJgMmEAsOSCaKpQ0Bhg2WDQILDQENDRWJNgFoNng2AjaIBkGmQQIIQU86ShuXIAEgiBoVUQA%2FM%2B1dMj8%2FXl3tXV0SOS9dXV3tK10BERIXOS8vL11dXV0Q%2Fc7eXe0Q7RDtXTEwAF1dXV1dAV1dXV1dXV1dXV0BFA4CBw4BFRQeAhceAxUUBiMiLgInNx4DMzI2NTQmJy4DNTQ2Nz4DNTQmIyIGFREjETQ%2BAjMyHgIB8g4VGQsXIA8YHQ8bLyMTYWYVKiQbBhAGGSEnFDIwLjkhKxgKLBkKFRILNDRDPV0cNlA0Nk4xFwJoGSchHAwZIRkPFhAOBw0eJzUlTlQGCQoDUQMKCgcsJScyGg8bHB4UKDYaChgZGg4sMUxD%2FdgCKjJSOiAYKzsA%2F%2F8AK%2F%2F1AcADCAImAEQAAAEGAENSAAATuQAC%2F%2FlACTk3HhNQAg84TysrNAD%2F%2FwAr%2F%2FUBwAMIAiYARAAAAQYAhkUAABBACwIrODYeE1ACDzdPKys0%2F%2F8AK%2F%2F1AcADAQImAEQAAAEGAGVLAAAQQAsCETs3HhNQAg84TysrNP%2F%2FACv%2F9QHAAt8CJgBEAAABBgBvTAAAEEALAhJFNh4TUAIPRE8rKzT%2F%2FwAr%2F%2FUBwALeAiYARAAAAQYAeksAABdAEAMCEThKHhNQAw9CTwIPNk8rKys0NAD%2F%2FwAr%2F%2FUBwAMLAiYARAAAAQYA1EsAAAy3AwIRPzYeE1ArNDQAAwAs%2F%2FQDJQIVADsATABVAQlArlZSAWM0AVQ0AXcrAaUlAQNICgF5AgGYGwEGEAEIAwEsDQECTYI5LZcnpycCiAMBZgMBVQMBRwMBBQMlAwInA5dVp1UCZlUBVUYyATKCF0I%2FF6dKAUqCIQ13LYctAgMbLQEbF3sXAjsNARwNAQIPDQEtFw0NFy0DV1YwVwFVhQ8xATExFxKIQgBFAUVFCFCIJ3cqAWgqAQcqAQgqUCAdiCEkUD88iAMIUTg1iDkAUQA%2FMu0yPzPtMj8z7TI%2FXl1dXTPtEjkvXTPtMjMvXe0BXRESFzkvLy9dX11dXV1fXRDE7V0RMzMQ7V0yXV05OV1dXV1dXRDE7V9dMTAAXV1dAV1dX11dXV1dBSImJw4DIyIuAjU0PgIzMh4CFzU0LgIjIgYHJz4BMzIWFz4BMzIWFRQGHQEhHgEzMjY3Fw4BJTI2Ny4BJy4BIyIOAhUUFiU2JiMiDgIHAmU5VSAKIS45IjJPOB4jPFAsDh4aFAQKGi0kLkURCxJTMEVRFCFaMWhxAf6cBlNWMUMRDRFV%2Fmo0PBAPEQELKiAVLycaQQH5AUE5IDEhFAMMGxoHEhELEihALSs%2BJhIDBQQBGhctIxUNB00IDy8qLSyEhQgLBQlQVREITgkUTxcLHEAkBQcGEyEbMybzP1EZKDQbAAEAM%2F9DAb0CFQA6ALJADU88Xzx%2FPAM8QBEWSAK4%2F%2BBAIAgSSBYMlgSmBAIEARISHiZgNoA2AjY8jzwBVy0BLYIeuP%2FoQEQOAE1XHgEPHh8eAh6rOydYKgFJKgEqiCYjUDVXMgFGMgEyiAAMCRgJEEgJDRAWATYZEBAATQkZGRkpGWkZeRkFCBkAUQA%2FMl5dKzLW3d4yzSsyEO1dXTI%2FM%2B1dXTIBEPZdXSvtXV0Q1l3GEjkvxc1dxM4xMAArAStdBQceARUUDgIjIiYnNx4BMzI1NCYvAT4BNy4DNTQ%2BAjMyFhcHLgEjIgYVFB4CMzI2NxcOAwE%2BCx0aChgoHRorDQsMGxEkFhQHBA4IM000GiNAXTkjRiAVFTcfTlMTKUAuJTwRDQgbIicMGQ4hIAsaFg4IBTkEBhcLDwgDDCIPBy1GWzY8ZEkpDA1PCgxiXipGMhsOCE4ECQcFAAAA%2F%2F8AM%2F%2F0AfkDCAImAEgAAAEGAENpAAATuQAC%2F%2FBACSclAAdQAgQmTysrNAD%2F%2FwAz%2F%2FQB%2BQMIAiYASAAAAQYAhlwAABBACwIhJiQAB1ACBCVPKys0%2F%2F8AM%2F%2F0AfkDAQImAEgAAAEGAGViAAAQQAsCCCklAAdQAgQoTysrNP%2F%2FADP%2F9AH5At4CJgBIAAABBgB6YgAAF0AQAwIIJzkAB1ADBDFPAgQlTysrKzQ0AP%2F%2FAAcAAADDAwgCJgDSAAABBgBDygAAE7kAAf%2FoQAkHBQEAUAECBk8rKzQA%2F%2F8APAAAAPgDCAImANIAAAEGAIa9AAAQQAsBGgYEAQBQAQIFTysrNP%2F%2F%2F%2FcAAAEHAwECJgDSAAABBgBlwwAAEEALAQAJBQEAUAECBk8rKzT%2F%2F%2F%2FmAAABGALeAiYA0gAAAQYAesMAABdAEAIBAAYYAQBQAgIQTwECBE8rKys0NAAAAgAz%2F%2FMCNAMIACIANwDjQJasIQGZIQGmHwGqGwFHNQGWIQEXIQFpGAEHEQF3EAFWEAGmHgEeHR0VIQEhAQSCIyYbGBYmLoIOJAA0AAJeGgFLGgEfGi8aPxoDDw4fDgIIACYaDg4aJgAEOThpGHkYAhgZdxYBFlUTAUYTAROIKXQhAWUhAVchATYhASsbAQEbIQMiGSkZKQkiTR2FHk8ziEkJWQkCCVEAP13tP%2B0%2FEjk5Ly8SFzldXV1dXRDtXV0yXREzXQEREhc5Ly8vL15dXV1dXRDtETMzMxDU7TIyXTIvM10xMABdXV1dXV1dAV1dXV0BBx4BFRQOAiMiLgI1ND4CMzIWFyYnByc3Jic3HgEXNwM0JjUuASMiDgIVFB4CMzI%2BAgI0ayAsFzhfSDtZOx0hPFc2M0wSDCt4F2YhMzsWOR18ZwEiSiApOSQREiM1Iy47IQ0CkSU2kF5Ce185LEheMzhaPyEgDlBEKT8jJyUzDjEkK%2F51CBAIHhYZLDoiJEExHSxIXP%2F%2FAFAAAAH0At8CJgBRAAABBgBvZwAAEEALAQAnGAAHUAECJk8rKzT%2F%2FwAz%2F%2FMCGwMIAiYAUgAAAQYAQ3IAABO5AAL%2F6EAJIyEJAFACDiJPKys0AP%2F%2FADP%2F8wIbAwgCJgBSAAABBgCGZQAAEEALAhkiIAkAUAIOIU8rKzT%2F%2FwAz%2F%2FMCGwMBAiYAUgAAAQYAZWsAABBACwIAJSEJAFACDiJPKys0%2F%2F8AM%2F%2FzAhsC3wImAFIAAAEGAG9sAAAQQAsCAC8gCQBQAg4uTysrNP%2F%2FADP%2F8wIbAt4CJgBSAAABBgB6awAAF0AQAwIAIjQJAFADDixPAg4gTysrKzQ0AAADADUALQH%2FAikACwAXABsAY0BAnxuvGwJgGwFPG18bAgAbEBsCCBsZAw9ADRJIDwkVgBmQGQIfGS8ZAlAVgBWQFQMZFRkVHRwSDBivGgAGUBoBGgAvXd7NEP3ezQEREjk5Ly9dXV0QwN0rwBDNXl1dXV0xMCUiJjU0NjMyFhUUBgMiJjU0NjMyFhUUBgchFSEBGhkjIxkZIyMZGSMjGRkjI%2F4Byv42LSEcHCEhHBwhAYIhHBwhIRwcIVxPAAMAM%2F%2FcAhsCKwAaACIAKgE8QNOXJaclAnYlAXgdqB0CZBgBUxgBVhcBSQ4BOw4Bqg0BWwprCgI2AUYBAqYAAacqAZslAXklAWslAVolAakiAZQdAXYdAWUdAVQdAVcYAVgKiAoCmB0BmA0BeQ2JDQIYHh0NDBkMDAsoIBWZCgGXAAF2AIYAAgomJQAaCxoVGaALAa8ZAQsZCxkQWSNpIwIjggOrLI8sAVYbZhsCG4IPEB8QAggQqyuYJQElHh5JIFkgAiCIGhkAGGYYARgVUJcdAR0mJkYoVigCKIgMCw0KaQoBCghRAD8zXREzxjLtXTIRM10%2FM10RM8Yy7V0yETNdARD2Xl3tXV0Q9u1dEjk5GS8vXV0SOTJ9h8TExMQBXV1dETMzETMQh8TExMQBXV1dMTAAXV1dXV1dXV1dXV1dAV1dXV1dXV1dXV1dXQEeARUUDgIjIicHJzcuATU0PgIzMhYXNxcFFBcTJiMiBgU0JwMWMzI2AeAcHyRBWjVPOjE1NhwfJEFaNSdFHTE1%2Fn0V1CQxRU8BKBbTIjNFTwG9I105PmRIJylAKEYjXjk9ZUgnFhRAKP9BLQESHGdZPi%2F%2B7htnAAAA%2F%2F8ASv%2F1Ae4DCAImAFgAAAEGAENnAAATuQAB%2F%2BhACRkXBwBQAQkYTysrNAD%2F%2FwBK%2F%2FUB7gMIAiYAWAAAAQYAhloAABBACwEZGBYHAFABCRdPKys0%2F%2F8ASv%2F1Ae4DAQImAFgAAAEGAGVgAAAQQAsBABsXBwBQAQkYTysrNP%2F%2FAEr%2F9QHuAt4CJgBYAAABBgB6YAAAF0AQAgEAGCoHAFACCSJPAQkWTysrKzQ0AP%2F%2FAAL%2FRAHfAwgCJgBcAAABBgCGQwAAEEALAS4jISATUAEJIk8rKzQAAgBQ%2F0cCGgMIABAAJQBoQEWZIwGaAgGJAAFaAAFJAAEAf6gRAZkRAVgRASkRORFJEQMRqycdCBl%2FG6omCAOIHiFQHU4aS4sJmwkCCQyIixmbGQIZFlEAPzNd7TJdPz8%2FM%2B0yARD27TIyEPZdXV1d7V1dXTEwXV0BNCYjIg4CBxEeATMyPgI3FA4CIyImJxUjETcRPgEzMh4CAblaSw8gHRYFEUAmKDkkEGEfPFc4LUURXV0aORo5X0MlAQNbYwQFBgL%2Bvg4aHTNFKDtkSCgYC9ADsRD%2B9wkLJkZl%2F%2F8AAv9EAd8C3gImAFwAAAEGAHpJAAAXQBACARQlNyATUAIJL08BCSNPKysrNDQAAAEAUAAAAK0CCAADAB9AEQAFEAUCCAWqA38BqgQCSQBKAD8%2FARD2%2FeZeXTEwMyMRM61dXQIIAAD%2F%2FwBCAAAAugLgAgYATAAAAAIAVQI%2FASMDCwATAB8AdEAnAEATH0gAoBQaQBMfSBqgCq8UAQAKEAqwCgMUChQKISC%2FDwGnDwEPuP%2FwQCYIDkgPp7AXAR8XAQ8XLxd%2FF58XBAgXba8dAR2nmAUBBRAIDkgFawA%2FK13tcT9eXXFx7StdcQEREjk5Ly9dXRDtKxDtKzEwARQOAiMiLgI1ND4CMzIeAgc0JiMiBhUUFjMyNgEjERwlFRUlHBERHCUVFSUcETIfFhYfHxYWHwKlGCUbDg4bJRgYJRsODhslGBodHRoaHR0AAf9ZAAABNwK1AAMAJ0AVAwEAAa4CAxQCAwIEAANBAkIBQgBBAD8%2FPz8BLxDUhyuHfcQxMAkBIwEBN%2F55VwGHArX9SwK1AAAAAgAdATMBSQLFAAsAHwA8QCYMsoAGAQUGFQYCBiEWspAAoAACjwABCgAaAAIIACAbswmxEbMDcQA%2F7fTtARDWXl1dXe0Q1l1d7TEwEzQ2MzIWFRQGIyImNzQuAiMiDgIVFB4CMzI%2BAh1QRkZQUEZGUOIHEh0WFh0SCAgSHRYWHRIHAfxhaGhhYWhoYRwzJhYWJjMcHTImFhYmMgAAAgAYATsBTwK9AA4AEwBGQCgTDgayBQFwA5ADAgMVrxIBEq8IvwgCCBQSCQGzAwcHAwWxnw8BDw5wAD%2FNXf3OMi8Q7TIyARDWXc1dENZd3TLtMjIxMAEVMxUjFSM1IzU%2BAzcHDgEHMwEdMjJEwQ4sNDodBB9AF3YCveo7XV0uGUBEQRlTIFEmAAMAIAEzAUYCxQAdACsANwCiQGE3GwE1LLK3GwGmGwEbGCaybwABEAAgAAIAOSkysrgLAakLAQsOHrJPCF8IAgg4pjW2NQI1uSkBKacbARuEC5QLAmsLewsCNAtEC1QLAwsLEyOzAxAMEEgDMAgLSAOxL7MTuP%2F4swwQSBO4%2F%2Bi0CAtIE3EAPysr7fQrK%2B0ROS9dXV0zXTNdM10BENZd7dQyXV3tMhDWXV3t1DJdXe0yMTBdARQGIyIuAjU0NjcuATU0PgIzMh4CFRQGBx4BBxQeAjMyNjU0JicOATc0JiMiBhUUFhc%2BAQFGTEgqOCIOJBgXGxMkMh8kNCEPHRckGt4IEh0VKSM5MxQYjx4lJR4qLxQZAaQyPxUgJxIcMQ8RLxoWKB4SFB4lEhwtEBEuIwgSDwofFBwfDA8ipg0gIA8UJgkOIAAAAAABADUA%2FwH%2FAU4AAwAStgEAAAQArwIAL%2B0BETMvzTEwEyEVITUByv42AU5P%2F%2F%2F%2FWQAAATcCtQIGANUAAP%2F%2FAFAAAAMRAwgAJgBJAAAABwBJAYYAAAADAFAAAAJAAwgAEwAXACMAf0AxGBMBdwiHCAJmCAEEEwENBIADAQMbFX8hF6olYCVwJaAlAw8lHyUvJQMICg5%2FEKokGLj%2FwEAfDBJIGIkeUxZJFEoPSg2HC0kEaAd4BwIHiAOHAAEATwA%2FXTLtXTI%2F7T8%2FPz%2FtKwEQ9u0yXl1dEPYy%2FTLWXTLGMTAAXQFdXV0BMhYXBy4BIyIGHQEzFSMRIxE0NgEjETMnIiY1NDYzMhYVFAYBGyMyCw0LJhtBNMjIXWIBgV1dLxkjIxkZIyMDCAkEUAQJRzwtTv5GAjdkbfz4AgheIRwcISEcHCEAAP%2F%2FAFAAAAJAAwgCBgDcAAAAAgBQ%2F%2FYCfwMIABMAHwB7QB0YEwF3CIcIAmYIARUgCAtIBBMBCAoOf0AQqiAhH7j%2FwEAuCAtIHxmADHADAQMXf1AZAQ8ZARkZTh%2BIFFEPSg2HC0kEaAd4BwIHiAOHAAEATwA%2FXTLtXTI%2F7T8%2F7T8BL11d%2FdZdxhoQ3ivGEPYa7TIxMABeXSsBXV1dATIWFwcuASMiBh0BMxUjESMRNDYBLgE1ETcRFB4CFwEbIzILDQsmG0E0yMhdYgHAVkhdCBIeFgMICQRQBAlHPC1O%2FkYCN2Rt%2FO4CRkoCcBD9jxceEgkDAAD%2F%2FwBQ%2F%2FYCfwMIAgYA3gAA%2F%2F8AUAAAA8YDCAAmAEkAAAAHANwBhgAA%2F%2F8AUP%2F2BAUDCAAmAEkAAAAHAN4BhgAAAAcAHf%2FxAvECxQATAB8AKwA%2FAFgAcwB%2FABlADyldPCMaFDBXegkFD0UFRgA%2FPxIXOTEwARQOAiMiLgI1ND4CMzIeAgUiBhUUFjMyNjU0JgE0JiMiBhUUFjMyNiU0NjcnDgEHHgEVFAYHHgEXNy4BNxc2MzIeAhczLgEnDgEjIi4CJy4BIyIDBx4BMzI2Nz4DMzIWFz4BNyMOAyMiJhMyNjU0JiMiBhUUFgLxOGKETEuEYjk5YoRLTIRiOP2eExwcExQdHQGQHxAWGx0TFBz%2BxiEdJh4uCgsPDwsKLh4mHSExIRwgHDEmFwJGAyEbBwoFDBcTDAEQGw8xCyEVLxkPGxABDBMXDAUKBxshA0YCFyYxHBEdqxAfGhYSHh0BWkuEYTk5YYRLTIViODhihRsdFBQbGxQUHf78FxYdFBUaH%2BclPRQ8Fz8nCBwRDxwJJj4YPhI94TwOFCMuGyZGGgEBCA8VDAUD%2Fqs6CwoDAw0VDwgBARpGKRwwIxQHAS0YFhEhGRgTHP%2F%2FAB3%2F8wZ0AYwAJwDWAAD%2BxwAnABEBZwAAACcA2AJd%2FscAJwDWA8T%2BxwAHANYFK%2F7HAAAADgBJAAADhwK1AAMABwALAA8AEwAXABsAHwAjACcAKwAvADMANwb5QIoGGyYbNhtGG2YbhhsGPQcbAQkGGiYaNhpGGmYahhoGPQcaAQkGExYTJhNmEwQGEhYSJhJmEgQZBQEZBAFJAVkBAkkAWQACCRdpFwIJFGkUAgYONg5mDgMGDTYNZg0DCQs5CwIJCDkIAj2aMaoxAmkxeTGJMQMxQAoOSAoxATEvgDQ7SC9%2FKI8oAii4AcCzLC9IKLgBgEAjKCtImiiqKAJpKHkoiSgDKEAKDkgKKAEoJIA0O0h%2FJI8kAiS4AcBAISsvSA0kHSQC%2FSQBmiSqJAJpJHkkiSQDJEAKDkgKJAEkNrj%2FgEAJNDtIBzYBNjUyuP%2BAQAwNE0gXMicyNzIDMiu4%2FgBAEi0xSDErASArAQErESsC8SsBK7j%2FgEAMDRNIFysnKzcrAysnuP4Asy0xSCe4%2FkCzKCxIJ7j%2FgED%2FDRNIFycnJzcnAyccCCABCSAiLxWPFQIJFQE7jxWfFf8VA14VAb0V7RX9FQNbFQFMFQEbFQGqFboVAnkVAQgVGBUCChUJEwE7Ew8MPwxvDAM9TwyPDL8M7wwEHgwBrQztDAJ7DAFMDAELDAGqDNoMAjkMaQwCCAwBCwwvCF8IAgkIATvpCAGPCL8IAh4IAe0IAXsIAUwIAaoI2ggCCAg4CAILCGYahhoCBxoBMxoZABdgFwI9UBeAF5AX8BcE4RfxFwKyFwFTFwEUF0QXArUXAXYXphcCBxcXFwIKFwAPMA9gDwM9gA%2BwD%2BAPAxEPQQ8C4Q8Bcg%2BiDwIED0QPAtQPQD0BpQ8BZg8BBw83DwILDwAKMAoCPZAKwAoCIQoB8QoBggoBVAoB5AoBtQoBRgoBFwoBCgAEOeYDAQMGEBkfuP%2BAQAk0RUgfIiw1ODW4%2FgBAOTE7SGE1AUA1UDUCITUxNQIQNQEBNQHhNfE1ArI1wjXSNQOTNaM1AoI1AWM1czUCVDUBQzUBlTUBNbj%2FgEAMDRJIBzUXNSc1AzUiuP8AQBMYHUi1IsUi1SIDpiIBIjPdLAEsuAFAQAwgJkhLLFssAjwsASy4AQBAFhccSKosuizKLAMsgA4TSCxACg1ILB%2B4AgC3MkVIHzExJjS4AgCzMDtINLgBwLMsL0g0uAGAQC0jK0h8NIw0Ams0AVw0AUs0ATSADxNINEAKDkg0GyMrIzsjAyPAFBpIIykrQi24%2FoCzJCdILbj%2BwLMeI0gtuP8AsxodSC24%2F0CzERlILbj%2FgEAJCxBIBy0BCi0euP4AQKoyRUgeJCZBIBlAGVAZYBmAGaAZBhcZAQAZATvAGeAZArEZAWAZgBmgGQMBGREZIRlBGQThGQGiGcIZAoMZAXIZAWMZAQQZJBlEGQOlGcUZ1RnlGQQ2GUYZZhmGGQQHGScZAgsZIAYBBwYBPNYGAYAGAXcGAeEGAUMGAaUGAQcGAQwGaReZFwIIFwEzFx8QLxA%2FEH8QBAgQATyvEO8QAp4QAU8QjxACDRABELgBYED%2FKklMEFwQbBCsEOwQBQsQAcsQAaoQuhACGRApEGkQAwgQAQsQDwMfAwJB2AMBjwOfAwL9AwHuAwFMA1wDArsDAaoDAQkDGQMCDwMXFQEVFQoPGC8YTxhfGG8YjxivGAc7vxjPGO8YA64YAU8YbxiPGAMOGB4YLhgDjRitGM0Y7RgETBhsGHwYAwsYKxgC2xjrGAKKGKoYyhgDSRhpGAI4GAEpGAEIGAELGA8HAT7ZBwGOBwF4BwHtBwFLBwGqBwEIBwEMB2YNAQcNATMND0IAERARIBFgEQQ9gBGQEaAR4BEEARFBEQLhEfERAqIRAVMRYxECBBFEEQJlEaURtRHFQDoRBBcRJxECBhEBCxEAAhACAkHgAvACAqcCAVACAUECAbICAaMCAQAAAhACAgFgAnACAgICrwgBCApBAD%2FNcjJfXV9xX3FxcnJycl5dMl5dXV1xcXFxcnJeXT%2FNXl1dMl5dXXFxcnJyXl0yXl1dXV1dXXFxcXJycnJeXRI5L10zXl1dXXFxcXJyXl0zXl1dXV1xcStycnJyXl1dzV5dXTJeXV1xcXJycl5dXTJeXV1dcXFxcXFxcnJycl5dXV0%2FzTIrMl5dKysrKys%2FzTIrcTIrK3FxcXErKysSOS8zKzMrK10rcXErcc0yXV0rMl0rXXFxcXFxcXFycnJycisBEN4y3DIr3jLcMnLOzTIyXV1dXXFxcXJyXl0yXl1dXV1xcXFycl5dMl5dXV1xcXFxcl5dEM1eXV0yXl1dcXFxcnJyXl1dMl5dXV1xcXFxcnJeXTJeXTJeXV1dcXFxcXJyXl1dEM1eXTIyXSsrKzJdK3FycnIrMl0rEM1dKzJdK11dcXIrcisyXStdXSsrcjIrMl0rXV0xMABeXV1dXV1dAV1dXV1dXV5dXl1eXV5dAREzEQMRMxEBNSEVATUhFQERMxEVNSEVAREzEQMRMxEDETMRATUhFQE1IRUBETMRFTUhFQERMxEDah0dHf6vATT%2BzAE0%2Fq8dATT%2Brx2bHR0d%2Fq4BNf7LATX%2Brh0BNf6uHQFrASr%2B1v60ASr%2B1gJ5HR39aB0dAWsBKv7WHx0d%2FtMBKv7WAUwBKv7W%2FrQBKv7WAnkdHf1oHR0BawEq%2FtYfHR3%2B0wEq%2FtYAAQAAAOUBAgAOAKQACgACABAALwBZAAAFIAb5AAUAAQAAACIAIgAiACIAWABkARIB2gKIA6wDzAQYBGQFDAU6BWwFkAW4BdoGega8B3AIIAh6CQgJmAnGCrALWgtwC4YLygv0DDoMxA20Dk4O5A%2BSEAQQNhBeEQIRNhFWEbQSThJsE1ITvhR8FNQVohYiFvgXKBd6GAAY5hmCGegaMhpSGnQalBrgGvobJBveHFYc5B1wHh4eYh8cH3wfsiAGILAg5iG6IhgiiiL8I44jwiSiJPglRCXEJ%2BQoYCkQKVAptinOKjYqlitCK3YrgiuqK9AsTiyOLL4syizWLQAtFi0uLYgtrC5WLlYujC76L3gwDjCWMLgxsjHsMsAzVDNgM34zhjSQNKw1EjVeNcQ2OjZkNqg26DbyNzo3aDfGN9I34jfyOAI4dDiMOKI4uDjOOOg5xjo2Ox47NjtMO2Q7fjuWO6w7wjvcPFI8aDyAPJY8rDzCPNw9Oj68PtQ%2B6j8APxo%2FMD%2BGQFBAZkB6QI5AokC6QMxBykJ4Qo5CokK2Qs5C5EL4QwxDJEPoQ%2FxEEkQmRDpETkRmRMRFqEW%2BRdJF5kX%2BRhJGgEaYRrRGvEcmR0pHmEfcSIBIlkieSKpJIkkqSZxJpEmwSbxKfkqYTnwAAQAAAADMzN0PKsFfDzz1ABkD6AAAAADJirZYAAAAAMqgwH3%2FWf9DDZgDwgAAAAkAAgAAAAAAAAH0ADIAAAAAAOcAAADnAAABFABHAaIASAKbADECNAA9A1oAMgKaAC4A8QBIAUQATgFEAAcB4AAlAjQANQD2ACYBKwAaAPYAOAGA%2F%2BgCNAAyAjQAXQI0ADgCNAA3AjQAKAI0AEECNAA%2FAjQAPAI0ADcCNAAzAPYAOAD2ACYCNAA1AjQANQI0ADUBlAATA7YAQQKXAAgCgwBWAmwAOgLJAFYCOwBWAhkAVgKgADoCwQBWAQ0AVgH0AAgCdQBWAgcAVgNnAEMC2ABWAwoAOgJgAFYDCgA6AnUAVgIUACECNQAQArAAUQKQAAoDoQAcAncAFwJWAAYCPQAhAUkAYAGA%2F%2BgBSQAIAjQALwHs%2F%2FwBeAA9AgoAKwJNAFAB0QAzAk0AMwIvADMBggBQAkIAMwI7AFAA%2FQBCAP3%2FvAIKAFABEQBOA10AUAI%2BAFACTgAzAk0AUAJNADMBggBQAb4AKAGSAEoCPgBKAfYAEAMJABIB%2FwAQAfEAAgHXACQBTQAoARcAYAFNAAgCNAAqAjQALQDrACEBoQAhAXgANAEjAB4D4QA6AOsAPADrACEBoQA8AaEAIQFoADQB8P%2F%2BA%2BT%2F%2FgF4ABcBIwAoA7MAMwDnAAABFABHAjQAVgI0ADwCNAAsAjQAEQEXAGAB7AArAXgAIwMuAEEBhQAmAfMAHgI0ADUBKwAaAy4AQQF4AC4BTAAaAjQANQFnAB4BZwAgAXgAfwJEAFAChgArAPYAOAF4AEUBZwAyAb4AKwHzACgDagAyA2oAMgNqACABlAAcApcACAKXAAgClwAIApcACAKXAAgClwAIA6cABgJsADoCOwBWAjsAVgI7AFYCOwBWAQ0ADwENAEQBDf%2F%2FAQ3%2F7gLWABMC2ABWAwoAOgMKADoDCgA6AwoAOgMKADoCNABQAwoAOgKwAFECsABRArAAUQKwAFECVgAGAmEAVgJvAFACCgArAgoAKwIKACsCCgArAgoAKwIKACsDWwAsAdEAMwIvADMCLwAzAi8AMwIvADMA%2FQAHAP0APAD9%2F%2FcA%2Ff%2FmAk0AMwI%2BAFACTgAzAk4AMwJOADMCTgAzAk4AMwI0ADUCTgAzAj4ASgI%2BAEoCPgBKAj4ASgHxAAICTQBQAfEAAgD9AFAA%2FQBCAXgAVQCU%2F1kBZwAdAWcAGAFnACACNAA1AJT%2FWQMIAFACgwBQAoMAUAKXAFAClwBQBAkAUAQdAFADDgAdBpIAHQPRAEkAAQAAA6T%2FQwAcDbf%2FWf9dDZgAAQAAAAAAAAAAAAAAAAAAAOUAAwJaAZAABQAAArwCigAAAIwCvAKKAAAB3QBPAPoAAAILBQQDBgIDAgTgAAL%2FUAAgWwAAAAAAAAAAREFNQQBAAAD7BAMI%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%2FDxImJiYnEyEZNxohJhU3GRYmGBgZJysQGRgeITs7OxssLCwsLCw%2FKiYmJiYTExMTMTE0NDQ0NCY0Li4uLicpKiMjIyMjIzofJSUlJRISEhInJigoKCgoJigmJiYmISchEhIZChgYGCYKNCsrLS1FRzRxQwAAAAACAAAAAwAAABQAAwABAAAAFAAEANwAAAAuACAABAAOAAAADQB%2BAP8BMQFTAsYC2gLcIBQgGiAeICIgOiBEIHQgrCISIhXg%2F%2B%2F98AD%2F%2FwAAAAAADQAgAKABMQFSAsYC2gLcIBMgGCAcICIgOSBEIHQgrCISIhXg%2F%2B%2F98AD%2F%2FwAB%2F%2FX%2F4%2F%2FS%2F6EAAP2f%2Ffr9k%2BBaAAAAAOBKAADgkeBj37bex97FH%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%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%2BAYoqxQECKcEVgaDotLCCKI0lkiiNTWDwbIVktLEtSWH0belktLLASAEsBS1RCLSyxAgBCsSMBiFGxQAGIU1pYuRAAACCIVFixAgFCWVktLEUYaCNLUVgjIEUgZLBAUFh8WWiKYFlELSywABawAiWwAiUBsAEjPgCwAiM%2BsQECBgywCiNlQrALI0IBsAEjPwCwAiM%2FsQECBgywBiNlQrAHI0KwARYBLQC5AQMBArJkHxBBDQECACABAgAwAQIAAwBQAQIAYAECAAL%2FwAECsjY5RrgBArICEx%2B5AQEBALKmHyC6AQAAAQEAsgETH0EXAQgAkAEHAFUBBgCQAQkAVQEFAJABBABVAC8BBwABAL8BBADfAQQA%2FwEEAAP%2FwLP%2FGihGuP%2FAQBr%2FEBhG37Pvs%2F%2BzA6%2Byv7ICQLCQsKCwA6%2BuAbj%2FwECyrQgMRhCsoKwCQKtQq2CrA2CqAaCqAaekCB%2BmpAgfpaQVH6QCBx%2BjAgMfop4GH6GeCx%2BgngYfnwECH54BBR%2BdAQQfnJnIH5uZFR%2BamH0fmQIHH5gCRx%2BXAjIflpEaH5WRfR%2BUk6YfkwEBH5IBGh%2BRARkfkI5IH4%2BOZB9PjgFvjn%2BOAq%2BOAUCOODtGjgICH42LFh%2BMi30fiwETH4qFPh%2BJhyIfiIdkHxCHIIcwhwNQh2CHArj%2FwEDKhzY5RocCEx%2BGAgMfr4W%2Fhc%2BFA4UCAR%2BEgBkfg4HJH4J%2Fph%2BBAcgfgAEDHyB%2FAX8BEx9%2BeTMffXs3H3x5ZB97Ai0fegICH195b3kCeQISH3h0GR93dX4fdnNkH3UBfR90AQUfYHNwcwIQcyBzwHMDcwETH3J%2Bb1VxfnBVaFtbVWdbX1VmW11VZFtaVWNbXFViW1lVWENWVVdDVVVTkE1VUpBLVVGQSlVQkElVT5BOVS9OAb9J30n%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%2Fx%2F%2F4DRwIIAAD%2FR%2F9vAuIDAQAHAA3%2F9f%2F%2B%2F%2F4CqQAAArX%2F8QAQ%2F0cDDwK1AAAAYgDxAUgB9QJOAAD%2F8wAA%2F2cAF%2F%2FvABAAFP9DAkQCaALeAwgBOwK9AAj%2F%2BABhAJwASQBmAE4AsABPAQwAOgBUAEMAbgBdAMQARABgAEwAsABFAN4AVABQAGsAPQBcAGAAMwBMAFEAdQBZADIARQBIAF0AtQBPAD4AhQBCAG4AhgCyAJwBCgAvAHIAUgDEAIUAbgA9ACoAWAA8AFIANQCiAOYATgBQAMMBhABIAD4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYgBdAGAAVABQAggADf%2F1AwEABwAAAAAAAQAAGawAAQRFGAAACgGeAAoAJP%2BxAAoAJv%2FnAAoAKv%2FnAAoALf%2BFAAoAMv%2FnAAoANP%2FnAAoAOQANAAoAOgAKAAoAPf%2F0AAoAmP96AAsADABQAAsAJv%2FiAAsAKv%2FiAAsAMv%2FiAAsANP%2FiAAsANv%2FuAAsANwALAAsAOP%2F2AAsAOQAPAAsAPAARAAwAJP%2FsAAwAJv%2F0AAwAKv%2F0AAwALf%2FzAAwAMv%2F0AAwANP%2F0AAwANv%2F0AAwAN%2F%2FkAAwAOf%2FeAAwAOv%2FuAAwAO%2F%2FkAAwAPP%2FVAAwAPf%2FqAAwAmP%2FsAA0AJP%2FAAA0AJv%2F2AA0AKv%2F2AA0ALf%2BGAA0AO%2F%2FnAA0AmP%2BfABAALf%2FkABAAN%2F%2FOABAAOf%2F0ABAAO%2F%2FhABAAPP%2FOABAAPf%2FwABAAmP%2FyABEAJAAZABEAJv%2FjABEAKv%2FjABEALQAcABEAMv%2FfABEANP%2FfABEANgAOABEAN%2F%2B6ABEAOP%2FpABEAOf%2BzABEAOv%2FYABEAOwAKABEAPP%2BwABEAPQATABEAmAATABIAJP%2FOABIAJv%2FlABIAKv%2FlABIALf%2B5ABIAMv%2FmABIANP%2FmABIANwAdABIAOQAgABIAOgAWABIAOwAUABIAPAAiABIAPQAJABIAmP%2BzAB0AJv%2F1AB0AKv%2F1AB0AMv%2F1AB0ANP%2F1AB0AN%2F%2BrAB0AOP%2F0AB0AOf%2FrAB0AOv%2FzAB0APP%2FYACMAJP%2FmACMAJv%2F2ACMAKv%2F2ACMALf%2FmACMAMv%2F2ACMANP%2F2ACMANv%2FuACMAN%2F%2FUACMAOP%2F0ACMAOf%2FcACMAOv%2FpACMAO%2F%2FZACMAPP%2FKACMAPf%2FeACMAmP%2FaACQACv%2ByACQAC%2F%2FtACQADf%2FBACQAEQAaACQAEgARACQAI%2F%2FxACQAJAAhACQAJv%2FtACQAKv%2FtACQALQAdACQAMv%2FtACQANP%2FtACQANgAQACQAN%2F%2FAACQAOP%2F2ACQAOf%2FCACQAOv%2FuACQAOwAZACQAPP%2BxACQAPQATACQAQP%2FlACQARv%2FzACQAR%2F%2FzACQASP%2FzACQASv%2FwACQAUv%2FzACQAVP%2FzACQAVgANACQAWP%2FxACQAWf%2FrACQAWv%2FtACQAWwAaACQAXP%2FxACQAXQAPACQAXv%2FsACQAYP%2F2ACQAYwAZACQAZv%2FtACQAaP%2B5ACQAaf%2B8ACQAmAAiACQAwv%2FzACUACv%2F1ACUAC%2F%2F1ACUADP%2FnACUADf%2F1ACUAEf%2F2ACUAEv%2FrACUAHf%2F1ACUAIv%2FsACUAOf%2FtACUAOv%2FzACUAO%2F%2FrACUAPP%2FnACUAQP%2FeACUAXf%2FzACUAYP%2FqACUAmP%2F0ACYAC%2F%2FpACYAEP%2FiACYAEQAWACYAIgAPACYAI%2F%2FvACYAJAAWACYAJv%2FsACYAKv%2FsACYALQAXACYAMv%2FsACYANP%2FsACYANgAKACYANwARACYAOQARACYAOwANACYAPAARACYAPQAKACYAPv%2F2ACYARv%2FxACYAR%2F%2FxACYASP%2FxACYASv%2FxACYAUv%2FxACYAVP%2FxACYAWP%2FsACYAWf%2FwACYAWv%2FvACYAWwAOACYAXP%2FwACYAXv%2FkACYAYwASACYAZv%2FTACYAaQANACYAmAAWACYAwv%2FxACcABP%2FyACcACv%2FmACcAC%2F%2F0ACcADP%2FfACcAEf%2FfACcAEv%2FVACcAIv%2FZACcAI%2F%2F1ACcAJP%2FtACcALf%2FnACcANv%2F1ACcAN%2F%2FlACcAOf%2FvACcAOv%2FvACcAO%2F%2FoACcAPP%2FXACcAPf%2FxACcAPv%2F1ACcAQP%2FWACcARP%2FyACcAYP%2FjACcAY%2F%2FeACcAaP%2FsACcAaf%2F0ACcAmP%2FWACgACv%2FwACgAC%2F%2FrACgADf%2FzACgAEQALACgAI%2F%2FwACgAJAARACgAJv%2FkACgAKv%2FkACgALQARACgAMv%2FkACgANP%2FkACgAOP%2FqACgAPv%2FvACgAQP%2FVACgARv%2FwACgAR%2F%2FvACgASP%2FwACgASv%2FpACgAUv%2FvACgAVP%2FxACgAV%2F%2FyACgAWP%2FtACgAWf%2FnACgAWv%2FnACgAWwAJACgAXP%2FnACgAXv%2FtACgAYP%2FqACgAZv%2FpACgAaP%2FzACgAmAASACgAwv%2FxACkABP%2F2ACkAC%2F%2FoACkAEf%2B%2FACkAEv%2FEACkAHf%2FuACkAIgANACkAI%2F%2FuACkAJP%2FSACkAJv%2FzACkAKv%2FzACkALf%2BwACkAMv%2FzACkANP%2FzACkANwAOACkAOQASACkAPAAUACkAPv%2FxACkAQP%2FXACkARP%2FNACkARv%2FyACkAR%2F%2FyACkASP%2FyACkASv%2FyACkAUf%2FkACkAUv%2FyACkAU%2F%2FkACkAVP%2FyACkAVf%2FkACkAWP%2FnACkAW%2F%2FcACkAXf%2FmACkAY%2F%2BZACkAcP%2FrACkAmP%2B0ACkAwv%2FxACoADP%2F2ACoADf%2FnACoAQP%2F2ACoAWf%2FuACoAXP%2FuACoAYP%2F2AC0ADP%2F2AC0AEf%2F2AC0AEv%2FcAC0AJP%2F2AC0ALf%2FxAC0APf%2FvAC0AQP%2FeAC0AXf%2FuAC0AYP%2FtAC0AY%2F%2FzAC0AmP%2FnAC4AC%2F%2FuAC4ADf%2FfAC4AEP%2FRAC4AEQAQAC4AEgASAC4AIgALAC4AI%2F%2FtAC4AJAAiAC4AJv%2FdAC4AKv%2FdAC4ALQAbAC4AMv%2FdAC4ANP%2FdAC4ANwARAC4AOQAUAC4AOgAKAC4AOwAaAC4APAAWAC4APQAVAC4AQP%2FmAC4ARv%2FrAC4AR%2F%2FrAC4ASP%2FrAC4ASv%2FrAC4AUv%2FrAC4AVP%2FrAC4AVgAOAC4AWP%2FzAC4AWf%2FyAC4AWv%2FQAC4AWwAbAC4AXP%2F2AC4AXQAQAC4AXv%2FtAC4AYwAPAC4AZv%2FDAC4AmAAjAC4AwQAVAC4Awv%2FnAC8ACv%2BCAC8AC%2F%2F2AC8ADf91AC8AEP%2B7AC8AEQAXAC8AEgAMAC8AJAAcAC8AJv%2FTAC8AKv%2FTAC8ALQAcAC8AMv%2FTAC8ANP%2FTAC8ANgAOAC8AN%2F%2BNAC8AOP%2FuAC8AOf%2BcAC8AOv%2FTAC8AOwAUAC8APP%2BNAC8APQAOAC8AQP%2FgAC8ARv%2F0AC8AR%2F%2F0AC8ASP%2F0AC8ASv%2F0AC8AUv%2F0AC8AVP%2F0AC8AWf%2FYAC8AWv%2FbAC8AWwAVAC8AXP%2FsAC8AXQAKAC8AXv%2FoAC8AYP%2FxAC8AYwAOAC8AZv%2FZAC8AaP%2BIAC8Aaf%2BKAC8AcAALAC8AmAAdADAAOf%2F6ADAAPP%2F4ADIABP%2FyADIACv%2FpADIAC%2F%2F0ADIADP%2FkADIAEf%2FfADIAEv%2FWADIAIv%2FeADIAI%2F%2F1ADIAJP%2FtADIALf%2FnADIANv%2F2ADIAN%2F%2FlADIAOf%2FvADIAOv%2FwADIAO%2F%2FoADIAPP%2FXADIAPf%2FxADIAPv%2F2ADIAQP%2FWADIARP%2FzADIAYP%2FjADIAY%2F%2FiADIAaP%2FwADIAaf%2F2ADIAmP%2FYADMABP%2F1ADMAC%2F%2F0ADMADP%2FoADMAEf%2BmADMAEv%2FCADMAJP%2FOADMALf%2BhADMAO%2F%2FuADMAPv%2FxADMAQP%2FXADMARP%2FtADMARv%2FrADMAR%2F%2FrADMASP%2FgADMASv%2FrADMAUv%2FrADMAVP%2F1ADMAWQAKADMAWgAJADMAXAAKADMAYP%2FoADMAY%2F%2BNADMAZv%2FuADMAaQAKADMAmP%2BsADMAwv%2FwADQABP%2FyADQACv%2FpADQAC%2F%2F0ADQAEf%2FfADQAIv%2FeADQAI%2F%2F1ADQAJP%2FtADQALf%2FnADQANv%2F2ADQAN%2F%2FlADQAOf%2FvADQAOv%2FwADQAO%2F%2FoADQAPP%2FXADQAPf%2FxADQAPv%2F2ADQARP%2FyADQATQAPADQAY%2F%2FsADQAaP%2FwADQAaf%2F2ADQAmP%2FYADUAC%2F%2F0ADUAEQAOADUAEgAMADUAIv%2FzADUAI%2F%2FxADUAJAAcADUAJv%2F1ADUAKv%2F1ADUALQAWADUAMv%2F1ADUANP%2F1ADUAOf%2F2ADUAOwATADUAPP%2FtADUAPQAOADUAQP%2FfADUAWwAVADUAXQAJADUAXv%2FvADUAYP%2FwADUAYwAOADUAZv%2FsADUAmAAdADUAwv%2FxADcAC%2F%2FlADcADAAMADcAEP%2FPADcAEf%2B1ADcAEv%2BqADcAHf%2BsADcAIgAWADcAI%2F%2FNADcAJP%2FAADcAJv%2FlADcAKv%2FlADcALf%2BmADcAMv%2FlADcANP%2FlADcANwAYADcAOQAbADcAOgARADcAOwAPADcAPAAdADcAQP%2FgADcARP%2FTADcARv%2FJADcAR%2F%2FJADcASP%2FJADcASv%2FJADcAUf%2FYADcAUv%2FJADcAU%2F%2FYADcAVP%2FJADcAVf%2FYADcAVv%2FTADcAWP%2FeADcAWf%2FqADcAWv%2BvADcAW%2F%2FuADcAXP%2FqADcAXf%2FiADcAXv%2FoADcAY%2F%2B9ADcAZv%2BrADcAcP%2B0ADcAmP%2B4ADcAwAAVADcAwQAfADcAwv%2BxADgAC%2F%2F2ADgAEf%2F2ADgAEv%2FXADgAHf%2F2ADgAJP%2F2ADgALf%2FxADgAPf%2FwADgAQP%2FbADgARP%2FwADgAXf%2FuADgAXv%2F2ADgAYP%2FqADgAY%2F%2FrADgAmP%2FeADkACgAOADkAC%2F%2FfADkADAAQADkAEP%2F2ADkAEf%2B1ADkAEv%2B9ADkAHf%2FsADkAIgAYADkAI%2F%2FaADkAJP%2FCADkAJv%2FvADkAKv%2FvADkALf%2BhADkAMP%2F6ADkAMv%2FvADkANP%2FvADkANwAcADkAOQAfADkAOgAVADkAOwATADkAPAAhADkAQP%2FkADkARP%2FnADkARv%2FTADkAR%2F%2FTADkASP%2FTADkASv%2FTADkAUf%2FiADkAUv%2FTADkAU%2F%2FiADkAVP%2FTADkAVf%2FiADkAVv%2FxADkAWP%2FoADkAXf%2F%2BADkAXv%2FoADkAY%2F%2B2ADkAZv%2FVADkAaAATADkAaQAYADkAcP%2F1ADkAmP%2BuADkAvgALADkAwQAgADkAwv%2FNADoACgANADoAC%2F%2FwADoAEf%2FbADoAEv%2FRADoAHf%2F1ADoAIgAQADoAI%2F%2FtADoAJP%2FuADoAJv%2FwADoAKv%2FwADoALf%2FEADoAMv%2FwADoANP%2FwADoANwATADoAOQAWADoAOgAMADoAOwAKADoAPAAYADoAQP%2FbADoARP%2FjADoARv%2FqADoAR%2F%2FqADoASP%2FqADoASv%2FmADoAUf%2FrADoAUv%2FqADoAU%2F%2FrADoAVP%2FqADoAVf%2FrADoAVv%2FvADoAWP%2FtADoAXv%2FvADoAY%2F%2FaADoAZv%2FsADoAaAASADoAaQAZADoAmP%2FPADoAwQASADoAwv%2FmADsAC%2F%2FnADsADf%2FmADsAEP%2FkADsAEQANADsAEgAKADsAI%2F%2FmADsAJAAbADsAJv%2FoADsAKv%2FoADsALQAUADsAMv%2FoADsANP%2FoADsANwANADsAOQARADsAOwASADsAPAATADsAPQANADsAQP%2FeADsARv%2FuADsAR%2F%2FuADsASP%2FuADsASv%2FuADsAUv%2FuADsAVP%2FuADsAWP%2F2ADsAWf%2F1ADsAWv%2FbADsAWwATADsAXP%2F5ADsAXv%2FmADsAYwAKADsAZv%2FRADsAmAAcADsAwv%2FjADwAC%2F%2FWADwADAASADwAEP%2FPADwAEf%2B%2FADwAEv%2BuADwAHf%2FaADwAIgAZADwAI%2F%2FGADwAJP%2BxADwAJv%2FXADwAKv%2FXADwALf%2BNADwAMP%2F4ADwAMv%2FXADwANP%2FXADwANwAeADwAOQAhADwAOgAXADwAOwAVADwAPAAjADwAPQALADwAQP%2FmADwARP%2FXADwARv%2FPADwAR%2F%2FPADwASP%2FPADwASv%2FPADwAUf%2FYADwAUv%2FPADwAU%2F%2FYADwAVP%2FPADwAVf%2FYADwAVv%2FsADwAWP%2FeADwAW%2F%2FxADwAXf%2FkADwAXv%2FqADwAY%2F%2ByADwAZv%2B0ADwAaAALADwAaQARADwAcP%2FgADwAmP%2BwADwAwQApADwAwv%2ByAD0AC%2F%2FrAD0AEP%2FEAD0AEQALAD0AI%2F%2FwAD0AJAARAD0AJv%2FcAD0AKv%2FcAD0ALQARAD0AMv%2FcAD0ANP%2FcAD0AOP%2FyAD0AOQAKAD0APAAMAD0APv%2FvAD0AQP%2FVAD0ARv%2FwAD0AR%2F%2FvAD0ASP%2FwAD0ASv%2FpAD0AUv%2FvAD0AVP%2FxAD0AV%2F%2FzAD0AWP%2FtAD0AWf%2FpAD0AWv%2FoAD0AWwAJAD0AXP%2FpAD0AXv%2FdAD0AYP%2FxAD0AZv%2FOAD0AmAASAD0AwQAKAD0Awv%2FxAD4AJP%2FkAD4AJv%2FVAD4AKv%2FVAD4ALf%2FkAD4AMv%2FVAD4ANP%2FVAD4ANv%2FmAD4AN%2F%2FfAD4AOP%2FaAD4AOf%2FjAD4AOv%2FbAD4AO%2F%2FcAD4APP%2FlAD4APf%2FXAD4AQABQAD4AmP%2FlAEAAJv%2F2AEAAKv%2F2AEAAMv%2F2AEAANP%2F2AEAAOv%2F2AEAAO%2F%2F2AEAAPf%2FwAEQACv%2FlAEQADP%2FoAEQADf%2FsAEQAIv%2FkAEQAQP%2FiAEQAWf%2FxAEQAWv%2F0AEQAXP%2FxAEQAYP%2FrAEQAaf%2FoAEUACv%2FbAEUADP%2FlAEUAEv%2FqAEUAIv%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plyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cb08AABFOAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAjPL%2FpQAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAAxADsAUABTACAAMAAwADEALgAwADAAMQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAHa4ADV2ADV8AC1cEs3pishg2FfJaEtxSn94IlU6ciwvljRcm9wVDgxsadLb0%2FGIypoPBp1Fx0xGTbQdxoAU51YoZ9RXGB0bXNPyK3JLMApRwa%2FIMy1PPoJDx39kimekZX1c%2BDSW41tEZBuFiwdwx1dRoPVA2vWPSlsSDqhNkqYfhrqlVUGD0J3HEAgZavmtLnDC5WBriSpD8Uk02KsUkJ9vCFz2CZXAd5viwGZ2xcVYRPa1bEIai51nMYlbL2ERuB2TCzLAbPWWRZ3%2FsZ%2FKBjLk8%2FgAZzK1OaxNw4oGBbsNhx6Reg5HRFVCrwa15kGmJEy5kX1YypVm%2FHo7TjKP3l%2B%2FnuCTOyiOa6S8QEJbuiGYNlCnM7tHChCHRRQHXQh7yPXASlcvc5KrNKol6orb35kbo%2BiEwl8d230cfWPwTy00bFDRYURYGchbwsoDcaR2AJsFGCrbWCzBdQ0qCobWwLfueqAzSkzaHX3yCjDlGYPV0VZqVXlqbr4poRaG5NNPMDv5MCHkLSFyABMRBhMiGSZiDgABYmwsLWDjUZCUnHwvXt0VAUy4%2FqjwVgjfUqp1evzUqutJB7DW99Cq5aEhGePsa4omKUp7LnSQUz%2BZq51pU72ApMxkaVZE9jF3IGKVnF0GlK1E6iqLJXFzy1BEv6Kr0ngCzIgVANRwCkVHYJj86Sv2dzqgVrjYKlg8W94FbPGQghEbTakZ4AoS9h0u39DOoRJTA2iirs9xa5m9Ir0KwkogoqFNVDbTIGO0%2FWuJYfp61Kq0bhgbcbnvbTDj4HBESsXMdvrvpp7Owf%2FS2EbndoEoglO%2FjigGAuLUTUmG07ZGlRCZ4QZLu81tPEgTtAvF99BxfnxNslgVYH5Jc4AfHwsiktDBO4wjgjizFMsIeBTGmIYrhtrK3tvtBu5tYB0PDMXILUkFYPM%2B5yLLGOU9DtoDLYWLQig%2B8wNRZbJZZWeK5JyKNwumLgP7X6pqgiZMFUvfCTl6Urx1gUOjzhcICumUUsiNESZCmTANkgHoh%2BKU0t1fgQYOBkNfp4MRiJysezS1Vz%2BahE7ohM0iJBn4I%2BT9RJN%2FuQk6SjQGwC%2Bom%2FSD7Zq2RxvcSjtdxHfo7NtktA46mEg5gBkTtMKu7oFb1KgqwqBLzQqZI%2Fs4QSRhYF3ani1URTcMD38kDDqlc3DChmYJTCR9WpaWrYFiXeNi7jYa1Oe%2BSP30gxVyeC10DYOakGpWaU1qkQ9OXlujEbM4A6A%2BQ243TA2QnI8mKzNXNo1HPN1CFawST6uC9dkyQTqq5bOKkkuGjz0nkj35Sr%2BQ%2FkLGg2iQMrTKImSGshnX7%2F0Jyh0JziDbTzxp3IrbosXgpchLcBJpLLUBrFbt%2FU6qXqAaNFJoeBItBoqgQlPZkpTq31s5VZsgIkFupAno5G9oKwCGRqv5294xFqOrEpewcjx%2Be06mxDkN85c0ckmWmMzbkFX9YHAf83p0EsnBywjZbcPxZw91kqg7voxasp3VfDhEDAufUx0MTddLO5kCyKkUBwOm%2FMurQH4bJQgK%2Bp14dkBBhgxpBUkTlSbTE8DDu9qKiDSCtvmPY7FRMn0wuS6XR7J%2BnpjjeaDNwiOgIKUWCP9aLb%2BXpc4toQCryp6gjCVq1Cl1Di%2BtEx4WnBp2hmGZpEksZwTSggXuqAWD4B7WAMcir2wmA%2BI43ECxUSeTDT%2BxgR3O4XFPOGxHh09lxpScampLahUbICao0db7%2F%2F%2B4CBMcCokQ7aOQ02MOzh0NEaJGGOvPR1t4sY1I55lNfQ3IkF04onnAomXxiNg2EbBbPTMBBlv%2BggPSmkb4GNFW6Ehd6nDzJlnIXFfzr1bVWQlfylDodFBbhCDf%2FVB13j441dc2rx3h4hfAWIJuZqbH%2Fwbuafg%2FnTQJ9bnMfR3U6TwYgDQJIOPOtmZW4ZHsR2VifZGaC6Pzv4ZzC2nSAaqSe09F9MxuI4UmUVA2RAUqBUIETEIAhB%2BtBhsxhVMXUmv3RPe1grhYWLM%2BPHwwO5W069qIgDXtETXZQ3741DSk%2BvO6y8k%2F7Mg6GFC5fSK02yXZ1XV0NZe8iknoIbSFnDm%2BbMP%2BBCQhoLKOeU3FVNQ%2BgPEntwkHT950cQYEYQc%2FXuL4nxwcRW2bIN%2ByskPaD5Rz6lN8LW5IDLYfuvhxlLcjWXSADeTshWsJk%2FHCuRbwjqLcx66fc6UlF33HipliQ1cvfKlgRpVcsmIsiYnJjzJIovUyqi%2F2OTQcIwt%2B3WW44BAQh%2F2GymOIscSpIt0YUePiwOCJN%2BQ60lqkSRbWi10lotzgWZsqzEI7OGFGeIoFyqli41%2FNn7LfLQ0qtxkaAwbsKZQ0D9s%2FwhBqoxhAlxCVNVF%2FJ6rsUBQ49MLtR3ICtKPLKZ%2FQPEeIRgWUNLbNR3v3tTaL8uiXO%2BOj3tSJc56KJHol9nGPuclbrQpCn4keT05ETSOQ%2FDwwKjCRLgIzBi7x%2BU7ByTZGUWcGLPY0VtXWC7kQ38C5thIGJx%2FxLM3KsoGyRwC5AFUNWPzU0QBCaVXo6z1NCclLuPVA8%2FwWuNhiAG9CPPSSlIOCuh2AQM1d6MyIEIQArIa9YenQ5ug37h9gPNDHVLImcjE91ewI7pz7HgOEVqTD%2FjREYRtEoDIfgxzDePkVvHWJAtcpCJ%2Bg2o4akZpphRUQ0U92lJcKZGJI9mnpl8UVzpMi8SBGXUAPpgcS2cDAusSE6q0hNibhCgZHLReLsA%2BSv8wF6LiDL3TogB1DsO%2FHeMg0ETucRO2hIOvbp1NVkFMclluNmBQbTy%2Bosr2bJ7oWdUtSHi1s%2B2vWxq8njY1eW4BCVr2EqSyIUuFKS9iwVF5FBGeg0hRcHwCKMtqYLBfvVSYXHdGZK3Ug95C2d%2B3t9ZWEYqq6F4TocgcDS4tI8cvEEXK1%2ByB9RofdKpHoA3eP9%2FB%2FEBG%2BIgvmwZ4Iir1SOYFEtUG%2FoVkECQqoPTdHoNpQKpA9K9k7TisAUBsKPtWgBriyeOxs1o%2B4KCIFumN744dH3Yf7X8F426LGtEhFLULhlKFgXF8Wq2D6kFSBkViLad9D9U%2FP6A%2Fr8SE7jsr9s%2Bsb5ODWI5uS6BkZJI5RQ5QDVZ5%2FKOG6Dzc1RLQKAdGkgwLiZLQdEYQ9yhQLtD5kWr4wGHTv4GqloWePyHYyuy3Ek3iQVQkdkAz0MxcsHzccdMLO8KFh7ywo4jXywraIhIicAI4dWdD5wEXBhsMklzbWc8zi8opC3IlPsLXHCWOqOgq4iPU6xyDS2tdSLnlhSFjsCCWvMRt5TAI30EHR4f16NYyzoG%2FoKgCUsW4182Wvj5gpb0bGGLe8YRIYRGVgF2hwIlEfm7eUwwLZWW%2BmmBZPggPnIKI0b6G%2BlHCncx0wkaVPRQpZKWIIrzh4yC4Ar2vTKzhVKSutNi1dSzmM73AGUXSBJrQH5J4Dfb%2BRU25tYrIVdLr4ogiwTE2i2hl39ffJWHNTSWq3PvVJplNqDTJgJcvCHYoQAuHeOew0gZGfmX3p4D0aSr1BhXbXRA1qipfS6LiggtiT1eDg4FTCWwCdAFC9FCOZ52EdrFetMBxUdC0dfnqpr0mELJE2m5FVMvwplyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFsYABEAAAAAoUAAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABgAAAABwAAAAcalXC8EdERUYAAAGcAAAAHgAAACABCAAET1MvMgAAAbwAAABDAAAAYGenS4RjbWFwAAACAAAAARsAAAJySvAJmmN2dCAAAAMcAAAACAAAAAgAKAOHZnBnbQAAAyQAAAGxAAACZVO0L6dnYXNwAAAE2AAAAAgAAAAIAAAAEGdseWYAAATgAABODAAAiTweHjMhaGVhZAAAUuwAAAA0AAAANgJiWP5oaGVhAABTIAAAABwAAAAkCjIED2htdHgAAFM8AAABFAAAAvTBwRGObG9jYQAAVFAAAAGrAAABuDSPVk5tYXhwAABV%2FAAAACAAAAAgAgQBoG5hbWUAAFYcAAABggAAA3zUr5ntcG9zdAAAV6AAAANAAAAIhLlGpmlwcmVwAABa4AAAAC4AAAAusPIrFHdlYmYAAFsQAAAABgAAAAZYr1LmAAAAAQAAAADMPaLPAAAAAM8MFvIAAAAAzwwJLnjaY2BkYGDgA2IJBhBgYmAEwltAzALmMQAADagBDQAAeNpjYGZpZJzAwMrAwszDdIGBgSEKQjMuYTBi2gHkA6Wwg1DvcD8GBwbeRwzMB%2F4LANVJMNQAhRmRlCgwMAIAC2EJ1gB42s2RP0vDYBDG723aSIrSUESsiHcIWqqDXbvFRe0gBJw6tTgUCx2Kk926dusixc0P4OiXaQZzjx2cnNRFhPiagENdHBx84P693P0O7iUihzLbJGM9mb6tTFrnTWhjSAEVyLfZCgnt060U5UDacrdd3vnYNVWvWlJHPa1oTRva1JZ2tKdDHesUHiqooYEjNNFCD0OMcY2bR0qSr10pcc8S6QfRaEF9Fa1roKElnutARzqBgQ9BHQFOEKKDAUaYYJoSTfKWzJMo6epSPI%2Fv44sHJ9qI1malWVEqsi5lWRZXiN%2F5lV%2F4mZ8YfMWX3Ocud7jNLT7jUz7mQw62ouwafyvj0jfW5KzLLTZkX5EpX6B%2FLXfxYfU3U5%2BPg2iWAAAAAI8AKAL4eNpdUbtOW0EQ3Q0PA4HE2CA52hSzmZDGe6EFCcTVjWJkO4XlCGk3cpGLcQEfQIFEDdqvGaChpEibBiEXSHxCPiESM2uIojQ7O7NzzpkzS8qRqnfpa89T5ySQwt0GzTb9Tki1swD3pOvrjYy0gwdabGb0ynX7%2FgsGm9GUO2oA5T1vKQ8ZTTuBWrSn%2FtH8Cob7%2FB%2FzOxi0NNP01DoJ6SEE5ptxS4PvGc26yw%2F6gtXhYjAwpJim4i4%2FplL%2BtzTnasuwtZHRvIMzEfnJNEBTa20Emv7UIdXzcRRLkMumsTaYmLL%2BJBPBhcl0VVO1zPjawV2ys%2BhggyrNgQfYw1Z5DB4ODyYU0rckyiwNEfZiq8QIEZMcCjnl3Mn%2BpED5SBLGvElKO%2BOGtQbGkdfAoDZPs%2F88m01tbx3C%2BFkcwXe%2FGUs6%2BMiG2hgRYjtiKYAJREJGVfmGGs%2B9LAbkUvvPQJSA5fGPf50ItO7YRDyXtXUOMVYIen7b3PLLirtWuc6LQndvqmqo0inN%2B17OvscDnh4Lw0FjwZvP%2B%2F5Kgfo8LK40aA4EQ3o3ev%2BiteqIq7wXPrIn07%2BxWgAAAAABAAH%2F%2FwAPeNq9vQlgG%2BWVADzfzEijWxpJo5FkS7IkS%2FIpxZJlxfGR4Nx3yM2dgIBCOMIVrhAghXK0CYZCKA0tKSyQco6UUNpu2G3pwoq26hnSQrcsvVhajm0hu90m8fC%2F981Ilu0Eyu7%2F%2F3EkzSXNe%2B973%2FvePQzLtDEM%2BQzXwnCMwKRLhMkMlgWefS9bMhr%2BbbDMsbDJlDg8bMDDZcHIHRssEzyeE6NiIifG28gs9e0%2F%2F5lrOfZmG%2FsThjBFpsgv4ZcwMtPKKExGsecUUlWsWaL4M4rnkGLIKu6qImRLAdLJTOvx5KOpgizmxIIsRKWoLKTEuCikCkXCPb%2Fj%2BQq8CKeO1TYPTzqgjtHLAA0G%2FtH7WplFTNnCMJ14c4He3JAtE8bSuW8W4cydRLFlFMshhc0q5qrCZ8tmC54yC%2BbOssWMmxbG3FmyU%2BgCJCrW%2Fsgo6SKj6mb14PiWupmMUpwN%2FBf4Z5gCs4VRshmlrVpuy%2BJPtaXNFI4ohSOSVQwZpTmnGKtKMKtIGcVaLUtWvFByIWTTM0qBQharlkKRLHy6St2kU3FllXRVcWZL%2FaSzFCuIbkXoV7rFsrUl29%2Ffj1Qs5HoL8UKur9CXy8o%2BOd6bZuMxBytEhahRgrcwn8sOs%2FmcUTDGY6k0SRWLlxleL96WWHzV%2Fg82Dxqfyy1ZFPb3z5npIdcX1YNGsgvezdP6Z%2Bek0KIluWciGzc9Xr26dcRGDhcz%2BeLjp%2B544fwLC%2BunBbxdpw4X033FRVeOdDqD09bln7rwqq%2Bmn7yGwTGpkFF%2BCXsA%2BMtNeYGrEoXPlAzayANRK9wtx7YiORlGH8Pavx7Y%2F%2BgD0sW%2FyJ%2FESEyEUbiM4qjCmBHFlynJ8BMlMye6S1axv39aD%2Bf15aLZvt5kPCakSTxmlLyygzguW86%2Bf80TT1yT7up67pIv%2FZIdWUPeX3H50x885dh49S8eCNgcm5Bt4FXkFeAdHuaBhbEzTJ7IBZIQzQYY9QpgcXQF6VIPsuvZ9TD6XUU8qG6u0L3DY4%2Byp6k20jW2F36H%2B%2BjDjz7kX%2BBfYFjGyLgYRkgAexN4Ffp6MyQZE%2Bxk6b%2Bfeog979DaQ%2BfY7Q84W532zf9%2BsnbgdHvK8YDdXqMFwKQwZibHlI3Iz0JV4WAaWTKK6RCQssyZkHM4AzCuicNNkxEY10qJK0aJCNMpHxV5BYE79iaQUhl7fez1YpFN4u87GBPMl2eZFoZJhklhmOTFhJgUHETW93qTgsFBJDhm5FtvX34SID9r0fJ1bvfty%2BetcljGXBYH7H%2F%2BkvagtKGDPXP32F9dcvDyvkJ7QD65wF1msXLf5TyWseWiP8AANeSP3uX%2FhX%2BA8TBNzClM2YEYuTOKXFWaNMZozijkkCJVFcmFskExwBwAdg9Ionsfx7rcrXK%2FYhCB85mS2wFTwNyvyKLi6lea3PsIIxjg%2FLQetwsQkrwC8XmdxBhLEWB31uVr6XMlW2TSDKzWvIx0CcLFJq9JPXj1zspnXyTu735XfZ%2B8h%2BfU31d2Xq0ehJMXCwLpWsZepP75xe%2FCFRp%2FFpn1%2FLP8HCYAHEmUYEZhDiFPOqqlJo2le4fZMJHhjZW8Dk5I88Xpp1599bVd066%2F5qr1fbOvvWXv8PDjt1w7m3PN2rKqm184Z%2B4CvnvVlln91950Y3nNmvKNN10LtProm8x8%2Fj4YewtjA5aKenKeKPGYiYcrLCD%2Ffg97D%2Fmluv1u9SZ1%2Bz33slwLFUV%2FUmcSj%2Foe%2BWf4pHzY%2BBs%2Bxs8odhC%2FVAzxwERypuSnIE%2F4dY8sFORUIZ4Spt5n9r8s%2FM73Fn33naXLjnNH9sqdv%2F3Cjj984ec%2Fn8C7HpjHeZKPJYdIb1%2FWFyJeY1wiEhlNzFT%2BR5mZIKN3EVK8L1fZqihbK7n7iupHdzE4%2F%2Bj3l9B1yco44Xdk4JoIE2dSTCeTgRmhMGUGOYivlvwtICiFjBLLKaaq0ppVrBklmVNsVaUtqzgzSkdOcVWVrqziySjpnOKtKtOyiH84m0NyEKWXjiNf3WeyubytclbhXSii9pntooS7%2FqrSDO8aV4aySkt1X2tb1zQ81eIqReHKRHt3D%2B5y1VIeBZPNCoIp2NTfrzjFUnMIBVQPXS%2Fz8fykF0pBmKVRcpxzvHJ0f7H%2BD8TN2F76OjB%2BkF%2FSeAnKUhBOx7bWjzC6fNPWRifQMMPMZEaYsh2p15UDtJFkoYwySEnTC7wxi9LDRddHP7y7Si2AOBCtvapMz5ZOonwD8qEPJK5PEmFMo7FkgEzcJ59wPgRcIIVCkroZ38e32fUnOpNtOMxe3LAz9sKJzjAgPSfjvpw5i7mUuZG5C9ZqSoXS4EU5pEOpt5hFSpSWbM0hLUpzroT97kzp9C%2FAfmu1tPqWLFBnFKlTciHTmYH52rKUQtNAUs1aAdv91dKC9fjpKm2AY5tvgu0LqqVrbs9mS3dTyuFcGCDabOgksaSY%2F%2Fh9kv%2F%2F9vqQVEGCneCNjP7fzmfre%2BTR420e%2B6%2F%2F6wWwztbkReMYr9V5fJDyeC%2Bd%2FktySnNVmYOjqpyOY6qshhHdMGFE5ROMaGnj8UfP64PdPjichF2jNPk8jgbyPZV9UY36H7f%2FydRmD%2BDO2Bx8P%2F52I83Hv%2F0pKIoyPMTE%2BA%2F4DsbAMLA4pEgqRB7hsgfHvvYj8rJ6OtcLWz%2FG6y5kLuTn8fNATuN1BTORzUQwkwtJUH3rIAmS4EH1LfoGH8MT9w%2FiNbBWTdANUszzE7QDUAviOSVUVaLZcnMI1ZzmJGg8oWbcDEVAv9b1h7YG%2FSEJAiucVRJVpSVbTiTx0kQcvpVM4GayGb6VqGsZ7TDSSdAyyqwPRHa%2FkhAVd78SAK1DdgUbtA7ZDVqHq78UAu1jH2OQ%2FHiuSSx7A6S%2F%2FxN0Dw7keU7KSXEpnv9YPWResQKi%2FhO0EfUUvAile412X6a0izBnHE%2BzapmqWUV1zeo51KyaQx%2BjWz2HulVz%2BBO1Kw6WsY%2FHDLUG8r6O2I5r1VcFH1xlqiH2InG9CIhRXZGR%2BQdAf2iC1X4ug4ajBdDoouuSUC0L1JgTwJhTBFfJBhh5qmWbBw%2FawI5Ck6lkE2CQZH9zHACnmpm7IIE26wVg873JVN7nBg2NjaVZok1g1HfpBJZ%2F9vj5W4%2BQs49sPf%2Fxn522%2B9V3X919Gvl1SCri7CjiAvU8aRt%2BrFC5pnTkSOmaSuGxYfWXz2%2BBq%2BBiYhtfs2DdLTIE5FKprsdMY8o8rjgmWy5HFFdG4REfRMIESFjBTNbQcVZLIpU2BbCwCRjHtb8iGCNdFRj6CkGDhBxWbWh5g6KwAukWZJr4XfwuZj5zMnM2g0ywoqoszih9QL2VlHoLqsoCV2kp3AMWtFVApwUM2lBDwPZLxX1OKdePrNDqLgUyYFiWVgDLlwRQYJTF4j4umpqLZ%2Fvcpc6TkOE93gjrixDBJxdkH2zmsjPZvplE7iukCn2wme%2FNsMkMKSRTQioJm%2FGYkzU6ScooyIIRNg2oFEpeYywZNN7POvzciu7BWw3dPYZkW6wpkzSmM4bPTZ%2B2gvM7yZcMhi8Rl8yt6Br8nKF7mlG7wpDtMtw6kF7BBezs%2FUZy%2FrbyNvjPnhJtTxp7ugyfG0ifzAXhlMFwP2sPcid3DX3O0NWDX27uThpzHYbPzciczAUc2s87AtzJmRmfM2TSxuS0wPpt29afsm0byEKBKX70Ea8YvKD3j2ugBeYOphzFudYK1v603iwsFalMuSPblwN1oakKKiYqFY4uOJ7JULsVzPvgIVx1Wl2lBM7DrNKGmmk50UYlFAMM3eZCeaRMAy3VVeqBre6s0ltV7Nlybw9e1OuBi3pdqGeCUESHAHWi4CJSX0kalhMPqJ3aFrKSB16obnr07aLTQjwD7aSrfYB4LE6H9ehbVkeRu2WgfWxO%2B0CRyhjkM03a8CRrcRbbBwba4XtZq8Nx7BJkvvbBwXb2wNgc9gBYxkf3a5uavQR%2FBon%2FNdggXUzZTPV14HvQz82HUFc3UT0bqQRMXzKYQPAQOlnNJF5zurCj7C0V9SD%2Bsaewt4xtHduLcoRdj%2FyOAvBtkBMORmRiTBmMpU6CXA%2FSzlgtGwkSzGgCieDRZhPVSesUISIpOqzkV2TUYT32jNXBriddAX6b1aHaxs4A9Dg34o4ylmd4%2Fin%2BKZjDHuCB7Qza4M6q4kUhW5a9eBtZgnHx4JBTwxAgsMOouUpeQHD8ogBcJLtwcsO8R6Ox5LWDnLJwDg%2FMrJIow46Zd6Kty5S8TtgTGLsLT3nwlJGINl0Qu1tbeLeL5Vta3boA9qCEkPg9xE3mEPeePer76gH1fd%2BHZO2HH6pPzAeR8Y3GE3v2sGepT3yIp8dUIOlBaiuwHz3IMAYv0BTlVVq3soQcYgwWlTNDHQ9O6nggZmRhlGO6tIqTHAd%2FJMrFOU%2BOixfJsz%2BVHvb%2BhDw79lb7B209bzY9wSvoQDm6gq4FhzW%2FD6vr6No9FzFlK95TuxuYICQ7LirLvIC35hlN9NuBjhYqK0sCD5KKNYOksosgX6lXDL0g6KcEiKjtRf6kvoHyU30Dtn7z7LPUjwciFP14lSKMM%2FAr2PfPAhQyE2bO0TVID53LBhjYCB1YsaqI2kIKymSzqyTBFiiOLbikiqJ7v513y1Q3aBaVcL8iuffbDB5fiI6p7IE1l5jMTFBfUXuH2WyYpUsRqY0kR4U9R87a88bhN%2FacpX2c%2FgFZ%2FcEH6lMrdlV2HSENJ%2BCDZdWnPsDzKp2uwLQTeTbIbKzxrM6iTQ0sipiAjeuv82UzYgJ8uR%2F50ouY%2BEVF6lfgCDKnj2LicSImRoGhetDxWZJrgokcF0%2FEk8uPHNl1Yq5Uz9ZwoUzC1n0KiE%2B7Lk2AR1xUsWcBJW8GRwKMbzOocjxxUJ0sL3qiTTVZmJOjhRwXrXAt%2F0pA1m23OiqVLOnKVjaOPRwgv0LJpSZg4pO3YJk9PIGGEuNn5jXMe6RhgNIQYLDVKRcEyok2IAxvtnA%2Bf30aU0L55CmE8uj6SAvMbMOJyPQhmUHaJ5OJ7IcRf%2BpJOH8nubsCnFvTmZqZVQy6mWFsxYwSADhDFE6A2%2BkqWTReDQOcFoTLwHu8cgCH2CyWJB%2Bu%2BSKOPGHMXgkPB0SFAu0Ns%2BhAjnuNLUkXOpmFqCjAiu4gsq4k7TqifhU0otsvuYeM3v2Vl0EjYn%2F0rqYWbQFVCTSqk%2FHMdaftZjQdj6Hwupgocx1TFnE8YX75qmVfBOe4Lwhz3I7zrmw34AE7gy7ymOacgIHXGDdYLcUBGReqMaK7HwF%2BXjBYLaCbRBH%2BoLtkNiFavggsL1EqHsx4nQGd6JrLDrApiNFCEt3GnBgVQJHpy%2BdgTsZjKcRuy8tfuVvd%2FMXNt5WOLCSj9HOXdphdj4rfdXh2xZESLk34SY8xsOoVdZ49nh9rBaOYMoo%2FhyIumMWgRHMOBy2URWnXkkMxE80ir7XmUHdPZKmJYzpUtrm8qGxYq2WH2wdbqDCAtMOVDZf7BBgaqYaXBxb8IRKVEnntpfmMuFuOvVlzEqEMHH%2Bh1wnd8tryr%2F0f21us7zbIa9SHQF7LOHYtmo7TCiN2CEalbAjioBlQUgdduNQpDgC4CQ86QIdBBYgpteCqxjsMQToz0Kc0MG4w5xq8SaDcaOo5KB9bVpKulVuoInJsK2guIMWp3Vpk17cPVFZu2bKyAgrMXjjP%2FmEX1cR1XYH6gTiYy7K2rmEkAbmJq9J4ApAQ5K5nmITZYVIQHSTNGQVAe%2FC2a7ZefmGxve36W0Zvu%2FJUL9KPjA5Os0ebDMtPJodPnmtpa7PMPRnJwuly6n7g6h7mJOZ8ppxB6gznlGRV6aPjHAIyjei2DK5iMaBNZ1XpdJWysDVQVQZcJRdVG0qzgbWznUAjmzXUTKVJMgOzU3B5vBxytw3WOifKObFuxQyTljCRxvfTbMzBSh5Rc0sgQdEtkZi077CCQrfj98T4%2Bx1085xH3nj7jUfOqdhMe0w2%2BsauH98mHpCSVtJ1408vv%2FynN6oHtb0r4QvwvSvHXiU%2FxAvVXnxv2NbleIVfz73NGEBnY8SoQTRESQHjIzJYBSkwczWpzyo7dsyv%2FSejILMr6pMNh2q%2FxW2mv%2BVk3AxVExyHUL6JehAEfz1RABtDSIGYrf%2F2gbmXXjh7rfar%2Ffnbnv36rX2X3HlPbfy%2BzZ%2FN%2FZX%2BZogZpHEr9BKH9d9G35BHEzwRGB0PrDb7XD5ZW94Jgwqcw6Pxc4BEE2KC1O8PVpIEhrNcQMuIaNCQeygsZwrXXy2cbNw5aiR3A1zH3qyQVwCwJ24tXHLn3VXT%2Fd99YZdpxFT%2B3VtlU90%2FroBUEYGbAwBnlNokM5nZYPstZpYTonF4adocMEbc1XJ7z1yUGcFMOdW5RDNQyonupXgslimxRjgSB4U8C2iWHAtgL1ot9S5Ef%2BcKNFpKhjD6RqslDqOSIc07ZkfTJldV8vDuKqWtnUoHdbnMypbT1M%2BSjpk7y8TqxLskXKUCfGfOAtgeqJaSi%2FDTVVoClBzJKsur5f6heSjLToaLpoVgZfJK%2FkDvTFR9CwlY0Nvap2OosxRrAgp7vNOpnRoXy%2FyMAfTZhN2zzAZfoK8wODRrNiV%2BT1SM59HdkstH0ZmuCUcCLw6EIofqYB5UQ%2BqRwYu0oyAAorirXc2BuCQoQuFFDqPdTUaLRdVGtSw0xFF4dmkWueajh%2B3K2F78wEuobNVlbBcIDYwhHsB3lKfsAfwlelGxAltF7m1kzgqK4814EQpo7hYQvnM0P765Pu5T15PNmvVT5r3NOLr2akkKZbP0GNVcW2nQUPMwoGyxZVGtRX0QTBQryhq0S%2FywBZZrk%2Bbxj1dRSJdcGMwQjP39pSaQ1jScgf5%2FNCGBPCmgmIwrDXxK8JnXP3Gfol2pryKVCvf20RWwxb19zF857bTjfk7Q9QRN1xN0jxZPtTxUZmCKU13PLQBrMEYr1fVED64cZsJ4ZV%2B20NebSsYEAhbu6%2BwBsOh%2BGQqvCofwDYbgVxYn6Ho27nerQ%2BFwCN%2Fo3Cdg%2B%2Fxav28A5Ld2Zzmn3VzxZmu2nVmL%2BNH774f7%2BwKU55rIVAgSOfSeElA4dUB%2Bod0Q3lhrEY9kyYsIktUxCSQF9%2BFkLfao0cQCs1y3yjS910qXEktVsUwwpy0MejQFE501Au4Q3kDplBMLoLuBnhMVi89yXymOuYvs%2Bzx59pgfWLBIhwBj93S9LMH9GMBA7E3COmyUKuRF8mJIOvamFCIH1Ln8Nm847G1Y91A%2Fl0G7OYUpJxBGsJAs1Ga0cLXUCkXIljnLBAuyjaIA3MlmkUFNmm9E0FyzJiuuf85UTPPkAYOhGg%2BTOyp5YItLE4RM4ArDBPWGCPFRlaEC6gCw3LE3823siuEzWZdFHba42JEk67GQwxYPm2StlrG9FhwDmLNzKhX2ta1byam4lh29%2BUsWh8OCbxPyA0JMgukGvriUZiOAqGvPKN2gpmVgMhFM%2FAAsItVyhPonIxnALuIqpejyXsoBKhEYBiXQX%2BpMAUrR1kQLXTS622EvEoujRahExVK6BwSax73fGghmpmmKkZbdoPko0zws2A7WSbMcGHSheY0x1uvD7IcYKn8D7dzbqBzt4p25wqqOSvvKGWmrbRdoSsXRyuhoxTi8fnh4PQHq4FWoNJma5uY7YUfyDrSDKQzXjJIWvGj4TKrv5enYPgs2cQxeGvfZc0q4WmZtKN2JEs%2BUWnUtNA%2BW7EwSlWEYpGg%2BmUoTWOviYAA5iJMQj%2BzJk4vbh1s9p5PbVro68%2BRrsTZ32GhUbzxDvczfbOtwOsmmcvrKEV9f15%2Fe6F43MkI6PGmHnXvrmHtaky0oCORfyStfUL8HPIf%2Bn%2B8Bz7WAlOhhzmXKLQhZtKp9Ejo%2BHVWlJ6NZusBhdJT4Q3ANLlZRWLYwvQa065ZDIjArHaVEGkbJBwPTHoWN5n6lQ4RdpQed%2F4BgniZHpLlU3oDeSxyJMA%2BfJJ5CuwGHKK9txEFHZc2%2B1gj5xTXflqMxqw0I3du55bHlla%2BdctMNZz70%2BSUX7d19ipBr4waa5ZDdKcwnyhcKZxTaTAJny520Zd7qu5dWNq4%2B9ebi9UtXbqzPS%2B5Sqnu36CNBaJgWxIADo94oovR0BAS14OnDIUjlo7KDcxLO6JOlytIrTE9b2uYbzQbydTY6IxYwGG6yTJvfL8zOcCdPb%2FUQjvT3m%2BKphM127F97B439DPvRc7pfyAxzYBtTbtLstTJr8OGCYwOut1GudwHXW4AjklRQahouWp0wW0AHCFFfQ9lPAzn%2BIAZy%2FDSQ0wRf82trk7OKc6ZkAdlaMlDbLdKkxSRsosLDEKRg9hfkuJgTPGIuCjswGp1ElIHdCiKYpJy0ePHiG26A15Fd7IFdWZeUjsYqRXVzsRKLZrwiqH2PP37szce5c3CZDaUkM3vsmWyxmOVWsmYpFaJrUPSjb%2FB3gfxDfO9gQO0CfNkqNVEb0bUA3E4NXcuhT49kCHSbfazJTFDRcfnRGgJRRsUCawL8GRDZSkQE2R2ksnsmASRlAWQfdwLUo49r2BU11L2ZcdTTkit3g0YZ7sd11Me2IOrsnTXU9XyBxngqowsgjHLSZeDT7ockfglaYkf34zvXgu%2B4hsD223TbTyOY62kEcy%2BoLris0IhL5WO3%2Fh5YxUn7nk84PxHW8e3KCcOtdXDI4fqmuvl4R48Db1L3MoJ%2B0Z3B4EAog4kYQxmlt4q5GHq%2BxRCZGCf25D%2FdfiMmxUayVxrHozJhELSQFwDPXlTfVN8%2B3tGPx0vDSMPuk%2FASP3W8%2FPijdaLtcaTIrE%2FYRJRMx80twPyRlTp%2BS2h%2BwRyaQ3M6zaFZTfMLLqL5BcUsYrw1hzGcK0FM3IQ6QtnVMi1Lsw3K%2FrbpaPXc%2FL%2FKDDle5sf%2FjZa1IOfHv6G%2FZcIc%2FsTtT0H1Y6OfZoSMH%2B3Wx8cImmqQ6WBmMItgZUSrPUmz3dJA9cUZNDQ185yuIBiOBylK42CfXrRNSPg495XHHkPzBmcOznOcOTDnu3BbPTg%2B%2F1VbDWoUBWAWohxWbfySo2%2F9vcIO%2FhmpjSI18ONswFdmyrOoP2txRpl1SJldpehO6yn8H3HD0LNHpulo5NPhx7UUo49XKn%2BvHD%2F6FjrV2OQNi4uLdR%2BRwr1NvTrdNHMRphhafx6qYoP15YW108zQfAymxNt15y%2B6XCammYHl0hVh3490dUXG3JEu7hYw1z3snXR%2FC7yTopa4yzXMcxkoe4o%2Bu2G5r4VyaR7FBN9zqIquED38gxkVoQBAJPYrkljyuKniApCVXR4JXRRNouLtn5o4N5MYUsQjRt26Jh908%2Bs9gYDn6F53sPhX0rGIJN495r9o2eZlyzZ3cYfdwaD7mA3e3%2FvGZx8kw%2Bqb5LD6Izy3DOh270cf8s38C4wXtPQhHYOQBnuEwq7lgKAz1qQFrJokIJ2dR1hDCKvA%2BKme4ekFayobJl6qtaf5VBJ5iZoYxnuv%2Fel11%2F%2Fk2oUL%2F7G%2F3xI754wrOme%2BeP%2BFm%2B6%2F%2F9Au9o9bf3Hztlf%2F%2B77L%2F3tkxBzbdOmexZ%2FfRc%2FcjzrVeHxNYhaOR9e8WnTN1xBd82hEldHDJuogYojCgJG9ktcDihJG0Y4bQ8uJsYmhs6VHFWVyxMysfvUItxZguorhuX83OBkf6HkMoM0VwkbU6oU0WwgTuZBmU8m%2BmcRBrppz8RVXhKWFy9ctnZlYtu3Lyy%2F%2F3k3bHKed5hJ8aYuTNZtPL5BdZ37za195acP8u7ZcfcVVn5t7ygPFAd545je3rbwoeLbRt6ht4Y7e3CW76vbyS%2FxnmGYmCmvlOqYcRGo4qxgVwAFL0QHToktIDV%2B11IbUsGEcOBRG01HxiaVIDAliwlCxFNJCHry4ryUWT%2BguSOA2XhYmKDoFSWO9RIGkzIBihrzzKLkk8m31FTQIyeH2gco7j6oHH32H7P8aefBydRP522WXSaejo%2F3Rd4ymb4ORaMMrb6j85tF33rkwSx68DK75n8suW9M8Hs%2B9D%2BZSM5NmFuiYwbSJZZRUVTFrs9mlGAHLDMXSXkWHIlOKuYAFpVArsGDJHIS5lOgv8Ub4TNantmwU4uO45GuokGEuQiQzkWIOAyBjYG9dc%2FdkZB4Pn3%2F%2BWeEQ%2BYz6gOBfMHvt7H4NoxUt62sYPZAHhEg02esxE%2FJTMocM%2F5J1yLnZF4%2FjtZf6AeK4%2FjfpeJm1EWvVcRkP%2B1P%2FGY3wC%2BEI%2Bi8wqyoK42UGRPf5wObXxqvUEm0QXicYLTObIinArWtopKw%2BMhm5taSJLdyhrrxHw0ku1VCaW%2Fk64rRD%2FT2eP%2FmLWn62lpckM3OZshux8FHrEewbs1ao4z2kcFnEzUQLdUouL0xCI5VpPswJssswH1mxxNio541IsEaYSRw%2BzCQPH2bMZ5bQGwoCN%2FU96g2tfE99Dd4r7Gmk7WXtyMvqL9WDL1eoM%2FXlcfv2FqCxB6isRQWtOer202gL8Ej1ZA4tgkHgT1OAKpp6cli10Y2spuWqi8l%2FHN1P3lYX1XIba7IeJX1ZQj%2B53YkZWX56F7GGNVMLSdXuMn4n%2FUY0e2DKzdSAfkN%2F%2FaaEub2up3j1eg7MhREy6O3CnBft129ne%2FE32SSbHP%2BVBn%2FcEuRCoC4tnMGrKF9WmCJ3C10vIwxTmJTQNjnBrXL85N1dx9fe9HsX%2BSX095nCuCtKcwg2fmkXLugN30M6t9Vwpr4KQyPOwDciLNDkMNs79kN%2BCSKMFSrk7SljRGMZINlzNebksyg0AvWfIZPiWXk8BsMzrrSiqoV3gNdz4yYn%2BQYOEtwPswy4t2t8R2F1aHznphVZ1qrOd9Hj3aur8U70PhPuocUfQafh26hO49Z1GlRoUFQgO0%2FWzaNkgpnGLxl7%2FUhdCydOthdo9C1G4P7Gf53m6GLqbepb7Io%2Fqw%2BRjX9m140pfyYbYQtxc330M34lfyVaMwkzKRBZkOAg2ag%2B9Bd2UCJjyov00rGXfISMlTRZxzEc%2FzT%2FNPXnjtDMB0cVS9YQav%2FUzIeAnvmwDzMfqP9CwjRZzHuYmvWQAzmBSmVcinN7iAfkrGfPHvU99YD63h0V%2Bo%2BmO9QP7tlDTqaHJ8DlBB1iPC8J4XI1wGXX4MLcIPsEuGwWjGcwsMTwsGqWiLF%2FMnjRfHQKWFh4cRygptIqS2HS9VV%2F3QmmUUggcEebC8Uoj2DYpSk3lwtyIVVICSlBngzEqld37Hh15054P7hjCixttTPwPhEmcSJM7gaYPHWYHOMwOafABJwlpCZDs4l4ye5n3z1vCiS3qO%2BQ3c%2B8e54WTx2HYzozyFxEIemvKoNUF4hS9bpXG70hOtfCVSWsuWUBvmGAL4zrTbQX1pu0uN%2FMt80YoMOYGtSHUYmKSgdmKpdtzjQq2r1iyUim4JDLood82DCD5hV4jYKDl3PDfL43zWGlpAf4cTKGr9zFOwUja2DNvIP3cB6fSTY4UyE%2FqRjc3bHmeM%2BcnqbsyXOTt0%2FlVzbAGm0WwcCxxOvy2VyElyNDSc7Snl2fzS1p8xv9%2BQ1qujiBPhjJHahxtEQXZDdN4vFVMdeIH3ftltxSjYd9qB1PRbcQxYgnJpwfD7H3yKgWeJwK%2BB1gEBUrcL4xDucHS%2FMCpuzV43ARWhDFZMqOQDRHi6jKtng3jQNMQ%2BlcNjS3o3%2FDUy0L4RT6N3pQcwUVYl8sns7Q4XPDniL1lxhMywn1K0Qsh9vatThePjdMCvmcFGZlQMBBBCmeT5NUDtRxONpXiOdzsAsH4VRO%2Bl7kts9X%2BrZscv%2FgB4HnT6l84fbwBv%2Bliyv39b5SCZy7qLL4Ev9PyeHKDfcFKxXvis%2FOrZy3N%2FrSS03337DwYv%2BPfjTtq5VFF%2Fp%2F%2BAP5ogUVjV8b8%2BYi4zlfnroJWM%2Bb0yUfGn6YM0VzvrzBiclxNC%2FgBMlxDVkPJ0r9WnjbbRfX%2Fp84ffNXDVcxU%2FNVJ%2BMQnIpD0wQc%2FJNwCHwMDoABORH0m3as%2B%2BFO9bETZ%2FjFd6yr7lQfnwJzkw6zj7I%2FXy8O1SR7QIOZFofWcu2cXgqz6NNhdjQdJ9dOJpji5aHpXb2pKVAPs9OLVz75YXXj5ic%2FHJiab3dFkS0Mfzj2wcCHT27eWKtXPof6KYxMkObcYThW125KrAEYm6HKMc1kxuLhsb1snLxEs5l%2FPuX7ep5J7ft1XxXWoIOWpH2fpqx2qQPqLNKtwfBDmKOLYI4aUU8yUC%2BQQHOw%2BCr%2BUIk3NKRVIyA%2FJC%2BSA%2BohrZx5bC%2FWFOLvfAYG4a3a7xhrv0PGfweWCcXYrxdug3ryGdIGsHSoM2u%2FU6PJv1K9DauowZhG0B2ccd4d%2F%2FEgXnWW8g%2FbK7e8%2BvT5JuLUsGBb51zVKGuM8E2xlp%2FlyJScuo4nE%2FjzwEuMyjeT5E719Zvf2UmSN6ubbyaj%2BgHOjEdgF34vxsT5nfxO%2BnsMGLpREWxd7VdiJIUXqa%2BRvTeT1E71tZsVOKJu3q6%2Bxubxx1I71Ne2Y20Nzal8gOrVcWY51dpaqkqc%2BrNDmVpmBWl0Cpk1qy%2FgwuxKm52PxrUs0DjIaTMuVyER6Nk%2F2W9BPGGCajrG61BNR7Fdy6zUPsjaGXzc2%2BeN8zN4v%2FfHkn8aSO%2Ffjp%2BHD3ae%2BjNvKOQlaXgfewlkuKFO1yTTwUxjemGF6WXKKZTkPTkMjqLUhsW4h3Zm6KWdGYYA%2Ft4e0b3favPkZ2hhdzEnRWlyDMDV15vMEKyyd8JUg02cVU7iIF6JlrN6YlgEQrNtCmHMAh%2FF9eRSwrEx61CqJfSfPrdHej8aSg1Z4ix7aRFzwYtczON%2B9Vewedf3DY4WN8u1aBZikVg9pqdS08RRu89vG3WnkyWT10Jo14DDG6Zl%2Fhm31M3cLS%2FZ2tf1Ix999Cf%2BWVo7lB3P88uBtoEpsKVeyk1SvpDGHL9hQ8HLOvg0Gxf6fIW%2BxDAmEaSSMS1WzQu%2FNJs4aXo6c8G5X77j7c90LfrMZZ%2B95vrTVznPcadmF8j0eRs3ndbu402CNyrGvzI0pN51iu%2Bu7%2BYHbzp7%2B8Dgmlx3uD%2F8gvrDn%2BxekzMbPc7CjfZVI3uTM8%2B6aXXWazaEArkrWlpe%2BiHOmUsZGz%2BH%2Fw9mAXAZURZSjppbVea6SoMwFIvgNTgX2GdGH029cXtlWAl9GrBhArCCLhMmiA9f6GNTPtmXSqbSbAE0nDDn4AUjbpNLDeZ58yIjM6a3OVsc3uVrRUPEZzE7Oc7kCLT6%2B%2Bdsmj3LmXz0H3xSar7TvXyVIWfvOH9R2snaeBMhVlfQnZwhC47emeRbI%2BHpN3ZLlsTwUHjkvdSKvcXowi6Pt8Upm6yEN3qbh2ZfMvspsurirjUPGVgh%2BrnX7jNFzrv48VW2gabepjY54OIt6aVrW9KXYJ3695gb%2BPf4HBNlZjLzmdkMLk%2FtVWWGlk66IKMMHlIK1dJCIEIBVL5ZFnMgaGpvyw%2FTPC%2BmZJ4hup9nxNZ0fnhkPhX6QBdjNmyIAFmMsbQhVQgbskCZNJ%2FSio3yvQWj7JMLmJgwFDQ2z1t%2BxZYvjn5xyxXL5zUbg5MP7BZmd5%2B2%2Brqbrl55StcCKymEZvSHe5T%2FUdLn3dpzwQWJJR4vu77N0rZu9Yb5mcz8DavXtWE26MR9y4ahM6d3tuVOH%2FiMgyxMzJ0RWHQKlryvWbjuiq2zL%2FBvaG3FaQq0YIAWCuOjGQ0nMV9jys6aJTqSwWxQpS%2F47aH%2F%2FM9fM1KnRXGmHYrtO4aSg%2FzNodi%2Fozhd%2B6xOm6dzn4u%2BB%2Bh7kL630vcEvpfhbMudLXfGjWCg9SuBfiXYr7T2K4l%2BxdrPPG%2B12V2BYGsirf8jsyxwyOGccDCdVmYFCaPRGtOncySOBC64kaw6gWcSmvoBVxSSMAgsDghfJ%2FiCocHEverL9yZWPnb5BSnL7p89n9h0FyVqRg7PcDpIW9LXnYlN82TIxs7U7JvT9mBQSJx67hfHyS3uPLriLunc86%2Bd7mxT3%2Brb4X78Eo2imXXNK5uCY4%2Ff7UxmkzP9s9gbp93Td9csb1ubrXfT2efR%2FghfBeHopvqGG3PRk9StkQKQBbSkQX2WxCaSj1%2FalDA4Ek3S2DbvdO%2FYtv8iL5AX1Mcx4DNnzvwWviVmaDl6qMj%2BfKy7iIIMu4PU8rUkKnfbYVZj8ihyc0dGSVA568OOOGUfzZL0%2Bc2d5YSvVqNa6gRO9yVguofCaJOZw7DpS1CnfL63r4DeB%2BqPChP0FaQSIvUZZEhcMGKfhYJoMCaLPifZtH7rerLJ6fOL6gPFoOfMN870BIvqA6KfGNu9PQ4yl6waXr9%2BWH1a%2FUdHj7fda7f9Tf3bab5ZXtG8bJlZ9M7ynUaEv9lwNa7le5sndWEowGpSzzllVjHr6tH3PJaZabkwmAUEhj5G3huz0j%2FltuYLObqfXxKSdmHi5C4pVPzYfzT2xL5fuzAkHVtUS2GH9ebv2tL89JzhGv4ZxsK4QJc%2BieppfhpZ9NCkGxNtL8Flx40zQmsRfLDUW8V9gt1N6yhcYKU5cBCHCPZ6QC8pcBmY%2BlFSkLV9WCDX%2FwUXvYfG9qpPEu66Hc%2BpT7KP7bjuITz4l0qFXf8yelBfQpXtup37x%2B7beb12oNFf5gZdUqsBdoEhmZvoZZoYHiNiPJasZYuNPaX%2BpIjZ9JgQpuXPIzcPMFqeFR3%2FBHBzN7OBKUfw983VspmWapgxj0%2FKYg5MRw7N%2Bk6gRpr6UpPYFAMbcXCukpvQDGEZSx5LGeByDlVckDxusRSOALlkTI7TekJJXhAgoKgPG1O9WLzgdRhAlNfaMYGNahTXpPKJgfaIaAUbG%2Fb9qaXnfuU7Xzl3acpvxPZMpItdXzlyWcvpAavV2xxv60r7RdORSsfKbRdetiKXW3HZhdtWkrVaIi9W%2BWG%2Fod8y3%2BQ%2Fw72BWhMno6Ms%2FDuygWz87dg%2Fkd2%2FQ3fZ79jZSOt5zOv8D3mmVhtvJvPYOezIb9WH1D1caOwFdvbv0Amn1dPNYIqGLaDjm2AetTGoU%2BtOGIGq1uiB4QVNRdc2HLiE0QpIGTQoeKFjXXyZdJKuH5Cbf3HsTXLvL8gNuN9ZYQ%2BQJvUPtBUR1rVtVv9AmlCdBxjdMGZPwJhlAdpyDMcrkFO6gPYuJallB2Pwha8im6BsAhWpFbMXMWWxWYtegLJUkpsBpG46KskC8A7oHOj7RM8n52A7CV1R6QjRzzgur3hZNJZMuQ1OR4C7KWloEs%2FEydj3CmsKuyOhjpB6EN6yNI1GnjZ2W0u3gXvS7%2FY8LEpw%2FNhM3u6jszervYe9UqjS4WMacoOxjsPZmAHHozau2LMUl3pFInZdIfmczvRRCQulK3rB62EsXuVu0Qpatd%2FW8n%2Fxt10wVvXfdtL8QYP222IGGbnBPsuRKBgW4%2FeIEmy1pB5suNMxP9aW1aYWOhoISQOPbacyNapVs9HOTtQzhz2cNLccb6YaH8lpTJCDr738MukgHS%2B%2FrB5CDteSyXX%2Bhf8ZsCe3w2820%2BoitEctGdoSajydjIZroihxotL4T8An%2FVH1Fy%2B91JADpgCVtfqChVqGuRKsYmeFEC1RMlDu8VZpWVZTzXcXoVZQOUKbqUWwmRpWaJkjoj69J6f8NBEtc1f2YNAIhGG8JpOO%2BdsHIk8ZWr3sHk%2FC8JR6V1FLtKdDBm9kDfs7X6pbHmul3iy6ru%2FWc3WtIKsKYPXQsmsGC4IVo6vUAbBladl0qcOIfs8gJTAHTDuTSGlezg9yw4YBkhATcY%2FPAfyMHv4US9vZ6HIzljw3GwjwjljQ9wphWd4g8BX2kbEzkhnWYnMauFXeEF%2Fg3g5JyLzYMCDrz9rtR1hB8kXXHAF92sILnIElR449U1HftVlJs0ROVn%2FLraS%2B%2FmdoXgvLLAL6nwf074R1Nq9XEeVoRmskhzLVny2305zq9gTWxfVmtNZD7TnAqqtHi%2BH3DZCCB%2ByCVJIGLASjFEYbuu4ABVTQkDOE0fA0xhY1ed1XzXRet8ptdHvP9ML7quucM7eI3qag2xQuXvjQ8hv%2BMmOGO0get02fN9067SJyphR6msxfus3b4glKnlbiuXGp%2Bq2nAfM2gbOGJHe71yu0%2F1vP0FCPEflTAp3oQf5B1Ldoqyn8S6G3uyCgwzslC7L0x2Vvdu%2Fe3f3msj%2Ft3%2F%2Bn2vYf95EX6Mc%2BevrBrt8s%2B%2BP%2B%2FX9c9puuB7U1uqjXRcSYNKx%2FNK7cdghj5lio4msD1vPWqhaId4BgRAwsbS3xpZ7v4mRBRxFnkpwkRCUunsLgZXFoq%2FNIOvF%2BaCilfrN5KJVtmubYcLe3uKsIhvOLRw7djm2ZPlxc7CfrI53FrpO3CLu6Tm5rfvlfggn1DnL1qwef%2Fc0N6h3FxtqwJTqcm5lyM45qgnp7mRw2A4BVU8CSZ2x2YanHx2G4Yf3kaU2GEtfam2gVu9grQNSwTMZFd9nU1oUucK1et92tdMKqkkYR3kE9STmxIPYOEOQG2aNPwHr6WIZNFiIwBVPRPBcX4jDTPgn3Ioq0sb03kFEggLp3MgEOvgrIk6vpvIT1r2iQ%2BDUU95OYaxl0pbbSFiXtNEPbi2iXe2idfA8t9Rw5Lu7dWnJtN82o7R4E6YJFct2A%2FH6b2OSdSY3CplZAOQ4UEBV%2Ff8nbA5SRB0%2Bi3uWCSIkA0z0BMzpCaElWtLaiEdoqQY566DJHk7ijHSzmv%2BTB8k0ZTJVis8NBLnRYRdvFDusakl6z6eLVm7YLTU71SeExsAESnzU2OX%2FgcrtdaoFYeTMn8DxrsHxxrfo07eC1mLWLCYv5LaNts8Prdly7ZHSZ%2BrQv9XDTKWSV1BaWvCFCWM7A2UwO%2B%2FOr3tVl2rnAN2uZMI2hXEs7omiZorBeT8f6CmVGtjydioPp%2FUi7IbqUYC0CbYIQ6dJqEZBxgNBeF0pAZVALsCQjWFJoa%2BbbKe1CrTThQomICtNf4qZj5Uiqi9KOBSFo0EiBJcCGMFvLYkpx8VgqhzI8WhATAha6CQ5DB0ElMyeeexESyuq42O6yOsjFgjf6lEl90tVk3L5pteUNZ5PxsySxSf36urstBmIAgpl5K1FbuT9W2NmjS8i1Lpdkv8xmfIu3p8SxD%2Bxfa5PIqmK7V336FPLf7636ptVpNXMYaSGqDSmszbWNqLMaHGCx9DEzwEIpF3CuTYc5NZBRpENKAfuZldslKkJnAHlycMCFein280O%2FSzsGWdx9%2Ff0lR4bmdTFY4RNma%2FHvVCKP6VCSmEoancSITVAKw4ZBrA7ow0nlkw2iEMUaso0UdNZuf7hNUp9eNrrkWmfCsdl32qrnfQEN8mwWkTpFNZvY9eyP1fuMnshjAlmLpLlw9SWb1qg%2FW20j9kt81rX3uO11GpEKutK%2Bj4RUXwe7paLHMFjGwr1n8MO63QscM5dR8loPmH6apJ7PKp3VcictwemcDphHs1h8mtKy0mcA5p0YB7SBSl5ypgBzO1X%2FjBEwOWeCrJCw1mSIJLQmkZgF4CScJyqLBgc1SZN5cRBWlVTSSVCTZ12%2Bzfak81qydHQZWeVtf9g%2B9iHMAMuX3yV%2FPUV9ytuWzXKC0e%2F75j2A%2BsKI7xI7sa7JVdOrL7pk9YU4mcha4bFmh1PdDqOuvr7d2Oz8vs%2Ft9pGKVXCZOLv7HvYyijiL%2BjjMkycZB6w0M3R9xUX1Nz2%2FjKbvac1DXTZ0yLq1Hn2Mh1aEl0ze%2FnpNuB42YEEFF8xEMpy159fXPVwPlb27hyRYUJ%2FIPeol7Jrrfr3nrD3qu3oA1EMsr7xIkqTjJV1ffRJgcsGqN123zGAK%2BjSYtGwGi5bKUPJi7yU35nWVLYzYr1Wpo0tLpBBhV0kKErqzC9E8QHRt450r5B4EST2oAUQB1gB6VH39xVdemkKjYZ1GYp06Wvadezz7TkRj1ubsp1aaQcIsPHfJ6tW6zk6gU4J2rSBT6QTraKUGUwORHkXLi4xOhmmpDpO3cdC0vMUGsKQJYLl0sESaHGjDdZBx9U8dyoSZ9qM4Doi0irNyHBg3oyWp6dd8HU4XczvzJebLzFZ9PDuqyvKM8oUMUXZTcDURu17rTvIg5oAx2sCuF2c5HAZPNN46uGjNNbfcese96BuwuGdZ7LHktIFTz9%2FywJepEF7eIbqf903rG5y3YM06vOYL4iyzhfGuv%2BjWHTvvpW5Oz0Se8Bh9gtcnZNE1LIfZgg9dcF4HQaHEFmA6wn%2FsCGwEZRCjqX1pgtuFvkKYlcMkAko5nCkMc4VkAUPlhTSX6kvhjIcrU8aUAyYptocV4D4OnPwFXxbevD4pzRVylKTXUuJpJP2bqckYIzaf3%2BRf7RvuD5nSXEe3kRg%2Bc1U4Fuccabtjnmgeakm7si6eGNt51hQIyh6P1egytjUZbW0Oh4fnE7zBIvh9Rpcp4pbNlvb4TJvVHOmzWYXMSqfH7ewMDpucQ05pmOM8hOshHBfkLKLVLcTMXc2tD5Mp84CcEr262TwvYPXwtrApE%2Bbdi7zzzUaPxcZdEWodjpiJILishLVa4zKbYe0mzpd0hwKh5rDLSIhg8STMJm6hJHdaHB3egNnt4cxWOSW1CCmDnTPwrXGfjeNsbqOFALWElNMqC7HLLrO1ChabyPvXENbI286qxclY%2Fln%2BVsbLMIOkQID0fbJBLoCSniLGWIY42fXRZRv%2BYac6dvdR%2Bz9cv33sKWen89K7Ol3sunNf6Tv93Dvf3vbs6fMyY0%2B5XJcwtAfiBuDNp%2FS8cM03t4LBfgO9VaVArePmDPaVAkMfphPol9OruBQmqjT%2Bkz9U6ukf72lZzlPrLY%2FWG3ZGxJjQ%2F6Jag2CzKdpwaqJRd6JtsEIPqzatdrpS%2BZdxQ%2B9bx9tUH0BHIHugON7fAW0ArNjS6rX0kKrWMVmzq5kSY9INd1ycDKCfCGKRe%2FvYIu4W9eEKv6SIBd7qQWwAwzTaFminJ5gN1INDaFVrKIeagj%2BG1e1aBWgyozgPKTHaddAZQwKCagJg0Ebeca0nIRaGxWlhGHYhZLO47jIlB6y4SkRzD0qNHVPrJR7Ua0fd0rQzbI12pGugvdI%2BgH4GIBz9Rw1jMto%2BoBEKSzppHTozwa7HuKgXtKNaZp47h1F2jKpr4VbMTCA0Fwk9lNgfhStgEFW4ubKzglFX7YN9Td%2BrYEi1gi84wLXQj2MPaCfZt7RrGL1X515%2BL813iYCNPsIsYX6md2x3VtFTviinBKrK3CyWBffTvgS92XIbpVnbSebOWvudpQ0pAbAk6IXrUWsndtpIaqb%2BcFUZdtG4yYLqvsKC2aZOpYdaGwXaB0%2FPfliGSjD2FbA4aFuN0jBI31L3IOg%2FBTAdnjM7PXxGyzoKaz25bKLWG2dfR%2FfgMG62aUk4SlwsZQZw4fGT43X58ekd7%2BKxPupPwC0ZbBGWes20%2FZSghakSWkEA2Ogn6gt0cSTd0iL7lsKrpcUnr2yPXEfM2o7DCXbOghwJt6QjcKqFlE%2FQPug27fzvu6It6ZZvt%2FQYthGHw4c7lrt9odZcbqWvBW9D%2FaO%2FZAb4bwPvLAftCvMdUlW04kCa9MGoVbFXBPZvYEopzDmY2a%2B0iPutzmBTD1JIcpeyOWpEGGG5yWVxVeorEIy3pjCALBDgN71%2FvYCNJeGwfgk26AnDwgNqZoTgN1C5jMdAwpKm4LT4lnkzp0dntafaRHJn3O9sOtVrTp8UV68X5pJrvR65Kel0tY79undh4VSzwTmtNen3ktN7hzb1BeSztpiE046NCXNZw%2Bzpouu85XNXbTq1bb7KkEP%2FtKx%2FVrOrp6OzC3%2F17Mw6kY3G1SuF2eQqn0tuTeEvjsyMD89qbZXw9wp5Xtx99rmn%2Fe0jRhgkH83ef8a6z7VGC374Mc23uILp47%2FHr4cZl2eGGJTC3dSPI2QJkg%2B0Bq2bWIemNWCzjI5mbH0uOLV8HC4HRDVofh0gCHVHYZ0CdlRMsylYhb24jPtkMG2HWbRcY05KJ%2BOKl2656%2Bzz77r5xfi62fNf2iC6Oi%2BfNzJ%2F9rr4E0PDs3xnbFx5mW3W7OEN%2FYtmbLk8t2DwHM712Zduvvmlz6ZPvWj%2BvH%2F%2BrCzPuHneSfPmX3RqumntScO%2BUy89%2FVLb8Lz1zfM3rnrymRXnaPj1f%2FQBfzX%2FT4wf1h7G4wVAYIwJbXqfErADPoyzYKQnQKgQkC7wCSZEL80fgBHvH9kx57QzRkjTyMgOk%2FWCg%2Br1fz3LnQ4VRg6O7BBsFxwkt8J%2BJhyKF0Z2jJxx1iz1DyNk41mzdo7gV749ssNswu%2F8z1nu7umFEcKfAd%2By0G9tEHtyofiFnXBkZET9wyxNrlf03oHYQSWEefA00hqgVrWbemSMWtcXGBnYd2tVGLSDBhU1Gdr%2BpckNyqbR4NMew1AQtZSOmlVciIpRt16ZXxk878EnvlycoYVWimio%2FsvQ2qGhtUXuW1I2HQ6ns9Kx%2BSCx%2F8IeOPYanhia0EvdP54VE8hgozEU0wTjR6lkb6EvKxOfV0DFgYCMJpkf%2FwxUWmvc6XR0OEgT%2FWhV%2F3jwJ2TrTw4SuRV2nU71LSd%2BxNX%2FUg%2F%2B7McMR1KMG%2BzK3zA9wKGzmfsZrGPsoPWNw9lyN089LuhJGKBOSHe17B7AY24Puhzm0AY5sNZlq7hAZl2lLtJZ5t0zMBUQDE5vZJhuuUqzgJAgOpqx5r6pWpqLTY4wu4jtV7pExYyBkG7Ybe9XBsRvMI6m1Awa61fcmDGI3nDQccU0iaf5fK%2BeKcjLcRAKooyV%2BEbMF8Sid483zAGXiQ7ioU1fSerzhnWLjB42PbIsE12%2FfXkxOXflYCf3kKlv0azY4IpCW3l38Utr2gJ7XWKHt1nghxb%2B6Yn1K0lp6TlOspwYHYFM%2F%2FrC6bfNFpYt5z1dMy8YWbDYrlYdgqdr8Nzhzz9hXbpMXNe2iQ2HuvySUQCz3m0aGOt03zprfkCPCZ%2FH3cs%2FDbTdweAqBHYDqChhGk1spo3srNWyhz4QxOPQaSocUvppD%2FrWbNlPux%2F6w%2BbOskD9XwKjVZQ7Na8FUtKPjW%2FZ6SOUYk7xOWukpaMnhzsOt9IFtO3owcUNjUtGLHUNaoHlHNILaEmlSryXPkJDAEsfKJ2T4j7YFoZJrg%2FntIx90ZI0QyY1bMAclOJjfq%2FFzGW6Nz528%2FcfmLOiNbHa2x6Vw395ySRJ7TPiZ0vRL0SXDGbbl6c65J9lU2tlf8EoWUSbaJ5ma2XXF3sKwYG1Z7atL1%2Fdu7g5JHX1N6%2FqyBWz20MDfVYHiYW%2FGJC5EZ4P2qx38w6j3SpaCnfNB5kD1jX%2FjK7LRGr9Px0ZxYbNRJAoJSsYg4qp3lQ0LppZfGdpDAXsZf2TRgFpSQUNhxzbyt2C0T1WlxFG%2BgwF7H7xmN4TxpgDIa1tmmhipbdaYnhgbzvNk6eeuXKIRkdDMrZAwz5LOIosbZah5dXhgwYwtc4uwxct%2BiNfLLSisNSE7chdoLIGstl9bpdkopVyHpRN2Gmjlo6nmERYE8DoNdFaSMUuKiIOqGaLR2nPaQz0aI2QOFFrilQAFZJ9uYIxpNEirb45p3gOOruLBFv2ICFo01c8q7U4OowNiiqVCTRx0DrDGLNHyxcsCRbsPER9koCrifpdQHK6qJO3mYpU31QCCICMUwJkgX9FJIDDReNfoRaNKB4gzr6gxWrSNLwMNt5qJICgESAWwWoD3oDVBqVmF5zzYx2mEmigho49N5kqDdRAGhTrJDlnnBoaBdgD41Q5thVLcigPLtF5xA1csqnWvxRNAnRfACEoU5TtDtrGUMSJLWcUEX1R%2Bzwu0UQf7SLQRyV5aB4hdmSyUuzxIR20A85%2BhuWMtEuEA6tdDTydt7o%2FgyLGYXBDREtL527kZRgzbUxrDA4aOiDCvQ1GDj4oosJ8HA4Chd6NmJTdtL%2B2G9t7YLFYleJgRd%2FVPg8dHTvFU6A4WDQcRMrCFAfPRBx8k3FIUOg1Bk014kBbEBfPacShSIHHP8qVuERSvtTtGgtwJmKxktERsNZqRFz6VKWwOw%2BVBasHlyRgUmw7Sbva%2BixmfAfpq8FtNaE45YxaFUuN4LQvWO2Vk2rihNaD0da2mLrYQHTatrv%2B%2Bt%2FBawV4nRReEDAgfCx0nbC4EV6L7xPgzUlcA8x1eCsakHoz3hODW%2B%2F5qNWdd4zX7jZPLY2W6wXRzNSC7Mn1Y1jQ%2BPD77z2CRYwVraRRq1zU6hi1E%2B%2BcVCt3HGivxdFqsMTQizkJmvhUaFBkyCAWaKklU2r24GbseCXjkyH0dJP3H0ZYsNLykfcmAFkhS9V9COkj72l1mI2QqrYjR8bjfv9reGPj8Eb%2FHnjlJiznrUM0Ad7ikSMatO8%2FPBnaIiCy9P932kY5hLU2zhNhJYePaEyAiACPzGwg7WaAlfKkwUvrC4PMIFNfjqnbBysH3FqnY1AHZNrVGESpQI2oWvaGIooKp%2BXYY0KEJ18HLl%2BDWnvUW6UGFxlFeBCuivqw%2BjA%2BBmxCIS7V814DQXeI%2FyoTxYwfh%2BYbgiWxmUakMRnC16wlQ5Q86PF34kwFwoAtF0HLuI%2FJ9YHhZuhzF%2FoyoImBrsUIxtdMMknvNhjMZrPDZmWdxGE2yerPyZdZVjCbnRb%2BgPrhYhcreNRFXslqctrZCzyiSHjyrM1pM7uMP1d%2FutZTn8uUbn4Y45lMmdPpZsKabCw1CtK8DhCL3vrUtmoTuhTk9MxExSUqzVrnURjSQj6KhZDUY4VZn7R5BKUpjm8mr9Uik8OOyhlnPPoOrkxU6AAxnd%2FS6PctXmDZd2hQQKNsQ01AiGllVutwOumiGgTAEg22mVXz5CRR5QOT7Dl%2FIBxpaUWtF%2FSDOFYtYzWLyRyOxenRoEi9bfUi65Sge920gt9CCvMVE7RqQJ9Ny66rVVQ%2FuGVR8dF3yJ%2FJxvvI4fvUJzSWOHvh9ffWGPS6Ze88aiZr71Nt96kPTaltObVWj9NEH9Lhaaxw0apysKUEWKKBuoeq9iC053mzxeF0e7RKRrkJjhCjYLO7ROaENTqs3ljiRFU68uV7Lr98z4mLdNx4%2BvIGOYa%2BUBfofmm9C7eod3CgD3mUaBGfCfQF%2BnQ6NzqQfbpPsSYLzCxt7Q%2BrvMPK%2B62uY37yAnkRMxmxWyY2l3Va1Pe4FuxGjGwytiXLHOf%2BnZPu%2F%2FfdXEshNE%2B4excWqauzyZnjd2cP0PDU4aNvTbj3jXBv7LtZw11boQ1aY0rQ6GxZvL1Iby%2Fi7aUpuIMZOuHmPezqL6vzdrG3Ntz8u%2BqP2dW71Xn3j83I1p%2FfhXijz9Zfx1yqYgDRoPUi91HMffTWPrx1cDLmciHFJTxYWdoIwMaDGzez3%2BKO3HfwPvKjBvL%2F29mvbjz2JmH%2F8T%2FhjGo%2FHhzpE8Dhyf4doHhkQS7I1gZQth48%2B1UO7%2FY3ck4DMYoHNx4k7wB8977HfuvoH7OaDJPxmRz8s6BJmbDOg6e2n5lWPhmr2HFOK6rHmh8zkfkH1IT6%2FeXsnrEN5CHOdfQV4lKvJnuq3BNj02sysUhxwyrMpQyuHkYYSCMdSHzmhiNbNor0gRI87Jmytef%2FgfwxUhHkpO4hWkrnNIrustkna14hTaTgcwhkWjGOOm4siXiHpCPA8ZWwd9fYnF2YKVYs7uLe9oapEDz2DBboq5tD3PkVyn%2FDtP%2Beh%2BbjzWJQPwQInRRCJxgVZaNzAnjh2kJtpJ2KmZIRRGDZKvv1xvh1oCSQfhSkRAHLo7hxwNR3r9mmfg4hm3v9NWTj9WriN40Qvq6%2Bd82NAGAJTm64Xm3lnqjU5orR4KCwykycPiuxtQGYRENnBY0qQorUaeJpKIvUwTj%2FCfWQ%2BrxOoOITT9xY%2Bz8OivohvYgS6%2Bir9ORNX8f3Cb1J3ZR2F9E1GWjnoLRzoEljdKHKHcRy5BJvytbJ5wQKM%2BOBHLpAOl3Iz9j9x5alTsAgtijxYGk5rOYlJLB2yDKRzvhATkpmTSnnaFa7RufNqo2ip4WhKw00BjsCsHqSCiPaPrhSYY6D02nHwel42EyAvxF4JSRinkcJi0LKVlsA%2FUOwSE5GQR8oLdZvJuMoVID6l1AcaLRfPdiIwyUVOjY6Fvj01BoOFZoPZ6acMrl2QNMQwVzR%2BrcdXcErev86MnqE5g4ni7RrxZib%2FiDGls6D33tGt6m0ztg3Y6Si5AvmsL1JSW6mD4ssWZ05fFxkyebSqSPUWuhaqSsNvRA%2BugpTIlktaFUZNOPVh40NeM7p0mNAbq8kah0btZJPUDlKxAv0MyFhtXRb%2BKNuB%2FqiehHuDmDWv6Zf0h7Ro3rTaLoJBtrL7LdpRA%2Fzcg%2B9TON79V72tbrsGVpdNi29ZmpOFdmKS9M%2Bs0s2Yf9njNWYqcZusVJHYY8mHkX9ycvam1DvZ5HwYrXOeA957UmwxWxW%2F08bhtBGgVYL3W4Nqu8GEuwB2h4ba2Bq75t%2FoJ5Ee2D8czGUj%2FqNBvUK2o9vZcQjXinOjTT2Nl7C5DBDhkYjbTklVy2zVgfau22Zsr85gZXmKXxWwniyaxsmuxpbaFPjaB4hp8%2F2kbAHLZenSX7APjRwiksPboc51OqdBCi8OEAOBxaTUZrJmw0E1M2LA6otsFjdXDsy3ZYkd6pbkrbp04nBLDm4a3wBwk6Hw%2BoWcmf98LHb8bAe29RsJz%2FTzLSAnZxmskzZr2sKvgz1mdNSH7cX3UCZjNb4yO2iLBVoplqcOMVKKuQS2K%2B5bsNHZSkB6KSiJJ%2BQtCxz0E4PotE0WrkAdJl1RfWOYq24AQswUxfQY6hQaVdrBs3YnMrZ6qFi3dwfrZx9dqU4Nu%2BRWx%2BBcXExLv4h%2FiF8RrA7zMlhLjeT9GHBJxAwTVKFYYLpw07iIpHhe847%2F6f3Xx%2BJPCIKnu94el1bdh47d4todHze0e1%2BmPt95x13HTzv%2FHuGouRhd9pxu4MXt5x7bOcWscf7oscoPjwxtx%2FXt4zeXwmf45qheXsnZUoj9AEvTWhfZmCCtYn7ku3ZWZRomJpQIPneYS6LzCvp9VE0K8Hrm0mGyRDpa5ElL3aIBo0%2FzcdjDt5JJAe5mPUlV25emfQRQi52SChkvOGQ9%2Fve0A4yvINuGIPFra9vO%2F2xq4ozu8zm7EFvzkEyWd7eEpC8Tc0WS1b9qSPnxXZ47JNSgI%2FyMekev%2F8eKQabAaliCpzU0y5F29qiVkt9%2FmIecKAefycTO%2FdGpUQ0hT1dJa2LPX2JMn16x0fcN26gj%2BPQus5Xdh2hT6TGf%2Brmou5zqN3DTDvatOoRdm%2BOSjvLIepN1vpUaQuB9gSReuO%2FWoubXH2riKkGtEKIfhT1Hc0cr6VwsAc0rqImJj5LSu%2Bph70UsGpsge5PcNXtnq4Gf4Ko%2BRPw%2BX2iiz4DoCXe1qF1I6D7wZZkW7tW5DmpjR6G2jzoPMxpXf0NOXQogjqBfeiVrewBrLccm7NVoZ2TKMNjLyqgm%2F6UM3IYr1Bt8I7VGns1IUa930XNoff%2FEk6BT4cTXTI0NiAfj5P%2BoIETYlShDj%2F69II6RvwEfJpAW5szBZvWqdgkdGz2eaXmUB2Xfd5gc%2BxjRgcYGEaH6J8nHBXqXKlo5tdEDCjkFH4qKibA7oO5NBX24FTYm8Zhl%2F3jsEs%2Bvc%2FFcWE3iJ5uclyIdSk7EVT14V14eNeuqTwjU645dRxSL40LxbPYojFBH%2FWjw97WALu3nvGPtVnRKrYubyW15zbomGnPATou%2FPTp0xiH7iQ8LuugxB0HG6Tx9va%2B%2FuR24ulr3749eZxpgXGQ1dvXrt2%2BGltmTZ4PcVrvOnkUOqaOQmfDHI%2B2pibMh1iy7e%2Bd43Ea24l%2F3Ay%2Fu4jRg8rHznD0fzPGCbhoVU8pZskUbNqmYtOuY%2FO8Vwo2hyOxRB2f573%2BYFO4pTX58RjFsdZV%2FgR8iufixD4BMqM0%2F7uODOrDE%2FAJ0fy1Tmb3OD7hTCnWDlpVhLIfNReQG5OUCVPZ40mzMBYQZHUG1MoE9wXkZhOtCwvi88aQH2P1fu%2BYzdJeVdpdNCkrgxn24wKRKTVhURho0V6s0zg%2BeWQYaz1OBySK0kgGhoqOS6Jziuf8K52TlSnyr0LHGhdIKiRpCGly35kQ1lZovrkQ9YIGaMhSp0N40mMdEeO6h46aUl7gX4vDw9GH%2FSmi%2BJzZybvlJsoMwRBWWdhdjFd%2FrJrUr%2Fjc%2B4w2kZy4vU6%2BEKW2Q%2FREzrvQuJJ34qc70gVBy%2F3j%2Bb06ri2gE%2BypYRvNgGaAiYtNmQYBlJiEcAucz5ZbaIlOSxgszBYXOoWVQBarUeQsPtdUHHfEtnhpSh11VmKTLh8%2BWHaf2enmKUFiUZ0gYf05yrI%2Fglan1ECUxiy6OlGAJ6hChM%2FuwdXxBKQJ4PxGnah4YtJ8GQ0W7R%2FW6n7w0YP8i%2FxJ9NkKl9MMN%2BpdVuyYqWLSqryjDWYWNtLVah4FzfkTw8A0PmtFkrFffymMqqobDJSSFZ9m4IxgfAQfdLqPsWhPXbaL%2B6xOSXsyIEclM4g2FNTxmECzMCSv7CBEi2A7Llv%2BBhm96Zonnrgm3dX13CVf%2BiU7sobMoBY3eX%2FF5cgHT3%2FwlGPj1b94IGBzbFLv0x8H2YAb%2BvzDDHXrhw%2Bh7kcbF4cBZjFo0BxWFA4zweSsiWBoyRMAhYl8lXzxzGsngqE5LygcX1UvDn6leM3TUyDBNYPouboXg33BAIMLoOhGSY5g01CSQ8OMnAlmmfrBE%2FgfvoSfld3d3bup92Cpuq9W73ot%2FE6B3w1rDuPx%2BmSsTMllCyL8oKg3eaI4pHQMfBJmw13bn%2BEvWDS9C%2B%2BS7lt0gTk7cP0pwS%2BeeeUjrMO86AL2QH695PItuiCs%2Fqi7m2TDFyzyR1rOGzr1s8ZTLnvkSt7Jsxcw%2Fw8un4kSeNpjYGRgYGBkcFz6%2F1NPPL%2FNVwZ5lg1AEYbzPJx6MPq%2F1f85rHdZJYBcDgYmkCgAaDML2XjaY2BkYGCV%2BN8CJq3%2BXWa9ywAUQQHzAYk7Bkt42mPcwaDBsoGBAQmnwNhMPQwMrBIImnETELsxMDDchOLFQD4LkPaA0CA5qP4TjF9A7P%2BfIGb9%2Fww1kw%2BIxSFq%2Fu%2BAYBS7YVgWiLOgasURehikoDQjlGYBmjEHqocRwgezWdD0ofgLB4aZeQJJzBQh9n8rkFbErvf%2FbKh%2BmNgUKL8Ui3qY%2BR1Q9glUOxl1oHbuBLIFgDQzDgzzJwuSv0H4DBBnIPGVoOEA4%2BcD3XsYiudjCRdQ3DwC0m5A2gpICyHCh1EPzc9eQCyKpJcDKp4IxExAzAoVZ0XCDCxHIPYwgMH%2FGwwBDJYMJ4DpRx0oxoQCEeAmmGRBEhFnQAUpYBMFIKz%2Fc1AhSOf%2FT%2F8%2FAeUqAVIarnx42mNgYNCCwjSGJfggowGjB2MAYwvjKsZzjP%2BYzJhmMJ1hesesw%2BzD%2FIWliDWFjY9Nic2F7RG7D%2FsG9hccUzgNOBM4p3BxcblwdXC94w7jnsD9jMeOZwOvDG8Z7yo%2BK74Evi6%2BO%2Fw3BHwEOgSuCfIJqglOERIRShC6JSwm3CfCI5IksktUTXSSWJLYLXEf8TbxFxIWEk0S%2ByR1JGdI%2FpHykqqR2iZ1TeqJtIi0gbSfdJv0NukH0l9k%2BGSiZJbIvJM1kj0kJyDXJfdG3k1%2BgfwB%2BUcKbApaCm4KeQoTFD4pZikeUXJSOqFcp7xORURlksozVRHVDtU5qptU36lFqXWo7VN7oc6nnqV%2BTsNCI0Vjh6aZ5hWtPK032hnam7R%2F6bjozNDdp%2FtML01vkt4vfSsDCYNthmqGdYaXjDiMdhgnGE8wkTJZZMpimma6z0zMLMZsizmXeYL5GvM%2FFgEWWywlLCMsp1lesZKwirNqs7pmrWNdYn3PxsEmw%2BaOrYvtCjsBOw%2B7PXbv7A3sZzkwOSQ4bHFUcuxw%2FODU5PTDucP5m4uNS4LLPABRwojtAAABAAAA2wCbABEAAAAAAAIAAQACABYAAAEAAQEAAAAAeNqtUs1OwkAYnBY0Eo0aSTj34MGLDSAiwsl4EP%2BJRtGjVCiVQomtVBKfwmfw4sWDR59A38On8GCcXRYkyMEYaXZ39ut8s7NTAMzhBRrEL44FzhFo0RjXK%2B56WMMS7hTWMYsHhSPYw7PCUeTwqfAEbrWswpPIak8KTyGhvSscI%2F5QeBqL%2BrzCM8QZhePEZwq%2FIqH3Pbwhqd%2BHYWjabrdddyyv5ZuW18QmPLTRxTUc2KgjgIFHjjSSSGGFqMK3BnZwgRbXXfI7xILfgMnKBlw%2BxpCCL3dVrlWuHc6XZG7x5ucooYhtnnqIAxyTV6SWixqHQ32bPUfk27hhRZySYmdSeinghKeX2VcYq%2FVTaXlE67cOjJG%2BU3kPn%2B89mcGwp5LU6O2%2Bq3UyA1iS3xl0mFjjXECTqg1qCk6NVXFyhYmbWJUjx9zTWP%2FjLcd%2FqfHVUD4mu11%2B5TZ9O8q1z6pAzX%2FjlOmyQueiGgwy2VeZ9v2lmZHIM4e8zDLPLDKD%2F2PmC43JiIUAAHjabdVV15RlAEbh2YBggd3drXO%2F89bYCPPZ3d0CioAoKnZ3d3d3YXd3x4E%2Fwp%2Bg%2BM32zPfkXjNrZj%2FPwTVrOmM6o8%2FfCzpF5%2F%2BePzsdxjC2M7YzsTOJcSzCeCawKIuxOEuwJBOZxFIszTIs2%2FmL5VieFViRlViZVViV1VidNViTtVibdViX9VifDdiQjdiYTdiUzdicLdiSregSCnqUVNQ0tPTZmm3Ylu3Ynh3YkcnsxBSmMmCEndmFXdmN3dmDPdmLvdmHfdmP%2FTmAAzmIgzmEQzmMwzmCIzmKozmGYzmO4zmBEzmJaUxnBidzCjM5lVnMZg6nMZfTOYN5nMlZnM18zuFczuN8LuBCLuJiLuFSLuNyruBKruJqruFaruN6buBGbuJmbuFWbuN27uBO7uJu7uFe7uN%2BHuBBHuJhHuFRHuNxnuBJnuJpnuFZnuN5XuBFXuJlXuFVXmMBr%2FMGb%2FIWb%2FMO7%2FIe7%2FMBH%2FIRH%2FMJn%2FIZn%2FMFX%2FIVX%2FMN3%2FId3%2FMDP%2FITP%2FMLv%2FIbv%2FPH%2BGkz58%2BZngnzZs3odrtThzu5%2B%2B8WC99w4xZuzy3dyq3dxm3dvjt5uMXIcKuRcYN5c2ePvqhGpoxu7WGNX2q6ox8eeImBlxh4iYGHDzx84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%2BwANawDs2wDs2xGwErADAxEzQ%2BAh4BFz4CHgIVFA4DBy4EZDhad3d5LDB7eHVYNkN5hKg%2BPqeEeUMDEEB2Uy0ESURFSAQtU3ZAOXmAf7JVVbJ%2FgHkAAAL%2FuABHBJUEsAAKAAwAAAMhEzMTIQETCQETAzdIAd%2BSApIB2P6Ckf6A%2FoKSlAIDIAGQ%2FnD%2B6v4%2FARP%2B7QG%2F%2Fj8BAAAAA%2F%2B4AEcElQSwAAoADAAWABgAsA0vsBMzsAHNsAQyAbAXL7EYASsAMDEDIRMzEyEBEwkBEwM3ExcHNxcnNyMnB0gB35ICkgHY%2FoKR%2FoD%2BgpKUAkPEScDDSsHrTU4DIAGQ%2FnD%2B6v4%2FARP%2B7QG%2F%2Fj8BAnSO4oyN5YzT0wAAAAEAAAAABLAEsAATAAAxNQE1IiY9ATQ2MhYdARQGIxUBFQGQJT%2Bw%2BLA%2FJQGQjwEBZJUzyHywsHzIM5Vk%2Fv%2BPAAANAAAAAASwBEwAAwAHAAsADwATABcAGwAfACMAJwArAC8AMwCwALIAAAArsATNsRggMjKwBy%2BwIjOwCM2wJDKwCy%2BwJjOwDM2wKDKwDy%2BwKjOwEM2wLDKwEy%2BwLjOwFM2wMDKwFy%2BxHjIzM7ABzQGwNC%2BwANawBM2zCAwQFCQXMrAEELEFASuzCQ0RFSQXMrAYzbAcMrAYELEZASuwHTKwIM2zJCgsMCQXMrAgELEhASuzJSktMSQXMrADzbE1ASsAsQwLERKxGhs5ObAPEbEcHTk5MDExESERJTM1IzUzNSM1MzUjNTM1IzUzNSMTIREhNSERIQEzNSM1MzUjNTM1IzUzNSM1MzUjBLD7tGRkZGRkZGRkZGTIAlj9qAJY%2FagCvGRkZGRkZGRkZGQETPu0ZGRkZGRkZGRkZPx8AZBkAZD8fGRkZGRkZGRkZAAAAAAEAAAAAARMBEwADwAfAC8APwBCALINAAArsCwzsATNsCQysB0vsDwzsBTNsDQyAbBAL7AA1rAQMrAJzbAYMrAJELEgASuwMDKwKc2wODKxQQErADAxNRE0NjMhMhYVERQGIyEiJhkBNDYzITIWFREUBiMhIiYBETQ2MyEyFhURFAYjISImGQE0NjMhMhYVERQGIyEiJh0VAZAVHR0V%2FnAVHR0VAZAVHR0V%2FnAVHQJYHRUBkBUdHRX%2BcBUdHRUBkBUdHRX%2BcBUdMgGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHf29AZAVHR0V%2FnAVHR0CbQGQFR0dFf5wFR0dAAAJAAAAAARMBEwADwAfAC8APwBPAF8AbwB%2FAI8AdgCyDQAAK7E8bDMzsATNsTRkMjKwHS%2BxTHwzM7AUzbFEdDIysC0vsVyMMzOwJM2xVIQyMgGwkC%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%2BwTDOwHM2wRDKwLS%2BwXDOwJM2wVDIBsGAvsADWsRAgMjKwCc2xFygyMrAJELEwASuxQFAyMrA5zbFIWDIysWEBKwAwMT0BNDY7ATIWHQEUBisBIiYRFBY7ATI2PQE0JisBIgYVPQE0NjsBMhYdARQGKwEiJgE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHQGQHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdMsgVHR0VyBUdHQGlFR0dFcgVHR0VyMgVHR0VyBUdHfz1yBUdHRXIFR0dAaXIFR0dFcgVHR0BpcgVHR0VyBUdHQAAAAEAHQAiBPIEKgAFAAATCQEnAScdAaMDMtT9oc4Bxv5cAzTU%2FaHPAAABAGoAagRGBEYACwAAEwkBNwkBFwkBBwkBagEa%2FubUARoBGtT%2B5gEa1P7m%2FuYBPgEaARrU%2FuYBGtT%2B5v7m1AEa%2FuYAAAMAF%2F%2FsBMQEmQATABsAJwC3ALIOAAArsBIvsBfNsBwvsCMzsB3NsCEyshwdCiuzQBwmCSuyHRwKK7NAHR8JK7AbL7ADzQGwKC%2BwAdawFc2wFRCxJgErsB4ysCXNsCAysiUmCiuzQCUjCSuyJiUKK7NAJhwJK7AlELEZASuwBc2xKQErsSYVERKyAhYbOTk5sCURsBI5sBkSswMXEBokFzmwBRGwBzkAsRcSERKwEDmxHQ4RErMABRUYJBc5sBsRshQZATk5OTAxEhAAIAAVFAcBFhQPAQYiJwEGIyICEBYgNhAmIAM1MzUzFTMVIxUjNRcBHAGQARxOASwHB20IFAj%2B1HeOyIPCARLBwf7uRmTIZGTIAe0BkAEc%2FuTIjnf%2B1AgUCG0HBwEsTgJs%2FvDCwQESwf5ZyGRkyGRkAAADABf%2F7ATEBJoAEwAbAB8AXQCyDgAAK7ASL7AXzbAbL7ADzQGwIC%2BwAdawFc2wFRCxGQErsAXNsSEBK7EZFREStQMCEBIcHSQXObAFEbAHOQCxEg4RErAJObAXEbAQObAbErUBAAcFHB4kFzkwMRIQACAAFRQHARYUDwEGIicBBiMiAhAWIDYQJiADITUhFwEcAZABHE4BLAcHbQgUCP7Ud47Ig8IBEsHB%2Fu5GAZD%2BcAHuAZABHP7kyI15%2FtUHFgdtCAgBLE4CbP7wwsIBEML%2BWcgAAAIAFwAXBJkEsAAbACsARQCwGC%2BwCs0BsCwvsADWsAfNsgcACiuzQAcDCSuwBxCxDAErsBPNsgwTCiuzQAwQCSuxLQErsQwHERKzFxgcIyQXOQAwMRM0EjcVDgEVFBYgNjU0Jic1FhIVFA4CIi4CARQWOwEyNjURNCYrASIGFRfSp2d8%2BgFi%2Bnxnp9Jbm9Xs1ZtbAd0dFWQVHR0VZBUdAli3ASg%2BpjfIeLH6%2BrF4yDemPv7Yt3bVm1tbm9UBDBUdHRUBkBUdHRUABABkAAEEsASxAAMABwALAA8AMACyBAAAK7EIDDMzAbAQL7AE1rAHzbAHELEIASuwC82wCxCxDAErsA%2FNsREBKwAwMTczESMBETMRMxEzETMRMxFkyMgBLMhkyGTIAQEs%2FtQB9P4MAyD84ASw%2B1AAAAIAGgAbBJYElgBHAFEAYgCwEi%2BwUM2wSy%2BwNs0BsFIvsADWsEjNsEgQsU0BK7AkzbFTASuxSAARErELPTk5sE0Rsw8VMzkkFzmwJBKxGS85OQCxUBIRErEHHTk5sEsRswMhJ0UkFzmwNhKxK0E5OTAxExQfAhYfAQcWFzcXFh8CFjMyPwI2PwEXNjcnNzY%2FAjY1NC8CJi8BNyYnBycmLwImIyIPAgYPAScGBxcHBg8CBgU0NjIWFRQGIiYaBpcCDhgDUC08hQUtMQUmKCIbLyYGLi8FhjgwUAMYDwGYBQWYARAXA1AsPIYFLTAGJigiGy8mBTIsBYU7LlADGQ0ClwYBZ36yfn6yfgJZISkmBjEsBYY7LlEDGg0ClwUFlwINGgNRLD2GBSwxBiYoIhwtJgYzKgWGOi9RAxkOAZgFBZgBDhkDUS86hgUvLgYmMBlYfn5YWX5%2BAAAABwBk%2F%2F8EsAUUABkAIwAnACsALwAzADcAiQCyIQAAK7AkzbIoMDQyMjKwJy%2ByKjI2MzMzsBvNsBcvsATNsQ4sMjKwLy%2BwCc0BsDgvsBrWsCTNsCQQsSUBK7AFMrAozbAsMrIlKAors0AlAAkrsCgQsSkBK7AwzbAwELExASuwLTKwNM2wDTKyNDEKK7NANBMJK7A0ELE1ASuwHc2xOQErADAxEzU0NjMhNTQ2MyEyFh0BITIWHQEUBiMhIiYTESERFAYjISImNzMRIxMzESMRITUhEzMRIxMzESNkDwoBEzspASwpOwETCg8OC%2FvmCw5kA4Q7Kf1EKTtkZGTIZGQBLP7UyGRkyGRkBAEyCg9kKTs7KWQPCjILDg78bgMg%2FOApPDwpArz9RAK8ASxk%2B7QCvP1EArwAAAAAAQABAAEFFQTdAAoALACyCQAAK7AEMwGwCy%2BwCdawCM2wCBCxBQErsATNsQwBK7EFCBESsAE5ADAxEwkBIxEhESERIREBApAChMj%2B1P7U%2FtQCWQKE%2FXz9qAGQ%2FnACWAAAAgBkAAAD6ASwAA4AEQAiALIMAAArAbASL7AB1rAGzbIGAQors0AGCAkrsRMBKwAwMTcRNDYzIREhERQGIyEiJgERAWQOCwHbAZAOC%2FyuCw4CWAEsGQR%2BCw7%2BDP1dCw4OAxIBLP7UAAADAAQABASsBKwACwATABkAggCwCi%2BwD82wFC%2BwF82yFxQKK7NAFxUJK7ATL7AEzQGwGi%2BwAdawDc2wDRCxFAErsBfNshcUCiuzQBcZCSuwFxCxEQErsAfNsRsBK7EUDRESswoDDhMkFzmxERcRErMJBA8SJBc5ALEUDxESswcADRAkFzmxExcRErMGAREMJBc5MDESEBIkIAQSEAIEICQSEBYgNhAmIBMRMxEzFQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqkdkyAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP9%2FgGQ%2FtRkAAAAAAL%2FnAAABRQEsAALAA8ALgCyAAAAK7AHM7AKL7AMzbAPL7ADzbIDDwors0ADAQkrsAUyAbAQL7ERASsAMDEjATMDMwMzASEDIwMTMwMjZAGv0RWiFNABr%2F3mKfIoMeAbqgSw%2FtQBLPtQAZD%2BcAH0ASwAAAAAAgAAAAAETASwAAsADwBKALILAAArsAzNsA8vsAnNsAEyAbAQL7AE1rAHzbIEBwors0AEAAkrsAcQsQ0BK7AKzbERASuxBwQRErECCTk5sA0RsQgMOTkAMDExESEBMxEhETMBIRElMzUjAer%2B3sgBLMj%2B3gHq%2FuGvrwGQASwB9P4M%2FtT%2BcMhkAAMAAQABBK8ErwAPABcAHgBjALINAAArsBPNsBcvsAXNAbAfL7AB1rARzbARELEZASuwHM2wHBCxFQErsAnNsSABK7EZEREStA0EEhcYJBc5sBwRsB45sBUStAwFExYdJBc5ALEXExEStQEICQAaHiQXOTAxEjQ%2BAjIeAhQOAiIuARIQFiA2ECYgAzMRMxEzAwFfoN703qBfX6De9N6gXPIBVPLy%2FqxQlsiW%2BgHe9N6gX1%2Bg3vTeoF9foAIC%2Fqzy8gFU8v5kASz%2B1P7UAAMABAAEBKwErAALABMAGgBhALAKL7APzbATL7AEzQGwGy%2BwAdawDc2wDRCxGQErsBjNsBgQsREBK7AHzbEcASuxGQ0RErQKAw4TFCQXObAYEbAVObARErQJBA8SFiQXOQCxEw8RErUBBgcAFRgkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAxsBIxEjEQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2Bqk%2F6%2BpbIAbYBRAESoKD%2B7v68%2Fu6goAJf%2Fqrz8wFW8%2F5iASz%2B1P7UASwAAAACAAAAAASwBLAACwATACkAsgkAACuwDM2wEDKwEy%2BwAs0BsBQvsRUBKwCxDAkRErIEAQ45OTkwMTUREyEbARQGIyEiJhMzFyE3MwMhyAMgxwEOC%2FuCCw7IyDIBLDLIYf2iGQHbArz9RP4lCw4OAebIyAH0AAMABAAEBKwErAALABUAGABGALAKL7APzbAUL7AEzQGwGS%2BwAdawDM2wDBCxEQErsAfNsRoBK7ERDBEStQQJCgMWGCQXOQCxFA8RErUBBgcAFhckFzkwMRIQEiQgBBIQAgQgJBMUFiA2NTQmIAYBEQUEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzAToBKQG2AUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FP%2BigGRyAABABcAFwSZBLAAHABTALAFL7ANzbARL7AZzQGwHS%2BwAdawD82wDxCxCgErsAnNsR4BK7EKDxEStQUEExQXGSQXObAJEbEVFjk5ALERDREStAEACRQVJBc5sBkRsBc5MDESFB4CMj4CNSMUBiAmEDYzMhcHIREHJiMiDgEXW5vV7NWbW5b6%2Fp76%2BrGIbpIBkJGdxnbVmwLO7NWbW1ub1Xax%2BvoBYvpRkgGQkXpbmwAAAAIAFwAABJkEsAAQACEAegCyEQAAK7AfL7AWzbIWHwors0AWGgkrsA0vsAXNsg0FCiuzQA0ACSsBsCIvsADWsBDNsBAQsRkBK7AazbEjASuxEAARErEREjk5sBkRtwcFCwoTFB8hJBc5sBoSsQkIOTkAsRYfERKwITmwDRGxCRI5ObAFErAHOTAxEzQ%2BAjMyFzcRITcmIyIGFQMRIQcWMzI2NTMUDgIjIicXW5vVdsadkf5wk3CHsfpJAZCTcIex%2BpZbm9V2xp0CWHbVm1t6kf5wk1D6sf2oAZCTUPqxdtWbW3oAAAoAZAAABLAEsAADAAcACwAPABMAFwAbAB8AIwAnAFAAsAgvsBgzsAnNsBoysAwvsBwzsA3NsB0ysBAvsCAzsBHNsCEysBQvsCQzsBXNsCUyAbAoL7AI1rIMEBQyMjKwC82yDhIWMjIysSkBKwAwMTMhESETESERJTUzFSc1MxUnNTMVJzUzFRMhNSE9ASEVJTUhFSU1IRVkBEz7tGQDhPzgZGRkZGRkZGQB9P4MAfT%2BDAH0%2FgwB9ASw%2B7QDhPx8ZGRkyGRkyGRkyGRk%2FahkZGRkyGRkyGRkAAIAAAAABEwEsAAZACMASgCyFwAAK7AgL7AJzQGwJC%2BwBdawGs2yBRoKK7NABQAJK7AaELEbASuwDs2yDhsKK7NADhMJK7ElASsAsSAXERKzBA4FGiQXOTAxNRE0NjsBNTQ2MyEyFh0BMzIWFREUBiMhIiYBITU0JisBIgYVOylkdlIBLFJ2ZCk7Oyn8fCk7AZABLB0VyBUdZAJYKTvIUnZ2Usg7Kf2oKTs7AuWWFR0dFQAAAAIAZAAABEwETAADABUAFwCyAAAAKwGwFi%2BwANawA82xFwErADAxMxEzERM%2BAR4CPgE3EQ4BLgMGB2RkZDyHeHxyamQpKHuEkId0WhQETPu0AZA8MA0hGwVPUQH0UUUKKCgKRVEAAAAAAwAAAAAEsASXACEAMQBBAGcAsi8AACuwPjOwJs2wNjKwDC%2BwHc0BsEIvsADWsAfNsAcQsSIBK7ArzbArELEyASuwO82wOxCxEAErsBfNsUMBK7EyKxESswwLHRwkFzkAsSYvERK0BxATFAMkFzmwDBGxCA85OTAxERQWOwEyNjURND4BIB4BFREUFjsBMjY1ETQuAiIOAhUTETQ2OwEyFhURFAYrASImJRE0NjsBMhYVERQGKwEiJg4LMgsOjeQBBuSNDgsyCw5jo97o3qNjyAwIoAgMDAigCAwCWAwIoAgMDAigCAwBEwsODgsBLH%2FRcnLRf%2F7UCw4OCwEsdN6jY2Oj3nT91QHMCAwMCP40CAwMCAHMCAwMCP40CAwMAAAAAgAAAMgEWAPoAAUAEQAAESEFEQUhATcnNxc3FwcXBycHASwBLP7U%2FtQCsI2NR42NR42NR42NAZDIAyDI%2FquNjUeNjUeNjUeNjQAAAAIAAADIA3AD6AAFAA8AEgABsBAvsA7WsAnNsREBKwAwMREhBREFISU3FhUUByc2NTQBLAEs%2FtT%2B1AK8RW9qQ1YBkMgDIMg5NYevqYU2boqSAAAAAAMAAAC6BGID9wAFAA8AHQA8ALAAL7ABzQGwHi%2BwDtawCc2wCRCxEwErsBrNsR8BK7ETCRESsxAWFx0kFzkAsQEAERKzCQ4TGiQXOTAxGQEhJRElATcWFRQHJzY1NDcXFhUUDwEXNzY1NC8BASwBLP7UAZJFb2pDVl4He3cHUQaOkAYBkQGQyPzgyAHJNYevqoU3boqRzQiXwb2WCEIIsuPmsggADQAAAAAEsASwAAcAEQAVABkAHQAhAC8AMwA%2FAEMARwBLAE8BAQCyAAAAK7EwRDMzsBLNsikxRTIyMrAaL7InK0wzMzOwG82yJS1NMjIysCIvsQIGMzOwI82wCDKwHi%2BxDkgzM7AhzbE0STIyAbBQL7Aa1rEFHjIysB3NsQMfMjKwHRCxMAErsQ0sMjKwM82wNTKwMxCxLgsrsCoysCXNsEAysi4lCiuzQC4iCSuyAQsPMjIysCUQsTcBK7FESDIysDvNsSZKMjKwOxCxTAErsEIysE%2FNsjk9RjIyMrFRASuxMB0RErUUFRgZND8kFzmxNyURErIoKTg5OTkAsSIbERKzExQ4OSQXObAjEbIEOjs5OTmwHhJACQUWGTY3PD1AQyQXOTAxMSERIzUjFSM1MzUhNTM1IxEhExEhEQERIREDNTMVAzM1IwE1IREzFSMVIzUjNTM1AzUzFQMzETMRITUjNTMRIRMRIREDNSEVATUzFRM1MxUB9MhkyGQBkGRk%2FgxkASz%2B1AEsyGRkZGQBLAEsyGTIZGRkZGRkyAEsyMj9qMgBLMgBLP7UZGRkAfRkZGRkZGQBLPu0ASz%2B1AK8ASz%2B1P2oZGQCvGT%2BDGT%2B1GRkZGTI%2FgxkZAPo%2FtT%2B1MhkAfT%2BcAEs%2FtT84GRkA4RkZP1EZGQAAAAACQAAAAAEsASwAAMABwALAA8AEwAXABsAHwAjAHAAsgwAACuyBBQcMzMzsA3NsRUdMjKyDAAAK7AFzQGwJC%2BwCNawC82wCxCxEAErsAwysBPNsA%2FNsBMQsRQLK7AXzbAXELEYCyuwG82wGxCxIAErsCPNsSUBK7EQCxESsQcGOTmxGxcRErEcHTk5ADAxNTMRIxM1IRUnETMRFzUzFScRMxEVNTMVNREzERU1MxUnETMRZGRkASzIZMhkZMhkZMhkyMgD6PtQZGTIA%2Bj8GMhbW8gD6PwYyFtbyAPo%2FBjIW1vIA%2Bj8GAAAAAIAAAAABLAEsAAHABMAKQCyBwAAK7ASL7AEzQGwFC%2BwAdawCc2xFQErALESBxESsgAGCzk5OTAxERM0NjMhCQEAFBcWMjc2NCcmIgcBDwoB2gK8%2Fgz92B0eUx4dHR5THgK8AdsKD%2F1E%2FgwD41QdHh4dVB0eHgAAAAMAAQAABd0EsAAHABMAGQAxALIHAAArsBczsBIvsATNsBQyAbAaL7AB1rAJzbEbASsAsRIHERK0AAYLFhkkFzkwMRsBNDYzIQkBABQXFjI3NjQnJiIHJTMJAScBAQEOCwHaArz%2BDP3XHh1UHR4eHVQdAgtkArz%2BDDIBwgK8AdsLDv1E%2FgwD41QdHh4dVB0eHrD9RP4MMgHCAAAAAAEAZAAABLAEsAAKAD8AsgAAACuwBy%2BwAs0BsAsvsADWsArNsAoQsQUBK7AEzbEMASuxCgARErICBwg5OTkAsQcAERKyAQQFOTk5MDEzETchEQcRIQchEWSvA51k%2FRJkAu4EAa%2F8GGQD6GT8GAAAAAABAMgAAARMBLEACgAAMwkBETQmIyEiBhXIAcIBwh0V%2FOAVHQG8%2FkUEfhQeHhQAAAADAAAAAASwBLAACwAXACcAWQCyJQAAK7AczbAKL7ADzbIKAwors0AKAAkrsAcysgMKCiuzQAMBCSuwBTIBsCgvsADWsAvNsAIysAsQsQgBK7AFMrAHzbEpASuxCAsRErMMDyciJBc5ADAxNREzFyE3MxEjNSEVExchNwMuASMhIgYHAzc%2BATMhMhYfARYGIyEiJshkAlhkyMj84DUoAlA%2BXgIQCv4%2BChACQiYCEwoB9AoTAiYCCwr9qAoLZAK8yMj9RMjIAtl8fAFaCw4OC%2FuBmAoODgqYCg4OAAAABAAAAGQEsARMAB0AJQAtADEAbwCwAy%2BwJc2wKS%2BwLc2wIS%2BwE80BsDIvsADWsB%2FNsh8ACiuzQB8vCSuwHxCxJwErsCvNsTMBK7EfABESsBk5sSsnERKzISQlICQXOQCxLSkRErMfIiMeJBc5sCERsS4xOTmwExK0CxkaLzAkFzkwMTUUFjMhMjY1ETQmKwEuBCsBIg4CDwEjIgYVADQ2MhYUBiICFBYyNjQmIiU1MxU7KQPoKTs7KZYEDzM3UyrIKVI6LgsMlik7AWSQyJCQyAY%2BWD4%2BWAFYZMgpOzspAlgpOwgbRTUrKTs7FRQ7Kf5wyJCQyJABIFg%2BPlg%2BXmRkAAIANQAABLAErwAeACIAHgCyAAAAK7ANM7AezbICDA8yMjIBsCMvsSQBKwAwMTMhNSIuAT8BIRcWBiMVITUmJy4BLwEBIwEGBw4BDwEBExcTNQFtKT4kE1wBh1IQKzUBoSIoEh4GBv5%2FXf5xGBwMKg8PAWuyLnRCFjYs6t4tV0JCASoTLg4NA%2Bb8EjAbDBoHBwHHAcmM%2FsMAAwBkAAADwwSwACAAKQAxAGUAsiAAACuwIc2yIAAAK7ABzbApL7AqzbAxL7ANzbANELALzQGwMi%2BwBNawIc2wKjKwIRCxLgErsBDNsCUg1hGwHM2xMwErsSUuERKwFjkAsSkhERKwHDmwKhGwFjmwMRKwEDkwMTM1PgE1ETQuAyc1BTIWFRQOAg8BHgQVFA4BIyczMjY1NCYrATUzMjY1NCYjZCk7AgkWJB8B13i6FyEiCwwIG0U0K3amT8ihWYB9Xp%2BLTGyom1kHMygDOxwXHQ0PB0cBsIw3XTcoCAcDDDNBdkZUkU3IYVRagWR7TVJhAAAAAAEAyAAAA28EsAAZACAAsgAAACuwAc2wGDKwCy%2BwDjOwDM0BsBovsRsBKwAwMTM1PgE3EzYmJy4BJzUhFw4DDwEDBhYXFchNcwitCihHBgkFAakCIToiGQUFgAowRzkHQy8DUTgkEwEDATk5CCMnJQwM%2FMc0PAY5AAL%2FtQAABRQEsAAJACUAfgCyGwAAK7AfL7ICBRYzMzOwDM2yHwwKK7NAHxAJK7AKMgGwJi%2BwAdawB82wBxCxCgErsCXNsCUQsR0BK7AYzbIYHQors0AYGgkrsh0YCiuzQB0bCSuwGBCxEAErsA%2FNsScBK7EKBxESsQUIOTkAsR8bERKwCTmwDBGwBDkwMSczESM3FyMRMwcTETMhMxEjNC4DKwERFxUhNTcRIyIOAxVLS0t9fUtLffqWAryWMhAVLiEiyGT%2BcGTIIiEvFBHIAyCnp%2FzgpwNjASz%2B1B0nFQkC%2FK4yZGQyA1ICCRUnHQAAAAIAIf%2B2BI8EsQAJACUAiQCyCAAAK7ACzbAfL7AWM7AMzbIfDAors0AfCgkrsA8yAbAmL7AK1rAlzbAlELEdASuwGM2yGB0KK7NAGBoJK7IdGAors0AdGwkrsBgQsRABK7APzbEnASuxHSURErMCCAkBJBc5sRAYERKzBAYHAyQXOQCxAggRErEABTk5sB8RsgEEGjk5OTAxPwEVITUXBzUhFQMRMyEzESM0LgMrAREXFSE1NxEjIg4DFSGnAyCnp%2FzgZJYCvJYyEBQvISLIZP5wZMgiIS4VEDN9S0t9fUtLA88BLP7UHScVCQL9djJkZDICigIJFScdAAAAAAQAAAAABLAETAAPAB8ALwA%2FAAA1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVHRUETBUdHRX7tBUdHRUDIBUdHRX84BUdHRUD6BUdHRX8GBUdHRUCWBUdHRX9qBUdMhQeHhRkFR0dFcgUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFQAEAAAAAASwBEwADwAfAC8APwAANRQWMyEyNj0BNCYjISIGFREUFjMhMjY9ATQmIyEiBhUTFBYzITI2PQE0JiMhIgYVERQWMyEyNj0BNCYjISIGFR0VBEwVHR0V%2B7QVHR0VBEwVHR0V%2B7QVHcgdFQK8FR0dFf1EFR0dFQK8FR0dFf1EFR0yFB4eFGQVHR0VAfQUHh4UZBUdHRX%2BcBQeHhRkFR0dFQH0FB4eFGQVHR0VAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wLS%2BwJM2wHS%2BwFM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYTNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJhM1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR1kHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHcgdFQJYFR0dFf2oFR0yZBUdHRVkFB4eAmxkFR0dFWQUHh7%2B6GQVHR0VZBQeHgJsZBUdHRVkFB4eAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wHS%2BwFM2wLS%2BwJM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0yZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAAAAAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AUgCyDQAAK7BMM7AEzbBEMrAdL7BcM7AUzbBUMrAtL7BsM7AkzbBkMrA9L7B8M7A0zbB0MgGwgC%2BwANayECAwMjIysAnNshgoODIyMrGBASsAMDE9ATQ2OwEyFh0BFAYrASImETU0NjsBMhYdARQGKwEiJhE1NDY7ATIWHQEUBisBIiYRNTQ2OwEyFh0BFAYrASImATU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0BLB0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHTJkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh78kGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgAABv%2BbAAAEsARMAAYACgAaACoAOgBKACAAsAAvsCYzsAHNsC4yAbBLL7FMASsAsQEAERKwBDkwMQM1MzUXBzUTMxEjExQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWOwEyNj0BNCYrASIGFWXJpqbIZGTIHRUCWBQeHhT9qBUdHRUBLBQeHhT%2B1BUdHRUB9BQeHhT%2BDBUdHRVkFB4eFGQVHQH0ZEt9fUv%2BDARM%2B%2BYUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRUAAAAABgABAAAFFQRMAA8AHwAvAD8AQwBKABcAskAAACsBsEsvsEDWsEPNsUwBKwAwMTcUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjsBMjY9ATQmKwEiBhUBETMRExc1MzUjNQEdFQJYFB4eFP2oFR0dFQEsFB4eFP7UFR0dFQH0FB4eFP4MFR0dFWQUHh4UZBUdAyBkIafIyDIUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRX75gRM%2B7QCJn1LZEsAAgAAAMgEsAPoAA8AEgAtALANL7AEzbAEzQGwEy%2BwANawCc2xFAErsQkAERKwEDkAsQQNERKxERI5OTAxGQE0NjMhMhYVERQGIyEiJgkBESwfAu4fLCwf%2FRIfLAOEASwBEwKKHywsH%2F12HywsAWQBLP2oAAADAAAAAASwBEwADwAXAB8AWQCyDQAAK7AfL7AbzbAXL7AEzQGwIC%2BwANawEM2wEBCxGQErsB3NsB0QsRUBK7AJzbEhASuxHRkRErARObAVEbETEjk5ALEfDRESshATFTk5ObAbEbAUOTAxNRE0NjMhMhYVERQGIyEiJj8BBScBExEhEjQ2MhYUBiIaEgRYExkZE%2FuoEhpk9wEqSgEl7PwYbE5wTk5wLAP0EhoaEvwMEhoa7baDnAE%2B%2FuAB9P7OcE5OcE4AAgCU%2F%2FMEHAS9ABQAHgA9ALINAAArsB0vsATNAbAfL7AA1rAVzbAVELEbASuwCM2xIAErsRsVERKxDQQ5OQCxHQ0RErIHABg5OTkwMRM0PgEzMh4BFAcOAQ8BLgQnJjcUFjMyNjQmIgaUedF6e9B5SUm7OTkKImNdcys%2Fwpdqa5eX1pYC6XzXgX7V9pVy9kJCCSJrb6BLi5Zrl5fWlpcAAAIAAQABBK8ErwAPABUASQCyDQAAK7ATzbAUL7AFzQGwFi%2BwAdawEc2wERCxEwErsAnNsRcBK7ETERESsQ0EOTmwCRGxBQw5OQCxFBMRErMBCAkAJBc5MDESND4CMh4CFA4CIi4BEhAWMxEiAV%2Bg3vTeoF9foN703qBN%2B7CwAd703qBfX6De9N6gX1%2BgAgn%2BnvoDVgACAHUABAPfBQ8AFgAlAAATND4DNx4GFRQOAgcuAjceARc3LgInJjY%2FAQ4BdURtc3MeFUlPV00%2FJU5%2Bmk9yw4B%2BDltbEAcWLgoPAgkJXDcBll64oZ3FYEePdndzdYZFWZlkOwQGXrh%2BUmwaYgYWSihJjTQzbpYAAAADAAAAAATFBGgAHAAhACYAVwCyGgAAK7APzbAIL7AEzQGwJy%2BwANawDM2wDBCxEwErsBbNsSgBK7ETDBESswYdHiAkFzmwFhGxHyI5OQCxCA8RErMVHR8hJBc5sAQRsyAiIyUkFzkwMRkBNDYzBBcHISIGFREUFjMhMjY9ATcVFAYjISImJTcBJwkBFzcvAeulAW4fuv7JKTs7KQH0KTvI66X%2B1KXrAbShAZxy%2FmsB%2BXFxFVwBkAEspesGCLo7Kf4MKTs7KX3I4aXr62oyAZxx%2FmsB%2BHFxVRwAAAAAAgAAAAAElQRMABwALgBIALIaAAArsBDNsCIvsCfNsAkvsATNsAQQsAbNAbAvL7AA1rANzbEwASsAsSIQERKyFR0kOTk5sQkaERKwJTmxBAYRErAmOTAxGQE0NjMhFwYHIyIGFREUFjMhMjY1NxUUBiMhIiYBPgMfARUJARUiDgXrpQEFAoVVkSk7OykB9Ck7yOul%2FtSl6wGnHmdnXx4dAWj%2BmQcYSENWQzkBkAEspetQIFg7Kf4MKTs7KZk1pevrASEmNBMJAQHRAUQBPtgCDhczQ20AAAAAAgAAAAAEqARMAB0AIwBSALIbAAArsBDNsAkvsATNAbAkL7AA1rANzbANELEUASuwF82xJQErsRQNERKzBx4fIiQXObAXEbAhOQCxCRARErMWHyIjJBc5sAQRsSAhOTkwMRkBNDYzITIXByEiBhURFBYzITI2PQE3FRQGIyEiJgkCJwEn66UBLD1Csv6jKTs7KQH0KTvI66X%2B1KXrAVYBGwI3if5SkgGQASyl6xexOyn%2BDCk7OylFyKml6%2BsBjf7kAjeJ%2FlGTAAABAAAAAQSwBLEAFwBFALISAAArsBYvsA4zsALNsAkyAbAYL7AU1rADMrAQzbAIMrEZASuxEBQRErEGEjk5ALEWEhESsQ0XOTmwAhGxAAw5OTAxEQEVMzUjCQEjFTM1CQE1IxUzCQEzNSMVASzIyAEsASfDyAEs%2FtTIw%2F7Z%2FtTIyAJbASjGyAEs%2FtTIxv7Y%2FtTGyP7UASzIxgAAAAABAMgAAAOEBEwAEwAdALIRAAArsAszAbAUL7AA1rANzbAIMrEVASsAMDE3ETQ2OwEyFhURAREBERQGKwEiJsgdFWQVHQH0%2FgwdFWQVHTID6BUdHRX%2BSwHn%2B7QB6P5KFR0dAAAAAQAAAAAEsARMABcAHwCyFQAAK7ENDzMzAbAYL7AA1rARzbAIMrEZASsAMDE1ETQ2OwEyFhURAREBEQERAREUBisBIiYdFWQVHQH0AfT%2BDP4MHRVkFR0yA%2BgVHR0V%2FksB5%2F4ZAef7tAHo%2FhgB6P5KFR0dAAABAIgAAASwBEwABgAUALIGAAArsAQzAbAHL7EIASsAMDETAREBEQERiAI0AfT%2BDAImAib%2BGQHn%2B7QB6P4YAAAAAQDIAAAETARMAAIAADMJAcgDhPx8AiYCJgAAAAIAyABkA4QD6AAPAB8AADcUFjsBMjY1ETQmKwEiBhUBFBY7ATI2NRE0JisBIgYVyB0VyBUdHRXIFR0BkB0VyBUdHRXIFR2WFR0dFQMgFR0dFfzgFR0dFQMgFR0dFQAAAAEAyABkBEwD6AAPAAA3FBYzITI2NRE0JiMhIgYVyB0VAyAVHR0V%2FOAVHZYUHh4UAyAVHR0VAAAAAQAAAAAEKARMAAYAFACyAAAAK7AFMwGwBy%2BxCAErADAxMREBEQkBEQH0AjT9zARM%2FhkB5%2F3a%2FdoB6AAAAQAAAAAEsARMABcAHwCyAAAAK7EQFjMzAbAYL7AU1rAEMrANzbEZASsAMDExEQERARE0NjsBMhYVERQGKwEiJjURAREB9AH0HRVkFR0dFWQVHf4MBEz%2BGQHn%2FhkBtRUdHRX8GBUdHRUBtv4YAegAAAEBLAAAA%2BgETAATAB0AsgAAACuwDjMBsBQvsBLWsAIysAvNsRUBKwAwMSERARE0NjsBMhYVERQGKwEiJjURASwB9B0VZBUdHRVkFR0ETP4ZAbUVHR0V%2FBgVHR0VAbYAAAIAZADIBLAEKAAPABIAEgCwDS%2BwBM0BsBMvsRQBKwAwMTc1NDYzITIWHQEUBiMhIiYRIQFkHRUD6BUdHRX8GBUdBEz92vpkFR0dFWQVHR0BDwI0AAEAuQAHA%2FkEqQAFAAATATcJASe5AlDw%2Fp8BYfACV%2F2w8AFhAWHwAAABARD%2F0gRSBHQACAAAJQkBNwEXBxUBARABYf6f8QI8FQH9sMIBYQFh8P3FFgEB%2FbEAAAAAAgADAAMErQStAAsAFwBCALAKL7AOzbAVL7AEzQGwGC%2BwAdawDM2wDBCxEQErsAfNsRkBK7ERDBESswQJCgMkFzkAsRUOERKzAQYHACQXOTAxEhASJCAEEhACBCAkEzMVMzUzNSM1IxUjA6ABEwFEAROgoP7t%2Frz%2B7YnIyMjIyMgBtgFEAROgoP7t%2Frz%2B7aCgAVHIyMjIyAACAAMAAwStBK0ACwAPAEkAsAovsAzNsA8vsATNAbAQL7AB1rAMzbAMELENASuwB82xEQErsQ0MERKzBAkKAyQXOQCxDAoRErEHADk5sQQPERKxAQY5OTAxEhASJCAEEhACBCAkEyE1IQOgARMBRAEToKD%2B7f68%2Fu2JAlj9qAG2AUQBE6Cg%2Fu3%2BvP7toKABUcgAAAAAAgADAAMErQStAAsAFwAyALAKL7AEzbAEzQGwGC%2BwAdawB82wB82xGQErsQcBERKxDBA5OQCxBAoRErENFTk5MDESEBIkIAQSEAIEICQTFzcXNyc3JwcnBxcDoAETAUQBE6Cg%2Fu3%2BvP7tU9WNjdWOjtWNjdSNAbYBRAEToKD%2B7f68%2Fu2goAEo1Y6O1Y2N1I2O1Y0AAAIAAwADBK0ErQALABEAMgCwCi%2BwBM2wBM0BsBIvsAHWsAfNsAfNsRMBK7EHARESsQwOOTkAsQQKERKxDQ85OTAxEhASJCAEEhACBCAkEwkBJwcnA6ABEwFEAROgoP7t%2Frz%2B7WsBFAGbr%2BxmAbYBRAEToKD%2B7f68%2Fu2goAGE%2FusBm67sZgAAAAADAAMAAwStBK0ACwA4ADwAbACwCi%2BwOc2wPC%2BwJ82wIS%2BwG82wNC%2BwBM0BsD0vsBLWsB7NsB4QsS4BK7AHzbE%2BASuxHhIRErEhNDk5sC4RtAkEKDo7JBc5ALEnPBESsQcAOTmwIRGwKjmwGxKyDA8uOTk5sDQRsQYBOTkwMRIQEiQgBBIQAgQgJBMzMhYyNjQ%2BBToBMzIWFRQGBw4EFzM%2BBDU0LgMjIg4CEzM1IwOgARMBRAEToKD%2B7f68%2Fu3JjwQPBwYCBQIJBA4EEwMTFggXBQ8nHRgByAUSLSIcIzFEMRsyUUUmiMjIAbYBRAEToKD%2B7f68%2Fu2goAIZAgYMCgcFAwIBFBAWDBABBBcfPSYDCikyWDIzTCgYBhg1YP4uZAADAAMAAwStBK0ACwAVABkAOwCwCi%2BwDM2wFS%2BwDjOwEs2wES%2BwFs2wGS%2BwBM0BsBovsRsBKwCxEhURErEHADk5sRYRERKxBgE5OTAxEhASJCAEEhACBCAkNyE1IxEhFTMVIxMzNSMDoAETAUQBE6Cg%2Fu3%2BvP7t7QGQZP7UZGRkyMgBtgFEAROgoP7t%2Frz%2B7aCgiWQBLGTIAZBkAAACAAAAAASwBLAAGAAvAGkAshQAACuwEi%2BwH82wHDKwAC%2ByDhkhMzMzsAHNsgwjLTIyMgGwMC%2BwFNayBRwpMjIysBPNsgceJzIyMrExASuxExQRErMiIy4vJBc5ALESFBESsBU5sQAfERKwHTmwARGyHigpOTk5MDERNTM%2BATc1MxUeAhczFSMOAQcVIzUuASczHgEXNTMVNjcjNTMuAScVIzUOAQczFcMfh4vINnZrEsvLGbdZyIyIHmAYb0vIlTTJyBllSshLbhjRAfTIfZUayMgUUINFyGaoIcXFG5d9SW0Yzs4wnshKahjMyxdsSMgAAAAAAwAEAAQErASsAAsAEwAfAEYAsAovsA%2FNsBMvsATNAbAgL7AB1rANzbANELERASuwB82xIQErsRENERK1BAkKAxQaJBc5ALETDxEStQEGBwAXHSQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiADNyc3FzcXBxcHJwcEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqpJh4dth4dth4dth4cBtgFEARKgoP7u%2Frz%2B7qCgAl%2F%2BqvPzAVbz%2FduHh22Hh22Hh22HhwAAAAMABAAEBKwErAALABMAGQBGALAKL7APzbATL7AEzQGwGi%2BwAdawDc2wDRCxEQErsAfNsRsBK7ERDREStQQJCgMUGCQXOQCxEw8RErUBBgcAFxkkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAzcXNxcBBKABEgFEARKgoP7u%2Frz%2B7hbzAVbz8%2F6qa41XzI7%2BpgG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BI41XzY7%2BpwAAAAMABAAEBKwErAALABMAGwBGALAKL7AWzbARL7AEzQGwHC%2BwAdawDM2wDBCxGQErsAfNsR0BK7EZDBEStQQJCgMPFCQXOQCxERYRErUBBgcADhskFzkwMRIQEiQgBBIQAgQgJBMUFwEmIyIGExYzMjY1NCcEoAESAUQBEqCg%2Fu7%2BvP7uFj4COGR0q%2FPNYXCr8zsBtgFEARKgoP7u%2Frz%2B7qCgAbRzZAI3PvP98jvzq3BhAAAAAAEAAABjBLAD6AAGABoAsAUvsALNAbAHL7EIASsAsQIFERKwADkwMREBESERIRECWAJY%2FagCIwHF%2FtT%2B1P7TAAABAAAAYwSwA%2BgABgAaALAAL7ABzQGwBy%2BxCAErALEBABESsAQ5MDEZASERCQERAlgCWP2oAZABLAEs%2Fjv%2BQAEtAAAAAAEAzAAABEoEsAAGAB8AsgUAACsBsAcvsAXWsATNsQgBK7EEBRESsAE5ADAxEwkBIREhEcwBwgG8%2Ftb%2B1AJYAlj9qP2oAlgAAAEAaAAAA%2BYEsAAGAB8AsgYAACsBsAcvsAHWsATNsQgBK7EEARESsAY5ADAxEyERIREhAWgBKAEsASr%2BPwJYAlj9qP2oAAAAAAEAAADHBLAETAANAAA1PgM3EQkBEQ4DBkaJ55wCWP2oX7CkgsiE1a1nCAEP%2Fjv%2BQAEtAiREdQAAAgAAAAAEsASwAAYADQARALIAAAArAbAOL7EPASsAMDExERcBFwEXExcBFxEhF4EBJo7%2B2oHrjgEmgf5wgQGQgQEmjv7agQMJjgEmgQGQgQACACIAIwSOBI4ABgANAAA3ASchEScJAREXARcBFyIBJ4EBkIH%2B2QGogQEnjv7ZgbABJ4H%2BcIL%2B2QI1AZCBASeN%2FtmCAAMAFwAXBJkEmQAPAB8AIwBPALANL7AgzbAjL7AUzbAdL7AFzQGwJC%2BwAdawEM2wEBCxGQErsAnNsSUBK7EZEBEStQUMDQQhIyQXOQCxFCMRErEJADk5sB0RsQgBOTkwMRI0PgIyHgIUDgIiLgEBEx4BOwEyNjcTNiYrASIGEzM1Ixdbm9Xs1ZtbW5vV7NWbAVY6BCMUNhQjBDoEGBTPFRgwyMgB4uzVm1tbm9Xs1ZtbW5sCRv7SFB0dFAEuFB0d%2FcVkAAAFAAAAAASwBLAAJgAqADAANAA7ADQAsicAACuwMTOwKs2wMjIBsDwvsDHWsAUysDTNsAcysT0BK7E0MREStAsMEzU6JBc5ADAxETMVIREzESE1MzUjNjQmLwEuASMiDwEGByYvASYjIgYPAQ4BFBUjEyERIRMiNj8BFxMRIREBNx4DI2QBkMgBkGRvAQMCIgs9JyAd7xYSExXuIR0nPQojAgJvZAGQ%2FnBkAyITEtXbAZD%2Bj8oFDiASAgMgyAEs%2FtTIZAELEwisJzARkA0WGAyQEi4msQgUCgH8fAGQAfRgMC%2B%2F%2FHwBkP5wA4TFDClXOQACAAD%2F6gSvBLAAGwAyABcAsgAAACsBsDMvsCfWsA%2FNsTQBKwAwMRU1Ny4CPgE3PgU3FAIOBC4CIwc2Fjc2JT4DNz4BJyYiBgcOAQ8BBAfYCQgDFTguL2llmonoaCxKaHGDeHtcUw9jEidDNwE4RmFrWykWBAgHFCERI509Pf6PWRaPwTU8gGKCOzxVMy0eOR69%2FszQm1UzCQYTDzd%2FDVNCqCY%2FX4BUMhQJBR0ZM3MgIMXMAAABAG8ADAREBOcASAAjAAGwSS%2BwAdawRc2wRRCxPAErsUoBK7E8RRESsTo2OTkAMDESFBceARcWPgM3PgEnHgEHDgEHDgQeAT4BNz4ENzYCJxYXFicmJy4CNw4EFx4DDgQHBi4CNw4BbwUJRkYfQjo4KA8gDhRPVhEFHxYKCQ8DAwgOGSQYOURrQ0APJqWkFhUnRw8ST1MFMw0qZ0ouDwIMBAgBAQsQGhImOhcHDjQ%2FAblCHjh%2FLRUKJT49HkLtJ1CoZCFJLBMUIA8XCAsBBAYUHD1DbkOsAVNtLFWfBQIHIYbZlQgfZm2nUww7GzQbKBcZEAQKLk1WIC5uAAAD%2F8MAfQTtBDMAIQA%2FAEcAQwCwGi%2BwKc2wOi%2BwCc0BsEgvsDzWsDfNsUkBK7E3PBESQAoJGRoIKSg1PkBDJBc5ALEJOhEStwARJC41PkJHJBc5MDEDNz4GMh4FHwEHDgYiLgUnNx4FMj4ENy4EJxYVFAYiJjU0NwYXFhc3LgEvAT0aBhxGT3N2k5CTdnNPRhwGGhoGHEZPc3aTkJN2c09GHAabB0MtW1R6gHdSWSxICwE3HTo5HjGw%2BLAuZoUxaWklTBMUAlgoCihXVGBHLy9HYFRXKAooKAooV1RgRy8vR2BUVygKKApgPV44KygzXDtoDgFJJUU6GUpZfLCwfFVJV3N8Q2kYYCQkAAAABP%2FDAAAE7QSwABYAIAApAEEAoQCyDwAAK7AOMwGwQi%2BxQwErsDYauj3v790AFSsKsA8uDrAMwAWxDgH5DrANwLAPELMLDwwTK7MQDwwTK7MZDwwTK7MaDwwTK7MkDwwTK7MlDwwTK7IQDwwgiiCKIwYOERI5sBk5sBo5sCQ5sCU5sAs5ALcLDA0QGRokJS4uLi4uLi4uAUAKCwwNDg8QGRokJS4uLi4uLi4uLi6wQBoBADAxAzc%2BBjMyFzczASM3LgQnNxIXNy4BNTQ3BhcWFz8BLgEvAQE3PgY3Jic3HgIfAQcOBD0aBhxGT3N2k0g9PCWU%2FsaUJVKmcmknCpvStyVrjy5mhTFpLxceOg8OASgmFi0vIjATLwFhKydDgS4NGhoHJVplkwJYKAooV1RgRy8RjvtQjxVlZ3k4Dyj%2B5jaNEqduVUlXc3xDL1ccUhsa%2FaeRDyYyJj8YQAJ%2FMJI2j0AUKCgMNGtiZgAAAAP%2FngAABRIErAALABIAFwAAJhYzITI2JwEuAQcBNwkBITUjFREbATUjbxslBQ4lGxX9fhQ4FP1%2B9QG9Ab3%2Bp8hkZMhEREcgBCAiBSD71mQC0%2F0tZGQBkP7UASxkAAAAAAEAZAAVBLAEsAApAEgAsB4vsAnNAbAqL7Al1rAFMrAWzbALMrIWJQors0AWGAkrsiUWCiuzQCUjCSuxKwErsRYlERKxHR45OQCxCR4RErEWJTk5MDETNTQ2NwERNDYyFhURAR4BHQEUBiclERYdARQGLwEjBwYmPQE0NxEFBiZkFg8Ba1h8WAFrDxYYEf6ZZBoTXt5eExpk%2FpkRGAEGKRQxDgFFAVM%2BWFg%2B%2Fq3%2Buw4xFCkUDQz5%2FvlbFkAUEQlOTgkRFEAWWwEH%2BQwNABEAAAAABEwEsAAJABsAHwAjACcAKwAvADMANwA7AD8AQwBHAEsATwBTAFcAADUUFjMhMjY1ESE1ITU0JisBNSMVITUjFSMiBhUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUdFQPoFR37tARMHRWWZP4MZJYVHWRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZDIUHh4UAu5klhUdZGRkZB0V%2FEpkZMhkZMhkZP5wZGTIZGTIZGT%2BcGRkyGRkyGRk%2FnBkZMhkZMhkZP5wZGTIZGTIZGQAAAMAAAADBXgErgAKABAAGQBBALAAL7AYM7ABzbATMrALL7AIM7AQzbADMgGwGi%2BxGwErALEBABESsREWOTmwCxGzBw0SFSQXObAQErEGDjk5MDE9ASEBMzUJATUjCQEhFzcnIQE3FzM1CQE1IwEDAljxASz%2B1J%2F9qP6rAQN6jbX%2BqwKmjXqfASz%2B1PHIyAJYxv7Z%2FtTF%2FagCWHqOtP2VjnvG%2Ftn%2B1MUAAAEAAAAABLAETAASABoAsg4AACuwEC%2BwDDOwBM0BsBMvsRQBKwAwMRkBNDYzITIWFREUBiMhAREjIiY7KQPoKTs7Kf2s%2FtBkKTsBkAJYKTs7Kf2oKTv%2B1AEsOwAAAwBkAAAETASwACUAKQAtAGAAsh8AACuwCc2yCR8KK7NACQEJK7AVMrAmL7AqM7AnzbArMgGwLi%2BwANawJjKwA82wKDKwAxCxEgErsCoysBfNsCwysS8BK7EDABESsCQ5sBIRsR4fOTmwFxKwGTkAMDETNSEVFBcWFxYzMj4GJzQ9ASEVFA4FIi4FGQEhESERIRFkASwGEVUnNSU7KR8RCwMCAQEsBhgnTWWdwJ1lTScYBgEsAZABLAJYyPpxIFwZCwsUHCMoLC4YEQj6yCpSfmpxUDMzUHFqflIBVgEs%2FtQBLP7UAAAAAAH%2F4gC4BGgD3gAFAAADFwkBNwEe4wFgAWHi%2Fb4Bm%2BMBYf6f4wJDAAABAEYA2gTMBAAABQAAEwkBJwkBRgJEAkLi%2Fp%2F%2BoAMd%2Fb0CQ%2BP%2BnwFhAAAAAAL%2FOgBkBXYD6AAIABEAKACwBy%2BwBM0BsBIvsAfWsATNsRMBK7EEBxESsAE5ALEEBxESsA45MDEDCQEjESEXIREBFyERIwkBIxHGASsBLMsBgdf84AGU1wF9xgErASvIArwBG%2F7l%2FnDIAlgBLMj%2BcP7lARsCWAAAAAEAEgAABKoEsAAyAEYAsiIAACuwGTOwLM2wLBCwJs2xFR0yMrAvL7AEzbAQL7AJzQGwMy%2BwJNawH82wHxCxHAErsBfNsTQBK7EXHBESsC05ADAxEyY3NjMhNz4BOwEyFhQGKwEDDgIrARUUBiImPQEhFRQGIiY9ASMiJjU0NjMhNyEiJicSBQ8OGQOAJgUbEV4UHh4UNskCCB4SHx0qHf7UHSodMhUdHRUCFzD9hyAtBQOrGBITohEVHSod%2FD8EDRYyFB4eFDIyFB4eFDIeFBUdyCoWAAAAAAIAAAAABLAETAADAA8AIACyAAAAK7ABzbAEL7AFzbANMrAJzQGwEC%2BxEQErADAxMREhEQE1MzQ2MyEyFhUhFQSw%2B1DIOykBLCk7AfQDIPzgA4RkKTs7KWQAAAAAAgABAAAF3QRMAAMAEAAoALIAAAArsAHNsA8vsA3NsAUysAnNAbARL7ESASsAsQEAERKwBDkwMTMBIQkBETM0NjMhMhYVIRUhAQEsBLD%2B1PtQyDspASwpOwH0%2FBgCvP1EAZACWCk7OynIAAAAAQEuAAADggSwAAkAIQCyCQAAKwGwCi%2BwAdawB82xCwErsQcBERKxBAk5OQAwMQEzESMJASMRMwEBLsbGASoBKsbG%2FtYBLAJYASz%2B1P2o%2FtQAAAAAAQAAAS8EsAOCAAkAHACwCC%2BwAs0BsAovsQsBKwCxAggRErEABTk5MDERARUhNQkBNSEVASwCWAEs%2FtT9qAJYASrGxv7W%2FtfFxQAAAAQAAAAABLAEsAAPABkAHQAhAEkAsgwAACuwGs2wHjKwHS%2BwIDOwBc2wEC%2BwFM0BsCIvsBvWsB7NsB4QsR8BK7AJzbIfCQors0AfAAkrsSMBK7EJHxESsBk5ADAxPQE0NjMhMhYdARQGIyEiJhsBPgEzITIWFxMBMzUjFzM1IzspA%2BgpOzsp%2FBgpOx%2BsBSQUAqATJQWs%2Fo9kZMhkZGRkKTs7KWQpOzsBVQLjFictF%2F0k%2FtRkZGQAAAAD%2F5sAZASwBEwACwApADcAJgABsDgvsADWsAbNsAYQsSoBK7AyzbE5ASuxKgYRErEMGjk5ADAxAzU0Nj8BFS4EFzU8Az4FOwElESUjExYOASMiKwEiJicCARE0NjMyFhURFAYjIiZlMhkZBA4iGhbJAQICBAUHBMgCo%2F1dJi8CCgwPBQNTFB0ENwPoHRUUHh4UFR0CWDIYMg0N%2BgIHFRYhVfoCDAQKBAcDBQIB%2BvyuyP7sDAsBHBUBUf7iA1IVHR0V%2FK4UHh4AAAIASgAABGYEsAArADMANQCyLwAAK7AzzbApL7AfM7ADzbAYMrIpAwors0ApJQkrAbA0L7E1ASsAsSkzERKxLDE5OTAxEzQ2OwE3Ez4BNycmNjsBMhYHBhUeARcTFzMyFhUUBgcOBCMiJi8BLgEFHgEyNjcGIkobFBJ1Pw96UxIGEhReFBIGElN6Dz92ERQbGhIIHmRqn0998To6EhoBpww4RjgLMGwBXhUdrQFHTX4UIBMaFRMkARN%2FTf65rR0VFCgHAwsdFRIpFBQHKdwxPT0xBgAAAQAVABUEnAScABcAABMXBzcXNxc3Fyc3JzcnNwcnBycHJxcHFxXpTuAtm5st4E7qtLTqTuAtm5st4E7pswG9LeBO6bOz6U7gLZucLOFO6bS06U7hLJwAAAMAAABkBLAEsAADACIALgAaAAGwLy%2BwKNawFs2xMAErsRYoERKwFDkAMDE1MxEjARQ7ARY7ATI3EzY9ATQmIyE2PQE0JisBIgYPAgYVExE%2FATMVByEVAyMnyMgBLGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiMgCWP3zS2Q5AVgfK2QsUXYHlixRKBzGxBol%2FokBd9XUr%2BF9%2FolkAAAAAAMAAAAABLAETAADACIALgBwALIcAAArsCXNsBUvsAAzsCjNsC4vsAfNsAEysCwvsArNAbAvL7AA1rADzbADELEEASuwI82wIxCxJgErsBjNsBgQsSkBK7ARzbEwASuxJiMRErIIKCw5OTmwGBGwFTmwKRKwKzkAsS4cERKwKjkwMRkBMxE3ETQ7ATY7ATIXExYdARQGIyEWHQEUBisBIiYvAiY3HwEzNSchNQMjByPIZGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiGQBkAJY%2Fah9AZBLZDn%2BqB8rZCxRdgeWLFEoHMbEGiXU1a%2FhfQF3ZAAAAAMACABkBRUEVQADACIAQQB5ALAgL7AkzbAbL7ApzbAxL7AUzbABMrIxFAors0AxAAkrAbBCL7AA1rADzbADELEEASuwI82wIxCxLQErsBjNsi0YCiuzQC08CSuxQwErsS0jERK0DBEbFD8kFzkAsRskERKwIzmwKRGwGDmxFDERErIXPEE5OTkwMTcRMxE3ETQ2PwElNjMyHwEWFRQPASEyFhQGKwEDDgEjISImNxchEz4BOwEyNjU0JiMhKgIuBCcmNTQ%2FAScFCMhkHA4OAWoOCxEMbQ4LVQEuVWttVGuCBxsP%2FqsHpmRkASWDBhsPyxASEhD%2BNwELBAkDBwQEAgUKk1b%2BrcgCWP2oSwINESUKCeYGDHAOFBIOeUyQTv6tFieiG1kBUxUoHhUUHQEBAgMFAwwIDg23U%2BwAAAAD%2F5sAZQSwBFYAHgA4ADwAeQCwGC%2BwJM2wHS%2BwH82wOC%2BwA82wOjKyOAMKK7NAODkJKwGwPS%2BwAdawH82yHwEKK7NAHywJK7AfELEmASuwFM2wFBCxOQErsDzNsT4BK7EmHxEStAcMHAQpJBc5ALEdJBESsCY5sB8RsAA5sQM4ERKyAScsOTk5MDECNDYzBScmNTQ%2FATYzMhcFHgIVERQGIyEiJicDIyInMzIWFxMhNxElBxcWFRQHDgUqASMhAREzEWVsVQEuVQsObQ0QCw4BbQcUIawI%2FqsQGwaCa1QK3g8bBoMBJWv%2BqVeRCgUBBQMHBAkECwH%2BJAPpyAJDkEwBeRAQFQ1xDAbmBA0nEf3yDaEoFQFTZCkU%2Fq1ZAfbtU7gLDwsJAwUDAgEB%2FgwCWP2oAAAAAAMAYQAABEwFDgAbADYAOgBHALI3AAArsDjNAbA7L7AV1rA3MrApzbIpFQors0ApOgkrsDMysCkQsS8BK7AOzbE8ASuxKRURErESNjk5sQ4vERKwETkAMDEbAR4CMyEyNjURNCYnJTU0JiIGFREnJgYPAQYXNxcWNz4FPAE1ETQ2Fh0BFBYXBREHIQM1IRVh5gQNJxECDQ2iKBX%2BrU6QTHkPJQ5wFltTtxYZAwUDAgEBMjIoFQFTWf4JCAJYAs%2F%2BlQYTH6YHAVYPGwaDalRua1X%2B0lQMAQ1uFgtWkhINAQUDBwQJBAsBAcgWEhMVyhAbBoL%2B2mT%2BcMjIAAAAAAMAAgAKA%2B0FGAAdADQAOABFALA1L7A2zQGwOS%2BwCtawNTKwK82yKwoKK7NAKzgJK7AhMrArELEnASuwD82xOgErsSsKERKxDB85ObEPJxESsA05ADAxEwYfAR4BPwEUBhUUFjI2PQElPgE1ETQmIyEiBg8BAxMhFxEFDgEdARQGJjURPAEuAScmDwETNSEVAhAWcA0mD3kBTZBOAVMUKaIN%2FfMRJQoKmuwB91n%2BrBQoMjIDBwYYFriSAlgCSR8Wbg0BC1UzzS5UbG5UaoMGGw8BVgemHA4P%2FoIBU2T%2B2oIGHA%2FKFhISFgHICwcQCAMNEpICccjIAAAAAgAFAAAEsASrAA4AFQA6ALIMAAArsBDNsA8vsAXNAbAWL7AA1rAQzbEXASsAsRAMERKxCRI5ObAPEbEAEzk5sAUSsQgUOTkwMRM0PgIzMgQSEAIEICQCARchBwkBFQVfoN16ogEToKD%2B7f68%2FuygASUCASwCAZL%2BbgJVet2gX6D%2B7P68%2Fu2goAETAQrJwgEmASrFAAIAAAAABKsEqwAQABcAOACyDgAAK7AUzbAWL7AFzQGwGC%2BwFNawCs2xGQErALEUDhESsBI5sBYRsgoAETk5ObAFErAXOTAxETQ%2BAjMyHgIVFAIEICQCNwEnITchNV%2Bg3Xl63aBfoP7s%2Frz%2B7aDIAZICASwC%2FtQCVXrdoF9foN16ov7toKABE6X%2B2sLJxQAAAAIABQAABLAEqwAOABUAPgCyDAAAK7ARzQGwFi%2BwANawEc2wERCxEgErsAnNsRcBK7ERABESsQwPOTmwEhGxBRU5ObAJErELFDk5ADAxEzQ%2BAjMyBBIQAgQgJAIlMxEzETMBBV%2Bg3XqiAROgoP7t%2Frz%2B7KABJ8jIyP7UAlV63aBfoP7s%2Frz%2B7aCgAROl%2FtQBLAGQAAACAAUAAASwBKsADgAVAE0AsgwAACuwFC%2BwBc0BsBYvsADWsBXNsBUQsRIBK7AJzbEXASuxFQARErEMDzk5sBIRsQUQOTmwCRKxCxE5OQCxFAwRErIIABA5OTkwMRM0PgIzMgQSEAIEICQCJQkBIxEjEQVfoN16ogEToKD%2B7f68%2FuygAScBLAEsyMgCVXrdoF%2Bg%2Fuz%2BvP7toKABE6X%2BcAGQASz%2B1AAAAAAEAAUAAASwBKsAEACIAJgAmgB8ALIOAAArsCrNsE8vsIwvAbCbL7AA1rAUzbAUELFYASuwCs2xnAErsRQAERKwEjmwWBFADg4FEyEjJDxKVXiEhYmUJBc5sAoSQAoNIiYwO1pndpmaJBc5ALFPKhEStxYwNjxGSFVXJBc5sIwRQAkKABRYbIWNjpQkFzkwMRM0PgIzMh4CFRQCBCAkAhMGFgcUFgcyHgEXFhceAjcWBhcWFxQOARcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQcGJyY3NjceAhceAR8BNDYnJjY3PgM3JjcyFjI2Ny4DJzYnHgE%2FATYuAScGJw4DBwYmBw4BBwYWBw4BJT4BNxYyPgE3FBYVLgM3MwVfoN16ed2gX6D%2B7f68%2Fuyg%2BQgbBiIBDBYYCBhUFj45HQguAyotBgEFaHUeIiMDDi4NDkYRCT0gLhAyEAQBBikEAggZGhcTEwsGEAYoGwYMKA4OEwQEJQQFCgcYFgYQCB8SFwkKKSM%2FDAsJHzYMCwcvUg8TEg8rGj4IDz0PFT4DAxMBAzEBAwMaAwoRCxIHIgksHCSiAQJVet2gX1%2Bg3Xqi%2Fu2goAETAVkhdxwJRhkLEwQMHggvHgQSShRHCQYTCgwDcx0kPh8JAQcHEAsBAgsLIxcCLwINCAMWJhIdGR0cHhAGAQEHChMlCQgDSRUXKwoOKhQZCRITAwkLFycVIAcnBQ0DBQQkIxYMAwMMEgYKAQMHBgcnDwsXByJxcQwkBwoMEQQYVQECBgQMXwAAAAABAAAAAgSvBIUAFAAAPAE3ASY2NzYXBRc3FgcGJwEGIi8BDwJYIU5gpI7%2B%2FZH7DaR7gv2sDysPb48rEAJXZck2XGWK6H6vXEYv%2FawQEG4AAAYAAABgBLAErAAPAB8ALwAzADcAOwBQALAML7A0zbA3L7AFzbAcL7AwzbAzL7AVzbAsL7A4zbA7L7AlzQGwPC%2BwNdaxMTkyMrAJzbEYKDIysjUJCiuzQDUACSuxECAyMrE9ASsAMDE9ATQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYBITUhEyE1IRMzNSM7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTsCWAH0%2FgzIASz%2B1GTIyMRkKTs7KWQpOzsBuWQpOzspZCk7OwG5ZCk7OylkKTs7%2Fplk%2FgxkArxkAAACAGQAAARMBLAAAwAJACUAsggAACuwAC%2BwAc0BsAovsAjWsAfNsQsBKwCxAAgRErAEOTAxEzUhFQUhAREHEWQD6PxKA4T%2BosgETGRkZP4M%2FtTIAfQAAAAAAwAAAGQEsASwAAkAIQAlAGAAsAcvsAHNsAovsB0zsA7NsRgiMjKwJS%2BwE80BsCYvsA%2FWsCLNsCAysg8iCiuzQA8LCSuwADKwIhCxIwErsB4ysBjNshgjCiuzQBgcCSuwAjKxJwErALEOChESsB85MDE9ASEVFAYjISImGQE0NjMhNTQ2OwEyFh0BITIWFREhNSMVETM1IwSwOyn8GCk7OykBLDspyCk7ASwpO%2F4MyMjIyMjIKTs7AVUBkCk7ZCk7OylkOyn%2BcGRkAfRkAAAAAAQAAAAABLAEsAAGAA0AFAAbABQAsgAAACuwEjMBsBwvsR0BKwAwMTERFzcXBxcBNxc3JzchATcXNxEhNwM3JyERJweByI7Igf5wgciOyIH%2BcALZjsiB%2FnCByMiBAZCByAGQgciOyIEDIIHIjsiB%2FJmOyIH%2BcIEC5siB%2FnCByAAABgAAAAAEqASoAAsAFQAfACkAQgBMANIAsgoAACuwD82wHi%2BwSjOwGc2wRTKwKC%2BwOTOwI82wNDKyKCMKK7NAKEEJK7AUL7AEzQGwTS%2BwAdawDM2wDBCxFwErsBvNsBsQsSELK7AmzbAmELEqASuwPs2wPhCxQwErsEjNszdIQwgrsDHNsDEvsDfNsEgQsREBK7AHzbFOASuxJhsRErMKDhQDJBc5sT4qERKxLTw5ObE3MREStQkPEwQvOyQXOQCxHg8RErMHAAwRJBc5sBkRsyotPD4kFzmwKBKxBgE5ObAjEbEvOzk5MDEYARIkIAQSEAIEICQTFBYgNjU0JiAGFjQ2MhYVFAYjIjY0NjMyFhQGIyIXNDY%2FAiY1NDYzMhYUBiMiJwcWFRQGIiYlNDYyFhQGIyImoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzbR8uICAXFk0gFxYgIBYXUikfegEJIBcWICAWDg83ETNIMwEeIC4fIBYXIAGyAUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FOHLh8gFhcg5CwhIC4guiAxBX4BDg4WISAuIAqRFh0kMzNSFiAfLiAgAAAAAf%2FYADsEugSwAE8AOgCwBS%2BwJ82wIC%2BwFc2wNi%2BwSs0BsFAvsVEBKwCxJwURErA%2FObAgEbQLDxobMSQXObAVErEyMzk5MDECBhceATMyNz4CNzY3AT4BJyYnJiMiBgcBBxcBNjc2MzIXFgcBBiMiJicmPgI3NjcBPgIzMhceAQcGDwEDHwEBPgEnLgEnJiMiBwYHARsaMCN2Rj84IUApJygRAYojGA8bWhQJLkEj%2FnsHRQF5FBMXGyYPECT93TRJN1oJBQ8wJCYYFAFcND1rNhkXX3YIB1v8%2FQdFAgVDOBEQZk9FU2taKEf%2BAAHWvk45QBwQMSorLBEBiiNiL1cRAiEj%2FnQHQwF1FhAXJCck%2Fd00Qj8jPkAkJBUUAVw0NzUEEZtiZVv5%2FwAHPAH%2FQ7RdV4YkITcYR%2F4AAAAAAAIAUAA2BMMEWAAbADUAPQCwMy%2BwLTOwA82wBzIBsDYvsADWsBzNsBwQsSoBK7AKzbE3ASuxKhwRErMDBw8YJBc5ALEDMxESsAU5MDETNDYzMhc2MzIWFRQOAgcGDwEnLgInLgM3FB4BHwEWFzY%2FAT4CNTQmIyIPAScmIyIGUMWEj2Jnj4HCI1dDR8VgERArckZCR0NXI6o9PkAWXWFScQxAQz5gOUo6dnIzSDxjAxCDxYGBxYMuWmxHRr%2BDFxc6gUZBRkdsWi4bVkE%2BFlpvXG8MPkZYHEdhU6uuUGMAAAAAAgA5%2F%2FIEdwS%2BABgAMwAAExQfARYzMjcBNjQvASYnBxcBJzcnJicHBhMUHwEWFzcnARcHFxYXNz4BNTQvASYjIgcBBjlCjURbXUIBG0JCjQgLadT%2Be%2FdfEi4dN0LUQo0HDGnUAYX3XxIvHh0jN0KNQl1fQP7lQgFhX0COQkIBG0K6Qo0JCGnU%2Fnv4XxItODdCAQRdQo0HCmnUAYX3YBExMx0jaitdQo1CQv7lQAAAAAADAMgAAAPoBLAAEQAVAB0ARQCyDwAAK7AZzbAdL7ASzbAVL7AGzQGwHi%2BwANawEs2wEhCxEwErsAvNsAsQsBvNsBsvsR8BK7EbEhESsgYFFjk5OQAwMTcRND4CMh4CFREUBiMhIiY3IREhEhQWMjY0JiLIPGacqppkOjsp%2FagpO2QCWP2oxD1WPT1WZAO5FTIuHh4uMhX8Ryk7O%2FECvPzZVj09Vj0AAAABAAAAAASwBLAAGAARALIAAAArAbAZL7EaASsAMDExATcnIQEnJjQ3NjIXARYUBwYiLwEBEScHAS%2FP0gEsAQsjDw8OKg4BGw8PDioOJP7p1NABfNDUARckDioODw%2F%2B5g8qDg8PI%2F71%2FtTSzwADAScAEgQJBOEAMQA9AEMAlwCwLS%2BwKjOwBM2wPjKyLQQKK7NALSwJK7A7L7AfM7ASzbISOwors0ASEwkrAbBEL7AO1rAAMrAyzbABzbAyELEsASuyBBI6MjIysCvNshQfPjIyMrArELFAASuwJ82wHCDWEbAbzbFFASuxMgERErACObAsEbAJOQCxBC0RErApObA7EbYADhsnOkBDJBc5sBISsBU5MDEBMx4BFxEuASMuBDU0PgE3NTMVHgQXIy4BJxEXHgQVFAYHFSM1JicuARMUHgMXFhcRDgETNjU0JicBJ4sFV0oGEwIuQk4vIViCT2QmRVI8KwOfCDZKQCI8UDcosptkmFUoGagQESoUHAcEPUnqqlhSAbFNYw8BTwEGDhkvOVg3XIdDB05PBBMsP2lCSEsN%2Fs0OBxMsPGU%2Bi6oLTU4RVyhrAh4dLBgVBgcCAQESCDv9KxKFQEcZAAAAAQBkAGYDlAStAEMAjQCwMS%2BwKs2wAC%2BwHjOwAc2wHDKwEy%2BwC82yEwsKK7NAEw4JKwGwRC%2BwB9awOTKwGM2wJDKyGAcKK7NAGB4JK7IHGAors0AHAAkrsBgQsQ8BK7AOzbFFASuxGAcRErMCOEJDJBc5sA8RtQsfICoxMyQXObAOErAsOQCxKjERErEtOTk5sAARsSw8OTkwMRM1MyYnLgE%2BATc2MzIWFSM0LgEjIgYHBhUUHgEXMxUjFgYHBgc%2BATM2FjMyNxcOAiMiJgcOAQ8BJz4FNz4BJ2SmGBQKCQMvLWGmgcqZRFAkJVQUKSEXHvHFCBUVKTojYhUhjCFMPDIpTycqF9IyJ1YXGDcGFQoRDBEJMAwkAlhkMTcaO1ZeKFiydzRLHB0VLDkcUyozZDKCHTs2Cw4BIh6TGRcDQgQEGgwLkQQOBg0LEQo3j0cAAgACAAAErgSwAAYADQAfALIMAAArAbAOL7AM1rALzbEPASuxCwwRErAIOQAwMRMJASMRIxEJAiMRIxECASoBKsbIAZIBKgEqxsgBLP7UASwDhPx8AlgBLP7U%2FHwDhAAABQACAAAD6ASwAAYADAAWAB4AIgCmALIHAAArsAYzsArNsgcAACuwCM2wEy%2BwFM2xAAQyMrANL7AOzbAdL7AfzbIdHwors0AdFwkrsBoysCIvsBjNsAIyAbAjL7AB1rAEzbAEELEIASuxDRcyMrAKzbEdHzIysAoQsRUBK7EbIDIysBDNsQsZMjKzEhAVCCuwE82wEy%2BwEs2xJAErsQQBERKwBjmwCBGwBTkAsRQIERKwEDmwDRGwETkwMRMzETMRMwEhNTMVMxUBNSEVIxUjNTM1AxEhESM1IxU3MzUjAsbIxv7WAZBkyP7UASxjZGPIASxkZAFkZAEsA4T8fP7UyGRkAZBkyGRkZAEsAfT%2BDGRkyMgABQACAAAD6ASwAAYADgAUAB4AIgCgALIGAAArsQcKMzOwDS%2BwH82wIi%2BwCM2wDy%2BwEs2wEM2wGy%2BwHM2wFS%2BwFs2wAjIBsCMvsAHWsATNsAQQsQcBK7EPFTIysA7NsREfMjKwDhCxCwErsR0gMjKwCs2xExcyMrMaCgsIK7AbzbAbL7AazbEkASuxBAERErAGObAHEbAFOQCxIh8RErMBBAUAJBc5sRwQERKwGDmwFRGwGTkwMRMzETMRMwEhESERIzUjFQM1MxUzFQE1IRUjFSM1MzUDMzUjAsbIxv7WAZABLGRkZGTI%2FtQBLGNkY2NkZAEsA4T8fP7UAfT%2BDGRkArzIZGQBkGTIZGRk%2FHzIAAAEAAIAAARMBLAABgAMABIAFgBrALILAAArsAwvsBPNsBYvsAjNsA0vsA7Nsg0OCiuzQA0RCSsBsBcvsBHWsBDNsxMQEQgrsAfNsAcvsA0zsBPNsBAQsRQLK7ALMrAKzbEYASsAsQgLERK0AAIDBgEkFzmxDg0RErEFBDk5MDETCQEjESMRBREhESM1AzUzESMREzM1IwIBKgEqxsgCWAEsZMjIZAFkZAEs%2FtQBLAOE%2FHzIAZD%2BDGQD6GT%2BDAGQ%2FHzIAAAABAACAAAETASwAAYADAASABYAawCyCwAAK7AHL7AIzbASL7ATzbISEwors0ASEAkrsBYvsA7NAbAXL7AL1rAKzbMTCgsIK7ANzbANL7AHM7ATzbAKELEUCyuwETKwEM2xGAErALETCxEStAACAwYBJBc5sQ4WERKxBQQ5OTAxEwkBIxEjESU1MxEjEQMRIREjNSczNSMCASoBKsbIAljIZGQBLGRjZGQBLP7UASwDhPx8ZGT%2BDAGQAZABkP4MZGTIAAAAAAUAAgAABLAEsAAGAAoADgASABYAUgCwBy%2BwCM2wCy%2BwDM2wDy%2BwEM2wEy%2BwFM0BsBcvsA%2FWsgcLEzIyMrASzbAWzbIWDwors0AWCgkrs0AWDgkrsRgBKwCxCwgRErMCAwYAJBc5MDETCQEjESMRBTUhFQE1IRUBNSEVATUzFQIBKgEqxsgB9AH0%2FgwBkP5wASz%2B1MgBLP7UASwDhPx8yMjIASzIyAEsyMgBLMjIAAAABQACAAAEsASwAAYACgAOABIAFgBSALAHL7AIzbALL7AMzbAPL7AQzbATL7AUzQGwFy%2BwC9ayBw8TMjIysA7NsArNsgoLCiuzQAoSCSuzQAoWCSuxGAErALELCBESswIDBgAkFzkwMRMJASMRIxEFNTMVAzUhFQE1IRUBNSEVAgEqASrGyAH0yMgBLP7UAZD%2BcAH0ASz%2B1AEsA4T8fMjIyAEsyMgBLMjIASzIyAAAAAACAAAAAARMBEwADwAfACoAsg0AACuwE82wHC%2BwBM0BsCAvsADWsBDNsBAQsRcBK7AJzbEhASsAMDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFeulASyi7u2j%2FtSl68g7KQH0KTs7Kf4MKTsBkAEspevto%2F7UpevrQSk7OykB9Ck7OykAAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMtAe6iASyl6%2Bul%2FtSj7cg7KQH0KTs7Kf4MKTvIAU3%2BswGQASyj7eul%2FtSl6%2BtBKTs7KQH0KTs7Kf4M%2BvoAAAAAAwAAAAAETARMAA8AHwAiAD4Asg0AACuwE82wHC%2BwBM0BsCMvsADWsBDNsBAQsRcBK7AJzbEkASuxFxARErEgIjk5ALEcExESsSAhOTkwMRkBNDYzITIWFREUBiMhIiY3FBYzITI2NRE0JiMhIgYVFxsB66UBLKPt66X%2B1KXryDspAfQpOzsp%2FgwpO2T6%2BgGQASyj7e6i%2FtSl6%2BtBKTs7KQH0KTs7KWT%2BswFNAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMhA%2BulASyl6%2B2j%2FtSl68g7KQH0KTs7Kf4MKTtkAfT6AZABLKXr66X%2B1KLu7T8pOzspAfQpOzsp%2FnABTQACAAAAAAUUBEwABgAaADwAsgcAACuwCM2wAC%2BwAc2wES%2BwEs0BsBsvsAzWsBfNsRwBKwCxCAcRErAFObEBABESsAQ5sBERsAM5MDEZASE1CQE1EzUhMjY1ETQmIyE1ITIWFREUBiMBLAGQ%2FnDIAfQpOzsp%2FgwBkKXr66UBkAEsyP6i%2FqLI%2FnDIOykB9Ck7yOul%2FtSl6wAAAAEA2QACA9YEngAhACgAsA4vsBPNAbAiL7Ac1rAWzbEjASuxFhwRErAZOQCxEw4RErAPOTAxExYzIQIHBh8BMzI3NgAzNicmIwU2Ejc2LwEjIgcOAQAHBtkIFwEumwUFCQkJDgwLAg8BDgkIF%2F7TAZoCAgcJCQ8KBLz%2B80sQAgcT%2FkoUFQsIDw0CaRMREQEEAa8MDwwJEAbT%2FtFWEwAAAAACAAAAAAUUBEwAGAAfAD8AsgMAACuwCM2wCBCwBs2wGS%2BwGs2wEC%2BwEs2wFM0BsCAvsADWsAvNsSEBKwCxGgMRErEdHjk5sBARsBw5MDERFBYzITI3NSEiJjURNDYzITUuAS8BIgYVAREhNQkBNeulASwvNf4MKTs7KQH0DshdXaXrAlgBLAGQ%2FnABkKXrD7k7KQH0KTu5BAcCAuul%2FtQBLMj%2Bov6iyAACAAAAAASwBLAAHQAkAFQAsgMAACuwD82wFi%2BwGs0BsCUvsADWsBLNsBIQsQsBK7AHzbEmASuxCxIRErUYGR4fICQkFzmwBxGwIzkAsRYPERK1CAkeIiMkJBc5sBoRsB85MDERFBYzITI2PQEnBxUUBiMhIiY1ETQ2OwE3JyMiBhUlASchEScB66UBLKPtTno7Kf4MKTs7KZx2SmSl6wHwAWGVAfSV%2FqoBkKXr66ViSXuUKTs7KQH0KTt6TuulCQFWlf4Mlf6fAAMABAAEBKwErAALABMAGwBaALAKL7APzbAbL7AXzbATL7AEzQGwHC%2BwAdawDc2wDRCxFQErsBnNsBkQsREBK7AHzbEdASuxGRURErcECQoODxITAyQXOQCxFxsRErcBBgcMDRARACQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiACNDYyFhQGIgSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqhdyoHJyoAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BEqBycqByAAAAAwAAAAAETASwAAkAEAAUAC4AsgkAACuwEc2wFC%2BwBc2wCzIBsBUvsBLWsAjNshIICiuzQBIACSuxFgErADAxMRE0NjMhMhYVEQkCIREhEQEzNSMOCwQYCxD8GAG9AcL%2B2f7UAfRkZAETCw4PCv7tAyD%2BDAH0AZD%2BcP12MgAAAAADAAAAAARMBLAACQAQABQAKwCyCQAAK7ARzbAUL7AFzQGwFS%2BwEtawCM2yEggKK7NAEgAJK7EWASsAMDExETQ2MyEyFhURASERIREhCQEzNSMOCwQYCxD8GAEsASwBJ%2F5DAV5kZAETCw4PCv7tArz%2B1AEsAfT75jIAAAAAAwAAAAAETAR%2FAAkADwATAC4AsgkAACuwEM2wEy%2BwBc0BsBQvsBHWsAwysAjNshEICiuzQBEACSuxFQErADAxMRE0NjMhMhYVEQkCJwEnATM1Iw4LBBgLEPwYATECVJr%2BRpYChWRkARMLDg8K%2Fu0Cwf7PAlSb%2FkaX%2FToyAAQAAAAABEwEsAAJAA0AFAAYACsAsgkAACuwFc2wGC%2BwBc0BsBkvsBbWsAjNshYICiuzQBYACSuxGgErADAxMRE0NjMhMhYVEQEXNycDJRMHJwcXATM1Iw4LBBgLEPwYYdRhcAK5A%2FqV1JUBzmRkARMLDg8K%2Fu0D3GLVYfzgAQK775XUlf4NMgAEAAAAAARMBLAACQANABQAGAAuALIJAAArsBXNsBgvsAXNAbAZL7AW1rATMrAIzbIWCAors0AWAAkrsRoBKwAwMTERNDYzITIWFREBFzcnExcHFzcXCwEzNSMOCwQYCxD8fNRi1QPvldSV%2BQFjZGQBEwsODwr%2B7QJk1GHUAev6ldSU7QK5%2B%2BkyAAAAAAIAF%2F%2F%2FBLAErwAFAAgAFwCyBAAAKwGwCS%2BwBdawBs2xCgErADAxEwERCQERFwkBFwSZ%2FiX%2Byk8CoP1gAZ8DEPvJARD%2BdwGgzQOq%2FTgAAAAAAgAAAGQETASwABUAGQBNALARL7AGzbIRBgors0AREwkrsA4ysgYRCiuzQAYECSuwCDIBsBovsADWsBLNsAbNsBIQsQ8BK7ALzbEbASuxDwYRErIJFhc5OTkAMDE1ETQ2OwERIREzFxEUBisBESERIyImATM1Ix0V%2BgH0ZMgeFJb9RJYVHQJYZGSWA%2BgUHv7UASzI%2FK4VHQGQ%2FnAdA2fIAAMAAAA%2BBRQEsAATABkAHQBAALAPL7AGzbIPBgors0APEQkrsgYPCiuzQAYECSuwCDIBsB4vsADWsBDNsAbNsR8BKwCxBg8RErILFxg5OTkwMTURNDY7AREhETMXFQEnByERIyImJTcXARcBAzM1Ix0V%2BgH0ZMj%2B7Hh%2B%2FoaWFR0CRXt4AWF7%2FiXhZGSWA%2BgUHv7UASzI2v7teH%2F%2BcB2xe3gBYHv%2BJAOqyAAAAAADAAAABgUOBLAAEwAXACMAFQABsCQvsADWsBDNsAbNsSUBKwAwMTURNDY7AREhETMXEQcnASERIyImATM1IxM3JzcXNxcHFwcnBx0V%2BgH0ZMhnqv7W%2FreWFR0CWGRkZKqqf6qqf6qqf6qqlgPoFB7%2B1AEsyP7zZ6r%2B1v5wHQNnyPvVqqp%2FqqqAqap%2FqqoAAAADAAAAAASwBLAAEgAZAB0AbACwDi%2BwBs2yDgYKK7NADhAJK7AGELAMzbAaL7AbzbEECDIyAbAeL7AA1rAPzbIPAAors0APCwkrsAAQsAbNsA8QsRoBK7AdzbAMMrEfASuxGgYRErATOQCxDA4RErEXGDk5sRoGERKwCjkwMTURNDY7AREhETMXESEVIREjIiYlCQEjESMRAzUzFR0V%2BgH0ZMj%2BcP4MlhUdAlgBLAEsyMjIZJYD6BQe%2FtQBLMj%2B1Mj%2BcB2r%2FtQBLAEs%2FtQCvMjIAAAAAAMAAAAABLAEsAASABkAHQBbALAOL7AGzbIOBgors0AOEAkrsBovsBvNsQQIMjIBsB4vsADWsA%2FNsAbNsA8QsRoBK7AdzbEfASuxGgYRErATObAdEbANOQCxBg4RErILDBk5OTmwGhGwCjkwMTURNDY7AREhETMXEScBIREjIiYlMxEzETMJATUzFR0V%2BgH0ZMjI%2Ftb%2BbpYVHQJYyMjI%2FtT%2B1GSWA%2BgUHv7UASzI%2Fm7I%2Ftb%2BcB2r%2FtQBLAEsAZDIyAAAAAADAAAAyASwBEwACQATABcAADUUFjMhMjY1ESE1ITU0JiMhIgYVEzUhFR0VBEwVHftQBLAdFfu0FR1kAZD6FR0dFQImZJYVHR0V%2FRLIyAAAAAYAAABmBLAErgAGAAoADgAVABkAHQCBALAFL7EWGjMzsALNsRccMjKwBy%2BxCw8zM7AIzbEMEDIyAbAeL7AH1rAKzbAKELELASuwDs2wDhCxFgErsBnNsR8BK7ELChESswIFBgEkFzmxFg4RErMEAw8QJBc5sBkRsxIUFREkFzkAsQIFERKwADmwBxGxARQ5ObAIErATOTAxEQEVIRUhFQM1MxUzNTMVMzUhNQkBNQM1MxU7ATUjASwBkP5wyGRkZGQBkAEs%2FtRkZGRkZAGQASrGyMYCusjIyMjIxv7W%2FtbG%2FgzIyMgAAAACAGQAAASwBLAAGAAvADoAshQAACsBsDAvsADWsATNsAQQsRcLK7AQzbMJEBcIK7AFzbAFL7AJzbAQELEKCyuwDs2xMQErADAxExE3FxEzETcXETMRNxcRBxEUBisBIiY1ESUUHgIfAREUFjsBMjY1ETQmBwUOARVkMjJkMjJkMjJkHRXIFR0CWBUdHQsKHRXIFR0kGv7sGSUCvAGQZGT%2B1AEsZGT%2B1AEsZGT%2BcMv%2BQRUdHRUBv2QdNSEYBgX%2BcxUdHRUEUh8TEXQRRR8AAAAAAQBkAAAEsARMADMAOACyAAAAK7AMM7AzzbICCw4yMjKwKC%2ByGBwlMzMzsCfNsBoyAbA0L7E1ASsAsSgzERKxBiA5OTAxMyE1IiY1ESERFAYjFSE1Ii4DNRE0Nj8BNSEVMhYVESERNDYzNSEVMh4DFREUBg8BZAGQSxkB9BlLAZAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGTgMJgGK%2FnYmDDg4AQUJFQ4DeBYZAQI4OAwm%2FnYBiiYMODgBBQkVDvyIFhkBAgAGAAAAAARMBEwADwAYABwAIAAqAC4AMgCyGAAAK7AQzbAWL7ASzbISFgors0ASFAkrAbAvL7EwASsAsRIQERKzBAMZHCQXOTAxERQWMyEyNjURNCYjISIGFRMhNzMlESEHIQM1IRUBNSEVASEXFSUzNSEnIQE1JRU7KQEsKTs7Kf7UKTtkAZDIaQEn%2Fldk%2FolkASz%2B1AEs%2FtQBkMgBJ2n%2BV2T%2BiQH0AZABLCk7OykB9Ck7Oyn9RMhi%2FtZkASzIyAEsyMgBkMhiYshk%2FUajhaMAAQAQABAEnwSfACAAABIeAxceAzM%2FATYmLwEmBg8BLgEnNz4BLwEuAQ8BEAEfPpJmZ9GXex8foxEHE8ATNBB2jvxldhEGDosOLRKiA%2BQriY%2FUZmeSPSEBohIuDogOBBF2ZfyOdhExFMITBhGiAAAAAAIAAAAABLAETAAdAEAALwCyGwAAK7AMzbAoL7A4zQGwQS%2BxQgErALEMGxESsSAvOTmwKBGzJikyQCQXOTAxPQE0NjcBNTQ%2BAzIeAh8BFQEeAR0BFAYjISImERQWPwE%2BAT0BNiAXFRQWHwEWNj0BLgQjIg4EDwEVDgFtAhYmUnBSJhYBAQFtDhUdFfu0FB4dFMoUHY0BPo0dFMoUHQYaZHzaflymdWQ%2FLAkJMtQUMw4BLzIEDSAZFRQbHAoKMv7RDjMU1BUdHQKrFRkEIQQiFZIYGJIVIgQhBBkVyAgZQTEpFSEoKCELCgACAGQAAASwBEwAAwAZABQAsgAAACuwAc0BsBovsRsBKwAwMTM1IRUlISc1NxEjFSM1IxUjNSMVIzUjERcVZARM%2B%2F8Dtn1kZGTIZMhkZGRkZMiW%2BmQBkMjIyMjIyP5wZPoAAAAAAwBkAAAEsARMAAkAEwAdACQAsgoAACuwFDMBsB4vsArWsBPNsBMQsRQBK7AdzbEfASsAMDEzIRE0JisBIgYVARE0NjsBMhYVETMRNDY7ATIWFRFkASw7KWQpOwGQOylkKTtkOylkKTsBkCk7Oyn%2BcAPoKTs7KfwYArwpOzsp%2FUQAAAAABf%2BcAAAEsARMAA8AEwAfACcAKwBIALINAAArsBDNsBMvsATNAbAsL7AA1rAQzbAQELERASuwCc2xLQErsREQERK1FBUgIygqJBc5ALETEBEStRQaICYoKSQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUzESM1IxMRMxFksHwCvHywsHz9RHywyAOE%2FHxkASzIyP7UyMgBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qAEsZGT%2B1GRkZAEsZP5wASz%2B1AAAAAAF%2F5wAAASwBEwADwATAB8AJwArAEgAsg0AACuwEM2wEy%2BwBM0BsCwvsADWsBDNsBAQsREBK7AJzbEtASuxERARErUUGSAjKCokFzkAsRMQERK1FBogJigpJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMzNTMVMxEjFSM1IwEzNTMRIzUjExEzEWSwfAK8fLCwfP1EfLDIA4T8fGRkZGRkZGQBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qMjIAfTIyP4MZAEsZP5wASz%2B1AAABP%2BcAAAEsARMAA8AEwAbACMARACyDQAAK7AQzbATL7AEzQGwJC%2BwANawEM2wEBCxEQErsAnNsSUBK7EREBESsxQVHB0kFzkAsRMQERKzFBocIiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETITUjETM1IQEhNSMRMzUhZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1AGQASzIyP7UASwB9HywsHz%2BDHywsBgCvP2oZAEsZP4MZAEsZAAAAAAE%2F5wAAASwBEwADwATABYAGQBEALINAAArsBDNsBMvsATNAbAaL7AA1rAQzbAQELERASuwCc2xGwErsREQERKzFBUXGCQXOQCxExARErMVFhcZJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMFERMtAWSwfAK8fLCwfP1EfLDIA4T8fGQBLGQBLP7UASwB9HywsHz%2BDHywsBgCvP6ilgEs%2FtSWlgAAAAAF%2F5wAAASwBEwADwATABcAHwAnAFoAsg0AACuwEM2wFC%2BwGM2wIzKwHy%2BwJTOwFc2wEy%2BwBM0BsCgvsADWsBDNsBAQsRQBK7AYzbAYELEcASuwIc2wIRCxJAErsBfNsBcQsREBK7AJzbEpASsAMDEDETQ2MyEyFhURFAYjISImNyERIRMRIRElMzI2NCYrAQQUFjsBESMiZLB8Arx8sLB8%2FUR8sMgDhPx8ZAK8%2FaiCKTY5JoIBEzYpgoImASwB9HywsHz%2BDHywsBgCvP2oAfT%2BDGRUglZWglQBLAAABf%2BcAAAEsARMAA8AEwAfACMAKQBIALINAAArsBDNsBMvsATNAbAqL7AA1rAQzbAQELERASuwCc2xKwErsREQERK1FBUgISQnJBc5ALETEBEStRQaICImKCQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUjEzMRMxEjZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1MjIAZFkZGNkZMgBLAH0fLCwfP4MfLCwGAK8%2FagBLGRk%2FtRkZGQBLP5wAfQABv%2BcAAAEsARMAA8AEwAZAB0AIQAnAEwAsg0AACuwEM2wEy%2BwBM0BsCgvsADWsBDNsBAQsREBK7AJzbEpASuxERARErcUFRocHh8iJSQXOQCxExARErcUGBobHiAkJiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNSMTNTMVFzM1IxMzETMRI2SwfAK8fLCwfP1EfLDIA4T8fGQBLMhkZWTIZGRjZGTIASwB9HywsHz%2BDHywsBgCvP2oAZBk%2FnDIyGRkASz%2BcAH0AAAAAAb%2FnAAABLAETAAPABMAHQAhACUAKwCbALINAAArsBDNsB4vsSIpMzOwH8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EPARABEQESAO8BEwEUARUBFgEXARgBGQEaARsBHAEdAR4BHwEgASEBIgEjASQBJQEmAScBKAEpASoBKwEsAS0BLgEvATABMQEyATMBNAE1ATYBNwE4ATkBOgE7ATwBPQE%2BAT8BQAFBAUIBQwFEAUUBRgFHAUgBSQFKAUsBTAFNAU4BTwFQAVEBUgFTAVQBVQFWAVcBWAFZAVoBWwFcAV0BXgFfAWABYQFiAWMBZAFlAWYBZwFoAWkBagFrAWwBbQFuAW8BcAFxAXIBcwF0AXUBdgF3AXgBeQF6AXsBfAF9AX4BfwGAAYEBggGDAYQBhQGGAYcBiAGJAYoBiwGMAY0BjgGPAZABkQGSAZMBlAGVAZYBlwGYAZkBmgGbAZwBnQGeAZ8BoAGhAaIBowGkAaUBpgGnAagBqQGqAasBrAGtAa4BrwGwAbEBsgGzAbQBtQG2AbcBuAG5AboBuwG8Ab0BvgG%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%2F4WwAY0AS7AIUFixAQGOWbFGBitYIbAQWUuwFFJYIbCAWR2wBitcWFmwFCsAAAABUuZYrgAA%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIC8%2BCjxnbHlwaCAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZDsiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDQiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzNCIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI2IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTciIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYzIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMjEyOyIgZD0iTTIwMCA0MDBoOTAwdjMwMGgtOTAwdi0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjVmYzsiIGhvcml6LWFkdi14PSI1MDAiIGQ9Ik0wIDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjYwMTsiIGQ9Ik0tMTQgNDk0cTAgLTgwIDU2LjUgLTEzN3QxMzUuNSAtNTdoNzUwcTEyMCAwIDIwNSA4Ni41dDg1IDIwNy41dC04NSAyMDd0LTIwNSA4NnEtNDYgMCAtOTAgLTE0cS00NCA5NyAtMTM0LjUgMTU2LjV0LTIwMC41IDU5LjVxLTE1MiAwIC0yNjAgLTEwNy41dC0xMDggLTI2MC41cTAgLTI1IDIgLTM3cS02NiAtMTQgLTEwOC41IC02Ny41dC00Mi41IC0xMjIuNXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDEwOyIgZD0iTTAgNTB2NDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWg0MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNDAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNMCA2NTB2NDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWg0MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNDAwIHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV6TTYwMCA1MHY0MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDQwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek02MDAgNjUwdjQwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoNDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MDAgcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMTE7IiBkPSJNMCA1MHYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek0wIDQ1MHYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDAgcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNMCA4NTB2MjAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgyMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTIwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMjAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNDAwIDUwdjIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDBxMCAtMjEgLTE0LjUgLTM1LjUgdC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek00MDAgNDUwdjIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV6TTQwMCA4NTB2MjAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgyMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjUgdi0yMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV6TTgwMCA1MHYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek04MDAgNDUwdjIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoMjAwIHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek04MDAgODUwdjIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAxMjsiIGQ9Ik0wIDUwdjIwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoMjAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0yMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV6TTAgNDUwcTAgLTIxIDE0LjUgLTM1LjV0MzUuNSAtMTQuNWgyMDBxMjEgMCAzNS41IDE0LjV0MTQuNSAzNS41djIwMHEwIDIxIC0xNC41IDM1LjV0LTM1LjUgMTQuNWgtMjAwcS0yMSAwIC0zNS41IC0xNC41IHQtMTQuNSAtMzUuNXYtMjAwek0wIDg1MHYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDIwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek00MDAgNTB2MjAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWg3MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTIwMHEwIC0yMSAtMTQuNSAtMzUuNSB0LTM1LjUgLTE0LjVoLTcwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV6TTQwMCA0NTB2MjAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWg3MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTIwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNzAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNDAwIDg1MHYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDcwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNSB2LTIwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNzAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDEzOyIgZD0iTTI5IDQ1NGw0MTkgLTQyMGw4MTggODIwbC0yMTIgMjEybC02MDcgLTYwN2wtMjA2IDIwN3oiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI3OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzEgMTIxLjUgLTI5Mi41dDI5Mi41IC0xMjEuNXQyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNXQtMTIxLjUgMjkyLjV0LTI5Mi41IDEyMS41dC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNMzUwIDYwMGwyNTAgMzAwIGwyNTAgLTMwMGgtMTUwdi0zMDBoLTIwMHYzMDBoLTE1MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI4OyIgZD0iTTAgMjV2NDc1bDIwMCA3MDBoODAwbDE5OSAtNzAwbDEgLTQ3NXEwIC0xMSAtNyAtMTh0LTE4IC03aC0xMTUwcS0xMSAwIC0xOCA3dC03IDE4ek0yMDAgNTAwaDIwMGw1MCAtMjAwaDMwMGw1MCAyMDBoMjAwbC05NyA1MDBoLTYwNnoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI5OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzIgMTIxLjUgLTI5M3QyOTIuNSAtMTIxdDI5Mi41IDEyMXQxMjEuNSAyOTNxMCAxNzEgLTEyMS41IDI5Mi41dC0yOTIuNSAxMjEuNXQtMjkyLjUgLTEyMS41dC0xMjEuNSAtMjkyLjV6TTUwMCAzOTd2NDAxIGwyOTcgLTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMwOyIgZD0iTTIzIDYwMHEwIC0xMTggNDUuNSAtMjI0LjV0MTIzIC0xODR0MTg0IC0xMjN0MjI0LjUgLTQ1LjV0MjI0LjUgNDUuNXQxODQgMTIzdDEyMyAxODR0NDUuNSAyMjQuNWgtMTUwcTAgLTE3NyAtMTI1IC0zMDJ0LTMwMiAtMTI1dC0zMDIgMTI1dC0xMjUgMzAydDEyNSAzMDJ0MzAyIDEyNXExMzYgMCAyNDYgLTgxbC0xNDYgLTE0Nmg0MDB2NDAwbC0xNDUgLTE0NXEtMTU3IDEyMiAtMzU1IDEyMnEtMTE4IDAgLTIyNC41IC00NS41dC0xODQgLTEyMyB0LTEyMyAtMTg0dC00NS41IC0yMjQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMxOyIgZD0iTTIzIDYwMHEwIDExOCA0NS41IDIyNC41dDEyMyAxODR0MTg0IDEyM3QyMjQuNSA0NS41cTE5OCAwIDM1NSAtMTIybDE0NSAxNDV2LTQwMGgtNDAwbDE0NyAxNDdxLTExMiA4MCAtMjQ3IDgwcS0xNzcgMCAtMzAyIC0xMjV0LTEyNSAtMzAyaC0xNTB6TTEwMCAwdjQwMGg0MDBsLTE0NyAtMTQ3cTExMiAtODAgMjQ3IC04MHExNzcgMCAzMDIgMTI1dDEyNSAzMDJoMTUwcTAgLTExOCAtNDUuNSAtMjI0LjV0LTEyMyAtMTg0dC0xODQgLTEyMyB0LTIyNC41IC00NS41cS0xOTggMCAtMzU1IDEyMnoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ4OyIgZD0iTTEwMCAwdjg5cTQxIDcgNzAuNSAzMi41dDI5LjUgNjUuNXY4MjdxMCAyOCAtMSAzOS41dC01LjUgMjZ0LTE1LjUgMjF0LTI5IDE0dC00OSAxNC41djcxbDQ3MSAtMXExMjAgMCAyMTMgLTg4dDkzIC0yMjhxMCAtNTUgLTExLjUgLTEwMS41dC0yOCAtNzR0LTMzLjUgLTQ3LjV0LTI4IC0yOGwtMTIgLTdxOCAtMyAyMS41IC05dDQ4IC0zMS41dDYwLjUgLTU4dDQ3LjUgLTkxLjV0MjEuNSAtMTI5cTAgLTg0IC01OSAtMTU2LjV0LTE0MiAtMTExIHQtMTYyIC0zOC41aC01MDB6TTQwMCAyMDBoMTYxcTg5IDAgMTUzIDQ4LjV0NjQgMTMyLjVxMCA5MCAtNjIuNSAxNTQuNXQtMTU2LjUgNjQuNWgtMTU5di00MDB6TTQwMCA3MDBoMTM5cTc2IDAgMTMwIDYxLjV0NTQgMTM4LjVxMCA4MiAtODQgMTMwLjV0LTIzOSA0OC41di0zNzl6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0OTsiIGQ9Ik0yMDAgMHY1N3E3NyA3IDEzNC41IDQwLjV0NjUuNSA4MC41bDE3MyA4NDlxMTAgNTYgLTEwIDc0dC05MSAzN3EtNiAxIC0xMC41IDIuNXQtOS41IDIuNXY1N2g0MjVsMiAtNTdxLTMzIC04IC02MiAtMjUuNXQtNDYgLTM3dC0yOS41IC0zOHQtMTcuNSAtMzAuNWwtNSAtMTJsLTEyOCAtODI1cS0xMCAtNTIgMTQgLTgydDk1IC0zNnYtNTdoLTUwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDUwOyIgZD0iTS03NSAyMDBoNzV2ODAwaC03NWwxMjUgMTY3bDEyNSAtMTY3aC03NXYtODAwaDc1bC0xMjUgLTE2N3pNMzAwIDkwMHYzMDBoMTUwaDcwMGgxNTB2LTMwMGgtNTBxMCAyOSAtOCA0OC41dC0xOC41IDMwdC0zMy41IDE1dC0zOS41IDUuNXQtNTAuNSAxaC0yMDB2LTg1MGwxMDAgLTUwdi0xMDBoLTQwMHYxMDBsMTAwIDUwdjg1MGgtMjAwcS0zNCAwIC01MC41IC0xdC00MCAtNS41dC0zMy41IC0xNXQtMTguNSAtMzB0LTguNSAtNDguNWgtNDl6ICIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwNTE7IiBkPSJNMzMgNTFsMTY3IDEyNXYtNzVoODAwdjc1bDE2NyAtMTI1bC0xNjcgLTEyNXY3NWgtODAwdi03NXpNMTAwIDkwMXYzMDBoMTUwaDcwMGgxNTB2LTMwMGgtNTBxMCAyOSAtOCA0OC41dC0xOCAzMHQtMzMuNSAxNXQtNDAgNS41dC01MC41IDFoLTIwMHYtNjUwbDEwMCAtNTB2LTEwMGgtNDAwdjEwMGwxMDAgNTB2NjUwaC0yMDBxLTM0IDAgLTUwLjUgLTF0LTM5LjUgLTUuNXQtMzMuNSAtMTV0LTE4LjUgLTMwdC04IC00OC41aC01MHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDU5OyIgZD0iTTAgMjc1djY1MHEwIDMxIDIyIDUzdDUzIDIyaDc1MHEzMSAwIDUzIC0yMnQyMiAtNTN2LTY1MHEwIC0zMSAtMjIgLTUzdC01MyAtMjJoLTc1MHEtMzEgMCAtNTMgMjJ0LTIyIDUzek05MDAgNjAwbDMwMCAzMDB2LTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDYwOyIgZD0iTTAgNDR2MTAxMnEwIDE4IDEzIDMxdDMxIDEzaDExMTJxMTkgMCAzMS41IC0xM3QxMi41IC0zMXYtMTAxMnEwIC0xOCAtMTIuNSAtMzF0LTMxLjUgLTEzaC0xMTEycS0xOCAwIC0zMSAxM3QtMTMgMzF6TTEwMCAyNjNsMjQ3IDE4MmwyOTggLTEzMWwtNzQgMTU2bDI5MyAzMThsMjM2IC0yODh2NTAwaC0xMDAwdi03Mzd6TTIwOCA3NTBxMCA1NiAzOSA5NXQ5NSAzOXQ5NSAtMzl0MzkgLTk1dC0zOSAtOTV0LTk1IC0zOXQtOTUgMzl0LTM5IDk1eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEzNiA1NTBsNTY0IDU1MHYtNDg3bDUwMCA0ODd2LTExMDBsLTUwMCA0ODh2LTQ4OHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc4OyIgZD0iTTEwMCAyNTB2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek0xMDAgNTAwaDExMDBsLTU1MCA1NjR6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3OTsiIGQ9Ik0xODUgNTk5bDU5MiAtNTkybDI0MCAyNDBsLTM1MyAzNTNsMzUzIDM1M2wtMjQwIDI0MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDgwOyIgZD0iTTI3MiAxOTRsMzUzIDM1M2wtMzUzIDM1M2wyNDEgMjQwbDU3MiAtNTcxbDIxIC0yMmwtMSAtMXYtMWwtNTkyIC01OTF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4MTsiIGQ9Ik0zIDYwMHEwIDE2MiA4MCAyOTkuNXQyMTcuNSAyMTcuNXQyOTkuNSA4MHQyOTkuNSAtODB0MjE3LjUgLTIxNy41dDgwIC0yOTkuNXQtODAgLTI5OS41dC0yMTcuNSAtMjE3LjV0LTI5OS41IC04MHQtMjk5LjUgODB0LTIxNy41IDIxNy41dC04MCAyOTkuNXpNMzAwIDUwMGgyMDB2LTIwMGgyMDB2MjAwaDIwMHYyMDBoLTIwMHYyMDBoLTIwMHYtMjAwaC0yMDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDgyOyIgZD0iTTMgNjAwcTAgMTYyIDgwIDI5OS41dDIxNy41IDIxNy41dDI5OS41IDgwdDI5OS41IC04MHQyMTcuNSAtMjE3LjV0ODAgLTI5OS41dC04MCAtMjk5LjV0LTIxNy41IC0yMTcuNXQtMjk5LjUgLTgwdC0yOTkuNSA4MHQtMjE3LjUgMjE3LjV0LTgwIDI5OS41ek0zMDAgNTAwaDYwMHYyMDBoLTYwMHYtMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODM7IiBkPSJNMyA2MDBxMCAxNjIgODAgMjk5LjV0MjE3LjUgMjE3LjV0Mjk5LjUgODB0Mjk5LjUgLTgwdDIxNy41IC0yMTcuNXQ4MCAtMjk5LjV0LTgwIC0yOTkuNXQtMjE3LjUgLTIxNy41dC0yOTkuNSAtODB0LTI5OS41IDgwdC0yMTcuNSAyMTcuNXQtODAgMjk5LjV6TTI0NiA0NTlsMjEzIC0yMTNsMTQxIDE0MmwxNDEgLTE0MmwyMTMgMjEzbC0xNDIgMTQxbDE0MiAxNDFsLTIxMyAyMTJsLTE0MSAtMTQxbC0xNDEgMTQybC0yMTIgLTIxM2wxNDEgLTE0MSB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4NDsiIGQ9Ik0zIDYwMHEwIDE2MiA4MCAyOTkuNXQyMTcuNSAyMTcuNXQyOTkuNSA4MHQyOTkuNSAtODB0MjE3LjUgLTIxNy41dDgwIC0yOTkuNXQtODAgLTI5OS41dC0yMTcuNSAtMjE3LjV0LTI5OS41IC04MHQtMjk5LjUgODB0LTIxNy41IDIxNy41dC04MCAyOTkuNXpNMjcwIDU1MWwyNzYgLTI3N2w0MTEgNDExbC0xNzUgMTc0bC0yMzYgLTIzNmwtMTAyIDEwMnoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkxOyIgZD0iTTAgNTQ3bDYwMCA0NTN2LTMwMGg2MDB2LTMwMGgtNjAwdi0zMDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MjsiIGQ9Ik0wIDQwMHYzMDBoNjAwdjMwMGw2MDAgLTQ1M2wtNjAwIC00NDh2MzAxaC02MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MzsiIGQ9Ik0yMDQgNjAwbDQ1MCA2MDBsNDQ0IC02MDBoLTI5OHYtNjAwaC0zMDB2NjAwaC0yOTZ6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NDsiIGQ9Ik0xMDQgNjAwaDI5NnY2MDBoMzAwdi02MDBoMjk4bC00NDkgLTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk1OyIgZD0iTTAgMjAwcTYgMTMyIDQxIDIzOC41dDEwMy41IDE5M3QxODQgMTM4dDI3MS41IDU5LjV2MjcxbDYwMCAtNDUzbC02MDAgLTQ0OHYzMDFxLTk1IC0yIC0xODMgLTIwdC0xNzAgLTUydC0xNDcgLTkyLjV0LTEwMCAtMTM1LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NjsiIGQ9Ik0wIDB2NDAwbDEyOSAtMTI5bDI5NCAyOTRsMTQyIC0xNDJsLTI5NCAtMjk0bDEyOSAtMTI5aC00MDB6TTYzNSA3NzdsMTQyIC0xNDJsMjk0IDI5NGwxMjkgLTEyOXY0MDBoLTQwMGwxMjkgLTEyOXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk3OyIgZD0iTTM0IDE3NmwyOTUgMjk1bC0xMjkgMTI5aDQwMHYtNDAwbC0xMjkgMTMwbC0yOTUgLTI5NXpNNjAwIDYwMHY0MDBsMTI5IC0xMjlsMjk1IDI5NWwxNDIgLTE0MWwtMjk1IC0yOTVsMTI5IC0xMzBoLTQwMHoiIC8%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUwOyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTYwMiA5MDBsMjk4IDMwMGwyOTggLTMwMGgtMTk4di05MDBoLTIwMHY5MDBoLTE5OHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUxOyIgZD0iTTIgMzAwaDE5OHY5MDBoMjAwdi05MDBoMTk4bC0yOTggLTMwMHpNNzAwIDB2MjAwaDEwMHYtMTAwaDIwMHYtMTAwaC0zMDB6TTcwMCA0MDB2MTAwaDMwMHYtMjAwaC05OXYtMTAwaC0xMDB2MTAwaDk5djEwMGgtMjAwek03MDAgNzAwdjUwMGgzMDB2LTUwMGgtMTAwdjEwMGgtMTAwdi0xMDBoLTEwMHpNODAxIDkwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTU0OyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTgwMCA0MDB2MTAwaDIwMHYtNTAwaC0xMDB2NDAwaC0xMDB6TTgwMCA4MDB2NDAwaDMwMHYtNTAwaC0xMDB2MTAwaC0yMDB6TTkwMSA5MDBoMTAwdjIwMGgtMTAwdi0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1NTsiIGQ9Ik0yIDMwMGwyOTggLTMwMGwyOTggMzAwaC0xOTh2OTAwaC0yMDB2LTkwMGgtMTk4ek03MDAgMTAwdjIwMGg1MDB2LTIwMGgtNTAwek03MDAgNDAwdjIwMGg0MDB2LTIwMGgtNDAwek03MDAgNzAwdjIwMGgzMDB2LTIwMGgtMzAwek03MDAgMTAwMHYyMDBoMjAwdi0yMDBoLTIwMHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY3OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzAwaDMwMHYtMzAwaDMwMHYzMDBoMjk1bC00NDUgNTAwek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY4OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzA1bDMwNSAtMzA1bDU5NiA1OTZsLTE1NCAxNTVsLTQ0MiAtNDQybC0xNTAgMTUxek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY5OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgOTg4bDk3IC05OGwyMTIgMjEzbC05NyA5N3pNMjAwIDQwMGw2OTcgMWwzIDY5OWwtMjUwIC0yMzlsLTE0OSAxNDlsLTIxMiAtMjEybDE0OSAtMTQ5ek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTczOyIgZD0iTTAgMTUwdjEwMDBxMCAyMCAxNC41IDM1dDM1LjUgMTVoMjUwdi0zMDBoNTAwdjMwMGgxMDBsMjAwIC0yMDB2LTIxOGwtMjc2IC0yNzVsLTEyMCAxMjBsLTEyNiAtMTI3aC0zNzh2LTQwMGgtMTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNTgxIDMwNmwxMjMgMTIzbDEyMCAtMTIwbDM1MyAzNTJsMTIzIC0xMjNsLTQ3NSAtNDc2ek02MDAgMTAwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg0OyIgZD0iTTEwMCAwdjEwMGgxMTAwdi0xMDBoLTExMDB6TTE3NSAyMDBoOTUwbC0xMjUgMTUwdjI1MGwxMDAgMTAwdjQwMGgtMTAwdi0yMDBoLTEwMHYyMDBoLTIwMHYtMjAwaC0xMDB2MjAwaC0yMDB2LTIwMGgtMTAwdjIwMGgtMTAwdi00MDBsMTAwIC0xMDB2LTI1MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg4OyIgZD0iTS0xMDAgMzAwdjUwMHEwIDEyNCA4OCAyMTJ0MjEyIDg4aDcwMHExMjQgMCAyMTIgLTg4dDg4IC0yMTJ2LTUwMHEwIC0xMjQgLTg4IC0yMTJ0LTIxMiAtODhoLTcwMHEtMTI0IDAgLTIxMiA4OHQtODggMjEyek0xMDAgMjAwaDkwMHY3MDBoLTkwMHYtNzAwek0yMDAgMzAwaDMwMHYxMDBoLTIwMHYzMDBoMjAwdjEwMGgtMzAwdi01MDB6TTYwMCAzMDBoMzAwdjEwMGgtMjAwdjMwMGgyMDB2MTAwaC0zMDB2LTUwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTk0OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzEgMTIxLjUgLTI5Mi41dDI5Mi41IC0xMjEuNXQyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNXQtMTIxLjUgMjkyLjV0LTI5Mi41IDEyMS41dC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNNDAwIDUwMHYyMDAgbDEwMCAxMDBoMzAwdi0xMDBoLTMwMHYtMjAwaDMwMHYtMTAwaC0zMDB6IiAvPgo8Z2x5cGggdW5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+<style type="text/css"> + pre:not([class]) { + background-color: white; + } +</style> +<script type="text/javascript"> +if (window.hljs && document.readyState && document.readyState === "complete") { + window.setTimeout(function() { + hljs.initHighlighting(); + }, 0); +} +</script> + + + +</head> + +<body> + +<style type="text/css"> +.main-container { + max-width: 940px; + margin-left: auto; + margin-right: auto; +} +code { + color: inherit; + background-color: rgba(0, 0, 0, 0.04); +} +img { + max-width:100%; + height: auto; +} +</style> +<div class="container-fluid main-container"> + + +<div id="header"> +<h1 class="title">Example evaluation of FOCUS Example Dataset D</h1> +<h4 class="author"><em>Johannes Ranke</em></h4> +<h4 class="date"><em>2015-11-09</em></h4> +</div> + + +<p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look a the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p> +<pre class="r"><code>library("mkin") +print(FOCUS_2006_D)</code></pre> +<pre><code>## name time value +## 1 parent 0 99.46 +## 2 parent 0 102.04 +## 3 parent 1 93.50 +## 4 parent 1 92.50 +## 5 parent 3 63.23 +## 6 parent 3 68.99 +## 7 parent 7 52.32 +## 8 parent 7 55.13 +## 9 parent 14 27.27 +## 10 parent 14 26.64 +## 11 parent 21 11.50 +## 12 parent 21 11.64 +## 13 parent 35 2.85 +## 14 parent 35 2.91 +## 15 parent 50 0.69 +## 16 parent 50 0.63 +## 17 parent 75 0.05 +## 18 parent 75 0.06 +## 19 parent 100 NA +## 20 parent 100 NA +## 21 parent 120 NA +## 22 parent 120 NA +## 23 m1 0 0.00 +## 24 m1 0 0.00 +## 25 m1 1 4.84 +## 26 m1 1 5.64 +## 27 m1 3 12.91 +## 28 m1 3 12.96 +## 29 m1 7 22.97 +## 30 m1 7 24.47 +## 31 m1 14 41.69 +## 32 m1 14 33.21 +## 33 m1 21 44.37 +## 34 m1 21 46.44 +## 35 m1 35 41.22 +## 36 m1 35 37.95 +## 37 m1 50 41.19 +## 38 m1 50 40.01 +## 39 m1 75 40.09 +## 40 m1 75 33.85 +## 41 m1 100 31.04 +## 42 m1 100 33.13 +## 43 m1 120 25.15 +## 44 m1 120 33.31</code></pre> +<p>Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.</p> +<p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> +<pre class="r"><code>SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))</code></pre> +<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre class="r"><code>print(SFO_SFO$diffs)</code></pre> +<pre><code>## parent +## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" +## m1 +## "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"</code></pre> +<p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p> +<pre class="r"><code>fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)</code></pre> +<p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot</code> method for <code>mkinfit</code> objects.</p> +<pre class="r"><code>plot(fit, show_residuals = TRUE)</code></pre> +<p><img 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" title alt width="672" /></p> +<p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> +<pre class="r"><code>mkinparplot(fit)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> +<pre class="r"><code>summary(fit)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:06 2015 +## Date of summary: Mon Jul 20 14:51:06 2015 +## +## Equations: +## d_parent = - k_parent_sink * parent - k_parent_m1 * parent +## d_m1 = + k_parent_m1 * parent - k_m1_sink * m1 +## +## Model predictions using solution type deSolve +## +## Fitted with method Port using 153 model solutions performed in 0.66 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 100.7500 state +## k_parent_sink 0.1000 deparm +## k_parent_m1 0.1001 deparm +## k_m1_sink 0.1002 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 100.750000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf +## log_k_parent_m1 -2.301586 -Inf Inf +## log_k_m1_sink -2.300587 -Inf Inf +## +## Fixed parameter values: +## value type +## m1_0 0 state +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 99.600 1.61400 96.330 102.900 +## log_k_parent_sink -3.038 0.07826 -3.197 -2.879 +## log_k_parent_m1 -2.980 0.04124 -3.064 -2.897 +## log_k_m1_sink -5.248 0.13610 -5.523 -4.972 +## +## Parameter correlation: +## parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink +## parent_0 1.00000 0.6075 -0.06625 -0.1701 +## log_k_parent_sink 0.60752 1.0000 -0.08740 -0.6253 +## log_k_parent_m1 -0.06625 -0.0874 1.00000 0.4716 +## log_k_m1_sink -0.17006 -0.6253 0.47163 1.0000 +## +## Residual standard error: 3.211 on 36 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 99.600000 61.720 2.024e-38 96.330000 1.029e+02 +## k_parent_sink 0.047920 12.780 3.050e-15 0.040890 5.616e-02 +## k_parent_m1 0.050780 24.250 3.407e-24 0.046700 5.521e-02 +## k_m1_sink 0.005261 7.349 5.758e-09 0.003992 6.933e-03 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 6.398 4 15 +## parent 6.827 3 6 +## m1 4.490 1 9 +## +## Resulting formation fractions: +## ff +## parent_sink 0.4855 +## parent_m1 0.5145 +## m1_sink 1.0000 +## +## Estimated disappearance times: +## DT50 DT90 +## parent 7.023 23.33 +## m1 131.761 437.70 +## +## Data: +## time variable observed predicted residual +## 0 parent 99.46 9.960e+01 -1.385e-01 +## 0 parent 102.04 9.960e+01 2.442e+00 +## 1 parent 93.50 9.024e+01 3.262e+00 +## 1 parent 92.50 9.024e+01 2.262e+00 +## 3 parent 63.23 7.407e+01 -1.084e+01 +## 3 parent 68.99 7.407e+01 -5.083e+00 +## 7 parent 52.32 4.991e+01 2.408e+00 +## 7 parent 55.13 4.991e+01 5.218e+00 +## 14 parent 27.27 2.501e+01 2.257e+00 +## 14 parent 26.64 2.501e+01 1.627e+00 +## 21 parent 11.50 1.253e+01 -1.035e+00 +## 21 parent 11.64 1.253e+01 -8.946e-01 +## 35 parent 2.85 3.148e+00 -2.979e-01 +## 35 parent 2.91 3.148e+00 -2.379e-01 +## 50 parent 0.69 7.162e-01 -2.624e-02 +## 50 parent 0.63 7.162e-01 -8.624e-02 +## 75 parent 0.05 6.074e-02 -1.074e-02 +## 75 parent 0.06 6.074e-02 -7.382e-04 +## 100 parent NA 5.151e-03 NA +## 100 parent NA 5.151e-03 NA +## 120 parent NA 7.155e-04 NA +## 120 parent NA 7.155e-04 NA +## 0 m1 0.00 0.000e+00 0.000e+00 +## 0 m1 0.00 0.000e+00 0.000e+00 +## 1 m1 4.84 4.803e+00 3.704e-02 +## 1 m1 5.64 4.803e+00 8.370e-01 +## 3 m1 12.91 1.302e+01 -1.140e-01 +## 3 m1 12.96 1.302e+01 -6.400e-02 +## 7 m1 22.97 2.504e+01 -2.075e+00 +## 7 m1 24.47 2.504e+01 -5.748e-01 +## 14 m1 41.69 3.669e+01 5.000e+00 +## 14 m1 33.21 3.669e+01 -3.480e+00 +## 21 m1 44.37 4.165e+01 2.717e+00 +## 21 m1 46.44 4.165e+01 4.787e+00 +## 35 m1 41.22 4.331e+01 -2.093e+00 +## 35 m1 37.95 4.331e+01 -5.363e+00 +## 50 m1 41.19 4.122e+01 -2.831e-02 +## 50 m1 40.01 4.122e+01 -1.208e+00 +## 75 m1 40.09 3.645e+01 3.643e+00 +## 75 m1 33.85 3.645e+01 -2.597e+00 +## 100 m1 31.04 3.198e+01 -9.416e-01 +## 100 m1 33.13 3.198e+01 1.148e+00 +## 120 m1 25.15 2.879e+01 -3.640e+00 +## 120 m1 33.31 2.879e+01 4.520e+00</code></pre> + + +</div> + +<script> + +// add bootstrap table styles to pandoc tables +$(document).ready(function () { + $('tr.header').parent('thead').parent('table').addClass('table table-condensed'); +}); + +</script> + + +</body> +</html> diff --git a/inst/web/vignettes/FOCUS_L.html b/inst/web/vignettes/FOCUS_L.html new file mode 100644 index 00000000..9bb60153 --- /dev/null +++ b/inst/web/vignettes/FOCUS_L.html @@ -0,0 +1,867 @@ +<!DOCTYPE html> + +<html xmlns="http://www.w3.org/1999/xhtml"> + +<head> + +<meta charset="utf-8"> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<meta name="generator" content="pandoc" /> + +<meta name="author" content="Johannes Ranke" /> + +<meta name="date" content="2015-11-09" /> + +<title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> + +<script 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Lj%2F8LYMDgBMACAbuP%2FwtQ4ATRuPCbj%2F8EATDgBNCVcREA4ATRGPAxAOAE0DWAA%2FK%2B0rPyvtKwEQ3isrKyvtENYrKysr7TEwACsrKysBKysrKysrKysTNDYzMhYVFAYjIiYlNC4CIyIOAhUUHgIzMj4CMnttbXt7bW17AW4NHzQmJjQfDQ0fNCYmNB8NAVuuvLyurry8rjlmTC0tTGY5OWZMLS1MZgAAAAABAF0AAAFxArUADABQQDUDBIsHHwABAA8GHwYCCAYGDg2NB50HAl8Hbwd%2FBwMHAI4QDCAMMAxQDGAMcAwGDAwDBVUDVgA%2FPxI5L13tM11dARESOS9eXc5dM%2B05MTATPgE3MxEjEQ4DB105bC1CXQwjKS0VAiYWRTT9SwI0CxgWFAcAAAABADgAAAHqAsUALADlQC8DBxMXAxcTiwcDFAcDEwcXBxcQGowMAEAQAE0AQAgJAEwALiQlawp7CgIKjBAtJLj%2F8LMSAE0kuP%2FgsxEATSS4%2F8hAGBAATSQfCBEATR8QDxAATB%2BPByAODwBMF7j%2F6LQREgBMF7j%2F6LYPAE0HFwwluP%2FgtBASAEwluP%2F4tAkATSUquP%2FwtBESAEwquP%2FosxAATSq4%2F%2Fi0CQsATCq4%2F%2FBACQgATSpYCo4MVQA%2F7T8rKysrMysrEjk5Kysr7SsrMisrKwEQ3u1xxjIQ1isrxO0SOTkvLwDBhwUrEADBhwV9EMQxMAEUDgIHDgMVIRUhJjQ1ND4CNz4DNTQuAiMiDgIHJz4DMzIWAdMdMDweES0oGwE%2F%2FlwBHzI%2BHhktIxUTICsYHTAmGgcuCSQyPiRtbQIEJENBPx4RLjIuEU8GDAUvUUhAHhgvLzIcHiocDRAVFwZCCh0aEmUAAAABADf%2F8QHpAsUAOADDQB6nIQGWIQGHIQEOCA8ATQUYDwBNNhAPAE00CA8ATQK4%2F%2FBAYg8ATVkQaRACEIyPI58jryMDFyMBIyt7JgFdJm0mAgomCiY1agMBSQNZAwIDjIcrAR8rAQ4rAQgrOhgZNjU5JiYKjggIHjYAjzUQDwBNNTAIEBIATDBXGBWPGR4IDQ4ATB5YAD8rM%2B0yPyszK%2B0yEjkv7Tk9LwEYEN4yxjIQ1l5dXV3tXV0SOTkvL11dENRdXe1dMTAAKysrASsrXV1dNzI2NTQuAisBNTMyPgI1NC4CIyIGByc%2BAzMyHgIVFAYHHgMVFA4CIyIuAic3HgHiWU0gNEUkGCEZNSwcFCErGDBDFyQMJS41HTdQNBo%2BLBsyJRcfQWJDGjcwJAgSEU9ERjsmNB8NTAoaLSIcKBoLHA9HCBMQCxsxQyc2TRQIHi48JS1NOCAIDAwEUQgaAAAAAgAoAAACCgK1AA4AFQBrQD8oAgEIEQEOFSsFASgFOAUCCQUBDwUFDYsHCgkXDxQBO18UbxR%2FFAMUiwAADhYAFI4NDQeOCQkFC1UPjgQEBVYAPzMQ7T8SOS%2FtMy%2FtMgEQzjIQ7XJeXRDW3TLtMi9eXV1xMjEwAF5dAV03PgM3MxEzFSMVIzUhAQ4DBzMoE0JTXzBZUlJa%2FsoBNh49OjMU3PAsdXx4MP5ITLGxAZYgT1ZaKwAAAQBB%2F%2FEB6wK1ACcApkAQGSAPAE0BIA8ATRggDwBNFLj%2F8EA7DwBNDhAPAE0iahYBFowPAx8DAggDKQAjI4sgGxQgGxsgDg0oIyKOIACOGxAREgBMGwgQAE0bEA4ATRu4%2F%2BBAHg0ATRsQDABNGxsIIFYOEY8NIA8ATQ0ICBASAEwIVwA%2FKzMr7TI%2FEjkvKysrKyvtEO0yARDeMtY5hyt9EMQBGBDWXl3tXcQxMAArKysBKysTHgEVFA4CIyIuAic3HgEzMj4CNTQuAiM%2BAzchFSMOA8eXjR8%2FYEIaNi4kCBIRTTgtPSYRGkJuVAYHBgQCAU79AQQEBAGqBnhnLk44IAgMDARRCBoUIi0ZJjsoFTFWUlEtTw8zNzQAAAACAD%2F%2F8QIBArgAGgAtAJi1LQgPAE0kuP%2Fwsw8ATRq4%2F%2FizDwBNCbj%2F%2BEA5EgBNAxgPAE1XAwEFMAYBDwYBBgYAWSsBK4wHE3cTAhMvHoYLlgsCCyGMRwBXAAIALgsOjx4bGwUmuP%2FwQAsODwBMJo8YVwaPBbj%2F8LQOAE0FWAA%2FK%2B0%2F7SsSOS8z7TIBEN5d7TJdMhDWXe1dEjkvXV0zMTAAXQErKysrKxM0PgI3Fw4DBz4BMzIeAhUUDgIjIiY3IgYHDgEVFB4CMzI%2BAjU0Jj84Z5NcBzthTDQNGkEjO1I0Fxo1Ujhyd%2BAjPB4BAQwfNCghLh4NQQEWZZtqNwFOARg0Uz0MECQ7TSomUEIqnNANDQsXCyxOOyMcLDMYREgAAAABADwAAAH1ArUADgAlQBIOiwAABwWLCBAHDw5VCQWOB1YAP%2B0yPwEQzhDW7RI5L%2B0xMDM%2BAzchNSEVDgMHowYtQk8m%2Fq8BuSFQRzMGULGnjy1RTSaFpbtdAAAAAAMAN%2F%2FxAf0CxQAhADMARQEOuQAy%2F%2BizDwBNMrj%2F8EAJDgBNLhAPAE0NuP%2Fwsw8ATQO4%2F%2Fi0DxAATEW4%2F%2FCzDgBNQLj%2F8LUPAE00jBu4%2F%2FhAIRESAEwbAENBIEExLw8vEA8ATS8PQSAvIEGLDy8UDy9BD7j%2F4EA3DwBNUA8BICAPAE1fIAEPIA8gCiyMdgABDwABAEc%2BjBEIERIATBEijAoQEABNCkYPDyAgBTmPFrj%2F8LYICQBMFlgnuP%2FwtQ8ATSePBbj%2F%2BEALDgBNBRAICQBMBVcAPysr7Ss%2FK%2B0SORkvMy8BGBDeK%2B3UK%2B0Q1l1d7RI5OS8vXStdK8GHBCsQAcGHBH0QxAArhw7EEIcOxAEYENQr7TEwACsrASsrKysrJRQOAiMiLgI1ND4CNyY1ND4CMzIeAhUUDgIHFgUUHgIzMj4CNTQuAicOARM0LgIjIg4CFRQeAhc%2BAQH9HTlWOEFXNBYTHykXYh43TjA4UDMXER0lFHb%2Bmg0gMiYiMyAQGC0%2FKC0x%2Bg0cLCAgLRwNECI3JioytCpHNR0lOEMeHTUuJQ44bSZFNB8iNUAeHTMsIg04fxInIRYUHygVIDAkGQkZSgEwDyQfFRQfJRIWLCYeCRlDAAIAM%2F%2F%2BAfUCxQAaAC0AzkALJCAPAE0YIA8ATQK4%2F%2BCzDwBNArj%2F8LMOAE0CuP%2F4swgATQG4%2F%2FCzDwBNAbj%2F6LMOAE0BuP%2F4QCkJAE0eCSEIDQ4ATCGMAASbA6sDAncDAU8DAQMDEWgAAQ8AHwACCAAvK7j%2F8LMPAE0ruP%2F4QBEOAE0rjBEIEABNES4eG48JDLj%2FwEAYCgBNDAwDJggOAE0mjxZYBI8DCA4ATQNXAD8r7T%2FtKxI5Lysz7TIBEN4r7SsrEMZeXV0ROS9dXV0zEO0rMjIxMCsrKysrKysrARQGBycyPgI3DgEjIi4CNTQ%2BAjMyHgIHMjY3NjQ1NC4CIyIOAhUUFgH1zcoDP2VMMwwaQiM7UjQXGjVSODlXOx7gIz4dAQwfNCghLh4NQQGgztMBThg1VD0MDyM7TSolUEIqKExtiQ0NCxULK087IxwrMxhER%2F%2F%2FADj%2F8wC%2BAggCJgARAAABBwARAAABjwAQQAsBABEMBQBQAQgOTysrNAAA%2F%2F8AJv9hAL4CCAImAA8AAAEHABEAAAGPABBACwEOEw4HAVABABBPKys0AAAAAQA1AEwB%2FwIKAAYAYEAmBAUGAAQABq8FBBQFBAYFAwIBAAMAAa8CAxQCAwECAAUQDQBNBQK4%2F%2FBACg0ATQJQAIAAAgAAGS9dzSvNKwEYLy%2FBhwQrEAHBhwR9EMQBGC%2FBhwQrEAHBhwR9EMQxMBMFByU1JRecAWMa%2FlABsBoBK5JNt1C3TQAAAAIANQCZAf8BvQADAAcALkAaAwcHAgUFCQgCrz8ATwCvAAMABK8wBkAGAgYAL13t3l3tARESOS8zMy8zMTA3IRUhESEVITUByv42Acr%2BNuhPASRPAAEANQBMAf8CCgAGAGRAIgMEBQYDBgWvBAMUBAMFBAIBAAYCBgCvAQIUAQIAAQYEAQS4%2F%2FBAEA0ATQQBEA0ATQFQBoAGAgYAGS9dzSvNKwEYLy8vEMGHBCsQAcGHBH0QxBABwYcEGCsQAcGHBH0QxDEwEzcFFQUnJTUaAbD%2BUBoBYwG9TbdQt02SAAAAAgAT%2F%2FMBeQLFACEALQCSuQAJ%2F%2BCzCQBNCLj%2F4LMKAE0EuP%2F4sxEATQO4%2F%2FhANhESAEwigihAGzhIKCgSfwAYAQgYGANYH2gfeB8DH38HCycLNwsDCy8vLwECAy4VICsBKyVjArj%2F8LYPAE0CAIgDuP%2Fwtg8QAEwDBmgAPzMr7TIrP91xzgEQzjJdENZd7V0SOS9eXe0zLyvtMTAAKysBKysTIgcnPgEzMh4CFRQOBBUcARcjJjU0PgQ1NCYTFAYjIiY1NDYzMhauQj8aIlUwOUksERomLiYaAU4EFyQoJBc2CyQfHyQkHx8kAnciSBIWHzA7GyI1LiotNSEFDAUUFiA0LScoLBoqNf2%2FGygoGxsoKAAAAgBB%2F2cDdQLFAEUAVQD3QK2mRAGJQ5lDAmc%2FAVU%2FAVg1AZYpAZcjpyMCihkBaRkBCQIBqAMBA0lNfxMuLy%2FvEwGWE6YTAogTAVYTAROZHKkcAngcAVkcARx%2FkEGgQQKHQQEPQR9BAghBV1N%2F0AvgC%2FALA3kLiQuZCwMLdiaGJpYmAycmNyZXJgMmf%2BA3AZ83ATdWTVCHEDyHIRMQYEkXRocGLzKHLitpA3kDiQMDSwNbAwI6AwEDIAgLSAMABgAvMzMrXV1d3jLtMhDtMjI%2FM97tEO0yARDeXXH9XV3eXXHtENZeXV1d%2FV1dXc5dXV1xMi8zEO0yOV0xMABdAV1dXV1dXV1dXSUiJicOASMiLgI1ND4CMzIWFxEUFjMyPgI1NC4CIyIOAhUUHgIzMjY3Fw4BIyIuAjU0PgIzMh4CFRQOAicyNjcuAT0BLgEjIgYVFBYCsiAqDhY%2BHzBPOiAeO1U2M1UXFhIaJxkMLFJ1SUl6WDE0XX9MO00LChFZM1medkVEc5dUUpJuQB80SPAVKxEDAgYiFUdGRCsSDwwRHjtWOTBVPyQQCP7OIBokOkkmP29UMDFbhFRYhVktDgRKBg4zaaNwbKJrNjVlj1pEaEckUQkJDRoM6wIEWENHVAAAAgAIAAACjwK1ABEAGADcuQAP%2F%2FhANA0ATRAPDg0MEQwHCAkKCwYLBBgXFhUFFQUgDBIBTAsFFQVzBgsUBgsFBgECAxITFBUAFQC4%2F%2FBAQwwBTQwAFQBzEQwUEQwAERUVBhEaABoBQBpwGqAa0BoEDxoBDwYfBgIIBhkDBHkYXxVvFX8VAxIVEhUMEUIMQQtBBkIAPz8%2FPxI5OS8vXTPtMgEQxl5dXV1xEMYRORkvEADBhwUYK4d9xAErEIcOxMQFxMQOxMQQAMGHBRgrh33EASsQhw7ExAXExBCHDsTExMQQhw7ExMTEMTABKyEuASchByM%2BAzczHgMXAy4BJw4BBwIlER4Q%2FsY%2FZShGQ0IkWSRCQ0YoxSA%2FIiM%2FHy1XLbFuu6iZS0uZqLtuAQFXo0tLo1cAAAADAFb%2F%2BgJQArwAGAApADgAh7VXFQEwdhO4%2F%2FC0DgBNExC4%2F%2FC0DhIATBC4%2F%2FBADQkATRAkdhZACAkBTBa4%2F%2FhAEQ4QAEwWQAkATRYwCABNFjo6uP%2FAQBwJAU0ZOHMFqDk4NXwLEyp5KSkABgtFGh98BQBGAD8y7TI%2FMxI5L%2B05EO0yARD27TIrEN4rKysr7dQrKzIr7TEwXQUiLgInET4DMzIeAhUUBgceARUUBgEVFhceATMyPgI1NC4CIyczMj4CNTQuAiMiBgcBERUxMTAUFDAxMBU8aU0sNi09SJn%2B%2FwsPDSMXK006IxoxRCp9ZiRALxscMUIlJSoRBgIEBgUCoAUGBAISK0YzOUkSEk5HaGkBSfQBAQEBCxwyJiIvHAxOChoqIB4pGQwCAgABADr%2F8QJTAsUAJwDmQCMpQAwSSGgDAR4IEgBNhxuXGwICCBIATQIIDwBNEA8PIiMpGrj%2F8LMKAE0auP%2F4QBAJAE0adgUIEgBNBQgPAE0FuP%2F4tg4ATQWpKBC4%2F%2BhAFA0PAEwQFQgODwBMFQgJCgBMFXwPuP%2F4tBESAEwPuP%2FoQBkNDwBMDwoIEQBNCggOAE0KRSIYDQ8ATCIfuP%2F4tA4PAEwfuP%2F4QBMJCgBMH3wjGA0PAEwjAAgQAE0AuP%2F4tA4ATQBGAD8rKzIr7SsrMis%2FKyszKyvtKysyKwEQ%2FisrK%2B0rKxDGMjIvMzEwACsrXSsBXSsFIi4CNTQ%2BAjMyHgIXBy4DIyIOAhUUHgIzMjY3Fw4DAYBJeFYvNVp4RCtFMyAGHAkiKzMaN1g%2BIR06VzpDWBYZByQ2SA8wXIdXV4dcMAwQEARQBg4OCSZIZ0A%2BZkkoHAtQBQ8OCgAAAAACAFb%2F%2BgKPArwAEAAdAIJAHRQQDg8ATBQQCgBNFAgJAE0UEAgATRR2AEAJAU0AuP%2F4sxIATQC4%2F%2FBAGw4PAEwAQAkATQCpHxpzCKgeGhcICgBNF3wJDLj%2F%2BLYOAE0MRRsRuP%2F4QA0KAE0RfAgFCA4ATQVGAD8rM%2B0rMj8rM%2B0rMgEQ9u0Q9isrKyvtKysrKzEwARQOAiMiJicRPgEzMh4CATI2NTQmIyIGBxEeAQKPOGaOVSplKSllKlWOZjj%2Bh4qIiIopLQkJLQFbWoVXKwcKAqAKBytXhf6ajX9%2FjQIB%2Fe4BAgABAFYAAAIWArUACwA1QB0HAw8KHwoCCAoNBAhzAKgMBXkHBwEJeQtCA3kBQQA%2F7T%2FtEjkv7QEQ%2Fu0yEN5eXcbOMTAzESEVIRUhFSEVIRVWAaf%2BugEi%2Ft4BXwK1U85R8FMAAAAAAQBWAAAB%2BAK1AAkAKEAUBwMLBAhzAKgKBXkHBwEJQgN5AUEAP%2B0%2FEjkv7QEQ%2Fu0yENbOMTAzESEVIRUhFSERVgGi%2Fr8BHf7jArVT0VL%2BwQABADr%2F8gJeAsUAJwDRtQoQEABND7j%2F%2BEAsDwBNCRgPAE0XFidzAUAIAU0BQAgATQ8BjwECAagpKUAOAE0pQAsATQApAR%2B4%2F%2FizDgBNH7j%2F8EAQCgBNH3YMQAgBTQwIDwBNDLj%2F%2BEAMDgBNDEAIAE0MqSgXuP%2FwQBAOAE0XGhAKAE0afBEBAQcWuP%2FoQA4NDwBMFhEIDgBNEUUnJLj%2F%2BLMOAE0kuP%2FwtwoATSR8AgdGAD8z7SsrMj8rMysSOS8Q7SsyKwEQ9isrKyvtKytdKysQ9l0rK%2B3GMjEwACsrASsBMxEOAyMiLgI1ND4CMzIeAhcHLgEjIg4CFRQeAjMyNjcB%2FWEMLTtGJEl4Vi81WnlFL0k0IQYfHWAyNVc9Ih07VzsqOw0BVf68BAoKBzBchldXh1wwDBAQBFETGSZIZ0A%2BZkkoCQQAAAAAAQBWAAACawK1AAsAN0AfAwtzDwIfAgIIAqgNBQlzB6gMCnkEBAYIQQZCAkIAQQA%2FPz8%2FEjkv7QEQ%2Fu0yEP5eXf3AMTABMxEjESERIxEzESECCmFh%2Fq1hYQFTArX9SwFA%2FsACtf7fAAABAFYAAAC3ArUAAwAkuQAF%2F8BAEQgBTQAFAQWoAXMDqAQCQgBBAD8%2FARD2%2FeZdKzEwEzMRI1ZhYQK1%2FUsAAAAAAQAI%2F%2FEBowK1ABMAebkAA%2F%2FwQBQJAE1nEAERcxNACQFNDxMBE6gVFbj%2FwEAYCQFNCwpACQFNCkAJAE0PCgEIChQSQQsOuP%2F4QB0PAE0OfAoQDA4ATAoFCBAATQUQDwBNBQgIAE0FRgA%2FKysrMyvtKzI%2FARDGXl0rKzIrEPZdK%2B0xMABdASslFA4CIyIuAic3HgEzMjY1ETMBoxU1WUMiOS4iCiYVQzJIQmHfMlZBJQsREwlNDyFJWQHNAAABAFYAAAJvArUAGgDbQGAaCA8ATVkOeQ4CDRAUAE2IDQHYDAHYCAGnBgELGBMATQsgCxJICAsYCygLAwgLcwkJChYZGhUaBAMCAQAFABoIEwBNwBoByhoBGgUABXMVGhQVFRopADkAAggAGAACAAq4%2F%2BhALxQATTAKUApgCgMKHBURFHMSqBsaQcAQARB7wAUBBc8VAQ8VARUVERNBEUIKQgBBAD8%2FPz8SOS9dcTlx7XE%2FARD%2B7TIyENZdK8ZdXYcQK4d9xAFdcSsQhw7ExMTEEIcOxMQBETMYEO1eXSsrMTBdXV1dK10rAQ4DBx4DFyMuAycRIxEzET4DNwJXH05VVCUrYFxRG3IeTldZKmFhI1RSSRkCtSJTV1QkHlhkaC8vX1dKGv63ArX%2BziFTVE0dAAAAAAEAVgAAAfcCtQAFABtADQAHBHMCqAYDQQV5AUIAP%2B0%2FARD27RDGMTAlFSERMxEB9%2F5fYVRUArX9nwAAAAEAQwAAAyUCtQApAUq5ACX%2F8LMRAE0kuP%2FwsxEATSG4%2F%2FhAEhUZSGYhpiEC9yABpx0BZh0BGbj%2F6LYRAE1XGQEYuP%2FwQDkRAE0JFQGIDvgOAmkOAfcJAQgIGAgC%2BQgB2AjoCAKZCKkIAogIAWkIAVcDAQeWEaYRAocRARFzECu4%2F8C1CQFNK6kfuP%2FwtgwSAUwfcx64%2F8BAJAoNSB4imRsBG3OmHAGFHJUcAk8cARwpdk8AAQCJEJkQqRADELj%2FwEAmCg1IEAoQDBIBTApzDwsBCAupKhZAGh5IFnwpKRwfQhxBG0ERQSK4%2F%2BC0DQBNIge4%2F8C3FRkBTLAHAQe4%2F8BADBoeSKQHAQd8EEEKQgA%2FP%2B1dK10rMis%2FPz8%2FEjkv7SsBEPZeXe0r1Std1F391F1dXe1dMtUr7SvmKxDtXV0yMTBdXV1dXV1xXV1dXStdK11dXV0rKyslLgUnBgIHIz4DNzMeAxc%2BAzczEhMjJgInDgUHAY0KISYrJiEKCw4FXwQKDQ8JVRs%2BPjoYGDo%2BPhtVIhFfBQ4LCiEmKyYhCmAYSlZbUUARdv7tjFq3sahLLHiDgzY2g4N4LP6x%2FpqMARN2EUBRW1ZKGAAAAAABAFYAAAKCArUAEwCWQCCHDgEgAwF3CwEDCwAHAHMQCxQQCxBzEkAICQFMEqgVFbj%2FwEBDCQFNBBWEFQIIB3MJqBQTQosHmwcCegcBawcBWgcBSQcBhBCUEAJiEHIQAgJAEFAQAhQQNBACBxAIEUELQQpBCEIAQgA%2FPz8%2FPxI5OV1dX11dXV1dXV0%2FARD27V5dKxD2K%2F2HK4d9xAFfXTEwAF1dIS4FJxEjETMeAxcRMxECMRY5QkdGQhxfTS9raV0gXyVcY2ZfVCD94wK1MoWNiDUCAf1LAAACADr%2F8QLQAsUAEwAnAQe1EiAQAE0MuP%2FgsxAATQi4%2F%2BBAHRAATQIgEABNEQgSAE0RCA8ATQ0IEgBNDAgPAE0HuP%2F4sxIATQe4%2F%2FizDwBNA7j%2F%2BLMSAE0DuP%2F4QBUPAE0eCA4ATR4QCgBNHnYKQAkBTQq4%2F%2FizEgBNCrj%2F%2BEAPDwBNCkAJAE0KqSnwKQEUuP%2F4sw4ATRS4%2F%2FBAHQoATRR2AEAIAU0ACBIATQAIDwBNAEAIAE0AqSgZuP%2F4sw4ATRm4%2F%2FizCgBNGbj%2F8EAaCQBNGXwPRiMIDgBNIwgKAE0jEAkATSN8BUUAP%2B0rKys%2F7SsrKwEQ9isrKyvtKytdEPYrKysr7SsrMTAAKysrKysrKysBKysrKxM0PgIzMh4CFRQOAiMiLgI3FB4CMzI%2BAjU0LgIjIg4COjVbeURDd1o1NVp3Q0R5WzVmIDtVNTVUOyAgO1Q1NVU7IAFbWYhbLi5biFlZiFsuLluIWT9nSCcnSGc%2FP2dIJydIZwAAAAIAVgAAAj0CvAAOABwAUrkABf%2Fwsw0ATQS4%2F%2BizDQBNArj%2F8LUNAE0YdgO4%2F%2FhAGQ4PAEwDHgkRcwuoHRJ5CAgACkIRD3wMAEUAPzLtMj8SOS%2FtARD27TIQ1ivtMTArKysBMhYVFA4CKwERIxE%2BARciBxEzMj4CNTQuAgEOkp0rUXRJTWEpZTI%2BIUkyUDceHzVGArxvbjxVNRj%2B%2FwKrCgdVA%2F7wDR82Kig0HwwAAAAAAgA6%2F0cC0ALFAB4AMgELuQAN%2F%2BizEABNCLj%2F4EAREABNAiAQAE14HAENCA8ATQe4%2F%2FizEgBNB7j%2F%2BLMPAE0DuP%2F4sxIATQO4%2F%2FhAHQ8ATQ92GhoAKRAOAE0pEAoATSl2ChUVCkAJAU0KuP%2F4sxIATQq4%2F%2FhADA8ATQpACQBNCqk0H7j%2F8LMOAE0fuP%2FwQB0KAE0fdgBACAFNAAgSAE0ACA8ATQBACABNAKkzJLj%2F8LcJCgBMJHwPGrj%2F%2BLcOAE0aRhR8Fbj%2F8LQREgBMFbj%2FyEATCABNFS4QCQoATC58BQgOAE0FRQA%2FK%2B0rLysr7T8rM%2B0rARD2KysrK%2B0rKxDmKysrKzIvEO0rKxI5L%2B0xMAArKysrK10BKysrEzQ%2BAjMyHgIVFA4CBx4DFwcuAycuAzcUHgIzMj4CNTQuAiMiDgI6NVt5REN3WjUoR182ASc8SiUXNF9NNAk%2FcFIwZiA7VTU1VDsgIDtUNTVVOyABW1mIWy4uW4hZTnpZNwoXIxgQBEwHFyY7LAQzW4JVP2dIJydIZz8%2FZ0gnJ0hnAAIAVgAAAmMCvAAaACgAhUATCAgSAE2YCAGJCAFXAgELJHYAGLj%2F%2BLMPAE0YuP%2FwQAkOAE0YEA0ATRi4%2F8BALAoLAEwYBQZzZwSHBAIEBAUqYCoBDx1zEagpHRt8FR55CwAODhASFUUQQgVCAD8%2FPzMSOS8zM%2B0Q7TIBEPbtMl0QxjIvXe0Q1isrKysy7TIxMF1dXSsBHgMXIy4DJwYiKwERIxE%2BATMyFhUUBgMiBxEzMj4CNTQuAgGpEDE0MxJqFC8wLxMNGw5XYSllKpKZSdo%2BIUUyUDceHjNFASIUQU9WKCZORz4XAf7xAqsKB25tRWIBLQP%2B%2BwocMyonNB4MAAABACH%2F8QH1AsUAMAEfQDkFJwEIDgEIDQEKJgGXIgEFHwETqBIBmRIBiBIBEiUAIQGJCAEhHggFIQUIcx4hFB4hCB4eBRsCdiW4%2F%2FC0EBIATCW4%2F8C0Cw4ATCW4%2F%2FBAYQkATXAlASUyG3YMEBIATQwYEQBNDBAQAE0MEAkATQwuLUAIDkgtMZgFAXkFAT0FTQUCCwUBCHQeAUIeAQUeBR4PRy4BLmcAAQB8LQgPAE1YLQEtKEZ3E5cTAlYTARMWfBK4%2F%2FBAEQ8ATYYSlhICdxIBVhIBEg9FAD8zXV1dK%2B0yXV0%2FM10r7V0yXRI5OS8vXV1eXV1dXQEQ1isy1isrKyvtENZdKysr7RE5ORDBhwQrEAHBhwR9EMQAXV0BGBDEXV1dMjEwAF1dXQFdXV03MjU0LgInLgM1NDYzMhYXBy4BIyIOAhUUHgIXHgMVFAYjIi4CJzceAfeYGy47ISZEMh18bT9nGB8VUzccMCQVFig1IC1LNx6FeSlFNicLHRdeRWggLSEYDA0hLT4sW2YbEE8NGQwYJRkcJh0YDBIkMkUxW2ILDxEGUA0gAAAAAAEAEAAAAiUCtQAHADpAJAl%2FAY8BAgABAQEDc38EjwQCAAQBBA8GHwYCCAYIA0IBBnkAQQA%2F%2FcA%2FARDWXl3WXV391l1dxjEwARUjESMRIzUCJdph2gK1VP2fAmFUAAAAAAEAUf%2FxAl8CtQAXAFlALhYIDQBNFQgNAE0DCA0ATQIIDQBNEHMPEh8SAggSqBmfGQEHcwWoGBFBBkELfAC4%2F%2FizDABNALj%2F8LQLAE0ARgA%2FKyvtPz8BEPbtXRD2Xl3tMTAAKysrKwUiLgI1ETMRFBYzMj4CNREzERQOAgFYRGRAH2FcSiU9LBhhH0FjDypJZDoBs%2F5YaF4XMEs0Aaj%2BTTpkSSoAAQAKAAAChgK1ABIAx0AqiBEBeBABZw4BNw1HDYcNA3cMAbgKAYcJAXgJATgBSAEC1Q%2FlDwLGDwESuP%2FwQDAMEgFMDxIPDBJzAAUUAAAFDBAMEgFMDwwLBg8GDHMLBhQLCwYUzwABAAUgExsBTAW4%2F8C1ExZIBXMGuP%2FgQBwTGwFMzwYBBg8LHwsCCAsTEkEMQQtBBkIFQgBBAD8%2FPz8%2FPwEQ1l5d1V0r%2FSsr1V3GhxArEADBhwV9EMQBK4cYECsIfRDEASsAXV0xMAFdXV1dXV1dXV0BDgMHIy4DJzMWEhc2EjcChiZFQkEiWyJBQ0YlazZoNzdoNQK1bruomUtLmai7bp7%2B3IB%2FASWeAAEAHAAAA4UCtQAgAV5AJDggAeggAR8IEgBNOB8BeBgBNxIB5xIBdwkBhwIBhwEBmwABHbj%2F8EAKEQBNdB0BtB0BGbj%2F8EBVDBIBTB0ZFBlzGh0UGh2gIgEiGhQ3HgELHhseAjYRAYkRARQRAR4RFBFzAB4UAB4gFAHQFAEUAHsEAbsEAZkEAQgQDBIBTAQIDQhzBwQUBwcElgMBELj%2F8EBgEQBNNhABmhABKRBJEFkQAxQQAQMQDRBzAAMUAAMAcA0B4A0BDaAHAQ8HHwcCCAchHkIdQhpBFA0AIBEATZkAAXgAAWYAAZcNAWANAUQNVA0CAA0EEBEZQQhBB0EEQgNCAD8%2FPz8%2F1sAROTldXV1dXV0rETM%2FPz8BENZeXV0Z1V1xGNWHK4d9xAFdXV1xK12HGBArh33EAStdXXEYEMVdcYcrh33EAV1dcV1dGRDVGMZdhyuHfcQBK11xKzEwAF0BXV1dXXFdcStdcQEOAQcjJgInMx4DFz4BNzMeARc%2BAzczBgIHIy4BAdEqVithNVEjZg4eICARKFonVCdcKBAgHx4OYyRRNGEsVAHWhuRsmQFUyE6ck4Y5afuGhvtpOIaTnU7I%2FqyZbOQAAAABABcAAAJjArUAFwDjs6AZARm4%2F8BALAwQSIoUAQMIEQBNhhEBFBMKFxAREgsXEA4SCwAPCRMKBRMKAA8KEAwSAUwKuP%2F4tBESAEwSuP%2FwQBQMEgFMChILEnMTChQTChMXAQAPALj%2F8EBIDBIBTAAIEgBNEBAMEgFMABAXEHMPABQPDwBAF1AXYBeAF6AXBRcZkBkBDxkvGQIPDwsfCwIICxgXQhNBEkEQQQ9BC0IKQgBCAD8%2FPz8%2FPz8%2FARDWXl3GXV0Qxl2HECuHfcQBKysrhw7EARgQ1ocrh33EASsrKw%2BHDsQPDw8xMABdAStdK10hLgMnDgMHIz4BNwMzGwEzAx4BFwH2DyoyNhkZNjErD2std0PecKupb9pEeC4eSU9PIyNPT0keWLZgAUf%2B%2FgEC%2Fr1huFkAAAABAAYAAAJQArUAEACLuQAL%2F%2FBAUgwSAUwICwgFC3MMDxQMDw8QEm8MnwyvDAMMEHMABRAMEgFMCAUEAQgBBXMEARQEAQFvAJ8ArwADAA8EHwQCCAQRAQEPDwwQQgxBC0EFQQRBAEIAPz8%2FPz8%2FEjkvMy8BENZeXcZdMocrEADBhwV9EMQBKxgQ%2FdZdxhEzhysIfRDEASsxMDMRLgEnMx4BFz4BNzMOAQcR%2B0V6NnImXjIxXidsNnpEARZr0GRSqU5OqVJkz2r%2B6AAAAAABACEAAAIYArUAFQBNQCoMERILEgEABwcSCxJzAAcUAAcADwgfCAIICBcTCxYSABN5FEELBwh5CUIAP%2F0yxT%2F9xTMBEN7GEN5eXdaHK4d9xIcOxBCHDsTEMTABDgUHIRUhNT4FNyE1IQIOFz1ESEU%2BGQGG%2FgkVOUFGRUEb%2FpwB2wJoGklZY2VkLVNBKGBkZl5SH1MAAQBg%2F0cBQQMPAAcAHUANAgUJA38HCASHBgKHAAAv7S%2FtARDe7RDWwDEwEzMVIxEzFSNg4YqK4QMPSfzKSQAAAAH%2F6P9HAZgDDwADAClAFgEDAAOuAgEUAgIBAAQCA2ICYgFkAGQAPz8%2FPwEvEMaHECuHfcQxMAMzASMYXQFTXwMP%2FDgAAQAI%2F0cA6QMPAAcAHUANA38HCQUBCASHBgKHAAAv7S%2FtARDWwBDe7TEwFyM1MxEjNTPp4YqK4blJAzZJAAAAAAEALwE7AgUCtQAGAGxAQokFmQUCOAVoBXgFA6sEuwQCpAK0AgKGAZYBAjcBZwF3AQNKA1oDAgQEBQMCAgEDBgYDAAADAwcCBH8DjwOfAwMDBgAvxV3NMgERMxkvMxgvETMvGRDFMhgvGRDFMhgvMTAAXQFdXV1dXV0BEwcLAScTAUPCSqGhSsICtf6sJgEa%2FuYmAVQAAAAAAf%2F8%2F0cB8P%2BWAAMAGEAKAQEFAAAEAIcCSwA%2F7QERMy8RMy8xMAchFSEEAfT%2BDGpPAAAAAAEAPQJEAPkDCAADADeyAgEAuP%2FAQAkREgBMAAMEAwC4%2F8CzEgBNALj%2FwLUNAE0AAQIAL83dKyvNARDeGcUrGN0ZxTEwExcHJ3mAL40DCJoqiQAAAAIAK%2F%2F1AcACFQAOADUA2EALIQgKAE0sCBEATSK4%2F%2BCzCABNErj%2F4EAXCABNCggRAE0DKQgSAE0pfxRACAkBTBS4%2F%2FhAEQoATRQ4CAkATBSqNx83ATIzuP%2FAQA4KDABMMwyCH0AICQFMH7j%2F6LMSAE0fuP%2FwQBoRAE0fUAkKAEwfQAgATR82KSSIBAdAEgFNB7j%2FwEAKCAFNB0AREgBMB7j%2FwEASCABNBwcPAwCIFRpRMi%2BIMw9QAD8z7TI%2FM%2B0yEjkvKysrKzPtMgEQ1isrKysr7cQrMl0Q9isrK%2F0rwDEwACsrKysBKyUyNjc1LgEjIg4CFRQWEzIeAhURDgMjIi4CNTQ%2BAjMyHgIXNTQuAiMiBgcnPgEBACEzEQotIBUvJxpALzhNLxQMKzY%2FHyxKNh4jPFAsDh4bFAQKGi0kLkURCxJUQwMEmwUHBhMhGzInAdIdNUks%2FrsCBwYFEidALSs%2BJhIDBQQBGhctIxUNB00IDwAAAAIAUP%2F0AhoDCAASACEAiLUbIA0ATRi4%2F%2BBAKw0ATRkIDgBNGYIIEBIATQgIDgBNCBALDQBMCKsjEgAhfxCqIhJOExaIEA24%2F%2Fi0ERIATA24%2F%2FC1DgBNDVEhuP%2FgtxESAEwhHogAuP%2FgQAwREgBMAAMIDgBNA1AAPyszK%2B0yKz8rKzPtMj8BEPbtMjIQ9isrK%2B0rMTAAKysTPgEzMh4CFRQOAiMiJicRNxEeATMyNjU0LgIjIgYHrRFFLThXPB8lRGA8QWQgXQ4zJktaECQ5KCZAEQHxCxgoSGQ8P2VGJhIJAukQ%2FUkEB2NbKEYzHRoOAAEAM%2F%2F0Ab0CFQAhALe5ABf%2F8LMNAE0SuP%2Fwsw0ATQe4%2F%2FCzDQBNA7j%2F8EAKDQBNAggQAE0NHbj%2FwEAQDBZIHSM%2FI08jAiNADxZIFLj%2F%2BEAMDQBNFIIFQAgJAUwFuP%2FwQCMOAE0FQAgJAEwFqyIOEQgNAE0REAwATRGIDQoIDgBNClAcGbj%2F%2BLMNAE0ZuP%2FwtgwATRmIHQC4%2F%2Fi0DgBNAFEAPysy7SsrMj8rM%2B0rKzIBEPYrKyvtKytdENYrxjEwKysrKysFIi4CNTQ%2BAjMyFhcHLgEjIgYVFB4CMzI2NxcOAwEyP19BICNAXTkjRiAVFTcfTlMTKUAuJTwRDQgeJisMKEhjPDxkSSkMDU8KDGJeKkYyGw4ITgUJBwUAAgAz%2F%2FQB%2FQMIAA4AIQCtuQAX%2F%2FCzDQBNCrj%2F2LMNAE0KuP%2F4tAsMAEwFuP%2FgQBYNAE0OIX8QQAgJAUwQQAgJAEwQqiMIuP%2FwQAwOAE0IghlACAkBTBm4%2F%2FC0ERIATBm4%2F%2FBADQ4ATRlACAkATBmrIgC4%2F%2BC3ERIATAADiCG4%2F%2BBACRESAEwhHlAOC7j%2F8EAKEgBNC4gRFFEQTgA%2FPzPtKzI%2FMyvtMisBEPYrKysr7SsQ9isr%2FcAxMCsrKysBLgEjIg4CFRQWMzI2NxE3EQ4BIyIuAjU0PgIzMhYXAaARQCYoOSQQWksmMw5dIGRBPGBEJR88VzgtRREBmg4aHTNGKFtjBwQCpxD9BwkSJkZlPzxkSCgYCwAAAAIAM%2F%2F0AfkCFQAaACMA7LkAIf%2Fwsw0ATRm4%2F%2FCzDQBNGLj%2F4LMNAE0OuP%2FYsw0ATQe4%2F%2FBACQgATRgQDwBNB7j%2FwLQPEABMBrj%2F%2BEAVDwBNEl8TbxMCExuCCKslTyVvJQIjuP%2F4tQ4ATSOCDLj%2F%2BEAMDgBNDIIAQAgJAUwAuP%2F4sw4ATQC4%2F%2FBADgwATQBACAkATACrJBIPuP%2FwQAkNAE0PiBYjhwu4%2F8BACRMUAEwLCwUTFrj%2F%2BEATDgBNFlEeCA4ATR4QDQBNHogFUAA%2F7SsrPyszEjkvK%2B0Q7SsyARD2KysrK%2B0r7StdEPbtxF0yMTAAKysrASsrKysrEzQ%2BAjMyFhUcAQchHgEzMjY3Fw4BIyIuAiU2JiMiDgIHMyhCVi1pcAH%2BnAZSVzFDEQ0RVTZEY0AfAWUBQTkgMSITAwEDRWdEIoOGBhMIUVQRCE4JFClIY24%2FURkoNBsAAAEAUAAAAYsDCAATAERAIQQTAQgHEgEMAxUKDn8QqhQPSg2HC0kEBwgPEABMB4gDALj%2F%2BLQRAE0ATwA%2FKzLtKzI%2F7T8BEPbtMhDWxjEwAF1eXQEyFhcHLgEjIgYdATMVIxEjETQ2ARspOwwRDCwgQTTIyF1iAwgMBVAGC0c8LU7%2BRgI3ZG0AAAACADP%2FRAHyAhMAHgAtAOq5ACj%2F4LMNAE0nuP%2Fwsw0ATSO4%2F9izDQBNErj%2F8LMQAE0SuP%2FoswgATQu4%2F%2FCzDQBNCrj%2F8EAZDQBNAB4tfxBACAkBTBBACAkATBCqLxgXJbj%2F%2BLMOAE0luP%2FwswwATSW4%2F%2FhADAsATSWCCEAICQFMCLj%2F8LQREgBMCLj%2F6EAVDgBNCEAJAE0ISAgATQirLhgbiBcUuP%2FwQCEODwBMFFIfIogQDVAtIBESAEwtKogAIBESAEwADwMBCAMAL15dMyvtMis%2FM%2B0yPysz7TIBEPYrKysrK%2B0rKyvEMhD2Kyv9xDIxMCsrKysrKyslDgEjIi4CNTQ%2BAjMyFhcRFAYjIiYnNx4BMzI2NQMuASMiBhUUHgIzMjY3AZYMRS4vUz4kIkFeO0FhIXx%2BMVcgERxRLFNJAQ4vKEtRFyczGyU%2BEjQIGR4%2FXkE5X0UnEwj%2BMHhsEAtRCxFCSAGYBAdiUS1AKhQVDgAAAAABAFAAAAH0AwgAGQBuuQAJ%2F%2FC2CABNSxABCbj%2F%2BLQPEABMCLj%2F%2BEAsEABNDX8LQAgJAUwLQAgJAEwLqht%2FG%2B8bAk8bvxsCAhh%2FAKoaGUoMShgTiAa4%2F%2Fi2DwBNBlACTgA%2FPyvtMj8%2FARD27TJdcRD2KyvtMTAAKytdASszETcRPgEzMh4CFREjETQuAiMiDgIHEVBdGjsdPlIxFF0NHjIlDyAdFwUC%2BBD%2B9goLIz9YNf7cARAwRCoTBAUGAv5QAAACAEIAAAC6AuAAAwAPADNAEAAREBECCBGqDQN%2FBwGqEAS4%2F8BACwwSSASJClMCSQBKAD8%2FP%2B0rARD2Mv0y5l5dMTAzIxEzJyImNTQ2MzIWFRQGrV1dLxkjIxkZIyMCCF4hHBwhIRwcIQAAAAAC%2F7z%2FRQC6AuAADwAbAE%2B5AA7%2F6LQJCgBMDrj%2F2EAUCABNAB0QHQIIHaoZDH8TAwqqHBC4%2F8BADwwSSBCJFlMLSQQHiAMAUgA%2FMu0yPz%2FtKwEQ9sQz%2FTLmXl0xMCsrByImJzceATMyNjURMxEUBhMiJjU0NjMyFhUUBgUMJg0MChoLMCldWywZIyMZGSMjuwYFTAMEOzkB%2F%2F4CZGEDISEcHCEhHBwhAAABAFAAAAH8AwgAGgD%2FQBUVCBUATQkYEABNBggYAE0LEA8ATQu4%2F%2FBAGAwATQsQCwBNCggLAE0JEBgATQgIDwBNArj%2F8LMZAE0GuP%2FgtBQYAUwGuP%2FgtAgBTQYVuP%2FgsxsBTRW4%2F%2FBAGxQVAUwVCA4ATQAgGwFNACAWGAFMABAUFQFMALj%2F4EAbDwBNFQAWAH8QFRQQFQAQFhAIAU0WBRAIAU0FuP%2FgsxIATQW4%2F8BAIg0QSAUcEAx%2FDqobFkkQEAsMAEwQEAYVSQ9ODUoMSgZKBUoAPz8%2FPz8%2FEjkvKz8BEPb9wBDWKysrxisQAMGHBSuHfcQBKysrKysrKzIrKzEwACsrKysrKysBKysrAR4DFyMuAycVIxE3ET4DNzMOAwEMHkNBOhRtFTc8PhxdXRg6ODISbRc2OjsBHhdET1EjI0lDOBP6AvgQ%2FiYYOjw3FRk8PT0AAQBO%2F%2FYA%2BQMIAAsAPUATASAMAE0AEAgSAUwBIAgLAEwNC7j%2FwEAQCAxIC4AFfwOqDAVOC4gAUQA%2F7T8BEPb9Gt4rxjEwACsBKysXLgE1ETcRFB4CF%2BxWSF0IEh4WCgJGSgJwEP2PFx4SCQMAAQBQAAADEwITAC0BIUARIyANAE0aEBESAEwVIA0ATQ64%2F%2FC0ERIATA64%2F%2BCzEABNDrj%2F0LMPAE0NuP%2FwtBESAEwNuP%2FosxAATQ24%2F9izDwBNDbj%2F4LMIAE0MuP%2FwQBQQAE0JEBIATQkIEQBNCBAQEgBMBrj%2F%2BLMPAE0FuP%2FgtBESAEwFuP%2FosxAATQW4%2F9CzDwBNBLj%2F6LMQAE0vuP%2FAtAgJAUwvuP%2FAQDcJAE0vqhJ%2FEEAcAE0PEAEQBiB%2FG08eAQ8eAQgeK38tqi4sSh9KEUorJiYbEBAATRsYiAsAAwMGuP%2FwtAgATQYLuP%2F4tQkKAEwLUAA%2FKzMrMy8zEO0yKzIvMz8%2FPwEQ9u3cXl1xMv053F0r7eYrKzEwACsrKysrKysrKysrKysrKysrKysTPgEzMhYXPgMzMh4CFREjETQuAiMiBgceARURIxE0LgIjIg4CBxEjUCBpRTJEFwceLDYfPEwrD10JGSsiLz0LCAhdChkrIQ4gHRgEXQH5CBIbGgUSEQ0jQFg0%2FtwBEC5CLBUZCho%2BIv7cARAuQiwVAgMEAf5JAAAAAAEAUAAAAfQCEwAXAG65AAb%2F8EAOCABNDggNAE0NIA0ATQa4%2F%2FCzEABNBrj%2F%2BLMPAE0FuP%2FoQB8QAE0Kfw8IHwgCCAiqGb8ZARV%2FF6oYFkoJShUQiAADuP%2F4tA8ATQNQAD8rM%2B0yPz8BEPbtXRD2Xl3tMTAAKysrKysBKxM%2BATMyHgIVESMRNC4CIyIOAgcRI1AgakU%2BUjEUXQ0eMiUPIB0XBV0B%2BQgSIz9YNf7cARAwRCoTAgMEAf5JAAAAAgAz%2F%2FMCGwIVABMAHwCBuQAf%2F9izDQBNG7j%2F2EAbDQBNGSgNAE0VKA0ATRQQDgBNFIIAqyF%2FIQEauP%2FwQCMOAE0aggpACAkBTApACAkATAqrIBcIEgBNFwgOAE0XiA9QHbj%2F%2BLMSAE0duP%2F4tg4ATR2IBVEAP%2B0rKz%2FtKysBEPYrK%2B0rXRD27SsxMAArKysrARQOAiMiLgI1ND4CMzIeAgc0JiMiBhUUFjMyNgIbJEFaNTVaQSQkQVo1NVpBJGFPRERPT0RETwEEPmRIJydIZD49ZUgnJ0hlPVhnZ1hYZ2cAAAAAAgBQ%2F0cCGgITAA4AIQB6QFgNEA0ATQwgDQBNAhgSAE0CMA0ATQAQEQBNABgOAE0Afw8IEwBNDxASAE0PCA4ATQ8QCwwATA%2BrIwYXfxmqIgYDiBodUBhLByAREgBMBwqIFyAREgBMFxRRAD8zK%2B0yKz8%2FM%2B0yARD27TIQ9isrKyvtKysxMCsrKysBNCYjIgYHER4BMzI%2BAjcUDgIjIiYnFSMRPgEzMh4CAblaSyovDhFAJig5JBBhHzxXOC1FEV0hY0E8YEQlAQNbYgYE%2FrgOGh0zRSg7ZEgoGAvQArEIEyZGZQAAAgAz%2F0cB%2FQITAA4AIQC5uQAR%2F%2FCzDQBNDbj%2F8LMSAE0NuP%2Fgsw0ATQO4%2F%2FBAFw0ATRkZCH8XQAgJAUwXQAgJAEwXqiMAuP%2F4QAwOAE0Agg9ACAkBTA%2B4%2F%2BizEgBND7j%2F8EAUDgBND0AICQBMD6siCCAREgBMCAW4%2F%2FBAJg0ATQWIGiAREgBMGh1RGEsJDBARAE0MCA4ATQwQCw0ATAyIFxRQAD8z7SsrKzI%2FPzMr7SsyKwEQ9isrKyvtKxD2KyvtMi8xMCsrKysTFB4CMzI2NxEuASMiBgc0PgIzMhYXESM1DgEjIi4ClBAkOSgmQBEOLypLWmElRGA8QWMhXRFFLThXPB8BAyhFMx0aDgFIBAZiWz9lRiYTCP1P0AsYKEhkAAAAAAEAUAAAAXgCEwARACVAEgYFEwx%2FDqoSDUoMBgmIDwUAUAA%2FMjLtMjI%2FARD27RDGMjEwATIeAhcHLgEjIgYHESMRPgEBEAwfHhkGEAszKBozCF0hYgITAwUFAlEECwsC%2FksB8gwVAAAAAQAo%2F%2FQBlwIVAC0BOrkAJf%2FYswkATSW4%2F8hAEwgATQwwCQBNDEAIAE0JGBIATSu4%2F%2FBADxMATSYYCgwATCUIDgBNIrj%2F%2BLMSAE0iuP%2FgsxEATSK4%2F%2FhACQ8ATSEIFABNIbj%2F8LMSAE0huP%2FwswoATQ24%2F%2Bi0CwwATA24%2F%2BBADgoATQkQEQBNCBgKAE0GuP%2F4QEITAE0GCBIATQYIDxAATAUIDwBNBRALDABMCAUhHggeIX8FCBQFCCEFHgUaA3%2BfEwETJKsvPy8BGn8LKi4eCBUATR64%2F%2Fi2EABNBR4OK7j%2F8LMVAE0ruP%2F4QBQUAE0rAIgqJ1EUF4gTDggTAE0OUAA%2FKzPtMj8z7TIrKxI5OSsrARDe1u1dEPbEXe0ROTkQwYcEKxABwYcEfRDEMTAAKysrKysrKysrKysrKysrKysrASsrKysrNzI2NTQmJy4DNTQ2MzIeAhcHLgEjIgYVFB4CFx4DFRQGIyImJzceAck5NzY%2BHjcoGGZYFiwmHQgRD0AtJzoNGygcJTopFmtjRU4OERBGQx4hIigZDBkiMCNFUQUHCANQCBEfIREaFRIKDhskMyVIShcGUAYYAAAAAAEASv%2F1AYACqQAXAF5ADx8ZLxk%2FGQMTIAgATQwCDbj%2FwEAQDRhIDRlPGQEDF38VqhgMCbj%2F%2BEAZERIATAmIDRAICgsATBAQCQBNEFEXAocASQA%2F7c0%2FKysz7SsyARD27TJdENYrxjMxMCtdEzMVIxUUHgIzMjY3Fw4BIyIuAjURN6fFxQwYJBgqMwoSDkYtNUUqEV0CCE7wJzMdDBMETQcVGzZPNAHQEAAAAAABAEr%2F9QHuAggAFQBQQCEGGAgATVQMARN%2FDxUfFQIIFaoXvxcBCn8IqhYUSQlJEw64%2F%2FBADw0OAEwOiAADEA8QAEwDUQA%2FKzPtKzI%2FPwEQ9u1dEPZeXe0xMABdASslDgEjIi4CNREzERQWMzI%2BAjcRMwHuIGlFPFIyFl08Rw8gHBcFXQ8IEiNAVzUBJP7wX1ICAwMCAbcAAAEAEAAAAeYCCAASALu2DwgPEABMC7j%2F%2BEAPDxAATAYICgBNBhAJAE0BuP%2F4twkKAEyQDQENuP%2FAszxCSA24%2F8CzJi1IDbj%2F4EBKExZIDRINCBJ%2FAAMUAAADDQgHBA0ECH8HBBQHBwQUnwCvAAIAABAAAgADf58ErwQCAAQQBAIEDwcfBwIIBxMSSQhJB0kESgNKAEkAPz8%2FPz8%2FARDWXl3VXV391V1dxocQKxAAwYcFfRDEhxgQKwh9EMQAKysrcjEwASsrKysrAQYCByMmAiczHgMXPgM3AeYnaTFUMWknZAwhJSYRESYlIQwCCJL%2B9GpqAQySMW9tZigoZm1vMQABABIAAAL3AggAJAPKQAlAJgEdCBAATRa4%2F%2FCzEAFNFbj%2F%2BLMSAE0LuP%2F4sxcATQW4%2F%2FizEgBNBbj%2F8EATEQBNBSgIAE0BCBEATSMYDwBNGbj%2F8EAJEABNCBgPAE0kuP%2FwswkBTSS4%2F%2FC0ExUATCS4%2F%2Bi0DQ4ATCS4%2F%2BhACgkATUAkATQkASS4%2F8CzJipIJLj%2F4EAOICVIciQBQCRQJGAkAyS4%2F%2BBADxMXSCQgGyB%2FISQUISQmIbj%2FwLMOAU0huP%2FAtQ4ATSEbALj%2F6LMOAU0AuP%2FwQEoNAU0AIAoBTQAQExUATAAgDwBNABAKAE1bAAEAICgrSP0AAesAAdkAAbsAAZoAAYsAAX0AAV8AbwACSgABuwDLANsAAxYQEgFNFrj%2F8EAODwFNFigOAU0WGA0BTRa4%2F%2FBADwoBTRYgFhcATBYQFQBNFrj%2F%2BLMUAE0WuP%2FwQAkTAE0WGBIATRa4%2F%2FBADg8ATRYgDgBNFhgNAE0WuP%2FwQIYKAE02FgEEFgH2FgHkFgGXFrcWAnYWAWQWAVIWAUYWAfkWAbYWAQAWGxZ%2FAwAUAwA%2FG08bAn8b7xsCGwMHCA4BTQcYCQFNBxATFQBMBxAOAE0HGA0ATQcYCQBNTwcBOwcBB0AmKkgHICAlSH0HAU8HXwdvBwMHIBMXSAcLEAt%2FCgcUCgoHBrj%2F6LMOAU0GuP%2F4QAkJAU0GGBkATQa4%2F%2BC0FRYATAa4%2F%2FC0EhQATAa4%2F%2BCzDgBNBrj%2F6LYNAE1UBgEGuP%2FgQBInK0jWBgGEBgF2BgFEBmQGAga4%2F%2BBAExMXSBUQEgFNFRAOAU0VCA0BTRW4%2F%2FCzCgFNFbj%2F4ECmGQBNFRgWAE0VIBUATRUQExQATBUgDgBNFSAJAE02FQELFQH5FQG0FcQVApYVAWcVAVYVAUoVAfkVAcsV2xXrFQO5FQEGFRAVfwMGFAMGPwNPA18DvwPPAwV%2FA48D7wMDA0AQ0BACYBDwEAIQQAoBDwofCgIICiUkSiFJIEkWSRsQJBABZBB0EAIrAwGcAwGNAwFrA3sDAhADBhVJC0kKSQdKBkoASgA%2FPz8%2FPz8SOTldXV1xXXERMz8%2FPz8BENZeXXEZ1V1xGNVdcYcrh33EAV1dXXFxcXFxcXJyKysrKysrKysrKytxcXFxK3IrKysrKysrhxgQK4d9xAErcXErK3JyKysrKysrGBDFXXGHK4d9xAFdXXFxcXFxcXFycisrKysrKysrKysrKysrXXFxcXFxcXFxcStyKysrKysrGRDVKysYxocrh33EAStxcSsrcnIrKysrMTAAKysrASsrKysrKysrcSEuAScOAQcjJgInMx4DFz4DNzMeAxc%2BAzczBgIHAfkcPxoaPhxRLlgnZAocHyIPESEeGwpNCRoeIREPISAcCmAnWC5IuWRkuUhnAQiZL25uZiYvaWtnLS1na2kvJmZubi%2BZ%2FvhnAAAAAAEAEAAAAe0CCAAXAKlAJEYUARQTChAXERILEBdJDgEOEgsPAAkTCgUTCg8AChAMEgFMErj%2F8EAUDBIBTAoSCxJ%2FEwoUEwoTFwEPAAC4%2F%2FBAMAwSAUwQEAwSAUwAEBcQfw8AFA8PABcZDw8LHwsCCAsYF0oTSRJJEEkPSQtKCkoASgA%2FPz8%2FPz8%2FPwEQ1l5dxhDGhxArh33EASsrhw7EARgQ1ocrh33EASsrD4cOxA8BcQ8PcTEwIS4DJw4DByM%2BATcnMxc3MwceARcBhwsgJigTEyglIAtgI2EwrGl8fWOoMGUjFjY5OBkZOTk2FUSNQPe2tvFBkUUAAQAC%2F0QB3wIIACAA9bkAGf%2F4sw8ATRm4%2F%2Bi2DgBNBBgBFbj%2F%2BECIDQBNBw8BBwgQAE0HEA8ATQkHAQUIDQBNACAJFhUUExcTEBIXExIFFwUSfxMXFBMXIhNAERIATBNADQBNEwgMAE0TQAkATRMMDQ8KDwgHBgkGICYBTQYgJypIBgoPCn8JBhQJBgUPDwkfCQIICSHQD%2BAP8A8DDwYFAxIAA4ggG1ITSRJJCkkJSQA%2FPz8%2FPzPtMhIXOXEBENZeXRnFMocYK4d9xAErK4cOxMQQhw7ExAEZ1SsrKysYxocrEADBhwV9EMQQDsQQhw7ExMQBGBDGMjEwK10rK10rXSsrFx4BMzI2NyYCJzMeAxc%2BATczBgIHDgMjIi4CJxMLIxE3Phk%2FbSRkCx8mLBgmOBlgJFgzFCs0QSkLGBcTBGEFCTE3eAENii1obm4zac5tk%2F7xdi1BKhQDBQYCAAAAAAEAJAAAAbECCAARAEFAJQUOCQ5%2FAAUUAAUADwYfBgIGEwATAQgPCRIOAA%2BHEEkJBQaHB0oAP%2F3BxT%2F9xcEBEN7GXl0Q3l3WhyuHfcQxMAEOAwchFSE1PgM3ITUhAakVRU9PHgEe%2FnMYRk1JG%2F7%2FAXcBwhdUZnAzTj4samldIE4AAQAo%2F0cBRQMPACgAWkALCSABCQgBCA0bERe4%2F%2FBAKAgNSBd%2FiSiZKAJiKHIoAigGoCIBIhAIDUgiKSiIAAAUFAsbiB0NiAsAL%2B0v7RI5GS8zGC%2FtARDeK10yzl1d%2FSsyzjIxMF5dXRMyPgI9ATQ%2BAjsBFSIGHQEUBgceAR0BFBYzFSMiLgI9ATQuAiMoGCIXCxIsSTYEOTEjICAjMTkENkksEgsXIhgBTxEbIxK0KUArF0kpOppCSBQUSEKaOilJFixAKbQSIxsRAAAAAQBg%2F0cAtwMPAAMAFLcBrgMDBQQCAAAvLwEREjkv7TEwEzMRI2BXVwMP%2FDgAAAAAAQAI%2F0cBJQMPACgAXkAkZyABBiABBggBCBsNFxAQCA1IEH8ihgCWAAJtAH0AAgCvBQEFuP%2FwQBIIDUgFKgCIKCgUFAsbiB0NiAsAL%2B0v7RI5GS8zGC%2FtARDeK13OXV0z%2FSsyzjIxMF5dXV0BIg4CHQEUDgIrATUyNj0BNDY3LgE9ATQmIzUzMh4CHQEUHgIzASUYIhcLEixJNgQ5MSMgICMxOQQ2SSwSCxciGAEHERsjErQpQCwWSSk6mkJIFBRIQpo6KUkXK0AptBIjGxEAAAEAKgDZAggBfgAhAFhAOqYYAQkUAXkIiQgCqQcBBAMBCCGvUABwAJAAAwQAFAAkAAMAEK8JERkRKREDEREiACEWrwsREByvBQsAL9ztxDIQ%2FcQyAREzL13t1F1d7TEwXl1dXV1dAQ4DIyImJy4BIyIOAgcnPgMzMhYXHgEzMj4CNwIIBBEgMSMeMxcbMhwQFhAKBEAEEh8xIx4zFxsyHBAWEAoEAWUUMCsdGA4QHw8XGw0SEzErHRgOEB8PFxsNAAEALf%2FzAicCwgAzAMVAeZknAVgmARoZERAZEBkQLgcGISI1MypHGwEbGBIPpxUBFX8xKygApi4BmC4BBi4WLkYuhi4ELjSbIashAiEeiCUAAA8BCA%2BFETExERsoKCsYhRoRGhEaA4simyICIpclASVRhAeUBwIHCoiTBgGEBgEGmAMBDwMBA1QAP11dM11d7TJdP10zXRI5OS8vEO0yMy8zETMvEO1eXTIQ7TJdARDcXV1dMjIyMu1dMjIyMl3OMhDWMsYyEjk5Ly8RMxEzMTBdXRM%2BATMyFhcHLgEjIg4CBzMHIwYUFRwBFzMHIx4BMzI2NxcOASMiJicjNTMmNDU8ATcjNYkXiGosOh0UGDkeJjkpGgfpDuUBAdEOuxFZQyo9GBQRVDN1gBNaUgEBUgHYdXULCVAJCxYoOSNIDRoODRgLSFlGEAtPCRR%2FcUgLGA0OGg1IAAAAAQAh%2F4IArwByAA0ANLkABv%2FgQBoIC0gGAAOCDQcKCg4PPw1PDQINBxAGIAYCBgAvXTMvXQEREjkvzjPtMjIrMTA3HAEHDgEHJz4BNTQmJ68BBCYbSBUVAQFyCxUKNGYsFyxaJwoXC%2F%2F%2FACH%2FggFlAHIAJgBjAAAABwBjALYAAAABADQCRgFEAwEABQAuQBhQBQEFBV8BAQEBAwMGfwOPA58DAwMAAgQALzPdzV0BETMZLzIYL10yL10xMBMXBycHJ7yIJ2FhJwMBjyxYWCwAAAEAHgA%2FAPsB%2BQAFACu0BAIFAQO4%2F8BADA4SSAMABgQFAgEAAwAZLzPNMs0yARgQ1sUr1TLFMjEwEzcXBxcHHpxBb29BARzdI7q6IwACADr%2F%2BQO8ArwAEgAtAG9ASCMncwoKGCYiKS8HABcAAgB2eBiYGKgYA2YYARguCih5KiR5JiYgKkILInkgQQgOGA5YDmgOBA58HUUHBRcFVwVnBQQIBXwTRgA%2F7V5dP%2B1dP%2B0yPxI5L%2B0Q7TIBEN5dXe1dENbGxhI5L%2B0yMTATFB4CMzI2NzY3ES4BIyIOAgEiLgI1ND4CMzIWFyEVIRUhFSEVIRUhDgGhJkdmPxchCwwJFiwWP2ZHJgESUYplOTplilEXOBkBh%2F7JARP%2B7QFQ%2FmAZOQFbQGRFJQEBAQICEQMCJUVk%2Fl8sWIVZWYRYLAQDUdFP81EDBAAAAAABADwCCADKAvgADQBLuQAI%2F%2FCzEgBNCLj%2F%2BEAmEQBNBwY6BgELBhsGKwYDCAYAgg0KD0ANAQ0HkAYBLwY%2FBm8GAwYAL11dMy9dARDeMu0yXl1dETMxMCsrEzwBNz4BNxcOARUUFhc8AQQmG0gVFQEBAggLFQo0ZiwXLFonChcLAAEAIQH%2BAK8C7gANAClAFwYHDQCCCg6QDQEvDT8Nbw0DDQdABgEGAC9dMy9dXQEQ1O0zzjIxMBMcAQcOAQcnPgE1NCYnrwEEJhtIFRUBAQLuCxUKNGUtFy1ZJwoXCwAA%2F%2F8APAIIAYAC%2BAAmAGgAAAAHAGgAtgAA%2F%2F8AIQH%2BAWUC7gAmAGkAAAAHAGkAtgAAAAEANADiATQB4AATABO2CgAAFRQPBQAvzQEREjkvzTEwARQOAiMiLgI1ND4CMzIeAgE0EiEvHh4vIRISIS8eHi8hEgFhGi4iFRUiLhoZLyIVFSIvAAH%2F%2FgEEAfIBUwADABG1AQUDBAACAC%2FNARDGEMQxMAMhFSECAfT%2BDAFTTwAAAf%2F%2BAQQD5gFTAAMAE7UBBQMEAAIAL80BEMYQxDEwMTADIRUhAgPo%2FBgBU08AAAAAAQAXAmcBYALfAB0AVUA3aQd5B4kHAx0PAB8ALwADCAAODx4AHR2gC7ALwAsDCzQURBQCFK8avxrPGgMaBQ8ODjkFSQUCBQAvXTMvMxDdXdxdzV0zLzMBENYyzF5dMjEwAF0BDgMjIiYnLgEjIgYHJz4DMzIWFx4BMzI2NwFgBREaIxUUIxARGAsRHQgwBBIaIxUUIxARGAsRHQgCxwwgGxMOCAgIHBAYDCAbEw4ICAgcEAAAAQAoAD8BBQH5AAUAKEASAwEEAAJADhJIAgUHAwQBAAUCABkvM80yzTIBGBDezSvdMs0yMTA3JzcnNxdpQW9vQZw%2FI7q6I90AAwAz%2F%2FMDfQIVACcAMwA8AKBAaHcXhxcCJCUDE6c8ATxUHmQeAkYeAR6CKH8lAVkoaSgCJSglKAtpNAE0gncZhxkCGas%2BVi5mLgIugg8LAQgLqz08hR0dBhNnFgEWUDdJK1krAiuIEFAkISFGMVYxAjGIBlElA2gAAQBRAD9dMjI%2F7V0yETM%2F7V0yP10zEjkv7QEQ%2Fl5d7V0Q%2Fl3tXRI5OS8vXV0Q7V1dMl05OREzMTBdBSImJw4BIyIuAjU0PgIzMhYXPgEzMhYVFAYdASEeATMyNjcXDgEBNCYjIgYVFBYzMjYlNiYjIg4CBwK9T2kdIGI%2FNVpBJCRBWjU%2FYx8hZTZocQH%2BnAZTVjFDEQ0RVf7HT0RET09ERE8BYgFBOSAxIRQDDDozNjgnSGQ%2BPWVIJzszODaEhQgLBQlQVREITgkUARBYZ2dYWGdnij9RGSg0GwAAAAIAR%2F9HAM0CCAAJABUAI0AQEAQHfwoDCQkWFwMTDVAISwA%2FP93OARESOS8zxP0yxDEwNzQ2NzMeAR0BIwM0NjMyFhUUBiMiJlgMCToJDGQRJB8fJCQfHyQCVIlERIlUuwJ%2BGygoGxsoKAAAAAEAVv%2BTAeACtQAkAGxAQwcdfwYfQA8SSB8fAAsKGa8aARomkCYBVhEBEYIAJRlGFlYWAhaIIB4aCR0ZHQIdSgtJDlkOAg6ICgcFBggWCAIICFAAP15dM8Uz7V0yP10zxTPtXTIBEN7tXV0Q1l0yxjISOS8rM%2B0yMTATND4CNzUzFRYXBy4BIyIGFRQeAjMyNjcXDgEHFSM1LgNWGzFILVM6NBUVNx9OUxMpQC4lPBENDkQkUzBIMBkBIzFWRC4JkIsDFE0JDF5aKEMwGg4HTAgPAouQCS1DVgAAAAABADwAAAIHAsIAIgCVuQAf%2F%2BizDwBNH7j%2F8EBUDgBNFRAQAE0UCBAATRQICQBNCwgPAE0HFQEbGggBAREIJAcDInMNCggQEgBMChEwDwEPDwEPIxsdfBcQAHkCDg4AAhACAggCAgkaZxcBF0UHeQlCAD%2FtP10zEjkvXl0zLxDtMhDtMgEQ1l1d3cwrwP0yxRDGETkvEMYyMTAAXQErKysrKysTMxUjFRQGByEVIT4BPQEjNTM1ND4CMzIWFwcmIyIOAhX2vb0ICAEh%2FnYLEV1dHzlQMCw8HBc1PBssHxEBdUsIMm4yUEeNRw9LU0phORYQC1AZECZAMAAAAAACACwAbQIIAkkAHgAyAJJAYA80LzRPNAMIEgyHDAFlDHUMAgwTC6AfAR8PHAMdAogCmAICagJ6AgICrykBKQAzGhWIFZgVAmoVehUCFRsUFwQKCQWHBZcFAmUFdQUCBaAksCTAJAMkB68uvy7PLgMuFwAvzV3czV0zXV0RM84yEM4yM11dETMBEN7NXTNdXREzzjLczV3OMjNdXREzMTBeXRM0Nyc3FzYzMhc3FwceARUUBgcXBycGIyImJwcnNyYlNC4CIyIOAhUUHgIzMj4CWRxJOkovOzsuSzpKDg8PDko6SzA5HTYXSjpJHAE3EyArGBgrIBMTICsYGCsgEwFbPS1KOksdHUs6ShY1HyA0Fko6ShwODko6SS0%2BHC4hEREhLhwdLSERESEtAAABABEAAAIkArUAHgC1QG4AGBAYAhgcHg8SDwwScxMWFBMWBxYXFgIWHiBvE58TrxMDExkecwAPDAsIDwgMcwsIFAsLCAgIGAgCCA8GHwYCBgIEbwCfAK8AAwAPCx8LAggLHwMaeQIcHgYYeRYICBYWEx5CE0ESQQxBC0EAQgA%2FPz8%2FPz8SOS8zLxDtMhDeMu0yARDWXl3UXTLOMl0zXYcQKxAAwYcFfRDEARgQ%2FTLUXcYRM12HKwh9EMQBGBDOMl0xMDM1IzUzNSM1My4BJzMeARc%2BATczDgEHMxUjFTMVIxXtoqKiiDZhK2ckVC0sUSVlLGE2iaKiompHZkdYrVJOnEhInE5TrVdHZkdqAAAAAAIAYP9HALcDDwADAAcAHUANAQWuAwcHCQAIBFoCWQA%2FPxDGARI5L8DtMjEwNzMRIxEzESNgV1dXV8b%2BgQPI%2FoEAAAAAAgAr%2F5MBwgLFADsAUQELQLMlNgE3K6crAigXAagQAVdFARo3AQs3AaQ0AYYzAagyAagvAWUrARUYAQQYAakMAYgMAUpNgi0GgjUdHDgLATYqAQsqDqQyATcyATI3LQEtUz9CggA7DiOCFqoTARM4DgEOUh0dIIh3EwEGEwETP5gyAQkyAQgySpQqAXYqhioCayoBVioBiD8Bej8BbT8Bl0oBmQsBeguKCwJpCwFaCwELSj8qBJYcARwZU5k7ATs4iAADUQA%2FM%2B0yXT8zXRc5XV1dXV1dXV1dXV1dETNeXV0RM11d7TIvARDeXTJd1O0QxDLtMhDWXTJdXRE5OV1dxDLU7RDtMjEwAF1dXV1dXV1dXV1dXQFdXV1dFx4BMzI2NTQmJy4DNTQ%2BAjcuATU0NjMyFhcHLgEjIgYVFB4CFx4DFRQOAgceARUUBiMiJicTLgEnDgEVFB4CFx4BFz4BNTQuAkQeSDg4OTZAJEExHBEZIA8ZHWFYM1gaFRhFNCc5DBooHCRBMRwQGB8PHSRqYkNXGq8HDQcdJBcpOCEFCQUdJBcoNAENEiEkIycWDBwoOCgaLSUbCRQ0IkVRFAhNCRQhIRIZFRIJDBwoOCgZLCQdCRU4JUtNFQ0B4wIFAxMzHhojGxULAgMCEzIfGiMaFAAAAAACACMCaAFVAt4ACwAXACm5ABX%2FwEASDRJIFQ8DQA0SSAMJCRgSDAYAAC%2FNL80BETMvzSvezSsxMBMiJjU0NjMyFhUUBjMiJjU0NjMyFhUUBl0XIyMXFyMjpxcjIxcXIyMCaCEaGiEhGhohIRoaISEaGiEAAAMAQf%2FxAu0CxQATACcASQDbQJ43oEUBRbAUwBTQFAOZFAFoFAEUd1gAaAACAygAAQwAHAACAgBLvz7PPt8%2BA6Y%2BAT53jy0BLb8ezx7fHgOWHgFnHgEed1cKZwoCBgoWCiYKAwgKSsg4ATg7fTdwMgFhMgEyoBmwGcAZA4kZARl9Zw8BSA9YDwIZDwEPRURBfUV%2FKAFuKAEoryO%2FI88jA4YjASN9aAUBRwVXBQIWBQEFRgA%2FXV1d%2FV1d1l1dMu0yP11dXf1dXdZdXTLtMl0BEN5eXV39XV1d3l3tXV0Q1l9dXV9d%2FV1dXcZdMjEwARQOAiMiLgI1ND4CMzIeAgc0LgIjIg4CFRQeAjMyPgIFIi4CNTQ%2BAjMyHgIXBy4BIyIGFRQWMzI2NxcOAwLtOF58RER8Xjg4XnxERHxeOEkoR2M7O2NHKChHYzs7Y0co%2Fv8rRjMcHzRGJxorIBUEFww1IzNEPjwnMw4TBRciKwFbVYddMTFdh1VVh10xMV2HVUJtTioqTm1CQm1OKipObY0cNE0yMUw1HAgLCwM8BxNHRD5PEQc8AwsJBwACACYBSQFQAsIADgAxAJZAKkgfWB8CSBsBVxIBExIBBBIBA7AlwCXQJQMlk1cUZxQCJhQ2FEYUAxQzL7j%2FwEAVDRBIL78MzwzfDAMMlEkdWR1pHQMduP%2FAQCQIC0gdMi4rmg8lACIQIgIIIpoQByAH0AfgBwQHBw8AmhgvD2gAPzPU7RI5L13tXl0yEO0yARDeK13tXcQrENZdXf1dwDEwXV1dXV0TMjY3NS4BIyIOAhUUFhMyHgIdAQ4BIyIuAjU0PgIzMhYXNTQuAiMiBgcnPgHEFiwFCyIQESMcEi8iKTgjDxhSKSE3KBcaLDogDiIVBhIgGhc3FAoTPQGHAgJkAgIEDBUQHhkBOxQkMyDgBQkNHCwgHywbDAIDAw8eFw8GBz8FCgAA%2F%2F8AHgA%2FAcsB%2BQAmAGYAAAAHAGYA0AAAAAEANQA1Af8BigAFABxADACvAgQCBAcGBa8DAQAvL%2B0BERI5OS8v7TEwAREjESE1Af9Q%2FoYBiv6rAQZP%2F%2F8AGgEAAREBVwIWABAAAAAEAEH%2F8QLtAsUAEwAnAD0ASAFFQO5WNwE1N0U3AjU0RTRVNAPHDAHHBwHIAwF3NAEDKCi0QsRC1EIDQnc12Sv5KwIrODg9PZc1AWo1ASc1VzUCNbQUxBTUFAOYFAEUdygAWABoAAMMABwAAgIASiy%2FSM9I30gDSHXALgFZLmkumS4DQC4BKS4BLr8ezx7fHgOWHgEedwYKFgomClYKZgoFCApJ0D7gPgI%2BezggKzArAisrLUV9hjIBYTIBVTIBFjJGMgIyoBmwGcAZA4kZARl9Zw8BGQ9JD1kPAw9FPYktAW4tARktSS1ZLQMtryO%2FI88jA4YjASN9aAUBFgVGBVYFAwVGAD9dXf1dXc5dXV0yP11d%2FV1d3l1dXV3tEjkvXTPtXQEQ3l5d%2FV1d3l1dXV3tXTIQ1l9dXf1dXc5dXV0yfS8yETNdGBDtXTJ9LzEwAF9dAV1dXV1dXQEUDgIjIi4CNTQ%2BAjMyHgIHNC4CIyIOAhUUHgIzMj4CBy4BJyMVIxE%2BATMyFhUUBgceAxcnMzI2NTQmIyIGBwLtOF58RER8Xjg4XnxERHxeOEkoR2M7O2NHKChHYzs7Y0coqhc5FUNHGzwZVlonJAoaHBwL9SYtPDkoCxgLAVtVh10xMV2HVVWHXTExXYdVQm1OKipObUJCbU4qKk5thS1RGJYBhAYGQEElNw8MJCwwGNAZJyUdAQEAAAAAAQAuAn8BSgLHAAMAHkAPAQEAAASvAL8AAgAwAgECAC9dzV0BETMvMi8xMBMhFSEuARz%2B5ALHSAACABoB8gEyAwgAEwAfAGlASL8AzwDfAAMAlBS%2FGs8a3xoDGpQKwBQBDwoBCApACxBIFAoUCiEgzw8BD5rAFwFGFwE9FwEGFxYXJhcDF2jPHQEdmgUQCQ1IBQAvK%2B1dP11dXV3tXQEREjk5Ly8rXl1dEO1dEO1dMTABFA4CIyIuAjU0PgIzMh4CBzQmIyIGFRQWMzI2ATIXJjMcHDMmFxcmMxwcMyYXQyseHisrHh4rAn0gNCQTEyQ0ICA0JBMTJDQgIykpIyMpKQAAAAACADUAAAH%2FAmwACwAPAGBAOg0FAwevAQmvCwELD4AFkAUCHwUvBQJQDwEFCQ8PCQUDERAMrw5cBK8GBgpAAgECAK9PCAEIrwoBCl8AP13FXf3GXREzL%2B0%2F7QEREhc5Ly8vXV1dETNdEMDtMhEzMTATMzUzFTMVIxUjNSMRIRUhNb1Qvb1QvQHK%2FjYBo8nJT8nJ%2FvtPAAAAAAEAHgE7ATgCxQAhAF9AOxMgIyAzIAMTsmcAdwACVgABAAAJCLILCxscCRwJHCMiGxazHwizSQoBCrFmHHYchhwDVRwBHEYfAR9xAD9dM11d9F3tEO0yARESOTkvLxEzMy%2FtETMvXV3tMTAAXQEUDgIHDgEHMxUhJj4CNz4BNTQmIyIOAgcnPgEzMhYBKBMgKhgRIQO6%2FvYDDx0mFSIrIR0RHhgTBSQRRS1FQgJWFSgnJxUOIQ0%2FITMqIxIdMRgaGAkNDQQxEiM6AAAAAAEAIAEzATYCxQAtAGdAGhUkAQQkAQg3HGccAh8QsnYdAR0iCQkqA7IiuP%2FwQB8ID0giLxYXK2AqASouHwqzCAgaKwCzKiexFhOzFxpxAD8z7TL0Mu0yEjkv7TkBENZdMsYyENYr7RI5LxDUXe0zMTBdXl1dEzI2NTQuAisBNTMyPgI1NCYjIgYHJz4BMzIWFRQHHgEVFA4CIyImJzceAZA1KBIfKBUTGQ4fGRAlHRsuERkQQyJJPTcjKRIoPywfQBIPFjEBcSEaERgPBzkFDBUPGRUTCDcLFzsuMR8LMyMaKyESDgk8CA0AAAAAAQB%2FAkQBOwMIAAMAOEAOAQIDQBESAEwDAAUAQAO4%2F8CzEgBNA7j%2FwLYNAE0DgAIBAC%2FNGt0rKxrNARDeGcUrGN0ZxTEwAQcnNwE7jS%2BAAs2JKpoAAQBQ%2F0cB9AIIABgAOEAfC38JEhV%2FAH8XCRcJFxoZGEkWSxIwDRJIEgSID1EKSQA%2FP%2B0zKz8%2FARESOTkvLxDt7TIQ7TEwExEUFjMyPgI3ETMRDgEjIiYnHgEdASMRrT5FDyAcFwVdIGU8N0EUAwJcAgj%2B6l9MAgMDAgG3%2FgcIEhcWHDkfZwLBAAEAK%2F9HAiECvAAWADFAGxF1EwN1BRMFCQkFEwMYFxJDvwDPAAIADkUEQwA%2FP81dPwEREhc5Ly8vEO0Q7TEwASIGBxEjES4BNTQ%2BAjMyFhcRIxEuAQGCCBAGTm98LVR4SipgKU4OKgJvAQH82gG%2FC2hmN1I4HAcK%2FJwDIwIDAAD%2F%2FwA4AOMAvgFpAhcAEQAAAPAAAAABAEX%2FQwD%2BABQAHQAyQBkAGwOiERuhFQsLHhUVHx4ADKYPpggaaQhqAD8%2FEO3tMgEREjkvETkvEO0Q7REzMTAXHgEVFA4CIyImJzceATMyNTQmLwE%2BAzczDgHHHRoKGCgdGisNCwwbESQWFAcDDAwLA0AGEiUOISALGhYOCAU5BAYXCw8IAwobGhUFCyIAAAABADIBOwDuAr0ACgAuQBkAfwoBCgSyBgYMC4oHAXsHAQcKswAFsQNwAD%2Ft3u0yXV0BERI5L%2B3OXTIxMBM%2BATczESMRDgEHMiZKGzFGFjkUAm0NKRr%2BfgEmDhYGAAIAKwFIAZMCxAATAB8AWkA%2BvwDPAN8AAwCUFL8azxrfGgOWGqYaAhqUCrAUwBTQFAOZFKkUAgYUFhQCCQoZCgIIFAoUCiEgF5oPaB2aBWcAP%2B0%2F7QEREjk5Ly9eXV1dXRDtXV0Q7V0xMAEUDgIjIi4CNTQ%2BAjMyHgIHNCYjIgYVFBYzMjYBkxswQicnQjAbGzBCJydCMBtKNzMyODczMzcCBitGMhsbMkYrK0cxGxsxRys4Q0M4N0REAAD%2F%2FwAoAD8B1QH5ACYAcAAAAAcAcADQAAD%2F%2FwAyAAADUgK9ACYAiwAAACcA1QFrAAAABwDXAgP%2ByP%2F%2FADIAAAM7Ar0AJgCLAAAAJwDVAVYAAAAHAIQCA%2F7I%2F%2F8AIAAAA1ICxQAmAIUAAAAnANUBegAAAAcA1wID%2FsgAAgAc%2F0QBgQIIACEALQBhQD6HEAFmEAEKDgEIIoIoKBh%2FGxsOBXAGgAaQBgMGLwB%2FDkAJD0gOLhgrJVAFA4h5BokGmQYDBgkJGQkpCQMJUgA%2FXTNd7TI%2F3c4BENYr7RDGXTIROS%2FtMy%2FtXl0xMABdXRcUFjMyNxcOASMiLgI1ND4ENTwBJzMWFRQOBBM0NjMyFhUUBiMiJn43MUI%2FGiJVMDZILRMZJismGQFOBBchKCEXJSQfHyQkHx8kFyYxIkgSFhwsNxshNC8qLzQhBQkFFBMgNC0oKCoBxBsoKBsbKCgAAAD%2F%2FwAIAAACjwOkAiYAJAAAAQcAQwCWAJwAE7kAAv%2FnQAkcGgYRUAILG08rKzQAAAD%2F%2FwAIAAACjwOkAiYAJAAAAQcAhgCOAJwAEEALAh4bGQYRUAILGk8rKzQAAP%2F%2FAAgAAAKPA50CJgAkAAABBwBlAI8AnAAQQAsCAB4aBhFQAgsbTysrNAAA%2F%2F8ACAAAAo8DewImACQAAAEHAG8AkACcABBACwIAKBkGEVACCydPKys0AAD%2F%2FwAIAAACjwN6AiYAJAAAAQcAegCPAJwAF0AQAwIAGy0GEVADCyVPAgsZTysrKzQ0AAAAAAMACAAAAo8DRwAgACcAMwEiuQAa%2F%2FizDQBNCrj%2FwEATEx1ICi5AFBdILi4xFEATHUgUKLj%2FwEBSExdIKCgxMR4fICEiIyQdJBsaGRgXHBcdJB1zHBcUHBcdHAMEBQYHAgcAJyYlJAcBJAFzAgcUAgcBAiQkAhw1QDVwNaA1Aw81AQ8CHwICCAI0D7j%2F8LYPEkhXDwEPuP%2FoQC0IDUgPKzEgAHknQCGAIZAhA5IkAYMkAXokAVkkaSQCISQhJBwHBxcxQRxCAkIAPz8%2FxTIvETk5Ly9dXV1dXTPtMhDezStdKwEQxl5dXV0QxhE5GS8QAMGHBRgrh33EEA7ExAXExBCHDsTExMQQAMGHBRgrh33Ehw7ExMTEEIcOxMQFxMQOxMQBMxEzGC8rzSsRMy8rzSsxMCs3ByM%2BAzcuATU0PgIzMh4CFRQGBx4DFyMuAS8BLgEnDgEHEzQmIyIGFRQWMzI2rD9lJUI%2FPSETFhEcJRUVJRwRFhIhPj9CJmoRHhAcID8iIz8ftx8WFh8fFhYfsrJmsZ%2BQRw4qHBglGw4OGyUYGykOR5GfsWctWC1PV6NLS6JYAeAaHR0aGh0dAAIABgAAA4ICtQAVABoAg0BGEBESExQPFA0LGRYOFhQYCBJIFA4WDnMPFBQPDxQJFnMCBgYPFQQHHA8bAnkEGhl5CgsKBAoECggWAXkUQQ8PCA4OBnkIQgA%2F7TMvETMvP%2B0yEjk5Ly8RMxDtMhDtARDGENbGxhI5LzPtMocQK4d9xAErEIfExA7EEIcOxMTExDEwASEVIRUhFSEVITUhDgEHIz4DNyEFDgEHMwNp%2FtMBCf73AUb%2BWv7vGC8WaDltaGUyAb7%2BczVyPeQCZNFP81GyLFktbL%2BqmUdMSLZqAAAAAQA6%2F0MCUwLFAEABGEBMQkAMEkhkLgFTLgFCLgEjLjMuAhIuAQMuAVUqASMqMypDKgMSKgEDKgEIaAMBKQI5AgJqL3ovii8Dmi4BcyqDKpMqA2IqAUUqVSoCG7j%2F%2BEB%2BDxJIKj46PgI%2BNCw6OgUQDw8iI0IWGgEHGgEadmgFmAUCVwUBBalBMTg%2BKUoiWiJqIgMiBx8XH1cfZx8EH3xKI1ojaiMDIwBoJngmmCYDVyYBJkZFEFUQZRADEAgVGBVYFWgVBBV8hw%2BXDwJFD1UPZQ8DD4gKAWcKAVgKAQpFAD9dXV0zXV3tXTJdP11dMzNd7V0yXdbd3s0BEP5dXe1dXRDGMjIvMxE5L83Mzl0xMAArXV1dXV0BXV1eXV1dXV1dXV1dXSsFLgM1ND4CMzIeAhcHLgMjIg4CFRQeAjMyNjcXDgEHDgEHHgEVFA4CIyImJzceATMyNTQmLwE%2BAQFKPmRHJzVaeEQrRTMgBhwJIiszGjdYPiEdOlc6Q1gWGQ5rUgMFAh0aChgoHRorDQsMGxEkFhQHBA0LCDdbfU9Xh1wwDBAQBFAGDg4JJkhnQD5mSSgcC1ALHwIGCwUOISALGhYOCAU5BAYXCw8IAwwgAP%2F%2FAFYAAAIWA6QCJgAoAAABBwBDAIsAnAATuQAB%2F%2FJACQ8NAApQAQEOTysrNAAAAP%2F%2FAFYAAAIWA6QCJgAoAAABBwCGAH4AnAAQQAsBIw4MAApQAQENTysrNAAA%2F%2F8AVgAAAhYDnQImACgAAAEHAGUAbQCcABO5AAH%2F80AJEQ0AClABAQ5PKys0AAAA%2F%2F8AVgAAAhYDegImACgAAAEHAHoAbQCcABqxAgG4%2F%2FNADQ4gAApQAgEYTwEBDE8rKys0NP%2F%2FAA8AAADLA6QCJgAsAAABBwBD%2F9IAnAATuQAB%2F%2BhACQcFAAFQAQAGTysrNAAAAP%2F%2FAEQAAAEAA6QCJgAsAAABBwCG%2F8UAnAAQQAsBGgYEAAFQAQAFTysrNAAA%2F%2F%2F%2F%2FwAAAQ8DnQImACwAAAEHAGX%2FywCcABBACwEACQUAAVABAAZPKys0AAD%2F%2F%2F%2FuAAABIAN6AiYALAAAAQcAev%2FLAJwAF0AQAgEABhgAAVACABBPAQAETysrKzQ0AAAAAAIAE%2F%2F6ApwCvAAUACUAd0BNKRhpGHkYAxgYAQkYAQgYdpcAAVYAZgACDwABAKknACdwJ7AnAyMgHnMNCgioJgsfeQohIQUeGBsBG3wNVxABEEUjFxUBFXwIWAUBBUYAP10z7V0yP10z7V0yEjkvM%2B0yARD2zjP9zjNdEPZdXV3tXl1dXTEwARQOAiMiJicRIzUzET4BMzIeAgEyNjU0JiMiBgcVMxUjFR4BApw4Zo5VKmUpUFApZSpVjmY4%2FoeKiIiKKS0JgoIJLQFbWoVXKwcKATlLARwKBytXhf6ajX9%2FjQIB1UvyAQIAAAD%2F%2FwBWAAACggN7AiYAMQAAAQcAbwCxAJwAEEALAQAjFAkSUAEKIk8rKzQAAP%2F%2FADr%2F8QLQA6QCJgAyAAABBwBDANAAnAATuQAC%2F%2BhACSspAAlQAgQqTysrNAAAAP%2F%2FADr%2F8QLQA6QCJgAyAAABBwCGAMMAnAAQQAsCGSooAAlQAgQpTysrNAAA%2F%2F8AOv%2FxAtADnQImADIAAAEHAGUAyQCcABBACwIALSkACVACBCpPKys0AAD%2F%2FwA6%2F%2FEC0AN7AiYAMgAAAQcAbwDKAJwAEEALAgA3KAAJUAIENk8rKzQAAP%2F%2FADr%2F8QLQA3oCJgAyAAABBwB6AMkAnAAXQBADAgAqPAAJUAMENE8CBChPKysrNDQAAAAAAQBQAGEB5AH1AAsAiUBYC2AJsAkClgkBCQkBBZkDAQMDyQoBtgrGCgKWCgEKxgQBuQTJBAKZBAEEAQEMDQawCAGWCAEICAoCmQABAADJBwG2B8YHApYHAQcExgEBuQHJAQKZAQEBCgAZLzNdcXIzM11xcjMvXTIRMy9dcjMBERI5LzNdcXIzXXFyMy9dMxEzL11yMzEwJScHJzcnNxc3FwcXAaySkjiSkjiSkjiSkmGSkjiSkjiSkjiSkgADADr%2F3ALQAtoAGgAkAC8CbEAmRS5lLgI2LWYtAoksAXgeAWgdATkdASgdAUoYAWQXAWQWAWYVAQ%2B4%2F%2BC2EABNWQsBCrj%2F%2BEBUEABNRgqGCgJqCQFsCAEDIBAATXkumS4CWi0BWiwBZikBVSgBSCMBlh4BVh0BRR0BgxwBVBoBVxkBlhgBZRYBahUBWRQBmgoBagkBdwMBKRg5GAIHuP%2FwtAwNAEwHuP%2FoQBoLAE0YHR4HCBcIFxASAE0XGBEATRcQEABNF7j%2F4EAUDQBNFxYgBSUSFRAMAE0VGAsATS24%2F%2BizDQBNCrj%2F8EAKDABNFS4tCgkWCbj%2F8EAJEBIATAkFEggWuP%2FgQBoNAE0WCBYIACoQEQBNKggOAE0qEAoATSp2Dbj%2F%2BLMSAE0NuP%2F4QA8PAE0NQAkATQ2pMfAxARu4%2F%2FizDgBNG7j%2F8EALCgBNG3YACBIATQC4%2F%2FhAEREATQAIDwBNAEAIAE0AqTAduP%2Fwtw8QAEwdLi4luP%2F4sw4ATSW4%2F%2FizCgBNJbj%2F8LUJAE0lfBe4%2F%2FBAPRASAEwXFhgQCwBNGBUVEggSAE0SCA8ATRJGLRAPEABMLR4eIAgOAE0gCAoATSAQCQBNIHwJEBASAEwJCAq4%2F%2Bi2CwBNCgcHBbj%2F%2BLMSAE0FuP%2F4tA8ATQVFAD8rKzMRMyvGMivtKysrMhEzKz8rKzMRMyvGMivtKysrMhEzKwEQ9isrKyvtKytdEPYrKyvtKysrEjk5GS8vKxI5OTIrfYfExMTEASsrKysRMxEzETMrKysrEIfExMTEASsrXTEwAF1dXV1dXV1dXV1dXV1dXV1dXV0BK11dXStdK11dXV1dXV1dXV1dEzQ%2BAjMyFzcXBx4BFRQOAiMiJicHJzcuATcUFwEmIyIOAhMyPgI1NCYnARY6NVt5RGpSOz5AJy01WndDNV8pOj4%2BKC5mLAE%2FN081VTsg5TVUOyAWFP7CNQFbWYhbLjhNLlMtf1JZiFsuGxtLLlAtgFRtRgGdKydIZ%2F6sJ0hnPzVXI%2F5lKQD%2F%2FwBR%2F%2FECXwOkAiYAOAAAAQcAQwCjAJwAE7kAAf%2FoQAkbGQQSUAEGGk8rKzQAAAD%2F%2FwBR%2F%2FECXwOkAiYAOAAAAQcAhgCWAJwAEEALARkaGAQSUAEGGU8rKzQAAP%2F%2FAFH%2F8QJfA50CJgA4AAABBwBlAJwAnAAQQAsBAB0ZBBJQAQYaTysrNAAA%2F%2F8AUf%2FxAl8DegImADgAAAEHAHoAnACcABdAEAIBABosBBJQAgYkTwEGGE8rKys0NAAAAP%2F%2FAAYAAAJQA6QCJgA8AAABBwCGAGoAnAAQQAsBGhMRBAxQAQQSTysrNAAAAAIAVgAAAj0CtQAOAB4AUrkAF%2F%2Fosw4ATRS4%2F%2BhAIw4ATRV2Bg4PDxAbcx0GHQYdIB8QE3wLAHkaCxoLGhweQRxCAD8%2FEjk5Ly8Q7RDtMgEREjk5Ly8Q7TIyETMQ7TEwKys3MzI%2BAjU0LgIjKgEHNRU%2BATMgFRQOAisBFSMRt0kyUDceHzhOLxInExMpEwE3K1F0SU1h3g0gNiooNR8MAsRxAgHdPFM1GI4CtQABAFD%2F9AJHAwgARQDUQJKmRAEnRAEVRAGpMAGpLwGpLgErKAEcKAEGEwEGDwEHQwF4OAF3MAE5FAGFEAEGEhYSJhIDEoIjAIIzCIKoKwErGjl%2FO5gjAZgzAYAzAQ8zASMzOzszIwNHRngmmCaoJgMmEAsOSCaKpQ0Bhg2WDQILDQENDRWJNgFoNng2AjaIBkGmQQIIQU86ShuXIAEgiBoVUQA%2FM%2B1dMj8%2FXl3tXV0SOS9dXV3tK10BERIXOS8vL11dXV0Q%2Fc7eXe0Q7RDtXTEwAF1dXV1dAV1dXV1dXV1dXV0BFA4CBw4BFRQeAhceAxUUBiMiLgInNx4DMzI2NTQmJy4DNTQ2Nz4DNTQmIyIGFREjETQ%2BAjMyHgIB8g4VGQsXIA8YHQ8bLyMTYWYVKiQbBhAGGSEnFDIwLjkhKxgKLBkKFRILNDRDPV0cNlA0Nk4xFwJoGSchHAwZIRkPFhAOBw0eJzUlTlQGCQoDUQMKCgcsJScyGg8bHB4UKDYaChgZGg4sMUxD%2FdgCKjJSOiAYKzsA%2F%2F8AK%2F%2F1AcADCAImAEQAAAEGAENSAAATuQAC%2F%2FlACTk3HhNQAg84TysrNAD%2F%2FwAr%2F%2FUBwAMIAiYARAAAAQYAhkUAABBACwIrODYeE1ACDzdPKys0%2F%2F8AK%2F%2F1AcADAQImAEQAAAEGAGVLAAAQQAsCETs3HhNQAg84TysrNP%2F%2FACv%2F9QHAAt8CJgBEAAABBgBvTAAAEEALAhJFNh4TUAIPRE8rKzT%2F%2FwAr%2F%2FUBwALeAiYARAAAAQYAeksAABdAEAMCEThKHhNQAw9CTwIPNk8rKys0NAD%2F%2FwAr%2F%2FUBwAMLAiYARAAAAQYA1EsAAAy3AwIRPzYeE1ArNDQAAwAs%2F%2FQDJQIVADsATABVAQlArlZSAWM0AVQ0AXcrAaUlAQNICgF5AgGYGwEGEAEIAwEsDQECTYI5LZcnpycCiAMBZgMBVQMBRwMBBQMlAwInA5dVp1UCZlUBVUYyATKCF0I%2FF6dKAUqCIQ13LYctAgMbLQEbF3sXAjsNARwNAQIPDQEtFw0NFy0DV1YwVwFVhQ8xATExFxKIQgBFAUVFCFCIJ3cqAWgqAQcqAQgqUCAdiCEkUD88iAMIUTg1iDkAUQA%2FMu0yPzPtMj8z7TI%2FXl1dXTPtEjkvXTPtMjMvXe0BXRESFzkvLy9dX11dXV1fXRDE7V0RMzMQ7V0yXV05OV1dXV1dXRDE7V9dMTAAXV1dAV1dX11dXV1dBSImJw4DIyIuAjU0PgIzMh4CFzU0LgIjIgYHJz4BMzIWFz4BMzIWFRQGHQEhHgEzMjY3Fw4BJTI2Ny4BJy4BIyIOAhUUFiU2JiMiDgIHAmU5VSAKIS45IjJPOB4jPFAsDh4aFAQKGi0kLkURCxJTMEVRFCFaMWhxAf6cBlNWMUMRDRFV%2Fmo0PBAPEQELKiAVLycaQQH5AUE5IDEhFAMMGxoHEhELEihALSs%2BJhIDBQQBGhctIxUNB00IDy8qLSyEhQgLBQlQVREITgkUTxcLHEAkBQcGEyEbMybzP1EZKDQbAAEAM%2F9DAb0CFQA6ALJADU88Xzx%2FPAM8QBEWSAK4%2F%2BBAIAgSSBYMlgSmBAIEARISHiZgNoA2AjY8jzwBVy0BLYIeuP%2FoQEQOAE1XHgEPHh8eAh6rOydYKgFJKgEqiCYjUDVXMgFGMgEyiAAMCRgJEEgJDRAWATYZEBAATQkZGRkpGWkZeRkFCBkAUQA%2FMl5dKzLW3d4yzSsyEO1dXTI%2FM%2B1dXTIBEPZdXSvtXV0Q1l3GEjkvxc1dxM4xMAArAStdBQceARUUDgIjIiYnNx4BMzI1NCYvAT4BNy4DNTQ%2BAjMyFhcHLgEjIgYVFB4CMzI2NxcOAwE%2BCx0aChgoHRorDQsMGxEkFhQHBA4IM000GiNAXTkjRiAVFTcfTlMTKUAuJTwRDQgbIicMGQ4hIAsaFg4IBTkEBhcLDwgDDCIPBy1GWzY8ZEkpDA1PCgxiXipGMhsOCE4ECQcFAAAA%2F%2F8AM%2F%2F0AfkDCAImAEgAAAEGAENpAAATuQAC%2F%2FBACSclAAdQAgQmTysrNAD%2F%2FwAz%2F%2FQB%2BQMIAiYASAAAAQYAhlwAABBACwIhJiQAB1ACBCVPKys0%2F%2F8AM%2F%2F0AfkDAQImAEgAAAEGAGViAAAQQAsCCCklAAdQAgQoTysrNP%2F%2FADP%2F9AH5At4CJgBIAAABBgB6YgAAF0AQAwIIJzkAB1ADBDFPAgQlTysrKzQ0AP%2F%2FAAcAAADDAwgCJgDSAAABBgBDygAAE7kAAf%2FoQAkHBQEAUAECBk8rKzQA%2F%2F8APAAAAPgDCAImANIAAAEGAIa9AAAQQAsBGgYEAQBQAQIFTysrNP%2F%2F%2F%2FcAAAEHAwECJgDSAAABBgBlwwAAEEALAQAJBQEAUAECBk8rKzT%2F%2F%2F%2FmAAABGALeAiYA0gAAAQYAesMAABdAEAIBAAYYAQBQAgIQTwECBE8rKys0NAAAAgAz%2F%2FMCNAMIACIANwDjQJasIQGZIQGmHwGqGwFHNQGWIQEXIQFpGAEHEQF3EAFWEAGmHgEeHR0VIQEhAQSCIyYbGBYmLoIOJAA0AAJeGgFLGgEfGi8aPxoDDw4fDgIIACYaDg4aJgAEOThpGHkYAhgZdxYBFlUTAUYTAROIKXQhAWUhAVchATYhASsbAQEbIQMiGSkZKQkiTR2FHk8ziEkJWQkCCVEAP13tP%2B0%2FEjk5Ly8SFzldXV1dXRDtXV0yXREzXQEREhc5Ly8vL15dXV1dXRDtETMzMxDU7TIyXTIvM10xMABdXV1dXV1dAV1dXV0BBx4BFRQOAiMiLgI1ND4CMzIWFyYnByc3Jic3HgEXNwM0JjUuASMiDgIVFB4CMzI%2BAgI0ayAsFzhfSDtZOx0hPFc2M0wSDCt4F2YhMzsWOR18ZwEiSiApOSQREiM1Iy47IQ0CkSU2kF5Ce185LEheMzhaPyEgDlBEKT8jJyUzDjEkK%2F51CBAIHhYZLDoiJEExHSxIXP%2F%2FAFAAAAH0At8CJgBRAAABBgBvZwAAEEALAQAnGAAHUAECJk8rKzT%2F%2FwAz%2F%2FMCGwMIAiYAUgAAAQYAQ3IAABO5AAL%2F6EAJIyEJAFACDiJPKys0AP%2F%2FADP%2F8wIbAwgCJgBSAAABBgCGZQAAEEALAhkiIAkAUAIOIU8rKzT%2F%2FwAz%2F%2FMCGwMBAiYAUgAAAQYAZWsAABBACwIAJSEJAFACDiJPKys0%2F%2F8AM%2F%2FzAhsC3wImAFIAAAEGAG9sAAAQQAsCAC8gCQBQAg4uTysrNP%2F%2FADP%2F8wIbAt4CJgBSAAABBgB6awAAF0AQAwIAIjQJAFADDixPAg4gTysrKzQ0AAADADUALQH%2FAikACwAXABsAY0BAnxuvGwJgGwFPG18bAgAbEBsCCBsZAw9ADRJIDwkVgBmQGQIfGS8ZAlAVgBWQFQMZFRkVHRwSDBivGgAGUBoBGgAvXd7NEP3ezQEREjk5Ly9dXV0QwN0rwBDNXl1dXV0xMCUiJjU0NjMyFhUUBgMiJjU0NjMyFhUUBgchFSEBGhkjIxkZIyMZGSMjGRkjI%2F4Byv42LSEcHCEhHBwhAYIhHBwhIRwcIVxPAAMAM%2F%2FcAhsCKwAaACIAKgE8QNOXJaclAnYlAXgdqB0CZBgBUxgBVhcBSQ4BOw4Bqg0BWwprCgI2AUYBAqYAAacqAZslAXklAWslAVolAakiAZQdAXYdAWUdAVQdAVcYAVgKiAoCmB0BmA0BeQ2JDQIYHh0NDBkMDAsoIBWZCgGXAAF2AIYAAgomJQAaCxoVGaALAa8ZAQsZCxkQWSNpIwIjggOrLI8sAVYbZhsCG4IPEB8QAggQqyuYJQElHh5JIFkgAiCIGhkAGGYYARgVUJcdAR0mJkYoVigCKIgMCw0KaQoBCghRAD8zXREzxjLtXTIRM10%2FM10RM8Yy7V0yETNdARD2Xl3tXV0Q9u1dEjk5GS8vXV0SOTJ9h8TExMQBXV1dETMzETMQh8TExMQBXV1dMTAAXV1dXV1dXV1dXV1dAV1dXV1dXV1dXV1dXQEeARUUDgIjIicHJzcuATU0PgIzMhYXNxcFFBcTJiMiBgU0JwMWMzI2AeAcHyRBWjVPOjE1NhwfJEFaNSdFHTE1%2Fn0V1CQxRU8BKBbTIjNFTwG9I105PmRIJylAKEYjXjk9ZUgnFhRAKP9BLQESHGdZPi%2F%2B7htnAAAA%2F%2F8ASv%2F1Ae4DCAImAFgAAAEGAENnAAATuQAB%2F%2BhACRkXBwBQAQkYTysrNAD%2F%2FwBK%2F%2FUB7gMIAiYAWAAAAQYAhloAABBACwEZGBYHAFABCRdPKys0%2F%2F8ASv%2F1Ae4DAQImAFgAAAEGAGVgAAAQQAsBABsXBwBQAQkYTysrNP%2F%2FAEr%2F9QHuAt4CJgBYAAABBgB6YAAAF0AQAgEAGCoHAFACCSJPAQkWTysrKzQ0AP%2F%2FAAL%2FRAHfAwgCJgBcAAABBgCGQwAAEEALAS4jISATUAEJIk8rKzQAAgBQ%2F0cCGgMIABAAJQBoQEWZIwGaAgGJAAFaAAFJAAEAf6gRAZkRAVgRASkRORFJEQMRqycdCBl%2FG6omCAOIHiFQHU4aS4sJmwkCCQyIixmbGQIZFlEAPzNd7TJdPz8%2FM%2B0yARD27TIyEPZdXV1d7V1dXTEwXV0BNCYjIg4CBxEeATMyPgI3FA4CIyImJxUjETcRPgEzMh4CAblaSw8gHRYFEUAmKDkkEGEfPFc4LUURXV0aORo5X0MlAQNbYwQFBgL%2Bvg4aHTNFKDtkSCgYC9ADsRD%2B9wkLJkZl%2F%2F8AAv9EAd8C3gImAFwAAAEGAHpJAAAXQBACARQlNyATUAIJL08BCSNPKysrNDQAAAEAUAAAAK0CCAADAB9AEQAFEAUCCAWqA38BqgQCSQBKAD8%2FARD2%2FeZeXTEwMyMRM61dXQIIAAD%2F%2FwBCAAAAugLgAgYATAAAAAIAVQI%2FASMDCwATAB8AdEAnAEATH0gAoBQaQBMfSBqgCq8UAQAKEAqwCgMUChQKISC%2FDwGnDwEPuP%2FwQCYIDkgPp7AXAR8XAQ8XLxd%2FF58XBAgXba8dAR2nmAUBBRAIDkgFawA%2FK13tcT9eXXFx7StdcQEREjk5Ly9dXRDtKxDtKzEwARQOAiMiLgI1ND4CMzIeAgc0JiMiBhUUFjMyNgEjERwlFRUlHBERHCUVFSUcETIfFhYfHxYWHwKlGCUbDg4bJRgYJRsODhslGBodHRoaHR0AAf9ZAAABNwK1AAMAJ0AVAwEAAa4CAxQCAwIEAANBAkIBQgBBAD8%2FPz8BLxDUhyuHfcQxMAkBIwEBN%2F55VwGHArX9SwK1AAAAAgAdATMBSQLFAAsAHwA8QCYMsoAGAQUGFQYCBiEWspAAoAACjwABCgAaAAIIACAbswmxEbMDcQA%2F7fTtARDWXl1dXe0Q1l1d7TEwEzQ2MzIWFRQGIyImNzQuAiMiDgIVFB4CMzI%2BAh1QRkZQUEZGUOIHEh0WFh0SCAgSHRYWHRIHAfxhaGhhYWhoYRwzJhYWJjMcHTImFhYmMgAAAgAYATsBTwK9AA4AEwBGQCgTDgayBQFwA5ADAgMVrxIBEq8IvwgCCBQSCQGzAwcHAwWxnw8BDw5wAD%2FNXf3OMi8Q7TIyARDWXc1dENZd3TLtMjIxMAEVMxUjFSM1IzU%2BAzcHDgEHMwEdMjJEwQ4sNDodBB9AF3YCveo7XV0uGUBEQRlTIFEmAAMAIAEzAUYCxQAdACsANwCiQGE3GwE1LLK3GwGmGwEbGCaybwABEAAgAAIAOSkysrgLAakLAQsOHrJPCF8IAgg4pjW2NQI1uSkBKacbARuEC5QLAmsLewsCNAtEC1QLAwsLEyOzAxAMEEgDMAgLSAOxL7MTuP%2F4swwQSBO4%2F%2Bi0CAtIE3EAPysr7fQrK%2B0ROS9dXV0zXTNdM10BENZd7dQyXV3tMhDWXV3t1DJdXe0yMTBdARQGIyIuAjU0NjcuATU0PgIzMh4CFRQGBx4BBxQeAjMyNjU0JicOATc0JiMiBhUUFhc%2BAQFGTEgqOCIOJBgXGxMkMh8kNCEPHRckGt4IEh0VKSM5MxQYjx4lJR4qLxQZAaQyPxUgJxIcMQ8RLxoWKB4SFB4lEhwtEBEuIwgSDwofFBwfDA8ipg0gIA8UJgkOIAAAAAABADUA%2FwH%2FAU4AAwAStgEAAAQArwIAL%2B0BETMvzTEwEyEVITUByv42AU5P%2F%2F%2F%2FWQAAATcCtQIGANUAAP%2F%2FAFAAAAMRAwgAJgBJAAAABwBJAYYAAAADAFAAAAJAAwgAEwAXACMAf0AxGBMBdwiHCAJmCAEEEwENBIADAQMbFX8hF6olYCVwJaAlAw8lHyUvJQMICg5%2FEKokGLj%2FwEAfDBJIGIkeUxZJFEoPSg2HC0kEaAd4BwIHiAOHAAEATwA%2FXTLtXTI%2F7T8%2FPz%2FtKwEQ9u0yXl1dEPYy%2FTLWXTLGMTAAXQFdXV0BMhYXBy4BIyIGHQEzFSMRIxE0NgEjETMnIiY1NDYzMhYVFAYBGyMyCw0LJhtBNMjIXWIBgV1dLxkjIxkZIyMDCAkEUAQJRzwtTv5GAjdkbfz4AgheIRwcISEcHCEAAP%2F%2FAFAAAAJAAwgCBgDcAAAAAgBQ%2F%2FYCfwMIABMAHwB7QB0YEwF3CIcIAmYIARUgCAtIBBMBCAoOf0AQqiAhH7j%2FwEAuCAtIHxmADHADAQMXf1AZAQ8ZARkZTh%2BIFFEPSg2HC0kEaAd4BwIHiAOHAAEATwA%2FXTLtXTI%2F7T8%2F7T8BL11d%2FdZdxhoQ3ivGEPYa7TIxMABeXSsBXV1dATIWFwcuASMiBh0BMxUjESMRNDYBLgE1ETcRFB4CFwEbIzILDQsmG0E0yMhdYgHAVkhdCBIeFgMICQRQBAlHPC1O%2FkYCN2Rt%2FO4CRkoCcBD9jxceEgkDAAD%2F%2FwBQ%2F%2FYCfwMIAgYA3gAA%2F%2F8AUAAAA8YDCAAmAEkAAAAHANwBhgAA%2F%2F8AUP%2F2BAUDCAAmAEkAAAAHAN4BhgAAAAcAHf%2FxAvECxQATAB8AKwA%2FAFgAcwB%2FABlADyldPCMaFDBXegkFD0UFRgA%2FPxIXOTEwARQOAiMiLgI1ND4CMzIeAgUiBhUUFjMyNjU0JgE0JiMiBhUUFjMyNiU0NjcnDgEHHgEVFAYHHgEXNy4BNxc2MzIeAhczLgEnDgEjIi4CJy4BIyIDBx4BMzI2Nz4DMzIWFz4BNyMOAyMiJhMyNjU0JiMiBhUUFgLxOGKETEuEYjk5YoRLTIRiOP2eExwcExQdHQGQHxAWGx0TFBz%2BxiEdJh4uCgsPDwsKLh4mHSExIRwgHDEmFwJGAyEbBwoFDBcTDAEQGw8xCyEVLxkPGxABDBMXDAUKBxshA0YCFyYxHBEdqxAfGhYSHh0BWkuEYTk5YYRLTIViODhihRsdFBQbGxQUHf78FxYdFBUaH%2BclPRQ8Fz8nCBwRDxwJJj4YPhI94TwOFCMuGyZGGgEBCA8VDAUD%2Fqs6CwoDAw0VDwgBARpGKRwwIxQHAS0YFhEhGRgTHP%2F%2FAB3%2F8wZ0AYwAJwDWAAD%2BxwAnABEBZwAAACcA2AJd%2FscAJwDWA8T%2BxwAHANYFK%2F7HAAAADgBJAAADhwK1AAMABwALAA8AEwAXABsAHwAjACcAKwAvADMANwb5QIoGGyYbNhtGG2YbhhsGPQcbAQkGGiYaNhpGGmYahhoGPQcaAQkGExYTJhNmEwQGEhYSJhJmEgQZBQEZBAFJAVkBAkkAWQACCRdpFwIJFGkUAgYONg5mDgMGDTYNZg0DCQs5CwIJCDkIAj2aMaoxAmkxeTGJMQMxQAoOSAoxATEvgDQ7SC9%2FKI8oAii4AcCzLC9IKLgBgEAjKCtImiiqKAJpKHkoiSgDKEAKDkgKKAEoJIA0O0h%2FJI8kAiS4AcBAISsvSA0kHSQC%2FSQBmiSqJAJpJHkkiSQDJEAKDkgKJAEkNrj%2FgEAJNDtIBzYBNjUyuP%2BAQAwNE0gXMicyNzIDMiu4%2FgBAEi0xSDErASArAQErESsC8SsBK7j%2FgEAMDRNIFysnKzcrAysnuP4Asy0xSCe4%2FkCzKCxIJ7j%2FgED%2FDRNIFycnJzcnAyccCCABCSAiLxWPFQIJFQE7jxWfFf8VA14VAb0V7RX9FQNbFQFMFQEbFQGqFboVAnkVAQgVGBUCChUJEwE7Ew8MPwxvDAM9TwyPDL8M7wwEHgwBrQztDAJ7DAFMDAELDAGqDNoMAjkMaQwCCAwBCwwvCF8IAgkIATvpCAGPCL8IAh4IAe0IAXsIAUwIAaoI2ggCCAg4CAILCGYahhoCBxoBMxoZABdgFwI9UBeAF5AX8BcE4RfxFwKyFwFTFwEUF0QXArUXAXYXphcCBxcXFwIKFwAPMA9gDwM9gA%2BwD%2BAPAxEPQQ8C4Q8Bcg%2BiDwIED0QPAtQPQD0BpQ8BZg8BBw83DwILDwAKMAoCPZAKwAoCIQoB8QoBggoBVAoB5AoBtQoBRgoBFwoBCgAEOeYDAQMGEBkfuP%2BAQAk0RUgfIiw1ODW4%2FgBAOTE7SGE1AUA1UDUCITUxNQIQNQEBNQHhNfE1ArI1wjXSNQOTNaM1AoI1AWM1czUCVDUBQzUBlTUBNbj%2FgEAMDRJIBzUXNSc1AzUiuP8AQBMYHUi1IsUi1SIDpiIBIjPdLAEsuAFAQAwgJkhLLFssAjwsASy4AQBAFhccSKosuizKLAMsgA4TSCxACg1ILB%2B4AgC3MkVIHzExJjS4AgCzMDtINLgBwLMsL0g0uAGAQC0jK0h8NIw0Ams0AVw0AUs0ATSADxNINEAKDkg0GyMrIzsjAyPAFBpIIykrQi24%2FoCzJCdILbj%2BwLMeI0gtuP8AsxodSC24%2F0CzERlILbj%2FgEAJCxBIBy0BCi0euP4AQKoyRUgeJCZBIBlAGVAZYBmAGaAZBhcZAQAZATvAGeAZArEZAWAZgBmgGQMBGREZIRlBGQThGQGiGcIZAoMZAXIZAWMZAQQZJBlEGQOlGcUZ1RnlGQQ2GUYZZhmGGQQHGScZAgsZIAYBBwYBPNYGAYAGAXcGAeEGAUMGAaUGAQcGAQwGaReZFwIIFwEzFx8QLxA%2FEH8QBAgQATyvEO8QAp4QAU8QjxACDRABELgBYED%2FKklMEFwQbBCsEOwQBQsQAcsQAaoQuhACGRApEGkQAwgQAQsQDwMfAwJB2AMBjwOfAwL9AwHuAwFMA1wDArsDAaoDAQkDGQMCDwMXFQEVFQoPGC8YTxhfGG8YjxivGAc7vxjPGO8YA64YAU8YbxiPGAMOGB4YLhgDjRitGM0Y7RgETBhsGHwYAwsYKxgC2xjrGAKKGKoYyhgDSRhpGAI4GAEpGAEIGAELGA8HAT7ZBwGOBwF4BwHtBwFLBwGqBwEIBwEMB2YNAQcNATMND0IAERARIBFgEQQ9gBGQEaAR4BEEARFBEQLhEfERAqIRAVMRYxECBBFEEQJlEaURtRHFQDoRBBcRJxECBhEBCxEAAhACAkHgAvACAqcCAVACAUECAbICAaMCAQAAAhACAgFgAnACAgICrwgBCApBAD%2FNcjJfXV9xX3FxcnJycl5dMl5dXV1xcXFxcnJeXT%2FNXl1dMl5dXXFxcnJyXl0yXl1dXV1dXXFxcXJycnJeXRI5L10zXl1dXXFxcXJyXl0zXl1dXV1xcStycnJyXl1dzV5dXTJeXV1xcXJycl5dXTJeXV1dcXFxcXFxcnJycl5dXV0%2FzTIrMl5dKysrKys%2FzTIrcTIrK3FxcXErKysSOS8zKzMrK10rcXErcc0yXV0rMl0rXXFxcXFxcXFycnJycisBEN4y3DIr3jLcMnLOzTIyXV1dXXFxcXJyXl0yXl1dXV1xcXFycl5dMl5dXV1xcXFxcl5dEM1eXV0yXl1dcXFxcnJyXl1dMl5dXV1xcXFxcnJeXTJeXTJeXV1dcXFxcXJyXl1dEM1eXTIyXSsrKzJdK3FycnIrMl0rEM1dKzJdK11dcXIrcisyXStdXSsrcjIrMl0rXV0xMABeXV1dXV1dAV1dXV1dXV5dXl1eXV5dAREzEQMRMxEBNSEVATUhFQERMxEVNSEVAREzEQMRMxEDETMRATUhFQE1IRUBETMRFTUhFQERMxEDah0dHf6vATT%2BzAE0%2Fq8dATT%2Brx2bHR0d%2Fq4BNf7LATX%2Brh0BNf6uHQFrASr%2B1v60ASr%2B1gJ5HR39aB0dAWsBKv7WHx0d%2FtMBKv7WAUwBKv7W%2FrQBKv7WAnkdHf1oHR0BawEq%2FtYfHR3%2B0wEq%2FtYAAQAAAOUBAgAOAKQACgACABAALwBZAAAFIAb5AAUAAQAAACIAIgAiACIAWABkARIB2gKIA6wDzAQYBGQFDAU6BWwFkAW4BdoGega8B3AIIAh6CQgJmAnGCrALWgtwC4YLygv0DDoMxA20Dk4O5A%2BSEAQQNhBeEQIRNhFWEbQSThJsE1ITvhR8FNQVohYiFvgXKBd6GAAY5hmCGegaMhpSGnQalBrgGvobJBveHFYc5B1wHh4eYh8cH3wfsiAGILAg5iG6IhgiiiL8I44jwiSiJPglRCXEJ%2BQoYCkQKVAptinOKjYqlitCK3YrgiuqK9AsTiyOLL4syizWLQAtFi0uLYgtrC5WLlYujC76L3gwDjCWMLgxsjHsMsAzVDNgM34zhjSQNKw1EjVeNcQ2OjZkNqg26DbyNzo3aDfGN9I34jfyOAI4dDiMOKI4uDjOOOg5xjo2Ox47NjtMO2Q7fjuWO6w7wjvcPFI8aDyAPJY8rDzCPNw9Oj68PtQ%2B6j8APxo%2FMD%2BGQFBAZkB6QI5AokC6QMxBykJ4Qo5CokK2Qs5C5EL4QwxDJEPoQ%2FxEEkQmRDpETkRmRMRFqEW%2BRdJF5kX%2BRhJGgEaYRrRGvEcmR0pHmEfcSIBIlkieSKpJIkkqSZxJpEmwSbxKfkqYTnwAAQAAAADMzN0PKsFfDzz1ABkD6AAAAADJirZYAAAAAMqgwH3%2FWf9DDZgDwgAAAAkAAgAAAAAAAAH0ADIAAAAAAOcAAADnAAABFABHAaIASAKbADECNAA9A1oAMgKaAC4A8QBIAUQATgFEAAcB4AAlAjQANQD2ACYBKwAaAPYAOAGA%2F%2BgCNAAyAjQAXQI0ADgCNAA3AjQAKAI0AEECNAA%2FAjQAPAI0ADcCNAAzAPYAOAD2ACYCNAA1AjQANQI0ADUBlAATA7YAQQKXAAgCgwBWAmwAOgLJAFYCOwBWAhkAVgKgADoCwQBWAQ0AVgH0AAgCdQBWAgcAVgNnAEMC2ABWAwoAOgJgAFYDCgA6AnUAVgIUACECNQAQArAAUQKQAAoDoQAcAncAFwJWAAYCPQAhAUkAYAGA%2F%2BgBSQAIAjQALwHs%2F%2FwBeAA9AgoAKwJNAFAB0QAzAk0AMwIvADMBggBQAkIAMwI7AFAA%2FQBCAP3%2FvAIKAFABEQBOA10AUAI%2BAFACTgAzAk0AUAJNADMBggBQAb4AKAGSAEoCPgBKAfYAEAMJABIB%2FwAQAfEAAgHXACQBTQAoARcAYAFNAAgCNAAqAjQALQDrACEBoQAhAXgANAEjAB4D4QA6AOsAPADrACEBoQA8AaEAIQFoADQB8P%2F%2BA%2BT%2F%2FgF4ABcBIwAoA7MAMwDnAAABFABHAjQAVgI0ADwCNAAsAjQAEQEXAGAB7AArAXgAIwMuAEEBhQAmAfMAHgI0ADUBKwAaAy4AQQF4AC4BTAAaAjQANQFnAB4BZwAgAXgAfwJEAFAChgArAPYAOAF4AEUBZwAyAb4AKwHzACgDagAyA2oAMgNqACABlAAcApcACAKXAAgClwAIApcACAKXAAgClwAIA6cABgJsADoCOwBWAjsAVgI7AFYCOwBWAQ0ADwENAEQBDf%2F%2FAQ3%2F7gLWABMC2ABWAwoAOgMKADoDCgA6AwoAOgMKADoCNABQAwoAOgKwAFECsABRArAAUQKwAFECVgAGAmEAVgJvAFACCgArAgoAKwIKACsCCgArAgoAKwIKACsDWwAsAdEAMwIvADMCLwAzAi8AMwIvADMA%2FQAHAP0APAD9%2F%2FcA%2Ff%2FmAk0AMwI%2BAFACTgAzAk4AMwJOADMCTgAzAk4AMwI0ADUCTgAzAj4ASgI%2BAEoCPgBKAj4ASgHxAAICTQBQAfEAAgD9AFAA%2FQBCAXgAVQCU%2F1kBZwAdAWcAGAFnACACNAA1AJT%2FWQMIAFACgwBQAoMAUAKXAFAClwBQBAkAUAQdAFADDgAdBpIAHQPRAEkAAQAAA6T%2FQwAcDbf%2FWf9dDZgAAQAAAAAAAAAAAAAAAAAAAOUAAwJaAZAABQAAArwCigAAAIwCvAKKAAAB3QBPAPoAAAILBQQDBgIDAgTgAAL%2FUAAgWwAAAAAAAAAAREFNQQBAAAD7BAMI%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%2FDxImJiYnEyEZNxohJhU3GRYmGBgZJysQGRgeITs7OxssLCwsLCw%2FKiYmJiYTExMTMTE0NDQ0NCY0Li4uLicpKiMjIyMjIzofJSUlJRISEhInJigoKCgoJigmJiYmISchEhIZChgYGCYKNCsrLS1FRzRxQwAAAAACAAAAAwAAABQAAwABAAAAFAAEANwAAAAuACAABAAOAAAADQB%2BAP8BMQFTAsYC2gLcIBQgGiAeICIgOiBEIHQgrCISIhXg%2F%2B%2F98AD%2F%2FwAAAAAADQAgAKABMQFSAsYC2gLcIBMgGCAcICIgOSBEIHQgrCISIhXg%2F%2B%2F98AD%2F%2FwAB%2F%2FX%2F4%2F%2FS%2F6EAAP2f%2Ffr9k%2BBaAAAAAOBKAADgkeBj37bex97FH%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%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%2BAYoqxQECKcEVgaDotLCCKI0lkiiNTWDwbIVktLEtSWH0belktLLASAEsBS1RCLSyxAgBCsSMBiFGxQAGIU1pYuRAAACCIVFixAgFCWVktLEUYaCNLUVgjIEUgZLBAUFh8WWiKYFlELSywABawAiWwAiUBsAEjPgCwAiM%2BsQECBgywCiNlQrALI0IBsAEjPwCwAiM%2FsQECBgywBiNlQrAHI0KwARYBLQC5AQMBArJkHxBBDQECACABAgAwAQIAAwBQAQIAYAECAAL%2FwAECsjY5RrgBArICEx%2B5AQEBALKmHyC6AQAAAQEAsgETH0EXAQgAkAEHAFUBBgCQAQkAVQEFAJABBABVAC8BBwABAL8BBADfAQQA%2FwEEAAP%2FwLP%2FGihGuP%2FAQBr%2FEBhG37Pvs%2F%2BzA6%2Byv7ICQLCQsKCwA6%2BuAbj%2FwECyrQgMRhCsoKwCQKtQq2CrA2CqAaCqAaekCB%2BmpAgfpaQVH6QCBx%2BjAgMfop4GH6GeCx%2BgngYfnwECH54BBR%2BdAQQfnJnIH5uZFR%2BamH0fmQIHH5gCRx%2BXAjIflpEaH5WRfR%2BUk6YfkwEBH5IBGh%2BRARkfkI5IH4%2BOZB9PjgFvjn%2BOAq%2BOAUCOODtGjgICH42LFh%2BMi30fiwETH4qFPh%2BJhyIfiIdkHxCHIIcwhwNQh2CHArj%2FwEDKhzY5RocCEx%2BGAgMfr4W%2Fhc%2BFA4UCAR%2BEgBkfg4HJH4J%2Fph%2BBAcgfgAEDHyB%2FAX8BEx9%2BeTMffXs3H3x5ZB97Ai0fegICH195b3kCeQISH3h0GR93dX4fdnNkH3UBfR90AQUfYHNwcwIQcyBzwHMDcwETH3J%2Bb1VxfnBVaFtbVWdbX1VmW11VZFtaVWNbXFViW1lVWENWVVdDVVVTkE1VUpBLVVGQSlVQkElVT5BOVS9OAb9J30n%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%2Fx%2F%2F4DRwIIAAD%2FR%2F9vAuIDAQAHAA3%2F9f%2F%2B%2F%2F4CqQAAArX%2F8QAQ%2F0cDDwK1AAAAYgDxAUgB9QJOAAD%2F8wAA%2F2cAF%2F%2FvABAAFP9DAkQCaALeAwgBOwK9AAj%2F%2BABhAJwASQBmAE4AsABPAQwAOgBUAEMAbgBdAMQARABgAEwAsABFAN4AVABQAGsAPQBcAGAAMwBMAFEAdQBZADIARQBIAF0AtQBPAD4AhQBCAG4AhgCyAJwBCgAvAHIAUgDEAIUAbgA9ACoAWAA8AFIANQCiAOYATgBQAMMBhABIAD4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYgBdAGAAVABQAggADf%2F1AwEABwAAAAAAAQAAGawAAQRFGAAACgGeAAoAJP%2BxAAoAJv%2FnAAoAKv%2FnAAoALf%2BFAAoAMv%2FnAAoANP%2FnAAoAOQANAAoAOgAKAAoAPf%2F0AAoAmP96AAsADABQAAsAJv%2FiAAsAKv%2FiAAsAMv%2FiAAsANP%2FiAAsANv%2FuAAsANwALAAsAOP%2F2AAsAOQAPAAsAPAARAAwAJP%2FsAAwAJv%2F0AAwAKv%2F0AAwALf%2FzAAwAMv%2F0AAwANP%2F0AAwANv%2F0AAwAN%2F%2FkAAwAOf%2FeAAwAOv%2FuAAwAO%2F%2FkAAwAPP%2FVAAwAPf%2FqAAwAmP%2FsAA0AJP%2FAAA0AJv%2F2AA0AKv%2F2AA0ALf%2BGAA0AO%2F%2FnAA0AmP%2BfABAALf%2FkABAAN%2F%2FOABAAOf%2F0ABAAO%2F%2FhABAAPP%2FOABAAPf%2FwABAAmP%2FyABEAJAAZABEAJv%2FjABEAKv%2FjABEALQAcABEAMv%2FfABEANP%2FfABEANgAOABEAN%2F%2B6ABEAOP%2FpABEAOf%2BzABEAOv%2FYABEAOwAKABEAPP%2BwABEAPQATABEAmAATABIAJP%2FOABIAJv%2FlABIAKv%2FlABIALf%2B5ABIAMv%2FmABIANP%2FmABIANwAdABIAOQAgABIAOgAWABIAOwAUABIAPAAiABIAPQAJABIAmP%2BzAB0AJv%2F1AB0AKv%2F1AB0AMv%2F1AB0ANP%2F1AB0AN%2F%2BrAB0AOP%2F0AB0AOf%2FrAB0AOv%2FzAB0APP%2FYACMAJP%2FmACMAJv%2F2ACMAKv%2F2ACMALf%2FmACMAMv%2F2ACMANP%2F2ACMANv%2FuACMAN%2F%2FUACMAOP%2F0ACMAOf%2FcACMAOv%2FpACMAO%2F%2FZACMAPP%2FKACMAPf%2FeACMAmP%2FaACQACv%2ByACQAC%2F%2FtACQADf%2FBACQAEQAaACQAEgARACQAI%2F%2FxACQAJAAhACQAJv%2FtACQAKv%2FtACQALQAdACQAMv%2FtACQANP%2FtACQANgAQACQAN%2F%2FAACQAOP%2F2ACQAOf%2FCACQAOv%2FuACQAOwAZACQAPP%2BxACQAPQATACQAQP%2FlACQARv%2FzACQAR%2F%2FzACQASP%2FzACQASv%2FwACQAUv%2FzACQAVP%2FzACQAVgANACQAWP%2FxACQAWf%2FrACQAWv%2FtACQAWwAaACQAXP%2FxACQAXQAPACQAXv%2FsACQAYP%2F2ACQAYwAZACQAZv%2FtACQAaP%2B5ACQAaf%2B8ACQAmAAiACQAwv%2FzACUACv%2F1ACUAC%2F%2F1ACUADP%2FnACUADf%2F1ACUAEf%2F2ACUAEv%2FrACUAHf%2F1ACUAIv%2FsACUAOf%2FtACUAOv%2FzACUAO%2F%2FrACUAPP%2FnACUAQP%2FeACUAXf%2FzACUAYP%2FqACUAmP%2F0ACYAC%2F%2FpACYAEP%2FiACYAEQAWACYAIgAPACYAI%2F%2FvACYAJAAWACYAJv%2FsACYAKv%2FsACYALQAXACYAMv%2FsACYANP%2FsACYANgAKACYANwARACYAOQARACYAOwANACYAPAARACYAPQAKACYAPv%2F2ACYARv%2FxACYAR%2F%2FxACYASP%2FxACYASv%2FxACYAUv%2FxACYAVP%2FxACYAWP%2FsACYAWf%2FwACYAWv%2FvACYAWwAOACYAXP%2FwACYAXv%2FkACYAYwASACYAZv%2FTACYAaQANACYAmAAWACYAwv%2FxACcABP%2FyACcACv%2FmACcAC%2F%2F0ACcADP%2FfACcAEf%2FfACcAEv%2FVACcAIv%2FZACcAI%2F%2F1ACcAJP%2FtACcALf%2FnACcANv%2F1ACcAN%2F%2FlACcAOf%2FvACcAOv%2FvACcAO%2F%2FoACcAPP%2FXACcAPf%2FxACcAPv%2F1ACcAQP%2FWACcARP%2FyACcAYP%2FjACcAY%2F%2FeACcAaP%2FsACcAaf%2F0ACcAmP%2FWACgACv%2FwACgAC%2F%2FrACgADf%2FzACgAEQALACgAI%2F%2FwACgAJAARACgAJv%2FkACgAKv%2FkACgALQARACgAMv%2FkACgANP%2FkACgAOP%2FqACgAPv%2FvACgAQP%2FVACgARv%2FwACgAR%2F%2FvACgASP%2FwACgASv%2FpACgAUv%2FvACgAVP%2FxACgAV%2F%2FyACgAWP%2FtACgAWf%2FnACgAWv%2FnACgAWwAJACgAXP%2FnACgAXv%2FtACgAYP%2FqACgAZv%2FpACgAaP%2FzACgAmAASACgAwv%2FxACkABP%2F2ACkAC%2F%2FoACkAEf%2B%2FACkAEv%2FEACkAHf%2FuACkAIgANACkAI%2F%2FuACkAJP%2FSACkAJv%2FzACkAKv%2FzACkALf%2BwACkAMv%2FzACkANP%2FzACkANwAOACkAOQASACkAPAAUACkAPv%2FxACkAQP%2FXACkARP%2FNACkARv%2FyACkAR%2F%2FyACkASP%2FyACkASv%2FyACkAUf%2FkACkAUv%2FyACkAU%2F%2FkACkAVP%2FyACkAVf%2FkACkAWP%2FnACkAW%2F%2FcACkAXf%2FmACkAY%2F%2BZACkAcP%2FrACkAmP%2B0ACkAwv%2FxACoADP%2F2ACoADf%2FnACoAQP%2F2ACoAWf%2FuACoAXP%2FuACoAYP%2F2AC0ADP%2F2AC0AEf%2F2AC0AEv%2FcAC0AJP%2F2AC0ALf%2FxAC0APf%2FvAC0AQP%2FeAC0AXf%2FuAC0AYP%2FtAC0AY%2F%2FzAC0AmP%2FnAC4AC%2F%2FuAC4ADf%2FfAC4AEP%2FRAC4AEQAQAC4AEgASAC4AIgALAC4AI%2F%2FtAC4AJAAiAC4AJv%2FdAC4AKv%2FdAC4ALQAbAC4AMv%2FdAC4ANP%2FdAC4ANwARAC4AOQAUAC4AOgAKAC4AOwAaAC4APAAWAC4APQAVAC4AQP%2FmAC4ARv%2FrAC4AR%2F%2FrAC4ASP%2FrAC4ASv%2FrAC4AUv%2FrAC4AVP%2FrAC4AVgAOAC4AWP%2FzAC4AWf%2FyAC4AWv%2FQAC4AWwAbAC4AXP%2F2AC4AXQAQAC4AXv%2FtAC4AYwAPAC4AZv%2FDAC4AmAAjAC4AwQAVAC4Awv%2FnAC8ACv%2BCAC8AC%2F%2F2AC8ADf91AC8AEP%2B7AC8AEQAXAC8AEgAMAC8AJAAcAC8AJv%2FTAC8AKv%2FTAC8ALQAcAC8AMv%2FTAC8ANP%2FTAC8ANgAOAC8AN%2F%2BNAC8AOP%2FuAC8AOf%2BcAC8AOv%2FTAC8AOwAUAC8APP%2BNAC8APQAOAC8AQP%2FgAC8ARv%2F0AC8AR%2F%2F0AC8ASP%2F0AC8ASv%2F0AC8AUv%2F0AC8AVP%2F0AC8AWf%2FYAC8AWv%2FbAC8AWwAVAC8AXP%2FsAC8AXQAKAC8AXv%2FoAC8AYP%2FxAC8AYwAOAC8AZv%2FZAC8AaP%2BIAC8Aaf%2BKAC8AcAALAC8AmAAdADAAOf%2F6ADAAPP%2F4ADIABP%2FyADIACv%2FpADIAC%2F%2F0ADIADP%2FkADIAEf%2FfADIAEv%2FWADIAIv%2FeADIAI%2F%2F1ADIAJP%2FtADIALf%2FnADIANv%2F2ADIAN%2F%2FlADIAOf%2FvADIAOv%2FwADIAO%2F%2FoADIAPP%2FXADIAPf%2FxADIAPv%2F2ADIAQP%2FWADIARP%2FzADIAYP%2FjADIAY%2F%2FiADIAaP%2FwADIAaf%2F2ADIAmP%2FYADMABP%2F1ADMAC%2F%2F0ADMADP%2FoADMAEf%2BmADMAEv%2FCADMAJP%2FOADMALf%2BhADMAO%2F%2FuADMAPv%2FxADMAQP%2FXADMARP%2FtADMARv%2FrADMAR%2F%2FrADMASP%2FgADMASv%2FrADMAUv%2FrADMAVP%2F1ADMAWQAKADMAWgAJADMAXAAKADMAYP%2FoADMAY%2F%2BNADMAZv%2FuADMAaQAKADMAmP%2BsADMAwv%2FwADQABP%2FyADQACv%2FpADQAC%2F%2F0ADQAEf%2FfADQAIv%2FeADQAI%2F%2F1ADQAJP%2FtADQALf%2FnADQANv%2F2ADQAN%2F%2FlADQAOf%2FvADQAOv%2FwADQAO%2F%2FoADQAPP%2FXADQAPf%2FxADQAPv%2F2ADQARP%2FyADQATQAPADQAY%2F%2FsADQAaP%2FwADQAaf%2F2ADQAmP%2FYADUAC%2F%2F0ADUAEQAOADUAEgAMADUAIv%2FzADUAI%2F%2FxADUAJAAcADUAJv%2F1ADUAKv%2F1ADUALQAWADUAMv%2F1ADUANP%2F1ADUAOf%2F2ADUAOwATADUAPP%2FtADUAPQAOADUAQP%2FfADUAWwAVADUAXQAJADUAXv%2FvADUAYP%2FwADUAYwAOADUAZv%2FsADUAmAAdADUAwv%2FxADcAC%2F%2FlADcADAAMADcAEP%2FPADcAEf%2B1ADcAEv%2BqADcAHf%2BsADcAIgAWADcAI%2F%2FNADcAJP%2FAADcAJv%2FlADcAKv%2FlADcALf%2BmADcAMv%2FlADcANP%2FlADcANwAYADcAOQAbADcAOgARADcAOwAPADcAPAAdADcAQP%2FgADcARP%2FTADcARv%2FJADcAR%2F%2FJADcASP%2FJADcASv%2FJADcAUf%2FYADcAUv%2FJADcAU%2F%2FYADcAVP%2FJADcAVf%2FYADcAVv%2FTADcAWP%2FeADcAWf%2FqADcAWv%2BvADcAW%2F%2FuADcAXP%2FqADcAXf%2FiADcAXv%2FoADcAY%2F%2B9ADcAZv%2BrADcAcP%2B0ADcAmP%2B4ADcAwAAVADcAwQAfADcAwv%2BxADgAC%2F%2F2ADgAEf%2F2ADgAEv%2FXADgAHf%2F2ADgAJP%2F2ADgALf%2FxADgAPf%2FwADgAQP%2FbADgARP%2FwADgAXf%2FuADgAXv%2F2ADgAYP%2FqADgAY%2F%2FrADgAmP%2FeADkACgAOADkAC%2F%2FfADkADAAQADkAEP%2F2ADkAEf%2B1ADkAEv%2B9ADkAHf%2FsADkAIgAYADkAI%2F%2FaADkAJP%2FCADkAJv%2FvADkAKv%2FvADkALf%2BhADkAMP%2F6ADkAMv%2FvADkANP%2FvADkANwAcADkAOQAfADkAOgAVADkAOwATADkAPAAhADkAQP%2FkADkARP%2FnADkARv%2FTADkAR%2F%2FTADkASP%2FTADkASv%2FTADkAUf%2FiADkAUv%2FTADkAU%2F%2FiADkAVP%2FTADkAVf%2FiADkAVv%2FxADkAWP%2FoADkAXf%2F%2BADkAXv%2FoADkAY%2F%2B2ADkAZv%2FVADkAaAATADkAaQAYADkAcP%2F1ADkAmP%2BuADkAvgALADkAwQAgADkAwv%2FNADoACgANADoAC%2F%2FwADoAEf%2FbADoAEv%2FRADoAHf%2F1ADoAIgAQADoAI%2F%2FtADoAJP%2FuADoAJv%2FwADoAKv%2FwADoALf%2FEADoAMv%2FwADoANP%2FwADoANwATADoAOQAWADoAOgAMADoAOwAKADoAPAAYADoAQP%2FbADoARP%2FjADoARv%2FqADoAR%2F%2FqADoASP%2FqADoASv%2FmADoAUf%2FrADoAUv%2FqADoAU%2F%2FrADoAVP%2FqADoAVf%2FrADoAVv%2FvADoAWP%2FtADoAXv%2FvADoAY%2F%2FaADoAZv%2FsADoAaAASADoAaQAZADoAmP%2FPADoAwQASADoAwv%2FmADsAC%2F%2FnADsADf%2FmADsAEP%2FkADsAEQANADsAEgAKADsAI%2F%2FmADsAJAAbADsAJv%2FoADsAKv%2FoADsALQAUADsAMv%2FoADsANP%2FoADsANwANADsAOQARADsAOwASADsAPAATADsAPQANADsAQP%2FeADsARv%2FuADsAR%2F%2FuADsASP%2FuADsASv%2FuADsAUv%2FuADsAVP%2FuADsAWP%2F2ADsAWf%2F1ADsAWv%2FbADsAWwATADsAXP%2F5ADsAXv%2FmADsAYwAKADsAZv%2FRADsAmAAcADsAwv%2FjADwAC%2F%2FWADwADAASADwAEP%2FPADwAEf%2B%2FADwAEv%2BuADwAHf%2FaADwAIgAZADwAI%2F%2FGADwAJP%2BxADwAJv%2FXADwAKv%2FXADwALf%2BNADwAMP%2F4ADwAMv%2FXADwANP%2FXADwANwAeADwAOQAhADwAOgAXADwAOwAVADwAPAAjADwAPQALADwAQP%2FmADwARP%2FXADwARv%2FPADwAR%2F%2FPADwASP%2FPADwASv%2FPADwAUf%2FYADwAUv%2FPADwAU%2F%2FYADwAVP%2FPADwAVf%2FYADwAVv%2FsADwAWP%2FeADwAW%2F%2FxADwAXf%2FkADwAXv%2FqADwAY%2F%2ByADwAZv%2B0ADwAaAALADwAaQARADwAcP%2FgADwAmP%2BwADwAwQApADwAwv%2ByAD0AC%2F%2FrAD0AEP%2FEAD0AEQALAD0AI%2F%2FwAD0AJAARAD0AJv%2FcAD0AKv%2FcAD0ALQARAD0AMv%2FcAD0ANP%2FcAD0AOP%2FyAD0AOQAKAD0APAAMAD0APv%2FvAD0AQP%2FVAD0ARv%2FwAD0AR%2F%2FvAD0ASP%2FwAD0ASv%2FpAD0AUv%2FvAD0AVP%2FxAD0AV%2F%2FzAD0AWP%2FtAD0AWf%2FpAD0AWv%2FoAD0AWwAJAD0AXP%2FpAD0AXv%2FdAD0AYP%2FxAD0AZv%2FOAD0AmAASAD0AwQAKAD0Awv%2FxAD4AJP%2FkAD4AJv%2FVAD4AKv%2FVAD4ALf%2FkAD4AMv%2FVAD4ANP%2FVAD4ANv%2FmAD4AN%2F%2FfAD4AOP%2FaAD4AOf%2FjAD4AOv%2FbAD4AO%2F%2FcAD4APP%2FlAD4APf%2FXAD4AQABQAD4AmP%2FlAEAAJv%2F2AEAAKv%2F2AEAAMv%2F2AEAANP%2F2AEAAOv%2F2AEAAO%2F%2F2AEAAPf%2FwAEQACv%2FlAEQADP%2FoAEQADf%2FsAEQAIv%2FkAEQAQP%2FiAEQAWf%2FxAEQAWv%2F0AEQAXP%2FxAEQAYP%2FrAEQAaf%2FoAEUACv%2FbAEUADP%2FlAEUAEv%2FqAEUAIv%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plyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cb08AABFOAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAjPL%2FpQAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAAxADsAUABTACAAMAAwADEALgAwADAAMQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAHa4ADV2ADV8AC1cEs3pishg2FfJaEtxSn94IlU6ciwvljRcm9wVDgxsadLb0%2FGIypoPBp1Fx0xGTbQdxoAU51YoZ9RXGB0bXNPyK3JLMApRwa%2FIMy1PPoJDx39kimekZX1c%2BDSW41tEZBuFiwdwx1dRoPVA2vWPSlsSDqhNkqYfhrqlVUGD0J3HEAgZavmtLnDC5WBriSpD8Uk02KsUkJ9vCFz2CZXAd5viwGZ2xcVYRPa1bEIai51nMYlbL2ERuB2TCzLAbPWWRZ3%2FsZ%2FKBjLk8%2FgAZzK1OaxNw4oGBbsNhx6Reg5HRFVCrwa15kGmJEy5kX1YypVm%2FHo7TjKP3l%2B%2FnuCTOyiOa6S8QEJbuiGYNlCnM7tHChCHRRQHXQh7yPXASlcvc5KrNKol6orb35kbo%2BiEwl8d230cfWPwTy00bFDRYURYGchbwsoDcaR2AJsFGCrbWCzBdQ0qCobWwLfueqAzSkzaHX3yCjDlGYPV0VZqVXlqbr4poRaG5NNPMDv5MCHkLSFyABMRBhMiGSZiDgABYmwsLWDjUZCUnHwvXt0VAUy4%2FqjwVgjfUqp1evzUqutJB7DW99Cq5aEhGePsa4omKUp7LnSQUz%2BZq51pU72ApMxkaVZE9jF3IGKVnF0GlK1E6iqLJXFzy1BEv6Kr0ngCzIgVANRwCkVHYJj86Sv2dzqgVrjYKlg8W94FbPGQghEbTakZ4AoS9h0u39DOoRJTA2iirs9xa5m9Ir0KwkogoqFNVDbTIGO0%2FWuJYfp61Kq0bhgbcbnvbTDj4HBESsXMdvrvpp7Owf%2FS2EbndoEoglO%2FjigGAuLUTUmG07ZGlRCZ4QZLu81tPEgTtAvF99BxfnxNslgVYH5Jc4AfHwsiktDBO4wjgjizFMsIeBTGmIYrhtrK3tvtBu5tYB0PDMXILUkFYPM%2B5yLLGOU9DtoDLYWLQig%2B8wNRZbJZZWeK5JyKNwumLgP7X6pqgiZMFUvfCTl6Urx1gUOjzhcICumUUsiNESZCmTANkgHoh%2BKU0t1fgQYOBkNfp4MRiJysezS1Vz%2BahE7ohM0iJBn4I%2BT9RJN%2FuQk6SjQGwC%2Bom%2FSD7Zq2RxvcSjtdxHfo7NtktA46mEg5gBkTtMKu7oFb1KgqwqBLzQqZI%2Fs4QSRhYF3ani1URTcMD38kDDqlc3DChmYJTCR9WpaWrYFiXeNi7jYa1Oe%2BSP30gxVyeC10DYOakGpWaU1qkQ9OXlujEbM4A6A%2BQ243TA2QnI8mKzNXNo1HPN1CFawST6uC9dkyQTqq5bOKkkuGjz0nkj35Sr%2BQ%2FkLGg2iQMrTKImSGshnX7%2F0Jyh0JziDbTzxp3IrbosXgpchLcBJpLLUBrFbt%2FU6qXqAaNFJoeBItBoqgQlPZkpTq31s5VZsgIkFupAno5G9oKwCGRqv5294xFqOrEpewcjx%2Be06mxDkN85c0ckmWmMzbkFX9YHAf83p0EsnBywjZbcPxZw91kqg7voxasp3VfDhEDAufUx0MTddLO5kCyKkUBwOm%2FMurQH4bJQgK%2Bp14dkBBhgxpBUkTlSbTE8DDu9qKiDSCtvmPY7FRMn0wuS6XR7J%2BnpjjeaDNwiOgIKUWCP9aLb%2BXpc4toQCryp6gjCVq1Cl1Di%2BtEx4WnBp2hmGZpEksZwTSggXuqAWD4B7WAMcir2wmA%2BI43ECxUSeTDT%2BxgR3O4XFPOGxHh09lxpScampLahUbICao0db7%2F%2F%2B4CBMcCokQ7aOQ02MOzh0NEaJGGOvPR1t4sY1I55lNfQ3IkF04onnAomXxiNg2EbBbPTMBBlv%2BggPSmkb4GNFW6Ehd6nDzJlnIXFfzr1bVWQlfylDodFBbhCDf%2FVB13j441dc2rx3h4hfAWIJuZqbH%2Fwbuafg%2FnTQJ9bnMfR3U6TwYgDQJIOPOtmZW4ZHsR2VifZGaC6Pzv4ZzC2nSAaqSe09F9MxuI4UmUVA2RAUqBUIETEIAhB%2BtBhsxhVMXUmv3RPe1grhYWLM%2BPHwwO5W069qIgDXtETXZQ3741DSk%2BvO6y8k%2F7Mg6GFC5fSK02yXZ1XV0NZe8iknoIbSFnDm%2BbMP%2BBCQhoLKOeU3FVNQ%2BgPEntwkHT950cQYEYQc%2FXuL4nxwcRW2bIN%2ByskPaD5Rz6lN8LW5IDLYfuvhxlLcjWXSADeTshWsJk%2FHCuRbwjqLcx66fc6UlF33HipliQ1cvfKlgRpVcsmIsiYnJjzJIovUyqi%2F2OTQcIwt%2B3WW44BAQh%2F2GymOIscSpIt0YUePiwOCJN%2BQ60lqkSRbWi10lotzgWZsqzEI7OGFGeIoFyqli41%2FNn7LfLQ0qtxkaAwbsKZQ0D9s%2FwhBqoxhAlxCVNVF%2FJ6rsUBQ49MLtR3ICtKPLKZ%2FQPEeIRgWUNLbNR3v3tTaL8uiXO%2BOj3tSJc56KJHol9nGPuclbrQpCn4keT05ETSOQ%2FDwwKjCRLgIzBi7x%2BU7ByTZGUWcGLPY0VtXWC7kQ38C5thIGJx%2FxLM3KsoGyRwC5AFUNWPzU0QBCaVXo6z1NCclLuPVA8%2FwWuNhiAG9CPPSSlIOCuh2AQM1d6MyIEIQArIa9YenQ5ug37h9gPNDHVLImcjE91ewI7pz7HgOEVqTD%2FjREYRtEoDIfgxzDePkVvHWJAtcpCJ%2Bg2o4akZpphRUQ0U92lJcKZGJI9mnpl8UVzpMi8SBGXUAPpgcS2cDAusSE6q0hNibhCgZHLReLsA%2BSv8wF6LiDL3TogB1DsO%2FHeMg0ETucRO2hIOvbp1NVkFMclluNmBQbTy%2Bosr2bJ7oWdUtSHi1s%2B2vWxq8njY1eW4BCVr2EqSyIUuFKS9iwVF5FBGeg0hRcHwCKMtqYLBfvVSYXHdGZK3Ug95C2d%2B3t9ZWEYqq6F4TocgcDS4tI8cvEEXK1%2ByB9RofdKpHoA3eP9%2FB%2FEBG%2BIgvmwZ4Iir1SOYFEtUG%2FoVkECQqoPTdHoNpQKpA9K9k7TisAUBsKPtWgBriyeOxs1o%2B4KCIFumN744dH3Yf7X8F426LGtEhFLULhlKFgXF8Wq2D6kFSBkViLad9D9U%2FP6A%2Fr8SE7jsr9s%2Bsb5ODWI5uS6BkZJI5RQ5QDVZ5%2FKOG6Dzc1RLQKAdGkgwLiZLQdEYQ9yhQLtD5kWr4wGHTv4GqloWePyHYyuy3Ek3iQVQkdkAz0MxcsHzccdMLO8KFh7ywo4jXywraIhIicAI4dWdD5wEXBhsMklzbWc8zi8opC3IlPsLXHCWOqOgq4iPU6xyDS2tdSLnlhSFjsCCWvMRt5TAI30EHR4f16NYyzoG%2FoKgCUsW4182Wvj5gpb0bGGLe8YRIYRGVgF2hwIlEfm7eUwwLZWW%2BmmBZPggPnIKI0b6G%2BlHCncx0wkaVPRQpZKWIIrzh4yC4Ar2vTKzhVKSutNi1dSzmM73AGUXSBJrQH5J4Dfb%2BRU25tYrIVdLr4ogiwTE2i2hl39ffJWHNTSWq3PvVJplNqDTJgJcvCHYoQAuHeOew0gZGfmX3p4D0aSr1BhXbXRA1qipfS6LiggtiT1eDg4FTCWwCdAFC9FCOZ52EdrFetMBxUdC0dfnqpr0mELJE2m5FVMvwplyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFsYABEAAAAAoUAAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABgAAAABwAAAAcalXC8EdERUYAAAGcAAAAHgAAACABCAAET1MvMgAAAbwAAABDAAAAYGenS4RjbWFwAAACAAAAARsAAAJySvAJmmN2dCAAAAMcAAAACAAAAAgAKAOHZnBnbQAAAyQAAAGxAAACZVO0L6dnYXNwAAAE2AAAAAgAAAAIAAAAEGdseWYAAATgAABODAAAiTweHjMhaGVhZAAAUuwAAAA0AAAANgJiWP5oaGVhAABTIAAAABwAAAAkCjIED2htdHgAAFM8AAABFAAAAvTBwRGObG9jYQAAVFAAAAGrAAABuDSPVk5tYXhwAABV%2FAAAACAAAAAgAgQBoG5hbWUAAFYcAAABggAAA3zUr5ntcG9zdAAAV6AAAANAAAAIhLlGpmlwcmVwAABa4AAAAC4AAAAusPIrFHdlYmYAAFsQAAAABgAAAAZYr1LmAAAAAQAAAADMPaLPAAAAAM8MFvIAAAAAzwwJLnjaY2BkYGDgA2IJBhBgYmAEwltAzALmMQAADagBDQAAeNpjYGZpZJzAwMrAwszDdIGBgSEKQjMuYTBi2gHkA6Wwg1DvcD8GBwbeRwzMB%2F4LANVJMNQAhRmRlCgwMAIAC2EJ1gB42s2RP0vDYBDG723aSIrSUESsiHcIWqqDXbvFRe0gBJw6tTgUCx2Kk926dusixc0P4OiXaQZzjx2cnNRFhPiagENdHBx84P693P0O7iUihzLbJGM9mb6tTFrnTWhjSAEVyLfZCgnt060U5UDacrdd3vnYNVWvWlJHPa1oTRva1JZ2tKdDHesUHiqooYEjNNFCD0OMcY2bR0qSr10pcc8S6QfRaEF9Fa1roKElnutARzqBgQ9BHQFOEKKDAUaYYJoSTfKWzJMo6epSPI%2Fv44sHJ9qI1malWVEqsi5lWRZXiN%2F5lV%2F4mZ8YfMWX3Ocud7jNLT7jUz7mQw62ouwafyvj0jfW5KzLLTZkX5EpX6B%2FLXfxYfU3U5%2BPg2iWAAAAAI8AKAL4eNpdUbtOW0EQ3Q0PA4HE2CA52hSzmZDGe6EFCcTVjWJkO4XlCGk3cpGLcQEfQIFEDdqvGaChpEibBiEXSHxCPiESM2uIojQ7O7NzzpkzS8qRqnfpa89T5ySQwt0GzTb9Tki1swD3pOvrjYy0gwdabGb0ynX7%2FgsGm9GUO2oA5T1vKQ8ZTTuBWrSn%2FtH8Cob7%2FB%2FzOxi0NNP01DoJ6SEE5ptxS4PvGc26yw%2F6gtXhYjAwpJim4i4%2FplL%2BtzTnasuwtZHRvIMzEfnJNEBTa20Emv7UIdXzcRRLkMumsTaYmLL%2BJBPBhcl0VVO1zPjawV2ys%2BhggyrNgQfYw1Z5DB4ODyYU0rckyiwNEfZiq8QIEZMcCjnl3Mn%2BpED5SBLGvElKO%2BOGtQbGkdfAoDZPs%2F88m01tbx3C%2BFkcwXe%2FGUs6%2BMiG2hgRYjtiKYAJREJGVfmGGs%2B9LAbkUvvPQJSA5fGPf50ItO7YRDyXtXUOMVYIen7b3PLLirtWuc6LQndvqmqo0inN%2B17OvscDnh4Lw0FjwZvP%2B%2F5Kgfo8LK40aA4EQ3o3ev%2BiteqIq7wXPrIn07%2BxWgAAAAABAAH%2F%2FwAPeNq9vQlgG%2BWVADzfzEijWxpJo5FkS7IkS%2FIpxZJlxfGR4Nx3yM2dgIBCOMIVrhAghXK0CYZCKA0tKSyQco6UUNpu2G3pwoq26hnSQrcsvVhajm0hu90m8fC%2F981Ilu0Eyu7%2F%2F3EkzSXNe%2B973%2FvePQzLtDEM%2BQzXwnCMwKRLhMkMlgWefS9bMhr%2BbbDMsbDJlDg8bMDDZcHIHRssEzyeE6NiIifG28gs9e0%2F%2F5lrOfZmG%2FsThjBFpsgv4ZcwMtPKKExGsecUUlWsWaL4M4rnkGLIKu6qImRLAdLJTOvx5KOpgizmxIIsRKWoLKTEuCikCkXCPb%2Fj%2BQq8CKeO1TYPTzqgjtHLAA0G%2FtH7WplFTNnCMJ14c4He3JAtE8bSuW8W4cydRLFlFMshhc0q5qrCZ8tmC54yC%2BbOssWMmxbG3FmyU%2BgCJCrW%2Fsgo6SKj6mb14PiWupmMUpwN%2FBf4Z5gCs4VRshmlrVpuy%2BJPtaXNFI4ohSOSVQwZpTmnGKtKMKtIGcVaLUtWvFByIWTTM0qBQharlkKRLHy6St2kU3FllXRVcWZL%2FaSzFCuIbkXoV7rFsrUl29%2Ffj1Qs5HoL8UKur9CXy8o%2BOd6bZuMxBytEhahRgrcwn8sOs%2FmcUTDGY6k0SRWLlxleL96WWHzV%2Fg82Dxqfyy1ZFPb3z5npIdcX1YNGsgvezdP6Z%2Bek0KIluWciGzc9Xr26dcRGDhcz%2BeLjp%2B544fwLC%2BunBbxdpw4X033FRVeOdDqD09bln7rwqq%2Bmn7yGwTGpkFF%2BCXsA%2BMtNeYGrEoXPlAzayANRK9wtx7YiORlGH8Pavx7Y%2F%2BgD0sW%2FyJ%2FESEyEUbiM4qjCmBHFlynJ8BMlMye6S1axv39aD%2Bf15aLZvt5kPCakSTxmlLyygzguW86%2Bf80TT1yT7up67pIv%2FZIdWUPeX3H50x885dh49S8eCNgcm5Bt4FXkFeAdHuaBhbEzTJ7IBZIQzQYY9QpgcXQF6VIPsuvZ9TD6XUU8qG6u0L3DY4%2Byp6k20jW2F36H%2B%2BjDjz7kX%2BBfYFjGyLgYRkgAexN4Ffp6MyQZE%2Bxk6b%2Bfeog979DaQ%2BfY7Q84W532zf9%2BsnbgdHvK8YDdXqMFwKQwZibHlI3Iz0JV4WAaWTKK6RCQssyZkHM4AzCuicNNkxEY10qJK0aJCNMpHxV5BYE79iaQUhl7fez1YpFN4u87GBPMl2eZFoZJhklhmOTFhJgUHETW93qTgsFBJDhm5FtvX34SID9r0fJ1bvfty%2BetcljGXBYH7H%2F%2BkvagtKGDPXP32F9dcvDyvkJ7QD65wF1msXLf5TyWseWiP8AANeSP3uX%2FhX%2BA8TBNzClM2YEYuTOKXFWaNMZozijkkCJVFcmFskExwBwAdg9Ionsfx7rcrXK%2FYhCB85mS2wFTwNyvyKLi6lea3PsIIxjg%2FLQetwsQkrwC8XmdxBhLEWB31uVr6XMlW2TSDKzWvIx0CcLFJq9JPXj1zspnXyTu735XfZ%2B8h%2BfU31d2Xq0ehJMXCwLpWsZepP75xe%2FCFRp%2FFpn1%2FLP8HCYAHEmUYEZhDiFPOqqlJo2le4fZMJHhjZW8Dk5I88Xpp1599bVd066%2F5qr1fbOvvWXv8PDjt1w7m3PN2rKqm184Z%2B4CvnvVlln91950Y3nNmvKNN10LtProm8x8%2Fj4YewtjA5aKenKeKPGYiYcrLCD%2Ffg97D%2Fmluv1u9SZ1%2Bz33slwLFUV%2FUmcSj%2Foe%2BWf4pHzY%2BBs%2Bxs8odhC%2FVAzxwERypuSnIE%2F4dY8sFORUIZ4Spt5n9r8s%2FM73Fn33naXLjnNH9sqdv%2F3Cjj984ec%2Fn8C7HpjHeZKPJYdIb1%2FWFyJeY1wiEhlNzFT%2BR5mZIKN3EVK8L1fZqihbK7n7iupHdzE4%2F%2Bj3l9B1yco44Xdk4JoIE2dSTCeTgRmhMGUGOYivlvwtICiFjBLLKaaq0ppVrBklmVNsVaUtqzgzSkdOcVWVrqziySjpnOKtKtOyiH84m0NyEKWXjiNf3WeyubytclbhXSii9pntooS7%2FqrSDO8aV4aySkt1X2tb1zQ81eIqReHKRHt3D%2B5y1VIeBZPNCoIp2NTfrzjFUnMIBVQPXS%2Fz8fykF0pBmKVRcpxzvHJ0f7H%2BD8TN2F76OjB%2BkF%2FSeAnKUhBOx7bWjzC6fNPWRifQMMPMZEaYsh2p15UDtJFkoYwySEnTC7wxi9LDRddHP7y7Si2AOBCtvapMz5ZOonwD8qEPJK5PEmFMo7FkgEzcJ59wPgRcIIVCkroZ38e32fUnOpNtOMxe3LAz9sKJzjAgPSfjvpw5i7mUuZG5C9ZqSoXS4EU5pEOpt5hFSpSWbM0hLUpzroT97kzp9C%2FAfmu1tPqWLFBnFKlTciHTmYH52rKUQtNAUs1aAdv91dKC9fjpKm2AY5tvgu0LqqVrbs9mS3dTyuFcGCDabOgksaSY%2F%2Fh9kv%2F%2F9vqQVEGCneCNjP7fzmfre%2BTR420e%2B6%2F%2F6wWwztbkReMYr9V5fJDyeC%2Bd%2FktySnNVmYOjqpyOY6qshhHdMGFE5ROMaGnj8UfP64PdPjichF2jNPk8jgbyPZV9UY36H7f%2FydRmD%2BDO2Bx8P%2F52I83Hv%2F0pKIoyPMTE%2BA%2F4DsbAMLA4pEgqRB7hsgfHvvYj8rJ6OtcLWz%2FG6y5kLuTn8fNATuN1BTORzUQwkwtJUH3rIAmS4EH1LfoGH8MT9w%2FiNbBWTdANUszzE7QDUAviOSVUVaLZcnMI1ZzmJGg8oWbcDEVAv9b1h7YG%2FSEJAiucVRJVpSVbTiTx0kQcvpVM4GayGb6VqGsZ7TDSSdAyyqwPRHa%2FkhAVd78SAK1DdgUbtA7ZDVqHq78UAu1jH2OQ%2FHiuSSx7A6S%2F%2FxN0Dw7keU7KSXEpnv9YPWResQKi%2FhO0EfUUvAile412X6a0izBnHE%2BzapmqWUV1zeo51KyaQx%2BjWz2HulVz%2BBO1Kw6WsY%2FHDLUG8r6O2I5r1VcFH1xlqiH2InG9CIhRXZGR%2BQdAf2iC1X4ug4ajBdDoouuSUC0L1JgTwJhTBFfJBhh5qmWbBw%2FawI5Ck6lkE2CQZH9zHACnmpm7IIE26wVg873JVN7nBg2NjaVZok1g1HfpBJZ%2F9vj5W4%2BQs49sPf%2Fxn522%2B9V3X919Gvl1SCri7CjiAvU8aRt%2BrFC5pnTkSOmaSuGxYfWXz2%2BBq%2BBiYhtfs2DdLTIE5FKprsdMY8o8rjgmWy5HFFdG4REfRMIESFjBTNbQcVZLIpU2BbCwCRjHtb8iGCNdFRj6CkGDhBxWbWh5g6KwAukWZJr4XfwuZj5zMnM2g0ywoqoszih9QL2VlHoLqsoCV2kp3AMWtFVApwUM2lBDwPZLxX1OKdePrNDqLgUyYFiWVgDLlwRQYJTF4j4umpqLZ%2Fvcpc6TkOE93gjrixDBJxdkH2zmsjPZvplE7iukCn2wme%2FNsMkMKSRTQioJm%2FGYkzU6ScooyIIRNg2oFEpeYywZNN7POvzciu7BWw3dPYZkW6wpkzSmM4bPTZ%2B2gvM7yZcMhi8Rl8yt6Br8nKF7mlG7wpDtMtw6kF7BBezs%2FUZy%2FrbyNvjPnhJtTxp7ugyfG0ifzAXhlMFwP2sPcid3DX3O0NWDX27uThpzHYbPzciczAUc2s87AtzJmRmfM2TSxuS0wPpt29afsm0byEKBKX70Ea8YvKD3j2ugBeYOphzFudYK1v603iwsFalMuSPblwN1oakKKiYqFY4uOJ7JULsVzPvgIVx1Wl2lBM7DrNKGmmk50UYlFAMM3eZCeaRMAy3VVeqBre6s0ltV7Nlybw9e1OuBi3pdqGeCUESHAHWi4CJSX0kalhMPqJ3aFrKSB16obnr07aLTQjwD7aSrfYB4LE6H9ehbVkeRu2WgfWxO%2B0CRyhjkM03a8CRrcRbbBwba4XtZq8Nx7BJkvvbBwXb2wNgc9gBYxkf3a5uavQR%2FBon%2FNdggXUzZTPV14HvQz82HUFc3UT0bqQRMXzKYQPAQOlnNJF5zurCj7C0V9SD%2Bsaewt4xtHduLcoRdj%2FyOAvBtkBMORmRiTBmMpU6CXA%2FSzlgtGwkSzGgCieDRZhPVSesUISIpOqzkV2TUYT32jNXBriddAX6b1aHaxs4A9Dg34o4ylmd4%2Fin%2BKZjDHuCB7Qza4M6q4kUhW5a9eBtZgnHx4JBTwxAgsMOouUpeQHD8ogBcJLtwcsO8R6Ox5LWDnLJwDg%2FMrJIow46Zd6Kty5S8TtgTGLsLT3nwlJGINl0Qu1tbeLeL5Vta3boA9qCEkPg9xE3mEPeePer76gH1fd%2BHZO2HH6pPzAeR8Y3GE3v2sGepT3yIp8dUIOlBaiuwHz3IMAYv0BTlVVq3soQcYgwWlTNDHQ9O6nggZmRhlGO6tIqTHAd%2FJMrFOU%2BOixfJsz%2BVHvb%2BhDw79lb7B209bzY9wSvoQDm6gq4FhzW%2FD6vr6No9FzFlK95TuxuYICQ7LirLvIC35hlN9NuBjhYqK0sCD5KKNYOksosgX6lXDL0g6KcEiKjtRf6kvoHyU30Dtn7z7LPUjwciFP14lSKMM%2FAr2PfPAhQyE2bO0TVID53LBhjYCB1YsaqI2kIKymSzqyTBFiiOLbikiqJ7v513y1Q3aBaVcL8iuffbDB5fiI6p7IE1l5jMTFBfUXuH2WyYpUsRqY0kR4U9R87a88bhN%2FacpX2c%2FgFZ%2FcEH6lMrdlV2HSENJ%2BCDZdWnPsDzKp2uwLQTeTbIbKzxrM6iTQ0sipiAjeuv82UzYgJ8uR%2F50ouY%2BEVF6lfgCDKnj2LicSImRoGhetDxWZJrgokcF0%2FEk8uPHNl1Yq5Uz9ZwoUzC1n0KiE%2B7Lk2AR1xUsWcBJW8GRwKMbzOocjxxUJ0sL3qiTTVZmJOjhRwXrXAt%2F0pA1m23OiqVLOnKVjaOPRwgv0LJpSZg4pO3YJk9PIGGEuNn5jXMe6RhgNIQYLDVKRcEyok2IAxvtnA%2Bf30aU0L55CmE8uj6SAvMbMOJyPQhmUHaJ5OJ7IcRf%2BpJOH8nubsCnFvTmZqZVQy6mWFsxYwSADhDFE6A2%2BkqWTReDQOcFoTLwHu8cgCH2CyWJB%2Bu%2BSKOPGHMXgkPB0SFAu0Ns%2BhAjnuNLUkXOpmFqCjAiu4gsq4k7TqifhU0otsvuYeM3v2Vl0EjYn%2F0rqYWbQFVCTSqk%2FHMdaftZjQdj6Hwupgocx1TFnE8YX75qmVfBOe4Lwhz3I7zrmw34AE7gy7ymOacgIHXGDdYLcUBGReqMaK7HwF%2BXjBYLaCbRBH%2BoLtkNiFavggsL1EqHsx4nQGd6JrLDrApiNFCEt3GnBgVQJHpy%2BdgTsZjKcRuy8tfuVvd%2FMXNt5WOLCSj9HOXdphdj4rfdXh2xZESLk34SY8xsOoVdZ49nh9rBaOYMoo%2FhyIumMWgRHMOBy2URWnXkkMxE80ir7XmUHdPZKmJYzpUtrm8qGxYq2WH2wdbqDCAtMOVDZf7BBgaqYaXBxb8IRKVEnntpfmMuFuOvVlzEqEMHH%2Bh1wnd8tryr%2F0f21us7zbIa9SHQF7LOHYtmo7TCiN2CEalbAjioBlQUgdduNQpDgC4CQ86QIdBBYgpteCqxjsMQToz0Kc0MG4w5xq8SaDcaOo5KB9bVpKulVuoInJsK2guIMWp3Vpk17cPVFZu2bKyAgrMXjjP%2FmEX1cR1XYH6gTiYy7K2rmEkAbmJq9J4ApAQ5K5nmITZYVIQHSTNGQVAe%2FC2a7ZefmGxve36W0Zvu%2FJUL9KPjA5Os0ebDMtPJodPnmtpa7PMPRnJwuly6n7g6h7mJOZ8ppxB6gznlGRV6aPjHAIyjei2DK5iMaBNZ1XpdJWysDVQVQZcJRdVG0qzgbWznUAjmzXUTKVJMgOzU3B5vBxytw3WOifKObFuxQyTljCRxvfTbMzBSh5Rc0sgQdEtkZi077CCQrfj98T4%2Bx1085xH3nj7jUfOqdhMe0w2%2BsauH98mHpCSVtJ1408vv%2FynN6oHtb0r4QvwvSvHXiU%2FxAvVXnxv2NbleIVfz73NGEBnY8SoQTRESQHjIzJYBSkwczWpzyo7dsyv%2FSejILMr6pMNh2q%2FxW2mv%2BVk3AxVExyHUL6JehAEfz1RABtDSIGYrf%2F2gbmXXjh7rfar%2Ffnbnv36rX2X3HlPbfy%2BzZ%2FN%2FZX%2BZogZpHEr9BKH9d9G35BHEzwRGB0PrDb7XD5ZW94Jgwqcw6Pxc4BEE2KC1O8PVpIEhrNcQMuIaNCQeygsZwrXXy2cbNw5aiR3A1zH3qyQVwCwJ24tXHLn3VXT%2Fd99YZdpxFT%2B3VtlU90%2FroBUEYGbAwBnlNokM5nZYPstZpYTonF4adocMEbc1XJ7z1yUGcFMOdW5RDNQyonupXgslimxRjgSB4U8C2iWHAtgL1ot9S5Ef%2BcKNFpKhjD6RqslDqOSIc07ZkfTJldV8vDuKqWtnUoHdbnMypbT1M%2BSjpk7y8TqxLskXKUCfGfOAtgeqJaSi%2FDTVVoClBzJKsur5f6heSjLToaLpoVgZfJK%2FkDvTFR9CwlY0Nvap2OosxRrAgp7vNOpnRoXy%2FyMAfTZhN2zzAZfoK8wODRrNiV%2BT1SM59HdkstH0ZmuCUcCLw6EIofqYB5UQ%2BqRwYu0oyAAorirXc2BuCQoQuFFDqPdTUaLRdVGtSw0xFF4dmkWueajh%2B3K2F78wEuobNVlbBcIDYwhHsB3lKfsAfwlelGxAltF7m1kzgqK4814EQpo7hYQvnM0P765Pu5T15PNmvVT5r3NOLr2akkKZbP0GNVcW2nQUPMwoGyxZVGtRX0QTBQryhq0S%2FywBZZrk%2Bbxj1dRSJdcGMwQjP39pSaQ1jScgf5%2FNCGBPCmgmIwrDXxK8JnXP3Gfol2pryKVCvf20RWwxb19zF857bTjfk7Q9QRN1xN0jxZPtTxUZmCKU13PLQBrMEYr1fVED64cZsJ4ZV%2B20NebSsYEAhbu6%2BwBsOh%2BGQqvCofwDYbgVxYn6Ho27nerQ%2BFwCN%2Fo3Cdg%2B%2Fxav28A5Ld2Zzmn3VzxZmu2nVmL%2BNH774f7%2BwKU55rIVAgSOfSeElA4dUB%2Bod0Q3lhrEY9kyYsIktUxCSQF9%2BFkLfao0cQCs1y3yjS910qXEktVsUwwpy0MejQFE501Au4Q3kDplBMLoLuBnhMVi89yXymOuYvs%2Bzx59pgfWLBIhwBj93S9LMH9GMBA7E3COmyUKuRF8mJIOvamFCIH1Ln8Nm847G1Y91A%2Fl0G7OYUpJxBGsJAs1Ga0cLXUCkXIljnLBAuyjaIA3MlmkUFNmm9E0FyzJiuuf85UTPPkAYOhGg%2BTOyp5YItLE4RM4ArDBPWGCPFRlaEC6gCw3LE3823siuEzWZdFHba42JEk67GQwxYPm2StlrG9FhwDmLNzKhX2ta1byam4lh29%2BUsWh8OCbxPyA0JMgukGvriUZiOAqGvPKN2gpmVgMhFM%2FAAsItVyhPonIxnALuIqpejyXsoBKhEYBiXQX%2BpMAUrR1kQLXTS622EvEoujRahExVK6BwSax73fGghmpmmKkZbdoPko0zws2A7WSbMcGHSheY0x1uvD7IcYKn8D7dzbqBzt4p25wqqOSvvKGWmrbRdoSsXRyuhoxTi8fnh4PQHq4FWoNJma5uY7YUfyDrSDKQzXjJIWvGj4TKrv5enYPgs2cQxeGvfZc0q4WmZtKN2JEs%2BUWnUtNA%2BW7EwSlWEYpGg%2BmUoTWOviYAA5iJMQj%2BzJk4vbh1s9p5PbVro68%2BRrsTZ32GhUbzxDvczfbOtwOsmmcvrKEV9f15%2Fe6F43MkI6PGmHnXvrmHtaky0oCORfyStfUL8HPIf%2Bn%2B8Bz7WAlOhhzmXKLQhZtKp9Ejo%2BHVWlJ6NZusBhdJT4Q3ANLlZRWLYwvQa065ZDIjArHaVEGkbJBwPTHoWN5n6lQ4RdpQed%2F4BgniZHpLlU3oDeSxyJMA%2BfJJ5CuwGHKK9txEFHZc2%2B1gj5xTXflqMxqw0I3du55bHlla%2BdctMNZz70%2BSUX7d19ipBr4waa5ZDdKcwnyhcKZxTaTAJny520Zd7qu5dWNq4%2B9ebi9UtXbqzPS%2B5Sqnu36CNBaJgWxIADo94oovR0BAS14OnDIUjlo7KDcxLO6JOlytIrTE9b2uYbzQbydTY6IxYwGG6yTJvfL8zOcCdPb%2FUQjvT3m%2BKphM127F97B439DPvRc7pfyAxzYBtTbtLstTJr8OGCYwOut1GudwHXW4AjklRQahouWp0wW0AHCFFfQ9lPAzn%2BIAZy%2FDSQ0wRf82trk7OKc6ZkAdlaMlDbLdKkxSRsosLDEKRg9hfkuJgTPGIuCjswGp1ElIHdCiKYpJy0ePHiG26A15Fd7IFdWZeUjsYqRXVzsRKLZrwiqH2PP37szce5c3CZDaUkM3vsmWyxmOVWsmYpFaJrUPSjb%2FB3gfxDfO9gQO0CfNkqNVEb0bUA3E4NXcuhT49kCHSbfazJTFDRcfnRGgJRRsUCawL8GRDZSkQE2R2ksnsmASRlAWQfdwLUo49r2BU11L2ZcdTTkit3g0YZ7sd11Me2IOrsnTXU9XyBxngqowsgjHLSZeDT7ockfglaYkf34zvXgu%2B4hsD223TbTyOY62kEcy%2BoLris0IhL5WO3%2Fh5YxUn7nk84PxHW8e3KCcOtdXDI4fqmuvl4R48Db1L3MoJ%2B0Z3B4EAog4kYQxmlt4q5GHq%2BxRCZGCf25D%2FdfiMmxUayVxrHozJhELSQFwDPXlTfVN8%2B3tGPx0vDSMPuk%2FASP3W8%2FPijdaLtcaTIrE%2FYRJRMx80twPyRlTp%2BS2h%2BwRyaQ3M6zaFZTfMLLqL5BcUsYrw1hzGcK0FM3IQ6QtnVMi1Lsw3K%2FrbpaPXc%2FL%2FKDDle5sf%2FjZa1IOfHv6G%2FZcIc%2FsTtT0H1Y6OfZoSMH%2B3Wx8cImmqQ6WBmMItgZUSrPUmz3dJA9cUZNDQ185yuIBiOBylK42CfXrRNSPg495XHHkPzBmcOznOcOTDnu3BbPTg%2B%2F1VbDWoUBWAWohxWbfySo2%2F9vcIO%2FhmpjSI18ONswFdmyrOoP2txRpl1SJldpehO6yn8H3HD0LNHpulo5NPhx7UUo49XKn%2BvHD%2F6FjrV2OQNi4uLdR%2BRwr1NvTrdNHMRphhafx6qYoP15YW108zQfAymxNt15y%2B6XCammYHl0hVh3490dUXG3JEu7hYw1z3snXR%2FC7yTopa4yzXMcxkoe4o%2Bu2G5r4VyaR7FBN9zqIquED38gxkVoQBAJPYrkljyuKniApCVXR4JXRRNouLtn5o4N5MYUsQjRt26Jh908%2Bs9gYDn6F53sPhX0rGIJN495r9o2eZlyzZ3cYfdwaD7mA3e3%2FvGZx8kw%2Bqb5LD6Izy3DOh270cf8s38C4wXtPQhHYOQBnuEwq7lgKAz1qQFrJokIJ2dR1hDCKvA%2BKme4ekFayobJl6qtaf5VBJ5iZoYxnuv%2Fel11%2F%2Fk2oUL%2F7G%2F3xI754wrOme%2BeP%2BFm%2B6%2F%2F9Au9o9bf3Hztlf%2F%2B77L%2F3tkxBzbdOmexZ%2FfRc%2FcjzrVeHxNYhaOR9e8WnTN1xBd82hEldHDJuogYojCgJG9ktcDihJG0Y4bQ8uJsYmhs6VHFWVyxMysfvUItxZguorhuX83OBkf6HkMoM0VwkbU6oU0WwgTuZBmU8m%2BmcRBrppz8RVXhKWFy9ctnZlYtu3Lyy%2F%2F3k3bHKed5hJ8aYuTNZtPL5BdZ37za195acP8u7ZcfcVVn5t7ygPFAd545je3rbwoeLbRt6ht4Y7e3CW76vbyS%2FxnmGYmCmvlOqYcRGo4qxgVwAFL0QHToktIDV%2B11IbUsGEcOBRG01HxiaVIDAliwlCxFNJCHry4ryUWT%2BguSOA2XhYmKDoFSWO9RIGkzIBihrzzKLkk8m31FTQIyeH2gco7j6oHH32H7P8aefBydRP522WXSaejo%2F3Rd4ymb4ORaMMrb6j85tF33rkwSx68DK75n8suW9M8Hs%2B9D%2BZSM5NmFuiYwbSJZZRUVTFrs9mlGAHLDMXSXkWHIlOKuYAFpVArsGDJHIS5lOgv8Ub4TNantmwU4uO45GuokGEuQiQzkWIOAyBjYG9dc%2FdkZB4Pn3%2F%2BWeEQ%2BYz6gOBfMHvt7H4NoxUt62sYPZAHhEg02esxE%2FJTMocM%2F5J1yLnZF4%2FjtZf6AeK4%2FjfpeJm1EWvVcRkP%2B1P%2FGY3wC%2BEI%2Bi8wqyoK42UGRPf5wObXxqvUEm0QXicYLTObIinArWtopKw%2BMhm5taSJLdyhrrxHw0ku1VCaW%2Fk64rRD%2FT2eP%2FmLWn62lpckM3OZshux8FHrEewbs1ao4z2kcFnEzUQLdUouL0xCI5VpPswJssswH1mxxNio541IsEaYSRw%2BzCQPH2bMZ5bQGwoCN%2FU96g2tfE99Dd4r7Gmk7WXtyMvqL9WDL1eoM%2FXlcfv2FqCxB6isRQWtOer202gL8Ej1ZA4tgkHgT1OAKpp6cli10Y2spuWqi8l%2FHN1P3lYX1XIba7IeJX1ZQj%2B53YkZWX56F7GGNVMLSdXuMn4n%2FUY0e2DKzdSAfkN%2F%2FaaEub2up3j1eg7MhREy6O3CnBft129ne%2FE32SSbHP%2BVBn%2FcEuRCoC4tnMGrKF9WmCJ3C10vIwxTmJTQNjnBrXL85N1dx9fe9HsX%2BSX095nCuCtKcwg2fmkXLugN30M6t9Vwpr4KQyPOwDciLNDkMNs79kN%2BCSKMFSrk7SljRGMZINlzNebksyg0AvWfIZPiWXk8BsMzrrSiqoV3gNdz4yYn%2BQYOEtwPswy4t2t8R2F1aHznphVZ1qrOd9Hj3aur8U70PhPuocUfQafh26hO49Z1GlRoUFQgO0%2FWzaNkgpnGLxl7%2FUhdCydOthdo9C1G4P7Gf53m6GLqbepb7Io%2Fqw%2BRjX9m140pfyYbYQtxc330M34lfyVaMwkzKRBZkOAg2ag%2B9Bd2UCJjyov00rGXfISMlTRZxzEc%2FzT%2FNPXnjtDMB0cVS9YQav%2FUzIeAnvmwDzMfqP9CwjRZzHuYmvWQAzmBSmVcinN7iAfkrGfPHvU99YD63h0V%2Bo%2BmO9QP7tlDTqaHJ8DlBB1iPC8J4XI1wGXX4MLcIPsEuGwWjGcwsMTwsGqWiLF%2FMnjRfHQKWFh4cRygptIqS2HS9VV%2F3QmmUUggcEebC8Uoj2DYpSk3lwtyIVVICSlBngzEqld37Hh15054P7hjCixttTPwPhEmcSJM7gaYPHWYHOMwOafABJwlpCZDs4l4ye5n3z1vCiS3qO%2BQ3c%2B8e54WTx2HYzozyFxEIemvKoNUF4hS9bpXG70hOtfCVSWsuWUBvmGAL4zrTbQX1pu0uN%2FMt80YoMOYGtSHUYmKSgdmKpdtzjQq2r1iyUim4JDLood82DCD5hV4jYKDl3PDfL43zWGlpAf4cTKGr9zFOwUja2DNvIP3cB6fSTY4UyE%2FqRjc3bHmeM%2BcnqbsyXOTt0%2FlVzbAGm0WwcCxxOvy2VyElyNDSc7Snl2fzS1p8xv9%2BQ1qujiBPhjJHahxtEQXZDdN4vFVMdeIH3ftltxSjYd9qB1PRbcQxYgnJpwfD7H3yKgWeJwK%2BB1gEBUrcL4xDucHS%2FMCpuzV43ARWhDFZMqOQDRHi6jKtng3jQNMQ%2BlcNjS3o3%2FDUy0L4RT6N3pQcwUVYl8sns7Q4XPDniL1lxhMywn1K0Qsh9vatThePjdMCvmcFGZlQMBBBCmeT5NUDtRxONpXiOdzsAsH4VRO%2Bl7kts9X%2BrZscv%2FgB4HnT6l84fbwBv%2Bliyv39b5SCZy7qLL4Ev9PyeHKDfcFKxXvis%2FOrZy3N%2FrSS03337DwYv%2BPfjTtq5VFF%2Fp%2F%2BAP5ogUVjV8b8%2BYi4zlfnroJWM%2Bb0yUfGn6YM0VzvrzBiclxNC%2FgBMlxDVkPJ0r9WnjbbRfX%2Fp84ffNXDVcxU%2FNVJ%2BMQnIpD0wQc%2FJNwCHwMDoABORH0m3as%2B%2BFO9bETZ%2FjFd6yr7lQfnwJzkw6zj7I%2FXy8O1SR7QIOZFofWcu2cXgqz6NNhdjQdJ9dOJpji5aHpXb2pKVAPs9OLVz75YXXj5ic%2FHJiab3dFkS0Mfzj2wcCHT27eWKtXPof6KYxMkObcYThW125KrAEYm6HKMc1kxuLhsb1snLxEs5l%2FPuX7ep5J7ft1XxXWoIOWpH2fpqx2qQPqLNKtwfBDmKOLYI4aUU8yUC%2BQQHOw%2BCr%2BUIk3NKRVIyA%2FJC%2BSA%2BohrZx5bC%2FWFOLvfAYG4a3a7xhrv0PGfweWCcXYrxdug3ryGdIGsHSoM2u%2FU6PJv1K9DauowZhG0B2ccd4d%2F%2FEgXnWW8g%2FbK7e8%2BvT5JuLUsGBb51zVKGuM8E2xlp%2FlyJScuo4nE%2FjzwEuMyjeT5E719Zvf2UmSN6ubbyaj%2BgHOjEdgF34vxsT5nfxO%2BnsMGLpREWxd7VdiJIUXqa%2BRvTeT1E71tZsVOKJu3q6%2Bxubxx1I71Ne2Y20Nzal8gOrVcWY51dpaqkqc%2BrNDmVpmBWl0Cpk1qy%2FgwuxKm52PxrUs0DjIaTMuVyER6Nk%2F2W9BPGGCajrG61BNR7Fdy6zUPsjaGXzc2%2BeN8zN4v%2FfHkn8aSO%2Ffjp%2BHD3ae%2BjNvKOQlaXgfewlkuKFO1yTTwUxjemGF6WXKKZTkPTkMjqLUhsW4h3Zm6KWdGYYA%2Ft4e0b3favPkZ2hhdzEnRWlyDMDV15vMEKyyd8JUg02cVU7iIF6JlrN6YlgEQrNtCmHMAh%2FF9eRSwrEx61CqJfSfPrdHej8aSg1Z4ix7aRFzwYtczON%2B9Vewedf3DY4WN8u1aBZikVg9pqdS08RRu89vG3WnkyWT10Jo14DDG6Zl%2Fhm31M3cLS%2FZ2tf1Ix999Cf%2BWVo7lB3P88uBtoEpsKVeyk1SvpDGHL9hQ8HLOvg0Gxf6fIW%2BxDAmEaSSMS1WzQu%2FNJs4aXo6c8G5X77j7c90LfrMZZ%2B95vrTVznPcadmF8j0eRs3ndbu402CNyrGvzI0pN51iu%2Bu7%2BYHbzp7%2B8Dgmlx3uD%2F8gvrDn%2BxekzMbPc7CjfZVI3uTM8%2B6aXXWazaEArkrWlpe%2BiHOmUsZGz%2BH%2Fw9mAXAZURZSjppbVea6SoMwFIvgNTgX2GdGH029cXtlWAl9GrBhArCCLhMmiA9f6GNTPtmXSqbSbAE0nDDn4AUjbpNLDeZ58yIjM6a3OVsc3uVrRUPEZzE7Oc7kCLT6%2B%2Bdsmj3LmXz0H3xSar7TvXyVIWfvOH9R2snaeBMhVlfQnZwhC47emeRbI%2BHpN3ZLlsTwUHjkvdSKvcXowi6Pt8Upm6yEN3qbh2ZfMvspsurirjUPGVgh%2BrnX7jNFzrv48VW2gabepjY54OIt6aVrW9KXYJ3695gb%2BPf4HBNlZjLzmdkMLk%2FtVWWGlk66IKMMHlIK1dJCIEIBVL5ZFnMgaGpvyw%2FTPC%2BmZJ4hup9nxNZ0fnhkPhX6QBdjNmyIAFmMsbQhVQgbskCZNJ%2FSio3yvQWj7JMLmJgwFDQ2z1t%2BxZYvjn5xyxXL5zUbg5MP7BZmd5%2B2%2Brqbrl55StcCKymEZvSHe5T%2FUdLn3dpzwQWJJR4vu77N0rZu9Yb5mcz8DavXtWE26MR9y4ahM6d3tuVOH%2FiMgyxMzJ0RWHQKlryvWbjuiq2zL%2FBvaG3FaQq0YIAWCuOjGQ0nMV9jys6aJTqSwWxQpS%2F47aH%2F%2FM9fM1KnRXGmHYrtO4aSg%2FzNodi%2Fozhd%2B6xOm6dzn4u%2BB%2Bh7kL630vcEvpfhbMudLXfGjWCg9SuBfiXYr7T2K4l%2BxdrPPG%2B12V2BYGsirf8jsyxwyOGccDCdVmYFCaPRGtOncySOBC64kaw6gWcSmvoBVxSSMAgsDghfJ%2FiCocHEverL9yZWPnb5BSnL7p89n9h0FyVqRg7PcDpIW9LXnYlN82TIxs7U7JvT9mBQSJx67hfHyS3uPLriLunc86%2Bd7mxT3%2Brb4X78Eo2imXXNK5uCY4%2Ff7UxmkzP9s9gbp93Td9csb1ubrXfT2efR%2FghfBeHopvqGG3PRk9StkQKQBbSkQX2WxCaSj1%2FalDA4Ek3S2DbvdO%2FYtv8iL5AX1Mcx4DNnzvwWviVmaDl6qMj%2BfKy7iIIMu4PU8rUkKnfbYVZj8ihyc0dGSVA568OOOGUfzZL0%2Bc2d5YSvVqNa6gRO9yVguofCaJOZw7DpS1CnfL63r4DeB%2BqPChP0FaQSIvUZZEhcMGKfhYJoMCaLPifZtH7rerLJ6fOL6gPFoOfMN870BIvqA6KfGNu9PQ4yl6waXr9%2BWH1a%2FUdHj7fda7f9Tf3bab5ZXtG8bJlZ9M7ynUaEv9lwNa7le5sndWEowGpSzzllVjHr6tH3PJaZabkwmAUEhj5G3huz0j%2FltuYLObqfXxKSdmHi5C4pVPzYfzT2xL5fuzAkHVtUS2GH9ebv2tL89JzhGv4ZxsK4QJc%2BieppfhpZ9NCkGxNtL8Flx40zQmsRfLDUW8V9gt1N6yhcYKU5cBCHCPZ6QC8pcBmY%2BlFSkLV9WCDX%2FwUXvYfG9qpPEu66Hc%2BpT7KP7bjuITz4l0qFXf8yelBfQpXtup37x%2B7beb12oNFf5gZdUqsBdoEhmZvoZZoYHiNiPJasZYuNPaX%2BpIjZ9JgQpuXPIzcPMFqeFR3%2FBHBzN7OBKUfw983VspmWapgxj0%2FKYg5MRw7N%2Bk6gRpr6UpPYFAMbcXCukpvQDGEZSx5LGeByDlVckDxusRSOALlkTI7TekJJXhAgoKgPG1O9WLzgdRhAlNfaMYGNahTXpPKJgfaIaAUbG%2Fb9qaXnfuU7Xzl3acpvxPZMpItdXzlyWcvpAavV2xxv60r7RdORSsfKbRdetiKXW3HZhdtWkrVaIi9W%2BWG%2Fod8y3%2BQ%2Fw72BWhMno6Ms%2FDuygWz87dg%2Fkd2%2FQ3fZ79jZSOt5zOv8D3mmVhtvJvPYOezIb9WH1D1caOwFdvbv0Amn1dPNYIqGLaDjm2AetTGoU%2BtOGIGq1uiB4QVNRdc2HLiE0QpIGTQoeKFjXXyZdJKuH5Cbf3HsTXLvL8gNuN9ZYQ%2BQJvUPtBUR1rVtVv9AmlCdBxjdMGZPwJhlAdpyDMcrkFO6gPYuJallB2Pwha8im6BsAhWpFbMXMWWxWYtegLJUkpsBpG46KskC8A7oHOj7RM8n52A7CV1R6QjRzzgur3hZNJZMuQ1OR4C7KWloEs%2FEydj3CmsKuyOhjpB6EN6yNI1GnjZ2W0u3gXvS7%2FY8LEpw%2FNhM3u6jszervYe9UqjS4WMacoOxjsPZmAHHozau2LMUl3pFInZdIfmczvRRCQulK3rB62EsXuVu0Qpatd%2FW8n%2Fxt10wVvXfdtL8QYP222IGGbnBPsuRKBgW4%2FeIEmy1pB5suNMxP9aW1aYWOhoISQOPbacyNapVs9HOTtQzhz2cNLccb6YaH8lpTJCDr738MukgHS%2B%2FrB5CDteSyXX%2Bhf8ZsCe3w2820%2BoitEctGdoSajydjIZroihxotL4T8An%2FVH1Fy%2B91JADpgCVtfqChVqGuRKsYmeFEC1RMlDu8VZpWVZTzXcXoVZQOUKbqUWwmRpWaJkjoj69J6f8NBEtc1f2YNAIhGG8JpOO%2BdsHIk8ZWr3sHk%2FC8JR6V1FLtKdDBm9kDfs7X6pbHmul3iy6ru%2FWc3WtIKsKYPXQsmsGC4IVo6vUAbBladl0qcOIfs8gJTAHTDuTSGlezg9yw4YBkhATcY%2FPAfyMHv4US9vZ6HIzljw3GwjwjljQ9wphWd4g8BX2kbEzkhnWYnMauFXeEF%2Fg3g5JyLzYMCDrz9rtR1hB8kXXHAF92sILnIElR449U1HftVlJs0ROVn%2FLraS%2B%2FmdoXgvLLAL6nwf074R1Nq9XEeVoRmskhzLVny2305zq9gTWxfVmtNZD7TnAqqtHi%2BH3DZCCB%2ByCVJIGLASjFEYbuu4ABVTQkDOE0fA0xhY1ed1XzXRet8ptdHvP9ML7quucM7eI3qag2xQuXvjQ8hv%2BMmOGO0get02fN9067SJyphR6msxfus3b4glKnlbiuXGp%2Bq2nAfM2gbOGJHe71yu0%2F1vP0FCPEflTAp3oQf5B1Ldoqyn8S6G3uyCgwzslC7L0x2Vvdu%2Fe3f3msj%2Ft3%2F%2Bn2vYf95EX6Mc%2BevrBrt8s%2B%2BP%2B%2FX9c9puuB7U1uqjXRcSYNKx%2FNK7cdghj5lio4msD1vPWqhaId4BgRAwsbS3xpZ7v4mRBRxFnkpwkRCUunsLgZXFoq%2FNIOvF%2BaCilfrN5KJVtmubYcLe3uKsIhvOLRw7djm2ZPlxc7CfrI53FrpO3CLu6Tm5rfvlfggn1DnL1qwef%2Fc0N6h3FxtqwJTqcm5lyM45qgnp7mRw2A4BVU8CSZ2x2YanHx2G4Yf3kaU2GEtfam2gVu9grQNSwTMZFd9nU1oUucK1et92tdMKqkkYR3kE9STmxIPYOEOQG2aNPwHr6WIZNFiIwBVPRPBcX4jDTPgn3Ioq0sb03kFEggLp3MgEOvgrIk6vpvIT1r2iQ%2BDUU95OYaxl0pbbSFiXtNEPbi2iXe2idfA8t9Rw5Lu7dWnJtN82o7R4E6YJFct2A%2FH6b2OSdSY3CplZAOQ4UEBV%2Ff8nbA5SRB0%2Bi3uWCSIkA0z0BMzpCaElWtLaiEdoqQY566DJHk7ijHSzmv%2BTB8k0ZTJVis8NBLnRYRdvFDusakl6z6eLVm7YLTU71SeExsAESnzU2OX%2FgcrtdaoFYeTMn8DxrsHxxrfo07eC1mLWLCYv5LaNts8Prdly7ZHSZ%2BrQv9XDTKWSV1BaWvCFCWM7A2UwO%2B%2FOr3tVl2rnAN2uZMI2hXEs7omiZorBeT8f6CmVGtjydioPp%2FUi7IbqUYC0CbYIQ6dJqEZBxgNBeF0pAZVALsCQjWFJoa%2BbbKe1CrTThQomICtNf4qZj5Uiqi9KOBSFo0EiBJcCGMFvLYkpx8VgqhzI8WhATAha6CQ5DB0ElMyeeexESyuq42O6yOsjFgjf6lEl90tVk3L5pteUNZ5PxsySxSf36urstBmIAgpl5K1FbuT9W2NmjS8i1Lpdkv8xmfIu3p8SxD%2Bxfa5PIqmK7V336FPLf7636ptVpNXMYaSGqDSmszbWNqLMaHGCx9DEzwEIpF3CuTYc5NZBRpENKAfuZldslKkJnAHlycMCFein280O%2FSzsGWdx9%2Ff0lR4bmdTFY4RNma%2FHvVCKP6VCSmEoancSITVAKw4ZBrA7ow0nlkw2iEMUaso0UdNZuf7hNUp9eNrrkWmfCsdl32qrnfQEN8mwWkTpFNZvY9eyP1fuMnshjAlmLpLlw9SWb1qg%2FW20j9kt81rX3uO11GpEKutK%2Bj4RUXwe7paLHMFjGwr1n8MO63QscM5dR8loPmH6apJ7PKp3VcictwemcDphHs1h8mtKy0mcA5p0YB7SBSl5ypgBzO1X%2FjBEwOWeCrJCw1mSIJLQmkZgF4CScJyqLBgc1SZN5cRBWlVTSSVCTZ12%2Bzfak81qydHQZWeVtf9g%2B9iHMAMuX3yV%2FPUV9ytuWzXKC0e%2F75j2A%2BsKI7xI7sa7JVdOrL7pk9YU4mcha4bFmh1PdDqOuvr7d2Oz8vs%2Ft9pGKVXCZOLv7HvYyijiL%2BjjMkycZB6w0M3R9xUX1Nz2%2FjKbvac1DXTZ0yLq1Hn2Mh1aEl0ze%2FnpNuB42YEEFF8xEMpy159fXPVwPlb27hyRYUJ%2FIPeol7Jrrfr3nrD3qu3oA1EMsr7xIkqTjJV1ffRJgcsGqN123zGAK%2BjSYtGwGi5bKUPJi7yU35nWVLYzYr1Wpo0tLpBBhV0kKErqzC9E8QHRt450r5B4EST2oAUQB1gB6VH39xVdemkKjYZ1GYp06Wvadezz7TkRj1ubsp1aaQcIsPHfJ6tW6zk6gU4J2rSBT6QTraKUGUwORHkXLi4xOhmmpDpO3cdC0vMUGsKQJYLl0sESaHGjDdZBx9U8dyoSZ9qM4Doi0irNyHBg3oyWp6dd8HU4XczvzJebLzFZ9PDuqyvKM8oUMUXZTcDURu17rTvIg5oAx2sCuF2c5HAZPNN46uGjNNbfcese96BuwuGdZ7LHktIFTz9%2FywJepEF7eIbqf903rG5y3YM06vOYL4iyzhfGuv%2BjWHTvvpW5Oz0Se8Bh9gtcnZNE1LIfZgg9dcF4HQaHEFmA6wn%2FsCGwEZRCjqX1pgtuFvkKYlcMkAko5nCkMc4VkAUPlhTSX6kvhjIcrU8aUAyYptocV4D4OnPwFXxbevD4pzRVylKTXUuJpJP2bqckYIzaf3%2BRf7RvuD5nSXEe3kRg%2Bc1U4Fuccabtjnmgeakm7si6eGNt51hQIyh6P1egytjUZbW0Oh4fnE7zBIvh9Rpcp4pbNlvb4TJvVHOmzWYXMSqfH7ewMDpucQ05pmOM8hOshHBfkLKLVLcTMXc2tD5Mp84CcEr262TwvYPXwtrApE%2Bbdi7zzzUaPxcZdEWodjpiJILishLVa4zKbYe0mzpd0hwKh5rDLSIhg8STMJm6hJHdaHB3egNnt4cxWOSW1CCmDnTPwrXGfjeNsbqOFALWElNMqC7HLLrO1ChabyPvXENbI286qxclY%2Fln%2BVsbLMIOkQID0fbJBLoCSniLGWIY42fXRZRv%2BYac6dvdR%2Bz9cv33sKWen89K7Ol3sunNf6Tv93Dvf3vbs6fMyY0%2B5XJcwtAfiBuDNp%2FS8cM03t4LBfgO9VaVArePmDPaVAkMfphPol9OruBQmqjT%2Bkz9U6ukf72lZzlPrLY%2FWG3ZGxJjQ%2F6Jag2CzKdpwaqJRd6JtsEIPqzatdrpS%2BZdxQ%2B9bx9tUH0BHIHugON7fAW0ArNjS6rX0kKrWMVmzq5kSY9INd1ycDKCfCGKRe%2FvYIu4W9eEKv6SIBd7qQWwAwzTaFminJ5gN1INDaFVrKIeagj%2BG1e1aBWgyozgPKTHaddAZQwKCagJg0Ebeca0nIRaGxWlhGHYhZLO47jIlB6y4SkRzD0qNHVPrJR7Ua0fd0rQzbI12pGugvdI%2BgH4GIBz9Rw1jMto%2BoBEKSzppHTozwa7HuKgXtKNaZp47h1F2jKpr4VbMTCA0Fwk9lNgfhStgEFW4ubKzglFX7YN9Td%2BrYEi1gi84wLXQj2MPaCfZt7RrGL1X515%2BL813iYCNPsIsYX6md2x3VtFTviinBKrK3CyWBffTvgS92XIbpVnbSebOWvudpQ0pAbAk6IXrUWsndtpIaqb%2BcFUZdtG4yYLqvsKC2aZOpYdaGwXaB0%2FPfliGSjD2FbA4aFuN0jBI31L3IOg%2FBTAdnjM7PXxGyzoKaz25bKLWG2dfR%2FfgMG62aUk4SlwsZQZw4fGT43X58ekd7%2BKxPupPwC0ZbBGWes20%2FZSghakSWkEA2Ogn6gt0cSTd0iL7lsKrpcUnr2yPXEfM2o7DCXbOghwJt6QjcKqFlE%2FQPug27fzvu6It6ZZvt%2FQYthGHw4c7lrt9odZcbqWvBW9D%2FaO%2FZAb4bwPvLAftCvMdUlW04kCa9MGoVbFXBPZvYEopzDmY2a%2B0iPutzmBTD1JIcpeyOWpEGGG5yWVxVeorEIy3pjCALBDgN71%2FvYCNJeGwfgk26AnDwgNqZoTgN1C5jMdAwpKm4LT4lnkzp0dntafaRHJn3O9sOtVrTp8UV68X5pJrvR65Kel0tY79undh4VSzwTmtNen3ktN7hzb1BeSztpiE046NCXNZw%2Bzpouu85XNXbTq1bb7KkEP%2FtKx%2FVrOrp6OzC3%2F17Mw6kY3G1SuF2eQqn0tuTeEvjsyMD89qbZXw9wp5Xtx99rmn%2Fe0jRhgkH83ef8a6z7VGC374Mc23uILp47%2FHr4cZl2eGGJTC3dSPI2QJkg%2B0Bq2bWIemNWCzjI5mbH0uOLV8HC4HRDVofh0gCHVHYZ0CdlRMsylYhb24jPtkMG2HWbRcY05KJ%2BOKl2656%2Bzz77r5xfi62fNf2iC6Oi%2BfNzJ%2F9rr4E0PDs3xnbFx5mW3W7OEN%2FYtmbLk8t2DwHM712Zduvvmlz6ZPvWj%2BvH%2F%2BrCzPuHneSfPmX3RqumntScO%2BUy89%2FVLb8Lz1zfM3rnrymRXnaPj1f%2FQBfzX%2FT4wf1h7G4wVAYIwJbXqfErADPoyzYKQnQKgQkC7wCSZEL80fgBHvH9kx57QzRkjTyMgOk%2FWCg%2Br1fz3LnQ4VRg6O7BBsFxwkt8J%2BJhyKF0Z2jJxx1iz1DyNk41mzdo7gV749ssNswu%2F8z1nu7umFEcKfAd%2By0G9tEHtyofiFnXBkZET9wyxNrlf03oHYQSWEefA00hqgVrWbemSMWtcXGBnYd2tVGLSDBhU1Gdr%2BpckNyqbR4NMew1AQtZSOmlVciIpRt16ZXxk878EnvlycoYVWimio%2FsvQ2qGhtUXuW1I2HQ6ns9Kx%2BSCx%2F8IeOPYanhia0EvdP54VE8hgozEU0wTjR6lkb6EvKxOfV0DFgYCMJpkf%2FwxUWmvc6XR0OEgT%2FWhV%2F3jwJ2TrTw4SuRV2nU71LSd%2BxNX%2FUg%2F%2B7McMR1KMG%2BzK3zA9wKGzmfsZrGPsoPWNw9lyN089LuhJGKBOSHe17B7AY24Puhzm0AY5sNZlq7hAZl2lLtJZ5t0zMBUQDE5vZJhuuUqzgJAgOpqx5r6pWpqLTY4wu4jtV7pExYyBkG7Ybe9XBsRvMI6m1Awa61fcmDGI3nDQccU0iaf5fK%2BeKcjLcRAKooyV%2BEbMF8Sid483zAGXiQ7ioU1fSerzhnWLjB42PbIsE12%2FfXkxOXflYCf3kKlv0azY4IpCW3l38Utr2gJ7XWKHt1nghxb%2B6Yn1K0lp6TlOspwYHYFM%2F%2FrC6bfNFpYt5z1dMy8YWbDYrlYdgqdr8Nzhzz9hXbpMXNe2iQ2HuvySUQCz3m0aGOt03zprfkCPCZ%2FH3cs%2FDbTdweAqBHYDqChhGk1spo3srNWyhz4QxOPQaSocUvppD%2FrWbNlPux%2F6w%2BbOskD9XwKjVZQ7Na8FUtKPjW%2FZ6SOUYk7xOWukpaMnhzsOt9IFtO3owcUNjUtGLHUNaoHlHNILaEmlSryXPkJDAEsfKJ2T4j7YFoZJrg%2FntIx90ZI0QyY1bMAclOJjfq%2FFzGW6Nz528%2FcfmLOiNbHa2x6Vw395ySRJ7TPiZ0vRL0SXDGbbl6c65J9lU2tlf8EoWUSbaJ5ma2XXF3sKwYG1Z7atL1%2Fdu7g5JHX1N6%2FqyBWz20MDfVYHiYW%2FGJC5EZ4P2qx38w6j3SpaCnfNB5kD1jX%2FjK7LRGr9Px0ZxYbNRJAoJSsYg4qp3lQ0LppZfGdpDAXsZf2TRgFpSQUNhxzbyt2C0T1WlxFG%2BgwF7H7xmN4TxpgDIa1tmmhipbdaYnhgbzvNk6eeuXKIRkdDMrZAwz5LOIosbZah5dXhgwYwtc4uwxct%2BiNfLLSisNSE7chdoLIGstl9bpdkopVyHpRN2Gmjlo6nmERYE8DoNdFaSMUuKiIOqGaLR2nPaQz0aI2QOFFrilQAFZJ9uYIxpNEirb45p3gOOruLBFv2ICFo01c8q7U4OowNiiqVCTRx0DrDGLNHyxcsCRbsPER9koCrifpdQHK6qJO3mYpU31QCCICMUwJkgX9FJIDDReNfoRaNKB4gzr6gxWrSNLwMNt5qJICgESAWwWoD3oDVBqVmF5zzYx2mEmigho49N5kqDdRAGhTrJDlnnBoaBdgD41Q5thVLcigPLtF5xA1csqnWvxRNAnRfACEoU5TtDtrGUMSJLWcUEX1R%2Bzwu0UQf7SLQRyV5aB4hdmSyUuzxIR20A85%2BhuWMtEuEA6tdDTydt7o%2FgyLGYXBDREtL527kZRgzbUxrDA4aOiDCvQ1GDj4oosJ8HA4Chd6NmJTdtL%2B2G9t7YLFYleJgRd%2FVPg8dHTvFU6A4WDQcRMrCFAfPRBx8k3FIUOg1Bk014kBbEBfPacShSIHHP8qVuERSvtTtGgtwJmKxktERsNZqRFz6VKWwOw%2BVBasHlyRgUmw7Sbva%2BixmfAfpq8FtNaE45YxaFUuN4LQvWO2Vk2rihNaD0da2mLrYQHTatrv%2B%2Bt%2FBawV4nRReEDAgfCx0nbC4EV6L7xPgzUlcA8x1eCsakHoz3hODW%2B%2F5qNWdd4zX7jZPLY2W6wXRzNSC7Mn1Y1jQ%2BPD77z2CRYwVraRRq1zU6hi1E%2B%2BcVCt3HGivxdFqsMTQizkJmvhUaFBkyCAWaKklU2r24GbseCXjkyH0dJP3H0ZYsNLykfcmAFkhS9V9COkj72l1mI2QqrYjR8bjfv9reGPj8Eb%2FHnjlJiznrUM0Ad7ikSMatO8%2FPBnaIiCy9P932kY5hLU2zhNhJYePaEyAiACPzGwg7WaAlfKkwUvrC4PMIFNfjqnbBysH3FqnY1AHZNrVGESpQI2oWvaGIooKp%2BXYY0KEJ18HLl%2BDWnvUW6UGFxlFeBCuivqw%2BjA%2BBmxCIS7V814DQXeI%2FyoTxYwfh%2BYbgiWxmUakMRnC16wlQ5Q86PF34kwFwoAtF0HLuI%2FJ9YHhZuhzF%2FoyoImBrsUIxtdMMknvNhjMZrPDZmWdxGE2yerPyZdZVjCbnRb%2BgPrhYhcreNRFXslqctrZCzyiSHjyrM1pM7uMP1d%2FutZTn8uUbn4Y45lMmdPpZsKabCw1CtK8DhCL3vrUtmoTuhTk9MxExSUqzVrnURjSQj6KhZDUY4VZn7R5BKUpjm8mr9Uik8OOyhlnPPoOrkxU6AAxnd%2FS6PctXmDZd2hQQKNsQ01AiGllVutwOumiGgTAEg22mVXz5CRR5QOT7Dl%2FIBxpaUWtF%2FSDOFYtYzWLyRyOxenRoEi9bfUi65Sge920gt9CCvMVE7RqQJ9Ny66rVVQ%2FuGVR8dF3yJ%2FJxvvI4fvUJzSWOHvh9ffWGPS6Ze88aiZr71Nt96kPTaltObVWj9NEH9Lhaaxw0apysKUEWKKBuoeq9iC053mzxeF0e7RKRrkJjhCjYLO7ROaENTqs3ljiRFU68uV7Lr98z4mLdNx4%2BvIGOYa%2BUBfofmm9C7eod3CgD3mUaBGfCfQF%2BnQ6NzqQfbpPsSYLzCxt7Q%2BrvMPK%2B62uY37yAnkRMxmxWyY2l3Va1Pe4FuxGjGwytiXLHOf%2BnZPu%2F%2FfdXEshNE%2B4excWqauzyZnjd2cP0PDU4aNvTbj3jXBv7LtZw11boQ1aY0rQ6GxZvL1Iby%2Fi7aUpuIMZOuHmPezqL6vzdrG3Ntz8u%2BqP2dW71Xn3j83I1p%2FfhXijz9Zfx1yqYgDRoPUi91HMffTWPrx1cDLmciHFJTxYWdoIwMaDGzez3%2BKO3HfwPvKjBvL%2F29mvbjz2JmH%2F8T%2FhjGo%2FHhzpE8Dhyf4doHhkQS7I1gZQth48%2B1UO7%2FY3ck4DMYoHNx4k7wB8977HfuvoH7OaDJPxmRz8s6BJmbDOg6e2n5lWPhmr2HFOK6rHmh8zkfkH1IT6%2FeXsnrEN5CHOdfQV4lKvJnuq3BNj02sysUhxwyrMpQyuHkYYSCMdSHzmhiNbNor0gRI87Jmytef%2FgfwxUhHkpO4hWkrnNIrustkna14hTaTgcwhkWjGOOm4siXiHpCPA8ZWwd9fYnF2YKVYs7uLe9oapEDz2DBboq5tD3PkVyn%2FDtP%2Beh%2BbjzWJQPwQInRRCJxgVZaNzAnjh2kJtpJ2KmZIRRGDZKvv1xvh1oCSQfhSkRAHLo7hxwNR3r9mmfg4hm3v9NWTj9WriN40Qvq6%2Bd82NAGAJTm64Xm3lnqjU5orR4KCwykycPiuxtQGYRENnBY0qQorUaeJpKIvUwTj%2FCfWQ%2BrxOoOITT9xY%2Bz8OivohvYgS6%2Bir9ORNX8f3Cb1J3ZR2F9E1GWjnoLRzoEljdKHKHcRy5BJvytbJ5wQKM%2BOBHLpAOl3Iz9j9x5alTsAgtijxYGk5rOYlJLB2yDKRzvhATkpmTSnnaFa7RufNqo2ip4WhKw00BjsCsHqSCiPaPrhSYY6D02nHwel42EyAvxF4JSRinkcJi0LKVlsA%2FUOwSE5GQR8oLdZvJuMoVID6l1AcaLRfPdiIwyUVOjY6Fvj01BoOFZoPZ6acMrl2QNMQwVzR%2BrcdXcErev86MnqE5g4ni7RrxZib%2FiDGls6D33tGt6m0ztg3Y6Si5AvmsL1JSW6mD4ssWZ05fFxkyebSqSPUWuhaqSsNvRA%2BugpTIlktaFUZNOPVh40NeM7p0mNAbq8kah0btZJPUDlKxAv0MyFhtXRb%2BKNuB%2FqiehHuDmDWv6Zf0h7Ro3rTaLoJBtrL7LdpRA%2Fzcg%2B9TON79V72tbrsGVpdNi29ZmpOFdmKS9M%2Bs0s2Yf9njNWYqcZusVJHYY8mHkX9ycvam1DvZ5HwYrXOeA957UmwxWxW%2F08bhtBGgVYL3W4Nqu8GEuwB2h4ba2Bq75t%2FoJ5Ee2D8czGUj%2FqNBvUK2o9vZcQjXinOjTT2Nl7C5DBDhkYjbTklVy2zVgfau22Zsr85gZXmKXxWwniyaxsmuxpbaFPjaB4hp8%2F2kbAHLZenSX7APjRwiksPboc51OqdBCi8OEAOBxaTUZrJmw0E1M2LA6otsFjdXDsy3ZYkd6pbkrbp04nBLDm4a3wBwk6Hw%2BoWcmf98LHb8bAe29RsJz%2FTzLSAnZxmskzZr2sKvgz1mdNSH7cX3UCZjNb4yO2iLBVoplqcOMVKKuQS2K%2B5bsNHZSkB6KSiJJ%2BQtCxz0E4PotE0WrkAdJl1RfWOYq24AQswUxfQY6hQaVdrBs3YnMrZ6qFi3dwfrZx9dqU4Nu%2BRWx%2BBcXExLv4h%2FiF8RrA7zMlhLjeT9GHBJxAwTVKFYYLpw07iIpHhe847%2F6f3Xx%2BJPCIKnu94el1bdh47d4todHze0e1%2BmPt95x13HTzv%2FHuGouRhd9pxu4MXt5x7bOcWscf7oscoPjwxtx%2FXt4zeXwmf45qheXsnZUoj9AEvTWhfZmCCtYn7ku3ZWZRomJpQIPneYS6LzCvp9VE0K8Hrm0mGyRDpa5ElL3aIBo0%2FzcdjDt5JJAe5mPUlV25emfQRQi52SChkvOGQ9%2Fve0A4yvINuGIPFra9vO%2F2xq4ozu8zm7EFvzkEyWd7eEpC8Tc0WS1b9qSPnxXZ47JNSgI%2FyMekev%2F8eKQabAaliCpzU0y5F29qiVkt9%2FmIecKAefycTO%2FdGpUQ0hT1dJa2LPX2JMn16x0fcN26gj%2BPQus5Xdh2hT6TGf%2Brmou5zqN3DTDvatOoRdm%2BOSjvLIepN1vpUaQuB9gSReuO%2FWoubXH2riKkGtEKIfhT1Hc0cr6VwsAc0rqImJj5LSu%2Bph70UsGpsge5PcNXtnq4Gf4Ko%2BRPw%2BX2iiz4DoCXe1qF1I6D7wZZkW7tW5DmpjR6G2jzoPMxpXf0NOXQogjqBfeiVrewBrLccm7NVoZ2TKMNjLyqgm%2F6UM3IYr1Bt8I7VGns1IUa930XNoff%2FEk6BT4cTXTI0NiAfj5P%2BoIETYlShDj%2F69II6RvwEfJpAW5szBZvWqdgkdGz2eaXmUB2Xfd5gc%2BxjRgcYGEaH6J8nHBXqXKlo5tdEDCjkFH4qKibA7oO5NBX24FTYm8Zhl%2F3jsEs%2Bvc%2FFcWE3iJ5uclyIdSk7EVT14V14eNeuqTwjU645dRxSL40LxbPYojFBH%2FWjw97WALu3nvGPtVnRKrYubyW15zbomGnPATou%2FPTp0xiH7iQ8LuugxB0HG6Tx9va%2B%2FuR24ulr3749eZxpgXGQ1dvXrt2%2BGltmTZ4PcVrvOnkUOqaOQmfDHI%2B2pibMh1iy7e%2Bd43Ea24l%2F3Ay%2Fu4jRg8rHznD0fzPGCbhoVU8pZskUbNqmYtOuY%2FO8Vwo2hyOxRB2f573%2BYFO4pTX58RjFsdZV%2FgR8iufixD4BMqM0%2F7uODOrDE%2FAJ0fy1Tmb3OD7hTCnWDlpVhLIfNReQG5OUCVPZ40mzMBYQZHUG1MoE9wXkZhOtCwvi88aQH2P1fu%2BYzdJeVdpdNCkrgxn24wKRKTVhURho0V6s0zg%2BeWQYaz1OBySK0kgGhoqOS6Jziuf8K52TlSnyr0LHGhdIKiRpCGly35kQ1lZovrkQ9YIGaMhSp0N40mMdEeO6h46aUl7gX4vDw9GH%2FSmi%2BJzZybvlJsoMwRBWWdhdjFd%2FrJrUr%2Fjc%2B4w2kZy4vU6%2BEKW2Q%2FREzrvQuJJ34qc70gVBy%2F3j%2Bb06ri2gE%2BypYRvNgGaAiYtNmQYBlJiEcAucz5ZbaIlOSxgszBYXOoWVQBarUeQsPtdUHHfEtnhpSh11VmKTLh8%2BWHaf2enmKUFiUZ0gYf05yrI%2Fglan1ECUxiy6OlGAJ6hChM%2FuwdXxBKQJ4PxGnah4YtJ8GQ0W7R%2FW6n7w0YP8i%2FxJ9NkKl9MMN%2BpdVuyYqWLSqryjDWYWNtLVah4FzfkTw8A0PmtFkrFffymMqqobDJSSFZ9m4IxgfAQfdLqPsWhPXbaL%2B6xOSXsyIEclM4g2FNTxmECzMCSv7CBEi2A7Llv%2BBhm96Zonnrgm3dX13CVf%2BiU7sobMoBY3eX%2FF5cgHT3%2FwlGPj1b94IGBzbFLv0x8H2YAb%2BvzDDHXrhw%2Bh7kcbF4cBZjFo0BxWFA4zweSsiWBoyRMAhYl8lXzxzGsngqE5LygcX1UvDn6leM3TUyDBNYPouboXg33BAIMLoOhGSY5g01CSQ8OMnAlmmfrBE%2FgfvoSfld3d3bup92Cpuq9W73ot%2FE6B3w1rDuPx%2BmSsTMllCyL8oKg3eaI4pHQMfBJmw13bn%2BEvWDS9C%2B%2BS7lt0gTk7cP0pwS%2BeeeUjrMO86AL2QH695PItuiCs%2Fqi7m2TDFyzyR1rOGzr1s8ZTLnvkSt7Jsxcw%2Fw8un4kSeNpjYGRgYGBkcFz6%2F1NPPL%2FNVwZ5lg1AEYbzPJx6MPq%2F1f85rHdZJYBcDgYmkCgAaDML2XjaY2BkYGCV%2BN8CJq3%2BXWa9ywAUQQHzAYk7Bkt42mPcwaDBsoGBAQmnwNhMPQwMrBIImnETELsxMDDchOLFQD4LkPaA0CA5qP4TjF9A7P%2BfIGb9%2Fww1kw%2BIxSFq%2Fu%2BAYBS7YVgWiLOgasURehikoDQjlGYBmjEHqocRwgezWdD0ofgLB4aZeQJJzBQh9n8rkFbErvf%2FbKh%2BmNgUKL8Ui3qY%2BR1Q9glUOxl1oHbuBLIFgDQzDgzzJwuSv0H4DBBnIPGVoOEA4%2BcD3XsYiudjCRdQ3DwC0m5A2gpICyHCh1EPzc9eQCyKpJcDKp4IxExAzAoVZ0XCDCxHIPYwgMH%2FGwwBDJYMJ4DpRx0oxoQCEeAmmGRBEhFnQAUpYBMFIKz%2Fc1AhSOf%2FT%2F8%2FAeUqAVIarnx42mNgYNCCwjSGJfggowGjB2MAYwvjKsZzjP%2BYzJhmMJ1hesesw%2BzD%2FIWliDWFjY9Nic2F7RG7D%2FsG9hccUzgNOBM4p3BxcblwdXC94w7jnsD9jMeOZwOvDG8Z7yo%2BK74Evi6%2BO%2Fw3BHwEOgSuCfIJqglOERIRShC6JSwm3CfCI5IksktUTXSSWJLYLXEf8TbxFxIWEk0S%2ByR1JGdI%2FpHykqqR2iZ1TeqJtIi0gbSfdJv0NukH0l9k%2BGSiZJbIvJM1kj0kJyDXJfdG3k1%2BgfwB%2BUcKbApaCm4KeQoTFD4pZikeUXJSOqFcp7xORURlksozVRHVDtU5qptU36lFqXWo7VN7oc6nnqV%2BTsNCI0Vjh6aZ5hWtPK032hnam7R%2F6bjozNDdp%2FtML01vkt4vfSsDCYNthmqGdYaXjDiMdhgnGE8wkTJZZMpimma6z0zMLMZsizmXeYL5GvM%2FFgEWWywlLCMsp1lesZKwirNqs7pmrWNdYn3PxsEmw%2BaOrYvtCjsBOw%2B7PXbv7A3sZzkwOSQ4bHFUcuxw%2FODU5PTDucP5m4uNS4LLPABRwojtAAABAAAA2wCbABEAAAAAAAIAAQACABYAAAEAAQEAAAAAeNqtUs1OwkAYnBY0Eo0aSTj34MGLDSAiwsl4EP%2BJRtGjVCiVQomtVBKfwmfw4sWDR59A38On8GCcXRYkyMEYaXZ39ut8s7NTAMzhBRrEL44FzhFo0RjXK%2B56WMMS7hTWMYsHhSPYw7PCUeTwqfAEbrWswpPIak8KTyGhvSscI%2F5QeBqL%2BrzCM8QZhePEZwq%2FIqH3Pbwhqd%2BHYWjabrdddyyv5ZuW18QmPLTRxTUc2KgjgIFHjjSSSGGFqMK3BnZwgRbXXfI7xILfgMnKBlw%2BxpCCL3dVrlWuHc6XZG7x5ucooYhtnnqIAxyTV6SWixqHQ32bPUfk27hhRZySYmdSeinghKeX2VcYq%2FVTaXlE67cOjJG%2BU3kPn%2B89mcGwp5LU6O2%2Bq3UyA1iS3xl0mFjjXECTqg1qCk6NVXFyhYmbWJUjx9zTWP%2FjLcd%2FqfHVUD4mu11%2B5TZ9O8q1z6pAzX%2FjlOmyQueiGgwy2VeZ9v2lmZHIM4e8zDLPLDKD%2F2PmC43JiIUAAHjabdVV15RlAEbh2YBggd3drXO%2F89bYCPPZ3d0CioAoKnZ3d3d3YXd3x4E%2Fwp%2Bg%2BM32zPfkXjNrZj%2FPwTVrOmM6o8%2FfCzpF5%2F%2BePzsdxjC2M7YzsTOJcSzCeCawKIuxOEuwJBOZxFIszTIs2%2FmL5VieFViRlViZVViV1VidNViTtVibdViX9VifDdiQjdiYTdiUzdicLdiSregSCnqUVNQ0tPTZmm3Ylu3Ynh3YkcnsxBSmMmCEndmFXdmN3dmDPdmLvdmHfdmP%2FTmAAzmIgzmEQzmMwzmCIzmKozmGYzmO4zmBEzmJaUxnBidzCjM5lVnMZg6nMZfTOYN5nMlZnM18zuFczuN8LuBCLuJiLuFSLuNyruBKruJqruFaruN6buBGbuJmbuFWbuN27uBO7uJu7uFe7uN%2BHuBBHuJhHuFRHuNxnuBJnuJpnuFZnuN5XuBFXuJlXuFVXmMBr%2FMGb%2FIWb%2FMO7%2FIe7%2FMBH%2FIRH%2FMJn%2FIZn%2FMFX%2FIVX%2FMN3%2FId3%2FMDP%2FITP%2FMLv%2FIbv%2FPH%2BGkz58%2BZngnzZs3odrtThzu5%2B%2B8WC99w4xZuzy3dyq3dxm3dvjt5uMXIcKuRcYN5c2ePvqhGpoxu7WGNX2q6ox8eeImBlxh4iYGHDzx84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%2BwANawDs2wDs2xGwErADAxEzQ%2BAh4BFz4CHgIVFA4DBy4EZDhad3d5LDB7eHVYNkN5hKg%2BPqeEeUMDEEB2Uy0ESURFSAQtU3ZAOXmAf7JVVbJ%2FgHkAAAL%2FuABHBJUEsAAKAAwAAAMhEzMTIQETCQETAzdIAd%2BSApIB2P6Ckf6A%2FoKSlAIDIAGQ%2FnD%2B6v4%2FARP%2B7QG%2F%2Fj8BAAAAA%2F%2B4AEcElQSwAAoADAAWABgAsA0vsBMzsAHNsAQyAbAXL7EYASsAMDEDIRMzEyEBEwkBEwM3ExcHNxcnNyMnB0gB35ICkgHY%2FoKR%2FoD%2BgpKUAkPEScDDSsHrTU4DIAGQ%2FnD%2B6v4%2FARP%2B7QG%2F%2Fj8BAnSO4oyN5YzT0wAAAAEAAAAABLAEsAATAAAxNQE1IiY9ATQ2MhYdARQGIxUBFQGQJT%2Bw%2BLA%2FJQGQjwEBZJUzyHywsHzIM5Vk%2Fv%2BPAAANAAAAAASwBEwAAwAHAAsADwATABcAGwAfACMAJwArAC8AMwCwALIAAAArsATNsRggMjKwBy%2BwIjOwCM2wJDKwCy%2BwJjOwDM2wKDKwDy%2BwKjOwEM2wLDKwEy%2BwLjOwFM2wMDKwFy%2BxHjIzM7ABzQGwNC%2BwANawBM2zCAwQFCQXMrAEELEFASuzCQ0RFSQXMrAYzbAcMrAYELEZASuwHTKwIM2zJCgsMCQXMrAgELEhASuzJSktMSQXMrADzbE1ASsAsQwLERKxGhs5ObAPEbEcHTk5MDExESERJTM1IzUzNSM1MzUjNTM1IzUzNSMTIREhNSERIQEzNSM1MzUjNTM1IzUzNSM1MzUjBLD7tGRkZGRkZGRkZGTIAlj9qAJY%2FagCvGRkZGRkZGRkZGQETPu0ZGRkZGRkZGRkZPx8AZBkAZD8fGRkZGRkZGRkZAAAAAAEAAAAAARMBEwADwAfAC8APwBCALINAAArsCwzsATNsCQysB0vsDwzsBTNsDQyAbBAL7AA1rAQMrAJzbAYMrAJELEgASuwMDKwKc2wODKxQQErADAxNRE0NjMhMhYVERQGIyEiJhkBNDYzITIWFREUBiMhIiYBETQ2MyEyFhURFAYjISImGQE0NjMhMhYVERQGIyEiJh0VAZAVHR0V%2FnAVHR0VAZAVHR0V%2FnAVHQJYHRUBkBUdHRX%2BcBUdHRUBkBUdHRX%2BcBUdMgGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHf29AZAVHR0V%2FnAVHR0CbQGQFR0dFf5wFR0dAAAJAAAAAARMBEwADwAfAC8APwBPAF8AbwB%2FAI8AdgCyDQAAK7E8bDMzsATNsTRkMjKwHS%2BxTHwzM7AUzbFEdDIysC0vsVyMMzOwJM2xVIQyMgGwkC%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%2BwTDOwHM2wRDKwLS%2BwXDOwJM2wVDIBsGAvsADWsRAgMjKwCc2xFygyMrAJELEwASuxQFAyMrA5zbFIWDIysWEBKwAwMT0BNDY7ATIWHQEUBisBIiYRFBY7ATI2PQE0JisBIgYVPQE0NjsBMhYdARQGKwEiJgE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHQGQHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdMsgVHR0VyBUdHQGlFR0dFcgVHR0VyMgVHR0VyBUdHfz1yBUdHRXIFR0dAaXIFR0dFcgVHR0BpcgVHR0VyBUdHQAAAAEAHQAiBPIEKgAFAAATCQEnAScdAaMDMtT9oc4Bxv5cAzTU%2FaHPAAABAGoAagRGBEYACwAAEwkBNwkBFwkBBwkBagEa%2FubUARoBGtT%2B5gEa1P7m%2FuYBPgEaARrU%2FuYBGtT%2B5v7m1AEa%2FuYAAAMAF%2F%2FsBMQEmQATABsAJwC3ALIOAAArsBIvsBfNsBwvsCMzsB3NsCEyshwdCiuzQBwmCSuyHRwKK7NAHR8JK7AbL7ADzQGwKC%2BwAdawFc2wFRCxJgErsB4ysCXNsCAysiUmCiuzQCUjCSuyJiUKK7NAJhwJK7AlELEZASuwBc2xKQErsSYVERKyAhYbOTk5sCURsBI5sBkSswMXEBokFzmwBRGwBzkAsRcSERKwEDmxHQ4RErMABRUYJBc5sBsRshQZATk5OTAxEhAAIAAVFAcBFhQPAQYiJwEGIyICEBYgNhAmIAM1MzUzFTMVIxUjNRcBHAGQARxOASwHB20IFAj%2B1HeOyIPCARLBwf7uRmTIZGTIAe0BkAEc%2FuTIjnf%2B1AgUCG0HBwEsTgJs%2FvDCwQESwf5ZyGRkyGRkAAADABf%2F7ATEBJoAEwAbAB8AXQCyDgAAK7ASL7AXzbAbL7ADzQGwIC%2BwAdawFc2wFRCxGQErsAXNsSEBK7EZFREStQMCEBIcHSQXObAFEbAHOQCxEg4RErAJObAXEbAQObAbErUBAAcFHB4kFzkwMRIQACAAFRQHARYUDwEGIicBBiMiAhAWIDYQJiADITUhFwEcAZABHE4BLAcHbQgUCP7Ud47Ig8IBEsHB%2Fu5GAZD%2BcAHuAZABHP7kyI15%2FtUHFgdtCAgBLE4CbP7wwsIBEML%2BWcgAAAIAFwAXBJkEsAAbACsARQCwGC%2BwCs0BsCwvsADWsAfNsgcACiuzQAcDCSuwBxCxDAErsBPNsgwTCiuzQAwQCSuxLQErsQwHERKzFxgcIyQXOQAwMRM0EjcVDgEVFBYgNjU0Jic1FhIVFA4CIi4CARQWOwEyNjURNCYrASIGFRfSp2d8%2BgFi%2Bnxnp9Jbm9Xs1ZtbAd0dFWQVHR0VZBUdAli3ASg%2BpjfIeLH6%2BrF4yDemPv7Yt3bVm1tbm9UBDBUdHRUBkBUdHRUABABkAAEEsASxAAMABwALAA8AMACyBAAAK7EIDDMzAbAQL7AE1rAHzbAHELEIASuwC82wCxCxDAErsA%2FNsREBKwAwMTczESMBETMRMxEzETMRMxFkyMgBLMhkyGTIAQEs%2FtQB9P4MAyD84ASw%2B1AAAAIAGgAbBJYElgBHAFEAYgCwEi%2BwUM2wSy%2BwNs0BsFIvsADWsEjNsEgQsU0BK7AkzbFTASuxSAARErELPTk5sE0Rsw8VMzkkFzmwJBKxGS85OQCxUBIRErEHHTk5sEsRswMhJ0UkFzmwNhKxK0E5OTAxExQfAhYfAQcWFzcXFh8CFjMyPwI2PwEXNjcnNzY%2FAjY1NC8CJi8BNyYnBycmLwImIyIPAgYPAScGBxcHBg8CBgU0NjIWFRQGIiYaBpcCDhgDUC08hQUtMQUmKCIbLyYGLi8FhjgwUAMYDwGYBQWYARAXA1AsPIYFLTAGJigiGy8mBTIsBYU7LlADGQ0ClwYBZ36yfn6yfgJZISkmBjEsBYY7LlEDGg0ClwUFlwINGgNRLD2GBSwxBiYoIhwtJgYzKgWGOi9RAxkOAZgFBZgBDhkDUS86hgUvLgYmMBlYfn5YWX5%2BAAAABwBk%2F%2F8EsAUUABkAIwAnACsALwAzADcAiQCyIQAAK7AkzbIoMDQyMjKwJy%2ByKjI2MzMzsBvNsBcvsATNsQ4sMjKwLy%2BwCc0BsDgvsBrWsCTNsCQQsSUBK7AFMrAozbAsMrIlKAors0AlAAkrsCgQsSkBK7AwzbAwELExASuwLTKwNM2wDTKyNDEKK7NANBMJK7A0ELE1ASuwHc2xOQErADAxEzU0NjMhNTQ2MyEyFh0BITIWHQEUBiMhIiYTESERFAYjISImNzMRIxMzESMRITUhEzMRIxMzESNkDwoBEzspASwpOwETCg8OC%2FvmCw5kA4Q7Kf1EKTtkZGTIZGQBLP7UyGRkyGRkBAEyCg9kKTs7KWQPCjILDg78bgMg%2FOApPDwpArz9RAK8ASxk%2B7QCvP1EArwAAAAAAQABAAEFFQTdAAoALACyCQAAK7AEMwGwCy%2BwCdawCM2wCBCxBQErsATNsQwBK7EFCBESsAE5ADAxEwkBIxEhESERIREBApAChMj%2B1P7U%2FtQCWQKE%2FXz9qAGQ%2FnACWAAAAgBkAAAD6ASwAA4AEQAiALIMAAArAbASL7AB1rAGzbIGAQors0AGCAkrsRMBKwAwMTcRNDYzIREhERQGIyEiJgERAWQOCwHbAZAOC%2FyuCw4CWAEsGQR%2BCw7%2BDP1dCw4OAxIBLP7UAAADAAQABASsBKwACwATABkAggCwCi%2BwD82wFC%2BwF82yFxQKK7NAFxUJK7ATL7AEzQGwGi%2BwAdawDc2wDRCxFAErsBfNshcUCiuzQBcZCSuwFxCxEQErsAfNsRsBK7EUDRESswoDDhMkFzmxERcRErMJBA8SJBc5ALEUDxESswcADRAkFzmxExcRErMGAREMJBc5MDESEBIkIAQSEAIEICQSEBYgNhAmIBMRMxEzFQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqkdkyAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP9%2FgGQ%2FtRkAAAAAAL%2FnAAABRQEsAALAA8ALgCyAAAAK7AHM7AKL7AMzbAPL7ADzbIDDwors0ADAQkrsAUyAbAQL7ERASsAMDEjATMDMwMzASEDIwMTMwMjZAGv0RWiFNABr%2F3mKfIoMeAbqgSw%2FtQBLPtQAZD%2BcAH0ASwAAAAAAgAAAAAETASwAAsADwBKALILAAArsAzNsA8vsAnNsAEyAbAQL7AE1rAHzbIEBwors0AEAAkrsAcQsQ0BK7AKzbERASuxBwQRErECCTk5sA0RsQgMOTkAMDExESEBMxEhETMBIRElMzUjAer%2B3sgBLMj%2B3gHq%2FuGvrwGQASwB9P4M%2FtT%2BcMhkAAMAAQABBK8ErwAPABcAHgBjALINAAArsBPNsBcvsAXNAbAfL7AB1rARzbARELEZASuwHM2wHBCxFQErsAnNsSABK7EZEREStA0EEhcYJBc5sBwRsB45sBUStAwFExYdJBc5ALEXExEStQEICQAaHiQXOTAxEjQ%2BAjIeAhQOAiIuARIQFiA2ECYgAzMRMxEzAwFfoN703qBfX6De9N6gXPIBVPLy%2FqxQlsiW%2BgHe9N6gX1%2Bg3vTeoF9foAIC%2Fqzy8gFU8v5kASz%2B1P7UAAMABAAEBKwErAALABMAGgBhALAKL7APzbATL7AEzQGwGy%2BwAdawDc2wDRCxGQErsBjNsBgQsREBK7AHzbEcASuxGQ0RErQKAw4TFCQXObAYEbAVObARErQJBA8SFiQXOQCxEw8RErUBBgcAFRgkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAxsBIxEjEQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2Bqk%2F6%2BpbIAbYBRAESoKD%2B7v68%2Fu6goAJf%2Fqrz8wFW8%2F5iASz%2B1P7UASwAAAACAAAAAASwBLAACwATACkAsgkAACuwDM2wEDKwEy%2BwAs0BsBQvsRUBKwCxDAkRErIEAQ45OTkwMTUREyEbARQGIyEiJhMzFyE3MwMhyAMgxwEOC%2FuCCw7IyDIBLDLIYf2iGQHbArz9RP4lCw4OAebIyAH0AAMABAAEBKwErAALABUAGABGALAKL7APzbAUL7AEzQGwGS%2BwAdawDM2wDBCxEQErsAfNsRoBK7ERDBEStQQJCgMWGCQXOQCxFA8RErUBBgcAFhckFzkwMRIQEiQgBBIQAgQgJBMUFiA2NTQmIAYBEQUEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzAToBKQG2AUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FP%2BigGRyAABABcAFwSZBLAAHABTALAFL7ANzbARL7AZzQGwHS%2BwAdawD82wDxCxCgErsAnNsR4BK7EKDxEStQUEExQXGSQXObAJEbEVFjk5ALERDREStAEACRQVJBc5sBkRsBc5MDESFB4CMj4CNSMUBiAmEDYzMhcHIREHJiMiDgEXW5vV7NWbW5b6%2Fp76%2BrGIbpIBkJGdxnbVmwLO7NWbW1ub1Xax%2BvoBYvpRkgGQkXpbmwAAAAIAFwAABJkEsAAQACEAegCyEQAAK7AfL7AWzbIWHwors0AWGgkrsA0vsAXNsg0FCiuzQA0ACSsBsCIvsADWsBDNsBAQsRkBK7AazbEjASuxEAARErEREjk5sBkRtwcFCwoTFB8hJBc5sBoSsQkIOTkAsRYfERKwITmwDRGxCRI5ObAFErAHOTAxEzQ%2BAjMyFzcRITcmIyIGFQMRIQcWMzI2NTMUDgIjIicXW5vVdsadkf5wk3CHsfpJAZCTcIex%2BpZbm9V2xp0CWHbVm1t6kf5wk1D6sf2oAZCTUPqxdtWbW3oAAAoAZAAABLAEsAADAAcACwAPABMAFwAbAB8AIwAnAFAAsAgvsBgzsAnNsBoysAwvsBwzsA3NsB0ysBAvsCAzsBHNsCEysBQvsCQzsBXNsCUyAbAoL7AI1rIMEBQyMjKwC82yDhIWMjIysSkBKwAwMTMhESETESERJTUzFSc1MxUnNTMVJzUzFRMhNSE9ASEVJTUhFSU1IRVkBEz7tGQDhPzgZGRkZGRkZGQB9P4MAfT%2BDAH0%2FgwB9ASw%2B7QDhPx8ZGRkyGRkyGRkyGRk%2FahkZGRkyGRkyGRkAAIAAAAABEwEsAAZACMASgCyFwAAK7AgL7AJzQGwJC%2BwBdawGs2yBRoKK7NABQAJK7AaELEbASuwDs2yDhsKK7NADhMJK7ElASsAsSAXERKzBA4FGiQXOTAxNRE0NjsBNTQ2MyEyFh0BMzIWFREUBiMhIiYBITU0JisBIgYVOylkdlIBLFJ2ZCk7Oyn8fCk7AZABLB0VyBUdZAJYKTvIUnZ2Usg7Kf2oKTs7AuWWFR0dFQAAAAIAZAAABEwETAADABUAFwCyAAAAKwGwFi%2BwANawA82xFwErADAxMxEzERM%2BAR4CPgE3EQ4BLgMGB2RkZDyHeHxyamQpKHuEkId0WhQETPu0AZA8MA0hGwVPUQH0UUUKKCgKRVEAAAAAAwAAAAAEsASXACEAMQBBAGcAsi8AACuwPjOwJs2wNjKwDC%2BwHc0BsEIvsADWsAfNsAcQsSIBK7ArzbArELEyASuwO82wOxCxEAErsBfNsUMBK7EyKxESswwLHRwkFzkAsSYvERK0BxATFAMkFzmwDBGxCA85OTAxERQWOwEyNjURND4BIB4BFREUFjsBMjY1ETQuAiIOAhUTETQ2OwEyFhURFAYrASImJRE0NjsBMhYVERQGKwEiJg4LMgsOjeQBBuSNDgsyCw5jo97o3qNjyAwIoAgMDAigCAwCWAwIoAgMDAigCAwBEwsODgsBLH%2FRcnLRf%2F7UCw4OCwEsdN6jY2Oj3nT91QHMCAwMCP40CAwMCAHMCAwMCP40CAwMAAAAAgAAAMgEWAPoAAUAEQAAESEFEQUhATcnNxc3FwcXBycHASwBLP7U%2FtQCsI2NR42NR42NR42NAZDIAyDI%2FquNjUeNjUeNjUeNjQAAAAIAAADIA3AD6AAFAA8AEgABsBAvsA7WsAnNsREBKwAwMREhBREFISU3FhUUByc2NTQBLAEs%2FtT%2B1AK8RW9qQ1YBkMgDIMg5NYevqYU2boqSAAAAAAMAAAC6BGID9wAFAA8AHQA8ALAAL7ABzQGwHi%2BwDtawCc2wCRCxEwErsBrNsR8BK7ETCRESsxAWFx0kFzkAsQEAERKzCQ4TGiQXOTAxGQEhJRElATcWFRQHJzY1NDcXFhUUDwEXNzY1NC8BASwBLP7UAZJFb2pDVl4He3cHUQaOkAYBkQGQyPzgyAHJNYevqoU3boqRzQiXwb2WCEIIsuPmsggADQAAAAAEsASwAAcAEQAVABkAHQAhAC8AMwA%2FAEMARwBLAE8BAQCyAAAAK7EwRDMzsBLNsikxRTIyMrAaL7InK0wzMzOwG82yJS1NMjIysCIvsQIGMzOwI82wCDKwHi%2BxDkgzM7AhzbE0STIyAbBQL7Aa1rEFHjIysB3NsQMfMjKwHRCxMAErsQ0sMjKwM82wNTKwMxCxLgsrsCoysCXNsEAysi4lCiuzQC4iCSuyAQsPMjIysCUQsTcBK7FESDIysDvNsSZKMjKwOxCxTAErsEIysE%2FNsjk9RjIyMrFRASuxMB0RErUUFRgZND8kFzmxNyURErIoKTg5OTkAsSIbERKzExQ4OSQXObAjEbIEOjs5OTmwHhJACQUWGTY3PD1AQyQXOTAxMSERIzUjFSM1MzUhNTM1IxEhExEhEQERIREDNTMVAzM1IwE1IREzFSMVIzUjNTM1AzUzFQMzETMRITUjNTMRIRMRIREDNSEVATUzFRM1MxUB9MhkyGQBkGRk%2FgxkASz%2B1AEsyGRkZGQBLAEsyGTIZGRkZGRkyAEsyMj9qMgBLMgBLP7UZGRkAfRkZGRkZGQBLPu0ASz%2B1AK8ASz%2B1P2oZGQCvGT%2BDGT%2B1GRkZGTI%2FgxkZAPo%2FtT%2B1MhkAfT%2BcAEs%2FtT84GRkA4RkZP1EZGQAAAAACQAAAAAEsASwAAMABwALAA8AEwAXABsAHwAjAHAAsgwAACuyBBQcMzMzsA3NsRUdMjKyDAAAK7AFzQGwJC%2BwCNawC82wCxCxEAErsAwysBPNsA%2FNsBMQsRQLK7AXzbAXELEYCyuwG82wGxCxIAErsCPNsSUBK7EQCxESsQcGOTmxGxcRErEcHTk5ADAxNTMRIxM1IRUnETMRFzUzFScRMxEVNTMVNREzERU1MxUnETMRZGRkASzIZMhkZMhkZMhkyMgD6PtQZGTIA%2Bj8GMhbW8gD6PwYyFtbyAPo%2FBjIW1vIA%2Bj8GAAAAAIAAAAABLAEsAAHABMAKQCyBwAAK7ASL7AEzQGwFC%2BwAdawCc2xFQErALESBxESsgAGCzk5OTAxERM0NjMhCQEAFBcWMjc2NCcmIgcBDwoB2gK8%2Fgz92B0eUx4dHR5THgK8AdsKD%2F1E%2FgwD41QdHh4dVB0eHgAAAAMAAQAABd0EsAAHABMAGQAxALIHAAArsBczsBIvsATNsBQyAbAaL7AB1rAJzbEbASsAsRIHERK0AAYLFhkkFzkwMRsBNDYzIQkBABQXFjI3NjQnJiIHJTMJAScBAQEOCwHaArz%2BDP3XHh1UHR4eHVQdAgtkArz%2BDDIBwgK8AdsLDv1E%2FgwD41QdHh4dVB0eHrD9RP4MMgHCAAAAAAEAZAAABLAEsAAKAD8AsgAAACuwBy%2BwAs0BsAsvsADWsArNsAoQsQUBK7AEzbEMASuxCgARErICBwg5OTkAsQcAERKyAQQFOTk5MDEzETchEQcRIQchEWSvA51k%2FRJkAu4EAa%2F8GGQD6GT8GAAAAAABAMgAAARMBLEACgAAMwkBETQmIyEiBhXIAcIBwh0V%2FOAVHQG8%2FkUEfhQeHhQAAAADAAAAAASwBLAACwAXACcAWQCyJQAAK7AczbAKL7ADzbIKAwors0AKAAkrsAcysgMKCiuzQAMBCSuwBTIBsCgvsADWsAvNsAIysAsQsQgBK7AFMrAHzbEpASuxCAsRErMMDyciJBc5ADAxNREzFyE3MxEjNSEVExchNwMuASMhIgYHAzc%2BATMhMhYfARYGIyEiJshkAlhkyMj84DUoAlA%2BXgIQCv4%2BChACQiYCEwoB9AoTAiYCCwr9qAoLZAK8yMj9RMjIAtl8fAFaCw4OC%2FuBmAoODgqYCg4OAAAABAAAAGQEsARMAB0AJQAtADEAbwCwAy%2BwJc2wKS%2BwLc2wIS%2BwE80BsDIvsADWsB%2FNsh8ACiuzQB8vCSuwHxCxJwErsCvNsTMBK7EfABESsBk5sSsnERKzISQlICQXOQCxLSkRErMfIiMeJBc5sCERsS4xOTmwExK0CxkaLzAkFzkwMTUUFjMhMjY1ETQmKwEuBCsBIg4CDwEjIgYVADQ2MhYUBiICFBYyNjQmIiU1MxU7KQPoKTs7KZYEDzM3UyrIKVI6LgsMlik7AWSQyJCQyAY%2BWD4%2BWAFYZMgpOzspAlgpOwgbRTUrKTs7FRQ7Kf5wyJCQyJABIFg%2BPlg%2BXmRkAAIANQAABLAErwAeACIAHgCyAAAAK7ANM7AezbICDA8yMjIBsCMvsSQBKwAwMTMhNSIuAT8BIRcWBiMVITUmJy4BLwEBIwEGBw4BDwEBExcTNQFtKT4kE1wBh1IQKzUBoSIoEh4GBv5%2FXf5xGBwMKg8PAWuyLnRCFjYs6t4tV0JCASoTLg4NA%2Bb8EjAbDBoHBwHHAcmM%2FsMAAwBkAAADwwSwACAAKQAxAGUAsiAAACuwIc2yIAAAK7ABzbApL7AqzbAxL7ANzbANELALzQGwMi%2BwBNawIc2wKjKwIRCxLgErsBDNsCUg1hGwHM2xMwErsSUuERKwFjkAsSkhERKwHDmwKhGwFjmwMRKwEDkwMTM1PgE1ETQuAyc1BTIWFRQOAg8BHgQVFA4BIyczMjY1NCYrATUzMjY1NCYjZCk7AgkWJB8B13i6FyEiCwwIG0U0K3amT8ihWYB9Xp%2BLTGyom1kHMygDOxwXHQ0PB0cBsIw3XTcoCAcDDDNBdkZUkU3IYVRagWR7TVJhAAAAAAEAyAAAA28EsAAZACAAsgAAACuwAc2wGDKwCy%2BwDjOwDM0BsBovsRsBKwAwMTM1PgE3EzYmJy4BJzUhFw4DDwEDBhYXFchNcwitCihHBgkFAakCIToiGQUFgAowRzkHQy8DUTgkEwEDATk5CCMnJQwM%2FMc0PAY5AAL%2FtQAABRQEsAAJACUAfgCyGwAAK7AfL7ICBRYzMzOwDM2yHwwKK7NAHxAJK7AKMgGwJi%2BwAdawB82wBxCxCgErsCXNsCUQsR0BK7AYzbIYHQors0AYGgkrsh0YCiuzQB0bCSuwGBCxEAErsA%2FNsScBK7EKBxESsQUIOTkAsR8bERKwCTmwDBGwBDkwMSczESM3FyMRMwcTETMhMxEjNC4DKwERFxUhNTcRIyIOAxVLS0t9fUtLffqWAryWMhAVLiEiyGT%2BcGTIIiEvFBHIAyCnp%2FzgpwNjASz%2B1B0nFQkC%2FK4yZGQyA1ICCRUnHQAAAAIAIf%2B2BI8EsQAJACUAiQCyCAAAK7ACzbAfL7AWM7AMzbIfDAors0AfCgkrsA8yAbAmL7AK1rAlzbAlELEdASuwGM2yGB0KK7NAGBoJK7IdGAors0AdGwkrsBgQsRABK7APzbEnASuxHSURErMCCAkBJBc5sRAYERKzBAYHAyQXOQCxAggRErEABTk5sB8RsgEEGjk5OTAxPwEVITUXBzUhFQMRMyEzESM0LgMrAREXFSE1NxEjIg4DFSGnAyCnp%2FzgZJYCvJYyEBQvISLIZP5wZMgiIS4VEDN9S0t9fUtLA88BLP7UHScVCQL9djJkZDICigIJFScdAAAAAAQAAAAABLAETAAPAB8ALwA%2FAAA1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVHRUETBUdHRX7tBUdHRUDIBUdHRX84BUdHRUD6BUdHRX8GBUdHRUCWBUdHRX9qBUdMhQeHhRkFR0dFcgUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFQAEAAAAAASwBEwADwAfAC8APwAANRQWMyEyNj0BNCYjISIGFREUFjMhMjY9ATQmIyEiBhUTFBYzITI2PQE0JiMhIgYVERQWMyEyNj0BNCYjISIGFR0VBEwVHR0V%2B7QVHR0VBEwVHR0V%2B7QVHcgdFQK8FR0dFf1EFR0dFQK8FR0dFf1EFR0yFB4eFGQVHR0VAfQUHh4UZBUdHRX%2BcBQeHhRkFR0dFQH0FB4eFGQVHR0VAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wLS%2BwJM2wHS%2BwFM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYTNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJhM1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR1kHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHcgdFQJYFR0dFf2oFR0yZBUdHRVkFB4eAmxkFR0dFWQUHh7%2B6GQVHR0VZBQeHgJsZBUdHRVkFB4eAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wHS%2BwFM2wLS%2BwJM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0yZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAAAAAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AUgCyDQAAK7BMM7AEzbBEMrAdL7BcM7AUzbBUMrAtL7BsM7AkzbBkMrA9L7B8M7A0zbB0MgGwgC%2BwANayECAwMjIysAnNshgoODIyMrGBASsAMDE9ATQ2OwEyFh0BFAYrASImETU0NjsBMhYdARQGKwEiJhE1NDY7ATIWHQEUBisBIiYRNTQ2OwEyFh0BFAYrASImATU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0BLB0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHTJkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh78kGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgAABv%2BbAAAEsARMAAYACgAaACoAOgBKACAAsAAvsCYzsAHNsC4yAbBLL7FMASsAsQEAERKwBDkwMQM1MzUXBzUTMxEjExQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWOwEyNj0BNCYrASIGFWXJpqbIZGTIHRUCWBQeHhT9qBUdHRUBLBQeHhT%2B1BUdHRUB9BQeHhT%2BDBUdHRVkFB4eFGQVHQH0ZEt9fUv%2BDARM%2B%2BYUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRUAAAAABgABAAAFFQRMAA8AHwAvAD8AQwBKABcAskAAACsBsEsvsEDWsEPNsUwBKwAwMTcUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjsBMjY9ATQmKwEiBhUBETMRExc1MzUjNQEdFQJYFB4eFP2oFR0dFQEsFB4eFP7UFR0dFQH0FB4eFP4MFR0dFWQUHh4UZBUdAyBkIafIyDIUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRX75gRM%2B7QCJn1LZEsAAgAAAMgEsAPoAA8AEgAtALANL7AEzbAEzQGwEy%2BwANawCc2xFAErsQkAERKwEDkAsQQNERKxERI5OTAxGQE0NjMhMhYVERQGIyEiJgkBESwfAu4fLCwf%2FRIfLAOEASwBEwKKHywsH%2F12HywsAWQBLP2oAAADAAAAAASwBEwADwAXAB8AWQCyDQAAK7AfL7AbzbAXL7AEzQGwIC%2BwANawEM2wEBCxGQErsB3NsB0QsRUBK7AJzbEhASuxHRkRErARObAVEbETEjk5ALEfDRESshATFTk5ObAbEbAUOTAxNRE0NjMhMhYVERQGIyEiJj8BBScBExEhEjQ2MhYUBiIaEgRYExkZE%2FuoEhpk9wEqSgEl7PwYbE5wTk5wLAP0EhoaEvwMEhoa7baDnAE%2B%2FuAB9P7OcE5OcE4AAgCU%2F%2FMEHAS9ABQAHgA9ALINAAArsB0vsATNAbAfL7AA1rAVzbAVELEbASuwCM2xIAErsRsVERKxDQQ5OQCxHQ0RErIHABg5OTkwMRM0PgEzMh4BFAcOAQ8BLgQnJjcUFjMyNjQmIgaUedF6e9B5SUm7OTkKImNdcys%2Fwpdqa5eX1pYC6XzXgX7V9pVy9kJCCSJrb6BLi5Zrl5fWlpcAAAIAAQABBK8ErwAPABUASQCyDQAAK7ATzbAUL7AFzQGwFi%2BwAdawEc2wERCxEwErsAnNsRcBK7ETERESsQ0EOTmwCRGxBQw5OQCxFBMRErMBCAkAJBc5MDESND4CMh4CFA4CIi4BEhAWMxEiAV%2Bg3vTeoF9foN703qBN%2B7CwAd703qBfX6De9N6gX1%2BgAgn%2BnvoDVgACAHUABAPfBQ8AFgAlAAATND4DNx4GFRQOAgcuAjceARc3LgInJjY%2FAQ4BdURtc3MeFUlPV00%2FJU5%2Bmk9yw4B%2BDltbEAcWLgoPAgkJXDcBll64oZ3FYEePdndzdYZFWZlkOwQGXrh%2BUmwaYgYWSihJjTQzbpYAAAADAAAAAATFBGgAHAAhACYAVwCyGgAAK7APzbAIL7AEzQGwJy%2BwANawDM2wDBCxEwErsBbNsSgBK7ETDBESswYdHiAkFzmwFhGxHyI5OQCxCA8RErMVHR8hJBc5sAQRsyAiIyUkFzkwMRkBNDYzBBcHISIGFREUFjMhMjY9ATcVFAYjISImJTcBJwkBFzcvAeulAW4fuv7JKTs7KQH0KTvI66X%2B1KXrAbShAZxy%2FmsB%2BXFxFVwBkAEspesGCLo7Kf4MKTs7KX3I4aXr62oyAZxx%2FmsB%2BHFxVRwAAAAAAgAAAAAElQRMABwALgBIALIaAAArsBDNsCIvsCfNsAkvsATNsAQQsAbNAbAvL7AA1rANzbEwASsAsSIQERKyFR0kOTk5sQkaERKwJTmxBAYRErAmOTAxGQE0NjMhFwYHIyIGFREUFjMhMjY1NxUUBiMhIiYBPgMfARUJARUiDgXrpQEFAoVVkSk7OykB9Ck7yOul%2FtSl6wGnHmdnXx4dAWj%2BmQcYSENWQzkBkAEspetQIFg7Kf4MKTs7KZk1pevrASEmNBMJAQHRAUQBPtgCDhczQ20AAAAAAgAAAAAEqARMAB0AIwBSALIbAAArsBDNsAkvsATNAbAkL7AA1rANzbANELEUASuwF82xJQErsRQNERKzBx4fIiQXObAXEbAhOQCxCRARErMWHyIjJBc5sAQRsSAhOTkwMRkBNDYzITIXByEiBhURFBYzITI2PQE3FRQGIyEiJgkCJwEn66UBLD1Csv6jKTs7KQH0KTvI66X%2B1KXrAVYBGwI3if5SkgGQASyl6xexOyn%2BDCk7OylFyKml6%2BsBjf7kAjeJ%2FlGTAAABAAAAAQSwBLEAFwBFALISAAArsBYvsA4zsALNsAkyAbAYL7AU1rADMrAQzbAIMrEZASuxEBQRErEGEjk5ALEWEhESsQ0XOTmwAhGxAAw5OTAxEQEVMzUjCQEjFTM1CQE1IxUzCQEzNSMVASzIyAEsASfDyAEs%2FtTIw%2F7Z%2FtTIyAJbASjGyAEs%2FtTIxv7Y%2FtTGyP7UASzIxgAAAAABAMgAAAOEBEwAEwAdALIRAAArsAszAbAUL7AA1rANzbAIMrEVASsAMDE3ETQ2OwEyFhURAREBERQGKwEiJsgdFWQVHQH0%2FgwdFWQVHTID6BUdHRX%2BSwHn%2B7QB6P5KFR0dAAAAAQAAAAAEsARMABcAHwCyFQAAK7ENDzMzAbAYL7AA1rARzbAIMrEZASsAMDE1ETQ2OwEyFhURAREBEQERAREUBisBIiYdFWQVHQH0AfT%2BDP4MHRVkFR0yA%2BgVHR0V%2FksB5%2F4ZAef7tAHo%2FhgB6P5KFR0dAAABAIgAAASwBEwABgAUALIGAAArsAQzAbAHL7EIASsAMDETAREBEQERiAI0AfT%2BDAImAib%2BGQHn%2B7QB6P4YAAAAAQDIAAAETARMAAIAADMJAcgDhPx8AiYCJgAAAAIAyABkA4QD6AAPAB8AADcUFjsBMjY1ETQmKwEiBhUBFBY7ATI2NRE0JisBIgYVyB0VyBUdHRXIFR0BkB0VyBUdHRXIFR2WFR0dFQMgFR0dFfzgFR0dFQMgFR0dFQAAAAEAyABkBEwD6AAPAAA3FBYzITI2NRE0JiMhIgYVyB0VAyAVHR0V%2FOAVHZYUHh4UAyAVHR0VAAAAAQAAAAAEKARMAAYAFACyAAAAK7AFMwGwBy%2BxCAErADAxMREBEQkBEQH0AjT9zARM%2FhkB5%2F3a%2FdoB6AAAAQAAAAAEsARMABcAHwCyAAAAK7EQFjMzAbAYL7AU1rAEMrANzbEZASsAMDExEQERARE0NjsBMhYVERQGKwEiJjURAREB9AH0HRVkFR0dFWQVHf4MBEz%2BGQHn%2FhkBtRUdHRX8GBUdHRUBtv4YAegAAAEBLAAAA%2BgETAATAB0AsgAAACuwDjMBsBQvsBLWsAIysAvNsRUBKwAwMSERARE0NjsBMhYVERQGKwEiJjURASwB9B0VZBUdHRVkFR0ETP4ZAbUVHR0V%2FBgVHR0VAbYAAAIAZADIBLAEKAAPABIAEgCwDS%2BwBM0BsBMvsRQBKwAwMTc1NDYzITIWHQEUBiMhIiYRIQFkHRUD6BUdHRX8GBUdBEz92vpkFR0dFWQVHR0BDwI0AAEAuQAHA%2FkEqQAFAAATATcJASe5AlDw%2Fp8BYfACV%2F2w8AFhAWHwAAABARD%2F0gRSBHQACAAAJQkBNwEXBxUBARABYf6f8QI8FQH9sMIBYQFh8P3FFgEB%2FbEAAAAAAgADAAMErQStAAsAFwBCALAKL7AOzbAVL7AEzQGwGC%2BwAdawDM2wDBCxEQErsAfNsRkBK7ERDBESswQJCgMkFzkAsRUOERKzAQYHACQXOTAxEhASJCAEEhACBCAkEzMVMzUzNSM1IxUjA6ABEwFEAROgoP7t%2Frz%2B7YnIyMjIyMgBtgFEAROgoP7t%2Frz%2B7aCgAVHIyMjIyAACAAMAAwStBK0ACwAPAEkAsAovsAzNsA8vsATNAbAQL7AB1rAMzbAMELENASuwB82xEQErsQ0MERKzBAkKAyQXOQCxDAoRErEHADk5sQQPERKxAQY5OTAxEhASJCAEEhACBCAkEyE1IQOgARMBRAEToKD%2B7f68%2Fu2JAlj9qAG2AUQBE6Cg%2Fu3%2BvP7toKABUcgAAAAAAgADAAMErQStAAsAFwAyALAKL7AEzbAEzQGwGC%2BwAdawB82wB82xGQErsQcBERKxDBA5OQCxBAoRErENFTk5MDESEBIkIAQSEAIEICQTFzcXNyc3JwcnBxcDoAETAUQBE6Cg%2Fu3%2BvP7tU9WNjdWOjtWNjdSNAbYBRAEToKD%2B7f68%2Fu2goAEo1Y6O1Y2N1I2O1Y0AAAIAAwADBK0ErQALABEAMgCwCi%2BwBM2wBM0BsBIvsAHWsAfNsAfNsRMBK7EHARESsQwOOTkAsQQKERKxDQ85OTAxEhASJCAEEhACBCAkEwkBJwcnA6ABEwFEAROgoP7t%2Frz%2B7WsBFAGbr%2BxmAbYBRAEToKD%2B7f68%2Fu2goAGE%2FusBm67sZgAAAAADAAMAAwStBK0ACwA4ADwAbACwCi%2BwOc2wPC%2BwJ82wIS%2BwG82wNC%2BwBM0BsD0vsBLWsB7NsB4QsS4BK7AHzbE%2BASuxHhIRErEhNDk5sC4RtAkEKDo7JBc5ALEnPBESsQcAOTmwIRGwKjmwGxKyDA8uOTk5sDQRsQYBOTkwMRIQEiQgBBIQAgQgJBMzMhYyNjQ%2BBToBMzIWFRQGBw4EFzM%2BBDU0LgMjIg4CEzM1IwOgARMBRAEToKD%2B7f68%2Fu3JjwQPBwYCBQIJBA4EEwMTFggXBQ8nHRgByAUSLSIcIzFEMRsyUUUmiMjIAbYBRAEToKD%2B7f68%2Fu2goAIZAgYMCgcFAwIBFBAWDBABBBcfPSYDCikyWDIzTCgYBhg1YP4uZAADAAMAAwStBK0ACwAVABkAOwCwCi%2BwDM2wFS%2BwDjOwEs2wES%2BwFs2wGS%2BwBM0BsBovsRsBKwCxEhURErEHADk5sRYRERKxBgE5OTAxEhASJCAEEhACBCAkNyE1IxEhFTMVIxMzNSMDoAETAUQBE6Cg%2Fu3%2BvP7t7QGQZP7UZGRkyMgBtgFEAROgoP7t%2Frz%2B7aCgiWQBLGTIAZBkAAACAAAAAASwBLAAGAAvAGkAshQAACuwEi%2BwH82wHDKwAC%2ByDhkhMzMzsAHNsgwjLTIyMgGwMC%2BwFNayBRwpMjIysBPNsgceJzIyMrExASuxExQRErMiIy4vJBc5ALESFBESsBU5sQAfERKwHTmwARGyHigpOTk5MDERNTM%2BATc1MxUeAhczFSMOAQcVIzUuASczHgEXNTMVNjcjNTMuAScVIzUOAQczFcMfh4vINnZrEsvLGbdZyIyIHmAYb0vIlTTJyBllSshLbhjRAfTIfZUayMgUUINFyGaoIcXFG5d9SW0Yzs4wnshKahjMyxdsSMgAAAAAAwAEAAQErASsAAsAEwAfAEYAsAovsA%2FNsBMvsATNAbAgL7AB1rANzbANELERASuwB82xIQErsRENERK1BAkKAxQaJBc5ALETDxEStQEGBwAXHSQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiADNyc3FzcXBxcHJwcEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqpJh4dth4dth4dth4cBtgFEARKgoP7u%2Frz%2B7qCgAl%2F%2BqvPzAVbz%2FduHh22Hh22Hh22HhwAAAAMABAAEBKwErAALABMAGQBGALAKL7APzbATL7AEzQGwGi%2BwAdawDc2wDRCxEQErsAfNsRsBK7ERDREStQQJCgMUGCQXOQCxEw8RErUBBgcAFxkkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAzcXNxcBBKABEgFEARKgoP7u%2Frz%2B7hbzAVbz8%2F6qa41XzI7%2BpgG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BI41XzY7%2BpwAAAAMABAAEBKwErAALABMAGwBGALAKL7AWzbARL7AEzQGwHC%2BwAdawDM2wDBCxGQErsAfNsR0BK7EZDBEStQQJCgMPFCQXOQCxERYRErUBBgcADhskFzkwMRIQEiQgBBIQAgQgJBMUFwEmIyIGExYzMjY1NCcEoAESAUQBEqCg%2Fu7%2BvP7uFj4COGR0q%2FPNYXCr8zsBtgFEARKgoP7u%2Frz%2B7qCgAbRzZAI3PvP98jvzq3BhAAAAAAEAAABjBLAD6AAGABoAsAUvsALNAbAHL7EIASsAsQIFERKwADkwMREBESERIRECWAJY%2FagCIwHF%2FtT%2B1P7TAAABAAAAYwSwA%2BgABgAaALAAL7ABzQGwBy%2BxCAErALEBABESsAQ5MDEZASERCQERAlgCWP2oAZABLAEs%2Fjv%2BQAEtAAAAAAEAzAAABEoEsAAGAB8AsgUAACsBsAcvsAXWsATNsQgBK7EEBRESsAE5ADAxEwkBIREhEcwBwgG8%2Ftb%2B1AJYAlj9qP2oAlgAAAEAaAAAA%2BYEsAAGAB8AsgYAACsBsAcvsAHWsATNsQgBK7EEARESsAY5ADAxEyERIREhAWgBKAEsASr%2BPwJYAlj9qP2oAAAAAAEAAADHBLAETAANAAA1PgM3EQkBEQ4DBkaJ55wCWP2oX7CkgsiE1a1nCAEP%2Fjv%2BQAEtAiREdQAAAgAAAAAEsASwAAYADQARALIAAAArAbAOL7EPASsAMDExERcBFwEXExcBFxEhF4EBJo7%2B2oHrjgEmgf5wgQGQgQEmjv7agQMJjgEmgQGQgQACACIAIwSOBI4ABgANAAA3ASchEScJAREXARcBFyIBJ4EBkIH%2B2QGogQEnjv7ZgbABJ4H%2BcIL%2B2QI1AZCBASeN%2FtmCAAMAFwAXBJkEmQAPAB8AIwBPALANL7AgzbAjL7AUzbAdL7AFzQGwJC%2BwAdawEM2wEBCxGQErsAnNsSUBK7EZEBEStQUMDQQhIyQXOQCxFCMRErEJADk5sB0RsQgBOTkwMRI0PgIyHgIUDgIiLgEBEx4BOwEyNjcTNiYrASIGEzM1Ixdbm9Xs1ZtbW5vV7NWbAVY6BCMUNhQjBDoEGBTPFRgwyMgB4uzVm1tbm9Xs1ZtbW5sCRv7SFB0dFAEuFB0d%2FcVkAAAFAAAAAASwBLAAJgAqADAANAA7ADQAsicAACuwMTOwKs2wMjIBsDwvsDHWsAUysDTNsAcysT0BK7E0MREStAsMEzU6JBc5ADAxETMVIREzESE1MzUjNjQmLwEuASMiDwEGByYvASYjIgYPAQ4BFBUjEyERIRMiNj8BFxMRIREBNx4DI2QBkMgBkGRvAQMCIgs9JyAd7xYSExXuIR0nPQojAgJvZAGQ%2FnBkAyITEtXbAZD%2Bj8oFDiASAgMgyAEs%2FtTIZAELEwisJzARkA0WGAyQEi4msQgUCgH8fAGQAfRgMC%2B%2F%2FHwBkP5wA4TFDClXOQACAAD%2F6gSvBLAAGwAyABcAsgAAACsBsDMvsCfWsA%2FNsTQBKwAwMRU1Ny4CPgE3PgU3FAIOBC4CIwc2Fjc2JT4DNz4BJyYiBgcOAQ8BBAfYCQgDFTguL2llmonoaCxKaHGDeHtcUw9jEidDNwE4RmFrWykWBAgHFCERI509Pf6PWRaPwTU8gGKCOzxVMy0eOR69%2FszQm1UzCQYTDzd%2FDVNCqCY%2FX4BUMhQJBR0ZM3MgIMXMAAABAG8ADAREBOcASAAjAAGwSS%2BwAdawRc2wRRCxPAErsUoBK7E8RRESsTo2OTkAMDESFBceARcWPgM3PgEnHgEHDgEHDgQeAT4BNz4ENzYCJxYXFicmJy4CNw4EFx4DDgQHBi4CNw4BbwUJRkYfQjo4KA8gDhRPVhEFHxYKCQ8DAwgOGSQYOURrQ0APJqWkFhUnRw8ST1MFMw0qZ0ouDwIMBAgBAQsQGhImOhcHDjQ%2FAblCHjh%2FLRUKJT49HkLtJ1CoZCFJLBMUIA8XCAsBBAYUHD1DbkOsAVNtLFWfBQIHIYbZlQgfZm2nUww7GzQbKBcZEAQKLk1WIC5uAAAD%2F8MAfQTtBDMAIQA%2FAEcAQwCwGi%2BwKc2wOi%2BwCc0BsEgvsDzWsDfNsUkBK7E3PBESQAoJGRoIKSg1PkBDJBc5ALEJOhEStwARJC41PkJHJBc5MDEDNz4GMh4FHwEHDgYiLgUnNx4FMj4ENy4EJxYVFAYiJjU0NwYXFhc3LgEvAT0aBhxGT3N2k5CTdnNPRhwGGhoGHEZPc3aTkJN2c09GHAabB0MtW1R6gHdSWSxICwE3HTo5HjGw%2BLAuZoUxaWklTBMUAlgoCihXVGBHLy9HYFRXKAooKAooV1RgRy8vR2BUVygKKApgPV44KygzXDtoDgFJJUU6GUpZfLCwfFVJV3N8Q2kYYCQkAAAABP%2FDAAAE7QSwABYAIAApAEEAoQCyDwAAK7AOMwGwQi%2BxQwErsDYauj3v790AFSsKsA8uDrAMwAWxDgH5DrANwLAPELMLDwwTK7MQDwwTK7MZDwwTK7MaDwwTK7MkDwwTK7MlDwwTK7IQDwwgiiCKIwYOERI5sBk5sBo5sCQ5sCU5sAs5ALcLDA0QGRokJS4uLi4uLi4uAUAKCwwNDg8QGRokJS4uLi4uLi4uLi6wQBoBADAxAzc%2BBjMyFzczASM3LgQnNxIXNy4BNTQ3BhcWFz8BLgEvAQE3PgY3Jic3HgIfAQcOBD0aBhxGT3N2k0g9PCWU%2FsaUJVKmcmknCpvStyVrjy5mhTFpLxceOg8OASgmFi0vIjATLwFhKydDgS4NGhoHJVplkwJYKAooV1RgRy8RjvtQjxVlZ3k4Dyj%2B5jaNEqduVUlXc3xDL1ccUhsa%2FaeRDyYyJj8YQAJ%2FMJI2j0AUKCgMNGtiZgAAAAP%2FngAABRIErAALABIAFwAAJhYzITI2JwEuAQcBNwkBITUjFREbATUjbxslBQ4lGxX9fhQ4FP1%2B9QG9Ab3%2Bp8hkZMhEREcgBCAiBSD71mQC0%2F0tZGQBkP7UASxkAAAAAAEAZAAVBLAEsAApAEgAsB4vsAnNAbAqL7Al1rAFMrAWzbALMrIWJQors0AWGAkrsiUWCiuzQCUjCSuxKwErsRYlERKxHR45OQCxCR4RErEWJTk5MDETNTQ2NwERNDYyFhURAR4BHQEUBiclERYdARQGLwEjBwYmPQE0NxEFBiZkFg8Ba1h8WAFrDxYYEf6ZZBoTXt5eExpk%2FpkRGAEGKRQxDgFFAVM%2BWFg%2B%2Fq3%2Buw4xFCkUDQz5%2FvlbFkAUEQlOTgkRFEAWWwEH%2BQwNABEAAAAABEwEsAAJABsAHwAjACcAKwAvADMANwA7AD8AQwBHAEsATwBTAFcAADUUFjMhMjY1ESE1ITU0JisBNSMVITUjFSMiBhUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUdFQPoFR37tARMHRWWZP4MZJYVHWRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZDIUHh4UAu5klhUdZGRkZB0V%2FEpkZMhkZMhkZP5wZGTIZGTIZGT%2BcGRkyGRkyGRk%2FnBkZMhkZMhkZP5wZGTIZGTIZGQAAAMAAAADBXgErgAKABAAGQBBALAAL7AYM7ABzbATMrALL7AIM7AQzbADMgGwGi%2BxGwErALEBABESsREWOTmwCxGzBw0SFSQXObAQErEGDjk5MDE9ASEBMzUJATUjCQEhFzcnIQE3FzM1CQE1IwEDAljxASz%2B1J%2F9qP6rAQN6jbX%2BqwKmjXqfASz%2B1PHIyAJYxv7Z%2FtTF%2FagCWHqOtP2VjnvG%2Ftn%2B1MUAAAEAAAAABLAETAASABoAsg4AACuwEC%2BwDDOwBM0BsBMvsRQBKwAwMRkBNDYzITIWFREUBiMhAREjIiY7KQPoKTs7Kf2s%2FtBkKTsBkAJYKTs7Kf2oKTv%2B1AEsOwAAAwBkAAAETASwACUAKQAtAGAAsh8AACuwCc2yCR8KK7NACQEJK7AVMrAmL7AqM7AnzbArMgGwLi%2BwANawJjKwA82wKDKwAxCxEgErsCoysBfNsCwysS8BK7EDABESsCQ5sBIRsR4fOTmwFxKwGTkAMDETNSEVFBcWFxYzMj4GJzQ9ASEVFA4FIi4FGQEhESERIRFkASwGEVUnNSU7KR8RCwMCAQEsBhgnTWWdwJ1lTScYBgEsAZABLAJYyPpxIFwZCwsUHCMoLC4YEQj6yCpSfmpxUDMzUHFqflIBVgEs%2FtQBLP7UAAAAAAH%2F4gC4BGgD3gAFAAADFwkBNwEe4wFgAWHi%2Fb4Bm%2BMBYf6f4wJDAAABAEYA2gTMBAAABQAAEwkBJwkBRgJEAkLi%2Fp%2F%2BoAMd%2Fb0CQ%2BP%2BnwFhAAAAAAL%2FOgBkBXYD6AAIABEAKACwBy%2BwBM0BsBIvsAfWsATNsRMBK7EEBxESsAE5ALEEBxESsA45MDEDCQEjESEXIREBFyERIwkBIxHGASsBLMsBgdf84AGU1wF9xgErASvIArwBG%2F7l%2FnDIAlgBLMj%2BcP7lARsCWAAAAAEAEgAABKoEsAAyAEYAsiIAACuwGTOwLM2wLBCwJs2xFR0yMrAvL7AEzbAQL7AJzQGwMy%2BwJNawH82wHxCxHAErsBfNsTQBK7EXHBESsC05ADAxEyY3NjMhNz4BOwEyFhQGKwEDDgIrARUUBiImPQEhFRQGIiY9ASMiJjU0NjMhNyEiJicSBQ8OGQOAJgUbEV4UHh4UNskCCB4SHx0qHf7UHSodMhUdHRUCFzD9hyAtBQOrGBITohEVHSod%2FD8EDRYyFB4eFDIyFB4eFDIeFBUdyCoWAAAAAAIAAAAABLAETAADAA8AIACyAAAAK7ABzbAEL7AFzbANMrAJzQGwEC%2BxEQErADAxMREhEQE1MzQ2MyEyFhUhFQSw%2B1DIOykBLCk7AfQDIPzgA4RkKTs7KWQAAAAAAgABAAAF3QRMAAMAEAAoALIAAAArsAHNsA8vsA3NsAUysAnNAbARL7ESASsAsQEAERKwBDkwMTMBIQkBETM0NjMhMhYVIRUhAQEsBLD%2B1PtQyDspASwpOwH0%2FBgCvP1EAZACWCk7OynIAAAAAQEuAAADggSwAAkAIQCyCQAAKwGwCi%2BwAdawB82xCwErsQcBERKxBAk5OQAwMQEzESMJASMRMwEBLsbGASoBKsbG%2FtYBLAJYASz%2B1P2o%2FtQAAAAAAQAAAS8EsAOCAAkAHACwCC%2BwAs0BsAovsQsBKwCxAggRErEABTk5MDERARUhNQkBNSEVASwCWAEs%2FtT9qAJYASrGxv7W%2FtfFxQAAAAQAAAAABLAEsAAPABkAHQAhAEkAsgwAACuwGs2wHjKwHS%2BwIDOwBc2wEC%2BwFM0BsCIvsBvWsB7NsB4QsR8BK7AJzbIfCQors0AfAAkrsSMBK7EJHxESsBk5ADAxPQE0NjMhMhYdARQGIyEiJhsBPgEzITIWFxMBMzUjFzM1IzspA%2BgpOzsp%2FBgpOx%2BsBSQUAqATJQWs%2Fo9kZMhkZGRkKTs7KWQpOzsBVQLjFictF%2F0k%2FtRkZGQAAAAD%2F5sAZASwBEwACwApADcAJgABsDgvsADWsAbNsAYQsSoBK7AyzbE5ASuxKgYRErEMGjk5ADAxAzU0Nj8BFS4EFzU8Az4FOwElESUjExYOASMiKwEiJicCARE0NjMyFhURFAYjIiZlMhkZBA4iGhbJAQICBAUHBMgCo%2F1dJi8CCgwPBQNTFB0ENwPoHRUUHh4UFR0CWDIYMg0N%2BgIHFRYhVfoCDAQKBAcDBQIB%2BvyuyP7sDAsBHBUBUf7iA1IVHR0V%2FK4UHh4AAAIASgAABGYEsAArADMANQCyLwAAK7AzzbApL7AfM7ADzbAYMrIpAwors0ApJQkrAbA0L7E1ASsAsSkzERKxLDE5OTAxEzQ2OwE3Ez4BNycmNjsBMhYHBhUeARcTFzMyFhUUBgcOBCMiJi8BLgEFHgEyNjcGIkobFBJ1Pw96UxIGEhReFBIGElN6Dz92ERQbGhIIHmRqn0998To6EhoBpww4RjgLMGwBXhUdrQFHTX4UIBMaFRMkARN%2FTf65rR0VFCgHAwsdFRIpFBQHKdwxPT0xBgAAAQAVABUEnAScABcAABMXBzcXNxc3Fyc3JzcnNwcnBycHJxcHFxXpTuAtm5st4E7qtLTqTuAtm5st4E7pswG9LeBO6bOz6U7gLZucLOFO6bS06U7hLJwAAAMAAABkBLAEsAADACIALgAaAAGwLy%2BwKNawFs2xMAErsRYoERKwFDkAMDE1MxEjARQ7ARY7ATI3EzY9ATQmIyE2PQE0JisBIgYPAgYVExE%2FATMVByEVAyMnyMgBLGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiMgCWP3zS2Q5AVgfK2QsUXYHlixRKBzGxBol%2FokBd9XUr%2BF9%2FolkAAAAAAMAAAAABLAETAADACIALgBwALIcAAArsCXNsBUvsAAzsCjNsC4vsAfNsAEysCwvsArNAbAvL7AA1rADzbADELEEASuwI82wIxCxJgErsBjNsBgQsSkBK7ARzbEwASuxJiMRErIIKCw5OTmwGBGwFTmwKRKwKzkAsS4cERKwKjkwMRkBMxE3ETQ7ATY7ATIXExYdARQGIyEWHQEUBisBIiYvAiY3HwEzNSchNQMjByPIZGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiGQBkAJY%2Fah9AZBLZDn%2BqB8rZCxRdgeWLFEoHMbEGiXU1a%2FhfQF3ZAAAAAMACABkBRUEVQADACIAQQB5ALAgL7AkzbAbL7ApzbAxL7AUzbABMrIxFAors0AxAAkrAbBCL7AA1rADzbADELEEASuwI82wIxCxLQErsBjNsi0YCiuzQC08CSuxQwErsS0jERK0DBEbFD8kFzkAsRskERKwIzmwKRGwGDmxFDERErIXPEE5OTkwMTcRMxE3ETQ2PwElNjMyHwEWFRQPASEyFhQGKwEDDgEjISImNxchEz4BOwEyNjU0JiMhKgIuBCcmNTQ%2FAScFCMhkHA4OAWoOCxEMbQ4LVQEuVWttVGuCBxsP%2FqsHpmRkASWDBhsPyxASEhD%2BNwELBAkDBwQEAgUKk1b%2BrcgCWP2oSwINESUKCeYGDHAOFBIOeUyQTv6tFieiG1kBUxUoHhUUHQEBAgMFAwwIDg23U%2BwAAAAD%2F5sAZQSwBFYAHgA4ADwAeQCwGC%2BwJM2wHS%2BwH82wOC%2BwA82wOjKyOAMKK7NAODkJKwGwPS%2BwAdawH82yHwEKK7NAHywJK7AfELEmASuwFM2wFBCxOQErsDzNsT4BK7EmHxEStAcMHAQpJBc5ALEdJBESsCY5sB8RsAA5sQM4ERKyAScsOTk5MDECNDYzBScmNTQ%2FATYzMhcFHgIVERQGIyEiJicDIyInMzIWFxMhNxElBxcWFRQHDgUqASMhAREzEWVsVQEuVQsObQ0QCw4BbQcUIawI%2FqsQGwaCa1QK3g8bBoMBJWv%2BqVeRCgUBBQMHBAkECwH%2BJAPpyAJDkEwBeRAQFQ1xDAbmBA0nEf3yDaEoFQFTZCkU%2Fq1ZAfbtU7gLDwsJAwUDAgEB%2FgwCWP2oAAAAAAMAYQAABEwFDgAbADYAOgBHALI3AAArsDjNAbA7L7AV1rA3MrApzbIpFQors0ApOgkrsDMysCkQsS8BK7AOzbE8ASuxKRURErESNjk5sQ4vERKwETkAMDEbAR4CMyEyNjURNCYnJTU0JiIGFREnJgYPAQYXNxcWNz4FPAE1ETQ2Fh0BFBYXBREHIQM1IRVh5gQNJxECDQ2iKBX%2BrU6QTHkPJQ5wFltTtxYZAwUDAgEBMjIoFQFTWf4JCAJYAs%2F%2BlQYTH6YHAVYPGwaDalRua1X%2B0lQMAQ1uFgtWkhINAQUDBwQJBAsBAcgWEhMVyhAbBoL%2B2mT%2BcMjIAAAAAAMAAgAKA%2B0FGAAdADQAOABFALA1L7A2zQGwOS%2BwCtawNTKwK82yKwoKK7NAKzgJK7AhMrArELEnASuwD82xOgErsSsKERKxDB85ObEPJxESsA05ADAxEwYfAR4BPwEUBhUUFjI2PQElPgE1ETQmIyEiBg8BAxMhFxEFDgEdARQGJjURPAEuAScmDwETNSEVAhAWcA0mD3kBTZBOAVMUKaIN%2FfMRJQoKmuwB91n%2BrBQoMjIDBwYYFriSAlgCSR8Wbg0BC1UzzS5UbG5UaoMGGw8BVgemHA4P%2FoIBU2T%2B2oIGHA%2FKFhISFgHICwcQCAMNEpICccjIAAAAAgAFAAAEsASrAA4AFQA6ALIMAAArsBDNsA8vsAXNAbAWL7AA1rAQzbEXASsAsRAMERKxCRI5ObAPEbEAEzk5sAUSsQgUOTkwMRM0PgIzMgQSEAIEICQCARchBwkBFQVfoN16ogEToKD%2B7f68%2FuygASUCASwCAZL%2BbgJVet2gX6D%2B7P68%2Fu2goAETAQrJwgEmASrFAAIAAAAABKsEqwAQABcAOACyDgAAK7AUzbAWL7AFzQGwGC%2BwFNawCs2xGQErALEUDhESsBI5sBYRsgoAETk5ObAFErAXOTAxETQ%2BAjMyHgIVFAIEICQCNwEnITchNV%2Bg3Xl63aBfoP7s%2Frz%2B7aDIAZICASwC%2FtQCVXrdoF9foN16ov7toKABE6X%2B2sLJxQAAAAIABQAABLAEqwAOABUAPgCyDAAAK7ARzQGwFi%2BwANawEc2wERCxEgErsAnNsRcBK7ERABESsQwPOTmwEhGxBRU5ObAJErELFDk5ADAxEzQ%2BAjMyBBIQAgQgJAIlMxEzETMBBV%2Bg3XqiAROgoP7t%2Frz%2B7KABJ8jIyP7UAlV63aBfoP7s%2Frz%2B7aCgAROl%2FtQBLAGQAAACAAUAAASwBKsADgAVAE0AsgwAACuwFC%2BwBc0BsBYvsADWsBXNsBUQsRIBK7AJzbEXASuxFQARErEMDzk5sBIRsQUQOTmwCRKxCxE5OQCxFAwRErIIABA5OTkwMRM0PgIzMgQSEAIEICQCJQkBIxEjEQVfoN16ogEToKD%2B7f68%2FuygAScBLAEsyMgCVXrdoF%2Bg%2Fuz%2BvP7toKABE6X%2BcAGQASz%2B1AAAAAAEAAUAAASwBKsAEACIAJgAmgB8ALIOAAArsCrNsE8vsIwvAbCbL7AA1rAUzbAUELFYASuwCs2xnAErsRQAERKwEjmwWBFADg4FEyEjJDxKVXiEhYmUJBc5sAoSQAoNIiYwO1pndpmaJBc5ALFPKhEStxYwNjxGSFVXJBc5sIwRQAkKABRYbIWNjpQkFzkwMRM0PgIzMh4CFRQCBCAkAhMGFgcUFgcyHgEXFhceAjcWBhcWFxQOARcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQcGJyY3NjceAhceAR8BNDYnJjY3PgM3JjcyFjI2Ny4DJzYnHgE%2FATYuAScGJw4DBwYmBw4BBwYWBw4BJT4BNxYyPgE3FBYVLgM3MwVfoN16ed2gX6D%2B7f68%2Fuyg%2BQgbBiIBDBYYCBhUFj45HQguAyotBgEFaHUeIiMDDi4NDkYRCT0gLhAyEAQBBikEAggZGhcTEwsGEAYoGwYMKA4OEwQEJQQFCgcYFgYQCB8SFwkKKSM%2FDAsJHzYMCwcvUg8TEg8rGj4IDz0PFT4DAxMBAzEBAwMaAwoRCxIHIgksHCSiAQJVet2gX1%2Bg3Xqi%2Fu2goAETAVkhdxwJRhkLEwQMHggvHgQSShRHCQYTCgwDcx0kPh8JAQcHEAsBAgsLIxcCLwINCAMWJhIdGR0cHhAGAQEHChMlCQgDSRUXKwoOKhQZCRITAwkLFycVIAcnBQ0DBQQkIxYMAwMMEgYKAQMHBgcnDwsXByJxcQwkBwoMEQQYVQECBgQMXwAAAAABAAAAAgSvBIUAFAAAPAE3ASY2NzYXBRc3FgcGJwEGIi8BDwJYIU5gpI7%2B%2FZH7DaR7gv2sDysPb48rEAJXZck2XGWK6H6vXEYv%2FawQEG4AAAYAAABgBLAErAAPAB8ALwAzADcAOwBQALAML7A0zbA3L7AFzbAcL7AwzbAzL7AVzbAsL7A4zbA7L7AlzQGwPC%2BwNdaxMTkyMrAJzbEYKDIysjUJCiuzQDUACSuxECAyMrE9ASsAMDE9ATQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYBITUhEyE1IRMzNSM7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTsCWAH0%2FgzIASz%2B1GTIyMRkKTs7KWQpOzsBuWQpOzspZCk7OwG5ZCk7OylkKTs7%2Fplk%2FgxkArxkAAACAGQAAARMBLAAAwAJACUAsggAACuwAC%2BwAc0BsAovsAjWsAfNsQsBKwCxAAgRErAEOTAxEzUhFQUhAREHEWQD6PxKA4T%2BosgETGRkZP4M%2FtTIAfQAAAAAAwAAAGQEsASwAAkAIQAlAGAAsAcvsAHNsAovsB0zsA7NsRgiMjKwJS%2BwE80BsCYvsA%2FWsCLNsCAysg8iCiuzQA8LCSuwADKwIhCxIwErsB4ysBjNshgjCiuzQBgcCSuwAjKxJwErALEOChESsB85MDE9ASEVFAYjISImGQE0NjMhNTQ2OwEyFh0BITIWFREhNSMVETM1IwSwOyn8GCk7OykBLDspyCk7ASwpO%2F4MyMjIyMjIKTs7AVUBkCk7ZCk7OylkOyn%2BcGRkAfRkAAAAAAQAAAAABLAEsAAGAA0AFAAbABQAsgAAACuwEjMBsBwvsR0BKwAwMTERFzcXBxcBNxc3JzchATcXNxEhNwM3JyERJweByI7Igf5wgciOyIH%2BcALZjsiB%2FnCByMiBAZCByAGQgciOyIEDIIHIjsiB%2FJmOyIH%2BcIEC5siB%2FnCByAAABgAAAAAEqASoAAsAFQAfACkAQgBMANIAsgoAACuwD82wHi%2BwSjOwGc2wRTKwKC%2BwOTOwI82wNDKyKCMKK7NAKEEJK7AUL7AEzQGwTS%2BwAdawDM2wDBCxFwErsBvNsBsQsSELK7AmzbAmELEqASuwPs2wPhCxQwErsEjNszdIQwgrsDHNsDEvsDfNsEgQsREBK7AHzbFOASuxJhsRErMKDhQDJBc5sT4qERKxLTw5ObE3MREStQkPEwQvOyQXOQCxHg8RErMHAAwRJBc5sBkRsyotPD4kFzmwKBKxBgE5ObAjEbEvOzk5MDEYARIkIAQSEAIEICQTFBYgNjU0JiAGFjQ2MhYVFAYjIjY0NjMyFhQGIyIXNDY%2FAiY1NDYzMhYUBiMiJwcWFRQGIiYlNDYyFhQGIyImoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzbR8uICAXFk0gFxYgIBYXUikfegEJIBcWICAWDg83ETNIMwEeIC4fIBYXIAGyAUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FOHLh8gFhcg5CwhIC4guiAxBX4BDg4WISAuIAqRFh0kMzNSFiAfLiAgAAAAAf%2FYADsEugSwAE8AOgCwBS%2BwJ82wIC%2BwFc2wNi%2BwSs0BsFAvsVEBKwCxJwURErA%2FObAgEbQLDxobMSQXObAVErEyMzk5MDECBhceATMyNz4CNzY3AT4BJyYnJiMiBgcBBxcBNjc2MzIXFgcBBiMiJicmPgI3NjcBPgIzMhceAQcGDwEDHwEBPgEnLgEnJiMiBwYHARsaMCN2Rj84IUApJygRAYojGA8bWhQJLkEj%2FnsHRQF5FBMXGyYPECT93TRJN1oJBQ8wJCYYFAFcND1rNhkXX3YIB1v8%2FQdFAgVDOBEQZk9FU2taKEf%2BAAHWvk45QBwQMSorLBEBiiNiL1cRAiEj%2FnQHQwF1FhAXJCck%2Fd00Qj8jPkAkJBUUAVw0NzUEEZtiZVv5%2FwAHPAH%2FQ7RdV4YkITcYR%2F4AAAAAAAIAUAA2BMMEWAAbADUAPQCwMy%2BwLTOwA82wBzIBsDYvsADWsBzNsBwQsSoBK7AKzbE3ASuxKhwRErMDBw8YJBc5ALEDMxESsAU5MDETNDYzMhc2MzIWFRQOAgcGDwEnLgInLgM3FB4BHwEWFzY%2FAT4CNTQmIyIPAScmIyIGUMWEj2Jnj4HCI1dDR8VgERArckZCR0NXI6o9PkAWXWFScQxAQz5gOUo6dnIzSDxjAxCDxYGBxYMuWmxHRr%2BDFxc6gUZBRkdsWi4bVkE%2BFlpvXG8MPkZYHEdhU6uuUGMAAAAAAgA5%2F%2FIEdwS%2BABgAMwAAExQfARYzMjcBNjQvASYnBxcBJzcnJicHBhMUHwEWFzcnARcHFxYXNz4BNTQvASYjIgcBBjlCjURbXUIBG0JCjQgLadT%2Be%2FdfEi4dN0LUQo0HDGnUAYX3XxIvHh0jN0KNQl1fQP7lQgFhX0COQkIBG0K6Qo0JCGnU%2Fnv4XxItODdCAQRdQo0HCmnUAYX3YBExMx0jaitdQo1CQv7lQAAAAAADAMgAAAPoBLAAEQAVAB0ARQCyDwAAK7AZzbAdL7ASzbAVL7AGzQGwHi%2BwANawEs2wEhCxEwErsAvNsAsQsBvNsBsvsR8BK7EbEhESsgYFFjk5OQAwMTcRND4CMh4CFREUBiMhIiY3IREhEhQWMjY0JiLIPGacqppkOjsp%2FagpO2QCWP2oxD1WPT1WZAO5FTIuHh4uMhX8Ryk7O%2FECvPzZVj09Vj0AAAABAAAAAASwBLAAGAARALIAAAArAbAZL7EaASsAMDExATcnIQEnJjQ3NjIXARYUBwYiLwEBEScHAS%2FP0gEsAQsjDw8OKg4BGw8PDioOJP7p1NABfNDUARckDioODw%2F%2B5g8qDg8PI%2F71%2FtTSzwADAScAEgQJBOEAMQA9AEMAlwCwLS%2BwKjOwBM2wPjKyLQQKK7NALSwJK7A7L7AfM7ASzbISOwors0ASEwkrAbBEL7AO1rAAMrAyzbABzbAyELEsASuyBBI6MjIysCvNshQfPjIyMrArELFAASuwJ82wHCDWEbAbzbFFASuxMgERErACObAsEbAJOQCxBC0RErApObA7EbYADhsnOkBDJBc5sBISsBU5MDEBMx4BFxEuASMuBDU0PgE3NTMVHgQXIy4BJxEXHgQVFAYHFSM1JicuARMUHgMXFhcRDgETNjU0JicBJ4sFV0oGEwIuQk4vIViCT2QmRVI8KwOfCDZKQCI8UDcosptkmFUoGagQESoUHAcEPUnqqlhSAbFNYw8BTwEGDhkvOVg3XIdDB05PBBMsP2lCSEsN%2Fs0OBxMsPGU%2Bi6oLTU4RVyhrAh4dLBgVBgcCAQESCDv9KxKFQEcZAAAAAQBkAGYDlAStAEMAjQCwMS%2BwKs2wAC%2BwHjOwAc2wHDKwEy%2BwC82yEwsKK7NAEw4JKwGwRC%2BwB9awOTKwGM2wJDKyGAcKK7NAGB4JK7IHGAors0AHAAkrsBgQsQ8BK7AOzbFFASuxGAcRErMCOEJDJBc5sA8RtQsfICoxMyQXObAOErAsOQCxKjERErEtOTk5sAARsSw8OTkwMRM1MyYnLgE%2BATc2MzIWFSM0LgEjIgYHBhUUHgEXMxUjFgYHBgc%2BATM2FjMyNxcOAiMiJgcOAQ8BJz4FNz4BJ2SmGBQKCQMvLWGmgcqZRFAkJVQUKSEXHvHFCBUVKTojYhUhjCFMPDIpTycqF9IyJ1YXGDcGFQoRDBEJMAwkAlhkMTcaO1ZeKFiydzRLHB0VLDkcUyozZDKCHTs2Cw4BIh6TGRcDQgQEGgwLkQQOBg0LEQo3j0cAAgACAAAErgSwAAYADQAfALIMAAArAbAOL7AM1rALzbEPASuxCwwRErAIOQAwMRMJASMRIxEJAiMRIxECASoBKsbIAZIBKgEqxsgBLP7UASwDhPx8AlgBLP7U%2FHwDhAAABQACAAAD6ASwAAYADAAWAB4AIgCmALIHAAArsAYzsArNsgcAACuwCM2wEy%2BwFM2xAAQyMrANL7AOzbAdL7AfzbIdHwors0AdFwkrsBoysCIvsBjNsAIyAbAjL7AB1rAEzbAEELEIASuxDRcyMrAKzbEdHzIysAoQsRUBK7EbIDIysBDNsQsZMjKzEhAVCCuwE82wEy%2BwEs2xJAErsQQBERKwBjmwCBGwBTkAsRQIERKwEDmwDRGwETkwMRMzETMRMwEhNTMVMxUBNSEVIxUjNTM1AxEhESM1IxU3MzUjAsbIxv7WAZBkyP7UASxjZGPIASxkZAFkZAEsA4T8fP7UyGRkAZBkyGRkZAEsAfT%2BDGRkyMgABQACAAAD6ASwAAYADgAUAB4AIgCgALIGAAArsQcKMzOwDS%2BwH82wIi%2BwCM2wDy%2BwEs2wEM2wGy%2BwHM2wFS%2BwFs2wAjIBsCMvsAHWsATNsAQQsQcBK7EPFTIysA7NsREfMjKwDhCxCwErsR0gMjKwCs2xExcyMrMaCgsIK7AbzbAbL7AazbEkASuxBAERErAGObAHEbAFOQCxIh8RErMBBAUAJBc5sRwQERKwGDmwFRGwGTkwMRMzETMRMwEhESERIzUjFQM1MxUzFQE1IRUjFSM1MzUDMzUjAsbIxv7WAZABLGRkZGTI%2FtQBLGNkY2NkZAEsA4T8fP7UAfT%2BDGRkArzIZGQBkGTIZGRk%2FHzIAAAEAAIAAARMBLAABgAMABIAFgBrALILAAArsAwvsBPNsBYvsAjNsA0vsA7Nsg0OCiuzQA0RCSsBsBcvsBHWsBDNsxMQEQgrsAfNsAcvsA0zsBPNsBAQsRQLK7ALMrAKzbEYASsAsQgLERK0AAIDBgEkFzmxDg0RErEFBDk5MDETCQEjESMRBREhESM1AzUzESMREzM1IwIBKgEqxsgCWAEsZMjIZAFkZAEs%2FtQBLAOE%2FHzIAZD%2BDGQD6GT%2BDAGQ%2FHzIAAAABAACAAAETASwAAYADAASABYAawCyCwAAK7AHL7AIzbASL7ATzbISEwors0ASEAkrsBYvsA7NAbAXL7AL1rAKzbMTCgsIK7ANzbANL7AHM7ATzbAKELEUCyuwETKwEM2xGAErALETCxEStAACAwYBJBc5sQ4WERKxBQQ5OTAxEwkBIxEjESU1MxEjEQMRIREjNSczNSMCASoBKsbIAljIZGQBLGRjZGQBLP7UASwDhPx8ZGT%2BDAGQAZABkP4MZGTIAAAAAAUAAgAABLAEsAAGAAoADgASABYAUgCwBy%2BwCM2wCy%2BwDM2wDy%2BwEM2wEy%2BwFM0BsBcvsA%2FWsgcLEzIyMrASzbAWzbIWDwors0AWCgkrs0AWDgkrsRgBKwCxCwgRErMCAwYAJBc5MDETCQEjESMRBTUhFQE1IRUBNSEVATUzFQIBKgEqxsgB9AH0%2FgwBkP5wASz%2B1MgBLP7UASwDhPx8yMjIASzIyAEsyMgBLMjIAAAABQACAAAEsASwAAYACgAOABIAFgBSALAHL7AIzbALL7AMzbAPL7AQzbATL7AUzQGwFy%2BwC9ayBw8TMjIysA7NsArNsgoLCiuzQAoSCSuzQAoWCSuxGAErALELCBESswIDBgAkFzkwMRMJASMRIxEFNTMVAzUhFQE1IRUBNSEVAgEqASrGyAH0yMgBLP7UAZD%2BcAH0ASz%2B1AEsA4T8fMjIyAEsyMgBLMjIASzIyAAAAAACAAAAAARMBEwADwAfACoAsg0AACuwE82wHC%2BwBM0BsCAvsADWsBDNsBAQsRcBK7AJzbEhASsAMDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFeulASyi7u2j%2FtSl68g7KQH0KTs7Kf4MKTsBkAEspevto%2F7UpevrQSk7OykB9Ck7OykAAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMtAe6iASyl6%2Bul%2FtSj7cg7KQH0KTs7Kf4MKTvIAU3%2BswGQASyj7eul%2FtSl6%2BtBKTs7KQH0KTs7Kf4M%2BvoAAAAAAwAAAAAETARMAA8AHwAiAD4Asg0AACuwE82wHC%2BwBM0BsCMvsADWsBDNsBAQsRcBK7AJzbEkASuxFxARErEgIjk5ALEcExESsSAhOTkwMRkBNDYzITIWFREUBiMhIiY3FBYzITI2NRE0JiMhIgYVFxsB66UBLKPt66X%2B1KXryDspAfQpOzsp%2FgwpO2T6%2BgGQASyj7e6i%2FtSl6%2BtBKTs7KQH0KTs7KWT%2BswFNAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMhA%2BulASyl6%2B2j%2FtSl68g7KQH0KTs7Kf4MKTtkAfT6AZABLKXr66X%2B1KLu7T8pOzspAfQpOzsp%2FnABTQACAAAAAAUUBEwABgAaADwAsgcAACuwCM2wAC%2BwAc2wES%2BwEs0BsBsvsAzWsBfNsRwBKwCxCAcRErAFObEBABESsAQ5sBERsAM5MDEZASE1CQE1EzUhMjY1ETQmIyE1ITIWFREUBiMBLAGQ%2FnDIAfQpOzsp%2FgwBkKXr66UBkAEsyP6i%2FqLI%2FnDIOykB9Ck7yOul%2FtSl6wAAAAEA2QACA9YEngAhACgAsA4vsBPNAbAiL7Ac1rAWzbEjASuxFhwRErAZOQCxEw4RErAPOTAxExYzIQIHBh8BMzI3NgAzNicmIwU2Ejc2LwEjIgcOAQAHBtkIFwEumwUFCQkJDgwLAg8BDgkIF%2F7TAZoCAgcJCQ8KBLz%2B80sQAgcT%2FkoUFQsIDw0CaRMREQEEAa8MDwwJEAbT%2FtFWEwAAAAACAAAAAAUUBEwAGAAfAD8AsgMAACuwCM2wCBCwBs2wGS%2BwGs2wEC%2BwEs2wFM0BsCAvsADWsAvNsSEBKwCxGgMRErEdHjk5sBARsBw5MDERFBYzITI3NSEiJjURNDYzITUuAS8BIgYVAREhNQkBNeulASwvNf4MKTs7KQH0DshdXaXrAlgBLAGQ%2FnABkKXrD7k7KQH0KTu5BAcCAuul%2FtQBLMj%2Bov6iyAACAAAAAASwBLAAHQAkAFQAsgMAACuwD82wFi%2BwGs0BsCUvsADWsBLNsBIQsQsBK7AHzbEmASuxCxIRErUYGR4fICQkFzmwBxGwIzkAsRYPERK1CAkeIiMkJBc5sBoRsB85MDERFBYzITI2PQEnBxUUBiMhIiY1ETQ2OwE3JyMiBhUlASchEScB66UBLKPtTno7Kf4MKTs7KZx2SmSl6wHwAWGVAfSV%2FqoBkKXr66ViSXuUKTs7KQH0KTt6TuulCQFWlf4Mlf6fAAMABAAEBKwErAALABMAGwBaALAKL7APzbAbL7AXzbATL7AEzQGwHC%2BwAdawDc2wDRCxFQErsBnNsBkQsREBK7AHzbEdASuxGRURErcECQoODxITAyQXOQCxFxsRErcBBgcMDRARACQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiACNDYyFhQGIgSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqhdyoHJyoAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BEqBycqByAAAAAwAAAAAETASwAAkAEAAUAC4AsgkAACuwEc2wFC%2BwBc2wCzIBsBUvsBLWsAjNshIICiuzQBIACSuxFgErADAxMRE0NjMhMhYVEQkCIREhEQEzNSMOCwQYCxD8GAG9AcL%2B2f7UAfRkZAETCw4PCv7tAyD%2BDAH0AZD%2BcP12MgAAAAADAAAAAARMBLAACQAQABQAKwCyCQAAK7ARzbAUL7AFzQGwFS%2BwEtawCM2yEggKK7NAEgAJK7EWASsAMDExETQ2MyEyFhURASERIREhCQEzNSMOCwQYCxD8GAEsASwBJ%2F5DAV5kZAETCw4PCv7tArz%2B1AEsAfT75jIAAAAAAwAAAAAETAR%2FAAkADwATAC4AsgkAACuwEM2wEy%2BwBc0BsBQvsBHWsAwysAjNshEICiuzQBEACSuxFQErADAxMRE0NjMhMhYVEQkCJwEnATM1Iw4LBBgLEPwYATECVJr%2BRpYChWRkARMLDg8K%2Fu0Cwf7PAlSb%2FkaX%2FToyAAQAAAAABEwEsAAJAA0AFAAYACsAsgkAACuwFc2wGC%2BwBc0BsBkvsBbWsAjNshYICiuzQBYACSuxGgErADAxMRE0NjMhMhYVEQEXNycDJRMHJwcXATM1Iw4LBBgLEPwYYdRhcAK5A%2FqV1JUBzmRkARMLDg8K%2Fu0D3GLVYfzgAQK775XUlf4NMgAEAAAAAARMBLAACQANABQAGAAuALIJAAArsBXNsBgvsAXNAbAZL7AW1rATMrAIzbIWCAors0AWAAkrsRoBKwAwMTERNDYzITIWFREBFzcnExcHFzcXCwEzNSMOCwQYCxD8fNRi1QPvldSV%2BQFjZGQBEwsODwr%2B7QJk1GHUAev6ldSU7QK5%2B%2BkyAAAAAAIAF%2F%2F%2FBLAErwAFAAgAFwCyBAAAKwGwCS%2BwBdawBs2xCgErADAxEwERCQERFwkBFwSZ%2FiX%2Byk8CoP1gAZ8DEPvJARD%2BdwGgzQOq%2FTgAAAAAAgAAAGQETASwABUAGQBNALARL7AGzbIRBgors0AREwkrsA4ysgYRCiuzQAYECSuwCDIBsBovsADWsBLNsAbNsBIQsQ8BK7ALzbEbASuxDwYRErIJFhc5OTkAMDE1ETQ2OwERIREzFxEUBisBESERIyImATM1Ix0V%2BgH0ZMgeFJb9RJYVHQJYZGSWA%2BgUHv7UASzI%2FK4VHQGQ%2FnAdA2fIAAMAAAA%2BBRQEsAATABkAHQBAALAPL7AGzbIPBgors0APEQkrsgYPCiuzQAYECSuwCDIBsB4vsADWsBDNsAbNsR8BKwCxBg8RErILFxg5OTkwMTURNDY7AREhETMXFQEnByERIyImJTcXARcBAzM1Ix0V%2BgH0ZMj%2B7Hh%2B%2FoaWFR0CRXt4AWF7%2FiXhZGSWA%2BgUHv7UASzI2v7teH%2F%2BcB2xe3gBYHv%2BJAOqyAAAAAADAAAABgUOBLAAEwAXACMAFQABsCQvsADWsBDNsAbNsSUBKwAwMTURNDY7AREhETMXEQcnASERIyImATM1IxM3JzcXNxcHFwcnBx0V%2BgH0ZMhnqv7W%2FreWFR0CWGRkZKqqf6qqf6qqf6qqlgPoFB7%2B1AEsyP7zZ6r%2B1v5wHQNnyPvVqqp%2FqqqAqap%2FqqoAAAADAAAAAASwBLAAEgAZAB0AbACwDi%2BwBs2yDgYKK7NADhAJK7AGELAMzbAaL7AbzbEECDIyAbAeL7AA1rAPzbIPAAors0APCwkrsAAQsAbNsA8QsRoBK7AdzbAMMrEfASuxGgYRErATOQCxDA4RErEXGDk5sRoGERKwCjkwMTURNDY7AREhETMXESEVIREjIiYlCQEjESMRAzUzFR0V%2BgH0ZMj%2BcP4MlhUdAlgBLAEsyMjIZJYD6BQe%2FtQBLMj%2B1Mj%2BcB2r%2FtQBLAEs%2FtQCvMjIAAAAAAMAAAAABLAEsAASABkAHQBbALAOL7AGzbIOBgors0AOEAkrsBovsBvNsQQIMjIBsB4vsADWsA%2FNsAbNsA8QsRoBK7AdzbEfASuxGgYRErATObAdEbANOQCxBg4RErILDBk5OTmwGhGwCjkwMTURNDY7AREhETMXEScBIREjIiYlMxEzETMJATUzFR0V%2BgH0ZMjI%2Ftb%2BbpYVHQJYyMjI%2FtT%2B1GSWA%2BgUHv7UASzI%2Fm7I%2Ftb%2BcB2r%2FtQBLAEsAZDIyAAAAAADAAAAyASwBEwACQATABcAADUUFjMhMjY1ESE1ITU0JiMhIgYVEzUhFR0VBEwVHftQBLAdFfu0FR1kAZD6FR0dFQImZJYVHR0V%2FRLIyAAAAAYAAABmBLAErgAGAAoADgAVABkAHQCBALAFL7EWGjMzsALNsRccMjKwBy%2BxCw8zM7AIzbEMEDIyAbAeL7AH1rAKzbAKELELASuwDs2wDhCxFgErsBnNsR8BK7ELChESswIFBgEkFzmxFg4RErMEAw8QJBc5sBkRsxIUFREkFzkAsQIFERKwADmwBxGxARQ5ObAIErATOTAxEQEVIRUhFQM1MxUzNTMVMzUhNQkBNQM1MxU7ATUjASwBkP5wyGRkZGQBkAEs%2FtRkZGRkZAGQASrGyMYCusjIyMjIxv7W%2FtbG%2FgzIyMgAAAACAGQAAASwBLAAGAAvADoAshQAACsBsDAvsADWsATNsAQQsRcLK7AQzbMJEBcIK7AFzbAFL7AJzbAQELEKCyuwDs2xMQErADAxExE3FxEzETcXETMRNxcRBxEUBisBIiY1ESUUHgIfAREUFjsBMjY1ETQmBwUOARVkMjJkMjJkMjJkHRXIFR0CWBUdHQsKHRXIFR0kGv7sGSUCvAGQZGT%2B1AEsZGT%2B1AEsZGT%2BcMv%2BQRUdHRUBv2QdNSEYBgX%2BcxUdHRUEUh8TEXQRRR8AAAAAAQBkAAAEsARMADMAOACyAAAAK7AMM7AzzbICCw4yMjKwKC%2ByGBwlMzMzsCfNsBoyAbA0L7E1ASsAsSgzERKxBiA5OTAxMyE1IiY1ESERFAYjFSE1Ii4DNRE0Nj8BNSEVMhYVESERNDYzNSEVMh4DFREUBg8BZAGQSxkB9BlLAZAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGTgMJgGK%2FnYmDDg4AQUJFQ4DeBYZAQI4OAwm%2FnYBiiYMODgBBQkVDvyIFhkBAgAGAAAAAARMBEwADwAYABwAIAAqAC4AMgCyGAAAK7AQzbAWL7ASzbISFgors0ASFAkrAbAvL7EwASsAsRIQERKzBAMZHCQXOTAxERQWMyEyNjURNCYjISIGFRMhNzMlESEHIQM1IRUBNSEVASEXFSUzNSEnIQE1JRU7KQEsKTs7Kf7UKTtkAZDIaQEn%2Fldk%2FolkASz%2B1AEs%2FtQBkMgBJ2n%2BV2T%2BiQH0AZABLCk7OykB9Ck7Oyn9RMhi%2FtZkASzIyAEsyMgBkMhiYshk%2FUajhaMAAQAQABAEnwSfACAAABIeAxceAzM%2FATYmLwEmBg8BLgEnNz4BLwEuAQ8BEAEfPpJmZ9GXex8foxEHE8ATNBB2jvxldhEGDosOLRKiA%2BQriY%2FUZmeSPSEBohIuDogOBBF2ZfyOdhExFMITBhGiAAAAAAIAAAAABLAETAAdAEAALwCyGwAAK7AMzbAoL7A4zQGwQS%2BxQgErALEMGxESsSAvOTmwKBGzJikyQCQXOTAxPQE0NjcBNTQ%2BAzIeAh8BFQEeAR0BFAYjISImERQWPwE%2BAT0BNiAXFRQWHwEWNj0BLgQjIg4EDwEVDgFtAhYmUnBSJhYBAQFtDhUdFfu0FB4dFMoUHY0BPo0dFMoUHQYaZHzaflymdWQ%2FLAkJMtQUMw4BLzIEDSAZFRQbHAoKMv7RDjMU1BUdHQKrFRkEIQQiFZIYGJIVIgQhBBkVyAgZQTEpFSEoKCELCgACAGQAAASwBEwAAwAZABQAsgAAACuwAc0BsBovsRsBKwAwMTM1IRUlISc1NxEjFSM1IxUjNSMVIzUjERcVZARM%2B%2F8Dtn1kZGTIZMhkZGRkZMiW%2BmQBkMjIyMjIyP5wZPoAAAAAAwBkAAAEsARMAAkAEwAdACQAsgoAACuwFDMBsB4vsArWsBPNsBMQsRQBK7AdzbEfASsAMDEzIRE0JisBIgYVARE0NjsBMhYVETMRNDY7ATIWFRFkASw7KWQpOwGQOylkKTtkOylkKTsBkCk7Oyn%2BcAPoKTs7KfwYArwpOzsp%2FUQAAAAABf%2BcAAAEsARMAA8AEwAfACcAKwBIALINAAArsBDNsBMvsATNAbAsL7AA1rAQzbAQELERASuwCc2xLQErsREQERK1FBUgIygqJBc5ALETEBEStRQaICYoKSQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUzESM1IxMRMxFksHwCvHywsHz9RHywyAOE%2FHxkASzIyP7UyMgBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qAEsZGT%2B1GRkZAEsZP5wASz%2B1AAAAAAF%2F5wAAASwBEwADwATAB8AJwArAEgAsg0AACuwEM2wEy%2BwBM0BsCwvsADWsBDNsBAQsREBK7AJzbEtASuxERARErUUGSAjKCokFzkAsRMQERK1FBogJigpJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMzNTMVMxEjFSM1IwEzNTMRIzUjExEzEWSwfAK8fLCwfP1EfLDIA4T8fGRkZGRkZGQBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qMjIAfTIyP4MZAEsZP5wASz%2B1AAABP%2BcAAAEsARMAA8AEwAbACMARACyDQAAK7AQzbATL7AEzQGwJC%2BwANawEM2wEBCxEQErsAnNsSUBK7EREBESsxQVHB0kFzkAsRMQERKzFBocIiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETITUjETM1IQEhNSMRMzUhZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1AGQASzIyP7UASwB9HywsHz%2BDHywsBgCvP2oZAEsZP4MZAEsZAAAAAAE%2F5wAAASwBEwADwATABYAGQBEALINAAArsBDNsBMvsATNAbAaL7AA1rAQzbAQELERASuwCc2xGwErsREQERKzFBUXGCQXOQCxExARErMVFhcZJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMFERMtAWSwfAK8fLCwfP1EfLDIA4T8fGQBLGQBLP7UASwB9HywsHz%2BDHywsBgCvP6ilgEs%2FtSWlgAAAAAF%2F5wAAASwBEwADwATABcAHwAnAFoAsg0AACuwEM2wFC%2BwGM2wIzKwHy%2BwJTOwFc2wEy%2BwBM0BsCgvsADWsBDNsBAQsRQBK7AYzbAYELEcASuwIc2wIRCxJAErsBfNsBcQsREBK7AJzbEpASsAMDEDETQ2MyEyFhURFAYjISImNyERIRMRIRElMzI2NCYrAQQUFjsBESMiZLB8Arx8sLB8%2FUR8sMgDhPx8ZAK8%2FaiCKTY5JoIBEzYpgoImASwB9HywsHz%2BDHywsBgCvP2oAfT%2BDGRUglZWglQBLAAABf%2BcAAAEsARMAA8AEwAfACMAKQBIALINAAArsBDNsBMvsATNAbAqL7AA1rAQzbAQELERASuwCc2xKwErsREQERK1FBUgISQnJBc5ALETEBEStRQaICImKCQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUjEzMRMxEjZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1MjIAZFkZGNkZMgBLAH0fLCwfP4MfLCwGAK8%2FagBLGRk%2FtRkZGQBLP5wAfQABv%2BcAAAEsARMAA8AEwAZAB0AIQAnAEwAsg0AACuwEM2wEy%2BwBM0BsCgvsADWsBDNsBAQsREBK7AJzbEpASuxERARErcUFRocHh8iJSQXOQCxExARErcUGBobHiAkJiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNSMTNTMVFzM1IxMzETMRI2SwfAK8fLCwfP1EfLDIA4T8fGQBLMhkZWTIZGRjZGTIASwB9HywsHz%2BDHywsBgCvP2oAZBk%2FnDIyGRkASz%2BcAH0AAAAAAb%2FnAAABLAETAAPABMAHQAhACUAKwCbALINAAArsBDNsB4vsSIpMzOwH8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EPARABEQESAO8BEwEUARUBFgEXARgBGQEaARsBHAEdAR4BHwEgASEBIgEjASQBJQEmAScBKAEpASoBKwEsAS0BLgEvATABMQEyATMBNAE1ATYBNwE4ATkBOgE7ATwBPQE%2BAT8BQAFBAUIBQwFEAUUBRgFHAUgBSQFKAUsBTAFNAU4BTwFQAVEBUgFTAVQBVQFWAVcBWAFZAVoBWwFcAV0BXgFfAWABYQFiAWMBZAFlAWYBZwFoAWkBagFrAWwBbQFuAW8BcAFxAXIBcwF0AXUBdgF3AXgBeQF6AXsBfAF9AX4BfwGAAYEBggGDAYQBhQGGAYcBiAGJAYoBiwGMAY0BjgGPAZABkQGSAZMBlAGVAZYBlwGYAZkBmgGbAZwBnQGeAZ8BoAGhAaIBowGkAaUBpgGnAagBqQGqAasBrAGtAa4BrwGwAbEBsgGzAbQBtQG2AbcBuAG5AboBuwG8Ab0BvgG%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%2F4WwAY0AS7AIUFixAQGOWbFGBitYIbAQWUuwFFJYIbCAWR2wBitcWFmwFCsAAAABUuZYrgAA%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIC8%2BCjxnbHlwaCAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZDsiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDQiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzNCIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI2IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTciIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYzIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDVmOyIgaG9yaXotYWR2LXg9IjMyNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwYWM7IiBkPSJNMTAwIDUwMGwxMDAgMTAwaDExM3EwIDQ3IDUgMTAwaC0yMThsMTAwIDEwMGgxMzVxMzcgMTY3IDExMiAyNTdxMTE3IDE0MSAyOTcgMTQxcTI0MiAwIDM1NCAtMTg5cTYwIC0xMDMgNjYgLTIwOWgtMTgxcTAgNTUgLTI1LjUgOTl0LTYzLjUgNjh0LTc1IDM2LjV0LTY3IDEyLjVxLTI0IDAgLTUyLjUgLTEwdC02Mi41IC0zMnQtNjUuNSAtNjd0LTUwLjUgLTEwN2gzNzlsLTEwMCAtMTAwaC0zMDBxLTYgLTQ2IC02IC0xMDBoNDA2bC0xMDAgLTEwMCBoLTMwMHE5IC03NCAzMyAtMTMydDUyLjUgLTkxdDYyIC01NC41dDU5IC0yOXQ0Ni41IC03LjVxMjkgMCA2NiAxM3Q3NSAzN3Q2My41IDY3LjV0MjUuNSA5Ni41aDE3NHEtMzEgLTE3MiAtMTI4IC0yNzhxLTEwNyAtMTE3IC0yNzQgLTExN3EtMjA1IDAgLTMyNCAxNThxLTM2IDQ2IC02OSAxMzEuNXQtNDUgMjA1LjVoLTIxN3oiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMjEyOyIgZD0iTTIwMCA0MDBoOTAwdjMwMGgtOTAwdi0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjVmYzsiIGhvcml6LWFkdi14PSI1MDAiIGQ9Ik0wIDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjYwMTsiIGQ9Ik0tMTQgNDk0cTAgLTgwIDU2LjUgLTEzN3QxMzUuNSAtNTdoNzUwcTEyMCAwIDIwNSA4Ni41dDg1IDIwNy41dC04NSAyMDd0LTIwNSA4NnEtNDYgMCAtOTAgLTE0cS00NCA5NyAtMTM0LjUgMTU2LjV0LTIwMC41IDU5LjVxLTE1MiAwIC0yNjAgLTEwNy41dC0xMDggLTI2MC41cTAgLTI1IDIgLTM3cS02NiAtMTQgLTEwOC41IC02Ny41dC00Mi41IC0xMjIuNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDEzOyIgZD0iTTI5IDQ1NGw0MTkgLTQyMGw4MTggODIwbC0yMTIgMjEybC02MDcgLTYwN2wtMjA2IDIwN3oiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTS0xMDAgMGw0MzEgMTIwMGgyMDlsLTIxIC0zMDBoMTYybC0yMCAzMDBoMjA4bDQzMSAtMTIwMGgtNTM4bC00MSA0MDBoLTI0MmwtNDAgLTQwMGgtNTM5ek00ODggNTAwaDIyNGwtMjcgMzAwaC0xNzB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAyNTsiIGQ9Ik0wIDB2NDAwaDQ5MGwtMjkwIDMwMGgyMDB2NTAwaDMwMHYtNTAwaDIwMGwtMjkwIC0zMDBoNDkwdi00MDBoLTExMDB6TTgxMyAyMDBoMTc1djEwMGgtMTc1di0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAyNjsiIGQ9Ik0xIDYwMHEwIDEyMiA0Ny41IDIzM3QxMjcuNSAxOTF0MTkxIDEyNy41dDIzMyA0Ny41dDIzMyAtNDcuNXQxOTEgLTEyNy41dDEyNy41IC0xOTF0NDcuNSAtMjMzdC00Ny41IC0yMzN0LTEyNy41IC0xOTF0LTE5MSAtMTI3LjV0LTIzMyAtNDcuNXQtMjMzIDQ3LjV0LTE5MSAxMjcuNXQtMTI3LjUgMTkxdC00Ny41IDIzM3pNMTg4IDYwMHEwIC0xNzAgMTIxIC0yOTF0MjkxIC0xMjF0MjkxIDEyMXQxMjEgMjkxdC0xMjEgMjkxdC0yOTEgMTIxIHQtMjkxIC0xMjF0LTEyMSAtMjkxek0zNTAgNjAwaDE1MHYzMDBoMjAwdi0zMDBoMTUwbC0yNTAgLTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI3OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzEgMTIxLjUgLTI5Mi41dDI5Mi41IC0xMjEuNXQyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNXQtMTIxLjUgMjkyLjV0LTI5Mi41IDEyMS41dC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNMzUwIDYwMGwyNTAgMzAwIGwyNTAgLTMwMGgtMTUwdi0zMDBoLTIwMHYzMDBoLTE1MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI4OyIgZD0iTTAgMjV2NDc1bDIwMCA3MDBoODAwbDE5OSAtNzAwbDEgLTQ3NXEwIC0xMSAtNyAtMTh0LTE4IC03aC0xMTUwcS0xMSAwIC0xOCA3dC03IDE4ek0yMDAgNTAwaDIwMGw1MCAtMjAwaDMwMGw1MCAyMDBoMjAwbC05NyA1MDBoLTYwNnoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI5OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzIgMTIxLjUgLTI5M3QyOTIuNSAtMTIxdDI5Mi41IDEyMXQxMjEuNSAyOTNxMCAxNzEgLTEyMS41IDI5Mi41dC0yOTIuNSAxMjEuNXQtMjkyLjUgLTEyMS41dC0xMjEuNSAtMjkyLjV6TTUwMCAzOTd2NDAxIGwyOTcgLTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMwOyIgZD0iTTIzIDYwMHEwIC0xMTggNDUuNSAtMjI0LjV0MTIzIC0xODR0MTg0IC0xMjN0MjI0LjUgLTQ1LjV0MjI0LjUgNDUuNXQxODQgMTIzdDEyMyAxODR0NDUuNSAyMjQuNWgtMTUwcTAgLTE3NyAtMTI1IC0zMDJ0LTMwMiAtMTI1dC0zMDIgMTI1dC0xMjUgMzAydDEyNSAzMDJ0MzAyIDEyNXExMzYgMCAyNDYgLTgxbC0xNDYgLTE0Nmg0MDB2NDAwbC0xNDUgLTE0NXEtMTU3IDEyMiAtMzU1IDEyMnEtMTE4IDAgLTIyNC41IC00NS41dC0xODQgLTEyMyB0LTEyMyAtMTg0dC00NS41IC0yMjQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMxOyIgZD0iTTIzIDYwMHEwIDExOCA0NS41IDIyNC41dDEyMyAxODR0MTg0IDEyM3QyMjQuNSA0NS41cTE5OCAwIDM1NSAtMTIybDE0NSAxNDV2LTQwMGgtNDAwbDE0NyAxNDdxLTExMiA4MCAtMjQ3IDgwcS0xNzcgMCAtMzAyIC0xMjV0LTEyNSAtMzAyaC0xNTB6TTEwMCAwdjQwMGg0MDBsLTE0NyAtMTQ3cTExMiAtODAgMjQ3IC04MHExNzcgMCAzMDIgMTI1dDEyNSAzMDJoMTUwcTAgLTExOCAtNDUuNSAtMjI0LjV0LTEyMyAtMTg0dC0xODQgLTEyMyB0LTIyNC41IC00NS41cS0xOTggMCAtMzU1IDEyMnoiIC8%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDU5OyIgZD0iTTAgMjc1djY1MHEwIDMxIDIyIDUzdDUzIDIyaDc1MHEzMSAwIDUzIC0yMnQyMiAtNTN2LTY1MHEwIC0zMSAtMjIgLTUzdC01MyAtMjJoLTc1MHEtMzEgMCAtNTMgMjJ0LTIyIDUzek05MDAgNjAwbDMwMCAzMDB2LTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDYwOyIgZD0iTTAgNDR2MTAxMnEwIDE4IDEzIDMxdDMxIDEzaDExMTJxMTkgMCAzMS41IC0xM3QxMi41IC0zMXYtMTAxMnEwIC0xOCAtMTIuNSAtMzF0LTMxLjUgLTEzaC0xMTEycS0xOCAwIC0zMSAxM3QtMTMgMzF6TTEwMCAyNjNsMjQ3IDE4MmwyOTggLTEzMWwtNzQgMTU2bDI5MyAzMThsMjM2IC0yODh2NTAwaC0xMDAwdi03Mzd6TTIwOCA3NTBxMCA1NiAzOSA5NXQ5NSAzOXQ5NSAtMzl0MzkgLTk1dC0zOSAtOTV0LTk1IC0zOXQtOTUgMzl0LTM5IDk1eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEzNiA1NTBsNTY0IDU1MHYtNDg3bDUwMCA0ODd2LTExMDBsLTUwMCA0ODh2LTQ4OHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc4OyIgZD0iTTEwMCAyNTB2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek0xMDAgNTAwaDExMDBsLTU1MCA1NjR6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3OTsiIGQ9Ik0xODUgNTk5bDU5MiAtNTkybDI0MCAyNDBsLTM1MyAzNTNsMzUzIDM1M2wtMjQwIDI0MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDgwOyIgZD0iTTI3MiAxOTRsMzUzIDM1M2wtMzUzIDM1M2wyNDEgMjQwbDU3MiAtNTcxbDIxIC0yMmwtMSAtMXYtMWwtNTkyIC01OTF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4MTsiIGQ9Ik0zIDYwMHEwIDE2MiA4MCAyOTkuNXQyMTcuNSAyMTcuNXQyOTkuNSA4MHQyOTkuNSAtODB0MjE3LjUgLTIxNy41dDgwIC0yOTkuNXQtODAgLTI5OS41dC0yMTcuNSAtMjE3LjV0LTI5OS41IC04MHQtMjk5LjUgODB0LTIxNy41IDIxNy41dC04MCAyOTkuNXpNMzAwIDUwMGgyMDB2LTIwMGgyMDB2MjAwaDIwMHYyMDBoLTIwMHYyMDBoLTIwMHYtMjAwaC0yMDB2LTIwMHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkxOyIgZD0iTTAgNTQ3bDYwMCA0NTN2LTMwMGg2MDB2LTMwMGgtNjAwdi0zMDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MjsiIGQ9Ik0wIDQwMHYzMDBoNjAwdjMwMGw2MDAgLTQ1M2wtNjAwIC00NDh2MzAxaC02MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MzsiIGQ9Ik0yMDQgNjAwbDQ1MCA2MDBsNDQ0IC02MDBoLTI5OHYtNjAwaC0zMDB2NjAwaC0yOTZ6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NDsiIGQ9Ik0xMDQgNjAwaDI5NnY2MDBoMzAwdi02MDBoMjk4bC00NDkgLTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk1OyIgZD0iTTAgMjAwcTYgMTMyIDQxIDIzOC41dDEwMy41IDE5M3QxODQgMTM4dDI3MS41IDU5LjV2MjcxbDYwMCAtNDUzbC02MDAgLTQ0OHYzMDFxLTk1IC0yIC0xODMgLTIwdC0xNzAgLTUydC0xNDcgLTkyLjV0LTEwMCAtMTM1LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NjsiIGQ9Ik0wIDB2NDAwbDEyOSAtMTI5bDI5NCAyOTRsMTQyIC0xNDJsLTI5NCAtMjk0bDEyOSAtMTI5aC00MDB6TTYzNSA3NzdsMTQyIC0xNDJsMjk0IDI5NGwxMjkgLTEyOXY0MDBoLTQwMGwxMjkgLTEyOXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk3OyIgZD0iTTM0IDE3NmwyOTUgMjk1bC0xMjkgMTI5aDQwMHYtNDAwbC0xMjkgMTMwbC0yOTUgLTI5NXpNNjAwIDYwMHY0MDBsMTI5IC0xMjlsMjk1IDI5NWwxNDIgLTE0MWwtMjk1IC0yOTVsMTI5IC0xMzBoLTQwMHoiIC8%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUwOyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTYwMiA5MDBsMjk4IDMwMGwyOTggLTMwMGgtMTk4di05MDBoLTIwMHY5MDBoLTE5OHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUxOyIgZD0iTTIgMzAwaDE5OHY5MDBoMjAwdi05MDBoMTk4bC0yOTggLTMwMHpNNzAwIDB2MjAwaDEwMHYtMTAwaDIwMHYtMTAwaC0zMDB6TTcwMCA0MDB2MTAwaDMwMHYtMjAwaC05OXYtMTAwaC0xMDB2MTAwaDk5djEwMGgtMjAwek03MDAgNzAwdjUwMGgzMDB2LTUwMGgtMTAwdjEwMGgtMTAwdi0xMDBoLTEwMHpNODAxIDkwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTU0OyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTgwMCA0MDB2MTAwaDIwMHYtNTAwaC0xMDB2NDAwaC0xMDB6TTgwMCA4MDB2NDAwaDMwMHYtNTAwaC0xMDB2MTAwaC0yMDB6TTkwMSA5MDBoMTAwdjIwMGgtMTAwdi0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1NTsiIGQ9Ik0yIDMwMGwyOTggLTMwMGwyOTggMzAwaC0xOTh2OTAwaC0yMDB2LTkwMGgtMTk4ek03MDAgMTAwdjIwMGg1MDB2LTIwMGgtNTAwek03MDAgNDAwdjIwMGg0MDB2LTIwMGgtNDAwek03MDAgNzAwdjIwMGgzMDB2LTIwMGgtMzAwek03MDAgMTAwMHYyMDBoMjAwdi0yMDBoLTIwMHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY3OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzAwaDMwMHYtMzAwaDMwMHYzMDBoMjk1bC00NDUgNTAwek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY4OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzA1bDMwNSAtMzA1bDU5NiA1OTZsLTE1NCAxNTVsLTQ0MiAtNDQybC0xNTAgMTUxek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY5OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgOTg4bDk3IC05OGwyMTIgMjEzbC05NyA5N3pNMjAwIDQwMGw2OTcgMWwzIDY5OWwtMjUwIC0yMzlsLTE0OSAxNDlsLTIxMiAtMjEybDE0OSAtMTQ5ek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTczOyIgZD0iTTAgMTUwdjEwMDBxMCAyMCAxNC41IDM1dDM1LjUgMTVoMjUwdi0zMDBoNTAwdjMwMGgxMDBsMjAwIC0yMDB2LTIxOGwtMjc2IC0yNzVsLTEyMCAxMjBsLTEyNiAtMTI3aC0zNzh2LTQwMGgtMTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNTgxIDMwNmwxMjMgMTIzbDEyMCAtMTIwbDM1MyAzNTJsMTIzIC0xMjNsLTQ3NSAtNDc2ek02MDAgMTAwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg0OyIgZD0iTTEwMCAwdjEwMGgxMTAwdi0xMDBoLTExMDB6TTE3NSAyMDBoOTUwbC0xMjUgMTUwdjI1MGwxMDAgMTAwdjQwMGgtMTAwdi0yMDBoLTEwMHYyMDBoLTIwMHYtMjAwaC0xMDB2MjAwaC0yMDB2LTIwMGgtMTAwdjIwMGgtMTAwdi00MDBsMTAwIC0xMDB2LTI1MHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg4OyIgZD0iTS0xMDAgMzAwdjUwMHEwIDEyNCA4OCAyMTJ0MjEyIDg4aDcwMHExMjQgMCAyMTIgLTg4dDg4IC0yMTJ2LTUwMHEwIC0xMjQgLTg4IC0yMTJ0LTIxMiAtODhoLTcwMHEtMTI0IDAgLTIxMiA4OHQtODggMjEyek0xMDAgMjAwaDkwMHY3MDBoLTkwMHYtNzAwek0yMDAgMzAwaDMwMHYxMDBoLTIwMHYzMDBoMjAwdjEwMGgtMzAwdi01MDB6TTYwMCAzMDBoMzAwdjEwMGgtMjAwdjMwMGgyMDB2MTAwaC0zMDB2LTUwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTk0OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzEgMTIxLjUgLTI5Mi41dDI5Mi41IC0xMjEuNXQyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNXQtMTIxLjUgMjkyLjV0LTI5Mi41IDEyMS41dC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNNDAwIDUwMHYyMDAgbDEwMCAxMDBoMzAwdi0xMDBoLTMwMHYtMjAwaDMwMHYtMTAwaC0zMDB6IiAvPgo8Z2x5cGggdW5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0.04); +} +img { + max-width:100%; + height: auto; +} +</style> +<div class="container-fluid main-container"> + + +<div id="header"> +<h1 class="title">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> +<h4 class="author"><em>Johannes Ranke</em></h4> +<h4 class="date"><em>2015-11-09</em></h4> +</div> + +<div id="TOC"> +<ul> +<li><a href="#laboratory-data-l1">Laboratory Data L1</a></li> +<li><a href="#laboratory-data-l2">Laboratory Data L2</a></li> +<li><a href="#laboratory-data-l3">Laboratory Data L3</a></li> +<li><a href="#laboratory-data-l4">Laboratory Data L4</a></li> +</ul> +</div> + +<div id="laboratory-data-l1" class="section level2"> +<h2>Laboratory Data L1</h2> +<p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p> +<pre class="r"><code>library("mkin") +FOCUS_2006_L1 = data.frame( + t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), + parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, + 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, + 27.7, 27.3, 10.0, 10.4, 2.9, 4.0)) +FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> +<p>Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the models, please see the FOCUS kinetics report.</p> +<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>SFO</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> +<pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet=TRUE) +summary(m.L1.SFO)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:06 2015 +## Date of summary: Mon Jul 20 14:51:07 2015 +## +## Equations: +## d_parent = - k_parent_sink * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 37 model solutions performed in 0.087 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 89.85 state +## k_parent_sink 0.10 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 89.850000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 92.470 1.36800 89.570 95.370 +## log_k_parent_sink -2.347 0.04057 -2.433 -2.261 +## +## Parameter correlation: +## parent_0 log_k_parent_sink +## parent_0 1.0000 0.6248 +## log_k_parent_sink 0.6248 1.0000 +## +## Residual standard error: 2.948 on 16 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 92.47000 67.58 2.170e-21 89.57000 95.3700 +## k_parent_sink 0.09561 24.65 1.867e-14 0.08773 0.1042 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 3.424 2 7 +## parent 3.424 2 7 +## +## Resulting formation fractions: +## ff +## parent_sink 1 +## +## Estimated disappearance times: +## DT50 DT90 +## parent 7.249 24.08 +## +## Data: +## time variable observed predicted residual +## 0 parent 88.3 92.471 -4.1710 +## 0 parent 91.4 92.471 -1.0710 +## 1 parent 85.6 84.039 1.5610 +## 1 parent 84.5 84.039 0.4610 +## 2 parent 78.9 76.376 2.5241 +## 2 parent 77.6 76.376 1.2241 +## 3 parent 72.0 69.412 2.5884 +## 3 parent 71.9 69.412 2.4884 +## 5 parent 50.3 57.330 -7.0301 +## 5 parent 59.4 57.330 2.0699 +## 7 parent 47.0 47.352 -0.3515 +## 7 parent 45.1 47.352 -2.2515 +## 14 parent 27.7 24.247 3.4528 +## 14 parent 27.3 24.247 3.0528 +## 21 parent 10.0 12.416 -2.4163 +## 21 parent 10.4 12.416 -2.0163 +## 30 parent 2.9 5.251 -2.3513 +## 30 parent 4.0 5.251 -1.2513</code></pre> +<p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> +<pre class="r"><code>plot(m.L1.SFO)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /> The residual plot can be easily obtained by</p> +<pre class="r"><code>mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<p>For comparison, the FOMC model is fitted as well, and the chi^2 error level is checked.</p> +<pre class="r"><code>m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)</code></pre> +<pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge. +## Convergence code is 1</code></pre> +<pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:07 2015 +## Date of summary: Mon Jul 20 14:51:07 2015 +## +## +## Warning: Optimisation by method Port did not converge. +## Convergence code is 1 +## +## +## Equations: +## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 188 model solutions performed in 0.43 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 89.85 state +## alpha 1.00 deparm +## beta 10.00 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 89.850000 -Inf Inf +## log_alpha 0.000000 -Inf Inf +## log_beta 2.302585 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 92.47 1.422 89.44 95.50 +## log_alpha 15.43 15.080 -16.71 47.58 +## log_beta 17.78 15.090 -14.37 49.93 +## +## Parameter correlation: +## parent_0 log_alpha log_beta +## parent_0 1.0000 0.1129 0.1112 +## log_alpha 0.1129 1.0000 1.0000 +## log_beta 0.1112 1.0000 1.0000 +## +## Residual standard error: 3.045 on 15 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 9.247e+01 65.150 4.044e-20 8.944e+01 9.550e+01 +## alpha 5.044e+06 1.271 1.115e-01 5.510e-08 4.618e+20 +## beta 5.276e+07 1.259 1.137e-01 5.732e-07 4.857e+21 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 3.619 3 6 +## parent 3.619 3 6 +## +## Estimated disappearance times: +## DT50 DT90 DT50back +## parent 7.25 24.08 7.25</code></pre> +<p>Due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the chi^2 error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have p-values for the two sided t-test of 0.18 and 0.125, and their correlation is 1.000, indicating that the model is overparameterised.</p> +<p>The chi^2 error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same chi^2 error levels as the kinfit package. Furthermore, the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI is a widely used standard package in this field.</p> +</div> +<div id="laboratory-data-l2" class="section level2"> +<h2>Laboratory Data L2</h2> +<p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> +<pre class="r"><code>FOCUS_2006_L2 = data.frame( + t = rep(c(0, 1, 3, 7, 14, 28), each = 2), + parent = c(96.1, 91.8, 41.4, 38.7, + 19.3, 22.3, 4.6, 4.6, + 2.6, 1.2, 0.3, 0.6)) +FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)</code></pre> +<p>Again, the SFO model is fitted and a summary is obtained:</p> +<pre class="r"><code>m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) +summary(m.L2.SFO)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:07 2015 +## Date of summary: Mon Jul 20 14:51:07 2015 +## +## Equations: +## d_parent = - k_parent_sink * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 41 model solutions performed in 0.094 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 93.95 state +## k_parent_sink 0.10 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 93.950000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 91.4700 3.8070 82.9800 99.9500 +## log_k_parent_sink -0.4112 0.1074 -0.6505 -0.1719 +## +## Parameter correlation: +## parent_0 log_k_parent_sink +## parent_0 1.0000 0.4295 +## log_k_parent_sink 0.4295 1.0000 +## +## Residual standard error: 5.51 on 10 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 91.4700 24.03 1.773e-10 82.9800 99.9500 +## k_parent_sink 0.6629 9.31 1.525e-06 0.5218 0.8421 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 14.38 2 4 +## parent 14.38 2 4 +## +## Resulting formation fractions: +## ff +## parent_sink 1 +## +## Estimated disappearance times: +## DT50 DT90 +## parent 1.046 3.474 +## +## Data: +## time variable observed predicted residual +## 0 parent 96.1 9.147e+01 4.6343 +## 0 parent 91.8 9.147e+01 0.3343 +## 1 parent 41.4 4.714e+01 -5.7394 +## 1 parent 38.7 4.714e+01 -8.4394 +## 3 parent 19.3 1.252e+01 6.7790 +## 3 parent 22.3 1.252e+01 9.7790 +## 7 parent 4.6 8.834e-01 3.7166 +## 7 parent 4.6 8.834e-01 3.7166 +## 14 parent 2.6 8.532e-03 2.5915 +## 14 parent 1.2 8.532e-03 1.1915 +## 28 parent 0.3 7.958e-07 0.3000 +## 28 parent 0.6 7.958e-07 0.6000</code></pre> +<p>The chi^2 error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit and the residuals.</p> +<pre class="r"><code>par(mfrow = c(2, 1)) +plot(m.L2.SFO) +mkinresplot(m.L2.SFO)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p> +<p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p> +<p>For comparison, the FOMC model is fitted as well, and the chi^2 error level is checked.</p> +<pre class="r"><code>m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) +par(mfrow = c(2, 1)) +plot(m.L2.FOMC) +mkinresplot(m.L2.FOMC)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:08 2015 +## Date of summary: Mon Jul 20 14:51:08 2015 +## +## Equations: +## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 81 model solutions performed in 0.18 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 93.95 state +## alpha 1.00 deparm +## beta 10.00 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 93.950000 -Inf Inf +## log_alpha 0.000000 -Inf Inf +## log_beta 2.302585 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 93.7700 1.8560 89.5700 97.9700 +## log_alpha 0.3180 0.1867 -0.1044 0.7405 +## log_beta 0.2102 0.2943 -0.4555 0.8759 +## +## Parameter correlation: +## parent_0 log_alpha log_beta +## parent_0 1.00000 -0.09553 -0.1863 +## log_alpha -0.09553 1.00000 0.9757 +## log_beta -0.18628 0.97567 1.0000 +## +## Residual standard error: 2.628 on 9 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 93.770 50.510 1.173e-12 89.5700 97.970 +## alpha 1.374 5.355 2.296e-04 0.9009 2.097 +## beta 1.234 3.398 3.949e-03 0.6341 2.401 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 6.205 3 3 +## parent 6.205 3 3 +## +## Estimated disappearance times: +## DT50 DT90 DT50back +## parent 0.8092 5.356 1.612</code></pre> +<p>The error level at which the chi^2 test passes is much lower in this case. Therefore, the FOMC model provides a better description of the data, as less experimental error has to be assumed in order to explain the data.</p> +<p>Fitting the four parameter DFOP model further reduces the chi^2 error level.</p> +<pre class="r"><code>m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) +plot(m.L2.DFOP)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<p>Here, the default starting parameters for the DFOP model obviously do not lead to a reasonable solution. Therefore the fit is repeated with different starting parameters.</p> +<pre class="r"><code>m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, + parms.ini = c(k1 = 1, k2 = 0.01, g = 0.8), + quiet=TRUE) +plot(m.L2.DFOP)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:10 2015 +## Date of summary: Mon Jul 20 14:51:10 2015 +## +## Equations: +## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * +## time))) * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 336 model solutions performed in 0.793 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 93.95 state +## k1 1.00 deparm +## k2 0.01 deparm +## g 0.80 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 93.9500000 -Inf Inf +## log_k1 0.0000000 -Inf Inf +## log_k2 -4.6051702 -Inf Inf +## g_ilr 0.9802581 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 93.9500 NA NA NA +## log_k1 3.1210 NA NA NA +## log_k2 -1.0880 NA NA NA +## g_ilr -0.2821 NA NA NA +## +## Parameter correlation: +## Could not estimate covariance matrix; singular system: +## +## Residual standard error: 1.732 on 8 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 93.9500 NA NA NA NA +## k1 22.6700 NA NA NA NA +## k2 0.3369 NA NA NA NA +## g 0.4016 NA NA NA NA +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 2.53 4 2 +## parent 2.53 4 2 +## +## Estimated disappearance times: +## DT50 DT90 DT50_k1 DT50_k2 +## parent 0.5335 5.311 0.03058 2.058</code></pre> +<p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> +</div> +<div id="laboratory-data-l3" class="section level2"> +<h2>Laboratory Data L3</h2> +<p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> +<pre class="r"><code>FOCUS_2006_L3 = data.frame( + t = c(0, 3, 7, 14, 30, 60, 91, 120), + parent = c(97.8, 60, 51, 43, 35, 22, 15, 12)) +FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)</code></pre> +<p>SFO model, summary and plot:</p> +<pre class="r"><code>m.L3.SFO <- mkinfit("SFO", FOCUS_2006_L3_mkin, quiet = TRUE) +plot(m.L3.SFO)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<pre class="r"><code>summary(m.L3.SFO)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:10 2015 +## Date of summary: Mon Jul 20 14:51:10 2015 +## +## Equations: +## d_parent = - k_parent_sink * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 43 model solutions performed in 0.103 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 97.8 state +## k_parent_sink 0.1 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 97.800000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 74.870 8.4570 54.180 95.57 +## log_k_parent_sink -3.678 0.3261 -4.476 -2.88 +## +## Parameter correlation: +## parent_0 log_k_parent_sink +## parent_0 1.0000 0.5483 +## log_k_parent_sink 0.5483 1.0000 +## +## Residual standard error: 12.91 on 6 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 74.87000 8.853 5.776e-05 54.18000 95.57000 +## k_parent_sink 0.02527 3.067 1.102e-02 0.01138 0.05612 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 21.24 2 6 +## parent 21.24 2 6 +## +## Resulting formation fractions: +## ff +## parent_sink 1 +## +## Estimated disappearance times: +## DT50 DT90 +## parent 27.43 91.13 +## +## Data: +## time variable observed predicted residual +## 0 parent 97.8 74.872 22.9281 +## 3 parent 60.0 69.406 -9.4061 +## 7 parent 51.0 62.734 -11.7340 +## 14 parent 43.0 52.564 -9.5638 +## 30 parent 35.0 35.084 -0.0839 +## 60 parent 22.0 16.440 5.5602 +## 91 parent 15.0 7.511 7.4887 +## 120 parent 12.0 3.610 8.3903</code></pre> +<p>The chi^2 error level of 21% as well as the plot suggest that the model does not fit very well.</p> +<p>The FOMC model performs better:</p> +<pre class="r"><code>m.L3.FOMC <- mkinfit("FOMC", FOCUS_2006_L3_mkin, quiet = TRUE) +plot(m.L3.FOMC)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<pre class="r"><code>summary(m.L3.FOMC, data = FALSE)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:11 2015 +## Date of summary: Mon Jul 20 14:51:11 2015 +## +## Equations: +## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 83 model solutions performed in 0.187 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 97.8 state +## alpha 1.0 deparm +## beta 10.0 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 97.800000 -Inf Inf +## log_alpha 0.000000 -Inf Inf +## log_beta 2.302585 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 96.9700 4.5500 85.2800 108.7000 +## log_alpha -0.8619 0.1704 -1.3000 -0.4238 +## log_beta 0.6193 0.4744 -0.6003 1.8390 +## +## Parameter correlation: +## parent_0 log_alpha log_beta +## parent_0 1.0000 -0.1512 -0.4271 +## log_alpha -0.1512 1.0000 0.9110 +## log_beta -0.4271 0.9110 1.0000 +## +## Residual standard error: 4.572 on 5 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 96.9700 21.310 2.108e-06 85.2800 108.7000 +## alpha 0.4224 5.867 1.020e-03 0.2725 0.6546 +## beta 1.8580 2.108 4.444e-02 0.5487 6.2890 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 7.32 3 5 +## parent 7.32 3 5 +## +## Estimated disappearance times: +## DT50 DT90 DT50back +## parent 7.729 431.2 129.8</code></pre> +<p>The error level at which the chi^2 test passes is 7% in this case.</p> +<p>Fitting the four parameter DFOP model further reduces the chi^2 error level considerably:</p> +<pre class="r"><code>m.L3.DFOP <- mkinfit("DFOP", FOCUS_2006_L3_mkin, quiet = TRUE) +plot(m.L3.DFOP)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<pre class="r"><code>summary(m.L3.DFOP, data = FALSE)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:11 2015 +## Date of summary: Mon Jul 20 14:51:11 2015 +## +## Equations: +## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * +## time))) * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 137 model solutions performed in 0.322 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 97.80 state +## k1 0.10 deparm +## k2 0.01 deparm +## g 0.50 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 97.800000 -Inf Inf +## log_k1 -2.302585 -Inf Inf +## log_k2 -4.605170 -Inf Inf +## g_ilr 0.000000 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 97.7500 1.43800 93.7500 101.70000 +## log_k1 -0.6612 0.13340 -1.0310 -0.29100 +## log_k2 -4.2860 0.05902 -4.4500 -4.12200 +## g_ilr -0.1229 0.05121 -0.2651 0.01925 +## +## Parameter correlation: +## parent_0 log_k1 log_k2 g_ilr +## parent_0 1.00000 0.1640 0.01315 0.4253 +## log_k1 0.16400 1.0000 0.46478 -0.5526 +## log_k2 0.01315 0.4648 1.00000 -0.6631 +## g_ilr 0.42526 -0.5526 -0.66310 1.0000 +## +## Residual standard error: 1.439 on 4 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 97.75000 67.970 1.404e-07 93.75000 101.70000 +## k1 0.51620 7.499 8.460e-04 0.35650 0.74750 +## k2 0.01376 16.940 3.557e-05 0.01168 0.01621 +## g 0.45660 25.410 7.121e-06 0.40730 0.50680 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 2.225 4 4 +## parent 2.225 4 4 +## +## Estimated disappearance times: +## DT50 DT90 DT50_k1 DT50_k2 +## parent 7.464 123 1.343 50.37</code></pre> +<p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the chi^2 error level criterion for laboratory data L3.</p> +<p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</p> +</div> +<div id="laboratory-data-l4" class="section level2"> +<h2>Laboratory Data L4</h2> +<p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> +<pre class="r"><code>FOCUS_2006_L4 = data.frame( + t = c(0, 3, 7, 14, 30, 60, 91, 120), + parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0)) +FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4)</code></pre> +<p>SFO model, summary and plot:</p> +<pre class="r"><code>m.L4.SFO <- mkinfit("SFO", FOCUS_2006_L4_mkin, quiet = TRUE) +plot(m.L4.SFO)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<pre class="r"><code>summary(m.L4.SFO, data = FALSE)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:12 2015 +## Date of summary: Mon Jul 20 14:51:12 2015 +## +## Equations: +## d_parent = - k_parent_sink * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 46 model solutions performed in 0.101 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 96.6 state +## k_parent_sink 0.1 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 96.600000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 96.44 1.94900 91.670 101.200 +## log_k_parent_sink -5.03 0.07999 -5.225 -4.834 +## +## Parameter correlation: +## parent_0 log_k_parent_sink +## parent_0 1.0000 0.5865 +## log_k_parent_sink 0.5865 1.0000 +## +## Residual standard error: 3.651 on 6 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 96.440000 49.49 2.283e-09 91.670000 1.012e+02 +## k_parent_sink 0.006541 12.50 8.008e-06 0.005378 7.955e-03 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 3.287 2 6 +## parent 3.287 2 6 +## +## Resulting formation fractions: +## ff +## parent_sink 1 +## +## Estimated disappearance times: +## DT50 DT90 +## parent 106 352</code></pre> +<p>The chi^2 error level of 3.3% as well as the plot suggest that the model fits very well.</p> +<p>The FOMC model for comparison:</p> +<pre class="r"><code>m.L4.FOMC <- mkinfit("FOMC", FOCUS_2006_L4_mkin, quiet = TRUE) +plot(m.L4.FOMC)</code></pre> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<pre class="r"><code>summary(m.L4.FOMC, data = FALSE)</code></pre> +<pre><code>## mkin version: 0.9.40 +## R version: 3.2.1 +## Date of fit: Mon Jul 20 14:51:12 2015 +## Date of summary: Mon Jul 20 14:51:12 2015 +## +## Equations: +## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent +## +## Model predictions using solution type analytical +## +## Fitted with method Port using 66 model solutions performed in 0.146 s +## +## Weighting: none +## +## Starting values for parameters to be optimised: +## value type +## parent_0 96.6 state +## alpha 1.0 deparm +## beta 10.0 deparm +## +## Starting values for the transformed parameters actually optimised: +## value lower upper +## parent_0 96.600000 -Inf Inf +## log_alpha 0.000000 -Inf Inf +## log_beta 2.302585 -Inf Inf +## +## Fixed parameter values: +## None +## +## Optimised, transformed parameters with symmetric confidence intervals: +## Estimate Std. Error Lower Upper +## parent_0 99.1400 1.6800 94.820 103.5000 +## log_alpha -0.3506 0.3725 -1.308 0.6068 +## log_beta 4.1740 0.5635 2.726 5.6230 +## +## Parameter correlation: +## parent_0 log_alpha log_beta +## parent_0 1.0000 -0.5365 -0.6083 +## log_alpha -0.5365 1.0000 0.9913 +## log_beta -0.6083 0.9913 1.0000 +## +## Residual standard error: 2.315 on 5 degrees of freedom +## +## Backtransformed parameters: +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. +## Estimate t value Pr(>t) Lower Upper +## parent_0 99.1400 59.020 1.322e-08 94.8200 103.500 +## alpha 0.7042 2.685 2.178e-02 0.2703 1.835 +## beta 64.9800 1.775 6.807e-02 15.2600 276.600 +## +## Chi2 error levels in percent: +## err.min n.optim df +## All data 2.029 3 5 +## parent 2.029 3 5 +## +## Estimated disappearance times: +## DT50 DT90 DT50back +## parent 108.9 1644 494.9</code></pre> +<p>The error level at which the chi^2 test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> +</div> + + +</div> + +<script> + +// add bootstrap table styles to pandoc tables +$(document).ready(function () { + $('tr.header').parent('thead').parent('table').addClass('table table-condensed'); +}); + +</script> + + +</body> +</html> diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf Binary files differnew file mode 100644 index 00000000..a84b8c94 --- /dev/null +++ b/inst/web/vignettes/FOCUS_Z.pdf diff --git a/inst/web/vignettes/mkin.pdf b/inst/web/vignettes/mkin.pdf Binary files differnew file mode 100644 index 00000000..ce628c0e --- /dev/null +++ b/inst/web/vignettes/mkin.pdf |
