From 7ccc9165386f4ae5b10cf23961ec1f1b0f92de62 Mon Sep 17 00:00:00 2001
From: BethanyG <BethanyG@users.noreply.github.com>
Date: Mon, 6 Mar 2023 22:08:21 -0800
Subject: [PATCH 1/8] First draft of acronym approaches.

---
 .../practice/acronym/.articles/config.json    |  11 +
 .../Untitled-checkpoint.ipynb                 | 982 +++++++++++++++++
 .../acronym/approaches/Untitled.ipynb         | 989 ++++++++++++++++++
 .../approaches/functools-reduce/content.md    |   0
 .../approaches/functools-reduce/snippet.txt   |   4 +
 .../generator-expression/content.md           |   0
 .../generator-expression/snippet.txt          |   5 +
 .../acronym/approaches/introduction.md        | 183 ++++
 .../approaches/list-comprehension/content.md  |   0
 .../approaches/list-comprehension/snippet.txt |   5 +
 .../acronym/approaches/loop/content.md        |   0
 .../acronym/approaches/loop/snippet.txt       |   7 +
 .../approaches/map-function/content.md        |   0
 .../approaches/map-function/snippet.txt       |   4 +
 .../acronym/approaches/regex-join/content.md  |   0
 .../acronym/approaches/regex-join/snippet.txt |   4 +
 .../acronym/approaches/regex-sub/content.md   |   0
 .../acronym/approaches/regex-sub/snippet.txt  |   3 +
 18 files changed, 2197 insertions(+)
 create mode 100644 exercises/practice/acronym/.articles/config.json
 create mode 100644 exercises/practice/acronym/approaches/.ipynb_checkpoints/Untitled-checkpoint.ipynb
 create mode 100644 exercises/practice/acronym/approaches/Untitled.ipynb
 create mode 100644 exercises/practice/acronym/approaches/functools-reduce/content.md
 create mode 100644 exercises/practice/acronym/approaches/functools-reduce/snippet.txt
 create mode 100644 exercises/practice/acronym/approaches/generator-expression/content.md
 create mode 100644 exercises/practice/acronym/approaches/generator-expression/snippet.txt
 create mode 100644 exercises/practice/acronym/approaches/introduction.md
 create mode 100644 exercises/practice/acronym/approaches/list-comprehension/content.md
 create mode 100644 exercises/practice/acronym/approaches/list-comprehension/snippet.txt
 create mode 100644 exercises/practice/acronym/approaches/loop/content.md
 create mode 100644 exercises/practice/acronym/approaches/loop/snippet.txt
 create mode 100644 exercises/practice/acronym/approaches/map-function/content.md
 create mode 100644 exercises/practice/acronym/approaches/map-function/snippet.txt
 create mode 100644 exercises/practice/acronym/approaches/regex-join/content.md
 create mode 100644 exercises/practice/acronym/approaches/regex-join/snippet.txt
 create mode 100644 exercises/practice/acronym/approaches/regex-sub/content.md
 create mode 100644 exercises/practice/acronym/approaches/regex-sub/snippet.txt

diff --git a/exercises/practice/acronym/.articles/config.json b/exercises/practice/acronym/.articles/config.json
new file mode 100644
index 0000000000..56076f9126
--- /dev/null
+++ b/exercises/practice/acronym/.articles/config.json
@@ -0,0 +1,11 @@
+{
+  "articles": [
+    {
+      "uuid": "4c0e0a02-0bc0-4921-8016-20b0ae57804a",
+      "slug": "performance",
+      "title": "Performance deep dive",
+      "blurb": "Deep dive to find out the most performant approach to forming an acronym.",
+      "authors": ["bethanyg"]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/exercises/practice/acronym/approaches/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000000..3f02ee7f74
--- /dev/null
+++ b/exercises/practice/acronym/approaches/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,982 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "684ee076",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>input length</th>\n",
+       "      <th>regex_join_I</th>\n",
+       "      <th>regex_join</th>\n",
+       "      <th>regex_sub</th>\n",
+       "      <th>genex</th>\n",
+       "      <th>map</th>\n",
+       "      <th>reduce</th>\n",
+       "      <th>list_comprehension</th>\n",
+       "      <th>loop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "      <td>2.445</td>\n",
+       "      <td>1.943</td>\n",
+       "      <td>1.897</td>\n",
+       "      <td>1.050</td>\n",
+       "      <td>1.014</td>\n",
+       "      <td>0.990</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14</td>\n",
+       "      <td>2.260</td>\n",
+       "      <td>1.683</td>\n",
+       "      <td>1.526</td>\n",
+       "      <td>0.962</td>\n",
+       "      <td>0.913</td>\n",
+       "      <td>0.832</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19</td>\n",
+       "      <td>2.928</td>\n",
+       "      <td>2.204</td>\n",
+       "      <td>2.377</td>\n",
+       "      <td>1.133</td>\n",
+       "      <td>1.115</td>\n",
+       "      <td>1.159</td>\n",
+       "      <td>0.959</td>\n",
+       "      <td>0.864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20</td>\n",
+       "      <td>2.998</td>\n",
+       "      <td>2.242</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>1.165</td>\n",
+       "      <td>1.146</td>\n",
+       "      <td>1.197</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>0.894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25</td>\n",
+       "      <td>2.853</td>\n",
+       "      <td>1.973</td>\n",
+       "      <td>1.921</td>\n",
+       "      <td>1.058</td>\n",
+       "      <td>1.027</td>\n",
+       "      <td>1.014</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>30</td>\n",
+       "      <td>3.303</td>\n",
+       "      <td>2.259</td>\n",
+       "      <td>2.378</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>1.186</td>\n",
+       "      <td>0.970</td>\n",
+       "      <td>0.867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>35</td>\n",
+       "      <td>4.300</td>\n",
+       "      <td>3.079</td>\n",
+       "      <td>3.837</td>\n",
+       "      <td>1.457</td>\n",
+       "      <td>1.466</td>\n",
+       "      <td>1.664</td>\n",
+       "      <td>1.232</td>\n",
+       "      <td>1.222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>39</td>\n",
+       "      <td>3.612</td>\n",
+       "      <td>2.282</td>\n",
+       "      <td>2.492</td>\n",
+       "      <td>1.175</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>1.220</td>\n",
+       "      <td>1.013</td>\n",
+       "      <td>0.899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>42</td>\n",
+       "      <td>5.106</td>\n",
+       "      <td>3.851</td>\n",
+       "      <td>4.493</td>\n",
+       "      <td>1.636</td>\n",
+       "      <td>1.660</td>\n",
+       "      <td>1.994</td>\n",
+       "      <td>1.402</td>\n",
+       "      <td>1.397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>45</td>\n",
+       "      <td>4.412</td>\n",
+       "      <td>2.981</td>\n",
+       "      <td>3.329</td>\n",
+       "      <td>1.375</td>\n",
+       "      <td>1.361</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>1.121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>6.532</td>\n",
+       "      <td>4.751</td>\n",
+       "      <td>5.609</td>\n",
+       "      <td>1.997</td>\n",
+       "      <td>2.041</td>\n",
+       "      <td>2.523</td>\n",
+       "      <td>1.662</td>\n",
+       "      <td>1.827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>63</td>\n",
+       "      <td>6.717</td>\n",
+       "      <td>4.839</td>\n",
+       "      <td>6.875</td>\n",
+       "      <td>2.043</td>\n",
+       "      <td>2.089</td>\n",
+       "      <td>2.569</td>\n",
+       "      <td>1.711</td>\n",
+       "      <td>1.888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>74</td>\n",
+       "      <td>7.559</td>\n",
+       "      <td>5.414</td>\n",
+       "      <td>6.513</td>\n",
+       "      <td>2.310</td>\n",
+       "      <td>2.405</td>\n",
+       "      <td>3.021</td>\n",
+       "      <td>1.980</td>\n",
+       "      <td>2.221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>78</td>\n",
+       "      <td>6.327</td>\n",
+       "      <td>3.958</td>\n",
+       "      <td>4.507</td>\n",
+       "      <td>1.695</td>\n",
+       "      <td>1.721</td>\n",
+       "      <td>2.046</td>\n",
+       "      <td>1.455</td>\n",
+       "      <td>1.491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>93</td>\n",
+       "      <td>7.947</td>\n",
+       "      <td>4.840</td>\n",
+       "      <td>5.779</td>\n",
+       "      <td>2.004</td>\n",
+       "      <td>2.070</td>\n",
+       "      <td>2.548</td>\n",
+       "      <td>1.704</td>\n",
+       "      <td>1.886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>108</td>\n",
+       "      <td>11.166</td>\n",
+       "      <td>7.261</td>\n",
+       "      <td>10.266</td>\n",
+       "      <td>3.087</td>\n",
+       "      <td>3.199</td>\n",
+       "      <td>4.091</td>\n",
+       "      <td>2.519</td>\n",
+       "      <td>2.887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>120</td>\n",
+       "      <td>8.991</td>\n",
+       "      <td>4.932</td>\n",
+       "      <td>6.000</td>\n",
+       "      <td>2.129</td>\n",
+       "      <td>2.176</td>\n",
+       "      <td>2.680</td>\n",
+       "      <td>1.813</td>\n",
+       "      <td>1.938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>140</td>\n",
+       "      <td>14.260</td>\n",
+       "      <td>9.803</td>\n",
+       "      <td>12.876</td>\n",
+       "      <td>4.021</td>\n",
+       "      <td>4.206</td>\n",
+       "      <td>5.480</td>\n",
+       "      <td>3.329</td>\n",
+       "      <td>3.968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>150</td>\n",
+       "      <td>12.290</td>\n",
+       "      <td>7.211</td>\n",
+       "      <td>8.760</td>\n",
+       "      <td>2.986</td>\n",
+       "      <td>3.087</td>\n",
+       "      <td>3.943</td>\n",
+       "      <td>2.487</td>\n",
+       "      <td>2.776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>200</td>\n",
+       "      <td>19.407</td>\n",
+       "      <td>12.727</td>\n",
+       "      <td>17.067</td>\n",
+       "      <td>5.224</td>\n",
+       "      <td>5.540</td>\n",
+       "      <td>7.166</td>\n",
+       "      <td>4.210</td>\n",
+       "      <td>5.107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>210</td>\n",
+       "      <td>20.110</td>\n",
+       "      <td>12.974</td>\n",
+       "      <td>21.827</td>\n",
+       "      <td>5.352</td>\n",
+       "      <td>5.748</td>\n",
+       "      <td>7.257</td>\n",
+       "      <td>4.362</td>\n",
+       "      <td>5.258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>225</td>\n",
+       "      <td>21.177</td>\n",
+       "      <td>14.154</td>\n",
+       "      <td>19.314</td>\n",
+       "      <td>5.793</td>\n",
+       "      <td>6.017</td>\n",
+       "      <td>7.917</td>\n",
+       "      <td>4.653</td>\n",
+       "      <td>5.702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>260</td>\n",
+       "      <td>18.602</td>\n",
+       "      <td>10.110</td>\n",
+       "      <td>12.960</td>\n",
+       "      <td>4.189</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>5.640</td>\n",
+       "      <td>3.444</td>\n",
+       "      <td>4.067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>310</td>\n",
+       "      <td>23.087</td>\n",
+       "      <td>13.068</td>\n",
+       "      <td>17.437</td>\n",
+       "      <td>5.352</td>\n",
+       "      <td>5.693</td>\n",
+       "      <td>7.303</td>\n",
+       "      <td>4.326</td>\n",
+       "      <td>5.202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>360</td>\n",
+       "      <td>33.145</td>\n",
+       "      <td>21.459</td>\n",
+       "      <td>31.768</td>\n",
+       "      <td>8.962</td>\n",
+       "      <td>9.701</td>\n",
+       "      <td>12.506</td>\n",
+       "      <td>7.364</td>\n",
+       "      <td>8.998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>400</td>\n",
+       "      <td>25.931</td>\n",
+       "      <td>13.517</td>\n",
+       "      <td>18.251</td>\n",
+       "      <td>5.577</td>\n",
+       "      <td>5.939</td>\n",
+       "      <td>7.538</td>\n",
+       "      <td>4.644</td>\n",
+       "      <td>5.437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>750</td>\n",
+       "      <td>67.390</td>\n",
+       "      <td>44.480</td>\n",
+       "      <td>56.100</td>\n",
+       "      <td>18.607</td>\n",
+       "      <td>19.995</td>\n",
+       "      <td>26.182</td>\n",
+       "      <td>15.324</td>\n",
+       "      <td>18.628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    input length  regex_join_I  regex_join  regex_sub   genex     map  reduce  \\\n",
+       "0             13         2.445       1.943      1.897   1.050   1.014   0.990   \n",
+       "1             14         2.260       1.683      1.526   0.962   0.913   0.832   \n",
+       "2             19         2.928       2.204      2.377   1.133   1.115   1.159   \n",
+       "3             20         2.998       2.242      2.807   1.165   1.146   1.197   \n",
+       "4             25         2.853       1.973      1.921   1.058   1.027   1.014   \n",
+       "5             30         3.303       2.259      2.378   1.148   1.137   1.186   \n",
+       "6             35         4.300       3.079      3.837   1.457   1.466   1.664   \n",
+       "7             39         3.612       2.282      2.492   1.175   1.172   1.220   \n",
+       "8             42         5.106       3.851      4.493   1.636   1.660   1.994   \n",
+       "9             45         4.412       2.981      3.329   1.375   1.361   1.504   \n",
+       "10            60         6.532       4.751      5.609   1.997   2.041   2.523   \n",
+       "11            63         6.717       4.839      6.875   2.043   2.089   2.569   \n",
+       "12            74         7.559       5.414      6.513   2.310   2.405   3.021   \n",
+       "13            78         6.327       3.958      4.507   1.695   1.721   2.046   \n",
+       "14            93         7.947       4.840      5.779   2.004   2.070   2.548   \n",
+       "15           108        11.166       7.261     10.266   3.087   3.199   4.091   \n",
+       "16           120         8.991       4.932      6.000   2.129   2.176   2.680   \n",
+       "17           140        14.260       9.803     12.876   4.021   4.206   5.480   \n",
+       "18           150        12.290       7.211      8.760   2.986   3.087   3.943   \n",
+       "19           200        19.407      12.727     17.067   5.224   5.540   7.166   \n",
+       "20           210        20.110      12.974     21.827   5.352   5.748   7.257   \n",
+       "21           225        21.177      14.154     19.314   5.793   6.017   7.917   \n",
+       "22           260        18.602      10.110     12.960   4.189   4.370   5.640   \n",
+       "23           310        23.087      13.068     17.437   5.352   5.693   7.303   \n",
+       "24           360        33.145      21.459     31.768   8.962   9.701  12.506   \n",
+       "25           400        25.931      13.517     18.251   5.577   5.939   7.538   \n",
+       "26           750        67.390      44.480     56.100  18.607  19.995  26.182   \n",
+       "\n",
+       "    list_comprehension    loop  \n",
+       "0                0.878   0.743  \n",
+       "1                0.798   0.634  \n",
+       "2                0.959   0.864  \n",
+       "3                0.987   0.894  \n",
+       "4                0.889   0.760  \n",
+       "5                0.970   0.867  \n",
+       "6                1.232   1.222  \n",
+       "7                1.013   0.899  \n",
+       "8                1.402   1.397  \n",
+       "9                1.160   1.121  \n",
+       "10               1.662   1.827  \n",
+       "11               1.711   1.888  \n",
+       "12               1.980   2.221  \n",
+       "13               1.455   1.491  \n",
+       "14               1.704   1.886  \n",
+       "15               2.519   2.887  \n",
+       "16               1.813   1.938  \n",
+       "17               3.329   3.968  \n",
+       "18               2.487   2.776  \n",
+       "19               4.210   5.107  \n",
+       "20               4.362   5.258  \n",
+       "21               4.653   5.702  \n",
+       "22               3.444   4.067  \n",
+       "23               4.326   5.202  \n",
+       "24               7.364   8.998  \n",
+       "25               4.644   5.437  \n",
+       "26              15.324  18.628  "
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "data_to_plot = {'input length': [13,\n",
+    " 14,\n",
+    " 19,\n",
+    " 20,\n",
+    " 25,\n",
+    " 30,\n",
+    " 35,\n",
+    " 39,\n",
+    " 42,\n",
+    " 45,\n",
+    " 60,\n",
+    " 63,\n",
+    " 74,\n",
+    " 78,\n",
+    " 93,\n",
+    " 108,\n",
+    " 120,\n",
+    " 140,\n",
+    " 150,\n",
+    " 200,\n",
+    " 210,\n",
+    " 225,\n",
+    " 260,\n",
+    " 310,\n",
+    " 360,\n",
+    " 400,\n",
+    " 750],\n",
+    "'regex_join_I': [2.445, 2.26, 2.928, 2.998, 2.853, 3.303, 4.3, 3.612, 5.106, 4.412, 6.532, 6.717, 7.559, 6.327, 7.947, 11.166, 8.991, 14.26, 12.29, 19.407, 20.11, 21.177, 18.602, 23.087, 33.145, 25.931, 67.39],\n",
+    "\n",
+    "'regex_join' :[1.943, 1.683, 2.204, 2.242, 1.973, 2.259, 3.079, 2.282, 3.851, 2.981, 4.751, 4.839, 5.414, 3.958, 4.84, 7.261, 4.932, 9.803, 7.211, 12.727, 12.974, 14.154, 10.11, 13.068, 21.459, 13.517, 44.48],\n",
+    "\n",
+    "'regex_sub': [1.897, 1.526, 2.377, 2.807, 1.921, 2.378, 3.837, 2.492, 4.493, 3.329, 5.609, 6.875, 6.513, 4.507, 5.779, 10.266, 6.0, 12.876, 8.76, 17.067, 21.827, 19.314, 12.96, 17.437, 31.768, 18.251, 56.1],\n",
+    "\n",
+    "'genex': [1.05, 0.962, 1.133, 1.165, 1.058, 1.148, 1.457, 1.175, 1.636, 1.375, 1.997, 2.043, 2.31, 1.695, 2.004, 3.087, 2.129, 4.021, 2.986, 5.224, 5.352, 5.793, 4.189, 5.352, 8.962, 5.577, 18.607],\n",
+    "\n",
+    "'map' :[1.014, 0.913, 1.115, 1.146, 1.027, 1.137, 1.466, 1.172, 1.66, 1.361, 2.041, 2.089, 2.405, 1.721, 2.07, 3.199, 2.176, 4.206, 3.087, 5.54, 5.748, 6.017, 4.37, 5.693, 9.701, 5.939, 19.995],\n",
+    "\n",
+    "'reduce': [0.99, 0.832, 1.159, 1.197, 1.014, 1.186, 1.664, 1.22, 1.994, 1.504, 2.523, 2.569, 3.021, 2.046, 2.548, 4.091, 2.68, 5.48, 3.943, 7.166, 7.257, 7.917, 5.64, 7.303, 12.506, 7.538, 26.182],\n",
+    "\n",
+    "'list_comprehension': [0.878, 0.798, 0.959, 0.987, 0.889, 0.97, 1.232, 1.013, 1.402, 1.16, 1.662, 1.711, 1.98, 1.455, 1.704, 2.519, 1.813, 3.329, 2.487, 4.21, 4.362, 4.653, 3.444, 4.326, 7.364, 4.644, 15.324],\n",
+    "\n",
+    "'loop': [0.743, 0.634, 0.864, 0.894, 0.76, 0.867, 1.222, 0.899, 1.397, 1.121, 1.827, 1.888, 2.221, 1.491, 1.886, 2.887, 1.938, 3.968, 2.776, 5.107, 5.258, 5.702, 4.067, 5.202, 8.998, 5.437, 18.628]}\n",
+    "\n",
+    "plot_data = pd.DataFrame.from_dict(data_to_plot)\n",
+    "new_data = plot_data.sort_values('input length')\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "79c28644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>regex_join_I</th>\n",
+       "      <th>regex_join</th>\n",
+       "      <th>regex_sub</th>\n",
+       "      <th>genex</th>\n",
+       "      <th>map</th>\n",
+       "      <th>reduce</th>\n",
+       "      <th>list_comprehension</th>\n",
+       "      <th>loop</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>input length</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.445</td>\n",
+       "      <td>1.943</td>\n",
+       "      <td>1.897</td>\n",
+       "      <td>1.050</td>\n",
+       "      <td>1.014</td>\n",
+       "      <td>0.990</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2.260</td>\n",
+       "      <td>1.683</td>\n",
+       "      <td>1.526</td>\n",
+       "      <td>0.962</td>\n",
+       "      <td>0.913</td>\n",
+       "      <td>0.832</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2.928</td>\n",
+       "      <td>2.204</td>\n",
+       "      <td>2.377</td>\n",
+       "      <td>1.133</td>\n",
+       "      <td>1.115</td>\n",
+       "      <td>1.159</td>\n",
+       "      <td>0.959</td>\n",
+       "      <td>0.864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>2.998</td>\n",
+       "      <td>2.242</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>1.165</td>\n",
+       "      <td>1.146</td>\n",
+       "      <td>1.197</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>0.894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>2.853</td>\n",
+       "      <td>1.973</td>\n",
+       "      <td>1.921</td>\n",
+       "      <td>1.058</td>\n",
+       "      <td>1.027</td>\n",
+       "      <td>1.014</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>3.303</td>\n",
+       "      <td>2.259</td>\n",
+       "      <td>2.378</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>1.186</td>\n",
+       "      <td>0.970</td>\n",
+       "      <td>0.867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>4.300</td>\n",
+       "      <td>3.079</td>\n",
+       "      <td>3.837</td>\n",
+       "      <td>1.457</td>\n",
+       "      <td>1.466</td>\n",
+       "      <td>1.664</td>\n",
+       "      <td>1.232</td>\n",
+       "      <td>1.222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>3.612</td>\n",
+       "      <td>2.282</td>\n",
+       "      <td>2.492</td>\n",
+       "      <td>1.175</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>1.220</td>\n",
+       "      <td>1.013</td>\n",
+       "      <td>0.899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>5.106</td>\n",
+       "      <td>3.851</td>\n",
+       "      <td>4.493</td>\n",
+       "      <td>1.636</td>\n",
+       "      <td>1.660</td>\n",
+       "      <td>1.994</td>\n",
+       "      <td>1.402</td>\n",
+       "      <td>1.397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>4.412</td>\n",
+       "      <td>2.981</td>\n",
+       "      <td>3.329</td>\n",
+       "      <td>1.375</td>\n",
+       "      <td>1.361</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>1.121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>6.532</td>\n",
+       "      <td>4.751</td>\n",
+       "      <td>5.609</td>\n",
+       "      <td>1.997</td>\n",
+       "      <td>2.041</td>\n",
+       "      <td>2.523</td>\n",
+       "      <td>1.662</td>\n",
+       "      <td>1.827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>6.717</td>\n",
+       "      <td>4.839</td>\n",
+       "      <td>6.875</td>\n",
+       "      <td>2.043</td>\n",
+       "      <td>2.089</td>\n",
+       "      <td>2.569</td>\n",
+       "      <td>1.711</td>\n",
+       "      <td>1.888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>7.559</td>\n",
+       "      <td>5.414</td>\n",
+       "      <td>6.513</td>\n",
+       "      <td>2.310</td>\n",
+       "      <td>2.405</td>\n",
+       "      <td>3.021</td>\n",
+       "      <td>1.980</td>\n",
+       "      <td>2.221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>6.327</td>\n",
+       "      <td>3.958</td>\n",
+       "      <td>4.507</td>\n",
+       "      <td>1.695</td>\n",
+       "      <td>1.721</td>\n",
+       "      <td>2.046</td>\n",
+       "      <td>1.455</td>\n",
+       "      <td>1.491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>7.947</td>\n",
+       "      <td>4.840</td>\n",
+       "      <td>5.779</td>\n",
+       "      <td>2.004</td>\n",
+       "      <td>2.070</td>\n",
+       "      <td>2.548</td>\n",
+       "      <td>1.704</td>\n",
+       "      <td>1.886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>11.166</td>\n",
+       "      <td>7.261</td>\n",
+       "      <td>10.266</td>\n",
+       "      <td>3.087</td>\n",
+       "      <td>3.199</td>\n",
+       "      <td>4.091</td>\n",
+       "      <td>2.519</td>\n",
+       "      <td>2.887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>8.991</td>\n",
+       "      <td>4.932</td>\n",
+       "      <td>6.000</td>\n",
+       "      <td>2.129</td>\n",
+       "      <td>2.176</td>\n",
+       "      <td>2.680</td>\n",
+       "      <td>1.813</td>\n",
+       "      <td>1.938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>14.260</td>\n",
+       "      <td>9.803</td>\n",
+       "      <td>12.876</td>\n",
+       "      <td>4.021</td>\n",
+       "      <td>4.206</td>\n",
+       "      <td>5.480</td>\n",
+       "      <td>3.329</td>\n",
+       "      <td>3.968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>12.290</td>\n",
+       "      <td>7.211</td>\n",
+       "      <td>8.760</td>\n",
+       "      <td>2.986</td>\n",
+       "      <td>3.087</td>\n",
+       "      <td>3.943</td>\n",
+       "      <td>2.487</td>\n",
+       "      <td>2.776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>19.407</td>\n",
+       "      <td>12.727</td>\n",
+       "      <td>17.067</td>\n",
+       "      <td>5.224</td>\n",
+       "      <td>5.540</td>\n",
+       "      <td>7.166</td>\n",
+       "      <td>4.210</td>\n",
+       "      <td>5.107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>20.110</td>\n",
+       "      <td>12.974</td>\n",
+       "      <td>21.827</td>\n",
+       "      <td>5.352</td>\n",
+       "      <td>5.748</td>\n",
+       "      <td>7.257</td>\n",
+       "      <td>4.362</td>\n",
+       "      <td>5.258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>21.177</td>\n",
+       "      <td>14.154</td>\n",
+       "      <td>19.314</td>\n",
+       "      <td>5.793</td>\n",
+       "      <td>6.017</td>\n",
+       "      <td>7.917</td>\n",
+       "      <td>4.653</td>\n",
+       "      <td>5.702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>18.602</td>\n",
+       "      <td>10.110</td>\n",
+       "      <td>12.960</td>\n",
+       "      <td>4.189</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>5.640</td>\n",
+       "      <td>3.444</td>\n",
+       "      <td>4.067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>23.087</td>\n",
+       "      <td>13.068</td>\n",
+       "      <td>17.437</td>\n",
+       "      <td>5.352</td>\n",
+       "      <td>5.693</td>\n",
+       "      <td>7.303</td>\n",
+       "      <td>4.326</td>\n",
+       "      <td>5.202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>33.145</td>\n",
+       "      <td>21.459</td>\n",
+       "      <td>31.768</td>\n",
+       "      <td>8.962</td>\n",
+       "      <td>9.701</td>\n",
+       "      <td>12.506</td>\n",
+       "      <td>7.364</td>\n",
+       "      <td>8.998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>25.931</td>\n",
+       "      <td>13.517</td>\n",
+       "      <td>18.251</td>\n",
+       "      <td>5.577</td>\n",
+       "      <td>5.939</td>\n",
+       "      <td>7.538</td>\n",
+       "      <td>4.644</td>\n",
+       "      <td>5.437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>67.390</td>\n",
+       "      <td>44.480</td>\n",
+       "      <td>56.100</td>\n",
+       "      <td>18.607</td>\n",
+       "      <td>19.995</td>\n",
+       "      <td>26.182</td>\n",
+       "      <td>15.324</td>\n",
+       "      <td>18.628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              regex_join_I  regex_join  regex_sub   genex     map  reduce  \\\n",
+       "input length                                                                \n",
+       "13                   2.445       1.943      1.897   1.050   1.014   0.990   \n",
+       "14                   2.260       1.683      1.526   0.962   0.913   0.832   \n",
+       "19                   2.928       2.204      2.377   1.133   1.115   1.159   \n",
+       "20                   2.998       2.242      2.807   1.165   1.146   1.197   \n",
+       "25                   2.853       1.973      1.921   1.058   1.027   1.014   \n",
+       "30                   3.303       2.259      2.378   1.148   1.137   1.186   \n",
+       "35                   4.300       3.079      3.837   1.457   1.466   1.664   \n",
+       "39                   3.612       2.282      2.492   1.175   1.172   1.220   \n",
+       "42                   5.106       3.851      4.493   1.636   1.660   1.994   \n",
+       "45                   4.412       2.981      3.329   1.375   1.361   1.504   \n",
+       "60                   6.532       4.751      5.609   1.997   2.041   2.523   \n",
+       "63                   6.717       4.839      6.875   2.043   2.089   2.569   \n",
+       "74                   7.559       5.414      6.513   2.310   2.405   3.021   \n",
+       "78                   6.327       3.958      4.507   1.695   1.721   2.046   \n",
+       "93                   7.947       4.840      5.779   2.004   2.070   2.548   \n",
+       "108                 11.166       7.261     10.266   3.087   3.199   4.091   \n",
+       "120                  8.991       4.932      6.000   2.129   2.176   2.680   \n",
+       "140                 14.260       9.803     12.876   4.021   4.206   5.480   \n",
+       "150                 12.290       7.211      8.760   2.986   3.087   3.943   \n",
+       "200                 19.407      12.727     17.067   5.224   5.540   7.166   \n",
+       "210                 20.110      12.974     21.827   5.352   5.748   7.257   \n",
+       "225                 21.177      14.154     19.314   5.793   6.017   7.917   \n",
+       "260                 18.602      10.110     12.960   4.189   4.370   5.640   \n",
+       "310                 23.087      13.068     17.437   5.352   5.693   7.303   \n",
+       "360                 33.145      21.459     31.768   8.962   9.701  12.506   \n",
+       "400                 25.931      13.517     18.251   5.577   5.939   7.538   \n",
+       "750                 67.390      44.480     56.100  18.607  19.995  26.182   \n",
+       "\n",
+       "              list_comprehension    loop  \n",
+       "input length                              \n",
+       "13                         0.878   0.743  \n",
+       "14                         0.798   0.634  \n",
+       "19                         0.959   0.864  \n",
+       "20                         0.987   0.894  \n",
+       "25                         0.889   0.760  \n",
+       "30                         0.970   0.867  \n",
+       "35                         1.232   1.222  \n",
+       "39                         1.013   0.899  \n",
+       "42                         1.402   1.397  \n",
+       "45                         1.160   1.121  \n",
+       "60                         1.662   1.827  \n",
+       "63                         1.711   1.888  \n",
+       "74                         1.980   2.221  \n",
+       "78                         1.455   1.491  \n",
+       "93                         1.704   1.886  \n",
+       "108                        2.519   2.887  \n",
+       "120                        1.813   1.938  \n",
+       "140                        3.329   3.968  \n",
+       "150                        2.487   2.776  \n",
+       "200                        4.210   5.107  \n",
+       "210                        4.362   5.258  \n",
+       "225                        4.653   5.702  \n",
+       "260                        3.444   4.067  \n",
+       "310                        4.326   5.202  \n",
+       "360                        7.364   8.998  \n",
+       "400                        4.644   5.437  \n",
+       "750                       15.324  18.628  "
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data.set_index('input length', inplace=True)\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "d51b3964",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7faefd7ef250>"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vHEKbaaBBO1cmrWqFtV3JnHZbn6nn+MwTlG/rgWsLV9r/3g6bCjaYlSq5g5VF0aRSFEUpyAtGREQQGBhISEgI7u7uBXlpIYQQQjyFwaDw0+RT/DHvPADdR9binc8aoTEpuYNzL6+4wqHxh7F2teLVw73QmGuMHUmIFyIr7QohhBAlXFqylnmD9nNo013UGhXDFzah67Caxo5lNA9OPsDJ24kar1Xn4amH1BxUU4p9UaSV3LftQgghhOBBeAofttrBoU13KWVvxqxtbUt0sX/+mwts6bKNM5+fRa1R03JhC8rKtJuiiJMWfiGEEKKEunLsITN67iYuKg23arbM2BiIRw07Y8cyCkVRUKlUONYpjdpEjdpc2kRF8SHfzUIIIUQJtOePW4wN+JO4qDS8WpVjwcHOJbbYjzoazeb2W0iNTsW1hSuvHn4F7/e8jB1LiDwjBb8QQghRghgMCj9PP83sAfvITNfTaWh1Pt3RDtvS5saOZhSKonD6izPEnIvl3NcXACjlXsrIqYTIW9KlRwghhCgh0lN1fP7WAfatCUOtVvH2Z43oMapWiZxTPiMhg/v7IqnUtSL+i1twZcVVvN+XVn1RPEnBL4QQQpQAsfdTmd5zN9dOxGBlY8rE3/xp1LFkTo+tS9OxueNWEm8n0uGP9ri1dMVnbH1jxxIi30jBL4QQQhRz10/FMj0ohJh7qZSrVIoZGwOp6Olg7FgFTlEU9Ol6TCxNqBJUmTs772JTXrrviOJPCn4hhBCiGDuw4Q7zBu0nI1WHZ7OyTF0bgH0ZC2PHKnC6VB2HJhwm+X4KHf5oh/cYL+qNqouJhZRCoviT73IhhBCiGFIUhd/nnOenKacAaDuoKqO/9sWshC4glZmUSfjuCLQpWuIuxuFUzwm1RuYuESWDFPxCCCFEMZOZrmPBO4cI+fUWKhW8NbshvT70LJGDc29vvo15aQtcm7sQ+H0AZramlK5V2tixhChQUvALIYQQxcij6DRmvLKby0ceYmFtwviVLfHrVt7YsYwifFc4u9/eg4WTBa/sC6JcE2djRxLCKKTgF0IIIYqJ2+cfMbX7Lh7cTaGMhzUzNgZSxavktWanRqdi4WSBW2s33Fu74R7ojrlDyVxnQAiQgl8IIYQoFo5sDWfOa3tJS9ZRs7ET09YHULqclbFjFbh7e+8ROmwvtd+shc/Y+rT7rW2J7MokxP+Sgl8IIYQowhRFYd2XF1k+/gSKAq37VWbMd36YldTZZ1QqMuIzeHgmBoPeIANzhUAKfiGEEKLI0mbqWTziCDt/vA7AoBn16TexXolr0U57mMaRqcdoMr0Rbi1d6byhI86NnVGpS9bXQYinyVXBP3DgQOLi4jAxydo9ODgYLy8vtFotQ4YMYfjw4TRp0iRfgwohhBDiHwkx6Xzyaijn90djbqlh7E8taPFKRWPHMoojU49xa8MtDFoDgctbU65pOWNHEqJQybHgVxSFsLAwQkNDswt+gFu3bjFx4kQuXbqUrwGFEEII8bg7l+KZ1iOEyFtJOLpaMX19ANUbOhk7VoFSFIWHpx5StkFZmsxohCFTT9NPGhs7lhCFUo4d227dugXAm2++Sbdu3fjll18AWLt2LUOGDMHLyyt/EwohhBAi24md93i/+TYibyVR1ceRRYc7l7xi36Cwe0goW7ps496++1iVtSLw+wCsXayNHU2IQinHFv7ExER8fX2ZMmUKWq2W119/nUqVKjFu3DgAVqxY8cxjExMTH9sWFRX1kpGFEEKIkkdRFDZ/dYWlY45hMCi0eKUCH/3YAgurkjUcT1EUVGoVDjUduL8/En263tiRhCj0cvwtUb9+ferXr5/9ea9evdi7dy/NmjXL8eQrVqxgyZIlL5dQCCGEKOF0WgPffHCUrUuvAtB/Yj0GTq+PugQNSlUUhWu/XSds+x3a/hyI9xgvqvevRim3UsaOJkShl2PBf+LECbRaLb6+vkDWD9z/9uV/lkGDBhEUFPTYtqioKAYMGPACUYUQQoiSJ+lRBrP67uF0SCSm5mrGLG9OQL/Kxo5V4DITMjkx+yTpMenc/Sucih0rSLEvRC7lWLknJSWxaNEifv/9d7RaLRs2bGDGjBm5OrmtrS22trYvHVIIIYQoie5dT2Rq911EXEvEwdmCaesCqNW0rLFjFaj4GwlkxKXj3NiZ1ktbkXI/hYodKxg7lhBFSo4Ff+vWrTl79iw9evTAYDDQv3//x7r4CCGEECLvndkdySd9Qkl+lEmleg4EbwykbPmS1aIddzmOrV22obE0oceu7rg2dzF2JCGKJJWiKEpBXjAiIoLAwEBCQkJwd3cvyEsLIYQQRcL2766yZNQR9DqFpl09mLCyJZalTI0dq8DoM/WoNFnjE/7s8xcWpc1p/nkzzGzMjJxMiKKpZA3tF0IIIQoxvd7AsrHH2bjoMgCvfliHwZ/6oNHkOIt2sZEckczut/fg3toNn7H1absiEBMrkxK3erAQeUkKfiGEEKIQSEnMZHb/vRz/8x4mpmpGf+NL+zeqGTtWgUu8ncjD0w9Je5hG3eF1MLUuOU82hMgvUvALIYQQRhZ5K4mpPUK4eykeW0dzpq4NoG4LZ2PHKjAGvYHzX1+gWu+quLZwpdU3/ri1dJViX4g8UnKeEQohhBCF0Pn90bznt5W7l+IpX9ueRYe7lKhiH+DknFOcmHWSPSP2oSgKVXpUxqK0hbFjCVFsSMEvhBBCGMlfK64zod1OEmIyaNjejQX7O+FS2cbYsQpMSlQqAJ5Da1PaszTe79WTvvpC5AMp+IUQQogCZjAoLJ9wgs/fOohOa6DH6FoEbwrE2q7kzEJz4duLrG68hnv77mNV1ooef3fDtYWrsWMJUSxJH34hhBCiAKUla5n7+j4Obw5HY6JixKKmdH67hrFjFbjMxEwMmQYennqIW0tXVGpp2Rciv0jBL4QQQhSQB3eTmRYUwq2zjyjlYMaUP1rjHVByFpOKvRDLpe8v0+wzP7zHeOHS3AUX33LGjiVEsScFvxBCCFEALh95wIxXdvMoOh336rbM2BiIe3U7Y8cqMAatgV1v7CY5IpnSnqXxHFJbin0hCogU/EIIIUQ+C/39Fp+/dQBthgHvABcm/9EKGwdzY8cqELpUHYl3EildqzQtFjTn9ubb1HiturFjCVGiSMEvhBBC5BODQWHljDP8NussAJ3fqcHwBU0wMS0Zc2akPUxjx6t/khaTTo9d3XFt7oJr85LThUmIwkIKfiGEECIfpKfq+OzNA+xfG4ZarWLYF43pNqJmiZl2UlEUzEubY+FkiUFrQJuUCeWsjB1LiBJJCn4hhBAij8XcS2F6z91cPxmLla0pE3/zp1EHd2PHKhD6TD3Hgo9jbmeOz9j6BHzbCrW5BrNSsmquEMYiBb8QQgiRh66fjGFa0G5i76fiUtmGGRsDqVDb3tixCkzM2RgufX8ZjZmGGq9Vx9rF2tiRhCjxpOAXQggh8sj+dWHMf2M/GWl66jR3Zura1tg5WRg7VoGI2HOP0rUccG7kjO/MJjjVLyPFvhCFhBT8QgghxEtSFIVVn55jxbTTALR7oyqjv/bF1Exj5GQF4/rqG+x7bz/lmpaj45r21H6rtrEjCSH+hxT8QgghxEvITNfxxdBDhK66hUoFb81pSK8xniVicK4uXYeJhQlurVyxKmeFm7+smCtEYZTrgn/u3Lk8evSIOXPmcPr0aWbPnk1KSgo1atRgzpw5mJmZ5WdOIYQQotB5FJ3GjJ67uXz0IZalTJiw0p+mXT2MHatARB2NZs/wvbT4sjluLV3pdaAnptYyMFeIwihXEwEfPnyYDRs2AJCcnMyoUaMIDg5m27ZtAKxduzb/EgohhBCF0K1zcYxqupXLRx9Strw1X+zrVGKKfYD7++6Tci+FKyuuAEixL0QhlmMLf3x8PF9++SXDhg3jypUrHDx4EG9vb2rWrAnA5MmT0ev1/3psYmIiiYmJj22LiorKg9hCCCGE8Rzecpc5r+0jPUVHraZlmLYuAAdnS2PHyncZCRlcWn4Zr/fr4T3GC6tyVlTvW83YsYQQOcix4J86dSoffPABkZGRANy5cwcrKys++OADbt26hY+PDxMmTPjXY1esWMGSJUvyNrEQQghhJIqisPbzi3z/8QkUBQL6V+aDZX6YWRT/IXGKovD3wF1EH3sAQP0Pvak5sIaRUwkhcuOZXXrWrFmDi4sLvr6+2dv0ej0HDhxgzJgxrF+/nrS0NJYtW/avxw8aNIiQkJDH/v366695ewdCCCFEAdBm6vli6EGWT8gq9t/4xIdxK1oU+2JfURRSH6SiUqnwGeeDk5cTVV6pbOxYQojn8MzfUtu3b+fhw4d0796dhIQEUlNTURSFhg0b4uGR1U+xY8eO/PLLL/96vK2tLba2tnmfWgghhChACTHpBPcK5cKBaMwtNYxd0YIWPSsaO1a+06XrODT+MBG779FjVzdcm7vQbUcXmYlHiCLmmQX/jz/+mP3x+vXrOXbsGO+99x59+vQhMjISFxcXQkND8fT0zPegQgghhDHcuRTP1O67iLqdjKOrFTM2BlLNx9HYsQqE2lRN8r0UMpMzibsYh5WzlRT7QhRBz/0c0sXFheDgYIYNG0ZGRga1atVi/Pjx+ZFNCCGEMKrjf0bwaf+9pCZqqd7QkenrA3F0tTJ2rHx3e0sYSXeSqDeyLq2/8SctJo3StUobO5YQ4gWpFEVRCvKCERERBAYGEhISgru7e0FeWgghhMgVRVHYtOQy3354HINBoUWvinz0Q3MsrIp3f32AhJsJrGuxAUVR6P5XN5zqloynGUIUZ8X/N5cQQgjxHHRaA1+9d4Tty64BMGCyF69N9UZdzLuyJN9LRm2qxq6KHQ0m+GBqbYJjHWnVF6I4kIJfCCGE+I/EuAxm9dnDmdBITM3VfPh9c1r3Lf4z0kSfeMDfr++idE0HOqxuj9foesaOVOhdC4km7GAM7abKOEZR+OVqpV0hhBCiuIu4lsD7zbZxJjQSB2cLPtvdsdgX+//t1WtTvhRqEzUacw26NJ2RUxUNti4WhMy5TOT5eGNHESJHUvALIYQo8U6H3Oc9v23cu55IZS8HFh3uQs0mZYwdK1+lPUxjZ/+/ubfvPlZlrei6pTPtfm2LmY2ZsaMVSnqtgX0Lr5GeqAWgXG07Ru4LwNnTzsjJhMiZdOkRQghRom1bdpUlo45g0Cv4dvNg/M8tsSxlauxY+e76Hze4F3qP1MgUgnb3wKaCjbEjFVphh2NYO+wEkecTiL2ZTNAiHwA8GsoYB1E0SMEvhBCiRNLrDCwbe5yNiy8D0HtsHQbPalCsB+cqisLtTbep2LUidd71JP1ROp5Dasvc+k+RFp/J9onnOPztTRQFHCtbU7uLq7FjCfHcpOAXQghR4qQkZPJp/72c2HkPE1M173/rR9vXqxo7Vr47NP4wV36+ivfVeBqM96HxlEbGjlQoKYrC2TXhbHz/NElR6ahNVAR8VJO2U2pjaimlkyh65LtWCCFEiRJ5K4mp3Xdx93ICdk7mTF0bQJ3mzsaOla/0GXo05hoqdavE7S1hONaTufWf5ffBxzjxcxgAFf0c6bW0IS517I2aSYiXIYN2hRBClBjn90Ux2ncrdy8nUMHTnkWHuxTrYl9RFK7+do21zdeTGp2Ka3MXeh9/lYodKxg7WqFWLaAslvam9FrakBH7AqXYF0WeFPxCCCFKhJ0/XWdC+79IjM2gcUd3vtzfiXKVivlAVQVurr9FcngytzbdBsCsBAxIfl53jsSwYfSp7GlKGwysyIRrnfF9u0qxHtMhSg7p0iOEEKJY0+sN/PDxSdZ+cRGAoNG1GTq/IRpN8W3zSriZQOShKGoOrEHrb/y5v/8+VXpWMXasQictIZPtH/8zKLdyyzJ49fJApVJRysnc2PGEyDNS8AshhCi2UpO0zH19H0e2hKMxUTFycVM6Da1h7Fj5Kj0unc0dt5KZlIltJVtcm7tIsf//KIrCubURbHjv1GODcmt1cjF2NCHyhRT8QgghiqXoO8lMCwrh9rlHlHIwY8rq1ni3Lr4FnT5Tjy5Fh0VpC+q848mjq49w8nYydqxCJy4shfUjT3J5eyQgg3JFySAFvxBCiGLn0uEHzHhlN/EP0nGvbkvwpja4VbM1dqx8kxKZQsiQUEwsNHRY3R7vD7xABSqV9D//X8kP0/ms3p9kJOuwsDOly5x6NBkq/fRF8ScFvxBCiGJl9283+WLoQbQZBuoHujDp91bYOBTv/tgqtYrku8mozdSk3EvBpnwxH4z8nBRFyeqXX8aCBq9VIDVeS/cvvLEtZ2nsaEIUCCn4hRBCFAsGg8LP006zavY5ALq+W5NhXzTGxLR4Ds416A2c+fws5XydcW3hSrtf2lDKvRQWjhbGjlZopCVksmPSeUqVtaDdVE8Aghb7oC7GA7aF+De5/o6fO3cuEyZMyP5cq9UyaNAgjh49mi/BhBBCiNxKT9Eyq+8eVs0+h1qtYvjCJoxc3LTYFvsAN1bf5PQXZ9gzYh+6VB1OXk5S7P+HoiicXRvOPM8/Ofj1DULnXSY1LgNAin1RIuWqhf/w4cNs2LCBVq1aAXDr1i0mTpzIpUuX8jObEEIIkaOYeylMC9rNjVOxWNuZMmlVKxq0czN2rHwTeyEWh1oOVO1dhXv77lO9XzVMrOSB/X/F3fnPoNxtWYNyKzTNGpRrVbp4d+sS4llyfJsbHx/Pl19+ybBhw7K3rV27liFDhuDl5ZWv4YQQQohnuXYihlFNt3LjVCwuVWxYcKBzsS72r6y8yqb2Wzj92RnUGjWtv/HHraWrsWMVCnqdgT2fX2G+5w4ub4vEws6UV75uwMgDgbjWszd2PFEAVq1axapVq565z9ChQ4mOjn6h8wcEBBAREfFCxxpbjk0CU6dO5YMPPiAyMjJ727hx4wBYsWLFM49NTEwkMTHxsW1RUVEvklMIIYR4zL61YXw2eD8ZaXrqtXRmyprW2BbTLi3/HXRqWzFrMK4+Q5+9TWQ59esdtow9C4BXbw96fFkfWxcZlFuS9OvXL8d9vvvuuwJIUvg8s+Bfs2YNLi4u+Pr6sn79+uc++YoVK1iyZMkLhxNCCCH+P0VR+G3WOX6efhqADm9WY+SSppiaaYycLH/EXojl8MQjBHzXGtcWrryyPwi7ynbGjlUoaNN0mFpmlTINXqvApa33aTy4ErU6yVOPgnb06FHmz5+PwWDAzc0NKysrrl+/jl6vZ+jQoXTp0gWtVsu0adM4efIkzs7OqFQqhg8fTpMmTVi2bBk7duxAr9fTvHlzxo4dy+7du5k3bx6bN28mKiqKgQMHsmbNGpydnf81w+LFiwEYNWoUoaGhLFiwAIPBgIeHB8HBwTg5OREQEMDPP//MsWPH2L9/PwkJCYSHh9OsWTOmT59egF+xgvXMgn/79u08fPiQ7t27k5CQQGpqKp9++ikTJ07M1ckHDRpEUFDQY9uioqIYMGDAiycWQghRYmWk6fhi6EH2/H4blQqGzmtEz/drF+uW7hOzTxF97AGnPz9Ds3l+UuyT9abv/PoINrx3mt7fNaJWRxfUGjWD1jQzdjSj+XvlDf768Xq+nLvd4Gq0HVg1x/3CwsIIDQ3l22+/pWzZssydO5fk5GT69u2Ll5cXe/bsIS0tjT///JP79+/TtWtXAPbt28eFCxdYu3YtKpWKsWPHsnnzZrp3785ff/3F0qVLOXr0KOPHj39qsf+/YmNjmTp1KqtWrcLd3Z3ly5cTHBzMokWLHtvv9OnTbN26FY1GQ4cOHejXrx81ahTPlbifWfD/+OOP2R+vX7+eY8eO5brYB7C1tcXWtvgudCKEEKLgxEWlMqPnbq4ci8GylAkTfvGnaRcPY8fKF7pUHff23aNChwq0+LIZF765iM+4+saOVSg8upvC+lGnuLTlPgAnfr5NrY7FdwXloqRSpUrY2Nhw6NAh0tPTWbduHQCpqalcv36dgwcP0rt3b1QqFW5ubvj6+gJZk8OcO3eOnj17ApCeno6ra9ZTmkmTJtGpUyd8fHzo3LlzrnKcO3eOevXq4e7uDkCfPn1YtmzZE/vVr1+fUqVKAeDh4UFCQsLLfQEKMRnWL4QQotC7eTaOaT1CeBiegnMFa2ZsbEOlug7GjpUv9Jl6tnTdRtylONqvaod7KzcaT2tk7FhGp9cZOLDoOn9Ou0Bmig4LW1M6z65H03eqGDtaodB2YNVctcLnJwuLrDE0BoOB+fPn4+mZtfZBTEwMdnZ2rFu3DoPB8MRxer2eQYMGMXjwYCBrDKhGo8k+VqPRcOvWLTIyMjA3z3m2pf9/DUVR0Ol0T+z3v+dSqVQoipLLOy16cj0Zbc+ePZkzZ85j21auXEmTJk3yPJQQQgjxX4c232VMy+08DE+htm9ZFh3uUqyLfY2Zhgody2NX2RYrZxl0CnD3eCwLGv/N5o/OkJmiw6u3B+Mvd8Tv3aqo1cW3O1dR1bRp0+zZch48eEC3bt2IjIzEz8+P7du3oygK0dHRHDt2DJVKRdOmTdm0aRMpKSnodDpGjBjBzp070ev1fPzxx0yaNInGjRuzcOHCXF3fy8uLs2fPZs+o88cff5T4elVa+IUQQhRKiqKw5rML/DDxJIoCgQMq8/63fphZFL8/XfpMPceCj/Po8iM6rG6P9wde1BlWB7NSpsaOVijsW3CN+2ficahgxStfNZBBuYXcyJEjmT59Ol26dEGv1zN27FjKly9P7969uXLlCl27dqVMmTK4urpiYWFB48aNuXLlCr1790av19OiRQuCgoJYvnw5jo6OtGvXDj8/P7p06UK7du3w9vZ+5vWdnJwIDg5m5MiRaLVaXF1dmTVrVsHcfCGlUgr4+UVERASBgYGEhIRk960SQggh/ldmhp5Fww/z94obAAye6UOf8XWL7eDctIdpbAjYREZ8Bp02dMS5YVljRzIqRVGIC0vBsVJW/+rEqDQOLL5O4MTamFsXvzd8JcWePXtQFIXWrVuTlJREjx49WLduHfb29nly/k8//RRnZ2feeuutPDlfcSI/NUIIIQqV+IfpBPfazcWDDzC3MmHcihY0D6pg7Fj5ImJ3BGozDa7NXQj4rhVqUzVlG5TsYv/R3RQ2jD7Fjd0PGHuxIw4eVtiWs6TTrHrGjiZeUpUqVRg3bhwLFiwAYPTo0c9d7P/0009s2LDhie12dnaEh4ezfPnyF8539+5dRo0a9a+vzZw5k7p1677wuY1NWviFEEIUGmEXHzG1ewjRYck4uVkxY2MgVes7GjtWvrh/4D47Xt2JRWkLeu7pgWWZkt1fX68zcGDxdf6cmjUo19zGhIG/+8kMPELkAWnhF0IIUSgc3xHBp/33kpqkpXpDJ6ZvCMDRxcrYsfJcelw6ZnZmlPMth5u/K85NnLEopisE51b4iTjWDjtBxKlHANTr5U6PBT7YuZbsN0FC5BUp+IUQQhiVoihsWHSJ78aewGBQ8O9dkQ+/b465ZfH7ExV9PJrdQ/dQvV81Goz3od2vbVFrcj1hXrGTkaJjx8RzHPjqBopBwaG8FT2XNKB2FxmUK0ReKn6/TYUQQhQZOq2BJaOOsGP5NQBem+LFa1O9i+3gXH26nrQHaUQejsKgNaA2LbnFPoBao+LKzihUKmg5pgbtp3tiLjMTCZHnpOAXQghhFIlxGczsHcrZPVGYWWj48PtmtOpT2dix8lxGQgbHZhynwXgfXFu40v73drj4liuxxf6j8FTSEzJxqWOPqYWGfiuaoDFT416/eK6tIERhUDJ/2wghhDCq8KsJvOe3lbN7oihdzpL5uzsUy2If4OjUY1z77ToHxx4CwK2la4ks9vU6A/sWXGVe7R380v8Iem3WaqgVmjhKsS/yxKpVq7IX/HqaoUOHEh0dXUCJCg9p4RdCCFGgTu26z6y+e0iOz6SKd2mmbwikrIe1sWPlKUVRiL0Qh1NdRxpOakBaTDpNghsbO5bRhJ+MY+07/wzKLVvDhoxkHVYOZkZOJoqTfv365bjPd999VwBJCh8p+IUQQhSYLUuv8PV7RzHoFZr1KM+4FS2wsC5efbYVReHAmINc+/067Ve1w72VG+1/bWvsWEaRnqRl59QL7F98PXtQbtBiHzy7uhk7mshjR48eZf78+RgMBtzc3LCysuL69evo9XqGDh1Kly5d0Gq1TJs2jZMnT+Ls7IxKpWL48OE0adKEZcuWsWPHDvR6Pc2bN2fs2LHs3r2befPmsXnzZqKiohg4cCBr1qzB2dn5XzMsXrwYgFGjRhEaGsqCBQswGAx4eHgQHByMk5MTAQEB/Pzzzxw7doz9+/eTkJBAeHg4zZo1Y/r06QX4FStYUvALIYTId3qdgaUfHmPzV1cA6DO+Lm984oNaXfwG56pUKqzdrNFYaMiIzzB2HKO5deAhv/Q/TEJEGmqNSgblFoCvW+/+1+3DQwMA2Pj+Ke6fjX/i9e5f1sfN24FjP93mxIrbTz0+J2FhYYSGhvLtt99StmxZ5s6dS3JyMn379sXLy4s9e/aQlpbGn3/+yf379+natSsA+/bt48KFC6xduxaVSsXYsWPZvHkz3bt356+//mLp0qUcPXqU8ePHP7XY/1+xsbFMnTqVVatW4e7uzvLlywkODmbRokWP7Xf69Gm2bt2KRqOhQ4cO9OvXjxo1auTqXosaKfiFEELkq+T4DD7tt5eTf9/H1EzN+8ua0ea1KsaOledub77NjfW3CPy+Nd4feFH1lSrYVrI1diyjsXWxJCUmE49Gpem1tKH00y8BKlWqhI2NDYcOHSI9PZ1169YBkJqayvXr1zl48CC9e/dGpVLh5uaGr68vAIcPH+bcuXP07NkTgPT0dFxds6ZmnTRpEp06dcLHx4fOnTvnKse5c+eoV69e9gKvffr0YdmyZU/sV79+fUqVKgWAh4cHCQkJL/cFKMSk4BdCCJFv7t9MZGr3EMKvJGBXxoJpa1vj2SznFrqiRpui5fDko6Q9SOP25jCqBFUuccW+QW/g8Lc38e5THmtHc5yqlGLk/gDcvO1L9FoDBSmnlvgeC3ye+XrjNyrR+I1KL3x9C4usBeQMBgPz58/H09MTgJiYGOzs7Fi3bh0Gg+GJ4/R6PYMGDWLw4MEAJCYmotFoso/VaDTcunWLjIwMzM3Nc8zx/6+hKAo6ne6J/f73XCqVCkVRcnmnRY/8BAohhMgX5/ZGMdp3G+FXEqhYx55FhzsXu2I/OSKZ6BMPMLU2pfU3/jSd2YTKPV68YCqqIk7Fsch3F+tHnmLruLPZ2z0alJZivwRq2rRp9mw5Dx48oFu3bkRGRuLn58f27dtRFIXo6GiOHTuGSqWiadOmbNq0iZSUFHQ6HSNGjGDnzp3o9Xo+/vhjJk2aROPGjVm4cGGuru/l5cXZs2eJiIgA4I8//qBJkyb5dr9FgbTwCyGEyHN//nCNxSOOoNMaaNzJnQm/tMTatnjNyBJ/I4Gt3bahUqvo8Xc3XJq54NLMxdixClRGspad0y6wb2HWoFx7Dys8u8uA3JJu5MiRTJ8+nS5duqDX6xk7dizly5end+/eXLlyha5du1KmTBlcXV2xsLCgcePGXLlyhd69e6PX62nRogVBQUEsX74cR0dH2rVrh5+fH126dKFdu3Z4e3s/8/pOTk4EBwczcuRItFotrq6uzJo1q2BuvpBSKbl4frFw4UJ27tyJSqWiV69eDB48mNOnTzN79mxSUlKoUaMGc+bMwcws51/mERERBAYGEhISkt23SgghRPGg1xv4fsJJ1n15EYCe73syZG4DNMWoldeg/6e7wM6+f6E2UeP/VUssSlsYMVXBu7jlHutHniI+PBWVWkWL0dXoEFxHBuWKp9qzZw+KotC6dWuSkpLo0aMH69atw97ePk/O/+mnn+Ls7Mxbb72VJ+crTnJs4T927BhHjhxh8+bN6HQ6OnXqhK+vL6NGjWL58uXUrFmTMWPGsHbtWvr3718QmYUQQhRCqUla5ry2l6PbItCYqBj1lS8d36pu7Fh5Ku1hGnuG78W5kTM+4+oT+EMAptamqIrhbEPPsn7kSQ5+fQMA9wYOvPptQ9x9Shs5lSjsqlSpwrhx41iwYAEAo0ePfu5i/6effmLDhg1PbLezsyM8PJzly5fnQdLiJ1ct/FqtFlNTU+7du0f//v15//33CQkJYcmSJQDExcWh1+spU6ZMjheUFn4hhCh+ou8kM61HCLfPP8KmtDlTVrfCq1Xx695y/0Akf/beiYWjBb0O9sSsmHVTyq1z6yP4/Y2jdJxZl2Yjqko/fSEKuVwV/ACLFi3ihx9+oEOHDlSuXJkbN26g1Wq5desWPj4+TJgw4YmR04mJiSQmJj62LSoqigEDBkjBL4QQxcTFQw8I7rWb+AfpeNS0Y8bGQNyqFp8ZahRF4fIPl6nYpSJWzlbcWHsTl2blsHYpXqsDP0vE6Ucc/e4mQUsaoFZnzWaSEpNBqTIlqxuTEEVVrgt+gLS0NIYNG0ajRo347bff+OOPP3B1dWXSpEm4ubkxatSox/ZfvHhx9lOA/08KfiGEKPp2/XKTBW8fRJtpoEFbVyau8qeUfc7T5hUlp784w6l5pynnW45O6zugUpWc7jv/f1Bunx8av9S0jUII48ixD//NmzfJzMykVq1aWFpa0q5dO+bOnUuzZs3w8PAAoGPHjvzyyy9PHDto0CCCgoIe2/bfFn4hhBBFl8Gg8NOUU/wx9zwAXYfX5N0vGqMxKT5dO9Ji0rF0sqDGa9W5teEWnm/XLlHF/v8flNvyverUe0Ua6oQoinIs+CMiIli0aFH2fKohISEEBwfzxRdfEBkZiYuLC6GhodmLK/wvW1tbbG2Lz2NdIYQQkJ6iZd4bBzi44Q5qjYp3v2xMt+G1jB0rT1377RqHJx0l8McA3Fu5EbSnR4npp55wL5UN753m/PqsOczdfRzo9W1DPBrIoFwhiqocC35/f3/OnTtHjx490Gg0tGvXjh49emBvb8+wYcPIyMigVq1ajB8/viDyCiGEMKKHESlMDwrhxuk4rO1MmfR7axq0dTV2rDyXfD8FXZqO+/vv497KrcQU+2kJmXzmtZPUuEzMrE3o+Ekdmo2sVqye3AhREj1XH/68ILP0CCFE0XT1eAzTe4YQF5mGSxUbgjcFUr6mvbFj5ZmEmwlcWHYJ30+zVuS8v/c+7gEl7+/UlnFneXgtkaDFDXDwsDJ2HCHyTY0aNbh69aqxYxQIWWlXCCFEjvauuc1ngw+Qma6nnn85pqxuha1j8ZmhxaA38NfAXSTeSsS2og11361TIor9jBQdf02/gEqjosscLwA6fVoXtUZVosYrCFHcScEvhBDiqRRF4ddPzrIy+AwAHd6qxsjFTTE10xg3WB7RZ+pJCkvCvro9zef7ceWXa9QYWMPYsQrEpW33WT/iJI/upqIxVdPyverYulhK9x3xQo4ePcr8+fMxGAy4ublhZWXF9evX0ev1DB06lC5duqDVapk2bRonT57E2dkZlUrF8OHDadKkCcuWLWPHjh3o9XqaN2/O2LFj2b17N/PmzWPz5s1ERUUxcOBA1qxZg7Oz879mOHz4MPPnzweyFuL6/PPPSU1N5fXXX2f37t1A1gySQPbMklOmTOHcuXM4ODjw6aef4upa/LooAshPtRBCiH+VkaZjzmv7WBl8BrVaxTufNeL9pX7FpthPj0tnW48dbOu5g5TIFFyaudD6G3/MSpkaO1q+SrifxoreB/m+634e3U3Frb49Iw8EYOtiaexo4iVtC9rBtd+v5/nHuRUWFsaKFSuoUKECnp6erF+/nl9//ZWlS5cSHh7O77//TlpaGn/++SezZ8/m/PmsWb727dvHhQsXWLt2LRs3biQ6OprNmzcTGBiIt7c3S5cu5eOPP2b8+PFPLfYBvv76a6ZPn8769evx8/Pj0qVLOWZu1KgRmzZtom3btsyaNSvX91rUSAu/EEKIJ8RGpjI9aDfXTsRgZWPKhF9a0qSzh7Fj5SkzOzNMrU3QmGtIj00v9gtpGfQGDn97k+0Tz5OeqMXM2oQOwXVoPkoG5Yq8UalSJWxsbDh06BDp6emsW7cOgNTUVK5fv87Bgwfp3bs3KpUKNzc3fH19gayW+XPnztGzZ08A0tPTs1vaJ02aRKdOnfDx8aFz587PvH5gYCAjR46kTZs2BAYG0qxZMyIiIp66v4WFBd26dQOge/fuLFiw4GW/BIWWFPxCCCEec+N0LNOCQoiJSMW5YimCNwZSsY6DsWPlCYPewOnPzqBSq/AZW59W37RCpQaL0sVnPMLTXNiYNa8+QO2urvRc7IND+eL9Jqek6byhY75+nBMLi6yfI4PBwPz587OnbI+JicHOzo5169ZhMBieOE6v1zNo0CAGDx4MQGJiIhqNJvtYjUbDrVu3yMjIwNz86Qv7vfHGG7Ru3ZrQ0FDmz5/PuXPn6NatG/87P41Op8PEJKv8Vav/eaOrKEr29uJI3tILIYTIdmjTHcb47yAmIhVPv7IsOtS52BT7ALHnYjm78BxnFp4l6W4Slk4WxbrY16brs4udOkHuePctzxvrmvHmxuZS7It807Rp0+z1mx48eEC3bt2IjIzEz8+P7du3oygK0dHRHDt2DJVKRdOmTdm0aRMpKSnodDpGjBjBzp070ev1fPzxx0yaNInGjRuzcOHCZ1731VdfJSUlhTfeeIM33niDS5cuYWtrS3x8PHFxcWRmZrJ///7s/VNTUwkJCQFg3bp1+Pn55d8XxciK71sZIYQQuaYoCqvnneeHSVktwG0GVuG9pX6YmReP/vrRx6OxKW9DmfplaBLcGIca9tiUtzF2rHx1eft91o04SbfPvKn3igdqtYqBv/kaO5YoAUaOHMn06dPp0qULer2esWPHUr58eXr37s2VK1fo2rUrZcqUwdXVFQsLCxo3bsyVK1fo3bs3er2eFi1aEBQUxPLly3F0dKRdu3b4+fnRpUsX2rVrh7e3979ed8yYMUyYMAETExOsrKyYOXMmNjY2DBkyhF69elGuXDnq1q2bvb+trS27du1i4cKFODs7M3v27AL6ChU8mYdfCCFKuMwMPQuHHWLXypuoVDB4VgN6j61TbKZlvL0ljNB39+DcyJmOa9qjLub91RPup7Hpg9OcXRMOQK3OLgzZ0tLIqYSAPXv2oCgKrVu3JikpiR49erBu3Trs7e2NHa3YkxZ+IUSxdvv8I26cjiVgQGU0JWS11OcR/yCN4F6hXDz0AHMrEyasbIFf9wrGjpUnDFoDalM1zk3KYuloQRlvJyjQJq6C9cSgXCsNHYLr0nx0NWNHEwKAKlWqMG7cuOzBsaNHj37uYv+nn35iw4YNT2wvW7Ys3333XR6kLJ6khV8IUWw9uJvMsPqbSEnQMmCyF69Pr2/sSIVK2IVHTO0RQnRYMk7uVgRvDKSKt6OxY+WJ2AuxhL6zB99Pm+Lm70ZGfAbm9k8f7FfU3T8Xz5p3jnP3aBwAtbu4ErTYh9IVpJ++EEIG7QohiimDQWH+4AOkJGgB+HXmWY5sDTdyqsLj6LZw3m++jeiwZGo0cmLR4S7FptgHuPtXOAk3Ezn39QWAYl3sAxxccp27R+OwdbFg0Bo/3tzUXIp9IUQ2KfiFEMXSui8vcm5vFPZlLeg1JmtquHmD9nHvRqKRkxmXoiisX3CR6UG7SUvW0apPJebv7oCji5Wxo700XaqOc1+dx6A34PVePZp80pi2PwUaO1a+iQtLyf6485x6tPqoJuMudaTeKx7FZvyFECJvSMEvhCh2bp6N46fJWbPNfLCsGUPmNsSve3lSErR80juU9FSdkRMahzZTz8J3D/PtR8cxGBQGTvNmwi8tMbcsHsO5/h4cwvFPTnD6szOoNWrqDPXEpJjc2/9KjEzj576HmFt7BzE3kgCwKm1O13leWNqZGTmdEKIwkoJfCFGsZKTpmPv6PnRaA53fqUHTLlmtnR/90Bz36rbcPveIhcMOUcDDl4wuMTadiR3/Zsfya5hZaJi4yp/XpngXi5bg9Lh0ALxG1cO+uj2VulU0bqB8YjAoHPrmBnNr7eDs6nDUarh/Nt7YsYQQRYAU/EKIYuWHSSe5czEe9+q2vD2vYfZ2azszpqxpjYW1Cbt/u8Xmr68YMWXBunslntF+2zi3N4rSLpZ8FtoR/1crGTvWSzNoDRyZcpR1LTeQEpmCa3MXgkK7U7pWaWNHy3OR5+NZ0jyEdSNOkp6opVYnF8ZeyOq+I4QQOZGCXwhRbJz8+z4bF11GY6Ji/M8tsbA2fez1ip4OjFneDIBvPzzGxYPRxohZoE7+fZ/3m20j8mYSVeuXZtHhLtRo5GTsWHlDDY+uPCIzIZMHJx9mbSpmU69q0/VsnXCWLxr8xZ0jsdi6WPD6aj/e2tKC0hVlUK4QIneKX+dGIUSJlBibzudvHQDgtaneVG+YVdTqM/SEbQvDysUaF99y+L9aiStHY1i/4CIz++7hq+NdKV2u6A9Y/Tebv77MNx8cw6BXaBZUgXE/NX/iTVBRFBF6j0dXHlH33Tq0+tqfpPBkyvqUMXasfKHWqLjyZySKXqHZ8Kp0nFVX+umLQuvzzz9n586dODg4UKZMGQICAlCr1axYsQKDwYCnpyfTpk3D3Nyc5s2b0759e06ePIlGo2HBggV4eHhw7tw5Zs+eTXp6Og4ODsyYMQMHBwe6devGrFmz8PX15a233iIgIIABAwYY+5aLjFwV/AsXLmTnzp2oVCp69erF4MGDAdBqtQwZMoThw4fTpEmTfA0qhBBPoygKC989TOz9VDz9ytJnfNbS6Q9OPuDenvucmn8a15auuPiWA2DInAbcOBXDuX3RzOq7l7l/t8fEtPi0DOt1Br4Zc4wt/+m21HdCPQYF10etLvr99ZPCk/lr4N8oegXnJs6U9SmDZRlLY8fKU4lRaSRFpePm7YDGVE3fHxqjzzRQoWkxeTIj8s2pd3997HOXznVx6VKPsJ8PE3f4FqV9K1PxdV8it54jctv5x/b1+WYAGbHJXJy8CQDPmd0xdyzFqXd/xeebnAvr3bt3c/LkSbZu3UpaWhpBQUH4+vqybds2fv/9d8zNzfn888/5/vvvGT58OA8fPsTX15cpU6YwZ84cfv31V8aMGcPkyZNZunQprq6u7N+/nylTpvDTTz8xa9Yspk+fzuuvv45KpZJi/znlWPAfO3aMI0eOsHnzZnQ6HZ06dcLf3x+AiRMncunSpXwPKYQQz7Jr5U0OrL+DlY0p41a0QKNRo0vXEfJWKKlRqZjZmVGpW0UURUGlUqExUTNxVStGNNrChQPRLB9/gmFfNDb2beSJ5PgMZvXdy6ld9zE1U/PBd80IHFDF2LFeWtrDNABsPErRYJwPBr0ha+XcYsRgUDj63U22TjhHqTLmfHSuA6YWGtx9it+YBFH8HDp0iI4dO2JmZoaZmRlt2rRBURTu3LlD7969gayG4tq1a2cf06JFCwCqVavGiRMnCAsLIzw8nHfffTd7n+TkZAB8fX1p2rQpX3zxBTt27CjAOyseciz4GzduzM8//4yJiQnR0dHo9XqsrKz4+eefGTJkCCtWrHjqsYmJiSQmPj7ndVRU1MunFkKI/4i8lcRXo48AMHxhE8pVsgEgIy4D++r2WDhZ0OPvbk/MRuPgbMnkP1oxNuBPNiy6RI3GTrTuW7nA8+elezcSmdp9FxFXE7Eva8G0dQHU9i1r7FgvLeZ8LH+/vgvbirZ0XNMer9H1jB0pz0Wej2fNsBPcORwLQKVmTqQnajG10Bg5mShKntYSX/F1Xyq+7pv9uUuXerh0efLnyNyx1BPnyE3rPoBarcZgMDy2Ta/X07FjRyZPngxASkoKer3+n+uZZy2Ip1KpUBQFg8GAu7s7mzZtyj4+JiYGyHqSe/v2bSwtLbl9+zZlyxb9320FKVfPsE1NTVm0aBGdO3fG19cXZ2dnxo0bR5s2bZ553IoVKwgMDHzsnzyCEULkFb3OwPw39pOWrKPFKxVoM/CflmxrV2s6rm5P5w0dQYEL311kU/staFO02fvU9i2b3bL/5duHuH3+UYHfQ145uyeS9/y2EXE1kUp1HVh0uEuxKPYBrJwtUfQKikFBm6TN+YAiJDNVx7aPz2UNyj0ci005C17/I2tQrk1ZC2PHEyLX/Pz8+Ouvv8jMzCQ5OZk9e/aQlJTE33//TWxsLIqiMH369Gc2FFeuXJmEhAROnDgBwLp16/joo48A+O2337CysuLrr79mypQppKSkPPU84km5HrQ7evRohg4dyrBhw1i9ejV9+vTJ8ZhBgwYRFBT02LaoqCgp+oUQeeKPeee5eOgBjq5WjP7aN7sV/9rv14m/Fk/dd+tk9+8O2xJGzNkYbm26TY3+1bPP0WVYDS4ffUjILzcJfnU3i490oZS9uVHu50Xt+P4ai0ccRq9TaNLZnQm/+GNlU7QH52YmZrJ/zEFqDqyOm78bnTd0xKa8DepiNNYi/EQcP/c5RNztFFQq8Hu3Kp0+lUG5omhq1aoVp0+fJigoCDs7O8qWLUvlypUZOXIkgwYNwmAwUKtWLd5+++2nnsPMzIyFCxcya9YsMjIyKFWqFHPnziU8PJxvvvmGNWvW4OLiQvPmzZk/fz7Tp08vuBss4nIs+G/evElmZia1atXC0tKSdu3acfXq1Vyd3NbWFltb25cOKYQQ/9/V4zH8EnwGgA+/b46tY1ZrqEFn4PRnZ0iOSMbJy4nK3bPmm/cZ50NmYibl2z0+b7lKpWL0177cPh/HrbOPmP/GAaatDygSA1z1egPLx59k/YKLAPQa48mbsxugKQZTU177/TphW8OIuxTHK/uDsKtiZ+xIec6mnAUpDzNwqWvHq982lEG5okg7ffo0FStWZNu2bWi1Wvr06UPlypWpWbMmr7766hP7/28t2bNnT3r27AlA/fr1Wbt27RP7HzhwIPvjqVOn5sMdFG85/lWIiIhg8uTJZGZmkpmZSUhICA0aNCiIbEII8a/SU7TMG7QPvU4haHRtGrR1zX5NbaIm4LtW1HqjJpW6Vsze7trchYqdKqDolewBoP9lYWXC1DUBlHIw48jWcH6fc66gbuWFpSRmMr3HbtYvuIiJqZox3zVj6LxGRbrYVxSFu3/dxaA3UPutWni+XZv2v7UtNnPrGwwKR5bfJDEy6/vP3t2Kd0Nb88GJdlLsiyKvUqVKbN26lW7dutGzZ086d+5MzZo1jR1L/EeOLfz+/v6cO3eOHj16oNFoaNeuHZ07dy6IbEII8a+WjTtBxLVEKnja8+anPtnbDVoD6XHplKlfhjL1n5yX/d6+++wZvhcXv3IELGv92GsulW2Y8HNLpnTbxc/TTlO9gRMN27vl+728iKiwJKb1CCHsQjw2pc2ZuqY19fzLGTvWSzsefILz31zA6/16NJzQgKbBxWe656iLCawZdoKwgzF49fbg9d/9APBoIDPwiOLB3t6e77//3tgxxFPkqtlk1KhRbN++nS1btjBq1KjHXlu5cqXMwS+EKDBHtoaz7durmJqpGf9zS8ws/mm3uLH2Jqsbr+XcV+f/9Vj7qnZkPMogKSwJg87wxOuNOrrz2hRvFAXmDNxHVFhSvt3Hi7p4MJrRvtsIuxCPR007Fh3uXOSL/f/+X7gHumNqbYJd5eLTfUebpmP7pHN8Xn8nYQdjsClnQd0gd2PHEkKUMLLSrhCiyHgUncYXQw8C8MZMH6p4Pd46Gnc5Dn2GHivnf18519rVmp6hPbCrZvfENJ3/1X+yF1dPxHBsewSf9N7DF3s7Ym5ZOH5V/r3yBgvfOYQ200CDdq5MWtUK6yI+wPP25tucmH2SThs64drchT4nemPuULQGTT/N1b+jWPfuCWJv/WdQ7rAqdPq0Hpb2Rfv/TAhR9BSPjpFCiGJPURS+fPsgCQ/T8WpVjp7vez6xT9PgJgSF9qByj0pPPY99dXsyEzO59ONlDNonW/nVahXjVrTApbINN07FsmTUERRFydN7eV4Gg8IPE0/y2eADaDMNdB9Zi082tynyxb6iKFxZeZXE20lc++0aQLEp9rdOOMuy9nuJvZVCuTp2jDwQyCtfN5RiXwhhFFLwCyGKhO3fXePotghK2Zvx0Y8tHptFx6A3cHLuKZLCkyldywG1ybN/tf3Z+y8Of3yEOzvu/OvrNg7mTFnTGnNLDX/9dIPt313L03t5HmnJWj55NZQ/5p1HrVExcklThi9ogiaHeyzMku8lc23VdVQqFa2+9qfZPF+8P/Aydqw8VbV1WUwtNXSeXY8xJ9tR0VcG5QohjKfo/sUQQpQY4VcT+PbDYwCM+tqXsh7Wj71+e9Ntznx5lp39/spVa3y1PlVxbeGCZVnLp+5Txas07y3NGlj59XtHuXL04UvcwYt5EJ7Ch612cGjTXUrZmzFrW1u6Divas15kJmWyqf0W9o85wL1997EsY0nN12s+tYtVURF1KYE17xxH/5/xCDXbuzDpdhcCxtdCU4zWDhBCFE2Fo2OqEEI8hU5rYN6gfWSk6QkcUJlWvZ/sruNYz4kqr1TGtYVrrgrHWoNrUvvNWjnuFzigCleOPWTzV1f4pE8oXx3riv0z3iTkpSvHHjKj527iotJwq2bLjI2BeNQouoNZDXoD2mQt5nbm1H6rFtHHHuDoWfRnqNGm6dg16zKh86+g1xpwqWtP85HVAGSlXCFEoSEFvxCiUPv1kzNcOxFL2fLWjFjU9InXDVoD9lXtaPWVf67PqVKpSHuYxsXll0AFDSc8fW2Rt+c34sapOC4dfsCn/fcy+892+d6dZs8ft/j8rYNkpuvxalWOyatbY1u66PZtT4tJZ8/wvRi0BjquaY/3e1ndd1RFYHGzZ7n6dxTrhp8k9mYyAL5vV6HBaxWMnEoIIZ4kBb8QotC6eDCa3+ecR6WCcT+1eGKQqmJQ2NRxC46epWkyvfFzDfhMj03n7MJzmFiZUG9EXcxs/n0wpamZhsl/tGJEo82c3RPFj5NOMWRuw5e6r6dRFIWVM87w68yzAHQaWp0Ri5piUsS7hCgGA4+uPELRKySGJWFfteg+qQBIepDO5g/PcOrXrDEg5Txt6fVtIyr5ST99UXIdPXqUpUuXYmpqSkREBAEBAVhZWbFr1y4Ali1bxp9//smmTZtIS0vD1NSUzz//nMqVKxMQEECHDh04dOgQAJ9++im1a9c25u0UO1LwCyEKpZTETOa9sR+DQaHPuLrUbfnkXPMx52J5dPkRGY8yMLF6vl9nDjUd8BlXH5dmLpiWMn3mvo6uVkxa1YpxbXey5vML1GjiRIueFZ/rejnJSNPx2ZsH2LcmDLVaxdufNaLHqFpFtm+7oihc+PYipWuXxq2lK21XBGJVzgprF+ucDy7EtGk6PvfeSVJUOiYWGtpN9cR/THVMzDTGjiYEd2cseOprdv5NsWvVlLszFjz2cW72Lz/t/Vxd/+zZs2zbtg17e3v8/PwYP34869ev5+OPP2bbtm2EhoaycuVKLCwsWLhwIb/++itTpkwBwMrKio0bN7J7927Gjx/Pli1bnuPORU6k4BdCFEpLxxwj6nYyVeuXZuB073/dp4y3E70O9iQ5IgWN+fMXXPXHZJ03IyEDM1uzZxbXdVuWY+i8hnz74XE+f/MAFWrZU76W/XNf89/ERqYyPWg3107EYGVjysTf/GnUsWgvznRr022OTT+OhZMFrx7p9a8rHxdFppYm+L5ThbDDsbzyVQOcqpQydiQhCo3q1avj4uICgIODA76+vgC4urqSmJjI559/zrZt2wgLC2P//v3UqvXPWKrevXsDEBAQwIQJE4iLi6N06aI/zqewkIJfCFHo7F8fxl8/3cDMQsP4n1ti+i+tpw9PPcS0lCn21e2xrWj7wtc6OfcUF5ZepO3Pgbi2cH3mvkGja3Pl6EP2rg4j+NVQFh3ugpXNs58O5OTG6Vim9Qgh5l4q5SqVYsbGQCp6OrzUOY0p/no8tpVtqdS1Ine23aFyUGXMcniCUphp0/WEfHqJ9AQtPRb6ANBmUm3UGlWRffoiiq/ctMT/7z7Pu39OTE0f/1nXaP753R0ZGUmfPn147bXXaNmyJU5OTly+fDn7dROTf0pSg8Hw2LHi5RXtjqFCiGIn9n4qC4cdBmDI3Ib/2oquKAoHxx9inf8GwneFv9T11CZqdGk6oo5G57ivSqXig2XNqOBpT/iVBD5/68BLLcp1YMMdxvjvIOZeKp7NyrLwUJciXezfXH+TjW02c/qzM6g1agK+a03FTkV3EOu1kGg+q/cnf8+8xIGvbhB7K2twrsZELcW+EM/p/PnzVKhQgTfeeIO6deuya9cu9Hp99uvbtm0D4O+//6ZKlSrY2RXtsT6FjbTwCyEKDYNB4bM3D5AUl0HD9m50G/7vc87r0/SUqV+GjEeZuDRzealr1nqjJpW6VcS+mn2u9rcsZcq0tQGMbLKFA+vvsPbzi7z6UZ3nuqaiKPw+5zw/TTkFQNvXqzD6Gz/MXqBbUmFiWdYKg9ZAWkwaiqIU2aI4+WE6mz86w8mVWYNynWvb8uq3DXGsLN13hHhRzZs358qVK3Tq1AlFUWjUqBHXr1/Pfv3UqVOsXbsWS0tL5syZY8SkxZNKKeA14yMiIggMDCQkJAR396LdR1UIkbc2Lr7ENx8cw9bRnKVnuuPoYvXM/fUZ+hfqu/9vkiOSSbiZgJu/W672P7T5LjN67katVjH7z3Z4B+TujUdmuo4F7xwi5NdbqFTw5qcNePWjOkW2OE64mcChj4/QclELrMtZEXf5EaVrFc2nFIqicOzH22wdd5bUuMysQblTauP/YQ0ZlCtEPgoICODnn3+WujAfSZceIUShEHbxEcsnnADg/W/9nlrsR+yOIOSt3cRejMuzYj/+WjyrG69lz4h96DP0OR8A+HUrT98J9TAYFD7tv4cH4Sk5X+dBGuPb/kXIr7ewsDZh6roAeo+tW2SLfYDjM09wf999TszK+r8rqsU+wNW/olg95DipcZlUb+vM2PMdCPy4thT7QogiT7r0CCGMLjNDz9zX96HNMNDhzWo06/H0ft/nlpwn8lAUZRuWzbOVWu2q2eFY1xHbSjZkJmZiWSZ3q+m+PsOb6ydjOPn3fWb2DuWzPR2f2i3n9vlHTOuxi+g7KZTxsGbGxkCqeBXNGSj0mXoiD0TiHuBOs3l+WJa1otHkpy9eVphp0/WYmGf1ya/RrhwNBlagZnsX6vcrX6TfiAlRlOzevdvYEYo96dIjhDC65eNPsObzC7hUseGbk92wfMasLqnRqVxcfgnv970wtc672V8MOgPqF1hBNzE2nZGNtxB9J4VOb1fnva/9ntjnyNZw5ry2l7RkHTUbOzFtfQClyz27u1JhZdAZ2N5zB9HHH9D+t3a4t85dF6jC6FpINOuGn6DNxNo0GlTJ2HGEECLf5Oqv25IlS+jcuTOdO3dm3rx5AJw+fZrevXvTuXNnxowZQ2ZmZr4GFUIUT2dCI1n7xQXUGhXjV7R4arGvKAqPrjzCytmKRpMa5mmxD1mz9cRdiuPARweJOR+b6+NsHS2Ysro1puZqti+7xs6f/hmEpigKa7+4wPSgENKSdbTqW4n5uzsU6WJfbaLGrZUb1q7WmNn9++rEhV3yw3RWvXGUb9vuIeZ6Msd+vP1Ssy0JIURhl2PBf+jQIQ4cOMCGDRvYuHEjFy9eZMOGDYwaNYrg4ODsaZTWrl2b72GFEMVL0qMM5g/ej6JA/4n1qNW07FP3jTwYyfpWG9k7cl++5bm26jpXf7nG5R8v57zz/6jWwIlRS7IWmFk84jDXT8agzdSz4J1DfDfuBIoCg2bUZ8LKlphZFL2elAa9gVPzTvNnn50YdAa83/ciKKQ7ZX2K1mJaWYNybzG31g5O/ByGibmajjPr8s5f/tJ9R7wQJTPd2BGEyJUc//KUKVOGCRMmYGaW1ZJTpUoV7t27h7e3NzVrZk2ZN3ny5MfmUhVCiNxYMuoIMRGp1GzsRL+JXs/cN+lOMiZWJthVy7+5mWu9URNFUag16N+nA32W9oOrceXYQ7Z/d41PeodStnwpzu+PxtxSw9ifWtDilYp5H7iAZDzK5MovV0l7mEbUkShcm7tibm9u7FjPJfpKIuvePcHNvQ8BqNbGmV5fN8Cpqo2Rk4mixpCWQua5A2ScDEWlMcH2nZnGjiREjp6rD39YWBj9+vVj8ODB3LhxA61Wy61bt/Dx8WHChAmYmz/+ByAxMZHExMTHtkVFRTFgwADpwy9ECbd71S3mDtyHhbUJX5/ohlu1nFfLTY9NR22uKZCVW3XpOkyeszU+M0PPh/47uHYiBgBHVyumrw+gekOn/IiY76KPRWPQGnBp5kLUkSj0mQbcWj57NeLCatMHp9m38BqlypjT7QtvfPpXkFZ98dzSj/1N6tYfQKcFQGVuhd1HS1Bbv/hq3yXZwIEDGTlyJE2aNDF2lGIv13/Nrl+/zjvvvMO4ceOIioriwIED/PHHH7i6ujJp0iSWLVvGqFGjHjtmxYoVLFmyJM9DCyGKtug7ySwZmbWa7rAvGudY7B+fdQKPNh6Ua+Kc79nSY9PZ/+FBEq7H88r+nqjUuS8Kzcw1TFnTmnFt/qS0syUTV/nj5Gadj2nzT/SJB2wL2oG5vTlBu7tTrmk5Y0d6bnF3UihdIevr3z64Dio1tJlUG6vSRevphDAefcx9Mk7twaJJe9R2jmjKuoNeh0mVupg3aI1Z7SaozOT7SRR+uSr4T548yejRo5k4cSKdO3dmzZo1eHl54eHhAUDHjh355Zdfnjhu0KBBBAUFPbbtvy38QoiSSa838Nng/aQkaPHt5kGHN6s9c/+oo9GcW3yeKyuu0vd07zwfrPv/mdmbEXchjtToVOIuxeFYx/G5ji/rYc2PV3oW2dbjzKRMTKxMKFPfCdcWLpSuVRqL0hbGjvVckmMy2DL2DKd/u8sHJ9viUsceCxtTun1e39jRRBGifxBBwoL3AVCZW2LpH4RJhZrYjf0ajX3RGr9SEI4ePcr8+fMxGAy4ublhZWXF9evX0ev1DB06lC5dupCZmcmkSZO4cOECbm5uPHr0KPvYJUuWsHLlSgAmTJhA48aN6dmzJz/99BOrVq1Co9HQunVrxo4dS0xMDFOnTiUqKgqVSsWHH36In9+TM6SJf+RY8EdGRjJixAi+/PJLfH2zBqU1b96cxYsXExkZiYuLC6GhoXh6ej5xrK2tLba28phLCPGPdV9c5Ny+aBycLXj/22Y5FsYO1e2pP8YbE2uTfC/2AdQaNa2+aYlNBRusyr7YbDpFtdiPvRhHyFu7qRJUmQbjfWi3si1q06KzPqOiKJz4OYzNH50hNTYTE3M1907H41LH3tjRRCGnKAq6m+fJOLUHjbMHlv5BqMu4YVK+OuoybphWqQtk/WwX5mI/8bupj31u7tMa8watSdu7Ae2101h1HoyJayWSV32BITke26HBGJIekfz7l48dZ+EfhFn1+qRs/RF95G1shwbn6vphYWGEhoby7bffUrZsWebOnUtycjJ9+/bFy8uLv/76C4AdO3YQFhZGt27dnnm+c+fO8dtvv7Fu3TosLS0ZMmQIFy5c4Pvvv+eVV14hMDCQBw8e0L9/fzZu3EipUqWe46tVsuRY8H///fdkZGQwZ86c7G19+/YlODiYYcOGkZGRQa1atRg/fny+BhVCFH03TseyYuppAD5c3hz7Ms9uOdamaDGzN8NnXMG2zDo3yuo6FHshFgtHC6xdima3nOeV8SiD5LvJhIdE4P2+V56tZFwQHlxNZO27J7m55wEA1QKdeeXrBpSpJoNyxdMp6amoLKxQqVSk7vwV/b2bqB3KYtGyByqVCpt3ZhXZN/DGUKlSJWxsbDh06BDp6emsW7cOgNTUVK5fv86xY8fo06cPABUrVqR+/Wf/bj9+/DitW7fGxibr5/inn34CsmaQvHXrFosWLQJAp9MRHh5OrVq18unOir4cC/7JkyczefLkf32tVatWeZ1HCFFMZaTpmPv6PnRaA12H16RRx5wH7e8eGoo2RUfzz5thXzX/Zuf5N6e/OMOpeaepO7wOjac2KtBrFyRdqo4Ts09Sd0RdXJu70PbnQFyauRSZYl+vNbDr00uEzL6MPtOAtZM53b/wxmeADMoV/07JSCPzwmEyTu5BHxuJ/bilqDQaLJp3Rf/wHuY+rbK/d4ra99DTWuIt/YOw9P+ni3WpfmOyP1bbODz1OOsug5/r+hYWWY04BoOB+fPnZ/f+iImJwc7OjtWrVz+25oWJSVYZqlKpHtuu1WqzX//f/4Po6GgsLS0xGAysWLECe3t7AB48eICj4/N1vyxpis6zWiFEkfb9xye5ezkBj5p2DJnTMMf902LSeXg6htjzsVgYYZCleys3TG1Mn3umnqLm6IzjXPzuEvs/OACARxsPTCyLzj2r1HDlz0j0mQYaD67E+MsdafBaxSJXqIn8p711keS1S3g0eygp675GF3YJJT0FffRdAMy9mmPVpg+a0vk/OUBx17RpU1atWgVkFePdunUjMjISX19ftmzZgsFg4N69e5w6dQoABwcHwsPDycjIID4+npMnTwLQsGFD9u7dS0pKCjqdjg8//JALFy7QtGlTfvvtNwBu3LhB165dSUtLM87NFhFF57e6EKLIOrHzHpuWXEZjomL8zy2xsMr5V4+lkwW9j79K7NkYowwaLeNThv5n+2KSi6xFUfy1eOyr2+PzkTcJN+JpPDXnN2GFRXJMBo/upODRoDRqjZre3zUiNTaTKv5PX7hNlEyG9BTUFlld8tJ2r0F36wIAJhVqZs2yU8cXlUXRXPm6MBs5ciTTp0+nS5cu6PV6xo4dS/ny5enfvz/Xr1+nY8eOuLm5Ub16dQCqVauGv78/nTt3xs3NjQYNGgDg6enJa6+9Rt++fTEYDLRt2xY/Pz+qVKnC1KlT6dq1KwDz5s2T/vs5eK55+PNCREQEgYGBMg+/ECVEQkw6w7w3EReVxpuzfOgzvl6Ox8RdfkTKvWTcA92N2lKrKAr3990nIvQejac1KhatxoqicHT6cS4uu0j739rh3trN2JFy7X8H5ZpZmTDuYgfMC2BNBlH0aG+eJy10HfrIMOwnLENlakbmpWPoIm5i3qAVGkcXY0cUokBJlx4hRL5RFIWF7x4mLiqNOs2d6fVRnVwdd3LuKf56bRcXvr2YzwmfTZ+uJ3TYXi4svcjD0zFGzZJXVCoV5nZmqE3UpNxPMXacXHtwNZFvAvfw++BjpMZm4lStFOkJWmPHEoWEoihowy6jZGR16zCkJKG7dQFFm4ku8jYAZrUbY9WunxT7okQqns+qhRCFwl8rbnBwwx2sbE0Z+1NzNJqc2xgURaFcU2fiLsZR9ZUqBZDy6UwsTfAaVRddhh6b8kV7tpeI0Htc/eUqrb9thff7XlToWIHStRyMHStHugw9u+deYdenl9BnGrByNKP75940GCj99EUWfWwUST/NxBAbhXWvkZj7tMKsVkOUoHcwq+uX3aVHiJJMCn4hRL64fzORb94/CsDIxU0pVzH3BXPdYXWo87bnc61ym1/qDq+b/bFiUApFpuelS9Ox/4MDpEalcmP1Dar3r14kiv3I8/Gs6H2Ih1eTAGj0RiW6zPOilJOsbFqSKdoMMi8dR2VuiVnNBqjtnVDS01DZlgaDHgCVqRkWjdoaOakQhYcU/EKIPKfXGZg3aD9pyTr8e1ckoH/lXB0XdzmOfaP3U/+j+lRoXz6fU+Ze5KEoTsw+Sfl2HniNynkMQmGR9jCN5PBkyviUodXX/kQdiaJa32evbFyY2JSzIOVhBmWq29BraUOqtpJBuSWVoijow6+TcTKUzHMHUTJS0XhUw6xmA1QaE2zf+QR1aWdU6qIxnawQBU0KfiFEnvt9znkuH3mIk7sVo77yzXXXi0vfXyb2fBz3990vVAW/Pl3Hg+MP0KVoqTeybpHoSpJ0N4mt3bdjyDTQY1c3XPzK4eJXztixnklRFE7+cofKLcpQuqI1pcpY8M5f/pTztMOkiKwLIPKeISGWxO9nYIi5n71N414V8/r+KIqStfqtk6sREwpR+EnBL4TIU+f3RfHLJ2cAGPtDC2wcct/9ounMJpSuXZoKHQpPsQ/g1sqNlgubU7FL4e83rigKikHB2s0a++r26NN0UMgzAzy8nsTad09wY/cDanVy4a0tLVCpVLj7lDZ2NFHAFG0mmZdPoNJoMPNsgsrGAfQ6VDb2mHv7Y+bTChNnD2PHFKJIkYJfCJEn0lO0rJxxhvULL2HQK7zygSfeAbmfDSP6xAPK1Hei9puFb2l0lVpFtT7VUAwKD8/EUMbbydiR/lVGQgb7PziAQw0HGoz3IWBZK0ytTFGbFt4J2XQZekLnZQ3K1WVkDcr1elWKuZIscfl09OHX0JT1wLR2Y1RqNTZvTkFtXxaVRp70CPEiCu9fASFEkXFi5z3e9trE2i8uggI936vN4Fk+uT4+/kYC27ptZ1PbzRi0hnxM+uIUg8LGdpvZ3GELCTcTjB3nX8VdesTdP8O59MNl0mPTMbczL9TF/q39D/nC5y/+nHYBXYaBRm9UZPzlTjQaVKnQP0kRecOQ9Ii0/ZtIWvEp/10WyKx2IzSulTFv3BYMWb8PNI4uUuwXckePHmXgwIFMmjSJ8+fPP3W/RYsWceLEiQJMljcGDhzI0aNH8+383bt3z7dzg7TwCyFeQvyDNJZ+eJzQVbcAqOJdmveX+lG94fO1gKdGpWLtak2ZBmUKbYGqUqtwqudIRlw6SeHJ2FWxM3YkIKsLz43VN3D1d8PFtxzNP29GOV9nLBwLfnXi5/H3zIv8OTVr1VMZlFuyKDotqNWo1Boyzx0ibcdKAHRhlzGtVBuLFt2w9A8yckrxombNmvXM148fP06TJk0KKE3RsWnTpnw9vxT8QojnpigKf/98g2VjT5AUl4G5pYaB0+oT9F5tTF6gYHdt7sKrh19Bm1q4F1JqPLURpvP8UJsUnjclF5dd4ui0Yzg3dabTug5U71c0ZuGp4l8WE3M1ARNqETihlgzKLeYURUF//zYZp0LJPLOfUn3fx7SaN2beLdDevoi5TytMylcHkJl2iriBAwcycuRIKlSowEcffURqaipqtZrJkycTFhbGhQsXmDx5MkuWLKFGjRr/eo579+7x8ccfExcXh4WFBTNnzqRmzZqsW7eOH3/8EZVKhaenJ1OmTMHa2ppmzZoRGBjIuXPncHJy4pVXXmHlypVERUUxZ84cGjduzMCBA6lZsyYnTpwgIyODiRMn0rx5cyZMmEB8fDx37txh7NixODk5MXv2bNLT03FwcGDGjBl4eGR1M1y7di1z5swhMTGRSZMmERAQQExMDFOnTiUqKgqVSsWHH36In58fixcvJjo6mjt37nDv3j1effVV3n33Xa5cucLUqVPR6XSYm5sze/ZsKlasSI0aNbh69SppaWlMnjyZq1evolKpeOutt+jRowfr169n//79JCQkEB4eTrNmzZg+fXqu/1+k4BdCPJd7NxJZ9O5hzoRGAuDTxpVRXzXFtYrtC53v1LzTlCpfiqqvVMHcrnDPr25ub44+U8/N9TcxLWVK+XbGG1yckZCBuZ05VXpW5vJPl6nRrxrqXCxsZiwPryexZ/4Vghb7YGKuoXKLMky+0xWbsoX7SYR4OYbkBDLP7CPjVCj6qLvZ27U3zmNazRu1tS02r40zYsLiRbdj5L9uN+m4BMP1bRhu7HjiNXXVjqirdUa3Y+RjH///45/X2rVradWqFUOGDGHfvn2cPHmSt956i3Xr1jFy5MinFvsAM2bMoH379gwYMIC9e/fyzTffMHz4cJYuXcrq1auzC/ElS5Ywfvx4YmJiaNmyJcHBwQwcOJBdu3bx22+/sWHDBlasWEHjxo0BSE5OZsOGDVy+fJmhQ4eye/duAOzt7Vm6dCmZmZn06tWLpUuX4urqyv79+5kyZQo//fQTADY2NmzYsIHQ0FCWLFlCQEAAs2bN4pVXXiEwMJAHDx7Qv39/Nm7cCMDVq1f59ddfSUpKok2bNgwYMIAVK1YwePBgOnbsyIYNGzhz5gwVK1bMvvfFixfj4ODA1q1biYuL49VXX6VmzZoAnD59mq1bt6LRaOjQoQP9+vV75tfxf0nBL4TIFZ3WwLovLvDLJ2fJTNdj62jOO583JnBA5Rfub510J4kzC88CUM63HLYVCv9qtmHb77Bn+D5K13bAo62HUfqa3958mwMfHaL1sla4t3LjlX09C21XKF3mfwblzsoalFu6cikCJ2QNzJZiv3hS9DpAhUqjQXvlBKnbVwCgsiyFmXcLzBu0RuNSybghRb7z9fVl1KhRXL58GX9/f1577bVcH3v8+HG++OILAPz9/fH39+eXX36hdevWODhkLRrYp08fPv744+xjWrZsCYCbmxsNGjQAwNXVlcTExOx9evfuDUCtWrUoU6YMV69eBaBevaz1VcLCwggPD+fdd9/NPiY5OTn74zZt2gBQtWpVHj16BMChQ4e4desWixYtAkCn0xEeHg5AkyZNMDMzw9HREXt7e5KSkvD39yc4OJj9+/cTEBBA69atH7v3I0eO8OmnnwJQunRpAgMDOXbsGKVKlaJ+/fqUKlUKAA8PDxIScj+eTAp+IUSOrhx9yIJhh7h9PusXXJuBVXh7fiPsnF6uYLN2s6bFl81JvJ1YJIp9gIodK+DS3IXK3StlrbyrKfiCP/56ApmJmdzZfgf3Vm6Ftti/tf8ha4edIPpy1h/choMq0mRI7hZhE0WPITWJtNB1ZJ7Zh3X3tzGr0xSzun5kXjmJuXdLTGs2QGViauyYxdqzWuLV1TqjrtY5V8e+SIv+/9egQQO2bdvGnj172L59Oxs2bODHH3/M1bEmJv+Up4qicPPmTQyGxyd0UBQFnU6X/bmZmVn2x5qnDPD+3+0GgyH7OhYWFtnb3N3ds/vT6/V6YmJinjj+fxt6DAYDK1aswN7eHoAHDx7g6OjIrl27MDf/56m1SqVCURQ6dOhA/fr1CQ0N5aeffmLPnj3MnDnzsfv6//ep12etIP1v58utXP2VWLJkCZ07d6Zz587Mmzcve7tWq2XQoEH5OmpZiMLmwncXOf/1ee7svMuFZReNHSdfpSZp+fr9o7zffBu3zz/CpbINs/9sx9gfW7x0sZ+RkIFiUKjWuyoNxud+Rp/npU3TEXs7Oecdc0ljrqHT2g7UHFijQLvQJN9L5siUoxh0Brw/8CLw+9b4zfUtsOs/j9S4DFYPPc5X/ruJvpyIU7VSvBvSin4/NqGUU+HutiWejyElEf2DewCoTMzIPLEbJSUR7c2sWVpU5pbYvDYOszpNpdgvYebNm8fmzZsJCgpi6tSpXLp0Ccgqmv9bwD5Nw4YN2bZtG5DVgj5lyhQaN27M7t27iY+PB2D16tXPPfh3+/btAJw/f57ExESqV6/+2OuVK1cmISEhexahdevW8dFHHz3znE2bNuW3334D4MaNG3Tt2pW0tLSn7v/+++9z/vx5+vbty3vvvZf9dfnf861duxaAuLg4QkJCsrskvYwcW/gPHTrEgQMH2LBhAyqViiFDhvD3339TpUoVJk6c+ERQIYozRVE4//UFUiNTs7c51LDHzd/NiKnyx5Et4SwedZiYiFTUGhWvjvFkwBRvLKzy5sHgiVknCQ+JoOWC5ri2yJ9VMlPjMljULAR9hoExp9thaWeW80G5dOmHy1z5+QodVrfHqqxVnp333ygGhb8G/M2jK/FYOFng/Z4XFTtXzNdrvii91sCXDf8mLiwFjel/BuV+XAtTCxmIWdykbPiWjFOhmFSqje2bU1GZmWPVfSgaJxc0blWMHU8Y2cCBA/nwww9Zv349Go2GuXPnAtCiRQumTZvG3Llz8fH598aeqVOnMnnyZH777TcsLS2ZOXMmVatW5Z133mHgwIFotVo8PT2ZMWPGc2UKDw8nKChrBqgvv/zyiScBZmZmLFy4kFmzZpGRkUGpUqWycz/N5MmTmTp1Kl27dgWy3uj8t9vNvxk2bBiTJk3iq6++wtTU9ImBtyNGjGD69Ol07doVvV7PsGHD8PT0zO5+9KJUSg7PA65fv05KSgre3t4ABAcHU6FCBaKjo/Hx8WHFihWMHDky1++yIiIiCAwMJCQkBHd395cKL0RBM+gNhG29Q+ShKOwq2xJ/Ix6/T33/tUtFYlwG67+8yOWjD2nZqyLtB1d7oRlsClpsZCrffHCM/WvDAKje0JH3l/pRxdsxz65h0BnY1GELcRfjeGVfEPbV7PPs3P+l1xpY1mEvN0If0HpcTbrM8crT8+8aHMKdHXdpNKUh9UbUzdNz/5dBbyD5bjK2lWyJPBTF+a/P03Jhi0I/5WboZ1e4tPU+vb5piHOtFxvMLQofXXQ4upvnsfDrBEDKlh/IOPInptW9KfXaeJknXxRq/509qKROCZpjU121av9M8RYWFsaOHTtYtWpV9ojiFStWPPXYxMTExwZLAERFRb1gVCGMT61RU7l7paz+2//zXvnCtxep0KkCNh6lSHqUVehvXHyZ1KSsaSbP7I5kzWcXeG2qN637VUJTCGdTMRgU/vzhOsvHHyclQYuFtQmDguvTfWStPM+rNlHT469uPDj1MF+KfUVRWDf8JDdCH2BTzoJmI7J+j+l1BtQaVZ4MtPV6z4uqr1alfLv8WRU2MymTkLdCibsYR49d3XDxK4eLX7l8udbL0GXq2fPZVR6FpfDqskYA+H9QHf8xNVCrZfGsos6Qlkzm2QNknNqDPuIGAKbV66NxcsHSvweWLbujtsu7xgBRcsydO5dDhw49sb1OnTo5zuUvnl+un81fv36dd955h3Hjxj02fdCzrFixgiVLXn7ghxCFRciQUCzLWFB/jDeWZSwBOL/0AsemH+fqqmsktajApq8uk5qYVeg3aOuKb7fybFxyiYiricx/Yz9/zDvP69O9aR5UodCsJnr3SjwLhx3mwoFoABp3dGfkkqY4V3j6Y8kXlRKZQti2O9QYUB3nhvmz0NK+L69x9PtbmFhoeHNTcxw8rIi9lcyvA4/Q8LWK+L1b9aWvUcbbiTLeTugz9CRHJ1PKLW+/Vib/03Uq5X4K1i7WeXr+vHDrwH8G5V7KathpProaLnXsC/X0oCL3Urb+SMbRnaDPGhipMrfCrF4zUGf9/6ptSxsznijixo8fX6DXW7lyZYFer7DJVcF/8uRJRo8ezcSJE+nc+ekjvP+/QYMGZfeV+q+oqCgGDBjwfCmFKATSHqYRti0MjZmGRpMbZm9361gB1VcX2HUhmVsHzgFZc9O/NsULz2bOAHQaWp2QX2/yS/AZ7l6KZ2bvPVRr4MigGfVp2N7NaIV/Zoae1XPP8/ucc2gzDTg4W/Dugia07FUx3zKd++o8l5ZfJu7yI1p83izPz39xyz22jD0DQL+fGlO+UVbrY/iJOO4cjuXe6Xgq+5ehXO2XXyn3wckH/D0oBIfq9nRa3/Glz6coChe+vYg2WYvPR/Vp9bU/Bq2+0BX7qY8y2Tb+LEeWZ62w7FS1FK980xCXOvbGDSZeiv7BPTKvncKyeVZfZJWZORj0mFSth3mDAMxqN0JlKoOuhSiKciz4IyMjGTFiBF9++SW+vs83I4StrS22ttJ/UxQPFo4WdN3WhYTr8Zham5KSkMmGhZdYv/AiKQmZgAqfls60aeZI4CePj6jXmKhpN6garfpW5s/vr7Hq03NcPxnL5C678GxWlsGf+FC3ZcF217hwIJqF7x7i7uWseXw7vFWNIXMaYuOQv3/Q3Vq6EX3sAZ5DauX5uSPPx/PrgCMoCnSYUQfv3v8sjOXduzyXd0RyYkUYvw44wntH2rz06q721ezRperIiM9Am6LF1PrlZiGJu/SI48EnUBSFyj0qY1/15d+U5CVFUTj9+102fXCa5AcZWYNyx9ckcGJtGZRbxKUf30XqhqUAmFaug4lrJSx8O2HeuB0aeycjpxNCvKwcC/7vv/+ejIwM5syZk72tb9++9OvXL1+DCVHYKAaFsj5lsK5ix68zz7J+wUWS4zMB8G7tSv+J9bgx7Qhh313kWm0Hqver9sQ5zMw1dBtei3ZvVGPL11f4Y955Lh58wEcBf9KgrStvfOJD9Yb5+8c1OT6DHyadYtu3WSP+3avbMvprX7xaueTrdSHra1i+nQcebd3z5QmCnbsV5Zs6UqqsOW0m137i9aBFPtze/5D7Z+PZPuk83T7zfqnrmdmaERTaA5vypV7qfmIvxmHpZIGjZ2kaT29EKfdSha7YBwg7FMOvA44AULlFGXotlUG5RZFi0KO7eYGMU6FY+HXCxKM6ptW8UFlYYVbHN6tlH1Db2Bs3qBAiz+Q4S09ek1l6RFGkz9Czymc1Whsztt9IJzE+q49+Pf9yDJzqTT3/rNb5a79f59L3l2i7og3Wrjl3w0hJzGTDgkus+/Ji9gDfZj3K8/qM+lT0dMjTe1AUhQMb7vD1e0eJi0zDxFRN73F16fdxXcws8n8NvrSHaWzutJVab9Sk7vA6+dZlSK81oBiUp7be3zkay5LmIRj0Cm/v9KdG25d/spKRkMH11Teo3rcaZjbPN/VneEgEIW/tpoy3Ex3XdkBtUrj6v+sy9ag1qux++auHHqdCU0caDa4kg3KLGEWvR6XRYEiKJ37u22AwYN6oLdZB72S9rtPKXPm5oBgMpN8II+XMJRStjjIDehg7khA5kpV2hchBcnwG6z8+jjY2nfjodBLjVdRr6cyAKd54t368Vbx632pUfaUKalM1kQcjsSxr+cxZaKxtzXhtqjfdRtRk9fwLbP7qMgc33uXQpru07leZ16Z641b15VtQH0ak8NXoIxzenLXcd62mZXj/W788f1PxLDc33CI5PJnoY9F5Oo2loij8OfUCTd+ugoOHFZocpj6t0MSRdtM8+XPqBX5/4ygfnu3w0otBhQ7by73Qe6g1amq/mbuuSga9AbVGjVM9R8ztzLCraoeiVwrVb+XbB7MG5fq9W5Vmw7OeWPX+rpGRU4nnoWSkkXn+EBknQ1GXdqbUq6NQ29hj3qgt6lJ2mPm0yt5Xiv2n08UnknL2EilnLpFy7gqGlKy1WFQW5jj16YLKpBD94BrJ0aNHWbJkSYkfHFtYyXeoEE+R9CiDDQsvsXHxpaxpKlVQr7Ej8z9tnN2i/2/Upmru7bvPzv5/Ucq9FN3/7Iq5/bMLSltHC4bMaUjQe7VZNfscO767xu7fbrHnj9t0eLMa/Sd5Ucb9+Qdu6vUGti69yk+TT5GapMXKxpQ3Zzeg89sFP2Wi59Da2Fe1w8olbxep+mvGRXbNusSZ1XcZd7Ejmly0kAd+XIurf0Vx50gst/Y+oN4rLze1Zo3+1VF0Buyr5a4bTsLNBHa/s4dGkxvi3sqNHiE9sHzJlYvzUuqjTLZNOMuR77IG5R778Ta+w6pKi34RoRgM6G5fIuPUbjIvHAVtBgCqmEgUvQ6VxgTr7kONnLJwU/R6MiMfYO7uQvqdCO6Mn/PY66bOTlh718baqzYgPxei8JOCX4h/sX99GF8MOfif6TUVGjYrS99PGuR6YG3ZBmUoXbs0Ln7lMLXJfauZo4sVIxc1pdcYT36deZZdP99k+3fX+PvnG3R5pyZ9J9TFvqxlrs51+/wjFg47xOWjDwHw616eEYua4ORW8DO+3D8QiZOXI+4BeduN79SqO/wVfBGVWkWPL+vnqtiHrPUU+q9sSnJ0OuUbv/wc4pW6VqRS14q53v/21jDiLsRx5oszuPm7Fppi/7+DcjePOUNSdDoaUzWtx9WkzaTaUuwXAf8t5pWMNJJWzAJdVjdBk4q1MW/QGrM6TVFp5M/+0+gSkjCxs0GfmsatUdNQdDqqfjcXcw9XTErbY17eNbvIN3PJnymFi4Pbt28zdepU4uPjsbKyYtKkSdSrV4+YmBgmTZrE/fv3MTEx4YMPPqBly5YsXryY+/fvc/PmTR49ekSfPn0YMmSIsW+j2JGffCH+n/3rwvi0/14MegXvABd6vVmVc+/vI3zhaeq2zN3Ui6bWpnTZ2AkTKxMUg0J4SAQegbkvdstVtOHD5c3pPbYuK2ecZu/qMDYsusSO76/RY1Rten3o+dTZdDLTdfw26xyr559Hr1NwdLVixKImNOtRIdfXz0vpcensGrQLlamaXvt7Zq9f8LLuHInhjzePAdDtc29qdXJ9ruNLV7CmdIV/3vzoMvQvNWtPemw6F5ZdJONRBs3m+T3xuj5Tz9WVV6k5qCZeo+qhUquo9UbNQrMWQ+ytZNaNOMnVnVmLI1Zq7kSvpQ3zZPpSkb8yzh0k4+hOVFY22AwYi9rSGoumHcDUHHOfVmgcC9+CbYWBotOTdu1WVjedM5fIuHuPyks+wdTJAVMnBwyZWnQxcZi5OlN5STAqdeEaX/NvDFcGPva5yikIlVNPlMhlKAn7/3nB1Al1lS9RUi+j3P0UlV0LVC5vo8SsR4nZ8Ng51DWfr4vO2LFjefvtt2nXrh1nzpzhvffeY+fOnXzyySc0bdqUwYMHEx4eTr9+/di4cSMAFy5c4Pfff8dgMNCzZ098fX3x9PR8oa+B+HdS8AvxP/av/6fY7/dxPQYF1+fOn3cxLWX63HOhm1iZoCgKoe/s4faWMFouakG13s+34JNHDTsm/taKPuNiWTHtNEe3RfD7nHNsXXqFXh/WoceoWliW+ucJwpnQSBYNP8y964moVND13ZoMnumDtd3zDSTNSxnxGTjWdURjaZJnxX7cnRR+6HEAXYYB33eq0GL0kzMi5ZYuQ8+2j89x+8BDRh4IxMTsxYp+g87Aua/OgwLeY7yxLvd416XQYXu4s/0uqQ/SaPhxA7xG1XvhzPnh2I+3ubozCkt7U7rM86Lxm5WlVb+QUhQFfdQdTFwqAqC9fgbd7UuoLKxQMjNQmZlj1WmQcUMWUtqYR9l98VMvXMWQlp79msrcjIzw+5g6OeAx7X00Vv/8vioKxX5hkJKSQkREBO3atQPA29sbOzs7bt26xZEjR5g5cyYAHh4eeHl5cfbsWQC6dOmCtXXW39iAgACOHDkiBX8ek4JfiP84uPEOs/9T7PedkFXsq1QqKnasgMcldzL/s3ru81CpVLj6u3Jv330sSr/4wNAq3o4Eb2rDpcMPWDH1NGdCI/lpyik2Lr5En/F18X+1EiumnWbnj9cBKF/bnveX+uHpZ/zHznaV7ei8sRPalOf/+v2b9EQt33fdT/KDDKq1cSZokc9LtZJr0/Vc2HiPuLAU/px6gS5zvF7oPFbOVjSZ3hjHuo5YOf9TKGQmZmJma4bnEE/iLsRRvt3LjRfIS4/upuBQPuuPbJuJtchM0REwvhY2zoWji5F4UubVU6Ru+R7Do4fYj/sGtZ0jFk07YlK+BuZ1/bKn1BSPSwg9TNy23WRGRD623cytXFY3He/aWNasgto0qwHlf4v9ouRprfEql7dRubz95HarWqj+5xiVU09UTj1f+Pr/NvGjoijo9fonXvvvdgCN5p+GFoPB8NjnIm/IW1YhgEOb7jCr7x70OoU+4+ryxidZxX5mYiaRh6JQqVUv3M+6xoDq9Dr4Ch5tPEiLSScjPuOFc9b2Lcvcv9szZ2c7ajZ2Iv5BOt9+eJz+5Vez88frmJqpGTSjPl+f6Fooiv3zX5/n5NxTpMelv/SiVP8VdiiGB1cSKVPDhkGr/XKclScnlnZm9F/ZBJVaxZ75V7gRGv3C5/IcUptyTZzJTMxEl67j1LzTrG22npTIFFz8yvHKgZ6UbWD8/5e0+EzWvnuCT6ts4+7xWABMLU3o/kV9KfYLGSUzg4wz+9DHZ43FUVvbYoiLRm3rgD4u63vVxK0yFo3aoLLI2wHxRVnq5Rvcm/8tcVtDANCnpZMZEYnKwpxSDevhPKQvlRcHU+nzyZQd2BPrujWzi/2cKLp4lLidJP41jHu/LMvP2yhySpUqhbu7O3/99RcAZ86cISYmhmrVqtG0aVPWrl0LQHh4OKdOncLb2xuAXbt2kZmZSUJCAqGhoTRv3txYt1BsSQu/KPEObb7LzD5Zxf6rH9Vh8Kx/WozDQyLY8+5ePNp60G5lmxc6v0qV9WYh5nwsu94IoXTt0rRdEYjqJbpL1A90xTvAhaNbI/hp2ilun3tE3RbOvLfUD48ahaPPtTZFy5kF58hMzMTN35VyTfOmH3HNDi68s9Mfew8rLO3zpqtSpWZlaDOpNn9/cpHfXj/KR2fbY/WCT2TOLj7HmS/O0my+H9EnHpAWk8b9A5FUe7UqmhfsLpRXFEXhzOpwNr1/mqTodNQmKu6dekT5Ri8/cFnkHUVR0N29SubJUDLOH4KMNCwCXsWqTR80blWwGTIDk4o1UamlFRTAkKkl7coNUs5cQm1pgdOrnfk/9s4yPI7rbMP3mUXtSloxk8WyZMsyM8SQOLETx3GYmb9A21DTMDZtw9i0jdMwMxhiihllkGWWLGZcLc/5fqwtxY1BZjuZ+7p82dqZOXNmLWmfOfO8z+uzd9C+ch3e1nbCJo0leHABpqQ4LNlpBx2hKaWkY/MSvKWfYDJvwBhQAkgCQ6FlQx2q95oTrn/G8eSZZ57hoYce4sUXX8RgMPDiiy9iNBr585//zAMPPMBnn30GwGOPPUZUlH8BxGQycdFFF9He3s71119PevrB2V81Dowm+DV+1yz5uozHd4v9P+Rx9ZP99rSHSIkt3Ubc8MPvQmsKNuKxe3A1OXG3uTHZDu/RuxCCwZMTGXhGAlXb2ohNCzqhPNcGq4EJ74xj58yyIyL267a0EZkRBED6mOjDHu9/Gf+Xnmye6Y/q/PiGFVz24dBDsgqZbEa8Di8N6xoY/coomoobiRt+cAXFR4OGHe18dvNKin/wF+WmDIvg3Nf6E5N7YtwgavjxbFuP/YvXURu6rCe6xAx0Uf6ifyEEhlTN2+yuruvy4hdtQbr8Xc91ocGETzsda24m0ddeuCs2E/RhIejDQro1tpQqrp3L0XlWoO9xOfYdHex85Z9kX7LQv4MwQGABjuYcrH37Ase0f+kJy6BBgxg0aBDAXrP4o6Ojef311/d6bP/+/bn11luP6vx+72iCX+N3y5Jvynj0vDl4PSrn3JHL1U/1+5XAS5uaRtrUNFSfetjnC0oO4ozPJ2JLs6Ez6nC3ezAGHr7NRVEE8RmH35zrSOKxe/A6fEQPjCZ64OGL882za/jnxHmMujOLM57sfVSSbXR6hYvfGczf+/zI2k/KWf7WDgZemXrQ46Sdk0bMkJjOhmsBx1nsqz6VuX/bxIxHNuBx+PxFuU/nM/BqrSj3REB6XLiLlqOPT0MXEYsSaENtqEIEhWIqGOVP2YnSutJLVcVeuNH/Z80GPNV1e2w3Jcfv8uLngpQoAWZCxg7r9vjuuq04Nn4LrYsxB23EaNlVzBuRgzVtDC53Pg3b9ShhIwgdOQXFFIQVOPYhxxoah4Ym+DV+lyz9tozHdon9qbf15Nq/9v+ViGwqbqK+sJ6EUxKOWLpMWE4YUkrWvbqeda+sZ/J3kwhKDDwiY59IFP1rI2ueLWTQwwPIviz7sMaq3dTK2+cuRPX6V9GOZoxleGogU1/qy/tXLKP4x+pDEvwGq2G/3ZWPNUIRbJpRjcfho+CiJM76u+bTP1FQHXZanrkJ6bRjHj4Zy+mXo4tOJOjaR9AnZSF+54WL7soa7IUbsY0ditDrqX7lbXxtdgAUawDWXtl+kd87p9ur97vxtdUivFtQQodR9d06KLmL6AHbYdevemdzIB5PPsE9IxFCUPDqbUf46jR2o63sHxs0wa/xu2PZd+U8eu4cPG6Vs/+vJ9f9bcBeReSWj7ey7uX15F2fy6CHBx65CUionF+Jo85BxZzywxbEJyItW1vwOrwEpRzekwd7g4t/TV6Ao9lD3pR4Tn/i6EdZ9rs0hcAoM1mnnrzZ5Y5mN3Wb20gaGI4Qgmmv9adhezvZpx6+NU3j0FFbG3GtmQ9SEjDqbJQAK7rYZKTbhW5XxCaAoUfP4zfJ44jqdNFRtAVzahL6kGAqX/gPrpJyjHFRWPN7EjJ+BFJKf6JOespB3RBJ1YVQTFR8tgq18t/EDZiJVBRkwTKCMqIp+T4ZU5QJ1dAfU+oErKP7YTH8vm+4NH5baIJf43fF8h/KeWTaT3jcKlNuzeH6v+9d7ANE9AonYUw8yROTjugchCIY/cooalbUkjQ+ESnlCdN86XDxOrw46p2MfGEEvW7KIyQr5NDHcvuYPm0h9VvbiS8I4aL/Dj4mFhQhBNmndQljl92LyXpy/KqUUlL4cRlf3L4aqUru3ng6llAjkRlBnfUPGscWqaqdGe72z17Fs3k1wmzFPPQMhMFI0GX3IkwnZwTk4SKl9K/i72p85SjeivR4ib7mAkLGDSdoSD+McdEoVn/6UMR5k7o9tqr6cGxZjHvHDAzKGixhJZD3LW2ba3BuksT2U/C4szF7G7GmJ5HzzIvozEcmSUxD40Tk5PgU09A4Aqz4sYKHz/GL/TNvzuaGfwzcr9BOnZJK6pSDt3R0B1OoiaTxibiaXMy9eR49Jvcg88JDbx51ItC0qZmZl8/CFGxk8neTCM0OPeSxpJR8euNKts2rIzjWzFVfjjguonvtp2V8cuNKLv9kKGkjj3+c5v5o2NHOZ7esovh7f7FnytBwnC0eLKHHr+na7xUpJb6KbbhWzcVTvBLb7c8ijGZM/ceC3oCp3ymwK2Hn9yb2VacL+/pNnSLfW9/YtVEIzGnJKLsy8MPPGn9QY0tVUvXtWvRtr2KLW0yA1UnA7gAqCXSsI25KAc6qRGT2owSEhnUeq4l9jd86muDX+F2wckYFD02djcelMvmmbG56btB+xf7G6cU46hykTU3Flnr0UkzK55RT/lMFjRubSD27B3rzyfcj6WpxYQw2Yo23onpUfG4fjloH1rhDL2db9u8dLPvPDgwBOq76cgQhCccnX7xiTTP2ehfvXeqP6jxSMaBHEp9HZd6zm5jxsL8o12zzF+UOukYryj3WqG3NuNbMx71qLr6anZ2ve7YUYswdhDFvMMa8wcdxhsceKSWqvQNdoJW2paupfOEt2NVsCUAXFIg1Pwdrn55YemejD+7+kyhPYyX29d9j0BUSEN6Mkv02JW8tIn5ALREZTlzNQTjaciB4KMEDpqC3xRIcBsHZmrVN4/fHyacuNDQOkpUzK3lo6k94XCqTbsji5uf3L/adDU6WP7Icj91LZEHkURX8aVPTsFd20OOsk1Ps7/hqB4vuWcyQJwaTOiWViR+dSlBSEMphNsPKPzeRdV+UM/CKHiT2DzvwAUeJCQ/ksnlmNTuXNfLJjSu45L0hJ5T9qm5zG9PPXUjVuhYA+lyQxFn/6ENwzO9r1fh48kvLTsfX/8K9fjEAwhKMsWAEpr5j0P/Cn/97QHp9CL0OZ2kFFU+/ij48hORH/4gpKR5UFXNGj87utuYeiZ3v34HwOT2gqojGl/FVz0HxbSZ497d6G0hnGckXD0IhBWdoLOZ+vQno5tgaGr91Tj6FoaFxEKyaVclDZ8/G7fRxxvVZ3PzC4AMKNnO4mYkfn0b10hoSxx79OLzet/QCoGZZDZvf38Lwvw87rKZcxwLVp6LoFFytbpyNLkp/2EnqlFRsaUfm5sgcbODqr0Ycd3GtM/ijOv/RdwZrPiwjZ2Is/S/rcVzn9EsCo0y017kI62HlnJf77VF7oHF08TVU41z0He4NS7D9399RLEEY+5+C9Hkw9TsFQ2YBQv/7sIlIKXHtrOi06XjqGkh98RGM0RH4WtuRUkV1ezDGRpH+z6fQBXbv6Z/qVbFvWIC7dAb20ja2fZRA5h8mEJsxH53cjKoqtNck4VP6ETL6AjDFET818ShfrYbGyUm3BH97ezsXXHABr732GgkJCaxevZonn3wSu91OVlYWTz31FEbjifeoW+P3zerZlTw4xS/2T782k1te3H/RZ0dNB/P+bwHDnh5CZN9IIvtGHrO5eh1eZl89B0edg6gBUWRdlHnMzn0wqB6Vda+tZ/uXO5j8zRlkXZSJJSqAxPGH/yHbWuXgg6uWcc4r/QjvEXjcxf5uItKDmPJCXz68ahmf3bKKlGGRRKQdnyhVKSVrPy0ntpeNqKxgAkKMXPvdSCIzgzBatPWbo43a3gJ6PYrZitpch2vxdwB4Nq3CVDAKY2YBxsyC4zzLY4PP3kHHumLsazZiLyzC29TStVFR8FTVYoyLJuWv92KIiexcxd+f2JdSgqscXDuommdjy/Ozyb3sa8J6VmI2BLLVOw1nZTNi9G0gDCjmPthMv71YYw2No8EBPyEKCwu5//77KSkpAfzi/9Zbb+XNN98kOzubO++8k08++YSLLrroaM9VQ6PbrJlT1Sn2J16Tya0vDzmgn3n5YyuonFfJ0geWMf7tccdopn70AXpGvjCCirkVZJx74rYUl6pky0dbadnSQtnMcnpMTiFpwuGnGLk7vPx7ys+ULW/kyzvXcNXnw4/AbI8cAy5Pofj7Kgo/LuO9S5dw87xT0B2mbelgaSyx89mtK9n4bRVpoyK58acxCCGI73PoxdEa3UM6O2j/5CU8xSsJOPViAkacib5HLubRUzHmDkYXd+I89TnaNP04j7ZFq3Bs2QFqV0NCXWgwgfm5fi9+ryx0u5J1jHH7b7wnvU00L/gEtWE+AcHFmENbQbFiingfn91Fc2keupBkRNhwRnx/IYaQrhuGE2NJQEPj5OCAgv+jjz7iwQcf5K677gJg4cKF9OnTh+xsf3b4/fffj+8XBTi/pLW1ldbW1j1eq66uPtw5a2jsl8K5VTxw5ixcDh+nXZXB/71yYLEPMOSJwehMOvrd3fcYzPLXJIyJJ2FMPFL1N+ZKOSOZoKTjH6Xobvew6ulVhGSFkH1JFiOfG4671UPCmPgjMr6qSj64chllyxsJS7Fy7uv9j8i4R5LdWfYli+tprXTQXNZBeOqxWVn0eVXmP7eZGQ+tx93hL8rtc34SUsIJ8hDkN4m3eieyvRlDem8wBeCrLQcpUZvrARCKgmXCb3+hy752I60/L8c2ajCW3EwcW0pwbNoGOoWAnHSsfXKx9snBlBTfradynqZGWtYU4aw3E39OPrLwFGyBHbDrx0lVrSihwwmJDWHolzdjjj6xuohraJysHFDwP/7443t8XVpaisVi4Y477mD79u307duXe+65Z6/HTp8+nZdeeunIzFRDoxusnVfNX86cjcvh49QrM7jttaEHFPsrn16FLc1G+rQ0hv+t+63YjxZrX17HisdXsv2LHZz5/aTj7uevnF/Jhn8WYQozkT41jah+RzaecsZD6yn8uAxTkJ6rvx5BUNSJ2QXWEmrkmm9GEppkOWZpPaVLG/jkhhVUFjYD0Of8RM76RwHBsVpR7tFA7WjDXfgzrlVz8FVsRwmPwXbniwghCDz3FhRbJErwb/eJilRVnDvKsK8pwpQcT1D/3nRs3Err/GUoFguW3ExCTx1J0MB8LHlZ6CwH/j5UPR5aV/2Ep/wnDGI1gTFlqDvi2PbO6cSdXQDBQ/A01NHRko2pxwTM6UNQFL800R160JeGhsb/cNCmT5/Px88//8yHH35IXFwcf/7zn3njjTf22hr58ssv5+yzz97jterqai6++OJDn7GGxj5YN7+a+yfPwtXhZfzl6dz++oHFfs2KWtY8W4jQC6IHRBGUfPxX1LMvzaLk6xIK/tjnuIn9jpoOVv99DQMeGEDyxCR639LLnyR0hH3iK98tYeZjRQhFcNmHQ4nJPXqJSEeCuN4hnf/2eVS8bvWo9QeY9+wmvv7jGqSEsBQrU1/uR85ErSj3aCA9Lto/fhHPxhXg8wIgzBYMab3B4wKjGX3iiVlXc7h4W9vpWLuR9jVFdBRuxNfWDkBgv14E9e9N0OC+6CwBWPvmARCQsX/7kupVaSsqA0VPcJoDNl5EsNICv3D+mSN1JF86CNXtQ5f+MqYMgemoXaGGhgYcguCPiIggPz+fxER/kd7EiRN555139rpvcHAwwcHa4ziNo8+6BTVdYv+yNO5448BiHyC6fxRDHh+EYtKdEGIfwBRi4swfJyOEwNngpG5N/TFJC/ol8/5vAZXzKtEF6Bn88EAG3H/kbTY7Ftbx4dXLAZjyXMFJlTBTv62ddy5aTFRWEBe9fXRy1VNHRqIzKoy4LZMJD+RqRblHGF9NGb7mOoxZfREGE2pjLagqhsw+GPuOwZgzAGH47YVRSFXFua20M1HHuX0nSNm5XR8RhrVPT4L69wbAnByPOXnf9j0ppb+plXTiXHEPatNCqufk4PROpvczU0C68TjDcbTmoIsYiSV3IsEDotCUgYbGseWgP0GGDx/Oiy++SFVVFbGxscyZM4fc3NyjMTcNjW6x/uca7p80E6fdy9hL0rjjn8PQ6fZfUNm8tYX1r69n8KOD6Hl1z2M00+4jhMDZ6OSLCV/hrHcy6avTiciPOKrnbN7cTMOGRtLOTmXgAwNY9fQqcq85eu9Nc5kDJAy9MZ3ht5xcXYZVr0rNhhbKljeSfVosfS9KPuwxG0vtzH68iLOeK8Bo0ZPYL4z7SyYTFH1iWpxOZuxfvI5r2UxEcBiGu15FKDqsU65DCQpFsYUfeICTDG9zK85tpQT264WntoGdf/l75zah1+/y4vtz8Y1x0Qf04jsra2hfPwO14WdaiiVhp91N2OBUjPpViPA2wvKaaWmOQChG6D0Toz4C02+g4ER6OkBvBgTqvAeRtevQTXkHYdS8RxonPgct+GNjY3nkkUe44YYbcLlc5OTkcPfddx+NuWloHJANi2q7xP7FqfzhXwcW+1JK5t44l4Z1jZjDzfS/p98xmu3BYQ4zkzg2gYYNjQREHl3PdtvONj4f9yVCEUT2iSA8N+yoJxUVXJBEVHYQMXknto1nb0RlBXPWswV8fP0KPr1pJSlDIwhLObQPfZ9XZcHzm/nxQX9RbmC0mYmP+nszaGL/8JGqD8/WdbjXLcJ61rUIvQFdfBrCtBBjVl+ky4kIsKJPOHHTsQ4W6fPh3FqKOSMF6fWx/dYHkV4vaa8/gTEmEkuvbIyxkVjze2LJzUQxH9hQIz11tM5/BbVpAUFxFYSFqhAKRkM4jesqCB+Shkh9GE+7hYj+A4gUOgCE4djFGx9JpOqDllJk3QZkfRGyrgiat6M7czoiNBXZXgkdtciGYkTsifkZoqHxS4SUv3iWdwwoLy9n7NixzJ49m4SEY2tT0PhtUbS4lj+fPpOONg9jLkzlT28NP6DY303jxkZWPbOaUS+OxGA9cZvj+Fw+EKAz6qhf10BYz1CUbl5jd6heWoMtzUZAhJl5t8xHZ9Yx4P7+mEKOjqPW51XZ9EM1PSfFHZXxjyVSSqZPW8i6zytIGRbBTXPGoNMf3P/NzmUNfHzDCirXNAOQf14iU57VinKPBFJKhBC41i7E/sGzAARe/CeMuYOQHhdIEMbfjnPc29SCvdBv07GvK0a1O0h+8m7MPRKpeOZ1pNdH1OXnHDAmE/zvXcuyRbhKZ1G/IoDsh29HoRy5fqJ/uwrO5ji8Sl+MSeMxZ556wvTNOFR2f7+oxZ8jS+cg6zeCp2PPnYQOZfQjKMmj/TcARisEJyKE1s1X48RHM4VqnJRsXNIl9kdf0IM//efAYl9KyfLHVpBxbjphOWGM+/fYYzTbQ0dn8q+SbZxezOL7ltDntnz63nVkGvtseLOIJfcvJX1aGqNeGsmI54cf0ZuJvfH1H9aw4MUtTHggl1Mfyjuq5zraCCE4940BlC5tpGRhPT89uZHxf+mevdHZ6uG7P69l0StbkRJCky2c83I/ck4/+W+Ejieq04573SLcK+dgGnI6pvzhGLP744zrgbHnwM5VfGE4+YW+9PpwbNmBfc0G7GuKcJVW7LHdEBOJr9VfgBv3x+v2K8hVr0rbxmJE+1ICY7dDy0KClQroAY6teTSvKSNscCpqyKW47D0IyD4Nq/nktT5JrwsaNu1aud+ArCtCKbgGkT4R2bQNWbXSv6M1GhGZi4joiYjsCeFZCL3/qZuIPPGsoBoa+0MT/BonHcVL67hvt9g/vwd3vTWiWyurm9/bwrqX17Pt0+2cu+Qc9OaT59s/OCUIpP+DefdK1KEgpaSpuImwnDASxyaw6q+rCUoOQkp51MX+wle2sODFLeiMChnjDrzKeDJgDTdx4VsDeX3CPGY8soHM8dEkD95/rYWqSl4YOouaolYUnWD0nVmMfyD3qKX9/NaRqop3+3pcq+bg3rAUPG4AhCUIU/5whNGE7ZZnjvMsjwxSVRGKQtP3c6n/6BtUh7NzmzAasORm+r34+T0xxnRZaf7394WUEvv2Cjo2zKBmYQCNK6qI7ltI5nlLoM6/j+oLpKM5k7Cxp2PNT0IIgT7j/pNWNMja9ajbf0TWbYDGrSD37B8kG4ohfSJKxiRk3EBEZE+E5ejWTWloHEtO1p9djZOQ4mV1vP/kWq5+oh9JOSGHPMa9E2fQ0eph5Lkp3DW9e2IfIG1qKhXzK0k/J/WYiX2v20fjDjuRmUGH9cg7flQ85/x8NrZUG1KVOBudmMMPzt+t+lRmX/UTZbPKOWvGmYTnhnH+inMxBh/9JJJNM6v54rbVAJz3Rn9Sh5+cvt69kTkuhlF3ZvHzi1uo3tB6QMGvKILht2SwfPoOzn2tP3H5v91c96PJ7htfz+bVtL/9ZOfr+h65mPqNwZh3dNKTjgf2wo3UvvMZltxMoq84F12QFdXhxBgX3VlsG5CdjmLctz3RWd2E9HkJiI/EU7kAc831WGJVKqon4LPH0dGSTUdjI5bcSYjQYegsOQTv8uGfTEjVi1D0yOo1qGvfhohsdH2vQzbvQBZ/tmsvAaFpnSv3IjIXbCn+LRHZiIjs4zZ/DY2jhSb4NY4JPp/KP65dSOmGZiq3tfHikkmYDzJmcNPyeu7bJfZHTEvhnv+O7JbYr1leQ2NREzmXZ3PK66MP8QoOnu0L6vjgqmU0bGun/+UpTHu1PwbzoX+A2lJtuJpdzL15Ph01HUz++gz0AQd+D1Wfiqfdg8lmIjAhEL1FT1tpG+G5YcdE7NdsbOXt8xah+iRj78mh/2X7z/E+GTn98V70vyxlj5z+3fi8Kj+/sIWKwiYumu4XoYOvS2PwtalH/anKbw3p8+JeMx/XyjkYC0ZiHjAeQ0Y+uoR0DJkFmPqORhd2cj898tQ3dkZmCqOBuP+7EsVswl1W1ekVD+zXi9QXHsYQtW9bjZQSXDtpX/UFru0zCUoopaHkfOKvuB9DdD5qOTia40m6uD85+WeflB1tpc8DTVt32XL81hwRkoJu7NNI1YOsXAruVuh7HSK2H0rfGyCyp1/UG7RkHY3fF5rg1zgmzH5nG6UbmgHYWdTMP+9azq0vDen28ZtX1HPvaT9ib/EwfGpyt8W+q8XFrCt+wtngXxHvMSnlEK/g4AkIMdC4ww7Aiukl1Ba3cuVnww+vIFNAy7YW3C1uWra1EJ63fx9ty7YW5t48n4DIAMa/PZZ+9/aj9629scZYDn0OB0F7vYt/TZ6Ps8VDr6kJnPZYr2Ny3mON3qTbQ+y31zkJjDSzc3kDH1/fVZQ77KYMkgeF7+oRcXIXOR4rpKoi21v8HW6FoGPmB8jWRlB0mAeMR+j02G566nhP85BRPR4cxds6Rb67orpzmzAZkV4v5owUEv58KwHZqQAoAWaUgD2f8PmcHloKy2lcUYJo+ZbkU1egqFVYDWDN8u9jDi7zj6u3ofRbhlUfxMkme6WjEXXdf/3WnIYtoLr33K7uapwW2RNl9KOICL/XXgTFIXpfesznq6FxoqAJfo2jjtvp5e2H1gBw7h/y+OLFIr55bRP9JsQz9Myk/R8MbFlZz72nzcDe4mHY2cnc++4o9IburYyabCYG/KU/pd+Vknzagc91ODTsaOenJzcy+W99MAcbiO0Vwg0zR2EKMjB92kJ2Lm3k2QEzufLzYSQNOLSCN5PNxPi3xqK36AlKCkL1qCh7eS9Ur4qiV9BbDbRsa8FR24GjzoElyoIx8NilEv3wl3U0bLeT0C+UC6cP6lYztJMZn0flqz+sYeU7JfQ5L4kl/9zWWZQ79aV+JA86eQsdjwfOpT/inP8lwmzBduvfEIoOy7jzATD2GnqcZ3foSFWledbP2NcU0bFhM9LVJVqVADOWXllY8/1efKH3f0xbe2X9ehyfg6Y5n+LaORtL6CaKXh2Lu9VCRH47iloFuhAIHITT3hNz5kTCB3T1ixD6E6PR4L6Q7nZ/Uk5dEbJ+A9Jei/7Mt0DRI4s+6toxOGmXLSfPX0gbmgaAMFgRKaccn8lraJyAaIJf46jz1SvF1JXZ6dE7lKue7EdYbACv/3E5z167kKz+EYTH7Xu1ecuqBu45bQbtzW6GTUnivve6J/ZVj0rhC2vJuyGXzAsyyDg//ajFxjma3cx6vIgFL27B51YJjDIzcddKdvoYv73gtmXjefvcRWxfUMfLI3/ivDcH0O/ilEM6X2i23/Nds6KWeTfPZ8xro4gs6PLEV8yvZOFdixj5/AhiBkUz4Z3xhOWGHVOhv5vJz+QjVcmE30lRqqIXNJbacTR7WPzGNhSdYNQdWUx48Pdx/YeLdDvxbFqNIW8wQgikvRW1qRbFFoHa3oISaMPU/8RP19ob9nWbsK9aR9iUU9Hbgmj6fi6eqloATEnxXV78zFSE/tfWPyklHSUNNC4vQWn7gthhZdC+khCbB3Y9OIsd3YEMGkPowMnI9BtRQnohhMKxeZ53eEgpkZu/6rTn0FKKv4XvL/ax1yKsUSgDbwdbEiIiB2E6+axIGhrHA+0TSOOo0t7s4oOn1gFw9eP9UBTBlP/ryYoZFaycUclfr1jAkz9M2OvK79bVDdxz6o+0N7kZcmYi93ZT7AMsf3wF61/bQM2KWk57f8JREftet4/Fr21jxiMb6Gj0r9D1uzSZwdel/mrfoCgz188cxRf/t5rFb2yjpcJx2Off/tl22krbWP9GEWNeHdX5evXiatpK2ih6s4iYQdHEDDr2nmav24feqMMUaODc1wcc8/MfL4QQnP/mAF6fMA+zzcDZzxdoRbkHQEqJt2QjrlVzca9bBG4nwTc8gT4pE1P/seiTstCn5iGUk6vewV1dh33NBiy9sjHFx9D49Uw61hZj6pGEbeRAwqdMAFVizc9BHxay1zGcNa34mkuwhG5Admxj5fVWvK1OMs5bC22bAIGqz8Zp74kpdTyp9wxFKCd+s7bOzPs1/0atWolu1MMISwS+9e9AW6V/J0UPYZn+1fuIXYW1Fv/ChtLz3OM4ew2NkxNN8GscVT56Zj1tjS56j4ym/2nxgD+l5I//Hs4NBV+x5qcqPv3HBs79456Z7NvWNHDPqTNob3IzeHIif/5gNAZj9wtesy/NonJ+JX3/2OdIXk4naz8t49v71lK/xZ9znTYqksl/60Niv7B9HqM36pj2Wn96nZNA5i9iKX0eFV03b2R+ycAHBxCUHETPq3LY8tFWSn8oZeybp5B/W28CIgPIvvTXFoCjjb3Bxc8vbWHNhzu56ssRRGac2LaBo0FgpJk/rD71eE/jpMC18icccz5DbezyreuTMpE+vw9bCQ5DCd73z9SJhOpy07Fhc2fzK09NPQDh556B6ZyJ2EYPxpyahDk1EQDbqF+nCHlaHTSvXIe7ZDZl34fSsbOJrCt3ENB3HgBxpz+Cq9GMKScbX6yCPmYken0ogcfuMg8a6XFAQzFytzWnrgjd6McgKg9ZswZq1viLbZNHovS8AKTP77sPy0DoT/5+CRoaJwqa4Nc4ajRUdvDFC0UAXPVEPxqLmih8vpCBDwwgLCGQP7w5jAfOms1/7l9J/ugYMvv74wy3FTZy94QZtDW6GDwpkfs/7L7YL5tVRkBkABH5EUyZdRbiMDzjHqePmqIWqta1UL2+hRG3ZRKS4H84PuORDdRvaScyK4hJT+eTOzmu208RssbHdP57/ZcVfHtvIVd+PpyorIN7NK0z6ci7PrczW7/0u53snFlG8qlJ9Lwq56DGOlzKVjSy8JUtrP6gDK/Tn29d9HUlo+489jcdGicu0u3CXbwSY95ghKIgHXbUxmpEcBimglH+lJ3I+OM9zW4hpcRdWeMvti0swrFxK9Lj7dyuWC1Ye2cTkOb3zQcP7Q//U3bgc3qQ7nZ0cj3ustk4t/xIeEIj5EHFN5PRWWLpaOkFoQGI4KGkFUxE6E98C4tavgS5c77fmtO849eZ9/UbEVF5iF6XIHqej4jqDYCSc87xmK6Gxu8CTfBrHDXeeWwNLoePYWcnkzM4iuol1bRsbWH139cw4tnhDDojkbNuyeHLlzby1KXzeXn5ZKq2t3HPhB9pa3Qx8PQE/nwQYr9pUzM/XTsXgLNmTCYkI+SQ5q36VGY8vIGfni7G51E7X08ZFtEp+HudncDg69IYcl3aIa3Og18wzPlbMbXFbTw/eBaXvDeEnImxBz1Oy5YWGtY1MPL54SRNSDykuRwKHqePwo92svCVrexc1tj5evbEWIbfknFI16Lx28WzfQPt/30a6epAueoBDOm9MRaMQolKxJDeC6Gc+JnvqtOFp64BU2Ic7cvXUvmPf3ZtFAJzWjLW/BysfXpiTk/Zqw1JSh9NixZR+n4ZLWvLGfDwIgICN2IADAmgevW4HFnkPHAa1txTULrZZ+R4IB1Nnav21G1A9BiHkjkZWb0KuflL/05C92trjs0foKDEDTyOs9fQ+H2hCX6No0LZphZ++NcWFEVwxaMFeB1eDIEGGoua0Fu7ikeveaofhXOrKFnfzFOXzGfjklpaG1wMnJjAXz4eg9HUfREQ3COIHmemIFWJLd12SPO2N7h45+IlbJ5RjRAQlR1EbK8QYvNse6zAn/pQ3n5G6R5CCK77fiTvX7GMdZ+V86/JCzjjyd6M/mPWQdUchGSGMPHj0w57PgfL5pnVvH/FMsAfQTrwyh4MvTGdiPQT08ZzOB2KNQ4etaUB15r56BMzMKTmoYtNRvo86BLSO/dRrMEYM/scv0keACkl3oZmDBGhuCqqKb37KfRhNno8/xCWnDR0tiAseVn+gtveOehtQb86vmNHPS1rlqLWziN2lB3hWE6wXtK0ciogsNf0ICBKgeBhEDgYna0/VuXEtLLItkrkzgX+wtr6Imiv2nOHgDDInIySPAppDvWn5oRnIfQnfl2BhsZvHSGllAfe7chRXl7O2LFjmT17NgkJCcfy1BrHkMfOn8OCT0uZeE0mt782lMX3L6FyfhVZl2aSe03PPYRXyfombh38De5dVpABp8XzwCdjMHazG67H7mHHlzvIuDADAOmTh7QqVr+1jdfGz6WptANrhIlL3h9C5tijX/CqqpKZj25gxsMbAOh3STLnvt4fQzeaah0rVFWyZVYNaz8t45xX+6MoAtWn8tY5C8mdHE/BhUkYD7KR2tHGW1OGd/t6zEMmojraaf/vXwk49SIMyVoXzaON9HpofvxqpKsDQ+4ggi7+EwBqa+MJ78n3dTjoWL/Li19YhK+1nfQ3/4rQ69h2w30YoiJIuO9mdJaAfd5E1s3bSN3crTSu2E6fG97BEtW6x3ZpSKCp8SGC+/bDYAs4YW9EZUsp6sZPEQFhKPlXoJb8hDr3L1076AP8XWkjchGRu/5YtOhZDY0TkRPrE1rjN0HxsjoWfFqK0azjkr/4Yxkr5lXSsqWF2CExuFvdNBU3d6bHpOSFcsM/BvLCTYvpf+rBiX0pJXNvns/OH3bSXm6n710FCP2hfXjaEixYI0wERZu57ONhhCYemzA7RRGc+mAesb1CeP/ypax8p5TaTW1c+flwbHGH0aTrCOBodrP8rR0sfHVrZ4Fy3pQEcibGougUrvpixHGd375wr19C+8cvgNeDLioRz/b1eEuKaPvXw/4EmLjfXrff44WUEl/5Vlyr5iKddgLPvx2hN2DMH4Zqb8M0oCtG80QU+1JKXDsru7z4m7aBr8vKp7MF4ampw5QYR+qLj6CYurpTCyHwtDpoWrmTjp0NJI8vRDb+gKHFTPX3fQDwdATidXvx+PIxJo5FHzsaxZxIxLG+0P0gXa1+W059kd93rzehG/MEeDqQxZ8ibcko+VcgInshMiZ1intsKSeFFUtDQ0MT/BpHGCkl/75vJQBTbs0hIt7fx3HqT1OoXFiFOSKA93p9gN6s56J1F6DbZdk547osBk9KJCz24Fa7hBCkn5NGfWE9aef8Og7zQHicPloqHESkBWIw67jmm5EEhBjQH4SV6EjRe2oCEemB/GfKz9jrXOiMx8+7W1nYxMKXt7LqvVLcHf4nL7aEAIZen05CvxM/ZlIXnQhCwdh3NPrEdPQpOUh7C2pHG7qY5AMPoHFA1LYmXKvn4141B19tuf9FoaCecSVKoA3LWdedsCvXu6n/5DtaZi/E29TS9aIQBGSl+m06+T0xpSR0evEVkxGf04O3vQOjuRS1YQFVH81m66cDQEDCwEoURzGBsRFk3H4noQNSsSRehzCEYhQnjhdfehzIrd8hdwv81rI9d9AHIFUfhKaj9L0eInMBENZIdMPuPQ4z1tDQOFw0wa9xRFk5o5LCudUEhho5765eSCmpmFNB3Ig4Ekb70zdCs0MxBhlw1DkITOgKlNtfA669UfrjTmIGRdNjcgqJ4xPQd/OpwG4aS+xMn7aQjiY3dywfjyXMv7p/PInrHcJty8Zjr3cRGOH38Xoc3mNq75n//Ga+vGN159cZY6MZdlM6PSfHoTuBCwi9ZVvw7tyEedgkdJHx2G77B7rQqM7tlrOuA9WHUBRUpx3XslmYh08+6fLdTwSk6qPlhT8i7X6hLKw2TAUj/UW4gf76mRNR7LctWUXTD/MIGTec4OEDUDsceJta0IUGd3a2tfbKRhfY9btI9aq0FlXQtHIrSusXmCxFhOXUIA0OBBA3QqFx5yRsBZmooUZ08RehC+xL4oDj78OXUkJ7dVdhbdM2lAnP+m/Mlr8A6q5UIZ0RwrP3LKwVCkLRIXpfdnwvQkND44jQbRXR3t7OBRdcwGuvvdbpvb/rrrsYPHgwU6dOPWoT1Dh5UFXJv+5bAcAFd/cmKNRE3ao6frxoJqE5oZz901kIIZj8zRnoDiJTf29UzKtg9lU/EZwazFk/TMZgPbgussU/VPHuJUvoaHQTnmqlrdaFJez4f0ADBEaYusS+08crY+aQMjSCSX/NPyqCu7m8g7LljfQ62/9znXN6LD8+tJ7+l6Yw9KZ0orNP7BhAqao4F36D48d3Qaro4lIx9Oi5h9iHXQJUp0dKif2D5/BsXo23bAuB592KMJwY//cnKt6qElzLZ6O2NhB0yV0IRYexzwjUplpM/cZgyCxA6E6s9SNfux372mLshUWYeyQSetpovE2tOIq3oQ8LIXj4AEImjCR45CBMyfGdNyhSSlSvipBNdGz4lh1vrqB2RSwIybAnZmMIdPlPYEqC4CEoQUPIf/4UxAlSaKtWrYK6df7c+7oicDbuuUNLKSKkB6LXpYiAML+4D01DKCfW/5+GhsaRpVs/4YWFhdx///2UlJQAUFNTw4MPPsjixYsZPPjXzUM0Tm58XvWQhOXcD3ewvbCJiAQLZ97sL4x0t3sIybCRMLrrA1Vn1NGyrYXtX+4g7/rcgxbrALY0GyGZISRNSDyo41VVMuuxImY8vB4pIeeMWC56ezCWUOOBDz4OlCyqp3xVEzuXNVK9oYVLPxh6ROYqpWTb3FoWvrKV9V9UoBgUHiyfjCXMRGRGEA9VnYXBfOJ7c9X2FuyfvIRns/+JhGnoGegTM/Z7jBAC84gz8ZZuwrNhCW3/aiLw0rtRrCf2jc2xRu1oQ7H4U2fcaxbgWvI9AL6mOnShkVhOv/yEWsWXqoprRxnta/yNr5xbS2BXJoW7sobQ00YTOKgP+jAbll7+30/GmMjO4xuXFtO24msU3wqi+jdhMJQSAMQNi6atuiehA1Jw6fUoUXHoY0YhTMcuAnd/qNtnIauWo/S+HBEUh7r8RWjc3LWDyda5ck9kLlj9fUB0BdccpxlraGgcD7ol+D/66CMefPBB7rrrLgC+/vprxo4dS0hIyNGcm8Zx4Me3tvDancu4+sl+TLq++2kmHreP6Q+sAuCyBwsw7bKgxI+MY+r8s/G59my8Mu/WBdStqsOWZiP1rO4XUDrqHLSWtBI9IJrJ35yB/iCsLh1Nbt67dAkbv6tCCDjtkTzG3tcT5TCacx1tMk6J5sbZY5g+bSGbZ9bw/KCZXPXlCKJzDk2cOts8rHi7hEWvbqWmyJ8cougFuZPjcLZ5O59ynAxi31uxjba3n0K2NSECArFOuxljzoBuHWtI60XQ9Y/RPv1xvDs30franwm68n50YUc/lelEx1dTRseMd/FsWk3wrX9DH52Iqd8YpNeDqd8YdKF+kXwiiH3V5aZ9eaG/4HbtRnyt7V0bdTos2Wl+L36fngAYwkIwDCrA0+qgYU4Rjq0LSbx4IoolAUfJGhIHvtl1vDBBUH+CBg5myIXX7Xrx2Mff7kZ21O1atffbc5Q+V6HE9kWWzPY3uYopQATFoaSOQ0b37rLmBMWfEP9XGhoax5duqaXHH398j6+vuca/MrBy5cr9Htfa2kpr655xZNXV1fvYW+N4U7W9jZf/bymuDi+v3LaU5J6h9BrRPQH03Rubqd7RTlKOjXGXpgGw7bNtuJrdpJ+bhjFoz1XpzIsyCEm3EZzS/cx2r9PLzCtm07CugbH/OoWk8d1fYVNVyatj51C5phlLmJGL3x1M9qknR2Oo1BGR3LZsPP85+2cq1zTz/OCZXPLuEHpOijuoceb9YxM/PrweV5vftxsca2bwtWkMvi7tuKcBHQqKLQKQ6FNyCDz/dhTbwcUB6mOSCL7hCdqmP4GvuhTX0hlYJl56dCZ7guOtKkGYAvw3PHoDno0rQFHwlW1GH52ILioB6+Srjvc0/fatbTtxFG8ldNJYpMdL1ctvd67k6yNCdwn8XKy5mSgBXTU5TStLaVq2iYZlFXSUlDH4oY+J6OPGtbmegD73EjJwLM6dH6ELK8CQNA4R1BehmDheFR6yaTuyYkmnwKejds8dagshti8i4wxETAEi0t8bRMm7+DjMVkND40TnqJr2pk+fzksvvXQ0T6FxhFBVybPXLcTV4SU02kxTjZMnLpzLS8snEx67/2LajjYP7z5eCMCVj/dDp1eQqmTV39bQur0VS6yFlIl7JqNkX5JF9iVZBzVHRa8Q2ScCR42DyD4HF2qnKIIJf8ll1hNFXP7xMMJSrAd1/PEmLNnKLQvG8uFVyyj8uIx/n7WA05/szSl35ezzGJ9HpbXa2RkvarYZcLV5SR0ZybCb0ul1dsIhdwk+Xvia63GvmYd51FSUQBvB1z6KEhqF0B3aEwnFFk7wdY/iXPgN5tHnACB9vkMe72RCtbfiLlyAa9VcfJU7MA0+DeuZ16ALj8F63m0Y0vJQgo5/IpO3pQ174UaCBvdB6PVU/PU1fG3tBPbrhTEumtDTRqEP9wt9Y3wMQgikT6WtuBpXTSkR+Q3I1oUEtM6ivSOOto2DEXoTXpcNdALdrifV1h6R0OOzY359UqrQsrOzmZWI6ImScQZy5wLU1W907WiwIiJyILIr8x5ASRx+zOesoaFx8nFUBf/ll1/O2Wefvcdr1dXVXHyxtgJxovH9m5spnFuNLdLMKyvP5MmL57N2XjWPXzCXv846Df1+hOGn/1hPS52TnkOiGDLZv+oupaTfXQWUfFu6z5X4+sJ6Nr69iYTR8fSYnLLf+VX+XEnssFiGPD6Ygj/0wRx24DQdr8vHyndKGXhVD4QQ9Do7gdwz41B0J5fI3Y3JqufSD4YQ28vGDw+s32d0aGu1g6X/3M6i17cRkhDAbUvGA1BwYRJJA8OI7RVyDGd95PCUFtP+9lNIRztKcDimvqPRRRz+UxphthAw9jwApNtJ678exlQwGvPgUw977BMRX30VHT++g6d4Jfj8T3tEQCDC3HVjb+pz/PorSJ8P59bSzlx85/adAOiDA7H26Unw6MFIpwt2pStFXT4NKSXS40MIgauujfV/eIKMqXMIj29CbvePawyE8IJwLEPPI6RPIorxZoQucF/TOHrX525H1hT64zDri5B1G8HzCytSRz1knIGIG4Cw1/wi8z4JcQJFe2poaJxcHFXBHxwcTHCwVgh3olO7s5037/an69z8wiDCYizc994obh7wNRsW1vLm3Su44R8D93psc62DT5/1d4i96ol+nV5RIQSpU1JJnbLvbPy6NfVsfnczHVX2/Qr+LR9tZf7/LSDtnFRGvTSyW2K/qayDt89dyM5ljThbPIy60/804WQV+7sRQjD+/lxyTo8lvqBr9dXZ5qGysJmFr2xl3afl+Dz+xkGmID0djf4EIqNFf9KKfQBdeCzo9RgyCzBkFhyVc7g3rsBXtoWOsi0ogTaMeb+NUAJvTRlCp0MXEYcwmvEULQfAkFngT9nJ7o8wHL/CdW9TC/bCjX6Rv64Y1d7RuU0Y9AT0zEAY/cX5URdPAcBV20bVd2twbPkZ4VxOVP9GLH1vxxhxCl6PjcD4JlSfARHcHyV0OAQPJcCSjaVTNB+b65WOJuSOmSBVlNwLkLXrUWfftedOlih/YW1kT0R0H/91R/ZEF9nzmMxRQ0Pjt4+Ww/U7R0rJc9cvoqPNw7Czkxk5LQUAW7iJ+z8czZ9O+YHPXygie1AEo8//tXh/74m1ONq9DDojodPvb6+y8/Wkb8m+NIs+t+fv89wpZ6Rgr7KTtp+bAgBzuBlDoIGoflHdKj7bPLuGdy5cjL3eRWiShdSRkQc85mQjoW9Xx9I1H+3kvxcs7vxaKIK8KfEMuymdjLHRJ3XBnq++Ctea+QSMPc9v4bnhCRRbxFHLzjflDwevG/f6JRhy+h+VcxxLpOqj7Y0H8O7chLHvGAKn3YwSHIr1/NsxpGQft8630uvD19GBPjiIxq9nUffuF3tsN8REdja+svTM2KO7bdPqnVS99zrhWcsIz6hCP9TTNW7LApTQsfR9/TaEmIDOmnvM4jKllNBW0WnNwd2ObsRfwNWKuux5sESg5F6AiOwJ0QW7BH6u/2/Lb+93lIaGxonFQQn+n376aY+vn3rqqSM6GY1jz4zpW1k5s5KgMBNX3ZfHjxfOoL2sHU+Hl3MXnsP1fx/Ay/+3lGevW0RKXigpuV2rylXb2/j29U0IAVc93q/z9R1fl2CvsNO4oXFvp+wkIMJM/3v8x6k+9Ver762lbRgsehLHJnDOz1Oxxuy/lkBKyU9PF/P9/euQqiRzQgwXvzO4M9P+t4iUkpXvlgIQGGli0DWpDLk+jdCkk6tGYW94tq6l7Z2/gtuJLjwWU8HIX2XrHw1M/U7B2HeM3wvudtEx4z0Cxp6LEnDs7R8Hi/T58GwtRK2vwjzsDH/jpKBQhNmCYumav6n30GM+N9XtRjEaaV+9gaoX38Kan0PcbVf5O9kaDVhyM/2Nr/r07IzLbFlfQen0mfga5pIwzoWp4BGMoVako4bIfL/Vx+OOhsDBGBJPQQT7n8gYggOAvkf1eqTqRVathF0CX9YVgeuXHXt1yCF/AlsiImsKIiwTqfoQpmD0E7XaNg0NjWOLtsL/O6a+ws7rf1gGwI3PDqT8ix1UzK3s3F75cxWTb8ymeGkds9/dzqPnzuGFJZOwBvtX26Y/uBqvR2X8ZWmk5HXdCORe25Pw3DBMoQcW2q07Wll03xKkKpn4YZdn2tXkYsZFM/C5VCZ+fCrBPfZvDXO0uPngymWs/6ICgHF/7smpD+We9BaeAyGE4IpPh1Gxqom4/JB9+vpPRnQxyQhTAIasvhhy+h34gP3gbW4FRaAP7l4q1O6nIh3fvoVr+Uw8WwoJvvpBlODjX8S6Pzq++TeupT+C3oCx7yiUgECsk69CBFiPeXMx6fXi2LQd+5oi2guLUIxGkh/7I8bYKNQOB576JgAsPTNIf/OvCJ2OtuJq6hZtIe6UIrBkU/3DejrW/UD+LTP8g7aegSV5AonX3Q7RExAhQzGZEo7+taheaNq2a/V+I8qQP4HQoc69Hzxd9iPMoZ2r9kTkgqJHCAXdkD8d9TlqaGho7A9N8P9OkVLywk2Lsbd4GHRGAqdclIqrKR5joAFrnJXoQdGEpNsA+L9Xh7J9XRM71jbx96t+5i8fj2HbmkbmvL8dg1Hh0ge7/NStJa2YIwKIHbZnMaWzzYMpUI8Qgm3za0noG4op0IApzETVwioAXC0uTDa/KFG9KsZgIz63ijnywJGR85/bzPovKjDbDFz838EHHVl5MqPTKyQNPLhIyhMVb1UJ7rULCZhwEUqgDdvNT/tXqA/BlmQv3IguJBhTUhzVb7yHc2sJ0VedT9Dg7vv/zWOm4i0tRgTaEJYTa4Vfddhxr12Ie818Ai/6I0pQCMbcQXi2FmLqNwbwv2fH0rbjqW/sLLbtWL8Z1eHs3KYEmFGdLowxkaS++AiGyDDsJQ00Ld+KY/si9L6V2FJ2Et2jDrlFRSQ/QNSY4TSaJR61CX3cSIQ1FyEEwb3zgLyjdh3SXrMrDnNXJGZDMfjcXTtkTfEL+/QzQCi7GlvlQmDMSW2h09DQ+O0ipNwVYHyMKC8vZ+zYscyePZuEhKO/MqOxd356bxtPX7YAq83AG2unEKCANXZPG0h7RTtCEVhjrVRsbeXWQV9jb/Fw9ZP9WDOnipUzKpl6ey7X/62r2dH35/5A3ao6xk0fS9zwLtH9n6k/017rJCgmgHWflXPqQ3lMeMAfK1c+p4KI/HDMYWaklDRtbCKsZxhehxd3mxtL1P6tPABet49Pb1rJ2Ht7EpF2YgkzjQMjpcS19Ec6vpsOXg/W82/3++kPkebZC6n55/sE5KSTcNcNlD/zOo6iLQAEDS4g6qrzur3arzrsIEAxW5EeN96KbRhS9h2HejSRqg/vtnW4Vs7FXbQMvH4Rajn9cszDJ7P71/mxFJ1SSurf/4r2Vetwl+/ZZ8WYEIs1Pwdrn1wCslPxNDlx1bYSlO4GUwpLL3iD6LxZJJ+29hfjCaQxB13iVYjwycfmGuqKkFUrEcmjELYkvN/fDDVr9twpONEv8iN6+vezHFw0sIaGhsbxRFvh/x3SWN3BK7f7rTzX/20gwTYjH/b7iNCsECa8NwFjoIGi/2xkyZ+XknNVNkMeG0x8ejB3vTWSB8+ezb/vW4mUYAk2cOG9vTrH9Tq9qF6JVCG8V9eK8+ZZ1az/ogKjVc95t2Sy7rNy5jxTzJDr0wiKNpMwJh4pJa07WtnxdQkrn17F0KeHkH1J1j476XrdPr69Z60/anJAOHqjjvPf3HuSkMaJjepox/7Zq3g2LAXANGB8tzvm/hJfWzuti1cROmEkQYMKaPxyBtaCXITJSOL9t9I8ayF1735O25LVdGzYQvTV5xE0+MA+byXAfyMsVRX7xy/iLlqKdcr1mPqPPeg5Hi6OH9/DueDLzq/1qXn+moNc//f+sRL6LQuW0bZkFZEXTcEUH4Nj0zbc5dUIswlrr6xOL74hwv90oWPHFrY9dheW8M2E5dYgHW2IvO+JGpsDzja83jqEbSj6mNGI4EEIve2ozFuqPmgp6Vy5Fz3GosQNQC3+FLntBxSDBWFLQokbiNSb/XGYEbvSc0xa4pyGhsbJiyb4f2dIKXnp1qW0NbroNz6OCVekU7t8VwdHRWAM9EffRQ+IQugEqlvtPHbw5EQuvLc37z/pX40770+9CA7visjUm/Wc8flEHHWOTmuOz6vyxe2rARh3Xw4FFySx6v1Sir6uZMbD6znnlf5IKflq4jfUr6knfVoaUpWYw/cdvdlS6eDt8xZSsqiBom8quatoIjr9b9ur/1vFV1dB238eQ22uQ5gsWM6+HlPvYQc1hvR6aZqxgIZPv0O1OzBEhhNYkEvKP/6CY/sqNvzlKxzlTaRem0/KX++i5o2P6Niwmcrn/k3goNVEX3Ueels3VvuFQAmPAVXF/tmr+JrqCBh3/lET2dLlwL1uEa6Vc7Ce93/oQqMw5g3CvWEJpr6jMRaMRhd69NNdVLcHx8at2NcUETgwH0tOOh2FG7GvXI+lZwam+BjCzz0DIQTGlCRaN1bTsHwbW9/7itjJ+cT2fgdz4zekTfrFoPowcFeQet1IpByBEH88KnOXzmZkzVpk/QaoK0LWbwSvo2sHYyDEDUAkDgeDBRGWDoCSf/lRmY+GhobG8UIT/L8z5n9SwsLPSwkI1HP760MRQhA9MJrzV56Hs77rgzA8L5yL1l7wq8LbSx/qQ3VJO7U72zn7/7psDd4OL+teX0/mhZl7pOksenUrNUWthKdaGXmHPwv/jCd7s/HbKpb8czsjbsskKiuYoOQg2krbSDsnjd639iY0K2Sv8982r5b/XrCYthontoQALnp7kCb2T2KUkAiEKQBdQjqBF9yBLiy628dKKbGvXEftO5/jqa4DwNIrC0NEGLJtBe61D2MSW2lZNRVXkxV16zt4OnxEXPwYrm0F1L77Be1LV+Mo2kLUVecRNLhgv+JdCIHl1ItRQiLp+OpNnHM+QW2pxzrleoTecNjvBfifIuD1IIwmfI012D97FQD36vkEnDINXUIGtjtfPGqxpLtxV9d1efE3bEa6Pbvm58OSk45t7DACemYQWOC35bldZkr+swiD8ixxgzcQk9xB6fSpmKKDie0fB4oZn5KPLmokInQ4BGR2NpE6UjdM0uuEhk1+z70lHCV1AnL7TNRlz+25Y2Cs35YT2RMR53+SpKSMgZQxR2QeGhoaGicimof/d0RznZPren9BS52TW18ezKTrs9k5owyhEyScEr/XD976dQ1Uzq+k98299jJiF5vf38KCO34mqn8kk7/xL+W117t4KvNbHM0ervx8OHlnxXfu//F1y1ny5nZ6TU3gik+G+Z8KhJhQ9tHRV0rJvH9s4tt71qL6JOmnRHHJe0MIijpwEy6NEwu1rRnnz18TMOEihE6H2tKAsAYflGh2lpZT9/ZndGzYDIAhNoqoS85GCXVi7HgdxbEAAHe7mdptV6MEZhMWeh/msDYkCiLqYnymy6h+80s61m9CZwuix7MPoLMcuEAcwF28gvb3nwWPi4DxFxIw5pyDfyN+gVRVHD99jHvVHIz5I7Cc6u9Gbv/8NfRJWRjzBiNM3ZvboaC63XRs2IK9sAj7mqLOG6jdmFISsOb3JGhgPqohkMZlO7AXLydlmoJBv4HmhptZc9tnxI8qImPaMqQUtHr/TVCf/uiMTlDMCOXINbqSUoWWMmS935oj64ugcStIHwAipgDdaS8hGzahrngFInvuEvm5iIDj03tAQ0ND43iirfD/jnj19qW01DnJHx3D6ddmoXpVljywlLaSNsa/PZakCUl77O/t8PLdlO/w2L0kjkvc56o7QGhWCCmTUkg+rWuMHx9Yh6PZQ8a4aHLP3DM1Z8JDeax6r5R1n5WzY1E9PYbuuwDO2ebhw6uXsfaTcgBOuTuH0x7N01b2T0K81aW0/ftRZHszwhRAwCnTUGzdTxjyNrdS/9E3tMxZDFKiWC1ETDsdc+/eNM36B9HZHyMUQLFAzFXogy4maYxf4LlqC2hf/zSBQd9B7X/B9x1Bo28icND56ENt6CwB2NcV42trJ2hIv/2uPBuz+xN87cM45n6Gefikfe63P6TLga+uAn1COkJR8G4tRG2ux7ujqHMf69k3HNLYBzy3lHiqanFX1xHYNw/Hph1UPP1q53bFGoC1Vw7WPj3RJyTianATNqgHsvFHGme9RVhIMXGndkCbf39b2gXkPjaF0N7nIwzbEEEDCe304R++0JdeF7J6JTiaUDLOgLoifN9dv+dOQoHQdL+oj+njfyk8C92pzx/2+TU0NDROdjTB/zth0ZelzP1wByaLnjveGIaiCHwelexLs9g5s4yEU379tEVv0ZNzZQ4+lw+Ddf/fKpF9Ixn7Ztcj8dZqB0v/vQNFJ5jy7K+tEra4AEbekcWsx4vY9EPVfgV/e62LzTNrMAXpufCtQfQ6W3sydLKiC49BsQYjohIw9T/loI6VqkrZI8/jrqwBnULohFGEnzUEJdBCc2ErpR/7iLxHT2vtEEInPYlijOSXt4SmqEhMp/wN2XE1vi33oXMX0fDj17S3nEPPB/uhOpxUv/oO3sZmhF5P0MA++52PPiGdoEvu8s/N68H+xeuYh09GH5O832vwlmzEtWou7vWLEXojIfe8jtAbCJhwEUiJvkfuQb0v3UV1unBu34mlZwbeukZ23PkoijWA9DeeIiA7lYCsNL8nPzMdRzO0rN5Cyw9fgqeKmhW5jJx5B9R/TmiPVQD4fMGIoMHookYhbNlEj9u9cr7/ztkHQvrc0LjVX1jbXolu4G3gbkWd9ScwWBHpEyEsw2/NCc9EROzKvQ/PQhgOnOiloaGh8XtEs/T8Dqja3sadI7+jsdrBjc8OZMqtPY9ofN+SvyzF51bJv7UXgQldkZhV65vZ8XM9Q29I3+txzlYPlWubSR2+98LDX3bf3Ty7htBEC5GZ3YtSPFFwrZmPr6YMY+4g9Al7fx9+6/ia6nD+/BWW06/wW3jamvwWHuXATcKklLQtWY1iNhFYkEvL3MW0LSsk5IzxOIpeITRpNkrM2SjJD1IyfRFRo+KwpKQccFzV46Fp5vNseE5iigxlwKuDwFFI67ok2pYWknDvzSAl9sKN/qSfA/ycOOZ9juPHdxEmC0HXPIQ+fk/RK6XEOfczXCt/Qm2s6Xxdn5yN9fzb0IUc+eJbKSXuiupOm45j4zakz0fa60+gDw6i9C9/xxAZRsTFU1G9AlNUILJtDaUv/wNb8k6Ce9Sh6FV8bj0bP/8zWXdNxmhYBs4SsA3bw4d/OHOkvdJvy9mVe0/jFlA9nfvoLvgOYbbhm3MfBMWj5F+FMBw9e5OGhobGbxFthf83zqIvS/nbVT9jb/GQNzyaM2/2F9qWzSpn1V9X0feuviSNT9zvGDXLalj36npSz+pB6pQ9hYy73UPxO5vwOXzkXd9zj22xeSHE5oXsc1xzsGGvYt/nVfn2nrU07mjn8k+GIYQgc2z3izmPJ77GGny1ZRiz+wPgWj0P75ZC3IU/Y/vjS90Sub8lvBXbaPvXI0inHSU4jIBRZ6MEdb9bbfvS1VQ9/28MUeFYcu/H3Ls3descrLjpM/KuWoWiOMBRgZQqKZcP7fa4isFA+Ol/ZNCgdjxN7VB2E3RsQOdKxjTsORCCxq9nUf/+VwQOyCf66vPRh+w7ltE89Ay8FdtQm+vRRfprVaTbha+6FH1SJkIIPCVFqI01KLZwjAWjMfUdjS4idp9jHgqqw4l9/aZOke/d1c0WACEwpyXja25FHxxE+GWXsPOtD6m45Qn0kX3o89w02HINyePs/vlLgVfkoE8cSa+nJiP0QcDhRZFKVxuyYSPUFSGyzwFjIL7vbgRHw5472lK6Otbu+pnRjXnisM6toaGh8XtGE/wnEQ1VHXz013V43Co3PTcI/T4KXAG8HpV/37eST5/dAMDQs5L4w7/8Vh6ATe9upmFdIy1bW+AAgr+puInS73fibnX/SvAbAw2c+d0kqhZWY0u14XF4mfu3TYy4LRNzcPeKML0uHwtf2Yo1wkTm+GjeuXAx2+bVoegF5SubSOx/4hbZSSk7V387vv+vPyPdaCb0/v8g9AbMQ07HExaDLi4Foej8yTIfPochqx/G3kMRut/2j6AuKgHFFo7SIwfTgHHdOsbT0ETTD/OIPH8ygQPyseRmEjioDzWfPk+A/ksqPh2H6jLSUD4Ny4AeWLJPP+T5mcIDMYUHIptuxLvpL9SvsrHj2y8JG7yO+JFhKAFm2pcX0rFxC9FXnEvQsP57Xe0XBiOBF9yJdDsQRhOqvZWWZ29D+ryE3vtPhNFMwCnnwvAz0aflHbEbPykl+HwIvZ7atz+l6cf54PN1btcFB2LJz8GQkISz3UjLhnKCqtyYksDccSvZUwppLIqj5OdcEHqImAJSImxDEUGD0OkPPXteql5o2t7Vsba+CFpKO7crETko8YMQCUPA0eTPvI/siYjIQRi15nkaGhoaR5Lfttr4jWBvdfPJ39bz6XNFuDq8AOQNj+aUC/fula2vsPPEhfPYsKgWRSe45qn+TL295x5CZcxro9j60VZSp6Yd8PypU9NwtbjJOG9PS4qUEketg7CcMMJy/KJ8zjOb+PGh9WxfWM/1P4zq1vUV/1DNV39YQ2CkCUUvaK1yEhxr5tIPh55QYl86O1Ad7UhHO2prE57Nq/FsXk3wzU+jBASii09DmCwYsgqQjnZEUCjG7H4Ys/t1juHdWoh77ULcaxfimPEe5mFnYBow7qgmsBxL1LZmvCVFeLauxXLmtQiDiaBrHkJYgg5oi1GdLhq/nkXj17OQbg+GsBBCTh1Fwm0DkGV/xxa6DoD0i+sJGnEfwdkHXh2XzTuQFUtRci/Y734idDy6gkEYytejn7cEV9kKAkzzcI+6ELXcQMf6Yqpemk7b0tVEX33BXlf7haIgzP4mXY4Z7yE72tAlZqK2NqGLiMWQnH3A+XaH3TeZLXMWU//xt4SePoawSWPRBQeCqmLOTMWan4O0RlC/opSmbYsJtH9BaFYlwf0VahY9QtigHijBmah1JYQMHkj/yy/zX0PyA4c+r446ZO16RGgawpaEOvcvyJ3z99xJMUB4pj8xx+J/uqcbdu8hn1NDQ0NDo3toHv4TGI/bx7evb+K9xwtpqXcB0KN3KDvWNpHWJ4yXl0/+lYhaNauSpy6dT0udk/A4C/e9N4q84XvaYaoWVxM9MKrTH/+/SCkpWVRPj2G/tts4G5ydTbEqf67kh/NnkHN5NkOeGExTWQdPZ3+Hx+HjpjljSBsV1a3rlFLywtBZ7FzaCEDqyEgu/WAIwTHHTwQ7f/4a19qFKLZwgi7+EwBt/3kMz5Y1v9rXev7tmPKHI71+3/H+4iWl14N7zXwcC75Cravw72+2EjDufMxDD32l+njiLlqGe+MKvCUbURuqOl8PmHgpASPOOuDxUlVp/Xk59e9/hbepBQBr/3wINWAJeo+QHv5VYVWE4dZfTkDvqxHKft5j1edPbPG58H0wCbwOdGf9F2xJqHP+jEg7FZE0ap+r7O5GO/b512OLXw6A0z0Sj/scat+ehepwolgtRF+579V+8KcRCZ2+095zOEgpcZVWYF+zAfuaIsw9koi6/BxaF66g6sW3CBzYh+jrLqFh0WaaV+8k7eZx6AK8uJZcgl5XjKLr+hWvqmY8Cd8TEB+H9LWDYjkkH770dCDri6G+CCJyUGL74Vvyd2TxZyh9r0fpfRlq4XTUrd/58+4jeyIi8/wpOroj07NAQ0NDQ6P7aCv8JyCqKpn74XamP7Ca6h3tAOQOi+Kap/qTXhDGZWmfsG1NI6tmVdFvfFznMe89Xsg7j6xBSigYG8s9/x1JSNSeorlhfQPfnf094b3DOeuHyQilS7Dsvvd799IlrH5vJ1d+MZy8M/2CxdXiYsbFM2nZ3sqFq89HZ9LRsN4v0HffAHxzdyEeh4/8cxO7LfbBXzg89cV+vHPxYvLOSuD0x3uh249d6WghXY7OlXZv2RZ85VuRzo7O7Yot3N8oymxFBASiT87CmDMAXbz/KUl3cuSF3oCp/1iMfcfg2bQK5/wv8JYWd/qUpaqiNtWgCz+y3u4jha+2HM+WNfia67GecQUA7o3Lca+c49/BYEKflIkhNQ/zwAkHHK+jeBt1b3+Kc/tOAEypiUReOpWip35GuArp+4dSVNWCLvF6dNGXYdHtO4VFutqQW79FLf4U3bD7EDEFiOyp4LGDwYLcMRtZ9jOy7GewJaP0uhSROh6h7Plr0BhmxXDmf3AWPofBNR2zcT7mgELE1RfTNEPFuXmbf7V/8Sqir7kAfajtV3PZX1JPd/C1d2BfV+wX+YUb8TW3dm1rbcfnOgsREUvyE3dhjHGz87U/YLFtwuAMo3l1FhEjMjBYWsEDPl1PdJGjELah6AL7oN+Vhy903bPNSNUHLaWdthxZVwTN20H6u3CLzLMgth8ipi+0VkCg/3eS6H0peq1jrYaGhsYJgbbCfwKhqpJl35Xz30fWoN9YT5IJ6sKsTPn7EPqPj0Vn0iGE4P0n1/LWX1bRd1wcT/4wgZZ6J09fNp+VMyoRAi76cz4X/yUf3V5W8CvmVfDzHxeRPDGJwY8M6nx9yT+3sfH7Ki5+ZzCLXt3K138qxBJm5A9rTiUkwYKUki/GfUXbzjYmfnwakX38MZr26g70Jh0V61t5edRP6M067t44kbBk6zF73w4XX30ljjmf4C5aTsgfXkIJtOGt3onaWI0uJvmgur8eCp6dm9DHpiAMJtwbl9P+zl8x5AzAMukqdCH7jis92kiXA2/ZFrzlWzGPOhshBB0z3sM59zMQgpD730IJsOLZtg5fVQn6lBx0sSndqktw19ZT9+6XtC9dDYAuxIYpP5P4iSUIUxyVSwdQ/e1asq5zYO1zLsKwb2uXbC5FLf4EufV78Pq7RYvMs9ANvWvP/bwu/w3BunfAvispJzAWJe9iRPrpCL3pf4dGOnciSx+C1oV4nUaWPT6V6IEpULYB1eki8aHbsWQd2BbXHVSHk6bv59K+pgjnlh3wi1/NulAbxpQUvATTstONq3oTKRPXE9W/ETxdqT9uRwJqysdYksKQHRvBGI84SB++7Gjw++0BJWkE6pZvURf+T8Gs0EHYrsz7+CEoid0vmNbQ0NDQOPYc1gr/Tz/9xEsvvYTD4WDYsGHcf//9R2pevzvmfLCdDx9cRXJdGz4XJAbrCJE+znyiLxmTE/n5Twspm1XOsKeHMOmGLD54ai2rZlXy7RubeO+JQurLOwgON3H32yPpf+q+bQTxo+I5d9E5eF1dhX0bv6/i05tWovokxd9XMfKOLLbMrqH4h2reu3QJN8wajaJTGPPaKKxxVgxWA2WzywmMtxKaHYrqU/nidn8295g/ZZ8UYl9KCaoPodOjtjXjXj0fhIJn+wZMvYeij0mCmKQDD3QEMCRldf7b11ANig7v9vUoZv9qttrR5n+qoBzdpx5qWzPe0mIMWQUIgwnXqrl0fP0vAIy5g9BFxmPILEBta8KQktM5H0NaLwxp++/E/L9Uv/w2jk3bEUYDhswMFPNCovO+gDovUhdM7KSLiTsrf5+WGSlVZPkS5MaPkZXLOl8Xsf0QOeciEn4tQIXehMieisg8E7ntR9R1/4XWMtQlf4PC/6Ab9TAipmDPY8xJkPkvfBWfUzdjHe5mI9XLWxn43ECclTFYstLwNDRR9+4XRF1yNvqwkG6/B1JK2hatpGPDZqKvOh9h0NPw1Uyk0wU6hYCMNKx9cwnITmH7iy9jFN/StCaFlu3RmMNVovI3ggfQh0PwEETwUEzBQxAm/82RsOQceA5eFzRu7iqsrSsCe7V/Y3gWStIIfzGtNWaXLScXEbEr834vN0gaGhoaGicmhyz4y8rKePDBB/n4448JDw/n8ssvZ968eYwa1b1CTY0uvnxpI6/cvpQMsyQqEOLzQjnny4m0b2kmJCMEgKbiZjqqOjCFmQkKNXH6NZl89nwRL9y0GICcQZHc98FoohL3LbbXPFeIJcZC+jlpGAP99pPyVY28fd4iVJ9k3H096X2OP7HngrcG8ff8H9k2r47ZT2xk/F9yCckIQaqSstnlzLh4JgBnfj+JbStaqVjdjC0hgFPuPjKFiUcL6fPiXrcI54KvMPYeRsCos9Gn5BBw2qUYew1BF9p9K9LRIGD4ZEz5w/FWlSJ2CX77h8/ja64nYMw5mPqMOGLn8jVU492xAU9pMd6S4k7/fdA1D2FIzUPfoye6+DT0Kdmwa9XekJKDIeXAQvJ/kapKy0+LkKokdMIIws+dRPV/v0UErSd26OsYg5wAeBiEMfvPCP3erTvS50bojNBcgjrbX1uBzoRIOw0lZxoi9MBNn4SiR2ScgUg7DVk6F3Xt29CyE4L93/vqth8RCUMRJn/PByEE+oSpxF81lcCBFcjWRRha78cQkUrFu5vxlXpwrN0AikLcLfu2sEhVxbl9J/Y1RYSMH4HeFkTDp9/jrqwheMRALDnpBI87hca1FXjbt2MIsxB+5nikr52cS35AKBJLajQ+21mE9ktGeHtDYMGuPPwD99OQUkJrGbK1HCVxKLK1HN/nF4H07bmjPsAfhxnV23/9ISnoz/30gONraGhoaJy4HLLgnzlzJqeffjoxMTEAPPvss5hM2orPwbJb7AOMvr8vvXtYCE4Owhpqwjqwy0oy6cvTad7Sgs6sY8ObRQwpCOErg4LXo3L2//Xk6qf6YTDuO+rPXt3B6n+sQfWoRPWPIiTdRmOpnTcnLcBt99LvkmROezSvc/+gKDMXvj2IN06dx48PbyBtTBSpwyNZ9shy1r/mj/qM6BNBRJ8Iyoo7CIw0MfmvfTBaTsyyEOly4Fo+C+eib1Gb6wFwC4F55BSEEASMPHBx6bFCCQrFuCurXnXY8dWWo7bU4925uVPwS48bYTB2e0zp8+KrKsHXUI0pfzgAHd+/jaeoa3V8t/8e/OJRH5OM7eanj8g1OTZtp+bND1ACzAQN7kNriZPSeU5637gdY5ATjy8bQ879mGwD9j7/llLUjZ8it89AN+W/iNBURNpp/r8zJiNMBx8fKRQdosdYRMop0LwDYYlA1m9EXfCIv6NrzjkoPc9DmLv6Btjy4pFtaciSHuDcTmzmM1Q3paOLH0TY1DMAaP5pEYF9eqIPC8Hb0oZ97Ubsa4roWFuMr81fk2OIisDatzf6jFzcunB8znJkzWJCs2cTlrQCfYCHhk25wHV+r33YVAiIIrznWIR1d7+L/ScPSWeL35rTuAXR61Lw2P0CX9EjLp4BgbGgD4DA6M6VexGZC7bk312/CA0NDY3fOofs4X/wwQcxGAyUl5dTVVXF6NGjuf322/dYaWptbaW1tXWP46qrq7n44os1Dz97iv2bnxvI5JtzDrhSt+Xjrcy/dQEJY+JJvr0An1cld9iBPeaqR2Xb59tp2tTEwL8MoKPJzUvDZ1OzsZX0MVFc+/1I9Hu5YfjmnkLm/LWYkEQLf1g9gfYdLfx03Vx63ZhHz6u75uts9WAK0h+Rzr1HGrW1kZbn7kA6/Q2FlMh4zMMnY+oz8qBE876Qqoq3qQVPXSOeugY8tQ14d/+7rgHV4cSSm0Xw8P4E9u998OP7vLjXLvR75EOj8NVX0frKPZgGjMM89HQUW/ivj3E58FbuwNDDLw7tX/8L1+LvQW8k9IHpCL0B14rZuDetxpCSjT45u9v+++7irqyh/pPviL7yXBSLhZJHXyM4301ozhpIeY61d/9Awpk2wgoMiNCxv/rekVJFVixFbvwEWbGk83Vl6N0omWcesXnucc6GzagrXkJWrfS/oDcjMs9Eyb0IYe1KrZKqG9+Ol6D+TRTFh7vVTPFHZxI5fBTt336BYg3AGB2Jc0fZnl78EBuqJQKny0PzRifeDgNhPcvpfeOsPeahKkko0ZNQEm7r3rx9Hmja2mnLkXUboK2867znfIIIisX34/+BwYoy+I8IS3jXExMNDQ0Njd80h/zp7vP5WLFiBf/973+xWCzceOONfP7550ydOrVzn+nTp/PSSy8dkYn+1vil2L/lxcH0UHx8PPhT+t1dQNp+svETRsfT48wUkk9LIm3Qr2Mz90ZjUSPBqcGdOfpet4+3zllIzcZWYnKDueLTYXsV+wATH+3Ftnm1JPQNwxCgI7JvJOctm4aiU3DZvRgt/kLi7jbZOlZ4a8pQG6sx5gxACQ5DF50IQsE84kwMWX0P2g8vvV587R3oQ4JxVVTT9N0c9KE2Iqadjn31Biqeef1XxwidijGyDWO0E/uqZqTPh7VfL9yVNbjLqrAW5KKYDiy2hE6PqaDLKufeuBzptONc8CXORd9i7DMCU8Fo9CnZCEWHc/lMOr78J6gqIff+EyUoFH1SFp4theiTs/1pRLvSgkz9D69z6v8ivV5c5dW0zF1C88z54FNRpZ6KJU4MumKSz/gR2kA0vk+f52/c+xhuO3Lbd6gbP4XWMv+LOuMvbDtHpkh2b4jwTHSnvoCsXY+6djqyfBGy6CN8xZ8j0k9H6X0ZIjAGoRjRp92JjDsLb9E9oOygZZOe5vVLSByciLeizJ88pNOhhEURPrGAoCwX9k2z0HlmYY1rpvi94Tjso7EN6osqClFCByNsQyFoCHrTvlOapJTQXoWsK0LE9UeYQ/B9czU0bdtzR53J77WPzIVd3++6U1/Y83o1sa+hoaHxu+CQBX9ERARDhgwhLMxfIDZu3DjWrl27h+C//PLLOfvss/c4bvcK/++ZL14s4tU7/FaKW14czOQbs1n59CraSttQ9PsXogGRAZzyxphun6tqUTXfnfM9gfGBDHtmKAlj4lF0gvj8EOo2tXLNtyMJCNn3h77OoHDTT2MwBHR9qwidQErJ9GkLkRLOfb1/twp1d0dOKmExR/VJQMd303H+/DXCEozh7t7+5k9X/Lnbza2kz0fH+k3Y1xThrqzFXVWLp64Bc3oKyY/+Ael00TJ7IaakeEInT8DZ6gOTGcUaSPjwFsyRTajOLZgjGlH0/ujC1voH6WiMZvmlzxAW78VXWknoGacQdelUPA1N6KwWFHP3LHEBI87E0KMnzgVf4V6/BPfKObhXziH4lmfQx/VAF+6PRdQlpKO2t/gtQr2HdVp5jhSq04VzWynOknJcpeW4SipwVVSBz3/NCEHYxCyCBoex9bMifKFZuJw1mNLGQcylvxpPejoQBguyYgnq0uf8L1qjUbKn+m075l/HXx4tRFQeunHP+Ff81/0XWTIHuflLSJsAgTFIVyvCFIwISEPf90N0jgrSb6+n6utCUq5dhNeRxLpnjLRt9xCfuBFb5LuIRh/Bu+7RpTSTeXt/9MmX7vp6wr4LlN3tyPqNUFeErN9VWOtsAkAZ/RgiZQwiLBOpejttOSIyF0JTfxU5qqGhoaHx++SQPw3GjBnD3XffTWtrK1arlQULFjB27J6rhcHBwQQHH3pr9t8i/yv2z7guE6lK+t5VgDHYSMLYvducnK0eCj8uY+W7pRjwkjc0GGushd43/zodRaqS7V/uoMekFKIHRRHZJ4LoQdFEDfAXpCo6hbOeLWDsfTkERpoPOOdfiv3y1U3oTQqN2+1s+rEas82A0XJgv6/0emh/7+94ildgGjIRy6Srjpjolz4fnqJlGHL6I/QGfy6+wYix15BdXnfTAcW+VFUcm7bTtnglbUtW42tt33MHIVBdbv+/LRZ06fH49FWUPXsBrqYAKjb1xJIcRlrez+Dyr0pLKfB641FC8gkZcBG1f5tBdN4SEsZswF4azsZ342mq+gGzcyuubdsJLMglaHAB1j65BxT/+oR0Ai+8E19DNc6fv8azaSVqUy3E9UCfnEXoX6bvcc1H4r32NrfSPHMBqJKI8yfhLC2n7NEXfrWfsAbhU7xEji0nLG02OCIpeOk/BGUnI/Q3/dq642pDXfAIsnELummfIJJHIZJHI3qMQySNOK6iVYRnohv9KLK5FLlzLiK6D9LZgu/TaYi4gSgjH0LoDAhLIvFTEokd44LNT2IAgvP/hNvhxpzaG1gJ1j5gG4oIHoqw5iOUrhvt3e+JVL3QtB2MgYigOHzLX0Ju+AD4H+elKcRfWGv0Z+krw+7VfPcaGhoaGvvkkD9J8/Pzueaaa7jooovweDwMGzaMc84550jO7aSlobKDphoH6QV7eqv3EPsvDWbS9VksunsxXoeXEc8Op9eNeXvsr/pUNs+qYcV/S1j/eQUehz9N44w/Z1L0zzXY0m17FfzzbpnPts+201HdQa8b85j09RkoeoVl/9lOREYQqcP9y4zdEfu/ZNPMav41eQGRGYF4Xf5V3FMfzO3WOGpDNd4d/mxv1+LvUSzBBIw996DOvzd8teW0TX8CtakW67m3YioYhTFvCIb03ijW/d9sSikRu4T8jj88hre+sXObKSWYsBEmnLXllHwVQntVKLkXpqOuGY7BU0/qWV0CzF4TjS/wfGy5cRBlQkgPWHshrHnoftHcKOPO8bjW/AieYlSdGXsN2D9bRa8rNmAyttG+ro22JasRRiPWglyChxxY/OvCY7CedS1wbedrQqfvTNU5WFSPB3d5Na7ScpwlFbhKy5EuN8lP3IX0+mj49HsUiwVfRBoBsUGY05LRx4ZR8U0JboeOmJGbsKUXEZJWg87kRaIgbCMITgxH6LsEqfTYkdt+RKSdCsZAZHsluFqgYRMiMhfdmMcPaf5HCxGSjAjxJ/DIunXg84DXidAZkB0N0FaBiO6NYhuMzPwnsvwfpF0eTebdk5GqG9QbEfqgX40r7XXIuvWIgDBEdD7qmn8j105H9LoEXb8bEdYopKKHsIxdK/e7CmsD4/a4cdLEvoaGhobG/jispbNp06Yxbdq0IzWX3wwPnj2brasaeGrGqfQZ4/fi/q/Yn3xDNi3bWtj6yTakKsm7IY/wXL89qrXKwfznNrPy3VJaKx2d46aNiqTfpSn0mhJPsFUlfmR8p2h1Nblo2txMzKBo0qelUbWwisCEXat/eoWibyr56NoV6AyCuzeeTljKwWflpwwJJ6yHleoN/kLsqOwght2csd9jds9PF51I0FX349m+AceM93HM/hBhDcI8+LSDnofa1oRqb0Ufk4wSFo30ulHCYxEGvzAWOh3iAGK/8etZtMxZTPxdN6BiRMWAEuzD3mCmvSmcQS+cgq70SgKjoGnraShh8SgBIeCpQyDweUNQ9WnoI/oRmNGfnpN2e+z77vOcQhGY+/4d6X2Q4N51DMi1Ujt3A2EDv0D4Wog8ZRMdZaHYt0bRvqGV9qWrEUYD1oI8ggYXEDSozxHN4peqSkfRlk47jrO0HHdFdZcl5xf4HE50ISYs/S14WkvxrL2LIAGJ0+rBlMjWzwahDzET0XcVwQkV/vGDx6Ek3YkI6PLcy9Zy1OJPkVu+BY8dRUqUnHPQjXgQrFEIc8gRu76jhZI4HDHtY/D6o0TVDe/5V+GjC1DyL0PEjkDJHdm5v1CMoBiRHgc0bNrVsXaXNaejzr9Pj/HoovMRETnIoHiEwf/zKTImo8uaonntNTQ0NDQOC63T7hGmdmc7l6Z+AkBito1XV53Jt69v+pXY9zq86AP01K6sxdnowpYbgS3Ob8Fo2mnnsZRvAAhPC2TAZSn0vSSZ8B5dq8VSSnweFb1RR1tpG19O/BqAcxeegynU1Dl+zcZWFr26laX/2o7H4WP8/T057ZGDa5L0SyrWNPH84Fn43CrXfj+S7FP3U1zocdH+3t8xFozG1LurEZJz+Uw6Pn8dXUI6wdc/jtB1b3XSV1uB8+evcK2ehy42heAbn0QIga++CiUsar+rnJ76RloXrcSSm0lAWjJVL02n9eflyKhwFOs2IgtKsPWoY/tX/WlvnUzWn8Zjcj0JxkhE2BkIax5SesFdB4YIhNK9ImV/gy8vQrf3/aXqhqYfkI0/IJvmI4TH/7pPR8UPl+AorgSholhtpDz3IIoisa8pIrBvXrc9/1JVQQiEENR/+j3O7aXE3XYVQq9ny1V/8jd62r0voEoTHp+P0D5lGMPbqV6aTs7fnsYY2IBcN+HXJzDEILNnoTMbkK1LwdcGliyEKbHzPZBVy5FFnyDLF9FpT4nOR+l9OUr8oF+PeRKhrn0bdd274NllA4vo6S/uDYoHVysipg9q5TLUmX/8dea9IRARmYNIGIbS89zOG2QNDQ0NDY0jiVbRdYRZ8WMFeiR6BcqKW7jvtBmsnV8DdIn9Te9upvCFtZz20alE9Ing23vXsXjaMv5v0Thicm2EJlmZ/Ew+yYPDSRka8SsBsGlGNd/esYJw2U7CkChOeXMMoVmhCAXc7R70gQaKvqti4atb2fpTbedxg69N5dSH97QNHSzxfUK57vuRtFY79yv2Ado/eA7PplV4q0owZvdFGP3WH/OA8QhjAMbsfgcU+1JKvCUbcS74Ck/xCv+LQqAEh4HbCaYAdBF7n4e3uZW2JatpXbQS5+bt/kNjeuCLyiF1WiSRg0tQ5MxfnMtIypV90Kfuzjf/2x7jCaGHfaSnSHstsrUM2iqQrRX+v9v8f+PpQBnzOEryKGTjFgiIQAT4s92FYoTwMxHhZyJ97dA8F2/FVwidQtJD99G0qBhv0S2YQu2UPF2Oz5GDKK8kIDudpIdux9fhQChKp/hX3R7c5VV7FtLurCDxoTtxOwRti1birqim9vtF1PywgMDoaoJSvdh66dCZ6tk5I4OSLyPQmT30OvV7AAwxvf3R/KYECBoE5hSEOQ0C0sCcBsYYlF3foyJ4T/Eum7bhm/sAtJT4X1CMiNTx/rSd8Mz9/t+fLCi9L0NkT0UWf4Za9CHUF6H+dI9/Y3Ai+qkfIGw9/F+HZXZZcyJywZaIEF1PbTSxr6GhoaFxNNAE/2Hi7PDS3uQiIt7/CH75DxUMDoaweAvfbOz4ldhXfSqb3t1MW2kbO2eVsfSrBoq+rkTRCyoLm4nJ9SeRjP7DvjvW6k0KNZvasEU48XR4EUIwfvpYDEEGhBBsm1fL9HMXAWC06Oh7cTLDbkonLj90n2MeDOljDpz7D2Aeejq+6lICL7u3U+zvZndijFR9dHz3Nqbew3Y1ffofVBX7Ry+gttSD3oCpYDTm4ZPQRcbv9Zw+ewftywtpXbiSjvWbOjPQDRFujPG11Kw20LrcQ/rVE1BatiCFGYJHoURMRNhGInS/tjpJrws66pD2Wr8FY/e/2ytRCq5FhGfhm/8w1KzZ53shwvzWJ9+Cx6BpK7rTX0dE5aFWrUIEJyKskf4GS+GTMIRPYveDN2tWHEK0oNO3Epi0DFhGR6WN1pIWpK+d5hkLafjsBwKy0/A2teCuqAG1y5KjmDwYw+yUvXwXhhAHAf2vJfycSPS6b4i66r3/ea8hbkI+kdOuwpIYBg0hCEM0YXn9EQH+p0si++19XqP02KG9xu/Hb9rub/ZkjYGOWrBEoGRNRWSduUcjq98KwhiI6H0Zoud5yM1foW78GFQfIizDn55jjUR38QyE/uDqZjQ0NDQ0NI4EmuA/DDraPNw66Guqtrfx2Dfj6TUymrU/VXKqEfRNDiZcns7Md7dz43ODmHxDNq4mF6ZQE6d9dCpF/93MDy/spGpdCwGhRq74ZGi3hXTqyEhCUoOpLPeRHGFl2/xaVr1TytSX+6EzCFJHRpJ7VjwZY6Lof1nKfmM3jzTS7cRbVYIhORtDWi9sdzyP0PvtLK2LV9E6fylhk8dh6ekXwK5ls3At+hb36nkEX/coSmgUrlVz8GxbR+BFf0TodJhPOQe1uQHzkIkogXuPZvQ2t1Lzrw9oX70BvH7bhD7ETlBvO6H9mtEppQCYM5LR9bgQXVgCwvQqImgIQteVZiPttcjq1YjofERgDL75DyG3z9zbKf37p4xFhGf5vddSRQTF+a0cwQn+v4Pi/d1MdUa/8AsIRdqDICwDqfpQf7rXbwUJikNEF/jPG1Pg74IKGMODkQPnQcs8fNXfQtt8LHEtBMSsBgTu6joMQU14K5ejenQgrRgTYggbOBdTRB3GIOce821RAwke1A9fTQ1q+c8ogRlgTUeYUyEgDZM5FfPum574/9vzWj12aK+GwFiEwYK6/l1/k6f2Kv/rrpY99lcic1Fi+6E77eXfTUSk0JsRPc9D6XneXrdpaGhoaGgcD377n8BHkVfvWEr5Zn8B6+MXzuWap/rjbvdQl2AiJ8VC317BXFt9AYEhJmpW1PLjhTMY8sRg9HE2Pn9oC+11LiKzgrj6qxFEZvw6wWNfCCEYdHUq3967lplvVuJ92d9RM2NcNH3OS0IIwVWfH3zmutrRhggIRAjh9337PJ2FsN1Buhy0vf0k3rItBF16D4aMfITegOpwUvOfj3Gun401vY6yRzcQeek5hE4cjWnAODxbVqM21SICAnEu/g7Hj+8C4N2+AUNaHuYB4399Limxr1pP+8p1RF91Pq5mF20ri8DnxeU0E5RXScqUVV0HKFYIGUN41jREcLJ/DNsYaKtArVmDCMtAhGehrnwNuf1HlEF3IHKmgTEIFD1YIsESibBE+otLLRH+pJRIfydb3YBbDvwGCT26Cc8hVR9C0SGdLYioPGTtWmirRLZVIrd+69/XEoWI7Ysy/M+gBEDoaejDJiJ9DmhZgHBXInRWgsZNQG/6D+F55TQWJRJ6wbfoTCZci5di0DuR0ojUJ6MEZyIsaYRE+J+i6KJPh+jT93xP3XZoqUJtrwJ7NbK9GtqrUHpfgQjPxDfrbqhZjTLhOUTcAGTlCmTlsq4BdEawxiACY/1WlYAI/2X/Rqw7GhoaGhoaJyua4D9EFnxawoy3tpIXJEgO0/NjqYvnrl8ECERsGA2bamh6ahUxQ2IIHBRN1cIqPG0eNry7lUU/tOFzq2ROiOGyD4Yc0gp8/8tT+OGB9XhdKkHRZgZfm0qPYRGHfD2eLYW0f/wiAaOnYh56Oo6fPsZTvJKgS+9GsYUfeADAW7ENb+kmRKANJdQf/enYWkLls//CmriWxEs2U/FDL5BQ9/aneJtaibr4LAIvuAPp9aIEWP03AJtXYx58GvoeOXuMvztVxpySiKfDS+VrHyDbWggakIIpaAkJFy5i03tDkeG9MPYaihSbEGHjEGGnQfAwEEZo3oFa/DmyZg2yejU4GgAQuReiC89CJAwGdztY/U9blP63wKA79vBZHy67i4uF2YZu/N/92euNW3fNaQ2yZg101CKbtiGEgmwtx/fdjYiYPojoPv4nANHjALCmRRKgDkBt8WDrm41i9H8vGfOfAZ0VYYzvnLt026G9CrV9E7RXI+IGIEJS8C19FrntR3C37XW+MmkUIjwTYUtCOhtB9T9BET3PR2Sc4Rf4gbFgDtU86BoaGhoaGicgmuA/BOrK7Tx3g98jP+jcFFzzyumbGsCy7U5STJIecQbqykxEpFlQPSqqV6XPbfmEZIagiwpk6aw5DLkugzP/0QfdATrr7ovgmABumD0ae72LnNNj0RsPLYdb+rw4Zr6Pc/6XAHg2rcLUbwzuwgWoDdW0f/Q8wdc+0q2xDKl5BF78J3SR8Shh0TR8OYOmrz8jatw6AtPqUX0Cc04OlYshOqeEgHS/2JYoKAF+G4liCdrjfFJKnFtLaVu0gtYlq/A1tWLsO4TaxYWYTC5MkRkY4yNRSt/AEiPp/VA0hqwrAFC9pyM8TkRAKOrOBagLnwJX856TNoUgYvxxiABK6gRI7UqiEfruP+E4VISih4hsREQ25F6AlCo07+gU4LJuPTgbkSU/IUt+2jVvm9/+E52PEncdIvMpv+WmaRvSbENYslG3fo/c+UrnSv3/Cnpl8B8RISkgVf82nclv1wmMgcBdK/WBMYio3gDoht615/EJg4/6e6OhoaGhoaFx+GixnAeJqkrumfAjhXOrGT4ikgtvzWbpA8tIvzKHv/+tmBHSiQXJtkYzugA9uRkQlhvKqBdHYgr2r742ltgPKQf/SONrqKb9w+fwlW8FRSHglPMwjz4boehQO9qwf/YqllMv3meBLIB0dtD2/t8JOOVcDMldhcYtyzfS+smjRJ+2Dn2gG9VnQcbcg86Qgr3ZQkD71ShKC17LtZQ+30DsjZcQWJDbebxrZwWtC1fQMm85vuZmQGKMbMdW0EpgVj16Yy2N2/ujRtxP1NgcZNUbSHMmuG1QW+RfLa9ZiwjPQHfaS8j6YnzfXA0B4YiYgl0r5X3AlnLCr0pLKaF1Z+fqv6xe4y+E3YXImIRu2L34lj6H3PgxSv+bUfIuwrfiFeT6d7sG2kPQx6L0GIuIKfA3jhIKmENO+PdCQ0NDQ0ND4+DRVvgPguqSNl64aTGFc6uJjTASt6mOtS96OG/pNAxWA/+8Ips1fyvEW2fHUyIon1+Fs11hx+wqGm5ZwQXThyCEOCHEPoD989fwlW9FCYnAev7t6JOykC43Pp8XvTWIoEu6VnQdsz9G+rwEjDu/s/mT6rTT9p/H8ZVtpqO1ieBbn6Ft+TqaN7Wy4z9L6X1tJfpANwQNQDFNQ138T1RXMwHBCSg9T4W2j2lbVYraKil79St6PBSFfelKmn9aireurvPc+vA2Yievwxxu73xN6kIJG9oHEZ2IWvgWsnoDsu7DzmZInfs5mvwr5mEZ6KZ+CEHxJ52oFUKALRlhS4ass/w3AO1VyOrVyJpCRKK/XkPYkpG2FNhVHKr0GIeMyEZYY3ZZbvYu6IWle5YtDQ0NDQ0NjZMTTfD/D1JKFnxaysqZFSRk2MjoF05qfhgz3trC2w+twdXhJTDUyJX39qJq+kYi+0aiM+ko+baEps3NrPihmYZt7Ux8rBfl86uorfRQbzcQtrwZR5MbS9jhWUQ6ZryPtLdgOfMahO7g//ukywE6PUJvwHr29TjmfIrljCtwldVSdvdfcVTbqdlsJqJ/FKGJKrHXnY+QThxzPgHVh3Q7sU66EtVhp+0/j+66YYgk8NK7aflpMY2f/gtnWwg+RyTVW68ksM9O9Fn3QcMWv53GEAit5ahLyhHJY1CjzqWldTbS00DpG19B8SpMMa3YhtfSuCmWpp05hKWmYQpbDvpwCBmHbFShwY7ocyeoXtTCt0D1N6wiOKnL674raQfw58gHn3xPlPaGEMKf6hMUBxlndL6uZJ+Nkn12137hmVrBrIaGhoaGhoYm+H+Jqkpeu3MZ814tItUMM+3gY88V0dHn9+CGfwzEFmnmnUWtuEJsOOod/HT9XKRXIh06dKqJwdelse7zcrbNqyNzfDSXfTj0oItzfXUVuNcv8WfZX3inf45tjbhXzkEXnYh56BkHGGFPvBXbaf/gWYw5/bGcfjm68FgMg86h/G9v4dhQDIAp1M6o5xfTtiWKxoVpNM+MI+K8SQRdfi/2z1/HPHACqqOdtn8/iq9iG0poFAHn3Enj2iZCM3YQeNlSXK29CReXEe54DlbshOTrEBHZ6Ca9CaHpyI0fo675N7J0LaG6YoLuORXB1+gDl6NOcKIzNAMQNDiZ9KgRyNo1yIY+iJEvoQSE4V0xFew10LwdEZaB0u8Gf3JOVL62Wq2hoaGhoaGh8T9ogn8XPq/Ks9cvYub0rYwKgahe4cSlhrJ5QzPbC5sIj7dw8/ODGHRGIoUvrmXLggYKP6mh+Idq+l2Riq1vLM3LKgkN8BGcHIjBrOOKz4az4+c6sifGHrA4V21twluxFbWlEdOAcQidDvfG5Thmvu+f3/gL0UXEoo9Lxb1yzh7NlQ7E7jKNjm/+jdpQhWfrWtxV1TR8NoPWn5dhTashMMdH9fIcbMPOROq2EZxVQ1BGHSKyB/YdPan613xiLrwVXVQ87s1r8FXt8GfmR46i9pG/ETpyC4q1HPQQ0CMKS3oe6vwkpLvNn9Fuje4sjBV5FyFSx6OueBm5fSb6+oUomcngKEJnACmt0BGMrnYt6vo/dV1I3QZIGuEX+PoAf8Y9oORe8Ktr1tDQ0NDQ0NDQ8PO7L9pd/n057z2xlvZmFzs3tmCx6DgzVCK9KtMWTqVsVjlZl2eh0ysIIbBXd/Bhv49QvZIdzQFc9vVoAkINPD9oFoFGL1ajSsEdvRn/cH63zu9rqMI57wtcq+eBzwtA0PWPYUjOxldbgfPnr9BnFkBwAr62DnSx8eBswxQXs/9x6ypwzPkEb8lGAi+5C31cKr6GKhzzv6alzETbkmXYcioJHViKMcyOqlpRU77CEBWH74upCFsVIqgFkPhcJurnptG6Ph5zXi7xt1yMZ/Mqqt6bh16/iejT12MIciFVPbLSjIg6F92we5DOJtBb9pt0I6vXIH0uRExvZPFTqOu/B4ceEP78+4ieu9Jo+iCieyMMlu7+12poaGhoaGhoaKCt8LOtsJH/b+/eo6Mq7/2Pv/fM5AYhITEhpECL3C9FUEHMIf4QBAQCBrBctCIUOcBSpIUukVoWKoIttBoFPHRxqhZaPBjaILUCCmIpBEwBkduJ4SJ3Ei4pJCQkM5mZ5/dH6vSAQ6BCsuPk8/qL2fPMfj7rS/bs7+zZe8//bqu840n92DB+9vb/48usUxTuPc/qwWvxnLuMw+Wg3Zi2HP1bAbsyT3K6KIxwh5/Bb3Sjbb/KxrvntLZsejWPEg90fPh7NzT35Q3vUv7Jnypvi2g5cLW6A0fDRngvXKQ8/zMqzl/AJPfj/PufE356MRGNizENPBhfBM74MST27gBOF8brwXv0C7xHc4n8j4GEtboDHA48n28GwHssD9d3WlCeX8zp93KJ7XCY2584RlhM5QWuxkTDGYMrfidWUlOspn0w+/8HExGL1fgyzmg3RLoqb5G5bx9Hpr6Ir9yHZbzE9cuvbPYvuzAnG4CpD/USMMZgRcZdtwZW4y5YVH4LYU5ewIrtDm0q76BjJXbUr5OKiIiI3KQ63/AP/XEH/r72JGePl/Jwj9v45D+3cuafN4jp1DOW+KRIEu9M4O32mbgLyzj8j0iMcdHn5x24d3zLwHoGzu3Emf8twnJYJHeKveZ83lOHcTb+HpbThbNRM7AsrCadKL3QgJJP8qHiLGENdhDZuIjLx2/DdBmKr+ADvvf4p1esp3j3axQv/vr56s5GTSGuGVa9eJypP6Ro+2EKf/93oi8k0ej+9lhhLqLbnycsphxjEnC0eAYKwRxYBO7LADi6Polp0h3zRRbm+GZMlJvEhFPEDQaPcRLeKI+zG9oREeEl2unFn18fLtTHapOOo/PYyl+h/TdZloXzwQX/9utEREREpGp1/pQev8/Ppnn72f5fXxBfUQKAt20Tuv5nG5I6xNCkc0Mw8PvOmbgLy4npeTupMzrR9K74oOszfn/gtpWmwoMVVnmhrr+okJKs/8J7cDdRD02icG8ppdt34SgvICKpGPe5BvjLw6j33UKaDP8MgPKT3XB4o7ngSyWhxW+xSkqw3BbcVoJl+Sn6uDWmOBK/qwE+Z0O8nnDK8t0YXxmnj3wHnyecFvftJen+PBz1vDiin8Q69jH4joLLD1ZLnENXgM8Dxhf0dBlTegZ/3mrMgT9D+QWsNh6ssGL8l+vDkUjAwmreG8ddE7Bimt3y/x8RERERuTl1/gj/5lk7+fLNfbjKnRR4w+nwYBJDVj6Aw3Hl3Xke/MMDRMZF0vD2BkHXY4zBsiwur1lKyac7KDkXSVzzy0Td04uoB0ZQdrYEz6E8ME4Ovf4B9RoVktjtFNFtzuCqV4H3TDOsQj+WswzjtaDMRXhFHlwOJ+nBCTiS/4ovJwMrIR4aOSjJ2YrffZKiC9FEJF4istFJohtdotFtpTjre7j0ykDcvpYkdTlFRKMSjAFz4r+hJAKiGmM1H4CjVVrlLR6rOMfeqp+E864JmM4/whzbBOHhYO3GcWE/ptyJ4+7JgYtxRURERKT2uekj/CUlJYwaNYrf/OY3N3TEvrYd4S85VcK79/yR8rBIBvyhNy1SG93Q6zz7cyj9bCf5n7qxSs6S2PIi9XoPpGzt73E2vIQ/3A/n6mOsesTP/j3H5y7GHP87ka3Oc1vKEZyR3sC6jK8+Jt+CosjK5ju6MVbDllhxLaFhi8rz2SNi/jXe+PGtfhwuHoHvFuFoUHFFNuMHvvcqzsZpeFf0A+si+GKxGnfFaj0Iq0l3LMfNfdb76gOOiIiIiNRuN9X17d69m5kzZ3L06NFbFKfmRTeJZsyhx3BFVZbC7/FQtG4tnmPHiUkfTmSzpEBj63N78Ze5KV3zB/z71uMIr6BhTDj1by/G8jkoX/M7Ir5zkahuJ7EcUHExEn9BDP7sASS3NjialkB9D45IL8btwIrqjtX+WSh2YzVzY93WFiJir9tIW5YDZ+rP8W14BsouYywLvDEYRxJWeHOIaoUj7j4AnAPfgsg4rPBb++u+avZFREREvh1uquHPzMzk+eefZ/r06UGfLy4upri4+IplBQUFNzNltbi0/wjnV32CqziX6PoniGhQTrjfovCVHHwmlvAoD7jLOF8QR1xqe8LyPyau8wkcZS6isbCSSyDWTVluImFeoCQcYjyENSyHhpV3wnFE1QdfI6z470N4MtZ3+2EltMVyhmN9gztNWgntcQ5fBeUXIOo2LIcz+LgQ+XVZEREREflmbqrhnzt3bpXPL126lEWLFt3MFNXO5/ZQsernNE0+h6NdOa7byqCBB8J8NDgYDz4Hpecb4Ix1k9B3P46oT+BuL5bL4D/ZCCtpIIRtweIQVuMozn8xgORmrbBa9sF8MRNTugMroj2O7o9gNboPy6r6B7j+HZYzDOrf2ClIIiIiIlI33ZK79PTu3Ztly5Z97Zz8YEf4fT4fZWVltGrVCpfL/muGPccPY+0YibPZpSuWGxOFKe6LuXgcb3wEEQ1yrnyh87vQ4iUcDe+tHF+UDeGNsaJaIiIiIiJSW1Rrxx0TE0NMTMz1B9rIlZyIafbPC2idsRD3IFZ8f6z6nbBcldldBW9hyppgRd4OkbdDZAuIbHHFeexWbA874ouIiIiIVMn+Q+w2c4TFYDq9DxXnILozlvX1c+GtxuPQJaoiIiIi8m1U5xt+ACuyGUTqR6NEREREJPTU+C/tioiIiIhIzbl1t4wREREREZFaRw2/iIiIiEgIU8MvIiIiIhLC1PCLiIiIiIQwNfwiIiIiIiFMDb+IiIiISAhTwy8iIiIiEsLU8IuIiIiIhDA1/CIiIiIiIUwNv4iIiIhICFPDLyIiIiISwtTwi4iIiIiEMDX8IiIiIiIhTA2/iIiIiEgIU8MvIiIiIhLC1PCLiIiIiIQwNfwiIiIiIiFMDb+IiIiISAhTwy8iIiIiEsLU8IuIiIiIhDA1/CIiIiIiIUwNv4iIiIhICHPZHcBuXq+XgoICu2OIiIjILdS4cWNc8KkkggAADWVJREFUrjrf5ogAavg5dOgQ6enpdscQERGRW2j16tW0a9fO7hgitUKdb/ijoqIAWL58OY0bN7Y5DRQUFPDDH/5QeZRHeUI4i/IoTyjlqU1Z/m+er/bvIqKGH6fTCVR+9de0aVOb0/yL8lRNeaqmPNdWm7KA8lyP8lStNuWpTVngX/t3EdFFuyIiIiIiIU0Nv4iIiIhICFPDLyIiIiISwup8wx8TE8PkyZOJiYmxOwqgPNejPFVTnm9HFlCe61GeqtWmPLUpC9S+PCK1gWWMMXaHEBERERGR6lHnj/CLiIiIiIQyNfwiIiIiIiGsTjb8JSUlDBo0iJMnTwLwzjvvkJaWxsCBA5k3bx41eZbTxo0bGTZsGAMGDGDOnDm25rm6LgDTp08nKysr8HjHjh0MGzaMwYMHM2nSJIqKimoky65duxgxYgRpaWlMmzYNj8cDwPr16xk8eDBpaWnMmDEjsPxWWrRoEWlpaaSlpTF//vzA8oqKCsaMGUNOTk5gmV157KzP66+/zsCBA0lLS+Ptt98OLLerPsHy2FkfgHnz5jFjxozAY7tqEyyPnbUZPXo0aWlppKenk56ezu7duwH76hMsj531CbZ/APvqEyyPXfVZuXJl4P8pPT2du+++m9mzZwP2b18itZKpYz7//HMzaNAg07FjR3PixAlz/Phx07dvX1NaWmq8Xq8ZOXKk2bx5c41kOX78uElNTTX5+fnG4/GYRx55xCxfvtyWPFfXpaCgwEycONHccccd5k9/+lNgXJ8+fczBgweNMcb86le/Mq+88kq1Z7l06ZLp0aOHyc3NNcYYM3XqVLN8+XJTWlpqUlNTzblz54wxxvzkJz8xK1asuKVZsrOzzciRI43b7TYej8c8/vjj5qOPPjKHDx82I0eONJ06dTKffvqpMcbYlicrK8u2+uTk5JhRo0aZiooKU1ZWZnr16mUOHz5sW32C5cnNzbWtPsYYs3XrVtO9e3fz7LPPGmOMbbUJlsfObcvv95vU1FRTUVFxxXK76hMsj531CbZ/+Otf/2pbfYLlWb9+va3b1lcOHDhg+vbtawoLC23fvkRqqzr3S7uZmZk8//zzTJ8+HYBmzZrxwQcfEBYWxoULFygpKamxK/vXr1/PwIEDAz9FnpGRQUREBMOHD6/xPFfX5f333+eBBx6gYcOGV4xbs2YNYWFhVFRUcObMGdq2bVvtWbKzs+nSpQvt2rUDYObMmfh8PurVq8fGjRsJCwujrKyMwsLCW16rxMREZsyYQXh4OAAtW7bk9OnT7Nq1i/Hjx7N06dLAWLvynDp1yrb63HPPPSxbtgyXy8WZM2cC8y5btsyW+gTLk5uba1t9Ll68SEZGBpMmTeKLL74A4I9//KMttQmWx85t68svvwRg3LhxXLx4kREjRvDYY4/ZVp9geRITE22rz7X2D0uWLLGlPsHyfP7557bV5/964YUXmDp1KvHx8fz2t7+1bfsSqc3q3Ck9c+fOpWvXrlcsCwsLIzMzkz59+pCYmBh486pux44dw+fzMWnSJNLT03nnnXeIjY21Jc/VdRk/fjzDhw//2riwsDDy8vLo2bMnOTk5pKWlVXuWY8eOUa9ePaZOnUp6ejoLFy4MvFmHhYWxadMm7r//fi5cuEBqauotzdK6dWu6dOkCwNGjR1m7di09e/Zk+vTp9OnT52vj7cgTHh5uW32+mmPBggWkpaWRkpJCUlKSbfUJlufcuXO21WfWrFlMnTr1iubCztpcncfObau4uJiUlBTeeOMNfve737FixQqys7Ntq0+wPHbW51r7B7vqEyyPnfX5ytatWykvL2fAgAGAvduXSG1W5xr+axkxYgQ5OTkkJCSwaNGiGpnT5/Oxbds2Xn75Zd5991327NnDqlWrbMtzo9q2bcvWrVt58sknmTp1arXP5/P52LJlC9OmTSMrK4uysjKWLFkSeP6rDx+9evXihRdeqJYMBw8eZNy4cUyfPp3mzZtXObam89SG+kyZMoVt27aRn59PZmZmlWNrOo/H47GlPitXriQ5OZmUlJQbfk111iZYHjv/du68807mz59PgwYNiI+P5wc/+AGbNm2q8jU1neeVV16xrT5V7R+upabz1Ib3nhUrVvCjH/3ohsbWRB6R2qrON/z5+fns3LkTAJfLRVpaGnl5eTUyd0JCAikpKcTHxxMZGUmfPn3Yvn27bXmux+12s2HDhsDjhx56qEayJSQk0LlzZ5o1a4bT6WTAgAHs2bOHixcvsmXLlsC4wYMHV0uenTt3MnbsWH76058ydOjQa46zK4+d9Tl8+DC5ubkAREVF0a9fv2vOYVeeJUuW2FKfNWvWkJ2dTXp6OgsWLGDjxo28/PLLQcfWRG2C5Xnrrbds+9vZsWMH27ZtCzw2xuByBT/L1K48cXFxttUn2P5hz549QcfalWfx4sW2vjd7PB62b99O7969qxxXU3lEarM63/BfunSJZ555huLiYowxfPjhh9x99901MnevXr3YsmULxcXF+Hw+Nm/eTLt27WzLcz0ul4sXX3yRffv2AbB27Vruuuuuap83NTWV/fv3k5+fD8Ann3xCx44dMcbwzDPPcPr0aQDWrVt3y/Pk5+fz1FNP8etf//q6py/ZlcfO+pw8eZKZM2fi8XjweDx8/PHH1/x7tSvP7NmzbanP22+/zV/+8hdWr17NlClT6N27N88991zQsTVRm2B5/vznP9v2t3Pp0iXmz5+P2+2mpKSEVatW0bdv36Bj7cqzaNEi2+oTbP/QsWPHoGPtyjNhwgTb6gOQl5dH8+bNqVevXpXjaiqPSG1W5y7avVqbNm2YMGECo0aNwul00rVr1xv+evBmde7cmfHjx/Poo49SUVFBjx49GD16NBEREbbkuR6n00lGRgazZs3C5/ORlJTE3Llzq33e5ORkZs+ezaRJk3C73bRv355nn32WqKgoXnrpJSZOnIhlWbRq1YoXX3zxls795ptv4na7+eUvfxlYNmrUKB555JGvjY2Li7Mtj1316dmzJ3v27GHIkCE4nU769et3zQ9GNVGfYHmGDBlCw4YNbanPjaqJ2gRj57bVq1cvdu/ezZAhQ/D7/Tz66KPceeedQcfWRH2C5enatatt9Qm2f3j44YeDjq2J+gTL8+STT/L973/ftm3rxIkTgYuIq2LX9iVSm1jG1OBN50VEREREpEbV+VN6RERERERCmRp+EREREZEQpoZfRERERCSEqeEXEREREQlhavhFREREREKYGn4R+VbYu3cvU6ZMqZZ179mzh1mzZgV9rnfv3uzdu7da5h03bhz/+Mc/qn0eERGp29Twi8i3QqdOnViwYEG1rPvQoUOcOXOmWtZdlezs7BqfU0RE6h41/CLyrZCTk8OgQYMAmDFjBnPmzGH06NH07duXyZMnU1paCkCHDh3IyMhg2LBh9O/fn48++giArKwsJk6cGFjfV4/z8/NZsGABO3bs4Gc/+1mVGTZu3Mjw4cMZMmQIo0aNYteuXQAsXLiQGTNm8MQTT9C/f3/GjBnD2bNngcpvD4YNG8bgwYN56qmnGDp0KDk5OYG5xowZE/il0nfffZdhw4Zx//33k5GRcQurJyIidZkafhH5Vtq3bx9vvvkma9as4dSpU6xbtw4An89HVFQUWVlZvPbaazz33HOB02aCSU5OZsqUKXTt2pVf/OIX1xx39OhRMjIyWLJkCe+99x4vvfQSTz/9NJcvXwZgx44dvP7666xbt46oqChWrFiB1+vl6aef5sc//jHvv/8+o0ePJjc3FyAw19KlS0lOTgYgIiKCrKwsVq5cyVtvvRX4ICAiInIzXHYHEBH5Ju677z7Cw8MBaNOmDUVFRYHnHnvsMQDatWtHmzZt2L59+03Pl52dzdmzZxk7dmxgmWVZHD9+HIB77rmH6OhooPJbhqKiIg4cOABAz549Abj33ntp3br1Nef46huMxMREEhISKCwsDHwYEBER+abU8IvIt1JkZGTg35ZlYYwJPHY6nYF/+/1+nE7n18ZUVFT8W/P5/X5SUlJ47bXXAsvy8/Np1KgR69evD5rH6XReMefV2a7mcv3rLfnqvCIiIt+UTukRkZDz3nvvAbB//36OHDlCt27diI+P5+DBg7jdbioqKvjwww8D451OJ16vt8p1pqSkkJ2dzeHDhwHYtGkTDz30EOXl5dd8TcuWLQkPD+dvf/sbUHk+/4EDB7As64bnFRERuVk6wi8iIeezzz4jMzMTv99PRkYGsbGx9OjRg27dujFgwAASExPp3r07eXl5AHTp0oU33niDyZMns2jRoqDrbNWqFbNnz2batGkYY3C5XCxevJj69etfM4fL5WLhwoU8//zzvPrqqzRv3pyEhITAtwH9+/dn9OjRLFy48NYXQURE5J8so++MRSSEtG3blm3bthEfH293FADmzZvHE088QUJCAvn5+aSnp7NhwwZiYmLsjiYiInWEjvCLiFSjJk2aMHbsWFwuF8YY5syZo2ZfRERqlI7wi4iIiIiEMF20KyIiIiISwtTwi4iIiIiEMDX8IiIiIiIhTA2/iIiIiEgIU8MvIiIiIhLC1PCLiIiIiISw/w8isPY/FEeNGAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 782.6x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "data = new_data\n",
+    "#pallette = ['#782be8', '#d000ba', '#ff0c5c', '#ff5938', '#f47e1f', '#afc94e', '#95d678', '#79e8c8']\n",
+    "pallette = ['#FDFC4B', '#E8186A', '#F69805', '#D64624', '#AA0A81', '#5E18C7', '#0A6152', '#3F0F84']\n",
+    "g = sns.relplot(data=data, kind=\"line\",palette=palette, linewidth=2, height=6, aspect=1.5)\n",
+    "g.set(xticks=range(13, 750, 50))\n",
+    "g.set(yticks=range(1, 70, 5))\n",
+    "g.despine(offset=10, trim=True)\n",
+    "#sns.lineplot(data=data, palette=\"plasma\", linewidth=2.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49f8add7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/exercises/practice/acronym/approaches/Untitled.ipynb b/exercises/practice/acronym/approaches/Untitled.ipynb
new file mode 100644
index 0000000000..6fd3f74091
--- /dev/null
+++ b/exercises/practice/acronym/approaches/Untitled.ipynb
@@ -0,0 +1,989 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "dea90412",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>input length</th>\n",
+       "      <th>regex_join_I</th>\n",
+       "      <th>regex_join</th>\n",
+       "      <th>regex_sub</th>\n",
+       "      <th>genex</th>\n",
+       "      <th>map</th>\n",
+       "      <th>reduce</th>\n",
+       "      <th>list_comprehension</th>\n",
+       "      <th>loop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13</td>\n",
+       "      <td>2.445</td>\n",
+       "      <td>1.943</td>\n",
+       "      <td>1.897</td>\n",
+       "      <td>1.050</td>\n",
+       "      <td>1.014</td>\n",
+       "      <td>0.990</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14</td>\n",
+       "      <td>2.260</td>\n",
+       "      <td>1.683</td>\n",
+       "      <td>1.526</td>\n",
+       "      <td>0.962</td>\n",
+       "      <td>0.913</td>\n",
+       "      <td>0.832</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19</td>\n",
+       "      <td>2.928</td>\n",
+       "      <td>2.204</td>\n",
+       "      <td>2.377</td>\n",
+       "      <td>1.133</td>\n",
+       "      <td>1.115</td>\n",
+       "      <td>1.159</td>\n",
+       "      <td>0.959</td>\n",
+       "      <td>0.864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20</td>\n",
+       "      <td>2.998</td>\n",
+       "      <td>2.242</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>1.165</td>\n",
+       "      <td>1.146</td>\n",
+       "      <td>1.197</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>0.894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25</td>\n",
+       "      <td>2.853</td>\n",
+       "      <td>1.973</td>\n",
+       "      <td>1.921</td>\n",
+       "      <td>1.058</td>\n",
+       "      <td>1.027</td>\n",
+       "      <td>1.014</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>30</td>\n",
+       "      <td>3.303</td>\n",
+       "      <td>2.259</td>\n",
+       "      <td>2.378</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>1.186</td>\n",
+       "      <td>0.970</td>\n",
+       "      <td>0.867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>35</td>\n",
+       "      <td>4.300</td>\n",
+       "      <td>3.079</td>\n",
+       "      <td>3.837</td>\n",
+       "      <td>1.457</td>\n",
+       "      <td>1.466</td>\n",
+       "      <td>1.664</td>\n",
+       "      <td>1.232</td>\n",
+       "      <td>1.222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>39</td>\n",
+       "      <td>3.612</td>\n",
+       "      <td>2.282</td>\n",
+       "      <td>2.492</td>\n",
+       "      <td>1.175</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>1.220</td>\n",
+       "      <td>1.013</td>\n",
+       "      <td>0.899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>42</td>\n",
+       "      <td>5.106</td>\n",
+       "      <td>3.851</td>\n",
+       "      <td>4.493</td>\n",
+       "      <td>1.636</td>\n",
+       "      <td>1.660</td>\n",
+       "      <td>1.994</td>\n",
+       "      <td>1.402</td>\n",
+       "      <td>1.397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>45</td>\n",
+       "      <td>4.412</td>\n",
+       "      <td>2.981</td>\n",
+       "      <td>3.329</td>\n",
+       "      <td>1.375</td>\n",
+       "      <td>1.361</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>1.121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>60</td>\n",
+       "      <td>6.532</td>\n",
+       "      <td>4.751</td>\n",
+       "      <td>5.609</td>\n",
+       "      <td>1.997</td>\n",
+       "      <td>2.041</td>\n",
+       "      <td>2.523</td>\n",
+       "      <td>1.662</td>\n",
+       "      <td>1.827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>63</td>\n",
+       "      <td>6.717</td>\n",
+       "      <td>4.839</td>\n",
+       "      <td>6.875</td>\n",
+       "      <td>2.043</td>\n",
+       "      <td>2.089</td>\n",
+       "      <td>2.569</td>\n",
+       "      <td>1.711</td>\n",
+       "      <td>1.888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>74</td>\n",
+       "      <td>7.559</td>\n",
+       "      <td>5.414</td>\n",
+       "      <td>6.513</td>\n",
+       "      <td>2.310</td>\n",
+       "      <td>2.405</td>\n",
+       "      <td>3.021</td>\n",
+       "      <td>1.980</td>\n",
+       "      <td>2.221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>78</td>\n",
+       "      <td>6.327</td>\n",
+       "      <td>3.958</td>\n",
+       "      <td>4.507</td>\n",
+       "      <td>1.695</td>\n",
+       "      <td>1.721</td>\n",
+       "      <td>2.046</td>\n",
+       "      <td>1.455</td>\n",
+       "      <td>1.491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>93</td>\n",
+       "      <td>7.947</td>\n",
+       "      <td>4.840</td>\n",
+       "      <td>5.779</td>\n",
+       "      <td>2.004</td>\n",
+       "      <td>2.070</td>\n",
+       "      <td>2.548</td>\n",
+       "      <td>1.704</td>\n",
+       "      <td>1.886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>108</td>\n",
+       "      <td>11.166</td>\n",
+       "      <td>7.261</td>\n",
+       "      <td>10.266</td>\n",
+       "      <td>3.087</td>\n",
+       "      <td>3.199</td>\n",
+       "      <td>4.091</td>\n",
+       "      <td>2.519</td>\n",
+       "      <td>2.887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>120</td>\n",
+       "      <td>8.991</td>\n",
+       "      <td>4.932</td>\n",
+       "      <td>6.000</td>\n",
+       "      <td>2.129</td>\n",
+       "      <td>2.176</td>\n",
+       "      <td>2.680</td>\n",
+       "      <td>1.813</td>\n",
+       "      <td>1.938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>140</td>\n",
+       "      <td>14.260</td>\n",
+       "      <td>9.803</td>\n",
+       "      <td>12.876</td>\n",
+       "      <td>4.021</td>\n",
+       "      <td>4.206</td>\n",
+       "      <td>5.480</td>\n",
+       "      <td>3.329</td>\n",
+       "      <td>3.968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>150</td>\n",
+       "      <td>12.290</td>\n",
+       "      <td>7.211</td>\n",
+       "      <td>8.760</td>\n",
+       "      <td>2.986</td>\n",
+       "      <td>3.087</td>\n",
+       "      <td>3.943</td>\n",
+       "      <td>2.487</td>\n",
+       "      <td>2.776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>200</td>\n",
+       "      <td>19.407</td>\n",
+       "      <td>12.727</td>\n",
+       "      <td>17.067</td>\n",
+       "      <td>5.224</td>\n",
+       "      <td>5.540</td>\n",
+       "      <td>7.166</td>\n",
+       "      <td>4.210</td>\n",
+       "      <td>5.107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>210</td>\n",
+       "      <td>20.110</td>\n",
+       "      <td>12.974</td>\n",
+       "      <td>21.827</td>\n",
+       "      <td>5.352</td>\n",
+       "      <td>5.748</td>\n",
+       "      <td>7.257</td>\n",
+       "      <td>4.362</td>\n",
+       "      <td>5.258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>225</td>\n",
+       "      <td>21.177</td>\n",
+       "      <td>14.154</td>\n",
+       "      <td>19.314</td>\n",
+       "      <td>5.793</td>\n",
+       "      <td>6.017</td>\n",
+       "      <td>7.917</td>\n",
+       "      <td>4.653</td>\n",
+       "      <td>5.702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>260</td>\n",
+       "      <td>18.602</td>\n",
+       "      <td>10.110</td>\n",
+       "      <td>12.960</td>\n",
+       "      <td>4.189</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>5.640</td>\n",
+       "      <td>3.444</td>\n",
+       "      <td>4.067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>310</td>\n",
+       "      <td>23.087</td>\n",
+       "      <td>13.068</td>\n",
+       "      <td>17.437</td>\n",
+       "      <td>5.352</td>\n",
+       "      <td>5.693</td>\n",
+       "      <td>7.303</td>\n",
+       "      <td>4.326</td>\n",
+       "      <td>5.202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>360</td>\n",
+       "      <td>33.145</td>\n",
+       "      <td>21.459</td>\n",
+       "      <td>31.768</td>\n",
+       "      <td>8.962</td>\n",
+       "      <td>9.701</td>\n",
+       "      <td>12.506</td>\n",
+       "      <td>7.364</td>\n",
+       "      <td>8.998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>400</td>\n",
+       "      <td>25.931</td>\n",
+       "      <td>13.517</td>\n",
+       "      <td>18.251</td>\n",
+       "      <td>5.577</td>\n",
+       "      <td>5.939</td>\n",
+       "      <td>7.538</td>\n",
+       "      <td>4.644</td>\n",
+       "      <td>5.437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>750</td>\n",
+       "      <td>67.390</td>\n",
+       "      <td>44.480</td>\n",
+       "      <td>56.100</td>\n",
+       "      <td>18.607</td>\n",
+       "      <td>19.995</td>\n",
+       "      <td>26.182</td>\n",
+       "      <td>15.324</td>\n",
+       "      <td>18.628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    input length  regex_join_I  regex_join  regex_sub   genex     map  reduce  \\\n",
+       "0             13         2.445       1.943      1.897   1.050   1.014   0.990   \n",
+       "1             14         2.260       1.683      1.526   0.962   0.913   0.832   \n",
+       "2             19         2.928       2.204      2.377   1.133   1.115   1.159   \n",
+       "3             20         2.998       2.242      2.807   1.165   1.146   1.197   \n",
+       "4             25         2.853       1.973      1.921   1.058   1.027   1.014   \n",
+       "5             30         3.303       2.259      2.378   1.148   1.137   1.186   \n",
+       "6             35         4.300       3.079      3.837   1.457   1.466   1.664   \n",
+       "7             39         3.612       2.282      2.492   1.175   1.172   1.220   \n",
+       "8             42         5.106       3.851      4.493   1.636   1.660   1.994   \n",
+       "9             45         4.412       2.981      3.329   1.375   1.361   1.504   \n",
+       "10            60         6.532       4.751      5.609   1.997   2.041   2.523   \n",
+       "11            63         6.717       4.839      6.875   2.043   2.089   2.569   \n",
+       "12            74         7.559       5.414      6.513   2.310   2.405   3.021   \n",
+       "13            78         6.327       3.958      4.507   1.695   1.721   2.046   \n",
+       "14            93         7.947       4.840      5.779   2.004   2.070   2.548   \n",
+       "15           108        11.166       7.261     10.266   3.087   3.199   4.091   \n",
+       "16           120         8.991       4.932      6.000   2.129   2.176   2.680   \n",
+       "17           140        14.260       9.803     12.876   4.021   4.206   5.480   \n",
+       "18           150        12.290       7.211      8.760   2.986   3.087   3.943   \n",
+       "19           200        19.407      12.727     17.067   5.224   5.540   7.166   \n",
+       "20           210        20.110      12.974     21.827   5.352   5.748   7.257   \n",
+       "21           225        21.177      14.154     19.314   5.793   6.017   7.917   \n",
+       "22           260        18.602      10.110     12.960   4.189   4.370   5.640   \n",
+       "23           310        23.087      13.068     17.437   5.352   5.693   7.303   \n",
+       "24           360        33.145      21.459     31.768   8.962   9.701  12.506   \n",
+       "25           400        25.931      13.517     18.251   5.577   5.939   7.538   \n",
+       "26           750        67.390      44.480     56.100  18.607  19.995  26.182   \n",
+       "\n",
+       "    list_comprehension    loop  \n",
+       "0                0.878   0.743  \n",
+       "1                0.798   0.634  \n",
+       "2                0.959   0.864  \n",
+       "3                0.987   0.894  \n",
+       "4                0.889   0.760  \n",
+       "5                0.970   0.867  \n",
+       "6                1.232   1.222  \n",
+       "7                1.013   0.899  \n",
+       "8                1.402   1.397  \n",
+       "9                1.160   1.121  \n",
+       "10               1.662   1.827  \n",
+       "11               1.711   1.888  \n",
+       "12               1.980   2.221  \n",
+       "13               1.455   1.491  \n",
+       "14               1.704   1.886  \n",
+       "15               2.519   2.887  \n",
+       "16               1.813   1.938  \n",
+       "17               3.329   3.968  \n",
+       "18               2.487   2.776  \n",
+       "19               4.210   5.107  \n",
+       "20               4.362   5.258  \n",
+       "21               4.653   5.702  \n",
+       "22               3.444   4.067  \n",
+       "23               4.326   5.202  \n",
+       "24               7.364   8.998  \n",
+       "25               4.644   5.437  \n",
+       "26              15.324  18.628  "
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "data_to_plot = {'input length': [13,\n",
+    " 14,\n",
+    " 19,\n",
+    " 20,\n",
+    " 25,\n",
+    " 30,\n",
+    " 35,\n",
+    " 39,\n",
+    " 42,\n",
+    " 45,\n",
+    " 60,\n",
+    " 63,\n",
+    " 74,\n",
+    " 78,\n",
+    " 93,\n",
+    " 108,\n",
+    " 120,\n",
+    " 140,\n",
+    " 150,\n",
+    " 200,\n",
+    " 210,\n",
+    " 225,\n",
+    " 260,\n",
+    " 310,\n",
+    " 360,\n",
+    " 400,\n",
+    " 750],\n",
+    "'regex_join_I': [2.445, 2.26, 2.928, 2.998, 2.853, 3.303, 4.3, 3.612, 5.106, 4.412, 6.532, 6.717, 7.559, 6.327, 7.947, 11.166, 8.991, 14.26, 12.29, 19.407, 20.11, 21.177, 18.602, 23.087, 33.145, 25.931, 67.39],\n",
+    "\n",
+    "'regex_join' :[1.943, 1.683, 2.204, 2.242, 1.973, 2.259, 3.079, 2.282, 3.851, 2.981, 4.751, 4.839, 5.414, 3.958, 4.84, 7.261, 4.932, 9.803, 7.211, 12.727, 12.974, 14.154, 10.11, 13.068, 21.459, 13.517, 44.48],\n",
+    "\n",
+    "'regex_sub': [1.897, 1.526, 2.377, 2.807, 1.921, 2.378, 3.837, 2.492, 4.493, 3.329, 5.609, 6.875, 6.513, 4.507, 5.779, 10.266, 6.0, 12.876, 8.76, 17.067, 21.827, 19.314, 12.96, 17.437, 31.768, 18.251, 56.1],\n",
+    "\n",
+    "'genex': [1.05, 0.962, 1.133, 1.165, 1.058, 1.148, 1.457, 1.175, 1.636, 1.375, 1.997, 2.043, 2.31, 1.695, 2.004, 3.087, 2.129, 4.021, 2.986, 5.224, 5.352, 5.793, 4.189, 5.352, 8.962, 5.577, 18.607],\n",
+    "\n",
+    "'map' :[1.014, 0.913, 1.115, 1.146, 1.027, 1.137, 1.466, 1.172, 1.66, 1.361, 2.041, 2.089, 2.405, 1.721, 2.07, 3.199, 2.176, 4.206, 3.087, 5.54, 5.748, 6.017, 4.37, 5.693, 9.701, 5.939, 19.995],\n",
+    "\n",
+    "'reduce': [0.99, 0.832, 1.159, 1.197, 1.014, 1.186, 1.664, 1.22, 1.994, 1.504, 2.523, 2.569, 3.021, 2.046, 2.548, 4.091, 2.68, 5.48, 3.943, 7.166, 7.257, 7.917, 5.64, 7.303, 12.506, 7.538, 26.182],\n",
+    "\n",
+    "'list_comprehension': [0.878, 0.798, 0.959, 0.987, 0.889, 0.97, 1.232, 1.013, 1.402, 1.16, 1.662, 1.711, 1.98, 1.455, 1.704, 2.519, 1.813, 3.329, 2.487, 4.21, 4.362, 4.653, 3.444, 4.326, 7.364, 4.644, 15.324],\n",
+    "\n",
+    "'loop': [0.743, 0.634, 0.864, 0.894, 0.76, 0.867, 1.222, 0.899, 1.397, 1.121, 1.827, 1.888, 2.221, 1.491, 1.886, 2.887, 1.938, 3.968, 2.776, 5.107, 5.258, 5.702, 4.067, 5.202, 8.998, 5.437, 18.628]}\n",
+    "\n",
+    "plot_data = pd.DataFrame.from_dict(data_to_plot)\n",
+    "new_data = plot_data.sort_values('input length')\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "8968f0c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>regex_join_I</th>\n",
+       "      <th>regex_join</th>\n",
+       "      <th>regex_sub</th>\n",
+       "      <th>genex</th>\n",
+       "      <th>map</th>\n",
+       "      <th>reduce</th>\n",
+       "      <th>list_comprehension</th>\n",
+       "      <th>loop</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>input length</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.445</td>\n",
+       "      <td>1.943</td>\n",
+       "      <td>1.897</td>\n",
+       "      <td>1.050</td>\n",
+       "      <td>1.014</td>\n",
+       "      <td>0.990</td>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2.260</td>\n",
+       "      <td>1.683</td>\n",
+       "      <td>1.526</td>\n",
+       "      <td>0.962</td>\n",
+       "      <td>0.913</td>\n",
+       "      <td>0.832</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2.928</td>\n",
+       "      <td>2.204</td>\n",
+       "      <td>2.377</td>\n",
+       "      <td>1.133</td>\n",
+       "      <td>1.115</td>\n",
+       "      <td>1.159</td>\n",
+       "      <td>0.959</td>\n",
+       "      <td>0.864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>2.998</td>\n",
+       "      <td>2.242</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>1.165</td>\n",
+       "      <td>1.146</td>\n",
+       "      <td>1.197</td>\n",
+       "      <td>0.987</td>\n",
+       "      <td>0.894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>2.853</td>\n",
+       "      <td>1.973</td>\n",
+       "      <td>1.921</td>\n",
+       "      <td>1.058</td>\n",
+       "      <td>1.027</td>\n",
+       "      <td>1.014</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>0.760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>3.303</td>\n",
+       "      <td>2.259</td>\n",
+       "      <td>2.378</td>\n",
+       "      <td>1.148</td>\n",
+       "      <td>1.137</td>\n",
+       "      <td>1.186</td>\n",
+       "      <td>0.970</td>\n",
+       "      <td>0.867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>4.300</td>\n",
+       "      <td>3.079</td>\n",
+       "      <td>3.837</td>\n",
+       "      <td>1.457</td>\n",
+       "      <td>1.466</td>\n",
+       "      <td>1.664</td>\n",
+       "      <td>1.232</td>\n",
+       "      <td>1.222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>3.612</td>\n",
+       "      <td>2.282</td>\n",
+       "      <td>2.492</td>\n",
+       "      <td>1.175</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>1.220</td>\n",
+       "      <td>1.013</td>\n",
+       "      <td>0.899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>5.106</td>\n",
+       "      <td>3.851</td>\n",
+       "      <td>4.493</td>\n",
+       "      <td>1.636</td>\n",
+       "      <td>1.660</td>\n",
+       "      <td>1.994</td>\n",
+       "      <td>1.402</td>\n",
+       "      <td>1.397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>4.412</td>\n",
+       "      <td>2.981</td>\n",
+       "      <td>3.329</td>\n",
+       "      <td>1.375</td>\n",
+       "      <td>1.361</td>\n",
+       "      <td>1.504</td>\n",
+       "      <td>1.160</td>\n",
+       "      <td>1.121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>6.532</td>\n",
+       "      <td>4.751</td>\n",
+       "      <td>5.609</td>\n",
+       "      <td>1.997</td>\n",
+       "      <td>2.041</td>\n",
+       "      <td>2.523</td>\n",
+       "      <td>1.662</td>\n",
+       "      <td>1.827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>6.717</td>\n",
+       "      <td>4.839</td>\n",
+       "      <td>6.875</td>\n",
+       "      <td>2.043</td>\n",
+       "      <td>2.089</td>\n",
+       "      <td>2.569</td>\n",
+       "      <td>1.711</td>\n",
+       "      <td>1.888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>7.559</td>\n",
+       "      <td>5.414</td>\n",
+       "      <td>6.513</td>\n",
+       "      <td>2.310</td>\n",
+       "      <td>2.405</td>\n",
+       "      <td>3.021</td>\n",
+       "      <td>1.980</td>\n",
+       "      <td>2.221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>6.327</td>\n",
+       "      <td>3.958</td>\n",
+       "      <td>4.507</td>\n",
+       "      <td>1.695</td>\n",
+       "      <td>1.721</td>\n",
+       "      <td>2.046</td>\n",
+       "      <td>1.455</td>\n",
+       "      <td>1.491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>7.947</td>\n",
+       "      <td>4.840</td>\n",
+       "      <td>5.779</td>\n",
+       "      <td>2.004</td>\n",
+       "      <td>2.070</td>\n",
+       "      <td>2.548</td>\n",
+       "      <td>1.704</td>\n",
+       "      <td>1.886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>11.166</td>\n",
+       "      <td>7.261</td>\n",
+       "      <td>10.266</td>\n",
+       "      <td>3.087</td>\n",
+       "      <td>3.199</td>\n",
+       "      <td>4.091</td>\n",
+       "      <td>2.519</td>\n",
+       "      <td>2.887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>8.991</td>\n",
+       "      <td>4.932</td>\n",
+       "      <td>6.000</td>\n",
+       "      <td>2.129</td>\n",
+       "      <td>2.176</td>\n",
+       "      <td>2.680</td>\n",
+       "      <td>1.813</td>\n",
+       "      <td>1.938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>14.260</td>\n",
+       "      <td>9.803</td>\n",
+       "      <td>12.876</td>\n",
+       "      <td>4.021</td>\n",
+       "      <td>4.206</td>\n",
+       "      <td>5.480</td>\n",
+       "      <td>3.329</td>\n",
+       "      <td>3.968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>12.290</td>\n",
+       "      <td>7.211</td>\n",
+       "      <td>8.760</td>\n",
+       "      <td>2.986</td>\n",
+       "      <td>3.087</td>\n",
+       "      <td>3.943</td>\n",
+       "      <td>2.487</td>\n",
+       "      <td>2.776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>19.407</td>\n",
+       "      <td>12.727</td>\n",
+       "      <td>17.067</td>\n",
+       "      <td>5.224</td>\n",
+       "      <td>5.540</td>\n",
+       "      <td>7.166</td>\n",
+       "      <td>4.210</td>\n",
+       "      <td>5.107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>20.110</td>\n",
+       "      <td>12.974</td>\n",
+       "      <td>21.827</td>\n",
+       "      <td>5.352</td>\n",
+       "      <td>5.748</td>\n",
+       "      <td>7.257</td>\n",
+       "      <td>4.362</td>\n",
+       "      <td>5.258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>21.177</td>\n",
+       "      <td>14.154</td>\n",
+       "      <td>19.314</td>\n",
+       "      <td>5.793</td>\n",
+       "      <td>6.017</td>\n",
+       "      <td>7.917</td>\n",
+       "      <td>4.653</td>\n",
+       "      <td>5.702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>18.602</td>\n",
+       "      <td>10.110</td>\n",
+       "      <td>12.960</td>\n",
+       "      <td>4.189</td>\n",
+       "      <td>4.370</td>\n",
+       "      <td>5.640</td>\n",
+       "      <td>3.444</td>\n",
+       "      <td>4.067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>23.087</td>\n",
+       "      <td>13.068</td>\n",
+       "      <td>17.437</td>\n",
+       "      <td>5.352</td>\n",
+       "      <td>5.693</td>\n",
+       "      <td>7.303</td>\n",
+       "      <td>4.326</td>\n",
+       "      <td>5.202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>33.145</td>\n",
+       "      <td>21.459</td>\n",
+       "      <td>31.768</td>\n",
+       "      <td>8.962</td>\n",
+       "      <td>9.701</td>\n",
+       "      <td>12.506</td>\n",
+       "      <td>7.364</td>\n",
+       "      <td>8.998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>25.931</td>\n",
+       "      <td>13.517</td>\n",
+       "      <td>18.251</td>\n",
+       "      <td>5.577</td>\n",
+       "      <td>5.939</td>\n",
+       "      <td>7.538</td>\n",
+       "      <td>4.644</td>\n",
+       "      <td>5.437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>750</th>\n",
+       "      <td>67.390</td>\n",
+       "      <td>44.480</td>\n",
+       "      <td>56.100</td>\n",
+       "      <td>18.607</td>\n",
+       "      <td>19.995</td>\n",
+       "      <td>26.182</td>\n",
+       "      <td>15.324</td>\n",
+       "      <td>18.628</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              regex_join_I  regex_join  regex_sub   genex     map  reduce  \\\n",
+       "input length                                                                \n",
+       "13                   2.445       1.943      1.897   1.050   1.014   0.990   \n",
+       "14                   2.260       1.683      1.526   0.962   0.913   0.832   \n",
+       "19                   2.928       2.204      2.377   1.133   1.115   1.159   \n",
+       "20                   2.998       2.242      2.807   1.165   1.146   1.197   \n",
+       "25                   2.853       1.973      1.921   1.058   1.027   1.014   \n",
+       "30                   3.303       2.259      2.378   1.148   1.137   1.186   \n",
+       "35                   4.300       3.079      3.837   1.457   1.466   1.664   \n",
+       "39                   3.612       2.282      2.492   1.175   1.172   1.220   \n",
+       "42                   5.106       3.851      4.493   1.636   1.660   1.994   \n",
+       "45                   4.412       2.981      3.329   1.375   1.361   1.504   \n",
+       "60                   6.532       4.751      5.609   1.997   2.041   2.523   \n",
+       "63                   6.717       4.839      6.875   2.043   2.089   2.569   \n",
+       "74                   7.559       5.414      6.513   2.310   2.405   3.021   \n",
+       "78                   6.327       3.958      4.507   1.695   1.721   2.046   \n",
+       "93                   7.947       4.840      5.779   2.004   2.070   2.548   \n",
+       "108                 11.166       7.261     10.266   3.087   3.199   4.091   \n",
+       "120                  8.991       4.932      6.000   2.129   2.176   2.680   \n",
+       "140                 14.260       9.803     12.876   4.021   4.206   5.480   \n",
+       "150                 12.290       7.211      8.760   2.986   3.087   3.943   \n",
+       "200                 19.407      12.727     17.067   5.224   5.540   7.166   \n",
+       "210                 20.110      12.974     21.827   5.352   5.748   7.257   \n",
+       "225                 21.177      14.154     19.314   5.793   6.017   7.917   \n",
+       "260                 18.602      10.110     12.960   4.189   4.370   5.640   \n",
+       "310                 23.087      13.068     17.437   5.352   5.693   7.303   \n",
+       "360                 33.145      21.459     31.768   8.962   9.701  12.506   \n",
+       "400                 25.931      13.517     18.251   5.577   5.939   7.538   \n",
+       "750                 67.390      44.480     56.100  18.607  19.995  26.182   \n",
+       "\n",
+       "              list_comprehension    loop  \n",
+       "input length                              \n",
+       "13                         0.878   0.743  \n",
+       "14                         0.798   0.634  \n",
+       "19                         0.959   0.864  \n",
+       "20                         0.987   0.894  \n",
+       "25                         0.889   0.760  \n",
+       "30                         0.970   0.867  \n",
+       "35                         1.232   1.222  \n",
+       "39                         1.013   0.899  \n",
+       "42                         1.402   1.397  \n",
+       "45                         1.160   1.121  \n",
+       "60                         1.662   1.827  \n",
+       "63                         1.711   1.888  \n",
+       "74                         1.980   2.221  \n",
+       "78                         1.455   1.491  \n",
+       "93                         1.704   1.886  \n",
+       "108                        2.519   2.887  \n",
+       "120                        1.813   1.938  \n",
+       "140                        3.329   3.968  \n",
+       "150                        2.487   2.776  \n",
+       "200                        4.210   5.107  \n",
+       "210                        4.362   5.258  \n",
+       "225                        4.653   5.702  \n",
+       "260                        3.444   4.067  \n",
+       "310                        4.326   5.202  \n",
+       "360                        7.364   8.998  \n",
+       "400                        4.644   5.437  \n",
+       "750                       15.324  18.628  "
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data.set_index('input length', inplace=True)\n",
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "a37f56a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7faf09203280>"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1142.6x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"darkgrid\")\n",
+    "data = new_data\n",
+    "\n",
+    "g = sns.relplot(data=data, kind=\"line\",palette='icefire_r', linewidth=3.5, height=7, aspect=2)\n",
+    "g.set(xticks=range(13, 750, 50))\n",
+    "g.set(yticks=range(1, 70, 5))\n",
+    "g.despine(offset=10, trim=True)\n",
+    "#sns.lineplot(data=data, palette=\"plasma\", linewidth=2.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d79056ba",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a022cf79",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/exercises/practice/acronym/approaches/functools-reduce/content.md b/exercises/practice/acronym/approaches/functools-reduce/content.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/exercises/practice/acronym/approaches/functools-reduce/snippet.txt b/exercises/practice/acronym/approaches/functools-reduce/snippet.txt
new file mode 100644
index 0000000000..6de63fe147
--- /dev/null
+++ b/exercises/practice/acronym/approaches/functools-reduce/snippet.txt
@@ -0,0 +1,4 @@
+phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+acronym = reduce(lambda start, word: start + word[0], phrase, "")
+
+return acronym
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/generator-expression/content.md b/exercises/practice/acronym/approaches/generator-expression/content.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/exercises/practice/acronym/approaches/generator-expression/snippet.txt b/exercises/practice/acronym/approaches/generator-expression/snippet.txt
new file mode 100644
index 0000000000..b4d33e4b58
--- /dev/null
+++ b/exercises/practice/acronym/approaches/generator-expression/snippet.txt
@@ -0,0 +1,5 @@
+phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+words = (word[0] for word in phrase)
+acronym = ''.join(words)
+
+return acronym
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/introduction.md b/exercises/practice/acronym/approaches/introduction.md
new file mode 100644
index 0000000000..83c0f51c25
--- /dev/null
+++ b/exercises/practice/acronym/approaches/introduction.md
@@ -0,0 +1,183 @@
+# Introduction
+
+There are multiple Pythonic ways to solve the Acronym exercise.
+Among them are:
+
+- Using `str.replace()` to scrub, and a `for loop` with string concatenation via the `+` operator.
+- Using `str.replace()` to scrub, and joining via `str.join()`passing a `list-comprehension` or `generator-expression`.
+- Using `str.replace()` to scrub, and joining via `map()` or `functools.reduce()`.
+- Using `re.findall`/`re.finditer` to scrub, and `str.join` with a `generator-expression` or `list-comprehension`.
+- Using only `re.sub` (aka "only" regex)`
+
+
+## General Guidance
+
+The goal of the Acronym exercise is to collect the first letters of each word in the input phrase and return them as a single capitalized string (_the acronym_) .
+
+Strings are _immutable_, so any method to produce an acronym will be creating a new `str`.
+
+
+Forming an acronym is most easily done with a direct or indirect loops, although some regex methods can avoid looping constructs altogether.
+
+The challenge is to efficiently identify and capitalize the first letters while removing or ignoring non-letter characters such as `'`,`-`,`_`, and white space.
+
+
+
+## Approach: scrub with `replace()` and join via `for` loop
+
+```python
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace('-' , ' ').replace("_", " ").upper().split()
+    acronym = ""
+    
+    for word in phrase:
+        acronym += word[0]
+
+    return acronym
+
+```
+
+This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
+The resulting `list` is looped over to select the first letter of each word, which is then concatenated via `+` to the acronym string.
+
+For more information, check the [loop approach][approach-loop].
+
+
+## Approach: scrub with `replace()` and join via `list comprehension`
+
+```python
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    words = [word[0] for word in phrase]
+    acronym = ''.join(words)
+    
+    return acronym
+```
+
+This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
+A list comprehension is used to select the first letters of each word.
+The list of first letters is then concatenated via `str.join()` to form the acronym.
+
+For more information, check the [list-comprehension][approach-list-comprehension]  approach.
+
+
+## Approach: scrub with `replace()` and join via `map()`
+
+```python
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    acronym = ''.join(map(lambda word: word[0], phrase))
+    
+    return acronym
+```
+
+This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
+The first letters of each word are extracted via the built-in `map()` function, which is passed to `str.join()` to form the acronym.
+
+For more information, check the [map][approach-map-function] approach.
+
+
+## Approach: scrub with `replace()` and join via `functools.reduce()`
+
+```python
+from functools import reduce
+
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    acronym = reduce(lambda start, word: start + word[0], phrase, "")
+    
+    return acronym
+```
+
+This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
+ The acronym is created via `functools.reduce()`, isolating the first letters of each word, and joining them together in a new string.
+
+For more information, take a look at the [functools.reduce()][approach-functools-reduce] approach.
+
+
+## Approach: scrub with `replace()` & join via `generator expression`
+
+```python
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    words = (word[0] for word in phrase) # note the parenthesis instead of square brackets.
+    acronym = ''.join(words)
+    
+    return acronym
+
+```
+
+This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
+A `generator-expression` is used to select the first letters of each word.
+The generator-expression is then consumed by `str.join()` to create the acronym.
+
+For more information, check the [generator-expression][approach-generator-expression] approach.
+
+
+## Approach: filter with `re.findall()` and join via `str.join()`
+
+```python
+import re
+
+
+def abbreviate(phrase):
+    removed = re.findall(r"[a-zA-Z']+", phrase)
+    acronym = ''.join(word[0] for word in removed)
+    
+    return acronym.upper()
+
+```
+
+This approach uses a `regex` to remove non-letter characters, then uses a `generator-expression` passed to  `str.join()` to isolate the first letters of each word.
+
+The resulting string is capitalized using `.upper()`.
+
+For more information, check the [regex-join][approach-regex-join] approach.
+
+
+## Approach: use `re.sub`
+
+```python
+import re
+
+def abbreviate_regex_sub(to_abbreviate):
+    acronym = re.sub("\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)", "", to_abbreviate)
+    
+    return  acronym.upper()
+```
+
+This approach uses the regular expression module `re` to clean the string and identify the first letters of each word without the use of loops.
+
+`.upper()` is then called on the result to capitalize all the characters.
+
+For more information, read the [regex-sub][approach-regex-sub] approach.
+
+
+
+## Other approaches
+
+Besides these seven idiomatic approaches, there are a multitude of possible variations using different string cleaning and joining methods.
+
+However, these listed approaches cover the majority of 'mainstream' strategies.
+
+
+
+## Which approach to use?
+
+All seven approaches are idiomatic, and show multiple paradigms and possiblities.
+
+The `list-comprehension` approach is the fastest, although `loop`, `map`,  and`reduce`near identical in performance.
+
+The least performant for the input data was using a `generator-expression` , `re.findall` and  `re.sub` (least performant).
+
+To compare performance of the approaches, take a look at the [Performance article][article-performance].
+
+
+[approach-functools-reduce]: https://exercism.org/tracks/python/exercises/acronym/approaches/functools-reduce
+[approach-generator-expression]: https://exercism.org/tracks/python/exercises/acronym/approaches/generator-expression
+[approach-list-comprehension]: https://exercism.org/tracks/python/exercises/acronym/approaches/list-comprehension
+[approach-loop]: https://exercism.org/tracks/python/exercises/acronym/approaches/loop
+[approach-map-function]: https://exercism.org/tracks/python/exercises/acronym/approaches/map-function
+[approach-regex-join]: https://exercism.org/tracks/python/exercises/acronym/approaches/regex-join
+[approach-regex-sub]: https://exercism.org/tracks/python/exercises/acronym/approaches/regex-sub
+[article-performance]: https://exercism.org/tracks/python/exercises/isogram/articles/performance
diff --git a/exercises/practice/acronym/approaches/list-comprehension/content.md b/exercises/practice/acronym/approaches/list-comprehension/content.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/exercises/practice/acronym/approaches/list-comprehension/snippet.txt b/exercises/practice/acronym/approaches/list-comprehension/snippet.txt
new file mode 100644
index 0000000000..75799feb27
--- /dev/null
+++ b/exercises/practice/acronym/approaches/list-comprehension/snippet.txt
@@ -0,0 +1,5 @@
+phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+words = [word[0] for word in phrase]
+acronym = ''.join(words)
+
+return acronym
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/loop/content.md b/exercises/practice/acronym/approaches/loop/content.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/exercises/practice/acronym/approaches/loop/snippet.txt b/exercises/practice/acronym/approaches/loop/snippet.txt
new file mode 100644
index 0000000000..c8cec5aa21
--- /dev/null
+++ b/exercises/practice/acronym/approaches/loop/snippet.txt
@@ -0,0 +1,7 @@
+phrase = to_abbreviate.replace('-' , ' ').replace("_", " ").upper().split()
+acronym = ""
+
+for word in phrase:
+    acronym += word[0]
+
+return acronym
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/map-function/content.md b/exercises/practice/acronym/approaches/map-function/content.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/exercises/practice/acronym/approaches/map-function/snippet.txt b/exercises/practice/acronym/approaches/map-function/snippet.txt
new file mode 100644
index 0000000000..49eecf6143
--- /dev/null
+++ b/exercises/practice/acronym/approaches/map-function/snippet.txt
@@ -0,0 +1,4 @@
+phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+acronym = ''.join(map(lambda word: word[0], phrase))
+
+return acronym
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/regex-join/content.md b/exercises/practice/acronym/approaches/regex-join/content.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/exercises/practice/acronym/approaches/regex-join/snippet.txt b/exercises/practice/acronym/approaches/regex-join/snippet.txt
new file mode 100644
index 0000000000..2d08be6d14
--- /dev/null
+++ b/exercises/practice/acronym/approaches/regex-join/snippet.txt
@@ -0,0 +1,4 @@
+removed = re.findall(r"[a-zA-Z']+", words)
+acronym = ''.join(word[0] for word in removed))
+
+return acronym.upper()
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/regex-sub/content.md b/exercises/practice/acronym/approaches/regex-sub/content.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/exercises/practice/acronym/approaches/regex-sub/snippet.txt b/exercises/practice/acronym/approaches/regex-sub/snippet.txt
new file mode 100644
index 0000000000..076cb61b49
--- /dev/null
+++ b/exercises/practice/acronym/approaches/regex-sub/snippet.txt
@@ -0,0 +1,3 @@
+acronym = re.sub("\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)", "", to_abbreviate)
+
+return  acronym.upper()
\ No newline at end of file

From 395736c4ea9796cb4b57e2c75ae7896b848c6060 Mon Sep 17 00:00:00 2001
From: BethanyG <BethanyG@users.noreply.github.com>
Date: Mon, 6 Mar 2023 22:16:10 -0800
Subject: [PATCH 2/8] Performance directory.

---
 exercises/practice/acronym/.articles/performance/content.md | 0
 exercises/practice/acronym/.articles/performance/snippet.md | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 exercises/practice/acronym/.articles/performance/content.md
 create mode 100644 exercises/practice/acronym/.articles/performance/snippet.md

diff --git a/exercises/practice/acronym/.articles/performance/content.md b/exercises/practice/acronym/.articles/performance/content.md
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/exercises/practice/acronym/.articles/performance/snippet.md b/exercises/practice/acronym/.articles/performance/snippet.md
new file mode 100644
index 0000000000..e69de29bb2

From 61731948b7dcad2a1808a091bff90ed5f54507cb Mon Sep 17 00:00:00 2001
From: BethanyG <BethanyG@users.noreply.github.com>
Date: Thu, 9 Mar 2023 15:55:22 -0800
Subject: [PATCH 3/8] Added starter content to performance to clear CI check.

---
 .../acronym/.articles/performance/content.md  | 30 +++++++++++++++++++
 .../acronym/approaches/introduction.md        | 29 +++++++++++-------
 2 files changed, 48 insertions(+), 11 deletions(-)

diff --git a/exercises/practice/acronym/.articles/performance/content.md b/exercises/practice/acronym/.articles/performance/content.md
index e69de29bb2..7ff38a1bed 100644
--- a/exercises/practice/acronym/.articles/performance/content.md
+++ b/exercises/practice/acronym/.articles/performance/content.md
@@ -0,0 +1,30 @@
+# Performance
+
+In this approach, we'll find out how to most efficiently form an acronym from an input string.
+
+The [approaches page][approaches] lists seven idiomatic approaches to this exercise:
+
+1. [Using a `list-comprehension`][approach-list-comprehension]
+2. [Using a `loop`][approach-loop]
+3. [Using `functools.reduce()`][approach-functools-reduce]
+4. [Using `map()`][approach-map-function]
+5. [Using a `generator-expression`][approach-generator-expression]
+6. [Using a `regex` with `str.join()`][approach-regex-join]
+7. [Using `re.sub()`][approach-regex-sub]
+
+
+## Benchmarks
+
+To benchmark these approaches, we wrote a [small benchmark application][benchmark-application] using [].
+The benchmark checks the various approaches against
+Besides the regular CPU-time columns, the amount of memory used was also tracked.
+
+These are the results:
+
+[approach-functools-reduce]: https://exercism.org/tracks/python/exercises/acronym/approaches/functools-reduce
+[approach-generator-expression]: https://exercism.org/tracks/python/exercises/acronym/approaches/generator-expression
+[approach-list-comprehension]: https://exercism.org/tracks/python/exercises/acronym/approaches/list-comprehension
+[approach-loop]: https://exercism.org/tracks/python/exercises/acronym/approaches/loop
+[approach-map-function]: https://exercism.org/tracks/python/exercises/acronym/approaches/map-function
+[approach-regex-join]: https://exercism.org/tracks/python/exercises/acronym/approaches/regex-join
+[approach-regex-sub]: https://exercism.org/tracks/python/exercises/acronym/approaches/regex-sub
diff --git a/exercises/practice/acronym/approaches/introduction.md b/exercises/practice/acronym/approaches/introduction.md
index 83c0f51c25..8e48c7b4b4 100644
--- a/exercises/practice/acronym/approaches/introduction.md
+++ b/exercises/practice/acronym/approaches/introduction.md
@@ -4,23 +4,30 @@ There are multiple Pythonic ways to solve the Acronym exercise.
 Among them are:
 
 - Using `str.replace()` to scrub, and a `for loop` with string concatenation via the `+` operator.
-- Using `str.replace()` to scrub, and joining via `str.join()`passing a `list-comprehension` or `generator-expression`.
-- Using `str.replace()` to scrub, and joining via `map()` or `functools.reduce()`.
-- Using `re.findall`/`re.finditer` to scrub, and `str.join` with a `generator-expression` or `list-comprehension`.
-- Using only `re.sub` (aka "only" regex)`
+- Using `str.replace()` to scrub, and joining via `str.join()`passing a `list-comprehension`
+- Using `str.replace()` to scrub, and joining via `str.join()`passing a `generator-expression`.
+- Using `str.replace()` to scrub, and joining via `functools.reduce()`.
+- Using `str.replace()` to scrub, and joining via `str.join()` passing `map()`.
+- Using `re.findall()`/`re.finditer()` to scrub, and `str.join()` with a `generator-expression`.
+- Using `re.sub()` (_using "only" regex_)`
 
 
 ## General Guidance
 
-The goal of the Acronym exercise is to collect the first letters of each word in the input phrase and return them as a single capitalized string (_the acronym_) .
+The goal of the Acronym exercise is to collect the first letters of each word in the input phrase and return them as a single capitalized string (_the acronym_).
+The challenge is to efficiently identify and capitalize the first letters while removing or ignoring non-letter characters such as `'`,`-`,`_`, and white space.
 
-Strings are _immutable_, so any method to produce an acronym will be creating a new `str`.
 
+There are two idiomatic strategies for non-letter character removal:
+- Python's built-in [`str.replace()`][str-replace].
+- [`re`][re] module, (_regular expressions_).
 
-Forming an acronym is most easily done with a direct or indirect loops, although some regex methods can avoid looping constructs altogether.
+For all but the most complex scenarios, using `str.replace()` is generally more efficient than using a regex.
 
-The challenge is to efficiently identify and capitalize the first letters while removing or ignoring non-letter characters such as `'`,`-`,`_`, and white space.
+Forming the final acronym is most easily done with a direct or indirect loop, after splitting the input into a word list via [`str.split()`][str-split].
+Some `regex` methods can avoid looping altogether, although they can become very non-performant due to backtracking.
 
+Strings are _immutable_, so any method to produce an acronym will be creating and returning a new `str`.
 
 
 ## Approach: scrub with `replace()` and join via `for` loop
@@ -164,11 +171,11 @@ However, these listed approaches cover the majority of 'mainstream' strategies.
 
 ## Which approach to use?
 
-All seven approaches are idiomatic, and show multiple paradigms and possiblities.
+All seven approaches are idiomatic, and show multiple paradigms and possibilities.
 
-The `list-comprehension` approach is the fastest, although `loop`, `map`,  and`reduce`near identical in performance.
+The `list-comprehension` approach is the fastest, although `loop`, `map`,  and `reduce` have near-identical performance for the test data.
 
-The least performant for the input data was using a `generator-expression` , `re.findall` and  `re.sub` (least performant).
+The least performant for the test data was using a `generator-expression`, `re.findall` and  `re.sub` (least performant).
 
 To compare performance of the approaches, take a look at the [Performance article][article-performance].
 

From e402e5f00c40829ec38758928307dc4a2e694324 Mon Sep 17 00:00:00 2001
From: BethanyG <BethanyG@users.noreply.github.com>
Date: Thu, 9 Mar 2023 16:30:55 -0800
Subject: [PATCH 4/8] Adding snippet placeholder for performance.

---
 .../practice/acronym/.articles/performance/snippet.md     | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/exercises/practice/acronym/.articles/performance/snippet.md b/exercises/practice/acronym/.articles/performance/snippet.md
index e69de29bb2..b4873f6dbf 100644
--- a/exercises/practice/acronym/.articles/performance/snippet.md
+++ b/exercises/practice/acronym/.articles/performance/snippet.md
@@ -0,0 +1,8 @@
+| Method 	| Mean Time 	| Memory Allocated 	|
+|--------	|-----------	|------------------	|
+|        	|           	|                  	|
+|        	|           	|                  	|
+|        	|           	|                  	|
+|        	|           	|                  	|
+|        	|           	|                  	|
+|        	|           	|                  	|
\ No newline at end of file

From ca179e8b9600dcc27ca47b3abe42c5b1c1aa3f1f Mon Sep 17 00:00:00 2001
From: BethanyG <BethanyG@users.noreply.github.com>
Date: Wed, 2 Aug 2023 14:25:22 -0700
Subject: [PATCH 5/8] Updated and edited approaches for acronym.  Modified
 snipetts and corrected links.

---
 .../approaches/functools-reduce/content.md    |  54 ++++++++
 .../approaches/functools-reduce/snippet.txt   |   6 +-
 .../generator-expression/content.md           |  48 +++++++
 .../generator-expression/snippet.txt          |   8 +-
 .../acronym/approaches/introduction.md        | 121 +++++++-----------
 .../approaches/list-comprehension/content.md  |  46 +++++++
 .../approaches/list-comprehension/snippet.txt |   7 +-
 .../acronym/approaches/loop/content.md        |  40 ++++++
 .../acronym/approaches/loop/snippet.txt       |  11 +-
 .../approaches/map-function/content.md        |  49 +++++++
 .../approaches/map-function/snippet.txt       |   8 +-
 .../acronym/approaches/regex-join/content.md  |  70 ++++++++++
 .../acronym/approaches/regex-join/snippet.txt |   8 +-
 .../acronym/approaches/regex-sub/content.md   |  68 ++++++++++
 .../acronym/approaches/regex-sub/snippet.txt  |   7 +-
 15 files changed, 450 insertions(+), 101 deletions(-)

diff --git a/exercises/practice/acronym/approaches/functools-reduce/content.md b/exercises/practice/acronym/approaches/functools-reduce/content.md
index e69de29bb2..074db3fa28 100644
--- a/exercises/practice/acronym/approaches/functools-reduce/content.md
+++ b/exercises/practice/acronym/approaches/functools-reduce/content.md
@@ -0,0 +1,54 @@
+# Scrub with `replace()` and join via `functools.reduce()`
+
+
+```python
+from functools import reduce
+
+
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    
+    return reduce(lambda start, word: start + word[0], phrase, "")
+```
+
+
+-  This approach begins by using  [`str.replace()`][str-replace] to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
+- The phrase is then upper-cased by calling [`str.upper()`][str-upper],
+- Finally, the phrase is turned into a `list` of words by calling [`str.split()`][str-split].
+
+The three methods above are all [chained][chaining] together, with the output of one method serving as the input to the next method in the "chain".
+This works because both `replace()` and `upper()` return strings, and both `upper()` and `split()` take strings as arguments.
+However, if `split()` were called first, `replace()` and `upper()` would fail, since neither method will take a `list` as input.
+
+~~~~exercism/note
+`re.findall()` or `re.finditer()` can also be used to "scrub" `to_abbreviate`.
+These two methods from the `re` module will return a `list` or a lazy `iterator` of results, respectively.
+As of this writing, both of these methods benchmark slower than using `str.replace()` for scrubbing.
+~~~~
+
+
+Once the phrase is scrubbed and turned into a word `list`, the acronym is created via `reduce()`.
+`reduce()` is a method from the [`functools`][functools] module, which provides support for higher-order functions and functional programming in Python.
+
+
+[`functools.reduce()`][reduce] applies an anonymous two-argument function (_the [lambda][python lambdas] in the code example_) to the items of an iterable.
+ The application of the function travels from left to right, so that the iterable becomes a single value (_it is "reduced" to a single value_).
+
+
+ Using code from the example above, `reduce(lambda start, word: start + word[0], ['GNU', 'IMAGE', 'MANIPULATION', 'PROGRAM'])` would calculate `((('GNU'[0] + 'IMAGE'[0])+'MANIPULATION'[0])+'PROGRAM'[0])`, or `GIMP`.
+  The left argument, `start`, is the _accumulated value_ and the right argument, `word`, is the value from the iterable that is used to update the accumulated 'total'.
+  The optional 'initializer' value '' is used here, and is placed ahead/before the items of the iterable in the calculation, and serves as a default if the iterable that is passed is empty.
+
+
+Since using `reduce()` is fairly succinct, it is put directly on the `return` line to produce the acronym rather than assigning and returning an intermediate variable.
+
+
+In benchmarks, this solution performed about as well as both the `loops` and the `list-comprehension` solutions.
+
+[chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
+[functools]: https://docs.python.org/3/library/functools.html
+[reduce]: https://docs.python.org/3/library/functools.html#functools.reduce
+[str-replace]: https://docs.python.org/3/library/stdtypes.html#str.replace
+[str-split]: https://docs.python.org/3/library/stdtypes.html#str.split
+[str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
+[python lambdas]: https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions
diff --git a/exercises/practice/acronym/approaches/functools-reduce/snippet.txt b/exercises/practice/acronym/approaches/functools-reduce/snippet.txt
index 6de63fe147..34e930c76f 100644
--- a/exercises/practice/acronym/approaches/functools-reduce/snippet.txt
+++ b/exercises/practice/acronym/approaches/functools-reduce/snippet.txt
@@ -1,4 +1,6 @@
+from functools import reduce
+
+def abbreviate(to_abbreviate):
 phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
-acronym = reduce(lambda start, word: start + word[0], phrase, "")
 
-return acronym
\ No newline at end of file
+return reduce(lambda start, word: start + word[0], phrase, "")
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/generator-expression/content.md b/exercises/practice/acronym/approaches/generator-expression/content.md
index e69de29bb2..f5b590ccaa 100644
--- a/exercises/practice/acronym/approaches/generator-expression/content.md
+++ b/exercises/practice/acronym/approaches/generator-expression/content.md
@@ -0,0 +1,48 @@
+# Scrub with `replace()` and join via `generator-expression`
+
+
+```python
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
+    
+    # note the lack of square brackets around the comprehension.
+    return ''.join(word[0] for word in phrase)
+```
+
+
+-  This approach begins by using  [`str.replace()`][str-replace] to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
+- The phrase is then upper-cased by calling [`str.upper()`][str-upper],
+- Finally, the phrase is turned into a `list` of words by calling [`str.split()`][str-split].
+
+The three methods above are all [chained][chaining] together, with the output of one method serving as the input to the next method in the "chain".
+This works because both `replace()` and `upper()` return strings, and both `upper()` and `split()` take strings as arguments.
+However, if `split()` were called first, `replace()` and `upper()` would fail, since neither method will take a `list` as input.
+
+~~~~exercism/note
+`re.findall()` or `re.finditer()` can also be used to "scrub" `to_abbreviate`.
+These two methods from the `re` module will return a `list` or a lazy `iterator` of results, respectively.
+As of this writing, both of these methods benchmark slower than using `str.replace()` for scrubbing.
+~~~~
+
+
+A [`generator-expression`][generator-expression] is then used to iterate through the phrase and select the first letters of each word via [`bracket notation`][subscript notation].
+
+
+Generator expressions are short-form [generators][generators] - lazy iterators that produce their values _on demand_, instead of saving them to memory.
+This generator expression is consumed by [`str.join()`][str-join], which joins the generated letters together using an empty string.
+Other "seperator" strings can be used with `str.join()` - see [concept:python/string-methods]() for some additional examples.
+Since the generator expression and `join()` are fairly succinct, they are put directly on the `return` line rather than assigning and returning an intermediate variable for the acronym.
+
+
+In benchmarks, this solution was surprisingly slower than the `list comprehension` version.
+[This article][Oscar Alsing] from Oscar Alsing briefly explains why.
+
+[Oscar Alsing]: https://www.oscaralsing.com/list-comprehension-vs-generator-expression/#:~:text=List%20comprehensions%20are%20usually%20faster,difference%20is%20often%20quite%20small.
+[chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
+[generator-expression]: https://dbader.org/blog/python-generator-expressions
+[generators]: https://dbader.org/blog/python-generators
+[str-join]: https://docs.python.org/3/library/stdtypes.html#str.join
+[str-replace]: https://docs.python.org/3/library/stdtypes.html#str.replace
+[str-split]: https://docs.python.org/3/library/stdtypes.html#str.split
+[str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
+[subscript notation]: https://docs.python.org/3/glossary.html#term-slice
diff --git a/exercises/practice/acronym/approaches/generator-expression/snippet.txt b/exercises/practice/acronym/approaches/generator-expression/snippet.txt
index b4d33e4b58..eb4a143df8 100644
--- a/exercises/practice/acronym/approaches/generator-expression/snippet.txt
+++ b/exercises/practice/acronym/approaches/generator-expression/snippet.txt
@@ -1,5 +1,5 @@
-phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
-words = (word[0] for word in phrase)
-acronym = ''.join(words)
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
 
-return acronym
\ No newline at end of file
+    # note the lack of square brackets around the comprehension.
+    return ''.join(word[0] for word in phrase)
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/introduction.md b/exercises/practice/acronym/approaches/introduction.md
index 8e48c7b4b4..8512236e06 100644
--- a/exercises/practice/acronym/approaches/introduction.md
+++ b/exercises/practice/acronym/approaches/introduction.md
@@ -3,13 +3,16 @@
 There are multiple Pythonic ways to solve the Acronym exercise.
 Among them are:
 
-- Using `str.replace()` to scrub, and a `for loop` with string concatenation via the `+` operator.
-- Using `str.replace()` to scrub, and joining via `str.join()`passing a `list-comprehension`
-- Using `str.replace()` to scrub, and joining via `str.join()`passing a `generator-expression`.
-- Using `str.replace()` to scrub, and joining via `functools.reduce()`.
-- Using `str.replace()` to scrub, and joining via `str.join()` passing `map()`.
-- Using `re.findall()`/`re.finditer()` to scrub, and `str.join()` with a `generator-expression`.
-- Using `re.sub()` (_using "only" regex_)`
+- Using `str.replace()` to scrub the input, and:
+  - joining with a `for loop` with string concatenation via the `+` operator.
+  - joining via `str.join()`, passing a `list-comprehension` or `generator-expression`.
+  - joining via `str.join()`,  passing `map()`.
+  - joining via `functools.reduce()`.
+
+- Using `re.findall()`/`re.finditer()` to scrub the input, and:
+  - joining via `str.join()`, passing a `generator-expression`.
+
+ - Using `re.sub()` for both cleaning and joining (_using "only" regex for almost everything_)`
 
 
 ## General Guidance
@@ -20,12 +23,14 @@ The challenge is to efficiently identify and capitalize the first letters while
 
 There are two idiomatic strategies for non-letter character removal:
 - Python's built-in [`str.replace()`][str-replace].
-- [`re`][re] module, (_regular expressions_).
+- The [`re`][re] module, (_regular expressions_).
+
+For all but the most complex scenarios, using `str.replace()` is generally more efficient than using a regular expression.
 
-For all but the most complex scenarios, using `str.replace()` is generally more efficient than using a regex.
 
-Forming the final acronym is most easily done with a direct or indirect loop, after splitting the input into a word list via [`str.split()`][str-split].
-Some `regex` methods can avoid looping altogether, although they can become very non-performant due to backtracking.
+Forming the final acronym is most easily done with a direct or indirect `loop`, after splitting the input into a word list via [`str.split()`][str-split].
+The majority of these approaches demonstrate alternatives to the "classic" looping structure using various other iteration techniques.
+Some `regex` methods can avoid looping altogether, although they can become very non-performant due to excessive backtracking.
 
 Strings are _immutable_, so any method to produce an acronym will be creating and returning a new `str`.
 
@@ -34,38 +39,36 @@ Strings are _immutable_, so any method to produce an acronym will be creating an
 
 ```python
 def abbreviate(to_abbreviate):
-    phrase = to_abbreviate.replace('-' , ' ').replace("_", " ").upper().split()
-    acronym = ""
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
+    acronym = ''
     
     for word in phrase:
         acronym += word[0]
 
     return acronym
-
 ```
 
-This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
-The resulting `list` is looped over to select the first letter of each word, which is then concatenated via `+` to the acronym string.
-
-For more information, check the [loop approach][approach-loop].
+For more information, take a look at the [loop approach][approach-loop].
 
 
-## Approach: scrub with `replace()` and join via `list comprehension`
+## Approach: scrub with `replace()` and join via `list comprehension` or `Generator expression`
 
 ```python
 def abbreviate(to_abbreviate):
-    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
-    words = [word[0] for word in phrase]
-    acronym = ''.join(words)
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
     
-    return acronym
+    return ''.join([word[0] for word in phrase])
+    
+###OR### 
+    
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
+    
+    # note the parenthesis instead of square brackets.    
+    return ''.join((word[0] for word in phrase))
 ```
 
-This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
-A list comprehension is used to select the first letters of each word.
-The list of first letters is then concatenated via `str.join()` to form the acronym.
-
-For more information, check the [list-comprehension][approach-list-comprehension]  approach.
+For more information, check out the [list-comprehension][approach-list-comprehension]  approach or the [generator-expression][approach-generator-expression] approach.
 
 
 ## Approach: scrub with `replace()` and join via `map()`
@@ -73,15 +76,11 @@ For more information, check the [list-comprehension][approach-list-comprehension
 ```python
 def abbreviate(to_abbreviate):
     phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
-    acronym = ''.join(map(lambda word: word[0], phrase))
     
-    return acronym
+    return ''.join(map(lambda word: word[0], phrase))
 ```
 
-This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
-The first letters of each word are extracted via the built-in `map()` function, which is passed to `str.join()` to form the acronym.
-
-For more information, check the [map][approach-map-function] approach.
+For more information, read the [map][approach-map-function] approach.
 
 
 ## Approach: scrub with `replace()` and join via `functools.reduce()`
@@ -89,36 +88,14 @@ For more information, check the [map][approach-map-function] approach.
 ```python
 from functools import reduce
 
-def abbreviate(to_abbreviate):
-    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
-    acronym = reduce(lambda start, word: start + word[0], phrase, "")
-    
-    return acronym
-```
-
-This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
- The acronym is created via `functools.reduce()`, isolating the first letters of each word, and joining them together in a new string.
 
-For more information, take a look at the [functools.reduce()][approach-functools-reduce] approach.
-
-
-## Approach: scrub with `replace()` & join via `generator expression`
-
-```python
 def abbreviate(to_abbreviate):
     phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
-    words = (word[0] for word in phrase) # note the parenthesis instead of square brackets.
-    acronym = ''.join(words)
     
-    return acronym
-
+    return reduce(lambda start, word: start + word[0], phrase, "")
 ```
 
-This approach uses the `str.replace()` method to remove non-letter characters, capitalizes all the words via `.upper()`, and creates a word list via `.split()`.
-A `generator-expression` is used to select the first letters of each word.
-The generator-expression is then consumed by `str.join()` to create the acronym.
-
-For more information, check the [generator-expression][approach-generator-expression] approach.
+For more information, take a look at the [functools.reduce()][approach-functools-reduce] approach.
 
 
 ## Approach: filter with `re.findall()` and join via `str.join()`
@@ -129,38 +106,28 @@ import re
 
 def abbreviate(phrase):
     removed = re.findall(r"[a-zA-Z']+", phrase)
-    acronym = ''.join(word[0] for word in removed)
     
-    return acronym.upper()
-
+    return ''.join(word[0] for word in removed).upper()
 ```
 
-This approach uses a `regex` to remove non-letter characters, then uses a `generator-expression` passed to  `str.join()` to isolate the first letters of each word.
+For more information, take a look at the [regex-join][approach-regex-join] approach.
 
-The resulting string is capitalized using `.upper()`.
 
-For more information, check the [regex-join][approach-regex-join] approach.
-
-
-## Approach: use `re.sub`
+## Approach: use `re.sub()`
 
 ```python
 import re
 
+
 def abbreviate_regex_sub(to_abbreviate):
-    acronym = re.sub("\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)", "", to_abbreviate)
+    pattern = re.compile(r"\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)")
     
-    return  acronym.upper()
+    return  re.sub(pattern, "", to_abbreviate).upper()
 ```
 
-This approach uses the regular expression module `re` to clean the string and identify the first letters of each word without the use of loops.
-
-`.upper()` is then called on the result to capitalize all the characters.
-
 For more information, read the [regex-sub][approach-regex-sub] approach.
 
 
-
 ## Other approaches
 
 Besides these seven idiomatic approaches, there are a multitude of possible variations using different string cleaning and joining methods.
@@ -168,17 +135,17 @@ Besides these seven idiomatic approaches, there are a multitude of possible vari
 However, these listed approaches cover the majority of 'mainstream' strategies.
 
 
-
 ## Which approach to use?
 
 All seven approaches are idiomatic, and show multiple paradigms and possibilities.
 
 The `list-comprehension` approach is the fastest, although `loop`, `map`,  and `reduce` have near-identical performance for the test data.
+All are fairly succinct and readable, although the 'classic' loop is probably the easiest understood by those coming to Python from other programming languages.
 
-The least performant for the test data was using a `generator-expression`, `re.findall` and  `re.sub` (least performant).
 
-To compare performance of the approaches, take a look at the [Performance article][article-performance].
+The least performant for the test data was using a `generator-expression`, `re.findall` and  `re.sub` (_least performant_).
 
+To compare performance of the approaches, take a look at the [Performance article][article-performance].
 
 [approach-functools-reduce]: https://exercism.org/tracks/python/exercises/acronym/approaches/functools-reduce
 [approach-generator-expression]: https://exercism.org/tracks/python/exercises/acronym/approaches/generator-expression
diff --git a/exercises/practice/acronym/approaches/list-comprehension/content.md b/exercises/practice/acronym/approaches/list-comprehension/content.md
index e69de29bb2..3a7b0cd40f 100644
--- a/exercises/practice/acronym/approaches/list-comprehension/content.md
+++ b/exercises/practice/acronym/approaches/list-comprehension/content.md
@@ -0,0 +1,46 @@
+# Scrub with `replace()` and join via `list comprehension`
+
+```python
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
+    
+    return ''.join([word[0] for word in phrase])
+```
+
+-  This approach begins by using  [`str.replace()`][str-replace] to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
+- The phrase is then upper-cased by calling [`str.upper()`][str-upper],
+- Finally, the phrase is turned into a `list` of words by calling [`str.split()`][str-split].
+
+The three methods above are all [chained][chaining] together, with the output of one method serving as the input to the next method in the "chain".
+This works because both `replace()` and `upper()` return strings, and both `upper()` and `split()` _take_ strings as arguments.
+However, if `split()` were called first, `replace()` and `upper()` would fail, since neither method will take a `list` as input.
+
+
+~~~~exercism/note
+`re.findall()` or `re.finditer()` can also be used to "scrub" `to_abbreviate`.
+These two methods from the `re` module will return a `list` or a lazy `iterator` of results, respectively.
+As of this writing, both of these methods benchmark slower than using `str.replace()` for scrubbing.
+~~~~
+
+
+A [`list comprehension`][list comprehension] is then used to iterate through the phrase and select the first letters of each word via [`bracket notation`][subscript notation].
+This comprehension is passed into [`str.join()`][str-join], which unpacks the `list` of first letters and joins them together using an empty string - the acronym.
+Other "seperator" strings besides an empty string can be used with `str.join()` - see [concept:python/string-methods]() for some additional examples.
+Since the comprehension and `join()` are fairly succinct, they are put directly on the `return` line rather than assigning and returning an intermediate variable for the acronym.
+
+
+The weakness of this solution is that it is taking up extra space with the `list comprehension`, which is creating and saving a `list` in memory - only to have that list immediately unpacked by the `str.join()` method.
+While this is trivial for the inputs this problem is tested against, it could become a problem if the inputs get longer.
+It could also be an issue if the code were deployed in a memory-constrained environment.
+A [generator expression][generator-expression] here would be more memory-efficient, though there are speed tradeoffs.
+See the [generator expression][approach-generator-expression] approach for more details.
+
+[approach-generator-expression]: https://exercism.org/tracks/python/exercises/acronym/approaches/generator-expression
+[chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
+[generator-expression]: https://dbader.org/blog/python-generator-expressions
+[list comprehension]: https://treyhunner.com/2015/12/python-list-comprehensions-now-in-color/
+[str-join]: https://docs.python.org/3/library/stdtypes.html#str.join
+[str-replace]: https://docs.python.org/3/library/stdtypes.html#str.replace
+[str-split]: https://docs.python.org/3/library/stdtypes.html#str.split
+[str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
+[subscript notation]: https://docs.python.org/3/glossary.html#term-slice
diff --git a/exercises/practice/acronym/approaches/list-comprehension/snippet.txt b/exercises/practice/acronym/approaches/list-comprehension/snippet.txt
index 75799feb27..cf17c6ec67 100644
--- a/exercises/practice/acronym/approaches/list-comprehension/snippet.txt
+++ b/exercises/practice/acronym/approaches/list-comprehension/snippet.txt
@@ -1,5 +1,4 @@
-phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
-words = [word[0] for word in phrase]
-acronym = ''.join(words)
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
 
-return acronym
\ No newline at end of file
+    return ''.join([word[0] for word in phrase])
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/loop/content.md b/exercises/practice/acronym/approaches/loop/content.md
index e69de29bb2..e89001d381 100644
--- a/exercises/practice/acronym/approaches/loop/content.md
+++ b/exercises/practice/acronym/approaches/loop/content.md
@@ -0,0 +1,40 @@
+# Scrub with `replace()` and join via `for` loop
+
+
+```python
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
+    acronym = ''
+    
+    for word in phrase:
+        acronym += word[0]
+
+    return acronym
+```
+
+
+-  This approach begins by using  [`str.replace()`][str-replace] to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
+- The phrase is then upper-cased by calling [`str.upper()`][str-upper],
+- Finally, the phrase is turned into a `list` of words by calling [`str.split()`][str-split].
+
+The three methods above are all [chained][chaining] together, with the output of one method serving as the input to the next method in the "chain".
+This works because both `replace()` and `upper()` return strings, and both `upper()` and `split()` take strings as arguments.
+However, if `split()` were called first, `replace()` and `upper()` would fail, since neither method will take a `list` as input.
+
+After the phrase is cleaned and split into a word list, we declare an empty `acronym` string to hold our final acronym.
+The phrase `list` is then looped over via `for word in phrase`.
+The first letter of each word is selected via [`bracket notation`][subscript notation], and concatenated via `+` to the `acronym` string.
+When the loop is complete, `acronym` is returned from the function.
+
+
+~~~~exercism/note
+`re.findall()` or `re.finditer()` can also be used to "scrub" `to_abbreviate`.
+These two methods from the `re` module will return a `list` or a lazy `iterator` of results, respectively.
+As of this writing, both of these methods benchmark slower than using `str.replace()` for scrubbing.
+~~~~
+
+[chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
+[str-replace]: https://docs.python.org/3/library/stdtypes.html#str.replace
+[str-split]: https://docs.python.org/3/library/stdtypes.html#str.split
+[str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
+[subscript notation]: https://docs.python.org/3/glossary.html#term-slice
diff --git a/exercises/practice/acronym/approaches/loop/snippet.txt b/exercises/practice/acronym/approaches/loop/snippet.txt
index c8cec5aa21..bdf60c6e78 100644
--- a/exercises/practice/acronym/approaches/loop/snippet.txt
+++ b/exercises/practice/acronym/approaches/loop/snippet.txt
@@ -1,7 +1,8 @@
-phrase = to_abbreviate.replace('-' , ' ').replace("_", " ").upper().split()
-acronym = ""
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
+    acronym = ''
 
-for word in phrase:
-    acronym += word[0]
+    for word in phrase:
+        acronym += word[0]
 
-return acronym
\ No newline at end of file
+    return acronym
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/map-function/content.md b/exercises/practice/acronym/approaches/map-function/content.md
index e69de29bb2..f237bd823b 100644
--- a/exercises/practice/acronym/approaches/map-function/content.md
+++ b/exercises/practice/acronym/approaches/map-function/content.md
@@ -0,0 +1,49 @@
+# Scrub with `replace()` and join via `map()`
+
+
+```python
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    
+    return ''.join(map(lambda word: word[0], phrase))
+```
+
+-  This approach begins by using  [`str.replace()`][str-replace] to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
+- The phrase is then upper-cased by calling [`str.upper()`][str-upper],
+- Finally, the phrase is turned into a `list` of words by calling [`str.split()`][str-split].
+
+The three methods above are all [chained][chaining] together, with the output of one method serving as the input to the next method in the "chain".
+This works because both `replace()` and `upper()` return strings, and both `upper()` and `split()` take strings as arguments.
+However, if `split()` were called first, `replace()` and `upper()` would fail, since neither method will take a `list` as input.
+
+~~~~exercism/note
+`re.findall()` or `re.finditer()` can also be used to "scrub" `to_abbreviate`.
+These two methods from the `re` module will return a `list` or a lazy `iterator` of results, respectively.
+As of this writing, both of these methods benchmark slower than using `str.replace()` for scrubbing.
+~~~~
+
+
+Once the phrase is scrubbed and turned into a word `list`, the acronym is created via the [built-in][python-builtins] [`map()`][map] function.
+`map()` applies an anonymous function (_the [lambda][python lambdas] in the code example_) to all the items of an iterable (_'mapping' the function 'onto' each item_), returning a [lazy iterator][lazy iterator] of results.
+The application of the function travels from left to right, and function results are produced as needed.
+
+
+Using code from the example above, `map(lambda word: word[0], ['GNU', 'IMAGE', 'MANIPULATION', 'PROGRAM'])` would calculate `'GNU'[0], 'IMAGE'[0], 'MANIPULATION'[0]), 'PROGRAM'[0]` in order as a stream of data.
+ `word[0]` is the function, which extracts the letter at index zero for every word in the phrase list.
+This stream of data can then be 'consumed' - either in a `loop`, or by being 'unpacked' by another function or process.
+Here, the `iterator` from `map()` is immediately consumed/unpacked by [`join()`][str-join], which glues the results together with an empty string to produce the acronym.
+
+
+Since using `join()` with `map()` is fairly succinct, the combination is put directly on the `return` line to produce the acronym rather than assigning and returning an intermediate variable.
+
+In benchmarks, this solution performed about as well as  the `loops`, `reduce` and `list-comprehension` solutions.
+
+[chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
+[lazy iterator]: https://www.pythonmorsels.com/what-is-an-iterator/
+[map]: https://docs.python.org/3/library/functions.html#map
+[python lambdas]: https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions
+[python-builtins]: https://docs.python.org/3/library/functions.html
+[str-join]: https://docs.python.org/3/library/stdtypes.html#str.join
+[str-replace]: https://docs.python.org/3/library/stdtypes.html#str.replace
+[str-split]: https://docs.python.org/3/library/stdtypes.html#str.split
+[str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
diff --git a/exercises/practice/acronym/approaches/map-function/snippet.txt b/exercises/practice/acronym/approaches/map-function/snippet.txt
index 49eecf6143..ea7b5b521b 100644
--- a/exercises/practice/acronym/approaches/map-function/snippet.txt
+++ b/exercises/practice/acronym/approaches/map-function/snippet.txt
@@ -1,4 +1,4 @@
-phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
-acronym = ''.join(map(lambda word: word[0], phrase))
-
-return acronym
\ No newline at end of file
+def abbreviate(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    
+    return ''.join(map(lambda word: word[0], phrase))
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/regex-join/content.md b/exercises/practice/acronym/approaches/regex-join/content.md
index e69de29bb2..b580ca4048 100644
--- a/exercises/practice/acronym/approaches/regex-join/content.md
+++ b/exercises/practice/acronym/approaches/regex-join/content.md
@@ -0,0 +1,70 @@
+# Approach: filter with `re.findall()` and join via `str.join()`
+
+
+```python
+import re
+
+
+def abbreviate(phrase):
+    removed = re.findall(r"[a-zA-Z']+", phrase)
+    
+    return ''.join(word[0] for word in removed).upper()
+
+###OR###
+
+def def abbreviate(phrase):
+    removed = re.finditer(r"[a-zA-Z']+", phrase)
+    
+    return ''.join(word[0] for word in removed).upper()
+```
+
+
+This approach begins by using  [`re.findall()`][re-findall] method from the [re][re] module to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
+Python's `re` module provides support for [regular expressions][regular expressions] within the language, and has many useful methods for searching, parsing, and modifying text.
+Regular expression matching starts at the left-hand side of the input and travels toward the right.
+
+
+`findall()` searches text for all matching patterns, returning results (_including 'empty' matches_) in a `list`.
+The [`re.finditer()`][re-finditer] method works in the same fashion as `re.findall()`, but returns results as a _[lazy iterator][lazy iterator]_, producing matches _on demand_, instead of saving them to memory.
+
+
+The regular expression `r[a-zA-Z]+` in the code example looks for any single character in the range `a-z` lowercase and `A-Z` uppercase.
+The `+` operator is a 'greedy' modifier that matches the previous range one to unlimited times.
+This means that the expression will match any collection or repeat of letters (_word_), but will omit matching on any sort of space or 'non-letter' character, such as `\t`, `\n`, ` `, `_`, or `-`.
+
+For example, in `Complementary metal-oxide semiconductor`, the regex will match `Complementary`, `metal`, `oxide`, and `semiconductor`.
+The regex will not match on ` ` or `-`.
+The result returned by `findall()` will then be `['Complementary', 'metal', 'oxide', 'semiconductor']`.
+
+
+~~~~exercism/note
+`to_abbreviate.replace("_", " ").replace("-", " ").upper().split()` can also be used to 'scrub' `to_abbreviate` and turn the results into a `list`.
+The `.replace()` approach benchmarked faster than using `re.findall()` to 'scrub', most likely due to overhead in importing the `re` module and in the [backtracking][backtracking] behavior of regex searching and matching.
+
+[backtracking]: https://stackoverflow.com/questions/9011592/in-regular-expressions-what-is-a-backtracking-back-referencing
+~~~~
+
+
+Once `findall()` or `finditer` completes, a [`generator-expression`][generator-expression] is used to iterate through the results and select the first letters of each word via [`bracket notation`][subscript notation].
+Generator expressions are short-form [generators][generators] - lazy iterators that produce their values _on demand_, instead of saving them to memory.
+This generator expression is consumed by [`str.join()`][str-join], which joins the generated letters together using an empty string.
+Other "seperator" strings can be used with `str.join()` - see [concept:python/string-methods]() for some additional examples.
+
+
+Finally, the result of `.join()` is capitalized using the [chained][chaining] [`.upper()`][str-upper].
+Since the generator expression + join + upper is fairly succinct, they are placed directly on the `return` line rather than assigning and returning an intermediate variable for the acronym.
+
+
+This approach was less performant in benchmarks than those using `loop`, `map`,  `list-comprehension`, and `reduce`.
+
+[chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
+[generator-expression]: https://dbader.org/blog/python-generator-expressions
+[generators]: https://dbader.org/blog/python-generators
+[lazy iterator]: https://www.pythonmorsels.com/what-is-an-iterator/
+[re-findall]: https://docs.python.org/3/library/re.html#re.findall
+[re-finditer]: https://docs.python.org/3/library/re.html#re.finditer
+[re]: https://docs.python.org/3/library/re.html
+[regular expressions]: https://en.wikipedia.org/wiki/Regular_expression
+[str-join]: https://docs.python.org/3/library/stdtypes.html#str.join
+[str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
+[subscript notation]: https://docs.python.org/3/glossary.html#term-slice
diff --git a/exercises/practice/acronym/approaches/regex-join/snippet.txt b/exercises/practice/acronym/approaches/regex-join/snippet.txt
index 2d08be6d14..309665fdf2 100644
--- a/exercises/practice/acronym/approaches/regex-join/snippet.txt
+++ b/exercises/practice/acronym/approaches/regex-join/snippet.txt
@@ -1,4 +1,6 @@
-removed = re.findall(r"[a-zA-Z']+", words)
-acronym = ''.join(word[0] for word in removed))
+import re
 
-return acronym.upper()
\ No newline at end of file
+def abbreviate(phrase):
+    removed = re.findall(r"[a-zA-Z']+", phrase)
+
+    return ''.join(word[0] for word in removed).upper()
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/regex-sub/content.md b/exercises/practice/acronym/approaches/regex-sub/content.md
index e69de29bb2..ca2e73348e 100644
--- a/exercises/practice/acronym/approaches/regex-sub/content.md
+++ b/exercises/practice/acronym/approaches/regex-sub/content.md
@@ -0,0 +1,68 @@
+## Approach: use `re.sub`
+
+
+```python
+import re
+
+
+def abbreviate_regex_sub(to_abbreviate):
+    pattern = re.compile(r"\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)")
+    
+    return  re.sub(pattern, "", to_abbreviate).upper()
+```
+
+This approach begins by using the [`re.sub()`][re-sub] method from the [re][re] module to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
+Python's `re` module provides support for [regular expressions][regular expressions] within the language, and has many useful methods for searching, parsing, and modifying text.
+
+
+`sub()` searches text for all matching patterns, **sub**stituting a replacement string (_in our case, an empty string_).
+Regular expression matching starts at the left-hand side of the input and travels toward the right.
+
+~~~~exercism/caution
+While it is a fun experiment to see if the entire problem can be more or less solved with a single regex, the excessive [backtracking][backtracking] used in this solution slows down performance considerably.
+This solution tested the slowest of all solutions during benchmarking.  
+
+
+A more performant method of cleaning would be to use [`re.findall()`][re-findall] or [`re.finditer()`][re-finditer] to scrub the phrase of unwanted characters, and then process the results with a `list-comprehension` or `loop` to extract the first letters of words.
+`to_abbreviate.replace("_", " ").replace("-", " ").upper().split()` can also be used, and is even more performant here for cleaning test inputs. 
+
+
+However, if nothing but a regular expression will do, the third-party [regex][regex] module provides more tools for lookarounds, recursion, partial matches, and nested sets.
+Experimenting with that third-party library on your local environment (_the exercism Python track does not support third-party libraries_) could aid in optimizing this complicated regular expression and help with extracting first letters to form acronyms.  
+
+[backtracking]: https://stackoverflow.com/questions/9011592/in-regular-expressions-what-is-a-backtracking-back-referencing
+[re-findall]: https://docs.python.org/3/library/re.html#re.findall
+[re-finditer]: https://docs.python.org/3/library/re.html#re.finditer
+[regex]: https://github.com/mrabarnett/mrab-regex
+~~~~
+
+The regular expression `\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)` in the code example above has five alternatives for matching.
+For convenience and reuse, the regex is compiled using [`re.compile()`][re-compile].
+Alternatives are seperated with the pipe (`|`) symbol:
+
+
+1.  `\B[a-z',]+`, which starts searching at a [non-word boundary][re-non-word boundary], looks for any character in the group `abcdefghijklmnopqrstuvwxyz',`.
+The `+` operator is a 'greedy' modifier that matches a character in the previous group one to unlimited times.
+This means that this expression will match any collection or repeat of the letters (_plus `'` and `,`_), but will omit matching on anything else.
+2. `|-|` matches the character `-`, with no leading or trailing space.
+3. `| |` matches one **single** space character.
+4. `[A-Z]{2}\b` matches any character in `ABCDEFGHIJKLMNOPQRSTUVWXYZ` exactly twice (_that's the `{2}` part_) - but only right before a [word boundary][re-non-word boundary].
+5. `[^A-Z'](?<=_)` matches a single character that is **not** in `ABCDEFGHIJKLMNOPQRSTUVWXYZ`, and then asserts a [positive lookbehind][positive lookbehind] to match `_`.
+
+Because these matches are used in the `re.sub()` method, an empty string is _substituted_  - so the matches are _removed_ from the result.
+
+
+As an example, for the input phrase `The Road _Not_ Taken`, the regex will match `he`, ` `, `oad`, ` `, `-`, `ot`, `-`, ` `, and `aken`, replacing each match with ''.
+The result is the string `TRNT`.
+
+
+To ensure that all results are capitalized for any input, the approach then [chains][chaining] [`.upper()`][str-upper] to `re.sub()` on the `return` line to produce the final acronym.
+
+[chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
+[positive lookbehind]: https://www.regular-expressions.info/lookaround.html
+[re-compile]: https://docs.python.org/3/library/re.html#re.compile
+[re-non-word boundary]: https://stackoverflow.com/questions/4541573/what-are-non-word-boundary-in-regex-b-compared-to-word-boundary
+[re-sub]: https://docs.python.org/3/library/re.html#re.sub
+[re]: https://docs.python.org/3/library/re.html
+[regular expressions]: https://en.wikipedia.org/wiki/Regular_expression
+[str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
diff --git a/exercises/practice/acronym/approaches/regex-sub/snippet.txt b/exercises/practice/acronym/approaches/regex-sub/snippet.txt
index 076cb61b49..bd4626d075 100644
--- a/exercises/practice/acronym/approaches/regex-sub/snippet.txt
+++ b/exercises/practice/acronym/approaches/regex-sub/snippet.txt
@@ -1,3 +1,6 @@
-acronym = re.sub("\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)", "", to_abbreviate)
+import re
 
-return  acronym.upper()
\ No newline at end of file
+def abbreviate_regex_sub(to_abbreviate):
+    pattern = re.compile(r"\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)")
+
+    return  re.sub(pattern, "", to_abbreviate).upper()
\ No newline at end of file

From f3cfac60d2de7e4a0751a30512c94588e6e1f42c Mon Sep 17 00:00:00 2001
From: BethanyG <BethanyG@users.noreply.github.com>
Date: Tue, 27 Feb 2024 22:16:00 -0800
Subject: [PATCH 6/8] Finished up approaches and added benchmarks

Finished up approaches and added benchmarks.
Deleted unneeded Jupyter notebooks.
---
 .../practice/acronym/.articles/config.json    |   2 +-
 .../.articles/performance/code/Benchmark.py   | 157 +++
 .../acronym/.articles/performance/content.md  |  62 +-
 .../acronym/.articles/performance/snippet.md  |  17 +-
 .../Untitled-checkpoint.ipynb                 | 982 -----------------
 .../acronym/approaches/Untitled.ipynb         | 989 ------------------
 .../practice/acronym/approaches/config.json   |  56 +
 .../approaches/functools-reduce/snippet.txt   |   4 +-
 .../acronym/approaches/introduction.md        |  13 +-
 .../acronym/approaches/regex-join/content.md  |  56 +-
 .../acronym/approaches/regex-sub/content.md   |  31 +-
 .../acronym/approaches/regex-sub/snippet.txt  |   4 +-
 12 files changed, 349 insertions(+), 2024 deletions(-)
 create mode 100644 exercises/practice/acronym/.articles/performance/code/Benchmark.py
 delete mode 100644 exercises/practice/acronym/approaches/.ipynb_checkpoints/Untitled-checkpoint.ipynb
 delete mode 100644 exercises/practice/acronym/approaches/Untitled.ipynb
 create mode 100644 exercises/practice/acronym/approaches/config.json

diff --git a/exercises/practice/acronym/.articles/config.json b/exercises/practice/acronym/.articles/config.json
index 56076f9126..ad22d1e171 100644
--- a/exercises/practice/acronym/.articles/config.json
+++ b/exercises/practice/acronym/.articles/config.json
@@ -5,7 +5,7 @@
       "slug": "performance",
       "title": "Performance deep dive",
       "blurb": "Deep dive to find out the most performant approach to forming an acronym.",
-      "authors": ["bethanyg"]
+      "authors": ["bethanyg, colinleach"]
     }
   ]
 }
\ No newline at end of file
diff --git a/exercises/practice/acronym/.articles/performance/code/Benchmark.py b/exercises/practice/acronym/.articles/performance/code/Benchmark.py
new file mode 100644
index 0000000000..44ae8db7b2
--- /dev/null
+++ b/exercises/practice/acronym/.articles/performance/code/Benchmark.py
@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Script for timing Acronym Solutions.
+
+Creates timing table and timing graphs for
+multiple approaches to the Acronym problem in Python.
+
+Created Feb 2024
+@authors: bethanygarcia, colinleach
+"""
+
+import timeit
+import re
+from functools import reduce
+
+import pandas as pd
+import numpy as np
+
+
+# ------------ FUNCTIONS TO TIME ------------- #
+def abbreviate_list_comprehension(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    return ''.join([word[0] for word in phrase])
+
+
+def abbreviate_genex(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    letters = (word[0] for word in phrase)
+    return ''.join(letters)
+
+
+def abbreviate_loop(to_abbreviate):
+    phrase = to_abbreviate.replace('-', ' ').replace("_", " ").upper().split()
+    acronym = ""
+
+    for word in phrase:
+        acronym += word[0]
+
+    return acronym
+
+
+def abbreviate_map(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+
+    return ''.join(map(lambda word: word[0], phrase))
+
+
+def abbreviate_reduce(to_abbreviate):
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+
+    return reduce(lambda start, word: start + word[0], phrase, "")
+
+
+def abbreviate_regex_join(phrase):
+    removed = re.findall(r"[a-zA-Z']+", phrase)
+    return ''.join(word[0] for word in removed).upper()
+
+
+def abbreviate_finditer_join(to_abbreviate):
+    return ''.join(word[0][0] for word in
+                   re.finditer(r"[a-zA-Z']+", to_abbreviate)).upper()
+
+
+def abbreviate_regex_sub(to_abbreviate):
+    pattern = re.compile(r"(?<!_)\B[\w']+|[ ,\-_]")
+    return re.sub(pattern, "", to_abbreviate).upper()
+
+
+def abbreviate_regex_findall(to_abbreviate):
+    return ''.join(re.findall(r"(?<!')\b[a-zA-Z]|(?<=_)[^ _']", to_abbreviate.upper()))
+
+
+## ---------END FUNCTIONS TO BE TIMED-------------------- ##
+
+## --------  Timing Code Starts Here ---------------------##
+
+
+# Input Data Setup
+inputs = ['Ruby on Rails',
+  "Halley's Comet",
+  'First In, First Out',
+  'The Road _Not_ Taken',
+  'Portable Network Graphics',
+  'GNU Image Manipulation Program',
+  'Something - I made up from thin air',
+  'Complementary metal-oxide semiconductor',
+  'Ruby on Rails Ruby on Rails Ruby on Rails ',
+  "Halley's Comet Halley's Comet Halley's Comet ",
+  'First In, First Out First In, First Out First In, First Out ',
+  'The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken ',
+  'Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me',
+  'Portable Network Graphics Portable Network Graphics Portable Network Graphics ',
+  'GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program ',
+  'Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air ',
+  'Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor ',
+  'Ruby on Rails Ruby on Rails Ruby on Rails Ruby on Rails Ruby on Rails Ruby on Rails Ruby on Rails Ruby on Rails Ruby on Rails Ruby on Rails ',
+  "Halley's Comet Halley's Comet Halley's Comet Halley's Comet Halley's Comet Halley's Comet Halley's Comet Halley's Comet Halley's Comet Halley's Comet ",
+  'First In, First Out First In, First Out First In, First Out First In, First Out First In, First Out First In, First Out First In, First Out First In, First Out First In, First Out First In, First Out ',
+  'The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken The Road _Not_ Taken ',
+  'Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me ',
+  'Portable Network Graphics Portable Network Graphics Portable Network Graphics Portable Network Graphics Portable Network Graphics Portable Network Graphics Portable Network Graphics Portable Network Graphics Portable Network Graphics Portable Network Graphics ',
+  'GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program GNU Image Manipulation Program ',
+  'Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air ',
+  'Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor Complementary metal-oxide semiconductor ',
+  'The Road _Not_ Taken Graphics Portable Network Graphics GNU Image Manipulation Program GNU Image Manipulation Program Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Something - I made up from thin air Complementary metal-oxide semiconductor Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me'
+  "Ruby on Rails Halley's Comet First In, First Out The Road _Not_ Taken Portable Network Graphics GNU Image Manipulation Program Something - I made up from thin air Complementary metal-oxide semiconductor Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Ruby on Rails Halley's Coment First In, First Out The Road _Not_ Taken Portable Network Graphics GNU Image Manipulation Program Something - I made up from thin air Complementary metal-oxide semiconductor Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me"   
+  'Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me '
+  "Ruby on Rails Halley's Comet First In, First Out The Road _Not_ Taken Portable Network Graphics GNU Image Manipulation Program Something - I made up from thin air Complementary metal-oxide semiconductor Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Ruby on Rails Halley's Coment First In, First Out The Road _Not_ Taken Portable Network Graphics GNU Image Manipulation Program Something - I made up from thin air Complementary metal-oxide semiconductor Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Ruby on Rails Halley's Coment First In, First Out The Road _Not_ Taken Portable Network Graphics GNU Image Manipulation Program Something - I made up from thin air Complementary metal-oxide semiconductor Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me Ruby on Rails Halley's Coment First In, First Out The Road _Not_ Taken Portable Network Graphics GNU Image Manipulation Program Something - I made up from thin air Complementary metal-oxide semiconductor Rolling On The Floor Laughing So Hard That My Dogs Came Over And Licked Me"
+]
+
+
+# #Set up columns and rows for Pandas Data Frame
+col_headers = [f'Length: {len(item)}'for item in inputs]
+row_headers = ["loop with str.replace",
+               "list_comprehension with str.join()",
+               "map() with str.replace()",
+               "functools.reduce() with str.replace()",
+               "generator expression with str.join()",
+               "regex to clean with str.join()",
+               "re.finditer() with str.join()",
+               "re.sub() to clean and join",
+               "re.findall() 1st letters w/ str.join()"]
+
+# # empty dataframe will be filled in one cell at a time later
+df = pd.DataFrame(np.nan, index=row_headers, columns=col_headers)
+
+# #Function List to Call When Timing
+functions = [abbreviate_loop,
+             abbreviate_list_comprehension,
+             abbreviate_map,
+             abbreviate_reduce,
+             abbreviate_genex,
+             abbreviate_regex_join,
+             abbreviate_finditer_join,
+             abbreviate_regex_sub,
+             abbreviate_regex_findall]
+
+# Run timings using timeit.autorange().  Run Each Set 3 Times.
+for function, title in zip(functions, row_headers):
+    timings = [[
+            timeit.Timer(lambda: function(data), globals=globals()).autorange()[1] /
+            timeit.Timer(lambda: function(data), globals=globals()).autorange()[0]
+            for data in inputs] for rounds in range(3)]
+
+    # Only the fastest Cycle counts.
+    timing_result = min(timings)
+
+    # timing_result = [round(min(timeit.repeat(lambda: function(data), repeat=3, number=1000000, globals=globals())), 6) for data in words_II]
+    print(f'{title}', f'Timings : {timing_result}')
+
+    # Insert results into the dataframe
+    df.loc[title, 'Length: 13':'Length: 1114'] = timing_result
+
+# The next bit is useful for `introduction.md`
+pd.options.display.float_format = '{:,.2e}'.format
+print('\nDataframe in Markdown format:\n')
+print(df.to_markdown(floatfmt=".2e"))
\ No newline at end of file
diff --git a/exercises/practice/acronym/.articles/performance/content.md b/exercises/practice/acronym/.articles/performance/content.md
index 7ff38a1bed..d1fc4c9d29 100644
--- a/exercises/practice/acronym/.articles/performance/content.md
+++ b/exercises/practice/acronym/.articles/performance/content.md
@@ -1,25 +1,64 @@
 # Performance
 
-In this approach, we'll find out how to most efficiently form an acronym from an input string.
+In this article, we'll find out how to most efficiently form an acronym from an input string.
 
 The [approaches page][approaches] lists seven idiomatic approaches to this exercise:
 
-1. [Using a `list-comprehension`][approach-list-comprehension]
-2. [Using a `loop`][approach-loop]
+
+1. [Using a `loop`][approach-loop]
+2. [Using a `list-comprehension`][approach-list-comprehension]
 3. [Using `functools.reduce()`][approach-functools-reduce]
 4. [Using `map()`][approach-map-function]
 5. [Using a `generator-expression`][approach-generator-expression]
-6. [Using a `regex` with `str.join()`][approach-regex-join]
-7. [Using `re.sub()`][approach-regex-sub]
+6. [Using `re.finditer()` with `str.join()`][approach-regex-join]
+7. [Using `re.findall()` with `str.join()`][approach-regex-join]
+8. [Using `re.sub()`][approach-regex-sub]
+
+We'll include a ninth approach using `re.findall()` with a regex to select the first letters of each word, and `str.join()` to make the acronym.
 
 
 ## Benchmarks
 
-To benchmark these approaches, we wrote a [small benchmark application][benchmark-application] using [].
-The benchmark checks the various approaches against
-Besides the regular CPU-time columns, the amount of memory used was also tracked.
+To benchmark these approaches, we wrote a [small benchmarking script][benchmark-application] using the [`timeit`][timeit] module along with the third party libraries [`numbpy`][numpy] and [`pandas`][pandas].
+
+All approaches are `O(n)` - they require (_at minimum_) a loop through the entire input to create results, and the work scales in line with the length of the function input.
+That doesn't mean that all of these approaches take the _same amount of run time_.
+Despite being `O(n)`, overhead such as number of function calls, module importing/loading, regex backtracking, generator tracking, string concatenation, and `lambda` evaluation can add significant time.
+Some of the slowest strategies (_mostly regex solutions_) are _**10x or more times slower**_ than the fastest methods (_straight looping and list-comprehensions_).
+
+Of these variants, the `loop` approach is by far the fastest (and easiest read) for inputs under length 45.
+Above length 45, repeated string creation and concatenation via `+` starts to slow things down, and the `list-comprehension` approach becomes more efficient due to its loop optimizations and use of `str.join()`.
+
+
+At the largest input sizes, `map()` and `generator expressions` become more efficient (_as does `functools.reduce()` for certain inputs_), as they are not saving intermediary results to memory in the same way `list comprehensions` or string concatenation do.
+
+The least efficient and least readable are the regex solutions.
+While regex definitely has its place, the lack of readability and significant slowdown in this case become an issue.
+Of particular interest is the `re.sub()` vs `re.findall()` (_first letters_) solutions.
+Even though the `re.sub()` solution takes only 652 steps in the regex engine, `re.sub()` and its unpacking is slow enough that the 1766 steps for the first letters `re.findall()` solution is faster.
+
+
+
+||                              	| **Len: 13** 	| **Len: 14** 	| **Len: 19** 	| **Len: 20** 	| **Len: 25** 	| **Len: 30** 	| **Len: 35** 	| **Len: 39** 	| **Len: 42** 	| **Len: 45** 	| **Len: 60** 	| **Len: 63** 	| **Len: 74** 	| **Len: 150** 	| **Len: 210** 	| **Len: 360** 	| **Len: 400** 	| **Len: 2940** 	|
+|------------------------------	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:------------:	|:------------:	|:------------:	|:------------:	|:-------------:	|
+| **loop**                     	|   5.79e-07  	|   4.96e-07  	|   6.98e-07  	|   7.41e-07  	|   6.18e-07  	|   7.25e-07  	|   1.03e-06  	|   7.33e-07  	|   1.16e-06  	|   8.71e-07  	|   1.51e-06  	|   1.65e-06  	|   1.83e-06  	|   2.43e-06   	|   4.63e-06   	|   7.76e-06   	|   4.85e-06   	|    5.94e-05   	|
+| **list_comprehension**       	|   7.28e-07  	|   6.57e-07  	|   8.26e-07  	|   8.62e-07  	|   7.67e-07  	|   8.30e-07  	|   1.08e-06  	|   8.68e-07  	|   1.24e-06  	|   4.00e-07  	|   1.49e-06  	|   1.55e-06  	|   1.76e-06  	|   2.19e-06   	|   4.08e-06   	|   7.21e-06   	|   4.40e-06   	|    5.42e-05   	|
+| **functools.reduce()**       	|   7.93e-07  	|   6.65e-07  	|   9.50e-07  	|   2.43e-06  	|   8.19e-07  	|   9.56e-07  	|   1.36e-06  	|   4.12e-07  	|   1.64e-06  	|   1.21e-06  	|   2.03e-06  	|   2.14e-06  	|   2.45e-06  	|   3.15e-06   	|   6.03e-06   	|   1.03e-05   	|   6.19e-06   	|    8.10e-05   	|
+| **map() **                   	|   8.05e-07  	|   7.21e-07  	|   9.34e-07  	|   9.46e-07  	|   8.32e-07  	|   9.16e-07  	|   1.23e-06  	|   9.52e-07  	|   1.44e-06  	|   1.14e-06  	|   1.71e-06  	|   1.80e-06  	|   2.00e-06  	|   2.58e-06   	|   4.81e-06   	|   8.02e-06   	|   4.95e-06   	|    5.64e-05   	|
+| **generator expression**     	|   8.85e-07  	|   7.90e-07  	|   1.01e-06  	|   1.01e-06  	|   9.26e-07  	|   2.49e-06  	|   1.30e-06  	|   1.06e-06  	|   1.49e-06  	|   1.19e-06  	|   1.81e-06  	|   1.86e-06  	|   2.10e-06  	|   2.67e-06   	|   5.12e-06   	|   8.61e-06   	|   5.12e-06   	|    5.81e-05   	|
+| **re.finditer()**            	|   1.05e-06  	|   1.74e-06  	|   2.44e-06  	|   2.40e-06  	|   2.09e-06  	|   2.45e-06  	|   3.28e-06  	|   2.42e-06  	|   8.15e-06  	|   3.12e-06  	|   5.15e-06  	|   5.18e-06  	|   5.94e-06  	|   7.89e-06   	|   1.46e-05   	|   2.35e-05   	|   1.48e-05   	|    1.68e-04   	|
+| **regex with str.join()**    	|   1.62e-06  	|   1.42e-06  	|   1.85e-06  	|   1.91e-06  	|   1.66e-06  	|   1.88e-06  	|   2.61e-06  	|   4.41e-06  	|   3.14e-06  	|   2.47e-06  	|   3.92e-06  	|   4.11e-06  	|   4.61e-06  	|   6.24e-06   	|   1.13e-05   	|   1.86e-05   	|   1.19e-05   	|    1.36e-04   	|
+| **re.findall() 1st letters** 	|   1.63e-06  	|   1.57e-06  	|   2.04e-06  	|   2.12e-06  	|   2.16e-06  	|   2.50e-06  	|   3.18e-06  	|   2.90e-06  	|   3.73e-06  	|   3.41e-06  	|   4.84e-06  	|   5.22e-06  	|   5.94e-06  	|   1.00e-05   	|   1.54e-05   	|   2.48e-05   	|   2.28e-05   	|    1.95e-04   	|
+| **re.sub()**                 	|   2.35e-06  	|   1.10e-06  	|   3.06e-06  	|   2.94e-06  	|   2.51e-06  	|   2.92e-06  	|   4.10e-06  	|   2.91e-06  	|   4.95e-06  	|   3.80e-06  	|   6.48e-06  	|   6.39e-06  	|   6.90e-06  	|   9.29e-06   	|   1.90e-05   	|   2.98e-05   	|   1.83e-05   	|    2.03e-04   	|
+
+
+Keep in mind that all these approaches are very fast, and that benchmarking at this granularity can be unstable.
+
+Measurements were taken on a 3.1 GHz Quad-Core Intel Core i7 Mac running MacOS Ventura.
+Tests used `timeit.Timer.autorange()`, repeated 3 times.
+Time is reported in seconds taken per string after calculating the 'best of' time.
+The timeit module docs have more details, and [note.nkmk.me][note_nkmk_me] has a nice summary of methods.
 
-These are the results:
 
 [approach-functools-reduce]: https://exercism.org/tracks/python/exercises/acronym/approaches/functools-reduce
 [approach-generator-expression]: https://exercism.org/tracks/python/exercises/acronym/approaches/generator-expression
@@ -28,3 +67,8 @@ These are the results:
 [approach-map-function]: https://exercism.org/tracks/python/exercises/acronym/approaches/map-function
 [approach-regex-join]: https://exercism.org/tracks/python/exercises/acronym/approaches/regex-join
 [approach-regex-sub]: https://exercism.org/tracks/python/exercises/acronym/approaches/regex-sub
+[benchmark-application]: https://github.com/exercism/python/tree/main/exercises/practice/acronym/.articles/performance/code/Benchmark.py
+[note_nkmk_me]: https://note.nkmk.me/en/python-timeit-measure/
+[numpy]: https://numpy.org/
+[pandas]: https://pandas.pydata.org/docs/index.html
+[timeit]: https://docs.python.org/3.11/library/timeit.html
diff --git a/exercises/practice/acronym/.articles/performance/snippet.md b/exercises/practice/acronym/.articles/performance/snippet.md
index b4873f6dbf..e6d772912f 100644
--- a/exercises/practice/acronym/.articles/performance/snippet.md
+++ b/exercises/practice/acronym/.articles/performance/snippet.md
@@ -1,8 +1,9 @@
-| Method 	| Mean Time 	| Memory Allocated 	|
-|--------	|-----------	|------------------	|
-|        	|           	|                  	|
-|        	|           	|                  	|
-|        	|           	|                  	|
-|        	|           	|                  	|
-|        	|           	|                  	|
-|        	|           	|                  	|
\ No newline at end of file
+|                              	| **Len: 13** 	| **Len: 30** 	| **Len: 74** 	| **Len: 210** 	| **Len: 2940** 	|
+|------------------------------	|:-----------:	|:-----------:	|:-----------:	|:------------:	|:-------------:	|
+| **loop**                     	|   5.79e-07  	|   7.25e-07  	|   1.83e-06  	|   4.63e-06   	|    5.94e-05   	|
+| **list_comprehension**       	|   7.28e-07  	|   8.30e-07  	|   1.76e-06  	|   4.08e-06   	|    5.42e-05   	|
+| **functools.reduce()**       	|   7.93e-07  	|   9.56e-07  	|   2.45e-06  	|   6.03e-06   	|    8.10e-05   	|
+| **map() **                   	|   8.05e-07  	|   9.16e-07  	|   2.00e-06  	|   4.81e-06   	|    5.64e-05   	|
+| **generator expression**     	|   8.85e-07  	|   2.49e-06  	|   2.10e-06  	|   5.12e-06   	|    5.81e-05   	|
+| **re.findall() 1st letters** 	|   1.63e-06  	|   2.50e-06  	|   5.94e-06  	|   1.54e-05   	|    1.95e-04   	|
+| **re.sub()**                 	|   2.35e-06  	|   2.92e-06  	|   6.90e-06  	|   1.90e-05   	|    2.03e-04   	|
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/exercises/practice/acronym/approaches/.ipynb_checkpoints/Untitled-checkpoint.ipynb
deleted file mode 100644
index 3f02ee7f74..0000000000
--- a/exercises/practice/acronym/approaches/.ipynb_checkpoints/Untitled-checkpoint.ipynb
+++ /dev/null
@@ -1,982 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "684ee076",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>input length</th>\n",
-       "      <th>regex_join_I</th>\n",
-       "      <th>regex_join</th>\n",
-       "      <th>regex_sub</th>\n",
-       "      <th>genex</th>\n",
-       "      <th>map</th>\n",
-       "      <th>reduce</th>\n",
-       "      <th>list_comprehension</th>\n",
-       "      <th>loop</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>13</td>\n",
-       "      <td>2.445</td>\n",
-       "      <td>1.943</td>\n",
-       "      <td>1.897</td>\n",
-       "      <td>1.050</td>\n",
-       "      <td>1.014</td>\n",
-       "      <td>0.990</td>\n",
-       "      <td>0.878</td>\n",
-       "      <td>0.743</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>14</td>\n",
-       "      <td>2.260</td>\n",
-       "      <td>1.683</td>\n",
-       "      <td>1.526</td>\n",
-       "      <td>0.962</td>\n",
-       "      <td>0.913</td>\n",
-       "      <td>0.832</td>\n",
-       "      <td>0.798</td>\n",
-       "      <td>0.634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>19</td>\n",
-       "      <td>2.928</td>\n",
-       "      <td>2.204</td>\n",
-       "      <td>2.377</td>\n",
-       "      <td>1.133</td>\n",
-       "      <td>1.115</td>\n",
-       "      <td>1.159</td>\n",
-       "      <td>0.959</td>\n",
-       "      <td>0.864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>20</td>\n",
-       "      <td>2.998</td>\n",
-       "      <td>2.242</td>\n",
-       "      <td>2.807</td>\n",
-       "      <td>1.165</td>\n",
-       "      <td>1.146</td>\n",
-       "      <td>1.197</td>\n",
-       "      <td>0.987</td>\n",
-       "      <td>0.894</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>25</td>\n",
-       "      <td>2.853</td>\n",
-       "      <td>1.973</td>\n",
-       "      <td>1.921</td>\n",
-       "      <td>1.058</td>\n",
-       "      <td>1.027</td>\n",
-       "      <td>1.014</td>\n",
-       "      <td>0.889</td>\n",
-       "      <td>0.760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>30</td>\n",
-       "      <td>3.303</td>\n",
-       "      <td>2.259</td>\n",
-       "      <td>2.378</td>\n",
-       "      <td>1.148</td>\n",
-       "      <td>1.137</td>\n",
-       "      <td>1.186</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>35</td>\n",
-       "      <td>4.300</td>\n",
-       "      <td>3.079</td>\n",
-       "      <td>3.837</td>\n",
-       "      <td>1.457</td>\n",
-       "      <td>1.466</td>\n",
-       "      <td>1.664</td>\n",
-       "      <td>1.232</td>\n",
-       "      <td>1.222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>39</td>\n",
-       "      <td>3.612</td>\n",
-       "      <td>2.282</td>\n",
-       "      <td>2.492</td>\n",
-       "      <td>1.175</td>\n",
-       "      <td>1.172</td>\n",
-       "      <td>1.220</td>\n",
-       "      <td>1.013</td>\n",
-       "      <td>0.899</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>42</td>\n",
-       "      <td>5.106</td>\n",
-       "      <td>3.851</td>\n",
-       "      <td>4.493</td>\n",
-       "      <td>1.636</td>\n",
-       "      <td>1.660</td>\n",
-       "      <td>1.994</td>\n",
-       "      <td>1.402</td>\n",
-       "      <td>1.397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>45</td>\n",
-       "      <td>4.412</td>\n",
-       "      <td>2.981</td>\n",
-       "      <td>3.329</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>1.361</td>\n",
-       "      <td>1.504</td>\n",
-       "      <td>1.160</td>\n",
-       "      <td>1.121</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>60</td>\n",
-       "      <td>6.532</td>\n",
-       "      <td>4.751</td>\n",
-       "      <td>5.609</td>\n",
-       "      <td>1.997</td>\n",
-       "      <td>2.041</td>\n",
-       "      <td>2.523</td>\n",
-       "      <td>1.662</td>\n",
-       "      <td>1.827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>63</td>\n",
-       "      <td>6.717</td>\n",
-       "      <td>4.839</td>\n",
-       "      <td>6.875</td>\n",
-       "      <td>2.043</td>\n",
-       "      <td>2.089</td>\n",
-       "      <td>2.569</td>\n",
-       "      <td>1.711</td>\n",
-       "      <td>1.888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>74</td>\n",
-       "      <td>7.559</td>\n",
-       "      <td>5.414</td>\n",
-       "      <td>6.513</td>\n",
-       "      <td>2.310</td>\n",
-       "      <td>2.405</td>\n",
-       "      <td>3.021</td>\n",
-       "      <td>1.980</td>\n",
-       "      <td>2.221</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>78</td>\n",
-       "      <td>6.327</td>\n",
-       "      <td>3.958</td>\n",
-       "      <td>4.507</td>\n",
-       "      <td>1.695</td>\n",
-       "      <td>1.721</td>\n",
-       "      <td>2.046</td>\n",
-       "      <td>1.455</td>\n",
-       "      <td>1.491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>93</td>\n",
-       "      <td>7.947</td>\n",
-       "      <td>4.840</td>\n",
-       "      <td>5.779</td>\n",
-       "      <td>2.004</td>\n",
-       "      <td>2.070</td>\n",
-       "      <td>2.548</td>\n",
-       "      <td>1.704</td>\n",
-       "      <td>1.886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>108</td>\n",
-       "      <td>11.166</td>\n",
-       "      <td>7.261</td>\n",
-       "      <td>10.266</td>\n",
-       "      <td>3.087</td>\n",
-       "      <td>3.199</td>\n",
-       "      <td>4.091</td>\n",
-       "      <td>2.519</td>\n",
-       "      <td>2.887</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>120</td>\n",
-       "      <td>8.991</td>\n",
-       "      <td>4.932</td>\n",
-       "      <td>6.000</td>\n",
-       "      <td>2.129</td>\n",
-       "      <td>2.176</td>\n",
-       "      <td>2.680</td>\n",
-       "      <td>1.813</td>\n",
-       "      <td>1.938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>140</td>\n",
-       "      <td>14.260</td>\n",
-       "      <td>9.803</td>\n",
-       "      <td>12.876</td>\n",
-       "      <td>4.021</td>\n",
-       "      <td>4.206</td>\n",
-       "      <td>5.480</td>\n",
-       "      <td>3.329</td>\n",
-       "      <td>3.968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>150</td>\n",
-       "      <td>12.290</td>\n",
-       "      <td>7.211</td>\n",
-       "      <td>8.760</td>\n",
-       "      <td>2.986</td>\n",
-       "      <td>3.087</td>\n",
-       "      <td>3.943</td>\n",
-       "      <td>2.487</td>\n",
-       "      <td>2.776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>200</td>\n",
-       "      <td>19.407</td>\n",
-       "      <td>12.727</td>\n",
-       "      <td>17.067</td>\n",
-       "      <td>5.224</td>\n",
-       "      <td>5.540</td>\n",
-       "      <td>7.166</td>\n",
-       "      <td>4.210</td>\n",
-       "      <td>5.107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>210</td>\n",
-       "      <td>20.110</td>\n",
-       "      <td>12.974</td>\n",
-       "      <td>21.827</td>\n",
-       "      <td>5.352</td>\n",
-       "      <td>5.748</td>\n",
-       "      <td>7.257</td>\n",
-       "      <td>4.362</td>\n",
-       "      <td>5.258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>225</td>\n",
-       "      <td>21.177</td>\n",
-       "      <td>14.154</td>\n",
-       "      <td>19.314</td>\n",
-       "      <td>5.793</td>\n",
-       "      <td>6.017</td>\n",
-       "      <td>7.917</td>\n",
-       "      <td>4.653</td>\n",
-       "      <td>5.702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>260</td>\n",
-       "      <td>18.602</td>\n",
-       "      <td>10.110</td>\n",
-       "      <td>12.960</td>\n",
-       "      <td>4.189</td>\n",
-       "      <td>4.370</td>\n",
-       "      <td>5.640</td>\n",
-       "      <td>3.444</td>\n",
-       "      <td>4.067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>310</td>\n",
-       "      <td>23.087</td>\n",
-       "      <td>13.068</td>\n",
-       "      <td>17.437</td>\n",
-       "      <td>5.352</td>\n",
-       "      <td>5.693</td>\n",
-       "      <td>7.303</td>\n",
-       "      <td>4.326</td>\n",
-       "      <td>5.202</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>360</td>\n",
-       "      <td>33.145</td>\n",
-       "      <td>21.459</td>\n",
-       "      <td>31.768</td>\n",
-       "      <td>8.962</td>\n",
-       "      <td>9.701</td>\n",
-       "      <td>12.506</td>\n",
-       "      <td>7.364</td>\n",
-       "      <td>8.998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>400</td>\n",
-       "      <td>25.931</td>\n",
-       "      <td>13.517</td>\n",
-       "      <td>18.251</td>\n",
-       "      <td>5.577</td>\n",
-       "      <td>5.939</td>\n",
-       "      <td>7.538</td>\n",
-       "      <td>4.644</td>\n",
-       "      <td>5.437</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>750</td>\n",
-       "      <td>67.390</td>\n",
-       "      <td>44.480</td>\n",
-       "      <td>56.100</td>\n",
-       "      <td>18.607</td>\n",
-       "      <td>19.995</td>\n",
-       "      <td>26.182</td>\n",
-       "      <td>15.324</td>\n",
-       "      <td>18.628</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    input length  regex_join_I  regex_join  regex_sub   genex     map  reduce  \\\n",
-       "0             13         2.445       1.943      1.897   1.050   1.014   0.990   \n",
-       "1             14         2.260       1.683      1.526   0.962   0.913   0.832   \n",
-       "2             19         2.928       2.204      2.377   1.133   1.115   1.159   \n",
-       "3             20         2.998       2.242      2.807   1.165   1.146   1.197   \n",
-       "4             25         2.853       1.973      1.921   1.058   1.027   1.014   \n",
-       "5             30         3.303       2.259      2.378   1.148   1.137   1.186   \n",
-       "6             35         4.300       3.079      3.837   1.457   1.466   1.664   \n",
-       "7             39         3.612       2.282      2.492   1.175   1.172   1.220   \n",
-       "8             42         5.106       3.851      4.493   1.636   1.660   1.994   \n",
-       "9             45         4.412       2.981      3.329   1.375   1.361   1.504   \n",
-       "10            60         6.532       4.751      5.609   1.997   2.041   2.523   \n",
-       "11            63         6.717       4.839      6.875   2.043   2.089   2.569   \n",
-       "12            74         7.559       5.414      6.513   2.310   2.405   3.021   \n",
-       "13            78         6.327       3.958      4.507   1.695   1.721   2.046   \n",
-       "14            93         7.947       4.840      5.779   2.004   2.070   2.548   \n",
-       "15           108        11.166       7.261     10.266   3.087   3.199   4.091   \n",
-       "16           120         8.991       4.932      6.000   2.129   2.176   2.680   \n",
-       "17           140        14.260       9.803     12.876   4.021   4.206   5.480   \n",
-       "18           150        12.290       7.211      8.760   2.986   3.087   3.943   \n",
-       "19           200        19.407      12.727     17.067   5.224   5.540   7.166   \n",
-       "20           210        20.110      12.974     21.827   5.352   5.748   7.257   \n",
-       "21           225        21.177      14.154     19.314   5.793   6.017   7.917   \n",
-       "22           260        18.602      10.110     12.960   4.189   4.370   5.640   \n",
-       "23           310        23.087      13.068     17.437   5.352   5.693   7.303   \n",
-       "24           360        33.145      21.459     31.768   8.962   9.701  12.506   \n",
-       "25           400        25.931      13.517     18.251   5.577   5.939   7.538   \n",
-       "26           750        67.390      44.480     56.100  18.607  19.995  26.182   \n",
-       "\n",
-       "    list_comprehension    loop  \n",
-       "0                0.878   0.743  \n",
-       "1                0.798   0.634  \n",
-       "2                0.959   0.864  \n",
-       "3                0.987   0.894  \n",
-       "4                0.889   0.760  \n",
-       "5                0.970   0.867  \n",
-       "6                1.232   1.222  \n",
-       "7                1.013   0.899  \n",
-       "8                1.402   1.397  \n",
-       "9                1.160   1.121  \n",
-       "10               1.662   1.827  \n",
-       "11               1.711   1.888  \n",
-       "12               1.980   2.221  \n",
-       "13               1.455   1.491  \n",
-       "14               1.704   1.886  \n",
-       "15               2.519   2.887  \n",
-       "16               1.813   1.938  \n",
-       "17               3.329   3.968  \n",
-       "18               2.487   2.776  \n",
-       "19               4.210   5.107  \n",
-       "20               4.362   5.258  \n",
-       "21               4.653   5.702  \n",
-       "22               3.444   4.067  \n",
-       "23               4.326   5.202  \n",
-       "24               7.364   8.998  \n",
-       "25               4.644   5.437  \n",
-       "26              15.324  18.628  "
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "data_to_plot = {'input length': [13,\n",
-    " 14,\n",
-    " 19,\n",
-    " 20,\n",
-    " 25,\n",
-    " 30,\n",
-    " 35,\n",
-    " 39,\n",
-    " 42,\n",
-    " 45,\n",
-    " 60,\n",
-    " 63,\n",
-    " 74,\n",
-    " 78,\n",
-    " 93,\n",
-    " 108,\n",
-    " 120,\n",
-    " 140,\n",
-    " 150,\n",
-    " 200,\n",
-    " 210,\n",
-    " 225,\n",
-    " 260,\n",
-    " 310,\n",
-    " 360,\n",
-    " 400,\n",
-    " 750],\n",
-    "'regex_join_I': [2.445, 2.26, 2.928, 2.998, 2.853, 3.303, 4.3, 3.612, 5.106, 4.412, 6.532, 6.717, 7.559, 6.327, 7.947, 11.166, 8.991, 14.26, 12.29, 19.407, 20.11, 21.177, 18.602, 23.087, 33.145, 25.931, 67.39],\n",
-    "\n",
-    "'regex_join' :[1.943, 1.683, 2.204, 2.242, 1.973, 2.259, 3.079, 2.282, 3.851, 2.981, 4.751, 4.839, 5.414, 3.958, 4.84, 7.261, 4.932, 9.803, 7.211, 12.727, 12.974, 14.154, 10.11, 13.068, 21.459, 13.517, 44.48],\n",
-    "\n",
-    "'regex_sub': [1.897, 1.526, 2.377, 2.807, 1.921, 2.378, 3.837, 2.492, 4.493, 3.329, 5.609, 6.875, 6.513, 4.507, 5.779, 10.266, 6.0, 12.876, 8.76, 17.067, 21.827, 19.314, 12.96, 17.437, 31.768, 18.251, 56.1],\n",
-    "\n",
-    "'genex': [1.05, 0.962, 1.133, 1.165, 1.058, 1.148, 1.457, 1.175, 1.636, 1.375, 1.997, 2.043, 2.31, 1.695, 2.004, 3.087, 2.129, 4.021, 2.986, 5.224, 5.352, 5.793, 4.189, 5.352, 8.962, 5.577, 18.607],\n",
-    "\n",
-    "'map' :[1.014, 0.913, 1.115, 1.146, 1.027, 1.137, 1.466, 1.172, 1.66, 1.361, 2.041, 2.089, 2.405, 1.721, 2.07, 3.199, 2.176, 4.206, 3.087, 5.54, 5.748, 6.017, 4.37, 5.693, 9.701, 5.939, 19.995],\n",
-    "\n",
-    "'reduce': [0.99, 0.832, 1.159, 1.197, 1.014, 1.186, 1.664, 1.22, 1.994, 1.504, 2.523, 2.569, 3.021, 2.046, 2.548, 4.091, 2.68, 5.48, 3.943, 7.166, 7.257, 7.917, 5.64, 7.303, 12.506, 7.538, 26.182],\n",
-    "\n",
-    "'list_comprehension': [0.878, 0.798, 0.959, 0.987, 0.889, 0.97, 1.232, 1.013, 1.402, 1.16, 1.662, 1.711, 1.98, 1.455, 1.704, 2.519, 1.813, 3.329, 2.487, 4.21, 4.362, 4.653, 3.444, 4.326, 7.364, 4.644, 15.324],\n",
-    "\n",
-    "'loop': [0.743, 0.634, 0.864, 0.894, 0.76, 0.867, 1.222, 0.899, 1.397, 1.121, 1.827, 1.888, 2.221, 1.491, 1.886, 2.887, 1.938, 3.968, 2.776, 5.107, 5.258, 5.702, 4.067, 5.202, 8.998, 5.437, 18.628]}\n",
-    "\n",
-    "plot_data = pd.DataFrame.from_dict(data_to_plot)\n",
-    "new_data = plot_data.sort_values('input length')\n",
-    "new_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "79c28644",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regex_join_I</th>\n",
-       "      <th>regex_join</th>\n",
-       "      <th>regex_sub</th>\n",
-       "      <th>genex</th>\n",
-       "      <th>map</th>\n",
-       "      <th>reduce</th>\n",
-       "      <th>list_comprehension</th>\n",
-       "      <th>loop</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>input length</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.445</td>\n",
-       "      <td>1.943</td>\n",
-       "      <td>1.897</td>\n",
-       "      <td>1.050</td>\n",
-       "      <td>1.014</td>\n",
-       "      <td>0.990</td>\n",
-       "      <td>0.878</td>\n",
-       "      <td>0.743</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.260</td>\n",
-       "      <td>1.683</td>\n",
-       "      <td>1.526</td>\n",
-       "      <td>0.962</td>\n",
-       "      <td>0.913</td>\n",
-       "      <td>0.832</td>\n",
-       "      <td>0.798</td>\n",
-       "      <td>0.634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2.928</td>\n",
-       "      <td>2.204</td>\n",
-       "      <td>2.377</td>\n",
-       "      <td>1.133</td>\n",
-       "      <td>1.115</td>\n",
-       "      <td>1.159</td>\n",
-       "      <td>0.959</td>\n",
-       "      <td>0.864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>2.998</td>\n",
-       "      <td>2.242</td>\n",
-       "      <td>2.807</td>\n",
-       "      <td>1.165</td>\n",
-       "      <td>1.146</td>\n",
-       "      <td>1.197</td>\n",
-       "      <td>0.987</td>\n",
-       "      <td>0.894</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>2.853</td>\n",
-       "      <td>1.973</td>\n",
-       "      <td>1.921</td>\n",
-       "      <td>1.058</td>\n",
-       "      <td>1.027</td>\n",
-       "      <td>1.014</td>\n",
-       "      <td>0.889</td>\n",
-       "      <td>0.760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>3.303</td>\n",
-       "      <td>2.259</td>\n",
-       "      <td>2.378</td>\n",
-       "      <td>1.148</td>\n",
-       "      <td>1.137</td>\n",
-       "      <td>1.186</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>4.300</td>\n",
-       "      <td>3.079</td>\n",
-       "      <td>3.837</td>\n",
-       "      <td>1.457</td>\n",
-       "      <td>1.466</td>\n",
-       "      <td>1.664</td>\n",
-       "      <td>1.232</td>\n",
-       "      <td>1.222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>3.612</td>\n",
-       "      <td>2.282</td>\n",
-       "      <td>2.492</td>\n",
-       "      <td>1.175</td>\n",
-       "      <td>1.172</td>\n",
-       "      <td>1.220</td>\n",
-       "      <td>1.013</td>\n",
-       "      <td>0.899</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>5.106</td>\n",
-       "      <td>3.851</td>\n",
-       "      <td>4.493</td>\n",
-       "      <td>1.636</td>\n",
-       "      <td>1.660</td>\n",
-       "      <td>1.994</td>\n",
-       "      <td>1.402</td>\n",
-       "      <td>1.397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>4.412</td>\n",
-       "      <td>2.981</td>\n",
-       "      <td>3.329</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>1.361</td>\n",
-       "      <td>1.504</td>\n",
-       "      <td>1.160</td>\n",
-       "      <td>1.121</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>6.532</td>\n",
-       "      <td>4.751</td>\n",
-       "      <td>5.609</td>\n",
-       "      <td>1.997</td>\n",
-       "      <td>2.041</td>\n",
-       "      <td>2.523</td>\n",
-       "      <td>1.662</td>\n",
-       "      <td>1.827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>6.717</td>\n",
-       "      <td>4.839</td>\n",
-       "      <td>6.875</td>\n",
-       "      <td>2.043</td>\n",
-       "      <td>2.089</td>\n",
-       "      <td>2.569</td>\n",
-       "      <td>1.711</td>\n",
-       "      <td>1.888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>7.559</td>\n",
-       "      <td>5.414</td>\n",
-       "      <td>6.513</td>\n",
-       "      <td>2.310</td>\n",
-       "      <td>2.405</td>\n",
-       "      <td>3.021</td>\n",
-       "      <td>1.980</td>\n",
-       "      <td>2.221</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>6.327</td>\n",
-       "      <td>3.958</td>\n",
-       "      <td>4.507</td>\n",
-       "      <td>1.695</td>\n",
-       "      <td>1.721</td>\n",
-       "      <td>2.046</td>\n",
-       "      <td>1.455</td>\n",
-       "      <td>1.491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>7.947</td>\n",
-       "      <td>4.840</td>\n",
-       "      <td>5.779</td>\n",
-       "      <td>2.004</td>\n",
-       "      <td>2.070</td>\n",
-       "      <td>2.548</td>\n",
-       "      <td>1.704</td>\n",
-       "      <td>1.886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>11.166</td>\n",
-       "      <td>7.261</td>\n",
-       "      <td>10.266</td>\n",
-       "      <td>3.087</td>\n",
-       "      <td>3.199</td>\n",
-       "      <td>4.091</td>\n",
-       "      <td>2.519</td>\n",
-       "      <td>2.887</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120</th>\n",
-       "      <td>8.991</td>\n",
-       "      <td>4.932</td>\n",
-       "      <td>6.000</td>\n",
-       "      <td>2.129</td>\n",
-       "      <td>2.176</td>\n",
-       "      <td>2.680</td>\n",
-       "      <td>1.813</td>\n",
-       "      <td>1.938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>14.260</td>\n",
-       "      <td>9.803</td>\n",
-       "      <td>12.876</td>\n",
-       "      <td>4.021</td>\n",
-       "      <td>4.206</td>\n",
-       "      <td>5.480</td>\n",
-       "      <td>3.329</td>\n",
-       "      <td>3.968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>12.290</td>\n",
-       "      <td>7.211</td>\n",
-       "      <td>8.760</td>\n",
-       "      <td>2.986</td>\n",
-       "      <td>3.087</td>\n",
-       "      <td>3.943</td>\n",
-       "      <td>2.487</td>\n",
-       "      <td>2.776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>19.407</td>\n",
-       "      <td>12.727</td>\n",
-       "      <td>17.067</td>\n",
-       "      <td>5.224</td>\n",
-       "      <td>5.540</td>\n",
-       "      <td>7.166</td>\n",
-       "      <td>4.210</td>\n",
-       "      <td>5.107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>210</th>\n",
-       "      <td>20.110</td>\n",
-       "      <td>12.974</td>\n",
-       "      <td>21.827</td>\n",
-       "      <td>5.352</td>\n",
-       "      <td>5.748</td>\n",
-       "      <td>7.257</td>\n",
-       "      <td>4.362</td>\n",
-       "      <td>5.258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>225</th>\n",
-       "      <td>21.177</td>\n",
-       "      <td>14.154</td>\n",
-       "      <td>19.314</td>\n",
-       "      <td>5.793</td>\n",
-       "      <td>6.017</td>\n",
-       "      <td>7.917</td>\n",
-       "      <td>4.653</td>\n",
-       "      <td>5.702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>260</th>\n",
-       "      <td>18.602</td>\n",
-       "      <td>10.110</td>\n",
-       "      <td>12.960</td>\n",
-       "      <td>4.189</td>\n",
-       "      <td>4.370</td>\n",
-       "      <td>5.640</td>\n",
-       "      <td>3.444</td>\n",
-       "      <td>4.067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>310</th>\n",
-       "      <td>23.087</td>\n",
-       "      <td>13.068</td>\n",
-       "      <td>17.437</td>\n",
-       "      <td>5.352</td>\n",
-       "      <td>5.693</td>\n",
-       "      <td>7.303</td>\n",
-       "      <td>4.326</td>\n",
-       "      <td>5.202</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>360</th>\n",
-       "      <td>33.145</td>\n",
-       "      <td>21.459</td>\n",
-       "      <td>31.768</td>\n",
-       "      <td>8.962</td>\n",
-       "      <td>9.701</td>\n",
-       "      <td>12.506</td>\n",
-       "      <td>7.364</td>\n",
-       "      <td>8.998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>25.931</td>\n",
-       "      <td>13.517</td>\n",
-       "      <td>18.251</td>\n",
-       "      <td>5.577</td>\n",
-       "      <td>5.939</td>\n",
-       "      <td>7.538</td>\n",
-       "      <td>4.644</td>\n",
-       "      <td>5.437</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>750</th>\n",
-       "      <td>67.390</td>\n",
-       "      <td>44.480</td>\n",
-       "      <td>56.100</td>\n",
-       "      <td>18.607</td>\n",
-       "      <td>19.995</td>\n",
-       "      <td>26.182</td>\n",
-       "      <td>15.324</td>\n",
-       "      <td>18.628</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              regex_join_I  regex_join  regex_sub   genex     map  reduce  \\\n",
-       "input length                                                                \n",
-       "13                   2.445       1.943      1.897   1.050   1.014   0.990   \n",
-       "14                   2.260       1.683      1.526   0.962   0.913   0.832   \n",
-       "19                   2.928       2.204      2.377   1.133   1.115   1.159   \n",
-       "20                   2.998       2.242      2.807   1.165   1.146   1.197   \n",
-       "25                   2.853       1.973      1.921   1.058   1.027   1.014   \n",
-       "30                   3.303       2.259      2.378   1.148   1.137   1.186   \n",
-       "35                   4.300       3.079      3.837   1.457   1.466   1.664   \n",
-       "39                   3.612       2.282      2.492   1.175   1.172   1.220   \n",
-       "42                   5.106       3.851      4.493   1.636   1.660   1.994   \n",
-       "45                   4.412       2.981      3.329   1.375   1.361   1.504   \n",
-       "60                   6.532       4.751      5.609   1.997   2.041   2.523   \n",
-       "63                   6.717       4.839      6.875   2.043   2.089   2.569   \n",
-       "74                   7.559       5.414      6.513   2.310   2.405   3.021   \n",
-       "78                   6.327       3.958      4.507   1.695   1.721   2.046   \n",
-       "93                   7.947       4.840      5.779   2.004   2.070   2.548   \n",
-       "108                 11.166       7.261     10.266   3.087   3.199   4.091   \n",
-       "120                  8.991       4.932      6.000   2.129   2.176   2.680   \n",
-       "140                 14.260       9.803     12.876   4.021   4.206   5.480   \n",
-       "150                 12.290       7.211      8.760   2.986   3.087   3.943   \n",
-       "200                 19.407      12.727     17.067   5.224   5.540   7.166   \n",
-       "210                 20.110      12.974     21.827   5.352   5.748   7.257   \n",
-       "225                 21.177      14.154     19.314   5.793   6.017   7.917   \n",
-       "260                 18.602      10.110     12.960   4.189   4.370   5.640   \n",
-       "310                 23.087      13.068     17.437   5.352   5.693   7.303   \n",
-       "360                 33.145      21.459     31.768   8.962   9.701  12.506   \n",
-       "400                 25.931      13.517     18.251   5.577   5.939   7.538   \n",
-       "750                 67.390      44.480     56.100  18.607  19.995  26.182   \n",
-       "\n",
-       "              list_comprehension    loop  \n",
-       "input length                              \n",
-       "13                         0.878   0.743  \n",
-       "14                         0.798   0.634  \n",
-       "19                         0.959   0.864  \n",
-       "20                         0.987   0.894  \n",
-       "25                         0.889   0.760  \n",
-       "30                         0.970   0.867  \n",
-       "35                         1.232   1.222  \n",
-       "39                         1.013   0.899  \n",
-       "42                         1.402   1.397  \n",
-       "45                         1.160   1.121  \n",
-       "60                         1.662   1.827  \n",
-       "63                         1.711   1.888  \n",
-       "74                         1.980   2.221  \n",
-       "78                         1.455   1.491  \n",
-       "93                         1.704   1.886  \n",
-       "108                        2.519   2.887  \n",
-       "120                        1.813   1.938  \n",
-       "140                        3.329   3.968  \n",
-       "150                        2.487   2.776  \n",
-       "200                        4.210   5.107  \n",
-       "210                        4.362   5.258  \n",
-       "225                        4.653   5.702  \n",
-       "260                        3.444   4.067  \n",
-       "310                        4.326   5.202  \n",
-       "360                        7.364   8.998  \n",
-       "400                        4.644   5.437  \n",
-       "750                       15.324  18.628  "
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_data.set_index('input length', inplace=True)\n",
-    "new_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "id": "d51b3964",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7faefd7ef250>"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 782.6x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.set_theme(style=\"ticks\")\n",
-    "data = new_data\n",
-    "#pallette = ['#782be8', '#d000ba', '#ff0c5c', '#ff5938', '#f47e1f', '#afc94e', '#95d678', '#79e8c8']\n",
-    "pallette = ['#FDFC4B', '#E8186A', '#F69805', '#D64624', '#AA0A81', '#5E18C7', '#0A6152', '#3F0F84']\n",
-    "g = sns.relplot(data=data, kind=\"line\",palette=palette, linewidth=2, height=6, aspect=1.5)\n",
-    "g.set(xticks=range(13, 750, 50))\n",
-    "g.set(yticks=range(1, 70, 5))\n",
-    "g.despine(offset=10, trim=True)\n",
-    "#sns.lineplot(data=data, palette=\"plasma\", linewidth=2.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "49f8add7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/exercises/practice/acronym/approaches/Untitled.ipynb b/exercises/practice/acronym/approaches/Untitled.ipynb
deleted file mode 100644
index 6fd3f74091..0000000000
--- a/exercises/practice/acronym/approaches/Untitled.ipynb
+++ /dev/null
@@ -1,989 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "id": "dea90412",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>input length</th>\n",
-       "      <th>regex_join_I</th>\n",
-       "      <th>regex_join</th>\n",
-       "      <th>regex_sub</th>\n",
-       "      <th>genex</th>\n",
-       "      <th>map</th>\n",
-       "      <th>reduce</th>\n",
-       "      <th>list_comprehension</th>\n",
-       "      <th>loop</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>13</td>\n",
-       "      <td>2.445</td>\n",
-       "      <td>1.943</td>\n",
-       "      <td>1.897</td>\n",
-       "      <td>1.050</td>\n",
-       "      <td>1.014</td>\n",
-       "      <td>0.990</td>\n",
-       "      <td>0.878</td>\n",
-       "      <td>0.743</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>14</td>\n",
-       "      <td>2.260</td>\n",
-       "      <td>1.683</td>\n",
-       "      <td>1.526</td>\n",
-       "      <td>0.962</td>\n",
-       "      <td>0.913</td>\n",
-       "      <td>0.832</td>\n",
-       "      <td>0.798</td>\n",
-       "      <td>0.634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>19</td>\n",
-       "      <td>2.928</td>\n",
-       "      <td>2.204</td>\n",
-       "      <td>2.377</td>\n",
-       "      <td>1.133</td>\n",
-       "      <td>1.115</td>\n",
-       "      <td>1.159</td>\n",
-       "      <td>0.959</td>\n",
-       "      <td>0.864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>20</td>\n",
-       "      <td>2.998</td>\n",
-       "      <td>2.242</td>\n",
-       "      <td>2.807</td>\n",
-       "      <td>1.165</td>\n",
-       "      <td>1.146</td>\n",
-       "      <td>1.197</td>\n",
-       "      <td>0.987</td>\n",
-       "      <td>0.894</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>25</td>\n",
-       "      <td>2.853</td>\n",
-       "      <td>1.973</td>\n",
-       "      <td>1.921</td>\n",
-       "      <td>1.058</td>\n",
-       "      <td>1.027</td>\n",
-       "      <td>1.014</td>\n",
-       "      <td>0.889</td>\n",
-       "      <td>0.760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>30</td>\n",
-       "      <td>3.303</td>\n",
-       "      <td>2.259</td>\n",
-       "      <td>2.378</td>\n",
-       "      <td>1.148</td>\n",
-       "      <td>1.137</td>\n",
-       "      <td>1.186</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>35</td>\n",
-       "      <td>4.300</td>\n",
-       "      <td>3.079</td>\n",
-       "      <td>3.837</td>\n",
-       "      <td>1.457</td>\n",
-       "      <td>1.466</td>\n",
-       "      <td>1.664</td>\n",
-       "      <td>1.232</td>\n",
-       "      <td>1.222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>39</td>\n",
-       "      <td>3.612</td>\n",
-       "      <td>2.282</td>\n",
-       "      <td>2.492</td>\n",
-       "      <td>1.175</td>\n",
-       "      <td>1.172</td>\n",
-       "      <td>1.220</td>\n",
-       "      <td>1.013</td>\n",
-       "      <td>0.899</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>42</td>\n",
-       "      <td>5.106</td>\n",
-       "      <td>3.851</td>\n",
-       "      <td>4.493</td>\n",
-       "      <td>1.636</td>\n",
-       "      <td>1.660</td>\n",
-       "      <td>1.994</td>\n",
-       "      <td>1.402</td>\n",
-       "      <td>1.397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>45</td>\n",
-       "      <td>4.412</td>\n",
-       "      <td>2.981</td>\n",
-       "      <td>3.329</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>1.361</td>\n",
-       "      <td>1.504</td>\n",
-       "      <td>1.160</td>\n",
-       "      <td>1.121</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>60</td>\n",
-       "      <td>6.532</td>\n",
-       "      <td>4.751</td>\n",
-       "      <td>5.609</td>\n",
-       "      <td>1.997</td>\n",
-       "      <td>2.041</td>\n",
-       "      <td>2.523</td>\n",
-       "      <td>1.662</td>\n",
-       "      <td>1.827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>63</td>\n",
-       "      <td>6.717</td>\n",
-       "      <td>4.839</td>\n",
-       "      <td>6.875</td>\n",
-       "      <td>2.043</td>\n",
-       "      <td>2.089</td>\n",
-       "      <td>2.569</td>\n",
-       "      <td>1.711</td>\n",
-       "      <td>1.888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>74</td>\n",
-       "      <td>7.559</td>\n",
-       "      <td>5.414</td>\n",
-       "      <td>6.513</td>\n",
-       "      <td>2.310</td>\n",
-       "      <td>2.405</td>\n",
-       "      <td>3.021</td>\n",
-       "      <td>1.980</td>\n",
-       "      <td>2.221</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>78</td>\n",
-       "      <td>6.327</td>\n",
-       "      <td>3.958</td>\n",
-       "      <td>4.507</td>\n",
-       "      <td>1.695</td>\n",
-       "      <td>1.721</td>\n",
-       "      <td>2.046</td>\n",
-       "      <td>1.455</td>\n",
-       "      <td>1.491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>93</td>\n",
-       "      <td>7.947</td>\n",
-       "      <td>4.840</td>\n",
-       "      <td>5.779</td>\n",
-       "      <td>2.004</td>\n",
-       "      <td>2.070</td>\n",
-       "      <td>2.548</td>\n",
-       "      <td>1.704</td>\n",
-       "      <td>1.886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>108</td>\n",
-       "      <td>11.166</td>\n",
-       "      <td>7.261</td>\n",
-       "      <td>10.266</td>\n",
-       "      <td>3.087</td>\n",
-       "      <td>3.199</td>\n",
-       "      <td>4.091</td>\n",
-       "      <td>2.519</td>\n",
-       "      <td>2.887</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>120</td>\n",
-       "      <td>8.991</td>\n",
-       "      <td>4.932</td>\n",
-       "      <td>6.000</td>\n",
-       "      <td>2.129</td>\n",
-       "      <td>2.176</td>\n",
-       "      <td>2.680</td>\n",
-       "      <td>1.813</td>\n",
-       "      <td>1.938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>140</td>\n",
-       "      <td>14.260</td>\n",
-       "      <td>9.803</td>\n",
-       "      <td>12.876</td>\n",
-       "      <td>4.021</td>\n",
-       "      <td>4.206</td>\n",
-       "      <td>5.480</td>\n",
-       "      <td>3.329</td>\n",
-       "      <td>3.968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>150</td>\n",
-       "      <td>12.290</td>\n",
-       "      <td>7.211</td>\n",
-       "      <td>8.760</td>\n",
-       "      <td>2.986</td>\n",
-       "      <td>3.087</td>\n",
-       "      <td>3.943</td>\n",
-       "      <td>2.487</td>\n",
-       "      <td>2.776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>200</td>\n",
-       "      <td>19.407</td>\n",
-       "      <td>12.727</td>\n",
-       "      <td>17.067</td>\n",
-       "      <td>5.224</td>\n",
-       "      <td>5.540</td>\n",
-       "      <td>7.166</td>\n",
-       "      <td>4.210</td>\n",
-       "      <td>5.107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>210</td>\n",
-       "      <td>20.110</td>\n",
-       "      <td>12.974</td>\n",
-       "      <td>21.827</td>\n",
-       "      <td>5.352</td>\n",
-       "      <td>5.748</td>\n",
-       "      <td>7.257</td>\n",
-       "      <td>4.362</td>\n",
-       "      <td>5.258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>225</td>\n",
-       "      <td>21.177</td>\n",
-       "      <td>14.154</td>\n",
-       "      <td>19.314</td>\n",
-       "      <td>5.793</td>\n",
-       "      <td>6.017</td>\n",
-       "      <td>7.917</td>\n",
-       "      <td>4.653</td>\n",
-       "      <td>5.702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>260</td>\n",
-       "      <td>18.602</td>\n",
-       "      <td>10.110</td>\n",
-       "      <td>12.960</td>\n",
-       "      <td>4.189</td>\n",
-       "      <td>4.370</td>\n",
-       "      <td>5.640</td>\n",
-       "      <td>3.444</td>\n",
-       "      <td>4.067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>310</td>\n",
-       "      <td>23.087</td>\n",
-       "      <td>13.068</td>\n",
-       "      <td>17.437</td>\n",
-       "      <td>5.352</td>\n",
-       "      <td>5.693</td>\n",
-       "      <td>7.303</td>\n",
-       "      <td>4.326</td>\n",
-       "      <td>5.202</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>360</td>\n",
-       "      <td>33.145</td>\n",
-       "      <td>21.459</td>\n",
-       "      <td>31.768</td>\n",
-       "      <td>8.962</td>\n",
-       "      <td>9.701</td>\n",
-       "      <td>12.506</td>\n",
-       "      <td>7.364</td>\n",
-       "      <td>8.998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>400</td>\n",
-       "      <td>25.931</td>\n",
-       "      <td>13.517</td>\n",
-       "      <td>18.251</td>\n",
-       "      <td>5.577</td>\n",
-       "      <td>5.939</td>\n",
-       "      <td>7.538</td>\n",
-       "      <td>4.644</td>\n",
-       "      <td>5.437</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>750</td>\n",
-       "      <td>67.390</td>\n",
-       "      <td>44.480</td>\n",
-       "      <td>56.100</td>\n",
-       "      <td>18.607</td>\n",
-       "      <td>19.995</td>\n",
-       "      <td>26.182</td>\n",
-       "      <td>15.324</td>\n",
-       "      <td>18.628</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    input length  regex_join_I  regex_join  regex_sub   genex     map  reduce  \\\n",
-       "0             13         2.445       1.943      1.897   1.050   1.014   0.990   \n",
-       "1             14         2.260       1.683      1.526   0.962   0.913   0.832   \n",
-       "2             19         2.928       2.204      2.377   1.133   1.115   1.159   \n",
-       "3             20         2.998       2.242      2.807   1.165   1.146   1.197   \n",
-       "4             25         2.853       1.973      1.921   1.058   1.027   1.014   \n",
-       "5             30         3.303       2.259      2.378   1.148   1.137   1.186   \n",
-       "6             35         4.300       3.079      3.837   1.457   1.466   1.664   \n",
-       "7             39         3.612       2.282      2.492   1.175   1.172   1.220   \n",
-       "8             42         5.106       3.851      4.493   1.636   1.660   1.994   \n",
-       "9             45         4.412       2.981      3.329   1.375   1.361   1.504   \n",
-       "10            60         6.532       4.751      5.609   1.997   2.041   2.523   \n",
-       "11            63         6.717       4.839      6.875   2.043   2.089   2.569   \n",
-       "12            74         7.559       5.414      6.513   2.310   2.405   3.021   \n",
-       "13            78         6.327       3.958      4.507   1.695   1.721   2.046   \n",
-       "14            93         7.947       4.840      5.779   2.004   2.070   2.548   \n",
-       "15           108        11.166       7.261     10.266   3.087   3.199   4.091   \n",
-       "16           120         8.991       4.932      6.000   2.129   2.176   2.680   \n",
-       "17           140        14.260       9.803     12.876   4.021   4.206   5.480   \n",
-       "18           150        12.290       7.211      8.760   2.986   3.087   3.943   \n",
-       "19           200        19.407      12.727     17.067   5.224   5.540   7.166   \n",
-       "20           210        20.110      12.974     21.827   5.352   5.748   7.257   \n",
-       "21           225        21.177      14.154     19.314   5.793   6.017   7.917   \n",
-       "22           260        18.602      10.110     12.960   4.189   4.370   5.640   \n",
-       "23           310        23.087      13.068     17.437   5.352   5.693   7.303   \n",
-       "24           360        33.145      21.459     31.768   8.962   9.701  12.506   \n",
-       "25           400        25.931      13.517     18.251   5.577   5.939   7.538   \n",
-       "26           750        67.390      44.480     56.100  18.607  19.995  26.182   \n",
-       "\n",
-       "    list_comprehension    loop  \n",
-       "0                0.878   0.743  \n",
-       "1                0.798   0.634  \n",
-       "2                0.959   0.864  \n",
-       "3                0.987   0.894  \n",
-       "4                0.889   0.760  \n",
-       "5                0.970   0.867  \n",
-       "6                1.232   1.222  \n",
-       "7                1.013   0.899  \n",
-       "8                1.402   1.397  \n",
-       "9                1.160   1.121  \n",
-       "10               1.662   1.827  \n",
-       "11               1.711   1.888  \n",
-       "12               1.980   2.221  \n",
-       "13               1.455   1.491  \n",
-       "14               1.704   1.886  \n",
-       "15               2.519   2.887  \n",
-       "16               1.813   1.938  \n",
-       "17               3.329   3.968  \n",
-       "18               2.487   2.776  \n",
-       "19               4.210   5.107  \n",
-       "20               4.362   5.258  \n",
-       "21               4.653   5.702  \n",
-       "22               3.444   4.067  \n",
-       "23               4.326   5.202  \n",
-       "24               7.364   8.998  \n",
-       "25               4.644   5.437  \n",
-       "26              15.324  18.628  "
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib as mpl\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "data_to_plot = {'input length': [13,\n",
-    " 14,\n",
-    " 19,\n",
-    " 20,\n",
-    " 25,\n",
-    " 30,\n",
-    " 35,\n",
-    " 39,\n",
-    " 42,\n",
-    " 45,\n",
-    " 60,\n",
-    " 63,\n",
-    " 74,\n",
-    " 78,\n",
-    " 93,\n",
-    " 108,\n",
-    " 120,\n",
-    " 140,\n",
-    " 150,\n",
-    " 200,\n",
-    " 210,\n",
-    " 225,\n",
-    " 260,\n",
-    " 310,\n",
-    " 360,\n",
-    " 400,\n",
-    " 750],\n",
-    "'regex_join_I': [2.445, 2.26, 2.928, 2.998, 2.853, 3.303, 4.3, 3.612, 5.106, 4.412, 6.532, 6.717, 7.559, 6.327, 7.947, 11.166, 8.991, 14.26, 12.29, 19.407, 20.11, 21.177, 18.602, 23.087, 33.145, 25.931, 67.39],\n",
-    "\n",
-    "'regex_join' :[1.943, 1.683, 2.204, 2.242, 1.973, 2.259, 3.079, 2.282, 3.851, 2.981, 4.751, 4.839, 5.414, 3.958, 4.84, 7.261, 4.932, 9.803, 7.211, 12.727, 12.974, 14.154, 10.11, 13.068, 21.459, 13.517, 44.48],\n",
-    "\n",
-    "'regex_sub': [1.897, 1.526, 2.377, 2.807, 1.921, 2.378, 3.837, 2.492, 4.493, 3.329, 5.609, 6.875, 6.513, 4.507, 5.779, 10.266, 6.0, 12.876, 8.76, 17.067, 21.827, 19.314, 12.96, 17.437, 31.768, 18.251, 56.1],\n",
-    "\n",
-    "'genex': [1.05, 0.962, 1.133, 1.165, 1.058, 1.148, 1.457, 1.175, 1.636, 1.375, 1.997, 2.043, 2.31, 1.695, 2.004, 3.087, 2.129, 4.021, 2.986, 5.224, 5.352, 5.793, 4.189, 5.352, 8.962, 5.577, 18.607],\n",
-    "\n",
-    "'map' :[1.014, 0.913, 1.115, 1.146, 1.027, 1.137, 1.466, 1.172, 1.66, 1.361, 2.041, 2.089, 2.405, 1.721, 2.07, 3.199, 2.176, 4.206, 3.087, 5.54, 5.748, 6.017, 4.37, 5.693, 9.701, 5.939, 19.995],\n",
-    "\n",
-    "'reduce': [0.99, 0.832, 1.159, 1.197, 1.014, 1.186, 1.664, 1.22, 1.994, 1.504, 2.523, 2.569, 3.021, 2.046, 2.548, 4.091, 2.68, 5.48, 3.943, 7.166, 7.257, 7.917, 5.64, 7.303, 12.506, 7.538, 26.182],\n",
-    "\n",
-    "'list_comprehension': [0.878, 0.798, 0.959, 0.987, 0.889, 0.97, 1.232, 1.013, 1.402, 1.16, 1.662, 1.711, 1.98, 1.455, 1.704, 2.519, 1.813, 3.329, 2.487, 4.21, 4.362, 4.653, 3.444, 4.326, 7.364, 4.644, 15.324],\n",
-    "\n",
-    "'loop': [0.743, 0.634, 0.864, 0.894, 0.76, 0.867, 1.222, 0.899, 1.397, 1.121, 1.827, 1.888, 2.221, 1.491, 1.886, 2.887, 1.938, 3.968, 2.776, 5.107, 5.258, 5.702, 4.067, 5.202, 8.998, 5.437, 18.628]}\n",
-    "\n",
-    "plot_data = pd.DataFrame.from_dict(data_to_plot)\n",
-    "new_data = plot_data.sort_values('input length')\n",
-    "new_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "id": "8968f0c7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>regex_join_I</th>\n",
-       "      <th>regex_join</th>\n",
-       "      <th>regex_sub</th>\n",
-       "      <th>genex</th>\n",
-       "      <th>map</th>\n",
-       "      <th>reduce</th>\n",
-       "      <th>list_comprehension</th>\n",
-       "      <th>loop</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>input length</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.445</td>\n",
-       "      <td>1.943</td>\n",
-       "      <td>1.897</td>\n",
-       "      <td>1.050</td>\n",
-       "      <td>1.014</td>\n",
-       "      <td>0.990</td>\n",
-       "      <td>0.878</td>\n",
-       "      <td>0.743</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.260</td>\n",
-       "      <td>1.683</td>\n",
-       "      <td>1.526</td>\n",
-       "      <td>0.962</td>\n",
-       "      <td>0.913</td>\n",
-       "      <td>0.832</td>\n",
-       "      <td>0.798</td>\n",
-       "      <td>0.634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2.928</td>\n",
-       "      <td>2.204</td>\n",
-       "      <td>2.377</td>\n",
-       "      <td>1.133</td>\n",
-       "      <td>1.115</td>\n",
-       "      <td>1.159</td>\n",
-       "      <td>0.959</td>\n",
-       "      <td>0.864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>2.998</td>\n",
-       "      <td>2.242</td>\n",
-       "      <td>2.807</td>\n",
-       "      <td>1.165</td>\n",
-       "      <td>1.146</td>\n",
-       "      <td>1.197</td>\n",
-       "      <td>0.987</td>\n",
-       "      <td>0.894</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>2.853</td>\n",
-       "      <td>1.973</td>\n",
-       "      <td>1.921</td>\n",
-       "      <td>1.058</td>\n",
-       "      <td>1.027</td>\n",
-       "      <td>1.014</td>\n",
-       "      <td>0.889</td>\n",
-       "      <td>0.760</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>3.303</td>\n",
-       "      <td>2.259</td>\n",
-       "      <td>2.378</td>\n",
-       "      <td>1.148</td>\n",
-       "      <td>1.137</td>\n",
-       "      <td>1.186</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>4.300</td>\n",
-       "      <td>3.079</td>\n",
-       "      <td>3.837</td>\n",
-       "      <td>1.457</td>\n",
-       "      <td>1.466</td>\n",
-       "      <td>1.664</td>\n",
-       "      <td>1.232</td>\n",
-       "      <td>1.222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>3.612</td>\n",
-       "      <td>2.282</td>\n",
-       "      <td>2.492</td>\n",
-       "      <td>1.175</td>\n",
-       "      <td>1.172</td>\n",
-       "      <td>1.220</td>\n",
-       "      <td>1.013</td>\n",
-       "      <td>0.899</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>5.106</td>\n",
-       "      <td>3.851</td>\n",
-       "      <td>4.493</td>\n",
-       "      <td>1.636</td>\n",
-       "      <td>1.660</td>\n",
-       "      <td>1.994</td>\n",
-       "      <td>1.402</td>\n",
-       "      <td>1.397</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>4.412</td>\n",
-       "      <td>2.981</td>\n",
-       "      <td>3.329</td>\n",
-       "      <td>1.375</td>\n",
-       "      <td>1.361</td>\n",
-       "      <td>1.504</td>\n",
-       "      <td>1.160</td>\n",
-       "      <td>1.121</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>6.532</td>\n",
-       "      <td>4.751</td>\n",
-       "      <td>5.609</td>\n",
-       "      <td>1.997</td>\n",
-       "      <td>2.041</td>\n",
-       "      <td>2.523</td>\n",
-       "      <td>1.662</td>\n",
-       "      <td>1.827</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>6.717</td>\n",
-       "      <td>4.839</td>\n",
-       "      <td>6.875</td>\n",
-       "      <td>2.043</td>\n",
-       "      <td>2.089</td>\n",
-       "      <td>2.569</td>\n",
-       "      <td>1.711</td>\n",
-       "      <td>1.888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>7.559</td>\n",
-       "      <td>5.414</td>\n",
-       "      <td>6.513</td>\n",
-       "      <td>2.310</td>\n",
-       "      <td>2.405</td>\n",
-       "      <td>3.021</td>\n",
-       "      <td>1.980</td>\n",
-       "      <td>2.221</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>6.327</td>\n",
-       "      <td>3.958</td>\n",
-       "      <td>4.507</td>\n",
-       "      <td>1.695</td>\n",
-       "      <td>1.721</td>\n",
-       "      <td>2.046</td>\n",
-       "      <td>1.455</td>\n",
-       "      <td>1.491</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>7.947</td>\n",
-       "      <td>4.840</td>\n",
-       "      <td>5.779</td>\n",
-       "      <td>2.004</td>\n",
-       "      <td>2.070</td>\n",
-       "      <td>2.548</td>\n",
-       "      <td>1.704</td>\n",
-       "      <td>1.886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>11.166</td>\n",
-       "      <td>7.261</td>\n",
-       "      <td>10.266</td>\n",
-       "      <td>3.087</td>\n",
-       "      <td>3.199</td>\n",
-       "      <td>4.091</td>\n",
-       "      <td>2.519</td>\n",
-       "      <td>2.887</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120</th>\n",
-       "      <td>8.991</td>\n",
-       "      <td>4.932</td>\n",
-       "      <td>6.000</td>\n",
-       "      <td>2.129</td>\n",
-       "      <td>2.176</td>\n",
-       "      <td>2.680</td>\n",
-       "      <td>1.813</td>\n",
-       "      <td>1.938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>14.260</td>\n",
-       "      <td>9.803</td>\n",
-       "      <td>12.876</td>\n",
-       "      <td>4.021</td>\n",
-       "      <td>4.206</td>\n",
-       "      <td>5.480</td>\n",
-       "      <td>3.329</td>\n",
-       "      <td>3.968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>12.290</td>\n",
-       "      <td>7.211</td>\n",
-       "      <td>8.760</td>\n",
-       "      <td>2.986</td>\n",
-       "      <td>3.087</td>\n",
-       "      <td>3.943</td>\n",
-       "      <td>2.487</td>\n",
-       "      <td>2.776</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200</th>\n",
-       "      <td>19.407</td>\n",
-       "      <td>12.727</td>\n",
-       "      <td>17.067</td>\n",
-       "      <td>5.224</td>\n",
-       "      <td>5.540</td>\n",
-       "      <td>7.166</td>\n",
-       "      <td>4.210</td>\n",
-       "      <td>5.107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>210</th>\n",
-       "      <td>20.110</td>\n",
-       "      <td>12.974</td>\n",
-       "      <td>21.827</td>\n",
-       "      <td>5.352</td>\n",
-       "      <td>5.748</td>\n",
-       "      <td>7.257</td>\n",
-       "      <td>4.362</td>\n",
-       "      <td>5.258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>225</th>\n",
-       "      <td>21.177</td>\n",
-       "      <td>14.154</td>\n",
-       "      <td>19.314</td>\n",
-       "      <td>5.793</td>\n",
-       "      <td>6.017</td>\n",
-       "      <td>7.917</td>\n",
-       "      <td>4.653</td>\n",
-       "      <td>5.702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>260</th>\n",
-       "      <td>18.602</td>\n",
-       "      <td>10.110</td>\n",
-       "      <td>12.960</td>\n",
-       "      <td>4.189</td>\n",
-       "      <td>4.370</td>\n",
-       "      <td>5.640</td>\n",
-       "      <td>3.444</td>\n",
-       "      <td>4.067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>310</th>\n",
-       "      <td>23.087</td>\n",
-       "      <td>13.068</td>\n",
-       "      <td>17.437</td>\n",
-       "      <td>5.352</td>\n",
-       "      <td>5.693</td>\n",
-       "      <td>7.303</td>\n",
-       "      <td>4.326</td>\n",
-       "      <td>5.202</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>360</th>\n",
-       "      <td>33.145</td>\n",
-       "      <td>21.459</td>\n",
-       "      <td>31.768</td>\n",
-       "      <td>8.962</td>\n",
-       "      <td>9.701</td>\n",
-       "      <td>12.506</td>\n",
-       "      <td>7.364</td>\n",
-       "      <td>8.998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>25.931</td>\n",
-       "      <td>13.517</td>\n",
-       "      <td>18.251</td>\n",
-       "      <td>5.577</td>\n",
-       "      <td>5.939</td>\n",
-       "      <td>7.538</td>\n",
-       "      <td>4.644</td>\n",
-       "      <td>5.437</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>750</th>\n",
-       "      <td>67.390</td>\n",
-       "      <td>44.480</td>\n",
-       "      <td>56.100</td>\n",
-       "      <td>18.607</td>\n",
-       "      <td>19.995</td>\n",
-       "      <td>26.182</td>\n",
-       "      <td>15.324</td>\n",
-       "      <td>18.628</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              regex_join_I  regex_join  regex_sub   genex     map  reduce  \\\n",
-       "input length                                                                \n",
-       "13                   2.445       1.943      1.897   1.050   1.014   0.990   \n",
-       "14                   2.260       1.683      1.526   0.962   0.913   0.832   \n",
-       "19                   2.928       2.204      2.377   1.133   1.115   1.159   \n",
-       "20                   2.998       2.242      2.807   1.165   1.146   1.197   \n",
-       "25                   2.853       1.973      1.921   1.058   1.027   1.014   \n",
-       "30                   3.303       2.259      2.378   1.148   1.137   1.186   \n",
-       "35                   4.300       3.079      3.837   1.457   1.466   1.664   \n",
-       "39                   3.612       2.282      2.492   1.175   1.172   1.220   \n",
-       "42                   5.106       3.851      4.493   1.636   1.660   1.994   \n",
-       "45                   4.412       2.981      3.329   1.375   1.361   1.504   \n",
-       "60                   6.532       4.751      5.609   1.997   2.041   2.523   \n",
-       "63                   6.717       4.839      6.875   2.043   2.089   2.569   \n",
-       "74                   7.559       5.414      6.513   2.310   2.405   3.021   \n",
-       "78                   6.327       3.958      4.507   1.695   1.721   2.046   \n",
-       "93                   7.947       4.840      5.779   2.004   2.070   2.548   \n",
-       "108                 11.166       7.261     10.266   3.087   3.199   4.091   \n",
-       "120                  8.991       4.932      6.000   2.129   2.176   2.680   \n",
-       "140                 14.260       9.803     12.876   4.021   4.206   5.480   \n",
-       "150                 12.290       7.211      8.760   2.986   3.087   3.943   \n",
-       "200                 19.407      12.727     17.067   5.224   5.540   7.166   \n",
-       "210                 20.110      12.974     21.827   5.352   5.748   7.257   \n",
-       "225                 21.177      14.154     19.314   5.793   6.017   7.917   \n",
-       "260                 18.602      10.110     12.960   4.189   4.370   5.640   \n",
-       "310                 23.087      13.068     17.437   5.352   5.693   7.303   \n",
-       "360                 33.145      21.459     31.768   8.962   9.701  12.506   \n",
-       "400                 25.931      13.517     18.251   5.577   5.939   7.538   \n",
-       "750                 67.390      44.480     56.100  18.607  19.995  26.182   \n",
-       "\n",
-       "              list_comprehension    loop  \n",
-       "input length                              \n",
-       "13                         0.878   0.743  \n",
-       "14                         0.798   0.634  \n",
-       "19                         0.959   0.864  \n",
-       "20                         0.987   0.894  \n",
-       "25                         0.889   0.760  \n",
-       "30                         0.970   0.867  \n",
-       "35                         1.232   1.222  \n",
-       "39                         1.013   0.899  \n",
-       "42                         1.402   1.397  \n",
-       "45                         1.160   1.121  \n",
-       "60                         1.662   1.827  \n",
-       "63                         1.711   1.888  \n",
-       "74                         1.980   2.221  \n",
-       "78                         1.455   1.491  \n",
-       "93                         1.704   1.886  \n",
-       "108                        2.519   2.887  \n",
-       "120                        1.813   1.938  \n",
-       "140                        3.329   3.968  \n",
-       "150                        2.487   2.776  \n",
-       "200                        4.210   5.107  \n",
-       "210                        4.362   5.258  \n",
-       "225                        4.653   5.702  \n",
-       "260                        3.444   4.067  \n",
-       "310                        4.326   5.202  \n",
-       "360                        7.364   8.998  \n",
-       "400                        4.644   5.437  \n",
-       "750                       15.324  18.628  "
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_data.set_index('input length', inplace=True)\n",
-    "new_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "id": "a37f56a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7faf09203280>"
-      ]
-     },
-     "execution_count": 214,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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g6dckESl6+p9HRERERET8X8pxrHVvYx7cmq/kVGuBHXcrRFb0QTARkVxq0IiIiIiIiP9yPJg752Jum45hZ3uV3LBoyvS8naRyzcEwfBRQRCTXBTVobrjhBk6dOkVAQO7lEyZMwHEcnnnmGdLS0mjQoAHPPvssQUFBhRpWRERERETkQhlHd2OteQvjzEGvcdcwcBpdg9N6BEFVK0Jiio8Sioicdd4Gjeu67N+/n+XLl+c1aFJTU7nmmmuYNGkSDRs25J577mH69OmMGjWq0AOLiIiIiIj8rswUrE0fYn6/LF/JiamL3XEsxNTxQTARkd923gbNvn37ALjllls4c+YMw4cPJzY2lhYtWtCwYUMAHnnkEWzbLtykIiIiIiIiv8d1MfasxNo4BSPLe1WMGxiK02YkTsOeYJo+Cigi8tvO26BJTk4mLi6ORx99lJycHG688Ub69+9PWFgYf/3rX9m3bx+tWrXigQceKIq8IiIiIiIi+Z0+hLX2Lcyj3+YrOXXisdvfBGHRPggmvyc7I4NNU2dQqUE96nZs7+s4Ij5luK7rXswT3n33XZ555hnKly/PJ598QpUqVXj44YepWrUq48ePL9BwHo9NQIBVoK8pIiIiIiL+w83JJHPtx2RtmA6O96p+s2xlQq/+M4G1W/sonfyerbMXsfCl/5Jy4hTlqlfhr5+/S4D2NZVS7LwraDZv3kxOTg5xcXFA7p400dHRNG/enOrVqwPQq1cvPvjggwIPd/p0eoG/ZmGJjY0gsRRsLqZ5+hfN079onv5F8/Qvmqd/0TyLD+PgVqy1kzFSE73GXdPCaTaQnOaDyAoI+t1NgEvCPAtKcZvrnk07STlxCoBTB4+w5K2PaTN8kI9TXZzY2AhfRxA/ct6bL1NSUnj++efJysoiNTWVmTNn8tprr/HNN9+QkJAAwPLly2ncuHGhhxURERERESHtFNYX/yJg8bP5mjNO5cZ4Bv0fTusREKDVGMXJ6YOHyUxNzXscd9NIgsPLAFCmXDTh5cv5KppIsXDeFTTdunVj+/btDBw4EMdxGDVqFG3atGHChAmMGzeOrKwsGjVqxP33318UeUVEREREpLRybMxdizC3foyRk+lVckMisdvfiFu3MxiGjwLKuWQkp7D+/Y/ZPmserYYO4Io/3QxAaFQkHW+9gbRTp2l77RCCQkN9nFTEt87boAG4++67ufvuu73GunbtSteuXQshkoiIiIiIiDcj8QesNW9hnPwxX81u2AOnzSgIDvdBMvk9rusy/W8Pk7g39/O27bPZNOvXi7JVKgHQYkAfX8YTKVZ0vpyIiIiIiBRfWWmYaydhzX44X3PGLVcTT98ncTrepuZMMWUYBm1GDM57bOd4+GbhUh8mEim+LmgFjYiIiIiISJFyXYx9a7A2vIeRkeRdCgjGaTUCp3EvMHXqa3FyfM9eVr75Np1vG02lBvUAaNj9CrZ9NoeMpGQ6/2k09TrH+zilSPGkBo2IiIiIiBQvSQlYaydjHtmRr+TUbIvd4WYIj/FBMPkttsfD0n/9h28WfQGuy8rXJzP85WcwDAPDNOn7+P2ERUcTEBTo66gixZYaNCIiIiIiUjzYOZg7ZmFun4lh53iV3DIx2HG34NZs46Nw8nusgAAyziSB6wJweOc3/LBqHfWuyF0tE1mxgi/jiZQIatCIiIiIiIjPGUd2Yq2dhJGU4DXuGhZOkz44LYdCYIiP0smvuY7DnlXrqNuxPVZA7q+VV4y7mf2btuLYNrXataZczeo+TilSsqhBIyIiIiIivpNxBmvD+5h7V+crORUbYMePhXI1fBBMfsuhHd+w8o1JHPvuB7r/ZVzeSUzlalSn05gbialTi1ptW/k4pUjJowaNiIiIiIgUPdfB3L0Uc/NHGNnp3qXgcOy21+PW7wqGDp4tTrJS05j54D/IycgAYN27H9Hwyi6EhOeeovXLE5tE5OLofzsRERERESlaJ/djzXk095amXzVnnHpd8Qx5GbdBdzVnignHtvPeDg4vQ7uRQ/IeZ6enc+Tr3b6IJeJ3tIJGRERERESKRnYG5tZPMXfNx/h5M9n/cctWxY4fi1v5ch+Fk19zbJsdcxeyaeoMrn31OSIqxALQathAdsxdRJXGDek05kaiKlfycVIR/6AGjYiIiIiIFC7XxTiwCWv9OxhpJ71LViBOiyE4TfuDpV9PiouU44nMuP9xTh04CMDqyVPo9eA9AAQGB3PDpFfzbmsSkYKh/wFFRERERKTwpBzHWvc25sGt+UpOtRbYcbdCZEUfBJPfU6Z8ubzTmQC+XbKcloP7UalBPQA1Z0QKgW7qFBERERGRgud4MLd/TsCMe/I1Z9ywaDzd78Hu+aCaM8VE2qnTbJ0+C/fnW89My6LL7bcCEBgaSsdbb6B8LZ2mJVKYtIJGREREREQKlHF0N9aatzDOHPQadw0D5/JeOK2GQ1CYj9LJL3mys9k6fRYbP5pGdnoGZatVpU6HNgDUaNWcbuP/RP0uHSlTLtrHSUX8nxo0IiIiIiJSMDKTsTZ9iPn98nwlJ6YudsexEFPHB8Hkt5w+dITVk6fAzytnvnxzMjXbtMi7vanloL6+jCdSqqhBIyIiIiIif4zrYuxZibVxCkZWincpMBSnzUichj3B1A4LxUFmamreHjKxdWrRpNdVfD1/MQCu65KaeEInM4n4gBo0IiIiIiJy6U4fwlr7FubRb/OVnDrx2O1vgjDdHlMcnEk4xvznXufgVzu5+f03CQoNBaDjLdfz05avaD1sIM369/LaHFhEio6+8kRERERE5OJ5sjC/moG5Yw6Ga3uV3IiK2PFjcKs191E4+bUDW75i1iP/xJOVDcCmj2fQ8ebrAShTLppbPpiIaVm+jChS6qlBIyIiIiIiF8U4uBVr7WSM1ESvcdcMwGk+EKfZQAgI8k04OafKlzcgNCKclKxTAGz5dCatBvcjNCoKQM0ZkWJAN4GKiIiIiMiFSTuJ9cWLBCx+Nl9zxqncBM+g/8s9oUnNGZ87sOUrNn40Le9xUGgoV43PPTa7Qr26DHr2ibzmjIgUD1pBIyIiIiIiv8+xMXctxNz6CUZOplfJDYnCbn8jbt1OYBg+Cij/c/rgYVa+MZl96zdhmCa1O7Qltk4tAFoPuIZs16JepzgMbdgsUuzoq1JERERERH6TkfgDAbMfwtrwXr7mjN2wB56hL+Fe1lnNmWLi9OEj7Fu/CQDXcVj5xmTcn4/QNi2L+ld0VHNGpJjSChoREREREcnHyUzFXDsJ89slGLheNbdcTez4sbgV6/sonfyPnZND6slTRFWqCEDt9m2o0boFP235CoDwmHLYOTkEBOm2M5Hi7oIaNDfccAOnTp0i4Ofj1iZMmEDz5s3JyclhzJgx3HHHHbRv375Qg4qIiIiISBFwXYx9a0jZNAUr7bR3KSAYp9UInMa9wNSmsr7kui4/rFrHlxPfJSg0hOvefAnTsjAMgy7jbmHFfyZxxbibqVj/Ml9HFZELdN4Gjeu67N+/n+XLl+c1aAD27dvHQw89xK5duwo1oIiIiIiIFJGkBKy1kzCP7PzVmhlwarbD7jAawmN8kUx+ZeOHn7Lm7Q/yHu9avIwmva4CILZubYb96ylfRRORS3Temw/37dsHwC233EL//v354IPc/wSmT5/OmDFjaN68eeEmFBERERGRwmXnYG6dRsDMv2Me2elVcsvE4LnqPuwef1dzphi5vGd3rF/ctrT505m4juPDRCLyRxnu/3aM+g3btm1j6tSpPProo+Tk5HDjjTfy4IMP0rFjRyD39qc777yzUG5x8nhsAgK0dFJEREREpLDk7N9GxuL/4Jw67F0wLYLbDiKk43UYQSG+CScAZKdn8OW7HxMSHk6nG4fljS/+92RWTv6Q9iMGcuW4GykTXdZ3IUXkDzvvLU4tW7akZcuWeY+HDh3KypUr8xo0hen06fRCfx8FJTY2gsTEFF/HKHSap3/RPP2L5ulfNE//onn6F7+ZZ8YZrA3vY+5dna/kVGxAVJ+/cMqIITUpB8gp+nxFpLh/Pr9fsZrl/3mLtJOnCAwNpVr79pQpFw1A4wH9qBkfR7ka1Un3QPp55lHc51oSxcZG+DqC+JHz3uK0efNm1q1bl/fYdV2vvWhERERERKQEcR3MbxcTMP3ufM0ZNzgcT6dx2H3+gVWhto8Cyi+ln0ki7eQpAHIyMlj7zod5taDQUMrVqO6raCJSwM7boElJSeH5558nKyuL1NRUZs6cyVVXXVUU2UREREREpCCd3I8151GstZMwsr1Xqzv1uuIZ8jJug+5gnPfXBCkkpw8fISnhaN7jpn2vplyNagCERZelUiMdbS7ir867FKZbt25s376dgQMH4jgOo0aN8rrlSUREREREirnsDMytn2Lumo/xqy0o3bJVsePH4la+3EfhBCAzJZX1Uz7mq8/nUbtdawY8+QgAVkAAXW6/lcM7d9Fu1FCCwsJ8nFRECssF3at09913c/fdd5+zNmXKlILMIyIiIiIiBcV1MQ5sxFr3Dkb6Ke+SFYjTYghO0/5gaQsDX1v47EvsW7cRgL1rN/DT1u3UaJV7Ym7t9m2o3b6NL+OJSBHQ/8QiIiIiIv4o5TjWurcxD27NV3KqtcCOuxUiK/ogmEDu3p4AhmEA0OGGEXkNGoDdy1bmNWhEpHRQg0ZERERExJ84HsydczG3Tcews71Kblg0doebcWu1h58bA1L0Evf+yMo3JtOk11U0vLILAJUa1qdRj64c3rmLzreNpn7XTj5OKSJFTQ0aERERERE/YRz9FmvNJIwzB73GXcPAubwXTqvhEKQ9THzFdV2Wvfom22cvANfl9KHD1O3UgcDgYAC63nkbgSHBBAQF+TipiPiCGjQiIiIiIiVdZjLWpg8xv1+er+TE1MXuOBZi6vggmPySYRgEBAfDz7c3pRw/wdbps2h/3XAAQiMjfBlPRHxM5+eJiIiIiJRUrovx/XICpv81X3PGDQzFjrsFu99Tas74iOu67P5iJRlJyXlj7a8fTsjPjZiarVtQJ66dr+KJSDGjFTQiIiIiIiXR6YNYayZhHvs2X8mpE4/d/iYIi/ZBMAE48vW3rHhjMke//Y6Wg/vR7c7bAAgJD+fKv9xOUFgotdq1ztskWEREDRoRERERkZLEk4X51QzMHXMwXNur5EZUxI4fg1tNp//4kmPbLHjmXyQlHAVg+6z5NO/fm3I1qgHQoFtnX8YTkWJKtziJiIiIiJQQxsGtBMy4B2v7517NGdcMwG45FM/gF9Wc8RFP9tkTs0zLovNto/MeG6ZBwrff+SCViJQkWkEjIiIiIlLcpZ3EWv8u5v4N+UpO5SbY8WOgbBUfBBPHtvl6/mLWvvsR/f/xEFWaNAKg3hXxVG3amLDosnQeexNlq1b2cVIRKe7UoBERERERKa4cG3PXQsytn2DkZHqV3JAo7PY34tbtBNrHxCeyMzKYeue9nPzxAAArXp/EyNdewDBNDMNg8PP/yDtCW0TkfNSgEREREREphozEH7BWT8Q4tT9fzW7YA6fNKAgOL/pgkicoNJRy1avmNWiO7v6e779cQ4OuuXvMqDkjIhdDDRoRERERkeIkKw1z81TM3UswcL1Kbrma2PFjcSvW91G40i39TBLbPptDhxtGYAUGAtB57Gj2rduIYVm0GzmUOh3a+jiliJRUatCIiIiIiBQHrouxbw3WhvcwMpK8SwHBOK1G4DTuBablo4Cllyc7h22fzWHDh5+QnZZOSEQ4rYcNBKBs1cr0evBvVGnSiPCY8r4NKiIlmho0IiIiIiK+lpSAtXYS5pGd+UpOzXbYHUZDeEzR5xIAcjLS2fjhp2SnpQOwfsrHXN6zO6FRkQDU79rJl/FExE/omG0REREREV+xczC3TiNg5t/zNWfcMjF4rroPu8ff1ZzxgdSTp/LeDo2Kov0NI84+LhtFSuIJX8QSET+mFTQiIiIiIj5gHNmJtXYSRlKC17hrWDhN++K0GAKBIT5KV3qlHE9k9aT3+f7LtYx+5z9EVa4EQIuBfflu+Soa9ehK8/698vagEREpKGrQiIiIiIgUpYwzWBvex9y7Ol/JqdgAO34slKvhg2CSuPdHpt75dzxZ2QCsmvgufR9/AICAoEBGvf4iho40F5FCogaNiIiIiEhRcB3M3UsxN3+EkZ3uXQoOx257PW79rmBoFwJfialdk/K1anDsux8A+H7lGk7u/4nytXIbZmrOiEhh0v/+IiIiIiKF7eR+rDmP5t7S9KvmjFOvK54hL+M26K7mTBH7adsOPv/nv3Dd3OPMDdOk6x1jAIitW5uh//dkXnNGRKSwaQWNiIiIiEhhyc7A3Pop5q75GD83Af7HLVsVO34sbuXLfRSu9EpKOMqK/0xi79oNAFRo3IR6neMAqNq0MUP/70mqNW+CaelIcxEpOmrQiIiIiIgUNNfFOLARa907GOmnvEtWIE7LoThN+oGlH8d9wc7JYd/6TXmPv/zvO9Tp0CZv498arZr7KpqIlGIXvIbyueee44EHcjfI2rZtG8OHD6dPnz7cc889ZGdnF1pAEREREZESJeU41pLnCPjixXzNGadaSzyD/4XTfJCaM0XIzskhce+PeY/L1ahO8/698x5XvrwBOZlZvogmIpLngr4rrFu3jpkzZ9K1a1dSU1MZP348kyZNomHDhtxzzz1Mnz6dUaNGFXZWEREREZHiy/Fg7pyLuW06hu39B0w3LBq7w824tdqDNpotMq7rsnftBr787ztkpaRyy5SJBIeXASDuppEkJRyl119uJaRSNR8nFRG5gAbNmTNneOmllxg3bhy7d+9mzZo1tGjRgoYNGwLwyCOPYNt2oQcVERERESmujKPfYq2ZhHHmoNe4axg4l/fCaTUcgsJ8lK702rV4GYueeznv8YaPpnHFbaMBCI2KZNAzjxMbG0FiYopvAoqI/ILhur/arexX7rrrLkaOHElCQgIbN26kTp06/PDDD+Tk5LBv3z5atWrFAw88QHBwcIGH83hsAgK0MZeIiIiIFE9OehKZK94me8fifDWrUj1Cr7mLgEqX+SCZAORkZvFivxtIOnocgDLlynLfwo8JCg3xcTIRkfx+dwXNtGnTqFy5MnFxcXz22WcA2LbN6tWr+eSTT6hSpQoPP/wwEydOZPz48QUe7vTp9PNfVEyUls675ulfNE//onn6F83Tv2ie/iU2NoLE48kYe1ZgbfwAI8t7zm5gKE6bUeQ0vIpM04QS+jEpaZ/PnMxMtkz7nLSTp7jy7jvyxuNvuYGFz75Es75XEzf6OpJScyA1J69e0ub5R5SmuRaV2NgIX0cQP/K7DZr58+eTmJjIgAEDSEpKIj09Hdd1adOmDdWrVwegV69efPDBB0USVkRERETE1+zEA1jzXsE89m2+mlMnHrv9TRAW7YNkpde+9ZtZ+tJ/SE08AUDjXldRqUE9ABp2v4JKDeoRXb2qLyOKiJzX7zZo3nnnnby3P/vsMzZu3Mhf/vIXRowYQUJCApUrV2b58uU0bty40IOKiIiIiPiUJwvzqxmk7JyD6XjvwehGVMSOH4NbTccz+4IVGJDXnAFY+cZkhr/0DIZhYJimmjMiUiJc9Nl+lStXZsKECYwbN46srCwaNWrE/fffXxjZRERERESKBePgVqy1kzFSE73GXTMAp/lAnGYDISDIN+FKoTNHjpKZkpK3SqZm6xbU6dCWfes3EVo2iobdu4Dr6sQsESlRLrhBM3jwYAYPHgxA165d6dq1a2FlEhEREREpHtJOYq1/F3P/hnwlp3IT7PgxULaKD4KVTlmpaaz/4BO+mjmHqCqVuXHSvzGt3ENFrhh3C+Vq1aD9qGF5R2mLiJQkF72CRkRERETE7zk25q6FmFs/wcjJ9CoZYWXJaXsDbt1OWqFRxNa++yHbPpsDwKkDB9k5dxHNB/QGoFyNanlHaIuIlESmrwOIiIiIiBQnRuIPBMx6EGvDe17NGRcDu+FVRNw2EfeyzmrOFBHHPrvfT7tRwwgMOXtE9r4Nm3wRSQqR67q+jiDiM1pBIyIiIiICkJWGuXkq5u4lGHj/kuiWq4ndcSxuhfqYIRGQoqOKC9uJHw+w8s23qdyoAfGjRwFQplw07UYNZcfchXQaexMNu13h45RSUJysTFJXLCFlyXwqPf48VoSOr5bSRw0aERERESndXBdj35rcFTMZSd6lgGCcViNwGvcC0/JRwNJnzdsfsPGjabiOw+EdX9O0T08iYmMAaD18EK2GDSQwONjHKaUg2CnJJC+cQ/KC2TgpuV9/KYvnUnbISB8nEyl6atCIiIiISOmVlIC1dhLmkZ35Sk7NdtgdRkN4TNHnKuUiK1XAdRwAPFnZrJk8hWse+CsAAUE6Lcsf5Bw/SvLcmaQsW4ibleVVS1owi8i+gzHVhJNSRg0aERERESl9PNmYO2Zh7vgcw87xKrnhsdhxN+PWaOOjcKWL67p8v3I15WvWIKZ2TQAaX30l2z6bw4l9+6nRqjmthw30bUgpMFk/7iVp9nTS1q6En5twv2SWCSei+zVgewA1aKR0UYNGREREREoV4/AOrLWTMZITvMZdw8Jp2henxRAIDPmNZ0tBSvj2O1b8ZxIJu3ZTs01LBj/3DwzDwLQsrrz7drJSUqndoS2GNmQu0VzXJfPrr0iaNY2M7VvPeY1VPoaovoOJuPIazNCwIk4oUjyoQSMiIiIipUP6GayN72PuXZ2v5FRsgB0/FsrV8EGw0mvt2x+QsGs3AAc2b2P/xi3Ubp+7cqlqk8t9GU0KSNrGtZyZ/hHZP/5wznpg9ZpEDRhGeHwXjMDAIk4nUryoQSMiIiIi/s11MHcvxdz8EUZ2uncpOBy77fW49buCYfomXymSnZ5OQHAwppW74fIV425hym1/AdfFDAjg1E+H8ho04h8yd39zzuZMyOVNiRowjNCWWiEl8j9q0IiIiIiI/zq5H2vNW5iJe/KVnHpdsdteD6GRPghWuji2zTcLl7Lm7Q/oNOYmmvTqAUBs3do06XUVmckpdL5tNNHVqvg4qRS0qD4DSZ7/Odg2GAZh7eKJGjCMkHoNfR1NpNhRg0ZERERE/E92BubWTzF3zcdwXa+SW7YqdvxY3Mq6haYouI7DJ3+5n4Rd3wGw5u0p1O/akaDQUAB6/PWOvBU1UjLlnsj0GZgW5Uf/yasWUD6WiO5Xg+sS1W8IgVWq+SilSPGnBo2IiIiI+A/XxTiwEWvdOxjpp7xLViBOy6E4TfqBpR+Di4phmtRo1SKvQZN28hRfzZxLu1HDANScKcGyfvyBpFnTSVv3JTgORmAgZQcOxyob7XVd+bHjdRuTyAXQdyYRERER8Q8px7HWvY15MP8pMU61lthxt0BkRR8EK10ykpLZ8OGntL12CGXK5f6i3nbkEL6ev5istDTajBhCy0H9fJxSLtX/TmQ68/k0Mnd4f625OTkkLZhFuZGjvcbVnBG5MGrQiIiIiEjJZnswv56LuW06hp3tVXLDorE73Ixbqz3ol8RCZXs8fDVzLuunfExWaho5mZlcdc+dAASFhtL70XspW6UKEbHlfZxULoVr26StX03SrGm/cyJTLYJr1S3iZCL+Qw0aERERESmxjKPfYq2ZhHHmoNe4axg4l/fCaTUcgsJ8lK702TlvEVmpaQB8PX8JLQb2JbZOLQCqN2/qw2RyqZysTFKXLyZpzmd4jh895zUhjZvlnsjUoo1Wy4j8AWrQiIiIiEjJk5mMtelDzO+X5ys5MXWxO46FmDo+CFa6nD58hOiquScvWQEBXDHuFj5/aAIAkZUq5jVrpOSx01JJnv85yQtm46Qk57/AMAhr35Gy/YcRXK9B0QcU8UNq0IiIiIhIyeG6GHtWYG38ACMrxbsUGIrTZhROw6vANH0UsHRISTzJmrffZ9fi5Qz9vyep0bIZALXbt6HeFR2pfHkDWgzsS0BQoI+TyiXLySFp5ie4OTlew0ZgIOFdexLVbzCBlav6KJyIf1KDRkRERERKhtMHsdZMwjz2bb6SU6cjdvsbISz6HE+UgpR++gzv3jSOnMxMAFa+MZnr3vgXpmVhGAb9nnjAxwmlIFhlownvehUpS+YDYJYJJ+LqvkT1GpDvlCYRKRhq0IiIiIhI8ebJwtw2A3PnHAzX9iq5kZWw48fgVm3mo3ClT1h0WS7r1IFvl64AIPGHfRzY8hW127X2bTC5aK7rkrljG0lzPyPmT38hICbWqx7VbwgZX20hss9AIrpfjRmq/ZxECpMaNCIiIiJSbBk/bcVaNxkjNdFr3DUDcJoPxGk2EAKCfBOulDi4fSdbp8+i9yP3EhgcDECnMTfy/Zdria5WhS7jbqFmm5Y+TikXw7Vt0tZ9SdKs6WTv3wtA0vzPKX/jWK/rAitXpdpr72DolkGRIqEGjYiIiIgUP2knsda/g7l/Y76SU7kJdvwYKFvFB8FKj+TjJ5j9+Iv8sHodANtmzKbdqGEARFSIZcQrz1LhsjqYluXLmHIR7IwMkhfMJmnODDyJx7xqKUvmU3bISKwy4V7jas6IFJ0LbtA899xznD59mmeffRaAnJwcxowZwx133EH79u0LLaCIiIiIlCKOjblrIebWTzByMr1KbkgUdvsbcet2Ah3lW+gCQ4I5tOPrvMcbP5pG42t6UKZc7v4jlRrU81U0uUh20hmSF83h4KK5eJKT8l9gmIS1bIObkQG/atCISNG5oAbNunXrmDlzJl27dgVg3759PPTQQ+zatasws4mIiIhIKWIk/oC1eiLGqf1e4y4GTsMeOG1GQrB+eSwstsdDwje7qda8CQChkRHEj76OZa++CUDtDm1xXdeXEeUi5RxLIGnOZ6QuX4ybnZWvbgQGEd6tJ1F9BxNYWSvSRHztvA2aM2fO8NJLLzFu3Dh2794NwPTp0xkzZgzvvfdeoQcUERERET+XlYa5eSrm7iUYeDcA3HI1sTuOxa1Q30fh/J/ruvy4fhMr33ybpCNHuent/xBdPff45KZ9ryZh126aD+xDlcsb+jipXKisfXtImjWdtHWrwHXy1c0y4URe04/IXgOwosoWfUAROafzNmgee+wx/vrXv5KQkJA3dt999wGoQSMiIiIil851MfatwdrwHkaG920XbkAwTusROJf3AlN7nBSmg1/t5POH/5n3+Mv/vsOAJx8BwAoIoNdDf/NVNLlEqSu/IG3tynzjQRUrEt5r0M8nMoX6IJmI/J7fbdBMmzaNypUrExcXx2effVZUmfJER4cREFByviHHxkb4OkKR0Dz9i+bpXzRP/6J5+hfN05t96jAZi/+DZ/+2fLXA+h0J7fEnzMjYczyzeCjpn0/XdTF+3scn5qp4Nrdqyv6tOwE4tH0nQXYGEFHi53mh/G2ekTfewPaFc8DJPZY+tO5lVL52FOW6XYkZoHNiRIqr3/3qnD9/PomJiQwYMICkpCTS09N5+umneeihh4ok3OnT6UXyfgpCbGwEiYkpvo5R6DRP/6J5+hfN079onv5F8/wFTzbmjlmY22diOB6vkhseix13Czk1WpOeBRTTj1lJ/nzmZGWxdfosDn21k8HPT8hr0sSPGc2BO++laZ+exN00imwrd3VFSZ3nxSipn08nM5OU5Ytw09MpO2Skd9EqQ5mOV2CfPk3UgKGENm+NaxiYAQElcq7Fmb8198S3frdB88477+S9/dlnn7Fx48Yia86IiIiIiH8xDu/AWjsZIznBa9w1LJymfXFaDIHAEB+l83+HdnzDgqdfJOV4IgDfLV9Fw+5XAFCpYX3GTJ1MRGyMLyPKBbCTzpC8cA7JC2fjpKZgBAUTcVVvrMgor+tib78HIzDQRylF5FJofZuIiIiIFK70M1gb38fcuzpfyanYADt+LJSr4YNgpUt4+XKknTqd93jVxHep27E9gcHBAGrOFHM5R4+QNPczUpctxs3Jzht3s7NIXjSH6GHXe12v5oxIyXPBDZrBgwczePBgr7EpU6YUeCARERER8ROOg/ndUszNH2Fke9+67gaHY7e7HrdeVzBM3+Tzc0lHj3HqwEFqt28DQNmqlWk5qC9bpn1OSGQEbUcOwdJ+JMVe1t49JM2aRtr61b95IpMZog1/RfyB/kcWERERkYJ34kestW9hJv6Qr+TU64rd7noIifRBMP+XlZbOxo+msXX6LAJCgrllykRCI3P3yWh/wwgCgoJoPWIQIeHhPk4qv8V1XTK2byFp1nQyv/7qnNdYMRWI6jeYiG46kUlKlqlTpwIwcuTI37xm7NixPPnkk1SsWPGiX7979+68//77VKtW7ZIz+ooaNCIiIiJScLIzMLd+irlrPobrepXcslWx48fiVr7cR+FKh28WLmXT1OkA2Dk5rH9/Kt3uvA2AkPBwOt56gy/jye9wPR7S1q0iadY0sg/sO+c1QTXrEDVgGGXiOmNoBZSUQL/XmPmft956qwiSFD/6ihYRERGRP851yf5uDQGLXsdIP+VdsgJxWg7FadIPLP34WRg82dkEBAUB0KxfL7Z9NoekhKMAHN29B8e2MS3LlxHlAtgpSSS+/i/w5OSrhTRtQdSAYYQ2a5V3+pbIxdqwYQMvvPACjuNQtWpVwsLC2LNnD7ZtM3bsWPr27UtOTg6PP/44W7ZsoWLFihiGwR133EH79u2ZOHEiCxYswLZtOnXqxL333suyZct4/vnnmT17NkePHuWGG25g2rRpv7n65d///jcA48ePZ/ny5bz88ss4jkP16tWZMGECMTExeatgNm7cyKpVq0hKSuLgwYN07NiRJ554ogg/YkVL3yFFRERE5I9JOY617m3SD27l1782OtVaYsfdApEXv0xdzu/k/p9Y+ebbBJcJo8+j9wEQEBRI5z+NZsV/JtFpzI00urILhql9fkqCgOjyhF/RndRli3IHDJMycZ2J6j+U4Lr1fBtO/rDsnUvJ2rG4UF47uFlPgpr2uKBr9+/fz/Lly/nvf/9LhQoVeO6550hNTeXaa6+lefPmrFixgoyMDBYuXMiRI0fo168fAF9++SVff/0106dPxzAM7r33XmbPns2AAQNYvHgxb775Jhs2bOD++++/oFuTTp48yWOPPcbUqVOpVq0akyZNYsKECbz66qte123bto25c+diWRbXXHMNI0eOpEGDBhf/QSoB1KARERERkUtjezC/nou5bTqGne1VcsOisTvcjFurPeiv/YViy7TP+fK/7+A6uRvHthzUjypNGgFQr3M8tdu3yTuhSYqX/53IVHbISAKiy3vVovoNIW3NCsK7XkVUvyEEVqzso5Tir2rXrk1ERARr164lMzOTGTNmAJCens6ePXtYs2YNw4cPxzAMqlatSlxcHADr1q1jx44deYcHZWZmUqVKFQAefvhhevfuTatWrejTp88F5dixYwfNmjXL2ytmxIgRTJw4Md91LVu2JPznPbOqV69OUlLSH/sAFGNq0IiIiIjIRTOOfou15i2MM4e8xl3DwLm8F06r4RAU5qN0pUPFBvXymjMAK9+czLX/fgHDMDAMQ82ZYihr7/c/n8i0BlwHMySMctff4nVNULUa1HhrKmaovn78TVDTHhe8yqUwhYSEAOA4Di+88AKNGzcG4MSJE0RFRTFjxgwcJ/+JYbZtc9NNN3HzzTcDkJycjPXzrZMnTpzAsiz27dtHVlYWwRfw/8+v34frung8nnzX/fK1DMPA/dX+Zv5Eax1FRERE5MJlJmN9+ToB8x7P15yxKtfH0/9ZnA6j1ZwpYK7r8v3KNezftDVvrFqzxtS7omPu2y2acuVfbtfeJMWQ67qkb9tMwhP3c+SBu0hbtyrvuOzkxXNx0tPyPUfNGSkKHTp0yDtR6fjx4/Tv35+EhATi4+OZP38+ruty7NgxNm7ciGEYdOjQgVmzZpGWlobH4+HPf/4zixYtwrZtHnzwQR5++GHatWvHK6+8ckHvv3nz5mzfvp1Dh3K/l3zyySe0b9++0OZbEmgFjYiIiIicn+ti7FmBtfEDjKwU71JgKE6bUUR1HkTmyXQfBfRfR3d/z8o3JnN45y7KVq3MTW//ByswEIDOt42m0VVdqRvfXs2ZYib3RKYvfz6R6cdzXhNYsTKekycICitTxOlE4M477+SJJ56gb9++2LbNvffeS40aNRg+fDi7d++mX79+xMbGUqVKFUJCQmjXrh27d+9m+PDh2LZN586dGTRoEJMmTaJ8+fL07NmT+Ph4+vbtS8+ePWnRosXvvv+YmBgmTJjAnXfeSU5ODlWqVOGpp54qmskXU4ZbjNcHJSamnP+iYiI2NqJE5b1Umqd/0Tz9i+bpXzRP/1Li53n6INaaSZjHvs1Xcup0xG5/I4RFl/x5XqCinucXr7zJ9lnz8h53uf1WWg8bWOjvV5/PS+NkZJCybBFJcz/DPnH8nNeENG1J2QHDCGnWskgba6Xlc1qUYmMjfB2hwK1YsQLXdenWrRspKSkMHDiQGTNmULZs2QJ5/aeffpqKFSty6623Fsjr+ROtoBERERGRc/NkYW6bgblzDoZre5XcyErY8WNwqzbzUTj/lZ2RAUBQaCgAcTeNZPfSFWSlpWFaFpmpqb6MJ7/BTjpD8oJZJC+cg5N2js+RYVIm/ucTmeroRCYpvurWrct9993Hyy+/DMBdd9110c2Zd999l5kzZ+Ybj4qK4uDBg0yaNOmS8/3000+MHz/+nLUnn3ySpk2bXvJr+5oaNCIiIiKSj/HTVqx1kzFSE73GXTMAp/lAnGYDISDIN+H8lGPb7Fq8jDWTp3B5z+50vm00AGFlo2h//XAO79xF59tGU65GNd8GlXM6M/0jkhfOzjduBAUT3q0nUf0G60QmKRGqV6+etzfNpRo9ejSjR48umEC/UqNGDWbNmlUor+1ratCIiIiIyFlpJ7HWv4O5f2O+klO5CXb8GChbxQfB/N/sx55m37rcj/vWGbNo1u8aoipXAqD18EG0GTHYl/HkPCL7DCR50dy8DYDNiEgir+lH5NX9sKLK+jaciJQIatCIiIiICDg25q6FmFs/wcjJ9Cq5IVHY7W/ErdsJtBFtoanftVNeg8bO8bDhw2n0/HvuMn5tAFw8uK5Lxleb8SQeJ7JnH69aYKUqlOnQiawfviOq3xDCu/XE/Pk4YxGRC6EGjYiIiEgpZyT+gLV6Isap/V7jLgZOwx44bUZCcLhvwvmpjOQUNkz5mEZXdaNi/csAaHRlF7Z9NpsTP/5Em+GDaHutVswUF67HQ+qaFSTNnk7OT/sxgkMoE3cFVoT3BrHlx96JGVYGw7J8lFRESjI1aERERERKq6w0zM1TMXcvwcD7YE+3XE3sjmNxK9T3UTj/5Lou2z6bw7r3p5KVksqxPXsZ/tIzGIaBYZpcfd/dBIWFElmxgq+jCuBkpJPyxUKS5s7EPnl2PyY3K5OUxXMpO2Sk1/VWRGRRRxQRP6IGjYiIiEhp47oY+9ZgbXgPIyPJuxQQjNN6BM7lvcDUKoCCZhgGB7ZsIysl95Sfwzu+4YfV66nXOQ6AmNo1fRlPfmafOU3SglmkLJr7mycy2WdOF30wET/wvw2IR44c+ZvXjB07lieffJKKFSsWVaxiQQ0aERERkdIkKQFr7STMIzvzlZxa7bDbj4bwmKLP5ccS9+2nfM3qmD/f9nLFn25h/8atuI5DZKUKBAQF+jih/E9OwmGS5swgdcUS3JycfHUjKJiI7lcT2XcwgRUr+SChSMn3e42Z/3nrrbeKIEnxowaNiIiISGngycbcMQtz+0wMx+NVcsNjseNuwa3R2kfh/FPqiZOseedDvlm4lCvvGkfzAb0BKF+zOm2vHUJweBlaDu5HQJCOK/e1rD3fcWbWNNI3rgHXzVc3IyKJ7NU/90SmyCgfJBQpGBs2bOCFF17AcRyqVq1KWFgYe/bswbZtxo4dS9++fcnJyeHxxx9ny5YtVKxYEcMwuOOOO2jfvj0TJ05kwYIF2LZNp06duPfee1m2bBnPP/88s2fP5ujRo9xwww1MmzbtN1e//Pvf/wZg/PjxLF++nJdffhnHcahevToTJkwgJiaG7t278/7777Nx40ZWrVpFUlISBw8epGPHjjzxxBNF+BErWmrQiIiIiPg54/AOrLWTMZITvMZdw8Jp2henxRAI1GkzBcmTncMHf7qb9NNnAFj77oc0vLILweFlAOg05kYfppNfyjl9miOP3gO2na8WUKESUf0G557IFKyvEfnjvv3Lneccb/TKawAc+PfLpP/wQ756jTvvoky9+iQumMeJhQt+8/kXYv/+/Sxfvpz//ve/VKhQgeeee47U1FSuvfZamjdvzooVK8jIyGDhwoUcOXKEfv36AfDll1/y9ddfM336dAzD4N5772X27NkMGDCAxYsX8+abb7Jhwwbuv//+C7o16eTJkzz22GNMnTqVatWqMWnSJCZMmMCrr77qdd22bduYO3culmVxzTXXMHLkSBo0aHDB8y1J1KARERER8VfpZ7A2vo+5d3W+klOxAXb8WChXwwfB/F9AUCAtBvZl7TsfAJCRlMx3K1bTrO/VPk4mvxYYHU14p26krlyaNxZU+zKiBgyjTIdOOpFJ/E7t2rWJiIhg7dq1ZGZmMmPGDADS09PZs2cPa9asYfjw4RiGQdWqVYmLy90ja926dezYsYPBg3NPmMvMzKRKlSoAPPzww/Tu3ZtWrVrRp0+fc7/jX9mxYwfNmjWjWrVqAIwYMYKJEyfmu65ly5aEh+eeJFi9enWSkpLyXeMv1KARERER8TeOg/ndUszNH2Fkp3uV3OBw7HbX49brCobpm3x+6PDOb1jz9gf0eujvRMSWB6D1sAHsmLuQ4DJhXDHuFmq30y1kvvS/E5nCO3XDKhvtVYvqN4TUlUsJbd6KqAHDCGnSAsMwfJRU/Nn5VrrUHH/379Zje/UhtteFNUB+S0hI7mowx3F44YUXaNy4MQAnTpwgKiqKGTNm4DhOvufZts1NN93EzTffDEBycjLWzw3MEydOYFkW+/btIysri+Dg4PPm+PX7cF0Xj8eT77pfvpZhGLjnuA3RX1zQd+VXXnmF3r1706dPH9555x0gd5nR8OHD6dOnD/fccw/Z2dmFGlRERERELsCJH7HmPoK1dlK+5oxTryueoS/j1u+u5kwByUxNZe4/nuWTvzzAoe1fs2by+3m1wJAQhr34FDe89aqaMz5knznNqY/e5eDtN3LqvYkkL5yd75qgmrWp9u+3qfTI04Q2banmjJQKHTp0yDtR6fjx4/Tv35+EhATi4+OZP38+ruty7NgxNm7ciGEYdOjQgVmzZpGWlobH4+HPf/4zixYtwrZtHnzwQR5++GHatWvHK6+8ckHvv3nz5mzfvp1Dhw4B8Mknn9C+fftCm29JcN4VNBs3bmT9+vXMnj0bj8dD7969iYuLY/z48UyaNImGDRtyzz33MH36dEaNGlUUmUVERETk17IzMLd+irlrPsav/rrolq2KHT8Wt/LlPgrnv4JCQzl18HDe412Ll9FiUF8qNagHQHS1Kr6KVurlJBwmafZ0Ulcu9TqRKXnhHKIGDMcMDfW6PrCSPldSutx555088cQT9O3bF9u2uffee6lRowbDhw9n9+7d9OvXj9jYWKpUqUJISAjt2rVj9+7dDB8+HNu26dy5M4MGDWLSpEmUL1+enj17Eh8fT9++fenZsyctWrT43fcfExPDhAkTuPPOO8nJyaFKlSo89dRTRTP5YspwL2B9UE5ODoGBgRw+fJhRo0Zx991388UXX/Daa7nLs06dOoVt28TGxhZouMTElAJ9vcIUGxtRovJeKs3Tv2ie/kXz9C+ap38p1Hm6LsaBjVjr3sFIP+VdsgJxWg7FadIPrMK/s700fD4d2+bkrq+Jbdo8b+zAlq+Yce+jANTv2okrbruZyEoVfBWxwJTUz+f5T2SKouKD/yCkXkOg5M7zUpSmuRaV2NgIX0cocCtWrMB1Xbp160ZKSgoDBw5kxowZlC1btkBe/+mnn6ZixYrceuutBfJ6/uSCvlMHBgby6quv8vbbb3PNNdeQmJhIWFgYf/3rX9m3bx+tWrXigQceKOysIiIiIvJLKcex1r2NeXBrvpJTrSV23C0Qef6TNOTC/LhhM1+++TYnDxxk0DOPU7t9GwBqtm5B22uHUDe+PVWaNPJxytLJdV0ytm0iadY0MnftPOc1ARUqEdV/COFdr9KJTCK/o27dutx33328/PLLANx1110X3Zx59913mTlzZr7xqKgoDh48yKRJkwogqf+5oBU0/5ORkcG4ceNo27YtH330EZ988glVqlTh4YcfpmrVqowfP75Aw3k8NgEB2jVdRERE5JdcO4esjTPJXPMReLK8akZ4eUJ7jCOwQUfto1GATvx0iH/1vSFvc8oKdWpy14zJWAE6c8OXnJwcTi1bSsLHH5Hx475zXhNWvwGVR15Huc5dMPT5EpFi7Lz/Q+3du5fs7GwaNWpEaGgoPXv25LnnnqNjx45Ur14dgF69evHBBx8UeLjTp9PPf1ExUVqWC2qe/kXz9C+ap3/RPP1LQc7TOPot1pq3MM4c8hp3DQPn8l44rYaTHRQGJ1IL5P1dDH/7fDq2jfm/I5ZDo2jcqwdfz18CwOkjx/h2w04q1r/MhwkLV0n4fJ58/y2S58w4Zy20eeufT2RqjmMYnDidcc7rSsI8C0ppmmtR8cdbnMR3zrt9/6FDh3jkkUfIzs4mOzubL774ggkTJvDNN9+QkJAAwPLly/OO5hIRERGRQpCZjPXl6wTMezxfc8aJqYun/7M4HUZDUJhv8vkRT3Y2G6dO5/1b7yQnIzNvPP7m6wkqE0bbIX245YOJft2cKSkirrwGfrlSzDQp07kbVV54nUqPPEVoUx2XLSIlx3lX0HTp0oUdO3YwcOBALMuiZ8+eDBw4kLJlyzJu3DiysrJo1KgR999/f1HkFRERESldXAdjz0qsjR9gZHn/5dsNDMVpMwqn4VVg6tjsgpC490dmPfokyUePA7DpkxnEj74OgPDy5bj1w0nUqFtFqxCKWM6RQ2T/tJ8yHTp5jQdVrU5Y2zgytm8h4speRPYdRGCs9l0SkZLpgm7CHD9+fL79Zbp27UrXrl0LI5OIiIiIAJw+iLVmEuaxb/OVnDodsdvfBGFliz6XH4uqXBFPVnbe482ffEbTPlcTERsDQGikbmcoSpl7dpM0axrpG9dihIQS0rQFVplwr2vKjf4TZkgoVkSkj1KKiBQM7ZIlIiIiUtx4sjC3zcDcOQfDtb1KbmQl7PgxuFWb+Sicf0k+dpyftu6gSa8eAASFhdHxlutZ8uJrhERGEHfjSMKiy/o2ZCnzWycyuRnppCyZT9mBw72u14oZEfEXatCIiIiIFCPGT1ux1k3GSE30GnfNAJzmA3GaDYSAIN+E8yPZ6elsnDqdLdNm4Xg8VGxwGbF1agHQ+JoeZCSn0KzvNYREhP/+C0mBcXNySF27kqRZ08k5uP+c12Tu/qZoQ4mIzzVo0IDvvvvO1zGKhBo0IiIiIsVB2kms9e9g7t+Yr+RUaYodfytEVfFBMP90aMc3bPxwWt7jL9+YzODnJ2AYBqZl0W7kUB+mK12cjHRSli4gad5M7JMnznlNaIs2uScyNdbKMRHxX2rQiIiIiPiSY2PuWoC59VOMnEyvkhsShd3+Rty6nbxPqpFLkpWWTnCZ3FOuardvQ41Wzflp63YAMpKSyUpLIyRcK2aKiuf0KZIXzCJl0Vyc9LT8F5gmZTp2Jar/UIJr1Sn6gCJ+aMOGDbzwwgs4jkPVqlUJCwtjz5492LbN2LFj6du3Lzk5OTz++ONs2bKFihUrYhgGd9xxB+3bt2fixIksWLAA27bp1KkT9957L8uWLeP5559n9uzZHD16lBtuuIFp06ZRseK5bz9ct24dL7zwAgBRUVG8+OKLpKenc+ONN7Js2TIA/v3vfwPk7YX76KOPsmPHDqKjo3n66aepUsU//2Ch7f5FREREfMQ4voeAWQ9ibXjfqznjYmA3vArP0JdwL+us5swfdOqng3z+0ASm/fVBHDt3Tx/DMOhy+62Ex8Zw9X1/4bo3X1JzpgjZaakcGn8LSTM/ydecMYJDiOwziGqvvUOFu+5Tc0b8ysSb/8KWzxcU+NsXY//+/bz33nvUrFmTxo0b89lnn/Hhhx/y5ptvcvDgQT7++GMyMjJYuHAhzzzzDDt35u4F9eWXX/L1118zffp0Pv/8c44dO8bs2bO58soradGiBW+++SYPPvgg999//282ZwBef/11nnjiCT777DPi4+PZtWvXeTO3bduWWbNmcdVVV/HUU09d1HxLEq2gERERESlqWWmYm6di7l6CgetVcsvVxO44FrdCfR+F8y+7Fi9j0fOv4DoOAN8uWU7ja3I3BI6tW5tbP3wLK0A/Ehc1q0w4YW06kLZmRd6YGRlFZO8BRPbshxWh07JECkvt2rWJiIhg7dq1ZGZmMmPGDADS09PZs2cPa9asYfjw4RiGQdWqVYmLiwNyV77s2LGDwYMHA5CZmZm3kuXhhx+md+/etGrVij59+vzu+7/yyiu588476dGjB1deeSUdO3bk0KFDv3l9SEgI/fv3B2DAgAG8/PLLf/RDUGzpu5GIiIhIUXFdjH1rsDa8h5GR5F0KCMZpPQLn8l5gWj4K6H+qNW+CGRCAnZ17dPaatz+g4ZVdsAIDAdScKWSu45CxbRPB9RpiRUZ51aIGDCVtzQoCKlYmqv9Qwrv0wAwO9lFSkaJx2zuvFOrbFyIkJAQAx3F44YUXaNy4MQAnTpwgKiqKGTNm4Pzc1P4l27a56aabuPnmmwFITk7Gsqy851qWxb59+8jKyiL4d76WR48eTbdu3Vi+fDkvvPACO3bsoH///rju2T9YeDweAn7+/9k0z97447pu3rg/0i1OIiIiIkUh6QjWwicJWPFqvuaMU6sdniEv4TTpq+bMH+C6LntWrWPn/MV5Y5EVK9Bm2EAAqjZtTP9/PpzXnJHC4+bkkLJiCYf/fjvHnn2c5EVz810TXPsyKj3xHNVemURkzz5qzogUsQ4dOjB16lQAjh8/Tv/+/UlISCA+Pp758+fjui7Hjh1j48aNGIZBhw4dmDVrFmlpaXg8Hv785z+zaNEibNvmwQcf5OGHH6Zdu3a88srvN4yGDRtGWloao0ePZvTo0ezatYvIyEjOnDnDqVOnyM7OZtWqVXnXp6en88UXXwAwY8YM4uPjC++D4mP+23oSERERKQ482WSs+oCAdZ9gOB6vkhseix13C26N1j4K5z+Off8DK16fxOEd3xAUFkqdDm0pUy4agLYjh1Ch/mVc1qkDhvbzKVROehopXywkae5M7FNnT2RKXjCbqP5DMINDvK4Pbdy8qCOKyM/uvPNOnnjiCfr27Ytt29x7773UqFGD4cOHs3v3bvr160dsbCxVqlQhJCSEdu3asXv3boYPH45t23Tu3JlBgwYxadIkypcvT8+ePYmPj6dv37707NmTFi1anPP93nPPPTzwwAMEBAQQFhbGk08+SUREBGPGjGHo0KFUqlSJpk2b5l0fGRnJ0qVLeeWVV6hYsSLPPPNMEX2Eip7h/nIdUTGTmJji6wgXLDY2okTlvVSap3/RPP2L5ulfNE//YBzegbV2MkZygte4a1g4TfvitBgCgSG/8eySx5efz22fzWH5axPzHjftezVX3XNnobwvf/93+z8XM0/P6ZMkz59FyuJ55z6RybKo9OA/CW3eqoBT/nGl5fMJpWuuRSU21v/2S1qxYgWu69KtWzdSUlIYOHAgM2bMoGzZsr6O5ve0gkZERMTX0k5i/rAKN/Yy3CpNfJ1GCkL6GayN72PuXZ2v5FRsiB0/BsrV8EEw/5GTkUl2RkbeKplm/Xvx1ax5nD54GMM0CQgKwnVdrZgpZNmHD5I0ZwapK78AT06+uhEcQkSPXkT1GURAbAUfJBSRi1W3bl3uu+++vM1477rrrotuzrz77rvMnDkz33iFChV46623CiClf1KDRkRExJdOHyJg/uMYmbl/0bTbjMJpPtC3meTSOQ7md0sxN3+EkZ3uVXKDw7HbXY9brysY2gbwUrmOw64ly1k96X0qNazHgH8+AuRu9ttl3C1sn7OAK/50C+VrVvdxUv+W+d0ukmZNJ33zOjjHgnwzqixRvQYQ0bOvTmQSKWGqV6+etzfNpfrf/jJycdSgERER8ZXkowQs/GdecwbA2vwRZKfj9rrNh8Hkkpz4EWvtW5iJP+QrBTW9irRmIyAk0gfB/MvSl19n59xFAOxds4Gftu2gRstmANSJa0eduHa+jFcqnPnsY05PffectYBKVYjqP4TwK3Qik4jIxVKDRkRExBdSTxCw4J8Y6afzlawdn5MR4IEW12ulRUmQnYG59RPMXQswfrWSwC1bFTt+LGWbtSdN+z4UiCa9euY1aAA2TZ2e16CRohHWNi5fgyaobn3KDhxOWNs4DEsnkYmIXAo1aERERIpa+pnc5kxqYt6QGxbt1azJ3joXKykJ+4o7dOxyceW6GPs3YK1/FyP9lHfJCsJpOTT32GxLP25dqszUVDZM+YTKjRpQv2snACo3qk/DHl3Zs3INrYcNpO3IoT5O6b/stDQyv/2akEbee2MFVa9JWOv2pG/ZQGjLtkQNGEbI5U2134+IyB+knxhERESKUmZK7m1NvzjVx42sjKfPPzAPbMRcOxmD3FUY5t5VkJOB3e1uCAjyUWA5p5TjWGsnYx7alq/kVGuJHX8rRGhD1D9ix5wFrJ48hczkFCIqxFI7ri2BP98yc8Vto+l0yw1EVtLHuDB4Tp8ked4sflo6D9eF6m9OwQwN87om+rpbiB45mqCatX2UUkTE/6hBIyIiUlSy07EWPY1x+mDekBsei6fXoxBWFqdRT9ygMKyVr2G4DgDmT5thyXPYPe71q+OYSyzbg/n1XMxt0zHsbK+SGxaN3eFm3FrtQSsJ/rDTh46QmZx7W1jK8US2zZhNu1HDAAiPKe/LaH4r+/BBkmZPJ/XLZV4nMqUsXUBUvyFe1wZVr1nU8URE/J4aNCIiIkUhJxNr8bOYJ/bmDblh0bnNmfCYs2N1O2EHhhCw7CWwc39BMo/shAX/xL76QQgOL/Lokss4+i3WmrcwzhzyGncNA+fyXjitRkBQqI/SlXyJ+/YTHlOe0MjcE3/aXz+CbxZ9QWZyCpEVKxBdraqPE/qv3BOZppG+ad0566mrVxDZd7BuYRIRKWRq0IiIiBQ2Owdr6f9hHtudN+QGR+C55lGIrJTvcrdGG8oMm0Dq9CcwPFkAmIl7MOb/A881D0No2aJKLgCZyVgbP8DcsyJfyYmpi93xNojRbR6XKu3Uada+8yFfL1hCi4F96XbnWABCIsLpfNtoMs4k0WroAAKCdJtfQXIdh/QtG0iaNY2s73ad85rgatUJ7z2I8C491JwR8SMvvvgiixYtIjo6mtjYWLp3745pmrz33ns4jkPjxo15/PHHCQ4OplOnTlx99dVs2bIFy7J4+eWXqV69Ojt27OCZZ54hMzOT6Oho/vGPfxAdHU3//v156qmniIuL49Zbb6V79+5cd911vp5yiaEGjYiISGFyPFjLXsI8siNvyA0Ky220RFf7zacF1mqB3eux3FuistMAME4dIGDu43iueQQiYgs9eqnnOhh7VmBt/AAjK9W7FBSG02YUToMeYOqkrUvlui4z7nuME/v2A7B91jxaDOhNdPXc1TJNe/f0YTr/5OZkk7p6BUmzppNz+KdzXhN8WQOiBg6jZq+enDiVXsQJRfzbbdeO8nrcb+gQ+g0dwrtvvMnalV8S3+UKRt8+jjnTZzBn+gyvayd+/BEnEhN5aPxfAHj6368QExvLbdeOYuLHH13Q+1+2bBlbtmxh7ty5ZGRkMGjQIOLi4pg3bx4ff/wxwcHBvPjii0yePJk77riDxMRE4uLiePTRR3n22Wf58MMPueeee3jkkUd48803qVKlCqtWreLRRx/l3Xff5amnnuKJJ57gxhtvxDAMNWcukho0IiIihcVxsFb+J3cfmZ+5AcHYPR+EmDrnfEpmSiohEbm3MbkV6uHp84/cTYUzkgAwkhMImPdY7uqbslUKfw6l1emDWGsmYR77Nl/JqdsJu92NEFa26HP5Add181ZjGIZBh+tHMHfCcwA4ts3uZV8Sd9NIX0b0W05mJofuHot9MvGc9dBW7XJPZGrUBMMwdFy2iB9au3YtvXr1IigoiKCgIHr06IHruhw4cIDhw4cDkJOTw+WXX573nM6dOwNQr149Nm/ezP79+zl48CC333573jWpqbl/yIiLi6NDhw7861//YsGCBUU4M/+gBo2IiEhhcB2sNRMx9605O2QFYl91P27FBgAkJRwlMyWVivUvIycjk02ffMbmTz9j6AtPEtu1be6TytXA0/efXsdyG2knf27SPALlaxX1zPybJwtz2wzMnXMwXNur5EZWwo4fg1u1mY/ClXxHvtnNyjcm0eX2MVRp3BCAel06UqVJIzKTU7hi3K3Ubt/axyn9lxkSQnC9BqT/skFjWYR36kZU/6EE1ajls2wipcVvrXQZffs4Rt8+Lu/x/1bW/FpMbGy+17jQ1TMApmniOI7XmG3b9OrVi0ceeQSAtLQ0bPvs98Dgn0/QMwwD13VxHIdq1aoxa9asvOefOHECyG3C//jjj4SGhvLjjz9SoYJO27sYF7Qm95VXXqF379706dOHd955J288JyeHm266iQ0bNhRaQBERkRLHdTHXv4f5/bKzQ4aF3f1vuFWa5D52HBa98Cof3fE3vnj5dSZdN4b170/Fk5nFitcnef/wFFkJT59/4EadXTFjZCYTMP8JjGPfFdm0/J3x01YCZtyDteNzr+aMawZgtxyKZ9D/qTlziWyPh3n/fJ6Px99Lwq7vWPH6JFw39zh5wzDo+/gD3DDp39Tp0EZ7nRSQ7EM/Yael5hsvOyD3JCwjJJTIvoOp/tq7xN75dzVnREqJ+Ph4Fi9eTHZ2NqmpqaxYsYKUlBSWLFnCyZMncV2XJ554gvfee+83X6NOnTokJSWxeXPuCuEZM2bw97//HYCPPvqIsLAwXn/9dR599FHS0tKKZF7+4rwraDZu3Mj69euZPXs2Ho+H3r1706VLFwAeeughdu0696ZiIiIipZW55WOsXWeX9bqGgd3tLtwarfLGts9ewKGvdua9/UtHv/2O3SvWEtu0+dnB8Bg8fSYQsOgpjJM/AmBkp2MtfBK7x71qHPwRaSex1r+DuX9jvpJTpSl2/K0QpdvJ/ggrIADb48l7fPTb7/hu2Zc0vDL3Z8rw8uV8Fc3vZO7+JvdEps3riR45mrKDr/WqB1/WgJg7/05Y6/ZY4RE+SikivtK1a1e2bdvGoEGDiIqKokKFCtSpU4c777yTm266CcdxaNSoEbfddttvvkZQUBCvvPIKTz31FFlZWYSHh/Pcc89x8OBB3njjDaZNm0blypXp1KkTL7zwAk888UTRTbCEO2+Dpl27drz//vsEBARw7NgxbNsmLCyM999/nzFjxvxuZ01ERKS0Mb/6DGv7TK8xu/PtuLXjvMYOf332DxxWYCD9/vEgcx5/muDwcDrecj0Nu8Rx8tebc4ZG4un1GNaSZzF/XjljeLKwFj+L3e1u3FrtCmdS/sqxMXctwNz6KUZOplfJDYnC7nATbp2OoBUdF82xbXZ/sZIG3TpjBQYC0Pm20exbtwnH46HeFfFUaljfxyn9x2+dyJQ0/3Mi+w7G/NUJWBFdehR1RBEpJrZt20atWrWYN28eOTk5jBgxgjp16tCwYUOGDRuW7/rvvju7Unfw4MEMHjwYgJYtWzJ9+vR8169evTrv7ccee6wQZuDfDPd/60vP49VXX+Xtt9/mmmuu4ZlnnslbfnrDDTdw55130r59+wIP5/HYBARoczIRESkZsjbPImPpm15joT3/THCrvvmudV2XbXOXMOeZV+k29nquuPlavlu9gVotmxJcJux334+bnUnazH/i+XHr2UHDJKz3Xwlqql+8LoTnyG7SF/4b5/i+X1UMglr2IqTLaMwQrS64FN+v3cT8F17n2A8/0ue+P9PphrM/8K/7+HMq1atD7dZa8VUQnOxsTi5dTMLHH5H504FzXnPZE/+kXNfuRZxMRIqrM2fO8Le//Y3ExERc12XgwIHceuutvo4lP7vgBg1ARkYG48aNo3fv3owYMQIo3AZNYmJKgb9mYYmNjShReS+V5ulfNE//onn6lvHdMgJWezdn7LbX4zTr/7vPSzt1mtCoSMxfnZYSGxvB8ePJ2DkeAoIC8z/RzsFa8Srmfu994Oy4W3Auv+aS5uALRf75zErD3DwVc/cSDLx/BHLL1cTuOBa3QsGv7Ciu/24LWkSIwTNXDSc7LXf1V3BEOLdMmUhopH81u3z9+XTS0kheOp/keTOxT5865zWhrdtTdsAwghs2vuR9fXw9z6JSWuYJpWuuRSU21r/+fxPfOu8tTnv37iU7O5tGjRoRGhpKz549vZY5iYiIlHbG3tVYq//rNWa3HJqvOXP68BF+XL+ZloP6Ypi5+/SXKRd9ztc8umcfnz/1KuEx5bn6/rvzX2AFYne7G1b/F3PPirPD696G7HSc5oN0a84vuS7GvjVY69/DyEzyLgUE47QegXN5LzC1cvdiebJz8pqIIRHhtL9uBKsm5h4qYefkcOy7PdRq2+r3XkIukOfkCZLnf07ykvm4Gen5L9CJTCIiJdp5GzSHDh3i1VdfZerUqQB88cUXDBmS/7gvERGR0sg4sAlr5WteqzHsJn1xWnrfx+06Douff5XDO79hz5dr6XnfXURXPffGs5s+nsHqSe/jOg4YBi0G9aVi/cvyX2ha2J3H4QaGem1KbG35OLdJ0/Y6NWkAko5grZ2MeWRnvpJTqx12+9EQHlP0uUo4T3YOX82cw+ZPZzLyP/9HVKWKALQc3I+d8xZStWljOt5yPeEx5X2c1D+kfLGAE2/9B2xPvpoREkrEVb2J6jOQgPKxPkgnIiIF4bwNmi5durBjxw4GDhyIZVn07NmTPn36FEU2ERGRYs04vANr2UsY7tkjse2GV+G0uyFfY2TbzLkc3vkNAId3fsMXL7/B0Bf+ec7XrXBZndzmDIDrsvLNtxn+r6d/I4SJ02E0BIVhfTUjb9jaORty0nHixsDPq3VKHU825o7PMbd/juF4/1Lrhsdix92CW6O1j8KVbMnHjjPtnodISjgGwOq33qPPo/cBEBAUyA0TXyUwNMSXEf1O8GUN8jVnrKhoIvsMJKJnH6wy4T5KJiIiBeW8DRqA8ePHM378+HPWpkyZUqCBRERESgLj6LdYS573+sXfuewKnPhbz7lqxQywCAgOwpOVjRUUxJV3jfvN167ZpiUNrujAd1+up0ar5nS5/Tyb9xkGTusRuU2ajWe/L1u7l2JkZ2B3+TOYF/Qt328Yh3dgrZ2EkXzUa9w1LJym/XBaDIZANRAuVURsDCGREXkNmu+Wr6LV0AHExrYBUHPmD3Adh5yEwwRVre41HlSzDqEt2pDx1WYCKlclqv9Qwq+4Mt8JTSIiUnKVrp/WRERECoCRuBdr8bMYdnbemFOrHXbn28E492qVFgP6ULN1SxY9/zL1OscTXb3q776PPn+/g4ZX9aBOfLsL3uDTadoPNygMa/XEvFuuzH1rICcDu/s9EFAKfpFLP4O14b3cef+KU7EhdsexEF39HE+U35OSeIJdi5fRbtQwDMPAME263D6GT+9+gODwMsTdOJIKl9XxdcwSzc3JJvXLZSTNno6dnET1N6Zghng3uqJH3EBEj16EtemAYWm/JBERf6MGjYiIyMU49RPWoqcwcjLyhpxqLbG73n3eDWajq1Vh+EvPXNC7ia1dA8K9NxB2HSdvc+Hf4ja4EjswFGvFvzFcGwDz4FZY/Ax2j/sgKPSC3n+J4ziY3y3F3PwRRrb35qlucDh2u+tx63X9zQaanFt2RgabPp7Blk9n4snKpnzN6lzWKQ6Aas0ac9Xfx3NZxw6ERkX6OGnJZaelkrLk5xOZzpzOG09ZvoioXgO8rg2+rEHurU4iIuKX9FOKiIjIhUo6QsDCf2JkpeYNOZUbY1/5N7Dy/83j9MHDzJ3wHGmnzv7SZVpWviO1z8d1HL5esJT3bvkz6afPnP/6OvHYPe7Ftc4ez20mfIO1YAJk+uHxqid+xJr7SO4tTb9qzjj1uuIZ+jJu/e5qzlyClOMn2PjhNDxZuavFvvzvO9g5OXn1pr17qjlziTwnT3Dq/bc4ePuNnP7wba/mDEDygtm4rvsbzxYREX+kFTQiIiIXIuU4AQv+iZFx9ohmJ7Ze7qqUc9w65Ng2i55/hSPffMuBLV/RffyfaHhllwu+Xensu01k1qNPcXzPXgDWvvshPf765/M+z63RCvvqh7GWPJe32sc8sRdj/hN4rnkEws59vHeJkp2BufUTzF0LMH71i6xbthp2/Bjcypf7KFzJlX76DGHRZQEoX7M6zfr1YvuseQAEBAeTevJU3olNcvGyDx4gafZ0UlctP/eJTKFhRPboRWSfgRf9/4WIyPls2LCBN998k8DAQA4dOkT37t0JCwtj6dKlAEycOJGFCxcya9YsMjIyCAwM5MUXX6ROnTp0796da665hrVr1wLw9NNPc/nl+j5bkNSgEREROZ+0U7nNmbSTeUNuuZrYVz/4m7cMbZ0xmyPffAtAVkoqO+YspGH3Ky762OuwctHkZGTmPd45bzHNB/Qhtk6t8z7XrXw5dq/Hcm/J+nnVj3H6IAFzH8PT61GIqHBRWYoN18XYvwFr/bsY6ae8S1YQTsuhOE36nnNVk/y20wcP8+V/3+HIrt3c8v5/CQ4vA0DcTSP5aetXtBkxmMZXX3nRK8AEXNcla/c3nJk1jYwtG855jVU2msg+g4i4qrdOZBLxY9ddN+Y3a4MH92fIkP5cd90Yr7cv5PoPP5x0wRm2b9/OvHnzKFu2LPHx8dx///189tlnPPjgg8ybN4/ly5czZcoUQkJCeOWVV/jwww959NFHAQgLC+Pzzz9n2bJl3H///cyZM+fCJy/npZ9cREREfk9Gcu5tTSnH8obcqKq5q1CCf/uXqGrNm1C+Vg1O7v+JgOAget5313n3jzkXKyCALrffwucP/xMrKIg2wwYSVfnCVy+4sXXx9PkHAQufxEjPvYXCSDn2c5PmEShb7aIz+VTKcay1kzEPbctXcqq1xI6/teQ2nnxo/6atfP7QBBw7d9+ijR9No/NtowEIKxvF6Hdev6R/vwJuTg4J/7ifrO92nbMeWLkqUQOGUqazTmQSkaJRv359KleuDEB0dDRxcbl7i1WpUoXk5GRefPFF5s2bx/79+1m1ahWNGjXKe+7w4cMB6N69Ow888ACnTp2iXLlyRT8JP6UGjYiIyG/JSsttbJw5nDfkRlTIXX0SGvW7T63UoB7Xvfky69+fSnhMeaKrVrnkGLU7tKXTmBtpeGUXIiteQvMhujqePhN+bjQdB8BIP0XAvMfxXP0wxJSA03dsD+bXczG3Tfc6PQvADYvG7nAzbq32F71CSXJVbXo5YeWiSU08AcDWz+bQZsQgQqNy/52rOXPpjMBArMiy+caD6zUkauDw3BOZ9PEVKTUuZKXLL6+52OsvRGBgoNdj6xcrIxMSEhgxYgTXX389V1xxBTExMXz77bd59YCAsy0Ex3G8nit/nL4biIiInEtOJtaipzFO7c8bcsuUx9PrMSjz238p+t8KBICAoEA6jbmRFgP7/KEohmHQbtSwS2vO/E9kRTx9JuD+YsWMkZlCwPx/YBzd/YfyFTbj6LcEfH4f1uaPvJozrmFgN+6NZ8jLuLU7qDlzgVzXZe/ajax998O8scCQEDqNuRGAKk0aMfylZ/KaM3Lh7LRUnIyMfONRA4bmvR3auj2VJ/wflZ96iTLt4tWcEZFiZefOndSsWZPRo0fTtGlTli5div2Ln23mzcvdk2zJkiXUrVuXKH2vKFBaQSMiIvJrnmysJc9hJu7JG3JDovBc8/v7tpw8cJBZj/yT7neNo1bbVoUWL+3Uada++yExtWvRclDfC39imXJ4+jyBtehpzBP7ADByMrAWPond4++41VoUTuBLlZmMtfEDzD0r8pWcmLrYHW+DmNpFn6sEO/HjAZa/NpGD23aAYVA3vj0V618GQKMruxAaGUGtdq21Oe1F8pxMJGne56QsmU/ZoaMoO2CYVz2kweVEX3sTYe3iCKpeyzchRUQuQKdOndi9eze9e/fGdV3atm3Lnj1nfx7aunUr06dPJzQ0lGeffdaHSf2TGjQiIiK/ZHuwvngRM+GbvCE3qMzP+7X89m1Kjm2z6LmXOXM4gc/uf5wmvXvS/a4/EVDAe0psmzmXNZPfJzs9g+CIcBr+/Ev1BQuJxO71GCx5DvNo7pJlw85tSNld/5K7EsXXXAdjzwqsjR94HWkO4AaF4bQZhdOgB2jlwUVLO3kqtzkD4LqsfGMyw/71NIZhYJgmtdu38W3AEib7p/25JzKtXg4//4U5ed7nRPUegBHo/bVfdshIX0QUEfHSvn172rdvn/d42bJleW+PHz/+vM//29/+RrVqJWz/uhJEP9mIiIj8j2NjrXjVawNaNzAU+5qHoVzN333q9lnzObr7+7zHZ44kYAUU/N9BPFlZZKfn3kKRlZLK+venXvyLBIVhX/0QTrWWeUOGY2Mtfwnj++UFFfXSnD6INe8fBKx6M19zxqnbCc+Ql3Ea9VRz5gLlZGZy+tCRvMc127SkdofcJoxhmpSvWR07J/9Rz/LbXNcleftXHH3mMQ7/bRypK5fmNWcA7NMnSV37pQ8TiohISaUVNCIiIgCug7XqDcz9688OWUHYV92PG3vZeZ9++dVXcuLHA+yct4jAkBCuvvfSTm06n5aD+7NjzgKSEo4RXb0qNdtc4q1UAcHYPe6Fla9i/pg7Z8N1CVj1BnZ2Bk6T3gWY+gJ4sjC3zcDcOQfDtb1KbmRl7Phbcas2K9pMJZjrOOxe9iWr3nqP4DJh3PDWq3nHY1/xp1sA6Dx2NDG1f7/xKGe5tk365nUkzZpO1p5z79sUWKUaUf2HEh5/RRGnExEpfL9cbSOFQw0aERER18Vc+zbmD2f/6u2aAbn7slS+/IJeIrhMGFf97U7qXRFP+pkkoipXKpSoAUGBdP3zbSQdPUrz/r3/2CodKwC7690QOBHz+7M/dFkb3oWcdJwWQ4pk413jp61Y6yZjpCZ6jbtmAE7zgTjNBkKAjh++GBs+/JS17+RuApyaCDvnLaZ5/14AlK9ZnUFPP+7LeCWKk51N6pdfkDx7OjkJh895TXD9RrknMrVur01/RUTkkqlBIyIipZvrYm76AGv34rNDhond7e4L2jQ3/fQZwqLL5j0uzM2B/6dufLuCezHTxO70J9ygMKyv5+YNW1s/hex0nHY3FF6TJu0kaateIeD7NflKTpWm2PG3QtSlH09emjXp3ZNNU2eQk5kJwFefz6VZ36vVPLhIaZvWcfK/r2InnT5nPaxNB6IGDCOkYeMiTiYiIv5IDRoRESnVzK9mYO2ck/fYxcDucidurfM3QU78eICP7riHloP7E3fTKAKCAgsz6m86+NVOdsxdSK8H78m7jeWiGEZuIyYoLLcx8zPr67kY2em5pyUV5C/2jo25awHm1k/Jycn0KrkhUdgdbsKt01HHZl+grNQ0Nnz4KSER4bQblXt6UHj5crQdOZQNH3xMyyEDaD9qmJozlyCgfEz+5owVQMzV1xB8VX+CqtXwTTAREfFLatCIiEipZe6c49WQALA73YZbt9N5n+vYNouefxlPVjabpk5n39oNDH3xKcqUiy6suPmkHE9k2b8nsndN7h4y1Zo1ybuN5aIZBk7LoRAYlnuL08/M75dBTgZ2l/Fg/fEfG4zje7DWTMQ4dcBr3MXAaXgVTptrITj8D7+f0mLXkuWsfH0SGUnJBIQE0+iq7kTElgeg9bABNOrRpdBut/M3dmoKVrj3iWjBdeoR0rQlmTu3YYSGEdmzD5G9BlC5YW0SE1N8lFRERPyV/pQiIiKlkvntYqyNU7zG7PY34Ta48oKe/8OqdRz77oe8x2Viynvd6lQUDNPkwJazJ06tffdDslLT/tBrOk164+l8O+4vVq+YP67DWvoCeLIu/YWz0jDXTMKa80j+5ky5Wtj9nsTpOEbNmYvkycomIyk59+3MLNa8/X5eLTAkRM2Z83Bdl4xvdnD06Uc5dPdYnKz8/8bLDh1F9PW3UuONKZS7/lYCysf4IKmIiO/ccMMNbNiwwdcxSgU1aEREpNQx9qzEWjvJa8xufS1Okz4X/Br1unSk9yP3EhIZQVBYKD3/Nh6jiG/JCY8pT7uRQ/Me12zTEjsn5w+/rlu/G3a3v+KaZ2+XMg9tw1r0NGSnX+SLuRh7VxMw/W6s3YsxcM+WAkMIufI2PAOewa1Q7w/nLg1O/HjA69jsJr16EFOnFgARFWKodamnepUyrm2Ttn41CQ/dzdEn7iNj2yacpDOkrliS79rQy5tSdsAwzDJlfJBURERKE93iJCIipYrx43qsVa97jdnNBuI0H3Rxr2MYNOx+BdVbNCVx749EVqpQkDEvWOthA0ncu582IwZRuVGDAntdt3YH7MAQrKX/h2FnA2Ae/RYWTMC++iEIiTz/iyQdwVo7GfPIznwlp1Y77A43E1KrFim6VeS80k+fYe27H7Fz3iJqtmnB4Gf/AYBpWXS9YwwJu3bTathAAoODfZy0eHOys0lduZSkOTPwnONEpqQ5M4jo0QvjUvZyEhEpATZs2MALL7yA4zhUrVqVsLAw9uzZg23bjB07lr59+5Kdnc3DDz/M119/TdWqVTl9+nTec1977TWmTMldgfzAAw/Qrl07Bg8ezLvvvsvUqVOxLItu3bpx7733cuLECR577DGOHj2KYRj87W9/Iz4+3pfTL/bUoBERkVLDOLgVa8UrGO7ZVRz25dfgtBl5wRvSHtm1m0oN6uVtxlumXHSR7jvza4EhIfR74oFCeW23Wgvsax7GWvwsRk4GAOaJfRjznsBzzSNQpty5n+jJxtzxOeb2zzEcj/drhsdix92CW6N1oWT2V0v+9Rp71+QuL9+/cSs/btxC7Xa5H8MarZpTo1VzX8Yr9uzUFFIWzyNp/uc4SWfOec3/TmRSc0ZECtPf7nnf63HPns25+prmfDx1DZs27eX2O3py2WWVeOqfMzh1Oo0X/3Ujp06l8tSTn3k979pr42nb7jJe/88i9u49xov/uvGCM+zfv5/ly5fz3//+lwoVKvDcc8+RmprKtddeS/PmzVm8OPdkywULFrB//3769+//u6+3Y8cOPvroI2bMmEFoaChjxozh66+/ZvLkyQwZMoQrr7yS48ePM2rUKD7//HPCw3U7829Rg0ZEREoF48jXWF+8iOHYeWNOva44HUZfcHMmce+PfHr3g1SsfxlX3/cXytWoVkhpL11OVhZbp8+iatPGVGv2x4/+dSs1wtP7cQIWPYWRmbvSxThziIB5j+G55lGIrOh1vXF4B9baSRjJR71fx7BwmvbDaTEYAkP+cC5/57pu7u1hP5+8FD/6Ovau3Qg/Nxf3rtmQ16CR3+ZJPE7SvJmkLF2Am5WZ/wIrgPAruhPVf6hOZBKRUqN27dpERESwdu1aMjMzmTFjBgDp6ens2bOHjRs3MmLECABq1apFy5Ytf/f1Nm3aRLdu3YiIyN1o/d133wVg7dq17Nu3j1dffRUAj8fDwYMHadSoUSHNrOS7oAbNa6+9xoIFCwDo0qUL9913H9u2beOZZ54hLS2NBg0a8OyzzxIUFFSoYUVERC6Fcex7rCXPYdhn92dxasdjdxoHxoVtx2Z7PCx87mUcj4eEXbuZcttfuO6NfxFTu2Zhxb5o361Yxar/vkvyseNUuKwOo97416Udu/1rMXXw9PkHAQuexEg/BYCRcvznJs0jEF0d0s9gbXgPc9+afE93KjbE7jg29zo5r4Rvv2PFfybRrN81NL46d9Pq2Lq1adKrB4d37qLLuFuo3aGtj1MWb65tc+KNl0hdvRxsO18970Sm3gMJKFfeBwlFpLT6rZUu147syLUjO+Y9fvjRIXlvlysX/pvPu+PPV190hpCQ3D+UOI7DCy+8QOPGuX/QOXHiBFFRUXz66ae5fyj4WUBAbtvAMAyv8Zyf970LCAjw2ofv2LFjhIaG4jgO7733HmXLlgXg+PHjlC+v/3N/z3l/Kl27di2rV69m5syZfP7553zzzTfMnDmT8ePHM2HCBObNmwfA9OnTCz2siIjIRTvxI9bipzF+cQKRU6M1dtc7wbzwvfKP79nH6UNn96yo1rwJ5WsVr7+4/7B6PcnHjgNw/Id97Fq8vOBevGw1PH0n4EacXTFjpJ8mYN4TmFunETDj7nzNGTc4HE/ncdh9nlBz5gIt/r9Xmfrnv5OwazerJ71PTsbZVR9dbh/DjZNfo05cuyLfkLqkMSwLOyU5X3PGii7vfSKTmjMiPpeamsn6dd8z8c0lzJi+3tdxSpUOHTowdepUILd50r9/fxISEoiLi2POnDk4jsPhw4fZunUrANHR0Rw8eJCsrCzOnDnDli1bAGjTpg0rV64kLS0Nj8fD3/72N77++ms6dOjARx99BMAPP/xAv379yMjI8M1kS4jzrqCJjY3lgQceyFsdU7duXQ4fPkyLFi1o2LAhAI888gj2Of46ISIi4lNnDhGw6EmMX5w85FRpit3tr2Be3F2+lRvV58a3/s2i518hcd+P9PzbncXul+TOY2/ih9XrsbOzCYmMwLQK+LDGiAp4+k7IXUlz5iAARlYK1rZp+S516nXFbnf9hW0mLHl+eVR72slTbPrkM+JHjwIguEyYj1IVb65tg+NgBAZ6jUcNGEbG1o0ABFatQdSAoYR36ooRqBXfIr5k2w579x5lx/YD7NhxgL0/HMtblVGxYhRDhnbwccLS48477+SJJ56gb9++2LbNvffeS40aNRg1ahR79uyhV69eVK1alfr16wNQr149unTpQp8+fahatSqtW+featu4cWOuv/56rr32WhzH4aqrriI+Pp66devy2GOP0a9fPwCef/557T9zHob7yzVK57F//35GjhzJzTffzA8//EBOTg779u2jVatWPPDAAwQX8MkBHo9NQIA2ahMRkYtnnz5C6of34qaeyhuzql1O+PCnMIIufQ8Ux3FI3HeAipfVLoiYBe6LN94jMzWN7rfdQGhURKG8DycjmbRPH8NO+C5fzYypQVjPOwmo0bRQ3rc/cWybLbMW0qhLPOHlczeazkpL5//6XEfqydNc3r0Tve4ZR0zN4rfXUXHgZGVxYtECEj6ZSsWBg6k0bIRX3XVdDrz8IlHtO1C2Q3zefj4iUvROJCazceNeNm/ey7atP5Kaeo49oX723pQ/U6XKb2xCL+LnLrhBs2fPHv70pz8xfvx4jh49ypQpU/jkk0+oUqUKDz/8MFWrVmX8+PEFGi6xBB27GRsbUaLyXirN079onv5F8/yF1BMEzHscIzUxb8iJqYPd6zEIurhVCLuXfUnVpo2JiC3aWyGK/eczOwNr6fOYCd8A4FpBOC2H4jTpC9aFr04q9vMsIL+e509bt7Pi9Umc2LefZv160eOvd+TVfliznqCwMGq0bOaLqH9IUXw+7ZQUUhbPJWnBrLwTmazysVR/7R2MgKI5/6K0/rv1V6VlnuC7uc78bAPTPl33u9dYlkmDBlW47oYrqF27QhEl++NiYwvnjyFSOl3Qd7EtW7Zw11138dBDD9GnTx+mTZtG8+bNqV49937yXr168cEHHxRqUBERkQuScYaAhf/0as640dWxr374opszx/fsZeEz/yIwJISud47l8p7di91tTedz+uBh9q3fROthAwv2hYNCsXs+iPvdUshKxanXFSJKzg/UvuTYNl+88ganD+buabRz3iJaDOyTt+H0ZR21vP9cfu9EJvtkIqlrVhDRpYeP0omUbq7rcvjQKbbvOMCO7fsZNKg9DRtVzas3a17znA2aipXK0qxZTZo1r0njxtUICdEtiFK6nbdBk5CQwJ///Gdeeukl4uLiAOjUqRP//ve/SUhIoHLlyixfvjxv52cRERGfyUrN3R8lKSFvyI2slHscdMjF/YXLzsnJPbXJtslKS2PRcy9Tplw0tdq2KujUhSIjOYX173/M9lnzcGybyo0bUuXyhgX7TgKCcBr3LtjX9FNZaWf3QTItiyv+dAuzHvknAFZgAIn7fixWJ4IVJ9kH9nFm1nTS1qwAx8lXzz2RqS+hTX//GFgRKRyO4/C3v77HsWNJeWO1alXwatDUrl2B8IgQPDk2jZtU/7kpU4uKFaN8EVmk2Dpvg2by5MlkZWXx7LPP5o1de+21TJgwgXHjxpGVlUWjRo24//77CzWoiIjI78pOx1r4FMbpn/KG3DIxeHo9BmFlL/rl0k+f8Xpcu0MbarYpOb8AnvhxP9s+m533eMV/JjHytRdK3Aqgks7OyeGrz+ex4YNP6PePh6jeIndvnjpxbanRugVlykXTacyNRMTG+Dhp8eK6Lpnf7CBp1jQyvtp8zmus6PJE9R1ERI9emGFlijihSOlj2w57fzhKQsJpunQ9+8d50zSJiY30atDs2L6fkaM6eV3z+BPDqVgxSnuMivyO8zZoHnnkER555JFz1rp27VrQeURERC5eTibW4ucwT+zNG3JDy+Lp/RiEX9ovvhEVYrnujX+x/oNP2DFnIVfdU/xObfo91Zs35bLOcfywKndJuWmZZCanEBqlU5WKSnZGBh/86W7OHDoCwMrXJ3Hdmy9hmCaGYTDomceximjPlJIk87tdnHznTbL3fn/OemC1GkT1H0p4p275Tm4SkYL3ww9HmTtnC19//RPpaVkEBlp0iKtPcPDZr79mzWvyzde5p/tFRYVRo2Ystu1g/eI0wapVtfGvyPnopwIRESnZ7Byspf+HeezbvCE3OAJPr0chstIfemkrMJCON19P2xGDCQoreUccX3HbzZzc/xMdb76eel06lqgGkz8ICg2lUoN6eQ2a4z/s47vlq2h4ZRcANWd+i2meszkT3KgJZQcMI7RlW53IJFJIsrM9eDw2YWFnT+fNzMhm44Y9eY9zcmy+3XWIFi3PnmbYpk1dXMelWbOa1KgZi2nq+43IpdBPBiIiUnI5HqxlL2Ee2ZE35AaG4rnmYYiufkkvuWXa50RXq0KduHZ5YyWxOQNQtmplRr/zun6ZLSIpiSfZMm0mncbcSEBQ7kaXncbcyJ5V6wgMDqTddSO4rHO8j1MWL65tY1jetzuE1GtIyOVNydy1EwyDsLZxRPUfSkiDy32UUsR/5W3uu30/O7YfYPfuwwwa3J6Bg85+D2zQsArBwQFkZXnyxvbuPebVoKlcOZr+A9oWaXYRf6QGjYiIlEyOg7XyP5g/nd2fwg0Ixr76IYipc0kvefS7PXz533dwHYfLr+5O1z+PJSQ8vKAS+8QvmzOu67J72UqqN29KeEzRHhvuz3IyM9n8yUw2fTIDT2YWYdFlaTdyKACRFSvQ7/H7adypNRm29l34n5zEYyTPnUn61o1UffFNzCDvk1uiBo0goHJVovoNIajqpTVbReTcUlMz+XrnT+zYfoAdOw5w6lSqV33H9gNeDZrAwABata5DdrZHm/v6gQ0bNvDaa69Ro0YNrr32Wpo2bXrO61599VXi4+Np06ZNESf8Y/6fvfMOj6Ls+vA929J7770CCb13kKIgCqJYsGLX1y4WxF4/X8tr710sKKIooEiRKj0BUiCV9N6z2Tbz/bFhkyWhpzP3dXnJPPPMs2eyye7Mb845v4ULF3LXXXcxYsSITll/zpw5rFy5slPWBlmgkZGRkZHpjUgiyq0fosja2jKkVGO64GEkn5izWtKoN7D25TeQml1iUtauJ3TYEGInj++QkLubwpQ0Nr7zMcWp6fSbPoXpi+/t7pD6DCaDkX0rfsPYpANg5zc/0H/GVOzdXAEIHzUcR3cntGV13Rhlz0CXk0XNrz/SsHWTxZGpYfPfOE2ZaTXPfuBQ7Af2rpsCGZmezp9rk9ix4zDpaYVIknTCeUeOFNHYqLMqc7r7P7JjX1/j+eefP+n+Xbt2dZrI0ZvpTHEGZIFGRkZGRqaXIUkSih1foDi8vmVMUGKa/ACSf/tPgU5rXdFEYGJ/KnLMLlARo0cQM2ncOcfbE5BEkXWvvUN5Vg4Ah/5cz8BLZ+ETHdm9gfViaopLcPH1AcDWyZFR117Jhrc/BMDJx5uGqmqLQHO+I0kSTQeTzI5MSXva7K9ZuRzHSdPlUjwZmQ6koqIOd3dHq95j+fkVpKUWnPAYFxd7BiSEkJAYYtXcV6ZvcizTJCQkhAcffJDGxkYUCgVLliwhJyeHgwcPsmTJEt5++21iYtp/+FVQUMCjjz5KZWUltra2PPfcc8TGxvLTTz/x2WefIQgC/fr144knnsDBwYExY8YwZcoUkpOT8fT0ZN68eXz11VcUFxfz0ksvMXz4cBYuXEhsbCy7d+9Gp9Px2GOPMXbsWB555BGqq6vJzc3loYcewtPTkxdffJGmpibc3Nx4+umnCQoyZ1wuX76cl156idraWh5//HEmT55MeXk5S5cupbi4GEEQeOCBBxg9ejRvvfUWJSUl5ObmUlBQwPz587n99ttJS0tj6dKlGI1GbGxsePHFFwkNDSUmJob09HS0Wi1LliwhPT0dQRC46aabuOSSS/j555/ZvHkzNTU15OXlMWbMGJ566qnTfl9kgUZGRkZGplfRtPlLlCmrLduSIGCa9B+k4MHntK7a1pYp99xO1LjR/PPBZ0y9/84+01RXUCgYf+sN/Lz4SQAUSiUlhzNlgeYsqC4o4p8PPyNr+y6u+/Qd3AL9AUi4eCZHNm8jdspE+s+cikIplzNJJhONO7dSvfJH9JlH2p2jDgzGZc58OMnTfBkZmVNjMBhJSckneb+5bKmgoJJXX7sWf/8W56SExBDW/dXSs02pVBAT699cthRCcLDc3LcruP3jjHbH31sUyaq9lfy+t7LNvosGuzNrsDu3f5xh9e/jjz8bli9fzsSJE1m0aBH//PMPe/bs4aabbuKnn37irrvuOqE4A/D0008zffp0rr76ajZt2sR7773HHXfcwfvvv88PP/xgEU7efvttFi9eTHl5OePHj+eZZ55h4cKFrFu3jm+//ZYVK1bwxRdfMHy4ubSuvr6eFStWkJqays0338z69eaHcq6urrz//vvo9Xouu+wy3n//ffz9/dm8eTNPPPEEn3/+OQBOTk6sWLGCDRs28PbbbzN58mSef/555s2bx5QpUygtLeWqq67il19+ASA9PZ1vvvmGuro6pk6dytVXX80XX3zBDTfcwMyZM1mxYgX79+8nNDTUcu5vvfUWbm5urFq1isrKSubPn09sbCwA+/btY9WqVSiVSmbMmMGVV1550p9ja2SBRkZGRkam16BIWoFu93dWY6ZxtyOFjTrrNSVJshJiggcnmq2Q+4g4c4zQYYMJGzEUpUbN+FtuwDXAr7tD6nWUZWbzze33IxrNjTL/+eAz5jz7OGB2ZJr/2gt97vfmbBB1Ouo3/kXNbz9hLClqd45t3ABc5lwmOzLJyHQQtTVaXn7xF6ux5KRcK4GmX78ggoM9iY0LICEhhPh+gdjaapA5vxk1ahR33303qampTJgwgWuuuea0j921axevvfYaABMmTGDChAl8/fXXTJo0CTc3NwCuuOIKHn30Ucsx48ebS8cDAgIYMmQIAP7+/tTW1lrmXH755QDExcXh5eVFeno6AAkJCQDk5OSQl5fH7bffbjmmvr6ll9LUqVMBiIyMpKqqCoBt27aRlZXF//73PwCMRiN5eWZr+BEjRqDRaPDw8MDV1ZW6ujomTJjAM888w+bNm5k8eTKTJk2yOvcdO3bwwgsvAODu7s6UKVPYuXMnjo6ODBo0CMfmHoZBQUHU1NSc9s9UFmhkZGRkZHoFikOrUe5eZjVmGnUTUtTEc1p366dfYTIYGX3D1ahtzPX2ffUme/bTj6HSqLs7jF6LZ3govjFRFB4yW7pnbt1BRc5RPEKDgb77e3O6SKJIwZefUbT8R8Tadi5GBQH74aPNjkzRcV0foIxML6d1c19HJ1uuurqlDNfD04mAAHcKClqyL5KScpkxc5Bl285Owyef3U6Z3A+rWzlZpsus5uyY0zn2bDNmjmfIkCH8/vvvbNy4kT/++IMVK1bw2WefndaxKlWLnCBJEpmZmYjN/cVajxuNLQ5gmlZN4ZUnyDZtPS6KouV1bG1tLWOBgYGWfjAmk4ny8vI2x7f+XhZFkS+++AJXV1cASktL8fDwYN26ddjYtPRbEgQBSZKYMWMGgwYNYsOGDXz++eds3LiR5557zuq8jj9Pk8kE0O56p4v8yEJGRkZGpscjHF6Pcof1xYJp2DWI8dPPad2i1HR2LfuJPT+s4Otb7qEoNf2c1uvpHC/OaGtqyd2zv3uC6eFIkkTWjl2seel1y4WVIAhMuGMRAH7xMSx46/8s4oyMuZSu/sCBtuKMSo3TlJkEvP4hPg8+IYszMjJnwfIft3PrzR/wvzf/YOPGQ2zZnNrmpi8hMQSA0DBvLp4zzMqJSUbmRLzyyiv8+uuvXHrppSxdupSUlBTALHIcExxOxNChQ/n9998Bc4bKE088wfDhw1m/fj3V1dUA/PDDD2fcbPiPP/4A4MCBA9TW1hIdHW21Pzw8nJqaGnbvNjt5/vTTTzz44IMnXXPkyJF8++23AGRkZDB79my0Wu0J5997770cOHCABQsWcM8991h+Lq3XW758OQCVlZX8/ffflhKtc0HOoJGRkZGR6dEImVtRbv7Aasw06DLEhIvPaV2Twdq1qSqvgJqiEvzizs4FqjdhMhhIWvkH279chiSK3PjVh3JD21ZU5Rfy95vvcbRZvAobMdTSMNovLpoF/3sFv36xcsbMceWBAH5XXk3Nrn8BUDg44jTtIpxnzkHlduInwjIyMi1UlNeRnJzLoMFhuLo6WMZ9fV2tBJnq6kaOHi0nJMTLMjZr1hAunjMMFxf7Lo1ZpnezcOFCHnjgAX7++WeUSiUvv/wyAOPGjePJJ5/k5ZdfZvDg9vv8LV26lCVLlvDtt99iZ2fHc889R2RkJLfeeisLFy7EYDDQr18/nn766TOKKS8vj0svvRSA119/vU2mjUaj4c033+T5559Hp9Ph6OhoiftELFmyhKVLlzJ79mzALEwdK0Nqj9tuu43HH3+cd955B7Va3abR75133slTTz3F7NmzMZlM3HbbbfTr189SjnW2CNKZ5Nt0Mb0p/c7Ly6lXxXu2yOfZt5DPs2/R585TEhEyNqPc/D6C1PIEx2bYXOoHXAEdcHN8eOMW/n7zPbQ1tUSOG8Xspx7tMTfdnfl+Hvh9LX/9923LdsLsGUy9785Oea1T0RN/b6sKCvnihjstvWacfby5/ov3UGnOvldDTzzPs8HsyLSfmpU/Yj9kJM4zrYVST09HDj38ELZx/XGaOhOFXd+8Uewr7+epkM+z89HrjaSm5JOc1NLcF+CW2y5g4sR+lnk1NY3cfqvZKU6pVBAT488VV44hKurM+omdL+9pV+Ll5dTdIfQpjrlLna8W33IGjYyMjIxMz8JkRMjcjDL5V4QaaztQU+xUbCcvor68/gQHnxnRE8cSmNifzR9/ydibFvYYcaaziZ8+hd3fr6Aq3/zzPbovGYNOZ+nBc75h0OmoyM7FN9acQu0W4M+gS2ex58dfEBQKQoYNwqQ3nJNA09uRTCYa/t1Kzcof0WeZHZkMhQU4TbsIodWTTUEQ8Hn4ye4KU0amxyNJEvn5FWZBJimXtLQCDIa2ZSTJSblWAo2Liz1XLBhNUJCn3NxXpkt5+eWX2bZtW5vx/v378/zzz3dDRH0bWaCRkZGRkekZGJpQpK9DcfB3hIaKNrvFiHGIoxeds4hi1BtQKBUWG2R7N1emP/Sfc1qzt6FUqZhw+4388cJ/GXH1FQyaO/u8bB4sSRLp6/9h80dfoNdqufGrD7FzNj8JHbHwCupKyxixcAFe4aHdG2g3Iuqamh2Zfm7jyGQsK6Fhx2Ycx0zsnuBkZHoZkiTx8ENfUZDf1kb5eDIzituUEc65RO4pI9P1LF68uEtf76uvvurS1+tpyAKNjIyMjEz3oq1FkbIaRcoaBH1Dm92SoESMn444fCEI597bfusnX1JwIIXpD99zXjd4DRs5jEXffoKt04nrr/s6KWv/Zu0rb1q2//3qOybeeTMAto6OzHryke4Krdsx1dVSu+Y3alf/ilh3YkcmtX9Q1wcnI9PDMZlEMjOKycurYMrUAZZxQRDw9XU9oUATFu5NQkIICYkhREX5nTdZnTIyMi3IAo2MjIyMTPdQV4bi4G8o0tcjmPRtdksqG8SYqYj9LwJHzw55ycKDqexZvhIkia9vvZcJd9zEwDkXdcjavQ1BENqIM0Wp6XhHRaBU9d3Lg9ZPpGMmj2fHV99RU1QCQPrGLYy5aSHqZhvP8xFDaTG1q1ZQt34Nkk7XZr+gVuM4YSous+eh9g/shghlZHou2dmlrFyxk4OH8mhs0CEIAiNGRuHo2PKZkpAQwp7dWYC5bCkhMYSEhBAGJATj7Nw3ezbJyMicPn33CkxGRkZGpmdSeRRl8kqErK0Ikthmt2TjhNhvJmL8DLDpuOwOSZLY8PaH0Nwb32QwYO/i0mHr92ZqikvY/OHnHN64hYl33czguefmkNUT0TU0svPbH2msqmb6w/cAoNJoGHfz9fzxwn8ZNHc2I665/LwVZ/QFeVQv/5aGbZtAbPt3aXZkmoXzzItlRyYZGUCnM2AwmKzEF5PRxM6dGZZtSZI4dDCPESOjLGODB4ej1epJHBhKcLCnnCUjIyNjhSzQyMjIyMh0CUJxGorkX1Dk7W13v+TgiThgNmL0JFB3/E2yIAjMenIxf/7fW+TtTyZ64liiJ47t8NfpbRj1Bpbd+SCNVdUAbP9iGXFTJ1l6sfQFMrbuYN1r71jOccCs6fjHxwIQNWEMN8ZF4+zj3Y0Rdj9iXS0NWza0GVd6eOIyay5OU2b0WUcmGZnTJT+/gqT9OZbmvjMvHMSCK1u+R8IjfHB0tKW+vskylpVVYiXQeHg6cfGcYV0at4yMTO9BFmhkZGT6HMc31ZPpRiQRIW+fWZgpSW9/ilsQpoQ5SOGjQdG5X0sufr5c9uqzHPjjTyLHjurU1+otqDRqhsy/hM0ffg6Arq6ezK076D/zgu4NrAOxdXS0iDMAm979mAVv/R+CICAIwnkvzgDYxvbDJiYeXXoKAOqgUFzmXIbjmIkIfbjkTUbmTPjis40cOpRn2U5OyrUSaBQKBcOGR1Jb29jcSyYUHx85U1NGRub0kb9xZWRk+gySJLH+zfdIW/8PAy+dxZgbrunukM5fRCNC1jZzKVNVXvtTfGIREy5BChoEnSioGZqaaKqtw8nbCwBBoSBh1oxOe73eyKC5F5P822psnZyYcMciAhP6nfqgHkxFzlF09Q34948DIDCxP5HjRpGxeTuOnh4kXnyhudTtPBNyRV0T9Rv+pH7rJvyWvoSgtnbucpkzn9rfV+AyZz52A4fKQrfMecex5r7JybmkHMpn8aOXYGPT8neSkBhiJdDk5JRRU92Ai6uDZezmW6Z2acwyMmfKv//+y9tvv33euyX1VGSBRkZGps+QtX0XSb+uxsbRgdjJE7o7nPMTow5F+noUB35DaChvd4oYNNgszPjGdklIWz7+ikNr1zHh9pvoP/MC+aazHVQaNfNfewEnL08Exbk7ZXUXjdU1bP/iW5J/W4OLvy/XffI2ymYRYtwt1+MVHsbQyy9FbXd+9Zkx1dZQu/aYI1MtAPVbNuA0aZrVPPuhI3EYJmeWyZxflJfXsmvnEbZuSbc09z1GWmoBiQNDLdsJiSEs+3YLLi72DGh2W7KxVbezqoyMjMzZIQs0MjIyfYbk31YDMGzBPHJ27sEt0B+FUonYTsNLmQ5GV48iZQ2KlNUITXVtdkuCAiliLKYBF4N711lb5ycfYt+K30CS+OvVtyg4cIgZi+/rstc/XXQ5WVR9/Qk2UbG4XnYVglLZ5TEcX+ZjMhioL6/Axc+3y2M5W3Z+8wNJK/8AoDq/kKSVfzD4sjkAuAX4M+q6K7szvC7HUFJM7aqfqVu/Fklv7chU8+tyHCdMtRLkZPFS5nzAYDCSciif5KRckpNzKSho3/IazCVMrQWa4GBPXnz5aoKCPFEo5L8XmbPjjW2HrbZHBnkwMsiDP48Uk1JWaxl3tlFz45Aw8msaWX4on3gvZ6ZF+bIjr4IdeRVWa9w7OvqM48jOzmbp0qVUV1djb2/P448/TkJCAuXl5Tz++OMUFhaiUqm47777GD9+PG+99RaFhYVkZmZSVVXFFVdcwaJFi87uhyBzQk5LoHn77bdZvdp84zNhwgQefvhhAAwGA4sWLeKOO+5gxIgRnReljIyMzCmoLS4le+ceALZ8/CVR48cwZP4l1JaU8s0tzzLhzlsJTOzfzVH2QRoqUBxYhSJ9HYKxrSWvpNQgxkxG7D8bnLy6PLx9P/9mcW0CiBg9sstjOB00AUFIokj18m8wFBfiddeD3RaLJElkbvuXf97/FKVazcKP/oeiGwSj00GSJESj0ZIlM/zqyzm4Zh36hkYA8pIOWgSa8wlddgY1K3+kYdtmaMcpTeHgiP3w0UhGI4JG0w0Rysh0H01NBl55+ZfWXw3t4uPriourdWNsQRAICen67zIZmc7goYce4pZbbmHatGns37+fe+65h7Vr1/Lss88ycuRIbrjhBvLy8rjyyiv55ZdfADh48CDfffcdoigyd+5cRo0aRb9+vbssuqdxSoFm27ZtbNmyhRUrViAIAosWLeKvv/4iIiKCxx57jJSUlK6IU0ZGRuak2Lk6M+3Bu0n6dTUl6UdImD0DXX0DKx57horsXH56+AmmL76P2MnjT7yIaETI248iYyNCfhIo1YihI5FiJiN5Rpx3/SpOSnU+yuRfETI3I4imNrsljYPZKjtuBtg5d0OAZi5c8iA7vw3h36++J2rCGKLG9czyDUGtxueBJRQufYCGLRsQ1Gq8nniiW2LZ/f3PlobBAAd+/5PEi2d2SywnozjtMJve+wT//vGMu/k6AOxdXRhx9RUc+H0N42+9gYgxPVOQ6wwkSaLpwD6qVy6nKbl9pzSlhxcus+fiNHkGCju7Lo5QRqbrqKvTcvDAUZKTc9FoVNxw42TLPicnO8LCfcjKLLE6xs5OQ3y/QLm5r0ync6Jsl2lRvkyLapu1Guhib3XMsYybc6GhoYH8/HymTTOXug4cOBAXFxeysrLYsWMHzz33HABBQUEkJiaSlJQEwKxZs3BwMPdcmjx5Mjt27JAFmg7mlAKNl5cXjzzyCJrmJywREREUFhayb98+Fi1axBdffNHpQcrIyMicCrWtLf1nXkD/mRdQlpmNZ1gIm97/lIrsXABMBiP/fvMDUeNHo2ztSCJJUJGN4sgmFFlbrMtzjDqU6esgfR2SaxBi9CTEyHFgd/5etAmlh1Ekr0SRu6vd/ZKDB2L/ixBjpnaKVfaZolSpGHXtlUSOGYmj57ldzHQm+twsyj95F5qf6NZv+JNcF0fsr7q5y8tO4qZOZMeX32FoMtvE7ln+Cwmzpveo3jT/fPAZu7//GYDiNLMg6+LrA8DgebMZPG+2JaumryNJEg3bNlGz8kf02ZntzlEHh+I6Zz4OoyfIjkwyfZ5fV+7i+++2WjJk7O01XHvdRJTKls+wxMRQsjJLCAv3JiEhhPET4vDyckGl6pnZgjIyHY3UTgqZJEmYTKY2+46NAyhbZdSKomi1LdMxnPJqKyoqioEDBwKQk5PD6tWrLWVOU6fKXcplZGR6Hl4RYQgKBWNuvMbyBN3B3Y1Ln1/aIs40VqE48CuqFQ+iXvkIyhP0TjmGUJ2HcueXqJbdhnLdqwhH90I7mSN9EklCyN+P8venUP22pF1xRnIJwDjuDozz30LsP6tbxRmDtomsHbutxrwiwrBz6b5MntYYqyooffNlTPUtv2+akHCcZ1yMIf+oZaz0l5+p/Orjdi+iOhNHTw+GXTkPQaEgcc6FLPjfKz1KnAHwDAux/NtkMLDl4y8t20q1+rwRZ8BcclH31+p2xRnbfon4PPYcAa++h+P4KbI4I9OnKC+vZf3fBygvr7Ua9/N3sypfamzUk5FRbDVn2vRE3v/wFp5/4SquWDCGhIQQWZyROa9wdHQkMDCQP//8E4D9+/dTXl5OVFQUI0eOZPny5QDk5eWxd+9eix6wbt069Ho9NTU1bNiwgbFjx57oJWTOEkE6zSu/I0eOcOutt3L33Xdz6aWXWsYXLlzIXXfd1Sk9aIxGk/xhKSMjc0o2fvwNwYn9CBua2CbbQDSZWPvmxyTMmIR/dCiGI9vRH1hHwZ5duDmCRt1OdoJCiSp8GJK2FlPBics4BUd3NP2nokm4AKV7YEefVrcjiSYMaZtp2vEjYmlWu3OU/jHYjLwcddRIBKFn3MT/+sKbbF+2goGzLmD2I3dj30OEGQBtTjbpix9AX1KC08BBxLzyGopWPUDK/1xD1ovPWfXN8V94HYE33dKlceq1TVQVFuMTEdqlr9seosnE3l//xD8uEv/YKPOYKPLulbdRkHKYuImjmXn/bXiFdV3z6Z5G9c5/Ofzw/eYNhQL38RPxXXAVjrFx3RuYjEwHYzKJfPD+X+zenUneUXOT1Dvvms4llw63zGlo0DH3kv9DFCVUKgX9+wdz7XUTGJBw/n5GyMi05pjN9lNPPcVTTz1FdXU1arWaJUuWMHjwYEpKSli6dCmFhYUA3HPPPUydOpW33nqL3bt309DQQH19PTfddBPz58/v5rPpe5yWQLNnzx7+85//8Nhjj3HRRRdZ7etMgaas7MRPs3saXl5OvSres0U+z75FXzjP6sJiPr3mZgDcgwOZcPtNhI0Y2jJBknDT51OzazWK7G0I+kYqakS+/lOHi6PAZRNtcLQzizSiZzhS5ATEiDFg23xTX12A4vAGFBmbELQ1J4xD9IlDjJ6EFDay27JHOuz9NOpRHNmI4sCvCHWl7U4RAwc2W2XHdXlvnpOd59F9ySx/4HHLtndUBFe//3qPcKfRHkqi9JVnEBsbLGNO02fhueguq3m161ZT8cGbVmNuC67DdV73OhA1VlVj7+ba4eue7P3M23+Aje9+TFlGFoGJ/Zn/2guW97Io9TAGrZbgwYkdHlNncK5/n8ccmTThkW3ssSVJoujx+9CEReIyay5qP/9zDfes6QvfK6eDfJ6djyRJVFc34ubmYDX+4P1fUFhYZdkeNCiMhxZbNwP//fe9+Pm6Et8vEFvbUzfCPl/eTzi/zrWr8PJy6u4QuoS33noLgLvvvrubI+nbnDLXtaioiDvvvJPXX3+dUaN6ZnNFGRmZ85cDv6+1/LvyaD4qGxvzRl0ZioxNKDL+ob62mGO5eA1aiR836GnSQ1OlxFd/Grj89hk4j7iwfftn1wDE4dcgDl1gbiB8eD1C3l6E45xRFCWpKEpSkXZ8ihQ22izWeEf3rsbCugYUqWtRHFqN0NRWjJIEASlsNKaEOeAR2vXxnQbFqelW26Ouu6pHiDP1mzdQ9s5/wWS0jKl8/XG5aG6buc5TZyIZ9FR++p5lrOq7LxA0Glxmz+uSeFuja2jk329+YN9PvzL3lacJShzQZa+967ufKMswZ2/lJx0kc+sOIsear0X84s7cUrQ3oss6Qs2vyy2OTCpff3O5Uqu6f0EQ8HvutR5XiiYjc6a0bu57IPkooiTxzruLrD7HExJDrASa7OwSRFFE0er3/6KLBndp3DIyMjIdxSkFmk8++QSdTsdLL71kGVuwYAFXXtm9T/JkZGRkAOrLyi3/dgv0J8iuHOUfT6MoOtTu/DqthL7lHhn7wAjsJl0PtqfIelGokEKGYgoZCo3VKDL/QZG+AaGmwGqaYGhCOLwexeH1SC4BzY2Fx4O961meYRfQUIni0B8o0v5CMGjb7JaUasSoSYgDZoOzTzcEePoMv2o+fv1i+fOV/+HfP46I0cNPfVAnIkkSNb98T9W3n1uN20TH4bP4KZTO7Tecdpk5BweNQN7771rGKr/8CEGjwXn67M4M2QptbR1f3HAHjVXVAGx652Oueu+1TrPdbqqrR2NvZ1l/wm03krt7H5IootRoqCur6JTX7WlIkkRT8j6qV/5I04F9VvuMxYU07tqOw0jrun9ZnJHpjZhMIpkZxSQl5ZKclENWVkkb++u8o+UEt7K2ThwYSnp6YbPbUghRUX5W4oyMjEznIGfOdA2n3YOmO+hN6XfnS7qgfJ59iz5xnqJIzf5NHFzxM25iCYnhJ5nqE4MYOYFKmzBWPPkKkknkyrf/7+zLNiQJofSIOasmexuCoan9aYICKWiwOasmaBAoOqdR5xm/nzWFZqvsjH8QRGOb3ZLGHjFuOmK/mWDn2nGBniOnc556rRbJJGLj6HDSeZ2JZDJR8fHb1K1bbTVuP2IsXnc/hOJYttcJ8PJy4vC771P9w1dW456334fT5OkdHu+J+P25/yN9/T+W7dlPPUrU+NEdtr6XlxPFRVUk/foHO778jrE3LSRhdout999vvo+uoYGxNy3E2ce7w163qzmd31vJZKJh+z/UrFyOPqd9RyZNSBhu19yE/cCh7e7vbvrE98ppIJ/nuXH0aDk/L9/BwYNHaWzUn3TulVePZfbszv19P1/eT+j557pq1R+sXr2G2267hQED+nd3OKfF+VLiJNM1yO38ZWRkeieVR80lTJlb8GysYmJo+9MkRy9sE6ZSHzAKnH0BcAOufOsVmuobrMSZrO07KcvKYfhV80+vLEYQkHyiMflEw8jrEbJ3mPvVlKRaT5NEhKO7URzdjWTnghg5ATF6Irh2T2NhoSwTRfJKhJx/EWjHZtHeDbHfRYixU0Fj3w0Rnhn6xkZ2//ALwxbMRd2cCaWxs+vWmEStltLXX0C7z9rxyvmiS3G/9ubTznZwvewqJL2Oml9+sIyVv/8GCgcHHEZ0jXPCuJuvI2PLDuxcnBm76Foix47s0PVFUWTZnQ9SesQsSGz97BtiJo23iGuT776lz2eHiE1N1G/4k5rffsJYVtLuHNv+A3GZcxl2iUN6RNmejMzpotMZ0OkMODu3fJ8oBIGdOzNOeIxSqSAm1p+EhBCGDonoijBlupDq6mpSU9NITU0jOzubZ555yvK5VlxczL59+zlw4GCvEWhkZDoSWaCRkZHpPTRWo8jaguLIP+hLslEqQVC0vVGR1LZIoSMRoyYg+cZh5+1C/XFPi+xcXLBzaSkvKU4/wqpnX8HYpKO2uJQp995+ZmUcaluk6ImYoidiqik0N9k9sgmhscpqmqCtQXngV5QHfkX0jm5uLDwaNJ0sKEgSQuEBFMkrURQeaH+Ksx+mhIuRIseDsvfYFP/zweck/7aatL83Mv3hewgY0K9b4zFWVVDy4pPos1vdfAgC7tffisuFl5zRWoIg4HbVDUh6PbV//AKAOiAIm+iuc+dx9vHm0hefxC8u2iKAdSQKhYKIMSMsAo22uob9v6xixDVXAH27dMdUW0Ptml+pXfMbYl1t2wmCAoeRY3GZcxk2EedHzx2ZvkF+fgX79+WQnJxLeloBkyb35/obJln2BwS64+7uSGVlvWXM19eVhMQQEhJCTru5r0zPR6vVkp5+mJSU1GZRJpXCwiKrObfddisBAebm5nFx5u+31NTUNmvJyJwPyAKNjIxMz0YUEXJ2mMWOgiRLc95tB4yk5BhJiFCRGKnEyVGJ5J+AGDkeKWTYGTkpaWvr+OWxZzA26QBz42GPkCAGXzbnFEeeABd/xKFXIQ6+AqEgyVwClbsHQTJZTVOUHkZRehhpx+dIYaPMYo1PbMc2FhZFhNydKJJ/QVHevlW26BludmQKGQ697Gb46N4kkn8zlxBVFxTxx/OvcsOXH6LSdI/ApM/LpfiFJzCVt7hfCWoNXvcsxmHEmLNaU2gWdySDAd3hVHyfeAGli2sHRXx6BA9KsNqWJAmjTndWgk19RSU7vlzGiGsW4OTlAcDQyy/lwKq16LVaRlx9OYPmdl2fne5En5dD9Y/ftBkX1BocJ08zOzL5dp8jk4zM2fL9sq3s2dPynZOclGu1XxAERoyIoqy81tJLxtu7/Z5cMr0Ho9FIZmYWqalmMSYlJZXs7BxEUTzpcampqa0EmljCwsIICAjoipBlZHocskAjIyPTc2moQLnhTRQlaVbDRpPEgSwjWh1sO2jkSKUzCz94HRzcz+pl7JydGH71fDa+8zFIEiFDBpJ4yUXnHr9CiRQ0GFPQYNDWtjQWrs6zmiYYdQhHNqI4shHJ2Q8xeiJi5ISzPh8ATAaEI5tQHvgNobao3Smi/wDExEuQ/Pr3LrepVpiMRuzdXC1NbKfed2e3iTMAFZ9/YCXOKJxc8HnkKWzPMeNFEAQ8Ft2J1KRFYd99fXUASg5nsPHdj3H09OCiJQ+d9nEmg4Fd3/3MrmXLMTQ1YdIbmL74XgDUtrbMeuoRXP18O8XKu6diG5+ATWQMugyz+5jCwRHnGRfjPPPiLhfhZGTOhNbNfQ8cyGXxI5fg4NAi2CYkhlgJNMXF1ZSW1liJMAuvm9ClMct0PA0NDWzZstUixhw+fAS9/uQ9hY7H19fX6hh3dze++eaLjg5VRqbXIAs0MjIyPRLh6F6U/7yDoGvbyC692AatrqUhb/+5l52bmAEMnnsxzt5e7PruJ2Y99ShKVcvHo1GvR6U5x1RrO2fE/rMQ+12EUJ5pdnrK3NrGNUmoLUK5exmKPd8hBQ5qbiw8BJSn+XGtb0SR9heKQ3+0Ka8CkBCQwkZgSrgEPE/SUbmXEDZ8CNd9+jbr//chKhsNYSO6t3Gq990PUfjYvRjLSlD5+uP72HOo/TomA0JQKBDaEWckkwlDYT6aoJAOeZ2TsfPbH9nyyVccs1kZNHc2/vGxp3WsoFCQvnEzhibz3+6hP9cz8NJZeHkNAjjtdXobkiShTd5L5o5NON30H4RWny2CIOAyZz4VX3yIy+y5OE2ajqKb+yfJyJyKoqIqnnh8mVVz30MH8xg+IsqynZBg/jxycbG3lC05Ocm/272Z8vJyUlPTCAwMJCwsFIDa2jqefvq5017DxcWF+PhY4uLimv+Lxd3drZMilpHpncgCjYyMTM/CZESx+1uUB1dZDUtKNVLIMMTICQTaBzM2agMHVq2lobKSuGmTTrDYmRE5dhQRo0dY9bw4vGkr/7z/KXNffhr34A5o6isISF6RSF6RiCOuQ8j519xY+DhbcEGSEPL2osjbi2TrjBg5HjF6ErgFtb+uthrtpp9Q7fkNQd/YZrekUCFFTcA0YDa49K2SCTsXFy564iFMxrZOVF2N0tUNn8efo/LLj/C684ET2mh3FJLJRNnbr9K4azu+T7yAbUx8p76eX79YWnvgbv7gM6548+UTzi/PzsUzzHyjplAqmXj7Tfz08FIAPIIDMRm6/z3rLCSTiYZt/1Cz8kf0ueZMAkX8QBzHTbaaZz9sFPZDR1oJNzIyPQGdzsDOfzOorm5k0OAwy7iPj0ubRtVJSblWAo2Prysv/981BAZ6yE2teyGSJFneN51Ox4IFV1NSYs4Ove66hdx6680A+Pr64ObmRlVV2wdCtra2xMREEx9vFmLi4+Pw8/OTfx9kZE6BfDUgIyPTc6grRbnhDRRl1s4OknsIxkn3gatZWLAHhl95GcOumEvl0XxsHR07LITW4kzWjt388fyriEYjP9z3KPNeeQaviLCTHH2GqGyQIsdjihyPqba4pbFwQ4V1TE21KA+uQnlwFaJXJFL0JMTwMWaHpdpiFAd+Q3FkIzqTgeMveyS1HWLsBYj9LjznLKOeQlN9AyufeI4xNy603PwDVllPXYFkMiEZjW3ssjUBQfg++kznv77BQOmbL9P47xYAip9fgt+TL3VqM9mgxAFEjhtFxubthI0Yyvjbbmx3XnVhMVs++pzDm7Zy6UtPETZ8CAAhQwcRP30yfnGxDLho2pk14u4liE1N1G1YS+1vP2EsK7XaV7PyRxzGTrK6QRH64M9ApnciSRJ5eRUkJ+VamvsaDCbCwr2tBBqFQkH/AcH8u+MIAH5+bnh6trUZDgry7LLYZc4enU5HRkYmqamppKSYm/gOHz6M++67BwAbGxvU6pbS4dTUlrJzQRCIi4vl3393EhERbiXGhISEoJKFZxmZM0b+q5GRkekRCNk7UG55v032hyluGuLwa0HVtsRIUCjwCA3ulHgkUWTbp18hNmdlNFZVc3D1OibddXOnvB7OvohDFiAOuhyhMLm5sfAuBPG4xsJlGVCWgWLHF0hekQglqQhSO1bZti6I/S9EjJ0GNt3bs6Sj+ePVd8nc+i/Z/+5h1HVXMmzBvC6/0T9moy2o1Hg/8Hi33GRLkoTY2OKAImkbKX72MXyfegWb0M4rXxt/640kzJpB6LDB7e5vrKrmixvvxNTcU+Cf9z4hZMhAy3s0Y/F9nRZbd2KqqaZ2zW/UrvkVsb5taSYKBeqAYCRtY7ulajIy3UFdnZaDB46SnJxLclIuVVUNbeZkZ5VSU9OIi0uLTfaUKQPo1y9Ibu7byzCZTBw5ksnWrXssrkoZGRkYj8tAdTzuwVd8fBz5+QUApKWlW2XYLF78IM7Oztgc97DibKhp0nEoJ5/DRaWMiQohKrBvZfzKyJwOskAjIyPTvRj1KHZ+hTJ1rdWwpLbDNO52pLCRljGDTse2T7+m34ypVpkTnYGgUDD35af5afGTlGVkET1xLBNubz9boENRKJACB2IKHAhNtSgyt6A4vAGh8jgHDJMeoTilzeGSkw/igNmIURPbFbV6Ozm79rLrp98BEI1Gkn5dzcA5F2Hj2HU3vMfbaFd+8QHuN9ze5WnbCo0Gn4efoviFJehSDwIgNtRT/Oyj+D31Sqf1pHH198XV39dqTDSZLAKMvZsrsZPHc2jNOgAqcvM4ujfphIJOb8dQXEjNqp+pX/8nkqFtc8xjjkxh111Lndq5GyKUkWmf1X/s5euv/qEdjb8NmRnFDB7SIvz2HxBM/wGd84BEpmOQJIni4hKLtXVqahppaWk0NmpPeeyRIxkYDAZL5szUqVMIDQ0lLi6WuLhYq+87Ly+vs4rNIEpolC1Zy+tW/8EW53DzAw87T5SpR2SBRua8RBZoZGRkuo+aQlTr30CozLEaFj0jME26F5x9rMaPbNrKnh9/Yc+Pv+DfP47pD92DW1Dn2TDau7ky/7Xn2f3dz4y67kqrLI3qwuI2N6kdjq0zYr8LEeNnQkU2imONhfVtn3BK7iE4jF1AjcdAUPTdkgn34EAiRw4hY8ceAKY9eHeXijPt2WjXrVuD88xLOqwZ8JmgsLXF95GnKX7ucXRHzGnnYm2NWaR5+tVOj0k0mVj78hukrtvIDV99gFuzTeqYG6/h8MYteIQGM+GOmwjo37m9cboDXeYRalb+SMOOLSC1tZBVODqZHZlmzEbp4oqtlxN1Ze1k1sjIdDJlZbUcSM4lLj4QP7+WhqyBgR4nFGdcXe0ZkBDC2LExhIR64+xs3/5EmR7H+vUbWL16LSkpqe32hjkZDg4OxMbGEB8fh06nswg0Y8eOYezYMWcdU5NWy860DFLKqqnQi+jtnfGx13DHyH6WOZ6enjQezcchwPxwobC+6UTLycj0aWSBRkZGplsQMreg3PohgsH6C9jU7yLEYVe361qU/Nsay7/LMrKx74LO/7aOjoxddK3V2P6Vv7Px7Y+Y+dgDxEwa1+kxIAjgGY7oGY44/FqE3J3mrJqSdCTvaMSEi5ECEtF4O0MfvwF09vHmxg9fZf3ny6nKK+jSrAztoSRKX3kGsbFFIDtmo90d4owlBnsHfB5/juKnF6PPzgTAVFVJ0dOL8Xvm/1B7d46QWFdaxs+PPEVFzlEANr37CZc8/wQAjp4eXPnOq3iEBFn1depLVHzxgSVzqTUqL2+cZ88zOzLZ2rZzpIxM5yNJEl9/+Q/7k3IoKjTfpF+xYDRzLhlumRMTG4BGo0KvN6JSKYmJ8Tc7LiWGEBzsiSAIeHk5UdbHv1d6I01NTRw+fISUlFSOHDnCY489grL5IVJ+fgFbt2475RpqtZro6KjmrBhz75jg4CAU5/CZLUkSJbUNlBskYt0dUDWvdcfVV6AePB7/qXMAUAAVRoNVqVREdAwNW7+3CDT1xtNI7ZKR6YPIAo2MjEzXYtSh3P4ZisPrrYYljQOm8XcihbRvk6zXajHqW8oHYiaPx8ah65/opfy5nvVvvg/A78/9H3qtlgEXTuu6AFQapIixmCLGmt10zhM3hNYlNIIgkDBrRpe+fv3mDZS9818wtdTpq/wC8H302W4VZ46hdHDEd8kLFD21GENeDgCmijKKn3kUv6dfQeVx5inop8LBw7rp9NF9SdSVVeDk5QHQ6WWI3Y3rnPmUtBJoNKERuMy5DIdR4+XGvzJdiiRJVFbW4+HR0qhXEATS0wst4gxAclKulUCj0ai4+ppxeHo6ExcfiK2tGpmeh9FoJCsrm9DQEDQac+ny77+v5r//fd0y55prrrZYX8fHx7VZQxAES4nS8OEDCQoKIzIywqr575kiShI5xWUcyMkjt6qWKlGB6OSGwsZsp35z/yD8Hc0idXhUFGn52dbHq9TU6o242JhjcHJxYYCzBpeKLOKDA4idPfGsY5OR6c3IAo2MjEzXUZWHav3rCNX5VsOidwymSfeA44kdHzR2dlzzwRsUpx8h+bc1JF48s7OjbZfcPftbNiSJmqLibokDOG/Emab6er67+2EGz53NgC4WZiRJouaX76n69nOrcZvoOHwWP9XpNtpngtLZBb+lL1L05EMYCs1/Y8aSIoqffgTfp/8PlVvHungplEqmP3wPPz28FH2jlphJ41Gq+pYwITY1Ubd+DSp3TxxGjrXaZzdoGOqgEJSu7rjOmY9twiDZPlamy2hoaLK4LSUn5aLTG/ngw1tRturpkZAYQlZWiWU7L78Co9GEqtXf6QXTErs0bpmTI0kSBQUFlga+KSmpHD58BJ1OxwcfvMuAAf2BtiJMamqqRaCJjY3Bz8+X2NhYi6tSTEwMDs0Ptc42K6q6spJ9Bw6yv85ADWokZ3cUGhsQnMHd3F+rde5NUYPOItBExMSx56efAWgqK0ZZV4mfvQ0qhbUz5m03Xn/GccnI9DVkgUZGRqbzkSSEIxtQbvsUwWTdRNOUeAni4MtBcXofR74xUfjGRHVGlKfF9IfvQaXRcOD3tQy8dBZjblzYbbGcL2x652Mqc/NY9/q7HP5nG9e8+gTQ+Q2QJZOJio/fpm7daqtx+xFj8br7oTb22j0BpasbvktfoujJhzCWFAFgKCqwNA7uaEHJNzaa2376CpPBgMa+7/SoMNVUU7v6V2rX/oZYX4c6IAj74aOtyrUEhQL/515DITsyyXQDhw7l89b/rD+bMjOKiY5pyehLHBhKUlIOCQkhJCaGEhnlayXOyHQ/FRUVVmJMamoadXXtiyepqWkWgeZY9ovBYMDZ2ZnGxhYHTEdHR3766YezjklvNJGSc5QmG0d8nB0Ia3bv2rDmDz7//DMGP/M+AnAqObqooQkwf+eMmTQZ34AAQnxUhAwbbSnHkpGRaYss0MjIyHQuei3KbR+hyNxiNSzZOmOacBdS4MBTLqGtqcXW2alHPJ1WKJVMvf9OggcnEj1hjCUmSZLI2r6L8FHDekScfYW8/Qc4tPZvy3Z1fiEaOzuaGk0nOercOWajrd23y2rcedZc3Bcu6tF9VVQenvg++RJFSx+yNDM25OVS+t/n8H3qlQ7//VSq1SjPIU2+J2EoLqTmt5+p32DtyGQoyKNxz784DBtlNV8WZ2Q6k/LyWnOWTFIuAweHMXFiS0PV/v2DUCgERLGlT0dycq6VQBMT48/zL1zVpTHLnJiGhoZmRyWzq1JKSiqlpWWnfXxKSqrl32q1mpdffoGgoCD8/f3O+nPdIIqUNOjYuHMPhfVa6pU2CK5eKFQqoJpEncki0ETExqKvrsRQV4PayVrsl0wm9OXFaBpr8NAoGJ3QnwEhLaW1wWHhBIeFIyMjc2pkgUZGRqbzKM9GteENhNoiq2HRrx+mif8B+9Nr8rvisWcwNDYyYPYM4i+YjK2TY2dEe9oIgtCmOfD2L5ax48tlJMyeyZR7buvRN/C9AUkUERQKAgbEM3bRtWz7/FtEo5FpD/3H3HuosfOaVh5vow2AIOB+/a24XHhJp71uR6L28sFv6UsUPfkgpqpKFA6OuF+zSBYPT4Au83CzI9PW9h2ZnJwRG+q7ITKZ8wmdzkBqSr6lbKmwVf8Yg9FkJdDY29sQFeVHZmaJpbnvkKHyDXBPxGg0cv31N5GdnYN0Op7mzSiVSsLDw4mLM5cqJST0t9o/cuSIM4pDr9ORk5lBBnpyHIPIqG6gtFGPBODgAw5wfF5LUYPO8u+I6BgA6nKO4ODli21THV42SiJ9PEiMisB7TOwZxSMjI9M+skAjIyPT8UgSitS1KP79EkFsaaoqCQLioPmIiXPhNAWM0owsilPTAdj49kdUHc1nyr13dErYZ8vuH1aw48tlACT/thqDVsuMR++Xb4bPEEmSKDiQwq5ly/EMC2HcLdejUCoZftV8wkYOI3f3PoIHd36/BElvwFhZbtkW1Bq87lmMw4iztxjtDtR+/vgufYnS157H6+6HsQmL6O6QehSSJKHdv4ealT/SdCip3TkqLx9cZs/DcdI02ZFJplMoKKhk395skpNzSU8rwGBoPzsw5VB+m/4xt90+DRdXB7m5bw9h3bq/SUpKJiUllfj4OB544D4AVCoVgiCcUpwJDAyw9IyJj48jKioK27P83KmqrWN/RhYZxRWU1dWTv/JrjmZnYzIZ8fH3Y+bLH1PSqD/1OjoDJlFCqRBwcHTi1Y8+Iyg0DGeXntN/TUamryELNDIyMh2LrgHllvdQ5Oy0Gpbs3TBN/A+SX78THNg+jVXVKNUqTAaz0DPgoukdFmpHobG3Nzfsbb748omNksWZs2DNi6+Rum4jAAUHUxh+1XxsHM0lJF7hoXiFh3ZJHGofX3wffYaiJx9C0Nji88hT2Ea3dcXoDWgCgwl49T05o6sVktFIw7ZNVK/8EcPRnHbnaEIjcLlkPg4jx8mOTDKdyoqf/2Xb1vSTzrGz09CvfxB1dU24ubWU1fn4unZydDLHI0kSJSWlpKamotfrmT69xcVx+fKfSU4+AJizZloTFxdLZmaWZdvd3d1KjImLi8XZ2fmsYxIEgV3btvD3H6soFOzwuOgqwB7c7cEdjhYUYmp2ISwpLMJN2VYsMumaMFWW4GDQ4udgS7+QAIbEx6FUtFzP9EsceFYxysjInD6yQCMjI9NhCKVHUG54A6HeuqZaDByIafxdYHfmFx+hwwYz7eF7WP38f/GNjcY7qudlASTMmo7GzpY1L73OqOuuZPDci7s7pF5Ba+tsgICE/haBRt/QSNKvfzD8qvndEptNRDTeDy1F7e3XI2y0z4UTiTONu3dglzgEoY/0jzldmlIPUvbW/7W7z3bAINmRSaZDMZlEMjKKSU7K5UByLvc9MNtKZElIDGkj0AgChIX5kJAYQkJCiNzctxupra0lJcXcM8bcNyaNyspKAPz8fK0Emri4OItAk5WVTVNTkyUDZsKE8bi6ulrEGG9v7zP+jJEkifziEpKzcskur8Y1PIYKo8TUYE/6eThRmJfHpj/X4hAU3izQtOAQGErtkUMta5UXEeLki5+DDX6ONrgJIl6OdtjaDDirn5OMjEzHIQs0MjIy544kojj4O4pd3yJILenZkqBAHHol4oDZIJz+E/yGyiqUGjW2juZeM3FTJlJXUoZneNgpjuw+YqdMwCsiDPeQIMuYJIrsXLacgXMusmSCyIBeq+Xg73+yZ/kvzHryUfziogGInzaJ7Z9/Q0NlFRGjRxA0qAvKmSQJ7d6d2A0e3uZi2T5xSKe/fndR89tPVH75EfbDR+N932MIqvPncsC2fyKasAj02ZnmAUGBw+hxuFx8GTbh3ecQJ9P3qKioY/FDX9HYqpTkQHIu4yfEW7YTEkIAcHW1JyEhhITEEPoPCMbZue+4ovUWmpqaOHz4iEWISUlJpaCg4ITzi4qKqaqqxs3NFTBbX6tUKqKiIomPj0OrbRFoxowZzZgxo087FkmSqDeYKGpooqCuiU279mB0dEXt4g5Kd/Bxp6LBAEBhvY5+Hk5ExpozPRuL8hBNRhRK8+e6sbGe8AEDCRs6kMjYOIaOHIyDkxfK8+hzX0amNyH/ZcrIyJwbTbUo/3kXRd5eq2HJwRPTpHuRfKLPaLny7FxWPPo0rgF+zH3pKYs7THdlUpwJHqHBln9LksTfb75H8m9rOPLPNua+/DT2rnLNtramhs+uu52mWnOT313fLefipx8DQKXRcMGD/8HZxwvPsJBOj6W1jbbrFQtxu+zqTn/NnkD1yh+p+voTABp3bqPs7f/D6+6H+1wpj6GokLoNa3G74lqrcxMEAZc58yl/93UcJ03DZfY81D6+3RipTG/nWHNfvcHE8OGRlnF3d0dsbTVWAk1ykrVA4+rqwP/991r8/d3krK0uxGg0kpOTS0qK2U3pyJHDHD6cgcl0+g6BgiCQlZXFkCGDARg/fizr1q1Bo9GccSy5R49y9HA6mYfTUKs1LLztDt7an4Oh2aVLCAjnRLmOxY1NAIRHRePm7kF4dAzuhYcJ9PSkf1ggkUEJKCcPssz38nKirKzzGu3LyMicG7JAIyMjc9YIxakoN7yJ0FhpNS6GDMM07nawOTO3pbz9B1j5xHPoGxqpKy3jr9feZvrD9/bKi9Ztn39D8m9rACg9ksnyB5dwzQdvWJX0nC8Y9QZUGvOlpZ2LCz7RkeTu3gdAxpYdVB7Nxz04EIDwkUO7JKbjbbSrv/8Klac3ThMv6JLX705sQiMQ1Gokg/npa8PWTQhqDZ6339cnetXoMtKpXvkjjf9uBUlCExqB4+jxVnMcRo7Drv9AlC6u3ROkTJ9g164M/voz2dLc18/fzUqgEQSBAQnBbNqYAoCvr2u7fWMCAty7KuTzEkmSKCgoxMvLExsbGwDWrFnLCy+8fEbr+Pj4WPWMiY2NwcGhJTv22NonQ6/Xk5KZTcrRAvJrG6kT1ODmhbakgENvLAXAw8uL626/Ex97G/Lrm064ltDUSJSvFyHOdgDY2dvz7Zp1Z3ROMjIyPY/TEmjq6+tZsGAB77//PoGBgezbt48XX3yRhoYGYmJieOmll85YLZaRkenFSCKKpF9Q7P0BoZUlraRQIQ5fiBg/w1xEf4bYONgjmVrWKzyYSlNtLXa90C0gauwokn9djbamFgSB4VfNP+/EmbKsHHZ/9xNFaYe5/rN3Lec/7MrLyN29D1tnJwZdOhu7Ls4sOpGNtqht7NI4ugu7xMF4P7CEkv97BpqfFtdv/AtBrcHj5rt6pSDa4sj0A02Hkq321az8EYdR46zOS1AqZXFG5oyoq9NiY6NGo2m5dK6sqOfggaOW7aLCKspKa/DybvlMmzCxHxERviQkhuDt3fu+y3oz2dk5vPXWO6SkpFJbW8vbb7/J4MHmTJK4uJM3fnd2drYSY+LiYvHw8DjjGKqbDOTVaylq0LE/I5s6hQaVnQO4BIFLy42YfUCoxWygoqyMqooK/BxaBBobkx59SQGeaoEwLzcSIkIJ8JJLMmVk+iKnFGiSkpJYsmQJOTk5gFmsufvuu/n444+JjY3l/vvvZ/ny5Vx11VUnX0hGRqZvoK1GufFtFIXWN0GSkw/GyfeBZ/hZL+0dFcFFSxezcsmz+MXHMOfZx3ulOAPmc7nizZdY/tBSRl13JbGTx5/6oD5Eyp/rWfPS65bt9I2biZsyEYCggQOY+dgDRI4Zidqua62L9Xm5FL/wBKbyUsuY2Ub7YRxGjO3SWLoT+yEj8L73UUpffwFEsyha99fvCBoN7tfd0mtEmtNxZEISEetqUTr3zs8Sme6hdXPf5KQcsrJKuOe+WVYZMokDQ62OEQTIzCqxEmhiYwOIjQ3oqrDPOxoaGkhLSyc11dwz5tJL5zBsmDkT087Ojh07/rXMTUlJtQg0oaEh2NnZodVqsbGxoX//OCIjoy2iTECA/xl9DtbV1ZKUnkHJ4VSy01PJSE/jqf++yQGjDduKqsyTHNxOeOOltLHF1tsffVkRwWHhVFdWMMQ/hBg3B/wcbLFXK4Ezc8GUkZHpnZxSoPnhhx948sknefjhhwHYunUrAwcOJDY2FoAlS5acUb2mjIxM70UoPIBy4/8QtDVW42LYaExjbwHNmTU1NOoNbP3kS4ZeMRcHdzfAXOJy6YtPEpjYH1Uvz8xzDw7i+s/fRWNnZxkz6g38+er/GDr/kh7pSHW2SKKIUa9H3dwQMXTYYJQaDSa9uffCrmU/ETt5AoIgIAgCcVMndnmM2kNJlL7yDGJjg2VM4eSCz+InsY2JP8mRfROHkWPxuushyt56xWIRX/v7CgS1GrerbujRIo2o1VK3fg01v/2MqaKs3Tl2iYNxmTMf2/4De/S5yPQcyspqzYJMci6HDh616h0DkJyUYyXQ+Pq6Eh3jj6+PS3Nz3xCcne2OX1amgzAYDGRkZJKSktrsqpRGTk4uktRiGR0cHGwRaHx8vHF3d7e4LqWkpFrmKZVKnnrqCXx9fQkLC8XPz+20+7Lo9Xo0Gg11eiP/FFRSWKclv7oehcaJAz/9RENeNgAZ6an4JYw44TqSKGKqqcBO14CPnZqLn3+RuOhIbG1bfod8OHXZlIyMTN9CkFp/qp2EyZMn8+WXX/LHH3+QkZGBwWAgKyuLwYMH88gjj5xW3eWZYjSaZFtBGZkegCSaaNryDbpt3wGtPjJUGuym3oYmccYZ3wBpa+r46t4lZO9OIqBfDLd8+gYa+759YWsyGln24NMc+nsztk4OXP/uy4QM7N/dYZ0TJoORpDXr+efTb4kYOYTZi++27Fv5/Bvs+O4XQgcPYMJNVxEzbmS33SiXr/uT7JeeRzIaLWM2AYHEvPxfbAMDuyWmnkLZH6vIfuVFq7GAG24i4LobuymiE2OorKTk5+WUrPwZU107N1MKJe6TJuO34Cocos6sQbnM+ctHH6xj+/bD5OVVnHSej48LX31ztyz4dQGiKJKdnUty8kEOHDhEUtIhUlPTMTT3zjoRY8aM5PPP37NsL136PLW1dSQm9mfo0EEMGHD6WSiSJFFYWMTOg+mkFpRSmnmEIxvWEBETw/8++4QGvZHFa62zibOWfUDp9r8BuOGO27ns9jt5dkMKAuDjYENt7hGCXOzpH+LPsLgIHPv4dY+MjMyZc8ZNgk0mE1u2bOH777/H39+fxx9/nA8//JC777771AefIVVVvacfwPnSEV0+z77FaZ1nQyXKjW+iKE61GpZcAjBOvg+DezCU15/xa//y2DNk704CoOBQOl8/9Cyzn3r0jNc5HXrK+7n9y2Uc+nszAE11DXxx9+Ms+ubjDivz6Y7z/PmRJ8nZaXbwqsgvInHeXOxcnAEYcMnFhIweRUB/c3ZK+Vn8nrTHmZynJEnU/PI9Vd9+bjVuExWLz+KnqLNxoa4H/G60R5e9n8Mm4LGoloqP37EMFXz2CY0GcJ3T+e5pZ3KeRU893qbHDICgscFpynScL5qL2seXRqCxh72vPeVzqLPpyecpSRLl5XV4eTlbjR88mHdSccbPz42ExBASEkIoLa1DoRB69Hl2JF15ntu2bScpKZmUlFTS0tJpaGg49UGtcHd3x9HR2Sreu+/+j9WcE52Ll5cT+/ansS8tg+yKKsoNEgYHFzRefiiULuDlQkVBJSVFxeh0ekpLaxEEAVcbFdW6FuHfISgcx4M7iYiJw8XNC6FRz039AvGxt0GtVMCAFrdHbYMRbUPX/w6dL7+7XYmXl1N3hyDThzhjgcbT05PExESCgoIAmDlzJl9//XWHByYjI9P9CHl7UW56B0Fn/UUuRk3ANOomUJ+9sDDh9psoTEmjqbYOjb0dAy6afq7h9ngGz72Yo3v2U3AgBaVaxYWPPdDlPVjOlab6emwdW9y5YiaNtwg0xiYd+3/5nVHXXQmAk7cXTt5e3RInNNtof/Q2dX+vthq3Hz4ar/8sRtEJmZ+9Fefps5H0eiq//MgyVvX1JwgaDS4z53RjZNY4X3iJlUCjcHLBeebFOE+fJfeYkWmXhoYmkpJySU7K5UByLg0NOj78+DarZr8JiSGkpORbtu3sNPTrH0RCQojc3LcTqK2tIzU1lbq6OqZOnWIZ/+67H9i9e89prWFvb0dsbGyrRr5x+Ph4n1Z2k8loJD83l6CwMBQKBevzyjlyMI/iukZw8AcHf5TA8Tn8DkFhAFRXVlJZXoaHlzdBTnbYq/TYNtVjo63l8vkXE/vA7VZxBDrJWTJnSnKeFidbBWFe8ve0zPnHGQs0Y8eO5a233qKoqAg/Pz82bNhAv35y0yoZmT6FaESxexnKA79ZDUsqG0yjb0KKmnhWy+oaGrFxMPepcQsKYM6zj7Pm5Te5+OlH8YoIO9eoezw2jg7MfflpVj3zMgMumk7I0EGWfaLJ1KNdnmpLStnz40oO/LGWeS8/TUBzmnjs5PFs++xrtLV1DLhwOv1mTDnFSl1H2Vuv0LB1k9WY80WX4r5wEUIP/ll3Fy6z5yHp9VR994VlrPKzD7BLGIwmIKjL4pAkyWx/rlRinzjEap/90JGo/QKQTCZcZs/FcdI0FDa9S+SU6VoyM0p4+3/WIm16WgEDEkIs2wmJIez8N8OSJRMZ5SuX2HcQRqMRlarlduPVV1/j559/AcDDw50pUyZbxIz4+Lh2BRqVSkVkZCTx8S1iTHBwEMrT/Byvrm9g7T9bOFpVR5XeRMqnb6DTNfHx8l8ICA6hRmekuL4JBMVJ19E4uTBq6nQiI8JRKs3nNDfS97RikGmhVmsir9JAfqWe/CoDeZV68ioNvHiZP97O5p9rTaOJ3Aq9LNDInJecsUDj5+fHM888w2233YZOpyMuLo7Fixd3RmwyMjLdQV0pyg1voig7YjUsuQWbXZpcz9yNQpIk9i5fye7vf2bBW6/g4me+oAkY0I/rP38XpeqMP4p6LWpbWy55fqnV0zVdQyM/PbyU/jOnkjBrRjdG1z71FZV8uvBWxOb+LTuXLefSZoFGqVZz0dLFuAX4W0qbegpOk6fTsGOL2UpaEHC/7hZcLrq0u8Pq0bjOuxLJoKf6p2UgKPC8474uE2ckg4H6bZuoWbkcQ14O6qBQ7P472NoeW6HA5/HnUXl6ySKbjIXWzX3j4wOZPmOgZV9sXAAajQq9vqUMJSkp10qgCQ315rkXruzKkPskRqORnJxcSxPflJQ0iouL+eOPXy1iire3t2V+RUUlZWVllrG4OLMBSUhIsMXeOj4+jsjISDSnYRqgbWwk9fBhqiUlau8Aiht0FDY0mUuQ3MPBHWwA0dYOdE1kpqcTEByCn4MNyeXHZQrrmlDWV+GukAh2d2ZAaBAh3h4oxr/UQT+tvo0oSZTXGZuFGLMIk19lFmVqtGK7x+RX6i0CTaC7moMHmroyZBmZHsNp3xWtX7/e8u+JEycyceLEzohHRkamGxFy/kW5+X0EvXXdtyl2KuKI60F15q5KosnEhrc/Imnl7wCseOwZFvzvFWydzGUy55M4c4zWN5yGpiZ+efwZilPTKU5NR9+oZejl3S8iNFZVY+/mCoCjhzuhwwaTtX0nANk7dlOenYtnmPkGxz8+trvCPCl2CYPxvO1eKj56G6//PHRe2WifC65XXItkNKEJC8dxzMROfz1TYwM1q36mZtXPmCrKLeOGvBy0+3djP2iY1Xy1j/zEWqaFN17/nZ3/tjxQaKhvshJoNBoVcXEBHDqUT0yMPwmJIQweEt4NkfYtjjXQNQsxZkel9PTDNDW1vanOzT1KeLg5SzY+Ps5q3+HDRywCzciRI/jzzz9wbFVGeyLqamvJTE8jIz2NzLRU8nUSTpPnYOPmaZ5wkp5CDkHhVNfsISM9lfEXTCPQ0ZYYTydcFQJSVSkJYcH4uznLzaBPA4NJorrRhJdTy7XcO3+XsSG1Hp3xtHxoLORVGhgcav63v5ua+ibZJVjm/OT8uzOSkZFpi1GPYtfXKFPWWA1LajtMY29FCh99DkvrKThwyLJdmZtHyl/rGTz34rNesy+R8ucGCpJbfj47v/2R+GmTsXft+p4HkiiS/e9udi5bTm1xKTd98xFKtRqAYVfOI2v7TjxCgxm2YB5uQWeeSdUdOE28ALuEwajcPbo7lF6DIAi4X9P5Dk6m6ipq/ljJ0b9+x1TfniOTAn1OZhuBRub8Q5Ik8vIqOHjgKDNmDkShaClF8fV1tZqbnl5IU5MeW9uWBwo33jQZJ2d7bG3VXRVyn6OysorU1DQrQaampua0jk1JSbUINHFxsVx33UJLhoynp6dlno2NTbuusJIkUas3smbTFtILi2lQ2XF03a9Up+yzzHEICsdznmebY49H1DYwZNxE+l18If0HmsuMA53suDvcu7lxrs9pndP5RqNOJL9K3yYjpqjagKejik9uaml+LAjCaYszSgX4u6oJdFPj49JyW2qjUvDQhfJ7IXN+Igs0MjLnOzVFqDa8gVCRbTUseoZjmnQvOJ/b02qNnR2Xvvgk397xAA0VlYy75QYGXTr7nNbsSyTMnkFNcQm7v/sJjb0dc19+ulvEGYD9K/9gw1sfWLbT/t5EvxlTAQjoH8/8118gcEA/BMXJ6/S7g/rNGxC1jThPu6jNPlmc6Ti0h5KR9HrsBw096zUMhfnU/PYT9ZvWIbVjmSvY2OA0eQbOsy5F7S1ny5yv1NVpOXDgqKW5b1WVObMzKtqPqCg/y7zExBB+XbkLMDf37d8/iPr6JiuBxktu8ntWFBQU8uyzn7Bv3wGKi4vP6FiNRkNMTDTx8XGEh4daxh0cHLj11pvbPUaSJEqKCklNP0L+kVSuuukWS2nUBweOonUOBOdA7AHnnAwrgaaxKA/RZEShbLm1cVQr8XOwwc/BFleFiVA3Z1ztbBCEgWd0LucLkiRR1diqP0ylgbxmMaay4cTZLKV1RpoMIrZq87VBkHtbIdROLRDoriHQXU2Qm9rybz8XNSqlnKkkI9MaWaCRkelJ6LVg0IKDe9e8XMpGVKvfRDBYpySb+l2IOOxqUJ7d08by7Fy0tbUEJQ4AwMnLk0tffJLqgiKiJ4w557j7EoIgMO7m67B1csS/Xxy+MVGWfY3VNdg6OXZa82CDtgmjXm/pHRM7ZQJbPvoCQ3OK+q7vfyZ++hRLmvex97MnYWWjLShQuXtgP3Rkd4fVJ9Em7aXkladBkvB59BnsBgw8o+ObjqRRs/JHGnduA6nt01WFkwvOF16M8/TZKJ16Vj8jmc7HZBI5cqTI0ksmO6ukvV8TkpNyrQSaqGg/5s4bQf8BwURGys19zxSTycSRIxmkpJgzY6ZOncLIkcMBsLOzZfXqv065hkKhIDw8jLi4lr4x4eFhVs2B20OSJCqaDGQUl/P7+o002Tli6xuMrtLAgY8/ZOK0GQSFhiEIAn4ONmTVaC3H2ge2GAs4u7gSERuLc3kenq4uRPt5Ex8cgIvtmZdlnw+YRInCarMI42ynJNbP3GhdlOCmT49iPIvKooIqAxHe5uynOD9bZiY4E+RuzowJctfg4aiUS8ZkZE4TWaCRkekhCGWZKP98AaGpDlPcNMRRN57SUeCsMepQbv+MxsPraf11KWkcMI2/Aynk7EsKcnfv47enX0JA4Ir/vWzpU+IdGY53ZO+o+zcUF9KwYwsKBwccx0/tdDtmQRAYfuVlVmONVdV8f88jeEdFMOPR+zq0V4+2ppb9v/zOvhW/ETNxLFPuvQMAO2cnBsyazr6ffyN28niGLpjXoy+oJJOJio/fpm5ds0OLJFL6xov4v/x2l7oOnQ/ojqRT8vKTloyXkpefxPfx57GN639axzelp1C05P5299n4++N44aU4TrxAdmQ6T6mt1XL/vZ/R2Kg/5dysrBKrbZVKyWXzR3VWaH0KURTJy8tHoRAICjJ/Rur1ehYtuhVRNDdudXFxtgg07u7uBAT4UVBQZLWOv7+/RYiJj48jOjoKO7sTW0kbjQaOZmVx8EgmGeXV2PmHYOsfTHGDDr1oVuFU/YZxrPOMwsYGQaUmIy2NoFCzEOPnYGsRaKT6GoK8PLj8/14jMjYOT2+fHv1d1V00GUQKq8yf2eHeLdcxl76aSmmNeXxSnKNFoFEqBPxd1RytaJvZ2BqFAD7OqpaMGHc1no4t1yiRPjZE+sjuSzIyZ4ss0MjI9AT0jSg3vI7QZO7DoEz9E0QRcczN0NEXHVX5qNa/jlCdZzUsekebS5ocT13DfSKO7k1ixaNPI5rMj19WPPo0V737Xxzc3c4l4i5BMplo3LuTurWr0Ca12HzWrFyOxw23YT9kRJfF0lRXz08PL6Uqv4Cq/AIMTU3MenIxqtNwsTgdVi55jsJDqQAcXPM3o667ytIQeNiCeQyeezHOvt4nWaH7EbVaSl9/wWzH3AqnqRei9vXvpqj6LprQcOwSBtO4518AJJ2O4heX4vfEi9hExZzyeJvoONTBoRiO5rSsGR6Fy5z5hF40nfIq7YkPlukzNDUZSE3NR9uoY/SYlubizs52uLg4tCvQqFRKYmLNzX0TE0IICj7776jzjbKyMlJSWvrGpKWlU19fz+zZF/Hoo2YHVjs7O8LDw8jIyATM/WJaM27caI4eLbT0jImLi8XV1fWEr6lraiIrI4ODmdkUpB4gK/UQ2RlHMBoMBMy4jKALL6cOoO7EDj0KpQp7/2CKC/ItYwO9nAl3tsPP0RY7OUvKita21XmVBkuvmLJaIxIwNMyeJ+e0lIv6u2ksAk1epbUYE+SusQg0GqVAgJu6WYQxizGBbmoC3NRoVD2v1FlGpq8gCzQyMt2NJKHc+iFCXanVsDJ9HShViCNv6BiRRpIQjmxEue0TBJP1RbAp4RLEIZeD4tw+Evz7x+EbG225+Xf28eq08pyOwlhVQd3fa6hbt9rKQcayv6SIkpeexH7YKNyvv7VLemIUpx2mIueoZbssMxttTR1OXmfXS6UqvxDXAD/LE8aBl1xkeY9Mej37V/7B6OuvAugVYpq+opyiJx9Cn53RMigIuF93Ky4XXdJtcfVlBLUar/sfp/SVp9Am7QVA0jZS/Pzj+D75MjZhEQCI2kbqt2zAafIMKxtsQRBwufgyyt9+FbvEIbjMmY9t/0QEQUA4D53czjf27c3mjz/2kp5WiNFowt3dkVGjY6yyHhISQygqqgLAz8+NhMQQEhJCiIsPlJv7nga1tXWkpaWRmppmaeJbXt72Ow0gNTXNajsuLpaMjEzs7e2wt7e32vfss0uam+e2j1EUKW3UU9SgY82mzVSjwt4vCIVPLIe+W0ZdZovg05CffcJ1kCSE+mpcMBLk4sgtH36Mn7urZbennQZPu/O7ZEmrF0kpbDI36K00kF9l7g9TewLb6mPkV1pf84V62bA/x9zTqaBSjyRJlr/Fiwe5MCXeiSB3NV5OKpQKOTNJRqarka+KZGS6GeHIRhRZ29rdp0xZAwoV4vCF5ybS6LUot32MInOz9Wvbu2AYdydS4MCzXrr1F7tKo2HOc4+z7K6H8ImOYvriezos66Mz0GUeofDxe8F06oLrxl3b0e7fg8u8BbjMvgxFJ55X6LDBXPzsEn576kVsHOy57NXnzkqcKTyYys5ly8navpPLXn2O4MGJAERPHMvWT79CNJkYMv9S+l94QUefQqehz8sl5eWl6EtayhwEtQavex6WbbQ7GYVGg/dDSyl5cSlNh5IBEBvqKX72Ubzvfxxt8l7q1q5CbGxA6ezS5v1wHDMRTUg4NqG9o9RR5uyorWls46JUW9vIoYMtWZuVlfUU5FcSGNTyuTZuXByBge4kJITITX1PgU6ns+obk5aWxtGjeac+sJmsrGy0Wq2lLOmqq67kiisuJyQk2NKU93hqqqvITE8n/fBhMptE6hQ2hA0dTVmTHvFYr6DgGFobZDsEhVkLNHlZgDlj1dvehgBn++Ymvjb42NugUcpZGQaTRFG1wSLCDAmzJ7K5PKm83shTv5xZs2aAkhojeqNoyXqZluiGv5PCkhnTmnh/ucxURqa7kQUaGZnupLoA5fZPLZuSICDGX4ji0B8ImK94lAdXmTNphlx5diJNRQ6qDa8j1FjXkIt+/XCd+ygVTWcvNGhr6/jtqRcZdOlsosaZewDYubiw4H+vYOfi3CPdflqjCQ1H5e6BsaxV9pJSicPwMThOmkbjru3UrfvD0tBUMuip/u5LFBobXGbP69TYwkcOZe5LT2Hr7IRbYEvJTuXRPGwcHU+Z6VJbUsp39yy2xL5z2XKLQKNQKpn70tM4+3pbbLR7A9pDSZS+8gxiY4NlTOHkgs/iJ7GNie/GyM4fFDa2+Cx+muLnHkN32HzjJdbVUvz0Yqt5Nb/8iP3wMVYZEoJKJYszfRBJkjh8uIik/TmW5r633j6N8eNb/iYTEkKsjhEEyMousRJowiN8CI+QbXWPRxRFK1vx99//iG+++RbTaTxYaE1wcLBV3xh1q8/+kJAWi2RJkjAajYgKJcUNOhrVCsqyMrltgblPmtLegWEvfYY9UKI9ec8g55Ao3AYkEBkbR0R0DOExcdj4+uHvbI+qh18fdDatbatbZ8QUVRtaBC9ApRQsAo2fixqlAkwnSZix2FYfK0tqbtLbOhNmcJgjQY6nZ4MtIyPT9cgCjYxMd2HUm+2tjTrLkDjwMsTB85FcA1Bt/dAyrkz6BRRqxMHzT399SUKR9heKf79AMLXUGEsIiIMuQxw4D4WTCzSdOHX5ZFQXFrPi0aeoyiugODUdx9dewC/O3IviWD+TnkJ9WiqVq9fidvWN1jeMSiVOUy+katnnKD29cZ46E8fJ01G5mV207AcNxWnyNMo/fht95hEAVN6+OE2b1SVxBw20dk2qLizmxweWoLGz47JXn8XJ28uyz2Q0UpKZg8LZfMPj7ONN1LjRHPlnKwBH9+ynPDvX0rTZLSigS86ho6jfvIGyd/4LJqNlTOXrj+9jz6H2k3vOdCUKOzt8H3uOomceQZ91pN05hpJiTJXlqDy82t0v07f43xu/W2ywwey01FqgcXN3ZEBCMG6uDiQkhtB/QAjOziduKnu+k5aWzl9/rSMlJZWcnFx+/fVni6Di5uZySnHGy8uL+PhY4uLMYkxMTDROTk5t5kmSRFFBPpnpaWSmp5GRlka1fyS+wydgsjfPT/R1ZVZwMGqNBoNej6mxgabyEmw924ppSgEUtZV4ahSEe7kx4Lor8Lnz+nP/gfQBDhU0seVw/WnZVrcmr1V5kkop4OeiJr/K0K5tdZC7Gl/ZtlpGptcjCzQyMt2EYve3CJW5lm3RJw5x4FwApNipmESjVXaNct+P5kyaxEtPvbiuAeWWD1Dk7LAaluzdME24G8n/9JxXTkbyb6upyisAwKjTs/qF/3L95+/1mJ4zoq6Jhi0bqf3zd8tNpF3iYOwGDLKa5zRlBprgUOwGDbPqmXEMm8gY/J9/g7r1a6n69jM8bry9XVcnSRQ7NWOorqyC5Q8+TkNFJQ3A9/c8wmX/fR4HN1cOrv6L3T+sQDIZufGbjy1lZcOunMeRf7YSNnIowxZchkdo8MlfpAdiZaPdCpvoOHwWP4XSWS6F6A4UDg74Lnme4qcXo89t6Suh8vHDZfY8HCdOlR2Z+hA6nYGUlHySk3IpLa3hoYfnWPYJgsCAhBD+2ZRiGTuQfBRRlFC0emr/6GNzuzTm3kBVVTWpqeZ+MQsWXI6DgwMAGRmZLFv2vWVeZmYWsbHmByDx8XFWazg5ORIbe0yMMf/fy6v9Rsr5uTkcSkvjcGEpxY16GhQa0j9/w8r2PuzyeIs4A5Bf04gq1IvQiEiOpJrfY31pATYubrirINrP21Km5GmnQXEeuykVVOnZkdloyYh56hJfHG3N1xVHK/SsSqo94zWrG62FnIcv9MbJVinbVsvI9GFkgUZGphsQju5BeegPy7akccA08W5QtAgEYvwMMBlR7vzSMqbcvcycSTPgxBkcQlkGyg1vtGk6LAYkYppwF9h1zA3t2EXXUp6dQ87OvTh5ezL76ce6VZwRtVqMpcUYSotpOphE/ca/rEphAGrX/t5GoFG6uGI/dORJ1xaUSpwvuBDHMRNQ2Du02a/LzqDszVfwuOE27BIHn/vJtINRp0MSW/Ka1Xa22DjYk7xqDZve+8QynrL2bxJmzwTANyaKG7/6ENcAv06JqSvQZ2VQtewLqzG38RNwvuX+Trc/lzk5SidnfJe+RNWyzzHV1eI4ZoK5rKmHiLQyZ48kSeTlVZCclEtSUo6lue8xystr8fR0tmwnJJoFGj8/N0aMjCQq2h+QAPkG8hiNjY2kpx+2CDIpKakUFbX0E0lMTGDo0CFAWxEmJSXVItBER0czf/68ZlelOAIDA6xKoMBsnV1SWICbfxBFDToKG5oobtCRkleOwiUCXCLQABrAdtV3NJW1lEA35Fk38q3Q6tEaTVy96FYAImNjsXd1x1Z1/gkEOqNIQavmvHmVBm4a74GXk/l2Kqdcz+dbKi3z86sMxPqZPw+D3E9cTtzatjrIvbk8yc2cHXNM4DlGmJf8vScj09eRBRoZma6moRLlP+9aDZnG3Y7JxoXN735MdUERY25aiFd4qFmIEY0od39rmavc+aU5kyZ+hvW6koTi0O8odn2DILZcSEuCAnHIAsSEi0E4+wwPSZIoPJRKQH9z2rpCqWTW0sWs/98HjF10LY6eZ+cwdDYYK8qpXb3SIsgYS0sQ6079ZEqsr0Mymc76BrI9cUYSRSo+fgdDwVGKn3sMh1HjcL/ulg4v7XAL9OeKN19m+YNLkCSY93/PYufiTP+ZF7D9y2XoGxoB2L/yDwbMmmG5cO4t4owkSehzs1E6u6Byb/ldsomIwn3hIiq//AgA54suJfL++yivbOyuUGVaoXR2wfPWe7o7DJkOoLFRx/79OSQn5XIgOdeqZOl4kpNymTylpQRz8OAw3vzfDXh5u+Dl5XRS15/zAaPRSGZmppXFdU5OLqJ44uYhqalpFoEmJCQYR0dHfHy8iY+PIzS0pYePjY0N991n/TdXXVnJ5r//IiMtlczD6VQKGsKuvhNNsdFqnsKx7QMah6Bwi0ATEBxCmJ8nNg1VJISHEOBsT/8gD8QGHSPGjT/rn0dvw2JbXWXOhDmWEVPabFvdmqnxThaBJvC4hrv5lQZi/cyZhEHuGotttVmE0Vj6xPi7qmTbahkZGQuyQCMj05WIIspNbyHoWi5eTbHTkEKHs+Ozr9m7fCUAJYczuOmbj1BpNIiJl4DJYC5xaka5/VMkhQopdqp5oKkO5T/voMjba/VykoMHpkn3IvnEnFvYJhMb3vqApF9XM+2h/9B/ptn1R2Nvz4xH7juntU+G1Fxnf7ygIhn01Kz8sb1D2iDY2eM1fQbqcdPQBId2dIjUb1pnaZYK0LB9M417d+E6/2pcLrwEoQOb8Dr7eHPFmy9j0htw9DD3ybFxdCDx4gtJXrWGMVfPI3raBb3mqaZkNNKUeoDGXTto3L0dY1kprguuxW3eVVbznGfNxVhehsrbB5eLLpUzNGRkOoH8vAre/t/qk84RBAgL88H2OLtjW1uNlWvT+YQ52yi/2do6lZSUNI4cOYJef/IGusdTWFho+bdSqWTVql/QHOcWWF9XR2Z6OgpnF5QevhQ16Chq0BEoaXn3/16yzLP1CUDjcvJG8gCCTsvk2Rcz/NYbCYuKxsHRsc0cTwcbyhrP7Fx6A60dKAG+31bGn/srya/UU3MK2+rW5FXqGRpmtib3d1GjEMBeoyDIXY2dpmV9ZzsFP9wZKttWy8jInBJZoJGR6UIUyb+gKDpk2ZbcghBHXAtAv2lTyN29n+LUdBoqKq1KVcRBl4FoMDcLbka59SNMChU4+6Lc+CZCQ4XVa4nBQzGNvwNs2l5wnQmiycTKJ54je8duANa99g5O3l6EDBl4TuueCu2hJCo/+wDnmbNxmjLTap/Kw8ucDSSd+CJKExKO0/SLcBw7GZ9g7057oms/eDiOk6ZRv+FPy5ika6Lq60+o3/AnHjfdid2AgR32eu25Nw2/8jJGXHM5AZ14nh2F2NhA4/49ZtvyfbsQG+qt9jfu2t5GoBEEAY8bbuvKMGVk+iRlpTUkJ+eSnJRLZJQfsy8eatkXEemLvb2GxuNuxl3dHEgYECw3922mrKwcvV6Pl5f5wYfRaGThwusxGAynOLIFV1cXSwPfuDhz3xi345rrN9TVkXQ4jdSsXHIra6kSBXD1wiEoDJURqGwpj3LzdsbGxhadrgmAptJCTLomlK36QDlrVHhqFNgbGon19yXYzQknjQpIOOufRW/gmG31sbKkYxkxOoPEe9cFWeblVeg4VNB0Rmt7OimtzDXVKoEvbwnB2VbR5kGJIAjIvXtlZGROB1mgkZHpIoSSwyj2/mDZlpQajJPuBZX5CZmdizOVR/Ms+3N277MINAiC2WbbZDTbbgMCEsrN74EgILQSKiSFEnHYNYj9Ljw7W+7jUCiVeIWHWQQa0WSi8mh+pwk0hpJiKr/6mMZ/twBQuewLHEaOR+HQUl4kqNWovLxBklB5+6Ly9kHl44va27d529fixNTZKF1c8brjfpymzKDi43fQ52S2nEtBHsXPPILDmAm4L7wZlUf7jRvPFRvHtqVXPQljRTmNu3eYRZlDyWA88Y2MPvMIxsoKqzKnvkxJSQ07tqcz55Lh3R2KTB/n3bfXsGVLmmW7orLeSqBRKhX07x/M3r3ZxMT6k5AYQmJCCEHBnr0mK68zee65F9m5cxfl5eVMm3YB77zzCgBqtZrIyAhSU9PaPc7Ozo6YmGiLGBMfH4evr2+bn6kkSfz0zVcc2LuHzPRUKsrKGLj0LWwjhkEEnKx7XHGjnrCoKHIyM4iIjiUiJga1qZ5AV2cifT3xc7DBQd23L/mP2VabBZhmMaYd22qrY/Qi9hpzaVGoV/tNzZUKs731sbIkS3mSmxo7TduyJBc7OcNTRkbm3Ojbn9YyMj0FXQPKjW9aCSmmkdeDWxBGvQGVRm0pVSlOO8ywBfMIGWrdzBZBQBy+0NyTJmWNeQjJyn1BcvIxlzR5RXRo+GNuvIaaohIytu5g5qP3Ez1hTIeuDyBqG6le8T21q35GavUkUqyppnrFd7hfc5PV/MC3Pu1U16QzxTYmHv+X/kfdn79T9d0XVg2KG7ZuonHPTtwuvwbnmXMQVH3/o1dsaqLm959p3LXdYlF+MjQR0TgMG4X9sFEou0hc605qaxtZ8fNO1v2VjMkkEhbmQ0JiSKv9WrZuTWPUqOg2DUBlZE6EJEnkHS0nOfko06YnotG0fNb4B1j/XWVnlVBbq7XKiFl43QRuu2M6trYdV5rZW9DpdBw5kmFp4Ovo6MADD7SU8JaVlVFeXg5AWpq1GBMfH0dqahpKpZLIyAirzJjQ0BCUzWWZoihSmJfHpr/WUlJdR/TEaZYyJQE4vG0rSbt3WtY9kZ11azxs1XjaaXjqtTdxcnbp058XkiRR3WjCYAJvZ5Vl7NYv8iiqNp7i6LYUVBmI8jE33Y30tSXSx4agVrbVge5q/GTbahkZmS6m798lyMh0N5KEcuuHCPVlliExdARSzBQkUeSHex/BLSiAsTctZMyN15zcCUkQEEfeYM6kSV9ntUsMG4lp7G2gsT/nkHN27eXovmTG3XwdgiAgKBRMX3wPw47Owzsy/JzXb40kitRvWkfVt59hqq5qs99h3CScZ17cZrwniTPHEJRKnGdejMOocVR+/Qn1m1reI6lJS+WXH1F3rOypX99OKxfUampXrUCsP0HJlUqNXf9E7IeNwn7IiE7LLuqJ1Nc3cf99X9DYoLOMLVu2hf4Dgi22xF9/uYktW9JYu3o/t9x6AYFB50dGkczZUVPdwLJlW62a+4aEejFgQLBlTkJiCD98vw0AOzsN/fsHoW3UWQk0Hh5OnA+YTCZycnJJTU2zuCodOZKBydTSYN/d3Z3777/XkukSFxfHrl3mTNKjR/OorW35bJs371KmT59GVFQkNse5yxlFkX8PpbH8nTfITDmAttHc4Nx/ysWkBQ+0zFMIEBEXbyXQNORl4Rpr/q6QRBGNvhFPtUCopxvRvl74Odhgo+p7GRsmUaJRL+LUysHouV+LOZjfRINeZEKMIw/O9AbMpUM2Z9Bg19Veac6CcVNjq24RXgaGOvL6lQEddxIyMjIyZ4ks0MjIdDLC4Q0osrdbtiUHT0xjbwVBIG39JorTDlOcdpjDm7Yy45F7iZk4rmWuJKFraMC2deM+QUAcswgUShSpf4JKgzh8IWLsBR1S0nTg97Wse/1dJFHEwc2VIfMvAUCl0XS4ONOUnkLFZ++1m2FhExmD+w23YRsd186RPRulqxtedz1oLnv65B30uS22pYa8XMrefInAtz9HoendTTVFbSPapL00pSTjfsPtVin7glKJ/ZARViKVwsERu8HDcRg2CruBQ1DYnbuY2BtxdLRl6JBw/vmnpbm0SqmgrrYRF1cH9u/LtpSiZGQU8+gj37Dw2glMm57YXSHL9CCMRhN1tVrc3Fu+F+zsbdi+LR2DoUVgSE7KtRJoQkO9uWz+KOL7BRIZ6YuqD97Yt4ckSRQXF1syY1JT00hLS0er1Z70uMrKSkpLS/HxMWewDB48kJycHEt2jI2NDTqdWWT19/MjJzODv/74nSOFxQQkDsPWL5iiBh2lWh2ipCS/UW8RZwDqj2ZavZ4ogU/cAHwDAomIiSEyJg7P2P6ovOyJ8HbHx94GjbLnPZg4F47ZVudVGshrLk/Kr9RTUG0gIdCOpy9tcSFs1Is06M1ZyHmV1n2SgtzV5JS3jAmAr4uKgOaSpKCT2FbLyMjI9DRkgUZGpjOpzke5/VPLpiQImCb9B2wcMRkMbPnoy5a5kohvTBRg7vOSsXk7u777CRtHBy579TnrdQUF4uibzA5PNk6WPjbnSnl2Ln+99o6lbGrT+5/iGR7a4f1mjOVlVH79CQ1bN7bZp3Rzx+3qG3EcN7lHZsmcCbZx/fF/+W1q1/5G1XdfImnNF+fu197ca8UZY1VFcz+ZHWgP7Lf0k3GcNA2bsEirufbDRtKUcsCcJTNsFLax/c6L8q7WSJLE3j1Z9OsfZOVyc9nlo9i+/TDuHk5csWA0I0ZEWQSumppGbGxU6HTmlH2TScTb27lb4pfpGbRu7nvwYB5h4d4seeIyy36NRkVsXAAHko9axnJySq3WUCgE5s4b0WUxdyfZ2TmsX7/BIspUV1ef0fGOjo7ExcXS0NAiqAwfPozhw4fR2NBA1pHDrPh2Gfv2H6RI7YTezgn7wDDsfOIR/AZQC1BWa7WmS3g01Sn7LNuNBTmAOXPGx94GPwcbRgwYw8VTJ5/lWfdc2rOtzqs0UNaObfUx8quse5UFums4kG9u4ltQZUCUJBTNn5ljohwIau4LE+iuJsBNLdtWy8jI9FrOrytlGZmuxKhHteFNBFPLUx1x0Hwkn1gAlGo10x76D/+8/yllmdkMvHQ2Ln6+AGx892P2r1hlOa7kcAY+0dY3vwA4dGzZg2dYCONvuZ5/PvgMgNjJ4wkY0K/D1pdMJqp/XkbNLz8i6XVW+wS1GufZ83C95AoUdn3HJURQKnG58BJz2dNXn2CqrMBhzMQ28ySDoUMtuTsKSZIw5OearbB3bUeXkd7uvMZd29sRaEZjP3zMedtgND29kGXfbObw4SIumz/K6ubY09OZx5fMIzzCp00mw4SJ/YiNC+TLLzayb282Y8fGMnBQWFeHL9ONNDUZSE3NJznJLMoUFVmXf6anFdLUpLcS/YYMiUAUJavmvn0drVZLevphUlJSmTXrQpydzULmkSMZfPLJZ6e1hkajISoqytLANy4ulqCgQKteLk1GE8UNOt79vxc5+NfvSM0PMRRqDcP+78vTepgQNHA4MUITETGxRMbGEhYZTS1KPG01fcJ6WZQkyutM5Ffq8XVV4+9q/j4rqjZwy+d5pzi6LWW1RpoMIrZq8882McgWo0lqzojRmJ8jNf/YxkQ5Miaqo85ERkZGpnuRBRoZmU5CsetrhMpcy7boG4+YONdqTsiQgVz9/uukrttIxJiWm7d+06dYCTS7li1n1pOPdEqc2to61LY2qJozOoZcfik1RcXYujgz+vqrO/bmWqGgKeVgG3HGYdQ43K65CbW3b8e9Vg9D5eaB938eRjLo23XvKH7pSVRu7rhfcxNK17ZW2l2JZDLRlJ5C467tNO7ajrGk6OQHKFWIdW17zfT2DKizpaCgku+WbWHP7izL2KrfdjNl6gBcXFrKuqJj/E+4ho+PCy+/cjU//7SzjThjMom889ZqpkxNoF//oBOsINMbSU7K5bffdpOeVojRaDrhPJNJJOVQPoOHtJSdTpue2KfL4IxGI6Ioomn+rtq3bz93330vomguewkNDWH06FGAuWlvewiCQFhYqJXFdUREOCqVisryMjLS0vhnwzqyyqowObmTMHkGRY06KpvM2Rx2g8cj/dny3Swa9GiL87H3D27zWmqFgJ+DDb4Otvg72BCQGILnxdbZMb3xUcQx2+pjltWt7at1RrNwde1oN+YPN3+PeTurUCnhJL/OFjydlAS5tTgltcYswjie4EgZGRmZvsNpCzT19fUsWLCA999/n8DAQAAefvhhRo4cydy5c09xtIxMD0OSEI7uBpMBKXQEKDq2JlnI3W1xWgKQbBwxTbwbmm9YJUmy3KQrlEr6TZ9idbxPdCTBgxMpOHCI+OlTGHp55/yNVeQVsOyuh/CJjuTCxx5AUCgQBIHJ99zeKVkPgiDgcf2tFDx0J0gimtAI3G+4Dbv4AR3+Wj0VQd22tKlxx2aakvea/71rO24LrsNp2kUIJ2sY3UnUb/uHio/fRqyrPek8hb0DdoOGmcuXBg61skE/X6mqrGf58h1s3HDI8oT9GHq9kZRDeYwaHXPa6wmCwPgJ8W3G16zex44dR9ix4wiTJvfnqqvH4uDQvkWsTM+lrk6LUqnA3r6lqaxWq+fQwRNnGxxr7puQGEJ4xMndfXozkiSRn59v6RmTkpLK4cNHePzxR7ngAvP3ZWhoqEWcAUhNTbMINAEB/jg7O+PgYN8sxpgdlaKjo7G3t8NkMqJStWQs/vjPDrakZuAQGIZN7DiIBSVwqLLeKi6Vl5/Vtr2DI5rGWgSjAWfJQKCzPVG+XgQ42+Nuq7aU4PRWSmsN7MqvJCWn7rRsq4+R16o8SakQ8HdVc7TC0LwN/q7mUqRAN40lIybgBLbVMjIyMucbpyXQJCUlsWTJEnJycgAoKSnhySefZPv27YwcObIz45OR6XgkCcXWjywuSKL/AExTHwJ1B93gNFSi3Pyu1ZBp3O2WcqSK3Dx+f/YVxi66jrARQ04ohEz+z61oHBxw9Ogcy+HCQ2n8tvQ5GqpqqM4vxNXPlzE3LQToEHHGWFWBsaQY21jrEilNSBiucxeg8vLCceK0bhEhehKSQU/FFx9atsXGBio+fZe69WvxWHQntjFtb9A77LVFsU2Wi8rd44TijNLDq9kKeyS2cQN6ZElWd9DYqGPVb3tY/cdeS9+Y1gwfHsnlC0bj73/uf8slxdX8+ENL0/EN6w+yb182N9w4iWHD2imDlOkxSJJEWlqBuWwpOZfsrBKuv2ESF0xryXrp1z8IQRAsAp8gQFi4DwkJISQkhvTZ5r7l5eVWYkxaWhp1dfVt5qWkpFoEGjc3V/z8fCkqKgbMfWeOIQgCP/30Pba2thQczSUjPZ1/t2/jxz/WUi0JXBgXxqx58y3zJTcv3BNPXTIsqm2Yd/MdxISHMnzUENS2rhgk0CiEXlvK2aATySzVWbJgrh7lZmmku/+olrfWlZ/xmvmV1v1jrhll/uyTbatlZGRkTs1pCTQ//PADTz75JA8//DAAv/32G1OmTMHV1bUzY5OR6RQU+5ZbWVQrCg/AmucxTXsEbM4xC0AUUW56C0HXcmFpip+BFDLMsr35g88oz8rhl8eeJnjIQGY/+Qg2jm1f1z24c0sXDFot2lYXwCl/bWDIFZdaO0adBaJeT+3vP1P98/co7OwJfPPjNj1l3BZce06v0ZcQ1Bq87nyQik/exVDQ0uBTn5NJ0ZL7cZw0Dferb0Tp4tohr6cvyLOULmnCIvFcdKfVfpuoWBTOLoi1NQBoQiOwHzYS+2Gj0IRG9NqbkM6ioryOxx79lrq6to4wsbEBXHn1WKKi/No58uxwcLRl5Kho/tmUYhmrrmrg9f+uYsTIKK6/fiIurnI2U09EEAQ+fP8viourLWPJSblWAo2joy1Dh0VgZ6smITGE/gNCrGyw+wL19fWkpaU3CzKppKSkUVZWdlrHpqamWm0vWnQTarWK+Pg4fH19yTycTkZaKpnpaeb/Dqej9g0ietFDaAZHYgP4AIe3/261TriXB6kNbWOwESSCXBzwc7DBz8EWPwcbXEYsQhAEvLycKCurw6bNUT0PkyhRUmskv1mEmRjniLuD+RYgOU/LC6tKLHPHxTgS728WaI4vM2oPi211c5PeIHc1wR7Wx42KlD+TZGRkZE6X0xJonn/+eavtRYsWAbBnz56Oj6gVbm72vepJkZeXU3eH0CX05vPU7V+Ndt+PbcYVpemo/3wWhyueR+HgCpzdeTZtXUZT0aGWdb1CcbnwdoRml6XMf/eStWNXy35JJCDU55Q3vdraOv794VcEhYIJN155xnG1h9fM8Uja+/n5yf/DPy6a6955EWevs286LEkSVZs3UfjeO+iKCgEwNWkx/PULgTfd0iExnws9+vd28jgCx42k5KcfKPj8M8Smlpv9+g1/ot21ncCbb8V71sWnzDg6/jwlk4n6lENUbd1M9dbNNOW1lE+IleV4PrK4ze+fuPBaUCpxGz0OG9+e2Reop7yfnp6OhIV7k5zU0m8qJMSTmxZNYeSoqHMWtI4/Ty8vJ55YOo/duwfyxmu/U1JSY9n3744jpBzK4/Y7pjH1goReJab1lPfzXNFq9SQn5bJ7dyY52WW88uo1Vu/DiJFRrPyl5TsgJSUfV1d71OqWv+sXXuyYz/jupL338/nnX+Wff7aSlZVzRmsplUpiYqIYMKAfw4YNwsvLiSatlvLSUq5ZOI+KRh15NVq2V9Tz6+50inb8Q9mODZbjpepKNM6uVmuWaHVWMfZXK/k7uwhPNUT5eRDt60GQiz0utuqT/h31tN/bJoNIXrmOnLImckqb/1+mI69Ch97YUpcUH+pMTKg59gTUQItAU61XWM5roIMdUIhCAH83DSFetoR62Zj/87YlxMsGZ7u+086yp72fncn5dK4yMr2NHv2pWlXVeOpJPYRjT1L6Or35PIWju1Gue4vWl1qSygbBaG5YayrNoubLBzDOfAKv0NAzPk+hJA3llq8t60tKDbrx/0FXpQPMr6Fw8aD/hdM4tGYdkigy+ubrKS9vm8bdmj0//sL2L75F36hF42BPxKRJ7WbcnArRZGL39z8z8JKL0NibG5UOm3sRWp1IxJgR6NCc9Xury8mi8vP3aTqU3GZf+bZtaC66vFtLmXrL761qysUEDBxF5Rcf0bD9H8u4qb6O3NdfpeiXX/C4+S5so2LbPf7YeYq6JrTJ+8yZMnv+tWTDHI+hvIyCf/diExFtNa6ceBFAs1Vsz/u5def7mZ5eSFSUH4pWriuXzR9FclIubm4OXDZ/FOMnxKNUKk75t30qTnaeISHevPjy1Xz/3Tb+XLufYy1v6uqaeOXlX1m9ej933T0TJ6een33RW/4+T8X+fdm89t9VVs199+/PJTDQLHx7eTkR2ZxN5efnRkKiuWypvLyuVz2MOhl5eXlkZ2fw77/7UCqV3H//PZZ9R45kn5Y4ExQUSFxcnMVVKTo6isryMrZv2sjmLf/yw9p/aLJ1wD0yDufQKnSmlj409uFxOJeVWAk0+uoKDPW1qB1brOojh4y0+p2zkSQWj7JuLGyo11Feb93QvjU94fd2b04j+45qyW+2rS49iW11aw7l1BLraS5vtTFJKBVgEkGtFCgub6SsrCUD5pv/RGMr6tuxrRbR1WspO7ePuR5DT3g/u4rz6Vy7ClnwkulIerRAIyPTUQilh1GufwOhVeNOU+KliGGjUK15DqHJ3HNDqClEtWoppqtfAs7gw1ZXj3LD/xCklgtF06gbwDXQapqjhzvTHrybQXNnc3TPfnxjTu0Lqba1Qd9ozqjQNzSSvGoNwxbMO/3YAH1jI6ueeYWcnXvITz7EJc8/gaJZMImbOvGM1mqNqaaaqu++oO7vtdDq3AEUjk64XbEQpwu6p9ltb0Xl4YX3/Y+hTZpBxSfvYCgqsOzTZ2dQ9Ni9OE6ZgftVN6B0drHsM9VUU7ZrEyUbNqJN2tvGKet4lO6e2A8diWDb82/gewK5uWUs+3YLyUm53H7HdMaNb7mZi4z05d77Z5GYGIKNTdf15bG11XDd9RMZNTqajz5YR0FBpWVfba0WO7tTlyfInDm1tVoOHsjFw9OZmFZOXMEhXm2cl5KTWgQagAEDgnnzfzfg5e1Cb0aSJIqLS8jPz2fYsKGW8U8++Zw///wLACcnR+677z+WDJT4+Di2bt1mtY6Hhzvx8fEWMcbP14fSgnyCwsLw9m0pDcw6nM5O+wDsxo+gtcdda3HmGPaBYajUasIiIomIjSMyJhaduz0eHi4EuTria2+Dvdr6u7cnZpu1tq3OqzQ7JdVoTTw+uyWrcU9uI7/uO3lD9/YoqWnpEaNSCjxzqR/eziq8nFRtLL8jfOwoK2vbW0tGRkZGpnOQBRqZvk91Ico/X0Yw6S1DJc6D+e2dzRj1fzPzP9cRnP0tQkMFAEJ9GfXfPATTHge30+gDI0kot3yA0NDSSE8MG4kUPfmEh3iFh+IVHnpa4cdPn8K2z7+lsaoaBw93bM6iR8xvT79M7i6zS1DOzj1seOsDptx7xxmvcwzJYKB2za9UL/8WsbHBeqdCgfP02bjOvwalk/xE4WyxSxxMwH/fo2bVCqp/+hZJ1yK41P+9BpvIGJynzrSMFT3zCIajOSddUxMShv3QUeZ+MuGRPfKmpCfy8Ufr2LD+oCVL5ccftjFiZBQaTctX6PDh3degNzranxdeuopfVuzi15W7kCSJW269oM9kZXQ3RqOJjCPFJCfnkpyUS3Z2CZIEY8bGWgk07u6OBAV5kJdXYWnue3wGk0aj6pXiTE1NjaWB77H/V1VVoVarWbduDermhuHx8XEWgaaurp78/HyCgszfowkJAxg6dAhxcbHExcXi4+VJVVkJmUeOkJl3lJ0ZWUgunjgGheNpKGNxK4EmMiYO7aqN2PkGtg2uFW4qgfCwYJ7bsMVix93TsbKtrjKQX9nWtro1DToRBxtzNkug28nP8Xjb6sBmxyQXO+tsmIQgWaiXkZGR6SnIAo1M36axCtXa5xF0LamcYuBA9qe7UpG7FYBVr33KDe88h936lxDqzHXYUn0lqt+fxDT9cSSviJO+hJD+N4qcfy3bkqMXpjG3mu03mtn9/c/oGhsZtmAeGrszuxBSaTSMXXQtkigSd8FkVJozf0I/+vqryE86iEmvR6nREDx44BmvAeanptq9O6n84kOrzI5j2CUOwf36W9EEBp/V+jLWCGoNrpdegePYSVR88SGN/24BQBMRjdOkaVZz7YeMoOZ4gUahwDZugNkKe+hI1D49s59MT8fB3obWrtnl5XUcOpjHoMFh3RfUcajVKuZfPooRIyI5cqSIsDBvq/0HDxwlI6OYWbOHyMLNaVBWWmMRZA4ezEOr1beZcyA5F1GUrMrdLp07AlGS6N8/uNc299VqtRw+fISUlFSLIFNYWNjuXIPBQEZGJnFx5rLL+HhzZplarSYqKtLKiWlgYgL7YsLJLikmKb8IhYcvDkFh2MeNRzFARWvT8CagyWjCtvl31dvPD0NpAdDccF8ScRANhLg7E+zmjJ+DDb72NmiUPdem2SRKVtkp/11TypES3WnZVrcmv1JPjJ/ZdTLIXY1SAX4uaosI07phr2xbLSMjI9P7OCOBZv369VbbL730UocGIyPToegbUa19EaG+xZlB9IzANPl+EmNKqcjJI29/MvVl5ST9/S9DZz9jLneqMjdRFXT1KFc/g+mCxUh+J7A6rspHueNzy6YkKDBN/I+VG1RDZRXbv1iGoamJg7//yYTbbyJ2yoR2lxMbGhAbG1B5Wd9c9Z95wRmfvslgQNn8VNMvLoYLH3uAv998jznPLcEvLuaM1wOo+uYzalb+0GZc7ReA+3W3YDd4uJyV0QmovLzxeXAJjft2U/n5+3guurNN2Zj9sFHUrPgewdYOu4FDcBg2CrtBw+UspjNErzdiNJqwt2/xZpk9Zxjr1x+koUHHgAHBXHnVWEKPE0B6CsEhXgSHeFmNNTUZ+OijdZSV1rJj+2Fuue0CwsN9TrCCzEcfmjOmTkVjo56Skmr8/FqKbkaOij7JET0Po9FIVla2xVEpNTWNrKxsRLFt6dCJSElJtQg0EeFhvPn6C1SWVoBCSXRsy3eNUqVif2UjXjOu4nR8zYobdIS6mPulCYLAvTdeS43CBn9HW7ztNagVPV98+HZ7JSmFZgvr/gG2PHRhy9/d0Qo9BVWGkxxtjZ1aIMBdg7GVmhPnb8vy1X9tSgAAh0lJREFUO8Nk22oZGRmZPoScQSPTNzEZUf79X4TKHMuQ5ORjttJW2+Lg6U5ZVrZln8rWBuzdMF74FMq1L6AozwRAMGhRrn0e09SHkAIHWr+GUY9qwxtWpVPi4MuRfKzFj22ffYOhqQkwizW6+uNKgpppSk+h5OWnEOtqcZ49D49rbz7h6VUezUPfqMU3tv2bgeRVa9n700queOMl7FzMjRGjxo8mZNigM87gaY3DqHHU/Pojx9IJFPYOuM6/GufpsxHUXdd743zFftBQ7BI+aLenj01ENNGvvIYuIBJFL0nt70mIosjmf1L58YftDBkawQ03TrLsc3S05YYbJ+HkZMeAhJAOf21Jkti9ew///ruTiIhwpk6dYikZ6Qh+/GEbZaXmPhVHj5bzxOPfcedd0xk9pv1m0+cDkiSRd7ScpORcpkwZYCXIBQWd2M3Oz9+NhARzc9+4uEBsbXvP554kSZhMJlQq86Vfamoat99+F3p92+ygk+Hj40N8fBzx8eZGvjExMfy7+R+Wffk5Dd7B2PgG4xAUhp2nP+E1WqLcWh5YeKpPLiQIooizYCTIxRE7tfXnXLR/zxEVTaJEXoWO5KwGS0lSXqUBTycVj1zUEmdKoY6kPHMPubxKazEmyF1DVlnbn317ttVB7ho8HJVtHoAc3y/m/9m77/CoyuyB4987vWaSSe+VFDoE6b1ZELFg713Xbfpby+661tVdXV1773VtqFgAQQWk9046pPeeTCZT7++PgUmGhI4i8f08Dw/MbXPfuckw98x5zxEEQRBOfSJAI/Q9shflihdRVO7oWqQLwjnjHnJXbiZz6kR0JhNjrr6M1ppaRl52IfqgfVkGOjOeM/+BetkTeMp836BKHhfKJY/hmfwn5OTR/mMq1r+H1FTqf+yNHoB38Lk9Tie6fyZ71qzH1tiENTGeQWef3mMbd1MDtU88jLfNdxPV+vU8tEkpmCZOC9iuKieP9R9+RtHqdUSmp3LZi/8N+MAme72sfOM9NvzvMwC+uu8RLvjPw6j23bAfT3AGQJvaD9PkGbQv+x7z9DMIufgqlJbg4zqmcHQOVnBZUigIHjlKdGY4Brt3lfHO28soK/PVofrxhx2ccebQgMyInyOY0d7ezsKF3zFv3heUlna9l7z66htceeXlXHPNxSfkeeLiQtHrNf5pOkajlgEDf5vTENvbO3n/3eVs31FKc5MvWB4VFcxpp3XVEBo8pCsIp9drGDgowReUGZxwytWPsdlsvP/+h+Tk5JKTk8sf//h7Zs3y1a6Kj487bHDGYrGQlZVJv35pWMxGcDqoqqlh5NnnE5uQQMq+DBev10Pejm2c9vgdKDVdwa7S5raAAE1KRCj7vxpReD0EK7wkhASRaPVNUwrTaX5VQQeH20tFo4vypq4gTHmjk4pmFwfUgwagoT2wmG68Ve0P0FQ0uQKmOQ2I1WFzevcFYNTE7asVY9KJKYiCIAi/ZSJAI/Q5ig0foCha6X8sq7R4Zt7D5iXr+enlN9n65be+Tkrnnd37ATQGTBc9TNPHD6Eo3wKA5PWgXPoUHvetyP0mIxWvR5nzXddzaM14Jv0Bekm5HnjmdNInj2Pjx58T3T/T3z3Jv6/LRe2Tj+BpbgpYXv/qc2jTMlDHdBVF3PHtYopWrQWgJq+Qsi3bSRg+xL++s62d3B+W+x9X7NjNroXfM2TOWYd72QLYt22m5dsviPjLP3pkY4Rceg1BZ52LNinlqI4pCL9W7TaHPzgD4PF4+Wr+Bm6+ZeYh9jp+n3/+JS+//GqP5TU1NTzxxH957733ufTSS5gzZzZarbaXIxyZKVMHMmRoEm+98SObNu3hyqsmYdl3Y73fgbVU+gK320NLcwehYV3T/PR6DRs3FtHR0RWY2L6tJCBAEx0dwiWXjiM9I4a0tKhTomaPzWbzB2FSUlIYN24MABqNhg8//AiXy5e9sXt3jj9AYzKZSExMoKTEFxzU6XRkZKSTmpqCxWRE8rioq6+not3OpopaDHHJGOMz0fWfyI82iC9v8Ado0jKzwOulo6IEc3JXZmdxQwskd9W+uvLa68htshGm1xCqU6P4lUyJbev0RVvM+4IjTTY3d35cecRtq/erb/fQ4fRi2Ff7JTlcQ1KYxp8F4+4WoDlzcBBnDg461OEEQRCE3yARoBH6FMXOb1Hu+Nr/WJYUeKbeQZPLxOq33gegOiePj/98Dzd+9GZARklLVTWlW7Yz6KyZSGodnul3wrJnURT7AiKSLKP66UU8bbUodi0MeF7PxFvBaD3oeWn0esZec3mv6xrffQ1H3u4ey2VHJ7X/fZToR5/2B0lOu/h8di5c4p9iVLR6fUCARm8J4rx/3c9Hf7wLp62DUVdezOBzzuxx7INxVVXQ+O5rdGz0jbn1m88JPv+SgG1UIVZUIQcfqyD82nm9XhTdgqmnnZZKv37RFBRUoVYrOfOs4ZwzZ8QhjnD03G43y5YtJzMzk7i4WADOPvssXn/9Tdzu3lvY1tTU8vTTz/Luu+9z2WWXcN55c9AfYxac1Wrijr/MZveucvoPCOyEU1fbwr///SWXXTae7BGHLor+a9fQ0MbWLXv9xX2jY0L45yOX+tcrlQoGDExgw/pC/7LSkvqAY0iSxDlzTvvFzvloOZ1OCgsL/d2Udu/OobS0DHnf/wszZ073B2jUajXp6f3Ytcv3f0xOTk7AsS6//DK8Xq+vxXV0JLdddjHbFAqsQ0djTEpBmx1KYEWjLtU2B15ZRiFJhEVEMnzUGMwaBR7ZQ5haIslqITkksAaWJElkWY++E+GJIMsydd3aVpc3dWXEtNi9XDEmhItH+bLmgvRKGm2eIw7OmLQK/7Qkp1vGsO97jZkDg5g5UARhBEEQhCMnAjRCnyHtWY1i3bsByzzjb0GOH0bdT6sDlo+5+lJ/cKajuYV173/Ctq8WIHu9RGdlEB4+EJQqPFP+BCt1KAqW+fdVbvks8Dn6n4mcEHgz5/V4WPrcKww+56xDttNuX7WM1kVfdY1Bb0AdHYtzTwEAzpI9NL77KmE3/B6AkPhY+k0Yg6uzk9MumUvckIE0VVTidjj9zxOWnMg5D/6Ntto6Bpwx/fAvHL7ixM3zPqRlwXzwdN0sNn/+EabJM1BZD16TQRBOFS0tHXw+bx0lxbXc/+BF/umBkiRx2RUTWL5sF3MvHIP1BN5Aulwu3nnnPebP/4qGhkYuvPACbr/9TwBYrVamTp3MunUbmD17FmNGjWTdpi3M+/QzbLauWlWNjY08//yLvP/+B1x66SWcf/55GI2Ggz3lQUmSxICB8QHLZFnm9dd/oKqyiSef+JrRY9K5+prJPTJsThUrfsrhk4+73u/37qmhtdUe0FFp+PBkOjocDB6SyJDBicQnhJ2MUz0iHo+H0tIyfxHf3btzKSwsPGhQD3y1Zbrr3z+LlpYWUpKSsIZYeOflFygqq8CSnM6w02czKdbqz+qQkTHEJGEddPgAlcsr09jpIkyvQZIkHnnuRVxeL9ERQdTXtx92/5/L/rbV+6cl7a8RU9HkotN18JBL9xoxSoVEbIia4vrAKWBhZqV/KlJWgplgjZe4EDXBhp71YQRBEAThWIgAjdAnSJU7US5/Hqnb912e7EuQ0ycDvgK54WkpfP/UCzg77AydM8u/XXVuAVs+7wqSrHrjPbJGPuZ7oFDimXALslqPcndg1gyAbE3Ee1rPzJhdi75n21cL2f7Ndww4Yzrjb7gKQ3DP2gW6/oPRZg3EkeOrdxN+2/+hTU2n4s7f4W331RJp++4b9AOHYhw9HoCz/v4Xf3emip27mX/vP1FpNFz6wpOYw32BlO5ZNYciezy0/bCQxv+9g7elucd64+jxB615Iginis5OJwu+3cw3X2+is9N3E7ZubUFAx52MjBgyMmJO+HOrVCp++mkFDQ2NACxYsIibb74Rg8EXALn6qisYmJbE0kUL+Prd1xj+j2eZecff0daUsPDj/9HS0uo/VnNzCy+99AoffPA/Lr74Qi688AJMpuMLJq1elceO7V31b9auyael2cY/7r/wuI77c5FlmdLSerZv87XAPv+CUWT178oIGjw4MSBAI8uwc2cpY8d2FW+fNHkAkyYP+EXP+0jV1dWxc+cuf3vr3Nw8Ojo6juoYHR12bDYbRqMRWZa58uZbMCcks353Ho7oZFrjklEPDaIdWFHRSJbVSLTR17Y5NT2T/PI9vR5X43UTpVeTFBpMjElHtEmH+YBCvmqF4hcLVHQ4vZTvC8CMSjVi1Pqy4pbntvPMkrrD7N1TeVNgMGZafxOtdu9B21aHh5tFzS9BEAThhBMBGuHU11iC8vv/IHm7vlH0ZM3EO+S8gM2CY6K44PGH6GxrD6gDkzwqm7ghAynf5guStDc04Oj+gVhS4B19DWj0KLd+7l8sq7S4p/wZVIE1Wpx2O6vf+sC3jddL7o/LGXP1Zb2euirESvR9/6bxvdeRtFqMo8YBvkBNzWMP+Lerf+kpNMlpqCOj/MGZzvZ2Nn/2FZIk0V7fwJd/e5CLn/k3GsORffNt372DXe+9SkdhQY912oz+hF5zM9q0Y2vHLQi/Bm63h2VLdzHvs7W0tATe5H780SpGnJZ6QuuL2GwdLFr0HVu3buWhhx5AkiQkSeL888/j8cefAHwZNdt25+KOiGPpK0+zYekSfzZE6PCxaEIj6AA6QiIZ/6dEdFWl/PDp/2hubvE/T2trK6+99gb/+9/H3HvvX5k4ccIxn/Pw7BRmnj6EJYu3Icu+LJtLLz/24/0cWlvt7NzhC8hs315Cc3PXtUxOiQgI0CQlR2A263G7Pf7ivllZcb0d9qRraWmhuLiEIUMG+5e9/vqbfP31t0d8DIPBQFJiAiEWMyrZQ1tTA+V7iti8O5c9xkiqbA4cHi/EZhEbm9XrMapsDn+AZvLMM4itrKJedhNt1JFoDSLGqCPaqMWk+eU/MsqyTFOHh/JGF0lhGoL0vt/XTcUdPPBltX+7f81VMTDOlyUVZz2yzlr721b7CvSqSQ4PrPN07vDgEzMIQRAEQTgKIkAjnNra61F99yiSy+5f5E0ciXf0dSBJbP5sPumTx2MK82WWSJLU1bFpH0mSmHDTNSx89EnGXX8l6RPHoTUYwNbWfSO82ZeAxohi44eAhGfCrRAc2+OUHG3thCYnYmv0Ff0dcdF5/syW3kgqFaHX3uKvHwBgGDGaoFnn0frtF74FCgXu+lrUkV3FFtU6HanjRtFUXoG9pRVJocDV6ThsgMZVW03je6/TsXZlj3XK0DCsV1yPcdxkka4tnLJkWWbDhiI+/t8qqqqaeqyPjg7h4kvHoVT2LOp9LIqLi5k37wsWLvzOn+1w4YVzGTx4EACnnz6DL7+cz7jZ52LOGsIPbU48lS20WSICpqrETJ8TcFx3cCTtwZGMvT2NsI4mvnn9FRobuuqldHR0kJKSfFznrtdruObaKYwZm8Frryxh6LBk0tKiArZpaGgjJMT0ixYRzsurZNvWYrZvK2Hv3hrkg8xM2b6thMu6BZQUCokHHryIiEjLCbu+J9rChd/x5ptvU1FRgVKpZPHiBf7aQllZmQcN0KjVauLjYn3BGAk6JQm7QosqJp6Kwhyatq/3b1tRVEhx0pHVPml2dE3tmThjJhOPY2zHyuOVqWl1+zNiujomubA5vQD87exIxqT5OkJFWwKDMGWNrq4ATUjgOn/b6hBfJsz+jJiwXtpWC4IgCMLJJgI0wqnL0Y7qu0eQOrpuwLyRGXgm/xEUCopWr2fZi6+z5p3/MeHmaxl01gykXrosAURnZXDN2y/16LB0IO+g2XjTJvm6NWl7n1pgjgjngscfYu+6TWz+7EtGXHx+wHpZlnv9UHjgMuvl19GZuxNJoyXiT3ejCg0s1bjoX/8lb+kKAFLGjOSse/9yyDbaXrud5i8/pvXrecguV8A6SaPFMmculjkXotDqDv4CCMKvXH5eJR+8v4KCgqoe6ywWAxfMHc3kKQOOO3PG7XazYsVK5s37gs2bt/RYP2/eFwwePIjW5maWLV5EotWELXkgla0O/zZho6fAZ+/4zi0khLDqQuodnRhTAzMdPOYQaswhTHvwCfTVpSx64yVqq6qYOXMGcXFd2SEff/wptbW1XHbZJYSGHl3dqIyMGB79d8/pmk6nm38+PI9gi4Ebb55OTMwvUyD87beWUlJ86GkqwSFGklMi8Hi8AcGY6JiQQ+z183O73ezdW+wv4Jubm8tTTz1JeLjvywGVSkVFRQXgqzGTn1/gz6LJyvJde0mSiIuLZdCggWRmZTGgfxaRERFcfuY0GoBBd/6b4PiuTnoqvTEgQFOyezuGtNPo6N4LWpYxSV7iLUbiLCaijVqijVp0v3CXqqpmFwU1Dn99mPImFxVNLlyeQ5fkLW90Ar4ATaRFhUqJv9W1b52PSafkjtPDibKoRdtqQRAE4ZQjAjTCqcntRLnkMaTmCv8iOTgWz4y7QaXBYevgh2deAsBhs/H9f58nKrMfEWkHbw3dPTgjyzLFGzaTMHxIz6CN/vDfSkqSRMroEaSMDiwebNuwhvaliwm/7S8ojMZDH0OtJuqvD6MwmXutAzPlDzcTmpSIOSKMrOmTDxtccpbupeXzj3osN46bjPWK61GFHaxXhyCcOgoKq3sEZ3Q6NWfPzuasWcPR6TQH2fPINDQ08NVX3/Dll19RV9d7AMEcFIQcFMI/77mTdSuW494XEL3m4utooSvDTaE3MubM2cyYOoURY8ejVqtxu10sXPoTa6pb0KQOCAgqtzo9tFpjmXbn/ZR//BrnnnO2f53dbuedd96lubmFefO+4Oqrr+Taa68+qrFpepnCMu+ztdRUN1NT3cxf7/6A884fxdmzs487wNXZ6SJndznbthVTWlrPvf+4IKCz1uDBiT0CNCqVksysWAYPTmTwkETi40NPegaELMtUVFT6i/jm5OSSl5ePw+EI2C4nJ5eMjATAV7S3u927czDrtWxcs5r8vDwyBvTHYzSTOnIcEYOGU+7yMDfLlykVFRtHdUU5HdUVGLsFaAxxyahUKpLS+pGansGwUaNps5pwe71EGbXEGHVEGrVof6Gsola7x1+kt67VzRVjuwJ7C7a38uXmlkPs3buypsAivjdMDCVIryQuRE3MAVkzU7LMB+4uCIIgCKcEEaARTj1eL8plz6CoyfMvkg0huE//uz+rpaO5GZ3ZRHudbzrAkDlnHTI4013Fzt3Me+s9SrbsZMZf/sCgs2Ye0X51RXupyS+i/8wpvQZLXJXl1D33H2R7BxV//SORd/4DTXzSIY+ptAQfdJ0h2MLoKy8+onMD0GX0xzhuMrZVywDQpPQj9fY76Iw6vikSgnAyHZiRNnPmYL5buIX6+jaUSgVTpw3i/AtGHVdXIlmW2b59B/PmfcGyZcsP2kGn/7BhjDr/EtqDI2h1edn6xF/9wRmA4u+/QTH9IpKCDAyPCCIzxITytIcCjqFSqZk9YxpnyzK5ewpYWdFCrTYYd7c5PvXrl7N91TJ2r1vJpJlncN5lV7Bu4xZ/nRqn04lWG1hP41i0tdlZsnib/7HL5eGTj1ezbm0+N90yk+TkiGM67u5dZfz7X1/i7pbdsWdPbcDUqsFDEvn6q41Ex4T4AzJZWXHodEdWX+Tn0tDQ4G9vnZOTS05OLq2trYfdLycnhzlzZlBXU0NpUT4Txo8jJT2duMz+mGITmJ9XSJUxDv2s0SQpuz6aVdl8gZ42pxuzRkVaRibVFeU4qrsKO0uyTHxCAg8vW4VWc3wByKPhlWXqu7WtLmt0Ut7korLZTZMt8HdkznAL5n2ZLEdSI6Z72+r9fyeFBY5t1pCehfcFQRAE4VQnyfLBZnaffKdSdfzfSjX/kz7OjmaUa99CsXeNf5Gs1uM++2GwJgRs6nG52Pjx5+xc9ANXvPI02iNoS9vZ3s6rF12Du9P3odgUFsq1776MWnfoaT+yLPPZ/91L2dbthKcmM+nW6wM6KXntdir//mdcZSX+ZUFnnUvotbcc0bAPfK6ODWswZI86aIcld0M9yiALkjrwg7C7vo6qB+4i+IJLMU2aTkSkRfzcHkJeXiV1tS0Mz07BYDj+G96f20n//fyFhIebKSmp56v5G2lqaufW350esH7Vylw2bizioovHEh197NNd7HY73323hM8//4LCwqJet1Fr1GTPOJOsmbOp8ijo/h9qzeof2PvRKwAEWYI5fc65XHzzbRjVR/bdyP7raXO5WV/dwvrqZhSyl6V/vgy5W5AoqN8ABl5wFfnfL6Bo62aCgy3Mm/eJv65JcXEJ//vfx1x11RXExh5dp6qqqiZef/V7cnIqApYrFBKzzs7mgrmje828ga7ivpZgIwMGdLX3bm/v5OYbXwmouzX3wjGcf8Eo/2O320NTk43w8COro/JzcTgcfPrpPH+GTE1N7VHtr1arsAZbCAm24GisprGmBoBHn38JT2Im3+49sm5Dl2ZEkx5ioqSoCEkhoQuPpszmJNqoJcKgQXWQ6bsn2pebmymqdfqnJzncR/YR8rGLYugf4/t/dHdlJ3d/Ugn42lbHh3QFYeKsauKtGiz6X64b1LH6Lb3finH2Lb+lsf5S9k9hFYQTQWTQCKcGtxPFzm9QbP8SydXpXywrVHhm3NUjOAOgVKsZdcXFjLj4fH/no8PRmUxkzz2Xde9/DICtsYny7btIHpl9yP32rt1A2dbtgC+TZuMnn/sDNLIsU//SUwHBGW2/TKxXXH9E59Sdt8NG/SvPYlu9nOALLyfkoisD1zs6aflqHi3zPyHk4quwzL4gYL0qLJy4Z14XrbPxXZfqqmby8ivJz6skIsLCueeNDNhm0cItrFtbgFqtZOjQJCZO6k/2iNSTdMYCgMvlZt5na3n/vRW0t/veC2bMGExav2j/NuPGZzJufOYxP0dVVTUff/wJCxYsor29vddtIhOTSBo9AX36QLQhYVR6em4TOmwMEVX5zDjrbEaOn4D6CN+HDmRUq5gSH8q4mBBWb9rC7ohIqiu7AiYxM87FG5NM2lW3MeLSDiJsjei6BZXfeec9vvtuMQsWLOT002dw1VVXkpAQ39tT9RAdHcLf/zGXpT/u4MMPVmK3+2p9eL0yX3+1kQ3rC7nx5ukBnZK+W7SVFT/l+Iv7jjgtNSBAYzLpSE2LpLCgGkmC9PQYrNbAml4qlfIXDc44nU4KC4vIycklKiqKcePG7DsPFW+99Q52u/0wRwCFQkFwkAm9wYDCbMEQGYMpPhljXDL6yBjy3/wv7AvQFOXlMab/0EMfT/YSZdQRa9Zj3hcES0ztev+JMB285tjxWFNoI7/aVyMmyqLmhkld9YyW59korHEcYu+elApoaO8KKKaEa3jq0lhiD2hbLQiCIAiCCNAIPxePC6lkA1JDMXLsYOSYgcd2HNmLtGc1yg0fINkaAlch4Zn0e+ToAf5lW774Bkt0VEDtlyMNzuw34uLz2fntImIGDWDcdVdiTTh8i9bW2jqUGg0epxNJoWDizdd1rfvmc2xrfvI/VliCifi/e3tktxyO7PFQdd9fcJbsBaD5s/+hGzAY/YAhyLKMbfVPNL7/Bp563ze8TZ9+gGnitB7TpH6rwRmn083evbXk5/kCMvn5VbS1dd10JSaGBQRoOjudbNnse61dLg8bNhRhMGpFgOYk27u3lpdfWhKw7MMPV/KP++aesG/c6+rq+OSTz3pdFxsVQdSUswjNHnfQouNBGhVDw82kaoJJmPLfE3JOABqlgskjs5kwbz5rf1rGFx9+wN6GZoIzu7L1mtUGmoMN/Gf5FgxleUwYmMWSJd8DvoK0CxYsYtGixUyfPo1rrrmSpKSkwz6vQiExbfpghg5L5s03fvT/XgBUVzfz8IOfcf+DF5GR4cvOqaxsZM+eGv82u3aW4XZ7AurWnDPnNJxONwMHJpCaGvGLfpvr9Xppa2vDYumaInPJJVdQXe1r2zx58kR/gEapVJKZmcGWLVt7HCcs1Mqw7GwSho4gKD4Rh9bAruJydGGRvT6vMT6Zxq1rASjMy2GOQYMEyPiubZhaIj7Y5G9rHarXoDjBWSSyLNPc4fF3SCprcqJRSlw7oSsIs2hHK5tLfO+NiaFqoGtdvFV90ADN/rbV/aINhBvwZ8REW9SolF3j0KkVpEX++jMSBUEQBOFkEAEa4cRqqUSR9wOKgmVInb4P3PKO+XjOvC8gkHIkpJpcFOveRVFX2GOdHByLZ/S1yLGD/cvq95aw/KU38LrdZEydyJTf34Qh+OjnqGuNBm6f/w4d7iMPZAydM4vUMSNZ+cZ7qHU6wpITAbDv3Ebj+290bahQEHHH31GFhh31eUlKJUGzzqP+xX03fLKXumceI+y2/6P5sw9x5O4K2F62d9C6+BtCLrziqJ+rL1n6405WrcwlP78qoObFgUpLG+jocPinMpWU1HPgvdHoMekBjxvq2/jfhysZPTadIUMSUR/h1BXhyMmyjCzjb/Gcnh7DuHEZrFrlq0FltZqYNKk/skyP63Ukmpqa+frrb4iLi2Xq1CkADBo0kH790igo8L33aDUaQow62usqaS9vo7O+ukdwRvZ6yQw1kx1hITXYcFQ31rIsU9roYk+tg1CTigGxOpSHaGmtVCoZN2Ua46ZMY82O3WxxyNS5A7e368zY+43gf2WVpIyfQtGq5cgeXxaD1+tl8eIlLFnyPVOnTuaaa64iNfXwgcfQUDN/ufMc1qzJ5523lvkDnAMGxJOe3pXBNHhIEksWb/c/drk8VFY0kpDYVYh8xC8U6JRlmdraWn/dmN27c8jLyycrK5Nnn33Kv11aWqo/QJOTkxtwjKysTPLz8tGZjCCBvbIEpewhO2M4/7j/Xv61vohylwwu10GDMwAJw0cxIzGCtMxMUtMzUSsUXJIRQ6hOjVWnPqFTenprW+0r2uvC5vAGbGs1KgMCNHEhan+ApqLZhccr+38e40PUXW2rrRriQnydkuKtGkL3ta0W0ycEQRAE4diJuwnh+HlcSMXrUeR9j6JqV4/VkiyjXPUa7vP+A8ojyBpprUG58QMUe9f2WCXrzHiHX4w3YxoougIoXo+HJU8+h3dfXYa8H38ieWQ2/WdO7fUpXFWVNH7wBrLLhfWK69HEJwasN4YE09HtA6bD1oHH5TpkwMccEc6Zf70D2ev78Ouur6P2qUfB2/Vh2HrVTej7Dzr8a3AQpskzsO/Yim3FjwB4mhqp+effe2ynMFsIufRqzFNP77HuVOP1ypSW1tHcZMNqNQXc5DkcLj763yo6O510drro7HRx7nkj/d/kAzQ0tLF7d/khn0OtVpKaGkVrq90foMnIiOGlV25my+Y9rF2TT1FRTcA0DYC16wpYvTqP1avz0Os1jDgtleuun4pWe3ILmZ7K3G4Py5ftpry8gbLSesrKGrj+hqmMHNXPv811N0xl+/ZSzp6dzelnDD1oDZTD+e9/n2b+/K9xuVz065fGlCmTkSQJSZI49/zz+G7zLgxpWRR/8Tb28mL2v+PUrl1K7OkXICkUyO0tpGq8nDV8IKGmYytE/MZPjczf0tXV5tzhFq6feGRtsscM6s8YoKzNzqrKJvKabAHr9RExpJ57GXFTziTng1epKcz3r5NlmR9+WMoPPyxl0qQJXHPN1WRkpHMokiQxdmwGAwcm8N67y9m4oZAbbpoeEFwYMCCO+PhQ+g+IZ8iQRLL6x/1ivxOtra3divj6Cvk2NDT22C43Nw+v1+vvHJWamsLKlasAaKhvoKy0lPiEBHbUtxI89RwmDZ2ER6WhtSiX3c/cB0Bhbi4KSSLSqKWivbPHcwBokIkx6YgLMjBwZCqRikkB69NDDt3N73C6F8nucHp5bkndEbet3q/R5sHm8GLU+l6LOKsvsyfSoiLOqqHD6fUX+L3gtGAuHHly25gLgiAIQl8mAjTCsWuu9AVlCpYjOQ79bZnUUoli+3y8w+YefCNnB4qtn6PYtQDJG9gBQlao8A44C+/Q80HT8ybI43YTnppC1W7ft+oJ2UPJmjGl16ex79pG7X8exmvz1ZaoKsgl+v7H0CT27GbkdrrY/s1C1r33McmjR3DG3bcHrC9cuYbo/pkYrV0fWCWFAtnlpPbJf+Jt7brpMo6fQtBZcw4+/iMgSRJhN/4eZ2EerqqKnhsolQSdOYfguZehNJp6rj+FOBwuPv1kDevWFtDQ4Pv5mjZ9ENffMM2/jSRJfLdoa8B+EyZkQrcATXp6z6KoFouB9IwY35/0aJKTI3ptG6zTqRkzNoMxYzN6TNEAWLu6q5OY3e6kqLD6mIMFvyUul5vKyibKyhpQq5SMGt0VfFEqFXz4wQp/rROAsrKGgABNQkIYz71w/VG/1h6PB2W3KX4KhQLXvi5LBQWFrPjpJwaMHMPm2lYKY/sTHemrYxM5ZhrFn3ZlwkVazEQ2lDJmyACGxKcdceaDyyOzo9zOuqIOrp1gRaf23RBnxWiZv6Vru9GpXTftLXYPC7a1MinTREzwwYMc8WY9l2ToqbM7+WLTLiolHVK3bkBKjRZn8W4MHiderYlOd2AWxfLlK1i+fAXjxo3l2muvon///occS1CQntt+fwb19a2EhQXWitHpNDz2nysPsueJ43A4yM/P35cZk0tOTg7l5b28L/aivb2dV555mvqaSkqr62jXGMk68xwqF3+B7GijtqKc+IQEGuwuilrtoPJ1EDLGJflStWSZluYm2tvaiN4XoDGplUQbtUTvm6IUY9Ri1qj8Px/Hk1nS1unp6pTU6PJ3TpoxwMzFo3z//+jUEhv2dhxx4V4AtVIiNkRNi93jD9BMyTIxrb8JjarnFL4TPeVKEARBEIRA4k5COCJOu52Cn1ajV8voXPVE2Hahb8o76PZycDze9Mkotn6O5PR9o6vY9gXelLFgOeCG2evxTYva/LF/WlTA6uTReEZcDkEHTx1Xa7VMv/13ZE6dyLIXXmP67bf1etPU9sMi6l97DjxdU128ba1UPXQP0Q881qPt9bcPP07RKl8mz+7FS8m+8DzCU3zbtFbX8u3D/0GhUjHysrkMnzsH9b7Wtg1vvoSjsOv1USckEXbzn05ICrtCbyD89r9R9fc/I3dr4avPHkXoVTeijjl8zZxTgUajYv26ruAMgKPTFbCNWu1Lqe/eDabzgG3S+kWRkhJBckok6em+gExEpOWor8WBwRmXy43JrEepVODx+G52R49JDziuLMv84+8fkZwSwegx6WRlxfq/sf8t8Hq91NS0UF7WQFmZLxumvKyBqqomvF7fNUtJjQwI0EiSRFxcKAUFVf5lZWUNPY59NMGZiopKvvjiS777bjHvvPMmVqsVgPPOO5dPPvkMhUpFzNDT+KK0maXakh77h42YQP33XzBxylRmnH0OGQMGHvHPT6fLy+YSO2sKbWzY04HN6ftZGRyvY1w/XxA1O8mARinh9MiEmZVkxXTV51iVb+PDtU18uLaJ9EgtkzJNTEg3EmLsffzheg03jR9Gc6eTrzbvZo9bhaTRUrNyMd5OO2pA7mxBKSkJG3c6dUX5dNR11YtZtWo1q1atZtSokVx33dUMGnTojL8DgzO/hOXLV/Dmm2+zZ88ePJ6DT1s8kKRQEBQegTk6lqDYBPKtiZiGTCHO0BXMbtu1ibY9uRTm5ZA9ZizRpsBaKUqtjlseeJTBaSnEJyejUqmZoNExMdbqL+R7rGRZpq7NQ3lT17SkskZfNkxzR+/jLG3oCmQqJF+wZU+ds8d23dtW7++UFBeiJiJI1WM63f7AoSAIgiAIvzwRoBEOS/Z6+ebv91G8tWtevlYDf5qr89+k1DR68SpUWIeORTnwdOSIdJAkZI0R1cqXAZA8LpSrXsdz5j/8xSKksi0o17+L1NzzW09vWCre0VcjRx55N5a4IQO5/JWne9w8yR4PTR+8ScvX83rdz9vaQtWD9/gyabpNdxpyzpn+AA2yzMaPP+fMv94BwMo33sXjcuFxuVj1xnsERUWQNW0ybT8sou37hf5jKIwmIu+8D8VhWnUfDW1yKhH/dy+N77yKMtiK5fyLMQwdcfgdf2VkWWbv3lrWrcmntLSeu+4513/tJEli5Kh+LPh2s3/7Tkdg8EWSJEJDTXi9MjqdGp1O06MdtsGg5ZXXbj7hNRHUahV333MubW12NqwvZO3agh41agoKqtizp4Y9e2r44fsdBAcbGDmqH6NHp5OeEeOvq3Kq6z7NYr/33lnODz/swOl0H2Qvn4ryBrxeOeC1iIsPpbi4lphYK/HxYfTvf/RBR6/Xy9q165g37wvWrl3nD+J99dU3XHPNVXjcbj7+5GMGzrmIiBFjURt7b5GplCSi1DJ/+uwrwsxHNh2lrdPD+j0drCm0saXEjrOXqSZrizr8ARqdWsEfZoRj1CrQq6WALIVleV0/t/k1DvJrHLzxUwNDE/RMyjQxOtWIoZdOOME6DVeNHUqn28Pi3XswaTxUazS4nE4kwBhiJWPuVWQqJJxle9k870NaSruK/65bt55169aTnT2cm266/rCBmhOtpaWFDRs27puqlMsDD/yDyMiuIH1BQcFhj2E2GckekU3q4GF0JPenDRWuwySXGBNSkBqq/YGfaKOOEK16X2aM70+8ORWNsus1DzrK6Vsuj0x1i4tws8ofDFm/x8Z/FtbSebgTPEBZY+B74sA4HUF6pb8uTFyImjirr27Mr71ttSAIgiAIIkAjHEpDsa/Y7961jA6tQxGjYE+l79vfMIvC/2FPDoln5Q4Xhdv3woLviR9aw4VPPuJblz4Zb8EyFDW+4I6iaifeohXI1iSU699DUbGtx9PKxlA8Iy5DTh0H0qG/yctbuoKa/ELGXH0p6n0BkN4+hLYu+rpHcMYwajzu2mqce32FQL0tzVQ/eA9RDz4O4b70/sQRw0gYPoSavEJOu/QChp0/GwCXvZPawj3+Y0X0SyVzykQcRfk0vPFC15NIEuF/vAt1VM9pNsfLkD0KQ/aoE37cn5ssy5QU17F2bT5r1xRQW9s1Day8vIH4+K4CyqNH92P3rjJGj0knMzOW4F7qNTz7/NG3Kz+RzGY9U6cNYuq0njewa9fkBzxubu5g8XfbWPzdNqxWE6NG9WP02HTS0qJOqZunpsZ2Nm4s8tWJKWugvLyBZ5+7Dp1O499GrVEdNjgDvu5aDQ1tAS2VL79iAtddPxWl8ui/yW9tbeWbb77l88/nU1lZ2WP9l1/O54orLkOlUtESFEHskLG9Hidcr2F4RBCDw4IwqA9fMLy+zc3aIhtrimzsLO/Ee4j7bAlo7wzMiJic2XNKYofDQ5OtZ+aEV4bNJXY2l9jRqOoZlWJgcqaJYYkG1MoDsiFUSs4Z3A8G38P1N97Et/M+5Zt5nxA8dba/0LEmPoXRf76XtsXzWL1oQcD+mzZtJjd3ws8aoKmvb2DVqo2kp6cTHu77/S8sLOK++x70b7N7d44/QJOZ0S9gf0mhJDguHlNkNJa4BNqKC2jc8BOjBgzhoUf/SYfLw3827eGwZJk519zInMf+6f99DNKo+OOwpGMaV4fDi1IJ2n1ThXLKO7j3ozKqW1x4vPDIBdEMjve1yg42KI84OKNUQLTFV5w3OTwwIH3jpKMvQC8IgiAIwq+HCNAIPXk9KLbOQ7FlHhK+D4yJUUoiQhR8vcrJ3iovYSEqvP0m482cjhzej/ovbvbtK8v+QAkAkgLPuBuRvrgLSfbdaChXvQYeJ5Ic+GFUVmnxDjkP78BZoDp8C057Sws/Pvsy9pZWClasYcYdt5EwfEiv25pnnIVt9XIc+TkABF9wKcEXXYnXZqP6oXtwFhcB4GlpovrBu7E++wLoQpAkiel3/B6t0YDe0nUDqdbruPK1Z9nx7XeseftDJt16PZJCgSLIgjo+Cece37e7wRdejmH4yJ4n9BsjyzJlpfWsXVvA2jX5VFc397rd2jUFAQGatH7RPPrvy3+hszzxkpMjSc+IIT+vZ6CgsbGdhQu3sHDhFsLCzDz8z0uwBB9fwdATyel0U1HRSFlpPYlJ4SR2K85cW9fKW28uDdi+vLyRtLQo/+P4+J5Fbq1WE/HxocTFhxEfH0p8QhixsdYe05UOzII6Erm5ecyb9wVLlnyP09lzioc5Jo7IUCtzZk7Du69w98jYMLZ238jjJlEjMzUjiXiz/oiCZkW1Dl78oZ78g7Qe3k+lgMHxekanGhmdajjoFKXuDFolr14TT26Vg2W57awsaKfVHlg7xumWWZFvY0W+DbNOwfh+RiZlmsmK0faoFxJstXL5jTcz98qreXFjHq3d1sUataSNGca2bz7FodDgVviyQkJCQjjnnLP92xUXl1BVVcXo0aOOKahos3WQl5fnz4zZvTuHmhrfFKu//e1uzj57FgCZmRkB0xe/nT+fnWtXUlhQQK3dyYRLryI4IRl1WCR2pZbur0qDyUzzhuUU5vq+GDColVg0Klq6BwxlGatWRVyQwV83JsqoRXuUQcHubav3d0raPz2pod3DPbMi/JlSZr2SiqaujJeyRqc/QBNn1fQ4tk4t7cuA0XRlxPTStloQBEEQhL7juAI0P/74I88//zx2u51x48Zx7733nqjzEk4WWwPKpc+iqMnpsUqrhhkzkqlSp2EaOB7PkKEAuDo7aa6s9m+3v8X0/nUFGwpQyUMYgG+qiuQOvJGRkZDTp+DJvgQMwUd8qstfehN7i+8Wo6WyipJNWw8aoFFoNETeeR9V999J8NzLME3wdXdSms1E3fcvqh+8G2eJL73f09RI5TtvEnTz/wEQHBPV43iuzk7UOh1D58xiwOnT/EEpdXgk0Q8/ScPrz+NpbSH4gsuOeDx9VUNDG48+8jlVlU2H3C4+IYzQ0FO7sPGBJkzMYsLELBrq21i7roC1q/MoKqrpsZ1KpSTIElj8urW1A/MRBgmOR2/Tk/7x9/+xZ0+t/+b4vPNHBQRo4uKsPY5TVlofEKBJTYtixszBxMWFEh8fRlx8KCbTiZvmB74isT/+uJR5875g9+6e71lKrY7oYacRN2oClsRU4lytXDAh279+xpTJbFmyFlN4JGMTIxkeZUXXS7Ho/WRZprDWiVmnIMriC2AEG5QHDc5oVRLZSQbGpBkYkWTApDt8Js6BJEkiK0ZHVoyOGyeFsrXUzrLcdtYW2XoUg23r9LJwRxtLdrfx3o2JB30+rU7Hn8cNprjV1/mpqKWDcbFWXnnqA1R4UXk78cguMm/9G7bC3Xi7ZTK+8cab/PDDUjIzM7n22qsYP37cQX9GXS4XhYVF/mBMTk4OxcUlATWjutu5YxdJcTEU5eVQmJuLRqXA4XSjwMvmNatwXvN7Qq6/AOu+4sfOfX8OZIxPJj45mdT0TJxOJxqNhv6hJuxur3+aUqRBGzBN6XB6a1td1uiivKln2+ruyrtNQYoO0aBWSv7uSuXdgjUGjYILRliwGlX+GjFhJjEtSRAEQRB+a445QFNWVsb999/Pp59+SmhoKFdffTXLly9n0qRJh99Z+FWSSjei/OlFJEe7f9naXS5MkZGkz5iJlDKa5H4ZmA6o5aFUq7ny1Weo31tC/d4SEkcMA2DVm++z5fOvcHbYCYmLIWtWBIr22oB9vTGD8Iy8EkKTjvp8k0eNYO/6TdibWwiOjWb0VZf418kuJ5I68BtJZXAIsU++jKQK/LFXmoOI+se/qHrwHlxlxegGDSPpL/fQ2BY4tx+gqbySeXf+A5VWw1VvPI9CqQzMGMIXDAq79XZwu/xTCH5L2ts7A27EQ0JMdNp7u42CuLhQRo/px6jR6cTG9rzp/6V5vV5aWlppbGygsbGJoCAzqampqFTHl2wYGmZm1qzhzJo1nNraFtatLWDNmnyK9/p+Hw5WXFilVjJmTDqjx6QTF3dkbZcPRpZlGhra/IV6y0rrKStvQKtR8cBDFwdsq1QpA26iy8vqA9YbjTpCQ820tnYQF+8LwEREBragj4oK5trrem9zf7zKyyt5440P+Oabb2lubul1G0n2MuzKGwntP9S/rEzW4PJ6Ue/7vTQYjfz17MlolIqD3gh7vDK7KjpZU2RjbZGN+jYPc4ZZuGGS73qEmlRkRGnJq/YFacw6BSNTDIxJNTI0Ue+f3nIiqJQSI5INjEg2YHd6Wbeng+W5bWwusQdMqTotOTAY9PG6JpQKiYkZJiKCfD/LkiSRbDGQbDFQY3NgUUFaRibFhQU4OjsJHzyC0MxBhGYO4rntpYyMDCbK1cqPPy4DIDc3l48++oQJE8YDvt+d0tIyf2vr3btzKCgo9HfJOhylRkN+XRNPfvApxrgUNCnDUH/7DVpk9l8Zr9uNQnno30UFMknx8Tz8v89QdXv/ndktwHgoDreXyiYXlc0uf+YLwHurG5m3sfeftUPpHoRRKiROH2hGrfJlxmREBf7fcc344/sdFwRBEATh1HfMdx1LlizhrLPOIirK943pU089hVZ79Gnpwq+Ax4ViwwcodwXWHmh3qVm504lnawU/bfmaiTeHE94vo8fuCqWS8NRkwlMD21TLXi/ODjvgC2yURd1Kwp73kbxuZEsMnpFXIscP9xcMPloZUyaQMHwIy19+g/4zp6HWan3p5p99SMe6VUQ//AQKfWBWwoHBmf2UlmCi7/83TZ9+gPXKG1DqdHBAgKZgxRq+vv9R/+OdC79n8Nmn93o8SZJA3TNlva+qqGhk7Zp81q7NR6FQ8NjjV/jXKRQSo0b3Y9HCrQDExIQwekw6o0enE9fLNJgTTZZl2ttt1NfXExbWNX3q88+/JDc3l4aGRhobG2loaKSpqalHVxiDwcDgwYMYOnQIw4YNJSsr87gCNhERFmafM4LZ54ygqqqJdWsLGDkqLWCboqIa6up82WGfz1vH5/PW8bvbTmf8hKwjfp7S0npydpdTVla/r4tSQ0Dr6v3UaiVerzegu1R8fGjAtKzyisYe+z30z0uwWPS/aFeqmpoannzyKVavXuufprSfSqfH43KicDvQeF2oZTeNG5YHBGgkjY6i5g4yrV033tpeMmacbi9bS32dl9bt6aCtM/C51hTZuH6i1R/UmTHQTL9ILaPTjAyM1fXoivNz0GsUTM40MTnTREuHh5UF7SzPbSenysHkzK6Cx063l883NdPhlHlnVSMDYnVMyjAxPt2IeV8QJ9Lo+7/793f/jatvuY0FX85jqynefwy728vyikYk2UvGuZdSvOw7OpsauPzyS/3bXHPN9RQWFh3Ruat0emSvF40ChmdnM2LEENSJmewgMINOF2TB1drsf+yoLgs8jkIiyqAlal9L62ijjnC95ohe/4O1ra5tdbM/1vX+TXosBt9rFBdyZO/n+9tWx1vVxIWoyYoJDMLcPEXUiBEEQRAE4eCO+S6jpKQEtVrNLbfcQlVVFZMnT+bPf/7zCTw14RfRWo1y6dMo6gMLKMrWRDZWpeBx+4I2bbX1cJT3HAPOmM76Dz8FQKXT0tihIWbu00jt9ciR6aA4+h+/9voGtn/zHaddfD5qvQ69JYgz7r4dAK/TSf2L/8W2ahkAdc88RsSd9yEpj2xagdISTNgNtx10fezALNR6PS67L+i08vV3SOufim3xN4Rec8sJ7dJ0Kln6405ee/X7gGUVFY0BGTETJvZHp9MwZqwvE+REpe07HA7q6uqpq6ujtrYWlUrFtGldWRsPPPAwP/20gs7OTgYM6M9rr73sX7dq1WrWrFl7yOObouPobGpg7dp1rF27DgCdTsegQQP9AZuhQ3ufVnckoqNDOPe8njWK1h1QXFiSJAYNSghYVlXZhNvjwel0U1bWwOjRgYVT167J58sv1h/2HFwuDzU1LURHh/iXDR+egsGg9deJ6b5uv5BeCjafaG1tbdTXN5CcnARAUFAQW7duDwjOBCelEjd6ElFDR1D44cs0bV7lX9e4bT1ypx2VRsPg8CBGxoQSZez9iwSbw8uGvR2sLbKxqbjjkAVba1vdlDa6SAz13bSfPvCXbzXdncWgZNYQC7OGWKhucRHarb7Nhr0ddDi7xrKropNdFZ28uqye7CQDkzJNnJZs8HcTMlsszL3yWsLK6lhX1Yyr2xu/LClInDCN+LGTqdm6jpdefJ7CHVs4+4KLSExM6DVAozaaCIpNIDghGXN0HOa4RAzhkRR+8BIJdPLIf/5NeLiZ3SX17NheGrDvgMkzSDZqSMvMIjUjE1VYFDsaO/zTlML0mh51dnqzo8zO3npn17SkRict9oNPS9qvrNGJxbC/Rkxgp6be2lbHW9WEm3u2rRYEQRAEQThSxxyg8Xg8bNy4kffeew+DwcCtt97KF198wfnnn3/CTi4kxIDqEPUAfm3Cw3tv0/pr5dy9jI5Fz4LTHrBcM/xs9FNvxPDiu6h1WlydDkJioxhz3kzgyMcZHp7BmEvPIzozjcGnT0Fr3J/NknrU59pW38CCJ19mx6KleNxuIuIjGXPJuf71rsZG8h/5K7bdu/zLOjatQ124g5DxE476+Xzn7xunp6OD4qefJO76G5l992188eCTIEmc9acbaHn+cTrLSvHsLaDfw4+iiz36dsAHOrDl8M/taH5uy8sbUCgkYmK6gi8TJ2X1CNDs2F7M0KFdtYjCw82MHHn01x2gqqqa9es3U1ZWTnV1LdXVNdTU1FJTU0tTU3PAtmlpKVxyyRz/Y61WRWdnJwCNjY3UlBdRvGcvxXv2UNvYQGh6f4wRURgjoqjZsYXGgsA6JsOuvQ29NYy2ynKa9uRTtXktLaV72bBhIxs2bCQ1NZlFiz73b19SUkpERDh6vf6YxrrfeeePxBJsYNmy3VRWNDJ0WBJp/QJrIb326hKW/tj18z5gXyvq/ddzwIC4QwZogoL0JCdHkJQcQUREUMDPwczTBzPz9MHHNYYjIcsydXX1VFZWM3RoV5egV155k9dff5fm5hZMJhObN/+EJElUV7TRPz2ZLbl7iM4eQ8rEaWhDI/z7RY2dRtPmVcTGxzPrgvM4+4LzcZlCiDLpes2UcXtkvt7UyPLdLWzc0467l3bY3fWP1TNpgIVJ/S0khf8yAdmj/X8l/ICZPNpyF+FBaupaAzMC3V5Yt6eDdXs6MGgUTB5g4fQhIWSnmFApJS6JDOI8t4fVpQ38WFRDU2fX/gqlkujssZA9ljUrvuOzc8/CHBkLkkRY5kCCYhOIzRqIKSYer7b33wVTXBJ7l80nLMyXNZOZEIp6Zxkur4xWIZEQYmTmvX8lKzww+DX0IG8jZQ0OCqvtFNc6MGgVXDy264X4YF41u8o6jvQl9Gt2Kf2vvyHIwF3nSCSG60iK0GI1qo4p0HyqfU44VmKcfYsYZ9/zWxqrIJxqjjlAExYWxpgxY7BafTdq06dPZ/v27Sc0QNPUdPQfqE6W8HAzdQfUZvnVcnWiXPsWivzALiyyxohnwi24kkZha3Iw7NKLyTzrDLZ9tRBzeBiNTfYjGqcsy7irK7Fv20S62oU+yEhrhwc6jv31cdm95Cxfg8ft68Lx0zufkDplCpJCgbNkL9X/vh9Pfbf6NgoFodffhjtj6DFdl/3j9No7qH7kXhx5u2nZto24Bx7nmndeBlnG+b836CjzfeNr31NE4VNPEXn3A8c8xtzcCt59exn19W2cedYw5pw78mcP1BzJ9aypbmbdOl/3peLiOqZMHciNN033r1cqlaSmRlJUVENEhIXRY/oxYGDCMf8+NDQ0sGnTFjZv3sKmTZupqKg44n0rKqpYs2I9aZm+qUAGoxlzTDzGiGgMEZH8c/4K9BHR6JLHkJY5JWBfb4cNbUMlF115FaGhVrSWEH70+D7ABMUlEBSXQHt1BS2le/37DBo6jKKKJry2VqrKyvj3k8+wd28xWVmZnHHG6Zx33hyOhcGo4+zZI5h1djYlxXW4Pd6A19PhcLF6VWCWzY4dpfQfEOffzmLxZbhotWri4qzEx4cRn9DVQcliMQTcYP5c719ut5uamlrKy8upqKikoqKCiopKysvLqaysorOzE0mSWLp0CRqNLyOls9Ptry3T3t7OW6+8zZql31PU2EbYmGlMufT3SL3UIrGkD+QfL73B6GFD/VOv1C4vrd3+L+l0ef3ZIrIs8/bSampae28HrpBgYJyOMalGRqUaCTfvf04XdXVHVlvleJyI/1dGxKl57Zo4dlV0siy3ndUFNmzOwOyRDqeXBVuaWLCliWCDkonpRqYPMJMcrmWgWU/W4ER2NrSxoqyeBmfgFMCOimJcThd1ZaXoJCWDL7sBtdEXdDlUjkrSiLFMGjuE2tpWIiMtNNS3c0lGDMFaFSFatf9ns/v4O5xeX5HeJhdOt8wZg7qCN499WcXGYt+XDfFWNVP7dQXQoswKukKZPXVvWx2YEaMKeP4JKVpAxmvvpN5+8OMdzCn1OeE4iHH2LWKcfc9vaay/FBHwEk6kYw7QTJkyhbvvvpvW1laMRiMrVqxg2rRpJ/LchJ9DYymqpU8hNQfe9HrD++GZ8if27CymbftChpxzJgB6i4XRV17S25ECeNra6Ny5Ffu2zdi3b8Jd1xUsaV04n+iHnkCX0d/f/ehQnHY7uxZ+T3t9AxNuugbwtbUePOt0Nnw0DwDZK9NWW4+qYi+1T/8bubPr07LCaCLijr+hHzz8iF6SQ6l/7TkcebsBcNdUUfXAXUQ/+B/af/qejg2r/dspQ0IJvemPx/QcXq+XL79Yz7zP1vkLs376yRqKimr43W2nH1PL4RPpww9WsGFD19SFDRsKufa6KQHZbZddMQGtVk1ycsQxT1+aN+8L5s37nOLikkNvKEmoDUY0RjNqowmNyeT/t8fRyZ+uu4pvVq1HkiSGDx9Gc/Y0OExhUQBzRCTt9hYuumgukiSxo74NCqsDtuko2IHa6yI4Moa6ujriho3kqc170bodlG1chy04AnVQMzt27CTcGsIZp89Ab/Bljr377vukpqYwePAgzOYj+49ckiSSkiN6LN++vQSHIzBAUFbWEPA4OiaEp565lvDwoJ890OdwOPzBl/Lyim5BmAqqq6t71PU5kCzLfPDmG3idDq657Q/ExsYGrP9w8VISz7qU/pExBz1GgknLiKhgsqypPerilDY4WVVgY3WhDa1K4olLfMeXJInRqUbmb+kq/qpRSgxL9LXDHpliIEh/6mRxHoxSITE4Xs/geD23TgljY3EHy3Lb2bC3w99RaL/mDg9fbW0l2KgiOVzr339IeBCDw8wUNHewoqye8g4nnvZW6tb/5NsGL0rZS0f5HiwZvWdfGZQSsWY9UUYdcaYY0g+YJpdiMfjbVvs6Jbkoa+qqEVPf3vVzFGxQBgRo4qwaf4CmstmF2yP7W1HH72thrVdLxO6bihQXItpWC4IgCILw63PMAZohQ4Zwww03cNlll+FyuRg3bhwXXHDBiTw34ShIud+jKNuMN2MackJ2zw1kGUXe9yjWvo3kCbyx8wyeg2fYhaz9YB5r3vkQSaHAmhhH/JBBPY+z/3BuN46CXOzbNmHfvgVHYT7IvX9fKpnMlG3eRuHniyhYsZorX32WkLjeb7R2fPsdP738Fg6bDYVSydDzzsYc7iuqOPS8s6nOK2DY+bNJHjWC9kVfUfPu6wHPq4qKIfKeB9HExvd6/KNlvfw6HPm5uGuqAF+QpvLvt+Np7HYzrFQR8X9/RxVy9F2IGhraePH5ReTk9MwS2bxpD/+49yPu+L/Zv0iHo7q6VtavK2DS5AEBXZhGjUkPCNC0t3WSm1PBwG41UbKyjnxqV3t7O1u3bmPHjp3ccstN/oCOzWY7aHBmyNW3YoqKRWsyozIYDxoE6qguZ/uy+Tg6O9Hp9UwYP47cHaVU2npvg7yfwutl0LBh3H/BZ/5l/YINXJIezZ5mGwV1TbQ53QxPS6DerOXFDz+hqqqadW1eaLThUGmJGD2FiNG+rBx7UyNNRTnceu8DhHgdJEZG8MnX3wG+oEBaWhrDhnXVsLFYLL2e18FkZ6dy/4MXsaeoBotFT1x8GDExgXVilEoFkZFHd9xDaWtr8wdfxowZjdHou7levPh7HnjgoaM+nkqnRxcSij7Eii44lFX1HehDQnl9ewlpUYnMnj2L2NhY9ublUNBsQ99LcMagUjI0PIjhEUGE6ruKuB7YOnzB9la+3dbqf9zQ7ibU5Pvvb0yage93tXFaioHRqQayk7rqsfRFapXEmDQjY9KM2Bxe1hTaWJbbzvYyO91DNZMyuoInxfVOnllSx+QMExMzjFw/OJGyNjutnS5Of+RfLP5qPk0N9SSkpGKICqcOMKsVxJj0/nox0UYdZk3Pjxwuj8z7K2rJK2v314g5VNvq/Zo7PLR1evyFjrvXiDHrlDTaPP6OVdP7m5mYYRJtqwVBEARB+NU7rt6xc+fOZe7cuSfqXIRjJFXuRLXqVd+/Szfhmf4X5MTTujZw2FCuegXF3sCCqLLOgmfS75HjhlD402rWvPOhb7nXyzcPPsYVrzyDObyry47XZqNm5WLqVq3Gvms7sv3wU9DUMXFI1/yRz//v7/5lu777nvHXX9V1Ht1upgwhIThsNt/zeTxs/eIbfxaNOTyMC598BNntpuH1F2j7YWHAc+n6DyLiL/9AaT5xBTtVoeFE3/8YVQ/chbvWl03haagL2Cb02lvQZfQ/6mNv2byXl178jvb2zoDlkgT7OxxXVTaRn1f5swVoamtbWLhgK2vX5lNY4Buf0aRj8uQB/m2GD09GrVZiNuv3dV/qR2pa1MEOeUiLFi3mn/981F/kdebMGYSFhvDuyy+yaesOwrIGYQiLoHTFDyDLKPGgkj1oVEpMkdGHPb7a6MtMaWttRbevDkyoXuMP0Gi9bqxaFdEWExEmPWE6NWF6DUGanvUkdColGVYTGVYTZ6ZE4vHKKCc8589yio6OorK294CSPsSKfsQ4GDEOgOr6WvhmMcgysixTUFBAQUEBn3ziCwilpCT7gzXDhg31Tx09GIVCIiMjhoyMg2eUHC1fG+4GfybMqFEjCQ31/f6vWrWGO++827/tK6+8wKBBg5Blmbbmnh2eJIUCpUaLuzNwHsiw636P3hqOLsSK+oAOa/tVOzwkhJj46199z/f9t1+z/tF/4p57DSq9L2iQEWZmUIiRzBCTvyCryyOzvczXeWnD3g6evTzO34FnTKoxIECztsjGrCG+4FVWjI73bk5E/RvMoDBqFUwfYGb6ADMN7W5W5Ps6QWlUCiKCugIey3PbKaxxUFjj4M0VDbx8dTzxwXow62HKNMZN6cqebXe6kSQwqn0fL/a3rd6yt9PfKWlwvJ4zB/vep1UKePPHGjqchw/KdCcBVc0uzFG+azwy2cDjF8UQZ1X7gzb77f85EARBEARB+LU7rgCN8OugyFns/7eEjHLZs7hn/xOsiUh1hSh/fAqpPTCo4I0ZhGfSH8AQDEDahDFkTJlI3lJfunq/CWPRWwIDHbLHTemzT3VFD3qjVKJL749+yHD0Q4ajSU4DhYKQ+FiaynxZIrsW/cDoKy9lz+p1bPpsPiMuOo9+E8cCkDJ6BMFxMTSXV6ILMqM1d7Vd9TQ30fTxu3RsWoenKfCm0DT1dMJu+D2SOrDTxomgCo8g6oHHqL7/zoCpWwCmyTMwz5x1TMd1udwBwRmNRsXV10wmLj6Up//7DU1NNqZNH8SUqQOP6/wP1NjYzrq1Baxbm09+flWP9evWFgQEaHQ6Df9+7Aoio4KPaKqMw+Fgx46d/hoyt9/+JzIzM/B4PKiVUkAHnk2bNjNkynTK0k4jaeJckhUKZK+X5jVLoNPm7x/TXrQb64Bhh31urcnEfU8+jTmo62d3fEwIZ2bFouh0oVUeeWaExyujkPAHbvYHAroHcs5KCqe4zU5+bRPVDg+y1PvxOxtqe/ze9Jt1AS5bO41FeewtLmHPnr3Mm/cFAAkJCf4Mm2HDhhJ+YOXXY7S/Hkz3OjDd68LsL6gM8OgjD5GWkkxtdRVFOTsDjlNSXsmgQYOQJIkPX34BgMRJM4kYOAx9SChaSzD28r0ot64kNjYWBTJfvPcWlth4tCGHbzPc4uiqCTN81GgefeZ5aq1RKFQqhkdYSI+3UlfXht3pZVOxjTVFNjYe0K1o3R4bM/d1VxoQq8OkVWDWKRidZmRAbFfhWoUkoRD374SaVJw7PJhzhwfT6er6HfXKMsvz2v2PY0PURFm6Pjqs32PD44XMaC1VLW5/EKa3ttX7yeAP0EiSRGK4lpyK3ou67G9bHbevdfX+aUkxIWq0qq7ftxCjihCj+EgjCIIgCMKpTXyaOdXZW5FKNwQsktwOVEsex5s+FcWWz5Dkrnn7sqTAO/wivIPPpWJ3LjEDLEiShCRJzPzLH2iurCRr5FAy+qei0gQGO5RBFozpGdjycgOWq2Pi9gVkstH1H4Sil2/GB5wxnZWvvUPC8CFkzZjCO9feSktVDQCb5833B2gkhYJx112Bo62drBlTAurVSDo97cu/R3Z1m6IlSVivvIGgs8//WVPX1eGRRN3/OFX33+nPoNEkpxF6w++P+XlHjurHtOmD+OH7HSQkhPGHP53lz5R55F+XMf/LDVx+RWAHKlmWcbk8aHqZKnA4P3y/nZUrcsnLqzzkdhXlDbjdnoAaM9EHTJ/pzuVysXt3jj8gs3PnLpxOp3/96y+9iNbroqCkFHVMIpaYOFoqy31deaqrGaPTYoxL9m8vKRQEJ6UR1NnKgCFD6T9kKJFZg8lxazCqVRjUSgwqxb6/9/1RKzGqlKiVCiAz4PwiDFrCgw0BBfFa7B7qWt002ty4PDLj+nUFAt/4qYFlue20dHjQqiUSQzUkhWlIDvf9nRiqwbTvG/oki4Eki4HJcaF4vDKVtk6KW+0UNrRQYXPi2RewsRXtxuxuY8rs89EHhbBtx07iJ81EofJdR3dnJ83FhTQW5dG0J5+y0mJKS0uZP/9rAGJiYhg2bCh//ONth61f43a7KSsrDwi+7J+aVFV1+HowSBLaIAtPv/wqpuBgNNZwtCFhDLvuD+itoeiCrWzSGzjD60WlUBAVE01j4V6CrCFYU9P9h0nK7M/vLvbVsqqrqeHrd1/H1dJ00ACNAgjSqLBo1UR2q7tkDQvHGtYVoGqxe/hmUyPfbWlga6m9Rw2V/dYUdfgDNCqlxPNXxmE1iikuR6L7FC+HS2ZUqoGV+TaaOzxMyjQFvIbvr25ib72zt8McVHlj4PZJ4TpK6x0BAZj9NWIigkTbakEQBEEQfjtEgOYUpyj6Ccnb84ZLaq9DufnjgGWyMRTP5D/hCe/HytffZeNH8xh/49WMvNQ3TU3hcTFjSBK2hR9Rv9yILnMgSktwwDGCThuJvaIC/aBh6IcMQzd4OOrwyMOe56BZp5MxZQKWqEhkWaZsy3Z/gKZix26q8wqIyuiHq6qCqNYanBVlqHVnBo5Vp0M3aBj2zb7WwZJOT/gf78J42pgjfr2OhzoyiuiHn6D54/dAocB62bUotEdWwFeWZbZtLWbwkKSALJQrr5qE1Wpi1tnZAUGX4GAjV18zucdxfvh+B4sWbuGOv8wOaHV9JLZuKT5ocCY42MDIUf0YPTqd9IyYQ2bKuN1u8vML2LRpM5s2bWb79h0BmRf76a1hhKSk05achjo1gwHhvmlR5d9/xV233UR2djZBQWY8XhnJ60HulsZw0/2PMDU1sFDsoZo+y7JMp0umsd1Fi91Di91Lo81NY7uHRpsbjbaZG8cH+7d/e0UD3+/2ZQWEGJQBARqXx1ekFKDTJZNX7SCvOrCGTbhZRVJYYOAmJlhNvFlPvFnPhFgrXlmm2uagpM1O1B9upW7mRBJTUomMiWFvSwfvdqs7pNLpCMscSFimL1vK43LSUrKHxiJfa+/Kykrq6+uYNXMqWYMGo9XpWLNmLaWlpXR2Ohg6dAhDhvheoZaWFi6//CoORqHW+Ou+tJYX4+qw+dfFjhpP/wuu9AeODqXV6caq0xAZHcOe/DxcjYHZZS0Ol3/6Ymh4OB8t/pENLW7q7C6CtCqCtSosGjUWrS8oY1IrUfQSPJFlmbo2D2uLbKwtsrGrohPvIZL4JKB/rI4RSYHtnffXnBGOjl6j4ObJYdwwMZRtpXYSw7pq/ZQ0OI86OBNmVhIRpA6Y2nrPuXHcOilYBM8EQRAEQfjNE59YTwXODpSrX0dqLMEzcDZy+mTfcllGkbeUVpsXrUZCY7aAWo/UVtPjEN6EEXgm/g5ZY2T+3x5i77qNAKx64z0i0lII7Wik8d3X8Lb6upl4be00vvsa4X+4M+A4MZddifbsi5GURzcnQB9kRtlUR/W/78d42miGz53D7sU/olSr6HfaUDp/WEj5S4/jqijz7xM897IewR/jqHHIdjuG00ZjHDcZlTX0wKf6WanDIwn//V+Oap+ODgdvvv4jq1fncdnlEzh7dlcRZ41GxXnnjzqi4+TmVvDO28vweLz84+8f8bvfn0F2dkrANi3NNtavL2TDhiL+fPusgA5Qo8aks2nTHv/jIIuBSZP6M3RoEhmZMT063+zn9XopKtrDpk2b2bx5M1u2bMNms/XYzhAeiTU1g5DUdEJSMtAfpGhyUFIacVERBAX5MkGUConsaCtKSSIpSE+CWY9B7fv58soyHi/++iAuj8ybKxpotXtotXv3/e0LyBwskwJAo5K4YZzFfwNo7Xaz3mL3+OrL7AtKhRgP/7Nd1+amrs3Nhr1ddZguGx3CpaN9mUayLLOjrJOkMA1jon1ZYMnhXdlQHW4PRrUSm6v3bBalWoM1LRNrWiat5SV01NXg7bTz19tuRmM0k5aZSUV9C6UVvilqI4YOIjUuinMvOo/o5Cwi+mUi6Y1dRXhDfJkv+pBQNKauDJzNrz2Nrr0Jr8tJXWU5UnvzEQVnwDcNyarTcM3vfs/Vt95GqyGYDQ0dWPZlwVg0KmR8AROFQoElOITpwV37251emjo8NLd5KK3upLnD43ts8zAyxcDIFF+9mQ6nzPVvlh7yXFRKGBqvZ3SakVEpRoJFzZETTqmQGJ4UmB1ZVONAqQCP98Bte29bHReiRq/p+T6jVStEcEYQBEEQBAERoPn1c3Wi/O5fKGrzAFCteBFPazXe7IuR6ouQmstYvtVNbomH2GQzaVNGcpr6eySXbz6/rFDhHXkl3v5ngCQhAcmjRvgDNLLXS/4Lz9HP1rMWidfhQHa7kbrdsCkNBiTbYaZI9KL+lWdo+2ERyDLO4iJCr/sdY7IzsdaVoslbg5wHrgP2sW9ch/rMcwKWmaeejnnq6Uf9/Adqabbx4487cbk8TJ8xGKvVdPidjkFhYTXPPbuAulpfgdKPP1pFVv9YUlMDi+yWlpbx+utvcsXll2A2GYmKiQ24YZFlmXf3BWcA7HYnT/7nK+68aw7DhifT0mzj+ecWsXt3ub+I7caNRUyc2FW8ePjwZEJDzQwdlsToMelkZcUSGWkJmPpzoGeffZ6FC7+jpaXloNvsN+TKWwiKSzjsdtZ+/UnqFxhYmpUcQXunh5IGJ0vL2tlb56S43klJg5PrJ4b62+mqFLBgW+shMyh643TLtDu8/uKh1m5BGK/s6wizP8MiPVLLjAFmQoxKWu0eiut959LpOvSTJnXLLKhpdXPv51X+59qfbbP/T7rVRP/hJho6XZS02ilusbGnqZ0OuedN6lljR/BJ/g7kfVlK1uzxmM+/mpiKUkxlxVRvXc+2zZsp2NhJar9ktNH9GHbrnT2O05t7//kwp0Vbmf/x/3j9mf8Soup9jCoJgrVqgnXqrgCM1vd6JSR3Xcv0sJCAuiC5VZ1sKbHvC7y4fX93eGiyeXC4D/56BumV/gCNQSOhUUo4DwjA6dUS2UkGxqQZGZFkwKDtu52Xfq2m9jeTnWxgw54OWuweYoLVom21IAiCIAjCcRABml8ztxPlksdQ1Ob5b7olSUK57XMkexMAXq/MnkoPXhnK9tShDCsi87rf41z5HtbIEDyjroawFDxuN8p9gZYhc86iancu+UtXMMzoJv6A4IwqIorQ63+HYfjIEzYURZDFXyTV01BP7X8e4lB9gJQhVuQepSVPjFUrc3n9tR9wOHwhoe8WbeWyy8czbfqhJtEcm61b9vqDM7DvehXV+AM0tbW1vPnmO3zzzbeY45NpWrENvcVClGM1t19+IevWrSc+Pp7Y2BjuvPtcnnnqG39h3379ohk4yNdO3Bykp7y8wf9zArBuTUFAgMZg0PLs89f1+KZalmUqKirZtGkzFRUV/O53t/jXOZ1OWlpakBRKguIS9mXHpGOKjGHFv/6G5PV1WVLJbtoLd/YaoFFKEG/Wk2jWkxikJ9qgpbrVQ3Gdnb37gh/F9U7q2tw99gVfi9/9JEkiSK/0T0E6EiatggiLho5uAZrhiQb+erYKq1GJ1agKyLgYlmhgWGJgpoBXlqlpcfvPdW+9g+J6J9XNXQVQuwdoup9zo81Do83O5pKuIqgqBcRZ902RCtOQHBbM5AHhKFQyJW2dlLTaKWm1o1ZIXHHheaz+8iNqq5oBMKdlISkUWOKTsMQn4bJ30JK/C4CqikqCtUf+tt62L0hy5nkXcPbci3DKsLKiyTftaF8gxqBS0unwTfvan+UyNsnszzj6cXcbH69vpqnDjd0p8+ltSf4aJjmVnXy4tumIz2e/7tdXkiSCDUpq29wE6RWMSjFyxvAwkiwyGpUIypxsFr2S6QMOXRdJEARBEARBODIiQPNr5XGh/OEJFFW7aLfLfLbMQW2TzMQhKkYPUKPIXwpARYOXzm4lAEo3bua17TuJ7JfGRU8/gNfjYfmzr9BaXcOcf96LpFBg37KBrLLtxJg6Cer+E6BUYTlnLsEXXIJCq+NEspwzl7bvvsFraz/oNuqEJAwjRmM8bQyalH5IB5lyc7zi4kNxOrvydex2J66DTDU5XuedP4pdO8vIy6vEajVx2x/OICsrjpaWFt577wM+/XQe2tAIhlzzOyIGdnUpagNe217Cohdfp6FkD5deeglXXXU59943l3feXsamTXv48x1no97XylahUDByVD+WLN4GgNGoJTTMHFDnAeh1GsEbb7zFm2++7X/cXlvBHfc9SEW7g7CRk8gOTyE4KRXVAT8TEeEhdFaWoNPpyBw4nMSkWGoBjUIiwawnIUhPgllHrEmHSqFgw94Onvm2kdIG1yGnIx2o+IAaF5FBKnRqCYteiVmvJEinIEivxKJXEqT3/TtIr8RqVBJiVKJVKQgPNwdkCkUHq4kOPvKOXwpJ8u8zJs3oX253eilt8AVtIrt1tjnwnA/k9uIP9izrtnx0qoG/z45iaLgvY6ik0YFKp+ad+Quora5ix9YtrDLEB4QugyxBmLx2zEEWlEolWqUCjVKBc1+2lRKZII2KEJ0GS/cMGI0Kewds3NvRbXqRe9+/nf5Ml3ZHz/bHI5IM/owjt1emsrnr96m5w0OUxfe7eyTTxQ4UpFf0yL64YmwI4WYVWTE6lAqpx/UUBEEQBEEQhL5ABGh+jbxulEufQVG+FVmWWbjWSU2jjFqjwosUcNMdFqxgeoZEQbGHEocCr1fG2+mgYscu8pauYNv8BVTs8H27vvrVN0lrr6Zj3UqAgOCMrv8gQm/8A5ojmKJyLJRGE8EXXk7j2690LVQo0GUNwnDaGAwjRqGOjD7hz+v1eqmtbSUqKti/LDExnLHjMlm10teNKjEpnBkzh5yQ56uqaiI6uqvjkVKp4LY/nMEnH6/hyqsmolTKvPXWO3zwwYd0dNhRajSM/MM9qHvpfFVldzHo5jsoWb6EDz76GJVS4oYbb+D6G6Yx98IxWCyB+4wbn4HT6WL06HQGDIwP6MLU0NDApk1b2Lx5C5s3b+bFF58jLCyMNct/4ttPPvRvZ45NoKbfSP69oQiPDGiCCMsYQG9m3XAbE1NiSE1PR6VS43B7yKnrYFOhg7xyF4vq23h0rgnVvkCbBBTVHllBUV237kmZ0YGBoScuiT3IXr88vUZBRrSOjAPO8fSBZlLCNf4gTHG9k4om12GnZkUEBb4lP/B5NY0239SR0weZOef0M4lpslHSamdPUxv1DjdDx47n8esuRqPTozbqKSptYbglhE4HODqh1e6lpdlLns1DZoyac8Z01Qa6YV4pNa29Zy4dSvcpYQfWe2m2eYiyqAPWmbQKgg2+gFmwQUmIQUmwUeX726Ak2OhbZtEre50aMyVLZGgIgiAIgiAIfZ8I0PzaeL0olz+PosTXqWjXXg97Kn3fYA89fw7RiSao+gI8vm+stWqJMJcXs8lDms7DDy1dWQFr3v6Q1pqugsHrPpmPOdiFudv9lCLIgvWqGzFNnPazF2kMOutcJLUGV3kJ2n6Z6IedhtL089x4ybLM1q3FfPy/VbS22Xnq6WvQartemwsvGkNWViydnS7S06NRKgOzdZ59ZgGxsaFMmJh1RK+L2+3h449Ws+Dbzdx1zxyGDEnyrwsLC+KGG6cwf/7XvPnmW7S0dE158jidlCxfQtoZc/zLJFlG3vecCqWK5KlnEjX0NJZ+9Apqt4O5V16F2dyVxbFfenoM6ekxgK+Tz/6215s2bqakNLDI6vrN20g7bTRyUCj25gZQmkCS8HTaCUrN5FAJLnqVgmClFlXEEL4ugfPDvKREgFalRCer+XJTnX/b4non8Vbf1J/uU4D8YwWiglX7pvpo/TVaIi2qXjv6nCpCjCpGpqj8dVQAHG4vZY0uiuu6pkjtrXPS1tmVoZIc1lXUua3TQ327L7OrvMmF3SmjkCSyrCYyQoxc/0MHoUYt9W6ZZRvraLV7DhsAUh2Q0BJsUB5TgKapWx2q6GA14/oZ/cGW7lkzA2P1fP77ZNSqU/daCoIgCIIgCMIvRQRofk1aq1Fu+ABF8Tr/ok63EqXKi8ftYdOnX5L58lN4hg5CueQxJEc7jnYPjhbfDVaQCmI0XlrcEgOTIxj1/DPk/LCcJU88h1qnY7i2sys4I0mYp59JyKXXojT/Mt9OS5JE0MxZP/vz1NW28PJLi8np1sb4u0VbOWfOaf7HEREW+g+IwO12ExcXmLmzbVsxX3+1CYBly3Zx3XVTiYs/eLeomupmnntuIXuKfMGwl15czGOPXY4l2IjH42Hx4iW89tqb1De3kDTlDBxrf6KzqQEASfZS8+N8kifNQI2a6moDze1e+mdJuFVdN866YCud7S189NbrLPj8M4LiU0lKTeXWW2/Bag2hvb2drVu3sWnTZtav38DevcW9nmvqzNlEDBzOBnM8G/IqGRNvxWg04eroRCF70TbY8draUBi7fiY0kgKlW0VrC5TVeGls8QLOfX9gQKyOlAhfYCHhgCBMcb2TCem+f4ealIxI0hNlUfsDMQmhml67uvRFWpWCtAgtaRFawPf6yrJMU4eH4n1FkQfEdmXiHDhNqnuAq7bVTX2bh/q2o5ua13RAge8DpyBpVRIh+7JZgg/Icume/dK9C1a8VcM9swK7re0nCsUKgiAIgiAIwpETAZpfg4ZilNu/RNq7BqlbkVdZUjL0tr8QJ0ew6MF/ERMVijXcSqvDxcaaQaRZ26gvKcXotbGvJifDjB5UEiiaK7Et/oZBsy/A1tBEv4ljUaxbRvOnH6BJTCH0pj+gS886SQP+eZnMeioqGgOWLVq4hbNmDcftdrF06TLmzfuC3btzABjQP4srrryC8ePH4vHIvP3mUv9+uTkV/PWeDzhr1nDOO38UOl3PuiWffrrGH5wBaG3pYO3aAnT6Nl599XVKyitIGD+VidPOQm0wog0KZvdHb6CV3SQMP52QEReyO0ePwwngy6ZYu9FLSKhESgpISpmq5QvoKC8GoLGllTJbPrty81m+fAVxcXHk5eUFFAjWWoIJikuibtfWwNcmMiagkO+eRhtzLrkUrVZLUHI2DapYip0dtDV7qKn30tYq4XRKQPcb+8Cb7u6BBINGQWyIGoUEyWEaUsK7ggqSJHH/uSd+GtupTJIkrEYVVqOqRwvjeKuGO04P90+R6v5a7q07sqliABql5J9ClBAaGEC7ZFQI52UH+4Mwv5VgmSAIgiAIgiD8Gkly97u6X5lTqQhkr0Urm8p9gZeyzeD1IFtiwBKNbInx/VulRZHzHYryrT2OJ0sSnim3400cSdMHb9I8/zPcMuxRmMlv9+J2dOtsg0yEWmZCihVvWyuyw+FbrtYQ98JbqEJ82R9ep5P25d9jnno6kvLoi3cedJz4MgEaG9opLKxGUkiMHJl2TMc/Fh0dDgwGbcCy7xZt5Z23lyFJEpMm9eeCuaP5bvG3vPXWO9hsNpAkLPFJALSWlyB7vcTGxjJ37gV4XOEsWrQNtzuwOGpYmJmrrpnMiBGpAcvb2uz89e4PaGxsR6/XMG16KosW/Y/Coj0ADLv+j0QM6KpxI3u9lPywCYd1BG2uQ8dI1SqZq2caUeet58NXXqC+thab0gBaPYMuv5E9S76htbwEfWg4ISnpWPd1WTKERQCw9B9/wtPegkp2o5I9ZJ5zKWHTulqXS8BfslMwqJXM29jM2ysbD3ImvbPoFUzKNHHjpDD/Mo9X9nf4+bXoa0Vlc6s6WbSjleoWNwZNV32XuAgDaq+7W7aLCr1G+tmnL/7S+tr1PBgxzr5FjLNvEePsW34r44Tf1lh/KeHholaecOKIDJqfg+xFsXMBio0fInm7pqlI9UVQX3TY3W36eJYXBjHGkIztyX/SsX41kgTIUNhoxy0f0CYZCQmZoNNnI2m1NL75EsrgEEJv+qM/OAOg0GgImnHWCRliR4eDvXtqKSysorCwmqKiGpqbbAAkJYX3CNC43R4UCgnFCezM1N7eydfzN7B48XYefuQS4uK6xjpt+iD27q1h1tnZxMf7ggc6nc4XnAFSZ8z2132xNzVQvmY55etW8Mwzz2I2m5g9+zzqak0U5HdlxtTXt/HfJ75mwIB4brp5OuERFgDMZj23/f4MPv5oFfFxDp5/4ZGA8yxd+UNAgMblUuCIHUVba8+b5jCzklCjirxqX5BtVLKJOemRkH4O02bM5JF3VrErby/R/YyEDxpOxIChONpa0VmCe32N+k+/CI0qDIITaPaaqfZ60bf7MmPa2yT+e1k/DGpfICoxtGeNmP1USkjY1xa6+58QY8+3kF9bcKYvyozW9SieDOJDlyAIgiAIgiCcykSA5kRrr0f50wsoqnYFLJZlmRXb3AxMUWIN6j1I4Y0egHfwHJa8tZi8ZSvIX3kTg3VOYjUgSaBWQH+Dh622npctUiNjmjAFZUgockcH5plnn/DaMkVF1bz79jJ27iyjoqKBg+VelZbW43C4/EV5m5ttPPv0AjKzYrno4rHHdQ6yLFNf18ratQXMn7+BDpsvkPHxR6v4v7+cgyzL7Ny5k08/+Yxly1ewbU0M973wKgaNmpkzp/Pccy/gcDio2b7JH6DRh4TS76zzST39HGq2b6Zs9VI+/PA9JEliyKCJuN0xtLc7/Oewa1cZlZVN/gCNLMtk9Y8jNqyKJau2Yk3pR+Oegv1nzGkpcUTrVFTZPZSWQG2NhNwtyKaQYFSKgZkDgxiWqEepkMiv7uSrLa2cPTSoa+xKDYVyKsrMJMIG+YIqkkJx0OAMAOnjKSpR+Pp2IwMSOTu7sqcq6p2ER/t+npL3TaEJMyn3BWD2Fe0N1xAbrBb1RARBEARBEARBEH5GIkBzvFqqUOxeiCO+H5JdRrn2TSRnR8Am3rBUlm+0sX7XXrYVeblwipooa1eQxpt4Gt7Bc5Aj0ilcuYa8pSsAX9eXnA4l0Ro3SqUKZZCFpMYGKp1e0qdPJTkzhT1ff015WQ2JgzJRhYYDEHzBpcc8HK/XS05OBdu3FnPJZeMDpkaUFNexaNHWwx5DlmU2b87FZJLQaqw8/fS3NDfZyM2tIDU1kuwDpggdSnl5A7t2llFaWk9ZWT0V5Y3Y7T3rb2zauIfcnDJyd6zh8aefR28NI37STEzDRvLqrgrGRocwPSGUc6+4hQbCKWpQYO+A7t2tFUoV0cNGEj1sJO3VFZSuWsqOjWvwOt0kxI9CIUX6g1KffrKG8Agt77zzHi6Xi2tvv4OO7GlkjZyFraqc9R8vRJ84lqS6BVx31eUYI2OpbnZz17oq//NFWVTMHBjE9P6mHpko6VE6/nJmYIbE0px22jq9aDTQ0gTBVnrweMDWjj9Dpv0QyRRalUSzzcP+twGrUcmHtyRi1h3b9DdBEARBEARBEATh2IkAzXFS/fAEUlMZ9t2LAl7MTodMab1E7Ogz2bq9kfWrdgLQ0enloxUqrn/mb+gVTuSQOAiKAsDbYYOSQkwqaHcDyAw3eVCbzUTceR+q0HCqH7ybc/94FaaJ0wAIn30ew8pKkJ1HXjS0N83NNr79ehOrV+fRtG+q0mmj+pGWFuXfxhyk73VfnV6JVuvG4WjAbm+gorKQ//vLF4QEBzNm+BSam7q2ffGF7/jno5cSHR3iX+ZwuCgvb6CstIHsESmYzV3Ps2XLXv73wcpDnrteryAuspN//OEK2ltbUSr1DLzkWqxpmf5t1lQ08/7idhptw3wLNFBeKhNkkYmOBK8iMB3IFBVL/wuuIP3suaz570OUlK5Cow4mIX4sbreOPXtquP66v+IxOuh35vm8uasclL6AijE6jsSzb6KxXsHpF08mLjEYgJAoNVnRWsLMKk4fGMSgeN1RtZIO0iuIC1FT3uSisljJ1SPC2FbfilohUd8Iq3d30mEjIDtnvyiLKmBqUnKYlkiLiqjIIP+UGEmSRHBGEARBEARBEAThJBEBmuPltPdY1Grz8vGPHhpb3ZhXzCNe68WsVNDm8d04Z2f3R2mKRA6PpK2unk0vvEZyVAjeT99G6XEzNQhyOhTIQGRCDFF/fQh1dCwAsU+/hkITWCtEE594QoayYMGWgE5AP36/jdbWKqqrqmhuaaGysg1JIQMdtLfXYLPV4nA24fHYCUnNIDxrENaIIcRHXo6jtYUNLzyGNdhFZ0sROosvWOJ0utlTVEN0dAiNje08/NBn1NY0+zNT7g45lyFDk/znkBAfxsF4vU7aHMW4o4JZ/9P34HYBElpLHPVVdqzdyuDIChmH5KF7B6KWZomWZokbhkdiMMtsrGmhqCUw+8lh66SjrmbfuTdRk/8pWn0kSmsYCWdPJ3r4KKQD6urIMuj3xZgW72xjzjCLPxPpXxfGHHONlnH9TIxJM7KlxE5Du5uhEUEMjfBNgfopr50l7Q4MGqlralKYhuQwDQlhGgyiO48gCIIgCIIgCMKvmgjQHCfPmOtQ/vAkkuxrQ9zZ5uWbFdDY6isO3OaRyLcrmBDkZodNSaRGJnLnatwNF1CwZRcrXnsHW0MjraOzGeTx7aOUYKDRizZzIJF33YfS3FWH5MDgDPgK8L7z9jK2bythwsQszr9g1EGL8ba0dLB2TT52u5NzzxvpXx4cbGTAgDh27izzL/v22yW8/e56VHoDOksIuuAQDPFRGCOiKFuwAre9K5gRmjmE5Kmn+x9rgqyYJ93JGlUsjlgJZX0tSnUQSUlexo7LACAoSE9DfVtALZvNm3Npai6hID+fstJSKirqgQy8XjdOVwtuTzuSUYE+0kLooP5kZMxGoVKx29ZEW4MXxWk3o9IHY1PJeL1eXC5obJBorJfodrqEmpQMitOjkGBQnAG1UiLTauKrHY0sLGgkLFxGpQapqgqd5KXTK6FWa1AmjmTwDZejNvVe36epESrKFHTaJTRKiX6RWjpdMnqNLyhzvAV0FZJE9gHtmAFGJBl447p4ws2qPtexRxAEQRAEQRAE4bdABGiOk5w4AveMu6h56QlqcxswqSQGIdOkUNHuldBJMmOD3FhUMC7IgwQoDEZ0/TLJ7JdJwYo1FK1aS+G6zfRLj0FXXwlKFebpZxJ69Y1I6oN31tnvk49X88P3OwD4fN469u6t5fd/OBO9vmvf3NwK5n+5gR3bS/B6ZbRaNUOGRvPJ/95n+/YdVFRVE6qPQm3JQhPmRRtrJTJ1BoOjrkGl1fZ4zqY2Cza7FoUxDKUhDCJV+IrQ+qi1agxx/Wl2SaALornuG5IvuoYdW9by+1t+x9VXXkZTQz0atQu3uyuY9MXn31HfuMn/OH7cFCyxweisVgxh4eiCrT0yVgDCho8jZOdOKvTBAHjcErt3KOi0A0ioFDA4Xkd2ooHsJAMJoepeAxlV9V7KSxVUlMlERsB/Lz8LLpzMlx9/xLcbq1GMuhy1ydtjv9YWX2DG1i6RFKbhjNFmJmWYMP1CU4YMWgUGrciSEQRBEARBEARBOFWJAM1xstdUs+qhl9he1EqoSsmEIA9aBYwNcrOlXcm4KSOJOfcCPG2t2Ldtxr51I5qUNFAoWPKfZylatRYAa3wsmkkTiEhOQD94OIp91WtlWWb3rnK++moD1dXNTJs2iHPmnOZ//i2b9/LN15sCzmnL5r089MAn/OWuOYSG+jI96uua2La12L+Nw+Hih+83Mv+bhf5lyqxIMq+6qNcAyIEs/bJx1XVt12mXkWUZRyfY7dBp9wU/rAaQa75HO/cKYkeNIzp7JDnvvMDD9/8DXVgkpoRsjGFRaEKC8ACVS0rQpkxGoQtC0gaRMGUkJqvxsOeTkD2K22+8jGteL6Wt0xdACVKpmTJIT3aSgUHx+iOa5nPzlDDOGWZhV62HukY7Zp0KdEFceeNNlEVVU6NoD9je1g7lpQpcdgUTM0ycPtBMv0ityGIRBEEQBEEQBEEQjooI0BynH/71JPlFvs48HV6JVg9YVBA6dBgXXXEd2pR+/m2NI8ciyzKy00HF9l3sWbOemAFZZF90HmnjetYyKSyo4v33V5CfV+lfVpCbS+fMgej0ehoa2njheV+AxavW4jUGo2hvQuF2UlJSz9/ufh2XYytl5RXYOp3Ex81CYzKjj4/AlBZNjcmCMftqlJY4lEGxuI16JEXP7JADeTygPCAxxN4Bm9cr/AVqzToF0WYlF40MRnvG8yyubAFAoVIz4Po/93pctwsqOgYHLHPKXrpn5nTn9YLkUHLxkAj6BRtQKRScnx2MSgnZSQbiQnrPkjmc6GA1g/tZ/cVzATxembxqB0oTaLW+OjPVVQrC1FquOi2IiRkmUedFEARBEARBEARBOGbHHaBpb2/nkksu4eWXXyYuLu5EnNMpZfSf/0DBdbchAx4ZdHEJRN1wC/ohw/3bdHQ4UKmUaDS++iCSVkfckIHc+sUHvR5TlmVyc/P47NMl5Od5kAGv3oxH8vD916+z4OtXCI+MJihkDB0dvqCAV2fCnjkKvB5UDZWoqvPJzf8ShUGJdcg4UodNIjgmCr1Z7X+emnoJXeoU/2OXCzrtoNtX4LbTDuMSgwjRaQjSKFm83caq3A5cTtBrFCSFqYkMUvn+WHz/jghSERmkDphu4/YaKbG7ydvXHepgVGpQKmU8nq6giqMT3G7f3w6H5Pt7379t7ZBg1ZAx2ejvhjT3tOAjuWxHTamQeP3aBLaX29leZkellLjlDCOpET2nfwmCIAiCIAiCIAjC0TquAM22bdu49957KS4uPkGnc+oJS0pg6AXnsPmz+TiRaMieBCoN3z3xCjt3VdNu0yAp9MiyjMddw4RJcaSmpRIdHU1IiJXvv1tOQX45bW0eJFQEqfdy5a1/4MY/3IM2djAJ42dhjgujuFgFSCgHTkHdUER9TQ71DXuJzeiPt1bG2VBEZ34HuvQZuMPjyJoRQ4Y8CrW+99bYAGazjC87pSsgUlEuIXslJJeC+BAt08aFo9+XGRI8VMc14+PQeJ2YtIojzk5RKRRc2C+K+38sRm3y9LqNXqmktsmDUunL0AEwahTIbSocHgXBehUWg5Jgq5JggxKLXklKhJaYYHWvx/s5qFW+Ar29FekVBEEQBEEQBEEQhONxXAGaTz75hPvvv5+77rrrRJ3PKWnSLdfx3IJFdDoVLP10NdofmpDM4XhDE1CkBKEONoHsxdnQyFf5e/Bu24nXvgKt04tVFQXokZVqHPGZlLgTeHojDLv9aaxWGeW+K1RX76t3IimUqMPT0cf0Y2i2bzqSvbqJTS/+i9vPn87bOW70IQq0OoCDB2dkGZwuMGgl4oK1JIZpSAz1tWZODNMQbOhZ3DbeqiE83EBdXe9BlkNRKhTcPjqedzbV0uBwokLBxJQgBsUYCNGqcbohp7KT4CFKLAYlFr0CjUpMGRIEQRAEQRAEQRB+G44rQPPII4+cqPM4pX395QK8Y/+GSqVFY9ZiskiYzWAKktHpum9pYvP6JLxeX+aJq2Qd4f2sqC0GVCY9qhALak3Cvm0D666EhsvY2rsyViIiu9bro0K49PEnCDJEkNrUihzawYFkGVydEgbURBl0ZIQaSOuvI2Kayj896OcWalJzx6TYXteplTAiWWSmCIIgCIIgCIIgCL9NkizLvVdgPQpTp07l3XffPeE1aNxuDyrVL9Om+Fi53R6mn/cfki+bidF06G2dTti+uWs89t3fMHruZNRBB890AUCGSK2RcbGRbChsZ01BCxHJDjTdyp/MSItkdmYMAAt211Da1o5HlkmxGkkLNZMUbED7K38tBUEQBEEQBEEQBOG36lfdxampqWcmyK9NTU0zpuwRhw3OAOCWMGokbE5fTGz6hGy8Cg+ug2weqtEwMsbCkDCzP7gyKNLC3BEGPsqtpsreiQKJM5LCOC0siIZ6XwvoURFGRkV0a00tQ+sJei3Dw80B3Y36KjHOvkWMs28R4+xbxDj7FjHOvkWMs+/5LY31lxIebj7ZpyD0Ib/qAM2pICzcQkZ2GA4C21MrJYlYo5Zki4EooxavLKNSKEifaqTT5aXR5sGkTWRpZT3l7Z0EaVT+PxatiuQgAyG63gvgBmnV3DQkng6XB4UEOpEZIwiCIAiCIAiCIAinNBGgOU5KhcRFWdFsbmjD7fIQb9aRaNYTbdShVPRe20WnVhAT7CuAOyc18pif26AWgRlBEARBEARBEARB6AtOSIDmxx9/PBGHOWWlWAyMSosU6YKCIAiCIAiCIAiCIBwT0cdYEARBEARBEARBEAThJBMBGkEQBEEQBEEQBEEQhJNMBGgEQRAEQRAEQRAEQRBOMhGgEQRBEARBEARBEARBOMlEgEYQBEEQBEEQBEEQBOEkEwEaQRAEQRAEQRAEQRCEk0wEaARBEARBEARBEARBEE4yEaARBEEQBEEQBEEQBEE4yUSARhAEQRAEQRAEQRAE4SQTARpBEARBEARBEARBEISTTARoBEEQBEEQBEEQBEEQTjIRoBEEQRAEQRAEQRAEQTjJRIBGEARBEARBEARBEAThJBMBGkEQBEEQBEEQBEEQhJNMBGgEQRAEQRAEQRAEQRBOMhGgEQRBEARBEARBEARBOMlEgEYQBEEQBEEQBEEQBOEkk2RZlk/2SQiCIAiCIAiCIAiCIPyWiQwaQRAEQRAEQRAEQRCEk0wEaARBEARBEARBEARBEE4yEaARBEEQBEEQBEEQBEE4yUSARhAEQRAEQRAEQRAE4SQTARpBEARBEARBEARBEISTTARoBEEQBEEQBEEQBEEQTjIRoBEEQRAEQRAEQRAEQTjJRIBGEARBEARBEARBEAThJBMBGkEQBEEQBEEQBEEQhJNMBGgEQRAEQRAEQRAEQRBOMhGgEQRBEARBEARBEARBOMlEgEYQBEEQBEEQBEEQBOEkEwEaQRAEQRAEQRAEQRCEk0wEaARBEARBEARBEARBEE4yEaARBEEQBEEQBEEQBEE4yUSARhAEQRAEQRAEQRAE4SQTARpBEARBEARBEARBEISTTARoBEEQBEEQBEEQBEEQTjIRoBEEQRAEQRAEQRAEQTjJRIDmGLW3t3P22WdTXl4OwIcffsisWbM466yzeOyxx5Bl+SSf4fH78ccfOf/88znzzDP55z//CfStcR54DQHuuusuPv/8c//jjRs3cv755zN79mxuueUWWlpaTsapHpcDx7llyxYuuugiZs2axR133IHT6QRgyZIlzJ49m1mzZnHPPff4l58Knn/+eWbNmsWsWbN4/PHH/ctdLhdXX30169at8y/ra+Psi9fzmWee4ayzzmLWrFm89dZb/uV97Xr2Ns6+eD33e+yxx7jnnnv8j/va9dyv+zj76vW88sormTVrFnPmzGHOnDls27YN6HvXtLdx9sVr2tvnPeh717O3cfa16/npp5/6f17nzJlDdnY2Dz30END3rqcg9FmycNS2bt0qn3322fKAAQPksrIyubS0VJ4xY4Zss9lkt9stX3zxxfKKFStO9mkel9LSUnn8+PFyVVWV7HQ65UsvvVT+4IMP+sw4D7yG1dXV8s033ywPHjxYnjdvnn+76dOnywUFBbIsy/J//vMf+cknnzxZp3xMDhxnW1ubPG7cODknJ0eWZVm+/fbb5Q8++EC22Wzy+PHj5bq6OlmWZfnPf/6z/NFHH53MUz9iq1atki+++GLZ4XDITqdTvuqqq+TFixfLRUVF8sUXXywPGjRIXrt2rSzLcp8b5+eff97nrue6devkSy65RHa5XLLdbpenTJkiFxUV9bnr2ds4c3Jy+tz13G/16tXyqFGj5LvvvluWZbnPXc/9uo+zL77fyrIse71eefz48bLL5QpY3teuaW/j7IvXtLfPe8uWLetz17O3cS5ZsqTPXc/u8vPz5RkzZsgNDQ197noKQl+mOtkBolPRJ598wv33389dd90FQHx8PN9++y1qtZqmpiba29sJCgo6yWd5fJYsWcJZZ51FVFQUAE899RRarZYLL7ywT4zzwGv49ddfM23aNIKDgwO2W7BgAWq1GpfLRU1NDRkZGSfhbI/dgeNctWoVQ4cOJTMzE4B7770Xj8eDwWDgxx9/RK1WY7fbaWhoOGWubXh4OPfccw8ajQaA1NRUKisr2bJlCzfccAPvvPOOf9u+Ns6Kioo+dz1HjhzJu+++i0qloqamxj+ed999t09dz97GmZOT0+euJ0BzczNPPfUUt9xyC7m5uQB89tlnfep6Qs9x9sX3W4A9e/YAcN1119Hc3MxFF13EFVdc0eeuaW/jDA8P73PX9GCf91599dU+dT17G+fWrVv73PXs7oEHHuD222/HarXy+uuv96nrKQh9mZjidAweeeQRRowYEbBMrVbzySefMH36dMLDw/1v9qeqkpISPB4Pt9xyC3PmzOHDDz/EYrH0mXEeeA1vuOEGLrzwwh7bqdVq8vLymDRpEuvWrWPWrFm/5GketwPHWVJSgsFg4Pbbb2fOnDk899xz/v+M1Wo1y5cvZ/LkyTQ1NTF+/PiTddpHpV+/fgwdOhSA4uJiFi5cyKRJk7jrrruYPn16j+370jg1Gk2fu57gO/dnn32WWbNmMWbMGCIjI/vc9YSe46yrq+uT1/O+++7j9ttvD/jg3xev54Hj7IvvtwCtra2MGTOGF154gbfffpuPPvqIVatW9blr2ts4++I1Pdjnvb52PXsbZ1+8nvutXr2azs5OzjzzTKBvvucKQl8lAjQn0EUXXcS6desICwvj+eefP9mnc1w8Hg9r1qzh0Ucf5eOPP2b79u188cUXQN8a55HIyMhg9erV/O53v+P2228/2adzXDweDytXruSOO+7g888/x2638+qrr/rX7w9ETZkyhQceeODknegxKCgo4LrrruOuu+4iKSnpkNv2lXH25ev5xz/+kTVr1lBVVcUnn3xyyG37yjidTmefu56ffvop0dHRjBkz5oj36Svj7Ku/n8OGDePxxx/HbDZjtVqZO3cuy5cvP+Q+p+JYexvnk08+2eeu6aE+7x1MXxlnX/0dBfjoo4+49tprj2jbU3mcgtAXiQDNCVBVVcWmTZsAUKlUzJo1i7y8vJN8VscnLCyMMWPGYLVa0el0TJ8+nQ0bNvS5cR6Kw+Hg+++/9z8+55xzTvnxhoWFMWTIEOLj41EqlZx55pls376d5uZmVq5c6d9u9uzZp9RYN23axDXXXMP//d//cd555x10u742zr54PYuKisjJyQFAr9czc+bMg557Xxvnq6++2ueu54IFC1i1ahVz5szh2Wef5ccff+TRRx/tddu+Ns4333yzz11P8BXPX7Nmjf+xLMuoVL3PmD+Vx9rbOENCQvrcNe3t89727dt73bavjfOll17qc9cTwOl0smHDBqZOnXrI7U71cQpCXyUCNCdAW1sbd955J62trciyzHfffUd2dvbJPq3jMmXKFFauXElraysej4cVK1aQmZnZ58Z5KCqVigcffJCdO3cCsHDhQoYPH36Sz+r4jB8/nl27dlFVVQXA0qVLGTBgALIsc+edd1JZWQnAokWLTpmxVlVVcdttt/HEE08cdgpaXxtnX7ye5eXl3HvvvTidTpxOJz/88MNB32f62jgfeuihPnc933rrLb755hvmz5/PH//4R6ZOncrf/va3Xrfta+P86quv+tz1BN9nnscffxyHw0F7eztffPEFM2bM6HXbU3msvY3z+eef73PXtLfPewMGDOh12742zptuuqnPXU+AvLw8kpKSMBgMh9zuVB+nIPRVokjwCZCens5NN93EJZdcglKpZMSIEUecVvhrNWTIEG644QYuu+wyXC4X48aN48orr0Sr1fapcR6KUqnkqaee4r777sPj8RAZGckjjzxysk/ruERHR/PQQw9xyy234HA4yMrK4u6770av1/Pwww9z8803I0kSaWlpPPjggyf7dI/IG2+8gcPh4N///rd/2SWXXMKll17aY9uQkJA+N86+dj0nTZrE9u3bOff/27m/EBnbP47j72mmZSO0zarNicKSKGoXm0SKRvkzRkoy7WYPFLurHC0HFFtyYtYOKUU586emLaX1J0VN2mzEkiwi0mSL2gOiXTO/Az3z62FWz9Nj3Xa9X0cz91xzfb/3XDXNfLruOx4nHA6zevXqYYO30byepc4zHo8zZcqUMbWe/8ZoXs9SxuL3LXz9o3v//n3i8Tj5fJ6tW7eycOHCkmNH85qWOs+ampoxt6alfu9t2rSp5NjRvJ6lznPnzp3MmzdvTK0nwOvXr4s3Q/6R0bye0lgWKhQKhaCbkCRJkiRJ+pN5iZMkSZIkSVLADGgkSZIkSZICZkAjSZIkSZIUMAMaSZIkSZKkgBnQSJIkSZIkBcyARpKkX6y3t5eWlpYRmfvBgwfs37+/5GsrV66kt7d3ROpu376d9+/fj3gdSZKkscqARpKkX2z+/Pl0dHSMyNzPnj3j7du3IzL3j2Sz2V9eU5IkaSwxoJEk6Rfr7u5m7dq1ALS2ttLW1kYymWTVqlU0NTXx4cMHAObOnUsqlSKRSBCLxbh69SoAmUyGHTt2FOf763kul6Ojo4Oenh727t37wx5u3LjB5s2bicfjbNmyhXv37gGQTqdpbW2lsbGRWCxGfX09/f39wNfdOYlEgnXr1rFr1y42btxId3d3sVZ9fT25XA6A8+fPk0gkWLFiBalU6id+epIkSWOTAY0kSQF7+PAhp0+f5vLly7x584auri4Avnz5Qnl5OZlMhvb2dvbt21e8jKiUqqoqWlpaqKmp4fDhw8OOe/nyJalUilOnTtHZ2cmhQ4dobm7m48ePAPT09HDs2DG6urooLy/n3LlzDA0N0dzczO7du7l06RLJZJLHjx8DFGudPXuWqqoqAMaNG0cmk+HixYucOXOmGNxIkiSptEjQDUiS9KdbtmwZZWVlAFRXVzMwMFB8bdu2bQDMmTOH6upq7ty585/rZbNZ+vv7aWhoKB4LhUK8evUKgEWLFjFx4kTg6y6egYEB+vr6AFi+fDkAS5YsYdasWcPW+GuHUGVlJdFolHfv3hXDG0mSJH3PgEaSpICNHz+++DgUClEoFIrPw+Fw8XE+nyccDn83ZnBw8F/Vy+fz1NXV0d7eXjyWy+WYOnUq165dK9lPOBz+W81ve/tWJPL/nxjf9itJkqTveYmTJEm/sc7OTgAePXrEixcvqK2tpaKigqdPn/L582cGBwe5cuVKcXw4HGZoaOiHc9bV1ZHNZnn+/DkAN2/eZP369Xz69GnY98yYMYOysjJu3boFfL0fTV9fH6FQ6B/XlSRJ0vDcQSNJ0m/s7t27XLhwgXw+TyqVYvLkySxdupTa2lrWrFlDZWUlixcv5smTJwAsWLCAEydO0NTUxPHjx0vOOXPmTA4ePMiePXsoFApEIhFOnjzJhAkThu0jEomQTqc5cOAAR48eZfr06USj0eJum1gsRjKZJJ1O//wPQZIk6Q8QKrjnWJKk39Ls2bO5ffs2FRUVQbcCwJEjR2hsbCQajZLL5diwYQPXr19n0qRJQbcmSZI06rmDRpIk/SPTpk2joaGBSCRCoVCgra3NcEaSJOkncQeNJEmSJElSwLxJsCRJkiRJUsAMaCRJkiRJkgJmQCNJkiRJkhQwAxpJkiRJkqSAGdBIkiRJkiQFzIBGkiRJkiQpYP8DpoemEOhEEw4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 1142.6x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.set_theme(style=\"darkgrid\")\n",
-    "data = new_data\n",
-    "\n",
-    "g = sns.relplot(data=data, kind=\"line\",palette='icefire_r', linewidth=3.5, height=7, aspect=2)\n",
-    "g.set(xticks=range(13, 750, 50))\n",
-    "g.set(yticks=range(1, 70, 5))\n",
-    "g.despine(offset=10, trim=True)\n",
-    "#sns.lineplot(data=data, palette=\"plasma\", linewidth=2.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d79056ba",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a022cf79",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/exercises/practice/acronym/approaches/config.json b/exercises/practice/acronym/approaches/config.json
new file mode 100644
index 0000000000..a3ed8e6d93
--- /dev/null
+++ b/exercises/practice/acronym/approaches/config.json
@@ -0,0 +1,56 @@
+{
+  "introduction": {
+    "authors": ["BethanyG"]
+  },
+  "approaches": [
+    {
+      "uuid": "8ee6ac18-270b-4a62-80e6-5efb09139274",
+      "slug": "functools-reduce",
+      "title": "Functools Reduce",
+      "blurb": "Use functools.reduce() to form an acronym from text cleaned using str.replace().",
+      "authors": ["BethanyG"]
+    },
+    {
+      "uuid": "d568ea30-b839-46ad-9c9b-73321a274325",
+      "slug": "generator-expression",
+      "title": "Generator Expression",
+      "blurb": "Use a generator expression with str.join() to form an acronym from text cleaned using str.replace().",
+      "authors": ["BethanyG"]
+    },
+    {
+      "uuid": "da53b1bc-35c7-47a7-88d5-56ebb9d3658d",
+      "slug": "list-comprehension",
+      "title": "List Comprehension",
+      "blurb": "Use a list comprehension with str.join() to form an acronym from text cleaned using str.replace().",
+      "authors": ["BethanyG"]
+    },
+    {
+      "uuid": "abd51d7d-3743-448d-b8f1-49f484ae6b30",
+      "slug": "loop",
+      "title": "Loop",
+      "blurb": "Use str.replace() to clean the input string and a loop with string concatenation to form the acronym.",
+      "authors": ["BethanyG"]
+    },
+    {
+      "uuid": "9eee8db9-80f8-4ee4-aaaf-e55b78221283",
+      "slug": "map-function",
+      "title": "Map Built-in",
+      "blurb": "Use the built-in map() function to form an acronym after cleaning the input string with str.replace().",
+      "authors": ["BethanyG"]
+    },
+    {
+      "uuid": "8f4dc8ba-fd1c-4c85-bcc3-8ef9dca34c7f",
+      "slug": "regex-join",
+      "title": "Regex join",
+      "blurb": "Use regex to clean the input string and form the acronym with str.join().",
+      "authors": ["BethanyG"]
+    },
+    {
+      "uuid": "8830be43-44c3-45ab-8311-f588f60dfc5f",
+      "slug": "regex-sub",
+      "title": "Regex Sub",
+      "blurb": "Use re.sub() to clean the input string and create the acronym in one step.",
+      "authors": ["BethanyG"]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/functools-reduce/snippet.txt b/exercises/practice/acronym/approaches/functools-reduce/snippet.txt
index 34e930c76f..190d5d4aef 100644
--- a/exercises/practice/acronym/approaches/functools-reduce/snippet.txt
+++ b/exercises/practice/acronym/approaches/functools-reduce/snippet.txt
@@ -1,6 +1,6 @@
 from functools import reduce
 
 def abbreviate(to_abbreviate):
-phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
+    phrase = to_abbreviate.replace("_", " ").replace("-", " ").upper().split()
 
-return reduce(lambda start, word: start + word[0], phrase, "")
\ No newline at end of file
+    return reduce(lambda start, word: start + word[0], phrase, "")
\ No newline at end of file
diff --git a/exercises/practice/acronym/approaches/introduction.md b/exercises/practice/acronym/approaches/introduction.md
index 8512236e06..38b606b4a2 100644
--- a/exercises/practice/acronym/approaches/introduction.md
+++ b/exercises/practice/acronym/approaches/introduction.md
@@ -53,6 +53,7 @@ For more information, take a look at the [loop approach][approach-loop].
 
 ## Approach: scrub with `replace()` and join via `list comprehension` or `Generator expression`
 
+
 ```python
 def abbreviate(to_abbreviate):
     phrase = to_abbreviate.replace('-', ' ').replace('_', ' ').upper().split()
@@ -120,9 +121,9 @@ import re
 
 
 def abbreviate_regex_sub(to_abbreviate):
-    pattern = re.compile(r"\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)")
-    
-    return  re.sub(pattern, "", to_abbreviate).upper()
+    pattern = re.compile(r"(?<!_)\B[\w']+|[ ,\-_]")
+ 
+    return  re.sub(pattern, "", to_abbreviate.upper())
 ```
 
 For more information, read the [regex-sub][approach-regex-sub] approach.
@@ -138,9 +139,11 @@ However, these listed approaches cover the majority of 'mainstream' strategies.
 ## Which approach to use?
 
 All seven approaches are idiomatic, and show multiple paradigms and possibilities.
+All approaches are also `O(n)`, with `n` being the length of the input string.
+No matter the removal method, the entire input string must be iterated through to be cleaned and the first letters extracted.
 
-The `list-comprehension` approach is the fastest, although `loop`, `map`,  and `reduce` have near-identical performance for the test data.
-All are fairly succinct and readable, although the 'classic' loop is probably the easiest understood by those coming to Python from other programming languages.
+Of these strategies, the `loop` approach is the fastest, although `list-comprehension`, `map`,  and `reduce` have near-identical performance for the test data.
+All approaches are fairly succinct and readable, although the 'classic' loop is probably the easiest understood by those coming to Python from other programming languages.
 
 
 The least performant for the test data was using a `generator-expression`, `re.findall` and  `re.sub` (_least performant_).
diff --git a/exercises/practice/acronym/approaches/regex-join/content.md b/exercises/practice/acronym/approaches/regex-join/content.md
index b580ca4048..f6ca2f4844 100644
--- a/exercises/practice/acronym/approaches/regex-join/content.md
+++ b/exercises/practice/acronym/approaches/regex-join/content.md
@@ -4,18 +4,38 @@
 ```python
 import re
 
+###re.findall###
 
-def abbreviate(phrase):
-    removed = re.findall(r"[a-zA-Z']+", phrase)
+def abbreviate(to_abbreviate):
+    #Capitalize the input before cleaning.
+    removed = re.findall(r"[a-zA-Z']+", to_abbreviate.upper())
     
-    return ''.join(word[0] for word in removed).upper()
+    return ''.join(word[0] for word in removed)
 
-###OR###
+#OR#
 
-def def abbreviate(phrase):
-    removed = re.finditer(r"[a-zA-Z']+", phrase)
-    
-    return ''.join(word[0] for word in removed).upper()
+def abbreviate(to_abbreviate):
+    #Capitalize the result after joining.
+    return ''.join(word[0] for word in
+                   re.findall(r"[a-zA-Z']+", to_abbreviate)).upper()
+                   
+###re.finditer###
+
+def abbreviate(to_abbreviate):
+    #Capitalize the input before cleaning.
+    removed = re.finditer(r"[a-zA-Z']+", to_abbreviate.upper())
+
+    #word.group(0)[0] (first letter of Matched word) can also be written as
+    #word[0][0], with the first bracketed number referring to Match group 0.
+    return ''.join(word.group(0)[0] for word in removed)
+
+#OR#
+
+def abbreviate(to_abbreviate):
+    #Capitalize the output after joining.
+    #Use bracket notation for Match group.
+    return ''.join(word[0][0] for word in
+                   re.finditer(r"[a-zA-Z']+", to_abbreviate)).upper()                          
 ```
 
 
@@ -24,11 +44,14 @@ Python's `re` module provides support for [regular expressions][regular expressi
 Regular expression matching starts at the left-hand side of the input and travels toward the right.
 
 
-`findall()` searches text for all matching patterns, returning results (_including 'empty' matches_) in a `list`.
-The [`re.finditer()`][re-finditer] method works in the same fashion as `re.findall()`, but returns results as a _[lazy iterator][lazy iterator]_, producing matches _on demand_, instead of saving them to memory.
+`re.findall()` searches text for all matching patterns, returning results (_including 'empty' matches_) in a `list` of strings.
 
 
-The regular expression `r[a-zA-Z]+` in the code example looks for any single character in the range `a-z` lowercase and `A-Z` uppercase.
+The [`re.finditer()`][re-finditer] method works in the same fashion as `re.findall()`, but returns results as a _[lazy iterator][lazy iterator]_ over [Match objects][match objects].
+ This means that `re.finditer()` produces matches _on demand_ instead of saving them to memory, but needs to have both the iterator and the Match objects _unpacked_.
+
+
+The regular expression `r[a-zA-Z']+` in the code example looks for any single character in the range `a-z` lowercase and `A-Z` uppercase, plus the `'` (_apostrophe_) character.
 The `+` operator is a 'greedy' modifier that matches the previous range one to unlimited times.
 This means that the expression will match any collection or repeat of letters (_word_), but will omit matching on any sort of space or 'non-letter' character, such as `\t`, `\n`, ` `, `_`, or `-`.
 
@@ -39,20 +62,24 @@ The result returned by `findall()` will then be `['Complementary', 'metal', 'oxi
 
 ~~~~exercism/note
 `to_abbreviate.replace("_", " ").replace("-", " ").upper().split()` can also be used to 'scrub' `to_abbreviate` and turn the results into a `list`.
-The `.replace()` approach benchmarked faster than using `re.findall()` to 'scrub', most likely due to overhead in importing the `re` module and in the [backtracking][backtracking] behavior of regex searching and matching.
+The `.replace()` approach benchmarked faster than using `re.findall()`/`re.finditer()` to 'scrub', most likely due to overhead in importing the `re` module and in the [backtracking][backtracking] behavior of regex searching and matching.
 
 [backtracking]: https://stackoverflow.com/questions/9011592/in-regular-expressions-what-is-a-backtracking-back-referencing
 ~~~~
 
 
-Once `findall()` or `finditer` completes, a [`generator-expression`][generator-expression] is used to iterate through the results and select the first letters of each word via [`bracket notation`][subscript notation].
+Once `findall()` or `finditer()` completes, a [`generator-expression`][generator-expression] is used to iterate through the results and select the first letters of each word via [`bracket notation`][subscript notation].
+Note that when using `finditer()`, the `Match object` has to be unpacked via `match.group(0)`/`match[0]` before the first letter can be selected.
+
+
 Generator expressions are short-form [generators][generators] - lazy iterators that produce their values _on demand_, instead of saving them to memory.
 This generator expression is consumed by [`str.join()`][str-join], which joins the generated letters together using an empty string.
 Other "seperator" strings can be used with `str.join()` - see [concept:python/string-methods]() for some additional examples.
 
 
 Finally, the result of `.join()` is capitalized using the [chained][chaining] [`.upper()`][str-upper].
-Since the generator expression + join + upper is fairly succinct, they are placed directly on the `return` line rather than assigning and returning an intermediate variable for the acronym.
+Alternatively, `.upper()` can be used on `to_abbreviate` within `findall()`/`finditer()`, to uppercase the input before cleaning.
+Since the generator expression + join + upper is fairly succinct, they can be placed directly on the `return` line rather than assigning and returning an intermediate variable for the acronym.
 
 
 This approach was less performant in benchmarks than those using `loop`, `map`,  `list-comprehension`, and `reduce`.
@@ -68,3 +95,4 @@ This approach was less performant in benchmarks than those using `loop`, `map`,
 [str-join]: https://docs.python.org/3/library/stdtypes.html#str.join
 [str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
 [subscript notation]: https://docs.python.org/3/glossary.html#term-slice
+[match objects]: https://docs.python.org/3/library/re.html#re.Match
diff --git a/exercises/practice/acronym/approaches/regex-sub/content.md b/exercises/practice/acronym/approaches/regex-sub/content.md
index ca2e73348e..4f1b447d81 100644
--- a/exercises/practice/acronym/approaches/regex-sub/content.md
+++ b/exercises/practice/acronym/approaches/regex-sub/content.md
@@ -6,12 +6,17 @@ import re
 
 
 def abbreviate_regex_sub(to_abbreviate):
-    pattern = re.compile(r"\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)")
-    
+    pattern = re.compile(r"(?<!_)\B[\w']+|[ ,\-_]")
+ 
     return  re.sub(pattern, "", to_abbreviate).upper()
+    
+###OR###
+
+def abbreviate_regex_sub(to_abbreviate):
+    return  re.sub(r"(?<!_)\B[\w']+|[ ,\-_]", "", to_abbreviate.upper())
 ```
 
-This approach begins by using the [`re.sub()`][re-sub] method from the [re][re] module to "scrub" (_remove_) non-letter characters such as `'`,`-`,`_`, and white space from `to_abbreviate`.
+This approach begins by using the [`re.sub()`][re-sub] method from the [re][re] module to "scrub" (_remove_) unwanted characters such as `'`,`-`,`_`, white space, and all but the first letters of each word from `to_abbreviate`.
 Python's `re` module provides support for [regular expressions][regular expressions] within the language, and has many useful methods for searching, parsing, and modifying text.
 
 
@@ -20,7 +25,7 @@ Regular expression matching starts at the left-hand side of the input and travel
 
 ~~~~exercism/caution
 While it is a fun experiment to see if the entire problem can be more or less solved with a single regex, the excessive [backtracking][backtracking] used in this solution slows down performance considerably.
-This solution tested the slowest of all solutions during benchmarking.  
+This solution tested the slowest of all solutions during benchmarking, taking 652 steps in the regex engine to find and replace 82 matches.  
 
 
 A more performant method of cleaning would be to use [`re.findall()`][re-findall] or [`re.finditer()`][re-finditer] to scrub the phrase of unwanted characters, and then process the results with a `list-comprehension` or `loop` to extract the first letters of words.
@@ -36,18 +41,17 @@ Experimenting with that third-party library on your local environment (_the exer
 [regex]: https://github.com/mrabarnett/mrab-regex
 ~~~~
 
-The regular expression `\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)` in the code example above has five alternatives for matching.
+The regular expression `(?<!_)\B[\w']+|[ ,\-_]` in the code example above has two alternatives for matching.
 For convenience and reuse, the regex is compiled using [`re.compile()`][re-compile].
 Alternatives are seperated with the pipe (`|`) symbol:
 
 
-1.  `\B[a-z',]+`, which starts searching at a [non-word boundary][re-non-word boundary], looks for any character in the group `abcdefghijklmnopqrstuvwxyz',`.
+1.  `(?<!_)` is a [negative lookbehind][negative lookbehind], which ensures that `_` followed by letter characters (_see the pattern explanation below_) is **not** matched (_for example, `_none` is **not** matched, but ` _` with a preceding space **is** matched_).
+2.  `\B[\w']+`, which starts searching at a [non-word boundary][re-non-word boundary], looks for any character in the group `abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ_'`.
 The `+` operator is a 'greedy' modifier that matches a character in the previous group one to unlimited times.
-This means that this expression will match any collection or repeat of the letters (_plus `'` and `,`_), but will omit matching on anything else.
-2. `|-|` matches the character `-`, with no leading or trailing space.
-3. `| |` matches one **single** space character.
-4. `[A-Z]{2}\b` matches any character in `ABCDEFGHIJKLMNOPQRSTUVWXYZ` exactly twice (_that's the `{2}` part_) - but only right before a [word boundary][re-non-word boundary].
-5. `[^A-Z'](?<=_)` matches a single character that is **not** in `ABCDEFGHIJKLMNOPQRSTUVWXYZ`, and then asserts a [positive lookbehind][positive lookbehind] to match `_`.
+This means that this expression will match any collection or repeat of the letters (_plus `'`_), but will not match on anything else.
+3. `[ ,\-_]` matches any of the characters ` -_,` (_space, hyphen, underscore, comma_) once.
+
 
 Because these matches are used in the `re.sub()` method, an empty string is _substituted_  - so the matches are _removed_ from the result.
 
@@ -58,11 +62,14 @@ The result is the string `TRNT`.
 
 To ensure that all results are capitalized for any input, the approach then [chains][chaining] [`.upper()`][str-upper] to `re.sub()` on the `return` line to produce the final acronym.
 
+To play with this regex and see a more in-depth explanation, you can use it on [regex101][regex101].
+
 [chaining]: https://pyneng.readthedocs.io/en/latest/book/04_data_structures/method_chaining.html
-[positive lookbehind]: https://www.regular-expressions.info/lookaround.html
+[negative lookbehind]: https://www.regular-expressions.info/lookaround.html
 [re-compile]: https://docs.python.org/3/library/re.html#re.compile
 [re-non-word boundary]: https://stackoverflow.com/questions/4541573/what-are-non-word-boundary-in-regex-b-compared-to-word-boundary
 [re-sub]: https://docs.python.org/3/library/re.html#re.sub
 [re]: https://docs.python.org/3/library/re.html
+[regex101]: https://regex101.com/
 [regular expressions]: https://en.wikipedia.org/wiki/Regular_expression
 [str-upper]: https://docs.python.org/3/library/stdtypes.html#str.upper
diff --git a/exercises/practice/acronym/approaches/regex-sub/snippet.txt b/exercises/practice/acronym/approaches/regex-sub/snippet.txt
index bd4626d075..ebfb0cf30b 100644
--- a/exercises/practice/acronym/approaches/regex-sub/snippet.txt
+++ b/exercises/practice/acronym/approaches/regex-sub/snippet.txt
@@ -1,6 +1,6 @@
 import re
 
 def abbreviate_regex_sub(to_abbreviate):
-    pattern = re.compile(r"\B[a-z',]+|-| |[A-Z]{2}\b|[^A-Z'](?<=_)")
+    pattern = re.compile(r"(?<!_)\B[\w']+|[ ,\-_]")
 
-    return  re.sub(pattern, "", to_abbreviate).upper()
\ No newline at end of file
+    return  re.sub(pattern, "", to_abbreviate.upper())
\ No newline at end of file

From 84a22c66b7cc060ae97de216a06336c5233a3ee1 Mon Sep 17 00:00:00 2001
From: BethanyG <BethanyG@users.noreply.github.com>
Date: Tue, 27 Feb 2024 22:18:41 -0800
Subject: [PATCH 7/8] Update snippet.md

Removed genexp so file would be 8 lines.
---
 exercises/practice/acronym/.articles/performance/snippet.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/exercises/practice/acronym/.articles/performance/snippet.md b/exercises/practice/acronym/.articles/performance/snippet.md
index e6d772912f..00e1067fd9 100644
--- a/exercises/practice/acronym/.articles/performance/snippet.md
+++ b/exercises/practice/acronym/.articles/performance/snippet.md
@@ -3,7 +3,6 @@
 | **loop**                     	|   5.79e-07  	|   7.25e-07  	|   1.83e-06  	|   4.63e-06   	|    5.94e-05   	|
 | **list_comprehension**       	|   7.28e-07  	|   8.30e-07  	|   1.76e-06  	|   4.08e-06   	|    5.42e-05   	|
 | **functools.reduce()**       	|   7.93e-07  	|   9.56e-07  	|   2.45e-06  	|   6.03e-06   	|    8.10e-05   	|
-| **map() **                   	|   8.05e-07  	|   9.16e-07  	|   2.00e-06  	|   4.81e-06   	|    5.64e-05   	|
-| **generator expression**     	|   8.85e-07  	|   2.49e-06  	|   2.10e-06  	|   5.12e-06   	|    5.81e-05   	|
+| **map()**                   	|   8.05e-07  	|   9.16e-07  	|   2.00e-06  	|   4.81e-06   	|    5.64e-05   	|
 | **re.findall() 1st letters** 	|   1.63e-06  	|   2.50e-06  	|   5.94e-06  	|   1.54e-05   	|    1.95e-04   	|
 | **re.sub()**                 	|   2.35e-06  	|   2.92e-06  	|   6.90e-06  	|   1.90e-05   	|    2.03e-04   	|
\ No newline at end of file

From a756ba5aea44bc7b07f9850ab9dfa992b5fc4948 Mon Sep 17 00:00:00 2001
From: BethanyG <BethanyG@users.noreply.github.com>
Date: Tue, 27 Feb 2024 22:46:44 -0800
Subject: [PATCH 8/8] Update content.md

Fixed benchmark table and broken link in performance article.
---
 .../acronym/.articles/performance/content.md         | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/exercises/practice/acronym/.articles/performance/content.md b/exercises/practice/acronym/.articles/performance/content.md
index d1fc4c9d29..2fbfbf207e 100644
--- a/exercises/practice/acronym/.articles/performance/content.md
+++ b/exercises/practice/acronym/.articles/performance/content.md
@@ -39,13 +39,13 @@ Even though the `re.sub()` solution takes only 652 steps in the regex engine, `r
 
 
 
-||                              	| **Len: 13** 	| **Len: 14** 	| **Len: 19** 	| **Len: 20** 	| **Len: 25** 	| **Len: 30** 	| **Len: 35** 	| **Len: 39** 	| **Len: 42** 	| **Len: 45** 	| **Len: 60** 	| **Len: 63** 	| **Len: 74** 	| **Len: 150** 	| **Len: 210** 	| **Len: 360** 	| **Len: 400** 	| **Len: 2940** 	|
+|      **String Length >>>** 	| **13** 	| **14** 	| **19** 	| **20** 	| **25** 	| **30** 	| **35** 	| **39** 	| **42** 	| **45** 	| **60** 	| **63** 	| **74** 	| **150** 	| **210** 	| **360** 	| **400** 	| **2940** 	|
 |------------------------------	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:-----------:	|:------------:	|:------------:	|:------------:	|:------------:	|:-------------:	|
 | **loop**                     	|   5.79e-07  	|   4.96e-07  	|   6.98e-07  	|   7.41e-07  	|   6.18e-07  	|   7.25e-07  	|   1.03e-06  	|   7.33e-07  	|   1.16e-06  	|   8.71e-07  	|   1.51e-06  	|   1.65e-06  	|   1.83e-06  	|   2.43e-06   	|   4.63e-06   	|   7.76e-06   	|   4.85e-06   	|    5.94e-05   	|
-| **list_comprehension**       	|   7.28e-07  	|   6.57e-07  	|   8.26e-07  	|   8.62e-07  	|   7.67e-07  	|   8.30e-07  	|   1.08e-06  	|   8.68e-07  	|   1.24e-06  	|   4.00e-07  	|   1.49e-06  	|   1.55e-06  	|   1.76e-06  	|   2.19e-06   	|   4.08e-06   	|   7.21e-06   	|   4.40e-06   	|    5.42e-05   	|
+| **list&nbsp;comprehension**       	|   7.28e-07  	|   6.57e-07  	|   8.26e-07  	|   8.62e-07  	|   7.67e-07  	|   8.30e-07  	|   1.08e-06  	|   8.68e-07  	|   1.24e-06  	|   4.00e-07  	|   1.49e-06  	|   1.55e-06  	|   1.76e-06  	|   2.19e-06   	|   4.08e-06   	|   7.21e-06   	|   4.40e-06   	|    5.42e-05   	|
 | **functools.reduce()**       	|   7.93e-07  	|   6.65e-07  	|   9.50e-07  	|   2.43e-06  	|   8.19e-07  	|   9.56e-07  	|   1.36e-06  	|   4.12e-07  	|   1.64e-06  	|   1.21e-06  	|   2.03e-06  	|   2.14e-06  	|   2.45e-06  	|   3.15e-06   	|   6.03e-06   	|   1.03e-05   	|   6.19e-06   	|    8.10e-05   	|
-| **map() **                   	|   8.05e-07  	|   7.21e-07  	|   9.34e-07  	|   9.46e-07  	|   8.32e-07  	|   9.16e-07  	|   1.23e-06  	|   9.52e-07  	|   1.44e-06  	|   1.14e-06  	|   1.71e-06  	|   1.80e-06  	|   2.00e-06  	|   2.58e-06   	|   4.81e-06   	|   8.02e-06   	|   4.95e-06   	|    5.64e-05   	|
-| **generator expression**     	|   8.85e-07  	|   7.90e-07  	|   1.01e-06  	|   1.01e-06  	|   9.26e-07  	|   2.49e-06  	|   1.30e-06  	|   1.06e-06  	|   1.49e-06  	|   1.19e-06  	|   1.81e-06  	|   1.86e-06  	|   2.10e-06  	|   2.67e-06   	|   5.12e-06   	|   8.61e-06   	|   5.12e-06   	|    5.81e-05   	|
+| **map()**                   	|   8.05e-07  	|   7.21e-07  	|   9.34e-07  	|   9.46e-07  	|   8.32e-07  	|   9.16e-07  	|   1.23e-06  	|   9.52e-07  	|   1.44e-06  	|   1.14e-06  	|   1.71e-06  	|   1.80e-06  	|   2.00e-06  	|   2.58e-06   	|   4.81e-06   	|   8.02e-06   	|   4.95e-06   	|    5.64e-05   	|
+| **generator&nbsp;expression**     	|   8.85e-07  	|   7.90e-07  	|   1.01e-06  	|   1.01e-06  	|   9.26e-07  	|   2.49e-06  	|   1.30e-06  	|   1.06e-06  	|   1.49e-06  	|   1.19e-06  	|   1.81e-06  	|   1.86e-06  	|   2.10e-06  	|   2.67e-06   	|   5.12e-06   	|   8.61e-06   	|   5.12e-06   	|    5.81e-05   	|
 | **re.finditer()**            	|   1.05e-06  	|   1.74e-06  	|   2.44e-06  	|   2.40e-06  	|   2.09e-06  	|   2.45e-06  	|   3.28e-06  	|   2.42e-06  	|   8.15e-06  	|   3.12e-06  	|   5.15e-06  	|   5.18e-06  	|   5.94e-06  	|   7.89e-06   	|   1.46e-05   	|   2.35e-05   	|   1.48e-05   	|    1.68e-04   	|
 | **regex with str.join()**    	|   1.62e-06  	|   1.42e-06  	|   1.85e-06  	|   1.91e-06  	|   1.66e-06  	|   1.88e-06  	|   2.61e-06  	|   4.41e-06  	|   3.14e-06  	|   2.47e-06  	|   3.92e-06  	|   4.11e-06  	|   4.61e-06  	|   6.24e-06   	|   1.13e-05   	|   1.86e-05   	|   1.19e-05   	|    1.36e-04   	|
 | **re.findall() 1st letters** 	|   1.63e-06  	|   1.57e-06  	|   2.04e-06  	|   2.12e-06  	|   2.16e-06  	|   2.50e-06  	|   3.18e-06  	|   2.90e-06  	|   3.73e-06  	|   3.41e-06  	|   4.84e-06  	|   5.22e-06  	|   5.94e-06  	|   1.00e-05   	|   1.54e-05   	|   2.48e-05   	|   2.28e-05   	|    1.95e-04   	|
@@ -57,9 +57,9 @@ Keep in mind that all these approaches are very fast, and that benchmarking at t
 Measurements were taken on a 3.1 GHz Quad-Core Intel Core i7 Mac running MacOS Ventura.
 Tests used `timeit.Timer.autorange()`, repeated 3 times.
 Time is reported in seconds taken per string after calculating the 'best of' time.
-The timeit module docs have more details, and [note.nkmk.me][note_nkmk_me] has a nice summary of methods.
-
+The [timeit module][timeit] docs have more details, and [note.nkmk.me][note_nkmk_me] has a nice summary of methods.
 
+[approaches]: https://exercism.org/tracks/python/exercises/acronym/dig_deeper
 [approach-functools-reduce]: https://exercism.org/tracks/python/exercises/acronym/approaches/functools-reduce
 [approach-generator-expression]: https://exercism.org/tracks/python/exercises/acronym/approaches/generator-expression
 [approach-list-comprehension]: https://exercism.org/tracks/python/exercises/acronym/approaches/list-comprehension