Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n",
    "\n",
    "This notebook analyses the final set of papers found by the snowballing and coding selection process. \n",
    "\n",
    "The list of papers analysied is the spreadsheet CodedPapers.xlsx, containing the results of coding all the rounds.\n",
    "\n",
    "Copyright &copy; 2021.   \n",
    "Shared under the Apache 2.0 License"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "colab_type": "code",
    "id": "VD37eEggwAcN",
    "outputId": "016164cc-d8df-42fe-8797-48482a2513bb"
   },
   "outputs": [],
   "source": [
    "# !pip install google-search-results\n",
    "from serpapi import GoogleScholarSearch\n",
    "from ScholarUtils import GetPapers, GetPaper, WellCitedPapers, InitScholar, RelatedQuery, RelatedRef, NumCitations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "colab_type": "code",
    "id": "bg0FZIzozs9Q",
    "outputId": "7f97b83a-f557-46f8-c472-ce361aa121f6"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "import os\n",
    "import statsmodels.stats.inter_rater as ir\n",
    "import networkx as nx\n",
    "# Warnings break anonymization...\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Change to an appropriate location:\n",
    "imageDir='.' \n",
    "\n",
    "# Some image defaults:\n",
    "sns.set_palette(\"colorblind\")\n",
    "plt.rcParams['font.size'] = '14'\n",
    "plt.rcParams['figure.figsize'] = 8,4\n",
    "\n",
    "# Reload ScholarUtils every time before executing code \n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def Savefig(identifier):\n",
    "    # Save the current image as an image file identified by the string identifier\n",
    "    plt.savefig( os.path.join(imageDir, identifier + '.pdf'), bbox_inches='tight' ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "InitScholar(\"APIKey.yaml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n"
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       "      <td>zkjagHr-p8cJ</td>\n",
       "      <td>Society is increasingly reliant upon complex a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>443</td>\n",
       "      <td>6871428980662632846</td>\n",
       "      <td>5</td>\n",
       "      <td>77</td>\n",
       "      <td>2017</td>\n",
       "      <td>Security and privacy in the internet of medica...</td>\n",
       "      <td>F Alsubaei, A Abuhussein…</td>\n",
       "      <td>https://ieeexplore.ieee.org/abstract/document/...</td>\n",
       "      <td>joG4Y0A4XF8J</td>\n",
       "      <td>As the number of devices (things) connected to...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>480</td>\n",
       "      <td>7192314918966201656</td>\n",
       "      <td>5</td>\n",
       "      <td>701</td>\n",
       "      <td>2017</td>\n",
       "      <td>Internet of Things security: A survey</td>\n",
       "      <td>FA Alaba, M Othman, IAT Hashem, F Alotaibi</td>\n",
       "      <td>https://www.sciencedirect.com/science/article/...</td>\n",
       "      <td>OA258U880GMJ</td>\n",
       "      <td>Abstract The Internet of things (IoT) has rece...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>516</td>\n",
       "      <td>4744011031270295363</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "      <td>2012</td>\n",
       "      <td>Towards metrics-driven adaptive security manag...</td>\n",
       "      <td>RM Savola, H Abie, M Sihvonen</td>\n",
       "      <td>http://asset.nr.no/images/5/5a/SeTTIT2012_Savo...</td>\n",
       "      <td>Q4dm6eUc1kEJ</td>\n",
       "      <td>• SECURITY CONTROL EFFECTIVENESS• Security eff...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>518</td>\n",
       "      <td>8690943492523216829</td>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>2014</td>\n",
       "      <td>Game-based adaptive security in the Internet o...</td>\n",
       "      <td>M Hamdi, H Abie</td>\n",
       "      <td>https://ieeexplore.ieee.org/abstract/document/...</td>\n",
       "      <td>vc_WUg9vnHgJ</td>\n",
       "      <td>The Internet of Things (IoT) is enabling the d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>521</td>\n",
       "      <td>427003935600987829</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>2015</td>\n",
       "      <td>Risk-driven security metrics development for a...</td>\n",
       "      <td>RM Savola, P Savolainen, A Evesti…</td>\n",
       "      <td>https://ieeexplore.ieee.org/abstract/document/...</td>\n",
       "      <td>tc4wK9EF7QUJ</td>\n",
       "      <td>Security and privacy for e-health Internet-of-...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Missed this one.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>529</td>\n",
       "      <td>15128102205622477107</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>2015</td>\n",
       "      <td>Threats identification for the smart internet ...</td>\n",
       "      <td>K Habib, W Leister</td>\n",
       "      <td>https://ieeexplore.ieee.org/abstract/document/...</td>\n",
       "      <td>M2Huy83Q8dEJ</td>\n",
       "      <td>The Things in the smart Internet of Things (Io...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>536</td>\n",
       "      <td>6262309459127610254</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>2014</td>\n",
       "      <td>An evaluation framework for adaptive security ...</td>\n",
       "      <td>W Leister, M Hamdi, H Abie…</td>\n",
       "      <td>http://qmro.qmul.ac.uk/xmlui/handle/123456789/...</td>\n",
       "      <td>jqveIEYx6FYJ</td>\n",
       "      <td>—We present an assessment framework to evaluat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>538</td>\n",
       "      <td>6984330372398901370</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>2017</td>\n",
       "      <td>Towards composable threat assessment for medic...</td>\n",
       "      <td>S Darwish, I Nouretdinov, SD Wolthusen</td>\n",
       "      <td>https://www.sciencedirect.com/science/article/...</td>\n",
       "      <td>eujOGHhT7WAJ</td>\n",
       "      <td>Abstract The Medical Internet of Things (MIoT)...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>572</td>\n",
       "      <td>2355436589644192734</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>2017</td>\n",
       "      <td>Assessing medical device vulnerabilities on th...</td>\n",
       "      <td>E McMahon, R Williams, M El, S Samtani…</td>\n",
       "      <td>https://ieeexplore.ieee.org/abstract/document/...</td>\n",
       "      <td>3huzQBkxsCAJ</td>\n",
       "      <td>Internet enabled medical devices offer patient...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>575</td>\n",
       "      <td>11056082201899200765</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>2015</td>\n",
       "      <td>Security analysis of a patient monitoring syst...</td>\n",
       "      <td>K Habib, A Torjusen, W Leister</td>\n",
       "      <td>https://www.researchgate.net/profile/Wolfgang-...</td>\n",
       "      <td>_XC4Wy8Yb5kJ</td>\n",
       "      <td>Things in eHealth can be established through t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>695</td>\n",
       "      <td>6724629180905251087</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>2019</td>\n",
       "      <td>Ontology-based security recommendation for the...</td>\n",
       "      <td>F Alsubaei, A Abuhussein, S Shiva</td>\n",
       "      <td>https://ieeexplore.ieee.org/abstract/document/...</td>\n",
       "      <td>D2EVhpyuUl0J</td>\n",
       "      <td>Security and privacy are among the key barrier...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>698</td>\n",
       "      <td>10710897600124378357</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2019</td>\n",
       "      <td>Security and privacy for the internet of medic...</td>\n",
       "      <td>Y Sun, FPW Lo, B Lo</td>\n",
       "      <td>https://ieeexplore.ieee.org/abstract/document/...</td>\n",
       "      <td>9Vz_WJ7ApJQJ</td>\n",
       "      <td>With the increasing demands on quality healthc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>729</td>\n",
       "      <td>5021234964518621637</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>2019</td>\n",
       "      <td>IoT smart health security threats</td>\n",
       "      <td>SA Butt, JL Diaz-Martinez, T Jamal, A Ali…</td>\n",
       "      <td>https://ieeexplore.ieee.org/abstract/document/...</td>\n",
       "      <td>xVHnuZgCr0UJ</td>\n",
       "      <td>The Internet of things (IoT) is an active area...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Dummys guide to basic attacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>870</td>\n",
       "      <td>16125298375562168501</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>2012</td>\n",
       "      <td>Risk-driven security metrics in agile software...</td>\n",
       "      <td>RM Savola, C Frühwirth, A Pietikäinen</td>\n",
       "      <td>http://jucs.org/jucs_18_12/risk_driven_securit...</td>\n",
       "      <td>tQiR5G-RyN8J</td>\n",
       "      <td>The need for effective and efficient informati...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Like the risk-driven bit.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>970</td>\n",
       "      <td>16205524198307687761</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2020</td>\n",
       "      <td>Operationalization of privacy and security req...</td>\n",
       "      <td>O Tomashchuk, Y Li, D Van Landuyt, W Joosen</td>\n",
       "      <td>https://link.springer.com/chapter/10.1007/978-...</td>\n",
       "      <td>UW0g12WW5eAJ</td>\n",
       "      <td>Abstract The Fourth Industrial Revolution impo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>1113</td>\n",
       "      <td>9619492135223642694</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>2015</td>\n",
       "      <td>A value blueprint approach to cybersecurity in...</td>\n",
       "      <td>G Tanev, P Tzolov, R Apiafi</td>\n",
       "      <td>https://timreview.ca/sites/default/files/artic...</td>\n",
       "      <td>RkqrdyFNf4UJ</td>\n",
       "      <td>Cybersecurity for networked medical devices ha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Emphasises stakeholder value</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                   Key  Round  Citations  Year  \\\n",
       "0              0   2158158161114378793      1        123  2016   \n",
       "1              1    856727435201930563      1         31  2019   \n",
       "28            28   7884234468971956873      2        103  2018   \n",
       "41            41  15098848452248305004      2          6  2020   \n",
       "44            44  11161626713660218806      2          7  2018   \n",
       "142          142  15490167577412579022      3         41  2018   \n",
       "143          143  16163323237889647504      3         26  2019   \n",
       "151          151  16937427700442842878      3         22  2019   \n",
       "156          156    402742775937259293      3         19  2019   \n",
       "158          158    705795199300173541      3         20  2019   \n",
       "160          160  17027613155545731121      3         22  2019   \n",
       "168          168   6230626243802260829      3        120  2017   \n",
       "172          172  15110423003858188256      3        113  2015   \n",
       "174          174   8005757708775614339      3         18  2016   \n",
       "178          178   3717699742540737694      3         34  2005   \n",
       "186          186  14324492556190021166      3         16  2020   \n",
       "194          194   1776037729542888110      3         24  2020   \n",
       "202          202  16248860325541214466      3          7  2016   \n",
       "227          227   3404010353886548903      3         10  2018   \n",
       "312          312   2030156502610964061      4        194  2018   \n",
       "325          325   1340038700140404557      4        149  2012   \n",
       "336          336  18231763146764638745      4         13  2017   \n",
       "341          341  14386747336759593166      4         58  2019   \n",
       "443          443   6871428980662632846      5         77  2017   \n",
       "480          480   7192314918966201656      5        701  2017   \n",
       "516          516   4744011031270295363      5         68  2012   \n",
       "518          518   8690943492523216829      5         75  2014   \n",
       "521          521    427003935600987829      7         24  2015   \n",
       "529          529  15128102205622477107      5         21  2015   \n",
       "536          536   6262309459127610254      5         25  2014   \n",
       "538          538   6984330372398901370      5         26  2017   \n",
       "572          572   2355436589644192734      5         35  2017   \n",
       "575          575  11056082201899200765      5         41  2015   \n",
       "695          695   6724629180905251087      6         21  2019   \n",
       "698          698  10710897600124378357      6         37  2019   \n",
       "729          729   5021234964518621637      6         54  2019   \n",
       "870          870  16125298375562168501      6         23  2012   \n",
       "970          970  16205524198307687761      6          2  2020   \n",
       "1113        1113   9619492135223642694      6         17  2015   \n",
       "\n",
       "                                                  Title  \\\n",
       "0         How to measure anything in cybersecurity risk   \n",
       "1     Cyber threat intelligence sharing: Survey and ...   \n",
       "28    Future developments in cyber risk assessment f...   \n",
       "41             Cyber risk measurement with ordinal data   \n",
       "44    A risk index for ordinal variables and its sta...   \n",
       "142   If you can't understand it, you can't properly...   \n",
       "143   Definition of cyber strategy transformation ro...   \n",
       "151   Cyber risk impact assessment-assessing the ris...   \n",
       "156   Methodology for designing decision support sup...   \n",
       "158   Design principles for cyber risk impact assess...   \n",
       "160                           Cyber Risk in IoT Systems   \n",
       "168   Security risk assessment in Internet of Things...   \n",
       "172   Systems engineering framework for cyber physic...   \n",
       "174   Value analysis of cyber security based on atta...   \n",
       "178   Aligning information security investments with...   \n",
       "186   Future developments in standardisation of cybe...   \n",
       "194   Cyber risk at the edge: current and future tre...   \n",
       "202              Cyber value at risk in the Netherlands   \n",
       "227   Emerging risks in the IoT ecosystem: Who's afr...   \n",
       "312   Cyber and physical security vulnerability asse...   \n",
       "325   Risk-based adaptive security for smart IoT in ...   \n",
       "336   Bridging the gap from cyber security to resili...   \n",
       "341   Fundamental concepts of cyber resilience: Intr...   \n",
       "443   Security and privacy in the internet of medica...   \n",
       "480               Internet of Things security: A survey   \n",
       "516   Towards metrics-driven adaptive security manag...   \n",
       "518   Game-based adaptive security in the Internet o...   \n",
       "521   Risk-driven security metrics development for a...   \n",
       "529   Threats identification for the smart internet ...   \n",
       "536   An evaluation framework for adaptive security ...   \n",
       "538   Towards composable threat assessment for medic...   \n",
       "572   Assessing medical device vulnerabilities on th...   \n",
       "575   Security analysis of a patient monitoring syst...   \n",
       "695   Ontology-based security recommendation for the...   \n",
       "698   Security and privacy for the internet of medic...   \n",
       "729                   IoT smart health security threats   \n",
       "870   Risk-driven security metrics in agile software...   \n",
       "970   Operationalization of privacy and security req...   \n",
       "1113  A value blueprint approach to cybersecurity in...   \n",
       "\n",
       "                                               Authors  \\\n",
       "0                               DW Hubbard, R Seiersen   \n",
       "1          TD Wagner, K Mahbub, E Palomar, AE Abdallah   \n",
       "28      P Radanliev, DC De Roure, R Nicolescu, M Huth…   \n",
       "41                S Facchinetti, P Giudici, SA Osmetti   \n",
       "44                           S Facchinetti, SA Osmetti   \n",
       "142       JRC Nurse, P Radanliev, S Creese, D De Roure   \n",
       "143               P Radanliev, DC De Roure, JRC Nurse…   \n",
       "151   P Radanliev, D De Roure, JRC Nurse, R Nicolescu…   \n",
       "156        P Radanliev, D Charles De Roure, JRC Nurse…   \n",
       "158               P Radanliev, DC De Roure, JRC Nurse…   \n",
       "160      P Radanliev, DC De Roure, C Maple, JRC Nurse…   \n",
       "168                    JRC Nurse, S Creese, D De Roure   \n",
       "172          D DiMase, ZA Collier, K Heffner, I Linkov   \n",
       "174                         M Akbari Roumani, CC Fung…   \n",
       "178                                         G Rodewald   \n",
       "186               P Radanliev, DC De Roure, JRC Nurse…   \n",
       "194        P Radanliev, D De Roure, K Page, JRC Nurse…   \n",
       "202                                            J Buith   \n",
       "227                 LM Tanczer, I Steenmans, M Elsden…   \n",
       "312                                     B Ali, AI Awad   \n",
       "325                              H Abie, I Balasingham   \n",
       "336    PE Roege, ZA Collier, V Chevardin, P Chouinard…   \n",
       "341                                   I Linkov, A Kott   \n",
       "443                          F Alsubaei, A Abuhussein…   \n",
       "480         FA Alaba, M Othman, IAT Hashem, F Alotaibi   \n",
       "516                      RM Savola, H Abie, M Sihvonen   \n",
       "518                                    M Hamdi, H Abie   \n",
       "521                 RM Savola, P Savolainen, A Evesti…   \n",
       "529                                 K Habib, W Leister   \n",
       "536                        W Leister, M Hamdi, H Abie…   \n",
       "538             S Darwish, I Nouretdinov, SD Wolthusen   \n",
       "572            E McMahon, R Williams, M El, S Samtani…   \n",
       "575                     K Habib, A Torjusen, W Leister   \n",
       "695                  F Alsubaei, A Abuhussein, S Shiva   \n",
       "698                                Y Sun, FPW Lo, B Lo   \n",
       "729         SA Butt, JL Diaz-Martinez, T Jamal, A Ali…   \n",
       "870              RM Savola, C Frühwirth, A Pietikäinen   \n",
       "970        O Tomashchuk, Y Li, D Van Landuyt, W Joosen   \n",
       "1113                       G Tanev, P Tzolov, R Apiafi   \n",
       "\n",
       "                                                   Link       Related  \\\n",
       "0     https://books.google.com/books?hl=en&lr=&id=Aw...  KTJVwmlR8x0J   \n",
       "1     https://www.sciencedirect.com/science/article/...  Q-WKLBC14wsJ   \n",
       "28    https://www.sciencedirect.com/science/article/...  icr2IHptam0J   \n",
       "41    https://link.springer.com/article/10.1007/s102...  bH23kaviidEJ   \n",
       "44    https://onlinelibrary.wiley.com/doi/abs/10.100...  tl0691sQ5poJ   \n",
       "142   https://digital-library.theiet.org/content/con...  zi7zWlch-NYJ   \n",
       "143   https://www.researchgate.net/profile/Petar-Rad...  kKM6ENmoT-AJ   \n",
       "151    https://www.preprints.org/manuscript/201903.0109  _sbBVcHUDesJ   \n",
       "156                                                 NaN  HVeYv2vUlgUJ   \n",
       "158   https://www.researchgate.net/profile/Petar-Rad...  5X52iv58ywkJ   \n",
       "160    https://www.preprints.org/manuscript/201903.0104  MVAS5fI7TuwJ   \n",
       "168   https://ieeexplore.ieee.org/abstract/document/...  XWUPto6hd1YJ   \n",
       "172   https://link.springer.com/article/10.1007/s106...  4Lfb3qkBs9EJ   \n",
       "174   https://researchrepository.murdoch.edu.au/id/e...  g2dqRTgqGm8J   \n",
       "178   https://dl.acm.org/doi/abs/10.1145/1107622.110...  nqyz9zfslzMJ   \n",
       "186   https://link.springer.com/article/10.1007/s424...  LvZu3xXSysYJ   \n",
       "194   https://link.springer.com/content/pdf/10.1186/...  rloYuefApRgJ   \n",
       "202                                                 NaN  AnnSPGCMf-EJ   \n",
       "227   https://ieeexplore.ieee.org/abstract/document/...  p9Mos2F5PS8J   \n",
       "312                         https://www.mdpi.com/270606  XW6TOJWQLBwJ   \n",
       "325   https://www.balasingham.net/Content/files/pdf/...  TR8lExnGmBIJ   \n",
       "336   https://link.springer.com/chapter/10.1007/978-...  Gdr2Z_w7BP0J   \n",
       "341   https://link.springer.com/chapter/10.1007/978-...  zkjagHr-p8cJ   \n",
       "443   https://ieeexplore.ieee.org/abstract/document/...  joG4Y0A4XF8J   \n",
       "480   https://www.sciencedirect.com/science/article/...  OA258U880GMJ   \n",
       "516   http://asset.nr.no/images/5/5a/SeTTIT2012_Savo...  Q4dm6eUc1kEJ   \n",
       "518   https://ieeexplore.ieee.org/abstract/document/...  vc_WUg9vnHgJ   \n",
       "521   https://ieeexplore.ieee.org/abstract/document/...  tc4wK9EF7QUJ   \n",
       "529   https://ieeexplore.ieee.org/abstract/document/...  M2Huy83Q8dEJ   \n",
       "536   http://qmro.qmul.ac.uk/xmlui/handle/123456789/...  jqveIEYx6FYJ   \n",
       "538   https://www.sciencedirect.com/science/article/...  eujOGHhT7WAJ   \n",
       "572   https://ieeexplore.ieee.org/abstract/document/...  3huzQBkxsCAJ   \n",
       "575   https://www.researchgate.net/profile/Wolfgang-...  _XC4Wy8Yb5kJ   \n",
       "695   https://ieeexplore.ieee.org/abstract/document/...  D2EVhpyuUl0J   \n",
       "698   https://ieeexplore.ieee.org/abstract/document/...  9Vz_WJ7ApJQJ   \n",
       "729   https://ieeexplore.ieee.org/abstract/document/...  xVHnuZgCr0UJ   \n",
       "870   http://jucs.org/jucs_18_12/risk_driven_securit...  tQiR5G-RyN8J   \n",
       "970   https://link.springer.com/chapter/10.1007/978-...  UW0g12WW5eAJ   \n",
       "1113  https://timreview.ca/sites/default/files/artic...  RkqrdyFNf4UJ   \n",
       "\n",
       "                                                Snippet    C   P  AgreedScore  \\\n",
       "0     A ground shaking exposé on the failure of popu...  NaN NaN          7.0   \n",
       "1     Abstract Cyber Threat Intelligence (CTI) shari...  NaN NaN          7.0   \n",
       "28    This article is focused on the economic impact...  NaN NaN          7.0   \n",
       "41    The paper proposes a new methodology to measur...  NaN NaN          7.0   \n",
       "44    Measuring risk when data are available only on...  3.0 NaN          7.0   \n",
       "142   Security risk assessment methods have served u...  NaN NaN          7.0   \n",
       "143   Abstract The Internet-of-Things (IoT) enables ...  NaN NaN          7.0   \n",
       "151   We present an updated design process for adapt...  NaN NaN          7.0   \n",
       "156                                                 NaN  NaN NaN          7.0   \n",
       "158   Digital IoT technologies present new cyber ris...  NaN NaN          7.0   \n",
       "160   In this paper we present an understanding of c...  NaN NaN          7.0   \n",
       "168   Information security risk assessment methods h...  NaN NaN          7.0   \n",
       "172   As our infrastructure, economy, and national d...  NaN NaN          7.0   \n",
       "174   It is challenging to ensure security and to mi...  NaN NaN          7.0   \n",
       "178   Technology continually places greater demands ...  NaN NaN          7.0   \n",
       "186   In this research article, we explore the use o...  NaN NaN          7.0   \n",
       "194   Digital technologies have changed the way supp...  NaN NaN          7.0   \n",
       "202                                                 NaN  NaN NaN          7.0   \n",
       "227   Rapid technological innovations, including the...  NaN NaN          7.0   \n",
       "312   Abstract The Internet of Things (IoT) is an em...  NaN NaN          7.0   \n",
       "325   ABSTRACT Emerging Internet of Things (IoTs) te...  NaN NaN          9.0   \n",
       "336   This chapter describes an evolution of practic...  NaN NaN          7.0   \n",
       "341   Society is increasingly reliant upon complex a...  NaN NaN          7.0   \n",
       "443   As the number of devices (things) connected to...  NaN NaN          7.0   \n",
       "480   Abstract The Internet of things (IoT) has rece...  NaN NaN          7.0   \n",
       "516   • SECURITY CONTROL EFFECTIVENESS• Security eff...  NaN NaN          7.0   \n",
       "518   The Internet of Things (IoT) is enabling the d...  NaN NaN          7.0   \n",
       "521   Security and privacy for e-health Internet-of-...  NaN NaN          9.0   \n",
       "529   The Things in the smart Internet of Things (Io...  NaN NaN          7.0   \n",
       "536   —We present an assessment framework to evaluat...  NaN NaN          7.0   \n",
       "538   Abstract The Medical Internet of Things (MIoT)...  NaN NaN          7.0   \n",
       "572   Internet enabled medical devices offer patient...  NaN NaN          7.0   \n",
       "575   Things in eHealth can be established through t...  NaN NaN          7.0   \n",
       "695   Security and privacy are among the key barrier...  NaN NaN          7.0   \n",
       "698   With the increasing demands on quality healthc...  NaN NaN          7.0   \n",
       "729   The Internet of things (IoT) is an active area...  NaN NaN          7.0   \n",
       "870   The need for effective and efficient informati...  NaN NaN          7.0   \n",
       "970   Abstract The Fourth Industrial Revolution impo...  NaN NaN          7.0   \n",
       "1113  Cybersecurity for networked medical devices ha...  NaN NaN          7.0   \n",
       "\n",
       "                        Unnamed: 13  \n",
       "0                               NaN  \n",
       "1                               NaN  \n",
       "28                              NaN  \n",
       "41                              NaN  \n",
       "44                              NaN  \n",
       "142                             NaN  \n",
       "143                             NaN  \n",
       "151                             NaN  \n",
       "156                             NaN  \n",
       "158                             NaN  \n",
       "160                             NaN  \n",
       "168                             NaN  \n",
       "172                             NaN  \n",
       "174                             NaN  \n",
       "178                             NaN  \n",
       "186                             NaN  \n",
       "194                             NaN  \n",
       "202                             NaN  \n",
       "227                             NaN  \n",
       "312                             NaN  \n",
       "325                             NaN  \n",
       "336                             NaN  \n",
       "341                             NaN  \n",
       "443                             NaN  \n",
       "480                             NaN  \n",
       "516                             NaN  \n",
       "518                             NaN  \n",
       "521                Missed this one.  \n",
       "529                             NaN  \n",
       "536                             NaN  \n",
       "538                             NaN  \n",
       "572                             NaN  \n",
       "575                             NaN  \n",
       "695                             NaN  \n",
       "698                             NaN  \n",
       "729   Dummys guide to basic attacks  \n",
       "870       Like the risk-driven bit.  \n",
       "970                             NaN  \n",
       "1113   Emphasises stakeholder value  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all the coded papers, and filter out those we've accepted:\n",
    "allPreviousPapersDf=pd.read_excel('CodedPapers.xlsx', \n",
    "                                # Want Key as string, not number, to match WellCitedPapers() output:\n",
    "                              dtype={'Key':np.str_}) \n",
    "acceptedPapersDf=allPreviousPapersDf.query('AgreedScore>6').copy()\n",
    "print(len(acceptedPapersDf))\n",
    "acceptedPapersDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    491\n",
       "7    275\n",
       "5    263\n",
       "4    157\n",
       "2    140\n",
       "3    131\n",
       "1      2\n",
       "Name: Round, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allPreviousPapersDf['Round'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acceptedPapersDf.Year.value_counts().to_frame('Count').sort_index().plot.bar(legend=None)\n",
    "plt.ylabel(\"Papers selected\")\n",
    "Savefig('PapersByYear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inter-rater reliability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 2: 100% agreement\n",
      "                  Simple Kappa Coefficient\n",
      "              --------------------------------\n",
      "              Kappa                        nan\n",
      "              ASE                          nan\n",
      "              95% Lower Conf Limit         nan\n",
      "              95% Upper Conf Limit         nan\n",
      "\n",
      "                 Test of H0: Simple Kappa = 0\n",
      "\n",
      "              ASE under H0                 nan\n",
      "              Z                            nan\n",
      "              One-sided Pr >  Z            nan\n",
      "              Two-sided Pr > |Z|           nan\n",
      "\n",
      "[[140.]]\n",
      "Round 3: 100% agreement\n",
      "                  Simple Kappa Coefficient\n",
      "              --------------------------------\n",
      "              Kappa                        nan\n",
      "              ASE                          nan\n",
      "              95% Lower Conf Limit         nan\n",
      "              95% Upper Conf Limit         nan\n",
      "\n",
      "                 Test of H0: Simple Kappa = 0\n",
      "\n",
      "              ASE under H0                 nan\n",
      "              Z                            nan\n",
      "              One-sided Pr >  Z            nan\n",
      "              Two-sided Pr > |Z|           nan\n",
      "\n",
      "[[131.]]\n",
      "Round 4: 100% agreement\n",
      "                  Simple Kappa Coefficient\n",
      "              --------------------------------\n",
      "              Kappa                        nan\n",
      "              ASE                          nan\n",
      "              95% Lower Conf Limit         nan\n",
      "              95% Upper Conf Limit         nan\n",
      "\n",
      "                 Test of H0: Simple Kappa = 0\n",
      "\n",
      "              ASE under H0                 nan\n",
      "              Z                            nan\n",
      "              One-sided Pr >  Z            nan\n",
      "              Two-sided Pr > |Z|           nan\n",
      "\n",
      "[[157.]]\n",
      "Round 5: 100% agreement\n",
      "                  Simple Kappa Coefficient\n",
      "              --------------------------------\n",
      "              Kappa                        nan\n",
      "              ASE                          nan\n",
      "              95% Lower Conf Limit         nan\n",
      "              95% Upper Conf Limit         nan\n",
      "\n",
      "                 Test of H0: Simple Kappa = 0\n",
      "\n",
      "              ASE under H0                 nan\n",
      "              Z                            nan\n",
      "              One-sided Pr >  Z            nan\n",
      "              Two-sided Pr > |Z|           nan\n",
      "\n",
      "[[263.]]\n",
      "Round 6: 100% agreement\n",
      "                  Simple Kappa Coefficient\n",
      "              --------------------------------\n",
      "              Kappa                        nan\n",
      "              ASE                          nan\n",
      "              95% Lower Conf Limit         nan\n",
      "              95% Upper Conf Limit         nan\n",
      "\n",
      "                 Test of H0: Simple Kappa = 0\n",
      "\n",
      "              ASE under H0                 nan\n",
      "              Z                            nan\n",
      "              One-sided Pr >  Z            nan\n",
      "              Two-sided Pr > |Z|           nan\n",
      "\n",
      "[[491.]]\n",
      "Round 7: 100% agreement\n",
      "                  Simple Kappa Coefficient\n",
      "              --------------------------------\n",
      "              Kappa                        nan\n",
      "              ASE                          nan\n",
      "              95% Lower Conf Limit         nan\n",
      "              95% Upper Conf Limit         nan\n",
      "\n",
      "                 Test of H0: Simple Kappa = 0\n",
      "\n",
      "              ASE under H0                 nan\n",
      "              Z                            nan\n",
      "              One-sided Pr >  Z            nan\n",
      "              Two-sided Pr > |Z|           nan\n",
      "\n",
      "[[275.]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the Inter-rater reliability (Cohen Kappa)\n",
    "\n",
    "for rnd in range(2,allPreviousPapersDf.Round.max()+1):\n",
    "    papersDf=(allPreviousPapersDf.query('Round=={}'.format(rnd))\n",
    "              [['P','C']]\n",
    "              .fillna(0)\n",
    "              .applymap(lambda x: int(x) > 6 ) # We're only interested in accept or not.\n",
    "             )\n",
    "    matrix,bins= ir.to_table(papersDf)\n",
    "    proportionAgreed=np.trace(matrix) / matrix.sum().sum()\n",
    "    print( \"Round {}: {:.0%} agreement\\n{}\\n{}\".format(rnd,proportionAgreed,ir.cohens_kappa(matrix),matrix) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# After Round 3, we plot the number of accepted papers we get for different citation cut-offs.\n",
    "\n",
    "def CitationsNeeded(year, **kwargs):\n",
    "    req=kwargs['req']\n",
    "    return req / 2 if year >= 2020 else (2020 - year) * req\n",
    "\n",
    "round3Df=acceptedPapersDf.query('Round<=3')\n",
    "pd.Series({ req: (round3Df.Citations >= round3Df.Year.apply(CitationsNeeded, req=req)).sum()\n",
    " for req in range(8, 30)}).plot()\n",
    "plt.xlabel(\"Cut-off point\")\n",
    "plt.ylabel(\"Papers selected\")\n",
    "plt.axhline(y=30, color='C2', linestyle='--')\n",
    "Savefig('CitationLimits')\n",
    "\n",
    "# Points on the graph: How many citations as a result of chosing 19, 18?\n",
    "((round3Df.Citations >= round3Df.Year.apply(CitationsNeeded, req=19)).sum(), \n",
    " (round3Df.Citations >= round3Df.Year.apply(CitationsNeeded, req=18)).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "#So, choose 19:\n",
    "chosenPapersDf=(acceptedPapersDf.assign( citationsRequired=acceptedPapersDf.Year.apply(CitationsNeeded, req=19) )\n",
    "                .query('Citations >= citationsRequired'))\n",
    "print(len(chosenPapersDf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering\n",
    "\n",
    "Use the 'related articles' list as a closeness measure for clustering the papers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving 101 papers for {'q': 'related:KTJVwmlR8x0J:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:Q-WKLBC14wsJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:icr2IHptam0J:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:zi7zWlch-NYJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:kKM6ENmoT-AJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:_sbBVcHUDesJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:HVeYv2vUlgUJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:5X52iv58ywkJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:MVAS5fI7TuwJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:XWUPto6hd1YJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:4Lfb3qkBs9EJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:LvZu3xXSysYJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:rloYuefApRgJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:XW6TOJWQLBwJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:zkjagHr-p8cJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:joG4Y0A4XF8J:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:OA258U880GMJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:D2EVhpyuUl0J:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:9Vz_WJ7ApJQJ:scholar.google.com/'}\n",
      "Retrieving 101 papers for {'q': 'related:xVHnuZgCr0UJ:scholar.google.com/'}\n"
     ]
    }
   ],
   "source": [
    "# Now we want, for each paper, in a column, the related papers as a map, key the Related ID, \n",
    "#   value the position of that related paper in the list.\n",
    "\n",
    "# This takes a long time.\n",
    "def RelatedPaperMap(relatedId):\n",
    "    allRelatedPapers= GetPapers(RelatedQuery(relatedId))\n",
    "    return {RelatedRef(p) : idx for idx, p in enumerate(allRelatedPapers)}\n",
    "\n",
    "papersWithRelatedPapersListDf=(chosenPapersDf\n",
    "                              .assign(Associations=[RelatedPaperMap(paper.Related) \n",
    "                                                   for paper in chosenPapersDf.itertuples()])\n",
    "                              )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The Google queries take a long time and can cost money. \n",
    "#To make this repeatable we 'comment out' the slow parts by changing the cell type to 'Raw NB Convert'; \n",
    "# to redo the queries, convert them back again to Code.\n",
    "\n",
    "with open( \"papersWithRelatedPapers.p\", \"wb\" ) as outFile:\n",
    "    pickle.dump( papersWithRelatedPapersListDf, outFile )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open( \"papersWithRelatedPapers.p\", \"rb\" ) as inFile:\n",
    "    papersWithRelatedPapersListDf=pickle.load( inFile )\n",
    "\n",
    "len(papersWithRelatedPapersListDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a unique short name for each paper:\n",
    "def CiteDescription(paper):\n",
    "    # E.g. Acar+2016\n",
    "    authorSurnames=re.split(r'[ ,]*[A-Z]* ', paper.Authors)[1:]\n",
    "    authorCite=(authorSurnames[0] if len(authorSurnames)==1 \n",
    "            else '&'.join(authorSurnames) if len(authorSurnames)==2\n",
    "            else authorSurnames[0] + '+')\n",
    "    return '{}{:d}'.format(authorCite, paper.Year)\n",
    "\n",
    "#Add 'a','b' etc to the end to differentiate repeated descriptions ('Acar+2016a')\n",
    "baseDescriptions=[CiteDescription(p) for p in papersWithRelatedPapersListDf.itertuples()]\n",
    "\n",
    "# Kludge: Google Scholar doesn't have space for third author in Bednar, Spiekermann et al. \n",
    "papersWithRelatedAndCitesDf=(papersWithRelatedPapersListDf\n",
    "            .assign(Cite=[ # 'Bednar+2019' if 'Bednar' in name else\n",
    "                name + ['','a','b','c','d','e','f','g'][len([earlierName for earlierName in baseDescriptions[0:idx] if earlierName==name])]\n",
    "     for idx, name in enumerate(baseDescriptions)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster, set_link_color_palette\n",
    "from scipy.spatial.distance import pdist\n",
    "# Perform Agglomerative clustering\n",
    "\n",
    "# pdist is the distance metric between two papers: \n",
    "# pdist's distance function receives two arrays containing the elements of two rows \n",
    "# of the dataframe. The array index of the items we want is the column index in the dataframe:\n",
    "theAssociationsLoc=papersWithRelatedAndCitesDf.columns.get_loc(\"Associations\") # e.g. 11\n",
    "theIdLoc=papersWithRelatedAndCitesDf.columns.get_loc(\"Related\")\n",
    "NOT_ASSOCIATED=101 # Distance for items not in the 'associated papers' list of either papers considered.\n",
    "\n",
    "def Distance(paper1, paper2):\n",
    "    return min(paper1[theAssociationsLoc].get(paper2[theIdLoc], NOT_ASSOCIATED),\n",
    "               paper2[theAssociationsLoc].get(paper1[theIdLoc], NOT_ASSOCIATED))\n",
    "\n",
    "# The linkage method choses how each two clusters are compared for distance:\n",
    "#       single, complete, weighted, average, ward\n",
    "#‘ward’ minimizes the variance of the clusters being merged.\n",
    "#‘average’ uses the average of the distances of each observation of the two sets.\n",
    "#‘complete’ or ‘maximum’ linkage uses the maximum distances between all observations of the two sets.\n",
    "#‘single’ uses the minimum of the distances between all observations of the two sets.\n",
    "\n",
    "Z = linkage(pdist(papersWithRelatedAndCitesDf, Distance), 'ward', optimal_ordering=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bit more magic, to get the cluster numbers (1,2,3...) for each paper:\n",
    "clusterCutoff=NOT_ASSOCIATED-1 # Tweek to get an acceptable number of clusters.\n",
    "clusteredPapersDf=(papersWithRelatedAndCitesDf.\n",
    "                             assign(Cluster = fcluster(Z, t=clusterCutoff, criterion='distance') # t chosen to correspond to the x-axis separate point above.\n",
    "                                   )\n",
    "                            )\n",
    "# Most of the clusters aren't interesting. Create 'groups' from the ones with more than 2 elements:\n",
    "clusterSizes=clusteredPapersDf['Cluster'].value_counts()\n",
    "groupMap=(clusterSizes[clusterSizes>2].to_frame()\n",
    "            .assign(Group=lambda df: range(1,len(df)+1))\n",
    "            ['Group']\n",
    "                   )\n",
    "\n",
    "def Group(cluster):\n",
    "    return groupMap.get(cluster,0)\n",
    "    \n",
    "NumGroups=groupMap.max()\n",
    "NumGroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NodeColour(n):\n",
    "    # Answer a colour for this node. \n",
    "    # There's a colour for the unclustered nodes:\n",
    "    if Z[n-len(clusteredPapersDf)][2] > clusterCutoff: \n",
    "        return 'C0'\n",
    "    # otherwise follow the tree to a paper and see what group that is in.\n",
    "    while n >= len(clusteredPapersDf):\n",
    "        n=int(Z[n-len(clusteredPapersDf)][0])\n",
    "    return 'C{}'.format(Group(clusteredPapersDf.iloc[n].Cluster))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dj+asqgDeriya6tFVnyxYHxP0zmlGqEpciLkAnoG53a1Ih1vVjAA+y1/EXIWmwaxP3orBPRwtbhFJTFobiwt1IvB8DO7lGNzepXK+hYkL1WdcWE0Vz7erez0Bs0DZGBMIx8bgLkzWXWWqVod5g+MzdqOeBdWxNg4TQ3qaWuVA57r3Rn7NjteCRmOsqzp8C7Pk+bhSTiGQVMtqhmLO7FSvNF7G0oSrVw/Q3T5Ty5J2HJ2f2fx0tE+Z/1Y+NzuOJtLVPJDSvIkJPHulQ1thbXpOKauLMXfhlTAR+N0Y3N/Ti41GzInFyTqCSfveL4E5KnPi+5X6N5oDoKMdu1s/IcRXhAceeIAHHnig3dUQQog+iyyrukFyLTsRiydRKz5UEaDWVd4S11vUTGKdkt4U7wvsm9xSdgaOxmIWnY0taCJmeVSLV0p5PVo+EYMbC8xaPV4lBrcgFj/kQ8yV6cPyeZ/ln0YLQrt0jcuXwnYEnBx2Bt6hG64PXZHci27FRB2w9gSL2TKixiXFcywsvBagdqwuSM8fW+CWafr5d4OLMYFmECYU/o/OsX8a0VT5TfTJgiMwK7ZbYnA/81l+f6N8k9XDJsDgsjtQDG7ZGnW4E3N5nAGzkDsGi/HT32f5Oz7LXwZ+niyilsMWj2fF4F7xWX4L9pzfxtzsavFcM/fqs3w8ZmV4cnLt2xCLmzMTJnRNLk3V82tMs+O1YHLG2FhsTK9a53xdC7AGFELRfFh8P2Ciy/ZcdNzfV5XRdAiKZTrFDOzuOOpqHkjJzsJc439Isjr1pV0Tk7h9DnBOtJ1u18G+06/ABKJ6vI9tqvFnbN6tVb/J2VBkcusnhPiK8PzzzwOwyiqrtLkmQgjRN5FY1X3OwmKOHFfj3Kvp7zKkALsp1swqWFDllvBZ/hxwaAxur5Qn2K6EWwAfV+Iu9QgpdkyxCFzbl3YoqnADsHMMbvZSXJ6fYFZf3XZjSW5c6wBnpQVNd/OZFehXFdqiBSBfjA7LhucwgW9pn+UnUZ8HMAuLX1A/TtV/MUuDZSrHN2ip8pMyDtsxbRJSXKKh2KJsFiwezYe10vYEdfpkwXhMRL0MGBaDW78US6sWM2CWCtXnPKhB+eOAO2Jws2AWFJ4OIbFY6I2KttvWbqmOt2Dj5lfAazVch+qV1ehe8Rbk/rwY3Pq1zneTluvZBYW1UM3+08fJmbTezY7XnmAYFgh8dp/ltTY/KGiljR9K6belc7D3bbDv5ckV+tvNQ8D6MbiZSjGr5seEpZpWbt0ZR43mAZ/ld8Tg/o2JXz9mUve8cj7vAVekuF3lwOyTzLk+yz9J8beWAx7vCWGqKxrUTwghhBBiqkViVTdJcTqOoXbsoluAD4BzY3BHYYvxAzGRo0ticLNjQtFQLI7FeMzyZA464qQMJe2clGJoPYFZ8iyKuVRs2mx8ixrlfwMTYvpjQeQXTFZWBc+UhJBTsJ24bkjWZrNju1o9gu2QV853y/TfYrekn6WAwmN8llcXZztg/bOuC2AM7kUsQPNPG9zOEtgb+EuwReHbmIvK7tiCaB8wcSMGty9wfQzOAVdii555MZHxNZ/lp/os/ygGdwjwpxhceVe37wOf+Sz/U8rrCiwm1vPYwnoDYPUG9WyGZ4A5k1vbo6m8f5XOn0XHQqZZF8CmaLJPTsRn+fgY3LYp/S0xuA1qPOMi7QcxuIeA38bgRmPtvitmvVauw16Ya9zNwH+woNmHYJYtT8Xg/g8Lun0F8CImgA3CYnIVAZhPwwSBe2Nwp2HPZmZgSWBVn+WbNHOvMbjrsTH3OObO8wPMSq/sZjQ5dFnPFvN7HmuHXaMFBh8HPOezvGXxvA08A2wQgxuGtfWbPsvfbGa89kThKSbcZcDVMbhTsbntS2x+XB84yGf587TQxin24alYvKZPsD79Pezlx31MGk/qq8ZxWPy54TG4P2DfgUdgQv5Ecac746ireaCS/C/YnPAOtgtnOZ+/YnP3g9j3wuLATnSez54BVo3BbYjFL3vHZ/kr2Iuqe9L9nY8JcHNjwd+n9Vl+cBft0yVN1k8IIYQQYqpFMasmjwuBagBnfJa/j7kzfIktok7Etky/s5q2Dp9hP973wLaqvhbbGWgHn3ZsS9ZG6wLnYlY+N2OL650x65+ugtQ2Yl5s0TBDyvPByr/li4Q+y9/Agpbn2GLgL9h9rl/jjfNV6V8RR+Ss9PnoGnXYGQtq3Wjr9+kwQaIRL2KC2jLAmZhocQ4WwHsrn+UT3dd8lt+MLYJmxrYZH44Jb/Ol+y7SnYnFQFkQa59rsIVZLJW7H/B3YDAmnsyIWcpMDudhAZhPwBbMnXbN8ln+JLZgfrSLdusOXfbJKin+zvaYhd3NsbS1fQ22Ax7DXGuGYAvDqgvcE9izORFbsJ2JtfmaKWjyW9gOXfunMi8Dvo3trPZYqtMHmJhxM2YtMxzbKXITOsZnM/da7IB2PmZ5szfWVw5scI9N02Q9W8lvLOYSuRxmtTOSDtG4r/NL4BOsv4/E5rumx2sPsSM2lrfELHiuTvV6gRSHqRttfBjWV3+GxRE8GHMr26A3rHWmJMndbgNsJ7wrsdiAZ2JjvLzhQHfGUVfzQJkixtuQZIVV5n7s+ZyFxWU8DAt4v3MpzSGYUHwl9jwHp/srYj6OxeLs3YqJYsume+oJmqmfEEIIIcRUS78JE3oiXI4Qot3E4BbHLIH28Fl+frvrI4QQBclV70XgJp/lu/VSmXtgLycWT4HZvxassMIKEx591EJSFrsBFrsDCiH6DkOHWmjRHXao66UshBBTPf369XtswoQJK9Q6JzdAIb7iJBfN72IWaqOBv7W3RkKIrzsxuD9hVr5vYhaO+2GutKc3uq6Hyl4Kc4k/Grju6yRUCSG+OkikEkKIxkisEuKrz+7AkZgL4PY1XGGEEKK3mRHb6W9ezE38EWCt5K48pTkLc6N9AHPNFEIIIYQQXzHkBiiEEEIIMZUgN0AhvhrcfbftOzNw4MA210QIIdpHIzdABVgXQgghhBBCiF4kxkiMseuEQgjxNUVilRBCCCGEEEIIIYToM0isEkIIIYQQQgghhBB9BolVQgghhBBCCCGEEKLPoN0AhRBCCCGEEKIXmWmmmdpdBSGE6NNIrBJCCCGEEEKIXmTrrbdudxWEEKJPIzdAIYQQQgghhBBCCNFnkFglhBBCCCGEEL3IiBEjGDFiRLurIYQQfRa5AQohhBBCCCFEL/L666+3uwpCCNGnkWWVEEIIIYQQQgghhOgzSKwSQgghhBBCCCGEEH0GiVVCCCGEEEIIIYQQos+gmFVCCCGEEFMp48Y8weir1mp3Nb6SzLzktsy27O7troaYSpltttnaXQUhhOjTSKwSQgghhBCiRD7mCQCJVWKKsfnmm7e7CkII0aeRWCWEEEIIMZUywzzLMf9WI9pdja8cskYTQggh2otiVgkhhBBCCCFELzJs2DCGDRvW7moIIUSfZYpYVsXgBgEXlg6NB/4DXAEc47P8sx4qpz8QgV18lg/piTxrlDEYOMpneb/SsQnA0T7LB0+JMrtDDG4PYDNgOeCbWLtcBJzuszyvpF0IOA1YG+gHjAAyn+WvldLMChwFrAAsD8wKrOGz/K4aZS8MHAusAcwNvA5cCZzos/yTbtzL4FR2LRbzWf5ik/l8E8iAG3yWP95qPXqb0rjxPstfaWM9hgCr+yzv36469CYxuNWB1bG56cseyO8uAJ/lq09uXj1Jeq5r+SxfsN11qRKDewW4y2f5oCbT98fmuD18lp835WomWiU9m0HAxT7LX24i/TTAqcA2wLzYfL3pFKrbpsAiPstPrRxfHbgTWNtn+RQzQeqLvx2EEO3jrbfeancVhBCiTzOl3QC3woSLWTEh5ZD0/19N4XKnNAOw++pLHAncBlwAjAV+gglIP8KeAwAxuJmAO4BxwM7ABOA44M4Y3P+VxKW5gF2Bx1O+NR3rY3AzY2LX9MARwGvAisDRwGLYAqS7/AT4onLsPy1c/01M9Hoduw8harE61k+OAyZbrAL26YE8vm5sBnzY7kqIHqE/Np7uA7oUq4Atgf2A3wIPYt9fU4pNgbUwcawd9MXfDkIIIYQQfZIpLVaNKlnB3BaDWwzYLQa3X09YMLQLn+UP9WZ5ydJoUBeWLsv7LB9T+nxnDK4fcHQMbpHSG+49gEWAJYpnE4N7EngB2JOOH/Gv+iyfM51fizpiFfBjTJRa12f5raWy5wR+F4ObyWf5py3cbpmHfZZ/3s1rpxgxuBl8lo9rdz1E38Rn+TPtrkO7aHVsFOl9lv9zStZL9Gm+l/6GHrJs7LPzc2//dhBCCCGE+CrT2wHWH8feas4NvA0Qg1sHc9X6ATA79ib2QuyH60SrmmQR9AfMUmcGzDro99UCYnArAgcBK2PWQa8B1wDH+iz/XyndXdj9D075LJnKPtxn+XWNbqKWKX8MbjnMkmlVYMZ0rwf7LL83nT8Qs9yY32f52Ep+zwDPT47rQ0WoKhiZ/i5AxxvujYGHyq50PstjDO5+YBOSWOWzfEKTRbv0t2oV8T4WE60fPUw9d7myy2bJTQjg3Bjcuen/u/gsH1LP7aj6bEsuicsCf8TEuduBTVKfPArYGmvjN4DzMPfHhouuGNwiwJ8w18lPgL8BNUWO5OL5S2AJ4GPgeuAAn+XvpvNPA8/6LN+ict1KwEPAZkWf7qqfNqjv/MDJwPqYdeRzwO99ll9aSjMIey4Dgf2xsT4OuBz4XTH+Ss9mb+A7wC7AzOm+fgF8G/gz1tZvYWP3okp9uryPwu0N2Ag4A/gh9oz+6LP8LynNYDpcTsfHYN059aHp0rntsef7MfBsKue+Bm11V8pj9fR5dczFaBNgHWBbzKJxOPBLn+Xv18urlOceqb2+B3wGPIlZqj6GWWpc6rP8N5VrBmHP43s+y58tHV8FOB3r02+l9vhT5VqPzVfrALMB/8bGxbWlNIOpMzbq3MMQ7HlsldL/APgrsF91PMbg5sP629rYPP5uutddfZa/XSf/uYGbUn3XLbs1V9I1+x2xbrq/pYFpsb4z1Gf5Men84qmOP05lvg08DGxXiOypTsdi8+7cWL8/1Wf5X0vlNLzXZvphar/7sD51OLAw8ChmHfsmNq9vCXwOXAocVH4R0GQ9B2H9aQBmHb1RqsvVwIE+yz8r9XWwF1TF5fVcyF/B5gCAL1L6Yo5udc75VWrDV4Dv1yhrCGZNXMzzYC9l+peSzRSDO5MGYzQ9jwNSXh6zBLsMOKyrEAdyAxRCCCGEaJ7eDrDeH/iAzmb+i2ALnF2BDbA4S4OB4yvXngPsjv3o3hz74fq3GmUsDIwC9gLWwxZlu9I5hlbBoul8kedo4OoY3HdbuakY3PLAA8CcmOXSFukeR8TgfpiSXYoteLapXPtDbAF6SStlNslAzK3p+dKxpYGnaqR9GliqG2WMwKyyTo7BLRWDmyUGtybm1vGXcsyqGNxdaXHSLNPG4KYr/Wulv46mwxrsRGyBNQBbzHaH64G7scXcaWnBMhzrk6cDP8OEqiOAUxplFINzmGvlD4B9sfguHltkVtOeBJyFtfPG2CJpPeCWGNy0KdklwIYxuDkql++ILXxvTnk1009r1XfmdO8/Aw7FXGn+BVwSg/tFjUsuBV7E2v+0VNbZNdIdgglTO2NurNsAfwGuxZ7TZpgoc2EMbulSfVq5j9mweeJSTEQZCZwdg1sjnT8POD/9/yd09BMwQeM3mNC1Liaq3Z7K7Q6nYwvg7YFjUr1P7+qiGNwfMFHncUwY3RG4B1g4WZBcCOwcg5uxcumewN1loQprjyuweXZT4C7gjLToL8pbCBNdlsPuf+NU9jUxuI1rVLHT2OjidmbHxMvLsP5Uaw4H69MDsP6+NvBrTJSbqVbiJIDej7XvT+oJVYkuvyOSmHwDJtpsk+7tVExULbgRE4/2xvrHwZg4O03KY7ZUpw2w77QNgH9g/a/sCt/VvTbbD1fDXFAPwsbUopgINxT4CBNg/ooJyRPHbQv1LNf3JWx8n43NYYekc4+nz6T7KMZTPTfszYAh6f8T5+huzDlDsWe1JfYcanEsNheOKZW1WSVNM2P0Umyu/hvWVicCu6U69AnGv/c8o69aiw//pXBuQvRl5pprLuaaa652V0MIIfosU9qyatq0qC9iVm2BBfKeaDFVWDgAJLe1ezFrnd/F4A71Wf5lDG4J7MfjYT7LT0rJb43BzYItOCjld00lv/sxq5+LY3D7Vqya5gZW81n+Qkr/OCZybA2c0MJ9noK9nV+zCGYegxuOiUJHAJv6LH8zBncHsBMmPhTsBLyHLXyKek9LZ4ukYvHT6Xk1cpGLwf0fJhhd4LP8v6VTc6byqrwLVMWOLklv03+CLYqeLp06D7MGKvMF9ma/WapvqYdiC/Vm6jUuBle4Fr3cA+4XZ/gsn7hoicHthIkbA32W35MO354sA46KwZ1czwIEW0guAgwo6hWDuwVbjE0kLcAPwN7EH1M6/jxmRbERcB3WLsdj/faclGZ6bHF6RSnAfpf9tE59d8FcPcvWEbfE4OYFjovBnV8e08DNPst/l/5/a7ImOCYGd4LP8rJw+pLP8p3T/4fH4FbFxsNOhfVEDO5RTCjYko7+1cp9zArs47P8zpTuHsxaaDvgTp/lr8fgihgyVbfTAcCt5eeOLeK7yz0+y4vF/61pXts9BjeoniVjEs5/A5zms3z/0qmy6Ho2Fu9nK5Loncb/yth9lpkV+IXP8svT52ExuAUwd+GLUj0GY/PPwNJ8OTyJWMdgIk6ZMypt1IhZgB19ll/fRboBwKE+y8sCwFW1EiYru1swAWrLrtyOm/yOWB77HtrbZ3lhNXpH6bq5sTGxic/ycnuUxbf9MKuhZYvvGExQ/SY2R5yd+ltX99psP5wFWM9n+QepjvNhQssjpfF4WwxuA6yvFN9DzdZz4j36LD+qlG4lrJ8d5bP8w2iWwgD/7mre9Vn+zxjcG+n/E9PG4H5Ja3PO1T7LD+yirJdicGOAvEG9Go7RNEdtA+zss/ziUhu8C1wag/u+z/JRjerRW+Rjnmh3FYQQXbDRRhu1uwpCCNGnmdJi1bOVz2f5LD+zfCCZ+g/G3nB/u1Knb2FuKithgs2VlfwupyJWpbfEh2GL24WwwN8Fi9HZquuF0o9zksvF29ib96aIwX0Ds2A6AfiyIiiNAHYofb4EuCgGt5jP8hdS2m2BK33nGBsv0eEaUWZ8peyau8alNr0+5bN/9Tz25rhKt9z1kjXHFdiz2gkTEX6EWcp8jlkdAOCz/KctZr8ynQOsT8nAu11xbeXzesCrwAOVZ34r5j61MpMu6gsGAP8pL5iSKHslNhYK1sb6/dBKGQ9ji+vVgOt8lv8nBnc31v7nlOo3N3AxtNxPq6wGvFHDjedSzBplKToLbbXG6XFYvyiLVbdU0hXzxfDigM/y99KYXKib9/FpIVSl/MbF4F6guTE+EjgkBnd8qusjvrKzZotUrfr+hbk0z4vNc7VYC+sDf61zvnDjHY5ZUhUWmntiFiR/ryT/AhOWy1yOicsLYBY962EWKB9U2nc4cEoMbraSgAOTjo1GfE5JmG/ASOCAJCbdATxVR9BbDXMPvwFzm+tSDG/yO2IUNt9eHoO7ABMxyuLzWMy1+qQkoNxV/i5JrIeN1VijHXfHxs2TTdxrs/3wwUKoSkwynkrHf9SNehbU6sdr1ajP5NDqnNNKH2xEV2N0PSDHrAyr8z5YvUf1UF26zfRzLI6b55vtroYQQgghxGQxpd0AN8N2hlsfW0juE4P7eXEyuXXdAGyILWbXTOkLF8DCrWX+9LdsIVTrM9gP2b0wl4m1U36FW0LVTebdGtePq5GuEXNi7n1HYIub8r9fAnOU3NeuweITFdZB62A/gqsugBulehf/zsUsvlas/HuzWpkY3FyYi1k/LG7LR5Uk71HbjWkOaltcdcVu2G5q6/ssv9Rn+T0+y/+AWXrslaweustjPssfLf2LXV8yxRhd+fwtTFCsPvNH0vlGdt3zU7vvVo99K/19sUY5s1XKuBj4cbRYQ2DC1YslQayVflplTia9f+gQWKr9qd44XaByvNrf8gbHizHZ6n3U6tPNjvETsFhBG2MWn2NjcBcmq5ruUJ1vCoG6UV2KZ9zVDmJnYc9/meRCtSNwYQ1R4z2f5eMrx6rP51vAz5m0fQv31mrfrtU36vF2xSKmHttg3w0HYkLJGzG4I2v00fUxi6JzmhGqEl1+R3iL6bcu9h15CfBWDO7hGNzAdH5CuvZRzA3s+RjcyzG4vUvlfAsTL6rtWFhNFe3Y1b022w+7M55aqWdBrX48Az1Lq3NOK32wEV2N0W9hFncf07mtCiFT/jxCiKb5xz/+wT/+MTkG20IIMXUzpS2rnvIdO87dgf0QPyUGd02KZbQosAIlt5+UtmoXW/wQnZfOW2HPW06UrHw2AQZXXLaW7aH7qcX7WFyoP5OsWKr4FGzbZ/knMbhrMeuPo7AF5cs+y++vpK+6g22IuS482qgiyWJgOPaDeVWf5W/USPY0FreqylLUCfDdBctiC+CXKscL0eZ7QE/7IxTuga5yvJWFwmfV66PtYFiPqlXHWCxGytZ10r/SIK/R1H4G81Y+F5Zk61BbdClbml2D9cEdY3CnY4LniaXz79NkP63Bu1hw9yrz1agH2H08XfkMFqB6cnmf7t9HSyRR52QsHtt8mKh+KhZLaJtG1/Yg76S/C2Bx+upxM9bn9sTG26zUtsaaIwY3fUWwqj6fsZgocnKdsqoiebObMTSdNlkx7Qvsm1yxdgaOxqzFyvHPjsDGxy0xuJ9V59IqrXxHJIu8O2NwM2BB1I/B4in191n+jrcdVn+eLKKWw8TSs2Jwr/gsvwVrx7cxN7taPNfMvfZCP2yqnr1Mq3NOK31wchiLfXesWuf8JC+QhBCiHmPHttNhQAgh+j69thtgcr85AHNP2wd7S18EkJ24cEqxdqquPA9jC9StgZNKx7etpJsBs7qoWg4Mmpy6NyIJUPdii5XHm1goX4IJCutii6aGwbibJdrOdDdhgbpX96Xd/ircAPwhBrdIWmwVsZF+TP3AtI14C1sAf7dS5krpb08IFFVeTX+XIbmVJZeMdSrpirfi36iTxzKVYxu2UIdhWAy2j33nANbN8CCwSwxu5VLMqmmYVPi6Dev3C/ssv61Rhj7LP4rBXY9ZVL2JWQJcUjrfaj8tczewVQzuxxUxYHtskfvvSvqtKcX3wcbpl3QImN1mMu+jHuV+UrVELMp9CzgvBrc+k/abKckIrO1+gVkr1iS5kZ6DjeFVgRE1BGSw+XELzPWvYFvMfbcYq8MwV9WnfWl3vHbhs/w54NAY3F5M2vbjsf52GRZ/a33feGfLlr8jkov2HdFiJF6PzbHvlM5PAEbF4PbHLE2Xwdz1hmE71L3m68evq5bV6F6nVD9suZ5d0GjebZZW55xW6jY59RqGBbCf3Wf57ZORjxBCCCGE6IJeE6sAfJbfEIMbiQVPPxP7wfkqcHwM7gtsAfGbGtc9F4P7GxakeRoshsfamAtIOd0HMbiHgN/G4EZjC4pdmdT9qKfZH9uda3gM7nzMcmZuLEjvtD7LyyLQCExMOB8T6y6lZ7gGE5z2A2aOwa1cOveSz/Ix6f/nYhYA18fgDsfeSB8L/IeOeEcAxOB+hu1+VVgdDEyuJ58kywGwnZz2B25OMVVew6zljsC2X7+/lN/twHd8lre022INRmLxuE5J/WEcJoBWXVH+i70J3zYG9yTmghlTAOXLgQticKdhMXSWozVRcyhpV64Y3B8xaxaHWQtujAXVrxfo+SJMVPh7DO5QbPG1F+baN5EUEPhk4MxkcXE39lZ/Iaz/n1eOx4RZGm2HWWXcV8NtspV+WmYI1q/+HoM7DHNJ2yHVYc8abl3rx+BOweK4/AizIrzYdw6uPjl09z7qUVgU/jZaoPsvfJY/msS/J7CdzN7Ddm9cj8o4mZKkPnAasH8MblZMbP4Ca9dnfZZfUUp+PhbzbDlMkKrFR8Dv0zh+AesvawHlIO9HYsLiPWmefgVzE14GWMRn+a49d4eTEoObHZsnh2LxlcZjwv4cdMQGmojP8vExuG1T+lticBv4LL+7Vt7NfkcksWg1zGLtP1j/OgSbu5+KFsD+dCxe34uYADYIi8lVCLWnYZZP96Zn+Bw2ny6JWb5u0sy99kI/7LKeLeb3PNYOu0YLPD4OeK6GS3ojhtDanNMszwBzJnfNR4HPqpbMjfBZflcM7jJs1+BTsXHyJbbT8frAQa3Mc8mt9HYs3loRX/A72PfbMb7zxhqfAxf5LN+t2fyFEEIIIb7KTOmYVbU4HIv7sFeKp7IpZp1zMebacw+dracK9sQWY7/Dgqkuib1lrbIdJpL8GfvB+xb13Rt6BJ/lj2NxT8ZicVBuxRYyy2L3U077JbZj1AJYQNx6FlCtsh4Wp+oMzHKn/G+DUvmfYLHBnscsb4otv9f0Wf5xJc+zsbglR6bPg9PniW44KcD7ylhQ2eOwxd0emAvS2hXLl2npAYE0xabZBFtEDsGe9W10bIFepPsSCxA8B7YgHIm5x4EJRkdh26//A4tPU93GvFEdxqdrzsWsXm7G2nJn4AE64sXUujbHFl2jsFhDF2HP4LgaaQ9N+a+GBS6/Hnuz/x4mNpS5DevvCzBpHLSW+mnluk+woOa3YmPzekwQ2clneS1Xsx2BxbFx+lusjfapl3+rdPc+GnAj9hz2wcbLyHS82DnwfMyiYm/g91hsoV7D205u+2Dj7Bqsn62BCcPldGMwQXM09YP7f4hZUu2MPcc1gP18ll9UyqcQnJ/A4iXdho35gXS2mJtSfIYJM3sAV2P9aACwg6+zi2CaE7bH7vvmGNwaDfJv5jviCUywORHrX2fSMU/+L13zGiac3oBZdn0b2NBn+WOpTh8Aq2Bzw0GYi/YF2NxViMzN3OsU7YdN1rOV/MZiL0SWw/rjSOCHLebR6pzTLOdhLypOwISm7gSL2RH7Ltwy1etq7H5foHYswkb0w74Xp+niGOnYtK1XVwghhBDiq0m/CRN6K9SDEGJqJgY3CAtevVgPirCiSWJwc2ACSvBZfkS76yOEaA8rrLDChH8c9M1Ox+bfakR7KvMVZvRVtsml2k5MKYYNGwbAeuut1+aaCCFE++jXr99jEyZMWKHWuV51AxRCCNGzxODmwYJR74dZY5zV3hoJIYQQoiskUgkhRGPa4QYohBCi59gA273vR8DOPstHd5FeCCGEEEIIIfo0sqwSQvQIPsuHUIkbJqY8anchhBDiq8ff//53ADbffPM210QIIfomEquEEEIIIYQQohf58MMP210FIYTo08gNUAghhBBCCCGEEEL0GSRWCSGEEEIIIYQQQog+g8QqIYQQQgghhBBCCNFnUMwqIYQQQgghhOhFFlxwwXZXQQgh+jQSq4QQQgghpjLGjXmCfoCbZ7l2V0UIUYO11lqr3VUQQog+jdwAhRBCCCGmQtw8yzHzktu2uxpCCCGEEC0jyyohhBBCiKmMGeZZjvm3GtHuaggh6nDllVcCsPXWW7e5JkII0TeRWCWEEEIIIYQQvcinn37a7ioIIUSfRm6AQgghhBBCCCGEEKLPILFKCCGEEEIIIYQQQvQZJFYJIYQQQgghhBBCiD6DYlYJIYQQQgghRC/ivW93FYQQok8jsUoIIYQQQgghepGBAwe2uwpCCNGnkRugEEIIIYQQQgghhOgzSKwSQgghhBBCiF5k6NChDB06tN3VEEKIPovcAIUQQgghhBCiFxk/fny7qyCEEH2aqU6sisENAi4EPgC8z/L3SuemA8YDR/ssH9yWCvYwMbhpgd8A6wNLAzMBLwB/Bi70Wf5lJf3SwGnAKsA44Abgtz7L3y2lWRA4CFgBWA74BtaWr9Qof2ngWGBlYHbgFeAC4HSf5Z93436GAGv5LF+wdGw+4K/AT4A5gN/4LA+t5j21E4O7C3jFZ/mg9Hl1YHXgmGo/aCepnvgsX70Xy1ydNrZFKv9OYA2f5Xc1SDcEWN1nef/0uT8wCLjYZ/nLU7iaUx2p/SKldo/BZcBrPsv/3r6atY9Sm+zis3xIe2vz1aBWm6XfGtP4LL+gRvrBwFHA9N35HhRCCCGEEFO3G+DsmOAytfMN4HDgKeAXwKbYovhc4ORywhjct4G70jVbAvsCawE3xuDKfeG7wNbAe8C99Qou5bcIkAEbAdcBpwDHT9ZddeZIYCCwGzAAuLwH856aWR1bME3N47xZVuer0RbHApuVPvfH6r1IW2ozdZIBm7e7EuIrxWjsu+em0rFBwK5tqY0QQgghxNeAqc6yqsStwK9icMFn+VtTooAY3PTA5z7LJ0yJ/FMZQ4D+DaxQ/gcsUraMAm6Pwc2B3f+RPsv/l44fAEwPbOSz/P2U/5vA3ZjIVVga3OOzfN50fndgnTplbwjMDfzYZ/nz6dgdMbhFgZ/Tc2Lh94AnfJZf20P5iamEGNwMPsvHtbsePYXP8pfaXQfRM/TG90MrxOD6YfO/aJE0xzzU7noIIYQQQnydmJrFquOAYcBhwK/qJSrM9X2W96scH8Kk7jgRs0bqD+wIzAfMFYObAbNiWhuYC3gXeAzY1Wf52+n6mTALia2BBYA3gPOAEyfHLcln+RepvCojgV0wMek/6djGwE2FUJWuvycG9xqwCUmsaqE+Lv39sHL8fXrAgqXU5sXnYtE3iUtichV8DTjAZ/nplXODgQOBBXyWv5cWbRmwF+CBscA1wKE+yz+slN3JVaZZd66UdiBm9fYjbKy9CJzhs/z8dH5bzBpuWcza7QUg+Cy/qJLPBMxS7W3M5XNu4HFgH5/lT9cpezDW3wDGx2CPqtrPS+mbvt/kxjcdMBj4PbAk8DJwuM/y6yr5bpvS+XT/h9cpf27MqmjjdH8RONVn+V9LaQZhLr4DsTG9NuZ2+v0Y3IrACcDyWFu+BQzzWb5PV20RgzsaswpcFHMTfhLrCxMXp6V22AQTb7cFJgDDgV+Wx1QMbh7gdEzM/RJztW3K5aw875TKBLitqDdN9L1Sfr/D+s6CPsvHlI73A14CHvZZvl06Nj82j60PzAo8B/zeZ/mlpesG08R82aA+0wG/BXbGrMU+wubKzGf5szG4GYETsWfbH/gYm8sO8Fn+bCmfQVhfGID1hY1S2quBA32Wf1an/FeA7wDficHtkA5fVLjO1khflDMQ2B+zRB2HWXf+rngR0MX3w/t0Md+kPCZgffhTYG/su2Qk8Guf5aMq9docm9P+D8iB2zB37tcq93ofcEdKuyj2/fPPSl5N95E6bfRLYAdgCWzefxY41mf5TaU002FjcHvs++/jlO5gn+X3pTTbYy9UFgO+wObzM32Wn1PKZyBmafujVNZ96b6fKqVZN5W1NDAt9l071Gf5Men84lg//zEwGzavPgxsV89lrzo/pjlwYDpXfC/d3ZuuzUKIrz6LL754u6sghBB9mr7uEjM5jAbOBH4Rg/tOD+Z7GLA4JjJsBnwGXIItmg7AFlm/Bl7H4kcVP9SHA7tji9ifYULVEZjL3JRgICYajU51+Aa2UHqqRtqngaW6UcZVwDvAmTE4H4ObLQa3GbAT8MdywhjcK0WsohYoXC+exBZYA9K/0dWEyXruOmDPSrnTYu6DV5bilx0PnIot8DbCBJdBwE0Vd8huE4PbBLgdE/T2xESOC7CFcsEi2OJ6B8yy7R/AeTG4vWpkuSOwAbAfJkIuDFyf+hZgMaBKi+7zgPPT/39CR9v1FItifflUzKVqNHB1DO67RYIY3FrA3zARbnOsr5+OLWoppZsNuD/d3+D09x/A2TG4WkLzUGzhuCVwcAxuFmx8fYE9x/WBY+gQ47tqiwWwOG6bpuvfBu6Jwf1fjbJPx0Sq7VMZW6RjZf6OCVWHAtsAnwN/qpFXVzyOiR9gc0pR78dbyOMCTDDbpXJ8HWw+OAcgBjczZmH5s1TvTYF/AZfE4H7RjbrX43Js/N2cytgDeAaYP52fARPKjsP6wd7AjMBDSZCucgkmqGwOnI211yHFSZ/lr/gs71cS9zbDhMzhdLTnsU3U+1JMbN0c6yt7pPKq1Pp+aGW++TnWf3+Z0syLWcrOWSRI88M1WLttic0vywB3x+BmreS3BiayHQ2sh82lVZrqIw3oj42xrbD+/ijmWv6zUpqDMKH9DGDdVNbtwJzpnn6CtXFh5bsV5sr+zdJ9b5Cu+RibD7fH+sq9MbiFUppFMHE4prpsjLX9zKW63IiN+b1TXQ7GBMhW5v59sO+kJ+noR/u0cP0UZfx7z5OPeaLd1RBCdMEqq6zCKqus0u5qCCFEn2VqtqwCe3u6J/aWtadiS/wX2Kzs2hGDG4C9JS/vP3tV6f/bYYvkgT7L70nHbk+WEkfF4E4uWWBVn0k/oF/1eKOgrenN8tbAEaV0c6S83qtxybtUBIRm8Fn+33Tv12OWNWAL+cE+y39fSf45Jia0kv84bJH6EeZO05UbxlnAnTG4VX2WF7G2NgAWBP4CkBZ9+2PWFL9MaYbH4MZgC98NscVOt0kWCacDozArmMJSbUTl/k4oXTMNFv9rfmwR9ZdKtuOBDX2Wj0/pwfrYj4AHqnXwWf56DO719PHhKRDkd25gNZ/lL6T6PI4JVltj1iFgC+RngU2KNojB/Rtzp3mulNd+mIi3bJEfMCIG901sfJxdqf/VPssPLD7E4FbA+veBPsvLi/Eh0HVb+CzfvZTXtJhF5tOYyLlf5b7v8VleCGi3xuCWAHaPwQ3yWT4hBrc2Nta381lexFYbHoO7BeuHTeOz/MMY3DPp47+b6P+18ng3BncFJtqfUpq39gSeK4k4u2AWLWWrrVticPMCx8Xgzk9WnN0mBrcmJu7t57P8jNKp60r1/QAT9YtrpsWEpf9i8+hplWz/5rO8sJobEYNbKaU7ihr4LP9nDG4c8E6L7Xmzz/Lfpf/fmqxpjonBnVBygYbK90M35ptvAOv4LP8kXf8wJvb+BjgiCbMnY5tnTPxOS+mex/psKOU3B/DDsit8shIqt0mzfaQmpXYp5rHbMcFuL+CWdGoAcGvF6vUfpf+vDLzvszwrHbu1UtTpmPXSJqXy7sS+e36LWa8tj70g2LtktXZHKf3cWD/fxGd5ud3/1ugeq/gsfyYG9yEwXZ1+NB77LpysMTM5uHmWY+Ylt21X8UIIIYQQk81ULValH+F/JAlC2Bv4yeW6GjFIRgIHJJHiDuCpSpr1gFeBByqi062YBcHKdCxY6u1jWz1ez51rKeAyTPg4uUb6WvFTaubVFcnd6e/AJ9gb/rHAmsDhMbhxPssnlu+z/Lu1c+k5fJbflRb3e9IRGH5P4MnSgmJlzHrj0srll9Ph7jNZYhUm/H0HOKmRS2UMbjHMOmc1zGWoeLNfKwbTbYVQlfhX+rswNcSqXuCFkrCEz/K3Y3Bvp/oUIsOKVNrAZ/nDyT2pzHqYG06sjI/CGnEpOluEVGOXvYBZEZ4Tg/sztqD9D02SLMAOw1yq5iyfqpH8psrnf2H9aV7MYmcAtkC9ppLucuw+28FZmNvdTzFBZ37MwufAUprVgDdqCBOXYuNiKTr6XHdZB5t/zm2UKAa3NSY+LIFtlFFQS1Cv9TzWmow61uPKyufLsbn7R5hIVFD9fmh1vrm5EKrALMNicA/RYQk4AHNdG1oZK69jwvBqdBarHvLNxWxspo/UJAb3Q0yYXhGYh47vk7IgPRI4JAZ3PCZgPeKzPK+cnyMGdynWNvdVXGsXw6w5T6jc96fAg9h9g70gGA9cHoO7ABOX3y6lH4uJWyclIfau8jzWg3wEfFzjt0KvMP0cizP/ViO6TiiEaCtDhgwBYNCgQW2thxBC9FWmZjfAgtMwy6Fjeii/SVzQMHeDG7Af9k8Cb8Tgjiy5eHwLEy/GV/49ks7PVcprxcq/GzG3n+rxSUguELdhi+xNKxYk72ELxTlrXDoHteNedcWBmAvIuj7Lr/FZfpfP8iMxd69j01vs3uZsYMsY3FzJ/XM9OlspFfff6TmmthpL7fZpleJ5vl4vQbKQuA1YDnNDWRV7rhdgi9sq1edTCFozTlZNu0+t/jKOjvrMjQVz/m+NdNVj38IWm9XxUVgnzlVJX312H2DuTm9ii+7XYnBPxeC26OomYnDLYy5pH2NWKStjz+EJardtV89hfuC9irAItduhV/BZ/gjmmlW4l+6OWTqWY6PNSe257a3S+cllLuBd37HhwyTE4DYCrgD+jbl5rYQ9jzE0/zxqjZ/Jpfr8is8LVI5X27DV+abeeCnK+Vb6O4JJx8uydDFW6tFkH5mE5H5XuPP9ClgFe17D6Py8TsCs3TbGXiSMjcFdWHxH+Cy/G3P9WwgTo8fE4EaUXHGL+z6/xn1vWNy3z/IXMde+aTDLtbdicA9Hi3VFEo/WTvd6IvB8DO7lGNzezbRTC3yAzSlCCCGEEKKbTNWWVQA+yz+OwZ2IxVCqFR/qM4AYnKu86a3+6C+Y5E1penO7L7BvcgvaGXvTPAYTT8ZiAtLWdfJ8pZTXo+UTMbixwKzV41VicAtii4YPgfXKgXtTvp8mi5ala1y+FBYrpFWWBV4sxYIqeAQTKr6LxbTqTS7GFiGDMBHuf1iMo4JicTsf5uoFTHS/nAt7VpD6BR1B5Avq9YsyxT1XF7JlBmAC5qpFgOFSPdrB5NxvLd7BFpLz1jg3L2ZpWDAWixNVdbkreK7yudYYHAVskdpvBSxu0ZUxuOXKwZdrsAW2KN+8LDBF203z/QbX1WM0ZiEyfUWwqtUOvcnZmOXZApgQcZXvvINoPVfgIk5Up3HRwnxZ5h1gzhjcNxoIVttic8qg4kC0XfV6QiybHOalNF/Q8TzfqKSr9s1m55tqvtVjRTlF+kGV+hR81EV9GtFVH6nFepj129Y+yyeK89E2FJlIGgsnAyen2GMbYrGkZsJe9uCz/Gos7t0swOop/bD03Vbc9yFU3KkTE/uiz/I7MXfwGbAg6sdg8cH6+yx/x2f5y8DPkyX0clh8sLNicK/4LL+lRt4t422TjIZCnxBCCCGEaMxUL1YlzsLihhxX41yxaF6GFLg4xcpZhUl/+HeJz/LngEOjBcFdJh0ehi2KP/alHa16iuSOV/yAX9uXdnSqcAOwcwxu9mSNUgS2/Q7dc317C1glBjdHRbBaKf2tLuSmOCnOz1DM/W8WLKZNWbh7CLO+2BYT9wq2wcZDIdr9N6Vbhs5s0EQ1nscEyN1jcH+t4wpSLOaqAskmNdJ2l8Lq5xt03Zcn534nwWf5FzG4kZiV2+BSzKqVMGu8slg1DLPKeK3istOdcj/H4pwdgVlxfA/bVKBeW8yEue2VY9Ctibkz1nID7IoHsR3ItsDcmQq6GzymXO/J4TLgD1hsnoWZNCba3cBWMbgf+yy/v3R8e0xI/Hf6PDnz5a2YFeHu1A84PxMmHpbZCWvTnmIcrbfn1pRiH2HP80s6rGPr0ex8U7B+DG7mUsyq/pi130np/ANYO3/XV3YN7QG66iO1qDWPLY6JRDUtS5Nb4nkxuPWZdL7BZ/nHWID2RbA4VXNhgvUrwNI+y0+qXlOnnHHAHUn8uh4LFv9O6fwEYFQMbn/MqnIZOmJsNcM4LMC7EEIIIYSYAnwtxCqf5eNicMcAf61x+hbMZP/cGNxRmAvJgTRpwh+Dmx0TioZiMUPGY4LDHHQEiB1K2v0oxdB6ArNgWRRbUG/qs/zT7txbtF3+hmMCwK7AgulNdMEzJbHmFGwXpRuStdns2M5Uj1CJAxSD2zL994fp789SUOAxyWUDbDGzAxZw+BTs7ffqwO+Aa8txg2JwLwKv+iz/aXfus0XOomNXwE4LrhTH7FQsfsonmAvY9zAh8z5SDJwULPsKYLcY3PPYYmkD7P4akq7NsHhed8Tg/oJZ2X0P+FYKCP0AZgX359TvZgYOxxZTs9fMuHWK4Ny/jRbg+4t6FnqTc78NOAobA9fF4M7B4tkcTYdrWcFp2OL93hjcaansmYElMcuzhgJeDG5DbPe16zCBaWZs97yPMPEI6rfFMCww85AY3IVYYOgj6KbQ6rP8thjcfZiFytxYPK1tqLEob5LnMfFm1xjcu9gC+Tmf5R/F4AZjbex9lr/SRb3+F4MbggXq/pfP8mqcsyGYZdvfY3CHYULDDpjL1J6l4Ordni99lt8Zg7sGODW5j92BWWCuBtyU4mUNAzZN/eBGbP75Nd2zcqvHM8Cqqd+8hQVbf6WLa9ZPc9ytWJyqo4CLK8HVJ6HZ+abE/+iYT2fAxsuHpMDySYw/AJs35qHjeSyAxb+6y2d5S8HCS3Xtqo/UYgTWPy9O323zpzq/RinMQAzueux773HMJf0HmFVWsRvlMZgF2Z2YO++C2HMfVbx8icHti+2A6rAYYu+ka1bBhO5T00ui1bB2/g/mjnxIyvOp5FZ4OuZq+iImgg5K91AWI5vhGWCfGNw2WDzMj9LLKmJwRwJHAjP4LjYmiMHdDnynHNcxBnc+sLPP8ulKx4o8F/VZ/uqkOQkhhBBCTF18HWJWFVyILRw7kYK4boi9Jb8ScyH7E/ajuRk+w36A7wFcjYk+A4AdfJZfn8oYj8XROBdbVN+MCVg7Y6JFPmm2TTMv9sN/hpTng5V/yxcJfZa/gcX2ybEA0H/B7nP9GoHAr0r/ihgmZ6XPR5fyewiLtTQGWwDcgG3tfgy20C0zHT1rHVEXbzvCPQ886rP88RpJDsMs7X6GLYgPxtwHN6i0w36Y4DQYW9zMiFkANVOH67GFPliclRuwZ/9KOj8G29p+WqzfnIht/14NxDw53Ig9t32wvjCyi/Tdvt9a+CwfgfWDJVK+B2DC0HOVdB9gC86bsS3uh2OxuzahuXH4ArbIPwJbvF+ILT7XLrkm1WwLn+XDsUXxj1OaXYGfYwvZ7rJ5upcTsXacDnM1ahmf5WPTtcthVjgj6RCQZ8bEq/ebzK6IAXZOjXI+wcSOWzErnutTmTv5LP9rKd37TN58uS3WvzbFxsQFmGtyEVvpXOB4TOD7ByaYboQJMj3FIVgfvBJrz8FNXLMjJmReiwV/PxfrS83Q7HxDOn4TcCbmRjYG+GnZHc9n+TnYS44lsLhMt2Dz8nRYgPHJoW4fqYXP8qexMV5Y5x6I3d89laT3YAH2z8cEyb2xFyVFAPeHsRcup2Gx/E7G+vtEy06f5TdjQtTM2Fw5POUxHx2i9BPp/IlYXz4TE7DXTK6nb2FC2v6pvpcB38Z2Wn2smXsucTJmLXce1o/KbTYNNrc3s3nJtEz64nBaJv2+bCVPIcRXgKWXXpqll64VnUMIIQRAvwkT2rJZjRBTjOSG8iywh8/y89tdHyGmBDG4BzDLk6ZEk2g7se0HfLsa007UJgY3CBM/F0vBu6dkWROA432WHz4ly+miDuojUwErrLDChEcfbRjmUjTB6KtsY1HtrCiEEEJMOfr16/fYhAkTVqh17mvhBii+HiT3x+9iVgajsdgrQkx1pADWywHbNZH2B5gVzn7AXyVCiCrqI0II0fuMH2/h/qaffvo210QIIfomEqvE1MTuWEyP54HtG+w4JsRXmhTjbuYmk1+LuQsPx2ItCVFFfUQIIXqZoUNts+pBgwa1tyJCCNFHkVglphp8lg+mufgzQnxt8Fnev911+Kris3wIFny+N8pqWywi9REhhBBCCNHX+DoFWBdCCCGEEEIIIYQQfRyJVUIIIYQQQgghhBCizyCxSgghhBBCCCGEEEL0GRSzSgghhBBCCCF6ke9///vtroIQQvRpJFYJIYQQQgghRC8isUoIIRojN0AhhBBCCCGE6EU+/fRTPv3003ZXQwgh+iwSq4QQQgghhBCiF7nyyiu58sor210NIYTos0isEkIIIYQQQgghhBB9BolVQgghhBBCCCGEEKLPILFKCCGEEEIIIYQQQvQZJFYJIYQQQgghhBBCiD7DdO2ugBBCCCGEEH2NLZ/ZAnfWA+2uhphKmeez2QC4SH1MCNHH2G75BfjFyt9pdzUkVgkhhBBCCCFEbzJmxvnaXQUhhJiEUW9+ACCxSgghhBBCiL7I1Utdw/xb7d3uaoiplA8+sAXh7LPP3uaaCCFEB2v0IWtPxawSQgghhBBCiF7k2muv5dprr213NYQQos8isUoIIYQQQgghhBBC9Bkm2w0wBjcIuLB0aDzwH+AK4Bif5Z9NbhmpnP5ABHbxWT6kJ/KsUcZg4Cif5f1KxyYAR/ssHzwlyuwOMbg9gM2A5YBvYu1yEXC6z/K8knYh4DRgbaAfMALIfJa/VkozK3AUsAKwPDArsIbP8rtqlO2BU4C1gOmBR4ADfJY/2qM3WYMYXD9ge2BX4PvAbMB/gfuAc3yW39lCXv2xdtvDZ/l5PV7ZyaA0przP8leavGYIsJbP8gVbLKs/U3hcTWnSPQwCLvZZ/nJ7ayNicN8HNgXO8Fn+bntr0x5qfZf0UrmrA6tj371flo73p43zXXfmmdI8uJjP8hdbLG9m4C/AusA82Hdj1koeQgghhBCivfSkZdVWwABgA2A4cAgmanzVGQD0KTEDOBJ4C9gP2BATBo8FhpYTxeBmAu4AlgR2BnYCFgPuTD/mC+bCBKDPgdvqFRqDmwsThpYB9gS2TafujMF9b7LvqgExuGmBKzFR7hVgN+CnwEHAjMDtMbipxen/Jqzfje6Fskansm7qhbKmFP0xsXWRNtdDGN/Hnsecba5HOzkPG1e9zepY2/c1q+nenmf2BbYDfpfKPa2XyhVCCCGEED1ETwZYH1V6+3lbDG4xYLcY3H7lN7xfNXyWP9Sb5aU38oN8lvdvkGx5n+VjSp/vTFZHR8fgFilZl+yBLeCXKJ5NDO5J4AVMbDo1pXvVZ/mc6fxawOZ1yt0bmBcYWMrvDuBl4Ghg61butUUOAbYEtvRZfk3l3NAY3DqYVV+fIQls/XyWf95k+umBz9OzHdNV+p7AZ/k4oFf7uBBTOz7LXwdeb3c92k36Xpq+DfPM94A3fZZf3ChRDG6GVDchhBBCCNHHmJK7AT6OuYrNDbwNkASFDPgBMDsmclwIBJ/lXxQXJougPwDbADNg1kG/rxYQg1sRs6xZGbMOeg24BjjWZ/n/Sunuwu51cMpnyVT24T7Lr2t0E7XcAGNwy2GWTKtiVj2PAwf7LL83nT8QOA6Y32f52Ep+zwDP+yzftFG5jagIVQUj098FsHsD2Bh4qOxC4bM8xuDuBzYhiVU+yyc0WfTKwAuV/D6Jwd0LbBiDm64QZmJwRwMbAYtiItKTwKHdEf9icA74LXBTDaGqqMetKe3vgOOBBcvtlBZNLwEP+yzfrnSpi8GdCuwIzIL1tV9W3e+S6+UvgSWAj4HrMffHd0tpJgAnAB9hYuDCmGvlP2vcU3/MLWZfzDJoR2A+YK4Y3CZU3ABjcNsDB2CWcV9gff1Mn+XnNGi3XYC/Akf4LD+pTpqiHhPdcwqXQpIrFzZeXwP291l+Uwxuf+DXmOXMncDulbYu2uFTTOCcC+ufv/ZZPqqUrqn5IKXdI+X1PeAzrD8dArhUBzCRvLikphtrK+V21eZp/jkBc539BmbtOMxn+T6lPDw2F6yDua3+G5tPri2lWRw4GfhxSvM28DCwnc/yz2NwswAnYmN2XuB94F9YP3221ObHA+9hz2Zu4C5gl1TMmZhL1IfpHk6utEkz9RyMWe0sDgRgIDAWOB84zmf5lxW38BdKz6OuS2sMbsZ0f2tjY+FjrL8cUNxfSjdfaqe1sT71LvAYsKvP8rdjcNOl+m2PzYMfA89ic/N9pXyaGcv7AXul+nyGzR3HF+0Rg1s3lbU0MC3wBjDUZ/kx5baq4VJ+PPZ8f4M9o8eBfXyWP11KNy0m/u+G9c+HgH3oeCaD67Tj4FQngPFF21dcEaeNwR2Dvcj4BnAvsHcS18p5ddlGderwCmZ9ewdwIDb/bx2D+yeTzjNdjp8a+f8QuBm4H9i+VpiB1M7V/6+R/t4JbAH8DJvfpge+mV4UHIXNw98G3gQuxdp7fMqrf7qHvYHvYGNr5tQ2v0jX/Rkbx29hv0MuatReQoivNwMGtMMAVwghvjpMSVeB/sAH2GKmYBHgdszlbAPMpWsw9gO+zDnA7piYsjnwHPC3GmUsDIzCFhXrAaenvC+skXbRdL7IczRwdQzuu63cVAxueeABbKG+B/bDdywwIv2QBvuROy0mtpWv/SG22L6klTKbZCDwJfB86djSwFM10j4NLNWNMr4A8hrHx2GLjUVLxxbAXC82xeIJvQ3cE4P7v/KFMbhXkpjYiBWw2Fw3NFHHC7B22KVyfB3AY32rzCGYGLELJhz9ELg1LV6KOp4EnIXF+9oYEzDWA25JC8syg7C+/bv0980u6nsYtvj/BRaHrNbi6ydYn7oba8+tgHOxNqlJDO6QdK971BOqumA24GLMnWkz7PldE4P7I7bw2xcTfNbAFmhVfg6sjy14B2Eiy+0xuLJrWFPzQQzuD5jo9jhmvbcjcA82/h9PdQETaQakf483uLcuy+2qzZOANBwbE4PSvR5D6QVAihf3MBZb7jdY33kca8eNS/W5ERsve2OC0sHYmCrm59PSfR+NCTV7YfPeNyv3tROwJiZs/AoT0y8GrsXEvS2whf5JMbj1u1HPgmsxMWJT4LpUr53TuZsw0Qs6XMO7cmmdAYuTdxz2PPbGXgI8lASqgktSXgekdvg1Zr00Uzp/UKr/GVg77oI954l9rpmxHIPbAfgjcBn2XHcAri7yicEtgs1FEZvjN8a+V8qu1fXYMd3jfql+CwPXJ6Gt4GjgUOzZbYL1s2bmvvMw4RDgJ3S0fZlDgO9ifX+/dL7qPt7KfFeLNYD9032sh/W9TjQzfmpcsw4mwF4LbNUgHuaAlPdb1J4P/oTFb9wplQ02BxyMtfmG2G+Ig9LxKodgwtTOmEv+Nlh8rGux/r9ZuucLY3BL17sfIYRYYoklWGKJJdpdDSGE6LP0pGXVtOkH96zYj7UtsEDeEy0VfJb/pfh/snS5F7OM+F0M7tD0Zn4J7M34YaVF9q3px+1e5QLLVjYpv/sxy4GLY3D7Vqya5gZW81n+Qkr/OLaA2hp7u9ssp2AWFmsWwcxjcMMxUegIYFOf5W8m97idsB/9BTthlg83luo9LfbDuWCadLzTs2nkSpYEoP2AC3yW/7d0as5UXpV3gTka32ZNngPWjsHNVbRtDG4a4Eel8or67l6q37TAMEwk2y3VteBzbMHSiIXS31e7qqDP8ndjcFcAv4jBnVKyGtsTeK6Gtc1HwCaFq2oM7nnMMuDnwPnpbfoB2Bv2Y0r3VKTbCFuwF/QD1ilb9nXBf4HNytZtJWuUgpWB9ysBgm+tlVl6HoVou5nP8u7GiJkV2Mtn+T0p3zeBJ7CF3FLFuI7BLQP8KgY3bcUa6htYO3yS0j2MuZ/+Bhsnzc4H303XnOazfP9S/jeVrn0m/fffzVjuNVMuXbf5ktgYOtBneXkxPqT0/8FYfxhYmouGJ3HoGOCGGNzcmFi6ic/ysiBRFucHYFY755eO1drrelzKp7BuXIbU3j7Lj0vH7sLm560w4aqpelbK+aPP8uKFwIgY3JpYfKALfZaPicG9lM6VXcPr4rP8A+zlBKmO02Jiw39TvkW8oQGYdWZZXLmq9P8BwK0+y08vHftHKd/+NDeWBwBPltPQ0VZglkAOs0j6MB27o6v7TIwHNixZ6xT38CPggRjcHJgI/Bef5Qela26LwY3HBLS6+Cx/PQZXWEg9XOc741Wf5dsXH2Jw8wCnxOC+nb63+tPafFeLOYAf+ix/q3R9/0qaZsbPRJKAeCFwks/yIxsV7rP8oRjcO8C48nxQmlcfqXw/LYP1s7LV2q0xuC+AY2NwJ1Xq+JLP8kKcHR6DWxX7bt/JZ/mlKc9HMaFvS+x7r1cZ/97zjL5qLWZecltmW3b3ri8QQrSFd955B4C55567zTURQoi+SU+KVc9WPp/ls/zM8oEY3PzYwmg97M1kufxvYW9CV8IEmysr+V1ORayKwc2GWaZsiQka05dOL0Znq64XCqEKILmNvI292W6KGNw3MAumE4AvK4LSCOwNfMElwEUxuMV8lr+Q0m4LXOk7x8h4CXMpqNIp/lIMrqYbTWrT61M++1fPA7Vc/Lq7Q9VfMGuGi2Nwv8bcvA7DLJbALJqKeq2Vzv0fnQMtx3KGPstbsmxrkrOwt94/xRbT82OLrANrpL3al2Kq+Sy/Py34BmBWCmtj/XFo5Xk/jAmjq9F58TasBaEK4Lom3DBHAnPE4C7FxsF9Psvfr5FuunT+p9jOgA+2UI8qnxRCVaIY3yMqotSzqdz56Ryj5+ZCqALwWf5KDO4hSpYeTc4Ha2Ht/9fJuJdONFluV23+AuaOd04M7s/A3T7L/1Mpaj1M5Pig0neGYwLBbNgc9TJm7TQvcFd5nkqMBAalBfitwD+rbpKJ2yoCRfHMhhcHklvhi3QIwE3VsyTKwKRBsp/CXCobUkOY/7IkEm+Nufougbm+FZRfOY8EDkgC4x3AU5WxMxI4JAZ3PHALJkqULUGbHcsjgX1icH/C5tYHfJZ/Wko/CpufL4/BXQDc47P87a7uP3FbIVQl/pX+LoxZ7C6LWWhdVbnuaroQq5qk+uzK5b9J6/NdLR4qC1V1aGb8FGTYy4Zf+yw/u4t8m6Eq9K6W/l5aOX4p5u4/kM7WYbdU0tUaZ++l3xcL0SbyMU8ASKwSog9z44327nrQoEHtrYgQQvRRetINcDNgRcycfwT2Y//nxclk8XEDZplxHOausiIdrjczpr/zp79lC6Fan8HetO6FuX2snfIrXIJmrKStFWtjXI10jZgTc+87AluslP/9ElvcFm16DfAJ5vYB5oY2L5O6AG6U6l38Oxez+Fqx8m8Sd7Jou/Pdhi0A1/VZ/lElyXvU3pFrDmpbXDXEW+D2HTBXuRdTnco7LY1O9VoeW/x+jFlSrZzu4Qlaa++CYhFTS9SrVc9HgEfpEDd3xyy4arl01OpX/8XcssDEC7D7rT7z2bDYOWVa3cGvy/Q+y+/GLGEWwhZaY2JwI2LFpTLVZwNs0ftIi/Wo8n6lDsWiv9pviuPV59qwXVuYD4r27ZFg1c2W21WbJ2ugNbAxcBbwWgzuqRjcFqXivoVZ6FX7TbFL6lxJbFkb668nAs/H4F6Owe1dyudXmEvnrpiQ8nYM7rRosf3K1Hs2tY6Xn1eX9axcX51Lm51Hb6/kfyRADG4jbEfTf2NWtSthz2RMJd9tsGd3ICYevBGDO7I0556AxR3aGLOWGxuDuzBZrxX3CV2P5YsxV8SVMAHi3Rjc3wvroGQtti72/XkJ8FYM7uEY3MAm2qBW28Gk339V8avWeOoOXZXf6nxXi2bmtGbGT8G2WEywmvEKu0G1fnPWOf5W5XxBd8dZrzH9HIvj5lmuHUULIYQQQvQYPWlZ9ZTvvEPck9hb+WuShcWiWOyhiabyKe1GlXyKH4zz0hEovPg8kWhBeTcBBpfdPmJwy/bQ/dTifcx66M/YgmYSCksBb4HHr8XEnSJw68s+y++vpP9X+XMMbkMg91n+aKOKJKuM4djiYVWf5W/USPY0FreqylLAMzWOd4nP8mticNdhcZZyn+UvxeDOBv7js/y1lGwLTBzavGxFkFxc3u9GsY+m6zaieQubs7G39gtgYtVVvnZw4HnrHBuV/l9Y561DbYFvbOVzs8HqW0rvs/xqLMbaLNj29CcDw2JwC5Ysw97F+tmNwGUxuO0buY9OYeq1a9FPm50P3kl/F8DcUCeXZsvtss29BYvfIlmgrIDFsrkyBrecz/KnsL5xb7quFm+mcl4Gfp4shpbDhO+zYnCv+Cy/xWf5xynvQ2Jw38EsSU/CFsMH1c66JZqqZw+wJ+ZeWs13W+BFn+WDihPRYsZ1EgmS9dK+wL7R3MV3xuIijQHOTnPNycDJ0WJdbYjFkpoJE7qaGstJQDwHmz/mSOn/iAlqK6U0d2K7sM6ABdQ+BrgpBtffZ/k7NfJuluL771t0dh+rNZ6mBK3Od7Vodk4bRePxU7AFNu/fFYNbswmrrVbrV3wvzIdZKFP6DM3dsxBCCCGE6GGmyG6APsvHxeAOwFwo9sHe0BdWAGXxYno6u86BuRt8icWSKgeG3raSbgbMyml85figyal7I3zHznfLAY+X3cfqcAmwY7Sdozahw1JhskgWFTdh7nerN4gLcwPwhxjcImlBXMQO+TEWTLZbJBekf6f8vo0tBMv3NhMWh6och2lNzNWkkxtgk+Xl0QJ7HxuD28LX2BEwBrc2cH/JXecybEfJv6Vy/1K9JrFlDG5wyR3px8CCQOFCdxvWHxf2WX5bq3XvaZJwcWO0IM+nY2LlmNL5u2JwP8Ms2y6PwW3bJsFq/RjczL4jZlV/zMKuGNPNzgcjsPb/BeYmVovCOuQbTdSr2XIn0kSbf44FAz8Cs+r5HuYaNwyzPHy6GdfQJJKMirbb4m7AMlRcjnyWvwr8MVoMn2W6vNvmaKmeTVDzefgsryc2zoSJ22V2wub3mqS8Do3B7UWNdkiCxnnRAskX51seyz7L3wOuiMGthIlt1fPjgDuSoHk9NidPjlj1L8widys6drkkfW6GcttXLW2bodfnuwbjp+ANTCwuBMI1fZa3asHaiLvT323pvLlDMSfcgxBCCCGE6HWmiFgF4LP8hhjcSCxo8ZmYuPEqcHy0wKXjseC/1euei8H9DTgmuXeMxNxk1q+k+yDFwPltDG40tkDYlQ73rSnF/tiP1+ExuPOxN+FzY0F3p/VZXhaBRmDWA+djC7JqTIzucg0mOO0HzByDW7l07iWf5cVC+lzMSuP6GNzhmHh0LOZW12lXvCRwzIzFTAEYmNxnPvFZfktKMz3we+zH/YeY1dYhmAVAOZ7KMCzOyJAY3IWYFdYRdFjVlMt9EQv6+9Mu7vlETCS8IgY3BAuc/C4mLG2B7fA4MWi8z/L/pXS/Af7ls/yBOvnOClwXgzsHmCeV8wLJci5Zjp0MnJmsOe7GduxbCOuX5yUriylGtK3m58UWa29i9/xrLID1mGp6n+X3xuDWw4SOK5JgVRV1pzT/w4IUn4IJy0djfaZwGW12PngpBncasH8MblZMgP0CC0j9rM/yK7AdMD8Hdo3BvYst2J+r4RbbdLldtXmygPwFFr8nYmPn15hAUAidR2LumPekOfAVrI8uAyzis3zX5FZ4Oma18yIm0AxK93NHqsuD6b7/hbnWDsTGQi231u7QZT1bzK+w2tw3BncR1sZPVuJHlRkGbJqe842Ym/GvKVlhxuBmx+bToViMoPHYC4A5SIHvY3DXY67Gj2NWQT/A4nGdA82P5RjcX+l4jm9j89dOpXL2wuIc3YzNpXNj8+Cb1N59tWlSrKOACXEfpXteHhMvoRQXsA5F2/82BncL8EVXVrqV8ntlvmty/JTrNToGtzrmSnpXDG4Nn+U9YvHns/zpGNxlwOBk5fUAJt4eAVzmOwdX71GSpeRLwDG+c0D7z4GLfJbvVjrW7HelEEIIIcRUQU/GrKrF4Zg7w15pobIpFgfiYsyV7h46W08V7IkJPL/D4sUsicUyqbId8FjKa0jKe78a6XoMn+WPY/FUxmKxsm7FFpvLUnkDm6x1/oYJaA82sIBqlfWwOFVnYD/sy/82KJX/CRaT53nMymsotjBYM1mLlDkbC+pb7LQ0OH0uB7SdgAWuPwcTQjLgAixe1sSFqM/y4djC48fY4nNXLCZOrfufjgYWFKU8v8Cs7QZhrlxDsMX8KdjCdWCKg1KmCFJ8DvU5MdVrCBY75fF0PxPFHZ/lh2ILq9WwwP/XY+5X72HC1pTmYaA/JvTchrk63U3pWVdJ7qbrYgHKr4rBTbLF4BTmYsz670xMVBkD/LRwxWxlPvBZ/jvMQnNlTKgdisW7eS2dH4uJssth7TISEzwmoYVyu2rzFzBB7ghsLFyICUxr+yx/PZX1Gube9AQWT+k2bDwNpGP3uLfSfeyPCVKXYUHfN/RZ/lhKcw/W94emNt0S+I3vvOtdt2mynq3k9wQ2f2yE7SA3ErunepyLWbRsg4nQG6Rry+P5M2xs7oEFG78WExR28Fl+fUpzD+a+dj4mgO2NiesTN1Zocizfj/Wfs7C2OAx70VDsAPcEJq6ciM3/Z9Ixr/aEZdpRKe+dsT7xMzoshqtzXJUbU733wb4PRrZaeC/Nd12Onxr1egsb9zkmWPXki6mdsTG+KyZC7pY+79zooh6gH/b9V/0tNi2Tfi829V0phPjqsNpqq7Haaqt1nVAIIb6m9JswodUQO0J8NYi2K9h+wLd9593MxBQkBjcBON5n+eHtrosQUwMxuK0w4Wg1n+X3trs+om+zwgorTPjHQd8EYP6tRrS3Ml9hRl+1FqA2FEII8fVijbPMIenOfVbplfL69ev32IQJE1aodW6KuQEK0S5icD/AtrzfD/irhCohxFeFFB9rA8y67zPMyutg4CHMUk0IIcRUwFtv2X4R8803XxcphRDi64nEKjE1ci0Wb2g45lIjhBBfFT7GXPD2BWbD4mZdCRySgvALIYSYChg2bBgAgwYNam9FhBCijyKxSkx1+Czv3+46fJ3xWd6v3XUQ4quKz/Knsd3vhBBCCCGE+NoypQOsCyGEEEIIIYQQQgjRNBKrhBBCCCGEEEIIIUSfQWKVEEIIIYQQQgghhOgzKGaVEEIIIYQQQvQiP/3pT9tdBSGE6NNIrBJCCCGEEEKIXmShhRZqdxWEEKJPI7FKCCGEEGIqY9yYJxh91VrtrkaPMPOS2zLbsru3uxpC9Cj/+c9/AIlWQghRD8WsEkIIIYQQfZJ8zBN88uzl7a6GED3O7bffzu23397uagghRJ9FllVCCCGEEFMZM8yzHPNvNaLd1ZhsphbrMCGEEEK0hiyrhBBCCCGEEEIIIUSfQWKVEEIIIYQQQgghhOgzSKwSQgghhBBCCCGEEH0GxawSQgghhBBCiF5kvfXWa3cVhBCiTyOxSgghhBBCCCF6kfnmm6/dVRBCiD6N3ACFEEIIIYQQohd5+eWXefnll9tdDSGE6LPIskoIIYQQQgghepF77rkHgEUWWaTNNRFCiL6JLKuEEEIIIYQQQgghRJ9BYpUQQgghhBBCCCGE6DPIDVAI0YkY3CDgwtKh8cB/gCuAY3yWf9ZD5fQHIrCLz/IhPZFnjTIGA0f5LO9XOjYBONpn+eApUWZ3iMHtAWwGLAd8E2uXi4DTfZbnlbQLAacBawP9gBFA5rP8tVKaWYGjgBWA5YFZgTV8lt9Vo2wPnAKsBUwPPAIc4LP80SbqPQjrK95n+Sst3HI5j6WAA4A1gfmAz4AngauAv7bS32JwdwHT+Sz/SXfqMrnE4IYAO5cO/Q94FjjTZ/kFNdKu7rO8fwv5vwLc57N8xxbrNYjJfE518lvMZ/mLTV4zGOuTXVF3PojBzQscBqwHLAR8ArwK3Acc6LN8XDN1aYY0Pw0CLvZZrqAyQgghhBC9jCyrhBD12AoYAGwADAcOwUSNrzoDgPPaXYkKRwJvAfsBG2LC4LHA0HKiGNxMwB3AkpgoshOwGHBnDG7mUtK5gF2Bz4Hb6hUag5sLW+gvA+wJbJtO3RmD+95k31UXxOC2Ah4HlsXudx1gO+AB4OhUp68aY7A+NgC7l/eB82NwW1bSHYsJlL3BTak+o3upvFqcR0e7DACOS8e3qhy/qdbFMbjZgIex8XEasD7WP24GNgK+0cP17Y+JawomI4QQQgjRBmRZJYSox6iS1cRtMbjFgN1icPv5LP+ynRWbHHyWP9Sb5SWLkkFdWNAs77N8TOnznTG4fsDRMbhFSpYde2CL5yWKZxODexJ4AVu4n5rSveqzfM50fi1g8zrl7g3MCwws5XcH8DImFm3dyr22QupPFwO3AFv5LP+8dPrmGNwfgMWnVPndJQY3QxcWPHm5j8XgbscsE3cHri6O+yx/acrVsjOpb43pMuGUrcPrwOvF5xjckum/o5q0ztoS+A7wfZ/lT5SOXxODO7LnaiqEEL3Dhhtu2O4qCCFEn0ZilRCiWR7HXMXmBt4GiMGtA2TAD4DZMZHjQiD4LP+iuDBZBP0B2AaYAbMO+n21gBjcisBBwMqYddBrwDXAsT7L/1dKdxc2fw1O+SyZyj7cZ/l1jW6ilhtgDG45zNJlVWDGdK8H+yy/N50/ELMEmd9n+dhKfs8Az/ss37RRuY2oCFUFI9PfBbB7A9gYeKi8uPdZHmNw9wObkMQqn+UTmix6ZeCFSn6fxODuBTaMwU1XiEgxuEWAPwFrYO5XfwOeabKcWvwGe4b7VISqoh5jgDExuPmwfnCAz/LTy2mSEHggsIDP8vdKxzcBjsesziJwpM/yKyvXNnzmKc0QrM9vBfwR6+d/xSzgmsJn+ccxuOeBhSvlD6HkBhiDmw6z5Nkee+YfYy6EB/ssv69W3jG4aYGzMYu4zXyW314n3SAqboCFSyFwYyp3YeDfmEtpzfIakcTVDNgL8MBYbOwe6rP8w1bzq8Gc6e9b1RPl/h6DuxH4ts/y5Sv188BLwN4+y89J/epkzJ12LuBd4DHMInEp4M506W0xuCKbia60yXX3l8AS2LO6Huuj75bKnID1w/eAX2Nz513ALinJmcC6wIeYq+jJpWvr1s9n+dtdNZYQou8z99xzt7sKQgjRp5EboBCiWfoDH2CL0IJFgNuxBd4GWJylwdgCrcw5mGXJqZiVz3OY2FFlYWAUtuBdDzg95X1hjbSLpvNFnqOBq2Nw323lpmJwy2NuZ3NilktbpHscEYP7YUp2KTAtJraVr/0h8D3gklbKbJKBwJfA86VjSwNP1Uj7NLbAbpUvgLzG8XGYW9WiADE4h7kT/gDYF4vl44HDqxfG4AbH4CakmD+NWAsY6bO8oWuaz/K3gOuouAQmoWY34MqyUAV8FzgDE5c2B14ELo/BrVG6tplnXjA7cDlwGfAzavfbuqR6LoQJJY04CBPwzsAEjF2wsTVnrcQxuG9gYtAmmOhVU6jqglWB3wJHYH17WuDGGNw3u5HX8dhYvA1zy/s91k9uisH1xG+NR9Lfy2Nw61bcXsucBfwgBvejyvFf0CGygo3ZAVi8tLUxMel1YCZMuNw3pfs1HS6KjwPE4E5K5YzABOQDsPnqlvS8y+yExWPbB/gV1uYXA9disdm2wFwZT4rBrV+6rlH9GjL+vefJxzzRVTIhRJt57rnneO6559pdDSGE6LPIskoIUY9pk7XHrFhsnS0wq4uJFlM+y/9S/D9ZVtwLOOB3MbhDfZZ/GYNbArMWOcxn+Ukp+a0xuFkwUYpSftdU8rsfszq4OAa3b8WqaW5gNZ/lL6T0j2OC1dbACS3c5ymY5c6aRTDzGNxwTBQ6AtjUZ/mbyT1uJ2yRWrATZjVxY6ne02KBzwumScc7zbe1rIlKefwfZr1zgc/y/5ZOzZnKq/IuMEfj26zJc8DaMbi5irZNwkKx0C+Ekp0xYXJA4eIWg7sF+FeNPL/ERLCurLsWwixFmuEszDVy1ZLl0wbAgsBfKmnnrdRzGCbmHYMJBdDEMy/lNwuwo8/y65usa/lZfws4FBO8jqt/BWDCxK0V67F/1Ml/DuAG4NvAj5sNcl6D2TC3uvdSvm9hFn3r04IoF4ObE9gfuMhn+S/T4eExuDGY6LJhqm+38Vl+TwzuKEwgHQZ8EYMbhY294LP8/ZR0GGaJuCdJ4IrBTY+Jf0N9ln+U0g3ArL7KceGuKt1TYTX474pbZ39MQDraZ/kxpePPY5ZqG2HiasE4YJOSheIymCh5hM/y49Kxu7A5ditMuOqyfl3h5lmOmZfctuuEQoi28eCDDwKwxBJLtLkmQgjRN5FllRCiHs9iOwG+C5wPnOOz/Mxyghjc/DG4c2Jwr2IWOuOxRfk3sYU6wErYXNPJDQuzVulEDG62GNzJMbiXsEXeeGyx2w9z6SrzQiFUASTXmLepuFs1IlmnDMQWgV/G4KZLQkOxy95qpeSXACunWEuFILEtZtlTjmH0Uqp38e8ILNZO+dj4epZHMbj5MZeilzABoEotEahfjWPN8Bfs2Vwcg1s0lX0GZjUFJjyBLZz/U160p7hl1WeKz/JjfJZP57P81W7WaRKS69UzdLau2hN4skYMsmo9v8Ce749icNO0+MzBgtTfSPMsQMdzfgOzqNnVZ/nDXVw3Elg/Bnd8DO4nyZqtFt/GROFZmDyhCuDBilVaIT42PYYSK2PuvZdWjl+Otd/A7lWvM0kcWhiz0rwEc487Cngq2k6BRb88B9g2Bjd7unRTTMQ8p5TdSOCAGNx+MbhlkzjeDGtjY2Zo0XdS/3kYE9ar/ee2ijD9bPo7vHRfn2MWgAv1QP2Yfo7FmX+rEcy27O7NXiKEEEII0eeQWCWEqMdmwIqYlcUIYJ8Y3M+Lk8kC5wbMauI4zNVlRTpcAGdMf+dPf8sWQrU+g7n77YUJJmun/Ap3nBkrad9lUsbVSNeIOTHXpyOoiElYPJo5Si5M12BuRDumz+tgC+CqC+BGqd7Fv3Mxi68VK//erFYm2u58t2HCybolK5CC96jtFjYHtS2uGpICt+8A/BBbLL+JCVOnpSSFi9781H5etY41y38wEa9Zzga2jMHNFYP7DuZ2VbWqqlen/2IWf/PQ2jMHeLtsTdgEb2PPdyXMojACF5QCitfjBEx42RgTo8bG4C6MwVWDmvwf5g56eXKRnBw6jaGS6NrKGIKOPtnJpTOJMGOp48rYHXyWv+Wz/Hyf5bv4LPfYM1sAs3YqOB/7fbNT+rwX8IjP8n+W0myDzV8HYu54b8TgjmzCZbEQ4V9k0v4zGyaglamOy7zB8XK7d7d+QgghhBBTBXIDFELU46nKDnFPAqfE4K7xWf4JFs9oBWAnn+UTLSpicBtV8ikWsPPSESi8+DyRGNyMWPydwWVXqBjcsj10P7V4H7Me+jMWR2YSip0PU+DxazFx5yhMtHrZZ/n9lfSdXONicBtiO8Q92qgiMbjZMGuLuYBVfZa/USPZ05hQUWUpuhns3Gf5NTG467Cd93Kf5S/F4M7GLJReS8lG1yl33hrHmmUEsHsMbr4mRZeLgROxOEhzAP8DhtZIV6tO82JiwBgsFldTzzzRbLD6gvGlZ/1Ick99EouhtUG9i3yWj8cCap+cgmtviMWAmonOsdKGAU8Av4/BfVYNOt8mCtFrPqyPAhOtD+eic5y7HsVn+Z9jcMdSitnms3xsDO4qYM/k3rkGZo1Vvu5tTAjfN7kq74ztgDkGE0brUdzLOtQWiHvkXiejfkIIIYQQUwUSq4QQXeKzfFwM7gDMPW0fLOZPEeh3fJEuxYbZoXL5w5g4sDVwUul4NaDKDJjFy/jK8UGTU/dGlHa+Ww54vCJS1OISYMcY3LqYsHZKT9Qj2m6JN2Hud6s3cO26AfhDDG6RZBVVxND5MXBwd8tPlkP/Tvl9GxNHyvf2ILBLDG7lUiyoabBn2l1Ow+IInRWD26pqvZQsipYoxECf5R/G4IZi7n+zAH+rs8vcQpV6TovFAnokPd9Wn/lk4bP8uRjcn4HfxOBW9Fk+solr3gLOSwG3l6lx/pQY3OdAiMFN47P8tEky6V0ewqwat8WCwhdsg/3OuHtyC0gC3jvVWG/JdXV2KlZdWJyzB4HzMPe8SdyOC3yWPwccGoPbi472LqzMvlFJfhs2ny3ss/y2btxKy9SpnxBCCCHEVI3EKiFEU/gsvyEGNxILnn4mJm68Chwfg/sCE5l+U+O652JwfwOOSQLHSMzFb/1Kug9icA8Bv43BjQbewXYCXGBK3hcWF+oeLCD0+diid25geWBan+VlEWgE5ip3PibWVWP0dJdrMMFpP2DmGNzKpXMv+Swfk/5/Lub2dH0M7nDM6udYzKWuHI+HGNzPgJmBwjJtYBKAPvFZfktKMz22a9vd2IJ+aeAQzDrmj6XsLsLEsL/H4A7FXN32wtyeOhGDOxI4Eli0Udwqn+UvJLfSS4GHYnB/AV5IdV4VE6WOwYLsF5xFR9yqWi6AYC5/V6Rg3GOAvTGrsb1LaVp55j3BSdhudEdibqKTEIO7HrOYehyz2PkB5up4Tq30PstPS+MuxOCm9Vn+hx6uc9P4LH83BncqcEgM7hMsSPj3MPfg+zAhdnLZEfh1DO5CTBz7FHuuv8Ws5v5cqdNDyaptNeBPPss/Lc6lWFYjMMu8IjbfJpjF3q0p2fNYvK1dY3DvYuLVc8ny8GTgzGTxdDfwGRZvam3gPJ/ld07OjTZZPyHEV5zNNtus3VUQQog+jWIfCCFa4XAsZsteaRe1TYG3MHeqP2MCwEk1rtsTE3h+h23ZviQWz6fKdtgOcX8GhqS89+vJG6jis/xxLMbQWCxW1q3A6ZjIc08l7ZfYLmkLYMGpJye4dZn1sDhVZ2DWIOV/E13HkvvlmthC+hJsMRuxXe0+ruR5NhZE/Mj0eXD6XHYhmoAFrj8HuAXIgAuweFlFbB3S/9cGRmGC0UWp3Fo73E2DWch1GRDaZ/lVmED0NOZaOQKzgFkViyn1l0r6J9O9P5qeWy1eBH6F9bW/p/vbriwgtPLMe4Lk0nUGsGEM7gd1kt2DuZadj7n67Y0JiQc2yPcM7F5/H4Orm66XOAwTAX+GBaQ/GJsXNugh67WbMFF3Y0zgHIGJmU9hgeZr9Yer09+q4PcZJgrukdJci8Vq26HY9dHb7pi/xCzw7sZE9h+mc4di4uNq2CYD1wMHYSLjC0w+XdZPCPHVZ/bZZ2f22WfvOqEQQnxN6TdhQqvhOIQQQojeJwa3OGZpsofP8vPbXR/Rt4nB3Q986bN81XbXpTdZYYUVJjz6aMMQeV8pRl+1FgDzbzXia1Gu+Prw1FNPAbDMMvLuFUL0HdY46wEA7txnlV4pr1+/fo9NmDBhhVrn5AYohBCiTxODWxD4LhZgejRm3SbEJMTgZsCs9dYCVsHc54QQos9RiMoSq4QQojYSq4QQQvR1dsfcGZ8HtvdZ/r8210f0XeYHHsB2+jzBZ/kN7a2OEEIIIYToDhKrhBBC9Gl8lg/GYm4J0RCf5a/QRLw0IYQQQgjRt1GAdSGEEEIIIYQQQgjRZ5BYJYQQQgghhBBCCCH6DHIDFEIIIYQQQoheZOutt253FYQQok8jsUoIIYQQQgghepGZZpqp3VUQQog+jdwAhRBCCCGEEKIXGTVqFKNGjWp3NYQQos8isUoIIYQQQgghehGJVUII0RiJVUIIIYQQQgghhBCizyCxSgghhBBCCCGEEEL0GSRWCSGEEEIIIYQQQog+g8QqIYQQQgghhBBCCNFnmK7dFRBCCCGEEEKIrxM77LBDu6sghBB9GolVQgghhBCizzJuzBOMvmqtXi0zH/MEbp7lerVM8fVi+umnb3cVhBCiTyM3QCGEEEIIIUq4eZZj5iW3bXc1xFTMyJEjGTlyZLurIYQQfRZZVgkhhBBCiD7LDPMsx/xbjWh3NYToUZ5++mkAVlxxxTbXRAgh+iayrBJCCCGEEEIIIYQQfQaJVUIIIYQQQgghhBCizyCxSgghhBBCCCGEEEL0GZqKWRWDGwRcWDo0HvgPcAVwjM/yz3qiMjG4/kAEdvFZPqQn8qxRxmDgKJ/l/UrHJgBH+ywfPCXK7A4xuD2AzYDlgG9i7XIRcLrP8rySdiHgNGBtoB8wAsh8lr9WSjMrcBSwArA8MCuwhs/yu2qU7YFTgLWA6YFHgAN8lj9aStM/1WkPn+Xn9dA9rw7cmT6u67P81sr5/sDL6R5bLjf142l8ll9Q4/iFwGI+y1/sTt2bLH8IsLrP8v4tXjcIq5/3Wf5Kj1dsMonBDQACsAwwE/ADn+WjerH81bF+U7M/T2beQ+jGM+trZbRCDO4uAJ/lq0+BvIfQ4r3G4F4B7vJZPqin6/N1JQaXAa/5LP97u+syOfT23Fj8fgCm91n++ZQuTwghhBDi60yrllVbAQOADYDhwCGYqPFVZwDQI4JLD3Ik8BawH7AhJgweCwwtJ4rBzQTcASwJ7AzsBCwG3BmDm7mUdC5gV+Bz4LZ6hcbg5gLuw4SHPYFiK5w7Y3Dfm+y7ao6PsPuo8nPg48nIdxDWBqJnOR8TvjfCxtLz7a1Oj3IsJhp/1cvoK3TnXjdL14meIwM2b3clhBDi68ygQYMYNGhQu6shhBB9llZ3AxxVsjy5LQa3GLBbDG4/n+Vf9nDdeg2f5Q/1Znnp7eygLqwLlvdZPqb0+c4YXD/g6BjcIj7LX07H9wAWAZYonk0M7kngBUxsOjWle9Vn+Zzp/FrUX6jsDcwLDCzldwdm0XQ0sHUr99pN/g5sGYOb2Wf5J6XjOwHXYKKT6APE4KYBlgCO91l+Rw/k1w+zWsi7TNwL+Cx/aWooo6/QnXv1Wf7PKVGXryMxuBl8lo9rdz2EEEIIIYToilbFqiqPY65icwNvA8Tg1sHe2v4AmB0TOS4Egs/yL4oLk0XQH4BtgBkw66DfVwuIwa0IHASsjFkHvYYJFsf6LP9fKd1d6X4Gp3yWTGUf7rP8ukY3UcsNMAa3HPY2f1VgxnSvB/ssvzedPxA4DpjfZ/nYSn7PAM/7LN+0UbmNqAhVBSPT3wWwewPYGHio7L7mszzG4O4HNiGJVT7LJzRZ9MrAC5X8PonB3QtsGIObruL+4GJwpwI7ArNgz/GXk+mS8XdgC0xQuwQgBrcKsCgmwA2qXtDE87oLGJj+X7TF3RVXp7ljcEdjFkIfA1cDB5bdXGNw8wMnA+tjrpTPAb/3WX5ppT4/xawOlwLeoEbfbiW/GtdNj7mj7Ah8G3gTuBTrx+NL6RYBzgRWT/d0SSrjHJLrTAzuRuDbPsuXr5ThgZeAvX2Wn1OjDoPocA8+IgZ3BCaK9k/ndwQOwMSsj4FbsPYcXcrjFcyS7w7gQOwZbw1cW+e+5wFOx6wNvwRuwPpLrbSbpzz/D8gxi8LfVtxjt091XAz4Aptfzizut5bbWjNtWrm3G7FntTDwb8xF975Sfp3KKLnY7oWN9T2AbwD3Ys/i9dK1MwF/TG3mMBfgU4D7acKdOga3LTZneuBF4PA66ebGxtfG2HwfgVN9lv+1ks4Dx2AuyXNgff8fPsv3q3Ov06W22T7d68fAs9jYva/Ujp3cAGNwPwJOwOarfsBDwCE+yx8ppRmCfT9tBJwB/DDV548+y//SRbvMApyIzaHzAu8D/8LmtmfruazXckktfTedAhyP9bUIHOmz/MrStYNTW/xfqu9KwAfAucDg8guhGNwSwEnAGtj35xMpzbAa+S2L9ZEfA7enufI7wHdicDuk5BfVc7OMwc2HzVFrY9/B7wKPAbv6LH+7nhtuLRe90pi4G/teXxB4BhuXd5auHYI9u62x8b4sZmn8R5/lf6pVz3Rdt+ayUrp5sJcyGwHfwn7X3AXsXhH5fAwuYN8pYzHr0uOKZxSDmxHrP2sD/bF+PRJzp3+2XvlCiK8XDzzwAACrrLJKm2sihBB9k8kVq/pjP6bLYs0iwO3An4DPsBhJg4F5gINL6c7BhKqjsR9xawN/q1HGwsAoYAjmHrY05iK3CB0uagWLYj9sTwTeAX4LXB2DW7KVWEQxuOWxheE/sYXip9jCcUQMbhWf5Y9hwsCJ6R7OKl37Q+B7wBHNltcCA7EFetnNamng+hppn8bcNlvlC2xhX2UctmBeFFuYFxyCPZ9dsB/3JwC3xuCWLkST0qKp2bgin2KC5E4ksQpzAbyfDpFuIk0+r32wZzYtJngBfFjJ6hLgMkwkG4D12/dS3UlulXdji/BDsbhtOwKXxOBmKhbuyV3yZuBRrI/OkPKaBWtfWsmvDhdhC7kTsMXfAExoWARb+BODc5hAM2O6/7eB3YEtK3mdBdwUg/tRebEP/AL4hNrjEuAm4Cep/PMxV9pxqexfYGP8CqyPfDvVdaUY3PI+y8vunGsA38fmgreBVxrc99+xOG6HYtaD22BzTSdicHsBZ2OL5WMwIXAwcHcM7v98ln8Ug/sJ1ifOwASraTCR+5v1Cm+hTQtWxcS6I7D58Fjgxhhcf5/l7ze4T7B2ewBzXf0WJjgMJYmuib9i43ww1t9+SsVVuMG9rIU925uwubIQAqenNMZjcLNhY+8bqZwIrAucnSx1/pTSeSy+3afYmHkBWAhYp0E1DgJ+AxyGzSOzYd8Zczao9/9h4+YZTLiegH233B2DW9ln+ROl5LOlewxYP9gl1fu5sjhSg9MwYa7oZ3NhYs83G1zTiO9i/Www1mf2Bi6PwY2pUY/rgAuw75d1sb7zZbqWGNy3sTH3EfBL7Dt4X2wMb+iz/JZKftdj4/PklM8H2Pz0RJEnUOvlSMElmLh1ADZHzYv1s5mau/VJGIgJh4dh88VBwC0xuOV8lpe/W2bD5o+TMSF1W+CMGNxHDUTY7s5lxODmwMbbnNiLqCexcbcJJgSXxaprsbnlNEzYOhprm0K8nwGbc44DRqc89wEeSr9H3qpXj55g/HvPM/qqtaZY/jMvuS2zLbv7FMtfiK8Lzz9vP+clVgkhRG1aFaumTW/CZ8XiiGyBWQlMXICX31gnl557sR96v4vBHeqz/Mv0Vnh74DCf5Sel5Lemt9l7lQv0WX5NJb/7MZHh4hjcvhWrprmB1XyWv5DSP479UCwW9c1yCmZhsWbhjhSDGw48hS0cNvVZ/mZyj9uJkliVPr+HWVMU9Z4We/tfME063qn9GwVsTQu0/YALfJb/t3RqzlRelXcxEaRVngPWjsHNVbRtcvX6Uam8Mh8Bm5TeKD+PLaR+ji2QwBZIX2CLyma5GHM1XQATHrfGFjW1aOZ5PROD+xCYroHb5998lh+V/j8iBrcSsB1JrMIWuovR2YLglhjcvMBxMbjz01g4HGuXdQo3xhjcA9ib/TdL5TWbXydicMukepWtAW+NwX0BHBuDO8ln+ZPYQn4RYKVi4RaDuwUTBRYuZTkMEwH3xMSGwnJrF2Coz/KPajWWz/IxMbii771etGvq78di1jATBeUY3LPYfLArtnAvmAP4YVcLuBjc2pg4tp3P8svT4eHpnhYspZsFW+Be6LN819LxhzGhdzdMvFgZeN9neVYqplNQ/xoMork2LZgN+L7P8vdS2rcwcX59GiycE6/6LN++VP95gFNicN9O808xjx7ss7yw3LstWVv9qou8wRbYz9J5/P4bs1Iqiwb7YWLFssXcio2PbwJHxeDOTnPX0ZigtZzP8nI/v6hBHQYAt/osP7107B9d1PtITDj4aSH4xeBuw0TOo+js4jwrsE8hCMXg7sHEs+3o2MyhXr2G+iw/v3SsprVfk8wLDCiNkWHYC4VjMEGzzLmV78XZgN/G4EK63/2xMTPAd7hq34yJd8djFoxlzqi0LzG4ccA7DebCMgOAQ32Wl0XQq5q4rh7zAj8uLBxjcLcDr2LzZjlW4azAL0pjfVj6Pjg6BndRHWvhbs1lid9gY3sF39n19LIaaf/os7wQpkbE4NbE+tSFAD7LP8BEbFIdpsXifP43pTutQT36NPkY04IlVgkhhBBiStOqWFU1Xz/LZ/mZ5QPR3JoGA+th1hTlMr6FmfKvhAk2V9KZy6mIVemH+mGY5cJC2Fv/gsXobNX1QmkxRXJReJvai8iaxOC+gb35PQH4siIojQB2KH2+BLgoBreYz/IXUtptgSsrLgMvYYu9KuPLH2JwNS2PUpten/LZv0Y+tX6096txrBn+AvwaEwN/jVlJHIa5CYEJT2WuLrun+Cy/Pwb3OrbAOT8dOwZblLXCncDr2GI8YovgK6kIcC0+r664qfL5X5grSsFqwBt+0h3nLsUWKUulawYAN/tSvC2f5f+J5prpS9c1m1+V1Urpqtcdi7XHk5gY81rZwsBn+YQY3DWYq1Fx7MsY3DmY8LB/Wmhtii0q67rMNGAJbKwfVj7os/y+GNyrqX5lseqhJi0NBmCi5zWV45dj80053WzA0Ep/eB2bw1bDxKqRwBwxuEtTHvc1Ye3UVJuWeLAQqhLF82xmTqrVH4tr38Tm0X5MKhxcTRdiVVo8rwicVBm/DydXrTLrAQ8DsdKew7EF+VJYf1sHuLEiVHXFSOCQGFwhsjziu45Xtloq5/1SvT+Mwd2AWbmU+bRsueSzfFwM7gW6bv+RwKAY3DuYgPnPWsJxC/ynLAz5LP8iBncVcGAMbhrfOeZjre/F3bFNL+7D7r/q+v1FDO4y4MgY3Gw+y8tWo5MjskFyX0svi+4AnqojFDXLQ77kipusHG/Cxm2ZemP9PMxl9PXKucmdy9YBRvrmYqRVx+ZTWOiDicTgtsYsFpfAQiIULNFE/pPF9HMszvxbjZgieU9Jiy0hhBBCiDKt7ga4GbbAWR8TAvaJwf28OJkscG7A4skcB6yZ0h+fksyY/s6f/pYthGp9Blu074UtbtdO+e1bya/g3RrXj6uRrhFzYq5iR2BiUvnfL7HFbdFu12CuBTumz+tgP4ovoTMbpXoX/87FLL5WrPybZJEXbXe+27BF6bo13gy/R22XmTmobXHVEG+B23fA3DReTHUaQMeb4NGVS2o9s/9ii4lukxZDQ7E37TsDN6SFR5VWnldXVPvPOMydo1xW9f7BBNjiPFj/rtcu1bo3k1+V4nj12lr1eLuJeoAJi9PQYdmwFyYcdCe4db36FXWs3letdLWYH3jPl2JyJar38630dwST9ollMZcufJbfjbnQLYQt6MfE4EYkK8ZGdWi2TaHSp0oidjNzUq3+WL62mEer9alXlzJzY8J/M/30W5hAUm3LQiSbq/R3EgGhC07ArKE2xqzuxsbgLowWI6sejcZN1Zq01hzYzHfCrzBxY1dMrHk7BndaslrrDvXa2WHul43SFp+LObXR/fdj0jZodnzVYxvse/1ATJR8IwZ3ZAvzapVmvzMajfVG3y/dncta6b+1xubEPhWD2whzYfw39sJlJew7fgyt/R4RQgghhPja0qpl1VO+8w5xT2JuKdckK5JFsXgjO/lSgOj0w61M8eN5XjrHIJq3nChakNJNsMCxp5eOL9tivVvhfcx66M+YK9okFG/BvQUevxYTd4pg1y/7LL+/kr6TdUwMbkMg91n+aKOKJKuy4diP6FV9lr9RI9nTWNyqKkthbiEt47P8mhjcdcDiqZ4vxeDOxqwDXqskn3eSDOzYqO6UXeFiLG7P0thithbv0+Tz6gHepfZb8fnS38LKbzT126U7+dWqR5GuvLtarXos1UQ98Fk+Nll67JlcKNeg5MbSIuX6VZkPi61UplkrjdGY+Dh9ZRFbvZ/i/gdh46PKRMHXZ/nVWFy7WbCA6Sdj7kYL1uk3TbdpL1DMo9/CrA9bqcs7mOBUr5++Wvo8FhPE9quTV+Ey+A4titTpOZ4MnBwtkPeG2KYQM2EiSS3epX7fqvXComW8xVQ7BLP6+g5m2XsSFs/vICz+GJjYVGYualOvnXMmjRdV73uxmP8b3f8EJm2DybGCwmf529gLon2T6+nOmMvnGCwuXE+1RfX7rdFYr/VdWNS3u3NZy/23AdsCL/rOmwJMT4NYbEKIrx/TTz9914mEEOJrTHffjBYWAgdgC6V90uHirXN5N7LpmdQV62FMYNi6crwaMH0GzGqm+nZ1ULcq3QRJdLsXC+L8uM/yR6v/KpdcAiwag1sXE9aqVlXdIr3BvwlzG1vH1w8QfwOwcrQdyopr+2PBgG/obvk+y7/wWf7vJFR9G1s4nl0j6ZblN+wxuB9j8YMe7G7ZpTo8i4lQV2OiXa00rTyvIkh8d7kbWDDdY5ntscX8v9PnB4H1owVQByAGtxD2TLqTX616wKTjpRhn96S/DwELR9s5rahHPyzWXC3OwlyNzsPiwl1eJ11XPIdZQHSqX7QdHb9Tqn+rPIjNB9X6V9vhAUyQ+m6t/uA7B3EGTJzwWX4jZk0zP/UX2q226ZTkYUyIqG6k0OXGCsmlbSSTjt+VsI0zygzDAs+/Vqc9C/HvVmzH0PnpBj7L3/JZfh5mEbdMg6R3AxvE4GYt1XtWzIK1u32rUb1e9Vn+R8wNs6jXf7H5pFrPDepks1AMbuXiQ3LD3Aqz+KmKorW+Fz/GXM3A7nHlNM+X89sGc1dsFJepoFtzoc/y53yWH4pZrBX3Xgib1bZYv042K6f5EJj47DZg0u+MemP9NRqIVYnuzGW3Aj+Ktlvi5DITUI1BuRN2T0IIAcAOO+zADju0Eq1CCCG+XkzWboA+y2+IwY3EgqefiS2uXwWOjxbseTwWtLR63XMxuL8Bx6SFUrEb4PqVdB/E4B7CgsuOxt587krPvf2sx/7Ygn94DO58zIJhbmB5YFqf5eVdDUdgrnLnYz9Qq3GEuss1mLixHzBzeaEDvOSzvHgbfy7m7nZ9DO5wbPF6LLYzUacYHTG4nwEzY65QAAOTu80nPu0glcTF32MLog8xq6ZDMAuVP9ao56zAdSlOyDzYDlYvULJyisEdiQVFXtRn+as18qiLz/JfNpGs2ef1DOa6ug1mkfRRLeGiAUOw5/H3GNxhmMvIDljf3bMU0+Y4bCF6awzuFMzi4GgmdX9pNr9O+Cx/OsWnGZxiCD2AuWoeAVzmLbh6kf9BpfzHYBYGhZvQl5V8H4q2KcFqwJ98ln/afNN0yueL9MzPSfGgLsXG7PFY37iw0fUN8r0tBndfynduOnYDXKaS7sMY3AHAn6MFJb8F2wFtASxe1l0+y/8WgzsGs9S4ExvDC2Lx2kaVxleVIbTQplOS0jx6bJpHH8NcrwtL1q7qchS2QC+P36PpcCctOA1r53tjcKdhYuTMmIC1qs/yTUr5bQA8EIM7AXMjXgBYz2f5jtQgBnc9tivd45gA8gMsRlaj+ELHYhZYt8fgTsbmvIOw+bfV2Hg1icE9iIn9/8KEooGYIH4RTIxTdgWwW7RNJZ7D7n31Oln+F7giBncU1mf2xixX966Rdo/S9+K6WP8aXIrRdRr2wua2lN+H2AujxakvllV5Blg1Wfm+hQVbf6VGO8yOfccNxeK9jcdeysxB2ozAZ/noGNzdmBXaO5jQviNmaV2vLW6NtktssRvgzNhzLfMR8PvSWN8OiyE4qKuYWd2cy07DXhSMiMEdhz37udP97tWkCFgwDNg0jZcbMbf6X2OWwBNJVnsvAcd4i+1YHP8cuMhn+W6lYy9imy78tIV6CCGEEEJ8Zem2ZVWJwzHrqr1SYNxNsR+/F2NWMfdg7hNV9sQEnt9h8WKWxH4oVtkOW4T9GVsovkV9d5QewWf541h8ibFYrKxbsS3dl6XDaqVI+yW2q9cCWDDlehZQrbIeFn/kDOyNc/nfxAVJsixaE9vl7BJsURGxnfE+ruR5NhZn5sj0eXD6XLaYmoAFrj8HW+Rn2Dbq6/ragY9PxBalQ7C32Y+ntGVruGmwN8rdDfrekBae18nA7djb9pG0GDw8tfXAlP9JWND75TC317+W0v0bE15nwuKWnIQF9L69O/nVYed0P7ti29Dvlj7vXMo/x+KoPYkFzr8IEzH/nJLUigF2dfrbncDqE0n13wl7BtdjAuhtwMAa/bIVNsfu90SsbafDxNpq+edgrqNLYOPiFkyImY4OF9WHMSui01LdTiZZ7TS4r+606ZTkF9j4PBCbR5emI6Zfw7r4LC82IFgC+DtmKZvReSfAYmezVbB2PwizcrwAW8SXg5e/gsXmeQh7PsMw8aie8Ac2PtfBvguGYeLN79P91Kv3k5go9CHW/peQBCWf5U80uucWuAezcBqKWbhuCfzGd95Vbz+s3QZjfXFG6ge2fzGd+126ZjFsV8taOxJuggnWN2Ciz3GUhBxvAex/gr1AOBsbs3MCG/gsH9bk/R2CPecrsblwcJ10n2Fz+h6pnGsxYXwHn+XXl9LtiD33M7DvgtdSvWtxN/bi4wQ62u1nPsufr6T7ELOk2hmbQ9YA9vNZ3mh3yTItzWVJDPwxdo8HY/3xj5iFVFdB/6uci4nz22C7W26AicjVMdkP+26s/hablkmtsKarcUwI8RXm7rvv5u67e9wgWAghphr6TZgwWeEshBBfMWJwNwLf81k+ieVDtB0Lv/RZvmrv1+yrS6M2bUNdDsCEt/41YsyJXiYGdxcwnc/yn3SRbjBmnTa9z/KqC9lUQbSdJu+rZ2lXSjcEWMtn+YKTUdbXdi5bYYUVJjz6aMOQmN2m2A1wSu022FfKFKI3GDJkCACDBg1qaz2EEKLMGmc9AMCd+6zSK+X169fvsQkTJqxQ69xkuQEKIfo2Mbj9MauTFzCXza2wt/x7l9LMgLlMroVZ0GwyaU6ioJk27cW6bIi5QY7C3P5Wxax3rpRQJb5uaC4TQgghhJh6kFglxNTNOCxu3MKYC8lzwO4+y88vpZkfi3v1PnCCz/JuB+b/mtBMm/YWH2Gu1wdjcX/ewFyxjmpDXYRoN5rLhBBCCCGmEuQGKIQQQggxlSA3QCG+GsgNUAjRF5EboBBCCCGEEEJ8TZlpppnaXQUhhOjTSKwSQgghhBBCiF5k6623bncVhBCiT1PdLlkIIYQQQgghhBBCiLYhsUoIIYQQQgghepERI0YwYoRisQkhRD3kBiiEEEIIIYQQvcjrr7/e7ioIIUSfRpZVQgghhBBCCCGEEKLPILFKCCGEEEIIIYQQQvQZJFYJIYQQQgghhBBCiD6DYlYJIYQQQgghRC8y22yztbsKQgjRp5FYJYQQQgghhBC9yOabb97uKgghRJ9GboBCCCGEEEIIIYQQos8gsUoIIYQQQgghepFhw4YxbNiwdldDCCH6LHIDFEIIIYQQQohe5K233mp3FYQQok8jyyohhBBCCCGEEEII0WeQWCWEEEIIIYQQQggh+gwSq4QQQgghhBBCCCFEn6HLmFUxuEHAhaVDnwJjgH8ClwFX+yz/spS+PxCBXXyWD0nHBgNHAZ8B8/ks/6BBGYv5LH+xOzfTDmJwKwC/AFYDFgbeAe4FDvdZHitppwEOAvYE5gOeA47xWX5NJd3OwMbACinPi3yWD/p/9s47Xo8pfeDfGEZbJXqXYXV+gljCErFK9E70O3pbBqMvorMGw+5GNwkJNnpPIkh0gg2rBxM9RG8rEyO/P54zuXPnvvW294bn+/ncz3vfmTPnPHPaO+eZ53lOibKHAPuXEOtyx0u8Nt5Pb+B0YF1gUeAr4E3gLsdLrmhLnhXK2gTYBKmDXyunVuLQngSMLdUXugumT27ieEmvBpR9AHAKsCzwk+Ml83e1DDMTubn6YMdLrmvD9acChwFLAK86XtK7QwXsYNp7v12JGetPOl6yTxeW1ylzSxzaYwEcL9mko/NuFOa36zGgv+MlY80xD/jA8ZI7GydZ1zB1yst8ettmXVZeMuVl7IXX7LLyFKWrWHDBBRstgqIoSremHsuq3YC+wNaIMmMqoqwaHYf2nLl0n5p0D5TIYxqwa4nj+wHf1yFLd2IgsBpwBbAVcDKwNvBCHNpLF9KeAwwC/mnSPgvcFof21oV0+wDLAw8D31UpfwpS3/m/y9pyI3For2tkWgg4EdgSOAFRqu3UljyrsAmixFQLv9rYCelD3Zlz6Jy+UpE4tJcArgGeBjYFum4l9TskDu0/AecBtyKK+n0bK5HSTmaGuaU78RLyW/tS7pgH7NwQaX7j2AuvydwrD2y0GIrS4Wy33XZst912jRZDURSl21LPboATChZPN8WhfRtwG/B34K8AjpdMRRQepbgTWdRcnx0wCp1+wI1AUx3ydDq5N/Ez3p6W4CLHS6YUrnvKXHcwcIY5tgjgAxc6XhKYpI/Fof1H4ELgwVwWW2aWRnFoD6giZuJ4Sbn6rpe/At8AW5h2zBhmrMKUBuJ4yX8aLUM54tCe3fGSqY6XvNsgEVYALMQK8clyieLQ7gHM5nhJ0mWSdTOyOmhnNquYz6scL3mvnXnN6D/tzafRxKFtAT0cL/ml0bLUQ3eeW7oTufb9jvLPOb95Zl94TRbfbUyjxVAURVEU5TdOPcqqVjheckcc2vcAB8ehfZLjJT+VcgPMcSMwJg7tZR0ved8c2xf4AHicgrIqDu0tkLeVawHzAe8h7oKh4yVpLt1eiAXQCkBq8vun4yVX59L0QxRHf0IseZ4Ejne85NV21sGUEsfej0N7CrBk7vCWgA0MKyQfBtwQh7aTuQ020CVuAeDrUovGnPJsduAjYJjjJcfm0+TcOVdxvORNY6l1PmJpNicwGRjpeMkROddQgGlxaGfl9DB5zWXO747U48fAdcAFOVk2QVwxdkIs1XYDegBDEMXg2oiV2VrAJOA4x0tG5eQtK1+lSopDeyHECmF7xAotBi51vOSaEnXRF1ECbgf8ANwOnOh4yc+5tMsh1nabmDQ3IdZsVwOO4yWTTLpJ5Fx16iyjan224d76mXI3N/Xbu+gGmJsPDjPlHmzq+gngcMdLPirIeImR0QbGABcDT1F6PsmuG0KzO+wjpi8NdbykKXOnAh5FrAWXN/nfFYf2WabOlkesPl8BTs0rf9vbx0weFeeeOLR94CygZ6ZEi0P7DsRKY3PHS8aYYwcDVwILmMUycWjvg8x9KyFt/xDS9p/myi9XB60UFKb9HwDmRZTmH5RIMxZpe4B3TX2f5XjJoDi050XG1M7AgqZOrkLm7OmFOt3F1OmOiPJs/mJZuTId4Gykr/VE+u99jpccY+rvPGCp/HxslHLvAs85XrJnLjs7Du1LEQvWP5h6OSobZ7nrDwaOorlu7wFOcLzkq1ya6eZ+v0fcu5dB3LdL1W0vZCwcCfyxWvnmmoHIuF0GeAPwMmVsrfcdh/asJo+9kDH4A+LafXIur0kU3AAr1bk5vy7i1r4+0tYfAHcA5zhe8r/ivVSiFhlNulraZFbgeGROWA5pmxdN3b2Zm7+cfJ1nv0nZb5A5VrJ949Cej5wboKm/ZYFl49De21w+FLgfeaHX2/GSlwv3PBaY3fGSvvXUlaIovx3uu+8+ALWuUhRFKUO7lFWGB5HFRh9E4VSJJ5DFy97IAyCIsmoYML1E+uWAR4B/IPGu+iBudAsj7nbEof1nc/0VyKJtFmBlcgufOLS3QR5qH0AWCCAP2U/Eof1/jpd8WNOd1kgc2qsAiyCLi4zVENfJYjyu18znqshCpl4WiUP7C+R+30Os1oKCMq8JeTivZCEG8DywTRzaVwE3AC8VLQQcL5kah3YEHBSH9il5hQjyMD/OLAj+AIwyeTYhD/u9gA1M2uuApYADgT8jSsZM3lnNtasiipP/Igui0xGF2vEFuUPEam8PxCXpb0jf3gxRdnxsjt1pFKVf1CBfScxi/ClE4TIIabMtgSuNdcg/CpfchLjL7owolQYBX2MUdXFo24i75xzAEcDnwEGUdpctR7UyaqrPNtzbcFPurlSfS05BXPQOQMbGJeb6frk01yDKoEHAC8BfTJpqnIMsRq9AFAEvIe6xGf2B3ohC6HNkDgJZFF+GKF/nRuaGx+PQ7uN4ySuFMkLq7GNQ89zzqMljfVN+D1Mv/0NcGjMThk2BF3OKqkMQhea/kfpdAplX14tDe23HS36ooQ5mYJQpo5C+82fHS74spjEcYe7lFKTPfQp8ZKwvH0AUeGcg/Wwb4FJkzj61kM8/EOXavkj/L4lRmjyPxEs8E5gILA1sYZLcgPQBF7HyzdgCcJA+l+cUYIJJvwhSZ6Pj0F7N8ZJppswLkXGR/a4sCZwLrB6H9gb5+RWZP95DlJc/Ap+Uu5dayzdshChlTkd+/84B7o9Du5fjJd/Ucd8nAccCp5ly50V+SxcoJ2ANdQ6iuJmAKG6/R37jzkB+t+v12aoqYx1tcivyTBIiY2cOZMwujijA6qWJ1u07XyHNTsiz0MvI/AUyB71v0h+KjJvsXlZCxrjbBnmqMu3rtzskptTcKw9k3jUO6gCJFEUpxZdflvuZVRRFUaBjlFXZm/fFa0g7HVEs7Qucb+KerIxYXG1YTOx4yVXZ/2YB9wRiceHHoX2qsQhZH/imEFB8dCGryxElyg65/B5DHkCPR6y3sgDoeXc3K/s0C/6MX8tZP5l0VyEPqtfnTi1g5Cwq5b7Kna+XCcgi/TXkgXwn4ALEwiz/hPkrogwqpRDMczFiIXKo+ftfHNpPIm+Gb8gtBq5E6m03RFFCHNr/h7RFZsGwMvI2/sTCwn8IgOMlH8WhnVnVPFdQiu2JKLD6OV6SKUAzi5kz49C+yPGSz3PpH3W85Djz/8NGQXAUsFHOcuBTZCGxDfLGu6J8FTgGeYO+huMlE82xMXFoz29ku7JwLzc7XnJmLt165v6yY03I4m49x0ueN7I+hLTtMlVkqbWMWuuz3nu73fGSE2uU8X3HS/bKvsShvTBwcRzaSzhe8olZvO2FWFJkC++HjbXVXytl7HjJu3FoZ4rh10u4xfYE1nG8ZHLhuhljJBb3npHIWDoQqYs8beljUNvcMwFREPVHFP5rIvPB5eZYxiZZvkbecxBrmBmKgTi030TmyQOQRX3ZOjDKqez/NRHF0QRgV8dLfqIMjpe8Hod25vr3n5zl37ZIP8tbwY2OQ3tu4Pg4tC/NlHiG5/NtUIGzEAXqmo6X5BVBQ408X8Wh/W/gkDi0L87NsYcCb5VQ0H8P7JCz0HwbsTzbD7je1MsJiLXY2dlFuXTbAXfn8uuBuE7Xak1UsfxcunkRi5yvTbrJwHgkbuTNddx3X2C04yWX5/K+r4qMFescxLI6+9/8Pj+FxFi8MQ7tIysoO0tRUcZa2yQO7U0Ri71jnJYbgtxdhyxFWrWveSE1A8dL/hOH9lTgi+L8E4f2tcCxcWif4HjJj+bwoYjL/b/bIVenkkwRQzBVVimKoiiK0ig6QlmVmcxXU4Rk3AicblwI9gOedbxkYhzarZRVcWgvjrylHIBYDeTlXQRx2xoP9IxDexjyRvVJ89Y5y2MFxO3l/ILC6SfgGeSNa8YZNC/w8xSDM5xF89vTIv9ErHO2yRYZhh6UrqMeJY7VhOMlYeHQg3Fo/wB4RgEx0aS7Ean3avn9D9gpDu1VkQVRX2SxvDmwaxzaAxwvme54SRyH9ijkgfsmc/mhiIIu2wlpIvIwfnUc2v9CFuy1WrANQN5IP11os9HIm/T1gXtzxx8qXP8msKLTMnZR9kY9C3rfVvkGAM8BcUG2UYiCcFXEnSyjuNHAf2kZ/Ht9ZAep57MDjpdMj8UN7P9qkKeWMmqtz3rv7a4a5SsnI4hC7hNgPWQs3FZIdztVlFU18GxRUQUQh/ZmiCXH/9FSWVzKwrHuPlbr3ON4ya9xaD+OWE6dZT5fAUYAR8WhPY/JczHECgvE4mYRI/8MHC95Mg7t9xGrjfxivWQdGDYGAqQPHFC0pqyDjRHF+C2F48MQBWBfWipJau0/WwD3F5QmRQYjbl9/QRSsiyMKjFLK1BY72Dpe8pRRnPdFlEWbIy8thhfa7TlEGbMxLZUfI+t0e6tWfsYzhd+Q/JjJqOW+xwOnxKF9HtKPn3eqx2yrWufGEvM0xLJyaVrGQVsBqEdZVU3GWttkC+R39to6yq5Gve1b5BqknvYErotDew6kzW5sZ75lma3niu2OKdWVu/0piqIoiqKUoiOCZmeL/08rpjI4EqT9GWTxMpAyShRj5XQvsC2yoN4UWBeJ0QHGbcTxknGIhc/SyOJnShzaY4ylD8iCDmQRMK3wty0SayPjGlNG9re9OX5Y4fg1lCAO7QuAQ5AFX9G66ytEqVZUTvXMne8IsoVin7Zm4HjJ646XBI6X7IIoCYchi4BtcskGAxvGob26sZzYB4iyBYbjJd8iiq5PTNoP4tB+NQ7tXWoQYRHEwqfYXplCp7jX79eF7wmiiMrfU7bwyfpNW+VbBFkYFWXLlCxF2YrtOhWYPfd9ccQtq8hnVeSop4xa67Pee6tpzFeQEZrdvzLLzGJd1FMP5WglZxzaayNuOz8gc9H6yNh+mdIuaXX3Meqbex4F1o9lZ9X+SDyc8Yj710bm2DTEegWalWul2mAyrS01K7XV1kjspKvboajKZPrKaR3zbnLufK0y5VkQcdUsi1H2voDM1SDK1V/IWQLlKNWnPqM5xmDWbu/Qut3mpX3joJbyM1qMmVy9zpE7Vst9n4+8hNkesbr7Mg7tKJb4ZOWoWueIa/lhiFJ0c2T8HFmUsUaqyVhrmyyI9MGOVALV274tMAq/e2huo92QsXB12YsURVEURVGUDrGs2gZZUL1YxzU3Av9CHqrLmcEvjyhc9nW8ZEZQ8ji0W0UhdLzkduD2WOIQbQJcBIyMQ3spmt/unkJrCymQRWeWzyfk4o3k3GTecrzkhUo3FIf2aUgcraMdL7mpRJLXEAXC8rSMW7Wq+Xy9Uv51UK+lW0UcL/k5Du2LEWXUqkjAWJCF/iTEouplYB4KSjzHSyYAu5g34X2QNhgRh/aaTuXA9l8i1i27lzk/qS33UqSN8n2JKFSKbmIZb9Upxqc094E8i9aZTyVqrc96761D+pghWxAuQkvLpo6oh1Jy7oLMPzs7uThBcWj3pKCEagc1zz2IcspGlIUbA9c4XvJLHNpPIIp6B7E2ydyIMkXGYiXyXQxRYOSp1FanI8roh+LQ3srxkqcqpK3EV8ACcWjbBauYTMaipU2t/ecLWitySnElYim5JKK0uc3JBd7OUapPLYq4QObl3ILWSsr8+Yx6x0G18uul4n2b/n0RcFEc2oshitJLgbmQGGylqFjnxjpoB2BQ3nUvDu012nIDNchYa5t8gfTBOSsorLI4i3bheFEJmdER89xgxPV6HeQ38wnHSzrqN19RlJmUxRYr9ROuKIqiZLRLWRWH9s7Im9DLK8U4KcG/kcDNr5RZTIA8pIK8Oc3Kmw0Jzl4SRwIK3x/L7mqXIw+fbyGL8dUcL7mwDhlrJg7toxHrr9Oc1kGoM0Yii9O9EVefjH2AVx2zE2AHsBfycD2+3gvj0F7Kye3OlmNl8znjDbNxXboaUdBtBIxxvOTdUvkaa41n49A+HekvqwCv0mxdMycSxyVjJKJM+MHxkrYExK2LCvKVYiTilvaB0zJuVlt5FnDj0P6T0xyzqgdy/x1FrfXZ0fdWD88h/XY3WgaL3q2TypuLQhw3E+9mGdq20UEp6pl7XkXcaE9Agr1nscUeReaMpZEFbz7vzxDr1BmuY3Fob4BY0V1Sh5zTEEXmLYiSf2vHS56o4/qMcUb+3WgZGH9vZO4rxhKrldHAznFoL+7kdjkswS2IO+PNSDteVSbdrnFoD8rFjNoQ2ezhGXP+YcSdcRnHSx5uo8yVqFZ+vdR63xhX0Ovi0N4aWL1CntXqfHYkpuO0wvGmOuSuR8Za22Q08pt0EBLAvxTZTsSrA2/DjFiTW5RJXytTkd+yVjhe8qiJq3cpEp+z7HOMoii/HwYMGNBoERRFUbo19SirehuTfBt5IN4WWZQ8jFgO1IyJw7FTlWRvIA+V58WhnSIPxccWE8WhfTbyVvoxxCpqKeBoYIJjtvOOQ/tI4J5Ydl4bgbx9XRSJLfWB4yWX1iN/ofyByK5DI4FH49BeP3f6u+ztqeMln8ehfRkSl+N7ZMeyPRCriR0Kea5Ks7XNnMh22NnucOMcL5kSh/aySLyoWxFLrdmROm1C3HnezeW3H7Jz1F+M22Q5ropDe1GT76vIYmRdJP7Ju7SOMXM9ErtrTQrKlViCLR+CxBGJkQX40YhSKluUZW+Wj48lqHhqLNiGI7skPRKH9iWI5ZaNWKVtD+xYp3K0FTXKV4rLkHZ7wrTnW+balZFg2ztUuLYUQ5CdsO401nlTkIVW5h5aMpB/ndRanx19bzXjeMlbcWjfDJxjXIBfRMZGZknZEfWQZyQS3HxILLtbrohYGH3cUQWY2GM1zT0m7VhkTh1v3FRB5rWLc/9neadxaJ+BWNQMQ1x1l0TcpCciLlr1yDrNzGXDEQurbarMFaV4CAl2fVUsAfRfQ1wMDwIucFoGV6+HMxEL3qfj0D4fme+WBAY4XpLtsIjjJf+LQ3sI8jvxX8dLni6T3zxIMO6rkV0KL0Dq7EaTz7txaF8E/DOWwP/jEGucpRF3t+scL3msZM61UbH8eql233Fo34OM+ZcQq6S1kPh0ldzQKta54yXfxqH9LDJ3f4r06wOozQKuFdVkrLVNHC95zMT7uzQO7aURZe9siLXiA44EnR+P/J5dbOaaqchOfXnX6bbwOrCR+W2ZjARbn5Q7fxXyEu0LIB+c/hdgqOMlB+aOvYNsSvGX3LFHgGUdL/ljO+VUFEVRFEWZKagnZtVtyCJ+FLIgmh15qz/A8ZKfK13YFowbyY7IQ1/mNvg4ULRQeA7ohSy0H0ZcCcaRi6/keMmDyMPq3MB15h7+jrintPVtdsYAxPVugMkr/ze4kPY0xALrGCPDhsDujpcUd2baHanv25DYFpvkvq9m0nyPuN2chMT2GoFsTX80zXFDMmZBFE/Vgrn/A3ngPhKJsXE/Es9nGLChsVybgVEGjkMsru5tmRUTgf8hCoCHkMXzL8DmOeut+5E6OgKpr/Em32mI5d21iELpQWQRvT/wNC3dp9pKLfK1wigRNjAynYS04w2IwrHuBazp51sgAbWvQmLNfIj0d4Bvy1xaTxk11WdH31sbOMSUdyKiGF2N5r7c7nrI43jJKGSsbIj0wwOQDR/eqXRdG8qpZ+7J6vjR3LH/IIv3qcX0jpdcg+ysugYyXv+OzIH9imO1Rll/QSwz70U2a+hf5ZLi9b/SvBPiSUhQ/W2A4ygEgq8z30lIAP5nEcXOSOBsRLFbJIuvVkkRcwHSzkOQ+eclYMu8O6jjJaci/XFjZG69B7mnr5G5oz1ULb8NVLrvx5E55nqk7g5H+krZnTxrrPM9EaXyv5B7mUx5F+JqVJWxjjYZiLxE2RHpyzcgc8mnJp9fkDntQyP3v5BxM6SNsmecgij4RyC/ZYMK57M2GlKI62bRvPNwxqwljll0TOgGRVG6CXfeeSd33nln9YSKoii/U3pMn96RYWeU3xMmvs8HQOh4yemNlue3RBza9wOrOF6yfKNlaSRxaJ+AKKB7OV7yQaPlUbo3Zje5Y4AlHC/5rtHy5DExEGPgYMdLruvgvLvtfStCHNoHI8rEFR3ZaKbT6NOnz/QXXqgYZrMq2W6AxV0Fyx1XFKV+hgwZAkBTU1ND5VAURcnTf7AY6T92xAZdUl6PHj1enD59esnN4fQtnVI3xsVnJWRxNAutLciUOohD+zhkV7qJiIvQbohFyuGNlKurMe4zqyOBpn9FYqH5wAhVVCmViEN7LZrnpGt+Lwqb3+t9z0wYt/7lkViVd3e2okpRFEVRFOW3giqrlLawDeI29wGwf5Wgx0p1piLxZpZBXD3eAg5yvOT6ilf99vgecd05GXGb+xi4AomfoyiVuAuJBTaK31d/+b3e98zEYMS9+mngqAbLoiiKoiiKMtOgyiqlbhwvGUL743soBsdL/kVzjKrfLSag9/pVEypKAcdLejVahmqYOFDV4gbWm2evjsxP6XgcL9mk0TIoiqIoiqLMjKiySlEURVEURVEUpQtZaqmlGi2CoihKt0aVVYqiKIqiKIqiKF3IZptt1mgRFEVRujWzNFoARVEURVEURVEURVEURclQZZWiKIqiKIqiKEoXMmLECEaMGNFoMRRFUbot6gaoKIqiKIqiKIrShfz000+NFkFRFKVbo5ZViqIoiqIoiqIoiqIoSrdBlVWKoiiKoiiKoiiKoihKt0GVVYqiKIqiKIqiKIqiKEq3QWNWKYqiKIqiKIqidCGO4zRaBEVRlG6NKqsURVEURVEURVG6kH79+jVaBEVRlG6NugEqiqIoiqIoiqIoiqIo3QZVVimKoiiKoiiKonQhw4cPZ/jw4Y0WQ1EUpduiboCKoiiKoiiKoihdyLRp0xotgqIoSrdGlVWKoiiKoihKC6ZOeZlPb9usxbFkysvYC6/ZIIkURVEURfk9oW6AiqIoiqIoSlXshddk7pUHNloMRVEURVF+B6hllaIoiqIoitKC2Rdek8V3G9NoMRRFURRF+Z2iyipFURRFURRFUZQuZMUVV2y0CIqiKN2aTlNWxaHdBES5Q9OAD4F/A2c7XvJzB5XTC4gB1/GSIR2RZ4kyBgFnOl7SI3dsOnCW4yWDOqPMthCH9sHATsCawPxIvQwFLne8JCmkXRq4DNgc6AGMATzHSz7IpZkHOBPoA6wNzAP0d7xkbImyHeBiYDNgNuB54ATHS15o470MMmVn/ABMBsYDQx0vGdWWfE3e2wHnAysAswM9gbsBHC/ZpA35tegLcWjvCCzneMmlhXSbAI9Rpg4bTRzacwNXAVsCCyP9xuvkMicBTzpesk9nltNWzDw2i+MlN3RC3h7wgeMld3Z03h3FzCAjlB7Tjpd801ChaiAO7fkBD7jX8ZKXCufGQtvmpJmV3HOD43jJpMZKUztxaC8GXAP8Gfk9OdbxkrChQimK0u3ZYIMNGi2CoihKt6YrYlbtBvQFtgFGAacgSo2Znb7AdY0WosAZiELnGGBbRDF4DtBiX9w4tOcCHgVWBvYH9kUWeY8ZhUXGgsABwC/Aw+UKjUN7QeBJYHXgUCALaPFYHNqrtPOe/ozU9Q7AhchCYGQc2jfFoV13/41De1akPj4GtjB5fw8cYf7aQrEv7AgcVyLdSybtSyXOdQeOBPYEfETOyxorTregCRkDnYEH7NxJeXcUHt1cxgpjemZgfkQpv3aJc+2Zk5Su5QygH3Ag0v9ubaw4iqIoiqIoMz9d4QY4wfGSd8z/D8ehvQJwYBzaxzhe8msXlN8pOF7ybFeWZyyNmhwv6VUh2dqOl0zJfX8sDu0ewFlxaC/neMl75vjBwHLASlnbxKH9CjARUTZlFkHvO16ygDm/GeUXrYcDiwL9cvk9CrwHnAXsXs+9FnjO8ZJfct+vj0P7WCPjBOCSOvNbErEQG+F4yeO546+3VcBa+4LjJd8BXdpv6mQV4BPHS25stCAdSRzaszteMvW3Uk53pYH3X25Mt4k4tC2gR2He6XIcL2nznNSRxKE9G/CL4yXTGy1LN2YV4GXHS+5qtCCKosw8DBkyBICmpqaGyqEoitJdaUTMqpcQV7GFgM8B4tDeAnmDvxYwH6LkiIDQ8ZI0u9BYBAXAHoirx6PA34sFxKG9LnASsD5iHfQBcAdwjuMl/8ulG4vUwSCTz8qm7L85XnJ3pZso5QYYh/aaiCXTRsAc5l5PdrzkCXP+ROBcYHHHS74s5Pc68LbjJTtWKrcSBUVVxnjzuSRybwDbA8/mlIg4XhLHof0UYsF0qTlW6+JkfWBiIb8f49B+Atg2Du1ZO3Lh53jJZXFo74v0mRnKqji0F0Lqf3ukf8XApY6XXGPOD6LZtfD6OLSvB8Y5XrJJ0eUm57K3A2KtMRCYjlgHHpV3Mcr3hTi0hyDWatlxEKVfr1JugEaZ6AGHAQ7wJdJXTzXKrXwZ5yFj5lhzfy8BRzhe8lq1OotDex/gBGAlxKXyIeBEx0s+Lcia/7+su6KxwDsdsZxcCvgaeAqxBFkKeAHY0fGSewrXDUHG/7KFsX0wMmaXQhSHxzte8ljh2n6IBcOfEKvQJ026V3NpxiJj+iLgbGBV4GTKWInFob2XqZcVgBSZK/7peMnVJq9+hTrJ+ksTMkf1A/6KuNNOAnrXMv8Y98dlgWXj0N7b5D3U8ZImc77iXJKT/xikPywGvIL0pZuBsY6XNMWhvQ51tkXufFkZc2NpDWQMbgg8AuxQx3w+CWnD+01eywBvIO7IT+bSrYu4+K0NzIlYj450vOSIKmO6nrF1PmKNdaiRo08c2juYvFcBLkesPL9EXMIjMwedhvTZ8cBBjpe8m8t3IHCIqaM5kZcBoeMlQ835XsgcBXBtHNrXmv9dx0uGlHIDjEN7JcTCtD/yG/gyMMjxkpG5NFmdrAiESB/9ErgeOLfSS6KcTEcCvYB9kL61YBza39RYn0cBeyNzzSzAm0jff6BQ1nLAP8y9/Ij021YKulw/GQX8DWmfFxCLx0+Q36tdEQvgYcBJ2e9NHNpzABcg47MXMveNR1zU38yV0UTzeD4OGRdTEQspP//cUKa+su/ZPOE4XjIpDu0/IX1rfcTd/lngFMdLni+Vn6IoiqIoitJMV7gBFukFfIs86GYshyx0DkDcBYciCqTzCtdeDRyEPJzuDLyFPOAWWQaxujkMGIAsNA6gZQytjOXN+SzPT4Hb49D+Yz03FYf22sDTwAKI5dIu5h7HmAUjyIO0hSjb8teugyyIbqqnzBrpB/wKvJ07thrwaom0ryGL+3pJgaTE8anIIm357EAc2mPN4qO9PAQsFYf2MibfeRFlyTZI39kGuA+4Mg7tv5prrkOUKyBKw75Ud7O5HFFS7YUoP3Yxx8pxDvAgMMXk3xeJI1aO85C+9zCwHaI0bQIeKOHmuI+5r2MAF+nn9xg3qLLEoX0I0rfeQPr4yUhcqnFxaP/BJOuLLAYn5+Qu6a4Yh7Zt5D0aGIK4nB4FfIXECnoRWRAeWrhufsTK7rqCciRbIJ6GKAWnAg+ZhXl27TbIHPGDqYe9EGuaJ0z8tTwrAlcgC+EtzXWl7uPPyJgch7hu7gZci7hmgfSN/yBKoKxOiv1lOLJY3RWpV6ht/tkJqetRubzPMXLVMpcQh/ZBiDJiDKJUHYLMh5n8tKEt8pSVMcc9SP1tT7NCsNb5HEQZdzyi+NwDmR/vN/Jh+ucoZI5pArZGxmHW5yuN6XrGVpOR1Tefn+TO3QY8gPSRF4Eb4tA+H7EoPRkZiyvR+rdoOeB2RHGzIzIfXReH9mHm/Kc0W6teQHMdP0AJ4tBeAlHarImMt92Bb8z9bFXikruQFzo7IjH5zsIo0mvgNGQcHYL0g5+pvT570dwueyCKpfvzMubmkLUQxVgTogD7Wxl5Nkba9SRzD8sjirLhiJJxIBIz6jgjc8bsyDxxLtKuhyPK32dNnKkiw4B3kHa5DBl/V5aRCaQN+yJzxH9obsNP49D+P2Rs9DT3tx8wLzLvrlkhz3Yz7eu3+fS2zfjuv90tUoGiKIqiKErtdIVllWUW0/MgD727IG/OZyyQHC+5KvvfvA1/ArABPw7tUx0v+dUsXPcCTnO85EKTfLRZzGQP/1l+dxTyewr4DrgxDu0jC1ZNCwEbO14y0aR/CXkA3R15I1orFyMWFJtmwczj0B6FKIVORywbPjHucfsCg3PX7otYptyfk9tC3sRmzGKOt2izShZL5mH5GOAGx0s+y51awJRX5Cvkwbpe3gI2j0N7waxuzeLlT7nyMlLkDXh7yQLBL27+PwaxAlkja0tkcT8/cGYc2lc6XvJRHNoTzLl3a3Tfe9zxkkzZNdr0w4Pi0G4qZXnmeMm7cWhPAZJq+cehvQCyuBrqeMlR5vAoc/1NiBLo3twl04BtHS+ZZq4HWUj/CVFulCrDQhQMYx0vGZg7/iYyzg4ArnC85Nk4tL8AptZQL/tg4og5XpKX7/bc/4MRS5dlHS953xzbDxnXxRXUosCGWXD/OLQfAd5HFq77mjSXIxYzO+Tu4THEaud4xOIjYyFgC8dLJlS5j/WBbwpB5Edn/zhe8noc2t8Bs1aok9sdLzkxf6CW+cfxkv/EoT0V+KJE3lXnEjO+zgQecrzkoFx5k5FFfJ562iJ/H5VkzLjC8ZLLC9dVnc9zyecFejte8nVO/vGIUupmxNq1J2IF+EruuiGmrJJjug1jqwfSZ/KWt9m/F2eusXFov4Aoag5FrGe+M8cXBy7P17HjJefn8poFGIvMV4cDVzleMjUO7f+YJO/VMO6OM3XR12l2t34QsUY6D1Hg57nE8ZJMQTomDu1NkZh0pV7aFPkM2Cmb4+qpT8dL/MJ9P4Iovg7Lybg/oszrm2uzh4D/lpHnD8AAx0u+NWkXQ+aE53PlPWyU2rthfl9N+vz4sBDl52emLooWlw/m8httLKXOjkP7fMdL3i6kxRG312fj0P4ecZWc0YZxaJ+BKN7/klnixqH9MGKBeSadHAsumfIyAPOucVCVlIqiKIqiKN2TrrCsehNZZH+FuCFc7XjJP/MJ4tBePA7tq+PQfh+x0JmGvAmdH1jEJFvPyDuikH+rQKZxaM8bh/ZFcWi/izwsTkMeqHsg7j55JuaUGzhe8jniarVMrTcYh/aciHXIbcCvcWjPapRK2S57G+eS3wSsH0vsrkz5NBCJt5KP9/KukTv7Ox1RxuSPTTNuCKVkWhyxeniX0sG+S7n49ShxrBauQtrmxji0lzdlX4G8KQex7ALA8ZK/OF5Sl9VaGTJZs/sYADwHxFn9m7odhbhitcViDFpbOfwXeVu/aBvzy7O+yWtY4fitiEKvX+H4w5miKicLVO6rKyFjqEWQfeNm9X6JMmphC2ByQVFV5FbE6uPg3LFDgQccL/mokPZZJ7cLpeMl3yP13hfAjJXlgeGFtv0JeIaW4wtgUg2KKhClSM84tIfFob1tZs1TJ61i1NQ5/xSvrXUuWcr83VbI4h5aK4PraYt6KXX/tcznGc9kiipDsU9PNLJfHYf2PiWs6MpR79gaWc7Vi5wSyMj6OdJnv8ulyVzKZsgXh/YKcWjfEof2xzTP2QchY7ItbExr9+0UuAVxP523kL44d71K7b9rdxeU8TXXZxza68ShfX8c2p+Zc9MQN7z8ffcFPswrd4wSs/j7nvFMpqgyZPVd3BX2TXJtYOTZPQ7t52JxY/wFcTn8A6XbodTzRf7FSz1sDNzv5FzGTZ+5l7bNuzUzW88VsRfuVOMtRVEURVGUTqcrlFU7Aesib8rHAEfEob1fdtK8eb0XeTN7LrCpSZ+5jMxhPhc3n3kLoVLfQd4cH4YoTDY3+R1ZyC/jqxLXTy2RrhILIO4rp1NQJiHuGj1zbhJ3IA/L+5jvWyCKj6IL4HZG7uzvWsTia93C3yeF64hld76HkQXulmbxn+drWlo7ZfSktMVVRRwJ3L43sA7iQvEJLXeT+7TePGsgW5BkeS+CLA6K9Z8t5hdsYznF/pEpFOvpH+XI2qBF/RhruS9p3UZtkaVkGYbJJcqohQWRndfK4njJz8g4PNAoXDZCFIZXlUheagx/hsRZg2YFx/W0bt9tad22NfU3x0vGIVYYSyNKlylxaI8xFom1UqqseuafIrXOJdl8+HnhnlLgi8KxetqiXlrcfx3zeUaLPp1T2M9hvn+LxDT6BLGW+SAO7Vfj0N6lilz1jq1KfaY4JyZljs2Q21j8Poy47J2MuDuuC9yAKH3awgJl5JyMzPVFq9hS80Wt81axnJrq0ygTHzHf/wpsgNz3yELZi1N+3JeiXH2XOj6jnDi0t0N2xX0Dscxez8gzhdJ1Ue75Ysliwhqo1F5tsWBWFOU3xmqrrcZqq63WaDEURVG6LV3hBviq03KHuFeAi+PQvsPxkh8Ri4k+wL6Ol8x4a2seMvNkD32L0hwoPPs+g1gCqu6ABJ29PHd8jQ66n1J8g1gP/QsouZNa5vriSODxuxDlzpmI0uo9x0ueKqRv4Q4Rh/a2iGvZC5UEMW/XM2uijRwvKaVUeA2JW1VkVdq4K57jJXfEoX034u6RGHe4K5G35x9UvrpNbA184HjJh+b7l8jC/Zgy6d/qBBnaS7aYXAxpE2CGtd2CtIzr1hFlFFkMiSdTL18Aq9eQ7krEqm8HRGk9idaWEFDaSm1RmhViWT2cgii8ixTjpdW8a5njJbcjMer+AGyCBGYfGYf2Uk5tu5W2KKsD5p9vqGEuiUM7r6TNl28hbpBFam2LeinWda3zec0YK7ldzLjog/SDEXFor+nkgusXqHdsdfROd30RS9iNnJbB4tvzm/sV5cfxdEq/eGkrxfqotT4HIEH1d89b7cWyOUqeTyn9G9QRFqt5BgLvOGbjAiPLbJRX0i9K7v5y8lRUzpehUnt1ZFspijKTsu666zZaBEVRlG5Nl+4GaGJ0nIC4qhyBxGbJHmJnuDeZh8m9C5c/hyzidkd2Q8oYWEg3O2KZMK1wvKk9slfCad75bk3gpRoWuTcB+8ShvSWyeLy4I+QwC4IHEPe7TfLuIgXuBYI4tJczVlHZrkYb0hwkum6MVccbJr8lkOC6HXJveeLQPhboTcs4RSORN/kfGFfORpIFlq/GsybtQFoGAd8DGZvjOkCWtxDrgIGIZRIAcWhvgCymLylzXSVGAwPj0N7O8ZL7yiUyCsvRyG57vYGzy4yN9ePQXjpTPMahPQ8SDDlzY3oLUa6s5jTHq+tQHC/5AQkCvRwSC2dBxPpiKhJvr1bqmX9a9ZM65pKPzN9utIxBtCMl5vU62qKqjFWodT6vG2PF82wc2qcjAd1XofRGEdA1Y6sSpeqhJzLf58ksyWqp43GAF4d2L8dLJpk8sw07/lPCgrYjqbU+S933ishvS97l9BnAjUN7/VzMqlmQ3/eOZC5au8Xui4zRUuyOBKXPGIg8d7Rl975xwDZxaM+TtY2Z27ZD4pcpivI7Z9o0mSpnm222BkuiKIrSPelSZRWA4yX3xqE9Hgm2+09EufE+cF4c2inykHtsieveikP7ZiTY6SxIvJnNEQubfLpv49B+FjjeWB98gQSRbosZfz0cBzyOBJ29HnlzvBCy3brleEleCTQGcWu5HnmYLsYBaSt3IIuCY4C549BeP3fuXcdLppj/r0Vciu6JQ/tvyFv0c4APkR0XZxDLDk5zI9uvA/SLQ3sh4EfHSx4yaWZDdoYahwSSXg2xfniNgjIkluDZy9YRt2o90y/mQALy7gpshewwdkUu3WXIwumJOLQvQxQccyMBmjdycoG5u4DXgQXi0D4csVz6uWgpB+B4yVdxaF8KnBKH9o/ILoKrIO5TT1JmV7B6cLwkjSXQ79VxaA9D+tqSiFvWRGoLtlxkGBL/6JY4tC9AFMnzIDvvhU5uS3jEdeseZFzfUCa/z5BgxoOQBfFJSNudY+5hehzaRyL91UbiynyBWD1sgCgoL633JuLQPtvk8RgyHpdCdjickBsrryOuy3sg8d++d7ykrJVenfPP68BGxmpyMhLIfBI1zCXGuuos4No4tK9D3F2XQ5TN35KLE5ejlraoVcZy1DSf14op9xBkN7sY6RdHIzvAPVPuuq4YW1V4GpkL/xWH9plG7r8h/WG+XLrPEKukgXFov4K4iMdOy01AMi5DlJ4Pmzy/Q176rIgodzuNOupzDKIcujEO7UsQd7+zkA0D8mEHhiJ99c44tE9FrGIPQwLudyQjkQ0JLkM2MFkH6T/flEm/dRzaFyMK+T8h1s83lgquXgPnIO6wj8ShfRHyO3sS8pt/dpbIjPH9HS+ZNXfsDOAMYPksYL8Jn3ADErC9s5WtiqJ0AcOHSzjRpqamxgqiKIrSTemKmFWl+BvivnKYI7td7YgshG5E3F8ep6X1VMahiILHR2LMrIzEoSiyJ7LF+L+QXaMmU949rENwvOQlJBbGl4gSZTRiobEGcj/5tL8iO10tiQSOLWcBVS8DkNglVyALufzfjMWMcb/cFHgbsfIajiwENzUWJnmuRBbCZ5jvg8z3/Hbe05HA0VcjwYg95KF6S9O+eSzqU5I+aeR/ADgVWWQMcLykxW58JrbNBsgi6iTExekGxJLhsTrK6wiuQwLzno+8kS9rfYRsEX8cooC7H1nA3QhsU6PlS1UcL7kGsSZYA1FW/B2Jp9OvRHvXkt80JNbalYgi4UFEEbIQrd1bHkACod/jeMnkMlmOQ5Sa5yPxZeYAtsovEB0veRCJSTY3Ur+jzH0sRgWlRRWeA3ohSoCHERfAcbRc+F+EWJJchyjIr6Y6tc4/pyBK1REm70FQ+1zieMl1iCJoc6RdD0QsmKYjCqsitbRFTTKWo875vBYmAv9DYng9hChXfwE2ryE4fKePrXIYZedOyHx3O3AB0oeGFdL9igRd74koesYjljel8vwE+DPyEuBKk+8CyP2M7JQbaUnV+nS85DWkDy6LWPCeaNIVfwMTpN9OQOaOochv0LkdLPO1iGJ+D2Qe3gap31LjA8Qtf0Xk+eJ4c/0RbSnYkd0rN0GUikOR39ofkHn35VxSi9aWXrOYY8UdgYvHFEVRFEVRfrP0mD69o0N1KIqiCHFob44oWzZzvOSRaumV9hGH9rqIgnQ/x0tuKpzTtlCUEsSh3YQoQlfowJdHDaNPnz7T7ztpfgAW361UmL/qfHrbZu26XlGU6gwZMgRQyypFUboX/Qc/DcBjR2zQJeX16NHjxenTp/cpda7L3QAVRfntE4f28ohb2mVI7CVVjnQwcWg7yC6DTyDWG6sg1ocx4hKcpdO2UBRFURRFURRlpqJRboCKovy2ydy2pgL7NViW3yr/Q3ZlvBaxmBqEuFtt4njJT7l02haKoiiKoiiKosxUqGWVoigdjtkqvqnBYvymMXGnBtSQrgltC0Upi+MlQ5D4coqiKF1G7969Gy2CoihKt0aVVYqiKIqiKIqiKF2IKqsURVEqo26AiqIoiqIoiqIoXchPP/3ETz/9VD2hoijK7xRVVimKoiiKoiiKonQhI0aMYMSIEY0WQ1EUpduiyipFURRFURRFURRFURSl26AxqxRFURRFUX5jTJ3yMp/etlmbrk2mvIy98JodLJGiKIqiKErtqGWVoiiKoiiKMgN74TWZe+WBjRZDURRFUZTfMWpZpSiKoiiK8htj9oXXZPHdxjRaDEVRFEVRlDahyipFURRFURRFUZQupE+fPo0WQVEUpVujyipFURRFURRFUZQuZPXVV2+0CIqiKN0ajVmlKIqiKIqiKIrShXz77bd8++23jRZDURSl26LKKkVRFEVRFEVRlC7krrvu4q677mq0GIqiKN0WVVYpiqIoiqIoiqIoiqIo3QZVVimKoiiKoiiKoiiKoijdBlVWKYqiKIqiKIqiKIqiKN0GVVYpiqIoiqIoiqIoiqIo3YZZGy2AoiiKoiiKoijK74m+ffs2WgRFUZRuTcOUVXFoNwFR7tA04EPg38DZjpf83EHl9AJiwHW8ZEhH5FmijEHAmY6X9Mgdmw6c5XjJoM4osy3EoX0wsBOwJjA/Ui9DgcsdL0kKaZcGLgM2B3oAYwDP8ZIPcmnmAc4E+gBrA/MA/R0vGVuibAe4GNgMmA14HjjB8ZIXOvQmW5Y5Fuhnvv4KfAe8DzwBXOV4yWudVG4T0rcdx0smdUYZnUUc2psAj1GmHTsg/7EAjpds0oZru3RMxaHdFwiB1YG5gLUcL5nQFWW3FVO/szpe8udGy1Kk3vbL9cXNHS8ZU2dZY6Ft/ay7EIe2B3zgeMmdjZZFmfE7/7jjJY/WmP4A4BRgWeAnx0vm7zzpFEWZGVlppZUaLYKiKEq3pju4Ae4G9AW2AUYhD3cXN1SijqEvcF2jhShwBjAZOAbYFlEMngMMzyeKQ3su4FFgZWB/YF9gBeCxOLTnziVdEDgA+AV4uFyhcWgvCDyJLPoPBQaaU4/Fob1Ku++qMq8gbbEhsAdwI9AfmBCH9hGdVOYDpsxPOyn/zuQlRPaXOin/I8zfzMD1iEJ/O6RO3m6sODM9XTknzkz9rBwesHOjhVBmcCawaS0J49BeArgGeNpcs1knyqUoykzKF198wRdffNFoMRRFUbot3cENcILjJe+Y/x+OQ3sF4MA4tI9xvOTXRgrWHhwvebYryzNvfZscL+lVIdnajpdMyX1/LA7tHsBZcWgv53jJe+b4wcBywEpZ28Sh/QowEVE2XWrSve94yQLm/GaUX1gdDiwK9Mvl9yjwHnAWsHs991on3xfaYnQc2v8AbgH+EYf2eMdLxndkgaaOp1RN2I2IQ9sCejhe8h3QaX3X8ZLXOyvvjiQO7VmAlYDzarWkqJJfD2C2ogXjzEgc2rM7XjK13vRdOSfOLP1M+c2yAmABQx0vebJcot/SvKAoSv3cf//9ADQ1NTVWEEVRlG5Kd1BWFXkJeQu5EPA5QBzaWyBvmdcC5kOUHBEQOl6SZhcai6AAsaCZHbEO+nuxgDi01wVOAtZHrIM+AO4AznG85H+5dGOROhpk8lnZlP03x0vurnQTpVxe4tBeE7Fk2giYw9zryY6XPGHOnwicCyzueMmXhfxeB952vGTHSuVWoqCoysgUNUsi9wawPfBsTomI4yVxHNpPATtglFWOl0yvsej1gYmF/H6MQ/sJYNs4tGd1vOQXAGO5dTpicbcU8DXwFHCE4yWf1VheRRwvmWasqrYDjkYsxzDlV2wjk2Zd4HzE9XFOxFptpOMlR5jzTRTcAE3fvARRzNmIW+XF5t5muKjGoT0E6f/bAVcA6wAfA5c4XnJVpfvKuU3taq7fAVkw3Qccne9Tpn+eD3yPKCCXAfrEoT0fBTfAesaBqb9BwMaI69wHwBDHSy7I5TXDPasemcvcc7vbq0SeTTS7KJ8eh/bpiGK2lzm/D3ACosz6AXgIONHxkk9zeUxCrAkfBU4Elkfa/q4yZc4KHI9YMi6HtMuLyLz3DVKPJzhecnnhukEm/yUdL/k6d3wH4Dxk0RwDZzheMqINdTcE6Y+7If13LcRi5Jgy9zEW6SsXAWcDqwInA5cV58Q4tFc06TYE5kXm++eAPbP5oET+yyEWuJ8D2+bvuYQcpfrZTsBW5n56AEMAH+kbl5n7mwQc53jJqBL1sDtwObAG0o8ucbzkH7l0CyNzeH9k/voScTs+wfGSjwsylh0rpv8sCywbh/be5pKhjpc0lbnfmsqtVudxaP8BuAAZh4sife+/wFGOl7xp8pgV6f/7A44p6xbgtMx936Q5E9gL+W35AXgT6V9PmjR7mXxWAFJz//90vOTqQp3viMyFa5k0xzle8kAc2sch8/cCSNselP+Nq1HOXsj4OMzIeTAyRzwBHO54yUcmXfZbd1oc2qeZ/0u6tBq59zdfH4lDG0zb1TsvKIqiKIqi/J7pDm6ARXoB3yIPlhnLAY8gLmfbIHGWBiGLsTxXAwchypSdgbeAm0uUsQwwAXlAHYAsPg6gZQytjOXN+SzPT4Hb49D+Yz03FYf22ohLwALIA/Eu5h7HxKG9jkk2DFmo71G4dh1gFeCmesqskX5IPKe8i9NqwKsl0r6GLD7rJQVKvTmeiiwMlgeIQ9tG3AmPRhaR2wJHAV8BPbOL4tCebhYEbcbxks+BF5BFW5Zv1TYyi7lR5p6agK2RRXk1xe81SB8LaO6bw8uknRfpt8OQReN44Mo4tPvXeHshMB3YEzgNUT7eXiJdEzKefPP5SYU8q46DOLT/BDxj0h5r8rwUWTx3lMwz6MT2egDIYj5dj7iv7WTyOwQZh28g9XAysCUwzpSVpz9wHGI9OABxSS3Hrch89iCyOD8YeB1RXE8G7kaUivn7t4ADgREFpc0fkcX9JUbGd4Bb8/2nxvkoYz4j3y2IoqfUnJpnRVP+P5C6eaRMuvsRBcHhJt3JyJxQ8ncpDu21jMxvAJuVU1RVIQR+RObYfyLKwBBxD74Bqa+vgDvj0F6ocO28iOv0UKSNxgJXGOVmxgLAz4g7+wCalTFPxaE9R+5eqo2VnRBl2Cik//VFFIvlqKlcqtf5ZYjy5CwkXuFhyG/l/Lk8hgF/Q/rBNohy60Bazmcnmfu6wpTjIv0gs8T9s8lnHFKXuwHXFsoBqfMbEffRnRDl2h1xaF+CjK8jkTbsD/yrcG0tcmacgoybAxBFbN9CuiwK8hCa26OcS+s5yG8YRr5i29UzLyiKoiiKovxu6Q6WVZZ5AzoP8jC6CxLIe4bFVN6ixJjNP4FYp/hxaJ/qeMmvcWivhLzFPc3xkgtN8tFmAXlYvkDHS+4o5PcUEnz7xji0jyxYcywEbOx4yUST/iVkob47Yq1RKxcjb4U3zUz+49AehSiFTgd2dLzkE+Mety8wOHftvoiF0f05uS3EMiBjFnO8RZuWs1Awaf8PeTC/oWC1tIApr0gLpVEdvAVsHof2glndGjerP+XKA9gHebDfwfGSe3PXF5UWqflrLx8gFhUZVdsIsSrqiVjS5BcZQ8oVkuubJztekln6PWysrf5a4pJ5EEuyx8z1jwNbIIqcx2q4r9ccL3HN/yPj0P4KGBaH9l8cL8krDnoAWxSsCcvFEKtlHASIwmN9x0t+MsdqdaGrVeY8ndJejpdMiUM76/8fZe5rZsydA4x1vCSLu0Yc2m8ic9IByOI8oyewjlE2lSUO7U2Ree8Yx0vy19+d+38w4ra7Uc7yaRtEuVG0uFsU6JuTeySiaD4bsaKC2uou4w/APo6X3FPpPnIshPSrCeUSGEXQCrQe6yUVYXFo/wWxPrkNOCT/+1Anjzpecpz5/+E4tLdBFOIb5Sx+PgVepvnFSMY8puxbzfeRcWgvibhRD3W8ZLrjJW+RszozfeYppK63otmCpuJYcbzkP3FoTwW+qMV9spZya6zzvsBwx0uuzx2bYfUTh/ZGiKJvf8dLbjSHx+TGa2/T7n2B0QVLwPty/68PfON4iZc7NrrErc0DHOZ4yeOm/E+QttkWWDXrB3Forw78NQ5ty/GStA45M953vGSv3H0uDFwch/YSjpd84njJs8ZC6uNq7eF4ybtxaL9hvr5eIn1N80J7mPb12yRTZsdeeM3OKkJRFEVRFKXT6Q7KqjcL3wc7XvLP/IE4tBdHLKkGAEvQUu5FkDfQ6yEKmxauLohFQAtlVRza8yLWG7sCSyO702WsQEurronZAh3EIicO7c8R66yaiEN7TsSC6Xzg14JCaQywd+77TcDQOLRXcLxkokk7ELGeyMeJeRdxEykyrVB2yR3pTJ3eY/I5rngesXIp0qPEsVq4CnnTfGMc2kcDPyH175jzWWyyLYDJhYVUKxwv6ah+2wNzn3W00UTENebqOLT/BYxzvOTDKuWsZ8q6rXD8dkorq37KFFUAjpdMjUN7IrX3ueIYuA2xTuhLSyuXkXlFVRUqjgOjeNsQuDi3+K6HWmXGlNeZ7VWOlZD55rT8QcdLnoxD+30jT17Z9GyNC9ItkH54bbkEjpeMjcUV+FBEMYb5/5USi+EP88fM4v024ESjJJ6d2ucjkA0U7qd2JlVSVBm+RFxJL4xDe1FEATixTNrdEKu4Sx0vOaUOOUrxUOH7m8CKTsu4Qtlv0tKFtCniLp7nVsTCZkkgcxk7HPnNWR7Ib0ixkjnf3rFSkmrlUludjwea4tD+AlEe/aegGByAWMneUeg3maJpY8QSazxwShza5yF1/rzTMi7TeKBnHNrDkDp80vGSb0rc1o+ZosqQtc2YglxvIs8FiyPtUKucGQ8Uyv2v+VyGyhanbaHWeaFd2AuvydwrD6yeUFEURVEUpZvSHdwAdwLWRdxzxgBHxKG9X3bSLK7uRd6knovsrLMuzS6AmYvD4uazGNeoVJyjCHmovwJxdVgXMdfP55fxVYnrp5ZIV4kFEPe+0xFlUv7vKOShPWuLOxA3lX3M9y0QS4miC+B2Ru7s71rE0mXdwl+rB+1Ydud7GFGgbOl4yfeFJF/TbO2UpyelLa4q4kjg9r2R+EvvGJn6Ii4n0Lxr3oJIfKauYulc2TW1keMl3yJuHJ8g1i4fxKH9ahzau1QoJ+ubnxeOl4vBVaqO6+lzLfI1i8SvkQV1nnp2K6w2Dnoi88lHdeSZp1aZMzqzvcqRjYlS9TaZ1mOm1vpdEPiqBsXhlcCucWgvGIf2ssiCvFQcs1L96jPEGnVh6puPAD6v05Kp6n07Eu9uc8QV9wLg7Ti03zMKlyK7AP+jtJt2vRTHVoIoM/OyZUqV4nj72vGSaYVjWV0vCRCH9l+RfjYGcSn8E2JFlM+vvWOlFbWUW2Od/xVxpz8AUSh9Hof2ZUbBBqKstZEYVPl+k81tC5rP85GYVdsjytUv49COMtdKx0vGIUrIpRHLrSlxaI8x1r55vsl/ybVNqXacca91yJlRnN+yF0P1/M7XSqfvEjtbzxVZfLcxzLvGQZ1dlKIo7WDjjTdm4403brQYiqIo3ZbuYFn1qtNyh7hXEPP7Oxwv+RF5S9wH2NfxkmHZRXFob1fIJ3sAXJTmQOHZ9xmY+B07AIPyLgpxaK/RQfdTim8Q66F/IdYirXDMzoeOBB6/C1HunIkord5zvOSpQvr/5r/Hob0tkDhe8kIlQYxV2SjkYX0jpxD01/AaEreqyKpIHJ26cbzkjji070bi2STGVeJKxArkA5PsC2D1tuRfL3FoL4L0q8yl5xtqb6MJwC7mjX0fJN7JiDi013S8pFSsr6xvLoIE881YtETajqDY521kgVxs61oD5NfC10j9lVMuVaNWmTO+ofPaqxzZgnaxEucWQ5QAeWqt3y+ABeLQnrOKwupGRMnQhNTN/ygdf6dUv1oUWdBPQeLE1VR3hnr7SU3pjRJ7P+OKvSaiKBsch/Ykx0vyFlCHIHHVxsahvaljAn03gJ5xaM9WUFhldZ3104HAI46XHJ8liEPboSXtHSulqKXcqnXueMkPyPg4xShEdwUuRPrOSYh11s80u5MW+cSUMw0J5H5RHNqLIS+bLkUCye9h0tyOxL37A7CJST8yDu2lnPbvBFyTnA2iI+ddRVFmYpZbbrlGi6AoitKt6Q7KqhkYd6cTEPe0I5C4Ktkb3RkLhDi0Z6O1q8pzyAJgd+ThOqNoBz87YlVQfEPe1B7ZK+E073y3JvBSDQ/iNwH7xKG9JaJYu7gj5DBvxx9A3O82cXK78xW4Fwji0F7OLG6yXZM2RALytgljnfGGyW8JZNGSv7fRwMA4tLdzvOS+Ell0CKb/DEb6/xVGtnrbKIsH9mwsu8VtjwTBL6X8eA5ZoOxGy90pd2vPfVRgdyRYdL6cWZCAzp2C4yU/xaH9JNJvz67DvTCjLpk7ub3K8RZiSTMQCbwOQBzaGyAuuZfUkVee0ci4OggJSl4Sx0u+i0N7OOL+9wfgZsdLviuRdOk4tNd3Wsba2g1xxfoVqLvuOhNj8TMhlt3dDkQU1nll1XdIkO6HkLhdmzpe8kbrnDodC7HyujV3bCASFypTVs2FyJvHzX+pY6xkG1DUQtVyCzJUq3McL3kfuCSW3QizlwgjEaXVfE75WHLFsiYD18WhvTUlXkYYBdn9sez0eDnyIqXUzrX1ULecNZBQe3soiqJUZfJk8QhebLFS78AURVGUbqWsAnC85N44tMcjwdP/iSg33gfOi0M7RZRMx5a47q04tG8GzjYuLOMRl4etC+m+jUP7WeD4WALpfoG4PHTkW+5SHAc8DoyKQ/t6xNpmISTAt+V4SV4JNAZ583s9sggZRsdwB6JwOgaYOw7t9XPn3nWat/2+Fnnjfk8c2n9DFC3nAB8iLiIziEN7KyQ+SmaZ1s+4evyYWUcY5dDfkZ2fvkOstk5BLLjyC/xhyM5kt8ShfQGi5JkHWaiGTvPW6b8gW4EfWMM9z5O7z3mMnC4Sx+UIx0tezKWt2kbGgu0QJPh1bO79aOB7yitWsr55jumbLyLurJl1YEcrC1aLQztCFtUrIi6z4zpw0VYOH2njZ2LZresjZCfP3o6XlIrNlactMndKe5XDxH46A4l/NQzpr0saWSfSRjc1x0sei0P7DuDSOLSXRgJtz4bE1XnA8ZKxueSDad4VsJQLIIhC7d9xaJ+JLPoPR+o07+5Vz3zU4Rh3r8uR3fXeQRRBTUh8rFZB+R0v+T4O7QGIsv2xWALvv9aZMpbge+DvZn6biGx4sBnQZJQ/YJQkcWifCjyPjPNdS+RVy1h5HdjI9OHJSLD1SWVkq1puLXUeh/YzyMuK/yIudP0QpeZQmBE77RbEIupSU9avyC6+WwMnOV7ydhza9yCB0F9CLMnWQtxWrzblnI1YpT2G/NYthYzLCbnfoTZTq5x1Zvs6sE0sGxZ8DXzieEm7LbRiCXtwA/AX4x5JHNr9MDsgOyZAvLF0exc42/GSs9tbrqIojWfkyJEANDU1NVYQRVGUbkp3iFlVir8hLlOHmRgVOyIP6zciriuP09J6KuNQRMHjI3EwVkZ2YSuyJ6Iw+BeyK9hkcjspdQaOl7yExJD6ErHmGY0sHNZA7ief9ldkh6YlgWcqWEDVywAkTtUVyEI9/7dNrvwfkcXO24iV13Bkob+peQue50okGPYZ5vsg8/3KXJrpSOD6q5G39x7ycL5lPuiucR3Zwlx7CPAgsjhfiJYxRSzzVwv/Z+7vaSSg+X7IlvNrOV7SQvFWYxtNRNyvTjf3EiGLvc0dL6kUg+YQc88nIn1zNZrjpH1b473UyjFIO/8biR1zP6UXzB2K4yXjEWXoh4iF0IPACdQWm6dumTu5vcqVeQ2yO+caiAXo35H4b/1KjI16GIiMnR0RZcENSB9pEd/GkR0N3wZeMPdfineQ2EM+cCcy9vZ0Wgbtr3k+6iQmIxZJxyH3ewuyeca2BQXyDEz9bo0oDR6NZQe4ruQ7pJ32R9q+P7KDY37HwLORee5YZJz/H6Jsb0GNY+UUxJpvBPLyZVAF2Wopt5Y6fxyxchyOKAZ3BY51Wu7qt4+RZVdTD7cjLzcm0hzDK9vB9HpEkXY4MlZONOefQxRHlyHj5yJEeTfjd6gDqEXOejgKiSd5H9Ieh3SIlPIcVtzdt4c5NkuVY4qiKIqiKL9ZekyfruETFKURGJfXi4Beubhd7clvE8RSYXPHS8a0N7+uYGaUuZHEob0isvPZwY6XXF8tvdIxxKE9BNjM8ZKlGi2LolSjT58+0194oWL4SkVRugFDhgwB1LJKUZTuRf/BTwPw2BEbdEl5PXr0eHH69Ol9Sp3rdm6AivJbxLjyrI5sl/4rEvjXB0Z0hKJK+W0Th/ZSwB+BsxBrq5sbK5GiKIqiKIqiKErnoebkitI1fI+4eN2KuNfsi7hfNTVOJGUm4iAkrtCiwF5tCGCvKIqiKIqiKIoy06BugIqiKIqiKL8R1A1QUWYOPvzwQwCWXnrpBkuiKIrSjLoBKoqiKIqiKIqi/E5RJZWiKEpl1A1QURRFURRFURSlC/nwww9nWFcpiqIorVFllaIoiqIoiqIoShfyyCOP8MgjjzRaDEVRlG6LKqsURVEURVEURVEURVGUboMqqxRFURRFURRFURRFUZRugwZYVxRFURRF+Y3w5ZdfMmTIkBbHVlttNdZdd12mTZvG8OHDW13Tu3dvevfuzU8//cSIESNane/Tpw+rr7463377LXfddVer83379mWllVbiiy++4P777291fuONN2a55ZZj8uTJjBw5stX5v/zlLyy99NJ8+OGHJd2iBgwYwGKLLcZ7773HcsstV+n2FUVRFEX5jaCWVYqiKIqiKIqiKIqiKEq3ocf06dMbLYOiKIqiKIrSAfTp02f6Cy+80GgxFEWpwuTJkwFYbLHFGiyJoihKM/0HPw3AY0ds0CXl9ejR48Xp06f3KXVO3QAVRVEURVEURVG6EFVSKYqiVEbdABVFURRFURRFUbqQ9957j/fee6/RYiiKonRb1LJKURRFURRFURSlC3n88ccBdNMARVGUMqhllaIoiqIoiqIoiqIoitJtUGWVoiiKoiiKoiiKoiiK0m1QZZWiKIqiKIqiKIqiKIrSbVBllaIoiqIoiqIoiqIoitJt0ADriqIoiqIoiqIoXci2227baBEURVG6NaqsUhRFURRFURRF6UIWWmihRougKIrSrWm4ssqK/B2B44CVgXmAz4H/AFelbjCygaK1wor8vwAu0BdYAvgEGA2cmbrB54W0cwDnAPsA8wMTgJNSN3i8kO44oD/QB1gMOCt1g0Elyp4LOA/YHVgQmAhcmLrB8HbcTz/gFOD/gAWAL4BXgZvak29nYkX+EGCT1A16me+9gCbgxtQN3muYYG3AivwmIAKc1A0mdXJZYwFSN9ikM8vpLKzIPwDpq8sCP6VuMH8nldMb2BG4InWDrwrnpgPnpW7wt84o25QxFupvJyvy5wc84N7UDV7qaLk6kkp13M58VwYGI3PpPMBOqRvc3VH5dwRmvooBN3WDIebYEHJzWgeXNwj5fepRQ7rHUzd4tHB8CLBZ6gZLVbm+iS6ay2YWumK+UBRl5uatt94CYKWVVmqwJIqiKN2ThsassiL/aOAuRPFyILANcK45vWmj5KrAYYii6FxgAHABsD3wrBX5fyikvR44GDgD2Bb4FBhlFmp5DgYWAe6uUvadwAHAhabMp4BhVuTv25YbMUrCx4CfgaPM/ZyGKKy2bkueXcQ5wE65772AM4HlGiLNzMMR5m+mw4r8JYBrgKeReWGzTiyuN9KfFujEMirR1naaH5F77Q6VpnPoTefU8aXIPLA78kJhXAfn31kU57RGcCbt+819AKnzTztGHEVRlN8+zzzzDM8880yjxVAURem2NNqyygfuTt3gwNyxR4FrrcjvUkWaFfmbIMqbSm+Gj0jdYEru+zgr8t9GFkW7AzeYvNYE9gIOSN0gMsfGAa8BZyPKpozVUjf41Yr8WRFlWCnZ/gxsSe5tPDDaivylgIusyL85dYO0zls+DrFg2yl1g+m540O7uu5rwYr82VM3mJq6wbuNlmVmIldvrzdalnawAmABQ1M3eLK9mVmRPxvwS6Hfdwu6UztZkd8DmC11g6TRstTIKoh1ULeyyK3Gb2FOM7+LU6omVBRFURRFUZQaabSyagFgcqkTqRv8mv1fzpWhjEtYjCh9lkSsluYEngAOT93go/YIW1BUZYw3n0vmjm0PTAP+nbv2FyvybwVOzhQI5vivVGd98/lQ4fhIxBptfcTSqh4WAD4stWAvymRF/kLI2//tgYWQOr40dYNrCukcRBm3OdAT+Bi4L3WDY8z5sSb/TQrXTQLGpm7QZL43IS4l/YC/mvwmAb3zbZ5TMAI8bEV+lmV/RBG6ROoGLSxNjIzvIv3h6lIVY1w4LzDl9gJ+QNr5hNQN3syly+Tsa+TczqS9HTgxdYOfc2mXA/5hZPsRuBmoSTFh6m1W4GLEFXQFpA3OSN1gRC7dIMRCYg3gEmBD4BFgh3zdW5G/OPAhcGzqBv8olHUSYjm4ROoGU6zI3wJxL1sLmA94z9xzWFSQWpF/MHA4ojT4GXgFcd17EfgIGJa6wbGFa7I6XCVft7nzQ4D9zddHTBsPTd2gySidzkRcbTO33GGIK+00c30vU1dHIm25D+JuuyDwdRlZACbm+lMLBbaxCD0WGQsvIUrs1wp57QyciLjYJsDDwPGpG3xQvMfCdWOheYzk+vgOwBbAQGA6MAo4KnWDb3L3CKLov9b8n3c1qyqPGYdPIi8MTgSWB3a3In8+au/ncyFtsjsyJ34MXAdcYJTyTdRQx4U6qdjOhXmgV2ZtWs71LSfDhkjf3gr4CenTF1iRn1nNroiM0cNSN3ixkEct9TkXEAB7ALObev17CXmGUHADtCJ/buB0YDdgKaSvPoX0tc+syF8YGaf9zfkvkd+5E1I3+LjUfZfDuKsBnGZF/mnm/xbu6FbkrwVcAayDtOklqRtclTvfRMENMNef7kfabxngDcArKp2tyD8GGVOLIfOGh8yR+d+FxYCLkHl5QeArZG45oOiGX8h7VuAEZB5xkLq6BTgt67v1PDtYkT8QOASZZ+dELMPD1A2Glii+h6nTw43M44GjUzeYUE5eRVEURVEURWi0Bc3zwP5W5J9gRf6KHZjvKcAfEbe5Y5BFVmfFYOpnPt/IHVsNiFM3+KmQ9jXANrLVQ6YUKFo4TDWfq2cHrMgfZEX+dPPwXYnngS2syD/Xivz/M1YUrbAif15kkbQNMMh83gdcaUX+X3PpHJPnxsjCZCvgLGRB31aGIwuIXYGTS5x/CVFCAByNtHNfc3wwsJYV+X8qXHMIzcqicsyOxLw5F7nfw4E5EHfPxUqkvwlRgO0MXGlkOiU7aUW+jSxm1zLnmpBFUz2xTP6ILBYvMeW8A9xqRX7/EmnvQaz9tgcuK55M3eBTYAxQyoV0H2BkTjG7HKLwOgCpi6FIPzgvf5EV+QHiqvcSoqjYB3gcWMYoZiNkrM9RKO9QYFwpRZXhHKRtQequrzmGkeVk4EbE1TYCTjLHi5yGKB8OQVyufi6R5gGa3ZB3o7k/5V2b9kHq4Rgkft0ywD1mQQyAFfmHAXcgio5dzT2ujlhizlPmPqtxOaKk2gtRCO9ijmHk29n8f0FO7gfaIE9/xOryLMQ1+JXcuWr9fFZEiXaQkW0rRFF1OqJohdrquEi1dn7J5DEFeDCXZzWGAv9F+sPdwPlW5F9kZL0IUTLNDdxtxnB2n7XW59WmLi5F6uwtKs87Wf7ZfHE0MMTc81GIcqanSbYA0odPQdrpBESJ/VSJMVaNrK6G0Fx31+XOz2vkHoYoTccj83+puafIRsDxSB/YA7GQvN/EWAPAivyDgBCZk3YwctyMuLbmucnIdgKisDoaUYLPVUWGYchcezMydi9Awg6Ueiao5dlhOURRuzcSe+0+4DrTL4rsh7jVH4XM+4siSvdOdTN++9spbPrQ4Hb/XfPWs50ppqIoiqIoSkUabVl1GPLQ93fg71bkf4k8pEepG4xuR77vp26wV/bFvIW+2Ir8JVI3+MQcm4WWyjor+8wvPIG0nLuQWZiEiKLq7typBShYbRi+yp2vh7fM5/q0tK7KFhn5/H5FlFvVXJxORh7KTzN/31mR/xhwc95aB3lgXxZYI3WDiebYGLPYONOK/CtTN/gFWdzOCayZ1bGhlOKgVm5P3eDEcidTN/jOivzMOumN1A1mPFlbkT8SsQI6FFGiZRYaLjA8dYPvK+T7LbLIzPKykEX4Z8CetFYA3Zy6wZnm/zFW5K9n0mXH9kcWOH0zGa3IfwhZKNfKooXrR9LsVrpRIe0VqRtcTmVuQmKerZS6wVsmz97IojtTBlGwnuiBWBrYgG9F/qnGWuaPiFXEZakbHJcr44Hc/1cii9bdTNlYkf9/SJ/es5yQqRu8a0V+pgh+PXf/q5vr8hYgo63IT4FzrMi/MHWDvKLlM1q7vBbLmmJFfuaSNSF1g3dKJJsGbJuz3AK4DfgT8LQlsesuQuawA7KLrMh/DngbWSSH5WSowOOpG2TK4dFW5K8EHGRFflPqBlOtyP+POfdeYRzUK09PYJ3UDSbn0mb9q1o/3xP4M9Avbd5IIrOGO9OK/ItSN/i8hjqeQR3t/KwV+QkwJX//VbgpdYNzTDljEaXVccCKqRvE5vgsiPK3L6KMqqk+TfvshVjvXJiT+w+UcffOsY8pb4fUDe7NHb89+8eM2WNy5VvIS4UPECXhXTXWAakbPGva6OMydTcPYtH1mCnrccTKb0+aLdrKMS/QO3WDr821kxFl19bAzaZ+zwQeSt0gP+dORhSCefoCp6YtN/+4rVLhpu/uAeyfusGN5vAYK/K/Qua/3gUrp6rPDqkbnJ87PwswFlgcealxFS2ZE9gidYMfTfrnEEusYxEFXrdlwlfyM37ISutXSakoiqIoitI5NFRZlbrB28a9YEPk4Xd9ZMEw0Ir801M3OLdiBuV5oPA9Uwosg7iQgMSX2p/WFBdPLvKmtwVGoXUL4jKwoVHYZPSgtLKo4o5MFRiNKMSusCJ/P+BN5E19tsif4baXusHZiAKjIsZtYmMr8tdF3syvhwSu3sGK/M1TNzjYJB0APAfEBSVeZkGxKmJ9sQVwf0FR1V5qXnAVMUqUq5FF8nFGAbUjovQp6f6Xx4r83RHlykqI+1tGqS1bSvW3fBDwvojL5YyFoJFvBGKlVAvF61Mr8m8DTrQif5aC62Yt9XYX4sq1L80WXvsC3wIzFsiWuAwOQvrBErScMxZB3Hg3QxS/LdxC86RuEFuRPwpRHt5kDh+KWMPcWYO8RTY2n8MKx4chyrZ+tLQKuruSoqoOHs4UVYb83PI00tbzAsML4+UjZNxuTNuUVaX62OxIfy7pSm2oV55n84qqGmTI9/MBwPuI0i5f1mjEmmp9cn2rRupt53qYofhPxU37HWC+TFFlyCz+ljaftdbnesiYyCv+AW6lurJqC2ByQVHVCivyDzd5LY9YgGV09LZSP2WKKgCjHJ2I9PlqPJMpqgz58QLiwrgUshFJnnuAXwrHxgMnGKX5o8CrNYzpAYhF8h0l+iRIe03IHa/67GBF/grIb+zGiNti9tJrKq15MFNUAaRuMMmK/GepzfKvzaw438I8ulX79tPY9KHBHSSNoijl2GmnRu+toSiK0r1ptBsgqRukqRs8nrrB31I32AyxQPkvomToWeXychS3Q88eIvPuEYOAdXN/2QJi+8Lx+4qZm7epQ5GF2o4FC46s/FLWUz1z52vGKMJ2RdzXnjbXn0ezC06bd2BK3WB86gbnpG6wLbJoeASx2MhcCxdBHsqnFf6yN9oL5j7bFROsBO3dWep6pI9n7m6HAc+nbvCf8peAFfnbIfHG3kCsI9ZD+sIUWvahjFL9bfbc98URy54ipY6Vo9z1NrBw4XjVejMuqncAe1uR38NYZuwJ3JaL4zILolzYFlE2bIrUQ+YCmNVF1geqtf9gYEMr8le3JCbPPoiFSlsCeGfjq3ivkwvnKZOurVSbWxYxn2NoPWbWoLmuOrrcctQrT6V6qtbPF0GsMIvlPG/Ot+Xe623neihavyZljkH97bu4+SyO21rG/IJIXKiyGBfswUaOnRHLvswEpl43wGqUshKeWmM5LfqMcQkmd21WT58X0qXIzrR59kDmoxMRBeXHVuSfYVXeEGQRZI78gZZtlZVX7JMVx5mxjHsYWBOxTt4ImRNvoOVYyCg3by9Z4riiKL8z5ptvPuabb77qCRVFUX6nNNoNsBWpG3xiRf51SMyTFZCFTrZ4tgsL27Yu/DBBYCdl381DKMB/0/K7AWZchTw475q6wSMlzr8G7GRF/lyFuFWrIoufiq4vZeR9HQkw3gt5i/42zXFq6g2uXq6Mb6zIvwL4i5H1VSQY7efkXE4KZC6KX1D9AfxnxCqhSLkFZ7ssYVI3+NJYHx1qrHr6k3Pvq8BA4J0ssC/McCFs68L4UySOWZFF68ijVNpFkf5UDPxfa73dhFgX/hlxV1mcZqsnEIuNPsC+qRvMsGwxyrw82aJySZr7QykeRMbcocDLiHtRWWusKmSLysWQOErkvoP02zxdtfNfVm4TMg8UKet+2knUK0976ulLJMbc7mXOT2pDnvW2c2dTa31myrVFEXdkct+r8QW5OIRlGAg8krrB8dkBS+IGzmxk9bRI/qBRnreId2isgY8EjjRulvsj7udTEDfjUnyJ/O4UXaUz6rUE7osoZDfKB4kvWG3lKTdv1xUEX1GU3yavvvoqAKuvXm3KVxRF+X3SUGWVFflLp27wYYlTK5vP7O35++ZzdSSYLiZm0gZ08eLPivxLEIXH/qkb3F0m2b3IQ/RumJhN5mF2D2B07u1y3aTNOy3NhgRtHZ22YevzGuo+W0SMRHYA+yCtsOMS4laxsxX5i5sA3qV4H9glr3S0In9jRGnRVrK6nLPM+cHAM0jA4O8QN5xqzEVrF5R9aY5rVi/PAK4V+evnYi7NQvlFfSmWLlxvIf3r+bS2HSVL8RhiDbUvUn+TkJhUGVng4hlub6bf7V3IZwziinoI4jpZkpxrZmaRMKYtfdcwznwOpGWw90y2x2kb1fpTNZ5G5qQ/pqV3B+ssysndlfKMRAK//5CWD5gP9dVxZ7VzW6m1Pp9DxsTuwIW54wNrKGM04gq/XeoGrSx7DXMh81ket4a8y5HQ9j7fHj4yf7vRvEskiMt22ecTE7PrVEuCmlda5Y1EgvHPV+bFUr2UmhN7IoHhS7G1Fflz52JW9UIs4C4sk15RlN8RL7zwAqDKKkVRlHI02rLqVUuCet+FvJGfFwm8ehgwIm3eBvwhJJbOtVbkn4mY25+ImPZ3GVbkn4QE4L0B2XY9H3l0SrbwTt1gghX5/0YC7c6G3NvhyA5wexfy7AP0otklc1Ur8nc1/z+YWWZZkX8Kouz5BImfcaT53LCQ3xlI/I/lUzd4n/I8aEX+54i721vIQmVjRNnwDM3WWpchSrYnrMi/zKSdG1FqbZS6QfaQfiay09LTVuSfj1iPLQkMSN1gH5PmVkShcYMl27U7pj6/rSBnNd5GFEsHmKC5U4G3UhNA3QQPfsnc2z/S1js0lmIksKO53/uR7dqPBr5po4zZbmZ3WpF/KmKpdhilrczK8Rnwb9P/pyD9aUXz2SaM8mg4Yuk0GxIgPW9Z8wbS584zAa2nIYGBi/m8a+rqOEs2HbgXCfL/J+DN1A3+nUt+PeKCuyai2Gir7K9ZkX8LMMgogrN4UacDt5Rwza2VLGD/kVbkD0Xu+ZVaXRVTCfp/AvAvE5w5m7uWROIrjU3doOqOcG3gM8SKZKAV+a8gLsOxsS7sKnmGIwqTR4xS/2XEBWt5xL16RzP+aq7jTmznNlFr+6Zu8JYV+TcDZxvF9HhkB7utayhmGHAwcIsV+Rcgiq95gC2B0CgCRwInmfnkecRFd9cy+dXC68A2lmzc8DXwSQfHHyyJmYPOQn7br0Pcy5dD5stvMfEYrcifD1GKD0dig01DFEQ9aY4/VSr/sab/3G5F/qVIXf2K/OZuDZyUusHbdYj8NKIk/JeZi+dGYv59QcvYhhn/QwLrX4w8t5xlrp+xSUep32wTm/IG4C+pG4wzx/phdmbNBYtXFEVRFEX5zdLomFUnGRnORh44/40sRE6mOc4QqRt8g8TN+RUJWHsB8A+q70TU0WxlPg9AFDr5v+LOPi7ypvhcJGjr0oji5qVCuqOQB/RsQb+b+X4bLV0j5kYsC0YhW6u/CfwpdYO8iwlIfVpUD+Z+LrK4PQlZcN2FvM2+BNgys9Yxgck3QFy4TjLl34AsFPJBdychsZ2eRdpnJNKuU3JpHkOUNOshscBcJG7RN1VkLUvqBl8idbgmYoUxHlEu5cl20aoaWN1wLVLXexg5twG2o41KNbMI3xwJ5DsYUV7FSBvUyjuIhZuPBCRfAdgzH/i4jdyEbBE/N4Ug1kbuHRELxxuBfyGWLK2sAlI38IEjEKuBO5BFZX9kd7J8uilIO31K/cG2i+yP7Mx2ANI/DzTfS22cUBOpG7yMKNO2A55E+tMSdeZxNaKcWQmp34eQReqstAzm3GGY8XoQsngfg8i9XVfKYwLPb4mMn0OQNhmOtMfTmPhPbajjDm/n9lBHfR6KKGd9ZH5dGYmBVy3/aUiQ9StprsfBiFtc5hZ5NjKfHWvy/j+k7tvKUYiC8z6kPQ5pR151kbrBdch9bI4EVj8QeakzneY592fEqvpgZD6/C3lW2Dt1g3uqFLEP0t92NfnfjtzvROqLG5jNXzshv7G3I79119F6A4CMG5Hf/38i8/4URAGVj41V6je71LEe5lijn9sURVEURVG6hB7Tp3dVKBdFaQxW5D8F/Jq6Qbm4Jd0aK/LHArOmbvDnRsvSXozLzAeIhUi33rpdUZTGYHapfR7YL3WDm6qlV1rSp0+f6Zl7UVvJdgNs766CiqKUZ8iQIQA0NTU1VA5FUZQ8/Qc/DcBjR2zQJeX16NHjxenTp/cpda7RboCK0ilYkT87sDayY+MGlI8ponQBxmVqJSRQ/yyIpYiiKL9zTGD4I5F4ed8BqwCnItandzRQNEVRFEVRFKWBqLJK+a2yOOJ69A1wfuoG7XU5U9rHNohb7AfI5gTlgvArivL74n9IkPT9EDfWrxFX1pNrjDGoKIoyU7L77vXss6MoivL7Q5VVym8SE0OrWtyumYLUDTZptAztJXWDIcCQBouhKEo3I3WDycCARsuhKIrS1cw111zVEymKovyO0UCdiqIoiqIoiqIoXciECROYMGFCo8VQFEXptqiySlEURVEURVEUpQtRZZWiKEplVFmlKIqiKIqiKIqiKIqidBtUWaUoiqIoiqIoiqIoiqJ0G1RZpSiKoiiKoiiKoiiKonQbVFmlKIqiKIqiKIqiKIqidBtmbbQAiqIoiqIoiqIovyf23nvvRougKIrSrVFllaIoiqIoiqIoShcy22yzNVoERVGUbo26ASqKoiiKoiiKonQh48ePZ/z48Y0WQ1EUpduiyipFURRFURRFUZQu5LXXXuO1115rtBiKoijdFlVWKYqiKIqiKIqiKIqiKN0GVVYpiqIoiqIoiqIoiqIo3QZVVimKoiiKoiiKoiiKoijdBt0NUFEURVEURWnBf15cmP7x040WQ1F+s/T+5jsA+g/WcfZbZ8+1l+SQ9ZdttBiKMtOhyipFURRFURRFUZQuZML8fRotgtIFTPjkWwBVVilKG1BllaIoiqIoitKCtdaZwqNb7dZoMRRFUWZq1HJOUdqOxqxSFEVRFEVRFEXpQp5++mmefloVGYqiKOXoMMsqK/KvAw4ELkvd4LgS5wcBZ6Zu0KOjyizmDcyWusEvHZ1/tXKL92RF/sLAJcCWwJzAG0CQusFtVfKrqQ7pwPu0In86cF7qBn/riPy6CivyewEx4KZuMKSLy+4DHAJsDCwDfAE8AfwtdYO4kHYW4CTgUGAx4C3g7NQN7iik2x/YHuhj8hyaukFTIU0v5J7LsWfqBre24X56A6cD6wKLAl8BbwJ3pW5wRb35dQXFsWdF/vyAB9ybusFLjZOsfqzI3wR4DOifusHYTi5rCLBJ6ga9OrOczsCK/CYgApzUDSZ1QH47AsulbnBp4fgmSHtsnrrBmPaWU6H86cBZqRsMqvO63sCOwBWpG3zV8ZJ1HOXquAPy3Q44H1gBmB3ombrBNx1ZRnsp1V+tyJ8EjC3O7Yqi/D55++23Adhggw0aLImiKEr3pEMsq6zInxPIbMX3tiL/9+JeeB3Qt8Tx4cAWwAlIvYwG1q+U0e+4DtvKp0jdP9CAsgcCqwFXAFsBJwNrAy9Ykb90Ie05wCDgnybts8BtVuRvXUi3D7A88DDwXZlys3su/j0CTEX6WV1Ykb+ukWkh4EREwXoColTbqd78upDi2JsfUeSu3RBpZh7OoXu3ayUeQNr80w7Kb0eg1UuBLqQv0o/rpTfS1xfoUGk6hx3p4Do2v43DgY+R39m+wPcdWUYnshMyBhVFURRFUZQqdJRCZCdgXuBBYGtgAHB/B+XdbUnd4CPgo/wxK/LnBTYHjk/d4EZzeFQN2f0u67CtpG4wFVGyNIKLUjeYkj9gRf5TiNXTwcAZ5tgigA9cmLpBYJI+ZkX+H4ELkbbO2DJ1g1/NdQNKFVrqnq3Inwv4E3BfG60s/gp8A2xh8s8YZqzCuhVW5M+eusHUUmNPKU+u3t5ttCxtxYy5KVUTziSkbtCo+asVVuTPBvySusH0RstSA0sC8wAjUjd4vNHC1EPqBv9ptAyKoiiKoigzCx2lrNof+BpoAt4H9qMGRYsV+ccAhwG9gJ+BdxGXtLvM+UmUMJmv4D6xihX5VwDrAd8C1wKDckqAOYALEGVSL+AHYDxwQuoGbxbKcIBzkTe38yKufGdlspk0g2jtBjjd/K1Q7f4L1FOH1e6ziRLuMpVcMa3IPxo4FrGweQk4InWD13LnJ1FDW1iRvyJwEbAhUm+fA88hLmq/WJH/B6QNdkBczr4B/gsclbWBeXN+gqkTB/gSuAU4LXWDn02aXhTcAI2L02bAdojV0zrI2/dLUje4qkxd1k1RUWWOvW9F/hRkIZWxJWADwwrJhwE3WJHvZG6DWdu1gZ2RhdvQNl6/APB1QVFFKZmMYuxMYHfkPj9GLEMuyKc1brBnIe2wCNIHxgIHpW4wtZwrmhX5Y025m5jvmyDuWLsgVmk7ArMB8+f7csE98lor8q81/7uIW+VuwFKpG0zLlfUHxELnn6kbnFKucqzIz+5jeWAa8Apwal7RkJNzB2S+GIjMAaOQfv1NoW4uB7YFfgXuBe4sV35BliFI/97d5LEGMBnp3//IpWtCxn8/RBm5OTAJ6J2veyvyZzd1EKVucHyhrD2AW4G1UjeYYCzwTkIsRBcEPgDuAM5J3eB/hWt3QpS0a5p7fBM4N3WDe63I/y/wTuoGOxWuyepwQOoGJZX7FdyqnkTmyjMRF9o3AC91gyer1OX+5v9MQfN+oU/OZUX+P6ncnlXnqgoyFOfOQeYeVgRCpP2+BK5H6u/XXB0ATLQiP8vOSd1gUp1z55HI7+A+iIvyglbkX0aNc2i138ga67hYJ4sjvx9bI/PaW8DfUzcYVqgjgOutyL8eGJfNGSXyG2LuZ0dzP2shffe41A0esCL/OOBoZB58DJmjpuSur6l9rchfDvgH0B/4EbgZeL2EPJNQN0BFURRFUZSaaLflhBX5SyAPg/82D3l3A9tbkd+zynV7I3GdbkEeTPcGbqd9rg13A2OQB9ObkTg8Z+TOz448AJ8LbAMcDswBPGtF/mI52ZZGFCxrIgqc7REFzh1W5G9fSYDUDb43chxcLW2uvHrrsNp91ss+SH0cgyzwlwHuaaMr4v2IIuNwRFlzMuKilvW1y5DF9lnIIvowYALixpUxDPgbcm/bIMqtAxHXj2rMa64bhigPxgNXWpHfvw33UjNW5K+CKGbeyB1eDbn3dwrJMyXgqh1Q9P6IMmhkQZ4mK/KnGyVAJZ4HVrYi/yor8v9Urs3N8VHAQYiiZCtEUXU6cHEuXU/gaWAP4FJkbJ+IKJnsuu9O+AfQA9gXUeYW+RRR2oH0lcw98gFgMNIuRde3vYG5EUVvJZZE+uyOpuzPgcetyP+/EmkvR5QaewFnI0q2ywtp7kQUVacidfSLub9amRf4N6Kc3BFRAl5hlBhFhiNKiV2RcdgCo6AcAexlRb5VOL0P8GrqBhPM92WQcXoYYvV5OXAAzYoTAKzI/6u5x8+RvrkbcBeiFAG4EtjWzHl5DjWy1u3KCmwEHI/0xT0AC7jfxDErxzmIZeMUmvtLsY/U0p7tmavKcRfwKNK+dyNz5f7m3API7xdI3WayZ66R9chzGqIYOwS590z5UnUOrfE3spY6noEV+XMD45C55VRz//8FbrIi/xCT7Dqa3eXPNXkeUS7P3P3caK7dCembd1iRhDJXfQAA7VdJREFUfwmiXDoSiXfXH/hX4dqq9WlFvo24b69l8mpCFFsNiwP59rdT2PShwRX/rnmr2xj2Kcrvltlmm43ZZput0WIoiqJ0WzrCsmpfRBGRubwNBfZEFg2VrFn6Aq+kbnB27tiD5RLXyLWpG1xo/h9tXPKOtyI/TN3gm9QNvkUW2wCYBdoo4DMj82Xm1CBkcdwvdYMvzbFR5gH9bMQaoiRW5C+FPKi+B/zbivxtUjd4tIrc9dZhxfusUlYppgHbZpYn5m39bYh7Wc3blFiRvxBiUbZD6gb5Oro5939fYHjqBtfnjuWt1TZC7nv/nBvlGCvyv0Jc03rnFtClmAexCnvM5Pc48uZ/T+TNeYdjFDlXIYuy/H0tAHxTwrXmq9z59pS7JLApcHmJgPu/Aimy2K7Excgi61Dz9z8r8p9E2v+G1A1Sk25P4M/ImMhcbx4xfeVMK/IvSt3gc2ThuhzQp+Dycktb7tHwfOoGB5U7aay1srLeK7hXTbEif5y5txG544cCo1M3eK9SwflyzXwxElE2Hogod/M8nrrBX83/o63IXwk4yIr8ptQNpluRvzlSh/lA+KOsyH8IWKqSHDnmAQ7JXT/S9IOzrMgfWuhrt6ducGKV/G5C6mIzjLuysf4agCgzAEhzGwJYkd8DeAqJrXajFflHpm7wpZmHzkcC8+/cXEQLN+ibEBfYAzGxe8y8sTNiKdcWN7R5gd6pG3xt8puMKFi2puXcM4PUDd41lpBJBXe8au3Z3rmqHJekbpApAcdYkb8pMv6i1A2mWJGfuXJOSN1ghiK8DfJ8BuyUr3MznmuZQwdR5TeyxjrO4yK/H/mNBh6yIn9R4Fwr8q9P3eAjK/Kze3i3xnznAQ7L5i0r8j8BXkaUxqtmc5wV+asDf7Ui30rdIK2jPvdH5ry+mTxmTP+3BtkawoSvPgHgkJUqhtJUFKWT2XvvvRstgqIoSremI5RV+wETUzd4xnwfA3xijldSVo0HjrAi/x/APcDTqRv81E5ZRhS+34oop1ZHXEWwIn935C38SsB8ubQr5f4fgCjOvi1YmowCLrYif97UDVoFwTZvWB9C7u1w5K34PVbkb557iL0OWC91gzVyl9Zbh1Xvs04ezrtI0fyQvQx1KKsQF4n3gAvNAmNs6gYTC2nGA01W5H+BWFH8J6cQAan7BHnzna/7zOJiY8TCoxw/ZYssmKHImGjupbP4J7ABsE22YDb0oLSyqKN2xMyUnK1cAM3i6sZWV7RO9z9gJyvyV0UW930RC4PNgV2tyB9gFrMDEPfUp0u0y7mIe9i9yKJ2fNqxsVnuqp6kIoOBW63IXyF1g4mWuLStRbM1VlmsyN8MUdr8Hy2Vi6V2ZSwG+/8vYs25KOKu1xdRIN5RSHcrUr+1UO766xArsHwcr6r1lrrBU0b5sS/NSqWBSL/KW4/Mi9TDrsDSiKVcxgrI2N8A+ANwTYXyvrcifxii9DnPuI+6yJiIyl1XhWcK4y4/f7WHau3Z3rmq1nJfRfprNeqV5+4yysFa5tA2/UZWYWPg47T1jpjDkL6xKm1TAP2Ytoxtlbn8jyn89ryJPBMtjoyjWuuzL/BhXnFmXDZHIEq9LmfF+Rbm0a3KG5xt+tDgLpRGURRFURSlbbTLDdAs+lYF7rQif37jdjEP4gbS15L4ReW4EVHorIc84H5lRf6dlsTTaCuflfm+pJF3O8SF5g3EtWM9YF3EImaO3HWLIIqiaYW/zN1pwTLl74gojM4zLjY7Ia4SD1qRv6axSNiAnKtLG+uw4n22gWJg7ix+0RzFhJUwC5/NgRcQd4m3rch/z4r8w3PJ/gpcjbgQjQc+tyL/MkviIYHUvY3EE8vX/efmfLm6z/i6xLGp9d5LrViRfwHiRnNA6gZFF6avgJ6m3fP0zJ1vD/sh1hUvtzMfUjd4PXWDIHWDXYAlkAXiFojrC0i7LEvrMfG8Ob9g7rOjA5+3d/e3uxDlwqHm+2GIMvi+ShdZkb82siD/AbEEWh+ZL16mdH+qNo4WR+KDTSukK47nSlS6vjj+a623YYjC8g/m+77Ao6kbfJxLEyH1dgUyxtdFXJ6g+f6yPlCt/Qcjio+tzdg4BLHGqqce8rSo97Q5/lp7x3y19mzvXFVPubXcS73ylOsftcyhbf2NrMQCZWSanDvfFr7Jf0ndIDH/Fu8zO15v+y5O6THc1v6sKMrvhHHjxjFu3LhGi6EoitJtaa9lVRZH4yTzV2Q/ysRtMIqNq4GrTZybLZAYVv9GlEggMTRaxLmxIr/SA+uiiGVP/jtIgFgQi4F38sFNLdkFqZjnl8ATSKDXUnxS5vhy5vNbgNQNfjbxOx5CFFRXIC6C+bgnbanDaveZxR4pxghq6+Ipy7NqWxi3qv3MInRN4ChgsBX5k1I3eCh1gx+AU4BTrMhfFrHUuBBZKJyE1P3PSByaUpSr+y7HivzTkFhAR6ducFOJJK8hlhjL0zJuVRarqlUA3jrKXhdYBXG761BMv70YiVu0KhKH7EvEmmj3MpdNMp9fUF1p2qovGRY05RRp1w5lqRtMMxaNR1iR/3dkHrikhOtkkV2QmFI7py2Ds/eksACukU8R5eVsBYXTouUuKEGl6z8upK213m5CglbvZEX+c4giKpuXso0pdkA2cbg8d3yNQj5fmM8lEWugkqRu8KoV+U8gysOfgT/SrEicmehuc1W98rRnXLX1N7ISX9HSwjkjiydZam7oTGqtz0+R+IRF6hnXiqL8DoljMdLu169fgyVRFEXpnrRZWWVc3gYilkOtgvci8Z/2tSL/9GpxSIwLx7+tyF+PlouW9xFLpTzbVshqd0TxkTEQeSuaLZzmQhafefZFAvLmGYmY9r+WFna6qkIWOHtvjEIqdYOfrMjfBon1cS6y4PsA2lWH1e7zffO5OvC2KWtWRCHYVupqCyPvBEt2WzrQXPtQIc37wCWWBNvP8h6JKK3mS93gkXbI26lYsnviuciuUOUCZI9ElHB7I0GSM7Lg1aVcyWplf6Qvl4zJUytW5C+VukEpS5iVzWdm6TASUd78kBZ2ziwwGvibFflrVrD4eh9Y1Ir8hVI3+MLIsTyyUK3H7TRPZvUyZ5nzVyNK0tsQBWK1wOog80WLuF8mftAylHYDrMYzyFyzC+K6lzGwjjzKXf8BrZVVNWFiCz2DzIUrIruZ5XconN2UW7Toaip8fxqZhw6hZZyqUgxGLLp6Am+n1eP6dQZTKd9faqFRc1W5vt6V8tT6G1lPHY8DdrMif8PUDZ7KHd8LsWZ6o/RlnUat9fkM4FqRv37O3X8Wyiv2FUVRFEVRlBpoj2XVtoglxPElYkxgRf7VyM5Pm1AisLUV+dcA3yMPep8ji6R9abkb1K3ADZZsp30/YqnTVEGmg81D4nhkJ7qDEOXQN+b8SGDHXH7rINtWf1PI5wzEvelxS7Yun4QsqlYHlkvd4IAy5T+AxJsKLNnKeiQS26U/ss38R8BhVuQPN4Fx21qH1e5zPPAuEjtkFmTBcASy6GwrVdvCkh3SLkes495BFrhNiFLlUZPmGSS20X+RhW0/k9dQgNQNxlqRfwtwuxX5lyLt8Cuym9jWwEmpG7zdjvvIZP0FGJq6wYG5Y9cjwXRnzR3rBzyCuPndaI4NRLaWHwk8akV+Pkrtd6kbvG7u5XNTX6dYkf89slvWHkhQ9B0K8qxKs8XVnMCyVuTvar6PS1tupz4boqB4KJWg5qXubz/gBuAvqRtUsjG/ysQXuwlRdlqIZc2JSB/K4h4NR2ILPWLJLlovI9ZRyyM7ge1oYs5dhiwux1iRfy7SzguZ+z0sld0yb0OCaw83bbwQokjKLHPawmeIJcRAK/JfQRQucRb8OXWDj63Ivw9xzb0vdYMPa8hzJLJL2BAr8iNkjjqdtiuFHrYkeP3VlgQVn4j0h6ISuBLfA3/PXb8nEhy9qdpLgSrciOyEtgbikvdDTu5vrch/FtnE4VOknQ6gYEFn4lGdAvzDivw7kD7zPdAb+Lmg1L0DGUMbIjEEG8HrwALGTfkFRMaaYyJ11VxVgswi80gr8ociSsRXulieWn8j66njIcimBXcaq9WPEEX/5sChhfhSnU4d9TkUedl0pxX5pyLPM4chgf8rYqyL3wXOTnObzZT5fXoHeD91g7900C0qiqIoiqJ0a9oTs2p/ZCFyW5nztwD/I+dOUuApRFk0GNn2+TTkTXs+/VDEPWVnJL7MllTY+hpZEG+OKEP2QSxfzsmdvxY4D1kg3ofE49kO47aXYSyf+iAL8vONfFciipWyFgAmWPC2yG5IWyKB44ciFiPbIUqZ75GF/NK0vQ4r3qdxb9oB+BBZAPzL3MOQcrLXQC1tMRmx8DjOyHYLEv9o29QNXjRpHkfeOA9HlHu7Asfm3YvMPQ0y5+4BbkfcCSfScXFALFpb1JU61sMcy4+VAeb4AETZmv8rRq49DWmfYxBrkw2B3VM3KMZL2h3pB7chbqmb5L4XXUwyJWerwOo5ZjFyVwvm/g9kQXkkUtf3I5Zww4ANM6WFcTvbEhlDhyCxnIYjffNpTLwXozDdEFFynYwofC5BFJZZmneQtl0S2YTgRKTPtHkhbcbeQciCeQyisN2ukCwbZ1fXmOcoRJm9IVIvByBuue9Uuq4KOyN1dwGi1J0V6du18h2iqNwfaa/+wDGpG1TqC7Xwb6SNFkMUl0X2BF5E5pIhyFgv7oZI6gb/BHZDdjccjiildqVgiWb60z2IIr29sreV6xAl/PmIIqJiDLMydMVc1QJjsTgI6d9PIn19ia6Up47fyJrrOHWDH831oxHL4XuQ38x9UzcoG7S/k6lanyYG1uZIsPXBSH+OkXm/GqV+X6D0b9GsJY4piqIoiqL8ZukxfXq7wsEoiqLMFFiRPxxRPC1nlFszFVbkDwE2S91gqUbL0l6MW/I7wBOpG+zbaHkU5bdEnz59pr/wwgtlz2e7AdayY2ClNIqitI8RI2Rz7913V6/h3zL9B0uEi8eO2KDBkihKbXR1n+3Ro8eL06dP71PqXHsDrCuKonRrjJtmb8Si8riZUVH1W8GK/HkRV7G9gKURqztFURRF+d2hSipFUZTKqLJKUZTfOs8g8dGG0tpNU+la1kbi732OuC9OaKw4iqIoiqIoiqJ0R1RZpSjKb5rUDarF7ZopSN2gqdEytBezkcRvoj0URVEUpT2MGTMGgM0226zBkiiKonRPVFmlKIqiKIqiKIrShXz00UeNFkFRFKVb057dABVFURRFURRFURRFURSlQ1FllaIoiqIoiqIoiqIoitJtUGWVoiiKoiiKoiiKoiiK0m3QmFWKoiiKoii/IyZ8+QmbPlR+c9QJX31C7wWW6EKJFOX3x7zzzttoERRFUbo1qqxSFEVRFEVRZtB7gSUYuNzajRZDUX7T7Lzzzo0WQVEUpVujyipFURRFUZTfEb0XXIJHtzqi0WIoiqIoiqKURWNWKYqiKIqiKIqidCEjR45k5MiRjRZDURSl26KWVYqiKIqiKIqiKF3I5MmTGy2CoihKt0YtqxRFURRFURRFURRFUZRugyqrFEVRFEVRFEVRFEVRlG6DKqsURVEURVEURVEURVGUboPGrFIURVEURVEURelCFlxwwUaLoCiK0q1RZZWiKIqiKIqiKEoXst122zVaBEVRlG6NugEqiqIoiqIoiqIoiqIo3QZVVimKoiiKoiiKonQh9913H/fdd1+jxVAURem2tMkN0Ir864ADgctSNziuxPlBwJmpG/Ron3glyx4EnAnMlrrBLx2df7Vy23tPVuTvCuwJ9AEWAT4A7gTOT93g+0LansDFwI7AnMAzwLGpG/y3kO58k986wAKAm7rBkBJlLwT8HdgO+APwCnBG6gaj2nlPWwDHAn8C5jb3dBdwYeoGX7cxTw/4IHWDO9t4/ViA1A02acv1nYkV+ZsAjwGbp24wpsHiVCUnb//UDcY2VpqOo9hHrMjvjYy1K1I3+KpRciltp63ztBX5vYAm4MbUDd4rnJsEjE3doKlDhKxdpr5ACKwOzAWslbrBhE4oZ37AA+5N3eClwrmxwKypG/y5o8vNlTGINv62tvd3oquoVMeKovx++fLLLxstgqIoSrembssqK/LnBHYzX/e2Iv/3EvfqOqBvB+TjAylwKjAAuBI4HHjYivwZ7WFFfg/gXpPmr8AuwGzAY1bkL1XI86+IMuv+coVakT878KjJ70RgZ+BD4H6jjGgTVuSfCowCfgYOArYErkIWfuOtyF+6jVl7RkZF6SyOMH8ZvRFF+AINkUZpJL2Qtl+uxLmdgHO6VBrheuSF0nbIb8/bnVTO/Mi9r91J+VejPb+tHjPH78T8NLaOFUVRFEVRZjraomjaCZgXeBDYGlF+lFWS/FZI3eAj4KNKaazIbwKiKm+It0vdYEru+zgr8r8ChgKbIAolgO2BPwObpm7wmMn/GSBGlE1H5/KYL3WDX63I/yOwX5lydwPWIGcdY0X+SOBlxNrqT5XurRRW5PcHzgXC1A2OLdzTXcCLwI1A/3rzVpTOwor82VM3mJq6weuNLL8RZSv1k7rBf7q6TPPiYiXgvNQNHq2Wvob8eiDWyEm7hetgavlt7Up0fCqKoiiKonQP2qKs2h/4GrGceR9RjlRVVlmRfwxwGPIG+2fgXeRB/C5zfhIlXC2syJ8OnJW6waBClqtYkX8FsB7wLXAtMCh1g1/NdXMAFwCbmzJ/AMYDJ6Ru8GahDAdRumyBKOLeMGXelUsziA5wAywoqjLGm88lc8e2Bz7JFFXm2m+tyL8P2IGcsiq75yqsD/wPGJe7broV+aOB463IXzJ1g49rvxNAlGZfAacUT6RuEFuRfyFwkRX566Vu8BzMaM/zgM8R18GFgJeAI1I3eM2kmQQsCyxrRf7eJsuhWd+wIn8A8pa6N5AgLmonpW7wViVhrchfCbgQUZ7NjijqBqVuMLKQbk+Tfy9gInAacJy5r02syF8McXU8IXWDywvXDjL1smQNLpDzWZE/BGlPC7gPODp1gxl24VbkHwXsjSxcZwHeBM5J3eCBXJpZjbx7IX3oB5Pu5NQNnsylOxg4yuT1A3CPuYevcmkWBi4HtgV+Raz7anKxsSJ/XeB8xHpgTmAyMDJ1gyNyaaqONZNuTWAQsDHiAvUBMCR1gwvM+UnUMF/k3IbXAC4BNgQeAXbIuwFmimaTzUQr8rMsHaRd3kndYKdCWZsgfW9AOVfaXL79EAvIzYFJQG/Tbicgc6oDfAncApyWusHPuTzmBk5HFM5LIfPvU8iY+cyk+RNS9+sDPYBngVNSN3g+l88QYDOMqyOwlqnX41I3eMCK/OOQeWUBc18H5eer3Nj92qRbCBgLuCbJPxHLyu+Af6ZucFGhLmqeZ4EVERe4fqZergfOzc91VuSvZe5jXZPmKnPvxTaoOIZy7Qhi4Zpd2j91g7Gl+lqd9b2dkXMd4GPgktQNrirKmbuuiea+eLoV+acD76du0Muc3wfpN9k4fgg4MXWDT3N5TAKeRF5+nAgsD+yOuGfny+qFvAABuNaK/GvN/y1cya3I3wx5qbEy8B7wt9QN7i7ktSZigbYRMAcyr5+cusET5e7VXDeIwm9rB/1OVJUn10a7IfPDWsA1VuRfZurlMGROPRiZ054ADjcKtvw9lJ1ba61jRVEURVEUpSV1uQFakb8E8mD3b7OIuRvY3sRWqnTd3siD4C2INdbewO20z93mbmAMsvC6GVnMnZE7PzswD7I42gZxtZsDeNYoGzLZlgaeA9ZEHoq3Rx5q77Aif/t2yFcP/cznG7ljqwGvlkj7GrCMFfl/qLOMFJiWusH0wvHsDfLq2QEr8oeYxUJZzEK7H/BwfmFd4F7zuWnh+D5ImxyDLHSXAe7JuZTuhCg6RiHuIX0xbjhGUfUAsiDYA2nX1YEnrcjPK/uK8i6BLN7WRBYVuwPfAA9Ykb9VLt3mwHBkQbsLECCL5hWzNKkbTEb636GFMiwkltuIGmN1hcB0JIbZaUjfu72QphfiJrObud8XENfNrXJpTkL67hWIssBFFDIzxpdRHA5Gxsz2yGJ3APCQkTvjTkRRdaop7xfgH9VuxPTHUUg/a0LG+dnkFOK1jjWjCHgGWWAfi/SVSxFFTVu5B1HUbg9cVuL8A8hcAVLXWb/7FHHV3db0oTyHIovQ0TWUP9yk3RU42RwbBvwNmb+2QZTrB5q0AFiRbwMPI8qhIUjbHIUoiXuaNP9n7q0nUvf7IcqgcWbBnmdexNrxOmScfY7U/yWIEvdIxLWqP/CvEvexLzKej0CUbxuZ/O5CYuDtgljdXmhF/ta5+6h3nr0LUbTsiIy1sxClXpbfQub8Qub4kUh/PqBEXr2oPIZeMteD1HPW9iVjC7Whvm9G2noH5MXElcYqtRwPIFa1IEq6vkhbYUX+IcBNyG/Fzkhf2tKUXfxN6I8o2c9C6uaVEmV9SrMb3QW5e38gl2Z5RIF9qUn7KXC7seTFyLU28DQy5xyM9IMvgTFW5K9T4V4r0Z7fiXrkmQ+4FXk+2Qppr4xTgD8i/eoYU8bw/MU1zK211LGiKL9DFltsMRZbbLHqCRVFUX6n1GtZtS+i4LrRfB+KLLT3QN5ql6Mv8ErqBmfnjj1YZ9lFrk3d4ELz/2gr8udFLITC1A2+Sd3gWySGEjBDkTAK+MzInC1YByFvxvvlLFpGmcXV2TQrXFph8sy/yZ/FHC/Wa1pCSZTlsaQpZ0zqBi/kTi2AWGAUyaxgeiIKm1p5C5jXivxVUjfIK8WyWCF5xWFq/iqxIPKmuZSMGdm5YtyqacC2qRtMAzCWDLchrohPp27wHyvypwJfpG7wbOHac5E3+1tlAfYtcY98GzgeYwFVguOQOuubusE75roHgdeRN/gPmXRnmWM7ZW1mRf5/EZfGfMyYwUj8sI1yb+q3QRQqlcZCntdSN8isUkZa4g46zIr8v6Ru8AhA6gYzzDwscQ16BFGcHZaTuS8wumDldV/uul7IAuqs/Bi0Iv9tRIG3HXC3UdT9GdgzdYNbTbJRVuQ/RHVF0cpI/Z6YukF+UTwk9/8gahtrAbKwXD91g5/Msfa6Ql1RtILLk7rBFCvy3zVfJ2R9BMCK/JsQi7wDaV4ML4QsQM8sN7YL3J66wYm5PDdC5s39UzfI5tMxuT7Q2wTT3gdp3x1SN8jPRXml5hmI0vkvqRt8Y/J/GBl/Z9Iyps88wGGpGzxu0n2CWBhuC6yaukFqjq8O/NWKfCs7ZphqZPkll+5Y4PTUDc41x8YiioTdaJ7nB1HfPHtJ6gaZddEYK/I3Rebt7NixyGYOW6Zu8EHunt8v5FN1DKVu8J0V+ZlL6Bsl5pwi9db3ETlX7scRy7I9abbmKso7xYr8TNn9USaP+b05B7HyGpi7pzcRi58DEIV1Rk9gHaNcL0nqBlOtyM/cHN8rc+8LARunbjDRlPcSooDZHbEuA9kI5APEbT0x6UYhL1xOR5SO9dKe34l65PkDsE/qBvdkB8ycCWLRtlfu+MLAxVbkL5G6wSe1zK2pG9xdQx13KG9/O4VNHxpc9vyErz6h9wJF3buiKF3NgAEDGi2CoihKt6ZeZdV+wMTUDZ4x38cAn5jjlRbo44EjrMj/B2Lh8HRuEdpWRhS+34oop1ZHHhKxIn93RIGxEvL2NGOl3P8DkAXVtwUl0yjkoXTe1A2+KyPDIzRbReWZVvjeH3GXaYF5E34PYr3iFk73QKxuirTVDfFmZME41Ir8A5HFxiGImxWIyxcAqRsciCzMK9Eed8iHswWIIdvdcBnkbXhJLHGHWhvZOXHGTpDG5fApSrdFxsbAs3klROoGqRX5twBnGGXnj8iuihfkFRCpG7xkRX6cz8y4B72OWNdkyqpDEaVsfnGZr6df05Yum8U+fBuiCO6L9C2MFcBZiKvTwrn88i6P44FTrMjPlG7Ppy1j02yOKFKHF/r4c4jL1saI9UpfREl5R0GuW5FxUomJiKXa1Vbk/wsYl7rBh4U0VccaMhY2BC7ugDkiz13Vk5QmdYPvrcgfBhxkRf55pg1dpC2iyleXLX8A4sJ6R6EuMiutjYEJiGJjckFRVWRj4P5McWJk/s6K/HsRRWSeHzNFlSFziR5TUEq9ifw+LE7LeEIPpy13Yc2un+EGmbrBL1bkv0NLJXW982zR6uRVxEUroy8ynj/IlfujJW7STfkLaxxD9VBPff+UtnTlnmpF/kRkrquXlZAdZE/LH0zd4Ekr8t9H5r+8surZSoqqOpiYKapMeZ9bkf855h4s2XSlH6K4+rXQvmMQS+q20NbfiXrl+YXyoQyK/TAvwyfUPrd2K3ovsAQDl9NY74qiKIqidG9qVlZZEo9mVSQG0fy5U3cCR1mRv2LqBuV2K7oRccE7EHEfmWasWo5L3WBSWwRHLKRKfV/SyLsd8G/E+uss4AtEIfOgkSVjEUTZVi4w+YLIQ2cpDkXenGdsi7xZX7eQrtWiyJKYWvciu0/1K8bAQCyoSrlJZi6XtbiZzSB1g2+syN8FqY/M8uVdRIF1DqK8qocvkBhYvSqkyc4VlRZfFb5nrohzUJmeyEKzlKyTkfgl5VgAKBUoebLJsydiKTYb4hpVpNjfQNzDAkvisf0BWZAflTtfVGaehdR3yTxTN0iMRUXWh5c2ebyOuFx9gCyszgFWyV16PhIHbh/Efe8HK/JvR2KmfIH0cYB3KM2C5nNx4OvCArGVnKVIJZ5af8RqYTAwjxX5ryGWR5nyq5axliCLv44OuFxv/y4yGHE53dqK/AcQRe9dqYkZ1YbyFwFsyltHLpj7rBZLboES+YP07aKL9jf5L6bPQev5JFN2FsdkuXSljrdnni01R+TzW5zSbtIt2qOOMVQP9dR3qXm6eC/1lEuFsou/F+3t8xnFtoCW97AAEnPvdPPXCivyZ0lri61YqdxafyfqlefzgqK2HhlqnVu7lBXnW5hHtzqiekJFURrKnXdKSNCdd54ZNjVVFEXpeuqxrMrihZxk/orsh8RfaYWxUrkasbroiVgLXIIok9YzyX5GFm8zsCK/UkyrRRF3sPx3aF7YDUSCIjfl8puN1g/0XyKWMRdRmk/KCZAWAnoblxgK7nytMHLcgbgzbJa6wX9LJHsNqaciqwIfpG5QjwtgJu8TVuQvj8TgsBC3thMQpVPJ+CwV8vrFuLRsbkX+HGXiVmWxaNq9m5Xha8TarJSD/2JIW5bjqwrXTTfnf0Ks4hYpkW5RZKGb50YkBkkTskj9Hy3jmRSVmcW+tGj+iyXxiXrS3IcHIBaBu+eVmVbkz5W/ziiXLkIUyYshStNLkcDke9BcL1tQevGcnf8U6GlF/mwFhdWiJa5phXFb28VYGPRB4r2MsCJ/zdQNXqW2sWYhSuWy8ccM9c4XtbjqlSV1g1etyH8CadOfkTF0aOWrKpb/pclnozLps77yBbl4cmWo1LdLKRoaQZvn2TJ8Sul+WTxW0xiqk0bVd5Z3ubKLvzvt6vN18A0yZv9Fc4iAFrRBUdWV8rSnnmqdWxVFUVrx3Xfl3oUriqIoUKOyyiyiByKm7SeXSHIZsK8V+adXi9+SSuDpf1uRvx4tF3vv03pRtm2FrHZH4shkDESsFLK37XMhb9Dz7IsshvOMRFxKXkvd4H+VZO8ITMyU4cBfgG0qxK+4F3CtyO+XusE4c+28iJvJzWWuqYppnyz2yB+Q4LM3tUX5hcQFGYNY9rSIFWXJzl8nAY+nZifAOpmKWDrNwLj5vAjsZkX+oFx8nWWBDagcCHwc4FmR3yuz5jNuensA/0nd4Htz7AVE4TIobY5ZtQ6yW1sLZZVx/RmO9OM/ADfnXZmKyswS7A7ckPu+G2JVlLnZZgvqGYojK/JXRNzkSloeGbef6ywJbp2Np4eRxdsyqRs8XEGeZ5DxsQvi+pcxsHTy0hg3sWct2cVse8SC5VVqHGtW5D8J7GNF/tkV0tU7X9RCZjUxZ5nzg5FA2T2Bt1M3aI8SdiQyPuZLTXyyMowGBlqRv13qBveVSTMO2MaK/Hly/XgeZK4Y2w4ZO5KOnmefAU6wIn/pzN3UuAkX3fBqHUPV2j5Po+r7LcRybCASeB1T9gaIVeklbcy3nntvhZmXn0CC57/UxYqpcr8TXSVPrXNru+pYURRFURTl90itllXbIubsx6duMLZ40or8qxGXqE0oETTWivxrgO+RBcbnSHDbfWm5i9atwA2WbBl9P/Kg2VRBpoON4mc8shvSQcCgXByRkcCOufzWQXZ6+qaQzxnA88DjVuT/EwmS2xNZCC+XukGp3aXaw78QpcR5wI9W5K+fO/dR7u3/vUh9DbMi/wTkre0piMva3/MZWpHfD4nFkr1x72NF/g8AqRvcnkt3ARIo/AvEMuQEZBF3SiG/65HAzxX7R+oGj1iRfwZwtgk0e6ORc21Eqfkt0s5t4XVgIyvyt0VcXL4wSqbTkTgi91uRPxhREp1lyqq0WLsM6U8PW5F/JuJydATSF7fJpTsT6Zd3mX67EOK6N5lcXK8cg2lWutYaWD1jNSvyI6Tvr4j0iXE55cUYROF6oyW7tS2O3OsH5HbytCL/HiRI9ktI/a+FWJRcDZC6wbtW5F8E/NOK/JWQxfbPSEyhzYHrUjd4LHWDh42i6GpLAohPRJR51Sx7MO10CBKfJUaCXx9N87iH2seab2R8xtz3R4i7bO/UDf5q0tQ7X9RCFmT7SCvyhyJj45Vc/K87kB0cN0Ri4bUZE/PsFmRXtUuRevkVcZ3dGjjJuFUPQxTKt5jx+xxirbclEKZu8Cbi0rYt8Ihp5+mIImwuJHh5d6Cj59nLkPE72or8QYgyILMSzVPTGEKsTH8BDrAkyP1U4K1MGVWgIfVtYuydgYzPYUjfWBKZNyZSe/y0Ip8hFkADrch/BYndF6fNgfBr4TjgcSRo/vWI5dtCyG+BlbpBqZdcHUG534kukafWuZUKdWza9Axg+dQN3gewIn8/5EXGX3Ivq/ohLq0HpM2bMiiKoiiKovxmmaV6EkBcAL9HAkCX4hZkkbB/mfNPIcqiwcibyNOQB+18+qE076R0H7IY26mCTDsgD4P3IrF6zsXs1GW4FnmI38Pktw3y5vvbfCYmQG8fZLF/vpHvSiTWUEe5r+XJtks/DVnE5/9m7F5o3gZva+QZjARoToH+JQJXn4W0TWZZdKT5XmyvRZHF9mhEATMa2DB1g6LrikVrC7SSpG5wjrmnuZHF0mhkEXkj0CcfALlOTkEsCUYgCslBpryRSFvOb85dhWzj/ufUDSq5bH6C7HT3GtK+tyMuoduYPLN0DyMBeFdB6vwkRDExmULfMelfQRa6L6RuUJcrJbIVeg/EHfZ8ROmyay7v14wsyyL9/ERECfh4IZ9sh7HrESXt4YhC88RcXqfSHFB/BBLY/yREuTUxl9fOSFy3C4xcs9IyDlc5JiJzwOlIkPcIWfxvnilgax1rqRuMRxRCHyJ9+kFEEZG3hKl3vqhK6gYvI/1sO2SThvHAErnz05B6m2rKby/7mPJ2NfnejtT1REzsJVPmFkg9HYLUxWBk4f2VSfMK8qLgOyPXTYiVaT9zTw2no+dZE4vtL4jifSjyEmAkLS0Vax5DRjHz/+ydd7gdRfnHPxma0ntHAohRQMkPgxKQXozAShEQpAWkg4IwdJBQBJGlqpFOaNKlDCUJARI6ghhRkN6F0KsIBCa/P97ZZO/mnHPPLbnnBL6f57nPuWfP7Oy7szN7znz3fd/ZBxM9x2HX/rt1jt2y9v58p/xs7AHAt7E+8zusLdfspnds8V2zCyYejsHOveqh1lkdD2O5Gt/CkryPBk5PdlbvV71Jve+JPrOnmXtrJ23ssO/b6srC1W390rZmf7cJIYQQQkzX9Js0qa/SWggxfTLDBX5xLIHub5I4V/7sG9iKaLt+vlN+Xq39xReDlIvraeCuz3fKu+sxKIQQ05RBgwZNeuihhqkzhRBtwJgxYwBYb731WmyJmJasPdwWsL1jr1VbbIkQzdHXfbZfv35/mzRp0qBan3UlwboQX3jSsuenYE+/38TCzw7Ckq+fWyq3OBZKeTQWYtLtPGKivUm54lYAfoaF93Q3N5AQQgghBCCRSgghOkNilRAd+RzL/fUHLE/bf7FVzLb8fKe8vBT8LliekSeBn/VFcn7RMlbCcvG9DuybVj0UQgghhBBCCDGNkFglRImUTLvT3Eef75QPI+VHEV9s0qIS/TorJ4QQQgjRLFdeeSUAW221VYstEUKI9kRilRBCCCGEEEL0IR999FGrTRBCiLZGq8oIIYQQQgghhBBCiLZBYpUQQgghhBBCCCGEaBskVgkhhBBCCCGEEEKItkE5q4QQQgghhBCiD1lqqaVabYIQQrQ1EquEEEIIIb4gvPXWW4wYMaLDtuWXX56VV16ZiRMncumll061z8CBAxk4cCAfffTR5BXKygwaNIgVVliB9957j2uvvXaqzwcPHsyAAQN48803ufHGG6f6fI011mDppZdmwoQJjBw5cqrP1113XZZYYgleeuklbrvttqk+HzJkCAsvvDDPPvssSy+9dKPTF2K6Yc0112y1CUII0dYoDFAIIYQQQgghhBBCtA39Jk2a1GobhBBCCCFELzBo0KBJDz30UKvNEEJ0QuHluO2227bYEjEtWXv4vQDcsdeqLbZEiObo6z7br1+/v02aNGlQrc8UBiiEEEIIIYQQfcjEiRNbbYIQQrQ1CgMUQgghhBBCCCGEEG2DxCohhBBCCCGEEEII0TZIrBJCCCGEEEIIIYQQbYNyVgkhhBBCCCFEH/KNb3yj1SYIIURbI7FKCCGEEEIIIfqQVVfV6nBCCNEIhQEKIYQQQgghhBBCiLZBYpUQQgghhBBC9CEjRoxgxIgRrTZDCCHaFolVQgghhBBCCCGEEKJtaFnOKufDUOCC0qaJwEvAFcAxMc8+7qXj9AeeA3aKeTaiN+qscYxhwFExz/qVtk0Cjo55NmxaHLM7OB92BTYDVgTmxtrlQuD0mGefVsouAZwKrA/0A8YA+8U8e7FUZl1gJ2AwsCjwCjAaa4vXK/V9BTgW2C4dezxwcMyzO7t5LsOAo+p8vGzMs6ebrGduYD/ghphnD3fHlr6kNG6Winn2fAvtGAGsFfOsf6ts6EucD2sBa2H3ptgL9Y0FiHm2Vk/ravJ4QwEX8+z8vjhejeMPo3KPnIbHeh4YG/NsaHo/lGk0ZpwP+wEvxjz7S2X7MProfLuD8+GbwHBgEDAHsFnMs+um4fH6Y983u8Y8O3daHac7dKd/pPvfejHPFu/isfozjX+PCCGEEEJ8UWgHz6otMbFjI2AUcChwUkst6h0GA231oxz4NTAB2BfYGBMGjwUuLRdyPswK3A58E9gR2B5YFrjD+TBbqegewHzAccAQ4ATgx8D9zofZK8c+D9g12bAx8CowyvkwsIfn9AOsrct/L3Vh/7kx0WulHtohvtishfWT3rpn7pX++oqhwM59eLx24ibsvvDqNKh7P2DzGtvPTcdsV04Blga2wuwc11pzWsq07B9VXk3HuqkPjiWEEEIIMV3TDqsBji95wdzqfFgW+LnzYd/e8GBoFTHP7u/L46Un+UM78XRZKebZG6X3dzgf+gFHOx+Wjnn2bNq+KzaRGVBcG+fDI8BTwO7YRAdgr0p945wPT2ITn62A89O+KwI/A3aOeXZB2jYOeBQ4BhO4ussDMc8+68H+0wTnwywxzz5ptR2iPYl59lirbZhecT7MBHwW82xSM+XTPeqNTgv2IjHPXgZe7stjdpFvAXfGPBvZakN6C+fDDEC/Zr8PSv2oz/pH+k7o098GQgghWs/4V95j7eH3ttoMIZpi/CvvMXDRuVptBtAeYlWVh4H1gPmB1wGcDxtgT7D/D5gLeBZz2z8t5tnnxY7JIygHfgrMgnkH/a56AOfDysDBwCqYZ9CLwDXAsTHP/lcqNxZro2Gpnm+mYx/RWchErTDAJNocC6wOfCWd6yExz+5Knx+EeSktEvPsrUp9jwFPxjzbtNFxG1ERlgoeTK+LYecGyTuqHEoX8+w558M9wCYksaqJ+gp+jIV5XlGq7zPnw+XAIdNC2KkX2lEOzymFZACc43w4J/2/U8yzEdVwolIdHa5tKSTx28DJwGrAbcAmqU8ehYl3iwH/wbwuTuhMjHU+LA38Hlgb+C/wZ6CmyJFCPPcBBgAfAtcDB8Y8ezt9/ijweMyzn1T2+z42eZocBtRZP21g7yLAicCGWGjRE8DvYp5dUiozFLsuawL7Y2P9E+BywBfjr3Rt9gSWxMJNZ0vntRsWdvpHrK0nYGP3woo9nZ5HEc4DZMAZwHexa3RyzLMzU5lhTAk5neh8ACD1oRnTZz/Dru+HwOPpOHc3aKuxqY610vu1gDuw8bUBsDUwCfM23Sfm2bv16kr7/ww4EPOA/By7p/0h5tlZ6VhrpnKFwDMu5tlazocFsHvO2sDiwFvAXVjf+U+p/qINvgGclup7C/OYPK7cl50P/5facuVU5kwslLhq8z7AtlifdVi7HRvz7KZSmf5YP9gb6I+FES+M3bffcT7sC/wqbftX+r96nKFU7gWN2it93ul3RLo/LAks6XzYNh3uwphnQ+uEhs8JHI95Ys0HPJ/a5rRCeOuFfjATdp22Y0po9iXY/WpiqX6A/s6H7cH6coM6l8IeKqwPzIONjxDzbF/ngwd+Ayxe/j5ID0GewR4mbFOqbmbnwynJvtmx7+h9quF3nd3PUplJWHt+gD1E+RoW1vj3GufQnzr9yPmwCV3sH3XaaSfgbODImGe/rVOmsENhgEIIll9++VabIIQQUzFw0bnYZqXFOi/YB7SjWNUfeA+b5BQsjU3+fw98jP0gHQYsABxSKncWJlQdjYkm62MT/Cpfw3ImjcB+6C6PhactjU0OyiwDnI6FuL0JHABc7Xz4ZrN5kQCcDythk8C/Y55LH2FhdGOcD6vGPPsbNqk4IZ3D8NK+38WehB/Z7PG6wJpABJ4sbVsemxxUeRQL2+ysPoB/V+p7LubZRzXqmxn4evq/mMT370IupBkK8SARu+CR9yo2cfwL1u43pO3PNLl/leuxyfuJQExCxihgOUw0+Sc2+T0SmBfrSzVxPswM3Ap8FZtgvY5NyKYKOXI+/DbVdQY2wVoMEyBWSH3rc+BizINunphn75R23w54G7g51dVMP61l72yYR908wGFYKOZ2wMXOh1ljnp1d2eUS4Eqsn38PG3+zYeFqZQ4FxmLhqMthonHEhOtzMHF6T+AC58NDMc+KftSV85gTu0+chk3KdwL+5Hx4IubZHZi4uDjwcyzs9PPSvgdjAsnh2D1lTuz+NG+tdmqC04EbMfFrQDrfz9P518T58AOsPYvr7zBhfe5UZK/0+QxYHwJ4P73Oi91TD8W8SxbF+tI96R5XzR14LTaxPxUT+I7GrnXhMTk/JkBMSDZ/kmz6Wg3T+2Nt+zz2XZQBNzofNox5dkul7OHYPX23dB4fOx9+jl2zEZgQ/nXgMkworUsT7QXNfUdsho2bf2DfR1DHQ8f54LDQr5VSPf/EQt9Pwb7HDqvs0uV+kLgQE8aPB+7GQs6OSHb/DBNsB2P3ugex+1JdklD1V2z8HIV51y6BCWlg3rPHYmOm/GBoA2Appg49PRRr152ABZOdo50Py8c8m5iO2cz9rGAo9pDFY4L+K43Ohxr9qMY5N9M/qvscio2FXdtBhHrijQ87fYK/zUqLsdsqS/aRRUKIWqy88sqtNkH0EQMXnYs79lq11WYIMd3RDmLVDGlSPwf24/8nWCLvyT9ICw8HmPzE9i5M5PDOh8NinkXnwwDsx/jhpaeao1PupD3KB4x5dk2lvnuwydtFzoe9K15N8wNrxDx7KpV/GBM5iglBs5yEPZ1dp0hm7nwYhXkDHAlsGvPsFefD7ViOqOGlfbcH3sEmL4XdM9DRW8Gl7R2uaaOQCOfDd7D8VefHPHut9NG86XhV3sbEiHr1zYFNHv8NXNdkfcXnBZ8DXQnrq042LsVEkk6JefaJ86F4Cv9sL4RunhHz7PTiTfJa+AGwZimR/G1JXDvK+XBiNRF9iR2xCebgwi7nwy3YJHcy6Un9gZjnxDGl7U9ik9UMuxaXYh4QW2GibuGFsTVwRZySYL/TflrH3p0wL4S1Y56NTdtucT4sBBznfDivMsm8OeaZT/+PTl4Sxzgfjo95VhZOn4l5VkzQRzkfVsfGw/aFx5bz4SHMe28LkujZxfOYAwtpvSOVuxObbG8D3BHz7GXnQxHSVQ07HQyMLl93oIN62kXujHn2i/T/6HRf28X5MLRB2NsqwLsxz/YrbRtd/BPz7DHnw/vAjNU+HvPsCeweAEy+r9yDtd2PMHGqzMlFKC8m/K2DtVOx7VeY6PjDmBZjcD7cCrxQNbp0/Qsx5zbMc2sPoCpWvYZ5/00qlR8GjIp5tlOpnjcwL71GNGyvZFun3xExz/7ufPgEeLOJe8eG2L2g7FEzOom8BzgfTol59mapfJf7gfNhBexalD16RzsfPgeOdT78NubZI1hOwU+BN5qw+2hMMF8x5llZCLoQIObZ286HK4DdnA8nlWzbHXiidC8o+ADYpHigULpP7QCc14X7WUE/YINY8ojuhA79KNVdLdNp/yjt6zBhcedU73SRi2r8K+8BSKwSosVMnDgRgJlmmqnFlgghRHvSDmLV45X3w2Oe/aG8wVl40TAsifeidLR7Qewp/vcxwebKSn2XUxGrUjjG4djkdgmg/C2xLB29up4qhCqAmGevOx9ep7anQE2cD1/FPI6OZ4rHTcEYLBSm4GLgQufDsjHPnkpltwaurITKPYOFoFSZWDl2zRWOUpten+rZv0Y9tSZEjUJFZsS8GhYDVqtM6Ps1W1/Ms3XrHaMOq9DR0+WtegX7gOrEfgg2Sb+3cs1HY54CqzDFm6vKYOCl8mQyibJXMsWLA8x70AGXVo7xADa5XgO4LubZS87yhG1PEquSffMDF0GX+2mVNYD/1JicXoIJGcvRUWirNU6Pw7ysymJVVbQo7hejig0xz95JY3KJbp7HR4VQler7xPnwFM2N8QeBQ50Pv0m2/jVWVtbsItXJ7j+xkOaFsPtcPRvmcT5cgrXj3Z2Fi5VxPuyJ3SOXwYSmggFN2PcvzMutYDAWQjx51dCYZ/91PgQqXnPJY/RoLFxwAabcD56ocdzrKiLN4umvuiLoNXQudnfaXl38jmiGNTCPwMsq2y/BPPYG01Hk7E4/WKNUZ/UYx2Jj4pEuWW2i7Y0VoarKcExcXxcTMBfBRKWDapS9uuz5GvPsniQED8a8Upu6n5W2j+yCUAVT96NaNDueZkyfr4utDHhfF+yYpgxYYPaGT/CVN0WI9uDSS219o6FDh7bWECGEaFPaYTXAzbDJyobYRHIv58MOxYfpyeUN2ApyxwHrpPK/SUW+kl4XSa9lD6Fa78Emz3tgbv7rp/r2rtRX8DZT80mNco2YFws5OBITk8p/+2A/jItrcQ0WzlB4B22ATVAurtSZJbuLv3Mwj6+VK39TTTKcD/NhIWb9MA+IDypF3qF2GNM81PCQSrZfiOX+2TQ9vS/zdoP6is+7y99inj1U+nuu812mGdXVpBbEBMXqNf9r+ny+BnUtQu2+W922YHp9usZx5qwc4yJgtRTaAyZcPV0SxLrST6vMS+3VtCaUPm90HsX7aoB0tb992mB7MSa7eh61vP6aHePHY4LJjzGPz7ecDxekcLjuUB0LhUBd15aYZ+Ow8NwlMMH0DefDmOQ52RDnwy8wsWEMFmL6PUxErXfMWvaVyzXVb50PS2CeVPMCvwBWxe5XI+sct9q3at7vk0jeUEhqsr268h3RDPMCb8epc/PVGx9d7gelOqptVe8YzTAfnSSKj3n2V+AhpjwU2gUTDC+sUbxe3yjGfVfuZ9D1Ffw6Ld+F8TQnFsp5L1Pu6UIIIYQQopdoB8+qf8UpK87djj35Pcn5cE3Ms/9iT/sHUQr7SWWzSj3Fj9CFmJIovHg/GefDV7DktcMqIVvf7qXzqcW72FP1P5K8WKoUT5uTF8K1mPdHkSj32Zhn91TKV8PBNgY+jXn2UCNDksfAKOxH/+qxlES5xKNYjpYqy1E7wfeZWJ6tLWKe3Vanvs2c5S4q561aDhMZms791QWK8MCZK9sbCUS16uiwv/Oh0YSv+sT+LSyZ7lZ1yj/foK5XqX0NFqq8LybmG1BbdClP3K/B+uB2zofTMcHzhNLn79JkP63B29T2xFm4hh1g5/Fo5T1Y8uae8i7dP48ukfLsnAic6HxYGBPVTwFmxcZEnxDz7Gosl97swFrJppHOh8U7Odetgdtink3On1YSM7vDq0zdR6mxbQi2WMZW0VbOK449a516q2OrfL+fTPLG6XSMN2ovbMz39nfE28C8zoeZK5539cZHd49R1FnOu9eTY7zJ1AJyLf4EnOV8WAwTq66KpWToJer1jfEVG5u5n0Ftj91GNLuCZDPj6W3s+/lG4DLnw88ahd0LIYQQQoiu0Q5i1WRS+M2BWHjaXljemWLyMjm8LeXaqYbyPIBNULcCyivxVBOmz4J5XUysbB/aE9sbkQSou4AVgYebmChfjAkKP8QmTSf1hh1pIngTlvh2rVg/QfwNQO58WDrm2bNp3/7YymvlhPY4H07GJic7xvorJN6AhftsSXraniaVP8Xy/fTqSoCJIkfOCqSwsnTMDSrlimN/tU4dK1S2bdwFG0ZiOdg+jHlWDXftjPuAnZwPq5RyVjmmFr5uxfr912Ke3dqowphnHzgfrsc8ql7BvDQuLn3e1X5aZhywpfNhtYqw+jMsOfy/K+W3whJxF2ydzqPHHgo9PI96lPtJ1ROxOO4E4Fznw4ZM3W/6hJhnH2JJypfGcunMhyX9/oTaicdnZUqy9YKdapRrlvuAA50PS8Q8ewkmJ9+vPlyodV//BnaPaejJk3gZS+y+FZbku+AndOF7rU57fUrz3xGfUPveUWUclotpSyx/XMG26Xg9zZdXHANsLP2mtL34rryTrjMa2Nz5sEjMs0ZeSZdhix38GQufPbNOuS2cD8NKOatWw8I5ixC6pu9nfUGD8VR8Ptb58CMs0f7lzoetJVgJIYQQQvQObSVWAcQ8u8H58CCWPP0P2CT3BeA3KVHsRGosTx7z7Annw5+xJM2OKasBblgp957z4X4sqe2r2JPjnWnu6XFP2B+bLIxyPpyHeQbMj60ONUPMs7IINAYTE87DJnXVHCTd5RpsMrgvMJvzYZXSZ8/EKUuPn4OFS13vfDgCexp9LDY5nLx0t/Ph4HRe5wNPVep7I+bZMwAxz8anJLynJaHxOWwFt6WoiI7Oh9uAJWOefb2H5/og5l1wUuoPn2AC6CyVcq9hT+u3dj48goVgPhctyf7lwPnOh1Oxp+cr0jVR81Js4n9bEvX+gXltLIOFjW0ap14hseBCTBj8i/PhMEzw2QMLPZlMzLNnnA8nAn9wloR5HOYRtgTW/88t52PCPI22wcTDu2uETXaln5YZgfWrvzgfDsfEhG2TDbvHjsnVATZ0PpyETYa/h3kRXhQ7JlfvCd09j3oUHoUHOEt0/3nMs4eS+PcPbJW1d7D8TUMojZNpjfPhGMw75Q7svrE48EtgfGlMP4aFWP8UGxcfREuuPhI4OPWxv2Jh1lv0wJxTsXE22vkwjCmrAVbzCo3BQsUuSmNjEaxPvkgT4ekpf9vRmDh4ATZWv46tNlcV3zrQTHt14TviMWD15Nk6AUu2/nyNcrdgCcLPdD4sgHkVbogJ/SfEjsnVu0XMs0edD5cBw5Iwfy+WC+pI4LIa4dnNcBQp1M35cDzmBbsYMCTm2eSFLGKe/c/5MAL7bv5nzLN6SZHmAK5zPpyF5Sk7AVth8KJUT1fvZ71Ok+NpMjHP7nI+DMGu8RVJsKoKnY2OtwP2HbpuCkHE+bAmFia7c8yzIqfgktjYPSaWks8LIYQQQnxRaYecVbU4AstdsUcKmdgUmwhchIX23ElH76mC3TGBx2O5Jr6JeXZU2Qb4W6prRKp73xrleo2YZw9jeU/ewvKgjMae1H6byhPv9NT5z9ik4L4GHlBdZQiWp+oM7El2+W+j0vH/i01an8Q8by7FBKZ10pPmgh+l151r1Hdk5dg7YXlgjsO8u5bAJjwPV8rNQC+IqOnp9iaYwDYCu9a3pv/L5SI2YZwHm0A/yBQvkAuxydrmWPLjH2I51pq1YWLa5xxsqfSbsbbcEZtI1k3Enfr9+lh4zPBky3NY+1XLHpbqXwNLXH49cDAmnjxVKX4r1t8XY+o8aF3qp5X9/oslcB6Njc3rMXFv+5hnZ9fYZTts5bdrsWXqz8FEjl6hu+fRgBux67AX1r8fTNuLlQPPw4SfPYHfUTu59LTiAaA/JhTdioUsjaM0ptO224BzMdsLMe2Y9P+vsGvxHazPdoskuqyLCTwXYuNuJB29n4h59igmZi6JeV4ehImzTV+bmGfnAfth96rrsXvM1tQOHyvTTHs1+x1xKJYQ/kqsXYfVsTWm+i/ExuZN6f3+WCL33mJH7Hx2xu43P0/vd2y0Uz2S8PZ9zPPrBOxaHkPJu6jEVem1kVB7AiZ4jcDG08NY3sTJ4k4X72fTgmb6RweSN+kPsbyNVzkfquHnjXDY9155wZF+aZvrZJsQYjpm4MCBDBw4sNVmCCFE29Jv0qSupnwQQoju4XwYiomWy/aiCCuEaDHOVsTcF1g05llD7zYxbRk0aNCkhx6qn76yWA2w0YqBQgghegfdc4VoTL9+/f42adKkQbU+a7swQCGEEEJMHzgf/g9bXGFf4GwJVUII0RwffWSZIGadtd7aIkII8eVGYpUQQgghusu1WI6nUVjYtBBCiCa48sorARg6dGhrDRFCiDZFYpUQos+IeTaCSt4wIcT0S8yz/q22QQghhBBCfPFQok4hhBBCCCGEEEII0TZIrBJCCCGEEEIIIYQQbYPEKiGEEEIIIYQQQgjRNihnlRBCCCGEEEL0IYMG1VypXQghREJilRBCCCHEl4jxr7zH2sPvbbUZQgiAOzUWe4ttVlqM3VZZstVmCCF6CYUBCiGEEEIIIUQfMsvnHzPL5x+32owvDONfeY/LHv5Pq80QQvQi8qwSQgghhPgSMXDRubhjr1VbbYYQX2pGjBgBwNChQ1tqxxcFeYsK8cVDnlVCCCGEEEIIIYQQom2QWCWEEEIIIYQQQggh2gaJVUIIIYQQQgghhBCibZBYJYQQQgghhBBCCCHaBiVYF0IIIYQQQog+ZPDgwa02QQgh2hqJVUIIIYQQQgjRhwwYMKDVJgghRFujMEAhhBBCCCGE6EPefPNN3nzzzVabIYQQbYvEKiGEEEIIIYToQ2688UZuvPHGVpshhBBti8QqIYQQQgghhBBCCNE2SKwSQgghhBBCCCGEEG1DyxKsOx+GAheUNk0EXgKuAI6JefZxLx2nP/AcsFPMsxG9UWeNYwwDjop51q+0bRJwdMyzYdPimN3B+bArsBmwIjA31i4XAqfHPPu0UnYJ4FRgfaAfMAbYL+bZi6Uy6wI7AYOBRYFXgNFYW7xeqe8rwLHAdunY44GDY57d2cunORXOh37Az4CdgYHAnMBrwN3AWTHP7uhCXf2xdts15tm5vW5sDyiNqaVinj3f5D4jgPVini0+jWwaCGwKnBHz7O0myg8D7ox5dntl+wimoZ29gfNhLEDMs7VaasgXkNS3Xcyz81ttS0Gz48354IBTgJ8CCwE3xDzbtAvHGUaNMdFKnA9zA/th5/Jwa63pSK3vYyGEEEIIMf3RDp5VW2Jix0bAKOBQ4KSWWtQ7DAbaSswAfg1MAPYFNsaEwWOBS8uFnA+zArcD3wR2BLYHlgXucD7MViq6BzAfcBwwBDgB+DFwv/Nh9sqxzwN2TTZsDLwKjEpixjTD+TADcCUmyj0P/BxYFzgY+Apwm/NhrmlpQx9yE9bvXm21ISUGAkcB8zZZ/ihgnWlmjZheGYqJzdMjW2D33JOA1YCDurh/O46JuTG7VmqxHUIIIYQQ4gtKyzyrSoyPefZ0+v9W58OywM+dD/vGPIutNKwnxDy7vy+Pl54mD4151r9BsZVinr1Ren9H8jo62vmwdMyzZ9P2XYGlgQHFtXE+PAI8BeyOeQkA7FWpb5zz4UlgHLAVcH7ad0WSZ1PMswvStnHAo8AxmMA1rTgUmyxuEfPsmspnlzofNsC8+tqGJLD1i3n2WZPlZwI+S9fijc7KC9FTnA+zxDz7pMb2oi9OaoFZ7cq30utp7fKdVu/6fVGOJ4QQ0wNrrLFGq00QQoi2ph3EqioPA+sB8wOvAyRBYT/g/4C5gGex8IvTYp59XuyYPIJyLNxiFsw76HfVAzgfVsY8a1bBPINeBK4Bjo159r9SubFYGw1L9XwzHfuImGfXNTqJWmGASbQ5Flgd8+p5GDgk5tld6fODMC+lRWKevVWp7zHgya6Ej1SpCEsFD6bXxbBzg+QdVRIRiXn2nPPhHmATkljVRH0FP8YEoStK9X3mfLgcOKQ8kXE+HA1kwDJpn0eAw7oj/jkfZgYOAG6qIVQVdoxOZT3wG2Dx8nklMe8Z4IGYZ9uUdp3Z+XAKFtY4O9bX9qmGA6XQy32AAcCHwPXAgeWQuNRXjgc+wMTArwGDgL/XOKf+WBji3kD/dPyFgfmcD5tQCUtyPvwMOBDzjPsc6+t/iHl2VoN22wk4Gzgy5tlvG5RreK0qob5POR+KXWuGTaV2ADjc+XB4+r86hv4POAP4LvAf4OSYZ2dW6lkKG0cbYCGf/071XFvvXLq6r/Nha+y+sBTwNHBEnfpWAk4HVsaExD9h96ZfV8KGZ8Su046pzreAy4DDuxsS7XxYM9n1Pew+9jQWjnle+rzWPao/lbDpIgQT84I9GbsPn+18OJU6fRF4x/mwOeZF9B3gU+BW4IBKKPHzWDjujZinztewNt8v5tndqcxYYM2SzQDj6oVbppDjE7AQ5v7YuHsQG3ePl8oNxfrnYOAXWF/+ELgaOKjc7s6HpYHfA2sD/wX+DDxW6/gVW54HlkxvP09jYCfMy/MOYO2YZ2Nr2LRUzLPnG42JemGn6ZhjY54NrdS5ZjrP9dPxB3an35X6CMA5zodz0v87xTwbUfrePBF7GLEccAhwajPjqwjjA74BnJbsfgvzzj2uLPiV7gcrpzJnYmHrVZv3xTyB+wMfY/f033R2T3A+bJfap7h/34L1jVdLZZ6nkz4shBC1WHrppVttghBCtDXtEAZYpT/wHvbDs2Bp4DYsDGQjLKRrGCYulDkL2AUTUzYHnsAmFVW+huVM2gMLXzs91X1BjbLLpM+LOl8FrnY+fL0rJ5Umrfdi4VC7Aj9J5zjG+fDdVOwSYAZMbCvv+13s6fzFXTlmk6wJRODJ0rblgX/VKPsoNvHorD6wH+vl+p6LefZRjfpmBsptuRiWK2tTLPTndeBO58N3yjs6H54vJmsNGISFq9zQSTkwL7CITSTLbIBN4qrizqGYALQTNln/LjA6eZYUNv4WGI7l+/oxNukZAtySvKfKDMX6tk+vr3Ri7+HYZG43LA/ZVBNL58MPsD41DmvPLYFzsDapifPh0HSuuzYSqhKdXaubsIkpTAn3bRSmODi9jiiVLYfSzomN50sw0fRB4E/Oh7VL9i8BPIDlZfsV1u4PA9c4Hxp68DW7r/NhvWTHU9g94STsHjGgUt/82H1rXmAHTCj4IdZWVS7BhKU/Y9f/BCxktRqiOzZNjhuShMvbsPG1O9Ze5zNFOOkqcwGXY0LGj+h4X52qLzof9sAeADyGeTbuDqyAeV/OUal7dUxUPhK7980A3JjyIgHshQm3jzClX+zVwNZZgDmwvrcRsCf2cOB+58PCNcpfjIkXm2Ni4t7Y+AYmi963YiLd3tj1W4o6AmWFzbD+TMn2m5rYr6CzMdEVLsVEpi0w8Qia7HcVXsXailS+1nl9AxORfo/1+du6MTavxR4CbApcBxyNiWrA5PF1O/Zga0fs2gyhEi7qfNgWE1kvAzYEtsUEyYahyc6H3bC+8e90voekcxlXI8y9sz7cpzzxxoesPfzeun/jX3mvFWYJISpMmDCBCRMmtNoMIYRoW9rBs2qG9HR3DuyH/U+wJ5KTPabKnhPJ0+UubBLmnQ+HxTyLzocBWKjZ4aVJ9uj0o3KP8gHLXjapvnuA94GLnA97V7ya5gfWiHn2VCr/MPZjfSvMG6ZZTsK8WtYpkpk7H0ZhotCRwKYxz15xPtyO5YgaXtp3e+Ad7MltYfcMdHyC7NL2Dte0UShZEhX2Bc6PefZa6aN50/GqvA3M06C+ObAn4f/GJhfN1Fd8Xti7S6m+GYCRmKj182RrwWeYp1AjlkivL3RSjphnbzsfrgB2cz6cVApj2h14ouz9kPgA2KR4yp/CH+/GRInzkvfBgZjXwDGlcyrKZXRso37ABmXPvk54DdisHG5V8lwqWAV4N+bZfqVto2tV5iwJdCHabhbzrNMJdWfXKubZG86HZ1KRcrhvvfruT+fwnzqedHNgoad3pGPeiYmJ22BeKmAidj9gzdI4HpUmysfQWLhsdt+jgcfpeP3/DdyPCeQF+wOzAT+MefZyKjcK82qZjPNhdWyCu2PMs4vS5jHOh7eBS5wPA2OejU/bP8f6fl3SPe10TJBfu+SJMqbRfp0wO7BdzLPrS8fpn/7t0BfTPfdE4IKYZzuXyj+AieI/x+4TBXMCA2OevZPKTcCEyA2BP8c8e8z58D4wYzMeljHP3sMeWhTHnQHLh/ga1ldOrezy55hnR6X/xzgfvp/KFdt2xB6YDC55Dd4C/LMJW/7ufPhP+n+y7TXGar39OxsTXeHqmGeT82V1sd+VbfrE+VB4fT5bx675sfvZ5P2dD+fRtbF5chE2nuxaB7suxbZfMWV8vZiOcStT3+8HA4+U78PAzTVsnkzqM8diHmpbl7Y/jv3+2BkT4woa9uFGx2oFAxedi21WWqzzgkKIacrIkSMBGDp0aGsNEUKINqUdxKrHK++Hxzz7Q3mD82ERbCI5BFt1rmz3gljS8O9jgs2VlfoupyJWOR/mxLwBtsAEjZlKHy9LR6+upwqhCiDm2evOh9cx76ymcD58FfM4Oh6IFUFpDPakt+Bi4ELnw7Ixz55KZbcGrqzk/HiG2l4SHfIvOR/qhVwtgoWkPYNNqqvUyjlTd3WlZOdlmLfNahWRrF+z9SWvlcOx0KHyk+/nyuVinnXJs61JhmMT03WxydEimKhUKyHy1eVwlJhn9zgfXsYmRudhoTYOy4tVvt4PYMLoGnQUq0Z2QagCuK6JvEAPAvM4Hy7BxsHdMc/erVFuxvT5utiKe/c1Y0Cz16oX+SiWVm5Mk+an6DgWh2AT0fcq7T4KOMn5MGfMs/fr1N/pvlgI2MrAbyvX/4EaHk+rAPcVQlUq9z/nw0109OAbgoXJXVM5biEsroEJT8Q8W7eO7WUGYPeGDjb2kM8oieUVqn1xMDZ5r/b9l7H7/Rp0FKvuKyb5iUIEavoeW8X5sBXm6TIA8worGFCjeFWY/ScW9lgwGHipLMqkByRXYt9L0wvVkLem+103eL6G0NXVsVm9Lv/CvNsKBmPh6pPDSmOe/df5EOjovfggsJfz4ffYd969Nbx8qwzAflscXt4Y8+xu58ML2Pd5Wazq9T7cEwYsMDt37LVqKw4thBBCCNFrtEMY4GbY5G9DTLjZy/mwQ/Fh8vi4AVtB7jhsVaSVmRIC+JX0ukh6LXsI1XoP9mR2D+zH5vqpvr0r9RW8zdR8UqNcI+bFwgKOxMSk8t8+mKBQXItrsAnxdun9Bthy59UQwCzZXfydg3l8rVz5myqczPkwHxbW0g97Kv1Bpcg71A6RmIcaHlLJ9guxCd6mMc8eqRR5u0F9xedFqOTNWG6Qn2OT/ZWBf9C19i54Kb02FfoU8+yvwENMETd3wSbpF9YoXqtfvcaUXF0Lptenmfqaz4nl9SnT1RX8Oi0f82wcFn63BDZRfcP5MKYaUpns2QgLU/1rMwefBteqGWp551XH4oKYd1u1zYsVRqvtXqaZfefHxO1617/MIqS8e52UWxDzFP2wctxi30Y216Io/3LDUl3j9bK3a4VqXyz6/himbstvM/X5dLjHlkT5bvUj50OG5cf7N+Zt+32m5AyrVWf1Hv8JFkpYsAjNXe92p9Z16s1+1+hYxfG6MjZrXZfy9Wv2ulyEhYJ+HxPG3nY+/KXkGViL4vuq1nlMYOrvs17tw0IIIYQQoj08q/4Vp6w4dzuWl+Qk58M1Mc/+i+WMGgRsH/PskmKnNCEpU/yoXIgpicKL95Nxlnx3E2BYzLPTS9u/3UvnU4t3sXxIf8R+OE9F4QGRngxfi3lbHYWJVs/GPLunUr5DCIrzYWPg05hnDzUyJHmHjMImBqvHPPtPjWKPYnmmqixH7aTCZ2LhJFvEPLutTn2bOR9mrTzRXg57sl+Eh/0EE4c2j3k22UPM+TAP1oZd5aG0X4YlDG+GPwFnOR8Ww8Sqq2IpGXqJhepsG5/+L7zzNqC2yPJW5X1XV09rqnzMs6uxHGuzA2th4VkjnQ+Ll7xu3sb62Y3AZc6HnzUKH0309rXqLd7CwnROrPN5o1xgzez7GTbBrnf9yyFIrzJFuKmWqx73YyzvTb3jdoU302tncT6fYGJFmXoCRaP+Vv2s6NtDsbFfpSqO9zZbA08XCcZh8iqFDXMUNeBVat8Pa/WBZilyzDXb/vXqmLPG9nrnWes69Wa/a3Ss4njdHZu1eJX643AyyevvLOy+Pg92Tz4ZEzS/X6fu4p5fK8fZwth3ixBCCCGEmIa0g1g1mRTWcyDmqr8X9sR11vRxeUI8Ex1D58DCqyKWS6qcGHrrSrlZMC+niZXtQ3tieyOSAHUXllj24SZCcy4GtnM+/BAT1k7qpHxTOFst8SYsOfBaDXII3QDkzoelY549m/btD6zGlMS8RZ0nY6LOjrH+Cok3YHl+tiR5KaUwkJ8Co0tPoWfFcvKU8zCtg4VSdDm0LObZp8m+Y50PP4k1VgR0PqwP3FMS0S7DVpT8czrumdV9Els4H4aVchatBiwOFCF0t2L98Wsxz27tqu29TcyzD7GEv0tj+YzmwzxNis/HOh9+hHlLXe582LoTwarZa1Vc2682aeqnXShbi5FYeNCjXQyrbHpf58ODTH39v48tDlEWq+7H8uotXspZ9VXMi6163IOBueqIvV3lSSwv1i7Oh7MbhIu+gCU9L1O1rTvciwlSX495VssrsTt8guUsa4ZZmTqv1/bYfb873Afs5HxYpZSzymHfNd2l6Ccr0DGP3IY1ytYbEy8AP3E+zByn5EFcg+bbqSf9rqvjujhed8dmLe4DDnQ+LBHz7CUA58Ns2MOJmqRQvSvSeN29Qd1PYB5aW2Nh3aT6V8U8dU/uuflCCCGEEKIRbSVWAcQ8uyFNBr3z4Q9YKMcLwG+cD59jItOvauz3hPPhz8AxaSLxIBbit2Gl3HvOh/uBA5wPr2JeCDvTuRdCT9kfuBNLKHse9lR4fmAlYIaYZ2URaAz2lPk8bOJ1Cb3DNZjgtC8wm/NhldJnz8Q8K8SLc7DwxOudD0dggsSxWFjd5FXxnA8Hp/M6H3iqUt8bMc+eAYh5Nt5Z8vLTktD4HBaWsRQdRceRwH7ACOfDBdiKUkcCU3l/OR+eBl5oIofPCZhIeIXzYQQQsKfmi2PeQZtTShqfcgqNwPrYP2Oe3Vun3jmA65wPZwELpOM8RfKci3n2jPPhROAPzpL/j8O8GJbA+uW55fxL0wLnwzGYl8EdWH9aHPglluz8jWr5mGd3OR+GYMuzX5EEq6qoW9DstSo88fZ2PlyIjd9Hisl1DR4DNnI+jMQ80l6JedYVj4tfY6GMd6b7x/PY9V0BWDqWEn73YN+jMIGhfP2PxsKDypyC9fNRzoejsQn+/ul1soCUhMLLMA+4U5INERO/NgQOjnn2JIDz4TZgyUY522KeTXI+7Af8Bbjd+XAmJkx+C1gwTkkmfjlwhPPhcExYWx1LYN0jYp69nx46/NH5sADWn97D7rFrYkmru5p0+jEsRPynWJ69D2KePVGn7EhgU+fDqZi34Hexfv9ul0/GuBAT6f/ifDgMC5Pbg9peTU0R8+xV58M44FDnw5upzu0wT+Iq9cbE5dgKjOene9ZSWP9qaqm3rvS7GryGeUpt7Xx4BAtdfy52XJykSk/GZi1OxR5qjXY+DMPG1YFAByHM+XA2Jp7eh7XzNzDxsuZiEwAxzz53Pvwa88a6BPsOXgxLP/AUtVcO7jLOhzVJKx3HlOTe+bAk1sePiR0X5/gMuDDm2c9749hCiNaz7rrNpKEUQogvL+2Qs6oWR2DhM3ukSe2m2ETwIiyU7k46ek8V7I4JPB7L0fNNLGdJlW2Av6W6RqS6961RrteIefYwljflLSxX1mjMw+Xb2PmUy0bMs2cxLHFrw1XUusAQLE/VGdgP9/LfZI+KFH65DuahcTFTljxfJ3noFPwove5co74jK8feCfuBfxzm3bUEMCS1S3HcUdikcjVskrkzluOk1vnPSBOeEinPzlaY59wy2PW+HfNWm4itTFWd3F2VXs+iPicku0ZgidkfxvJ/TRZ3Yp4dhk0m18AS/1+PeTK8g014pjUPYBPPUzFPrxMx0ayu90wKN/0hln/sKudDNUypKNfUtYp59g8sCXWGrYL4ILZIQj32wSa+IZXdreEZTm3Xi1jY8D+wBQ1uxUI718Sue4/3jXlWLIowABOEDsSEuycq9b2JJa1/B7t3DceE6GuZWlDYDmunLbB+cjXWFk/RMQfPDDTxkCHaqn3rp7fnYd6Nu9FxJcITgD+k41yHiVnbd1Z3M8Q8Owv4MdZGF2OC1dHJ9vHdqPJEbFJ/LtYvGo3NczBR4adYP9oI639NiThV0nfQ+pjdwzHx6jnsXtYTtsNEwjOw+8iLdeqsOSaS2L0HFsoWsHvsdnRNlGu233UgfUftgolNY5JddT2a0j7dHpt16ivG15vYNfkjJlSeXyl6DyZYDk/HPBwTn3bspP6zsfHwbaxtfpf2X7PyPdgT+mFj2nWyjbStu96BQog2ZIkllmCJJZbovKAQQnxJ6TdpUldT5Qjxxcb58BtMvFw01l85Togu43yYARM232zCK1AIIbrMoEGDJj30kNJqCdHuvPSSrQMkwap3WHu4BUO022qo7WqXEO1Cv379/jZp0qRBtT5ruzBAIVqF8+H/ME+QfYGzJVSJnuJ8OBbzNnsByxO2C/AdaucmEkIIIcSXhNtus3SBQ4cOba0hQgjRpkisEmIK12I5nkZheYmE6CmTsFw9i6b/HwE2jXl2S0utEkIIIYQQQog2RmKVEImYZ/1bbYP4YhHz7NeYWCWEEEIIIYQQoknaNcG6EEIIIYQQQgghhPgSIrFKCCGEEEIIIYQQQrQNCgMUQgghhBBCiD5kyJAhrTZBCCHaGolVQgghhBBCCNGHLLzwwq02QQgh2hqFAQohhBBCCCFEH/Lss8/y7LPPttoMIYRoW+RZJYQQQgghhBB9yJ133gnA0ksv3WJLhBCiPZFnlRBCCCGEEEIIIYRoGyRWCSGEEEIIIYQQQoi2QWKVEEIIIYQQQgghhGgbJFYJIYQQQgghhBBCiLZBCdaFEEIIIYQQog/ZeOONW22CEEK0NRKrhBBCCCGEEKIPmX/++VttghBCtDUKAxRCCCGEEEKIPuSJJ57giSeeaLUZQgjRtsizSgghhBBCCCH6kPvuuw+AAQMGtNgSIYRoT+RZJYQQQgghhBBCCCHaBolVQgghhBBCCCGEEKJtaFkYoPPhXODnwKkxz/av8fkw4KiYZ/1K2yYBR8c8G1YpuwXwa+DrwFvAWGDvmGfvNzj+EsDzwOfAojHP3uzZGfUc58PzwNiYZ0PT+3WBnYDBwKLAK8BorF1er+z7FeBYYDtgbmA8cHDMszsr5fYH1gYGAQtToz1TuVmBg4FtgCWAN4E7gF/HPHu+B+e4HHAgsE46/sfAI8BVwNkxzz7uQl1jgRljnv2gu/ZMD1T7RS/UNwn4TcyzI3qjviaPV7Of9ULdz9OLbdMOOB+GAhcASxVjLd0P74x5dnvrLKuP82Et7P6wdsyzsa21pj7N9v10byHm2Vrp/VpUzq9GmYHApsAZMc/e7mXTxTSiq2PL+bAzcCiwJPBRzLO5p511QgghhBBfXlriWeV8+CqwZXq7rfOhWdFsMHBupa7VgCuBvwMZcBiwCDBPJ3XtgJ3/TJgg047sAcwHHAcMAU4Afgzc73yYvVL2PGBXTLTbGHgVGJUmUGV2BRYEruvk2OdiotI5wIbAEcAawG01jt0UzoctgYeBb2PC2gZY298LHA3s3p16Rdsz1bgVDbkJa7NXS9uOwgRe0Tfslf66UmYgdp3mnUY2iWlD02PL+bAocDb2nbUOsN40tEsIIYQQ4ktNqzyrNgPmBG7GhJAhwI2d7RTz7P4am38CvA/sFPMspm0XN2HDDsC/kh07Ar9vYp++Zq+YZ2+U3o9zPjwJjAO2As4HcD6sCPwM2Dnm2QVp2zjgUeAYTOAqWD7mWUwC4R61DprExK2A38U8O6m0/TXgFmA1YFRXTsT5sCxwUdp/y5hnn5U+vtn5kAPf6EqdfYHzYZaYZ5+02o7pmTrjVlRwPswEfJbG/BudlRfTbnzGPHusN8qILxzLAjMAF8Y8u7unlZXG/KQeWyaEmO7YbLPNWm2CEEK0Na0Sq3YE3gGGAi9gwlGnYlWdcKIIzIGFlL3SzMGdD4MxYeQgYC7gcOfD8jHPHk2fb4GFpS0R8+zltO1kYH9g15hn56Zt62NhecvHPHvM+fB17CntD5I9r2KizmExz96p2LAv8KtU7l/p/w5UhKqCB9PrYqVtPwYmAleU9v3M+XA5cEh5QlcS9BoxI/aDvBpG+W5hfhN1VPlVqnevilBV2PsG8IbzYWHgReDAmGenl8ukcI2DgMXK7el82AT4DTaReA4LVbyysu+KmDfX6sBXMA+vQ2Ke3VUqMwJ7Ur4lcDLwf9hT9H1rnZDzYQNgv1RuLuBZLHzrtJhnn5fKPQ/cjfXxo4CvAf8G9qtOeJrpF3VsWQsLU9oC8zDcBLuGAfhlzLO3auzzy1T//Kk99iqNgT+kdlg85tnE0j6zY/36DzHPDk3vT0jHWwjrI/8E9ol59njaZ6pxm67HMMxbb1bsmo+IeXZC+ryptm2ybZqxcUbMk3BHYCksnPgy4PByaKrzYTbgyKJtsPvYPantXqsVvpz2GwGsFfOsf3rfH+urewP9sfDdhYH5Un+eHAaY2g/sPnV4+v9o4EOs3y9evlc4H/oBzwAPxDyr6TXqfHgUeDzm2U8q278P3A9sFvPsuqrdpXJjYUoIXJ1jjMXG/DDgd8A3set4RMyz6yplezQ+nQ9bA7thXptfBZ7C+sqFNUzrl9pxT8xz9UFsjIzvxvkR82ytUugmwFPOh6LYUtgYfDrm2WaV/dfCxuyQmGc1xX/nwwKYZ+3aWH97C7gLuz/+p1TuG8CJ2IOEOYHXgQeAbdJ3Qa+MgVTmKOzhyGJYH3wcu1Z3pzI/S/Usi4XZv4jdL85Kn4/AruOmwBnYdXwR2D/m2U0pVP2XmIfaHcAulf7djJ39sfG1R7JzV6xf3AXsWfperzm26oTGj0jHBPMwBhOthibR6ShsHBch+5ekuiZWbJpqzGP3ESHEl4y55pqr1SYIIURb0+diVXKjXw/LT/SG8+E6YHPnwzxVQadJLsEm3Jc7HzZoMufRjpjIdSkwO3A4JpgdnD6/A5iEuflflLatA/wvvZ5b2vZ66Qn7osDL2CT7HWBpLCzxZiysBwDnw8+B04ARmMD0dezH9hxN2L5mev13advywHMxzz6qlH0UmDnV/2gTdQMQ8+wD58PFwC+dDw9gk7klgZOAfwC3lc5lGPYjfXJ+nTqsBzwY8+zVBmWIeTYh9YndgclilfNhBizH2ZWVfvJ1bMIzDJug7Yn1hTdint2R9l0Jm6T8HZu0fIRNYsY4H1aNefa3Un1zAZcDOXbt/tfA3KWxtvg9lntrULJjAeCQStnVgQGY0PExNjG/0fnQP+bZu8nOnvSLgtOAMVh45bLA8Vi/XLtSbjvgCUyImxm7ttc7H76ZxMTh2KRqMyzMtmBbYDYsPBTgVEwsPQwTCObDJsxz1zPQ+fA9LK/c09jYfTnZ+p1Ssa60bWc0Y+MlmMh3Ihbi8y3sGvXHvDdxPswM3IqFe52AiTpzAT/Ewo5f66JdYPeeBzGhZQbsXKsMBu7D+sVZadvLWD8+Fstr97tS+Q2wSfzODY57MXB0jfvudsDb2D2rN1gGG8cnYHnvDgCuTv3saei18bk0cDXwW+zevgZwrvPhqzHPzqzYtAMmjuwDzIJ5n97mfFi2B7mmbsJEpSMwMe3ltP1V4E/A6c6HRWOelR+o7I6JF6Mb1Dsv1icOxbztFsXa8J7UhkV/uREToPbE2nkxzGu5eLDQK2MA+478FdZvx2PC2KBkJ86HH6R6zsAEJYeJlOXjkPa7CLuOr6T6rnE+/BF7kLQ3JqqdBvwR8/Ttip0Fh6YyO2Ph7ydj3/vF92i9sVWLY4G/pXPbGxNUCxHtwmTj8diDicFYX1gaE/bKNDPme8QTb3zI2sPv7e1qxRecbVZajN1WWbLVZnyp+Ne//gXACius0GJLhBCiPWmFZ9X22A/YQgS6EJtY/xSoTiqaYU3gJewH89XOh83KniBVnA+zpGONKSYOzof7ge2cD4fGPIsxz95yPvwTm+Bf5HyYF5tIn0bHH55rY8IWACmZ+eSE5s6He7EJ+V3Oh/+LefZ354PDJt2jYp7tVCr7BjYJq4vzYY5kw7/pmHNqXmo/mX279HlX2Qn7UV5OOvsAsH7Ms09L2yL29LyzMIYlsB/6zTAcuMP5sHrJs2IjzLOg2kcWAgYXoWbOh5FMCX9cPZU5CZucrlPY7nwYhXkuHYk94S+YHdgu5tn1nRlZngQnb5a7MOHHOx8Oq3ixzQkMLIQB58MEbMKyIfDnnvSLCo+W9h/pfHgbuMT5sG7Ms9tK5SYCG5ee+oN5E34PuDd5Co7DJtRlsWp3YHTMs2fT+8HApTHPziuVubYTG3PMG2KVksDaIblxF9u2Mxra6HxYHbsn7BjzrLgvjSm13cDkdbNdqmuTmGc3lOq6ugu2VHkN82KaPH5KXjmAhVGmbf+phlQ6H64AdnM+nFSqY3fgidg40fmlmFfWVqRJevIO2Rq4ojLGe8L8wBoxz55Kx3gYE3CKiT30wviMeVbURRpLY7HchXsy9T3jq8AGMc/+m8o/gAk4v0rH6zLpwcsz6e34QohL9V+MiWg/xwQPnA/zA5tjXnh1750xzwpBuahrBsyT70XgR8C1qa5lmbpf/rn0f2+NgcHY+C97vZY77CrAuzHP9ittqyXGzQHskb4zcT68gj0I2RhYrvCedD6sAPzC+TBDzLPPu2BnwQsxzyZ/ZydPtZMK4bDR2KoS8+wZ50PxkOix0nfOCthvmLJH1mjnw+fAsc6H38Y8e6RU1VRjXohWM/6V9wAkVvUxDz30ECCxSggh6tEKsWoH4KmYZ/el92OwJ6s70EWxKoUbnICFESwJ3ID9YN0mWl6mxTEha4uYZ9ek3TbBnvJeVKrqQuzp93pM+WF9B+ZVArAW8B5wCrC/8+Fb2NPX75LyRiV7ZgZ8OpclsXCWggGY58Di6e+oyulcA0wVHleqe0bMy2YxYLXYMZSuH7XFon41tjXLcdjk3GOiyteSzbc4H9YsJnoxz47BhKFeI+bZWOfDY9ikuxCrdgceqTGheKm8LU1orgIOSpPWWTBB83igyNVVMAbzFCrzGU2EpAI4HxbBBKYhmMdDue4FgQml9/dVPFj+mV6/ll671S9qcGXl/VVYXx9MySMOuLUi6pbtKR7JD8e81JaNefaU82FlbKxtXtrvQWCo8+FNbOz8PTYI03O2yuRqwEk1PAHL5brStp3RmY1DgE8xz47ycYp7wRqYF8kGwISKINBTruvhpHU45im6LjZpXwTzOjmo0U4xz15KYuT2TPEoGYKJSxfV3bHrPFUIVem4rzsfXif1e2f58Xo8Pp3lxDsGu1YLM8WjqFY+q5uL+1ey6fn0wGJwjbI9JnmqXgLs4nz4TRJad8Luzxc03hucD3tinmbLYF6NBQPS61tYeOVvnQ8LYatjPtWxll4bAw8ChzoffoPlH/xrRdh8EJgnne/lwN2F52iF/8aOK9U+nl7HVOx6HBv7i2Dfuc3aWXBT5bjl+1xTaQOaYI30ekll+yWYOLkmtuJtQU/HfKcMWGB27thr1Wl5CPEFQ554Qggh2pE+FavSZHc54ETnw9ylj/4C7ON8+EbMsye7UOURwDXp6fMTzoefpLrOcT7sguWOmkjJ+wmb2H2Eee4UNoxK5XZkyo/e27FcKEtjHlTjYp79x/nwRHr/AtZ+5bpPAH6BTZruBT7ABIi/MEW4WiS9dggZipZXZKq8QjDZU+BCTEzbqPKUFsyD6mtT7ThlRcQuhbY4H5bHQq12KT+JTx4ITwK7UArRa5KXMAGvWf4E5M5yOM2OTVL2qVGuVujVa5gXzgJMyb91JHW8JpwPruSp83ojsaW8DyaOLoqJKo9jIUmbYmEeX6ns0uEaxDz7JD3R73a/qEN1/0+dD+/QMcfZVPYwZVJftvtaTBTaHRMt98AmeGVPil+kMjtjnjpvOx8uwvLH1BKj5sGEhHqhNt1p287ozMYFsf7yYZ395yu9/qdOme7SMCy2M2Ke/dX58BApbA4bm59h94vOuAi4wPmwVMyz5zDh6unOPEy6SK17zydMuYbz0sPx6Swf063Yff0QLF/Xp5hXVa1QyHr3jOUbnknPGJ7s2dD5cBMWAnZtzLOGoaPOh19gHq6nYGF172Dj535SG8Y8m+Qsf+Iw7DtoPufDc5gg/KdUVW+NgeOxsLXtsJDCD50PV2M5tN6MeTbO2aqvvyB5biVRdP/K99a75crTfQqm9hAuhLCivzRrZ0Ez97meUnguV8fyhMrn1CknhBCilxj/ynttJ76Of+U9Bi6q/GRCdIe+9qwqkpMezJT8UGV2wASoZlmaUvhQtOSsP8W8ST7ERKWzizwk6anzBth515p0buZ8mCPm2QfYinsRy0u1DlO8vm5P71/AQgfKT7C3Bi6KeXZcsSFNpMoUP1QXKm9MT4mrP7QLzsRCH7aohHIVPJpsn7UiECyH/dh/usY+jfh2en2wvDF517yL5QjpKmMwz4KFY5414xVzETbxGooJHP/DQpeqLFRn26dYPpGvYtfxj9TxGKmElDX7xHsZLPR0+5hnk5+oOx+yJvev0p1+UYvq/jNj7ddlkSXm2UTnw7nAXs6H32H9++SyV1/Msw+xvDCHOh+WxBK8/xZr/1pj/B3selTFszK92rZN2PgWNgFfvU4VhQfGm0BnvvpFgueZKx4n9a5hb3hY/Ak4y/mwGCZWXRWby710DTYutnM+nI55ZJ1QKfMxJg5UmQ9rt57yLj0fn4MxIXz1WFqwoOJ5U6bePaO3hcjJxDz7l/PhLkz4/RjLR7d7E7tuDdwW8+yAYoPzYaka9T8L7JBCZlfEhP3hzofnY57d0ltjIHljnog9cFoYC9s7BVsk4aepzNVYSP7smFfyiVhI8uJdDN+tRbNjtS8pxtrCmFBK6T1MPU4U/ieEEF8iBi46F9us1OhnrxCiHn0mVqVJ89ZY3qNaCZJPBbZ3PhzZBRf5R4GNneWweR8g2gpW22EhCO9jT38LtsPOeU+mhB0UrIjlg9oSOD/m2XvOh78nm5djiih2OyYevUwlzw72g72aL2unyvuXMS+jrSiFEGKJYae6Hs5WIdwFy9FxXfXzxA3Y6mBbkjwq0kTtp1h+ka4u7V6ISd+jFL7gbMWpuenepO5UrC2GOx+2rOEdMT8wIObZPQAxz953PlyKTehmB/5cXOMKSzgfVinlD5kBa4e/ponRf9MkcUXg4V6YLBXMml7LK+XNxNRhS83SpX7RgOr+W2KeGPfVLt4pZ2GT3KuwkMpz6hWMefYCcLLzYVvqiDoxzz5yPtyNCSTHxDyrlcC+t9u2MxtHYhP2ueqIwQWjga2dD1nMs1CnzAvpdQUsATPJg3NVzNOyu3yKCa+1uAzLA/ZnzMOyqXDqFJ52PeZR9QrmbXJxpdgLwELOh/ljnr0J4HxYBgtB6/Gjy5hnvTE+a/WXebCQ71ps6HyYLU7JWdUfy7X0224cu0xxn613nYZjoWHzAE/GPKt+f9RiVqZelbX6nTKZ9N053tmKej/H+uEtlTI9GQPleiZgSew3pMZ4TwLZjck7+XRM4Ky1wm1X6LKdTdBobDXDuPS6Nea1VlDcr+5ECCFEnzBw0bkUBi3EF4i+9KzaGPuxekCskfjX+XAW5iGwFh1D6xpxEPZD/D7nQw48j3ll7I15qiwI/BoLYQLz3HoOOKsqiKUJ00GY91cx2b8dC714PeZZsZreWMytfz6mDoUbCezoLDn701hunw53zJRL62jsR/4FmKj2dUwQ6DApcT4cDOyf7HnK+bBK6eM3Yp49k+oc7yzR8mlpUv8cJsgtRWWC73wYhK2aVOR0Wc75sEX6/+bkmXUXluz25DTpewibBB+B5e66sFTfr7E2XiZNgmqSvLJ2wCZr9zsfzsSSGs+GPSXfHQufvKe023CmeB/Um4C/BlzhfDgKmwjtia0mtWepzP7YhGGU8+E8rG/MD6wEzBDzrKury4EluX8B+I2zRLoTsQTN3aIr/aITli/t/w1s8jSuuxO7FPoasPxtIebZS+XPnQ/3YWLpPzFvxjUx4aFRGJrHJnj3JTH2ZcxLcmDMs1/Qy23bmY0pR9plmDfIKcBfMW+f/lgC/INTePIl2Gp1lzkfTsCE9zmw1QBPi3n2OHY/eg8LRT4KE/gOon7YUrM8BmzkbAGBd4BXYlogIubZ/5wPI7A2+mfMs66ISBeRkkNj+YWeq3x+FZZ359LUNvNjffLNnpxMhZ6Oz3uxMfLH1OazYfeqN7HVA6v8D0uAfRJ2fY5O+5/aw/MoVoXd2/lwIdZvHyl52F2DPRBZDVvRrxlGAgc7Hw7D+uU6mFfUZJwP38G+i67AvndmwDxSPyM9UOmtMZDEzX9gQuw7WA67IUxJ0n8M5qV2ByaALg78Eks631OhqitjtSvUHVtN2vRosmlYekh0L+btdyRwWY2w/Q6kPr9jzLMZS9um+l5N35/nA+vGPBtXszIhxHTFVltt1XkhIYT4EuM6L9Jr7Ih5FlxV5/PLsEnEjnU+n4o0Af8Bllz2FCz31IHYsuvLAfsBBzgffu18+D9sRb8LanluJU+fEcDqpTCLOyqvJO+Cf1a3J36BTQh+g00c5sAmgtVjnZdsWwe4HntSvjVT5+v4UXrdGfOMKf9V87vshCXrPQ5LKrsEMCTm2cOVcvtg1+CK9H7L9P4qTNwr2mJd4Fwst8rNqd6Hge/HPHuxVJ/DJkedJnOPeXYVNgF9FEskPgYTVVZP53NmpfwjWI6sh2qcR8HTWLt7LDfYssA2Mc/K1+xhYGUsHOMMzEPmdCzcsVtPvdMEdFPMC+0iLIzpTnrgndGFftGIfbFrcQWWX+ZGKpPbblCM2bNqfHYn5s11KdbvtgB+FTuuFtaBmGcPYhP2l4DfY/3rQFIeq2nQts3YuB2W82cLrO2vxsbKU6QcRykEagNMVC/GxXBMWHk7lXkXE+Yjluz+hHSOzQrw9dgH+C+WL6xY9r5Mo2vUiFuxdl6Mqb2qiLaq3Rbp8+sw4W1/bFz2Cj0dn0kE2Qy7D12Ntfm5TJ3wuuAirB/8ARNr3sAEgC7l9qthxz+wPpQBd2PXadHS5xOxvvUJzeUUAxPwz8KEyGux77AfVspMwFYH3B/7/rksHXfjmGfFCqy9MgZSPRsA52FC2p7A75iS0P8BTDg6FetbJ2LC9EZNnm8zNGNnV+hsbDXDjti57ozdF36e3jfze2aG9Fem1vdq09+1Qojpg1lnnZVZZ52184JCCPElpd+kSUqfINqTFHb4OLBr7LjkuqjgfFgLE0TWj3k2ppfrvhQTl5buxTBK0Ys4W51tX2DROuGyosUkr5ungbtinm3fanvEF5dBgwZNeuihh1pthpiOKBJSK3yqbxk/fjwAAwcObKkdXxTUj4WYPunXr9/fJk2aNKjWZ32dYF2ITnE+LI6FwB2NhQT9ubUWfTlJYacDsdxn+0uoaj+Sx+gATKg6W0JV++F8mBPL6fQzzOP15NZaJIQQoh2QWCWEEI2RWCXakV2wfB1PAj+rk4RbTHvuw/LbXIiFu4n241osR9AoLLRWtB8rYV6PrwP7xjwb31pzhBBCCCGEaH8kVom2I+bZMCwniWiStGhBr+YyiXmm3ChtTsyz/q22QTRmWoxNIYQQQgghvuj0ZYJ1IYQQQgghhBBCCCEaIrFKCCGEEEIIIYQQQrQNCgMUQgghhBBCiD5k2223bbUJQgjR1kisEkIIIYQQQog+ZKaZZmq1CUII0dYoDFAIIYQQQggh+pAHH3yQBx98sNVmCCFE2yLPKiGEEEIIIb7EjH/lPdYefm+rzfhSMfDdhwA46MGJLbak52yz0mLstsqSrTZDCPEFQ55VQgghhBBCCCG6zPhX3uOyh//TajOEEF9A5FklhBBCCCHEl5iBi87FHXut2mozvlSMGPEkAKcOnb7bXR55QohphTyrhBBCCCGEEEIIIUTbILFKCCGEEEIIIYQQQrQNCgMUQgghhBBCiD5k6NChrTZBCCHaGnlWCSGEEEIIIYQQQoi2QWKVEEIIIYQQQvQh9957L/feq+TkQghRD4lVQgghhBBCCNGHPPnkkzz55JOtNkMIIdoWiVVCCCGEEEIIIYQQom2QWCWEEEIIIYQQQggh2gaJVUIIIYQQQgghhBCibZix1QYI8WXH+XAu8HPg1Jhn+9f4fBhwVMyzfl2stz/wHLBrzLNze8HUbuF82ALYBhgELAi8CPwFOD7m2QeVsvMAJwGbAl8F7gN+FfPsn5Vyx6f6vgvMC+wU82xEpcxQ4IIGpi0S82xCF8+lqHPZmGdPd3HfTYGlY56d0pX9WoHzYT/gxZhnf+nleg8D9gAWBf4V82xg5fOhNL5mBUfHPBvmfJgE/Cbm2RGdHHcsQMyztbpstJhM9V7kfJgb2A+4IebZw31w/K8CE4A5gYExz/4xrY8phBDTiplmmqnVJgghRFsjzyohWkiafG2Z3m7rfPgiCsge+Bw4DBgC/AnYE7jV+TD5HuR86AfckMr8AvgJMBNwh/Nh8Uqdv8DErBsbHPcmYHDlb1XgLeDBrgpVvcCmwFRiZJuyH7B5b1bofPge8BvgcmANYPsaxarXbO+0/ZeV7V0VX/dKf6JnnIu1f8HcwFHASn10/M0xoQpghz46phBCTBO23XZbtt1221abIYQQbcsXcWIsxPTEZtjk62ZgQ0yoaSTAtBWFJ0wnXl9ZzLM3Su/HOR/eBi4E1gJuT9t/DPwAWCfm2R2p/vsw77CDMMGiYK6YZ9H58HXqTFrTMcvHxfmwOjAfNsGe7nE+zBLz7JNW29Ek30qvZ8Y8e7ZWgeo1cz58Jf3775hn93f3wDHPHuvuvmJKP4t59jLwcgtN2RF4G3gKE/cPinn2eQvtmd7GoBBCCCHEdIPEKiFay47AO8BQ4AVMeOlUrHI+7ANsCwzAPCQfB46NeXZTjeIzOx9OAbYDZsfEoX1inj1fqm9rYDfg25jH0lPAaTHPLuzuiRVUhKqCB9PrYqVtPwZeKYSqtO97zocAbEJJrIp5Frtpzo7Ap5h3T6+QQsxmBIYBvwO+CTwLHBHz7LpUZkQ6Nil0DeCFmGf907b5gWOxNpgfE+hOiXl2duk4Q7EQuTUxz7L1geeBgc6H54G7sb5zFPA14N/AfjHP7q7Yuybwa+B7WN+5Gzgg5tm/0ufPA0sCSzofike+F8Y8G9qgDb4HHA+sAvQD7gcOjXn211IbrZmKP+N8gBTKV6/OruB8+CXwK6ztHgb2inn2aOnzsTAlDND5sBZwB9avNgC2BiYBo7Cx8W5p3wWAM4CNMA/B64BrgeuBtWOejU3lfoi1/fLADMB/gEtjnh3TwO6FgROxazkfJsT8Ddg55tnrqcysqd6tsPHyH8zD6YTyOEh2Hg1kWLjt68BYYJeYZ5+kPrhW0eeaaJufAD/CPAJnAuYuhwGWwowBznE+nJP+3wkL0d0SWDzm2cTSsWYHXgX+EPPs0HrtUqetFgPWBc4E/g6cA/wQE/o727fhtelp2zgfdgOuAFaMefZIpY5bsJDjgen9jMCB2P1gKczT8zLg8JhnHzsfZsEEwUtinv2qUtdQ7B7wrZhnj3d23kKI9mbcuHEArLnmmp2UFEKILycSq4RoEc6HRYH1gLNjnr3hfLgO2Nz5ME/Ms3c62b0/NmF9HhvHGXCj82HDmGe3VMoeCozHJpELYqLCaOfD8qWJ5NLA1cBvgYiFaZ3rfPhqzLMze3SitSl+mf27tG154F81yj4K7OB8mD3m2YfdPWAp5PLGmGdvVT4bAezY1bxgJZYBTgdOAN4EDgCudj58M+W2OhZYAFgZE6QAPknHnhO4BxMJh2ECwA+BPyWvjd9XjnUpNrndgo738NUx8fJI4ON0zBudD/0L8cX5sBEmstyEiZcABwN3OR++E/PsJczb72bgH8keqHiolXE+fAcYBzyGia6TgEMwD7pVUl6hvdLxDsVCuV6l9zx0tgOeAPYFZsZynl2f2v6zTvY9HRP4foa13e8wQWrHUpm/YCLuocDTmFDR4Zo4H5bGQlivBo7BBNFlsXHViIsxYfBA4CVgIUyQmTXVOyMmoC2HXc9/YoLgkViutgNSuXmAe9O244BHsLG+SWqT7nj+/B64BQvX/EqNz1/FruVfsH5/Q9r+DPBXLIRzM+DK0j7bArNhQlMh3DYUQktsj4mrF2H3jd9j16mhWNWDa9OIatuMBN7D+uJBpWMvhN3jDyntewl2vz4Ru2bfwq5tf+AnSVi8ANjF+XBozLOPS/vuDozrTKh64o0PWXv4vT04PfFlY/wr7zFw0blabcaXjueeM71fYpUQQtRGYpUQraM8+QILi9sG+CnmPVCXmGe++D/lfboN+AaWvLoqVn0AbFJ4YTgfnsS8aXYAzkv1HV+pbyywCJZb6szSZzNgnjOTN6Xt1XvJ5zHPJlGD5CFxDDAm5tlDpY/mxcS3Km+n13mAbotVmBfEnFg7V/k8/XWX+YE1Yp49BeB8eBibzG+FJZJ/xvnwBvBpjXC2fTHB4tvF/sCYlLz6KOfDnyqiy9Uxzw5iaoqk0+8kGyZgHmwbAn9OZU7HJrubFDs5H+7APMEOwDyx/u58+AR4s8nQu19jYsi6JVHsVuxaHgVsHvPsMedDEfr397JXXy8wEdi4EF6T19ZVmOdYZzP2O2Oe/SL9P9r5MAATCYbGPJvkfNgAC039acyzQnQZ5Xy4AfNeK1gJE4X2jHn2ftp2O50zGDgs5tmlpW1Xlf7fJh1/zZhnd6Ztt6VzPMr5cGLywPoVJr4Minn299L+lzVhQz3+GvNsl3ofJlGlONazlb7yhvNhHCaulMWq3YHRpTDQroy7HYAnYp49AJDE/c2cD3OXPeFq0N1r04ip2sb5cBXwM+fDISWPt22w++WfU5nVsfv7jjHPivv+mBQWfYnzYWDMs/FYXr8DMHH94rTvdzChcpse2i7EVAxcdC62WWmxzgsKIYQQfYjEKiFaxw7AUzHP7kvvxwCvpO0NxSrnw3exkJ+VMY+dQkB6okbxq8vhQjHP7nE+vIxNlM9L9S2LCUhrAAszZfGFqkfGbUzxiiozsfJ+bUzwqto9O+bZ8xnm6VWmH+aVU6W73k5VdsQ8hKbyxIh59nNsRcbu8lRJaCLm2evOh9fpKGjUYwjwAPBcRfQbBeyCedWUQ4uurVPPfRWPvGIFxa/B5Gu8DHB85TgfYasurtGErbVYA/NWe7fYEPPs/SToZN2ssyvcWg41o+N5dyZWVcNm/wnMgnk4TcDEgc+Zus2vpuO5jcfGwOXOh/MxEez1Jmx/EDgwLS5wO7ZCYnkMDMHCg++tXLPRmAfVKpjX0AbYogFloaqn1OtnzTIca49lY5495XxYGfg/Son7Y5419RskhZl+Cyiv+nghFr65FXB2rf0S4+netWlErba5GBuv62D3crAHEmNinr2a3g/BPLuuqXE9wcbS+JhnzzkfRmHi3sXps92x+1enK3QOWGB27thr1S6cjhBCCCFE+6HVAIVoAWnithzwF+fD3MmLZg5sIjLY+fCNBvsugYlG82K5i1bFRKuR1A7Xea3OtsVSfbMDtwIrYuEqq6f6zscm7mV2T58Vf0en7StX/v5Ww+6vYBPrpYEfpmTNZd5O51RlnvTaWWhkXZwPi2DhOJc2ERrWHd6use0Tal+PKgtik9SJlb/Cw2a+SvlXqU0HG0pJnwsbFkyv59U41sY1jtMs89axaQJTrt20pNr21fPuyb6LAO9UxDCojKkU6vlD7Dv1YmCC8+GBlB+sET/FxsRBmCD5H+fDr92UVTIXxLzuqtfrr+nz+UqvvZ34vF4/a5ZrsT6we3q/BybGh27UVYRlhtL98kFMvNmx7l706No0olbb3IV5E24P4Hz4FubVdXGpzIKYl9eHdLyehXhWHoPDgdWcDys4H2bDQgwviHn2aQ/sFkIIIYSYbpBnlRCtoZhgHZz+quxARy+CMkOAuYCtyoJPSsRci4XqbBuf/h+MTYhXLyfjrhHaR8yzDp5bzocV0vaHqmUr5WYCrsFCs9aLefbPGsUexTxEqiwHvNiTfFXYRG8GaocAtpq3sMnqvnU+r3rL1QyvbPI4YLmXxtT4vLuT4Lcxb7wqC1NbxJueeBWYx/kwU0WwmmpMpYUB7kgJslfDPBVvSjnD3qxVefLw2RvYO4Ug7ogJwG9goWBvYTnMtqpj3/Pp9U06LlZQi48xoaTKfEzpG2W6288AiHk20flwLrCX8+F3mBfUyV0Vi50PM6d9wfKoVVnA+fD1JErVs6Wza9Pjtklho5cA+zkf9sREqw/p6IX1VjrW6nVMfaX0/83Y9d0dO+85aOxBJoSYzph11no/24QQQoDEKiH6nNLk6wE6Jt4tOBXY3vlwZJ28T8Wvm/IqW9/AJmG1vCu2cD4MK+WsWg1YHAv9qlffPFhy5h6TvEQuxRJHb9QgD9INwE7OhzVjno1L+86JhVv9uc4+zbID8EjKB9MqPsGSqFcZiXnIvdgL4UmNeAKb/C4f8+y3nZStZ2stxgEbOR/miHn2AYDzYQ7suo3tnqltw/2YyFlNFL5lvR2SR9vtpZDXpTAxqSFJCD7M+bAHsELaPBJL6P5hJ0m1RwNHOB9WTAnta/ECsJDzYf5CPHM+LIMllu9uNu7CE61eXzkLE0evwrw0z6lTrhEZ5r13NFP3p4WwlT13wHKnNaTBtemttrkYe8iwOZZM/pqYZx+VPh+JPZyYK+bZbZ3YGp0PZzHF23VMzLNnumCLEKLN2Wqres8hhBBCgMQqIVpBEXJ1QEzL3pdJE5Q/AWthy6RXGYPlfLrI+XAyFqp0NPAitUN75wCuS/UugK3c9RRTErvfC7wP/NH5cBS2WtcR2CSuN5YH+iM2uf8N8F/nwyqlz14ueYfdgAlolzgfDsTC/g7Fclb9rlxhCuFZgCkePYOcDx8CxDy7ulJ2JWzyf0A9A50P52FJj6flPfExYN7kdfEQ8HHyMDsVCwe7y/lwKiYqzQZ8E/N26xXRMHl+7I2tlDczJr68iU34V8XEslNKtq7ufNgYC+V6s0FS9GOxPn2b8+FEzOvkYEwEPaY3bG8VMc9GOx/uBs52PsyPrQa4BRYyC7ZyJklgWgPzhnkJS7h/KOYpU2uFS5wPc2Fj+VLgcUws3gQLnSxyGF2K5Xa7LY31f2AeQMtgq0pumsSQU7EVDcc4H47Dcm/Nn+rbI4mIV2HX6lLnwyklGzsV0hrwGuYttLXz4RHgv8BzxWqbMc/+43wImNgX0mqT5Tb4DFsNsFG+uB0xD6W8lnel8+FX2GqhR9US95u8Nr3SNjHPnnQ+PICtqroYHUMAiXk21vlwGbZS6ClYOGfEVgLcEDg45tmTpV3Ow1bkXBETLYUQQgghvjQoZ5UQfc+O2Ap9V9X5/DLgf9TJxRLz7FHsqf2STMl3cwhwZ63ymDj1NDACy4PyMJYzamKq7w1sMjkDljj6BOBcbIn13uBH6fVwTIwq/01eUSt5fm2M5c8ajoXPfA6sXZ3kYuLcVdgS8mChVFdRu013xMS9S2t8VjBD+puWnIt5gRyPTVIDQMyz9zCx6GZM5BmF5QvbhNpiZbeJeXYzNnGfLdkzChMCF2aKpx3YRP0JTNB6EJsw16vzEUxYfR8Ls7wYExfWbODlMz2xOeYRcyLWHl8BjkyfvZde/4G16QmY0PQHLHxvnZhn/6tT78fYWNwVG3fXYiG528Y8ux4slA7Lt3QOsBvWRy7F+vS9pNDNlNx+tVTHIcnek7F+X5QphLbFgOuw+8b+QFkc6RJpzO6CCWxjsL5STapfjMmzalTRcNw5HxbA7h9XNggDPg+7F9bLQdXptenltrk41fMfao/f7bDxtAXm3XU1sA/2AKGaC+0NzHPxVexeL4T4AjFmzBjGjKkVlS+EEAKg36RJPUpLIYQQQnypcD78ERgKzFtKZC9q4Hy4FBPSli6vSio6J4VjvwicFvPsyM7KFwwaNGjSQw81TCMohGgDRowYAcDQoUNbakdPWXu4RUu3ehXSdrFDCNE1+vXr97dJkyYNqvWZwgCFEEKIOjgfhmLhsI9iIXhDsJXtTpJQVZ8U7jsQC3HdX0JV8ySPsgHYogsO8zQVQgghhPhSIbFKCCGEqM9/gf2wPFGzYCFkhwEntdCm6YH7sHDQC5HY0lU2Ai7AvKp2jHn2aovtEUIIIYTocyRWCSGEEHWIeVYvF5poQMyzfq22YXol5tkILMegEEIIIcSXFolVQgghhBBCCNGHzDnnnK02QQgh2hqJVUIIIYQQQgjRh2y++eatNkEIIdoa12oDhBBCCCGEEEIIIYQokFglhBBCCCGEEH3IyJEjGTlyZKvNEEKItkVhgEIIIYQQQgjRh0yYMKHVJgghRFsjzyohhBBCCCGEEEII0TZIrBJCCCGEEEIIIYQQbYPEKiGEEEIIIYQQQgjRNihnlRBCCCGEEEL0IfPNN1+rTRBCiLZGYpUQQgghhBBC9CFZlrXaBCGEaGskVgkhhBBCCCGE6BbjX3mPtYff23IbBi46V0ttEEL0LspZJYQQQgghhBB9SAiBEEKrzfjCMHDRudhmpcVabYYQoheRZ5UQQgghhBBC9CFvvfVWq03oNQYuOhd37LVqq80QQnzBkGeVEEIIIYQQQgghhGgbJFYJIYQQQgghhBBCiLZBYpUQQgghhBBCCCGEaBuUs0oIMRXOh6HABaVNEZgA3AMcGfPsiW7UORDYFDgj5tnbpe1zA/sBN8Q8e7i7NpfqGwTsBqwBfA14E7gLOCLm2XOVsg44GNgdWBh4Ajgm5tk1lXI7Aj8GBqU6L4x5NrTGsWcADgN2BhYFXgT+GPPstG6eS3/gOWDXmGfnNrnPWsAdTRSteQ7TA/X6UoPyzwN3xzzbbtpaVvO4Y4t2TtdmLayPxVK5/nTxOk/PNNsuPTzGpsDSMc9OqWxfCxsf68c8G9Mbx+opzodhwJ0xz25vtS1CiL5j4YUXbrUJQgjR1sizSgjRiC2BwZjwcyjwf8BtzofurA08EDgKmLeyfe60faVuW9mRrYHlgTOAHwGHpLofcj4sUSl7LDAM+EMqez9wlfNhw0q57YBlgFuB9xscezhwBHAesDFwFZA7H47owfl0lYexa1b8bZ62n1DZfmwf2tTbDKR2X2p31sLs/rJ/925Gx/63Fr3fLpsC+/difdOSo4B1Wm2EEKJvGTJkCEOGDGm1GUII0bbIs0oI0YjxMc+eTv/f43x4BRNsVgVu6WtjSh4oa8c8G1un2Ikxz96o7HdP2m9X4Ndp24KAB34b8yxPRe9wPnwd+C1wc6mKHxYeH86Hmr8snQ9fA3YBjo15dlzafKvzYU7gcOfD8Ga8gHpKzLP3MdGtsKt/+vfZmGf319xpOiF5rvVrtR2iezgfZol59knMs7+32pbuUPS/mGeftdoWIYQQQogvOhKrhBBdofAqmqnY4HwYAawV86x/uaDzYSxAzLO1KmGFTzkfimJLYSISwDnOh3PS/zvFPBvRHQOrQlXa9oLz4Q1gsdLmHwIzA5dUil8CnO98WKoIG2wyNOl7mGdIVcQbCeyNeW5d2tRJdEISzI7CPIw+xcKaDu5OeGad+n+Y6l8emAH4D3BpzLNj0ufD0uffAk4HfgC8BRwV8+wC58P2wOHA4sCDwC4xz54p1b81Fqr5beCrwFPAaTHPLqzYMQk4HvgAC9X8GiYwFqFdHfpSzLPnOzmvrZPdXwP+DewX8+zuSpk1MUGzuJ53AwfEPPtXqcwGWOjq/wFzAc9i/fu0mGef1zl20WYAEwu7Y56VxbcZnA/HYKLqV7Hw1T1jnr3cyXk1vF6pzIqYN9PqwFcwD7xDYp7dVeP8j0jnPyPwNBZueV76fBJwdMyzYaV9+mPjePK4TfeF9TDvzJNTW50N7FsOA6zXLsnGl4FLYp79qmLjUKy9vxXz7PEa7TEC2LFkL8ALlXvUrM6HP2CemJOAUcA+Mc/eLdVTq/8NAv7eW/2kZN/hzofD0/9Hxzwb5nxYOR1/Jaw/TABGxjzbq3rOQojpj7/85S8AbL755p2UFEKILydf9lAEIURjZnA+zOh8mMX58C1s4vQ6MLaL9dwEFN5GRWjhYOBVaoep3dRDuzuQbF8QEygKlgc+wSbjZR5Nr8t18TCFSPFpZfsn6XWFkj1DnQ+TUv6cLpGEqpuAD4GfAnumuu92PizWaN8m618auAETH36K5eo6BZitRvGrki2bAn/DRL7jk02HADsBA4A/V/ZbGrga2DbtG4BznQ971DjGUGAjTKTaKNVVry81YnXgAODIdF4zADemnGnFuW8E3Ia17XbAz4A5gLsqIaRLp3I7J5suxMJJf9Pg+Odi4aFg4l5hd5lDga+nevdNnzcUOJu5Xs6HlYB7sbDJXYGfYOLiGOfDd0vlNknnNTMmzmwCnA8s2ciGBswFXA5chom11X4Addol5tknmLCzo/PhK5V9dgfG1RKqEsdinpFvMKWdN6uUOR0TqX4GHIO1yek16hpKx/73Si/3k6IPjCjZeq7zYXZMQPs82bBhslMPGYX4gvD+++/z/vuNMgsIIcSXG/3oEUI0ojoZfAXYOIWaNU3MszecD4VnTTm0EOdDERI0VZhaSoBeFtVnKF6dD+X7V6zn/ZTKnYlNXM8rfTQv8G7Ms0mVXd4ufd4VCq+mVYBymFMxGS3XF7FJaPXYzXAc5qHxoyIcyflwH/AkJsb0NE/PSphYsWfpOtdL/HxSzLOLkg0PARkmJCxV7Ot8WAQ43fmwZMyzFwBinh1fVJCu8VhgEUzkOrNyjH7ABjHP/lfap2Zf6oQ5gYExz95JdUzAvL42ZIqIcjomgmxSOtYdWHsfgHnJEPPszNLn/TAPqJkB73w4rFZfjHn2svOh8JB6oE4o2Qsxz35WqnsB4CTnw6Ixz16pc17NXK+TsGT/68Q8+zTVPQr4FybebZrO43RgPBZmW5xDT5KQzw5sF/Ps+noFOmmXP2HtviVwcbL7O9gY26ZBnc8kT8pPG4S+3hnz7Bfp/9HOhwHALs6HoZV7Qq3+12v9JObZ/cmb7D9lW9NCEfMAB8U8e6Rkz4h6513wxBsfsvbwezsrJoRoMQPftVt2MV63WWkxdlulu88GhBDii4fEKiFEIzbDQnH6Yavb7QPc7HxYI+bZvxvu2Tv8mikhQmWqE+ijMY+FWvwBy7G1USFUJPpRWyzqVk6kmGePOR9uBY52PjwLPACsTZq4YgJVUfYi4KKuHsP5MBsmThxfntTHPHsu5eVaszu2VxgPTAQudz6cj03qX69TdnLIY8yzd5wPrwN/r4iZheC5BPBCOo9lMS+RNbBVGAtB8hOmZmRZKOgB91Wu/z/T69dKNi0DHF8RQj8C7ku2ksougvW3Idi4KJdfEAvX6g5Vj8KyjfXEqvE0uF7Oh69i/eJ4IFbObQzm3QbmAbcklsOtV1bkAz4Dbuzuzqlfj8IE0IvT5t0x4fkvPbStVlvPAixEx+s3siJU9VU/eQp4FzjL+fBHTBx7qZkTE0JMf4x/5T0AiVVCCFFCYpUQohH/qnhBjQZewiZgP+2D459Nx8nuIljI0x5Y2FlBzYm88+EELDfSjjHPRlc+fhuYx/nQr+JJMU/p866yExa2NTK9fx84CPMW6ixMrRnmwcS0WnVNoPvhWpOJefZ0yoF0MCYQzOJ8eBDz8BhXKf5O5f2ndbaB5SAihTfdik3uDwGeSWX2xMKlqvRGu0HlesY8+yR5tBQhZgum1/Po6IFX8CJM9gS7ARMfhmFi3P+wcMbDS/X12EamiHd162zies2LeSQemf6mIp3TfOltw/xYXeT1ejm8usBwIDgfVsBCHbcDziw8xHpAs21d7X990k9inr3nfFgbu2bDgTmcD49ieeGuabTvgAVm5469Vm1URAjRBowY8SQApw5dVd6QQghRA4lVQoimiXn2v+Q19J3S5o+x0JYq82F5cXpyvFcoCVFuysp2T8Q8e6jRvilZ8SHAL2OeXVyjyKOYJ8UydMxbVeSqeqwb9v4HWMv5sCgmEjzDlLa6u+6OzfMO5g22cI3PFqaH7V0Q8+wObGXEWYDVMC+om5wP/WOevdnD6gdjotrq5eTmFS+VMt0JlewORdsdSu3Qt0IcWQZLsr19zLPJyfmdD9m0Na8+ja4X5p0TgT9Sx5sv5ll0PhTXtbO8Z58w9Xifr1ZBeufa3Qw8j3lU/QPLDXV2L9TbLNVz6LN+EvNsPPCTNDYGpWNe6XxYsZzIXQgxfbL44ou32gQhhGhrJFYJIZrG+TArNgl7tLT5BWAh58P8hZDhfFgGCysqPyosPBe+Wqm23vae2PlLLLfT4THPfl+n2EhsYrktFkZYsB3mUfZczb2aoBDZUp6a/TCvirHdra9U73+dD38DtnQ+DCutKLYkFupY71y7e7xPgNuTN9T12OqNPRWrZk2vE4sNzod5sGTezdLrfQbLOfY8sHzMs982KFfL/pmYEk7XiLLdH3TDxobUul4xzx50PtwFrAg83CDE70ns/HdxPpxdI5dbwQuUFgtIbNRD0+u2SxLSzsKE59WBMeWVJTupszf7R8G06Cef0sDWFPJ7v/PhSCyB/rewfGNCiOmY9dZbr9UmCCFEWyOxSgjRiIHOh/mx0LNFsJxV89JRFLkKW33rUufDKcD8mAdAVdQoPJX2dj5ciE3iHgFew7wVtnY+PAL8F3gu5lm3vIScD1sDp2Fi1O3Oh1VKH78f8+wxgJhnrzsfTgUOdT58ADyMhTauQ0U4cT4sxxSPq68CSzoftkjvx8U8eyOV2xPzNHsO83TaEVvhbN2ySOB82AFbZW3dGqF1nXEklm/nRufDcCyJ9dHAe8DJXaxrKtKKfGtgHi0vMeV6vkLvTJDvxcIj/+h8OApbte4IrL/M1WQdNftST0LDYp5Ncj7sDVzvfJgZuDLZtBAmBL4Y8+wUbEXJF4DfOB8+T8f+VRftPsD5cAvweWcegp3R5PXaH7gTGOV8OA8LbZsfy382Q8yzQ9L574flgrrd+VAsSvAtYMGYZ0XuuMuBI5Ln4v2YgFQ32XmTdNYu52GhdCtiq/Y1W+e8aUw+BHwc8+yfnezTKdOonzwGbOR8GIl5T76CXZvdgOuw+8lswC8xMe++np6HEEIIIUS74zovIoT4EnMVNjG6lymrtA2JeXZVUSDltNoCCx+6DsvRtD/mqUGp3D+wCWeGhcQ9CCyaRJxdsHxMY9L2noRUDcHEtSHJ9vLf8ErZwzEPrH2xZeJXA7aKeRYq5bbC2uIqTKxbq/R++VK5GTAPkJHpWP8DVol59tdKfS6V7XIy95hnIzFPlrmxifKZ2MT4Bw1WjOsK/8AmxicAo7EE9c9hK8n1ONF5EvY2w87/6nScc4FLGu1XqaNmX+oF227GhJ/Zkk2jgN9hwuN9qcynWN6hCVhY3R8xIaiRl03BjVi/2CvV92BPbaaJ6xXz7GFgZUwUPiOVOx34drKdVO56YP309jws59JumCdRwQnpGPtg4/1bwPY9PIeG7ZL6zDhMZLuhyTrPxYS144G/AtUx3W2mQT/ZBxPpA3buu2EJ1v+HidO3ABdgCevXj3nWm3nFhBAt4sorr+TKK69stRlCCNG29Js0qa/SgQghhBBCdI0UJvoicFrMs5pJ4sUUBg0aNOmhh3rksCeE6ANGjBgBwNChQycnWJ8eF0eYnm0XQrSefv36/W3SpEmDan2mMEAhhBBCtB3OhwWw3Hf7Yt6IVc9IIYQQQgjxBUVhgEIIIYRoRzYC7gK+B+wY8+zVFtsjhBBCCCH6CHlWCSGEEKLtiHk2AhjRYjOEEEIIIUQLkFglhBBCCCGEEH3IUkst1WoThBCirZFYJYQQQgghhBB9yJprrtlqE4QQoq1RziohhBBCCCGEEEII0TZIrBJCCCGEEEKIPuTSSy/l0ksvbbUZQgjRtigMUAghhBBCCCH6kIkTJ7baBCGEaGvkWSWEEEIIIYQQQggh2gaJVUIIIYQQQgghhBCibZBYJYQQQgghhBBCCCHaBuWsEkIIIYQQQog+5Bvf+EarTRBCiLZGYpUQQgghhBBC9CGrrrpqq00QQoi2RmGAQgghhBBCCCGEEKJtkFglhBBCCCGEEH3IiBEjGDFiRKvNEEKItkVilRBCCCGEEEIIIYRoGyRWCSGEEEIIIYQQQoi2QWKVEEIIIYQQQgghhGgbJFYJIYQQQgghhBBCiLZhxlYbIIQQQgghhBBfJpZffvlWmyCEEG1NS8Qq58NQ4AJg2ZhnT9cpMwJYK+ZZ/27U/zxwd8yz7bpvZZePORtwPPATYB7gaeCsmGfDa5RdC7gDWD/m2ZjS9qWB29PbdWKePdvksYcBd8Y8u72yfSjgYp6dX9m+LrATMBhYFHgFGA0cFfPs9UrZrwDHAtsBcwPjgYNjnt1ZKbc/sDYwCFgYODrm2bAats4K/AbYCpgPeAr4bcyzS5s51xr1rYW1ZS12jXl2bnfq7aYtYwFinq3VV8ecFtTrN13YfxjWl/r1pl3TI86HgcCmwBkxz97uw+POBpwJ/BBYADg95tl+0/iYz9PH992+pPS9tVTMs+fTtueBsTHPhtYr80WgGNPATDHPPuvivkPpwf2kq0zL+4/zYW5gP+CGmGcPVz4bC9P//V8I0XesvPLKHd6Pf+U91h5+b4us6T7jX3mPgYvO1WozhBBfQNo5DPBYYLNWG9EFTgV+jgkxmwCXYWJQUzgfBgB3Ap8BazQrVCWOAtapsX0osHON7XtgQtFxwBDgBODHwP3Oh9krZc8DdgV+DWwMvAqMSpPwMrsCCwLXdWLrX5JNv03HvAe4xPmwfSf7dcYvsfYu/13fwzq/rAyldr8RXWcgNj7n7ePj7g1sA3hsLJzax8f/InIT1pav9rDMl42hfHHuJ3Nj43mlGp/tlf6EEKIpJk6cyMSJE1ttRo8ZuOhcbLPSYq02QwjxBaRtwwBjnj3Tahu6yBbA+THP/pTej2lUuIzzYXngNuAdYN2YZ69MA/vK7BXz7I3S+3HOhyeBcZjH0/nJrhWBnwE7xzy7IG0bBzwKHIOJTQXLxzyLzocZMTFsKpwPP8A8PXaKeTYibR7tfFgcONH58OeYZ59385z+HfPs/m7uK8QXjW8Br8Q8u6jVhvQmzodZYp590mTZfpgn0Ke9cex0z3yjp2VEfbpyfduNmGePtdoGIcT0xaWXWlDB0KFDARN97thr1RZaJIQQ7UXbilXVMEDnQ3/gOUwIWQzz5PkqcBewZ8yzlxvUNQPwJ2BrYLOYZ7c5HxYBTgQ2BOYAngB+F/PskrTP94AHgB/HPAuV+v6EiVOLxjwrHol8DizbjfMcCNyKPYlfr0YY3nbAgcAA4EPgFuCgmGevps8npaKHOx8OT/8fDawFrFkpMy7m2VoVoargwfRafjTyY2AicEWxIebZZ86Hy4FDyhOLmGexidNdJb3eUtk+EtgofX5PE/U0TanflAWycvjg2jHPxqZtY7ExMQz4HfBN4FngiJhn11Xq3TqVWwoL+TyixrG/gnmtrQ/0x67fg8CBMc8eL5UbioUOrYaFmPwI+Ag4LebZCc6HwvvtG8BjwB4xz/5WOdbmwEHAd4BPsT51QMyzF0tlngfuBm7EvAO+Bvwb2C/m2d2lNqjZb5wPC2DeeGsDiwNvYePvwJhn/6mefzM4H3YF9sTElY+BR4BDY57dmz5vOE4r7TcY+AWQYW19NTZWPi6VnQ04EtgyncM7WJ/bK+bZa6nMUuk8NwDmTG10dMyza0v1DEtt+B3gDOD7wHvAOcCwJNwWdgE85fzk28hSMc+edz7si93P+qdzfwb4Tfk4ddqs2XtC+f/J/bxGfXXbJL1/CNg05tn1lf1GAOsBS5ZF5nRND077Pob1wzsq+66JeWt+D/PwvTuV+1epzFhsPJ6IiePLAYdQx0us1L9vx8bCMpj4fm0S3o8FVge+AjwMHBLz7K7S/itjodwrYd8tE4CRMc/2Sp8PpZMQv3plUpvsw5Rrdj02bt4ulZmEeea+DvwKmD/ZuVfMs0crx9kM85pbEYjA48BxMc9uSJ/PiPWRHbF71FuYt+/h5fHQE3rhfjIUa6s1sXG7PvA8MND5MCd2LTbHvICfx8JaT4t5Vu7f/4eNv5XTOZ4JdAj/68p3QNpes22xe9Nzqdg5zodz0v87xTwbUSsMMHlM/xa7Z84C/AO7P4ys3apCCCGEEKKgbcWqBhwK3IuFFSwInAxcSvpBXMX58FWmhOStFfPs4TQ5G4flljoMeAnLyXSx82HWmGdnxzz7q/PhCWB7IJTqmxmbAP25JFQBXAz8yvmwd8yzPzZ5LitjE4rnsfxVb1Vs3w04CxOLDsXySx0PfN/5sFLMsw/Ted0HjEhlAV4GrgQuAWYAdk/b329gS9F+/y5tWx54LubZR5WyjwIzA19P/zdLMaGtejoUT9JXIIlVJTGg2dwvLk3QCiZ100trGeB0TBx6EzgAuNr58M0iv5rzYT3gz1jIzwGknEDATJiYUjALJrAch4mR82ICwP2pvgmVY18IXAScjQkHx6ccKRtik9gPMRHtOufDMoXHiPNhD0yMvQCb1M+BCWnjnA/fiXn2QekYq2MT5iMxgeRY4EbnQ/+YZ+8m++r1m3nTPodi3iOLpvO/J51PlybBzoc87X8edq0jJlh+Dbi3mXFaqfJibKxvjo2LYZjwclQ63syYiDcQu773A3Nh3n7zAK85H5bAROpCMHgD+ClwjfNh00IMKHEd5ol4QqrnyHQew7D+cRwmZG6JjUuAV50P22L3rmMwwe+rmPDVMFywC/eEYdhkuwilrun10VmbxDz7m/PhQawvXF/ab27sPvi7yjhbE/gucDg2rg8GbnE+rBjz7Im070aprpuw60kqd1fqry+V6vsGJkYciwnHneX9Wjudy9HYNXze+bAS1sZ/xx5yfISJhGOcD6umc5wdGAX8FQtb+wATEXv8iNv58Fusn5+B3e8Xw/rFCun45fbbDruH7IvdY08Crk/j67NU3y9SXddhYtSHmMDWv1TPJZhoeyL2ffktrA37Y3kVC9vGAv27kxsy0ZP7ScGl2LjdApjR+eCwvrESJmj+E3uYcQp2rz0s2T4/JkxOSO3wCda+X+vmuXTWtqOwe8tfsLFS3AtqeoE7HxbFxLwPMKHyPSw89ybnw8Yxz6oPbXqNJ974cLrMeyPEl42B79rtcO3h9zL+lfdYcPZZWmyREEK0F9OjWPVCzLOfFW+St8dJzodFq+Fzzod5sB+UiwKrlZK574R5QZWfqN7ifFgIOM75cF6aQFwMHOF8mCvm2Xup3IbYhPLi0nHmxiaaTwFnOB/ebTJh+PHYD9haQtUM2A//sTHPti5tfxybeO2MJW2+P3ls/KcaBud8eB+YsbPwOOfDHMBpmFB1XemjebHJfpW3S593hULIWYWO3lVFbq9yfRETtybRHKMq7/+DeXZ0lfmxnGFPATgfHsaEpq2w6wU2EX4c2KTwKHM+/Bub6E8Wq1Kf2aV4n67pKOA1LJ9Q1UPk4phnx6ayYzGhYX/gGzHPnkvbHTbRH4yJUbNjE9ILYp5NzgvjfHgAeBLLo3Za6RhzAgNjnr2Tyk3AvL02xATYx+r1myQ27Fs5n3uAFzFvsIYeQWWcD1/HxKBTY57tX/roptL/zY7Tgj/HPDsq/T/G+fB9rJ2Lbdth7bZJRXS6uvT/MMwzY83SmByVRKxjmDJBLTgn5tlv0/+jk0fIAc6H02KeveF8KCay40v3H5wPg4FHYp4dU6rrZhrQxXvCm8AnTYTGNtMmw4HznA9Lxjx7IW3bARNTqgsYLITda19Mtt0GvIAJdkVeutMx75pNSudwByZGHYB5FxbMD2wQ82x8J+dRMA/w3bIQnGx4EVu0ohB4RwH/wkSWTTEvynkwD7VHSvWNaPK4NUlePQdinnnHlLY/iQkZGR3vuROBjYsHIenefhXmgXZvyePo2phnm5f2m3z/cz6sjgmsO8YpYaBjnA9vY/kBB5ba83MsT2J36fb9pMTVMc8OKtm/MfADpg4Xnw0bW6fEPHsTu3/MBvyw1N9uxfpbl2mybf+e/n22ibG1P9anBpcedNyMCce/YWoPYyHEl5gPP+nJrVgIIb6YTI9i1U2V9/9Mr1/DVrUrWBSbwE3EJk9lL5Y1MHFnbKWuSzDvlOVSvZdgk8MtmTIp2x54IubZX0v7XYGJKsthXg8jnA//LULHnA9HYGEpc5VDGNK5bISJbT+vfDYA8xw7vLSNmGd3Ox9ewDwYzqCHJG+ky7Cn/avFjis99aO2WNTdVZZGY4LYGc6HHTDBZ3NMUAATqOwfm9gdM1UN9dkb84oo6G6emqcKoSrZ8brz4XXS0/okGKyMrWBYtveBFBbTAefDVtgEfADmsVIwoMaxJ09eooVbPo31medKZYrwwSXS62BswnhpxbPs5VR2DTqKVfcVE8tEefx0ivNhT8wrZRlsotjofBqxHhb+VfWOKtPsOC2odW9Yr/R+A2BCDe+oMkMw0ei9SnuOwsbpnDHPyp4hV1b2vxwTKFfAxIh6PAjs5Xz4PSY+3lvDg7HKtLgnNNMml2NeYLsyJdx1d+CmOHX49f2xFHoa8+wD50ORdBznw7JY3zm+0r4fYR6ia1Tqe74LQlVx/LJQ9VWsXY4HYuWYY4Bt0/9PAe8CZzkf/oiJaWUPr+6yPtbPq+PzAczDaA06ilW3Vjx2y+PzXszTa3Yaj5sh2P3vmsoxR6fXNbBVXYl5tm4XzqUWPbqfJKoi9xrYd8Flle2XYOL7YMzbeTBT97f/Oh8C5h3XVZpp266wBmbfZJE65tnnzofLgF/XuJf0GgMWmF15b4SYDhgx4kkATh26KvMcIf1aCCGqtPNqgPWohoEUIWRfqWz/DhbGdnmNcKt5qb1a04TS5yQvgjtJHgHJg2ojOnpVfQ+b8P02CT27Yj+yL3c+rJ+K/QCbhFSFn9Mwr4+dMO+Fqo00sLPHq4slL50LsQn9phWPArC2rnWceUqfN01qny2A/2ITr7exJ8yHpiI9WUHryZhnD5X+qufSLLXO6ROm9K/5sXC/12qU67DN+ZBhQua/sUT138eErjeYur/C1F5sn9bZRmn/BdPrGEyYLf99G8v3UqbD+cUpyYxr2dOBFCIzPB1rc8zbo8hD1un+FQq76uaao8lxWqLWvaHsUz8f5nHXiAUxr6FqW55UqqNMtR8U7ztbFuciLFfX9zEh7G3nw1+SJ049psU9odM2SeGdFwA/dz7MmDx3lsPyA1WpNy6K9ij663lM3cYbM3X7dvWeUC0/LxaCdmSN4+0DzON8cMkLcm3sgcdw4EXnw7+cDz+hZxTn+3SN489JJ+OTqb/fmhk3C2Jebx9WjlfkQ6wesyd0+35SotY1eztOnWi9Ou4XoYn7cBdopm27QqP7Vz+mfI8KIb6kDBw4kIEDB7baDCGEaFumR8+qZhmJJTP9nfPh45hnp5c+e5vaniALp9dySN7FWDLVJbE8LjNjOTYKlk6v74ElGnc+7JTKXecs6fn6TAl160DMs2OcD7MAhyU7f1WysWxT1c6HatXXRc7EwkW2iHl2W43PHwU2c5YfqOz1sRwmmjxdY5+GRFsxaWCalM+GhaoVIRe9mlw9UeRRmrmyvbsTtjexid9CNT5biI4hKFsDT8c8G1pscD7MRC8IjSWKvjqU2vnDPqixrbtsDdwW8+yAYoOzZOTd4c30uhgd83yV6co4bfaYK3RSpkgaf2Kdz6srdS6Eha+V30PnAtAkzAvzrBSuvAHmvXQFJmDVYlrcE5ppE7CcaPsDm2Dhqc8zdegt1B8XRXsU1+xQaq+YWvWIbDYMuF75dzEvnT9iAuFUFB6SyYPrJ8kbaVCy8cqUb+tftfZtguJ8N6B2SHV3+jDYuKln01vYfW/1Op9P69Vmu0r1mr0NzOt8mDl2XMmxOu5fpX5/K9Psd0AzbdsV3qb+WJ1EFx/2CCG+eEioEkKIxnyRxSpinp3kfPgMOC09PS9yBI0DtnQ+rBbzrCyQ/Ax7+lxOMn4V8HssXORHwJ2xY8LvQiDYljRZTK7+22HhDacCIyphg1U7D3e2ctz+SbA6FJvAv4YJBOcVZZ0PqwJLYhPbgk+xBM1VPsGSbU+F8+FkLFxpx1hZ6a7EDVh+pi0xD6wibPCnwOgaT76bpmjDJN7sk+qrmai2h7yGtUN1Qr5RdypL1/ZBYAvnw7BSzqrvY0l4y2LVrEydD2Z7zNOjt7gXE6S+HvPswl6qs16/mZWpkyPv1M1jjMFEhN2wMMladGWcNsNoYGvnQxYrK3yWGIkJy4/GPPtfE3Vuha32VbA15tFSTHaLMVJrfAKQwqiuSH1o93rl6No9oVmaaRNinj3jfBiN5V8aCBwTa68AuorzYYkihC7lw9uIKSGaT2BC1/KlXF/TjBQWdheWbP7hOjZX9/kMWwThSGxF1G/RffHiVqyffy3m2a3drKPMvVj/2o3aYiFYHz4YCyGu9RCir6n7PVSHcVg/25KOD4a2xb7rilxR9wEHVvrbbFgesDLNfgc007adjufKeeznLNn888m+GbDvz7/HjgtfCCG+hHz0kT0HnnXWWVtsiRBCtCetFquGpISsZd7rpR/1AMQ8O9X58DkmWM0Q8yzHkubuC/wleT69jP0QXh/YPZaSNsc8e9/5cAOWE2kRLMyvXP8/nQ8XYD9K58MSE0/EJrxrpLq3cD6c1Sgha8yzA5KH1SHOh09ing1zPvwa87y4BMvXsRgWNvcUFpZT8BiwkfNhJPb0/pVoyeYfw/Li/BRbseiDmGdPOB8Oxrwkzgeecj6sUqrrjUI0ink23vlwRWq7mbBlu/fElkLftrQPzodBmFhThJYu53zYIv1/c+GZ5Xw4FBN0XsHymuydXler1PdrbCWoZeKUpM5dJubZpHQOP09JjZ/AJilrdbdOLHRzNOY5dxa2QtXRTAlTKRgJbOp8OBVb3v27wC8xb49eIfXPA4E/Olts4BbMy28xLFfP2Jhnf+5itTX7DWkS7Hw4DMsPtg4W1tkdu59J7bJ/EjRuwJI9fw94PObZFXRhnDbJJaQwXefDCVjeoDkwj8nTYp49jvW5vwJ3Oh/+gAkr82AT3aVjKYl9YtcUTvtgqmcXbGn6d9PnxSp8ezsfLsTuDY8Af8BExvsw4e0bmJA5mjokobTZe0JvtknBcCy/1kTs3lGL17Bk2MOYshrgbFjuv2I87o2tcDczlvPrTcwbZlXgxZhnp3TjPBqxPxbOPcr5cB7mkTM/tsrbDDHPDnGW1Hs3LH/Uc8nmXzLlGnWL1M9PBP7gfBiACRgfYznn1gfOjXl2Rxfq+yDdQ3/vfLgGE3M+wATEj2Oe/T7m2diUF+lq58MpWH+O2P15Q+DgmGdPwuTk80vGPPt6d8+xCerdT+pxC5bv7cx0T3s02b0LcEK05OpgD4L2omN/OxDoIDI3+x3QTNti/fstTOB9BAtpfy5WFkgp2TcUuNX5cBQm9O+FjfXJQlmt7zpnOR3PB9aNeTYubVsTuA3YOU5JnC+EmI658kpLezl06NDWGiKEEG1Kq3NW/R7zXCr/VVdI6zExz84AfoGFBB4U8+y/2ER+NOYVcT325H37mGe1kqtejCVs/4SOq2QV7JLqXxGbfF2JCVW7Al/HwhFvdj6s2Impv8ASuR/lfDg42bI9lnvoeuB32JP6NaMtUV+wD/ajOWCT5t3S9hOxH7fnpu1npe0/Sq87YxOx8t+RFZt2wibBx2HeEUsAQ2KePVwptw92/a5I77dkyjVdsFRuNmxyPQrLA/Q48L2YZ+VQKrC+OQPdT+ZeZl9sufFhyb6vYG3dLWKeFYmZB6R6D8RWMKtOwM7BzvWn2LXZCHvq/x69SMyzszAPkAFYX70FE89mJCVS7iL1+s0x6f9fYV6D38FEje7a7bHJ2ypAMTlcG1u5jW6M086ONxELx/oTNkZuxgSY+UkhOSlZ8yBszB6Pjbc/JTtur1HtJpjocAO2st5xJGEm1fcPrN9l2AT8Qexecg8mXg5PxzgcE4527OQcmr0nNEUzbVLiJiwR+vU18gAWjMM8vI5nylj7USGOpGPejN0fZ8P62Kh0HgvTA2GoHuletTImMpyB9afTsTa8MxV7ChM5jsTGzwWYV+T6ceok8l09/mFY266BfTdcj4l476TjdrW+P2D318WxMXMNJhqXF2LYDut3W6TjXY3do5+iY06nGZj2D63q3U9qkrzfNsK8eQ9mykIk+1NaXCCJVutiYueFWKjnSGoLqU19B3TWtsm2XTABe0w6n6onV1HXK1i+ykex8XU1FgK+UcyzkaWitb7ram3rl7a1+nebEEIIIUSf0G/SpK6mBBFCiC83yZPjKGCm2HEFzS8sacGI0cB6bRJeJoSowaBBgyY99FBvpLUUQkxLRowYAZhnVbEa4DvH/ajBHkII8cWjX79+f5s0adKgWp+1OgxQCCFEG+N8WAZbSOJULO+ThCohhBBCCCHENEXu5EIIIRpRhMZ9AuzQYluEEEIIIYQQXwLkWSWEEF0k5tkwLP/NF56YZ0OxRNFCCCGE6CUGDaoZ9SKEECIhsUoIIYQQQggh+pAVVlih1SYIIURbozBAIYQQQgghhOhD3nvvPd57r1cXiBZCiC8UEquEEEIIIYQQog+59tprufbaa1tthhBCtC0Sq4QQQgghhBBCCCFE2yCxSgghhBBCCCGEEEK0DRKrhBBCCCGEEEIIIUTbILFKCCGEEEIIIYQQQrQNM7baACGEEEIIIYT4MjF48OBWmyCEEG2NxCohhBBCCCGE6EMGDBjQahOEEKKtURigEEIIIYQQQvQhb775Jm+++WarzRBCiLZFYpUQQgghhBBC9CE33ngjN954Y6vNEEKItkVilRBCCCGEEEIIIYRoGyRWCSGEEEIIIYQQQoi2QWKVEEIIIYQQQgghhGgbtBqgEEIIIYQQQrSIgYvO1WoThBCi7ZBYJYQQQgghhBB9yBprrNFqE4QQoq2RWCWEEEIIIYQQfcjSSy/dahOEEKKtmS7FKufDUOACYNmYZ09XPpsRmAgcHfNsWBfr7Q88B+wa8+zcTso+D9wd82y7rhyjt3A+DAOOinnWr7J9AeBk4IfAV4F/A3nMs6vq1LMpsD/wTWAO4HXg78CZMc9G9oZN0xPOh0WAXwLrA8sCnwKPYP3pzhrlNwWOAr4FvAacA5wQ8+zzUpkfALsAg7B2fjnmWf8GNuwA/AJYHvgEeBTYM+bZP7txPs9Tp586H44DDm/l9Up95s6YZ7e3ygbR99S77s6HEcB6Mc8W72T/odh3wFIxz56fNlbWPO5awB3A2jHPxvbVcTujK99dXzZq9ZW+vO90p88U36XATDHPPptmxgkhWsqECRMAWHjhhVtsiRBCtCdKsP7F41JgA+BAYEtgNLBKrYLOh18C1wJPAT8HNgKOSx+v041jnwsM7sZ+7cR3gZ8C1wNbAEOBj4GxzoeNywWdDz8ErgEeBH4EnA4cARxfqXNdYHVMdPp3o4M7H44HzgRuwq7HdsBtwKw9OKd25ii619fE9E1Pr/tN2L3m1d4xp2keTsd9uI+PK7pPrb7Sl/cd9RkhRE1GjhzJyJFdei4shBBfKqZLz6ovC86HWWKefdKF8nNiHkEHxDy7KG0e1WAXD1wX8+znpW23A+c4H7osZMY8exl4uav71cL5MBPwWcyzSb1RXxe4G/hG+Wm282EUJjQdBNxYKvtbzGtpt/T+DufD7MARzodTY55NSNuPjXl2dKrrEuAHtQ7sfBgMHAJsHvPsutJHN/X8tMQXDedDP8zz4tNW29LXxDx7A3ijBcd9H7i/r4/bDjgfZgD6TS+ePqXvkJb0lVJ7fWn7jBBCCCFET/hSiFUNQuZGAGvVCMma2flwCubVMjsm4OxTK9zE+bArcDCwOPAYJhTdUfp85fT5KsB8wIuYN86xMc/+Vyo3FrseJwLHAMthwsWpzof/A84AVgbewjxvaoVuTUp/yzZskCnMC0yo9UHMs1g5z6Uwr6sNgDkxD6GjY55dWyozjEo7p7DMA4EdgaWS/Zdh4WcfpzL9sRCWvYH+WLsvDMznfJgFa5P1sfZ7G/gbsHPMs9fT/rNiT8q3AhYD/oN5eZ1QnEcpFGOTdA5bp7YahV3bd9N5v1ujLT5zPozHwviK8/r/9s47zo6q/P/vOSAgCAiEKCWAsUQFvoT2lRo60kaKqFQJKEXKD5GhiwRCERgFpImCBCmCgpRBCCG0IAQIJVL8EsDQpIXQI1LP/f3xnMnOzs7de3ezu/cufN6v177u3pkzM2dOm3s+8zzPGQaMBPYqJb8YOBaztLqwqiy74SfA0yWhakAJE7xjsDpYEngRuASr6w9DmkeBe3wa/zh8Xxirl5eK7lsuye4CXvBp/P0618qFyKNckh0V/p/tvuuSbBes7YwAZgE3Aof6NO7WmqbQl47HBMURwOPAPljbOQ7YHZgXuA7Yz6fxfwrHN9Oe5gNOwtrlciF/U4BDfBo/XjjXF+mm/dZzEarjuvQMJqbeigmnXw55vNol2UrAWMyCbz7MiuNwn8Z3Niirr4R7XQfrcy9hfeJIn8ZvFNKNAzYGYmwsWjWUy698Gv82pFkVuB/YxqfxtaXr5McvC+RiR2W9h/T5mNflOk2Uz/XhnpbBxqmf+jT+eyk/BwIHhXt+GPgpcBlwu0/j0d2U1/qU6qvQ3sYAp2CuvtOBnzfTl8PYehzWRhYJ95v5ND7QJVkCnAAsHUSX/JgI+Bdwr0/jHQuna+rZFZ5b+9PRt67F2u7rhTQ1zEr0HWBvrDxXc0n2JNb2twa+ALwJPBKu83g4tuG4H9ItAByNWQEvDbwB3AXs69P4lWaf3Q2eIVtTaCv1xp1QDj0p6050U14L07XNfBtro8sDc2F1fqlP4+O6Of9mwJXAOOD/9eC5IoQQQggxKBnsYtVc4Udxp219cN4jgKnYhHYo9gN0gkuy5fMJe2A9bDJ1FBZb6DDgRpdkK/k0nhbSLBPONQ77Ebs88AtgOCaYFPkaNkEbi012XndJNgSbcLyM/fB/H5sELFPOtE/jd1ySXQPs6ZLsRp/G1zW4z/uA3VySTQeu9Wn8RFWiIMzci8WzOgh7S/0D4CqXZNs0uM4l2AT3ZOBuLLbTWGxC8d1S2qOwCf9eWD2+B/wZm+AeAjyPTY42IrjFhfq/CRP3xmKTpjWwCdCiwMGla5yBTWZ3wiZqpwAfY2VbiUuyeTA3jocLm5cPn48W0/o0ftol2bshPz1lHWCqS7JDgQOxe30cm8jPjjlWmJg1G5ctqugnUC14XoQJICdiE/81MdfG4ViZgbXHokvk+li7XMol2dd8Gj8RJqGrY/VfjzWByVjfOC9s+zeAS7K9wrYrsP64ZMjTt1ySreLTeFY35wX4CnAqNvmchdXzdeFvbsy98xshzQxM/OlJe5oXi/F2PCbwLArsC9zjkuzrBau6i+mm/faCDTCR9NiQ72dckq0C3InFmtsTeBcT5ia6JFvLp/ED3ZxvSazMf4oJBcOBI4Eb6OrSuxAm6JxOh+B3rkuyaT6Nb/Np/IBLsinYRH22WOWS7PNYmzrFp/HHwYKwst6buU6D8lkX69dHY+PHWOB6l2TL5UK0S7Ifh3NfAPwFE/0uAz7f4Nzd8WVsbDkJmIm1kytDW3iq3kFBqLoPq7NjMJfsYZigDvCHcA+7Y204Z1NMBNqjdMqGzy6XZL8M+fsN1i6XwtrxCqG9fFw432jsWZQA/8HE69OA72Dt5ElMhF2bzuXXcNwP4+rNWHs+CbM+WhiLt7gIFgOwp1Q9Q8rUa3/v0rOyrmI0Xcur03r0LsmGY+PQlVj7/gB7wVQ30rKzOIbnYy+5xjbKxLRXZ7HBOXc3kV0hRCsZ+ebbAGxwzt1MffEthn5u3hbnSAgh2ovBLlY93jhJr3gH2LpgRfEENnH/ITbByfkCsLZP4+dCuluAZ7HJ/a4APo2vyhOHN7R3AW8Df3RJtp9P49cK5xsCbOrTeGrhmBOABYBvF65zc7hOJ1ySLY39qJ4OXOGSbMsGAWT3wX4wnwKc4pLsNWzycKFP4wmFdGMwYWO9Qn5vCiLWcdgP7y64JFsXE7V2K7glTnRJ9jpwiUuykcV7xSYn2xZd/8LE9kifxpcW0hWDxe+IiTzrFQKg3+KSDOAYl2Qn5xZYgUk+jQ8I/09wSTYC+LFLstHduByOwd7671zYtmj4fKNLatu2aMX2RiyJtYGVsUnkq9ik689BFMwFgBomsDX7Zn0nOoSmurgkWwErz6IINsEl2cfAWJdkv/Rp/DBmJXCAS7JlfRo/iwkoE7EJ6QbAE5ho8JmQthKfxveEenrBp/FsN5ngPjMWs3LZobD9cUyU2QObaHfHYsBaPo2nh2MdJqB8yafxxiHNTS7JRmFWHYeGbU21J5/Gb2FB84t5vglrwztiE3qwiXF37benLAKsWhDD8nHnOWDD3CXQmevqo5hos029k4V7nL1wgEuyu4GngDtdkq3s0/ihQvIFMYuX20LaSdhEfkc66vkc4IJC2wAbN+fBJtt1672H16nHQsDI3CrMJdnLmHixBXBZaAfHADfmloGFdFdVnK9ZhgCjfBo/Gc73ICZi5sJvPY7FFsJYyafxi4XtFwH4NH7dJdkVwF4uyU4tjFF7A9N814Dd3T67gtB9CNbHZ1vxFNLFwDWF80XYM6loBbwmZgVUfBYWLWybHfd3wfrH1qUXHlfWK6wmqHqGdErQXfvrYVlXUVVe3yilWQXrDz/x5iII9gKgkvDy4oSQXsHzhfiEMuv9QeFhLYQQA8pgF6u2pWuMpLmY8/gQVxZN7H0a3+WS7N/YD+viD/R7cgEppHvHJVkezBWYHUfqKCxY9zBsAp/zVcw9IueZknhDOFf5Ov9xSZZhb3Hz68yDuUlNwdzJrgGudUm2Sf6D3CXZ+cC3fBqvGM7zhDN3m7WxyeAaWJnu4JLsaJ/GebD1zTBLi7dKFjo3Aae6JFuo8KO7yGbYW+OrSsflQtgozAog55oKwWgKcEgQ+m4FHi2l2QwT7u6uuMbx4Z6KE6Fy/KdHMCuZL1DhEumSbCfMHXNsyaUqt0qqErh6u7qewybq6/s0fjBc/xbMoutIgrVKEAF60ndvxKz5yvyEztYCo8Jn2RrqEkw8Wi/k5Q5MKNsQc6/ZELMAeSn8f174fMkXXOJ6wAjMKuSo4kafxn93SfZsyEcjseqJXKgK5Pkox3B7HIhdkkWhXTXdnlySfR+zUBlBZ+uJEYX/G7XfnnJPSaj6LFYeJwK+lOeJdBZYuxDGjQQTM5bFXAiL91EUq94tWjb5NH7fmUtY0crzcmw10j0x0R5ssv83bzHtmqGZ69Rjsi+4L2L9m8KxS4e/cn+4lg73xN7wZC5UAXhz8ZxB4zxvClxfEqrKnINZfm6EiT5LYKLSoRVpGz27NsHGmUtLbeVe7CXKKDqLVeOLwktgCjDaJdlMrF88VLLGanbc3xR4uQkL4J5Q9QzpCT0p6yqqyqvMVGzF4stdkv0Be4Eyo07a0zBRfHtfcq3tjhGLf47b9l2r2eRCiBbx/PPDABg2bBiL/PzGFudGCCHaj8G+GuCjPo3vL/5h8WDmlCr3g1cwd4meprsQs2D6DTZRWB2LqwGdJ4ZQvbLVEt1cp8g2wArACd6Csm+LTUBucEm2Upgsr0XHhAEAn8Yf+zSe5NP458HiZDg2wTvGJdkiIdlQbDL7Yenv1LB/sYr85cfNg7lhFY+bUee4qvv/ASYOHIoJJS+4JPuF6wgAPxSbZJfzdl+da7xe+p4HsC/XBS7JYsxV5AKfxsfUOU+VBdXnK67TDK8Br+dCFcyOd3UL5irTW14v95PQV8rlnd9LefvLxf3e4tr8A9jAmZvqCpjFy22YSyCYhVUjK5h61MtHnpdmrNbKFm8fdLN9bjrch5tqT6FtXIHFRNoJ+BbWt1+lc1tq1H57SlWdzYVZUJXzvD+wSINrnYRZDl6CrT75v8B2YV+5T1RZEb5fTOctHtGFwI9cks0drGy+icXZa5aG1+mGTv3OdyxQkR+7RPicUUr3Mea+11uq+nszeV6MBotS+DS+D4sFtk/Y9GNMWLuoInmjZ9LQ8PkUXdvLQjQ3Jh+ACdJ7YMLVDJdkpzmL9ZZfo5lxfzEsVlNfMkerQ/awrHt1fW9uod/Gfn9dDLzskuxel2TrVSTfEVvcY2KT1xdCDCKGDRvGsGHDWp0NIYRoWwa7ZVWz5IG85/GdV86qJ7J8oc62qU2meyFcbz4sCO0Yn8Zn5Alckq1Y57pVb4Rf6uY6RfJ4F2+BTRpdkn0Hs6qZgIllX8LiqtTFp/GLwQLrDMzy6z5MRLkTiz9SRT2rgNewsl+3yeO63H9447wfsF9w2dsNc515FTg3XONpzN2mimfqbO8Wl2QbYe5aV2OWIWUeC5/LY/FP8uOWw+IR/bMXl32MalEqorpt9DX5hPuLWEBhCt+hsxXgbZgQs0HY/jDWVoe6JFsbc2U8j95RzEeZL2KTyf6i2fa0A/CULwTjdhacvpOQ1kT7zWPqzFO6Tr2xqdwO3sSs3M4G/tglNQ0D/O8A/LFgRYmzFS3nhHOBn2Fj37ZYmXW3KulAkosJQ4sbgxvnkIHPDjPp+hKkinOB81ySLYUJKH/xhWDoBRo9u/I+vCnVouBrpe9VY/IsLDbWES7JlsWshn+JCb+H0fy4PxMTurujp8/uvhgnmy3rXl8/WA7e5mwBkbUxd/q/OYutVhRNN8Ke3ze6JNvCN47VJ4QYRDz//PMAEqyEEKIOnxaxKo+dsgK2SlYe9HctLMZHme1dko0pxP1YG3MdmVxKt4ZLsmE+jZ8P6RbErBNyV7N5MauHD0vHje5B3idjbkTF6yyAuSYUycWTnQmClE/jd12SbYkJC8djotlsd8LiOUt8PXzmFjXjMTeSx5pwcSgyHpu8LOzT+JYeHFeJt6D1R7ok24eOSc54LGDvrF66nHUhxGS5FrNo2qVqsu/T+DmXZP/AyrsYR2QXrL57Y899NbCRS7LVguVTHmtpY8yCob+5I3zugMVIycldySYVtt2GCRJ7Y7GlapiFxWOYGDMX3cRhKfABFrOnyDTMGmQHCm63LsnWwqyeftXMzfSSZtvT/HR1G9uVbhZ4qNN+i2NT0epxi2YyG1yC7wRWAh5sIExVMT9dx6fde3iOcp7+5ZJsAhYbaSRwXEW+qup9IPh3+PseYbXOwDa05nk4AdjOJdkSvvtVLv8EpFgg+GWob6nW6Nl1MyZuLuPT+OY5zXxwSf6VS7Kd6TwmNzPuT8BczmOfxlmdND19djdLd+2v2bKeY4Ll361BIL4We6FUFKsew6xVbwXGuyTb3KfxnNy3EKKNuOUWGyJHjx7d2owIIUSb8mkRq27ELI5+75LsGExEOhRzU6hiQeAal2TnAYtjrjJP0tVy4RUsAPUYOlYDXACL74NP47dckt0DHOyS7CXsR+geNPcmPec0bJWx4nUOAcqi0d8wV4HU2WpD47H4WBsAK2ITtH1ckl3qO1anetQl2W2YSPI05gayBeYC8eeCsPULzMJqkkuyszBLiUWwCcRwn8aVqyT5NL7dJdmfsFWxfh3O4bEVobYADvN1ViAEcLbk90TgUiy20IeYtcYidEzsL8Um17e4JPsV5p42D7Y613eAbXwav1vvGhXX/DpWljMxN8dViwF6SwF5j8RWGjsPm+CsjMXpOaMUV2hxLK4Q2ORnfpdk24fv//RpnFthXYBZ4VzlkuznIQ97YbGDNi2cb1nM8uk4381S5z3Fp/Fjob7GhFgzd2Mi5dHAn7wFV8+ZhAV534gOt1YwEWt/4LlSzKh6/BPY0iXZeMzS48Vg3fcLzLrhEsxFbSlMQHuSziJDX9NsexoPbOOS7DRsdclVgf+HWToBzbVfn8YvuSS7A7NSmYm5Su0SrtcsP8Pq4yaXZBdg1kNDsEDOc/k0PrybY8djK4I+grmGbYcJAXPKOdjk+0MsnlmZynrvg+t2i09j75LsWOxZcD5mPTkci0v3Fs0vWtBXHIO94LjbJdmJWB0sBWzm03iXQr7/65JsHLYa6yM+jest9dbtsysIiScDZwVLvzsw66VhmJv6+b7BiosuySZjrq2PYM/Q9TCxNA8K3+y4fwkW2+xPLslOwtzWF8Rc5E4PYnFPn93NUrf99aCse0UQq0dhcSCfx/rqEZjF2aPl9D6N/88l2frY2DreJdlmEqyEEEII8WlgsMesagpvS5Zvhf1g/jP2A/5M6sfUOQmbNIzDJl0PYqvxlS0Q7sCsPE7E4tfMB2xeEmB2xOJonR3O9zJwYA/yPhMTBGZik4GzsQnmH0rpfLjH47Af+9eG9CMwK6yVsDfRE52t4gcmrrlwzIRwD2tiE7ddC+d+DlgNm7ifiL2dPxebpDSyntkFi4mzfcjTlZiY8SSNlyZ/Dyv7PcNxV4f87ZwHmw118m3g95iwcwMmDuyGiS0fdD1tt6yBiQnLYu1jculvNj6Nbwj3tQbm5nQQVj5lcWB5bFL8F8w1ZvHC9+8XzvceVtd3YtZxV2Nub5uXLBQizIKnP/rvbpi75x5YWf4ofN+tmMhbQP08PlyxDeT/Nxuvan9sifeMjiXn8Wn8O6wNroi1m1Owdrdef7rC9KA9/R4Tz34Q8r4l1s/eKpyuYfsN7IItCvEbbIx4DrOEbDbPD2Lxsl4L55iAtZ8V6WwNV8UBmPBwAtb/F8TGrDnlb8C7wLVF4bZAZb0PBN5WVDsIE2euxdr4zpgL11vdHNofeXkGi3d2D/bcGY+Nx69WJM9XkezOvbbhs8un8ZFYeY/CnofXYs+CN7BxuRGTsHHrUqyetwcOKrq608S4H/K0KfYsyfvaOZh483pI8yY9e3Y3S6P210xZ95Z/YC+1TsL66lnYy6IN61kuB6vM9bDn0gRnC7cIIYQQQnyiiWq1gQiFI4QQ4tOCS7JNsIn4xn3hAtzfuCRbHbMA+qFP44tbnZ8qXJKdgL3oWNJXr74q+ojBXtarrbZa7f77+zO0nxCiLxg3bhxgboD5aoBvHL95C3MkhBADTxRFD9RqtdWq9n1a3ACFEEL0My7Jvoy51Z2GxdBqO6HKJdmXMNfVO4G3gW9gLr1PA1e1MGuVuCRbGbOQPRD43WAUTwYLKmshhBBCiPZBYpUQQoi+4mjMBewfwA9bnJd6/BeLt/dDzOX3DSy22OE9iW83gFyNreh3ExbjSvQfKmshxICx2WabtToLQgjR1sgNUAghhBDiE4LcAIUYfMgNUAjxaaU7N8BPRYB1IYQQQgghhGgXpk+fzvTpzSycLIQQn07kBiiEEEIIIYQQA8ikSbZg7/Dhw1ucEyGEaE9kWSWEEEIIIYQQQggh2gaJVUIIIYQQQgghhBCibZBYJYQQQgghhBBCCCHaBsWsEkIIIYQQQogWMXLJhVudBSGEaDskVgkhhBBCCCHEALLVVlu1OgtCCNHWSKwSQgghhBBCiAFkyJAhrc6CEEK0NYpZJYQQQgghhBADyLRp05g2bVqrsyGEEG2LLKuEEEIIIYQQYgCZPHkyACNGjGhxToQQoj2RZZUQQgghhBBCCCGEaBskVgkhhBBCCCGEEEKItkFilRBCCCGEEEIIIYRoGyRWCSGEEEIIIYQQQoi2QQHWhRBCCCGEEGIA2XbbbVudBSGEaGskVgkhhBBCCCHEALLwwgu3OgtCCNHW9KlY5ZJsR+AyYD2fxpMK278AvAzM8Gn8hdIx+wFnASv6NH60L/PT7rgk+xqwH7ABMBx4B5gCHO3T+B8V6fcEDga+BDwDnObT+LelNDGwI7Aa8BVgkk/j9etc/yfAgeF8rwAXAcf5NP5wDu5pSMhjHM4bAf8C/gac4dP4pR6cawxwDPAZn8Yf9TZPvcUl2TbAcJ/Gvy5tXx+4DdjEp/HEgc5XFaGsJvk0vrXJtMf4NI56cZ2fAs/5NP5rT48Nx48D1vdpvFz4vhzwNLC7T+NxVWk+zbgkux2Y26fxOgNwrWeA230aj+7vazWDS7L1gCOA/wEWBWYCjwIX+zS+tJV5G8y4JBsNXAh8yafxM63NTXtQKJOv+jR+qo/OuT72nNjAp/HtYdvtAPWeyUKITxePPmrTnhVWWKHFORFCiPakr2NW3RE+R5W2jwLeBYa6JPt6xb7XgMf6OC+DgU0xoeoiTNzZF1gcuNcl2arFhEGoOg+4CtgM+AtwThCcimwDjATuAf5d78IuyY4AzsZEpK2AMzGR6dze3oxLsm8CU4EfAhcD3wn3dRHwXeCc3p67RWwD/KzVmWiSY4ANm0x7PrBmL6/zU2C7Xh7bLGMB2cYPPNtiZd9yglB8G/AesD825h2FCVZbtC5nQswR+4Y/IYTg/vvv5/777291NoQQom3pU8sqn8YvuiSbTrVYdSvwjfD/44V96wJ3+jSu9WVeBgqXZPP6NH6/zr4aBYuRCi4Hzi7eu0uyWzGrqQMx0QeXZHMDJ2AWBUeFpLe5JFsSGOuS7PyCNdSePo19OO7vdfI1H3Ak8EefxgeHzTeH/J7ikuw0n8Y9Eg9DHq/CJpdr+TSeUdh9i0uy04HNe3LOgaC7+uun680FRK2wFMvxafxvuhEyW41P43+1Og+fJvI+4NP4oVbnpcDPgIeAbUvPhotckvXJS5Z26IuDjYEeL+eUdsuvT+N/tjoPQgghhBCDhf6IWXUH8D2XZHMXJgGjgEsxC6pRwO8AXJJ9FVgCmBS+rw4cBqwBLAY8hwkgY30a/ze/QJhkHAv8CFgYsyLaF/g/4FifxmNCujGYxcnXgNOB9UIeLgCOz0WdkHYIZlXwHWAI5pr0a5/GvyukGY25CqwHHABsgglLI3tTUD6NZ1Zse8sl2RPAUoXNa2IWV5eUkl8M7A6sg1khULynblgB+BxwY2n7eOBUzKKop5Zu2wFfB75XEqoI+foIyABckj0CPOXTuJP1TMFtYjOfxjcVdn3DJdlvgG8BbwG/B8b0Z/0FV7Tdwv/5ZPnZkmva/C7JzgJ2AGrATcD+Po3fLJynBpyIuXjuDSyDuWg+FNycfgH8L2bl+Hfg4KI7rEuyTTFrppWxtj493MPpPo0/LuXvKJdkuZg5ux9U3NsYSm6A4RwnADOAg0IZPgjsmwuXwU1sWWBZl2Q7h0Mvyt3GXJKthNXBusB84fjDfRrfWZWPelS5AbokG465C68PzMLa/jTM2rCTO1OwQtwfGBHSXgsc4tP49Z7cbyHttkACrAR4TGw/3qfxdWH/3MAhWHv5EjbG/Ak4yqfxew3u9UBgH2A5TOj9F3CCT+OrS+k2Bk7B+th04Oc+ja8p7P8KNtatA3wReAlrj0f6NH6jkG4csDHwPeBXWLv6HXBg2Q2w0F/WxPpLHMrzSuDQ4r31pH6aZFHg+aqXGKV+n+ex3AbGUN3Gy31xHZdkdwIH+TQ+s3gdl2SHAccDS/o0fjVs2w44FHNN/AC4Geuzz4X914f0q5TO9SWsbn/i0/i8qhsOLxFOwsal5bBynIK13ccL6fJ7brZezsQseP+Duek3JZjkLqjAycBxwDeBw4HTwv0cj1kHL0THs/fqwvFfC8euHdLMAO4Fdsx/GzQ5bi8errUBsDTWv+4M5fJCId0YrA+siLXttYFbgK1dki0AHI21+6WBN4C7sP7+SuG2h7gkO7a7Mq1TVosDZ2BWyh64DujiKi03QCGEEEKI5ukPsWoSJqCsAtznkuzzmDhyJ/Yj85hC2lGFY8AmD1OBcdiEYnlsMj8cEwRyjsUsg04FJoZrXddNnq7Gftyfhv0IPRZ4PmzDJdlC2A/XzwJjsB/M3wbODW9mzyyd71JsMro9fR/3a1GsvC4sbF4+fJZjeuWT6m8SxKom+Th8flDanr+Bnu08XxCQurMQA5sAfwzc0MT1zwXOcEm2pE/jFwvb98bKfkIp/TXAH7CJ3LexSYfH6qq/6m8sJhCujk2koKN8cs4Argd2woSRU7Ay2K2UbjQmMCTYhPFFl2RbYiLK34BdQrrDgDtdkv2PT+Pnw7bh2ITrTEzMWC3c4+LYxBFs0joZ6zf5RLg3llO7YALDgcA8WP+61iXZ18Pkclusfv8R8gCQT+JXwfr4Q8CemNvvPsBEl2Rr+TR+oBf5IZx7HkwUmA8TpWcAP8bqr5z2l5g7628wAWkpbKK7QsjHx4Xkje4Xl2QHhHNdg9XrLGy8Wa5wnkuwceVk4G7MgnRsSPPdbu5rZ2xSfRxWdp+lIz5TkS9jbe0kzA3uYODKkM88vs6SWJ3/FJuID8fGyBvo6vK5MGbVmYY0/6V7Lsb6y3bhXGPCNY4J99GT+hkTjmskYN0H7OaS7Hjgz8AjfWR9O5rOfXE69gzZFetjRXYBxheEqn2wsetCrM4WxMrijtBn38Fcnf/mkux/fRrfVzjXXnSIRfWYN5zzeExsXBQrz3tCXb9cSt9svXwWi404Axtje+LG+zWs/Y/Fyup1l2TDMNEpF3pfBX4AXOWSbJtcxMXGxjeBn2DtdinMhdOF/DU7bi+KjX1HhGstifWBu0K5lIWka7EXUicDvlAOI7E+dA/WB74NLILFa8zptky74a+YmH0k8GQoj3J7GjCmvTqLDc65u1WXF0I0ycg33wZgg3PuZuqLbzFySQVcF0KIIv0lVoEJUfdhVhbvAw9gYtUwl2TLhYnKKOBtTKDCp/FV+UlckkXYD9m3gT+6JNvPp/FrLskWwSZkv/VpfFhIfrNLsg+xiV8Vv/JpnIs/E12SbYgFIc+3HYhZjKzo0/jJQrrPA8e4JDu35CpypU/jQ4sXCPmdq+LaLlhe5PgG1k9nYkHJTy9syyevb5TSvl7a3yxPYmLPGpiQl5NPaovnq2ECTCOLrWHAqz6N323i+hcDv8Qs48bC7Dfs22HWEOVJ6e99Gv8y/D8hTHIOdkl2erBimuP6K+PT+F8uyV4FPvBpfE+dZJN8Gh9QyNcI4McuyUaX7iECNi1ZB54B3OHTeOvCttuwCeHBWBvHFwLohzZ2JyasJC7JjvRp7H0a3+OSDOCFbvLaDB8CW+UupeGcf8Esv+72afyQS7L3gZkV1zkVs4Tc0KfxB+H4mzCB9WjMWq+3jMbEl2/lAoBLshuxcWOZPJGzYO2HYBYexxW2P4FZrcWY6NTU/YZ2diJwtU/j4gR/ttWfS7J1sYnpbj6N/xg2T3RJ9jpwiUuykT6Np9a5rzWBh4t5pVrsHQKMytu2S7IHMTHj+yF/eFvQorioxd3AU5j4uXLJxe9zwC4+ja+tk68yl/k0zifrE12SfQsbP/Nto2mifgIeG08aCU+HYwtEHBX+3g794zKfxn9uMt9VVPXFi7G6GuHTeFrYNhIT7fPx6XOY+HGhT+M9CsfeCzyBjWWnY9ap0zFRKC+Lz2AvcC4NglYlPo3fwkS+/NxzYW3tFay8Tysd0qhedsPqZc28v4Z6eaRRIRUYgpXX1EK+LsDKcT2fxq+FzTcFEes44Lownn8V2LogXkFnsa6pcTvUyYGlcrkLG282p/MzDOA3Po3PKKTfA+tr5bxcWXG/jcq0Cy7JNsEsGnf0aXx5oTxuxKy4hBCiISOXXJgdV1mqcUIhhPgU0edilU/j6S7J/o0JUWn4vDdMYJ9wSTYjbHsmfN5VcGdaCJuYbI+JH58pnPqrmNi1IrAANqksciX1xaq/lb4/irm/5GyGvSl+uiQs3YRNHr4JPFzYXv5xDDYxuLBi+wXhL+cibHLXBWdBz3cCfuQ7r0iUu7L0SVwvn8azXJL9AdjfJdlD2ARrZeytcydhyqfxHfR9bLN3XJJdggk7JwTxbnfsPqvKsDw5vRyrlxUwEaIv6q83lNvVI5h1RL76Zc740uT4q5i1zIml/L6LWUiNKqRdAnu7vxlmUVBMP7R0nTnlZt95Jch8UrsMZjFUiUuyz2KulSdilgzFPE4Edq48sHnWwFYgnG2p4tO45pLsKswSKWcTzGrj0lIe7sVE71F0Fqsa3e9amLDzO+qzGWaheFXpmrl14CiCGF/BFGBfl2RnYtYgd9cRe58sTObxaTwjjKNFoW4ezFroh5gAMF/h+BGYxVvOR5jVS7NUtfONC9+brR+CMFcU5yrx5ko8yplr+GaYC/DGmEvXJj6N9+xB/ot06ouBqzGLuV2Bn4dtu2Iux7m4sSbmylZuW//G3EJHYa653iXZeZjY8rMgQG2DjQmV7n9FXJJ9HxOrR2DWPzkjKpI3qpc1MVfK2cJyyN+f6bCMbMQzFWLrZpio+lbFeHtqeI6/hol2v3S2GvDtxTZcOE9T47azhUT2wcbNBQppq8qlPL5vCrxcEqrq0ahMq1gTe25eVdp+OXaPA86IxT/Hbfuu1YpLCyF6wLvvjgRg/vnnb21GhBCiTenr1QBzJmGxQCLsR3wxZs3fsUnI0pibzKTCvguxH6S/wSaeq2PuC9Ax+VoifJbjIr1CfV4vfX+fzpO5oSGfH5b+ckFssdLxL1VcIwv5Lf6BuRwWt42pymBwMTkRi0Xzhzr5L1tQLVra3xMOxiYFl2EWWzdglgFvUH1/jXgeWNwlWbNP3HOwyfYWoZ3shVmwVNVjeVv+PX8F1Rf11xuq2hV0bltV1xsaPi+ga563IuTXWSDp68K247HV/lbHYi1VXWdOafZ+yiyKWRUeTdf72R9YxM1ZUOwl6NrfoWu7yMv1qYp8LETXdtDofvP03blUDsUs3WaVrpfnt3zNIn/EXKS+hfXF112S/TVYiHWXzzyvxXo5CRtbLgG2xKzDcmuwcv3NKLlDNqKqnOYtfG+2fnqMT+MpPo3H+jTeCrNSuQUTuXu7zneXvh8EwquAnV2SRcFyZ0fgLwUXs7xtTaRr21qRzvV8AfZs3TV83we4zzcIYO+SLAauwOI/7YS1i9Ux17eqPthMvTQznnZH1Vg5FBNFy+Vwati/WLAs3QS4H2ubT7gkm+46r17b1LgdXHHPwcp+O6xtrxHSVJVLOc+LAS9UpKuiUZlWsQTwRkn4hj5o/0KITzbzzz+/hCohhOiG/nADBBOgdsJ+UK5Cx9tqMOFqX8wSAywgex5cdmsscHbRhH/F0rnzH6JD6RwE/AtzkN/XsMnWgXX2Tyt9rwr6+1o4z2yCW9EzPo27XZfWJdmu2I/xX/k0PqEiSX6fy9P5h/g3w2ePVxjyafw2sJ2zwLBfxCzd5sfiLlWuItiAiVisos3p+oa56vqPOgtsvDcWj+Qr4f8qvoC9pS9+h44JyBzXXz9Tvl7eTo7Ayq1MHkvsy1iMql19Gs8Orh8mte3Em5g13tmYANOFBq6vjXiJjrZepNzn83LdlK4us8X9zZIvgLAUXePFFc/5HubuXMWLdbYTJvTnAecF9+ZNMevQKzChoifsgK3ueXy+IbiuVdHX7b/Z+pkjfBq/6WyhhY3C9R7Fyh5MMCxSTySsd+8XY9ax62AxlJYI23LytjOa6sUnZrv3BXf1vwB7B1fYDSi493XDDtjCE6PzDcGFsKdu3jkv0RHvsEhP6qWqvPIA5yfXOeZFMCtr4IfhZcRKmHB9jkuyZ3wa30jz4/YOwC2+Y+XaPGB9s3meSSEOYz/wEibIf6YkWPVp+xdCfPKYOnUqACNHjmxpPoQQol3pL7HqjvB5OObaNbmw7+9Y7I3vY25PuZAzL2adUX47Obr0/REsUO336BxU/HtzkN/x2KpKz/mKlez6E2crjV0InO/TOKmTbDL2g3tnOosbu2Bvgu/q7fVD8OA8gPBR4TplF8tm+Cs2uTjZJdmkPChxTnDz+LZP46KbxTmYJcgiwBM+jW+tc+7vYzGucnbALFlyAaG/6u99bOLa10zDxMHlC7G4qshft83uE2HyWuVW9wH9k9cyXcrEp/F/gvC4EvDgHApTVdwD7O4KQavDBLgcvPxmTDRbxqfxzX1w3buxdrYXhThVJcZjgfEX9ml8S28v5G3FvitCjJx6om13zE/XsXP33uanhzRbP03jkmyY71hkoMjXw2cu2j8bPlfAYkflY82mPbzkbZgF3a5Y+36GzhbBd2OC1Fd8Gl/UxPnOwcbt8zEX1Mu7Tw5YHX5U2rYr1bEQm2EyVi9rFGJWOWw8nRPGY65vj1W4VHYhiLJTXZL9DIvttQK2Em2z4/b8WBkW6UnbngDs4JIs9mmc9eC4ZpmM1dF36VzPO1QnF0IIQ2KVEEJ0T7+IVT6NHw8xVWLgAZ/Gswq7H8ImgDFwW/4m0qfxWy7J7sECZ7+EiSZ70OHqlZ/7DZdkpwNHuiR7h47VAH+UJ+lFlk/DgiTf6ZLsNExMWACbGK1bDILdl7gkG4WtPPQwMM4l2RqF3e/nbiM+jT90SXY09lb6BeyeN8TK54A8oHU457J0uCAuhsUQylflmuLT+NmQ7gfYG/tpmFi0LVYG3y0GAXZJth7merNHIYB0F3waf+RsWfebsYnJGXQIkSthE/7H6RwT5CrM9XBtzC2xHnuGSdYUbAWnH2MWeG+G/f1Vf/8EFg2uK/cD7/k07klw4kpCPJ/9sJXn5sFics3E3sSvhU3efo25Az0LnOCS7GNMjDiom7xu6ZJsPGZV9KLvvNJiX/FPYF2XZFth8bJmelss4WeYReVNIQDzS1hw5lWAuXwaH17nfM0wDhOE/hoE1VexNrBI2O9hdlD8k4GznAW7vwOzvBmGuSSd79O46VUzQ2y1I4AzQ/ylSzHBYiTWFs70aXy7S7I/Yavz/RoLqu0xF+ctgMN8Gj9RdX6XZL8L55uMWZh8DRMnyqthNsN4bPW8RzA3yO2wtjQQjKOJ+gFwSfYLbIXXL+djUR1uCM+QK7D+/FnMZexgrLxygX4K8C8sVpLDxNR9aey61YkQy+lSTCj8DHCaLyyS4NP4bZdkhwBnB2vUG7GYVkthVsK3+zS+rJD+HmeB8EcBZ9aJRVZmPLBNGMOuB1YF/h9mudgbLsJeGP3VJdmRWBvbB3OJnRN+gbXzSS7JzsKEvUUwEWq4T+M9XJL9D7aC5RVYe5wLe/H0EZC/lGh23B4PHBbu4T7s2ddlpcluuASz+v2TS7KTsDhZC2LPktN9Gj/e0wIo4tP4Zpdkf8csJIfQsRpgU9ZcLsk+Ai7yafyjwrangGd9Gm9U2HYLsKxP46/MSX6FEEIIIQYL/RWzCmzimq9eNpsQK2Vy2DepdMyO2KqBZ2MToJepdhE4BouDsRsW02dzOiyw3uppRkMQ3LWwuE2HYVYUf8DcEpue3PaCDbFJ1crY5Gty4a9TkFhvq8L9BHsrfhNWVvv7ND67dM4NMMuov2A/+r9Z+L5BIV0Nm9Rdj1l2DQXWrwhCm69y2LCt+DT+JyZMXYzVx/WYOLU7Znm1dyn9h1hg6fexiVU9tsbEhuswa7LjCat0hfP0V/2dj70pPxGbJPXZW3mfxjdgE9kFwnVuwlwwv0iwRAwi5DZYP/gj1i8m0dnKLGd/zOIwwybwe/VVXkscgU0q/xyuMybk9UFMJH0Nizk3AZusrkjXft4jQjlsiom6v8XayvNYeUChz/s0PhK791Ehj9dibeINbBLZ02ufhVltLo2JVVdhE+WnC8l2wcph+3C9K7H6eJLu49bchQkS52Ai71HYxHq3nuYTs1C5DotndgU2Gd+xF+fpMT2pH2wcmYuORSPqcTzWlg7DhKGrsb7wK8xCMxcoP8L6+fPYM+NsrCzH9eJWLgY+j/XJS8o7fRqfB3wHC+p9ccjXsdhLn6kV58tXm2sYWD3we6z+foD14y2xlzo9fqaF/H6AjZtTsTZ2EdZuj+/msGbO+xzmnvwPbGy8GTgXE+1yIeplbLW+n2Ht8k/YAhFb+TR+IJyn2XH7OKwMD8Lawf9gQlOz+f0Qa5/nYmPDDVh5DKF38R6r2C6c9ySs/82NjQHNMBddrefmrtg2F/1nDS+EEEII0XZEtdpAh+/pH1ySfQ+bnI7yaXxno/Si9QR3naeAO30a79oovRBFXJJdD3zDp/GXW50X0ZVPe/24JLsL8D6N68UzE6JfWG211Wr3399tqEwhRBswbtw4AEaPHt3SfAghRCuJouiBWq22WtW+QfmWLsR12RIz538Ps044HIud0pvg4GIACUubr4AF4R+GWUsIUZcQ72YWZq20IGbttCVmbShajOrHcEk2L+b6ujFmNdQvLuRCCCGEEEJ80hmUYhU2KRoF7IfF35iBWVUdUYwzItqWVTA3jxnAgT6Np7Y2O2IQ8D7mBrQM5g4zDfixT+MLWporkaP6MZbAgrG/CZxY4VYthBBCALDzzlXr5QghhMj5xLgBCiGEEEJ82pEboBBCCCEGC925AfZngHUhhBBCCCGEECWmTJnClClTWp0NIYRoWyRWCSGEEEIIIcQA8thjj/HYY4+1OhtCCNG2SKwSQgghhBBCCCGEEG2DxCohhBBCCCGEEEII0TZIrBJCCCGEEEIIIYQQbYPEKiGEEEIIIYQQQgjRNkS1Wq3VeRBCCCGEEH1AFEXvANNanQ/R5wwBZrY6E6LPUb1+clHdfjJRvfY9y9ZqtcWrdsw90DkRQgghhBD9xrRarbZaqzMh+pYoiu5XvX7yUL1+clHdfjJRvQ4scgMUQgghhBBCCCGEEG2DxCohhBBCCCGEEEII0TZIrBJCCCGE+OTwu1ZnQPQLqtdPJqrXTy6q208mqtcBRAHWhRBCCCGEEEIIIUTbIMsqIYQQQgghhBBCCNE2SKwSQgghhBBCCCGEEG2DxCohhBBCCCGEEEII0TZIrBJCCCGEGOREUbRvFEVPR1H0XhRFD0RRtG6r8ySaJ4qiMVEU1Up/Lxf2RyHNi1EU/TeKotujKFq+lXkW1URRNCqKouuiKHoh1OPo0v6GdRlF0bxRFJ0ZRdHMKIr+E8639IDeiOhEE/U6rqIP31NKo3ptI6IoOiKKoilRFL0dRdGrURRlURStUEqj/tpCJFYJIYQQQgxioij6AXAGcCKwMnA3cGMURcu0NGOip0wDlij8rVjYdyhwMHAAsDowA7g5iqIFBzqToiGfAx4FDgT+W7G/mbo8HfgusCOwLrAQcH0URXP1X7ZFAxrVK8BEOvfhLUr7T0f12k6sD5wDrAVsCHwETIyiaNFCGvXXFqLVAIUQQgghBjFRFN0LPFyr1fYsbHsSuLJWqx3RupyJZomiaAywfa1WW6FiXwS8CJxVq9VOCNs+i02aklqtdt5A5lU0TxRFs4D9a7XauPC9YV1GUbQw8Cqwe61WuzSkGQY8C2xeq9VuGvg7EUXK9Rq2jQOG1Gq1reoco3ptc6Io+hzwFrBNrVbL1F9bjyyrhBBCCCEGKVEUzQOsCkwo7ZqAvS0Wg4fhwcXo6SiKLo+iaHjY/iXgixTquFar/ReYhOp4sNFMXa4KfKaU5nng/1B9tzvrRFE0I4qiJ6Io+n0URUML+1Sv7c+CmD7yRviu/tpiJFYJIYQQQgxehgBzAa+Utr+C/cgWg4N7gdHA5sCeWN3dHUXRYnTUo+p48NNMXX4R+BiY2U0a0X6MB34IbIS5jf0vcGsURfOG/arX9ucMYCowOXxXf20xc7c6A0IIIYQQYo4px3WIKraJNqVWq91Y/B4CM08HdgPyIM2q408OvalL1XcbU6vVLi98fSSKogcwV7Atgb92c6jqtQ2IoujXwDrAOrVa7ePSbvXXFiHLKiGEEEKIwctM7K1u+Q3uULq+DRaDhFqtNgt4DPgqkK8KqDoe/DRTly9j1pJDukkj2pxarfYi8G+sD4PqtW2Joug0LDj6hrVabXphl/pri5FYJYQQQggxSKnVah8ADwCblHZtgq0KKAYhURTNB3wdeAl4GpsQbVLavy6q48FGM3X5APBhKc3SwDdQfQ8aoigaAiyF9WFQvbYlURSdAeyECVWPl3arv7YYuQEKIYQQQgxufg1cHEXRfcBdwD7AksBvW5or0TRRFKVABjyHvZE/GlgAuKhWq9WiKDodOCqKoseBJ4CfA7OAy1qTY1GPsKLYV8JXBywTRdFI4PVarfZco7qs1WpvRVF0AXBqFEUzgNewPv4wMHEg70V00F29hr8xwFWYOLUccBK2atzVoHptR6IoOhvYFdgGeCOKotyCalatVpvVzNireu1folpNrpRCCCGEEIOZKIr2BQ4FlgAeBQ6q1WqTWpsr0SxRFF0OjMJcSV7F4lQdXavV/hn2R8AxwN7AIlhA9v1qtdqjrcmxqEcUResDt1XsuqhWq41upi6D9capmMXHZ4FbgH3DKmOiBXRXr8BPgGuAlYHPY4LVbVgfnl1nqtf2IoqiekLIsbVabUxIo/7aQiRWCSGEEEIIIYQQQoi2QTGrhBBCCCGEEEIIIUTbILFKCCGEEEIIIYQQQrQNEquEEEIIIYQQQgghRNsgsUoIIYQQQgghhBBCtA0Sq4QQQgghhBBCCCFE2yCxSgghhBBCCCGEEEK0DRKrhBBCCCGEEEIIIUTbILFKCCGEEEIIIYQQQrQN/x8nWSCShNvq/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 12))\n",
    "longLabels=['{}: {}'.format(p.Cite, p.Title) for p in clusteredPapersDf.itertuples()]\n",
    "MAX_TITLE_LENGTH=70\n",
    "truncatedLabels=[label[:MAX_TITLE_LENGTH] + ('...' if len(label) > MAX_TITLE_LENGTH else '')\n",
    "                 for label in longLabels\n",
    "                ]\n",
    "                            \n",
    "dn = dendrogram(Z, \n",
    "                orientation='right', \n",
    "                leaf_font_size=16, \n",
    "                #count_sort='descending', # Tweek sorting to give good arrangement. \n",
    "                link_color_func=NodeColour,\n",
    "                distance_sort='descending'\n",
    "               )\n",
    "\n",
    "# Kludge the labels to the group color used in the cluster diagram, and add a separator between groups\n",
    "\n",
    "yTicks, yTickLabels = plt.yticks()\n",
    "LabelOrder=[int(i.get_text()) for i in yTickLabels] # Top to bottom.\n",
    "\n",
    "currentGroup=0\n",
    "for i in yTickLabels:\n",
    "    itemNumber=int(i.get_text())\n",
    "    thisItemsGroup=Group(clusteredPapersDf.iloc[itemNumber].Cluster)\n",
    "    if thisItemsGroup != currentGroup:\n",
    "        if True: # currentGroup != 0:\n",
    "            # Draw a line here:\n",
    "            plt.axhline(y=i.get_position()[1] - 5, color='grey', xmax=0.1, linestyle='--')\n",
    "        currentGroup = thisItemsGroup\n",
    "    i.set_color( 'C{}'.format(thisItemsGroup)) \n",
    "    i.set_text(truncatedLabels[itemNumber])\n",
    "plt.yticks(yTicks, labels=yTickLabels)\n",
    "\n",
    "plt.axvline(x=clusterCutoff, color='grey', linestyle='--')\n",
    "\n",
    "Savefig('clusters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://ieeexplore.ieee.org/abstract/document/8057728/\" style=\"color:#de8f05\">Nurse+2017: Security risk assessment in Internet of Things systems</a><br><a href=\"https://www.sciencedirect.com/science/article/pii/S0166361518301817\" style=\"color:#de8f05\">Radanliev+2018: Future developments in cyber risk assessment for the i...</a><br><a href=\"https://digital-library.theiet.org/content/conferences/10.1049/cp.2018.0001\" style=\"color:#de8f05\">Nurse+2018: If you can't understand it, you can't properly assess it! ...</a><br><a href=\"https://www.preprints.org/manuscript/201903.0109\" style=\"color:#de8f05\">Radanliev+2019a: Cyber risk impact assessment-assessing the risk from ...</a><br><a href=\"https://www.preprints.org/manuscript/201903.0104\" style=\"color:#de8f05\">Radanliev+2019d: Cyber Risk in IoT Systems</a><br><a href=\"nan\" style=\"color:#de8f05\">Radanliev+2019b: Methodology for designing decision support supply cha...</a><br><a href=\"https://link.springer.com/article/10.1007/s10669-015-9540-y\" style=\"color:#de8f05\">DiMase+2015: Systems engineering framework for cyber physical security...</a><br><a href=\"https://www.researchgate.net/profile/Petar-Radanliev/publication/331684145_Definition_of_Cyber_Strategy_Transformation_Roadmap_for_Standardisation_of_IoT_Risk_Impact_Assessment_with_a_Goal-Oriented_Approach_and_the_Internet_of_Things_Micro_Mart/links/5c87cfab299bf1e02e29c385/Definition-of-Cyber-Strategy-Transformation-Roadmap-for-Standardisation-of-IoT-Risk-Impact-Assessment-with-a-Goal-Oriented-Approach-and-the-Internet-of-Things-Micro-Mart.pdf\" style=\"color:#de8f05\">Radanliev+2019: Definition of cyber strategy transformation roadmap fo...</a><br><a href=\"https://www.researchgate.net/profile/Petar-Radanliev/publication/331683680_Design_principles_for_cyber_risk_impact_assessment_from_Internet_of_Things_IoT/links/5c87c8ad458515b59e468003/Design-principles-for-cyber-risk-impact-assessment-from-Internet-of-Things-IoT.pdf\" style=\"color:#de8f05\">Radanliev+2019c: Design principles for cyber risk impact assessment fr...</a><br><a href=\"https://ieeexplore.ieee.org/abstract/document/8936335/\" style=\"color:#029e73\">Sun+2019: Security and privacy for the internet of medical things enab...</a><br><a href=\"https://ieeexplore.ieee.org/abstract/document/8110212/\" style=\"color:#029e73\">Alsubaei&Abuhussein…2017: Security and privacy in the internet of medi...</a><br><a href=\"https://ieeexplore.ieee.org/abstract/document/8689354/\" style=\"color:#029e73\">Alsubaei+2019: Ontology-based security recommendation for the internet...</a><br><a href=\"https://link.springer.com/article/10.1007/s42452-019-1931-0\" style=\"color:#0173b2\">Radanliev+2020: Future developments in standardisation of cyber risk i...</a><br><a href=\"https://link.springer.com/content/pdf/10.1186/s42400-020-00052-8.pdf\" style=\"color:#0173b2\">Radanliev+2020a: Cyber risk at the edge: current and future trends on ...</a><br><a href=\"https://www.mdpi.com/270606\" style=\"color:#0173b2\">Ali&Awad2018: Cyber and physical security vulnerability assessment for...</a><br><a href=\"https://www.sciencedirect.com/science/article/pii/S1084804517301455\" style=\"color:#0173b2\">Alaba+2017: Internet of Things security: A survey</a><br><a href=\"https://ieeexplore.ieee.org/abstract/document/8853599/\" style=\"color:#0173b2\">Butt+2019: IoT smart health security threats</a><br><a href=\"https://link.springer.com/chapter/10.1007/978-3-319-77492-3_1\" style=\"color:#0173b2\">Linkov&Kott2019: Fundamental concepts of cyber resilience: Introductio...</a><br><a href=\"https://books.google.com/books?hl=en&lr=&id=AwD0BgAAQBAJ&oi=fnd&pg=PR9&dq=How+to+Measure+Anything+in+Cybersecurity+Risk.+Douglas+W+Hubbard&ots=qrrm-FlaIl&sig=T7lQFiPqlbod4W_j34Tp6SPj5lY\" style=\"color:#0173b2\">Hubbard&Seiersen2016: How to measure anything in cybersecurity risk</a><br><a href=\"https://www.sciencedirect.com/science/article/pii/S016740481830467X\" style=\"color:#0173b2\">Wagner+2019: Cyber threat intelligence sharing: Survey and research di...</a>"
      ],
      "text/plain": [
       "<__main__.Shout at 0x7fdd541af310>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of triplets: URL, color, text for each label above, in top to bottom order:\n",
    "Links=[p.Link for p in clusteredPapersDf.itertuples()]\n",
    "LabelList=[(Links[LabelOrder[i]], matplotlib.colors.to_hex(item.get_color()), item.get_text()) \n",
    "           for i, item in enumerate(yTickLabels)][::-1]\n",
    "# Kludge to get Jupyter to display html:\n",
    "class Shout(object):\n",
    "    def _repr_html_(self):\n",
    "        return '<br>'.join(['<a href=\"{}\" style=\"color:{}\">{}</a>'.format(*triplet) for triplet in LabelList])    \n",
    "Shout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Citation map\n",
    "\n",
    "We use the 'cited by' functionality to track citation links between the papers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving 124 papers for {'cites': '2158158161114378793'}\n",
      "Retrieving 34 papers for {'cites': '856727435201930563'}\n",
      "Retrieving 104 papers for {'cites': '7884234468971956873'}\n",
      "Retrieving 41 papers for {'cites': '15490167577412579022'}\n",
      "Retrieving 26 papers for {'cites': '16163323237889647504'}\n",
      "Retrieving 22 papers for {'cites': '16937427700442842878'}\n",
      "Retrieving 19 papers for {'cites': '402742775937259293'}\n",
      "Retrieving 20 papers for {'cites': '705795199300173541'}\n",
      "Retrieving 22 papers for {'cites': '17027613155545731121'}\n",
      "Retrieving 120 papers for {'cites': '6230626243802260829'}\n",
      "Retrieving 113 papers for {'cites': '15110423003858188256'}\n",
      "Retrieving 16 papers for {'cites': '14324492556190021166'}\n",
      "Retrieving 24 papers for {'cites': '1776037729542888110'}\n",
      "Retrieving 195 papers for {'cites': '2030156502610964061'}\n",
      "Retrieving 60 papers for {'cites': '14386747336759593166'}\n",
      "Retrieving 77 papers for {'cites': '6871428980662632846'}\n",
      "Retrieving 200 papers for {'cites': '7192314918966201656'}\n",
      "Retrieving 21 papers for {'cites': '6724629180905251087'}\n",
      "Retrieving 37 papers for {'cites': '10710897600124378357'}\n",
      "Retrieving 56 papers for {'cites': '5021234964518621637'}\n"
     ]
    }
   ],
   "source": [
    "def CitingPapers(paperSeries):\n",
    "    # Use WellCitedPapers, because we're only interested in links between those papers:\n",
    "    allCitingPapers= WellCitedPapers(GetPapers({'cites': paperSeries.Key}))\n",
    "    return [paper.Key for paper in allCitingPapers.itertuples()]\n",
    "\n",
    "# This takes a long time...\n",
    "papersWithClustersAndCitedByDf=(clusteredPapersDf\n",
    "                                .assign(CitedBy=[CitingPapers(paper) \n",
    "                                                 for paper in clusteredPapersDf.itertuples()]\n",
    "                                       ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open( \"papersWithCitedBy.p\", \"wb\" ) as outFile:\n",
    "    pickle.dump( papersWithClustersAndCitedByDf, outFile )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open( \"papersWithCitedBy.p\", \"rb\" ) as inFile:\n",
    "    papersWithClustersAndCitedByDf=pickle.load( inFile )\n",
    "len(papersWithClustersAndCitedByDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "workingDf=(papersWithClustersAndCitedByDf.copy()\n",
    "           .set_index(['Key'], drop=False)\n",
    "          )\n",
    "\n",
    "edges=[(cFrom, paper.Key) for paper in workingDf.itertuples() \n",
    "         for cFrom in paper.CitedBy if cFrom in workingDf.Key.values \n",
    "     ]\n",
    "\n",
    "G = nx.DiGraph()\n",
    "G.add_edges_from(edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use a columns layout, where each column is a year, with the latest years on the left:\n",
    "def Subset(paper):\n",
    "    return 2022-paper.Year if paper.Year > 2015 else 2022-2015\n",
    "G.add_nodes_from([(paper.Key, {'subset': Subset(paper)}) for paper in workingDf.itertuples()])\n",
    "positions=nx.multipartite_layout(G)\n",
    "\n",
    "# Move the items for some years up slightly so the texts don't overlap:\n",
    "#delta=0.05\n",
    "#positions={k: (np.array([pos[0],pos[1]+delta]) if workingDf.loc[k].Year in (2020,2018,2016) else pos) \n",
    "#           for k, pos in positions.items()}\n",
    "\n",
    "# And move Hadar+2018 out of the way so it doesn't look as though it has lots of links:\n",
    "#hadar=positions['9873211921093443394']\n",
    "#positions['9873211921093443394']=(hadar[0],hadar[1]-0.85)\n",
    "\n",
    "fig=plt.figure(figsize =(18, 15))\n",
    "\n",
    "# We want two lots of node labels, one with the group numbers large, in white, and underneath the correct ones.\n",
    "# So draw the group labels in the main network drawing process, and add the paper titles afterwards:\n",
    "\n",
    "nx.draw_networkx(G,\n",
    "                 # Need to specify explicitly the nodes, even though they're in G, to ensure ordering:\n",
    "                 nodelist=[paper.Key for paper in workingDf.itertuples()],\n",
    "                 node_color=['C{}'.format(Group(paper.Cluster)) for paper in workingDf.itertuples()], \n",
    "                 pos=positions, \n",
    "                 labels={paper.Key: Group(paper.Cluster) for paper in workingDf.itertuples()},\n",
    "                 font_size=30,\n",
    "                 font_color=(1,1,1,0.7),\n",
    "                 node_size=1200,\n",
    "                 arrowsize=15\n",
    "                )\n",
    "\n",
    "nx.draw_networkx_labels(G,\n",
    "                 pos=positions, \n",
    "                 labels={paper.Key: paper.Cite for paper in workingDf.itertuples()},\n",
    "                 bbox=dict(facecolor=(1,1,1,0.7), edgecolor=(1,1,1,0.7)),\n",
    "                )    \n",
    "\n",
    "groupNames={0: 'Unclustered', 2: 'Pressures', 1: 'Individual Developers', 3: 'Privacy'}\n",
    "plt.legend(handles=[matplotlib.patches.Patch(facecolor='C{}'.format(i), label='{}: {}'.format(i, groupNames[i])) \n",
    "                    for i in range(0,NumGroups+1)])\n",
    "\n",
    "## Magic to remove bounding box:\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_visible(False)\n",
    "Savefig('citationLinks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List of authors and venues\n",
    "\n",
    "Google Scholar isn't good at author lists and venues. Save as a spreadsheet and fix them manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "papersWithClustersAndCitedByDf[['Key','Cite','Round','Citations','Year','Title', 'Authors','Link','Snippet']].to_excel('SelectedPapers.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open SelectedPapers.xlsx\n",
    "assert(False) # And STOP!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open SelectedPapersSaved.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... then add Venue and Authors columns to SelectedPapers.xlsx and save it as SelectedPapersSaved.xlsx\n",
    "This has to be done manually, as Scholar truncates the author list and isn't very clear on venues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "venuesDf=pd.read_excel('SelectedPapersSaved.xlsx')\n",
    "len(venuesDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of venues:\n",
    "venuesDf.Venue.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Table of all authors with more than one paper. \n",
    "# Save to a spreadsheet as the easiest way to create a table.\n",
    "(venuesDf\n",
    "    .assign(Author=venuesDf.Authors.str.split(','))\n",
    "    .explode('Author')\n",
    "    .assign(Author=lambda df: df.Author.str.strip())\n",
    "    [['Author']] \n",
    "    .value_counts()\n",
    "    .to_frame('Count')\n",
    "    .query('Count>1')\n",
    "    .to_excel('Authors.xlsx')\n",
    ")\n",
    "!open authors.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open ScholarUtils.py"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "NLP_example_clustering.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}