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<title>Reproducible Research: Peer Assessment 1</title>

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</head>

<body>
<h1>Reproducible Research: Peer Assessment 1</h1>

<h2>Loading and preprocessing the data</h2>

<p>Unzip the &ldquo;activity.zip&rdquo; file to your working directory and read in the &ldquo;activity.csv&rdquo; file</p>

<pre><code class="r">unzip(&quot;activity.zip&quot;)
data &lt;- read.csv(&quot;activity.csv&quot;, header=TRUE)
</code></pre>

<p>Our dataset has the following structure:</p>

<pre><code class="r">str(data)
</code></pre>

<pre><code>## &#39;data.frame&#39;:    17568 obs. of  3 variables:
##  $ steps   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ date    : Factor w/ 61 levels &quot;2012-10-01&quot;,&quot;2012-10-02&quot;,..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ interval: int  0 5 10 15 20 25 30 35 40 45 ...
</code></pre>

<p>To take a look at the first few rows of the <code>data</code>, do:</p>

<pre><code class="r">head(data)
</code></pre>

<pre><code>##   steps       date interval
## 1    NA 2012-10-01        0
## 2    NA 2012-10-01        5
## 3    NA 2012-10-01       10
## 4    NA 2012-10-01       15
## 5    NA 2012-10-01       20
## 6    NA 2012-10-01       25
</code></pre>

<h2>What is mean total number of steps taken per day?</h2>

<h3>1. Make a histogram of the total number of steps taken each day</h3>

<p>Calculate the total number of steps taken each day by splitting the dataset by date and summing up all the steps taken on that date.</p>

<pre><code class="r">totalsteps &lt;- as.numeric(tapply(data$steps, data$date, sum))
</code></pre>

<p>Plot histogram of the total number of steps taken per day</p>

<pre><code class="r">hist(totalsteps, main = &quot;Histogram of total number of steps&quot;,
     xlab = &quot;Total number of steps taken per day&quot;)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk histogram_NA"/> </p>

<h3>2. Calculate and report the mean and median total number of steps taken per day</h3>

<p><strong>Mean</strong> total number of steps taken per day is:</p>

<pre><code class="r">mean(totalsteps, na.rm = TRUE)
</code></pre>

<pre><code>## [1] 10766.19
</code></pre>

<p><strong>Median</strong> total number of steps taken per day is:</p>

<pre><code class="r">median(totalsteps, na.rm=TRUE)
</code></pre>

<pre><code>## [1] 10765
</code></pre>

<h2>What is the average daily activity pattern?</h2>

<h3>1. Make a time series plot (i.e. type = &ldquo;l&rdquo;) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)</h3>

<p>Calculate the average number of steps taken in each 5-minute interval across all days. </p>

<pre><code class="r">nsteps &lt;- as.numeric(tapply(data$steps, data$interval, mean, na.rm=TRUE))
</code></pre>

<p>Create new data frame <code>av.activity</code> that stores the values of 5-minute intervals and corresponding average number of steps taken</p>

<pre><code class="r">av.activity &lt;- data.frame(interval = as.numeric(levels(as.factor(data$interval))),
                          nsteps = nsteps)
</code></pre>

<p>Show a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)</p>

<pre><code class="r">plot(av.activity$interval, av.activity$nsteps, type = &quot;l&quot;,
     main = &quot;Average number of steps across all days&quot;,
     xlab = &quot;5-minute interval&quot;, ylab = &quot;Number of steps&quot;)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk average_interval_steps"/> </p>

<h3>2. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?</h3>

<p>The 5-minute interval with the highest average number of steps is</p>

<pre><code class="r">av.activity$interval[av.activity$nsteps == max(av.activity$nsteps)]
</code></pre>

<pre><code>## [1] 835
</code></pre>

<p>This interval corresponds to the interval between 8:35 AM and 8.40 AM.</p>

<h2>Imputing missing values</h2>

<h3>1. Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)</h3>

<p>Total number of missing values in the dataset is 2304.</p>

<pre><code class="r">sum(is.na(data))
</code></pre>

<pre><code>## [1] 2304
</code></pre>

<h3>2. To fill the missing values in the dataset use the average number of steps for that 5-minute interval calculated above.</h3>

<p>First, split the dataset by 5-minute intervals. Then, in each set of &#39;step number&#39; values identify NA values, look in <code>av.activity</code> for the &#39;number of steps&#39; value that corresponds to the 5-minute interval that set is for and replace NAs.</p>

<pre><code class="r">newdata &lt;- split(data, data$interval)
newdata &lt;- lapply(newdata, function(x) {
    x$steps[is.na(x$steps)] &lt;- av.activity$nsteps[av.activity$interval == x$interval[1]];
    x})
</code></pre>

<h3>3. Finally, use the <code>unsplit()</code> function to reverse the effects of splitting and return lists of data into a new data frame called <code>newdata</code> that is equal to the original dataset but with the missing data filled in.</h3>

<pre><code class="r">newdata &lt;- unsplit(newdata, data$interval)
</code></pre>

<p>Here is what the new dataset looks like (the first few rows):</p>

<pre><code class="r">head(newdata)
</code></pre>

<pre><code>##       steps       date interval
## 1 1.7169811 2012-10-01        0
## 2 0.3396226 2012-10-01        5
## 3 0.1320755 2012-10-01       10
## 4 0.1509434 2012-10-01       15
## 5 0.0754717 2012-10-01       20
## 6 2.0943396 2012-10-01       25
</code></pre>

<h3>4. Make a histogram of the total number of steps taken each day with new dataset.</h3>

<pre><code class="r">new.totalsteps &lt;- as.numeric(tapply(newdata$steps, newdata$date, sum))
hist(new.totalsteps, main = &quot;Histogram of total number of steps (missing values imputed)&quot;,
     xlab = &quot;Total number of steps taken per day&quot;)
</code></pre>

<p><img 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" alt="plot of chunk histogram"/> </p>

<p>Calculate and report the <strong>mean</strong> and <strong>median</strong> total number of steps taken per day with new dataset.</p>

<p><strong>Mean</strong> total number of steps taken per day is:</p>

<pre><code class="r">mean(new.totalsteps)
</code></pre>

<pre><code>## [1] 10766.19
</code></pre>

<p><strong>Median</strong> total number of steps taken per day is:</p>

<pre><code class="r">median(new.totalsteps)
</code></pre>

<pre><code>## [1] 10766.19
</code></pre>

<h2>Are there differences in activity patterns between weekdays and weekends?</h2>

<h3>1. Create a new factor variable in the dataset with two levels &ndash; &ldquo;weekday&rdquo; and &ldquo;weekend&rdquo; indicating whether a given date is a weekday or weekend day.</h3>

<pre><code class="r">weekpart &lt;- weekdays(as.Date(newdata$date)) %in% c(&quot;Sunday&quot;, &quot;Saturday&quot;)
newdata$weekpart &lt;- factor(weekpart, labels = c(&quot;weekday&quot;, &quot;weekend&quot;))
</code></pre>

<p>The dataset <code>newdata</code> now has the structure:</p>

<pre><code class="r">str(newdata)
</code></pre>

<pre><code>## &#39;data.frame&#39;:    17568 obs. of  4 variables:
##  $ steps   : num  1.717 0.3396 0.1321 0.1509 0.0755 ...
##  $ date    : Factor w/ 61 levels &quot;2012-10-01&quot;,&quot;2012-10-02&quot;,..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ interval: int  0 5 10 15 20 25 30 35 40 45 ...
##  $ weekpart: Factor w/ 2 levels &quot;weekday&quot;,&quot;weekend&quot;: 1 1 1 1 1 1 1 1 1 1 ...
</code></pre>

<p>and looks like:</p>

<pre><code class="r">head(newdata)
</code></pre>

<pre><code>##       steps       date interval weekpart
## 1 1.7169811 2012-10-01        0  weekday
## 2 0.3396226 2012-10-01        5  weekday
## 3 0.1320755 2012-10-01       10  weekday
## 4 0.1509434 2012-10-01       15  weekday
## 5 0.0754717 2012-10-01       20  weekday
## 6 2.0943396 2012-10-01       25  weekday
</code></pre>

<h3>2. Make a panel plot containing a time series plot (i.e. type = &ldquo;l&rdquo;) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).</h3>

<p>First, split the data into 2 sets, one containing the data for weekdays and other for weekend days, and in each set calculate the average number of steps taken per 5-minute interval across all days that belong to the set.</p>

<pre><code class="r">average &lt;- lapply(split(newdata, newdata$weekpart),
                  function(x) as.numeric(tapply(x$steps, x$interval, mean)))

interval = as.numeric(levels(as.factor(data$interval)))
av.weekday &lt;- data.frame(interval = interval, nsteps = average$weekday,
                         weekpart = as.factor(&quot;weekday&quot;))
av.weekend &lt;- data.frame(interval = interval, nsteps = average$weekend,
                         weekpart = as.factor(&quot;weekend&quot;))
</code></pre>

<p>Bind those values into one data frame called <code>weekActivity</code></p>

<pre><code class="r">weekActivity &lt;- rbind(av.weekday, av.weekend)
</code></pre>

<p>Show a panel plot comparing the average number of steps taken per 5-minute interval across weekdays and weekends (here is used <em>lattice</em> plotting system).  </p>

<pre><code class="r">library(lattice)
xyplot(nsteps ~ interval | weekpart, data = weekActivity,
       type = &quot;l&quot;, layout=c(1,2), ylab = &quot;Number of steps&quot;)
</code></pre>

<p><img 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MBHxMOdJEACJEACJBBMAhT4YD43lpoESIAESIAEIhKgwEfEw50kQAIkQAIkEEwCFPhgPjeWmgRIgARIgAQiEqDAR8TDnSRAAiRAAiQQTAIU+GA+N5aaBEiABEiABCISoMBHxMOdJEACJEACJBA7gXfeeUcuueQSGTBggHz11VfmAsuWLZM+ffpIp06dZNSoUWbb/v37ZdCgQdKhQwcZOnRo7DeKcEaJCPu4iwRIgARIgARIIEYCmzdvlieffFKmTJkiO3fulHPPPVe+/fZbGTx4sDz00ENSr1496datm3Tp0kWef/55adKkiTz99NNy1VVXyYQJE8y+GG8Z8vBAC/ycOXNk4sSJhSr2008/FdrGDSRAAiRAAiQQD4FVq1bJkiVLZM2aNYVOHzJkiBQrVqzA9goVKsjHH38spUqVkjJlysi6devk8OHD5m/Lli3Nse3atZNZs2bJ1KlT5aWXXpKSJUtKv379ZPLkyRR4EKpWrZqccsopBcDiTaVKlQpt4wYSIAESIAESiIdA+fLl5dhjjw2pN6GuV7p0acnJyRGE3+GV33PPPbJjxw4j9vbxVapUkU2bNsnq1aulatWqZrO9zT4m0b+B9uCPO+44wY/b/vvf/7o38T0JkAAJkAAJxEUAAlynTh3p0aOH5/P37NljPPKuXbvKzTffLAcPHpTdu3fnnb9r1y7TCIBDmpuba8Tf3pZ3UIIvmGSXIECeTgIkQAIkQAJOAgjHX3zxxXLZZZfJbbfdZnaVKFFCypYtK+if//3332X27Nmm7x0h+5kzZ5pjZsyYIc2bN3deKqHXgfbgE6o5TyYBEiABEiCBFBD44IMPZNq0abJhwwZ59NFHzR2++OILGT58uAwcOFD27t0rPXv2NFEB9OHfeuut8swzz0i5cuVk2LBhSSsRBT5pKHkhEiABEiABEhDp37+/+XGz6Nixo+AHAo/kO1jdunVl5MiRgpA+PPxkGkP0yaTJa5EACZAACZBAFAK2uDsPS7a449oUeCdhviYBEiABEiCBLCFAgc+SB8lqkAAJkAAJkICTAAXeSYOvSYAESIAESCBLCFDgs+RBshokQAIkQAIk4CRAgXfS4GsSIAESIAESyBICFPgseZCsBgmQAAmQAAk4CVDgnTT4mgRIgARIgASyhAAFPkseJKtBAiRAAiRAAk4CFHgnDb4mARIgARIggSwhQIHPkgfJapAACZAACZCAkwAF3kmDr0mABEiABEggSwhQ4LPkQbIaJEACJEACJOAkQIF30uBrEiABEiABEsgSAhT4LHmQrAYJkAAJkAAJOAlQ4J00+JoESIAESIAEsoQABT5LHiSrQQIkQAIkQAJOAhR4Jw2+JgESIAESIIEsIUCBz5IHyWqQAAmQAAmQgJMABd5Jg69JIMkEXp4isik3yRfl5UiABEjAAwEKvAdIPIQE4iXw4pcimynw8eLjeSRAAgkQoMAnAI+nkkA0Ajv3RjuC+0mABEggNQQo8KnhyqtmKYHffxeZt1Jk/uroFdx/UKRYMRGcQyMBEiCBdBOgwKebOO8XaAIQ7MW/ibQdJvLpvMhVgfd+dBmRg4cjH8e9JEACJJAKAhT4VFDlNbOewJ+6iBwV5b8HAl+xrMhhCnzWfx5YQRLwI4EoX1F+LDLLRAKZJXDgkEi5UiJ79kcuR+4+FXj14A9R4COD4l4SIIGUEKDApwQrL5rNBNC3DoHfeyByLXfusY6jwEfmxL0kQAKpIUCBTw1XXjWLCexTgS+rAo+/kSxXQ/SlStCDj8SI+0iABFJHgAKfOra8cpYSsD34aCH6nRqiL1lcBZ5Z9Fn6SWC1SMDfBCjw/n4+LJ0PCaAP3njwUUL08OBL04P34RNkkUggPQSWL18uvXr1yrvZ+PHj5ayzzsr7Wbt2rezfv18GDRokHTp0kKFDh+Ydm4wXFPhkUOQ1ihQBePDlS2uSXRSBRxY9Q/RF6qPBypJAHoFx48ZJ3759ZdmyZXnbpkyZIvfcc498+eWX5qd27dry+OOPS5MmTWTSpEmyePFimTBhQt7xib5Q/yK4BhizZs0qVAG0mmgkkCoCEPgyJaMLPI6jwKfqKfC6JJA+AuvXr5fVq1frkNfCQ2KuuOIKndBKJ8hwWW5urowdO1bOOeecvD3ff/+9nHTSSfLss8/K5ZdfLjVr1pSpU6fKSy+9JCVLlpR+/frJ5MmTpVu3bnnnJPIi0AIP2AcOFHajfufUYYl8JnhuFAJIroPA740yTA4CX1xjZCG+E6LcgbtJgAT8RACacvDgwZB6E66c8N5xjtOKFy+uM1v+boS9Xbt2AsFHw6Fq1armsCpVqsimTZucpyT0OtACj7AGftyGfg4aCaSKAPrgjcAXblsWuOV+PY5JdgWQ8A0JBJLAMcccI/Xr15eBAwcmVP4xY8bknT9nzhwZPXq0VKpUSeDtlylTRnbt2iU5OTl5xyT6gn3wiRLk+UWOQCwh+hL6H8Zx8EXuI8IKk0AhAog49+zZU3bv3m32oYv5lFNOkZYtW8rMmTPNthkzZkjz5s0LnRvvBgp8vOR4XpElYIfo90Xx4A9odM548IW77YosOz9XfOkGkXXb/FxCli3IBI7Sua0vvfRS6dOnj3Tu3Fnq1KkjzZo1kyFDhsiIESOM+EP00Q+fLAt0iD5ZEHgdEoiFAITba4i+BMbBU+BjwZuxY//wmsifu4r0Oi1jReCNs4xAiRIlZP78+Xm1uuqqqwRJeXv27JHy5cub7XXr1pWRI0eabWXL6uIVSTR68EmEyUsVDQLoW/ck8NoQCKLAowvioNaxKNlyzWtCHjSeF40EUkkAnrwt7s77JFvccW0KvJMwX5OABwIIzZfWLHp45pG8czQETB98wGay+8cnIi986QFEFh0ydbHIWSeKoHFDI4FsIUCBz5YnyXqkjQCEu5R6emWirCiHUL4ZBx8wb3i3TrF7OGCNkkQePkR99FyRYypp5ILdKYmg5Lk+I0CB99kDYXH8TWDOcl1kRj14hOgr6mx2uyOMhYdwBDGLfpfWqSiN3V+zVSTnaGvlv6LWNeHv/zaWLlECFPhECfL8IkXgihetMC4mrqpdRWTVlvDVD+o4+KLmwWNKYUw9jGgL5jigkUC2EKDAZ8uTZD3SRsCOXteuLALvL5zBg9eJqyL204c7N5Pbc4tYiH7HHo3GlLFmHaQHn8lPHu+dbAIU+GQT5fWylsBeDc2X13532+qoB782wrhpCHwQx8EbD74I9UXDg6+oo5PwrOjB259u/s0GAhT4bHiKrENaCGD9dwiBvdRBJA8eniD634trKD9Spn1aCh7jTdAHX5TWsN8BgdcQPYbIMckuxg8LD/c1AU504+vHw8L5iQA8eITk7bC88eDDhOhN/7v+d2GxmSAJPKIORwWwUZLI5yRXBf5o24PX+tNIIFsI0IPPlifJeqScANZ/73qySO4L1q1qaR/8xp2hBRxCiaQtI/B2p33KS5j4DdD/jv7ootQXvVP74I/WOiNETw8+8c8Qr+AfAhR4/zwLlsTnBBCiL6vD42yDeFevKLJ+u70l/68ReBUMM0wuIJnZu1Tc691uZZQjApFMe2i0SIu/JfOKybuWCdGrB4/nyT745HHllTJPgAKf+WfAEgSEAAQek9s4DV78mhCJdhAKePA6K2VgQvTwXuvXsHIMku3BH6vLXdfUseZ+NITo0QfPYXJ+fDosUyIEKPCJ0OO5RYoAQvRODx6VRz+83SfvhGFn0AcpRA9R37ZLpLKugZFsTxYz4yF5D2LqN9uOYXLqwSPawhC9354Oy5MIAQp8IvR4bpEisBchepcHXwcefIhEOzOdrd0HH5AhZxD4LppjMKRH8gUejaMTc0SWbUzfRwbJjX2fjjzbIEqD2QiRd2D64JUBjQSyhQAFPlueJOuRcgIQKUxR67RwY+ELJNkFReC1nBA5/KD8yTQ0jk6sKYI119NlWB1uyW8in/0Q/o6o5/bdItUqWMPkkh25CH9n7iGB1BOgwKeeMe+QJQRChegxFn5tCA8eC81AKIM0Dh7ihrHgWEgn2X3wYNdQBf7XNHrw6BLYp89hwsLwH8DfNEES89DDEKKnwFss+Ds7CFDgs+M5shZpIBAqRF+zksjm3MKJdEEM0UPc0CiByCdb6BAGR0Li0jQKPGbka9NApJL2r3++IPQHZJ0mSKJcMNQ92Q0b68r8TQKZIUCBzwx33jWABEKF6JFEV0OHykEonAaBRDgf+4Oy9CrEDV4shC7ZAo9JgqqUsxbnSdfEP2hUlNOciWvbi7zxdeg6rVMP3inwya638zPB1ySQbgIU+HQT5/0CSyCUB4/KYFU5d6Id+nYRnofAB8UrRDkh7vhJ9jh4DDGE2GLN9ZWb0/MRQIge92xcy0rwGzO38H0Roq+lZYLRg7c48Hf2EKDAZ8+zZE1STGCvijaWFXVbqEQ7WyyDNg7e9MFr9n+yPVlb4BvoOPt0ZdKbex55Xte0E3l3lgi2OQ0zEaKbBYaV/zhMzmLB39lBgAKfHc+RtUgDgX0aZoZH7rZQi85AKCAYJskuIFPVQtTzBF4bM8k0072h3vQJOlQuXZn0mJkPHjzsuOoipx8n8uFs6739G+P+MQ89zEQuklxv68r8TQKZIRDi6yozBeFdScDvBCCAyDB3WygPHsdCMNAgSFefs7tcsb5HowR98LBklxueMyYJMh58mobKOT141Omqs0U+miOC9d9twyQ3lR0CTw/eJsO/2UCAAp8NT5F1SAsBCDU8XLeFms3OTlhLtlC6753M93ajBNdEPZM5Fh7D1TBJkPHg05RJ7/TgUSeE4js2tkL1eA+DwFfS5D8Yh8lZHPg7ewhQ4LPnWbImKSYAwQsl8DmaRb9VQ70QSNtssQySwNt5A6gDIhXO+tj1ivb32pdF2g4reBQ8aczzDqusYorIBvq+U224b/kjIXr7Xpe10XHxOmTOXgUQx2AWOxjKdcjxDK2toX8vWivy0pTQ+7iVBPxCgALvlyfBcvieAMK3EAG3IZEOk6U4h8rZ3n6QBB6CbofoUc94BL5pHZHOTQoSck8QdAIS7dIQpkcWvXtq4arlRXo0F3n7G8t7t8UdJUbjzWuIfvx8kZlLC9aT70jAbwQo8H57IiyPbwk4BdBdSHeY3hyrgoEku0CNgz/SgDFi59GbdbJAWPx3V1IhkhOdow+MwKchTG88+BCjHvq1Epm+xPLi7fC8XQevDZv5q608Bfs8/iUBPxKgwPvxqbBMviQA0bZDze4CugUenqAZB6+CGZQkO7tRgrp5FTo3B4jqYa2703JV9J2edLoy6dHYcN7XLlMFDcn3UZF/fXp+gp29DxEXdFVEMqyIh2jNUZjsXg2h+gqDrNf8TQJ+IkCB99PTYFl8TcCE3cP8x7gz6e3+bAjGhh1WONft2fqtss4uiHgFHmHxQy4PHlPGOvvCG6R4VTlkyX/3qwhmz6sQwoMH94tbisxdoQ021+JBqHe0SX5+UO+9WV1rnntcC/fo2AivaCTgLwJhvq78VUiWhgT8QCBckh3K5l50xukNT9Vw8FszRIod8fj8UJdQZbAz/7EP0+yiDrEaxNztAZsZ5RxCW1dn/sP8/fD2U2E/rRP5vw+t8qOBFcpKa/3uvUikSa2CeyHw7vIXPELkh1UiLevllx8Nip3q1V//isiCNe6j+Z4EMkcgzMc/cwXinUnArwScHq67jGa6Wsd89PD2IRbVdRnSmfeHTs5zXyPT7+2oA8qBsqPvPFbD/O/unINdKn7OPng0dOoj0S5F/fB4TuCP+oQTeNSr7YkiyKp3mpeGzbyVKvA6aQ4iBLBtutwsghZzNCIQrXFgTuCvIkNg+fLl0qtXr7z6Llu2TPr06SOdOnWSUaNGme379++XQYMGSYcOHWTo0KF5xybjBQU+GRR5jSJBAB4thC+U1dQseqwrbo8dx7G2uEDc4MX63UyZj9QPZYdQxmqoZyGB121Ogcc1U5loB5FF2fETalhjpDqhzx597OEM4+Y36BC/Rsfkh+gRjUBDDmP9fR6kCVctbk8BgXHjxknfvn0Fom7b4MGD5b777pORI0fKsGHDZNu2bfL4449LkyZNZNKkSbJ48WKZMGGCfXjCf0skfIUMXmDVqlUGiLsI69evd2/iexJImEAkgcfFsSrZWvXij9dpUZ3eMKZLReja74b62TP1xTJkzFkv1NOda+Dug8fxEPhULR0LYUcjA/Wxh/05yxjptXlWERpjCM9jKCCGRoIRGnRbdlnj+7GqYFASKiMx4L7CBLZu3SpLly6ViRMnFtrZtWtX7X4r3LTLzc2VsWPHyjnnnJN3zrp166RlS00AUWvXrp3MmjVLpk6dKi+99JKULFlS+vXrJ5MnT5Zu3brlnZPIi0ALPIR85syZheq/eXOalqsqdGduyGYCB/TLPFwWPept+uGPCLwRlyPeMEQjCB680+ONZ1Y3iB0yy1F3p6HuGH/uNGTST1jo3JK818jit734cBGXcHcrp9EWdDOEMwh8i2OtvWX02xNj/LGSIFasw1wI7gTDcNfh9mAR2Llzp/zyyy9SoYKGalwGgQ9l8N4PHtR/iiOGRkKZMkdmVdJtVapUkU2bNsnq1aulatWq5ih7m31Oon8DLfBnnHGG4MdtP/zwg3sT35NAQgTgmdnDosJdyLlsLMTSFheEp7HUrN/NGXWAdxrKG0UCG8QslGE4HIag4TpOQx/8sdb3V97m+hrlWKHtcIgxvOFkGsoNDx7liMeDR586htjVu13km/tFGtbML908Ffhzm1nvEc7/YpHIt7+KtDpBF63R8H0oZvln81VQCdSrV08aNWokAwcOjLsKFStWlN279cN1xHbt2iU5OTlSqVIlgbcP8be32cck+jfJ/1qJFofnk4A/CRixUNGLZHUQoldvDuYUF0Tv4PmnKmvcumPiv40Hf+QbAY0Ttyf+P5397f+9JfLwGJH1uo662yCKmBnOfR48eHcfPMSxmnr1azTikWxDPfADscWKfrEYoi0TNbJw5zsakdFsf2c+AYQf/e0Y5gdDJv6VL2oj4G8iD1yiXTPa7UCBt9jwd2ECJUqUkLJlywoizL9rP9bs2bNN3ztC9nYkesaMGdK8efPCJ8e5hQIfJzieVrQIQLRsjzxczZ2T3RixdIgLBA4ebjxmJ+7Fc24s56CO8Nxh8Hzd87JD3P7U1coxuPlNkVe+sjxd6wwrtF1JV2YrJPBab+c4ePv4VE14g8YVIgNenpldFvsvBH6HRhzQGMP5Tvbw3pvXzR/uiNXxTlSxx3OHgRmeu1fr+XjBhW+8nsfjgktg+PDhJgrQuXNn6dGjh9SpU0eGDBkiI0aMkJ49e5qcMvTDJ8sCHaJPFgRehwSiEfAiFvD4kGQHw/HO8DD6duPx4DHf+QMfi4zVcHGqzRl1gOfrFiv0TSMEf1FLDVOfIvLGdB37PUJk4Fki56vTsVND1GjIuL1ynIf6u82ek75TY/eexN6jHxxld9bH6xXLa/12qKd+QM9HY8U5VBDD45ofm38lNPiQWGnPloeRB7F48Cgfnu+A1vnX5KvsIgCvff58XbjgiHXs2FHws3fv3rz++Lp165qs+j179hgP3z42GX/1I0kjARKIRsCLwCOLGhOeYHw0vrydHj88WISwYzUIVaSkr1ivF+l4Zx3ROMF7p6H8tieOpLlbu4s8os7GjF9ETh9qha/RAEAyotMw7Mwdosd+hLpTkUmPcifiwSPSgryBo1XgkURnm0mwq2e/s3IyqjlyriDwuK8Xw2p2VfXc1VusRW+8nMNjsoeAM9nOrhXC98k2evDJJsrrZSUB4w2qxxbN7KFyEBk73I1zomVnh7suBN6eUCXcMcnajnvZZUbjBO+dFsoTx5DAh/paYrdJQ/ih+uAxPjyUwJvJblKwqhy6FpDzgGdmz0XgrEek12bEgwo8nh/Wj7c9eAyFwxh4RB1sA6sCAq+5Fl49+OWbrGv9qpP9eG0U2PflXxLwSoAevFdSPK7IEoDA4gvf6ZGHg2H3w+OL3nk8hCMeTxzXSZfAw/O2y2z6k7XOToMHj3qEMnRPQAAxL7vzXIjXVhXHyiGcE3vcOPYn0+zGFeaUjzSsMdQ9bYFH46ai1sVmP3dl/vA4+zwkTzqH/2E0gFeBX6ECjwgGGgnuhpR9ff4lgUQJ0INPlCDPz2oC938o8tNaS/gqu8Zyh6q4yaTXfngjMo7mc7whenijEA0IZbKHk7nL7/TgITxuscKENeEWb0F/9Uod9lbtiGhV/pPIKk0iQ3849oUruwnTqxd/Rn13aeJ/bzfGVH9jNkQa0JCpq8P60B9vh+h/UIF39r/jwj//JnKcRjBsM33wWl8vhmgHZj9Eg8rdpeHlfB5DAl4IOL6CvBzOY0igaBFYtEbkug76pa+JYrZ3G4mASbTToXLukD76puNJsoMXGm94P1I5Q+3DkDB7rL8RHrcHHyZZDteCiGOqXjQAIHSY9AURC2TeO8PY7vsikx5z0mN9ddtbdh8T63vTUNEyxBqex31Kq8uDzHl0NaAudogeK8jZE9zY5XlvsMi/BtjvrPu5G0X5ewu+srlkyoP/42sis5cXLBPfZR8BCnz2PVPWKIkE0Oeag+Q5DT97EXh48JjZzOkNozjwDJGAF6tBbCA08YT3Y7kXkr2wxjkmoIHB40YjxWnw4MOF6CuV0ylbVeBRTwhrFX2P+tpC5ryO87XJpFeBv/RZEfRHJ8PADMIZ6xh43LukCjwaHagHsuPhwSMhDl6901sPVU7jwWukxYtt0YYPkuxCNaS8nJ/oMet35I/4SPRaPN+/BCjw/n02LJkPCJgEMfXm0N9qe7eRigUPPk/gHf9dEIx4xsHDg8e5qRb4d2fpGumnWZO3oH4QHmffMLxriF84rxgePPrS0RgBq8oq8MiejybwCNGjCwRih2VXk2HwojGnvpfn5b4fGhzj77TEHXVBvTE87tR67iMLvy+u9XYyK3xE/hZwwQI1mRJ4rBeARgYtuwk4voKyu6KsHQnEQwDeIMQCQ6a8CAbC0RDj/RBEPc82iDSGXsVq6J/FufGE973eC7PSYTw2xrfb5h4m5xwiZx/j/As+EAx0RYAT8hXQoIkm8JjCFlPWInKwVSMAyTD0wcN7Rx3itZNr6/S61awQvZng5tjoV0Ljx2tGvM0lYwKv1Ul2cmN0Qjwi3QQS+BdId1F5PxJIPwGIBTKx4aF6NWTSQ9ycAg9vMC4PHiF6Fc1UevDw3s9vYTUk7Dqi7E5vFPe3J3Sxj3H+NR68CrTx4HUHQvTw4CH66I8PZxBF7MdCPckSHDvJLly0IVxZnNtv6aaJdvocEaL37MFrXbz0wWO0AViCsXPEgfP+qX5tPPgkNahSXVZeP34CFPj42fHMIkAAIXoIPJKuEHr2YhArhNadxyMjG15wrGZC9CoGqRJ4jO+esljkktMLlgzC45yqFmVHQyOcwYPfptdCtAH99wjRow8e5UbiWiRr00AXsFGPGWVJhpkERy1DIgKPcpTRqWiRewHRtqejjVQ+3M+LwIMTuiRg6PZAgyTdhmR/RBFo2U1AP5I0EiCBcARMiB4CH4MHD4F3f9HH68Hbk8SkKkT/wbfW6mgQaKe5p6rF/VGHcAZxe+X6I0Pi7D54bRS4kw1DnX/budakL1jMJRmGRpFJskvw2w2LySDBroWH/neUGw0b93MPVZ9fNljz+WOf8eC1ARGLPTlB5KoXYzkjxLGq8MmKmIS4Ojf5hECC/wI+qQWLQQIpImA8eA2lIgTt9Mgj3Q6z2SEE6jTTBx+HB2/3wcfj/TvvH+o1QsWfLxDp26rwXnffMLoX4NFGss5NLJFDoAMePHIObG860nnYV0X77JMlOIg82OHvaPeNtB+LycDLdY9/D3cOkuy8rAePJXcbHWNdBeVEI9Kr/ab5Eu/P0qVsNT8gEWOSXSL0gnMuBT44z4olzQABfPlC2DbosCJMduLFwnrwKnixGu6PxkGyxog77//RbJGOjQvOxmbvh0fuHCYHDx7l8GJzVlhhZzQK4NF6CZWjzz4ZHvyxt1nDv3BPL/eNVB88dwwddI9/D3cO7ufFg18Mga9lXQUC7+UcHINoy4VPqPd+tvfGZriyoiGCqM2zX1irAoY7jtuDTSBK71iwK8fSk0AiBPClioxweO6PXur9ShhH7cxIx5lIqoJYxzojHcLNSFZb8pt+uZ8auR/cewmtfICx80SeuiL0WRAeZ5IdIgjhxsC7r/DDMCsxDevHQ0gQLo9mdp99tOOi7T/lWEsw3eWPdl6o/Xhm3U+xVowLtd+9zYvAo78dowawzCwM5YzWBw+P/z8alkeUY4w2YODFL9Mwf7yGzyG6BiDwa3XOhlin8433vjwv/QT0MdNIgARCEcAXIb6AYzUk5N3fq/BZ8IBjzaRHGc5TkcFwslm/Fr5mvFs++V7kzAbWgiqhruHO7kaynFcPHteDeOzURoHXED0aD2jIJGoo594jAoY6JGKYZ/7BPt6vgPtF88Z/WW+F121RReMHAo8GnLuLAnV5ZpLIPz4W6d/aKgsWwLHP8V6ygkfifkjuwzNC1CQV0aGCd+S7TBFI8F8gU8XmfUkg9QTgPdtfxMm4G7LQY+1Lt7+MkXWdrIlJMP3qR3Mir0PuFiuITaRhcm4+4Ib7IArgJVSOBDWck2gyIc7HfSGCXuYtcJc7kfeoZ7Rx8BDyk47Jv4vtwT84WmTqkvztmJfghhFWg+GFa0Q6Nc7fB074bMZraDTacztA4BNlHm85eF7qCVDgU8+YdwgoAQhFtCFesVQNWegQyljMLgMWJsH0osmwsT9Y/cpYUCWcub1EJMzF4sGjgYDGCTxaXMuL2REOMHpkrJczCh6D8xAJgEcK4Ywn+lLwirG9MyF6V3Kl+wqLVeAbOwTe5oRkTqcn3XW4yF8vFMF4fPfwRIgzRDpesxuuSBw1Aq+8aNlJgAKfnc+VtUoCAQhUMj149OU7+7W9FNH+MsZkMEj0S9RQp5GarHVpm8hXgig7y2qLZ+Sz8vea8/VeuAZEzItB4BHhwDjx16bF7llizDr6qVFW3N/rqAcvZfNyjJdhcs4EO1wTny90Y0CwnaLd5eT8fnr3vW2v373d63t7WWA7RI9GJC07CXj818vOyrNWJBCJgD3JTaRjYtkHoXNmpns5F1/G8NiS5cFjWFyDmta480j3d5cVq+l5TbLDdSGwxoNX8fIq8IhwQODRd49hYKs1ASwW26FRBvSbmxC9svZ631juEenYaEl2yDHAdLzIp7AN59gC7/Tg8dzDDUtE/7mzMWBfy+tfu9EIgcf8//aSuF7P53HBIUCBD86zYknTTABfosn04KMJQKjq4csYX+hIrsKc8YkakrzaN4x+FVug7SPRT4tla70axBUZ9F774HFde7Y/eOKIWGDRnlgM50HgIVjwcsE7nWbGwWvEIpzZ49+dkQWUE88Yn7VvNYmy8V+s13jm4cz0wevx8Zr9uS6v3RkI/4MbLTsJpPlfIDshslbZScB8EeoXcLIMohkty9p5r/p36pCqTVYjA94zQsCJJkQhKuFlGVUIj7Os8KwhCF7NDiPjGl7uh+vi+rgPPPEaFa0hXF7vh+NwHkL0e4+E6NMu8GjURBB4d4IdygxO8ODxXNCoqaiNKHjyZSIJvJ6D6Ei8ZkeF0GA7WgUejYlEP1fxloXnpZYABT61fHn1ABOAwEfypGKtGrK6IwmA+3qtT7CSoBCih8FDhLeXiJk6HblepOuY0LFDrEwffKwevJYVQgQR82IQHNwHoexVW2JfrxzhZoSdv7nfumdGBD5Ckt1PmmBnT3Bj87AbQuCErgVEHyD2mCY3nMGDT0Tg7agQxuIPPMuagjjW4Zvhysbt/iJAgffX82BpfEQAX4TJzKKHB+9MXItWVUwniqFpOA+GL30IdCKGOtkNhkjXcffBx5pkh2gDtM6E6LVh4sXs+fqxSA2mvYVHHovhPGSGwxNGYyjdAu9m5i471r1vXKvgVgg8nimmxTXl1xn9EMWI9LlDvQ4rXPd0yAWvHP4d7ofPAEZRXHm2FabHPWnZR4ACn33PlDVKEgHzRRghVBrrbaIJgPt6CJtOvzffA8aXMsKriZgJz3qok+1Z2vdC2DuWYXI4z+7Htxso9rXC/YUHj+F4EDqEjmOt6yIV0IY1ratv2Jm8YYXhyuveboTXEfVw7kf+BDxv5Ag4DWzgsSP/APWupvuNwEfw4HG+3TBwXsvra9PIc3wGwJoevFd6wTrO8ZiDVXCWlgRSTcDuq0zWfSAAXj14iDv6rp3iCIGAGCRiaLR46XbAfe2yoixeznGXCyKEc9Gw8WLw4DEUEI0JLM+KssZiSGK7q6d1xkN9YzkzOceaYXJhQvQ/atnc3jvuCs7oc0f+AQQeOQQQ+Eh98DjPJNohwhSlIYBj3eZu5Jnhcjo0kZZ9BDz+62VfxVkjEohGAGKKL9JkGQTeax+87cU6721/qTu32a9Xa5/1+PkimP89UujWa4geQ7TsIX0QIISQYzUIO/qKnY2USNdAYwCz9SE0j+l+UVbYxIUilz1nvQ73Gxn3SFBDiD5TFun5hkqwQzkRlUFDBtERPHN4+Jh8Jtrnzj4vnrqaRp7e17ZK2i2AlQVp2UcgiV9f2QeHNSraBCBO4cYix0MGghdtKlP7uviyLzSDmf634ssZAo41xResFlm4RmS+/kU5m9YRmb3cmtYUS9aGMpwfTTzs89B1DpE3Aq8eZqyG+nrsfjeXxrK1mK1v2hKR3qfnRyuQeLZso5V852ZilwkLuGB8fyYtksBjgpur2xUuHRo/tsAjuRAePMbKR/PM8QzjTbRzh+gra6Nou96Tln0E9GNCIwESCEUAa4HDu0mWIeRuh72jXRNf9ugbdZrx4FWguz+qCVIawj7teJGzG4oM6mwNK8OxN78ZuT8VogDvz4uV0kYDxB0CG09DB4KHn1hscFdrUh8Iud0Hj0VYWh6njZcVurxto9BXw6pox+hcAZm0cOPgEbVZqg0UOz/AWUZ0Y4AxhD3Pg9f6Rkqyw/loGMQj8BMWiFzxgsiTA/NLgc84Ri3Qso8ABT77nilrlCQCW/SLtn6NJF1MLwOP1qvAI0zt9lbtsGyTWroQSUeR5scWLpvJRNdzw5kJz3r8r4fIwNtD6DyuEL2KUKwCj3LDk0fjCmWFQeDPPlHku18jCLyu295Ah31l0sJ58Mt1LgPMRBhqsR7bg8c8BxBsTIKD4X7RGlR2Yy/W+tqRgZ9/yz8TS/UiGkTLPgIxtq+zDwBrRALhCEBY3FnP4Y71sj2cAIQ6Fx58RVd/svlSPyIC9he1+1x7wRb3dvu98eBjEXgVWXiYZeII0WPcPxLP4jG7rjh3sz6H42tYM72Fu9Y6FfjaYbolwp2T7O14vmiYoNvEaQjPh0qwwzGIUmDIG8T/Rm204biNOgIA9Y9kdmMv0jGh9qGxNqy3LubTP38vBB79/rTkEnj44YflrLPOMj+XXHKJufiyZcukT58+0qlTJxk1alRybxjialE+RiHO4CYSKCIE4MEjdJosgwDYiWuRron7ztFwdC1XyNmInootQubhPGp7Pvdw1zd98OpZezHcz0y+koAHH++SrYge2CMG0NCCB4wEunDJamtV4MPlHXipazKOQYQGAvrH10S++mv+FbGCXKNj8t87XyEKg3wKcHrles2nWGWNJHBPiOM8B6/dwxjd+8O9x3wG7saaEXgm2YVDFvf2sWPHypgxY6RChQoamdEHrDZ48GB56KGHpF69etKtWzfp0qWLVK6cupZpoAV+y5Ytsm6dNo9dtn27DjqlkUCCBCC0yfTgIQCYnz2avTdLZO5K7SsdUPBIM/ZZPXiIblgPXvuu4f1f+IQ1iUn/1gWvEasHD5HFT7SQccG7WO/QoMFPPIbGhbMPHg0tzOw3a1nB9dRxbSQdohGQ8T54rSseL56x87OD4XsXtQxNAd/7Ux2Ngeo6RS/G8Efrg8ewxe9XWP36ztA/Ghi9nxK554LQ3RmIxrgXDTJZ9PTgQz+gI1t37dolmzdvloULFxY6rmnTpoW2HdZsWugTRB6vBwyw/pmhVy1bWh+Gdu3ayaxZs6R79+6Fzk/WhkAL/Pz58+X9998vxGLp0qWFtnEDCcRCAEKI8dgYI5wsMwKv141m8EaxDvhx1QseafrEVWwj9YnDg4e4YPU31AH26TxdwayaZtnXtsTQq+iiEQFxx/3iEXjUF4ln8ZrtpSJ8jIZWq/rq5X6lDZ+2Ba+IrPNYJ+EpeIXkvEN3BHg1ryuySMP07U6y2GFkwPGuZxnujhD4jXp8uAacfR7Y3DtSpG3Dgsl7L0xWgT9D5PkvRE6tV3jYIDx492cayZyICt0wwir/GzfZd+FfmwCEefbs2bJqlYZYXPaf//xHu6L04Tts/fr1UqNGDfOzYMECOf/882XkyJFSpozCPmJVqlSRTZs0QSOFFmiB79ixo+DHbXZryb2d70nAKwGIZDLD87gvhDXaOHh4WD+uFbnjvMIlNSF6NDz0mHCCi8S8V6daQ+Xg1cI+mStyVgNLCGKZsMbcDwKv93N6idZVo/9GfV3fe9FPchyBsqJPG3XCtTAMEEvIYsy2c7w7ktKSOdrBUYSYXqKMeDZoECGxDgL/83pN/qvhPZIB4UYIHdeKZOjqQQMQjVCnIdyPkQhoWD07SeQv6sk7DQLvjnQgioD8BUSGwnUlOK9RFF+feOKJcsopp8jAgQM9Vb9WrVoyZcoUc2yPHj3kvffek507d8ru3fmhEkQFcnJyPF0v3oOifIzivSzPI4FgE8DEMVjRLJnmZZjcG19b4uwUMLsMRnD1SxiNBLwOZfBkkaiFjHI7cQqCgy/2WPrfcW0kcsEjhYiE6/MPVQZ7G8QqEQ8e98fQMZsFRK+leqXIpnca6llFE8UybSgfGHdrlj9xjFkiVp9HLFatQvT5A/AZaKjPGM/VNjQu0ADCcDxEOZaq2H/9i73X+muiP9qAcBuEHbPtae8CLQkEfv31V7nqqqvMlfbv3y+5ublSp04dKVu2rAn1/679SogINGnSJAl3C38JCnx4NtxThAm8PUPkktOTCwBiF2mYHLxVzEZ3WZvQ94WomzHpIb6g7TMQoseYZqwUZgs8pj7FF7t7ghP7nHB/ESWwVzhzJ2aFO8e5HYlj0TxR5/Hu16gvZrZz5kHY/fDOY1FPd9jZuT9dr/F8IbIoL6IKMJNgF6PAY332aIu/YDEeePDoirEN2foQagy9A7vbzhV5ZqKVk2EfgwaBuw8e+07QKAMmu/E6EZN9Pf4NTaB+/fpSrVo16du3r7Rq1UruvvtunXq6uAwfPtxEATp37izw7CH6qbQwfkAqb8lrk4C/CSBEjhA9JpFJpuGLN1KI/i1tVPRsXlDQnPeHRwsBieRNI5yNWd8QhrUF3vbg3XOQO68d6rXd5497RpsbPdT5EPdEBB4RADR6nF0lZ9QXeWmKJUR2+B/ChEzwTBvqiogHPPA8gVfRvalwL2LEok66O+Jus7O7RgnQeMCztW2TsnJGndCl0b6R9sdPFrmzh3UUjg8l8KcfrxMmdUr/Aj122bPx7xNPPCF79+6VEiVKmB/U0e5WxnZnf3yq6k8PPlVked3AEhj5neW9HxnZkrR6QADCCTzGcWOK1v6tw98OXhk86nD97zgTogxxgcgg+QyGlcLguSHpDo0Mr4Y+cBOixz0jRA3CXS9RgTcevDa0nAIP7xhD5hapcNqGPnm/CDy6dlBGlAmNRITsa2pjKxWGBWqcnj6eu3typGvbWePyvz3SrYHPQajPD5aOPU8bl4gQ0ZJHACIOgXdbOsQd96TAu8nzfZEm8JuOsPxhlci56iEl25BVHi5Ej753dAm4v6CdZYDgmRC9hs7DGfref33Uus5+/bKGx4YvdAhBrH3wtgePa0SKGoQrCxoTtpcd7phI23F/48G7vHNk0zv74RGpsPvpI10v1fvwfHJfsBL+ILbof29cO3V3ReKj04MPNb0xsvERqv/PBKuRF86DRynxjJ3XS13JeeV0EaDAp4s07xMIAqNmi5zfIvowpXgqE26YHDKuMaY5Wp8/BMSEyyMIvLNc8HyRSQ/vFslqcfXBqwdqugXi8eCLqcDrT7wWyoPHtdz98Car3tUIiPeeyTgPjQ0IvJnBTvvEU2UItTuT7EJNb4x7YzIdMHvpS+v4cqVDlwhRGjxrWvYQoMBnz7NkTRIkAA/oi0U6SUyYSUkSvLzxZkN58P+dJjLgzNChU+c90QcPDytUiNV5nP0aY98xbMoWeNMHH2OIHuK5cnPhoVX2PSL9NR58AgKf1wfvEm97Old49zCIqR9C9FZprAQ3RC6QyxFtRjr7nHj+ugXeTG+sORih7IYO1kqDG3SMfbhoDD5X9OBD0QvuNgp8cJ8dS55kAmN0Qhgk1jmztpN5i1B98BCBZSrCF2jUIJrF6sFjchUMlUJ9diNEr33wsYyDR4j8Q41oILHNPXY6WlmxPxl98MhLqKr5BE5DbgTC9Ha/MjLt/STwKCu8+Fna7+11ghtn/by+hifu9uArhhF4hPPvu0jkm6XhEx8h/PTgvdIPxnEU+GA8J5YyxQQwccgn31vrkKfqViZEr+OXnYZJaa5s6y35zU568zrpzHHqwSOjHv368KaRW4BGgleDwGO4HZZqjcfMTHYJfMPg/MraOKmnCWBuQ6MD09bCkEzoN4H/9Addzle5IdkxVWY8eG242WY8eG1YhLOTtLsAOQLhPgPYjiRQDpULRzB42xP49wteZVliEghH4Isfrclh3NPDhjs+nu0QWWeIfs5yK9O668nerlZZv7yRbQ/h82Lwup8YL3JKXZE2DUTenxVaLMNdC1/4CPHHuwwrwtSJTHQDVmhghErUO+N4kXnaYMGoAjTO/DBVrZPjxqdEHr/MuSX5r90ePPIsKobpX/d6d/TDO6MCXs/jcf4kEEN73p8VYKlIIBkEMDTuj12ScaXw14Awo4/TJIWpWL86TeRqHcYUSsBCXQVDmR7up2OVNdPfiyG5Ch4bDIud3N2z4MQo1p7wv+0+8Do6jWk8hvp6rVuo60/SfAjMzR7KMLENPHuIvHMYXahjs3UbPHjnMDkj8MolETOZ9NpoijSaI5Hr89z0EvAk8MuXL5fjjz9eFi1aZJa/69mzpzRr1iy9JeXdSCBFBDDkCt41FudIpaFPGh74oP+K/Lmrdaf2J8V2x0TKCLEN10cbqhQI+eInXpFGfb1GG0Ldf8pfQm3N34bM8Bnap+y38Hx+CVP7CgKPeQpgWFEPeRaYyTARQ6Id++ETIeivc/VfMLLdeeedcs0118i+ffvM2rXTp083K+OEWlUn8pW4lwT8Q8BMIXqP5VHDe+9zRurLBsE7rF/EGLr29jcq9J1Tf89E7tBTE/+W/DP+K2DK3IVr4z8/2pnoh//656Ir8GjYIMEQBk8e3RSJTs6E/A4KvMU0G35H9eDfffdd47lPmjRJsLzdxx9/LH/9619l4sSJcu2112YDA9ahCBIYrQl1yJjHSmsQoo6NUg8BAm+vXW6WENUkuGy2f/RObe2wqAomufFb/3tqa51/dXyeEEoHA3yekjFjHhIrMexwyW9W9AZj6xH6x/z4SBg895T8+/OV/wlEFHiseHPw4EGpUKGCfPLJJ9KnTx9TI6yOU7FimM4x/9eZJSyiBDCbF/qV0deOGesGnmWtL9779PBDh5KJCt0AmC7WzlZmP2fidDFt7aadiV8nqFewJzNC1w+WfE3UkPn/2DhLzNGdg1wH81dfvzhFu7F0P5jTgkEgosAX03hPmzZt5MILL5Rp06YJwvOvvPKKvP7663LvvfcGo4YsZZEmsGKTDv3RsDhWy8Lc4I9+Zgk7BB4eIMYpX3BqehAhoxxj0bG0qXNRkPTcPTvv8vpN2Vkvr7XCHAf4XEPgayVB4C/XRi9+QhnyHZxJfaGO4TZ/EdAgT2R76623zJJ3CMk3bdpUateuLXPmzDHh+shnci8JJI8A+gUx6Uks9vN6kUufE/nsB+sseO/XaNY6+t8RhjxWs7Af6ut9ZrhY7h3qWHjwmA++EgU+FB5ui4MABB45HUbgdVhkKg0JfLs0XE8LDoGoAl+uXDmTZNegQQNZsWKFWcO2bt26wakhS5oVBLBK181veq/KnOUiHR4S+dtF+cumIiM8R8OL+DKEB51oQpL30lhHos8U47YRVqUHHys9Hh+KAD5L8ODRaE2GBx/qHvY25DpgZUJacAhEFfhff/1VzjzzTKlevbqcfPLJ5i8S72gkkE4CWEXN6wQco+eK/EtD8VhX+wSdKMUeN44vJwjrV4u1v7JKSRF6PAAAQABJREFUOktv3QtDxjCs6dZu1gpf6S8B75htBCDw170iMnZe/BMSeWWCnBGG6L3S8sdxUQUemfK9e/eWDRs2yLZt22TkyJFyxx13mMx6f1SBpSgKBOwxvpG+YDDF5rNfiIxRgf/35SIn17YSgpBhDIMHj0xjeCJ1MiDwGE+OJLtYxqJbJedvEghNAHkdWHxn5WOpH01ADz70M/Dz1ohJdig4PPi77rpLw5n6SVLr2LGjXH311fLVV18Zj95s5C8SSDGBXToDHPquIdKhhkXBu39wtFWIJ1TcMQkIDB4OZo9DaBzDfeDBr9MEO4yhTrfBg0cZQpU/3WXh/bKDQK/TRPCTDjN98BoFu+1tkS4nW59jDKf7Q+d03J33iIdAVA/+1FNPlREjRpjhcrjB+vXr5dNPP5W2bdvGcz+eQwJxEYDnjok9MM2r2xCC/3/6pVNXvfJ/XJIv7vZx8No36FAqCD2G+KzR/nwcm25Dkp2JRGiok0YCQSNgPHhtoGLJ2e069h55MR/PCVotilZ5owo8xrzfcMMNUq1aNTM9ba1atWTx4sXSv39/ady4sdx4441FixhrmxECEHgML4MH7zRkpd8wQudaP9Ua3x5qWlWsqoaMeszShaVH4cFjXvd0Gzz4DmmYUCfd9eL9igYB24NHFAoRM4xsQU7J5iOz6RUNCsGqZdQQ/SOPPCIPPPCA8eBzc3PNpDclSuSfVqlSisdmBIsnS5siAvB8EaLHzFpO+36FlRkfaSw7VkNDKBF931X1GhdrSDMTWezwgIr6uG3ns+PrYBEor/8/SFRFFA3/j5jTAVMvz18t0qlxsOpSVEob1YOHgL/wwgvSq1cv+eabbwTj4jFtbatWrczPSSedVFRYsZ4ZJIAvFoTo4cHPXalTdA6y1jrfpN5Do1qRC2YLPLKA4eHf3yvy8dxLAiRQmAA8eHjvMFvoh/QQ+XyBtY2//UcgqsBfd911csIJJxgvHsVHwt3bb78tS5Ys8V9tWKKsJYAQfaWy1hfMVP3oYXrZa14SmfJT9P50NADGzU9sZbOsBcuKkYBHAohAYd57RL8wURReYzbIpRussfgeL8PD0kggosBjLvqFCxfKkCFDpEwZdX/U6tWrJwMGDJAvvtDxSDQSSAIBDG/7cLY1hCzc5SDwpXUpy0VrrRXEMGnMHA3PIzwYbUw7GgbvDRa5qVO4q3M7CZBANAKY6x6JdVh0BmF6JNohpwXD9BatiXZ2+P1YgOmJ8SJPTrAWYwp/ZP4eRPHQ0KdFJhBR4DE0DgvNzJ+v7s8RO6zfxmPGjBEk29FIIBkE3p1l/YMPeTe8JwCB37jTOu7hfiKv3qBhwhesf3Ivi180qyPSpHYySstrkEDRJIAk1RPVY8ea8RB4ePBIfIXAYya9eM2eUXLpemuo6yFt8EcziPtr06Idxf0RBR54hg8fLh06dJDHHntMPvzwQ+PBYyW5Cy64gPRIICkEED5/U/vU2zQQueXN0F8WGAd/9dmWqGOBGNsg8hRumwb/kkBqCSBBtZnOVL5Np8fFsE9E0qqrF//1z4ndF42Fy9pYiXvDP9XkvSgiv1e/D1ZsTuyeReHsqAKPJWJnz55t1n5HaH7y5Mny+eefS/Hi+nRpJJAgAcyjjeE29arpwjBn6nzz52gS3CiRiQvzL4xJapBch2VWaSRAApkj0PVkkRs76rBT7Xf/daNVDkx6891y/b/9MP5ymWiATkqFtSOQwPfouMgijwgC1q5Hki0tPIGoAr9582Zp1KiR3H777XL33XdLw4YNzbS12E4rugQgyi9NSbz+y/SLAlnutp2pXvyjl4q8M1PkhcnWP/nLX4l0biJyDEdk2pj4lwQySuDT20W+/7tVBHjxb/1BE2BVdOM1rIiHWSex4uPQi63wP9alR/98KINjgNwafH/QwhMIK/B79+4V/HTq1Mn8td/v3r1bbrnlFhk9enT4q3JP1hPAxDGvqMBjFjm0plveL3JQx8XGakvVC8Ba7U7DMq6YS36lJvTc/b7I5B9F+rVyHsHXJEACmSTQ6BhryKldhlra+P5NvwviNQg8lr6F2SKPCXSQfBdK5CHw3ZpZSbfWWfwdikBYgT///POlbNmysmDBAvMXr/GDpLvp06dzqloHTXyw4XFmo2E4zAffirQdpi12zVp/5SvrHw4Cj/ncv1kq8qNmtqMffPov3gg8+pnIvJXW7HLXvyJy+vGFz8OY9WG9rX/2O3tYY+ALH8UtJEACfiCAzHp7djsv5XE6A6b7TRP3IOy2oTvuAf3/x3frk5/bW62/SMLDOadoLgC+e/xq6Mru2bOnnHPOOfLTTz9lpJhhBX7ixIly4MABueyyy8xfvMbPwYMHZdWqVcIJbvKfFzzMf2tL0+typmu26nwC7xZsmdqtVC8ZpPl3jv8Vkli+/VXHsd4dutz4x4EQX6sCvFJ7Y/6vl2atTteZ2PRntZYfM8Nh4ouBmuQ2bYk1PzU8+hHaAJi1LPSyktj+5zd0/urvrex3tM7fH6ze/3Gh64Hs2of6ipwWZn/os7iVBEggEwTQhRbKi8d3DTzu99VRgCHjvvKfLIcBQr1VE+wql7X2OX9D5B/pp6Nn1JPHEs+24VpYTMrO3o+WkGefl86/O3fulHvvvdfMGYME9Ztuuimdt8+7lyIMbRgihylpManN8uXL5fjjjzdLxGKIHFolzZppfCTDtm/fPtm1S5+2yzB/fqpt+SZrHmbVIOPFtm1oebfdmuavzYwPL4aU4AdDTJAUgr9oEEAk3/xaPWM9D94wPsAXtbS23XauNQVkqDrgfgf1HwbjwVscK4IGAfrA7AYC5lkvr/eAOGI7Zm47Sl/jBy1k+ziILEJgmB/9rH9of/q11vhW1GuUjklHlur5Or87VoqCN40ZrO4bKXKhlhFePBJs7jlfpH9rnV/9YZHxd1pTwWJcOsa0PzRGx6fruFm0spF1i7q/+KXVqGh7osh/p1lLtvZoHqqWBbfhH51GAiTgbwIIsWNdenjnm/VreYt+v0CMEQXENNH1a4jM+EXki0X6HXK9ft/9W78DtDvurp7W92Ko2uH7q38rkXYPWX3+cKL+pkm49rLPWBL6+S+txD9nBCDUtRLZBuf20KFDsmWL9hu6rGpVrYTL4LG3bNlSKleubH62b98u0KVSpfTLOY0W9avzzjvvlO+++07Gjx8vXbp0kTPPPFOeeeYZmTZtmhx7rCpMBg2T7bz88suFSoDyptrWbtMW6SxLiPHBHnyOCt57IgvX6JK6jawhHBg+guxQJKTZPxjPXVKpPzlQV0B7S71d9aJxXJ8zRF6eYv19XD1bCHIkw3hUNBQg4jC0Yn/Xv1jnHA0LCD/mibb/4jWOgfBD5E/RR4fV1b6+Txsb+plFwhzOzzlaF2/pUNirxj/oI/1FsHDLqffrve/JH56GoWq2NT/ykcB90XBZsFpkkv5D4x5Y4OXl66wjX50q0v4k+yz+JQESCDqBISrUY+fq91lFqz8dk+DguxFj5SHUWBjqaxX4/7vYSpDD9x5mxcN3wVVnh699i3oi/xqg/fGfW1HDW7vrcrVNrOMRWbxXHQ98l5UMEQUIf9XY9vz8888ydepUM4LMfeb777+v9TvyRXxk5+rVq8Up/BD6TZs2Se3a2iJJoxXT2erwvR7WIOKLFi0ylbvjjjvkxx9/lL/+9a8mm/7aa68Ne14md2A431NPPSU5OTmZLAbvTQIkQAIkkAUE4EgiB23gQPXMPBicz1GjRsnTTz9tjm7durVxitPtwRdsdrgKDu1HnzsS6z755BPBmHgYQg2Y7IZGAiRAAiRAAiRQkEDTpk1l7ty5Gi39XRCexwyw6RZ3lChiiB798G3atJELL7zQtD6QPf/KK6/I66+/bhIIClaJ70iABEiABEiABGrWrGm8fcz4umHDBvnnP/+ZESgRBR4lekuXh33vvfdk6NChglbJypUrZc6cOVKlinb20kiABEiABEiABAoR+OMf/yjXX3+9SVZ399EXOjhFG6IKfLly5eSaa67Ju32PHj3yXvMFCZAACZAACZBAaAKZCMs7SxKxD955IF+TAAmQAAmQAAkEh0BIgf/qq69kxIgRphZTpuj4KRoJkAAJkAAJkECgCIQM0SMpAIl0nTt3FvQjYAU5t2FcX+nSpd2b+Z4ESIAESIAESMAHBEJ68Jg7F3bqqafK4sWL5YQTTij08+mnn/qg+CwCCZAACZAACZBAKAIhPXh4519++aU5vnv37iFn7wl1MW4jARIgARIgARLwB4GQAu8sGlbEwSD9FStWmAlusB58plL+neWK9BpL2qJbAXPp00iABEiABEggEQLISzv3XF0kJGAWVQExB2/fvn1l4cKFUr58eTOz3eOPPy6DBg3ybVVXLF8mf/vLXVKmDHMEfPuQWDASIAESCAiB5avWyEUXXRSQ0uYXM6rAY5k7zGSHuXURuv/mm2+kX79+0r59ezn55JPzr+SjV40bnihP/fU6yanOyXh89FhYFBIgARIIJIGX3xkjWL00aBZV4LHQDFaSswfsn3322YJFZrCyjl8FPmgPgeUlARIgARIggWQTCJlF77wJ1n3HpPlOQ39E9erVnZv4mgRIIGAETmg/QOYt0vU7o9gxrS6WpSt0HWQaCZBAoAhE9eAxDr5bt27SqVMnqVevnvHmEaoPYn9EoJ4MC0sCJEACJEACCRCI6sEjwW7SpEnSqlUrE6a/5557zMpyJUuWTOC2PJUESMALgSdHvC8PP/umOfTQoUPSsuf1Mmn6bPN+zoIlMuDPQ83rKd/MlRY9rpXKzXtK70H3yaYt28x2LFc57KnXpW6bPlLnzN7y4NOvmyUszU7Hr8F/e0LufPBZs2XlmvXS4+ohUr3lhXLr3/9jRtHYh772wThp0vVKqXDyuXLa+TfIt/N+1L7J/aZcq9dtsA+Te/75grz98YS893xBAiSQfgJRBR5FOuOMM+S+++6Txx57TK677rq8/vj0F5d3JIGiRaB5kwby4fipptJzNZw+f/GyPIEfN2WmnHhcHdm4eZtceMM9MuSmy+THiW9IpYrltVHwljnnjVHj5c2PPpfRrzwsH734oPzvk0kya+6PBSD+7bGXzXX/ccf1ZvuVtw+ThvXryrzPRsheFe9NW7ab7T//ukoG3/+EvP2fv8mqGR/IGc0byX16bunSpeSEerVl1LivzHEHDx6UEe+NlQ6tWxS4D9+QAAmkl4AngU9vkXg3EiABm8DZp58iS1RYd+zcJV/Nmie9z+sg076bb3ZPmPqdnNextRHWpg3ry0XnnC3ly5aRe/98pXw6+RtzzGsjx8m1fXtIg3p1pNEJ9eS6/j1l9KSv7cvLC29/Im99NEFGv/ywlNVhpVu27ZCps36QewdfKXWOqSH333J13rE1q1eVmR89Ly2bniRHFSsmJzc8XtZt2GL2X3phFxk5zlq34kuNJuBedWvl5J3LFyRAAuknELUPfs+ePVK2bNn0l4x3JAES0GhZSWnfqrmK+g8yZeZcufPGS6XXjX+Vrdt3yqJflstZpzWVz6d+azzwRl2uKEBszW8bZc36TfKvF9+Rf4/4IG/fac1Oyns9/qtZsllFHcdVOrqC/LJ8jRH2mjWqmmMg8rVrWgm1FSuUk3fHfGF+duzcLSceXycvfH9B17Zy41/+Jes3bpH3P/1SBlzQJe8efEECJJAZAlEFvnHjxjJhwgQ56aT8L4XMFJV3JYGiSeDcDq0FXvH3C3+WNi1PlhZNTpQnXnnPhMAxW2PrFk2k7enN5PM3HssDtFYFu1ZONWnVvLE57sbLLjT7cnftlkOHDucd9/rj95qQ/x/ve0ymvPuU1KuTIzh3246dUvnoirJr9x7ZsHmrOf71kePlg0+nyMjn/iHNGp0gn0yYJn8Z/qLZB+//gi5nyScTp8sYjRA8cNt1effgCxIggcwQiBqir1u3rsyfb4UEM1NE3pUEijaBczu0kte1L/2k+sea6Zc7nNlCnnz1A+nR6UwD5px2Z8jMuYu0AbDEvH/zw8/lPE2SwxTTvbq1k1ff/8x4/Ei4u+K2YfKEJu7ZVqFcWbnrD5fJqrW6gqSG84+pUU0aN6gnSKaDvTP6C5298pB5vWX7DtM3D3HHtf6rxxw4sg8HXHphV3n85ffM+XYEwJzIXyRAAhkhEFXga9asaaaqzcnJkaZNm+b9YPIbGgmQQOoJNFRhR986QvWwjirwO3N3a/+7JfDwnh8acpO073ezNOoyUB596R15/sE7pHjx4tKzcxsV7apyfLv+0rDT5cZ7v/sPlxcodBld9vmJv90sQx5+zjQE3nvm7/LUa6OkXtu+KuKfybG1rb70yy86R5Apj2z9pt2uNkl26zZslt179prrdW/fStZv2sLwfAG6fEMCmSNQTFviv0e6/Zw5c8wiM+5jELKvWtXqp3Pvy/T7/r17caraTD8E3j/tBDCMbtuOXKlWpVKheyPUDiuvHrtX27x1e8hrYXuVShULLTq1Z+8+04iYP/6/Zr/X+/A4EvA7AUxVW+7YFjJw4EC/F7VA+aL2wZ922mnmBAx92bZtm5mPnqu0FWDINyTgCwLw2EOJOwoXi7DblQl3rVDb0S3wxofjpV/PThR3GyD/kkCGCUT14JcvXy7Dhg2TTz75RG6++WZZv369oF8eE9741VqfcZqUL3WUVCjv3Vvxa11YLhIIAgEM49u3/4BUq1rJDKELQplZRhLwSuD7BYvlyaefkz59+ng9xRfHRfXgMbHNOeecIw888IBs3LhR7rrrLrngggukd+/evs2sr123ntxyyy2ckMcXHzEWggRIgASCTQCrqe7da+WaBKkmEQUe3fNYBx4JdW+9Zc2MhfnoBwwYYJaP9evQudKaNIRFcpAYSCMBEiABEiCBRAj89NNPiZyesXMjZtEX09mqKlSoUGCYHIbejBkzRmrVqpWxQvPGJEACJEACJEACkQlE9OBx6vDhw6VDhw5Sv359wQIzL7zwglkHHmF6GgmQAAmQAAmQgD8JRBV4JBUg3D127Fg5cOCA6Xtv2LChP2vDUpEACZAACZAACRgCUQUeRzVq1EiqVKliZtHy69h3Pk8SIAESIAESIIF8AhH74HHYjz/+KKeffrpgRrvjjjtOunTpIv/73//yr8BXJEACJEACJEACviMQ1YO/5ppr5KKLLpJPP/3UePHTp0+XK664QhCmxzrxNBIgARIgARIgAf8RiOjBY5jcr7/+Kvfee6/x4EuVKiWdO3eWG264QWbOnOm/2rBEJEACJEACJEAChkBEgccwubZt28qrr76qi1RYK0rt3r1bvv76a2nfvj0RkgAJkAAJkAAJ+JRASIGHd96kSRPz8/3338tNN90kNWrUkObNm5u56OfOnSubN2/2aZVYLBIgARIgARIggZB98BD31157LSKdE088MeJ+7iSBbCTw0GiR284VKVsqG2vHOpEACWQTgZACf/TRR0vr1q3z6olV5DBVX5SVZfOO5wsSyFYCb38jclkbkfo1srWGrBcJkEC2EAgp8M7K3X///fLMM8+YJDssR2nbY489Jt27d7ff8i8JFAkChw6L7LCWVi8S9WUlSYAEgksgqsC/8sorsmTJEqlWrVpwa8mSk0ASCOigEl0KVWQbBT4JNHkJEiCBVBMImWTnvCkWlcnNzXVu4msSKJIE9h0UKV9aBX5Xkaw+K00CJBAwAlE9+KefflrOPPNM6dGjh1SvXj2veldeeaXJqs/bwBckkOUE9qvAl4PA787yirJ6JEACWUEgqsDffvvt0rRpU6ldu7YcdVS+w+/sj88KEqwECUQhAA++nGbPU+CjgOJuEiABXxCIKvA///yzrFmzRjCLHY0EijKBAyrwGB63lR58Uf4YsO4kEBgC+S55mCJ37dpVJk6cGGYvN5NA0SGwXydzpAdfdJ43a0oCQScQVeD37t0r559/vul/b9y4sdg/48aNC3rdWX4SiIkA+uD37BfJ3RvTaTyYBEiABDJCIGqI/u9//7tZbMZdOs5k5ybC99lOAB58nSoiW5hFn+2PmvUjgawgEFXg7777bvntt98KVfZf//qXdOvWrdB2biCBbCWAPvjS+h8DT55GAiRAAn4nEFXg4cHv369xSTX8XbBggbz33ntcC97vT5blSzoBCHsp/Y85YC2smPTr84IkQAIkkEwCUQUeY+CdhqS71atXy6RJk6Rv377OXXxNAllNACH6MiVV4OnBZ/VzZuVIIFsIRE2yc1cUC86sWLFCtm/f7t7F9ySQ1QTgwUPgD+p89DQSIAES8DuBqB48+tnXr19v6gFxxzrwJUuWNAvQ+L1yLB8JJJMABL6k/scgTL/vgPbHq9jTSIAESMCvBKIK/MMPPyz79u3LK3/ZsmWlWbNmnPgmjwhfFBUCpg9eF1QsqT/oh6fAF5Unz3qSQDAJRBX4M844I5g1Y6lJIMkE0AcP7x0/eE0jARIgAT8TCCnwyJTHOvCR7M4775S2bdtGOoT7SCCrCMCDxzC5UvDgmWiXVc+WlSGBbCQQUuCrVq0q3bt3L1TfnTt3yvDhw+XgwYNy3333FdrPDSSQzQQg8FhNDv3w9OCz+UmzbiSQHQRCCjxWjvvDH/5QoIaYj/6GG26Q1q1by4svvih16tQpsN/Lm3feeUfeffdd038/ePBg6dChgyxbtkyGDBlikvduueUW6d27txlvf/PNN8uPP/4oXbp0kaFDh3q5PI8hgZQSgMBXLmf1weM1jQRIgAT8TCCkwDsLnJubawQY4vzEE0/INddc49zt+TWy75988kmZMmWKIBJw7rnnyrfffisQ+oceekjq1atnZsaDoD///PPSpEkTwVr0V111lUyYMIGz5nkmzQNTRaBAHzwFPlWYeV0SIIEkEYgo8F988YVcf/31RmzRLx+P126Xs0KFCvLxxx8b771MmTKybt06OXz4sPnbsmVLc1i7du1k1qxZMnXqVHnppZfMcLx+/frJ5MmTQwr8xo0bZeXKlfYt8v5u3bo17zVfkECiBLD++9c/W1PUov/d9MEzyS5RrDyfBAJDAE4ppmyfPXt2oTKffvrphbb5ZUNIgd+9e7cgie7tt9+Wxx57TK677jpTXoyDt61YsWL2S09/S5cuLTk5OSb8Dq/8nnvukR07dgjE3rYqVarIpk2bzEx5yAOA2dvsY5x/lyxZIh9++KFzk3kdSvQLHcQNJOCRwIpNIo+MFenR3Op/N33w9OA90uNhJBB8Ahs2bJB58+bJtm3bClUGDupRR8U8Z1yh66RiQ0iBnzlzpjz33HPmfuh3x4/bMB89vOtYbM+ePeYcTHeLPnYk66ExYduuXbtMI6BSpUqCrgGIv73NPsb59+yzzxb8uG3VqlXuTXxPAnET2KuT2mCZ2D36t7wm2dGDjxslTySBQBJo0KCBmf9l4MCBgSp/SIFHqBwtlkh29NFHR9pdaB/C8RdffLHpU7chlShRQjBxDvrn4bEj/HHbbbcJWkRoZGAd+hkzZkiLFi0KXY8bSCBdBPaptw5xxzrwRuD1v4ZZ9Omiz/uQAAnESyCkwGMq2ho1asR7zZDnffDBBzJt2jTTcHj00UfNMejjx7A7CP7evXulZ8+epp8fWfW33nqrmQ63XLlyMmzYsJDX5EYSSAcBeO9ldVraHXtEKmqPkllRjiH6dKDnPUiABBIgEFLgE7he2FP79+8v+HFbx44dBT8QeLs/vm7dujJy5EhBSB8ePo0EMklgtwp8BRV2JNvBg8dUtRwml8knwnuTAAl4IeCbzABb3J2Fprg7afB1pghA4I/WduZ29eCPVqG356LPVHl4XxIgARLwQsA3Au+lsDyGBDJBAAIPzx1WAsPkNO71yfcii9Za2/ibBEiABPxIgALvx6fCMvmKwG5dTLFcKe2H1x8YsugXrrF+rC38TQIkQAL+I0CB998zYYl8RsDug4fIw+DFr9vOBWcsGvxNAiTgVwIUeL8+GZbLNwR2HfHgyx8ReMz3VKuSyC4N3dNIgARIwK8EKPB+fTIsl28IYJicM0S/ViezwpyOOzXpjkYCJEACfiVAgffrk2G5fEMAnjqS7Ow++Dt7iDypE1rt0IlvaCRAAiTgVwJpGwfvVwAsFwlEIwAPvnEtkVPq5h+JcfH04PN58BUJkID/CFDg/fdMWCKfEaheQaTrydbwOLtoGA+/U/vmaSRAAiTgVwIM0fv1ybBcviHwiy7LgLHvTsOUtfTgnUT4mgRIwG8EKPB+eyIsj+8IHAix9jtmttvJPnjfPSsWiARIIJ8ABT6fBV+RQCEChw6LFCu0VaS4/ufAq8cYeRoJkAAJ+JEABd6PT4Vl8g0BeO8lXeF5FO6gbm+QI/L+LN8UlQUhARIggQIEKPAFcPANCRQkYAReZ65zG2azG9Zb5IdVIk9PdO/lexIgARLIPAEKfOafAUvgYwLhBB5FLq1rxA/rI4IkvBe/9HElWDQSIIEiSYACXyQfOyvtlcCBg9bysOGOx+Q3D6rIw5N/dWq4o7idBEiABNJPgAKffua8Y4AIHNQkO6z/Hskwy93D/URmLRN58+tIR3IfCZAACaSPAAU+fax5pwASQIge/e3RDOPiH+kvMmWxyLtMvIuGi/tJgATSQIACnwbIvEVwCeyPEqJ31qySjo3/p4r8+PkiH8527uFrEiABEkg/AQp8+pnzjgEiAA++lAcP3q5S1fIij10qMvI7kVxOhGNj4V8SIIEMEKDAZwA6bxkcAhjvHq0P3l0bHL/3gAgWpKGRAAmQQKYIUOAzRZ73DQQBr33wzsqgzx7JeTQSIAESyCQBCnwm6fPeviewP04PHp4/jQRIgAQySYACn0n6vLfvCZg++BBT1UYqOOapxxz2NBIgARLIJAEKfCbp896+JwBPvEQc/yXoh0cGPo0ESIAEMkUgjq+uTBWV9yWB9BOABx9rkh1KyX749D8r3pEESKAgAQp8QR58RwIFCBiBjzFEjwvA68e5NBIgARLIFAEKfKbI876BIBCvBw+vn4l2gXjELCQJZC0BCnzWPlpWLBkE4hV4E6KnB5+MR8BrkAAJxEkg7QK/fPly6dWrV15xx48fL2eddVbez9q1a2X//v0yaNAg6dChgwwdOjTvWL4ggXQTiLaaXLjywINniD4cHW4nARJIB4G0Cvy4ceOkb9++smyZLrt1xKZMmSL33HOPfPnll+andu3a8vjjj0uTJk1k0qRJsnjxYpkwYYJ9OP+SQFoJGA8+nj54TnaT1ufEm5EACRQmkFaBz83NlbFjxxYoxffffy9bt26VZ599VrZt22b2TZ06Vfr37y8lS5aUfv36yeTJkwucY7/ZtWuXwON3/+zZs8c+hH9JICEC8Ybo6cEnhJ0nk4CvCOzbt8/olFtr8N7PFodvEn914L0fPFhwcHDx4sXl999/l5o1a0q7du0Egr969WqpWrWquVGVKlVk06ZNIW86ffp0ee211wrt++GHHwpt4wYSiIcABD6ecfA4h0l28RDnOSTgPwKIOs+YMcP8uEv3xhtvyFFHpdVXdhch7Pu0CnyoUowZMyZv85w5c2T06NFSqVIlgbdfpkwZgZeek5OTd4zzRffu3QU/bhswYIB7E9+TQFwEINKl4vgvYRZ9XLh5Egn4kgC6jE877TQZOHCgL8sXrlAZbXYcPnxYevbsKbt37zblQ3/7KaecIi1btpSZM2eabWg1NW/ePFz5uZ0EUkog3hA9suhxLo0ESIAEMkUgowKPsMall14qffr0kc6dO0udOnWkWbNmMmTIEBkxYoQRf4g++uFpJJAJAiZEr2Idq1HgYyXG40mABJJNII7gY2JFKFGihMyfPz/vIldddZVcccUVgsS48uXLm+1169aVkSNHmm1ly5bNO5YvSCDdBLDsa+k4/ktMiJ4LzqT7cfF+JEACDgIZ9eDtcsCTt8Xd3oa/FHcnDb7OBAH0wReL48acqjYOaDyFBEggqQR8IfBJrREvRgJJJIBlX7H8a6wGD55LxsZKjceTAAkkk0AcX13JvD2vRQL+JnD49/gEnh68v58rS0cCRYEABb4oPGXWMW4COtBDjoojRl9S++2ZRR83dp5IAiSQBAIU+CRA5CWyl8Ah9eDjmcOCE91k72eCNSOBoBCgwAflSbGcGSGQSB88PfiMPDLelARI4AgBCjw/CiQQgQD64OMJ0WMcPJPsIoDlLhIggZQToMCnHDFvEGQC9OCD/PRYdhIo2gQo8EX7+bP2UQhA4OP14BmijwKXu0mABFJKgAKfUry8eNAJxOvBc5hc0J88y08CwSdAgQ/+M2QNUkggXoHnanIpfCi8NAmQgCcCFHhPmHhQUSXwOya6iWMcPJLsuB58Uf3UsN4k4A8CFHh/PAeWwqcEEvLgudiMT58qi0UCRYMABb5oPGfWMk4C8U50U4oz2cVJnKeRAAkkiwAFPlkkeZ2sJBD3VLUaot93MCuRsFIkQAIBIUCBD8iDYjEzQwAefDyryWEN+f0U+Mw8NN6VBEjAEKDA84NAAhEIxO3BU+AjUOUuEiCBdBCgwKeDMu8RWALxevClNERPDz6wj50FJ4GsIECBz4rHyEqkikC8WfRIstt/KFWl4nVJgARIIDoBCnx0RjyiCBOIN0QPD/4A++CL8CeHVSeBzBOgwGf+GbAEPiYQrwdfkh68j5+qf4u2fY/Ixp3+LR9LFiwCFPhgPS+WNs0EsFxsPFn0JkRPDz7NTyv4t3t/lsg/xwa/HqyBPwhQ4P3xHFgKHxKINzyPqjDJzocPNABFOqizH+buC0BBWcRAEKDAB+IxsZCZIBBvBj3KSg8+E08s+PfEEsO7KPDBf5A+qQEF3icPgsXwHwF48MXiWGgGNUFYX6P7gsVqaCTglQAWKKLAe6XF46IRoMBHI8T9RZZAIh48oMGL33ugyOJjxeMgQIGPAxpPCUuAAh8WDXcUdQLxZtDb3Ew/PMfC2zj41wMBhOjZB+8BFA/xRIAC7wkTDyqKBJBBf1ScIXrwYj98UfzUJFbnvTryolwpkd37E7sOzyYBEKDA83NAAmEIJOzBa4iek92EgcvNIQns0y6dSmVFdup4eBoJJEog7QK/fPly6dWrV165ly1bJn369JFOnTrJqFGjzPb9+/fLoEGDpEOHDjJ06NC8Y/mCBNJJIFGBL6mz2e3UjOj3v01nqXmvIBNAzkblciI79ga5Fiy7XwikVeDHjRsnffv2FYi6bYMHD5b77rtPRo4cKcOGDZNt27bJ448/Lk2aNJFJkybJ4sWLZcKECfbh/EsCaSOQyDh4FNIO0d/9XtqKzBsFnAAE/mj14HMp8AF/kv4ofloFPjc3V8aOLThN07p166Rly5ZSrVo1adeuncyaNUumTp0q/fv3l5IlS0q/fv1k8uTJYWn9ruOQ3D9hD+YOEoiBQKIePNaEt0OumIKURgLRCNijLujBRyOV/v1unbHfp78k3u+oX0HpM3jvBw/mz9+5detWKVOmTF4BqlSpIps2bZLVq1dL1apVzXZ7W95BjhcffvihPPXUU44t1suFCxcW2sYNJBArgWQk2e1Rj6x6RZGtu6y+1VjLwOOLFgEIfIMckc25Ravefq/tvHnzTER5xIgRhYo6ceJEOeqotPrKhcoQbkNaBd5diIoVK8ru3bvzNu/atUtycnKkUqVKAm8f4m9vyzvI8eKSSy4R/LhtwIAB7k18TwIxE0jUg0cf/G7tg69ewRL446vHXASeUMQIQOCrHfm8FLGq+7q6LVq0kDZt2sjAgQN9XU534TLa7ChRooSULVtWNm/ebMLss2fPNn3vCNnPnDnTlHXGjBnSvHlzd7n5ngRSTiAZE90gNG++sPPbsSkvN28QXAL7NcBpNwiDWwuW3C8EMirwgDB8+HDTKurcubP06NFD6tSpI0OGDBGEQnr27GmS7NAPTyOBdBNIOMlOPXgkS9kh+nSXv6jfb9B/RSYtCg6FPTr2vXRJkarlRbZolw6NBBIlkPYQPbz2+fPn55W7Y8eOgp+9e/fm9cfXrVvXZNXv2bPHePh5B/MFCaSRQDI8eAyTq6LDnrbRg0/jk7NuNXu5yJqtIh0biZTQxpbfDeH5sirwVSjwfn9UgSlfxj14m5Qz2c7ehvA9jQQyRSDRPniE5rdostQvG3RcM7Po0/oY1+8QaVxLBVNnhUOiYxBsn4bnyxwReCRl0kggUQK+EfhEK8LzSSDZBBLNoq+rA0E2qNB0bkKBT/aziXa9pdqoOiHHEkx76Fm0czK9H0Mq0SDBTHbzV4uggUkjgUQIUOATocdzs5oA+uCx7Gu8VreKyMrNOnGJjgTdz0Vn4sUY13krNulwsxrap62dkEEReAyNwyx2WKIYf695WeTlKSKLf4sLAU8iAUl7HzyZk0BQCMCDSmSxGXjwK1Tgy5XmnPTpfuZ29wo84iAJPLp1YF/fJ7JcGylTF4s8+pk1YVJ7zSVo11CkSW3rGP4mgWgEKPDRCHF/kSVgBD4BDx79qfiSLqHXoAef3o8Rll1FwwrPICgCv0UTMZGQaRvmTcDPlWdrQxFiv0TkPl2uY5l2P3z/gH0U/5JAeAIJfH2Fvyj3kEA2ELC9wETqkvuCNQ4e45tp6SNwUKMvaFjZ0wWn787x3wkhetuDd1/lOBX6K9qKvPNHkVqV3Xv5ngRCE6DAh+bCrSQgSLIrnsB68DZCLDoDj5KWPgJoUGEmQXjwQcmix4iLqkdC9OFIoU7lNTLBURnhCHG7kwAF3kmDr4sMgZlLRb79NXJ1k+HB4w6l9Es5KOvCg8nfP4rMJQh74cFDDNEHj+z0IBgmt8EkN9GME+FEI8T9NgEKvE2Cf4sUAcxwNmFB+Cq/NUOk5+OJZdHbVy+pHnxQ+uBXbxH5LkrDx66Xn/8e1IgJBD7dWfRvTBepMMg7GRy788jSsPDgw4XonVeEwHOcvJMIX4cjwCS7cGS4PasJQHAhAqEM88eP/E6k5XGJZdHb14bQBCVEv0tn3tukQhN0Q4ges9elO8kOs9Cd09QbPZSxtvanY4ranfqZw2cS89BHMzQCuNpcNErcDwL04Pk5KJIE8OUaLrsaXlj3ZpYoJzJMzgaLED3uFwRD4wbzoWMO/SCbHaJPt8Cjvz9cw9HNM1cbU0frpDb4HL7/rUjv071NqQsPfjNnunPj5PsQBCjwIaBwU/YTgEcNb9VtmJgGw5GuOMsasoREu0QtSEl2CP3WUa8SU70G2fB8ETlJu8CrN+41WoNGFAQejQJMZtOinjfiiBIwRP//2zsXuKum/P9/aRBJSkoXhES6iSISCsOEn5TUhAzj+jP9XH7S5GdGfmaY/4yfuwzRJNSgYpRIpVKkqKSkhAmp6OpSjMs8/+97r/bz7Oc857LP5TnPPud8v6/X0zln77XXXuuzV/uzvpf1XeGwKvVSRvClPgIKpP+kHm1zY+4ai0aNaTRWHpopcp6S++6afY4XaS4InsxkWAII2ou6QBwk6Cl0gi/3was1ghzv+RLGVFhrDb53j+D1GmIf9t8rXCsx0X9pmxeFA6vESxnBl/gAKJTur/vSaWS5ai8v/W0xBE8E+ed6n9M7uLuQLjQXBE9taJNhX/y56mMm9WxW4kDrXV8EGrzng9coo0SumEzwSXUN90pHgyfvPDsNNtpDV1uEjIjaXZfJ4UoxMQRSIWAEnwohOx8JBNZucf7JXJEky9aCBI92/dAMkcu7V0TO50qDB0CP4BME9UUC4O2NIMDuwL3juy+i1M5UbYFkSXRTO8+pahlTjKWyEK4dfPB11VLE8rgDFPOwgnWp0GMkwvbVymWHgBF8dvjZ1XlCYI0S/N51c6e5oMEHTbcvLHZaVOcDKjpE2lA2nMmFoJ3lanKSi/bEqwPt0/Nb55kU47Ul22MQPJjnywePhYmdAyFtEtGEsRpA0hD88wtF9lW3SFjhGu5jYgikQiCkUShVNXbeEKheBNZsdgRPBi+IPlshsAlTp68Jse79z+dWrrX/0bnLguYF2uXRF1y5J+F+4RPeTcm9tr4VNkW8ral6BMF7kxXtSz4S3dw3TSeIOi63KfGSA5/xRZKdZOJPBg5q7Ig+WdngOTR4f+188Lh9NwRiETCCj0XEfkcSgbWqIUHsuXqxfaem1IZaH5H0z6kGdVwrDXJqWLnrTetX/p3NL8gmrG82m/tkcy1YQPAsk4u6tSFVPwmyw0QP2ebDXw123ynxbtVxxcQxzKSCsUzZTzXArp5ai8IK1zCRMDEEUiFgJvpUCNn5GkeAFyF/EHImL2uCmMgY5hMsL18CsDB14nMmq93ArtXbTc9Er6QTZYGkIA/aGnRfRLnNidrmafDaj4MaOU1+0ceJSubmOJYgzPKeBq+TpDAmevCuo2MQH3xwF7lULWJVBtYB3/qUqrydL10EjOBL99kXTM/ZHpMXNUuKMtlkY7Wa97se7LbcpNMEQuGbRWsa/orIuUfpd627OqUQNHhwKbQtVhM9M1+D5/xp7UReWpKoZG6OMwFlUuSb3cMSPBMqktako8HTYv4vcE8TQyAZAkbwydCxc5FA4KP1Lsp4D3yP6oNv+Bu3z3rYxq3TAD0mCB9vFPnVCJGVn7tAqJvOFLm4m8taF7auTMsVwoYzaIQQjrfFaoH74En7yqQK6d5a8+uvymxy6FUQ4h+wYw38T3pf3BxhCZ6APHIPpDvB5Doj+BAPpsSLGMGX+AAohO5D8K00EAmtBXNmM/WN++b2MO0nAr+lEjzJRJatcXWgwWPmPLKFqzdMPdmUKYQNZ3wN3nMnFDjB+4lueGaQ4TEHOVdMNs8w0bWstCCoDu0dTTxs5D4TAp/gybmQjniR9KbBpwNZSZY1gi/Jx15YnfaCv/QlvU89kX8q2ZPxK14WukS9Yg092s4/N2hGOR3xmPl3VYLPpxSCid4zL+ukJ987sFXHc2ACyETFl1Or0UxPXIifPpY4kbAEz4QKrPmrleabGIL/2gLt/MdrnwkQSHNYJajFDhsC1YgA5k6WbrXf1+3hTtYvSD+ssEa5T2eRBZqpjsnBrBXuhRz2+lyUKwStmAAxgr4gKPzJf58nst91ueh9futAo9Y4NCEYzZd2zV0CmvfUgpNr8QheNXCCOUkj6+OX6j7euFas66plKl1B488kHiXd+1j5wkbACL6wn19JtJ6od5ZuYVJvsqfzV6L9IKyPH/dm8oQ0mPX31hfvzloHkwPWK+diLb1rQbh/C8EH70V1b9fgwRzz8VEHhutflEqhvbNKIlbQ4qdUQ7AdRMu4wg/PTm+M1TAWJsz6rZuKTBsc29LUvxm/WKZMDIFkCBjBJ0PHzkUCAV6EaEXIE5e7gDuf4H/SlKB/my0y+KnEa7c36VK4BkrwaO91lMC++Nq9kF2N+fkXwiHwK8oS64OH8HOViz+f/faWyMUh+JMPE5mzMhz5ptNe8vdjMq+l94TgCbILs8yQSQBl401GUt0fdxWWKRNDIBkCRvDJ0LFzkUAAU6bvM8eXjWbpEzzm0UObiJCU5tbnq+7YhlaF5s51d/5SZMAxbiOVxvqCzKdgfYh68hh88ETR+z5kCL4QdsCLfY6JCB4/OVuy4qLJpbCzG8F1+NIxneOOSRVFz3nKBd0I6bSJ8VvoO/6l018rmxkCRvCZ4WZX5RCBjaphL1iVuELPV6kE6Qtrtbcqca9XTZxlc0TXX/tzl7nsz5Mrb/SBXzSYRIQXsKfBq4kznxKVwDWwflDX/o9+TeSp+ZoHfZHI1KUis9/XpD+KJ9jSViYjTKKCufj/sVBk4tv5RC2zeyUieGqLtyb+2jGZJVDyWwfB76ljcP5HzuLhT5D88/E+0d79SWu886mOmQafCiE7DwJG8DYOahwBAp9++4zI3A/iN8Uj+O0mekpgZn9VtbADrndmSgie6Pgbz9QXtb5syQvuC+bT4BIkNC22QsVnmk/hpR/GL1vdbfrLZGcWxqLBJIk0qe+sdng21fiGgxs5LInqxvqBC8SXVboKYewb/q/ofnpJbrR/8aRTCzeRoS++PK+TFvqaqTDGGFffPOS2GuZZp0pV6xG8juNMBZcAk690gk0zvZddV7gI6Fy9ZmXKlCkybNiw8kaMHz9eGjZsKIMGDZL33ntPevToUel8eUH7UjQIfLJRpNcRTrM8Yn8XpBTsHAQf1HYwd+NTv/xEkU+UoCB4BNK6uZebLOCXv6ibSyKCadaXuqqh/m/vimv849X9yUsfi0NNCzn9r/65C1ZM1hbW7WOyD5romUR9ttlpu+kmZkl2r1yfS6bBYxI/ta3LbHdFd9cXEihlM/kiyC6IRxhrTTCuJNP+o8VjkcFlZWIIxEOgxjX4WbNmyW9/+1uZOXOm99e0aVO58847pXXr1jJ9+nRZsWKFTJ06NV7b7ViRIIA21aaZCEuZ2NUtKJiK2TQk6KvkhYbpvdMBLooebcYXyP8PfdQa8KGLrqdcUIOHpPp08kvn75MJChOVmhRIDDxYiZBKWJboafCqJfrCsziyhWaF0+WGUZYftc3JAtd+3k7klWUuWRIWHwLjsiF4b4wFJpFE0ad61hA8AXbZCNdbNrtsECz+a2uc4BctWiSbN2+W4cOHy5YtWzzEZ8+eLeeee67stNNO0rdvX5kxY0bxP4kS7uEq1eBbNBS55ASnWWE29iXWPM9xXmzsvb3fXk6jDGpPnIfwbz9HZJKaXkfNcS9wjtek1NY2p3rpV3f7SNULZmEEksL8C1n6AgmybK4QCB4tOpE0VvdMy8Yir610Ex6sQRBupuIR/HYrEnUwmUsVRQ+WTEazEXIWmIk+GwSL/9ok/w3y0/laurakrKxMGjduLMcdd5xA+KtXr5YGDRp4Dahfv75s2KAqXhyZMGGC3HvvvVXOLFum03OTgkEAsib9LCb2C7qqD32q7s3ezzWfF2/sixCCx1QMWbGRTLxEISQcGXWp20zmxENrHgpe+tloidn04MrHHDGDCROpMAJBstMemrxv8maCwlLDCW+5QMagVSVMnfkqg9k6dtIXe2+C7V58R831+gku/qqM2HJhfrOSAx+8L0THh/LB65jIRljxgBvFpPoRWLx4sUybNk1GjBhR5WZYmuGxKEqNE/ykSZPKcVm4cKFMnDhR6tWrJ998843Url1btm7dKo0aNSovE/zSu3dv4S9W+vXbzg6xJ+x3JBHgZQi5I2d0EHl5qcsbftJh7kWJNhkUAsDYMIagsM/Vpxw00QfL8f0/e8QeqZnf9CGVVlddLQNbrCJT3xW57Zxwd+EazNdE1WOa5zft3121Rghx+VqXpCVcbfktRRDl3qqlJ5NjW4o8MN1ZgpgMZGpdIUYBXIJj0Iui12PJhMleLkz0ti98MpRzd65Dhw7SpUsXOe+883JXaR5qqlET/b81DLRnz56ybZu+SVTwt7dr1046duwo8+bN847NnTtX2rdv7323f4oPAY88AtNMtMJBJ4s8Msv5gHnxokUG5YC99ZxGLEP0aPeFEGTkvfS1L+kIe9iTpS9bIRL+uYUizdVKElaDv0InRq9/4MzNPCPEexY6UemssQ8LVnmHcvLPm+rTz+V+7QQzpspUiI8eyw57G0DOmWrwaO+s6ghKqCh6HQtMnrIRJlumwWeDYPFfW6MEv6NGPPXv31/69Okj3bt3l2bNmknbtm1l8ODBMnLkSI/8IX388CbFiUA8Am+1j8hxrTRD3ZwKUknU+4W3iByohB91ycRET1KWYIBgpn1Ey8TMnk7a2cOaukkU5mY/Ax9aJ+RFcCOknAshTuLWf+hSvU9zUZurIwzBU7LvUSL/WOQmiN9p3zIRPw9D8FowS2URAMtkcQLB+hJ9Z2Lr++AH/FWEXRdNDIEgAjG6UfBUfr4PHDhQzj//fPn222+lTh0Xitq8eXNhuRzHdt01EL2SnybZXfKIAObNWBM8t7+wq8hlo1xkPQFqhS70MdVLP9hHCBkS8LXn4Ll0v+MCaVJPBLN0uoJp/oftGjzPCoJnxQOxD57vOcF/T6wPq+9KPkEhMcyYN0RYrvbuZy5/wWNzNIhvWLqtrFw+LME3VFfDrKHOuoE7Il359aNu06KgeZ46wqyY4NmigWcj+ODZChmB3FmuRy4Jlo3yjEwMgRrV4H340eR9cveP8WnkHkSjOL9DPvE0GV5+vz5e5NFX3Quz0HvvafDa17CCH5lo760ZapbB+6DljfuNS+cbPB7me1CD958VrpGOal1IFE3P0i2sKhBOIvnwC5H/e8nlLdivoW6csj2Wom0OiImgzbCJjJjA4AvPxERPuliWcMYSPJO5VBMz7pe1D14J3vfBY4Fg0kCmwZnLE6Fux0sNgUgQfKmBbv2tQACtEBKJJwTZfbxBZJ5qeoUuxAqkYwYmgh2C91/g2fQfgs80TsEj+O0avO+Dpy2Y6RMRPClvScKChh9P0LB/P8El3DlkHxcsiSbKuvvvtt/Lv45j/Yb7v1J/YvnAL72XMwamvkBLZBIfQcW4PnhOsRH7TBo4F0zzG9sQyDh2dUhsmVS//Sh67sOmQEwayOJH3SaGAAgYwds4qFEEfLNvoka8eqOaUP8r0dnCOY7Wy9+EBWo+1T7xIk4maKEkpMk2iApSJisd985E2OYWbVRXsnpr4v3JGARPoB3HY8WbnCjB+xo8fUb7RyCh3ym5n9O5wmXgJ5pB8/fLudJKVnodKyaWrPaPJP9cvsZZD0hoFFbQpDMhRXBhK+J4yzQ9l0zMZCXYHnDIluB9HzxjBLM8kyHyFlC3iSEAAmn8NzDADIHcI+CbfXNfc/Rq5KWPSZegtfFKeskELReCz1aD97R3JbBMhckBRAbRoun6gv/aXy7nH/M/fQ3ez7I29BmXphet9g/Pi7TfV+TsI/3S7rN5A0eWsaZtJihMdsKYndloaOg4EYI005FMCR4yheDj+dJxOyWLuWBCERt9n06bKetp8ErqPGOsCHxiwUh233TvYeULGwEj+MJ+fgXf+kRBdgXfsTgdwA9P1rNLT0iswfPiP/nPTnNHQ8tWG+P6bJZjeRr8dtIIEjzdS7RcjskJ5I+JHsLGekAsAQmMMF9fcWJVcHBHkKAmlpwgre6tHcGz810yoSwTAQIK0xGCODPJZAeZbopjoufe4H7nS45047WF89lq8EwswIt+Y0VgQoVlKNsxE6+9dqwwETCCL8znVjSt9ghetZ1SEF7oEDzm1EQm4afnuxc1L2le4Ly8sxGuz9T/zn19HzzxA/EInkj4WEGDR8PHRA/poF0+/prbpvd3/+F2q4u9BmsFBI9pPei+ACfW7w+/wGWeu3uK01Jjr+c3E4vFt4qcdUS8s4mPoUnHTiwSl644w+oCxm88fDlGZDu7JMZaJaghFwSP5eErxZe6CPTjWTPpMIKveEal/s0IvtRHQA33/3vVQOJF0ddws6rl9hC7Z9LVl388jRGSeWGxS8HLVq6YYF/7QLPxjc68OZj4s4nWhuAhMoLfYgn+sGaaMliD42KD6bYosZPfHYLHL8xufk2VpAmoS7QJDFkJIWgwCq4cACcmRkSs3zXAWTZueMrh6KPyx4kibDm8VttCcF+6Qq4B4gbSFdpG3+Lhi7Xh6ANdat87XqxaM886Ww2eZ1OmbgJ/EoUvnslROsGcVVtmR4oJASP4YnqaBdgXiAPfdClIo7puL3LPtKpaV6yQohf/NBovL2u0wPpKPqNV+539fmzpcL8hy3gaZrirnUmdmAEIKZbgMb3HWy7HpIJ2Q/xf63f6g1Yf3LY39v4QM9YNz5eskwJf0OB9AuX+N6kFgIQ9//WES5eLxvqPhS4ID+0/k77yPIgPiGdVYUKWSCj/xz4iBzeuWoJMi2zLe43+YZnAghEUtGy/X8Hj6X7H/cJECg2eyRTPyjT4dFEs3vJG8MX7bAuiZ6UUZMfabC/qWl/GsRo80ejj33LR5bz4fdM6xPjgQDU9f5LZ46Se2EqNEYYAABqFSURBVHXa6dTkm+gTbeASb7kcBAP5QTj8sRFLKoLHB48GDkEzKfngc9dK6orVdH/ZRQn+FF1D/6zIZLV4tG6q27++l5n27mOBSyGoxZNrn22ML1CiTpQhDvM8SzmxOiQSLBa/7yUybVnlQEFPg9cJS7YCXkyk/Emhp8HrZMzEEAABI3gbBzWKAP7JUjHRY2aG6Hgp84IPypyVzm99aBOn2UHMmOi/0PIkgsF8nYmwGU+qvOzJ6uXZoN2+ru07+qCqJeMtl6NvZFKjr6RyRZunHyyHSyRE0W8e7vr+ywc1jewDrqRH8HGIEC3+//qL/M94XSd/lMuXkEmmPr89YER7ffnLZE2VPFufiR6/cZzI++v8M+6TCQyTnzAC+d5ytk7UXtH9NrQe/9pc7MbHWMJE77k2dMygwbMmnmdmYggYwdsYqFEEIIOd47zAa7RR1XRzz0Sv5lpMwrzkg4L23qezO4LGCsGjyZNrnyCzTAme67IhePzT1PHWKpFjWgZb7L6j+UKCaLy+QMoQzmY1b3sm+u0afDKC968lyp66CLrDqoE/OVaD98syKZh/s4uyR5M+V4k+U6EPI5XQ395uKUGbZ+0928ped6pq4WotWLamonYvNiCNccsmP9f/Qon+OeeKyIV5ntYQIEibwQi3DhYxz1yv300MASN4GwM1igBmztjd4mq0QdV4c7YwxZyKZh400S/9zGm6x2zXkH2C5xOChaBriuBxK5BlDk0eTTSedGpROasdBI9mSaAdy8joA2vZWTqXStA+2U3Qx4C6kpEh5ygLeTI5yFSYfExZ4ja9wa2A4CZgTT3WgiE9RYYpyfub4iSbeLirq/7LskIS/JDoJzaeoWrpcEfwwfNciGGA3ImLAHuCNKtDnprvJinVUbfVmXsEjOBzj6nVmAYCng8+DU0ojaojV5RI8UcvVkLS/gYjnce/KdK7k4hvsoWw0Mb4RNCGcWXEmvXd2eT/ZqvBM7n4bLPTqBPdCeLyd5cjbepPaibGfM21fswB14YxaQ/sqoFpp7pJEL74VASfqE3pHocYWRXARGTVRpcNb/oQkcP3czV13F+J+SyXqGfBKjdBy4Skex/pJg2fbkq3hfHLQ+brcMPoRIyJFP1gvHzwRfzy2R7F5eNvcJNtXXZ99SNgBF/9GNsdkiCAzzDMiz9JFQVzipcve5AHE6tAnu+tFTmlTUU3MLuiRQY1V34/u0Ck220V5eJ9w6xNNDqCHxa/MubnTAWSXq1kxOQkkUCMxBCccLtquGrW9icmaP+zVzgf8X57Jbq68nFiEAi4o+9E4+PK8OurXDK3v5hYEBHP8/hUCR6TOn3nmfnSrrnzpf/lRfX5a5ng8/HLhPm8oafI1MFhSqYuA8Gv0TaDGcROe68/TeT+aW6ykrqGihL/7wURliAmEyL2+TMpDAQCw7cwGmytLC4E0Eoz0YQKGYXgznL43s/oUHmS4+MRJBfSok5715EKhBu79tzHg2jy0+9yv4Y87bStbCZQLG1jwgCBJBIityEVzNgkovGDJjF7k4iFILhJ1ya6Ov5xz8ycRw2eVjTTWAc06wWrRNoqmccTIvZv7a0Bfkry2eAar+5MjjHJwErC5jp7aGwHqZCJTSCe4+6X068Rl1kyYdwlGnvJrrNzNYOAGtJMDIGaQ+CjL0T27VZz96+JO0MMmLEhzlmq4Y78deVWcD6WPCBIkuDgAz5DCRwyQmND09xf/7xP1ZIxlaPFo70TfPX4ZZXrzuQXQVtos4mEPAYDjnFWh+GvuLZR9sgWbl1/svXvier0/cj50uBpBybul5a4bHBX9kjUMrfu/Z7zFGPFuaaFuAPaTQ4AliP6k8K+6vKZ874IuRV+3jZcK4kLSbbun1oInGRCh8sodoyGu4uVyicCRvD5RNvuVQkB9gTnhbSvahylJpjpyXZ2/CFVg9fQ4GNfnpBs/6PVT6wa5PnHuk8ivT/Z4HzGZHJ78R23nAuTPL5SJgFonNnK4J7qi94/dS2sDggue8RvnalA8MQh4IOn3nzJ+N+IfLg+dUBgy8aJU+bmq63cB4L3gxeZ8PmZAiH8/z5NrSpPixzRwi3BTNUu4kIIikwm5EMgoI9PVjqYRBsBI/hoP5+ibh3a5nGtirqLCTuHGfu5hboBy/lVi0Dwvpk+9iyZ7nzxlqjt7l7g/jE+Bz6sfvPN2SV+CdbHUrGwAtn4JBP2mnjlIHjInUkM6+jzJadqXwn8DCPZRO2HqT9MmXY6HsAKIZq+1g7uO/9i1Tmro8i9U0X+9+yK44m+EdSIiT6Rds7zQIit4LkYwTs8ovyvzvNMDIGaQcBP0FEzd6/ZuxJ7QG52tOxYQXvPhjzQ5AgWyyQve2xb0v0NwWfTdv9+ngav/nvy4OdTg+f+hZQ6mTH0i/Y+amrR0RiCoPRTq8/6r0SmLwsejf+d4DlwT7QkE+2eaH0sRGjwJtFHwAg++s+oaFv4pfqg8R+WopC85qJu8Xve9WCRJy+Pfy7M0ZokeNa++37gMG1NVAaiIUYhTHKcRHWU2nFcN68MqdxrngU5Ah6Zldq/TvDcoo9F5n5QuQ7/F5NGgi3JDfDuZ/5R+4wyAkbwUX46Rd42XuCJkqcUedflXjXN86KMJ7yUs9FaIXj88aTGzbd8vEFk3ofZ35WlghAOEwaT7BA4qJHLyPfA9MT1YJYnh8GCWzQtsRL89X93y++CV7DeHqsQSxn9zIW4g8a+ESxl36OEgBF8lJ5GibUFkyBkZJJbBAjII2kO5tt8y2OXqjk4RovMpA2YyVn7bxp8JuhVveY8XeXAEsBXV1Q9xxEm21jTWC1Bjn+CP68ZI/L0fEf8lFmrBE8+BHbPY6kmkwIS6vBpEk0EjOCj+VxKolVoaKWqwVfnA2ZtNJOneP796rxvLusmmBBTcam6cHKJJXUR+HhLL5cAh6WHu6sLiLSzvni7Dm6fbDM5/I+OGgB6gVtqOehJt6Meu/2hwRNjQYAd5vyWah1gcmASTQSM4KP5XEqiVR7Bq9ZgklsEMKUWerYxNMnFn7qMcblFp3Rrg5Rx/xANT1Y+P+c+iGByP3Dvytjgb7/tHJGzjxAZ+oxbgulHzpOZcMQskc4HJg7Kq1xb5V9kzDvohsrH7FfuEbBlcrnH1GoMgYDn89NEIYmWg4WowookQGDoGfpC1r9Clw33F3oPotd+NHCC5bCMsN+9L+yc5+fd94/5nye3cUmLJr3tlt5xfH8l+I80XwB5GdIJuLtarQFMLDZqwpyuLf072Gd1IWAafHUha/UmRWCzmvXyub45aWPspCFQIghA8OTRP1BN62SR/MPzLv88JN22WWIQyEZ4QdeK86TDXfyJy1TIfgdI7BbI7mjlf3EF4Bq45Hg17asbyaR6ETCCr158rfYECDCLtwC7BODYYUOgmhDwCF419xaqgZPt7p+qhRN4RzBj0/rhb0r2ST+FMUlvzr5X9xtYnPx6Egixxv7LBzXd8uHOlx90EyS/2s5mgoARfCao2TVZI8Be22TaMjEEDIH8IQDB3zNVyVkD6si3sGyN2wZ38C/SawP/d6dt96EP6KKb23QSmZyC4DHpc52fJ4GkSJYwJz3c0y1tPvh0EbPyOUGA5TWszzUxBAyB/CHAhjRsjXvZCeqL36KZ63TFxekdsrv/ZSe6659d6HIXJFoZM3aeSI/WFfci1TIEz2ZJJtWDgBF89eCadq3sAMbylKgLO0kFc40vXKUvCV13nWrNNVHdD81wEbyYBkl7ecKhUe+ttc8QKC4Euhwkwh+CmT3oV3dHM/+XfelfWSbS6YCqG0jhCmBjpGBO/GQaPEG4rNsnEPATjRkgR/4V3SvaxpI9P6K/4qh9i0UgkgT//fffy6BBg+S9996THj16yLBhw2LbXeO/Y9dw41tKtqVmsgaz6UoTNZ0RuJKtvKV18R8srMQSdrLr2Cpy3kcuKxa7l3W+xW1mQST85fqfj6U3u6pGEE+eXeBMg6yZveokkZnL3RKceGXtmCFgCBQeAsMHikxZKjJRo+35f96qscvWSMbGFetEumnynKBA8Lw32ZOC7IdE9/uEvl4VgGZ7Ou2eiP039PwSXTbJ5jooCne97M4/fVXFbnrBuu27QyCSBH/nnXdK69at5f7775eBAwfK1KlT5ZRTTonUMxv8lDM39WzvBmXfB9ymDx33UwJ7XCNMb3UmaPblJrqUvZYJMuFzm0aStlUyxExNesjrxroczw9e6H7XqpV8e8dp74pM1T/WqFK3v7XouDd1hvy8I2DWsJJ1Cp8bM10+2QUqKOzdjQbOzLhNs6rkzCza+1OtnYnAsOdcPmt2QqPtl54gMl8nFETTsslFryOcJk/wHH8sxeETDCa/4zDhPzV9/kOf3GxKEuyPfTcEDIGaQ4Dtgf0tgiHt95XU+ZumWj2Kx5NXVG7bCUr4vFP+qVr6+Lc0Pe5pIicd5kgdcsfS5wvvodsmuU1zeHe+coPuda/vwDtedC6G7/X9FDT/+9eV+ucOZSpRA+H000+XESNGSNOmTWXChAny1ltvyW233ValmZy79957qxxftmyZLF26VBo1alTlXK4OYDa6+VnnQ2IgDuvlkkU8PFOkb2eRB5U8mXkCLhqu/4eGS6YxEkuQK/y19zX69FoRtPg/TnTrTZkIfKcDOp58raZuAmR6dRT5vd7/yBbbk1fopIF7jb5M5IuvNK3kFhESnvBJikk+WaJCFirKEehCkA3mOrRrNHLO/aQnv9f7/6gkvJMS+c56jOO0+5Q2blnM+Q+5XaduOVuJ/nOnvVM/M3f+Y2PdwCTPZjJ8f22lyLjf6Ay+Vbwe2TFDwBAoVQQuGyUyZq5q73cnz2o5R9+T//133fr2PJGjD3JowVxdbnXR/LxDqzOnBhbladOmSePGjas8qunTp0sttLIISiQJvkOHDjJv3jypXbu2zJgxQ8aOHSsPP/xwaPj69esn9913X7USfOjGWEFDwBAwBAyBgkbgkUcekV133VXOO09nGAUkASNIdFpdr149+eYbdcKobN261Yg6Oo/GWmIIGAKGgCFQIAhEkuA7duzoafBgOHfuXGnfXh3dJoaAIWAIGAKGgCEQGoFIEvzgwYNl5MiR0rNnT1mxYoX07ds3dIesoCFgCBgChoAhYAjokuYogtC8eXMZP368fPvtt57fI4pttDYZAoaAIWAIGAJRRiCSGrwPGEENJoaAIWAIGAKGgCGQPgKRJvj0u2NXGAKGgCFgCBgChgAIGMHbODAEDAFDwBAwBIoQASP4Inyo1iVDwBAwBAwBQ8AI3saAIWAIGAKGgCFQhAgYwRfhQ7UuGQKGgCFgCBgCkVwml+1jIfr+7LPPljp16mRbVcLryXXftm3bhOftRAUCX3yhO9OoVOfeABV3K/xvNrbCP8PPP/9ct1newcZWSMhsbIUESosxtnbUjUb23ntvWbx4sUyZMiX8xREpGclc9BHBJmkzTjzxRJk5c2bSMnbSITBq1CjvJXzhhRcaJCEQsLEVAqTtRUiItdNOO8kFF1wQ/qISLmljK/zDZ8MzlMQBAwaEvyhiJc1EH7EHYs0xBAwBQ8AQMARygYARfC5QtDoMAUPAEDAEDIGIIWAEH7EHYs0xBAwBQ8AQMARygYARfC5QtDoMAUPAEDAEDIGIIVCUUfT5wLh37975uE1R3KN169ZekF1RdCYPnbCxFR7kww47TGrVqhX+ghIvaWMr/ABo06aN7LLLLuEviGBJi6KP4EOxJhkChoAhYAgYAtkiYCb6bBG06w0BQ8AQMAQMgQgiYAQfwYdiTTIEDAFDwBAwBLJFwAg+WwTtekPAEDAEDAFDIIIIGMFH8KFYkwwBQ8AQMAQMgWwRMIJPE8Hvv/9eLr/8cjn++ONl2LBhaV5dvMXXr18vxxxzTPnfCy+84HX25Zdflp49e8rJJ58sy5cv94599NFH0qdPHyFt5oQJE4oXlAQ9u/HGG8XHhyJ333239OjRw0uJ+fXXX3tXxcOtFMfeqlWr5KyzzipHknzgwXG2Zs0aSYRLPAzLKyqyL+z3cOWVV8oZZ5whjC8wQWxsFdmDTrc7ZSZpIXD77beX3XXXXWX6H6isf//+ZfoSSev6Yi2sL96y6667ruy7777z/n766aeyr776qqxTp05lmzdvLnv77bfLunXr5nX/tNNOK1u4cGHZhg0byjp27OidL1Zcgv1S8i7TnOllTZo0KRs3bpx36vXXXy8755xzyn744Yey++67r0xfzglxK7Wx9+KLL5YdeeSRZbqpUzmMQ4cOLXvuuefKxxkn4uGSaOyVV1RkX4YMGVI2duxYr1dg9MQTT5TZ2Cqyh5xBd0yDT3NGNHv2bDn33HO9DS769u0rM2bMSLOG4iy+aNEib83oPffcIytXrvR2YUJjVwKXPffcUzp06CBffvmlp1msXbvWO77XXnvJcccdJ/Pnzy9OUGJ6tWnTJk9Lv/jii8vPzJkzx9v58Gc/+5l3jvGUCLdSG3vffPNNJUsHoDHOdMIow4cPly1btng4xsMlEYblwBfZl6uvvlp0ouj1it00P/vsM7GxVWQPOYPuGMGnCdrq1aulQYMG3lX169cX1ULTrKE4i2/bts1LZtOuXTu59NJLZdasWRLEil5D9B988IHUrl27HIRSwnC//fYTtV6U950vQYzAZ+PGjZWOUYbjjLNg2VLADcJiEhgUktqoIiONGzf2JodMAuLhEjwWxDBYVzF9V6uQMElkh0u1DnluxCAGNraK6WmH74tlsguPlVeyXr16wksFktq6davtQ70dv1tuuaUcSTXPy5gxY6Rfv34eVv6Jb7/9Vlq0aCFMBnwpdQz98QQe4MLe08FjHAe3hg0blh8v5bE3adIkIPFE3TwyceLEuLgkwtC/thg/X3rpJfnd734nkydP9jAJYmBjqxifeOo+mQafGqNKJTA5z5s3zzs2d+5cad++faXzpfpD/e/y5ptvet1fsWKFoMmT6lF9757GhXn+3//+t+y2226CCRFNFU1swYIFQirbUpV44ykebjvvvLPn1ijlscf4IWDTnyD64ywdDIt1nE2dOlX+9Kc/CYGFzZo187qZDi7xyhYrVqXUL0tVm+bTxuyFvwutCrJ65plnLM+6YohvVIN7PD+8Bjh50fGYkR988EFB6yLKlxfQSSed5JnvNTBKNCDPe2HfcMMNaT6Fwi5+0003eWTNSgKsHYMGDfLMzOvWrZPnn39e9tlnn7i4leLY+/HHHz2slixZ4j300aNHiwaTeWPnkEMOkb/+9a8edvH+T8Ybe4U9chK3npz8//rXv2SPPfbwCmE9Gzx4sI2txJCVxBkj+AwfMwSPJmpSGQGWedWtW7fSQZbs4B/cccfKBiMIPuiPr3RRif2IN54S4RavbCnBhSYPBnXq1KnU7Xi4JMKw0oVF/iMdXOKVLXJ4irp7RvBF/Xitc4aAIWAIGAKlikBllapUUbB+GwKGgCFgCBgCRYaAEXyRPVDrjiFgCBgChoAhAAJG8DYODAFDwBAwBAyBIkTACL4IH6p1yRAwBAwBQ8AQMIK3MWAIGAKGgCFgCBQhAkbwRfhQrUuGgCFgCBgChoARvI0BQ8AQMAQMAUOgCBEwgi/Ch2pdMgQMAUPAEDAEjOBtDBgChoAhYAgYAkWIgBF8ET5U61JxIzBkyBAhn30qufnmm4VUrdUt11xzjdx6663VfRur3xAwBNJEwFLVpgmYFTcEahqBLVu2eBscsR1oImETG/L/k1u8uvP9Q/Ds285WpSaGgCEQHQRMg4/Os7CWGAKhEHj00Uflb3/7m1eW3flGjRolLVu2lCZNmsjw4cO94/379/c+O3ToIBs2bJD169dL7969Zc899xSOvfrqq975xYsXy69+9StvV79DDz1ULrnkEnn66ae9c/zD7naXXXaZ9/uxxx7ztvbdfffd5YgjjijfHri8sH0xBAyBSCFgBB+px2GNMQRSI8DWu5A28uGHH8qYMWO8LXlHjBgh1157rXz55ZfCd2TWrFmedn3xxRcLGv/y5csFjfuiiy7yzqPhP/HEE3LUUUfJHXfcIV27dpXHH3/cO8c/nOvcubOsXLlSrrrqKu9en376qXTq1CmUm6C8IvtiCBgCeUfgZ3m/o93QEDAEcooA+36jffMHia9Zs0ZatWrl3QONffPmzTJ58mRZunSp7LbbbsI+9CNHjpR33nnHK4MJf9iwYd53zP9MANj2FxP/1KlTvT3X+T5v3jxp06aNN4Fg//E33njDu8b+MQQMgWgiYAQfzedirTIEQiPQuHHj8rLskf7DDz+U/+bL6tWrPZ99jx49Kh1//fXX5fDDD5fmzZuXH2dC0L17d3nhhRdk5513lmOPPVYaNGggZWVl8tRTT3l/X331lecSYF92E0PAEIguAkbw0X021jJDIBQCO+ywQ9Jyvma/ZMkSadiwoVcWnzza/sKFC6VWrVqVrsd//+yzz3oavO/LHz16tIwbN07Gjx8vbdu29XzzQ4cOrXSd/TAEDIFoIWA++Gg9D2uNIZATBCDtXXbZxTOno4kTjPfAAw8IWve6desEEzv++Hhy5plnymuvvSYzZ86UXr16eUU2bdokBx98sEfuaPME9sVaCuLVZccMAUOg5hAwgq857O3OhkC1IoCpHfP7u+++K2jbTz75pBxwwAGe2f3666+X9u3bx70/Zv5u3bpJly5dpG7dul6ZAQMGeKZ+IvDxwxNkt3btWtm2bVvcOuygIWAI1DwCtg6+5p+BtcAQqDYEtm7dKhC2L5jmMdOnMuv75WM/N27cKPXr15cddzTdIBYb+20IRA0BI/ioPRFrjyFgCBgChoAhkAMEbBqeAxCtCkPAEDAEDAFDIGoIGMFH7YlYewwBQ8AQMAQMgRwgYASfAxCtCkPAEDAEDAFDIGoIGMFH7YlYewwBQ8AQMAQMgRwgYASfAxCtCkPAEDAEDAFDIGoIGMFH7YlYewwBQ8AQMAQMgRwgYASfAxCtCkPAEDAEDAFDIGoIGMFH7YlYewwBQ8AQMAQMgRwgYASfAxCtCkPAEDAEDAFDIGoIGMFH7YlYewwBQ8AQMAQMgRwg8P8BFbbR/pPYDFsAAAAASUVORK5CYII=" alt="plot of chunk time_series_panel"/> </p>

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