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<title>2. Confirmation of Bayesian skills</title>
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<h1 class="title toc-ignore">2. Confirmation of Bayesian skills</h1>
<div id="TOC">
<ul>
<li><a href="#correlations">Correlations</a>
<ul>
<li><a href="#frequentist-version">Frequentist version</a></li>
<li><a href="#bayesian-correlation">Bayesian correlation</a></li>
<li><a href="#bayes-factor-bf">Bayes Factor (BF)</a></li>
<li><a href="#visualise-the-bayes-factor">Visualise the Bayes factor</a></li>
</ul></li>
<li><a href="#t-tests"><em>t</em>-tests</a>
<ul>
<li><a href="#versicolor-vs.-virginica">Versicolor <em>vs.</em> virginica</a></li>
<li><a href="#compute-the-bayesian-t-test">Compute the Bayesian <em>t</em>-test</a></li>
</ul></li>
<li><a href="#logistic-model">Logistic Model</a>
<ul>
<li><a href="#fit-the-model">Fit the model</a></li>
<li><a href="#visualise-the-model">Visualise the model</a></li>
<li><a href="#performance-and-parameters">Performance and Parameters</a></li>
<li><a href="#visualise-the-indices">Visualise the indices</a></li>
<li><a href="#diagnostic-indices">Diagnostic Indices</a></li>
</ul></li>
</ul>
</div>
<p>This vignette can be referred to by citing the package:</p>
<ul>
<li>Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. (2019). <em>bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework</em>. Journal of Open Source Software, 4(40), 1541. <a href="https://doi.org/10.21105/joss.01541" class="uri">https://doi.org/10.21105/joss.01541</a></li>
</ul>
<hr />
<p>Now that <a href="https://easystats.github.io/bayestestR/articles/example1.html"><strong>describing and understanding posterior distributions</strong></a> of linear regressions is not that mysterious to you, we will take one step back and study some simpler models: <strong>correlations</strong> and <strong><em>t</em>-tests</strong>.</p>
<p>But before we do that, let us take a moment to remind ourselves and appreciate the fact that <strong>all basic statistical procedures</strong> such as correlations, <em>t</em>-tests, ANOVAs, or chi-square tests <strong>are</strong> linear regressions (we strongly recommend <a href="https://lindeloev.github.io/tests-as-linear/">this excellent demonstration</a>). Nevertheless, these simple models will provide a good pretext to introduce a few more complex indices, such as the <strong>Bayes factor</strong>.</p>
<div id="correlations" class="section level2">
<h2>Correlations</h2>
<div id="frequentist-version" class="section level3">
<h3>Frequentist version</h3>
<p>Once again, let us begin with a <strong>frequentist correlation</strong> between two continuous variables, the <strong>width</strong> and the <strong>length</strong> of the sepals of some flowers. The data is available in <code>R</code> as the <code>iris</code> dataset (the same that was used in the <a href="https://easystats.github.io/bayestestR/articles/example1.html">previous tutorial</a>).</p>
<p>We will compute a Pearson’s correlation test, store the results in an object called <code>result</code>, and then display it:</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>result <span class="ot"><-</span> <span class="fu">cor.test</span>(iris<span class="sc">$</span>Sepal.Width, iris<span class="sc">$</span>Sepal.Length)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>result</span></code></pre></div>
<pre><code>>
> Pearson's product-moment correlation
>
> data: iris$Sepal.Width and iris$Sepal.Length
> t = -1, df = 148, p-value = 0.2
> alternative hypothesis: true correlation is not equal to 0
> 95 percent confidence interval:
> -0.273 0.044
> sample estimates:
> cor
> -0.12</code></pre>
<p>As you can see in the output, the test actually compared <strong>two</strong> hypotheses: - the <strong>null hypothesis</strong> (<em>h0</em>; no correlation), - the <strong>alternative hypothesis</strong> (<em>h1</em>; a non-null correlation).</p>
<p>Based on the <em>p</em>-value, the null hypothesis cannot be rejected: the correlation between the two variables is <strong>negative but non-significant</strong> (<span class="math inline">\(r = -.12, p > .05\)</span>).</p>
</div>
<div id="bayesian-correlation" class="section level3">
<h3>Bayesian correlation</h3>
<p>To compute a Bayesian correlation test, we will need the <a href="https://richarddmorey.github.io/BayesFactor/"><code>BayesFactor</code></a> package (you can install it by running <code>install.packages("BayesFactor")</code>). We can then load this package, compute the correlation using the <code>correlationBF()</code> function, and store the result.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(BayesFactor)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>result <span class="ot"><-</span> <span class="fu">correlationBF</span>(iris<span class="sc">$</span>Sepal.Width, iris<span class="sc">$</span>Sepal.Length)</span></code></pre></div>
<p>Now, let us run our <code>describe_posterior()</code> function on that:</p>
<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="fu">describe_posterior</span>(result)</span></code></pre></div>
<pre><code>> Summary of Posterior Distribution
>
> Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior | BF
> -----------------------------------------------------------------------------------------------
> rho | -0.11 | [-0.26, 0.05] | 92.25% | [-0.05, 0.05] | 20.84% | Beta (3 +- 3) | 0.509</code></pre>
<p>We see again many things here, but the important indices for now are the <strong>median</strong> of the posterior distribution, <code>-.11</code>. This is (again) quite close to the frequentist correlation. We could, as previously, describe the <a href="https://easystats.github.io/bayestestR/articles/credible_interval.html"><strong>credible interval</strong></a>, the <a href="https://easystats.github.io/bayestestR/articles/probability_of_direction.html"><strong>pd</strong></a> or the <a href="https://easystats.github.io/bayestestR/articles/region_of_practical_equivalence.html"><strong>ROPE percentage</strong></a>, but we will focus here on another index provided by the Bayesian framework, the <strong>Bayes Factor (BF)</strong>.</p>
</div>
<div id="bayes-factor-bf" class="section level3">
<h3>Bayes Factor (BF)</h3>
<p>We said previously that a correlation test actually compares two hypotheses, a null (absence of effect) with an alternative one (presence of an effect). The <a href="https://easystats.github.io/bayestestR/articles/bayes_factors.html"><strong>Bayes factor (BF)</strong></a> allows the same comparison and determines <strong>under which of these two models the observed data are more probable</strong>: a model with the effect of interest, and a null model without the effect of interest. So, in the context of our correlation example, the null hypothesis would be no correlation between the two variables (<span class="math inline">\(h0: \rho = 0\)</span>; where <span class="math inline">\(\rho\)</span> stands for Bayesian correlation coefficient), while the alternative hypothesis would be that there is a correlation <strong>different</strong> than 0 - positive or negative (<span class="math inline">\(h1: \rho \neq 0\)</span>).</p>
<p>We can use <code>bayesfactor()</code> to specifically compute the Bayes factor comparing those models:</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><span class="fu">bayesfactor</span>(result)</span></code></pre></div>
<pre><code>> Bayes Factors for Model Comparison
>
> Model BF
> [2] (rho != 0) 0.509
>
> * Against Denominator: [1] (rho = 0)
> * Bayes Factor Type: JZS (BayesFactor)</code></pre>
<p>We got a <em>BF</em> of <code>0.51</code>. What does it mean?</p>
<p>Bayes factors are <strong>continuous measures of <em>relative</em> evidence</strong>, with a Bayes factor greater than 1 giving evidence in favour of one of the models (often referred to as <em>the numerator</em>), and a Bayes factor smaller than 1 giving evidence in favour of the other model (<em>the denominator</em>).</p>
<blockquote>
<p><strong>Yes, you heard that right, evidence in favour of the <em>null</em>!</strong></p>
</blockquote>
<p>That’s one of the reason why the Bayesian framework is sometimes considered as superior to the frequentist framework. Remember from your stats lessons, that the <strong><em>p</em>-value can only be used to reject <em>h0</em></strong>, but not <em>accept</em> it. With the <strong>Bayes factor</strong>, you can measure <strong>evidence against - and in favour of - the null</strong>. In other words, in the frequentist framework, if the <em>p</em>-value is not significant, we can conclude that <strong>evidence for the effect is absent</strong>, but not that there is <strong>evidence for the absence of the effect</strong>. In Bayesian framework, we can do the latter. This is important since sometimes our hypotheses are about no effect.</p>
<p>BFs representing evidence for the alternative against the null can be reversed using <span class="math inline">\(BF_{01}=1/BF_{10}\)</span> (the <em>01</em> and <em>10</em> correspond to <em>h0</em> against <em>h1</em> and <em>h1</em> against <em>h0</em>, respectively) to provide evidence of the null against the alternative. This improves human readability<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a> in cases where the BF of the alternative against the null is smaller than 1 (i.e., in support of the null).</p>
<p>In our case, <code>BF = 1/0.51 = 2</code>, indicates that the data are <strong>2 times more probable under the null compared to the alternative hypothesis</strong>, which, though favouring the null, is considered only <a href="https://easystats.github.io/effectsize/reference/interpret_bf.html">anecdotal evidence against the null</a>.</p>
<p>We can thus conclude that there is <strong>anecdotal evidence in favour of an absence of correlation between the two variables (r<sub>median</sub> = 0.11, BF = 0.51)</strong>, which is a much more informative statement that what we can do with frequentist statistics.</p>
<p><strong>And that’s not all!</strong></p>
</div>
<div id="visualise-the-bayes-factor" class="section level3">
<h3>Visualise the Bayes factor</h3>
<p>In general, <strong>pie charts are an absolute no-go in data visualisation</strong>, as our brain’s perceptive system heavily distorts the information presented in such way<a href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a>. Nevertheless, there is one exception: pizza charts.</p>
<p>It is an intuitive way of interpreting the strength of evidence provided by BFs as an amount of surprise.</p>
<div class="figure" style="text-align: center">
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TsfBCtgdlFHAolDC5Fo%2FTKI2Ci5bBUrY8IgYjZRRx%2FyUg2PCRQxGyTIGAQbBIYDAJAMBsmTAYcKRa30%2BEKH0yi5D6dyDYJznMGA1UUMAiUD6YUqh9PhAuHCKGKgBtRS48IkKbQpyqBs7pVKbOCgU2HZAuHHdFDArK0p9NAhsRSmysKBT6Y2Uq5wQ%2BnwopDZwgXHcKKQ2IENqKU2hRUzZwUCGw7IENmrLK4IbEqpmwqLSm1SLSGzx2UyqWCAG0KFTNo20RambWRQYfeoR5YAHDLtK5UwC1GVBa3xQpwFWaoLTsgpbY%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%2BnypVb6aUD6aUbBCtgnOczY8IBjwhAxUqxsUAwQDBCl%2BnwyNcjfTKIXAqKGBRcZDAKAfT5UA%2BmqFPpjZRaXBAMOEhS4qKU2qKU2IENqkUptKBTagQ2KmCGw0WctENh2SHMU%2BmdkCmw7KZXCZsUi4IfT5QpTZupnDWMpmxSCZ9PhKqZs4UCG3hFIbOyKniQ7qZXCZtdQTusKKmbdGUVM2KKXE7IPJYeK7YcOQ4tcqiwtooGFvDBawyqLeyCltp7bKphW21%2BdkFBatYwzlQWFCqCzRkS4OLFUqosQUFiuDKgsTCZyoLOFUOLOEKcWDZXBk4t4VjJxYgcWgaIU4tGyIYWuhnBxZwqhxYgYW8OgYWnZA2B2VT4nHp7pUHBCmwQMLCqUR6aJTD0wgYWDZUwOI2UgZikBxKsBwKIbBFHBQMLeFYlbDhMGcmwOyfE%2BA4FCjghyjglBwQjYIDhwhBx4VBw4CDYqKOJQbBEwOKK2BQo4FCtgdkKOB2QrYKFHBCh9NUo%2FTUK30ylAwQHDhBsChAxKhGxT4rORsTsg2J2QbE7INidlBseEUMUGxSLQwUGwCFbC1ChgEyYDDgKDYoFxOyK2JQDHhADahC4BADYiwuHCmcmMBgdkaDA7ImAw4UilPppClwSLS%2FTQKfTUC%2FTGyQD6Y2UyuCH0xsikNhChgptUUhs4RSG1AhtRSGzhQIfTKKQ2Hbuoqd1iipmwqZyENiLjKZ9MKKmbNlFTNvCKmbOFAhCmcKndafwUVI2oSlb3U0UivGFq71wqgtVRYW8JgycWuzaqotbaPvVRQWpgyoLVWcrC1kDi3uiKC1WIoLVYZypbbREqotNKq8hynFpVQ4sKqHFhKqKj01KHFiBxYgcemESmFoEMqhxZwqHFiBxZwkS%2FAwtViHFqYwoi1WIbBAwsCpnBsRsoQcSdFcpgwsKZMZN9MoG%2BnygOClDYKoOAQNgNkyco48IZwOKEHEoo47JUg4oo48Oqg4cKA4oDjwgOKJytirSDiosbFSLRxCqQcRshnDY8IQceEwRsUi8zYoQWQjY8Ikw2PBRcxsTsosHHhBsTslSDigGCA4hFbEKDYhEjNwgzcIc7Nwis3CAMg2KDYhQDHsiwMUGwQgfTOyAYHZRaGB7oNgqBhypFbAKIGA2RQwGyQuWw4RSmxRcFNqIDKqDbqUgYhCAbOEUhsOyZMFw7KKBsKBfp8sopT6fsEoU2KKQ2cKBTYNkMZhDZwovOQ2MikNqiwhsUEzayqkNqixM2cIENnCy0mbOFBM%2BmSipmzdRUzZ%2BKhUjYi85DYCpVSutCKmbRRlBPEPRZi14wC7uCotMQtMqC0nRUq1tqFVFlFcJlS2yiqK22dglRUWDXwRFbbBsmTGVBYBo%2FC0igs4VRUWJEUFqsDi1EyoLFU5Ti3ZIHFiooLG0SIcWHQKwOPTKYMnw6KpkwsRKcWIGHpq0OPTCIfEDRSgtwqQwtRDCxRTCxKQwtVQcUyYMLeEIItegVDYHZReQRYqlNgiURYEKOI5Sg4jZAcRshBxQgi1Fg4pgzhsUBxCIItHVFHDhEoiw7JRsOAlBwKVRw5RK2A3Sg4DlQo4jZVK2I2UyuGYbKozDZRWbhAW4QZgi8rMiMyK2IVRmCitj1QHHhBsFItb6Z4VhWw6IVj6aFbBIUMAorYBVGw4CAYnZRWYhEZlMYXIMhGYJBseEWtg%2BnioB9PhDGQPp%2BOiKH0yotKbNygGCDYhQgYDZADYNkAwQKbNlGiYoAyAYhQLhwqFwOyjRTZwopTYdUQuCKU%2BmsqmfTVoU27qKQ2BQqZs4UyuMkNqikNvCGMpG1FIbXSCZsKipG3hRUzapFTNhFFFSNnHZQTuseVOZedI26IuEzZCLhPFSLXji3Rd3nVFvZWCgt2VxhMq22qxFrbUFRYyrOcq22cIK22LTKgsVFR6aCgsVRUWURKcWBEUFnCqKizhA4tCEOLSVUOLAgcWrUQ2CmDKg9PhVLg4sQNhwimxKVDD0zqhnJh6Y3RD4BCmFg6olHEbOnKpseEQwsVIbBAcUBxQgi1DODY8FCDgdlQcEBwUwZMLFStgFKDgFUoi0dUKOA2UoOHAQpsVUbHlRRxQjYqo2IUWDiFSNiEgzDZRRx4VQceEI2J2SKOJSDYpBseUBxCQy2ISGBxGymeQ52x4SDMhjDMixmCJBZAMQgOI2RWw4UGwVGw5Uo2G5SgYHdBsEo2HDqVQxGyDYBRWwGgCqBgooYIBiixsShGY7IQMeEGw4UAwShfplRQPpoYyH0whS4BT%2F4v%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%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%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%2BCi4LjyhAxO3dAGKitiiBh9zqLnJTb2ShTYi%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%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%2FFDCN1sqLjKZCKhdaoqVwUELgpGkbrVFwjih8HnW28Loxle207Ksr22FKi9thVFhZolRa2xWpla2wKorbYNlUqwtA0VTn51BamEysLQAiHAVFLbVUyoLeOigZlQ4t%2FBUhxb9wRDYomMHFuyKYWqoOO6BhbsiGFiUEWiYZSrkcUBYOgItKqQ2I1KEEAbdFFjIGbhVBFpQy2KKLIkFkBwOyA4FAcAhzCLRshzDj96iiyqMyUFuEGZBmRYLVSIzd0GYKjMkG8tlFbhAVRpQZCMyDMpgyzbShBZKRm8EVmaqVOdm8EGbVTmW0WGqtRm%2FBRWbhKCx2VRmUI3dFZCChGRIzIrMpBuio3VQZAKaKKyoKIEaqKzBVANvLKKBBQBj9qqMgCi5ZkKDVQBUZRQZ9EAbZCA1XQBIAykGIHZAG1CtQpG6BSKsi4yBt7IENrfcpFoIFNqgQivGiLSEfgi0jKLkhtQTNqhUyFIqd1qCRFYUaSIQRutUXGUiFFRutTK4QuCyqFwSKm0rK8rgtHC6Yw55WtCqZXtCuEXtthDOF7bYWmXTZ6Hq3B7fTuuB%2FiALLOdppxz5w1jZ6s82Mun0%2FwBH%2BpvfD9P6t7VAsJ%2BCzniNnp59WOnDWNhtNXNpz0ZdNn7d%2BuuLW%2Fo%2FXuJoB6dz%2B5Yzxmxxi%2F79PThccJts5n%2BzV0ZdNv7T%2B5a%2Ft36n%2Fur%2FALFj%2FIcNvNHax1t%2F0PEbvV2c9TpH7H%2B8Q37R%2BsO3%2Bg9T%2FNWf8rwm%2B0drT1tf4zi91r7Oep0Wfy9%2B%2B3h7P2X9fcN7f03qn%2FmrGfOeB08%2B32eP59PW1jyjjdXLjYbTsaup0Wfy1%2B%2Fkgf7j%2FXh9T%2Bn9QDxNqmfPOAx%2Bvs%2B3p61x5Nx%2BeT%2Fo2nY1dS4%2Flb%2BYtP2T9b1%2Bjf8AYs%2F5%2FwAv3%2Bz7WGseRcfuNfZyvZ%2FKP8yXBx%2By%2Fqu%2Fpl%2FBc8%2FcXl2P19HS6Y%2B3%2FMM%2Fo6%2Bhf0%2F5O%2FmW8sP2b9QCzzaLX8SFnV9zeW4%2FX09K6ftzzHP6OpUfyX%2FM%2FwD%2Bn9aObf8AOWfVHlm%2B0%2Fj1NemfMtzq%2FDrX%2FwD%2BF%2Fmqv%2B6L%2B%2Fqel%2FnrHq3yvfY6NXU3j7V8y3OenT1q2fyF%2FNl%2F5f2g%2FwDG9b0R4P6gWdX3h5Vp%2FW7uvwtaftPzPP6Pe09a39v%2FAObf%2FwBT1%2F0%2F6f8A1iz6y8p33d1%2BFfSPmm572jxK%2FwBu%2FwCbGH%2Fl1k6fW9L%2FADlz9a%2BVbzPZ1dTp6O8z3eO1p6z2%2FwBOv5pN2J%2FQ%2BnbvcfW9Nh4XEqZ%2B9vK8Y5Npns6uox9m%2BZ5%2FJjtaetb%2B2%2F8ANAj%2FAGb0B%2F21qx638sz%2BbV2cunozzL5dPawpZ%2FTX%2BZ73f0%2F01h0B9YH3ArOr758sx8dWf5WtP2V5jn4acfzHH9Mv5nYHH9Kx%2FwCtp%2F7Kz688t%2F59n%2BK%2BiPMP%2BPT%2FAAXH9Lv5ih%2FV%2FRAmtp9W5x4WFYz9%2FwDl2PhtOzjxN%2BhuPz8dHTnqNb%2FS%2FwDmEkf6b9CJbI%2Brf8PTdTP7geXY%2FLtOjHiXH2Lx%2Bfjs%2BnPhUH9LP5gf%2FwC8%2FbwJn6nqtH%2FZLn7heX%2FJtejT43T0Hx3z7Pp1eFS3%2Blf74Tdn%2Bu%2FQWgNS71bnf%2Fswpq%2FcPgfhs9p0afFldP2Fxvx17Pp1eFT%2B1X7yB%2F6h%2BiPQ%2Bp%2FmLPuHwe72nd62s%2FYPGbzR3uow%2FpX%2B7v8AN%2B4%2Fo7RoR9Qv%2FwCwFM%2FuJwnw2Wvu9a4%2BweL%2BO00d7qV%2FtT%2B5mn7n%2Bmivy3%2B9mWPcTht1r6cNe3%2FE73R0ZEf0p%2FciQ%2F7p%2BmAJqLbyZUz%2B4nDbrX04XH2BxG909GVh%2FSj9XT%2FfHo%2F91d%2FnLHuLsdzq7WOpv2%2F22%2B09Geth%2FSn9Wf8A8t6L7fSu%2B1PcXY7nV046j2%2B22%2B09Geta3%2BlHrn%2F81YNx9Ax%2F8xc%2FcbRuM9r6XT2%2B17%2FHZ%2Bpj%2FSn1nA%2F31YQYf6Br%2FwB4nuNo3Ge19J7fa9%2Fjs%2FUsP6TXMH%2FfQCaj%2FZoH%2FwA0LGf3HxeTh%2B%2F9DeP29zOXiO59Tf2mJLf7%2B1n%2FAMLp%2FwB8p7j%2FANv3%2FoPb3%2B47n1ns%2FpPY5%2Bp%2B%2FXNo36YW%2B%2F1Ss6v3Hz8OH7%2F0taf29x8dv3PqP%2Faj0XP%2FAJ3fVv8AoB%2FrFn3G17jHaz4Wvb7Rv89n6hH9J%2FRdj%2B9%2BpGv%2Bzj%2FWJ7ja9xjtfSvt9o3%2Bez9TD%2Bk%2FoBif3u8iMh9ADzzKZ%2FcbafDYY7Weo9vtnv8APZ%2Fip%2Faj9G%2F%2FAKt61P8A4Vv2rHuLttzp6c9Tft%2Fsd9q6MdYj%2BlH6N2P7v64f%2Fq7ftT3G22509Oeo9v8AY77V0Y6wP9Kv0QLH949cR%2F8ACtf3p7jbbc6enPUe3%2Bx32rox1rf2q%2FbP%2FwBn%2BqdtrPcy5%2B4vE7rR05b9AcPvdXRhj%2FSr9rH%2FAOT%2FAFVWfGz7FPcTid1o6cr6A4be6%2BjDf2p%2Fa2%2F9U%2FUvwLNOye4nE7rR%2BPWegOH3urowpb%2FSz9mxe79w%2FW3Xat9MeWBWM%2FuHxl5Nns%2B91t4%2BweEnLtNfd6h%2FtZ%2Byn%2F8Av%2FrXff092%2FwKe4fGbvZ97xL6C4Tea%2B71B%2Faz9llv3D9aSKT6f%2BYr7h8Zu9n3vEnoLhN5r7vUJ%2FpZ%2Bymn7h%2BteIf09f8AiJ7h8Zu9n3vEegeE3mvu9Qf2t%2FZiW%2F2%2F9a%2Bk%2Bn3%2FAIE9w%2BN3ez73WegeE3mvu9Qn%2Blv7LbX9w%2FWePp9%2F4FPcPjd3s%2B94j0Dwm8193qE%2F0s%2FZRP8AvD9aQefT1%2F4ie4fGbvZ97xL6B4Tea%2B71B%2Fa%2F9lkH9f8ArQ38T%2Bm3BmxX3D43d7PveJPQPCbzX3epX%2B137BAP6z9eCQ7%2FAFPS930viuef3C4%2F5Nn0avG36C4H59p06fC39r%2F5fdj%2Bs%2FcAYLfU9L%2FUqe4XmHybLo1eNfQXA%2FPtOnT4W%2Ftf%2FLzT%2Bs%2FcGP8A1npcf9SnuDx%2FybLo1eM9BcD8%2B06dPhEf0t%2FYCx%2F2v9wD0%2Bf0v9UnuFx%2FybLo1eM9BcD8%2B06dPhb%2B1%2F8AL7Fv1f7if%2B09L%2FVJ7hcf8my6NXjPQXA%2FPtOnT4S%2F2u%2FYWP8A4z9f0Pqel%2Fqk9weP%2BTZ9GrxnoPgfn2nTp8Jv7X%2Fy%2BxP%2B1%2FuMf9Z6X%2BqT3B4%2F5Nl0avGvoPgfn2nTp8Lf2v8A5f8A%2FwDL%2FcHnH%2FSelp%2F2Se4PH%2FJsujV4z0HwPz7Tp0%2BEl39Lv2Nhj%2Bu%2FXWlpe%2F0i9Kf6MLWn9wuO%2BOz2fRq8TOr7C4L4a9p06fCH9rP2b%2F8AYfreC%2Fp%2B7BX3D4zd7PveJn0Fwm8193qA%2FwBLv2bT9f8ArCWkP6df%2BQr7h8Zu9n3vEeguE3mvu9Tf2u%2FZm%2F8Av%2F1zyCx9P%2FMT3C4zd7PveI9BcJvNfd6m%2Ftd%2BzMT%2FALf%2BtOon0%2F8AM1T3D4zd7PveI9BcJvNfd6m%2Ftd%2Bys%2F8At%2F63kP6f%2BZKe4fGbvZ97xJ6C4Tea%2B71N%2Fa79lj%2Fx%2FwCt8fTo3%2FAT3D4zd7PveJfQXCbzX3eoP7Xfs8f%2BP%2FWks7g%2Bm3nYnuFxm72fe6z0Fwm8193qKf6WftbuP3P9WLZYY2E%2B5bx%2B4nFfHZaPx62M%2FYPDfDa6%2FwAOov8Aa39rr%2FvT9SxDgG30x8FfcTid1o6cp6B4be6ujAD%2Bln7aW%2F8ANP1Lk%2F4bNn2T3E4ndaOnJ6B4fe6ujAXf0r%2FQP8v7t%2BoAOh9Own3hbx%2B4u3nLsdPTlnP2BsLybbV0YJ%2Fav9EI%2FwB7%2Bu%2F%2FAPFa3vV9xdtudPTnqT0Bsd9q6MdYj%2BlX6KP%2FADb1wTp9O2I6p7i7bc6enPUegNjvtXRjrJf%2FAEp%2FTv8AJ%2B8%2BraG%2Fi9G0z%2Fywtaf3F2k5dhjtZ6mNX7f7O8m2z2cdaY%2FpV6JD%2FwC%2B7%2Bf9AC3%2FAMxX3F17jHaz4U9v9G%2Fz2fqN%2Faj0CP8A1y%2Fp9Af6xPcXXuMdr6T2%2FwBG%2FwA9n6k7v6U2FhZ%2B%2BEA6n9OC%2FT%2FSBax%2B42r48P3%2FAKcs5%2Fb7Hw2%2Fc%2Bop%2FpQQP%2FXf%2FwDV%2Bz1lr3H%2FALfv%2FQz7ff3Hc%2Bsp%2FpUZb9%2Bn%2BEH9Ka7f9Krj9xv7fv8A0Ht9%2Fcdz6if2q9aP%2FOrOf9Af9Yt%2B4ujcZ7X0se3%2Bvf47P1E%2FtX6pp%2B9enR%2F%2BhLf%2B%2Br7i6NxntfSnt%2Fr3%2BOz9SX9q%2FwBZP%2Fm%2FouP%2Bru%2B1b9xNjudXTjqY9AbXfaejPW39qv1pdv3f0ILH%2FR3af8ZPcTY7nV046j0Btt9p6M9aJ%2Fpb%2B5v%2FAOpfpuuN%2FwBi6e4fDbrX%2BDHoHiN7p%2FEP7XfuLOf3P9MOcb%2Feye4XDbrX04PQPE73R0ZS%2Ftd%2B8EOP1%2F6PHSfU%2FwAxb9wuD3e07vWx6C4veaO91B%2Fa395dh%2Bv%2FAEQ0r6n%2BYnuFwe72nd6z0Hxe80d7qSu%2Fph%2B%2FB2%2FWfoCB%2Fl%2BqD%2F8AS5W8fuDwHx0bTo0%2BJzz9icbjm17Pp1eED%2FS%2F9%2FFpP%2B0%2Ft5Oto9T1X%2F8ApK4%2FcHy%2FP5Np0afEmfsTjvn2fTq8KX9sf5hL%2FwCl%2FRQf%2Fi3%2FAOrXT195d%2FptOjHiZ9Dcf%2Fro6c9SV%2F8ATX%2BY7aH9JeBU2%2BqY8bQtafvzy7Pz4%2Fl%2Fixq%2ByPMMfJn%2FAPf4FP8ATf8AmQQ36arf9Lv%2FAMVX115b%2FwA%2Bz%2FFPRXmH%2FDp%2Fggf6efzMH%2F8AD%2BjczuR6tq6et%2FLfm1dnLGfszzH5dPawW7%2Bnf8zin6X0rz%2FhHrWfEhax97%2BWZ%2FPns5Zz9m%2BZY%2FJjtYSP9Pv5pgH9BZx%2FpvT%2FAM5b9a%2BV7zPZ1dTPo%2FzPd47WnrSu%2FkH%2BagWH7aL4qPX9Bp63hax95eVZx%2F6z%2BXX4Wc%2FaPmeM%2FwDl3tPiSu%2FkL%2BawD%2F5SYq3regfL6i1j7w8qzyf93d1%2BFM%2FafmeOX%2Fq72jxIn%2BSP5pH%2FAOIvH%2Fael%2FnrfqzyvfY6NXUx6W8y3OenT1o3fyb%2FADMCRd%2B0eq9uxsPmLlrH3R5Znl%2F7tP49TGftrzHGf%2FHV%2BHWlf%2FKH8yWs%2FwCz%2BuXowB9xW9P3N5bq%2FX0%2Fj1M5%2B3PMdP6Or8Otzn%2BVP5jf%2FwBH%2FVf8ha9R%2BXb%2FAEdKen%2FMNzq6ET%2FLP8wiv7L%2Bs%2F7m%2FwCxb%2Fz3l%2B%2F0drDH%2BD4%2Fca%2BzlG%2F%2BXP3604n9k%2FXE7D9P6h91q1jzvgM%2Fr7Pt6etnPk3HY%2FQ2nZ1dTnv%2FAGD98tD3fs3660bn9P6o%2FwCatY844LPJjb7Pt6etnPlPG459jtOxq6nOf2b93Yk%2FtX6wAVP0PU%2FzVr%2FKcJnm22jtaetn%2FG8Vj9LX2c9Tmu%2Fav3Gf%2FL%2F1P%2FdX%2FYt%2F1%2FDbzT2sdbP9DxG71dnPU5j%2Bh%2FWAn%2FwnrBoI%2BndHkt44vY55tenpwxnhdt8dGroy57%2F0vr2lr%2FQ9S07G0j4LWNvs845NWOnDOdjtMZ5dOehy%2Bp6V9jZ2XWvuGW9OrGrmzhM6c458Oe4IiFwRUbgsrhFpSK%2FSPp%2FtH7Vbc9v7Z%2Blt0j0fTH%2FNXwPV5jxWccu115%2Fmz1vuWny%2FhtPNs9HZx1O30v2z9vtuBt%2FQ%2Fp7TMj07A3ksZ47iM4mdpq6c9beOD2GM3GjT0Ydtn6H9IGP%2Byei4m35A76aLnnitrn8%2Brpy3jhtlj8uOjDut%2FT%2BjH%2BisfX5RHkuf%2Fbr%2FANc9LeNlp%2F0x0Ou21mB2grm6Omy0gB42UHRbbpXbQqKrbadSWfyQVttPytXQH7kqui214Zvb7VBYW6CA%2BygsLTDGKMgYSSB2ZWBxaQQ0g%2B9KKgMRX7FA4tJfhw7oHtFO6mQzOHEg1KKYWsQGg16ohsXINxIZ%2FPkIKMBAknT2CgAtYf4hAYQw1VocAgM07jnwUUWNwI3iVBmIgHx07KkGSCRrx56ICAbtGGgMopmcA8QSgGFzCkSOqAuxd6jWPaqAsQNRQczqgOJO7Q4MfegbFqBxx4oARpIB1ozfggYW8kmeiDOTIpqZFaUQbgCBTzQY3UALyB4pEEk6s4lhv8EUCQHYsdHogIoA8x28UADg8bxO6DPdsedNe%2FvQGjvoICAA3kBhWpkoMBQ1cxttVA0nEQfDkIMQZevfRKMbINDOp328UoAADh9A%2Bmn2JQRazAkPoY3pVKNjMWhtendKA35dXNQ%2FtogaJLkBnGygNA80l%2BUAAtf8oJBgdEG0m12MaoMQILVYEbahAbbQBAjU0QbEP3mUoXEC1z8zePeqUaHi0Bi7oMwAyL2isQe6oN1ujPPIUowZ3iTXmnKDYgNuddtdEAYw4l5I0VGa38p3gPyg2JgOZp4apRmmpB1J2HglCsXEyKE7MdOyAkB8X5Z0GwJ145SjMxilS0sgwclwxEjJuiAmRIBfRAIN0V1YoMCADL8nVAoJDB3PjFVUa3EUe2rindA5Lcb91FATIGMwgW4APJcAttsqjANIgOfl61eqijiDJtrz9qBTaGDgwXMA7nRAJ1qLXJQC4OTEatJQagGw0O7oNiBIDk6oExhrav8Al9%2FuVGa4EDF4kjR%2FwUAZ2Jky%2ByAAXUJjLSea8IjAEh209oQhQcrS4x%2FwzPiUQXFwJA2cuyKUi6Q%2B7OiAbaWkYkCNQeFQpdizEMVFTxmrTrv1VQOCKVCKTGPlBEflZKiZBZhOw0fqqExLky0gWoJ3DYcyVQpfsNS2kFIJ3WBwDM1UCXWk00h0HNjW7WW9gtCVwcGDu6COHVvtRIh6loc7tIfdMIhdbUU28FRy%2BpZZda1wF1p0uDhaxnOM3CZxjPO5L%2F03oXZA%2BjZc%2FwCYYgv5Lpjba8c2rPSxnZaM8%2BMdDjv%2FAEP6Mv8A%2BF9FjX%2FR208FvHFbbH59XTlz%2Fptln8mnow5P92ftzt%2FsH6fr9Kxvcun9dxG81drPWx%2FRbDd6ezjqVstee1F5c5ep1WW613Uo67Rw7e9QXtBgv3Sq6bLa66KVXRYKU4FVEXtmIce3xRVhbE0eUqr2W1JgNVSiotegaD9ilF7QSQxjQ%2B9SigBNIksFQ4kMJJpt1SigteCGIkqUNawtcSGjsqKW6zN1AdnUoZhaATSHI4UFMWc0qSZjzSqbWKgOiGxBcEy8NVKCA5h25Hm7KUOwLEiSlUJImLSW0%2BKAgN0FRbHVWqNo02060SjYAC4fwu5tShmHynkl9j5JUNUGBSX4bhKoWviARj1iEBxl3c6h2ShjaCMSZoSfaUoLl6SNQ2qDEU1tKUYmhILExbqgwLfK8Bg4GvVABJD%2FAJnLdISguzyAR%2BY7OgwyeaHTtwlB0MMRUHyUozaAUZwfsVoIH5pYXFx5KUCkMeWlKM1ptmXLt0ShrRaRu4iJlM5BLyB4k91KAMiBURVWjPIDA7jT3KAvkdRi1DugzSDA6VSgMTjq1W2SjCRbLA156pQHALSDbDzsqNaC1XcNIShqGbmfnyUoQCLixLn82ytBLYv%2BLoCAxcggtDUCUYkChd5JPsEGccAw4d9WSjQD8oZpuaOyUYQA5gMBuxQYNpERv26JQTDh6a0%2FFQYUAEPV69koWpDaByRXwroqDL46s8CFKN8rgsxAgfYqM%2BogbmlfvQC55ALi4bVqYKAvqDWQTpuUBElxBox08FKM%2BuTQQ6ASJE8EiuyoIkEDSAezuoA%2Bpt6A79SqM1Q8vHs6UKBbaADt5pRrgDLuBr1TGRiNQzDXoEo0gbmAJh9VaAwektN2qUbKt1LeYFUBtJOMPAc%2BKBHDs5BuZ2p49kQwoHLPMdQlUHYgAyXOO5fdAQ5ca6HTghKNUNqP4Z%2FFKAwc5SeRHu5SjG01tNS5Jn3JQJ0c%2B%2BfsQAE0IPU6DRApYl688JQMaMSADI56pQsux3n3pRmFMvy1l6pRhvLajUcMogHkO%2Bn4pQDDh59veUoFJYnbl1aFOJhmLoEuZpNDBQIbSH2ShCxyFr7E%2BZVqEbcM%2FwCY%2FilErrQ53eo8VaEIDgksZcO%2FZKJs%2FwApFPblMhSBIIZ6dOylEbrXmRqzVVojeHdnfeioheCXDdExkRLPdxp%2BCI57rZIZpVo5vUAEsz6FXGUc9%2FsVaOe8aHsERz4F%2B1VaOex4KK67Aw82Kg6bNnrTdB0WmmxqorosBgVI1UHTaTu70AUVa0U0ajTQqmF7ciWnZ1FdFuTbuoLWvT26qCgkvU6D26KikwB3HVQPaDAmJ28UFANKW2wyBgGDUPnKgpbV2cE%2BcophubWIL2vxuiKyR8v2dUUXYOTAE7%2BSgYZPBdBhbucqwfBAQQbQCWDSH26oM0s1NHoGQEEW3QIb83EKwO5NfAxPVRWJJ3J0cKjEQ4hxBLAdUQw%2FMJ%2BaUUSSSwJGhcbIA5dyYkAe8eSIx%2FiuckioPDoC5MhiN%2BRsisLi7Cm3TRIDboxJDCPbopkAC5q0%2FM%2BiAuDqaS1JkIH3aTx1UANACWGp9oAQY6yx3b7NVQXk5UDMXUGdxdNabIFBJAAIeZYeKoZwZy2kbfioBkXxBLvt4lIMKkiMQ2SAgloLvr9iAFy%2FzAG6oIdigLkknR3fp%2BCAA2g7PvWEGyuIAaoI4SAlgWZzJZkGkNoGJxrKEYm4iC%2BgLfYg3FpFp1j4IQACzEB2Da06oQH1BrLiv2qwa00LO4fWuiBiXi5QZyGDjRwKoAMgWgszdPuQNxt2HbxQKb2IBJEVaO1UgxJJjUV0QZ3I0JLkINkQ4DckoCXNZhwW8YQYXEmrsHhIN%2BV2rqgxLOxq2qAZQQflBjp2QY3H%2BEZZflEU7pAXNDz3D0QZySMg5I2QAEhxHTb3IC%2BReQf8TfagFxJa0Frmf3JgYGkmWDkbdeqDXFiCbqAEDp4Kgu1C32qDElhLA1Jj7EAuy%2FhtkMARoqGq8cNwVAuumRFNYVCkMDcCYeIjVCMZOgNLSPFAJAi4sZB6qg69II%2BCAFyHf%2Fgzrp5oQDeRDl9%2BUgFQwDAwdC6JDFrt9noig4LAMWmZQAB%2F4g9WGqELdkMgQwoSOURnIg1ltiilalTNAW%2BzVEbR7aCCCVBi5fUmgNEAY8uRL08kANQCJPHtuoFi4Sa0J52VIRyAWPSde0qoS4Egs769%2BiKQw0tTb4DhEBwQQa0O6KmQRDz7oq6qJ3ONXPCCVRMEe2qoUiKnkugkQWpN2yggcgTLg6KiFxL7AVQQud9ix9qLSZS9QPWD7bJgc1%2B1DVEct4Jnw6LQhc%2F2BEc%2F8XxbyVHN6emr6qjrsH2rNHSKAVlm1UV020fQU04RXTYJah0PmpR0WOGed303UFrYq4BZM5VawTqTKDotA1hqaUWVWtDGvVEVenSDKBgC2xfqlVQCgthpLmfilRXEGlWYtzVSqe0N7vwQUtcNDUgTCBgx7U55QEAkn5uQ9VAbXc3M%2BgZBU8QdB8FBvzaEgluyo3yhiNfBiqofnmoaI8pTmGrp%2BapMa6IKEGCAHMMdeiBndqFt2ooMT2Z3uZAKEmpFG0j7koLatQ6JQoOVXmhNOiob5idH12PClwCGYRo4NYNfJKMwNrkPPu1OyUOTTR9CoA8FrXfT7eroAzkS9rwCPsVozlsgPlqdyoDL7zThACXJdmZiNUBo0uCacIB8pFpuYjf23QBid33H2QrQzggEOW08%2FcoMKFtKfcgAJIqSSRNOUBbIu0DWPBKByBJ0aoJ8EoNWf5hBZAAZxdrWZuqAw1p376IFa4swLGhEM6oYPDuIkS8qAhzIalUGxaMfCPxSjC2AxZvYJRgCCAA3PRBiKPDGmhKBmcOxb%2BDRAMQWBOXB%2BxKMRaQwdneiDG0OwBoGP3hBiLQIMhgH99EAuBctRnch6VQA2s4mTo3TqlGa4V1hvvQEg7FpLadkAkEOGYQxHdBsfzABy09dnSjH5nkwZHxhAOCDHVKC9uggUIFEAuIBDgktGtHQEmSPEGnPvQA4ki54q%2BytBjJjXaWZQYsHf3%2Bb1QCRaACMjAuPjqlBq7GGg1fpKDCTALPzVtXQEnj5TU%2B9AA%2FykGlSWjhAIqSSLqkUCAsAJPyiCK9EoP8A70sD1QAkiNNSfgyAEvBGsbvVAGDMbQCzEhnr8VaMbRoNmq%2B%2FsyUJUQYGk91aGOgqSXr3UAdyAXltlRouMOAX0jugNNKCilABJBYEAeMKjM7w5EzA1dQJcGd36VDdOUAmkGwCdUoxh3NBDaDx3QKDRydAwOtUQD8xL9RUcIA0QYP5Y2%2B9ApEQAGp9miDXBtCzGRVAjPoREUZlRIgfmMEOlCOdfPQRVBO6h0I0KokS4PEOyIWazBgFBK7%2BICoNd0Erg0V96K577flIh1qohcWZg2k%2FYmBG6szylRzXAEw5ZUc94F3LHTlUc90DdqhERYZNqqjlsDfFB12gPSdCoq9tWLyfYIOq3rH3qZV02wHo6mR0WwQ4qouFrAH5feqDottLDV9NgoL21O%2B0qKoBDUI9tVUPaBk2nuUVQOxkzA68KiweXkmFkOJl3YzwgcEB7rZGyCgD9D7bIHgEu4eUGDMS72iBvogaMnILUOohQG2jOABokUwMgbnwZAtoJIkyI5HZUU0khzDb%2B3RAXrod7fFQbUGZGmsfcqCXDMztUqBQHMgPL86UVGJcCoLwgYWOJAiG2frwpRhbDj5S0BolA7a1BUAFxLaBpf21QAO7sSDLO8%2B3CBgweGAFUUDW6f8AhFkRsQzQYHL%2FAHIMXDSQKUf7UDSAA7e0IpIIpSBRj7wqg7gyK%2FYoMTJxECkxuEBoSD%2FF7MijiYmpd43RDYwZoaBFDEdDv08kQwE0IbeX2RQcQQH47FEBjAAZgIDfegYuWFvV9DqgDal321YoCa0%2BVn7lBnkbEsdkVrKCY7SdUyhSQQ4qQwarqg0eGA0ZQEEyJB8WQABxPzS87oMJDgkho5PigJ1JctIAQKwgHXSnl3QNi8%2FmLu2j8QgDhxIh3D6lBhVzPhxRBocMC5AoGeio1pYMXpOvCgIth63fwvo2iAQJLx%2BY1CAwYAYCG5QAuTIoe%2B6A2tzMgdaoEYwzDGs7dFQSQS0ZM9PeoCLQDoG%2BKKUCGIxJYNKqMQ5AN2MSA0MoDiRJlpL6dEAFpBkyHcNy8FACaauWA3b8EBigEeCKRwSQWyGldWVQTkadYLlQbU1q2yA1rNpjYorOTIGviiAWqLgzgluyAsIIjYjXqgGU4MDSOdhTZAflhx72bp3QBzDwSwf4yEGAyEwZBIjVAuNNIb2ZWgOPzEkRy3ZUZwXat3SfeoBIHXcuFQeCCxl6oASRp0avDiEGNo0GsjSZlQTJYgVLUdjKoxr%2FAJTS%2FsHQY1LGuoUQpdgLXhjcKIMQKkA9dECEV%2Fw6g%2FcqFuAlnAuKBCNolqa7oJ3HXxLIEIJehcVfRUTuDEnU6%2B3RBHUuenKBQDLl2TKYSYVE90VC4TOvwVRzGCwoXcDwVESNi0PCIjfR68K4HLcHBjofYq4HNdUhvFBJjk3mjLk9PFnA5DarWR12e%2BizlV7dxXQorrtHGyg6LSJu3%2BCmRa1hyNKfBFdFgh3AO%2Big6AGB1YexRV7awYUDhpA7omDi0wwqavulVcAGGpT4KUMHe40GsoLWgXON6jogaT8rhxXwQM9Tw7MUDM8mem6UPSgDYsQYpuoG63AOJ2dFYHWHP8PCDDQVOgoPuQUh3l7aj7HQAOQAQ2xgwqCXOrA%2Fl4Oig1ammvJ0IQAObi8XA0G7CVQwAGUtj%2BYKUOzPc00A6qVQ1JuDbbyiMKsGcB2afFAoOpDlncasfigf80GWqNEAFGDEEdkVopN4PxRGIgMBDG0bIMbS4LdtAlGYF99BXxFEoNhuu0YDSqZDAER0HPVAuJLxIEPuKHVAwAAEEk18PBBiQXFDbPt2QEgkCARUgz1ZnQF2Ichh7UQBg7iprd06pRhi1tQ8OYMboGaGnX3opXGINDi%2B6I1rULPby%2BpCZCuwh4OnxBVDgsw%2FCaKAQWMbgAPPvQakEBh%2BZtgNOiA6uQeAemiKX8xIfSWRDEPSCAGQByCz5C6QHHRAHDPSZujXdkBqB7E90CtBtNz8%2B8qhpnUjTR%2BygIZncAeUINkzirksXQKN9ndudYQZquGyYBxpRkBEMf4jXR26INEOWNCaOg38MkAET33QAw4H5WILN3QYlySN8Rzx5oD%2FAJOpd7ttigxMkuwDDhBmMtA014QDU%2FwnetX5QNBho8kUrsXxtyIjfuiGFXBfYaMgAcmRy0oMQCXZo2%2FBBsXkH4opWufcEsQ4hEKbTLmQGfhKC4Ji75RM%2FegzOxl4f4yOiDcFtvZ0UALaPsAdR9iI0vc4BIimiBmFaEvKKUkWsHPM7wiMAxyDMZJ3SgORDEkOANa1hAxxeWM7oJkEDUQ90K0Coa3%2BGIfgcqjQHYEN4AqAkvrIgdwgxIobnrwgBtBuLu%2Fj96BADaQN9Pjugwc3F9OrFBpBjSs9UCzLiTQyoicWuxLWj8u3VaG6FiRQoEuabSW2HHggQuYdte6CVwAM0JogQg0JPmrRMiCasgjo2oMgFVMkuDhzWjlRULiGubUP9quEc9wYFo1AKCBDOSXaSqIXBoZtvBEc14DyQTwtDlvFvmdFRJi7%2BbKMuOw9CrnCumy6BqVIOqwjYy0%2BaK6LC8iPPRSDqsu1aCFmC1pBPcPqFYro9My0O9OikF7btAK1CmcKvZcK0DMkDO5iHoPYIKWkEkVYedEHQDiCanQKQNaTV4adfZ1IKi5stBQH3pA9pLGjs79eiQYyGxIep%2BxUUEPFa6Dus5Gc1knW0H4SrA2WptbKoPCRRehI7DcoHBttABbYBIGyFBJP2JApuyYYtpt2hINkHcmsWnrskBNziQZhhykBB1LhtAG%2BGqgd2LggAbqKUXUa1m%2FMA3h4qxByAcsW2fnqykVn1IA2c0VRsmNxm5qJBsgHto5jfwSASa1MbHdBsqAWgEHz1SA5Ro7QXiSkGyB2YT8UimlhkSKexKRDAtLGfthSDG4O5tIectfcrBnucmS4YSg2TD5jJgDk7KQDID5WbQgzGzqwHNsizBngxqfNIBk1rCjSa%2B3ikB53kndBjcZNtuQO32pAchbS2dBrypFC4hw4BeATGvKuEC64mgo%2Fy1NExgA3CtdZaOUgd5MAtQ691FK8uzvroQ1fNVGBJuJxgD2hAciATozgEpAMyDEghwY8UgwZzB3tnQoNmMQKEVmQkByYgsJhzHZikGeeedgUVheSBcYBl%2FblIACA%2FUFhCINtzn8rOKPDdEgxugl4MBp%2B1IGMijFRSky5EGC6Ixu4L6nRWDZCfmkiNQkGBgDEZEDybqkGNxFAza7e5IC4cHwL1Uigbg9GYxMUorEDLUQQKc90gd3YHvyopXeAG6e9EYXR%2BVgHfXsrAcgCHDloevtKkVjcWdg%2BlvwSIAukHRmDUqrBiSaTbqDq%2FKAwDSbtIZxKig4ufGG7F1UA0LQ1C%2FeiQKchDeE7cIFBGNoLXC08F%2FFAQaSxtj4IMSwh3ckDkx8UBfRg%2B%2B2zqRQyIeDdrb76qwHL%2BKgAn2ZIFBDXR2BJRAclvlcDUP8AFIGeCGZ7iwFUBF%2BpEN8s18VIpCCGLcMaNqqhcgDq8lzR2CsBzNo0FeiQC69jsAapAQSR4ueaIFdySCCQwrKQJkA8Npd4DZIAbsiwraXSAW3OLiIdpJ%2B5TOEY3AswYkv5JAj6WgETQtRagW4yzFm1NVIEuIAHytowSCZul3gwCkCXEsHHUdVcCNxuIIg7EQrBN8niC0KCd9zAG7VmKsErrg70cJjA5yTD1aVRzXGWbjaPBWIjddXc0DpEc110tWrqwctxMnX4KwQmnd%2BVUcdkiRGlw%2BCI6rXbfd1Kros3Z6HZkquqw6aCGUo6LZAZrtIUHTa3yk7pWl7DAeN67aKC9sVnUn4KUXD%2FADtVSghjcHB3f3K0WtpIlqdVKKiAxZgYPTlSioIAFHNAgIZgBI1G7q0VqwIDv8qlDProTTdA7sHaOfHlQMDPaTzsigBi8PNPN1aCxeS%2BpqPilFaBnIlShXJnFwDO6oImBc7yfJKMaNaWgy%2ByUC4GB3BnR9ExkOdAAwZmFXUo2RBORjTf3oDbJNNvCvvUoAcEB6VAaOXhWhqADs6isHe6K0PsyULSloBkl57q1B%2BYtMN3UozvNvVt6MqN8sG3t8WShrRLXB6kOpVNuwerAFh0CUCagggRa32lWoLNq4IZm0GiVQGI%2BYyGcFkqC%2FZy%2FwBxUUQ7k0c%2FdKUAPUOZo%2B6UEPBoGp8AlABJAOhFXnogIAIa1gKgijpRgauG0bx1QZhUAT8UoUHZ3PBffVVGBhx%2BYhA1oLEXTvt5qZypSXucbflJbXZVBaG%2FKJmg8EoBD3B5LOB1Sgt%2FEBJLufBSjPbLyJcvsgwg1NzOXjTwVqjlGTAv7BQLa2zBmII06q1GD94y8Eo2jEE3SACz8TRKMSAYDBw%2BjaOgIEFqbBmPailBBdyY0KKTQgGZPsVahizAEuTQxKgAuORJoNacjRUEcTTVi6g0s9ZesJRtiTiDLK0As5BAYtRKMAKyxkkJQwOhgtThRQBD3giT%2Bb8VUY3AHnbqoBAgwKDrV9laC7gEgTQ0dRQLNAdgId44VqMTqYZ5E0QEwWJcHQ6%2FBBpb5o3I81KC8gjZjxsigQ7iAlCAh22Jcu0%2BKBdgAfl7N0VqG7Et7V7qKAaX%2FKeyVGId7iC4p20VoIbsKMN1KpS4NGD4g90RnpqNOsz5KhnJIgiHdRQfUy7P0QAAGYJE%2BTVVqEGRL%2BfG0OrQQzgTEh5ShQYuB1j7uyB60e3inKilDsQ4jQTXRVEwaM1JhASWBhqygQOGLPEnkyVM5RmeSQXodeyUIdS7irHlWhbmLAyQZPZKFLdC8mvZ0olcAQW1Na%2BKUTuFIke1VcZE7ibiRvp96CUww7IEPBHBSiFxdmdgS4VwOe8h3BmaKohcXIimqojcSTBYio4RHNdBow1KUcl9DC1RJ5rG76ojjt1mqqOq2exhRXTY0Grsyiumw6Me6g6LXaQ5aCmR0WzzKjS9mpmC7%2BSgvbpc2rPRMi9tSASXOvKge2WL13p2QVtedf8AEEFQQ2x0P3KBwSRNrA1lBS2LcTMR%2BCCo3kCg196DOB%2FEwB6oKOCx0mNpqsjWs4NsbRvKq4B3FGMEPMPXdUMHA3LU2%2BCB5YgU0MuoCRk0s0n8CgwpBe0%2Fmh5QEUrxkR2ZBsgGeuw0PsUimBoCamC%2F2qAl2Z23Blh1QaaFmuAGOj1KAvFpAjc6BBjc2rHmiQBgCB3M%2BCBQ5LCLCxDBEYBqxEcIHJhtRXSqKV5IyYtA0RBEEUEmUU4kWuTNbgd0QXbXJ3IbYooGkF7aElEFyDbkdPxQa6AYIeSaoo99Y%2BCBWDmHgOKdSiDaQIGmhO5RQDy4dpB1k0QZnYlydRXZEY1t2BcHzlAZoQQ5rsigxLEVg1imqINI8%2FvRSs4tdiBQDy8EQTbI%2FwAWpZAQwc9wNepPKKDuQWckM9sxr5ojNrUGBEAIrGKzOj%2BLIgxR8W%2FKH19iit8zagiGJnrygzh2AJZp0qgAYgir66n2qiMazSeBR0GnEkCojp2RWA%2BaTBp70BJcZAtFBqgBNooSC7tUojESW02AroEBDucQwENCKAkwCAeGY%2BwRGclsXo4gIppGnB16IAGNWNpmJfvwiAA71BoxneqDC7oQPzDd%2BEgIHzQwADAIoSLon%2FJdy3L8oGMD8ooxeA3mgABtZy4cNDMiFcM4l6mQOdEDGdBawh9OiKBgtWYI328ERmIYPQSNB4oC%2Bhd9xCKxMNTT4IIngviHI9t0Q0h2L2iD0RWYAhywOjs2yDOIAkD2jsgLiTUGuqAamMWEalAQWABk%2FegECsRJhEZnFu7ObTygwq4tYmt3R4RQIIAmgmpRCQaBjPloFQpYPDwHD7aMyoYTWd6UZQHSJeCSJ%2BCKBa20gHrNH3REyKSdRkPNAflZ3dy%2FXRAvzCIbiiIBMcCR0lAheAAHNQ234qhbg0tLV%2B9BO6A1XLkdEC3YlhvLBQRNGrrvRUIQ1Rlz38VRETs5od0CElwfLzQQuIZ2nurhEbyH2JPxRXNdvzELSI38AGKKYRzXs8zs%2FmqOX1BWrw7KiGuPl8VUcfpmntJTKOq00ALNVRXRaWHaOyVXXbqA3DbrI6bZaHISi1ruQ1Gb7Uqui2eG0UqugF6g%2FepkVDggklzolFQSxY6QVBQEOG4YCk6pRUGvB7h0FAJLV0Cge0uBb47exSigLmjw4ShiCMpLmUoayDWA5dTOQxeLd4bblKGJMPHdGgYAbkyT96tQRcKTMA7ToyBmoGDO59xYJQwZn%2Fi1r7lKoOBiwYbqo1WY6wRvqlGt%2BW5nYElgmchxcAMhIGg9pWVMXMAt8EoVyCII0Ar47KozU0bT3bbKUbQMOdCA1KJVEOBEhtISjB9wSGFfbdKFpazjLQsQCPYq1Bcu7UPl71FYxG4qA9G2ShnMGS9SPBKMLnxcO1XrwlDZPP5WDgFAdnqanVKNQAAsfYpQMoNQ0c%2B0oMGGuRDEqoMk0gROqlVuTXUapQRzJJpVkoW65raaaSyqMaTB6OW1UUfyi6XILj70ozi16TQ7tugznQ6M9UG1LeDJRiRBaYr1%2B9KA4IBOoyJ8zylQGYC1mapNCrQQRWQxE7%2BKiieWIMHdKA5tBeWmRSOFQQQHD4tPRQAEh7ZJ3%2B9ASIIfYUHuSgFnkEb7dUo35QR%2BYAszPXRKMznmpHTv0SgioNQBB3fqlBZmcvSvglAau5Md4dKAHA%2BZneo9%2FmrQQ8AjcnhSjUDiO1T5pRodzEta2qUEVpIgFKAAem46pQCwrcfmBlKMCcm0P5njuFQcplwXZlACCXINQwIKUEVZ%2FwDhBKNwQxIlAAzC7FgHbulQX3iZPGiKUnEOBI5j7EoXWTQiKpQdiXAdAGIA4EsPclBkxBZ6zwlCsQboGLTNd%2FcrUE4gfNIAYk6gKVSn8xuya3UfFlahiSSw6j2bhRRIDiu4CUYbmlQUoXIiSGA3hUKxcu9zUSjAGAKDn20ShQw%2Fyd2%2BPirUM2MBgKkqVQFwe4vFFQLiHfmVKJkwbmJ4BKqDMF2h7koX8txiKm56AqVAuIBbFuEwFmCHJIrwrQpIPQ%2BEpRO6Tszz70oS4muo9mQSJDgbv8wnzCoiToPbxShXBgCdQgjc06gQ4VoncWNOjJjI5biQ5FGrVURuJftTWVajnvDF9NvxSo5r2LsRqFRzXnjhWiL%2FADM%2FtVKjhsNA79FR1WFh4qDpti6hPtyorqtJ3bRQdFpAmZ3QdFpFBzKirWNoeoKmVdNmhc9T7lMi1h0YhpPPgoKaM1FQ9rsADIFUFrCbgDXcJkOGa3FneNUFQRFSeeFkMC7Cj0I3CooCCHqABTdQM50JJofeimcEZuwURoteal3KKIfToake9UE5MCJimqKd7uhqx%2B5AQwBbQhj7FBgRoIJem5QYEjVwD7hKAvQs7weOEGtg1kgFMgPaWNA8katx2SIpR3l6AU8lFByCZcOSRqgzN%2BYOHqUBdyxgs8H3KAAkC6pYwTqXVBMl3AHXxogDQQCKFw7Ud%2FMoA9Cx0Zq9KohjSDpDMorBjLM1UAAa4kQ5o6obIkBy5qbT7cIMCTyDRBhdWIp22QOLrXggadh%2BKg0FnIkBh8FQJ%2BZyBsdkG%2BUuCKnmS26DNBe0R%2BXaEBipI6kKDFjBqacPrKoHLfLcX4QF6%2FK2ooFANCGAJ09zqgi4OXOxHfhSAG6TXjTTdUH%2BHRw2LoMGoDUT8GQBoLEBjXw1QaHDmszHtVAfmDbDfRAIuILmDSmqDSSAZLSWQYXWuWEhmfwSAtJIAJFa8IAxLzLSAIpTZAS7OSxGvTiUBIJY0q5hx71AtsCJGg253VGMSzA7V5EINBMdepr8EGBunIEtIAZAdXq5fSIqgFRczToKnugxJk21Z3aD0QZzLCKkblkCk6RPyx7FEOKHQ3b%2B2iig4IeNiQ%2FuQAXwAC5aRuqNlVwAH8UCm64AkeI51hAXnln691AopBDav4P0VGJkSHGj%2BKDbwK1G%2FQoA5cVj8zeVEB%2F4rgU435QEuQGNWJP2KAC1tzjIfToqM5I0IMcIDHNaNOygVw5DgB4bgKhQWdgS0t14VG%2Fi3u4KAXAgEhwAPzatqiG4aA0nhFAFw8ULHbugDAOAcXnKiIVySRQEsZk8eSARtUOX7orNIeRsiBJ1lm1ooFyAcFqO4norAtXkh0QLiAwcDaECEiCwaiBHYOJf2KCReXkifF1oTuu7mjn7kEbmoeHOiDGmj1ZQc5gUymiolcDxx8VRz3GrS%2FvVHPdcJBoKOiI33OC8AVZVHLfQidh7FUc190ttVIIsc3iqtRwWXAblyqOqy4EuzyoR023NJpRRXTZf7exRXTZfD8QoR0W3CnkoL23hxqQVFX9O8Oz6KCwuAkOYgIKi7tVm3Qh83Y7mm4QWtvJ0%2BU9FFh7S48sfwVpD23M4pvH4qZFQRWbhRQh82Lu%2FFPehFMrfGvhqgEO2L7IQwIuIban3qcxDZOWbWPNBnypG5aHnQq1RyHIPRkGF4YRBbYQ33IRTN2%2BbE7cn3oMbiCYnp4IMbqQzAECD2CAOGYFjQgVhCNvptoZbzQhnbICuhQjZkwzBoIFFIDkLhUh9NYlCGe2SZ69VCM4qA1DCEA32u5Yc%2FBUF6PaW2jzUGcngnVCMSAxekv0qhAdgDi21ux%2B9UjM0hz5JSC4lxyTFVCMTQgF3nw8kIBapJGpPbRUjAg5Pb%2BbwLhCDkxerVpDfaoBmQIMES9CqQxvAEhgDTdlAMiSajUt08FSDmLWf%2BI7oQwvEj5mhoUWBnboCxk8uiRsxpUy34KkY3gk1BFWgygY3D5hbqKHwooQcg58kWFytnXXLqiQcrWd2EsSWQgA23M%2B8FCCLnE2kBtd0AoQwkRa4jyVIxuYF3IME7uoNkJe2akDhUYgAUadD5hSkZ7Rabdaxo6pANwaHcboQ2Vtsy7UjT8VFjZWnsQ%2FtKJC5T0cA1r3lUjZ2AsHBPvQgZnUQZJY%2BEVQEepbL2u5g1cBQDIEYm01BYtDyqRswdC5p7VUIxvtBNrByI8%2BioN18Aij%2B9QhcwavEbchCFe0uzHQH4KkEXPuHJZ0BBJaOhPwUILw5ltojxRYR7SRD6Y92VQzhy%2FBFuqhABADFiHpSB96pBcQcd2ajKBXcUIc1HTVUgkgRBDwDoyEC675XB6jQR0Qa0%2FKSATB08qoQ7h5Mj2oosIDaxO4D6%2BeyqRs3JZ%2BtKJApIlixMyemyoBJYfxHUcaoQwumQRRggUEgTNadZQhiavR5UIRxaa8sA%2FdWkY3AhnIxiqEKbnZgQXkgfagDvNsA1CUAXGIfihUQMm%2BYTHzShALGWJerKkC68WuS8SgmSD80yJaAhE3rcQ4EjRUhMnOrAxVAuYEUArsgibw1HbTmsOhEyQGn2KqRO4gaOzbOhEiX%2BaQ%2BnkhELrwC5glm9yohddoCRzqqIm4T1REL7gHL1og5byJ6y3itEcpugzKqIv8AM%2Br1blB59umj1VR1WR41UV1WXMA7qDotu1BpJUV0WkRNNd0HVafx5UVUEkB4fyUF7bpA32eEVcFzWDpRlBa0yKHbuoKhhI1KAu7ih2QVtLSIfX2KCoZjU6EIGBpMaHZA9vys5qXlRThwxYRLBA4IJEi3UPtsgM2zUVPRA7ijDrxopAQdOIBKgIBEGhNK6K0Yy0ToVVGrmf8AJ%2B3zUBF1CXIuLqhgXJYMRTSDRQABgwDu86eSoLMxrzvsgYCej%2BagUwXJ0Ymk8qgACA4JMGdp%2BKBssSNH8t1IGyLQH60SDChoxp00DICTowMT8FAAKzq8RXog0QRqdPYIMAD8wcZPBG9UB2LPsDugLGLqHUIMGA%2BURuKboF%2FMzh3h990BtB5Y9imRiAWB%2Fhn7EAJalIfqN2QEkEggiH1QAmTUkUd%2B3FUBnQVPQd0A1BJDk09mQGdiTSfegABIo13s%2BnwQYOzsa0130QYORc%2FzA0OjICwJ%2BZwQ7z8UGL6FoqgBud%2BKDy5QAHIhjAeW16qh5tGzmBTsoMSwDjkBApu%2FMSTDEtp0KA%2FM5aAK8e9AA7sXHPU8boCCdQSaA6x9qAECT%2FCYbRAHB%2BUyYZ9e6o1vzOD8suBrKBi4LGXoGqygBctMgtaXZz2QEksCNNYQbJxFAWYhIMBNpLkjX3oAasZP8Q08UBIDyeQCHhBuMXtO0oBN0AgjxQF67SzFkG5kP9roAZbfSNW5EICSAa11hAAHj5WeNw2%2FRBtw4%2Fym2lAXucEMAIPXyQapIYhxIqgF2xq2zpgYEEyBIhtlRhcDT%2Fja9lIFFw0Alzl3furBmMPJq7IM%2FDvp0NEAeh%2FNsX1VG5brb0QDn%2BE19mQG4OQ12LFMDE0AJPM12dQKXuh2HmqFAdhOOr790AItrUdkGBJFrOdz%2BKmUrEvaXDlnIQKS3zCZEoAA5BedBsFQjiJFdD7kALuGoHcoJm6QAWGvLIEJihAGnsVRMuLiHrTdEKSBHiTsdHRUSQ9QQIA6IhSW6DbhBI3czugiSGMxLaKiF1zmAY3VErjo5eRoiZc9xZ20lUc9xBMU4VwOe403VRzXkS3cboiLz5qjgtOnRBey4kbEVGio67LmjusjotILEDpsouHRbdXnyQdNtwiXGiiuiw9Nn2UFQ3jBSqvaQwO2%2FvUFgQ0CnhCC1hatApVO4%2FhlyA9UQzj8paKOgsLnG3tsopw06lq6cK0PkzNMcqBhUlhNT8EofNi7vQcqBxdQOztKDBrqyTUKh8mYCmrqDAiS8vACBy9AIOte6lC7sxFR15VoYXUbzEAaorbkTDIAHBLOAJ9grQ4uAEuJ%2BbZ1AXB%2FirIND5oNjDlnma9EoaSJcRXYoMSAdBugEAxJqT%2BJ1SjOxJuLhoB4QAQ2lAwQb%2FJctEjcVZA70YAAaqAPbVhBY7hUZyHALvDtVAwYuA8u51hQFyG68j7kAECHcvG0IMbrp1aS2z%2FcnICwEUl6JQpBcc0EjqlBaTAcwD7cJQddJG%2B%2FuQCks%2BMjfmUoYuDVqv0QD%2FJ0L4ilPNBh%2Fkhnl4TOQANCPy0SjfKA1YDb8IM%2FzOxcCj86INQMSIgPwlDO7fLMA61QLV2i4Q4%2FBKDSAw03hKAXajzA47pRt7WYuXPHggDEAziSfHRKGcQ1QW8UGMsdz8PNKAxktk%2Bh4Sgh%2BDHy9W0QaZLtsN9n6oBNSWLgM7INRwT35CDEEyBNCD1dKDjaH0IDk8eSUaQGMAsPHolG5obqv7kAAIJEEHsyUFgzM5YkxD0SgA2nr11QE0BEdeUoANpbj8ohkDEnRp1qEAi126jZxCUAk1cWtRBoBJJ1lnQDIO%2FjogUkzkKGCH3qyoLgmXgwgx3dpd%2FwSgGCHa4Ma8V0QaoYEF5380GFTrFdX3SgO7hoeVQYmpqBPKgPUhxTolCG7cAvQ%2FBADc5k0LR0QKXrAA3PgVaM4qLhrPvjspRnYAgHE1ZKjABiASC7P1UoV8S4D6eSoBLFycQgWATRz%2BX8FQLrgYIcB%2FmqgmSDFtAYQKZq23hslCEm41HcUVQDWrk1KVUriKUqyUSykh2brPREK7SPw%2BKCd12oAoWCCdxZ9ZGSYVC64Se7qoidz%2BCtELriTBB3LojnuuE7Es6ohfdBIh5VHMSQ%2Flsqy57yJ02TAg%2Fz%2FBUcNh4QXtNPJB02EwenZQdNhgPHfVQdNpAPWqir2HbSohTKui0sXNG9iguC7RFXUFRcXd2H8I5SKtaW7SVBedNJoiqAg67OVBTKHZuapgEXWkS816%2BwViK5As8ifJRTvDl28wgclg%2FLhBnYhxNGFOB5qIrlvw5UU766GppCAi6m4gbFUEkia%2FBATBNG9nUDC4EVJ%2FwhICCSS4BGyDDQu4BnryoCKlux3GvvVWi5c%2BKDCWJAHPdAPmYsfmFDCB8mlhSu%2ByA5W3DsenZAWaP4Wk%2FYgwc1YNBG%2B6BWMAt9jbKghv4gwFCW8FBqtQcEVVBcy1JDqDO4JB%2F5MlBqsYPtzsgwNaOwBCAuYcToDKAGgdy7x2QHMuAd%2Fl37skGdyYYj%2BJ%2Fi7oC4drY0cqAgyRLM59ggwLi4sw0PxQHQPazSihZwYDUkdkyg6HEhz4P96BTaCHfkNHSSgJcCAKdHZAdQ5rQIAzuDShLoMZfia88ICSWYaQR%2BKAM2nzXVQGCKV1E0%2FBBmioI25q6AAzwK8NPxQYxWBd0qKIGJLginigSLWMV6tvKc40kMLWDGWmiAhi5BYmh0ICAA7watIdAzQQwmpaEAtta0jLWXHwTIxFzk1eDOiAF9DsO9JQM5DA6%2B3KBXBcbQG37INkCCR0fvyyQGCGagQKSwdvm1uH2qwLmCzEguGHwSA5FtXrMT7BIAROQLj7WQaQXGsHsgYkiGfkxRApALyXaG6oC8Eg5TH2IMBUnQu40QEiWMx0QBjLAQ7asgDSS7hm31QA3h3Fr7nnZAQTo8jXRAlHEAmSd0BcSNRtOiBXBMl2luqDSzg8nuiDIdug8tlAlpA%2FyQGg%2Feg1zmdqGkqgVZ33YoFl3baWVAy%2BUkPq%2BmpQKbiJu1o0lQLk413QTdiS%2BR2%2FFUK8TdAiOyoU3EDikwgmSaab0QJk%2BsVJVgQzSYUoW%2B6vRMYErjEliaqjnuuYVZoICIhdcDr3VxgJdczyx1CIhcW44VHNddroKhUQuIejIiF9w67lVHLfSQqOdy%2BXkqOGy7nWQqOq09hqsi9lzBnGqDptLN1ooOq0vrPCiremSdZ%2B1FXtuIDbwoL23O%2FwAUFxd96iqAmRQsJQWtu7cxpEqCwMAE10KCttzan%2Fg%2FcoKQaAkEymFF8WDx2V50OLgXbeg51hRTi4QKDUGeUFDcd2EUTAYOOpllARoCWek7IHzDZaa86ICLtTpTiuiB7TMgvvv2UBFDMmoM%2FYqCQQPzNsXZAcj0ippKkBYUYc9HQFjcB8VAQWmm6AOxfQ7096oIL6sGYN70WtqCzNLinO6AmASKmsFAQTONwJZ2QYXXUPEcdkBJNJf%2FABBARdTnWnigwFdrvE%2BKAmWmN6U8EGmpoJG6BcmIJDMKhWBgxf5Z6VdQZ2Zq%2B27IA%2FYwCO2iAQZIm6gd%2FDRA0s38JEEa1QaXjWjzSqDF2JqW3p3QF2uEs%2FmUAFwdn%2FKYFa0QYPpe4aorXogzu5obY6IA5tJYO%2BnXlA2RYvtDlICDA%2FijyUCuQ7AuAdKkqggtQNaPyoNnQs7aUZIGyYCIAbhlIBkXJYEavVWBYgEBiaCA6A6HU06dEGygQCGkT7kgGQkU3J25lICDq8W%2Fm29mQB63A0BHtCBgbntfaB9qDZGtWgJAlxOhZzr4pgFyHq%2F%2BIINqLiAxqemqDAF9CdfZ0G%2FLOjyNuUGJo7R%2BZAGMOAYo6DFhOm8ICQMpMtDoMSADO7nUcoDo0hhEIAAYP5uD7FAAImnMOgJLMWa7RzXugV%2FzEtAmKsgJuDFwRHaECEmhck1ID86qjEs9zna4iNIUGJoLQ5GsIMIfkMbvxQLUEAIggsdRsis4rVqs9QogktJLCSgmbh3Z3VgGR4Dltm2Vg0ZEEmjv8HQK7HrDjeqAEuPloWIMoASCZ1qgQ3RBx3%2BMoFJFS5Ya9UCk3Pta1FQpuGgcvTT2lAhu%2FwAp7h7UQTuI%2BwcDRETeTuSCD1p5qgEtDNlp7BQIbpIJ%2B5BI3ANMqiV1zxq0oI3XSdeFRElpEaP9qIjddJJPboqOe66QHfb4KiFxhn7fFBK65hp9iI5b7h74VRzX3HuahURnbyVR59t7NzRI06bLxGnVSDotuGrd1EdFl%2FCK6bLmbXdTJF7b9d4P2KRV7b5SC9t4D9a7qRV7bmf5oOsBBYXuJBo4UFLb7RDzskVWy5g21PemcEXtuBEud3lQhhfQ09tkgqLgwdjo%2B6hDO8g9OqpDAl3Ad6O6kByAh32mXQPbcHcflPsIQVzBIccuYogJuxpTVIGBAl2LUUhD5M8EhvJRWzq0nY6KwG28AvbtI5RD%2FU1aD2lIrWsRBLmpLVZEhnDSHANSgIuLgMQ3RSKwvG1NjMcJBhcDqzy9EiGBd%2Fmnz3UWBk9B2b8KqjR8xqx%2FLQezIGyBkSwE6R1QYlyXjf3IoltWcCT9yEbKHALkJBjc51tpISDC%2BcZpBPs6DG9nOm41kKkNmHYCQIHtRQgi4ESOI6INkALmD9tX2SDChfn5RwhGMEyeAHhCNGoJLF0IPygDxHCEY6jQUHnCED5XAY8ARyhGdq0pT3oC4cDWnavdIBENUCDqhBBDs0NRCCC80fVCFFwf8xLQwSAAmflYGAqGoNyNT03UIAud4%2BV2hIjAuNbnAn7kVhc4ly%2BujeaQEmBE%2BJQbJtDokGcOxPLdSgxkHV6gBCMCzljMkalBnli5b%2BJAHckaVY8oRhNrh2IjVvN0IJajG48oRjcGpFSyQCQXD6hzX2dCNlABM78gKwHUSWM%2B7ooRtuB0QjC4Fn1MDzlIBkAxduIQgZSQAS8EaOyEb6jmJFEgGRIuYMX1%2FFAM%2BX2SDPoWIEY7JAuTOSzEQPuSBwbQBqbY5QKb3FC%2FtuyRGFxiCJcqAZS0gbaN3QbIUDlmYinkkAzAmrnqKwkCm8G4NUvLOrEYXGhHAqEgV2H5Q%2BsdlRs7JGrUSDG%2BZaRpuopcixhhWFUhcwSGpo0aIFyG7Ws%2FZFhcsWkxUUSIV6gTEj2KpGybncosTNwALU2O6JCZaswJfQIFJJd4YwgU3B3Ylj%2BbZUTN476%2BNVAl1xAqQ8kmVYJ33EAtJ1O%2FZCI3XhpqzEhWCZuES5r1QSJFrsHOvt3TnRG6%2BWfpburEQuvmndIIX3kRxX7lRC64STroiOe%2B8SGfVlcYEL75bhiqOa64OTrwqiP1A9fbqkV5wu1Wh0WXGNlB02l9ZUyL2Hsg6Bc7CnKiui256%2B%2FyQXsNQzgyoLW3Gru8htUVe26XJnQdFBYXkUkioQWFzgOK6cKB3AYaiQiq231aSD7kirW3vr82kKCoNNCDRQMCTpTQQCgplMQBLe9QPkYL1ZuVUM566GFCjO7inKKIuMB22ZUOC1QwoFBRx0c6oMbjyWYR5pAwMvMmVBstTt14QPkBx5dUBBY8En26IGyMw1PM7IDlE1EPKBoPJoUAAMfws1D5IGm2hjWEABLiGAMIGyrDEDnVSDFmcSaBAaMIHDIM%2FLOw6%2B9FZv4dH2SjVuLiHmfglGcB3cQSwPc%2B9UM4dhEO52CisxIEkaoBpcwoxAVRtKEDQdeiDEkEgXSzse6A51AMceKQEEkBrpbiiKUGIaDXkohjcZDs0ugxIFs1qgZ3JDHvIRQu0IjQlpCIOQY86fFRS%2FLD6u6qGLMxID69UAcMJYaFBnA5iEGyx2IP5qoA4BM0JnZBgbdbgWfI09qoGe0AaAaj4qKDgUkEgv3VGcCskl%2FGiIzWkB9NduiUAF4JqGeJZAcrY2erNKDXEAcGAPuQLk%2Bjy2qDEksYtGp19nQbNg5LAFgPhqgxNzSXLV2%2B5FaRJOTkDbtCIFxltRr3QYD%2FABGnt5oGYQ%2FYaBRStRtD5aKgXADgGo0RBcXM55kT0UG9iDRKA%2BrIVuAe5qgBZnYfNqIoURhAcl3rKDOBqYNaIA4IOqoXK4sKxLO%2FgkDBiHBijoFJEvABFeUCvbTQRX23QA3nbGQ2tKpACZLnhh5IFyYEi2jtPigAJaHHXpogVw%2Bpd3OyBctXFpqOSrBriQxqaOUCPt8uqonkxAFRTXhAMiYq1TshQdmDtx%2BKIE0gkMAgmbmhpanARSEh9zLoEytNCXJdBI3FoiVRM36gnU%2FckREkihd6KhDeN%2B6QRJaIGwVRC66SMp2QQJFXHLKiRJBYn2CI577ndq0KDnvuMjfdVELrt3O4Co57ywaGdMCOerndUedbfpDaKo6QW%2BKiumy6k%2BwUyrosugAaKIsLqDyQdFt3kiui24001UF7bn9veoKi%2FuUVYXmN9HhQXF%2Fn%2BKiqW3OwBYRDaIigJqO3wCVVLbiGO9RoEyKW%2BpTmh67KKrnE0QPbcdz7eKgpbeXYjrrKB8jBlm9tVKGBES54VqQzxs87IMLjActTaiBxcep3G0qKbIlmLv7QrQRe8QHkeKgYEMSHtag2KAuXAFDI%2B5A%2BWm0FQEXl4MfxeCA5EAwQalvggNpoA9tZ0QEXEfxOedUBN8vi5Ghqg2Rt8YbmVQRXYuXea0UoYEl2DABtkGybtQ7%2BwQYEgkiQTIQHJhMaugNpB37saqA5CR4kR7URRc1cFzHKAOwiSIAp2lBiXdixO2roDqGZz0olAJd5d%2BNkpRBl7paQSlGJYkm13En4USgk7n7Iq5ZKBlIhiX%2B5CjAIfR2SgFhqZYMFaoBiXEm4yaQlQwuDOKeXdRWJbbLQdVRgdtaH3lkBd8gILUKgWj0PEDxZWo1WActr4QiszByPmYT7OlRiXL6N%2BaiDGK6b76JQXOhrpy%2FVFZ5gvEAIAZEN0OgHdEYwbaTAAQGOpGilVnDBi%2FT7kQCRMzq9aJRiSHOr6fGqUAtUtwXborQS%2BkkGaa8lSjOzDEjbfxSjF%2B%2B9H%2B1KACTr2bfdKCSWqHA1%2BxKMXJZnfV0oGVS4cH8UQpOjGPt4VGyaWZhHRAci8kP7UUUoNavpCqMbiHYSfgg0WuwYsKJQNPzZe1O6ULkDo0wxZUbMuxECH1hQYksJd9UC5NAZ2lj4ooZXP10KIU3mZir8DZUKbwKv4mEGJeAQAa90ANwtkzPUygU3EMwc0ShM7gaSduqoBuJI21MhQJcZp8CqgSHlhoNNUCuzGmzsGSkKb5%2F5yKQ3kPtJLoEJJ1qgQ3GN%2FNUTN%2FzAUQSN5Ys71KombxqXendEqd1zzoK8IIm6mpGqqIG4sS5BnoqJXXmdjvCGco3XO50CIhdfuKKjnuvZ2M0VETdq8oiF91Zog577mHwVVzZl1UcNt1PeqjosuLB4UF7Lmg90V02391FdFt9C7cqRF7bmZvFBW2%2FQIrosv1Md1M4Fxc1TGg1UFRcxPvRVBds1Z7IL23uPe8LIqLtjOpf3oKi4GlRuHRTg3NvuFFUF5YacOkFBc1SeqCgvejka%2B9A4vp3b7FAwvIoxGyQOLoLD7UDgxo9SyiQRdkN5f2dAXJBqR%2FCVQz%2FKwHUcKKYXiKDp7kByblwYQNk9DwSgIuc3B3F3tsgMDoKDZAzzR32UBdxV9bXQEXCSZeTtRARfWg%2B5AXAB5YR8aoGtuNRqabdXRRFxq%2FG6Agu3iCEGBaWLbD2CIOVtXqYZBoJO3mgwIIYNHKAs5Id%2FbhAXcmh2CkVg9styeyIO8TQ3IpQflNz1d2%2B9WIZ2ABruSpFZw4J0cMkABA4AaJ17IGcAuavCAAjzejH4IDkJYuAHZ596DOJJYtJQb5Q4dn35QYEVgywKg0EE%2FwAOrVVRiC5gEAflFUUwbjnrRQLqC7AVJ8VQXnWKjfzUGx27iqABxW2ZoqM7nY0OiBXDEO1KndEMLgzu7OT3lIrMMefeUANwtJklqpEGHqG8T7kUruxduAYRBcaS1B8EAyNdNQdkgwMzazlwNeqRWfWjVOzBkAF0Yt3ZEAE3Nx2qKqjAM0voW2HRASBXYx2RQcFi%2FIO4RABAMPu1EC5FzdV9NPjogLk1YXFn9yAEuJrps6AZB3fTYvCBcgNNoHCDZXBoeafggGQAIOjBAj2mGZjvvoqDkaAMalSDEl5nYKhCedK8HlACREQUCm9%2BjvHHVAuQLhmCBXNNQZKAVoRNURiRALMNCgU3NrUSPwQhDeJBDaTCRSXXuSJcKwIbh8xiNECm%2BR8zcIJm8ikA1%2FFBI3EhquqEycgPoURM3ASOjcqid13LkoiRuDtTjzVETeewQRNz18RyqiRvh9BoEETerBz33ly32JgQuO7cqolfcR96DmvuaWVwrmuuBJoqhMkSvMtvb4KjpsuoPFQdAucNqlF7b2ijKK6bb%2B%2FRRV7b36iqiLW3M0pRYXe3KKtbfQbKC1vqD7W%2B9BYXCJp%2BYKKoLqcGQyUVtvpyJ%2BKC4udgeqzVUF7kzT8UDi53cNPigplL%2BW6VTj1HPtRQP9QPa%2FYGsoHF8hy7nr5oKG%2BAz8tKA5kNUnb2CCgunpyoHyoNpUoIu0h%2BNuVaQzvFOqDWx93xRDZF9izsotEXs1PdPRUEXOHkTQ6qUHKAXBG9FaDkLWeY8%2BEocXB93NW1ClGegd%2BffCBsyBPn%2BCDZVggoMbg9oiGhA2bsMnNQ6gxLg5DHXslDO2s%2B2qVRDMdCaslAcSx0EaylQ4uD1BmT1SqF1xE7OwNUQzkS3sUqg4HQCLhwlQ2Q3c6Hr0RQfafwSoI4oN57JVAXWgEwwqyqDDkwWrp59FKrC05Eg7%2BKVGbEAVHilGkfMxJb5bde6Kzi1311EVTnBeXcAUBPHVBiagFruCgBdgHcmnxSoMtB4aiVRd9Sgzk1IHd0CgmvDjSOUQSSS9WEjUJRgfeKlFBmks9UqMYl6UFEqi8UYtrM9EAiBtLMlQdnPwSgEiguYvR39mSjEiBDUKVQBiCeW9ilRnBDCXqyUAk%2F4X4PXdKDlMxu1JSqDl30FD1SgZyHdAHDOTRy%2FKVAJHyiIiUqgCKNXRKjOTbSu%2BqVQN7C0P3fREDIXFw5aiKGY6MqgF3G1uh12QBzuzz8KFADcKAdiFaBkG0nXRKBddo7UbVSgC5gBL7JQuREnSs7pQt1JltD9quMjO0mI9pUKXggdVakbKu%2B6VYTKmwepUoU%2BprR6EKiZLyI4JQKbmBPEP5q0Ib2FTw6UTNwJMj7igmb3LAPOvkqFuJkulCXXgMw7IhDfI%2FDyQSN7ZHwKojdewarbKold6g3%2BxBM3Rv5oI3XM4Bd5CtRzm7o6tEjfI02QRuvd211RELrhQFtm4Vohdd3VVz33DfslRC64B5nZWolnz7Og84EKovZeISK6bTTyWRe24EQaoq1twfdQXtvcxpCRV7b94OqguLq0PKIsLnIUVS2%2FkNuguLxBBHI0UgsLoo%2FUoKC6hZ9lFUtvoxdggsLxEztXRRVAdjOh0QUF7NEbBBQHwCgLyGr7e9FUFwA21I4QOLgDz8KoKfUoQHOpCkDA03ZkDggiYQEXA8bHokD5NUu9Ad1BQXblzoygwugVejmoVDOwklgKoC4M0Iq%2BiILh58N0WtwS5ZwgzwBQGSUDEvUxsfNA4voAWhwFFEXFqto9aKozvd49UURcDLtxuiMGcMXbQcIGe5o13UUQTVi7R7QgxuFBroffCQaCK5ceSIPMA7OimdyZnVtYUAfUBVBdjOsvqFFFxX8zxvEoNlqWGz6eSAuJ2KBXHzGm569VQQ4YO5IjhAXEfM0nVQZ3cC4M71VDEmQ46eagWKtFSXVBBpM08FBpoS77oNJYv0JLIBOTO7aKg8SNj5KDaSWgh%2FegGRaC%2B%2BkDdVBd2IuIGiijlO7eKAGWYuAOEAg6iBLboNkKguIbSUAd7iduXQHK0BmYeXdAou3G%2Fi6DZ1faQ2qqNbdRqCQTUplRFwLb8e2qgQXtrTWOisRi0AGpnfZFbIS9NUgAIuBaJnWU5gAajrHgkAe6rxLN96oBIgRaZd0QMpI2FWQMbtPBlFI7V6Ame6qFJBapb4dEAJDyHNtEGclhTcBAjhoZzt7BAQe4iUKWAQPGK6IgEgww6fdCAEh%2Bu3j5osBwARI20QIbpc3TodECm4EEs43okC5B23oVQl14diWqgBuDIIm%2FaN9a9FYFJBcmW3QI9D4klEKbwJJZ0hUzeC3NFYIm7XfTp4IiZuIG5KonddLAsYJRU7i4L%2B3iiJXXAQINRvsqiJv0PbZII3XipLbBUSNwL7IIG4gEP3V50SuLcsioXXb9FRz3XB2HsERG64NWlVRz3XUYzurhEckHm23k60WmV7biWe7RRV7bzvRIrot9Q7tupB0W3neAoK23kaoL2%2BoYmtCKKRpa31DXLSgUFhfuURcXdSVFUzP3oK2%2BoQWeKBBYeoZnuFBUXnUxogoLzvEsotVFxiS5qUFB6hIIBnR9eykU31DLFyzwiK53dZkoqn1CaHhSB86dUDC7Is8EIGzJivaPNFpheXckSfdwge31C7gxSNtFBQepc7kuKMUDC44mWIQE3khn%2BKBxdcB%2BZpUB%2BoaAjmHSBhcNTOoQHK5vzA7goQwIIlh23SkM9WuqdN0B%2BZ%2BEAycNyzQgwvLTDiZ0QHIvV0BzuZnbogP1KtAQHMt%2BYF6nRA2ZAkyJMIALmdoZygY3NAJfQoMLi5uccoMC1CH1HxQEXFqlpLkoCLjNr9R70ByuLtBNSFFDK4O5cH4oNlcAzDV0QRfcwDzsitk5mKP0QNlDEtFVAMiJLknTRVC5kEBteNEgbOTEakwkUAWMFnDx7kByrIfaI2Ugz1kF36qg5E9FBsneQW8fBAuRLE1NFRjcz13NKINlrVvggIvJL8VSAC81Hc6cpAc7gWhmhIFF2LkCTqiBmXY66MPPxVgIvuIqQdWHXqpBhdcwZwHdigDmJI4Z9FRnIjIOKOg2RqJmGZRQyJdpDCPgqhcySSaVH3INncHLkPNH7IBmRJvHKKGRdzDs6IXMw4g1DaaIML7gILgGjIA9zBzWoLeaDAlm2qyDZEjadPwQAlxWkM6DfUgYl0C5l5IcCRCBc2Z7hugXO4vID18EAN5g5bwkC5EPKBDeZaHOoVCi81yjRkyE%2BpcSS8GhQTNzlgXdUA3k1JbnVApJH8TDZKhD6jNvq26QqZ9S5g5bZmdWBTfcAPbwQTPqefmiJn1CJy3Vgmb7mm7vRBM%2BoXI9yFSN50LblVE7rz03%2B9BG71bvaisErry4kxqgldfqNIQRuv8VURu9QjWEVE33KohdfV0EbrzR4VRz3XnfyVELryXD9FRHIv8dUR59ty1lF7bt67KKvbf24UFrbj1RV7b%2BWhQdFt%2FlopBa2%2FYu%2BikVa26jd0Frb534UVYXyBXWqQWF%2B5fhQVyhwQOUFLb4r30QUtvYzFOiCtt53DPBUFh6lJ0dlIHFw1ZtUWqAkSKIGF%2Bh9nRVcwdZ0dQOL6seUD5Unuge31JclgpBQE8e5QMLgXGo0VBfo2oRTZFo9vFA4vJ1aVIGz53Y8oCLyw23QO7iZ2ZARcTUhzHdIDk8abaJA4uY9VATeQ8MBsimF%2BLCUQcrqjXyQHIGof4IMLgHdyftQMQDMB4dBgHcvHgg2g0ZhugaXh6qKXLSrCaEqoJIo4ZobzQFzQEG7dAHd%2Flrt7bINlq2roHzcP3qorZmRoBThUFy4nqiNMHx0KAi94AgeSg2RdneXNfbRVQNx1IaqIOWniNUAG7DhKGF00oaqKBurruOiqMSWgCPYIC%2B4oXQK5G25QF6FjB1QZ%2FyiAUAButHxHvQF%2B5NAUUDexFN26og5NDdkUCTDOQ%2FsUQM3JlyPJFDIjffr4IgOWY0CDZGDQiHqgDnQQ2nkgzkyPfCAOWaST2QEw5csdlAHDw44pwqBUMIf7GZAfyuW2p1RQyOzNQIgZVj7ZQDJgC3RpQA3mNW9qKKXIlnAYvJlVAN0cawgQ3R8xfqqAbrnBFPZ0gV2IamroFN7u1N0AzrudTr7kCZVjsKIFyNRuwPVUI5lvlq7ogG7l6QKoUmcc1lIFuvAGgGzpAhuMsfFUIboApWiBLrmER04QSuv0VQmTjcIJm%2BpBdEpDfNegQSuvAeqoldcX25%2BCCV16ondcxDmUEieyCN18wa7KwRuv%2B1BI3M502VRG68pEc917l1oQvv8lRG66r%2BKIjdcyCOU1lVHCC9DKtVUXtXdEdFt%2F4KKvbe%2FZQVtu1B7IL23wfMKKtbdMIL237dlkWF6iqW3KiwvLg8qKuLxuygpbe48KIKZCATXRA9t9PBBUXmJEQSoKi%2Bal2hBQXnbzUFcx32RTC6YrUoHyL8UZ0Di%2BstOzMop8g8xow2ogYepNWejhEUFzM8j4qKf6mrkhqpA%2F1BUwaBARdGoNIQNlV9aNFNEoLkayBKKYXXUqDVAx9QBuKbypAc31L7jZA4v1p1QNnt1aiA5vUO3vQEXAw4fUH2CA5xXuNFA2YFYBpogIvodUBzYE7Ul0BF7gEeNaoGy3Pf8FFbNydkQR6gkx4hUAlpM%2BdEByDwEBBFXbsoMDk4neY9mVGe0EOWfdBnDSYKAns4KgM0oSae9BnLs51CowOx4IUGfTTRAAS0nnsqGc7vqe6ig5aoLOgORk02BhEDKPtH2qjZHQnpqoM8tl2HiyAFyK1q6o2RpRy9ZGiAvMzIUGLy0ooOCdy6IxL7dEArQgDaqoJNoc8zCgBIlj2Z0o2Qq8VlVWyFsmC6iBbcIoH0TIGZr4KjH1GMmqgUXVkH8KxuqpTeHgjlyiNk3V3dAM7mu9zV3QKbyCJbg68oBnq4A38lQDdzAQDKhpEqBchGWgqFQn1DUxLT5IBdcBqw4p0QLkYYdRxoqA9xl24U5ApuYkmay%2ByqUuZrR6bRygX6ndygX6jO6BDcakoEy2LkVVCZPwzONECG%2Bh1ogS664irKoQ3aHs23ZAh9SfeURM%2BoW5VEjeAKwPwQTuvLxu5LoJG%2Bu5VCG6ZPmgmb%2FNBG6%2FbsNFRG71D2nugkb0ETc3DhVEbrw%2FIVRG655Mkaq8wjf6nkmBC66JVRG69kgjdeqJZoOK25EWFwKiqW3FUWtuCgvbfypBa24aFBW29vcoq9t4%2B5Ba29meikFrbtB3UVUXCJdCqi6NkVW2%2BkzsoKi%2FSr6aIKi%2Bmu6gcXMILIKC7qx2QUF7wCgoLwQON1BQeoNIoSkDi9wzOygpkH6%2FBFNbc%2BrEmUDZkPXcsgceoQz9winF4NWflQPlSfsCAi6rVOn3FA4vAGg26KQPlViYCBxcDp%2BKgOQHQGSgL1Y61VqmN%2FNd1CiLoZ%2Bu6BhdXyCA5liGqgYX8t0QNnIrwSigLhL1FSEQ2TwY2lAchR2N0FARe7Tq6gOYoQJ0KQFyQzQZqimF79tUgGe1ddUgJ9Rm2SDZgzrPTukQRedex9nRRyAL6DpyoCbyRNCzQgw9RueIqg2T6SDRVBzIcuGMeCigLwXL9w9FUH6jksYCK2etEGy3rp%2BCIJvFY4UUBfAdnpsqjZM%2BrBBjcWYnuoNmKQPhyqoG9oo77%2BKI31KAGtaIoi4%2FNJIOigGWmpmNUC5B4pUuqC9p43DqBc5dwW3oqNmd%2BERjfqO5bZIoZcEvUMiBlEkRL1SAPJLhyWPVADcHkyOFQLi5fbRMDZAPNHl0UDfQ7KBfqfNEDUKoB9SheqKXMVB096IBviuvdAjxNWY91RjcwEPSQgBu4rCgXNme5viqhTc9Ke%2FhAv1GhzyUgQ36CvG%2FdIFN2gL9S6oXIAoEN8NSY6KwIbxpXhAh9R%2Flp18EiUhut1OqFKb2l35QJd6lZkHoiJ3XhhLvAVxgTu9RtezoJm4CQWVCZa71LIJm4O7uQyCd14Vgkb6zzKCV1%2FbR3QSuuqdd1RI3BETu9QTqRokELr6tqqiN16ojdc7z0QRN2%2FZVEbr%2B7qwRuvEpBC65UI44TlRxW3BUWtIUFhcGQOC3IQXtuG6iq23aoLW3DrwoKi7wRVrb1Be24Rwoqovb3uiKi%2BAHUgrbdFUVQXs2hSKoLm8FBUXu24QVF1PIKCgu0egr0QO57IGF7auge2%2Fdmr7FMiovBgyNlA%2BfMGiQOPUkiVA4u5cU8EU2UuNoKFM51A9tEU%2BRjU7oGF40LMaBA2TauRpRA4uEB2ah%2BxQML5JeNvBAwvL8DX3qQMbhV5QML9a6pA2RZQEXwK88oGyFXpVAH1rNSfbZVaJu3JZ%2FghTi7WpUAF24beXVgbJ9e6gObOO%2ByBsg5Lvx0RRFzBhBaWRAygh2eJ1CBhczDwfRFE3hoIfxIRGFwgxygOoYMwZvvQDJ206IGe06t4hQAkFhWZZUE3aExMKAu01I1KoAukS%2B8oM%2Br9dnQYEAkg8M6KwgEOW1RGdy4LAc1QYFnAMjlBif8rSbnAQYkbtLHlBgQ8E%2BxRQdq3ayiM4AaGaiDPEB%2BsoA8HQBy46IBkGoxoEBztrDS%2FLoAboDkBtkUMgKO5UQHakHY0VAyEmp0CDG7YsZQTN1KnzVByllAM9AaUNdFYUMjSv3IA7ULV%2FFEAlzv9miAG7VjGigXLVhq7TVUDPUd%2B6BTedQPgkC5GtTuUCm%2FlmgTurAmQLAnRApv8pCoU3tSen3IFN8gIEc11bVEKS1NWhApuAeWJ1QIfUL%2FABREzcKEto6sCG4Eb8oJm9jwdVRM3OOqBTcZ0mOiBMx3CCZvMh%2BjqwSuvl9kwJm%2BuvKQSNwI3V5ghu%2B8IlSuuhiUEbvU%2B5WIjdfXzVgjde78IJG51SpXXSiI3XOghdfKsEiSa0VRO66uqCb%2FADIrhB5VRUXcoLW3U96gtbcoqgO0qiguRFrbwoq1t%2FioKi4HWlUFbb29zKLVbb0FRd5ILW3w1VBYX0HmopxcPBBQXS%2BpRVLb5rRQUF%2BlSB7kFReDL0UD56%2BCBxc%2BwdKKZH70DC7V2ZQPbfTVUPmN%2FbsoHF4dhEdUD57kcEKBxfV44RTZCJogYEVoaIUz0cTygYX9jsopheANxv8AiqCLw4Y0dA%2BYkcQFICLrqk16oGF43GjIGyepf%2FCoDmw7oGyB6%2FagOes1%2B1Aw9QTqdQpBsgzEPqUDZPL9OqAgwHNUABNSRyEBcTQdKpVbJn1hUog0lwEAF2wLmoQpgXr2JUGzBnQUdATczac%2BaDZFifAfg6A5MBABKAZaTRggYXggMYbXZIBnz0BQEX0makfFBhe4YgEbIBmCZLvQMyA5g6sDogU3MQxjZvigIvihHCQbJoBZAuZFDrqXVGNzsCRyPxUGylvBBsncjug2XLNsgGXgKsgAuO7lkKGTawNSFSs4hqEqDZQJ7BELkIPmqAS4OjFo2UGPqNwEgxuLN7SgQ3kjY6KgZh9xoUCm5n2KBTfUvUCFQMoIfglApvEkHwQKSB4wgXPsQqBmTXp2UCv%2BCqENwcuS40QpTcJNSUCm%2BrVRC5aP0KBDfXrKoTMUJdBP6jvzUqhTdXVBPKo0p4oEN5DZHugU3sHeuqCZvYTTUoJm%2FwC5USN7jZBM3Pr1KoQ31colTuvG%2FRBG71HoqiRvVgldeBTsgjkS6BDcByiI3XOqI3XqiF13dVEyWQTuueiCN1xZ0VN9XVRxi5%2FtRFAUVUXKKsL4QVF50UFRc%2FCBxcR0RFbbqaoqwvKgrbfo7qCoukNCCgvLToirW3qB7b9EFhfDarKqW38oKC9BQXS7qKcXvXRBQXvrATmFMwJKCgv13UgcXt96BxdDu2qBhdVA4uNOyBh6nNKlQOL%2BWmUD5pA2T0L7uophe0v0QOLq6bnXZAwveD0ZAXAMSgYFyOJQo5EEBxPiiiLyDWEDm%2FV%2BFAfqQ79GVBzqBrHRQMLzDmXkhINmT2qBukD5HQvOygObfagOZpM0YoAb36BUPmLQS9BXooNk9D1KBsywkg6GqDfU2kjR0gOb3CWGiDZ6aNRAc3%2BIKDH1GqWmqQbLvKQNkDXwUgwIaZ1ZIAbiHYqjfU3LJCsbngdEGytI9wdBnkbBpQHINAPQqQDIt7grAMndmFDyyQA3tQuduEgbL3uoBm7iK6KwA%2BoxDpAPqNDhjqgGfNdUAzFCxBpVBvqEVKBcyXPYFAMjMvcgzjwpWEAyIFYAZAubaz1VgGQPxSBTe8wYd9kgBvakE6oFzI44QDLzqqFN2phlACeYVQMg9eGQoG8DXzQIfUoN%2ByBDe%2BuqBTcaaIhTewLTurAhv%2FBApvPdFTyM1mIVQhI1KBchPzQ9ECm%2FSiCZvcmqQIfUZ%2Fegmbup1VCG8s57oJm7sFRM3%2BKJUjfyqJm9666IJ3Xz0SCV1%2BmisRK65UTNyIldfygkbueFRE38pBI3EmqoS65kErrkEbr1RG650QjyqOIXeK0KW30UyLW3pkVF1FlVBcVRQXwoqttyCou8VEiguQUF1PIoVUXfeiq23%2BepUFRe4HKkFBfxKCguoyLVLb9VMqpb6m%2BmqQUF%2BtFBQXnpNEgoL9SzoHy5bZRTi5m8UFBefBA49SdkDi8791BQX8dEBFw02olDC59eyB8jLS2igIvZthqVQ4vMVhRT%2FU7bIgj1Nan7UgcX1UgbI6mlEU2Z9t0BF5nUGqBs2D6lAchImEDZQ%2FKgOQ%2BKFEXFhQgqqwJfuoDmfJUNm3bhQbOm4D9EDD1G6hAc9kBPqeSQbNj3bVIDmB3080gAvfbnlIGzPQJAXgCdqqDG4kHTsrBhfseyg2Uma6fag2f%2BUOUgOTbxQ%2FakAyuq8ahBvqamldEhWzbVgKQkBF0BkAzq0b8INnMk%2FY6sGNxLSRukAzhyTKQA3MY2o6A5PqVAuerUoOqsGF7aUglIBmQweldEAPqDdtHSAfUOzjVAM%2FDQAIBkdCzIFF1NBV1RsjrqoUhuapNvO6qVsmBINd3QrG4MdFAp9SO0qhTfQ6oFN5ADEg%2BKIU3B0UuYoNKqwL9TVIFN4EVHX70QmfLcfagU3wZgvRULdcZ5TAXKJPcIFzAoXRSm9n9qohTeJ96Kmb3REzfu%2FBqqEN0l%2FFAmb8oEN%2ByIkb37KiZ9SKsgnddXfdUIbuSgmbvLRVKmb6gIJXX%2BCRErr9lRI3%2BSombtaIiZuQSN3EoJm9VUjegkbifsVRM3BBK69ETzmqo5AVRUXIHF1PeoKi8JBYXKQOCiqW3IKC77lBYXqKoLtXRFBdygcXIK237aqZVS2%2F70FRf%2BKgpbed%2FFBQXjwRacXa%2BaBxf06KKoPVivVIKW3xVlBQX%2BXvSBxfvRQOLtaIHyoQYJRTfUP2pBT6jtNVAReOQ1UFBdzUSimF6IYXg9tEDi5qHpsoo5btyqgi5mksCgYXw71UDC9zuQgb6lfIoG%2BpzOiRRzcdNkgYXAto0hEMLiorC96RzQqob6laB9H1UUc36IDnQ%2FBAcw1Q2qAi8MHkoDluZ0QbIMz9kKwLOH6IUciRVrkBBYMCGQHI7wotE3yeKoBkYadEByIAMcngINlLQgwvu4CDZnXxQbMuAD1lAMrqvzCoORp5qAZND1QF4%2BCAZVDV2hBsgYq1AqBlcX7Qd0ShkdT1QrZ6dki1stT4olbJQoZCYd6iFQMxv1QA3hnE6oB9Rojp9qAZuw8AQg31GMmSgXMBtTqgQ%2BrWWCsRvqaeSQKb2LkDqigfUEbV6IFzk68IhMzwNmQDOunRULk%2FfRRSuxOiqAbxv3UC56eaqlN%2BtAiEN25HOqBTeKbopDezz5qoQ36pAmTPPUoFN3fmEEzeDGuqqFN%2FLtRSCRvfnzVCG%2FlBM38qwIb9URM3tqqlTN9UE7r5rTRMYErr9aKid1%2FKCZv7KwTN34oiZubVBM37VVVM3pBE3OqhCVBM3KokbpQSNyoTJUcwLohnQVFyiqC5BQXqCttyCguBUVQXMgoL0VQXIKC9QUF2yiQ4uVooLlKp7b2AbTRBS2%2FfwQUF1N1BQXs2qUUtvdn1UVQX8vuED5nZFOL%2BXUDi%2FzQUFw35KBxfO%2FCgcX7w%2BqBxfALdQgYXE6simFzIGzYbclA4vrKBhfQKBhdPWnZA9t8QwGuzKKbPempKIOTopspd%2ByVByAFXZFNkZNEqALw0E9O6obJi7zqophe0xwgJv69UQTfHvSqb6gG8BQH6hPXVOQN9QbRugOYavJQDKXLHl5QEXjQk6bogm8hgJhFHIPVygw9SjeOqBs4r0UC5VMDchUEepSS40dAc3l%2FbupQR6h4YoALywb2ZKDmdISjZlya7IMbyAPigGcTU0lBs67jlAM6sQSqNmYHLBAD6mhLmpPRBsz20Cg2ZVAN4DOZ0HCAZgkz1lAueSIOaKXN9eycgGaBR6gI381QPqN8UQBfFZRSm%2FUSCgXIQ26qAbieqlAJDMS6tGNwOp56KUKbxFQ2qoGZOrTKilN4KIX6mjsqpDeAPgiEN408VQpvL1pooFyJ1V5AhuBk12QA3gMiEN5nVAhvndAhv4VEz6joJm%2FtogQ3nRUIb90SkN6tRM3oJm8boJm9UTN78oEN3KCZu28UqENypEzfyipG5UTN6CZLoiZuRE7r1RI3coJm77gqJm5UI%2FKg5wVUUFygZA%2BUJFUF1FA4u%2FFUVtuUzgVFyiqAoGFxCCgvBUVQXcoKC4b9lBQXcpA4uoiHF33oHFyiqC%2Bkud0Di%2BsugoLqSoKC8tVA4vetUU4v581CnFze90U4u%2B8IG%2BpRIKD1KbCrpA2YCgoL2FUDC%2BRrypA2T0qEDC7lkU2XvhA2fKAi6vmgcXs%2FKimF6IOcmaIGF4qPBkDD1BR6QUimzUGzp5lVBFwp4Iovs8ICDz4INlsQdkDC4tuyg2bbxoiNnAnx4RRyNXZ6jlAcueiDZRWiA5w7g8FARe%2BqAZy2T7h0Bzn3ygw9TR2O3CA%2FUJbVIjZ7l3oisb%2FAMAg2ZFUBy5YIFzfUazsg31K68IDnz5oB9R4eQgxvO7IEzViDmVIrG47sgGfikAz7uYViBlR%2FJRWyZgGA0KAZWjWNFQDcTqeQg2dW7FAM66bpAuew6JAue%2FvQKb9awg2Y3ZAhuHHKqFPqAcdEgU36SWCQLn2QDPlkKQ3D7VShmygTPdVCm9izoFzr5ugnmOh3VCm%2FugQ3%2BVEEzd7tVUKbncOhSG9pQTN%2FwBqIQ3oJm8VVCG%2FaEEyeVQhu5QIbiiJkosIb9kgmbpq6omb0EyXZVCZAIJm9ESN0KiZPZUIboUEyXVwEJRMly47oOcOqHBKZDgnlQUdAwKKcE8oKAnYqClpKCgJmCyCgJ2KinHgUDgnZFPaTsgqCdAoHBOxUFASdEDgnYqIcEzBCoZzsop3u2PKCgN2xQODcNDygoDdsfgoHBuGjhA4uNGoopgTVigZzs6BgTEFFUBu2fZAwuu%2FwlA2R2QODdDA8qBgbtigYG4aE7oHBu0HVQEkxCKYE7VQF7pgoCCeaoC91GKBwb5a3uii9wqCdnQFzEFEF%2BPJA73agqKz3ag8IjEl5DnR1Q73MYUUAb5g90Q2V2x6IrPdsUQSSzEP7dUVnumJ7ojPxCAudB02RWc62lBnZ6xxwgznQRqOEQXLMxRWc7FQB3o46bKozn70UX2B6hQBzt%2BCqA5aiDORpVBgbtujIASa3AtsUVnuFAeEGyuMAFtUAJueiAE3bEnRAHucBkAe7YoA921yBXuYwiA5Gh8FRsrqsZUikN10QfB1QCTsW1qiFOT0lADlFUCvc8glAHOgKBSbho6IV7tBHCoBN2yBCbnoe6Bcrpg%2BCBCbnoVQhN2oIKBCbmoUCudvJEKSdkCG4iGJ7KhCTsiFJOxRU3uahbogQm%2FYqokSdpTAVzsqEJOxKIUk6COFBNzsqpCTMFBMk7FUTJOgKqEJOqKQlEISZgoJEnZVEyTsUCEnbuipknY9VUIXQITwiEJPLoFnlUf%2F2Q%3D%3D%22%3E%0A%20%20%20%20%3C%2Fdiv%3E%0A%20%20%20%20%3Cdiv%20class%3D%22position%2Drelative%20d%2Dblock%20my%2D0%20mx%2Dauto%20overflow%2Dhidden%22%20style%3D%22width%3A%20940px%3B%20height%3A%20370px%3B%20clear%3A%20both%22%3E%0A%20%20%20%20%20%20%3Cimg%20alt%3D%22404%20%26ldquo%3BThis%20is%20not%20the%20web%20page%20you%20are%20looking%20for%26rdquo%3B%22%20class%3D%22js%2Dplaxify%20position%2Dabsolute%22%20data%2Dxrange%3D%2220%22%20data%2Dyrange%3D%2210%22%20height%3D%22249%22%20width%3D%22271%22%20style%3D%22z%2Dindex%3A%2010%3B%20left%3A%2072px%3B%20top%3A%2072px%22%0A%20%20%20%20%20%20src%3D%22data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAQ8AAAD5CAMAAAAOTUC8AAAAA3NCSVQICAjb4U%2FgAAABDlBMVEX%2F%2F%2F%2FMzMzFxcUAAAC2traTk5MAAADW1tbMzMy7u7uvr69mZmZUVFROTk4AAADW1tbMzMyZmZlCQkLW1tZra2tmZmbW1tbFxcWvr6%2BFhYXe3t7W1ta2traZmZne3t7W1tbFxcWlpaXe3t62travr6%2Fm5ube3t7MzMzFxcW7u7vm5ube3t7MzMzv7%2B%2Fm5ube3t7W1tbv7%2B%2Fm5ube3t739%2Ffx9Pbv8vTv7%2B%2Fm5ub%2F%2F%2F%2F39%2Ffx9Pbv8vTv7%2B%2Fj6e3i6Ozf5ejV3%2BTU3uHR2%2BDH1NvG09nF0de6ydK6ydG3xs%2BsvcedtL6RqLWEna10lKVpipxmiZxbgJNafpRQdYxKc4tCa4M9aoM2YnsyYXowXXjFq0N%2FAAAAWnRSTlMAERERIiIiMzMzMzMzMzNEREREVVVVZmZmZnd3d3eIiIiImZmZqqqqqqq7u7vMzMzM3d3d7u7u7u7%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F9H2B9VAAAACXBIWXMAAAsSAAALEgHS3X78AAAAHHRFWHRTb2Z0d2FyZQBBZG9iZSBGaXJld29ya3MgQ1M0BrLToAAAIABJREFUeJztXY1jE7eSTx53vNwX3OGWuxcO7siDd7xcoYQXrpVCaQNrB%2Bw4MSHx7v7%2F%2F8hp9DkzGq29tgm0RS3Yu5Y0Mz%2FNjEbS7LK19dnKYLAXy2Dw%2Bfj4AsrOg6cvtdbKFv%2Fx8tmDnc%2FNV6%2FymLDvv64gws7eIe8olP%2FbW6G%2FxypjbCW%2B%2BpZD7WhqnWgf9O5l8Cy21hIkT3tbzmGCQ4cu%2B%2FPVuwwi99r9gY9Ht2716mTnWRxIjoUO9571G9tBrh26N18rlH1JgDu3bvbp48EhapwpR0BEP1iBL03%2B9ORrlRLNxf8H5fmtWzeW72H7wLOeGYpm3w%2B2e%2FCF2gfN7cfXSmXXEyQD%2B7CPWt4%2BwLohuA507%2BB2L75Md9ir9eNrtfJU4PuHWz3U8vahb4jVQ2d9%2BnL0r0t2%2B0zooBdfq5VtBoel%2Fl0Ptdw51JnYNABhv%2F3j0nzx9roPXyuWByorWn27vFpuH3TrAwnOrAL9%2BPd9%2BCLd9eBr1cLFsRz3UMunSerglMmNBEWMcb5fni%2BK8jWYyw4dBvfRY5LfFbQrVxJ2%2Fefl%2BMrc0TUEH3uSHMtP8ttoUoxWzidZ9MVNY%2Fqfe%2FCFsLym4MMTjXR7TPIhlkNTi9bcTMLkEyvq5wv7T%2FFdWgNcQ%2FBxOxLz2gn8Lz%2FJ31aCfZBARituLPbqTws7FsKYawg%2B9tMwRDF6eK3o9DS1Eio8vvS68mIBhX2hm2vwpkktEeHlJ%2Fkd2pIG13QhE%2B3Jf3zTTeIwjz16BUUrlrsqmr6OIvxpabVko5hkf%2FHk%2Fv1v79y%2F%2F9fvj5SSVEc%2F7xzquxKUy%2FO1cnlKCLq%2Flw8%2Btg8Rz0gFnpuwKZQ7T45yZQGI7nT1%2FAzVDA37BEUrlm3NldqUvy6tlruspXcOfwIcbt68YXq5cePGzW9%2BjMqHqz%2FqEG471kLr7uX5WrlkwRSQX36Sf0YbIjhID%2F%2F0o9MIOul836H8u7jX%2FnytXA7oxGgvvl96GLbFSAzgYB38wxHHHMqdMpkDaly21fJ8rVx2sjFQfSb5XWlR%2B10OB6zNUIwWLKdsMHjaiuUago89skTw6r6819rH2hGizzviMB4KA%2F68KN8erhYwvIbg46Wgxj0m%2BbD9jZwybFBIbD%2BgAvrwqkToJdHb3nytWm4nU4kTol5%2BkidaHYaxsEGxjSEPa51vCwN%2Bm651bMMefK1c9oli9A0%2B8tnWfBSbH8RqSdRHBQlJlHd9wQeO1eMKtMck%2F1jl5bvSKD5gJqCtbS3kK5ZrCD5YTOzKt8sPwwFu6dvfLzUfUELWEF7IIt5VPBIz37759OrxFOPgl%2BV9JvlDtC4Jq%2FpyzMT0w17KlZ9FxFLNawg%2BtnMGe23ISfvfP5TZ9tu0eC5SdyQ82H6%2Fw%2FwaNgp306SARmx5tRyEYQxe0vz%2FtzLbz%2FgSxjS4L9WW1hDXEXxk%2B%2Bqq3ynlXcqx%2FXhSxmOP66LB8aFU%2BwAPku7P14plBwkTS59Jfg839d%2BelIcxTjDI%2Fz4RpNzJuDLAXUPw8YDRBbXvtSG3F6RDrBenF2NeKPgIUZaExwOMmQ8Tr2WjUDPuVM9JnqxBfWddeKis6L8J1V%2ByRZHuy9dq5XYkGuNFmORvUd7TmOY9HPCFsQJ7K%2FK9w4wA%2FhLwuE3sxP0fFwGDbD4U%2BFqt4OAyrMHjJE%2FH0oIm4IH4DqVrHJXiu4aSfjxmCEPJ%2BELT%2FIbg2DrERho2JILXQngEDckdGoNMu1plPIRcCCEgO4xVUqgi8aUixY3AcTdKkfy9jsHlAJPUfuRlPOhIdnHH68JVhsdd9HPc5UZ8kSAGMNsQHvtUFEskTfJ4HLyCCHjkxtyJR4w1u%2BqnpW3yp4wvehiwGTy2GV%2B27%2Fuxbz4X2N2pDI%2F4K%2FYfZZq5O83rbwcY8BEw5YsGahvCYzeQRXijSX6QhdYL%2FIcvP3ZxdxDDWF3EYzfhq8M6APNFIx4t6u0qhe9fw7z2JDnDAVFJZ%2BkSHpragFnAd%2BKRaIWvvP6BUnwgFOGLMC3r7Qplhw2T%2FfJN6nqQD5OEBw%2Fn1IuuMPJAcKis1xCjJMg0SZFieMjjtEKhsbqj%2FgLFxIPkzTwoXfaSpFyEB5JD1Dq865wwRnyF4CChtRE8UF54HGC8wzAIaCT%2BcrrITsKfJfEo9nqIfsqCD8SXKvawUrnNaZLgYyvoh14Wj8jkMnh09Hobj4L23onwRdRWbwqPvwimT3YYwlZP5zgwTOHrj0vph0ry0F7%2FgrANcxHnizv6TeAh5Zrcxx3zdUJXPBamRStAFx5p0Esoo0cCgtvK%2BSJmupH59i4TFgjTHYZBik0Cd0vEH1qIvzM8FJlwSa93SWdZULSVxYkbml%2F2M5D5aeAAjbuv1jHfpjHvjAZyWZg0hC9fcr5Q683gsU1lcFffkH6xveiSXrLAeaE1a9Kp%2B4br5%2Fv9mudnD3BzvSE8dvkYqOw0cMC4EukeaLLMWLj6FpLISDy7i%2BnpIl90kbEBPKTnutjxxgANoirRTcdzqfKfytzR028nDzmeeEb6c984Xzmka%2BPBc02ySX5L3Oss4IGy9k1kLx%2BokD5TTKzofmHiCxmiwBfL21wbD5qJ4Qo%2F3qDzWoHuHhlJV%2B6X98cGvK7S5HjiAV8cSnxprtnrr%2BcOkrVEqHkqEo7X%2FZGygAdHVckHKkFeVE3H6gmPA7Ss8bSzvHG27t5EPHZbZUNv%2BHryP3u44AQMHaq4n9ITo7sItFD3uzIeeyo6mqgpjxIeaF89kZX5wgQFvvqVxyhGxyevufePzKPZTan0TPEgyRWbdCxgntKZWbmgOFZ%2FzOQURs2xzG4HLV71LQE4Vs%2BfDsXiyZ%2F%2FESXYZr9bh1rGgz3uZNvdSep0qPo%2BERB%2FArqIr36lRCbjQHyoR2OPKT2BXEoJ84R1%2BqOtM7xBfu6EIovVCPkOT96NR%2FJI2Dex4BFTQxv%2FmswgByR0cpXEE3soA%2BLE3QUOx7CIJOBiSBAcksHqlfEQhkBCvqQ%2BiO5jUl37GbJA9zGerHxf31E8JLLSY4q5B1GdM303HpqQovMbBqOgPYjubmAvaY8xgQJbB7xPpfD0EswpfxCC8sPQ2QAepMuC9hGuKXuILsvUcJ0%2BlB0ImU6js8F4RJn5TkOGTF70mniQrsQpJiXCJoPI%2FFZ8Lhs5%2BxeywezHnpNMJNmsJC%2Bf5Qrsr%2B0%2F8HQlkxI5I3T3cQcBv%2FsSX2KC%2FnMcW3aQlCc6P1Lh1GRt%2FSDemdFCzOC6nO5daTx%2F%2BBeB6gGvB0QfUjxyAw2M4AiSMRfPQzZlL1QiaaahoGG6Ysam%2Bj5nbJ%2B43NAjWbxm5HDNsrW4v9fxH3xGyGaTyIUwIzC6T8UZ4fu%2Fy%2BCQZgSaYour9NNYd72efmRictiliSenezf73aq03sUU75LEzqT5dPFK%2BSqrBIUllY3YC%2BIxV2iRATYOh3iIUIvD%2FQeDwfbWYLC7%2F1Lx4nuiW4ES3cCSsDGZlU3gQUe%2BAASuojkee7yFSj5Psz%2BsPKF7PWm2EHhYQpXXi8dSeCFNZoqohEJ8cj%2BO3%2B5Q4liYWgCzO3QnR%2BAh3llm1DY0vyxSCcUHiNHdU1oENcvzYvQg2%2BRmB18l%2ForrmVXx2Nq6tbjcz%2FlKPxK62yFGzbUtgSHJeSfb6FueLxyMFfj6hHg4cUp0HySN6rHagIxjvtBZki%2FW5%2Fp4LFEGSTcT3ULdbN8rAtGBzotbK%2B0D53nCm8o%2FXUBXUyNVHefoOymiYGGT8FoY36f%2BdrXhHJBNiu5x2mQZUMGU6jrnIMfyGMTyfvXDFZ9X4OcN14gHV%2FYOuv%2BV0CPBWXHL79Fq1sLOyfTCE%2BONlQGm6ETrovu%2FUWbBZeSm93xl5zdIbIU%2BrxMPLFcn3f9m2RMZIlhnHq0%2BFwxoZ9doL9nuYTfd%2F8wDrvgdeyFldz1WftgpzyO%2FRjyohAvo%2FtsLUTu8eieTefHtOpEC9qcL%2FfwGywAT1UuNw40%2F%2F1CwGHT1w6P1AqeIRwLkuvSDvVhoCbo3H%2F3QfQrs0FjnwcAsv%2FCLnG9DuXHr4XO%2BRE4fzx%2BuH1UPyNPd1%2Bk%2F6BAvS%2FeGWWI8eZGv8F88ub%2BRNcYgG6brwWN78O%2F3WVmS7o2bRuo79x8%2BieXh%2FTt%2BwXVz7RXX6nytW27ki8ulm968mTe%2BuT4Y6%2FK1JuGs9Gx%2BM5aeTT8pX1%2FL1%2FK1fC1fy9fytXwt11%2Fu7h%2BEJWZajZRKcZuLXPE9L755JHctnKzRRV86ZMrIkZoFKqHawf7dMho74vEI2tdAtzALPfb%2FGO9sWSt2QrkgP5f21fIuRHZd84M%2FFuB4UMpgpL0K28DZjcK5I%2F6qhbucSJI722LJ%2B46Vkf4sl2gv%2F7tMfyHNlstD4AIgHc6rdHUgQ5X3zX9PnHICTE3ERLvA12MBjnuBFB6UkuJ2fco0A%2BeETfI4rkCHCbFOCj%2Bryijey%2BD4w0tcFzfAnQjjLOi9rA1s1GXZSk9oxN%2FJbzq%2F6W8kvaI2LollymH2D3ftyiMkeYLoZviwSpRElyP7l47EXsQOGi%2BEVEaG8Jbjz692OR77GY%2FchRWsv%2BSxsipFb7hikkwar%2BWjAqXEqVyrfY4HMZfrUNGoYh1yMAX8hLP8S45Hltv1K1H0jJXCD5R9nd3heBDYaKOc8yzBFZ1lFxhkPWX6RQwg45k2XyYlO1YkfXKWkhgZHli2JVI4kfT9cvzzWyzHv6CAC%2FrtIimBgdPK4LOEh8hNrocIDUW%2Fdqg0j5RVAkFJQsuCUl1PFpANuxgDsz7juEt4cMxiDwIpFZU1t3bsORYNnswnJUgG4ROtB4v2klDxF7%2BPIFXWDzqiRSeEnQv%2BkQ0AYiy2%2FVI9b44HNwrxIt39jbnd8ny7sK%2FPbeqUclmTWLVuhRTwwJ2jYRV6SSQkNog1%2FFpskK9wEWOfxmFRnohEosIlwyckZZ0rUEHM5ZUoxvpejseXsLAqNtGkHTElolNYu4j1Un3Uitin%2BbjH8PjNrtUE%2FjAXsd97XD%2FkMctMWEeOCSeUjQIYxAPkP6TLAv9YlEzY5NCEEL3AK0b3XoaHQFgLd9M17%2FST7vtSuoIKZJIriX2hlsoA6bMWROJk9WVijLTES5wlllB1vlgpoYkIBtFK%2BNtayGQiYaKGv7s9s3sIDzw2RJm5AL9lyO5h%2FcgnIcoWMZGyGRLOugXnFlq01WRFuR%2Bk7K0Zrt3D8XpZx5j3UGrWtm09yWvJiirJJoOZydvhog0PTTPZrIv2JpOxSBfmIH3TNPbvumnPHR7tBHepG3SDx7mTpm0ECJaKc4eGIhsKR%2Be8qeErtwlyRbGKJDvi3HtkPSfpkKFsgGgtHjX8NdPqdG7wmZCKpsqkoJmAR73qYVNlOk4%2FaGDGgXB6BUOA1U0Ssqy1qTJRwXthvU%2Bsk7jRmYUDVAMAaWZWfMsW8jkNwwPb2KQBmVbzOUOjBlhzz5NSAMnN%2B5x76R%2BQyOvaMmvad6NqalCpqmFbz%2BBHozJUPyaTyUgU2NyozI9F%2F5TrJrHukUEeV58lu4wmyrRS%2FpoE47tDrMKAzreppv9bGzzMHaP0wFgT8KgnCb%2ByPhZMUOAbDyDaPZkwPJCfMpozIf5gIwuMgz90PKpvv74dVQqUHhRXvR2%2BgXvJn37izTKKh7Z%2BystnrHciU1tjs8zAUX6lhq9kRTGMNUkg61%2FN%2F2fWSMDFBD7HH1rrf9vQ68g6Y3vx03TufmtC3zBTXb4%2FMz3N3vvexxe2ysXYNKl8X62nO%2FOO3Y6Fc%2FNNc1Z5PqFl3c6nR1jsfgs9gGMrq8a%2B244mbe1FhP8tGDDjtGO4nrV2sCxsVt7a2pjtorIzNXTy07yGORuECP2f214sts3YEgM9tLXAGKrW%2F%2B4ZOfd0bdzhfDx051tCPABN56%2BSAP0CvoNttH4RXL9OnVrFjVUMYSdEPf%2FZXJ5%2BbJyDfQ1Q2CFsgwm%2FOYeZCfCYthao2mAXuDi9bBMir6GD0LP581q9mV36zlz901nAw1w49YC6V9DyZ6965s4UjSOTqHPtaLUDvdAafQYYIngTHAiA%2F5hW1QiGxPkRN55gVfVkWI18ZUfcmpopxlg%2BjEZVFVyC9j01Z6PhqQtg1MTAOamGI1BHe206fouYA2mP3JWBdWp6mzS%2BpdGOibm%2BMPgka0B2Qdx%2FZi9KBThKrxihtUH0dFVHG%2FZhhwtIbK0ja0BNah6gNLKcwqfR%2BrSqNA0gotG1i65ADXUEGPBxyhGKF14j2t6fBNRP29Si1%2BZTgGOL20pEEHfn55cwqj78sp8wvbQuOHpv9ON88oaOi4vHlLown9OTnyg%2FMGtqJH9QHm%2BA%2FjoZqh8IHXhQCQ9D5ecxxEkXHAQV%2FSrDBd2NcEgvgOeeV6dAwONRW58GWm2b%2B3FTH60v%2FDitUOswcmMb77ezyU%2BxW6W9xVlcVFBDHTUwqqUfpMYu4lTEwaumtvbSXNbWf4xd%2FWAlmgyPHyTsN3TyHcRekmHRTzu%2FNGkKNVIEXQ3%2BI0yCx3M77TTNaWpumwLNqfOG9eXbhHbjpeL6EfxJdKYOEDcQkSS6NbF9J3daMAydwMUFwSH9gzXcgIIhYynsnBfiMq%2FeSr8%2BvbQRA0yCHrxJXfvZZnxmJ472Mno4HVYhAY8QbMCKQON4zA0MWih4jxuRg8mmaechHMlGlhWyUMBwLPdq4OAE7B0dzCPoh44Bq6kdjdjrOJmqhxODV3sSqQXL8yvm5D8a2GHRAZ8w30WCyUSbOrY8mxyn8cylCB%2FE2cJfByTncqnmdBMjxETenyY%2F17R2fXPaxhjUL32sAHZt%2FKpNPtF5IOtHnAVG%2Fai9P609Wc%2B9VRvPQ%2B1stqnt3tTY1KxUr0Qp5Xsm2kHOo1AlhIy1l9YH3Voh8aP6hsVE214NhyYIaJt5HATTtHYuYl6Z3ybBEdrfWzRzm3snxq9OTaWpofAOpDQUpqPhq8DYpen4pDq2pPzU63k4umrrD%2B%2BqkSHxS1L2aJfINzJAmHbwV86KqI7PIfK7PHV3bFTZnB%2BffrTzhb1htHd2bJXYRfLg1Sx6x3Z30TRV4Ze2rl%2F7nk9t%2FDl7C06nraGDyxj6Wifz6speVH6QIF5rG0cS4vqZ48Eq3sSvZ0JIVBpZemH%2BotpB4g9dyAmaOB85cz%2FNWrvGqM7d6sqGX%2FD70BqQ20m7fOXZGbW29sxOoa3behwrHzW7%2BLupZi7Irwx8Vz5cnx9bfsY2CB8GRl5BEAN8zBy01czNZ8DY1G1qOjx6HLFv5XhwzeCQTJwkM%2BXxgIVkM5q5u3bLyDJn7QbE94tM6GZkWYRtE7eYM0H7iQrK7Ncjw5ldiFixf5nOTa359LUnPTxzPXurPgJ%2FPPM81C3wAE2trCdnjo9JbhNIrmzAOR6SQij1m0sDUtKKHz4zPAp8JihIP8z1yuT5z1qpzqVlzj%2FV1qwG8o8q9q0Kux2pliaN3Z8iHuUmcsH4eYHFKl0dELELs6I4LmoTTwNZDrrw6NIrnaTG%2FWZCaMQtZVmTFp9047Xcc5bZUfAfn8d4vwCQBTwIBh1DQ6xDRUZF604VMi0V%2B15Oji7mwt2eE4FoLzr7G0mV9pSQ0Wr0PyYSx6vLqPF46nQn44L4CC7XhvIJCv6Dm9XvJmEqt5cv1rQFabDyruY%2F%2BUB32IvQ1W8%2FJivNL7xDZmaa6iInPHSxO1PQJZZTAteiTek4HpIu5ffELgRyUvyxASbcudPSTEQNSG5Fk37jaIRhYJ4M81IcwwVXSrQX7BwCOfa1s0t3t2rdWhcJy1oHfSX0xD41lb1oolJXUncZ7qQs0I98xZ%2BbXOI5DqF6NQ2bYsibkDbUvUZamvdHe1aRN2aLtFeF1LlYg4nqruX5dn3vD3t9RPSNeP8I4Seb5iU8Ev%2FUikvySOtPvOXMDXmd9WeHmNjfSEzKzBOuC3gIrdhQLIyB8F54cSCxAiG1cASwUpOhSfT8XcGENWnr6yMFy7gI5ArzLWIo9gJf3LlGq3xygbJphk4Txhf2yMNnXPhNRXMd9grdpFPDjtnlqatk89Da5uK9pzm%2BCPkO7cx3aTq%2F8l0qtxFm%2Fh%2FaLcW6GWq7hQbbYFfTn5XbqzR9xi1HDF2GEAWxhAdHmha7N2jkd3uTLRw9wZav9ruIcHt%2B5PSjdrunzUWQpvI3AMgP9qbbGAybvvrUnWFaRM59lzYD5OqVl8PJW8NmLfRj5D2aN35nen7kck8sXo6VKgw8c0lMNHyZz7eisQTXfz6HMZ%2Bp2VVb1%2FOZNp9NMz%2BHXI3W7RW39RRIQo6CFbX2m%2BvKba7bHdV0QFP7VjC4r60Qtg%2FYvTdd%2Bj3yJiRxKLv1Dpv5div%2B%2FFhBHokfG6h0%2BhHUw260w1b9MVOOJeLlkj%2FFbgEb2lHrjogn%2FnNsPo%2BUPbJrJqNhPGwB%2FzEcjdLhCzSHU4BRNXx%2FaUwGro24NnEkHU9CusfUn4bC0fekgiSOeH6jlK%2Frj7i1umrbi2oUDnl0yC0Ag6ZJ09KsKNhOIf5QWYMw6Zy17Zn5BpZuPtWZ%2B4hni6c%2BLnW5Gy6VKKpsONyHTBm4VTc4YTNMSf4YX4dUkdNwTKc8Hg6XZoLxCSlt4VCsaUOuHS2L5oXcXhiWBENtjwKNQry2VnukjmqnJo6f1ynjwgujg3AKg4byJfQv42nAo3VH%2FxE1wAe6bFESB0v0CEec8QiVJudwIDSPdjOkcv3A07j7Rmb%2BV%2Fa43ma6NWNtzUU7IdpLZ%2F5j5fXC4ZESCDEe7mY1vXTW747vm4bVaj%2FG%2FICAh8VO21QRraOexLPehuDBhFiiZPqxcJI%2BA8%2F1wc63Z8pZj3LCNNY%2FTv11TUUPF8GY4BoO0ez8RI7z%2FVBbe3Guchp7cHjYs2F6aDyp3blwSFaK%2FoNFInhBtQweFADJ5YxhajPsnBthXrdNGxIcW5dxMXT1g%2FEbXGqMh5fYasLYTjfzs9amM8SsRYeaTQUAJbyKSRzeBWO%2F4XDRLoNOpfsxKaVvdCf6DyUvhVw5Ak9qBPnFkJ5ZJxLkP5u8waLbXKcJdoZeWYKnND75avw26HhUef9pU83OJscKjzFPMcP%2B1DaufU5I0BudYklSdPgt%2BBV%2FvxyP8dax1ZnVYrCVxn7aMrbhUaIb%2BDORYnKGYbKZwYF9TOvQPnljbMb%2BTWoK13CoTZnwub4jrB%2FOBN2zD5DjqSH288koXbogRfoyHtJkHa%2Bsmrfv1IkNJJ256FcmMPtwYgKBqvo5%2BHvz%2FQw%2FG2RH3sQkACRkS1w17eVJNRqFpA2Iw06qaupn459MIH7x3nQygiQOz4dpczGsxvMwj0Dc8a6q3s39JDS3fYznDXroQpSjsErriD%2F40IRyBHPt3NKO5uK024aKwMdbn4EAl%2FOUVDlpQ677FTQ7a5qwXGkg1jz12Q2wAPBzRuPSAJJoUx%2BdN86v%2BsGxf8Ywz01dvA%2F52y4%2BVUlBcgCCh01Fnm8xFNT8%2FAzTgMuHyOBMhS6nXhhgflS7rBCYMd%2Bk3iAPz96%2Fspwez13OCPxdD0030wBI64W34X0Q3XZzNPcwe3uxTPiYHiq8%2BuABgdujyFwORGGTMsODzC5K3AMGwzZLUsiwR5GBOjlzkgEerZfsYnIUW7uMZeMA4s3jM48I2JHt2SePhOy0d2dOoSaxC%2FUaUkIuU3K4Hn8AdbgYe%2F6OpmaN83Fi10%2BVSsMb4wg03pnWCPaC97EoNnknmvWo0zlNPiAqOdniXG5vhew0TC9jxK1T4h0edvowSie2ctPg%2FsD%2BVfSnpLbYFH1Q%2F5stCsJ3kmypRd60j04ErEjtJnvIVRoCOfKSReuYX3RWX55wuNzCaOOvmgQj4Qfu5%2FVF45Z54XfSoSf03qccpl7QQDGl5cOH72dwSfHYupvr1NTQrxEP4sgSrbdmbh2eNXUzztsGmV7BnD6eQ5AswZ8MWdIwYsrcgor2IiG6AUVx%2B0EfTwuWAE8r%2Bkn6IjTC%2FHvYR34DzZlLFkSsqyiy%2FyCCrAt5%2BD50ws6or8QA%2BgzDiyMkCPfZI%2FsgWO2n14wVfiGwnBRY8NlL4%2BEu1zkpqFwsNstlCFzNIG3yQo4rAyH7GB0sG4l06Yr5LCbxQte%2FAA9FtUILPgUzW%2Frkl8j3cC0OGw6SLmYjIBw4ynR17DpQ0ZpXtRQ78MBKm81LgusQGCn8JDshWluvkFxRDYdHUqWcD4lrSyzHQ%2FSFnHjGfa4JrvLILk%2FkSRPddLa0ni%2BcQCg%2F5D9zRaMWlY1MKR5bWsEy%2BpGM%2FWtol15CFcpRZQ8niIdr08q4QAUxp9yzg%2FEIKlbrq99C%2FCHIrfOmQodCLfXmoo7Lsw79evuhSdVcrQa9wmKhfrlHbk0JS5YV9MuWQvxB%2B8jYJ3xlP6RLuGZBKTe%2FcB02XFWYvNhrZzKwNf0b1gInEYSVD4IFPKRhZu3pcGV3kdvX6SlJHatJffOHSeGtGRUy2UVBc3jYMNxEUjAckJ0RSe39hfMt7aCIksBicHNYUDSbMlxitS4SHb9Z2MU1Ylp0Y9WgaqIDc0U8ZGdBuVtqTpz4R%2FQDxaI8yKyo8SMWPh2eHpHS%2FFJwP1q5fa%2Fmg3sq0q4mJtN56x8TdEkPNkMhOgZ4YxA0%2FXlqd3%2BupnY3FDZ2XELEa18N5he%2FPwb70LV7hBSi%2FHoy%2Bdi2l%2BG5ItfSnYv7%2BaRyCRBuyh5%2FsJtMF3avCtYAH0%2FgTAMOJKRRJvcW6kdoFIC3Hbs9Te1ecWQ4D%2BmEE%2Fdaoba5OoojFQzb7vTZI3t4B4U%2Bmvs9tNrupcJeYg14%2BEy886Z1Jyt21eM3TS9sLskv85An0TQRj9ZvEyrYT6r9vizgOfPf2bQfhKM2s%2BD5lxwWpVu%2FAexeSXHsyTV2j%2B91G1mZxkg8nKxO6%2Fierqn2G8NOLtiL9Qe9Z3Z79Nw%2BIujkeeNzOSxJu4ibtqmcv3F6%2FnZ26Z%2Ff07%2FUfmfbKNAv7ilF99wanubSKDMZC3gwv4RaQMrCcDSJ5whwUHhxUs3hON%2B%2BbmJUVR9ckoPXDz%2FfwonAEDIiXPrCvK1tosIHe%2BkTAeym9IddFYEYAAAHAklEQVRXtm0bnkSFXfn5uDq59HkPc5viMBr6IwXv7UEr3lpgbdrEqIosAuhno2qO39GRiRkmPikeo2AwH%2BgXqAkPGxTqc4gewsu0IFSMAW14uN2fRAfHFw5aXaKCdgeORmkav9qP8an2FILh6Sa89cC%2FT0IR2FNmYxvxgAcY7Ws3iFvOnKusH11nlb6RS0IIeLjX4QRs3I%2FNBfIfDcXDMd74BA50attOjc6f%2BTechJM47U97zad%2F9B%2BNOwrYwG0HdXLrpdYdbeqa1FNRo7Kzyi7%2FkQMYghebo9AkPOKz624EL52pj0P1%2BMaP1j9d7gEg8EC12p70p6P8FmtgRM7%2FAB2Fp9sdoZjgGTfx%2BRGvvRnYCpCkwdedeLC2qUxb96qeBmlz%2BA1kq%2B3P00QpZDw06SS%2BQVxC4p37tOkS6cC2QfZSJ0G1e0uIwcEf9QezjPZi8dbOAWmVFkIlncc%2BtRyf6gQJfpwFEmCaen4Wz4sdHzpMJVDmKEMhpbl4AMLhfoP9h297FZ%2FEtsPbIItEwIahiK%2F1sn%2Fg4XXLq31DQKqnlcOPzCQKK4XCU0aXfqRWqZ8Lw%2FL4ODFFEl5AJpehgDqx%2Fg3U2528B%2FFiIosPT0DfxzHGSPih1BqvATFlKrxPwvKH%2Fakz4JAqUtfiux4jHGTQRTw4lqiXJr5IgVk3FDhcrrheBnEoAFEq%2BDnCBLoU3hXiK2iNNQg%2BL%2F2pMTrn1tRe6gSoToSTYGF8MzlL%2F%2F5LACDfgJ7XJvqthpV%2FxY%2BNBltfx2UonJi5f1S9jpbt39qhISh5P6xOrky8opyinQyr9yFRwflDiNK8S00UUv4lOAYFr%2BM6NSy0bH7x%2FvS9cUPTyr4lo7FHVuHo2wgzm52fZiIRRGR7KefSn7m40DqK2THEpzVkz8YJt4F1BxoRd%2BgC6bXjJqYm2MTVeFmPlY7VzPqnvjoNb804fwsUGsjwPvZv2dAmXo8B%2FCRw%2BiaSsfrj41F4S4Z7y9v5OKgMOvqLPgQZUUk%2FkGWR82D3jqTWBe0VrBtgrjkPvbukh5DMpv1b9lwMN3XnDbVXgGnI9YDLoVuSnKufPjqezx1YlXvloUFmmE4rjq%2FCkiT6Bf8yPyvs2yu%2FEJjDC3PgLYBNetOTe00HdiH0CEWKx2iMErF0DY7PrmIGSmX3PQno7yAjqKnjayaqICioxAUsLS5CigRk77fthzH0O3LJ3ecuKWSm3Ytn4V0fkBllBnsGn%2BbLDMbo5%2BllE%2FMgdMTDvr9Yh5WzXTi7lPfwdiHtWCXaQURcKn9Mr7L1v1TJ%2FRnpQNrmiNU0i0%2FzTjpIan4n0FmwP5bvJJPodhmxuXqWuF7s%2Bjn7LU8R28TR2YL4Q7YbgUhyMclLaSJEBCa4p5LkJERgcKKr9y2ctohKLzJO2S%2BNMMdj3UGTGVhz0Fi1o2oEKYb2LUsMdGJqOaE4U2B%2BSBH0o9sBF8zkOp92gwet7Hx6EoQq981%2FT5xyAu57If7Iuenh6MplDUfnf4XPyk017Vhsvd7LAxbNLwz6HNuC3mQDrIXbkfF%2Bx0dDmEDn0%2BOcHu458afTnYwLIpAWzys%2Fu7zht08hLyZFvI9vIeqHZCmMjaAtOquTlTiZkINX3BDZQxHjDLi8WvYzuyE5Pq7%2FOR5fhIsIv3JwNuEiMoXEZVE8htGXRggNtcoglETHVZiCZ0qT6iYSCbplpBRYToYlaHcxPs1PagTquMoXNytn6pXpGu4k%2FF6IP6QecWMyaKK7iT8Sm5DGJBcJo5A6kRUiViBaqvhlgVL2k4hHHLovfDIoSsVvSJjI7cv6kQ%2BgQEro9HOsZaQrdKMIHUdGPm9AHP7a%2F3XO3lZU8KefS10F0jq%2FqzGe8VPHaoucBL5O6lrwH0x1dWiUg5FqdoKzmUBJ818%2BTeBc8h9Uf3stEXOjIPUQSOVxFP2VBKusDASOgo1Qywq3OuMx3%2BAL2%2F7AXza9%2FVF8P8zv1FwWvB%2BXMZWzTW0n1MSV%2BYDkN%2F2NBDqFn2sAud9tcWyoaIgqKXrpfUq%2Fs0kWFcFeeCcaS5sjJJqpbCU6DmqXF%2BICsi4EchsMADkeh78r75nT5Xi8DDUk3Spa6%2BeNoQTcc2ZURCfqCVEo9%2Bclx2Nfkd%2BTIkrKKsuATSpJKqy3xdCAC0a1tTzAyzkJikGuL%2Fscj13cyW%2FSIiKs0jJtl%2BPxhx8okUL%2FzH5%2BKz73cJvjsXUPk8GKII6QxAbZ4hDYVVFiyjxqRgXN1%2BX4go9dh1Fr4WfPsrt9L4Nja%2BuxKCRhtUjhV75r%2BFiAY2tr71em5EmxELAZGW7HjCBc7YlwbG398eBT7ujLGufHXkKQd51Y0fSGQtCE%2B5k%2BoJqUysFOAQ5wIvsHvhFCpItJTDLjQBAxOI%2FSaHJgCtqDfl9vpjt4JrmOra3%2FB72L99CCrFH3AAAAAElFTkSuQmCC%22%3E%0A%0A%20%20%20%20%20%20%3Cimg%20alt%3D%22%22%20class%3D%22js%2Dplaxify%20position%2Dabsolute%22%20data%2Dxrange%3D%2210%22%20data%2Dyrange%3D%2210%22%20height%3D%22230%22%20width%3D%22188%22%20style%3D%22top%3A%2094px%3B%20left%3A%20356px%3B%20z%2Dindex%3A%209%3B%22%0A%20%20%20%20%20%20src%3D%22data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAALwAAADmCAMAAABYgh8IAAAAA3NCSVQICAjb4U%2FgAAABgFBMVEX%2F%2F%2F9SOCxSOjH%2Fwp8AAAD%2BwJ4ICAhWPjL%2F7tDMQjj%2F%2F%2F%2F66834vZuZmZmVcl%2BbdmN7KCIxIRr39%2FdUQjpRS0nFQjhKMihptaVQRUEzJyAyIx46KSF8LSdAKyJSOCxUQjqcincQEBBSOjFSOjHzp4tSOCz%2FxqZSOjFkTEBUQjpSOjG7qJP%2F%2BPQpHhpSOjFSOjH87%2Bjz4cRqUURDMSlTSURQRUFUQjr%2F1r%2F%2F0bAaEg9UQjqHZFL%2F59nOTENTSURSOCyNfGojGhddV0wpKSnGl3yWlJKUh3d4YlM5OTkQEBDez7fLvKWdj4hzW0xIQj%2F05%2Bbbp4nWZlZTSURTSURSOjFSOjHo17yMgn5%2BbFwZLSlWPjL358v%2F4sL5w6XWxKyJcmF3VkYhFxIzMzP86MynnIi1jHRXmYwhISEYGBhSOCwICAj50rvehn%2BSblqRMClWPjIICAgAAACLZWJAa2I2XVRRS0kQEBBUQjpLOC%2Fxt5blqqWvhnBNh3spRD4ICAhWPjKmNNozAAAAgHRSTlMA%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FRBH%2F%2F%2F8i%2F%2F%2F%2F%2F%2F%2FuZv%2F%2Fd4j%2F3f%2Bq%2F1WZ%2F%2F%2F%2FZrv%2F%2F%2F%2F%2FIjN3%2F%2F%2F%2FM%2F%2F%2F%2FzPM%2F%2F8RM%2F%2F%2F%2F%2F8RiP%2F%2F%2F%2F8R%2F%2F%2F%2FEUTM7v%2F%2F%2F%2F%2B7%2F%2F%2F%2F%2F%2F%2F%2F%2FyL%2F%2F%2F%2F%2FRGa73f%2F%2F%2F%2F%2Bq7u7%2F%2F%2F8id5mq%2F%2F%2F%2F%2F%2F%2Bq7kFCNkwAAAAJcEhZcwAACxIAAAsSAdLdfvwAAAAcdEVYdFNvZnR3YXJlAEFkb2JlIEZpcmV3b3JrcyBDUzQGstOgAAAgAElEQVR4nMVdh0PUyBo32U2C7gLC0vvCozcBQREbFlTUs%2FfeFfXOe%2Fq84on%2F%2Bpv2lZkkm6x3nCNuSzL5zTe%2Fr8zMl2TXrsTSNrny2%2Fz8%2BPBgZX%2FyDv9e6Tp6rXKongMGPV0i%2BTI%2BeXWngGWUrsmpGxrGt2u5DxrxfIPel3%2B%2Bd2ywbQcxJpa2ym8DHit5hV9B6JGvP%2Fq%2BP145saNgrdJWmcKeV%2FLzvBs5xXfds4togUDvfRv5l%2Fhf%2Bc3Xvc763%2FNWch3b5bFjtOhNB3jjR3cY965dhzTN7aJ0L5fo57CxrPHw4Vh%2Bzfke5ILnvoNZ%2F%2BBH3mSeGqboCHV8RE2R%2F69Xdgj58oiloT7DLYXoX89TyXXebMZ7UJ0dkX7X8CjKyPfQTkAzVEvy8GYAm%2Bzro6EZRKJjdXmNbORzowytz87ma8lrEHl4g%2BqJGgu1iC%2BRadebfwz%2B1cFjFsURboy6v2dX1uazupSRjEhhwfCLjeP%2Fq11N1%2F5rlcrcysrwsPirXDvUldTrR1euE%2BqIREZv%2Bqzq%2B0AO8NhlkW1rfOgFKCNxOG37D1UGV6bujlr6AowbuDs%2BPCyadHT%2F%2F7r2L1eGx2tplniNfLua2uJS5494x%2BGJgUNIIfF%2FYO5aV9f%2Brv9VKpXJleH5eY93mpaYj8eyhoAsXBEDJyPEjhUq9cu2cye4cD2fNMCczo%2FYRgMjAl7yQyMmRdYkhg9%2B841Y9Aki4qkPnNe1DGfzBipTVUe%2BLSPeEj8muiSc%2FAPgswkR%2BbTVwGfHRsSt8ZzgtTx58f0EkFYA4kMfeMgAjipLhdRhXGljxMqhsVSlj5Jyz%2BvZtbqidrhvH%2Bfi9W0hcZ0AmgKkbDd1w1YoFhdDk2I89lJtcxJuEKvv0U54Ch90hfalmrsywYPPqFMqhmeugQNIYAd919LEFQfa7hgqL4%2B5eWPAcTLbCmTO6runBrvIdMySOjdc0lsTK2N7M%2Fn5aBz8wUzwvxjoltLwvuUA8Iw%2BmUDeIOgqiE886gVqTBSDzFvDOmokE%2FwUOxT5weqJuA0mYYGZt%2B2FhYP2jzxXx3lXxQ6Eo7Nt5UimAByzwtTSJcI%2F7OPmM8Gv6DPqIQBR1RlSghHynf9Og0mqrLeiyNpOW%2BLKazXTG80EP%2Bd9Bxv%2Fpc7IBD9p65rjCJnVifcI281qVIoAqANAa2JnUR8pUMv0UpP%2FgITidAVE3xeSmbco00tVoEZXBk707XPDYgTIxBwfxil4VEeER%2BBXlEliHCVK5tRLRVcVAQIK9hgzVAMtE2k1y%2BBKHkjbcrAoynoscpqmDsgcfFYStF3hwNA15hk5ENv%2FpEiQ9CSuEjy2cWPyzPigAtX73j8Rx%2BI3NiLjfVjXUDBzAqHyfSMFQKHtvwlcCAVYFmpFFA%2FhuAzs3jE%2FZgY3k17mzIEtniRr6LOT%2Bgwytzs8msiaOTDVZYIf%2FHsGgcB19M7OzvbMzvb2LnFQf0ejMkexw4YhkVOHJZU4GiZNeWD3algsFuV%2FXcLq6uxsP%2BsJvV%2BdUV5mWDluwdXHmblOV3I%2BBbke0ESet381DBVy86Y%2FqPfV2SUGCWvkNdeYe%2FktC%2Fw3u7U%2BVpU3HO%2BvorjNa0g%2FyNas9zo9ljsAmsrAfpUYEVcfP3WEhnt0rBZD4ouWPmOP7oJw1qnaMgDp9iIroJ%2F0Yqj52pSXETr0hiRs0wgjf6YDYrOGX2%2FokDXX%2BuZvDeWekJBrc0fA7yAB5OXOL7WxL%2FusMs%2FuSS9z%2BvEJx6slr1%2BgP7ANgj9hi6lZK2UE4vbNeQwK3p6M1ZHr3zOfYnbrWNXcCFkDGNvJ8IS6FFfzmC7qey%2FDVg7bpsmZFvasLW6%2FdBhItn1n1KcOgS1Vgu0lTAsn9EuNuBKXMdVx9QVcHSHyBGXNxB9SM4hIxWqHZ9WeGYwPpA%2BmjjFvmWNURuAjIXcuai5h3gKus%2BpLVVUV2TOzHtgC6mgPbEWqzsI4JNmC84pM5YzxVcaXkCMnNTWcKlp7PsEeJHbWnMFKC4tHI0veqJwWvy3ziRr8xGJLGBZDuxNCCzO3m90coE9St6TGYH1LToKokDlHcWiGZA6ze7jUUcQEWPcA3xiiViw5XjGiZkT2%2BfQ%2ByRbnGIPsk8g551OWjfqLaGjItIeED%2BnD2oXKLbwVqA6xkpxfnAtJcwjXOEmYNDAeAMJEakGWaxiGYoQqJNaDFjgdgvJfhZPyKXUfxc97WGFJ0tk3cU4Ys5O1MrXO8Vh6i8yxgjXud%2BX%2FWa%2B%2B8WZ8TbPLpYg1McO70t7Pl6QpukY%2B3o74RgrV%2Bplc4qbO%2Bs1PZP2I5UoTUKbOmIY2GO5U4yyxrCVyC2iChLd0zgCJUPiup2obYNpCr352rDNrCZHLOMwd66znH6bJL%2B5YtsL4HS81PFSHY8Pr8VDUjF6sP88ayzcH%2FG82WDKYxiRaLWGKFD0JwbyEYNKtIIzsumtsQmyisfbY4ySmlGlBe9KyTdvAhBkNHMYkW7MOznWSd4zr9herA9SxVZSIE1RxJmOxR%2BKV%2BA5cxumue9UJHUHwIRN2yBlSLPLAmf6v25h5S%2BI2wg4upxzkeZdgOwAcOSYrKkaVRbNO%2B4RWi4s9xgPWHuAbKNaUq7PunR0ReHrE8IRByR8RAGLLpIZ4whzRIZ8E6YpvtsQd1wVfe9eqRXfH8HCeA6EsDQmtHarWrG3tdT2%2BtjaIpi8iI5Uj8WPWRkyxo%2BNRQ6MAjgrzqEETJ%2Ff4jaXX%2FsJ%2B9%2BqYha868iVOuyFBnkFhsY5BISP9DWpiqnmiLTh3uWQxJCSVZGpqhQ1xvli7rFvW3IcGeAlzlyOc8gxu%2FtXVJ4ztFn2toACBuwaUN0sfu%2BTlXTs8Zll5ZBN2ExlEs01rA3O34GXqMIgMLm%2BCKVWrj3nX26Gl%2BIzgV3gDE2lj96f50IO6Z4mf2G3PsTLV4OaIYVcxDjt5rRkMHE%2FdNXvWmbexagPlcrSNO%2BgE9o%2B1iTf8CWMqnTxpQR01doDg1ZHD0zG7vr7%2B5Elv9ypxx26ALVZsB9Bsvbu3W5TeHhZxdjjGMXWKepD09XtWFx83idL8JQgKhYL8f%2FnUl6a1Cb93NTXEXO%2BdWFxrenHqsjigXNBFHlkon2o%2Bo0xXOJt35XnY1leGPVdSxPvm5qbm5lMIIdBAVDOaRTMWJyYmuruXxOvE4vumJgkZwAbwV9bfymVx8IvF3qoYjTPR%2BbF1QoIBAcJgDW7gIpnpOurVqFnIvbn5clAAIRIy8RIE5UAL2PwYBHwXLXA4rCx2Dy6LjpyYtbhhwbHY4N014CG5yWd%2BDA2LbbKY5pwRyAX8sjo5hxYooAHrCfld7QdtoQ1mL7GhXG6WPIzQoHlks%2BEL8zsQ3cxnCdn8ZE9ANEnoTV%2B%2BS8jyB%2FkaYBPlty%2BSh4%2FjQmbUIYUYYMYmTirWX2yKCed0Iyn3puZTdPIAcAcGbEDtgEYE8KEcAOvhiELhhazxMSpcZDse0mD9yYCHDkmYWIr5BhwprCnszZeJ7UEZVRHABhZ1At4jvFmyqeLrKUmbNUOQzNQBjf0q4IaIINcCnUQu%2FizpEoXxU5koXmaQA%2FMF2yrfLwstal7LmxzDRiIxgDEPi%2FWp2oWtkdi%2FlJkcOZIA2gToA7JEll6w98tSjR5zvhJL4pOOGvyyhzTzPBZ31TQ%2Ba5I0TcbKazDEg%2B80PhK8Ulgu81TjY8eUaYvmUAdENYr5zdpQXtYiLJPHIWxaK7UpItpos4ikKkOvKcmLvly0tK3GZD35KE4n7lbT8vknmjcunD59%2BsKBMbJ0ZGoMSRS8MvdcBbSPJO%2BxZ4f3zew7fMBIfiJvpKLBD1u%2Bx%2FnCq2Au4lGjKuf37DmMxC0DS6hIx19Gm1JAEqFqBw8O7zFl5v5lqUWR587Qeeh0NXZtGQfsaD6xAR70ml3faQ1%2BU570YAGQMyOJOhpIS47OwGCWPynzeGBmD5VNSUSSF5oOUEhrRm%2BUogPHrGflSixq7I2X9mj0EhGz8mQHUeDgnrSOGpUoH2gF4PLD5mmhSNTVqGZJntN7A%2BDt%2BMUWs2U6YcMtA16fe%2BZgGQwON%2BvM2aIhRWKpb2%2F3cOytm42nm98DaJOEQGd1ktJM%2BsoUp0m%2BcPg0Ul6VfdwOYhc4UQ1pLQQGYzMSdWsr4N9sbLywRhJMD4dlMdMHU3VfraJZc%2BvWeej1w4GKuIyQA3JI5QLiRZuPjnefOXzm8D3Fv4ei0jP6DNmJYYMo%2BXw9hRsEa07%2Ft2%2Fv3vYjl6XwBIYxsNWgkGXF7MAQPEDWY0gglFVDv%2FRutyhX%2Ftyz57wAv1jLQCuQ5qcK0QbhMQGnj92F1Pv69vb1yZMeVnw9bA1IHOtYpk30U9kI%2Fu2R3e2728XflUsSvDX8T8w%2BMB2yTApr9Q98AefmKvvFxgt9EvzP4rS7j6ge3%2FMAuexEORg5BGAqTWigtfWtFHu7%2Bn9FclFJrfYkqwZmJitXoDU%2B5xU3785S1a1GQRlRft6tOlwJ8Bk61gAMeoABQYE1RzdAcF55p0tH2ndjWRNstERYK5nCLC9MQmPyOubG%2FwrKCPDmvH9qgwMaikyHuEu%2FBXY%2FFMZapba8283K3luNtwxbfDypRQKCBQOpimfD9Ow%2BirHpzGkl%2BL6fzTk1ccasyFJLvUyiN3SSbSurjco%2FLZgqlBja%2B%2Foaj1uSI2njb%2BAzId2sy0GZ5WZv%2FXcvsaYdRH%2BgTjerFF0IXuqqacHevltnuIrVcrOQpXjC7pdaCReqEsP4vcjWK9rUB%2BQ70Rtpu17GyJ2asE%2BwZoaTRoDfuzbBBAgsTUy%2BwSnuUc9iOl9cUS9abyAl%2FvZpjb2PVE3yZl%2BgGcHsDHAGgmYarYgPrWBqdpPk9%2FZRoGLmM4Cs3EfKF1wE%2F40zi%2FVUcpLQhVsSOdgadeq3Uop1jTvKY5I1f3Lw7T8L66tPnpgkBO8azRyAX0le6TPHaouJOZsT2tb0EeUN6a2QvYz2HA1Owdgi3a6DMpqxbM3un4VMmHTTvIxeysNUs0HWouTCfNhxAX4vGErT4Ze1uUGiayMPgIMgYZQuY4PWK0h3Bj71%2FB4z%2FDTDPenFWoeAYxHS6cY1y1DK8k4iGeOhL40I2bxImUXHyr9eofYr8H19XsLyk0bjs94Q%2Fxl4J%2FikVtjXbIi%2FicbGtT5S13YELyVPYQDXVgjJ4LNqwwEZA1%2FZzYsCb3mXGuuSFUabyEGeOmo%2F3ig5b7NG0EaE5Q90OCmRlQMiiY7pywFTCEvyRBygjYPfpY7%2BGfM%2BRrz8yQanNfg%2Bpq6gsJruAcEGwcNbmfAHUmH3vGtXdbQj5x0M1ADf%2BsnHoFLlJHKmOMdYl69OyEGIlDyLp5Sp3DNDFp0FYwUdhCHtcRiIppJzXprKyLLZ%2BMm1mCD5Ey5yz2N6o19gdHVbgJdOqs9i6z7lpGj2CdleY3TFnJRlKnPaDlgMPMoCTwPcjupY1sFxGXM%2F3suNfLsMD1rlcIRiAHK0LCA2LTA7yQbPHIFYXoHv2%2FvYYxEAN9BAXvwdcg%2BGLeSs6QnZKhsS%2FH8pNGg3lG9tPVAoGIMelGHqGCdsiPzow3Rg1s6qkYFZjmwV%2FQK5laNO%2F7gtYN%2BjZgn%2BdN%2FPnPJH9kkcY9yka67XnCw7IMe%2B91j%2FtQvwjYt57%2FlgxiLLjCK8j8DG8Nn6xaYLSvSWuv4pse8LkOh8AMhMPU4a6O0P1CjwHZP87j49GLFwp83Wt5GhZDGb%2BcDMkxn3yrWKi02KN41XEH27Hga23gegEBWzPiDrif63EOiA%2FtIRhv69GgbG5muoNcxlGtbcQM5Y3Ipwf%2Fb7WrPizflLR1DR1DiqVUc2AbC%2BAO9lWjErwzDLbHqrZpveHkFXfUXO21y0U5IY0634YCBhEJi8dkblfXPTkJqivATO8colPfXB%2BZJj7UzanH1qyuftEQ29%2Fd3MTelELKNiJR3w1IJRCOZjWsp6AGMi%2FVUuh6gpytbWP%2BVJj%2FzZqqcZHzCn5AxWA7Q7aCe1Ih%2FUk3z73slulJNOl0TVFzxyk5RdE7sy39x8AvCCP%2BW%2BODaskkveG43nBXapo3qSTpb7ZaK2kXOOYdVhmKS8tKBmulsFeLOsQ1zRn9xh1RTamhotdCI2tQJ4YVNKW4scJlrLIO%2BCCRw1Q%2F64cuVdOUiL2MZmcJZVtUGAH5LTxNj56cvv%2Bg6Qc2lJXMQY1BJfr3o3L8CktCkzY8Zrwuo2yPs%2Fyg38oXkCIxLUYBOdUV2tjRdE%2FbqXU0dyWsR6IPW7LWjPGlCRpzY1vleraA9mtOiN3GYOkhdSbhYboC3qFYgNbF8bwJQfTnHvmZbVL%2FrW%2BS2zjZt0OD9gdwxNIXvg1SJWyZpaOFb9TZx5wJcPAmS0eDce2ORWIHAzcSx3edtq5KAc3Qu5PLqWx3DvN%2Fpaz8VFaxL7C3HSZzNGZDPPHqSvV2oPdMWeN6DQU34fOwzqM3Og8EXScs2zAZMNpBklX18sZS5YoBsievYhVj3eohTNKTkyGjugFyDHTI5BOWAeVvukQkE7%2F%2F%2BwBdoyCB4G5uXC2L3D%2B%2FbtO%2FxW%2FPRFJh%2B8Z%2B7ex7Q3br5974a2lG84UCs2c37WcYa16p2dRCNM%2BJF3VogA41imADjA%2BkPlM6xh%2F6dLdBwG37ZsPWgcqQlfF30vRHOKJiCz54QhKCgXaJEh4G8BzegryjdNcCFaaWUEydwrt200xhGLLKwP5VeZFHfKGu4FyHOVKGaYbadu5VspOaUznRLjEjCEpg0w5VTJFZFBDZGmTYLNzh%2BRBeVyEB%2B5BC9UworLWTci02hx%2BD0SD2rANLo94qvMsi9Jk74F6n0m18T1HNzHTOKrj398kZxZzLGeI0epdNXFL3xLQmcx7%2BXL2KyZtFMBKgesERhLIqMpnAFTBHoLRcC%2FLCjzfpHo6cjfg1BFayC%2FkP0XaJ5vtNOSuOXcfG%2BiuekPNIrklgJQT6sdRrwBabKhTxm9rvzpjxdNOksoZ0qwdUHvMPVL9g0aHje9sMjyXUkHpLRK7M3NkOCUnXQggnn78roKT%2FBzKES3TjE7nGn%2BI1uq5f%2FYxRjLoICHQOcIsTef4ZBNTOLkybBEP%2Feuzm1TddwT6PGLrEi9EBzZbZcraQubQuxrEdpAy7ty7KC9fuJdbo5et9iBeBOI4y2esgCYTqDWCOLEwNtO2EyLjD04tXGbx1ZuPBMzH2l36KmMMtTWMW6qkBdNHzh4cGwMwcRThYIYbQpOqtDYwfvPHl64zdIn%2BQlSU4VSb0o1OcpaGyXUSSIYKpUaSs%2FuyzaQcWQ2U8c2haDgaK9xWAcPPGsoLTy%2FHdUR0sodBmrdyuzQeAbh0AZcfNjQIBogyrP7b2ULTFCv%2BVBmfcBUQQfEB%2B%2FLw0rnH%2FGACs7o2z%2FYCpBwF2qrdA1aN6OukZg5dK9BgRc4RCccODhGuotqHJAFNU15cOCZOurmEE1k5Lz72Hz2HWZ37dq%2FcgyOZnLwuSiUOZqYLmnksoh31QBypNCMMuPN2H3dX6XpRWZcct2B95fcD01ouzYyCjyssSp65qFCYpogPyy8HUtf1zmooZcaNs%2FEPIkdCTAx6Y8jeaTOG1AZGWWVJdPx0abCrNivcJVK9w%2BOxRRUfn5wH7TkueVHs29oMT%2F5XQ86uTo5NZCRgXN8s4GKwibx40jJ2FDBddM3DTfPQMvJGtZYqR%2Bv9YiWn15vFYvF7XOvfkrZoUs2gJ%2FP9eAAv2Re5PvDxTAMZ%2BWVRN3qcqrFBeig8xMWSfBTUl7h9ZXlFFCGHFv6MqA71eK5k6l77R8cT7P6sgG3zwNoMEANDZsXq3h11Oo0dsoQ70LLyjsz26MjlUyyPFXXMX3t7Ox8GRa30uHv2rU8OJ%2BehHRxWhHe6oKbi%2BYSu6VN%2BGnhDE%2Bn4dN0XDSj44OH8j0OZ0teivVRgO9cF63Yflpr3xPL8vkO7iSgFtbi83tGH0H4pYZpdRuQ2%2FdK2BzOP5%2BTxLxdnxo8mibwtlefXznSPal69uvHT1V9fd7rjMaeWB4%2BZtkC4I4XDd1UNp8p70JUDC%2FoPhFfb06kz%2BPeuDsyWKn5WIWTH0QvVrfPWj%2B%2BpqtAq18%2FfQ230zSXNaAyPM%2FIQ7J79LAB7b7W0NvTaIoWFp29VZsH5kfmKokPw7DLU4nwZedfWzb6p9twBeJLRf3tzIpU2V%2BZm4IkKbp548VpQxHdAQv38OO9i1xX%2FA6v481wJb%2FvOSdB%2FtXZ%2BZcL77W5klVs6%2FxLED93jbsq3f396kpKWuW%2FSNJv2FwAM1Rq2ECpd%2FT39rR0txzLrp6VDxJgVYj3zitny8lftfCrd5R9O5e%2FzmGBoqVnie7EJ16mSyrgETZ9gTT4PCJv6e5pkUfVBf6svs6z%2BvGv6pa77ekWuwNAmKm1VNpaROkR%2F3o70P5FzzXm6QUTMcimXNTY%2B8Xu3d0tvT09vb31gD%2BJ18iHxZhFPLtlcN%2F5%2BPLjnV%2FPJlWQWLyOpaXebtmEln64FHfiubQ60wvAHvE3LTd19Lb0tvT3635iWW65wNOVwXHRPv2VlLazmpv2J4y961jq7%2BnpVuIXjv7RdMO96Zsl5nTPeFGHEDe2T%2BrstzoejHeyaK7g%2F%2Fg1SSefflB3T%2Fv6svPlp7DoakVa6UJPIxvQK%2BGLxkxsbE4%2F1LzXpmbT9%2FolcqMWZp0x%2B3b4WF6ZUOOO0NgY6eV2fh%2BOntF8D3zDq6yMREUD5I3Jjg9Nl0rM2w519BudtsZH2Y9RsMGHoQhkPhWTdvhMdx%2BbFd4jV6feReQY7SwJCZ8ZWmgooehLDRMdsN33%2BM0vczwwh4MvFoXkP4WJbnQbiFWVghnNMQToSh4LdUwMbUKALBtwk3cOjiTlX17058wF%2FeHLzjvFRHNyVnmCYvWTurOY%2FyYb%2FQhDYwEcOg%2FjW9mIaR4WQECm3qN8zPkJb5pQvRMWkwMYRfvi%2BldzpsyHY3S54kSQG9MsPi5tOOky1E4%2FGs8zxjtHN%2BIQ%2F1J22lYa2wPxXtY9mMc9YK%2BbAHD8uRn2qb%2BL1CnxlM3R7ADnFdxkRrmpJGsjy1lFLTpV7U6d8wi1T8SR%2BI9fKFFY0xBBEJk8%2FZ5FfBm9GCv4sfNOmBq9fBY7rLLBxniNIc0hm%2BYckH98yMzQqBkma7eE0epozYcRvqI7mihLmTre%2B0lynsO4nmrvlwdqXIz3aOgeDktUUFZjCCw3zac%2Bk6DtdZHirr9SfJQpn%2FFmdECGlBnCyRt8%2BsJ%2BEbQZuodzgWBsOLviCRqjc0nn6fp9lt965i8RsteIGn%2BCeyuRbG4MxsZnJ47eTZGhkf1x4aVwNue5LQ0DmewPNOTNoDWm6pLTXi14KxnZhjufwl9rWadzVatCfbpjg8SetqNzdzPvS3B8CCfSRHDA2mgcFGuJMVN01rt3f5mfP2bUuspvOyPfaobrZ6sp%2BUPyqYVTd%2B8yGRIJkAjQ5o0hbWnUiPw4p7zPeoGTzjQfh5XKmPo9GLGYO%2Bhs1XYL275Vpx8%2FK7Ek9dYQGxfAxoty2%2FFgebtA1L7Obgv1sfNjMSvcnQN3AxY8V6oj6aAaTQ09hzkyIfkzKGWf18KQu7ZIX9YiKnpClBGW5mPm%2BPoa07uEAv4mLkb2PRqa1gNAVS6y%2FSwrwz2bJRucea2GcBMxEU92Zg%2FxKraE67jxP2yJvItD0%2BheGxoWc2UqW5VBX9BN%2F%2B50vryT7p9s8J7FPqs5IKcayzGPhs7TMKphIseVb9AFznw6TAnoWbHsiYGKnSz0Xcq2MbTJRlITeXKb4YTWvQ89xfbqRzGsDnNNyVR85wzmlDUywt2GRUPSRwH8kqshbE%2FA7dQFY61%2BJXk5KVAthp%2BzsWva%2FB0z6fkXh54jaUQTorp7Dmxcvxn6ieFTmGsyqYLScKkN4vadc1gWRJbbQ9MwApTFURwzAnRahN6J98OsYvunr2H4IVNXEbzPqojZYHYm0Da7AdHxoYcUzpcavISHP8QOYmE%2B1uh3f%2Bz8qh1rzhkqnlKPp%2BD8ZOdlvGG4RGSzUCqRl3LAMk8VW1l3bqf89a%2FOr8LYbPV01wW%2BDnPmWcuDsqiwDGfLFupuPDqprx8FY7a6W3pacj0%2FXt%2BJnhH7ex6P%2BcgaBC442T%2BjlXEr%2B4cdyGuS79Vwq6Wlp6elpSXnc%2BsPeShQrKrOx0s%2Bem4mPhRtFvh2X93m8%2BrKQILVxx%2Fwt9EWOaMsZ3FzLkMc5TXFHD%2Be0IiMtYMc%2F%2B3nuCAiGnDTqsw32XmV33MklPW2SORyQj%2FnIvgyIbcpkSgq4r3hkPx28TkAL8H42yO%2FDffduzo3yqrBavk9cfu71TpEd29LPuz2g15coLampZg9b%2BK5ga7%2BNs2%2BAPAGo%2BgUmn63PiN5tXQh%2FnKCv2odbkdnvvUr%2FuRqnX%2BBpvoaNHiPUYSfrW3QpIdpEjnX1%2FW3dHf3yKWLNznB7wIg3FnD6XP6ro2SWZFViQhmhwhqcM63PJXqu5aEvirO554MH7UU3pGH6trRqbkRz4Po3s2BFaqycY%2FZ%2BfMkWo0spnxtmF8F9RlV6mgxJaellLfr8QGZbX81J80Ey4pX45KNRwu4lF8S4J0l8yTLsTwFDsua%2BVSkF8YmL3Z42Cc35uQYx0EGXTUco3d70xBevk7bPPNTHkfeNnmM9gNh9CtTWceyZ9u3BI%2BnQY3Q9FObR1EZcge%2BLWJgJpcFbSPr%2B6lZNF0j3zxm8cWHpZ5eGRzUsXA4h8n%2FVi77wLDV314NzxudZ5x%2FrpCwnIkaccqJyetWSzukl%2Brpzo9d3c%2BPeR9dfnd1BsQNpLBOOm1S%2FmC2z9OS0IFSbTm2TY57aPm9%2Fl5hLfMvvMmyf4Tjjrz5ufjymsUr8J7ww3OUu5ztc6xXJpYTFUgNE36qu7e%2BLAVZuiZXxudFGVlJyd9BMjB9RPOzwVLONnx22YF8y36Wujx%2FZXBYABgfnqwzLTFXIVEmWKfbmFXWUDruhu3ZjyPf8VLzruOL93CCu%2FTI8z37psc%2FGroxlWkZev7NBpztO8Nap6%2FUzre6vqPgaTAUc%2Fu1If8AAAMJSURBVAue9xAFb7JV%2BHRhbme%2FU%2BUq5zgKHH%2BB3DLB%2BQluadRe9SSr7EjZb1wXBlMQUuofhihLdAJ%2Bh9mqOhImdqh0GUhWB%2BAqk%2F8Ix4ENlOUMCl5HrsrOlKPcvjvgpLmh%2BXkPolQMtwayq9%2FZcg3D1sRMzwim%2B0r3aBuapjpynHak0HwsyNOitQcRvZz58NwLaGpduPKvgK%2B1JAW2sqSnbYBN6IB%2FtLmZ9NxiRzfnQV9vJhjUzEd67zz4%2BFQFK9MwfbCQsHrwo6MblLy77mGWao6flwmtpXsPN5Iux%2Fmuq0D%2BuaKn8Z0n9DJuyKQBufB4LuS4ofxgjV220fiI0OBcD0MN3knE0Pz5wRp7FGe4mSFhmYezYRHAJ8xeZTyQfKfLChOzj1ynJsisAZmGdy7s9yCmJPHfyKx%2FJ8v%2BrGfRrhZDlYZ3rtjLYKPd2YmxXe4yXjucV49eVQlVr2WyoB3Oy6%2BDWSfYwbIcZbDGF6xRKRonZc5afMXz96wz7GB5w%2Bc7jOKiUkqk%2FZCUdLa4yjsES74roXakDBCDuX0kdPKht3rddytkv5Oz%2FYFDQTMnlJ6cWC0Wf9W7fi522CMWXaZqn2DnysmtdUc9zTsGAh2CNSbR4Wmxx8JtFP0HDUjOyozjfkuMKFlY0pcuCi5M2V712LIIkCd9rngny9Mt8xxjK5yPeDs8PyxSyu%2FJsMPsYF3W%2BCMihLYt8wS99aX0rC0RlX2gLPdzT6zeMVr7I0j%2Fij0HfrV7ySOfymx5aOUQtm31Jyj1jwD%2FFBN7w61zJ3e1HVq5zqmvssl6UVvNMWEH7QGN%2FSGB5dPP29vb5z6%2FPom8aDs0MsAb0BG6V0eeFNGZMxgc%2FYFeyimwGiZLTxhPZTv562w%2Fb5839YPHUnY5F8729vf3d8vMqoR0sKdbQkVaWpb6%2B5f6u0d%2BiJlML08xYTnlUvKfztEzQOu47PNfKa%2FhwaofUjMIT27h9fj%2FJrIc5bPObv%2F1da2rsE9um1T%2Bfw1WvvLTtuBLjVsnmHL21faH8EPeyyZrlf8DbgJ4SzuJtLoAAAAASUVORK5CYII%3D%22%3E%0A%0A%20%20%20%20%20%20%3Cimg%20alt%3D%22%22%20class%3D%22js%2Dplaxify%20position%2Dabsolute%22%20data%2Dxrange%3D%2210%22%20data%2Dyrange%3D%2210%22%20height%3D%22156%22%20width%3D%22440%22%20style%3D%22top%3A%20150px%3B%20left%3A%20432px%3B%20z%2Dindex%3A%208%3B%22%0A%20%20%20%20%20%20src%3D%22data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAbgAAACcCAMAAAA6Xk4VAAAAA3NCSVQICAjb4U%2FgAAADAFBMVEX%2F%2F%2F9NmcCTfmuUe2OQd2KMdWGGcFqEbVqEa1JNmcCbhGucf2ibhGucf2iUe2NHhaiQd2KfinFJm8ajhWubhGucf2iDeW2Qd2KBdmuMclqKbllzYExJm8ajhWubhGucf2iMclqKbllJnctEnMubhGucf2icfWKQd2JChaxJnctEnMujhWubhGuegWWcf2iQd2KjhWubhGuegWWcf2iUe2OMclqKbllIodGjhWubhGuegWWUe2OMclpEpNdBoNOnimujhWuegWWUe2M8iriMclpsWkhDp92ljXOnimujhWulhGSegWWcfWKUe2M4i76VeF2Uc1mMclqOb1NCq%2BFDp92tjXCnimujhWulhGQyi8WMclpPrdxLrN1Cq%2BFAquM9quM%2FqOOvkG87peCtjXCtjGunimuqh2o2n9ujhWurhGSlhGQ2ltKcfWIvktAvjs0yi8Uqi8sticWUc1mTcVRpUkJkUUFardVTrdhPqNSvkG%2Byj3CtjGutiWenimuqh2qrhGSegWWcfWIyi8UxiL%2BUc1m9poq9pIa1nYJgrdNirNBardVqqsezmn2wmX5aqtCVnZWtlXq0k3NapMxTps%2Bsk3a0kW6yj3CvkG%2BzjmymkXZTositjGuljXNSncOtiWdQm7%2Bnimuqh2qfinFNmcCrhGSchnOjhWtQlrx5jpGlhGSbhGuUhHWegWWmfmGcf2iMgniVgW2ifF2cfWKTfmtIjrVCjLSceluUe2ODfnhAiLeVeF2PemR6enqQd2I7h7qZdFlChayUc1mMdWE6hbZ0eXw6g6%2BTcVSMclqOb1M6gKaGcFpqdX2KblmMa1OEbVphc4GEa1JecX%2BEaE4yeaKDZk98aFSDZEwxdJ5RbYF5ZFJ7YkswcJZ1YU9DaoN5XklzYEwubJN0XEkpapM5ZYFzWUNsWkhrV0MzYX8tX35pUkJoUj5kUUEpXH1jTzxgTj9hTDpbSjpRQjZSQjNMPzNLPDFHOS1CODBENyxANCs9NC86MCo3LSgwKSktJycvJyUrJCR%2F7i4wAAABAHRSTlMAEREREREREREiIiIzMzMzM0REREREREREREREVVVVVVVVZmZmZmZmZnd3d3d3d3eIiIiIiIiImZmZmZmZqqqqqqqqqqqqu7u7u7u7u7u7u7u7u8zMzMzMzMzM3d3d3d3d3d3d3d3d3d3d3d3d3d3d3d3d3d3d7u7u7u7u7u7u7u7u7u7u%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FWBVVlgAAAAlwSFlzAAALEgAACxIB0t1%2B%2FAAAABx0RVh0U29mdHdhcmUAQWRvYmUgRmlyZXdvcmtzIENTNAay06AAACAASURBVHic7Z0LYFtl2ce7DS8MxsdgE0VwoOxTUWFsMHCICKKuXJSbOoG0xSGKjG0OAbmo3NwYKDbYnKWkBjsGmtoOEyXIGNRUvrVOOktpCy2jI5hmTbuypDljY03p97z39z3nJM2lJIPmaZqce855f%2Bf%2FXN5zkpSUHBB20OzZcxacsuCURaWlpYtgYPbsaYXepaKlsumfWHBO6Q9%2B5LKyH5R%2Bfkah969oZptx0jnf%2FYUlMcl%2BfsHcQu9n0bhNnbOg9Ae14zEjtsFz57kfKPQOFw10tmB8mQmr9SC788wiuoLa1LnnZAANDGPzBNpfWHV4ofd98tqMLy7JCBrj9kI0FtP1gYWF3v9JaVPnlv48U2rUTwZjcV2P6%2FHo8YU%2BiMlns865PXNqLpcbc9P1WDyO4A1%2FvNDHMcns89ZF2viGuLWD2PS9%2B%2FfpMdBcoY9kUtmcLFykAOcHXvrIWCIxsgcGvlbog5lEdkrW2LDgQroO3MbGRscSe8BZFvpoJo%2FNzZ4binB%2BSEv2AbWxxNjY28CwKLk82fTMqjbFUErZAwEOmCFLjAG43YU%2BoMlipdlzQ56yfliPv4X8JLb9QK6YWObFDsuBGwLXoseGEwkc4ZC7LPrKfNlXcuCGQtx2Pb4HgCVGsbsc3avrNxX6kCaH3ZEjuKGYvo85SnjZH48VS7l82Cdy4IZzk1h8%2BO3RUUINnOXIcFz%2FcKEPajLYolzAbYBiIK7H9icgn0wkcIwbgRq8mJ3kwc7PBRy6mhOP6YgbqQbAYvF4MTvJg2XbR8nAtUH5jZJKym5sbE8xrcyLZd9LScC1AzhwkaOjOMSNjo3u0eM%2FLfRBTQbLodsEg%2BvS43GeUiKL63oRXB4sF24IXAdU3KSESyRwcrm3CC4vlksZR1xlTB9D5FiGsne4CC4flqurbIP0H4U45i6LMS5P9oMcwbXo%2BvA7VGyjkF4m9sSK5UA%2B7Ls5gtuEygGUmIyi7koAFyuWA3mxnHpO0PXvmB4fSZDrqOgawTvgOos36eXBcrj8Te7wig7r%2B%2BklnTHc5aXHi11eebCpaX46wMo2IHB9qB7A1HAHyv6YHiv0MU0Oy%2FjOZWH4HuY2dOfCKHaTyGHuLd67kCebkyM4vx6L7UdOchTjA09ZzE3yY1kXBBvIpz0G9fheEt8gsxyJx2PFEJcfm5UlNzfh5umAAuAd7CdH8bWB4gXwfNnnswNHuXm8wzHUeYJvGBop3iuUT5uTTb8XE5zH0wOJ5H7c3%2FUOupO5eONC%2FuyQzPtPajk3j28YdLYfFd9747FYUXB5tTnXZqs3sFYAF9u7fx8UBvqbhT6SSWdzMlKdR7FeQBZDH4%2FTo8WUMv8244vpxrpaj8GCyFvG9fhw8ROphbFZX0nj5iETNrDmaFyP6T89utAHMIlt%2BnhfumCBDVmg44pc5XbEEcd88pOfOvHEY%2F5nQo5kEtqMk0pZsuJ2j6s2bHede1QObzjliM9%2B%2BXJbuQ2svLzMZvv%2B5d%2F86meK%2BLKzGSed8d1fuOvBEJja2g0GVA0N9cQ8njtzoXbwMad%2F3VZeVlFuQ39ltvIKgGezVfzwhu999TMTdziTzA6pT8tOznLzUwBaWXk51hogKwO1lYPkkOoqyst%2FuHLlim8W2WVn6YG7KJtNT%2FnUly9D2kLkkMjK8T96YIYw8P3lK5ev%2FMlXiz4zC7szLXC%2BjL%2FG6%2BDPngWgyiuQvIjGsKsko%2BXEb1bYgNxKYFeUXeZ2V3qS%2B3RGGz34xK8jjVE%2BjBiWHFabjfhKCHaYHKD7XhFdhrZswn3llE%2BeVUZ4lSE0FQhhBXDC4Q3Toy%2BY7Q9XIndZRJexpQnurnS3d8zplwEs7BrLylhcox4SMcTRrgKll%2FhhK7%2BBSA7sm8VYl4FdlB64%2BuPS2djBn70cyayM0qqwEV6kAEAShEEmN1zUAdsfrsS2YvnyIrpM7Nw0wX1r%2FE0dc1YFJSIlIrYKChEqOQyLukqUtZRhjMup4lYgfl9994%2F4fWJnpgluPF855cTLy1n4QukHfiYe0kYrORbgysqI6pDnBA1ev5ImKPjle0XRpWenpgmuPmXfyRFfvqyszCalHsRb4jgGcMrKKFQ8BXd7oQEcA23fB2jLl9M4VxRdunZyuuDOTbqJKZD8Y1woaFWgBBKRw%2B6yrIJnkjYqP6ZLEuGQ01xO9UaEt7wourTsuHTBJfOVR5x%2BGe7KwjKiAJnCsMO0sX4utESFjURAlr6glxuo1JjkVq4sVgbj2%2BHpgrP2lcecJUoyBK%2BsDDRH0eAYV0b7TigmhBQXAwwtzL2BQaMVXdFdpmMfSBvcmaZ1p3zq61hURGi44K6wUXVVkHSkgl4WoB6yjFIus4mcxXb9cllv5LXoLse1tMEtM6w45cTLKLQKkoiUEQGV2cpInxaurytQtCvDAiOdzTaMWs5krmfEhK%2BE4qBIbhxLs7MSTPnVgYNPv7yiolxKOCoqSD9yGVVWWUUZ86FoHgtyVI%2FkEgHpx7x%2BpWyM3k%2BK5FLbsrTBnSpWOuIs2gViY30g5bR2oy82lFrCE4tvmFoFzVfoarRiKL%2BeslpBanAa71asKKYoKS3dPq%2F6%2Bju%2FsfjqxYu%2FAY8rr7rKRpqfyAklJTbGyMZkxy8H2OjFbyY9RBGVDTSJuV6IjSCEkmAFGiySS2FHXpg2uHqHww6P3z5w3333rb0P2%2F0P3nzLLbdceSVwvKqCwSKlAetHriBdJlDS2fAF8DJSfuM0hXRm3kD1Rvu%2BlmNyaLjoLSWbeuSRJ8ybd%2FY3Fi%2B9%2BupbHWCu9MG5GDbGDQbI0FrydP99999y8y1XXXkl8pI4kJH%2BShYJcTJjIxklml2BU9Ablq%2BglcBy0oeykjOc7OSmH3nsvHmLF199td1hx6pBT1g%2FDvu69MF5KDZrW4sea8kL1eP999%2BCBIkdK7pqYOPXw2HCVT8j9rvn%2FvnPp%2F%2BJ7Ll7nrvnnrvvRgGOOs0VUwrddIWwmZwWAsSI2SkwB%2BFnd6QP7k8PSCpTEKn41irjYvT%2B%2B5FjvQXB%2Bt1D%2BO93Dz30u4f%2Fz2D%2FAIL3gPZWrFi%2B8nuFbsQ82qHHzjtj8beXElYUl4O%2BCGyYJnle53rcMz61uj%2F98dGHDIgQlLX3SQTXqk8GpGuVldeCIG%2B%2B%2BeafPYzVhoAp%2BP55z93Id06CPpTps%2BadvXjpbURV5OFgGkPI7OSZDiiigyEN3QubGtsjjzxsAnefte4k52mpPRnu75988mlkTz739HPECEdQ3j3gNd%2FHqeX0mUDsahG2qDGx8QehSL0l056D%2BlEy4nRZ8wNsjz76yKOPCBJridpMzNZK2hP%2F0oJr%2BWQ6%2BiiCBg%2FK72ky%2BvTTz8HjuXvuvueUY2ceObXQbTzBNvNzoDEHp8JwMCp2iaPiM%2B2K4tgCwqFqTldtrSCIsD2CHw8aSBlwyAqjU34P9ujf%2Fvbkk%2F%2F4F9jLr776%2Bqvw%2F%2FquXbv60X%2F%2Fq6%2B%2B8carL7%2F80r%2F%2BBZ7yack4xj%2BRnbx16dJvL1589rx5nzvyyEML3fDZ26EnnEFEZmfeT0QwO%2FOQdsFMcYsO7i0ZX7G%2BQYyac91vH3gYDAF4%2BOEHefJoYQ%2FBIo8AoicRIuDzOnDZRQmRVzIOL3QSfmWzib3%2Bxhsvv%2FwybAB85ZNPIxU%2BWa2EaXpmakuXLl28ePG8efNmHjnrvSHIY%2BctXqplryZ82FyOQquyx5RWggLAWk0PAiYC6aXXX3%2F1DS6hXYxVv4RkJ5nWT6ftFKAYWvLYScZ29hPI%2Ff2vv%2FHyS%2F8wOnTxIg7hVszxjHnzTpg588AT5PTPnb1Uama73S6UhYeZppTM0W6XDlEkK0xhKj05d7E7fvsAzf3x84NIUBjU6wRLvwGAEBXj1s%2FI9DN99bOF%2BvlyQoFknZ0SUTSrxs49iJ3to90uOxh6RMLX3MYd68xZMwsLbdYZ37nVIUjIslCHpQMxzZNTSWVd1ShJUm4%2F9PtHQVgv0bjUL2BhFqLNmXIUpNIIVyDFhtftZ64TD4LSELV%2BBh0vuvPv8o7Zxemm%2BhGLQxBh3r506TXEsR47a%2Bb0PFI74exbxa6rHs3Y%2Flkfm%2Bwl0WDlA3%2F8279eelVQEGIRUus3jlBf2M8IcTq7%2Bvu5h%2Bxn7IRzZVvY2S%2F5UZjwyhPKycYOgz3bxf4qh2bnh%2B4wHB4%2B4luXXvMdIshZ72Kmc8J3bjMhko5BsJGkxOcpaYjKmrtMaYxupGFLxyu7WCOK5t%2FJ1dNPfRpTxi7h3tBinAiRUj%2Blv5OKqR8vL9FluPvlc%2BS%2Frzz%2FZ4VF0vM12WnMYr7KVTlu8nLNNd%2FGAfLYCcR4wq34PZzaOg1SPCfYOvyHHurearJDZJOUY5GyyWQHDYs4uwYG3uR%2BETfyTtzS%2FXScQlBdIs04cFrBVSPAMx%2Ba1tnw39defP4JiGyaeT%2FFeSaa3q7Rg9PUk5THfqlVDPQ189mgXXMNaPFz2SM8aNacBfNLx%2F%2BJlXXrnMobc3CmRNHukJuCHa3iWPCru%2BHvm1988bXXdrIG30XhGXJ3PqFfTkr6WX7ZT5UlgpxEqJ%2BnmExm%2Fbtee%2B2V%2F%2Fx785%2FXy%2BeZlCuRfdc0A0XpSBUHw4SlHJrZUpz04E7nnZAJv0Pmzi%2F9UYZf4%2BTkSGSHr%2FFpmvFINbHPVucp2cy6xx77%2B%2BZ%2Fv%2FjKa%2F%2Fl6cROpipGZKegwtJGlj%2BqpCXPSJT1Gth%2F%2FgOsNj%2Fx2GM1bE%2BZu%2BY7IyvPLrGRTkI1gskDMmJNLCu5T6U%2FV24kNuHW75xxwrgJzbQ5i76b9TfOO8WbSyCMg6ldv2axPDm89Y89sfl5LESLCEbJqJU1L8%2F6ESKQ04vwt3nz5r8%2FBpzUd9AMb6cJoXEBGRKRiTxBZV9k541j5%2B1hR%2FQ%2BkbzOn1Z6e7bQGLtxkxFNnqbxpqEuU1qDP8QWNLaoHaSI7InN2P79Ith%2Fnt8s25%2FJEutoG09I%2BLEYnoAz0rACO11U74mHrznjWEtus3L6dQej7OyG3StouFD2J70cSRplO60RAcnnXh5OUsUd3Ha2md0hE8HN5fG4yfsqISAX06zGNHlUS754UhP7ZL2ClmTu%2BJu3mxYx76QyT1Oz1%2FHe4fYzVJ95UE4%2F9UaNfFlJ0rd%2B77ZWMkt9HHzzFpbybDZm32wCXemcaRK4Bbljq%2FV4NgV3vxlpc5l3xdMWjASDbW2BTR4P3RVFldQPpdEM5iMzKk6Tjpcp1HxOaJrG6JpmGt5Sna%2FJ20%2B9r1beQmNHqiU7YDZLS7Z5113iJtOpt%2BeKjfz48zD6mruhx1nb0LdydYYjkcggsvAgvG4Ptr2waZOnVmpXtq%2BaeT%2BVcanxjAevJWtOaWuauTk0eU8Nrtg8QTO%2Bp3lXNXVc2rZmVrtYSt15vhGLhT1e77JDKLhTctabx1M%2FpMdj6GvuYlG3chS1IUSL%2FiOAEUIwMhgO9mxr3uTzuOTD15TDNYvTyDdJHLQUhOVprplwyVswn0rG08VIj6uev591ELAMCWxNumMWO7zBC3bXxwi4XCMc6K1hKK7H0deBgkXk08UVHCRqGyTkBtURMt4X7Gxr3lTvcRpOT%2BWoNe5nkuhKos3e3%2FIU5%2BpOGhs18aJuWjktNONqyZyo7KKtdtzBdsiwkvwmZLbbg7h5fXdhzc3InZsnCF5yeO%2F%2Bt%2FfqILuAdLp1ErVFJGJYcaqFw1SUOyAUgh%2F1WJ9uppaQXYwmPZSmVZaxEJgsG4t1pVhpcqLKuHyOJTu1lMXVzRn2QVkD74YLqPnwA7zlRKQmkE62ILGNJBJjo%2Ftiw3pUvGM995MUkEAmCzGs8kSYg70YYa3xICQ1pm1JpWWYaxk1DUsY9iFPHsGJteZD0Ag79N10F%2BQIDhxlFAQ3gn%2Bdb2xvTB%2F28x3YgWBQdlR4EYaGq09aJDLIl2Vow9t72logFLqNB5i5WYU4c1i0jnbGZY26UM4F68A2%2FulmpVSnizhIrDT26luWe4jz4F%2Fs1vGP4MDfO5CgBB1OhxNd%2BvERBmEOjKmK5JmRSFiSGQ%2BCimvFI2E0I9LX2wElBQqFEAzhCf2j98HmQP02eI4GYxqejlsXz9XwXCduG6fG1iDr41EHz%2FTJJtEKbCk8xofxdumGNbQf8E%2F3RCNHzd4LT3TQt3GQ9eHNHJqT7aeD74qDHoHmZHsA7%2BJyb%2FDItBg7GPWX5PgjtDjCRfX4Hvx7YaOgur3x4Sg5Gs3Zo%2FjESAQDI7AixhSFTli%2FurKq7qnmzu19IgulNMNcq0EaCp2s2fETvKGGGwC%2FoofGkBI2DrKURhqdrkea3kHbkzWyg54JGmlc0p6apvE3c%2FLV6TJ8Fn3FAOh0ypTtH56JBzUHm%2Buge47nI2INAIqT8spDePiDJdNzExxEuEA8pu9PEGyJMfTjRR5yWroiHJhIISNcceFBiR%2BDG179K2a%2Frnysrmnr9iCTY4RtSQqPwR5aFTo1RlFqfidmCVPdbtKxU1uLv1AYEdA0rjiNnfgaWYW1N5qDFyVLaFjDggPXj1CmRrZH%2FzBqHP34OaSx93Ly3SUKRjNdeDcbaBjzcWI%2BiR3RnvdbOSeV0BohSCXxb7yRX%2BmLxWPN5Jh9SiaiJpbgP8ODEeYzKWCUuzT9ymRrKmvqnm3q3q6Ax%2BuFuVz7emkoFJ5NahfS4C5XLXIQ%2BOAbGhpgcIMbgyRujumOtjrFZ%2BHZNKoz6leopjQHV7%2FG3tRBh5mbpN6QsUaDyGuhgsojqJicozQdm%2F9LHywpmZ0zuOG4%2FvYY%2BT1MkFxiTzzeQ07TZo6EiSvCBQM5iQVNVXImW11ZTd0oC3%2FCCQ%2BG6ZaDO9pwKFzHG4spUaON6FwHbYW%2BvFs6pxWQGheXxs8DIhXijkWopGGMh1o0m3pFTUziz%2BQFocI%2FaNIgwTCYTwlqsp%2F03fu1D6FqIDdw8P4B9CO0BBv6RdPEPl0Pkp3sMTDh4SyMJSOrjSYhaKAuOThhv6msq2vahgimyEpJKHzc42SujkpF462p0dMdncVen98PD%2FKE24ySxJrUmHSFpySnAY1vXHlc7w6NvRMWFRa7x9vgha2j8OXze%2FF74XdF4QxPRqPkBXYAT0ZLIfPiWX5%2FIByNRm%2F6WknJoTmBg%2FO2Jx7Xqdrw72LuH9Z3k0MIch9IuimFoxscpMEqzCQX4ZlkdzrgJDe6vq6pmbpR6oS5vrngd6BQ6EehkHopLhHuUeFYaiElaED8%2FDjG%2BMkDs4QWqwf%2FKkRJciCabPC45SBncy0RNJY0OQ%2F89IxAo3COwAvess%2Fr53MxFi97N3IKIX4%2BpDy0Ip7XGiX28ZKpuYILxXQdaW0MgQPVjcT1KDmMMMv6OZcIC0kRXgdEBDIyMbwmI3KKG23q7A7z80NKebgMw5DNNAd8HpdTU%2FMEjeuF5ggUmdfHWtzPz30iST%2Box%2BuhhkKUwINmC15eCYyEieqaAPH5DCt6qRrRal6yLhqn3G760UdLSnK6iArgduv6PlLDoV9WTCRGYnECzjW%2BugYFWN4PHU7LV6awSuxGu4MsaEpxVPTNhENBVBU2eFgSIjJ3rkJM0GvQgU80vg%2FJhg1yKIq6iI%2FzKtMliOoaRF2Cq9%2BrLIH85JsIW%2B%2FPq6o%2BUlKS%2BS%2B8SfYH9DvrUAyMkbQSQlxiRI8PY9dei5uKFG5SfwnJTSJcZqZiwSKvzM6YGxVBkJ84sgsXVSEjpwnnh%2FyhG5dVvAV97MXn83nldlbamyzklSZzLfn8NMDJMQxsy5bWjq5QKNS7RVGmdFIMALbB%2BqpqDG5RLuA8nno9po8kaFaJdDeix4bxIddKZ7kgI%2FV28VRC0l3GQS4dQ270mabO7WFZdRJK2sEzGEI9pAEE0cUTGmFuiIFeqUU5GC9zpF4f1ySWKI5RfKZfWo9LqbGlpa2jN9Q3MBCVrK9RBudlTrYNcftDFbKP5FjIeTz%2BOPoFWvIb6yjGje1nMc5tKt9EyR1J0YR9Ew5OGLjRvzZvw90yTO2DQo9h5tYRxiD40hcCUBp65DwG5OdpIJHIJ3Pw%2BaQkUQLDvJ1wgTAQ2NLa1hMMRRRYiu0OsK3J4XII5tRVMXDZ%2F%2BozBrdJB3CIGjVwlRgcHCTvpsKuEkuK9ZhwTGEFHC4IBlNUchNmvwY32ry1O5hmP2oQVYdEjI%2Bvw%2F2%2BjAdpVtnj%2BalHVEYDLVva2jt6gwMDg0lpyTYkTgpaO3h9KDNpQ9TAVUJyUjI3J3ABXY8TYgksu8S%2BmD6IP1%2BwLigHFBbhwiLSKWm7pLns0sosbXVlTd0zzZ3d9M4K4SFSXoMK7tgRDoWCoZ6u9vaurq72LS3YAi0taKgVT21v74AlBsIDQ2mhwhYOd3fv2I2Gggp3TDAEk2uI4DC4ktKsubkxOJ3%2ByDqR3Fu6HiaepY0Dk%2Fq1hODIqR3mC3HLKzhhKBtFCPuUMl6xiDIjfR7jqCvc19nZ1LSxjoipqqYPTQ34jQZAe6jgsKssmZr1lR2oBvzcVSZwnEvs1fUeAq5ZuEBx7Dif5EkmTTHDg8KrFgycsNW4okD5qFoKyr4CxnbnhKsv3Nm9tfmpuhpCAj9Vo0d1VU0kKkuOxrgtMLG%2BSlZcyUHZlgRQevr0eHwEZZO4iIMnGH8Bu0qnh3hGcvBheuzhMO0exg3AUgEl2Sw4OMnWQEJa14xrCtNlpizADYWD3Z1Nz26sYwCqqqurqxg3zq8OLdtoEFw7TKtmC32E3umVZU2AOg2G4%2FH9Y7izCzvMd3Q9Vk%2BzsJAIZJlILx%2FJSRb2a4iGf21u6uzuDpLdTi92QbAMdndva276y8aaKiurVhRHnrphxS4DuJ5oNMRX%2Bii7uXJONh0ouEMuGtffRld0UIwDyb0diw27MLZ1zgBhxXt9DfAi5pMYB%2F5CE0rLflNZWd3c3LwVOMKjGyQJ1tdJRjqbkf21DlBVW8ISepP4SSSR5MJ%2BtboP0ZxSBVdy0KLaTLmR31qP6PF9YwlCLjGa2KPHwqzucfWReyjDCjRDzI%2BQ2ywHmfYGOwvNJG2rFHqRBSTEQ9FUq55Q4aXIrrqaDUGUe1MJcn5%2FOBp9li8uwJWUHJJpdkn6V3sgOxklPSejo4mReFxv4RXrJt4DqWZmxkxtkMVBNPhsoXmkbZUKqmSiMujJer5hpLotasoroVx%2Fii8ggyspObQ0E9XR3xHeQm%2FNGxtFYe6tuB5zg6t04T9njzmn5n0WvHKTXClylzWF5pG2%2FToFAmtWxmmKp2SI0eBGANeugoOQupEv%2FpES1aZ%2FMe1YRxylx9MwHIu%2FRYqB0bERPa6HMDIU51xOd1BKGdXbYuXYhy8e0JKu7wDNTSxsjWViUW3wn9V8jqVZ67UawPWYwD3O6X60xGQnpdcFxrh5PGEd91bipHKfPhz3w1wn01xtj0g9pKwkHDFmKnxka6FxpG9rkoCQ%2FKOIbYKnhXOUQiBdBIJcyFR%2F1%2FHljIrDNvuc8WUn%2FXZ3QI%2Fpe0mQG9FjcZ6aUNEFQplV4b8pNI70bY2kLLNqpFkp5JZMsUGUVhoVx%2BvvKgvFYZtbmvp2S49sQ%2FHh2F7MDX3Syitzw6%2BeQGtPUHBKfV11W6FpZGCrjfoyZI1ydikGZYrJY16nCRwkJz6%2BzWTgSkqmzV2SNFOpVbh5AsPoswP796FPyOkdNL4xblh26Mnjb27fwa%2Ba0ktw0jVOIr2%2BA6nbZDxbbfRzCiVlptE9Wk3mrNG6W9kVAm4RlFWOpzhsU%2BdeYOUzDdg86MM6sRhoDf0PXSuk6uRP0sreQFtPkN%2BjZ%2FrgznsnpURG29ngL41ckvjMpM61GqFrikYjKjgowLfypVKCQ3bYoiVKu5upgdVDER4HfxmP7%2F4QaHX2%2FK%2Bcfy1m5hTkOEA8UL%2BppWPHwCCvvXGNjof6upvqaioLTSRNq04JwpRZsmmqRK3BPxuN7lbB9UajvXzJccEhw19dU2vNjJIL6dhP3vRhsdaMOQvOX2IZKTlDjy%2FQ1tsnkkop7QR%2Bf1l%2FwPOTkFQr%2F9VGmVklMcZCgNUNePpTJlfZBSj5smmBQ3b4yecuuzM5OU8g%2BObQTz9useLskxadf%2B3tjJZT5YdH3Q2B1o4d1lctd3Q3bzyA%2BZlxmAaqiaJSdVkac9Mk4FqgtHucLZA2OEJvzqnnXris3grcsnM%2F%2FYEUa06fveArS65NluwQ%2BW1qbtvOLzuHxU20WH%2FNf11feeBV5gYJmYBYjFjXD9VGKVY9Y8oqG6OszytjcNSmHnXyqWdedNFFS5YtW3bhhReee%2Bqnj0oFTbJD584%2F%2F9qUhUZtPSQvITnyDbIMJnzA8VudkpGx%2FDZBNaUu8kAzJCfkilwjGH5FNTnTZFbgcrWDZn8B3Kcb35ZPHvhjEC786sITGja1tAUHknS1AL%2BnDgh%2Bqw2gUvhDQ0ln4mzqatmGe04aGTYYQBfkon%2BgC1j2nOTJDpkz%2Fxxwn26ZoNslRtwujz%2FQ3qN%2BZFW6qSjc3VRgfqtFP5asnmqJkgrLWI1Xs8VFWsLmdkMOSZAhePg1EBVX5AqiONUOmzu%2F9Lo7XIwW0h8nR17qIXkJRnjaydBRPwr5Z6H85xojGcvyLbu6HPxiO%2BFFwDWSSi66nix0AIAjNm32F845%2FzomN%2BI73W5JgK4GX0t7L78Nk2uP3f5XCP2tkRo%2BRUWXatS4ATZeA4xamOAINn8jul1oAN%2F%2FUJ1mUpE3g%2BLvgiW%2FIAEPBz0KksnQtQGSl47goCo%2F6ZMIfdvzyM9wPS4JPnNvJRs0CFQehaRyd2MjCXDkBQ31InL1VVXrTy40qCQ2C4q%2F6%2B5ws6AnQh81Vz1EP%2FzBVJ6yyNV7eDC8PR%2F%2Bs9Ioltx7utjgdpRUMmp%2BDjBE70C6qdCEUtr0WSR7cblkZjJC1HMWFB8EZxdshRRBf3XvHj85wzCwS57sJ6GnGvKUPY2CGn9l5D48fvMV3Gb87%2Fzzl%2FzSrZgcAt0bcMc1728Rt0rTEBgJb9%2F6zLvAb7WxAkijIyXNHpWtwGaLX0BjCP2N7ehm2ejAhwpNJX1Dxd91bp5uGs3lbvAHOnr6eN1A07ooEQAAA8RJREFUnSbvB4URzG%2FirhupF8CN92lZic3UUWlZNIDgdqN%2BE8ZKGEa5pbWr695C08jYcPF3nSI7g3kDULurn%2BCS7v9D%2F91bn50Qfr%2BxVougKKBQTCZ9VVfJrpbXCujDA22Cm8BHKvHGxosLzSFbmzF3%2Fvk%2F%2FqWbdbqY%2FWetD9znDrV2pxU8Y7g9R35qv0mVrCALPEmEZ6oVAG4NZCboYhzlJEU4LryPFRpAbjYNXXr4sdtl1h6bAO6zrTc0GGF3%2Fpuz0Oz5rTE3feaX3cxg67biKNYqKCnksPq%2BVOiWnxg7bM58VvwlMeDX2iH8J75qG%2BGFILr7Jbx9W1NdXWUmdypVjpMbVlcpLtNCXvJyVY9vbN7WzT9IEmxMYe8TbsxmzwX53ZGMHfafKP71ivKPfAkVc6G0gu%2FuhBKiZvwcdE1yIAZxWfc%2B8z6SjU1bu4OGD9uFkzHb1Nh473GFbul3xabNRsVfUvnhmtC7CfLPYMR41U88Y18a7N7anEKElQYASdnwC9tqrllT92xz946w5Sd8epOr7V70PV7vY5sxZ36psfgzmRciIMpAkxYQpCgMb7cQ4RoDIakLK%2FUHPeqeam7r7rMGFo3uHgr1tgeSYzvt%2FY2NG2QvpdeNg8%2Ft9ngDre29wQH6NQzSh4vozWjsQ35UhKDC1VZSs%2Bq7IhNr6kBfW7u3J%2BMFJXUo2NPekiqyNd573nGFbs882yFzFvDiL7UEvYFAe0eQ9KJFFOUpV5TQ57%2FDYQiHnc3Nz2zcWLexrm69jK5m40Y09dmmpm0Aqy%2FVR1cHwqGu9taUwLCtOu%2F4QjdjwQyKvwt%2BPJ77FAi34K%2B3GGQ3UShf%2BJfbx78psJ6O1i3jE8PQPv0e6uJ6t2za7C8suuDH6eFD5sMqJAgjpCDM5QsXBgdCvV2tyaOYkdnFpx03ScJamob7ztKUH1OhL4AuBgbDGXxxCbXdkYFQqKenNXUQU%2B1eYPZeuAhQGJudgftktgF19La2d3X1hkKhoaHIkBnk0MDAUATD6upqTSOCGXV2aZFZWoaKv9J03WdD8gZvSTNoJbd7rzjv5Pd4R2QB7DB048Q4%2FDZ4c0STFNmqSxYeX5RZLnYYuM9k4S%2BF3LK2VVect%2FBjxbRxwgyFvyXXbngX5bbqiksWHnd4oY%2Fz%2FWoHzZ5LHehEYQsAsNNOPrqosfzYtKOOX7jw0kuvuHFVVrRuvPGKS85beNrxRxersgLa4Ucft%2FC08y655NKLV91446pVN95roISmwuPSiy%2B%2B%2BLyFC4%2F7WNEdHtj2wTwm8v8Pkm%2BrFsKSnCYAAAAASUVORK5CYII%3D%22%3E%0A%0A%20%20%20%20%20%20%3Cimg%20alt%3D%22%22%20class%3D%22js%2Dplaxify%20position%2Dabsolute%22%20data%2Dxrange%3D%2210%22%20data%2Dyrange%3D%2210%22%20height%3D%2249%22%20width%3D%22166%22%20style%3D%22%20%20top%3A%20297px%3B%20left%3A%20371px%3B%20z%2Dindex%3A%207%3B%22%0A%20%20%20%20%20%20src%3D%22data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAKYAAAAxCAYAAABQ69KMAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAHXRJREFUeNqEXW%2FEbld2X2u%2FrxBCCCEk7kjdunVJ3XHHMBVCKoRUxjC0OqaUofphVNMvjaE1NbSG9ktDCaH6IRKlY9IZMzoavVq9xFyuiUYuISbmaoiGEC7h2avrnL33Wr%2B19j5vbjx5z%2FOcc%2FbZZ%2B%2B115%2Ff%2BrP5h6%2F8KRUSov2j%2F2cmkUIsQsxVf2X9z8%2Fp%2F%2FZjpqr%2FFf1vu6bYNVs72%2Fft63b9dn78fvxPr4f22tUMbbYnCv5f%2B5H7V7Xfhavdsb8Hz%2B1t%2FdPXg2s59E%2F78bj24yHozwP6uWLtTn1rzxpNcW8F297HpN3zkd77wRjPbZy1vVt8wfj4e7RxEmJruZ3n8J77HLHsfaz7Pds8nGjd%2F7L3jtMc5fFqv0j%2Ff%2Bl3nvb29%2Bu2Bqr3o9JZeCb%2B83PFxinT2TkSpQ0CiT0AL2bBAadO0P12FnhAhQnyiWFcAPtgSyfGUyCqcRwHUGwi9uP%2BPKSpQJTc%2B0F0TS%2B6T9u51k9d1d%2Fv19MPax8u9Ql8RK95dPRzvFeBd6W0RGLfYEJgMdjQwKQjAW5EuWjvM23j7f4u97SP72x91H7d0XY%2B1d%2Fu6nUf6t%2F39fPxePa4vxGJz0Ve6IPI23zUNp87w5HDd2yLWPyerW9jzMcYdRoYRLe%2FG8cWfQyq0wmctbnXs%2BdErbH2UrW9mFBqdLxre7Hx4rSv1NiB8EL7X%2B7DUoG4qROW2KpqE9GuLX3gCNoaK3PiopWvabuXdYA2jnZJf7usnwdZ6lX9fn%2FmLsilkYBspLd%2BCRKKDtNGvAcT18bA7y%2BhzcFdauj3ipvA%2Bfu0jev2O9cnURLxxNHkjrb3qR7f1q58ov28oz%2Fe1o%2F%2B5U9ojJ10RrqNbSfOIRHau5XAdHDhsfWx2oJq0rOzC%2B600NsS5hXfP3jfQbBj%2FNrx%2BWDJBA%2FfxbmtKGgWuAFyOOnk59du955s%2Bga3bNwYWXgTqU4gPkAM5%2FXLA53wtgm73MXqZT11dbxIHADxQTcFIIvfg0kQiSKxCPxWYLWLccpGaFn8cTs%2F1Ixt8XdOg20MFSAuEpQ0oD5xXRHylT5%2F18dP%2Fu71o41A9Vi5Lr3diJXe0%2Bl9f4hjJwzvU1aJdsnWJZxJCKk2rkVAfPMJORn0B%2BcJVSdJdNO%2Bn89CarBYgcFtEyzsehHDgwdhj4kb322F2co7WVeNa3AQ0PfrPV%2FexK0eX9PjK3r6ih4%2FwhQHPagZ%2Fbm167Zb30oifO73chKp2F7kp9XGb3AHTmJxfPf3dXHHSUXa3l3qggjLzI2jxGmiefSBgmKD11WzD4DRPNw%2FT0Z9VT7Te97WFt7ZOO6mJugzbqqOeHdrtpg%2BK%2FZpDIATIVGwIUzn7GM%2B%2Bl%2BM6JFIV5zZ7ZXzqGQ3pTnoK5n9CiWi4DDZqGyjiiBGNGEyL%2BsTv6J%2Fv6wD9qQ%2B%2B5qLx5om2XUmVxEqiN9BTDh4tdN9VxFoVhGODK0qTUS5qGPj5iUYIDLxuaUB03Ve15TXKkIkINBNRSYVYTAQBr39yOAA1aCpCyTXuwTy9lnuams39bFv6Xve1Pl7S9v%2BjKyfMi0cNLSkc9rKpS8blIDIEGQyToPOKMAxZYilxMZdoQc2TrXT91Cu2YwYXA1utZlBdEkH9Fk9fkZPP9VWsyRr1pV3Rs6Kkz4WkJRm%2BUsWy2TiPC4wDhM58ADTs6Vzp%2B1dVCS5uOuLduduTWSZASazJjWrNTUYOn3JWL99ol19GhyHko4tNs6nRMA1EOWRJAgLRSZx%2Bqg%2B4%2Bv69%2BvO2eoN%2FfOm0sANpYsbUQLunAbEv4t873vijDImWpbqy%2BjTeeYYwwgx8QAK6XiFQbwFpgGNm43b7Fac8P369Gf1l2e1vY0YH0c9w59RTBMMnKy%2F7DTQXao1Ec1GDhW4yNCNBiG1SRMYqAEZDU58MopiUEVsgLvRQGixy7zqMxdGLumwi4tAvH8seJ4seDZuPxlNXCedlG3RNChoydlFApd3hsRJRO8M5KmOkmxGlhKn%2FEx%2F%2B4FO%2BAfURfxY8gWMKzGpBu%2BUFhwlzIXHmPzrK3%2Byd2qILVxJjlNy4pTFROPi34P6UWLk39Vrnm1644G4Cvjn6Bxa%2FAhr9IE3LFWgT7Q0HPA5JFG3C%2FAUcOW9fcPjuC%2ByutAB4xJziKtMXHmFsV6MV9LnYoGZI08GE0fi2%2Fu1UB2iYRLRzBkWi1q4qiK3lPm8oXTwekcBJgOn0ZMbwf4%2BbKpjXjiNUXQuWRAKoGIcx14SMSeuJlY6pd%2BnLWwi4Ef6%2BT%2F9vKbNf22DazgJNwHwYXC3apNZAxY3rq27yCYwACStOJ6mq4lbJ6IsNhyeiqgEiRPlgK4yiD70JFQTSodfRhvtWhufgNGKLW6XHEIO4fjknMK7be8ki3fFMR1QDSIJNtYSNeHmAKEgDbu1B6qFJEeIYxr6jOtKN9%2FVo3f1vd7VG%2F9Cf34kM5FqRtApMLZBR7zA0cv8ohwUJk4QysZBdqu8NXhVz%2F%2BtHv1Kifif9crntINnbTUUIA6fRoRvULxlCL0NDoD1ImCE0MQp86SxiaWIz41n7lIC%2BkEJq6Pk5XDCOZu9PeBwaP0d%2BpPrkNt9u6rBZKJ8qAZu4fOEOGCfd%2BB6YZAy9tiA7QISKHMlNKikj4Vr4FG1QIZSk43C4IXbIbzv6jv9r575kfb3eW3%2FjAGrdpYyoxB5HkrmZkIIi5TJEus6n4pq%2Bk%2F99X%2F02wt6x8PjJZuHoBHG1qHokcAn%2Bcv7ZJBN%2FJjYedVKGOBhiOwvL5zwXJm4qkFaE%2BGUlYUAepujCS5R6mT4BBCcx%2BKS7g0Z%2BJ5M%2BmGFsV4B0yiumxJ11t%2BJE9ccALgEaBy5Z%2FKhdclYgmwbiw7Rh%2FHetS9SJgFdv0uZsv%2FynH7%2FoR78Sj9%2F3tU7wGQ5oRVOeUNnLdEt5P5QF3Ft0HqnvqbHP9fjn2zYWNPdsp4ny5cb%2FndJBOCck4NP10QS1bCWMqENQhFyfQWJDPvWxGiyAhPWlrkuT%2B4DWLjMafI4Ep5IIP72Gx9oirIU1cihKSyj08QNwwIWwJ%2Bh3wPOi%2BOLcB9P3j0jRlucCw%2FYWEQ16Pcq1uWv9Vm%2F1OPv6eehRrhAH4KSR0xNKpJWU2Si3JwfVJ%2FSSfqF%2FvAvOiDX0deZDQoTKv3lxoANKCqMnRTUjpKokAjVLCZTEBFAX7DM4mJX8rkmJZt2ztImiQ3tdIJwWH%2FlZhsLzRfbyiXH0X0LhCoTsHQC7A%2BdFyUs3ov%2FRa9dvsdUDUlcFqQWwzg57lwnr9m8aKLHxxejPKjH39E2f6nH39Er7gsL34zGjhJvhqIEnU1scAfF6%2FE%2F6d%2F%2F0FNP4EAzGBhoUNiAcyOo3RvBbCKDGNqY3IkUjBaii9x1KSAC3GeTiAXgOd7TJ0j68hE2vzHCTnsblZdGC%2BK1JholCMX2Tot3jRYuEmoFRkEA%2BZAbFYixjjNSlj5pzoiCSICJogRxl3Sc31ndCR4vKRculq6%2FPqDtfk9b%2FoUePy1JvRoLYDfGSwexax8c%2BLfBPe%2FqTd%2FM%2BFzuGBIUupd4QBaSFW%2BhgYJm4nbCiBw0DopcsFJlNmjM2i0L7h6NFupgtXRdaFjRPHSfrMQLHRgtEtQN7iLS9NOuc5txVhPKwAJPKROURlTDmKAqEz1MURkqaQ55IYVEOATYBDdtJnsI94vBiV2C2YITpKErevzv%2BtmY3v0UEJ7uVh3cxF1vdKYvvFnar21Kq%2BDDF9YwAsBZD%2BRueXLQg6JxseuI3YIXC%2BHqA8Rsv8%2BDUvpE82Qhr1ZwsUCFDp%2BwTP4aBMQHsF%2B4ggZZzc%2BPQRzRuMCFKoELDiPBiFPExsh1Vu6OLQF%2F9CkYH0gowbgUn1gGCGi2ASCUcAoVFAt%2BGf1qc7BGUff5Yj58xvCmYUgkQIjf1N%2F%2BW48eRzRkN5xxYBv10r9px17wlV2J%2BZhFIxeMkzNHxGTPicESYMEPg2rXyYSDch5xQA%2FOCMHC4%2FksUx8HsaTVC9wIpYZAgG%2FkQua1YUrhYtFnfYQDcuJ2PJCMjmy43dG5tkmjOoEtQTXhGgKoaXG1v3OZdV3hpVcpEP0CvWChSZ%2BPqgMBE4mSucXJ8s83Yxqx8QIiYlOEXttk%2F%2BQPhVW%2BDMtaKNFECwuZ6xzymzidrWIGkIoBlC%2FiLrdkySHRsOFzEDCwIPIBJrdraDKcgovTruPJ6l6pDoMb%2BjvWJZF4DOrQyYff2IF2N8bEPHRHvMI5epbXkoJxOCExcsh%2B2tyJhSNGw0oORT5io43DC3ju%2BuyxPLQ5Z%2FT4iTFHZaxI%2FfHv9bLn%2BcCrwlQPQBsBOKlMCrJHHNEUsCCTSB1GU0nGGE2ruhktZck53JMihvU1YDt6oXYxzTXERNbgaSmgj0YYSYKTgBe%2BbbdqkYOhfj5Ep0NqjRgN%2BJ68P%2BBgEFnAbY4LB8eAyIyLdkJDo2sgDLPTpSYm4otfmA7uoaBbBjxUVlKXH9RzP2kQEzUcUz%2FP6RP%2BeOVnJgh0FSqTDjHyeob%2BVQh0ruFeWoSJxZUKxF%2B5p1ycAVH0%2FBWJoWPMx6kPA6pyNaMupNXczvDICEROFQg8yJjvalFQ8j2vIK9hHaNHrHBEAnzcxNJPUAJ4ny52QqwJBr17Yvp%2BkAy8Fu3oKg7pIZxw4JwHZoE5dUJGuvR9VD%2FfN6VHT35%2Ftnjjimw6k4tf4zIQp4cuyMBZhh%2BOUGcSEDmQV9QnB1MuBnRRuIZBmMQicDac9HVUTYZWSvCETO67AB9FXQt1NgbDDY2f4HOnMi3Qi4I0CCJ3MESR86IL3J4Xi6aAa7mE9BFKILxQDEyOTouYRsIH4XV1wmORuMu0cPpY%2FL7O41XV2MqT2wGKpzHBWdQUiBUc1liMpu6%2BZCabnLEinIAl5I6ESagdGskrdQFdDNbn8EZMCEO1oYKoqQCGS1c9gh%2BYKWW6lBD0IBBwEqLIJeKrA8vMqRYXGZEVUYbRtoxYzAODJxhoh7azqUct2asapmw5SUAwjkJIQDWyKxM9VYN7en8avSxBeHTCTIAVn%2Bl0vrCJ8qdXWXaSpAAnrjjEU%2BB6TJYhxws9CO%2BvpoVh3ktvR2gKt0VIA1MJPDyOAmfx1GGG3J%2BsmNd%2BLobHoY%2Bcekzqyv3aHAgYreTIxD5pwiZyObgPI9eQvpCHYWcKVRtEijH5C6s4hL3NVoB53pLUARHqgS0shqG6JOCsABizwEg0RB3cGD0dLEheqmKtX%2FTU1tKjCIwO60%2BozPpIWpUleon66j4DhsYgtkoYxIHwx2iWNUAdXXuYoTi%2F87DGMTrblXa0kN2TU2n2G8%2FIhEwii1N8I8I6Iy9%2Fwz1k4aUJrtQRrZWMlPY7T%2F7sHJzhzEImRwctJF5WKRAZQIeI47pue7QFVwIN7M8UXiIUY4FmaIohumpysrBc2s7cc5Y6%2FLXVQN05Vq5OCrGsuFvtVqZdXxeDEXUyg0kAoEaYJHsYOBCL666mizGHELhchIBTTGVW8mnSq0pSXWTponNssi0QxGiDFc6cgiBm3TJPdkxym8PHPDGQJySEJnEMDCYk9on5rEOoYsdnBxwmNiqnEEuaxyTgoUzBeK20ylOSe1tLd7IoDH5YBENZDvygMhFd00FpAZPUoEsZpzQ%2FtQRvhUfRtG%2Br5K3heXAiLSmnBaahpXwAB59z1V13qokb1gVxHJstwcuTC0t01QD1%2BGig0NKzFSe%2FTAsi6%2BDZykZGgFFgqIeiRPO5lRCmVzCoOzseEoQ1heQFF2mO%2BdzPvb25JN%2FQzykmt8ME15kb2oukUPoQgT6tRNDkWA70TdTlZGEg1PBsEykSLdARMLtaZF5lRA6MEI%2B0ljkMd9buJGO3nALDZOmFGf6raaFBqsHIaXK0Ik%2BkWKT8ygtEyUAJ%2BfEiIfGNl8lhtDSy8lgMHNtD6WT2NMEiY6HlIoPF%2B4PNJXlXr3zVNbwOJXDPAl6AwzkkXkJuJC%2F1DPfEsLndDBLqf9t1EfQOE8wcn820dvElH3WemByUa8o%2FWO8eh8lmqXKo8tNUlZGUhzGFAb7JgbGmckRuvPL5Z1GOoHks5VOX6kAMpcsqB0%2Foh0iZkYK9ryXEIOQI1aH%2BDacH6rliKED18etpM4UqRFCYdLmrn38oHYp4Ubv1iQWhcg0BnEexKAN0j0aDxzfmgShc3Z2XCB4j2d1rQwGfNHEm4HOWNVSyAsDHREwBIUygV1MgMFMRhGIFDsIQPjm2MBN3ZlyIUmBixXTEI%2Bw1BzrEsRCQJiVx7bUbNCarpcJcRlSnGMFaZKnKYFkut9alG3%2BzY4IxdkGCd%2FEv9eheaZ4O3qj097SVUyDGXvSqBpeYE%2BxYKSs9YhZ94mVSwCs0IohQRCDc4AOW6xwNmCbpUQEvjFYrogZzwO%2Fs98XgVWIJETsIi%2BAzs9VqYWsyA8qlQzh4PcJ2GN2zcuLlsXBpUqkZ18Uj1i%2FwhR9BTdyDqBm8eRTE8ME94pmtAzpcMbQJdGd%2BXb%2B90iLYO3alF%2F9Ub%2FlWcPj3qOscsznnupSlAn%2BRRwND9zOXyGB4nhjUq3LM5spqxXSNldXqPv2kkwparZOC7glt4oBS45I0caa14ZiirLrox5yi4OSQFA3KvDDYohTIkNFcX2kVkFig7zEqjJdFC2nCTEM%2FJYYFooQAx8VPdZz%2BwCrujUiePmH%2FqE19Vf9%2BGnOTeeHSuwjgPZqEE6STZtFCEEfIMDweeo9prrQIWi1JH8v6GhpykoyioQth1mBJ5QTX7%2BeE6PBVjVFIh8ZW9qHPsY3ZSPG%2BY9aoWDZpxEhdJYoitACawlNYnxzGbHqkfYlxlRNK4uZdCXCRqxuEMZsv611Kd%2BWzYUS766FPut72hn75on674w%2BWoAMugwFC%2FUOZ%2FKUClTcqFGwK4DHXUJ3Dc5M78UsSK6k3Pmld701cCgu1ogvWjKKerFYWabqrXHK0HZDAxlW5XudMlF5ORVKOe3ZhVsxQDMaOeP9qArLF1ZTot5YJbVgFIs9Sqpqw84UvSV0beV11GdmUDOeNEP9o%2B%2Bw1ktBjNPSBWASG3tOLv6S3v0TJ9I%2BFBGTh%2B3QOJL3y14wXQjGDrmNlQwX96Ty5sOTAkvT40V3tEOfEwfIFx5KH30XcrqAFynWpToQUZJbkaz4OpvWyM0JEcwXPKZ4SKjeX7M5EzxkuPOR0PZwuRMiHynV1CfOEcLpR%2BkWcI%2BfwP5RyGVxfLM7b2t5v6a8v10XYXMHqbgUSkbTprU7Ntzfuqd9vodUYSa1M9X1GOL13PoLDJVQlPpmSvXRBGtjMk%2FhFvC4bLzFCfoZNRrDsWr9lIoQ4unXqizJXzShLw61CqFeMbcVwPAruzmEUoLHi%2Beq%2BQKZCY0txmrxZlSFSPpZlzFY%2FLWJMTSXpXi0vWsYHkmsF%2BvMnek7pir%2Bo7d1a6%2FBm1AKYXDnUCtK%2Ft%2FX7l7TNb%2Bnx%2BwLpoaEKcciBcUA2exMQ3kBrPIrDXgZwCbtwCMnyGMW1qVUPMgfDX8lGUFm6KEsotlWWVTMQZLaA3ZIJMVu1fKHljF6tYkhGOYwpCB4wzCEfqSjB1Zg5eVmYNrwKT4N40QouzEIHNaXu6T0qgek39NxLWQUQkBS9dI2A%2FiaAL6bKuKJmPMuv6zXf0M%2FbFvLGvPRyILuvIdKcJn1qPw%2BF5UU8GWzlJ0Y4BWMCV5mchnJIWaaYDghrCq5gBi7O4KU6CwMvHHVr9K6Yw0DmwNtRyGpO9qcLC1mN9OoSUhxmw2ykfXDwZJVkzCRJMTJEVyFqVJY6PXr5Sk%2B2qzEAaKsb%2F3d6%2FAXaJbB8uEJgQvSViFc3yBAQB9%2BppTicdLBf1cZ%2BU899Vf%2F%2BeH6L2ZtQFpl4pu%2BMQgQIsbDQsp5RTiPgdaKV764AQsbyzTml7p5AxwWPSu9CtTqgJ7h%2BRilWNZAy1wh4pcXLyTIdZW1g1oVXKG26cIjjSlAFeBUtNRXlLYeespnT%2BrP6mLyv7byov39Bf%2FsznfCP1u9XQrEyY1orR%2Ft4ZXcN8ir66A1t6Hf081hD6%2BmuTBYjQTBqLKs3ezMqAnBzQDCUd2GoELeK2cQkq1xzUro17cSApf9QJ0ZHZYW6P2WxINZRQvm3kirmoTPiKG51RaSxekqNKQ1QojHjuBGyO9J7EQiVCfDHRR1dj3s797T9V%2FXdfluPf00%2Ff6N3fjxqj1YpM3KBBhIjB2ZZxAlCSWvGUoF5vZpv86%2B0k4%2FpGGuH%2BKW%2B3UcopJrxubqo8lZT9d0wgTyXvA6QToriWa9q18Hcul2BxPUg%2B1NS6USadMfMHZF7E0R3YxprsfFx%2F%2F%2FKETBlaY6YRqEAue26nvDk0o3v8nkIh1icaJgLJijFMyAf%2BrGe%2B0P9%2B5jOyTf075u5fM8usYqAPl3AzoB0mD4m54MIRhKWKfpcl3F%2BVsx11B3CgWR6Ux%2F0ph5%2FW%2B%2F5il73PO0VPbZKwgRh2VHMFDCO5nA1YPljhWGmXxY%2FHRvM%2BiZPVdOIiMr0naCQ%2FUUuvJJKU08uzeHOkzUMhvgp5pKvtm0ZkgaT1oSyTQBzZQVpS0AniNYbKeS8n2yxLzZS%2BFTb3KoKv6qf7e8nIT5XeL5H1s6WqULyKHWN%2BTpWo1c41Vx3w8YJeM0pwFK9qd9u6nNe1C%2BPs%2Bx115%2FRyXpaZ%2ByhodvZ9huhAoWXreZFTXezBmUOgsVwLyxStdpVLRe4ctBJltao9VNiBQ6%2FMu4gx1IDr5twXyjYgruboajEeut8YCkjV15VRHEVC7BXce5qxb4gh6ukKnl66ob%2BdkPn7Ibe9%2Baq2nILWRS3IWRd0tu3A6FUjtUjts6DNcRQ49IaknksWLoq40n9Y4ejOLhm1Gww08v6rJf7ith2S3hGr922TvmK3vxIqEBBEV%2BM9Snr5xS%2Bd104hrChjhlLZ6PICvvvbKKnFvBI%2BSP2ySqu8iQXW4JXYp15SjlAjZgL1EIXdznykdSoqX67E0TccQ25OhYfqyGvP%2B0%2B8pmO8Vs6xspc6L%2BUUDbR%2FKnHXA7O7fhC1qstS7X3kRc0ZdxevGr18ISdD0gjuskqbKx0mv3kkN8tYDxUi4qGPQ%2FZ3WLg%2BbmlE3DLV5tc0mep6D%2FthMptq4%2F7UPFueSVt0RSKosp356IAbsft6vJODnIoOn0Hj7PA0UMEe1A5eBKV%2BfuqTjovAk2CGJ5C6WTJgYK6Ilj2WnyjqL2mal0Q7n79e3q8gd0bId7UuXmrQBQY5ugzBK8YkSUJGne1OMrhgvHmWV04R9%2BzVTwb7LvUxXYhfdVImXxtBSxstNBtZUIN8I3biAXRygc6MR9op14HTrhtNDp2QXtCj6%2Fqs7bjh91TBQVH5SxZn6nimfRnhnxpDs4BOtjhYS7dSostWlLwhKEBdQraiHnrtNDt3B%2FdJGL00dewD2eFEgslbD5LM6x1SwntjrawEeK2S9qdfas%2FaqtvqD1jbnLtSlPnsJAYxwV2vGclhY1agw48kmlANThf56%2BwBQvPK3%2BOZFltJIS%2BbtvL0KJtyHQROhjs5mXaNvLkn7XB5hF8sJUSUQLla7T%2FpSvKTS9z29LvALjtJZiFpvykuBlV3GkhKuZ1Odi4zSDRvL9O3K2CPR9feHLxVtTtko8Atygpy6BkGjtDfKxtvKfXbftJvrNxQz3eCPC9DB0JGEACW%2FexZHdIXUihbn1U3mNHim2oRYEIc9l0NP7yBqwF1Irzw4CMvLvsAl6wjjJuRrrewJMBErGkNYsckuCUm2OiCbaf3ixAeYv23bso43TXu9i6ohdvWzlf2jms0EO6KB7XCdiq2V0N26eERSUhcJjJ9wMaBWFXbkOckChqxSY9MACJADcsxknfg%2F592KG5LSTxjj70pL%2Ff7rz0Hb3%2FHvcdeSURBG7klbfIs1pRY8FUV4tWUfkhEknE3JzZtRvF9wzm07SVChNuyHqeO37EAacBA0KksBHdabEpEy0gE4aa7LKMHMqbKg0D63hHXbrVjahbBOgCc3af7e%2B5bRN91tUDIGLTqR7Q4ytjb0V%2FJ74eYwli5t%2B80ZLhfh%2Fobx9RTIf9WLv4PqgkW8du9zbe0fP39J6d2OI23dy2Zk5qC6cM0zGcdez01t8t68EehUS2t6W5ZIVgN7YK0U4npeEzYyfHaE1Uk2yBjBjYNO9jD8%2F%2FF2AADp%2F9%2FkGB8WMAAAAASUVORK5CYII%3D%22%3E%0A%0A%20%20%20%20%20%20%3Cimg%20alt%3D%22%22%20class%3D%22js%2Dplaxify%20position%2Dabsolute%22%20data%2Dxrange%3D%2210%22%20data%2Dyrange%3D%2210%22%20height%3D%2275%22%20width%3D%22430%22%20style%3D%22top%3A%20263px%3B%20left%3A%20442px%3B%20z%2Dindex%3A%206%3B%22%0A%20%20%20%20%20%20src%3D%22data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAa4AAABLCAMAAAAf1ZMtAAAAA3NCSVQICAjb4U%2FgAAAAXVBMVEX%2F%2F%2F%2BznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGznW%2BznXGvF0qvAAAAH3RSTlMAEREiIjMzRERVVWZmd3eIiJmZqqq7u8zM3d3u7v%2F%2F6qauNwAAAAlwSFlzAAALEgAACxIB0t1%2B%2FAAAABx0RVh0U29mdHdhcmUAQWRvYmUgRmlyZXdvcmtzIENTNAay06AAAAnhSURBVHic7V2Lmqo4DF6Og%2Bggg4hYkWne%2FzGXW9skjddRATVn9xsubUn%2FP0nTFvW%2F%2F8YmQRjOw0ZmQ2vyESrzMIrjJMtypfZKKd0JtP%2B1R6CV2m7SVRgMrep7ShBGqzjNskIp0OZfx1FPUU9YyxWgC2qbrqKPwz1DapJqJ9qqA%2BaEydE7YNlrpCyyeBkO3aGXlJ6k0gKPvaUngR9inmQG26ulyn6W80%2BIvIeEi1W6Vb8MYhz6ulPABIAdtFyUhJ5LRpw7q1SexuHX0B2epsyiON10zgQOVuhocDyB40XTsoQVdxc0vUmZbUTtsjj6hMgLZbZMsn3psAULPqCwZnIHYLxwhqgLMkZdezx06nZgS5afLPKoBGG83irMhAhvj7BFGTCNzMfuQHe1z%2BoQ%2BWENSRDFWVE6mGz2Bgw87YYpfIu5Cq1AYqV0Lh860jvjUPk6%2FuT%2BYZy5PAIYdvjEOYr9w11OINcd21REuIezkjPTg3L3rrl%2Fw5Rg0x5CJCaSgOaVhv4%2FUwE4WZgc25DgkZxc%2Frw293%2BbEDlbpsrHSJNBy2V8GHSwTPGZlMUfgJ5jdngNO%2FghWnBAFf3YXety%2F5cOkVFaVAjhv1k5bcAMNAx%2FC%2B%2FpOHfkBvZqppQlfJ9nqxfM%2FcOfAo9BGBB%2F3QEAxTzARbk74IvnSGGjkz9AIsVITUdSz70fFspik7zMwBauS9JfBgV3HJ8boIXIuEaJesTsmdwkxdH6cWthlcrSVfhvaLz%2FIkFSepGNgmCt9kRokoKhUBxMWOSXJWvwHRLcrAzFPTqx4A%2FF%2FmaCgtqm8WKSi1pxRbvJwdDYkrt4iXl0to5asKFNYB%2FDTI8AGQTRhY9yJEgep%2BoYg9A%2FCia47p9aq3vWYoTvO8wxj609oUM6kaPeT0zNG%2BskmzyoLJnIun9BIRfBBs2jJPAopUWIHSrmKTRlQHD71NAZnOhEaFyy%2Bad7MKnsndgIYJX7rXP%2F1chz%2FyAtwTAgGjacQNRHkcQpi%2Fmgqx3eba844NCstcqzMS9qzdfH43wPCTd47m8IQOtUQnhDf1zjAgmeJl5Mow8AXI%2BVEubsiOkTM5du3X9ocnwJFEN3uo7hLss%2Bf0OmWalNMqZ1%2F%2BWh9xUfIAq%2BtEtPMAeTcXkWTsH2DEBwsb4iMCWGG2P3%2BVDv%2FITLOA5bNw%2BiBG03Wp1Hta3l%2BwbT56mZpKpz%2F%2B9nhUizezW1pI0bAq8K3FXRerPUAUlwc66TR7YYyv0m%2BX5o7h8s8%2BrdI9q9DbBqtkbnjwiRSfXyJt%2Fv0SCWHrarRhuoB7bNfQe2iMMA4smjFuk8p7EVHaiTT%2F3vmfuvKvcUo4C1GRqkSCFBhDiIHQB13MOCs3zf1STUH1NniBXOSmV%2Fz%2F1nWxBUAk2ugt%2F3XqlDscnW8Sr%2ByQ%2Bmf6dDmpQ5YrPQtAV3jVKPEea8GafkPkjGXxDrOvCtHlIHHKWmJWyNuHekq%2B151az7R7ev%2B4f5CTrA%2FeN0qIzaSp3%2B5%2BpDB3om6SeQ%2B%2FXAVty6od0ShrpxSQioUjkch6tMWXVxyOFp5d82OTQv7ed79jlCby6OHifMVX7eteZaqhteZv1KK9cwCJ3Gurl%2Bqm2ykDKfecMZSHUdYIJBTn54cnWutc1mQ%2Fua9%2F2D1UEMDjhfQEgi1apis5I4C%2BO0%2BNVceO7PnklyMwMOiFza%2BwgrIP%2FLRoeptYbjuYxvs4x9qjHWhkwruBpnllX3l6%2F7L%2FJKEwESJo4o2J0URx4x%2B27eo%2Fqk%2B6YSdUrCK27lsEuX50kL4r2kqGvTmTpXtzzR7GyZ7pglcBjYkgW6Z3p51LhNbeDzbTSMOG2l9RvUin30UaNCjfWqEXvxIxQAUYXphqo4PrubVZFG5973CdcVge%2FiNO5cktNw5t7W0dNK44CrpPFziesBaRWXpST6TyBKGVWL%2BFwa8s2DIvUnirSVi3LSJtk%2FMI21fuX1Q%2FdgUtk7QVGA%2B192NixGGfExwbIJrPXf4hK2HGel9pskrTtoGSb6DUPk9vw7kO6lUAOND69Tf3ft4krvZ2OPRd5NUhx5NXJUQAHAZYkGR78xqif2A1umWl4A6SyxM17aS6%2F36xuXwvpJtWtxPPNXe436h9OAoIvLPGS5Jb0Iz1lSsM5za6ylmN9GVi%2FzeKOclthtGIQGP%2BYxvjcy96IO6AlHV7PidIBj1outCc9CuGK0qqAMUplEE%2FPU5EI0%2Fy271INQjnDa3mVvYB5ne5LhHIcXa8IijlSvm2gjzIUgx7DB0AkgXzGku%2BBIypIQIvfPk8thrgea9kNDfrAo4ntuv9WT6l31iYWi2cHuOizDJK%2FAtV9P4%2FLvR7yW8PXtJtUOX6a9S70QA7yDpNfsjNQAygK7iQ%2FR86zhEHd94McPrwd7FiVZsf8tlcoe%2BwmbelJN%2FPkNs3e8wNEeLR6I9z3kXz9Bk%2Bflzn5JwNQkmBLEkG8BRWgS8%2FLV0HxcIsGi4QzbKwi9cX19kcRCU7obuWTuNQ5pF0IwBR6yOM7zm6T406a29hmSXWGlQHtxwkRD17FaJvZBwSBadRM0RhMPgsBuY5Ic9II%2F4VMAB5nvzjIDBGtDFkJeKA7GLX318TOaYvnQ%2BN8kYcPZi4Y7opQ1xu5vNd4PK52V9htZSPLx6unIbzQ05n8Vs%2BtJhyaNgyAZRDzUgB9qqbiHuCmP4qQ%2FojGD%2BOvksRrhR8pukY4z8LuMFxeIP7DUgxk9D5U8NgI9lMOwMEM2nJnwhh6AfZe2YsksJpZmnJavflI9tggmNOVUEovx0N02fNH2ydTka5lsS95vfkwABI0zP1LER98CyCo4t8GOR4ZKYP513VJnmUz6C1pOSr9T7VxKsHJ5VCLoMtYGnJIXr%2BhZVAK7eOWzIS0VskwBxOsax0rkPY98E0%2BlE87er5N%2FUbLBX784tUGszJfj%2BaT6s8R%2BZSYHa5Rr7kazQ37XbcSJSbMQ4gwfNEcRkTL4rkq5TaL38ypfmlf26fuTBj9vrGKQ0vxA4geTbDxHs%2F1j4ka%2BErX8XvSm9TvJzE7QOHAnUzYv46CH3mjVkewzjHhkc2W1eefwd1KaN0J%2Bn5rhaVQPUIlWyjwZ%2Bz7x8DJbrrelJWugja%2BqSBefgepiqSdo25Kn0Bxl7Gy0GM7BifuRVrxQCT1Tn4HqJmleJSa%2FZMCHLkyA24ZyWR8jkKUwwG630e%2BllmwHkK%2FvNC85uoyL3ttsfo5D37E0nbhVVWSrF9kJGYMEi5%2FO0eSE0AY97Dh4H82bGrjK9XzqE%2FweIuYHef42OFmpmm%2B0eN1F9bFIuGy%2BJ4R6F8tITq8rdr%2Fg8SHqmTIL42RT7BkpJ1fmK1VkSTyN78h%2BVQnCMI7T5keezW88A15p0krtsmwdx4tPKjFOmfU%2FoP48L%2FofeFiF96pE5uQAAAAASUVORK5CYII%3D%22%3E%0A%0A%20%20%20%20%20%20%3Cimg%20alt%3D%22%22%20class%3D%22js%2Dplaxify%20position%2Dabsolute%22%20data%2Dinvert%3D%22true%22%20data%2Dxrange%3D%2250%22%20data%2Dyrange%3D%2220%22%20height%3D%22123%22%20width%3D%22304%22%20style%3D%22top%3A%2073px%3B%20left%3A%20467px%3B%20z%2Dindex%3A%205%3B%22%0A%20%20%20%20%20%20src%3D%22data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAATAAAAB7CAMAAADEzSzaAAAAA3NCSVQICAjb4U%2FgAAABgFBMVEX%2F%2F%2F%2FjyZz02KW2pHvdxZu2pYLny5fv1qWFel7Vu4OlmnasnHuyn3uKfmLexZbVvpSEe2Lkx5Ts27LSuIXGs4vFsIrZxJbs0aLPvJO6p4Pp2rHlzZ3WvY3Puo7dy6DayaHOtoTdwZJuaFvJsn6llXPw1KC2o3fWxZ3ErYSomnuekm%2FHsoSekm%2BAdmKomnucjm3OtYvn17GcjnOllXO6p4PKsIjSuIt7c2O8rIWEfGmSh3ONg2V1cGGUinTt0Jydk3FzbGHPuo7kz6GJf2vn1q3q1aTfz6majnm2pHuyn3u2pYKyn3vPuo7s0aLOtYvSwJm8rIW3lXKllXq9q4ndxZvVvpTexZbPuo7SuIXErYR7c2N1cGGEe2Komnu2pYLPvJPGs4vWvY3OuJF8dWmNgmxybFuOhHG6p4OsnHvOuJGsnHuEe2LFrn3Gs4vHsoTFsIq6p4O2pYLKsIi2pYLZxJa2pYLayaHZxJbGs4usnHvbv4%2BvnHalkG6Jf2t%2FeWx8dWl0XqRGAAAAgHRSTlMA%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FEbv%2F%2F3f%2F%2F%2F%2F%2F%2F0Qid%2F%2F%2F%2FyJE7v%2F%2FIu4iRP8iRP%2F%2FEcz%2FM%2F%2F%2F%2FyJ3d4iqu8zd%2F%2F%2F%2Fd4iqu%2B7u7u4RESJEmarM3f8iMzNEVXeImZmqu7u7u7vMzN3d7u7u7v%2F%2F%2FxEREdqVUEYAAAAJcEhZcwAACxIAAAsSAdLdfvwAAAAcdEVYdFNvZnR3YXJlAEFkb2JlIEZpcmV3b3JrcyBDUzQGstOgAAAUrUlEQVR4nO1dC0MTVxbeMDNOrGQmEUgIECCKiooFQeUhKj62iI%2Buimhti692pbi2dq2P2tb2r%2B99nce9MwNJVIJdDiTzzGTuN9%2F5znfvjPiPf2z7%2BPe%2Bdp%2FBpxX%2FPv2fdp%2FCpxU7gDUZO4A1GTuANRk7gDUZT07%2Fcnvo9cr6artP5BOJtdeX9vZdGhw6NPhyB7LN4931vX17%2B%2FokYkODhx7sOLJN4i%2BJl4pLg4NDg0PnF9p9Rts7Fgy%2F%2BjRiIi8ftPuUtnesH9%2BrEZOTQYnY0Fq7z2k7x%2BLxvX0Csr3HJWp7Lw0NHRoafL0jY9mxLsXreB%2B8SYINDu2Uyux4LRX%2FeN%2Fx4zovv5BJObijYpmx8IWKQxD%2FUvGm3ae1bePi08ePe3sfPybAulXcmm%2F3mW3XWJRqL0OL%2Ft4vhNsXsaP6WfFOar4qkyr6vhhS5rXdp7WNQzvWPqn4ErEhJfor7T6rbRy3dbeoT7PskmCXiMvtPqttHIt3Z5TH1zEzMyg59le7z2o7xy9vtdxL1C69eiUB%2Bz%2B0YdPz383N3bpy5cr98zoevXn08MWLhw9%2Fui9WXvlhbm563lTCp2%2BPHzd49Q2%2BfSU0%2F%2FWf7T35LYz5i3O37j%2B%2FG0d%2Bf6VDRtjB48DZXRhRVB%2B4%2B%2BLZlan%2FvtWeQiA2OHRCMOz%2FomO07%2BKtpz%2F%2FGkccJwOWnIQIXMhBU3G087UwX%2BVyLMOrHvrb4zU9d%2Bvqr7FfQR6FOA0JrpBtrRBmJQlYKe7VEfd61d7z7W7Qx4yLt652x9EFCw787WAQhrANaHdWoyXiZOeuOFZoife%2FM2ACrIlyP4fK0qzQAszBTsVZSMldKht7dUr2lkuPlifb3bgPHfuePP017udcCm3NSgAU2pwzeta%2Fq37y9z9u1no1XDIl47JK1UfLs%2B1u5AeLfWeu1nJJTiUByQi248mbN2%2F%2BcWS8psCSqHUKhmEdvbb2d%2BiDP7ka51JajyJFW0KiV8j3gfew4%2BbN04X8kZFir4JLMcyLJ4apgpYeLX3amE3%2FcLcfWstSzyWXpV8mKuKH5tTPqT%2Fy%2BXxxZCSv89GkpGDdKHMdpfOff7KYzT3MIQqivTL6ReT6c34uJ19%2BVI5UlCO%2FXC7ruahsVkLoRbG2HAjA8gKwWFVImZJC9Cfyp%2F%2B4eaokgjAba3fTW4mlN%2F0CHwmMbLIvMRLRryGrXKiYCCmIUqF0aWpZr9O7VTqL%2BaICTOdjr7IVE8V8%2FvTRU8OnZAyI%2BO23en3gpzPtbn9z8c1yJLC5YPBI2CqeiEzVcE0Y0hqmYrWiYljR1Ejlw%2BJuSbvCyM0Bya1SVBcx8NvAwKlfr3w6qfnnAxuVkL071ssW%2FzQzwXePC5CSvdqIqZQUwiYgu%2Bl0oXaV6j99324kGop3t1MkPcNNhIldWekMk%2Bj1%2Fy4B0%2FoV665Rt8IrXywMu4gJzO5OtRuNTWPxtp12YXrbQw5kAsTUaqpz2veUhvVqJ2YYJqM4kARMxN3tXTQnb3dYqmOlmpuShAq5e7dbae%2BhZvqrWvBlpawSYL%2Fb3NqFRXPgh4V2w5IVbjK6TU7jXjqV0uYRN%2FHR%2FkhrWPWGAWw8JSExTt1qNzKp8fU6B4L71DCRYkki4b58x1QgYXJB%2BLme7nJt%2FPffR%2BtZWBme3d2GLmMtgz1pTU%2FtSbpkSoxksJTlH%2B%2FnyNBsyVodfbnNKubi9fTWZ9gEzsMUkBvpmBNuLmCpHCvF97eR%2Bv%2B1niLpYeKdVQIqAwypkP264G7ke23ASjQtWeu3T16udTjhjEMkYHTUyAEomYwuQno33HIgya9SGudKPy%2B0GyoZ39zOUpctI91ZO%2FscqrE124Fkqy4TnIqXZk5dsnQkwnJkYUeY2mmi7S5CmXJWv9JuvNabYUImXpScIa1J8NHJVXbIswTJZqC9aKv2%2F%2BkUx7TIqn7ppTAMzS9hmVkzQ%2FiEULHG483F9uG1aLEjzSWkN3dz2xBativ1cxbdeN6B4Ce1TC9GbTP%2Ba851phzMUqWNANpwU5iGG7v1e5ZgKSVdrFUNBKL324OXK18diZalAoayllYO4ICpRyES84xVrwMN6FeJoHveDrxuu21JX3A0jFdGO5%2BzInTdRkjAIeQVCYaViClwsU1tkH4u96HTPJsPDd8xSlsbgpYxxBhUuGojZqVh%2BOUWI7ZwvdlC1kikpGiDClnZMCPbj9jC9Q5KlA6SnSyrGtqk2SBrM3HM3k0i2IyvAMS2ErDrvAEWPDZWYWqXKdl6W8SdLXiERFbTlx5wiFRyZ0rJQZ8tfO5nJdU2WLMND2dZZc9dkbgMWR3O%2FmTelZKosXk5ebZVeD1IRSixziZEkhfpaLt11D50aukISfNT1KrE%2FYS9fWlr8FpHIbYqYoe72EAkiLXhAbK7TWGGTm3izUpbMnixukGr2pWXZwGeJvOy9M3Hx%2BvrClxecEJ4uZM4JnxTYtOG4xjpkTQpYRZem8ajjw%2FYS6t1WUmZDVuWlr1Pya1YQKXKlbMdN35o4X83OTm5qF%2BLk4vi94Fsc0UzrFLpqByoHBChHsRRs%2Bq9olao1WrxQn9FPsqjHncSczrUXL96Bkr9Vvpz6ske9StftdHRHvEjQ75P1B7XMmLipIjukypO1QbskMu12sApNVeDdXUTv93%2FPBHvo2x39GNKfs43z3TpiHxc9tWPnvi%2Bfu7LLOtdc7552MlsMQc0G9VCBBvMjvow3YHneYH6kVMvCAI9xRVqRi8Faqv5AG40n8C9vIAtsb0DOKaa%2Fvc9CIbt81ljJTBq2WeIqXb6gK0Fp8%2BOksshmrA%2FAk1IqjU90HYLMY8BEcDqgBqtNuoVgKjBxuyl8fVclIMAVgTV1gFbVW2LWEuoPZxjRAzebp%2Bv83MMKAMX4seZitET2KzwGIUYgzRAQcBJ4xAOYCJsGMSGuMA08V79rmXA1oEu2A5fE0yuiNgm4gjSiNCVOZcjUvo%2B7acTUrOW4apePYxc1CTOEVxDOzE%2BJvKP75FIVITe86pzLQO2AnQyVMEkguTEzNITBydb5wBgH5IR3hFwHzMeGBZQ4zBlHBCRVQRMgGARDCiGsArz0OPfIH%2BqrY9h3wGQGHlyvM0cH2sf1nYHSVUHUOsiJzMRUl8CZlOBpU1gsQbzLyDZI9AshNkmyErYkaZXW8XrXRIqyB4f1Rp2iajZFgcpB%2FFgzgJlNV0LsTDqMXVnOHgWmYxied2xLN39Fy5c%2BOc%2FL3Q2EPxI%2BiIgA1%2B1CtiaaVwE4sPUzKIUJi3DgSkV0I3A9sFSRFg%2BuD9R4I9yDnGSEaEgwcTMKMM%2B%2Bmzz6OTIswoiX9VWBxfXc1bwksj5ZnPFKXdsQddbKrDioxEDk%2BirdxhFqCzNDizAwEUFVfZ9DQEG%2FoTlIyxUW%2F2HSytcWVywInjLgfNkdAIUrKQjAGGGXEYS3NwoShJLQKvIsTZ6XkRXsUGGsXrCDiRVv1Wvf4dzhzkI34fciohqJrkiqKjQfqyrmIc%2Bw5TvAplpIB5lZQ5duOXu0VLIaUx%2BsZyF0sjIns7OPYeBYWgo0PSaL%2Fm8Nbwm2%2Bhateg341onkKW%2BZlghL3%2Fy%2BUKhoJ%2Fll289Pd3dAWNYwrWqL2ixc7RGOqRiw56fDSqlF2LF0DS9S8xim4yw143Gen6mPxN0G5H0gWEFCVChWDCPpecVegKvHg2YXX3Rm%2BmcbA2wdZswKZUfCJdjswllsvZitcBUYF0JCF3gm3%2FD8hBO5afeHyj2DfgGHwEr5PVvoaiIJkETgHUzwAJ%2BBTxjOPbsCRZaAmwlu9uSUgkBF2IVdKNA6ejjvsNLNk%2Bw3fBYojAPwS0FcEK%2BRXhdkGH5okRJZ6aCrICAfbYnNYxHa%2B0R4shQwE0qn6sV5RVAQIzjCPHEY3XRZh4b2JCAUQ%2FaUIr8GIxRGNmRrazjeTCG6XTUUibJ1tPd0%2B2dOzI%2Bvof6Suj%2F1cEUYC11jiZt7mxFbwg79jKqHrVAZwqPw2ZyGKkxEZfrcVm81ePx8XNHjiit16AVJbkAMK1hafTCY3lPWwFsTZ%2F7xx6UyMzuwE2UTmb3SauxNHTDIaJYM6xg4MoX1T%2FfUpBJhgUjR86d27NBdnveL60Atu5zVCR0AJ7VZM4lH6yERa%2Bcb61ixZF9Xm%2FjV6cKHW2LaC7DaFKN63Ec1%2BU%2F6j0iCAYM00ABcErDRkR0BjAyZncc3qNz9DKjIVThIN8%2BzoB1p40J8gw6NaxfrmZ050j%2Bc%2Bd4RDFMOQmslHnDMFYls21L9VgLgPnAq5yl5BuZc%2BIeS9yIOGXLIB4cP8A1X2gYXn3o72Wbc7kigotlRL8A1lVqmHYVOiWh8w19Lxy8xi9spXO0SPbbmHs0A1xzyI%2BB4rMEBcOmIfFpN%2FsoNFyBn5Apye1EABYVRvBxXNBDAGO4kAYwJV0mKw3TGGAeaVZyILKVMrm2NeaBV1ud7UC2qmserBzkK0xrJ%2BCCQNcoTy6sqB1%2FgbpGHh6akRgdSwtjiA9QpyJqlaVdDClX5p3sopLgI8c4oFY6A6ROQbTHUCGB0KSJn244ETKu%2BlUg3WcMs8ByR3dPNA%2FYSwWUsgNRAoacGeWzeEdo%2Bh8CbDh5yBp39BVKG7ZzFL47AlthOpAF2aPUmFFf0gYbq6RRgObL5DfUCjaqh0yhhjntlaOCPmqWbRrAn1rJrYsCLwZ6n4hreoAMYz7fI0LojXAB2GgF2n2dnXkrJbFA8tEKvara9D9%2FWH2fm9Y5BiiyDzfZGZgxdBHFVops5AGQJ2VTT4BhEqIi2Vc5dMFsBRNAGmpDvWz6r6tcdrOwdTFKHIgN3WbfRo8Sl52Pr3o4JMaUe8CcETFMewo1WFHEviSlpAbHtsS6s9XZdOfowZbkHfHV50jKtygj7xjpiHt67YTBGzVMm1UYrFCAQUpalhj7XuBWWugc3SE8GFJIgchHs4D%2BymxkyYuOAcoHpaXPduYZDx%2BNYp6CODLKRw0Dt7sU1OXfNeIDiIUC2HxjXhVgR8blaAVLSNIxGGbzmh1D%2FHoryZRjltfAF%2FnRRoMULjWUIgVV%2BVeg6qJHKbuS4wU15Jo3Bsx0xUnDiExYUvg3BtPNAbZKNsHp1WQ8a2OzkTjjs%2FJqPu4r0Ih8ZO4ZxcoxoQEko%2Fs6aPxZYQjknSN1GHD6haIeyjdjrmpMH2zFZy701gUImu4cXUYgGC6MArSIKWYTxgKOSqCV3yxd2f6GfxHIFk7QUSQNumlnLVLesYwaVtAWjPrfajxM8m%2BP3XtXs5xhnU12jlYwnSKGFruFhGzzuVTZXUurSjDjQXhnyJ2clAEiZwDxsDWIYQ9lTJTr5Tiu18bH5fAOqL2imevDPrNT%2FDA%2FjMrwJjtHdzZvUQpZLCh84JXZEe9hyk10Q5M%2BbPUVclGaUgX8STAiGIA6qhjm2zdBwL0WdF9S2Ipz546oIerAKilYefVbc52jdzlLV3AkYQuLZgSApIp%2FogqofaryTy6X6wOCYefOQfcRiSYLJe8aBUz3vUTRbK5ztIqXH0pji2M0CCISUm%2Fa%2FGGxOMkwbs2hCFhjNOYBC%2Bp8mxtHaMRkSvbgeBjdC2Z9VQSsqc7ROjSXewnOK1vDk2Q0295jpLaMzosKv2kejVkwF6s2lNX3QJXETIRBnoLVl2QpSCMiWIibK5MrCfXa6JYryRYg5TMq0udhH8tR4Ed4JVWAMfI4t1zZ6IKHKKrOkb5MhmFF6EkWjK0oJAYQvcwht%2BYesLhjTh1yiz%2BGZLiTs0I7AaZihoZcwQibRp7JKyO1PDKYAJCzAMlkxhAjlpJF8K%2FCixXZbbZO9nniL9o6saqZByzeNezHuUtgEm7hyKYGzQhxz7YtdfBcrh%2F3sMPtcYqp16g6ELvzbYiFOqaqJGmYxyMgLqucbAKwtVbdJiUlFgiGL7MW7Eh2CcZ6EvMhF3SZSbeJQidfVXUEdPr0LIrWsXyRj7hCkmNusuyXx2ri6XN49JDyCaVnS4Z25DRubmjH6JjKDEpJa2gnRcPotkCAagbaWH3SOGArQC5UGdvGEolo5kM%2FZRdjnrHbbUzCGLlYoYsZw%2FJ47xsHeqzOt3Vkqi6I4s9TU98fW2gMMGwZEcnpO3LKWcnILYb5HKamhqex%2B%2BcxFUHkFZkMrAZcfkTU5BGoL6kFrADPo%2BRt0bcPx%2B5EKbJWR7t27%2B7q6vpq%2F%2F6Dl6fGvp%2FdxMm%2Bm5ycWltbW19fP7%2By8qbceC75SDzcaJdKFEffvSjEYl8wjCSMyj4jQmounZQf5k8gFtHvqyEeAswhmIxqtXpCxL3Ro8MiugReKsxULAvsLh9slHf7ZicnV9fWlpfXH1w7%2F7IcWdXzQz%2BIImZj3rmzPBnhlxjoF2Uy5%2BcsW0EPohStu0ZVFSdmZmaOHj06fHS4a3h4NyCEWCFaFnLyTWIneXes8R7U%2FPzk6urq5eXlB9eunZd%2F3h2KJ2YVKRdLN7YOIaO90abkYoSKOyaQLtJ%2FrALaCzDAlNYrhp0WMT4%2BPjLydmbm3r0bXvXGqKQQA6JLTbrMbFcSpi5rxZf79z87eFCQTbCtCcjsWJie%2Fl4guLy8%2FPzatbsCwTJPMSaKDrPUcvLWUUyJxwyEw6jkrSP5vIDWMImPiBOCP4pAXTYCLn0ALURPwgY7vJAAXT44NjU2e2wzLWs59n03Pz03tbp0ZfnZj9fO1%2BJYkLCJznscEHcCpshpD1igAM2cuDc8UTtVlSAZ3eF4dNlM6epymQOxf%2F%2F%2BKwcPLo2NjR2bbd9%2FzzI9P31x7sza0tLy8x9%2FrA30qjT2UeQgMY3dH8BETNp9BU%2B1%2Bkoo0D2pQEKgh5EYu52USglLzrs0QDLDxkQtPPZ12wDaLPZ9Nz175szc0tLSsx%2Bff1mrDcSghJJ%2BNV4bg2qgCCQVWifYbrfNkEpdu51UA11ia74yEiQAOja7BX%2BK4WPF%2FPT8k7kzt35Yuv%2Fz84c9J17NnHh1T%2BNjANLMAf50ucg4DCIJAo0em%2Fq2dY3e7jHboNykpZrIsPsHLwsJmjo228qzhJ9k7OOJlrBDFphKoy8fXBNF7Njst%2B0%2B8bbFV1k8EhL0k9Jo6Ru%2F3b4avdWxH%2FllJEhk2Oy3f1sJev8QJmhMaHS7T6OB%2BB%2BC%2FKOr0h4pdAAAAABJRU5ErkJggg%3D%3D%22%3E%0A%0A%20%20%20%20%20%20%3Cimg%20alt%3D%22%22%20class%3D%22js%2Dplaxify%20position%2Dabsolute%22%20data%2Dinvert%3D%22true%22%20data%2Dxrange%3D%2275%22%20data%2Dyrange%3D%2230%22%20height%3D%2250%22%20width%3D%22116%22%20style%3D%22top%3A%20113px%3B%20left%3A%20762px%3B%20z%2Dindex%3A%204%3B%22%0A%20%20%20%20%20%20src%3D%22data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAHQAAAAyCAMAAAC6RQ9kAAAAA3NCSVQICAjb4U%2FgAAABgFBMVEX%2F%2F%2F%2FkyZm1pIDexpr73qqnmnallnGnmnn33aqjlXn12anszpi7p4PUvZPu1aXPuo6tm3zr0aLfxZbNt4xxbWOUim6yn3l7c2NsaF2%2BrIStm3yom4C2o3v22KSznW%2BrnIGejGuqlm3ZxJZxbWN0bFvUvZPv0JvnzZ2MfmPArYq7p4PXvo7Qu5LFsoq%2BrIRsaWDx057ErYKznXCsmnKFe2qom4CPgWSMhG3Qu5Kck3OllnGjlXl7c2NzcGeJf217c2OJf23FsoqUim6%2BrISck3PArYp%2FeWl%2FeGWfknrGsIzArYrGsIyck3PNt4zXvo67p4Pr0aLQu5K7p4Omk2zfxZbZxJbErYL12anu1aWwm2753KfmzaHny5aMg3N%2FeWmfknqajnmUiXSMhG2Mg3OrnIGjlXmUim6UiXSMhG2EeGN%2FeGW1pICajHOFe2rGsIy7p4OVhme%2BrIStm3ynmnmejGuUim6VhmfUvZPQu5LNt4zexprPuo7GsIy7p4Oyn3mmk2w%2B%2FMV0AAAAgHRSTlMA%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FRP%2F%2F%2FyJ3%2FyIR7v9E%2F%2F%2F%2FRHe7%2FxEiu%2F%2F%2FRHfu%2F%2F%2F%2F%2FxH%2F%2F%2F%2F%2FIjNEVaqqzMwRESIzVXeIu8zdIiIzZmZ3d4iZqru7u7vM3d3u7u7%2F%2F%2F8RESIiIiIiMzMzMzMzM0RERFVVVWZmZmZmZnd3d4iIiIiIiOmar1cAAAAJcEhZcwAACxIAAAsSAdLdfvwAAAAcdEVYdFNvZnR3YXJlAEFkb2JlIEZpcmV3b3JrcyBDUzQGstOgAAAFzUlEQVRYheWXh3saRxDFvSzHHQsczUEKCFVkiINKFEW25RZFcVwjJFvNTu%2B99%2Bp%2FPfNmCwfcxYosf8n3ZcB3gjvv797sm9nl1KlHxNyjbngCMf7U%2FwU6929Al%2F5L6X3nq43f5ud%2Fuf3ZpScARXp%2FGB%2F68q1fD4IHYXP5TC73em731psnDYXS9dMDX729nVZBmkIpVc4hXr188tCLA9CfAiIqftM%2F1WwTda90otC5IeilbahkoYAG9Ocyiz1J6JDSNw41Kg0wvRTOTVC3HgvzwcbG7ZJrfoNKLx8qTms6IBxT%2BcPyY1JvHqgmxrhlPpPSq54rm21WyFyXZDzGGVC%2FOSby8jYP1KRBdq9Y6Dnvgrn8cwACZRc%2B4rklU8FXIZv4xvGg20qLeEDUPS576kjnfAN9D5cUpxZYfeJsa6m7x2J%2BCSIkBCFV%2FZaFWqVbSrtI6dwq7SjODc9q7liFc2Dmyzw6EtxP71IjDKkdNFUzRDT5tkDBUUobOLd3VND46urqjQZi6mYYlsvl18oIQbE3T1ERUkiEwEngjIOsdrvdgrQx%2ByzHt6c5xuil3wlbgBJGEGY0Gk84hP4k%2Bidh%2BPxnPnWkGIuH2tG0AIvQWAsXRqFRiy%2BOxkyE2oGlyd%2BwPP1IfeE6wSeh9B%2FnmKB5sZjJZjNeDGuh6lPUk6BTFiVdIqWFWqR0avsZZ2g1m6FXDLRYoQuZzEQSVESsaU%2Fmi7JyEepTwE0%2BVRBaaYFGzmYnYqCyhku11PUE9zrvGJ3WREKG6HFK2TVFVyadU1QrZwGVkENDx0AXAa0nKG1EjCP7J6GhKtrfbc8LSKnQ0Br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alt="Wagenmakers' pizza poking analogy. From the great <www.bayesianspectacles.org> blog." width="80%" />
<p class="caption">
Wagenmakers’ pizza poking analogy. From the great <www.bayesianspectacles.org> blog.
</p>
</div>
<p>Such “pizza plots” can be directly created through the <a href="https://github.com/easystats/see"><code>see</code></a> visualisation companion package for <code>easystats</code> (you can install it by running <code>install.packages("see")</code>):</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(see)</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">bayesfactor</span>(result)) <span class="sc">+</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_pizza</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="100%" /></p>
<p>So, after seeing this pizza, how much would you be surprised by the outcome of a blinded poke?</p>
</div>
</div>
<div id="t-tests" class="section level2">
<h2><em>t</em>-tests</h2>
<blockquote>
<p><strong>“I know that I know nothing, and especially not if <em>versicolor</em> and <em>virginica</em> differ in terms of their Sepal.Width” - Socrates</strong>.</p>
</blockquote>
<p>Time to finally answer this crucial question!</p>
<div id="versicolor-vs.-virginica" class="section level3">
<h3>Versicolor <em>vs.</em> virginica</h3>
<p>Bayesian <em>t</em>-tests can be performed in a very similar way to correlations. As we are particularly interested in two levels of the <code>Species</code> factor, <em>versicolor</em> and <em>virginica</em>. We will start by filtering out from <code>iris</code> the non-relevant observations corresponding to the <em>setosa</em> specie, and we will then visualise the observations and the distribution of the <code>Sepal.Width</code> variable.</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">library</span>(dplyr)</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Select only two relevant species</span></span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>data <span class="ot"><-</span> iris <span class="sc">%>%</span></span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(Species <span class="sc">!=</span> <span class="st">"setosa"</span>) <span class="sc">%>%</span></span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">droplevels</span>()</span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualise distributions and observations</span></span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a>data <span class="sc">%>%</span></span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Width, <span class="at">fill =</span> Species)) <span class="sc">+</span></span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_violindot</span>(<span class="at">fill_dots =</span> <span class="st">"black"</span>, <span class="at">size_dots =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_material</span>() <span class="sc">+</span></span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_modern</span>()</span></code></pre></div>
<p><img 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" width="100%" /></p>
<p>It <em>seems</em> (visually) that <em>virgnica</em> flowers have, on average, a slightly higer width of sepals. Let’s assess this difference statistically by using the <code>ttestBF()</code> function in the <code>BayesFactor</code> package.</p>
</div>
<div id="compute-the-bayesian-t-test" class="section level3">
<h3>Compute the Bayesian <em>t</em>-test</h3>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>result <span class="ot"><-</span> BayesFactor<span class="sc">::</span><span class="fu">ttestBF</span>(<span class="at">formula =</span> Sepal.Width <span class="sc">~</span> Species, <span class="at">data =</span> data)</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="fu">describe_posterior</span>(result)</span></code></pre></div>
<pre><code>> Summary of Posterior Distribution
>
> Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Prior | BF
> ----------------------------------------------------------------------------------------------------
> Difference | 0.19 | [0.06, 0.31] | 99.75% | [-0.03, 0.03] | 0% | Cauchy (0 +- 0.71) | 17.72</code></pre>
<p>From the indices, we can say that the difference of <code>Sepal.Width</code> between <em>virginica</em> and <em>versicolor</em> has a probability of <strong>100% of being negative</strong> [<em>from the pd and the sign of the median</em>] (median = -0.19, 89% CI [-0.29, -0.092]). The data provides a <strong>strong evidence against the null hypothesis</strong> (BF = 18).</p>
<p>Keep that in mind as we will see another way of investigating this question.</p>
</div>
</div>
<div id="logistic-model" class="section level2">
<h2>Logistic Model</h2>
<p>A hypothesis for which one uses a <em>t</em>-test can also be tested using a binomial model (<em>e.g.</em>, a <strong>logistic model</strong>). Indeed, it is possible to reformulate the following hypothesis, “<em>there is an important difference in this variable between the two groups</em>” with the hypothesis “<em>this variable is able to discriminate between (or classify) the two groups</em>.” However, these models are much more powerful than a <em>t</em>-test.</p>
<p>In the case of the difference of <code>Sepal.Width</code> between <em>virginica</em> and <em>versicolor</em>, the question becomes, <em>how well can we classify the two species using only</em> <code>Sepal.Width</code>.</p>
<div id="fit-the-model" class="section level3">
<h3>Fit the model</h3>
<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><span class="fu">library</span>(rstanarm)</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>model <span class="ot"><-</span> <span class="fu">stan_glm</span>(Species <span class="sc">~</span> Sepal.Width, <span class="at">data =</span> data, <span class="at">family =</span> <span class="st">"binomial"</span>, <span class="at">refresh =</span> <span class="dv">0</span>)</span></code></pre></div>
</div>
<div id="visualise-the-model" class="section level3">
<h3>Visualise the model</h3>
<p>Using the <a href="https://github.com/easystats/modelbased"><code>modelbased</code></a> package.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(modelbased)</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>vizdata <span class="ot"><-</span> <span class="fu">estimate_relation</span>(model)</span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(vizdata, <span class="fu">aes</span>(<span class="at">x =</span> Sepal.Width, <span class="at">y =</span> Predicted)) <span class="sc">+</span></span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_ribbon</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> CI_low, <span class="at">ymax =</span> CI_high), <span class="at">alpha =</span> <span class="fl">0.5</span>) <span class="sc">+</span></span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>() <span class="sc">+</span> </span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">ylab</span>(<span class="st">"Probability of being virginica"</span>) <span class="sc">+</span></span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_modern</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="100%" /></p>
</div>
<div id="performance-and-parameters" class="section level3">
<h3>Performance and Parameters</h3>
<p>Once again, we can extract all indices of interest for the posterior distribution using our old pal <code>describe_posterior()</code>.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="fu">describe_posterior</span>(model, <span class="at">test =</span> <span class="fu">c</span>(<span class="st">"pd"</span>, <span class="st">"ROPE"</span>, <span class="st">"BF"</span>))</span></code></pre></div>
<pre><code>> Summary of Posterior Distribution
>
> Parameter | Median | 95% CI | pd | ROPE | % in ROPE | Rhat | ESS | BF
> -----------------------------------------------------------------------------------------------------
> (Intercept) | -6.15 | [-10.26, -2.13] | 99.92% | [-0.18, 0.18] | 0% | 1.001 | 2651.00 | 7.23
> Sepal.Width | 2.13 | [ 0.77, 3.60] | 99.95% | [-0.18, 0.18] | 0% | 1.001 | 2639.00 | 20.31</code></pre>
<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><span class="fu">library</span>(performance)</span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="fu">model_performance</span>(model)</span></code></pre></div>
<pre><code>> # Indices of model performance
>
> ELPD | ELPD_SE | LOOIC | LOOIC_SE | WAIC | R2 | RMSE | Sigma | Log_loss | Score_log | Score_spherical
> -----------------------------------------------------------------------------------------------------------------
> -66.265 | 3.071 | 132.530 | 6.142 | 132.519 | 0.099 | 0.477 | 1.000 | 0.643 | -34.666 | 0.014</code></pre>
</div>
<div id="visualise-the-indices" class="section level3">
<h3>Visualise the indices</h3>
<p>TO DO.</p>
<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">library</span>(see)</span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">rope</span>(result))</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="100%" /></p>
</div>
<div id="diagnostic-indices" class="section level3">
<h3>Diagnostic Indices</h3>
<p>About diagnostic indices such as Rhat and ESS.</p>
</div>
</div>
<div class="footnotes">
<hr />
<ol>
<li id="fn1"><p>If the effect is really strong, the BF values can be extremely high. So don’t be surprised if you see BF values that have been log-transformed to make them more human readable.<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
<li id="fn2"><p>An exception would be when the pie slices are well-labeled so that our brain’s perception system does not have to do the decoding work.<a href="#fnref2" class="footnote-back">↩︎</a></p></li>
</ol>
</div>
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