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<title>Using the loo package (version &gt;= 2.0.0)</title>

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<h1 class="title toc-ignore">Using the loo package (version &gt;= 2.0.0)</h1>
<h4 class="author">Aki Vehtari and Jonah Gabry</h4>
<h4 class="date">2025-12-22</h4>


<div id="TOC">
<ul>
<li><a href="#introduction">Introduction</a></li>
<li><a href="#setup">Setup</a></li>
<li><a href="#example-poisson-vs-negative-binomial-for-the-roaches-dataset">Example: Poisson vs negative binomial for the roaches dataset</a>
<ul>
<li><a href="#background-and-model-fitting">Background and model fitting</a>
<ul>
<li><a href="#roaches-data">Roaches data</a></li>
<li><a href="#fit-poisson-model">Fit Poisson model</a></li>
</ul></li>
<li><a href="#using-the-loo-package-for-model-checking-and-comparison">Using the <strong>loo</strong> package for model checking and comparison</a>
<ul>
<li><a href="#computing-psis-loo-and-checking-diagnostics">Computing PSIS-LOO and checking diagnostics</a></li>
<li><a href="#plotting-pareto-k-diagnostics">Plotting Pareto <span class="math inline">\(k\)</span> diagnostics</a></li>
<li><a href="#marginal-posterior-predictive-checks">Marginal posterior predictive checks</a></li>
</ul></li>
<li><a href="#try-alternative-model-with-more-flexibility">Try alternative model with more flexibility</a></li>
<li><a href="#comparing-the-models-on-expected-log-predictive-density">Comparing the models on expected log predictive density</a></li>
</ul></li>
<li><a href="#references">References</a></li>
</ul>
</div>

<!--
%\VignetteEngine{knitr::rmarkdown}
%\VignetteIndexEntry{Using the loo package}
-->
<div id="introduction" class="section level1">
<h1>Introduction</h1>
<p>This vignette demonstrates how to use the <strong>loo</strong> package to carry out Pareto smoothed importance-sampling leave-one-out cross-validation (PSIS-LOO) for purposes of model checking and model comparison.</p>
<p>In this vignette we can’t provide all necessary background information on PSIS-LOO and its diagnostics (Pareto <span class="math inline">\(k\)</span> and effective sample size), so we encourage readers to refer to the following papers for more details:</p>
<ul>
<li><p>Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. <em>Statistics and Computing</em>. 27(5), 1413–1432. :10.1007/s11222-016-9696-4. Links: <a href="https://link.springer.com/article/10.1007/s11222-016-9696-4">published</a> | <a href="https://arxiv.org/abs/1507.04544">preprint arXiv</a>.</p></li>
<li><p>Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto smoothed importance sampling. <em>Journal of Machine Learning Research</em>, 25(72):1-58. <a href="https://jmlr.org/papers/v25/19-556.html">PDF</a></p></li>
</ul>
</div>
<div id="setup" class="section level1">
<h1>Setup</h1>
<p>In addition to the <strong>loo</strong> package, we’ll also be using <strong>rstanarm</strong> and <strong>bayesplot</strong>:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;rstanarm&quot;</span>)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;bayesplot&quot;</span>)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;loo&quot;</span>)</span></code></pre></div>
</div>
<div id="example-poisson-vs-negative-binomial-for-the-roaches-dataset" class="section level1">
<h1>Example: Poisson vs negative binomial for the roaches dataset</h1>
<div id="background-and-model-fitting" class="section level2">
<h2>Background and model fitting</h2>
<p>The Poisson and negative binomial regression models used below in our example, as well as the <code>stan_glm</code> function used to fit the models, are covered in more depth in the <strong>rstanarm</strong> vignette <a href="http://mc-stan.org/rstanarm/articles/count.html"><em>Estimating Generalized Linear Models for Count Data with rstanarm</em></a>. In the rest of this vignette we will assume the reader is already familiar with these kinds of models.</p>
<div id="roaches-data" class="section level3">
<h3>Roaches data</h3>
<p>The example data we’ll use comes from Chapter 8.3 of Gelman and Hill (2007). We want to make inferences about the efficacy of a certain pest management system at reducing the number of roaches in urban apartments. Here is how Gelman and Hill describe the experiment and data (pg. 161):</p>
<blockquote>
<p>the treatment and control were applied to 160 and 104 apartments, respectively, and the outcome measurement <span class="math inline">\(y_i\)</span> in each apartment <span class="math inline">\(i\)</span> was the number of roaches caught in a set of traps. Different apartments had traps for different numbers of days</p>
</blockquote>
<p>In addition to an intercept, the regression predictors for the model are <code>roach1</code>, the pre-treatment number of roaches (rescaled above to be in units of hundreds), the treatment indicator <code>treatment</code>, and a variable indicating whether the apartment is in a building restricted to elderly residents <code>senior</code>. Because the number of days for which the roach traps were used is not the same for all apartments in the sample, we use the <code>offset</code> argument to specify that <code>log(exposure2)</code> should be added to the linear predictor.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># the &#39;roaches&#39; data frame is included with the rstanarm package</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(roaches)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="fu">str</span>(roaches)</span></code></pre></div>
<pre><code>&#39;data.frame&#39;:   262 obs. of  5 variables:
 $ y        : int  153 127 7 7 0 0 73 24 2 2 ...
 $ roach1   : num  308 331.25 1.67 3 2 ...
 $ treatment: int  1 1 1 1 1 1 1 1 0 0 ...
 $ senior   : int  0 0 0 0 0 0 0 0 0 0 ...
 $ exposure2: num  0.8 0.6 1 1 1.14 ...</code></pre>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># rescale to units of hundreds of roaches</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>roaches<span class="sc">$</span>roach1 <span class="ot">&lt;-</span> roaches<span class="sc">$</span>roach1 <span class="sc">/</span> <span class="dv">100</span></span></code></pre></div>
</div>
<div id="fit-poisson-model" class="section level3">
<h3>Fit Poisson model</h3>
<p>We’ll fit a simple Poisson regression model using the <code>stan_glm</code> function from the <strong>rstanarm</strong> package.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>fit1 <span class="ot">&lt;-</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">stan_glm</span>(</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>    <span class="at">formula =</span> y <span class="sc">~</span> roach1 <span class="sc">+</span> treatment <span class="sc">+</span> senior,</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">offset =</span> <span class="fu">log</span>(exposure2),</span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> roaches,</span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">poisson</span>(<span class="at">link =</span> <span class="st">&quot;log&quot;</span>),</span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">prior =</span> <span class="fu">normal</span>(<span class="dv">0</span>, <span class="fl">2.5</span>, <span class="at">autoscale =</span> <span class="cn">TRUE</span>),</span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a>    <span class="at">prior_intercept =</span> <span class="fu">normal</span>(<span class="dv">0</span>, <span class="dv">5</span>, <span class="at">autoscale =</span> <span class="cn">TRUE</span>),</span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a>    <span class="at">seed =</span> <span class="dv">12345</span></span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>  )</span></code></pre></div>
<p>Usually we would also run posterior predictive checks as shown in the <strong>rstanarm</strong> vignette <a href="http://mc-stan.org/rstanarm/articles/count.html">Estimating Generalized Linear Models for Count Data with rstanarm</a>, but here we focus only on methods provided by the <strong>loo</strong> package.</p>
<p><br></p>
</div>
</div>
<div id="using-the-loo-package-for-model-checking-and-comparison" class="section level2">
<h2>Using the <strong>loo</strong> package for model checking and comparison</h2>
<p><em>Although cross-validation is mostly used for model comparison, it is also useful for model checking.</em></p>
<div id="computing-psis-loo-and-checking-diagnostics" class="section level3">
<h3>Computing PSIS-LOO and checking diagnostics</h3>
<p>We start by computing PSIS-LOO with the <code>loo</code> function. Since we fit our model using <strong>rstanarm</strong> we can use the <code>loo</code> method for <code>stanreg</code> objects (fitted model objects from <strong>rstanarm</strong>), which doesn’t require us to first extract the pointwise log-likelihood values. If we had written our own Stan program instead of using <strong>rstanarm</strong> we would pass an array or matrix of log-likelihood values to the <code>loo</code> function (see, e.g. <code>help(&quot;loo.array&quot;, package = &quot;loo&quot;)</code>). We’ll also use the argument <code>save_psis = TRUE</code> to save some intermediate results to be re-used later.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>loo1 <span class="ot">&lt;-</span> <span class="fu">loo</span>(fit1, <span class="at">save_psis =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<pre><code>Replacing NAs in `r_eff` with 1s</code></pre>
<pre><code>Warning: Found 17 observations with a pareto_k &gt; 0.7. With this many problematic observations we recommend calling &#39;kfold&#39; with argument &#39;K=10&#39; to perform 10-fold cross-validation rather than LOO.</code></pre>
<p><code>loo</code> gives us warnings about the Pareto diagnostics, which indicate that for some observations the leave-one-out posteriors are different enough from the full posterior that importance-sampling is not able to correct the difference. We can see more details by printing the <code>loo</code> object.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(loo1)</span></code></pre></div>
<pre><code>
Computed from 4000 by 262 log-likelihood matrix.

         Estimate     SE
elpd_loo  -6247.5  727.9
p_loo       292.1   73.3
looic     12495.0 1455.7
------
MCSE of elpd_loo is NA.
MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 1.2]).

Pareto k diagnostic values:
                         Count Pct.    Min. ESS
(-Inf, 0.7]   (good)     245   93.5%   84      
   (0.7, 1]   (bad)        8    3.1%   &lt;NA&gt;    
   (1, Inf)   (very bad)   9    3.4%   &lt;NA&gt;    
See help(&#39;pareto-k-diagnostic&#39;) for details.</code></pre>
<p>The table shows us a summary of Pareto <span class="math inline">\(k\)</span> diagnostic, which is used to assess the reliability of the estimates. In addition to the proportion of leave-one-out folds with <span class="math inline">\(k\)</span> values in different intervals, the minimum of the effective sample sizes in that category is shown to give idea why higher <span class="math inline">\(k\)</span> values are bad. Since we have some <span class="math inline">\(k&gt;1\)</span>, we are not able to compute an estimate for the Monte Carlo standard error (SE) of the expected log predictive density (<code>elpd_loo</code>) and <code>NA</code> is displayed. (Full details on the interpretation of the Pareto <span class="math inline">\(k\)</span> diagnostics are available in the Vehtari, Gelman, and Gabry (2017) and Vehtari, Simpson, Gelman, Yao, and Gabry (2024) papers referenced at the top of this vignette.)</p>
<p>In this case the <code>elpd_loo</code> estimate should not be considered reliable. If we had a well-specified model we would expect the estimated effective number of parameters (<code>p_loo</code>) to be smaller than or similar to the total number of parameters in the model. Here <code>p_loo</code> is almost 300, which is about 70 times the total number of parameters in the model, indicating severe model misspecification.</p>
</div>
<div id="plotting-pareto-k-diagnostics" class="section level3">
<h3>Plotting Pareto <span class="math inline">\(k\)</span> diagnostics</h3>
<p>Using the <code>plot</code> method on our <code>loo1</code> object produces a plot of the <span class="math inline">\(k\)</span> values (in the same order as the observations in the dataset used to fit the model) with horizontal lines corresponding to the same categories as in the printed output above.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(loo1)</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAu4AAAHPCAYAAAAMBV/EAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAALuoAMABAAAAAEAAAHPAAAAAE2s/msAAEAASURBVHgB7J0HgBRF1scfLMkl55xzFgQOkCQigogBUTCiRI8TA4j6nSeHOaEioGJAQFEMeKhgAJQgShAlSs6wxCWHBZaF+epf2L3dvTO7Mz0zuzsz/6dDd1dXV/hV78yrqlevcniUCIUESIAESIAESIAESIAESCBbE8iZrUvHwpEACZAACZAACZAACZAACWgCVNz5IpAACZAACZAACZAACZBABBCg4h4BjcQikgAJkAAJkAAJkAAJkAAVd74DJEACJEACJEACJEACJBABBKi4R0AjsYgkQAIkQAIkQAIkQAIkQMWd7wAJkAAJkAAJkAAJkAAJRAABKu4R0EgsIgmQAAmQAAmQAAmQAAlQcec7QAIkQAIkQAIkQAIkQAIRQICKewQ0EotIAiRAAiRAAiRAAiRAAlTc+Q6QAAmQAAmQAAmQAAmQQAQQoOIeAY3EIpIACZAACZAACZAACZAAFXe+AyRAAiRAAiRAAiRAAiQQAQSouEdAI7GIJEACJEACJEACJEACJEDFne8ACZAACZAACZAACZAACUQAASruEdBILCIJkAAJkAAJkAAJkAAJUHHnO0ACJEACJEACJEACJEACEUCAinsENBKLSAIkQAIkQAIkQAIkQAJU3PkOkAAJkAAJkAAJkAAJkEAEEKDiHgGNxCKSAAmQAAmQAAmQAAmQABV3vgMkQAIkQAIkQAIkQAIkEAEEqLhHQCOxiCRAAiRAAiRAAiRAAiRAxZ3vAAmQAAmQAAmQAAmQAAlEAAEq7hHQSCwiCZAACZAACZAACZAACVBx5ztAAiRAAiRAAiRAAiRAAhFAgIp7BDQSi0gCJEACJEACJEACJEACVNz5DpAACZAACZAACZAACZBABBCg4h4BjcQikgAJkAAJkAAJkAAJkAAVd74DJEACJEACJEACJEACJBABBKi4R0AjsYgkQAIkQAIkQAIkQAIkQMWd7wAJkAAJkAAJkAAJkAAJRAABKu4R0EgsIgmQAAmQAAmQAAmQAAlQcec7QAIkQAIkQAIkQAIkQAIRQICKewQ0EotIAiRAAiRAAiRAAiRAAlTc+Q6QAAmQAAmQAAmQAAmQQAQQoOIeAY3EIpIACZAACZAACZAACZAAFXe+AyRAAiRAAiRAAiRAAiQQAQSouEdAI7GIJEACJEACJEACJEACJEDFne8ACZAACZAACZAACZAACUQAASruEdBILCIJkAAJkAAJkAAJkAAJUHHnO0ACJEACJEACJEACJEACEUCAinsENBKLSAIkQAIkQAIkQAIkQAJU3PkOkAAJkAAJkAAJkAAJkEAEEKDiHgGNxCKSAAmQAAmQAAmQAAmQABV3vgMkQAIkQAIkQAIkQAIkEAEEqLhHQCOxiCRAAiRAAiRAAiRAAiRAxZ3vAAmQAAmQAAmQAAmQAAlEAAEq7hHQSCwiCZAACZAACZAACZAACVBx5ztAAiRAAiRAAiRAAiRAAhFAgIp7BDQSi0gCJEACJEACJEACJEACuYiABEiABKwENm3aJF999ZU1yHaeO3duyZs3r+TJk0dKlSol1apVk0aNGkmOHDls8bxdXLx4UdasWSOrV6/WR5wnJiZK5cqVpUqVKlK9enW54447pEiRIt4eN8Nee+01SU5ONq+7deumy2AGWE4OHToky5cvN/Ndv369FCxYUOeHPNu2bSsdOnSwPBG+0wMHDsiHH35oZhAfHy8PPfSQeY2T9957Tw4fPmyG3X777bqsZgBPsgWBdevWyTfffGOWpUKFCnL33Xeb15F2wvcu0lqM5Y1ZAh4KCZAACVgI/O9///OoL8SAPvXr1/coZd+SStrTpUuXepo2bZphukpp9zz//POe06dPp03k7xCleNvSmTBhQpq4Z8+e9Tz55JMe1cmwxfVWt44dO3qWLFmSJo1QB6gOhK0sJUqUSJNF7dq1bXHmzJmTJg4DMofAH3/84bnqqqu8ZjZ16lRbO1155ZVe40VKYFa8d2fOnPE899xznjfffDNSMLGcJJDlBGgqE7NdNlacBEJHYO3atdKzZ08ZN26c10QXL14sSgHSI99eI1gCjx07Jkrhlttuu03UN6Tljv+nGNm/8847RXUA5Ny5cxk+OHfuXGnTpo0sWrQow7iMEP0EDh48KP3795cWLVrIb7/9Fv0VzoIaTp8+XerVqyf/+c9/RHXSs6AEzJIEIpMAFffIbDeWmgSyHQEo2Q8++KD89ddftrKdP39eevXqJUlJSbbwAgUKSMOGDaV58+ZSunRp2z1cfPfdd/Lqq6+mCfcn4P33309j7gNTnkqVKknLli2lTp062tTHmlZKSooup9VMxXqf57FBYM+ePVKrVi1RsziCDiAl9AQeffRR6dGjh2zfvj30iTNFEohyArRxj/IGZvVIIBQEYIueK1curchAET958qQenX7ppZfk6NGjZhZQ3p999ln5/PPPzTDYy+/evdu8xsnbb78t/fr1synPP/zwg7Zvx4i7IRgxx498zpyBjTG88cYbRhL62KpVK62I1a1b1wyHbf0jjzwin3zyiRmWkJAgkyZNkmHDhplhmX0yevRozdfIF50bSuYROHXqlBw/fjzDDJVpjHzxxRdmPGX2ZJ7zJH0C6BxRSIAE3BGg4u6OG58igZgigBFqKO5WgWnJgAED9OgkFoAaMm/ePONUH//880/b9fXXXy///Oc/bWG46Nq1q4wZM0buuece896JEycEiwAbNGhghmV0AsVr48aNtmhjx44Vq9KOmyVLlpSJEyeKsmO2xYdZT1ZKly5dsjJ75u0ngYoVKwo+FBIgARLITAL2X+LMzJl5kQAJRDyBokWLavOSt956y6wLRrKhPMMUBgITFKvs2rVL265780KD6XOYx+TLl0/y58+vP9YRfWs6vs6d+SHezp075YorrkjzCDzkwJZ5ypQpOi+UGZ5yghHYRKsFvoIOC7zINGvWTNq1ayd9+/b1K1nMYsDG2pD7779fd46Ma+MIzzwwCdqwYYPs3btXj9LDGw86JMjvrrvu0l56jPjejmpxoPZi8/vvv8uKFSukXLly2svOzTffrL30LFiwwOY55dZbbxXMXhiiFhbKkSNH9CXa7IUXXtDnmD1BB+7XX3/V74JalCzt27eXe++9N0PvQ5i1mT9/vp4JMeqGcmHmAd6L4HWocOHCRhG8HmHiohaPyqxZswTvG2Z80NZlypTR5lK9e/cWdJCsMznwUvTEE0+Y9TESxvs0dOhQfQlTq4cfflifg9fHH39sRJOqVavKkCFDzGvnCd6LH3/8UZuSoTMKz0xID2z+9a9/eTUXc6bh7frxxx8XzIJBihUrpm3G0a4zZ84UrN345ZdfdNqw14eHosaNG3tLJqAwt200Y8YM/V6AnVVgFofvDcjAgQO1KZv1Ps9JgAQsBNQfIIUESIAETALevMooxcC87zxRZik27xpKIfcoUwMzmlKKbffV149HKeielStXmnECPcnIq0yNGjVseSobeo/qXKTrqSbQMjjjKwVPe7FRyqAtb9QXH3gnUQqt7Z4brzIXLlzw3HfffbZ0jDysR6Xcen7++WdnMc1rtaDYo2YyvKajlHDPtGnTPMrtpu3++PHjzedxotx4mvfRJnhPVEfIDLOWB+edO3f2KAXNlob1YvPmzRl6HlJuFz3ff/+99THb+apVqzxqdsVnGYwywROS6vCYz6rOZobPKOXXjO+vVxmkqxRzD/4ujLydR/AeNWqUmXYgJ5dddpmZrnJv6lGmZh41G2aGWfOCh6V33nnHa/L+epUJpo2U2ZvXclnLmF7bei04A0kgxghg5ItCAiRAAiaBQBX3a665xvZjDKXJKmrE0wPFxPrjbJyrUUrP4MGDPd9++61H2c1bH0v3PCPFHYqSkYf1CMUFyuPrr7/ugeIaSlEj417ztOavZhFscdwo7sr+3paGNX3nuRpl9sANp1PQJs6yOJ9FB0QtHLbllZ7iDgXS+S4408Q12sabwPVioUKFbPl5e94IU95I0iSjZmc8al8Bv9NQJmAe5XVIpxMOxR1p++ocGfWwHtXajzR1yijAqrir2ZYMOz7I7+WXX06TrD+Ke7BtRMU9DXYGkEDABKi4B4yMD5BAdBPwV3GHoqPMP9IoScocIg0gtblLmnhWhQXnUDLVRkgeZf7hQdrpSUaKOzoBTkXEmR+uMXqr7O09auo+vewyvIdOQFxcnK2OGI2Gf2plHuD573//61VRDlRx379/fxq/9GrTH49aYOtR5gaeF1980eNkA2XJKcqMxlZWsFAmCh60vbL717MD3ng5R2utI+5GfGWuoZnCr79aqOxR5lS2vKBoqrULtiKhvZyzJEhPmbN4nnrqKT2Kr8xcbOlgBHvhwoW2dMDbKAeOyuTHA2VTmR55lOmSHtV2+vVXG2LpNOBTHJ06+GO3poF8EI6P8ppk5ufPiLsye7KlhXSVqY/n3Xff9QwfPtyDEXJrXmpDLo/yamTm4c+JVXE30sLfEjqSRj6YfTHu4Yh3BO+SVZx/L879A0LRRvgeAEdnW9asWdNkrMy2rMXiOQmQgIMAFXcHEF6SQKwT8Ka4K/tkrVQr22k9Cqvc5aVRVA3F4Ouvv/aKUNkP+3zGeNY4QtlT9rle00GgUzn1tgGTckvpl/Ju5Kn8xnuwaZMbgYJopIOj2gHWoxbs2pKC4qgW+NriBaq4Kz/znu7du3sMhRn5OgUdEWtZbrrpJlsUmJI4zTacI70wx4GCaU0H5xkp7qifsmu35Yf3wZmOUzlDh8MaB4on3kOrwMTGaQICZRMKtyHKd78tHWUrb9wyjzBJwUZg6HRC0QcPqyi7elsaaodg623zPCPFHWY4zlkNJz+YlDk7Nth8LBBxKu5gp+zpbUmoxdoedKisjDHTZZWMFPdQtRHyVGsMbGVRayOsReE5CZBAOgSouKcDh7dIIBYJeFPcrT/46Z1jp9L0BHbtyquMB8pQeungHkZGfe3G6o/ijnLAVAGjnjDJySg/3McOqs7R4PTqY9xTC1pt6U+ePNm4ZTs6FctAFXdrYrBl9mZepFwU2sri3PkTSrqVBUY7YZ/vFLWgM007ORVPowNhpPfAAw84k/GohaJpFFi1aNQWD2Uw0sDROrJtjbhlyxY9M2ONi3UDhjhNpLC24d///rdWZNXiUyNausdQKe6ffvqprU6oIzpEToH5EWapYL71008/penwOeM7r52KO0bavQl2KLVyg5mQVTJS3EPVRsiTiruVPM9JIDACgTlHVn/1FBIgARJwElBKtvaJDh/u6Qk8WsCzBLymwJMLdkeFFxRvgh1PcR+eSdyK6iAIvG5s27ZNe3nBjqzw8qLMWrwmCS8c2AE2EFFmPTYvMHi2U6dOXpNQZgJew90EwrMKvODAGweYYgfKtm3bCjymWEXNIlgvNQtrADzQeOOhzIjSuNC0PuftHJ5LnKJG97U3F2s42tYQ+M5XCx6NS3305i4UN9RMRhq22GPAENTfKvDqA0838LkOjyvdunXTewjA00y4ZdOmTbYssPGX1YuNcXPQoEHaLSn2FLj66qulePHixi1XRzUj4/U5NfNiC8ffhL8bTIWyjWyF4AUJkEDABKi4B4yMD5BA7BEoW7asGB+4sMPOkk2aNJEbbrhBlP227NixQ9SIYYau/gxyUDrV6LPeqAnKlRqJ1xs3wb2kVdQIpWBDolAI3O7BfeGyZcsEu6MqrynaJaAz7dmzZ6fZ/dUZx3oNBcgq6CyAlTcpX768t+CAw5RNvXZLqEZJtftKtAM2q0Inx6mMOZXFrVu32vJLzxc52joQ8aV0wlWkL8G7YxWUVy0wtQbZzpUtvO3aqrjDXaWvjhc6WMpjiXa9qGYK5NprrxW41AyXOBV3dIQyQ5TdvNdsnG0J95fOjdG8PqgCQ9lGvvJgOAmQgH8E6MfdP06MRQIxTQAjlM4NmPwBoiYAtV/sffv2CT5JSUly44032h7FiCxG4vGBz/LrrrtOK9dGJPi/DkQwmqsW3un8kCd8f2Ok1iroONxyyy3689FHH0mfPn2stwV5+rvpE+pkFdQZdfImhm97b/f8DcNOtOj0WEetjWeh8KKuamGhEZRmlNdZXsMHuPmA5QR1CUSU2YbX6Mru2ms4AtE5swpmb9D58SXKTMp2y7rLKZR+7IQLJVkthNTvmy2y5QIdNGWaon29+5ohsUQP+NRaLjzs7FAFnKCfD/hqM3Se8F5a78Pfuz8SyjbyJz/GIQES8E2AI+6+2fAOCZBAkASwAY6y49ab58BMBBssYYTdlyAudmO1CuI7FQfrfee5WkgrVdSoIzYKQn6YCUhPlFcWcY6EB7Ilu7NTAEVY2Z97zRImB8EIFE2YDxlKOxRVXGOzIWzChNH0xx57zJaF0wwGo81WSa9M27dvt0bN8NxN5w6bK1kFymR674izTM4OFpR+tbeATgPmWNiwydemWlCmlU98a/YhO3fODKATmRniaxQd+VuVdijx2DTKHwl1G/mTJ+OQAAl4J0DF3TsXhpIACYSAAOynMYJqCBSlSZMmGZdej8obiy0co+NO5dMWwXHhtCNXi23FOfppfQQ7Yzrv+zL5sD5nnMNGX/kfNy71EeY43gQmQcEIzHusI7dPP/20NjeCXbthnnP69GlbFk5TGXRqrKI2aRKYTTgFu5biE27B6Lj1HUF+2LHVm6BTtHjxYtst7KjqTTDCjJkJjMCjI4C6YIdfmExZBR0XX8ou4ll5W5/L6BzmZFZR3musl+b5fLVLrFr4KditFusUvvnmG/OemxMnHyMNmFdZxRt3633rube4oWgjIw+3jI3neSSBWCJAxT2WWpt1JYFMJqD8UuuFgdZsR4wY4VMxW79+vbz66qvW6Hq03haQwYXaBMgWA2YzymuHOBdpIhIUBmxhD/tnq/hSBq1xrOfOUd8xY8akUfiUtxq9MNL6XKDn8+bNsz2CdQZOccZxzlZggaZVoLg6mUP5Vx5iAprpsKYZyDnMa5SnIdsjUGCdJj2IMHbsWJu9NRR+LDyFQDnHAk+YWmEWRLlitNmwYz2AcoGo1wE4TXGsJiPOWQPwc6NYwkTLKlDcnW2D++jIKm85otxm6nUK6GgGI2DkbcbHuVZE7RzrdzahaiMjQyfj9My1jGd4JAES+JuAmjqjkAAJkIBJwJs7SPXDat4P9MSbH281Te9RCwk948aN80yfPt0Dl3j9+vVL435QfU15PvvsszRZpucOEmV17viJdNQiTI9aSOuBmz6kCb/U9erVgxG37YOdX5WilibP9AJmzpxpSwNpKrt5j1q4qh9TCyi97mgZqDtI5bXFlo9SUj1G2ygF0wNXkGqE3RYHzzgFm+A4661s/j3YIAd+xMHAeR/XGbmD/OWXX5xZ6evLL7/clh52yrUKNvtx5qcUSw/cRmI31HXr1ukNi/DeWONZ/X+j/s5dU5WXGY9S6M2sEOfjjz+2paFmV8z7OFFmUrb7yA/vjOpUetDOhmTkxx3xlO28La0iRYro/FEm+LIfMmSI7T7ySm//AiNv69HpDhJpgB3SR32ViYzewMrKDefOtsrIHWQo2sgotzKHs9Ubm14pMy+PWnfgUaZQRjQeSYAEvBCgH3cvUBhEArFMINSKO1iqUW3bD7VTifB1rdzjefUxnp7ijvzw4w8lyVe6vsLVSKDHuWMk0vNH4APeW7rYDdMIx+Y4arTXvA5UcR86dKj5rJEmdqHETqjKDjnNPcRBh8Up2AwqIz5QCJWJhC3NcCnuKB98txt18ueoZhvMTotRP/jPdz4LZR8bLnXt2tWjzK7S3MfurlZBR0gtIk4TD+nCb70h/iju6HCgzZ1l8nWNjlignUZviruRvjIX8pq3WiBuVMM8ZqS4I2Io2gjpeNtR1igzdu6lkAAJ+CZAUxn1bUEhARIIL4FXXnlFLxJUSpTfGcE+HmYDgdi3G4nDjluNPqdZdGrc93aEWQ/MFtx6GFG7t2rPOM60DZMP2MGrzYKC8tOtZgzEaaMOUyAswsTiVAhspa0uHmG/DVMMq8DOGzbKSum3BpvncMsJzzROcw83bWEmmsGJ2tFU1Ai0T4881sfhmQgsnSYX99xzT5rFyOrnT5YvX67jO9cywHUkfPtbBWliUbM3AeNAzGbUzIW2sXe6OfWWNlirmSC/6u/teSMMpmgwbYF4Mw+Dr321CZcRPaBjKNoIGcJtpy9Jb7G0r2cYTgKxRICKeyy1NutKAllEAO4AsUjwjz/+0P6znQqXUSwohq1btxY1cipYtOdc9GnE8+cIW3ds7INOgy8FFengXv/+/fXiRSxmdCtQqLEwEF5x0AkwBHXF5jsLFy7UG+wY4W6O4KFMHLz6KscCQri2RGcHri6tAvZOgaKI8o4cOVJ3VtQIvECpGz58uA6H7TiUXqvAbjxcgncEawOwWBaKOa6tgkW2ardP/W7AHlztimq9bZ7Dzn3RokWaka+OBuqORaBffvmlV0X5tdde05s1mYmqE3Q60cZYqxCIQEn966+/dIfKGz94vIHno6VLl4rT1WUg+Rhx8Q7DnakyAzOC9BGuSNExwnvo9BJji5jORajaCG5L0Ulx/n3j2tnu6RSHt0ggJgnkwGB8TNaclSYBEsgyAsr2VnvyUCYteqQYnmPgyQUjlFAgwyEYbUV+2DAJnmSQHxQYKIOBzAT4Uzakj42B4CEHSnsoFDJnvtgxFZ5SsLBW2ZCbXmWc8YK5xk6e2E3WEGWbLr525jTihOqI9sImRnDNiXbCAmBrh8iffMAGMw74YPQZrjChfON980cwm4FFpVB6sc9AsH748XOL90+Z0GilFQosXJE6Pf/4UzYjDphYF9du3LhRb5CG+1iwi9kGdOqwGDWYfIz8rMdg2wjejMAC7YOOBniE+m/RWl6ek0A0EKDiHg2tyDqQAAmQgJ8EMOsBUxAoscYHu4jCNMkqytZbK1JW0wV0FOCdhZJ9CKSnuGefUrIkJEACoSJAxT1UJJkOCZAACUQAAbh6xIgzZj0MgU08NsvCjAdEeXKRRx99VD788EMjih7RhxIf6lFbMwOeuCJAxd0VNj5EAhFLIFfElpwFJwESIAESCJgA7KyxSRUWdxpimCpg51r4RoeJBcx9rKI8gVBptwLhOQmQAAlkAQEuTs0C6MySBEiABLKSABaxwkzGKbDJh025U2mHXfvdd9/tjM5rEiABEiCBTCZAxT2TgTM7EiABEshqAhhZX716td4x1eo60loueMPBgkx4cMGiVC4atNLJPudoy5IlS5ofX550sk+JWRISIIFgCNDGPRh6fJYESIAEooAAvIPAR7naZVObw8DzCjyR+HLbGQVVZhVIgARIICIJUHGPyGZjoUmABEiABEiABEiABGKNAE1lYq3FWV8SIAESIAESIAESIIGIJEDFPSKbjYUmARIgARIgARIgARKINQJU3GOtxVlfEiABEiABEiABEiCBiCRAxT0im42FJgESIAESIAESIAESiDUCVNxjrcVZXxIgARIgARIgARIggYgkQMU9IpuNhSYBEiABEiABEiABEog1AlTcY63FWV8SIAESIAESIAESIIGIJEDFPSKbjYUmARIgARIgARIgARKINQJU3GOtxVlfEiABEiABEiABEiCBiCRAxT0im42FJgESIAESIAESIAESiDUCVNxjrcVZXxIgARIgARIgARIggYgkQMU9IpuNhSYBEiABEiABEiABEog1AlTcY63FWV8SIAESIAESIAESIIGIJEDFPSKbjYUmARIgARIgARIgARKINQJU3GOtxVlfEiABEiABEiABEiCBiCRAxT0im42FJgESIAESIAESIAESiDUCVNxjrcVZXxIgARIgARIgARIggYgkQMU9IpuNhSYBEiABEiABEiABEog1AlTcY63FWV8SIAESIAESIAESIIGIJEDFPSKbjYUmARIgARIgARIgARKINQJU3GOtxVlfEiABEiABEiABEiCBiCRAxT0im42FJgESIAESIAESIAESiDUCVNxjrcVZXxIgARIgARIgARIggYgkQMU9IpuNhSYBEiABEiABEiABEog1AlTcY63FWV8SIAESIAESIAESIIGIJEDFPSKbjYUmARIgARIgARIgARKINQJU3GOtxVlfEiABEiABEiABEiCBiCRAxT0im42FJgESIAESIAESIAESiDUCVNxjrcVZXxIgARIgARIgARIggYgkQMU9IpuNhSYBEiABEiABEiABEog1AlTcY63FWV8SIAESIAESIAESIIGIJEDFPSKbjYUmARIgARIgARIgARKINQJU3GOtxVlfEiABEiABEiABEiCBiCRAxT0im42FJgESIAESIAESIAESiDUCVNxjrcVZXxIgARIgARIgARIggYgkQMU9IpuNhSYBEiABEiABEiABEog1AlTcY63FWV8SIAESIAESIAESIIGIJEDFPSKbjYUmARIgARIgARIgARKINQJU3GOtxVlfEiABEiABEiABEiCBiCRAxT1MzXbhwgXp3LmzlC9fXhYsWBCmXJgsCZAACZAACZAACZBArBDIFSsVzex6PvfcczJnzhyd7dmzZzM7e+ZHAiRAAiRAAiRAAiQQZQQ44h6GBl2yZIk8++yzYUjZfZIJCQmCD4UESIAESIAESIAESCAyCXDEPcTtdurUKbnrrrskZ86cEhcXJ8nJySHOwV1yd999t35w3rx57hLgUyRAAiRAAiRAAiRAAllKgCPuIcb/8MMPy9atW2XkyJFSqFChEKfuPrkzZ84IPtEgZ84mS0rKhWioCutAAiRAAiRAAiRAAn4ToOLuN6qMI06fPl0mTJggrVu3lscffzzjBxjDFYGOdz0tDz070dWzfIgESIAESIAESIAEIpUAFfcQtdy+fftk4MCBUqBAAfnoo4+0mUyIkmYyDgKHjp6QxCMnHKG8JAESIAESIAESIIHoJkAb9xC0r8fjkfvuu08OHTok7733nlSvXj0EqaZNYtasWeLWRn3z5s0SHx+fNlGGkAAJkAAJkAAJkAAJRAQBKu4haKZx48YJlOru3bvLgAEDQpCi9yQmTZokn332mfebfoSeOMFRaj8wMQoJkAAJkAAJkAAJZEsCVNyDbJZ169bJY489JiVLlpT3338/yNTSf3zq1KkC5d2NFCtWTHLkyOHm0Sx9Zu/Bo7J4+UZRkxqmJJ05J3sPHJFpPywxw+LickjnNo0lf3w+M4wnJEACJEACJEACJBBNBKi4B9GacPV45513CjZYglJdunTpIFLz79G8efP6F9ERKxKVdlTh5fFfy7ufXdrIylqlw8eU281hY6xBMv7ZgXLvLR1sYbwgARIgARIgARIggWghQMU9iJbE7qgrV66UJk2aSK5cuWTmzJm21Awf7kuXLpXz589rxb558+a2OLxIn8Bzw3rL7TdcaYt025A3pFbVsvLc0N5meJzym9+kXlXzmickQAIkQAIkQAIkEG0EcqiFlRYjhGirXnjr06NHD4ELSH/lhhtukG+++cbf6CGNB283EGwQFelS8+ohWkn/YuzQSK8Ky08CJEACJEACJEACfhPItBH3nTt3SuXKlf0uWCREbNq0abqbGv388896pL1Zs2ZSokQJQXwKCZAACZAACZAACZAACbgh4Fpxx0JMfz2owEXio48+KtHm1eQ///lPusyxYBUuImFSc+2116YblzdJgARIgARIgARIgARIID0CrhX3hx9+WHn68OhNh3xlsHv3bunfv7/Mnj3bVxSGk0DABGpULiPV1YdCAiRAAiRAAiRAArFEwLXiDqX9/vvvF3g56dOnTxpmH3zwgQwbNswcZb/nnnvSxGEACbgh8MOHT7p5jM+QAAmQAAmQAAmQQEQTyOm29G+88YZ+tG/fvtoVopFOQkKCdOnSRZvRwDQGdu0//vijTJ482YjCIwmQAAmQAAmQAAmQAAmQQIAEgvIqg1H1gQMHSlxcnN7R8/jx4zJ06FDBMadyz/fAAw/I888/L4ZHkwDLxughJGC0QTR4lQkhFiZFAiRAAiRAAiRAAhFDICjFHbWcOHGitmO/ePGiWem6devKhAkTpFWrVmYYT7KWABX3rOXP3EmABEiABEiABEggWAKubdyNjO+77z494o4jlPfu3bvLtGnTJE+ePEYUHkmABEiABEiABEiABEiABIIk4NrG3ZovFp5+9NFHWoGHB5kFCxZYb/OcBEiABEiABEiABEiABEggSAJ+mcq0adNGDhw4kGFWWJh69uxZ7WmmYsWKaeJv3rw5TRgDMocATWUyhzNzIQESIAESIAESIIFwEfBLcS9fvrzs3bs36DLAhSQlawhQcc8a7syVBEiABEiABEiABEJFwC8b9/Hjx8uZM2dClSfTIQESIAESIAESIAESIAESCJCAXyPuAabJ6NmQAEfcs2GjsEgkQAIkQAIkQAIkEACBkCxODSA/RiUBEiABEiABEiABEiABEnBBgIq7C2h8hARIgARIgARIgARIgAQymwAV98wmzvxIgARIgARIgARIgARIwAUBKu4uoPEREiABEiABEiABEiABEshsAlTcM5s48yMBEiABEiABEiABEiABFwSouLuAxkdIgARIgARIgARIgARIILMJUHHPbOLMjwRIgARIgARIgARIgARcEKDi7gIaHyEBEiABEiABEiABEiCBzCYQtOLu8Xjk888/lyFDhkiXLl2kTp06cvToUV0PhK1cuTKz68T8SIAESIAESIAESIAESCDqCAS1c+qKFSvk/vvvl99//90GJjExUfLmzSuFChWSHDlyyKhRo2To0KG2OLzIXALcOTVzeTM3EiABEiABEiABEgg1Adcj7lDOu3btqpX2ChUqyIABA6RGjRpm+c6dOyeVK1cWjMgPGzZMvvzyS/MeT0iABEiABEiABEiABEiABAIj4Fpx/9e//iUHDhyQXr16yebNm+W9996TqlWrmrmXKFFC1qxZI/369dNhI0eO1Eq8GYEnJEACJEACJEACJEACJEACfhNwZSpz8eJFKViwoBQtWlQ2bdok8fHxOsPOnTvLnDlzBKPxUNwhFy5ckFKlSsmRI0dkz549Uq5cOR3OfzKXAE1lMpc3cyMBEiABEiABEiCBUBNwNeIOZT0pKUnatm1rKu2+ChYXFyfXXXedvr1t2zZf0RhOAiRAAiRAAiRAAiRAAiSQDgFXijtG0SG5cuVKJ+nUW1DeIcaob+odnpEACZAACZAACZAACZAACfhDwJXiXrt2bcmXL58sXLhQsAg1PTlz5ozMnz9foLzDVSSFBEiABEiABEiABEiABEggcAKuFHeMtLdr10527twpDz30kKSkpHjNOTk5WQYPHqzjtWjRQiv7XiNGQeDx48fliSeekH/84x9SsmRJufLKK+Xxxx+X5cuXR0HtWAUSIAESIAESIAESIIGsJuBqcSoKvX37dmncuLGcPHlSLr/8cunZs6d88cUXsnr1an3csWOHfPDBB3rxap48eWTZsmXSqFGjrK5vWPLfsGGD7shgUS781pctW1b27t2r88LMxGeffSY33nhjWPL2N1HDTOnUqVP+PsJ4JEACJEACJEACJEAC2YiAa8Uddfjuu++0//Z9+/b5rBI2YRozZoz06dPHZ5xIvgFToCZNmsjGjRulY8eOMnnyZIFf+y1btuiOy8svv6zNhH755Rdp3bp1llWVinuWoWfGJEACJEACJEACJBASAkEp7igBRnBfeuklmTt3rvbnfujQIb0ItWbNmtKyZUsZMWKElClTJiSFzY6JYJYBvuzh/hLKe7FixWzFvPPOO+XTTz+VRx55RF5//XXbvcy8oOKembSZFwmQAAmQAAmQAAmEnoB/bmHSyRcK4XPPPWfGOH36tOTPn9+8jvYTmARhhL1Tp05plHbUHaPsUNznzZsX7ShYPxIgARIgARIgARIggTASCFpxd5YtlpR21P2xxx7TH2xK5U0M3/V169b1dpthJEACJEACJEACJEACJOAXgaAVd9h4f/3117Jy5UpZsWKFYAS6Xr16csUVV+jPNddcI7lz5/arMJEcKWfOtA56sCD3/fff19W6+eabI7l6LDsJkAAJkAAJkAAJkEAWEwhKcYdnGSikq1atslVj8+bN8s033+iwq666Sr766ispWrSoLU60XsDOHQtUv//+e80FrjPHjh0rt956a9BVRsdo6dKlrtI5f/68eOtcuEqMD5EACZAACZAACZAACWQ6AdeLUxctWiTdu3eXI0eO6IWZWKBZtWpVfQ4vM1u3bpXPP/9cu4usVauWVmSrV6+e6RXM7Axh7//UU0+Z2WKBLmYkSpcubYa5PQHvmTNnun1ce7fx5XPfdaJ8kARIgARIgARIgARIIFMIuFbcmzdvLn/88Yf07dtXu3v0ZtsOpb5///4yffp06dy5s8yaNStTKpWVmaCOhw8flrNnz8oPP/wg06ZNkyJFimjPO4MGDQqqaFj4m5CQ4CoNuKyEj3mkQSEBEiABEiABEiABEog8Aq4U9z179mhPKthQ6c8//xSYg/iSpKQkqV+/vrZ9x3PlypXzFTUqw2Em8+CDD+q6/frrr3pH1ayoKN1BZgV15kkCJEACJEACJEACoSOQdkWlH2lDAYdgFD09pR1x4uPjtatEnGNToliTIUOGSMOGDXW1P/7441irPutLAiRAAiRAAiRAAiQQIgKuFPc6derohY779+/3qxiGeQZs4GNRmjVrpquNxbwUEiABEiABEiABEiABEnBDwJXiXqhQIYHNNDzHrF27Nt18d+/erW3ba9SoIRUrVkw3biTevOWWW6RUqVLyzjvv+Cw+dpOFYKMmCgmQAAmQAAmQAAmQAAm4IeBKcUdG2A00X758cu211+pzbxsQYbfQrl276gWR0WomgoWniYmJMnXqVK/8Dxw4IAsXLtT3jJF3rxEZSAIkQAIkQAIkQAIkQALpEHC1OBXp3XvvvbJmzRpZvny5Th6jzhhVx+JTeFXBRkyGaQg8zngbbc6bN28aH/DplDVb3sKC0/bt2ws6Lq+++qo8+uijZjkx0t67d2/5+eefpWbNmnqDKm/ed8wHwnjCxalhhMukSYAESIAESIAESCATCLhW3LHoFLumBiMYsQ82jWDyD9WzVt/t8NveqlUrOXjwoMyePVuPxhcuXFjmzJkjcKGZVULFPavIM18SIAESIAESIAESCA0B14r7F198IcFu5hMXFyfYuCkaBL7qH3nkEdm5c6dZHdQPO8uOGTNGypYta4ZnxQkV96ygzjxJgARIgARIgARIIHQEXCvuoStCdKWExbgbN26UkiVLCrzvwBwoOwgV9+zQCiwDCZAACZAACZAACbgnQMXdPbuIepKKe0Q1FwtLAiRAAiRAAiRAAmkI+N7yNE1U7wHnz58X+HM/d+6ceDweM9KFCxe0KQ12ToX5yLRp0+Tzzz837/OEBEiABEiABEiABEiABEjAfwKuR9zhReWpp56St99+W44dO+ZXjlbF3q8HGClkBDjiHjKUTIgESIAESIAESIAEsoSA6xH3cePGyQsvvOBXoeEK8vrrr/crLiORAAmQAAmQAAmQAAmQAAmkJeB6A6YXX3xRp9ajRw+ZOXOmrF69Wl/fdtttsmvXLu3+8IknnpAcOXJIpUqV9Mh82uwZQgIkQAIkQAIkQAIkQAIk4A8BV6Yyx48fF+wYWrVqVdm0aZPkynVp4L5atWp6l1TsFmrIBx98IAMGDJDx48fLoEGDjGAeM5kATWUyGTizIwESIAESIAESIIEQE3A14r5lyxZdDGw2ZCjtCGjQoIHeeAguEQ3p37+/1K9fX5555hkjiEcSIAESIAESIAESIAESIIEACbhS3PPnz6+zKVSokC272rVr62vDbMa42b59e9m7d69W6o0wHkmABEiABEiABEiABEiABPwn4Epxr169uh5p37Ztmy2nunXr6uslS5bYwg1Ff8OGDbZwXpAACZAACZAACZAACUQXgX2JR2X9loToqlQ2qY0rxT137txSs2ZNmTt3rixatMisCkxiILNmzbL5dMfiVUjRokX1kf+QAAmQAAmQAAmQAAlEJ4H/e/VT6TF4VHRWLotr5UpxR5lHjhwp2GSpY8eO0qdPH12NZs2aSZUqVWTZsmVy9913yxdffCE33nijrF+/XuLj4wUj9RQSIAESIAESIAESIIHoJXAu+bzgQwk9AdeKO9w+9u3bV++YOmfOHF2yuLg4vQgVLiA/+eQT6dWrl3z77bf63vDhw7XyHvoqMEUSIAESIAESIAESIAESiH4CrhV3oJkwYYKsXLlSHn30UZMURtqnTJmiPcxAgS9ZsqRW5rHLKoUESIAESIAESIAESIAESMAdAVd+3P3NKikpiaPs/sIKczz6cQ8zYCZPAiRAAiRAAjFIYPe+wzL+09ly4eJFs/Yz5/4pWKA6oFcnMwwnN3ZqLq2a1LKF8SIwApd2TgrsGb9jw66dQgIkQAIkQAIkQAIkEJ0EVqzdJq9NmOG1cqMnfmcLz6VMqqm425AEfBH0iLvH4xFsuHT06FFJTk5OtwDNmzdP9z5vho8AR9zDx5YpkwAJkAAJkEAsE7ioRtsvXvSYCO4aNkaWrtoim38aY4bBfDouLigLbTOtWD4JasR94sSJ8vjjj0tiYqJfDKHkU0iABEiABEiABEiABKKHQM6cOUX9bwqU9BzqKleuODOMJ6Eh4Fpx/+CDD2TgwIE2f+1oOAoJkAAJkAAJkAAJkAAJkEDoCbjWtF955RWttHfv3l1Wr14tZ86c0X7d4dvd1yf0xWeKJEACJEACJEACJEACJBAbBFyNuMM0ZvPmzcpWKU7Gjx8v5cqViw1arCUJkAAJkAAJkAAJkEC6BDq3aSzlSxdLNw5vuiPgSnFPSEjQubVr145KuzvufIoESIAESIAESIAEopLAfT2visp6ZYdKuTKVqV+/vuTJk0f27NmTHerAMpAACZAACZAACZAACZBA1BNwpbhDab/22mtl06ZN8scff0Q9JFaQBEiABEiABEiABEiABLKagGs/7rBzb9KkiZw6dUomT54sHTp0kMKFC2d1fbIs/xMnTsioUaNkwYIFsmXLFuUWKafUqVNHOnXqJA8//LDkzZs3y8qGjOnHPUvxM3MSIAESIAESIAESCJqAX4p7+/bt5cCBA2kyg/J+5MgRM7x06dJSpEgR89p5smHDBmdQVFwvW7ZMunXrZvqzL168uJw/f16gzENq1qwpP/74o1SrVi3L6kvFPcvQM2MSIAESIAESIAESCAkBvxT38uXLy969e4POMBo3YIJyjpmHbdu26VmHd999V2rVqqVZLV68WPr27SvosLRq1UoWLlyoPfEEDdJFAlTcXUDjIyRAAiRAAiRAAiSQjQj4pbh//fXXkpSUFHSx77jjjqDTyG4JQFG///77tXed9evXS6FChWxFhNvMBg0aSHJyssyePVuuueYa2/3MuqDinlmkmQ8JkAAJkAAJkAAJhIeAX+4gb7rppvDkHgWpzp8/X9cCjJxKO27ATAYj8kuXLpUVK1ZkmeKuC8l/SIAESIAESIAESIAEIpaAX4p7oLWD3fucOXPk4MGDcv3110vVqlUDTSJi4j/66KMCpd0wj/FWcIy2Q7BglUICJEACJEACJEACJEACbgj4ZSrjK+EffvhBxowZI507d5ZHHnlER9u4caO0aNHCXJiJwAcffFDefPNNX8lEdfj27du1Up+SkiIYncdC36wQmspkBXXmSQIkQAIkQALhIYB1gzly5AhP4kw12xJwPeL+559/yo033qi9p1SpUsWs4K233moq7TVq1NCuEaHcN2zYUPr372/Gi4UT/FHBFSSU9nr16ukFqsHUGzvW/vXXX66SQBk44u8KHR8iARIgARIggWxFYOgLk2XNhl0y56OnslW5WJjwE3CtuA8ePFgr7R07dpRBgwbpksKOe82aNfr8u+++k+uuu05whLnMCy+8EHOK+/Dhw+Xbb7/VnmQmTZqkd5sNpkkHDhwomOVwK3FxcW4f5XMkQAIkQAIkQALZhMCuvYdk197EbFIaFiMzCbhS3DF6i4WW2HBp1qxZkivXpWSgpEIwug6lHQL/5tiICC4Rjx07lq6fd/1AlPzz5JNPymuvvaZr88orr0jz5s2Drtl7770na9eudZUOZkc44u4KHR+KUQKffLNQ4i/LIzd3/keMEmC1SYAESIAEshsBV4o7dgbFBkPYFdRQ2lExYzS4S5cutnrCrhuKO0bj27Zta7sXbRfg0q9fP/n444911aC0Dx06NCTVrFChguDjRqzt5OZ5PkMCsUbgzcnfS9FC+am4x1rDs74kQAIkkI0JuFLcDZ/uxYoVM6uGnVVXrlypr52Ku7Fjav78+c340Xhy/Phx6dGjh8ydO1dy584tH3zwgdxzzz3RWFXWiQSingDWqETjpnFR33CsIAlEGYHk5BRZsmqTXLzoMWt2+OhJOXvuvMxfap+Fv7xuFSmiBhwo0UvAleJevXp1TQQLVA2ZPn26/pGD95I2bdoYwdo85rffftPXlSpVMsOj7WT37t3StWtXbcoCE6Jp06bpGYloqyfrQwIkQAIkQAIk4B+Bx16eIhu2Jsi37z3h3wNeYk359hcZPOIDL3dEutz3vC38/tuvkdFP3WcL40V0EXCluEMxhcnLwoULpXfv3oIRdth0Q3r27GkuwpwxY4aMGDFCe1Xp0KGDlChRIrro/V2bvXv3Cuq3bds2qVy5sl6QW79+/aisKytFAiRAAiRAAiTgH4EtO/fL5h37/YvsI9bt17eRsiWLyoULF80YL7/7tezYkyjvPDPADINnyOaNapjXPIlOAq79uC9evFiuvfZaOXnypEmmYsWKsmTJEilXrpxs3bpV4A4SEh8frzdkat26tRk3Wk4wlY56od7YJXXevHlSvnz5bFc9+nHPdk3CAmUjAp9++6u8OP7SrKFRLHhtiFObppUvk2oSiAXeLwy7Xa7veIURjUcSIAES8Emgx+BRsn5LgqyfPdpnHDc3ej7wmnIHuVM2/jTGzeN8JoIJuBpxR31btWqlR9xHjx6tF522bNlShgwZopV23Idv93z58knTpk1l7Nix+ojwaBPYsUNpz5Mnj7z11lvaXAj+1r0JlOciRYp4u8UwEiCBLCRQolhBqVaptM2mff+hY5I7V5wON4qWUw1pFS1M+1GDB48kQAIkQAKZS8D1iLs/xUxMTJSSJUv6EzUi48AtZpkyZeTw4cN+lR++76HcZ4VwxD0rqDPPSCbQ/OYntFeZ2ZO5wUkktyPLTgKZRSAl5YKsWLddLlxMNWn5v1Gfyo6ERJk6+iGzGNjttH6NilIgfz4zLNATjrgHSix64rsecfcHQTQr7ag/3GL6q7T7w4txSIAESIAESIAEIpPApP/NlwdGTvBa+A53jLSFD77rWnn9331sYYFclC1ZRA4eOh7II4wbJQTCqrhHCSOf1cDGUnQX5xMPb5AACZAACZBAzBDo1a21FCtcwOa28bUJMyRh/2F548l7bRyubFbbdh3oxZvKc4x1sWqgzzN+5BKg4h65bceSkwAJhJFAzpw51G7Dyk0DhQRIgAT8IFAw/2XS41r7TsufzvhVjp04LT27tvQjBf+jYKE8d0P3n1c0xaTiHk2tybqQAAmEjMBLw++SfHlzhyw9JkQCJEACJEACwRKg4h4sQT5PAiQQlQSuasm9GKKyYVkpEiABEohgAjkjuOwsOgmQAAmQAAmQAAlkWwIllatZuJulkECoCITVHWSoCsl0gidAd5DBM2QKJEACJEACJBAIAbiIPK8+l+XLE8hjjEsCPgnQVMYnGt4gARIgARIgARIgAfcEcqlN3PChkECoCAStuJ89e1bGjBkjv/32m2zcuFH27t0rxYoVk/Lly8t1110nffv2lbJly4aqvEyHBEiABEiABEiABEiABGKSQFCmMr/88otWzLdu3eoTXuXKlWXu3LlSrVo1n3F4I/wEaCoTfsbMgQRIgARIgARIgATCScC14o6R9QYNGsjRo0elaNGicu+990rt2rWldOnSsm/fPr2r6KRJk+TIkSNSoUIFmTdvntSoUSOcdWHa6RCg4p4OHN4iARIgARIgARIggQgg4Fpx79Gjh0yfPl26dOkin376qVbenfU9ceKEVugR7+6775aPPvrIGYXXmUSAinsmgWY2JEACJEACJEACJBAmAq4U94sXL0rBggUlPj5e27XDpt2XnDp1SurUqSM5cuSQ3bt3+4rG8DAToOIeZsBMngRIgARIgARIgATCTMCVH/ctW7ZIUlKStGnTRi9ETa+MUBg7duwoCQkJ2nwmvbi8RwIkQAIkQAIkQAIkQAIk4J2AK8UdSjsEI+7+yGWXXaajHT582J/ojEMCJEACJEACJEACJEACJOAg4EpxxyLUnDlzyqJFi+T8+fOOJO2XMKtZuHChNpWpX59biNvp8IoESIAESIAESIAESIAE/CPgSnHHCHqrVq1kx44dMnz4cPF4PD5ze+qpp2T9+vXSqFEjMeysfUbmDRIggbAT2LrrgOw9eDTs+TADEiABEiABEiCB0BJwtTgVRfj999+lZcuWWmm/4oor5KGHHpJatWrpzZYOHDggmzdvlrFjx8qSJUv06DxG3Vu3bh3a0jM1vwkYnSYsFqbENoHmNz8hVSqUki/HDo1tEKw9CZAACZAACUQYAdc7p7Zo0UImTpwoDz74oPz5559yzz33eK163rx55aWXXqLS7pUOA0kg8wmcPXdezp1LzvyMmSMJkAAJkAAJkEBQBFyZyhg59unTR1avXi2333673nwpV65L/QC4fqxYsaLcdtttsnbtWnn44YeNR3gkARIgARIgARIgARIgARJwQcD1iLuRV+XKlfUGTLhOTk6WPXv2aHOZfPnyGVF4JAESIAESIAESIAESIAESCJKAa8Ud9utly5aVnj17mkXIkyePVK1a1bw2Tp5++mlZtWqVfPHFF2KMyhv3eCQBEggfgVXrd8hXs5baFpAfOnpCUlIuyFNvfGZmDC9R997SQaoq23cKCZAACZAACZBA9iTgenEqfLhjcercuXMzrFmpUqUkMTFRe6HBCD0l8wlwcWrmM88OOT7/9lfy7Liv/CrKhBf/KXfe2NavuIxEAiRAAiRAAiSQ+QT8GnHft2+fbN261VY6+Gc/duyY/Prrr7Zw6wV8vMMGHko77N6LFi1qvc1zEiCBMBN4cvAtMrRvd1su8CpTuXxJmTZumBmeM2cOyZsnt3nNExLwl8DqDTt11EZ1OCjjLzPGIwESIAG3BPxS3OGn/brrrpOTJ0/a8lmxYoW0bevfCF3Dhg2lUKFCtud5QQIkEH4Cl+XLY8sEneg4pag7w22ReEECfhJ46NmJemBm7pT/+vkEo5EACZAACbgl4JdXmXLlysmzzz6rN1CCyYVhdhEXF2cLM+4ZxyJFikj58uXlmmuuka+//tptGSPyufHjxwsW6M6YMSMiy89CkwAJkIA/BFIuXNBrJvyJyzgkQAIkQALBEfBrxB1ZYIMlfAwJxMbdeCZWjticCjvKnjt3Ti6oHzVKZBN4a8osqVimmNzQqXlkV4SlJwESIAESIAESiGgCfivuzlo+8MADUqVKFWdwzF//8MMPcscddwh3KI2eV2HcRz9IvZoVokZxv77jFVKuVPZfb/LsuGlSrEhB+ddd10bPy8SakAAJkAAJkEAQBFwr7q+88koQ2Ubfo0eOHNEbTX388cfRV7kYrxHWeESTvPjoHRFRnemzf5fSJQpTcc9GrbVs9RZ577OfbO5Ft+0+KDlUGfv/3ztmSeFedGDvTtKsYXUzjCckQAIkQALBE3CtuBtZQ6mBf3Z4l9m8ebN2+bh48WLtQWbIkCHSr18/ufzyy43oUXncsGGDtG/fXg4ePCjwZf/iiy/KG2+8IQkJCVFZX1aKBEiQVNOwAABAAElEQVQgNgms25IgM+b+aVPcT50+q2Eg3BAsgG7TrA4VdwMIjyRAAiQQIgJBKe7wKnP//fcLbLqtArtueKAZN26cvPXWWzJq1CgZOnSoNUpUne/cuVMr7a1atdJ1btq0qYwePTqq6sjKkAAJkECfHh0EH6u07f2UGnHPIb989ow1mOckQAIkQAJhIOBacYdv9q5du8qBAwekQoUK+nzevHmyZcsWXUwszMRmS1Bqhw0bJhUrVpRbb701DFXI+iQrVaokP/30k1x99dVZXxiWICgCb38yS974cKYtjb0Hj8r+Q8ek5tVDzPCcakRx7Mh+0rlNYzOMJ8ER8GaSBCslZzhGcykkQAIkQAIkEIsEXCvu//rXv7TS3qtXL5k0aZJ2fdi5c2dTcS9RooSsWbNGHnnkEZkwYYKMHDlSevbsqf39RhvounXrCj7hltOnT8vu3btdZYMNsworLWi/Muuh+CZQxHNO6lcqKVar9kNHTkiBy/JKPRVuCDYsynVSKfTkaSAJ6vju14vkxY9+SpMGTDMuq3+nLfy1ITfKLVexw2SDkoUX58+cVd/rwr+FLGwDZk0CsUagTJ06sVZls7451GiWVUcxb6R3AiWwYMGC2o5906ZNAteQECjuc+bM0TulQnGHwGymVKlSgsWbe/bsEfiEjwXBKDyU7OnTp8tNN90Ukip3795dZs60jwYHkvAgvYQskCcYFwSmVb9eip47LlcnLCSQMBE4lK+obCtUWaWeOpq+tXAVyeVJkconEsxcc6guVa1jW6Vwsn0zODMCTzKdwMwq16hW80i3HWk7XpleGGZIAiQQEwTGey7GRD29VdLViDuU9aSkJLnhhhtMpd1b4gjDJk3YdXXKlCmybdu2mFHcffEIJhybYF1//fWuknjwwQdl8UWPjB031tXzsfzQjxOXSvnileWO/0SGN5bs3lbPLj0hxfPllMGNC6Rb1IFTlkmR+DzySo/r0o3Hm1lLoODqvboA3RvdkrUFYe4kQAIkEAMEXCnuxqZCuXL59ziUd4ix42oMcA1LFeGdx62HHqwz2KpK1W7QoLCULZoTzTdtnRSvUjHm2a3ZdkDy58sj1cql7wN+y54jki9PLqlQspDX1+LI8nclT+ECiqfdBMYZOf67rVJUuYPkO+skk72u22Wv4rA0JEACJBDVBHK6qV3t2rW1TfvChQv17qDppXHmzBmZP3++HnmvE8M2Sekx4r30CRw4ekpOJp1LP1IY78bF5RT4pY516ffqtzJ8/OwMMdz9wv/k4XE/ZhiPEbIHgR37j8muA8ezR2FYChIgARIggXQJuNJGMNLerl077THmoYcekpSUFK+ZJCcny+DBg3W8Fi1aaGXfa0QGkkA6BLo+/ok88tasdGKE99a4kf1lxAM9w5tJBKR+PuWCJKtPRnLu/AU5m+z9OyGjZ6338+TOpfZFyG0N4nkYCPQf9a0MfG1GGFJmkiRAAiRAAqEm4J+ti5dcx48fL40bN5Z3331Xli5dqj3GwDUkBG4hd+zYIR988IHAHh6bEiE+hQTcEDh1JlnwySq5qmX9rMo64vM9c+68rNuZqFw6plblzLkUOXH6nPyx8ZJtNO7AS0/DqqUkd65LZnUI+2jUA1TcASLMck51si6oWSUKCWRnAnhPz1+4qDx85cnOxWTZSCDsBFwr7lWrVpWpU6fKgAEDZOXKlfpjlPa2224zTqVQoUIyZswYadSokRnGExKIJQJJZ88rhTSnTSmNlfo/P2WhvP7l4jTVhXlGu4cm2sJfvf8a+ddNLcywWlVjwwOVWWGekAAJ+CQw6PWZsuPAMZn/xr0+44Tjxi+rdkrisdNyS/t64UieaZJAwARcK+7IqVu3bnpE/aWXXpK5c+fK5s2b5dChQ3oRas2aNaVly5YyYsQIKVOmTMAF4wMkEC0Euj4+RWpXKiHvDeseMVVaui5Bps79y7b50YGjp/XMx0NjfzDrceTkGT0CVrTgZWbYQRUPnZV/f/CznD6bLF1a1NB+vjs0riKliuaXJyfMlSL588nw3q3NZ7Ch1TXNqpvXPCEBEiABK4Gj6rsGn8yWN79aIpsSDlNxz2zwzM8nAdeK+8SJE7Vrx06dOslzzz1nZoBNgvLnz29e84QEAiGwcfchmf0H/N+kCsxktu49ImOnLzUD49Ri0ds61JcShS/tIWDeyIYnh04kSYnjSdmwZL6LtFgp7hN/XGGLcD7lohw/fVYmzVpphl9ULkYvqI9ToNCPnrbEFtyjbV3pdVUDeeWz36R0sfz63BaBF2EnsG5HouqQrbGZLiUkntCmSv9RHSpDsKHSnZ0aSR3V4aSQQCwTwLeb1dQvllmw7tmDgCvFHRsw/d///Z/eORX+2e+8M9WtG5X27NGwmVGKk2fOSV4sILTYJQeb7wffLZe3vl6WJpljp87K2zOWqJHcFEk8clbfx6jtHZ0aponLgOAJPNyzpeBjlSYDxmsXjzNeuMMaLFi0apUW/3xfyhUvKF8/19sMzqE0wVy0ozZ5ZNXJ3BXb5bUv0pouoTxOkya486TinlUtxXxJgARIwDsBV4r7unXrtNKOJDt27Og95RgP3bVrV9QTeOrjH6VW+ZIyuFuqyUOwlX5xQCd5sMc/bCMcVz0ySeqrhYulSyvf4MWLyH2dWugRQl9+woMtA58PjIB1QSmehJKOjzM8sFQZOxwEHri5hQy8/gqbCVTHYZMlt9prY9Yrd5lZov3y5E5dKGze4Em2I/D14r/klDJJu+uqptmubG4LBJOYH5ZukYuWoe69h0/qRe1T5qw2k1WvqXS4vIqUL+F9zwgzIk9IIIoIuFLca9WqJQULFpSTJ0/KqlWrpGzZslGEhFXxl0CS+rFIOpfsb3S/4mFUtmKpwra48KMenze3xCnPI5flzSWVStvv2yJn8QV+XH5dvUttAJ9qPgJTH4R/Pu8vs3Qw9emsbLoL5c9rhsXKSTFlD1+8UPY3cYrW9nAq5FhfAAUor9o0ixJ5BFZu2ysnks5GleL+xfy1Pl0AO12XPnTLPwQDPqGQ39bskv98OFdgAmgI7Nvh3ra9ZTE9Orb/vLEZzf0MSDxmKgFX39Rw7/jhhx/KvffeK3379pVx48bpkfciRYpkauGZWWAE7uzQQbbMX+Dzodzx8VKpeTM9WuqM5FEjH7uW/SHnk1JttVOUn/5TiYdsaRatXEmKK49D3uSs6uglrFB205YvxTTxlHJeoUkTyac6ht4kWeWfXh3wTKD18JaP23q88tM2mfJHqptDI+3EY0ly38vfGJf6+NS11eXWJmk7vduOn5V45YWmYskiAbWHLfG/L9zWw0wrg/Y4vH27HN1pn11KVutcknKmmO3kbI9vnrvdNJvx9l6ZeVtOsqIeluz1qbMe1vusRyqNUPyde3uvUnO4dMb2uMThzLFjckFyOvGY15HYHv2vayqt61e0jbg/9Ob3knDgiIzuUdesG06qFo8zv2vExfeVNbGE3cfl3KlTklut01PdWest/buIAZnkU6fFo8yF969bL1tyHLbFMS74ffU3iSDbA6mk93du8I61oyvFHZCwG+qVV14ps2fPlltuuUVzK168uF6w6gvi6tWpU1y+4jA8fAR6XtlGti/2bt+KXONUh6z85Y0llzo65YJS0reqnXJxNCTFU1pOHz6l0txiBEnSsaM+Fffje/bI9t8WmXF9nRQsVUry+dhl9+zx4yq/Nb4e1eGB1sNbYm7rcUM+j1ze5NKPaI2r2kuxylWk9zPTpHKZwvLywGvMrLb9skAKJW5Xddlhhhknn3tKSRFJkW55TwXUHsbz1qPbeljTMNoDC0tLFrGPlO9RM24H1m+wRpem+S9K4biz5rvmbI/4fKmbKnl7r2yJ/X0Rynp4S99bPZzxnPWw3mc9UmmE4u+c7XGJpz/v1RlPcUnJm+rVKbUlLp1FYntglrVhtdK2quTLeVFyJKt1Vdvs3/97t9miifF9ZQ+9dJXRe1VMRXuuTh5pN2Sg+TvYY8Tnsmn3YZk/+l5JOXdOfhn31qXfwRNb1Xec3ZGCkSe/rwwSElR7IJX0vndTc4mtsxxqpCh1TiiAuser0dkzZwJzzeQyqwBKxai+CBQoUEAtIs0liYmJvqJIDmXnmlOZcHiTfUdOyJod+/RIg3H/kwUrpXSRAtKpcQ0jSOLUQtVWdapI/nxplX9EuoBddtN75dQUZJwq5w9/bJAZv6/T6cKHLhbAnr9wQXKpkegi8fnM/DDNP6Bzc2lQuYwZll49EAmLqz0qrfQkpyoDpkN9ib/1wPP173tL6lQsIV8908tMDn8LF33sOPzg+zOkXLFC8n+3dfTZHkgos+thFt5ykl49jGjZrT2MclmPsVyPFz9ZqGdAhve+UiPhe5X6ZgTyd576VOpZON6r5PMptknLl75aICfPnpOX7r3OzBjfXXAcYEh2rIdRNusxve/dG5+cKtj/Yfn4/tZH7Od//37YA1Ov3LSHobj/NXGwToh/H6k8s8N7lVqa2DlL/csOsM7vvfeepPhQPAJMitEziUCyaq+43KmjnYFkCyV69vJNaR7ZfuCovD/b7gUmd1wuad+wWpq4CIBS7o8ULxgvFUtcMr0yjug4xOfJrUxIippJYMfNwgXzB1Qv3Tnx0UExE87gxN96+EoGP6y+2gL38PHViTLSzO71MMqZ0ZH1SCUUzvcqNRf72f/d2dYWwPZIxZEV7ZGa+6Uza3vMWbFJ3v3B7mbViH/XqKnGqT7e0aGJ9Gh9yetWdquHraCBXKixFF/fm/4kk973rj/PI461Pfx9xhkvWtojO9TDyTYWrv3ToryQuOuuVA8EXm4zKMoI3NepuVzXrI6tVv+dMluqlC4qjdUOl0ULXCZVyxRTJoY5pKwaLQ5WWtSuJPhY5f5xX0k1lcdjPa+yBvt9/vvGXVJElROecCgkQAIkEGkEGlYpKz2vbGSz/V64dpucU6PwnS6vZVZHfQ1LsxoVzOtoOKlbuaTe7C2z62Is3g53vp/9slL2HDouw3q0D3dWTD/CCbhW3CO83ix+gATg2q/C3yPgxqOXvLzklu+WrdfKcNsG3kfZjfhZfZz08x9SpVRR14p/RuVfv/uAFMiXVy8qtcatrcxkalYobg0yz1du2yNzV22xWQ8dO3VGezF47X8LzHiYWbi5VQPVUYIVZmTKUtVxukzNmDSqmnZBbmTWKHuV+rvf10tcXA7pcoW9g529SsnSBEOgTNGC0rv95bYkNu1J1F5lMMIezfLSwE5ZUj24J4a5Zrhlh1p4u+vgsXBnw/SjgEDQivv58+dl//79ck4t2oD9mCEXlA0xTGmSlBeQnTt3yrRp0+Tzzz83bvMYRQTQ7lb3h1ldNV8j67qcqa9oyIv59neLVeemsDzumBH4n8W23ZnpviMnZf3ugzbFPVltaJSi7PARbggU93YNkiJacceIUjE140HF3WjV0B5/+WubXgNCxT20XJlabBNo17hybANg7bMdAdeKOxZoPPXUU/L222/LMeWOyh+h4u4PpeiNM27Gb9q05voW9VxV8tKUpZoDzkAm/fSHNtsZfkuHDGKG9vYF9Tdh9f/rT+pdlfkRPlZ54J3penHqv3tdbQ2O+PNLHbzwVSPh0DG9kVBpNSpJIQESIIFYJrDn8HE5mXROOUYoFcsYorLurhV3+G5/4YUX/IJSoUIFuf766/2Ky0iRQ6B62RLK9KSYbErw7anGWpvVanHpmeTz4lZxH9j1H1I4v2+3Z0Ze2G3PuuOeEe7riBFumP1gUyRK5BIY8+2vUkh5HPpP76yZUo9ccix5JBOAGSO8blEihwAGMeCpzWKkoM0jMdMKO3erlCpaQA9IWMP8OZ+6YIVs339E3hrcw5/ofsfZsu+QHDmZJC1q2deg+Z0AIwZNwLXi/uKLL+rMe/TooTdhqlSpkjRq1Ehuu+02GTVqlGzcuFF+/vlnefnllwX3MDJPiXwC89dsFXg2MGSFstE+oXr1f+3cL09+9IMRrBep9rm6mdQoV8IMC/bk8mrl/U4CXywjP5lti3/s1FnZkHzQFg4vA0dPJUlttWD1n91a2+LzIrIIpFy4KPhQSCCWCNx/XSu5wPc+4CY/c+68djGMzn5my9zVW+QdZVrpTR567xtbcLfmdeW+a5rbwvy5uKA2OsQn1PLN4rWyee8hKu6hBhtAeq4U9+NqExzYtVdVO2TC/CXX3y7+cI2NmSpWrKg/nTp1kurVq8uAAQME7iMHDRoUQNEYNTsSgDmIVo4s3wc49Sh9KSUlVWmCTXYgo96hrivKefLMWduIBsqTotZeYHtwQ2B+c+rMOTluCTPupXfENuPoxFgzOHFabTqUclhGf/2L+Sg43NSygVRSi2JjSRav3ykzlQtRy2siB4+d0iM1/56c2sGD94vb2zWRBlXKyK9rt0t83tzSNMq8YYSj3dF5nqwWW1tH7ODuFXLnq5+aWYJvv84t5KpGNcwwnkQXAbjOTU+mzP1TTqjvuMEcmLBheuf7xXrU+9V+mW8NgNFq/O1aTStnLd+ovx9vb9/ELCf+fhspT0LZSQKd0c5OZY+WsrhS3Lds2aLr37JlS1NpR0CDBg1kxowZsnv3bq24I6x///4yevRoeeaZZ6i4A0iEy9WNawo+Vhk45kupVaGkPNqjgxmMP+6TShk+fjp1ky5MD0JxtoZBcS4YxIjHb+u2qxmAzWa+OEH6cI9W2aIsQ4E+qX68aijznsdvvcoW/59vfWW79ucC9tSrt++zRT3396LS1Tv2m+EqW7myXtWAFHfYvMO9ZqTIe8qvNMp7a9vGZpGNL3e0uSG6g6d/rFI7eJjxMBY2f7VojU6HirtBzPcR3oXaqPcKnA1Ztmm3num6omaqG0D8fVn/Doy4PMYOgS37Dgc8MBELdGC2iU9WSMHL8ir3nfbf0eVbEwSzAJ2bprr1zIqyMc/sT8CV4p4/f35ds0KF7P66a9eurRX31atXm4o7IrZv316byhw8eFBKqe3sKdFPYLJaIAo3kU75c8se6ffml7bgh29sK23qV9WKfh612591xz9bRC8XZ5JTfIysX0wzsm5VIr0kFVAQ7PSdtvqD3/6f3jQKO54GI5gajSRZu+uAlChkH/W7sl4V1WGpYqvGw2oKuJgaHRxx+zW2cPNCKaEWPdQM9nYCO0t4UbEO6R9VbjRPn02WD2f/bj6CjkHnJrWkvPL2E01SU5mg4WOVxw98p73KcGTVSoXn2ZUAdsLGiHMg3/fZtS5ZXS4MJE1f9Jc5CILybFMdtlPq+3DMtwttxWvXoLpcXq2cLYwXkUXAleIO8xeYx2zbpn44LVK37iWFY8mSJdKtWzfzjqHob9iwgYq7SSW6TzA1XyR/PqteJdMX/yUllOJm9feOEcEGlctoGCOUTXr1MsXlge6Xtl73hxBGLZwjF9ioCZtBOd0yYmQdC1EXrd9hUxAxOg+b+N/W7TCzxGLVxurLDX7HKdmPwHrVWfhh2Qbb+2WU8vs/NhinegQa70K0Ke5mBXlCAhFK4K2Zi+TQ8dPy3D1dIqoGX/66Sg4cPRXQ71S4K3hQcVyhzDetM3AYvb+obFiXb91rZq8mgPUMnL+K+7b9h+XT+Sts6e5Qu6WfOZcsz0ydY6aLE8zEOwdrbBF4ETICrhT33LlzS82aNWXu3LmyaNEiad360qK++vXr64LNmjVLm8ZgtAsyc+ZMfSxaNLbsfHWlY+Ef1c74zyrYURUfq/z450YpV7ywuQ239R7Ok9ToQDinLlHGfUdPyOvTU23QjTJgge0bFtt0hGPRl7NTYMTnMWsJdP9HfcHHKkPf/1Z7lRl5Z2drMM+zKQF41YCEYqflbFrFTC8WZhWxliTVgErUYEWKNlHcf/SkrTwlC+fPUk9acFV4Qq1DygyB+eQite7GqtgmHj8lSUoBtc4M41esSfXy6b6TW/YeFoxwh1ri8+bRa3zcpNtUlfnDh2+zPfrytHnaq8z4B26xhQdycVbNaGNQy8rtvHqfcI1wq5w6e856yfMwEnCluKM8I0eOlF69eknHjh31cfLkydKsWTOpUqWKLFu2TO6++2654YYb5JNPPpH169dLfHy8Xqgaxrow6SwicKfasa9k4QJZlLv/2fZs00gK5ssjhdRMgFVe/WqBYEfCuzs2NYPR6axe1vtup2Ykx4m96+K4GSWX+MLGzq5WxQALgc+rhcmHLV/kmElx2umDqcFooVqI+u2StWrmIzUlKBaJauTo0Q9mmLTwzJ1XNeXUrknE90lcXE7JFUEuTbE4EO3/7N2RNeI6T3kEwfueHW2Rf1CzTR/OWeb1JcH+EFa55cqGYl0Iab0Xbee/q/UfEywmdNb6TXTwuuFEktxz9RXWKJlyPqhrS/VeXciUvPzNpF6l0vL6gBts0V/9ar72KjN64I22cF5kHgHXijvcPmJk/cMPP5Q5cy5NmcTFxemR9j59+miFHUq7IcOHD9fKu3HNY/QQaN+weraqjNL1TAXRWrCOjWtYL83z3LlySoHL8kjtCsGtv7i2aW0pXii/mW52P5kyb7neyfQ6h009Zj3g096bb+jP1e6nX/22Jk3VoHQPGjvNFj6wS0ubctNLLV41TI/yKr/T8CCTqraLYAExlM941bkyBG3prRzGfR5TCeCH35jlTA3NvmfwToWN/CJN5q7aon1uZ0fFvVXdKvrvyNIflu//WK/L26N1QxvqK2LIexNmTmEeYvXi8vZ3i+SgGnUfeYdlhk5935TIou9w2PrT3t/2ivLCBwHXijvSmzBhgjz44IPaX7uRPkbaocDDz/vatWulRIkSMmTIEPn3v/9tROExRgnks3wxYZocGzJZNbezytb8gFIAZymTGkOgzLWqU1kp1nmNoAyPt7ZpLBm5SMswERcRbmhpN91wJoHRaow4Zxfl6veNu6RUkQLiVNyfm/qTmkHJr30H51J/y/ktinT7BtWUwg3FOlXl/mbJOhUnt82sCCPuzf72bgKbyLy546SlakdDWtSuJPhY5RG1eLVIgXj57x0+Fq9aI/M8DQF6j0mDJFsGYMbqgvouCMd3FGa5ulxRx1bvpervHO5unTs02yKF+QKLxlds3WObYcP+GbDDXohF5oao740GlUur2Tr7YnfjttsjvnOds8JwhIABiox2Wj504rRN4ceaKPhHh0mSIRhgwML7WNzEb8mGndqt5s2OjqHBhsfQEwhKcUdxGjdurD/Wot1xxx2CT1JSEkfZrWBi/HyEUsiMEVfYFcLm3Sn4gn9/1lJbMHYG7BDAqL6vkXVbollwgQ2hyhcvJIO6tsqC3P3P8qSyVcynFuW+8PlcKaEU+GpqwXBhZV6EUSusUbhRfawyd9VW7VXmRuWv3puMnfGr/lF7stfV3m6HLKxVXdXBy+d/By9kGTOhmCQA95vwjNJajXIHIm/N/E1OK4X1hT5dA3ksouPCA5QvU5U31Y7HVrlBrV3JClMVaxmM859XbhaYdHkTeBGzyg3/qKfK3cwalKXnbZWntlpqY8FwCAagDIHZIzZkouJuEAn/MWjFPb0iwq6dQgIGAesUZJ9OzeQ65a/cKiOmzNL+zvurDWMgGOmAAul0e2d9JhTnWByXGQvkDqv6GB0Xf8qNBVCov5WbP8+FKs6Z8+f1FPuCv7ZKyUIFbCPqgeQBG3h8wi2YaUlPMMuTS5niOEfe0nuG90JDYOfBowLbcKsJx6VFlB6x2hhj5LJDwxppFrZbS2HYAaNDn5UyQ20whhHjQBV37PeQrEZtnQLTIdj8Z3W9nOUKxTXMiqBEWte0QJE/phaNDru5vS2LiiWL2K6z8gKzhn3VrqXWxZk/KWX+uNpsD2sEDMHqnaY1yhuX+gilH6Y4WbWOwNt7iXpgNjQYgXtdf73SBJMPn/VNICDFHfaIq1atksWLF8uuXbukTp060qZNG6lRo4bvHHiHBBwEPlI7PlYoUUScI+OYZoRii1FdyItfztMLROHnPZxi+BU/cOyk5FMekzC6nB3ktf8t0B2Kx3peFZLiQHm2Kk4wdsEPqd4J9+8cgvxOD6qcmM7GhlXhktHfLJRCyuTqyd6dwpUF0/VBYEPCQbWTbtp9HRDd6tUD11go7vRIhXBDXpu+QC4oJTcz2hEbvE2co3aotZiGYadl/O3gVcWx35tfqPMccnuHy9NsTmeU2Z/jm+r9hN/tUJuKXabWkhidHX/KEY44+G43Fvuv331A77cAk7vTyqtLDcd+BMHmDxeGSapDZbgZdqaHNTrwKIMBIXwfpCeF81+WxpRwjdpgDzyd+3g408GC2N1q8CWrFHdnedBRApvn7wlupgdukilZS8Bvxf3IkSPSs2dPmTdvnq3E8Oc+bNgwefrppyVv3vT/CGwP8iJmCfyq/KXjS9OpuDuB4MvR28iUMx5sDjGSGqx94YtfzNU+bh+5qZ0ziyy5Pq+UE3xCIdN+XS2fqYWlTsEodO+Xp9iCsWgULJPOnpcjOZP08ag6QmHxttYgX57QLKq6Q3kn8jYjAcUOdvb+mkthZgPlL+LYfRa79iarT6TJH5t36/rATV2kChZu4wOB4oYOJBZHYzDopfu6BVQtuBFMyYQZHBSqVJGC2uZamTSb8pdamwMbZ+z1gGP9SmV0h7NsUfuGhOYDfp6cUHboUNwzEnhjgmLau/3lGUXV94eofTFQzswW7KiMWdN/O0zkoDgjvHjB8Czk//yXVYLF8m8O8u71BJ1IfO+N++fNmY3Er/w27D6oZyJQTsxUYe3KpWMxdTyiri8d/2tdVJtOypPVQNn2/Ue0M4DDymMOrrE5ExYrox2uduzgioE17Lb7zF3XppNq2lsfz/1Tm8zgtx2mM4E+nzZFhvgi4JfinqL8drZq1Uo2bdpkplO6dGk5cOCA4N7LL78s8O3+7LPPmvdj7eSCUggmTZqkP+CEHWKvvPJK7SrzqqtCM2IaLUxDPag64uNZesMl+F0PRpLPXxB0AjISTJPC0wlGsnzJSaXkblW7e1pHuM+p9OFPGIu0DMEoM1xuhdtzCpQ+eIuxlufnVZvFo37QkxzbfkMpSEq85KcYU9kQHN/5brEu687Eo3pU1PBS8cStHSW36jhBNu1JlPlrtlrXrgp2NEWa+CE3BPXuckVtsU6LN6tZ0bhtO2JqGpt5+au4j1IzFYXi80mwO9jaCpGFF18uXC151OJeQ3Gf9NMy1VGNk7uUm0xvAvMNDAd76wRZ42MEDh2ce5XZWmYKFHbre5iZeQeaF5SQa9V7ilmphlXK6sef+vhHbUKGDiv8XA+92XdHH+ZAC9dus5laYLQXgxLYyMcQjNgb3z2wncc5dpO2yjq16Rh8sv+5NUF3ov1V3C8tJremlDnnmME8pBRFp2CWL5ztj86g1bTFmX92v/5m6VqtqFdSJkPogGAW2NvR33rgWXyqqY3oIHAAcUC9l7NXbFK/UYfTKO64hzjeZKXa5AlrlqzeefB9g9nc79SMGo6bEhLV4uuL8rXacPGmVt7XPXlLm2H+E/BLcf/uu+9MpX3EiBEycOBAKV++vBw+fFh7jJk6daqMGjVK+vXrJ1WqVPE/9yiJic5L9+7d5ccff9Q1wkZTUN7/+usv7XkH3nfuueeeKKlt+KqBKdOqpS99uQSSCzwm4JNZgh3joHCmZ8KDH+Xv1c6eToEC/PznP9uCB3T5hzkaabsRwgtMUxtT1UayGMmF/bxWJJRigtGX9GTppl2CD0Tp3VJaeaS5sl5Vm7/2jWqUCO7yrAKlB5trzVU2zobAzrJOxVI2xd24F+wRSo+hBAWbVjien7V8o+6oYXfh9AQ+uWHvDDMNq6IDBS6Pmun0Ja98NV93pJwjnc746GRlhd93/OhDecupOg2RIFMXrJQjSgFFRx0KIZQglB+dT1wPs+47oCrUQ9k+G/bFy5WSjee9CUaGrVJK7YWBPGYuWyenzySnUdzxnYLZhvzKdW0kyf8WrdGd935/r10yyo6F+vA29f6PS/V3T6g72oZpIGZj3Qi+Q/DdZXjVgrczfFeiw+atU4yOCgYoMJoN23Z/Bd+7+H6spEbWIXi3tA2Wnwlg4S/yPqXemR0HjsjTfo6U63xUHniHYXrmXFtxacAlWTlUuGS6igEnKOWXV1VuNdUzGFXHAFXhAvn0e1kwPq/taNTHz2owWgAEfH/7WxIx/LHDVAYmMYYUL15csPHSb7/9pm3e4dd90KBBxu2YOcLVJZR2uL6cNm2atGvXTs6cOSPjxo2TJ554Qu69917d0bn66vB61Yhk4LC9gzKMKXDDpAP2jwmHj5vXqF+c+rHE1B5cb2Ukn6kfzDLFCqYZqYXCWkTZLrq1rcTItR7VTKcAWCjZtHoFFSN1ihpbfBcvFC+926VOcWOkra4acceX4G/rdtjin1SdEYweYYTVECgLMDmAHXAoBO42kWY5tUAXW49vUeUYp7xewHUnfrjgVxgzBTg2qFJGK+zLNyfoRbNO94PYmAMu7j57/C5b0R5Wbh7RXsZaAtvNAC6mLlihvcZ0V94bMhK0EaZu8RtoyNGTZ7Qpwkc//2kEaRMHvE/WxcngDqXS+MGGuzrU3+m+0kwkwJOfVmzWP/4ZKe5YzOnGrzOUgFxqb4JwCxQjSLAmakY5R6udi6EQDP17seLKbXuULbSaPXB0XGBSgnhfLFyllRwoFuoV1iPimGn58c8NShHxSLcWdY2kgzpqBUelH69cnuLPGSYyqLr+21HHAhZ3qZjpsLYZXDO2rV9Nl9coxEvKJA8mMTcq97GG8qQek2/VYld41QJXjOTj+wD1gikO1t2gzvjPl2CEE+n6monx9Vyw4bvV7By+K4zvU4y8GjN1SHvtzv1eR977/q3IP6vczyae8F/R9be845U3GHSyblfft5hVhHcsCGY70Hm07vwJzrBnty7cxPN7D5+Ql/t208/9uSVB9ioTm1fUNdKwymK1K+vrau2Ft9a57cWPrVGlV7vGamFrIzMM3++7FMMxg24ywwI5WazcMUJhr6IGvTBaDkH5YGqDemKGOFld+zrCnz3a8GWHyRpMavTvwd8mRZj5xPW7Q3rqPPB9j9+tGmUvmcbYjmpQALu5QmYsXSfbVfkevKGNvuY/wRPwS3HfsOHSyCEUUKfARKZHjx4yevRo2blzp/N21F8fOnRI3nrrLV1PbEbVvn17fQ6POo899pgkJibq2QjwiTXFHT80vjbsOax8C/d84aMM3w8oyLDPtgoWr15Zr4o1yOs5TDaqqulBp4kFdhYsqUaa61cuY3sOP5r4ooZCYAi+0Ds2runV5zLqZ/2iN57BsaBa9ORceQ9zmMLxl6nwtLbKq9QU5Ez1w+0UuI1zLuqrrn6AvCnu2L4bShRGiTDKZFUgnOk6r6G8Q1FEhwZKNrzI4MfUejRmGB569xvt/hELjK2ScOi4UjigaF0SozzGtfOIHwyYwNzRwW7yARd74OpUCLHYC/ExcwDTEfxQ+BLY43+jbIHTiLL8+VZNRVulrBr5syruL0+br5V2YwQQ6UCJD5Xibs0b5+AE/t5G8dQrlm1llNpxGIrq4y4XT/+jdkVbx+qg2jUXHVVDvlR/94dUmHVHXuMejta/U1xjg69b1Qfu6VLUzqahUtyRNtrm6Tsv2fz+95NZWrHG3zMUbGOEc68aZEhRihJMHKxidACNMGwyhjbH+5+evKE6MhCYG6CzDyX/0kf1H9R7gb9zq8CMAZ2aEmpwAB0eq9cTa7xQn8Mm+vDJ0//f3nkAylVU/39SSCUVEkkIkNBJAIGAdKRKkWKQJgpKUwFBpAh/FBAVVET8gaJIB6UoAoLAj6aA/ui9JbQUkpCElt7zkv2fz9x3Nnf37b63b9/j7b593wN5d2+bO/OZe2fOnDkzE3bffIPojveCKbgoiTQ2+A5n2jnietZ198VH4z7ENw5HhHUeUDRZV6K53xgGH3zmvQFEeBPMn5uGxEvvWcPPOJ9v0/AWkm/nLRgH53Sjh/qA+/Mluh51zz3KYGpmq8ICzWJ+WL99+8XN1s1ezHeeX+/AZpkZR+ithAt8FtiUvCjTGBHeM5dL4uJb/N0J/15bP4NyENbwS3oYMnHaTXrluC4tvBtI/vZFM2Tx2b1svUPdTZ/jPGOrGCtEDwJhIen3jWeRVsag4VZDPc2W8h/rP2uy0IBGWEfgA/s2SEP+txAv0J9mEyhJcZ83L/F36tcv6TLJf8qwYVgW7QOcNCluO9KfO++8M85Xj0/7vvs2HK197LHHRsX9gQceiHyGDx/eYfD86Kb/ja4Ynx8xJFZ8vqoovtUoiOmV+7Be7bb5evF6B/SDq++Ns0t8PzWrDD7iE2bMqrdOJ1dSSGM9SSzWyTEUv2LWKQp5lIH8ip87Kajzj69hA9R2toWH8uUCm76SXgJWCG2pHLLT5g0q2lOu+ocplH3CuYfl9tRQ+BeSn976iPUw9A0zzCqEYn/MXl+w7k9biTQumNTwDs6tYr7SLig8KOppgRXKFIU2lQiywvwX0/scKxSni27/V6M9I++aVYYFlxh8itWSFXjpjr7+kedyFNmkQTUnKisoR8yHz/MOMavVsEF9w2M2jzzdvD4TCRVZd+vOPtSYIh9bBYQP/pvvfxgHrKYt/6BEcZhslZx37SYrx65k7LrzJX9/LFY8J++/YwzX/2DVwwpb7jSTv7Jw8WOloYB/M5ZTnvmBNSLd2kw8f3h9ovjQQOL9xncaayzCeb6pQvnAeRqZVMRs6QKnpwclgX0qWuR5q8C7duocvmQ+3YQf89u2j5ovLOHm+6umraoxgAJ/sLbeaxa3tNA45tscO/mjeHjclH/HLcosPT24I1i0onUb5e/Mr+4aFbM4INWOX2vrPKQHh+5u5QYqLN8pi8G4YB0kfT6XNWxROGgQsqVxy3vMIGZcBWjo07u0y6Yj4vH0gmGEiT8wA5y/aXN1cx89Omm55qHnTIFZmh1si8WWd8kHu5OvKPoIs5mQX5zHiMCWNLGlkeBbFGLu499KmRN/5g8qX2PAqtGF6pm3Jkfla8v1hppitTy6pK28N/kFFxR94rvJWp+L6WExtlLkJXOZoFcH/3+mtmQxqbnmwnOjKfBpyXe9w+qK8K3wPqHUI+QF8XnL3DAIj/nHXTjOu5gWXLx4V2hs47qBwht9ru06yiuURsIkXmypWzhObwzXYVRB6aTHA+H9xyK9WzPWCknHh9/EhbqAKXwxaDCbzFr12+P33jb/8uw+acEyvsgaWj+6+cHscX5gtEEWL0vGGS217xZxN5yb/53Lm3KBuhAXu+bIPGsEIBfZuh1pcQWfaZqR9PuBe44r9D6gelJ9Q4HyFLnWvoe03GJjW75tqztLWk6gJMV9mc3njGBdLyT9+ycWBnzeO5o880wy4O7AAw+07umGODfZZJM4bSa9Fo888kg44YQTahYRLihYa+k2pPBCOWafAmWDNVePK3GSeCpX3CyOsflxESxLw21/7UED4r7/oXCnkk0rnrc+/nLBhZsoiN1C5fdj8UDo0qNQTbuoUKji37v24H62INIO8Tp8VddavV/Y1lb0pNt042GDTIlYEAvW9JR1WCGwisyyRgSWD3zWqQwY5EflQOGdrny9wRIfkvpD5U+l4hVTYaWrU+xmJDwUWHxg3efwA+OKv6WHT0G/wDjErV170V8fNT/0PuGMg7+YfSqV59PjJkXFaMOhg2yV485xlg/iQmWIpRKFngYMygyVGt2vM2bND8f8z9+y4UyfOS9nnxMoIlj7XAgDBbopQTFEgUdxH2b86SnAmumDAemiZp/KNb39z5vj4+DaGRY/BMtOVmxMHBbbtAy0Z6ADuM8ryjoKPwoW/pkojY0t4ESe97IGBrM+4IpCTwgz8PiMDed9ba/4OCpinnH3U2/kvAeujHmcLr3r8aiwM3tPVDzsBAotygqKBnu4fPgMKlgS03L5Pf+X3o0rDDNV29xFi+N3w+AzBEUZJZZ4FZKT/3B3zuEnrFLGrQArGe8nShquQ66449ISQebcFeIsMRxKWy0Ze/Lm5BkkJbLmPAMsSR3fCUJDC2HVz2BVDK5azoNvwgfVYeGHBd8CW75fts+b6xaNEBqhbD1/6ernvf7Ft/aL4dPTgiKLlTK95R1F4eHdolHz6sRp8fm4BiC4MRCfn9nYFpTBjWw+clRJvkfKJ86jIKLUp2dvwa0CdhgbOO4KL+8Q6eLdIYx3aGDYeVj7lrDsUP17EKMRFV5+UV7wjjBAmS0uCTTUaSTxjbgwCHiusb30+APiIVwQ8ZEnrTS6iBNxG9RvYmA1V3cJ8fvzt/5e3m89gxhKaKiy7WuNK8poykyU8Z7du9p2Wdbi3AcLtKWTFZqXmi7x7NtTLN1LAgotDcgbH30hxofZhiibaMgA+BF7f2eae1v+9Jh813yLGDUom0bYu0o5RVmH1Xd965GjTFjDGiK+nW7HUa6ZTYaVUkmLv8/MJkZerWllD4JCSn3CN8g/8haJX2RqPx60P/k9g3682BZlnXd+mw3Xiu+nv+uMceB5vIO8157PlFl2OL6fXIvLFhZuenQpv8Zbr8O8BUtiYxCLN2MHCIPpKinDMciQz8Sf78EbrMl3wIxsXeK7y4xhnKcO473kncIowRZuvS2PaVRTv3exBv4QO07+8z6Tn7wD3Meqtyj9zN2PvGHvJfl16M4r3YPiCf0pm0DTtaoFnVQiZT+jpm987bVEOWCwbjEZOnRoQHEfN25csUtq4jgt7A1NQcdygjXGJSnwfG/lFgUexR5FiAKjFPm6zaSxi1m/0+8kijmFMpYIfLVR0FE0mAcdoTvVC8f0M5aYArGsLpPtvqOgRJG9y6xb61gjAhcJLFLvW8WcL24tYot1BWHeYKa4ZFYTlGsUH7ZnmcUQQcFOL3NOVzHnT/py0nBg8A8NAPcVjTfZnytsZUGmmqNyobJiXmCUl0vNp5JCtZirwiKz7qVdVwjvBbM63mUKZb641cS3nGe2Gxo45BNdnHBHHjALOQ0I0s4CUbHnxNjR5Uqjhi5dBOWICmankcNjpR4PNvGHbmQYoch/z6axo8I687p/WqOgh1Xui2PjAKWDRgLbfn16xIpo1Z49wtmH7hZDP88sVzkDpSyecKVhgvzyjn/H/ckfzY7KBT0IVM4oAQt6JNfEC4v8oUeASor0U2nRTb3MKlCXGx5+PvQw5QWFPHYd23tpdW9Mi1fC+VsU9iMvuSUqASifXI9ixXvOb8QbdrBH6WNWntcmTjdFc3o8j68r/1zSs/hwjHvo2UD5ZKwIiiHvvLuU4ItKvEgPDTx6WtIDSEk317sfrT/HtyiOCPcjDDokXbxD+QJvLNeUDVjQXWGlcYPFlHTzLBhcat83mhzKgCvs6fB4RvJ/svVzKGeEy0I43Ge7dhlMc7dc4+UJv4k/+ygbiO8vX5EozPG4cULpJNuvt9l5iDeKfyH5/T+fjN8FZQuymY0V4bvAgg9vFClT06NiDjkUQdgTfztNxseXBx5IzJOYHou3bfn+drBVg/E3hl1Mt91LmepKJ/dhmU6+If+WeoAtNqRKmSaVdMyavzC+h9wH04QrA3XNPdAavyiIUz+hpyiZiSq9xeKalvNsNrC0uIX+xzZrDzLIVm2mMfa6Tb+JgYSVkRHyhn8xfdQb9js2QkxZJR8oF/juSKtvaawQN7Y0itICLxRceoLJfyzWGGwICzn8l39JX95gP+ek7fj34e/Pob+4Ob4r+LbvZIP5ma+fhoGPcUFhtihERZ6w6PmigYVxix6X4Tb94ySbBpLt2x98FJVj0kuDNS1M4YjQ+4F4zzGW8j6mjNMA5p2jzkUhZ3IB4kodQuOe944eIRiRdq83J2DEsefNX5wYDlhHIS1LjDuytC5pgPPuYgTxsUjUgyj4CO+PCw2E5jZ6/N6Ovi1Jce/okBpL/+zZidLGwNRiMnDgwHgKf/eWyuLFycfR3HAo6OgxYQacLlYYuhKQDocpLbmOiiFdYfs1sTKxj5J7CSNfOB8VcEqhIsIzEJ6DpWX9ocmAIb88/Qw/lt6isK5r/oTIzf9+KVpJsBhQqTNHLbM/nD/54XieSssrAwriQy++mdo2WguWLLXuSSug3jbrB4OjzNgVrXXxRos+yhyFJv6qP6xXCOM5+0PXIdYGrM7RijNrbmww+Lz0VPTpf37fT77+paiMUDBSYGHl6mXpecksyhRi+N7T8HDfau7zCooKkvRQgGLxxCUmPsPCWmKF7CpWUBaSeH99nlOQU2jf+sOvx3C5foXlx1k3PBArSaZuY5aZcyy9KAlYc8iP3TdfNyroa9YrYFhwsJpGy74pIG7ZJTx6Wc6x8FxoTDEe4YujhkcXnvR7x7tCOPhTLjFL3LRPZ5sS0zUqE1h20hUmvTZIoS1p6tfbfMTNkpq8u0lPDRZMONM1T3ria2l5S9hUTHUrlsdBa1R0uEgssUqLioXGC0oZC+sgVLKdeXHs/yScxDqKhRHF3eWqB56KvQXkEwoAglWLd5Y4kHfcD1OUOPb9OPmEoKh4vLmO9ORvUb5Rkg7abtNoVTvTeooI79QDd45hXGYNx2hVs/v5NlBKunQxFmYlS6xtyyMXns/+l80SijAI159HfJIYxVPx3cNn2BBE8XOWnOibzEEaMuTvL23hNNLlXe3pPE/uTpS6C4r4Hvs1vuUdgj/fAIKCDCP+o5GTtjJzPsbRI2r7V/3v0xyOFlkaL2kBu7PnOA1UBpCjwLC68zL7vu60WVF4P3z6x18ft38MAoWSb5BZU2BsMNNBZ3+jTC+297tXp1Xis3wa1QvNtY0weX+xVPJsGpf0+vAOJj7S5jNt+7yDND7nm2UTRQ8LKNZoBF/5XH/55Fuxvox4nvgdZAO6Yzrr37N4Iv3Hjl//8LNR6Tutfh0Lvk/uoRxAUL4+Mst28r3WxTjSo+DfZOLalQ4097cP4uWb5Ls/3AwszOV+55OvxXeFMpAeGtyISC8WaCzHzFvP+AcUd6+jckMOsSHkUxhyD1Jsi3GB9/Y161nZ3GZIQcgfprstRda1QaC4V+FqhNLflGDEWs/G4zCLFr0rrtine3EJI39/zkJLR17wsKPnkG8LIxmPx0WFBkoPe7cXW9ri1t7f/bbeKAxbrX/4j7nUUVch1D008BAYp7dmtI/iLjnJXmJA8N9s6RmjnuAd5Ds5eIdN48QFt9ssSRPNSPZJvZJ+whV/T98W8vdZOZ0ByugkSLl6Sc5DOshO4dq+gyS+NZI5Z05SOJaiuC9cuLK1Wc6zjzjiiPDXv/61nFvjPX/605/C9ddfH+gB2H//pPLxwCZMmBAeffTRuMsHxLN69+7tp60CWxZuueWWsHRpYpFkxdyRI0dmz+MPu9C66N+eMCksXN45TJzyQViW6Rq7BKnc6Bp88MkXw8svvxwWLugapkyeH1Z07R0LHg8k/xkjBq4V3F3CryGOxBV5ck6vMHJE0tNBpUYrv2e3LqH38gXx/PQlzLXePVaKVPQ+WBMr+oJF2LiswLaSj0GYWCC9DGZGoMmT54T5dZ3CR5mlscKnkMNaQUm6xArHKR+Yf6ils26RWeW69YwFGLNguGI3derUMG+ZWXIWzAljx46NrDiH4KPdeemCMNd8jjn/+6kzwgZrD42VpCsRpLHn4llhiVk+5y7uapVYKi+sQsUlZubsuWGuvX/H/OYWq8x7hEXm5oISj6WdLXky0xqWF/z+prD6wP5h/ILO0bqyly1Zfc8zb4RbHnvZnmmVs8VphjU+UHkojI/+zW1EMwoWvVF9loUh3VfEVZI9z+nOpmLvtNy6ZrssDEsN5oTFKJQ2+LZ+NgEs9V2N+9NPPR3ue8KmfFvWULGhCxvBAnTqn+6Nv+mS7dOrW9jG5nXHSpZUNEkNBsNOpoCSnyjHbOfbe/fO/AXht3+6IQzsgaKb2xgkX2fMmBH6dbEG1ULzge3SNyo/PIzGk1fw8eHZP8lsDL4LJXtDIiti0qUzjWBT/E1xYR9Oz73xbmDyRuvECQP79w2du3aLlvnB/TpHBe2jT2eGOnv3YNXZ3tUe3XtEpQgLIAr2Ys4tXmjvpT3JXpXVBvQ3Za57VHKY/pHGzexZM8N8a2AttOx69fU3Aq54nMN1h0Ym38fcObNM0bU4WkN99dUGhjpj5G4li5csDrNmz0mUW7tm0KBcgwPvw8cfs/5AktZ+fVYNPXpa74ZxKjZIlOkn07LA8iMsW2lgoGxMGwIIe/mihWELe6+QqSv6hDqLMd3tsOQrobHicYDtCstzV5KiRdnePfjzDSPpxgHjBbpaQ2XJEtwdkvC62jHKBxfCJj8WW3kGq87WYBzQv19sBBA+jYGr738yzLHxXatYhMiTNQavZMXAv6dsVdW333k3zDD9yl7F2HjuZIoVYxFYH4GxFUss/K7GlHdiWXxSQwuux8m3pJ+iAiWNeEYWHOOfHcNCSlmGRZpyiQbnymYWV+UKszHd9vhLoWfnpOGI60pa+I4wQD3zqg3qtF6Ffz/dK6y51trR8sy3weBb3qtp02yw5/JO9v5avCwAFOBc4ajLShdAGiA8o+vyJWEZW4vHKt17Z1cexSUJi3/dkoVhiZW985fMD8vt++KbQAgVDpSJxGPqHFNQLQ6r9THXqnqhQQaPyTM+iayTmDRkweV8R8hv7/x3WHuNQTEdNK5/9o19wvtTJpsB5QUr282VyV6XThbPH9e7wF3z4DOxF2bjzvaMOUvDF/p1ChuO3DS8+RG9DIlvO9seltMfzV0Utu5nbiOrDw5HH5q4av3BZhWjd3aToWZBt3juPNBcrSyNM3qtHT4wxfoPJx8c4/ULc70cO2lqqJtvg1PrOod5vVY2NmloY3l/6/1pYciC98P0xZ2tAZXkJ70SiG/vfXZc3Pc/NPYxNBEGbpZdl9uCX4aiVxcbHNulu7mTDojjaoaaqw0W+AEW7IfzrCHV1eobq8tHDR8W3XKurp9V5ic33x/L5tlvPReefrdL6NVnuL2X5INNbGCN7a/YGALq+6mm9iyy92r3TYYF1v9xYV2KdH1ejl7iYXW07cqSrISUM+1jIauxu4tw7r777isaUr6yWPTCdnSCAgXp1i23MEwnwc9RALdEGOg6YsSIsoK4+uqr47SdJ598slXWgxqEweDaLbfcMhaQWOZ7WmWdFvz3t9pqq+AWfz6ytNxqlcNi88c179p4+KOFWNaTKak4gH9t4mPbzSyjZnHp3DVWjrTY6brHKvWpVRIjNh4VK1wUtK03HxV8/EQM1P5svPHGoW/fvnH3uacm+uHYFYglathqfcLoNdaM6fjrS1Oi7/wHpoxiIcCVhQruf/FNNgV/kVnRcF/5Uv2KjtF3zyrGrlYp9rP0L51vVq4+feIz7jBr9QM5g36SdC415d5qHLMULQk/t8GYCBajz9lA7qVmEevXu1tsKMUT9X+obAfarEPdu9eZf2i3MGdx0tORvob8OGynzWI6rnlqQqzE0+fpSVhq2h3/kJkLk0IbpR3xbl5TScLbC/m3ICqPWEJRwjYeNtgGY40IM2fOCmNnzAndTfGrs7CowFYuhGRTptmKyZsP6RNW6909Jx1Y4XAXWdUaLfuOWj888Z7NiDBpZphpYefP4z4Xx+WUUHFEq5vlefL1pE7aT3zj+Ydf/UpJ0mmvGFoymlQivjW15rk53aOR0YbK2TSi9lSrFFE8Yte4VTz9eq9qFdVyswhZ78z8ZMpH4tIdi6IFjzUvaVxZo8BCj71H9Y9hg+qe9BdhocOKaoOiLXwswCiFizMWDoqWXfuhVdxdOi+NFnfCgftia8jV2TV1ds0StPelhRryiTsFz+thihGKe7QtW/xQTnv3YiAjvWadwq3PTww9X50WFfsVFi6zUPRfYy1Tij6xdFhUOjHjRDIFJhbrqNwZvzqz/EY3B4M5d/rMcPSlt1mcLU6WHjIkcdBIeM+bbXHkXwHpbnHAVWpf86XlwSFARgAAOmhJREFU6sdeGx+vopxbRkAmxBlrNWUBF6HQYiUPq3QLG6y/lvV4LA/vjadBbN3rFhbFKUorVjgvL1H6UVgH9OgZLdzwptyYaw1VF+5zWfnuJ2ng+FK73gfP+XXJtp635dHc+sF1hc4T0jJTdkgPLnj0AuBKQAvLH11Xn878sQiusOeGW3yP8EjPCotTIkk64nOMn1u2cRfrZfXO+jZOZ5ZZOWfYO8c1KINwhjdhYMlfSoPe8pvz46Z8HIP9wAyvCW9Lg6Wjk70vnP/zM++Fzs9OiIMRZ1sP1Kk2SH6h9fTOrbPzBEt0bMu1aYmNJztIo4nGNd8E1nl8nvkGu3fqbq+SuYhsODgs6NwjaeDY9RgNaCjx7S03hXypHVtheYvPO/dR1vEu/Oye5ywdvcKSei6v22BzBBfClUKDwfYsDPs/CmUV9TQD1HnHO1vckCX2GibuJYwrMUXeXFjq+K6tvKrrlDF7jH17nbvEBjHXUzYS1tajk3rwrlemholvTrMB8gM4nZVVV101zF6yImw/emROXevrjQxlELEZlLbYYmTsBX/o3WQwpwfQyRLAKvRD+vcMXa0OGmRukstt5WoGavezXgm2A6yXZgubxnb9JTary6sfWGOnLlrCKe/gTe/I1mv1j+ke/4k1IO1VOvnLO9qYrv7hOnNpjbzr7L2wd2aA1UGzFtvYESvPoUYPCm6OH5pST/0yZ3lXKy8SX3V6Sk6/5t76d8AYma6wxRabxHQ8P2NxGGB1HoPBKd8PtskDNujbKdz+3Pgwbc6icPiuoxvU511S9Xk5eokz62jbTvZC+/tdNO34b0+bNq3o+VJPlPCoUoOqmus222yzuNDStddeGxegKhSxb3zjG9FazSJMzHtfCdluu+3iY30wbUvj8NS4STabx3ux0MSXr8mXyB6I8kyXpFsEmorDKQfsZAMW1y14GQN8cHGhEEMxYouVgEqloSWoYBBFD1Luoxyg3KDYURBSY+EzScGN4s1zmvpyUNawjGEhw+LUlwGQVvjj07+6WYfo6h5oVqP0loFHDH6jh8Kt0XRPU6mRTrc083yEApIamv18l4F4QYE/37GR/VjdXaiYo5WkfvrH8762p5/KbvF/xnKH0IPCA/meeS4KMr+ppInWN3bfylb5HBZ+Zb7kuJOkV7RlNVn8shEGnVKZogAThs+zjPsSx4/aY3T07cdKxeBKZjLgPXOlJO7wQDtYyvvHM8sR8pHHMNVfWwgWSpQyOGN13NYGsRGHp956P/Ye8R5K2p5A0qhLBoWmn843aNkTG2yJKwfuBSvHB3AfVvxEMV0Rhpoi1tneb1zx7DJzu+hhjcZMOHn/HeKxm8yFkG8pUXwZkN3VypzF9Q2g9JML/+Y5qOhNlU+F7155lHElfGvM9ELZ4j0eK68o/AurLuln0CzlHt85DS3GfbBl1hgUS75pvijcPAgf163WEPKDchuhXGL6YJ5BvJjvHNck4oVwHNcVz5+kTEvKtm5WdnOcwa7JtYnv9+9PHBP3GdSatrQzIYPvU2b94aSDo0WacpNwmW2Fso58pQGBEs5+DNu2p1p9hzsQLljUCTuNGh7j6WV+/pa6CGFWKgbIM/VxenvV974az7vrnl/P/n9enxCubGI60niz/aEuJP40XGgUb2RGHwR2GC7cAk+4afc/7kMutUH4zP5z9amHxn39aTmBZlncW/642gsByzMrpGKZLCZ+zi3Fxa5rT8dRLCnwqGAYLIegMKNgUCimCxksZnz0+MatyPQwC1BiHf7Y7mcGk2XWnKdQwW+Q4hbF5HNmldjKpjNLS1QY6w8w5y+FiFvX3Jc9fb3/xo8Uf0mf+/0em/92gA1qZPAg/qMUMLjL+KAeinQqK2S5+UHjxuFCQYsCTiMESw+VZLGGAoUY5/ItcIQ115R/hIFU+dvp1gDJr8LMJh+v88rNytEoK61yyT5/cRtidpzos24NBNisbr6kn5iiQLzTSjvXY2GnAZH2Ved4WvDT9MGpFNhYrV54b0psiPhCGzTmyGcWnmIWH/IU9ul5rfntg7J+YAszMQgVqx7b84/cKz7Sj+9h8+cjDJJl0SmsYysHqSYD7Kj88PekgcRsQGluzH7BO+PTqsXASvgDP56DDy4DLuFCuDRSUOFLVWDyH4W7Ci4t0V/eQuTbISzPU7+ehm26cXvX04k7np/P3+JaRMsCP+lShHTg777b59ezniHrUbJ9vkMGY2M1pZJGwSr2Xuc/AxcF7im0vH3+tez3s+8Hf2YswShZfF8oWjwbP/n0AOlC9/sx8p68YpwBPuawZZEtFGK+D9JZdGuBUCbgYoFijCWRdygqmPbOprf4pNNLwCwnzCtPbxDhuqCEoSjnvxuFvk3umWKD9RCeT9mJ7zdp5j1H8GFGkf2HubPBg55EZmrZbqN14nGfB5zBngxyPd5WXkbYx0eeb5zymXcLt0C+b6LL4Hfm3ea7JM4wp+yFIenDQEHa2GeWllIbiOQ/7FCIUcgZXM5MOyw0Rhz8eyFs5OGX3olb/jSngUG5S3md9KJZA8hcOvi+GWTps8ew/d13vxLDv+j2R+PxAfYtT7cyCyGe6YWGmPEExZdwMB7Awbfe0OJdQGic2f+B3lfSxNS05E/cWv75lh4E8oVGApw9LwibupEt3/w8cw1L6j7c+6yuqHdDTbuH1rc/7DuJUWiw5Sh1bjQwWWN/ttWh+cI35vcn13cOW9mA5m+aYYR0PG49ZbjB0fvMlLE+///jptyzj38+acOSjtsiY7UQpkOl8eWSfk76eX5e29YjUJLi/uabb9qL5d12rffwWgjJXUYamwrTz6277rq1kOSYhn1tYAn/0vLtK+6waR+TjxtFj4/ct1Syrnj3tMITiQqEFSp1nRJXAo5TeXezQo+pvRgJ70KldHfOXMZ+puH2+wftFJiD2eeBxzed8Hy1un+98p4NAu0XC9Y4K4sNxqQATnzYQ/idzeIyyCoD/PSoXFHgkN/ZrAqD+/eOC7FQ+Z30h7vijBmTP54Vt2d99YvxOhYoirPKmGKPbzrz09P1+LH5JuND/tw7k+N1aYWNCoHKlS0DGvlFBYpC4Q0KO9iooAgRXxoDKENYQ+jSR3yGA7bplfzIA3o2djIrWGOKOwOr+JcWX4jJLerMHkCljdL+WQhWePd7zQ+fCtmnd/NzDLbEnQdFjgbE13bZMp66wQYBozBRuaa3nIR5HBhoLHmH+McsSbDlHUFJxK8dJYQBz6XIQKtMmYLOlcGjbTYVpk5lukgUXvIc8fxni6Ao8B5saN/S52w2oTcmzYjvBvPXI0zvR4Xd3RRP3scjd8XVLZ6Kf66x2VoYY8B75g1pKnmuRTn5zr7br7zYfv33jYnRF52yPioWZuV1RT7ZJu9nOp4oiqVyQEGF48Xf3C/2kPnDz7X1Hngmih9KDeF54z/ysZfU99nGaQSNDVPDonjwDrvwjqDcubLI/Z72/G33+J0ld/IuMLgRtz0UNu737cMvvx0vwq+d46zs6o1VTpxns6B43rLFEAAXXA9QnHkHWbmTgehsLzTFhygz+1ShRoor5ijuLsTFyyE/xjgV3hqPC7OhkH63ONM4mruwSzixftaqC299OCrjNEqnfDwnazRwq68r6l5W+HNo3NDoZNAjnMgnvgGfuYmy1Q0Q3MM3So/RNfUWVhRXXF4YQEvj7vLvHORBx5m3iDf5imWewbe8e+n89nwjPRgG0s/KBlTgx2CLV3wPjB3vbyHBfRK3QcplJgbAGONbZuqhHPWVfFFiGZjPNJtpccOAN5x9e8f/vWrGohFZRZi5+JlAgcHPvoXHQduNtB6VpcENFemwS/n9jd1Gx7JpXesZwOp92d3/afI2eqgP2HZUvI58p+6j7CNfGfSO9DQ3rKk2YQBxRcbYIFTcLF2O2n20/2x0y5iT7pZvktYjUJLinu9n3HqPb/8hbbBBYhV8/PHHCyZm1qxZ4ZVXXonn3F2l4IU1fpApqXxaRE/qd2zlOqY+pNXO1s9zPF9YUS/qJFZxujxklhsqISytSNy3bmcWFEExOX3MLrES9xX6/D4KIKxSV9tgIxesWsyBjdBFSsFGhcMcxSuPd7HCLRlQ6PexpVLg3yqmQPl+9Mu1nY9mLzCF/6l4vLE/pIrKGaWKipF65jhbQIlnf++Pdyez11ilQsFcTFGiwphYP2UXz4pKjKUrrVSgwGLRoRpDaaVS9Hmyo0XYKv5y5cdH7BnDK/V+3IZQCqwtUZKwkA18LrbBW6UKii3PIH99Vca//veVuA9jjvvWrdy4KnGPC/OS81ym/XNBMcG9wedC5p3BSorSFq13qS33poXGLP+YpYX8yM8f3//ZUftkb8NX/f/d+IDtr1RCsPAisafLGnhYIjnC/M40elEUsULjYuWDU30bb2zkD3EmjbzHKNW+TStAuFehGLOQDQrSH7Nd74mrBvFzSzQKJo0r7ufbKiS+3gDTBPJMFiUj3HuefjNxW6ORb8c3twXdkP22MauyKXmuWHOMd+qMr+4a48X3naxLwBoAKNSJZT1/y308h7Dt/5CxwZNsV9jWDkdll2s4zz7Kqwu9TBgovIERt6l0e/rZouDyfqCAIii4fKPFBH6ex8WuSR8/cb/tY16Tbt77xgSXvFXtH2NzUKrpYYRlIfGeF+8l5J1l7nu+Hd553Blp1L4yYbrFN9iCdNvG4x5WsfzmPAYOX6AI18f81Zqj8cTqDhoT//PtROH/uVnS2acHN7+R4c9ke0J9TwQNeL4NrOvp9zd9baHf6RWdUWxZwZv3JK6IbagY40DvI+8cjZ+RVu8gu1pPFt/rDTbIlTnr8wVfd8S3KMbH2uwq5QrGJ+tCjrd3tneK74x/pQrTsiK/zhtk7vOw+3F3ZSw1XL+O9VrgJGk9AqXnbus9s6ZCOuaYY8KFF14Ynn322TB+/Piw3nrr5aSPlVWZiYXBhqNHl9ZCzQmgHe9gfeXfeKusUUpbIq7spMP4v7GTbGBQf/OpTrjGffMzRClHihU0+a4i6TD9N13idPviz37GtclsJ0w1yUwtvk/hTCXeWCVJd+4PrAFBhe6CpY1ZWZh1wrdY5qlcqMjOPXwPv7TBFj9NFDHv1oQv9zFYiUGWWKN88BquGJxD3KWILb7iSL6vOyvU4n9ZruRXFiNs2rS4qE6RAM+1lUKZxhBFDUuwC5ayQveRdoSeHqyYTCPHa8W7lb7fw0lvG1eAVj6be/JX9/OGGwoIg0WxSqHY0nXvFvj0lnciXyFPxyX/N76pcB9nYx+KCY1SpsRDmNe/kPhx1joYY8u3N1dQamBpOmuOkFbOLUWbTQkN47SgwLREsBSnxQdJo7gXEl9hExcWi16i5trWXbPy8zxeY39yttxl99ATMNzeV1wb+OcLNNGI8ukf3bLuLnfEibIGKzANaowTKL98c+zzDtQxINS+ffZjQ5mH1ct399sufsv0vKxqlvl8OWznz8cGx/02mB5DRFNCgx85zpRAvv0bbN79YgJrvjEUTsoEfNn5htK9gPn3eiMDF0Pc0zy/3PUNfij5lE3lCEVAKfVEJLgSI5aT2GCI2/Tx+kj4qtesjOxTlOLGQg8ueYNfOGOvyHe+Xd9eVj9feTLNb6c4I0y6HIedT6xA/k6PM3NZQ9r47DN6Y2ucD4qKPtHgvbjOGv7UKT6uibIbFxX82uNA5/r4Ugfkl6X1p0raHIeibHFrLfHvpdzw+Lb4J2k9AuXX0q0Xh3Yd0pAhQ8KRRx4ZbrzxxoASf//992dnIsHF6LzzzovpO+200xqdeaZdQ/DIU4BawYlVj0L+50fvEwtiLOB0DxYSCi66i5PBpo/ESyhcGcDJSoUuFOhfM1eAYsq4X1doi5JXqOBgkA2+4A3E0oBCibKCUo5lCWG0P8d9fz0rjIgXs58UUxyp3Gh0pAWrG/HB4uJb4uhd1elr838THoV6dOmwRgVbumr7WHg/Mos3Xbv4siIo6MR1glmL2GIVY8vCPeTTF2zQY1pY8KM5wuwRXnkXuo+GQGPirlA0INIVVXoe+0L3e8OLbnW44eMK03whTP7tsMnGplwVL+p233z9OHDuSWsI4mZRTP7fYXtEbmdff39UFHAV4PmjzDcZpRXLngvvDoo+St2iVeqXsa/f+jWcR7nzBbjOuu4+P9VgiyL+pE0/iJX2xC8nLi64dLHPO8r39i2rsHkfC7oqWX7jrsa1nOcbzRcUfu7/uzWImB6VlSRJx5dsIDPbuGKq3YTFmOvyxfMQxSctWO6xONOjwTZf4JC29o5e3wY1phq6fr0PevR93x5ab5nPVzz33HKD6Pvus91wHYqpX8eWAce4s9AYxq2B+I2bbAvCmSJ2t81/npY9bRYPLMxpwe+X1X7TCj7rPNC4Y8VWUouFmH16FFHwXOixQE6yQamFxH3emaYvzhOfdxGKX7cCCr03cHmvcc1xIX9oIOJDzxgVeh6xhDKLCAYKxuLMmGm/La48j2+b+OZvGTyNAst85oNtLJJL2jfbjzVnO8bGEtBr+OCLiWsSdQbraeTL3lZ+UXZ6I4n5zHEnpBFFmVBMoqW8/iT1Dg1lwqDHgfcZBnHf0sx2rL0HvObUXxgLfFCqh58/OPVKG4yaFt4NFxqXuFohPCe9zV+Iyuc3jxeV8YdyNbG/N+/m+H4UaHzjOkODQ1I9BIrXZtUTx6qPybnnnhseeuih8N///jeMGjUq7LPPPnFe3AcffDDMs3mAd95553D66adXfTpaGsGvWsHLoNK+OySDxlyp8xXv8sNHGaDQjK4hdtIHt1Fps6qp73MfLh3pLur8sNjfbuO1o+96/rmf2jzEhazipxVRLI8yn0FmesCHFmXWu3Ldpz1fsWSl0DhAsP7B+EVjfaNRgK98MaHi5rrc6cwaXo2PPIxQ7PGXZslvZHt7DpXb/c+/lb2JblMfe+DbZNaYZAAhA8n8ePamMn98z3zjW2Kh98emKzg/VsqWAauNyQ8P2S0qae6a4NeiSNLg2WLdNaNCj3UMYfBtIYXU7/N3aB+rxOndwPWGd9gbDa6408WcNBiGx4ofHRQFwLce3jmH7h6P+75vcdPB9SYtWL1RTHDzWN8GiyE8g+M/PnxPe0AyXV36HmYuQqFnaXW+RbdgezrS1/Ibf1/krM/tWjA8LMak113a4sWpP/49MdgP8Qauu8Aw40YhOcN8xtPiY1H8GOlAsWYl3ULiPSL553bdLOn9fMIaVKTfF9vJvy5/311x8hX3L9aHl3897w0W+nwhbxC4YtX2nsD865raR0EvJIxNoWFQTNzS7OdP3n/HqJDTUOI+f9f9vUgWhFrFFnfrlx0w7gPH2fKdMm7mR430CPqzCm233WgdU7hX+kjnX+MNFeZ1p2GJ6w/xZF5z3jsXj6/vl7NltVmfDcXvxwWPsviEvbeNlvl8fn5dOdsdNhkeXTkZb7PbZuubscUaSjYVK37zLPSVFh/jkD7WFr/pqclvdPPcfMNTW8RFz2icQG7t0Pi1OluEAH7uL774YjjqqKMCvu7XXHNNvJK5z0888cRw8cUXx3lZi9xeM4exhDRHWAKayizfUuO+78UUBH8GFaMrTRz71p7b+KmcbSFre84FeTve0GDhEa/c8i7J2XVFyg+WOmjngvpZVPBhp2LCqkqlmi/5igzTRFKZu+U5rbjn35veR2ErprSlryv1N64w1SyF3G2Ir1vOvWHpaSjFHYFrsbwWEhRbLOyeL4WuSR/Lzwu65DlGgyytqPg9NEQKHee5haSp76fQPRwrFh6D9FCf8uPt4TArFMLgPsRdjvx6XCkKiZ8vdI5jjG1oieBjiwW1HEnpi0Vvbyq/cXn5LIR3YaU62/QTaKy6FLqvt/m8L6tbeY1f61sf5Or7zd26z3RT9x1b7+rh6TvQxlD4+JOm7m2N88UagsXCpmGGAaYxoQ6izGbQ8tE2mwvyS5sulx6GYg3CxsL7LM4Rx+bWlZ9FPBRm0wSkuDfNqKQrcJlhFTBWR2UwKkr7RhttFPrZQjySwgTcN7Lw2aaPnmOzweCq8FlJKUp7Oc9GsWFuYRdmQ2GQbamWpCPqZ7nx+7GiN2bZ9+uwiDKorKNLU4piuXyKWdBLDa+pnhAs7JWUtOLXWDxOOXCnxk63+blyG5gH2MJSzHLTXKHcoIewvQkWfBo4l9QPUhxivXc+VqMt08IMTGnJN+ykz1X6N42LA7YdWVI0htjMUPQcSUSgpQQqWxO0NPZVeH8vWxFzhx0K+yxWYXTbdZQYxNOWQlXcGsp8/uDTYhbcUtN2fv2S3MWuxwqM+wWDsySfHYG2tlax4Eu6x+mzS1nHDNlnIWpu6ulxwwWwvYk3DHHNwxLsDcmf3mZjjUrpemhvCW5hfCm3ffB/KUEV6xEu5V5dIwJpAlLc0zT0uyoIFHIHqIaIMW1WIVeWaohbY3H48RF7mHtDw8Gbjd2jc9VP4NQqs2xXP7G2ieF69Sttts3TWv8pvtCah7z/NpvEAfC+X6vbXa3B0hxFnLEjLREGwpfai9WS5+je2iPQyUaHl+f8V3ssajpFPof8M888U/XpZAQ+AzfTswBUfaQVQREQAREQAREokQBuWLjO0CMqEYHmEJDFvTm02vm1TE/pCnxLkvL666/b7ATLox9/S8LRva1DgHUC6KVYxVaok1SewDKbExpRflQ+L4iB8qM68sFjQX5gL+xmK3NKKk+gzmZwY0zepptuWvnINCMGY8aMCWeffXYz7qidS6W4105eNpoSXvLWksWLk6XQ+dgllSdAwcvKllIUK58XxID8QJQfEUPF/yg/Kp4FORHA6MPKuFLcc7BUbIfvwxu3FYuEHtwsAnKVaRYuXQyBYcOGhe233z7ccccdAlIFBJi9iBV7H3jggSqIjaKw1VZbxUXYnnjiCcGoAgKso7Fo0aLwwgsvVEFsFIW99947TJkyJYwdO1YwqoDAwQcfHKezfv/996sgNopCKQQaLp9Xyl26RgREQAREQAREQAREQAREoE0JSHFvU9x6mAiIgAiIgAiIgAiIgAiUR0CKe3ncdJcIiIAIiIAIiIAIiIAItCkBKe5tilsPEwEREAEREAEREAEREIHyCEhxL4+b7hIBERABERABERABERCBNiUgxb1NcethIiACIiACIiACIiACIlAeASnu5XHTXSIgAiIgAiIgAiIgAiLQpgSkuLcpbj1MBERABERABERABERABMojIMW9PG66SwREQAREQAREQAREQATalIAU9zbFrYeJgAiIgAiIgAiIgAiIQHkEpLiXx013iYAIiIAIiIAIiIAIiECbEpDi3qa49TAREAEREAEREAEREAERKI+AFPfyuOkuERABERABERABERABEWhTAl3b9Gl6WE0QOPzww8Omm25aE2mphUQccsghYdiwYbWQlJpIw5gxY0Lv3r1rIi21kIiDDjooLF26tBaSUhNpOOCAA8KHH35YE2mphUR8+ctfDuutt14tJKXDpKFTxqTDpFYJFQEREAEREAEREAEREIF2SkCuMu004xRtERABERABERABERCBjkVAinvHym+lVgREQAREQAREQAREoJ0SkOLeTjNO0RYBERABERABERABEehYBKS4d6z8VmpFQAREQAREQAREQATaKQEp7u004xRtERABERABERABERCBjkVAinvHym+lVgREQAREQAREQAREoJ0SkOLeTjNO0RYBERABERABERABEehYBKS4d6z8VmpFQAREQAREQAREQATaKQEp7u004xRtERABERABERABERCBjkVAinvHym+lVgREQAREQAREQAREoJ0SkOLeTjNO0RYBERABERABERABEehYBKS4d6z8VmpFQAREQAREQAREQATaKQEp7u004xRtERABERABERABERCBjkVAinvHym+lVgREQAREQAREQAREoJ0SkOLeTjNO0RYBERABERABERABEehYBKS4d6z8VmpFQAREQAREQAREQATaKQEp7u004xRtERABERABERABERCBjkVAinvHym+lVgREQAREQAREQAREoJ0SkOLeTjNO0RYBERABERABERABEehYBLp2rOQqteUSGD9+fPjZz34Wnn322TBz5syw9dZbhx133DGceuqpYdVVVy03WN3XCIGTTjopzJ49u5ErQrj22mtDr169cq5RXuXgaNHOVVddFU477bRwxx13hAMOOKBoWMuXLw833nhj/PfOO++EwYMHx+/j8MMPD7vttlvR+5RXRdEUPFFqfujbKYivxQfnzp0bLr300vDEE0+E9957L3Tu3DlsvPHGYc8994zfSffu3Qs+o9zvo9z7CkaiBg+Wmx/lfh/Kjyp5iTISEWiCwKOPPprp1q1bxl7ZjBXUmdVXXz3+Zn/bbbfNfPrpp02EoNPNJWAKe5YxnIv947q0KK/SNFr22xqpGWuURvZ333130cCWLVuW2WeffbJ5NGDAgOz30rVr18xNN91U8F7lVUEsRQ+Wmh/6dooibNGJ5557LjNo0KDse77aaqtl+vbtm93fYIMNMtYQbfCMcr+Pcu9rEIEaPVBufpT7fSg/qudFCtUTFcWkGglMmzYtWzgfd9xxGVcU33zzzczIkSNjob3LLrtUY9TbdZzMohXZUjn+5je/Kfpv8eLF2XQqr7IoWvzjgQceyPTv3z+rlDSmuJ911lnxOhq0jz/+eGbFihWZBQsWZH71q19lOnXqFP+hpKdFeZWm0fTv5uSHvp2meTb3ijlz5mTWXXfd+J7vuuuumbfffjsbxFNPPZUxq3s8t/3222fq6uqy5/hRzvfRkvtyHl6jOy3Jj3K+D+VHdb1IUtyrKz+qLjZe6I4ePbpB3D766KNMz549Y4H9wgsvNDivA+UTuPzyyyPX/fbbr+RAlFcloyp6Ib1HRx11VGSf7uUoprh//PHHGXNVitffe++9DcI988wz47n9998/55zyKgdH0Z3m5gcB6dspirPsE+aiFN/joUOHZlAa88Xcw7K9TA8//HD2dLnfR7n3ZR9c4z/KzQ+wlPN9KD+q64XS4FSrnSXFCfz5z3+OJ48++ugGF1m3adbv98orr2xwXgfKJ/Dyyy/Hm63BVHIgyquSURW88K233gqbbLJJgKO5hgXr6QjDhg0reK0fvPPOO8PChQujT/u+++7rh7PbY489Nv42i3GYNGlS9rjyKoui6I9y8oPA9O0URVr2CetJivd+5StfCeYe0yAcc5MJW265ZTzu/Nkp9/so974GEavRA+XmBzg8f5pTtyg/qutFkuJeXflRVbF5//33w4wZM2KcvvrVrxaM28EHHxyPP/TQQwXP62B5BF555ZV4I4OASxHlVSmUGr8GhtaLFKy7Pzz99NPh9NNPD+bq0uhNzzzzTDx/4IEHBvNnb3AtDQEG75n7THjkkUfieeVVA0wFD5STHwSkb6cgzhYdtJ6jcPvtt4fjjz++aDhLly6N5xiw6lLO98G95d7nz631bbn5AZfmfh/co/yAQvVIw5qmeuKmmFSYwGuvvRZjQEE8ZMiQgrGxrtN43Hx2g3Whhn79+hW8TgdLJ2CDgMLYsWPjDcOHDw8/+clPgvmRRqVy1KhRwXxMg403iDM6eKjKKydR/nbttdcO5ose9thjj5IDce5rrrlm0Xv4RrAejxs3Ll7j9+i7KoosnignP/TtNM603LNYZxuz0E6cODG8/vrrMfhtttkm+xh/15vzfXBzufdlH1zjP8rNj3K+D+VH9b1MUtyrL0+qJkY2EDXGxWbJyFES0xEcOHBgdveTTz6R4p6lUf4PlHasV1h7d9pppzBv3rxsYK+++mq49dZbg81UErcoN4jyKouo7B9Yx/nXHHHuNjC16G3+jZifaLzG79F3VRRZPFFOfujbaZzpZ3HWvH/jVJA2KDXYhAWxx8qf4+96c74P7i33Pn9uR942lh/lfB/Kj+p7m1b2aVVf3BSjChPAgo6UUuhyHb6+kpYTcB9ECuARI0bEudqx2Nr0X+H888+P/tdPPvlkOPLII6MLBk9UXrWcezkhNIe7fx/NuYc4+X3lxK+j3aNvp+1z3AZaBxuYHbp06RLXMWB8iEtz3vX0e17uff7cjrxtLD/K+T5gqfyorjdKFvfqyo+qig2KI5IuiPMjmD6HH6+k5QRsCsgwZsyY2Mtx3XXX5fRi0A293XbbBZttJqC833DDDdFtRnnVcu7lhNAc7v59NOce4uT3lRO/jnaPvp22zfEf/ehHcRA3T73kkktC2k2GY81519Pvebn38cyOLE3lRznfBzyVH9X1VsniXl35UVWxcf/1WbNmFY1X+lyh2QaK3qgTRQmwQuddd90V/v73v+co7X4Ds5fstddecRefbER5FTG0+Z/mcPfvozn3kCC/r80T1w4fqG+nbTINX2lmGrv44ovjA1HaGcydL81519Pvebn35T+/o+yXmh/lfB8wVH5U15skxb268qOqYuMf68yZM4vGK30uXfAWvUEnWoWADxR74403YnjKq1bB2uxAmsPdv4/m3EOE/L5mR043FCSgb6cglpIP4jZhKwXHaVNXWWWVON4G94xC0px3Pf2el3tfoTjU+rHm5EcpLPK/D+5RfpRCru2ukatM27Fud0/yjxXfQ1uhM/To0aNBGmyBlHjMVpkMPgivwUU60OoEbNGfGCZ+pYjyKmJo8z/O3b+DQhHwc7byZDzt9+i7KkTrsz+mb6d8xlOmTAn0+NnK2bE3kF7BPffcs2iA/q77N1DoQj/n3wfXlHtfofBr+Vhz86MUFvnfB/coP0oh13bXyOLedqzb3ZOYArJ3794x3r7gQ34iHnvssXjoC1/4QpNzXuffq/3CBOiCZjGTP/7xj4UvsKO25Hg8xxzhiPIqYmjzPyw8gxT7PnAl83mTGZuAKK8ihs/kj76dzwRrDJQpf5mKFqV9nXXWiWNsGlPauamc76Ml98WIdpA/5eRHOd+H8qP6Xigp7tWXJ1UTIwae+sqPt9xyS4N4MWDltttui8cLrRrZ4AYdKIkAg7RQ9nx1zfybKLDvueeeeNi5K6/yKbXN/jHHHBMHbz/77LNh/PjxDR7KioNM7Tl48ODsPNjKqwaYWu2Avp1WQ5kTEGU9i/BNmDAhKuMMjGdNiaaknO+DMMu9r6n41Mr5cvOjnO9D+VGFb429ABIRKErAlJGMLRSTMV/GzN/+9rfsdVYAZE499VSmncmYi0zG5hrPntOPlhF4+OGHMzaHe2R70UUX5QRmK3tmzOoVz2222WaZ5cuXZ88rr7IoWu3HWmutFVnffffdRcP81re+Fa/ZeeedM3Pnzs1eZ+MPMmussUY8Z4P4ssf5obzKwVHyTlP5oW+nZJTNuvDqq6+O77E1OjMwNheNov+slykn7HK+DwIo976ch9foTrn5Ue73ofyorheJaX4kItAogTPOOCMW2uZPnbHZTDKnnHJK5vOf/3z2mM3h2+j9Otl8Auecc07kS8PIpljLnHbaaRmbtz0zaNCgeHz99dfPWJd1g4CVVw2QtOhAU4oigb/zzjsZc3+J+cL1J5xwQubQQw/N9OnTJx5DobcxIg3iobxqgKTJA6Xkh76dJjE26wKbsSRj0whmyyPKpMb+nXTSSTnhl/t9lHtfzsNrcKel+VHu96H8qJ6XSYp79eRFVceEFj6W9XSBvdFGG2Xuv//+qo53e47c7bffnrGVUXOYowwedthhmXyrVjqdyqs0jZb9LkVR5AnmvpTZY489MjRu/Rvp2rVr5sQTT1RetSwLcu4uNT/07eRga9HOuHHjsu+0v9uNbfMVdx5e7vdR7n0tSnCV39wa+VHu96H8qI6XoxPRsI9QIgIlEZg4cWL0cxw+fHhc1dPcaEq6TxeVT+CDDz4I7777bjC3i7DhhhvGhZlKCU15VQql1r2GmWIYn2BKe7CGbcF5+As9UXlViErLj+nbaTnD1gyh3O+j3PtaM+61GFa534fyo7JvgxT3yvLX00VABERABERABERABESgJAIyl5aESReJgAiIgAiIgAiIgAiIQGUJSHGvLH89XQREQAREQAREQAREQARKIiDFvSRMukgEREAEREAEREAEREAEKktAintl+evpIiACIiACIiACIiACIlASASnuJWHSRSIgAiIgAiIgAiIgAiJQWQJS3CvLX08XAREQAREQAREQAREQgZIISHEvCZMuEgEREAEREAEREAEREIHKEpDiXln+eroIiIAIiIAIiIAIiIAIlERAintJmHSRCIiACIiACIiACIiACFSWgBT3yvLX00VABERABERABERABESgJAJS3EvCpItEQAREQAREQAREQAREoLIEpLhXlr+eLgIiIAIiIAIiIAIiIAIlEZDiXhImXSQCIiACIiACIiACIiAClSUgxb2y/PV0ERABERABERABERABESiJgBT3kjDpIhEQAREQAREQAREQARGoLAEp7pXlr6eLgAiIgAiIgAiIgAiIQEkEpLiXhEkXiYAIiIAIiIAIiIAIiEBlCUhxryx/PV0EREAEREAEREAEREAESiIgxb0kTLpIBERABERABERABERABCpLQIp7Zfnr6SIgAiIgAiIgAiIgAiJQEgEp7iVh0kUiIAIiIAIiIAIiIAIiUFkCUtwry19PFwEREAEREAEREAEREIGSCEhxLwmTLhIBERABERABERABERCByhKQ4l5Z/nq6CIiACIiACIiACIiACJREQIp7SZh0kQiIgAiIgAiIgAiIgAhUloAU98ry19NFQAREQAREQAREQAREoCQCUtxLwqSLREAEREAEREAEREAERKCyBKS4V5a/ni4CIiACIiACIiACIiACJRGQ4l4SJl0kAiIgAiIgAiIgAiIgApUlIMW9svz1dBEQAREQAREQAREQAREoiYAU95Iw6SIREAEREAEREAEREAERqCwBKe6V5a+ni4AIiIAIiIAIiIAIiEBJBKS4l4RJF4mACIiACIiACIiACIhAZQlIca8sfz1dBERABERABERABERABEoiIMW9JEy6SAREQAREQAREQAREQAQqS0CKe2X56+kiIAIiIAIiIAIiIAIiUBIBKe4lYdJFIiACIiACIiACIiACIlBZAlLcK8tfTxcBERABERABERABERCBkghIcS8Jky4SAREQAREQAREQAREQgcoSkOJeWf56ugiIgAiIgAiIgAiIgAiURECKe0mYdJEIiIAItA6B2bNnhzXXXLPgv3XWWSdsvPHGYddddw0//vGPw8cff9w6Dy0QyosvvljgaPs/BDv43n333a2WmGnTpoXp06e3WngKSAREQATKJSDFvVxyuk8EREAEyiCwYsWKgCJY6N/kyZPD22+/HZ544olw0UUXheHDh4czzzwz1NXVlfGkwreMHz8+7L777uHss88ufEE7P/rRRx9FtgsXLmyVlFx++eWxMQU3iQiIgAhUmkDXSkdAzxcBERCBjkrgN7/5Tdhxxx2zyV+6dGmYN29eVN7//Oc/h5dffjlwTZ8+fcIFF1yQva4lP+65557w2GOPhT322KMlwVTtvQcddFDYZpttwrrrrtsqcTzvvPNinrRKYApEBERABFpIQIp7CwHqdhEQAREol8BGG20Utt122wa377fffuHUU08Np59+erjiiivCz3/+88AxFFJJ4wR+8YtfNH6BzoqACIhAOyYgV5l2nHmKugiIQO0S6NKlS/jtb38b/d1xlTnqqKPC8uXLiyZ42bJl4a233gr4rs+ZM6fodc058cknn4Rnn302TJw4sdFnNyfMTCYTxo0bF15//fWwaNGikm/94IMPwnPPPRcYI9AaAtNXXnklTJ06tTWCUxgiIAIi0CYEpLi3CWY9RAREQASaT6Bz587R6s6d+L6/9957DQL529/+FkaOHBl69eoVNtlkk7D11luH/v37hyFDhkRrPT71yNy5c+M155xzTtzHXYZ7Pve5z8V9/8OA2K9//eth9dVXD4MGDQrbbbdddDvh2i996Uvh3Xff9UtL2m6++ebxOdx3/vnnx3CJL8cJ/4wzzijqw49if+GFF4YBAwaEYcOGxd4Jfo8YMSLcfPPNBZ+/5ZZbxufdfvvt2fMwIv7f/va3A42R/fffP/Tr1y9w7VprrRUZXHfdddnr+fHDH/4w3oPrErLnnnvG/d/97ndxX39EQAREoBIE5CpTCep6pgiIgAiUSGDfffcNq666apg/f34YO3ZswL3G5bjjjgvXX399wDrPgFP8ulHQn3nmmWgl//73vx8+/PDDONCVa1DCp0yZEhsAKK5bbLFFVEY9PKz1PA/lHYV2n332ic/G4v6vf/0rPPLII2H06NFh0qRJYeDAgX5bo9vFixdHy/opp5wSHnrooXj/8ccfH+Pxj3/8I1x22WUBKzzbtKC04xr05ptvhm7dusWeB9KHpf75558P3/zmN8M///nPgFLeqVOn7K1LliyJz0v3TmBdJ7wJEyaEnXbaKTDQlHTQwHnqqacCA1qJE0r6aaedFsNiYDC8/vOf/8TeBhob3iDKPkw/REAERKCtCViBKREBERABEWgjAp9++mnGyvn477777ivpqRtuuGG83nzds9ebm0c81r1798yTTz6ZPc4Pc5vJHHzwwfG8KZsZG/SaPW+DXeNxG5yaPeY/vvKVr8RzY8aMiWH4cbam1MdzxP2SSy5Jn2r09wYbbJC9z3z2M9YDkL3eFPCMNQDi+VtuuSV7nB/m4x+Pr7322pkXXngh59wdd9yRMQt6PH/NNdfknLNeh3j8L3/5S/Y4YTtzs9ZnTPnPnrOGRcaU+eyzTOHPnuOHDQyO5/773//mHNeOCIiACFSCgFxl2rqlpOeJgAiIQDMJrLHGGvEOLO4uWMBxhzn88MPDDjvs4IfjtmvXruHYY4+Nv/EJnzFjRs75QjtY6rFE41Ly61//OhBGWrbaaqvohsMxfOmbK8xPT7hp6zhuPbikILjEuOAW5C4pv//976N13M+xPeSQQ8JZZ50VD+H60xxfeaZ33HTTTbPBWcMnWEMk7jMdJz0SEhEQARGoVgJS3Ks1ZxQvERABEagnsGDBgvjLrDtZJsw4w1zwN910U/ZY+odZqrO7uI80JX379g2vvfZa4Fnrrbdeg8sJw6zn8Xgp4eUH8L3vfS/gs58vuPsg+O/jDoS89NJL0X2GeBxwwAHxWP6fH/zgB/GQ9WCU3JCg0bD33nvnB5UzdaTHocFFOiACIiACVUAg16RSBRFSFERABERABHIJ+OI/DMrMF3y5zZUk+oIzWwvW6jfeeCP6uPu1aYXfjzW2pUGAvzsWfsLjH3PKu2W7ueHxLHP3KfhIBsG6Dz8NB3oP3KLvDYVCN+KjT08EvQnEj4GmTQk9FPjL5wtxcEn7xvsxbUVABESgWghIca+WnFA8REAERKAAgVmzZmWnQMxfVIhFmpiphcGiaRk8eHA47LDD4sDN9PGmfjOQlfnj77zzzpzpHxnYynzzrEbKFIrlyNChQ4vehuKMpZt0oLh7elC0GxNmxEFxp6FRirCQlUQEREAE2jMBKe7tOfcUdxEQgZonwKwnLsxs4nLrrbfGmVWwfjP7Ci4gzILCNIvMCIOvNjOulCq4yLDIE24qKLg2QDWu6opv+6hRo0LPnj0Dbi3lKu64tBQT98HnOYgr+TQkGpPp06fH06XOcNNYWDonAiIgAu2BgBT39pBLiqMIiECHJIDbxtlnnx3TjivI9ttvn+XACqEo7cy5juU9PeiTi3AfcSnF/ePBBx+MSjuDUp9++umorPv9vvUwSwnP7/EtVvRddtnFd7NbFlZiysgePXpkn+kuMo3NGc9gWqZxRNZcc81sePohAiIgArVMoOFIoVpOrdImAiIgAu2EAEq5Tf8YfdeJsivw/MZlhfnNkQMPPLCB0s5xZp1xYR5zF9xeEFZaTQurkiIozW75Tp9HSWYOdSQdXvqaxn7nL3Dk1/7xj3+MP+kp8JlscMuhIYLiToOikFxxxRXxML0DO++8c6FLWuVYMV6tErgCEQEREIFmEpDi3kxgulwEREAEWosACx1NnTo1+49BmQ8//HCwucmj28tPfvKT+Cibkz1OgejPZcrG1VZbLe7aHOV+OLu99tprw69+9avsPoq+S+/eveNPZnFJH/dZaBjgykDUtMycOTPstddecXEnjqfvS1/X2G8WMvrtb3+bcwkLOjE9I8rxT3/60+w5Gg4+nSWz0XgjxS9AmfcpHJkOEmv9ZyXOi4GzEhEQARGoOAGz6khEQAREQATaiEB6ASarALILAxX7/bWvfa3BYkhE1RTd7L0sOmTzmmdOOumkjCm9GbNWZ2yQZ2bYsGHxGvN1z6aOxZr8Wea3nhk0aFDGZovJmEU9Y7O0xHOmrGZYjMkaDhnze8/YVJFxwaP9998/njdf+2x4Tf0wC368xxdaMmt6xlZRzdiqrBlT2OO5K6+8skEwxMdWiY3nWWyJZ7Mo02677RbTRxrOPffcBvc1tgAT4RUS60GIzyHMV199NecSa7Bkz8EKJhIREAERqBQBfCQlIiACIiACbUSgMcV9lVVWybBSqPmyRyXcrLxFY2V+5pkLLrggu4KoK+M2sDNz1VVXxRVKv/vd70al02aYyQkH5dOmU8wqpK6smqU9YwNcs8cJkzihNE+YMCFjg0Gz52yqyJwwi+244m7zzWdOPPHEDCu9elxRhC+77LJit8YGBaut2nSR2XtsOse40qm5yhS8r7UVd9JtbjzZxgKrykpEQAREoFIEOvFgK0QlIiACIiAC7ZAAbivM8860kaxOylSQpQhFPyuFMh96//79c25hwKgprGHAgAHBrNTBlPec883ZYf52fNVvu+22cMQRRwQWb8IVh1lqiC8rlzYlxJXBrUz7yCBdXIXaWhgMC2MGwrovflvHQc8TAREQASnuegdEQAREQAQ+MwL5ivtn9iAFLAIiIAIdgIAGp3aATFYSRUAEREAEREAEREAE2j8BKe7tPw+VAhEQAREQAREQAREQgQ5AQIp7B8hkJVEEREAEKkUAH3ama/T50CsVDz1XBERABGqBgHzcayEXlQYREAEREAEREAEREIGaJyCLe81nsRIoAiIgAiIgAiIgAiJQCwSkuNdCLioNIiACIiACIiACIiACNU9AinvNZ7ESKAIiIAIiIAIiIAIiUAsEpLjXQi4qDSIgAiIgAiIgAiIgAjVPQIp7zWexEigCIiACIiACIiACIlALBKS410IuKg0iIAIiIAIiIAIiIAI1T0CKe81nsRIoAiIgAiIgAiIgAiJQCwSkuNdCLioNIiACIiACIiACIiACNU9AinvNZ7ESKAIiIAIiIAIiIAIiUAsEpLjXQi4qDSIgAiIgAiIgAiIgAjVPQIp7zWexEigCIiACIiACIiACIlALBKS410IuKg0iIAIiIAIiIAIiIAI1T0CKe81nsRIoAiIgAiIgAiIgAiJQCwSkuNdCLioNIiACIiACIiACIiACNU9AinvNZ7ESKAIiIAIiIAIiIAIiUAsEpLjXQi4qDSIgAiIgAiIgAiIgAjVPQIp7zWexEigCIiACIiACIiACIlALBKS410IuKg0iIAIiIAIiIAIiIAI1T0CKe81nsRIoAiIgAiIgAiIgAiJQCwSkuNdCLioNIiACIiACIiACIiACNU9AinvNZ7ESKAIiIAIiIAIiIAIiUAsEpLjXQi4qDSIgAiIgAiIgAiIgAjVPQIp7zWexEigCIiACIiACIiACIlALBKS410IuKg0iIAIiIAIiIAIiIAI1T0CKe81nsRIoAiIgAiIgAiIgAiJQCwSkuNdCLioNIiACIiACIiACIiACNU9AinvNZ7ESKAIiIAIiIAIiIAIiUAsEpLjXQi4qDSIgAiIgAiIgAiIgAjVPQIp7zWexEigCIiACIiACIiACIlALBKS410IuKg0iIAIiIAIiIAIiIAI1T0CKe81nsRIoAiIgAiIgAiIgAiJQCwSkuNdCLioNIiACIiACIiACIiACNU9AinvNZ7ESKAIiIAIiIAIiIAIiUAsE/j9gqP5VhY/zDgAAAABJRU5ErkJggg==" width="70%" style="display: block; margin: auto;" /></p>
<p>This plot is useful to quickly see the distribution of <span class="math inline">\(k\)</span> values, but it’s often also possible to see structure with respect to data ordering. In our case this is mild, but there seems to be a block of data that is somewhat easier to predict (indices around 90–150). Unfortunately even for these data points we see some high <span class="math inline">\(k\)</span> values.</p>
</div>
<div id="marginal-posterior-predictive-checks" class="section level3">
<h3>Marginal posterior predictive checks</h3>
<p>The <code>loo</code> package can be used in combination with the <code>bayesplot</code> package for leave-one-out cross-validation marginal posterior predictive checks <a href="https://arxiv.org/abs/1709.01449">Gabry et al (2018)</a>. LOO-PIT values are cumulative probabilities for <span class="math inline">\(y_i\)</span> computed using the LOO marginal predictive distributions <span class="math inline">\(p(y_i|y_{-i})\)</span>. For a good model, the distribution of LOO-PIT values should be uniform. In the following QQ-plot the LOO-PIT values for our model (y-axi) is compared to standard uniform distribution (x-axis).</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>yrep <span class="ot">&lt;-</span> <span class="fu">posterior_predict</span>(fit1)</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="fu">ppc_loo_pit_qq</span>(</span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">y =</span> roaches<span class="sc">$</span>y,</span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>  <span class="at">yrep =</span> yrep,</span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>  <span class="at">lw =</span> <span class="fu">weights</span>(loo1<span class="sc">$</span>psis_object)</span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div>
<pre><code>Some PIT values larger than 1! Largest:  1 
Rounding PIT &gt; 1 to 1.</code></pre>
<pre><code>Warning in .loo_pit(y = y, yrep = object, lw = lw):</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAu4AAAHPCAYAAAAMBV/EAAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAALuoAMABAAAAAEAAAHPAAAAAE2s/msAAEAASURBVHgB7N0HfBTFHsDxfyoh9N57CYTeuwiIKEVUHgp2RVGeCtgVELEgVkRQ9CkqAg9EwfqQIiIqRRBBQGqk9w4BUkjh3X/1zpACu5dcsnv3m8/j3d3uzOzMdyP8Mzc7E3TelYSEAAIIIIAAAggggAACthYItnXraBwCCCCAAAIIIIAAAggYAgTu/CAggAACCCCAAAIIIOAAAQJ3B9wkmogAAggggAACCCCAAIE7PwMIIIAAAggggAACCDhAgMDdATeJJiKAAAIIIIAAAgggQODOzwACCCCAAAIIIIAAAg4QIHB3wE2iiQgggAACCCCAAAIIELjzM4AAAggggAACCCCAgAMECNwdcJNoIgIIIIAAAggggAACBO78DCCAAAIIIIAAAggg4AABAncf3aS4uDhJSEjwUe1UiwACCCCAAAIIIBBoAgTuPrrjhQoVko4dO/qodqpFAAEEEEAAAQQQCDQBAvdAu+P0FwEEEEAAAQQQQCDXBHQGxty5c3PkegTuOcJIJQgggAACCCCAAAIIXCjwxRdfSN26daVXr16ybt26C0968YnA3Qs0iiCAAAIIIIAAAgggkJXAxo0bpWvXrnL99dfLzp07JSUlRaZPn55VdtPHCdxNU5ERAQQQQAABBBBAAIGsBU6dOiUPPfSQNGrUSBYuXGhkrFq1qnz++efy0ksvZV3Q5JlQk/nIhgACCCCAAAIIIIAAApkInD9/Xj788EMZNmyYHD582MgRGRkpTz75pDz22GMSERGRSSnrhwjcrZtRAgEEEEAAAQQQQAABj8DQoUNl/Pjxns833HCDvPbaa1KpUiXPsZx4w1SZnFCkDgQQQAABBBBAAIGAFbjvvvskNDRUGjRoID/88IPMnDkzx4N2xWXEPWB/xOg4AggggAACCCCAQE4I6MoxGrC3adNGQkJCcqLKTOsgcM+UhYMIIIAAAggggAACCJgXaN++vfnMXuZ05FSZ06dPy44dOyx1OTU1VTZs2GA84Xvy5Mksy5rNl2UFnEAAAQQQQAABBBBAwAcCjhpxP3PmjHz66afGn1tvvVWqVatmimTTpk3y7LPPSp8+faRmzZryzDPPSHR0tNx7770XlDeb74JCfEAAAQQQQAABBCwIpLgGE2P2HZVNew7L9oPH5HRcgpxy/Ul2rfWdev6vinSVkqCgIBH9/M+LcdJ1ynXOdervPO5XTxNc58+7zrv+90+ev6rJtLxeM9jInCaTkdNzeVc97mv+9aqn9dhfOfRYkKvt5131BP2TV09r+iujkSdtW7X4X200MhldNcr/fVyPulPavH/1/Z92ePK4rpNVO/Q6mrQpae1CQ4KldJGCUrN8SenYoIaULVbor4x////Zs2dl7Nixog+fFip04bkLMubSB8cE7jpKvmbNGlFA/WM2HT16VJ566inp3bu39O3b1yhWpUoVI4ivUKGC9OzZ0zhmNp/Z65IPAQQQQAABBBBITkmV9TsPyJ/7j8q+Y7Gy+8gJ2X/slCS7I3SI8lxA78ua7ftl9tL10rd9Q/mX64/+AjBjxgxjKcd9+/ZJbGysvPrqq3neVscE7kWLFpVOnTpJsWLFjBF3s3KTJ08WDfo1cHen4sWLS+fOnWXixInSrVs3CQsLE7P53HXwigACCCCAAAIIpBfQUecdB4/Lii27ZPW2fbLnyCnREXaS/QX03s38ea3EbN4oc6dMlCVLlngavW3bNs/7vHzjmMDdjWR8beT+cIlX3V72xx9/NJbj0WA9bapXr54sWLBAli9fLu3atTOV77LLLktbBe8RQAABBBBAAAHZduCYfLNyo/wWs0fizyUj4lCBxLgzsuG7WTL718We6T21a9eWcePGydVXX22LXjkucLeitn//fmO0Xeezp0/ly5c3DukDq9Vcc+V1VP5S+Qjc0yvyGQEEEEAAgcAVOH46Tv4zd7n89ue+wEXwg56fd30jsn3lItmw8HNJiv9rOna+/JHy3KhnjLnt4eHhtumlXwfux48fN6B1y9n0yb31rG5LazZf+jr0s/5ysHLlygynChQokOEYBxBAAAEEEEDA2QJn4hPlq182yHdrtsqZhHPO7gytNwT2b14jv38z1aNRuXFb6XbTPfL4Y3d4jtnljV8H7u6HWDP7TUnntWtKTEz0POx6qXyZ3TQdsR85cmSGUyVLlsxwjAMIIIAAAggg4FyBXYdPyOhPFsrxM/HO7QQtzyBQIbqZlKhcS1KSk6Rxr1td72tKcH57DsD6deDuDp7Pncv4G7EG7JoqV64sZvNluNOuA127dpV169ZlOKW7ZpUqVSrDcQ4ggAACCCCAgPMETrtG2l9wBe0nCNqdd/NMtLjNLYMlPH9BCQoONpaoLF20oIlSuZ/FrwP3MmXKGKJxcXEZZN3HqlatKmbzZaiEAwgggAACCCDg9wJJySny6qwfCNr9+E7nK1DY0ztdM75ZzYqez3Z649eBe5EiRUQfQtV56OmTzm3XpA+mms2Xvg4+I4AAAggggID/CiQmJctnS9bJNys2uJZ01HCO5O8CulFT4cgIubJJbVt2NdiWrcrBRt14442ye/duOXDgwAW1bt682VgmslatWsZxs/kuqIQPCCCAAAIIIOCXAvGJSfL01Hny5fI/CNodeoeTEuJl3bfTZd+GVaZ7kC88VJ7o20ny5/vrWUjTBXMpo+MCd/cKMO6pLumdjhw5Iv3795dJkyYZp7p37y66U+r06dM9WbWOpUuXyqBBgyTYNZdJk9l8nkp4gwACCCCAAAJ+K/DevF9ku2sjJZLzBM67NlLa+dtPMn/s4xKzdL6snfNfSUnK+Lxj+p41qVFeXr2rp9SuYN9nFB0zVSY+Pt7YJGnWrFmG87x586REiRLSvn17KV26tMde12Pfu3evxMTEGMd02ce33npLRo8eLSNGjJC6devKqlWrZPDgwdKhQwdPObP5PAV4gwACCCCAAAJ+KbD36En5ecOOXOlbkch8El25rERVLCX5QkNcI73hkpCUJPnDw0RH/SNcr0muDSVDgoJEN6HU9/nCQo1zkRHhEpeQJPnCQyQl5bxxXscjk5JceVwjx3Gu8pFa37kkCXeV0R1cdSpIcHCQnHPl0WvEuRbwyO9ap1zzaL3uXV5DXHkSNY9r5Pmsa9lLrSfR1a4w1+IbusOoBse6EEeia8OpAhGax9XmfKHG5zBXPamua2meUFefElx5IrU9rmvoNbXe0BAdOD1vfJuhdWqfI7U9ickS4apHnysI+ftB0WRXXUYeVz16DbdLsssiOMj1MKmrU8kpqa4+hsjq31bJO6+Olk3r13ruX8uWLeWuLo2lQKEif9lpPa5rufusjtXKFJNiBTMuH+6pxCZvglyofjdpS+ev606poaEX/l6SkJAgJ06cMB5GdY+0Z3YfzObLrKz7mP4wN2/eXFasWOE+xCsCCCCAAAIIOEDgq1/+kKmLVud4S0sVKSBVSheTCiUKS+mihaR+lbKu90Vy/DqBWOGhQ4fkySeflI8//tj4hUEN6tevL+PHj5dOnTr5DcmFka2fdCvtCHzaLumoerly5dIeyvS92XyZFuYgAggggAACCDha4Mipv3bPzE4ndERZR3GrlyvpWqGkgkRVKO0ambbPDpzZ6Zudyia5Ruo1OH/uueckNjbWaFrRokXl2WeflX//+98ZBnHt1HZv2uKXgbs3EJRBAAEEEEAAAQRUIMI11cPblN81JaRfxybSo0Vdb6ugnAUB3Wzz5ZdfNoJ2nU0xYMAAefHFFz179FioyhFZvf/JdET3aCQCCCCAAAIIIGBNoJaXDyc+0LOttKtXzZiPbe2K5PZWQEfXNVD/6KOPZMKECdK0aVNvq3JEOb+c424Heea42+Eu0AYEEEAAAQSsC+iDkfe/84WcOB3nenzSXLq1c1Pp3bq+uczkylEBfVxTH9wNhOS45SAD4abQRwQQQAABBBDIO4Ew10oo9/doK7oEy6XCQY0X+7umxhC05939CpSgXYUJ3PPu54wrI4AAAggggIBNBRpVLy9P/CvrjXh0qcJ20VVl3MDe0qddA5v2wvnN0mUlSf8IMMf9HwveIYAAAggggAAChsC6Hfvlpz92GCvBpLrmy4SGBBlrmVd1LefYpEYFuax+dWNNcLh8I6CbZer+O/v27ZOvvvrKNxdxYK0E7g68aTQZAQQQQAABBHwjoPPb3/l2mRG0p72CTpk5E3/OtbHQeel3WWOC9rQ4Ofg+xbWp0nvvvWcE7Rq8a5ozZ4706NEjB6/i3KqYKuPce0fLEUAAAQQQQCCHBf4z95cMQbtewv2Q6rHYs/Ls9O/kxJm4HL4y1f3000/SrFkzY/11Ddp17vott9wiTZo0AedvAQJ3fhQQQAABBBBAAAGXwMbdh2Tx+m0XtdAA/lRcgsxY/PtF83HSvMDevXulf//+0rFjR1m7dq1RUJd1XLJkiUydOlXKly9vvjI/z0ng7uc3mO4hgAACCCCAgDmBH9b9aS6jK9fPG3dIYlKy6fxkzFxgzJgxUqdOHfnkk0+MDCVLlpT//Oc/8uuvv0rbtq6VfUgXCBC4X8DBBwQQQAABBBAIVAEdcTebdC78geOnzWYnXxYCO3bsEN39NDQ0VB588EGJiYmRgQMHiu6CSsoowMOpGU04ggACCCCAAAIBJvDdmq1y6OQZS71OTk2xlJ/MGQVGjx4thw8flhdeeEHq12cDq4xCFx4hcL/Qg08IIIAAAgggEGAC2w8ek/fnrbDUa11lpkzRQpbKkDmjQKlSpeTLL7/MeIIjmQrwPUSmLBxEAAEEEEAAgUAR+OzndXL+vHvdmEv3WoP2+lXLSqH8+S6dmRwI5KAAgXsOYlIVAggggAACCDhLIMW1M+ea7fs8yz2aab0uU3jz5U3NZA3oPDoFhpSzAgTuOetJbQgggAACCCDgIIHYuERJTkm11OIHerWTmuVLWioTSJk1YB8wYIBUr15ddu/eHUhd93lfCdx9TswFEEAAAQQQQMCuAt+v3WqpaQUiwuWy+tUtlQmUzMnJyfLGG29I7dq15cMPPzRWixk5cmSgdD9X+snDqbnCzEUQQAABBBBAwG4CK7fslk9+/GvDHzNt07nt9SqXMZM14PJ89913MmTIENm0aZPRd13O8a677pIXX3wx4Cx82WECd1/qUjcCCCCAAAII2FJAH0advHCVaDBu9rFUzXdVsyhb9ievGqXrsD/88MMXrAzTpk0bmTBhgjRr1iyvmuW312WqjN/eWjqGAAIIIIAAAlkJ7Dh0XA6fOmM6aNd6ujSqKQ2rlc+qyoA7/v7770t0dLQnaC9XrpxMmTJFli5dStDuo58GRtx9BEu1CCCAAAIIIGBfgQPHYy01rkzRgjLw6taWyvh7Zg3aExISJDw83Jgm8/TTT0uhQqxt78v7TuDuS13qRgABBBBAAAFbCoS45mBbSdXLlhCrZazU78S87dq1k9dff1169uxpPJDqxD44rc0E7k67Y7QXAQQQQAABBLItULFEEUt1VC1TzFL+QMms89tJuSdg7dfN3GsXV0IAAQQQQAABBHwiEH8uSSZ+u8x03cGuDZfa16tmOj8ZEfCVAIG7r2SpFwEEEEAAAQRsKfDe3F9k676jptv2r/YNpUzRwJq7vXz5cklKSjJtRMbcESBwzx1nroIAAggggAACNhDYffiE/Lxhh+mWdG9eR/q6AvdASfv27ZObb75Z2rZtK+PGjQuUbjumnwTujrlVNBQBBBBAAAEEsiOgK8m8O3e5pSrqVy0rQa6pMv6eEhMTZcyYMRIVFSXTp083uvvOO++I7oZKso8AD6fa517QEgQQQAABBBDwgUBSSop87Npsad5vWyzXfvjkGctlnFbgm2++kYceeki2bdtmND0kJEQGDRokzz33nISGEira6X5yN+x0N2gLAggggAACCOSogO6QOvbzn+TXmD1e1ZsvzH9DpS1btsjQoUNl3rx5HpvLL79cxo8fLw0aNPAc4419BPz3p9E+xrQEAQQQQAABBPJIYNHaP70O2rXJNcqVyKOW+/ayP//8s3Tp0sXzAGrlypXltddek759+/r2wtSeLQHmuGeLj8IIIIAAAgggYGeB/63cJN7MUNcy1csWd/3xz8C9TZs2UrNmTYmIiBDd8XTTpk0E7Xb+Qf67bYy4O+Am0UQEEEAAAQQQsC5wNuGc7Dl60npBVwndJfW+7m28KuuEQjp3fdq0aVK8eHGpWrWqE5pMG10CBO78GCCAAAIIIICAXwqcOpvgdb8e6NXOb0fb3ShNmzZ1v+XVIQIE7g65UTQTAQQQQAABBMwJJCYly+yl6+R/KzaZK5AuV9ECEeyUms6Ej/YQIHC3x32gFQgggAACCCCQAwI6PWbUfxfIjkPHva6tZ8tor8vaoeDChQulYMGC0rp1azs0hzbkoAAPp+YgJlUhgAACCCCAQN4KTJyzLFtBe52KpaVHy7p52wkvr75z507p06ePdO3aVe69915Jca1fT/IvAQJ3/7qf9AYBBBBAAIGAFfjzwFFZsWW31/1vF11VhvfrImGuDYiclOLj4+WZZ56RunXryueff240/fDhw/Lnn386qRu01YQAU2VMIJEFAQQQQAABBOwv8OtW7zZZKl2koDzap6MjH0b97LPP5NFHH5Xdu//6hSUsLEyGDBkiI0eOlEKFCtn/ptFCSwIE7pa4yIwAAggggAACdhU4cuqM5abpeu26yZLT1mtfv369DB48WBYvXuzp81VXXSXjxo2TqKgozzHe+JcAgbt/3U96gwACCCCAQMAKhLvWJreazrsKlCpSwGqxPM1/+vRpad++vcTGxhrtqFGjhrzxxhvSq1evPG0XF/e9AHPcfW/MFRBAAAEEEEAgFwRqli/p1VWa16rkVbm8KqRTYJ544gkpUKCAjB49WjZs2EDQnlc3I5evS+Cey+BcDgEEEEAAAQR8I9CmThWJzBcmOv3FbGpYrZxEVy5jNrtt8j3yyCOyZcsWGTZsmOTLl8827aIhvhUgcPetL7UjgAACCCCAQC4JFIgIl3u6tRKd/mImeK9QorAMuaZDLrUuZy+jwXqFChVytlJqs72A9clgtu8SDUQAAQQQQACBQBXoUL+6JKWkyvvzV0hScubrmIeGBMuVTWtLv8sau0bowwOVin47UIDA3YE3jSYjgAACCCCAQNYCnRvVlMbVy8vC32Nk055Dcjou0QjQK5YsIm3rVpHark2WwkPtuVb7nDlzZOXKlfLss89m3UHOBKwAgXvA3no6jgACCCCAgP8KFC8UKTd0aOSYDsbExMjQoUPl22+/laCgIOnRo4e0bNnSMe2nobkjwBz33HHmKggggAACCCCAQAYBXdpRV4ipX7++EbRrhooVK8qpU6cy5OUAAoy48zOAAAIIIIAAAgjkssD58+dl2rRpRtB+4MAB4+oRERHGLqhPPfWUREZG5nKLuJwTBAjcnXCXaCMCCCCAAAII+I3Ab7/9Jg8++KAsX77c06drr71Wxo4dK9WqVfMc4w0C6QUI3NOL8BkBBBBAAAEEEPChwAsvvOAJ2uvWrStvvvmmdO3a1YdXpGp/EWCOu7/cSfqBAAIIIIAAAo4QeP3116V06dKir2vXriVod8Rds0cjGXG3x32gFQgggAACCCAQIALVq1eX3bt3s+NpgNzvnOwmI+45qUldCCCAAAIIIICACQHd+ZSEgFUBAnerYuRHAAEEEEAAAQQQQCAPBAjc8wCdSyKAAAIIIICA/wnMnj1bmjVrJsePH/e/ztEjWwgQuNviNtAIBBBAAAEEEHCqwIYNG6RLly7yr3/9S1avXi0jR450aldot80FCNxtfoNoHgIIIIAAAgjYU+DkyZMyZMgQady4sSxatMhopD54euWVV9qzwbTK8QKsKuP4W0gHEEAAAQQQQCA3BVJTU+WDDz6Q4cOHy5EjR4xL606nw4YNM3Y+5cHT3LwbgXUtAvfAut/0FgEEEEAAAQSyIbBy5UoZNGiQMSXGXU2/fv3k1VdflYoVK7oP8YqATwQI3H3CSqUIIIAAAggg4I8C+/bt8wTtjRo1kvHjx8tll13mj12lTzYUIHC34U2hSQgggAACCCBgT4HrrrtObrzxRiNYv/feeyUkJMSeDaVVfilA4O6Xt5VOIYAAAggggICvBD755BNfVU29CFxUgFVlLsrDSQQQQAABBBBAAAEE7CFA4G6P+0ArEEAAAQQQQCCPBc6ePSvnz5/P41ZweQSyFiBwz9qGMwgggAACCCAQIALTpk2T2rVry9SpUwOkx3TTiQIE7k68a7QZAQQQQAABBHJEQHc6bdeundx6662yf/9+eeKJJyQ+Pj5H6qYSBHJagMA9p0WpDwEEEEAAAQRsL3D06FHRVWFatGghy5YtM9obFRUlkydPlvz589u+/TQwMAUI3APzvtNrBBBAAAEEAlIgJSVFJkyYILVq1ZL33ntPdBfUwoULy2uvvSbr16+Xbt26BaQLnXaGAMtBOuM+0UoEEEAAAQQQyKbA1q1bpU+fPvLHH38YNQUFBcntt98uL730kpQpUyabtVMcAd8LELj73pgrIIAAAggggIANBMqXLy/Hjx83WqJTZHTkvVWrVjZoGU1AwJwAgbs5J3IhgAACCCCAgMMFChYsKG+99ZacOHFC7rzzTtERdxICThJwVOCu89A2bdokBw4ckObNm0vRokVNWetXYlo2s1S2bFkpXbq051RycrJs3LjR81nfHDp0yLhesWLFLjjOBwQQQAABBBBwlsB1113nrAbTWgTSCDgmcNeA/dlnnzXmptWsWVOeeeYZiY6ONp4IT9OfDG81CB80aFCG4+4DWmfnzp3dH2XhwoUyevRoz2d9U69ePenatesFx/iAAAIIIIAAAggggEBuCjgicNclm5566inp3bu39O3b1/CpUqWKEcRXqFBBevbsmaXZV199Jf369ZOmTZtesLzTjh07jK/LWrdufUFZzT9y5EgJDv5rwR39Gk1/USAhgAACCCCAgH0FTp06JfqncuXK9m0kLUMgmwKOCNx1TdWTJ08agbu7v8WLFzdGyidOnGgs3RQWFuY+5XnVaS/VqlUzAnfPwb/f/Prrr8barZGRkZ5TK1askAYNGjC67hHhDQIIIIAAAvYW0KmwH330kTHAV79+fVm0aJG9G0zrEMiGgO3Xcdf1Vn/88UepVKmSaLCeNukUltOnT8vy5cvTHva8Dw0NzTRo1ww//PCDXH755Z68+mbGjBkyd+5cefTRR+XTTz+VxMTEC87zAQEEEEAAAQTsI6D//uuqMHfffbccOXLE+Lc9q5jAPq2mJQh4L2D7wF23H9bRdl3CKX1yH9uwYUP6Uxf9vG3bNmNbY93i2J10eSh96FWvpSPvukTULbfckuUvBe5yvCKAAAIIIIBA7gocPHjQWH9d/x1ftWqVcfGGDRvK4sWLpU2bNrnbGK6GQC4K2H6qjHu91bRTWtw+ERERxtvDhw+7D5l61dF2nfNeqFAhT34dzZ85c6axRJQ+0DplyhRjdZlhw4bJpEmTpEaNGp68ad+sXbvW2B457TF9X6pUqfSH+IwAAggggAAC2RBISkqScePGyfPPP298465V6Ypv+vm+++6TkJCQbNROUQTsL2D7EfezZ88aiuHh4Rk03fParU5p0d/I00+TcVeufwHob/Bvv/22MeKu8+R1zdes0vnz50X/Ikn/J6v8HEcAAQQQQAAB6wLnzp2Txo0by+OPP24E7bqIhAbrMTExcv/99xO0WyelhAMFbD/iXrJkSYNV/4NNn9wBu5UnyHU1md27d0uHDh3SV3fBZ50ff++998qSJUtEp+JogJ7ZRg36l0hmgf0777wjuvINCQEEEEAAAQSyL6ADeFdccYXxbXj79u2NKa36bzAJgUASsP2Ie5kyZYz7ERcXl+G+uI9VrVo1w7msDug0mUaNGhlfrWWVJ+1xDfD1lwYdUSchgAACCCCAQN4J6N4rupDEzz//bIy+511LuDICeSNg+8C9SJEixoOp+pBq+uSe265LPppNGrh37NjRbHYpUaKEsSZsZlN1TFdCRgQQQAABBBDItoDumK57s5AQCFQB2wfuemNuvPFGY3qLrvqSNm3evNlYJrJWrVppD2f5fufOnaJ/LrvssizzpD+hD5/26NEj/WE+I4AAAggggAACCCCQqwKOCNy7d+9uzBefPn26B0dXm1m6dKkMGjTIs8upntR1XPv372+sBOPJ/PcbHW2Pjo6W0qVLpz9l5H/zzTdl7969nnPLli0z1nK/4YYbPMd4gwACCCCAAAI5K3Ds2DH58ssvc7ZSakPADwVs/3Cqmuuyj/oA6OjRo2XEiBFSt25dY93WwYMHZ3jIVNdh1+BbnzJPnzRwv+qqq9IfNj7Hx8fLrFmzZPbs2dK2bVsjuK9evbqMGTMm04dSM62EgwgggAACCCBgWkA3WXz33Xfl6aefFl1F7o8//hCz36KbvggZEfAjgSDXainnndSfhIQEY611fWhVl4LKLOncd12XXVeGcSftpm68pJs2ZbYmvOaLjY01Rux1DfbChQu7i3r1qmvJNm/e3NjMyasKKIQAAggggIAfC+jSzDoAt379eqOXunKbrtGux0gIIJC5gOMC98y7Yb+jBO72uye0CAEEEEAg7wX27Nkjjz76qHz66aeexuhAl+5Y3rp1a88x3iCAQEaBzIesM+bjCAIIIIAAAggg4LWAfmOuO5zWqVPHE7TrM2e6O/nKlSsJ2r2WpWAgCfwzlySQek1fEUAAAQQQQCBXBW6//XZPwK5TWR944AEZNWqU6LLPJAQQMCfAiLs5J3IhgAACCCCAQDYEHn/8cePZNN39VJdafuONNwjas+FJ0cAUYMQ9MO87vUYAAQQQQCBXBZo1a2asCNekSZNcvS4XQ8CfBBhx96e7SV8QQAABBBCwsQBBu41vDk1zhACBuyNuE41EAAEEEEAAAQQQCHQBAvdA/wmg/wgggAACCGRD4ODBg/LII4+IrhpDQgAB3woQuPvWl9oRQAABBBDwS4GkpCR5/fXXJSoqSsaOHSuvvPKKX/aTTiFgJwEeTrXT3aAtCCCAAAIIOEBg/vz5MmTIENmyZYvRWt3J/MSJEw5oOU1EwNkCBO7Ovn+0HgEEEEAAgVwT2LZtmzz88MPy9ddfe67Zrl07GT9+vDRt2tRzjDcIIOAbAabK+MaVWhFAAAEEEPAbgbNnz8rw4cOlXr16nqC9fPnyMm3aNFmyZAlBu9/caTpidwFG3O1+h2gfAggggAACeSwwc+ZMefHFF41WhIeHy0MPPSQjRoyQggUL5nHLuDwCgSUQdN6VAqvLudPbkJAQad68uaxYsSJ3LshVEEAAAQQQ8JFAamqq8W+ajrKPGzdOatas6aMrUS0CCFxMgBH3i+lwDgEEEEAAAQREHz5dvHixFC5cGA0EEMhDAea45yE+l0YAAQQQQMApAgTtTrlTtNOfBQjc/fnu0jcEEEAAAQQQQAABvxEgcPebW0lHEEAAAQQQsCawd+9e6d+/v8ybN89aQXIjgECeCDDHPU/YuSgCCCCAAAJ5J5CYmCivvfaasVJMXFycrFmzRtavXy9hYWF51yiujAAClxQgcL8kERkQQAABBBDwH4GvvvrK2ERp+/btRqdCQ0OlW7ducu7cOQJ3/7nN9MRPBQjc/fTG0i0EEEAAAQTSCmzevFmGDBkiCxYs8Bzu3LmzseupbqxEQgAB+wswx93+94gWIoAAAggg4LXA6dOn5ZFHHpGGDRt6gvYqVarIrFmz5Pvvvzd2Q/W6cgoigECuCjDinqvcXAwBBBBAAIHcFYiPj5cPPvhAkpKSJH/+/PL444/LE088YbzP3ZZwNQQQyK4AgXt2BSmPAAIIIICAjQVKly4to0aNkiVLlsjrr78uOtpOQgABZwoEnXclZzbd3q0OCQkxtodesWKFvRtK6xBAAAEEEEAAAQQcIcCIuyNuE41EAAEEEEAAATMCSSkp8sfOg7Lz8AlJTk6RssULS9MaFaRARLiZ4uRBwNYCBO62vj00DgEEEEAAgYsL6DKO4eEEpaq0KmaPvDdvhRw/HXcBWnhoiPRt31CubVNfgoKCLjjHBwScJMCqMk66W7QVAQQQQACBvwV0HfZrr71WbrvtNkxcAnNWbpSXPvshQ9CuOOdcI+//XbxG3vl2OVYIOFqAwN3Rt4/GI4AAAggEmoDudDpixAiJjo4W3Uxp5syZ8tNPPwUag6e/Kamp8q4rIP9o4SrPsazeLFr7pyzduCOr0xxHwPYCBO62v0U0EAEEEEAAgb8ENEiPioqS0aNHS2JiojFFRpd3bNq0aUAS6foar8z6QRb+HmOq/zpJ5otlf5jKSyYE7CjAHHc73hXahAACCCCAQBqBdevWyYMPPnjByHr37t1l3LhxUqtWrTQ5A+dtwrkkGT3ze9m057DpTusyevrQ6un4RCmUP5/pcmREwC4ClgJ3fQBm4MCBops5JCQkGJs5vP3221KtWjW79Id2IIAAAggg4DcCqa5pIIMHD5Z3331XUlyrpWiqWbOmEbD36NHDb/pptSOJSckyctp82X7wuNWiRv7YswkE7l7JUSivBSwF7snJyfLxxx9L0aJF5YUXXpB+/fpJiRIl8roPXB8BBBBAAAG/FAgODpZjx44ZQXvBggVl+PDh8vDDDwf8KjJTF/3mddCuPyiFIhlt98v/YAKgU5YCd7eHfjV3++23uz/yigACCCCAAAI+Enj11Vclf/78xoBZ+fLlfXQV51R7Oi5BFqze6lWDdY57hZJFpHBkhFflKYRAXgt4FbhfccUVXrf7wIEDUq5cOa/LUxABBBBAAIFAEqhYsaJ8+OGHgdTli/Z1w+5Dkurlpu86x13Xcich4FQBr1aVKVWqlFf9PXPmjLz33ntelaUQAggggAACCCBw4ky81wjto6vK5Q1qeF2eggjktYBXgbu3O7T973//E126iYQAAggggAACrhVOdu6EwaKAt6vBXN0sSh68pr3Fq5EdAXsJeDVVpmXLlpZ7ceLECdm2bZuMHDnSclkKIIAAAggg4E8CW7ZskSFDhsjy5ctl69atUqZMGX/qnk/7El3ZutU93VpJN1fgTkLA6QJeBe6//vqr1/0OCtJHQ0gIIIAAAggEnkBsbKw8//zz8uabbxpLKqvAK6+8Iq+//nrgYXjZ4+KFIqWda8rL0o07L1lDcHCQPNGnkzSrVfGSecmAgBMEvArce/bsaalvug7t8ePHZdWqS29HbKliMiOAAAIIIOAAAZ0mOmXKFHnyySfl4MGDRosjIiJEdz3VYyRrAgOubClb9h6Ro7FnsywY4gran72lm9SpWDrLPJxAwGkCXgXu33zzjVf9nDNnjmRntN6ri1IIAQQQQACBPBTQf/d019MVK1Z4WnH99dcbo+xVq1b1HOONeQFdznHMHd1lwjdLZN2OAxkKli9eWAb3bi81y5XMcI4DCDhZwKvAXXdN1ZECq6lTp07y22+/WS1GfgQQQAABBBwp8Nxzz8moUaM8CzNER0fL+PHjpUuXLo7sj50aXaxgfhnZv6vE7D8qv2/fJydOx0uBiHCp65oD36haOQlxbV5FQsDfBLwK3A8dOiRVqlSxbBEZGWl8LWi5IAUQQAABBBBwoECrVq2MoL1IkSJGAP/AAw9IaKhX//Q6sPe50+Ra5UuK/iEhEAgCXv066u1UGQX1ZqQ+EG4EfUQAAQQQ8D+Bbt26yYQJEyQmJkaGDh1K0O5/t5geIZCrAl792v/oo4/KsWPHpF+/fhIVxfJKuXrHuBgCCCCAgKMEdJSdhAACCOSEgKUR97CwMOPp9/vuu0/2798vo0ePlh07duREO6gDAQQQQAABBBBAAAEELiJgacQ9JSXFGGG/4447LlJl1qe0/EcffSR333131pk4gwACCCCAgAMEdKW0q6++WoJ5CDJP7pYusXno5BmJSzwnxQtGSlHXw6okBPxdwFLgruux//DDD+Jt4K47xe3bt8/fTekfAggggIAfC6xfv14GDx4sixcvlnfeeUf0W2hS7gkkJafI1ys2yre/bpJTcQmeC1ctXUxuuKyRtKxd2XOMNwj4m4ClwF07P3PmTClVqpTUqVNHwsPDTXnob8UnTpyQ//73v2J18yZTFyATAggggAACPhbQf8dGjhxpBOv6DbKmyZMnE7j72D1t9SfPJsjIqfNk//HYtIeN97sOn5BXZi2W3q3rya2dm2U4zwEE/EHAcuCemJiYra2ZCdz94ceGPiCAAAKBI6DfNr///vsyfPhwY2EG7XmBAgWMzw8//HDgQORxT5dt2injv14iySmpmbbk/N9Hv/plg1QsWUQ6NayZaT4OIuBkAcuBu5M7S9sRQAABBBCwIrBkyRJjWsyaNWs8xW666SZ55ZVXpEKFCp5jvPGtwPzVW+T9ef/sPHuxqwW5Tk5btFouq1+dTZguBsU5RwpYDtxLlCghb731lldTZSZOnOhIJBqNAAIIIBB4Ap9//rn06dPH0/HGjRsba7K3b9/ec4w3vhfYf+yUfDB/pekL6ci7zn3fuu+I1K1UxnQ5MiLgBAHLgXv//v2N9du96Zzutvrhhx96U5QyCCCAAAII5KpA9+7dpVq1ahIbGysvvPCCDBw4kBVkcvUO/HWx/63cJKmuZ+WspoMnThO4W0Ujv+0FLAXuuk1zr169vO5UpUqVpEWLFl6XpyACCCCAAAK5JaA7fc+ePVuqVq0qxYoVy63Lcp10Aut2Hkh3xNzH8NAQcxnJhYCDBCwF7rqKzJVXXml0T5+u16WwNm7cKPXq1ZM2bdpImTKX/kpKRzBICCCAAAIIOEGgSZMmTmimX7fxlGslGW9StTLFvSlGGQRsLWApcHf3ZN26daIBeNo12fPnz2+MTOhmFCQEEEAAAQQQQCAnBArmD5f4c0mmq9KHU6MqlpbyJYqYLkNGBJwiEGy1oXFxcdKlS5cLgnatIz4+Xnr37i1pn7y3Wjf5EUAAAQQQ8LWA7i0ydepUWb16ta8vRf3ZFNAHTE+eibdUS5hriszd3VpZKkNmBJwiYDlw180mjh49Ks2bN5c33nhDfvzxR3n33XeNue9JSUnGijNO6TztRAABBBAILIFVq1ZJu3bt5LbbbpP7779fNIgn2VPgyKkzMnrm95KUxbrtmbU6IjxURvS7QqqW4ZmEzHw45nyBINdfWpb+1urYsaMcOXJEfvnlFylcuLBHQKsZMGCAfPLJJ8YT+PogayCnkJAQ45ebFSvMrTsbyFb0HQEEEPC1gP67NWzYMGNlM91QSVPdunXl+++/l3Llyvn68tTvhYButvTTH9tNl6xRroSM7N9VCkSY29XddMVkRMBGApZH3Hfv3i0vv/zyBUG79icoKEhee+01Y8rM/v37bdRFmoIAAgggEKgCycnJ8uabb0rt2rVl0qRJokF7kSJFZOzYsaLPaxG02/Mn41xyiizfvMt043Sn1Jfu6E7QblqMjE4VsBS466i6PpDaqFGjTPtbvHhxYye5vXv3ZnpeD/7+++9ZnuMEAggggAACOSWgo+n679XQoUPl5MmTxgDTXXfdJVu3bpWHHnpIAv2b4Zxy9kU9h0+eliRX8G42paSeN+6v2fzkQ8CpApbms+gDqLqubeXKlbPsb/369eXMmTOZnte58V9++aXo7nMkBBBAAAEEfCVw8OBB6dGjhyQmJhqXaNWqlbHrKXuJ+Eo8Z+t1xeGWksVZv5bqJjMCdhKwNOKuDde/BA8cOGC86vv0f1JSUoyHfdIf179EdYqNTqkhIYAAAggg4EuBsmXLGqPq+vrRRx/J8uXL2QDQl+A5XHeZogUlJNhciKJRRaWSRXO4BVSHgD0FLI24axfOnTsn5cuXv2hvFi5cmOX5UaNGZXmOEwgggAACCOSUwNNPPy1PPfVUhmeycqp+6vGdQL6wUGlZu5Kpee46ON++XlXfNYaaEbCRgLlfZ23UYJqCAAIIIICAGYHIyEiCdjNQNs1z0+VNRAP4S31PH1WxlLStW9WmvaBZCOSsgOURd13m8Oabbzae0A8PN7fkkk6fOXHihHz++ec523pqQwABBBBAAAG/FChXvLA88a9O8srsHyThXHKmfaxetrg83qcT03Az1eGgPwpYDtxvuOEG+fjjj72yuOmmm4yHU70qTCEEEEAAgYAX0OUcP/jgA9HVy5599tmA9/B3gIbVysm4gb3l86XrZZlrecgz8X89bFyhRBG5onFNuapZHdGdUkkIBIqApcBdl87S3ea8TQ0aNDCWk/S2POUQQAABBAJXYNmyZfLggw/K6tWrRb/97du3r+hKZiT/FihZuIAMvLq18Sc+Mcm1jGewhLnuPwmBQBSwNMddp8ZcddVVppx0vfdjx45dkDfY9YR49+7dLzjGBwQQQAABBC4moJv63XrrrdKuXTsjaNe8GrDrYgmkwBLIny+MoD2wbjm9TSdgKXBPV/aiH3Wt9iFDhlw0DycRQAABBBDISkADc11GOCoqSqZNm2Zk043+Jk6cKL/99ps0bdo0q6IcRwABBPxSwNJUGb8UoFMIIIAAArYTmDNnjrEOe0xMjNE2nRpz7733yvPPPy8avJMQQACBQBQgcA/Eu06fEUAAAZsLjB07VtxB+2WXXSbjx4+XRo0a2bzVNA8BBBDwrYCjAnddTWDTpk3Gzq3NmzeXokXN75SWnJwsGzduvEDz0KFDovUUK1bMczw71/BUwhsEEEAAgWwJaKDeq1cvefHFF6Vfv37ZqovCCCCAgL8IOCZw14Bdl/7q06eP1KxZU5555hmJjo42vjo1czN0N9fRo0dfkLVevXrStWtXz7HsXsNTEW8QQAABBLIloH8/64i7TpEhIYAAAgj8JeCIwP3o0aPGttW9e/c2lv/SplepUsUI4itUqCA9e/a85P386quvZOTIkaIr22gKCgoyfgFwF8yJa7jr4hUBBBBAIPsCBO3ZN6QGBBDwLwGfBe66fJc7SM4u2eTJk+XkyZOigbs76cNJnTt3NlYX6Natm4SFhblPZXhdsWKF6BryaUfX02fK7jXS18dnBBBAAIHMBc6fP89Ol5nTcDQTgaOxZ+XEmXgpGBEuZYsV4mcnEyMOBY6AzwL3iwXSVnhTUlLkxx9/lEqVKmVYSUC/Sl2wYIEsX75c9OGlrNKMGTNk27Ztsn37dmnZsqXxC0C+fPk82XPiGp7KeIMAAgggkKmAPmv0zjvvyLvvvis6oFKwYMFM83EQARX4cf12mbV0nRw4HusBKVogv/RqFS09W9aVkL+/Qfec5A0CASCQo+u4JyUlyfHjx3OUTUfudbS9fPnyGep1H9uwYUOGc+4D2p4DBw4Ydeg/FBMmTJBbbrnFCPbdebJ7DXc9vCKAAAIIZC6waNEiadKkiQwePNhYKOCFF17IPCNHA15Av5GZOGeZTPhmiRxME7QrzMmz8TJ10W/y/IyFkpiUHPBWAASeQLZG3PU/Lp1i8t1338n69etly5YtosF72bJlpWHDhsYf/UtaR8u9Te5fBCIjIzNUERERYRw7fPhwhnPuAzqlZubMmXLixAnjH4spU6YYr8OGDZNJkyZJjRo1PL9seHMNfej18ccfd1/O81q5cmXPe94ggAACgSqwa9cuefTRR2XWrFkeAv3m8/rrr/d85g0CaQW+WbFRFq390zh0Pu2JNO//2HVQPpi/Uv7ds22ao7xFwP8FvA7c9S/ju+66S3QUJX06ePCg6B+dxvL+++8b6+/edttt6bOZ+nz27FkjX3h4eIb87uk4iYmJGc6lP6BLPup22a1atZIPPvjA2IXvrbfekjfeeEOyc406derIk08+mf5ycs8990jp0qUzHOcAAgggEAgC8fHx8sorrxg7n+p7TWXKlJExY8bIHXfcwTzlQPgh8KKPCeeS5NOf15oquWjdn9K7dT2pULKIqfxkQsAfBLwK3H/66Sfp0aOHnDlzxjDQFVpKlSpljKzrnMW9e/fKvn37JCEhQU6dOiW33367MSo/depUy2YlS5Y0yujW1+mTO2C3MrodGhpqLCG5ZMkS0Sk2+q1Bdq5RsWJFueGGG9I3Tfr375/hGAcQQACBQBD49ttv5d///rfoAI8mHWR58MEHjWV8CxcuHAgE9NFLgfU7D0qChSkwK2P2yHUE7l5qU8yJApbnuOvIiY60a9Cuq7n8/PPPosd0M6NVq1bJ4sWL5c8//zSO6dzx5557ztjgaNq0afLf//7XspGO0GiKi4vLUNZ9rGrVqhnOXepAhw4dRH8Z0Kk9vrrGpdrAeQQQQMAfBY4dO+YJ2q+88kpZt26dvP7660LQ7o93O2f7dOjkaUsVHjphLb+lysmMgA0FLAfuuha6jqh///33Mm/ePGnfvr2kXaElbR/LlSsnTz/9tPEX+NVXXy0PPfSQZz552nwXe1+kSBHjwVT9JSB9cs9tr1atWvpTl/xcokQJ0ZF6nYLjq2tcshFkQAABBPxQQBcA0G8dv/jiC5k/f77olEISAmYEwkKtbbgVbjG/mTaQBwE7C1gO3PWBzrfffttYQ91sxwoVKiTTp083AuS0DyiZLX/jjTfK7t27jdVh0pbZvHmzMT2nVq1aaQ+ber927Vpjuo87sy+u4a6bVwQQQCCQBHT6pP6df+211wZSt+lrDgjUKFvCUi3Vyha3lJ/MCDhdwFLgvmfPHtGR6gEDBljud9GiReWxxx4zVp+xWrh79+7GTqn6D4E76WozS5culUGDBl2w0dORI0eMkR79BUOTvr755pvGtwTussuWLROdH592brqVa7jr4RUBBBBAAAEEck6gRjnXt+GlikrQJarU8wVcGzK1imIFt0tQcdrPBCw9nKrzFNu29X7ppTZt2ng1z12XfdQVYEaPHi0jRoyQunXrGvPpdalJnaueNuma7zqVJyYmxjis8+91lH/27NlG23Wll+rVqxsrG+iokDtZuYa7DK8IIIBAoAkcPXrU80B/oPWd/vpeQP9dvq97G3l66nxJTU2VrJaD1OMDrmwpkfkyrjjn+1ZyBQTyTiDItapKVv9dZGjVl19+KXPmzDGWeMxw0sSBrVu3yp133mmMlJvInmkWXalG12TXB0qDs9g1Tee+6/rtuoKMptjYWNGReF35xszDUWaukWnj0hwMCQmR5s2bG7sDpjnMWwQQQMCRAvow/7hx40Q3TtL9O1iH3ZG30TGN/n37Phn7xU8Sl5hkjL6nDVR0x9S7u7WUrk1qO6Y/NBSBnBKwFLhv27ZNrrnmGmMZRW8a8PHHHxuB7MSJE70p7qgyBO6Oul00FgEELiKgyzvq4gI6+KJJN67T91kNnlykKk4hYFrgdHyiLFi9Vdbu2C/HT8dJofz5JLpyGbmqWZSUKlLQdD1kRMCfBCxNldEpJjrPfe7cuaKrxFhJOqdcA3ZdSpKEAAIIIGB/AV3aVwP2//3vf57G6vTE8ePHE7R7RHjjKwEN1Pu0a2D88dU1qBcBpwlYejhV55716dPHePhz48aNpvuq89QGDhwof/zxh+iaviQEEEAAAfsK6D4dTz31lNSrV88TtFeoUMFYKUY34GvcuLF9G0/LEEAAAT8WsBS4q4NuoqHrtjdp0kTuu+8+2blzZ5Y8ycnJMmXKFImOjjZeX3rpJfFmzfUsL8AJBBBAAIEcFVi5cqVERUWJ/n2t89r17/thw4bJli1b2BE6R6WpDAEEELAuYGmOu7t6XaFF1z1PSUkxvi7VkZhKlSpJxYoVRdds11VddEqNrr2uIzeaunTpIt99952kXcnFXZ8/vjLH3R/vKn1CwP8F9OH/2rVri64e06tXL3njjTeMOe3+33N6iAACCNhfwKvAXbulozJ33HGHbNq06aK9DAsLM9Zv1x1UdcnFQEkE7oFyp+knAv4n8NlnnxmDMFdddZX/dY4eIYAAAg4W8Dpw1z7rsoljxoyRBQsWGCvNnD592qDQUfWqVasa02lGjRolDRo0cDCRd00ncPfOjVIIIIAAAggggAACmQtkK3BPW6UuB79r1y45duyY1KlTRwoUKJD2dMC9J3APuFtOhxFAAAEEEEAAAZ8KWFoO8mIt0VF2neuuGxwFetB+MSfOIYAAAnkloM8daapcmW3i8+oecF0EEEAgOwKWV5VJezEdZf/oo4/kpptuMqbDaMBeokQJKVeunHTr1s2Y264PqZIQQAABBPJOQKc1Pvfcc8a3oQ888EDeNYQrI4AAAghkS8DrqTI6LUY3U1q0aNFFG1CkSBFjs47bbrvtovn87SRTZfztjtIfBJwp8Pnnn8sjjzziWbo3NDTU2FNDl3wkIYAAAgg4S8CrEXfdgKN+/fqeoF2nyZQuXVqaNWsmHTt2NJYOc68gc+rUKbn99tvl1ltvdZYMrUUAAQQcLKCb5HXt2tXYNM+938YVV1wha9euNdZpd3DXaHoACBw/HSe/bN4lP6z7U9bt2C/nklMCoNd0EYFLC1ie4x4fH2+MtOv67DodZsSIEdKiRQtjk470lztw4IBMmjTJWAd42rRpokuL3Xzzzemz8RkBBBBAIIcEdLBEV/N66623RDfB06SrfI0dO1auu+66HLoK1SDgG4FjroD9wwUrZcWWv57HcF8lIixUrm/XQK5tU1+CXYOFJAQCVcDyVJnHHntMJkyYIN9++6107tzZlJsuE6kbNq1atUo2b94sxYsXN1XOyZmYKuPku0fbEXCmgAbtOgXm0KFDRgciIyPlySefNJ43cn8L6sye0epAEDh44rQM+3iuxMYlZNndlrUryaN9Lid4z1KIE/4uYHmqjI6gv/3226aDdgXU3VSnT58uOt991qxZ/m5K/xBAAIE8EdC/Y3v06GFcu2/fvsZASaBtfpcn8Fw02wKprsUuXp29WE5fJGjXi6zcuke+/mVDtq9HBQg4VcBS4K4rxOiqMQMGDLDc36JFixqjPuvXr7dclgIIIIAAAuYEdFO8H374QT799FOpVKmSuULkQiCPBVbF7JFdh0/IeRPtmL10vSSlMOfdBBVZ/FDAUuC+bt06adu2rdcMbdq0Ea2DhAACCCDgGwFdKODyyy/3TeXUioCPBFb/uc90zfHnkiRm31HT+cmIgD8JWArck5KSMn0I1SxIvnz5PA9LmS1DPgQQQAABBBDwbwFdRcbKM6fHYs/6Nwi9QyALAUuBe4MGDWTZsmVZVHXpw8uXL5dGjRpdOiM5EEAAAQQuENi2bRvPCF0gwgd/EoiMCBfXNHfTqYArPwmBQBSwFLhXr15ddJ773LlzLVslJibKxIkTCdwty1EAAQQCWeDs2bMyfPhwqVevnrEnBrtRB/JPg//2vU7FUqY7pyPztcqXNJ2fjAj4k4ClwF03WurTp4/0799fdHMPsyk1NVUGDhxo7NZ35ZVXmi1GPgQQQCCgBWbMmGEs7/jiiy+KDn6kuB7Iy863ngGNSedtLXA2/pzp9nWoV10KRUaYzk9GBPxJwFLgrh1//fXXjXnuTZo0kfvuu8+zjXZmKLr5x5QpUyQ6Otp4femll6RatWqZZeUYAggggMDfAr///rt06NBBbrrpJtm376+H9nr27GkMfuieGCQE/Elg+uI1MuOn3011qUShSLm9S3NTecmEgD8KWN6ASRFmz55tbKikoz/BwcFSoUIFY9mxihUrGmu2792715hSs3v3btEdVjV16dJFvvvuO9fDJ4Gx4xkbMBm3nf9DAAELAseOHTN2o37vvfdEv6nUVLt2bRk3bpxcffXVFmoiKwLOEIjZf1SemvytqcaWK1ZIRt18pZQoXMBUfjIh4I8Cod50SqfL6Ne1d9xxh2zatMkI0rOadxkWFmas366bgARK0O6NKWUQQAAB3TRJ12DXpBvX6d+bQ4YMkfBwHsTjp8M/BeasND/tVh9IJWj3z58DemVewPJUGXfVLVu2lNWrV8vIkSPU4fvaAABAAElEQVSldevWxj8y7nMaoOuUmOuvv15+++03GT16tLDdtluHVwQQQCBzgVGjRhkDHLfeeqts2bLFGPQgaM/ciqP+IbB+50HTHfnzwDE5l8zGS6bByOiXAl5NlclM4rxrHaddu3aJftVbp04dKVAgsL/KYqpMZj8lHEMAgUsJxMTESK1atS6VjfMIOF7gj10HZdR/F1jqx/uD+0qxgvktlSEzAv4k4NVUmcwAdJS9atWqxp+05++++25jtF3nZ+pufoEe0Ke14T0CCCCQXoCgPb0In/1R4MDxWHnp00WWuhYcFCSF8uezVIbMCPibgNdTZcxCvP3226JbcF977bVSokQJ0YeuSAgggAACCCAQuALTflgtCUnJpgF0WYsGVctKaIjPwxbTbSIjAnkh4PP/AvLly2fMg58wYYKxDvH+/fvzop9cEwEEEMgzAX2IX/eySEpKyrM2cGEE7CIQn5gkv27dY6k5uqnqdW0bWCpDZgT8UcDngbsbTdd812UjSQgggECgCMTGxsojjzxi7Bj9/vvviw5gkBAIdIEDJ2Il1fVcnJV0Y4dGUr9KWStFyIuAXwrk2Bx3Mzrt2rUzk408CCCAgKMF9GH9yZMny5NPPimHDx82+pI/f37R4yQEELAmoJsu9XUF7iQEEBDJ1cC9TJkymCOAAAJ+LbBixQp58MEH5ddff/X081//+pe89tprUqVKFc8x3iAQqAJlXRsp6YOmZkfd61QqHahU9BuBDAK5NlVGr1ywYMEMDeAAAggg4A8Chw4dkjvvvFPatGnjCdrr168vixYtks8++4yg3R9uMn3IEYHIfOHSrFZF03V1qFfNdF4yIuDvApYC95SUFGNTEG9RWArSWznKIYCA3QWmT59uTI/R6TBFixaV8ePHy5o1a6RTp052bzrtQyDXBW65vKmEh4aIrhZzsdSwWjlpXqvSxbJwDoGAErAUuCcmJsoHH3zgNVBqaqqxK6DXFVAQAQQQsKnAAw88INHR0XLPPfeIbqKk02VCQ3N1NqJNZWgWAhkFKpQsIk/8q5PkC8/434g7mK9TsbQ8fO1lGQtzBIEAFsj4X8wlMD799FPp16+fREVFWfpHSYN+nfvZokWLS1yB0wgggIDzBMLCwuS3334zNpxzXutpMQK5L9Coenl5c2Bv+XzZH/LL5l1yKi7BNbgnUqV0cenapJZc0biWhARbGl/M/U5wRQRyWcBy4L5r1y5p1qyZ180kcPeajoIIIGBzgYiICJu3kOYhYC+BEoULyD1XtTL+JLo2ZNINlgjW7XWPaI29BCwH7vZqPq1BAAEEEEAAAX8QyBdGSOIP95E++FbA8n8lwa6vrXr16iW1a9cW/WrYTNKHtU6dOiXffPONmezkQQABBGwjsHbtWhkyZIg8//zz0qFDB9u0i4YggAACCASegOXA/frrrzeWNvOG6o477pBvv/3Wm6KUQQABBHJV4Pjx4/L000/Lf/7zH9EVtQYPHmzMYdfBCxICCCCAAAJ5IWApcA8JCZE+ffp43c4mTZoYqy14XQEFEUAAAR8LaJD+3nvvyYgRI0SDd026B4U+lK8rYxG4+/gGUD0CCCCAQJYCQa5pLOzBnSWP9yf0l5zmzZsbK+l4XwslEUAgNwV++uknY2Rdp8doCnItcXHzzTfLyy+/LOXLl8/NpnAtBBBAAAEEMghYGnHPUJoDCCCAgB8IHDhwQB5++GH55JNPPL1p2rSpTJgwQdq2bes5xhsEEEAAAQTyUoDJmnmpz7URQMAWAsnJyfL1118bbSlZsqQxVebXX38laLfF3aERCCCAAAJuAUbc3RK8IoBAwApUqlTJmNOuI+/PPfecFC1aNGAt6DgCCCCAgH0FmOPuo3vDHHcfwVItAggggAACCCAQoAJMlQnQG0+3EUAAAQQQQAABBJwlQODurPtFaxFAwKKALpwVFxdnsRTZEUAAAQQQsJ8Agbv97gktQgCBHBJYuXKltGnTxljiMYeqpBoEEEAAAQTyTIDAPc/ouTACCPhK4NChQ3LXXXdJ69atjb0UPvroI/n99999dTnqRQABBBBAIFcECNxzhZmLIIBAbggkJSXJG2+8IbVr1xYN1nWajK4QM3bsWKlfv35uNIFrIIAAAggg4DMBloP0GS0VI4BAbgp89913MmTIENm0aZNx2eDgYGPU/cUXX5RSpUrlZlO4FgIIIIAAAj4RIHD3CSuVIoBAbgkkJCRI//795csvv/RcUue1666nzZo18xzjDQIIIIAAAk4XYKqM0+8g7UcgwAUiIiJEdz7VVK5cOZkyZYosXbqUoD3Afy7oPgIIIOCPAoy4++NdpU8IBJiAzmuPjo42dj8tVKhQgPWe7iLgPIHklFRZvnmXrN9xQE6cjZdC+fNJvSplpH10NckXRmjivDtKi3NLgJ1TfSTNzqk+gqVaBBBAAAFHC2w/eExe//xHOXTyjNGPoCBxPUj+V5eKFoiQob0vk/pVyzq6jzQeAV8JMFXGV7LUiwACCCCAAAIXCOw5clKenjpfDv8dtOtJd9Cu70/FJcjzn3wnm/Yc0o8kBBBIJ0Dgng6EjwggYB+B1NRUzyox9mkVLUEAAW8FJs5ZJueSkuXvAfYM1WgQn5p6Xt76ZqnodBoSAghcKEDgfqEHnxBAwCYCP//8s/GA6WWXXSYnT560SatoBgIIeCugU2Ri9h/NMmh316tBvU6j+X37PvchXhFA4G8BAnd+FBBAwFYC+/btk5tuukk0YNfdTo8ePSrvvvuurdpIYxBAwLrAlr1HLBWymt9S5WRGwKECBO4OvXE0GwF/E0hMTJQxY8ZIVFSUzJgxw+heyZIljaD98ccf97fu0h8EAk4gLjHJUp/jz1nLb6lyMiPgUAHWXHLojaPZCPiTwDfffCMPPfSQbNu2zeiWrso0aNAgee6556RYsWL+1FX6gkDACpQsHGmp7yUKF7CUn8wIBIIAgXsg3GX6iICNBQYOHCjvv/++p4WXX365jB8/Xho0aOA5xhsEEHC+QKNq5SXYtfZjatplZC7SraY1KlzkLKcQCEwBpsoE5n2n1wjYRqBbt25GWypXriyffvqp/PDDDwTttrk7NASBnBMoWjC/dG5U01SFraIqS5XSfNtmCotMASXAiHtA3W46i4D9BPr06SOTJk2S/v37S2Skta/S7dcbWoQAAlkJLN24U5Zu3JHVac/xSiWLyKDubTyfeYMAAv8IELj/Y8E7BBDII4EBAwbk0ZW5LAII5IbAzxt2yJtf/SyuTVIvmnRE/s4rWkj+fGEXzcdJBAJVgKkygXrn6TcCCCCAAAK5IBDr2g31P98uN4L2rDZecjdDA3uCdrcGrwhkFCBwz2jCEQQQyAGB5ORkmT59eg7URBUIIOBkgR/Xb5eEi+yWmrZvi9dvk7jEc2kP8R4BBNIIELinweAtAgjkjMDChQulUaNGcvPNN8u0adNyplJqQQABRwps3nv4klNk3B1LST0vf+4/5v7IKwIIpBMgcE8HwkcEEPBeYOfOnaIPm3bt2lU2btxoVPTFF194XyElEUDA8QJWR9Ct5nc8EB1AwIIAgbsFLLIigEDmAvHx8fLMM89I3bp15fPPPzcylS1bViZPniyzZs3KvBBHEUAgIARKFCogl5rbnhaCjZfSavAegQsFCNwv9OATAghYFPjss8+kTp06xi6nCQkJEhYWJo8++qhs3bpVbr/9dglybbhCQgCBwBVoUqO8qc7r3xSF8ueT6mWLm8pPJgQCUcBRy0GmpqbKpk2b5MCBA9K8eXMpWrSo6Xt2+vRpWbFihZw6dUqqV68uTZo0ybSsPlDn/orfneHQoUPG9dh63S3CKwJ/Cbzzzjvy73//28Nx1VVXybhx4yQqKspzjDcIIBDYAq3rVJEKJdbJ/mOnLjryrqPy/2rfUEKCGVMM7J8Yen8xgaDzrnSxDHY5pwH7s88+a8yfrVmzpvEVfHR0tNx7772XbOLSpUtl9OjRosG7O2ngP2bMGImIiHAfMl7nzZtn5E17sF69evLuu++mPXTJ9yEhIUawr78skBDwVwH9b0qDdN046Y033pBevXr5a1fpFwIIZENgx6Hj8vTUeZJwLjnLWtpFV5WhvTvwLV2WQpxAQMQRI+5Hjx6Vp556Snr37i19+/Y17luVKlWMIL5ChQrSs2fPLO/l3r175aOPPpKXXnpJNODXIH7KlCmyatUqY7fGBx544IKyX331lYwcOVKC//6NX7/m13IkBBDIKFCoUCGZP3++1K5dW/Lly5cxA0cQQCDgBXTzpY+++zXLoD1/eJgx0n5Nq2iC9oD/aQHgUgKOCNz1AbeTJ08agbu7Q8WLF5fOnTvLxIkTpVu3bsa8Wve5tK9z586VUaNGScWKFY3DutqFBhuPPfaYEcSnDdx1dLxBgwbGihhp6+A9AghkLaD/zZAQQACBzAS+WbFRPv5+1UWXg7yqWZT0bl0vs+IcQwCBdAK2n0iWkpIiP/74o1SqVEk0WE+bdAqLflW/fPnytIcveK9BhTtod59o0aKFMT9eV8JIm2bMmCEa6OuDdZ9++qkkJiamPc17BBBAAAEEEDApsNM1PWaKK2jXdLE5uV8s/0M27DposlayIRDYArYP3Pfv32+Mtpcvn/GpdPexDRs2ZHkXW7duneGczj8PDw83vt53nzx+/Ljx0KuO7OvI+4QJE+SWW2656C8FWvbEiROyevXqDH+YNuCW5dWJAufOnTOml6V/UNuJfaHNCCCQNwJf/bLhogG7u1W6msyXruCdhAAClxaw/VQZDag16cNv6ZP7wdLDhw+nP3XRzzpnXss8/fTTnnw6mj9z5kwjENdgRefB6+uwYcOMufA1atTw5E37RoP8IUOGpD1kvC9XrlyGYxxAwAkCc+bMkaFDh8qff/4pugOq/iEhgAACVgXWbt9vqoiOxq/beVB0rQyWjzVFRqYAFrB94H727Fnj9ugIefqk60Vrsjql5eOPP5ZrrrlGGjdunL5K0SUf27VrJ61atZIPPvjA2K79rbfeMlbMyJDZdaBTp07y008/ZTilU3tKliyZ4TgHELCrgK67rgG7ThdzJ10eNTY2VgoXLuw+xCsCCCBwSQENwk/Hm59umuJa7vlswjkp6FrHnYQAAlkL2D5wdwe/+tV9+uQO2CtXrpz+VJaf//jjD9HpNy+//HKWefREaGiosdTkkiVLRKfiZDUSoFNiypQpk6EunZtPQsAJAvqcyAsvvGCsv+7+70x/8Xz11VflxhtvdEIXaCMCCNhMQEfOC+QPlzPxGf/tzqypwa78kREZB+gyy8sxBAJZwPZz3N1BcVxcXIb75D5WtWrVDOcyO6AbKX344YfGKjMamJtJHTp0EA1mkpKSzGQnDwKOEdBfRqdOnWqsw/7KK68YP+c6/WzEiBGyefNmgnbH3EkaioD9BE6djRcNxs0kzRVduYzp/GbqJA8C/ipgLnrNw94XKVJE9CFUHSVPn9xz26tVq5b+VIbPGuS//fbbxrx2XQ7SbCpRooToiH5mU3XM1kE+BOwooFNj7rzzTnF/O3TttdfK2LFjxcx/T3bsD21CAAF7CMQnJsnIafMlNs7cVBmd485ykPa4d7TC/gK2H3FXQv26fvfu3caqL2lJdVRQv9KvVatW2sMZ3uuUGt3V8f777zfmsKfNkHY+b9rj7vdr166VHj16uD/yioDfCOiOp4MGDZK6devKggUL5IsvviBo95u7S0cQyDuBmT+vlX3HYk034OrmdaRJjQqm85MRgUAWsP2Iu96c7t27y5dffinTp0+XRx55xLhfutqM7oL65JNPenY51RNHjhyRwYMHS5cuXeTuu+8WfbhOd0LVuehahzvpKKP+MqDBi6ZJkyaJPgjbp08fz7rvy5YtMx58veGGG9zFeEXArwR0R2H9Nsn9oLdfdY7OIIBArgskuf5tXbhmq+nr1nNNkRlwZUvT+cmIQKALOCJw13m3urLL6NGjjfm3OkK4atUqI0DXOehpk67DvnfvXomJiTEO69xdDcAzS8HBwfLwww8bp3QzplmzZsns2bOlbdu2Urp0aalevbqMGTOG5akyw+OYXwgUKFDAL/pBJxBAwB4Cuw6dkISkZNONudjGTKYrISMCASQQ5HpAzVH/3SQkJBhrretDqxp4Z5Z07ruuy272AVR3HbrsnY7YlypVKtvL3+kmT82bNzc2c3LXzysCCCCAAAL+KpCUnCKPffg/2Xv0lOkuVi1TTF4b0Mt0fjIiEOgCjhhxT3uTdPT9Upsb6Wi5N0nXqma9am/kKGMnAf32SL9p0l9s024yZqc20hYEEPAvge0Hj8kLnyw0/UCq9l5XkylZmG/9/Osngd74WsBxgbuvQagfAScL6FQvfQ5k165dxnMdN910k2S166+T+0nbEUDAPgKb9hySZ/+7QJJTrX2Br7mb1axon47QEgQcIEDg7oCbRBMRuJSAbhKmD2UvWrTIk7Vjx448n+HR4A0CCPhCINE1n/31z3+0HLRrW3S0/fIGNXzRLOpEwG8FMp8k7rfdpWMI+JeAPow9ZMgQady4sSdo14eqdQWl+fPnGw9Y+1eP6Q0CCNhJYPH6bXLybILlJoWFBMuTfTtJWGiI5bIUQCCQBRhxD+S7T98dK6DPlOsSpsOGDZOjR48a/YiMjDQ+P/roo8Y0Gcd2joYjgIBjBNbtOGDMVbc2SUbkiia1pGqZ4o7pJw1FwC4CBO52uRO0AwGLAh988IEnaO/Xr5+8+uqrnj0ILFZFdgQQQMArgZNn48Vq0K4XalydDZe8AqdQwAswVSbgfwQAcKJAUFCQTJgwQZo0aSI//vijzJgxg6DdiTeSNiPgcIFC+fMZI+5mu+FeSaZhtXJmi5APAQTSCDDingaDtwg4SaBFixayevVqJzWZtiKAgJ8J1KtcVlbF7DXfK1fk/u8ebSTMtdcJCQEErAsw4m7djBIIIIAAAggg4BLo3KimROYLMzXqHup6IPXxPp2kYbXy2CGAgJcCBO5ewlEMAV8KJCUl+bJ66kYAAQRyRKBARLgMvqa9sZuSToPJKpUoFCkT/329tKhdKassHEcAARMCBO4mkMiCQG4JnDlzRp566ilp2LChJCRYX2Itt9rJdRBAAAG3QPNalWTEjVdIMVdwnlm6rH51GXdvbymexfnMynAMAQQyFwhyLSvnzQPhmdfGUY9AiGv+XvPmzWXFihWeY7xBICsB/c9w+vTp8vjjj8v+/fuNbC+88IIMHz48qyIcRwABBGwlkJScImu27ZOt+49KwrkkKVWkoDSvVVEqlChiq3bSGAScLMDDqU6+e7TdLwT0AVPd9XTp0qWe/lxzzTXSv39/z2feIIAAAnYX0M2UWkZVNv7Yva20DwGnCjBVxql3jnY7XkA3Trr33ntFV4dxB+1RUVEyb948+eqrr9j11PF3mA4ggAACCCCQswKMuOesJ7UhYErgww8/lEceeUROnjxp5C9cuLCMHDnSGHkPCwszVQeZEEAAATsIbNh1UH7esEP2HP3r77NKJYtK+3rVpH6VsnZoHm1AwK8ECNz96nbSGacIJCYmGkG7bqR0++23y0svvSRlypRxSvNpJwIIIGDMY3/7f8tk+eZdF2hs2XtEFv4eI61c02Ye6NVO8oczGHEBEB8QyIYAgXs28CiKgLcCAwcONB5cHjRokLRq1crbaiiHAAII5IlAquuB+pHT5sv2g8ezvP6KLbsl3vWQ6oh+V0iwa5CChAAC2RcgcM++ITUgYFlAVx2aPHmy5XIUQAABBPJa4NjpOFfQPk8OnThzyaas23FAfly/TTo1rHnJvGRAAIFLC/Bw6qWNyIEAAggggAACLoEz8Yny9FRzQbuC6Tj7gtVb9S0JAQRyQIDAPQcQqQKBtAK7d+9O+5H3CCCAgN8ITF+8Rg6fvPRIu7vDulHMtgPHhC1j3CK8IpA9AQL37PlRGgGPgK4QM3ToUKlRo4YsWLDAc5w3CCCAgD8IJCYlyw/r/rTcFZ0Pn5ySarkcBRBAIKMAgXtGE44gYEkgNTVVJk2aJLVr15Y333xTkpOTZdiwYZbqIDMCCCBgd4Edh45LkhcBeOHICNHNmUgIIJB9AQL37BtSQwALLF++3FgV5p577pEjR44YEjfccIN88cUXAaxC1xFAwB8FElwrxHiTWkVV8qYYZRBAIBMBAvdMUDiEwKUEDhw4YKy/3q5dO1m1apWRvWHDhrJ48WKZOXOmVKrEP1SXMuQ8Agg4S6BEoQKWGxzuGmm/vm0Dy+UogAACmQuwHGTmLhxFIEuBuXPnyo033iinT5828hQrVkyef/55ue+++0SXeSQhgAAC/ihQqVRRKVm4gByLPSv60OmlUkhwkDxyfUcpVaTgpbJyHgEETAow4m4SimwIuAWaNm0quuNpcHCwEazHxMTI/fffT9DuBuIVAQT8VqB/x8amgnYdaX/x9u7SrGZFv7WgYwjkhQAj7nmhzjUdLVCmTBnjYdRatWpJ48aNHd0XGo8AAghYEejYoIb86Vrece6qzcYa7ZmNvBeICJcXb7taKpQsYqVq8iKAgAmBINfaqpn9d2eiKFkuJqBTJpo3b25sa3+xfJxDAAEEEEDAaQLfr42RGYt/l5Nn4z1N182WWtWpLHde0UJKuKbUkBBAIOcFGHHPeVNqRAABBBBAwK8FujSqJZ0a1pTtB4/JkZNnJSI8VKqXLSFFCkT4db/pHAJ5LUDgntd3gOvbSmDNmjWiU2HKly9vq3bRGAQQQMAuAroRU8z+oxIblyC6RnvTmhUkXxjhhF3uD+3wbwH+S/Pv+0vvTAocO3ZMhg8fLu+//76xYsz06dNNliQbAgggEBgCGrDP/GmtzP1tsyQlp3g6rQ+idm9RV27s0IiNljwqvEHANwKsKuMbV2p1iEBKSoq89dZbog+a/uc//xHdBXXOnDly6NAhh/SAZiKAAAK+F9DNl0ZOmy9fr9hwQdCuVz7nCuK/XP6HPPPf+aLBPQkBBHwnQODuO1tqtrmAbpbUpEkTefDBB+XEiRPGEo+33XabbNmyxZguY/Pm0zwEEEAg1wQmzV8p21yryVwsbd13VD767teLZeEcAghkU4DAPZuAFHeewJ49e4zpMJ06dZL169cbHdAVgJYtWyYff/yxlC1b1nmdosUIIICAjwQOnTgti9dvM1W7rjZz5NQZU3nJhAAC1gWY427djBIOFti7d6/UqVNH4uLijF6ULl1aXnzxRbnzzjuNDZUc3DWajgACCPhEYPW2fabr1QWmf9++X7o2qW26DBkRQMC8ACPu5q3I6QcCFStWlJ49e0poaKgMHTpUtm7dKgMGDCBo94N7SxcQQMA3Akdjz1qq+Mgpa/ktVU5mBAJcgBH3AP8BCMTujx07Vp555hmJjo4OxO7TZwQQQMCSQP7wMEv5I/NZy2+pcjIjEOACBO4B/gMQiN2vUKGC6B8SAggggMClBU7F/bM76qVzi9SuUMpMNvIggIAXAgTuXqBRBAEEEEAAgUAQmLNyk8xdtcVUV4NcuSqWKip1KpU2lZ9MCCBgXYA57tbNKGFTgRUrVshnn31m09bRLAQQQMBZAodPnpEpi1aZbnRoSLA80LOdBAdpCE9CAAFfCBC4+0KVOnNV4ODBg8aqMG3atJF77rlHjhw5kqvX52IIIICAPwos/D1GUlJdy8SYTCP6XSE1ypUwmZtsCCDgjQCBuzdqlLGFQFJSkrz22msSFRUlkydPlvOudciCg4M9a7PbopE0AgEEEHCowNZ9R8TK2Hm+cGbfOvRW02wHCRC4O+hm0dR/BObPny8NGjSQxx57TGJjY42AfeDAgcbyjp07d/4nI+8QQAABBLwSOHk2XsyPt4vEJyZ5dR0KIYCAeQF+PTZvRU4bCGzbtk0efvhh+frrrz2tadeunUyYMEGaNGniOcYbBBBAAAHvBXTTpX1HT1mqoGThApbykxkBBKwLELhbN6NEHgpcd911nqkw5cuXl1deeUVuvvnmPGwRl0YAAQT8Q+BccoqcS0qWU3EJ8ursxaZH23U6TemiBaVc8cL+AUEvELCxAIG7jW8OTcso8PLLL8u1115rjLoPHz5cChYsmDETRxBAAAEETAkkpaTI/N+2yHdrYmTfMWsj7O4L6HSafh35xtPtwSsCvhQgcPelLnXnuMDVV18tO3bsEB1tJyGAAAIIeC9w2jWyPvrTRfLn/qPeV+Iq2b1FHelQr1q26qAwAgiYE+DhVHNO5LKRAEG7jW4GTUEAAccKvP7FT9kO2rXzd1zRwrEGNBwBpwkQuDvtjtFeBBBAAAEE/t/emQBGWZz//8lNEo5wJBAg4RAiEDnlEApqEcQ/oEi5LaVKFaq1IEirloJSyyV4UFqqiBbsXyigclhEscolQpQqBQIoFZAi4Q4kkDvZ3zyzvC97JrO772723f2OhnfemWfmeeYzk7zPzs474yOBL7/9Hx38/oyPtRDFREfhwCWfKaICEFAnAMddnRUk/Uhgx44dNG7cOKqoqPCjFlQNAiAAAiDABD47dNxnEPxSaiscuOQzR1QAAp4QgOPuCS3IGk7g1KlTNGbMGLrjjjvo73//Oy1btsxwHagQBEAABEDAnsCxnIv2CV7c8Uup/TpleFESRUAABLwlgJdTvSWHcj4RKC4ulqeezpkzhwoKCmRdycnJ2CXGJ6ooDAIgAAJVE9h16ATl5OZXLViFRMcWqdTnFryUWgUmZIOAoQTguBuKE5WpENiwYYPczvHYsWNSPDo6mh577DGaNWsWJSUlqVQBGRAAARAAAS8I8KFKf9r4mRcl7YukJyfRtGF3Yn27PRbcgYDfCcBx9ztiKNAIHDlyhCZPnkxbtmzRkqhv3770pz/9iTIzM/U0REAABEAABIwnUFhSSrNXf0LlPr5LFCteSJ01dgDFx8YYbyRqBAEQqJQAHPdK8SDTSAKrV6/WnfZmzZrRiy++SMOGDTNSBeoCARAAARBwQSBb7CAz/52tVFBc6iLXs6RJ9/WmWvFxnhWCNAiAgCEEIiwiGFITKrEjEBUVRV27dqWsrCy79HC+KSwspC5dutDo0aPpt7/9LcXHx4czDrQdBEAABAJCYOW2r+m9zw/4rKu2cNYfHdSLumWk+VwXKgABEPCOAGbcveOGUl4QYEf94MGDxB9qEEAABEAABPxP4J9fHPLJaW9Ut5bc8rHzTU2o+83pWB7j/y6DBhColAAc90rxINNoAnDajSaK+kAABEDANYG8giJ6e+tXrjOrSOU92ju2bEy/H92vCklkgwAIBJIAHPdA0g5hXbziKiKC/9QjgAAIgAAIVDeBrCPf09IPs6i03LtD7XgN7d2dsUd7dfcj9IOAIwEcwORIBPceE+BdYtq3b09ffeXdzI7HClEABEAABEDAJQGeZX/2/39EC97bTldE3NtwW5t0uTTG2/IoBwIg4B8CcNz9wzUsauV92IcOHUoDBgyg7Oxs+vWvfx0W7UYjQQAEQCDYCOQXFtOeIydoytKNlH3yrE/m3dw0mSbd18enOlAYBEDAPwSwVMY/XEO6Vj7pdO7cubRgwQLiE1A5pKam0i9/+cuQbjcaBwIgAALBRiD3agEt+/ALyvr2pCGm1UqIo2cfuJt4r3YEEACB4CMAxz34+iSoLeK92KdNm0anTp2SdsbGxtITTzxBM2bMoJo1awa17TAOBEAABEKFwJVrRfTWp3tp+wHrCdRGtCs6KpKeHv5jOO1GwEQdIOAnAnDc/QQ21Kr95ptvaMKECbRjxw69aQMHDqRXXnmFWrdurachAgIgAAIg4D8CReL009U79tEHe4+IE1CNO4alfq0EemrEj6llo/r+Mx41gwAI+EwAjrvPCMOjAt4xZs+ePbKxrVq1kg77oEGDwqPxaCUIgAAIBAGBk+cv0+x//Isu5hcYak3vzBZiTXtvisTOYIZyRWUg4A8CcNz9QTUE68zIyKDp06dTXFwcTZkyhXiJDAIIgAAIgEBgCHxz6hzNWvkxlZSVG6qwWUpdemIIXkQ1FCoqAwE/EoDj7ke4oVb1zJkzQ61JaA8IgAAIBDWBwuJS+njft/TWJ/823E6eYH9kQA/D60WFIAAC/iMAx91/bFEzCIAACIAACHhE4OCJM/TZoeN0QFwv5V/z+gAlFaUPC6e9TVqKiihkQAAEgoQAHPcg6YjqNIO3dCwrK6PExMTqNAO6QQAEQCBsCbCjvnTzbsrJzfc7g7TkJDnT3i69od91QQEIgICxBOC4G8vTdLW9//77cs364MGD5QunpmsADAYBEAABkxHIvVpIuw+foIPfn6ETZy/RhbxrZOAGMW5pxMdG04wx/SijCWbZ3UJCBggEOYEIiwhBbqMpzYuKiqKuXbtSVlZWUNrP2ztOnjyZPvroI2lfdHQ0cVrLli2D0l4YBQIgAAJmIHBVnGBaVlFB5y5fJd66sbi0XF7ZWT93JZ/2Hcuhs2JWPdAP3qTEGjT3wYGUXAfnbZhhHMFGEHBHwFQz7hXij+Hhw4cpJydHOsVJSUnu2uWUrlpWVc5JgUkS8vLy6Pnnn6dFixZRaWmptDo9PZ0WLlwIp90kfQgzQQAEiHjOibep1a4VfC/AcFq5eFZERUbKHVj4BNDi0jJ5qNDVohK55SH/nS8Wu7Pw9ocFxSUUJQ4eKi4po0hRRtQs6ywrt165fq67UOTzleUowkJnLuXLmfLvxRaNF65cI3bYWU+gHXKVsfCjds3pwX7dqG7NeBVxyIAACAQxAdPMuLPDPmvWLBo2bBjxPuLLly+ndu3a0cSJE6vEq1pWVa5KhUIg0DPueQVFtFkcyPH1dz9QjniglFsqiB9OERGR/Bgii/ge9sRXO2nfB6up6OoV2YTI6Bhqe8cgyux7H0XHxIqHFT/0SH5ly48v/uo2MlJ7MFpbLWW0PE3WlYzIY538ENXqkQq4Gs5jXbIei3x48sNOewhzBufpMsL+KNEO+WBmA0WGVd7GVl2G28wy8n9re4QB/ICusJOxcmE5Fpc2SntE34n2MD9tT2NphwuZCiEjdWm2OsjcYCcYXjdI6qoQXDVZjZ2oQ+fCeZrNwj6tHiki8nR7hAw7J2pcrH1hx07T4ciFFVjNYaut/SSuzMMi22zDTqSL5pD9eFHlYu0jVqLKxdWYkjZqXNgeYb+0VcS1McX1c5DsxPXGWHDBRVQo22PHhRVY7bXa6tmYsvahCherDLdJ56LZw1dtvHBfXDeoKnZybAvz5ZiS5W9wYTVcXuciIm7HlDZemK8cd8JW8R+XtZK5Xg/rEoniIiwUErJ+KSTTdHtEkq7bKswtkqVkndfTZBL/YyejpyLigkCNmGjqcXM6dctIo7bi5dM6iXDYXWBCEgiYkoApZtwvXLhAzzzzDA0ZMoRGjBghQTdr1kw68U2aNCFen+0uqJZVlXOnpzrTvxLO+svrdogZIesMur0tFVRSeI0+W76Qck/dOBq7cbtbqcPAMZRYN5l4V+By8XWuy+Am2U7WKBm7Su1vSqV7aJ/meOeZDLubroM7FLbSKjISrG0hV3Ef2ZWWu2+Hps4oLlp97Pa6DT62R6/XqHr0Cp0jnnFxLq+l3KjHBFwUuKqMKTGlrTW/iqvVFXcUYuddBserraCWp6U53mvpuOoE+NuFkX060r092skPYHoGIiAAAiFDgCeWgj7w7Prly5el464ZW69ePerbty8tWbJEX/Kh5dleVcuqytnWHQzxb384T/PXfurGabdaGBufSFFiRp1DreTG1Gf8b6nnTydJp90qgX9BAARAAATMTCCjSQN644mRdH/PW+C0m7kjYTsIVEEg6B338vJy2r59O6WlpRE767YhMzOT8vPzaffu3bbJely1rKqcXnEQRV7bvEd+BV6VSZ0Gj6UOgx6gfr/+I6XclFmVOPJBAARAAARMQqBPZgt6/mf3UHxsjEkshpkgAALeEgh6x/306dNytr1x48ZObdTSsrOznfI4QbWsqpxLJdWYeOzMRfr+XO71daGVG1KnURq17jWAIsVuNwggAAIgAALmJ8Dvcjx8d3eaPKQPZtnN351oAQgoEQj6Ne6XLl2SDUlISHBqUI0aNWTauXPnnPI4QbWsqpwrJUePHqVNmzY5ZXmy441TYcWE42esbBTFIQYCIAACIBAiBBom1aSnhv+Y0lPqhkiL0AwQAAEVAkHvuF+7dk22IzbWukbbtlExMdavBfnkT1dBtayqnCsd58+fp08//dQpy9UHDSchHxNKxHZmZSXFdPXiWUpKTfexNhQHARAAARAIVgK8U0xtsRd7q9QG1LV1U+rVtjlFi20sEUAABMKLQNA77g0aNJA9UlJS4tQzmsPO+5C7CqplVeVc6ejVqxdt3LjRKYu3g9SW8jhlGpRwYPc22vLKdLEdm4UGTJlP0bFxBtWMakAABEAABKqbQDMxmz7x/90mTjpNrm5ToB8EQCBICAS9496wYUOJqqCgwAmZlta8eXOnPE5QLasq51JJNSQeOHCAJk2aRNu2bdO1nzqQRc1vvV2/RwQEQAAEQMB8BBrUThT7rzel/p0ysAzGfN0Hi0HA7wSC3nGvU6eOnLnmF0gdg7a2vUWLFo5Z8l61rKqcSyUBTMzNzaWZM2fSX//6V+KdcDikNk2j5ncOo8ZtOwfQEqgCARAAARAwgkB6chL169yaMtMbETvtiTWcl4UaoQd1gAAIhAaBoHfcGfOoUaNo0aJFlJOTQ6mpqTr5I0eOyG0iW7durac5RlTLqso51h+Iez4B9fXXX6fp06fTxYsXpcrExER5P3XqVHp7x3/ogy+PBMIU6AABEAABEKiCAJ8IGycOQ+Ilk7FiHXo0X8Ua9VrxcVS/doJ00Ns0TaFWjRtQ3Zo41bQKnMgGARCwIWAKx33gwIG0fv16WrlyJT355JPSfN4JZteuXfT000/L47e1NvHLoryM5K677qKHH36YVMuqyml6Annlk2E3b96sq3zggQfohRdeID41lsP4/t2pXVpDeuez/XRCbA+JAAIgAAIg4JoAO9UR4j8OkeJiEXHrO54R4j5CvDMk8kW6NY/jVll+EZTjUZGRFCWucbHRct/0xvVqU6r4SUuuQ+nJdWXctWakggAIgIDvBCLEi42mOEg6Ly+PZs+eTbyTTNu2bWnv3r10zz330IABA+wo8PaM48ePJ35pdP78+TJPtayqnJ1CNzc809K1a1fKyspyI6GevGLFCnrwwQepU6dOtHjxYurdu7fbwleuFVHu1UK520CF6Fq554B47vAR4/wgKiu3UEx0JJWWVQiZCD2dH2VSXpMRDyk++jxGyJSLeuSjSzysLKIifnBxHj/IyvgqKtYGEcvJekSazBMcysU3BpHaU5AtF0IVop5oWU+5tIft4oehhSuQD06hV5RjHaVi9xyeseJvHiJkPVZt/BCV7blua7RoV4WwJ1LUa+H/hA6Osx3cjlKhQ7aZ06/r4tHPVXI9nCftEAmsgdnxVcavy8RouoSs9TdH5Dpw0fmKeipEaXYShIhsM3Ow2hNFZaI9zFL+Cop88b9os5VvmVgKFSNm7Fg26ro90pDr9WjsmAtzYhkO1l9na1/qMqIe5qKxk22WdtwYC7KfhW7ppHBV3CxxKRNpN9gJvqKw7ZjidpV7OaaEGyT72+WYEpy1P01sB+uNshkLWputfSAEhFBlY4rrioyI1FlpY9PdmNLHtuhnrlfj4nZMCV62XLg75Ni8/rum9WEgxpT+uyZsYHb2Y4p/H3iMWce47GfRifJ37frvo6sxxe3nerTxH8vt4t8ZHgCiUTzG5d8F8bsaJ2aWi8vKxIxztJW36DcOsn9EnHfDqhETRUWl4iqc33IxNlknB/6diBdpxaVllBAXSwXFpeIaI8vweOYGlYnyCWI5SVFJmVxWUlBcInWyfdIc8Q/3L9vBfzu03yPuUwQQAAEQMDsB0zjuGuiiIuGYirXe/EIpO2WuAq9951NWo8WDwzaolGV5VTnbuh3jRjru7HSsXbuWhg8f7rbNjvpxDwIgAAIgAAIgAAIgEFoETOe4mwW/kY67WdoMO0EABEAABEAABEAABPxHwPWUtf/0oWYQAAEQAAEQAAEQAAEQAAEvCMBx9wKaUUXy8/Np6dKlRlWHekAABEAABEAABEAABEKYABz3auhcXrP+1ltvUUZGBk2cOFHumFMNZkAlCIAACIAACIAACICAiQjAcQ9wZ/FuOLzjzc9//nM6c+aM1L5169YAWwF1IAACIAACIAACIAACZiMAxz1APcb7yz/yyCPUo0cP2rNnj9TK21pu2bJFHi4VIDOgBgRAAARAAARAAARAwKQE4Lj7uePKxH7GfOorn+66bNkysZdxBdWpU4deeukl2r9/P/Xv39/PFqB6EAABEAABEAABEACBUCBgv9F5KLQoyNrAh0Y999xz0io+xOShhx6iuXPnUkpKSpBZCnNAAARAAARAAARAAASCmQAcdz/1Dr+AygdBJSUlUUJCgjwwasyYMdSiRQvauHGjn7SiWhAAARAAARAAARAAATMQ6NChA3Xv3t0jU3EAk0e41IV/85vf0MKFC9ULGCDJhz7xBwU+WZaX5CCAgC8EYmJiqHbt2nTx4kVfqkFZEJAEatSoQfxz+fJlEAEBnwnwhBifjp6Xl+dzXagABGrVqkU84Xr16tWAwpg2bRotWLDAI51w3D3CpS5cWFhIq1atUi9ggOTZs2dp+fLlNGHCBKpbt64BNaKKcCbw7bff0rp164g/hEZG4nWYcB4LRrT9yy+/JP557LHHjKgOdYQ5gX/961/Ez7yf/vSnYU4CzTeCwLvvvkuxsbF07733GlGdch3t27enbt26KcuzIJbKeIRLXTg+Pp7Gjx+vXsAAyUOHDknHfcSIEZSenm5AjaginAl8/PHH0nHn9zL42xwEEPCFAL/jk52dHfC/i77YjLLBS+D06dPSuEA/Z4OXCCzzhQBPKtSsWdMUf58wjeZLT6MsCIAACIAACIAACIAACASIABz3AIGGGhAAARAAARAAARAAARDwhQAcd1/ooSwIgAAIgAAIgAAIgAAIBIgA1rgHCHQg1PD6rN69e8udGwKhDzpCm0D9+vXleOK1yQgg4CuBpk2bypOjfa0H5UGACdx0001yVxnQAAEjCLRr14743UQzBOwqY4Zego0gAAIgAAIgAAIgAAJhTwBLZcJ+CAAACIAACIAACIAACICAGQjAcTdDL8FGEAABEAABEAABEACBsCcAxz3shwAAgAAIgAAIgAAIgAAImIEAXk4Nwl6qqKigw4cPU05ODnXt2pWSkpKUrVQtqyqnrBiCQUvAl77Oz8+nrKwsunLlCrVs2ZI6d+7ssp1lZWXEB4DZBj7VkMcvTvG1pWL+uC/jSXWc+KLD/ITDqwXe9vXBgweJy7oKjRo1opSUFD1LddzpBRAxPYH9+/dThw4dPGpHbm4u/fvf/6bU1FRq27at2xPDVeU8Uu6BMF5O9QBWIETZYZ81axYNGzaMWrVqJU9C5bedJ06cWKV61bKqclUqhEDQE/Clr3ft2kWzZ88mdt61wI743LlznXYu+vDDD6WsJsfXzMxMevXVV22TEDc5AV/GEzddZZz4qsPkiMPKfG/7micJKnsm8jO0b9++OkuVcacLI2JqAvv27aNly5bJyc9PPvlEqS0Wi0U+v/Ly8mjs2LF04MAB2rRpEy1cuJAaN26s16EqpxfwUwQz7n4C6021Fy5coGeeeYaGDBlCI0aMkFU0a9ZMOvFNmjShwYMHu61WtayqnFtFyDANAV/6+tSpU/S3v/2N5s2bJz9AshP/1ltv0d69e+Ufxccff9yOw4YNG2jmzJn6DAVvIckfPBFCh4Av40mjUNU4MUKHpgvX4CbgS1/zOBo9ejR16dLFbgu/48eP05///Ge67bbb7Bpf1bizE8aNaQl8/fXXcqKJn1+eBH7W7dy5k9atW0cJCQlypp7H51NPPSUnnxITE2V1qnKe6PZGFmvcvaHmpzLLly+ny5cvS8ddU1GvXj05c7BkyRIqLS3Vkp2uqmVV5ZwUIMF0BHzp682bN9Nzzz0n/4DxH7L+/fvTr371K8mAnXjbwEtp2rdvL2Xuuusu4h+e7UpPT7cVQ9zkBHwZT9x0lXHiqw6TIw4r873ta1720qJFC/n3qGfPntSpUyf9h52tbt26SedLg6ky7jRZXM1NgJdy3n777R49e3jM8FgcOHCg3bgZPnw4nThxgjZu3CihqMoFgiAc90BQVtBRXl5O27dvp7S0NGJn3TbwkgNerrB7927bZD2uWlZVTq8YEdMS8LWv2RHnA3NsAz8Q+X2LwsJC22RatWoVsaM/bdo0WrNmDRUXF9vl48b8BHwdT0ygqnFihA7zkw6PFvjS19HR0XK23RWprVu30p133mmXVdW4sxPGTUgQ8OTQwG3bthEvgXFcD8+rHPh599FHH0kmqnKBAAjHPRCUFXScPn1azrbbrqfSimlp2dnZWpLdVbWsqpxd5bgxJQFf+9rxq2aGEBUVRbGxsZSRkaEzuXTpknyJmr8p4pmtxYsXyzWC7j5k6gURMRUBX8eTyjjxVYepgIa5sf7o6++++4643h/96Ec6XZVxpwsjEpYENL9K87NsIfBLqjyuioqKSFXOtry/4nDc/UXWw3r5DwwHXpbgGGrUqCGTzp0755gl71XLqsq5VIJEUxHwR1/zV4U8Bh944AGdBX87tHr1avl1Iq+H5xepz5w5Q7/73e/kHzxdEBFTE/B1PKmME191mBpwmBnvj77m2XZe816rVi2dpsq404URCUsC2liMj493ar/me50/f55U5Zwq8UMCHHc/QPWmymvXrsliPKPpGGJiYmSSuyUIqmVV5Rz14958BPzR1ytWrKD77rtPrid1JMJbPvJM11/+8hc5487rUPklMYTQIGDUeKpsnBilIzSIh3Yr/NHXvJTBcZmMRrGycafJ4BqeBLSxGBcX5wRA88fY91KVc6rEDwlw3P0A1ZsqGzRoIIuVlJQ4Fdccdncv+6mWVZVzMgAJpiNgdF/znsn8NfSUKVMqZcHrT3mbtubNm8uvFnntIIL5CRg9nlyNE6N1mJ966LbA6L7m3WROnjxJffr0qRSaq3FXaQFkhjyBysYiL5Hh9fL87qGqXCCAwXEPBGUFHQ0bNpRSBQUFTtJaGjtDroJqWVU5VzqQZi4CRvY1H6T05ptvyl1m+MGnEvgByh9CK9sJSaUeyAQHASPHk22LbMeJv3TY6kM8OAgY3de8TKZjx47Kh73ZjrvgIAIrqotAVWOR17nzbLyqXCDaAcc9EJQVdNSpU0du9M+zmo5BW9vOW2C5CqplVeVc6UCauQgY1df8oZGXv8yYMcNu7WhVNOrXry+35NK+aqxKHvnBTcCo8eTYSttx4i8djjpxX/0EjO5rdtzvuOMO5YbZjjvlQhAMSQJ8QioHV74Xr23XJkxV5QIBCY57ICgr6hg1apT8ui8nJ8euxJEjR+RXNa1bt7ZLt71RLasqZ1s34uYk4Gtf8xKtl19+We6XzGtEbQNv/1hZ+M9//kODBg2qTAR5JiPg63hy1VzHceIPHa70Iq36CRjV17zXNv/w/t2qwXHcqZaDXOgR4HNH+IMc74pmG3iTBd4tTTuBV1XOtg5/xeG4+4usF/XyAQB8UurKlSv10vwmMx948+ijj+qnUnImfxIcM2aMPMWS71XLqspxnQjmJuBJXzuOJ365lE9CZed9/fr19Nprr8kfPgjs6aefljvHMB0+WnrRokVke1Ld559/LsuNHDnS3ABhvR0BX8aT6jjxRIedcbgxHQFP+trx75NtY3m2nXezSklJsU2WcdVx51QQCaYmwH4TnxWgvR/o2Bg+AZVP3uVloLz5B7+XtWXLFnmvyfLBSzzLzg47B1U5rbw/rxHi5TG8PeZPwh7WnZeXR7Nnz5aDhAcNHzF/zz330IABA+xqOnr0KI0fP5569epF8+fPl3mqZVXl7BTixpQEVPvacTzNmTNHHqrkqtGRkZG0du1a+aDkfdv50CV+gYfHIj88W7ZsKXefYTmE0CLg7XjyZJyo6ggtsuHZGtW+dvz7ZEtr3Lhx8hlpu02tlu/JuNPK4GpeAocOHZIz5/xOFgf+1pe/ieFnk23g7Yp37txJr7/+OrVp00Zmffjhh/Tee+8Rn2HCjv+VK1do6tSpTu9NqMrZ6jM6DsfdaKIG1cdvM+fm5soXItw5QLz2nfepdXxhUKUsm6kqZ1CTUE01ElDpa3fjqSqz+eHLM2LJyclUu3btqsSRHwIEvBlPno4TFR0hgBJNEARU+trV3yeed+QDcvjwHFdnoDBcT8cdOiT0CfA3yuycu/qWhpfI8FkAiYmJlYJQlau0Ei8z4bh7CQ7FQAAEQAAEQAAEQAAEQCCQBPBddiBpQxcIgAAIgAAIgAAIgAAIeEkAjruX4FAMBEAABEAABEAABEAABAJJAI57IGlDFwiAAAiAAAiAAAiAAAh4SQCOu5fgUAwEQAAEQAAEQAAEQAAEAkkAjnsgaUMXCIAACIAACIAACIAACHhJAI67l+BQDARAAARAAARAAARAAAQCSQCOeyBpQxcIgAAIgAAIgAAIgAAIeEkAjruX4FAMBEAABEAABEAABEAABAJJAI57IGlDFwiAAAiEIAE+wVI18KmFCCAAAiAAAt4RiPauGEqBAAiAAAgEA4E1a9bQ3r17XZoyfPhw6t69u553/vx5WrhwITk62j179qShQ4fqcp5Ejh49Sn379pV63n33XZdFr169SnPmzKFr167RypUrqVmzZjRx4kR65JFHXMojEQRAAARAwDWBCPEHXH2qxHUdSAUBEAABEKhGAt999x2NHDmSvvrqK2nFL37xC1qwYAHVrVvXyarLly/TvHnzaP78+dSmTRt6++23qXPnzhQREeEkq5Kwbt06+slPfkJ16tQh/mAQExPjVGzs2LHUrVs3mjx5Mu3Zs4duv/12ioqKInbo+YoAAiAAAiCgRgCOuxonSIEACIBAUBN44YUX6KmnnpI28gz8rbfe6tbekpIS6dSz8/7444+7lVPJ4KUvq1atkh8C2Dl3DBs2bKD7779ffiug2bRlyxbptLPDjwACIAACIKBOAEtl1FlBEgRAAASClkBSUpJum21cT7SJxMbGUkpKCjVt2tQm1btodHQ0/exnP3Nb+PPPP5d5tWrV0mXuvvtuPY4ICIAACICAOgG8nKrOCpIgAAIgEDIEEhMTiX/8HQ4dOuRvFagfBEAABMKGABz3sOlqNBQEQAAEbhBwt6Y9Pz+f/vGPf5C2+8v//vc/Wrp0KX388cdUWlp6owKbGMv+85//pOLiYj1127ZttHXrVtq/f79M47XtfM8/eXl5upwWOXbsGPHLrW+++SZ9+eWXVFRUpGU5Xb/55hvKysqS6T/88IO07+TJk7rcxYsX6YsvvpD3FRUVtG/fPlqxYoWsVxcSEX7Fi78RYL3nzp2zzUIcBEAABIKSAJbKBGW3wCgQAAEQCCyBTz75hBYtWkS8/pwd8P79+9OkSZPonXfeIV4Tz4HXqr/33nv6i6w7d+6UTj7LsOObm5tLcXFxUnbjxo3ETjM70RzY8a9fv76M33TTTVS7dm0Z53IPPfQQXbhwgR588EEpP3fuXCooKKDXXnuNBg8eLOXY2ecdafhl2l27dtGsWbOIX8odN24clZeXU69evWjq1Kkyf9OmTTRmzBji5TmjR4+m48ePE38g4fDHP/6Rpk+fTi+//DK9+OKL0mbWVaNGDeIPGz169JBy+AcEQAAEgpEAZtyDsVdgEwiAAAgEmEDv3r1p1KhR+qw5O77Dhg2jK1eu0AcffEANGjSg9evXy7hmWlpaGnE5V7PVL730Er3yyiu6g84vzvI9/6Snp8sqLl26JHe04SvPyD/66KP0+9//nrZv3y7zX47EowAABh1JREFU7733XjkDzzf8YYLLsbPOM+Uss3v3bunMd+jQQe5aw2v2WY4/aLBz/4c//EF+8GCnn535yMhI6bjzBwR21nkXHm7fs88+K2f4WT8CCIAACAQ1Ad4OEgEEQAAEQMDcBMTsNG/tK3/++9//VtmYW265xSJm1+3kxJIXi1hCI+sQTrFdnpi5lumzZ8+2Sxd7s+t6xYy7XR7fpKamyvwDBw445YltK2Xep59+6pQndqqReeJFW8vp06f1fM2Otm3bWsSMvp6uRcRSG1lO7HBjETPxWrK8im0oZZ6YdbdLF469TBeOvUU4/nZ5uAEBEACBYCKAGfeg/lgF40AABEAgcAR4T3WelebA+7Lbhvbt28vb77//3jbZ5b7tdgJubnjpCq9n56DVbSvKy3LYBt53fu3atXqWthPOkCFD9CU7eqaIaC/c8h72Wlu0fD74iUNycrKWJK+8pIbTeGkPz8AjgAAIgECwEoDjHqw9A7tAAARAwI8ExAySk2NbmTrtoCR2bo0I2dnZcslLQkKCXIbjWCevOed16xxYVguaHdpVS1e5si53QVubX9lLse7KIh0EQAAEAkUAjnugSEMPCIAACPiRgO0+6bzOu6rAM8uuTlatqpxR+Tk5ObIqtlXbwcaxbl5Dz0GTdcz39N5xBt7T8pAHARAAgeomAMe9unsA+kEABEDAAAKNGjXSa+EdWioLPNvOu73wIUzVFW6++WapmneE4S0dXQXNoc/IyHCVjTQQAAEQCDsCcNzDrsvRYBAAgVAk0LFjR+ITUTnwPuiVhb1790qnvUmTJpWJ+TWvdevWpC1d2bx5s0tdvNMMhy5durjMRyIIgAAIhBsBOO7h1uNoLwiAQEgSqFevHg0dOlS2bcmSJXT27FmX7eSlKTNmzJB7prs6hIln410Fd2vbecZcC7ZxLU2bNdeuWnpMTAzNnDlT3movqWp5fOWXUg8fPkyZmZk0cuRIPUuzz509uqCLiLaEyNEWF6JIAgEQAIGgJADHPSi7BUaBAAiAgOcE3njjDXlwEp9C2q9fP7lPueakslN98OBBeYgS766iOc22Wo4cOSJ3VuE025NI+f7UqVN8oaNHj8qr9s+ZM2e0KNnGOZH3T9cOYHKsj/OffPJJ6tmzp/yGYM6cOZykhzVr1sgZ+VdffZWio2+cFXjo0CEps2PHDl3WNvL111/LW1f6tCU5fJKqbWA7tXX0zA4BBEAABIKWgJi9QAABEAABEAgRAmJW2TJhwgSL2DZR7k0ulqNYxHaLFrFNokWsFbeIw4ZctlQ4whZxmJIsIx5Yls6dO1vESaUWcZiRRZygauF6OF040ZYnnnjCIl5utSxevNjSp08fvYzYBcYiPjzI/dXFrL/lzjvv1POaN29uEQciWYTTbKef7Z02bZqsV8ysW5YuXWoRJ6BaOnXqZBG7yeiy+/fvt4wdO9YilgPJOnm/+eHDh1vef/99KVNYWGiZN2+eRazb13Wy3bx/PO9rP2XKFKmD21CzZk2LOGzJcv78ebmX/YABA/QyYsmRtEFXjAgIgAAIBBGBCLYlaD9VwDAQAAEQAAGvCfBLqjwDzVsrCkeYbHee8bpSPxUUBzkRz4SzzeJDg366qp/UoVoQAAEQMCUBOO6m7DYYDQIgAAIgAAIgAAIgEG4EsMY93Hoc7QUBEAABEAABEAABEDAlATjupuw2GA0CIAACIAACIAACIBBuBOC4h1uPo70gAAIgAAIgAAIgAAKmJADH3ZTdBqNBAARAAARAAARAAATCjQAc93DrcbQXBEAABEAABEAABEDAlATguJuy22A0CIAACIAACIAACIBAuBGA4x5uPY72ggAIgAAIgAAIgAAImJIAHHdTdhuMBgEQAAEQAAEQAAEQCDcCcNzDrcfRXhAAARAAARAAARAAAVMSgONuym6D0SAAAiAAAiAAAiAAAuFGAI57uPU42gsCIAACIAACIAACIGBKAnDcTdltMBoEQAAEQAAEQAAEQCDcCMBxD7ceR3tBAARAAARAAARAAARMSQCOuym7DUaDAAiAAAiAAAiAAAiEGwE47uHW42gvCIAACIAACIAACICAKQn8H2J3Nj/hy3EwAAAAAElFTkSuQmCC" width="60%" style="display: block; margin: auto;" /></p>
<p>The excessive number of LOO-PIT values close to 0 indicates that the model is under-dispersed compared to the data, and we should consider a model that allows for greater dispersion.</p>
</div>
</div>
<div id="try-alternative-model-with-more-flexibility" class="section level2">
<h2>Try alternative model with more flexibility</h2>
<p>Here we will try <a href="https://en.wikipedia.org/wiki/Negative_binomial_distribution">negative binomial</a> regression, which is commonly used for overdispersed count data.<br />
Unlike the Poisson distribution, the negative binomial distribution allows the conditional mean and variance of <span class="math inline">\(y\)</span> to differ.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>fit2 <span class="ot">&lt;-</span> <span class="fu">update</span>(fit1, <span class="at">family =</span> neg_binomial_2)</span></code></pre></div>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>loo2 <span class="ot">&lt;-</span> <span class="fu">loo</span>(fit2, <span class="at">save_psis =</span> <span class="cn">TRUE</span>, <span class="at">cores =</span> <span class="dv">2</span>)</span></code></pre></div>
<pre><code>Warning: Found 1 observation(s) with a pareto_k &gt; 0.7. We recommend calling &#39;loo&#39; again with argument &#39;k_threshold = 0.7&#39; in order to calculate the ELPD without the assumption that these observations are negligible. This will refit the model 1 times to compute the ELPDs for the problematic observations directly.</code></pre>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(loo2)</span></code></pre></div>
<pre><code>
Computed from 4000 by 262 log-likelihood matrix.

         Estimate   SE
elpd_loo   -895.6 37.8
p_loo         6.7  2.7
looic      1791.3 75.5
------
MCSE of elpd_loo is NA.
MCSE and ESS estimates assume MCMC draws (r_eff in [0.7, 1.4]).

Pareto k diagnostic values:
                         Count Pct.    Min. ESS
(-Inf, 0.7]   (good)     261   99.6%   378     
   (0.7, 1]   (bad)        1    0.4%   &lt;NA&gt;    
   (1, Inf)   (very bad)   0    0.0%   &lt;NA&gt;    
See help(&#39;pareto-k-diagnostic&#39;) for details.</code></pre>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(loo2, <span class="at">label_points =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>Using the <code>label_points</code> argument will label any <span class="math inline">\(k\)</span> values larger than the diagnostic threshold with the index of the corresponding data point. These high values are often the result of model misspecification and frequently correspond to data points that would be considered ``outliers’’ in the data and surprising according to the model <a href="https://arxiv.org/abs/1709.01449">Gabry et al (2019)</a>. Unfortunately, while large <span class="math inline">\(k\)</span> values are a useful indicator of model misspecification, small <span class="math inline">\(k\)</span> values are not a guarantee that a model is well-specified.</p>
<p>If there are a small number of problematic <span class="math inline">\(k\)</span> values then we can use a feature in <strong>rstanarm</strong> that lets us refit the model once for each of these problematic observations. Each time the model is refit, one of the observations with a high <span class="math inline">\(k\)</span> value is omitted and the LOO calculations are performed exactly for that observation. The results are then recombined with the approximate LOO calculations already carried out for the observations without problematic <span class="math inline">\(k\)</span> values:</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">any</span>(<span class="fu">pareto_k_values</span>(loo2) <span class="sc">&gt;</span> <span class="fl">0.7</span>)) {</span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>  loo2 <span class="ot">&lt;-</span> <span class="fu">loo</span>(fit2, <span class="at">save_psis =</span> <span class="cn">TRUE</span>, <span class="at">k_threshold =</span> <span class="fl">0.7</span>)</span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div>
<pre><code>1 problematic observation(s) found.
Model will be refit 1 times.</code></pre>
<pre><code>
Fitting model 1 out of 1 (leaving out observation 93)</code></pre>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(loo2)</span></code></pre></div>
<pre><code>
Computed from 4000 by 262 log-likelihood matrix.

         Estimate   SE
elpd_loo   -895.5 37.7
p_loo         6.6  2.6
looic      1791.1 75.4
------
MCSE of elpd_loo is 0.2.
MCSE and ESS estimates assume MCMC draws (r_eff in [0.7, 1.4]).

All Pareto k estimates are good (k &lt; 0.7).
See help(&#39;pareto-k-diagnostic&#39;) for details.</code></pre>
<p>In the print output we can see that the Monte Carlo SE is small compared to the other uncertainties.</p>
<p>On the other hand, <code>p_loo</code> is about 7 and still a bit higher than the total number of parameters in the model. This indicates that there is almost certainly still some degree of model misspecification, but this is much better than the <code>p_loo</code> estimate for the Poisson model.</p>
<p>For further model checking we again examine the LOO-PIT values.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>yrep <span class="ot">&lt;-</span> <span class="fu">posterior_predict</span>(fit2)</span>
<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ppc_loo_pit_qq</span>(roaches<span class="sc">$</span>y, yrep, <span class="at">lw =</span> <span class="fu">weights</span>(loo2<span class="sc">$</span>psis_object))</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="60%" style="display: block; margin: auto;" /></p>
<p>The plot for the negative binomial model looks better than the Poisson plot, but we still see that this model is not capturing all of the essential features in the data.</p>
</div>
<div id="comparing-the-models-on-expected-log-predictive-density" class="section level2">
<h2>Comparing the models on expected log predictive density</h2>
<p>We can use the <code>loo_compare</code> function to compare our two models on expected log predictive density (ELPD) for new data:</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="fu">loo_compare</span>(loo1, loo2)</span></code></pre></div>
<pre><code>     elpd_diff se_diff
fit2     0.0       0.0
fit1 -5352.0     709.2</code></pre>
<p>The difference in ELPD is much larger than several times the estimated standard error of the difference again indicating that the negative-binomial model is xpected to have better predictive performance than the Poisson model. However, according to the LOO-PIT checks there is still some misspecification, and a reasonable guess is that a hurdle or zero-inflated model would be an improvement (we leave that for another case study).</p>
<p><br></p>
</div>
</div>
<div id="references" class="section level1">
<h1>References</h1>
<p>Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. <em>J. R. Stat. Soc. A</em>, 182: 389-402. :10.1111/rssa.12378. (<a href="https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378">journal version</a>, <a href="https://arxiv.org/abs/1709.01449">arXiv preprint</a>, <a href="https://github.com/jgabry/bayes-vis-paper">code on GitHub</a>) <a id="gabry2019"></a></p>
<p>Gelman, A. and Hill, J. (2007). <em>Data Analysis Using Regression and Multilevel/Hierarchical Models.</em> Cambridge University Press, Cambridge, UK.</p>
<p>Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. <em>Statistics and Computing</em>. 27(5), 1413–1432. :10.1007/s11222-016-9696-4. <a href="https://link.springer.com/article/10.1007/s11222-016-9696-4">online</a>, <a href="https://arxiv.org/abs/1507.04544">arXiv preprint arXiv:1507.04544</a>.</p>
<p>Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto smoothed importance sampling. <em>Journal of Machine Learning Research</em>, 25(72):1-58. <a href="https://jmlr.org/papers/v25/19-556.html">PDF</a></p>
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