Avoid merge() and data.frame() in cost function

also for deSolve and eigenvalue based solutions. This noticeably increases performance for these methods, see test.log and benchmark vignette.
author: Johannes Ranke <jranke@uni-bremen.de> 2020-05-11 05:15:19 +0200
committer: Johannes Ranke <jranke@uni-bremen.de> 2020-05-11 05:18:32 +0200
commit: 234c9059a95e104917e488a6ddd2313234a96cdc (patch)
tree: f6e54098f79d94578434ef727b62f7cc5d5e79b7
parent: d113cd79b178fdc91aecb894707ed356129dfb75 (diff)
36 files changed, 799 insertions, 715 deletions
diff --git a/R/add_err.R b/R/add_err.R
index a523e9c2..9235223f 100644
--- a/R/add_err.R
+++ b/R/add_err.R
@@ -76,6 +76,8 @@ add_err <- function(prediction, sdfunc, secondary = c("M1", "M2"),
 {
   if (!is.na(seed)) set.seed(seed)
 
+  prediction <- as.data.frame(prediction)
+
   # The output of mkinpredict is in wide format
   d_long = mkin_wide_to_long(prediction, time = "time")
 
diff --git a/R/mkin_wide_to_long.R b/R/mkin_wide_to_long.R
index bef0e408..971f5273 100644
--- a/R/mkin_wide_to_long.R
+++ b/R/mkin_wide_to_long.R
@@ -21,6 +21,7 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value"))
 #' @export
 mkin_wide_to_long <- function(wide_data, time = "t")
 {
+  wide_data <- as.data.frame(wide_data)
   colnames <- names(wide_data)
   if (!(time %in% colnames)) stop("The data in wide format have to contain a variable named ", time, ".")
   vars <- subset(colnames, colnames != time)
diff --git a/R/mkinfit.R b/R/mkinfit.R
index e1089673..f6691b1b 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -592,12 +592,8 @@ mkinfit <- function(mkinmod, observed,
                          method.ode = method.ode,
                          atol = atol, rtol = rtol, ...)
 
-      out_long <- mkin_wide_to_long(out, time = "time")
-
-      out_merged <- merge(observed[c("name", "time")], out_long)
-      out_merged$name <- ordered(out_merged$name, levels = obs_vars)
-      out_merged <- out_merged[order(out_merged$name, out_merged$time), ]
-      observed$predicted <- out_merged$value
+      observed_index <- cbind(as.character(observed$time), as.character(observed$name))
+      observed$predicted <- out[observed_index]
     }
 
     # Define standard deviation for each observation
diff --git a/R/mkinpredict.R b/R/mkinpredict.R
index 75582fac..90cd45fb 100644
--- a/R/mkinpredict.R
+++ b/R/mkinpredict.R
@@ -32,12 +32,13 @@
 #'   is 1e-10, much lower than in \code{\link{lsoda}}.
 #' @param map_output Boolean to specify if the output should list values for
 #'   the observed variables (default) or for all state variables (if set to
-#'   FALSE).
+#'   FALSE). Setting this to FALSE has no effect for analytical solutions,
+#'   as these always return mapped output.
 #' @param \dots Further arguments passed to the ode solver in case such a
 #'   solver is used.
 #' @import deSolve
 #' @importFrom inline getDynLib
-#' @return A data frame with the solution in wide format
+#' @return A matrix with the numeric solution in wide format
 #' @author Johannes Ranke
 #' @examples
 #'
@@ -70,7 +71,7 @@
 #' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
 #'       seq(0, 20, by = 0.01))[2001,]
 #'
-#' # Check compiled model versions - they are faster than the eigenvalue based solutions!
+#' # Comparison of the performance of solution types
 #' SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
 #'                   m1 = list(type = "SFO"), use_of_ff = "max")
 #' if(require(rbenchmark)) {
@@ -92,15 +93,11 @@
 #'       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
 #'       solution_type = "analytical", use_compiled = FALSE)[201,])
 #' }
-#'     analytical = mkinpredict(SFO_SFO,
-#'       c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-#'       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-#'       solution_type = "analytical", use_compiled = FALSE)[201,]
 #'
 #' \dontrun{
 #'   # Predict from a fitted model
 #'   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
-#'   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "analytical")
+#'   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
 #'   head(mkinpredict(f))
 #' }
 #'
@@ -127,29 +124,34 @@ mkinpredict.mkinmod <- function(x,
   map_output = TRUE, ...)
 {
 
-  # Get the names of the state variables in the model
+  # Names of state variables and observed variables
   mod_vars <- names(x$diffs)
+  obs_vars <- names(x$spec)
 
   # Order the inital values for state variables if they are named
   if (!is.null(names(odeini))) {
     odeini <- odeini[mod_vars]
   }
 
+  out_obs <- matrix(NA, nrow = length(outtimes), ncol = 1 + length(obs_vars),
+    dimnames = list(as.character(outtimes), c("time", obs_vars)))
+  out_obs[, "time"] <- outtimes
+
   if (solution_type == "analytical") {
     # This is clumsy, as we wanted fast analytical predictions for mkinfit
-    out <- data.frame(time = outtimes)
-    obs_vars <- names(x$spec)
     pseudo_observed <-
       data.frame(name = rep(obs_vars, each = length(outtimes)),
       time = rep(outtimes, length(obs_vars)))
     pseudo_observed$predicted <- x$deg_func(pseudo_observed, odeini, odeparms)
     for (obs_var in obs_vars) {
-      out[obs_var] <- pseudo_observed[pseudo_observed$name == obs_var, "predicted"]
+      out_obs[, obs_var] <- pseudo_observed[pseudo_observed$name == obs_var, "predicted"]
     }
+    # We don't have solutions for unobserved state variables, the output of
+    # analytical solutions is always mapped to observed variables
+    return(out_obs)
   }
 
   if (solution_type == "eigen") {
-
     evalparse <- function(string) {
       eval(parse(text=string), as.list(c(odeparms, odeini)))
     }
@@ -163,8 +165,8 @@ mkinpredict.mkinmod <- function(x,
     }
     o <- matrix(mapply(f.out, outtimes),
       nrow = length(mod_vars), ncol = length(outtimes))
-    out <- data.frame(outtimes, t(o))
-    names(out) <- c("time", mod_vars)
+    out <- cbind(outtimes, t(o))
+    colnames(out) <- c("time", mod_vars)
   }
 
   if (solution_type == "deSolve") {
@@ -208,21 +210,23 @@ mkinpredict.mkinmod <- function(x,
     if (sum(is.na(out)) > 0) {
       stop("Differential equations were not integrated for all output times because\n",
      "NaN values occurred in output from ode()")
-      }
+    }
   }
+
   if (map_output) {
     # Output transformation for models with unobserved compartments like SFORB
-    out_mapped <- data.frame(time = out[,"time"])
+    # if not already mapped in analytical solution
     for (var in names(x$map)) {
-      if((length(x$map[[var]]) == 1) || solution_type == "analytical") {
-        out_mapped[var] <- out[, var]
+      if((length(x$map[[var]]) == 1)) {
+        out_obs[, var] <- out[, var]
       } else {
-        out_mapped[var] <- rowSums(out[, x$map[[var]]])
+        out_obs[, var] <- out[, x$map[[var]][1]] + out[, x$map[[var]][2]]
       }
     }
-    return(out_mapped)
+    return(out_obs)
   } else {
-    return(as.data.frame(out))
+    dimnames(out) <- list(time = as.character(outtimes), c("time", mod_vars))
+    return(out)
   }
 }
 
diff --git a/R/plot.mkinfit.R b/R/plot.mkinfit.R
index b2cb4890..48df9483 100644
--- a/R/plot.mkinfit.R
+++ b/R/plot.mkinfit.R
@@ -148,6 +148,7 @@ plot.mkinfit <- function(x, fit = x,
              solution_type = solution_type, atol = fit$atol, rtol = fit$rtol,
              use_compiled = FALSE)
   }
+  out <- as.data.frame(out)
 
   names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars
 
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 0623c6ed..2e6770d3 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -78,6 +78,7 @@ reference:
       - mkinresplot
       - mkinparplot
       - mkinerrplot
+      - create_deg_func
   - title: Analytical solutions
     desc: Parent only model solutions
     contents:
diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html
index e9c6b005..0a9ddf4a 100644
--- a/docs/articles/FOCUS_D.html
+++ b/docs/articles/FOCUS_D.html
@@ -31,7 +31,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.11</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50</span>
       </span>
     </div>
 
@@ -97,7 +97,7 @@
       <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset D</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2020-05-07</h4>
+            <h4 class="date">2020-05-11</h4>
       
       <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/FOCUS_D.Rmd"><code>vignettes/FOCUS_D.Rmd</code></a></small>
       <div class="hidden name"><code>FOCUS_D.Rmd</code></div>
@@ -159,10 +159,10 @@
 <div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">SFO_SFO</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"m1"</span>), <span class="kw">m1</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</pre></body></html></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
 <div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">SFO_SFO</span>$<span class="no">diffs</span>)</pre></body></html></div>
-<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>
+<pre><code>##                                                    parent 
+##                          "d_parent = - k_parent * parent" 
+##                                                        m1 
+## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</code></pre>
 <p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p>
 <div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">fit</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOCUS_2006_D</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)</pre></body></html></div>
 <pre><code>## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value
@@ -175,85 +175,83 @@
 <p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p>
 <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p>
 <div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">fit</span>)</pre></body></html></div>
-<pre><code>## mkin version used for fitting:    0.9.49.11 
+<pre><code>## mkin version used for fitting:    0.9.50 
 ## R version used for fitting:       4.0.0 
-## Date of fit:     Thu May  7 08:59:27 2020 
-## Date of summary: Thu May  7 08:59:27 2020 
+## Date of fit:     Mon May 11 05:14:41 2020 
+## Date of summary: Mon May 11 05:14:41 2020 
 ## 
 ## Equations:
-## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
-## d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1
+## d_parent/dt = - k_parent * parent
+## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
 ## 
-## Model predictions using solution type deSolve 
+## Model predictions using solution type analytical 
 ## 
-## Fitted using 389 model solutions performed in 1.031 s
+## Fitted using 421 model solutions performed in 0.167 s
 ## 
 ## Error model: Constant variance 
 ## 
 ## Error model algorithm: OLS 
 ## 
 ## 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
+##                   value   type
+## parent_0       100.7500  state
+## k_parent         0.1000 deparm
+## k_m1             0.1001 deparm
+## f_parent_to_m1   0.5000 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
+##                     value lower upper
+## parent_0       100.750000  -Inf   Inf
+## log_k_parent    -2.302585  -Inf   Inf
+## log_k_m1        -2.301586  -Inf   Inf
+## f_parent_ilr_1   0.000000  -Inf   Inf
 ## 
 ## Fixed parameter values:
 ##      value  type
 ## m1_0     0 state
 ## 
+## Results:
+## 
+##        AIC      BIC    logLik
+##   204.4486 212.6365 -97.22429
+## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
-##                   Estimate Std. Error  Lower   Upper
-## parent_0            99.600    1.57000 96.400 102.800
-## log_k_parent_sink   -3.038    0.07626 -3.193  -2.883
-## log_k_parent_m1     -2.980    0.04033 -3.062  -2.898
-## log_k_m1_sink       -5.248    0.13320 -5.518  -4.977
-## sigma                3.126    0.35850  2.396   3.855
+##                Estimate Std. Error    Lower    Upper
+## parent_0       99.60000    1.57000 96.40000 102.8000
+## log_k_parent   -2.31600    0.04087 -2.39900  -2.2330
+## log_k_m1       -5.24800    0.13320 -5.51800  -4.9770
+## f_parent_ilr_1  0.04096    0.06312 -0.08746   0.1694
+## sigma           3.12600    0.35850  2.39600   3.8550
 ## 
 ## Parameter correlation:
-##                     parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
-## parent_0           1.000e+00         6.067e-01      -6.372e-02    -1.688e-01
-## log_k_parent_sink  6.067e-01         1.000e+00      -8.550e-02    -6.252e-01
-## log_k_parent_m1   -6.372e-02        -8.550e-02       1.000e+00     4.731e-01
-## log_k_m1_sink     -1.688e-01        -6.252e-01       4.731e-01     1.000e+00
-## sigma              5.287e-10         3.306e-09       4.421e-08    -3.319e-10
-##                        sigma
-## parent_0           5.287e-10
-## log_k_parent_sink  3.306e-09
-## log_k_parent_m1    4.421e-08
-## log_k_m1_sink     -3.319e-10
-## sigma              1.000e+00
+##                  parent_0 log_k_parent   log_k_m1 f_parent_ilr_1      sigma
+## parent_0        1.000e+00    5.174e-01 -1.688e-01     -5.471e-01 -3.214e-07
+## log_k_parent    5.174e-01    1.000e+00 -3.263e-01     -5.426e-01  3.168e-07
+## log_k_m1       -1.688e-01   -3.263e-01  1.000e+00      7.478e-01 -1.410e-07
+## f_parent_ilr_1 -5.471e-01   -5.426e-01  7.478e-01      1.000e+00  5.093e-10
+## sigma          -3.214e-07    3.168e-07 -1.410e-07      5.093e-10  1.000e+00
 ## 
 ## 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(&gt;t)     Lower     Upper
-## parent_0      99.600000  63.430 2.298e-36 96.400000 1.028e+02
-## k_parent_sink  0.047920  13.110 6.126e-15  0.041030 5.596e-02
-## k_parent_m1    0.050780  24.800 3.269e-23  0.046780 5.512e-02
-## k_m1_sink      0.005261   7.510 6.165e-09  0.004012 6.898e-03
-## sigma          3.126000   8.718 2.235e-10  2.396000 3.855e+00
+##                 Estimate t value    Pr(&gt;t)     Lower     Upper
+## parent_0       99.600000  63.430 2.298e-36 96.400000 1.028e+02
+## k_parent        0.098700  24.470 4.955e-23  0.090820 1.073e-01
+## k_m1            0.005261   7.510 6.165e-09  0.004012 6.898e-03
+## f_parent_to_m1  0.514500  23.070 3.104e-22  0.469100 5.596e-01
+## sigma           3.126000   8.718 2.235e-10  2.396000 3.855e+00
 ## 
 ## FOCUS 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
+## parent     6.459       2  7
+## m1         4.690       2  8
 ## 
 ## Resulting formation fractions:
 ##                 ff
-## parent_sink 0.4855
 ## parent_m1   0.5145
-## m1_sink     1.0000
+## parent_sink 0.4855
 ## 
 ## Estimated disappearance times:
 ##           DT50   DT90
@@ -266,10 +264,10 @@
 ##     0   parent   102.04  99.59848  2.442e+00
 ##     1   parent    93.50  90.23787  3.262e+00
 ##     1   parent    92.50  90.23787  2.262e+00
-##     3   parent    63.23  74.07320 -1.084e+01
-##     3   parent    68.99  74.07320 -5.083e+00
-##     7   parent    52.32  49.91207  2.408e+00
-##     7   parent    55.13  49.91207  5.218e+00
+##     3   parent    63.23  74.07319 -1.084e+01
+##     3   parent    68.99  74.07319 -5.083e+00
+##     7   parent    52.32  49.91206  2.408e+00
+##     7   parent    55.13  49.91206  5.218e+00
 ##    14   parent    27.27  25.01257  2.257e+00
 ##    14   parent    26.64  25.01257  1.627e+00
 ##    21   parent    11.50  12.53462 -1.035e+00
@@ -279,7 +277,7 @@
 ##    50   parent     0.69   0.71624 -2.624e-02
 ##    50   parent     0.63   0.71624 -8.624e-02
 ##    75   parent     0.05   0.06074 -1.074e-02
-##    75   parent     0.06   0.06074 -7.382e-04
+##    75   parent     0.06   0.06074 -7.381e-04
 ##     1       m1     4.84   4.80296  3.704e-02
 ##     1       m1     5.64   4.80296  8.370e-01
 ##     3       m1    12.91  13.02400 -1.140e-01
diff --git a/docs/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/articles/FOCUS_D_files/figure-html/plot-1.png
index a7944b84..306244b3 100644
--- a/docs/articles/FOCUS_D_files/figure-html/plot-1.png
+++ b/docs/articles/FOCUS_D_files/figure-html/plot-1.png
diff --git a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
index 97c61a16..158e3c50 100644
--- a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
+++ b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png
diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html
index 42eb6c0a..b828b7dc 100644
--- a/docs/articles/mkin.html
+++ b/docs/articles/mkin.html
@@ -6,19 +6,19 @@
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
 <title>Introduction to mkin • mkin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha256-916EbMg70RQy9LHiGkXzG8hSg9EdNy97GazNG/aiY1w=" crossorigin="anonymous">
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+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
 <script src="../pkgdown.js"></script><meta property="og:title" content="Introduction to mkin">
-<meta property="og:description" content="">
-<meta name="twitter:card" content="summary">
+<meta property="og:description" content="mkin">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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     <div class="container template-article">
       <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
   <div class="container">
@@ -31,7 +31,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.6</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50</span>
       </span>
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@@ -74,7 +74,14 @@
   <a href="../news/index.html">News</a>
 </li>
       </ul>
-<ul class="nav navbar-nav navbar-right"></ul>
+<ul class="nav navbar-nav navbar-right">
+<li>
+  <a href="http://github.com/jranke/mkin/">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul>
 </div>
 <!--/.nav-collapse -->
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@@ -87,12 +94,12 @@
       </header><div class="row">
   <div class="col-md-9 contents">
     <div class="page-header toc-ignore">
-      <h1>Introduction to mkin</h1>
+      <h1 data-toc-skip>Introduction to mkin</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2019-11-01</h4>
-      
+            <h4 class="date">2020-05-11</h4>
       
+      <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/mkin.Rmd"><code>vignettes/mkin.Rmd</code></a></small>
       <div class="hidden name"><code>mkin.Rmd</code></div>
 
     </div>
@@ -104,34 +111,34 @@
 <h1 class="hasAnchor">
 <a href="#abstract" class="anchor"></a>Abstract</h1>
 <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 has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="st">"mkin"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb1-2" title="2"><span class="co"># Define the kinetic model</span></a>
-<a class="sourceLine" id="cb1-3" title="3">m_SFO_SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">parent =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>),</a>
-<a class="sourceLine" id="cb1-4" title="4">                         <span class="dt">M1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>),</a>
-<a class="sourceLine" id="cb1-5" title="5">                         <span class="dt">M2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),</a>
-<a class="sourceLine" id="cb1-6" title="6">                         <span class="dt">use_of_ff =</span> <span class="st">"max"</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb1-7" title="7"></a>
-<a class="sourceLine" id="cb1-8" title="8"></a>
-<a class="sourceLine" id="cb1-9" title="9"><span class="co"># Produce model predictions using some arbitrary parameters</span></a>
-<a class="sourceLine" id="cb1-10" title="10">sampling_times =<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)</a>
-<a class="sourceLine" id="cb1-11" title="11">d_SFO_SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinpredict.html">mkinpredict</a></span>(m_SFO_SFO_SFO,</a>
-<a class="sourceLine" id="cb1-12" title="12">  <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="dt">k_parent =</span> <span class="fl">0.03</span>,</a>
-<a class="sourceLine" id="cb1-13" title="13">    <span class="dt">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="dt">k_M1 =</span> <span class="kw"><a href="https://rdrr.io/r/base/Log.html">log</a></span>(<span class="dv">2</span>)<span class="op">/</span><span class="dv">100</span>,</a>
-<a class="sourceLine" id="cb1-14" title="14">    <span class="dt">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="dt">k_M2 =</span> <span class="kw"><a href="https://rdrr.io/r/base/Log.html">log</a></span>(<span class="dv">2</span>)<span class="op">/</span><span class="dv">50</span>),</a>
-<a class="sourceLine" id="cb1-15" title="15">  <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="dt">parent =</span> <span class="dv">100</span>, <span class="dt">M1 =</span> <span class="dv">0</span>, <span class="dt">M2 =</span> <span class="dv">0</span>),</a>
-<a class="sourceLine" id="cb1-16" title="16">  sampling_times)</a>
-<a class="sourceLine" id="cb1-17" title="17"></a>
-<a class="sourceLine" id="cb1-18" title="18"><span class="co"># Generate a dataset by adding normally distributed errors with</span></a>
-<a class="sourceLine" id="cb1-19" title="19"><span class="co"># standard deviation 3, for two replicates at each sampling time</span></a>
-<a class="sourceLine" id="cb1-20" title="20">d_SFO_SFO_SFO_err &lt;-<span class="st"> </span><span class="kw"><a href="../reference/add_err.html">add_err</a></span>(d_SFO_SFO_SFO, <span class="dt">reps =</span> <span class="dv">2</span>,</a>
-<a class="sourceLine" id="cb1-21" title="21">                             <span class="dt">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,</a>
-<a class="sourceLine" id="cb1-22" title="22">                             <span class="dt">n =</span> <span class="dv">1</span>, <span class="dt">seed =</span> <span class="dv">123456789</span> )</a>
-<a class="sourceLine" id="cb1-23" title="23"></a>
-<a class="sourceLine" id="cb1-24" title="24"><span class="co"># Fit the model to the dataset</span></a>
-<a class="sourceLine" id="cb1-25" title="25">f_SFO_SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[<span class="dv">1</span>]], <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb1-26" title="26"></a>
-<a class="sourceLine" id="cb1-27" title="27"><span class="co"># Plot the results separately for parent and metabolites</span></a>
-<a class="sourceLine" id="cb1-28" title="28"><span class="kw"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(f_SFO_SFO_SFO, <span class="dt">lpos =</span> <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span>))</a></code></pre></div>
+<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="st">"mkin"</span>, <span class="kw">quietly</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+<span class="co"># Define the kinetic model</span>
+<span class="no">m_SFO_SFO_SFO</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>),
+                         <span class="kw">M1</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>),
+                         <span class="kw">M2</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),
+                         <span class="kw">use_of_ff</span> <span class="kw">=</span> <span class="st">"max"</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+
+<span class="co"># Produce model predictions using some arbitrary parameters</span>
+<span class="no">sampling_times</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span>)
+<span class="no">d_SFO_SFO_SFO</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/mkinpredict.html">mkinpredict</a></span>(<span class="no">m_SFO_SFO_SFO</span>,
+  <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">k_parent</span> <span class="kw">=</span> <span class="fl">0.03</span>,
+    <span class="kw">f_parent_to_M1</span> <span class="kw">=</span> <span class="fl">0.5</span>, <span class="kw">k_M1</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html">log</a></span>(<span class="fl">2</span>)/<span class="fl">100</span>,
+    <span class="kw">f_M1_to_M2</span> <span class="kw">=</span> <span class="fl">0.9</span>, <span class="kw">k_M2</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html">log</a></span>(<span class="fl">2</span>)/<span class="fl">50</span>),
+  <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">parent</span> <span class="kw">=</span> <span class="fl">100</span>, <span class="kw">M1</span> <span class="kw">=</span> <span class="fl">0</span>, <span class="kw">M2</span> <span class="kw">=</span> <span class="fl">0</span>),
+  <span class="no">sampling_times</span>)
+
+<span class="co"># Generate a dataset by adding normally distributed errors with</span>
+<span class="co"># standard deviation 3, for two replicates at each sampling time</span>
+<span class="no">d_SFO_SFO_SFO_err</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/add_err.html">add_err</a></span>(<span class="no">d_SFO_SFO_SFO</span>, <span class="kw">reps</span> <span class="kw">=</span> <span class="fl">2</span>,
+                             <span class="kw">sdfunc</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) <span class="fl">3</span>,
+                             <span class="kw">n</span> <span class="kw">=</span> <span class="fl">1</span>, <span class="kw">seed</span> <span class="kw">=</span> <span class="fl">123456789</span> )
+
+<span class="co"># Fit the model to the dataset</span>
+<span class="no">f_SFO_SFO_SFO</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="no">m_SFO_SFO_SFO</span>, <span class="no">d_SFO_SFO_SFO_err</span><span class="kw">[[</span><span class="fl">1</span>]], <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+<span class="co"># Plot the results separately for parent and metabolites</span>
+<span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">f_SFO_SFO_SFO</span>, <span class="kw">lpos</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span>))</pre></body></html></div>
 <p><img src="mkin_files/figure-html/unnamed-chunk-2-1.png" width="768"></p>
 </div>
 <div id="background" class="section level1">
@@ -222,29 +229,11 @@
 </div>
   </div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
 
-        <div id="tocnav">
-      <h2 class="hasAnchor">
-<a href="#tocnav" class="anchor"></a>Contents</h2>
-      <ul class="nav nav-pills nav-stacked">
-<li><a href="#abstract">Abstract</a></li>
-      <li>
-<a href="#background">Background</a><ul class="nav nav-pills nav-stacked">
-<li><a href="#derived-software-tools">Derived software tools</a></li>
-      <li><a href="#recent-developments">Recent developments</a></li>
-      </ul>
-</li>
-      <li>
-<a href="#internal-parameter-transformations">Internal parameter transformations</a><ul class="nav nav-pills nav-stacked">
-<li><a href="#confidence-intervals-based-on-transformed-parameters">Confidence intervals based on transformed parameters</a></li>
-      <li><a href="#parameter-t-test-based-on-untransformed-parameters">Parameter t-test based on untransformed parameters</a></li>
-      </ul>
-</li>
-      <li><a href="#references">References</a></li>
-      </ul>
+        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
+    </nav>
 </div>
-      </div>
 
 </div>
 
@@ -255,7 +244,7 @@
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 <div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.4.1.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
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       </footer>
diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
index e21b4233..bdc067c1 100644
--- a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
+++ b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html
index 4fb76fa2..ad7cf62c 100644
--- a/docs/articles/web_only/benchmarks.html
+++ b/docs/articles/web_only/benchmarks.html
@@ -6,19 +6,19 @@
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
 <title>Benchmark timings for mkin on various systems • mkin</title>
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 <script src="../../pkgdown.js"></script><meta property="og:title" content="Benchmark timings for mkin on various systems">
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-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.6</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50</span>
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-      <h1>Benchmark timings for mkin on various systems</h1>
+      <h1 data-toc-skip>Benchmark timings for mkin on various systems</h1>
                         <h4 class="author">Johannes Ranke</h4>
             
-            <h4 class="date">2019-11-01</h4>
-      
+            <h4 class="date">2020-05-11</h4>
       
+      <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/web_only/benchmarks.Rmd"><code>vignettes/web_only/benchmarks.Rmd</code></a></small>
       <div class="hidden name"><code>benchmarks.Rmd</code></div>
 
     </div>
@@ -103,192 +110,143 @@
 <h2 class="hasAnchor">
 <a href="#systems" class="anchor"></a>Systems</h2>
 <p>Each system is characterized by its CPU type, the operating system type and the mkin version.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1">cpu_model &lt;-<span class="st"> </span>benchmarkme<span class="op">::</span><span class="kw"><a href="https://rdrr.io/pkg/benchmarkme/man/get_cpu.html">get_cpu</a></span>()<span class="op">$</span>model_name</a>
-<a class="sourceLine" id="cb1-2" title="2">operating_system &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/Sys.info.html">Sys.info</a></span>()[[<span class="st">"sysname"</span>]]</a>
-<a class="sourceLine" id="cb1-3" title="3">mkin_version &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/character.html">as.character</a></span>(<span class="kw"><a href="https://rdrr.io/r/utils/packageDescription.html">packageVersion</a></span>(<span class="st">"mkin"</span>))</a>
-<a class="sourceLine" id="cb1-4" title="4">system_string &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span>(operating_system, <span class="st">", "</span>, cpu_model, <span class="st">", mkin version "</span>, mkin_version)</a>
-<a class="sourceLine" id="cb1-5" title="5"><span class="kw"><a href="https://rdrr.io/r/base/load.html">load</a></span>(<span class="st">"~/git/mkin/vignettes/web_only/mkin_benchmarks.rda"</span>)</a>
-<a class="sourceLine" id="cb1-6" title="6">mkin_benchmarks[system_string, <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"CPU"</span>, <span class="st">"OS"</span>, <span class="st">"mkin"</span>)] &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(cpu_model, operating_system, mkin_version)</a>
-<a class="sourceLine" id="cb1-7" title="7"></a>
-<a class="sourceLine" id="cb1-8" title="8"><span class="cf">if</span> (mkin_version <span class="op">&gt;</span><span class="st"> "0.9.48.1"</span>) {</a>
-<a class="sourceLine" id="cb1-9" title="9">  mmkin_bench &lt;-<span class="st"> </span><span class="cf">function</span>(models, datasets, <span class="dt">error_model =</span> <span class="st">"const"</span>) <span class="kw"><a href="../../reference/mmkin.html">mmkin</a></span>(models, datasets, <span class="dt">error_model =</span> error_model, <span class="dt">cores =</span> <span class="dv">1</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb1-10" title="10">} <span class="cf">else</span> {</a>
-<a class="sourceLine" id="cb1-11" title="11">  mmkin_bench &lt;-<span class="st"> </span><span class="cf">function</span>(models, datasets, <span class="dt">error_model =</span> <span class="ot">NULL</span>) <span class="kw"><a href="../../reference/mmkin.html">mmkin</a></span>(models, datasets, <span class="dt">reweight.method =</span> error_model, <span class="dt">cores =</span> <span class="dv">1</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb1-12" title="12">}</a></code></pre></div>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="co"># Parent only</span></a>
-<a class="sourceLine" id="cb2-2" title="2">t1 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span>), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(FOCUS_<span class="dv">2006</span>_C, FOCUS_<span class="dv">2006</span>_D)))[[<span class="st">"elapsed"</span>]]</a>
-<a class="sourceLine" id="cb2-3" title="3">t2 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span>), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(FOCUS_<span class="dv">2006</span>_C, FOCUS_<span class="dv">2006</span>_D), <span class="dt">error_model =</span> <span class="st">"tc"</span>))[[<span class="st">"elapsed"</span>]]</a></code></pre></div>
+<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="no">cpu_model</span> <span class="kw">&lt;-</span> <span class="kw pkg">benchmarkme</span><span class="kw ns">::</span><span class="fu"><a href="https://rdrr.io/pkg/benchmarkme/man/get_cpu.html">get_cpu</a></span>()$<span class="no">model_name</span>
+<span class="no">operating_system</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html">Sys.info</a></span>()<span class="kw">[[</span><span class="st">"sysname"</span>]]
+<span class="no">mkin_version</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html">as.character</a></span>(<span class="fu"><a href="https://rdrr.io/r/utils/packageDescription.html">packageVersion</a></span>(<span class="st">"mkin"</span>))
+<span class="no">system_string</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span>(<span class="no">operating_system</span>, <span class="st">", "</span>, <span class="no">cpu_model</span>, <span class="st">", mkin version "</span>, <span class="no">mkin_version</span>)
+<span class="fu"><a href="https://rdrr.io/r/base/load.html">load</a></span>(<span class="st">"~/git/mkin/vignettes/web_only/mkin_benchmarks.rda"</span>)
+<span class="no">mkin_benchmarks</span>[<span class="no">system_string</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"CPU"</span>, <span class="st">"OS"</span>, <span class="st">"mkin"</span>)] <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">cpu_model</span>, <span class="no">operating_system</span>, <span class="no">mkin_version</span>)
+
+<span class="kw">if</span> (<span class="no">mkin_version</span> <span class="kw">&gt;</span> <span class="st">"0.9.48.1"</span>) {
+  <span class="no">mmkin_bench</span> <span class="kw">&lt;-</span> <span class="kw">function</span>(<span class="no">models</span>, <span class="no">datasets</span>, <span class="no">error_model</span> <span class="kw">=</span> <span class="st">"const"</span>) <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span>(<span class="no">models</span>, <span class="no">datasets</span>, <span class="kw">error_model</span> <span class="kw">=</span> <span class="no">error_model</span>, <span class="kw">cores</span> <span class="kw">=</span> <span class="fl">1</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+} <span class="kw">else</span> {
+  <span class="no">mmkin_bench</span> <span class="kw">&lt;-</span> <span class="kw">function</span>(<span class="no">models</span>, <span class="no">datasets</span>, <span class="no">error_model</span> <span class="kw">=</span> <span class="kw">NULL</span>) <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span>(<span class="no">models</span>, <span class="no">datasets</span>, <span class="kw">reweight.method</span> <span class="kw">=</span> <span class="no">error_model</span>, <span class="kw">cores</span> <span class="kw">=</span> <span class="fl">1</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+}</pre></body></html></div>
+<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">FOCUS_C</span> <span class="kw">&lt;-</span> <span class="no">FOCUS_2006_C</span>
+<span class="no">FOCUS_D</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html">subset</a></span>(<span class="no">FOCUS_2006_D</span>, <span class="no">value</span> <span class="kw">!=</span> <span class="fl">0</span>)
+<span class="co"># Parent only</span>
+<span class="no">t1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_C</span>, <span class="no">FOCUS_D</span>)))<span class="kw">[[</span><span class="st">"elapsed"</span>]]
+<span class="no">t2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_C</span>, <span class="no">FOCUS_D</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"tc"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]]</pre></body></html></div>
 <pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge:
+## iteration limit reached without convergence (10)
+
+## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge:
 ## iteration limit reached without convergence (10)</code></pre>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="co"># One metabolite</span></a>
-<a class="sourceLine" id="cb4-2" title="2">SFO_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../../reference/mkinmod.html">mkinmod</a></span>(</a>
-<a class="sourceLine" id="cb4-3" title="3">  <span class="dt">parent =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"m1"</span>),</a>
-<a class="sourceLine" id="cb4-4" title="4">  <span class="dt">m1 =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</a></code></pre></div>
+<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="co"># One metabolite</span>
+<span class="no">SFO_SFO</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(
+  <span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"m1"</span>),
+  <span class="kw">m1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</pre></body></html></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1">FOMC_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../../reference/mkinmod.html">mkinmod</a></span>(</a>
-<a class="sourceLine" id="cb6-2" title="2">  <span class="dt">parent =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"FOMC"</span>, <span class="st">"m1"</span>),</a>
-<a class="sourceLine" id="cb6-3" title="3">  <span class="dt">m1 =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</a></code></pre></div>
+<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">FOMC_SFO</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(
+  <span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"FOMC"</span>, <span class="st">"m1"</span>),
+  <span class="kw">m1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</pre></body></html></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1">DFOP_SFO &lt;-<span class="st"> </span><span class="kw"><a href="../../reference/mkinmod.html">mkinmod</a></span>(</a>
-<a class="sourceLine" id="cb8-2" title="2">  <span class="dt">parent =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"FOMC"</span>, <span class="st">"m1"</span>),</a>
-<a class="sourceLine" id="cb8-3" title="3">  <span class="dt">m1 =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</a></code></pre></div>
+<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">DFOP_SFO</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(
+  <span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"FOMC"</span>, <span class="st">"m1"</span>),
+  <span class="kw">m1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</pre></body></html></div>
 <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1">t3 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(SFO_SFO, FOMC_SFO, DFOP_SFO), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(FOCUS_<span class="dv">2006</span>_D)))[[<span class="st">"elapsed"</span>]]</a></code></pre></div>
-<pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data</code></pre>
-<pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data
+<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="no">t3</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOMC_SFO</span>, <span class="no">DFOP_SFO</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_D</span>)))<span class="kw">[[</span><span class="st">"elapsed"</span>]]
+<span class="no">t4</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOMC_SFO</span>, <span class="no">DFOP_SFO</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_D</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"tc"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]]
+<span class="no">t5</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOMC_SFO</span>, <span class="no">DFOP_SFO</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_D</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"obs"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]]
+
+<span class="co"># Two metabolites, synthetic data</span>
+<span class="no">m_synth_SFO_lin</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(<span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>),
+                           <span class="kw">M1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>),
+                           <span class="kw">M2</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),
+                           <span class="kw">use_of_ff</span> <span class="kw">=</span> <span class="st">"max"</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+<span class="no">m_synth_DFOP_par</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(<span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"M1"</span>, <span class="st">"M2"</span>)),
+                           <span class="kw">M1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),
+                           <span class="kw">M2</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),
+                           <span class="kw">use_of_ff</span> <span class="kw">=</span> <span class="st">"max"</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+<span class="no">SFO_lin_a</span> <span class="kw">&lt;-</span> <span class="no">synthetic_data_for_UBA_2014</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="no">data</span>
 
-## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data</code></pre>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1">t4 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(SFO_SFO, FOMC_SFO, DFOP_SFO), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw"><a href="https://rdrr.io/r/base/subset.html">subset</a></span>(FOCUS_<span class="dv">2006</span>_D, value <span class="op">!=</span><span class="st"> </span><span class="dv">0</span>)), <span class="dt">error_model =</span> <span class="st">"tc"</span>))[[<span class="st">"elapsed"</span>]]</a>
-<a class="sourceLine" id="cb13-2" title="2">t5 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(SFO_SFO, FOMC_SFO, DFOP_SFO), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(FOCUS_<span class="dv">2006</span>_D), <span class="dt">error_model =</span> <span class="st">"obs"</span>))[[<span class="st">"elapsed"</span>]]</a></code></pre></div>
-<pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data
+<span class="no">DFOP_par_c</span> <span class="kw">&lt;-</span> <span class="no">synthetic_data_for_UBA_2014</span><span class="kw">[[</span><span class="fl">12</span>]]$<span class="no">data</span>
 
-## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data
+<span class="no">t6</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_SFO_lin</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_lin_a</span>)))[<span class="st">"elapsed"</span>]
+<span class="no">t7</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_DFOP_par</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">DFOP_par_c</span>)))[<span class="st">"elapsed"</span>]
 
-## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data</code></pre>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" title="1"><span class="co"># Two metabolites, synthetic data</span></a>
-<a class="sourceLine" id="cb15-2" title="2">m_synth_SFO_lin &lt;-<span class="st"> </span><span class="kw"><a href="../../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">parent =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>),</a>
-<a class="sourceLine" id="cb15-3" title="3">                           <span class="dt">M1 =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>),</a>
-<a class="sourceLine" id="cb15-4" title="4">                           <span class="dt">M2 =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),</a>
-<a class="sourceLine" id="cb15-5" title="5">                           <span class="dt">use_of_ff =</span> <span class="st">"max"</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb15-6" title="6"></a>
-<a class="sourceLine" id="cb15-7" title="7">m_synth_DFOP_par &lt;-<span class="st"> </span><span class="kw"><a href="../../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">parent =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"DFOP"</span>, <span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"M1"</span>, <span class="st">"M2"</span>)),</a>
-<a class="sourceLine" id="cb15-8" title="8">                           <span class="dt">M1 =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),</a>
-<a class="sourceLine" id="cb15-9" title="9">                           <span class="dt">M2 =</span> <span class="kw"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>),</a>
-<a class="sourceLine" id="cb15-10" title="10">                           <span class="dt">use_of_ff =</span> <span class="st">"max"</span>, <span class="dt">quiet =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb15-11" title="11"></a>
-<a class="sourceLine" id="cb15-12" title="12">SFO_lin_a &lt;-<span class="st"> </span>synthetic_data_for_UBA_<span class="dv">2014</span>[[<span class="dv">1</span>]]<span class="op">$</span>data</a>
-<a class="sourceLine" id="cb15-13" title="13"></a>
-<a class="sourceLine" id="cb15-14" title="14">DFOP_par_c &lt;-<span class="st"> </span>synthetic_data_for_UBA_<span class="dv">2014</span>[[<span class="dv">12</span>]]<span class="op">$</span>data</a>
-<a class="sourceLine" id="cb15-15" title="15"></a>
-<a class="sourceLine" id="cb15-16" title="16">t6 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(m_synth_SFO_lin), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(SFO_lin_a)))[<span class="st">"elapsed"</span>]</a>
-<a class="sourceLine" id="cb15-17" title="17">t7 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(m_synth_DFOP_par), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(DFOP_par_c)))[<span class="st">"elapsed"</span>]</a>
-<a class="sourceLine" id="cb15-18" title="18"></a>
-<a class="sourceLine" id="cb15-19" title="19">t8 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(m_synth_SFO_lin), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(SFO_lin_a), <span class="dt">error_model =</span> <span class="st">"tc"</span>))[<span class="st">"elapsed"</span>]</a>
-<a class="sourceLine" id="cb15-20" title="20">t9 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(m_synth_DFOP_par), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(DFOP_par_c), <span class="dt">error_model =</span> <span class="st">"tc"</span>))[<span class="st">"elapsed"</span>]</a>
-<a class="sourceLine" id="cb15-21" title="21"></a>
-<a class="sourceLine" id="cb15-22" title="22">t10 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(m_synth_SFO_lin), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(SFO_lin_a), <span class="dt">error_model =</span> <span class="st">"obs"</span>))[<span class="st">"elapsed"</span>]</a>
-<a class="sourceLine" id="cb15-23" title="23">t11 &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="kw">mmkin_bench</span>(<span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(m_synth_DFOP_par), <span class="kw"><a href="https://rdrr.io/r/base/list.html">list</a></span>(DFOP_par_c), <span class="dt">error_model =</span> <span class="st">"obs"</span>))[<span class="st">"elapsed"</span>]</a>
-<a class="sourceLine" id="cb15-24" title="24"></a>
-<a class="sourceLine" id="cb15-25" title="25">mkin_benchmarks[system_string, <span class="kw"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span>(<span class="st">"t"</span>, <span class="dv">1</span><span class="op">:</span><span class="dv">11</span>)] &lt;-<span class="st"> </span><span class="kw"><a href="https://rdrr.io/r/base/c.html">c</a></span>(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)</a>
-<a class="sourceLine" id="cb15-26" title="26">mkin_benchmarks</a></code></pre></div>
-<pre><code>##                                                                                                       CPU
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 AMD Ryzen 7 1700 Eight-Core Processor
-##                                                                        OS
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 Linux
-##                                                                         mkin
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 0.9.48.1
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 0.9.49.1
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 0.9.49.2
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 0.9.49.3
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 0.9.49.4
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 0.9.49.6
-##                                                                        t1
+<span class="no">t8</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_SFO_lin</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_lin_a</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"tc"</span>))[<span class="st">"elapsed"</span>]
+<span class="no">t9</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_DFOP_par</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">DFOP_par_c</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"tc"</span>))[<span class="st">"elapsed"</span>]
+
+<span class="no">t10</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_SFO_lin</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_lin_a</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"obs"</span>))[<span class="st">"elapsed"</span>]
+<span class="no">t11</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_DFOP_par</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">DFOP_par_c</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"obs"</span>))[<span class="st">"elapsed"</span>]
+
+<span class="no">mkin_benchmarks</span>[<span class="no">system_string</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span>(<span class="st">"t"</span>, <span class="fl">1</span>:<span class="fl">11</span>)] <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">t1</span>, <span class="no">t2</span>, <span class="no">t3</span>, <span class="no">t4</span>, <span class="no">t5</span>, <span class="no">t6</span>, <span class="no">t7</span>, <span class="no">t8</span>, <span class="no">t9</span>, <span class="no">t10</span>, <span class="no">t11</span>)
+<span class="no">mkin_benchmarks</span>[, -<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>:<span class="fl">3</span>)]</pre></body></html></div>
+<pre><code>##                                                                        t1
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.610
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 8.184
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 7.064
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.296
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 5.936
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 5.901
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.683
 ##                                                                         t2
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 11.019
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 22.889
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 12.558
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 21.239
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 20.545
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 36.164
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    3.862
 ##                                                                        t3
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.764
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.649
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.786
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.510
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.446
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.510
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.369
 ##                                                                         t4
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 14.347
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 13.789
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  8.461
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 13.805
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 15.335
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 30.849
-##                                                                         t5
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1  9.495
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  6.395
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.675
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.386
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  6.002
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.545
-##                                                                        t6
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 2.623
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 2.542
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 2.723
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 2.643
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 2.635
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 2.563
-##                                                                        t7
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.252
-##                                                                        t8
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 7.525
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.632
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.862
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.02
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.737
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 7.865
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    6.104
+##                                                                        t5    t6
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 9.495 2.623
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 6.395 2.542
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 5.675 2.723
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.386 2.643
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 6.002 2.635
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   2.718 0.752
+##                                                                        t7    t8
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587 7.525
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128 4.632
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478 4.862
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374  7.02
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259 4.737
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.214 1.276
 ##                                                                         t9
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 16.621
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  8.171
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  7.618
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 11.124
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  7.763
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 16.195
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    2.858
 ##                                                                       t10
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 8.576
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 3.676
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 3.579
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 5.388
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 3.427
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 7.729
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   2.032
 ##                                                                        t11
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 31.267
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  5.636
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.574
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.365
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  5.626
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.787</code></pre>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" title="1"><span class="kw"><a href="https://rdrr.io/r/base/save.html">save</a></span>(mkin_benchmarks, <span class="dt">file =</span> <span class="st">"~/git/mkin/vignettes/mkin_benchmarks.rda"</span>)</a></code></pre></div>
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    2.973</code></pre>
+<div class="sourceCode" id="cb12"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/save.html">save</a></span>(<span class="no">mkin_benchmarks</span>, <span class="kw">file</span> <span class="kw">=</span> <span class="st">"~/git/mkin/vignettes/mkin_benchmarks.rda"</span>)</pre></body></html></div>
 </div>
   </div>
 
-  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
 
-        <div id="tocnav">
-      <h2 class="hasAnchor">
-<a href="#tocnav" class="anchor"></a>Contents</h2>
-      <ul class="nav nav-pills nav-stacked">
-<li><a href="#systems">Systems</a></li>
-      </ul>
+        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
+    </nav>
 </div>
-      </div>
 
 </div>
 
@@ -299,7 +257,7 @@
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.4.1.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
 </div>
 
       </footer>
diff --git a/docs/news/index.html b/docs/news/index.html
index bc69fd2d..1e13041d 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -145,7 +145,6 @@
 <ul>
 <li><p>Support SFORB with formation fractions</p></li>
 <li><p>‘mkinmod’: Make ‘use_of_ff’ = “max” the default</p></li>
-<li><p>Implement analytical solutions for some coupled models, e.g. SFO-SFO, DFOP-SFO, SFORB-SFO</p></li>
 </ul>
 </div>
     <div id="mkin-0-9-49-11-2020-04-20" class="section level1">
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 32bcaa05..b3cd3d30 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -10,7 +10,7 @@ articles:
   NAFTA_examples: web_only/NAFTA_examples.html
   benchmarks: web_only/benchmarks.html
   compiled_models: web_only/compiled_models.html
-last_built: 2020-05-07T20:11Z
+last_built: 2020-05-11T03:18Z
 urls:
   reference: https://pkgdown.jrwb.de/mkin/reference
   article: https://pkgdown.jrwb.de/mkin/articles
diff --git a/docs/reference/create_deg_func.html b/docs/reference/create_deg_func.html
index e14857cc..67016be0 100644
--- a/docs/reference/create_deg_func.html
+++ b/docs/reference/create_deg_func.html
@@ -167,7 +167,21 @@
     <pre class="examples"><div class='input'>
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
-  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div></pre>
+  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>FOCUS_D</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>) <span class='co'># to avoid warnings</span>
+<span class='no'>fit_1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>fit_2</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='co'># \dontrun{</span>
+<span class='kw'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/library.html'>require</a></span>(<span class='no'>rbenchmark</span>))
+  <span class='fu'><a href='https://rdrr.io/pkg/rbenchmark/man/benchmark.html'>benchmark</a></span>(
+    <span class='kw'>analytical</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
+    <span class='kw'>deSolve</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
+    <span class='kw'>replications</span> <span class='kw'>=</span> <span class='fl'>1</span>)</div><div class='output co'>#&gt; <span class='message'>Lade nötiges Paket: rbenchmark</span></div><div class='output co'>#&gt;         test replications elapsed relative user.self sys.self user.child
+#&gt; 1 analytical            1   0.198    1.000     0.198    0.000          0
+#&gt; 2    deSolve            1   0.350    1.768     0.348    0.001          0
+#&gt;   sys.child
+#&gt; 1         0
+#&gt; 2         0</div><div class='input'># }
+</div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
     <nav id="toc" data-toggle="toc" class="sticky-top">
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 75820166..7e7fff29 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -557,6 +557,12 @@ kinetic models fitted with mkinfit</p></td>
           <p><code><a href="mkinerrplot.html">mkinerrplot()</a></code> </p>
         </td>
         <td><p>Function to plot squared residuals and the error model for an mkin object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="create_deg_func.html">create_deg_func()</a></code> </p>
+        </td>
+        <td><p>Create degradation functions for known analytical solutions</p></td>
       </tr>
     </tbody><tbody>
       <tr>
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index 62823c58..9974b66b 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -78,7 +78,7 @@ likelihood function." />
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.11</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50</span>
       </span>
     </div>
 
@@ -174,7 +174,6 @@ likelihood function.</p>
   <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
   <span class='kw'>atol</span> <span class='kw'>=</span> <span class='fl'>1e-08</span>,
   <span class='kw'>rtol</span> <span class='kw'>=</span> <span class='fl'>1e-10</span>,
-  <span class='kw'>n.outtimes</span> <span class='kw'>=</span> <span class='fl'>100</span>,
   <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"const"</span>, <span class='st'>"obs"</span>, <span class='st'>"tc"</span>),
   <span class='kw'>error_model_algorithm</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"auto"</span>, <span class='st'>"d_3"</span>, <span class='st'>"direct"</span>, <span class='st'>"twostep"</span>, <span class='st'>"threestep"</span>,
     <span class='st'>"fourstep"</span>, <span class='st'>"IRLS"</span>, <span class='st'>"OLS"</span>),
@@ -325,13 +324,6 @@ is 1e-8, lower than in <code>lsoda</code>.</p></td>
 is 1e-10, much lower than in <code>lsoda</code>.</p></td>
     </tr>
     <tr>
-      <th>n.outtimes</th>
-      <td><p>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.</p></td>
-    </tr>
-    <tr>
       <th>error_model</th>
       <td><p>If the error model is "const", a constant standard
   deviation is assumed.</p>
@@ -422,7 +414,87 @@ estimators.</p>
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'>
 <span class='co'># Use shorthand notation for parent only degradation</span>
-<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in (function (t, parent_0, alpha, beta) {    parent = parent_0/(t/beta + 1)^alpha})(t = c(0, 1, 1.2020202020202, 2.4040404040404, 3, 3.60606060606061, 4.80808080808081, 6.01010101010101, 7, 7.21212121212121, 8.41414141414141, 9.61616161616162, 10.8181818181818, 12.020202020202, 13.2222222222222, 14, 14.4242424242424, 15.6262626262626, 16.8282828282828, 18.030303030303, 19.2323232323232, 20.4343434343434, 21.6363636363636, 22.8383838383838, 24.040404040404, 25.2424242424242, 26.4444444444444, 27.6464646464646, 28, 28.8484848484848, 30.050505050505, 31.2525252525253, 32.4545454545455, 33.6565656565657, 34.8585858585859, 36.0606060606061, 37.2626262626263, 38.4646464646465, 39.6666666666667, 40.8686868686869, 42.0707070707071, 43.2727272727273, 44.4747474747475, 45.6767676767677, 46.8787878787879, 48.0808080808081, 49.2828282828283, 50.4848484848485, 51.6868686868687, 52.8888888888889, 54.0909090909091, 55.2929292929293, 56.4949494949495, 57.6969696969697, 58.8989898989899, 60.1010101010101, 61.3030303030303, 62.5050505050505, 63, 63.7070707070707, 64.9090909090909, 66.1111111111111, 67.3131313131313, 68.5151515151515, 69.7171717171717, 70.9191919191919, 72.1212121212121, 73.3232323232323, 74.5252525252525, 75.7272727272727, 76.9292929292929, 78.1313131313131, 79.3333333333333, 80.5353535353535, 81.7373737373737, 82.9393939393939, 84.1414141414141, 85.3434343434343, 86.5454545454545, 87.7474747474747, 88.9494949494949, 90.1515151515152, 91, 91.3535353535353, 92.5555555555556, 93.7575757575758, 94.959595959596, 96.1616161616162, 97.3636363636364, 98.5656565656566, 99.7676767676768, 100.969696969697, 102.171717171717, 103.373737373737, 104.575757575758, 105.777777777778, 106.979797979798, 108.181818181818, 109.383838383838, 110.585858585859, 111.787878787879, 112.989898989899, 114.191919191919, 115.393939393939, 116.59595959596, 117.79797979798, 119), parent.0 = c(parent = 85.1),     alpha = 1, beta = 10): unbenutztes Argument (parent.0 = 85.1)</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0.001</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; <span class='error'>Error in summary(fit): Objekt 'fit' nicht gefunden</span></div><div class='input'>
+<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.50 
+#&gt; R version used for fitting:       4.0.0 
+#&gt; Date of fit:     Mon May 11 05:14:26 2020 
+#&gt; Date of summary: Mon May 11 05:14:26 2020 
+#&gt; 
+#&gt; Equations:
+#&gt; d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
+#&gt; 
+#&gt; Model predictions using solution type analytical 
+#&gt; 
+#&gt; Fitted using 222 model solutions performed in 0.043 s
+#&gt; 
+#&gt; Error model: Constant variance 
+#&gt; 
+#&gt; Error model algorithm: OLS 
+#&gt; 
+#&gt; Starting values for parameters to be optimised:
+#&gt;          value   type
+#&gt; parent_0  85.1  state
+#&gt; alpha      1.0 deparm
+#&gt; beta      10.0 deparm
+#&gt; 
+#&gt; Starting values for the transformed parameters actually optimised:
+#&gt;               value lower upper
+#&gt; parent_0  85.100000  -Inf   Inf
+#&gt; log_alpha  0.000000  -Inf   Inf
+#&gt; log_beta   2.302585  -Inf   Inf
+#&gt; 
+#&gt; Fixed parameter values:
+#&gt; None
+#&gt; 
+#&gt; Results:
+#&gt; 
+#&gt;        AIC      BIC    logLik
+#&gt;   44.68652 45.47542 -18.34326
+#&gt; 
+#&gt; Optimised, transformed parameters with symmetric confidence intervals:
+#&gt;           Estimate Std. Error    Lower   Upper
+#&gt; parent_0  85.87000     1.8070 81.23000 90.5200
+#&gt; log_alpha  0.05192     0.1353 -0.29580  0.3996
+#&gt; log_beta   0.65100     0.2287  0.06315  1.2390
+#&gt; sigma      1.85700     0.4378  0.73200  2.9830
+#&gt; 
+#&gt; Parameter correlation:
+#&gt;             parent_0  log_alpha   log_beta     sigma
+#&gt; parent_0   1.000e+00 -1.565e-01 -3.142e-01 4.758e-08
+#&gt; log_alpha -1.565e-01  1.000e+00  9.564e-01 1.007e-07
+#&gt; log_beta  -3.142e-01  9.564e-01  1.000e+00 8.568e-08
+#&gt; sigma      4.758e-08  1.007e-07  8.568e-08 1.000e+00
+#&gt; 
+#&gt; Backtransformed parameters:
+#&gt; Confidence intervals for internally transformed parameters are asymmetric.
+#&gt; t-test (unrealistically) based on the assumption of normal distribution
+#&gt; for estimators of untransformed parameters.
+#&gt;          Estimate t value    Pr(&gt;t)   Lower  Upper
+#&gt; parent_0   85.870  47.530 3.893e-08 81.2300 90.520
+#&gt; alpha       1.053   7.393 3.562e-04  0.7439  1.491
+#&gt; beta        1.917   4.373 3.601e-03  1.0650  3.451
+#&gt; sigma       1.857   4.243 4.074e-03  0.7320  2.983
+#&gt; 
+#&gt; FOCUS Chi2 error levels in percent:
+#&gt;          err.min n.optim df
+#&gt; All data   6.657       3  6
+#&gt; parent     6.657       3  6
+#&gt; 
+#&gt; Estimated disappearance times:
+#&gt;         DT50  DT90 DT50back
+#&gt; parent 1.785 15.15     4.56
+#&gt; 
+#&gt; Data:
+#&gt;  time variable observed predicted residual
+#&gt;     0   parent     85.1    85.875  -0.7749
+#&gt;     1   parent     57.9    55.191   2.7091
+#&gt;     3   parent     29.9    31.845  -1.9452
+#&gt;     7   parent     14.6    17.012  -2.4124
+#&gt;    14   parent      9.7     9.241   0.4590
+#&gt;    28   parent      6.6     4.754   1.8460
+#&gt;    63   parent      4.0     2.102   1.8977
+#&gt;    91   parent      3.9     1.441   2.4590
+#&gt;   119   parent      0.6     1.092  -0.4919</div><div class='input'>
 <span class='co'># One parent compound, one metabolite, both single first order.</span>
 <span class='co'># Use mkinsub for convenience in model formulation. Pathway to sink included per default.</span>
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
@@ -430,86 +502,105 @@ estimators.</p>
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
 <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>,
                            <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)))</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt;        User      System verstrichen 
-#&gt;       1.526       0.000       1.526 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt;      parent_0 k_parent_sink   k_parent_m1     k_m1_sink         sigma 
-#&gt;  99.598483069   0.047920122   0.050777612   0.005260651   3.125503875 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; $ff
-#&gt; parent_sink   parent_m1     m1_sink 
-#&gt;    0.485524    0.514476    1.000000 
+#&gt;       0.407       0.002       0.409 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt;       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
+#&gt;   99.598483222    0.098697734    0.005260651    0.514475962    3.125503875 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; $ff
+#&gt;   parent_m1 parent_sink 
+#&gt;    0.514476    0.485524 
 #&gt; 
 #&gt; $distimes
 #&gt;              DT50      DT90
 #&gt; parent   7.022929  23.32967
-#&gt; m1     131.760712 437.69961
+#&gt; m1     131.760715 437.69962
 #&gt; </div><div class='input'><span class='co'># \dontrun{</span>
 <span class='co'># deSolve is slower when no C compiler (gcc) was available during model generation</span>
 <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fit.deSolve</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>,
-                           <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)))</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt; <span class='message'>Ordinary least squares optimisation</span></div><div class='output co'>#&gt; Sum of squared residuals at call 1: 18915.53
-#&gt; Sum of squared residuals at call 2: 18915.53
-#&gt; Sum of squared residuals at call 6: 11424.02
-#&gt; Sum of squared residuals at call 10: 11424
-#&gt; Sum of squared residuals at call 12: 4094.396
-#&gt; Sum of squared residuals at call 16: 4094.396
-#&gt; Sum of squared residuals at call 19: 1340.595
-#&gt; Sum of squared residuals at call 20: 1340.593
-#&gt; Sum of squared residuals at call 25: 1072.239
-#&gt; Sum of squared residuals at call 28: 1072.236
-#&gt; Sum of squared residuals at call 30: 874.2615
-#&gt; Sum of squared residuals at call 33: 874.2611
-#&gt; Sum of squared residuals at call 35: 616.2375
-#&gt; Sum of squared residuals at call 37: 616.237
-#&gt; Sum of squared residuals at call 40: 467.4386
-#&gt; Sum of squared residuals at call 42: 467.438
-#&gt; Sum of squared residuals at call 46: 398.2913
-#&gt; Sum of squared residuals at call 48: 398.2913
-#&gt; Sum of squared residuals at call 49: 398.2912
-#&gt; Sum of squared residuals at call 51: 395.0711
-#&gt; Sum of squared residuals at call 54: 395.071
-#&gt; Sum of squared residuals at call 56: 378.3298
-#&gt; Sum of squared residuals at call 59: 378.3298
-#&gt; Sum of squared residuals at call 62: 376.9812
-#&gt; Sum of squared residuals at call 64: 376.9811
-#&gt; Sum of squared residuals at call 67: 375.2085
-#&gt; Sum of squared residuals at call 69: 375.2085
-#&gt; Sum of squared residuals at call 70: 375.2085
-#&gt; Sum of squared residuals at call 71: 375.2085
-#&gt; Sum of squared residuals at call 72: 374.5723
-#&gt; Sum of squared residuals at call 74: 374.5723
-#&gt; Sum of squared residuals at call 77: 374.0075
-#&gt; Sum of squared residuals at call 79: 374.0075
-#&gt; Sum of squared residuals at call 80: 374.0075
-#&gt; Sum of squared residuals at call 82: 373.1711
-#&gt; Sum of squared residuals at call 84: 373.1711
-#&gt; Sum of squared residuals at call 87: 372.6445
-#&gt; Sum of squared residuals at call 88: 372.1614
-#&gt; Sum of squared residuals at call 90: 372.1614
-#&gt; Sum of squared residuals at call 91: 372.1614
-#&gt; Sum of squared residuals at call 94: 371.6464
-#&gt; Sum of squared residuals at call 99: 371.4299
-#&gt; Sum of squared residuals at call 101: 371.4299
-#&gt; Sum of squared residuals at call 104: 371.4071
-#&gt; Sum of squared residuals at call 106: 371.4071
-#&gt; Sum of squared residuals at call 107: 371.4071
-#&gt; Sum of squared residuals at call 109: 371.2524
-#&gt; Sum of squared residuals at call 113: 371.2524
-#&gt; Sum of squared residuals at call 114: 371.2136
-#&gt; Sum of squared residuals at call 115: 371.2136
-#&gt; Sum of squared residuals at call 116: 371.2136
-#&gt; Sum of squared residuals at call 119: 371.2134
-#&gt; Sum of squared residuals at call 120: 371.2134
-#&gt; Sum of squared residuals at call 122: 371.2134
-#&gt; Sum of squared residuals at call 123: 371.2134
-#&gt; Sum of squared residuals at call 125: 371.2134
-#&gt; Sum of squared residuals at call 126: 371.2134
-#&gt; Sum of squared residuals at call 135: 371.2134
-#&gt; Negative log-likelihood at call 145: 97.22429</div><div class='output co'>#&gt; <span class='message'>Optimisation successfully terminated.</span></div><div class='output co'>#&gt;        User      System verstrichen 
-#&gt;       1.084       0.000       1.085 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt;      parent_0 k_parent_sink   k_parent_m1     k_m1_sink         sigma 
-#&gt;  99.598483072   0.047920122   0.050777612   0.005260651   3.125503874 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; $ff
-#&gt; parent_sink   parent_m1     m1_sink 
-#&gt;    0.485524    0.514476    1.000000 
+                           <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)))</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt; <span class='message'>Ordinary least squares optimisation</span></div><div class='output co'>#&gt; Sum of squared residuals at call 1: 15156.12
+#&gt; Sum of squared residuals at call 2: 15156.12
+#&gt; Sum of squared residuals at call 6: 8243.645
+#&gt; Sum of squared residuals at call 12: 6290.712
+#&gt; Sum of squared residuals at call 13: 6290.683
+#&gt; Sum of squared residuals at call 15: 6290.452
+#&gt; Sum of squared residuals at call 18: 1700.749
+#&gt; Sum of squared residuals at call 20: 1700.611
+#&gt; Sum of squared residuals at call 24: 1190.923
+#&gt; Sum of squared residuals at call 26: 1190.922
+#&gt; Sum of squared residuals at call 29: 1017.417
+#&gt; Sum of squared residuals at call 31: 1017.417
+#&gt; Sum of squared residuals at call 33: 1017.416
+#&gt; Sum of squared residuals at call 34: 644.0471
+#&gt; Sum of squared residuals at call 36: 644.0469
+#&gt; Sum of squared residuals at call 38: 644.0469
+#&gt; Sum of squared residuals at call 39: 590.5025
+#&gt; Sum of squared residuals at call 41: 590.5022
+#&gt; Sum of squared residuals at call 43: 590.5016
+#&gt; Sum of squared residuals at call 44: 543.219
+#&gt; Sum of squared residuals at call 45: 543.2187
+#&gt; Sum of squared residuals at call 46: 543.2186
+#&gt; Sum of squared residuals at call 50: 391.348
+#&gt; Sum of squared residuals at call 51: 391.3479
+#&gt; Sum of squared residuals at call 56: 386.4789
+#&gt; Sum of squared residuals at call 58: 386.4789
+#&gt; Sum of squared residuals at call 60: 386.4779
+#&gt; Sum of squared residuals at call 61: 384.0686
+#&gt; Sum of squared residuals at call 63: 384.0686
+#&gt; Sum of squared residuals at call 66: 382.7813
+#&gt; Sum of squared residuals at call 68: 382.7813
+#&gt; Sum of squared residuals at call 70: 382.7813
+#&gt; Sum of squared residuals at call 71: 378.9273
+#&gt; Sum of squared residuals at call 73: 378.9273
+#&gt; Sum of squared residuals at call 75: 378.9272
+#&gt; Sum of squared residuals at call 76: 377.4847
+#&gt; Sum of squared residuals at call 78: 377.4846
+#&gt; Sum of squared residuals at call 81: 375.9738
+#&gt; Sum of squared residuals at call 83: 375.9738
+#&gt; Sum of squared residuals at call 86: 375.3387
+#&gt; Sum of squared residuals at call 88: 375.3387
+#&gt; Sum of squared residuals at call 91: 374.5774
+#&gt; Sum of squared residuals at call 93: 374.5774
+#&gt; Sum of squared residuals at call 95: 374.5774
+#&gt; Sum of squared residuals at call 96: 373.5433
+#&gt; Sum of squared residuals at call 100: 373.5433
+#&gt; Sum of squared residuals at call 102: 373.2654
+#&gt; Sum of squared residuals at call 104: 373.2654
+#&gt; Sum of squared residuals at call 107: 372.6841
+#&gt; Sum of squared residuals at call 111: 372.684
+#&gt; Sum of squared residuals at call 114: 372.6374
+#&gt; Sum of squared residuals at call 116: 372.6374
+#&gt; Sum of squared residuals at call 119: 372.6223
+#&gt; Sum of squared residuals at call 121: 372.6223
+#&gt; Sum of squared residuals at call 123: 372.6223
+#&gt; Sum of squared residuals at call 124: 372.5903
+#&gt; Sum of squared residuals at call 126: 372.5903
+#&gt; Sum of squared residuals at call 129: 372.5445
+#&gt; Sum of squared residuals at call 130: 372.4921
+#&gt; Sum of squared residuals at call 131: 372.2377
+#&gt; Sum of squared residuals at call 132: 371.5434
+#&gt; Sum of squared residuals at call 134: 371.5434
+#&gt; Sum of squared residuals at call 137: 371.2857
+#&gt; Sum of squared residuals at call 139: 371.2857
+#&gt; Sum of squared residuals at call 143: 371.2247
+#&gt; Sum of squared residuals at call 144: 371.2247
+#&gt; Sum of squared residuals at call 149: 371.2189
+#&gt; Sum of squared residuals at call 150: 371.2145
+#&gt; Sum of squared residuals at call 153: 371.2145
+#&gt; Sum of squared residuals at call 155: 371.2138
+#&gt; Sum of squared residuals at call 156: 371.2138
+#&gt; Sum of squared residuals at call 157: 371.2138
+#&gt; Sum of squared residuals at call 161: 371.2134
+#&gt; Sum of squared residuals at call 162: 371.2134
+#&gt; Sum of squared residuals at call 165: 371.2134
+#&gt; Sum of squared residuals at call 166: 371.2134
+#&gt; Sum of squared residuals at call 168: 371.2134
+#&gt; Negative log-likelihood at call 178: 97.22429</div><div class='output co'>#&gt; <span class='message'>Optimisation successfully terminated.</span></div><div class='output co'>#&gt;        User      System verstrichen 
+#&gt;       0.349       0.000       0.350 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt;       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
+#&gt;   99.598480759    0.098697739    0.005260651    0.514475958    3.125503874 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; $ff
+#&gt;   parent_m1 parent_sink 
+#&gt;    0.514476    0.485524 
 #&gt; 
 #&gt; $distimes
 #&gt;              DT50      DT90
-#&gt; parent   7.022929  23.32967
-#&gt; m1     131.760712 437.69961
+#&gt; parent   7.022929  23.32966
+#&gt; m1     131.760731 437.69967
 #&gt; </div><div class='input'><span class='co'># }</span>
 
 <span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, FOMC</span>
@@ -518,31 +609,33 @@ estimators.</p>
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"FOMC"</span>, <span class='st'>"m1"</span>),
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
 <span class='no'>fit.FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='co'># Use starting parameters from parent only FOMC fit</span>
-<span class='no'>fit.FOMC</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in (function (t, parent_0, alpha, beta) {    parent = parent_0/(t/beta + 1)^alpha})(t = c(0, 0.757575757575758, 1, 1.51515151515152, 2.27272727272727, 3, 3.03030303030303, 3.78787878787879, 4.54545454545454, 5.3030303030303, 6.06060606060606, 6.81818181818182, 7, 7.57575757575758, 8.33333333333333, 9.09090909090909, 9.84848484848485, 10.6060606060606, 11.3636363636364, 12.1212121212121, 12.8787878787879, 13.6363636363636, 14, 14.3939393939394, 15.1515151515152, 15.9090909090909, 16.6666666666667, 17.4242424242424, 18.1818181818182, 18.9393939393939, 19.6969696969697, 20.4545454545455, 21, 21.2121212121212, 21.969696969697, 22.7272727272727, 23.4848484848485, 24.2424242424242, 25, 25.7575757575758, 26.5151515151515, 27.2727272727273, 28.030303030303, 28.7878787878788, 29.5454545454545, 30.3030303030303, 31.0606060606061, 31.8181818181818, 32.5757575757576, 33.3333333333333, 34.0909090909091, 34.8484848484849, 35, 35.6060606060606, 36.3636363636364, 37.1212121212121, 37.8787878787879, 38.6363636363636, 39.3939393939394, 40.1515151515151, 40.9090909090909, 41.6666666666667, 42.4242424242424, 43.1818181818182, 43.9393939393939, 44.6969696969697, 45.4545454545455, 46.2121212121212, 46.969696969697, 47.7272727272727, 48.4848484848485, 49.2424242424242, 50, 50.7575757575758, 51.5151515151515, 52.2727272727273, 53.030303030303, 53.7878787878788, 54.5454545454545, 55.3030303030303, 56.0606060606061, 56.8181818181818, 57.5757575757576, 58.3333333333333, 59.0909090909091, 59.8484848484849, 60.6060606060606, 61.3636363636364, 62.1212121212121, 62.8787878787879, 63.6363636363636, 64.3939393939394, 65.1515151515152, 65.9090909090909, 66.6666666666667, 67.4242424242424, 68.1818181818182, 68.9393939393939, 69.6969696969697, 70.4545454545455, 71.2121212121212, 71.969696969697, 72.7272727272727, 73.4848484848485, 74.2424242424242, 75), parent.0 = c(parent = 100.75), alpha = 1,     beta = 10): unbenutztes Argument (parent.0 = 100.75)</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0 0 0.001</span></div><div class='input'><span class='no'>fit.FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-  <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.FOMC</span>$<span class='no'>bparms.ode</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE, parms.ini = fit.FOMC$bparms.ode): Objekt 'fit.FOMC' nicht gefunden</span></div><div class='input'>
+<span class='no'>fit.FOMC</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>fit.FOMC_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
+  <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.FOMC</span>$<span class='no'>bparms.ode</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'>
 <span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, SFORB</span>
 <span class='no'>SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFORB"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>),
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
 <span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='no'>fit.SFORB_SFO.deSolve</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>,
                                  <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='co'># Use starting parameters from parent only SFORB fit (not really needed in this case)</span>
-<span class='no'>fit.SFORB</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"SFORB"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='error'>Error in (function (t, parent_0, k_12, k_21, k_1output) {    sqrt_exp = sqrt(1/4 * (k_12 + k_21 + k_1output)^2 + k_12 *         k_21 - (k_12 + k_1output) * k_21)    b1 = 0.5 * (k_12 + k_21 + k_1output) + sqrt_exp    b2 = 0.5 * (k_12 + k_21 + k_1output) - sqrt_exp    parent = parent_0 * (((k_12 + k_21 - b1)/(b2 - b1)) * exp(-b1 *         t) + ((k_12 + k_21 - b2)/(b1 - b2)) * exp(-b2 * t))})(t = c(0, 0.757575757575758, 1, 1.51515151515152, 2.27272727272727, 3, 3.03030303030303, 3.78787878787879, 4.54545454545454, 5.3030303030303, 6.06060606060606, 6.81818181818182, 7, 7.57575757575758, 8.33333333333333, 9.09090909090909, 9.84848484848485, 10.6060606060606, 11.3636363636364, 12.1212121212121, 12.8787878787879, 13.6363636363636, 14, 14.3939393939394, 15.1515151515152, 15.9090909090909, 16.6666666666667, 17.4242424242424, 18.1818181818182, 18.9393939393939, 19.6969696969697, 20.4545454545455, 21, 21.2121212121212, 21.969696969697, 22.7272727272727, 23.4848484848485, 24.2424242424242, 25, 25.7575757575758, 26.5151515151515, 27.2727272727273, 28.030303030303, 28.7878787878788, 29.5454545454545, 30.3030303030303, 31.0606060606061, 31.8181818181818, 32.5757575757576, 33.3333333333333, 34.0909090909091, 34.8484848484849, 35, 35.6060606060606, 36.3636363636364, 37.1212121212121, 37.8787878787879, 38.6363636363636, 39.3939393939394, 40.1515151515151, 40.9090909090909, 41.6666666666667, 42.4242424242424, 43.1818181818182, 43.9393939393939, 44.6969696969697, 45.4545454545455, 46.2121212121212, 46.969696969697, 47.7272727272727, 48.4848484848485, 49.2424242424242, 50, 50.7575757575758, 51.5151515151515, 52.2727272727273, 53.030303030303, 53.7878787878788, 54.5454545454545, 55.3030303030303, 56.0606060606061, 56.8181818181818, 57.5757575757576, 58.3333333333333, 59.0909090909091, 59.8484848484849, 60.6060606060606, 61.3636363636364, 62.1212121212121, 62.8787878787879, 63.6363636363636, 64.3939393939394, 65.1515151515152, 65.9090909090909, 66.6666666666667, 67.4242424242424, 68.1818181818182, 68.9393939393939, 69.6969696969697, 70.4545454545455, 71.2121212121212, 71.969696969697, 72.7272727272727, 73.4848484848485, 74.2424242424242, 75), parent.0 = c(parent_free = 100.75), k_1output = 0.1,     k_12 = 0.1, k_21 = 0.02): unbenutztes Argument (parent.0 = 100.75)</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 0.001 0 0.001</span></div><div class='input'><span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<span class='no'>bparms.ode</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt; <span class='error'>Error in mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode,     quiet = TRUE): Objekt 'fit.SFORB' nicht gefunden</span></div><div class='input'><span class='co'># }</span>
+<span class='no'>fit.SFORB</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"SFORB"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<span class='no'>bparms.ode</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Initial parameter(s) k_parent_free_sink not used in the model</span></div><div class='input'><span class='co'># }</span>
 
 <span class='co'># \dontrun{</span>
 <span class='co'># Weighted fits, including IRLS</span>
 <span class='no'>SFO_SFO.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
-                      <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f.noweight</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.49.11 
+                      <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f.noweight</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.50 
 #&gt; R version used for fitting:       4.0.0 
-#&gt; Date of fit:     Thu May  7 08:59:03 2020 
-#&gt; Date of summary: Thu May  7 08:59:03 2020 
+#&gt; Date of fit:     Mon May 11 05:14:31 2020 
+#&gt; Date of summary: Mon May 11 05:14:31 2020 
 #&gt; 
 #&gt; Equations:
 #&gt; d_parent/dt = - k_parent * parent
 #&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
 #&gt; 
-#&gt; Model predictions using solution type deSolve 
+#&gt; Model predictions using solution type analytical 
 #&gt; 
-#&gt; Fitted using 422 model solutions performed in 1.106 s
+#&gt; Fitted using 421 model solutions performed in 0.124 s
 #&gt; 
 #&gt; Error model: Constant variance 
 #&gt; 
@@ -566,6 +659,11 @@ estimators.</p>
 #&gt;      value  type
 #&gt; m1_0     0 state
 #&gt; 
+#&gt; Results:
+#&gt; 
+#&gt;        AIC      BIC    logLik
+#&gt;   204.4486 212.6365 -97.22429
+#&gt; 
 #&gt; Optimised, transformed parameters with symmetric confidence intervals:
 #&gt;                Estimate Std. Error    Lower    Upper
 #&gt; parent_0       99.60000    1.57000 96.40000 102.8000
@@ -576,11 +674,11 @@ estimators.</p>
 #&gt; 
 #&gt; Parameter correlation:
 #&gt;                  parent_0 log_k_parent   log_k_m1 f_parent_ilr_1      sigma
-#&gt; parent_0        1.000e+00    5.174e-01 -1.688e-01     -5.471e-01 -2.443e-07
-#&gt; log_k_parent    5.174e-01    1.000e+00 -3.263e-01     -5.426e-01  3.181e-07
-#&gt; log_k_m1       -1.688e-01   -3.263e-01  1.000e+00      7.478e-01 -1.369e-07
-#&gt; f_parent_ilr_1 -5.471e-01   -5.426e-01  7.478e-01      1.000e+00 -2.287e-08
-#&gt; sigma          -2.443e-07    3.181e-07 -1.369e-07     -2.287e-08  1.000e+00
+#&gt; parent_0        1.000e+00    5.174e-01 -1.688e-01     -5.471e-01 -3.214e-07
+#&gt; log_k_parent    5.174e-01    1.000e+00 -3.263e-01     -5.426e-01  3.168e-07
+#&gt; log_k_m1       -1.688e-01   -3.263e-01  1.000e+00      7.478e-01 -1.410e-07
+#&gt; f_parent_ilr_1 -5.471e-01   -5.426e-01  7.478e-01      1.000e+00  5.093e-10
+#&gt; sigma          -3.214e-07    3.168e-07 -1.410e-07      5.093e-10  1.000e+00
 #&gt; 
 #&gt; Backtransformed parameters:
 #&gt; Confidence intervals for internally transformed parameters are asymmetric.
@@ -648,18 +746,18 @@ estimators.</p>
 #&gt;   100       m1    31.04  31.98163 -9.416e-01
 #&gt;   100       m1    33.13  31.98163  1.148e+00
 #&gt;   120       m1    25.15  28.78984 -3.640e+00
-#&gt;   120       m1    33.31  28.78984  4.520e+00</div><div class='input'><span class='no'>f.obs</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.obs</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.49.11 
+#&gt;   120       m1    33.31  28.78984  4.520e+00</div><div class='input'><span class='no'>f.obs</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.obs</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.50 
 #&gt; R version used for fitting:       4.0.0 
-#&gt; Date of fit:     Thu May  7 08:59:06 2020 
-#&gt; Date of summary: Thu May  7 08:59:06 2020 
+#&gt; Date of fit:     Mon May 11 05:14:32 2020 
+#&gt; Date of summary: Mon May 11 05:14:32 2020 
 #&gt; 
 #&gt; Equations:
 #&gt; d_parent/dt = - k_parent * parent
 #&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
 #&gt; 
-#&gt; Model predictions using solution type deSolve 
+#&gt; Model predictions using solution type analytical 
 #&gt; 
-#&gt; Fitted using 979 model solutions performed in 2.604 s
+#&gt; Fitted using 978 model solutions performed in 0.336 s
 #&gt; 
 #&gt; Error model: Variance unique to each observed variable 
 #&gt; 
@@ -688,6 +786,11 @@ estimators.</p>
 #&gt;      value  type
 #&gt; m1_0     0 state
 #&gt; 
+#&gt; Results:
+#&gt; 
+#&gt;        AIC      BIC    logLik
+#&gt;   205.8727 215.6982 -96.93634
+#&gt; 
 #&gt; Optimised, transformed parameters with symmetric confidence intervals:
 #&gt;                Estimate Std. Error    Lower    Upper
 #&gt; parent_0       99.65000    1.70200 96.19000 103.1000
@@ -780,23 +883,23 @@ estimators.</p>
 #&gt;   100       m1    31.04  31.98773 -9.477e-01
 #&gt;   100       m1    33.13  31.98773  1.142e+00
 #&gt;   120       m1    25.15  28.80429 -3.654e+00
-#&gt;   120       m1    33.31  28.80429  4.506e+00</div><div class='input'><span class='no'>f.tc</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.tc</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.49.11 
+#&gt;   120       m1    33.31  28.80429  4.506e+00</div><div class='input'><span class='no'>f.tc</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.tc</span>)</div><div class='output co'>#&gt; mkin version used for fitting:    0.9.50 
 #&gt; R version used for fitting:       4.0.0 
-#&gt; Date of fit:     Thu May  7 08:59:16 2020 
-#&gt; Date of summary: Thu May  7 08:59:16 2020 
+#&gt; Date of fit:     Mon May 11 05:14:32 2020 
+#&gt; Date of summary: Mon May 11 05:14:32 2020 
 #&gt; 
 #&gt; Equations:
 #&gt; d_parent/dt = - k_parent * parent
 #&gt; d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
 #&gt; 
-#&gt; Model predictions using solution type deSolve 
+#&gt; Model predictions using solution type analytical 
 #&gt; 
-#&gt; Fitted using 2552 model solutions performed in 10.544 s
+#&gt; Fitted using 1875 model solutions performed in 0.642 s
 #&gt; 
 #&gt; Error model: Two-component variance function 
 #&gt; 
 #&gt; Error model algorithm: d_3 
-#&gt; Three-step fitting yielded a higher likelihood than direct fitting 
+#&gt; Direct fitting and three-step fitting yield approximately the same likelihood 
 #&gt; 
 #&gt; Starting values for parameters to be optimised:
 #&gt;                   value   type
@@ -820,6 +923,11 @@ estimators.</p>
 #&gt;      value  type
 #&gt; m1_0     0 state
 #&gt; 
+#&gt; Results:
+#&gt; 
+#&gt;        AIC      BIC    logLik
+#&gt;   141.9656 151.7911 -64.98278
+#&gt; 
 #&gt; Optimised, transformed parameters with symmetric confidence intervals:
 #&gt;                 Estimate Std. Error     Lower     Upper
 #&gt; parent_0       100.70000   2.621000 95.400000 106.10000
@@ -868,14 +976,14 @@ estimators.</p>
 #&gt; 
 #&gt; Data:
 #&gt;  time variable observed predicted   residual
-#&gt;     0   parent    99.46 100.73434  -1.274339
-#&gt;     0   parent   102.04 100.73434   1.305661
+#&gt;     0   parent    99.46 100.73434  -1.274340
+#&gt;     0   parent   102.04 100.73434   1.305660
 #&gt;     1   parent    93.50  91.09751   2.402486
 #&gt;     1   parent    92.50  91.09751   1.402486
 #&gt;     3   parent    63.23  74.50141 -11.271410
 #&gt;     3   parent    68.99  74.50141  -5.511410
-#&gt;     7   parent    52.32  49.82880   2.491201
-#&gt;     7   parent    55.13  49.82880   5.301201
+#&gt;     7   parent    52.32  49.82880   2.491200
+#&gt;     7   parent    55.13  49.82880   5.301200
 #&gt;    14   parent    27.27  24.64809   2.621908
 #&gt;    14   parent    26.64  24.64809   1.991908
 #&gt;    21   parent    11.50  12.19232  -0.692315
@@ -902,8 +1010,8 @@ estimators.</p>
 #&gt;    50       m1    40.01  41.34199  -1.331985
 #&gt;    75       m1    40.09  36.61471   3.475295
 #&gt;    75       m1    33.85  36.61471  -2.764705
-#&gt;   100       m1    31.04  32.20082  -1.160824
-#&gt;   100       m1    33.13  32.20082   0.929176
+#&gt;   100       m1    31.04  32.20082  -1.160823
+#&gt;   100       m1    33.13  32.20082   0.929177
 #&gt;   120       m1    25.15  29.04130  -3.891304
 #&gt;   120       m1    33.31  29.04130   4.268696</div><div class='input'># }
 
diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html
index 2f2e89d9..81b62ae1 100644
--- a/docs/reference/mkinmod.html
+++ b/docs/reference/mkinmod.html
@@ -237,7 +237,7 @@ in the FOCUS and NAFTA guidance documents are used.</p>
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'>
 <span class='co'># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</span>
-<span class='no'>SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))
+<span class='no'>SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))
 
 <span class='co'># One parent compound, one metabolite, both single first order</span>
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(
@@ -252,7 +252,7 @@ in the FOCUS and NAFTA guidance documents are used.</p>
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinmod</span>(
   <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; Compilation argument:
-#&gt;  /usr/lib/R/bin/R CMD SHLIB fileb6a4eaab60.c 2&gt; fileb6a4eaab60.c.err.txt 
+#&gt;  /usr/lib/R/bin/R CMD SHLIB file1a1abf38df.c 2&gt; file1a1abf38df.c.err.txt 
 #&gt; Program source:
 #&gt;   1: #include &lt;R.h&gt;
 #&gt;   2: 
diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html
index 21c13156..e48a0cdb 100644
--- a/docs/reference/mkinpredict.html
+++ b/docs/reference/mkinpredict.html
@@ -251,7 +251,8 @@ is 1e-10, much lower than in <code>lsoda</code>.</p></td>
       <th>map_output</th>
       <td><p>Boolean to specify if the output should list values for
 the observed variables (default) or for all state variables (if set to
-FALSE).</p></td>
+FALSE). Setting this to FALSE has no effect for analytical solutions,
+as these always return mapped output.</p></td>
     </tr>
     <tr>
       <th>...</th>
@@ -262,7 +263,7 @@ solver is used.</p></td>
 
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
-    <p>A matrix in the same format as the output of <code>ode</code>.</p>
+    <p>A matrix with the numeric solution in wide format</p>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'>
@@ -270,149 +271,147 @@ solver is used.</p></td>
 <span class='co'># Compare solution types</span>
 <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
       <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>)</div><div class='output co'>#&gt;    time  degradinol
-#&gt; 1     0 100.0000000
-#&gt; 2     1  74.0818221
-#&gt; 3     2  54.8811636
-#&gt; 4     3  40.6569660
-#&gt; 5     4  30.1194212
-#&gt; 6     5  22.3130160
-#&gt; 7     6  16.5298888
-#&gt; 8     7  12.2456428
-#&gt; 9     8   9.0717953
-#&gt; 10    9   6.7205513
-#&gt; 11   10   4.9787068
-#&gt; 12   11   3.6883167
-#&gt; 13   12   2.7323722
-#&gt; 14   13   2.0241911
-#&gt; 15   14   1.4995577
-#&gt; 16   15   1.1108997
-#&gt; 17   16   0.8229747
-#&gt; 18   17   0.6096747
-#&gt; 19   18   0.4516581
-#&gt; 20   19   0.3345965
-#&gt; 21   20   0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+#&gt; 0     0 100.0000000
+#&gt; 1     1  74.0818221
+#&gt; 2     2  54.8811636
+#&gt; 3     3  40.6569660
+#&gt; 4     4  30.1194212
+#&gt; 5     5  22.3130160
+#&gt; 6     6  16.5298888
+#&gt; 7     7  12.2456428
+#&gt; 8     8   9.0717953
+#&gt; 9     9   6.7205513
+#&gt; 10   10   4.9787068
+#&gt; 11   11   3.6883167
+#&gt; 12   12   2.7323722
+#&gt; 13   13   2.0241911
+#&gt; 14   14   1.4995577
+#&gt; 15   15   1.1108997
+#&gt; 16   16   0.8229747
+#&gt; 17   17   0.6096747
+#&gt; 18   18   0.4516581
+#&gt; 19   19   0.3345965
+#&gt; 20   20   0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
       <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)</div><div class='output co'>#&gt;    time  degradinol
-#&gt; 1     0 100.0000000
-#&gt; 2     1  74.0818221
-#&gt; 3     2  54.8811636
-#&gt; 4     3  40.6569660
-#&gt; 5     4  30.1194212
-#&gt; 6     5  22.3130160
-#&gt; 7     6  16.5298888
-#&gt; 8     7  12.2456428
-#&gt; 9     8   9.0717953
-#&gt; 10    9   6.7205513
-#&gt; 11   10   4.9787068
-#&gt; 12   11   3.6883167
-#&gt; 13   12   2.7323722
-#&gt; 14   13   2.0241911
-#&gt; 15   14   1.4995577
-#&gt; 16   15   1.1108996
-#&gt; 17   16   0.8229747
-#&gt; 18   17   0.6096747
-#&gt; 19   18   0.4516581
-#&gt; 20   19   0.3345965
-#&gt; 21   20   0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+#&gt; 0     0 100.0000000
+#&gt; 1     1  74.0818221
+#&gt; 2     2  54.8811636
+#&gt; 3     3  40.6569660
+#&gt; 4     4  30.1194212
+#&gt; 5     5  22.3130160
+#&gt; 6     6  16.5298888
+#&gt; 7     7  12.2456428
+#&gt; 8     8   9.0717953
+#&gt; 9     9   6.7205513
+#&gt; 10   10   4.9787068
+#&gt; 11   11   3.6883167
+#&gt; 12   12   2.7323722
+#&gt; 13   13   2.0241911
+#&gt; 14   14   1.4995577
+#&gt; 15   15   1.1108996
+#&gt; 16   16   0.8229747
+#&gt; 17   17   0.6096747
+#&gt; 18   18   0.4516581
+#&gt; 19   19   0.3345965
+#&gt; 20   20   0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
       <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#&gt;    time  degradinol
-#&gt; 1     0 100.0000000
-#&gt; 2     1  74.0818221
-#&gt; 3     2  54.8811636
-#&gt; 4     3  40.6569660
-#&gt; 5     4  30.1194212
-#&gt; 6     5  22.3130160
-#&gt; 7     6  16.5298888
-#&gt; 8     7  12.2456428
-#&gt; 9     8   9.0717953
-#&gt; 10    9   6.7205513
-#&gt; 11   10   4.9787068
-#&gt; 12   11   3.6883167
-#&gt; 13   12   2.7323722
-#&gt; 14   13   2.0241911
-#&gt; 15   14   1.4995577
-#&gt; 16   15   1.1108996
-#&gt; 17   16   0.8229747
-#&gt; 18   17   0.6096747
-#&gt; 19   18   0.4516581
-#&gt; 20   19   0.3345965
-#&gt; 21   20   0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+#&gt; 0     0 100.0000000
+#&gt; 1     1  74.0818221
+#&gt; 2     2  54.8811636
+#&gt; 3     3  40.6569660
+#&gt; 4     4  30.1194212
+#&gt; 5     5  22.3130160
+#&gt; 6     6  16.5298888
+#&gt; 7     7  12.2456428
+#&gt; 8     8   9.0717953
+#&gt; 9     9   6.7205513
+#&gt; 10   10   4.9787068
+#&gt; 11   11   3.6883167
+#&gt; 12   12   2.7323722
+#&gt; 13   13   2.0241911
+#&gt; 14   14   1.4995577
+#&gt; 15   15   1.1108996
+#&gt; 16   16   0.8229747
+#&gt; 17   17   0.6096747
+#&gt; 18   18   0.4516581
+#&gt; 19   19   0.3345965
+#&gt; 20   20   0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
       <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>)</div><div class='output co'>#&gt;    time  degradinol
-#&gt; 1     0 100.0000000
-#&gt; 2     1  74.0818221
-#&gt; 3     2  54.8811636
-#&gt; 4     3  40.6569660
-#&gt; 5     4  30.1194212
-#&gt; 6     5  22.3130160
-#&gt; 7     6  16.5298888
-#&gt; 8     7  12.2456428
-#&gt; 9     8   9.0717953
-#&gt; 10    9   6.7205513
-#&gt; 11   10   4.9787068
-#&gt; 12   11   3.6883167
-#&gt; 13   12   2.7323722
-#&gt; 14   13   2.0241911
-#&gt; 15   14   1.4995577
-#&gt; 16   15   1.1108997
-#&gt; 17   16   0.8229747
-#&gt; 18   17   0.6096747
-#&gt; 19   18   0.4516581
-#&gt; 20   19   0.3345965
-#&gt; 21   20   0.2478752</div><div class='input'>
+#&gt; 0     0 100.0000000
+#&gt; 1     1  74.0818221
+#&gt; 2     2  54.8811636
+#&gt; 3     3  40.6569660
+#&gt; 4     4  30.1194212
+#&gt; 5     5  22.3130160
+#&gt; 6     6  16.5298888
+#&gt; 7     7  12.2456428
+#&gt; 8     8   9.0717953
+#&gt; 9     9   6.7205513
+#&gt; 10   10   4.9787068
+#&gt; 11   11   3.6883167
+#&gt; 12   12   2.7323722
+#&gt; 13   13   2.0241911
+#&gt; 14   14   1.4995577
+#&gt; 15   15   1.1108997
+#&gt; 16   16   0.8229747
+#&gt; 17   17   0.6096747
+#&gt; 18   18   0.4516581
+#&gt; 19   19   0.3345965
+#&gt; 20   20   0.2478752</div><div class='input'>
 <span class='co'># Compare integration methods to analytical solution</span>
 <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
-      <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt;    time degradinol
-#&gt; 21   20  0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
-      <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"lsoda"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt;    time degradinol
-#&gt; 21   20  0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
-      <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"ode45"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt;    time degradinol
-#&gt; 21   20  0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
-      <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"rk4"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt;    time degradinol
-#&gt; 21   20  0.2480043</div><div class='input'><span class='co'># rk4 is not as precise here</span>
+      <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt;       time degradinol 
+#&gt; 20.0000000  0.2478752 </div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+      <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"lsoda"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt;       time degradinol 
+#&gt; 20.0000000  0.2478752 </div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+      <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"ode45"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt;       time degradinol 
+#&gt; 20.0000000  0.2478752 </div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>), <span class='fl'>0</span>:<span class='fl'>20</span>,
+      <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"rk4"</span>)[<span class='fl'>21</span>,]</div><div class='output co'>#&gt;       time degradinol 
+#&gt; 20.0000000  0.2480043 </div><div class='input'><span class='co'># rk4 is not as precise here</span>
 
 <span class='co'># The number of output times used to make a lot of difference until the</span>
 <span class='co'># default for atol was adjusted</span>
 <span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>),
-      <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>))[<span class='fl'>201</span>,]</div><div class='output co'>#&gt;     time degradinol
-#&gt; 201   20  0.2478752</div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>),
-      <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.01</span>))[<span class='fl'>2001</span>,]</div><div class='output co'>#&gt;      time degradinol
-#&gt; 2001   20  0.2478752</div><div class='input'>
-<span class='co'># Check compiled model versions - they are faster than the eigenvalue based solutions!</span>
+      <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>))[<span class='fl'>201</span>,]</div><div class='output co'>#&gt;       time degradinol 
+#&gt; 20.0000000  0.2478752 </div><div class='input'><span class='fu'>mkinpredict</span>(<span class='no'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_degradinol</span> <span class='kw'>=</span> <span class='fl'>0.3</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>degradinol</span> <span class='kw'>=</span> <span class='fl'>100</span>),
+      <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.01</span>))[<span class='fl'>2001</span>,]</div><div class='output co'>#&gt;       time degradinol 
+#&gt; 20.0000000  0.2478752 </div><div class='input'>
+<span class='co'># Comparison of the performance of solution types</span>
 <span class='no'>SFO_SFO</span> <span class='kw'>=</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>),
-                  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"min"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='kw'>if</span>(<span class='fu'><a href='https://rdrr.io/r/base/library.html'>require</a></span>(<span class='no'>rbenchmark</span>)) {
-  <span class='fu'><a href='https://rdrr.io/pkg/rbenchmark/man/benchmark.html'>benchmark</a></span>(
-    <span class='kw'>eigen</span> <span class='kw'>=</span> <span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
+                  <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='kw'>if</span>(<span class='fu'><a href='https://rdrr.io/r/base/library.html'>require</a></span>(<span class='no'>rbenchmark</span>)) {
+  <span class='fu'><a href='https://rdrr.io/pkg/rbenchmark/man/benchmark.html'>benchmark</a></span>(<span class='kw'>replications</span> <span class='kw'>=</span> <span class='fl'>10</span>, <span class='kw'>order</span> <span class='kw'>=</span> <span class='st'>"relative"</span>, <span class='kw'>columns</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"test"</span>, <span class='st'>"relative"</span>, <span class='st'>"elapsed"</span>),
+    <span class='kw'>eigen</span> <span class='kw'>=</span> <span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>,
+      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.15</span>, <span class='kw'>f_parent_to_m1</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>k_m1</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
       <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
       <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>)[<span class='fl'>201</span>,],
     <span class='kw'>deSolve_compiled</span> <span class='kw'>=</span> <span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>,
-      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
+      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.15</span>, <span class='kw'>f_parent_to_m1</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>k_m1</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
       <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
       <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)[<span class='fl'>201</span>,],
-    <span class='kw'>deSolve</span> <span class='kw'>=</span> <span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent_m1</span> <span class='kw'>=</span> <span class='fl'>0.05</span>, <span class='kw'>k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>, <span class='kw'>k_m1_sink</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
-     <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
-     <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)[<span class='fl'>201</span>,],
-    <span class='kw'>replications</span> <span class='kw'>=</span> <span class='fl'>10</span>)
-}</div><div class='output co'>#&gt; <span class='message'>Lade nötiges Paket: rbenchmark</span></div><div class='output co'>#&gt;               test replications elapsed relative user.self sys.self user.child
-#&gt; 3          deSolve           10   0.229   28.625     0.229        0          0
-#&gt; 2 deSolve_compiled           10   0.008    1.000     0.008        0          0
-#&gt; 1            eigen           10   0.026    3.250     0.025        0          0
-#&gt;   sys.child
-#&gt; 3         0
-#&gt; 2         0
-#&gt; 1         0</div><div class='input'>
-<span class='co'># Since mkin 0.9.49.11 we also have analytical solutions for some models, including SFO-SFO</span>
-<span class='co'>#    deSolve = mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),</span>
-<span class='co'>#     c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),</span>
-<span class='co'>#     solution_type = "analytical", use_compiled = FALSE)[201,],</span>
-
+    <span class='kw'>deSolve</span> <span class='kw'>=</span> <span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>,
+      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.15</span>, <span class='kw'>f_parent_to_m1</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>k_m1</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
+      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
+      <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)[<span class='fl'>201</span>,],
+    <span class='kw'>analytical</span> <span class='kw'>=</span> <span class='fu'>mkinpredict</span>(<span class='no'>SFO_SFO</span>,
+      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.15</span>, <span class='kw'>f_parent_to_m1</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>k_m1</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
+      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>20</span>, <span class='kw'>by</span> <span class='kw'>=</span> <span class='fl'>0.1</span>),
+      <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)[<span class='fl'>201</span>,])
+}</div><div class='output co'>#&gt;               test relative elapsed
+#&gt; 4       analytical     1.00   0.004
+#&gt; 2 deSolve_compiled     1.25   0.005
+#&gt; 1            eigen     5.00   0.020
+#&gt; 3          deSolve    54.25   0.217</div><div class='input'>
 <span class='co'># \dontrun{</span>
   <span class='co'># Predict from a fitted model</span>
   <span class='no'>f</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-  <span class='fu'><a href='https://rdrr.io/r/utils/head.html'>head</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>f</span>))</div><div class='output co'>#&gt;   time   parent       m1
-#&gt; 1  0.0 82.49216 0.000000
-#&gt; 2  0.1 80.00563 1.179963
-#&gt; 3  0.2 77.59404 2.312596
-#&gt; 4  0.3 75.25515 3.399443
-#&gt; 5  0.4 72.98675 4.442000
-#&gt; 6  0.5 70.78673 5.441717</div><div class='input'># }
+  <span class='no'>f</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)
+  <span class='fu'><a href='https://rdrr.io/r/utils/head.html'>head</a></span>(<span class='fu'>mkinpredict</span>(<span class='no'>f</span>))</div><div class='output co'>#&gt;     time   parent       m1
+#&gt; 0    0.0 82.49216 0.000000
+#&gt; 0.1  0.1 80.00562 1.236198
+#&gt; 0.2  0.2 77.59404 2.422818
+#&gt; 0.3  0.3 75.25514 3.561476
+#&gt; 0.4  0.4 72.98675 4.653740
+#&gt; 0.5  0.5 70.78673 5.701130</div><div class='input'># }
 
 </div></pre>
   </div>
diff --git a/docs/reference/plot.mkinfit-1.png b/docs/reference/plot.mkinfit-1.png
index 0fe3c263..c6a415d7 100644
--- a/docs/reference/plot.mkinfit-1.png
+++ b/docs/reference/plot.mkinfit-1.png
diff --git a/docs/reference/plot.mkinfit-2.png b/docs/reference/plot.mkinfit-2.png
index 72bc331e..5dd3731e 100644
--- a/docs/reference/plot.mkinfit-2.png
+++ b/docs/reference/plot.mkinfit-2.png
diff --git a/docs/reference/plot.mkinfit-3.png b/docs/reference/plot.mkinfit-3.png
index 6910ae47..59cf8f8d 100644
--- a/docs/reference/plot.mkinfit-3.png
+++ b/docs/reference/plot.mkinfit-3.png
diff --git a/docs/reference/plot.mkinfit-4.png b/docs/reference/plot.mkinfit-4.png
index 8d399598..d9867952 100644
--- a/docs/reference/plot.mkinfit-4.png
+++ b/docs/reference/plot.mkinfit-4.png
diff --git a/docs/reference/plot.mkinfit-5.png b/docs/reference/plot.mkinfit-5.png
index 20f30221..1109c8df 100644
--- a/docs/reference/plot.mkinfit-5.png
+++ b/docs/reference/plot.mkinfit-5.png
diff --git a/docs/reference/plot.mkinfit-6.png b/docs/reference/plot.mkinfit-6.png
index 0b3769ce..230c90e9 100644
--- a/docs/reference/plot.mkinfit-6.png
+++ b/docs/reference/plot.mkinfit-6.png
diff --git a/docs/reference/plot.mkinfit-7.png b/docs/reference/plot.mkinfit-7.png
index ad2ffa8c..b23427b5 100644
--- a/docs/reference/plot.mkinfit-7.png
+++ b/docs/reference/plot.mkinfit-7.png
diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html
index 33dc52b7..2ede2dec 100644
--- a/docs/reference/plot.mkinfit.html
+++ b/docs/reference/plot.mkinfit.html
@@ -10,23 +10,27 @@
 
 
 <!-- jquery -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
 <!-- Bootstrap -->
 
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha256-916EbMg70RQy9LHiGkXzG8hSg9EdNy97GazNG/aiY1w=" crossorigin="anonymous" />
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" />
 
-<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script>
+
+<!-- bootstrap-toc -->
+<link rel="stylesheet" href="../bootstrap-toc.css">
+<script src="../bootstrap-toc.js"></script>
 
 <!-- Font Awesome icons -->
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/all.min.css" integrity="sha256-nAmazAk6vS34Xqo0BSrTb+abbtFlgsFK7NKSi6o7Y78=" crossorigin="anonymous" />
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/v4-shims.min.css" integrity="sha256-6qHlizsOWFskGlwVOKuns+D1nB6ssZrHQrNj1wGplHc=" crossorigin="anonymous" />
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" />
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" />
 
 <!-- clipboard.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script>
 
 <!-- headroom.js -->
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/headroom.min.js" integrity="sha256-DJFC1kqIhelURkuza0AvYal5RxMtpzLjFhsnVIeuk+U=" crossorigin="anonymous"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
 
 <!-- pkgdown -->
 <link href="../pkgdown.css" rel="stylesheet">
@@ -39,7 +43,6 @@
 <meta property="og:description" content="Solves the differential equations with the optimised and fixed parameters
 from a previous successful call to mkinfit and plots the
 observed data together with the solution of the fitted model." />
-<meta name="twitter:card" content="summary" />
 
 
 
@@ -57,7 +60,7 @@ observed data together with the solution of the fitted model." />
 
   </head>
 
-  <body>
+  <body data-spy="scroll" data-target="#toc">
     <div class="container template-reference-topic">
       <header>
       <div class="navbar navbar-default navbar-fixed-top" role="navigation">
@@ -71,7 +74,7 @@ observed data together with the solution of the fitted model." />
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">mkin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.9</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50</span>
       </span>
     </div>
 
@@ -115,7 +118,12 @@ observed data together with the solution of the fitted model." />
 </li>
       </ul>
       <ul class="nav navbar-nav navbar-right">
-        
+        <li>
+  <a href="http://github.com/jranke/mkin/">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
       </ul>
       
     </div><!--/.nav-collapse -->
@@ -130,7 +138,7 @@ observed data together with the solution of the fitted model." />
   <div class="col-md-9 contents">
     <div class="page-header">
     <h1>Plot the observed data and the fitted model of an mkinfit object</h1>
-    
+    <small class="dont-index">Source: <a href='http://github.com/jranke/mkin/blob/master/R/plot.mkinfit.R'><code>R/plot.mkinfit.R</code></a></small>
     <div class="hidden name"><code>plot.mkinfit.Rd</code></div>
     </div>
 
@@ -141,7 +149,7 @@ observed data together with the solution of the fitted model.</p>
     </div>
 
     <pre class="usage"><span class='co'># S3 method for mkinfit</span>
-<span class='fu'><a href='https://rdrr.io/r/graphics/plot.html'>plot</a></span>(
+<span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(
   <span class='no'>x</span>,
   <span class='kw'>fit</span> <span class='kw'>=</span> <span class='no'>x</span>,
   <span class='kw'>obs_vars</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span>(<span class='no'>fit</span>$<span class='no'>mkinmod</span>$<span class='no'>map</span>),
@@ -303,7 +311,7 @@ chi2 error percentage.</p></td>
     </tr>
     <tr>
       <th>...</th>
-      <td><p>Further arguments passed to <code><a href='https://rdrr.io/r/graphics/plot.html'>plot</a></code>.</p></td>
+      <td><p>Further arguments passed to <code><a href='https://rdrr.io/r/base/plot.html'>plot</a></code>.</p></td>
     </tr>
     <tr>
       <th>standardized</th>
@@ -327,29 +335,22 @@ latex is being used for the formatting of the chi2 error level, if
 <span class='co'># parent to sink included</span>
 <span class='co'># \dontrun{</span>
 <span class='no'>SFO_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Parent"</span>),
-                   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span> ))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.html'>plot</a></span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_res</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_res</span>(<span class='no'>fit</span>, <span class='kw'>standardized</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='img'><img src='plot.mkinfit-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_err</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-4.png' alt='' width='700' height='433' /></div><div class='input'>
+                   <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span> ))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_res</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_res</span>(<span class='no'>fit</span>, <span class='kw'>standardized</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='img'><img src='plot.mkinfit-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_err</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-4.png' alt='' width='700' height='433' /></div><div class='input'>
 <span class='co'># Show the observed variables separately, with residuals</span>
-<span class='fu'><a href='https://rdrr.io/r/graphics/plot.html'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>),
+<span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>),
      <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-5.png' alt='' width='700' height='433' /></div><div class='input'>
 <span class='co'># The same can be obtained with less typing, using the convenience function plot_sep</span>
 <span class='fu'>plot_sep</span>(<span class='no'>fit</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>))</div><div class='img'><img src='plot.mkinfit-6.png' alt='' width='700' height='433' /></div><div class='input'>
 <span class='co'># Show the observed variables separately, with the error model</span>
-<span class='fu'><a href='https://rdrr.io/r/graphics/plot.html'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_errplot</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>),
+<span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_errplot</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>),
      <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-7.png' alt='' width='700' height='433' /></div><div class='input'># }
 
 </div></pre>
   </div>
-  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
-    <h2>Contents</h2>
-    <ul class="nav nav-pills nav-stacked">
-      <li><a href="#arguments">Arguments</a></li>
-      <li><a href="#value">Value</a></li>
-      <li><a href="#details">Details</a></li>
-      <li><a href="#examples">Examples</a></li>
-    </ul>
-
-    <h2>Author</h2>
-    <p>Johannes Ranke</p>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
   </div>
 </div>
 
@@ -360,7 +361,7 @@ latex is being used for the formatting of the chi2 error level, if
 </div>
 
 <div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.4.1.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
 </div>
 
       </footer>
diff --git a/man/mkinpredict.Rd b/man/mkinpredict.Rd
index 1ba5b5f8..2c9192b7 100644
--- a/man/mkinpredict.Rd
+++ b/man/mkinpredict.Rd
@@ -85,13 +85,14 @@ is 1e-10, much lower than in \code{\link{lsoda}}.}
 
 \item{map_output}{Boolean to specify if the output should list values for
 the observed variables (default) or for all state variables (if set to
-FALSE).}
+FALSE). Setting this to FALSE has no effect for analytical solutions,
+as these always return mapped output.}
 
 \item{\dots}{Further arguments passed to the ode solver in case such a
 solver is used.}
 }
 \value{
-A data frame with the solution in wide format
+A matrix with the numeric solution in wide format
 }
 \description{
 This function produces a time series for all the observed variables in a
@@ -129,7 +130,7 @@ mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
       seq(0, 20, by = 0.01))[2001,]
 
-# Check compiled model versions - they are faster than the eigenvalue based solutions!
+# Comparison of the performance of solution types
 SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
                   m1 = list(type = "SFO"), use_of_ff = "max")
 if(require(rbenchmark)) {
@@ -151,15 +152,11 @@ if(require(rbenchmark)) {
       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
       solution_type = "analytical", use_compiled = FALSE)[201,])
 }
-    analytical = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "analytical", use_compiled = FALSE)[201,]
 
 \dontrun{
   # Predict from a fitted model
   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
-  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "analytical")
+  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
   head(mkinpredict(f))
 }
 
diff --git a/test.log b/test.log
index 2a116058..7822efdb 100644
--- a/test.log
+++ b/test.log
@@ -2,9 +2,9 @@ Loading mkin
 Testing mkin
 ✔ |  OK F W S | Context
 ✔ |   2       | Export dataset for reading into CAKE
-✔ |  13       | Results for FOCUS D established in expertise for UBA (Ranke 2014) [2.5 s]
+✔ |  13       | Results for FOCUS D established in expertise for UBA (Ranke 2014) [1.2 s]
 ✔ |   4       | Calculation of FOCUS chi2 error levels [0.4 s]
-✔ |   7       | Fitting the SFORB model [9.0 s]
+✔ |   7       | Fitting the SFORB model [3.5 s]
 ✔ |   1       | Analytical solutions for coupled models [0.2 s]
 ✔ |   5       | Calculation of Akaike weights
 ✔ |  10       | Confidence intervals and p-values [1.0 s]
@@ -14,21 +14,21 @@ Testing mkin
 ✔ |   1       | Test dataset class mkinds used in gmkin
 ✔ |  12       | Special cases of mkinfit calls [0.6 s]
 ✔ |   8       | mkinmod model generation and printing [0.2 s]
-✔ |   3       | Model predictions with mkinpredict [0.4 s]
-✔ |  16       | Evaluations according to 2015 NAFTA guidance [1.3 s]
-✔ |   9       | Nonlinear mixed-effects models [12.1 s]
+✔ |   3       | Model predictions with mkinpredict [0.3 s]
+✔ |  16       | Evaluations according to 2015 NAFTA guidance [1.4 s]
+✔ |   9       | Nonlinear mixed-effects models [7.5 s]
 ✔ |   4       | Calculation of maximum time weighted average concentrations (TWAs) [2.4 s]
 ✔ |   3       | Summary
-✔ |  14       | Plotting [3.6 s]
+✔ |  14       | Plotting [1.7 s]
 ✔ |   4       | AIC calculation
 ✔ |   4       | Residuals extracted from mkinfit models
-✔ |   2       | Complex test case from Schaefer et al. (2007) Piacenza paper [4.2 s]
+✔ |   2       | Complex test case from Schaefer et al. (2007) Piacenza paper [1.4 s]
 ✔ |   1       | Summaries of old mkinfit objects
-✔ |   4       | Results for synthetic data established in expertise for UBA (Ranke 2014) [6.3 s]
-✔ |   9       | Hypothesis tests [17.7 s]
+✔ |   4       | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s]
+✔ |   9       | Hypothesis tests [6.5 s]
 
 ══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 66.3 s
+Duration: 35.1 s
 
 OK:       157
 Failed:   0
diff --git a/tests/testthat/FOCUS_2006_D.csf b/tests/testthat/FOCUS_2006_D.csf
index d41f0a9f..c0c5c10c 100644
--- a/tests/testthat/FOCUS_2006_D.csf
+++ b/tests/testthat/FOCUS_2006_D.csf
@@ -5,7 +5,7 @@ Description:
 MeasurementUnits: % AR
 TimeUnits: days
 Comments: Created using mkin::CAKE_export
-Date: 2020-05-10
+Date: 2020-05-11
 Optimiser: IRLS
 
 [Data]
diff --git a/tests/testthat/test_mkinpredict_SFO_SFO.R b/tests/testthat/test_mkinpredict_SFO_SFO.R
index 347aa50b..d0dc7cdb 100644
--- a/tests/testthat/test_mkinpredict_SFO_SFO.R
+++ b/tests/testthat/test_mkinpredict_SFO_SFO.R
@@ -5,26 +5,28 @@ test_that("Variants of model predictions for SFO_SFO model give equivalent resul
   # Do not use time 0, as eigenvalue based solution does not give 0 at time 0 for metabolites
   # and relative tolerance is thus not met
   tol = 0.01
-  SFO_SFO.1 <- mkinmod(parent = list(type = "SFO", to = "m1"),
-         m1 = list(type = "SFO"), use_of_ff = "min", quiet = TRUE)
-  SFO_SFO.2 <- mkinmod(parent = list(type = "SFO", to = "m1"),
-         m1 = list(type = "SFO"), use_of_ff = "max", quiet = TRUE)
+  SFO_SFO.1 <- mkinmod(parent = mkinsub("SFO", to = "m1"),
+         m1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
+  SFO_SFO.2 <- mkinmod(parent = mkinsub("SFO", to = "m1"),
+         m1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
 
   ot = seq(0, 100, by = 1)
-  r.1.e <- subset(mkinpredict(SFO_SFO.1,
+  r.1.e <- subset(as.data.frame(mkinpredict(SFO_SFO.1,
              c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1),
-             c(parent = 100, m1 = 0), ot, solution_type = "eigen"),
+             c(parent = 100, m1 = 0), ot, solution_type = "eigen")),
                  time %in% c(1, 10, 50, 100))
-  r.1.d <- subset(mkinpredict(SFO_SFO.1,
+  r.1.d <- subset(as.data.frame(mkinpredict(SFO_SFO.1,
              c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1),
-             c(parent = 100, m1 = 0), ot, solution_type = "deSolve"),
+             c(parent = 100, m1 = 0), ot, solution_type = "deSolve")),
                  time %in% c(1, 10, 50, 100))
 
-  r.2.e <- subset(mkinpredict(SFO_SFO.2, c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
-      c(parent = 100, m1 = 0), ot, solution_type = "eigen"),
+  r.2.e <- subset(as.data.frame(mkinpredict(SFO_SFO.2,
+      c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
+      c(parent = 100, m1 = 0), ot, solution_type = "eigen")),
                   time %in% c(1, 10, 50, 100))
-  r.2.d <- subset(mkinpredict(SFO_SFO.2, c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
-      c(parent = 100, m1 = 0), ot, solution_type = "deSolve"),
+  r.2.d <- subset(as.data.frame(mkinpredict(SFO_SFO.2,
+      c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
+      c(parent = 100, m1 = 0), ot, solution_type = "deSolve")),
                   time %in% c(1, 10, 50, 100))
 
   # Compare eigen and deSolve for minimum use of formation fractions
diff --git a/tests/testthat/test_plots_summary_twa.R b/tests/testthat/test_plots_summary_twa.R
index 13d1dd0f..bc3d29a8 100644
--- a/tests/testthat/test_plots_summary_twa.R
+++ b/tests/testthat/test_plots_summary_twa.R
@@ -13,7 +13,7 @@ test_that("Time weighted average concentrations are correct", {
                 odeparms = bpar[2:length(bpar)],
                 odeini = c(parent = bpar[[1]]),
                 outtimes = outtimes_10)
-    twa_num <- mean(pred_10$parent)
+    twa_num <- mean(pred_10[, "parent"])
     names(twa_num) <- 10
     twa_ana <- max_twa_parent(fit, 10)
 
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index ff9d4596..38c597b0 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2020-05-06" />
+<meta name="date" content="2020-05-11" />
 
 <title>Example evaluation of FOCUS Example Dataset D</title>
 
@@ -365,7 +365,7 @@ summary {
 
 <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">2020-05-06</h4>
+<h4 class="date">2020-05-11</h4>
 
 </div>
 
@@ -423,101 +423,99 @@ print(FOCUS_2006_D)</code></pre>
 <pre class="r"><code>SFO_SFO &lt;- mkinmod(parent = mkinsub(&quot;SFO&quot;, &quot;m1&quot;), m1 = mkinsub(&quot;SFO&quot;))</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 
-## &quot;d_parent = - k_parent_sink * parent - k_parent_m1 * parent&quot; 
-##                                                           m1 
-##             &quot;d_m1 = + k_parent_m1 * parent - k_m1_sink * m1&quot;</code></pre>
+<pre><code>##                                                    parent 
+##                          &quot;d_parent = - k_parent * parent&quot; 
+##                                                        m1 
+## &quot;d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1&quot;</code></pre>
 <p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p>
 <pre class="r"><code>fit &lt;- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)</code></pre>
 <pre><code>## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value
 ## of zero were removed from the data</code></pre>
 <p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p>
 <pre class="r"><code>plot_sep(fit, lpos = c(&quot;topright&quot;, &quot;bottomright&quot;))</code></pre>
-<p><img 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width="768" /></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 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" 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" width="768" /></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 used for fitting:    0.9.49.11 
+<pre><code>## mkin version used for fitting:    0.9.50 
 ## R version used for fitting:       4.0.0 
-## Date of fit:     Wed May  6 19:54:54 2020 
-## Date of summary: Wed May  6 19:54:54 2020 
+## Date of fit:     Mon May 11 04:41:12 2020 
+## Date of summary: Mon May 11 04:41:12 2020 
 ## 
 ## Equations:
-## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
-## d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1
+## d_parent/dt = - k_parent * parent
+## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
 ## 
-## Model predictions using solution type deSolve 
+## Model predictions using solution type analytical 
 ## 
-## Fitted using 389 model solutions performed in 1.041 s
+## Fitted using 421 model solutions performed in 0.149 s
 ## 
 ## Error model: Constant variance 
 ## 
 ## Error model algorithm: OLS 
 ## 
 ## 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
+##                   value   type
+## parent_0       100.7500  state
+## k_parent         0.1000 deparm
+## k_m1             0.1001 deparm
+## f_parent_to_m1   0.5000 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
+##                     value lower upper
+## parent_0       100.750000  -Inf   Inf
+## log_k_parent    -2.302585  -Inf   Inf
+## log_k_m1        -2.301586  -Inf   Inf
+## f_parent_ilr_1   0.000000  -Inf   Inf
 ## 
 ## Fixed parameter values:
 ##      value  type
 ## m1_0     0 state
 ## 
+## Results:
+## 
+##        AIC      BIC    logLik
+##   204.4486 212.6365 -97.22429
+## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
-##                   Estimate Std. Error  Lower   Upper
-## parent_0            99.600    1.57000 96.400 102.800
-## log_k_parent_sink   -3.038    0.07626 -3.193  -2.883
-## log_k_parent_m1     -2.980    0.04033 -3.062  -2.898
-## log_k_m1_sink       -5.248    0.13320 -5.518  -4.977
-## sigma                3.126    0.35850  2.396   3.855
+##                Estimate Std. Error    Lower    Upper
+## parent_0       99.60000    1.57000 96.40000 102.8000
+## log_k_parent   -2.31600    0.04087 -2.39900  -2.2330
+## log_k_m1       -5.24800    0.13320 -5.51800  -4.9770
+## f_parent_ilr_1  0.04096    0.06312 -0.08746   0.1694
+## sigma           3.12600    0.35850  2.39600   3.8550
 ## 
 ## Parameter correlation:
-##                     parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
-## parent_0           1.000e+00         6.067e-01      -6.372e-02    -1.688e-01
-## log_k_parent_sink  6.067e-01         1.000e+00      -8.550e-02    -6.252e-01
-## log_k_parent_m1   -6.372e-02        -8.550e-02       1.000e+00     4.731e-01
-## log_k_m1_sink     -1.688e-01        -6.252e-01       4.731e-01     1.000e+00
-## sigma              5.287e-10         3.306e-09       4.421e-08    -3.319e-10
-##                        sigma
-## parent_0           5.287e-10
-## log_k_parent_sink  3.306e-09
-## log_k_parent_m1    4.421e-08
-## log_k_m1_sink     -3.319e-10
-## sigma              1.000e+00
+##                  parent_0 log_k_parent   log_k_m1 f_parent_ilr_1      sigma
+## parent_0        1.000e+00    5.174e-01 -1.688e-01     -5.471e-01 -3.214e-07
+## log_k_parent    5.174e-01    1.000e+00 -3.263e-01     -5.426e-01  3.168e-07
+## log_k_m1       -1.688e-01   -3.263e-01  1.000e+00      7.478e-01 -1.410e-07
+## f_parent_ilr_1 -5.471e-01   -5.426e-01  7.478e-01      1.000e+00  5.093e-10
+## sigma          -3.214e-07    3.168e-07 -1.410e-07      5.093e-10  1.000e+00
 ## 
 ## 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(&gt;t)     Lower     Upper
-## parent_0      99.600000  63.430 2.298e-36 96.400000 1.028e+02
-## k_parent_sink  0.047920  13.110 6.126e-15  0.041030 5.596e-02
-## k_parent_m1    0.050780  24.800 3.269e-23  0.046780 5.512e-02
-## k_m1_sink      0.005261   7.510 6.165e-09  0.004012 6.898e-03
-## sigma          3.126000   8.718 2.235e-10  2.396000 3.855e+00
+##                 Estimate t value    Pr(&gt;t)     Lower     Upper
+## parent_0       99.600000  63.430 2.298e-36 96.400000 1.028e+02
+## k_parent        0.098700  24.470 4.955e-23  0.090820 1.073e-01
+## k_m1            0.005261   7.510 6.165e-09  0.004012 6.898e-03
+## f_parent_to_m1  0.514500  23.070 3.104e-22  0.469100 5.596e-01
+## sigma           3.126000   8.718 2.235e-10  2.396000 3.855e+00
 ## 
 ## FOCUS 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
+## parent     6.459       2  7
+## m1         4.690       2  8
 ## 
 ## Resulting formation fractions:
 ##                 ff
-## parent_sink 0.4855
 ## parent_m1   0.5145
-## m1_sink     1.0000
+## parent_sink 0.4855
 ## 
 ## Estimated disappearance times:
 ##           DT50   DT90
@@ -530,10 +528,10 @@ print(FOCUS_2006_D)</code></pre>
 ##     0   parent   102.04  99.59848  2.442e+00
 ##     1   parent    93.50  90.23787  3.262e+00
 ##     1   parent    92.50  90.23787  2.262e+00
-##     3   parent    63.23  74.07320 -1.084e+01
-##     3   parent    68.99  74.07320 -5.083e+00
-##     7   parent    52.32  49.91207  2.408e+00
-##     7   parent    55.13  49.91207  5.218e+00
+##     3   parent    63.23  74.07319 -1.084e+01
+##     3   parent    68.99  74.07319 -5.083e+00
+##     7   parent    52.32  49.91206  2.408e+00
+##     7   parent    55.13  49.91206  5.218e+00
 ##    14   parent    27.27  25.01257  2.257e+00
 ##    14   parent    26.64  25.01257  1.627e+00
 ##    21   parent    11.50  12.53462 -1.035e+00
@@ -543,7 +541,7 @@ print(FOCUS_2006_D)</code></pre>
 ##    50   parent     0.69   0.71624 -2.624e-02
 ##    50   parent     0.63   0.71624 -8.624e-02
 ##    75   parent     0.05   0.06074 -1.074e-02
-##    75   parent     0.06   0.06074 -7.382e-04
+##    75   parent     0.06   0.06074 -7.381e-04
 ##     1       m1     4.84   4.80296  3.704e-02
 ##     1       m1     5.64   4.80296  8.370e-01
 ##     3       m1    12.91  13.02400 -1.140e-01
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index 8786aa05..2ba360f4 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -1,17 +1,17 @@
 <!DOCTYPE html>
 
-<html xmlns="http://www.w3.org/1999/xhtml">
+<html>
 
 <head>
 
 <meta charset="utf-8" />
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
 
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2019-07-05" />
+<meta name="date" content="2020-05-11" />
 
 <title>Introduction to mkin</title>
 
@@ -1288,7 +1288,7 @@ window.initializeCodeFolding = function(show) {
 
     // create a collapsable div to wrap the code in
     var div = $('<div class="collapse r-code-collapse"></div>');
-    if (show)
+    if (show || $(this)[0].classList.contains('fold-show'))
       div.addClass('in');
     var id = 'rcode-643E0F36' + currentIndex++;
     div.attr('id', id);
@@ -1404,7 +1404,6 @@ code {
 }
 img {
   max-width:100%;
-  height: auto;
 }
 .tabbed-pane {
   padding-top: 12px;
@@ -1466,6 +1465,7 @@ summary {
   border: none;
   display: inline-block;
   border-radius: 4px;
+  background-color: transparent;
 }
 
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li {
@@ -1478,56 +1478,12 @@ summary {
 }
 </style>
 
-<script>
-$(document).ready(function () {
-  window.buildTabsets("TOC");
-});
-
-$(document).ready(function () {
-  $('.tabset-dropdown > .nav-tabs > li').click(function () {
-    $(this).parent().toggleClass('nav-tabs-open')
-  });
-});
-</script>
-
 <!-- code folding -->
 <style type="text/css">
 .code-folding-btn { margin-bottom: 4px; }
 </style>
-<script>
-$(document).ready(function () {
-  window.initializeCodeFolding("hide" === "show");
-});
-</script>
-
-
-
-<script>
-$(document).ready(function ()  {
-
-    // move toc-ignore selectors from section div to header
-    $('div.section.toc-ignore')
-        .removeClass('toc-ignore')
-        .children('h1,h2,h3,h4,h5').addClass('toc-ignore');
 
-    // establish options
-    var options = {
-      selectors: "h1,h2,h3",
-      theme: "bootstrap3",
-      context: '.toc-content',
-      hashGenerator: function (text) {
-        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase();
-      },
-      ignoreSelector: ".toc-ignore",
-      scrollTo: 0
-    };
-    options.showAndHide = true;
-    options.smoothScroll = true;
 
-    // tocify
-    var toc = $("#TOC").tocify(options).data("toc-tocify");
-});
-</script>
 
 <style type="text/css">
 
@@ -1541,6 +1497,12 @@ $(document).ready(function ()  {
 }
 }
 
+@media print {
+.toc-content {
+  /* see https://github.com/w3c/csswg-drafts/issues/4434 */
+  float: right;
+}
+}
 
 .toc-content {
   padding-left: 30px;
@@ -1621,7 +1583,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Introduction to mkin</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">2019-07-05</h4>
+<h4 class="date">2020-05-11</h4>
 
 </div>
 
@@ -1658,7 +1620,7 @@ f_SFO_SFO_SFO &lt;- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)
 
 # Plot the results separately for parent and metabolites
 plot_sep(f_SFO_SFO_SFO, lpos = c(&quot;topright&quot;, &quot;bottomright&quot;, &quot;bottomright&quot;))</code></pre>
-<p><img 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86cyYqRAXoEjiqAJk2aJC8vHxwcbGhoyGAwcnNzcUE0sJS7u/uXS6LGxMTExcWFhoYSBPH58+exY8fu3r37y+8XDAwMOhjZwcGBeVvy2LFjExMTAwMDt23bxlz/7v3798w+tra28vLyBEEkJiaWlZVNnjyZIIj6+vrHjx9LS0vb2try8/MTBKGmptbY2CggINA1rxn+h8FgXL58+cGDB2pqatOmTWtX4/4KCoWyZ8+eFStWVFRU9O3bl4+Pr6tGBoBWnFMAKSgoxMTEHDt2rLi4eNasWUuXLsUdDcBqcnJycnJy7Rr19PTOnTu3bds2Go127tw5T0/PYcOG/ezIzc3NrRt8fHwJCQl+fn6zZ8/28vI6f/488ykhIaHWjYCAgFmzZhEEQafTKRRKXFzct9YS5kKHDh2aPXt2lw/r7++flpbm5OR08+bNgwcPJiUldW2V+dVPFwB0Fc4pgMaOHTt37tzU1FQdHZ3ExERZWdnVq1cfO3aM7FzAdYSEhFJSUo4dO1ZYWLhs2TInJ6dODHL9+vXPnz8zGIwzZ84cOXLkypUrHh4e8+bNe/Lkydu3b9td5m9jY7NixQovL6+mpiZDQ8Nbt259dcwuv5OIbTEvhGq1Zs0a5p9DXl5eXbWL8vLyc+fO5efnM8vQ0aNHX7582dXVtavGB+Aer169OnHiRENDg4uLS3fOZsw5BVBsbKy9vb29vX1RUZGnp+fcuXPj4+NjY2NHjRpFdjTgOsLCwv7+/r8yAo1Gs7Ozq6qq8vLyMjExERYWnj9//pUrV/r16+fu7r5hwwYzM7PWzkOGDBk2bNjAgQMbGhqCgoLU1dXz8vJan5WXlxcUFJSWlq6pqVmxYsWaNWt+JViPcP369YsXL3p6ejKrk/r6+kePHhHfLoAOHjz4+vXrti3FxcUdn84pKSlRUVFpPQmnq6v75s2brkkPwE0ePXo0YsSIGTNmCAsLjx07dt++fZ37o7ETOKcAKiwspNFozItDCYLYsGHD4sWLfXx8Hj9+jNs7ocexsLBgTlLHpK+v/+V5nbbTrK9YsWLFihVtn2p9tvUGn8ePH7MqLps5derU+fPnt2/fHhISYmFhcevWre3bt3fQX1RUtN03hlQqlUKhdPAjenp6JSUliYmJVlZWZWVlly5dOnPmTNekB+AmoaGha9euZX5JbWVltWTJEhRAP83c3DwgICAwMFBCQiIxMfHDhw9Tp0598ODBggUL8EUYALeZOHGitbW1n59fVFRUQ0NDx509PDzatURGRoqIiHTwIwICAuHh4W5ubgICAu/evfvzzz87uKcPAL6ltLS0T58+zO2+ffuWlJR02645pwCys7NzdnbW0tJSUlJ69+5deHg4Ly9vaGgojUa7cOGCi4sL2QEBftTcuXPJjsAJ5OXlL1y4cPz48SdPnrBifHt7+/z8/IKCAkVFxdbvwgDgpwwZMuTQoUMWFhZ8fHx79uyxsbHptl2zsACi0+mZmZmlpaUtLS1KSko0Go3Vs/KsXbt24cKFJSUlffr0YV7zKCQkFBYWNmHCBCsrK+YNwwDAVTw9PT09PVk0OC8vr5aWFosGB+AGS5Ys8fLyUlBQ4OPj09fXj4iI6LZds6oAiouL8/b2FhQU1NXVZTAYOTk5dXV1x44ds7W1ZdEemaSkpKSkpNq2mJube3l5+fr6Tpgw4ebNmzIyMnPnztXU1GRpDAAAAPguAQGBs2fPvn//vrm5uctnPO8YqwqggICAuLi4tnVGcXGxi4vL/fv3WbTHDqxevVpVVTUlJUVfXz8/P9/MzOzBgwfq6urdnwQAAADaIWXeMlatBdbS0qKgoNC2hcQZvXh5eT98+FBZWTlkyBALC4u6urpdu3aRFQYAAABIx6ozQP7+/jQazdLSUldXl0KhZGdnp6SkzJ8/n0W761hNTU1TU9OGDRsuXLiQnJycmpqKpVLhF71///7Vq1dkp/hVlZWVZEcAACAHqwogPz8/R0fH2NjY0tJSCoViYmKyYsUKNTW1b/U/efJkcXFx25aSkpKuWluHSqVSqVSCIOTk5AIDA58/f25sbNwlIwN3UldXDw8Pj46OJjtIF8BymADAnVh4F5iMjAxzJtY3b94kJCRUVVV1UADV1dW1LvHIRKfT283332mioqIDBgw4evRocXHxtWvXJCUlf/vtty4ZGbjT9OnTp0+fTnYKAADoPFYVQMePH583bx4fH9/KlSt37Nhhbm6emJi4YMGCBQsWfLX/lxXJnTt3REVFuyrPuXPnpk2bVlpaKiAg0NjYaGpq2lUjAwAAQI/DqgJo1apV2dnZvXr1UlVVvX79uoWFRVVV1aBBg75VALGalpZWcnJyTU2NoKDgqlWrPDw8bt26xfxeDAAAALgNqyoAOp0uJSUlLi4uLCwsLS1NEISIiEjHa+t0AxERER4enpUrVzY3N2/ZsoXcMAAAAEAWVp0BcnV1NTU15ePjs7e3nz59+pQpU65fvz58+HAW7e6n8PLynjp1avDgwebm5t056zYAAACwCVYVQFu2bImPj+fl5bW0tLx161ZsbOyYMWPY534TZWXlkydPenh4pKamKikpkR0HAAAAuhUL7wJrPblib29vb2/Puh11ztChQ318fCZNmnT37l0+Pj6y4wAAAED34eqrgJcvXy4uLr58+XKygwAAAEC34uoCiEqlnjhx4sKFC2fPniU7CwAAAHQfFn4F1iNISUldvHjRzs6uT58+AwcOJDsOAAAAdAeuPgPENGDAgIMHD06cOBHrIgEAAHAJFEAEQRATJkyYOHGiu7t7c3Nza2NMTIyVlZWOjo6Pj8+7d+9IjAcAPys9PT0qKurjx4+tLZyxdhsAdBUUQP+1adMmQUHBhQsXMh+mp6fPnj37zz//vHLlCpVK9fT0JDceAPy4kJCQCRMmREREGBoaZmRkMBtnzZpFbioAYCvcfg1QKyqVevLkSQsLi/379/v6+kZGRvr6+o4cOZIgiN27d0tLS1dXV3fh2mQAwDp79ux5+PChtLR0Zmami4tLRkZGx/94P3/+3NDQ0Lalubm5qxZjBgD2hALoX6KiopcuXbK0tOzbty8PD0/r12F0Op0gCCwcBtBTCAoKMiseQ0PDOXPmLFiw4MiRIx30d3V1TU5ObttSXV1N+tI9AMBS/xZA9+/f//JpAQEBQ0PDbsxDMk1NzbNnz06aNOnQoUO+vr59+/bV1tbeuXPniBEjhIWFyU4HwOGysrISEhJmzZq1Zs2a69evr1u3bujQoZ0Yx9nZmUaj+fj4zJ8/PzAw0NnZ2c3N7fPnz9/q/+XlQaamprKysp3YNQD0FP8WQDt27CAIoqSkJDEx0cjIiJeXNy0tzcfHZ8+ePeTFI8GQIUN27dq1cOHCAwcOHDp06O3bt3Z2dpgsEaAbTJ48ecmSJU+fPo2Ojt6xY4efn9+jR486Mc7GjRsdHR3Ly8sJgqBSqZGRkWfPnpWUlOzqvADQg/1bAJ0+fZogCAcHh6ysrD59+hAE8fr169mzZ5MWjTyTJ0/Ozs5ev379nTt3hISEyI4DwC1qa2s9PDxWr17t4eFhYWHR7rqcn2JlZdW6zcPD4+Hh4eHh0RUZAYBDtL+upbCwUFtbm7mtrq5eUlLS7ZHYQnBwsL6+vpubW0tLC9lZALiFqqpqYGBgWFjY1KlTd+3ahT8/AL6KeWUq/KL2BdDgwYM9PT3v3r179+5dLy+vQYMGkRKLdBQK5a+//qJSqT4+PmRnAeAWERERvXv3PnPmjIyMzLt3706dOkV2IgD2cu7cOQ0NDQEBATMzs8zMTLLj9Gzt7wI7fPjwgQMH9uzZQ6VSraysfuUrMDqdnpmZWVpa2tLSoqSkRKPReHh4fi1tt+Lh4Tl58uTQoUNXrVq1atUqsuMAcDJfX9/W7ZcvXx47dowgiB07duzfv5+8UADs5fnz5wsXLrx8+bKhoeHp06ednZ1zcnJ4eXE3dye1f+MEBARGjhzZu3fviRMnFhQUCAgIdG7cuLg4b29vQUFBXV1dBoORk5NTV1d37NgxW1vbX43cXT59+nT8+HEbG5sjR47IysrOnTuX7EQAHGvMmDFkR/gJ1dXVJ06cKC8vHzZs2JAhQ8iOA9zi9u3bLi4uzGUrp06dGhIS8uLFi/79+5Odq6dqXwCFhYVt2rSpvr7excXF3t4+KChozpw5nRg3ICAgLi5OU1OztaW4uNjFxeWrN9uzoffv35uYmFhaWmppafHz8wcHBwsJCc2YMYPsXACcydHRsV0Lg8HYsGHDl+2k+/Dhg4mJiZmZmba29m+//TZr1qygoCCyQwFXEBcXf/v2LXO7ubm5qqpKQkKC3Eg9WvtrgLZt25aUlGRgYEClUjMzM7du3dq5cVtaWhQUFNq2yMnJdTIjGY4ePWpnZxcWFrZixYrExMSmpqY///wzMjKS7FwAnGzHjh1KSkqCgoJqampCQkKvXr0iO9FXMM9kh4eHr1y5Mj4+ft26dW3XEARgnXHjxt2/f3/JkiURERGurq6DBg1SUVEhO1QP1v4MUE1NTevXXnx8fL169ercuP7+/jQazdLSUldXl0KhZGdnp6SkzJ8//5fCdqM3b97o6ekxtxUVFQUFBf/++++pU6dKSEgMGzaM3GwAnOr48ePPnz9fu3bt9OnTGQzG7t27yU70FcXFxfr6+gRBMBgM5sHh3bt33/0Dj06nFxQUSEpK4k926DQJCYnk5OTt27dfuHDB2traz8+P7EQ9W/sCaMqUKePGjSsrKzt06NCpU6dcXV07N66fn5+jo2NsbGxpaSmFQjExMVmxYoWamtq3+m/cuDE/P79tS35+PonXdllaWoaEhHh7e4uJiV28eFFKSsrBweHixYvOzs4RERE2NjZkBQPgYO/evRMXF7e2to6Pj/f393/+/DnZib7CwsIiKChoy5Yt796909fXFxQU/G71k5mZ6erq2tTU9P79+2nTpu3cuRPrbEDnKCgobN68mewUHKJ9hREcHHzjxo3ExMTy8vK1a9daW1t3euhevXrNmjWLh4cnLS0tKyurpqamg87GxsbS0tJtW27evCkoKNjpvf8iFxeX5ORkNTU1GRkZBoNx9uxZgiAsLCyYReGVK1cGDx5MVjYATmVrazt16tStW7cOHz68urqaPecBkpKSev/+PZ1OV1dXLywsVFJS+u6PeHp6rlq1yt3dvbq6etSoUWfOnHF3d++GqADQEcb/U1RU9PHxSUhIoNPpjF+wc+dORUXF2traNWvWaGhoeHp6qqqq7tu378dHGDx4sKOj469k+HXv3r17+fIlc13oVlevXpWXl3/w4AFZqQBIUVRUpKyszNJdNDc3p6WlMRiM+Pj44ODgZ8+esXR3Hejg+LN48eItW7ZUVVVlZWU1NTVJS0tXVVV1MNTHjx/FxcVbH+7du9fPz6+L4wJwOlYcf9pfBP3ixQsrK6vt27f369fvjz/+ePr0aefqqtDQ0BcvXggJCR08eDAtLS0sLCwtLa3Tl1STRVJSUltbu930RaNHj/7777/Hjh3bU+5oA+gpPnz4oK6uXlVV1a9fv4CAgB85udL9mHfiSElJ6ejo1NfXNzY2dnymSkREhEqlFhcXMx++ePFCWVm5W5ICQEfafwUmLi4+ZcqUKVOmZGZm+vv7b9q0icFgdGJcGRmZpqYmgiCkpKSYBYSoqCh7ntDuhJEjRx49etTJyenSpUumpqZkxwHgECNHjmRuVFRUFBQUeHp6hoWFkRvpS1OnTjUzM5OQkNDW1j548KCnp2fH86VRqdQVK1YMHTrU09MzPz8/Li4uNTW129ICwLe0PwN07969oKAgfX39uXPnTpgwoaCgoHPjbty40dLSMjAwsG/fvra2tosXLx40aJCXl9cvB2YXo0ePPnbs2Lhx4xISEsjOAsAhUv8nPz8/PT2dPae4VVVVTUhIKC8vv3jxopub286dO7/7IwsWLDhy5EhjY+OAAQMyMjJkZGS6IScAdIzS7gTPsGHDXFxcJkyYoKio+ItDf/r0KTo6uqCgoKamRk5OzsHBgXnv6A8yNTmRl30AACAASURBVDWVlZWNjo7+xRgsFR8fP3ny5OPHjw8fPpzsLACsVVxcbG5uXlRU1G17NDY2Tk9P77bdtdUjjj8A3IMVx5//+wOrurp67ty5Li4uXTK0mJiYh4dHlwzFtmxsbC5cuDBhwoTDhw+PGzeO7DgAPdvChQtbt58/f45J3gCAdf6vAOrVq1dQUJC1tXXPmrWZXJaWljExMWPHjv3w4YOnpyfZcQB6MCsrq9ZtOzs7TLgFAKzzfwUQPz+/lZXVwIEDbWxsWi9YPnz4MBnBehJjY+O4uLiRI0eWl5djVSCATmDeU9nu9qhXr14ZGhqSlKgr1dfXh4aG3rx5U0ZGJjAw0MLCguxEAPDFXWBeXl6cdKlyt9HV1U1ISBgxYkRVVdXGjRsxzSvAT9mxYwdBECUlJYmJiUZGRry8vGlpaT4+Pnv27OncgHQ6PTMzs7S0tKWlRUlJiUajtZvPojv5+fm9f/9+3bp1r169mjhx4rVr1wYMGEBWGABgal8A2dnZZWVlPXnyZOLEiQUFBerq6mSk6pFUVFSSkpKcnJxcXV3Dw8M7vjMWANo6ffo0QRAODg5ZWVl9+vQhCOL169ezZ8/u3GhxcXHe3t6CgoK6uroMBiMnJ6euro65iGkXZv5BjY2N58+fLy8vFxQUtLa2Li4uPnPmDAogANK1L4DCwsI2bdpUX1/v4uJib28fFBQ0Z84cUpL1RJKSktevX/fw8Bg7duyFCxfExMTITgTQkxQWFmprazO31dXVS0pKOjdOQEBAXFycpqZma0txcbGLi8u3Ji9dvnz5y5cv27a8ePEiLy+vdTHEefPmMdcFunr1atupiX6k/ejRo3V1dZ6enhQKZd68eczFUzsxDtrRzs3tBw4c+PjxI9Gl2hdA27ZtS0pK8vLyolKpmZmZRkZGKIB+iqCg4Pnz5xcsWGBlZRUdHa2qqkp2IoAeY/DgwZ6enjNnziQI4ujRo4MGDercOC0tLQoKCm1bOr6xY9SoUTQarW1LamqqqKjopEmTmA+ZJ6UIgjAwMGht/MF2Nze3goKC6urq8ePHv3v3bteuXSdOnFBRUfnZcdCOdm5ud3R07PLVF9rPA6SlpfXkyZPJkydfuXKlsbHR2Nj4yZMnXbvLH9TT5+E4dOjQ2rVrIyMjTUxMyM4C0AW6YR6g+vr6AwcOJCYmUqlUKyur2bNnd+6r5H379u3YscPS0lJXV5dCoWRnZ6ekpMyfP9/Hx+cHR+ja409VVVVQUNDNmzdlZWX/+OOPtod4APgRrDj+tJ8JesqUKePGjXv9+vWhQ4eGDx/eegYYftbs2bP3798/ZswY5kry35KVlZWQkFBdXd1twQDY0KhRoyorK+fNm/fs2TNJSUlxcfEnT57Mmzevc6P5+fndvHnT1NT08+fPnz9/NjExiY2N/fHqp8tJS0sfPXq0qKjo4cOHqH4A2ET7r8DWrFlz9erVxMTE8vLytWvXMr9+g84ZM2bMrVu3xo8fn5GRsWHDBir1/8rNlpYWd3f31NRUFRWVly9fnjx5ctiwYWRFBSCXm5ubkJCQm5tbVw2opqZGYsUDAOyv/RmgrKyskpKSjRs3MhiMpUuX3rlzh5RYHKN///7//PNPSkqKi4tLu9M84eHhHz9+fPny5b179yIiImbMmEFWSADSeXl5CQkJ9e7d+9WrV3Z2dklJSStWrGj3NwMAQBdqf3yZPHmysLDw06dPo6OjQ0JC2s5MD50jKyt78+ZNBQUFU1PT7Ozs1vaMjIwxY8Ywl3u0trauq6urqKggLyYA+XD8AYBu074Aqq2t9fDwuHDhgoeHh4WFRUNDAymxOAw/P//+/fsXLVo0ZMiQqKgoZqOWllbrNe0vX76k0+lYIxq4HI4/ANBt2l8DpKqqGhgYePHixQcPHuzatat1QQz4dd7e3v379580aVJycvKGDRt+++23v//+28HBQVtb+/Lly9u2bcP80cDlcPwBgG7T/gxQRERE7969z5w5IyMj8+7du1OnTnV6aDqdnpGRERMTc+XKlfT09JaWll+LyglMTEzS09OfPn06bNiwDx8+3L9/39fX19DQ8Pbt21hIFaALjz8AAB1rfwZIRkbG1NT03r17KSkpzs7OOjo6nRuXraaiZyvS0tLR0dEbN24cNGjQkSNHJkyYQHYiAHYhIyMzevToJ0+emJiYTJ8+HUvxAADrtD8DtGLFiunTp5eVldXW1k6dOpW5QmEnMKeif/HixcWLF6Oiop4/f56QkLBkyZJfDswJqFTq8uXLL1686O/v7+/vX1hYSHYiALYQFhbm7Oy8ePFiBoNhb29/8OBBshMBAMdqXwCdPHkyIyMjNDR02bJlKSkpnT4A/exU9FzI1NR0/PjxBw8e1NbW7tOnz9OnT8lOBEAy5lI8BgYGzKV4tm7dSnYiAOBY7b8CU1BQaJ17g0KhiIuLd25cf39/Go325VT0vxSWs0RFRcXHx5eXl0dHR/v7+48YMaK4uBjXQQM3q6mpaV37go+Pr1evXuTmAQAO9m8BtHPnToIgtLS0DA0NHR0dGQzG1atXOz1ru5+fn6OjY2xsbGlpKYVCMTExWbFihZqa2rf6z507Nycnp21LVlZW53bdU8THx0+bNk1KSsrT09PU1NTAwGDEiBFhYWGKiopkRwMgB3MpnrKyskOHDp06dQpL8QAA67Q/A2RsbGxsbMzcnjdvnqSkZKeHbjsV/aFDhzqofgiCmDt3bklJSdsWf3//X9k7+5OXl8/NzWVuS0pKCgkJmZmZGRkZbd++3d3dndxsAKTAUjzQ5aqrq0VERHByHb70bwEUEBBQWVm5bdu29PT0uro6IyOjxYsX9+7du3PjhoWFtX24Zs0a5tlsLy+vr/bX19fX19dv2yIuLs6cJZlTeXt7m5iY0Ol0bW3to0ePLlq0KDg42MnJycvL6/z583v37m13ERUAN3B0dHR0dGRu19TUiIiIkJsHeq6MjAwvL6/8/HweHp7169f7+fmRnQjYy78XQb969crIyIhCoSxbtmzz5s2KiopmZmZtl274KdevX/fx8UlOTn706NGjR4/q6+uZG10UmxPIycmlp6erqKgUFRVt3rw5ODiYIAhjY+P09HR9fX1DQ8Pjx4+TnRGgm2RkZIwcOdLExGTlypVVVVW///776NGj+/TpQ3YuThYZGWlsbKyiojJ9+vTKykqy43QxOp0+ceLEP//889OnT48ePdq6dWtKSgrZoYDNMP5n3Lhx586dY7Rx9erVsWPHMjrr3LlzFhYWSUlJDAbDwMDgZ3988ODBzEuRuFNGRsbAgQMdHBxyc3PJzgLAYDAYRUVFysrKLBrc0NBw3bp1iYmJs2bNUlRUXLly5Z07d16/fs2i3X0Xxx9/UlJSVFVV79y5U1RUFBAQMGrUKLITdbHc3Fxtbe3Wh8uXL1+3bh2JeeAXseL48+8ZoMePH7eblG/06NEvXrzodGk1ceLEyMjIrVu3Ll68GGv6/CwajXb//v2RI0eam5tv2rSpqamJ7EQALFRZWbl8+XJLS8uNGzc2NzevWrXK1tb2VyZCTE9Pj4qK+vjxY2tLdHR0FwT9tsLCwoCAABcXl23btrH/ES8qKsrPz8/W1lZZWXnbtm3JycnV1dVkh+pK0tLSVVVVnz9/Zj4sLCyUlZUlNxKwm38LIF5e3ubm5rbPNTc3t94S3zny8vIXLlwwMDDQ0tL6lXG4Ey8v7++//56ampqcnGxoaHjnzh2yEwGwCh8fH3NDWlpaWFj4F0cLCQmZMGFCRESEoaFhRkYGs3HWrFm/OGwH3r59a2lpKSUl5eHhER8fz/4r2/Dz89fX1zO3m5qaGAwGh11zKSEhMWnSJFtbW29vb2dn58TERNxUCO38+4m3s7PbsmXLn3/+2dqyd+9eKyurX9+Hp6cn+x8O2Jaamtrly5cvX77s7e1tbm6+ZcsWZWVlskMBsLU9e/Y8fPhQWlo6MzPTxcUlIyNDVFSUpXuMjIwcMWLEypUrCYIYO3assrJyRUUFO59ycHNzs7OzU1NT09bW3rFjh5OTk6CgINmhuhiNRouIiPj48WNtbW3fvn1xQT208+8Jni1btkRHRw8fPjwkJGTHjh3jx48/evRoSEgIieGg1bhx4549e6alpUWj0TZs2ND6pxsAZygoKBD9n7bbnRtNUFCQ+bOGhoZz5sxZsGBBl4b9io8fP8rIyDC3+fn5xcXFP336xOqd/gp9ff2oqKgrV6788ccf/fv357xVR+rr65ctW/bkyZPs7OyioiIqlXrhwgWyQwF7+fcMkJiYWEpKSlRUVHp6elNT0+TJkydNmsRhJ0V7NCEhobVr13p7ewcGBurr62/atGnSpEmY3AI4Q11dXReO5uzsTKPRfHx85s+fHxgY6Ozs7Obm1no5yJeeP3/ebh6yjx8/SkhI/PgeR4wYMXbsWFdX1/79+//111/8/PyampqdfwHdwtTUlINrgoKCAgUFBeb5cgqFYmVlxfGT68LP+r/6hkKhODs7Ozs7k5UGvktDQyMyMvLu3buBgYE7d+4MDQ01NzcnOxTAr+raVS82btzo6OhYXl5OEASVSo2MjDx79mwHE6vu27ev3ZQfpaWlP1UA0Wi00NDQSZMmFRUVWVlZRUZG4o8TcmloaFRUVLx48UJPT6+pqenGjRuLFy8mOxSwFwqDwSA7w9eZmprKysqy+saNnotOp4eHhwcHB5uYmGzYsEFHR4fsRMDhiouLzc3Ni4qKyA7SHX7k+MNgMM6dO/fPP/+oq6vPnDnz16/dhq4VERExb948Y2Pj7OxsCwuL48ePoyrtuVhx/Pmlm7yARFQq1cvLKzs729TU1Nraes6cOcXFxWSHAmBHtra2rBh29uzZO3fuVFFRefDggbm5edd+iwe/ztXV9fHjx/7+/pcuXQoPD0f1A+2gAOrZBAUFFy9enJ2dLS0tTaPRfv/997dv35IdCoC9tLS0dPmYZWVlly9fvnXr1sKFC0+cOKGmpnbp0qUu3wv8Inl5+dGjRxsYGJAdBNgRCiBOICkpuWHDhmfPnrW0tOjr6wcFBaEMAmjFimk4SktLe/fu3XrreJ8+fcrKyrp8LwDAOiiAOIe8vPzOnTsfP37c2Nior6//+++/t7uxBYA7sWIKRH19/fLy8ri4OIIgiouLL168iLXrAXoWFECcRklJiVkGUSiU/v37+/r65uXlkR0KgNP06tXr1KlTM2bMUFFR6d+/f2BgoLGxMdmhAOAnoADiTEpKSlu3bs3OzpaRkTE3N3dzc3v48CHZoQA4io2NzevXr1NSUsrLy/39/cmOAwA/BwUQJ5ORkVm7du2rV69MTU3Hjx8/bNiw6OhoOp1Odi4ADkGlUpWVlfn5+ckOAgA/DQUQ5xMREVm4cGFeXt6sWbNWr16tp6e3e/duDlv5GQAA4KegAOIWfHx87u7uqampR44cSUhIUFdXDwgIwNzwAAA9y/Pnz2NiYnCPy69jYQFEp9MzMjJiYmKuXLmSnp7Oiqk4oBOsrKwiIiIyMzPFxMSGDRtmb29//vz5pqYmsnMBAMB3zJkzZ/To0Xv37jUyMjp27BjZcXo2Vq11GhcX5+3tLSgoqKury2AwcnJy6urqjh07xqIpWeFnKSsrr127Njg4ODIycu/evfPmzZs+ffrMmTO1tbXJjgYAAF9x8+bNBw8eZGdn9+rV6/Xr14MGDZo4caKoqCjZuXoqVhVAAQEBcXFxbddDLi4udnFxuX//Pov2CJ3Az8/v5ubm5uaWk5Nz+PBha2vrvn37ent7u7i4iIiIkJ0OAAD+9fjxYzs7O+bKwRoaGhoaGjk5OZh/odNY9RVYS0uLgoJC2xY5OTkW7Qt+Xd++fUNCQgoLC3///ffIyEhVVVUvL6/bt2/jljEAUvzzzz8uLi5WVlYrV678/Pkz2XGALejo6CQnJzMPy+Xl5a9fv9bS0iI7VA/GqjNA/v7+NBrN0tJSV1eXQqFkZ2enpKTMnz+fRbuDLsHHx+fk5OTk5FRRUXHq1KklS5aUlpa6u7tPmTLFyMiI7HQA3OL58+fOzs6bNm3S1tbetWvXb7/9durUKbJDAflGjx599OjRgQMHGhkZ3bp1688//5SQkCA7VA9GYTAYLBq6oKAgNja2tLSUQqEoKCiMGjVKTU3tW50dHR1TUlLatlRXVxsaGqalpbEoHvyIFy9enDp16tSpU3x8fJMnT3Z1de3Xrx/ZoYAcxcXF5ubmRUVFZAfpDqamprKystHR0aTsfdWqVQwGY/Xq1QRBNDY2ysrKlpaWCgkJkRIG2E18fPzr168HDx6sr69Pdpbuw4rjD6vOABEEUVlZqaCg4O7uLi4uzmyJjo4eM2bMVzufO3euoaGhbYu9vb28vDzr4sGP0NPTW7t27dq1a1NTU8+cOTNq1CgREZGJEye6uLgYGhqSnQ6AMzU2NrZe2crLy0ulUpubm8mNBOzDxsbGxsaG7BScgFXXAIWEhEyYMCEiIsLQ0DAjI4PZ2MGShEJCQpL/j5eXl0KhsCge/CwTE5OtW7cWFBQcPXq0rq5uwoQJWlpaQUFBiYmJmOAAoGuNHz9+//79cXFxRUVFv//+u5mZmZiYGNmhADgNqwqgPXv2PHz48NSpU5cuXZo0aRLmHeYMFArFzMwsJCQkLy8vMjJSWFh4/vz5ioqK3t7eFy5c+PTpE9kBAb7i0KFDZEf4OYMHD969e/eSJUssLS0/ffoUHh5OdiIADsSqr8AEBQWZp3ANDQ3nzJmzYMGCI0eOsGhfQApDQ0NDQ8NVq1YVFhZeuXLl8OHD3t7eJiYmo0aNGjlyJC4VAhKFhYW1fbhmzRrmncNeXl4kJfppzNsRyE4BwMlYdQbI2dmZRqPt2rWLIIjAwMDKyko3NzfczMmRVFVV586de+3atZKSkoCAgLy8vLFjx6qqqv72228RERFVVVVlZWWYaRq60/Xr1318fJKTkx89evTo0aP6+nrmBtm5AICNsOoM0MaNGx0dHcvLywmCoFKpkZGRZ8+elZSUZNHugB0ICwuPHTt27NixBEHk5OTcuHFjz549bm5uFAqFSqV6eHjs3bsX8ytCNzh16tT58+e3b98eEhJiYWFx69at7du3kx0KANgLC2+D/0Xk3oYKv45Op0tISCgpKY0ePTouLu7x48cCAgJGRkY2NjZDhgyxtLTEDO49S4+7Db68vNzPz09LSysqKionJ6eDnl5eXs+ePWvbkpWVpaenl5qayuKMAPBDetht8MDlMjMza2tr09PThYWFCYLo37+/hobGwoUL4+Pjt2zZkpaW1qdPHysrK0tLS0tLy969e5OdFziNvLz8hQsXjh8//uTJk457rl69uqqqqm3L9OnTZWVlWZkOAEiGAghYq+0pRh4enqFDhw4dOpQgiMbGxrS0tKSkpJMnT/r7+wsKClpYWJiZmZmamhoZGTEvWQX4dZ6enp6enh33UVdXV1dXb9siJCREpbLqEkkAYAcogIBVDA0NRUREjI2N/f39U1JSsrOzt27d2vosPz+/hYWFhYVFUFAQQRAvX75MSUm5f//+8ePHs7Ky+vXrN3jwYBMTE2NjYz09PR4eHvJeB/R4tra2d+/eJTsFALAXFEDAKlQq9datW+PHj1+yZAlBEGvXrh0+fPi3Ovfp06dPnz7Mv9Tr6uoyMjJSU1Nv3ry5adOm4uLi/v37G/2PgYEBzg/BT8FcnQDwJRRAwEImJiZv3rx5+/atlJQUL++PftiYX4dZWFgwH1ZXV2dkZGRkZCQkJOzevfvly5fa2toD/sfAwEBZWZllrwA4wXe/AusSt2/fXrlyZVFRkaWlZUhICC5rA2BzKICA5eTk5H7lx0VFRYcMGTJkyBDmw8bGxqdPnz5+/PjJkydbt259+vRpfX29gYFBv379+vXrp6+vr6enp6Sk1BXBgUN0sAhPV8nOzp46depff/1lYGBw5MiRCRMm3L9/n9U7BYBfgQIIehh+fv6BAwcOHDiwtaWqqurJkycvXrx4+vTpxYsXnz9/Xl9fr6Ojo6enp6Oj07dvX+b3a4KCgl0SICMjY/Xq1fn5+aampmvWrMGSvUAQxNWrV6dMmeLo6EgQxJo1a44fP15QUKCmpkZ2LgD4JhRA0ONJS0vb2tra2tq2trx//z4rKysrKys7O/vEiRM5OTmvXr2Sk5PT1tbW1tbW0tJi/ldTU/Nn5yJ68+aNo6Pj+vXrBw4ceOLECScnp5SUFA5Ytbe6ujomJqaxsXH48OEo6TqBl5e3oaGBuU2n05uamvj4+MiNBAAdQwEEHEhSUtLc3Nzc3Ly1paWlpbCwMDc3Nzc3Ny8vLyUlJTc399WrV8LCwpqamhoaGswbodXU1Jj//dbpopiYGEdHxxkzZhAEYWho2KdPn9zc3D59+nTP62KRwsJCKysrY2NjISGhoKCgS5cumZqakh2qhxk/frypqWn//v0HDBhw5MgRAwMDfA8LwOZQAAFX4OHh0dDQ0NDQcHBwaNteVlb2+vXr169f5+fnp6enX7hwoaCgoLCwUExMTEVFRUVFRVVVVUVFRVlZWVlZWUVFpbGxse09+VQqlU6nd/ur6WLr1q3z8/NbunQpQRAXLlxYsmQJbhr/WaqqqteuXduwYcOxY8esrKxCQkLITgQA34ECCLiagoKCgoJC23NFTGVlZUX/U1hYmJaWVlxcXFRUVFpa2tLScu3aNQ0NjY8fP1ZXV8fFxWVnZ8vLyyspKcnJyfXEW/Rfvnw5ZcoU5raFhcW8efPIzdNDGRoanj17luwUAPCjUAABfAWzMDIxMWnXzmAwrl27tm7dupcvX6qqqnp4eGRmZl67dq28vJx5w7+IiIi8vLycnJyCgoKcnJysrKycnJy8vLxMG6S8og7QaLTLly/b2NgQBHHp0iUjIyOyEwEAsBwKIICfQKFQampqSkpK3r59q6ysPG3aNENDw7YdqqqqysvLKyoqSkpKKioqKioq0tLSKioqKisrmQ8/ffokIyMj/TVSUlJSUlKSkpJSUlISEhIiIiLd86KCg4MdHBxoNJqIiEhJScm1a9e6Z78AACRCAQTwE54/f75w4cLLly8bGhqePn3a2dk5Jyen7RyPzFKmgxGam5srKyurqqoqKyvfvXvH3KioqMjOzn73P+/fv//w4UNjY6OEhISkpKREG5KSkmJiYuLi4uLi4mJiYmJiYqKiopKSkqKioqKiogICAp14UVJSUg8ePEhLS2toaBg8eHDnBgEA6FlQAAH8hNu3b7u4uDBnIZo6dWpISMiLFy/69+//4yPw8vIyv1/7bs/GxkZmJfSlN2/efPz48dOnT58+faqurv7w4QNzg06ni4iISEpKioiICAsLCwsLS0pKCgsLCwkJiYqKiomJCQoKCgsLi4mJCQgIiIiIiIqK9urVS0xMTEhIqG/fvhISEhxwSz8AwI9gYQFEp9MzMzOZF40qKSnRaDQsaQk9nbi4+Nu3b5nbzc3NVVVVEhISLNoXPz+/vLz8T83K09jYWFNT8+HDh5qampqamtra2g8fPtTW1tbW1tbU1Hz8+LGysrK2tvbjx4/19fW1tbWfPn1qaGiorq7+/PlzQ0NDcHDwwoULWfRyAADYCqsKoLi4OG9vb0FBQV1dXQaDkZOTU1dXd+zYsbaz1QH0OOPGjVu1atWSJUuMjY3PnDkzaNAgFRUVskP9i5+fn3khEdlBAADYHasKoICAgLi4OE1NzdaW4uJiFxeXb62PU19fX1dX17alubmZRdkAOk1CQiI5OXn79u0XLlywtrb28/MjOxEAAHQGqwqglpaWdlc5dLwi5vjx4x88eNC2pbq6GhdjAhtSUFDYvHkz2SngO8rKymRlZXl4eNLS0rKysoyMjPr160d2KABgI6wqgPz9/Wk0mqWlpa6uLoVCyc7OTklJmT9//rf6f3nn7eLFi2VlZVkUDwA42K5duzZt2pSbm7t169Zjx45ZW1svX7586dKlvr6+ZEcDAHbBqgLIz8/P0dExNja2tLSUQqGYmJisWLECayMDQDcIDQ198eKFkJDQwYMHHz9+LCUlVVFRYW5ujgIIAFqx8C4wNTU1Hx8f1o0PAPBVMjIyTU1NBEFISUkxbz4VFRUVEhIiOxcAsBHMAwQAnGbjxo2WlpZjxozp27evra2tg4NDTEzMjBkzvtX/2rVrhYWFbVsqKipERUVZnxQASMO+BRAPD8+uXbs6WFywpqbm1atXfHx83ZmqA42Njfz8/GSn+C92C8PHx8cmM+w1NTXx8PBQqVSygxAEQTQ3N1MoFDaZH6ulpUVERERdXf1bHRobG9kk6neNGDEiNTU1OjpaRkZGR0dHTk4uIiJCX1//W/0fPXr0+vXrti1UKvXhw4eDBg361o/g+NMBdguD489X4fhDYTAYXTtiV/nw4UNeXl4HHR4+fBgaGhocHNxtkTrw4cOH4ODg3bt3kx3kv2bOnLl//342OQb98ccfc+fOVVZWJjsIQRDE9u3bbWxsmFM5k+7kyZPS0tIjR44kOwhBEMTNmzc/ffr0559/dtBHUlKy7dwWHAzHn1+B48+34PjzLeQcfxg9VkJCgpWVFdkp/qukpERRUZHsFP8SEhKqra0lO8V/GRgYPHnyhOwU/+Xk5BQVFUV2iv9auHDhtm3byE7xX7t37/b39yc7BUvY2Nh0+Zg4/nQAx59vwfHnW0g5/rDFiTgAANZpaWkhOwIAsB0UQADA4Tw9PcmOAABsBwUQAHC4WbNmkR0BANgOCiAAAADgOjyrVq0iO0MniYiISEtLGxgYkB2EIAhCQEBAUlLS2NiY7CD/JSUlZW5uziZ3foqIiJibm7PJLSGCgoKDBg0SExMjOwhBEISQkJC+vr68vDzZQQiCIERERNTU1Dq4DRXawvGnAzj+fAuOP99CyvGHfW+DBwAAAGARfAUGAAAAXAcFEAAAAHAdFEAAAADAdVAAAQAAANdBaB6M8gAAIABJREFUAQQAAABcBwUQAAAAcB0UQAAAAMB1emoBtG/fPn19/QEDBty5c4esDMuWLdPT01NXVw8NDWWfVO7u7mFhYaTnuX79urGxsZqa2p49e0gPs2TJEg0NDS0trePHj5MY5uHDh+7u7q0Pv8zQnanahWHPDzPbYoc3hz1/ZTj+fAnHn++GIe3D3M2rz3eJ/Px8bW3t2tra3NxcdXX1lpaW7s+QkJCgp6fX0NBQUVGhqqqanp7ODqnOnTsnICDw999/M0h9lz59+tS3b9+3b99++PBBUVGxtLSUxDDx8fE0Gq2+vr60tFRMTKympoaUMKtWrdLQ0HBzc2M+/DJDd6ZqF4Y9P8xsix3eHPb8leH48yUcf74bhsQPc488AxQbG2tlZSUkJKSlpSUgIJCZmdn9GcrLy318fPj5+WVkZCwtLQsLC0lPVV5evn379taFr0nMEx0dPWbMGFlZWXFx8dzcXFlZWRLD8PDw8PHx8fHxCQgI8PHxUSgUUsKYmJhMnz699eGXGbozVbswbPhhZmfs8Oaw4a8Mx5+vwvHnu2FI/DD3yAKorKxMV1eXua2rq1taWtr9GVxcXObPn08QRGZmZlJS0tChQ0lP5efnFxISIiQkxHxIYp78/PyCggJDQ0MVFZVt27bx8PCQGMbS0rJv375KSkoqKiorV64UEhIiJczo0aMtLS1bH36ZoTtTtQvDhh9mdsYObw4b/spw/PkqHH++G4bED3OPLIAYDEbrMnsMBqOlpYWUGHQ6PTQ0dOLEiRcvXhQXFyc31fHjx7W0tCwsLFpbSMxTU1OTm5sbHx+fmZl59OjRtLQ0EsPcvn07Ly8vPj7+2rVrO3bsKCkpYYfPz5cZyE3FVh9mNscmbw5b/cpw/PkWHH9+BFkfZl4WjctSioqKqampzO2cnBwlJaXuz9DS0jJx4kRJScn09HTm0r7kpjp9+nReXl5sbGxpaem5c+caGhpIzCMnJzd8+HAJCQmCIKytrbOyskgMExMTM2XKFB0dHR0dHXNz84SEBHb4/HyZgcRU7PZhZnPs8Oaw268Mx59vwfHnu8j8MLPo2iKWys/P19fXb2hoKCoqUldXb25u7v4MJ0+enDx5MrulYjAYCxYsaL0Ikaw8z549MzAwqK+vZ17U9vTpUxLDHDhwYMyYMfX19VVVVWpqamlpaWSFuXXrVtuLENtl6OZUbcOw7YeZPbHDm8O2vzIcf9rB8ee7YUj8MPfIM0Bqampz5861srIiCOLo0aM8PDzdnyEhISE2NlZRUZH58PDhw2PGjCE9VVskvkv6+vrTp083MTGpra1dsmRJv379CIIgK8zMmTMfPnyop6fHYDACAgKMjY1JDNPqy98Oib8v9v8wsxUcf34Ejj9MOP58F4kfZgqDwWDR0AAAAADsqUdeBA0AAADwK1AAAQAAANdBAQQAAABcBwUQAAAAcB0UQAAAAMB1UAABAAAA10EBBAAAAFwHBRAAAABwHRRAAAAAwHVQAAEAAADXQQEEAAAAXAcFEAAAAHAdFEAAAADAdVAAAQAAANdBAQQAAABcBwUQAAAAcB0UQAAAAMB1UAABAAAA10EBBAAAAFwHBRAHysvLc3BwcHJy2rNnD9lZAAAA2BGFwWCQnQG62LJly0aNGmVtbT1kyJB79+6RHQcAAIDt8JIdALresmXLhIWFi4uLpaWlyc4CAADAjvAVGAcSERGJjo729fXdu3dvN+wuPj7e2dm5+38WAACg01AAcaAbN24kJiZevnxZSUmJ7Cxd78GDB4MGDVJUVJw+fXpLS0vbp7S1tXl5efn4+Pj4+CIjI1vbQ0JCpk2b9tXRNm/efOvWLdYmBgAA9oMCiANFREQ8e/bM1dX1W//X/yoGg1FVVfXldsc9u1lDQ4Orq+vRo0fz8/PfvHkTHh7e+lRjY+Pnz5+bm5ubmpqampomTJjAbE9PT1+/fj1zOy8vz8LCwtDQMCQkhCCIysrKR48e2dvbd/8LAQAAcqEA4kB//fVXdHT0uXPn2tYHbe3atUtTU7NPnz6LFi2i0+n37993cXExNjYODQ1tu00QxPr167W0tDQ1NYOCgtr1/O6wI0eOjIqKYj5lYmLCvBy7XZ+ffWmxsbGmpqYDBgzo1avXlStXXF1dW596+fKlgIDA1KlTBw8eHBISwry6v6amxtfX948//mD22b9//7p16zIzM6Oiourq6tavX798+fKfzQAAABwAF0H3eHPnzn3+/PmNGzf4+Pg8PDxsbGzmzJnTQf+7d+8eOXLkn3/+ERYW/u2337Zv325lZXX9+vX09HQdHZ379++3bl+/fv38+fMZGRm9evUaM2bMiRMnmI3MZ787rJubW2Rk5Pjx41+9evXu3Ttra+sv+wwaNKjtIIsWLcrNzW3bYmpq2lq+EASRl5fX0NBgYmJSVFRkY2Pz119/tT5VVlamqam5Zs0aHh4eZ2dnOTk5Ly8vf3//wMBAgiCePn1KEISvr6+np2dQUNDkyZOLi4sbGhoMDAw6+8YDAEAPhgKox/P29lZUVIyJiREXF8/Pzz958mTH/ePi4j59+jR58mSCICorKxkMhpWV1eDBg1trmtbtuLg4Ly8vMTExgiBmzZoVGxuro6PTtmfHwx48eHDp0qVNTU1nz5718vKiUChf9mlXAH31xFJb1dXVOTk5MTExsrKyXl5eW7ZsWbt2LfMpOzs7Ozs75vbvv/9+5coVfn5+KpU6efLks2fPMtu1tLSSkpKY256enps3b+54dwAAwKlQAPV4xsbGBEE0NDQsWLAgPDycQqF03F9YWNjb2zs4OJggCDqdTqfT09PThYWF23ZgbjAYjNbRqFQq84rjtj07HpaXl9fMzOzOnTsRERHMS5K/7NNajvwgeXl5Ozs7dXV1giCcnZ3Pnz/f+lRqaqqgoCDzjI6QkBAvL29ERMT9+/c1NDRqa2s/f/7s6Oh49epVZuekpCRNTU1FRcVly5bFxMRISUmFhYWpqKj8VBgAAOi5cA0Qh0hOTh4yZEj//v2/29PBweH06dPv379vbm52dXU9dOjQt3ra2NiEh4fX1NQ0Njb+9ddfQ4cO/dlh3dzcNm7cKCkpqaGh8SO7XrRo0fj/t3HjxrYdRo0adfPmzdevX9fU1Jw5c8bMzIzBYGRnZzc3NxcWFk6bNu3Dhw/MtCNHjrx48WJJScnr1693797t7OzcWv0QBBEaGrpo0aI3b96kpaU9evTI39//77///u5bBwAAHANngDhBampqfHz8/Pnzf6TzwIED582bZ2ZmxjwpMnv27PT09K/2dHR0TE9Pp9FoDAbDycnJ09MzLS3tx4clCGLs2LEzZ87ct2/ft/q0OwP03a/A1NXVly5damdn19jY6OjouHDhwsbGRl1d3fz8fBcXl5SUFD09PREREVdX16lTp35rkHPnzo0aNUpERERERGTgwIHGxsZiYmKnT5/ueNcAAMBJsBRGj9fc3Dz4P+zdeVyN6f8/8Ndp0WpJQoXshVBCkzL4hB9iDFljsu9JJIYx9n2rMRmGsWTGvlPT2LIUpplSTZayhpS1ol3L+f1R3+Qg5Jxzn+X1fPSYx32urq77VZ25vbvv677utm3Xr19vYGCQlpZWsWJFW1tboUMREREpNJ4BUnpr1qyxt7d3cHAAMGHChEePHh07dkzoUERERAqNZ4CIiIhI7XASNBEREakdFkBERESkdlgAERERkdphAURERERqhwUQERERqR3FvQ0+Kyvr8ePHQqcgojcMDQ2rV68udAp54PGHSNFI/fijKLfBd+rU6dy5cxKNurq6ZmZmQsQhIkn5+fmampp3794VOog8zJ49+9dff61SpYrQQYgIkM3xR1HOAJ09e1aixd7e3sTEJDAwUJA8RGXJysKuXRg9WugccpWYmFi03qY6yM/P//777318fIQOQkSAbI4/nANE9PlWrMCYMQgJEToHERGVEwsgos/08CF++QW+vpg8Gfn5QqchIqLyYAFE9Jm8veHpCS8v1KqFTZuETkNEROXBAohk79dfkZIidAgpuXgR4eHw9gYAX18sWIAXL4TOREREn40FEMlYaCgmTMDcuULnkIbCQnh5YfVq6OsDQNOmcHXFokVCxyIios/GAohkqbAQ06ZhwwYcOIDYWKHTfLF//kFEBAYMgEhU/LFhA7ZsQV6e0MmIiOjzKMpt8KSatmxBhQoYOxb5+fDywpkzQgf6Ml99BcVYN4uIiL4QzwCRzKSnY/58/PQTRCKMH49nz3DkiNCZiIiIABZAJEMLFsDFBa1bA4CmJvz84O2NnByhYxEREfESGMmIWIydO/H4MTZvftOooYFLl/C//wkXi4iICGABRLIiEiE5WegQRMosPR0REejUSegcRKqJl8CIiBTS3LlwccGDB0LnIFJNMjwDVFhYGBMTk5ycXFBQYGZmZmNjo6mpKbvdERGpjrg47NyJ0aMxYwb27BE6DZEKklUBFBISMnLkSD09PSsrK7FYfPPmzezs7G3btnXs2FFGeyTFkp2NJ0+QnIxnz/DiRfHHy5d4+RLp6cjKQloasrORk4PMTLx+DQDp6R98tJa+PnR0AMDQENra0NWFnh4qVoSODipVQqVKMDAo3qhSBUZGMDJC1aqoWhXGxjA0lN93TSQt06Zh9myMG4emTXH+PDp0EDoQkaqRVQE0ZcqUkJCQ+vXrl7QkJia6urqGh4fLaI8kjCdPcOcO7txBQgIePMCDB3j0CI8eITcXNWrA1BTVqsHYuPijdm1UroyKFWFggCpViusYieLmvUqKpIwM5OUVV06vXiE3F+npePkSmZl49QpPn+LmTaSmIjUVKSl48QIpKcjPR7VqqFYNNWuienWYmMDcHNWrw8wMNWvC3ByVK8vvx0X0KQIDce8eJk2CtjaWL4eXFyIiwDPoRFIlqwKooKCgZs2apVuqV68uo32RnIjFSEhAbCyuXcP164iPR3w8KlRAgwZo0AD16qFtW/Trh1q1YGYGIyNp7trAAAYGAMozbE4Onj3Ds2d48qR449EjREUhKQnJyXj0CPn5xZlr10bt2jA3R506sLBA7dqoUkWa3wXRp3j9GtOnw9e3+O+BgQOxYQO2bsWYMUInI1IpsiqAPDw8bGxsHB0draysRCJRfHz85cuXPT09ZbQ7kgmxGLdu4Z9/8O+/iI5GdDSqVIG1Nayt0bkzJk9G48ZKUCLo6hZXNh+SlYUHD5CUhMREPHyI2FgEBxef0BKJYGGBunVRr17xR/36qFevuBojkoUjR3DzJjw83rS8fIkVK1gAEUmXrAqgiRMnuri4BAcHJycni0SiNm3azJ0718LC4kP97927l/L2A8MzMzONpHsWgT5Fbi7CwxEaiosXcfkyjIzQti1at8Y338DWFlWrCp1PBvT1YWUFK6v3fCo1FQkJuH8f9+7hzh2cPo27d3H3LipXRoMGaNjwrQ++XUkq+vbF7duSjZzKRiRtMrwLzNTUdMSIETo6Oo8ePbpy5crrojkcH7BgwYKrV6+WbklISNAveuY2yVpBASIjcfIkQkIQEYFmzeDoiLFjsX071PzCZdF8altbyfakJNy+jTt3cPs2jhzB7du4fRsVKqBRI1haonFjNG4MS0s0alQ8vYkEtWnTprFjxwqd4pNpaaHU7EkikhFZFUA7duyYNGlS1apVfXx8Zs2a5ejoGBsbO2/evA8dhrZv3y7RYm9vb2JiIqN4BAAvXiA4GMeP48wZmJuja1dMn4727VGxotDJFJ6ZGczM8PXXbzU+eYL4eNy6hZs38ccfiI9HQgLMzGBlhSZNYGmJJk3QpIlqnkVTMAEBAaVfLly4UEdHB8CwYcMESkRECkdWBdC8efOuXr1avXp1IyOjjRs3Dh8+PCUlxd7eXpn+DlNVDx/i8GEcOoSoKDg7w8UFfn4wNRU6lvKrUQM1arxVFeXn49493LiBuDhcvowtWxAXBx0dNG2KJk3QrBmaNIG1NVjoS9uJEycOHz7s7u5edBY5JycnOjoaHy6AXFxcLl++XLolIyPDwcHBx8dHDmmJSBCyKoAKCwvNzMy0tbXHjx/v7u4OoHLlyvkfWuWF5ODxY+zfj927cesWevXC9Ono3Bm6ukLHUmlaWmjUCI0a4Ztv3jQmJeH6ddy4gf/+w549uHYNGhpo3hxNmxZPMG/WjNOJvtCuXbsOHDjg6+u7atWqdu3anT592tfXt4z++/fvz83NLd3SuXNnQ067IVJpsiqA/ve///Xs2XPJkiV+fn4A7t69u2jRItt351KQrOXk4MgRBAQgPBy9emHuXHTuDC0+A044RZfPOnd+0/L4Ma5dw9WruHIFAQG4fh2VKqFZM7RoAWvr4tpIDnOJli/HrVvYskXmO5KLfv36tW/ffuLEiUeOHJEobt6lr68vMeNQS0tLJBLJMiBReYWFYeBAXLumBDfhKjZZ/UP422+/HThwoOTl/fv3mzRpMmnSJBntjt7j6lX8+it274adHYYNw6FD0NMTOpOqyMvD2bPo2lU6o9WsiZo14ez8piUhAdeuITYWJ05g9Wrcvo169dC8OVq0KP7vh2+oLKfERKxZAx0dXLwIR0cpDy6QGjVqHDx4cMeOHbGxsUJnIZKSwkJMmQJjYyxYgDLPa9JHyaoA0tTUHDhwYMnLTp06deIzjeUjLw8HD8LfHwkJGDUKUVFlLYFD5bN+Pby98e+/aNVKJuPXrYu6deHiUvwyLw83biA2FrGx+OUX/PcfsrLQsiVatECLFrCxQbNmX1rdzpiBSZPQuDEmT0ZEBDRU5zHJ7u7uRVfhiVTB1q3Q1cXJk2jaFKNHo1kzoQMpMV4KUSEpKdi4EevXw9IS3t7o1YuXumTi+XMsXYpp0+DlhfPnIYcLJdraxbVO6QwxMYiJQVgY1q9HfDzq1UPLlmjZEjY2sLFBjRqfMf7lywgLw5w5yM2FoSECAjBihNS/CdWUkYGvvsL27WjdWugopAbS0zFvHo4ehbExZs2ClxdOnRI6kxLjP5Aq4cED+Ppixw58+y2Cg9/6l5Kkbs4cDBmCFStgb499+1DqTKf8VKsGZ+c3V83y8nD9enFJtHIloqNRoQJsbGBrC1tb2NigYcMPFmpFZ9RXrMCkSbh/H7t2oW9f9O3LR6R9kmXLkJMDLy+EhsqjFCY1t3AhevQorrY9PPDbbwgKenOqmD4TCyAld+8eli7F4cMYORKxsTAzEzrQOwoKMHAg5s+HtbXQUaTh2jUcPozr16GhgZ9+wqBB6NlT+CdjaGsXn/4p8eBB8dNLdu/GjBl48QItWxYXQ7a2aNYMFSoU99yxA3FxCApCfDyqVsW8edDVxfLlWLZMkG9Fmdy9i02bEBUFV1fs3g03N6EDkUq7exfr1sHNDd9/X9xSsya8vdGtGx+UWz4sgJTWgwdYuBBHj2LiRNy8qbjL623dirNnkZaG06eFjiINU6diwQIYGwNAu3Zo1w6rV2PePKFjvaNOHdSp8+b2+9RUREcjKgpnz8LXF3fuwMqq+PxQXh5GjEBAAHr0QJUq2L4dw4dzYsEnmT4d06ahVi34+WHAAPTuLXwpTCpMTw8LF77V0qULdHR46rHcWAApoadPsWQJdu7ExIm4fVuhL1W8eoX58/Hnnxg7FocPo08foQN9mcuXceoUbt7EqlXFLZmZCArC7NnFD+5WWEZG6NQJJTciZGcjNhZRUbhyBVFRiI6Gnh5EIgwahKpV8eABhg4VNK4yOHsW0dHYtQsAHBzQvj1WrcL8+QKnIhVmaoqZM4UOoVJYACmV7Gz4+WHtWgwdihs3lGAF4QUL0LMn7O3h64sxY9C9u3IvvWhvjzt3JBt1dBS9+nmXnh7atkXbtgDw6BFsbLBjB548ga4uZs9GkyYIC4OTk9ApFVhhIby80KQJfvqpuMXYGCtXYtQo3nRJpCxYACmPvXsxYwbatsXff6NBA6HTfILbt7FjB4qWYPnf/9CiBfz83ly9VkYaGir4lMr165GRAQ+PNy2pqfD1ZQFUFrEY336L3Fykpha3GBjA0xNisaCxiOgzsABSBlevwtMTaWnYuVOZ/lmaOhUODggNLX5pb4+lSzFsGJ87pljmzMHo0ZKNRZOc6EM0NbFggdAhiOiLsABSbFlZmD8fAQGYPx9jxyrTVH+xGMbGyMrC/v1vGrt1w+PHLIAUi76+Cp7WIiL6GBZACuyvvzBxIpyccPWqEkz3kSASYft2oUMQERG9HwsghZSaCi8vhIVh06a3nppJRERE0qA6T/xRHUFBaN4clSrhv/9Y/RAREcmCopwBOn78eHJycumWp0+fGqjDqmJiMdLSYGQEAFlZ8PbGiRPYuRMdOgidjIg+wS+/wN0dhoZC5yCiz6MoBdCtW7fi4+NLt2RlZeXl5QmVR35++QXLlyMuDnFxGDIEbdsiOhqVKgkdi4g+wcmT8PBAYiKWLv1IT7EYLi6YPBndu8slGRF9hKIUQNOmTZNoiY6OrqzIaxxLRUoKFi5EkyZwdcWVK/j5Z2GerElE5ZCfD29vbNqEWbMwYgQaNSqr8549+O8/TJkCZ+c3D2IjIuFwDpCg5s1D797Q08OZMzhwgNUPkTJZvx41amD0aEyd+pEVPrOzMWsWdu+GldWbxaOJSFCKcgZIHV2/jl27ULkyevVC27bw98fXXwudiYg+TUoKli0rfsTv9OmwtsbJk+ja9f2dly+HoyPat4eZGb76CkOHcjUsIsHxDJBwBg/G69eYNw8//YRZs/Dvvzh/XuhMRCoiMjLyyJEjL1++LGkJDAyU5g7mzMHAgbC2BoAKFbB8OaZORX7+e3omJuKXX4onCTVogBEj8OOP0kxCROXCAkgIhYUYMAD//YcuXXDpEsaNw5QpqFoVPj5CJyNSBatWrerbt+++fftatmwZFRVV1DhmzBip7SAvDzt3Yt06iETFH66uuHEDERHv6ezjA0tLnDiBTZuwaROqV0dAAK5ckVoYIioXXgKTu4wMfPcdkpKwejUqVnzTbmeHmjWFi0WkOvz9/a9cuWJsbBwTE+Pq6hoVFVWx9P9r78jOzs7JySndkp+fLy7jyaba2ih1bukjLC1RqRIiI9+0jByJ168/9cuJSDZYAMnXgwfo1QsODti3D9raQqchUk16enpFFU/Lli3HjRvn5eW1ZcuWMvr37ds3PDy8dEt6errU0syfL7WhqBzi4tC4MTR4uYMk8T0hRzExcHTE8OHYuJHVD5Hs9OnTx8bGZt26dQC8vb2fP38+aNCgrKysD/UPDg5OeVurVq1MOU9ZBTx6hNatsWmT0DlIEb05AyTxB1ARXV3dli1byjGP6jpzBm5u+OknDBr0VvutW9i/H7NnCxSLSIHExcWFhoaOGTNm4cKFJ06cWLx4cadOncoxzrJly1xcXJ48eQJAQ0Pj0KFDe/fuNSpab53Uyvffo0cPzJ+PQYNQpYrQaUixvCmA/Pz8ACQlJYWFhdna2mppaUVERIwfP97f31+4eKpi925MnYqDB+HkJPmpKVNw5gw6dICjoxDJiBTIwIEDZ86cefXq1cDAQD8/v4kTJ0ZHR5dvKKdS/69pamq6ubm5ublJKSYpicuXce4c4uIwfToWLICvr9CBSLG8uQS2e/fu3bt3V6hQIS4uLiIi4u+//3738RRUHhs3wscHJ0++p/oJCsK9e9i0CZMno7BQiHBECiQzM9PNze3gwYNubm7t2rXLzc0VOhEpLbEYXl5YvhwGBliyBLt24do1oTORYpGcA/TgwYOGDRsWbdetWzcpKUnukVTLkiVYswahoWjRQvJTeXnw9sbatcVPUty+XYB4RIqkTp063t7eAQEBQ4cOXbdunb6+vtCJSGkFBEBDA0Wn/apWxaxZ8PISOhMpFsm7wNq2bevu7j5q1CgAW7dubd26tRCpVMWcOTh2DKGh77+/fd06NGxY/GTEn36CiwtcXaHyjz8j+rB9+/bt2LFjz5491apVS0lJ2bVrl9CJSDmJxZgzBxUrvlmbOy8P58/jwgUuuE8lRBJrXeTk5GzcuDEsLExDQ8PJyWns2LG6urrlG7qwsDAmJiY5ObmgoMDMzMzGxkZTU/PTv9ze3t7ExETKi7fK048/IjAQp06hWrX3fPbpU1hZYedOWFoWt0yZgqZNsWKFPDMSfbrExEQHB4eHDx/KYvAJEya8t33Dhg2y2N1HKf3xh/7+GxkZko1ffQVDQyHS0JeSxfFH8gyQrq5ut27dzM3N+/Xrd//+/XJXPyEhISNHjtTT07OyshKLxTdv3szOzt62bVvHjh2/NLJS8PbG+fM4cwZVq76/Q3g4jIzg4fFWY2amHKIRKaCePXsKHYFUy1dfCZ2AFJ1kARQQELB8+fKcnBxXV9fOnTv7+PiMGzeuHONOmTIlJCSkfv36JS2JiYmurq7vvdlecf31F5YvR0jI5y2iNWsWzp/H6dNl3XXZqxd69frygESqwcXFRaJFLBYvXbr03XYiUkGBgXj4EB84Eywjkv+ur1279uLFi9bW1hoaGjExMWvWrCnfuAUFBTXfnvhSvXr1cmYUSl4evLyQkICtWz/jqxYsQFAQTpzgmhNEn8vPz8/MzExPT8/CwkJfX//u3btCJyIi2cvKwsSJ+OEH3L8vz91KngHKyMgoueylra2to6NTvnE9PDxsbGwcHR2trKxEIlF8fPzly5c9PT2/KKycFU1SXrQIPXqgf/9Pmp68ahX27sW5czA2ln0+IlWzY8eO69evL1q0aPjw4WKx+OeffxY6ERHJ3vLl+PprNG6MGTOwd6/cditZAA0ZMuSbb755/Pjxpk2bdu3aNWDAgPKNO3HiRBcXl+Dg4OTkZJFI1KZNm7lz51pYWHyo/5w5c27evFm65fbt2581aVrKnj7FihUIDYWlJXr2xOLFWLXqI1/y22/YuBGhoVC6c11EiiElJaVy5crt27c/f/68h4fH9evXhU5ERDL28CE2bEB+vLwUAAAgAElEQVRkJExM0LQpzp9Hhw7y2bNkAfTjjz+ePHkyLCzsyZMnixYtat++fbmHfv78ec2aNQcPHlz5/86dBAYGfmiqY7du3SSeuREVFSXkKiBz5sDdvfgWrSVLYG2NMWPQuPEH+x89irlzcfYszMzklpFIxXTs2HHo0KFr1qzp2rVreno61wEiUn3e3vD0RJ06ALB8Oby8EBEBuZz+kLwN3szMrHfv3kOGDHF0dBSJROUed9WqVf7+/o6OjpcuXTp8+LCtrS0AU1PT5OTkTxxByNtQY2LQujWWLHkzj2fvXhgY4Nix9/c/exaDBuHECdjYyC0jkZzJ9Db4IgUFBdHR0XZ2dhcuXDh9+vSgQYOaNm0qu92VgbfBE8nDhQv45hsEBkJPDwDEYgwZgunTMWaMREd53AZ/48aNwMBAX1/fsWPHFlVC1tbW5RjX39//ypUrxsbGMTExrq6uUVFRFStWlEZguSgsxMiRuHPnTUvDhu9fzBDAtWsYPBj79rH6IfpCaWlpdevWffHiRbNmzZo1aybkRXAikoP//kPDhm8t0l2xIt6eDyM7kgVQ5cqVhwwZMmTIkJiYGA8Pj+XLl0ucIvpEenp6RRVPy5Ytx40b5+XltWXLFinklQ9bW/z66yf1TE5Gz55YtUpu1yyJVFi3bt2KNp49e3b//n13d/eAgABhIxGRDHl4SK6HJ0eSt8FfuHDBx8enadOmkyZN6tu37/3y3pPWp08fGxubdevWAfD29n7+/PmgQYOysrK+NK9CyciAiwvGj8d33wkdhUgV/Pt/EhISIiMjtbQk/0JTdGIxEhKEDkFEn0Ty+DJ//nxXV9czZ86Ympp+ybjLli1zcXF58uQJAA0NjUOHDu3du9fIyOhLxlQshYVwc0ObNpg5U+goRCqoVatW0dHRQqf4TNu2YcoUxMXB3FzoKET0EW8VQOnp6ZMmTXJ1dZXK0E5OTiXbmpqabm5ubkUP5lUNs2YhLQ0HDgidg0h1TJ06tWT7+vXrtWvXFjDMZ3v1CnPmoEsXzJqFHTuETkNEH/FWAaSjo+Pj49O+fXvlW7VZzn7/HQcOIDwcFSoIHYVIdZT+q8nZ2bmDck2tK1o0dd06NGmCsDCU+l6I6COio/HsGbp0kec+3yqAKlSo4OTk1KpVqw4dOpSswLF582Z5BlICf/8NHx+cPfv+x7wT0ecrekpgrVq1SjfevXtXYnkwxXXnDrZvx3//QV8fixfDywv//PN5zxB814sXCA3Ft99KKSKRoioowIgRSEpCfLw8nyIlOQdo2LBhw4YNk9vulc+TJxgwAFu2oEkToaMQqQ4/Pz8ASUlJYWFhtra2WlpaERER48eP9/f3L9+AhYWFMTExycnJBQUFZmZmNjY2sr2pfupUzJiBoqmTQ4di0yb8/ju+8Fg6axYCAhAbW9YSrEQqYPNmGBqid28sWoTyPoG0HCQLIGdn57i4uNjY2H79+t2/f79u3bpyi6IE8vMxcCDGjAGfUE0kVbt37wbQpUuXuLi4Ro0aAbh3797YsWPLN1pISMjIkSP19PSsrKzEYvHNmzezs7O3bdvWsWNHKWZ+49IlHD+Oly9x8mRxy7NnmD0b7u4o93Ky0dE4fhwzZ2LaNMh0Pca8PGhry3B8orK9eoWFC3H8OOrUQdOmGDkSzZrJZ8+SBVBAQMDy5ctzcnJcXV07d+7s4+Mzbtw4+URRAtOmoVIl/PCD0DmIVNODBw8aNmxYtF23bt2kpKTyjTNlypSQkJD69euXtCQmJrq6uhZdaHvXvXv3UlJSSre8evVKU1MzMjKy6KWVlZWBgQGAtLS0O6WWSC1ub9487fDhO6WWubeqXdvAyAgi0fv7f2ic0u1jx2L0aKtp0wy++grBwWkODuUcp+z2q1cNVqxAdHRaVpZMxmc72z/avnatVY8eBnZ2ANK8vO6MGoX169/tHxsbm5+fD+kSv61FixYvXrzo2bOnWCzOyMho1KiRWCBt27Z1cXERau/vsXu3uFEjcVqa0DmIhPHw4cNatWrJdBdDhw4dOnTo2bNnz549+91337m7u5dvnCZNmmRmZpZuyc3Nbdu27Yf6Dxs2zO5tOjo6BgYGJS+DgoKKev7xxx+lu8mqvX59O319u1atgoKCxMePi62s/ggIkP5+bW2DTEzEzZuL16yR0/fFdrZLtDdrZqelFfTHH8XtAQF2urp2DRu+27958+b6+vpiqZJ8FliDBg1iY2MHDhx4/Pjx169f29nZxcbGSrnm+jQK8SwesRhpaTAywu3bcHTEX3/B1lbIPETCkcOzwHJycjZu3BgWFqahoeHk5DR27FhdXd1yjPPLL7/4+fk5OjpaWVmJRKL4+PjLly97enqOHz/+E0eQyfEnIgJWVjA0/Ei37Gw0bYrt29+sL9+jB7p2fetxAVLx449ISMC8eXBwQGzsB5/2QyQ7P/6IxYslG/v3x759Em2yOP5I3qQwZMiQb7755t69e5s2berateuAAQOkuDPls3EjWrTAixcYMACLFrH6IZKR7t27P3/+fPLkydeuXTMyMqpcuXJsbOzkyZPLN9rEiRNPnTplb2+flZWVlZXVpk2b4ODgT69+ZOLpU3TujB9//HjP48dx/z769EHVqsUf58+jvJPBP+jhQ2zciCVL0LAh3N0xb56Uxyf6FIsWQSyW/Hin+pERyTNAAIKCgsLCwvT19Tt27Ni+fXv55HiX8GeAUlPRpAkaN0ZmJiwtsWuXYEmIFIBMzwAFBAT079//8uXLEu3Ozs6y2N1HSf/4M2YM8vMRFIQLF2Bl9ZHOqamSLbq6xY/Llpb+/dGyJebMAYBXr9CkCY4eRevW0twFkfTI4wxQXFxcUlLSsmXLxGLx999/f/bsWSnuTMnMnw9XVwwdipgYTnwmkqlhw4bp6+ubm5vfvXvX2dn54sWLc+fO1fjCdXQUR1QUAgPh54dZs/App7WMjCQ/pFv9hIXh4EE8e4bvv8f332PpUpibY8YMae6CSOFJHl8GDhxoYGBw9erVwMDAVatWlV6ZXr3cuIHduzFuHObOxahRWLBA6EBEqk9ljz9eXli0CJUrY/JkJCcjKEjgPNWrY9kymJm9KbBcXTFkiMCpiORL8jb4zMxMNze3BQsWuLm5tWvXLjc3V5BYwps2DbNnY+pUTJ0KT080bYrz56FcC/MTKRvVPP7s2YP0dIwYAQBaWvDzw4QJ6NwZOjqCRWrcmE9xJpIsgOrUqePt7X348OF//vln3bp1JQ/EkDVXV1eJJz8/evTI2tpaPnuXdOoUTp7Es2dITkaDBvDyQtWqmD4d//4rTB4i9SDU8Ue2Fi9GUtJbqznfu4f9+zF0qHCZiOidAmjfvn07duzYs2dPtWrVUlJSdslr5u/mzZvT0tJKt/Tt27dGjRry2bukZs0wezZ++gk//AAjIwCwswMfEEskY0Idf2TrzBlkZko2mpkJEYWI3pAsgKpVq2Zvb3/hwoXLly/36dPH0tJSPjmqVq1atWrV0i06Ojqicq8i/4WMjXH0KDZuhJubMAGI1FK1atV69OgRGxvbpk2b4cOHq8ijeIT6Q46IyiQ5CXru3LnDhw9//PhxZmbm0KFDi55QqHZ+/BENGrD6IZKzgICAPn36zJgxQywWd+7c+ddffxU6ERGpLMkzQDt37oyJiTE0NATg6enZpk0bL6kvP6rgLl3C778jJkboHERqZ+3atRcvXhw2bJiGhkZMTIytrS2fRUhEMiJ5BqhmzZola2+IRKLKlSvLPZKgsrMxYgQ2buSMHyL5y8jIKHn2hba2to6A90kRkap7cwbop59+AtCgQYOWLVsWPYU0KCiof//+wmUTwrx5sLND795C5yBSR0WP4nn8+PGmTZt27dql7o/iISJZkrwEVvQI1qLtyZMnGxXdA6UmoqJ48YtIQAsXLix6FM+TJ08WLVok4KN4iEjlvSmApkyZ8vz587Vr10ZGRmZnZ9va2s6YMcPc3FzAcHKVn49Ro7B2LS9+EQnIxcXFxcWlaDsjI8Pwo89OJyIqlzdzgO7evWtraysSiWbPnr1ixQpTU9OvvvoqPj5ewHBytXw5atXC4MFC5yBSR1FRUd26dWvTps28efNevHgxbdq0Hj16NGrUSOhcRKSy3pwBmjp1qq+vb79+/YpeOjg4tGjRwsfH59ixYwJlk6Pbt+Hnh4gIoXMQqakRI0b079+/Y8eOAQEBzZs3Hzt27IwZM1RkHSAiUkhvCqD//vuvb9++pT/Xo0ePKVOmyD2SECZOxA8/gEdbIoE8f/78hx9+AGBlZXXkyJH58+cLnYiIVNybS2BaWlr5+fmlP5efn19yS7yKmDEDkZGSjbt24ckTeHgIEYiIAEBbW7tow9jY2MDAQNgwRKQO3tQ3zs7OK1euLP259evXOzk5yT2SzJw9i/Xr4eEBsfhN48uXmDkTGzfi/46/RKQCIiMjjxw58vLly5KWwMBAAfMQkaJ5cwls5cqVXbt2vXDhQpcuXbS1tc+dO3fv3r2zZ8+We+jCwsKYmJjk5OSCggIzMzMbGxtNTU1pZC6XggJ4eWHHDqxahT/+wHffFbf/8AO++QYODoIFIyLg/v37FStWLNrOzMws2U5PTy/HaKtWrfL393d0dPTy8jp8+LCtrS2AMWPGJCcnSyswESm7NwVQpUqVLl++fOTIkcjIyLy8vIEDB/bv319LS3KhoE8UEhIycuRIPT09KysrsVh88+bN7Ozsbdu2dezYUTrBP9emTTA2hqsrzM3Rvz/69IGhIa5exYEDuHZNmEhE9H+ys7OlOJq/v/+VK1eMjY1jYmJcXV2joqJKKioioiJv1TcikahPnz59+vT58nGnTJkSEhJSv379kpbExERXV9fw8PAvH/yzpaZi4UKcOAEAX32Fjh2xYgUWLYKHBxYtgrGxAJGIqBTpPvVCT0+vqOJp2bLluHHjvLy8tmzZIsXxiUgFlPMEz0cVFBTUrFmzdEt1ARcYXLAALVsiL694BnT//nBzg7Ex0tMxcqRgqYhINvr06WNjYzN+/HhPT09vb+8+ffoMGjQoKyvrQ/1DQ0MfP35cuiUlJYUnjYhUm6wKIA8PDxsbG0dHRysrK5FIFB8ff/nyZU9PTxnt7iOSk/H8OUo/VrpxYyxZguPHIeC0JCKSjWXLlrm4uDx58gSAhobGoUOH9u7dW8aDfU6ePCmx6GtKSoqQf7MRkeyJxKVviZKq+/fvBwcHJycni0SimjVrdu/e3cLC4kOdU1JS0tLSSrf07dvX3Nw8KChIJuHmzEFiIrZvl8ngRKooMTHRwcHh4cOHQgeRB3t7exMTE944RqQgZHH8kdUZIAAWFhbjx4//xM5jxoyJjo4u3fLo0aNyT8H+iMRE/PorrlyRyeBEpGA6dux47tw5oVMQSYlYjPv3uXLvl5NhAfRZDh48KNFS9BeYTHbm4wNPT9SuLZPBiUjBFBQUCB1BURUUIDkZtWoJnYM+x969GDECN26wBvpCsiqABn/gqaK7d++W0R4/SW4uoqJw8SJ++03IGEQkR+7u7kJHUFS+vli1CjdvonJloaPQp8nKwsyZ6N4dPj7Yv1/oNMpNVgXQ9OnT+/fvP3z4cEdHRxnt4rPl5aFVK+TkYMkScK19IrUxZswYoSMopCdPsGIF2rTBokVYvVroNPRpVq6EoyO2bkXTpjh1Cl26CB1IicmqALKzsxs2bFjr1q2dnZ1ltIvPtn49srLw6BF69RI6ChGR0GbPxsiR8PaGtTXGjIGlpdCB6GMSE7F+PSIioKuLlSsxdSqioyGjybJqQIbPOp03b16PHj1kN/7nSUnBsmUQi9G5M5YsEToNEZGgoqIQHIzZs1G9OmbMwPTpQgeiT+DjAw8PFN1P3a8fTEzAFT6/gGo97L0Ms2fD2hpWVti+HQEBeHvNDyIi9eLlhUWLiqf+eHri1i389ZfQmahM0dHYu7f4slfRx9On+PFH5OUJnUxZqceps2vXcOgQNDTw559v/tw5flzoWEREQjh2DGFhMDEpfkAQAC0tfP89unUTNBaVqVEjnDwp2VihArS1hUijCtSjAPLygrY2qlTBvn3Ytw+vX+PPP3H6NDp3FjoZEZHctWuHPXskG7nytYIzMOC/WdKlHgXQt9/i0iUMHYqStfCXLuU6QESkpqpVQ//+QocgEph6FED372P4cKxYIXQOIiIiUghqUAA9fowtWxATI3QOIiIiUhRqUAAtW4Zhw7jWOwnowIEDy5cvFzrF+40YMWLSpElCpyAikjdVL4CSkrBrF65eFToHqbWrV6/a2dmNHTtW6CCSjh49GhUVJXQKIiIBqHoBtHAhRo9GjRpC5yB1Z2pqamdnJ3QKSdHR0UlJSUKnICISgEoXQA8e4MABrnlIREREElR6JejlyzF2LIyNhc5BREREikVWZ4AKCgo0NTUBJCcnh4WFaWhoODg4mJmZSX9PeXlwdcXatWjY8K325GTs24fr16W/RyIiIlJysjoDVLNmTQBnz55t1arVsWPHTp061alTpwMHDkh/T/7+uHgR3t7FLwsLERcHAMuWYeRIrm1KRJs2bRI6AhEpHNnOAVq5cuXRo0fbtm0L4MWLF87Ozv369Xtvz9DQ0MePH5duSUlJqVix4kd28OwZli3DuXPo1w8nTuD//T9s2YKpUxEaij/+wLVrUvo+iEiZBAQElH65cOFCHR0dAMOGDRMoEREpHNkWQFWqVLGwsCjarlSp0suXLz/U8+TJk/Fvz1ZOSUmp/tHzN3Pm4Lvv0Lw5Vq3C1Km4cAFz56J7d7i5YdgwmJp+8XdARMrnxIkThw8fdnd319fXB5CTkxMdHQ0WQERUiqwugWlqatrZ2cXFxY0ZMwbAvXv3evfu3bFjxw/1X7Ro0b63NWzY0Kjk0V3vFR2NY8cwYwY6dkSjRqhTBwMG4Jtv4OeHmzfx9dfS/p6IZOnuXQwahIKCcnxpeHi4SCQKDAwsaVm0aJFIJMrNzS16eeXKlcGDB0snpzLYtWvX77//fvXq1f79+/v6+pqamvr6+vr6+gqdi4gUiKzOAD1+/DgtLe3WrVtPnz4FkJKSMmDAgO+++06a+/DyQvv2mDYN0dEYPBgdOuDnn7F+PTZvRocOWLQIvXtDQ6VvcyNV4u2NM2ewZQvKtV5itWrVDhw40LNnz6KXgYGBJVeQFyxYEBAQYG9vL7WoyqBfv37t27efOHHikSNHSgrBD9m5c2diYmLplqSkJD09PVkGJCKBybA+qFKlSps2bVxcXADY2dkNHz686L4w6cjLQ+3ayMnBwYNwcEBCAn7/HS1aIDcXGzdiwwYYGmL7dqntjkimQkLw3384dQrz5iEtrRwDtGrVKioqKi8vD8Ddu3dNTEwMDAyKPtWmTZvhw4dLMayyqFGjxsGDB62trRs0aFB2z+zs7NS3ValSpV69evLJSbI1cyYePBA6BCkipV0IUVsbv/+O4cMxbRqWLoWfH6ZORbNmGDgQADw88OwZ5szBiBEQiYTOSvR/srLw9mR/ACgowMSJmDkTJibo2BHTp2P2bMk+mpqoU6eMN7OGhkanTp3OnDnTrVu3gwcPurq6RkZGFn2qR48eOjo6N27ckOY3ojzc3d3d3d3L7jN69GiJlsLCQhMTE5mFInk5fBh+frh/H3v2CB2FFI7SFkAAIiNx8mTxTe+jR2PvXlhZ4dYtzJuHRo0AQF+f1Q8pFj8/bNki2fjqFTIzsWIFVqxAQQESE3HyJLS1Jbvt2AFHxzLG7tev3/bt27t163b06NGjR4/OfreKUlcdO3Y8d+6c0ClI7l6/xsyZ2LcPXl44fx4dOggdiBSL0hZAYjEmT0anTjhxorilZ08sWQJ7e0yYIGgyog+bPVvy7E5KCho3xtKlsLYubtm6FS9fIijoc8du167dhAkTEhISdHV1jbkAeikF5ZpaTkpvzRpYW6N3b+TkwMsLERGQ4jQMUn5KWwAVFMDSEpmZ2L//TWOFClDLuQ6kxB4+hK0tgoLeqnj09FBQ8LkHaw0Nja+//nrKlCl9+/aVckgl99FLYKSCnjzB2rW4dAkABg7EL79g2za8c62T1JnSFkBaWti27a2Wv/5CXBx4pCPl0rIlTp2S1mD9+vVzdnbesGGDtAZUDUWLcZB6+f57dOyIV69QNBlu6FDMmYP+/VG5stDJSFEobQH0rjVr4OPDST+khuzt7YODgwF06tSpsLCwqDE5Obmkg7Ozs7OzszDhiORPLMa9e8jIwLhxbxpr1cLVq2VPpCO1oioFUGwsbtwovgWMiIjUmUgETnunj1GVdQJXr8bkyahQQegcREREpARUogBKSsLx45zdRkRERJ9IJQqgX37BkCHgfb9ERET0aZR/DlBuLrZs4eVeIiIi+nTKfwZo1y60agVLS6FzEBERkdJQ/gJo/XpMnix0CCIiIlImMrwEVlhYGBMTk5ycXFBQYGZmZmNjI82nwRe5cAGvXqFrVykPSyRtFy9eXLFihdApJP37779VqlQROgURkQBkVQCFhISMHDlST0/PyspKLBbfvHkzOzt727ZtHTt2lOZufv4Znp7QUP7zWKTSunfvnpOTk5qaKnQQSQ0bNvzf//4ndAoiIgHIqgCaMmVKSEhI/fr1S1oSExNdXV3Dw8Pf23/t2rXx8fGlWxISEnR0dMraR3IyQkKwdas08hLJkL29vb29vdApiIjoDVkVQAUFBTVr1izdUr169TL6N2rUyNDQsHTL1atXmzVrVtY+KlbE/v2oWPELYhIREZE6klUB5OHhYWNj4+joaGVlJRKJ4uPjL1++7Onp+aH+vXr1kmi5ffu2iYlJWfswNATP3hMREdHnk9XsmYkTJ546dcre3j4rKysrK6tNmzbBwcHjx4+X0e6IiIiIPp0M7wKzsLBgxUNEREQKSKFXgr59+/bp06c/9Nn8/PxLly5JzBwSUGpqqpGRkdApiilamCpVqohEIqGDAMCrV68MDAykvyJDuWRlZWlqan5ksr+8vH792tzcvHbt2h/q8Pz5c3nmERyPP+WmaGF4/HkvHn9EYrFY6oNKxZ49e7Zs2VJGh2fPnl2/fr2iYkyCFovFL1++VJwlVRTqAPTy5UtDQ0MF+X8+IyNDR0dHW1tb6CAAkJWVpaGhoaurK3QQAMjNzdXV1bW1tS2jT/369X/99Ve5RRIQjz9fgsefD+Hx50OEOf6IlVZoaKiTk5PQKYolJSWZmpoKneINfX39zMxMoVMUs7a2jo2NFTpFsd69ex85ckToFMWmTp26du1aoVMU+/nnnz08PIROoTR4/CkDjz8fwuPPhwhy/OESgkRERKR2WAARERGR2mEBRERERGqHBRARERGpHSUugHR1dRVk+jqAChUq6OvrC53ijYoVKyrIXQ9QsN+UooXR09MTOkUxPT09xfnJKD6FeiPx+FMGhfpNKVoYNT/+KO5t8J8iNzdXQdYwAMN8GMN8SF5enoaGhoL8U1FYWFhQUKAgN+gqBYV6LzHMhzDMh/D4o9wFEBEREVE5KPElMCIiIqLyYQFEREREaocFEBEREakdFkBERESkdlgAERERkdphAURERERqR1kLoF9++aVp06YtWrQ4e/asUBlmz57dpEmTunXrrl69WnFSDR48OCAgQPA8J06csLOzs7Cw8Pf3FzzMzJkz69Wr16BBgx07dggY5sqVK4MHDy55+W4GeaaSCKOYb2aFpQg/HMX8lfH48y4efz4aRrA3s5yfPi8VCQkJDRs2zMzMvH37dt26dQsKCuSfITQ0tEmTJrm5uc+ePatTp05kZKQipNq/f7+uru727dvFgv6UXr161bhx46dPn6alpZmamiYnJwsY5vz58zY2Njk5OcnJyZUqVcrIyBAkzPz58+vVqzdo0KCil+9mkGcqiTCK+WZWWIrww1HMXxmPP+/i8eejYQR8MyvlGaDg4GAnJyd9ff0GDRro6urGxMTIP8OTJ0/Gjx9foUKFatWqOTo6PnjwQPBUT5488fX1dXd3L3opYJ7AwMCePXuamJhUrlz59u3bJiYmAobR1NTU1tbW1tbW1dXV1tYWiUSChGnTps3w4cNLXr6bQZ6pJMIo4JtZkSnCD0cBf2U8/rwXjz8fDSPgm1kpC6DHjx9bWVkVbVtZWSUnJ8s/g6urq6enJ4CYmJiLFy926tRJ8FQTJ05ctWpVySOBBMyTkJBw//79li1b1q5de+3atZqamgKGcXR0bNy4sZmZWe3atefNm6evry9ImB49ejg6Opa8fDeDPFNJhFHAN7MiU4QfjgL+ynj8eS8efz4aRsA3s1IWQGKxWCQSlWwXFBQIEqOwsHD16tX9+vU7fPhw5cqVhU21Y8eOBg0atGvXrqRFwDwZGRm3b98+f/58TEzM1q1bIyIiBAxz5syZO3funD9//q+//vLz80tKSlKE98+7GYRNpVBvZgWnID8chfqV8fjzITz+fAqh3sxaMhpXpkxNTf/999+i7Zs3b5qZmck/Q0FBQb9+/YyMjCIjIytVqiR4qt27d9+5cyc4ODg5OXn//v25ubkC5qlevXrXrl2rVKkCoH379nFxcQKG+fPPP4cMGWJpaWlpaeng4BAaGqoI7593MwiYStHezApOEX44ivYr4/HnQ3j8+Sgh38wymlskUwkJCU2bNs3NzX348GHdunXz8/Pln2Hnzp0DBw5UtFRisdjLy6tkEqJQea5du2ZtbZ2Tk1M0qe3q1asChtm4cWPPnj1zcnJevHhhYWEREREhVJjTp0+XnoQokUHOqUqHUdg3s2JShB+Owv7KePyRwOPPR8MI+GZWyjNAFhYWkyZNcnJyArB161ZNTU35ZwgNDQ0ODjY1NS16uXnz5p49ewqeqjQBf0pNmzYdPnx4mzZtMjMzZ86c2axZMwBChRk1atSVK1eaNGkiFounTJliZ2cnYJgS7/52BPx9Kf6bWaHw+PMpePwpwuPPRwn4ZhaJxRJb1l8AACAASURBVGIZDU1ERESkmJRyEjQRERHRl2ABRERERGqHBRARERGpHRZAREREpHZYABEREZHaYQFEREREaocFEBEREakdFkBERESkdlgAERERkdphAURERERqhwUQERERqR0WQERERKR2WAARERGR2mEBRERERGqHBRARERGpHRZAREREpHZYABEREZHaYQFEREREaocFEBEREakdFkBERESkdlgAqaA7d+506dKld+/e/v7+QmchIiJSRCKxWCx0BpKy2bNnd+/evX379l9//fWFCxeEjkNERKRweAZIBc2ePdvJySkxMdHY2FgOuzt//nyfPn3k/7VERETlxgJIBRkaGgYGBk6YMGH9+vVCZ5E+b29vMzMzc3PzqVOnFp2/3L9/v5WVVYMGDdasWfNu/xcvXrRo0eJDo61YseL06dMyjEtERAqJBZAKOnnyZFhY2LFjx8zMzD79q8Ri8YsXL97dLrunnAUFBZ06deratWs3btw4derUn3/++ezZs8mTJ588eTIqKmrz5s2RkZGl+/v5+f3vf/9LT08vennnzp127dq1bNly1apVAJ4/fx4dHd25c2cBvhMiIhIUCyAVtG/fvmvXrg0YMOC77757b4d169bVr1+/UaNG06dPLywsDA8Pd3V1tbOzW716deltAEuWLGnQoEH9+vV9fHwken502G7duh05cqToU23atCmajSTR53O/tdq1a2/YsMHIyEhfX79OnTqamppBQUFdu3atU6dOpUqVBg8eXLLHItbW1p6eniUvN2zYsHjx4piYmCNHjmRnZy9ZsuSHH3743AxERKQCtIQOQF9q0qRJ169fP3nypLa2tpubW4cOHX777bcy+p87d27Lli1///23gYHB6NGjfX19nZycTpw4ERkZaWlpGR4eXrJ94sSJAwcOREVF6ejo9OzZ848//ihqLPrsR4cdNGjQoUOHvv3227t376akpLRv3/7dPq1bty49yPTp02/fvl26xd7eftasWSUviy5mrVy58scff+zZs2e3bt2WLFlSp06dos/WqVMnLCys9Jd37tz50aNHixcvLno5YcIEd3d3Hx+fgQMHJiYm5ubmWltbf96Pm4iIVAILIKU3cuRIU1PTP//8s3LlygkJCTt37iy7f0hIyKtXrwYOHAjg+fPnYrHYycmpbdu2JTVNyXZISMiwYcMqVaoEYMyYMcHBwZaWlqV7lj3sr7/++v333+fl5e3du3fYsGEikejdPhIF0HtPLL1rxowZnTt3HjFixPbt2yVuYywoKCjjCxs0aHDx4sWibXd39xUrVnzK7oiISPWwAFJ6dnZ2AHJzc728vH7//XeRSFR2fwMDg5EjR/74448ACgsLCwsLIyMjDQwMSnco2hCLxSWjaWhoFNUWpXuWPayWltZXX3119uzZffv2HTp06L19SsqRT7Rv374aNWp06NChVatWY8aMCQ0NdXJyOn/+fNFnExMTzc3NP2Wcixcv1q9f39TUdPbs2X/++WfVqlUDAgJq1679WWGIiEh5cQ6Qirh06dLXX3/dvHnzj/bs0qXL7t27U1NT8/PzBwwYsGnTpg/17NChw++//56RkfH69evffvutU6dOnzvsoEGDli1bZmRkVK9evU/Z9fTp079927Jly0p3SE1NnT9/fn5+/suXL0NCQmxtbV1cXE6fPv3s2bOcnJx9+/b17dtXLBbHx8fn5+eXkXb16tXTp09/9OhRREREdHS0h4fH9u3bP/aTIyIi1cEzQKrg33//PX/+fOnZvmVo1arV5MmTv/rqq6ysLBcXl7Fjx0rcOVXCxcUlMjLSxsZGLBb37t3b3d09IiLi04cF0KtXr1GjRv3yyy8f6iNxBuijl8BGjRr1zz//NG7c+PXr13369Bk/fryWltaaNWs6d+5cWFg4fPhwOzu73NxcKyurhIQECwuL9w6yf//+7t27GxoaGhoatmrVys7OrlKlSrt37y5710REpEq4ErTSy8/Pb9u27fr16w0MDNLS0ipWrGhrayt0KCIiIoXGM0BKb82aNfb29g4ODgAmTJjw6NGjY8eOCR2KiIhIofEMEBEREakdToImIiIitcMCiIiIiNSOrOYAFRQUaGpqAkhOTg4LC9PQ0HBwcPisR1MRERERyYiszgDVrFkTwNmzZ1u1anXs2LFTp0516tTpwIEDMtodERER0aeT7V1gK1euPHr0aNu2bQG8ePHC2dm5X79+Mt0jERER0UfJdg5QlSpVShajq1Sp0suXL2W6OyIiIqJPIavb4GvWrGlubl5YWFi7du1jx47du3dv0qRJNWrU2LZt2yeOcPr06f3798siGxGVj4WFxezZs4VOIQ88/hApGqkff2S4DlBaWtqtW7eePn1a9ESF2NjY7777rmhm9LtcXV2jo6NLtyQnJ9eqVWv69OkyikdEnyU1NdXf3//hw4dCB5GHGTNm3L17t2vXrkIHISJANscfRVkIMTU1NTU1tXRL3759zc3Ng4KChIpEpFZC0kMevH4w3Hj4hzokJiY6ODioTwFkYmLi4+MjdBAiAmRz/FGUR2EYGRkZGRmVbtHR0RGJRELlIVI313Oux+fEl1EAERGpElkVQIMHD35vO5+5TURERIKTVQE0ffr0/v37Dx8+3NHRUUa7IKIvdzb97K3cWwAuZlx8kv9k0/NNAEy0TPpU6SN0NCIiGZJVAWRnZzds2LDWrVs7OzvLaBdE9OVu5t68knUFwMO8h2n5aZFZkQDqVqgrcCwiIhmT4RygefPmyW5wIpKKcdXGFW34P/OPz4n/ufbPwuYhIpIPPgyViIiI1I6i3AVGRMKqrlX9pTbXaicidcECiIgAYIDRAKEjEBHJDy+BERERkdphAURERERqhwUQEZEiup5z/buE74ROQaSyWAARESmiF/kv7r++L3QKIpXFAoiIiIjUDu8CIyJSICdenTiUdgjA47zHt3JvjXswDoCBhsFSs6W6GrpCpyNSHSyAiIgUSA3tGnb6dgBu5d66kXOjaFtLpKUl4uGaSJr4fxQRkQKx0bOx0bMBEJoRGp4ZPrbaWKETEakmzgEiIiIitcMCiIhU3KZNm4SOUB7m2uYdDDsInYJIZfESGBGpmoCAgNIvFy5cqKOjA2DYsGECJSqP+jr1F5ktEjoFkcpiAUREqubEiROHDx92d3fX19cHkJOTEx0djQ8XQCNHjvzvv/9Kt9y5c8fe3t7Hx0cOaYlIECyAiEjV7Nq168CBA76+vqtWrWrXrt3p06d9fX3L6D9nzpzU1NTSLcp1roiIyoEFEBGpoH79+rVv337ixIlHjhzJzc0tu3P9+vUlWgwMDLS0eHgkUmWcBE1EqqlGjRoHDx60trZu0KCB0FmISOGwACIiVebu7h4cHCx0CiJSOCyAiIiISO2wACIiIiK1wwKIiIiIhJQnzssqzJLzTlkAERERkZC2vdg2J2mOnHfKAoiIiIiE9Fr8Ok+cJ+edsgAiIiIitcOVvojU3e3c2+GZ4f9k/dPBsEPfKn2FjkNE6sLnkc/Z9LMAnuY/zS3MvZx5GUBjnca76u2Sw95ZABGpnVcFr8Izw//O/Ds8Kzw8M1xfQ9/ewN5e395W31boaESkRjxNPAcZDQKwL3VfwuuEGTVmAKikWUk+e2cBRKQWbufevpR56VLGpUuZl+7l3mul38rBwGG08ejNdTabapsKnY6I1FHtCrVrV6gN4HLm5azCLDt9O3nunQUQkWoqEBdEZ0eHZoSGZYRdzLyoBS1HQ8d2Bu1GVxtto2ejJeL/+0Sk1ngQJFIdr8Wv/83890LGhQsZFy5lXqqtXbu9Yftvq3y7ptYaiwoWQqcjInq/DoYdmug2kfNOWQARKbc8cV54Zvi5jHPn08+HZ4Vb6lh+bfj1BJMJf9T9w1jLWOh0REQf11yvufx3ygKISPkUiAuuZF8JSQ8JSQ+5nHnZUseyY8WOXtW92hu2l9v8QSIipSbDAqiwsDAmJiY5ObmgoMDMzMzGxkZTU1N2uyNSeXE5cafTT59JP3M+/bx5BXPnis6TTCbtrbe3imYVoaMRESkZWRVAISEhI0eO1NPTs7KyEovFN2/ezM7O3rZtW8eOHWW0RyKV9CL/xen00ydfnTyVfkoEUeeKnftX6b+x9sYa2jWEjkZEpMRkVQBNmTIlJCSkfv36JS2JiYmurq7h4eEy2iORyigQF/yd+fdfr/46mX4yPie+g2GHLpW6zKgxw1LXUuhoREQqQlYFUEFBQc2aNUu3VK9eXUb7IlINyXnJf736K/hV8Jn0MxYVLLpV6rbSfGU7g3baIm2ho5EUvBa/riCqIHQKIiomqwLIw8PDxsbG0dHRyspKJBLFx8dfvnzZ09NTRrsjUlKFKPwn85+gl0F/vvoz4XVCl4pdXCq5/FzrZ17h+kKRkZEPHz7s1KlT5cqVi1oCAwN79uwpYKT+d/t/X/N7BwMHATMQUQlZFUATJ050cXEJDg5OTk4WiURt2rSZO3euhcUHVyLJzc3Nysoq3ZKfny+jbESCSy9IP5F+IvBl4J8v/6ypXdOlsotfLb92Bu00RbxRQApWrVrl7+/v6Ojo5eV1+PBhW1tbAGPGjElOThYwVVZhVmZhpoABiKg0Gd4FZmFhMX78+E/s3Lt373/++ad0S3p6ugxCEUlBWkHa1MSp2yy2fe4XPnz98NjLY8deHvs7828HA4delXvNN51ft0JdGWRUa/7+/leuXDE2No6JiXF1dY2KiqpYsWIZ/desWXPz5s3SLQkJCRUq8HIVkSpTlHWA/vrrL4kWe3t7ExMTQcIQle1e7r19qfs+vQCKzY49knbk6Muj91/fd6nsMrba2IP1DxpqGMo0pDrT09Mrqnhatmw5btw4Ly+vLVu2lNHf0tJSokI6efKktrYUpl7linNH3h+ZJ84DEJsdu+jxok3PNwH4pvI3Q6sO/fLxiajc3hRA770/S1dXt2XLluUYd/Dgwe9t3717dzlGI1IomeLM14Wvy+5TiMLLGZcPvzx8JO1IobiwT5U+a2utdTRw5EWuMsTFxYWGho4ZM2bhwoUnTpxYvHhxp06dyjFOnz59bGxsxo8f7+np6e3t3adPn0GDBklcZC/t3blBW7Zs0dfXL8euJeiIdIZWHZpRkAEgLjuug2GH5rrNAbQ2aP3lgxPRl3hTAPn5+QFISkoKCwuztbXV0tKKiIgYP368v79/OcadPn16//79hw8f7ujoKLWwRIKKy4k7+vIogITXCYUoXPFkBQAdkc4o41EVNYvPH+SL889lnDuYevDoy6MmWiZ9qvQ5WP9gS73y/BWhhgYOHDhz5syrV68GBgb6+flNnDgxOjq6HOMsW7bMxcXlyZMnADQ0NA4dOrR3714jIyNp5/0k3St1L9rY9HzT14Zfd67YWZAYRCThTQFUdG6mS5cucXFxjRo1AnDv3r2xY8eWb1w7O7thw4a1bt3a2dlZKkGJBPdv1r9HUo8ASC9MF0NctA0NuFRy0dXQDUkP2Z+6/+jLo/Uq1HM1cg1tHNpAp4HAiZVNZmamm5vbggUL3Nzc2rVrl5ubW+6hnJycSrY1NTXd3Nzc3NykkZGIVITkHKAHDx40bNiwaLtu3bpJSUnlHnrevHnlz0WkeNrqt00ySgKQ8DrhRs6Nb42+LRQX3n99f/HjxUGvghrrNO5n1G+u6dw6FeoInVRZ1alTx9vb+/Dhw//888+6deukchFKcXxb5dvGOo2FTkFExSQLoLZt27q7u48aNQrA1q1bW7fmhWqiYpa6ljN1ZwK4kHFh87PNd3PvHk473FCnYX+j/ovNFteuUFvogEpv3759O3bs2LNnT7Vq1VJSUnbt2iV0ImmaZDJJ6AhE9IZkAbR58+aNGzf6+/traGg4OTmV+xIYkeoRQ/x35t+7U3bvSt0lhriRTqMIqwie75GKCRMmlGzfunVr27ZtAPz8/DZs2CBcKCJSZZIFkK6ubrdu3czNzfv163f//n1dXV1BYhEplKvZV3el7tqdsltfQ3+Q0aCTDU/6P/OfXmO60LlUh7BrNBORGpIsgAICApYvX56Tk+Pq6tq5c2cfH59x48YJkoxIcI/yHu1O2f1Hyh8pBSmDjAYdaXCk5H6urRZbhc2mYlxcXCRaxGLx0qVL320nIpIKDYnXa9euvXjxorW1tYaGRkxMzJo1awSJRSSg9IL0gBf/v737jmvq+vsAfpKQhL1RQGWIIqh1UUTFgXXUqo9WgbrR2sdRt/Wn/h5bq221dVUcoBbrgFbR1gpalNZZEWu1oCKKA2Q4gLBHJhn3+SO+KI0FLULOJfm8/zo5Xu79mFyOX2/uPSd6aObQ7ve6Zyoyd7Tbkdc1b1ObTXiavblt27bN1dXVzMzM3d3d3Nw8OzubdiIAMFi6BZBYLK792ovP5wuFQr1HAqBDzah/rfp1au5UtztucRVx85zmPXvj2Tdu3wy0HMghHNrpjEJMTExGRsa8efMSEhKuXbvG5eoOUC3al4VfZimyaKcAgOd0x5cpU6aMGTMmJycnKipq+PDh7733HpVYAPqUIc9Y+Wyl2x23NQVr+lr0zeqSFe8VP952vJCD/wDoVVlZmY2NzYABAy5dutStW7eMjAzaiZrSxeqLuTW5tFMAwHO69wCtXr36zJkzycnJIpHoiy++GDBgAJVYAHpQoa6ILYs9WHbwWc2zaQ7Tznc872PqQzuUUQsKCpo6derXX389fPjw6upqA5sHCABYRbcAcnd3Hzt27JQpUwIDAzkcXPYHA6QhmovVF/eV7EusSnzb+u3PXD4bZjUMS3Tl1OSUqkrfNKc59de+fftu3brl7OwcERFx7ty57du3UwzTJBjCfF/2vUwjI4Q8Uz47VXkqW5FNCOlv2b+zaWfa6QCMmm4BdO/evYSEhPDw8NmzZ2sroa5du1JJBtDkntQ8OVB64EDpAXsT+5kOMyPdIu14dNaHYqFTlaceyB/QLYAqKio8PDxKS0u7dOnSpUsXHq8FVKUfPf3oM5fPaheD06FklH9K/lQwCkJIlboqU5Ep1UgJIa1MWqEA0o/fJb/7m/vzOXzaQYB1dAsgGxubKVOmTJkyJS0tbcGCBRs2bGAYhkoygKaiZJQnK0/uK9l3XXp9kt2kOK+4HmY9aIeCfzBixAhto7i4OC8vLywsLDo6mm6kl4qriFvUalF9BZCAI9jRboe2PSxz2JJWS7AYqp7NeTzniOeRLqZdaAdpSpXqShueDe0ULZ5uAZSUlPTzzz+fOnXK3t4+ODj40KFDVGIBNIksRda3Jd9Gl0X7mPr8r8P/Hm9/3JSLuT3/Rq6RyxgZIUSqkSoYRbm6nBAi4AgsuBb6D/Pnn3/Wtm/cuBEZGan/DGB4DOy/8fnK/MEPBz/o8oB2kBZPtwBau3ZtcHDw+fPnXVxcqAQCeH01TE18RXxUSdQd+Z0w+7BL3pewCGV95j2ZF18RTwhRMAo1oz5WfowQ0tWsa5J3Et1gvXr1unXrFt0M9ZFpZLHlsSpGRQipVlfHlsc68Bw4hPO29dsNLI3iwnfBV67w+kRK0VPlU9opDMHfCqDq6ur58+cHBwfTSgPwmrIV2XtL9x4sPehr6jvHcc4423ECjoB2KFbb775fO6t1RHHEA/mDne12UgyzdOnS2nZGRka7dixdX7ZKXXVdcp0hDCGkhqlJl6Vbca0IIb6mvg0UQDEeMfqLaNxESpHHXQ+5Rq59+ca9N7SNKLeoWY6z6OVqGhJGUqOpoZ3CEPytABIKhcuXLx8wYECrVq1oBQJoBDWjPl11enfx7hRpyjT7ab91/K2TaSfaoeBf69+/f217yJAhgwYNohimAa35rfe47dG2z1Sd+dL1Sw+BByFEopHElMWE2YfRDAeEtOa3lvWQadtv3Hsj1iO2q1mLf5onoTJhbcFaQohYI1YT9Zv33ySECDiCo55H2wlY+l8FlvtbASQQCPr379+rV69BgwbVzsCxd+9eGsEAXkmRqujbkm+/KfmmDb/Nh04f4i6fRuMRHpdDbebla9euEULatm1btzM7O7t795a0/EheTd7Gwo0ogKA5WPIsu5p2JYQUqYoy5ZnaNofDwUXuRtO9B2j69OnTp0+nEgXgX/ld8ntkcWRiZWKIXcgJrxN4sOs1TbafrH1Cm4pt27YRQvLz85OTk3v27GliYpKSkjJ37tyIiIjG7VCj0aSlpRUUFKjValdX1x49ejTTQ/XtBO2o3DAOr4hDOIYxp10bfhtfM19CiFmNGSFE2xZwBBY8nH6NpFsADRky5P79++np6SEhIXl5eR4eHjRSAdRLppEdLj8cWRwpUUs+dPowsl2kLc+WdihDYMOzofhgbWxsLCFk2LBh9+/f79ixIyEkJydn9uzZjdvbhQsXZs6caWZm5uPjwzDMw4cPZTLZgQMHgoKCmjCzVpJ3kkQjSZWmEkKya7LlGrm2LeQIDeBrFwOw331/J6EhfCHeUdhxZeuVhJBkSfK3xd9q2/A6dAug6OjoDRs2yOXy4ODgoUOHLl++fM6cOVSSAejIrcndXbz7QOmBPhZ9NrhuGGY9DGuUGpjHjx936NBB2/bw8MjPz2/cfhYvXnzhwoX27dvX9jx9+jQ4OFj7RduLzpw5k5ubW7cnPz+/qqoqKipK+3LUqFFt2rQhhGRlZV24cKF2M23/ycqTX/75ZfUf1UpGWawqDuYH2wyyEbYWnvA6IcmTvLh9fftBf3P037hw4wa5wZ48r9/vyHO0zLeMSoliSR799P/0009yuZw0Kd0CaOvWrVeuXJk+fTqXy01LS+vZsycKIKDuYvXFncU7k8RJMxxm/NHpj/bC9i//GWiBevfuHRYW9sEHHxBC9u/f/+abjZyWWq1WOzs71+1p+MGO1NRUnQJILBYrlcrU1FTty27dumkH4pycnNrO2v5JdpMcOY7Hnh0rV5efqzr3tt3b7wvf7+PThxByNufsi9vXtx/0o/9V+l34Ln0q+rAnj37609PTlUolaVIcnRmivLy80tPTJ0yY8PPPP9fU1Pj5+aWnpzftIV9RQECAk5NTQkIClaMDG8g18kPlh3YU7VAxqoWtFk6zn4abLSh6+vRp3759nzx50nyHkMvle/bsSU5O5nK5/fv3nz17tqlpY25p37Vr17Zt2wIDA318fDgczoMHD65evbpo0aK5c+e+4h4aN/5kyDNCs0Pvdr777yPTUamuvCK+MtJmJO0gAC/RHOOP7kMfU6ZMGTNmTE5OTlRU1PDhw997770mPBjAK8pX5n+S/4nHXY/4iviv2359p/OduY5zUf0YsHfeeaekpGThwoV37961s7OzsbFJT09fuHBh4/Y2b968s2fPBgQESKVSqVTq7++fmJj46tVPo5lwTEw4upfV2SxNlrZRtJF2CgA6dH9XP//881OnTiUnJ4tEoi+++GLAgAFUYoEhCXoYdNDjoHailJdKkaZsK9qWWJU4xW5KsndyB2GHZk4HrDBx4kRzc/OJEyc21Q7d3d31UPHo8BZ6n+94Xs8HBYDG0b0CdP/+/fz8/K+++ophmP/+978XL16kEgsMSam6VKwWN7yNhmjiKuIGPhwYkh3S06xndpfsHe12oPoxHtOnTzc3N2/Tpk12dvaQIUOuXLny6aefcrnU5iVqNEcTR9oRXk6mkWUrsrMV2fnKfLlGrm2LlCLauQD0Snd8mTBhgoWFxZ07dxISEjZv3lx3ZnqA5iDRSCKKIzrd7bRJtGmh08KsLlnLWi/DQsfGCeOPfuwp2TMsa9iwrGHLni67Lb+tbQdlBkk0EtrRmt603Gn5ykY+TgiGTfcrMIlEMnny5M8++2zy5Mn9+vVTKBRUYkFLV64uX/hkYQ1TQwh5UvNk2bPnNc1sx9lDrYZqtylQFuws3rm3ZO8gy0HRHtH9LPrRTAwsgPFHP5a2Wrq01VJCSJI4aXX+6kvel2gnaka3ZLfKVGWufFfaQYB1dAsgNze3ZcuWxcXFXb9+fceOHbULYgD8K5Zcy3G24zSMhhDyh+SPt63e1q5W4yX0IoTckd35uujrk5UnJ9tNvtbpGh5rBy2MPwCgN7oF0A8//BATE3PkyBFHR8eysrLDhw9TiQUtHZ/DD7YN1rY/L/x8uPVw7ay4F6ovzHs8L02WtsBpQWbnTHsTe6oxgV0w/ugZ3QXgmo9EI9ko2qhdMl2kFIUXhTuZOBFCQu1C/cz9aKcDttAtgBwdHQMCApKSkq5evTpu3LhOnQxhBnGgTsNojpYf3SzaLNPIPmr9UbxXvJAjpB0KWMfR0XHkyJHp6en+/v4zZszAUjzNLcAiIMY9hnaKZmHFtdJwNYQQLodrw7OxM7EjhGDueKhLtwD69NNPY2Njx44da29vP3Xq1A8++GDJkiVUkoFhkGqkbny3Mdlj3AXua13WjrIZhTEI6oOlePTMhGOi/W7awFhwLZa3Xq5tf1/2/UyHmViXDV6kWwAdOnQoLS3N0tKSELJo0SJ/f38UQNA4parSyOLIXSW7Ai0Cj3ge6WPRh3YiYDssxQMAeqNbADk7O9fOvcHhcGxs8DQy/GtPap5sLdoaUxYz3nZ8kneSt9CbdiJoGcRice3aF3w+XyjE96Twunqb93YwcaCdAtjorwJo+/bthBAvL6/u3buPGjWKYZhTp06FhobSywYtT4Y8Y5NoU0JlwkyHmem+6dpHT/Nq8twEbvjmC15KuxRPYWFhVFTU4cOHsRQPvL597vtoRwCW0r0C5Ofn5+f3/Cb5hQsX2tnZ6T0StEh/SP7YULjhmvSadjJDW55t7R+NfTQ21jPW19SXYjxoEbAUDwDozV8F0OLFi0tKSrZu3ZqamiqTyXr27LlixQrtSvRGq1pdnSJNGWw1mHYQVjtTdWaDaEOOIuc/rf8T6xlrxjXT2UBN1GpGTSUbtDijRo0aNWqUti0Wi7X3IwIANLm/ZoDIzs7u2bMnh8NZtWrVxo0bXVxc+vTp8+DBrSrWEQAAGIBJREFUA4rhqLslu7WmYA3tFCylIZrjFcf97/t/9PSj9x3ez+ySOd9p/ovVD8CruHnz5ogRI/z9/desWVNaWvrRRx+NHDmyY8eOtHMBgMH66wrQ0qVLw8PDQ0JCtC/79u3brVu35cuXnzx5shH7VavVPB6PEFJQUJCcnMzlcvv27evqisnIDYGSUcaWx24o3GDFs/rY+eMxtmO4LywqJ1KKBjwcoCZqQshT5dN3st4RcAWEkNXOq2c4zNB/ZmC5999/PzQ0NCgoKDo6+o033pg9e/aKFSswDxAANJ+/CqDbt2+PHz++7p+NHDly8eLFjduvs7NzcXHxxYsXJ0+ePHToUAsLi1WrVq1fv762wGIzJaMUa8SEkGpNtYpRlavLCSE8wrPmWdOORpmCUewv2b9ZtNlT6Lmz3c4hVkPq27I1v/W5judUjIoQ8s6jd7a32e5t6k0IwYo88I9KSko+/vhjQoiPj098fPzatWtpJwIAA/dXAWRiYqJSqQQCQW2PSqWqfSS+cTZt2nTixInevXsTQkpLS4cMGVJfAZSXl1dSUlK3RyqVajSa1zl6o+0o2rG+cD0hRMWoZIzM644XIcSKZ5XROcOCa0ElEnVijTiqJGqraKufuV+sZ2yARcBLf8RN4KZtCDiCtoK2WPALGsDn87UNBwcHC4sm+C1LTU198uTJ4MGDa+fySEhIGD169OvvGQAMw18F0JAhQzZt2vTJJ5/U9kRGRvbv3/919m5ra+vu7q5tW1tbV1ZW1rflp59+evfu3bo9OTk5tTOC6Nmy1suWtV5GCLksvvxx/sdJ3klUYrBEubo8oigiojjiLau3Tnc43c2sG+1EAC+xefPmiIiIwMDAJUuWxMXF9ezZkxAya9asgoIC2tEAgC3+KoA2bdo0fPjwpKSkYcOG8fn83377LScn5+LFi43bL4/H8/Pz02g0s2bNOnnyZE5Ozvz584OCgurbPjo6WqcnICDAycmpcUeHJlGsKg4vCo8qiRprMza5U3JHYSPvSP3c5XPtIvAA9cnLy7OystK2JRJJbbu6uroRe4uIiLhx44aDg0NaWlpwcPDNmzdrdwgAoPVXAWRtbX316tX4+PjU1FSlUjlhwoTQ0FATE92Jgl5RYWFhRUVFZmZmUVERIaSsrOy9996bNm1a06TWFy7hvnh7rzEoUhVtFW2NKokabTP6j05/dBB2eJ29jbMd11TBwFDJZLIm3JuZmZm24unevfucOXOWLFmybx9mwwOAv/lbfcPhcMaNGzduXNP8c2Vra+vv769t151fsQXpbdE7yj2Kdgq9yq3J3STadLT86Az7GXc733Xhu9BOBEahaVe9GDduXI8ePebOnbto0aJly5aNGzdu4sSJUqm0vu2LiorEYnHdHrlcTuseRADQj0Ze4DESfA7feNaxylRkflX41cnKk3Mc59zvfN/JBN8/Qkv11VdfjRo1SiQSEUK4XO7x48ePHj3awLz2CxcuTElJqdvz7Nmzuk+EAIDhQQEE5K787peFX56rOrfAaYHOKhYALVTdBzh4PN7kyZMnT55c38ZHjx7V6cE9iAAGzxhvcIFaN6U3g7ODh2YO7W7WPatL1mqX1ah+wPA08PgFABgtXAEyUtck19YVrrslvfWf1v/5zuM7c6457UQAzUWtxlJ0AKALBZDRSRInrStclynPXOm88lj7Y0JOU958CsBCYWFhtCMAAOugADIiZ6vOritcV6As+D/n/5tqP5XP4dNOBKAPs2bNoh0BAFgHBZDhYwhzqvLUusJ11erqVc6rJtpN5HF4tEMBAADQhALIkGmIJq4ibn3heoZhPnb5eLzteOOc1xEAAEAHCiDDpCGaU5Wn1hSsYRhmlfOqELsQDuHQDgUAAMAWKIAMTQ1T833Z9xsKN7jwXTa6bhxmPYx2IgAAANZBAWQ4ZBrZvtJ9m0WbfU1997nvG2A5gHYiAAAAlkIBZAjEGvHu4t3hReG9zXsfa3/M39yfdiIAAABWQwHUspWry3cW7YwsjhxiNeRMhzNdzbrSTgQAANACoABqqQqVheFF4ftK971r8+6VTlc6CDvQTgT1un37dmJiIu0U/ywwMLDuslkAAPr3SPGoVFXa26K3Pg+KAqjlyanJ2SzafLT86BS7KTd9brYTtKOdCF7i+PHj586dY2Gdcfv27czMTBYGAwCjkliV+ED+AAUQ1OuO7M5G0cbEqsQ5jnPudb7XyqQV7UTwqoYOHbp27VraKXTt27fv6tWrtFMAAFCAAqhl+F3y+8bCjdel1xc7LY5sF2nNs6adCAAA4LVUqitLVaWEkBJVSZW6KluRTQixM7Gz49np4egogFiNIczpytMbRRvzlfn/af2fo55HTbmmtEMBAAA0gZXPVp6tPksIqVJX1TA1yZJkQoiX0OtMhzN6OLpxFUA/Vfw00HKgk4kT7SAvp2SUR8qPbBZtNuGYrGi9ItQ2FAt4AQCAIdnjtkfbiCiOeCB/sLPdTn0e3bgKoD3Fe2x4NkOthtIO0pBqdfXe0r3bi7Z7C72/bvM1pnIGAABoclgak0WeKZ+tfLbS865niiQlvn382Y5nUf0AvL6oqCjaEQCgITzC43L0XZAYxRWgB/IHYo2YEFKlrspUZGrvruog7GDDs6Ed7blbslvhovCEqoRp9tNSfFI8BB60EwG0YNHR0XVffv7550KhkBAyffp0SokAoCHTHKbJNXI9H9TwCyAFo5iRN0PJKAkhmfLM8KJwa641ISTMPmxRq0V0s2mIJrEyMbwo/IH8wcJWC7e3227Ls6UbCSgqVZU6mDjQTmEIfv3117i4uLCwMHNzc0KIXC6/desWQQEEwFaWXEtLrqWeD2r4X4EJOcKrna6m+KSk+KT0tui9q90ubZtu9SPRSHYX7+6c0XnFsxWlqtLsrtkrWq9A9WPMcmpyBjxs5Pq1165d43A4CQkJtT1ffPEFh8NRKBSEkFWrVvn6+np4eGzZsqVpsrLe4cOHv/vuuzt37oSGhoaHh7u4uISHh4eHh9POBQAsYvhXgNgmtyY3sjjyYOnBQZaDvnX7VkM0q/NX8zl82rmAMhWjUjGqRv+4o6PjsWPHRo8erX2ZkJBgZWVFCElOTo6Pj09LS6uqqvLz83vrrbd69erVNInZLSQkZMCAAfPmzYuPj9cWgg0YMWLE9evX6/ZUV1f7+fk1Z0AAoMy4CiATjokJpb8yQ5jz1ecjiyOTxcnvO7yf4pPiLnAnhCSJk6jkAQPTq1evmzdvKpVKPp+fnZ3t5OT0+PFjQohIJJo7d65AIHB0dAwMDHz8+LGRFECEkNatW//0008xMTHp6ekNbxkfHy+Tyer2rF27tm3bts2ZDgAoM64C6KD7QSe+vicBqlRXflf23a7iXXwOf77T/O89vrfgWhwtP7r82XJCSLGq+J783ns57xFC+Bz+t27fmnHN9JwQ9GZr0dbDZYfr9pSrygtVhYQQhjByjdziloW230voJeAI6m4Z2S4ywCKgvj1zudzBgwefP39+xIgRP/30U3BwcGpqKiEkODhYu0FaWtqVK1d2797dtH8j9gsLCwsLC2t4G1NTU1PTv00xqr1pGgAMmHEVQK35rfV5uFRpalRJ1LGKY8Othu9x2zPQcmDtH71p/iaXcAkh9+T3REpRqG0oIcSSZ4mJng3bDPsZgywH1e2Ra+Q5NTmEkEJV4deFX29uu5kQIuAI2gvbcwindjMeh9fVtGvDOw8JCTl48OCIESNOnDhx4sSJVatWafs1Gs3WrVu/+eabuLg4Gxu2PPmoT0FBQb/99hvtFADALsZVAEUWR/6Pzf+4Cdya9SjV6uoj5UeiSqJKVCWzHGdl+Ga8WHh5Cb28hF6EkCRx0vnq86F2oc0aCVjC3sTe3sRepzOQBBJCMhWZUcVRU+2nNnrn/fr1+/DDD3Nzc01NTR0cnj9NplarQ0JC7OzsUlNTra2NdAk5tVpNOwIAsI5xFUDxFfGdTDs1XwF0VXJ1X8m+4xXHg6yCvnD9Yrj1cK4RPGcHLMHlcgcOHLh48eLx48fXdh49elQoFO7fv59iMOpe+hUYABgh4yqAmkm+Mv+7su8Olh5kCDPTYea9zvde/bs2L6HXu7bvNms8aBHa8tuucl71mjsJCQkZMmRI3Rt9Ll++nJiY6OLion25d+/e2ifFjMesWbNoRwAA1jGKAiixKlGsFhNCREpRkjipXFVOCHnT4k1Pgefr7FaikcRXxH9X9t2f0j+DbYP3ue/rZ9Hv3+6kDb/N0lZLXycGGAYzrtkMhxmN+9mAgIDExERCyODBgzUajbazoKCAELJ7924jvPEZAOClDKEAKlOVvXhfRS0Fo/i+7HvtTNBFqqJL4kv35fe1/Z72jSmAlIzyTNWZ2PLYU5WnAi0D33d4P759PG5eBgAAaEEMoQAKeBBw0ftiW/4/T9oh5AgPeRzStodlDlvpvLJxq8ErGeWF6gs/lP9wsvKkj6nPJLtJ4W3DnUz0/VA9AAAAvD5DKIBecwrdhkk10l+rfo2riDtVdcpH6BNqF/qZ62f1FVsAAADQIhhCAfTqLLgWFlyLV9kyrybvl6pfEioTksRJARYB79q8u6HNBle+a3MnBAAAAD1oqQVQXk1eSHYIQxhCSL4yf8yjMdppc5e1WibWiGc5/vNDHz+2/7GBVbfEGvGl6kvnqs+dqTpToip52/rtafbTDnkcsuYZ6ewpAAAAhqqlFkBuAre97nvVjJoQMubRmPWu6135rlzCZQgzLXdafQXQi9VPpbryd8nvSeKkpOqk27LbvS16D7EaEu0R3cu8F6bwAQAAMFTNWABpNJq0tLSCggK1Wu3q6tqjRw8ej9dUO+cQTg+zHtq2gCN4w+wND4EHISRDntHwD8o0stuy2zekN1KkKX9I/niifOJv7j/QcuA613V9LPpgHS5oJuXl5dnZ2bRT6CopKaEdAQCAjuYqgC5cuDBz5kwzMzMfHx+GYR4+fCiTyQ4cOBAUFNQch6tSV6VKUwkh2TXZco1c2+YSbhtBmxxFTqYi86Hi4T3ZvXR5+uOax76mvr3Me71p/ubCVgu7mnY14bTUy2DQUnTo0GHNmjUJCQm0g/yDuXPn0o4AAEBBc/3bv3jx4gsXLrRv37625+nTp8HBwdeuXfvH7ZOTk7XzttUqKytreOkiBaNIqEzQMJogq6CdxTt/qfxFTdQKjaJSXTk4c7CSUdYwNTY8Gy+BV0dhxw6mHULtQtearfUWejdwGxBAc5g6derUqY1f5AsAAJpccxVAarXa2dm5bk+rVq0a2D4xMTEzM7Nuj0wms7S0bOBHylRlP5b/qCEaK64Vl3An2k+05FoqNIqYspgYjxgXvou7wN2ca/46fwsAAAAwSM1VAC1YsKBHjx6BgYE+Pj4cDufBgwdXr15dtGhRfduvX79ep2fFihVOTg1NM+jCdznieUSnM0OecaLyxFtWbzU6OQAAABi85nrQad68eWfPng0ICJBKpVKp1N/fPzExUQ93Gwg4Au3z8AAAAAD1acb7f93d3fV/f2UHYYffvH/T80EBAACgZWH1A1BlZWUNPzn88OFDMzO2PLgukUgsLF5pmmk9QJj6SKVSMzMzDodDOwghhCgUCh6PZ2LCil9DtVptY2NjZ2dX3waFhYX6zEMdxp9GQ5j6YPypD5XxhxV/83/k6em5ZcuWH374ob4NpFKpSCRiyYfHMIxarWZJGEKIUqk0MTFhya+ZSqXi8XjsCcPlcrlcVsxyqVarORwOS8JoNBqBQODi4tLANt26ddNbHrow/rwOjD/1wfhTHzrjD9NiXb58uX///rRTPJefn+/i4kI7xV/Mzc0lEgntFM917do1PT2ddornxo4dGx8fTzvFc0uXLt26dSvtFM/t3LlzwYIFtFO0GBh/GoDxpz4Yf+pDZfxhRekHAAAAoE8ogAAAAMDooAACAAAAo4MCCAAAAIxOCy6ATExMmnB5+dfEqjCEED6fz5J7+wkh7HnSkiBM/dh2DrMcq94uVoUhGH/qhzD1oXIOcxiG0fMhm4pGoyksLHR1daUd5Llnz561adOGdorn2BbG1dWVJY+hikQiBwcHlvzal5eXCwQClkxSIpVK5XK5vb097SAtA8afBrAtDMaff4TxpwUXQAAAAACNw5arlAAAAAB6gwIIAAAAjA4KIAAAADA6KIAAAADA6KAAAgAAAKODAggAAACMTkstgHbt2tW5c+du3bpdvHiRVoZVq1b5+vp6eHhs2bKFPakmTZoUHR1NPc+vv/7q5+fn7u4eERFBPczKlSs9PT29vLxiYmIohrlx48akSZNqX76YQZ+pdMKw82RmLTa8Oez8yDD+vAjjz0vDUDuZ9bz6fJPIzc3t0KGDRCLJysry8PBQq9X6z3D58mVfX1+FQlFcXOzm5paamsqGVD/++KOpqenBgwcZqu9SVVWVt7d3UVFRRUWFi4tLQUEBxTCXLl3q0aOHXC4vKCiwtrYWi8VUwqxdu9bT03PixInaly9m0GcqnTDsPJlZiw1vDjs/Mow/L8L489IwFE/mFnkFKDExsX///ubm5l5eXqampmlpafrPIBKJ5s6dKxAIHB0dAwMDHz9+TD2VSCQKDw8PCwvTvqSYJyEhYfTo0U5OTjY2NllZWU5OThTD8Hg8Pp/P5/NNTU35fD6Hw6ESxt/ff8aMGbUvX8ygz1Q6YVh4MrMZG94cFn5kGH/+Ecafl4aheDK3yAKosLDQx8dH2/bx8SkoKNB/huDg4EWLFhFC0tLSrly5MnjwYOqp5s2bt3nzZnNzc+1Linlyc3Pz8vK6d+/erl27rVu38ng8imECAwO9vb1dXV3btWu3Zs0ac3NzKmFGjhwZGBhY+/LFDPpMpROGhSczm7HhzWHhR4bx5x9h/HlpGIonc4ssgBiGqV3YhWEYtVpNJYZGo9myZUtISEhcXJyNjQ3dVDExMV5eXv369avtoZhHLBZnZWVdunQpLS1t//79KSkpFMOcP3/+0aNHly5d+uWXX7Zt25afn8+G8+fFDHRTsepkZjmWvDms+sgw/tQH48+roHUys2JJtn/LxcXlzz//1LYfPnxIZT1CtVodEhJiZ2eXmppqbW1NPVVsbOyjR48SExMLCgp+/PFHhUJBMU+rVq2GDx9ua2tLCBkwYMD9+/cphjl9+vSUKVM6derUqVOnvn37Xr58mQ3nz4sZKKZi28nMcmx4c9j2kWH8qQ/Gn5eieTI3071FzSo3N7dz584KheLJkyceHh4qlUr/GQ4dOjRhwgS2pWIYZsmSJbU3IdLKc/fu3a5du8rlcu1NbXfu3KEYZs+ePaNHj5bL5aWlpe7u7ikpKbTCnDt3ru5NiDoZ9JyqbhjWnszsxIY3h7UfGcYfHRh/XhqG4sncIq8Aubu7z58/v3///oSQ/fv383g8/We4fPlyYmKii4uL9uXevXtHjx5NPVVdFN+lzp07z5gxw9/fXyKRrFy5skuXLoQQWmE++OCDGzdu+Pr6MgyzePFiPz8/imFqvfjpUPy82H8yswrGn1eB8UcL489LUTyZOQzDNNOuAQAAANipRd4EDQAAAPA6UAABAACA0UEBBAAAAEYHBRAAAAAYHRRAAAAAYHRQAAEAAIDRQQEEAAAARgcFEAAAABgdFEAAAABgdFAAAQAAgNFBAQQAAABGBwUQAAAAGB0UQAAAAGB0UAABAACA0UEBBAAAAEYHBRAAAAAYHRRAAAAAYHRMaAeAFm/8+PE3b96Uy+UVFRXOzs6EkHfeeScnJycxMZF2NAAwcBh/oNE4DMPQzgCG4NKlS+vWrTt79iwhRCqVSqVSR0dH2qEAwChg/IFGwBUgaHpXr149cuTIxIkTv/nmG0tLyzt37oSGhmZkZBQUFAQGBq5evZoQsmbNmkOHDllaWi5dunT69Om0IwOAgcD4A68IBRA0oxs3bmRmZt67d69Hjx7Pnj2ztbX18PD45JNPTp8+feXKldu3b8vl8sDAwICAAB8fH9phAcCgYPyBhqEAgmYUGBjI4XDatGnj6+vr5ORECDE3N9doNMnJyYWFhRMmTCCEyOXy27dvYwACgKaF8QcahgIImhGXy9VpaJmbmy9evHjWrFmEEI1Gw+FwKIQDAIOG8QcahsfggYJBgwYdOnSopqZGIpF4e3vn5eXRTgQAxgLjD2jhChDolbu7O4fDGThw4FtvvdWrVy+FQrF8+XIPDw/auQDA8GH8gbrwGDwAAAAYHXwFBgAAAEYHBRAAAAAYHRRAAAAAYHRQAAEAAIDRQQEEAAAARgcFEAAAABgdFEAAAABgdFAAAQAAgNFBAQQAAABGBwUQAAAAGB0UQAAAAGB0UAABAACA0UEBBAAAAEYHBRAAAAAYHRRAAAAAYHT+H4K3fVxphqtgAAAAAElFTkSuQmCC" 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Z09e7YXB/fx8fHx8enFAQH6O5YqgKZOnSojI7N69Wp9fX0ajZafn48LooGh3N3dP18SNT4+PjExMTw8nCCIhoaGCRMm7N279/NvHPT09LoZ2dHRkYuLiyCICRMm3Lt3b+nSpTt27KCvf/f+/Xt6Hzs7OxkZGYIg7t27V1FRMX36dIIgmpqanjx5IiEhYWdnx8PDQxCEsrJyS0sLLy9v77xm+Fdzc/OpU6cKCgqMjY0nT55MoVB+ckAKhbJv3741a9ZUVVUNGjQId6oDMBTrFECysrLx8fHHjx8vLS2dN2/eihUrcI8DMJq0tLS0tHSXRh0dnXPnzu3YscPAwODcuXOenp4jR4780ZHb2to6Nri5uZOTk/39/efPn+/l5XX+/Hn6U/z8/B0bgYGB8+bNIwiCSqVSKJTExMSvrSXMhg4fPjx//vzeHbO1tdXOzk5WVtbU1HTHjh1Xrlw5duxYr4z8xQ8VAPQ61imAJkyYsHDhwrS0NC0trXv37klJSa1fv/748eNk5wK2w8/Pn5qaevz48eLi4pUrVzo7O/dgkGvXrjU0NNBotL///vvo0aOXL1/28PBYtGjR06dP37592+Uyf1tb2zVr1nh5ebW2turr69+8efOLY/bWLUXMj34hVIcNGzbQ/znk5eXVW7tISEjg4+OLiYkhCGLJkiXq6upFRUW9uN4zADOrr68/fvx4UVGRubl5r5z+JAXrFEAJCQkODg4ODg4lJSWenp4LFy5MSkpKSEgYO3Ys2dGA7QgICAQEBPzMCAYGBvb29jU1NV5eXiYmJgICAosXL758+fLgwYPd3d03b95sbm7e0Xn48OEjR440MjJqbm4ODg5WUVEpKCjoeFZGRoaPj09CQqKurm7NmjUbNmz4mWD9wrVr12JiYjw9PeknyZqamh4/fkx8vQA6dOjQ69evO7eUlpZ2/6VhWVmZtrY2fZuXl1dFReXNmzcogIAdNDY2WlhYDB061MDAIDw8/ObNm/10Xk3WKYCKi4sNDAzoF4cSBLF58+aQkBBfX98nT57ghk/odywtLZcuXdrxUFdX9/PzOp2nWV+zZs2aNWs6P9Xx7KlTp+gbT548YVRcJnP69Onz58/v3LkzLCzM0tLy5s2bO3fu7Ka/kJBQl28MOTg4uv9HraWlZWhoaGlpqYKCwsOHDwsKCjruYAdgbTExMaqqqvQDi7+/v7Ky8vr16/vj97asUwBZWFgEBgYuXbpUVFT03r17Hz58mDlz5sOHD4OCgvBFGAC7mTJlio2Njb+/f2xsbHNzc/edPTw8urRER0cLCgp28yNDhw4NCQkZOnSohIQE/esAISGhnw0N0B9UVFRoamrSt/n5+QcOHFheXt4fCyDWmQfI3t7excVFXV19yJAh06dPP3nyJBcXV3h4eEpKyoULF8hOB/ADFi5c2Pn0D/SMjIzMhQsX9PT01NXVGTH+woUL37x5c/Xq1aKiInzVDuzD2to6JiamrKyMIIjExMSqqiodHR2yQ/UEA88AUanUrKys8vLy9vZ2eXl5AwMDRs/Ks3HjxiVLlpSVlWlqatKveeTn54+IiJg8ebK1tTX9hmEAYCuenp6enp4MGpyPj49B1RUA0zI1NV28eLGurq6wsDCFQjl9+jR9xo1+h1EFUGJioo+PDx8fn7a2No1Gy8vLa2xsPH78uJ2dHYP2SCcuLi4uLt65xcLCwsvLy8/Pb/LkyTdu3JCUlFy4cKGamhpDYwAAALCqwMBAX1/ft2/fDhw4kIOjv36VxKgCKDAwMDExsXOdUVpa6urq+uDBAwbtsRvr169XUlJKTU3V1dUtLCw0Nzd/+PChiopK3ycBAABgAQMGDFBUVCQ7xU9hVOHW3t4uKyvbuYXEK6S4uLg+fPhQXV09fPhwS0vLxsbGPXv2kBUGAAAASMeoM0ABAQEGBgZWVlba2toUCiU3Nzc1NXXx4sUM2l336urqWltbN2/efOHChfv376elpWGpVPhJ79+/f/XqFdkpflZ1dTXZEQAAyMGoAsjf39/JySkhIaG8vJxCoZiYmKxZs6abWcIiIyNLS0s7t5SVlXVZP7LHODg46F9SSktLL1269MWLF8bGxr0yMrAnFRWVkydPxsXFkR2kF8yZM4fsCAAAJGDgXWCSkpL0mVjfvHmTnJxcU1PTTQHU2NjYscQjHZVK7TLff48JCQkNHTr02LFjpaWlV69eFRMTmzt3bq+MDOzJ29vb29ub7BQAANBzjCqATpw4sWjRIm5u7rVr1+7atcvCwuLevXtBQUFBQUFf7P95RXL79u1enFjs3Llzs2bNKi8v5+XlbWlpMTMz662RAQAAoN9hVAG0bt263NzcAQMGKCkpXbt2zdLSsqamZtiwYV8rgBhNXV39/v37dXV1fHx869at8/DwuHnzZv+9eQ8AAAB+BqMqACqVKi4uLiIiIiAgICEhQRCEoKAg6QvGCgoKcnJyrl27tq2tbdu2beSGAQAAALIw6gzQtGnTzMzMuLm5HRwcvL29Z8yYce3atVGjRjFodz+Ei4vr9OnTpqamFhYWtra2ZMcBAACAvsaoAmjbtm1JSUlcXFxWVlY3b95MSEgYP34889xvoqCgEBkZ6eHhkZaWJi8vT3YcAAAA6FMMvAus4+SKg4ODg4MD43bUMyNGjPD19Z06deqdO3e4ubnJjgMAAAB9h62vAl61apWIiMiqVavIDgIAAAB9iq0LIA4OjlOnTl24cOHs2bNkZwEAAIC+w8CvwPoFcXHxmJgYe3t7TU1NIyMjsuMAAABAX2DrM0B0Q4cOPXTo0JQpU7AuEgAAAJtAAUQQBDF58uQpU6a4u7u3tbV1NMbHx1tbW2tpafn6+r57947EeADwozIyMmJjYz9+/NjRwhprtwFAb0EB9P+2bNnCx8e3ZMkS+sOMjIz58+f/9ttvly9f5uDg8PT0JDceAHy/sLCwyZMnR0VF6evrZ2Zm0hvnzZtHbioAYCrsfg1QBw4OjsjISEtLy4MHD/r5+UVHR/v5+Y0ZM4YgiL1790pISNTW1vbi2mQAwDj79u179OiRhIREVlaWq6trZmZm93+8DQ0Nzc3NnVva2tp6azFmAGBOKID+IyQkdPHiRSsrq0GDBnFycnZ8HUalUgmCwMJhAP0FHx8fveLR19dfsGBBUFDQ0aNHu+k/bdq0+/fvd26pra0lfekeAGCo/wqgBw8efP40Ly+vvr5+H+YhmZqa2tmzZ6dOnXr48GE/P79BgwZpaGjs3r179OjRAgICZKcDgO/i4uJiYGDg6+u7ePHipUuXuri4uLm5NTQ0fK3/55cHmZmZSUlJMTgmAJDpvwJo165dBEGUlZXdu3fP0NCQi4srPT3d19d337595MUjwfDhw/fs2bNkyZI//vjj8OHDb9++tbe3x2SJAP1IaGiok5NTZWUlQRAcHBzR0dFnz54VExMjOxcAMJH/CqAzZ84QBOHo6JiTk6OpqUkQxOvXr+fPn09aNPJMnz49Nzd306ZNt2/f5ufnJzsOAOvz8/P7YvvBgwd7NqC1tXXHNicnp4eHh4eHR8+GAgCW1PUaoOLiYg0NDfq2iopKWVlZn0diCqtXr379+rWbm1tMTAwnJyfZcQBY3Pjx48mOANDXqFQqri4lUde33tTU1NPT886dO3fu3PHy8ho2bBgpsUhHoVD+/PNPDg4OX19fsrMAsD6nz4wbN+7x48dk5wLofVQqNSQkRFRUVEhIaMaMGZ8+fSI7EZvqWgAdOXLE2Nh43759Bw4cGDZs2KFDh3o8NJVKzczMjI+Pv3z5ckZGRnt7+89F7WucnJyRkZFZWVnr1q0jOwsAW9i1a5e8vDwfH5+ysjI/P/+rV6/ITgTQ+/bv35+RkZGXl/f27Vt+fv6lS5eSnYhNdf0KjJeXd8yYMQMHDpwyZUpRUREvL2/Pxk1MTPTx8eHj49PW1qbRaHl5eY2NjcePH7ezs/vZyH3l06dPJ06csLW1PXr0qJSU1MKFC8lOBMDiTpw48eLFi40bN3p7e9NotL1795Kd6Mtqa2tPnTpVWVk5cuTI4cOHkx0H+pmEhITg4GBpaWmCINavX29qakp2IjbV9QxQRESEi4tLSEgIjUZzcHDo8RmgwMDAxMTE7OzsmJiY2NjYFy9eJCcnL1++/KcD95H3798bGRmlpaUJCQnx8PCsXr36+PHjZIcCYHHv3r0TERGxsbFJSkoaOnToixcvyE70BR8+fDAyMrp//z6FQpk7d25YWBjZiaCfERUVpd+iSBBEZWWlqKgouXnYVtczQDt27EhJSfHy8uLg4MjKyjI0NFywYEEPxm1vb5eVle3cQq92+4tjx47Z29vT67958+YNGjTot99+ExERmTx5MtnRAFiWnZ3dzJkzt2/fPmrUqNraWua8B5N+JvvIkSMEQcyfP19bW3vJkiVcXJhUFr6Xn5/f9OnT6asLbN26NTAwkOxEbKrrH21dXV3H117c3NwDBgzo2bgBAQEGBgZWVlba2toUCiU3Nzc1NXXx4sU/FbYPvXnzRkdHh74tJyfHx8f3119/zZw5U1RUdOTIkeRmA2BVR48effz4says7L59+27evLl7926yE31BaWmprq4uQRA0Go1+cHj37l33/8D7+PFjTU2NsrIybikFgiBsbGxiY2OPHDnS1NS0ZcuWiRMnkp2ITXUtgGbMmDFx4sSKiorDhw+fPn162rRpPRvX39/fyckpISGhvLycQqGYmJisWbNGWVn5a/1DQ0MLCws7txQWFpL4jyorK6uwsDAfHx9hYeGYmBhxcXFHR8eYmBgXF5eoqChbW1uyggGwsA8fPqioqNTU1AwePHjw4MHMWS5YWloGBwdv27bt3bt3urq6fHx83Vc/wcHBf/75p5iYGAcHx9mzZ42NjfssKjAtU1NTXPpDuq4VxurVq69fv37v3r3KysqNGzfa2Nj0eOgBAwbMmzePk5MzPT09Jyenrq6um87GxsYSEhKdW27cuMHHx9fjvf8kV1fX+/fvKysrS0pK0mi0s2fPEgRhaWlJLwovX76Mzy5Ar6MvP0wQRFVVVVFRkaenZ0REBLmRPicuLv7+/XsqlaqiolJcXCwvL99N5+jo6KSkpKKiImFh4ejoaA8Pj9zc3D6LCgDdof0vOTk5X1/f5ORkKpVK+wm7d++Wk5Orr6/fsGGDqqqqp6enkpLSgQMHvn8EU1NTJyenn8nw8969e/fy5Uv6utAdrly5IiMj8/DhQ7JSAZCipKREQUGhz3aXkZHh4+PTZ7vropvjT0hIyLZt22pqanJyclpbWyUkJGpqar42zi+//LJjx46Oh9LS0m/fvu39uACsjhHHn653gWVnZ1tbW+/cuXPw4MG//vrrs2fPelZXhYeHZ2dn8/PzHzp0KD09PSIiIj09ffv27T9dsPUpMTExDQ2NLufhx40b99dff02YMOGLy8cCQK8wMjJizokQRURE3r59Ky4urqWl1dTU1NLS0s3F2gMHDszOzqZvV1ZWNjU1YUkyACbR9SswERGRGTNmzJgxIysrKyAgYMuWLTQarQfjSkpKtra2EgQhLi5OLyCEhISY856OHhgzZsyxY8ecnZ0vXrxoZmZGdhwAFrFkyZKO7RcvXigqKpIY5mtmzpxpbm4uKiqqoaFx6NAhT0/PbuZLmz17tomJibe3t4aGxqlTp1auXIn7xQCYRNc/xbt3716+fPnKlSvi4uKurq6RkZE9Gzc0NNTKymr8+PGDBg2ys7NzdHSMj4+fPXv2TwdmFuPGjTt+/PjEiRPPnz//M1dKAUCHziuY2tvbM+fdBkpKSsnJybt373769Kmbm9ucOXO66SwmJvbo0aOIiIi3b98ePHhwxIgRfZYTALpH6XKCZ+TIka6urpMnT5aTk/vJoT99+hQXF1dUVFRXVyctLe3o6Ei/d/Q7mZmZSUlJxcXF/WQMhkpKSpo+ffqJEydGjRpFdhYAxiotLbWwsCgpKWHE4F/8QpmXl1dfX58Ru/umfnH8AWAfjDj+/M8ZoNra2oULF7q6uvbK0MLCwh4eHr0yFNOytQF4h+YAACAASURBVLW9cOHC5MmTjxw5grkcAHps165dBEGUlZXdu3fP0NCQi4srPT3d19d33759ZEcDANb0PxdBDxgwIDg4+O3bt2Sl6Y+srKzi4+N9fX1PnDhBdhaA/urMmTNnzpzh4eHJyclJT0//559/Xr58iTvGAYBx/ucMEA8Pj7W1tZGRka2tbccFy/QZ36EbxsbGiYmJY8aMqaysDA4OJjsOQH9VXFysoaFB31ZRUSkrKyM3T694+PBhWFgYfeXUkJAQlrkXBKC/63oRtJeXl5eXFylR+jVtbe3k5OTRo0fX1NSEhoZSKBSyEwH0P6ampp6envTLio8dOzZs2LAeD0WlUrOyssrLy9vb2+Xl5Q0MDEiZVzonJ8fZ2XnTpk2ampp79+6dO3fu6dOn+z4GAHyuawFkb2+fk5Pz9OnTKVOmFBUVqaiokJGqX1JUVExJSXF2dp42bdrJkye7uTMWAL7oyJEjf/zxx759+zg4OKytrefPn9+zcRITE318fPj4+LS1tWk0Wl5eXmNjI30R017N+21nz56dN2+ej48P8e+F1Q0NDTgJBMAMuhZAERERW7ZsaWpqcnV1dXBwCA4O7tlq8OxJTEzs2rVrHh4eEyZMuHDhgrCwMNmJAPqHsWPHnjx58tdffyUIgj5V4NOnTxctWtSzr+ADAwMTExPV1NQ6WkpLS11dXb82eemqVatevnzZuSU7O7ugoKBjMcRFixbRZ7u4cuVK59U5vtn+4sWL7OzsnJwc4t9p9+kTpP3oOGhHO5u3//HHHx8/fiR6VdcCaMeOHSkpKV5eXhwcHFlZWYaGhiiAfggfH9/58+eDgoKsra3j4uKUlJTITgTQD7i5ufHz87u5ufXKaO3t7bKysp1bul+vdOzYsQYGBp1b0tLShISEpk6dSn+oqalJ39DT0+to/J72GTNm+Pj40CcWuXz5spGRkYiISA/GQTva2bzdycmp91df6LI0hpqaWn19/fjx42k0WnNzs56eXu8uvfH9mGEtsJ9x6NAhBQUFLBkGLKMP1gLLzs4+fPgwjUZbv369paVlYmJiz8bZv3+/pqamt7f3li1btm7d6uPjo6Ojc/Dgwe8foRePPzExMUZGRgMHDvT09MRCYAA90xdrgc2YMWPixImvX78+fPjwqFGjOs4Aw4+aP3/+wYMHx48fT19J/mtycnKSk5Nra2v7LBgA05o+fbqAgMCzZ8/i4uLCwsI6r4zxQ/z9/W/cuGFmZtbQ0NDQ0GBiYpKQkODr69u7ab/TpEmTMjIySktLIyIipKSkSMkAAJ/r+hXYhg0brly5cu/evcrKyo0bN2KRh58xfvz4mzdvTpo0KTMzc/PmzRwc/1Nutre3u7u7p6WlKSoqvnz5MjIycuTIkWRFBWAG9fX1Hh4e69ev9/DwsLS0bG5u7vFQysrKZFU8ANAvdD0DlJOTU1ZWFhoaSqPRVqxYcfv2bVJisYwhQ4b8888/qamprq6uXU7znDx58uPHjy9fvrx7925UVBQrLZQG0DNKSkpLly6NiIiYOXPmnj17cLcUADBO1wKot05BQwcpKakbN27IysqamZl1ntk2MzNz/Pjx9KWhbWxsGhsbq6qqyIsJQL6oqKiBAwf+/fffkpKS7969w5Q5AMA4XQsg+inoCxcu/PwpaOjAw8Nz8ODBZcuWDR8+PDY2lt6orq7ecU37y5cvqVSqpKQkeRkByCcpKTlu3LiioiIajebt7a2lpUV2IgBgWV0LIJyCZhwfH5+4uLigoKCQkJC2tra5c+e+ePHC0dHRz8/Pzs5ux44dmD8a2FxERISLi0tISAiNRnNwcDh06BDZiQCAZXUtgHrxFDSVSs3MzIyPj798+XJGRkZ7e/vPRWUFJiYmGRkZz549Gzly5IcPHx48eODn56evr3/r1i1PT0+y0wGQjD4PmZ6eHn0esu3bt5OdCABYVte7wCQlJc3MzO7evZuamuri4tLjU9DMMxU9s5GQkIiLiwsNDR02bNjRo0cnT55MdiIAZlFXV9exhgw3N/eAAQPIzQMALKxrAbRmzZozZ844OzuLi4vPnDlzzpw5QUFBPRj3R6eiZyscHByrVq1ycHDw8PAYO3ZsSEgIJowGIP6dh6yiouLw4cOnT5/GPGQAwDhdC6DIyMisrCxBQUGCIBYvXmxiYtKzAuhHp6JnQ2ZmZpMmTdqzZ8/hw4eVlZVjYmL09PTIDgVAJsxDBgB9pmsBJCsr2zFfH4VCoS9b0wMBAQEGBgZWVlba2toUCiU3Nzc1NXXx4sU/FZa1xMbGJiUlVVZWxsXFBQQEjB49urS0FNdBA5tzcnJycnKib9fV1dH/MQYA0Ov+K4B2795NEIS6urq+vj59EZwrV650Xpbsh/j7+zs5OSUkJJSXl1MoFBMTkzVr1igrK3+t/8KFC/Py8jq30NdPZmFJSUmzZs0SFxf39PQ0MzPT09MbPXp0RESEnJwc2dEA+lpmZuavv/5aU1Mzbty4xYsXb9q0KScnJzMzs7y8nOxoAMCaup4BMjY2NjY2pm8vWrRITEysx0N3noqe/i1PN50XLlxYVlbWuSUgIOBn9s78ZGRk8vPz6dtiYmL8/Pzm5uaGhoY7d+50d3cnNxtAH5s9e/bUqVPt7OwiIiKGDBkyf/78kJAQFRUVsnMB82poaODh4aHPJQvQA/99dAIDA6urq3fs2JGRkdHY2GhoaBgSEjJw4MCejRsREdH54YYNG+g3dHh5eX2xv66urq6ubucWERER1v5k+/j4mJiYUKlUDQ2NY8eOLVu2bPXq1c7Ozl5eXufPn9+/f3+Xi6gAWFh1dfWqVasIgtDW1o6NjV23bh3ZiYB5FRcXe3p6pqWlcXBwBAQEbN68GRcPQA/8Nw/Qq1evDA0NKRTKypUrt27dKicnZ25u3nnphh9y7do1X1/f+/fvP378+PHjx01NTfSNXorNCqSlpTMyMhQVFUtKSrZu3bp69WqCIIyNjTMyMnR1dfX19U+cOEF2RoA+ws3NTd+QkJAQEBAgNwxLiomJMTY2VlRU9Pb27u+r7syZM2f06NF1dXWFhYVJSUmnTp0iOxH0T7R/TZw48dy5c7ROrly5MmHCBFpPnTt3ztLSMiUlhUaj6enp/eiPm5qa0i9FYk+ZmZlGRkaOjo75+flkZwGg0Wi0kpISBQUFBg2uoqLyxW2ysNjx559//lFUVLx9+3ZJSUlgYOCYMWPITtRzra2t/Pz8LS0t9Id//fWXl5cXqYmgLzDi+PPfGaAnT550mZRv3Lhx2dnZPS6tpkyZEh0dvX379pCQEKwp9qMMDAwePHgwZswYCwuLLVu2tLa2kp0IgIGKioqE/tV5u8cDZmRkxMbGfvz4saMlLi6uN5J+WUtLy+7du6dMmbJo0aLXr18zbkc9Exsb6+/vb2dnp6CgsGPHjtTU1NraWrJD9RAXF5egoGDHNaPFxcVSUlLkRoJ+6r8CiIuLq62trfNzbW1tHbfE94yMjMyFCxf09PTU1dV/Zhz2xMXF9csvv6Slpd2/f19fX//27dtkJwJglMbGxup/dd7u2WhhYWGTJ0+OiorS19fPzMykN86bN6/38nY1d+7c69evu7u7y8jIWFtbd7mlg3Q8PDxNTU307dbWVhqN1q+vsAwODh41atTs2bPd3Nz279+/YMECshNBv/Tf34C9vf22bdt+++23jpb9+/dbW1v//D48PT2x0FWPKSsrX7p06dKlSz4+PhYWFtu2bVNQUCA7FEAv691VL/bt2/fo0SMJCYmsrCxXV9fMzMyfOZn0TZ8+fYqLiysvL6e/ioqKiqioqJ5NIcsg06dPt7e3V1ZW1tTU3L1798SJE/n4+MgO1XO6urqVlZX3799vbm4WFRXFGSDomf9O8Gzbti0uLm7UqFFhYWG7du2aNGnSsWPHwsLCSAwHHSZOnPj8+XN1dXUDA4PNmzd3/GMOAD7Hx8dHr3j09fUXLFjA6FqktrZWQECgo4aTlJT89OkTQ/f4o3R1dWNjY69cufLrr7/q6ekdOnSI7EQ/JSgoKCEhITc3t7Cw0M7Obu/evWQngn7pvwJIWFg4NTXVz8/v48eP5eXl06dPz8jIEBcXJzEcdMbPz79x48a0tLT09HRdXd2oqCgajUZ2KABm5OLiYmBgsGfPHoIgli5dWl1d7ebm1tDQ8LX+L168uPm/Pn78+P0X3g0cOFBSUnLXrl0tLS1Pnjz566+/xowZ0zuvpPeYmZmdP38+OTl57dq1/Pz8ZMfpuba2tuLiYjMzM/pDGxubHt+tDGzuf74GplAoLi4uLi4uZKWBb1JVVY2Ojr5z587SpUt3794dHh5uYWFBdigA5hIaGurk5FRZWUkQBAcHR3R09NmzZ7uZWPXAgQNd/idaXl4uKir6/Xs8f/68r68vfe60jRs3mpqa9jg8dI+Li0tTU/P69ev0e9ni4+MNDQ3JDgX9EoVpzyKYmZlJSUkx9MaNfo1KpZ48eXL16tUmJiabN2/W0tIiOxGwuNLSUgsLi5KSErKD9IXvOf40NjYeO3asoKBg2LBhbm5uP3nLCHy/lJSUKVOmDB06tKysTEJCIiEhoV9f0gTfgxHHH/zF9lccHBxeXl65ublmZmY2NjYLFiwoLS0lOxQAM7Kzs+v1MZubm4cPH56cnKyoqHjo0CHc59GXrKyssrOzf/nll2PHjt2+fRvVD/QMCqD+jY+PLyQkJDc3V0JCwsDA4Jdffnn79i3ZoQCYS3t7e6+PefXqVVFR0b///nvJkiXXr19PSkpiwul/WJioqOjo0aNNTEywCAb0GAogViAmJrZ58+bnz5+3t7fr6uoGBwejDALowIjTMxUVFRoaGvTtAQMGKCkpVVRU9PpeAIBxUACxDhkZmd27dz958qSlpUVXV/eXX35httnYAEjBiCkQraysrly5UlhYSBBESkrKq1evhgwZ0ut7AQDGQQHEauTl5ellEIVCGTJkiJ+fX0FBAdmhAFiNnp7e6tWrjYyMFBUV3dzcTpw4ISgoSHYoAPgBKIBYk7y8/Pbt23NzcyUlJS0sLNzc3B49ekR2KACWMm/evMrKytTU1KKiIkdHR7LjAMCPQQHEyiQlJTdu3Pjq1SszM7NJkyaNHDkyLi6OSqWSnQuARXBzcysoKOAGeID+CH+3rE9QUHDJkiUFBQXz5s1bv369jo7O3r17++9a0AAAAD8PBRC74Obmdnd3T0tLO3r0aHJysoqKSmBgYE5ODtm5AADYTltb2717927evFlfX092FvbF9e0uPUWlUrOyssrLy9vb2+Xl5Q0MDDg5ORm3O/hO1tbW1tbWpaWlhw4dGjlypK6urq+vr7OzMzc3N9nRAABY37t370aOHMnNzS0kJJSfn5+QkDB48GCyQ7EjRp0BSkxMVFNT8/DwOHLkyNGjR2fNmqWhoXHnzh0G7Q5+lIKCwsaNGwsLC+fOnbt//34lJaVff/01Pz+f7FwAACxu06ZN9vb2aWlpiYmJGzZsCAoKIjsRm2JUARQYGJiYmJidnR0TExMbG/vixYvk5OTly5czaHfQMzw8PG5ubrdv305KSmpra7OxsbG1tY2IiKirqyM7GgAAa8rKyho3bhx928nJKSsri9w8bItRBVB7e7usrGznFmlpaQbtC37eoEGDwsLCiouLf/nll+joaCUlJS8vr1u3buGWMYC+9M8//7i6ulpbW69du7ahoYHsOMAQWlpaycnJ9O3k5GRtbW1y87AtRl0DFBAQYGBgYGVlpa2tTaFQcnNzU1NTFy9ezKDdQa/g5uZ2dnZ2dnauqqo6ffr08uXLy8vL3d3dZ8yYYWhoSHY6ABb34sULFxeXLVu2aGho7NmzZ+7cuadPnyY7FPS+3377zdraOi0tTUhI6M6dO5cuXSI7EZui0Gg0Bg1dVFSUkJBQXl5OoVBkZWXHjh2rrKz8tc5OTk6pqamdW2pra/X19dPT0xkUD75Hdnb26dOnT58+zc3NPX369GnTpuFiPbZVWlpqYWFRUlJCdpC+YGZmJiUlFRcX15c7XbduHY1GW79+PUEQLS0tUlJS5eXl/Pz8fZkB+kZDQ8PVq1dbWlrs7e2lpKTIjtMPMOL4w8C7wKqrq2VlZd3d3UVEROgtcXFx48eP/2Lnc+fONTc3d25xcHCQkZFhXDz4Hjo6Ohs3bty4cWNaWtrff/89duxYQUHBKVOmuLq66uvrk50OgKW0tLQICQnRt7m4uDg4ONra2siNBAzCz88/efJkslOwO0ZdAxQWFjZ58uSoqCh9ff3MzEx6YzdLEvLz84v9Ly4uLgqFwqB48KNMTEy2b99eVFR07NixxsbGyZMnq6urBwcH37t3r729nex0AKxg0qRJBw8eTExMLCkp+eWXX8zNzYWFhckOBcCyGFUA7du379GjR6dPn7548eLUqVMx7zBroFAo5ubmYWFhBQUF0dHRAgICixcvlpOT8/HxuXDhwqdPn8gOCPAFhw8fJjvCdzE1Nd27d+/y5cutrKw+ffp08uRJshMBsDJGfQXGx8dHP5err6+/YMGCoKCgo0ePMmhfQAp9fX19ff1169YVFxdfvnz5yJEjPj4+JiYmY8eOHTNmDC4VAhJFRER0frhhw4YBAwYQBOHl5UVSou9FvwuB7BQAbIFRZ4BcXFwMDAz27NlDEMTSpUurq6vd3NxwVydLUlJSWrhw4dWrV8vKygIDAwsKCiZMmKCkpDR37tyoqKiampqKiorW1layYwIbuXbtmq+v7/379x8/fvz48eOmpib6Btm5AICJMOoMUGhoqJOTU2VlJUEQHBwc0dHRZ8+eFRMTY9DugBkICAhMmDBhwoQJBEHk5eVdv3593759bm5uFAqFg4PDw8Nj//79goKCZMcE1nf69Onz58/v3LkzLCzM0tLy5s2bO3fuJDsUADAXBt4G/5NIuQ0VehGVShUVFZWXlx83blxiYuKTJ094eXkNDQ1tbW2HDx9uZWXVccML9Av97jb4yspKf39/dXX12NjYvLy8bnp6eXk9f/68c0tOTo6Ojk5aWhqDMwLAd+lnt8EDm8vKyqqvr8/IyBAQECAIYsiQIaqqqkuWLElKStq2bVt6erqmpqa1tbWVlZWVldXAgQPJzgusRkZG5sKFCydOnHj69Gn3PdevX19TU9O5xdvbG7OzALA2FEDAWJ1PMXJyco4YMWLEiBEEQbS0tKSnp6ekpERGRgYEBPDx8VlaWpqbm5uZmRkaGtIvWQX4eZ6enp6ent33UVFRUVFR6dzCz8/PwcGoSyQBgBmgAAJG0dfXFxQUNDY2DggISE1Nzc3N3b59e8ezPDw8lpaWlpaWwcHBBEG8fPkyNTX1wYMHJ06cyMnJGTx4sKmpqYmJibGxsY6ODicnJ3mvA/o9Ozu7O3fukJ0CAJgLCiBgFA4Ojps3b06aNGn58uUEQWzcuHHUqFFf66ypqampqUn/l3pjY2NmZmZaWtqNGze2bNlSWlo6ZMgQw3/p6enh/BD8EMzVCQCfQwEEDGRiYvLmzZu3b9+Ki4tzcX3vh43+dZilpSX9YW1tbWZmZmZmZnJy8t69e1++fKmhoTH0X3p6egoKCgx7BcAKvvkV2M+rqKgIDg5OTk6Wl5dfs2bNmDFjGL1HAPhJKICA4aSlpX/mx4WEhIYPHz58+HD6w5aWlmfPnj158uTp06fbt29/9uxZU1OTnp7e4MGDBw8erKurq6OjIy8v3xvBgUV0swhPb5k2bZqlpWVSUlJ2dvacOXOuXr06ZMgQRu8UAH4GCiDoZ3h4eIyMjIyMjDpaampqnj59mp2d/ezZs5iYmBcvXjQ1NWlpaeno6GhpaQ0aNIj+/RofH1+vBMjMzFy/fn1hYaGZmdmGDRuwZC9UVla+fPkyKSmJQqEoKyt7e3tfvnwZBRAAk0MBBP2ehISEnZ2dnZ1dR8v79+9zcnJycnJyc3NPnTqVl5f36tUraWlpDQ0NDQ0NdXV1+n/V1NR+dC6iN2/eODk5bdq0ycjI6NSpU87OzqmpqSywam9tbW18fHxLS8uoUaNQ0v0oLi6utrY2KpVKv1q/ublZVFSU7FAA8A0ogIAFiYmJWVhYWFhYdLS0t7cXFxfn5+fn5+cXFBSkpqbm5+e/evVKQEBATU1NVVWVfiO0srIy/b9fO10UHx/v5OQ0e/ZsgiD09fU1NTXz8/M1NTX75nUxSHFxsbW1tbGxMT8/f3Bw8MWLF83MzMgO1Z9ISEhYWFj4+Pj4+vpmZ2dHRkampKSQHQoAvgEFELAFTk5OVVVVVVVVR0fHzu0VFRWvX79+/fp1YWFhRkbGhQsXioqKiouLhYWFFRUVFRUVlZSUFBUVFRQUFBQUFBUVW1paOt+Tz8HBQaVS+/zV9LLff//d399/xYoVBEFcuHBh+fLluGn8R506dWrz5s1Lly4dOHBgXFycmpoa2YkA4BtQAAFbk5WVlZWV7XyuiK6ioqLkX8XFxenp6aWlpSUlJeXl5e3t7VevXlVVVf348WNtbW1iYmJubq6MjIy8vLy0tHR/vEX/5cuXM2bMoG9bWlouWrSI3Dz9kbCw8JYtW8hOAQA/AAUQwBfQCyMTE5Mu7TQa7erVq7///vvLly+VlJQ8PDyysrKuXr1aWVlJv+FfUFBQRkZGWlpaVlZWWlpaSkpKWlpaRkZGshNSXlE3DAwMLl26ZGtrSxDExYsXDQ0NyU4EAMBwKIAAfgCFQqmrqysrK3v79q2CgsKsWbP09fU7d6ipqamsrKyqqiorK6uqqqqqqkpPT6+qqqqurqY//PTpk6SkpMSXiIuLi4uLi4mJiYuLi4qKCgoK9s2LWr16taOjo4GBgaCgYFlZ2dWrV/tmvwAAJEIBBPADXrx4sWTJkkuXLunr6585c8bFxSUvL6/zHI/0UqabEdra2qqrq2tqaqqrq9+9e0ffqKqqys3Nffev9+/ff/jwoaWlRVRUVExMTLQTMTExYWFhERERERERYWFhYWFhISEhMTExISEhISEhXl7eHrwocXHxhw8fpqenNzc3m5qa9mwQAID+BQUQwA+4deuWq6srfRaimTNnhoWFZWdn/9CML1xcXPTv177Zs6WlhV4Jfe7NmzcfP3789OnTp0+famtrP3z4QN+gUqmCgoJiYmKCgoICAgICAgJiYmICAgL8/PxCQkLCwsJ8fHwCAgLCwsK8vLyCgoJCQkIDBgwQFhbm5+cfNGiQqKgoC9zSDwDwPRhYAFGp1KysLPpFo/Ly8gYGBljSEvo7ERGRt2/f0rfb2tpqamoYN+MLDw+PjIzMD83K09LSUldX9+HDh7q6urq6uvr6+g8fPtTX19fX19fV1X38+LG6urq+vv7jx49NTU319fWfPn1qbm6ura1taGhobm5evXr1kiVLGPRyAACYCqMKoMTERB8fHz4+Pm1tbRqNlpeX19jYePz48c6z1QH0OxMnTly3bt3y5cuNjY3//vvvYcOGKSoqkh3qPzw8PPQLicgOAgDA7BhVAAUGBiYmJnaeDKO0tNTV1fXBgwdf7N/U1NTY2Ni5pa2tjUHZAHpMVFT0/v37O3fuvHDhgo2Njb+/P9mJAACgJxhVALW3t3e5yqH7FTEnTZr08OHDzi21tbW4GBOYkKys7NatW8lOAd9QUVEhJSXFycmZnp6ek5NjaGg4ePBgskMBABNhVAEUEBBgYGBgZWWlra1NoVByc3NTU1MXL178tf6f33kbEhIiJSXFoHgAwML27NmzZcuW/Pz87du3Hz9+3MbGZtWqVStWrPDz8yM7GgAwC0YVQP7+/k5OTgkJCeXl5RQKxcTEZM2aNcrKygzaHQBAh/Dw8OzsbH5+/kOHDj158kRcXLyqqsrCwgIFEAB0YOBdYMrKyr6+vowbHwDgiyQlJVtbWwmCEBcXp998KiQkxM/PT3YuAGAimAcIAFhNaGiolZXV+PHjBw0aZGdn5+joGB8fP3v27K/1v3r1anFxceeWqqoqISEhxicFANIwbwHEycm5Z8+es2fPfq1DXV3dq1evuLm5+zJVN1paWnh4eMhO8f+YLQw3NzeTzLDX2trKycnJwcFBdhCCIIi2tjYKhcIk82O1t7cLCgqqqKh8rUNLSwuTRP2m0aNHp6WlxcXFSUpKamlpSUtLR0VF6erqfq3/48ePX79+3bmFg4Pj0aNHw4YN+9qPkH78oVKpVCq18yzkfay9vZ0gCBI/EqT/LZN+ZGtubiZ39WUqldpn9xYw4vhDodFovTtib/nw4UNBQUE3HR49ehQeHr569eo+i9SNDx8+rF69eu/evWQH+X9z5sw5ePAgk9RAv/7668KFCxUUFMgOQhAEsXPnTltbW/pUzqSLjIyUkJAYM2YM2UEIgiBu3Ljx6dOn3377rZs+YmJinee2YGHMf/y5e/dubm7uvHnzyAoQFRXFx8c3YcIEsgLs3r3bysqqmyKV0VasWLFo0aKBAweSFcDLy+v48eNklYBVVVXbtm2Li4vrsz32/vGH1m8lJydbW1uTneL/lZWVycnJkZ3iP/z8/PX19WSn+H96enpPnz4lO8X/c3Z2jo2NJTvF/1uyZMmOHTvITvH/9u7dGxAQQHYKhrC1te31MUk//hw9etTHx4fEACtWrAgNDSUxgIuLS3R0NIkBBg8e/OzZMxIDcHFxtba2krX3goICNTU1svbeK5jiiwAAAMahf1kDANAZCiAAYHGenp5kRwAApoMCCABYHIkXygAA00IBBAAAAGyHc926dWRn6CFBQUEJCQk9PT2ygxAEQfDy8oqJiRkbG5Md5P+Ji4tbWFgwyZ3ngoKCFhYWTHJLGh8f37Bhw4SFhckOQhAEwc/Pr6urKyMjQ3YQgiAIQUFBZWXlbm6Dh85IP/4ICgoqKCioq6uTFUBAQEBbW1tOTo6sAKT/LQsICFhaWpJ4ZJOUlDQzMyNr73x8fGJiYoaGhmQF+HnMexs8AAAAAIPgKzAAAABg+709jQAAIABJREFUOyiAAAAAgO2gAAIAAAC2gwIIAAAA2A4KIAAAAGA7KIAAAACA7aAAAgAAALbTXwugAwcO6OrqDh069Pbt22RlWLlypY6OjoqKSnh4OPOkcnd3j4iIID3PtWvXjI2NlZWV9+3bR3qY5cuXq6qqqqurnzhxgsQwjx49cnd373j4eYa+TNUlDHN+mJkWWW8Ok/yaSDzIkH5gIfFgQu4BhDWPGGQvR98ThYWFGhoa9fX1+fn5Kioq7e3tfZ8hOTlZR0enubm5qqpKSUkpIyODGVKdO3eOl5f3r7/+opH6Ln369GnQoEFv37798OGDnJxceXk5iWGSkpIMDAyamprKy8uFhYXr6upICbNu3TpVVVU3Nzf6w88z9GWqLmGY88PMtMh6c5jk10TiQYb0AwuJBxNyDyCsesTol2eAEhISrK2t+fn51dXVeXl5s7Ky+j5DZWWlr68vDw+PpKSklZVVcXEx6akqKyt37tzZsfA1iXni4uLGjx8vJSUlIiKSn58vJSVFYhhOTk5ubm5ubm5eXl5ubm4KhUJKGBMTE29v746Hn2foy1RdwjDhh5mZkfXmMMOvidyDDOkHFhIPJuQeQFj1iNEvC6CKigptbW36tra2dnl5ed9ncHV1Xbx4MUEQWVlZKSkpI0aMID2Vv79/WFgYPz8//SGJeQoLC4uKivT19RUVFXfs2MHJyUliGCsrq0GDBsnLyysqKq5du5afn5+UMOPGjbOysup4+HmGvkzVJQwTfpiZGVlvDjP8msg9yJB+YCHxYELuAYRVjxj9sgCi0Wgdy3zSaLT29nZSYlCp1PDw8ClTpsTExIiIiJCb6sSJE+rq6paWlh0tJOapq6vLz89PSkrKyso6duxYeno6iWFu3bpVUFCQlJR09erVXbt2lZWVMcPn5/MM5KZiqg8zkyPxzSH310T6QYb0AwvzHExIP4CwxhGDi+wAPSEnJ5eWlkbfzsvLk5eX7/sM7e3tU6ZMERMTy8jIoC9HTG6qM2fOFBQUJCQklJeXnzt3rrm5mcQ80tLSo0aNEhUVJQjCxsYmJyeHxDDx8fEzZszQ0tLS0tKysLBITk5mhs/P5xlITMVsH2YmR9abQ/qvifSDDOkHFuY5mJB7ACH9o9hrGH+ZUe8rLCzU1dVtbm4uKSlRUVFpa2vr+wyRkZHTp09ntlQ0Gi0oKKjj+kSy8jx//lxPT6+pqYl+idyzZ89IDPPHH3+MHz++qamppqZGWVk5PT2drDA3b97sfA1jlwx9nKpzGKb9MDMnst4c5vk1kXWQIf3AQu7BhNwDCEseMfrlGSBlZeWFCxdaW1sTBHHs2DFOTs6+z5CcnJyQkCAnJ0d/eOTIkfHjx5OeqjMS3yVdXV1vb28TE5P6+vrly5cPHjyYIAiywsyZM+fRo0c6Ojo0Gi0wMNDY2JjEMB0+/+2Q+Pti/g8zUyHrN8WEv6Y+fitIP7Awz8GE3AMIE34Ue4ZCo9HIzgAAAADQp/rlRdAAAAAAPwMFEAAAALAdFEAAAADAdlAAAQAAANtBAQQAAABsBwUQAAAAsB0UQAAAAMB2UAABAAAA20EBBAAAAGwHBRAAAACwHRRAAAAAwHZQAAEAAADbQQEEAAAAbAcFEAAAALAdFEAAAADAdlAAAQAAANtBAQQAAABsBwUQAAAAsB0UQAAAAMB2UACxoIKCAkdHR2dn53379pGdBQAAgBlRaDQa2Rmgl61cuXLs2LE2NjbDhw+/e/cu2XEAAACYDhfZAaD3rVy5UkBAoLS0VEJCguwsAAAAzAhfgbEgQUHBuLg4Pz+//fv398HukpKSXFxc+v5nAQAAegwFEAu6fv36vXv3Ll26JC8vT3aW3vfw4cNhw4bJycl5e3u3t7d3fkpDQ4OLi4ubm5ubmzs6OrqjPSwsbNasWV8cbevWrTdv3mRsYgAAYD4ogFhQVFTU8+fPp02b9rX/638RjUarqan5fLv7nn2subl52rRpx44dKywsfPPmzcmTJzueamlpaWhoaGtra21tbW1tnTx5Mr09IyNj06ZN9O2CggJLS0t9ff2wsDCCIKqrqx8/fuzg4ND3LwQAAMiFAogF/fnnn3FxcefOnetcH3S2Z88eNTU1TU3NZcuWUanUBw8euLq6Ghsbh4eHd94mCGLTpk3q6upqamrBwcFden5z2DFjxsTGxtKfMjExoV+O3aXPj760hIQEMzOzoUOHDhgw4PLly9OmTet46uXLl7y8vDNnzjQ1NQ0LC6Nf3V9XV+fn5/frr7/S+xw8ePD333/PysqKjY1tbGzctGnTqlWrfjQDAACwAFwE3e8tXLjwxYsX169f5+bm9vDwsLW1XbBgQTf979y5c/To0X/++UdAQGDu3Lk7d+60tra+du1aRkaGlpbWgwcPOravXbt2/vz5zMzMAQMGjB8//tSpU/RG+rPfHNbNzS06OnrSpEmvXr169+6djY3N532GDRvWeZBly5bl5+d3bjEzM+soXwiCKCgoaG5uNjExKSkpsbW1/fPPPzueqqioUFNT27BhAycnp4uLi7S0tJeXV0BAwNKlSwmCePbsGUEQfn5+np6ewcHB06dPLy0tbW5u1tPT6+kbDwAA/RgKoH7Px8dHTk4uPj5eRESksLAwMjKy+/6JiYmfPn2aPn06QRDV1dU0Gs3a2trU1LSjpunYTkxM9PLyEhYWJghi3rx5CQkJWlpanXt2P+yhQ4dWrFjR2tp69uxZLy8vCoXyeZ8uBdAXTyx1Vltbm5eXFx8fLyUl5eXltW3bto0bN9Kfsre3t7e3p2//8ssvly9f5uHh4eDgmD59+tmzZ+nt6urqKSkp9G1PT8+tW7d2vzsAAGBVKID6PWNjY4Igmpubg4KCTp48SaFQuu8vICDg4+OzevVqgiCoVCqVSs3IyBAQEOjcgb5Bo9E6RuPg4KBfcdy5Z/fDcnFxmZub3759Oyoqin5J8ud9OsqR7yQjI2Nvb6+iokIQhIuLy/nz5zueSktL4+Pjo5/R4efn5+LiioqKevDggaqqan19fUNDg5OT05UrV+idU1JS1NTU5OTkVq5cGR8fLy4uHhERoaio+ENhAACg/8I1QCzi/v37w4cPHzJkyDd7Ojo6njlz5v37921tbdOmTTt8+PDXetra2p48ebKurq6lpeXPP/8cMWLEjw7r5uYWGhoqJiamqqr6PbtetmzZpP8VGhraucPYsWNv3Ljx+vXrurq6v//+29zcnEaj5ebmtrW1FRcXz5o168OHD/S0Y8aMiYmJKSsre/369d69e11cXDqqH4IgwsPDly1b9ubNm/T09MePHwcEBPz111/ffOsAAIBl4AwQK0hLS0tKSlq8ePH3dDYyMlq0aJG5uTn9pMj8+fMzMjK+2NPJySkjI8PAwIBGozk7O3t6eqanp3//sARBTJgwYc6cOQcOHPhany5ngL75FZiKisqKFSvs7e1bWlqcnJyWLFnS0tKira1dWFjo6uqampqqo6MjKCg4bdq0mTNnfm2Qc+fOjR07VlBQUFBQ0MjIyNjYWFhY+MyZM93vGgAAWAmWwuj32traTE1N9+/fLyDwf+zdeVxUVf8H8DM7M8Ow74ggaqKZooioaC6oqVim4F5olrmm9qQt9pStj5W5PD20mgtWmgqKhpIb7pILAmEqpIiKDOssDMww6/39Mf2QUEiR4czyeb/8Y+ZwuPfDMB6+c++554oVCoVEIunVqxftUAAAAFYNR4Bs3urVq6Oiovr3708ImTdv3p07d/bu3Us7FAAAgFXDESAAAABwOJgEDQAAAA4HBRAAAAA4HBRAAAAA4HBQAAEAAIDDQQEEAAAADsd6L4NXq9WlpaW0UwDAXc7Ozj4+PrRTtAWMPwDWptXHH2u5DH7o0KHHjh1r1Ojk5BQQEEAjDgA0ZjAYOBxOYWEh7SBtYfny5d9++62bmxvtIABAiGXGH2s5AnT06NFGLVFRUd7e3mlpaVTyADSD0epqTl2QxAygHaRNFRcXm9fbdAQGg+HNN99ctmwZ7SAAQIhlxh/MAQJ4aMrUQ1Vfb63LK6AdBAAAWggFEMDDMVTKVb+e9JgZV7VhB2M00Y4DAAAtgQII4OHIk3ZJYge7jB3K9XSvOXSKdhwAAGgJFEBgcapDp0w1atopWof2aqH2zyLXp2MIIR4vxCl2pptUtbRDAQDAQ0MBBJZVd+V61Xfb5T/bxWR2hpFtSnafMYEl4BNCeO38RP3CFcnptGMBAMBDs5arwMA+MYx8c4rny5MV2/dJRg7kt7ftRQ20fxZpr9+qWL2hokEj20ngnjCexeFQiwUAAA8PBRBYkOrIGcLlSoZHE6NJtinZb8Ui2okeieCxDiHJibRTAABAK8ApMLAUk6ZOsWO/x6x4wmJJRg40KWvU536nHQoAAIAQFEBgOYqd6aLe3QUd2xNCCJvt8UK8LGkXo9fTzgUAAIBTYGAhDFN74rxRUa06fPpuI4ulvXrD6YnH6MUCAAAgBAUQWAqLFfT9f2iHALA9Jk2d7votp+74nABgWTgFBgBgRRTb95X952tDpZx2EAA7Z8EjQCaTKTc3VyqVGo3GgICA8PBwDi4VBgBomv5OWe2J85LhA+Q/7PZ+dRbtOAD2zFIFUEZGxqxZs4RCYVhYGMMwBQUFGo1m06ZNQ4YMsdAewaowOr1RUW1UVBuVNaaaWqOq1qSqNak1JrWG0WhNWp2pVsPodIzewGi1jN5ICDHV1ZEmbq3FEvBZXC4hhC0UEA6HxeOy+Hy2UMDicdlCJ5bIiS0QsEVOLKETWyxki0UcZxHbWcx2FrElYraToE1/coBHINuc4jrhKcnIgXeWfFR3+U+nbp1pJwKwW5YqgBYvXpyRkREaGlrfUlxcHBcXd/bsWQvtEagwKlSGsgp9aaWhospYITdUyg0yhVGmYPQGjpsLx82F4+LMlojZEjHHWcz1dGOLhSwnJ7YTny0Wsng8Fp/HEvBZPC4hhOUkaGo5QUarYwwGQohJoyVGI6PTM3q9SV3H6A2mOq1JrWHqdCaNxqRUGUrKjbVqU63apFKbCy9iMrElzhwXMcfNheMqYbs4cz3c2K4Srrsrx92F4+HKFgnb9CUDaII665KhvEoy+kkWh+M+fZxsY0rAZ68TNiYqAFiEpQogo9Ho5+fXsMXHx8dC+4I2wjCGCpnuZon+tlRfLNWXlOvvlLG4XK6fF9fPi+vjye8cLOrfi+vpxvFwZYtFrbhnloBvvvtECzbL6PVGZY2pusaoVBmrVSZljUGmMN24bZArjfJqY5WCMZm4nm4cd1eulzvH053r4crx9uB6e3A93dli1EbQRhiDUZ602+OFOPPHAHF0b9WBk6qMTMnwaNrRAOyTpQqghQsXhoeHR0dHh4WFsVis/Pz8zMzMRYtseyFgh8MwemmF9tpN3bWbuqJi3Y1itljIax/Ab+/v1CNMMmYIz9/H+ksEFo/H9XInXu5NdWC0OkOl3ChTGmRyY6VCd6vEmH3ZUCEzVMgIi8X19uD6eHB9PLk+XlwfT66vJ8/Xy1yNAbQi9blcvbS86vsd9S0mtcaQeggFEICFWKoAmj9/fmxsbHp6ulQqZbFYkZGR7777bnBwcFP9b9y4IZPJGrbU1ta6uzf5RwsshNEbtH8Waa9cr7t6XVtwgy0WCToH8zsGu0U+we8QxHZuzeM6VoIl4PMCfXmBvvd+yVSrNpTLDBUyQ3mloayi7ver+rJKQ1kVW+zE9fXm+Xlz/b14ft5cP2+ev3frHvQCRyOKCm+XuKJRIwsz2AAsxoJXgfn7+7/wwgsCgeDOnTsXL17U6XTNdH7//fcvXbrUsKWoqEgkwl+UNmEyaQtva3Ku1F0q0F2/xQvyE4R1lIwY6LXweY6rhHY4mthiEb+DiN+hXaN2o0ypL60wlFXqpRXqc7/rSysM0goWl8v19+YF+vL8fXgBPtwAH56/j3l6E9D13Xffvfzyy7RT/AMWh8319aKdAsCBWGp03rJly4IFCzw8PJYtW/bWW29FR0fn5eWtWLGiqWFo8+bNjVqioqK8vb0tFA8IISZVrTr7suZCniYvn+vhJuwZ5vpMjKBrR7bQiXY0a8fxcOV4uJJunRo2GhUqfUmZQVqhl5bVnDinLyk3lFdxPFx5gX78QF9uoC8/0I/Xzs8uj6JZm6SkpIZPP/jgA4FAQAiZMWMGpUQAYHUsVQCtWLHi0qVLPj4+7u7u33zzzcyZM2UyWVRUlPV/DrN7hkq5+lyu+rdc3Y3bTj26iHp393ghjuPuSjuXzeO4SThukoZVEWM0Gcqr9MWl+jtl2vwbNUfO6O+UsXhcXjt/XqAfP8iP186P1z6A4+JMMbZdOnDgwO7duxMSEsxHkevq6nJyckjTBVBsbGxmZmbDlpqamv79+y9btqwN0gIAFZYqgEwmU0BAAI/Hmzt3bkJCAiHE1dXVYDBYaHfwj4yK6toz2bWnLhikFcI+3V3GxQh7dGHxeLRz2TMWh83z9+b5e5PIJ+objTKlrrhUX1yqu1lSezpLd1vKYrF5wQG8dn789gG89v78IH9MJ3pEW7duTU5OXrt27apVqwYMGHD48OG1a9c203/nzp1arbZhy/Dhw52dUZgC2DNLFUDDhg0bO3bsxx9/vG7dOkJIYWHhhx9+2KtXLwvtDprC6PXqc7/XHDurLSgS9XnCbeJopx5hLA5WFqGG4+Eq9HAV9uhS32JUVOtvS3W3pLrC2zXHftPfLmWLnHhB/vzgQF57f377QF47vzaYS6TcfVAvrfCaP93SO2ob8fHxgwYNmj9/fmpqaqPi5l4ikajRjEMul8tisSwZEBydYvs+XVGxzxtzaAdxXJYaVb///vvk5OT6pzdv3uzateuCBQsstDu4l+5WSc2h0zWnLghC2zsPifJZNpvFx/Ge1sEYjXWXCoQ9u7bK1swrRjo9cbckMpRX6W9LdbdKNDlXqvcc0ZdWcH08+cEB/OBAXvsAfnAg19ujVXZ9d49Viuq9GYTH1V4tFISF/vM32AJfX9+UlJQtW7bk5eXRzgLwN4byKtWvJ1kiJ032ZWGvbrTjOCgWwzC0M9yfeRJ0Wloa7SA2hjEa1b/lVKcfN5TLJDH9nWMGcJteAgdapjrtqCxpV8Cnr/NDg9pgd4zRqC8u1d8q0d0s0d28o7t5h9Hq+MHt+CEBvOBAfkg7fpD/I1a3Fes28wJ8eP4+yr2HAz57g9zv4EdxcXH//v1v3779KDuyFRh/wKLKP/9e0CGIFxIo37I7YM3yphbBh3qWGH9wja79MNWoVQdPVv96ghfg6/p0jLDPEzjVZQlGVY1y1wHXp4fJNiX7fbDkvrVC62JxOPzgQH5woHjQ3Qz6oju6ojvaK9dV6Sf0JWVcH09+SDt+SCA/pB0/pB3H7SHWL9AW3NBeve4WP4rR69lOgppjZ52H9rPIT2J3dLdKKj7f4L9yqfWvCArWo+5Sge7aTe9XElgCvurXE6pfT7rEDqEdyhGhALIHhkp5dVpGzbGzor49fd+ezw8OpJ3Inim2pomfjHR//lnpm6tqz2SLo3u3fQaOxJnzRJf6s2aM0ai/Xaq7WawruqPcc0h3o5jF5fI7tON3aMfvEMQPacfz82qyUGMY2cZk9+fGVa3fbqiQeS+ZWb7qe1FUT9wi7UHINqUwJqNiZ7rHzAm0s4CNMJlkm1M8ZsaZF5T3mDGh9N114if7cCSYdN/WUADZNkN5lTLlQO25XMmw/oFr3uZ4WN/V7CZT+ZqNbpPG8NsH0I7SCvS3pepzuYHr/k1YLI9Z8RVrN4n6dKd+ZwwWh8MPCeSHBJLBf7UYKuW6G8W6ouLaUxfkP6SaVLX84EB+aDt+SDt+hyBekD+L+9ch95pjZ/V3yjRZf+hLytgSsWLHfhaPq9x9yH36M9R+HhuhPptjlCv9P3qt5LX/SGL684L8aScCG6DKyDSUVmqv3dReu2luYTkJlDvSPV6cSDeYA0IBZKsMlXLFzv2ac3mSUYPa/W+F1S6vp8rIrLtUIKvV+K14hXaWViDbnOI2OZYtERNCBF1CBV06KPccdps0hnauxrhe7lwvd9H/X35vqlXrbhTrbhTXXSqoTjuqL63gBfryOwTxO7QjRqNkWD/V0bPC3o+zxcLao2fFQ6Pwt/wfMQaj/Mc9Hi9N4rhJXMePkG1K9n3XHt7hYGn8dv6u8aMatkhGDuQF3OdWPGBpKIBsj1GpUqYcqDl53uWpJwO/XGHNpypMmjrFjv2+b8+v+mab+myuKKon7USPRFtwQ5N7VV9Srtxz2NzCaLWarD9c456y8jmMbLHIqftjTt0fMz9ldHrdrRLdjdu6wtu6G8XaomI2n0dYRBwdwXYWGSvlzk9G0g1s/ar3HuG1DzBfCSgZPUR1JFNz8Q9h78dp5wJrJwgLtZsLLW0dCiBbwuj01WlHq3/JEA+ODPzvO9a/grBix35RRHdB5xCPmXGV32wV9u5m00svCjqHtPvyvUaNLB7Xyqufe7H4PEGnYEGnYEKIUaa4869PvBc/b1SoWHye24Sn7iz+sO7qdaewjrRjWi+jQqXcfVA8sI8y9ZC5hefnJdu8K6BnV1x5AGArUADZjNrTF+U/7BZ0DvFfuZTrZwM3TdSXVtQeOxewdjkhxOmJx/jBAdVpR13Hj6Sd6xGwWPZ3u8rqX08wWm3V9zvrW0y1mupfjqIAao7JJHlqECHEVKM2N/AC/fjBgcRaVxUBgHuhALIBulslso3Jplq115KZNvRnSb4pRdClQ92V6+angs4hyl0HnYdE4b5jVsUtbpQkZkCjRvMkJ2gKx8PV/blxtFMAwCNBAWTVGK1OsWN/zbGzbpPGSEZEE7btHF1nGLZEzGh16jMX69uE4V2NChUKIKvCEvDt77AWAMA/QgFkvTTZl6vWb3fq2jFg7dvWP92nMRbLa+HztEMAAADcHwoga2SqVcs2ptRdve45d1rDu2YCAABAq0ABZHU0WZcqv/1ZHNUzcM1y6ivsAQAA2CVrKYB++eUXqVTasKW8vFwsdoCZmAxjUmvYYhEhhNHqZEm7NDlXvJfMcOrWmXYyAPgHjE6vOnzGZczgf+4KAFbGWgqgP//8Mz8/v2GLWq3W6/W08rQZ1YGTyt0HA794V3+ntGJdkqBzcMDqt9hCJ9q5AOCfKfceVmzfz/V2F0X2aKZbXV6+cvdB33cWtsGtcwHgAVlLAfSvf/2rUUtOTo6rq51fLmSqUSt2pvMCfctXrdcV3vZ4cRKVO2sCQAsYZQrVvuNe86bJNu8Shndj8e4/nDJGU9XGZFOtpubYWeeh/do4JAA0xXYuq7ZHiu37RJE9WAJ+XV6+z9KXUP0A2BDZD6mSUYOch/Xntw+o3ne0qW6qAyc4bi6+b86Rb/3FpNa0ZUIAaAYKIGr0xaU1py7U/X6V5+/jMuGp6vTjtBMBwIPSFtzQXrnu+uwIQojHzAnVe44Y5cp7u5lq1MpdBzxeiOOHBgnDuyp3HWzzpABwfyiAqKlYt4noDK6TxnjMinebMFJ7/Vbd5T9phwKwE1lZWampqUrl3aIkLS2t1bbOMLKNye7Tx5mv0+T6ejnH9JNv/eXejvJtaeIBEfz2AYQQ9+njajIy9dKKVosBAI8ABRANDFOxeoOu6I5TeJg2v7Dq222yjclsZ5F8SyrtZAD2YNWqVRMmTNixY0fPnj2zs7PNjbNnz26t7etulmiv36r47+ai+IXmf8rUw7VnLjJaXcNu+ttS1eFTHHcX1aHTqkOn1ed/5wX5y3/Y3VoxAOBRWMskaMdhqtNWfpFklCndZ0xgOwnq2/mh7TluLhSDAdiNxMTEixcvenp65ubmxsXFZWdnSySSZvprNJq6urqGLQaDgWn6zqb8kMCQnf/7xxiMXi8Z1t9QXkXKq8wtvAAftsTWVnUHsFMogNqUoVJevvIbwWMdvF97kcXh0I4DYJ+EQqG54unZs+ecOXOWLFmyYcOGZvpPmDDh7NmzDVtUKtWjx+CHtvec0/7RtwP3ZSiv4ri5sPg82kHAVuEUWNvRFd0pfXu189AozzlTUP0AWM748ePDw8O/+OILQshrr71WWVk5ZcoUtVrdVP/09HTZ3/Xu3dvf378NI8PDYXT60nfXybfdZ94VwAO6ewSo0QcgMycnp549e7ZhHrtVl5dfsW6zx6x4cXREw3a9tEKdedF1wlO0ggHYn5UrV8bGxpaVlRFC2Gz2rl27tm/f7u7uTjsXtBpl6iFekH/t8XOSEdG8AF/accAm3S2A1q1bRwgpKSk5depUr169uFzuhQsX5s6dm5iYSC+enag9dUG2OcV72UtOYR0bfUm2cWddXr5Tt86CsFAq2QDs0sCBA+sfczicadOmTZs2jWIeaEWGKoUq/YT/Z6+rM7NlSbt935pLOxHYpLunwLZt27Zt2zY+n3/16tULFy789ttv996eAlpAdfCUbEuq7zsL761+NFmXDOVVnnOnVW3YQZqecQkAAPXkW3ZLRj/J9faQxA4xSCs02ZdpJwKb1HgS9K1btzp16mR+HBISUlJS0uaR7Ioy5UDN0d/8P3qV6+PZ6EuM0ShL2u3xQpwwvGvNkTM1R39zHtafSkgA6ubNm3ff9q+//rqNk4CV0xbc0OYXes2fTghhcTgeM8bLNqcE9OiCiZXwsBoXQH379k1ISHjxxRcJIRs3buzTpw+NVHZCvu0Xzfk8v49eve/17ap9x7j+3sJe3QghHrMmlv3na1G/cLZI2OYxAegbO3Ys7QhgG+Q/7iWElH/6bX2LobSy9tg55xh8gISH07gAWr9+/TfffJOYmMhmswcOHPjyyy+3eNMmkyk3N1cqlRqNxoCAgPDwcI4jVeiKn9M0WX/4vr+Ic79lP4xKlSLlgPeSmYaySkIIW+QkCA1S7jrg/tyzbZ4UgL7Y2NhGLQzD/Oc//7m3HRycx6x4Y3VNwxaXZ0fygwNo5QHb1bgAcnJyGjVDzeFgAAAgAElEQVRqVGBgYHx8/M2bN52cnFq23YyMjFmzZgmFwrCwMIZhCgoKNBrNpk2bhgwZ8qiRbYEsaVfdH9f83lvEdhbdt4P2zyK2s6jq+x0NG01/X0YWwNGsW7fus88+k8vlPj4+5eXlmLYM9+KHBNKOAHaicQGUlJT0ySef1NXVxcXFDR8+fNmyZXPmzGnBdhcvXpyRkREaevfKpuLi4ri4uPtebG+1NNmXlbsP+b2/iLBYD/5d8p/21v1xzW/FK2xxk+ezRH2eEPV5ojUyAtiPLVu2XL58+cMPP5w5cybDMP/73z+vtgwAVBhlCsXOdM+XpzzU30er0nghxDVr1pw+fbp79+5sNjs3N3f16tUt267RaPTz82vY4uPj08KMlDBGo2xTiqGiSpWR+eDfpdixX5N1ye+dBc1UPwBwXzKZzNXVddCgQcePH+/Ro8fly7i6B8BKyX/cU3PsXM3xc7SDtFzjI0A1NTX1p714PJ5AILjnWx7IwoULw8PDo6Ojw8LCWCxWfn5+ZmbmokWLHils2zJPUnafMrbs46/E/Xs9yPRk5Z7DtWcu+r2/mC0Rt0FCADszZMiQ5557bvXq1SNHjlSpVCLR/c8gAwBd2oIbdX/86fvOgoq1m0RRPdnCFs6WoatxATR9+vRnnnmmtLT0u+++27p166RJk1q23fnz58fGxqanp0ulUhaLFRkZ+e677wYHBzfV/9///ndBQUHDlmvXrlGcNG1UqpSph/w+epUX4CuK6K5M/tU9YXzz36I6ckZ18JT/h69yXJu77SIANGXDhg05OTl+fn6JiYmHDx/+73//SzsRANyDYWQbk92fG+fUrZOwZ5hy90H3ac/QztQSjQugd9555+DBg6dOnSorK/vwww8HDRrU4k1XVlb6+flNnTrV1dXV3JKWltbUxa6jRo1qdM+N7Oxsip//FNvSxEOizCusu017uuTVj52HR/MCmjyLpz7/u+LnfX7vL+J4uLZhTAC7olAoQkJCqqqqHn/88ccff9yhrhsFsBU1x84SNks8sA8hxP25Z0te/dh5aH+evzftXA+tcQEUHBw8bty46dOnR0dHsx5hZtOqVasSExOjo6OXLFmye/fuXr16EUJmz54tlUrv27/huvVmn3/+eYuvQXtEuqI7qqOZ7lOfUR06bW7hBQfIt+z2efP+88HrLhVUfbPN950FuCUNwKMYNWqU+UFFRcXNmzcTEhKSkpLoRgKAhkyaOvnWX9ziR+lu3Da3CPt0l/+Y6rNsNt1gLdC4ALpy5UpaWtratWtffvllcyXUvXv3Fmw3MTHx4sWLnp6eubm5cXFx2dnZEontnBhiGMmw/oayivoGnp/3fRczJITob0sr1m3yfm0WP6RdW+UDsE/nz5+vf3zx4sUvv/ySYhgAuJehtJLj7qo6kqk6cvfyIDafxxhNLE7jy6qsXOMCyNXVdfr06dOnT8/NzV24cOEnn3zCtOgeVUKh0Fzx9OzZc86cOUuWLNmwYUMr5G0T/A7tPOdMfZCeRrmybOU37s+Pd+rW2dKpABxK7969c3JyaKcAgL/hd2gX8NnrtFO0jsYF0IkTJ3755Zd9+/Z5eHjExcX99NNPLdvu+PHjw8PD586du2jRotdee238+PFTpkxRq9WPHNiKmOq0Zf/5WjJyoPPgvrSzANiDV199tf7x5cuXg4KCKIZpAUOFjOvlbrvLogA4lMYF0HvvvRcXF3fkyBF/f/9H2e7KlStjY2PLysoIIWw2e9euXdu3b3d3d3+UbVoXhqlct1nQKdj12RG0owDYiYZzAWNiYgYPHkwxzMMyKlUlr610n/a0ZNSTtLMAwD/7WwGkUqkWLFgQFxfXKptuOJZxOJxp06bZ08L28p/2mGrV3ktfoh0EwB6Y14hv1+5vE+kKCwsbXRxqzRTb0pwe76xIThcP7NPUPXAAwHr8rQASCATLli0bNGiQza3a3MZqjp+rzczx/2Qpi4vLdAFawbp16wghJSUlp06d6tWrF5fLvXDhwty5cxMTE2lHeyC6omL1hbzAL96R//SLYsc+j1kTaScCsCyTpq72xHnJUy1fK4e6vxVAfD5/4MCBvXv3Hjx4cP0aPOvXr6cRzHppC27If9jt997i+97mHQBaYNu2bYSQESNGXL16tXPnzoSQGzduvPzyy7RzPSjZxhS3KbFskdB96tg7Sz50Hh7Nb9/C+5ObVLV1V66L+vZo3YQArUuZckC55zDX30fYowvtLC3UeA7QjBkzZsyYQSWKTTAqVBVrNnrNm85r5/fPvQHgYdy6datTp07mxyEhISUlJS3elMlkys3NlUqlRqMxICAgPDzccssq1p6+aFJrJMP6E0LYziLXCU/JNqX4rXilZVuT/7S35thvAWvebmblVQC6DGWVNUcyPV+aJNu4M2D1WyzbXLO0cQEUExNz9erVvLy8+Pj4mzdvhoSE0EhlpRijqWLNRufhA4QRLVkbCQCa17dv34SEhBdffJEQsnHjxj59+rRsOxkZGbNmzRIKhWFhYQzDFBQUaDSaTZs2DRkypDXj/j/51r0sNqvso79WLWKMpro//qzLK3B64rGH3ZT5VJrrsyNlSbt835rb2kkJYzTa6N8qsCqyzbtcxsVInhqkPv+76uApl9G2dL1CvcYFUFJS0ieffFJXVxcXFzd8+PBly5bNmXP/5Y8dkDwphS1ycosbRTsIgH1av379N998k5iYyGazBw4c2OJTYIsXL87IyAgNDa1vKS4ujouLM0+1vteNGzdkMlnDlurqag6Hk5WVZX4aFhYmFosJIQqF4vr16/XdzO3ei2fIKyqv375d3/7YmMH8jkFN9W+mveirH8siOgs7+lSkprlu3dlz3Jjm+z9Ue+3prFs/7q59YVz9gnUt2w7aHbxdd+2WIivLe0h419paj5lxpSv+q3+iU1FZqUX3m5eXZzAYSKtqXACtWbPm9OnTM2bMYLPZubm5vXr1QgFkVns6S5N92f/T17HIB0CrGz169A8//PDWW28RQszrZeTl5b3yyistm4NoNBr9/P52krr5Czvef//9S5cuNWy5cePG7du360e/Dz74YMyYMYSQffv2rV27tr6buV3wWIcj5880bheFN9W/qfba0xcP/pGzOeMa2UBMao3x6N7/uAhjx4592O3ct310zHD5D6lHbv25Yfpz9TdsbsF20I52fXEpx92VvSDL3C7q3yv5/U/XZ5+x6H4//fTT6upq0qpYjRZ67tixY15e3uTJk3/55RedThcREZGXl9e6u3xAUVFR3t7eaWlpVPb+F4YxqTVssUhfWlH69hrffy/gd8D9LsBBFRcX9+/f/3aD4xytKCkpaeLEiZmZmY3aY2JiWrC1r776at26ddHR0WFhYSwWKz8/PzMzc9GiRXPnPuhJpdYdfxitTndbKugU3Fwfnf7Oko+8Fj5Xv6x82cdfCXt2dRk7tFUyKH7epy+rdJs8pvSt1QFrlzd1bx+A5mnzC6X/Xkv+XjmwRcKgTZ9a9FYYlhh/Gh8Bmj59+jPPPFNaWvrdd99t3bp10qRJrbgzm6M6eEq560DA6rcqVm9wmzIW1Q+AhZivvQgMDDx58uTs2bM/+OCDAwcOfPTRRy3b2vz582NjY9PT06VSKYvFioyMfPfdd4ODm6s/LEqR/Ksq/XjgF+9wPNya6qO+kGeokJV/eveIF2Mw6KXlrVIAGSrlqgMn/Ve9wfVyFw/pq9i+7wHv9gPQiKBLaMjO/9FO0ToaF0AffPDBvn37Tp06VVZW9uGHHw4aZMOX+D8iU61asXM/z9+n7INEXqCfZEQ07UQAdm7y5MlvvPHGpUuX0tLS1q1bN3/+/BbfDiw4OPjBj/dYlKGssubwGXF0hPzHPV6LmrzGVjygt7BnWKNGFo/XKhnkSbsksYO5Xu6EELdJY+4s/tD5+i1Bx/atsnEAG9X4gNXVq1dLSkpWrlzJMMybb7559OhRKrGsgWL7flFUL/GTkbqiYtcJI2nHAbB/tbW106ZNS0lJmTZt2oABA7RaLe1ErUCWtMvlmWEes+LrLl+ru3q9mZ5ssajRPxa/FQog7dXC2t9yTNW18h/3yH/co0w5wPVwk/+Q+uhbBrBpjQugyZMni8Vi8yewVatWNbw3oUPRF5fWnrrgPDJasX2fOGaAcmc67UQA9q99+/avvfZaUlLSc88998UXX9Qvx2q76vIKdDdLXMYOZQn4blOflm1MaTR5og2wXZzdpz/D8XBlO4vM/0T9wp0HRbZxDABr0/gUmPkT2Pvvv29Pn8BaQJa0yzXuKfnmFJexw1zGDLmz5KO6y3/WT04EAEvYsWPHli1bfv75Zy8vL5lMtnXrVtqJHo3JJNuU7DFjgvlMlvOTkTWHTtccP+c8JKotU/ACfHDPZoB7NS6AzJ/Adu/efe7cubb8BBYXF9foZP+dO3e6d6ez3qAm96om54pJqTIoqnl+3rJNyWxnkTxpt/+nr1PJA+AgvLy8xowZk5eXFxkZOXPmTFtfiLX2bK7utlSWtEuWtMvcYqpVK5LT27gAAoD7alwA0foEtn79eoVC0bBlwoQJvr6+bbP3RvhB/q4TnlLtP+YS9xRHLCKE8EPb16+cAQAWYmcLsYoie7RLXNGosVWm9QDAo2tcAHl5eUVFRZ04cSIzM3P8+PFdurTRTc48PDw8PDwatggEAhalJQfZErHm/O+eL08RD2rhSvwA0AJ2thAri8vh+nrRTgEA99d4EvS77747c+bM0tLS2tra5557bt26dVRi0aX4OY3r54XqB6CN1dTUODk5mR/zeDyBQEA3DwDYscZHgH766afc3FxnZ2dCyKJFiyIjI5csWUIjGDXa/MKa4+cCVr9FOwiAw8FCrADQZhoXQH5+fmz2X4eFWCyWq6trm0eiidHpK7/80XPOFMz4AWh7WIgVANrM3QLov//9LyGkY8eOPXv2jI2NZRhm3759EydOpJeNAsX2ffzQ9qLIHrSDADio2NjY2NhY8+Oamhrz0WgAgFbX+AhQRERERESE+fErr7xivi2zg9DdKMbJLwAqsrOz33rrraqqqjFjxixatOjjjz++evVqdna2VCqlHQ0A7NPdSdCLFy+ePn16WVnZ/v37U1JSCgsL4+PjExISKIZrS4zRVPnVjx4zJ+DkF0Dbe+GFFwYNGrRu3TqpVPrEE0+4uLi8/vrr994cHgCgtdw9AlRYWDh48OCEhITly5fz+fzjx4/369fv8OHDbXYlPF3K3Qe5nu7igbjyC4CCysrKt99+mxASFhaWmpr63nvv0U4EAHbubgH06quvrl27Nj4+3vy0f//+PXr0WLZs2d69eyllazv60orqfUcDsNAzACW8/7/tuaenp1gsphsGABzB3VNgv//++4QJExp+bcyYMVeuXGnzSBTI1m93i3uK6+NJOwgAAAC0hbtHgLhcrsFg4PP59S0Gg6H+knj7IP8hVRzdmx/avmFj7ckLRoVKMnowrVQAcPPmTYnkr+l3tbW19Y9VKhW9UABgz+7WNzExMZ999lnDr3355ZcDBw5s80iWUnepoPrXE1Xf7yAMU99oUmvkP6Z6zpnC4nAoZgNwcBqNpvL/NXzc4g1mZWWlpqYqlcr6lrS0tNZICgB24u4RoM8++2zkyJEnTpwYMWIEj8c7duzYjRs3jh492uJNm0ym3NxcqVRqNBoDAgLCw8M5FIsMk0m2KcX7lQTlnsM1J847D+5rbpZv/UXYp4fgsQ7UggEAIa1714tVq1YlJiZGR0cvWbJk9+7dvXr1IoTMnj0bF9UDQL27BZCLi0tmZmZqampWVpZer588efLEiRO53MYLBT2gjIyMWbNmCYXCsLAwhmEKCgo0Gs2mTZuGDBnSOsEfkurQabZELOoXzvFwrVi9QRTVk+0k0N0qUf+WHbj231QiAYCFJCYmXrx40dPTMzc3Ny4uLjs7u/6cGgCA2d/qGxaLNX78+PHjxz/6dhcvXpyRkREaGlrfUlxcHBcXd/bs2Uff+MMy1aoVO9N931lACBE81sHp8c7VqYfcpoyVfb/TbcpYtgSXnADYFaFQaK54evbsOWfOnCVLlmzYsIF2KACwLi08wPOPjEajn59fwxYfHx8L7esfKXbs54cEEqNRV3iLECLq37ti3Sa2RGyqq5MM608rFQBYyPjx48PDw+fOnbto0aLXXntt/PjxU6ZMUavVTfU/efJkaWlpwxaZTIaDRgD2zVIF0MKFC8PDw6Ojo8PCwlgsVn5+fmZm5qJFiyy0u+YZ5dXG6trKb36ub+H6+ypTfvV5cy6xr8vcAIAQsnLlytjY2LKyMkIIm83etWvX9u3bm7mxz8GDB/Pz8xu2yGQyip/ZAKANsJgGl0S1rps3b6anp0ulUhaL5efnN3r06ODg4KY6y2QyhULRsGXChAmBgYH79u2zRDb5tl+MVQqvhc9bYuMAdqm4uLh///63b9+mHaQtREVFeXt748IxACthifHHUkeACCHBwcFz5859wM6zZ8/Oyclp2HLnzp0WT8FunqFKUXPwtP+qNyyxcQCwNkOGDDl27BjtFAB/Y6rTMjo9x8WZdhDHZcEC6KGkpKQ0ajF/ArPEvuRbdktiB3O9HOhG9wCOzGg00o5gHUwmg7ya6+lGOwcQQojs+x26WyUBn75OWCzaWRyUpQqgqVOn3rd927ZtFtrjg2D0Bt2N29r8Qq/50ynGAIC2lJCQQDuCVVCmHa3eczjwf++yRULaWRyd9tpNze/5XC93VUamJGYA7TgOylIF0NKlSydOnDhz5szo6GgL7eJhMUZjybKVjN7gNvVploD/z98AAHZh9uzZtCPQZ1SoqlMP8Tu2VyT/6pHQCmudQMsxjHzzLvdpT/ODA8s+/krcvxdKUiosVQBFRETMmDGjT58+MTExFtrFw1Kln2C0eoNMKerTnXYWAIA2Jd+213loP5dnYkpe/VgyfAAvwJd2IsdVc+K8Sad3HtyXsFiiiO7K5F/dUZLSYMGLwFesWDFmzBjLbf+hmGrUyt0HCWGEPcKUKQdoxwEAaDu6G8Wai5dd457iuEpcnh0hT9pNO5HjYrQ6xbZfPGbFmaf+uE17uubYWX1JOe1cjshaJkFbmvynvfwgf8LleC18ruTVj53xAQgAHIZsU7L7lLHm8ywuY4aUHD6jyb4s7NWNdi5HVL3/mFFVq9yx/+59etksxfZ93q++QDGVY3KIAkh/W1p7NofFZvsun1f/AcjnrQe9RB8AwHapL+TVXbnOdnHW5Fz+q4nDlv+0BwUQFeJBkfyOjZfE47q7UAnj4ByiAJJtSmZxOGyxqPbMxdozFxmDUX3xD83v+cIeXWhHAwCwLKcuod7/mtWwRUQIxxXLz9DB9XLHIixWwiEKIGFUj7r8G86DI9nOInOL+/RnuF5YDAMA7B9bIhb370U7BYDVcYgCyFgudx7Sz/25Z2kHAQAAAKtg/7cCNSqqVUfOuE4YSTsIAAAAWAv7PwKk3HXQeUg/rP4OFCUnJ3/yySe0U9zfCy+8sGDBAtopAADamp0XQEaZsvbkhYC1b9MOAg7t0qVLERERL7/8Mu0gje3Zsyc7O5t2CgAACuy8AFLsTHcePoDjJqEdBBydv79/REQE7RSN5eTklJSU0E4BAECBPc8BMlTKa3/Ldn3GWu7FAQAAAFbCngsg5e6DkhHRbImYdhAAAACwLpYqgIxGo/mBVCrduXNnSkqKhY60M0Zj+Sff6ksrGgeQK2vPXHSJHWqJnQIAAIBNs1QB5OfnRwg5evRo79699+7de+jQoaFDhyYnJ7f6jlTpx+uuXpcn7frrOcPo75QR8+GfYf05rpj9A+DovvvuO9oRAMDqWHYS9GeffbZnz56+ffsSQqqqqmJiYuLj4+/b8+TJk6WlpQ1bZDKZRPIP5Yuxuka566DfB0sqPv9ek3NFGN5VdeSMbFOK30f/qjl+PnAdLv4CcERJSUkNn37wwQcCgYAQMmPGDEqJAMDqWLYAcnNzCw7+665vLi4uSqWyqZ4HDx7Mz89v2CKTyXx8fJrfvmLbL+LBffntA9yfHy/bnOL34RLFz/tEvR+vXLfJeWgUx9310X8EALA5Bw4c2L17d0JCgkgkIoTU1dXl5OQQFEAA0IClToFxOJyIiIirV6/Onj2bEHLjxo1x48YNGTKkqf4ffvjhjr/r1KmTu3tzd4zTFRWrz+e5jhtR+u5/ef7eXC/3itUbRZFPeMyM00vLnbp2bvUfCsByDGWVFWs3EpOpBd979uxZFouVlpZW3/Lhhx+yWCytVmt+evHixalTp7ZOUFuwdevWH3744dKlSxMnTly7dq2/v//atWvXrl1LOxcAWBFLHQEqLS1VKBR//vlneXk5IUQmk02aNOn5559vxV3INqY4de0oS0rR3SyuWLdZ0K1jzf4Tni9NUh05I+zWWZG8X9T3CcJiteIeASxHlrSrLq9AdSRTMiK6Bd/u5eWVnJw8duxY89O0tLT6M8jvv/9+UlJSVFRUq2W1BfHx8YMGDZo/f35qamp9IdiUn376qbi4uGFLSUmJUCi0ZEAAoMyCl8G7ublFRkbGxsYSQiIiImbOnMnhcFpr44zRyPVyY/QGdWaOoHOIobyy9sR5XnAAozeoDp70mD2Z7SSoOfpba+0OwKLq8gp0N0t8312o2L7PVKtpwRZ69+6dnZ2t1+sJIYWFhd7e3mLxXwtAREZGzpw5sxXT2gpfX9+UlJTu3bt37Nix+Z4ajUb+d25ubh06dGibnNAy1XuPaK/fop0CbJitrgTN4nC8Fs2oTPzB5Zlh7tOeUe47Kt+Uwg7yr1izkRAi27DDWF0j35bmPLQfDgKB9WC0OqOiunGryVS1/mfXZ4dzXJyduneWbdnlNuGpxn3YbK6XezNvZjabPXTo0CNHjowaNSolJSUuLi4rK8v8pTFjxggEgitXrrTmT2I7EhISEhISmu/z0ksvNWoxmUze3t4WCwWPSld0R/5zGj/I3/+TZRjkoWVstQAihOgKb2lyrwZ+8Q4hRBIzQH3qIifQT19S7jZpDNffhxDCFvDwHwOsSvW+o6ojmY0aTWoNo9UpUw8rUw8zJsZUJdPkXmXdc7jU+5UEQVhoMxuPj4/fvHnzqFGj9uzZs2fPnuXLl7dyeps1ZMiQY8eO0U4BrUm2KdnjhbjaE+drjv7mPKw/7Thgk2y2AGKYqu93OnXvrMn563OtqE93Rcqvgs4hkqcG0Y0G0BTXCU+5/v3ojqlGXfzK++7TnuG1DzC31GRkmtQa3+XzHnbjAwYMmDdvXlFRkZOTk6enZ+sktgv167KCfajNzDZW1zgPGyDoFFL2n69F/cLZIkzYgodmqwUQY2J4gb5MnVZ95uL/NzEsLlc8FB8FwJYYKuWCDu00Fy9pLl6qb2Tx+cRkIuyHm6LHZrOffPLJxYsXT5gwobVj2rZ/PAUGNoTR6+U/pHrOmcrisPkd2gnDuyp3HXR/bhztXGB7bLUAYnHYXguea9iiyb6sLymXDI6kFQmgBfghgb7vvtJaW4uPj4+Jifn6669ba4P2wbwYB9iH6r0ZHFcJRyLSFd4ihIj7hZev2eg8fADPD3O24OHYagF0r+pfjriMG45JP+CAoqKi0tPTCSFDhw41/f9KQlKptL5DTExMTEwMnXAArUp/p5Qxmiq/+bm+hRfop7t+CwUQPCw7KYB0t0p0xWU+0RG0gwAAgAV5LcJy3tA6LLgOUFuq3nPEZcxgFrfV1hkCAAAAO2YPBZBRplRfyJPEDKAdBAAAAGyDPRRA1QdOOD8ZyZaIaQcBAAAA22DzBRCjN9QcyZSMfpJ2EAAAALAZNl8A1Z66wA8N4gX40g4CAAAANsPmC6Dq9BMuowfTTgEAAAC2xIKXwZtMptzcXKlUajQaAwICwsPDW/Fu8GZ1l68xGo0wvGvrbhag1Z0+ffrTTz+lnaKx8+fPu7m50U4BAECBpQqgjIyMWbNmCYXCsLAwhmEKCgo0Gs2mTZuGDBnSintRpR+XjBmCxQ/Byo0ePbqurk4ul9MO0linTp2GDRtGOwUAAAWWKoAWL16ckZERGnr35tXFxcVxcXFnz569b/81a9bk5+c3bCkqKhIIBM3swihXavLyPedPb5XAAJYTFRUVFRVFOwUAANxlqQLIaDT6+fk1bPHx8Wmmf+fOnZ2dnRu2XLp06fHHH2/mW1hCJ5/XXmILnR4lJwAAADggSxVACxcuDA8Pj46ODgsLY7FY+fn5mZmZixYtaqr/008/3ajl2rVr3t7N3duF7SRweuKx1okLAAAAjsRSV4HNnz//0KFDUVFRarVarVZHRkamp6fPnTvXQrsDAAAAeHAWvAosODgYFQ8AAABYIau+G/y1a9cOHz7c1FcNBsOZM2cazRyiSC6Xu7u7007xF2sL4+bmxrKOi/Wqq6vFYnGrr8jQMmq1msPhND/Zv83odLrAwMCgoKCmOlRWVrZlHuqsfPzR6/VarZZiAI1Gw2azKb57VSqVUCjkcqn9FaM+stEd5xmG4XA44eHhbbM7S4w/LIZhWn2jreLnn3/esGFDMx0qKiouX74skUjaLFIzGIZRKpXWs6SKVRVASqXS2dnZSmqOmpoagUDA4/FoByGEELVazWaznZysYiK/Vqt1cnLq1atXM31CQ0O//fbbNotEkfWPP1qt1mAwiMXU7oGo0WhYLBbFdy/1/8vURza647zJZKqpqWndpW2a1/rjD2OzTp48OXDgQNop/lJSUuLv7087xV0ikai2tpZ2ir907949Ly+Pdoq/jBs3LjU1lXaKv7z66qtr1qyhneIv//vf/xYuXEg7hc2gPv5s2LBh1qxZFAO8+eabK1eupBhg/Pjxu3btohjg8ccfv3TpEsUAXC5Xr9fT2vv169dDQ0Np7b1V2PytMAAAAAAeFgogAAAAcDgogAAAAMDhoAACAAAAh2PDBZCTk5OVXD5DCOHz+SKRiKNZvycAACAASURBVHaKuyQSiZVcdUWs7DdlbWGEQiHtFH8RCoXW88pYP+pvJGsIQPfdaw2vAN0Arq6ubDa1P+ICgcB6hq+Wsd7L4B+EVqu1kjVUCMI0DWGaotfr2Wy2lZSqJpPJaDRayQIBNoHue8lkMhkMBj6fTyuAwWBgsVgU373U/y8jAPUAj8i2CyAAAACAFrDhU2AAAAAALYMCCAAAABwOCiAAAABwOCiAAAAAwOGgAAIAAACHgwIIAAAAHI6tFkBfffVVt27devTocfToUVoZli9f3rVr15CQkM8//9x6Uk2dOjUpKYl6ngMHDkRERAQHBycmJlIP88Ybb3To0KFjx45btmyhGObixYtTp06tf3pvhrZM1SiMdb6ZrRatF8dKfk0UBxnqAwvFwYTuAGKfIwbt29G3RFFRUadOnWpra69duxYSEmI0Gts+w8mTJ7t27arVaisqKtq3b5+VlWUNqXbu3Onk5LR582aG6qtUXV392GOPlZeXKxQKf39/qVRKMczx48fDw8Pr6uqkUqmLi0tNTQ2VMO+9916HDh2mTJlifnpvhrZM1SiMdb6ZrRatF8dKfk0UBxnqAwvFwYTuAGKvI4ZNHgFKT08fOHCgSCTq2LGjk5NTbm5u22coKyubO3cun8/38vKKjo6+desW9VRlZWVr165NSEgwP6WYJy0tbezYsd7e3q6urteuXfP29qYYhsPh8Hg8Ho/n5OTE4/FYLBaVMJGRkTNnzqx/em+GtkzVKIwVvpmtGa0Xxxp+TXQHGeoDC8XBhO4AYq8jhk0WQKWlpWFhYebHYWFhUqm07TPExcUtWrSIEJKbm3v69OmhQ4dSTzV//vxVq1bV35KMYp6ioqKbN2/27NkzKChozZo1HA6HYpjo6OjHHnssICAgKChoxYoVIpGISpgxY8ZER0fXP703Q1umahTGCt/M1ozWi2MNvya6gwz1gYXiYEJ3ALHXEcMmCyCGYVgsVv1jo9FIJYbJZPr888/j4+N3797t6upKN9WWLVs6duw4YMCA+haKeWpqaq5du3b8+PHc3NyNGzdeuHCBYpgjR45cv379+PHjv/7667p160pKSqzh/XNvBrqprOrNbOUovjh0f03UBxnqA4v1DCbUBxD7GDG4tAO0hL+///nz582PCwoKAgIC2j6D0WiMj493d3fPyspycXGhnmrbtm3Xr19PT0+XSqU7d+7UarUU8/j4+IwcOdLNzY0QMmjQoKtXr1IMs3///unTp3fp0qVLly79+/c/efKkNbx/7s1AMZW1vZmtHK0Xh/qvifogQ31gsZ7BhO4AQv2t2GosP82o9RUVFXXr1k2r1d6+fTskJMRgMLR9hp9++mny5MnWlophmCVLltTPT6SV548//ujevXtdXZ15itylS5cohvnmm2/Gjh1bV1dXVVUVHBx84cIFWmEOHz7ccA5jowxtnKphGKt9M1snWi+O9fyaaA0y1AcWuoMJ3QHELkcMmzwCFBwcvGDBgoEDBxJCNm7cyOFw2j7DyZMn09PT/f39zU/Xr18/duxY6qkaovgqdevWbebMmZGRkbW1tW+88cbjjz9OCKEV5sUXX7x48WLXrl0Zhlm8eHFERATFMPXu/e1Q/H1Z/5vZqtD6TVnhr6mNXwrqA4v1DCZ0BxArfCu2DIthGNoZAAAAANqUTU6CBgAAAHgUKIAAAADA4aAAAgAAAIeDAggAAAAcDgogAAAAcDgogAAAAMDhoAACAAAAh4MCCAAAABwOCiAAAABwOCiAAAAAwOGgAAIAAACHgwIIAAAAHA4KIAAAAHA4KIAAAADA4aAAAgAAAIeDAggAAAAcDgogAAAAcDgogAAAAMDhoAACAAAAh4MCCAAAABwOCiA7dP369REjRowbNy4xMZF2FgAAAGvEYhiGdgZoZcuXLx89evSgQYOefPLJEydO0I4DAABgdXAEyA4tX7584MCBxcXFnp6ebbC748ePjx8/vu2/FwAAoMVQANkhZ2fntLS0efPmffnll7SztL7XXnstICAgMDDw1VdfNR+/3LlzZ1hYWMeOHVevXn1v/6qqqh49ejS1tU8//fTw4cMWjAsAAFYJBZAdOnjw4KlTp/bu3RsQEPDg38UwTFVV1b2Pm+/Zxvbt23fo0KE//vjjypUrhw4d2r9/f0VFxSuvvHLw4MHs7Oz169dnZWU17L9u3bphw4apVCrz0+vXrw8YMKBnz56rVq0ihFRWVubk5AwfPpzCTwIAAFShALJDO3bs+OOPPyZNmvT888/ft8MXX3wRGhrauXPnpUuXmkyms2fPxsXFRUREfP755w0fE0I+/vjjjh07hoaGLlu2rFHPf9zsqFGjUlNTzV+KjIw0z0Zq1Odhf7SgoKCvv/7a3d1dJBK1b9+ew+Hs27dv5MiR7du3d3FxmTp1av0ezbp3775o0aL6p19//fVHH32Um5ubmpqq0Wg+/vjjt99++2EzAACAHeDSDgCPasGCBZcvXz548CCPx5s2bdrgwYO///77ZvofO3Zsw4YNv/32m1gsfumll9auXTtw4MADBw5kZWV16dLl7Nmz9Y8PHDiQnJycnZ0tEAjGjh37448/mhvNX/3HzU6ZMmXXrl3PPvtsYWGhTCYbNGjQvX369OnTcCNLly69du1aw5aoqKi33nqr/qn5ZNZnn332zjvvjB07dtSoUR9//HH79u3NX23fvv2pU6cafvvw4cPv3Lnz0UcfmZ/OmzcvISFh2bJlkydPLi4u1mq13bt3f7iXGwAA7AIKIJs3a9Ysf3///fv3u7q6FhUV/fTTT833z8jIqK6unjx5MiGksrKSYZiBAwf27du3vqapf5yRkTFjxgwXFxdCyOzZs9PT07t06dKwZ/Ob/fbbb9988029Xr99+/YZM2awWKx7+zQqgO57YOler7/++vDhw1944YXNmzc3uozRaDQ2840dO3Y8ffq0+XFCQsKnn376ILsDAAD7gwLI5kVERBBCtFrtkiVLfvjhBxaL1Xx/sVg8a9asd955hxBiMplMJlNWVpZYLG7YwfyAYZj6rbHZbHNt0bBn85vlcrn9+vU7evTojh07du3add8+9eXIA9qxY4evr+/gwYN79+49e/bskydPDhw48Pjx4+avFhcXBwYGPsh2Tp8+HRoa6u/vv3z58v3793t4eCQlJQUFBT1UGAAAsF2YA2Qnzpw58+STTz7xxBP/2HPEiBHbtm2Ty+UGg2HSpEnfffddUz0HDx78ww8/1NTU6HS677//fujQoQ+72SlTpqxcudLd3b1Dhw4PsuulS5c++3crV65s2EEul7/33nsGg0GpVGZkZPTq1Ss2Nvbw4cMVFRV1dXU7duyYMGECwzD5+fkGg6GZtJ9//vnSpUvv3Llz4cKFnJychQsXbt68+Z9eOQAAsB84AmQPzp8/f/z48YazfZvRu3fvV155pV+/fmq1OjY29uWXX2505VS92NjYrKys8PBwhmHGjRuXkJBw4cKFB98sIeTpp59+8cUXv/rqq6b6NDoC9I+nwF588cVz58499thjOp1u/Pjxc+fO5XK5q1evHj58uMlkmjlzZkREhFarDQsLKyoqCg4Ovu9Gdu7cOXr0aGdnZ2dn5969e0dERLi4uGzbtq35XQMAgD3BStA2z2Aw9O3b98svvxSLxQqFQiKR9OrVi3YoAAAAq4YjQDZv9erVUVFR/fv3J4TMmzfvzp07e/fupR0KAADAquEIEAAAADgcTIIGAAAAh4MCCAAAAByOpeYAGY1GDodDCJFKpadOnWKz2f3793+oW1MBAAAAWIiljgD5+fkRQo4ePdq7d++9e/ceOnRo6NChycnJFtodAAAAwIOz7FVgn3322Z49e/r27UsIqaqqiomJiY+Pt+geAQAAAP6RZecAubm51S9G5+LiolQqLbo7AAAAgAdhqcvg/fz8AgMDTSZTUFDQ3r17b9y4sWDBAl9f302bNj3gFg4fPrxz505LZAOAlgkODl6+fDntFG0B4w+AtWn18ceC6wApFIo///yzvLzcfEeFvLy8559/3jwz+l5xcXE5OTkNW6RSabt27ZYuXWqheADwUORyeWJi4u3bt2kHaQuvv/56YWHhyJEjaQcBAEIsM/5Yy0KIcrlcLpc3bJkwYUJgYOC+fftoRQJwKFdqfpfpKqM9hjXVobi4uH///o5TAHl7ey9btox2EAAgxDLjj7XcCsPd3d3d3b1hi0AgYLFYtPIAOJqSutul2pJo0mQBBABgTyxVAE2dOvW+7bjnNgAAAFBnqQJo6dKlEydOnDlzZnR0tIV2AQCP7mpNXplWSgi5VntVaVAcrzpICJFwXXq79qMdDQDAgixVAEVERMyYMaNPnz4xMTEW2gUAPLpSbclNzXVCSJW+QmNUF2muEUK8+D60cwEAWJYF5wCtWLHCchsHgFYxxPMp84MjlftKtSXTA2fTzQMA0DZwM1QAAABwONZyFRgA0OXCddMY1bRTAAC0ERRAAEAIIZFuuF4BABwIToEBAACAw0EBBAAAAA4HBRAAgBUpqbu9/tZa2ikA7B8KIAAAK1JjVFXpKminALB/KIAAAADA4eAqMAAA+i6psrOUmYQQpUFeppUmFX9FCBGwneL8n+Ox+LTTAdghFEAAAPS5cN1ChJ0IIWU6qVRTbH7MZrE5hEM7GoB9QgEEAEBfe2GH9sIOhJCC2suFtfmDPUfSTgRg5zAHCAAAABwOCiAAsHPfffcd7QgPwZ3n2cW5O+0UAPYPp8AAwN4kJSU1fPrBBx8IBAJCyIwZMyglegjefN/xftNopwCwfyiAAMDeHDhwYPfu3QkJCSKRiBBSV1eXk5NDmi6AZs2a9fvvvzdsuX79elRU1LJly9ogLQBQgQIIAOzN1q1bk5OT165du2rVqgEDBhw+fHjt2ubWVv73v/8tl8sbttjEsSIAeBQogADADsXHxw8aNGj+/Pmpqalarbb5zqGhoY1axGIxl4vhEcCeYRI0ANgnX1/flJSU7t27d+zYkXYWALA6KIAAwJ4lJCSkp6fTTgEAVgcFEAAAADgcFEAAAADgcFAAAQAAwENTG2tpR3gkKIAAAADg4cj1VR//+QbtFI8EBRAAAAA8HANjMDB62ikeCQogAAAAcDhY6QvA0ZVrpYXqgkL1n12cH49w7U87DgBYr2zl2V/KdhBCDIxBrq/6oOA1QgiXzZsXvMyd50k73cNBAQTgcDRGdaG6oFBdcF1dUKguELAFoaLHQkWPBQsbL4gMANBQmPMT7nwvQohcL/ux+OuEoPmEEBYhbjwP2tEeGgogAIdQrpVeU1+9Vnv1Wu3VCl1ZsLBjJ3GXJz1GvNBugSvPnXY6ALANQo4oRNiRECLmOHNZPPNjG4UCCMA+mRjTrbobf9ZcLqi9fK32KpvF7izu2kkc9qTHiPbCDmwWh3ZAAACaUAAB2A8DY7ih/rOg9o+CmsvX1Fc9eF6dxd16u0ZNCZjlyfemnQ4A7Icr1320zwTaKR4JCiAA22ZkjIXqgqs1efm1fxTWFvg5BXYRPz7Ua9Rs0avOXAntdABgn/hs/hDPp2ineCQogABsj4kx3dRcv1KTd6Xm9+u1+X5OgWHi7iO9nu4c3E3IEdFOBwBgAyxYAJlMptzcXKlUajQaAwICwsPDORxMOwBoOam2+LIq90rN7/k1f7jxPLo594zxGjMveJmII6YdDQDAxliqAMrIyJg1a5ZQKAwLC2MYpqCgQKPRbNq0aciQIRbaI4BdqjGoLtfk/qHK+UOVw2Kxujn37OMandBungvXjXY0AAAbZqkCaPHixRkZGaGhd5cVKS4ujouLO3v2rIX2CGA3TIzpujr/kir7kiq7VHuni7j745Keo33G+wkCaUcDALATliqAjEajn59fwxYfHx8L7QvAPij18jzVxTzVxcuqXE++zxOS3pP8Z3YSh3FwybrtMzAGLgtzLgGsiKX+Qy5cuDA8PDw6OjosLIzFYuXn52dmZi5atMhCuwOwUQxhCtUFv1dn/V6dVakre1wS3kMSMT1wNs5wPaKsrKzbt28PHTrU1dXV3JKWljZ27FgqYW5pCpOlP/wrdAWVvQPAfVmqAJo/f35sbGx6erpUKmWxWJGRke+++25wcHBT/bVarVqtbthiMBgslA2AujqT5pIqO7f6wu/VF1y57j1c+kwNfLGTKIzNwv2JW8GqVasSExOjo6OXLFmye/fuXr16EUJmz54tlUqp5NGatFpTHZVdA0BTLHhINjg4eO7cuQ/Yedy4cefOnWvYolKpLBAKoBWojbXbSja8GPTQRzRl+soc5bns6nOF6oKOoi7hLpHjfKd48XF2uJUlJiZevHjR09MzNzc3Li4uOztbImluSaTVq1cXFBQ0bCkqKuLz+RaOCQA0Wcs56V9//bVRS1RUlLc31q4Fa1SpLT+vOP3gBVBx3c1s5dls5dlKfUVPlz5DPJ9aGPKmgO1k0ZCOTCgUmiuenj17zpkzZ8mSJRs2bGimf5cuXRpVSAcPHuTxeI8Y47zi9AXlGUJIjUEl1RZ/fXMVIYRN2DODFuC3D0Dd3QLovtdnOTk59ezZswXbnTp16n3bt23b1oKtAVgVLVNnZP7hFC1DmGu1Vy8qf8tWnjURJsK135TAWZ1EXXGSqw2MHz8+PDx87ty5ixYteu2118aPHz9lypRGJ9kbundu0IYNG0SiR11SMlT0mPmBVHtHoa/q4zqAECJgC/hswSNuGQAe3d0CaN26dYSQkpKSU6dO9erVi8vlXrhwYe7cuYmJiS3Y7tKlSydOnDhz5szo6OhWCwtAlVRbnK08Rwip1JWbGNP+8l2EEB6LN8hzuBNbaO5jYoxXay5dUGZmV5+VcFwjXPstCHkzSBhCMbYDWvl/7d15fEznwgfwZ9ZMJvsqCxIRxHIRESFLRYOqeKskSsNNtEpVrbdV6rZoVbm4uK1ufGi5L6229pDaQiRpbEkksUZkQ5LJvsy+nfeP8UY6lmokec7M/L5/nTkmJz8z4/GbmeecZ82a6OhoiURCCOFyufv379+7d6+TU0cveu8idDMswXZbduNaU3awIwZDABZ5WIAMn82MGjXq5s2bPXr0IIQUFRXNmjWrdccNCgpKSEgYPHhwVFRUmwQFoK5IXpDdcJ4QotQrCCGGbYZw+tsNFlgJb0hzL9WnZzdedBN2CnIY9qH/Gnehx58cEdpNeHh48zaPx4uLi4uLi6OYBwDYxngOUGlpqb+/v2Hb19e3rKys1YdesQLnfIJZ8RP3qHcYSgipVleWK+8FOgxlGH2NpupI5c+5jZc7ibwGO4S+0mky1l1vhXfeeeex+7/55psOTtIe3IUehu+/AIA9jAvQkCFD4uPjZ8yYQQjZsWPH4MGDaaQCYCMPK++x7hMJIbek187VnqhSV2Q1nHe38gx2CJvoOdVZ4Eo7oAmjdYWejuEgcBrl9j+0UwDAHxgXoG3btn377bdbtmzhcrnh4eGt/goMwPwwhCmU5V+oP3e+LpUhTCcrr+U9/o3Pe9pEdHS00R6GYT7//PNH9wMAtAnjAiQSicaMGePt7R0bG1tSUiIS4VxNAHJfWXq+7tyF+nNCrlWIY8R7fitP1xwd4/Yq7VzmZvPmzevWraurq3N3d6+srMSsHQBoP8YFaOfOnWvXrlUqlTExMSNHjly8ePHbb79NJRkAdXWamgv1qRl1Z2Va6RCniHm+y5rP53pTPI9qNPO0a9eu69evr1q1avr06QzDfPnll7QTAYDZMi5AGzduTE9PT0hI4HK5OTk5gYGBKEBgaZR6RWZ9Rkbd2VJFUZDjsKneM3vY9OEQDu1c5q+2ttbBwSEiIiIlJWXu3LnXr1+nnQgAzJZxAZJKpc1fewkEAisrXLALLIWe0V+X5vxedza38XIv234jXF8eYD+Yz3neywHDs4uMjJw2bdq///3v0aNHNzU1Pf+lCFmiRl2VVnt6vMcU2kEA4CHjAjR16tRXXnmloqJi69ate/bsee2116jEAuhIZcq76XXJGXUpzgLXUKfIOK+3bPlPWzoK2sn27duvXLni4eGxZcuWU6dO/ec//6GdqG3UaqqvS3PGExQgABYxLkAff/zxiRMn0tLSJBLJqlWrIiIiqMQC6ABynexCfWpa7el6Te0wp8jF3T/1tOpMO5RFq6+v9/X1ramp6du3b9++fXk8Hu1EAGC2jAuQj4/P+PHjp06dGhYWxuFg0gOYIYYwN6R5qTUn85qy+tkFvurxel/bgViiq1otkWqbfMX+FDOMGTPGsFFVVVVSUhIfH79z506KeZ7TfWVpgewmIaRSVd6grU+pOUEIseKKhjiG4/UGQJ1xAbpx40ZiYuKmTZtmzZplaEL9+vWjkgygzdVqqtNqT6fVnrbh2UU4R03r/LYNz5Z2KLbIabxcoSqjW4AuXbrUvJ2VlfXVV19RDPMs1Hr1L+U7p3rPfOyfVqsrixUFhJAGbb1KpzBs8zj8QQ5DhRxhhwY1R3cVxS5CNzHPhnYQMFXGBcjBwWHq1KlTp07NycmZO3fu2rVrGYahkgygregY3ZXGi+dqThbJb4c4vTDX98Ou1t1oh4I/MWjQoCtXrtBO8SeUevml+vQnFaAB9oMH2A8mhNyW3fhVuyuh85yOTWfmDkl+CnUaMcghhHaQVtIzOi2jFXJxphE1xgXo3LlzR44cOXr0qLOzc0xMzO7du6nEAmgTlaryc7Un02vPeIq8X3AeNbfbUgHeef+RhlGr9WpCiFqv0jAamU5KCOFz+FZcChdBXbRoUfP29evXu3Tp0vEZwKSY8Pvz3+vOliqK4rzfoh3EchkXoJUrV8bExJw+fdrT05NKIIDnp2W02Q0XUmqO31OWhjqPWOK/2sPKi3Yolvrvve+yGy4QQjSMmmFIZv3vhBBvUdel/p93fJiWS7hHRUUNHz684zM8izpNTW5jJiFEqZer9WrD5B4ehxfsGPbY4ijm2TgKnDs6JbBbkTy/RFFEO4VF+0MBampqevfdd2NiYmilAXhOVWpJSs2J9NpkT1HnSJeXBjkM5XOMWz609GaXeaTLPELI6eqjFaqyJ32b094uXLhACOnc+Q9n4RUWFg4YMIBKnqer09QYJvRoGLWO0Rq2CSF97QY+tgB5i7q+47O4QyOaKYYw/7j2ZoO2znAzq+G8YeNl9wmTPBPo5WoNhV6u1Ctpp7Bof/i/wcrKavHixREREe7u7rQCAbSCntHnNmWeqU4qVhSEOo1Y4v+Zh5U37VDwF2zevJkQUlZWlpaWFhgYyOfzL1++PHv27C1bttCO9hh+4p5+4p6EkEZt/dXGK82Te87XpQywD7bmmcn1G1mIQzib+n5v2N5SvDbUKXKQw1C6kf6qfeX/e60pmxBSrZao9OpP898jhLhZeaAid7w/FCChUBgeHj5o0KDhw4c3X4N127ZtNIIBPJNGbcO52pMpNccdBc4jXF7GLJ9W43J4FJf7+PHHHwkho0aNunnzZo8ePQghRUVFs2bNopWndY5XHfYUdfax7k47CLCXg8DR29qHECLTS3VaxrBtz3ekncsSGX87kJCQkJBgYh8kgmUqkN1MrjmW15g12DF0nu8ynNj1nIY6vqDWq+hmKC0t9fd/cB6+r69vWVlZqw+l1+tzcnLKy8t1Op2Xl9fAgQPb47KKAo7QRejW5oeFZ8EhHGKCK/R5WHkbTju4qyhUclSGi686YYoYDcYFKCoq6ubNm3l5ebGxsSUlJb6+vjRSATyRWq++UH/udPUxtV45wuXlad5v40IgbcKaJ6b+3c2QIUPi4+NnzJhBCNmxY8fgwYNbd5zk5OQ333zT2to6ICCAYZj8/HyFQvH9999HRka2ZVxCrHnij3usv68s1TAaQoharypT3TOcmORp5U3lTDrL8arH665C05ut0c8usJ9dICHknrJIy+jHuk+knchyGRegnTt3rl27VqlUxsTEjBw5cvHixVgNHliiWl15piYprfZ0d3GvSZ7xfewGYIV2M7Nt27Zvv/12y5YtXC43PDy81V+BLViwIDk52c/Pr3nPvXv3YmJiDFOtH3XixIni4uKWe8rKyhobG7du3Wq4GR0d7e3tTQgpKChITk5uvlt0dLRDJ/sdd7+sKa4tPF/aoK2/wrvG4/B7RvpN6Ds5zPnFR+//pONgv6Xtv9J4wyPMxbDNhjws379v3z6lso3njBsXoI0bN6anpyckJHC53JycnMDAQBQgoO6mNO9U9dF86bUw5xc/6rHeTdiJdiJoYy+//PJ///vfDz/8kBDi5ORECMnLy5s3b17r5iDqdDoPD4+We55+YkdmZqZRAZJKpRqNJjMz03Czf//+hoG4qKioeef/7x/6cY/1J4tP/nrv17KG851tPGz59rGCN4Y6D33C/Z90HOy3rP11moa+f3uw0AIb8rB8f15enkajIW2KY3Sh5+7du+fl5U2ePPnIkSNqtTooKCgvL69tf+UzCgkJcXNzS0xMpPLbgQ00jPp83bmTVYl6ohvpGj3MKRLfKVB07969YcOG3b17tz0OvnPnzkmTJmVkZBjtj4qKasXRvv76682bN4eFhQUEBHA4nFu3bmVkZMyfP3/27NnPeITWjT+f5L83vcsc1k6CVujkt2U3+tsH0Q4C8Je1x/hjvCDf1KlTX3nllaKioq1bt44ePfq1115rw18G8IzqNbX7K3Yvvj4rq+H8FK83VvX6ItJlDNqPGUtISBCLxd7e3oWFhVFRUenp6cuXL+dyW7li6Jw5c06ePBkSEiKXy+VyeXBwcFJS0rO3n1bjcrhcDntXsL+rLDpWuZ92CgC2MP4K7NNPPz169GhaWppEIlm1alVERASVWGBO/nXnnzO6LHjG6YrF8oIT1UfyGjOHOQ1f5r/G3QpXJLcgkydPXrJkydWrVxMTEzdv3jxnzpxWLwfm4+PTAY3HyKJuy234WF4XwDQYv8G6efNmWVnZmjVrGIZZunTpsKm5yQAAHQpJREFUmTNnqMQCcyLVNqn+7IKnDGGyGs6vLVj2Vcm/fKz91vXeGuc9E+3H0shksri4uH379sXFxYWGhqpUlE/L/6ts+XYsnJiv1qur1JIqtaReU6dhHmw3autp5wKgzPgToDZ8BwbwLFR6ZVrt6ZPViXY8+9FurwQ5DGXzlwjQrrp27free+8dOHDg4sWLX3zxRfPlWOF5pNQcP1WdSAjRMGqZVrbhznJCCJ8jWN5zg0l/rfxr+X+DHIZ2E/egHQRMlXEBMrwD++STT0z0HRiwhEwn3X1/m47REkJqNdV7y743XGNmuPPoPnYPVndq0NSdqj6aUnuil03ft7os8LcJoJkYWODnn3/etWvXTz/95OrqWltbu2fPHtqJzMEot/8Z5fY/hJB82bX95XuW+q+mnahtVKju12lquhEUIGgl4wKEd2DQJkRc6yCHoXpGTwi5I7vV126gs8CVEOJu5UEIua8s/a3q4JWGi0Odhn+M09rh/7m6uo4dOzYvLy84OHj69Om4ECsAtB/jAoR3YNAmeBxekMMww/Zhyd5+doHeoq6EkBvS3P/e+65UWRTlGr2297c2PMwYhYdwIdZ2xSFcLod1U5T+EoYwxyr3KXRyQsh9ZWlG3dlCeT4hJMD2b4bLKwM8O+MC5OrqGhIScu7cuYyMjAkTJvTq1YtKLDAzekZ/sT7tt8oDakb9ktv4eU4f8jkC2qGAdXAh1nbVXdzzra4Laad4XlZcEYdwCSE8Dt+KKxLzbAkhmDgIrWBcgJYvX/7jjz+OHz/e2dl52rRpM2bMWLjQ5P/BAEVqvcpF4PZF0WoXoft4j9f72wex8DQZYAmpVCoSPZiWKxAIrKys6OYxM1wOz/BNtOniEM5I13GG7UJ5/iCHoYMchtKNBKbLuADt3r07JyfH1taWEDJ//vzg4GAUIGgdqbYpueZYcnVSD5ves33f7y7Gp4nwJwwXYq2oqNi6deuePXtwIVYAaD/GBcjDw6P56qscDsfBwaHDI4HJq9VUH6869HvtmSCHYUv9P/ew8qKdCEwDLsQKz66LyNdJ4EI7BZiwhwXoP//5DyGke/fuAwYMiI6OZhjm6NGjkyZNopcNTE+Z8m5S1YGchkvhLiNX9frCUeBMCKlRVzkLXfHNFzyL6Ojo6Ohow7ZUKjV8Gg3wqPEeU2hHANNm/AlQUFBQUNCDpfLmzZtnWJYZ4E/dkd86Vrm/UJYf5Ra9tve3Yp5N8x99Ubx6dtf3PUWdKcYDlsvOzv7www9ramrGjh07f/781atX37x5Mzs7u7y8nHY0ADBPDwvQggULqqurN27cmJmZqVAoAgMDP/jgA8NK9BZLqVcUywsCbP9GOwirXWu6crRyX7VaMsbt1be7vifkCo3uoGf0eqKnkg1MxRtvvDFp0qTIyMidO3f+7W9/mzVr1gcffIDrAAFA+3lYgAoLC4cPHx4fH79s2TKhUJiSkjJ06NBTp05Z8pnwpYqigxU/LvVHAXoMw+pdRyv3afTql90nDnWMwJmo0GrV1dX//Oc/CSEBAQEHDx5cuXIl7UQAYOYeFqBFixZt2rQpNjbWcHPYsGH9+/dfvHjx4cOHW3FcnU7H4/EIIeXl5WlpaVwud9iwYV5emAxrDnSM7kL9uaOV+0Rc63HukwIdhjw6v6dRW7+mYJme0RFCajU1mwo/4XH4hJBXOk0Oc36RQmhgN4HgwXWhXFxcbGxsnn5nAIDn97AA5ebmTpw4seWfjR07dsGCBa07roeHR1VV1ZkzZ+Li4kaOHGljY7Ns2bLVq1c3Fyw20zE6pV5BCFHqFTpGJ9NJCSFcwjWsZmXJtIwmtfZ0UuUBN2Gnqd6z+tj2f9I97fmO7/t9Yvjma2PhJ1O9Z3ay8iKEOAowqwwAAOh7WID4fL5WqxUKH07g0Gq1zafEt866desOHTo0ZMgQQkhNTU1UVNSTClBJSUl1dXXLPXK5XK+nM3HkVHViouQXQoiO6DQ69dIbswkhIp71Z72+NOnFk5+HSq88W3P8RNVhH+vus33e8xP3/NMfcRG6GTb4HL6TwAULfsFTlJSU2NnZGbZlMlnzdlNTU+sOmJmZeffu3REjRjRfyyMxMXHcuHHPHxUAzMPDAhQVFbVu3bqPPvqoec9XX30VHh7+PEd3dHT08fExbNvb2zc0NDzpnsuXL7927VrLPUVFRc3XhO1gL7mNf8ltPCEkX3Z9f/n/LvX/nEoMlpDppMnVx05XH+1t23+h38ddRL60E4EZUigUbXi09evXb9myJSwsbOHChQcOHAgMDCSEzJw5E+eUAUCzhwVo3bp1o0ePPnfu3KhRowQCwdmzZ4uKis6cOdO64/J4vKCgIL1eP3PmzMOHDxcVFb377ruRkZFPuv/OnTuN9oSEhLi5ubXut0ObaNI2nqg6nFJzPNAh5EP/NZ1aez3DCR5xbkKPts0GZqZtV73YsmVLVlaWi4tLTk5OTExMdnZ280dKAAAGDwuQvb19RkbGwYMHMzMzNRrN5MmTJ02axOcbXyjoGVVUVNTX19++fbuyspIQUltb+9prr/39739vm9QdhUM4HBNfPLl1GrUNJ6oOpdScGGA/+KMe69ytPJ/naFisBzqYtbW1ofEMGDDg7bffXrhw4fbt22mHAgB2+UO/4XA4EyZMmDBhQpsc2tHRMTg42LDd8vqKJsRP3DOh87u0U3SoanVlUuX+i/Vp4c5Rn/X60gFzlsEETZgwYeDAgbNnz54/f/577703YcKEKVOmyOXyJ92/srJSKpW23KNUKmnNQQSAjtHKD3gsBI/Ds5x1rCSqsqOV+640XIx0eenzgK/t+Pa0EwG00po1a6KjoyUSCSGEy+Xu379/7969T7mu/bx58y5fvtxyz/3791ueEQIA5gcFCMh9ZenRyl+vN+VEuRqvYgFgolqewMHj8eLi4uLi4p5057179xrtwRxEALOHAmTRShWFRyS/FMhujnZ7Jb7zOyKuNe1EAG0vMjLy7NmztFMAALugAFmoQnn+EcnPpYqiMe6vzuy6UMhty3NwAFhFp9PRjgAArIMCZHHyZdeOSH6RqMrGuk9813cJnyOgnQigfcXHx9OOAACsgwJkQa41XTki+aVBWxftHjPMKZKHtUvBMsycOZN2BABgHRQg88cQJrcx84jkZ6VeMc49dohjBJfzXCucAAAAmDoUIHPGECar4fwRyS+EMOM6TQpyGPbosu0AAAAWCAXIPDGEyWm8fLDiR0KYaPfYwY6hqD4AAADNUIDMjZbRZtSdPVa534HvNMkzvq/dQNqJAAAAWAcFyHyo9erU2pO/VR30tOr8Rpe5PW360E4EAADAUihA5kClV56pTjpefdhP3HOOzwfdxD1oJwIAAGA1FCDTJtNJT1cfTa4+1tu2//t+n3iLutJOBAAAYAJQgExVg7buRNXh1JpTgQ4hy/zXult50k4ET5Sbm5uUlEQ7xeOFhYW1XDYLAOBZaBltTuOlIIdhtIO0HgqQ6alWS5IqD1ysTxvmNHxlr03OAlfaieBP7N+//9SpUyzsGbm5ubdv32ZhMABguTpNzc9lP6AAQQe5ryw9VrkvrzEr0uWl1QFf2fMdaCeCZzVy5MiVK1fSTmFs+/btGRkZtFMAAFCAAmQaCmQ3j1XuL5LfHuU2bpr329Y8Me1EAABgcXSMrlZTTQip1VTrGF2VWkII4XP4TgIX2tH+MhQgVmMIk9eYeaxyf72mdoz7q+/4vi/gCGmHAgAAC3W54ff95f9LCNExugZt3YY7ywkhHMJZ3H2Vi9CNdrq/xrIKUGZDRk+bvnZ8e9pB/pyO0V2oT/2t8gCXw3vZfUKwQxgW8AIAALpCHCNCHCMIIVVqyYY7y//V+zvaiVrPsgrQmZrfrLniPnYDaAd5GqVekVJz4lR1Yicrr8leb+BSzgAAAG3OsgoQy9Vpak5VJ6bWnOprN3Ce74ddrf1oJwIwB1u3bp01axbtFABmhUu4XA6PdornYhEFqEJ1X6lXEkIUeoVEVSbm2xJCOgk92TOVuFRRdKLqcE7jpVCnEct7/ttV6E47EYAJ27lzZ8ubn376qZWVFSEkISGBUiIAc+MsdH3PbyXtFM/F/AuQltFsL/1Cx2gJIRXqspNVR0S11oSQUOcRI13H0c1mmON8oupwuer+SNdxcd5viXk2dCMBRVJtky3fjnYKc3D8+PEDBw7Ex8eLxWJCiFKpvHLlCkEBAmg7HMIx9ffq5l+A+BzBP3v8y7C9oXDFWLeJbJgDpNIrf687e7LqCJfD5XP463pv5Zn4Z4nwnKrVkk1Fq1b32tKKn71w4cLQoUOPHDkybtyDTr9q1arly5crlUorK6tly5YdOHBAoVDMnTv3/fffb9PULLVnz55ff/1106ZN69evDw0NPXXq1KZNm2iHAgB2Mf8CxDbV6srk6mNpdcm9bPq+0WUuQ/T7y/eg/YCO0ekZfat/3NXV9ddff20uQImJiXZ2doSQtLS0gwcP5uTkNDY2BgUFvfjii4MGDWqbxOwWGxsbERExZ86cgwcPqlSqp995zJgxFy9ebLmnqakpKCioPQMCAGWWVYB4hEdr0hZDmBtNuck1x/JlN8Kdo1b0+Lfhkgn5smtU8oCZGTRoUHZ2tkajEQgEhYWFbm5upaWlhBCJRDJ79myhUOjq6hoWFlZaWmohBYgQ0qlTp3379u3atSsvL+/p9zx48KBCoWi5Z+XKlZ07d27PdABAmWUVoBld59t1+PIRCp3897ozyTVJPMKPch07s+siK67oYn3az+U/EEKkuoYy5b1vStYTQngc3vTOc4VcXOrQbB2vOnSh7lzLPTKdtFFTTwhhCKNhNO/kTiaEMBzSSejB4/zhn+e0zm/7iXs+6chcLnfEiBGnT58eM2bMvn37YmJiMjMzCSExMTGGO+Tk5KSnp3/zzTdt/pdiufj4+Pj4+KffRyQSiUSilnsMk6YBwIxZVgGy5zt25K8rVtxJqTl+uf73fnaBCZ3f6WnTt/mPfMX+HMIhhJSr7tarGwY7hBJCRDxrAVfQkQmhg4U7R/Wy7ddyj0avrtJUEkIaNHUnqg5N8ppOCBFw+K7CToZXiAGXcDuLuj794LGxsT/88MOYMWMOHTp06NChZcuWGfbr9fqNGzd+9913Bw4ccHCwxPXjIiMjz549SzsFALCLZRWg5OpjA+yD2/ty3Uq94kJdakrtCam28QWX0asDtjxavNyFHu5CD0JIvuza9aa8YMewdo0ELGHDs7WxtjXa2YP0JoRIVGXnak6EOg5v9cFDQ0Pfeeed4uJikUjk4vJgXR6dThcbG+vk5JSZmWlvbwLXQG8POp2OdgQAYB3LKkBZjRc8rLzbrwDdkd06V3syq+F8L9t+Ezzi+tkFtnwTD9CuuFzuCy+8sGDBgokTJzbv3Lt3r5WV1Y4dOygGo+5PvwIDAAtkWQWondRran+vO5tem8wQJsJ55GM/8nkSN6HHIIeQdo0HJsFJ4DrOPfY5DxIbGxsVFdVyok9qampSUpKnp6fh5rZt25rPFLMcM2fOpB0BAFjHIgpQXlOWUqcghDRq629Jr8l0UkJIN7G/q7DT8xxWpVdmN1z4ve5skfz2YMfQN7rM9bcJ+KsHcRK4jHZ75XligHkQcoVhzi+27mdDQkKSkpIIISNGjNDrH5xLX15eTgj55ptvLHDiMwDAnzKHAiTTSW14xvMqmmkZTUbdWR2jI4Q0ahvyZdcq1PcN+1tXgHSM7lpT9vn61NzGy/42vcOdo+Z1+1DAwalbAAAAJsMcCtCq24uXdP/MSeDy2D/lcwSzuv7DsP08V4LWMbob0txL9enZjRc9rbxDHF943WuGHd9CZ5UCAACYNHMoQHpGZ/iApz2o9aqrTdlZDedzGi97ijoHO4S96vH6k8oWAAAAmARzKEDPzoorsuKK/vx+hNSoq/KasnIaL+XLrvuJewbah8R6xjsKnNs7IQAAAHQAUy1ANeqqr0r+RRiGEFKvqf2iaDWfwyeEvOT+qlKnGO4y+rE/Ncfng6esuqXSK29Jr16T5lxruiLVNvazCwx1GjGr6z+seeJ2+lsAAAAAFaZagJyFrtM7v6snekLIF0WrJ3pOcxQ4cwmHYci2u5ueVIAebT8KnbxAfvOW9Fq+7NpdRbGfuGdvu/5vdVngI+6OS/gAAACYq3YsQHq9Picnp7y8XKfTeXl5DRw4kMdrs4VIOYTT1bqbYZvP4XcW+bgK3QkhZcq7T/9BtV59T1lcoigslhfckd+qVVd3E/foadtnosdUP3EvrMMF7aSurq6wsJB2CmPV1dW0IwAA0NFeBSg5OfnNN9+0trYOCAhgGCY/P1+hUHz//feRkZHt8esUOnmx4g4hpEol0TBqwzaXcBwFLtVqiURVVqEqK1feu6csqdVUeVp18bH28xX7R7lGdxZ1pbU+PFgOf3//FStWJCYm0g7yGLNnz6YdAQCAgvYqQAsWLEhOTvbz82vec+/evZiYmAsXLjz2/mlpaYbrtjWrra19+tJFWkaT03hZz+h72fY7XX30amOWnui1jEauk68r+EjHaHVEa821cbPy6CT0dLfyDHYMGy+a4mHl/ZRpQADtYdq0adOmTaOdAgAAHmqvAqTT6Tw8PFrucXd3f8r9k5KSbt++3XKPQqGwtX3i5Q0JITKt9FJ9OkMYEdeaw+EMcYqw4om0eu3vdWfe6rrAke/sInQTcq2e528BAAAAZqm9CtDcuXMHDhwYFhYWEBDA4XBu3bqVkZExf/78J91/9erVRns++OADN7enrVrqIHCa7fO+0c4y5d3sxgu9bfu3OjkAAACYPW47HXfOnDknT54MCQmRy+VyuTw4ODgpKakDZhvwOXzD+fAAAAAAT9KOXcHHx6fj51e6W3ku6W78YRIAAABAS6z+sKS2tvbpZw7n5+dbW1t3WJ6nk8lkNjY2tFM8gDBPIpfLra2tORxWXORJpVLxeDw+nxX/DHU6nYODg5OT05PuUFFR0ZF5qGP5+KPT6dRqNcUAKpWKy+UKBAJaAaj/W6Y+slEPQAjp0qVLx/yi9hh/WDHyPla3bt02bNjw888/P+kOcrlcIpGw5D8PhmF0Oh1LwhBCNBoNn89nyX/zWq2Wx+OxJwyXy+Vy2+vL379Ep9NxOByWhNHr9UKh0NPT8yn36d/fUmbXsX/80ev1DMO04cXV/irqr17qAaiPbBqNhmIBZRhGr9f7+vp22G9s+/GHMVmpqanh4eG0UzxQVlbm6elJO8VDYrFYJpPRTvFAv3798vLyaKd4YPz48QcPHqSd4oFFixZt3LiRdooHvvzyy7lz59JOYTKojz/bt29/8803KQZYunTpmjVrKAaYMGHC/v37KQbo27fv1atXKQbg8/kajYbWb79z546fnx+t394mWPHWEwAAAKAjoQABAACAxUEBAgAAAIuDAgQAAAAWx4QLEJ/Pp3gGhBFWhSGECAQClpxbRAhhz5neBGGejG2vYZaj/nBRD0D91YsAQqGQ4jlo1F+Bz4/DMAztDK2k1+srKiq8vLxoB3ng/v373t7etFM8wLYwXl5eLDkNXiKRuLi4sKR21NXVCYVC6lfyMJDL5Uql0tnZmXYQ00B9/FEqlVKp1NXVlVaAhoYGHo/39BUb2xX1f8vUh1kEeE4mXIAAAAAAWoct35IAAAAAdBgUIAAAALA4KEAAAABgcVCAAAAAwOKgAAEAAIDFQQECAAAAi2OqBejrr7/u06dP//79z5w5QyvDsmXLevfu7evru2HDBvakev3113fu3Ek9z/Hjx4OCgnx8fLZs2UI9zJIlS7p169a9e/ddu3ZRDJOVlfX6668333w0Q0emMgrDzhcza9F6cFjyNFEcZKgPLBQHE7oDiHmOGLSXo2+N4uJif39/mUxWUFDg6+ur0+k6PkNqamrv3r1VKlVVVVXXrl0zMzPZkOqXX34RiUQ//PADQ/VRamxs7NmzZ2VlZX19vaenZ3l5OcUwKSkpAwcOVCqV5eXl9vb2UqmUSpiVK1d269ZtypQphpuPZujIVEZh2PliZi1aDw5LniaKgwz1gYXiYEJ3ADHXEcMkPwFKSkoKDw8Xi8Xdu3cXiUQ5OTkdn0EikcyePVsoFLq6uoaFhZWWllJPJZFINm3aFB8fb7hJMU9iYuK4cePc3NwcHBwKCgrc3NwohuHxeAKBQCAQiEQigUDA4XCohAkODp4+fXrzzUczdGQqozAsfDGzGa0Hhw1PE91BhvrAQnEwoTuAmOuIYZIFqKKiIiAgwLAdEBBQXl7e8RliYmLmz59PCMnJyUlPTx8xYgT1VHPmzFm/fr1YLDbcpJinuLi4pKRkwIABXbp02bhxI4/HoxgmLCysZ8+eXl5eXbp0WbFihVgsphJm7NixYWFhzTcfzdCRqYzCsPDFzGa0Hhw2PE10BxnqAwvFwYTuAGKuI4ZJFiCGYZoXlmIYRqfTUYmh1+s3bNgQGxt74MABBwcHuql27drVvXv30NDQ5j0U80il0oKCgpSUlJycnB07dly+fJlimNOnT9+5cyclJeW3337bvHlzWVkZG14/j2agm4pVL2aWo/jg0H2aqA8y1AcW9gwm1AcQ8xgxWLEk5F/l6el56dIlw3Z+fj6V9Qh1Ol1sbKyTk1NmZqa9vT31VD/++OOdO3eSkpLKy8t/+eUXlUpFMY+7u/vo0aMdHR0JIRERETdv3qQY5tixY1OnTu3Vq1evXr2GDRuWmprKhtfPoxkopmLbi5nlaD041J8m6oMM9YGFPYMJ3QGE+kuxzbT/NKO2V1xc3KdPH5VKdffuXV9fX61W2/EZdu/ePXnyZLalYhhm4cKFzfMTaeW5du1av379lEqlYYrc1atXKYb59ttvx40bp1Qqa2pqfHx8Ll++TCvMqVOnWs5hNMrQwalahmHti5mdaD047HmaaA0y1AcWuoMJ3QHELEcMk/wEyMfH59133w0PDyeE7Nixg8fjdXyG1NTUpKQkT09Pw81t27aNGzeOeqqWKD5Kffr0mT59enBwsEwmW7JkSd++fQkhtMLMmDEjKyurd+/eDMMsWLAgKCiIYphmjz47FJ8v9r+YWYXWM8XCp6mDHwrqAwt7BhO6AwgLX4qtw2EYhnYGAAAAgA5lkpOgAQAAAJ4HChAAAABYHBQgAAAAsDgoQAAAAGBxUIAAAADA4qAAAQAAgMVBAQIAAACLgwIEAAAAFgcFCAAAACwOChAAAABYHBQgAAAAsDgoQAAAAGBxUIAAAADA4qAAAQAAgMVBAQIAAACLgwIEAAAAFgcFCAAAACwOn3YAMHkTJ07Mzs5WKpX19fUeHh6EkJdffrmoqCgpKYl2NAAwcxh/oNU4DMPQzgDmICUl5bPPPjt58iQhRC6Xy+VyV1dX2qEAwCJg/IFWwCdA0PYyMjJ++umnKVOmfPfdd7a2tlevXp00adL169fLy8vDwsI+/vhjQsiKFSt2795ta2u7aNGihIQE2pEBwExg/IFnhAIE7SgrK+v27ds3btwYOHDg/fv3HR0dfX19P/roo2PHjqWnp+fm5iqVyrCwsJCQkICAANphAcCsYPyBp0MBgnYUFhbG4XC8vb179+7t5uZGCBGLxXq9Pi0traKiYvLkyYQQpVKZm5uLAQgA2hbGH3g6FCBoR1wu12jDQCwWL1iwYObMmYQQvV7P4XAohAMAs4bxB54Op8EDBcOHD9+9e7darZbJZD179iwpKaGdCAAsBcYfMMAnQNChfHx8OBzOCy+88OKLLw4aNEilUi1evNjX15d2LgAwfxh/oCWcBg8AAAAWB1+BAQAAgMVBAQIAAACLgwIEAAAAFgcFCAAAACwOChAAAABYHBQgAAAAsDgoQAAAAGBxUIAAAADA4qAAAQAAgMVBAQIAAACLgwIEAAAAFgcFCAAAACwOChAAAABYHBQgAAAAsDgoQAAAAGBx/g+fLTu0teWiZQAAAABJRU5ErkJggg==" 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 <div id="background" class="section level1">
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diff --git a/vignettes/mkin_benchmarks.rda b/vignettes/mkin_benchmarks.rda
index 32223710..dbd65850 100644
--- a/vignettes/mkin_benchmarks.rda
+++ b/vignettes/mkin_benchmarks.rda
diff --git a/vignettes/web_only/benchmarks.html b/vignettes/web_only/benchmarks.html
index c80a7e44..337ccbb3 100644
--- a/vignettes/web_only/benchmarks.html
+++ b/vignettes/web_only/benchmarks.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2020-05-10" />
+<meta name="date" content="2020-05-11" />
 
 <title>Benchmark timings for mkin on various systems</title>
 
@@ -1583,7 +1583,7 @@ div.tocify {
 
 <h1 class="title toc-ignore">Benchmark timings for mkin on various systems</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">2020-05-10</h4>
+<h4 class="date">2020-05-11</h4>
 
 </div>
 
@@ -1662,63 +1662,63 @@ mkin_benchmarks[, -c(1:3)]</code></pre>
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 7.064
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.296
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 5.936
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.658
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.678
 ##                                                                         t2
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 11.019
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 22.889
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 12.558
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 21.239
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 20.545
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    3.820
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    3.837
 ##                                                                        t3
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.764
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.649
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.786
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.510
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.446
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   3.424
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.372
 ##                                                                         t4
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 14.347
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 13.789
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  8.461
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 13.805
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 15.335
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   18.375
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    6.071
 ##                                                                        t5    t6
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 9.495 2.623
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 6.395 2.542
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 5.675 2.723
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.386 2.643
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 6.002 2.635
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   7.480 2.188
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   2.709 0.746
 ##                                                                        t7    t8
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587 7.525
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128 4.632
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478 4.862
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374  7.02
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259 4.737
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    3.69  3.87
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.208 1.268
 ##                                                                         t9
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 16.621
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  8.171
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  7.618
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 11.124
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  7.763
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    8.872
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    2.836
 ##                                                                       t10
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 8.576
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 3.676
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 3.579
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 5.388
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 3.427
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   6.257
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   2.019
 ##                                                                        t11
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 31.267
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  5.636
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.574
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.365
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  5.626
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    9.289</code></pre>
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    2.951</code></pre>
 <pre class="r"><code>save(mkin_benchmarks, file = &quot;~/git/mkin/vignettes/mkin_benchmarks.rda&quot;)</code></pre>
 </div>
author	Johannes Ranke <jranke@uni-bremen.de>	2020-05-11 05:15:19 +0200
committer	Johannes Ranke <jranke@uni-bremen.de>	2020-05-11 05:18:32 +0200
commit	234c9059a95e104917e488a6ddd2313234a96cdc (patch)
tree	f6e54098f79d94578434ef727b62f7cc5d5e79b7
parent	d113cd79b178fdc91aecb894707ed356129dfb75 (diff)