Remove reference to archived kinfit package

from vignettes/mkin.Rmd Static documentation rebuilt by pkgdown
author: Johannes Ranke <jranke@uni-bremen.de> 2019-05-02 18:54:22 +0200
committer: Johannes Ranke <jranke@uni-bremen.de> 2019-05-02 18:54:22 +0200
commit: 6ddb7575f37d9d534f014cbd105b2f07660d59c6 (patch)
tree: 5718770eb493344425b1a1842aa020fe3887fe1f /vignettes/FOCUS_L.html
parent: a4ca3451f1b5c37d10c6a41cb18a99b1631e8aa2 (diff)
1 files changed, 182 insertions, 169 deletions
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index 5e7e7c74..968ebf0c 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2019-04-04" />
+<meta name="date" content="2019-05-02" />
 
 <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title>
 
@@ -1327,9 +1327,7 @@ h6 {
 </style>
 
 
-</head>
 
-<body>
 
 <style type="text/css">
 .main-container {
@@ -1361,8 +1359,6 @@ summary {
 
 
 
-<div class="container-fluid main-container">
-
 <!-- tabsets -->
 
 <style type="text/css">
@@ -1526,6 +1522,16 @@ div.tocify {
 
 </style>
 
+
+
+</head>
+
+<body>
+
+
+<div class="container-fluid main-container">
+
+
 <!-- setup 3col/9col grid for toc_float and main content  -->
 <div class="row-fluid">
 <div class="col-xs-12 col-sm-4 col-md-3">
@@ -1543,8 +1549,8 @@ div.tocify {
 
 
 <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1>
-<h4 class="author"><em>Johannes Ranke</em></h4>
-<h4 class="date"><em>2019-04-04</em></h4>
+<h4 class="author">Johannes Ranke</h4>
+<h4 class="date">2019-05-02</h4>
 
 </div>
 
@@ -1563,32 +1569,32 @@ FOCUS_2006_L1_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L1)</code></pre>
 <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>&quot;SFO&quot;</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p>
 <pre class="r"><code>m.L1.SFO &lt;- mkinfit(&quot;SFO&quot;, FOCUS_2006_L1_mkin, quiet = TRUE)
 summary(m.L1.SFO)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.49.1 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Thu Apr  4 17:00:02 2019 
-## Date of summary: Thu Apr  4 17:00:02 2019 
+<pre><code>## mkin version used for fitting:    0.9.49.4 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 18:43:50 2019 
+## Date of summary: Thu May  2 18:43:50 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 98 model solutions performed in 0.237 s
+## Fitted using 133 model solutions performed in 0.283 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##               value   type
-## parent_0      89.85  state
-## k_parent_sink  0.10 deparm
-## sigma          1.00  error
+##                   value   type
+## parent_0      89.850000  state
+## k_parent_sink  0.100000 deparm
+## sigma          2.779827  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##                       value lower upper
 ## parent_0          89.850000  -Inf   Inf
 ## log_k_parent_sink -2.302585  -Inf   Inf
-## sigma              1.000000     0   Inf
+## sigma              2.779827     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -1601,9 +1607,9 @@ summary(m.L1.SFO)</code></pre>
 ## 
 ## Parameter correlation:
 ##                     parent_0 log_k_parent_sink      sigma
-## parent_0           1.000e+00         6.186e-01 -3.757e-07
-## log_k_parent_sink  6.186e-01         1.000e+00 -5.541e-07
-## sigma             -3.757e-07        -5.541e-07  1.000e+00
+## parent_0           1.000e+00         6.186e-01 -1.712e-09
+## log_k_parent_sink  6.186e-01         1.000e+00 -3.237e-09
+## sigma             -1.712e-09        -3.237e-09  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1614,9 +1620,9 @@ summary(m.L1.SFO)</code></pre>
 ## k_parent_sink  0.09561   26.57 2.487e-14  0.08824  0.1036
 ## sigma          2.78000    6.00 1.216e-05  1.79200  3.7670
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
-## All data   3.619       3  6
+## All data   3.424       2  7
 ## parent     3.424       2  7
 ## 
 ## Resulting formation fractions:
@@ -1641,8 +1647,8 @@ summary(m.L1.SFO)</code></pre>
 ##     5   parent     59.4    57.330   2.0699
 ##     7   parent     47.0    47.352  -0.3515
 ##     7   parent     45.1    47.352  -2.2515
-##    14   parent     27.7    24.247   3.4527
-##    14   parent     27.3    24.247   3.0527
+##    14   parent     27.7    24.247   3.4528
+##    14   parent     27.3    24.247   3.0528
 ##    21   parent     10.0    12.416  -2.4163
 ##    21   parent     10.4    12.416  -2.0163
 ##    30   parent      2.9     5.251  -2.3513
@@ -1652,80 +1658,87 @@ summary(m.L1.SFO)</code></pre>
 <p><img 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" /><!-- --></p>
 <p>The residual plot can be easily obtained by</p>
 <pre class="r"><code>mkinresplot(m.L1.SFO, ylab = &quot;Observed&quot;, xlab = &quot;Time&quot;)</code></pre>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p>
-<pre class="r"><code>m.L1.FOMC &lt;- mkinfit(&quot;FOMC&quot;, FOCUS_2006_L1_mkin, quiet=TRUE)
-plot(m.L1.FOMC, show_errmin = TRUE, main = &quot;FOCUS L1 - FOMC&quot;)</code></pre>
-<p><img 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" /><!-- --></p>
+<pre class="r"><code>m.L1.FOMC &lt;- mkinfit(&quot;FOMC&quot;, FOCUS_2006_L1_mkin, quiet=TRUE)</code></pre>
+<pre><code>## Warning in mkinfit(&quot;FOMC&quot;, FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:
+## false convergence (8)</code></pre>
+<pre class="r"><code>plot(m.L1.FOMC, show_errmin = TRUE, main = &quot;FOCUS L1 - FOMC&quot;)</code></pre>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre>
 <pre><code>## Warning in sqrt(diag(covar)): NaNs wurden erzeugt</code></pre>
 <pre><code>## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt</code></pre>
 <pre><code>## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
 ## finite result is doubtful</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.49.1 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Thu Apr  4 17:00:04 2019 
-## Date of summary: Thu Apr  4 17:00:04 2019 
+<pre><code>## mkin version used for fitting:    0.9.49.4 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 18:43:51 2019 
+## Date of summary: Thu May  2 18:43:51 2019 
+## 
+## 
+## Warning: Optimisation did not converge:
+## false convergence (8) 
+## 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 414 model solutions performed in 0.966 s
+## Fitted using 599 model solutions performed in 1.239 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##          value   type
-## parent_0 89.85  state
-## alpha     1.00 deparm
-## beta     10.00 deparm
-## sigma     1.00  error
+##              value   type
+## parent_0 89.850000  state
+## alpha     1.000000 deparm
+## beta     10.000000 deparm
+## sigma     2.779868  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##               value lower upper
 ## parent_0  89.850000  -Inf   Inf
 ## log_alpha  0.000000  -Inf   Inf
 ## log_beta   2.302585  -Inf   Inf
-## sigma      1.000000     0   Inf
+## sigma      2.779868     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
 ## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
-##           Estimate Std. Error  Lower Upper
-## parent_0     92.47     1.2820 89.720 95.22
-## log_alpha    12.01        NaN    NaN   NaN
-## log_beta     14.36        NaN    NaN   NaN
-## sigma         2.78     0.4618  1.789  3.77
+##           Estimate Std. Error  Lower  Upper
+## parent_0     92.47     1.2810 89.720 95.220
+## log_alpha    10.66        NaN    NaN    NaN
+## log_beta     13.01        NaN    NaN    NaN
+## sigma         2.78     0.4599  1.794  3.766
 ## 
 ## Parameter correlation:
-##            parent_0 log_alpha log_beta     sigma
-## parent_0  1.0000000       NaN      NaN 0.0004281
-## log_alpha       NaN         1      NaN       NaN
-## log_beta        NaN       NaN        1       NaN
-## sigma     0.0004281       NaN      NaN 1.0000000
+##           parent_0 log_alpha log_beta    sigma
+## parent_0  1.000000       NaN      NaN 0.003475
+## log_alpha      NaN         1      NaN      NaN
+## log_beta       NaN       NaN        1      NaN
+## sigma     0.003475       NaN      NaN 1.000000
 ## 
 ## 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 9.247e+01      NA     NA 89.720 95.22
-## alpha    1.649e+05      NA     NA     NA    NA
-## beta     1.725e+06      NA     NA     NA    NA
-## sigma    2.780e+00      NA     NA  1.789  3.77
+##           Estimate  t value    Pr(&gt;t)  Lower  Upper
+## parent_0     92.47 72.13000 1.052e-19 89.720 95.220
+## alpha     42700.00  0.02298 4.910e-01     NA     NA
+## beta     446600.00  0.02298 4.910e-01     NA     NA
+## sigma         2.78  6.00000 1.628e-05  1.794  3.766
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
-## All data   3.860       4  5
+## All data   3.619       3  6
 ## parent     3.619       3  6
 ## 
 ## Estimated disappearance times:
 ##         DT50  DT90 DT50back
-## parent 7.249 24.08    7.249</code></pre>
+## parent 7.249 24.08     7.25</code></pre>
 <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p>
 <p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline"><em>χ</em><sup>2</sup></span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline"><em>χ</em><sup>2</sup></span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p>
@@ -1745,7 +1758,7 @@ FOCUS_2006_L2_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L2)</code></pre>
 <pre class="r"><code>m.L2.SFO &lt;- mkinfit(&quot;SFO&quot;, FOCUS_2006_L2_mkin, quiet=TRUE)
 plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,
      main = &quot;FOCUS L2 - SFO&quot;)</code></pre>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAJACAMAAABlpiR1AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dB3gU1deHbyCBFAgJJYReAoYuEJAmTaqgUhQbKCpFAQUhYkBAQER6EzCUv6CigHSVHmkihCp8oCI1AjaKoUQgkGTvNzO7Gzabnd2Znbnsndnf+zxM2b1zzmHzPrM7d8olFACOIb4uAAB3QFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EleF1YmeHsGZZ81R0UIlOqy3WN29PaxQZWuvVk+JyPCErxflKQgYJs6/igvKEVn/+UHagCYQkZq/89ESZgvXG33GV0HFDt8n9CwgqQw5HbrS3Lbe7Lr53prJ1Le9M6izoF/atptgDOQq6Na/0Xt303PlybOguuZ8BQWUQHOmXKPEnpZ0JCe0yvEsoIY9lUvpfFUIKdx/VJQ8J2OwsaBlCnlz0Wf9gkvcnWyAHQS2VScjspC6EfJQ7X44N3ST3NyCoDIIj39mXNxBS8Rdh/mtFQj6ldBohNS9SScnHnQS9Skgj8Zv4S0Im2zZ2EPRPQoZSeqsgaZUrXc4N3ST3NyCoDI6OPELIFmlhEyH1KW1IyHZp9fGYmhk5Bb1JSBtxJW306G9sGzsI+gMhXwqzaqRSrnQ5N3ST3N+AoDIIjry5UORnmhlMyltftJQjwZmWSFLM4Xgl51d8Y0LaL/rLMZCDoPeuXBF+fd4II61z58uxoXxyPf+LhgCCypB9nDKbnhTcsb3ahpBTlwl5xKFhTkHP1Be3KdV14TX7+zmO4gXSn7NtkJMcG8on1/O/aAggqAwOjvwm/daUeJyQY5cIaejQ0KmbKXPnsGYFhc2i99redxL0n0bWdiLfDhCYY1tx3FA+OYv/KtdAUBkcfgZm5CcxtsWKJPCuJYKUcGjoJKhI5vY+hMTaVnIKergMyTPZ/gNhhNR55BDLvqF8ch3+Z8YCgsrgeJxSh5Cd0sJOQmpR2oCQ/dJq53IVbtKPCFkoriwkZBw90LOndSthb3fDunEOQZcEk8JbstccBM25oZvk/gYElcHRkTmEVDkjzM9UIWQWpZMJaSz+Utybl9SmdDMhHYRdoqUDIRvpz4Q8miG8lVWfhNkOaBwF3RBAKp9zmS7nhm6S+xsQVAZHR+4JvxsLPv/+88JPxEbCl2xaJULKD57WK5iQRZTeLCf8Jh05oiEhZW/QzKqE1Ju1akErQrrZNhYE7S4dkS/8L1No+pa4tCpXupwbuknub0BQGRwdoVeb2Q5aml8VV0+Wt62+I67tL2BdKZAsvlXU9laVVNu2E7JPW17ca1+qnjtfjg3dJfczIKgMORyhWUs7RAVGdViWZV1Nm1CvYGiNF2xnMy+/ExcRERd/WVq5ObFJqXzlWyzMPt3uIOhnbgTNsaHb5P4FBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQ1BtOt3ryY1/X4CdAUG8Ytos+6usa/AQI6g03sy50ct9ih4v3Xb0GPABBveKbjn+4b6BA0Cs1pNmk7rb17IUJW7UVZyogqDdsHprjvHjWFeu/+2sKBJ1es5w4Oxhu81JcON2w5iR6+Tm9yzUyEFQdX5c8fK/i5tc6PC1pNbN8zJDM5C61E8R/lnEVysdLa1SSUXqz7RpK43Zal3MKunVhOWF6s94Eq6DSwpDvaaNbg/zvvg43QFB1zP9p8KKW9ls2ttf8J+25KcmhJ6j4b9PD1++0+kxcoqKg1jcX9aBnKmRZlyVBy0mXM4nXJl8sJ0xeXrbMKqi0cLpRnYkn3/DF/4tbIKg60umImAP2lZHlmjev/mxyC0rFf0OnU7q8p7hERUGtb16Lujt+jK2h09e+KOhXr1CroNkLtMefD+r/YgggqFrGPJu9OGEspZn3kjsKggr/3plB6Yoe4hIVBbW+SZ/a/PBZW0MXgnaKLlc0VLxrM3th9/uWYbVanH9w/x/OgaAquV3my+zlQ1X+vdd1jl3Q7+reTG/7abag1jfp0uYt7A1dfcVTcceZdeKedYFSS6ebF1vTVWMf9H+LWyCoSiY+N/D+ytzKpfpm70EtYypWeDsjW1DrmzQtZLG9oYs9KJW8vENSqE3Qr+dR+m6d5jmeTeLXQFB1/Fvkty6/zPV1FX4EBFXHkJ7067AVvq7Cj4Cg6rgl/Fr0y5vXfAUEBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANToLOiQOAAU8+o9vBG24+BAAnqn8fz4SNFnfeMCk1IKggGc4EvTolFGrMvTNAgwPN4JmvVXmnQ9a1TijbxpgdLgRdH6TNGE62w8HOgfu4EbQJkni1FLxV33zAIPDjaDlU6RZ2y25mgJ/hhtBG+2QZpWP65sHGBxuBJ3d8o4w/bS2xVVr4DOydiWxx83YotwImvlqzJiZnWLwE5QzjhVszZxK78nn50ZQSn98f9DidBdNgS85Upt9jgnD5N/jR9C709s1egPdoLwBQW1cr/3Mpr0fRa3XNw3QCgS1May3OE0ueU/fPEAjENTGw4ekWd39+uYBGoGgNiqco3+sobQNRlbhCwhq4/HlNLk+TY+6qG8eoBEIamNz+Z9vFPjvtW76pgFagaB2lpRoFlys50190wCtQNBs0nbV/lzfJEA7ENSBQdP0TQK0A0EdmNdb3yRAOxDUgV1N9E0CtANBHbgaoW8SoB2Ggna3DwdlGEFpsb/1zQI0k0tQy5p3Bi9XO36J5YSL69QMKGjzbfpmAZpxFvRa08ZTpreod0l2gxRyvFuR2A8Fha/3KZGvwgcWSkt99RhJoYvqhlZfJDQotXRc9fCnr9IGhJBr0ibGEbTfbH2zAM04C9qnv3jPQ8JzshukkIcWXF4dLjj3crFJ3wwhywQlY9t/dntm4Ij1A8gnwlrczPSTxd+gV7u2+9u6JzaOoB/31zcL0IwkaHqqnX8LnhJnfxT4O/slpy/vFNJHmE4PuUY7i93atYYKSta10LRIcdzbPqWFtVbCwuuNDPkV/31LfbMAzUiCdoi0ExFgnecplP1S45wbpJA1wvQk2StMLRc+C4oXlHyP0n1kz5UrV5aRO7TU+8I78Q0MKeifxfXNAjTj/BVf+A9xei1c9uacFLJbmKaSVfSntkULtY0SBZ1G6XJi5RQtNYPyIWjGJTcXH8s8PCzSzf19wBc4Czr4BeGvmtm3l+wGKUQcQvcYSb4W/NzeLFpfFFRQchuxX6fGh6BHehUPIAHFXz0q876MoI12q0sDWOMs6O2uVRKG12x/Q3aDFCJekTY09PpWUcmb4TZBLwXNE16e0c3Ch6A/BMcmJC5NHFEreI/rBjKC9pqvKg1gTu6O+t0TP9ruZoMUkn/A+oSAEfRsnn57vmsY1viEVcmhBaZsGZ13ssMe9JVqB61PM/SBoI92sabO6t3MdQMZQae+rSoNYI7qM0nCQVKHQpXHZ1H6RUxogzXb42ZYlcyaUi2kylyLTdBR/SjdXr7AdWkTHwhacLVt4cdw1w1kBN3YTlUawBwvBD2oNocPBK39jm1hcl3XDWQETSmjKg1gjkkF/ZS8sOrQqcPregV85rqBjKCWgvK/voEvMKmg9IsaUq9XjSUy78s9oz4Otx3zhWkvt7OcT96QfN7pWXVp2c8zq/SD681eWqwyD2CLaQUVsZxOzfnCgewHmgXK3N3x0bte5AHsMKugazp1TbI8TUgvmVNiBRa6fn3dE+ryAMaYVNClpFHLkL4PrZxbcKrrBnKCnoxRlQewxqSCPvwmpWPIHiF3DdcN5ATNDL2lKhFgjEkFDdkgXmB1m9ItIa4byAlKax5RlQgwxqSCxkyi9H/kN0oXynxlywr63FeqEgHGmFTQccHxwwvWa/f7oQoDXTeQFXTMSFWJAGNMKui9d6NKTvuvKiF1r7huICvoiq6qEgHGmFRQSi0WStO/3XRH5m1ZQX+OVZkIMOXnfBWZU3iUfH5f3fIhK2hGGB5wxxUpZ9njpueGO0HpI7ioHtyHP0HfmKVvJmBo+BN0QU99MwFDw5+gh2rqmwkYGv4EvYuTneA+/AlK6+zTNxUwMhwK2vsTfVMBI8OhoHPxIHCQDYeCJsvcCAr8EQ4FvROKUeOBHQ4FpTUP6ZsLGBgeBe25QN9cwMDwKOisfvrmAgaGR0F3P6JvLmBgeBQ0LdTNk2+Bf8GjoLSK0qKA6eFS0BcX65sMGBcuBZ36lr7JgHHhUtDtGFQW2OBS0OsFMvXNBgwLl4LSSr/qmw0YFj4FfVbuubfA31ApaJH7dPC0hduBvNwLOmGIwqqA2VEpaGJi4oywcvHT340p7f7uYE8DebkXdGsLhVUBs6P+K/71lneFaUa7vu5aexzIy72gVwpZ3L0N/Af1gpZcKc3WlnDX2uNAXu4FpTE/KywLmBz1gpaYKc1muR3RyONAXh4EfXmewrKAyVEv6Gvh6y3Usr6Q2694jwN5eRB0YQ+FZQGTo17QG01JZPVI0tztM748DuTlQdAT5RSWBUyOF/2glqQJgybv8HAU42kgLw+CWqIuKKwLmBuvOurvnKYej7JdDuR1vK+doBnuN++yVGFdwNx4IegnpQmh3aYo6Aj678TdnC9cWTjfRnCi+02n9VdYFzA36gX9lPRZRuj0PG6f/5E1vQu980YAyf9hlusGHr7i6QE8QgyIqBe06pv0irAyqrq71hPIEDoydMKmUUEyd2h6EjQj/F+FhQFTo17QkA2SoJtlBkCyUnYopRUnCAvv13LdwJOgtNV3CgsDpka9oLVHSYJOkhlCzkrEakoLrRcWNoa5buBR0DEJCgsDpka9oP8LHLuXXJgXJjMIp5W23S207RhhYaR3HfWUft9YYWHA1KgX1DIjkhCS712Zox8r+0PbLf+68NQdY4Nkzll6FPRWGJ5jC7zrB03bt3zbJQ/N93fKI3bUl5kt875HQekjuxRWBsyMekH77VF2Kdy1Y5t3nMqQe9ezoPEfKkoDzI0Xl9uR8iNPaM7rWdC17TUnAcZHvaBZe4aUJ3HT/9KW17OgVwrh1k7g3bl4y6HhD+VpqymvZ0Fp1Z80ZQCmwLu7Oq9//WK+PJryKhC0z8eaMgBT4IWg5+e0DQpsM9/Tcbx7FAj6eTdNGYAp8OJMEsnXcZHmE+UKBD0XrTUJMD6qBU2vvfC6DnkVCEpLn9YhETA2qgX9lXyhR14lgvbEiF5AtaCWxx7X4551JYIuf1KHRMDYqP8N+lVs3HvTZghoyqtE0NRCGDDJ71EvaCk7mvIqEZQ2TtKUA5gAPp9uZ2McniHm9zC7q9MDigQ9VFVjFmB4mN7V6QZFglpKnNWUBBgfRnd1ekSRoOhoAozu6vSIMkHR0eT3MLqr0yPKBEVHk9/D6K5OjygTFB1Nfg+juzo9olBQdDT5O4zu6vSIQkHR0eTvsLqr0xMKBUVHk7/j5ZmkU+sva8urUFB0NPk76gU93+ZNujEviTigKa9SQdHR5OeoF7RT9ArapMmZDh7vCtYwkNd90NHk56gXNHIevRawlC4r6ra5toG8bNzePG9ro60KCwSmRL2ghZbSr8k/dIXMY+usaBzIy8rOii1fb16ks8ICgSlRL2ib5nviGtMbT9Zx11rrQF4if0VtE6af5flTYYXAjKgX9GhxErKT1ghc46611oG8RKa8Ic2i8LR6f8aLbqZbBy9RutL9iO5aB/IS6WftYepSTVF9wJx40w965YdVB9Lct9Y6kJfIiPel2YCQ2wpLBCZEvaAZg/IRQiImur9gWeNAXiIHy06uF1V/askGqz02BaZFvaCjyNDjqcfiySz37V0O5LWdZDPJY0bLQ8GDvhyQv9aCZxWWCEyIekFj4qVZvJILltfKnrJXsAddH7e3f8c391VfF+Hh9wQwMV501H8jzb6NULKR7OWcCgSNt+5kx47o+JWCVMCceHGqc6A0e6ODu9bdrZDHund33UCBoAOsz7efEr8E5+P9F5WCHjlyZGuRF9cmr+kW7bafqTEp20CAVGnQwHUDBYIu6CrNHv/yZiGMOue3qBT0/kEOaeKu9b34CLEjX9NX/K1KH92j6aOrpdNn/qewSGA6VAp6+j4X3bdfF/H2XW2C0vOdIutEPPMHpSvbKCwSmA7Vv0GzlvV5tNaLc+66bSxyLu6R37UJSmnqT9fE2Z3C/yhpDUyIWkF/a0jKtOkUS6oe9rhFev9IrYLa6SE3HhgwOyoFvVW1wndi5/u+mhVuet5m1aDf5N5SJ2hSbTWtgYlQKeioMJtyFyOGa8qrTlBLrMxlpcDsqBS02Rv29bebasqrTlA6U6Y7FZgdlYKGL7CvLy6kKa9KQa9H4jDJP1EpaMXx9vXJFTXlVSko7TPecxtgQlQK2q2p7fokS7sumvKqFfRoWYzc6ZeoFHRbwIdWQxeQDZryqhWUNl6nKR8wKGr7QYeTpsuPnljbhfTWlle1oF+205YQGBO1glpWlxVPxEcu0PbsMPWC3o0+qS0jMCTqL7fLPLV2+THPZzo9oFpQOhxPYvRHuB6GJgfni/6nbwnACBhHUPoULrrzQwwk6JaaegwSCoyFgQSlDVbpWwMwAEYSdEN1jV0HwHgYSVDsQv0QQwm6EbtQv8NQgtImK/WtAnCPsQTdhF2ov2EsQemjK/QtA/COwQTFLtTfMJig2IX6G0YTdEu1DH0LAXzDUFBdxknKxePTc65b1sQP/BJX25sWVoLqMk6SK04X+8Nx9UbzRlNntXn4Ly+jAd5hJKgu4yS5ZsTzjmv9+4pXkIzGYEpmhZGgeoyTJMPtit87rBWRRlG6E46HMJsURoLqMU6SHN9Uvj98570g67zCOa/DAa5hJKge4yTJ8uSE+8vRkplp4be8Dwd4hpGgeoyTJMv5oinZywlP/zBlQlL/l7yPBriG1VG8DuMkyTO2a/bi9bJB9RuFFPldQzTAM8z6QV2Ok3R54XwbwYkq4zmSXmW5fTHhmR8mf/T9AOxBzQozQW//LvWe3/rb8cXjfe0EzVAZLwfHou0HRfgNanIYCXr3rbyk0l5hYaHMdpq+4imdVd96Zz6O4s0OI0En5hv7RdMCKcwEtXQaZl1AP6jJYSRo7Fjh2z2uMzNBaWr5rdJ8QG/xZ+772h61B/iFkaDBm4XJoYD9zASlu0pIv25vtGg49ePWtXEu3qwwErTyR+K0Z43bzASlI9tJXQSWtfEDv8LVTKaFkaDj8r+3m9Kr0e3fZiZoRtMRWkMA/mEkaHpCcKww+60aYSYovRo7R3MMwDvM+kEzL4jTrB/muX5bB0Hp2ZJ46rLpMdotHzk4WCxZhyiAZwwtKF0ffUqPMIBfjC0onRt7VZc4gFcMLih972GM8GVqjC4onVjxrE6RAI8YXlA6pzyG/zAxxheULikld28zMD4mEJSuLI7Buk2LGQSlm6NkTgcAw2MKQenpWj1wSb05MYeg9M6rtXEwb0pMIiils4qv1zki4AHTCEp/rPBKqt4xgc8xj6D0VkLJz3UPCnyMiQSldG+1Jy4yCAt8iKkEpemjio7H4bypMJeglP7et/hEzYPZA34wm6CUHmz10EoMBWIazCcopVvrxyZ68UU/unZMJ9y+zBtmFJTSQy8VHqjycCmtREiX3pXyyj2ND/gIcwpK6ek3C3f7Ts2INR2jxJ+u7+TDKDd8YVZBKb2+4NHibx9R3DxsrjTLj4HC+MK8ggqceb9C7Ls/KnvsSOBuaVbkI5YFueXH9wctSvfczM8wtaCUWg6PrlPs1ZWXPbeM+ECcZgRuZV2SDJmvVBo7o3PMLz5Kzy0mF1Tk/JwnImoOXOvh9s/XQ09RmtW60IOpKTcfPybuPRc/bPHY0r/wA0EFMg9Mfjy8cveZe+/It2maJ7Z+WMEDD66onDTa9umz7RP+rHzcVwVwin8IKpL1y2f964VUfXbcmjOuf5Vuf+mJSb7r4S9Xr83yTcOi6m3xWQW+5tj0MWty/2UMN5isNu4dWzrsiQrBsU8M/mTjL1ydtS/zmDj9PtBf96CWfgWKhBeplOuyc8MNJqsH6b+sndK3XdWQonWf6jd24XcHznOgapla4lPMPw496OtCfMTU/P1/vvhlREXn1403mKyOXDr4zSfv9+oYVyY4tGy9ts/3GzFl/orNe39OSXWz62dFhZ5l3vmwVY1HfdWL4GtKPCtOf83rvEcz3mCyTEg7t3/z0rnj4vt0a9uwWrnIoKDIsjFxDVs/2e2lvm8lDJs4cdb8+V+tWLEpKWn7oUOHfjor8GeqiH7H3G3WHJ08YuXtEim6RTQWQZukWaHpTq8/2MFkD7S2EzhNVbwHzb3U388c2pv07YrP58+aOCEhYWDfvi9069audeuWcXFxtSsKlIwUCZDG0yMFI22UqJiDh+M80Dj7A3k4tEnr1q3KFm3tp+Sp0bq1IGeo847rwQ4mm5Zkp9IPquJxzs1UG3+dzcHRQx7Yk/2BJL0V0bBVdJM1SX5KxbKbks7S+XmdnwXnq8FkG+LRs85c+XaJvx7CC+wJKT1+TsfQ55xf99VgshAU5CSpbESx0IRcd0M82MFk7wNBgROWcz+5ONHnqzNJEBQoAoICroGggGt8Jehj2V2H9wkjAcxgGJolBi07T+4/rrcEKh3HRWdB76TmZmmrc8wos4tZ6JGvMQt9KpBZ6HM9PmAW+vtKLv66XnJdqVE6C+qKb59kF7vCOWahpw9mFjojkFlo2n8us9AnqjALLQ8ElQOCOgNB1QNBnYGgqoGgzkBQ5UBQOSCoMxBUPRDUGQiqGgjqDARVDgSVA4I6Y1ZBTzMcjmt0GrPQu9cyC215x3Mbb1m5j1no6+OYhZbnAQgKgPdAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAdewF3RR3fBmu5lEXiU9yKwXg8iD4qUZi9KtoRmUnv5hbEjVSeJTunQvOzs0u09cFuaCLiGDV3cOVj52oQomRiUK7NA9btbGUMkiBqXbQzMoPSH/+E2j873Fouzs0Kw+cTewFtRS83lKM2J7s4jduw2LqHRNAUJEixiUbg/NoPTM4OHCdGzgbf3Lzg7N6hN3B2tBz5NVwjQhmkXsFgP+PZyqf9jU48dLixYxKN0emkHpF2LFb/UvyO/6l50dmtUn7g7WgiYTcQCWxAAWw2GXjg0ipIuCATtVEyNaxKZ0KTSr0m81jclk9ImLoRl+4rKwFnQDEZ8StZRc0j/0neC2v95YV/gp/SNbLWJTuhSaUemH4wrsYVS2FJrhJy4L+z3oYWE6LyDXM6D1Yga5pn9Q2x6URenWPaiEzqVffSWgcwqbsm2hrTD5xGVh/xt0nTAdUZxZgo1E6YP8VBBj/Q3KonQHQfUt/WSJ6vvFOYOy7aGtMPnEZWF+FF+9F6VZtV5lEDopf5IwHRnuegxZTUgWsSldCs2g9Kxq7W5LC/qXnR2a4ScuC/N+0C8Cpu7pEyw3tqcWMuuWmrTxvSAW94Fbd3NMSpdCMyh9N3l7ocgt/cvODs3wE5eF/ZmkxbULNpUZ2VMjf78SHVb/a/2GLbyP7XuYRenW0PqXPs86OB75W/+y74dm94nLgnPxgGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAanQVdNxEABUy55RtBG76UAIBniv2fjwRN1jceMCm1ICjgGQgKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrjC/ojg9Hb3igw0mAB4nRBb3dqfqIMfWbXtUnGuCOByuoJdVOnE6Cxr+YIYQd/Lw+0QB3PFhBd0baCZipRzxKo86L0//ClV7zAgyGr77iCyzUJcy9IOu8wjldwgHuMLigtOhFcXo7PE2fcIA3jC7ooJ5ZwnRYN32iAe4wuqBpbet8NLlZ/Uv6RAPcYXRBKd04fOiqLL2CAd4wvqDA1EBQwDUQFHANBAVcA0EB10BQwDXGF3T/tEk79IoFuMPogqa/GDPk3ZrtrusTDXCH0QUd9nQ6pZn9XtYnGuAOLwU9tf6ytrx6CRotXcZ0M/y2PuEAb6gX9HybN+nGvCTigKa8uNwOKEK9oJ2iV9AmTc50aK8pr1570MJ/idP08Jv6hAO8oV7QyHn0WsBSuqyoprx6Cdr/dfGGuTGd9YkGuEO9oIWW0q/JP3RFmKa8egl6vWnjGXPb1vxTn2iAO9QL2qb5nrjG9MaTdTTl1a0f1LJqUP8lGToFA9yhXtCjxUnITlojcI3nba6dypR7C2eSgCK86Ga6dfASpSt/ddu61w5KLz9JSPhcmWcqQFCgCEYd9SSR0s6FZ2waGbTCdYPcgmYueKZ1/B+qsgDzo1LQRAfctk6k/5JvhYXhj7hukEvQtEbtVyaNiPpeYTnAT1ApaBEH3LZOpIeIeCvwhhwH+ztINpOctni/h9SgLI53gCPsvuKvBp4QFiaWdN0g1x609n5p9vBBVXmA2fFa0F9nuG0d1WbAQ+0oTSr1musGuQQtbz1Z2WarwnoYcGYXdt/coV5Qy9Kh8QKN6rlrvWpi7xalgynNH/eP6wa5BG27Wpzei/5dYT26szAsIDCgw11fpQeuUS/o2IBaQVH1C4bt8LjJHWEPKtcRmkvQbyqfofTum08pLEd3luYZSunuyDhf5QeuUS9o+Xi6uB292WCtpry5u5kSox5/sdzTqZqiaqC09ATHcwGnfFUAcIl6QfN9Qy9EZNFvGmrK66Kj/t+NX/2iKaYmgqw/fiOn+q4E4AL1gkZPppYi++gePi4W0Y1866RZwTk+rgPkRL2gvaM+p22fv/hGZU15uRO0WjNxujPgis8q+PnjD76RvXbBb6dmcsIAAArdSURBVFEv6PWXO9OfgkneJZrycifogbzN9v0zMshnzxK3xJd6a1TTOim+ys8rXvaDXtt6Vlte7gSlRx7KGxA+3mfp/9fghjCdpu2XvQkx+l2dpqHpFnFqKX/C14VwhnpBO9oYqikvBHWifIo0a7vFt2Vwh3pBewq83L7QQ59qygtBnWiwS5rFHvNxHbzh7Vf89ZbzNeWFoE7MaHNPmC6piUHzcuL1b9DtNTXlhaBOZLxYsXPnpuWwA3XCa0HXm6yj3tdYhhWpU6di3Hlf18Eb6gVdIjGzTHNNeSGoE4vrXROmE5v4ug7eUC9ofomQhu7vmvMEBHWi2WZxain3m68L8Rn/N2306twX5PLUD3rhZz++GtPfu5ksg0oPGdWyZq7zP/wIur1qmeqFRt/TN41xeOQHaVZF6d/DbCyo26VQSK1Xc91j6f1Ncx08bZFxyY1tuQTdX0L4jvvz8f4KyzEd09qLX2/Lq/lrN1O9wlOvZ+ysXtj5l6P6245nhJWLn/5uTOndbpsf6VU8gAQUf/WozPu5BO0qvXAjUuNzRw3LvW41pnzavcwRX9fhKwpIZyYvBq1zel39V/zrLcVfihnt+rpr/UNwbELi0sQRtYL3yNTjLGjMGWn2mP/eGL8l/rXZ//m6CJ8R8pnj7D7qBS25UpqtLeGu9aNdrMdjWb2buW6QS9Bq1i7qhu53zMCsFGuYLkw/C9vm9Lp6QUvMlGazyrhrXXC1beHHcMeXd0TaISGRkUUuUNoz0jbPHyzOf8sf4fQ65v4xzxeYJ/SDruWCnP/+gTvVCvpa+HoLtawv5PYrvvY7toXJdXO8fi3VRoFZqaniBZB3U23zE6UHHPp7WdkFqU6vY+4f8+Rig4e8Nbb+e86v11C9B73RlERWjyTN3T50+1PywqpDpw6v6xXg/JvCRu5upqsDH4pujy94v+Voy7BipeblGljdi35QS9KEQZN3eOgO+aKG9ASmGnI3huBMEnDmjqseHGYd9ZbzyRuSz8tqDEGBItQK2nP2DDua8kJQoAi1gpKnS9nRlBeCAkWoFTRNpyHdIChQhNGHQgQmx+BDIQKzY/ShEIHJMfpQiMDkGH0oRGByDD8UIjA3TIdCdAMEBYpgNBSiRyAoUIRX/aB3TlOtt85AUKAILwT9pDQhtNsUbYpCUKAI9YJ+SvosI3R6nk805YWgQBHqBa36Jr0irIyqrikvBAWKUC9oyAZJ0M0hmvJCUKAI9YLWHiUJOqmGprwQFChCvaD/Cxy7l1yYF6ZtxCsIChThxWCyMyIJIfnezXV7kyogKFCEN/2gafuWb7ukMS8EBYp4sE+321nRTp5JCprv6vVY77165AWGRa2gN7csPmih9NbxrR29yJZ17qyNMAV70ISYxG1zyo/2Ig8wDSoF/aWM8Pvzjf+LDRBmmvIq+IpPrnBdmP5bRu4BecAfUCnoE4W/OrasaMmGy7bsS9OUV4Ggw8dIs2EfaEoEjI1KQYuOEyYTSIrmvAoE7Wc9mTp9sOZkwLioFJQsFyYrifbHACsQdHpvadYjUXMyYFzUCio+HHStDkf0CgS9VPw7YbqqRKr2bMCwcCwo3Rvb8OX61Q9qTwaMC8+C0ns7P9+de+Qc4E+oFTQwf/78gUQay0tTXpxJAopQKegIBzTlhaBAEfwM5AWACyAo4Bq+Bb1y4Kq+aYHRYCiouqEQXZDSsUj9yM4XVCcGJoKVoKqHQszNfxWnZNC742LvqMsMTAUjQdUPhZib+c9Is45yA4UAf4CRoOqHQszNgNnSbGq8qszAXDASVGYoxPsoEDR+ojQbM1JVZmAuGAkqNxRiNgoE3VhHPMhKr6Z0sEZgRhgJ6sVQiLmwdGm56+/tj76oKjEwGayO4vUYCjFrfsPoxou03d4MDA6GQgRcw/eZJOD3QFDANQ9W0B/j7OSdpkc8YHoYCRp/H8eX7x0+ZKO6zBkmAHLASNBBIaR0jBXXDRomq4oH/BVWX/HryXG370NQoAhWgmaFQlCgA8wOkla7v50dggJF+KqbCYICRTAVNEH+MbcQFCiCqaDkhOxbEBQoAoICroGggGuYCpr0n+xbEBQoAkfxgGt8Juiw+bkY364HMx7rzix05yeYhe7Rkl3ojl2ZhX6hc+4/rreU8pGgC/vmpkWBqswIqsQsdPHCzEJXCWAWumpkNLPQFUNc/HW9ZOAN3wjqim+fZBe7wjlmoRk+RD8jkFlo2n8us9AnqjALLQ8ElQOCOgNB1QNBnYGgqoGgzkBQ5UBQOSCoMxBUPRDUGQiqGgjqDARVDgSVA4I6Y1ZBN3VhF7syuwc1zx7KLHRmGLPQdOACZqFP12AWWp4HIGjmNXaxGT7r/o78JTGaYVh2Wjq72L4YWeABCAqA90BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB17AXdFHd8Ga7mUReJY1614tB5EHWMSJYlG4NzaD09A9jQ6pOuksZlJ0dmt0nLgtzQZeQwas7Bx9hEXpiVKLADt3jZm0MlSxiULo9NIPSE/KP3zQ631ssys4OzeoTdwNrQS01n6c0I7Y3i9i927CIStcUIES0iEHp9tAMSs8MHi5Mxwbe1r/s7NCsPnF3sBb0PFklTBOiWcRuMeDfw+4fme8VqcePlxYtYlC6PTSD0i/Eit/qX5Df9S87OzSrT9wdrAVNJgeFaWJABoPYpWODCOlymUHkGNEiNqVLoVmVfqtpTCajT1wMzfATl4W1oBvIKWG6lMg/1t5r7gS3/fXGusJP6R/ZahGb0qXQjEo/HFdgD6OypdAMP3FZ2O9BDwvTeQF3WSWYQRjc8mTbg7IoPeb+IJI6l371lYDOKWzKtoW2wuQTl4X9b9B1wnREcWYJNko7DJ2Jsf4GZVG6g6D6ln6yRPX94pxB2fbQVph84rIwP4qv3ovSrFqvMgidlD9JmI4Mz9Q/tGQRm9Kl0AxKz6rW7ra0oH/Z2aEZfuKyMO8H/SJg6p4+wUcZRM6sW2rSxveCWDyowLqbY1K6FJpB6bvJ2wtFbulfdnZohp+4LOzPJC2uXbApm0G6/34lOqz+1xYGkW3fwyxKt4bWv/R5xMrf+pd9PzS7T1wWnIsHXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUL152vagGDLO15WYAgiqN3tXrlxZurEw+TWN9PR1McYHgrKg+nPi9PbTs31diPGBoCywCkpLzaC03Of9y1SY80eHImWXUnEIo9Dqi3xbm8GAoCxwFLTchsyxpMr+jB7Bt+nMwBHrB5BPfFydoYCgLHAU9BVK/yAfUrqNnEyLHCu82qe0b4szFhCUBY6CTqQ0Qxy66Dg5sY/suXLlyjJyx8flGQkIygJHQaeIgq6VBF1u64B6kIMQGB0IygIZQbeRiz4uzHhAUBbICHopaJ7w6oxuD/Qh7wYHgrJARlA6tMCULaPzTvZxdYYCgrLApaA1/6BZU6qFVJmLHagKICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgmv8HGKHWHxp5+IgAAAAASUVORK5CYII=" 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dkgXmB1m9ItIa4byAlKax5RlQgwxqSCxkyi9H/kN0oXynxlywr63FeqEgHGmFTQccHxwwvWa/f7oQoDXTeQFXTMSFWJAGNMKui9d6NKTvuvKiF1r7huICvoiq6qEgHGmFRQSi0WStO/3XRH5m1ZQX+OVZkIMOXnfBWZU3iUfH5f3fIhK2hGGB5wxxUpZ9njpueGO0HpI7ioHtyHP0HfmKVvJmBo+BN0QU99MwFDw5+gh2rqmwkYGv4EvYuTneA+/AlK6+zTNxUwMhwK2vsTfVMBI8OhoHPxIHCQDYeCJsvcCAr8EQ4FvROKUeOBHQ4FpTUP6ZsLGBgeBe25QN9cwMDwKOisfvrmAgaGR0F3P6JvLmBgeBQ0LdTNk2+Bf8GjoLSK0qKA6eFS0BcX65sMGBcuBZ36lr7JgHHhUtDtGFQW2OBS0OsFMvXNBgwLl4LSSr/qmw0YFj4FfVbuubfA31ApaJH7dPC0hduBvNwLOmGIwqqA2VEpaGJi4oywcvHT340p7f7uYE8DebkXdGsLhVUBs6P+K/71lneFaUa7vu5aexzIy72gVwpZ3L0N/Af1gpZcKc3WlnDX2uNAXu4FpTE/KywLmBz1gpaYKc1muR3RyONAXh4EfXmewrKAyVEv6Gvh6y3Usr6Q2694jwN5eRB0YQ+FZQGTo17QG01JZPVI0tztM748DuTlQdAT5RSWBUyOF/2glqQJgybv8HAU42kgLw+CWqIuKKwLmBuvOurvnKYej7JdDuR1vK+doBnuN++yVGFdwNx4IegnpQmh3aYo6Aj678TdnC9cWTjfRnCi+02n9VdYFzA36gX9lPRZRuj0PG6f/5E1vQu980YAyf9hlusGHr7i6QE8QgyIqBe06pv0irAyqrq71hPIEDoydMKmUUEyd2h6EjQj/F+FhQFTo17QkA2SoJtlBkCyUnYopRUnCAvv13LdwJOgtNV3CgsDpka9oLVHSYJOkhlCzkrEakoLrRcWNoa5buBR0DEJCgsDpka9oP8LHLuXXJgXJjMIp5W23S207RhhYaR3HfWUft9YYWHA1KgX1DIjkhCS712Zox8r+0PbLf+68NQdY4Nkzll6FPRWGJ5jC7zrB03bt3zbJQ/N93fKI3bUl5kt875HQekjuxRWBsyMekH77VF2Kdy1Y5t3nMqQe9ezoPEfKkoDzI0Xl9uR8iNPaM7rWdC17TUnAcZHvaBZe4aUJ3HT/9KW17OgVwrh1k7g3bl4y6HhD+VpqymvZ0Fp1Z80ZQCmwLu7Oq9//WK+PJryKhC0z8eaMgBT4IWg5+e0DQpsM9/Tcbx7FAj6eTdNGYAp8OJMEsnXcZHmE+UKBD0XrTUJMD6qBU2vvfC6DnkVCEpLn9YhETA2qgX9lXyhR14lgvbEiF5AtaCWxx7X4551JYIuf1KHRMDYqP8N+lVs3HvTZghoyqtE0NRCGDDJ71EvaCk7mvIqEZQ2TtKUA5gAPp9uZ2McniHm9zC7q9MDigQ9VFVjFmB4mN7V6QZFglpKnNWUBBgfRnd1ekSRoOhoAozu6vSIMkHR0eT3MLqr0yPKBEVHk9/D6K5OjygTFB1Nfg+juzo9olBQdDT5O4zu6vSIQkHR0eTvsLqr0xMKBUVHk7/j5ZmkU+sva8urUFB0NPk76gU93+ZNujEviTigKa9SQdHR5OeoF7RT9ArapMmZDh7vCtYwkNd90NHk56gXNHIevRawlC4r6ra5toG8bNzePG9ro60KCwSmRL2ghZbSr8k/dIXMY+usaBzIy8rOii1fb16ks8ICgSlRL2ib5nviGtMbT9Zx11rrQF4if0VtE6af5flTYYXAjKgX9GhxErKT1ghc46611oG8RKa8Ic2i8LR6f8aLbqZbBy9RutL9iO5aB/IS6WftYepSTVF9wJx40w965YdVB9Lct9Y6kJfIiPel2YCQ2wpLBCZEvaAZg/IRQiImur9gWeNAXiIHy06uF1V/askGqz02BaZFvaCjyNDjqcfiySz37V0O5LWdZDPJY0bLQ8GDvhyQv9aCZxWWCEyIekFj4qVZvJILltfKnrJXsAddH7e3f8c391VfF+Hh9wQwMV501H8jzb6NULKR7OWcCgSNt+5kx47o+JWCVMCceHGqc6A0e6ODu9bdrZDHund33UCBoAOsz7efEr8E5+P9F5WCHjlyZGuRF9cmr+kW7bafqTEp20CAVGnQwHUDBYIu6CrNHv/yZiGMOue3qBT0/kEOaeKu9b34CLEjX9NX/K1KH92j6aOrpdNn/qewSGA6VAp6+j4X3bdfF/H2XW2C0vOdIutEPPMHpSvbKCwSmA7Vv0GzlvV5tNaLc+66bSxyLu6R37UJSmnqT9fE2Z3C/yhpDUyIWkF/a0jKtOkUS6oe9rhFev9IrYLa6SE3HhgwOyoFvVW1wndi5/u+mhVuet5m1aDf5N5SJ2hSbTWtgYlQKeioMJtyFyOGa8qrTlBLrMxlpcDsqBS02Rv29bebasqrTlA6U6Y7FZgdlYKGL7CvLy6kKa9KQa9H4jDJP1EpaMXx9vXJFTXlVSko7TPecxtgQlQK2q2p7fokS7sumvKqFfRoWYzc6ZeoFHRbwIdWQxeQDZryqhWUNl6nKR8wKGr7QYeTpsuPnljbhfTWlle1oF+205YQGBO1glpWlxVPxEcu0PbsMPWC3o0+qS0jMCTqL7fLPLV2+THPZzo9oFpQOhxPYvRHuB6GJgfni/6nbwnACBhHUPoULrrzQwwk6JaaegwSCoyFgQSlDVbpWwMwAEYSdEN1jV0HwHgYSVDsQv0QQwm6EbtQv8NQgtImK/WtAnCPsQTdhF2ov2EsQemjK/QtA/COwQTFLtTfMJig2IX6G0YTdEu1DH0LAXzDUFBdxknKxePTc65b1sQP/BJX25sWVoLqMk6SK04X+8Nx9UbzRlNntXn4Ly+jAd5hJKgu4yS5ZsTzjmv9+4pXkIzGYEpmhZGgeoyTJMPtit87rBWRRlG6E46HMJsURoLqMU6SHN9Uvj98570g67zCOa/DAa5hJKge4yTJ8uSE+8vRkplp4be8Dwd4hpGgeoyTJMv5oinZywlP/zBlQlL/l7yPBriG1VG8DuMkyTO2a/bi9bJB9RuFFPldQzTAM8z6QV2Ok3R54XwbwYkq4zmSXmW5fTHhmR8mf/T9AOxBzQozQW//LvWe3/rb8cXjfe0EzVAZLwfHou0HRfgNanIYCXr3rbyk0l5hYaHMdpq+4imdVd96Zz6O4s0OI0En5hv7RdMCKcwEtXQaZl1AP6jJYSRo7Fjh2z2uMzNBaWr5rdJ8QG/xZ+772h61B/iFkaDBm4XJoYD9zASlu0pIv25vtGg49ePWtXEu3qwwErTyR+K0Z43bzASlI9tJXQSWtfEDv8LVTKaFkaDj8r+3m9Kr0e3fZiZoRtMRWkMA/mEkaHpCcKww+60aYSYovRo7R3MMwDvM+kEzL4jTrB/muX5bB0Hp2ZJ46rLpMdotHzk4WCxZhyiAZwwtKF0ffUqPMIBfjC0onRt7VZc4gFcMLih972GM8GVqjC4onVjxrE6RAI8YXlA6pzyG/zAxxheULikld28zMD4mEJSuLI7Buk2LGQSlm6NkTgcAw2MKQenpWj1wSb05MYeg9M6rtXEwb0pMIiils4qv1zki4AHTCEp/rPBKqt4xgc8xj6D0VkLJz3UPCnyMiQSldG+1Jy4yCAt8iKkEpemjio7H4bypMJeglP7et/hEzYPZA34wm6CUHmz10EoMBWIazCcopVvrxyZ68UU/unZMJ9y+zBtmFJTSQy8VHqjycCmtREiX3pXyyj2ND/gIcwpK6ek3C3f7Ts2INR2jxJ+u7+TDKDd8YVZBKb2+4NHibx9R3DxsrjTLj4HC+MK8ggqceb9C7Ls/KnvsSOBuaVbkI5YFueXH9wctSvfczM8wtaCUWg6PrlPs1ZWXPbeM+ECcZgRuZV2SDJmvVBo7o3PMLz5Kzy0mF1Tk/JwnImoOXOvh9s/XQ09RmtW60IOpKTcfPybuPRc/bPHY0r/wA0EFMg9Mfjy8cveZe+/It2maJ7Z+WMEDD66onDTa9umz7RP+rHzcVwVwin8IKpL1y2f964VUfXbcmjOuf5Vuf+mJSb7r4S9Xr83yTcOi6m3xWQW+5tj0MWty/2UMN5isNu4dWzrsiQrBsU8M/mTjL1ydtS/zmDj9PtBf96CWfgWKhBeplOuyc8MNJqsH6b+sndK3XdWQonWf6jd24XcHznOgapla4lPMPw496OtCfMTU/P1/vvhlREXn1403mKyOXDr4zSfv9+oYVyY4tGy9ts/3GzFl/orNe39OSXWz62dFhZ5l3vmwVY1HfdWL4GtKPCtOf83rvEcz3mCyTEg7t3/z0rnj4vt0a9uwWrnIoKDIsjFxDVs/2e2lvm8lDJs4cdb8+V+tWLEpKWn7oUOHfjor8GeqiH7H3G3WHJ08YuXtEim6RTQWQZukWaHpTq8/2MFkD7S2EzhNVbwHzb3U388c2pv07YrP58+aOCEhYWDfvi9069audeuWcXFxtSsKlIwUCZDG0yMFI22UqJiDh+M80Dj7A3k4tEnr1q3KFm3tp+Sp0bq1IGeo847rwQ4mm5Zkp9IPquJxzs1UG3+dzcHRQx7Yk/2BJL0V0bBVdJM1SX5KxbKbks7S+XmdnwXnq8FkG+LRs85c+XaJvx7CC+wJKT1+TsfQ55xf99VgshAU5CSpbESx0IRcd0M82MFk7wNBgROWcz+5ONHnqzNJEBQoAoICroGggGt8Jehj2V2H9wkjAcxgGJolBi07T+4/rrcEKh3HRWdB76TmZmmrc8wos4tZ6JGvMQt9KpBZ6HM9PmAW+vtKLv66XnJdqVE6C+qKb59kF7vCOWahpw9mFjojkFlo2n8us9AnqjALLQ8ElQOCOgNB1QNBnYGgqoGgzkBQ5UBQOSCoMxBUPRDUGQiqGgjqDARVDgSVA4I6Y1ZBTzMcjmt0GrPQu9cyC215x3Mbb1m5j1no6+OYhZbnAQgKgPdAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAdewF3RR3fBmu5lEXiU9yKwXg8iD4qUZi9KtoRmUnv5hbEjVSeJTunQvOzs0u09cFuaCLiGDV3cOVj52oQomRiUK7NA9btbGUMkiBqXbQzMoPSH/+E2j873Fouzs0Kw+cTewFtRS83lKM2J7s4jduw2LqHRNAUJEixiUbg/NoPTM4OHCdGzgbf3Lzg7N6hN3B2tBz5NVwjQhmkXsFgP+PZyqf9jU48dLixYxKN0emkHpF2LFb/UvyO/6l50dmtUn7g7WgiYTcQCWxAAWw2GXjg0ipIuCATtVEyNaxKZ0KTSr0m81jclk9ImLoRl+4rKwFnQDEZ8StZRc0j/0neC2v95YV/gp/SNbLWJTuhSaUemH4wrsYVS2FJrhJy4L+z3oYWE6LyDXM6D1Yga5pn9Q2x6URenWPaiEzqVffSWgcwqbsm2hrTD5xGVh/xt0nTAdUZxZgo1E6YP8VBBj/Q3KonQHQfUt/WSJ6vvFOYOy7aGtMPnEZWF+FF+9F6VZtV5lEDopf5IwHRnuegxZTUgWsSldCs2g9Kxq7W5LC/qXnR2a4ScuC/N+0C8Cpu7pEyw3tqcWMuuWmrTxvSAW94Fbd3NMSpdCMyh9N3l7ocgt/cvODs3wE5eF/ZmkxbULNpUZ2VMjf78SHVb/a/2GLbyP7XuYRenW0PqXPs86OB75W/+y74dm94nLgnPxgGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAanQVdNxEABUy55RtBG76UAIBniv2fjwRN1jceMCm1ICjgGQgKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrjC/ojg9Hb3igw0mAB4nRBb3dqfqIMfWbXtUnGuCOByuoJdVOnE6Cxr+YIYQd/Lw+0QB3PFhBd0baCZipRzxKo86L0//ClV7zAgyGr77iCyzUJcy9IOu8wjldwgHuMLigtOhFcXo7PE2fcIA3jC7ooJ5ZwnRYN32iAe4wuqBpbet8NLlZ/Uv6RAPcYXRBKd04fOiqLL2CAd4wvqDA1EBQwDUQFHANBAVcA0EB10BQwDXGF3T/tEk79IoFuMPogqa/GDPk3ZrtrusTDXCH0QUd9nQ6pZn9XtYnGuAOLwU9tf6ytrx6CRotXcZ0M/y2PuEAb6gX9HybN+nGvCTigKa8uNwOKEK9oJ2iV9AmTc50aK8pr1570MJ/idP08Jv6hAO8oV7QyHn0WsBSuqyoprx6Cdr/dfGGuTGd9YkGuEO9oIWW0q/JP3RFmKa8egl6vWnjGXPb1vxTn2iAO9QL2qb5nrjG9MaTdTTl1a0f1LJqUP8lGToFA9yhXtCjxUnITlojcI3nba6dypR7C2eSgCK86Ga6dfASpSt/ddu61w5KLz9JSPhcmWcqQFCgCEYd9SSR0s6FZ2waGbTCdYPcgmYueKZ1/B+qsgDzo1LQRAfctk6k/5JvhYXhj7hukEvQtEbtVyaNiPpeYTnAT1ApaBEH3LZOpIeIeCvwhhwH+ztINpOctni/h9SgLI53gCPsvuKvBp4QFiaWdN0g1x609n5p9vBBVXmA2fFa0F9nuG0d1WbAQ+0oTSr1musGuQQtbz1Z2WarwnoYcGYXdt/coV5Qy9Kh8QKN6rlrvWpi7xalgynNH/eP6wa5BG27Wpzei/5dYT26szAsIDCgw11fpQeuUS/o2IBaQVH1C4bt8LjJHWEPKtcRmkvQbyqfofTum08pLEd3luYZSunuyDhf5QeuUS9o+Xi6uB292WCtpry5u5kSox5/sdzTqZqiaqC09ATHcwGnfFUAcIl6QfN9Qy9EZNFvGmrK66Kj/t+NX/2iKaYmgqw/fiOn+q4E4AL1gkZPppYi++gePi4W0Y1866RZwTk+rgPkRL2gvaM+p22fv/hGZU15uRO0WjNxujPgis8q+PnjD76RvXbBb6dmcsIAAArbSURBVFEv6PWXO9OfgkneJZrycifogbzN9v0zMshnzxK3xJd6a1TTOim+ys8rXvaDXtt6Vlte7gSlRx7KGxA+3mfp/9fghjCdpu2XvQkx+l2dpqHpFnFqKX/C14VwhnpBO9oYqikvBHWifIo0a7vFt2Vwh3pBewq83L7QQ59qygtBnWiwS5rFHvNxHbzh7Vf89ZbzNeWFoE7MaHNPmC6piUHzcuL1b9DtNTXlhaBOZLxYsXPnpuWwA3XCa0HXm6yj3tdYhhWpU6di3Hlf18Eb6gVdIjGzTHNNeSGoE4vrXROmE5v4ug7eUC9ofomQhu7vmvMEBHWi2WZxain3m68L8Rn/N2306twX5PLUD3rhZz++GtPfu5ksg0oPGdWyZq7zP/wIur1qmeqFRt/TN41xeOQHaVZF6d/DbCyo26VQSK1Xc91j6f1Ncx08bZFxyY1tuQTdX0L4jvvz8f4KyzEd09qLX2/Lq/lrN1O9wlOvZ+ysXtj5l6P6245nhJWLn/5uTOndbpsf6VU8gAQUf/WozPu5BO0qvXAjUuNzRw3LvW41pnzavcwRX9fhKwpIZyYvBq1zel39V/zrLcVfihnt+rpr/UNwbELi0sQRtYL3yNTjLGjMGWn2mP/eGL8l/rXZ//m6CJ8R8pnj7D7qBS25UpqtLeGu9aNdrMdjWb2buW6QS9Bq1i7qhu53zMCsFGuYLkw/C9vm9Lp6QUvMlGazyrhrXXC1beHHcMeXd0TaCQiJjCxygdKekbZ5/mBx/lv+CKfXMfePeb7APKEfdC0XlOvvr1rQ18LXW6hlfSG3X/G137EtTK6b4/VrqTYKzEpNFS+AvJtqm58oPeDQ38vKLkh1eh1z/5gnFxs85K2x9d9zfr2GakFvNCWR1SNJc7cP3f6UvLDq0KnD63oFOP+msJG7m+nqwIei2+ML3m852jKsWKl5uQZW96If1JI0YdDkHR66Q76oIT2BqYbcjSE4kwScueOqB4dZR73lfPKG5POyGkNQoAi1gvacPcOOprwQFChCraDk6VJ2NOWFoEARagVN02lINwgKFGH0oRCByTH4UIjA7Bh9KERgcow+FCIwOUYfChGYHMMPhQjMDdOhEN0AQYEiGA2F6BEIChThVT/ondNU660zEBQowgtBPylNCO02RZuiEBQoQr2gn5I+ywidnucTTXkhKFCEekGrvkmvCCujqmvKC0GBItQLGrJBEnRziKa8EBQoQr2gtUdJgk6qoSkvBAWKUC/o/wLH7iUX5oVpG/EKggJFeDGY7IxIQki+d3Pd3qQKCAoU4U0/aNq+5dsuacwLQYEiHuzT7XZWtJNnkoLmu3o91nuvHnmBYVEr6M0tiw9aKL11fGtHL7JlnTtrI0zBHjQhJnHbnPKjvcgDTINKQX8pI/z+fOP/YgOEmaa8Cr7ikytcF6b/lpF7QB7wB1QK+kThr44tK1qy4bIt+9I05VUg6PAx0mzYB5oSAWOjUtCi44TJBJKiOa8CQftZT6ZOH6w5GTAuKgUly4XJSqL9McAKBJ3eW5r1SNScDBgXtYKKDwddq8MRvQJBLxX/TpiuKpGqPRswLBwLSvfGNny5fvWD2pMB48KzoPTezs935x45B/gTagUNzJ8/fyCRxvLSlBdnkoAiVAo6wgFNeSEoUAQ/A3kB4AIICriGb0GvHLiqb1pgNBgKqm4oRBekdCxSP7LzBdWJgYlgJajqoRBz81/FKRn07rjYO+oyA1PBSFD1QyHmZv4z0qyj3EAhwB9gJKj6oRBzM2C2NJsaryozMBeMBJUZCvE+CgSNnyjNxoxUlRmYC0aCyg2FmI0CQTfWEQ+y0qvtVJUZmAtGgnoxFGIuLF1a7vp7+6MvqkoMTAaro3g9hkLMmt8wuvEibbc3A4ODoRAB1/B9Jgn4PRAUcM2DFfTHODt5p+kRD5geRoLG38fx5XuHD9moLnOGCYAcMBJ0UAgpHWPFdYOGyariAX+F1Vf8enLc7fsQFCiClaBZoRAU6ACzg6TV7m9nh6BAEb7qZoKgQBFMBU2Qf8wtBAWKYCooOSH7FgQFioCggGsgKOAapoIm/Sf7FgQFisBRPOAanwk6bH4uxrfrwYzHujML3fkJZqF7tGQXumNXZqFf6Jz7j+stpXwk6MK+uWlRoCozgioxC128MLPQVQKYha4aGc0sdMUQF39dLxl4wzeCuuLbJ9nFrnCOWWiGD9HPCGQWmvafyyz0iSrMQssDQeWAoM5AUPVAUGcgqGogqDMQVDkQVA4I6gwEVQ8EdQaCqgaCOgNBlQNB5YCgzphV0E1d2MWuzO5BzbOHMgudGcYsNB24gFno0zWYhZbnAQiaeY1dbIbPur8jf0mMZhiWnZbOLrYvRhZ4AIIC4D0QFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDXsBV1UN7zZbiaRV0mj3vViEHmQdYwIFqVbQzMoPf3D2JCqk+5SBmVnh2b3icvCXNAlZPDqzsFHWISeGJUosEP3uFkbQyWLGJRuD82g9IT84zeNzvcWi7KzQ7P6xN3AWlBLzecpzYjtzSJ27zYsotI1BQgRLWJQuj00g9Izg4cL07GBt/UvOzs0q0/cHawFPU9WCdOEaBaxWwz497D7R+Z7Rerx46VFixiUbg/NoPQLseK3+hfkd/3Lzg7N6hN3B2tBk8lBYZoYkMEgdunYIEK6XGYQOUa0iE3pUmhWpd9qGpPJ6BMXQzP8xGVhLegGckqYLiXyj7X3mjvBbX+9sa7wU/pHtlrEpnQpNKPSD8cV2MOobCk0w09cFvZ70MPCdF7AXVYJZhAGtzzZ9qAsSo+5P4ikzqVffSWgcwqbsm2hrTD5xGVh/xt0nTAdUZxZgo3SDkNnYqy/QVmU7iCovqWfLFF9vzhnULY9tBUmn7gszI/iq/eiNKvWqwxCJ+VPEqYjwzP1Dy1ZxKZ0KTSD0rOqtbstLehfdnZohp+4LMz7Qb8ImLqnT/BRBpEz65aatPG9IBYPKrDu5piULoVmUPpu8vZCkVv6l50dmuEnLgv7M0mLaxdsymaQ7r9fiQ6r/7WFQWTb9zCL0q2h9S99HrHyt/5l3w/N7hOXBefiAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EFRvnrY9KIaM83UlpgCC6s3elStXlm4sTH5NIz19XYzxgaAsqP6cOL399GxfF2J8ICgLrILSUjMoLfd5/zIV5vzRoUjZpVQcwii0+iLf1mYwICgLHAUttyFzLKmyP6NH8G06M3DE+gHkEx9XZyggKAscBX2F0j/Ih5RuIyfTIscKr/Yp7dvijAUEZYGjoBMpzRCHLjpOTuwje65cubKM3PFxeUYCgrLAUdApoqBrJUGX2zqgHuQgBEYHgrJARtBt5KKPCzMeEJQFMoJeCponvDqj2wN9yLvBgaAskBGUDi0wZcvovJN9XJ2hgKAscClozT9o1pRqIVXmYgeqAggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuOb/AWOM1j7HJ62VAAAAAElFTkSuQmCC" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.</p>
 <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p>
 <p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p>
@@ -1756,36 +1769,36 @@ plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE,
 <pre class="r"><code>m.L2.FOMC &lt;- mkinfit(&quot;FOMC&quot;, FOCUS_2006_L2_mkin, quiet = TRUE)
 plot(m.L2.FOMC, show_residuals = TRUE,
      main = &quot;FOCUS L2 - FOMC&quot;)</code></pre>
-<p><img 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" 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.49.1 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Thu Apr  4 17:00:05 2019 
-## Date of summary: Thu Apr  4 17:00:05 2019 
+<pre><code>## mkin version used for fitting:    0.9.49.4 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 18:43:52 2019 
+## Date of summary: Thu May  2 18:43:52 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 107 model solutions performed in 0.249 s
+## Fitted using 240 model solutions performed in 0.483 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##          value   type
-## parent_0 93.95  state
-## alpha     1.00 deparm
-## beta     10.00 deparm
-## sigma     1.00  error
+##              value   type
+## parent_0 93.950000  state
+## alpha     1.000000 deparm
+## beta     10.000000 deparm
+## sigma     2.275722  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##               value lower upper
 ## parent_0  93.950000  -Inf   Inf
 ## log_alpha  0.000000  -Inf   Inf
 ## log_beta   2.302585  -Inf   Inf
-## sigma      1.000000     0   Inf
+## sigma      2.275722     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -1799,10 +1812,10 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -0.1150844 -2.085e-01  3.562e-06
-## log_alpha -1.151e-01  1.0000000  9.741e-01 -5.400e-06
-## log_beta  -2.085e-01  0.9741278  1.000e+00 -5.088e-06
-## sigma      3.562e-06 -0.0000054 -5.088e-06  1.000e+00
+## parent_0   1.000e+00 -1.151e-01 -2.085e-01  1.606e-08
+## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.168e-07
+## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.029e-07
+## sigma      1.606e-08 -1.168e-07 -1.029e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1814,9 +1827,9 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 ## beta        1.234   4.012 1.942e-03  0.6945  2.192
 ## sigma       2.276   4.899 5.977e-04  1.2050  3.347
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
-## All data   7.086       4  2
+## All data   6.205       3  3
 ## parent     6.205       3  3
 ## 
 ## Estimated disappearance times:
@@ -1830,12 +1843,12 @@ plot(m.L2.FOMC, show_residuals = TRUE,
 <pre class="r"><code>m.L2.DFOP &lt;- mkinfit(&quot;DFOP&quot;, FOCUS_2006_L2_mkin, quiet = TRUE)
 plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
      main = &quot;FOCUS L2 - DFOP&quot;)</code></pre>
-<p><img 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" 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" /><!-- --></p>
 <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.49.1 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Thu Apr  4 17:00:07 2019 
-## Date of summary: Thu Apr  4 17:00:07 2019 
+<pre><code>## mkin version used for fitting:    0.9.49.4 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 18:43:54 2019 
+## Date of summary: Thu May  2 18:43:54 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -1844,18 +1857,18 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 489 model solutions performed in 1.185 s
+## Fitted using 587 model solutions performed in 1.211 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##          value   type
-## parent_0 93.95  state
-## k1        0.10 deparm
-## k2        0.01 deparm
-## g         0.50 deparm
-## sigma     1.00  error
+##              value   type
+## parent_0 93.950000  state
+## k1        0.100000 deparm
+## k2        0.010000 deparm
+## g         0.500000 deparm
+## sigma     1.413899  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##              value lower upper
@@ -1863,46 +1876,46 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE,
 ## log_k1   -2.302585  -Inf   Inf
 ## log_k2   -4.605170  -Inf   Inf
 ## g_ilr     0.000000  -Inf   Inf
-## sigma     1.000000     0   Inf
+## sigma     1.413899     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
 ## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##          Estimate Std. Error      Lower     Upper
-## parent_0  93.9500    0.99980    91.5900   96.3100
-## log_k1     3.0480  972.30000 -2296.0000 2302.0000
-## log_k2    -1.0880    0.06285    -1.2370   -0.9394
-## g_ilr     -0.2821    0.07033    -0.4484   -0.1158
-## sigma      1.4140    0.28860     0.7314    2.0960
+## parent_0  93.9500  9.998e-01    91.5900   96.3100
+## log_k1     3.1330  2.265e+03 -5354.0000 5360.0000
+## log_k2    -1.0880  6.285e-02    -1.2370   -0.9394
+## g_ilr     -0.2821  7.033e-02    -0.4484   -0.1158
+## sigma      1.4140  2.886e-01     0.7314    2.0960
 ## 
 ## Parameter correlation:
 ##            parent_0     log_k1     log_k2      g_ilr      sigma
-## parent_0  1.000e+00  1.367e-06 -4.360e-10  2.665e-01 -1.520e-08
-## log_k1    1.367e-06  1.000e+00  2.264e-04 -4.454e-04 -2.092e-05
-## log_k2   -4.360e-10  2.264e-04  1.000e+00 -7.903e-01 -4.817e-10
-## g_ilr     2.665e-01 -4.454e-04 -7.903e-01  1.000e+00 -2.532e-09
-## sigma    -1.520e-08 -2.092e-05 -4.817e-10 -2.532e-09  1.000e+00
+## parent_0  1.000e+00  5.434e-07 -9.989e-11  2.665e-01 -3.978e-10
+## log_k1    5.434e-07  1.000e+00  8.888e-05 -1.748e-04 -8.207e-06
+## log_k2   -9.989e-11  8.888e-05  1.000e+00 -7.903e-01  5.751e-10
+## g_ilr     2.665e-01 -1.748e-04 -7.903e-01  1.000e+00 -7.109e-10
+## sigma    -3.978e-10 -8.207e-06  5.751e-10 -7.109e-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  93.9500 93.970000 2.036e-12 91.5900 96.3100
-## k1        21.0700  0.001054 4.996e-01  0.0000     Inf
-## k2         0.3369 15.910000 4.697e-07  0.2904  0.3909
-## g          0.4016 16.800000 3.238e-07  0.3466  0.4591
-## sigma      1.4140  4.899000 8.776e-04  0.7314  2.0960
+## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100
+## k1        22.9300 4.514e-04 4.998e-01  0.0000     Inf
+## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909
+## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591
+## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
-## All data    3.16       5  1
+## All data    2.53       4  2
 ## parent      2.53       4  2
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50_k1 DT50_k2
-## parent 0.5335 5.311  0.0329   2.058</code></pre>
+## parent 0.5335 5.311 0.03023   2.058</code></pre>
 <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p>
 </div>
 </div>
@@ -1920,7 +1933,7 @@ FOCUS_2006_L3_mkin &lt;- mkin_wide_to_long(FOCUS_2006_L3)</code></pre>
 mm.L3 &lt;- mmkin(c(&quot;SFO&quot;, &quot;FOMC&quot;, &quot;DFOP&quot;), cores = 1,
                list(&quot;FOCUS L3&quot; = FOCUS_2006_L3_mkin), quiet = TRUE)
 plot(mm.L3)</code></pre>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAMACAMAAADMiSWtAAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dB3wURdvAD/leEZAaUkhIoxkINUGlSXglghAhFEOAhCCiqCCgIIQiHQyCgiKgEAlNQKqgIihqUFEQEEQUUBR8BaSEkgJJSLn5dq8kYbN7M7sze7dz9/x/Py53u889O/vwT3a2zZoQABgYk6sbAACOAEEBQwOCAoYGBAUMDQgKGBoQFDA0IChgaEBQwNCAoIChAUEBQwOCAoYGBAUMDQgKGBoQFDA0IChgaEBQwNCAoOrIMVlBaGe1Cqb/e6RImDavkslUMV54YzJdRmisqSJCk/7PZKoQ9Jf1OybTQcvPvhVMFcKLXNZ0PgFB1SEIWkmgJpouWHqPyVStCCUJLlYwmTqXEfQrYdK9JlMV63dsgr4rRJtMPVzZeg4BQdWRY7JWLPceU707aFUFU/z1CqamRWi4yVRUKmiM6V6ETplM5yyxNkFbmx5EnU11XNZ0PgFB1WEXdJKpQq7wI9p07yxThTvCu3ZNzpUK2sdUYWER2rb8uiXWJuiRNf98XtM0wFUt5xQQVB22PuiA9qbq4sctJlNX+5a8zCb+ckUhpnLrI/bJ1j6ouOWvdMf5beYaEFQdtj7o9HamGuLHHSbTY6aq9pmlO0k5g3xER/fZJtsEvdqwgqmuCxrNMyCoOuyb+InWDXt3070zTRXEPfN2jY8IJgr77SMEQTeM3YvQwXtNEZZYm6C/fXUdfWeq4KqWcwoIqg67oDkVTA2K0IcVTN2vVjB1QmihyZSD7jHFIxRoqoVamKoK0tYxPWKJtQkaIOwkLQZBVQKCqsMuqPAn1HKYqUoRGiC8+T+TqSkS9tFNFYVp89Bnwsz77indxFeqXLmy/0yT6V6TqaELG88jIKg6SgRFW6pWMFV8WNy6Txe8q9hVfNddkPI/44U3qVUFRaumWiOt+1UV0ZP3mCo0yHFNu7kFBAUMDQgKGBoQFDA0IChgaEBQwNAYUtAzXXoudnUbAGNgSEEnfoM6uroNgDEwpKDZxf/EuroNgDFwjaD7H64bOrEImWp7eXm9gYpfb16n456y83fGXHCcIF1GYLlpAO+4RNBbvp8VZXZbikzW0yrPRZ8r2he0tXT+nvHFmAx4QReG+CXdsr0iNLNixYr3tLbOSfmCoumAk3GJoKd8ihD6abNN0BM+2cLrV/VL79Z5uke/hDLhxRnWf6Wf8IIeq52R2X269dU2afKGM22bv46uxjNbD0B/XCJocfOOaf+IC7cIuuQZ8dVc95zwusn/p4L6JVv7t0IajC060KdVsvjPPDs0ZJzlE7LIaJnZdTtCkfus7+8SdOcUhFITrK/WKacHo7Ffona3x/zipLU0DLgOlYTU5LKfXN1xck0f9M77/Wp0PmUpWSh6ZZZlWruvhJflR19O+6/ZFvV188s58QsOVDmFxH+7W2bmdVktvkNi2awz0xLRn6HF1veWUgZbLswoFGOuRmwreRV+AXr8hc60az3v9+edvbKuBtehKmVLuPgKglq41PYxW8kWjrBMCD0pvOSjKQ0O2UNeDY6KCu9/oDNC4r/xCxH6cIj4Dolls8686XNn7gxb4N2lNK8KWWV/Fdljuxko8aLOK2Y4HHWoSntOIpl/iK8gKNo8XXg5XMtWsoOBecLrEb8Cy7wZ/UvCUmYiVFRwIEYQVPj3yiLhi4niOySWzToT9drT8i9b4F2lLEroedH+amHIBsuP76aZJ7bo/D+dV9BQKHeoLP0lS/coO9Yv+COhzAUvBrQalrxb6KYnp5rH+nn3z0+Ptc5zFS7aSfrefG36E/aNTnzfS+ZDjVdb3ucGflASdiTsekHfJXZBP4nIzu+6skRQ60y0IaqzPfCuTfy2LmJHwfpafKoA5de5IX7PHJt9PhptnenU1XU1ih0qsb9k7R4t61e8f4hQ5uWP5l4Pswl6Miq3oM2+9FjrPFfhmk38x63r+CZctgtaMD3Mp+0O65x58aNLw5Y2Chhe8hfUPKN+6EuFJYJaZ6KcyqvsgXf9BX2pYqVKlRKtr3mmc+hja9dg03sITWgd9a/+q2gs5DtUYn/J2j36ud7EfWahzAOEvulrNkFRxp45XrvTY63zXIXBziRd9zrd57elrm6Fe6HcoRJ/221dpZsrerc3H4gZKOxRzrcIOjL1YJMFB2IFQa3zXNV6gwk6dgjaVHWzq1vhXih3qERBrd2jWWPN16veOBCzIjovq3lyetOC6yGpc0ehi8Efp8da57mq9QYT9Lbwi407iwSoRLFDZekvWbpHf7etG/y2uJM0Mqj13Dfykxp1n7H5bOumT8yPTI+1znMVBhMUAO4GBAUMDVbQQ1OGJk0+7IymAEB5cILObzx95cqZ4W84pTEAIAUnaEiW+Job7oSmAEB5cII2tJwVvNLCCU0BgPLgBN3omzh5cpL/h05pDABIwe4kXUmbNXvlFenUS8s9mBXZzP8boJ5KaNyLTw0b7rnU+5xKRjmgnkpo3Itf8Szl/wjPdHN0Pbo2oJ5KqN2Lzx0cJ9K0JYuWcQoIyhYqQaV78cUfbRbpVN/6MfuN4e/k0TaQN0BQtlAJqrAXPzjM8uNWk6HLn2xXQNM6DgFB2UIlqMJevE3QlXHCS9THmpvGJyAoW+gElccm6DTx3olRnjbQFwjKFh0F3fFwAbr9wA/acnALCMoWKkGbBVuRTLYJak5snBD8EkXbnMDYWiyYUCYjCMoWKkEzmqeeF5FMtgmK0OEPTmhvmVOIX3mDnuVlh+IBQdlCt4l/U3aorRJBjU88i+sIPgBB9UPHPigPgKBGBwSlBwTVERCUHhBUR0BQekBQHQFB6QFBdQQEpcdZguanTVpf5DjU/QBB6XGSoPkPxr4W3dXThlYBQelxkqBrn0CouO1u5umNDQhq5+bQWt7j7qj69s1gyw8nCTpVHLxzNFx8UwaPEnTQM9cvxEzCxN89KrZzBd0cZUYFLfYxT29sQNBv5ll4rdLsefNeqWP9MO9YSUTqqH6dB+VYR7wuHRX7QNyE2J5X4u4dKIY4SdDimAdHNUtint3ggKB7ky2Mv3dccvJzNa0fJh4piUgNzETjZ1pHvC4dFftAratoyFLmf0Hl75K178WbP1/8LUVyPgFBS94+n301dkK5iNTnETrWyTridemo2AceRWj2AtaCwl2y5QFB7dxIvL/W6PxyEanDhT9sHawjXpeOii2+Yy+owlhXIKgSHiWoAqk+F4oSplhHvC4dFdsmaIAlhJmgCmNdgaBKgKCCoLHtA+OzrSNel46KbRW06CHLs0OYCapwlywIqgQIKn20WlmK7XdUs9tJktwle7tXtEhIU4qUvAOCYlAWtARmgt6aliA+OzTR/tmcvlckuoH2lNxDJ6j8YRG3EpQAZoImdk+NWI1QJclkjurJHipBFQ6LcFRQYwnqm4WuhlwEQcvCdPAwGxwV1FiChlxDKK2XGQQtA9PBw2xwVFBjCfqm1xRkHhhdUTKZo3qyR8fBw3jAWIKiYzsRKto6RjKVo3qyh+ngYfljLIPiNgpk0TKnwMXADSCoEjhBi7duN6/pN/22/XNRmmVY8bYhbNrmBF5iMvTNK2UygqBsoRJ0VGSHXo9vHzxUMhkKyhaopxI4Qf1y7lQ/jbL8JZOhoGyBeiqBEzQgI7vSYXSlnmQyFJQtUE8lsAfq/QLHRaZ0kp4MhIKyBeqpBHYv/ugv6JsJa6R3wkJB2QL1VML9LxZhDwjKFn0F3a8tAc+AoGzRVVBzg8+0ZeAYEJQt+v4FTa93U1sKfgFB2aJzH/TZF7Sl4BcQlC06C5oZmK4tB7eAoGzRey9+V/0cbUl4BQRli+6HmYaN0JaEV0BQtuguaFYw+/8xIwOCskX/A/VfBHvUnrwOYzOBoEqwOZP0YqJCnFuiw9hMIKgSbATNC2dxXwUv6DA2EwiqBKNz8Yd8L2pLxCM6jM0EgirB6mKR2VGeM/S/DmMzgaBKsBK0+L8LtGXiEJZjM6Ffjoj0aETdKn5xzuV2//P9SVsq/mB5mOlWu0gRLz92KbnDSdeDbm6UrS0Xd8BxUJXkXXc421kXLA/vry0Xd9AI2izYimSyOwuaP7Rq7bZnHAQ4S9DcZmnakvEGjaAZzVPPi0gmu7Og0/reNr/dxkEAVtBkG9Kjxw6QLehv3ifIM3AM1Sb+zS/kpvIj6JdT33a8wS7Hg+J5M98rygFYQVNTU4c1SXm92WtyAWpOzW1olOWwqW6CJ/dBJ4XPejpA+vffMZ2/QqigpoMdFJJNfKsbCGW2lpmv7tTcMx7RDfVgQa963URo+mhV30lrsf/U4DgHASSC+ucK+1rBMvPVnZrLa+0Jz5n0YEEPtBNedj+u7ksr2oSNc3SEh0TQEY9u3RY9SmY+0am51NBKj/5mefe3nwc8KA0naKwd8pS8CHqj9iWEXhnPNimJoAXLBwxcUSAzn+TU3GeNjucvb5Bnef9FgPuflMcJmm6HPKUrBFX32GcbbwaOfqLxNbYNIRG0aEH/giWFcgGSU3MFb1iexBoRXCbkmVThpYPtBvmZHTWtOE8QbOLz9+3e/XEkeUrnC/rno1VqTDGr/97Piz/MY9wUEkFfjg8v6vuczPyClTlZk2NTSp4feGem5YhUeFCZmLsENcc9Rdtgo0Mg6NNtvGKDVFyd4HxBWy4s/PeRJc5eqiwkgtbPiUIF0iHsRAZ3yX2y77qY4dLJd2/if7lj38QjlB3+ruam8gGBoA0K5hy80JM8pdMFPRcqvOx9zMlLlYdE0KArUehaY5n5vnnIOwdlOx5+MTXk3i6/lXw645euqZ3cQCCob+7uN5CKp8c5XdALAcLmfVd3Jy9VHhJBF7UInhG2TGZ+6+Mo4hy6Iq2fw4J+6+fozCv/EAj6UlRG2KgI8pTO38R3TL76c8u1zl6qLETn4r96NUX2crmDQXE9m4xstEoy2XFB32uaSd48/iAQ1HwSHZx7jjyl8wW9NMi78TvOXqg8JIKmK14Pn7d9wdSlZ6VTMQUd85jcISt3gUDQVJHN5Cl5OQ6qCySCRobOKCehIzAFLY59Wk02ziAQNDk5eWTITPKUIKgS9k38mbktO68mz4kr6K2IFPJkvEF4qvNaD/KUIKgSJdeD3tkTVZk8J7agF4PXkWfjDEJBC1TcZwSCKmETdHuiT68NKsYAwxf0pO/n5On4gkDQeIEmSeQpQVAlbIJ2TVM3eA1BQb/1OaoqJT8QCLpbID0fG1YCCKqEVdAM6WNmcMgX9K+5Uw6Wfvq47mmVWTkBJyirOxQ8BQJBi1qdUpdTtqAHfV6ZHby89PP7Df5Vl5UTcII6vENBHhBUCdsmvnfVzjExMeQ5ZQvabSNCf3qVuUImpRnjC7OMAcEmXvEOBQVAUCVsgh6wQJ5TtqAN/xRe6mSUmTKxlTuOy0ggqOIdCgqAoEpgrweVR7agfZYhdOTuy0peiLotE8g5BIIq3qGgx/igJx6r3fIjiu+7GqrrQRWQLejv/v2e9vn4rknm4dGsL291PQSCKt6hoMP4oNnB71/fV+9H7QlcDdX1oArIFzRzw/v/SCYVDeqh4mgLH2AFDf8jxoLMLOlNiLcHxokEVxFehC784Tj1Px9pIfxs8YDm77v8Zwua60EVIP+NL4yLcTdDsYJuyVTs00tvQjR/ulmkU13h5SxCN7eo/znxUeHnUzGav+/yn51orgdVQMUmqSjhcTfbypOd6jTfkJuqw/igN/x3mH+rv097AldDdT2oAmoKWti/e66K1MaHQNDtQ8ztq8qOESAdH9QK1U7SgdaVAlZSfN/VEA4eJv8br4CqghYN6eRWIzMSCBq0P733tVDylJSHmfi+kZboYhHF33h51BW0eFhHd7rEnkBQv+LkFWZ/8pRwHFQJ+06Svr/x5tERV1V9wdAQCNqzX2Dmy+3JU4KgStgE1eE3Pv+NJ4aWdmunhkkPQPELgaBZy35Gs/5HnhIEVcImqA6/8U/2+mSx75GSjwuDfnMQzBUEgjI5M+cpkAiq/Buv9dTcef8ChJYNLZ2wzu8H3Hc4gUBQJmfmPAUSQW+Of/iRWXKnzTWfmjsiDvq8u+zIALt9eD5hXAYCQdmcmfMQSATtN/DQ4UFDZOZrfnRfru9hVBg/t+ykIwHGGAuIFpLDTLqdmXNDiAawFfpLhcEy87U/uu9Tv44hT9x9lvNsk7Hu8Dw6AkF1PDPnfpAI2umi0GtsKzOf4tRc9nflbvm42SX2Fv6LRofkVKeOZ+bcDpKnfAzwHT7ce5JcgOTUXFHacpG2IdraUjCsFf+Hm7CCnv0hF6HMU2+TpwRBlRAFfdfGRqWgMrs6+WOGizQK1NqahQHc78zjBF1UIch7V2D1ep3IU4KgSthumjv71Un50ZQSRKokJEgmUxT0M5/3NX/XGOAErXsU7TOtU9XbBkGVsAi6JbRuuxC/NXLz51dP2bixzkbpH1eagv7eZIQbX9wgUBMhc80iVSlBUCVEQT8L/lp4Pdx4p1zAoTYbUXC5qVQFzerd7gLF110OTlAv4Z+vupQgqBKioO2tj475sZ1sRHbSU+WfFU1XUPNr/l/TfN/F4AStlZGR4S38y3AcVhYQVAlR0DrWzVGx0m/9+vIP/KEt6Jf+0yY2Dk9Rtx00CjhBK9khTwmCKiEKGv635e2/zhxT/aK/z/5jUbMps7gGqifN6XDbMe9gBU15TLzgPa+PMwdcLa45PmDvKRUnAw0EjaA63HbMPVhBi4f7PDNrRGC8imG7qQtacH/+V4EvqLicwkDQCKr52gY3huA46C9LXll40EFUOegL2mti0YUgb5VjlhkDGkGJrm34e/0eFdeScg/hTXOqoBf00uPV7o9/23uxhqfxuRoaQUmubVjrO6hDc8ktjOta+PVXcYU+VxhTUIRy8hA60z6av7JT7SThbzvOrX0GoZfG3TX/s4bfX5rZ0k3/rBpVUAtF83xSefsjqvPz4o+Jwzamd757vniar/lx5ss1BIYWFKHfHoxW9QQc18NU0BNHRGLKHM+4XlvYkXpr6F1R8ZuElwcPMVyugTC4oKhoQZ1FXB2xpxG0WbAV++dbbSNFvMqeJHk58p1kn7t3Hz+IvGBeW9/dBrmyYXRBhZ5olzY8PXCBRtCM5qnnRSST767nthenSa+anVO96kNuuoXnQFBkXu07JotpRj2h2sS/+YXcVLl6Fi7r2X9vySezG44EbIMDQRG69mzAel52lnTeSbIzqvPHa4JkrzBzM7gQFKEDkZ1+Zp5UF2gFlXlYuUw986tlI7SrC92iuIATQVHxct/nuBjBiVbQyPKTZOp5WbzI8UQzukVxAS+CInTzZe/5HOypOkdQ1HgnMo8eQbcoLuBHUIR+7x26wfBdUVpBl5efJFfPQ0FtGj7ijo/xkcKToAjteyhiLz7KpThpJwnlHTpp+F9WFvAlKDJvCXtUxSPFXICzBPUUOBMUocKVwTFH8GEuAwRlC3eCInRnab1e5W6LKINrHxsCgrKFTlAX3UOT905gzPcK89bXu+8B2fMxTgIEZQuVoK67hyZ/eYPOu+V2En6q9wv62ld6NtuJgKBsoRLUlffQFK5v0fKD8jdKzZoqvCTJjoPiHEBQtlAJqn18UBaYP3s0cL70UGBKsvAyYINTGiALCMoWKkF1eHSfOn4aXHvk3ddGnvLblbHO34UnRUFQttDtJEnuoSneYX34aX0WLSPj36l+j+0oe0Xzl23rdHHl9aMgKFuoBL01LWGb8CPR/jk30fr46AAmTSPkzgftA2dddOYSHQKCsoVK0MTuqRGrEZIOM+T0gh5/oXbsJwa5MQQEZQuVoL5Z6GrIRdcLKvwtX9nef1K5Ue9dAQjKFrrDTNcQSutlNoCgAifH12237LorlnwXMHgYW6gEfdNrCjIPjK4omeyqghbtGlgzdpOLHz8Pg4exhW4v/thOQYutYyRTXVjQrNVdayZ8rPW65rw5bbvQHkKFwcPYwuHFIhiuLOtce/AOTX9Hh/X6YVfYWrrF6z54mIfhfoIKXFr6aI249aqvN8+vdhuhrx+hW7beg4d5Gm4pqEBGWmz1Lm/9qeo718UHHNDeiabz4GEeh7sKKnB7xzC/sHFfquiQRryPCoa+TLdUGkGlJz5sGKSersGNBRUwH5nVrnrPd0gPkJ5sFlpH0/NCNzf6v0jbBao0gkpPfOQOcsGZOYPh3oKKXN88LDDo6Q/+JYk1n9V0mcmReocKd9a1bpppBJWe+DB/bLm24ale2lNyj/sLKnJ6Sd/aTV/YdEmn9NPEJ0z032R5T3WYSf7Ex4pntafkHs8QVKD46KJetR8YtlpmaBlqps8QXuI2W97TCKpw4gMEVcKdBBUp/mXJgHq+feZ/x/hs0zH/73I3+VufHUe1Fy9/4gMEVcLdBLXwz8YxD1eJeGHVrwwvf9rZrHJH2wjHOgweBoIq4ZaCiuQfeDuhcbWOo9ecYP7oAR3GZgJBlXBbQS1kfb1gwANVHnx2ybeZDLOCoGzxYEEt5Hy/7Lm294fETFx3lM2ADzoMHgaCKuEBgloo/nPH3AEtKzfslZz2A+0VpTpcsAyCKuEpglop/H37a0kP1qjTfuhrW47laM0CgrIFBJVyaV/qhD7NK/t2SJqx5rvzxWq/DoKyBQRV4MI3q6YmdPCv1LDL0zNWf32G+JITEJQtIKhj8n//InVqUlT9Sr6RvUbOXfPlSdwDcUBQtoCgZJj/PbzznUmDH21SvUrDjnGj56R9evgf2d1+EJQtIKhabv/x3aa3Jw+NiQi4t3rjDrHPTlm07rND50qepAWCsgUEpeDm6e+2vzt7TEK3iKBO9mkgKFtAUMaAoGwBQRkDgrKFyyHAjQwIyhZOhwA3LiAoW5gOAW7+Zq9IdEMWLeMUEJQtTIcAv90jWiTkARYt4xQQlC16DAEOBWUL1FMJtUOA24CCsgXqqYTGw0xQULZAPZUAQdUDgrIFBGUMCMoWPQTdbPJgKrB/WjjUUwmNgpZwuwpRWM79RGHZ1YjCMmsQhd2oRRR2zYso7Ko3URglJPV8YRk25AzBcepeO7Eh+6Lwado4eiy1lfWD8GmUAEFBUGVAUAkgKAgqAQQFQZUBQSWAoCCoBBAUBFXGDQQtep4orPAFlmEFI4jC7owkCssnDHuRKIwSknqu+Q4bkvkKPs3Ck9iQv+fg00zDD2x9FP8bpQitoACgKyAoYGhAUMDQgKCAoQFBAUMDggKGBgQFDA0IChgaEBQwNJSCbmocugAXszDEL+kWSeTrCQQJD0b4JhXiw+YH1B1VjAvLEJ/LbY1xFGkJI14LKrD5iZqBLyRBFfEVJCgffenoBL3i83dmY8y52GO1MzK7TyeIPFw9AZ8wL+jnvC5p2LBjfhk5kVsxYQubB9tXwVGkJYx4LajArxdJM/CFJKgivoIE5WNQOjpB0xIRmj7FccxOYX5qAj4yu01KAj7h9jiEcm9hw44HZt/psA0T9kVqsH0VHEVawkjXgg5sfpJmEBSSoIr4ChKUj0Hp6ASdPRmhlUOxYVcjtuEjkzZuTMAnXNAr0icuC59t7H3Ve5lxYeeD7avgMFIMI10LOkjyY5tBUEiSKuIrSFA++tLRCTpLXGoSJsi8KmQVPnL9U0ioKzZsWtjZnL5TsGHfRB461W0tLkwsnzXGYaSlymRrQQk+P74ZJIUkqCJBBQnKR186OkHFJc6c5DimKKHnRYLIWL/gOlW6Y8OWjkRoXSw2bMIsYTvWDxcmls8a4zBSDCNcC0qw+QmaQVJIgioSVJCgfPSloxP0sv+V3HBMz3dbFzNhpPCLjw072/iv7JgUbNiaVlezBk/HhYnls8Y4jBTDyNeCBmx+smZgC0lQRYIKEpSPvnSUh5k2Ng/HHTt4qWKlSpUSSSKFuuLD0kL8n83HhhVP8K/71G1cmGUDZI1xFCmGka8FFbj8ZM3AFxJfRYIKEpSPvnRwoB4wNCAoYGhAUMDQgKCAoQFBAUMDggKGBgQFDA0IChgaEBQwNCAoYGhAUMDQgKCAoQFBAUMDggKGBgQFDA0IChgaEBQwNCAoYGhAUMDQgKCAoQFBAUNjXEHPdOm52NVtAFyOcQWd+A3q6Oo2AC7HuIJmF/8T6+o2AC7H6YKaant5eb2Bil9vXqfjHoROmN4SJl6pMAUVz2nmFXW8TOTOmAuOU6XLCCw3DeAY5wuaY/nxXPS5on1BW9GJKp2FT2lVpqAxT1wuTg3KLQncM74Ykwor6MyKFSve01p4Y55ZL3CW2TaeqoWUL7SvAuBEXCToCZ9s4fWr+kUnWjS8jlDv3lMu1L4pTIn9tiTw6R79Esp8rzjD+q/0E9lf0MkbhJftzS5d7PildTzVM22bv46uxrNaH0BfXCTokmfEV3PdcydajlmL8hrMnbKlR0nIJv+fCurvsX96K6TB2KIDfVoli//Ms0NDxlk+IYuMlpldtyMUuc/6Xiro6cHi64RXEVo8xjqe6tgvUbvbY37RfUUNAXmHSkJqctlPLuw4uaQPGopemWX50O6rEy2/7od2PZMyZdGwkpDlR19O+6/Z9uHr5pdz4hccqHIKif92t8zM67JafIfEsllnpiWiP0OLre8tpQw2iRQK78w9/hIjV7e5nvFYf+tYVmfatZ73O9kzmvmHuENVypZw8dWDBbWUbKH1gdqhJ0+0LAzKe/7jlCnre4kTTpwQXvLRlAaH7PGvBkdFhfc/IBRW/Dd+IUIfDhHfIbFs1pk3fe7MnWELlJRyzwDLj6IRvmGJz5QM+IsSL+q7koaBqENV2nMSyfxDfPV0QQ8G5gmvR/wKTrREiZ82up0y5bSXWMRuKy1BM/qXxKfMFPwqOBAjCCr8e2URQpsTxXdILJt1Juq1p+VftkBJKYdssPzIvy2YnlIi6HfTzBNbdP6fzmtqBAg6VJb+kqV7lB3rF/yRUOaCFwNaDUveLXTTk1PNY/28++enx1rnuWINnL5A60Ynvu8l86HGq5Eg6JaHeqGUKSix31svTYMAACAASURBVDXzFu+r4szcwA9K4o+EXS/ou8Qu6CcR2fldV5YIap2JNkR1tgfevYnPr3ND+ANxqmBfo+zLoX/bBTXHZp+PRltnOnnNXQFBh0rsL1m7R8v6Fe8fIpR5+aO518Nsgp6Myi1osy891jrPFWvg9AVaBS2YHubTdgcSBc2u9L4oaMGERj4dfrTMnBc/uvQLSxsFDC/5C2qeUT/0pcISQa0zUU7lVfbAu/+Cfiz2BfJM58yT/JptRXZBN70n7Da1jvpX71U1AAQdKrG/ZO0e/Vxv4j6zUOYBQqleswmKMvbM8dqdHmud54o1cMVCMVz3Ot3nt6WuboVbQNChEn/bbV2lmyt6tzcfiBm4DaH5FkFHph5ssuBArCCodZ4r1sAFy8QxdgjaVHWzq1vhFhB0qERBrd2jWWPN16veOBCzIjovq3lyetOC6yGpc0ehi8Efp8da57liDVywTBy3C4SOo6sb4R4QdKgs/SVL9+jvtnWD3xZ3kkYGtZ77Rn5So+4zNp9t3fSJ+ZHpsdZ5rlgDVywUAEgBQQFDgxX00JShSZN1fDQQADgCJ+j8xtNXrpwZ/oZTGgMAUnCChmSJr7nhTmgKAJQHJ2hDywnBKy2c0BQAKA9O0I2+iZMnJ/l/6JTGAIAU7E7SlbRZs1dekU79Z54Hs5D9AWuopxIa9+JXtEz2XEL3yBWKCqinEhr34lc8S/k/wjPddBAU6qmA2r342wPjRMI8ebceBGULlaDSvXjzp5tFOjWwfrwxM3FeDm0DeQMEZQuVoAp78YPDLD+yG764Likin6Z1HAKCsoVKUIW9eJugqQOFly47NDeNT0BQttAJKo9N0OnibROj3tGWg1tAULboKOinkbdisxr+qC0Ht4CgbKEStFmwFclkm6BoeMg9/pO1N41PQFC2UAma0Tz1vIhksl1Q9Gvtb6VfcXtAULbQbeLflB1lq0RQ1PKYhibxDQjKFh37oAJd9mrLwDEgKFv0FTR+g7YMHAOCskVfQUd63kDyIChb9BV0+jRtGTiGRtCClTlZk2NTpCffQFAlqAV9Z4S2DBxDI+jgLrlP9l0XM1wyGQRVglrQD/s7inNLaAT1zUPeOSi7nv2zed9ekRE04xuatyQv5fmKHX0F/fK/2jJwDI2grY+jiHPoSkn5bveMFqnrT9GeAR3nDWqcSZHAxegr6M/NtWXgGBpBDwbF9WwystEqyeTSeqrnpwcKEBq2QHsCV6OvoBfrasvAMVR78XnbF0xdelY6lUbQTWIna4W0V8sR+gp6516XDBvpSnQ4zEQj6MkgoQPaj+PhKvUVFFXnuPejDYMJiiY0GNGhA8dXjessaP0z2lLwi9EERYeW7uJ5uEqdBX3ogLYU/GI4QTlHZ0FjPtGWgl9AULboLOiQNG0p+AUEZYvOgo6bry0Fv4CgbNFZ0JQJ2lLwCwjKFp0FTX1aWwp+AUHZQieo/OBhZQq6o5e2ZvELCMoWKkEVBg8rU9D97bU2jFdAULYwHTzMRpmCnm6krVn8AoKyhengYTbKFPRaLW3N4hcQlC06Dh4mYv5PgdaWcQoIyhamg4fdmWoZFDc8sDTC5zJN6zgEBGULlaDFW7eb1/Sbftv+uXCRZVjxiODSkKYnqJrHHyAoW6gEHRXZodfj2wcPlUwuW9BO6draxS0gKFuoBPXLuVP9NMqS3jJTtqBPetozaqgF7V5uCgiqBE7QgIzsSofRlXqSyWULOmGuxobxCo2gCSJVEhIkk0FQJbAH6v0Cx0WmdEqWTC5b0FTp9t/doRF0fvWUjRvrbNxo/0x6V+eG1gEDpEMMugt0e/FHf0HfTFgjvWC7rKDpj2hrF7dQbeIPtdmIgks/2u6Lj27o+Fu76397YXrrIorlGhidLxZBF/y05eAWuj5odtJT5QuG28QnrRZemh2nWa5x0VtQc9VsbUl4hXYnaX35YURwgvbfJLw8dIhuuUZFb0FRcw8bw9YFh5nWPfgvWh/K8Z2bjtBd0L6btSXhFVccB51ZvWobd/07oLugnnacySUH6otvMV+oUdBdUE87zgRnktiiu6CedpwJBGWL7oJ62nEmEJQtugvqaceZQFC26C4oan5UWxZOAUHZor+gHnacCQRli/6CJnvWcSYQlC36C5r6lLYsnAKCskV/QQ9GaMvCKSAoW/QXNL+q+57mkAEEZYv+gqKHv9OWhk9AULY4QdDRHD8ERT0gKFucIOj6J7Wl4RMQlC1OEPTPQIU4t4RG0FvTErYJPxIlk0FQJURBk21Ih7BzgLSgPhe1tIxTaARN7J4asRqhSpLJIKgSoqCpqanDmqS83uw1uQDs+KAWYrbTtJAzqB4mm4WuhlwsI+j5v0R6N2bQLl4h2cS3uoFQZmuZ+fjxQS3Mkt6X7M7QCBpyDaG0XuYSQW81qS9SzZdFwziFRFD/XITygmXm48cHtfB5Z62t4xAaQd/0moLMA6MrSibDJl4Jm6AjHt26LXqUzHz8+KACn3QI/c857Q3kDZygsXbkZh7biVDR1jGSqSCoEjZBC5YPGLhCbpxP/PigCH0XuOdPvwaeM0ooTtB0O+QpQVAlbIIWLehfsKRQLkAyPmjR6uUibUPLhLywBKGpfm5607YMBJv4/H27d38cSZ4SBFXCJujL8eFFfZ+TmV+wMidrcmxKyR3ZeS8OF2lUdjSxIWsQOlg1nUVbuYBA0KfbeMUGqTi9BoIqYRO0fk4UKpAOYScyuEvuk33XxQyXTi5b0E2Rl9EnFX+layVHEAjaoGDOwQs9yVOCoErYBA26EoWuyR2L881D3jko29HwiwjNqF4nLOYdqkbyBIGgvrm730BNyVOCoErYBF3UInhG2DKZ+a2Po4hz6Iq0fpKCFmagrY/TtJErCAR9KSojbJSKq2RBUCXs5+K/ejXlJ7n5B4PiejYZ2WiVZHL5guZU95hbOwkENZ9EB+eeI08JgiphEzRdOgBoCXnbF0xdelY6Vaagj32kvml8QiBoqoiKWwlL6vnjup/t0/LmdHjMI+5GJBE0MnRGOQkdISNoaj81CXiGQNDk5OSRITPJU9rqaR4Ulhgy2jZtaK/9nzTeoKF9vEG0iT8zt2Xn1eQ5ZQTNqX1VVbP4hfBU57Ue5Clt9dzRthDlhn1veZ9bPRehvVFqG8chZNeD3tkTVZk8p1yfachCFY3iGUJBC1Q8xNRWz2mzhJdRiy3vr/oIL8el55jdERJBtyf69NqQQ55TTtBvmqlqFr8QCBov0CSJPKWtnmninQlRn1intViDCoaO09A+3iARtGvaTVU5Zfc6w35UlYNbCATdLZCuYkBkWz1vNx3yXr/2tlPOJ5rU9+7lCbfLEgiaofZyTllBU4ZuTHPXR6WUBScoxR0KOW8+t7TE6+I/PeMpqASCFrU6pS6nrKDf3xMzxOdjdYl4BCeowzsU5IHjoErYNvG9q3aOiYkhzylb0L6dpqDDcif03QyCTbziHQoKgKBK2AQ9YIE8p2xBG35b5yaqk6GmbVxCIKjiHQoKgKBKYK8HlUe2oN0+HDL3Ly+zmrZxCYGgincoEN6E6AQubf9axX+4nlBdD6qAbEEP+gyrFrxcTdP4hEBQxTsUCG9C1J+tPn0ebnnd2UuVhep6UAXkC/rX7Gae8MAPrKDhf8RYkJklvQkx/1XLDn94DeFlL0Knk530c3zlp5P3vvyS05bn6GcbmutBFVD6jb9QR9UpfT7BCrolU7FPL70JsfCteSIRXsLLQYT+N89JP8f6CD/3d3Da8hz9bEtzPagCipuk2R4wShPZqU7zDbmpJDchOoPsmpkILR/k5KXKQ3U9qAKKBc0N/VpFGj4hEHT7EHP7qovl5khuQrTh/D7ouAdTp/v8jI9zAoSDh8n/xiugXNCdDd3+7ByBoEH703tfC8WGleB8Qc2bn5lwxtkLlYfoYhHF33h5HBR0yAjyNHxCIKhfcfIKsz95SjgOqoR9J4ndb3xm8GfkebiEQNCe/QIzX25PnhIEVcImKMvf+C/rWa9x2D1xgXtewkwgaNayn9Gs/5GnBEGVsAnK9Dd+Rvs7wmtyi5Tn6/5NnpIfCARlcmbOUyARVPk3XsOpOXO/FxHK8MpEaI5b9kcJBGVyZs5TIBH05viHH5l1W2a+plNzmQ8sQz8+JLz5vKuahvICgaBszsx5CCSC9ht46PCgITLzCccHlXA2YFNW7QsIjR1P2kieIDnMxOjMnEdANICt0F8qDJaZTzQ+aHl+9vlyScBz3cKMcTUCYwgEZXZmzhMgEbTTRYTOt5WZr/XU3Lc+X5x8b9sd8lZyBMmpTlZn5jwBkqd8DPAdPtx7klyA9NTc/r0i0Q1xS93vvUNtQ3kBK+jZH3KFjvipt8lTgqBKiIK+a2OjUlD30re3u0eL1K2LXeyPvqvI28gVOEEXVQjy3hVYvV4n8pQgqBK2m+bOfnVSfgjvBJEqCQmSySQFPdN4tHteXY8TtO5RtM+0TnG0KzlAUCUsgm4JrdsuxG+N3Pz51VM2bqyzUfrHlaigV9o+6ZYj3uEErYmQuWaRqpQgqBKioJ8Fi5fIHW68Uy7gUJuNKLjcVLKC5g8PO0kSxxk4Qb2EfyqfewSCKiEK2v5by9sf28lGZCc95VduImlBV3m74T1KOEFrZWRkeAv/5O5vhWd1lgcraB3r5qhY6bd+ffkH/hAX9HRkr3/l52wdmPQFYRKDgRO0kh2ZefCszvJgBQ23XtLxry5jqhdM9Xlfbl9pfosP3g9dT75EA8H0WZ1WQFAlREFTHhP3ZfL6qB9wlYjjD3Y6Xn6qj/Bb8X0r8iwGguWzOm2AoEqIghYP93lm1ojAeBXPilNV0OLlviOlV4beqSL0K674qMhiHFg+q/NWDZMFbxYN4xSC46C/LHll4UE1OVX+xl9/qc4cyeijD21AaEEfVVmMAo2g8KzO8hDeNKcK1QX9a5Dvgrvupjse3CGyiYprzg0ElaACMveqgaBKOEtQhH7r7zOz7MVNud8e5PT5s7SCyjzDEwRVwnmCIvT7sNojfte2PEMBgrLFMIIidGmq7+M71J0ENCC0gsqcuwBBlXCuoAjlr+sQMOVPjV82CLSCygCCKuFsQQV+G+fb4d1r2r/vckBQthhNUIQKPx1Ys/sqbm8HAUHZYjxBBW592K/GY0v5fCYICMoWQwoqcHvbkDqtJ3/P3z4TCMoWowoqUPTdpNa1+i37g0kypwGCssXAgopcWTekXmDSKo727EFQthhcUJE/lg+q5x/31iE+Ti2BoGzhQFCRc2ufb3F/x3GbjD/IPQjKFk4EFcn+KqVPoNdjyZvPqLon0sl4tKDbx8xRuEVCMxwJauHyp7N6h1Tr8Px7+zP1WwgNnizo6DZvjfFjvJHjTVALN/ctfvbh+4Mef+X9g+oeFO4EPFjQy945CM1lPKgmnaCufHSf+ewn84Y8WN2v8/A3Pvm9dAfqZELbZ115iJ9a0O7lpvAi6AFx/K7dj7NNSiWoER7dd/7LZWMeb3Bfg24j3txx4ja6UPedAzMaunBICBpBKUZqMQKZXv9DaORktkmpBNU2PqgeFPy+a/GYJ5re5xvSeNLyPf9VHEhKf2gEpRmpxQgs9xvSqSXjfQMqQTWOD6ojF596cs4zj9X4j0+bvmPe3PT9P85/ZC/VJp5mpBYjcGb1LtYVpxLUKI/uK8PXD1xBZ/x/v/TjlkUvxbUPuNf/odgX567+/FenXcFH1we9e6SWojXLRdrWp20Ux9DtJEnHB826IRL/AIOGaeW12s28V5V8Krp48KPFk5IeC69dKbhd7IgZ7+74/qy+D7uj3UkqO1JL3sjhIo1UjGfvdlAJKh1L6Fa9WiL3uvSO9qxfc+Um5/39w46l05+LbRdauUpo255PT1q4bvfRf2RDqYC7OtlCJSinYwnlnPl+Z+qcMQldWwbcVzW4zeMJo2cu2fj5T38z2feHm+bYQiWoG4wllHPux8/WLnp1RHyXVkH3/8e3SfsnksbMeHvdp/t/vSj34B08IKg6brz/tsNROOkOM7nZWEL5l3777uPVi6a9mNCjfdO6le/1afxQ17jh4+e8s3bnvqN/XSe6elrvuzovDvQLe49uEUbij7qDR9WVHR3ZBpWg0rGEbHArqIT8y6cP7tm0/PXJIxN7dmpVv9Y9Vf0aR0b3SXpx0rxla7fvPXzqvMyJVp1PdZoffvXykXDpcRN+GbBYkLS2g6t/6Pbi3W0soYPT5/3jYHbOv6cP7922+p3Xkp9P7B0d+UBADdP9vvUjO3WPeybFHqOzoH+Jh5w+6s18Ga6ixS/CS5CDUY64vFhEL94NmfGSt5onGCGUfenPI/s+27xiu32CzoKeFt/v6sF8Ga7iSaG7craWg84TCFpKca1zCK15gi6JzoIWN11a9G/7FcyX4SpO+j43OdDR6oCgpVwWT+KcpjzLoPfldqcfqVJzuhs9wefy23OPOpoPgpZi9hU6RO/E0SXR/3rQPOYLMDIgaBm2+g6PC5A5lYPlfy8+8aptj96DL1jWBRC0LGff+yBLw9cuBcz69PnW+Zb3IChbQFAGvDlSeInabXkPgrIFBGXA+AXCy1OrLO9BULaAoAzY0eYWuhhw2vIeBGULCMqCUXWjfN6xvgVB2QKCMuHvffZnPYGgbAFBGUMjaMHKnKzJsSn5kslQTyVAUPXQCDq4S+6TfdfFDJdOhnoqAIKqh+phsnnIOwdlS29BgnoqAYKqh0bQ1sdRxDl0RVo+qKcSIKh6aAQ9GBTXs8nIRqvsn2+1ixTxqsugXbwCgjKGai8+b/uCqUvLDA934ojIxHjqVvELCMoYHW47XvEsXUquAUEZo8NdnSCoEiCoekBQtoCgjNHhtmMQVAkQVD06nOoEQZUAQdUDgrIFBGUMCMoWfseoNyggKFu4H6PeaICgbGE6Rv0tf8v4oJX8lL/i9oCgbGE7Rn22ZYTlRUMZNIxXQFC26DFGPRSULVBPJVSPUW8FCsoWqKcSGg8zQUHZAvVUAgRVDwjKFj0ETa0fZ+XJmvWIqM5/mLdtleP8PtdWNFb1rOOLDQmogU9T2w8bUrcWPk1Nf2yIX8M4Rzisp0ZBMzbbWPefl0l4kSxs5L1kYZWIwkaQhb1QmSjs+ar2dd7M/jFMqurZsgs2ZGhNfJoGsdiQuHr4NL6DsCHdIzY7xFE9NQpawu0qRGE59xOFZVcjCsusQRR2oxZR2DUvorCr3kRhlJDU84Vl2JAzDfFpeu3EhuyLwqdpU+40YznWD8KnUQIEBUGVAUElgKAgqAQQFARVBgSVAIKCoBJoBb3jTxSWF0AUlkv20N/bgURht4KJwrJDiMIynfLEbJJ6jl2NDTnfEp8mfi825NDj+DSdfsWGfDQMn0YJWkHRHQhjCsFSChw8tU1FGpL1YZPGTFE6akEBQE9AUMDQgKCAoQFBAUMDggKGBgQFDA0IChgaEBQwNJSCbmocugAXszDEL+kWSeTrCQQJD0b4JhXiw+YH1B1VjAvLaIbsS3QUaQkjXgsqsPmJmoEvJEEV8RUkKB996egEveLzd2ZjzLnYY7UzMrtPJ4g8XD0BnzAv6Oe8LmnYsGN+GTmRWzFhC5sH21fBUaQljHgtqMCvF0kz8IUkqCK+ggTlY1A6OkHTEhGaPsVxzE5hfmoCPjK7TUoCPuH2OIRyb2HDjgdm3+mwDRP2RWqwfRUcRVrCSNeCDmx+kmYQFJKgivgKEpSPQenoBJ09GaGV+DEcrkZsw0cmbdyYgE+4oFekT1wWPtvY+6r3MuPCzgfbV8FhpBhGuhZ0kOTHNoOgkCRVxFeQoHz0paMTdJa41CRMkHlVyCp85PqnkFBXbNi0sLM5fadgw76JPHSq21pcmFg+a4zDSEuVydaCEnx+fDNICklQRYIKEpSPvnR0gopLnDnJcUxRQs+LBJGxfsF1qnTHhi0didC6WGzYhFnCdqwfLkwsnzXGYaQYRrgWlGDzEzSDpJAEVSSoIEH56EtHJ+hl/yu54Zie77YuZsJI4RcfG3a28V/ZMSnYsDWtrmYNno4LE8tnjXEYKYaRrwUN2PxkzcAWkqCKBBUkKB996SgPM21sHo47dvBSxUqVKiWSRAp1xYelhfg/m48NK57gX/ep27gwywbIGuMoUgwjXwsqcPnJmoEvJL6KBBUkKB996eBAPWBoQFDA0ICggKEBQQFDA4IChgYEBQwNCAoYGhAUMDQgKGBoQFDA0ICggKEBQQFDA4IChgYEBQwNCAoYGhAUMDQgKGBoQFDA0ICggKEBQQFDA4IChgYEBQyNYQU906XnYle3AXA9hhV04jeoo6vbALgepwtaaKpd22dwlrDk2l5eXm+g4teb1+m4R5hRsbZXtY5/lMRlF/8T6zhTusx8uWkAz7hA0DyU9dzjwpJzLJ+fiz5XtC9oqyBoDrqZ8ERp4M6YC44z4QW1jpxqQRzTtf49FStutX5M+ULzCgBOxSWConyf32yCnvDJFl6/ql8kCop2h5bE7Rl/1wP/ijOs/0o/4QW1jpxqeSuO6ZrvJ44RdKZt89fR1Xhm6wPoi2sERbEbbYIueUZ8Ndc9JwqaPbi78GGT/08F9fc83aNfgjjrrZAGY4sO9GmVLP4zzw4NGWf5hCwyWmZ23Y5Q5D7r+7sEtY6cKr6zjOl6ImRQm9eLx36J2t0e84tzV9plkHaopKQml/3kyo6TiwR9ZpGlZKHolVmWqe2+QhW9fau0OS28X3705bT/mm3hXze/nBO/4ECVU0j8t7tlZl6X1eI7JJbNOjMtEf0ZWmx9byllsEmkUIy5GrFN/GEZ03Xvo3+ea7XqTLvW835/3tlr7SqIO1QlbAkXXz1e0NgNtpItHGGZGnrSsom3kI+mNDhkD381OCoqvP+BzgiJ/8YvROjDIeI7JJbNOvOmz525M2yBd5fSMnIqso/pKrI2TnxNvKjnGhoJkg5Vac9JJNPyZ9XTBb3j+6utZAcDRV+P+BWUCorQjP4lb1NmIlRUcCBGEFT498oihDYniu+QWDbrTNRrT8u/bIF3ldI6ciqyj+n6o7Bd3zpQ+PjdNPPEFp3/p/uqGgB8h8rSX7J0j7KFMn0klLngxYBWw5J3C9305FTzWD/v/vnpsdZ5rsAlgt4a2bVkoxPf95L5UOPVqIyguYEflIQfCbte0HeJXdBPIrLzu64sEdQ6E22I6mwPvGsTbx05tfhUAbIMmbml5Y38x9cI/z+x2eej0daZzlxrV4HvUIn9JWv3aFm/4v1DhDIvfzT3ephN0JNRuQVt9qXHWue5AhcI6uPjOyizRNCC6WE+bXegsoLOix9dGr+0UcDwkr+g5hn1Q18qLBHUOhPlVF5lD7zrL6h15NQ80zlkEdQ81rfBpGJhJ+w9hCa0jvpX7zU1AvgOldhfsnaPfq43cZ9ZKPMAoYv6mk1QlLFnjtfu9FjrPFdgwDNJ171O9/ltqatb4R7gO1Tib7utq3RzRe/25gMxA4X9yvkWQUemHmyy4ECsIKh1nivWwICCjh2CNlXd7OpWuAf4DpUoqLV7NGus+XrVGwdiVkTnZTVPTm9acD0kde4odDH44/RY6zxXrIEBBb0t9BmL8WEAAfgOlaW/ZOke/d22bvDb4k7SyKDWc9/IT2rUfcbms62bPjE/Mj3WOs8VGFBQACgFBAUMDVbQQ1OGJk3W8dFAAOAInKDzG09fuXJm+BtOaQwASMEJGpIlvuaGO6EpAFAenKANLWcEr7RwQlMAoDw4QTf6Jk6enOT/oVMaAwBSsDtJV9JmzV55RTq1+OxfnosOl5lAPZXQuBe/tlp9z6XyPhoXZYF6KqFxL37Fs5T/IzzTbQ/zlFBPJdTuxRevXS4S141FyzgFBGULlaDSvfi8kcNFGtWzff5x3c+UzeMPEJQtVIIq7MUPDrP8MA8KSwwZXf5b7g0IyhYqQRX24m2C7mhbiHLDvtfeNi4BQdlCJ6g8NkGnzSqKR6M8bQwlEJQtOgqa9qT5PwVRn2jLwS0gKFuoBG0WbEUy2Sbo7aZDqj3RvpCicTwCgrKFStCM5qnnRSSTbYKinDdrTMunaBuXgKBsodvEvyk7zJZdUITa/qChSXxDI+itaQniWCeJkskgqBJ0fVCB7p9qy8AxNIImdk+NWI1QJclkEFQJakET1mnLwDE0gvpmoashF0sFzZ+SLNLxIRYN4xR9BR3lmpv9XAmNoCHXEErrZS4RtHDxPJGIEBYN4xR9BZ02Q1sGjqER9E2vKcg8MLqiZHJpPT0QfQVdNEZbBo6h2os/thOhoq3SooGgSlALumawtgwco8NhJrcW9NbWNQ6vSdZX0I/lRkF1b0BQVfwv+InBPtscBOgr6P722jJwDAiqisGvI3Tcx8HIY/oK+psbl1YBEFQVLcQHAgT9oxygr6CXfLVl4BgQVBW9VyN0sYaDCzb0FfTOf7Rl4BgQVBU/+Uxc0MjR0DT6Coqq5jiIc0tAUHWcnfFKuqP5Ogtaz0Hvwj0BQdmis6AtPO6uORCULToLGvW1thT84ixBf1+2+ibzJRkQnQXt4+gYrFviJEE3+r0Q73+G+aKMh86CDkvVloJfnCOouc5JhN6JY74o46GzoK/M15aCX5wj6MUA4eVkE+aLMh50gsoPHlamoHMnaWsWvzhH0OKa5xBa2Zv5oowHlaAKg4eVKei7z2ltGK84qQ/6XtDUUT7HmS/KeDAdPMxGmYJ+2B95GM7ai/9x5hse8VhmpoOH2ShT0C+itTWLX+A4KFt0HDxM5HCE1obxCgjKFqaDh+W/arkLMTywZMpfoVSt4xAQlC1UghZv3W5e02/6bfvnwreldyHerEHZPu4AQdlCJeioyA69Ht8+eKhkcpmCFv8fjM1EDQiqBE5Qv5w71U+jLH/J5LIFrX1NY8N4BQRlC5WgARnZlQ6jK/Ukk8sWtOEfGhvGKyAoW+gO1PsFjotM6ZQsmVy2oA8e1NgwXgFB2UK3F3/03pxPHQAAEp5JREFUF/TNhDXFkqllC9ptt7Z2cQsIyhadLxZBg1dry8EtIChb9BZ0qqeNzgSCskVvQd+XHoNyd0BQtugt6N5HteXgFhCULXoLeqa+thzcAoKyRW9B71Qq0paEV0BQtugtKKqnwwPUjQwIyhbdBe3wrbYkvEIjqPS5U7faRop4ed4IV6XoLmjCWm1JeIVG0HLPnfr1iEiPxgzaxSu6CzpllrYkvEK1icc9d8oD0V3QFcO0JeEV6IOyRXdBv+iiLQmvgKBs0V3Q3xtqS8IrIChbdBc0/z7pxU7uDQjKFt0FRf7ShyG7NyAoW/QXtN1+bVk4BQRli/6CDvxAWxZOAUHZor+gk+Zoy8IpIChb9Bf0Pc962jkIyhasoMk2HD0qRMLdBU3voK1lnAKCsgUraGpq6rAmKa83e00uADs+qMCt++/QNZEvQFC2kGziW91AKLO1zHz8+KAiLQ/RNJA3QFC2kAjqn4tQXrDMfPz4oCIJr3jS07xwgsbaIU8JgiphE3TEo1u3RY+SmY8fHxSh4iE1vHy30zSRL3CCptshTwmCKmETtGD5gIErCmTm48cHRWj5Y8ca/Oxzna6VHEGwic/ft3v3x5HkKUFQJWyCFi3oX7BEdpQ6yfigt3tEi9StWyYkYb251uXovQyaygcEgj7dxis2aAF5ShBUCZugL8eHF/WVexpCwcqcrMmxKfn2z+Zv9opEl72A6aXXUbedTTxhuH8rBII2KJhz8EJP8pQgqBI2QevnRKEC6RB2IoO75D7Zd13McOnksgX91Xfp4OaPes4VTQSC+ubufgM1JU8JgiphEzToShS6JndfjG8e8s5B2Y6GX0To2ODWodlUbeQKAkFfisoIG6Vi8H4QVAmboItaBM8IWyYzv/VxFHEOXZHWT1rQ6zU86N54AkHNJ9HBuefIU4KgStjPxX/1aspPcvMPBsX1bDKy0SrJ5HIFffhzja3jEAJBU0U2k6cEQZWwCZqu2IPM275g6tKz0qnlCvpOgpam8QmBoMnJySNDZpKnBEGVsAkaGTqjnISOKFfQ6zUz1bWKYwhPdV7rQZ4SBFXCvok/M7dlZxUD0ZYvaJ+V6lrFMYSCFjQiTwmCKlFyPeidPVGVyXOWL+iOKDVt4hoCQeMFmiSRpwRBlbAJuj3Rp9cGFRd8lC9ogc9fKtvFLQSC7hZIz8eGlQCCKmETtGvaTVU5ZQo6Tu5aE7cEJyj1BeAeBoGgGdLHzOCQKegN35/faFp/tAccsMcJ6vACcHlAUCWsgha1OqUup1xBl9Vve+zPxIHqEvEIwSZe8QJwBez1LNw0e6dZY7O4hWQT37tq55iYGPKccoIWVxX+ZORXy1PVOB4hEFTxAnAFbPUseCT61baxnmYoiaAHLJDnlN0k1ff+CxXXcP9tPIGgiheAO77Ha303YWsWKTtAoxtDdz2oPLKCToxsfG2mBzzyg0BQxQvAHd/jNVUcaXX029Qt5Auq60EVkBU07+n/VOziAcM0YQUN/yPGgsyscvd4Zd0QiW8kvBSgDY/duJHRUkhvvmH57Bk/u9JcD6qAwl5n/sB2V8mT8ApW0C2Zil0m6T1et/xridxbUXh5HRW2+r/7/u/eeQjtrWX57Bk/G9NcD6qA0mER86sNfiPPwilkpzrNN+SmYu7xKtr22i5P20eiux5UAeXjdmvrLHP3ChMIun2IuX3VxXJzJPd42aA7Dnr+019ovu5qqK4HVcBBQU8/2P2cikwcQiBo0P703tdCyVNSCfq2T0xovNPuuLnFfEmEg4fJb5IUcFTQgtfqzFVxGpo/CAT1K05eYfYnT0kj6Nm6/6KCLqu0J1DD8TZVq01lvIkkulhEcZMkj+OCnoutv9mNt/MEgvbsF5j5cnvylDSCbokTXt5TcQiGgoIG69DltmlskxLdNMd4k/RV63b7Ck666R49gaBZy35Gs1Q8IZJG0B9aCX8MXp6tPYEKfm4lvGzvwzYpiaDMN0nF6/3vC6k9RMWxf34gEJTJiQ9Civ/bf+OkgH+1J1DBqQeEl/WMr7cgEZT9Jinf/+Wgxzu8SZ6SHwgEZXLig5TcNwckX6T4vgqKH5xy4duGn7JNSiKo8iaJZHxQOY5GoDtLvb1/IGwkTxAIyujEh/G4mBAQsZ5xThJBb45/+JFZt2Xmk40PKsO5oGKEVrSt/+iXxA3lBZLDTIxOfHgEJIL2G3jo8KAhMvPJxgeV4/GkHzYGfFu4tumD29xsVBwCQdmd+PAAiAawFTr0hcEy80nGB5Una3K7nl8LP4s/ejjsfbc6LkpyqpPdiQ/3h0TQTkIn+3xbmfkk44NiSe/u/5qa0wAGByvo2R9yEco8peKyubvr+cPcd9XdIsY3JE/5GOA7fLj3JLkAybnjWwHWq2981LXhlyG1R/2p7ivGBSfoogpB3rsCq9frRJ7yLkFTGkweXO8fTU3jEqyg79rYqBTUvcx72/WLD6htxb+TvPt8o/ZLxgQnaN2jaJ9pnaqOd1lBs2oKfw9mP6+lZXxCctPc2a9Oyl3/jVCCSJUE6chLWvpMt98Na73KHTqjOEFrImSuqW60v7L1PCoO27ivs/p28Qpe0C2hdduF+K2Rmz+/esrGjXU2Sv+4auvUm3c/7juF/0vucYJ6Cf981aUsW8/sWv8iNH2k6mZxC1bQz4LFve3DjXfKBRxqsxEFl5uqea/z9OjaT37N+YUkOEFrZWRkeAv/MshT3lXPhUHj4oOcdGrICGAFbf+t5e2P7WQjspOe8is3keKwSPayZk0XZyL093vvl7tulw9wglayQ57y7nr+9MYqeO6UDVHQOtb+UrHSZml9+SdS0R23+2ZgrWGLfZ5N8jlCk8VlwJPm2IIVNPxvy9t/nTjo/5V5/2n0btaH/6XL4iJAULZgBU15TBxsIa+PM0cEvnPf53G1BtagzOIaQFC2YAUtHu7zzKwRgfHyB5pkoS9oyE/o6tOVH5jnnOsYmQKCsoXgOOgvS15ZeFBNTvqCbq47aZzPN98Pq/XEVt6OjYKgbCG8aU4VDAp6fM48ccjb22sfrTPyR+pszgQEZYtBBS3l79mNH5h9jmFCnQFB2WJ4QQUOvujTcamKA9suxRWC/jEm4V0V+whcwYOgCBV+OrBGzDouxm50gaCnfOZ+0C2e+WKNAR+CCuSsf6LGk1tydcjMFhcIOmIBQnfqqXqUFT9wI6jAjZVdaw7YZnBHXSBorHidxCPfMl+uIeBJUIGM5dE1+m++pVt+elwg6GsDi9Fpbzd9mB9nggpkpD5eo/ea63ouggYXCJrXJayHzwbmizUG/AkqcHNd3xpdlhjzvgcaQZsFW5FMxtfzyC43HUiIU0EFcnc85R0xQ829kU6CRtCM5qnnRSST4TioEgYWVKDo2/EP1HvuU4PtNFFt4t+8+xke5p2bRTrVp2sS13AsqMjpBZ2rxSw957Tl4WHZB81NiBMJDmCXkjs4F1Tg5qYhvk3H7TXKRSW0gp4pPwk28UpwIahA8eHZ7avHvHPayYuVhVbQyPKTQFAleBFU5MaWZ4KCn/nQ5afsQVC20AmqdfhFnTi1uGeNps2bv3DJRcsXoRV0eflJIKgSOEE1D7+oH//U6dry3sqT97msSwqX27GFSlDtwy/qxlsvIHT7ob5tq/135jcukdQ9BP1yolFut6ESVPvwi7rx6hzh5dkVKHvXhIfv7zT1C6ffQu4Wgs5qkjLK1xD7nHSCMhl+kS17m15HfwecsrzP+WJqp/vbvLTVqV1SdxD0Vg1hX/OtJCcvVR66nSTpo/u0jm7HkGlebeq8V/ox/7uUJ2o3GPzeL84ayNkdBP1NHAThexWPzdARKkFvTUvYJvxILPlcT9P4oGy5djhLMsX824qnHqjx2NRd15yweHcQNL/2n8Jv+gtOXqo8VIImdk+NWI2QdJghQ+51Xvv01egajRMX/3hH3+W4g6Bold+zPRpcdvZSZaES1DcLXQ25yIegIsW/pj3XuspDI1edUDdApxrcQlB0ZsXWPKcvVBa6w0zCNjOtl5kbQS3kfv9WwgPVOoxec0KXJ925h6DGgUrQN72mIPPA6IqSyRwUNOvrBQMaV237worDrP9QgKBsoduLP7YToaKtYyRTeSlo9rdvPdWycrPEBV/YD0R88ty4XyiTgqBscZuLRbRy52jaS/+t7dd1/Nqj+XObvTvXl/JhDiAoWzxeUCvnd6UMbF75np4zt77dlS4TCMoWELSUC14fTu4dVKHFgNlbftM8kgwIyhYQtAxB+xGaO+joB5P7PFD5gT4TV/+o4Ql4IChbQNAy7PXt3anxBcvbO79tnZvYpppPp2fm7ziFPbZ/sEeTgbZn5YGgbAFBy3Jt+xd3y3jhq3df7tHovvrdXnx71++Knp7xXX9yQX3rGVYQlC0gKAEFf+x6a2S3BpVCo597fevR8mPMvJYsvDyxw/IeBGULCEpOwZk9y8b1aVmtdpu4Ce/uOV16iH9SivCS+IHlPQjKFhBUPVcPfpgy/LFGleq2G5C87NNfc1B6o/PoJz/rUDwgKFtAUM2YL+5f/9rz3ZtWqdUivFIt/4+sU0FQtoCg9Fw79vFbr9kviAZB2QKCMgYEZQsIyhgQlC0gKGNAULaAoIwBQdkCgjKGRlDpTYg2oJ5KgKDqoRGUp5sQnQUIyhgaQXm7CdEZgKCMoRGUy5sQdQYEZQyNoNKbEPNGDxdpFMiiYZwCgjKGai9echNiUdpykX5dGLSLV0BQxugwRv2KZ+lScg0IyhgdhgAHQZXgbQhwIwCCssXdhgB3OTqMUQ+CKqF2CHDzN3tFohuyaBmn6HCqEwRVQu0Q4Ld7RIuEuHIAW1cDgrJFjyHAoaBsgXoqoXoIcCtQULZAPZXQeJgJCsoWqKcSIKh6QFC26CJo0g0bF896Cpftq/yoDoK6dz2v33CEw3pqFPSjWnZMFYhwg7AKJet8SFvRWNaTwTqRLIlRmoq1HOKonhoFLeF2FaKwnPuJwrKrEYVl1iAKu1GLKOyaF1HYVW+iMEpI6vnCMmzIGYLj1L12YkP2ReHTtCl3mrEc6wfh0ygBgoKgyoCgEkBQEFQCCAqCKgOCSgBBQVAJICgIqowbCFo8niisaALLsMJkorCCiURhdyaxDKOEpJ6bD2JDsmfg07z3Bzbk/CJ8mtfLnQcvxy+r8WmUoBUUAHQFBAUMDQgKGBoQFDA0IChgaEBQwNCAoIChAUEBQwOCAoaGUtBNjUMX4GIWhvgl3SKJfD2BIOHBCN+kQnzY/IC6o4pxYRnNkH2JjiItYcRrQQU2P1Ez8IUkqCK+ggTloy8dnaBXfP7ObIw5F3usdkZm9+kEkYerJ+AT5gX9nNclDRt2zC8jJ3IrJmxh82D7KjiKtIQRrwUV+PUiaQa+kARVxFeQoHwMSkcnaFoiQtOnOI7ZKcxPTcBHZrdJScAn3B6HUO4tbNjxwOw7HbZhwr5IDbavgqNISxjpWtCBzU/SDIJCElQRX0GC8jEoHZ2gsycjtHIoNuxqxDZ8ZNLGjQn4hAt6RfrEZeGzjb2vei8zLux8sH0VHEaKYaRrQQdJfmwzCApJUkV8BQnKR186OkFniUtNwgSZV4WswkeufwoJdcWGTQs7m9N3Cjbsm8hDp7qtxYWJ5bPGOIy0VJlsLSjB58c3g6SQBFUkqCBB+ehLRyeouMSZmMvQihJ6XiSIjPULrlOlOzZs6UiE1sViwybMErZj/XBhYvmsMQ4jxTDCtaAEm5+gGSSFJKgiQQUJykdfOjpBL/tfyQ3H9Hy3dTETRgq/+Niws43/yo5JwYataXU1a/B0XJhYPmuMw0gxjHwtaMDmJ2sGtpAEVSSoIEH56EtHeZhpY/Nw3LGDlypWqlQpkSRSqCs+LC3E/9l8bFjxBP+6T93GhVk2QNYYR5FiGPlaUIHLT9YMfCHxVSSoIEH56EsHB+oBQwOCAoYGBAUMDQgKGBoQFDA0IChgaEBQwNCAoIChAUEBQwOCAoYGBAUMDQgKGBoQFDA0IChgaEBQwNCAoIChAUEBQwOCAoaGH0F7eNUyeXk95+pmuA2c1JMfQRHKqCq8bAl3dTPcBi7qyZ2gmfhnUwBkcFFP7gQ9EHMgZmjH52b1anYRvRXSYGyRq1vFL1zUk0dBq2UXeqWiV97+uvnlnHhd7wJ2b7ioJ4+CRiPU8m/0zvRXg6Oiwvu7ulX8wkU9eRS0m1DQ80JBU2YiVFTg6lbxCxf15FnQI2HXC/oucXWr+IWLevIsKFraKGC40X7jOYKLevIk6N0UG62UnGPQevIrKOARgKCAoQFBAUMDggKGBgQFDA0IChgaEBQwNCAoYGhAUMDQgKCAoQFBAUMDggKGBgQFDA0IChgaEBQwNCAoYGj+H8DSRDDWqCh9AAAAAElFTkSuQmCC" 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" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level considerably.</p>
 </div>
 <div id="accessing-mmkin-objects" class="section level2">
@@ -1928,10 +1941,10 @@ plot(mm.L3)</code></pre>
 <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p>
 <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p>
 <pre class="r"><code>summary(mm.L3[[&quot;DFOP&quot;, 1]])</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.49.1 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Thu Apr  4 17:00:09 2019 
-## Date of summary: Thu Apr  4 17:00:09 2019 
+<pre><code>## mkin version used for fitting:    0.9.49.4 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 18:43:55 2019 
+## Date of summary: Thu May  2 18:43:56 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -1940,18 +1953,18 @@ plot(mm.L3)</code></pre>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 172 model solutions performed in 0.424 s
+## Fitted using 372 model solutions performed in 0.761 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##          value   type
-## parent_0 97.80  state
-## k1        0.10 deparm
-## k2        0.01 deparm
-## g         0.50 deparm
-## sigma     1.00  error
+##              value   type
+## parent_0 97.800000  state
+## k1        0.100000 deparm
+## k2        0.010000 deparm
+## g         0.500000 deparm
+## sigma     1.017292  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##              value lower upper
@@ -1959,7 +1972,7 @@ plot(mm.L3)</code></pre>
 ## log_k1   -2.302585  -Inf   Inf
 ## log_k2   -4.605170  -Inf   Inf
 ## g_ilr     0.000000  -Inf   Inf
-## sigma     1.000000     0   Inf
+## sigma     1.017292     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -1974,11 +1987,11 @@ plot(mm.L3)</code></pre>
 ## 
 ## Parameter correlation:
 ##           parent_0     log_k1     log_k2      g_ilr      sigma
-## parent_0 1.000e+00  1.732e-01  2.282e-02  4.009e-01  2.438e-07
-## log_k1   1.732e-01  1.000e+00  4.945e-01 -5.809e-01 -1.076e-07
-## log_k2   2.282e-02  4.945e-01  1.000e+00 -6.812e-01 -6.155e-08
-## g_ilr    4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.930e-08
-## sigma    2.438e-07 -1.076e-07 -6.155e-08 -7.930e-08  1.000e+00
+## parent_0 1.000e+00  1.732e-01  2.282e-02  4.009e-01  1.660e-07
+## log_k1   1.732e-01  1.000e+00  4.945e-01 -5.809e-01  6.635e-08
+## log_k2   2.282e-02  4.945e-01  1.000e+00 -6.812e-01  3.880e-07
+## g_ilr    4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -3.822e-07
+## sigma    1.660e-07  6.635e-08  3.880e-07 -3.822e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1991,9 +2004,9 @@ plot(mm.L3)</code></pre>
 ## g         0.45660  34.920 2.581e-05  0.41540   0.49850
 ## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
-## All data   2.452       5  3
+## All data   2.225       4  4
 ## parent     2.225       4  4
 ## 
 ## Estimated disappearance times:
@@ -2011,7 +2024,7 @@ plot(mm.L3)</code></pre>
 ##    91   parent     15.0     15.18 -0.18181
 ##   120   parent     12.0     10.19  1.81395</code></pre>
 <pre class="r"><code>plot(mm.L3[[&quot;DFOP&quot;, 1]], show_errmin = TRUE)</code></pre>
-<p><img src="data:image/png;base64,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" 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" /><!-- --></p>
 <p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the <span class="math inline"><em>χ</em><sup>2</sup></span> error level criterion for laboratory data L3.</p>
 <p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</p>
 </div>
@@ -2029,35 +2042,35 @@ mm.L4 &lt;- mmkin(c(&quot;SFO&quot;, &quot;FOMC&quot;), cores = 1,
                list(&quot;FOCUS L4&quot; = FOCUS_2006_L4_mkin),
                quiet = TRUE)
 plot(mm.L4)</code></pre>
-<p><img 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" 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" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p>
 <pre class="r"><code>summary(mm.L4[[&quot;SFO&quot;, 1]], data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.49.1 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Thu Apr  4 17:00:10 2019 
-## Date of summary: Thu Apr  4 17:00:11 2019 
+<pre><code>## mkin version used for fitting:    0.9.49.4 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 18:43:56 2019 
+## Date of summary: Thu May  2 18:43:57 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 114 model solutions performed in 0.253 s
+## Fitted using 146 model solutions performed in 0.291 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##               value   type
-## parent_0       96.6  state
-## k_parent_sink   0.1 deparm
-## sigma           1.0  error
+##                  value   type
+## parent_0      96.60000  state
+## k_parent_sink  0.10000 deparm
+## sigma          3.16181  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##                       value lower upper
 ## parent_0          96.600000  -Inf   Inf
 ## log_k_parent_sink -2.302585  -Inf   Inf
-## sigma              1.000000     0   Inf
+## sigma              3.161810     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -2069,10 +2082,10 @@ plot(mm.L4)</code></pre>
 ## sigma                3.162    0.79050  1.130   5.194
 ## 
 ## Parameter correlation:
-##                    parent_0 log_k_parent_sink     sigma
-## parent_0          1.000e+00         5.938e-01 9.573e-08
-## log_k_parent_sink 5.938e-01         1.000e+00 6.838e-08
-## sigma             9.573e-08         6.838e-08 1.000e+00
+##                    parent_0 log_k_parent_sink      sigma
+## parent_0          1.000e+00         5.938e-01  4.256e-10
+## log_k_parent_sink 5.938e-01         1.000e+00 -7.280e-10
+## sigma             4.256e-10        -7.280e-10  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -2083,9 +2096,9 @@ plot(mm.L4)</code></pre>
 ## k_parent_sink  0.006541   14.17 1.578e-05  0.005455 7.842e-03
 ## sigma          3.162000    4.00 5.162e-03  1.130000 5.194e+00
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
-## All data   3.506       3  5
+## All data   3.287       2  6
 ## parent     3.287       2  6
 ## 
 ## Resulting formation fractions:
@@ -2096,34 +2109,34 @@ plot(mm.L4)</code></pre>
 ##        DT50 DT90
 ## parent  106  352</code></pre>
 <pre class="r"><code>summary(mm.L4[[&quot;FOMC&quot;, 1]], data = FALSE)</code></pre>
-<pre><code>## mkin version used for fitting:    0.9.49.1 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Thu Apr  4 17:00:11 2019 
-## Date of summary: Thu Apr  4 17:00:11 2019 
+<pre><code>## mkin version used for fitting:    0.9.49.4 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 18:43:56 2019 
+## Date of summary: Thu May  2 18:43:57 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 111 model solutions performed in 0.257 s
+## Fitted using 224 model solutions performed in 0.451 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##          value   type
-## parent_0  96.6  state
-## alpha      1.0 deparm
-## beta      10.0 deparm
-## sigma      1.0  error
+##              value   type
+## parent_0 96.600000  state
+## alpha     1.000000 deparm
+## beta     10.000000 deparm
+## sigma     1.830055  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##               value lower upper
 ## parent_0  96.600000  -Inf   Inf
 ## log_alpha  0.000000  -Inf   Inf
 ## log_beta   2.302585  -Inf   Inf
-## sigma      1.000000     0   Inf
+## sigma      1.830055     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -2137,10 +2150,10 @@ plot(mm.L4)</code></pre>
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -1.022e-06
-## log_alpha -4.696e-01  1.000e+00  9.889e-01  1.556e-06
-## log_beta  -5.543e-01  9.889e-01  1.000e+00  1.437e-06
-## sigma     -1.022e-06  1.556e-06  1.437e-06  1.000e+00
+## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.473e-07
+## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.429e-08
+## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.183e-08
+## sigma     -2.473e-07  2.429e-08  5.183e-08  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -2152,9 +2165,9 @@ plot(mm.L4)</code></pre>
 ## beta      64.9800   2.540 3.201e-02 21.7800 193.900
 ## sigma      1.8300   4.000 8.065e-03  0.5598   3.100
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
-## All data   2.192       4  4
+## All data   2.029       3  5
 ## parent     2.029       3  5
 ## 
 ## Estimated disappearance times:
author	Johannes Ranke <jranke@uni-bremen.de>	2019-05-02 18:54:22 +0200
committer	Johannes Ranke <jranke@uni-bremen.de>	2019-05-02 18:54:22 +0200
commit	6ddb7575f37d9d534f014cbd105b2f07660d59c6 (patch)
tree	5718770eb493344425b1a1842aa020fe3887fe1f /vignettes/FOCUS_L.html
parent	a4ca3451f1b5c37d10c6a41cb18a99b1631e8aa2 (diff)