Generic Club Recommendations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommendation Notebook \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes\n",
    "\n",
    "### 2/25\n",
    "- Added graph analysis for recommendations\n",
    "- Organized visualization charts along with steps for building word2vec model\n",
    "- Fixed lack of reproducibility when training new Word2Vec models\n",
    "\n",
    "### 1/12\n",
    "- Tweaked hyperparameters to get optimal performance (for now)\n",
    "- Blank descriptions are now replaced with club name respectively\n",
    "- Clubs that aren't confirmed are not included in dataset\n",
    "\n",
    "### 1/10\n",
    "- Create 3rd temporary recommendation system based off of favorites and interest lists. Basically just added case where favorites_list does not exist, so then tags are taken into consideration instead. This version still does not take tags into account when a favorites list does exist, however. We can improve this part if we want in future versions.\n",
    "\n",
    "### 1/8\n",
    "- Added .env variable support for use with version control\n",
    "- Added frequency distribution graph for words from descriptions\n",
    "\n",
    "### 1/6\n",
    "- Created 2nd temporary recommendation system based off of favorites, would like to include tags incorporated, even though we use the recommendation function that takes into consideration tags already\n",
    "- Figure out how to recommend even with no favorites, just tags\n",
    "\n",
    "### 12/31\n",
    "- Added various graphs for visualizing performance of model\n",
    "- Fixed environment by list dependencies without specific versions\n",
    "- Re-added directly importing data from MongoDB\n",
    "\n",
    "### 12/30\n",
    "- Will most likely want to give slack for k when recommending, since we don't want to have the exact same results each time\n",
    "- (Possibly pick from distributions?)\n",
    "- favorite_recommendation does not work as well as hoped, need to reevaluate how well it works + other strategies\n",
    "\n",
    "### 12/28\n",
    "- Switched from euclidean distance to cosine similarity\n",
    "- Fixed runtime for vectorizing the table\n",
    "\n",
    "### 12/23\n",
    "- Annotation, a lot\n",
    "- Recommend function takes into consideration tags\n",
    "- Notebook formatted\n",
    "\n",
    "### 12/10\n",
    "- Started\n",
    "- Issues importing from Tejas's environment, unable to connect to DB so exported directly from MongoDB\n",
    "- Used nlp/gensim to vectorize description, use K-nn approach to recommend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load .env variables\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Native imports\n",
    "import os\n",
    "import json\n",
    "import random\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "# MongoDB Python driver import\n",
    "import pymongo\n",
    "\n",
    "# 3rd-party data science imports\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "# pd.set_option('display.max_rows', None)\n",
    "\n",
    "# NLP Pre-processing tools\n",
    "import re, string, unicodedata\n",
    "import nltk\n",
    "#import contractions      # https://github.com/kootenpv/contractions\n",
    "#import inflect           # https://github.com/jaraco/inflect\n",
    "from nltk import word_tokenize, sent_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import LancasterStemmer, WordNetLemmatizer\n",
    "#import flashtext         # https://github.com/vi3k6i5/flashtext\n",
    "\n",
    "# Main NLP tool kit\n",
    "import gensim           # https://radimrehurek.com/gensim/auto_examples/index.html\n",
    "\n",
    "# Model training\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import bs4 as bs\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize # tokenizes sentences\n",
    "import re\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tag import pos_tag\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import numpy as np\n",
    "\n",
    "# Graph network analysis\n",
    "import networkx as nx\n",
    "\n",
    "# Fancy progress reporting\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Extra tooling\n",
    "import dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 0: Fetch data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directly from MongoDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn_string = os.getenv('MONGO_URI_PROD')\n",
    "client = pymongo.MongoClient(conn_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Advocacy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ASUC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CalGreek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Community Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Computer Science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Consulting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Cultural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Design</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Environmental</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Health</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Performing Arts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Political</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Pre-professional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Religious &amp; Spiritual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Research</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Sciences</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Social Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Sports &amp; Rec.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Publication</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                   name\n",
       "0    0               Advocacy\n",
       "1    1                   ASUC\n",
       "2    2               Business\n",
       "3    3               CalGreek\n",
       "4    4      Community Service\n",
       "5    5       Computer Science\n",
       "6    6             Consulting\n",
       "7    7               Cultural\n",
       "8    8                 Design\n",
       "9    9            Engineering\n",
       "10  10          Environmental\n",
       "11  11                 Health\n",
       "12  12                  Media\n",
       "13  13        Performing Arts\n",
       "14  14              Political\n",
       "15  15       Pre-professional\n",
       "16  16  Religious & Spiritual\n",
       "17  17               Research\n",
       "18  18               Sciences\n",
       "19  19                 Social\n",
       "20  20            Social Good\n",
       "21  21          Sports & Rec.\n",
       "22  22             Technology\n",
       "23  23            Publication"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ALL_CLUB_TAGS = []\n",
    "\n",
    "# Fetch list of tags\n",
    "for tag in client['production-db']['tag'].find():\n",
    "    ALL_CLUB_TAGS += [{\n",
    "        'id': tag['_id'],\n",
    "        'name': tag['name']\n",
    "    }]\n",
    "\n",
    "all_club_tags_df = pd.DataFrame(ALL_CLUB_TAGS)\n",
    "all_club_tags_df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>link_name</th>\n",
       "      <th>description</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EthiCAL Apparel</td>\n",
       "      <td>ethical-apparel</td>\n",
       "      <td>&lt;p&gt;EthiCAL Apparel is a &lt;strong&gt;student-run so...</td>\n",
       "      <td>[2, 8, 20]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UC Rally Committee</td>\n",
       "      <td>uc-rally-committee</td>\n",
       "      <td>The UC Rally Committee’s members are the offic...</td>\n",
       "      <td>[20, 21]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Codebase</td>\n",
       "      <td>codebase</td>\n",
       "      <td>Codebase is a community of developers that emp...</td>\n",
       "      <td>[5, 19, 22]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Berkeley Phi Beta Lambda</td>\n",
       "      <td>berkeley-phi-beta-lambda</td>\n",
       "      <td>Berkeley Phi Beta Lambda (PBL) is a chapter of...</td>\n",
       "      <td>[2, 6, 15]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BEAM- Berkeley Engineers and Mentors</td>\n",
       "      <td>beam--berkeley-engineers-and-mentors</td>\n",
       "      <td>BEAM- Berkeley Engineers and Mentors</td>\n",
       "      <td>[4, 18, 20]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Womxn Ignite @ Berkeley</td>\n",
       "      <td>womxn-ignite-berkeley</td>\n",
       "      <td>&lt;p&gt;Hello Bears! We are Womxn Ignite @ Berkeley...</td>\n",
       "      <td>[0, 14, 20]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Delta Consulting</td>\n",
       "      <td>delta-consulting</td>\n",
       "      <td>&lt;h2&gt;Overview&lt;/h2&gt;\\n&lt;p&gt;Delta Consulting is UC B...</td>\n",
       "      <td>[2, 6, 22]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Biofuels Technology Club</td>\n",
       "      <td>biofuels-technology-club</td>\n",
       "      <td>&lt;p&gt;Biofuels Technology Club (BTC) seeks to cre...</td>\n",
       "      <td>[10, 17, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>GRC (Global Research and Consulting)</td>\n",
       "      <td>global-research-and-consulting</td>\n",
       "      <td>&lt;p&gt;Global Research and Consulting (GRC) Group ...</td>\n",
       "      <td>[2, 6, 20]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Eggster Organization</td>\n",
       "      <td>eggster-organization</td>\n",
       "      <td>Eggster Organization</td>\n",
       "      <td>[4]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name  \\\n",
       "0                         EthiCAL Apparel   \n",
       "1                      UC Rally Committee   \n",
       "2                                Codebase   \n",
       "3                Berkeley Phi Beta Lambda   \n",
       "4    BEAM- Berkeley Engineers and Mentors   \n",
       "..                                    ...   \n",
       "141               Womxn Ignite @ Berkeley   \n",
       "142                      Delta Consulting   \n",
       "143              Biofuels Technology Club   \n",
       "144  GRC (Global Research and Consulting)   \n",
       "145                  Eggster Organization   \n",
       "\n",
       "                                link_name  \\\n",
       "0                         ethical-apparel   \n",
       "1                      uc-rally-committee   \n",
       "2                                codebase   \n",
       "3                berkeley-phi-beta-lambda   \n",
       "4    beam--berkeley-engineers-and-mentors   \n",
       "..                                    ...   \n",
       "141                 womxn-ignite-berkeley   \n",
       "142                      delta-consulting   \n",
       "143              biofuels-technology-club   \n",
       "144        global-research-and-consulting   \n",
       "145                  eggster-organization   \n",
       "\n",
       "                                           description          tags  \n",
       "0    <p>EthiCAL Apparel is a <strong>student-run so...    [2, 8, 20]  \n",
       "1    The UC Rally Committee’s members are the offic...      [20, 21]  \n",
       "2    Codebase is a community of developers that emp...   [5, 19, 22]  \n",
       "3    Berkeley Phi Beta Lambda (PBL) is a chapter of...    [2, 6, 15]  \n",
       "4                 BEAM- Berkeley Engineers and Mentors   [4, 18, 20]  \n",
       "..                                                 ...           ...  \n",
       "141  <p>Hello Bears! We are Womxn Ignite @ Berkeley...   [0, 14, 20]  \n",
       "142  <h2>Overview</h2>\\n<p>Delta Consulting is UC B...    [2, 6, 22]  \n",
       "143  <p>Biofuels Technology Club (BTC) seeks to cre...  [10, 17, 18]  \n",
       "144  <p>Global Research and Consulting (GRC) Group ...    [2, 6, 20]  \n",
       "145                               Eggster Organization           [4]  \n",
       "\n",
       "[146 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIST_CLUB_INFO = []\n",
    "\n",
    "CLUB_SEARCH_PARAMS = {\n",
    "    'role': 'officer',\n",
    "    'confirmed': True,\n",
    "    'club.reactivated': True\n",
    "}\n",
    "\n",
    "for user in client['production-db']['new_base_user'].find(CLUB_SEARCH_PARAMS):\n",
    "    club_name = user['club']['name'].strip()\n",
    "    club_link_name = user['club']['link_name'].strip()\n",
    "    club_description = user['club']['about_us'].strip()\n",
    "    club_tags = user['club']['tags']\n",
    "    \n",
    "    LIST_CLUB_INFO += [{\n",
    "        'name': club_name,\n",
    "        'link_name': club_link_name,\n",
    "        'description': club_description,\n",
    "        'tags': club_tags,\n",
    "    }]\n",
    "    \n",
    "club_db_df = pd.DataFrame(LIST_CLUB_INFO)\n",
    "club_db_df = club_db_df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)\n",
    "club_db_df = club_db_df.reset_index(drop = True)\n",
    "\n",
    "for (i, row) in club_db_df.iterrows():\n",
    "    row['description'] = row['description'] if len(row['description']) != 0 else row['name']\n",
    "\n",
    "club_db_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Club Recommendation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Clean table descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_table(table):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "    Cleans club dataframe descriptions into another column containing a lists of significant words in each description.\n",
    "    \n",
    "    Input:\n",
    "    table - club dataframe\n",
    "    \n",
    "    Output:\n",
    "    cleaned_table - table with a new column: \"cleaned descriptions\"\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    def clean_description(description):\n",
    "        \"\"\"\n",
    "        Description:\n",
    "        Clean single description into lists of significant words.\n",
    "\n",
    "        Input:\n",
    "        description - string of club description \n",
    "\n",
    "        Output:\n",
    "        new_description - list of significant words in description\n",
    "        \"\"\"\n",
    "        \n",
    "        try: \n",
    "            # Remove punctuation\n",
    "            new_description = re.sub(\"[^a-zA-Z]\", \" \", description)\n",
    "\n",
    "            # Tokenize into words (all lower case)\n",
    "            new_description = new_description.lower().split()\n",
    "\n",
    "            # Remove stopwords\n",
    "            eng_stopwords = set(stopwords.words(\"english\"))\n",
    "            new_description = [w for w in new_description if not w in eng_stopwords]\n",
    "\n",
    "            # Remove \"uc\" and \"berkeley\"\n",
    "            uc_berkeley = ['uc', 'berkeley', 'also', 'providing', 'various', 'well', 'provide', 'one']\n",
    "            new_description = [w for w in new_description if not w in uc_berkeley]\n",
    "        except TypeError:\n",
    "            return [\"\"]\n",
    "\n",
    "        return new_description\n",
    "    \n",
    "    clean_descriptions = []\n",
    "    \n",
    "    for i in np.arange(len(table)):\n",
    "        clean_descriptions += [clean_description(table['description'][i])]\n",
    "        \n",
    "    cleaned_table = table.drop(['description'], axis=1)\n",
    "    cleaned_table['clean_description'] = clean_descriptions\n",
    "    \n",
    "    return cleaned_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>link_name</th>\n",
       "      <th>tags</th>\n",
       "      <th>clean_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EthiCAL Apparel</td>\n",
       "      <td>ethical-apparel</td>\n",
       "      <td>[2, 8, 20]</td>\n",
       "      <td>[p, ethical, apparel, strong, student, run, so...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UC Rally Committee</td>\n",
       "      <td>uc-rally-committee</td>\n",
       "      <td>[20, 21]</td>\n",
       "      <td>[rally, committee, members, official, guardian...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Codebase</td>\n",
       "      <td>codebase</td>\n",
       "      <td>[5, 19, 22]</td>\n",
       "      <td>[codebase, community, developers, empowers, st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Berkeley Phi Beta Lambda</td>\n",
       "      <td>berkeley-phi-beta-lambda</td>\n",
       "      <td>[2, 6, 15]</td>\n",
       "      <td>[phi, beta, lambda, pbl, chapter, largest, stu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BEAM- Berkeley Engineers and Mentors</td>\n",
       "      <td>beam--berkeley-engineers-and-mentors</td>\n",
       "      <td>[4, 18, 20]</td>\n",
       "      <td>[beam, engineers, mentors]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Womxn Ignite @ Berkeley</td>\n",
       "      <td>womxn-ignite-berkeley</td>\n",
       "      <td>[0, 14, 20]</td>\n",
       "      <td>[p, hello, bears, womxn, ignite, collegiate, c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Delta Consulting</td>\n",
       "      <td>delta-consulting</td>\n",
       "      <td>[2, 6, 22]</td>\n",
       "      <td>[h, overview, h, p, delta, consulting, strong,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Biofuels Technology Club</td>\n",
       "      <td>biofuels-technology-club</td>\n",
       "      <td>[10, 17, 18]</td>\n",
       "      <td>[p, biofuels, technology, club, btc, seeks, cr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>GRC (Global Research and Consulting)</td>\n",
       "      <td>global-research-and-consulting</td>\n",
       "      <td>[2, 6, 20]</td>\n",
       "      <td>[p, global, research, consulting, grc, group, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Eggster Organization</td>\n",
       "      <td>eggster-organization</td>\n",
       "      <td>[4]</td>\n",
       "      <td>[eggster, organization]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name  \\\n",
       "0                         EthiCAL Apparel   \n",
       "1                      UC Rally Committee   \n",
       "2                                Codebase   \n",
       "3                Berkeley Phi Beta Lambda   \n",
       "4    BEAM- Berkeley Engineers and Mentors   \n",
       "..                                    ...   \n",
       "141               Womxn Ignite @ Berkeley   \n",
       "142                      Delta Consulting   \n",
       "143              Biofuels Technology Club   \n",
       "144  GRC (Global Research and Consulting)   \n",
       "145                  Eggster Organization   \n",
       "\n",
       "                                link_name          tags  \\\n",
       "0                         ethical-apparel    [2, 8, 20]   \n",
       "1                      uc-rally-committee      [20, 21]   \n",
       "2                                codebase   [5, 19, 22]   \n",
       "3                berkeley-phi-beta-lambda    [2, 6, 15]   \n",
       "4    beam--berkeley-engineers-and-mentors   [4, 18, 20]   \n",
       "..                                    ...           ...   \n",
       "141                 womxn-ignite-berkeley   [0, 14, 20]   \n",
       "142                      delta-consulting    [2, 6, 22]   \n",
       "143              biofuels-technology-club  [10, 17, 18]   \n",
       "144        global-research-and-consulting    [2, 6, 20]   \n",
       "145                  eggster-organization           [4]   \n",
       "\n",
       "                                     clean_description  \n",
       "0    [p, ethical, apparel, strong, student, run, so...  \n",
       "1    [rally, committee, members, official, guardian...  \n",
       "2    [codebase, community, developers, empowers, st...  \n",
       "3    [phi, beta, lambda, pbl, chapter, largest, stu...  \n",
       "4                           [beam, engineers, mentors]  \n",
       "..                                                 ...  \n",
       "141  [p, hello, bears, womxn, ignite, collegiate, c...  \n",
       "142  [h, overview, h, p, delta, consulting, strong,...  \n",
       "143  [p, biofuels, technology, club, btc, seeks, cr...  \n",
       "144  [p, global, research, consulting, grc, group, ...  \n",
       "145                            [eggster, organization]  \n",
       "\n",
       "[146 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_table = clean_table(club_db_df)\n",
    "cleaned_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency distribution from combined BOWs from cleaned descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOWs = cleaned_table['clean_description'].tolist()\n",
    "total_BOW = [word for bow in BOWs for word in bow]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total # of words:  6799\n",
      "Total # of unique words:  2179\n"
     ]
    }
   ],
   "source": [
    "print('Total # of words: ', len(total_BOW))\n",
    "print('Total # of unique words: ', len(set(total_BOW)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.FreqDist(total_BOW).plot(50, cumulative=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Add vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorize_table(table, yield_model = False):\n",
    "    MIN_WORD_COUNT = 20\n",
    "    VECTOR_SIZE = 100\n",
    "    CONTEXT_WINDOW_SIZE = 10\n",
    "    \n",
    "    \"\"\"\n",
    "    Description:\n",
    "    Uses cleaned table to create another column containing vectors using gensim's word2vec.\n",
    "    \n",
    "    Input:\n",
    "    table - cleaned table\n",
    "    \n",
    "    Output:\n",
    "    vectorized_table - table with a new_column: \"vector sum\"\n",
    "    \n",
    "    Run word2vec model\n",
    "    \"\"\"\n",
    "    \n",
    "    list_vectors = []\n",
    "    \n",
    "    model = gensim.models.Word2Vec(\n",
    "        table['clean_description'],\n",
    "        min_count=MIN_WORD_COUNT,\n",
    "        size=VECTOR_SIZE,\n",
    "        window=CONTEXT_WINDOW_SIZE,\n",
    "        compute_loss=True,\n",
    "        sample=1e-3 / 2,\n",
    "        seed=42,\n",
    "        workers=1\n",
    "    )\n",
    "    \n",
    "    for i in range(len(table)):\n",
    "        ith_description = table['clean_description'][i]    \n",
    "        \n",
    "        ith_vector_list = []\n",
    "        for ith_description_word in ith_description:\n",
    "            if ith_description_word in model.wv:\n",
    "                ith_vector_list += [model.wv[ith_description_word]]\n",
    "        \n",
    "        if len(ith_vector_list) == 0:\n",
    "            description_sum_vector = [1e-6] * VECTOR_SIZE\n",
    "        else:\n",
    "            description_sum_vector = sum(np.array(ith_vector_list))\n",
    "            \n",
    "        list_vectors += [description_sum_vector]\n",
    "        \n",
    "    vectorized_table = table.copy()\n",
    "    vectorized_table['vector_sum'] = list_vectors\n",
    "    \n",
    "    if yield_model:\n",
    "        return vectorized_table, model\n",
    "    else:\n",
    "        return vectorized_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Loss: 4426.64013671875\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>link_name</th>\n",
       "      <th>tags</th>\n",
       "      <th>clean_description</th>\n",
       "      <th>vector_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EthiCAL Apparel</td>\n",
       "      <td>ethical-apparel</td>\n",
       "      <td>[2, 8, 20]</td>\n",
       "      <td>[p, ethical, apparel, strong, student, run, so...</td>\n",
       "      <td>[-0.0004437992, -0.04965993, -0.011035232, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UC Rally Committee</td>\n",
       "      <td>uc-rally-committee</td>\n",
       "      <td>[20, 21]</td>\n",
       "      <td>[rally, committee, members, official, guardian...</td>\n",
       "      <td>[-0.017726174, -0.001097559, -0.0051284945, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Codebase</td>\n",
       "      <td>codebase</td>\n",
       "      <td>[5, 19, 22]</td>\n",
       "      <td>[codebase, community, developers, empowers, st...</td>\n",
       "      <td>[-0.010212343, -0.0064652627, -0.0034119352, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Berkeley Phi Beta Lambda</td>\n",
       "      <td>berkeley-phi-beta-lambda</td>\n",
       "      <td>[2, 6, 15]</td>\n",
       "      <td>[phi, beta, lambda, pbl, chapter, largest, stu...</td>\n",
       "      <td>[-0.021244504, 0.003627569, -0.0051328447, -0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BEAM- Berkeley Engineers and Mentors</td>\n",
       "      <td>beam--berkeley-engineers-and-mentors</td>\n",
       "      <td>[4, 18, 20]</td>\n",
       "      <td>[beam, engineers, mentors]</td>\n",
       "      <td>[1e-06, 1e-06, 1e-06, 1e-06, 1e-06, 1e-06, 1e-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Womxn Ignite @ Berkeley</td>\n",
       "      <td>womxn-ignite-berkeley</td>\n",
       "      <td>[0, 14, 20]</td>\n",
       "      <td>[p, hello, bears, womxn, ignite, collegiate, c...</td>\n",
       "      <td>[0.009286533, -0.027399413, -0.008770574, 0.03...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Delta Consulting</td>\n",
       "      <td>delta-consulting</td>\n",
       "      <td>[2, 6, 22]</td>\n",
       "      <td>[h, overview, h, p, delta, consulting, strong,...</td>\n",
       "      <td>[0.0066390336, -0.018419366, 0.009206503, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Biofuels Technology Club</td>\n",
       "      <td>biofuels-technology-club</td>\n",
       "      <td>[10, 17, 18]</td>\n",
       "      <td>[p, biofuels, technology, club, btc, seeks, cr...</td>\n",
       "      <td>[-0.005082991, -0.007874878, -0.021106554, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>GRC (Global Research and Consulting)</td>\n",
       "      <td>global-research-and-consulting</td>\n",
       "      <td>[2, 6, 20]</td>\n",
       "      <td>[p, global, research, consulting, grc, group, ...</td>\n",
       "      <td>[-0.0002745057, -0.035861474, -0.003897474, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Eggster Organization</td>\n",
       "      <td>eggster-organization</td>\n",
       "      <td>[4]</td>\n",
       "      <td>[eggster, organization]</td>\n",
       "      <td>[-0.00079523295, -0.0022420275, 0.0016288372, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     name  \\\n",
       "0                         EthiCAL Apparel   \n",
       "1                      UC Rally Committee   \n",
       "2                                Codebase   \n",
       "3                Berkeley Phi Beta Lambda   \n",
       "4    BEAM- Berkeley Engineers and Mentors   \n",
       "..                                    ...   \n",
       "141               Womxn Ignite @ Berkeley   \n",
       "142                      Delta Consulting   \n",
       "143              Biofuels Technology Club   \n",
       "144  GRC (Global Research and Consulting)   \n",
       "145                  Eggster Organization   \n",
       "\n",
       "                                link_name          tags  \\\n",
       "0                         ethical-apparel    [2, 8, 20]   \n",
       "1                      uc-rally-committee      [20, 21]   \n",
       "2                                codebase   [5, 19, 22]   \n",
       "3                berkeley-phi-beta-lambda    [2, 6, 15]   \n",
       "4    beam--berkeley-engineers-and-mentors   [4, 18, 20]   \n",
       "..                                    ...           ...   \n",
       "141                 womxn-ignite-berkeley   [0, 14, 20]   \n",
       "142                      delta-consulting    [2, 6, 22]   \n",
       "143              biofuels-technology-club  [10, 17, 18]   \n",
       "144        global-research-and-consulting    [2, 6, 20]   \n",
       "145                  eggster-organization           [4]   \n",
       "\n",
       "                                     clean_description  \\\n",
       "0    [p, ethical, apparel, strong, student, run, so...   \n",
       "1    [rally, committee, members, official, guardian...   \n",
       "2    [codebase, community, developers, empowers, st...   \n",
       "3    [phi, beta, lambda, pbl, chapter, largest, stu...   \n",
       "4                           [beam, engineers, mentors]   \n",
       "..                                                 ...   \n",
       "141  [p, hello, bears, womxn, ignite, collegiate, c...   \n",
       "142  [h, overview, h, p, delta, consulting, strong,...   \n",
       "143  [p, biofuels, technology, club, btc, seeks, cr...   \n",
       "144  [p, global, research, consulting, grc, group, ...   \n",
       "145                            [eggster, organization]   \n",
       "\n",
       "                                            vector_sum  \n",
       "0    [-0.0004437992, -0.04965993, -0.011035232, 0.0...  \n",
       "1    [-0.017726174, -0.001097559, -0.0051284945, 0....  \n",
       "2    [-0.010212343, -0.0064652627, -0.0034119352, -...  \n",
       "3    [-0.021244504, 0.003627569, -0.0051328447, -0....  \n",
       "4    [1e-06, 1e-06, 1e-06, 1e-06, 1e-06, 1e-06, 1e-...  \n",
       "..                                                 ...  \n",
       "141  [0.009286533, -0.027399413, -0.008770574, 0.03...  \n",
       "142  [0.0066390336, -0.018419366, 0.009206503, 0.00...  \n",
       "143  [-0.005082991, -0.007874878, -0.021106554, 0.0...  \n",
       "144  [-0.0002745057, -0.035861474, -0.003897474, 0....  \n",
       "145  [-0.00079523295, -0.0022420275, 0.0016288372, ...  \n",
       "\n",
       "[146 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector_table, w2v_model = vectorize_table(cleaned_table, yield_model=True)\n",
    "print('Model Loss:', w2v_model.get_latest_training_loss())\n",
    "vector_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TSNE plot from word2vec mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source: https://www.kaggle.com/jeffd23/visualizing-word-vectors-with-t-sne\n",
    "\n",
    "PERPLEXITY = 60\n",
    "\n",
    "def tsne_plot(model, perpl):\n",
    "    \"Creates a TSNE model and plots it\"\n",
    "    labels = []\n",
    "    tokens = []\n",
    "\n",
    "    for word in model.wv.vocab:\n",
    "        tokens.append(model.wv[word])\n",
    "        labels.append(word)\n",
    "        \n",
    "    tsne_model = TSNE(perplexity=perpl, n_components=2, init='pca', n_iter=1000, random_state=42)\n",
    "    new_values = tsne_model.fit_transform(tokens)\n",
    "\n",
    "    x = []\n",
    "    y = []\n",
    "    for value in new_values:\n",
    "        x.append(value[0])\n",
    "        y.append(value[1])\n",
    "        \n",
    "    plt.figure(figsize=(16, 16)) \n",
    "    for i in range(len(x)):\n",
    "        plt.scatter(x[i],y[i])\n",
    "        plt.annotate(labels[i],\n",
    "                     xy=(x[i], y[i]),\n",
    "                     xytext=(5, 2),\n",
    "                     textcoords='offset points',\n",
    "                     ha='right',\n",
    "                     va='bottom')\n",
    "    plt.show()\n",
    "    \n",
    "tsne_plot(w2v_model, PERPLEXITY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Create distance table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import spatial\n",
    "\n",
    "def create_distance_table(table):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "    Uses a vectorized table to create a pivot table containing distances between each club.\n",
    "    \n",
    "    Input:\n",
    "    table - table with vectorized descriptions\n",
    "    \n",
    "    Output:\n",
    "    distance_table - table containing all distances between each club\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    dict = table[['link_name', 'vector_sum']].set_index('link_name')['vector_sum'].to_dict()\n",
    "    distance_list = []\n",
    "    \n",
    "    for club_1 in dict:\n",
    "        vector_1 = dict[club_1]\n",
    "        distance_dictionary = {}\n",
    "        \n",
    "        for club_2 in dict:\n",
    "            vector_2 = dict[club_2]\n",
    "            \n",
    "            cosine_sim = 1 - spatial.distance.cosine(vector_1, vector_2)\n",
    "            distance_dictionary[club_2] = cosine_sim\n",
    "            \n",
    "        distance_list += [distance_dictionary]\n",
    "        \n",
    "    distance_table = pd.DataFrame(data=distance_list)\n",
    "    distance_table.index = table['link_name']\n",
    "    \n",
    "    return distance_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ethical-apparel</th>\n",
       "      <th>uc-rally-committee</th>\n",
       "      <th>codebase</th>\n",
       "      <th>berkeley-phi-beta-lambda</th>\n",
       "      <th>beam--berkeley-engineers-and-mentors</th>\n",
       "      <th>berkeley-finance-club</th>\n",
       "      <th>eta-kappa-nu</th>\n",
       "      <th>bridgeusa</th>\n",
       "      <th>taiwanese-student-association-</th>\n",
       "      <th>launchpad</th>\n",
       "      <th>...</th>\n",
       "      <th>phi-delta-epsilon</th>\n",
       "      <th>underwater-robotics-at-berkeley</th>\n",
       "      <th>reach-asian-and-pacific-islander-recruitment-and-retention-center</th>\n",
       "      <th>unicef-at-berkeley</th>\n",
       "      <th>volunteers-around-the-world</th>\n",
       "      <th>womxn-ignite-berkeley</th>\n",
       "      <th>delta-consulting</th>\n",
       "      <th>biofuels-technology-club</th>\n",
       "      <th>global-research-and-consulting</th>\n",
       "      <th>eggster-organization</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>link_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ethical-apparel</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.472766</td>\n",
       "      <td>0.519438</td>\n",
       "      <td>0.604482</td>\n",
       "      <td>0.031979</td>\n",
       "      <td>0.515900</td>\n",
       "      <td>0.579951</td>\n",
       "      <td>0.278761</td>\n",
       "      <td>0.494814</td>\n",
       "      <td>0.552593</td>\n",
       "      <td>...</td>\n",
       "      <td>0.911999</td>\n",
       "      <td>0.884631</td>\n",
       "      <td>0.941973</td>\n",
       "      <td>0.723832</td>\n",
       "      <td>0.799435</td>\n",
       "      <td>0.917771</td>\n",
       "      <td>0.939218</td>\n",
       "      <td>0.743391</td>\n",
       "      <td>0.899749</td>\n",
       "      <td>0.194280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uc-rally-committee</th>\n",
       "      <td>0.472766</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.445712</td>\n",
       "      <td>0.574576</td>\n",
       "      <td>0.068295</td>\n",
       "      <td>0.383032</td>\n",
       "      <td>0.456974</td>\n",
       "      <td>0.372048</td>\n",
       "      <td>0.595409</td>\n",
       "      <td>0.515927</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480237</td>\n",
       "      <td>0.376013</td>\n",
       "      <td>0.421655</td>\n",
       "      <td>0.545613</td>\n",
       "      <td>0.535308</td>\n",
       "      <td>0.467366</td>\n",
       "      <td>0.398352</td>\n",
       "      <td>0.476069</td>\n",
       "      <td>0.551734</td>\n",
       "      <td>0.137788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codebase</th>\n",
       "      <td>0.519438</td>\n",
       "      <td>0.445712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.745471</td>\n",
       "      <td>0.053717</td>\n",
       "      <td>0.589146</td>\n",
       "      <td>0.451945</td>\n",
       "      <td>0.533903</td>\n",
       "      <td>0.564562</td>\n",
       "      <td>0.531473</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424375</td>\n",
       "      <td>0.476573</td>\n",
       "      <td>0.529263</td>\n",
       "      <td>0.626834</td>\n",
       "      <td>0.542423</td>\n",
       "      <td>0.531709</td>\n",
       "      <td>0.551922</td>\n",
       "      <td>0.552001</td>\n",
       "      <td>0.579506</td>\n",
       "      <td>0.428825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>berkeley-phi-beta-lambda</th>\n",
       "      <td>0.604482</td>\n",
       "      <td>0.574576</td>\n",
       "      <td>0.745471</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049623</td>\n",
       "      <td>0.719927</td>\n",
       "      <td>0.557223</td>\n",
       "      <td>0.415469</td>\n",
       "      <td>0.587087</td>\n",
       "      <td>0.611022</td>\n",
       "      <td>...</td>\n",
       "      <td>0.523691</td>\n",
       "      <td>0.540259</td>\n",
       "      <td>0.546176</td>\n",
       "      <td>0.597019</td>\n",
       "      <td>0.601675</td>\n",
       "      <td>0.527231</td>\n",
       "      <td>0.638179</td>\n",
       "      <td>0.572636</td>\n",
       "      <td>0.649920</td>\n",
       "      <td>0.302754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beam--berkeley-engineers-and-mentors</th>\n",
       "      <td>0.031979</td>\n",
       "      <td>0.068295</td>\n",
       "      <td>0.053717</td>\n",
       "      <td>-0.049623</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057894</td>\n",
       "      <td>-0.100665</td>\n",
       "      <td>0.014852</td>\n",
       "      <td>0.002738</td>\n",
       "      <td>-0.180876</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043044</td>\n",
       "      <td>0.014529</td>\n",
       "      <td>0.043513</td>\n",
       "      <td>0.072335</td>\n",
       "      <td>0.016744</td>\n",
       "      <td>0.079452</td>\n",
       "      <td>-0.034114</td>\n",
       "      <td>0.105029</td>\n",
       "      <td>0.059410</td>\n",
       "      <td>-0.010883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>womxn-ignite-berkeley</th>\n",
       "      <td>0.917771</td>\n",
       "      <td>0.467366</td>\n",
       "      <td>0.531709</td>\n",
       "      <td>0.527231</td>\n",
       "      <td>0.079452</td>\n",
       "      <td>0.507036</td>\n",
       "      <td>0.450607</td>\n",
       "      <td>0.358599</td>\n",
       "      <td>0.391529</td>\n",
       "      <td>0.459251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.947656</td>\n",
       "      <td>0.930395</td>\n",
       "      <td>0.913004</td>\n",
       "      <td>0.754017</td>\n",
       "      <td>0.798232</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.868605</td>\n",
       "      <td>0.696952</td>\n",
       "      <td>0.921158</td>\n",
       "      <td>0.265224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta-consulting</th>\n",
       "      <td>0.939218</td>\n",
       "      <td>0.398352</td>\n",
       "      <td>0.551922</td>\n",
       "      <td>0.638179</td>\n",
       "      <td>-0.034114</td>\n",
       "      <td>0.526196</td>\n",
       "      <td>0.512986</td>\n",
       "      <td>0.258246</td>\n",
       "      <td>0.459299</td>\n",
       "      <td>0.451607</td>\n",
       "      <td>...</td>\n",
       "      <td>0.842802</td>\n",
       "      <td>0.858627</td>\n",
       "      <td>0.944788</td>\n",
       "      <td>0.644530</td>\n",
       "      <td>0.754822</td>\n",
       "      <td>0.868605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.687323</td>\n",
       "      <td>0.851817</td>\n",
       "      <td>0.275906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biofuels-technology-club</th>\n",
       "      <td>0.743391</td>\n",
       "      <td>0.476069</td>\n",
       "      <td>0.552001</td>\n",
       "      <td>0.572636</td>\n",
       "      <td>0.105029</td>\n",
       "      <td>0.408595</td>\n",
       "      <td>0.530240</td>\n",
       "      <td>0.316312</td>\n",
       "      <td>0.564152</td>\n",
       "      <td>0.548210</td>\n",
       "      <td>...</td>\n",
       "      <td>0.575114</td>\n",
       "      <td>0.588059</td>\n",
       "      <td>0.711163</td>\n",
       "      <td>0.900914</td>\n",
       "      <td>0.733793</td>\n",
       "      <td>0.696952</td>\n",
       "      <td>0.687323</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692924</td>\n",
       "      <td>0.239363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>global-research-and-consulting</th>\n",
       "      <td>0.899749</td>\n",
       "      <td>0.551734</td>\n",
       "      <td>0.579506</td>\n",
       "      <td>0.649920</td>\n",
       "      <td>0.059410</td>\n",
       "      <td>0.566351</td>\n",
       "      <td>0.429407</td>\n",
       "      <td>0.330644</td>\n",
       "      <td>0.523535</td>\n",
       "      <td>0.506126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.904572</td>\n",
       "      <td>0.913155</td>\n",
       "      <td>0.876624</td>\n",
       "      <td>0.741902</td>\n",
       "      <td>0.792002</td>\n",
       "      <td>0.921158</td>\n",
       "      <td>0.851817</td>\n",
       "      <td>0.692924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eggster-organization</th>\n",
       "      <td>0.194280</td>\n",
       "      <td>0.137788</td>\n",
       "      <td>0.428825</td>\n",
       "      <td>0.302754</td>\n",
       "      <td>-0.010883</td>\n",
       "      <td>0.147657</td>\n",
       "      <td>0.461840</td>\n",
       "      <td>0.641801</td>\n",
       "      <td>0.528933</td>\n",
       "      <td>0.099171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146155</td>\n",
       "      <td>0.217269</td>\n",
       "      <td>0.219002</td>\n",
       "      <td>0.220060</td>\n",
       "      <td>0.350786</td>\n",
       "      <td>0.265224</td>\n",
       "      <td>0.275906</td>\n",
       "      <td>0.239363</td>\n",
       "      <td>0.162670</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 146 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      ethical-apparel  uc-rally-committee  \\\n",
       "link_name                                                                   \n",
       "ethical-apparel                              1.000000            0.472766   \n",
       "uc-rally-committee                           0.472766            1.000000   \n",
       "codebase                                     0.519438            0.445712   \n",
       "berkeley-phi-beta-lambda                     0.604482            0.574576   \n",
       "beam--berkeley-engineers-and-mentors         0.031979            0.068295   \n",
       "...                                               ...                 ...   \n",
       "womxn-ignite-berkeley                        0.917771            0.467366   \n",
       "delta-consulting                             0.939218            0.398352   \n",
       "biofuels-technology-club                     0.743391            0.476069   \n",
       "global-research-and-consulting               0.899749            0.551734   \n",
       "eggster-organization                         0.194280            0.137788   \n",
       "\n",
       "                                      codebase  berkeley-phi-beta-lambda  \\\n",
       "link_name                                                                  \n",
       "ethical-apparel                       0.519438                  0.604482   \n",
       "uc-rally-committee                    0.445712                  0.574576   \n",
       "codebase                              1.000000                  0.745471   \n",
       "berkeley-phi-beta-lambda              0.745471                  1.000000   \n",
       "beam--berkeley-engineers-and-mentors  0.053717                 -0.049623   \n",
       "...                                        ...                       ...   \n",
       "womxn-ignite-berkeley                 0.531709                  0.527231   \n",
       "delta-consulting                      0.551922                  0.638179   \n",
       "biofuels-technology-club              0.552001                  0.572636   \n",
       "global-research-and-consulting        0.579506                  0.649920   \n",
       "eggster-organization                  0.428825                  0.302754   \n",
       "\n",
       "                                      beam--berkeley-engineers-and-mentors  \\\n",
       "link_name                                                                    \n",
       "ethical-apparel                                                   0.031979   \n",
       "uc-rally-committee                                                0.068295   \n",
       "codebase                                                          0.053717   \n",
       "berkeley-phi-beta-lambda                                         -0.049623   \n",
       "beam--berkeley-engineers-and-mentors                              1.000000   \n",
       "...                                                                    ...   \n",
       "womxn-ignite-berkeley                                             0.079452   \n",
       "delta-consulting                                                 -0.034114   \n",
       "biofuels-technology-club                                          0.105029   \n",
       "global-research-and-consulting                                    0.059410   \n",
       "eggster-organization                                             -0.010883   \n",
       "\n",
       "                                      berkeley-finance-club  eta-kappa-nu  \\\n",
       "link_name                                                                   \n",
       "ethical-apparel                                    0.515900      0.579951   \n",
       "uc-rally-committee                                 0.383032      0.456974   \n",
       "codebase                                           0.589146      0.451945   \n",
       "berkeley-phi-beta-lambda                           0.719927      0.557223   \n",
       "beam--berkeley-engineers-and-mentors               0.057894     -0.100665   \n",
       "...                                                     ...           ...   \n",
       "womxn-ignite-berkeley                              0.507036      0.450607   \n",
       "delta-consulting                                   0.526196      0.512986   \n",
       "biofuels-technology-club                           0.408595      0.530240   \n",
       "global-research-and-consulting                     0.566351      0.429407   \n",
       "eggster-organization                               0.147657      0.461840   \n",
       "\n",
       "                                      bridgeusa  \\\n",
       "link_name                                         \n",
       "ethical-apparel                        0.278761   \n",
       "uc-rally-committee                     0.372048   \n",
       "codebase                               0.533903   \n",
       "berkeley-phi-beta-lambda               0.415469   \n",
       "beam--berkeley-engineers-and-mentors   0.014852   \n",
       "...                                         ...   \n",
       "womxn-ignite-berkeley                  0.358599   \n",
       "delta-consulting                       0.258246   \n",
       "biofuels-technology-club               0.316312   \n",
       "global-research-and-consulting         0.330644   \n",
       "eggster-organization                   0.641801   \n",
       "\n",
       "                                      taiwanese-student-association-  \\\n",
       "link_name                                                              \n",
       "ethical-apparel                                             0.494814   \n",
       "uc-rally-committee                                          0.595409   \n",
       "codebase                                                    0.564562   \n",
       "berkeley-phi-beta-lambda                                    0.587087   \n",
       "beam--berkeley-engineers-and-mentors                        0.002738   \n",
       "...                                                              ...   \n",
       "womxn-ignite-berkeley                                       0.391529   \n",
       "delta-consulting                                            0.459299   \n",
       "biofuels-technology-club                                    0.564152   \n",
       "global-research-and-consulting                              0.523535   \n",
       "eggster-organization                                        0.528933   \n",
       "\n",
       "                                      launchpad  ...  phi-delta-epsilon  \\\n",
       "link_name                                        ...                      \n",
       "ethical-apparel                        0.552593  ...           0.911999   \n",
       "uc-rally-committee                     0.515927  ...           0.480237   \n",
       "codebase                               0.531473  ...           0.424375   \n",
       "berkeley-phi-beta-lambda               0.611022  ...           0.523691   \n",
       "beam--berkeley-engineers-and-mentors  -0.180876  ...           0.043044   \n",
       "...                                         ...  ...                ...   \n",
       "womxn-ignite-berkeley                  0.459251  ...           0.947656   \n",
       "delta-consulting                       0.451607  ...           0.842802   \n",
       "biofuels-technology-club               0.548210  ...           0.575114   \n",
       "global-research-and-consulting         0.506126  ...           0.904572   \n",
       "eggster-organization                   0.099171  ...           0.146155   \n",
       "\n",
       "                                      underwater-robotics-at-berkeley  \\\n",
       "link_name                                                               \n",
       "ethical-apparel                                              0.884631   \n",
       "uc-rally-committee                                           0.376013   \n",
       "codebase                                                     0.476573   \n",
       "berkeley-phi-beta-lambda                                     0.540259   \n",
       "beam--berkeley-engineers-and-mentors                         0.014529   \n",
       "...                                                               ...   \n",
       "womxn-ignite-berkeley                                        0.930395   \n",
       "delta-consulting                                             0.858627   \n",
       "biofuels-technology-club                                     0.588059   \n",
       "global-research-and-consulting                               0.913155   \n",
       "eggster-organization                                         0.217269   \n",
       "\n",
       "                                      reach-asian-and-pacific-islander-recruitment-and-retention-center  \\\n",
       "link_name                                                                                                 \n",
       "ethical-apparel                                                                0.941973                   \n",
       "uc-rally-committee                                                             0.421655                   \n",
       "codebase                                                                       0.529263                   \n",
       "berkeley-phi-beta-lambda                                                       0.546176                   \n",
       "beam--berkeley-engineers-and-mentors                                           0.043513                   \n",
       "...                                                                                 ...                   \n",
       "womxn-ignite-berkeley                                                          0.913004                   \n",
       "delta-consulting                                                               0.944788                   \n",
       "biofuels-technology-club                                                       0.711163                   \n",
       "global-research-and-consulting                                                 0.876624                   \n",
       "eggster-organization                                                           0.219002                   \n",
       "\n",
       "                                      unicef-at-berkeley  \\\n",
       "link_name                                                  \n",
       "ethical-apparel                                 0.723832   \n",
       "uc-rally-committee                              0.545613   \n",
       "codebase                                        0.626834   \n",
       "berkeley-phi-beta-lambda                        0.597019   \n",
       "beam--berkeley-engineers-and-mentors            0.072335   \n",
       "...                                                  ...   \n",
       "womxn-ignite-berkeley                           0.754017   \n",
       "delta-consulting                                0.644530   \n",
       "biofuels-technology-club                        0.900914   \n",
       "global-research-and-consulting                  0.741902   \n",
       "eggster-organization                            0.220060   \n",
       "\n",
       "                                      volunteers-around-the-world  \\\n",
       "link_name                                                           \n",
       "ethical-apparel                                          0.799435   \n",
       "uc-rally-committee                                       0.535308   \n",
       "codebase                                                 0.542423   \n",
       "berkeley-phi-beta-lambda                                 0.601675   \n",
       "beam--berkeley-engineers-and-mentors                     0.016744   \n",
       "...                                                           ...   \n",
       "womxn-ignite-berkeley                                    0.798232   \n",
       "delta-consulting                                         0.754822   \n",
       "biofuels-technology-club                                 0.733793   \n",
       "global-research-and-consulting                           0.792002   \n",
       "eggster-organization                                     0.350786   \n",
       "\n",
       "                                      womxn-ignite-berkeley  delta-consulting  \\\n",
       "link_name                                                                       \n",
       "ethical-apparel                                    0.917771          0.939218   \n",
       "uc-rally-committee                                 0.467366          0.398352   \n",
       "codebase                                           0.531709          0.551922   \n",
       "berkeley-phi-beta-lambda                           0.527231          0.638179   \n",
       "beam--berkeley-engineers-and-mentors               0.079452         -0.034114   \n",
       "...                                                     ...               ...   \n",
       "womxn-ignite-berkeley                              1.000000          0.868605   \n",
       "delta-consulting                                   0.868605          1.000000   \n",
       "biofuels-technology-club                           0.696952          0.687323   \n",
       "global-research-and-consulting                     0.921158          0.851817   \n",
       "eggster-organization                               0.265224          0.275906   \n",
       "\n",
       "                                      biofuels-technology-club  \\\n",
       "link_name                                                        \n",
       "ethical-apparel                                       0.743391   \n",
       "uc-rally-committee                                    0.476069   \n",
       "codebase                                              0.552001   \n",
       "berkeley-phi-beta-lambda                              0.572636   \n",
       "beam--berkeley-engineers-and-mentors                  0.105029   \n",
       "...                                                        ...   \n",
       "womxn-ignite-berkeley                                 0.696952   \n",
       "delta-consulting                                      0.687323   \n",
       "biofuels-technology-club                              1.000000   \n",
       "global-research-and-consulting                        0.692924   \n",
       "eggster-organization                                  0.239363   \n",
       "\n",
       "                                      global-research-and-consulting  \\\n",
       "link_name                                                              \n",
       "ethical-apparel                                             0.899749   \n",
       "uc-rally-committee                                          0.551734   \n",
       "codebase                                                    0.579506   \n",
       "berkeley-phi-beta-lambda                                    0.649920   \n",
       "beam--berkeley-engineers-and-mentors                        0.059410   \n",
       "...                                                              ...   \n",
       "womxn-ignite-berkeley                                       0.921158   \n",
       "delta-consulting                                            0.851817   \n",
       "biofuels-technology-club                                    0.692924   \n",
       "global-research-and-consulting                              1.000000   \n",
       "eggster-organization                                        0.162670   \n",
       "\n",
       "                                      eggster-organization  \n",
       "link_name                                                   \n",
       "ethical-apparel                                   0.194280  \n",
       "uc-rally-committee                                0.137788  \n",
       "codebase                                          0.428825  \n",
       "berkeley-phi-beta-lambda                          0.302754  \n",
       "beam--berkeley-engineers-and-mentors             -0.010883  \n",
       "...                                                    ...  \n",
       "womxn-ignite-berkeley                             0.265224  \n",
       "delta-consulting                                  0.275906  \n",
       "biofuels-technology-club                          0.239363  \n",
       "global-research-and-consulting                    0.162670  \n",
       "eggster-organization                              1.000000  \n",
       "\n",
       "[146 rows x 146 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_table = create_distance_table(vector_table)\n",
    "distance_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap correlation between all clubs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='link_name'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7z7DWuVj2hYvZDLZvcpora+88IPNYN1SPbsGx+BqIR0658dxouvqet8m3KxBfX7VpbzSdmM7llK7hOsX5wl4zenLe1ZP4Vup8gmuX8s38dEWLObY9qaOLr67O53jTqgpuw6wTXCXCuzlOOKY1x0iXfrAxmm52YVPOe+SxZFeOfSWaXvk892Onk/jPrGXr3dial8i+G8fkkF3y8vxoOieT6xuMgZV4Nz6yj+N7qOcH+WT7Y91bHMcxr6vf474Jb3Cx+bG9OZY2nLuT7ORzXCD8kLz55ItJ4bj9mA4udjm+dUvyJU75huz4LDeWMtO5/zvs4XKb93VjZ918jjH39z8ArHjJja3G4H4L6iBoiWunxk24L8LVAR2KYjcO09K5fREot3KLewtobBPut9gsHh/rqtz9Fte7Pfmq8laRLfHuPNKMx1VcMbfLutmu3MbtuL4Sw+PBP/+ljGb9h+K35nJe39wYiuP72H8t34bbKLUd18H/vZSSzzHxqRewGEPK8tUuPZfn+ZikZLL9cfuxKXzO+PY89zVNdv1WvoO/A7SK2zC7oxuzwl8fkJjAOCtyc1ZcU9agqNjE1xrXISuart7O7RnbgmP6v9nN30VdYBzM2OLcMA5RROSQun9rW5j/l7QD8DMA32lxbhg/FvwL8/rwL8wNwzAM46DA1NrrxLa1G0bDEysiz4nIIhF5U0SSRWSAiEwRkbkiMl5EmgOAiEwWkTtFZAqAX/kLEZE7RGSciMSIyM0iMtsr82+1nbS2PF4Zv/Ll+YeI3FjLsZNF5AER+UpElojIYJ+7h+df6z9WRIqC5fgYKSLTRGSViJzh5Q+JyL2+OtZIlt8N4GgRWSAivxGRdt6x87x/9f4IICJXe+UuFJG3RCTZ+3yciDwmIpO8+h8rIs+IyHLvCb+/jH9555soIjneZwO8MmcAuN6X9zvX0TAMwzAMwzi8sMW5YTQ8XQGMVdU+AAoQWdQ9BOA8VR0A4BkA//Dlz1DVY1X1XzUfiMg/ATQBcDmAkQA6AxgMoB+AASJyjP+EInJSHXmeBnCZlycGwAUAXqqj3ine0/BfeHWsoRuAk72y/yoicbUdHKAdgGMBnA7gcYnsi74SwF5VHQRgEICrRaQ9gD8AmKaq/VT13wDyAJyoqkcAOB/Ag/txvrdVdZCq9gWw3DtXDZkAjgfwGwAfAPg3gJ4AeotIv5prBzDPO+cUAH/1Pn8WwI2qOjRwvv2qo4hcIyJzRGTOs8s21ZbFMAzDMAzj0EbDDf/vIOWQ2hZrGD9SNqnqdC/9IoA/AegF4DOJvFg4BMAfKPpa4Pi/AJilqtcA0YX3SQBqgv5SEVmIT/UdU2seVZ0qIrtEpD+ApgDmqyq/0NrxCgB4xzQSkQzv849UtRxAuYjkeeVsrqOMGl7XiHTnahFZi8gC/yQAfUTkPC9PuncdFYFj4wA87C2cq7F/4VS9ROTvADK8ax/v832gqioiiwHkqupiABCRpYj8iLAAQBiuH14E8LaIpCPyw8kU7/MXAJz6XeqoqmMBjAWAwl+cqrXlMQzDMAzDMH6c2OLcMBqe4CKsEMDSWp6+1lAcsGcj8uQ7S1V3I6KmcpeqPrGPc+4rz1MAxgBoBu+JuIg8C6A/gK2qelod9a6x/SpM1QjMMyLyD0SekENV++2jLAHwS1X1L5whIiMCeX8DIBdAX0R2A5Xt4zw1jAMwSlUXisgYAP4ya+ofDlxLOHgttdS3rgV1rXXcF9d+5BO9EY4brpqyjmy/CNxnC8ZyOQN/R/b8MvfbRseENPJVj2fRmBkFTpipVxqLsaVNY4GcNjHOLgEL4mwPc7O1iHHCO+c8wdEOLUNcbqF+5M4Z2ISxrILFdKpjnODP0OnNybc0IDLVPJSFuiibyt2zrcq1S/by3eRrHMMiTgXaKJpOmc3lbKgqJLss7FS9WsVx3rwqHkr94pyo09yYHeTrMZsFn6p9w7DiDX46sDEgJNZqB4tkFUxzt1u8NCLfmvB2slvPdrHvRWEWXyuo5uvpneBE1SYXribfCWkcQ18w2Y2BlsKiTev/xb/zNY5x9S9THneJwuM59JobL0ngcxZ+toLsWGRH0x/fx0JXzR/m+69ZnGun6sBvh5UTeINifEJBNJ0jLPj0qzv52vLC7r6vBPfjnkVc7tC4JtH0NuW23xtol7bVrtz1H/OYTIxn+5E33L1aGuZ7aFAcj7up5bOi6Xbx3L5xH3B9t7/7VTTdOMTtEHyedfWcRnX6KgP1vXqamwM2ffMR+U7oezXZneJcHRM+ZKWu5VV8n+fscnXYUc1fwX1iWUBy0xSfAJgEBODG8xVkxbg5OAdch12BsfTZgnuj6d49ziffyGQW61tetced4+st5EsAz6OfLfhPNH3TwD+RrwAssljybyeOuTUwn2WF+Pskbpnr85bSjHyfLbgTdXHpgN+SnRqY94vU1amD8DxecgffQ+8XuR1ojeO5fj3X8dySg4xoOjfQ9rsX8/wWCzceGsewKOGOJ3jsNBI3PkrBE3BS4E+KZN+ctVu5DtmB+Xj1IicC1zvE9+LewHnyErivToNxMGPb2g2j4WkjIjUL8QsBzASQU/OZiMSJSM86jwY+RSQO+yMRSUPkKfAVIpEVnYi0FJEmgWP2lecdAKcgspV8PACo6uXeNnL/nH6+d+xwRLaf70t+NYqq3uKV1c/38WgvVr4jgA4AVnrn/nnNtngR6SIiKYj8eOH/lk0HsM178n4JIjsN6jpPDWkAtnllX7Q/9Q4QA6Dmif7PAHypqvkA9nrtgUC5tdbRMAzDMAzjsCNc3fD/DlLsyblhNDzLAVwmIk8AWI1IvPl4AA96W6VjATwAYGldBajqG97C/H1EfhR9GcAM71f7IgAXIxL3XJN/goh0ry2PqlaIyCQA+aq6r9lrj4h8BaARgCv+mwv3sRKR2O2mAK5T1TIReQqRbeTzJFLJHQBGAVgEoEpEFiLyBPxRAG+JyGgAk/DtnQW18RcAswBsALAYvNjfH4oB9BSRuQD2wvuhApGY/2dEpAS8Vf6/qaNhGIZhGIZxGGGLc8NoQFR1PYAetbgWADgm+KGqjgjYY3zpZ+CE2f7j/Qsen+pL15rHE4IbAmB0PdV/S1X/GCj/toDdy5eu9Z1O/msIfB5GJP7+T7W4TwjYfXzpP6IW/HVT1ccAPLavunh906sOX821/CVw/FxEtq7XcJv3+er9qaNhGIZhGMaPnoNYkK2hEVXTHDIMI4KI9ADwIYB3VPX/9pFvMoCbVHXOD1W3w42dJx8bnZw3Lc8gX8eTS8mO7ef05W64l+OCn5jzT7I/6+l+60gIhMiXBSKdujdxWoBzd3JM27E9OLYvLsPFVsa15zjmT17i32VW+UIrzwpxNET7KzLIRshFAFSt4tjJOR9zrOfKeBebeOFgrl8oi+M5t0x2v03vKeKY154n5ZNdssb9EVFWyL9pp7XguMDNy9y1N2nJcX7Vldy+e3e586Y35j5NaBSIGVznNncsLuP2PbbdVrJzN3NsYqefuBjN99/mGM2zLuJNHDFNXZvufmMt+cLVHD+7eUtGNN114E7yvbGgNdndKlzM5uYQx2huDrzP4ahyl7dr9zzyNbqKf7OsnOzinKvzuS8qdvL4TurgxkDVbs4b34nbRdJcPOpXj3BfHPVz7scFj7v2TUvk2NTdpaxJMCfBXfuOGN6YdNv9vcl+/3q3WapjiPspNZnrX1Tirq37VTyeP3yC69s/w93X2/fwpqGv47lvTotzscvJaXzOnHM4Wmrzyy7WdnIR35vnH833rsS7OsW25bnlnGd5ThhT7frmhO58X69dzucprnaD6aUkbt8HTub78dFP3Xl/cRKPs/WfcTtUh119F1Rzmx2dzhoQubudv2MXvi8SW3Nf7F7k5pOUxty+CU35fvvtLHetv4svIV92ex4fSUc6zQ0t4rzhvTzX/Hqim0/ubMP13ZvHYymnuysrJpGvZfXUDLI7D8+Ppit28kLsD2u4z/38LWcP2el9+DwVm107+a8TABDP8/OCh53+ws5ATP+Jv+E+3vi0i6ePi+exk96adRwq9ro6lRXyBJbejueAal/zxwYeUZTtCGhSpLl2KtnF15KYzvNQyDe15K7gglsM4T5+4Etup9s2vMSDqwGoWD+nwReg8e0GNng71IY9OTcMI4qqLkMk5ru+fCP+97UxDOP74l+YG4ZhGIZxcGOLc8MwDMMwDMMwDOOHIWzb2uvC1NoNwzAMwzAMwzAMo4GxxblhGAcNIlKb+Ft9x4wRkYf/F/UxDMMwDMMwDiyq4Qb/d7Bi29oNwziY+BOAOxu6EgcDcS2cyEzWDhb72T2bNUwyCpxw1PwyFhXyC8ABwIlLXfPuPo/fgNfokoFka6UTfGp6G4uOrV7Kgj6hGPdFt2JmMvlG38ivdf/kCXc9HR88nnxF/3mP7MSeTtwstnU2+QadsYvsnqtdfPWeVSz+0+SUFLITEl0dOrdjEaSyjWRi2epm0XTbxvnkC6VyX7Tp58SsJJ59CUe2Jzs7w11b1aJV2BdFy9317A1xuapsd7nKiQOVz2cBrUHp/AfJRy+yoNbII1ZH0zu3B4T9wEJzp8YXRNMVe/i3/jNb8XjZk+vGRGoFx8EXhFnMqFmmE+7a8k0G+YrvnkW2X6QuLpH/pNmyjeuftMYJKmWy/htSinaT/fxKN16uvYjFlQq/KCA7X53Y0ooqFoC74FoWcVr2rEuXBQQZ8/7+Bdkjj3Dt9NXcFuQ7egCLpu1d6e77kuksqNVWuI8373b9eORVXIdXXuL5o/Xprn0rN7CvYAL3cZPerqwRi/nerC7gcRfXzPWVZHE/nRDD/dg9xrV3cN6ZF8ftfcVVrr1vGruNfN98zPOHfzRvnMjzRVZTnnPFN7zPv6wd+RbdzoJrSXGu3zatySRfp4DY2d69TnAtKAgXruA2m1/mrqcqhsvduCyD7Kxc1/6NR3AbbZnKAmb+chesZCHHVilFZPuFE6s255MvNZmF0Pz137iS6zu/ivuG4frmzeC5Ja2JG5PPPMtjZRhY9K9rf1fHI244m3zb//RR4LwBZUofSUNbcc51Tjxu12QWlssKiHkWbXH1L17PeROSOG98uhOeS+/OddizmMdoSo4bL017cD/lzWMhvytyWCjWOLixJ+eGYTQIInKxiHwtIgtE5AkRuRdAkme/5OV5V0TmishSEblmP8o8XURmiEi2iFwtIrNFZKGIvCUiyV6ecSLyuIhME5FVInKG9/kYEXlPRD4VkZUi8ldfufXWwzv+be/41SLyT5+vyJc+T0TG/bftZhiGYRiGYfw4scW5YRg/OCLSHcD5AIapaj8A1QAWAyhV1X6qepGX9QpVHQBgIIAbRQKPgLjMswH8AcBpqroTwNuqOkhV+wJYDuBKX/Z2AI4FcDqAx0Wk5qf6wQAuAtAPwGgRqXmUvL/16OddV28A54tI6zry1XUN14jIHBGZM27llvoPMAzDMAzDONQIhxv+30GKbWs3DKMhOAHAAACzRQQAkgDk1ZLvRm/RDQCtAXQGsKuWfMchsnA+SVVr9kD2EpG/A8gAkApgvC//6xoJOFotImsBdPM+/0xVdwGAiLwNYDiAOd+hHhNVda93/DIAbQFsqqsRgqjqWABjAWDv5SMb/B2ghmEYhmEYxg+HLc4Nw2gIBMBzqvpH+lDkJl96BICRAIaqaomITAaQ6C2Sa7acX+X9vxaR97N3QWQxDQDjAIxS1YUiMgbACN+pggtfrevz71gPf9BdNdwc6y+XA+rqYOInLt67TSzHNHYYwHGL2+e7+NiOCWnkSyjjS/LHmWe9+Qz7Rl9OdnmhLy4UHDPY8zj+beK56S2j6QLhc1Yu4CDuBLi40bxbPyZfKI7j0+P3unhfLeaYxqc/a0p2q0p33rRwNfmK3uAY0gW+ePpzTuY4xUkvcsz8wM4uXu+Bzc3Id8lcPnZ8VUY03T4QM9pzMe+GSExe7+rbluOwizfz13MTXzzq5wUcp9h4fXOyj53Cceb5W1384erd3I/H9eK8O1a7sbS9jNvh9CSOc46Jce2degTH9G/6iMfAhmI3Lmcn8qa9cuF22rDbxSBXBDb4NU3l9v7Kd+3pgT7fFeKxFOtrYsnlOPe2O/geOyneXev9bwVi7au43MYhNy7bxHE/Ln+G8y6JdXGjZeDr3r2T2zC1eX403SObY+Jnf9GE7PVxLl6229Yy8m2I4Skn1xf2uvAFcmFpNccCV+e6Oj62gONuL8rk31OXTXXxyBtjOX636RyOK++Q7uLI42atJ1/7Co4NnxDv2r8JNycGV3O/fTbWXWvHBN7gtLqC+3xTgru2vSXcRqWbuf5rw+5eOLOM23cdOL63cYUbh8ExKLP4vlgIV6ecUh7be3M5xrhjgrPnlPOYPKktzy3Vle6+2TkpMB4KuF06+rQadlXyvLPEN58BwHmfuVjrUBzfm/OKWcihx1zXhrmV3EYdE+vcBIcpW/jasqt5vmta5L4T8mO4PZdU8fff3MXOLvn5HPKd3YTvvx0+XYzFAS2Dbo+wHsC2kPseqGbJD8gMrtM3Jb7xK/wdtikw7oanufuvYDOPnbxdPH7X5bk2bVzNc19sYG4ZPIr9BwUHsSBbQ2Pb2g3DaAgmAjhPRJoAgIhkiUhbAJUiUvONng5gj7cg7gZgCACo6jve1vd+qlrzbbsBwDkAnheRnt5naQC2eeXVbJOvYbSIxIhIR0QW9Su9z0/06pIEYBSA6d+xHnWRKyLdRSQGwNn15DWMA4Z/YW4YhmEYxsGNPTk3DOMHR1WXicifAUzwFqyVAK5HZEv3IhGZB+AKANeJyCJEFs8z6ylzpYhcBOANETkTwF8AzEJk4b4YkcV6DSsBTAHQFMB1qlrmba//EsALADoBeFlV54jI4u9Sjzr4A4APEdnivgRA6r6zG4ZhGIZhGIcbtjg3DKNBUNXXALwW+HgmgN/77FP3o5xxiGxhh6rOB9DDcz3m/auN6ar6m1o+z1PVGwLll3/Xenj2Gb70mwDerK8MwzAMwzCMHz3hg3Cr/UGCbWs3DMMwDMMwDMMwjAbGnpwbhnFYoapj6vh8HHxPvhuaYd22RtNJXRPIFy5ioZgWxzrBnOrx7CsL/Abb6JKB0XRQAC7rjWfJ3nuR8ydu41+5l05iQZ/Lz3TiUEs+DIjSjeEw+7RZM6LpJg9cTL7dv3+e7LihvaNpSeFogGuaryY7nB99nTxKFrO4Ukzg267RDieSFDrmZPIdtfIDsifMdUJYlyXlk69JpyKyf36qE8Yrn7aSfAnDOpMtrXxv2kvLIN/O37JQXsvznKjQxU/yxRw9iEXdQqmuz0O5LE7UIoEFtN5byW/7G320E5ZKWskiSI/uziH7dz3dGNViFq/K6cDHthvdJZru8+Qi8mUMY+G5ULsW0fT0u/LJt2wt16FPphNKKw7UARUcb98204m8ZXfmdojvyCJUf3/XjbVbRrLwWbiEBaq2z3Xneb2cBfdufqA72R1+uyKaXhpTSr7GTXgsJV88Ipre8LsF7BO+H392mRNXyvuQhft2BYTF2le4vun3S277017gvAmnuPvvrNnLyCcBMa6hNzlBu/L7uH2HDt9Otla5YxMGsNDciqfJxP/d4Oo0NVDuyliu/9mjdkbTz33Mom6ZgSd1fxrlRCJXvMH3VI8r+Ng2X/rusbje5GsZ5rHef7Qrd+YbPBeGYrnNzh7gXuZRup3n7mbDWTCrerw7tkuYx876dSzGVhx21zPsZq5D2RNbya4ud+eNV67fCeAxmX2qu0+qt+aTb1DRTrKz2rg67l7AwmfVWvfLSE4bxC842b2aj03N9omqbeDvhCPTWKg0o5mrQ0wcn7NkV2B8JLvvhJOT+fsjszOL6nX2CXau38T3fPOefGzxXDd+E+J47uiXxOM5qZkbo4lVAVHTQv47YFi2u88bdeVrq87nY8uXkGlxdQc5tjg3DMMwDMMwDMMwfhhMrb1ObFu7YRiGYRiGYRiGYTQw9uTcMAzDMAzDMAzD+GEI25PzurDFuWEYhxQiUqSqP0jIlIisBzBQVXfWl/c7lnsbgCJVva+uPAlNJZquWM/xbhIIrQ1lupi8GQXfkO9frTmmWCubRNPlhfwV4I8xB4D0l1wMetNTriZfo478xTrz/abRdCWEfOtv/JTszepi58Kfs88fcwcAVbNcsFzldo7tDDXizV8rprjYv8xGHIva+hqOa935mIutLrj/Q65DR27gMy9xcYu5H3HMYHw7jofc8fwaV784rl98PsciImlHNFk9ld/Q1/LiJmSvesLFF66J5/oduYfPk7uIY0w7/czFVup7HEP60yOLyY5Jd7dWRiLHtf45YQ/n9cXMb391B/mW7GxJ9pDK+dF0chOOj3zknXSyT6hyMd49OnD8dOYFXcje+4bLm9Wa4zdTNvF48VOax/G9WlVA9o3Ni30+Hs/xQ7qSnZW3PJr+RSy354Y/cZuVihujKYE/wTIGsP3GTW4snXkUt0PCUNYv2PrUxmg6rUkl+QYlc7z37u2+2PA53G//KWtK9p2fzo6mm/fke758F4+7wo9dfTukp5CvYB1fW/YZ2c6I4fZtEhBxXvGgiyNu24ivrWsy27FHD4mmZ7zKL8h4vD/fU4+848bvNYM5zrlsAdehMNfd5wlfLiVfciyP34oNbgz06MBzd8aJXIdlT7s2zMnhe7NoKV/bjAJXx77X9yJf/hcca515ac9oumrOCvJlteL7YsZ8dw/dnNSDfOVVgftkj298B9ZWSalc36TBbiy1XMdz34y8daiLHSvbkd1sENd3y1dubPUu57lkTyXrTOR0dXNCwuC25Ct7dwvZ2a1c++/YxHNo82H8PSoz3Pds5i7u44TOPB7a7nW6GAU7+PsiPoEHe+KQDtF05QoekxnZPL+Fq9zY0Qpu+2quEv6xpjnZ/4FxMGPb2g3DMAzjR4p/YW4YhmEYxsGNLc4NwzgkEZFUEZkoIvNEZLGInOV93k5Elvjy3eQ9qYaITBaRe0TkaxFZJSJHe5+HROQ+r5xFIvJL36l+6TtHNy//bSLygoh8ISKrReTqfdXJ890iIitF5HMA/OjNMAzDMAzjcEHDDf/vIMW2tRuGcahSBuBsVS0QkWwAM0Xk/f04LlZVB4vIaQD+CmAkgGsAtAfQX1WrRMT/XpqdqnqEiPwCwE0ArvI+7wNgCIAUAPNF5CMAeXXU6QgAFwDoj8i8Ow/A3GDFROQary7494AuGNOxRTCLYRiGYRiG8SPFFueGYRyqCIA7ReQYRKLfWgJouu9DAABve//PBdDOS48E8LiqVgGAqu6uI/85vs/fU9VSAKUiMgnAYAAf1VGnowG8o6olAFDXjwiqOhbAWADIP/+4ul8EaxiGYRiGcahignB1YotzwzAOVS4CkANggKpWeuJtiQCqwCE7iYHjyr3/q+HmQAFQ12K4tvyoJb/uo0615d8nW+c4QbPUTFZ32ZXHengJy5xIWa+0NuSbuzOb7Ka3bY2mBZnkS9zG4jR+Ebjmnz5Jvg3H/JzsZklOKGhSmAVxjj2L454LX3fiNbnvsIBWfn4G2clJLm9+EYv09L6e26HkC9dOCSUsmlYyfhXZuQVOmCk1o5x8pYE9DYmN3G811dU8nPKmcbeu2uXaO11YPC554TauwytOhKxJN26Hkllcp1ZdXVndF7Ho0d48tmdXN4qmm89kUaFd+Y3JLvic+7y40vXVDnB792/K4mHxyb6xJPznRCZYoCi5lxvPy97mNmSpJcCvJ1i4i/PufoBFnPJL3fX0Gsa6jeVlXKfqcEydPuSy+ZlvDJ+4jcXYMIXrEFaXNxxmcbPYEP8BGuu7uDSw2NbKD1nE8IiE/Gh68xzuC5nLY6m03B1bWMRtNjnQjyPgxLmWzGCBsqNj+F7VKje+p8xmkb/kwLbQzjlO/G5aSRb5hlRyG8Z84sZS8d4E8l1wHI/9RZ+5eaqymttsZuA8x949K5oOzoV563lOaO0bogXr2FeQz21YVe3GzuwpPKf2jGcxwcQj3Yhe/WhAxOsTvodSk921V1UEBPb28vzmv56Nr/N8kZDI47n47UUuvYOvraiAx0OvNHeteyo4byjwtbV3fn40nb+Dx2vH2/uSnf/4Vy5dxN81wb6h4wp4PsNsNsPq7qEZiVy/4WU8lnLWuGub9zXPdQObcHtv2eLu48ow+7aN43m0pNi1YWUV5y1dnE92Zanrm+Jirl8RawAi7t0N0XTWzSPJVzDlK7IrKt29kL+H2yErh8dHS7Xl3qGExZwbhnGokg4gz1sEHwegRoo1F0ATEWksIgkAztiPsiYAuE68FUZgW3tdnCUiiSLSGMAIRP6EqKtOUwGcLSJJIpIG4Mz9vEbDMAzDMAzjMMF+SjEM41DlJQAfiMgcAAsArAAAb2F8O4BZANbVfF4PTwHoAmCRiFQCeBLAw/Uc8zUi29jbALhDVbeKSF11micir3mfbQAwbf8v0zAMwzAM48eDanX9mQ5TbHFuGMYhRc07zr13jw+tI8+DAB6s5fMRvvROeDHnXqz5b71//vztfOk5iDwhr2GVql4TyL+vOv0DwD/quCzDMAzDMAzjMMcW54ZhGAch+SUuVq6wlOPUOvTcRXZcExd7ljYthXzH9thM9uqlOdF0z+O4nKWTOB65UUcXUxqMMW879TGyX+l7azQdCgRMzXmB4xj7Jzk7OZPjd2NCHDvXeIRrh5TpHLc660H+CpuY5OxRlRzzHAzi2hjr2rRfJ46d3bmQy005wsUXLn6F4yybJXNMqf/IbcL91rtZQP5gmUsm9OI41vi2pWTnfuFKXhSIL80oDMSg+8KGX1jTGuek50XtXdXcF4N7c+xyVamL54xZxXVKya4gO/W8PtF02aeLyLdoB8cub5vo2rg8zHHD27lKGJDoYo41oNTQ7kz+YMVb7unLuq9Z66CokgtululirVfu4RjY3s04Xv30Elfulr0coztgRB7ZcT3dWxUqFvJ4/nQGx2lXxbn6t6jmQdlpyB6yl3/l7scmmRycmpDCegYbNro6tk0oJF+XYn5CVRFy7V+uXIcJ8axvcXqWa8OEQGekBDQVUrJcv/XZzffF9hK+bxrHuetpNoDH+qLPeB5aF3L3TZMqPudxzVksoLTYjbs04XEWn8DHbvc1S0U55y2t4PG7TFz921Tz3NKiO8ec73jTtWFJNY+zUBzPNXuK3L2rynoFItzeab4qlZZxv6XncBvGN3NzRFUpX3dRAc9LaeL6uCAwee8NcZ26lLhyExO5Hb75C88B2b6hX1LNc1ZabOCm97EWPJ/1D/OYDPl0HFIC91D3LL6Pt+x0+huJ4LbfnMvzRbWv/beEuI2yy3k8F5a5+heFeaw0LeL7L3ebuzerA7HsxYF2aR5yY2nTLVPJt7eENQj83y+ZYe7j1BLWbTgoOYhfZdbQ2OLcMAzjO6KqtzV0HQxjf/AvzA3DMAzDOLgxQTjDMAzDMAzDMAzDaGDsyblhGIZhGIZhGIbxw2DvOa8TW5wbhnHIIyLtAHyoqr0Cn08GcJMn5vZdyrsNQJGq3ref+dcDGOgJwh0QWrXMJzvn5mOi6a138PtOs3zv6X7myELcNrtZ1I7L4Pi3UIz7QlwxJROzYl0s5eVn8hbome83Jdv/LnN/jDkAXLjwdrJLbnZaeZXbOTbxsWUuDvNXGzLw5EhXbsnXHONYucHF4KW0Avxhg/1acbxpzgIXX4h4oEUPd2xsFn/d0TukZ6Wg9TAXs5nRlmMcX37TxekjDhiR6mL1/e0JAEMDMdE7Jrk4bUniOMZmfVzfVK4qQXw3d55Zb3Cc8xHHuGu9Fpuxarp721/nIawd0CORN8XlL3fXPnwUxzXPe5ffc913uHsPc8JajmN8+ht+I/mFT7gXIaTyUPkWrW7sHE3vuYPjsq8cxnZcZ1fY2hf4fb2r3+FrS012Y1946HyLxh1cH59wXCvylU7aSvZjS128/a86bidf0Xo+0UxfaOjaeC73mqP52t790gXirgzEH1cX8djp0Nn16+4trCWRlM731LFXuHFWvoTv+TcXcWztiDIXXz3kQo6l7bOAj9UKl7eR8Dk7d+HpLm2ku7aZgTa6dBhrXxSscWOycieXmxvQajjXd20rx3Es7X35/NbL27q6vmqzsjn5ios5LvvK4a5vNs3keN5uJ7C+RdI0V8cS5RjjTUszyG7Wxs07CcJ9vGsbn6fnSfnON4/j3jM68LW2Weiup7qSfeXFPL9VLHbpTH79OPbmcZ3ahN282SrM5fZP5fGRku38CS0C2hGz2Y6JdeM5JcRzSZsYHs9+TgnMB4XruNzdO9yxvcr5nsnsw3N3SYmLDQ8F3oHeMTB+F61035sDG/GcmprJ7ZKSznbxXld2dSWP/dRk1urwk1zJ15afy/H2jbLd9VSU87U1T2GtA7+eQUZvbpctU7j9jYMbW5wbhmEc5PgX5vXhX5jXh39hXh/+hXl9+Bfm9eFfmNfLd/ih3b8wrw//wrw+/Avz+vAvzOvDvzCvD//CvD78C3PDMAzjwOJfmB9I/Avz+ggKDR4SmCBcnVjMuWEYPxZiReQ5EVkkIm+KCK08ReRCEVksIktE5B7f56eIyDwRWSgiE4OFisjVIvKJiCSJyMUi8rWILBCRJ0QCcsCR/N/KIyJXisi/A2Xef6AbwDAMwzAMwzh0scW5YRg/FroCGKuqfQAUAPhFjUNEWgC4B8DxAPoBGCQio0QkB8CTAM5V1b4ARvsLFJEbAJwJYBQi70Q/H8AwVe0HoBrARYH83evI8yqAn4hIzX7IywE8G7wAEblGROaIyJyXd2wJug3DMAzDMIwfMbbfzTCMHwubVHW6l34RwI0+3yAAk1V1BwCIyEsAjkFk8TxVVdcBgKru9h1zCYDNAEapaqWInABgAIDZEgluTQIQfE9VrXlUtVhEvgBwhogsBxCnqosDx0JVxwIYCwAbB56gQb9hGIZhGMYhT7i6oWtw0GKLc8MwfiwEF7N+uy6pKKnluBqWIPKUvRWAdV7e51T1j/uow77yPAXgTwBWoJan5kEKdjthmLiHJpMvZ1g82SKJ0XQJ+Asvrj2LQa2Y6Xb7Fwhf+pIPWYSs0tdsk8JcTiiw78ofZ55871jyzej1e7I3hpwQz+bJHPee0ZQFiGKSXeTA2qlcv5gYrv8kdcJGJyzl+jXvzDHoO4td++7+jEV4uvfm31yqfaMnvQnHp2/f0IjskvddnZq2Z9GgZa+zGFRxtRNjazKfr3tRQgbZoakuJn1uAsc4NlrCsYnlFe6rPSOT61uVx3n7jeC4+Iqdrv5BISPhy0H+HtduU3ewIlxqQCBg+6Mro+mEuETybZvD7Z+1fVs0XVLemHztu3PMf2GuK6uwkNslv4LtzM3uvtkxlgXgmvbmKeKWE9wYrdzO1xLfnsWs8ra6Y7uUc96tc3h8r/bFhvas4JsosR9f69Rxrr7NE7gf87bwvbDyGXef9OjK1zJH88k+Cu7Yglms+bB+XTbZzXY6gbieQ3eQL64p91v1FtdmIWUNhd3LuS+yB7l5as98ns++SuA57PglTgyxuJLLLQ4IjZUXuHYIzoWxIe4b/7hr3oXnh0Wf8XlS450g3Gbl604v5zpUrXP9Wh346iks42sNF7n2LSrksaJr+Fj/9SQlsIheaRGXW1Xt6lA2g9shIYmPLalw/lDgOyG/iO/V+J3uWtetDM5nLJQXF+/KLQ9zBFiwb/zsWspjZeMu/u4p8236PfVf7clX+gbvOMvMcfPqhk1c3x4BEcvyVa698wu4jysruP5lvjlWlfspPYfv1V0Frl/ri7JW33gpK+f2rA7zedIy3Fy+YWsm+eKTWeyuECYIdyhh29oNw/ix0EZEhnrpCwF86fPNAnCsiGR7ceIXApgCYIb3eXsAEBG/0tZ8ANcCeN/bFj8RwHki0qQmr4i0DdShzjyqOgtAawA/A/DKgbpowzAMwzCMQwoNN/y/gxRbnBuG8WNhOYDLRGQRgCwAj9U4VHUbgD8CmARgIYB5qvqet839GgBvi8hCAK/5C1TVLwHcBOAjRLaw/xnABO8cnwFoHsi/rJ48rwOYrqr8PivDMAzDMAzjsMe2tRuGccijqusB9KjFNcKX52UAL9dy7CcAPgl8dpsvPR7AeM98DYEFvJennS9dax6P4QD+XYfPMAzDMAzDOIyxxblhGMb/GBHJAPA1gIWq+q3XtdVGVjMXC5rUkn0VGzluOJTqYtG2h3la/+QljtcbfaOLnatcsJF8CWPOJnv9jZ9G08eexfFvc17gGMfK7S6OMRhjPnTJPWwP/FM03f5vHcm359GvyI7r7AIDu3bnuD9J4xjC9GfWoC5iG3G8XvPMomi69eX8bvi8V/LJTvXtfpuwphUGJrqND9nNiiiv+PajVZWFkJjtYit7nMP9Jmn+uMYESJKL7xz57F7K22q4i03tC+CJOa1c/TI4GLxFL44V3zPfXXtlPrnw2UoeXCNau1js0jLu880xHKuanu5iK09vw/WNa8GxqhJyfVfxOcc/VlZyvya2cmO4ajlv8Ju9pAXZLRPdfZLZmOP2C7Zy7GpFuSs3syXHWgd3OP5pootwuWNQLvk2j+e8bX0xuysS+FpOup7rW/WgGy+L4znudvD7XP8Rd7iombV3rybfqhLWOhjW3fXbnq0cuzwCHI+aJG4c+scnAKzYxP3W7xzXZrOe4EZq3ojHfmvfHNG0imOXM9rz2Ncy16+Z/bnNzv6Ex0fYd2h1IL53QDXXNzbBaRJsD3Ps7+5yjiNu7GuHvG94LslpxH3h15roNJDbYcUbPOd2Geni12d9yrHrmYEY+cp8105tjuT21DC34fZZrg7FFTz/piTyPb+91OkiDD2N783Nn/F97W+nHcrjqnUs3yc5x7nzpq9h/Yf4tjzudn1V97bhYN/4SUrneaZ5JbdL0x6uTg/cwm0/OoXHQ3Zf1y7di3eTr5xvazSJcfNoeiqP10bZXN9Qgru29StZKyK5PY/RxjvdWArOdaEYbqOUdFeHnCu7kW/rIzwHxCW6e7drN9aDqCrnefPhn7NGxUFB+ODdVt7Q2OLcMAzjf4yq5gPo0tD1ML4//oV5fQQXPvvCvzCvD//CvD78C3PDMAzDMA5uLObcMAzDMAzDMAzDMBoYe3JuGIZhGIZhGIZh/DAcxGrpDY09OTcM4wdHREaIyFH15DlRROaKyGLv/+N9vtEislxEJu3j+HYissR3vg8P3BV8+xy1+G4XkZEH+pyGYRiGYRjGjxNR1fpzGYZhHEBE5DYARap63z7y9AeQq6pbRaQXgPGq2tLzfQrgHlXd5+IcwIeq2ktERgC4SVXPOHBXwec4kOUCwKmtT41OzpXgX5hLwyyYMyLOCZrtBPvaKAsHrREnOJMQ+H02DSxWs1mdKE6hcrn9Y1hobqU6kZ6NVSxANDSuKdn3zLkzmv5J/+vJ1zOUQfZWXx3ShDd7xQXqn+yzTyzlNnsikQV9qn3ffSnCAklxwnHaJepix5eUbSNfr0R6mx7yql07KPj7tXtsFtkpvvbeoCxA9dcQx6vHiCvr0pIC8nWOZ0Gict94ya1iMaUY8LWlhVg07cZyJwj1WAIfG6RZjBPYuraaBal+qywkVel7SlJSzQJ2GbEsJJUdcnawj0+pSiF7WpwrKzkwfoP3TaE6Ma7YQB/HBs6zR125y8tYOapPoM/998acgrXkOyq9M9kLizdH0xsKuNzrWxxNdr7vXj4izGJmj5SuIDs11mkWnJHQlny/O6eQ7P+85cShQoHxMCnMIl9VvrG/vZLv6y4JTcj2j/2ucTzWy5THc7GvzXJiEgM+Fk3bVu3G4eBYFlj72/29yT7pl05vs0egDnlhFvnK99mNYvg+aB3D42x6meu3bglchx7gvHvEXesO5fsiP2Dvrnb3ffNYFqXrIFzuXrh2CQqqJQfmRvH1a0ZgfqsI3Bf+MVAW8BWEub5JvvP4+wUA4oTvv3Rfm2YIfw/t68ng+kC5JwrPb1W+ITsfnHewcht+HM6LpisDY7B/HPfjnEqXt0UsC6gF53I/JYHxGlxXtQu5sooCeQsC4yHR174VgfrGCrdaka9vigP91CaWv5+bBNr//vWvNrgYSdn0lxp8AZo47KIGb4fasCfnhmEcMETkXe8p91IRucb77BQRmSciC0VkoregvQ7Ab0RkgYgc7T0JX+LlmQoAqjpfVWskiJcCSBSRBBG5FZFXkj0uIveKSMj7f7aILBKRa+upY5ZXz0UiMlNE+nifLxaRDImwS0Qu9T5/QURGikhPEfnaq/MiEan5qzskIk961zxBRJK848aJyHleer2I3OMd/7WIdDqQ7W4YhmEYhmEc+ljMuWEYB5IrVHW3t0CdLSLvAXgSwDGquk5Esjz/4/A9OReRxQBOVtUt3mvHgpwLYL6qlgO43dvifpOqzvF+BNirqoNEJAHAdBGZANT5U/ffvLJGeeU8D6AfgOkAhgHYAGAtgKM93xAAPwdwF4D/qOpLIhIPIASgKYDOAC5U1atF5HWvri/Wct4CVR3sLfofAHBAn+IbhmEYhmEYhzb25NwwjAPJjSKyEMBMAK0BXANgqqquAwBV3V3HcdMBjBORqwHemyoiPQHcA6CuJ+InAbhURBYAmAWgMSIL5roYDuAFrz5fAGgsIukApgE4xvv3GIDeItISwG5VLQIwA8CfROT3ANqqas2+wnWqusBLzwXQro7zvuL7f2htGUTkGhGZIyJzNhVt2sclGIZhGIZhHKKEww3/7yDFnpwbhnFA8OK6RwIYqqolIjIZwEIAXes7VlWvE5EjAZwOYIGI9FPVXSLSCsA7AC5V1TV1nRrAL1V1fKA+7faR/1tVADAVwPUA2gC4BcDZAM5DZNEOVX1ZRGZ5dRwvIlch8oTdH0BbDYCDQ/kctaXdh6pjAYwFgKKbz47m0eKyQE6Oj4xp4X5nPecJjsH7ZRzHAXZ8MKqrh7xbPyZfkwcuJjv8+afRdO47HEOcnLmF7c4urnHzZI4hbv+3jmT748zfn/8I+fIvvJzs1DHHoC7Cazi+d+ML7h3kTftzfZ8byHG4JZM3uHP87EjyVUydR/aWqa69mw3h+NhQI47njT+2bzRdvewbznsCayDqpnVkx/Rz/sqXX+C8xS6m8AskYvaHLp72yHM5/hQhf5+no3K9a5fdy3ns7NzN19Ohj4upf7EjD+VP3+PYz1MvcmNt1xfc3i8ncrxkxiVOlqHk/UXk0yqOl0xo46vf13y7pjTm98yf7EtLiG+rrd8E4i5bur5Kas5547tx/OnHz7g40TP+wVEoFdMWkq0V7o+8FVO6k6/vA4PJfu5Gpx8ZTupBvotP4Bj0UFsX0z3zEdZ8+Hokx8TGH9klmt46dj35/vl2Np8n2cWVt7q6NfmOuZfv3f6PDIimS5+jKRZVRax9kDq6XzS99p88Xbcbxc+CNOz6NXYQt9nfb+Fjn7rMzS3LxvFc+MBvlpE9/q99oumz7+C4/MezON43tYkvZncHuZCUwfNofI6rQ1wnnlMLp20lO9E3tjbP4X5qfRRrS8SkunIrNnJMf0J77vPzPnDpf8dze2a1zEddNDqzA9kFH/C8edk6d5//rZrv+ebNeG7JuaanM8pZO6Lo/eVkpxzl9Cs2vcbt+avSusONXz+e54OqnfxDdSjDtf8nk1j/4ejW3BfXDHLzW+ww/k284IkpfJ5y16YJjfLJl3xyF7J1j+ur3Hc5b9Of8Lyz4yM3Z4XieEGY3ITHZFwLNz/HZLDmwO5JfL+l+26b2PY8f1Wu5Llk09cci28c3NiTc8MwDhTpAPZ4C/NuiGwHTwBwrIi0ByLx3l7eQgDRv1pEpKOqzlLVWwHsBNDa297+EYA/qur0fZx3PICfi0RUb0Ski0hASYeZCuAiL+8IADtVtUBVNwHIBtBZVdcC+BLATfAW5yLSAcBaVX0QwPsA+ny76H1yvu//Gd/xWONHjn9hXh/+hXl9+BfmhmEYhnEwoFrd4P8OVuzJuWEYB4pPAVwnIosArERka/sORLa2vy0iMQDyAJwI4AMAb4rIWQB+iYg4XGdEnmpPROSJ+y0AOgH4i4j8xTvHSaqaB+YpRLaSzxMR8c45ah/1vA3As149SwBc5vPNgttWPw2ROPMvPft8ABeLSCWA7QBuB9AI+0+C9+Q9BsCF3+E4wzAMwzAM4zDAFueGYRwQPLG2U+twfxLIuwr85HlaLcf83ftX27lG+NJhAH/y/vnZC6CXl2cygMleejeAs+oo9xJf+iv4dhep6l2ILNb97K45h5fnPl96TCDvI6r6t9rOaxiGYRiGYRi2ODcMwzAMwzAMwzB+GA5iQbaGRlQb/B3whmEYRoCyGa9EJ+eSfz5OvlAq/64a17d9NH3Dw/nke+gqFvwqmeLEdcoLWNhIAiokSc1cTNb6ORnky2rMwkbhaifwlJjGQkZB7t/aLJr+fWcW8Ml45VmyK564zRnVHCO24bl8sgtKnZhO3+dP4pOGAr9Fb3WCcBvvmEOuts9fQfb6i12d/NcJAB1e5byLR7m36PW6k7UQC59nqYH4bNfgJQFx/lA8fzcX74qPpifkNyHfZU9zvPrOW96KpjOPZ3Gita9x37Q9lvsxbqgTbtvz3GLybd3IZVWGXf2bN2GxonXbM8nOTHDiUUUVceT7Op7H6Bk+wbK248bwOV98juz462+OpjV/O9gZ0Gb0DfCSv/2LXMl/vpHsvb97MJretJqvpft1LK701UOuTROE/+DMTuH2fasqI5reLTye73rpTLKXnP9GNN2kGQsP5u9k4baERCcs1fYlfrHFslF8T3W/221aWvknFrd7U/jarkx3SmmVFTxfNDsmMEaXOiGvlze2JN8vng0IO273DfgWLNZ45ZiPyL60zN3XI97mNpp8zgdkN01wAmYPxfA4e/TtS8l+ZNTrrn5PDiHfhl+yWGazAa7csi183Y1+1pfsxbe7a+v71mjyaRkLJ+oSJz4Zc8Qw8iGF77dfjBoXTT90CU/WMSeexsducWKT0p6FByWd54+fn+pEOR959hQup5jvayS6cadLF5BLd/LLWGKOd3Nw8Jy/OOd51MXD/8d5pS0L2lEd5s/mvIO5DcvHvhRNr56aQb7eH11JdtWLT0bToRNP5HMG5ihU+IQJK1nADql8npgsdy9oMbeRFrMIINavcum4eHLF9D+O7PB2J5xYPWki+WLP+inZ40e9R/ZZ21+uTRj3B6V08jMNvgBNGnFFg7dDbdiTc8MwDMMwDMMwDOOHQe3JeV2YWrthGIZhGIZhGIZhNDC2ODcMwzAMwzAMwzCMBsZizo0fPSLSDsCHqtor8PntAKaq6uc/QB2eAnC/qi77X5+rlnN/DOBnqpr/Q5/7QCIiYwAMVNUbvkcZLQA8qKrn/ZfH/xrAWFUt8ex9tm0w/3dh7+Ujo5OzxPLvqHGjR3FmXzz1pVdwvOa46zLIDue6mLfw3lLyxQ3tTXbVrCXR9NZpHL/Z8jSOP63c4GITY5LZF9e5KdlXP+vi9Z6+k25LhNdvIDv+2ttcfd57lHwFL3K84a6NLl625THl5Ivt2IzsslkuLjT54uO5DkuWct4FuT4nuRDflmN/K7e5rk7oxdcdDOqvXOPeCphwNteh4oNJZIdaZJD90TgXA3na2RzHWLqSh1vywMbR9NIXuEo9RvEYiGnizlM2m/UAti1PI7vlgCJXvwyOj6zYzO2fNKh5NL3safZ1GsLvYo/v7fIWTd5GvtTjOZa5arWLM68uqiJf4QYes5n9XXhh1U6OE026YATZZW9OiabDZfx3UvJPh5K94yEeh36yL2hP9tR7XZstT+DIwmv/nMMHF7g48/WPcTs078exy3EdXFx8OJ99H3zEMbw9Yl25HU8tI9+C9/jtkIP+1S2aLnxmKvlShgRigzPc+PjyPh6DR13KfS5p7r6J6cgxxb++ZQXZ/zrHHbvxPY7TX1HMcdnHH+va6eqZfC2P98onOybRjYdQFo9fhLnPtcrd+BVbeZwV7+BjGx/l+nXndM6bPYz7XBJ5jNI5K/jYKz5wef+RzO2Z04v7MZTl4vTjjhlMvooJX5F91WQ3b96dxuUkZ/B9knGd07eonseaFHum8bjLGuXu1b0fsajGr7bU/RbSZ87mSbZ0Mce9J/fPiKZ3Ty4iX+NRzcnO/8jNYcFd1OlH8txdsc7dF0H9lbi2PPeVr3Z5S3dyn2adyd81xVM2oy7isjjkOb67Oza8m6+7cgu3r/97NiaVx5EksF28gNup2dTJDR5rXTpxbIMvQJNOuKbB26E27Mm5cdiiqrf+EAtz71xXNcTC3Dv3af/LhbmIHDLaFaq69b9dmHv8GkD0G30/2pbyG8aBwL8wrw//wtwwDMMwjIMbW5wbhwshEXlSRJaKyAQRSRKRcSJyHgCIyN0iskxEFonIfd5n40TkcRGZJiKrROQM7/N23mfzvH9HeZ+PEJHJIvKmiKwQkZdERDzfZBEZ6KVP8Y5bKCITa6usiAwQkSkiMldExotIc18594jI116djvY+TxaR1736vyYis3znWy8i2V69lwfbwcvTUUQ+9c43TUS6eZ/niMhbIjLb+zfM+/w2ERkrIhMAPC8iPb06LfDq0LmWaxosIl+JyHzv/67e52NE5G3v/KtF5J++Yy73rnMKgGHBMr08Ia+vlojIYhH5jfd5JxH53Gvned41thORJb7j7vWua5GIXLuvfhSRGwG0ADBJRCYF2jZFRD7yzrVERM4P5q+rnoZhGIZhGIcVGm74fwcph8wTL8P4nnQGcKGqXi0irwM4t8YhIlkAzgbQTVVVRDJ8x7UDcCyAjogssjoByANwoqqWeYvQVwAM9PL3B9ATwFYA0xFZUH7pO1cOgCcBHKOq67xzEyISB+AhAGep6g4ROR/APwDUvLMpVlUHi8hpAP4KYCSAXwDYo6p9RKQXgAXfoR1eBDAWwHWqulpEjgTwKIDjAfwHwL9V9UsRaQNgPIDuXlkDAAxX1VIReQjAf1T1JRGJBxDCt1nhXXeViIwEcCdcP/Tz2q4cwEqvvCoAf/POsxfAJADzaym3H4CWNWELvv57CcDdqvqOiCQi8mOkfx/mlQD2quogEUkAMN37sQGopR9V9UER+S2A41R1Z6AOpwDYqqqne3VIV9W9/vwiMqCOekYRkWsAXAMADwzthjFdWwazGIZhGIZhGD9SbHFuHC6sU9UFXnouIovuGgoAlAF4SkQ+AvChz/e6qoYBrBaRtQC6AVgH4GER6QegGkAXX/6vVXUzAIjIAu88X/r8QxCJc18HAKrKwaIRugLoBeAziTx4DwHwBxy+Xct1DEdkIQ1VXSIii2pthVraQURSARwF4A3vfABQE7A2EkAP3+eNRKQm+Op9Va0JWJ0B4BYRaQXgbVVdXcu50wE85/2goQD8QVETVXUvAIjIMgBtAWQDmKyqO7zPXwO3dQ1rAXTwFvQfAZjg1bGlqr7jtUmZV4b/uJMA9BFv94RXv84AKlB/PwZZDOA+EbkHEX2DaftTz2AGVR2LyA8lFHNuGIZhGIZh/PixxblxuOBXT6kGkFRjeE9yBwM4AcAFAG5A5KkxEFlE+lEAvwGQC6AvIk9j/QoqwfME7zGppUyIyHgATQHMQWSRvVRVhwbzBc7hL39/RS1qa4cYAPmq2q+W/DEAhvoW4TX1BYCoOomqviwiswCcDmC8iFyFyBP2q70spwG4A8AkVT1bIiJ9k/dRr5rrqq2tQoj8sABEfiC4VUT6AjgZwPUAfopIrHd9CIBfqur4QPkj9lGfWlHVVd6T8dMA3CUiE1T19kCePbXU84pvlxYh1Dglmg4XskhPxUtvkR3Xo000nSYsBFO1agvZsa2zXZ2KAyJNKalkV253YkD5RSyIkzJ9L9stXHrtVM7btTtvpEjbl0xBNQs++UXgYs/6BflKHryWbIlxw6Uqr5J8sa1ZXInOMW0W29tYzKpsp6t/uIpvtcQBLEgVn5bk8u7lckJtWCgo4bx+0bRu3ki+oAhg8Uy3WaNfekDIaFPdAnAVq3aRryrMomN5U3lrX/NLXDx74hEsaLdmLvdbyio3fhq14bEUE9DXqlzpRPXadOFzvvJ1a7KPW+R+s0zN4LGz91MWqUvr5+pbsZ37WMPcV5Lm8sZn8VgPL2FJkG9mOoG1nBwWU4qdPIfslKbuvNPm8m6XEXPXkJ0X6/yrYlhsCyXcj7rH3WPFZdygO5bxfdKiixsvksR5YwPCv4kJ7t7YOonb96NE7uN+X8x0ddjJ5Sbt4XYpnekEDrfHcp9qMc8X1dud2FXCCaeRr0BZaGzey07crE2zfPKVlPJ9Up7nxlZwLtTAFPDK162i6XM7sWBZ7joWLGs9wAmApZzejXx7x64ne/UHrp2atOD7onIb93neUjfPtwz8HisprC2RJq6vVLmccAkfW57rzps+gttIy/j+43bi7xr/nAoA4VVuPMd0bEO+6i9Wkl0204l7xsTyvRjsGypnG89ZiZ24HSTZzbF527ncrEK+h5Kaufsk/5sE8gVF06p9hwZ3PMeW8/dJQltXh9Kd7KvewhvrwpWujjFxgT6O574J5+W7cgq5j4PjNybZ3avBez68h9shIaA1eVAQPni3lTc0FnNuHPZ4T47TVfVjRBZ1/Xzu0SISIyIdAXQAsBKRJ6zbvCfql6D2Ldx1MQPAsSLS3jt3FgCo6smq2k9Vr/LOkSMiQ708cSLSs55yv0RksQcR6QGg976zO1S1AMA6ERntHS/eIhKIPN2NqqN7uwW+hYh0ALBWVR8E8D6APqr6iHdN/VR1KyLtVrNSHLMfVZsFYISINPa2+o/26lvtK/dWEckGEKOqbwH4C4AjvGvaLCKjvPoliEhQmG08gJ97ZUNEuohICvZNIYC04IcSUYEvUdUXAdwH4Ihg/trquR9tYBiGYRiGYRwm2JNzw4gsnt7z4pIFkSfjNawEMAWRp9rXeXHmjwJ4y1vMToLvCXJ9eDHk1wB4W0Ri4MWvB/JUeFutHxSRdETu0wcALA2W5+NRRLaML0IkLnsRInHa+8tFAB4TkT8jst38VQALAdwI4BGv3FgAUwFcV8vx5wO4WEQqAWwHcHstef7p1fG3AL6or0Kquk1EbkPkB41tAOah9h9CWgJ41mtPAPij9/8lAJ6QyCvzKhFZ3Pt/qn0Kke3q8ySyFWAHgFH1VGssgE9EZJuqHuf7vDeAe0Uk7J3r58H8iPzwU1s9DcMwDMMwDh8OYkG2hsYW58aPHlVdj0gMd419Xy3ZBtfyGQBMV1VS1fbiqfv4Pvqj9/lk+LZq+9/HraojfOlPAHxST50XADimls/95eyEizkvA3Cx9+NBRwATAWzw8tXk2Yk62sGLgT+llvPtRGThHfz8toB9F4C76rmmGeCY8b94n48DMM6X7wxf+lkAz9ZT7kLU8hTa66fjv31EpA28nQ9/8v75mYy6+/EhRMT6aux2XnK89y9YB8pfWz0NwzAMwzAMA7DFuWH8WEhGRE0+DpGn/z/XYFCacUhRMj8/ms7fmkS+gkLeWd9+99poelkFx+DN+Zjjhged4eL5nv6Mfdc0Zx2/UCMX+dT7eo7RnfVgIDa1lYspjgnEKUoaHxsHF6MXXrOWfBueyye7cZvN0XQwxrzVxCfIXjv8+mi6mkMnUbk6l+xNi1yseKsq3mQSSuU2/Gp982j6xOO3kW/P+DyyM45x5RbO4U01qcUbyI7ZvsMZcdyeaz/nPm833MVOTl7N7y4/t4LjC/dOzfdZISRmuWPbtmUNysdzOQ7+ho/WRdMrVnKg4hdJ/KRjRD9nr56YSb6CSo6BPPJGFzdaNZXbYXQ3jvdN6udeYrHkJY433RHmuNGOuS52OaMpR+rt3sWRLIXvufo2yiwkX1pbjht9MNa18dgbOEqo5L25ZH811wkuLE3gsXN8Kse1LopzfZEV+BPsmwc4nj6rqRs/e6qakA8FbKZNc/26Zxtfd3kM18kfi9/25k7ku+huvh8lwdWxUSseZ6Ur+SsmdZSLvhr5ynLyVWzgsRPf2cV0F989lnx3ZfLYyRnt7r9VT/A5dwT+ik0b1T2aXnbPCvJtXclx5P5xt2cz32/NOnIDb1vg5rCcAi53z15+6UqvO7tG08v/zO2QuJMDh1uNcNez7N3AvN6dY6/9c3tGe55jF8zh+zjk2yTW/2PWMt21lNt3WYWLkd6xm69lTz63S6eWbh4tCsx9TS7tSHbJZ6ui6VWreC5ZFrMDdZG3iO/5jBYkeYMNH7p4+pkx/N2iL/Kc2/18Nx9nJbNGQu4nPJ7X73Bt2DqLvxMSW/H8UJHn+nH7Vm6z1HZ7yC4vcoO0eC/PX2WreACnp7svrmYXsW7D6od5TGZnu+uJjQ/ojjThcXbP0hZk/xPGwYwtzg2jDlR1TEPXYX9R1UK417kZhmEAAC3MDcMwDOOgwATh6sQE4QzDMAzDMAzDMAyjgbHFuWEYhmEYhmEYhmE0MLY4N4wfEBGZLCL/9fZzEckRkVkiMl9Ejj6QdQucp52ILDnAZfYTkdN89k9E5A8H8hxeubeJyE3fs4yi+nNF844RkYe/z/kMwzAMwzAOG8Lhhv93kGIx54ZxaHECgBWqetn+HiAiIVU9GAJP+yESF/8xAKjq+4i8E/2gwXulmtSb8Qdg4RInTtMmk4VgNlax4FPpbDeVV8ewSM/KeBak6rnaCdu0qmRRoXA+/yaxYooT+Sr5ghXWJibx10fOAie2NElZeCn9mTVkJyM7mt74AovnFJSyGBA2uqQEhOb8AnAA0OHLR6Lp5QN/xXmXsthSsxh3PdWlAcGsKj7P5jjnnzyRhZdywAJVG15w6UrNJl/3BO6b2GR3bNE27qdZ1XyezOVO8OeERiymFBS6mlfpRJBarmUhoyUJfJ544WutKne/2c9NYPGinwWE596c6IS6zh22hXxzJ7OAWe6r26PpKbtZ6OiYTL6eUKoTN9ugrci3KZ77KqHI9WtJOV9bXiWPpYpKd21xJSwk1TafBeISxOVdeOt68rVowWN/Vbx7w+N0ZcG9X3doTnYYTszquYLF5BsR15PstDI3RlfG8zkLYvitkiO+cXb7DiwkNqyAxaEKC12/lryzgHwdL+X6lsxwAoizF7EvL5brdOSD66Ppj8tbku+kbSywVbbAHRuKySDfMyHut5vfckJ5Gyp4XFXyEMWEu9xcWR3Df3yvKuf7JGWbu/9W7mFBwxOubUv2krvcPBW7mb9ON4R5Pk77x6JoOq+Cz1lWyc/EKj53c25pNffpni1cbrXvra1zl7HAV2FgPCSou68rAyJ0m3awgFl1yIm8bVYWgEuo4jZsvMzV4ZvtXE7+vTw/dIrLiKa3CYvQVaPuhVFBIff/+iUZZOckOIG44sC39fJwCp/nVVenSXHcZqcGVBU3xbr5I24Pf1/s/YLbMK/K1TEOPIeums7tUh52fVOlXOGYwLExhc6ufp5FTIuruE6h3W58bKvgsdJxL99vRwcEUo2DG3tybhgHABF5V0TmishSEblGREIiMk5ElojIYhHxv45ttIh8LSKrap5+e0+qp4nIPO/fUbWcox8iIpunicgCEUkSkQu98peIyD2+vEUicruIzAIwNFDOABGZ4tV3vIg0932+UERmALjel5+eDIvIhyIywkuf4tV3oYhM9D4bLCJfeU/3vxKRriISj8i7z8/36n6+v1wRaSsiE0Vkkfd/G+/zcSLyoFfOWu/97/tDXxH5QkRWi8jVvrrfLCKzvfP8zdf2y733188D0NqXP1tEZojI6d6uhbe842eLyLBa+uhbeUQkxqtHjpcnRkS+EZHs4PGGYRiGYRg/ejTc8P8OUmxxbhgHhitUdQAiT4ZvROQpcUtV7aWqvcHv6o5V1cEAfg3gr95neQBOVNUjEHmv+IPBE3jvPr8VwGuq2g9AJoB7EHmXdz8Ag0RklJc9BcASVT1SVaPvUfFetfYQgPO8+j4D4B+e+1kAN6oqLebrwltsPgngXFXtC2C051oB4BhV7e/V907vtW7Ruqvqa4HiHgbwvKr2AfBS4PqbAxgO4AwAd+9P3RB5D/3piPwwcauItBCRkwB0RuSd9v0ADBCRmnfJd/XO319VN3jX1xTARwBuVdWPAPwHwL9VdRCAcwE8Vct5v5XHe5/6iwAu8vKMBLDQe4c84f2wM0dE5nxUuiboNgzDMAzDMH7E2LZ2wzgw3CgiZ3vp1gDiAXQQkYcQWeBN8OV92/t/LoB2XjoOwMPe0/FqAF3245yDAExW1R0AICIvATgGwLteGW/VckxXAL0AfBbZwY0QgG0ikg4gQ1WnePleAHBqPecfAmCqqq4DANXofs50AM+JSGcA6l1bfQwFcI7v3P7XcL7rLXCXeQvm/eE9VS0FUCoikxBZkA8HcBKA+V6eVEQW6xsBbFDVmb7j4wBMBHC9r01GAujhtRsANBIR3mdWd55nALwH4AEAV4B/rImiqmMBjAWAz5qer7XlMQzDMAzDMH6c2OLcML4n3hbvkQCGqmqJiEwGkACgL4CTEdki/lNEFmUAokGH1XD34G8A5HrHxAAo88r+ByJPgOE9LadT76NaZTVx5iIyHkBTAHMQebK7NPh0XEQyANS1GKwC77KpCbaSOo65A8AkVT1bRNoBmLyPetaFv9xyX/pb1ywi1wOo2bpeIzgXrJd6x96lqk8Ejm8H+IL5IlQh8uPJyQBqFucxiPRxqT+jbyFeZx4AhSKSKyLHAzgS7il6nQx/3un95f7+PfIN6bCN7OT2rg5Dp3Nc6IUDNpO9Z5WL/UsLc+xkyWKOu81s5OLYEko4ZnBUJccyt+jn4vdOWIp9cmKp207WtD83fbvrf0J2xUtvRtNVeXzO6kAcnT/OvPuc/5Cv8wePc+YYN6QLnp1JrrR7/o/sXqe+FE0nCrfZEY+xvuP22yZG001+yn1RuYQ3q+1a7No050jeYnd+Ccdwx7ZxMbEvvszxhZdPuJLs1J/eRXZGB3cL5c7meO+h4KGa2smlj93CGgSrwfGcA0Kuz0u5ulgd0DrolupiNkMclo3qar6ty3e4dvrJy8eRr+T+F8hO+d1VzkgI6BWEuA66zmlcbvrLl+Rr/fA5ZJ/3sy9csQncRsk5HH/adoubbs4Ncbxp6dT1XCf44vQb9SLPsJs50mXH0y52vE0lj4/gBNe2rWvUzF8eQ769t84mu+tDzl/y5AfkW/4M31Qdj3R9kwwe+2f04nmootD1W8omrl/n1y4lu3rih9G0NOLfOEvu3U52XKI77ynvnkW+ZufwJqxUn47D0DDff6c/2J/sTb//PJo+4S+Nybf7+WVkd2rt2iFvO9f39KcGkb3l5k+i6WPfOIV82MO6E4j3jdmtG8kVM+hEsof+5JloekhfbvukK88kO7zU6RnEDDiffEMycrjcn7o59vTn+ZwoCNysSU6rIeejD9kXx0uK2J9eHk13D3zXTB/9Ouqixw3cvtK9Vx05gfYvs2xN/HDWbSh8zX3/pWzg749OT/Dzh+Z3ujql38Fz6rdke/ztUsm6I8Ht0tKqs8/g7wDN3UB29ZRp0XRME46Rb30k39fY5HbXdfqCv8Pir72G7DdGv0s2j5YG4iAWZGtobFu7YXx/0gHs8Rbm3RB5opwNIEZV3wLwFwBH7EcZ27wnxJcg8kQbqnqLtw28Xy3HzAJwrBcXHQJwIdxCMoqqnuyVcRWAlQByRGQoENnmLiI9VTUfwF4RGe4d5l88rgfQz4uVbo3IU2gAmOGdv71XVs1fpekAav5UH+MrpxBA8ElzDV8BuMB37i/ryBdFRFZ41/dITRupao1y0FkikigijQGMADAbwHgAV4hIqnd8SxFpUlvZiPztewWAbuIU5ScAuMF3/n61HLevPE8hsr399YNEoM84DPAvzA3DMAzDOLixJ+eG8f35FMB1IrIIkcXvTAAtAUwWif5M+sd6yngUwFsiMhrAJHz7Se63UNVtIvJHL78A+FhV36vnmApPVO1Bbyt7LCJbrZcCuBzAMyJSgshCtobpANYBWAxgCSKiaVDVHSJyDYC3vevMA3AiIlvSnxOR3wL4wlfOJAB/EJEFAPjRXiRO/xkRuRnADq8udeKJqe1r58DXiIQTtAFwh7do3yoi3QHM8J52FwG4GECtC2VVrRaRCwB8ICIFXh0f8fo5FsBUANfVch115Xkfke3stW5pNwzDMAzDOCw4iAXZGhpbnBvG90RVy1F7fPZ/ask7wpfeCS/mXFVXIyJiVkOti3lVHQdgnM9+GcDLteRLDX7m8y1AJDY9+PlcRLbV13Cb97mijm3YqvoJgE8Cn80Ax8z/xft8NyJx8n7Geb71iAjbBcsfE7BrrmsIgEeC+b08t9X2uef7D2rpF0Ti8L91Hk/I7mSfi/cHgvvE69Nv5fHoi4gQ3Iq66mcYhmEYhmEcHIjIKYj83RhCROT37oA/HZFdkW0QWVffp6rf6yGMLc4NwzjkUNUP68918OBtjf859iPW3DAMwzAMw2hYvJDRRxDZFboZwGwReV9V/aIU1wNYpqpnem8xWikiL3kPd/4rbHFuGIbxP8b7pXV/XwMXIeSm5yZjOpCrZDw/fI8f4kRwlk75hovJYiG3Jqc4Ua+iNzh6IibwjdD6mla+c65iZ0CxJDbLHdy8cwH7GnEEwhP5TmDruYFtuaBQQFSoYzOXbs1CXJWrc8leu9RJGgQF4GLP5AiEiidui6bLi0Lk063chv37OYEqv+gVABKWA4C0Fu77WGK53Liuzchu1t0dG965l/O2ZnGwba87AaJ2Fdyn4aXTyW7UysWZJwxoRb6z+/DfC+EdJWTHtHIiRB1KWNUrcSG3f05bJyC4ezOLxfWo5Fj3+FQXPXJ8q63k+2QLCx+dmeMTMdzKgknx7Vm2QgucaBqqAn8LlfD4llZO7a75cdxm2MkCW21S3bW1GMxtFNsyg+ye3+xx9UvgKJmqYh77LcJufCcGVN2kLd/nZSVO8GnEiTzWJZHHVuk3vvOmcBstKM4ku83sr6Np5S7FZyHedNU1xwnEde3Mb3+MzeKxnzTQjdl2T3HBuo4F1mJynACb9DqSfJ2VReoqSlyb6Zol5FsDFkc8+9r0aHrpQwGVwsA22imFrr4Xp/J1787l8dx6gBsPyTl7yKdLFpJdVeXrm208fpHJYmzhubOi6ZiWfB+Ev5lL9tIqd96EXgG5lGpu75h27ZxRysKOGrD95eq8GVxuiMeZtGrjztGxDfk0l8eH7vEJ+wXuY/85g2gRjyvZvjmQwdePVXwTSUv+PkkZ5I5tH9y3l8fjI96nCagr55NP+vAbZv1tqFt5npRmLTmvf47atBb7QtLduJPmLGiInTxvosx9j8Zk8n2g27m9Y/cZBdhAHBqCcIMBfKOqawFARF4FcBYA/4SmANIkEi+ZCmA3IqLC/zUmCGcYhmEYhmEYhmEcNojINSIyx/fvmkCWlgD8v75s9j7z8zCA7gC2IqLN9CtP3Pm/xp6cG4ZhGIZhGIZhGD8MB4EgnKqOBTB2H1lq23IQfJPlyQAWIKKb1BHAZyIyTVULggfuL/bk3DAMwzAMwzAMwzAcmwG09tmtEHlC7udyAG9rhG8QebtRt+9zUntybhiHCSLSDsCHqtor8PlTAO4PCFx8l3LHABioqjfsI88IABWq+tV/c446zjnB917z4LluUtUzvkf5k70y5uxH3naopV2/L9LIBcCtfoAvs1V/zls+1XVd81AW+bZMLiM7IdHF4S6o5LjFRjs4787HXExebgHn3RibQPYRCfnuuOIk8jXP5BjHanU/PJdM5ti41HZsl83ieD4/mxalk90sxlf/QCy4P8YcAOKvdfbOx39DvpxKjl1+dZn7bv5pl0B8YWv+Dt62Ylo03eX+S8m3++rbyd6b59opLp5jlbfs5JC1EFwccbNEjoFGHMegb1/pizleuRfNe7p42TcWtKa8adUc235CJxejuWNTI/JNr+L2brfMxenujeHY1J7JHEO/YbmLe26UxuMsLvAcYvHKptH08GSO/c39vJLsVqPdfUKxnQAQ4vPoKhcbXDCfY+IbX8r9OLlidTQ9dDr/qdS8HV/bxDIX158ReAnmidkcL9ur3PXr2ngut3ral2TvzHfX/vbUxuQbVs717zkoL5oOz5pFvtQwnyemk4ttXzKWG7+H8NOsnTPcuCwu5L5IK+H2LZjp7LmJPHaO6zWc7OoPX4ym9avPyfeV5pM9eJub05oGnrZNjud2aH6/u3ebJwZeWlIY0HXwXbru4rGzqpzr3/kiF3O88Ab+Wug9qJTsrXvdedsG42p3sLaB+OephETsi+Y+PQCt4PkhvHgx2ZUr3XkSLh7FBQXmRn+5ksn6BKERPyW7eu28aLrq00mctzl/92CDT7ujVbvAOVeiLmIv+RWf872nyY45/qxouuz5eeSL37Se7KqN+dH0pq95PHTqSyZiW7t5M+Y4vu7wfB6j8N1/oaN/wnlXs1YA8n2x+IH5LDTkTD52rHvLrG7h733pw1/8Md3dmNTdL2BfNKn1hbHGfjAbQGcRaQ9gC4ALAPwskGcjgBMATBORpgC6Ati3uEA92OLcMA5zVPWqH+A0IxB5r/gBWZwDGIPIO9e/tTj/vnjqnIbxo8C/MDcMwzCMg4JDQBBOVatE5AYA4xF5ldozqrpURK7z/I8DuAPAOBFZjMg2+N97r9X9r7Ft7YZxeBErIs+JyCIReVNEkkVksogMBAARuVJEVnmfPSkiD9dWiIhc7uWbAmCY7/MzRWSWiMwXkc9FpKn3ZPk6AL8RkQUicnRt+eo4z60iMltElojIWIlwHoCBAF7yykuq5dBGIvKOiCwTkcdFJMYr7yQRmSEi80TkDRFJ9T5f753rSwCjfeeP8drr7yISEpF7vfosEpFra6lvrXlE5AUROcuX7yUR+UnweMMwDMMwDOPgQFU/VtUuqtpRVf/hffa4tzCHqm5V1ZNUtbeq9lLVF/ddYv3Y4twwDi+6Ahirqn0AFAD4RY1DRFoA+AuAIYi807HWmBkRaQ7gb4gsyk8E0MPn/hLAEFXtD+BVAL9T1fUAHgfwb1Xtp6rTastXR30fVtVB3pbxJABnqOqbAOYAuMgrr7SW4wYD+D8AvRER6DhHRLIB/BnASFU9wivjt75jylR1uKq+6tmxAF4CsEpV/wzgSgB7VXUQgEEArva2OvmpK89TiMQlQUTSARwF4ONgpf3KoU+/83nQbRiGYRiGcegTDjf8v4MU29ZuGIcXm1S15gW/LwK40ecbDGCKqu4GABF5A0CXWso4EsBkVd3h5XvNl68VgNe8BXw8IsIYtbG/+Y4Tkd8BSAaQBWApgA/qyOvna997KV8BMBxAGSI/JEyPvI4S8QD8L3V9LVDGEwBer/mlFMBJAPp4T+4BIB1AZwD+F4DXmkdVJ4jIIyLSBMA5AN5SDb5hmJVDy75+I6gIahiGYRiGYfyIscW5YRxeBBd8fru2V0bUxGDXKJy8D2BeLeXU8BAi4nLve8Jst32XfCIyHkBTRJ5q3wDgUUTE5jaJyG0AvqWYIyJHIrKQBoBbEdkRUNt1CoDPVPXCOuoUkHLCV4j8OPAvVS3zjv+lqo4PnL+d36wtj8cLAC5CRFDkijrqEGX5eW5nVKdz2BeT0YLsqjV5qIs9Rbzrv3M7Fwp1zskcjxw65mSyC+7/MJpOzWDhpX6d+FdniXUbsXZ/xudsfXkzslMedcJMqT87knwb72CxpTZ/Oz6arprGQletqljgqbrUDeGCZ2eSr7yIpQT8InC95/+bfAWXX072qBbu2BlL+RWnXS94nOwmbZ1Y26Zz7yZfszNZbCl+mROhSrn1RvIl3PAI2Vk/c7+T/fN+7rdfv/4p2btLnHhY2hYW7WpZycpA3bN2k11e5P4saNGL3wQzcD63Ybcxrs8lhft81VMBgbgHB0fT036+iHw/vTWDbITcsXN+zuOhbSv+TWvB+e9E08nxLBaXW5pMdoJP7KxtC944WPn8OLI7VzhBsMzmLMCX1JH/dBq53bXh3iKeotLO5g1IU9a7Ni0G17f4axYlO+Iud2/E3vIN+SoCGx+ThjuRt6UP5pNva0C88ZPfb4ymTziF+790HY+P9NudqOGU8yeQr43wNNvhctdmxz3G427X1X8nOzbJ9UWIuwkXV7Yie+htTmjso5vXk+8ksBhinyN8AnzLWETvy//jNvzprTnR9Kp/biTfaf/HApjrbp4eTbfvwl8V88ayqOKR17rxMeXXLHzWLp3vqebDnIDd3je4DnGJwSd7bv6Y+jz3aftGe8hOTHb3UPYHn5Bv/SfBP/3join/2ACAZP0n2cOv8I3ZMPf/8qd4E1uX051I3aw/BYQ996F9l3fBLWSXFwfq+9RD0eQbxYGxcnsu2ekJbkwWVvBY2XD7fLKrqtw9VfXOneRr3p77LW+DE4/LK1lFvr5HbCd7+Xw3znIa8VxSVMJ16PoT9z1bPo+/1zc+zPdqWdWUaLrHMeyr+mIN2Uen1Rb9Zxys2LZ2wzi8aCMiNRKfFyKyvbyGrwEcKyKZIhIL4FwAUNVqb/t4P1W9FcAsACNEpLGIxMEXo43Ik+Iaie/LfJ8XAj656TryqerJ3nmugvv63unFhp/nOz5anqrO8tXvfc8/WETae7Hm53vXORPAMBHpBABevH1tOwNqeBqRredveO0xHsDPvWuGiHQRkZTAMfvKMw7Ar706L93HeQ3DMAzDMH68qDb8v4MUW5wbxuHFcgCXicgiRLaJP1bjUNUtAO5EZPH9OYBlAPYGC1DVbYg86Z7h5fO/y+Q2RBaz0wD41So/AHB2jSDcPvL5z5MP4EkAiwG8i8grLWoYB+DxfQjCzQBwNyKK7usAvONtwx8D4BXv+meinndRqur93vW9gEjc+DIA80RkCSJP64OPIOrMo6q5iLT/s/s6p2EYhmEYhnF4YtvaDeMwwRNm61GLa4Qv/bKqjvWeFL8DYEIt+aGqz6KWRaaqvgfgvVo+XwWgT+Djb+Wr5bg/IyLiFvz8LQBv1XHMZACT6/B9gYhQW/DzdgF7hC/9V5/rT94/P3sB9PLyhuvIAxFJRiRG/ZXa6mYYhmEYhmEc3tji3DAMP7eJyEhEtpRPQOSJtfE98dr0GUTi7L+1G6E2ur/+s2i69F+PkS9UyPGcse1cbGXZVPb1PInjI8t8IYWTXuRgz6NWstZeUkcXo1c6l1zYuZC/PjLauvN2782xcnmv5JMdJy4OsGLqPPK1fZ7fUFf9wRvRdNU2jtcLpbJMgla5bWpp9/wf+7ZyvGlOpYv1DMaYN3qWf3faNtjFgw/utI18GXdeRnber51WQMtbBpOvatpssuELKa18+knsi5JPl7s6lDUnX9Jfb2d78r9cfXLT0P1i105p4zhmOzGN454zT3axtvmfcT8mJ3DewumuP0ryeZwVVaSRXfaK+52vW2uOR9/8cD7ZTfq4uMtB715HvvLHxpLd9Bk3XlQ5XrqLcsyu5rnBX3AX6z/GXXkD2a0+c+dp1L6C8w7m3xm3f+xiTGMCyh0VX3HMceOw0ywIBfYuNvrNmWSv+fVkV/dAbHUwdrVs5o5outez53N9r2ZNgq5PjnTHPfM2+Qp2cDBw8gvut8T+PQLt0IhMVG9xY2sTOA77iP9w++rOLc4o5nje6YsXk33C+06j4Iy3eTy8ct5HZFcVuw4oA4/1oz++lOyNFzu9iK6PnkK+8pffJ7vFEJde/yVf+MAHeTwUPTkxmh7xCc9DKGW9CK10Yz0nl+OypVNfssvOecaV+yeOiZf2A/k8BS4GXTpx/br8X1su93gXX33q6ydxORX8fSKN3HdN9Xuvk6/HTVlkh05x32FHX8lzyROjX0BdZP9iAH/Qsh3bpUXR5C+nTSdXqFtHssPb3Xm3v7qDfC0eGk12xfOvRtMJ115NPknOILvRbjd+O1XzOJPGrAkzOMbF9GsZj3UtCYyHVW7spwzhe7FHT9Zn0Xx3beEpX5Av6RLWMLn7xIfIvg8HAQexWnpDY4tzwzCiqOpNDV2HHyOq+jmANg1dD+Pww78wNwzDMAzj4MYW54ZhGIZhGIZhGMYPgz05rxMThDMMwzAMwzAMwzCMBkb0IJaSNwzD+K6IyGgAtwPYrqrH/RfHjwBwk6qecYCr9p34qOmFNDn3bu3iyySwUzkpi2PezlriYnrfbM9z/LLVHKvYo7Mrd/JajpU78xJ+b23RDPcu1ZQjOKb45TfTya721TE18AP5R7FFZP811sUyx4S4vjldAzHzO921fbWeY683x3HD9Cp35fbvx++efXVZa7JHtdgaTefv4Fj8rl8/SPbknn+Mpls14pjBT8s57rKxLwz6J8O3kG/T16lktx7i2iV+eE/yfXPvBrJfgjt2ZCk38MxE3hR3dVt33tfW83uBr/59Btkl73O8b5Xv3fGNLuxNvurl68gumO3ioJ/cyn1zQQK/h3laiWun9bFc/66V/Nygh7h26X4Fx2HOfILHS1aCi5HdVs4vclDw+Aj7zNYJPM46jea8c15wMd99juL4WQkEmse2dH0z7p0M8k2J4ZjTFr6XTfxtAL+jedUMHkvtu7v3oFcUcdz+ovV8X3fMdNIWi/ZyORWBCaR12MU9P5nAcfv3d+F3r5cXuPNu3pRBvs3K7X10N/fO8WkreNx1CMwBLTvlR9N3r+OxM6ScrzXO93frwFbcZtWBsXPxTnc9D4DnqHfjuL7nw93LiUmsr/D1Xo6h9zUD+lZyXHaPYfwCkjlfNo2mu7fjuOcpm3jO7QR3D20MtOcDMax34b+eFm1YyiTj5KZkb3rZ+UtK48j3yzDPYQ/FuLl9ivA8f3FXjovPXeP8S0u5fUd05flu/vJm0fQdsXwPBbkWrl1OP4LPuXQ2j/W1IffO90bVPJe0DvF32Ep1bz/tFsNjMDGev0dnVWRE00cl8XvENxWw7sCGODfnHteI+3jDrgyyi8QNnriALsbRZ+eTvWO682/ZwedcEctzYX+461Hle/y1OM573/pXGjzeqfSlvzT4AjTpojsavB1qw7a1G4bxY+NKAL9Q1UkNXZEDhX9hXh/+hXl9+Bfm9eFfmNdH9Xf4uvMvzOvDvzCvD//CvD78C/P68C/M66Nxdf15avAvzOvDvzCvD//C/LviX5jXh39hXh/+hXl9+Bfm9eFfmNdH+DuMUf/CvD78C/P6aFHrWyBrx78wrw//wrw+/Avz+vAvzOvDvzCvD//CvD7ivsMDJf/CvD78C/P6+A7NQAvz+vAvzOsj+EPDvvAvzOvDvzCvD//CvD78C/P68C/M68O/MK8P/8K8PvwL8/rwL8zrw78wrw//wrw+/AvzQwa1be11YYtzwzAOWUTkXQCtEVGX/w+AZgCGA2gvIu8D2Aagl6peISK9EXmN2WBVLQmUcwqABxB55/o83+eDvc+TAJQCuFxVV4rIGAA/AZAMoCMi71H/na+sOwGEAOxU1RNEJAXAQwB6IzLv3ua9ds4wDMMwDMMwANji3DCMQ5srVHW3iCQBmA3gWADHI7ItfY6IxACYLCJnA7gFwLW1LMwTATzpHfcNAP87llYAOEZVq7zXod0J4FzP1w9AfwDlAFaKyEMAyryyjlHVdSJS84jwFgBfeD8SZAD4WkQ+V1XaSysi1wC4BgBuSBuIU5I6fd/2MQzDMAzDOLgwQbg6scW5YRiHMjd6C28g8gS9s9+pqmHvKfciAE+o6nR8m24A1qnqagAQkRfhLZABpAN4TkQ6A1AA/mC9iTXvLBeRZQDaAsgEMFVV13nnr9kLfhKAn4hIzavqEhF5tdpyX3lQ1bEAxgLfjjk3DMMwDMMwftzY4twwjEMST7htJIChqloiIpMRWfQG6QygCHCBbCIyHkBTAHMAPIzIwrs27gAwSVXPFpF2ACb7fP5gzWpE5lOpoywBcK6qrqznsqIMG+mEjmZ/nkO+o64NBMwmuJjY7OUcG15WyDGlbRvnR9MPbOY4wMuS8snO/cgJ5FRXc9MufoVF00ZkupjY9CYswrN7C8f63VPkBJOaDeFyty8JxOz6flwPV/F1n3g8CyRNnuiuJ1E43rSikIWiftrFiQzNWNqSfIM7Bcr1xZmPWHoX+QqvvJzsy5Pdtce2ZAGf0oX8pCAj223iKFjN9ZszifuxWlxZt15RQb61L3Os9dnihmBQIGtQJffNS3w5+Ekn18YLVvL4WHQ3i7pd3sXF6b6zmQX2fjOSBfi2TXdj9IJz8sn36tsZZHeH21CSEMf9uOtjFltq38TFcFZV8bUml3OseJsmLvY2MY01CRKbct88/bq7nquu51u6fD6ZWOkTbkuI5fr+JIfb4YpTM6Pp3Pe5Pfes4vr6BcEmfMOCajcez0Jogxq5Mbt5ZQb5NgSEEk+Lc+dt3IZjVc8KCLdVl7tjG99+DvlSn3+b7K5lbswqa2thRB+OQU9o766tagfXL2stx+X2THL9tq2Y55Krc7nNHklz/ZgdiEfOTOWY7nYFrqysliwI+MVmjns+40g3XyyZwYJkyfE8luLauns1cTqPh8QMtk9IcO2yZxvPqb3A93V2ufN3G8XnnPEa1yn2MXeevoP52oq38Z/+2Xmu3MQ47riz03ichXzTc3IKz0NnXMR12vCya9+mCTzvZIOv1c9pPVkArmgLC9i1zHaiiuGASFqH1jxvZg5352k2nef1IJVlbtyNzOD7NjaB54fuGe57tW9KoI/bc/sWfO76sVGY56iWbfPJ3vKFuy9yOnOblZWwff4Id18ULed+i0/n+lYvZ5FF4+DGXqVmGMahSjqAPd7CvBuAIcEMIpKOSCz6MQAai8h5AKCqJ6tqP1W9CpGt6+1FpKN32IWBc9Qoao3ZjzrNAHCsiLT3zl/zV/t4AL8Uicgki0j//b9MwzAMwzCMHxGqDf/vIMUW54ZhHKp8CiBWRBYh8oR7Zi15/g3gUVVdhYiK+90iQo8YVLUMkW3sH4nIlwD876z6J4C7RGQ6IgJv+0RVd3hlvS0iC+Hi1+9AZEv8IhFZ4tmGYRiGYRiGEcW2tRuGcUiiquUATq3FNcKX5wpfehOAWhXWVPVTRGLPg5/PANDF99FfvM/HARjny3eGL/0JgE8C5ZQCuLbuqzEMwzAMwzhMMEG4OrHFuWEYxkFI0p//Ek332nA7+cL5HHcpsW4TVOMYjuFOa8Hv7w2luvjOS+ayr0knjj+Nb+fKypvGW8CaJXP8ZijGfdFu38BxgNnNuNxeVS7+LdSI69Dh1SvIrnz2qWg6cQC/23fPeH5Pew44BvKIxwY6I4Y3iklr91vMyQDWX/B41M648zLK2+rK16Ppb4begKY9XAxn2tPPUt6do650xqYCZP77hqiZUDiW8gbf0Fu908UxDkvj95Pn++JRN78F7Nrr7P5PnUB5C+59l+zUk9tG0yse4DjWo9M4RjNlkIuJ7pTHMdGhXRlkF+9w8b7DYwrIt/VLjgVOTHbxqCWLuZ8qhcuN9Y2lzq9dSr7yRx4nO+E3N0bTuovfV986+B7dcnftFa9/wOXccAPZfc94NZrOfYPfE93kRB6HsbPdeZISOe7WH8cKAJ8/7eJny4S1JM68ld//3v3eddF0+wr+c61oBV9bou/QbvceQT753Vyy2993dDS97qZp5FseeKX7iBR33+ff8Rb5UgeyPkT+dDcnfLSdY1zHvHse2bphWTQdm8TlbJv7Jdl5xW6sDx83jHzrr/6a7LhEFyvcuITfw93y36eQXXmV22zV+A8nka//b1k7NPH4ntF096Kl5EsZ1ZvsrY+sjqYHPs/nRDzPz7o0+uZOZPQbin3R+KJ3oulQd27fox/M5nLzXKx4zJDzub4xPCYbn/tiNN0+OHZakcYqtMynB/HZx+SLadeG7PYPO1uasM9/ziApv2dtg5TdHP+NbKcHkH4Xz78p5w4gu2qe01wNV7O2QZN/XcDlrl/l6ttzEPsqec7SXS5+XVp14bwhvle7X+arfzHPkzHteOxkrvEJWpSyVkCHrnxtqHAx6JmrFpBLuvQju8c59ubWQwnb1m4YhmH86KCFeT34F+b14V+Y14d/YV4f/oV5ffgX5vXhX5gbhmEYhnFwY0/ODcMwDMMwDMMwjB8G29ZeJ/bk3DAMwzAMwzAMwzAaGFucG4cFInK7iIz8H59jjIi02I9842pe6fU/qMNPROQP9eQZIyIPf8/ztBCRN79PGd/j3LeJyE315PmftLGIZIjILw50uYZhGIZhGIcNGm74fwcpogfxe94M40AgIiFVrf4BzjMZwE2qOqeefOMAfKiqDbW4HQNgoKruf0DsQYSI3AagSFXv20eecfgv21hEYlW1qg5fO6/cXt+hvP9q/O0+69jo5FywgVWaqir4d9XGPV288tVfsxjbnwPltunnxK2end+afD//LQvN7Xh+TTS9dAsLDgVjooae70Tf1r3PwjuNm3Oc9sWbnCDRx7exIM6Sv64ju+NgJ1gW36UxnzTM318LX3DltmjKIl5pLVjQZ9sK105N2rIoXVkBX93bO5tF05cPY6G20k1cpex3n46md/zkSvKl9owjO3aQE5mSOPYtuuUbsrud6K6nbAMPp/IiFnhq1Nn90RGsX/FuHkufFLIo2XBxbbG5gmPbZyZyv/68qRM6mr+hKfnC4Lwn/sGJc5VPX0m+6iLux9gsdz3zPudxVxZ4ptAh3Qks7S5MIl9+mNt0UB8n4rR7E19bdkcWOPz7Uie4dcd5peQr+IpFnRoNcdc27g2+/8aM5rxvvub8uwIvZzymmu+Tps3dsTO2NCPfkOa5ZCemOyG6Jcs475tJ3L6XVbjpred5fF/kfs6Cdk1HujbcNZV1EVKb87FJx7SLpj//D5czrC/fN5XFrh/ThmWS7+7XWDTtD+e5dpnwIs9RHyZyHR48x9ljWL8ON1Xyfd2p765oes1Cnlsy03k8xMW7ey7rCG7PFeO5z1u2zo+mv1nP5WYlsSBji66uj9cs5rwde+8i+7oVTojw91U8eIqr+NrKfG/+HH4p99vaN/jr7XZ19+rZlXwtHcHt0L6Lm48TW3MdCldxu4jvVl29lq/t4cS6NTYeCAiI7tjGwn6lvn6cF8v3/BmN+b4oK3Hjt8WxfN0rP+axlJbi6lRWzu3Zqht/n+za4OaPoAZIq5b5ZK/e5OawnCRuz4RErpOGXV/EJ7BvfV4G2W1zXJ0S0/h+S27BC8/b5/Kc8O/1r/IE3QCUPvXbBl+AJl11f4O3Q23Yk3OjwRCRi0XkaxFZICJPiMiRIrJIRBJFJEVElopILxEZISJTReQdEVkmIo+LRKZ9ETlJRGaIyDwReUNEUr3P14vIrd57q0f7n6R6vju94+aIyBEiMl5E1ojIdb763Swis706/c37rJ2ILBeRJ736TRCRJK/sgQBe8q4nyTv/bBFZIiJjRWSfk4CINPeuc4F3zNHe5xeKyGLvs3t8+U/xrnuhiEz0Pos+FReRM0VklojMF5HPRaRp7WeOlhfy2mmJd77feJ938o5f6J2vo9cOS3zH3etrq2u9z0eIyGQReVNEVojISzVtICKDROQrr8yvRSStrnLqqfPVXv6FIvKWiPi/JUeKyDQRWSUiZ3j5E0XkWe/65ovIcb52e0NEPgAwQURSRWSid72LReQsr8y7AXT0+uheiXCvr83O9137JBF5GcBiEblDRH7lq/c/RORGGIZhGIZhGIaHCcIZDYKIdAdwPoBhqlopIo8C6ArgfQB/B5AE4EVVXSIiIwAMBtADwAYAnwI4RyJPqv8MYKSqFovI7wH8FkDNe6fKVHW4d77A+0ywSVWHisi/EXlf9TAAiQCWAnhcRE4C0Nk7rwB4X0SOAbDR+/xCVb1aRF4HcK6qvigiN8D35FxEHlbV2730CwDOAMDv72F+BmC8qv5DREIAkiWyTf4eAAMA7EFk4TgKwHQATwI4RlXXiUhWLeV9CWCIqqqIXAXgdwD+bx/n7wegZc1TYZHo+41eAnC3qr4jIomI/KjXxHfclQD2quogEUkAMF1EJni+/gB6Atjq1XmYiHwN4DUA56vqbBFpBKC0rnJUlR+lMm+r6pNeff/ulfGQ52sH4FgAHQFMEpFOAK4HAFXtLSLdEGnPmvegDAXQR1V3i0gsgLNVtUBEsgHMFJH3AfwBQC9V7eed81yv3foCyAYwW0SmeuUN9vKuk8gT97cB/EciPyxd4PkNwzAMwzAOKzTc4A/OD1pscW40FCcgsuCc7T1MTQKQh8jCejaAMgD+J4tfq+paABCRVwAM9/L0QGQRBwDxAGb4jnltH+d/3/t/MYBUVS0EUCgiZd6i9CTvX81LJ1MRWZRvBLBOVRd4n89FZBFYG8eJyO8AJAPIQmThv6/F+WwAz4hIHIB3VXWBiBwPYLKq7vCu/SUAxwCoBjC1ZuGqqrtrKa8VgNdEpDkibbOvRS4ArAXQQUQeAvARIgvXNEQW7O945ynz6uE/7iQAfcTFeKcj0lYViPTbZu+YBYi01V4A21R1tldmgeevq5x91buXtyjPQKSPxvt8r6tqGMBqEVkLoBsi4+Yh77wrRGQDgJrF+We+dhQAd3o/yIQBtARQ286D4QBe8bat54rIFACDABR4117TP+tFZJeI9PfKma+qu4KFicg1AK4BgPv7dMZl7ZoHsxiGYRiGYRg/UmxxbjQUAuA5Vf0jfSjSDJFFVhwiT7Jrgs2CP7GpV8ZnqnphHefY1wuJa4KLwr50jR3rlX2Xqj4RqF+7QP5qRH5YQCBfIoBHEYnt3iSROOnEQJ4jAdSUf6uq1jydPx3ACyJyLyKLvNoQfLtNgjwE4H6v3BEAbgucP4TIjwsA8L6q3ioifQGcjMgT5p8C+HU956ipyy9VdTx9GDlnsK1q2ra2utdVzj8QaRPUPLH2MQ7AKFVdKJFY+hE+X11jpi784+UiADkABng7O9Yj0H++Ou9PeQDwFIAxAJoBeKa2A1R1LICxAFA245Vo/Xde8Rzlazk6gw+MdbF/KbM5prFJC/7dRuJdldtXcFxa+TSOBQ7FucindOH4t22SQPaOSS7Ws2l7jieMTeXq6iZXbvUyjq3udWcPssPfrHXpvRyvVziHm7hSXWxfk5/yDxsSy/GRXe6/NJredO7d5Gt5C29qaPw79/tQbEuOycz87aVkB+PMc953MehF115BvpIX55Md64sNDsVwHO7yz9LJToxz/dHlj+3JV/jagmg6Jg5I7u1iNue/xp3RFdyv7Y5xcYwZq7i9l+Tz71PbNrs6VQZuheOP20b21rHut6isrnxrTp/fkuwOSS7ufejd7ci358nZZGf+/tRouk0un1N38O9fEtshmg7NXEu+5D+yBMfxP3k7mq7awtNwWj+eCt591Y2J1oHXA1VtYT2DviHXTiuquC96XMPl5r7lxvf2OG7f6kqOSkzq5O7HIwfwHLD9ZY7Z7fMrF2ub+/Jm8n21l/v4tJVOtCCjO58zlM5ffWVfrY+m94RYzyL1b6yjqRtWOKOQ43lX6hqy17/t4r1Pvobb9+3n2M6d4OaeFOH27fN71lf47C7X3if+H7f95PtYm+G454dH05t+/SH5up/OMdKbv3DtMuhGbiPJ4PbVAjc++vyyL/u2smBEyp0bo+keY7gvwrt4nJVvcDoJoWPPIl/ngZw35feLoulR17CeRejk0Vynwj3RdOUb75Iv81SOK5dUN86OzGHtiGf/uhp1kfPMLWRnPP8I1+mEE6Lpbo/xM5jEEd25vnvdtRZ9wboHvW7vwyf2C4R1Cfg28ZhMKXX3Zpv23ThvHI+d7K+/dEY1x6fHjOS+CX890RnCfdw8kzdIxnQf4o5bw98lKGOdjJO/5O/2gwJ7lVqdWMy50VBMBHCeiDQBABHJEpG2iCxM/oLIVup7fPkHi0h7b0vw+Yhs2Z6JyDbpTl4Zyb4tyt+X8QCuEBfD3rKmrvugEEDNX0E13/Q7vTK+pRyuqrNUtZ/3733v+vO8bdpPAzgCwCwAx4pItreYvhDAFER2CBwrIu29+tW2rT0dQM230WW1nL/ad/5bve3bMar6FiJ9cIT3VHuzt5UeIpIgHNdd01Y/9574Q0S6iEgK6mYFgBYiMsjLn+ZtI6+1HFW9paaetZSVBmCbd8xFAd9oEYkRkY4AOgBYCWBqTT5vrLTxPg+SjkhfVEokLr2t97m/j+GVd75E4uVzENnV8HUd1/0OgFMQebI+vo48xo8I/8K8PmKT6vutzeFfmNeHf2FuGIZhGMbBjT05NxoEVV0mIn9GZOt0DIBKAO8BqFLVl72F6Ffetu4wIovRuwH0RmRB9I6qhr2npa94McpAJAZ91QGo3wQvLn6Gt4W7CMDFiDz9rYtxiMSrlyISv/wkItvm1yOyZb0+RgC4WUQqvfNdqqrbROSPACYh8pT2Y1V9D4hugX7ba788ACcGyrsNwBsisgWRHzLaY9+0BPCsVx4A1OxquATAEyJyOyL9NBqRPqnhKUS2q8+TSGPtADCqrpOoaoVEhNMeEpEkROLNR37Xcjz+gsgPGBsQaWv/SmQlIj9kNAVwnaqWSUTb4HERWQygCsAYVS2Xb2v1vQTgAxGZA2ABIj8oQFV3ich0iYjhfYJIHP9QAAsReTL/O1Xd7sWz13bdkwDk/xBvDzAMwzAMwzAOLWxxbjQYqvoa6ogL9xYvRwLR7dElqnp+Lfm+QORJZPDzdgF7TG0+VR2HyKK6Nt9/APynlur18uW5z5d+C4D/5S1/xrffZEV1CXz+HIDnavn8ZQAv1/L5J4gsEP2fjYN3Pd4i/r1ajovmCXy+EJGn9cHPVwM4vpYq9/L8YQB/8v75mez9qynnBl96NoAh+Da1lROsz22+9GMAHqslz5g6ji1DZGt58PNx4HGwE5FFd21l/Czw0c3eP3+eyfBdOwB4P3oMQeTHDcMwDMMwjMOTg/g94w2NbWs3DMP4HyMiPQB8A2Ci92OHYRiGYRiGYRCialL2hmEYBxt7LzmhzslZq/gX59hWGdH0Ga+w2M8LzbmYxue76IZNT7FATutrWvF58l1Z5QtZbCu2GQsoSZITwVn2ehz5epzDAlW/+9AJNd3/yFHkK7z/HbJThjlhN2nEAk9VyzeQXbrWidIlNOPfnuO6NiO74PPt0XTaMBZf04DwXPUeJzJVuZvbPqFDQCfQ93qY6t0V5Ep9gnUAy++9KZoOjRjB5WxZT2bpR3Oj6bJcvraUznX/zl62gSMoNn2TweVW8Qa6vme5Pi9YUEm+7VtYDK/HTU7cKrxtB/l2judxmPMTJxZVOovH0vbVHBfftCMf6ye+Bde3YJlLJ2VzLP6Ob1j6IinFXU9aS+6b2CY8Zt+b4MbLORdyfbSC2zS814kvzf2URbGO/DULgr3xsDt2ZRyPpT8cl0d2XM920fSc+1g0rf95LIao5e7awyXcDismZZDdZZATyovvwUJdy5/nOvX6d/9oOv/BSeSrKudxlzHUXeuyN1kUq+tx+WSHMt19E+rZkXwf3c4ilsf2cKJ1hbl8v83bxfU//fdujjjtPhbxei6gGlOY78pKSWMRy4wuPPYL1rhxl9qcfaEMFpvUMteGm+by2G57NM8t5VtdX1UH2jO5M5d71mcu/UZ3rkMMNzeN51AzFpMsms5CiResddd2d5jv8ewMHmfNznLtq4UsOla+mvMmdHdlbf2Ux+S1RVx/P+8cyXm1ir/DYrPdxT4/gQX2TknjeSi7r7vPg2KeVZtY6DF/pWuHUODezDye27B6u7sf/XMQAGQey99T+dPc/FFVzn2alMnzkF/YURJ4rtv9FedNa+3aMJTF85cGxF4//4IFUs/b9tK+xGx/EEoe+2WDL0CTf/5Qg7dDbdi2dsMwDMMwDMMwDOOHwd5zXie2rd0wDMMwDMMwDMMwGhh7cm4YhmEYhmEYhmH8MNh7zuvEFueGcRjjvR5tqqp+LiLrAQz0lMpr/O0AfKiqveooIljedYgo6z+/n/l/DWCsqpbUlzdwXJGqptafs9ZjxwCYoKpb/5vjaynvJwB6qOrdB6K8KLEuFCq2bQ65Nr+aT3azRBfTVhbmeL29u7iZsjNc3F1i8nrySavWXIckF7+X+wrH5yEQZ9esj6tDcTUHd0oax92mwMXd6aZ15IvP5g1dlWtcHG7Cef3IF7Od4wtjk11M3q7FHITZrDuXuzfP1Sl+GcdgIvA3w9Zlrs0ysnmoJnBWxA7qGU2XvDiffHG+GHMASLg5+rIHVDx+K/mKJ20iu7yAv6737Eh2x5ZwTH96RxeLGM8h0OjSkeNCN07iKwg1z4qmk/P4Ftm7nvOWT1/p6sBdgZhYjq2szt0TTSd05TjcL5dlkH12lRtLues4TjQ+EEOf7buegs1cv6oqrkNGT9dOsU05hrR8Jcd0l/iGS9UWHvsSX/emw0Lhfir+fD3ZleLusVxwDKmW8bVtftrFWvcfxeepWMfxvmFfUXGBeygkvH10yyLXpq1ieey/F9eC7I4vTXTnqOb2TMwIxAYXu7jt7YHY5Y47A2+PrHL1j01mbYDNcXvI3rPFjfVwNYeItg8FYu+LnD84F1ZX8pzwZWVGNH2K5HI5gThnfwxy8oXDybfylsVkN+/gxlJwvvDHmAPA1m/cOGzWNjDHBupQFnZtKIEhWLCZry25xA2ItH78/VFdye3rb6eCai4nsZjrW7XW6QGEmgc0QEq5b8KL3fWUl/F4KAvz+PVTuIHHWaOOPCHnL3R2cBGT1oS1AyTR5ShfmU++ij3ciNVVzi4q4LkkI6hDUuDaZVcezyWpG3guqa50dagIxJzHFPIVJPr0LMLfisvnYyv2+P5GqAjoIKRyX2SF647xNw4+bHFuGIcxqnpr/bm+U3mPf8dDfg3gRQDfaXH+PRkDYAmAA7I4V9X3Abx/IMoyjP3BvzA3DMMwDOPHg8WcG8ZhgIi0E5HlIvKkiCwVkQkikiQi40TkvEDeJBH5VESuDnzeQUTmi8ggEeno5ZkrItNEpJuX5zYR4ceDkc9TROQjEVkoIktE5HwRuRFACwCTRGSSl6/Id8x5IjLOS7cXkRkiMltE7giUfbP3+SIR+Vs913segIEAXhKRBSKSFChrhIh86LMf9p60Q0TWi8jfRGSeiCz2XfMYEXnYS7cVkYleXSaKSBvv83Ei8qCIfCUia4NtbhiGYRiGcdgQDjf8v4MUW5wbxuFDZwCPqGpPAPkAzq0lTyqADwC8rKpP1nwoIl0BvAXgclWdDWAsgF+q6gAANwF4tJ5znwJgq6r29bbIf6qqDyLy9Po4VT2unuP/A+AxVR0EIPoOLBE5ybuuwQD6ARggIsfUdb2q+iaAOQAuUtV+qlr33rra2amqRwB4DJHrDvIwgOdVtQ+AlwA86PM1BzAcwBkAat0CLyLXiMgcEZkzbuWW2rIYhmEYhmEYP1JscW4Yhw/rVHWBl54LoF0ted4D8GwgZjzH+/xiVV0gIqkAjgLwhogsAPAEIgvPfbEYwEgRuUdEjlbVvfXkDzIMwCte+gXf5yd5/+YDmAegGyKLcmD/rve78nY95Q0F8LKvnv7gxHdVNayqywA0/daRAFR1rKoOVNWBY7q2PADVNQzDMAzDOMhQbfh/BykWc24Yhw9+pZRqAEm15JkO4FQReVk1OnPtBbAJkQXyUkR+1MtX1X51nUhEWiPyBB4AHlfVx0VkAIDTANwlIhNU9fZaDvXPlon78EVPBeAuVX0icP522I/rFZEjEflxAQBuBbAb/KNlsA41ZVZj/+ZPf5399ZFgxiBVeU7AJZTJoketLg6ofJU78Z9WcQFxsMZ8bNWiVdF0WtuASExaBpnVU2dG0026cTkJvbLJlhgn6tRkPksISBIL5mzQaPQCYvodRb6S5xeRnfnb46Np3byR6xvHXVC0LS6azjmSt6yFd/LvQXHxTngn5dYbyVf59JNkt27k6luwOiAitJMFiOLiXB1ikwKiUiNGkO0XgYu/jm8HLb6Z7LQuHaPpNX9gEb32o8hE6WI3HuKbcH2/mcDx6o1zuF+LpjgxtpjACG+awnkTTxkYTcctXUW+vHf42nN+eqbz3fw6+S75BQsdSVK3aLp03Fry+cXuAGDvGidglZDK4lVFhSzqVLDSJ6C0rpB86cexiGGPua5fQ01Z+EoC405S3DTRpykr46VdOIDsktudwGHfcHB6YVrf4o5dc+tC8jVpz3lTjm8bTee+up18s2JYcO2kxKjmJ+KPaEO+63NZiDD59t9H0wWX/Zt8Sc1Y5C1uaO9ouv3E1exrEUe2xLi+0O0sxpYR0I5rc1P3aHrhrevJt1l5Wu/hu/9axbEIWWUljx3/uNv5Bo/XhL4sjFe0yfVb+SczyNeqB1c4eUSHaHrboyy417o5z0OdfubqW8zFfuvxmf96tq3i+rboFRAt9B9bzfWTgECgv9yUgIieKn9VxQ9y85CW8MYzieF7yj8mY9dzOwT7xk9yTkBQLaBIk5Tp5nbhoY787TweGlW578PEpnwtEsPtkNTIzZux8YFBGM/3amW+S8eG+LsmNiCUV7bYldUom7+fqyu5TqEWmdF0eHdRIG9AILDAjZ3GPXgORQyXW2HPYg8prLcMw/BzK4Bd4G3qFQBGAbhURH6mqgUA1onIaACQCH39hajqJm/beD9vYd4CERX3FwHcB+AIL2shAL90c66IdBeRGABn+z6fDuACL32R7/PxAK7wnuZDRFqKCP+V/W2i51TVWb56vg9gA4AeIpIgIukATqinrCBfBer55Xc83jAMwzAMwzhMsSfnhmEE+TWAZ0Tkn/AW6apaLCJnAPhMRIoRWXg+JiJ/BhAH4FUAC+soDwB6A7hXRMIAKgH83Pt8LIBPRGSbF3f+BwAfIvKkfgkiMfAA8CsAL4vIrxCJfYdXrwki0h3ADBEBgCIAFyPyZLsuxgF4XERKAQz1x52r6iYReR3AIgCrEdku/124EZG2uxnADgCXf8fjDcMwDMMwftwcxIJsDY0tzg3jMEBV1wPo9f/snXd4HcX1/t+jXm25yL3IveJuwAUwGAi9g6kBkkAIoYQE+CVfmkmAQCC0QKgBQ+i9F1NcwMYd915k3GXZKpasfs/vj13d2Xd9JRlikIHzeR4/ntmZnT5zNbtz3g3474kRJyfgDW4q+/vhhQCGB64fEyON8XXk/zG8t9zh6/8C8K+A/zUAr8WItw6ePXctdwbCHoAnGBcmZn1V9XUENvgx8roewPUxrucE3HMAjPHdE+Bt+Gvb+YgY914U8n+nb7QbhmEYhmEYP11sc24YhrEfUpbvbMgK1rFdc2VFAfnbH+rC86pDtpNN2IYwSOlG/gnI/+MHnO75zkJg90wuQ1Jntjec+aqzTliYnEVhRz7NNoS3xLsyVr3wXwqLT+LyV747KeqWBLbEWvsp2xfOrGkTdY/bzWr3iR3ZRn5TvmuX5CseRn00/2W/qHvOpJ0UNiqT81l5w+qoOz6uGYX125RL/tJJzr43bGOe/Ke7yV/yu19F3WsSO1BYl6k8HnYVBixF1gEtOzjbxTXV/FyoU+tC8sdnOFvFkly2Y8zuxDaQxc/NjrrTDsiksGnVWeSXS96JumdXtKGw1Y+xLfDgCtc3Q1qy7WTyIb3IX7HE6RCktahEfRTtcPb21TUh7YCJ3K+d2rjwdW+xvWl2h0K+N2A3WrqLbfrX3MG2+MvjXHihcnkvCo3v4ienBXzcb4lZ3C4rHnVzrPMAnqsnp2wh//atLq2ST1jH4c8bWc/ioUfcByc2b2M74coK1iAonbsm6q6u4bVl2wz2p6S7Pm/Rhft/a+gv0zduDtjpp3Cb/eJoNkiuXuvWj7xqXvtE2O79jsC4OyeO+3j7k7y+batya2G7PM6zZWueFxsedmMpbLOd0JwrN+lpp5nQpzlrJJTu4LUwr9q19/Zq7qfsIrb3zhjRPOre/TGPwZICXjeD6daAy5Bfwe3SZXagj4v5zeeyZWxRNrjL5qhb4pIoLJhnmNULuG49BrO9ejCpjNDL14QEvrB+rVuD+/bjPKu+4fFRtMO1S0k5l7dZGfcxAv1atJvbaPfCfPInJbtxt2Mz6z/Ehez/M3Ld2KnK57pUlPO9ZeUu3bg5XL7dxdyPK5J53O3xZqUxiOy/gmyNjdmcG4ZhGMZPlODG3DAMwzCM/RvbnBuGYRiGYRiGYRhGI2PH2g3DMAzDMAzDMIwfBjVBuLqwN+eG8TNGRJ4Ukb7fY/qXicgvffdF/ifV9kW6g0TkuHrCc0WkZV3he5H+RSLy0LeIP1lEhjUc0zAMwzAMwzBiY2/ODeNnjKr+Zl+kIyLxqrrH58tU9dGA9yJ4n0fbHI73HRgEYBiADxqI960Rkf1iXdy6yYkv5fRjwa/qMhYZ0kr3nHVQYjaF5a1jEZySZU7oplVIQKv9GSz4tPIxJzLVoReL52z7nJtpyKHbou74qVyGDqNZrGjLfCdso6VchtIdLMSTPsIJjZXOYKGdnNE85Jotc0JNCZ1YjG3LKyz4FQ+XbvNze1LY7o+WkX/13euj7hrhNircwgJgvY9ybbbsk6YUVvb+XPJXFLs2zOzZjcKCAnAAkPHIU1H32mE3cljbkKDWVlfGzBLup/C7iu2rWWSoZY4bL19uYuG2VjU8Bvr1cn0eFuvLi2Oxn27jD4i6P75xA4XdcA23KcqdoNnSf3H7pr2xgvyFxU74KiwGVVbJAmBZTZzI144CFljr0p3vnfa5E7dqncjiYMktOG5VsXN/WZVFYecevY38+Z86kb2dNZzujvkswNdypOu70pU8Lz74ikUBR7fZGnXnreQ+jQ+1S3GlS6vXEe0p7J9fbSS/NHXiXEviWUjsiHjWM+g61o2dVz7hsXNsExaFLN/l+qbgXf5JyIjwM9xTr3f1+exOnvPNF24lf3G+K+OgxCwKW89LAG74PzfuVt7Ldenze567lQ+68GbNWRAuUsPrcY/zXL999RTPv5pPeZ4ceuz2qPubydy+7foXk3/QLtem25TT7Z/I823ru64vWh/K6Zav4HkxKNHVNb6G27cGXDdJceXfvYbr0qEFlxcBcdKS3SxQNiiRBSSDtEwrJP+mJbw+pKW534xKLt4eY32luPVj9bu8loxskUf+/FLXTs1Tyyls65dc1w07W0fdBXHcF63zuK67SpxgXEI8l29XSHiubIMbW+EmKijhfiyJBPqRlxl0HlBI/uSFzbHfYYJwdWJvzg1jHyAi6SLyvogsEJHFIjLOf3t7l4jM8v919+N2FpHPRGSh/3+nBq5PEJEHRWS6iKwVkTPqKMOEYJiIlPj/j/Hf7L4mIstF5HnxPwpe+8ZXRH7nf9e89t6LRORfvvt8v/zzReQxEYmvTV9E/ioiMwGMEJE7RWSpX/57/DjjReRav1zDADzvp3O8iLwZyO8oEXkjRp0O9Ov9tf9/LxFJAvBXAOP8tMbV0S3XxWj7bBF5XURm+/9GBcr5uIhMBPBsqAzHi8hXItJSRI723fNE5FUR2eOTaLHiiMjYvamvYRiGYRiG8fPFNueGsW84BsBmVR2oqv0BfORfL1bVAwE8BOB+/9pDAJ5V1QEAngfwYAPXAaAtgNEATkDgG9/fgsEA/gCgL4CuAEaFwl8DcFrAPw7AyyLSx3ePUtVBAGoAnOfHSQewWFUPArAUwKkA+vnlvy2YuP/98jkAzvPT+QBAHxGpfcV6MYCnY5R7OYBDVXUwgJsB3KGqlb77ZVUdpKov11HnWG3/AID7VHU4gNMBPBmIPxTAyap6bu0FETkVwJ8B1B6hvxHAkao6xK/PH4MZ+kfpY8X5fG/qKyKXisgcEZnzRmluHdUyDMMwDMMwforsF8c3DeMnwCIA94jIXQDeU9Uv/JfTL/rhLwK4z3ePgNsI/xfAPxq4DgBvqWoEwFIRaY1vzyxV3QgAIjIfQA6AL2sDVXW7/1b+YACrAPQCMA3A7+FtWmf79UkFUHsWrAbA6767GEA5gCdF5H0A79VXGFVVEfkvgPNF5Gl4df9ljKhNATwjIj0AKIDEGHHqIlbbHwmgr18XAGgiIrWHx95R1eA508Phve0/WlWLReQEeA83pvn3JwH4KpTnwbHi7G19VfVxAI8DwJwOp9iZL8MwDMMwfnJoxATh6sI254axD1DVlSIyFN4b1r/7x6MBb0OJGG58y+sVAXftkfTbARzv5z8IQDX80zD+sfWkOu6vQey5/zKAs+C9rX7T31AKgGdU9S8x4pfX2pmrarWIHAhgLICzAVwB4Ig66lXL0wDehbepf9VP4/cALvHDjwPwNwCTVPVUEckBMDlWQiLyMYDWAOYE7OhjtX0cgBGhTTj8jTQbZwNr4Z0y6AnvDbgA+ERVz6mnTvXF2aO+9aRDtmkvLe1IYV0qQ7bWq5wN3ty47RS2SNuSvyjeGel9WsxFOP8JHhZrktwQ6rOQ7d0WpnDcw6c5W7m5yWxzN2MO28e+VulsuO96j+1LVyXx85dmE1w3DmrKNoOTV7Ug/9gmru7PvcBxcyrZtq9NiivvP+5lm/gDy7nNpqS6Nrv5V2wjv+ApzidrvRtaKYncvuXb+LBawXZ375o/r6OwNYncZkE78/Fz6GAKnht4M/klYIe5bFsadgRMmau4GfBCBZfp9MBY2xFaJWpCz8aWrnNlTFnLcS/vx3blE693/Xh+n00UdvV9rcjfNrB0dVe2HPlwPdtTDw/YyG7NT6GwLYlctwMKnb9JEvfjlEnc5/9IcLbM46tYQ+GD2TwfO9S45XVlKvf51ulsRz7hYtfn9z7NdZtUwAa0Ke8693uJbBN9WybP812Fru7Ly9he+u0ktm3/U4or793/4QFxTiqP591LXPv2C9nIL87n+YcPnXN6CsftvZ5tXr+Jc+UdWs3G4AvjK8h/6/2uDIPiuE9Py+Wfzxsiru5z49kQ9zjwOLv6vh1R98AI2zW//BjrOJygrm8mb+fn4yGTc6Q8EwgLzaFM5XSf/dTZkTcLqbbkzubxEazPcTVcl8dW83rRJFDeUe/x2Pmmmvt4brwb69nJPA8OKudCvfmpC8+p5n4Kz6kPJzuj6Wahv3LmVtUtPbOrhI2t50XYP7DY1Wd+aI1tvYXHZM94t84vF6739h3cvqWBP4sKyznPCJugIxLn2rd7Ev9+rN3BeidrkwLaItxk6BXHffPROqcB0aqa235HAq8lh7Z242HtFs6zfB7r4X6VzPPxMhj7M3as3TD2AeKpkO9W1ecA3ANgiB80LvB/7VvW6fA2sIB3RPzLBq7HRFVv8I91D/Iv5cJ7yw0AJ+PbvWUGgDcAnALgHHgbdQD4DMAZItIKAESkuYh0Dt/o2143VdUP4B2fHxSOA2AX4FS4VHUzPHG4GwFM8K89XFsnP7wpgNq/5C+qJ61f+PcEBe5itf1EeA8Oassdq5y1rId3kuFZEekHYAaAUQH79TQR6Rm6p844seprGN83O+IbjmMYhmEYPygRbfx/+ym2OTeMfcMBAGb5R8ZvgLO5TvYF064GcI1/7SoAF4vIQgAX+GH1Xd9bngBwmIjMAnAQ9nwTXC+qWgDPdryzqs7yry2Ft5mc6JfrE3j272EyAbznx5kCV9cgEwA86ou41b6GfR7ABj+fWPwD3kmEaQCC24xJ8I6n1ycIV1fbD/NF65aigQfIqroC3oOSVwE0gfeA4EW/njMA9A7F395AnIbqaxiGYRiGYfxMsWPthrEPUNWPAXwcvOYflX5YVW8Nxc1FjCPf9Vy/KOTfQyHcv74Nns1zLX/xr09G4Di4ql4RcI8JpXFCjHRfhnuTHrMcqroFwIEx4owPuF+Hs1GvZTS8hwoxUdWv4B0rr+Um//pOAMPruS/Hd4bbPh/ujXrMcvr+CXBv87+GZ0cOAGti5RtsR1X9vJ6y1VtfwzAMwzAM4+eLbc4Nw2gURGQuvLf7f2rssvwQ/NzqaxiGYRiGERM1Qbi6sM25YXxPBN7eGjFQ1aENx/rp8G3r2/u1C6PujuMfpLDwb1p8hhOn6TubxasOa8fCOxoQCmqRyxYKhwzfSP6DCpzlU1EeC8Jl7WJ/j4OduFKTxayek5HFKjgL8pxoz0Gns1DNyGMOI3/1W04Vq3LDbgo7vZL9m1c4UaeLJ/6awiJLppEfiU4I6w+vfERBqbf8laMe8q+oe+0LXLfBT44l//ZbXHl7/qULhVV9tZj8lbtdWl1O4eJ1mVpA/oy2TkgqLAB3/gIu7+Ihzqqky6hiCvt4MgvwHR0SGerQm4XcgsxZzOPl4CxXxuJdLMb20SIWTTthrBuH8z5lQa2bOu4gf3KGE0JqeufvKKzoz4+Qv+m9ga8ZhsTCJIGFCSNrF0TdO//+AYX1vfds8mee8mbU3bUtC5YN78UiVFOmuHY5sownZ6sRPPbvfdqVMWxXeO6dLOex4fa5UXdVMQs85e/ku/ue4uZRt+HtKaziBl4Dej0yIOr+/UPvU9j2tXwwq+tYZx217nMW1BqcXUj+tGzXLnmh/h/+8bnkHzrVfdBDmnJc3LyavNf3c+VPv+lyCls17h3yD2rlxM36bmdxsMOeP5r8fa99PurOvooPfhU8OoP8yS0C/RpaY4f8ewj5t9wyOepu99RvKQzVLJo2SFw/6hpeH+IOGE3+d05yX/4ccXQ+hR12+nHk1w1OYFJ6j6Gw/s15DfjwZPdVz2ufPQr1IelZUXdkUuijLKEfpv6nO6uxSOFWCpty2n/rzKP3Lbxu9u7YlSOUu3X/ry/z+E0cwHOoaqETTmy/vIjCWt5yPPnLnng76k6//UYKQzxvl7TMicBpMfdFL74Th7V041trWBBQi/LI32fapKhbmvH4jRvC40F3bIm6s+fOorCEky4gf7uTnoHx48Fszg3DMAzDMAzDMAyjkbE354ZhGIZhGIZhGMYPw36slt7Y2JtzwzAMwzAMwzAMw2hkRNWeXBjGD4mI5AAYqaov7KP0xgC4NpbSej33TPbvmbMvyhBINwfAe6raX0SGAfilql7ll7FSVafvgzyyAJyrqv+uI3yCX4bXvmP6Of79/fcy/ngAJap6z3fJry62Hjomujhv35BJYUnJbPOalunsGG/Z3ozCroywjVvP3zib0tIpbGOe1Iqf1+ZOd/nOrmlCYUWhR7u/GbUp6g7eBwBdjmDb8IsDtqvPnsU2zwWT2UY6uYmra9owtsErmlpI/k++cbaUhzZnW74mHdj2d+sKV8adu9l+PjWe2/ejBFfeU+PZbjG7UwmXt62z6a/YyjaYKZ34sFqkxNlWVxXyb3HeOm7D0nJnI79M2PZ3YPwu8vefdx+ndeJvou6aKqGwV/PYjvzoQP2mVzWlsA/iCsn/uwo3lqqE0+3bku20i4qcTfrWSm7vVcncLr0qXPu3TGQb//wqtm1vFu/GfnIi99vWcm6nNiluHCYl1FBYeE49vrt51H1V620UtiCXbebzAnb76xK5H/94ENt7vzDd2YMXxHHcQyt4jPbs4+xl31zNdtlnDWFtgOWznE16UjzX7aUEbu8jyly+Bw7aQmHjV7Qh/59buzJs3cRrQJ+Lk8i/7a3CqHvqzlYUdlxIyyBonpzUnMfOGzM7kL9Llevj8tA7pRdT2Ib7gUFOv+DaBWwbfkYZj7PguBtWyeNsXiKPs0Pi3LrU849ssz3jDh7rGxNcuzSt4TWgRzqvH7vKnC5Cu/Yclt6Ox2SwPqeXJVJYn1as27Blu+ur3sO3U9iqubyOPp7k2vSicu6LZqncLq1yXDtoDcddv7o5+dMDfRNcvwDgsaS63w3enMXtsHMHz+OsLKevMDuf+7hPMv9+BJelzkfzWClbwfNt5XI3hzKS+HezTSdOt6LEjZ1dRaE1qRV/xXbjhqyoOzw3w5RUuXbK6cDjauf2dPJnNXPtsHsXt29FVUIoLv8Gd1nwCXdeI1A6/pxG34Cmj3+x0dshFvbm3DB+eHIAnNtQpMZGRP4nsxdVnaOqV/neMQBG/s+F8sgCcHlDkb4LIhLfcCzD+PEQ3JgbhmEYhrF/Y5tzw9hHiMj5IjJLROaLyGMicpCILBSRFBFJF5ElItIfwJ0ADvHjXSMi8SJyt4jM9uP/1k9vjIhMFpHXRGS5iDwv/sfTReQY/9qXAE4LlCFdRJ7y0/paRE72r6eKyEt++i8DSN2zBoCIXCQir4rIuwAm1pNejoh8ISLz/H97bLz98r/nv4m+DMA1fp0PEZF1IpLox2siIrm1/sD9GSLymZ/+otq8/fbr5qd1dx3dcaRfvpUicoKfXn3tPElEXgCwKFSGrn69h4tINxH5SETm+mn3jlHnPeKISObe1NcwDMMwDMP4eWOCcIaxDxCRPgDGARilqlUi8m94X9R4B8Bt8DbDz6nqYhH5MwLH0EXkUgBFqjpcRJIBTBORiX7SgwH0A7AZwDQAo0RkDoAnABwBYDWAlwNFuQHA56r6K//49ywR+RTAbwHsVtUBIjIAwLx6qjMCwABV3Skid9SRXh6Ao1S1XER6AHgRwLBYialqrog8isDRb/9Y/fEA3gJwNoDXVbUqdGs5gFNVtVhEWgKYISLvAPgzgP6qOqieOuQAOAxANwCTRKQ7gF+i7nY+0E9znf8wASLSC8BLAC5W1fki8hmAy1R1lYgcBODf8PogyOPhOKp6xF7Wt3YsXAoA/+jeAxe0bReOYhiGYRiG8ePGBOHqxDbnhrFvGAtgKIDZ/svtVHgb2L8CmA1vo3lVHfceDWCAiJzh+5sC6AGgEsAsVd0IACIyH96mswTAOlVd5V9/Dv6Gzk/rJBG51venAOgE4FAADwKAqi4UkYX11OUTVa01dqorvc0AHhKRQQBqAPSsJ71YPAngenib1YsBXBIjjgC4Q0QOBRAB0B5A6xjxYvGKqkYArBKRtQB6o+F2Xhe4PxvA2wBOV9UlIpIB71j+q+KM2Ogjyg3E2Zv6QlUfh7fBJ5tzwzAMwzAM46ePbc4NY98gAJ5R1b/QRZE2ADIAJMLb2JbWce+Vqvpx6N4xAIKKJTVwc7aujZvA21CuCKUV8x4RORXALb631jg1WMa60hsPYBuAgfDMY1g1pgFUdZp/NP4wAPH+iYKDADzmR7kZQHN4m+Sh/mmEXHhtGK7D7fDeSiPwNj1cV0X97RzulyIAGwCMArDEr2NhA2/r64wTq771pAMAyBgVEO2ZxmI/aX3ouQDiMp0wV+WrLEDU/SR+QV/xtROBK9zM1g3x27jZup/rTt63ncGCTqX5XIbCZe7nJChUAwAFX7PmyrZqJ6JWlcvlbXYEiwoFqVzJ7ZASitp+ratrVlcW+0keyiJTbRNcO2Ru4uHb4igWvur4lRO7q6lia7CMX3Qmf/H737iwISzqtnshi8clBbo4LMbXsorjZgYEiL7MZ4GkLqNYrChoZ97q3ScpbMWB/IzwN4ezIFhQpG5k6HzNcS25X6vKncDS7lIWJFq+nTtnWB+Xj6zldIuqM8jfpZkThGoznEWcendgWYhIvmunuExeHjqt47rFN0kIxOXxK+kstnT1VCcCJ8Lz4rALWFwp9w3XZif1Y3G+whXcLnkBEbh+XDUMOpvH7FcvOrG+kakskqWsFYZuvdzcSOnIbTT8U65bzxb5UXfZTv4zsEuErW3SO7n52TGtgMLiuh5E/ibtZkbd2XksfJU2kMUF4/u557mR3G8orHA2eZGd5sZdy/Y8L3LXtyd/VbFrh0rltWX0SSGRwvedmNiQJ9kyq8PNH3AZftkt6t7yxDoK65nDa2y3wBrRpBt3VGJXnhdVa12ZElrzvI7wUKL6jDiYhQbjs7gfW/dwc0GSu1FYv448Lyrfd78DfQ7gNTasPJM6ygkTVq/gdHp34TGa0M2JC1aF4lZ+mYW6yOrHYycrwutbZLdrh8OabaKw5NDj+9JvXF/sCr2SyBzAa0D/bDcvtJrnfPIBLJxZuWxr1N0U3P9JA/m3psk6t5ZEyrluEhLGi2/l5q404bHdbBML+0VKXTs0KeZ5kdiZf8OeeZ0b5grsB4Tmp+Ewm3PD2Dd8BuAMEWkFACLSXEQ6w3sLehOA5wHc5cfdBSD4V/vHAH4XsEnuKSL81xSzHEAXEan9xT0nlNaVIlHb9MH+9akAzvOv9QcwAABU9U1VHeT/i6XcXld6TQFs8d9OXwCgISG1cJ0B4Fl4x+Gf9ssyM1CWd/w88vyN+eEAOsdKS1VvqL0vkPaZIhLnt1FXACvw7dq5EsApAH4pIueqajGAdSJypn+viMjA4A17EYfqaxiGYRiGYRhBbHNuGPsAVV0K4EZ4ImoLAXwC4EIA1f4n0+4EMFxEjgCwEEC1iCwQkWvgHXleCmCeiCyG9/a4zlMtqloO7xj7++IJwq0PBP8N3lv6hX5af/OvPwIgwy/b9QBm7WXV6krv3wAuFJEZ8I60xzoREORdAKeKLwjnX3seQDN4G9ZYPA9gmG9jfx68hxJQ1R3w7MUXS92CcCsATAHwITwb8HJ8+3YuBXACPCG7k/0y/FpEFsB7m35yjNvqi9NQfQ3DMAzDMIyfMXas3TD2Ear6MlicLRhWAyB4BnBsKMr/+f+CTPb/1aZxRcD9ETw76nA+ZfDE32JdP7u+8vvxJgCYsBfprYL/9t3nL/71XAD9fXe0/Kq6MhQfAEYDeE1VC+soSz48cbpYYXV+ik5VL6rjegR7187BOhQCGB6Ie0yMdMcH3OtixfGpt76GYRiGYRg/C0wQrk5E1RrHMIwfFhH5F4BjARznb9x/0nyX+s7pcEp0ce57cxcKe3M8254d0s7Z853Ppn34rbKt3PCmzq5u1c5mFNYumW1pMzOcLfaOQrYA2FHDtrSjT3H2qNV5bMNdVchlOmOley48oRk/Iy4t4XTLKp0NbHWED3t17sw2pK9ucer22SGb3FN/zQa+/53g8mlfxXaAmSGD3gXJzjZxeBXbXZdFuPydWxRG3St3cPu2SuR26fkLd+Bk9US2N10TssMOWuetS2Ib/i6V/Ds+ov1W8hfvdDalvWY9SGEbx+7x7C3KezvZTnF9PLfTuCpXn9wIl79CuIwHZ7kxW1DEcWfE8dj65ZGu/BM/4jKUxXG6zWpcmWrAYSVxPF6O6uV0Eyp2cb+t2NCS/FNSnaXOlV3YrvWblWw3vEJd+XeEXnl0reQ2+2IP1QzHWdU8tpaqGwOn/4rHb+n0beR/baWzBT62OYfdXcTjsL26OfXb3hsp7K8r2pD/V+rKtL2cNSqyErhM/f7g8nn7Prafb1HD7dCzbcA2vIKtohKTOK5GXL9O39GKwmYksb3vHWPdmnDCR2zT+scaHksbEt34ODjCh7/yqrmjguN5dD8eD58s60j+rEBdy0JjMDXCZWqV4OZQQjyH7arktfDGeFe3B+J5fQjfW1rh+jg+pJmwqYb78d54N15Oj+ffi2bcFWhV7dbGlqH1LC6UT0Gla8PViaxl8LyGfqgC/L9q7uMNSTw+gj3eNmQbProH903+RtdO4fLllrClXW6Sm7yJoa3RYLBNd5CN1byedUjg39Gyapfu+rjQuAqdX86ocRmH1/HlG7LJXxVY77YncBsNSy4k/6MRXmMfyn2ZF8tGoPSGMxt9A5p++6uN3g6xsDfnhmH84KjqlY1dhh+Sn1t9jf2H4MbcMAzDMPYHNGKCcHVhNueGYRiGYRiGYRiG0cjY5twwDMMwDMMwDMMwGhk71m4YhvE94X+bfRi8b92/p6r9G7dEhmEYhmEYjYwJwtWJbc4NwzD2Q1SdTklk5VoKO2Esi9No4Eeuw/amFHbyWSx09P5zLaLuw/uzGNTbK1jY6KyD3L3Fn7Iy0IEHsKDPvLeciM+gMSwU9cmK9uTPTHDCRvk7WSCn91FF5I9Ld6JIeVPZRu3RbSxelRQQ/BkBFteKbA+J3dU4AbA+zVlYLiWTRaYO/GXfqPv5v1MQDsnkez/c5UR7eoGF5YLCQADwzSQnNNcim/upU+tC8m9f7QR9XgipCB0dEgN6Nc+JOv3mcO6nsABch88eI3/xhRdH3ee020Bh6acPJf+WBxY7DzcDnklkMauBZc7/biILKKWG/kab84Ebo0Nbsfhh26v5+dbm+5dE3Ykp3N5FOzif6nLXThs3ZVFYn5yQyOK/fhd1P3/a2xR21sk7yN9ljat8cp8mFPbM6zwfl0accGKnkBBe3+t4PE/5p5vn0oJF3arLuLypgamR0YrnX6dC7otuAQHBSQs7UFhFMt8bFBpLlpCYWetd5J9/rxuX65NY66BThPsmUuPWt6ZteK7+bi232YUVTtSrnXL5rsng9WLB++7eDsm8Zg1onk/+ZcVuzeoxupDCMudy+TcVOfGw8Do5PLGY/BWBP62bpLNoWquePM9XzHPrUIcuXIbqdVnk75DgxlZROa8lw37J7RvXzZVx++NLKCw7wr8fHQpcuocq9+nKCAvPDe7jRMrKClnkbfJ2Ftw75cjNLs9pPNanKM+TIPEhkbdjWrDA4Tdbs1x5zuD2LJnP+l7zKlzc4tBZ4dND4nEdN7m6ri1jsbjm2fz7sTXfhYcVxbJbcfuWBUROM8r5t6W6hgvV6zh3b/FibocebXjdWbXVrZMdqzndtOY8T9J2cX2M/RvbnBuGYewFIpID4CMAXwI4GMACAE8DuBVAK3jfOF8D7zvm2fC+JR/83U4QkWcADAawEsAvVZV/8Q3DMAzDMH7q2JvzOjGbc8MwjL2nO4AH4H2zvTeAc+F9v/xaeN9PvwXAl6o6GMA7ADoF7u0F4HFVHQCgGMDl4cRF5FIRmSMic94szf0+62EYhmEYhmHsZ9jm3DAMY+9Zp6qLVDUCYAmAz1RVASwCkAPgUADPAYCqvg+gIHDvBlWd5rufg7epJ1T1cVUdpqrDTk3P+f5qYRiGYRiGYex32LF2wzCMvaci4I4E/BF462k1gLrOaoWv13umq9uBASPe+BYUVpXP9oXpV50edRd/8TGFxbVuS/4jh6yKurevYjvAMw9hG7y4ps4Gr7SK7Qury9jSbuBoZwNbmc9VG9NxM/lb5jpbz64D2CY6ccQg8uvWvKi77QVsn37F++u4TAFb7IzuFIS4Du3IP7a7s7evKOGfwma/aEX+3e8sirpP6s71Th/OtsCjX3Y2mzmHsj1sfPNk9rdtHnWXTGFbz/gMzqdljrOtPH0p27x26M224UevdbbBkRK2uw0/kw/amANAk2eejrqLzuOweTd+Q/5uPZ1dY8uRXN5z3mY7+PRsN3V+34LH2Qer2O55yGhnY1q6kctbs2Y9+bNHOXf5ap4XCUls+5k5zI33QaewzatW81z45sL/RN2nDGab6IRTxpF/6gVfRN15i7neI+O4X4sky91Xw4b60qIH+UdXu7Ff9TXPk51bubzH9XRjIOOiQynsirkLyL9touuLFgPZVnX9FzxPUtKcPfWgoziu7uZ53vxgZ6WT9THbWrcZxH2RfHBO1C3Nsiis9Z1c126BMrTswOmECZa+uJjndcuDOW6bD5xbknictR3FNrvJ81xfDerAYeF1tNMgN+/L8ng8KA9RtMxy8zoxnW36ex3DdS3+xNkuD7+S8yyfmUf++I2FLo/TWPOjZgPrFRR/6OrTsQ/n2WwbW16l9XZrcFoK/yaM/Jht+oNrT/PWbBtevIVt+oMM6c/zIm0gaxA0WePqWjiT+7jF+b3If8rCNVH34g94zmeOyCJ/4VtuXgztvJXCwr8nWbtdXVsu57o0G8pjqUmha8PSbzgsLj70Z0Cca9PkJrx2h/3Du7sylm/j9TdjCI+PlqH1eL9A7TvndWFvzg3DMPYdU+HZnkNEjgUQ3Ll1EpERvvsceLbrhmEYhmEYhgHANueGYRj7klsBHCoi8wAcDSD4qnEZgAtFZCGA5gAeaYTyGYZhGIZhGPspdqzdMAxjL1DVXAD9A/6L6gg7OnDbNf7/+QD6wjAMwzAM4+eOqbXXiW3ODcMw9kPKt7mDTVrJ3zctXM+21zV3vRF1Jwnb1e18lb+Rnr/V2e9tLQ99b3oF21JmpThb2+3g76TGrWxJ/uS1bEyZkebSKitn28RHkgN2jSsy8dxx7t6CZxZR3JTmbGeXMsR9S3f5imwKm5scsOneAhwW+J5v191sl719A7dTu/7OrrXwk5D9ZiLb881f4b5H3T2vgMI2Vjo78o1fpKF/a2ebmJgS+hZtnrPFT8wCagLNUpLLNoJfbnJ5JkKxNSH8dV3H9KqAjebMphiZ6GxgJ1ez/Wb4W+ZBO/Omzz9NYZ2P/w354xLdH1dvvMd9kS78h1enFNeP4e8jL0/kPn5sjrNBPzrCNttJs9iuNS7wCe/yIv6TZkcef6N5a6Cq3Y5g+/mKjTx+Ww1w82/t9CwK63DXy+RPh9Mo6F7DfVxWw3XdFvgWe/M41iBAFd+7Qtz8LPmI02maWEH+zGO6Rt3L/7KQwv4bx/P89HLXaNMm81qyI5HtQEtKXRl3f8x9WlHF6W6vdnHzErgvRq7kPo5f4+xlWx9TSGHLqtk+eXmNm/O9N1IQVu7meTzmIKdnkDSfx3rh12wHHxx322dzeb8o4vE8NDCH1m5tTmG7hOfq5hlu3OW0KqSwxDRuhxUFzvKp427+Hn3CKu6LJHFjcsVjPC/i4riuHXq78pa8y5ofGuG1I0lcv61ZxPomifFc3swNhVF3cn/u/wWF3BeHlbjO2r6Ffz+SpO6N2dY1nE7XgRxeuNaVNyWT58zq+1jPosNg9zuUlsRxd33FbVhc6vqiJvT98cyeId2Jlq4MBUXcDumLC8m/I1D3iHLbN83idFMLXXm3r+X1q7Kax1mzFs6WvfmBPH43vM/tW1W/xI2xn2Gbc8MwDGOfEtyYN0RwY94QwY15QwQ35g0R3Jg3RHBj3hDBjXlD1Ox9cevdmIcJbswNwzAMY39A7c15nZjNuWEYhmEYhmEYhmE0MrY5NwzDMAzDMAzDMIxGxjbnhrGPEJGrRGSZiDz/P6aTKyItG475/SIiw0TkwQbiDBKR4wL+k0Tkz99zucaIyMh6wr/FAeGY948XkWu/Rfz9or8MwzAMwzB+FES08f/tp5jNuWHsOy4HcKyqrmvsguwLVHUOgDkNRBsEYBiAD/x73gHwzvdbMowBUAJg+r5OWET2mzVx2rp2UXe3Dfy8QUImxzsK06PuNZGtFBapSSX/h3BiO8ensj3yv3eyCNKNyU7sbHDr7RSW3pLtyv+zuqMrH2tVYWMcC/Eg4gSfPnqbBYhy4naTP3Wbu3fNXO6ez1NZMOncSnfvKqRTWMoCtm2fFhBGG/Y1C+2kJXN5c651YlsL72QBuPgdWeSfkeo6Z3Eh28gfFTIsL8p1okKt01kEK7sTx21V48pfAxYHm7O4Lfk/SC6Muo9ryYJD67ezyFD66UPJP+9GJ5QWFoBr/f6T5N9yzCVRd2/lfns2mfuq/cYsl0cCj8m8eC5jWWAahgWU0kdym8583Lm7tOG+Kapgga0ice329WcsUNW2isfHYb9xQljvz+B0Lizivhl4qJsbCW25fT96lYW6NkScvkGC8PuR4mdnkv+TBDc37unJopCl+Swm99RDLt/OIaG2jkk8vhPi3Nydn8j1PrScyxSX4v6A/bKG6xJ+vbM11cWtAAuJJRbx88usmoAY2wu8YGQn8tydleDmeWJIAO6gLrzefTXDrZtrZD2FlQTE1wAgD64dKit4vA6MY7GwuQGRxVmpvPadWsbrUEqcq9vOAu6LtGZ8b26i65uUcp4X+fFcpjUR96dFnnaisL4deX1OO2VA1L36byySlhDH5V1T7QQwtyCHwlYmcicfs8TdW7mA09kR0sLQGufPr2DhwTXCQpRBvkroSv7Mz7aRf3memxc12znPYHsCQNNZrt9WhMQOT1/P91ZHXF0nV2VR2MjJnO6aap7nQZI28djfXuH6NT4kzFa4ledxVs/AWpLE6eSV8FhastXNhYy3uG7NweNsYTL/vhj7N/bm3DD2ASLyKICuAN4RkT+JyFsislBEZojIAD9O8zqutxCRiSLytYg8BiCm2pOIHCgi0/1400Wkl3+9n4jMEpH5fto9RCRdRN4XkQUislhExvlxx/r3LxKRp0Q8mVYRGe6nucBPK9N/Q/1eXXmLSBKAvwIY5+c9TkQuEpGH/Hs6i8hnfpk+E5FO/vUJIvKgn85aETmjjvqeKCIz/Tw/FZHWIpID4DIA1/h5HlLHvf8UkXl+vtn+tW4i8pGIzBWRL0Skd6A894rIJAB3hdK5REQ+FJFUETk/0M6PiYQker34e8QRkV+LyH2hNO+NVW7DMAzDMIyfPJFI4//bT7HNuWHsA1T1MgCbARwOIAfA16o6AMD/AXjWj3ZrHddvAfClqg6G99aZH4k7lgM41I93M4A7/OuXAXhAVQfBe4u9EcAxADar6kBV7Q/gIxFJATABwDhVPQDeyZnf+ZvslwFcraoDARwJgF9lxchbVSt998uqOkhVXw7d8xCAZ/36Pg8geES+LYDRAE4AcGcd9f0SwMF+ni8BuN7/nvijAO7z8/wixn3pAOap6hAAU+C1LwA8DuBKVR0K4FoA/w7c0xPAkar6p9oLInIFgBMBnAKvT8cBGOW3cw2A84KZikifOuK8BOAkkehru4sB8DeqDMMwDMMwjJ89+80RTsP4CTEawOkAoKqf+2/Gm9Zz/VAAp/nX3xeRgjrSbQrgGRHpAUCB6NnWrwDcICIdALyhqqtEZBGAe0TkLgDvqeoXIjIQwDpVXenf9wyA3wP4DMAWVZ3tl6EYAITPTteVd32MqK0XgP8C+Ecg7C1VjQBYKiJ1fR+rA4CXRaQtgCQAe2suEIH3sAEAngPwhohkABgJ4NVAvYLnyV5V1eAZsgvgPeQ4RVWrRGQsgKEAZvv3pwLgj2EDMeOoaqmIfA7gBBFZBiBRVRchBiJyKYBLAeDSJgfiyLTue1llwzAMwzAM48eObc4NY98T61i61nM9+L9LROT3AGqNOo8D8DcAk1T1VP9492QAUNUXRGQmgOMBfCwiv/E3/0P9+/4uIhNRty24xMo/RMy8vyXBPIJGhgIAInI7vDrAf/P8LwD3quo7IjIGwPg9Cu4dLZ/re99R1ZvryDcOQKGfbizCBlmL4dnTd4D3UEAAPKOqf6njfjQQ50l4pyWWo5635qr6OLw3/JjX8WT1TOuBdl3YNjx9CNvLVm9w4R1ns13lxk1s63dskrN5jYvjbr++32byx3VwduRJaRyWccYA8p/z2PKou7CAbSebNuWDGHcWZbnynMf2uwuf55+l7kNdePpKtk0dM4iPpb32mbO9HhrP3y7P7sw2pDlLnc1g74v4ENmuaWw/XbPMPRe6uCenU7qd7ZH7JLOd4JaNzuax73X8HKpi2grypxwzLOoufm42hfXr5ewu+wF4dV2HqP/gLH6e97sdbgws+SYDPVvtjPrHVZVz+R5YTP5uPZ2tYlwij4+gjTkAtP3oCVfeA6+isLN3s9XHwEvd87z+W/i51hGf87O+JgE7+ZICHr81eTyWEsXNhfx8tgPdjZCWQOD52/AE7scWHXkJmPsfZ9d65dgtFBafyX0e166NK98m/rZ9v7RC8h9Y7fpmdAX3RdBGFwCuE6d9EBf6a61Zz5Dd82Y3P2eva0NhXSp5nmRnuzl1QyeeU5/O60D+bpe48rb7km24NyzJIn/OcLcOVbIJNDas5rjdRhRG3WVbuN5XrWUdin4HuboFbcoBoKSAbXbHXOr6uOMzvBYWlPK6dF26G0tJyWx7n9GM+6bLBtfnBwhrUnQewPMv/TS3Nn45nhuiZTGP9dM7OnvwZWtaUVjvBF4bO8LVZ8RYHmdFoce9ZR8sdPd15nlQlMft0DHOpZtYwXO+cyX72w9x62rhep6bJ2fx71RCuru3MnRQt2MS902QgdW8/mb15PbO3Ob66pMU7v9+ofIfPdq17+A5/LtZXMX3Bm3xu1fyeGjdldeLuM0un+IKTqfLCP7tyZ/s2il8kDpF6v7TKyWd612zk9uwRUCHpHdb1qRISuPyt9jMejL7BfuxIFsQETkGwAMA4gE8qap7nPb0/0a9H96Lq3xVPex/ydOOtRvGvmcq/CPP/oTN999G7831YwHvF1hVH/aPbg9S1c3w3l7X/tJcVJuZiHQFsFZVH4S3AR8gIu0A7FbV5wDcA2AIvI1hjojUvo69AN6x7+UA2onIcD+9TNlTGC1m3gB2AchEbKYDONt3nwfvmHqdqOoNtfWNkeeFsfJU1ZpAG9VuzOMA1NqxnwvPZKAYwDoROdOvo/gnCeriawC/hach0A7e6YIzRKSVf39zEekcuqfOOKo6E0BHvzwv1tcOxk+H4Ma8IYIb84YIbswbIrgxNwzDMAxj7/BfAD0M4FgAfQGcIyJ9Q3Gy4JlJnqSq/QCc+b/ma5tzw9j3jAcwTEQWwrOnvrCB67cCOFRE5gE4GsA3iM0/4L0FnwbQK6FxABaLyHwAveHZsh8AYJZ/7QYAt6lqOTx751f9Y+8RAI/6tuPjAPxLRBYA+AQAPxKvO+9JAPr6AmjjQvdcBeBiv74XALi6jnrVxXi/rF8ACL4meBfAqfUIwpUC6CcicwEcAU+0DvAeEPzar+MSACfXl7mqfgnPNv19eEfYbwQw0a/PJ/Ds5oPxlzYQ5xUA01S1LrMFwzAMwzCMnz6N/Rm1vXtzfyCA1aq61v9b+SXs+bfjufBMSr8BAFUNmzx+a+xYu2HsI1Q1J+DdY+OnqjvruL4D3qa8lmvqSP8reMJltdzkX/87gL+Hon/s/wun8RmAwTGuzwZwcOjyZLij83XlvRPA8NB9E/ywXHib43BeF4X8Mb9JoqpvA3g7xvWVAAbsecce6d0Uur4OnlBeQ+UZH3AH2/FlOFv2YPycgDtmHJ/RAO6rI8wwDMMwDMP4gQjq/Pg87psX1tIeQPC7fxsBHBRKpieARBGZDO9U5wOq+iz+B2xzbhiG8T3iH3maBWCB/3DEMAzDMAzDaESCOj91UJ9WVC0J8MSAx8ITAv5KRGYExJe/NbY5NwzD+B5R1ULwqYO9Iq/SifY0K2KBnMov2Z/S1P1WlERY4KnXMBYVqixw1kwZQ9K5rKUsdLX1JSdmFJYhKP9oIfkzAlpnU7ez8NnxnVgo6LfznZjVjs9ZiKttK/4tjM9yZWoSEq9a9RmLCp0+yon/lG2iIOzcyHUtinPWGZLOAkm7C7lMkdmuvd/c2JHCRsex+M+GCpdPVeh3vc8WFocKimYlLuHf8bQDWMpBEly/paylIBTvYiuUqsCXFnaH+jQ3ksY3h0zSW450977xHosI9VYed0ERuF6zHqSwGf2vJ/+OD1xln9rOgmXHVLHIW9tOrv2X5XI7tC1nIawydeMyVVgEKTn0N1R8QI4psykLflWU8PjeEOdEntZMZhGy43psJP/CN12+axO5zY5tw2Jyke2uTH+K4/Hw2m4uQ9cznX/ZC80pbHs1i1BlBuo+9hTu1OrtXNfPprePug9K2UphZXE8ZrXAzd3XFvPYH6a8thSvceWdtI37eEgirwF5S9ycy2zO5StVboeVs51AXIt4XgOy2vKYnPeEm38lCRy3VHksxZe5Niwt4QNcI67i8k/9qxPcOrQNt+/yRdznXYqXRN0VoY+RFOzk+VeZ59ah9k1ZdKxpK54XJbku7mcfsnhcx3iO27W1K2NqKxapzF3H611JQmHUvT2BxeMqQluT0m2ubyQkZrZsFbdDTiuXbnxoLoZ/p4IIeGwv+5IFAts2d+1UsZsF9topi6i9Pd3pc2wN7Xh+1W8D+ectdlZou+K4HTZ/w/ohedVuzd0dxxbCG2fzb01mvCtTWhKXb3U5j8kum1yDa4QbPyOe7y2pcXVftYXXh57tWSDuj2k8//YHVH8UgnAb4ekF1dIB3meTw3HyVbUUQKmITAUwEMB33pybzblhGIZhGIZhGIZhOGYD6CEiXUQkCZ7IcfjLR28DOEREEkQkDd6x92X/S6b25twwDMMwDMMwDMP4YfgRfEpNVatF5Ap42kPxAJ5S1SUicpkf/qiqLhORjwAshCe0/KSqLq471YaxzblhGIZhGIZhGIZhBFDVDwB8ELr2aMh/N4C791Wetjk3jB8hInIRgIn+989/qDxPAbDS/2TYt7lvDIBKVZ3+PRTrO+G33zBVvaKeODkARqrqC75/GIBfqupVdd2zL+mQ7uw55+SxfeEvjuMvdZStcbZpxTVsv/nq/Bzyn9jBDZkN7/OT6+yuleRfnO9sU5uB7d0WbmdbvyAZAdteAEhsxzbRf/za2RS/kML2kMvXsN1i5sZtUXccm0+juIovzJ3s2mlVEpevbxXbOPYL2OCtfJLtC0sq2Q7ws13OBvKaI9lGd/OXXIZIwEjziMPZ3jj/Y/7JjQvYd1ZMLUXRDmePOq06i+LmxQX6Kl5xeW9n9/zRIrYFHtGSbZmXb3f2iBXxbMf4TCKX/5y3XZnSQzalzyZz+c/e7eKGbcwPXvwP8r884Oao+7qLuS/++Dy3/+AZzk60rfBYevNT+oIhWsHZWm+qZnverYlsudem2qVVU8VhOwrZTvSlgB3uo+3ZrnndSrbvzIt3Yy27mssbzucPF7vwoY9wXb7eyHEP/MqNn7cTOe4vm7Ddc2KSm0cfvc3rxYJk7sdhAVvPl7dxuosS2Xb5kI/cenJ8G7YxL9rBtss7810bLkhk+/+DEnmeZ7Vx+RRu5XQ+T+UxmqkuvHM1h/1zLad7Ptx4Dq+FCaF16ekkN+6OquCx/eDf2WZ3XLZbh8pLeW0pBPu3bHT2yTvieWx3DM2pLRG3NrbJYP2K1avZ1rpY3NozKJu1F6bu5D6fvdD1xUnt6v8zIdhOeaE19uAKnqu7i91auKGI18nenbhMn252Yyszjusd7psgLZpxm31d0JL8OVlu7G/dzb9ZxcoVOPN3zv0CbaeAzavZjrxZvEsrvob7dEeE7eBTAmOpdcjev/1gnicffOXWs+QKboduSRw3d51bW9q0ZA2C8HvmgUPceNi+mtevjZuzOO653E7G/o3ZnBvGj5OLALRrKFIQCSt6fXtOAdD3O6Q9BsDI/zHvxiAH3vcrAQCqOueH2pgbPz+CG/OGCG7MGyK4MTcMwzCM/YLG/sb5fnys3jbnhvEDIiJ3icjlAf94EfmTiFwnIrNFZKGI3OqH5YjIMhF5QkSWiMhEEUkVkTMADAPwvIjM96/likhL/75h/vcWa9N/XEQmAnhWRLJF5HU/r9kiMqqOct4pIkv98twjIiMBnATgbj/PbiIyWUTuEJEpAK4WkRNFZKaIfC0in4pIa//t82UArvHvO6SuMvjXPxGReSLymIisF5GWIvI3Ebk6ULbbRWSPTbKIXBUo80v+teYi8pZ/bYaI7PF9dBGZ4Ldprb/2Ufad8EQ+5ovINSIyRkTeqy9dv72f8ttmbW05RSRdRN4XkQUislhExjU8WgzDMAzDMIyfE3as3TB+WF4CcD+Af/v+s+BtAkcDOBDeNxXfEZFDAXwDoAeAc1T1EhF5BcDpqvqcL1BxrarOAQCRWJ9ijDIUwGhVLRORFwDcp6pfikgneCIXfYKRRaQ5gFMB9FZVFZEsVS0UkXcAvKeqrwXyzFLVw3x/MwAH+/f8BsD1qvonEXkUQImq3uPHq6sMtwD4XFX/LiLHALjUL9J/ALwB4AERiYOnlnlgjHr+GUAXVa0Q79viAHArgK9V9RQROQLAswAG1ddYofSuVdUT/HKPCYTVl25vAIcDyASwQkQeAXAMgM2qeryfFp+n8xGRS2vrfXOLA3Bmk057WVTDMAzDMIwfB7ofv7lubGxzbhg/IKr6tYi0EpF2ALIBFAAYAOBoAF/70TLgbcq/AbBOVef71+fCO2r9bXlHVWuNoo4E0DewmW8iIpmqGjRuKgZQDuBJEXkfwHv1pP1ywN0BwMsi0hZAEoB1ddwTswzwHlCcCgCq+pGIFPjuXBHZISKDAbSGtyneESPdhfBOE7wF4C3/2mgAp/vpfC4iLeraGH9L6kv3fVWtAFAhInl+mRcBuEdE7oL3gOOLWImq6uMAHgeAxV1PsF8uwzAMwzCMnxG2OTeMH57XAJwBoA28N+k5AP6uqo8FI/lHwoNqLDUAWDnHUQ1nppISCisNuOMAjAhs1mvz+hjeJnKOqv5GRA4EMBbeW+orABxRR77BtP8F4F5Vfcd/yzy+jnvqKkN9r/+fhGdn3wbAU378pwEMhvdG+jgAxwM4FN7x+5tEpB+8kwhhwpveaNv5ZUja4449qS/dcJ8lqOpKERkK4DgAfxeRiar61/oySM90yRx/VQaF7f6YbY4TA48bDkhuTWG9y1jQp2Cbs21eX8qCPjln9iT/wVVfR91p/dkmestnLK7U4aoeUffWf6+gMAmJIlWpE5fLuqA/hTX7+zfkTx3uRIWqVmyjsIOu4qG+7SUnkNM7gwWpkjJYOGr9smZRd78H+SBG+YsTyZ+9wNV9yzQeHilpLJR31O9dm25+nJ8htT2jGflrthW4PM46kcLkEv6UarfxB0TdE6/nIXzCWBZ8WjvVlWFYHxal25XHwkYDy7g+6dluvHQKifW135jF917qRJN2fMAidEEBOAAYt9AN9w2HX0Zh9986hPyocuJFy25dw2VQnnoJKW4cpqZyXzQt4HnTqZUTAUxvwQJJ2cN5vIz7xI279E6lFNZvIAvE9S5w/ThtchtO9yieN3dMcOXfFirvrW1Z5C29j2vfszayOFSzDixClXZETtSd8xALiy0I/Wykq+vXy6/i8lUtYcHDxAPdwaoZ43n+tUrnduj1J9dmB97F5W3SlgXANLB8tB7IYb+czfd2u8dJlnx4OX+haGgCr2FHXubq9sYEXgsTynne3H9rt6h73V8XUdhxl7LA2tbnXL9paAw2D4ll9rvJnXgqvoXbMyk0p8ae6uq6+g0W9erWldePAza7+mzazmU4OCQQ2LSVGx8ZA3idbJLHvwkHqEu3S0iwLCEkyNh2hLs3eyfPizCnD3LCpTPeYe2LA1Jbh6NHyerMwnIHVBeQPyiaNiCRx3bH1CLyr3/alX9YHG95wv2YnuLWhDTlPm2Vw3OqYJPrq+3FPIcqQ68OesU50bfqCFsTx8dx+/Yd4/pRkjjuxo/5d3T1AicY2GNw6LemCa8PN7zB7f3AvTD2Y8zm3DB+eF6Ct+k9A95G/WMAvxKRDAAQkfYi0qqe+wFgF7xj07Xkwju+DvhvdOtgIrzNNvy8BgGAqv5CVQf5G/MMAE39z0f8Ae64djjPME0BbPLdF9ZT1phlAPAlvGP+EJGjAQR3Mm/COxo+HF57QVUv9st8nH/cvaOqTgJwPYAseCcQpgI4z09zDIB8VeVfWW67k4Go9G599d2bdKP4JyV2q+pzAO4BMKSuuIZhGIZhGD9pGlsMbj8+Vm9vzg3jB0ZVl/jHuDep6hYAW0SkD4Cv/JfHJQDOh/fWtS4mAHhURMoAjIBnA/0fEfk/ADPrue8qAA+LyEJ4838qPMG2IJkA3haRFHhviK/xr78E4Alf5OwM7Ml4AK+KyCYAMwB08a+/C+A1ETkZwJX1lOFWAC/6YmlTAGyBt0GGqlaKyCQAhaoaq13iATznHy0XeDbthSIyHsDTfl67wQ8NannCr+8sAJ/BnQZYCKBaRBbAa++vA/fsTbpBDoAnphcBUAXgdw3ENwzDMAzDMH5m2ObcMBoBVT0g5H8AwAMxovYPxLkn4H4dwOuBeF8A4DPJXrzxIX8+gHqVwv0HBnsIrqnqNPCn1MaEwt8G8HaM+1bCs6sPEqsMRQB+oarVIjICwOG+7Tb8N+MHAzizjjJXwbMDD1/fCe9tePj6BHgbbqjqNj/tWv4SSHNs6NbJDaQ7PuSv7b9c+G/8DcMwDMMwDCMWtjk3DGN/oROAV/yNeCWASwBARPrCE6V7U1VXNWL5flASA7aJ8+9iG7z2bRPJn9XPHc+avIub6Khkfi6SUels6WansGXTgCcWkj+tlUt36Rtst1gRYfu3gr9tirqTEzlu5adsz7u7xtkt7n6H8yypzCb/0v8EbKB7sn1e9dT15J+ys2PUHc8mmDiiA9tlN8l0dq5f/I7L0Lsj1+2L3c7G8ezTCils9yK2Xa6Y5uztm/fiY3NlM9n+O7mXs5rIu+4VCptdwbbLH9+4wXmS4nB+N+ef9ynbE5YHLNa2LktF22Rnf1hdw33+biLbS/6+hevHskIeZ/MS2L6z/xZnU/rUdi7vdRezXWvQzrzjpEcp7OZhN5J/QKWzBW0fYZv46pCdaKskZ/e8JWRjnh/H97ba7eqT1oz7betMjvtxokv3tBH83LNmOY+7nUtdukngMbr8JS7vUVVu/pWH/gTbtpGtaD75xvlb1PCBocRc9me84XQoViv3RYsIl6F9hrPL/eCfXIYNiXzvcV+tjrqHHMl9unxSFvkrvlzu3NKBwnZsCdlTX+DGUtkstmV/r4bTPeB3zh48IyQZsl54balY4NKavIttle/o2ZL8f73F9ds5ISvPB/7NNsfjmji3htqzSLkNv7nXrcGJwvUur+S4s190GhApcdynG3JZo2JyhUv30fuOp7A3/ryB/OnFbhwOKef2zWjCbTZ5oxvPh6by70Wbaq7r2klujrVqz9oAaR147M9+x5Wfc9zzdypI3sqO5Jc47vN4cf6iUFhJBc/jwU8eEnWvuPxzCiuv4r5ITnSlzCvltS51G68XGsg2K5XnRVrvkBbKMpdWYmj8Nk1kvYWtc13c5JAmRfgUdveBzs68muUfUMmm90iJKZPTyEQajvJzxTbnhmHsF/gb78Exri8F0PWHL5Fh7J8EN+YNEdyYG4ZhGIaxf2Obc8MwDMMwDMMwDOMHwb5zXjem1m4YhmEYhmEYhmEYjYxtzo1vjYhM34s4fxCRtIbi7YOyDBKR4wL+k0Tkz99DPiUNx4pdpm9x33gRufbb3uffmyMi536Xe/8XROQU3yZ8X6R1kYg8VEfYByKStS/yMQzDMAzDMIz9ETvWbnxrVHXkXkT7A4Dn4H1maq8Qkfg6PpNVH4MADAPwgV+2dwC88y3T2NcMQqBMPxA5AM4F8MIPmCcAnAJPrG3p3t4gIgmqGtaHqRdV/dYPO/ayLN9lzAXv/9Z12Vte39Iu6j6hxXYKCwsSrQqI9IzNZBGhjaGTY8URF7dCWJElaxQ/T3v4zaZRN0v0AFtZdwe/HuWExLbMYTGdqioWWMtKcPloNQvtzEpiMZ1fDXdCVy/O4lKc2Zttrw9t5tqppobb6MNN7cifGGiXs27OorCNDxWSPzfBtdNLb3DcqtAzo7NSXJmmfd2ewrqlFZP/y6Xu3gsu5zZa/RiLAd1wjVOkuvq+VhR2U8cd5H8zr23UXVQdEkmL53ZJDY2PD1Y5Ia/liTw18uLZfv2Iz52g1jFVHPbH57k+9986JOoOC8D9dc5t5K+e/Z6777JZHBYSVEoMiIclJFMQKoTjbqhycyNxHc+TU9qxYGDLcjf2t/5nHYUlJHO7fFLg2ntRSMTpzjO5vS99xS0XaaHyDa9mQbhzT3Wqhve9y+WdXpVF/j9kuPkX7uOFcdw3u6vdvX+4gvsprgPPk8g6V9cXnuS47UIidUkHuPFeMoOC9hDqmva4eze0LpHn9bYEXlKvmfzbqPsvxz5GYZsj/CfGpvlunozNZHHJB7awwOHtgXH3xOCbKezqq3kdevefLq2SUPtGOFkccrRzP/MeByYrt8O1v3KCYE88z+vm4AoWGhub6YQJb7+ZRQm7Cb9rWxYofs52FqWbEWlC/rGZbqwXhES65ofEPY/NcCJwM9exeOD2DVyGI7PdevzfXSzGN1b2+LhMlNJyHlcfJfDvUlyovam8IdHKSZfPjrpbhOpdzcMZNVUun5zQb+POnS3IvyTZjYHwHxCD3+J5vSwgvNosFHlZRXPy96lwY79gFxdwbQqPuxlL3bwZVM5zpl1qKfnXKPv3C+xYe53Ym3PjW1P7FllExojIZBF5TUSWi8jz4nEVgHYAJvnfpoaIHC0iX4nIPBF5VUQy/Ou5InKziHwJ4Ezff6sfb5GI9PbjHSgi00Xka///XiKSBOCvAMaJyHwRGRd8+yoinUXkMxFZ6P/fyb8+QUQe9NNZKyJn+Ncz/Hi1ee/xqawYbXGmiCwWkQUiMrWOMtEbcT9+ju++QURWiMinAHoF4nQTkY9EZK6IfBFoh5hlB3AngEP8PGu/Sx4s52QRuc8v4zIRGS4ib4jIKhG5LRDvfBGZ5afzmIjE1/a5iNzu13OGiLQWkZEAToL3/e75fpkH+eELReRNEWkWyP8OEZkC4OpwuwWK2s6v9yoR+UegXLki0lK8EwLLReQZP4/XJMYJDX9sTvXLsFREHhVPBb62Ln8VkZkARojIH/2yLBaRPwTSuMnP6xMRebG2D2PU5UQRmemPzU9FpLUfb7xfzol++U8TkX/4Y+sjEannTwzDMAzDMAzj54Ztzo3/lcHw3pL3haeoPUpVHwSwGd53qg8XkZYAbgRwpKoOATAHwB8DaZSr6mhVfcn35/vxHgFQu6ldDuBQVR0M4GYAd6hqpe9+WVUHqerLobI9BOBZVR0A4HkADwbC2sL7LvYJ8Da2AFAO4FQ/78MB/FNEGvr+xM3wvs09EMBJe1GmKCIyFMDZ8NrwNADDA8GPA7hSVYf6bfDvBsr+ZwBf+HneV0eWlap6KIBH4X2P/PfwvqN+kYi0EJE+8L4/PkpVB8F7IHyef286gBl+PacCuERVp8M7pXCdn+8aAM8C+H9+my8CcEsg/yxVPUxV/xlut0CcQX4ZDoD3gCP8whbwHmI87udRDODyOup7IIA/+Wl1g9fGtXVZrKoHASgDcDGAg+B96/wSERksIsMAnA7XN8NCaQfr8iWAg/2x+RKA6wPxugE4Ht430Z8DMMn/xn2Zf50QkUtFZI6IzJle8rP5apxhGIZhGD8nIvvBv/0UO9Zu/K/MUtWNACAi8+Edr/4yFOdgeJv3af5eNwnAV4Hw8Ab2Df//uXAbqqYAnhGRHgAUwN68dRwRuP+/AP4RCHtLVSMAlta+6QQgAO4QkUPhTdv2AFoD2FpPHtMATBCRVwLl3lsOgfft7t0AICLv+P9nABgJ4NXAs4Hggc1YZd8bao/7LwKwRFW3+PmthXdqeTSAoQBm+/mmAqj9kHElvOPrgNcvR4UTF5Gm8DatU/xLzwB4NRAl2M91tdtnqlrkp7cUQGcA4e9GbVDVab77OQBXAbgnRn1nqepaP60X/fq9Bu+hw+t+nNHw+qDUj/cGvH6JA/C2qpb5198NpR2sSwcAL4tIW3hjO3gG9kNVrRKRRQDiAXzkX18Eb64Qqvo4vAczeKDT+XbmyzAMwzAM42eEbc6N/5WgUVQNYo8pAfCJqp5TRxphY5jaNIPp/Q3eW8dTxTsSPvk7lDW42QmWu3YHfB6AbABD/Q1VLgAyuBKR2+G/8fTfFl8mIgf51+aLyKAY+VaDT6kE04y1AYsDUOi/vY5FrLITIvI0vLe+mwP22rX3RUJpROC1swB4RlX/EiPJKlWtLWtd/dwQ0X6up932ZjyF20z9tGqNEW+G90Z9j3j+/+UBO/O6TkY0dGIiOGb/BeBeVX1HRMYAGB8IqwAAVY2ISLANa9u8TkbWuCzanMX2ekUfsX1sj7HODrB4MtsXjqzgqrRp5uKu39mUwuJz2N50bHWe84RaZGgK20Mm9nDPiZpv3UJhKR24qi2nOjvo5E6c7gkb2H466QBnz3v4wp0UljqI7fXiM1x4xXY+GHZi9kbyL1oReK4Vz7Z9rQZw3Xp96dLqE1quEuL48XtCc5dW19RdFNa6G/tPrXZ+Se1NYYMrQlIG5a5MbcF2q8kZbMjYawPf26VZUdTdZgTbRM/5gG0ph4zeFnU/NqcDhZUJ92OTls6WuW0nbpfBM/heVDltgQGVPJiCNuYAkDD8hKi7VwUbL1eFDjO1jrh2KQ1NqZA0A/plu7EV1kHI7MvjpelWF95iELdvfBu2XW3xnFtqshI43chO1hLtKG4ubwXrLRyWxuMbgbTOSi6goDVFPHeDnNOXn2c2X8QHkEoCVY3ry3qeFa9NJH/SoJyoe5ByGZpns713/NG/iroH/Ps1CmvXhvUWMtq6uvfezv3WcQPbimu+m7tDKjluYhLb6bfu6tbG4tXcRieH7N6D4+5A5X6SHJ6P7XR+1N2lPffTilwur2Q6u+ehofU3UflnKS7LjaUR1UUU1rY9t1nxDmfBdU45j7NmidwXLctdGXLGct12f87vNj6Nc2tC18rQ2hIqb5O2zka+dwWXt3sNz6GMbBe3V2hoL07ksR+keTNef3vvYMu1YBtuT+A8j++0ifyr17r5tlu5zdKF18mt4t6BtIkrp7Am6exvvcuVaYuw2EX/lvwb1man+00uq+EyFIWs2zo1dX3ePZnLl52XRf7KwJ+VTeJ4Xe8wkMdOxwVtYfx4sGPtxvfFLgC1v5ozAIwSke4AICJpIvWogcSmKYDaVfeiOvIJMx3esXHA23iH3+jHyiPP35gfDu+tLaGqN/ib8kGAZxuuqjNV9WYA+fDeQIfLlAtgiB9/CIAu/vWpAE4VkVQRyQRwop9HMYB1InKmf4+IyMAGyk55qurFfjm/jZDaZwDOEJFWfr7NRWSPNqgrX/+Nd4GIHOKHXQBgSqyb6mi3vaWTiIzw3ecA+NJPa5D/r/aEwIEi0sW3NR+H2P0/FcAp/phMB3AqgC/8uCeKSIp/kmGPI+gBgmPzwm9RD8P43gluzA3DMAxjf0Aj2uj/9ldsc258XzwO4EMRmaSq2+FtqF8UkYXwNuu967s5Bv8A8HcRmQbveHAtkwD0FV98LXTPVQAu9vO8AMDVDeTxPIBhIjIH3mZ++V6U625f4GsxvI3eghhleh1Ac//Y/+8ArAQAVZ0H73j0fD/OF4F0zwPwaxFZAGAJPJvl+lgIoFo8gbU9BOH2BlVdCk8bYKLfZp/As2+vj5cAXOeLoXWDtzm9279/EDxxvFjEare9ZRmAC/08msPTJojFV/Bs8hfDO2r+ZjiC3wcTAMwCMBPAk6r6tarOhmcGsADesfs5AOra5YyHZ4LwBbwHDYZhGIZhGIbxrbFj7ca3RlUz/P8nI3C8XFWvCLj/Be+4b63/c7DgWe31nLr8qjoHwBjf/RWA4Nv2m/zrO2OkO8EPywVwRIw8L6qjPvnw7NT3oDZOjOunxbgcq0xHx4gHVb0dwO0xrq8DcEyM6xfFKpeqVgEYGysPP3xMwD0Z3G/BsJexpwYA1V9VX4Nnuw3f9jv8nfOD68vf98dqtwn+v9o4JwTcOUDUHj+iqpfFuD/MblUNP7DZoy9V9V4A98a4/x5VHS+eGvxUAP+soy5vwxPYC+czvq58w2GGYRiGYRg/G/ZjQbbGxjbnhmEYsXlcRPrC0wh4xn/LbhiGYRiGYRjfC6K6/565NwzD+LlSPv+96OK8/uyHKaxZ+917xK/l9jVsifB/XVicbdPqrKh7SyUL7SSFHmX37bo96t61g7QREf7pqK521ia7K1jkpjrCFlQPJzuBrduasJBYuyd+Tf6iPzurhcpSFtPZtIkFn9arE0E66YXDuYCb17M/zYn0zPndHAoa/taZ5F98xotRd3Iii4P1ePmX5J9+ovtAwYg7cyis5IWZ5N+yyolBNcliwaFIDQtJ7SxwfbU0dJDnrNdPrLO8PQ5nEbqJn7Yh/9CW28mflOZEiPI2s5xHRLlMGWlO1GlDEcfdLdxXHRNdP5dUsejUtGQWVOpV4cbhiYtvo7CS3/6K/Kk3uy8XSnzoIx7JLI4YWTc/6l72648prM/Dh5B/2e+dldGOSh77A/ryBzy+WemECUV4YlRU8zuQGUkurfBfX5ffyMJiH9ziBBnjQhOuT2Yh+UvLXJv2u52txr64Ppf8h35wtgs77kUKm5vCbXhyklPyyt/F60W7ljy2Nue7MfBJCvfpXybwwS5dxHMuyM3/ZOG5QZVuLJ0RKDsAPHbSS+QfVunm0Ssp3PZ3vXI6+e870x14+sO/BlHYlEu4fAce7NbRyTPbU9ixL/IBvelnfxJ1j/6Ex6tWs9iZLpoedUvf0IG7Sl4T/t957qMhd5wXEik87hROd1tgvRNef+P6jSb/9Uc/FHXf9TyvJVrDQmPYVRh1Vr3xPgUlDONxFzfY5RPXnsOuO+TvqIu//5k/QiMdu3CERDfWK1/kj+Qk9mWF0YoZa6PuBV/y/Dro+dCYXPy1K+9BYzjPCP82allAzDM7pGqqHFcC65BWhPSPa1j0TZfNdp6UVAqLH8IHMGtWuzEaLDsAxB3BlpB3n8zz/Kb1zzckevu9U3D6mEbfgDZ7fXKjt0Ms7M25YRiGYRiGYRiG8YOwPwuyNTYmCGcYhmEYhmEYhmEYjYy9OTcMwzAMwzAMwzB+GEwQrk5sc24YxndCRLIAnKuq//6B8z0DwGUAWgEoBPAfVX0mED4BwHu+qvx3ST/Hv7//XsYfD6BEVe/5LvnVxa7r7o66IzVsO5t5zlDya6mzQc/950YKa/KbQ8lfeqeze26dwTajS9eyTV6zs90HEnbev4nCck7kI2mr3nQHsbr02UFhsxe3I/8x1c7uNr0F25dWPfcM+TOOcPadRR9tprDtEbZr3ZDkzMd23/tfCkvqwjbR2z51tpSdO7DdX8Ujj5O/z69cu+z4gG20Kx5+lPzlcHELnphNYek9+Cc3ab2zG23ajW07kw/pRf60N1ZE3R+u5/EQtMsHgPwqZ1eePzEFo3/l0i6LYxO7tlfzMK9Z42xVk2bxlwHTR7ItaE1emUunnOO++SlrH7QP2KtXh2zXq0PW11XiwsM25hmPPUX+ogsujroTW3H7lq7iv/6CdvwtW3MZqt6dSP7ef3Zjf/5trNuQcXQO+TtUfRN1Nzm2I4UteriE8wlk26+C7YZRxWNgd6Cvju7G8y+lIx98XPq5s08tf2cGhfXN4bhbfuU0LFoksR15nnC7tBng1pbmO1jrIqkFF7+g0JXhkDLWHNj1N57XaQOdXoRW8vzbCS5Torryb/vV/RR24Shuw21fu7UldzeXoewu1u6ohltbaqZ+QWHdW7C9d/5KV7eK0BzadRuvNd3aOpvo7ZfcRWHNjuU5tOUVZ9Of1fkrCgtvXnIjbg2ryuW+qH6c50XS8Ye58r45iRMC6y3kRtx6svky7qfiYtZb6HayK1T1zkoK2/7wSvK3OXVb1F0yaxuFBesSJv9JXmOzBi4lf8U3Lt+QOT0qN64h/9olbpB2zC6msOo3+CMrpYvdelb8GGuUtBnNnbFrsRuzNVU8HpqN5vG7e37gdzakSaEhbZGMUa68lUt5TY385xPyV+1ylQ+XIf0r/nMkAR1g/HiwY+2GYXxXsgBc/kNmKCJ3AjgVwG9UdQCAUwAMEZH79lH68Q3HMowfD8GNuWEYhmEY+ze2OTcM47tyJ4BuIjJfRO72/y0WkUUiMg4ARGSMiEwVkTdFZKmIPCriPesWkRIR+aeIzBORz0Qk279+iYjMFpEFIvK6/51xiMgYAJ1V9Tz/G/ZQ1Z2qejWAliISlLo9UkS+EJGVInKCf3+8X8bZIrJQRH4bKOMkEXkBwKJgBUWkq4h8LSLDRaSbiHwkInP9tFl+1ou/RxwRyRSRdSKS6MdpIiK5tX7DMAzDMIyfExpp/H/7K7Y5Nwzju/JnAGtUdRCAGQAGARgI4EgAd4tI7bnWAwH8CcABALoBOM2/ng5gnqoOATAFwC3+9TdUdbiqDgSwDEDtt7UuBXCLiKSJyIsiMktEbheRawH8E8AFgbLlADgMwPEAHhWRFD+dIlUdDmA4gEtEpPYbLQcCuEFV+9YmICK9ALwO4GJVnQ3gcQBXqupQANcCiHWcf484qroLwGS/LABwNoDXVXWPV5oicqmIzBGROc9u3BIONgzDMAzDMH7CmM25YRj7gtEAXlTVGgDbRGQKvA1wMYBZqroWAETkRT/ua/As6l72738OQO0HS/uLyG3wjs1nwBnIdVDVlSJyNYCZqnqOiDwIoArACngb/1peUdUIgFUishZAbwBHAxjg26wDQFMAPQBU+mVcF7g/G8DbAE5X1SUikgFgJIBXxdlkksFzA3GeBHA9gLcAXAzgkliNqKqPw9vgY/tRh9l3RgzDMAzD+OmxH7+5bmxsc24Yxr5A6gkLbzLr2nTWXp8A4BRVXSAiFwEY41+vXcp7A3jAd38I4CB44nB5DeQp8N5qkxqOf1y+NBS/CMAGAKMALIF3yqjQPyVQF3XGUdVpIpIjIocBiFfVxfWkAwBo8u+/R92lZ95CYcXPzSN/ajd3Qr5FXFMKq5o8k/xBUazpuSzaNaDZTvIXveqatLCM1Z+Wv85CTBlpFVH3rm0sItQ+hZv3LXHPNX4BJun315G//I7bou7MQSFxom0s8JNc4kSG0q//DYVpMYvUdTjT1Wf+uDcprPVTvyX/l0c5saUurViWIPmaq8jf9St3oKLZ/zuWwvJvYyGmlt1cuxStSaKwiiXfkL+wuHnUPbyG277pvX8kf7MTJkTdkXwWJGtWw/lsvn8J+bNHOXccR8VM1slDorj2LlP+c6IVWOQrIcX9JdYqicWsEmuyyN864sZS6s3XU1hQAA4Amv736ag7kr+BwlISWTBQS9z4LvoDC3UlXXYl+dee9YQrX6ghqpey6OKiVU7kK7KKRbLapfIBmdTAH6Rzk/nw4pE9+pI/LTIt6l64og2FNV3N+XTq5IQVU35zGoVNvZgF4o5+zolEbjj/MwrrKNyPm77OQF20imdByfRUV6aP41m08NAHbiR/zZfvRd3SvQ+FFbzzPvmT1bV/m5fGU9jTY1mQ8eA4tya0CPVb2m1/In/iMf+JuuPHjqWwdf+dRv6DT3fp9niH17PMX7Ho5oI/OmG0w2byehbJW0f+9p0DopHtOlGYtOlK/hYnu3UooQPXLX7kweTH9q1RZ8rVl1FQXAceZy1G3+qKMIHXzfYpLNwWLH/cFB47bUdxn8eNODzqbnJxD87ziH+gLrKvP5AvtOF2SUpy4nw1n77LeQ7le/u+/UHUvfqD0O/SReeTPz0gRtrsdzxWtIL7vEWNW98kMZXjFuWRP+vyLs5THvpTI4XbrOY9V4aUY1hINX7sOM4nsJ7VvPcyhSWcdhHnczwL/Rn7N3as3TCM78ouALW/3FMBjPPturMBHApglh92oIh08W3NxwH40r8eB6D2Lfa5geuZALb4NtnnBfLbJiLd4L0lP9q/9gt4DxlvhPf2vZYzRSTOj9/Vv+djAL8L2H73FBH+ZXRUwhOb+6WInKuqxQDWiciZ/r0iIgODN+xFnGcBvAjgaRiGYRiGYRhGCNucG4bxnVDVHQCmichiACMALASwAMDnAK5X1dpH91/BE49bDGAdgNrXlKUA+onIXABHAPirf/0mADMBfAJgeSDL/wC4B8ATAEaJyCwAJf69U1Q1+Bh/BTw79g8BXKaq5fCOli8FMM8v82Oo5/SQqpYCOAHANSJyMrwHBb8WkQXw3qafHOO2+uI8D6AZvA26YRiGYRjGz5LGFoPbnwXh7Fi7YRjfGVU9N3TpuhjRdqvquBjXoao3wduMB689AuCRGHEnisiJ8Dbov1fVfBFpAiAXwDuBeBfVkVcEwP/5/4JM9v/VxssF0N93F8Kzna/lmBjpjg+418WK4zMawGt+moZhGIZhGIZB2ObcMIwfDap6pYicD+A1f2O+A8Dj/pHy/RYR+ReAYwEct7f3aGnAnqyaDznVVLKJf02hs2st15pQGNumJqa4Zb9phOOWlrIdY/OOzja4/6h8Cls3i23bJVCkXbvY1rdZC7YxTtuZ5u6LZ3kALdxK/poSZ9tXuZXtmLNac7vsrgh8nS6Z7QtRze0QtEFPS2K7YA21YfPkcpdMqC90x2by79zl7A87bWPF/dSWXP7ija6dkjM4LK0FlzchwT3m35ofqlsclyk50aW1dmIKup/uwmpC8hCJKZxv+WrnLy/iPxG6tCkgf36+s0dOFU5nU3Ua+VMDttdbCtiOOYGHC0oDf5pIPH9xMLEVlyloZx7XsiOHhWw/dVtu1F2ynduwST7bkVP54vkVS3gcBkObxPFYatmObf7zNjsb3viwVMeObeRNDrzaqQrFTUvmfEqLXCO22LmdwhI0JMGR4OZ5ajzXpWMVrwHN2ru5u/kbnvPh8VFV5fQYMpTLq5VlqJPN68k7WJqQX4ISIlUVFJbJUxUJia7NykPaDOEyJASbJcJ9HB+SLakpcvnurOJ2QAbbZbdIcuuFVpZz3CS2TyaquS8QKm9wbddSbgek8HxDttMo0CIeD5rNds/0m1ETWgt38bovgcmqFbxGxXVnG3lkNHNxQ+tv+HeK8izkdUay24UiuL7SIq6Lrl3J/sDYT0vjMoSJFLj21jLWU0B8aLsUaCcNh4XWYwnMN43nftPdhewPtGncgKF15gkACJRRC7m8YRv5NJOX/VFhm3PDML43VHUyAm+lQ2F1Kw3Vn+ZzYPvy/R5VvbLhWIax7wluzA3DMAxjv2A/Plbe2JjNuWEYhmEYhmEYhmE0Mvbm3DAMwzAMwzAMw/hB2J8F2Robe3NuGMbPHhEpaThWnfdOEJEzGo5pGIZhGIZhGHVjb84NwzD2R2qcOFDZbhbFqqyIJ391pROZSREOq8xnJZhNW5yY0Y54jotKFitK3+DEaSrK+eeiJCQcFaSwkhW+ijezvyrFPTLfvJrFlZqEBJN2rXd11wiLTO3cwSJIeVUBka+QkBh2s0AO4p1Q07YyTqdn6JH+lgpXprQKrnfHUNzCSKC823dQ2PbV6eSvrnbtXxIS0QtTVunS3ZIYFhzie7eWu/p0WseidCVxLLZVFGrDhCT3nGpHHstCFIXqvhuu/MkhAa2toTI2DYjA5cdxOhXC91I3J3Obla7i9k5JdHUPC8DFNW1Ffm3SwpU9JH6IchYtzC917ZInHLdFAT/LK4pz7RDSFcPyNdnkl8DwjoTaDOUsAJaX4NJtVs0Jf7Ob+6ZVUHgsJFAm4XwCbK1hYbz1STzHOq7Pirrza3icZRazuNXOMpfWptSQ4FdI3EwLCp0nJNz2jXC6neJdutKkJYUVhZaw9aWu7impLKAVnic07sKCkSEBvl0rnb9EQu+1QmJsm6qcv38ar29aspP8SAuM71AZJJn7mNb2SKhPU0MSLvkBccEEHr8SEjALpitpWfWWNygupwUhMbaiIs4nUB8JtUP4d4oIjg0ACLW3lhYF3CHBvTjut6qNLnxlIQvLddrNImpVee43Nzks8hYWiAtSw0J+YTE5SeX5SHFLQ+2702nb6qolnE7XIeSPBNqhehPnmRDq861x9pr6x4S9OTcM4yeBiOSIyHIReVJEFovI8yJypIhME5FVInKgiGSIyNMiskhEForI6YH7/yki80TkMxHJ9q91F5FPRWSBH9ZNPB4SkaUi8j6AVoE0ckXkVj/uIhHp7V8/UESmi8jX/v+9fvAGMgzDMAzD2A9o7G+c78/H6m1zbhjGT4nuAB4AMABAbwDnwvu++LXwvm9+E4AiVT1AVQcA+Ny/Lx3APFUdAmAKgFv8688DeFhVBwIYCWALgFMB9AJwAIBL/OtB8v10HvHzBYDlAA5V1cEAbgZwR6zCi8ilIjJHROb8542J370VDMMwDMMwjB8ddqzdMIyfEutUdREAiMgSAJ+pqorIIgA5ADoCOLs2sqrWflA1AuBl3/0cgDdEJBNAe1V9049b7qd7KIAX1fsY9mYRqd3g1/KG//9cAKf57qYAnhGRHgAUQOjMdbQ8jwN4HADK575lXyY1DMMwDOMnx/785rqxsc25YRg/JYLGipGAPwJvvYsA9RhgOhQIGR3uGd5QGWrg1ti/AZikqqeKSA7q+PY7Ue7s+TqNZVvEnbM5++ZjnT1f/Mtc7NSubHuWusbZxyWwSSY6N2ObwSA1ET5o1aYZ27i16OpsSptt5DwrK/inZleZM7xt1T5kyxeyL2w22NVHMtk+dtfb/OteWeXu1XWLOdkO3cmvKxdE3cnC6WjeN+wPDIVOrUJtVME2j8MHOBtvSehKYanp3OBZ/dy9xSu438K24FlNXPseUMhtFFm7gPxtUpz9dHwTbvujem0gf3U5235mDnM2sFs5KoqEnymlqbMVjg99tLZNyEY62G6tQhoKG6qakb9ftrPVj6ybT2GRGm6noE2sbsvlsICNOQDEdx8edbfr+29wILfTbnXt0k64jzO7sI103x3OTnTDbraRzwxNsuyImxu7wytMJusBtKlyHdCrBdum7irhuZCSHMgnZANdFNKW0GVfu/LEsX33SvC9aYF0m5TxuEtvyvf27+bWrI+Xtec8C7aRP2gzLT36UlAiWDsgp9rlU7NyJoUVh85/tkpwfRUf+hNXizndNA10QGgeh/cN6R3cutkjP7RmbcxFXUTWL+Qy5PGk0p35Ube0bsv3FrJeRHzwJykhZLNdHLJd3uLujevUk9PNW1dnupFVczndxJA2w05ncx7XImRLXcHjIbgGR0L23fH1/byGtVBW81qOFk5LQivY3jv+kJM47geu/TOV4yJvI3mD0hK6cSXHrWBNClovwvbpu1mTombLKpduPucZ3qFGCt0ciisLjclAOgCAkkKXTHXoT5KQHXwTtYPSPyastwzD+DkxEcAVtR4Rqd0VxAGoVVw/F8CXqloMYKOInOLHTRaRNABTAZwtIvEi0hbA4XuRb1MAm3z3Rf9rJQzDMAzDMIyfHrY5Nwzj58RtAJr5gnEL4DbWpQD6ichcAEcA+Kt//QIAV4nIQgDTAbQB8CaAVQAWwbMrn7IX+f4DwN9FZBqAemRqDcMwai26sAABAABJREFUDMMwfuKoNP6//RQ71m4Yxk8CVc0F0D/gv6iOsAtj3Fv7HZqbQtdXwdush7kixjWoak7APQfAGN/9FYDg2ULKxzAMwzAMwzBE1TSHDMMw9jcKzhxDi3PSkM5R9/aXN1Pc5sP5CfCNnzgb3vGDt1JY8Tq2IZyzzdnvHT6M7eHK8vgl/85tzp52fTl/V3fstWxr+83jrozN2vP3cP+8jr9VfO9QZ2O8h+1ciKSezaPuHZ+xHePiTfxN6R7NC6LutodzGxV/zfaR1RXuIFlKU7YTnrWMv487tKtr0/QefABt60xu3+w+zlZRWToAyb3427/FM1x9tm0MfY889I3xJkkusdYduB2K8vhb8R2PdW1aMIPrvXFTFvkH/cHlW7WUbe9f+4xtYocnODvyzKZsH1lTxe2S2crlGxYCem1dB/Ifle7GQ2noe+QtW7M9Z3Kms60s2c522OFvmbfr62zDmzzzNIXtuuRi8j89t2PUfUqT7RTWtCPX9aXFLm6XSrZHbxnP7Z2c6Mo7SbmPf3NKIfm/etnNsT6duAwJydyIWX/6RdRd+cYnFPZuqN/a1bix0zyF6/I+2I74zDTXF/HxPDdbH89zvnyhizthSUcKu3gg21onNHfvhuKasZ37HW9xGU4od+XNbsb9P2kXryVHNXN25ffvYi2D61tzG7612c3rE7N5nSwP6SJkNnftVLSd59e6Yu7HPm0C43cXf1s9uxOXf/s3ro8zs7gvUpvxOnTbmjZR9/9ry3Up2BbSqMh2605m75CeBX8+G/fkuzY8O2TD3a51MflTmrgypQ/i34CdX3L5NeLy3VXEc/MJ5fIGuXU46xOEfxN2rXd9E17zRx3B/Vixxd27LZfHVYeBXLdVs51GRatsXlObdODFOy7F1a1gJa8zGa14zpcV8FgqLnDjJ6sl27InZXD71wR+lypK+X1quEzBs9CR0OffH17Ja+z49c83+mvjrYeOafQNaJupkxu9HWJhx9oNwzD2c4Ib84YIbswbIrgxb4jgxrwhghvzhghuzBsiuDFviODGvCGCG/OGCG7MGyK4MW+I4Ma8IYIb84YIbswNwzCMxiO4MW+Imm/xu2S7uZ8W1p2GYRiGYRiGYRiG0ciYzblhGIZhGIZhGIbxgxA0ezAYe3NuGIZhGIZhGIZhGI2MvTk3jEZCRHIAvKeq/RuKW08a7QA8qKpnNBh5HyIiFwGYqKp7b1z83fI5E95nzbaq6t58T3xf5HkKgJWqunQfpzseQImq3rM38VP/+Nuou+Svj1JY9rgc8sd1dGIvuz5ZTmFJ3dlOO71kZ9TdeXtIjKYbCxtpdUAwhzV6cECbfPKXTXJDofUB/EQ8LACWEHgunNSbBX3iTzmb7129MOqOLOYuyezMgkmdC53ddseHTuNM87eQt8Uve0fdVc9OoLDEX7MYf8fTnoq6U1pzZZKv4Lgt8x+MutP+wmEVjzzG/hVOUK3p4Wz/XzNxJ/m7dHf5TpnEAl997+U2K/v1hKhb0lkrYMUGFkXqk8PCUlrt4ldsZHGitlXsb9HRCf1VlPCfEzsKOd/s4e7esGjeKe14GcnsGxgfp46lsKp3J5I/6bIro+4m+SxoiPKQzX+8K2NYAC7zCRaIO/Xwy6Lupp1Y4Cn1xEHk7z/PlT8CHvt9T+IyvPCeE9+qCn1UMeFo/jDE4GVvR93LF/E8aZrCZUr/aFLUnfSLgyms5cQ15B9xR6eou+y92RR23Fwuf7tjXV8FxysA6C5+v5PS3601/efxWMm4/f/IH1nzdeBGFgfb/BZ/nbLf4W4uJJ90KIXFXcd1Sw4Iau3aFRqv408h/ynj33JhfzyMwoofmUT+zNP7Rt1lj3Geh13F47lijtOEaHMepxumSWpAVC20UEpT7vNdF7jyZh3RgsKadWhDft3u2iz+5PMorFUFC3TuOuulqHvwAwO5DO27hcrk1qmaKW9TWMuWrPMRN+aoqLt1GQvh7fott2+Q4G8fAOhaXvdT23eJug/78D0Kiz/0ZC7DOx9G3U0KQ4J7vx9H/v4zprl0jv8ll6Ewj/wocP62WSxKiCQWv8ssdn3RKpHHijTntVyXzXGeSp7j6NSd7810+jK6hOexDBxF/h4nv4n9jfDfBYbD3pwbxo8YVd38Q2/MfS4C0C5WgIjsy+94/xrA5Xu7Md9HeZ8CoG9Dkb4NImIPQg3DMAzDMIx6sc25YTQuCSLyjIgsFJHXRCRNRHJFpCUAiMgwEZnsuw8Tkfn+v69FJFNEckRksR9+kYi8ISIficgqEflHbSYicrSIfCUi80TkVRHJ8K/fKSJL/fzv8a+dKSKLRWSBiEwNF1hEzgAwDMDzfllS/TLfLCJfAjhTRC4Rkdl+Gq+LSJp/7wQReVBEpovIWj8tiEhbEZnqp7dYRA4RkZsBjAbwqIjcLSLx/v+z/fL+1r93jIhMEpEXACwKlTVHRJaJyBMiskREJopIqh/WzW+ruSLyhYj0FpGRAE4CcLdfloNEZK4ff6CIqIh08v1r/P7qLCKf+WX6LBA+QUTuFZFJAO4KlesSEfmwtiyGYRiGYRiGYZtzw2hcegF4XFUHACgGcHk9ca8F8HtVHQTgEABlMeIMAjAOwAEAxolIR3+jfyOAI1V1CIA5AP4oIs0BnAqgn5//bX4aNwP4haoOhLdRJVT1NT+N81R1kKrWlqNcVUer6ksA3lDV4X4ay+C9Aa+lLbxN9wkA7vSvnQvgY79uAwHMV9W/BvK5zk+jSFWHAxgO4BIRqT3fdiCAG1Q11hvvHgAeVtV+AAoBnO5ffxzAlao61G/bf6vqdADvALjOr9tMACki0sRv8zkADhGRzgDyVHU3gIcAPOu34fMAHgzk3dNv9z/VXhCRKwCcCOCUQNvVhl0qInNEZM5/3vosRlUMwzAMwzB+3KhKo//bXxFV+waqYTQGvs35VFWtfdN6BICr4G2wh6lqvogMA3CPqo4RkT/D20w/D2/zuzFot+7bgY9S1Uv89D4EcDuALAATANQaZCYB+ArAbwHMhbfhfN9Pp1JEHgXQDcArfj57fIjaf5t/rarO8f25AA5T1fW+/zB4m/0sABnwNt6XicgEAJ+o6vN+vF2qmikihwJ4CsBzAN5S1fnhfETkNQADANQacTb161AJ4JZYR9/99vlEVXv4/v8HIBHA/QC2A1gRiJ6sqn38Mr7nP4SAiDwB4A0AFwN4EcAxAL4AMEBVrxeRfABtVbVKRBIBbFHVln46k1T1GT+d8X7/bYS3MWeD6RAftD47ujgngtfpYaPZALxsi/uRuWUz27/d/yu2cXv0SRf36Hi2IX1OM8h/VVuXz0ub2Irh+BS2iX66smnUfcNYtkf/v8/Y7n1r4JnEuEq2gT60N9sNJzV35V09g7/h/mAC/7gmi3vefEYZWxJ0yuDviE+udGn1qOSu6NCE43Y8OZH8/3nFlXlgBdsFvp3KlhVHlLsyFsRz2O7Q4/G+VS6tTm0KKWzJFu7XOxJc3/ytmsM+CpXh6kA/PrKFbVNvfOVU8n9z4X+i7lYDuG6J3TmfuS4qNsQlU9hLCYXkH1edFXV/nMh22C2Fx2hTdeU/KcK2qr3/3JH8ufeuQ13kl7It8+5AukuSeXyc2pRtSjtOcjoP/xh6E4VdMYa/db9+isunVSceO4+ub0/+j6udfXqbeJ5vz1/Vmvw3POTq/rvUQgrbWcR1m5To/Kcm89x8pjKL/P0r3cDLCxnczBPum9MrnP1sNioprHNnzuelLW6NqAr93dungg1Mu6c5PYvUNJ5/Z+Rzn5+V7GyMw+l0TeH2XhhYh6Yk8fi9PMLlfzbOHVz6cxfWpPhqGfdbSZxrs4VJNRQ2spwrWxqImxNhO+f2bXjNnb3N2XB3FH7evqOG59Rbqc6G/sBqDjuuI+s2NB3hxtaCF3h+NUvnfO6rcWPn4lD7Lornw12HZ7q1PT+kK/FWMufzx06uTdes5LVjQkrdG6ORVVy3g1LYln1lqevjX9zOv0sLb1pL/imBeREX2u6c3ZbbbP169zu1Lo7txvvG8zjbWunaZVsCT6IDhMdvUaA+8eD2rQSv1QP7ujZbvJTX6hThcbcd3N5BmijrLbycypV/LPfVRt+ZbhpxRKNvQNt/9Xmjt0Ms7M25YTQu4cVJAVTDzc3oL4Sq3gngNwBSAcwQkd7Yk+BfIzXwRB8F3gZ1kP+vr6r+WlWr4b1xfh2enfVHfj6XwXvT3hHAfBFpISJP+8e8P6inLkGVmQkArlDVAwDcGqxHqIzi5zkVwKEANgH4r4iwGouLe2WgHl1UtVYdqhQA/JMCtUf/axWdYrVJHIDCQFqDVLVPHfX6At5b884A3ob3Zn80gD2O/PsE+7Q0FLYYQA6ADjB+lAQ35g1xRPne/+4HN+YNEdyYN0RwY24YhmEYxv6Nbc4No3HpJCIjfPc5AL4EkAtgqH+t9gg2RKSbqi5S1bvgve2OtTmPxQwAo0Sku59Omoj09O3Om6rqBwD+AO+NfW0+M1X1ZgD5ADqq6sX+BvY4P81dAOrbpWQC2OK/ST6vnni1das9Jv4EgP8AGBIj2scAfuenCb8O9OheVTcENtuPxkijNl4xgHXiqcFDPGplasN1mwrgfACrVDUCYCeA4wDUSrtOB1Arl30evD6si6/hve1/RzylfcMwDMMwjJ8VGmn8f/srtjk3jMZlGYALRWQhgOYAHoH3pvkBEfkC3pveWv7gi6UtgGdv/uEeqcVAVbfDU1d/0c9nBryNfSaA9/xrUwBc499yt4gsEk9obiqABTGSnQBPqG1+HaJmNwGYCeATAMtjhIcZA+8t/dfwHkg8ECPOkwCWApjnl+0x/G+fgzwPwK/99lwCoPYbLC8BuE480b1uqprrX699U/4lvLfutWftrgJwsd+OFwC4ur5MVfVLeDbu7/t6AIZhGIZhGIZh3zk3jMbC3/TFEjD7Ap6QWDj+lTHi5gLo74dPgLdpro1/QsD9OTwRtTAHxsjntBjxwnFeh3ccvpacUPgj8B40hO+7KOTP8P9/BsAzMeKPCbgjAP7P/xdksv8vVjlz4beP778n4F4Hz348fM80hPqlVhfAd98B4I5QHvyBYsSs6/iA+2N4JwEMwzAMwzB+VmhkvzT33i8wQTjDMIz9kPxfHBZdnIs3s0BOWGW0zTlOSKrnPXMpbEJSf/KPOM+JAd37Olsm/OFIFsXSanfuKyh6BQDby9jfp5u7N7kZnxeLS+Pyjp7qyjDn9lEUtmQ8C/p0GejEgHZtYgGc1lccQP4FN+e6MiSweE6XUcXkXzetSdTdrDmLYDXpwsJRC6c70abhv+cybHt1O/lb/cIdJKnexHkmtG9C/mB4fGsWB1v3FnlRWe2EgwpCgkk92rIwV0Kyq3tcPP/Gxydz37y3igXWThm8IepePz+Lwt6P4zJeOdqJF705ma00ju7EYkvpnVw5kkbws8et/2FRtxaDXPlnTeTDJelxLHSUGOfqkxDPdVtTxeVtByfO1boFizZldWbhrkcXO1mI6+f+jcIqH+TngwuecIJm60IHiU46iwXAnnrDiVllhI5VjkrjfozUuHmzaHcWhR3Shds3IdUltmpJNoW9ksLvYc6pdON7wPjOFLbiNp5/3U9xfZEwmp/lFtw/ifzNbjg56q544R0K272Ox2HWad1c3K9WU9gTc1iS4/Jz3Xox8Wke+y8n89x9+mpX9x5/m05hX3biMRrcHOws4PUsLZlF6hICInBpTXh9+OYbFqrsf5yb11++14LCujThNaHNAFf+RdO431o1YcmSsdvXR93LTuO6zAjlEx+QPgmuzQBQUsBtePhWN+efT+L3BYnC/db3ECcIJ3G8rpdv4bjB9p25ui2F/b4i1oE8jyltuf9rInzId0uBm9fb43g9bg+ex4OfPMSVZ9liClt6PwuXBvNJTeL+7zSC14uaEjffls/mfhtwGvfb/Dec9V34uHJyAq9nmRlOeyS7F4/tr2ewQNygYU6YsrqU+yJSzf4bNvL4eCr3tUbfGW8YPrbRN6AdZ3/W6O0QCzvWbhiGYRiGYRiGYRiNjB1rNwzDMAzDMAzDMH4Q7OB23dibc8MwDMMwDMMwDMNoZOzNufGDIiI5AN5T1f4Nxa0njXYAHlTVM/ZZwX4i+N8hP1dVC7/HPE4BsFJVl37L+8YAqFTV6Q3EOwlAX/+77j8oIpIFr/3+/S3vK6kVt6snTi6AYaqaX1+8WuIznClU875s/5Y0vBv54w46LOpu+xDb7478LT+D3fW5s3k8NmDHDACR3Wz/lnRwL+eZsonCho4J2THmuvImdaEv3GFjSPpuQEpW1F35Bdse9rmMbRPjOrovBiZMnkNhu99m+/p27dxPWlo21yWhfRb52+Y4W+DUbvxTmHjgAC4vFkbdFV9TEFod1ZT8xdNd+2YOSqEwrWQ7eElyfRMp2I34Vs4mPbtDIcVNbsEGyh/Mdrbiw3txXadNZtvEwy5wtovLXuTxcNbJO8ifcMq4qLvDXS9T2IVFbHcZn+nsPY/rsZHC1q1sTv5+A10Zapavp7CgjTwAxLdx7TCg71YKyzg6h/zVS12+lVu5HVoUcHkzu7h8akr4tU3qiYPIf0VLZ58atjFPuuoO8rd89XJX9iJOd9csLkMV3HiZmlBGYedc24v8uXetjLpHduB2iEvgfIKmt4N+zWaU3b7ge0vync1xzbxlFFZWxXNXSwP99jXP1cwhbF+vuc52fPmkLArrOZzHmfR2ts3JoW8a5c/ldqnOdbb4A5qzjXHTHZxPxVerou62KTwGs8dyeXdOdbbBvc/iMkg6t0PxJKctUbqTy5CVxnbOkpoYdR/QiTUpMruGv9/k+qpXH15TE1miAm13ufokjhxIYQPXziZ/Wi9X17gO7bkMeYWc7qsu3U4tWSMhqHsAAIk5Afv6kM15ZEMB+Zsc4uIO3s11a7uF+yZIsy6h9gztVNK3OrvsA1rx2pHYjtfc6vfdj8/6j/j3rsdYHmcVG936UbWb48Y14T4v3+TK0Lr5Lo7bjm3Qex/gbPojvEQBIf2YjDHu969yEZevX69t5I9v4tZySeJxldSbbcwzXuT67A+YIFzd2Jtz40eHqm7+MW3M/W9o/09zTUT2amVV1eO+z425zymIrTJfJyKSAO9zaSMbiquq7zTGxtwnC8DlDUUyjH1NcGPeEMGNeUMEN+aGYRiGYezf2ObcaAwSROQZEVkoIq+JSJqI5NZ+81lEhonIZN99mP8t7fn+d6czRSTH/841ROQiEXlDRD4SkVUi8o/aTETkaBH5SkTmicirIpLhX79TRJb6+d/jXzuz9hviIjJ1jxJ7cbqLyKd+nHki0k1EMkTkM9+/SERO9uPmiMgyEfk3gHkAOorIdSIy28/31kC654vILL+Oj9VuxEWkRET+KiIzAYyIVe4YZcwVkZaB/J8QkSUiMrGO75E3lP/tfn1niEhrERkJ4CR430Kf77dBN7/954rIFyLS279/gojcKyKTALwM4DIA1/j3HSIiJ4rITL9fPxWR1oE+fSiQxoMiMl1E1orIGf71MSIyRUReEZGVftuc59djkYh08+Nli8jrfrvPFpFR/vXxIvKUiEz2073Kb447AXTzy3h3jLZqLSJv+m2ywG+PYPgYEXkv4H9IRC4KRLnOL+MsEekeqz8MwzAMwzCMnye2OTcag14AHlfVAQCKUf+bymsB/F5VBwE4BEBZjDiDAIwDcACAcSLSUbyN/o0AjlTVIQDmAPijiDQHcCqAfn7+t/lp3AzgF6o6EN7mMxbPA3jYjzMSwBYA5QBO9fM4HMA/RaT2rE4vAM+q6mDf3QPed8UHARgqIoeKSB+/7KP8OtYAOM+/Px3AYlU9CMDSOspdHz388vYDUAjg9HCEvch/hl/fqQAu8Y+kvwPgOlUdpKprADwO4EpVHQqvv4JHwnvC64PTATwK4D7/vi8AfAngYL99XgJwfR31aAtgNIAT4G2eaxkI4Gp4/X4BgJ6qeiCAJwHUfhP+AT/P4X79nwzc3xvAL+D1yS0ikgjgzwDW+GW8LkZZHgQwxW+TIQCW1FHmuij2y/gQgPvDgSJyqYjMEZE5E9Zt3uNmwzAMwzCMHzsakUb/t79iNudGY7BBVaf57ucAXFVP3GkA7hWR5wG8oaob3d43ymeqWgQAIrIUQGd4x5P7Apjmx08C8BW8hwHlAJ4UkfcB1L7lnAZggoi8AuCNcAYikgmgvaq+CQCqWu5fTwRwh4gcCiACoD2A2o9Or1fVGb77aP9frcVqBrzN8wAAQwHM9suZCqDWOKsGwOu+u65y18c6VZ3vu+cCyIkRZ2w9+VcG8pkL4KjwzeKdRhgJ4NVAvwQ/ovqqqtaE7/PpAOBlEWkLr3/W1RHvLVWNAFha+3bdZ7aqbvHLsQbARP/6IngPSgDgSAB9A2Vr4vclALyvqhUAKkQkD67f6uMIAL8EAL9eRfVH34MXA//fFw5U1cfhPexAweljTMvUMAzDMAzjZ4Rtzo3GILzpUADVcCc5oooeqnqnvxk9DsAMETkS3iY1SEXAXQNvXAuAT1T1nHDmInIgvE3p2QCuAHCEql4mIgcBOB7AfBEZBOAeAIMBbPbjxuI8ANkAhqpqlXiiX7XlLw3EEwB/V9XHQmW5EsAzqvqXGGmX125sVbU6XG4ROQrephkA3lHVm0P3h9slVUQ6AnjXv/aoX6668q9SjX7sorZdw8QBKPTfuseitI7rAPAvAPeq6jviicWNryNesB5Sx/VIwB8JlDUOwAhVpRMX/mY91rhBKN7t8MYE6qljkOA4BgJj2UfrcO/Byq8C4j/x/Hyj6ZIN5K96/MWou01IRWj+oywmV6hOcKZFfAWFbZ3Llg/N85xYVERZ+CyxXzvyzwgYg+Rt5gdonUNCaLuSnWiaVrKQzfR/cXl7dXRCR+mtWU1n+lwuw8okJ83QeRM3b7/VLFb0WXmHqPvIrTspbOsHLKA1cLSrz4qvWMgoYTaXv/dZmVH3Wy9xX5x41BbUhaTzUKmp4jasck2GDjXcb1OmsIjezgQ3BHPf4LZfoZnk77KG6z71gi+i7nS0orCBh7K4VVw7Jzy38E3um7z4RPL3LnDtv3Mph31SwOVv8Zzru86h830dqr4h/6JV7plaWGqrKI6lOvrucI04pZrHc/95fFKleWpa1F1SwWMyKAAHAF2+cIeFWl5yMYVN+4rHaEmyq9uQCM+3DfcuJ//KUieo1SeRa5eqleT/bHVASGohBWFhIgsEnlftfkKXvJVGYV8n87jruLAw6p63IZnCasD+9nGuDd9I4rAxM7iPk2fMjLoP6M/zrSA07iZPcvduTuQB0RvcDnmLXJu2CcWd9TzPsVWBtbLH85xOShzPm4KIa8PwutnzIBa72/Ch0wZdVcrPfHsWFpJ/S7GLWwYer1nC465NkvsZ+/QGFug8qB95UbHOxa1azHGXruL2bRMYk28UNaOw8KuQo151z6MTE7mNqqpYE3X3u+7nf+HOUJ5JdT/X/nxeFvnbKbd3TaBU723jn+1Rs7nEqQGxwYLQerDyY16f28W5eTE/nufFAev4z87tcIJr2xJ4nI16qpD888udRkhGhOdxi9D47VDg9GJ3FfFavbKCy6tO+xApIVHFnPksRJm1x58ijY99Sq1u7Fi70Rh0EpERvvsceMebc+G9wQUCx69FpJuqLlLVu+AdTe+NvWMGgFG1dr3i2bX39N/0NlXVDwD8Ad4R89p8Zvob3HwAHVX1Yv9483GqWgxgo3hK5RCRZBFJA9AUQJ6/MT8c3lv7WHwM4Ffi7N7bi0grAJ8BOMN3Q0Sai8geacQqt6rW+OUbFGNjHhNV3RC459G9zT/ELgCZfnrFANaJyJn+/SIiAxu6z6cpgNq/Gi7cm/J/BybCewADAPAfutQHlVFVb6htL//SZwB+56cVLyJhFa/18N7UJ4tIU3gPU4KMC/z/1beoh2EYhmEYhvETxzbnRmOwDMCFIrIQQHMAjwC4FcADIvIFvLeYtfxBfKE2ePbmH+5NBqq6HcBFAF7085kBb2OfCeA9/9oUANf4t9ztC4kthmdfvWDPVHEBgKv8e6cDaAPPDn2YiMyB9xZ9eYz7oKoTAbwA4CsRWQTgNQCZ/ufIbgQw0U/3E3g21mHqKvf/xLfIP8hL8ITNvvaF184D8Gu/j5YAOLmO+94FcKovtnYIvDflr/p9vlefF/sOXAWvfxb6Jg+X1RdZVXfAM4VYHEsQDp6N++F+H84F0C90/wYArwBYCG9shD68hWTxBP6uxj7qQ8MwDMMwDOOngR1rN35QVDUXsT/D9QU88bBw/CtjxM0F0N8PnwBgQiD+CQH35wCGx7j/wBj5nFZfuf04q+DZHIcZEeMaassYuP8BeAJl4XRfhqdmHr6eEXBvQYxyx7gnx3fmB/NX1Zjq7t8i/9fgPVCArxcQ7sNjYtx/Uci/Ep6NfZC3Y9w3AX6fxkgjw/9/MoDJgetjAu5omP9NcffhZhdnfMgfbKtzw/EDYdsQ4+FDqK2uRwxxu0Df3BoOMwzDMAzD+LmwPwuyNTa2OTcMw9gPKatx9nEVEbaV63VyEvlXPOvs1mpCNmyZKWyvt7za2Z51SmSbxlcq2N7w8gRnMxgJ/ZBWLmA7xrVJzoa7ZwXbvy1P5vLPKV7rwqb0obBkCVsOO76Y2578S0L2sdPU2U+fHs+24UnJbB+ZFVBDKCphe7y40N8MCe2dLWVyAqeTmsJtOOFVl2/HkH3h3I9akH+XuJ/gAa3Znrt0F9s8flmV5TxJwMp4Z+N9ZBnnsy7RVWBdpCmuPmBj1P/ZgiyKm9yHLTPyFru+6l7DdUtoyzalNZvcgZe1idkUll3NZZo22dnsJoWswxelcj5ZCa4MOdVsmNjkWP7Ge2SVG+9N4jidUPNjw+70qLsLuB8jIevaVp12ufKt7kBh8UVcpqCdeeYTT1PY/KE3kb9rtcvnzFvZDjf3Lrb/nxkYlgNC9r3JmWzjH7Rl3RKytV4V2UX+FRE3z+emcDrLatgW+PImLrxfc7atzi9MJ/8ycWO2bajtk8F9kyKuPquW8NjZkcxr1spkZ7/ejpPBxgReC8sKsqLumqRCCstM4JsXJbi+OLoFa1JMyWdb8a4Bu+c3Q/b0v10dssWPuPbPCOmhZndnGZaaFS5uSTnXZb2yJkGNuntbxXEbVZdxn2cc4NL65gNe31LjuM9rAvbKXau444rjOd30Jm6+bc5nm+imod+alEzX3tk7+XepRute5xNDxsjN0vkjPbklLt/myr8t+aFdTavA+lEdEhPOjNSlVQs0C607aUk8dqTStW/farZHb9mV7b2br3DrZvi4cnwc55MSmNdFhVzeXinF5F9T5toh3GYFZdznJSl119XY/7DNuWEYhmH8iAhuzBsiuDE3DMMwjP0BVXtzXhdmc24YhmEYhmEYhmEYjYxtzg3DMAzDMAzDMAyjkbHNuWEY+wwRyfEV77/r/RNE5IxvEb+k4ViGYRiGYRjG/oJGGv/f/oqofQXeMIx9hIjkAHgvqH7+Le5NAPCkf/9re3lPSVAp/afE5pGHRxfnsmIWCmrRj8V14jKcfMhpE/mZ642VLPg1/LdOQGfZUyxk0+/+YeRf/3/To+7KShbeWRJK99hDNruyz2Exs5zftyP/+Q9si7qfv38kha279gu+93fuy36Vc9dQWFxGIvnju7q4ZVNzKay6lO3bJNBMmaf2prDK6SvI/9/pTojupOytFFZVzu3S5hgnDlW9iYW4Egd1In/pp66MmecMpbA1d6wkf6fDndjS1umcZ6tBLMQU1FsrXMHSMou2tiT/+kROayRcmcuquH03Cwtf9UsrjLozmvFYqqnicZh9lBsTy1/ivuh9DvsjO90zt0XvN6Ww+Dj+iyoo1NSyHT+rW76GhcaCgmCRkL1j35N2k/+fHzrxvj+dykJMu2ZxPnPXOrG7+clc7z/P/Rv5bx12Y9RdDv7766I4Hi9dr3Dj+YEHWBTrwHIWqBoy2s2pRdO43u+l8Bi4LNMJu7U6hkXd4nJYdFFyukfd6676hMNCYladznZL8YzHuX2HHcNfy6ze6XQT4lI47k2zWYztH1e5tebLu7iNXkpl/YV7D3J1O2M6r5u3VrGA2cALnEjW6ldYMKttF+5ziKurhF5rrV3GQo853Z2w36IVXJf2GTx22o9086ZkNbdneif2B+vzl8osCguLvJVFXJ8feCqL/G2ZQl5cXuLyCafbrgm3d6fz3HysXLKFwtbP4t+EnIPdvUunskDnzQl1P1d/ZQivJWvnslBpl4FOvC8sEto5iQX3Wndw/ZjRh+dB6Qpus20b3Pho2YbLl5HDfRFcVyvKOd1W/Xiurp7hyt+sKa8ziSGhtqCCebPBnOc3k1kgsG1vV7c4HuqIcBOidDuv5Z3nfdroBt+r+/6i0Teg3Zd+3OjtEAt7c24Yxr4mQUSe8b8t/pqIpInIUBGZIiJzReRjEWkLACIyWUTuEJEp8L79HUVE/ua/SY8TketEZLafZsxPkcWK46dxdSDO7SJyVYx7L/HvXSAir4t4ssN+/g+KyHQRWVv7Vl887va/h75IRMb5118WkeMC6U4QkdP9EwVfiMg8/9/IcBkMwzAMwzB+DkRUGv3f/optzg3D2Nf0AvC4qg4AUAzg9wD+BeAMVR0K4CkAtwfiZ6nqYar6z9oLIvIPAK0AXAzgSAA94H3nfRCAoSJyaDBDETm6jjj/AXChHycOwNkAno9R5jdUdbiqDgSwDMCvA2FtAYwGcAKAO/1rp/n5DPTLd7f/wOEl+N9VF5EkAGMBfAAgD8BRqjrED38wVsOJyKUiMkdE5jy3bXOsKIZhGIZhGMZPFPuUmmEY+5oNqjrNdz8H4P8A9AfwiXjfGY0HEDwP93Lo/psAzFTVS4HoxvtoAF/74RnwNuJTA/fEjKOqU0Vkh4gMBtAawNeqyh/r9egvIrcByPLv/TgQ9paqRgAsFZHaM4qjAbyoqjUAtvlv/ocD+BDAgyKSDOAYAFNVtUxEmgJ4SEQGAagB0DNGGaCqjwN4HOBj7YZhGIZhGMZPH7M5Nwxjn+HbnE9R1c6+/wgAVwJoo6ojYsSfDOBaVZ3j+ycAqAYwGN6b5p0i8k8AK1X1sRj3l6hqRgNxxgEYCaANgGdU9QMRedrPY7OqHici6wCcoqoLROQiAGNU9SK/PFEb+EB+9wNYqKpP+df/C+BVVX2n1g3vLf2LqvquiIyHt+m/Ht6JpXJVrffh6Eetz44uztNT2S740tZs67c0t1XUfX8y20oeAbb1Swss+Yvj2Xa9a4QN18oCdpYJ4CNg1SF72Y7VLnxVYqTeuK+VOJvu61L7Utj2kB3r8HJnk5eXwE22MJHt9SKo+/esXYTv7V/h7A2npPIhshYR9s8Q16bPnM9215/+h235CuLdvQPj2V5zQQ3bvFYFmnR36Bzb8jjum3x1duUTLmab/nufDpef+yov0Ka7hftmaYTHywjJirq3CdtkboiwveSB4uxPw23/h4s5nzsmuDIdVcb20o+ncD4dJSXqbhXhsV8VOomYGsg2L2SPHj60mB3o18OE6z2zhu1lJ+imqPvseLZr5dIDJYF50rWac10Zmgu3zLkt6v6q//+jsE9TeP7VBNp0Tk0Bhb08mo1Kdy524/CNolYUtiqONQmy4eIWg+fQRmV72aMjrl1GJbDtcmkFj/2ZCW5cTgbHvaCSx/7mRNcXGSFhpkmJXIbg6G4Pnn9vlrEOxZkpzkZ+lnKbHRVaC2eLs0/uKmzPy60CHF7mCjk/ZMMf1pU6ILBmzQytLWeAx92kiJtDyaHlqzi0JkxWZ8t+bjXXZX4SlzhD3c2DKzjhZSFdhKmBdDvHsYTLmFAfb0104zu0VGN4Da8P7yW5eZwVWlM/x07Uxc3VKeR/M4n7fEBA/+Tp+O0UdqKw3kKTQLNsTuACt6nhuZoYCA73fzEvQ9TnPSs4doVwuouTnT+UDPqE+mZTYF4Uhxq4WWhdHxpo77Xg8VsQz3HXxnMZ78l9sdHPdK/ofWyjb0B7Lf+w0dshFnas3TCMfU0nEandiJ8DYAaA7NprIpIoIv3quf8jeMfH3xeRTHhvsX8lIhn+/e1FpFXonvrivAnvLfZwPx5U9WJVHaSqtfbhmQC2iEgigPP2oo5TAYwTkXgRyQZwKIBZfthL8I7jHwL3Br4pgC3+G/gLsOdvtGF8L+SF/4I2DMMwDGO/xY61G4axr1kG4EIReQzAKnj25h/DO+7dFN66cz+AJXUloKqv+hvzdwAcB+AFAF/5x+JLAJwPz467Nv5EEekTK46qVorIJACF/jH0WNwEYCaA9QAWwdus18ebAEYAWABAAVyvqrUy3hMBPAvgHVWtff35bwCvi8iZACYBKIVhGIZhGIZhBLDNuWEY+wxVzQXQN0bQfHhvl8Pxx4T8FwXcT8ETjwOAB/x/4fszAu6YcXwhuIMBnFlPuR8B8EiM6xeF/Bn+/wrgOv9f+J4qAC1C11YBGBC49Je6ymIYhmEYhvFTRiP75Yny/QI71m4Yxk8WEekLYDWAz/wNsmEYhmEYhmHsl+z1m3PxzoqeB6Crqv5VRDrBE3ma1cCthmEYjYKqLgXQtbHL8V3YkOiW521gQae4eLYjnhcQKMoOCRttD0nblAdEpspDUkZL4liIKT3wE5EZMpNvV8PPdlcEhK/6VXLYopBY0fribVF3JCQIt1M47rJkV4aVIZG05qGfsGeKF0XdpzfpT2EpIdPrtUnu3tKQxFd86LF1u4DYzrZ3WGSqPCRAtCPQTMurWVxpRRK39za4+gyMsAhSoXJdd9a4vrn3aU43/JS9IGBn3o+TwWzWVkKnuHTyT61xQk3N4zhygnBOoyucKNmf4liYaegjbcm/LdW1cXmo39KEO2droF2yQ0JH/ULiS3MD4lbxIQm4sEjd7kDwJGUBuKqQCkQbdyhnD8GyqQk8T4ZEXBnPvJXlMG66jcUbgyJwIxbfRWGvDOMDNUFBuE7x3OevfkGHc1ARqNv0OBYd+3DbAvL/pdXoqHszuC5lEZ4LgwKiU4mp3PYrK5uSP3hnvzi2DCoJTaqgFteKkGje+moWkxuR4Nq0MtSnu6pYhKwoUMbwWhjS3kKauE7vHVqzHpZN5B8a1y7qng0WevxdOc+Tr5OdsF8ktMYuCrXZIQHRyEnCbVYcEm8M1if80jGnhgfwNwEBsBXJHBZeL7LjXLrtlAXgtidwRmmBIuUmcPlqqjjloAhceP3Njue+CTI/FJYVundLYPloEerjvFCbNQn8ToWFMlck1C2i1zwUlyUrgfLAmiWhMZkVsp6rFFfg1NC3tXckcJttCPRbM+V+axIyylsc5wQYt4b+Jgi39849ZCwbH9Mjr5tv8+b83/BsLM/x/bsAPLzPS2QYhmEYhmEYhmEYPzO+jc35Qao6RES+BgBVLRCRpIZuMgzDMAzDMAzDMAyjfr7N5rxKROLhKRPD/3xQ+POOhmEYhmEYhmEYhhETE4SrG9G9PPQvIucBGAdgCIBnAJwB4EZVffX7K55hGMaPGxHJAfCeqvZvKG6Qsmf/El2ca+bzV+e0ki3g4gf0irqvvmMjhT1472Dy5932edS9M5/tjVu0KiF/1lD3/HbFe2kU1v1gtr2uKXG/JSmD2B528ztsF/pwWVbU/bexOygs6VcXkT+yZI7z7OZ0Vt+/mfzbylwZR93MtuDSmaUHar74MuouncVlaHLNieQvf+a9qLtgJR8Ya3N5L/Ivuc21f99L2I68ej3bZWt5XV/2AyRki7hjvrM/nFTAds3n3tmZ/HOuXUH+QWc7zYIVr7EdY9/r2nC+LQJ9V8V2isXPziS/BgyHK3dzul9vbE3+YV22Rt3bNrJt7Wxl/2Fpzu6907XcvuEySY+AZsGObRy3nO2pkenszKunzqCghKOPIL8uXxp1f/M4j7NwmTbcuzzqjtTwH5w1IW2GVyLO5nhnyJL1vjl/J3/QPr1Lx50Utod9bz83lpJ/eRqFffHLL8k/8iJn07/xzXIKe7+8OfnHtXV1T0zl9zEpXfj9Tvk6V58PV3WgsLNu4jUhOJdl8EEUdPqFb5D/5mo3jwaM57E+/5ZvyN+pk1uXbt3GeT54z0DyP/sH12+/DM2hrXeznFJmO9dm6xZyG4Xn+dpn3Dra/eY+FIY0XnNr5syPuuNac3mlBedz1a3rou67R/GalfwLbkOk1G3TjRRey6+6ys3rB67hMugu/k2I69LFhW3Po7CyT5aTP+3Xv3Bx83ntC/9OBbnvDBbKiD94OJdpp6u7pLMWAzJ4Lal84+Ooe91kbvteD48h/6773nbJHN+TwqRDxzrLG1nO621cO177kO7KFCx7LLR4V8DNbR/fvQvHrXTres2i1RSWcPAg8v/7xg3k/9M3zzX6znhpt+Mb3eq875r3G2wHETkG3peA4gE8qap31hFvOIAZAMap6mv/S7n2+s25qj4vInMBjAUgAE5R1WX/S+aGYRiGYXx/BDfmhmEYhrE/ENFGfz7QIP6J8YcBHAVgI4DZIvKOLzYcjncXgI/3TOXb820/pbYNwBcApgNIFZEh+6IQhmEY3yci8paIzBWRJSJyqYjEi8gEEVksIotE5Bo/XncR+VREFojIPBHp5l+/TkRmi8hCEbnVv5YjIstE5Ak/3YkinnSsiPx/9s47zKri/OPf73ZggaU3gQWkF0GKoojYu9i7sUWjUYkaY4xGQzS2aOxGRH+IvaOxiw2UJr1Kld7rUrfv+/vjzL1z3uO9u4uii/p+noeHmTNzpp+5O+fM+52eLo3xAK6usoobhmEYhmEYP4Q+ABaJyGIRKQLwKoCBCeJdC+AtAOsThO02lV6ck7wTwEwAjwL4j/v3wJ4ohGEYxk/MpSLSE0AvAIMAdAfQTES6iEhXAM+6eC8BeEJE9gNwEIA1JI8G0BbBJN0dQE+S/V38ti5+ZwB5AE53158FMEhE+u5OId2Lg8kkJ//fl9N/UEUNwzAMwzCM8gn/zeX+XRGJ0gxA2CZgpbsWTqMZgFMBDNlT5dodQbizALRxbw4MwzB+SQwieapzNweQAaA1yccAfABgJMmaCBbsbwOAiBQAgFucHw1gmrs/G8GifDmAJSIy3V2fAiCXZG0AOSIy2l1/AcBxlSmkiAwFMBTQNueGYRiGYRi/FmQv2NYe/psrCYkKGf3b7GEAfxWRUnLP1Gl3BOHeAnCViOyRT/aGYRg/ByQHAPgXgKNFZBfJUQAGA5gM4BgAFwPYAOA6AN+KyD6R+/8DYIGIPBW5nouQ0BvJGxEs3B8GMENEWrrr3QC8LCJdSD4LoAeA1SJyfHnl/qLRWfHJuWWDrSqsbkdtR1wU0tq5YrEWyDmvuLbyH7l/ciGe6hcMUP43bvwu7t4/M0+FbcvPVP7Wbb3QzdT5WmRswJ1NlP+qu72I038v1WJKc4fuVP5O19WPu2WLboctn2lxneICL0qWkqp/2wp2pSv/xjwvDrT/PVos7rs7lTkZdhZ4EbjlooWWOlbTZapV14uQlRRqkbTGF7VQ/pX/5/ui+a09Vdi2Z8Yqf7X2vr3fe0+L3fWuvVH5azf2ZZjxrW77tWn6nfwGXUT0K/FCXfOphaM+TdOCfH+hF2drfaZOd/v4POWv0dG3/4gPtaDd6adqsTOk+UJ98lotFbQrRf/xU73M93OmaMGy9WmR9i/2omnpkb+venRbo/x3L/TtdnnqdpTHgp3+GftGD2fcNkiLUN31qB/fUUG4cwt0mfrOvi/ufmT/21VYZuRPtzNaroq7N67Sc8BbZboNc0t8G57QSQtFrZyfo/wdHvDj8o3rF6iw3tV1vzU90M9Lr36px91RdfSfjkWFfrzUa6mf+f9boMW3rj7BP+dv/08Llk1M1wKBN9X3z8K16/WzepzkKP/5A3353x9RR4Ud0VHPk8vmeXG2lh10vafP1nXt3NrX9c1VTVVYwxLdcQNae8G9gu36GYrOYYM2+sF1OHJUWJdC3Q4LM/zzdlYv3ceTx+nyPpHphceOL9PpNi7Rz1T/g31518/R7btqixZja1jDzxczCnS6r6flIRn/ydZ1mZRXX/kPzfVleHC1/q05uUA/Uww953nQvwFta+m5e/02/6wyMj+kUvu3lPnfhB0pehNyr3p6Pp66yZc/p0yXTyJrv32y/VyTXUeLNc5fodshi34+a1pfz1GF+XosNT9Ol7/m4x9W+cp4VquTqvwDRNcl75XbDiT7AhgsIsc4/98AQETuCcVZAr+Irw9gF4ArROSdH1qu3flyfg+AaSRnA4jPwCJy8g/N3DAM42egNoAtbmHeAcCBCCbQFBF5i+R3AIaLyDaSK0meIiLvkMxEoM75CYA7Sb4kIjvcFqbiZJmJSB7JrST7icgYAOeHwi75KStqGIZhGIaxt1PJb8NVzSQAbUm2ArAKwDkAzgtHEJG4jD7J4Qg+2rzzYzLdncX5cwiU6GbBzjc3DOOXw8cAriQ5E8B8BEddNAMwimTslfff3P8XAniK5B0IFuBnishIkh0BjHdblnYAuABA8nOwgEsADCO5C3tIvdMwDMMwDMP4eRCREpLXIPg7LhXAMBGZQ/JKF77H7MzD7M7ifKOIPPpTFMIwDOOnQkQKkdjm+5EEcRcCODzB9UcSxQfQJRTngZB7CoDwobqDK19iwzAMwzAMo6oRkQ8BfBi5lnBRLiIX74k8d8fm/EEE29nfhd7WPnVPFMQwDMPwvNT0gvjkfOqftK3ctIe0fVmDOt5m8Lqd2v7tjpBtHACsKfF2gp3qa9vJrVu1wWyrA71N3srJ2p6wWjWtDRq2IS0s1u9909P0JoPPS3Li7qitZNvm2l5vxxZfprDtNwBsKdF27/MzfL4tivUGrwFHrVP+/37l7S4PL9a21NFfxdYdvc3rU4uUJAGOK9X2st+VervFtenanK1HkdYK6HGK78dlI8t/Vx6u+8Ppum7HlGgb44LIOSwHpfp+bHOhthNlPW1rWzxtUdw9+WNt49il01rlTwkVeem3dVXY/9J1PmchVNcCXd5pmbrAZ2Vuibu/3Zajwg5qt1r5Z4b0DYoj9puFEXGebnV9P2bW0LafS1fodmgUeqam5Wk754P20e2wa7vvm/R0PdZf26bt60eV+TK0SNXtcHsD/Ty+ts6P0T9NvUOFzev9J+V/O2RXfn5N/QyN3aI1CrJL/fiJSA5gRJYeo1cV+adhUqrWIGhbpNuwbQPfb8N26DZrX6z7uFWpt6etHclzZJnWyehW6PPZRZ3OEWdtU/5l73v3TRF76auKdLrhcXdaqrY/TkvT9369y4/v7qKf+VWleqwfsK/XL5i6UNtE16Rus1Upfuy0Rr4KKynTdb0rw89T0Xl9n9Z5yp9e0/fbkhl6bG8r1vc+ELI5P1n0c7wpRc+GfSI23WE2puo5rEOaT3dGqf79eD01ojMRYmCZLsMhofkAAL4o8vXJiexf27+ajjtnV07cvS5Nzwe9S/W8vwh+fC/XP7noW6DH6MpU/9uzNjJ1h3U7AGB7qU9sabpOOHpk1tFN/NjZvln/Hq/aoeeLmZk+rezIfuae2KH8T6fpfJ9a+kaV25xPb3lylW9s777s3Spvh0TszpfzHu7/A0PXBAm+MhmGYRiGUfUcFFl0GIZhGIax91LpxbmIHPZTFsQwDMMwDMMwDMP4dbM3HKW2t7I7X85B8gQAnQHE91qIyB3J7zAMwzAMwzAMwzAMoyKi5g5JITkEwNkArkVwntuZAFr+ROX62SH5IcmcCuIsJVm/gji5JM8L+S8m+fgeKuYPojLl/hnKMIpkrx947wCSB+3pMu1G/ieTvLmq8q8M0XFXQbzZScLuIHnkni9d5fgh45TkKyRnkrw+cn04yTN+RFmStlOS+IPdOeeGYRiGYRiG8YPYnS/nB4lIN5IzReSfJP8DYMRPVbCKIJkmIsmVKXYTETl+DyWVi+AMvJf3UHo/OyRTRaS8Y6Ji8fZoH5TDAATHV437GfL6HiLyLgIhRMXPWP/KkIsfOe5E5PY9UZCfq11INkYwL+3Rl4QkoxpJVcKR7VbG3btGad2UDr113HBrb5mp37lm19BxD+npbZAnfaHFqqpTP/aZfdvG3ZyyRodFBLWq1fbCbutXafGfBbtqKf8TpfPi7olH6rjLx2vRm+Y9vZDYhm8j09K2iDfFd11UaYZZulsPLvQCP0WR99QNamlBn6Id/t5Bh2thuR3ztBJPg0IvxFMaEcFqcKj+yS1a4gWgGrZSQUjP0Vv+Phzvhej+VXODCtu4WefTdn8vOhZ9EneO041Wkq/T2rzWD5ja6VoEaedGLcBXp50XBdwQEef7XS0t+FRnH1/X9KW6H8cV5yj/d1u9cFfHWnkqLKu5rmvtRb4M1TO1uODyXXosbd/hBZZq1NXtEBUl27zVi0Md0kqL0KWk6dFVTXwZMmvqBu+zXpfpqsO9ENobX2vRtOh4yQxlExWA6zBJHx5xyXGXx90b1+l6r478pdemzI+tgzusVGFvLtZlapHrBbZaZeg+TUnX7bBzgxca61Sk69I5Xbd3gxb+OamRq9M5fIy+d3GKrk+YZ97JUf4/XOvLsOXhJSqsSZoWQhtH38dlZfp5y9uhxbgOzPTzZmkkbkZEeK5gu2/wjMjJwz3OKVD+4ld8XaPjKjVF37ul1D9DmRl6Plu/XM+jbS72bdZw3XoVVrhSC8SF041+sutfqJ+L5k18OxQW6IHVkrr8OU19uhtn6/bcEhG/C3NcE/1bs2a1FvLrWOqfqW8ztNDZpp1anK9tpv/9OHagbs/5I3T5e2X7ebPhFv2b1e1A3YbNv/P1KSrUfVG3jRYMXDrfP1MNS3TcFhk6brW6fv4oLdaiqzV26bnl+FDdMjL1nLp9u56P80TPQ3sDv5BzzquESn85B+JP0i6STRGcAdyqnPgV4r5OzSP5nPv69SbJ6iR7khxNcgrJT0g2cfFHkbyb5GgAf6og3n0kJ5JcQPIQd/1ikiNIfkxyIcl/h8oS/2pH8h2X5hySV+xmte4FcAjJ6aGveU2T5Hk0yfEkp5J8g+T3foHcV+P3Q/7HSV4cKvM/3f2zSHZw1+uRHElyGsmnAC9hS/IC1y7TST4VW4iQ3OG+nH4DoC/Jy1zbjSL5dOzrv/si+SDJLwHcR7IPyXEur3Ek27t41Ui+6vr1NQDVQmXYEXKfQXK4c59E8huX1mckG5HMBXAlgOtdmQ8h2YDkWyQnuX8HJ+qIHzA+viHZOXT/KJdGfPdDgvp3JznB1fNtknUqyONiN77eI7mE5DUkb3B1nkCyrovXxo2ZKSS/DvXtcJKPurZeTP91WI07Bs/W125sTGUldh4w9LWZ5BGuTLNIDiOZGRpzseekF8lRzj2Y5FCSIwE87/zDXDssJjkolM9uPV8ks0g+68oyjWRM/2IkgIaxcZHg1iNdGywgeaJLK5Xk/W7czCT5B3d9AMkvSb4MYFYk/9Yu397J+iUS/3txSNZ0/Z3u4tRybZkevd8wDMMwDMP4bbI7i/P3GWz7vh/AVABLAby6B8rQHsBQEemG4DvI1QAeA3CGiPQEMAzAXaH4OSJyKIBHK4iXJiJ9AFwH4B+h690RbM/vCuBsks0TlOlSl2YvAINI1otGYLANvmmCe28G8LWIdBeRh5Ll6RY4fwdwpIjsD2AygBsSNVAFbHT3Pwkgtq32HwDGiEgPBF98W7gyd3TlOFhEugMoBXC+u6cGgNkicgCAxQBuQ6DMfxSA6AKknSv3nwHMA9Df5XU7gLtdnKsA7HL9eheAnpWoyxgAB7q0XgVwk4gsBTAEwEOuTb9GcN70QyLSG8DpAJ6JJuQWPbs7Pl4FcJa7vwmApu686ijh+j8P4K+unrOgx1qyMdgFwVfuPq5Mu1ydxwP4nYszFMC1ruw3Avhv6P4mAPoBOBHBohz4/rhbD+AoNzbORvC8VAqSWQCGAzhbRLoi2GFzVSVu7QlgoIjEttd3AHCMq+c/QgvRCp+vCFcDgCvLuQCec2U8GcB3oXERJRfAoQBOADDE3XMZgK1u7PQGcDnJ2EvGPgBuFZFOobZoD+AtAJeIyCSU3y8xvhdHRLYDGOXKAgDnAHhLRL/OJnkFyckkJ7+wWn+tMwzDMAzD+DVQJqzyf3sru6PWfqdzvsXgS26WiOyJM1pWiMhY534RwC0IFi+fMjijNBVAeI/La+7/9hXEi225n4Lgj/QYn8fKTfJbBHbzKyJlGkTyVOduDqAtgE3hCLu5DT5RnjkAOgEY68qfgWBxtruE63mac/ePuUXkA5KxfWlHIFhATXJ5VkOwiAOChfpbzt0HwGgR2ezK/AaCBWmMN0Lb3msjWCy1RbCTNLYA6w+3IBSRmSRnVqIu+wB4zS2MMwAsSRLvSACd6M+wrUWyplsAxfgh4+N1AJ8iWEifBeCNJPm/ISKlJGsjeFk02l1/LnJPsjH4pSvrdpJbAbznrs8C0I3BDoqDALwRqmN4j9I7IlIG4FuSjZKUMR3A4yS7I+jbdkniJaI9gCUisiBUr6sBPFzBfe+KSHiv2gciUgigkOR6AI0ArEQlnq8I/RC8aIGIzCO5zNVnWzn3AMDrrp0WklyM4GXB0QjaOLbjoLbLvwjARBEJj7kGAP4H4HQRmVOJfkEFcZ4BcBOAdwBcAuByRBCRoQgW91g3YIBt+jIMwzAMw/gNsbtq7QchWGSkOT9E5PkfWYboH6DbAcwRkb5J4scMNFhBvJihTCl0PcMGNNEwkByAYPHXV0R2uW272lhGxz8AwFPOezsSLxgS5UkAn4rIuRWktxl6h0O0LMnqmegPewJ4TkT+liCsILTgruh1UthI5k4Ei81TGWxBH1VBGaLXw/V5DMCDIvKu64fBSe5PQdA/ymiJ5CcIFoCTEXxd363xISKrSG4i2Q3B1+Y/JLl3Z5LrFeYRuQ4AZSF/mYuXAiDP7W4oL10geV9dD2AdgP1cegXRCCSfBdADwOrIy6by+r8EfjxGx2K0Xb437ivzfJG8Gn7henwF5YndcxfcV+lQu0XHn7i0rhWRTyL3D0hQ/q0IXtwdDGAOKu4XlBdHRMYyMDc4FECqiJQrOJfRwLvTm2ubu+IVepphhp8i+qZrO/Idu3TXb53v7diWputd9eddpG3yVj+zPO7OL6yuwpZFbBwPvdSnO39YxL67o94FkD3fd3nGAfq9Uea05cqf3trbRzZtpzd71fxa28AOWOTzbdlSh+Uv0jZ5nXt7G8Jq/VqrsIIJ2g57wrgmcXfvWtoeMquu8qJkl8+n2r7a7k8Ktc1gWciksMbhWjph/hD97rtf47Vx9/Y8/eh1OkXbb856W+sqtmnv33299d0+Kqya7nIc386/q655rG6XYY9ry6tTQ7s7alLXLT1Dt3f1w3Pj7uwR2s75uuxVSMaKhdo+9tsvtE1pixbeJnrnVt3eDYv02M8K2aTn/PkYFVbj4y+V/4mP/TN3WTX97jBFmy7j80V+fGSX6Qbt30+P/c2z/c2FkZktp7NuszOW+XZ5eoXeqBe2MQeAxh89HXdv7TNIhe0q01NhzTKfj5TqQtwQkZvJ7urLu226tlutXlvHrd3Wh4s20UVuvx3KX7ze35taR88tD6Xo+eOx/r4NZ47WY/st6jKd+15e3N03vbEKq5mln6nrmvixs2axnmObNdNxw7bA82c0UGFZEbvyWi38g52zUdsNF8zRc3f3Xt6/foEWCKlRW9t7993o38UXl+hnvqBE/zk/9xkf3v5EPc8XF2xX/r47fLrdCnW6ua31PBoeD6n19Xyw5m1dt7RQcP1M/Sz2TUn2XQHI6blF+Ws00c9f4Sb/O7BoeTMV1qyBLkN2Q9+GG77U80OtiD5EYYFvpx7d16qw1Fq6fevt58fdltm6/9P0cEaNLD8G2kCPhwYtdV+khO4tizybOdV1GzZo7f9kqdZHt2f2GK2N0nGxnveNvZvdUWt/AcADCL5ixbaE/iD17QgtSMYWUOcCmACgQewayXSG7IBDzK9kvN2lNoAtbuHQAcHW7qSIyDduW213Jxy2HUDN8u5xTABwMMl9AYCBrX27BOktQ/CVONN9qT2iEml/BbddneRxAGJ/3XwO4AySDV1YXZKJxLQmAjiUZB2SaQi2jiejNoDYXxAXJylDFwDdQmHrSHYkmQLg1ND1cFoXha5H23QkgGtiHvd1GCJyjGu33+OHj49XEXzdrC0is8qL6HZDbKG3d74QwOhybqkUIrINwBKSZwIAA/ar4LZoG9UGsMZ9Ob4Qwc6BaD6XuPaK7gKZByA3Njah67UU3kShvHGRjAqfLxF5IvQMrIYeS+0QmGnMj9xza+ye0OUzSaaQbAOgtbvnEwBXhWy/25GMSKbFKQJwCoDfkTyvMv1SiTjPA3gFwLMVtJNh7BHCC3PDMAzD2BsQYZX/21vZHZvzXghslf8oIte6f4MqvKti5gK4yG17rgtnJ4xAbGsGgOkItokqRKSoMvF+AB8j+MI3E8FX4QmJIjG5zflMACUkZzByvFMYEdmAYDH7istrAr5v2w0RWYFgu/VMAC8BmFaJOvwTQH+SUxFs413u0voWgZ37SJfnpwjsl6N5rkJgO/4NgM8AfIvgK2Ii/g3gHpJjoReATwLIdvnchGDBH+NmAO8D+AJ6q/lgBNuBvwawMXT9PQCn0gt/DQLQi4Gg17cIBOOidfih4+NNBPbAr1ciLhC8RLjf1bM7gDsqeV9FnA/gMlf2OQAGVhA/Ou7+i+C5moBgC3hlv/aLiBQg2Hb9BslZCL7oD3Hh/wTwiOujChX9E1Cp5yvCfwGkurK8BuBit12+IuYjeKnwEYArXb2eQTCepzI4Ku0plLODSER2IrDtv57kQFSuX8qL8xKCl2WvVKL8hmEYhmEYxm+I3dnWPhtAY+jF1J6gTESii6vpCGyWFSIyIOKvMJ6IbISz9xWR4QiErmJhJ4bcuaEkjktU0HCcZDbnTuAp+nU7WZ5fINiBUC4ichOCBW555ZmM4MgxiMgmBIvyGNeH4r0Gb7cfTiuqFP+yiAx1X87fRvC1GiJyceS+8dD2zLe56/kIFrmJ6vMmgkVw9Pr/ENj4Rq8vgP7yDgTbzstld8eH869D5LkIj5sE9Z+OxF9/E+aRYAyG8w7nswTAsQnSjeaf7f5PNO7CbfY3F28pAlv8RNRDYEYBEfkcwZb3aP5fI4H9uogMrsAfzrPC5ytyvQB6V0bs+lIkqUu0nULXyxDoWtwSCRqFkElGOG0RyYN+ThP1y+CQO2HfOfoBeNOlaRiGYRiGYRhxKJU8aI7B0VHdEXwBjX+1EpGTf3DmgY3y+5E/3I29AJIPILANzkKwMP+TVHawGL84SA5DIFR4rMheeCDmrwCSjyF4MXG8eLG9pOS/dJt63krGTo67N47VtnL1D9M2yFe87TexPHOJttHdNVbbwM6b5e0n92mkN8hUr6OHwuqQXWZ6mt440aSztp0r3uq3jOWt1UZ4LxdpO+LfVfcbZfZ5QR8MUDREC+Kzmrd5XPFB5FzdRvp88jrXht7N1dDWRmXffKP83w73bdrl2aNU2PJB7yn/rl2+DB3u31+FjRv0rfIfcKG3EyxZo9sIEVvgzbO9zWPtFnpzSNQedc4O34Yn3K3tLufcpodX28N8vptn6g1z2Q21DWT2xb7N5v1N63iuLNL9WBqSgzjiFG2b+vH/9CEMuSm+bxaJTmdjqt5eeG4nb/de4zwtG1Lwrt5sk/X707xns9YKQHbEwizL51v04tsqKOMY/Y512R1+k9qmbbq83S/T5X19uLdlLYzsR+xUrPtxYoaPO47aPvaloUcr/6IrvTRG9Rq6n7Zt0898+HlsP1EfzrHh5MuUv9ahXihh6yht3ztstd5Md1Y13a8MnWVdv522Tw4/8wDwasjO9Yp/6o2GZWu1TWxKh45x923Xz1BhR+fr5+SQof6Z++gP01VYbrreJHZ/ii/TsH/r983P3LhQ+S+7xT9TxeP0c7x1rvKiOHTGd+OztB381P/qPu/1SFfvKdBtVvbdUuVnEz8fs5m2Orz02jHKP6R/6JzrAbpuUqDtk7lPCyTj0kHjlH/Y7fvG3bJDawUgRQ/wkpmLfNzIee8Z/fXciNDYv/SWcq0G8fQ5ofPfm2gbf2TpsV86b3HcnXaoPll360MfK/+s+d42u98wPbdsHKzn+XqX+aUJq0cMySN/DsuW0HOUqq0I2TViAbzMz8/qPgCli7X+RkpN//ud0kd/y5PVWst62yu+TWv/WX8/fGSQnstvXvZile/p/qbpaVW+pjhg9Ygqb4dE7M6X88F7OvMKvuIZVYiI3FhxLOPXgohcWtVl+LUjItf+0HvDC/OKCC/MKyK8MK+I1RHBpPKI/pFeHuGFeUWEF+YVoRbmFRBemFdEeGFeEeGFeUWEF+YVEV6YV0R4YW4YP4bwwrwiXt0NAarwwrwiwgvziggvzCsivDCviPDCvCLUwrwCGF2ElkN4YV5huuUszKOEF+YVEV6YV0hGZsVxHOGFeUWEF+YVEV6YV0R4YV4R0QV2uSyr8L18nPDCvCLCC3Pjl8/uHKVWrtAVyfGSXBnbMAzDMAzDMAzD+I1T5Z/N92J2RxCuIpIeN2YYhmEYhmEYhmEYRnL25OLcXoIYhmEYhmEYhmEYxg+g0oJwFSZEThWRyhsDGUYEkncA+EpEPksQNhyBeOD3VN6TpHWLiNy9h8t3MYBeInJNgrAPAZz3U6pwk+wF4HeJjjAkOQDAjeHTACpI6xQAC9zxej8LJJciaL/KGxkH9/VAcPxeJwAFAD4AcJ87ESBR/FEI2qLyhtr6/gHYvbYcjt0Ym5XllabnxyfnAmrbyeyIkFjn6nlx943FOuyiYm1L2ZK+2RZBi9xkl2lBn96t1sbdr6/Sgk7tCrUg3KQs/653cuQxGABdhivP8EJY/x6hRbtOKdUCRHNLfXha5PeqMGJTenAdLwhWVKht76fv1GUI13V1mo57QKoW6lpY5MuwLF3neWRE1Osp+k1k/Yq0fXobaMG61JAN7zcpWvBtYGN9MEr+Dm+Tfucu3W/HFmv/tAzfN70jCmWjM7XIXwvRZbzmdF+f20fodJuXaUu4VkW+DQ89WAsN/mdKopNGA+qV6TLNTNGP8pFF3tayQanWA+iUu175pyxrHHdHxwcj3wy2hoSawmJ2AFA/ks/oar6Mu6Cfi7+1WKv89y33ZVhYpm2BO6XU0mWAz+f5dRNV2P9qacvAcVm+vK1LdHlXR4wSd4XG0pVNdV80ePf/lP+9Ln+Pu6MCav9K0W3WDX7s1yvTZYiqW6xN9fe2jJQ3SzchelTzdrprd+ixPzZL6y9soG+zAQU616NO0/a+d3/on/MZZXkq7PySusr/WYYfd+fm6zw7NNfigm+s90J5TUt0G1Uri447T1Fk7q4emWPHV/PhvSISFemR8fxEphfsfL6j7rf3ZzdX/hYlXkCwfjX9fK3N1+39cKZ/5qNtVBQx229S4vtidqYehM0jvz1Z5awvnsrISxp2TqkuQ5tS3TDhFnwzS5fhxIh44MxM36/LUvTcd0ihvndH6PckWvbob+Pa0G9GWqSazUr0XFIjNH6Xp2jb++g81J6+XxeXlf/7vD5UhqgQZYciLUr4STU9vu9Z+nKVC6GNa3J6lX/UPWjNW1XeDonYk1/O98oKGr8cROT2JAvzyitceaJHZf2kiMjxP/XxWCIyOcnCfHeEHWOcgmCxu1dD8mQAjwN4GMHRcAcDWA3gA5KVV5ipfH4/pC0NwzAMwzAM40dT6cU5ye+dTUwyfD75hXukRMZvApK3kZxH8lOSr5C8keRwkme48KUkbyc5BsCZkXvvJfktyZnuyLdo2vcCqEZyOsmX3LULSE50156KLfhJPklyMsk5JP8ZSqM3yXEkZ7j7Yp8PmpL8mORCkv8OxV9Ksj7JXJJzST7t0hxJsloozZkkx5O8n+TsJG2TMB7JASTfd+7BJIeSHAng+cj9h7p6Tic5LVT2WPhBAE4GcL+L04Zkd5ITXL5vk0woXUvyWJJTXbt87q7VJfmOu3cCyW7uej1X/2kkn0LoBV6y/ojklQPgdgDHiMh4CSgSkaEAXgLwvRcVIS5w/TebZB+XXg2Sw0hOcmUa6K5fTPINku8hODYw2hfTSLYm2ZPkaJJTSH5Cskk000RxXPtODcVpS3JKOWU3DMMwDMP41SLCKv+3t7I7X85vI3l4zEPyrwAGxvwiknChYRhRGGzPPh1ADwCnAeiVJGqBiPQTkVdD99YFcCqAziLSDcC/ojeJyM0A8kWku4icT7IjgLMBHCwi3QGUAjjfRb9VRHoh+Cp7KMluJDMAvIbgbPf9EJz3HtsX1t2l1RXA2ST1XrKAtgCeEJHOAPJcXQHgWQBXulMNShPch92M1xPAQBE5L3L9RgBXu7oeEio7AEBExgF4F8BfXBt9h2CB/1fXprMA/COaGckGAJ4GcLprl9hLk38CmObuvQX+ZcE/AIwRkR4uvxYunfL6I8xZAJ4SkR0MXuZMdS8r/g/AcwjODE9GDRE5CMAfAQxz124F8IWI9AZwGIKXE7H9fX0BXCQi4TnuIABDEMxzKwA8BuAMEenp0rwr0j7pieK49t1KsruLegmA4YkKTfIK97Jo8ue7duOYGsMwDMMwDOMXz+5s4TwZwPsk/wLgWAAd3DXD2F36AfhfzGbYfbFMxGsJrm1DYHf8DMkPALxfifyOQLCQncTA/qsagJjh4lkkr0DwLDRBsNVbAKwRkUkAICLbXDkB4HMR2er83wJoiWDhFmaJiEx37ikAct1X4JpuYQwALwP4nk1zZeM53k1idz0WwIMMdg2MEJGVSe6P5VkbQE7ouMTnALyRIOqBCDQBlgCAiGx21/vBvYAQkS/cF/PaAPojePkCEfmAZMw4sLz+CLMfgCEk90PwUqQXgu34g0SkhCz3recrLt+vSNZy7Xo0gJNJ3ujiZMG9MADwaag+ANARwFAAR4vIapJdAHQB8KnLNxWANgoG2pcT5xkAl5C8AcGLiT6JCu12BQwFgPz3H4zbY330e20+v2+WtmtNCdt6lmarsB45m5R/5WZvA7suYhjQqqhI+Tev9baJA6DzLErVmx3CtqAHQduRV6O2GXzkLR9+QXVdvuZ/7638/NvMuDsrYi8tERvY7dt9hdo/ps85bzFJ2/em7Nta+T/663J/79NHqrA5l/l7m5Roe9lWDxyi4l50dTifEnT7kz8fvmCcruuqmb4vDsVWZGb5+m1Yq/txW8h+/TIAOVneprD9k91U3DpXTA35StGuni/vop31Vdw2Rdrsb91In+7pBdoePS1Fj48GDbw+wOfjmqmwXhGbzRri3zM2y96qwnaV5Cj/jtBng9PfO0eFrbn0CeU/+sVQP6eVfwa9zJ0Wd08YvE6F9b1bnwW96TZvV94Gevzu2KgfnPNLfPj8Mr3pqG89PbUN2+7b/28N+6mwg87R0/nqF73NaXaptjdtExn7Nct8+9Y6VNvshm3MAeCk2f9KGvZn6D6uW9OPnZXb9HOdRf3ueN8i/ydl2GYfAP7+7w7KXzrJ90UL6Of6uTd0ugOKvQbB8W8co8I+PvMj5T9MfDvlVdPP0GmR52TpoBlxd7+/6bgj/q3/PD67ubfjH71C6ykc8+wByj/tUn/ycI/njlBh2KTH3YClS+PulGYRnYYGenPW+1eNibsz2+qoZ5+j7y2d51/upjTVz2a7Pocq/9vnvh13n/ZwO51wqe4L2eht8Xu+O0eFVT9En22f0qZN3M3W2oru41CeUU47Xz8HqX30bwJC9tQdh32hgjL20XNAnxI/HiZ/rOe+g57uofylo77yefY7MGn5AACFIZvuSD+hWD9DCNmKf0+YK02Ps7LJ/vejc47Wq2D7yNnr2/yfLGVTpqmglP66j//3hzEwfjnszjnnGxnYf36GYMFxhuwpNTnjt0Zl95LsjF5wi7I+CBZ45wC4huRRCMYkECxYb0+Q33Mi8jd1kWyF4CtzbxHZwkDYK8vFTza2wyobpUj8DEXjVEM5dSb5LIJdBKuR+AtyMr7XPgAgIve6FxfHA5hA8kgEZicnuPDulUncbTWPtyuAyUjcLonqJpH/o/ET9cep8F/sf+/ilSJ4YfKpiJSR/AiR7ezh9hOR45PkKy6900VkfuT+A/D9tlyDYCzE+oUA5rjdDMkoL85brm5fAJgiIpsSxDGqiPDCvCLCC/OKCC/MKyK8MK+I8MK8IsILc8MwDMPYGyirOMpvlgq3tZPcTnIbyW0AFgFoh2A7a+yaYewuYwCcRDKLZDbcorEyuPi1ReRDANcB6C4ipW57dvfQwrzYbTMGgM8BnEGyoUujLsmWAGohWJRtJdkIfpv0PAS25b1d/Jr8kUJhIrIFwHaSsdex54TCLnFlP768eJWFZBsRmSUi9yFYUHcQkVtjbeSibQeCz5tuJ8AWkrFPgBcCGJ2gXccj2PrfyuUT+zzzFdxLBQZK5xvdboPw9eOAuGR3wv4QkbdD+U1GsL2+L4D5AI4gmQLgGHfPRQh2CKj2CzXD2S5ePwBbXR0/AXAt3WdtBirwychDMC7vdnWaD6AByb7u3nSSnSP3JI0jIgUu/ycRmC0YhmEYhmEYhqLCBYeI1KwojmHsDiIyieS7AGYAWIZgAbm1/Lvi1ATwP5KxL9zXJ4k3FMBMBkf8nU/y7wBGugVeMQKb7AkkpwGYA2Ax/GKviOTZAB5jIOaWj8Du/MdyGYCnSe4EMArJ61zZeMm4juRhCL46fwvgowRxXnV5DAJwBoCLEGwhr46gLS6J3iAiG5wJwAjXjusBHAVgMIBnSc4EsMulBQS26K84MbTRAJa7dL5N1B8IxkKY1wF8jWAL+BwE4+RzBP3eFsAd5bTBFpLjELyAudRduxOB6vtMt0BfiuQmAxCRdSRPQtB+l7p2etRt2U9zac0JxS9iIGiYLM5LCLb5K9E5wzAMwzCM3xJih3wlZbe+BpJshsDGNn6fiHyV/A7DSMoDIjLYLQa/AvAfEXk6FigiueHIInJxyJvQXjcS/68A/hryv4YENuyRdMPXJyGwsQ4zHCEhLwmdgx0q70YEdsex62E1+TlONA0kb0aw2ExEwngiMgrBYh0iMjhS3nDYtUnSDccfi+8fpVaBkRUgIh8hsth3ttoDE8TdhMDOO8b1obCE/RG9n4GC/rsIXqYMdrshjgWwTESKktw3IMn1fAB/SHB9OHS/joJvy+UAwl/ItSEz9BhyWgPfi+PoB2CYiJQmCTcMwzAMwzB+w7CyZuMk70OwVfRbeAVpEREThTN2G5IvI1gcZiGwP76niov0k+O+xv8NwcutZQAuFpENPzTebwW3xf9mBC8GdwD4AMBDIlJ5w9sqhuTbANoAOFxENlbmnqXdj4pPznW6auusVeOylH+fI0vi7rM/1NZKfyvWQkc9L/PuoS9oYasr/qDvLZzsNzPMHt9Qh4mOe+C5u+Lubd9oE/6s+vp9xNMz/SEHg/5UTYUteEQLJrU+1nfz6i+1CF2LG7Qq0q63p8fdjGiDSYn2z57q69Pj2M0qrGyXLu+srxvE3e07aoGvTct1+zY/wX8N2DhaD9EGx2uBn+IFXnogY38tSLbjU20Hn324F3W6//9UEK5upzUfGeqa/M36HXy1urohvpypRZyOOtzrHL47SotMTU/X997a2cfdtV7n89o6LZL0x0F+zH74Hy34dNw1ul9TOvn3hmOu1EJH9TIi4mzFvqOrperyrS3Vz0mDFN8fWZG6tNhfb1B6boofo3/4ewMVVjp1rvJ/8Y4XYBudpZ/VO87UY+CWN3x5V0f0PP+VqctUr6V/jsbN0qJeB3ZcpfxS6sddabF+Ntet1BsglxZ7ocewOBwArD/p98pf+2B/b+km/VzvmKfrWveag+Puwg/HqrCdi5UX1Zv6e8sK9N+h46bocXfkk/vF3W9drQ8G2hkxzrzgKn/hjCH6Z/PyYi3Wd8y1fsxOf2yXCutxk4476wH/rDbP1ToOK5bquJ0v8n3x8XA9BnNTdRu2P8W/Yx7/hp4f2jXR0iRXbfLl/XuoD4Hv2+9WS/NjqXVvXd55E7Qw2p1pvu63RNKNst8J/jlZ/qX+/Sgu0c9xbm8fNyoY+X9pes4NM6SB1t/YvFGXaWOhb9ONqXre2a9anvLXqO2fv7r9dF9MeU3/9mwPWS/mVtuhwpp101a8O1b5uLNW6N/Gbi30b0RY3HNjkc4zSuuQRkhmDT0fzFmu56E6qX7s1Kym55naDfXc8tfVOcr/4rIRVf7Z+qvGZ1a5bln/tW9UeTskYne+nJ8CoP0v6Q9iY+9Fvn/816+eynwt3p14vxVEZAKC+ecXi4icWtVlMAzDMAzD2Bsoq/Kl+d7L7pxzvhhAeoWxDMMwDMMwDMMwDMPYLXbny/kuANNJfo7QUVEiMij5LYZhGIZhGIZhGIYRUGaCcEnZncX5u+6fYfzskBwnIgftRvwBAG4UkRNJngygk4jc+1OVb2+B5MUAeonINXsgrQEAikRknPNfCWCXiDz/Y9PeE4TrGi6buz5SRFa7eM8AeFBEvq260u4+b21uFHd3HKltz3amaNu+hf/z+8OyMrarsIkZ2i7wlZe8ndqc0jUq7PgXtKH2IwW+DIek6I1TIyO2v92m+3yXLtE2jfNWaFu/L+ltKfvfX12FfZipbbiP/p8v/wdZ+ifr/Hu1IWub33k757nDdPk+TdXpdqK30sxfom3Mt23Q5X0604cPnKdttOdGbNvP/cxLCozb2kiFdXxe25v+Lz1kWzulBFfv4/vj5pX1VNz/jPd25dd0AjYt8e22YbGu239Ltb9Vme+7ZRu0PWdhptZVXPa1L9OmdG3J2r9Ab7b7bKpvi/wU/YfWrHRt81g8Z23cvSK9sQpL2UfbGBe+6Q80mJKlw9ZT59M8ZCfavFh3xrIMHXcBfJsVQO+pPH6KjjuV3g53/r/0OMuP2OVOy/T3zi3VtuspuW2Uf6V4LYH8Mt0XHxToPj98vu+3EZGz7d9crOPeENKZfJ+6/6el6Dnhz/B9HrUxb/jeM8q/808+/P++1n2xk7oNr3jq67h7yDJtY9y/QNd15XLfV8vSdDozM7W97xFLvvNx03XcL8u0hEeDx3y7ZGXo+WJxhh6/4XE3NV3rNuz73gLln0uvQTBstZ5/u6TqObb+e97WfVGGtjGeRj3fDZqdF3ePy9K264Vr9TyaleHbZVSmzjM1stDZQj8eDo1olKxP0+2SRT8eZqTruJHmhnzg81meosuwNrKi6Dfet9PCyLOYJcmXH4tWadv76Zn6tycz9JhHU/kwoiuQHZIdWPs/XZn2uhuxMJRNab7WaZg5qbbyLw01TNM0XbcvV2u9jZB5OppUsOpats3bldfZoss7V3cNWhX7sZSar8dZ2xW6zeaXVUrqxthLqPTiXESe+ykLYhjlsTsL8wT32oulH8YABAJs4wBARIZUaWnKIVK2iwHMBrDahf0+0T0/JSTTRLwEWdRvGFHCC/OKCC/MKyK8MDcMwzAMY++mQptzkq+7/2eRnBn999MX0TAAkjvc/wNIjiL5Jsl5JF9yZ1aD5LHu2hgE50nH7r2Y5OPO3YbkBJKTSN4RS9eF/cVdn+mO8ALJXJJzST5Ncg7Jke7sc5Ds7tKaSfJtknXc9VEkezl3fZJLnbszyYkkp7t7tNR0EKcPyXEkp7n/24fqMILkxyQXkvx36J5LSC4gORrAwdE0w21I8i6SM1y5G7nrJ5H8xuX5GclGJHMBXAngelfeQ0gOJnkjyY4kJ4bSzY3NBSR7khxNcgrJT0g2SVKW37k2mEHyBXetJcnP3fXPSbZIVr4E6cXKdgaAXgBecuWuFumPc91cNpvBCRTlts3u4PK52/XDnxL4e7s+neHGQc2K0jQMwzAMw/i1IWCV/9tbqYwg3J/c/ycCOCnBP8P4uekB4DoER7G1BnAwySwATyMYk4cAaJzk3kcAPCIiveG+rAIAyaMBtEVwhnp3AD1Jxs6rbgvgCRHpDCAPwOnu+vMA/urOJJ8F4B8VlPtKl3d3BAvIlQnizAPQX0R6ALgdwN2hsO4IjjPsCuBsks3d4vefCBblR+H7Z5eHqQFggojsh+Bs+cvd9TEADnR5vgrgJhFZCmAIgiPLuotIfL+iiMwFkEGytbt0NoDXGZxB/hiAM0SkJ4BhAO6KFoJkZwC3IjhWbD/4OeZxAM+79nwJwKPJypesgiLyJoJz4c935Y7vrSXZFMB9AA5H0Ja9SZ5SQdvsLjkicqiI/CfsR9AurwH4k8vjSAD50ZtJXkFyMsnJE3Ys/IFFMAzDMAzDMH6JVLitXUTWuP+XVRTXMH4mJorISgAgOR1ALoLt10tEZKG7/iKAKxLc2xf+WK6XATzg3Ee7f7FDdbMRLMqXu3Snu+tTAOSSrI1g4TXaXX8OwBsVlHs8gFtJ7gNgRKysEWoDeM59VRfoExI+F5Gtrn7fIjj3uz6AUbFz0Em+BqBdkvyLALwfqsdRzr0PgNfcQj8DwJIK6gEArwM4C8C9CBbnZwNoD6ALgE8ZbGZIBZBor+7hAN6MnfctIrEDT/vC73h4AUBsd8APKV8iekO31UsA+gN4B8nbZneJHoEX87cHsEZEJgGAiGxDAkRkKIChAPCfFhfYQSOGYRiGYfzqKKs4ym+WChfnJLcDSPRHIgGIiNRKEGYYPyVhZZxS+HH8YxYzBHCPiDylLgbbu6P5aeWN71MCvyslLuEhIi+T/AbACQA+Ifl7AB3hv9IeD+BOAF+KyKku71GhdCtdb5KpCBaZAPCuiNwOoFhEJMH9jyEQTHuXgQjc4ArqBwSLzjdIjgiqJgtJdgUwR0T6RsrSHMB7zjsEbu6oRB6xOD+kfIkobw/T99om2oYIXtzEdkf8HsDVCHZxrBaR4911rfjl/ZWtc5xz91kVd2c10j9jWQM6Kn/ZsvgmEDzxhhZeOj5ji/I3P8E3Q+k6nW7msV2V/+6PJ8XdUqKLf0JdLb4lRV5dp/FGLUDV/TT9U/PyMC9W1OOJniqsyT+/VP7Gdx3o0/liggpjpk5313j/LqjNAbq72zfQAnEbx/sy1L7jdyqs+guvKP+Dy72AXWmhTndADd0umW29tcLx81eosBqXHaH8bV763Od5x19V2ONPPqr8rO2FrnbN0QJ2rY/Qw+7mb/2mjBotdB8vm6h/stNSI2Orun9vtGOnFnxKydJ1bXO5F1+SLVoI7ZCPdXun9/Fj9vjxi1RYWUSQL6N7btw9cOJaFda42y7lXzXNi5/VaabDmi/LUf7qmf7ZyMzSz0nT4/R4Pv1lr76071n6XZrsjOQzMy/u/mMtLS3B3H2V/+gyH7d7qU5nn+arlb/enwfE3VddN12FtcjVz3V2V1/+s0Zrkb/S/LrKX7emv7f2wdq6JiwABwA1HvECceeefJkKS6+hx072Ob3j7gsfmqXz7KMF7XrXC6lbRcQEvxihRb0QEj8bUKjrnZXRQPmPucK3/5BndR8fnarHaHjcDWy0XoVV76xF/47Y4eeWE+vrPt6xRo+dBif58vd/Rm+Q2reTFuaqcZC3oLoKq1RYeh39vD022f/pMTBN16Vmjn7eMqr7utXso/t40yg97l7e4OfRTkV67NRK1/6O1/r5o/tS/WzunKvbu+bBvh2yXtXP0Jei44bp1k2n22qtnodq1PNlemeRFug8HPq3p2FT789urduzcI32983z30LKSvWYrN9Hx900OWHRg7iHaeW2Ze/6e2s31OOhpEhvYK7Xy7tZTeuF9Pxat1lKaijd9noOZUT88MFPGiYvsLHXUZkv52YXafwSmAegFck2IvIdgHOTxJuAYFv6awDOCV3/BMCdJF8SkR0kmwFI+ushIltJbiF5iNvyfSGA2Ff0pQB6ApgI4IzYPW4b+GIRedS5u4nIwwCeCMWpDcR/oS+uRL2/AfAIyXoAtgE4E8AMESlFsHW7MoTzvCh0fTuAhC/fROQ7kqUAboP/OjwfQAOSfUVkvNvm3k5E5oTL4ra1v03yIRHZRLKu+3o+DkGfvADgfATb2csrXzK2A0g0b8Xaqj6ALQjGyGPJEknShm+H3JdUoiwx5gFoSrK3iExy9ub5JhJnGIZhGIZhxKiMzblh7PWISAGCbewfMBCES2aGcR2AG5ygWRMAW939IxFscx9PchaAN5F4gRfmIgD3OzG07gDucNcfAHAVyXEItp3HOBvAbLcVvwMCm/Uo/wZwD8mxCLaFl4szOxmMYMv8ZwCmVnRPAgYj+Ar+NYDwa/33AJwaE4RLcN9rAC5AsMUdIlKE4GXEfSRnAJgO4Hsq+26xfheA0S7egy5oEIBLXHteCG+Lnqx8yRgOYEhMEC6U7xoAfwPwJYAZAKaKyP8qkd6PxrXN2QAec3X+FKFdFYZhGIZhGL8VqloMbm8WhNudc84No8oQkWz3/yiEtnqHz/MWkY8RLHqj9w5HsGADgi+wB4qIkDwHgXhYLN4jCATjonQJxXkg5J4O4MBoZBGZB6Bb6NLf3fV7ANyTpIqxe8dD24zflqAOEJETQ+5nATxbXrouXnbI/SaCFxBwC9TvLVJFZEGkHl9Hwh+At9mPXZuOwI67orI8h8BOP3xtKQJ79GjcZOUbDtcmIjI4dP0tAG+Fog4Ihb2M4CVMNK2EbbM7iMiACvyTkGC8GIZhGIZhGAZgi3Pjt0dPAI8zUCzLA3Bp1RbHMBKTkubtyRZ8U0+Fdar7nfKndW0dd+eXaXve6jW1LVrxMm+v9+R0ba83cNK3yt+ks7cpHT2pmQrLFG2DV4veCqRz3w0q7JuntG3qWnh7yfznPtHlK9KSDtuHfRV379yobTtr7aNtJyfN9Cf3VYeud/u2etPFzu3epnT02SNVWI9O2s6yeJffxFLvjtNUWN6dbyn/jq+8bW1OR705Le9RbU9fVurT3XbRQyps9TptUTInVbdL51Jvu7jkC33ueUlpKN9VQPO23k53Q4FOJ5O6b7of5ftx1ye6j8eU1lb+pmP8BqU3ZzdXYSc03qH8Ewavi7v3P1LbH7/8jN4k1F18eQtKdd3qbtJ9Hmb1cl2+jaXaVrVWvm+XpjV0HxfO1za8DeDHWlq/PiqsdNoM5Z+6wufTue4mFbZx0KfKf3CaL0N6NT1G06vpvnjj+gVx99ZU3Q6tMjYr/7bpvt9I/ZzUK9NfiFZu85vCmm7S5f2/r5sqf9jOvMG7/6fCdl2vD7Uo/GxK3N3kUm1rv/75pbpMzbw9bdFCbY/8Qqa2te021M8n66BtzCdTj7PNH3kb4/yI4lSterq9X37Gj5fDc/SfwzPf1O3drLFv30Uz9HycSv2c1Fnp+2ZnmY4bnksAYMN7fqyvW6+1AaIiJfn0Y7TlybqNJr6i86mW4i2mWpZqO/3wvAMA+WW+bq2b6LgbN2rb+5J53i6+cLW2ysrfqp+3XR94LYxthXo+y0+NyrN4Ni3TedZppuN+N89vSDy4Wp4KKy7Rdat7rj+xtuDLeTqfFdnKX1jsx0Bmuq5bwVI9zlJSfftv3azn1DprdNwdBd72fscq/WxmZ+l5qOwb3xclEZ2X/HydT/MufjzkzdXpMkXf+2xkn16irZDG3oMtzo3fFM4+fL+qLodhGMYPJbwwr4jwwtwwDMMw9gZMrT05ZnNuGIZhGIZhGIZhGFWMfTk3DMMwDMMwDMMwfhbsy3ly7Mu58YuB5HCSZ1Qc83v3nUzy5p+iTLsLyVySs/dUvJ8i758bkrdUIs7hJN8jOYvkeJLXuXPIk8Vf6o5M+6Flupjk47sRfxTJXhXHNAzDMAzDMIzE2Jdz41ePiLwL4N2qLoeRlFsA3J0skORVAAYCuFFEZpOsgeCYtVdJniUiUd2cHwXJvWJezGriRZwyl2oho9TGOcpfts6LnfVO14JJDU7T7zC2jVwdd59fZ70Ki4rIFG7y72+ri37PXYNaMKdtO1+G9EZauKZJLS2Q066wYdxdskOLQTXur8uQWt/HrbZFp5M/X4vprE/zXXdilzUqLK2ufhddc1dB3N0iIuiUrrWLsGR+Ttyd/fwIFZbdS4sKpc/39UmtrduhZFGx8mfl+Das1lj3cVGhFkE6PNXXffZGLf7Uo0Ge8tc7zoentD5AheX8faHyN2y0Xflll2+LwmItihV9nb9iTk7c3Ut032zdpOvesIYXcpv3ZY4Ka1qq6163QXLRtwxddTRM9eUv2Kof3ZrbtPBcjdreX+dwLR4n23XlWm70ol5bHtZCfjX313UrhRfC2pinxaxq1yhQ/p35XkhqQZEuw8BWejz3XuXLsGybHpQp6XrMVq/t27B6Y61JkBo5YDOLPu6Oefq53hl9Fmr48KgAXPWHnlb+bRddEnd//O/I+D0iMoe18QKC6fla4LL1HN2G+Tv8ve2q6/miW2mOLm9IIKx3emOdZ7oWqmxa7NOt216Ple3btbhZmA6HaB2HhWN0GbZM83N389r6+cqqr9shLVS3Ftk63cx6um96z/CCl5tGaSG/jm3081dS5MdzrQN0e+6aoduw91afbmmJLkOdHD2WpMiXKU1PfUjZpMdOdhPfpqlpkbrk6d+pMDmN1+o8S7WgYdNGvvx1eug8i1frOXblU74MKdSqaM1P0s/8hs99XaO/hRE9RsgmX6bsmnrsROfJhvV832TV1OUrLtC/z1m1/G9CaZGud43SSD4houWtXk/n02eDndz6S8K+nBsVQvI2kvNIfkryFZI3kryc5CSSM0i+RbK6izuc5JMkvyS5mOShJIeRnEtyeCjNo90X0Kkk3yCZnSBfknyc5LckPwDQMBTWk+RoklNIfkKyibs+yMWfSfJVdy3+FZRkG5ITXNnvIAOpV5ID3NfPN11dX3KK7tEyZZP83JV7FsmB7nquq+PTJOeQHBk7Y9uVdQbJ8QCuLqedE8YjmUryflfmmST/4K6/RvL4ULzhJE9PFj+SVxbJZ10dppE8LNRW/yP5Mcn5JP8Rqt88ks+QnO3a50iSY0kuJNnHxavh+nuSS3dgKN0RLt2FJP/trt8LoBqDM8lfSlDOtgDOAnCiiMwGABHZKSJ3A5iH4Fz1ZPyF5ET3b1+XXgM3Xie5fwe764NJDiU5EpHz50me4MZq/UqO2+/FIXkEybdDcY4iOSJ6r2EYhmEYxq+dqj7jfG8+59wW50a5MNiqezqAHgBOAxDbujtCRHqLyH4A5gK4LHRbHQTnVV8P4D0ADwHoDKArye4Mthv/HcCRIrI/grPGb0iQ/akA2gPoCuByAAe5MqUDeAzAGSLSE8AwAHe5e24G0ENEugG4MkGajwB4RER6A1gdCesB4DoAnQC0BnBwgvsLAJzqyn0YgP+EFvFtATwhIp0RHNN2urv+LIBBItI3QXphksW7DMBWV+beAC4n2QrAqwDOBgAGZ+ccAeDDcuKHuRoARKQrgHMBPEfGXyv3AXA+gO4AzqTfrr0vgvbrhuA8+fMA9ANwI4Kv3wBwK4AvXN6HAbifwZduuPTORtCfZ5NsLiI3A8gXke4icn6CNrkEwVf1MpJPMHgZM5jkIwAeBHBB8ubENhHpA+BxAA+7a48AeMiV73QAz4Ti9wQwUETOi10geSqCMRV7CVLuuC1nbH8BoCPJ2OeCS5DgbHqSV5CcTHLy8O+iw9MwDMMwDMP4NbNXbN809mr6AfifiOQDAMn33PUuJP8FIAdANoDwYcXviYiQnAVgnYjMcvfOAZALYB8EC+Cxbl2bAWB8grz7A3hFREoBrCb5hbveHkAXAJ+6+1MBxPawzgTwEsl3ALyTIM2+AE5x7pcBPBAKmygiK11Zp7uyjoncTwB3k+yPQM+iGYBGLmyJiEx37ikAcknWBpAjIqPd9RcAHBctVAXxjgbQjd7evjaCFwEfAXiUZCaAYwF8JSL5JJPF94fmBv36GACIyDySywC0c2GfisgmV64RLu47rn7hvvw81M+5obKeTPJG588C0MK5PxeRre7+bwG0BLAi2hYR9gNwD4CTABSLSE+SNwDIFZEtJGuWc+8rof9jh0gfCaCTf5+CWqE03o2Nc8dhCF5GHS0i20ieiIrH7YGJ4rh2egHABSSfRTAOfxctsIgMBTAUAPLOPmyPbtc3DMMwDMPYGyjbez9cVzm2ODcqItnjMxzAKSIyg+TFAAaEwmKGMWUhd8yfBqAUwQLwXJUReQCAp5z3dvd/ogUKAcxJ8iX6BASL+pMB3Eayc5LyJyJc1lIAaQnKVBdAAwA9RaSY5FIEC9BE91dzZU24yHKLtB4IvuCflyyeS+NaEfnkewHkKADHIPgi/Up58UnmRtJMRrQcMX+0L8P9HJtLCOB0EZkfyfsAJGjfaMYkr0awSwIIvlbTxe0A4GN3/SMELx8yY2mS/ATBS5LJIvL7BPWIuVMA9I0swuEW0tpIEliMYAdFOwRfwIkE4zZahXLiPItgJ0kBgDdEpCRBnDhFG72NXrsLM1RYwXRtK179tN5x91eF36iwlS/rnfcNu/pm+faruiqs743aNnH7h94WtG0DbYtYo662f6t5ZLO4u3TVRhXWfGC68q9/xdthZp/ZXYVtfXGm8tce6N+/5E/Q9c4+RT/eBzy6NO4uitgQV+ul9QG3TfC2wK0v0fa8pat016wUb2PcvmCzCssbq+2j656WG3cXjFuqwnL66ndJstO3YXrfrips55SIHe4RoeH5kQpC9Qa6vOveyQv5pqNWU59P5+vaqLjTH9TtVPdAX58NJdrudm01PTXk9t4ad2/7Tj/OmzfqsdT+z96utXDMPBWW0bWZ8qcefWncPemUN1TYljxt712jmrfTLi7W9pub87WdZZc2vg0LZmqb3awu+ll4dY03br/6Sa31KEsXKX+zFL/LZS61ceqZ5+h2eeY5/yxrq1CgYInux6YH+vbOmKafv50b9JxQu61PrUibVmNtqu63fYt8mepeozeIXfHU18qffY6fWwo/m6LCwjbmAFDrOb8ZqHM/bcFVslnbWmcU+n5jxILsmHxtn9zqSV/Gr6/S88PSapF56HA/zr56aqUKu050mw242o/99W/pOarNcdr+f81oP7bKinRYlHr9fFpT3tDPQdm3uq4tL6kTd698UY/JOpl6bvmqcFXcfUsj/dyuX6Tn+Z0Fvq45O/VPW/4W3Q7hdI/O189inVRd13op3p/eSs+b1SP6IanZvq4L1+jn66uUVUjGX8r0M7N4kRaaSEvx42PWR3qO6neI1hoJa5iUbdN1Wf2xbsO8bb4Na0W0Imqn6Xszs/2zumqp1o6ol6F1BgoLfH2ic9TOnbov6pb5Ps/pqOeDxWP070eTdN/eKZFnPDNXt8uihfr5M/ZubFu7URFjAJzEwEY5G8HiFwBqAljjtpgn2o5cHhMAHByyA65Osp2IfOO2N3d3Im5fATiHgQ11EwRfMgFgPoAGJPu6+9NJdiaZAqC5iHwJ4Cb4r/rRvGPbzc+pqKAJylQbwHq3MD8Mwdff8u7PA7CVZD936fxQ2CUu3ePLi4dgV8JVrq1Bsl1oq/irCLZIHwK/e6G8+DG+iuVBsh2Cr9uxBfVRJOsysJk/BcDY8uoY4RMA18a2+pPsUYl7imNlFZEnQu29GsAsBF+Z5yP4Kg8ELyMI4K8A3nT3HePu+X0o3bND/8e+cI8EcE0sAsnu5ZRrGQJTjufdS56E4zZyT9I4rj6rEWx7H15hqxjGHiC8MDcMwzAMY+/GFudGuYjIJARK5zMAjEDwBXErgNsAfAPgUwTCXLuT5gYAFwN4heRMBAuaDgmivg1gIYIF2pMARrv7ixAIgd1HcgaA6Qjs0VMBvOi2WU9DYFucF0nzOgA3kJwIoImry+7wEoBeJCcjWNxWpu6XAHiCgdBb/g+I9wyAbwFMZXAU2lPwX51HItgp8Jlrl4rix/gvgFTXVq8BuFhEYn/Fj0GwrX46gLdEZHIl6hjjTgDpAGa6vO+sxD1DXfzvCcIBeA6BzfkHCITjpiB46dIZwA4EegPJyCT5DQJl9+vdtUEI+m+m21qfSJcgjtsBcD6ANwDUQgXjthJj+yUAK0Tk2/LyNQzDMAzD+LVSBlb5v70V29ZuVIYHRGQwA0X2rwD8R0SmIlgwK0Tk4pB7KQLb8ERhXyAQK0uKOyLrmiRh0xEsSqP0i14QkeHwXypXATjQ2QCfg+BlA0RkFIBRoXuS5bsRwZfcRITr+kDIPQWB7XSMwUnSThhPRMoQCK597zxwESkGUC9yLVn8rbEyikgBgkVkItZH619BX8bD3Hbx76nDR/oAInJiyP1XBF/Bv4eIfEvyDQQ7BK4XkeXui/5CBDb2CU0BRCTXOf8Zub4R/ot6+PrgZOUVkWkI7MgB4DskGLciMiDkLm9s9wPwdJIwwzAMwzAM4zeMLc6NyjCUZCcEttXPuYX5L5WeAB53267zAFxafnSjqhGRBxgcGfc0yYYIXjK8guBFyy8G99V/J4A/V3VZDMMwDMMwqgpTvE0Ok3x4MgzDMKqQp/a5IOnkXLdUBx24z9q4+/r1Wl7gqFIt2jMg24sOjduuhXYalWgBmta1veDM17u0oE+3Mi1WNCHNC2FFtGnQqERf+CjT20HfkKKFdz4pyVH+roW+TGvT9PvkI1vq9zOvrPRiRjUiZcgt0nWbkuWFeQ4r0mVYAS0kNqCDF5ZK182J16c2V/4mxV6saEuqthzrKLrN1pb5fFplRUSEinVdp4dkI8alaeuYg0q0SFp6qO4NSrQQ0LZImZal6619J6d54bGJBXVU2Nx0ndYNuV586aOF+6iwGem6vfsU+fYujAiA7YgY2HUr9uPjsywtmHRIvk53YpZvp2zR6a5K0eVNDW1jrFemM+1SqNOdFkr3qn76WMN5X+Yo/4gML77UpEwLPnUr1LJvj4dEvjqnaIGnfYt1mQpD1VmepkXSOhXpuOHhviny2SVb34rFobT+cvBaFfbwmCbKf2FNL+7Y5NIWKuzjf2uhsc45fuy0HvOEChvS43bl7ys74u7Nxfp525qi23C/HD9nPZmfo8KyItaZZ5X5Mt1FPQn0gG7v2iG56GoV/Cm8JZTNvkW6Qadm6XF33X5+vrh3RlMVlluq6xaeKzfrIBRHli8z4eeIv5boyK+kawGwpiFRtXoRLbCiyG7eT1P9PD+oUKfzSZYWyrswIy/unr8tR4WtSdd9US8070/N1HVZGOr/KF0i/XQatdDcm/CTcEqk37IiW5XPrOnVEdOz9DM+bENj5W9S6u+NzkndI8/xmlTfLqt1E2HfQj0+FmT6xKK2xNuo43YPPfRpkfXZrExdt7RQXbsV6E6OzrGvRcQFX1v2TpXv6X6n8XlVvgA9Ze3LVd4OiTCbc8MwDMMwDMMwDMOoYmxbu2EYhmEYhmEYhvGzUFZxlN8s9uXcMAzDMAzDMAzDMKoYszk3DGOvheQtInJ3yD9ORA7aQ2kfAmAIgGIAfZ3SfGXvvRLALhF5nuRwAO+LyJsknwHwoFOZV2XfXQoXjIlPzhsv0vaa2R30e9WUGt4u94r3tI3ukwfp0wJLt/n31d9M1valfftp+9NtS/zmqm1btF3z2l3Vlb9X/3Vx9+a52m4xp5W26b52prdfH3p6xC74hBOVv/Tjj+Ju2anTKVqm7ehWf+ttEdu+9jsVJkv06XUpXfzBDpsu/5cKq/uIPqzh/ZNHxN0Duq1UYdl3X6/8O255yIf9848qrOCxp5S/ZKOve3pTbbi4boLe2Fa9VlHcvWSZtv/v/YnWtdx2w3363v1qx93jXtL9VoPaDrPjAd5Gc+MC3eeTttZX/vYp3m40I033Y3rEPr1Wfd93m9ZoXYQdhXrMNm3kbUwbPXyuCtt+53PKX/uRv8fdUhR5hCN+2eLH6K7/vqXCsu/Sh1t8dLTPp0/rNSosq4H+5jNhgn+OMiN2wr2O3aj8n3/SKO7eEbH/P+M2rQGx9om5cfeYzQ1VWLc0bYeb28/3ReaRvVTYiFu1zfxpD7eLuzc/NEqFzV+s+7hH//Vx99aFeozmdNZ9XLLZ+1+ZrbUYrpx2h477v/96T7a2Mf7dTVpz9t85/jlv/Nj5KuyO341U/ouzvN374Hw9Dw0bdrzyT7vgk7i7x71tVdiyu2Yqf4urvb39sseXq7Dcfx2g/Ev//k3c3Wq4Lu/3xmhp6Pkr0PMZm+sTZi85eWjcPfSEQhWWduiByl+26DsfNvBCXYYSfe8lA5+Ju4c9fbQKS2mzvy5vvrd7L/3gZR1WK1vf2y1UpgytK3DJGS8gGc++reczWaFPrE1p5Q+1yb/rXhWWeZL+86Bk4oy4e8s43fYN7tdzS9Hzr8bd1Qb/W4WVrftO+VHs52PW1M+M7Nys4zL0nEe0AVLqaE2C4uf970dKq5Y6ma66bsz2vwMlrw9RYanHn6X8753wmvKfuealKre1frPJ+VW+AD1jL2iHRNiXc8Mw9mbUX8x7amHuOB/BMYHdd2dh7soxRESeT3D996EzzL939J1h/NyEF+aGYRiGYezd2OLcMH4hkLyB5Gz37zqSuSTnkXyO5EySb7qz6EGyJ8nRJKeQ/IRkE3d9FMn7SE4kucB9PU6U174kPyM5g+RUkm0YcL/LfxbJs13cAS6v112a95I83+Uxi2QbF284ySEkv3bxTnTXLyb5eCjv912a9wKoRnI6yZdc2I5QnqNcneeRfMkdjweSx7trY0g+SvL9BPX7PYCzANzu7s0m+bmr6yySA0Nxf+fadwbJF9y1wSRvTJDuKJK9omUneSfJP4Xi3UVy0O70v2EYhmEYhvHrxhbnhvELgGRPAJcAOADAgQAuB1AHQHsAQ0WkG4BtAP5IMh3AYwDOEJGeAIYBuCuUXJqI9AFwHYB/JMnyJQBPiMh+AA4CsAbAaQC6A9gPwJEA7o8t+t21PwHoCuBCAO1cHs8AuDaUbi6AQwGcAGAISb3XLYSI3Awg333ZPj9BlB6uDp0AtAZwsEvvKQDHiUg/AA2SpP0MgHcB/MWlXQDgVBHZH8BhAP7jXkZ0BnArgMNdW/wpUXqVKPv/AbgIAEimADgHQRsrSF5BcjLJyc+89m5lsjIMwzAMw/hFIXvBv70VU2s3jF8G/QC8LSI7AYDkCACHAFghImNdnBcBDALwMYAuAD51H5NTESyuY8QMaKcgWCwrSNYE0ExE3gYAESlw1/sBeEVESgGsIzkaQG8ELwUmicgaF+87ADFDwFkIFrsxXheRMgALSS4GoI3qdo+JIrLS5Tnd1WUHgMUissTFeQXAFZVIiwDuJtkfgYhoMwCNABwO4E0R2QgAIrI5eRLJEZGlJDeR7OHSnSYimxLEGwpgKKBtzg3DMAzDMIxfP7Y4N4xfBslEK6ILOHFx54hI3yT3xNRgSuHmAJLPIvgSvRrBV93dKUM4TSBY3BaG3OF5JlF5S6B38ST9ml5OnrG6JC0jyU8QLIwni8jvI8HnI/jK3lNEikkudeVggjL/UJ4BcDGAxgh2M5RL2bTP4+6SQr3JKW1fLSKT0rpV3L32nXEqjBn63vTGvjta19aiUlKiq1r/RC90k/LRBhVWL32H8m/7zqdbv7cWipICXYadUuzDyiJdtnaFLn9NL2BWulaXN6NtLeUvmO7LUPq5tmZIaaDFtkrffzHuTqumBb5k4yrlb50WEttqpUXSZJkWmivemRIK00JGqXUiQ7vESx0wRbdDVo1i5S/Y7sW4lqfodHp+pesqoeqkdm6nwto1maH8ZaU638wDc/2932mBwJxS3a9t+ubF3evn6HbJaRwRYwuVqc2FOu7YoXp8ZDfxYksya7IKi9rQl475ntWKv3dLnr5Q5sd3Wl3950/Zd9OUf9/qfqzlnNZGhbFDJ+XPnOAFwLIYFUnTgnur031dS6Oz1S4tCFZU6MvYqlSLITZooZ+/4vUhccG161RYj2pblL90kq9r9aZ67K9crsX5etfzY61eMy0Il9pGi75lFPp+6ztLC+EpATgAaQO9WGLJO4+rsNOKI8JiqTvj7rIvPlFh9ctSlb9RD99Oa7/WbY/1+rneUabro8oXETgsmrjA55mrw4pHjlH+us38s1s6SpeXOXrOQn6oXyNzgKzWc+HaUt8OaaecqsJKP/1UpxtKq2zqFzosIsAXTrf0889UWNm4r3WyrXN9+Up0O0SHsyzwc41s1OMhnGeU0rciYnHpkWd1hk83pZbuw9I5es5N69kl7q6+cLwu3/xZSctQ8roek2zaTN+7Lc978vWzmXLAAF3e6WO9J1WP17Kt+jctPEehRI/fsrEf6zI19OKSsjnyW750tvLnp+yVumdGEmxbu2H8MvgKwCkkq5OsAeBUAF8DaEEytgg/F8AYAPMBNIhdJ5nutmcnRUQucVuwjxeRbQBWkjzF3Z/pbNm/AnA2yVSSDQD0BzBxN+txJskUZ4fe2pV1KYDu7npzAH1C8YvdNv3KMg9Aa5K5zn92qI7HuDpGF+YAUBvAercwPwxATCb1cwBnkawHACTrJrg3GdGyvw3gWAS7DT5JfIthGIZhGMavm7K94N/ein05N4xfACIylcGRXbHF8DMAtgCYC+Aikk8BWAjgSREpInkGgEdJ1kbwnD8MYM5uZHkhgKdI3oHgqLEzESwu+wKYgeBr8k0ispbk7mxNnw9gNIIv2FeKSAHJsQCWINgCPxtA+BydoQBmkpyaxO5cISL5JP8I4GOSG1H5lwcvAXiP5GQA0xEs8iEic0jeBWA0yVIA0xB8/a4MquyuX74EkOdMAwzDMAzDMAwjji3ODeMXgog8CODBmN99HS4TkSsTxJ2O4Mt29PqAkHsjEticu7CFCOyto/zF/QvHHQVgVJI8VBiAsSKiDoYWEUGwrTxROf4K4K8hf3aSPMMHU38pIh2cevsTAPS+WH/PxSH3RgQvHhLFew7Ac5Frg5OkMyBZ2Z0Q3IEIXnQYhmEYhmH8JolatBkeW5wbhvFr43KSFwHIQPCl+6kqLg9IdgLwPgJRv4WVuqmRt+es01NbIJUs0LaTGc29PVy9VG3Pm9ayuvKzrrfZTf9mqQrL7LmPLkPITm3n1kwV1Lintiku3ujtLLdM03ardXpoO7sGIZvptN4ddZ5NWypvuOaZRxyvwnbeO1T5U1Ny4m7W0naV7HKA8ss4b1uZqpsI2Knt95rtmxd3l2zQf1GkVdP2sTUPruM927fq8nXWtstp1Wv48kTshOu10jbnW95bHXf3LNG6hKwdsf2t68tY8OFkZHRqGPcXFeq+qB2xDWednLi70bF5KmzDy4XKn7/G51Ozrra7zFurx2Gj/Xx4/kRd1yXpuvwdNvg/TaJdI0XaDpP7hsbP6mU6csRGnm29rXjKho903CydU7Xqvv0Lxy9SYZmiN0R27eJt8xfO0QdEpGTp8ZIdunV+uk6HPfQYrdfS294WLdT9ViNX60Ok1vHlT+mgn6m1O6Yqfwv4upUV6HSWpUUkNkJzQNFC/Vyk53+ny08fd3Nxjk4nYucctjNPO+UaFTbhX7cof/sN/jlpUEdrDuxTvF35Uxv557Feavl//S8J2TL326brVlKi2zu1JkNunQ6z9dy4aYUfozXr1dFxQ3bCAFDylbeDTm2fq+M2aqL89VJ9exe/9rYuX0SHhNX8HMsO+6swWT4/kq5/VpmhlwWMtDdyvHZH1C62dKbW30jt09N7CvXcEf2dUnnmRObuuhFrsto5cWfRDP1bmH6KPh229Msv4+6sjrourB75bcwI9XnNSCdHfpdY1z/nMl9vSpRVkecix48BydP6D2ym+w3r1/u42/XYZrRMIZv0lEb1koYBQOOI39i7scW5YfxCEZGlCFTZfxGEvzD/xPk8BOChnyOvyiIi3yKwsTeMn5XwwtwwDMMwjL0bW5wbhmEYhmEYhmEYPwtl5R4A9NvG1NoNwzAMwzAMwzAMo4qxxblhGD8KkrkkZ1ccc++AZI5TdN9T6S0lWT/B9ZNJ3ryn8jEMwzAMw/g1IHvBv70VBkLJhmEYPwynGv++iPwo+3eSaSLyk6uW/JDykkxNdvwZyaUAejnF9z3GVblnxSfn/Uq1wNr4VC3ilRV6z7pBtPDOzjItLHZEiheOaVWk5/95Ohs0LPXbzs45bI0Km/mpFjpaRy+KNC5TN9WpBbpb/5vp/a2pRXmWiK5bTXrrq22i63JPnZ3K/0CeFw7aBV2GtpKl/OMkL+6+oFgLBY3N1OVNDW2/qytaKGoNdZnqhqzF5osu31lFWjxuZbpPNycyutZGjM7CQmIzU3UfRzmwxPdFXuQV/Gl1tBjbn/Miwm307TQ3IjzXILWG8g8qTI+7d4ou8BfV9JbF36V4caP3S3NU2Drq9j4qNASi6WxGRCivzLdFD9ZSYcup2yk99JzkRKz6VkPHnVngRd7OzWylwjZSd9YW+PJvijx/Tanbd3so7rISLRhYPUU/gP3px3NUp+1w0WJRD6X4cdmIWqCstuhBsIhenO+MAp3n85m7lP+CQt/nL2TuUGGtqcfDMfl+kG5O0e37eoa+97Ri/yxMyNDt+cDku5X/zl63xd1rUKTCqkM/jxcU+/a/J02nmxn5HtU4NNarR8LqlGn/N/TtfUKJFuZ6N02Lyd1Y5J+LxyJ1qxfpm/DcUhQ5eTm66Xet+H7rAz3Wp0CPh/5lvoyzI/PFrshP2c7QmNxcqvu/fkQts1rouUmnbqObq+k+fjI/J+5eGZnXS8tZGtWlHpPR8m4TPwZWFGmBtTaZ+j15J/pxlhJp0ehYSg+FLy/Tc3ebFN3n4b5qJOkqLKpCHq7rZui5bnGp7reuqTlx95lFut8e19lgR+j3sGGK/n3bR3QbzoTumzeW/a/K95S/2PSCKl+AXrD6xSpvh0TYl3PD+JVA8gaSs92/69wX7XkknyM5k+SbZLASItmT5GiSU0h+QrKJuz6K5H0kJ5JcQPKQJHn1JDmD5HgAV4euZ5F8luQsktNIHlbB9YtJvkHyPQAjI3kMcGV83ZXlXpLnu7LNItnGxWtA8i2Sk9y/g931wSSHuTotJjnIJX0vgDYkp5O8nwH3u3abRfLsUP5fknwZwCySNUh+4Oo9OxbPcS3Jqe7+DqG6Pe7cw0kOIfm1q8uJP6avDcMwDMMwjF8fJghnGL8CSPYEcAmAAxC8cP8GwGgA7QFcJiJjSQ4D8EeSjwB4DMBAEdngFpl3AbjUJZcmIn1IHg/gHwCOTJDlswCuFZHRJO8PXb8aAESkq1ukjiTZrpzrQHC+eDcR0Z/pAvYD0BHAZgCLATzjyvYnANcCuA7AIwAeEpExJFsA+MTdAwAdABwGoCaA+SSfBHAzgC4i0t213ekAuru86gOYRPIrd38fF3eJi7daRE5w94U/t24Ukf3ddvkbAfw+QV1yARwKoA2AL0nuKyLq/CmSVwC4AgD61+2JTjVN4N0wDMMwjF8Xds55cuzLuWH8OuiH4AztnSKyA8AIAIcAWCEiY12cF1289giOYPuU5HQAfwcQPuB6hPt/CoIFpcItSnNEZLS79EIouF/MLyLzACwD0K6c6wDwaZKFOQBMEpE1IlII4Dv4r+uzQmU7EsDjri7vAqhFMrYH7QMRKXRbztcD0IfM+jK/IiKlIrIOwUuN3i5soogsCeV5pNtZcIiIhPeklttmjtdFpMydc74YwYsDhYgMFZFeItLLFuaGYRiGYRi/LezLuWH8Okj2DjJq0yMu7hwR6ZvknpihUyncHEHyWQA9AKwGcF6CdCsqR3nvSHe6PA4A8JS7djuAbaGyAEBZyF8GP3+lAOgroo3aSIbrouqzu2UDABFZ4HYoHA/gHpIjReQOF/y9NktAor5Iytn5Pnj/UzaosFMXqA/uyKjrq3D5ZG2LeHFpXeXvmOLtI0dm6Lh/vkbbqc17dFPcHbUxX5KqbdxOv9Tb7x0+W9s1l+niYs0sbzz3zEXakO7rZ7RNZr/zvQ3k1Jd1WIMzmyj/X95aHXenZ2k7xaJdulv6rPHt0newbqMj3p2p/J9P9++uOlfTdsLrd2qbzD4XelvFpSN0GRp2Wqn8W1b5e1vc2FGFjbh9vfKfepO37/3nwzrdmzqvVv4Zk/U7qAbV/aMhkc8VFxVqO/g21f34mFeq05mYpm1iOx/g810wqZ4Kqyna1rrNAwfF3V2vmqXCrh/1B+WXjb6dNp/9iQpLj9hPZ4ZsKxl5pFpExmhuiZ8O0qntTTsfpt8PPjnO25n/8TxtL1uyVMcd9aUfhwsy9Ri9ZpBuh6sf9ff2TdNn0A8s0g9K51P88zf+La2LsDhF99tj/X1fTByl++39atrOdUCxL9ORT2rpjSOWfKf8CNWn21A9D+Xv0OOw1ZMHx90rbv1KhfWK2F6npPrnpP0GbbsetjEHgNsm3xl3D+lxuwqbmKJtpDsM9G2Y8oGeW44u0W123meXxd1jBzypwg68QZfp0Ef9uEtL1XbCDQp03O5/9fkc/R89dqqX6TF64iA/L339sLYxzq2tbdn/Xujj9izQ2gsHQs/dy0PT3T9b6X6bM1+Pjyez/Pj4S3EDFVZSoueLQ0PaI2sm67G9crP+Pfl7Lx/3nRnNVdhHqbpuYf6Wrdt3/gb927Mxzbd3SooOa10U0WNJ8+OsWpZusyXbdHl3hXQbWqfp34va2dq2PVym9Wm6jQ6soeeHedty4u70iGZJMXX590337ZJSTY+Vk3flKH+W+GezbS39u1Snqe7zG5fo37i9gbKKo/xmsS/nhvHr4CsAp5CsTrIGgFMBfA2gBcnYIvxcAGMAzAfQIHadZDrJzuUlLiKXiEh3ETleRPIAbCXZzwWfHynH+S7ddgBauPySXQ/n8Y3Lo7uIvLsbdR8J4JqYh2T3CuJvR7DNPVzms0mmkmwAoD+AidGbSDYFsEtEXgTwAID9d6OMAHAmyRRnK98akfobxk9BeGFuGIZhGMbejX05N4xfASIyleRw+EXlMwC2AJgL4CKSTwFYCOBJESkieQaAR90W9TQADwOYsxtZXgJgGMldCGy8Y/wXwBCSswCUALhYRApJJrv+A2usGATgCZIzEdTlKwBXJossIptIjmVw/NtHAG5CYPc+A8HX7JtEZG1M2C1EVwD3kywDUAzgqt0s53wEW+YbAbgyam9uGIZhGIZh/Laxxblh/EoQkQcBPBjzMzgyrExEvrdQFZHpCL4QR68PCLk3Ion9tIhMQSCgFmOwu14A4OIE8ZNdHw5geJI8RgEYlaRs8TBXzrByeizO4Ii/S8h9XiT6X9y/8vL/BPpFROx6bsg9GcAA5x4OXbexInJ99H7DMAzDMIzfElV+jtpejG1rNwzDMAzDMAzDMIwqhiL27sIwDGNvo2D8K/HJecqpb6mwtBQtpVKnthdFunmXFgYasq8Wilk4xwv+LIwIZjUu0cJRLWt5cZptO3XcLaU6n4YZ3rZ5Z7EWYioVbb7wYTW/aevMYr27v+vrZyj/4t+9HHdXr6lFvLZs1mJsy4q8UNCx7wxUYfLdbOgLvg0/+MtSFXTiCH0M/Yenvhd31xQtbNVv+MHK/9ElE+LuY67Q/bR2hBYVKir04kBbI+2bnaXrumSXl0nYkaLfq5864mTl/2Lg23F3j7ZanO/zxc2Uv2mpzqdTey9Et3GlFtD6bpcWUEoL/f1QL1WLWS2g7pvsUt8W2aLb5WOtK4X9i/z4OOPDc1TYuksfVv7Grw72nmJdBtaqr/ylC76Ju1dcM0KFNb/9AOX/5PqFcXdUuKhb3U3KP3K7f6Zq6eGBhqX6mfqqmu+7osi3o8G3NFb+N//pRZ1qlepSlERMghqK78feT/VWYR/9YbryH//GcT6PiODesnRdpgGFvk3XRUTH2lXXol6rd/rx8lGkT+9+/ljlL/vC58s6WuzuTw/p9u1W6oWvrpx2hwp7cT8tELdvqZ9PHsrUY/vld/UmslsGPh9333VHKxU28qZlyt851OfzN2lxrcOf0JIt7187N+4eOPEmFVa6ZJryy5wpcTdr6ueLXfXcct7JQ+Lu507S4yrt8H7KLxv92Immm9LnOOU/9/C74+6X37xIp5Ovxdmw1EulFI+erMvQQYu+sUcfn2fLbjrPI+9FMl64rKbypxx8qI5Q4H/vikd8UG4ZiiYvibvHjtLP1xHv69+avD8/Fnfn3KdFKhH5bUR4Dk7Vm5Bl4yodtaMXw4zOUWVrtQBjyetv+GRbacHT1OPPV/6yVb4vCp5+XYVVu+la5f/HmW8q/31LX6nyg8z+b58LqnwBetnKF6u8HRJhX84NwzAMwzAMwzAMo4qxxblhGIZhGIZhGIZhVDEmCGcYhmEYhmEYhmH8LNg558kxm3PDqEKcovr7YSVxkgMA3CgiJya5rbz0xonIQRXHLDeNpQB6ORX08uL1AvA7ERmUJDwXwEEi8nJl4v+Acr4CoDOAZ0XkoSRxBgPYISIPuKPm3heRNxPF/RHliOeRIOwH98eNuefGJ+d9S1NVWGHESqp1kTd0HZqhbQRvLdF2opNSvS1wn9JdKmx+xE74kPreXnnUxkYq7LAG2pb5gTxvh7kT2j6vZ6k2QL3skU5x98PXf6vCehToe3fRb/DaFbG13hB5vVwcapf+xbpu30HXbVSGt/07ulC30Y4U3cBHNFkTd1++Tsc9Q+op//g0b/NaFLGtHlik26FV6s64e2lpDRV2zEBtd7t9pi/vaUv17/bx6dqOfCG8/X/fMl3vb1OLlf/67Dzlr1HX5zNuQVMVdkCrtcq/Y4u3Bc5potv7msU5yt8T3o50GbUt8OoyfW+HFB+3aZke+xcdrO05Xx3j614zYu+9Vd+KbaHhUycSN7qN8IA0r9Vwl+iErijIVH6GbMdXpunxMSpdayp8k78i7t4eGaOvZrZT/pcz/QC/5+SdKuyZd3KUfyJ3xN1nFuo+P+o0rXXw+Yg6cfe6NF3zN7hB+Y+jt6efHMoDALpBaxIspR87daF1J6LUL/P57lOsx/O4TN05m0P29IeX6LpdMEPboA/p4W3QRzJPhQ1AjvJ/BR9+nOiwNkX6ORmW5eu2X6TezXVUdErzc/B40fbTUU2CT0NaHe1E6040L9Hz0BupvryHR+oSbcMedfzP96Q8rb2wKzK/vZPi0+2Rou3/l0GP34NK/By2JlXnuZW6cgeHNDWWpes8R4XaPkrjFD1P/iklX/m/KcqJuxek6Tz3LdHP6tw0/3uyEzru4ZF5f07osd4A3an7RTRWVofqvgZ6PutSpueHcDNtTdFttovaf3KRb+9pqXqs50UmqUX0cXMiz1uzyLy5IkX/rj649NUqt7V+ei+wOb98L7U5ty/nhvEr4scuzCsLyTR3bNjkcqLlAjgPwMtA/Jix8uLvTv6NESz8W+6J9H4qfq7+MAzDMAzD+KVgX86TYzbnhrGXQLI1yWkAeoeu1SA5jOQkktNIDnTXO5OcSHI6yZkk27rrO9z/TUh+5cJnkzwkQX6pJB8gOculEZb3vJbkVBfWwcUfTHIoyZEAnic5gOT7LuxQl9d0V86aAO4FcIi7dn0kfh+S41zccSTbu+sXkxxB8mOSC0n+O0lzjQTQ0KV9CMk27p4pJL+Olbmctj7C5T3LtW+mK9MIFz6QZD7JDJJZJBe764NIfuva69VQkp1IjiK5mOSgUD6x/hjg+uNtd/8Qkjb/GoZhGIZhGHHsy7lh7AW4xemrAC4BkAMgdnbIrQC+EJFLSeYAmEjyMwBXAnhERF4imQEgsoET5wH4RETuIpkKRPb0BlwBoBWAHiJSQjJ8PsxGEdmf5B8B3Ajg9+56TwD9RCTfbb+PcSOAq0VkLMlsAAUAbkZoe34k/jwA/V2+RwK4G8DpLqw7gB4ACgHMJ/mYiKyA5mQEW9S7u7Q/B3CliCwkeQCA/wI4PEGdQTILwHAAR4jIApLPA7gKwOMuXwA4BMBsBC9K0gDEzmC6GUArESl0/RGjA4DDANR0ZX5SRCKbHdEHQCcAywB8DOA0AGqLPckrEPQLjqrbC91q7puoCoZhGIZhGMavEFucG0bV0wDA/wCcLiJzIovYowGcTPJG588C0ALAeAC3ktwHwAgRWQjNJADDSKYDeEdEpifI90gAQ0SkBABEZHMoLHYI8BQEi8gY74qINgALGAvgQZIvufKsJMs15akN4Dn3xV8AZTD1uYhsBQCS3wJoCSC6OI/jXgYcBOCNUJ6ZyeIDaA9giYgscP7nELxYeJjkIpIdESykHwTQH8GLj69d3JkAXiL5DoB3Qml+ICKFAApJrgfQCMDKSL4TRST2Bf4VAP0QWZyLyFAAQwFgbf8BAgS2zuk1tWnWmrnajrHJft7G8fKv16uwnZn6fNlLf+9tzz4dqm0cTz1FywykHXJg3H3ovd+osPyd2sZtcHtvj1y4Tb8rSsvU9tNHX+ttuD/5hy7f/H8tVv5WffVZyoXr/Wa4mqd0VGEj7/Fx1yIL+4bOYT71D9qWssmDYTvBYnTb39vQl+zUYzd/q6/rkLolKC32mx7Ss7Qd9rkD9HuwdSO9rWqT3zdQYbLD59MRANN9PiWLdZ9v2+jtMIdnAwu3+fp0b6jLUKNp2L5wE4pDTXhBa207OeMD3S5hK/MBB2j77vETtA36gCu8DefUp7XN/AWRM7GPvNLHLZyh9QpWTdfnMDdqvTruXjhHt9m6aXrMHpjiK5eWrjdKLtupn5OGIT2AenW1DXdmtrbJ/HKZr+uzN2ob2MLxerpdP8uH52/JUWEP9tNj/+EJ/oXb1mraBrZFI/3s3lTqx8ey93UZ/nCtbt9z38uLu1cv0WPn7g/rKP9hIS2EC67SfwY2eExrKBxzhW+XzR9pPYv0annKX/Nwfy7zgmG6fatX13a5jXr4vkhtpG24W7+hy9RhoI87fYTecBS2MQf0Oeh37XuCCru9ht5QdXSGb9/iEl23nIb6Z+6GzX58tz9Wj9/PPtBjtEXXvLg7f6quS26rzcp/0Hb/zC9dr+fNlg22Kv9NK5fH3Y+23EeFrVynn+PazXybHVZfP8erl+i4t27zZ22fVqOnCjukRJe/e7ekP8XYtEzPAY0P8G04arQ+s/v+An2+d5hRDfWZ81FprBbF/r13Vpn+HWpSqsfZmYd4DYWMw7qrsMX36ue4daFPqyCi89L9HN0Xu6b78bJgnrbp77ifbqPvZvlnakeRLm9WxE6/ZTs/PnJT9dwx7VvdhsdX878tTdtrrYhtq/WfQP/crufCvQHZK6299w5sW6VhVD1bESw+D04QRgSL9u7uXwsRmetE1k4GkA/gE5LqK7GIfIVgYbkKwAskf0fy1NDW814u7WSCHLFZvxT6Jd7OBHEhIvci+LpeDcCEiraVA7gTwJdOCO8kBC8donnH809Q9jApAPJCbdRdRDoiOeX9JHwN4DgAxQA+Q7CA7gfgKxd+AoAnEOwgmEIy1jbfK3OCtKNtXeViKL80wgvziggvzCsivDCviPDCvCLCC/OKCC/MKyK8MK+I4so3g2EYhmEYVYwtzg2j6ikCcAqA35E8LxL2CQL7bwIAyR7u/9YAFovIowDeBaA+P5JsCWC9iDwN4P8A7C8ib4cWr5MR2G1fGVtgRra17xYk24jILBG5D4HoWwcA2wEke11bG8GLAwC4uKL0E5Q9HLYNwBKSZ7qykOR+5SQ3D0AuydgnrAsBjHburwBcB2C8iGwAUM/VZY6zEW8uIl8CuAmB+YH+5FM+fUi2cumcDWDMbtxrGIZhGIZh/Mqxxblh7AWIyE4AJwK4HsHCNcadCLZ8zyQ52/mBYHE3m+R0BIvH5yNJDgAwnYHA3OkAHkmQ7TMAlru0ZyCwU/+hXMdAeG4Ggq/5HyHYAl5CcgbJ6yPx/w3gHpJj8X17+R/C+QAuc/nPATAwWUQRKUBg2/8GyVkIREOHuOBvEGxJj30pnwlgpgRnTqYCeNHdMw3AQyKStxtlHI9AJG82gCUA3t6New3DMAzDMH4VlO0F//ZWzObcMKoQEVkKoItz58Ertf/PXcsH8IcE990D4J4E17Pd/88hsKUuL+8SADe4f+HruSH3ZAQLfYjI4Ei8UQBGOXdY6T3MERF/LP54AOFDfW9z14cjEGuL5ZHwrPdwuzn/EgDHJog3OOS+OOT+HF78LRw/HyF7dRG5IuQuRrDFPWkezh8uV/jL+i4ROTtRfQzDMAzDMAyDElVaMAzDMPYoTuTvxmQvGxJxXstT45NzDer3qCWRd75LSrxYzafTh6qwP/S6SfmnFXgxtjaZWvypNPJ7MH7bori7S80WKqwmtSBVixQvBrQLWuRmbZkWV2qa4sWtlpbuUGHNUrWo0PaQ6H1Narvsb4u0gF1pqF36ZmjxnDklW5S/SWpyi4QCaHGwNSW+jPVTteBbvRQtULZNvCBRjUh5l5VoUaGCMp/PPulaFG19iZZ36J7uRYemFGvxn07pkX4MyRkUiR4ryyNl2CdN5xsufwb1ppbvCnV7N8/wQmM7yrR9/bbSAuXvmtko7h61XQsxHVGznfKHy9CMur2XlunxEm7/AtHjLitS/tSQ3ES1yMbB7ZE+TwuFf7j1WxXWJEtbADUO9V1ppL2LI89quE0bpGiRt4xImdaHnptoOltK9TPVN9237xrRbb9VtEhWyxQ/9peWaiG0rMhcszP0/OWX6QMoeqdrIbSvCr3wWG6GFqFLj9Rtbakf3/VSdTtEv2illBNWHGnvSSFxsxWLPlBhR+x3ufLvm+7LmBkp39wSLdzWIDQvbSjVz2a3NP38rSjbFXdHhVGj5a2b4oW7GkREFDdB99vQyffH3V076fe8R1ZvHSm/n+/qRto3M7JZ7dkpD8TdN/a6RYVtg+7zXaFnbHVERC+aT3rotNDoc/zA5LuRjN/1VN8LkB2ZR3eExmTrSLq7IiPk3R0L4u56GdrKrnOGFnILt/+6SNtvjsxvaaG5pF6KFl/bUKafv1qh38r8yDxTLfKNtHpoftgceW7rU+ezsNTP5V1T9fO2NZLP+shv8PvLP6hyObbHm19Q5QvQa1a8WOXtkAjb1m4YhvETIyKjdmdhbhiGYRiGYVQtJI8lOd+d5nNzgvDzSc50/8ZVoHlUKWxxbhiGYRiGYRiGYRgOkqkITug5DkAnAOeS7BSJtgTAoSLSDYEu1FD8SMzm3DAMwzAMwzAMw/hZqPI97ZWjD4BFIrIYAEi+ikBwOG7rJCLjQvEnANjnx2Zqi3PDMH5SSOYCeD8slFZB/IsBjBSR1Xso/6UAeonIxh8TZ09BsimAR0XkjPLiDXvDi+cvPne4Csuuo23a6h7hBf6jdpXvn6in+UUfeju7hUXa7rpOmbbZHdKjYdy9fqm2h8zI1DZtO3d6m7a0VG33t7lQ2yI+kunLP6SuTqfJs7/T6d72n7hbdFSsnq/tpRcUev8Jj0b0/iK2ntju7fXG/HmRCjrkQ12GWSd7bcU62btUWLOHtA7hjEu+CGeKbn/1bbjhOW3vXVocbtMCFBd7e0NG7dVDJrAnoQFKQzaPh750tIr75dkjlb/fyf7mL95rpMK61dVDvv6B3p03TY+zHVu0XeOWnb5fd4q250yL2H6mFfg/xe5up209H1mj6zqw0LfLMe+eqsLy73tC+av/689xtxRpu0qmaRtN2bY+7t543dMqrN7gU5R/3h8+j7tvadFUhTU4Qo/niS95u/eaadpGt6hU2/dOzPBxCyN/nf7pPx2U/7nr58XdzYr1s9kkTT+PNbP82GrxzwNV2IjrtY3/aU/6kzc/+sN0FbY4Q2+oPDrVp1urni5DarrWPrhOfJle2KDHww3DDlV+rF+FZFx68wxdhhI/T537oT5U5ImTXlb+22v4NozOhZ/P0H1+a69b4+5/PtBZhX12zRzl77Ov1+qYPFfrWRx+r7b3/uLPi+PuI0ddocJku37eyiaNjruZo58vNG2pvOH6TP3HASqMTSPrgV0hu/iatVVQapf+SdP95JULdTr5WuMB2/O8e2ueCpKten5jD19G1tXPULRvwnz0x2bKn9J9fx2h1P8QlIz8QgWl9VanyuJfy/zzN3uInh+6PXOU8hc89WrcnXWlHmfM0M98GCnR9uhI1b+5KXVD9Yk8t2Xrl2j/J14ngU2b63T66nkeofmu6NlhKijj93rc/f3MN79fcAMkrwAQbqyhIhL+8t0MwIqQfyUA/fBpLkNwWtGPwhbnhmHsbVyM4LixPbI439twLx3KXZgbvw7CC/OKCC/MKyK8MK+I8MLcMAzDMPYGyvYCKTa3EC9vG3qiUib86E/yMASL8++d6rO7mM25YRg/B2kkn3OCGW+SrE6yJ8nRJKeQ/IRkE5JnAOgF4CWS00lWI3k7yUnuHPWhjMrfOki+49Ka496GRsNzSc6LliMU5VqSU0nOItnB3dPHCXxMc/+3T5L3KJIPuzizSfZx1w919Zju0qjpyjH7R7eoYRiGYRiG8VOxEkB4C8M+SPDhiGQ3AM8AGCgim35sprY4Nwzj56A9gu1C3QBsA3A1gMcAnCEiPQEMA3CXiLwJYDKA80Wkuzt3/HER6e22xVcDkEz1/FKXVi8Ag0jWSxAnWo4/hsI2isj+AJ4EcKO7Ng9AfxHpAeB2AMnPfwFqiMhBLs3YHrMbAVwtIt0BHAIgP8m9AIItViQnk5z8f29/Vl5UwzAMwzAM46djEoC2JFuRzABwDoB3wxFItgAwAsCFIrIgQRq7jW1rNwzj52CFiIx17hcB3AKgC4BP3YfwVABrktx7GMmbAFQHUBfAHADvJYg3iGTMQLU5gLYAom8wo+UYBCB2yOsI9/8UAKc5d20Az5Fsi2ArkzaO1bwCACLyFclaJHMAjAXwIMmXAIwQkZVJPvzD3RvfYlUw8Y1fiF6KYRiGYRhG5SmrOEqVIyIlJK8B8AmCv1OHicgckle68CEIPtzUA/Bf9/ddiYj0+jH5UsT+/jMM46fDCcKNFpGWzn84gGsBNBaRvgnijwJwo4hMJpkFYBkCsbYVJAe7aP8Hv0AfguAL978AHC0iu1wag0VkVEzsDUB2onKIyKlhQTiSvQA8ICIDSA4HMFVEHnX1GCUiuSSfBdADwGoROd7l908R+dKlvRxAVxHZSrIrgONdnY8EUIBKCOQ90uKC+OR8Yi0tvJSapn/WNm6sEXf/N02/c20nWcpfK3Triohw2y2nbFf+J972QkLNtc4V1kZe7V52sBd4WjNZi+eUlGh76kHFXmjs9Y5FKuz5+VoE59LeK+PuVyZq0aMzO6xQ/m1rfF1LS/VLkNHbtQhZeuin76xbclTYyse1QNxz+XXj7twSveGsOPKu5awuvkxjp2lhozbVtyn/mGKf74V/1G1091O6wW+93ovd/ekh/c7ptvrarvzt9V6wqmGJ/o3fmKoLvCOyf65xSHRvXroWAFsP3Vd/SfX9uC1fi689m6Hr8/A/28Tdd/xjuQq7Y/K/lL9k0vv+visn6rCIuV96yCQwLfLnTCH1heri46ZH4p7SVO9U/M86rxdwbU3dvmmZul0+Wuvbe1ZEEO7eM3WbXfF6qDzUbdS7RD+r553i833oPS0Wlh+p23Wt/PP3+iL9DE1N1Rt29g3NCdddocuQso8W7ipbsizufvkZHbdpRKRuwNV+MD37tB5n+5foMuwo8+85l6TryWRemlZ+vPejP8TdfzvuKRX2XZkWLPtXaHw/JHpM1o+8W71r8l1x99M9bldhF/9J98V7//Hl3xF5hqKLjHNC/Xb3+zkqLFP0vTee55+hp1/S82aPQi009nw13+dNoYXF2hTrB/m7dF+qM0S30YQyLaQ5MfQcdyrT9c6MPCfH1fS/RTM3681pG9J0GY7M8XFfiMy/yxkRUQtxVWRcfZxWXfnDuRRG5t/GkXl/S4qvQL2IkXNEXxThW3OLdK+WRuLOyWTSsB4Rpce5mb7EdSKRCyLzb8dCX6otKfp5W5yhyx/2dS/QtWlabafy3xEZd28ue7fKLb4fCv2NU1Vcv/zFKm+HRNi2dsMwfg5akIwtxM9FcNxEg9g1kukkY3K52wHEpH5jfylsJJkNJ6QmIivctvfu7s1lbQBb3MK8AwAtV5y8HGMqKHdtALG/ei+OXRSRS1zex4finu3q0g/AVrcwbyMis0TkPgTb9bUcs2EYhmEYxm+Msr3g396KLc4Nw/g5mAvgIpIzEWxNfwzBQvs+kjMATAdwkIs7HMAQktMBFAJ4GsAsAO8gsP9JxMcIROdmArgTweK/MuV4soJy/xvAPSTHItjSVB5bSI5D8CX/MnftOicQNwOBvfmPPmLDMAzDMAzD+HViNueGYfykiMhSAJ0SBE0H0D96UUTeAvBW6NLf3b/y8igEcFySsFwAcF/ey0TkymRxnHsygAHOPR5Au1DU28opxlsi8rdIutcmiLcUgb29YRiGYRiGYcQxm3PDMH4TOJvxCm29f2Dao+Ds5PdUmvmv3xGfnDc/NEqF1eqZFY0e56b3s5X/vqPylH/5595WcesunU5GqjaIa9PH205uW6JtHIsK9UaCwgJvz9mknbatXr9Il+lvIUPtR+pq28NG9w1U/uI3vTBq/iIdd+tabaM5f4u3yz3iNm0PyWxdBtnk7bYXPLJOhbX/7+HKv/Dqz+Puuo20LV+9m49W/plXexvprtfnqLB1L2ob+ag2IEP2kdvzdN+UlOqNbmNCdqOX3RWxMb5pofLv/8xBcffsP3ytwtr2z9NlyPD5bJik398XFWp/Rqa3c1y2KUeFbUnRcTtV9/l8ry6ltZW/T8hGtvN/9ZGxpV/p8qcecYT3lEU2KpZoe28Uetva9fd+pYIa/vUQ5d825Iu4e8VCbe/d4Sydz8SXfF+1arxFhW3N02P0Tfpx2KFIt8Pp/26p/O/ctDTu7pq1VYWVRexny0I2pe1u1umMGaw1K/r9zbf3tHu1fsHUdD3uBjbyOp3FBbpP67bXz2PeIj8HfLGpkQo77y6tFxFGtun54q7/5Cn/Efl+nPW9v40Ke+5W/Uz1gdfNeDpV25w/+EA35X/2+nlx9+XT7lBhk7r+Rfm7/93rCky5c60K2/9abRO9+Bk/Bvb9T0RWpUTbBstyr7/AxrrNQD0+brh1ftz9wO/1fJzSXLev7PLzFKvXUGHYp7VO9zJ/MsiD/9Rh4XQAgDX8+C36XOtBpHfR445t9vWeNF3eGwbpe8Pcf7Zuo5Rcrd2BQv9cS54eO6wX0Wb4eE7cvXCa/k3Y76H9lL94pLdySz9IhyHShpLnfxtZK0fHra3LoPpxm54fUKznqLLlfjyzYQOdTPNWugwrlsTdJd/o01nTzzhJ+aPaHX9bVvW21g/sBTbnN+6lNuf25dwwjN8E7gv+T/LFWkQG/BTpGr8dwgvzihgTEXQqj/DC3DAMwzCMvRtbnBuGYRiGYRiGYRg/C2V75TfrvQMThDMMwzAMwzAMwzCMKsYW54bxG4ZkLsnZCa6Pcud9V0WZLib5eJKwHYmu/4RlGUzyxj2QzgCS71cc0zAMwzAMw/itYtvaDcMwEkCySufHcdd6IZs+p2oxnS0TtBCThPaHzS3ZrMKWfqoFqcKCZvkr01VYp0u1v2C6d2+LCJTlF+m4HY7wglUzP9VCNg1q7VL+5qVeVKhajn7fsuzaD5W/6SHeFnvdEm1r3biNFgM64g9ekGjz89+qsM3rtKDPgkKf1vF/bqjCCl9+V/mzqvn2/2JlUxXW44axyl+ndnHcPeoB3W99+xcrv5T4umXup9Pd8Ey+8ne82ot4vfaUTmfLEH1y4NR0L6C0z+26PdeX6L6pOUWPjyYHe4Gir7fquPulbFf+7DpeYK3voMYq7NF7tNDY8Vf4Nn7kv7r8f/qTHlvM7RB3j75cayzuW69A+Ze84Ns/FdpuXxARTQu5W9TR43fbk18q//h5vg3b1dBibKyhx1JWihdSHL1Ri3od13al8pcu9WP/Ca5SYQffr4UJj+jo+2LzCp1n3g7dZs2a+TIWj9Njv4PWC8SIf/up7bSbtHjVvu8tUP7qnX2+M9/Uwmfbt2vBtTbHhcStIq8il901U/nT0nyblZRocck6ZXWV/8AbfBlG3rRM5ylaxDKnoX9u5m7RYmafXTNH+S++0acbFYDrPet+5f+88y1xd+u6es765lH9neuAK/wz9fVVut71M/Rz3bq/f6amfqbns32b62dobokfz2Mfq6/CGlfTdWvUyqeVtY/+KZv96SKdblpe3P3RX3Xc/BRdt6M6+vqsXqSFHNd/rOf53v1Hxt1ffq3nt7lp+ncqzMq39by5Ydsa5V9HP+7mZ+jydS7MU/6dKX7w74h8jqz+Vz23LCvwbVrj/eUqrEU9PQfM3+TH6Po0LfJ2QNZUXYYCX5+CUj3WN0PX9ZC+fk5YN1u30cbtWvxwU+jeushRYe0WjlD+NYyIDe4F7M3njFc19uXcMIw0ks+RnEnyTZLqL7Dw12qSZ5Ac7twNSL5FcpL7d3CixEkeT3IeyTEkH419QSZZl+Q7Lt8JJLsluLcVyfEu/TuTpN+G5NSQvy3JKc7dk+RoklNIfkKyibt+uUtzhqtDdXd9OMkHSX4J4D6X5H4kvyC5kOTlLh5J3u/OMJ9F8uzyrkfK25vkNJKto2GGYRiGYRjGbxdbnBuG0R7AUBHpBmAbgD9W8r5HADwkIr0BnA7gmWgEklkAngJwnIj0AxD+FPdPANNcvrcAeD5JHk+6PNYmCIeIfAdgK8nu7tIlAIaTTAfwGIAzRKQngGEA7nJxRohIbxHZD8BcAJeFkmwH4EgR+bPzdwNwAoC+AG4n2RTAaQC6A9gPwJEA7ncL/2TXY+1xEIAhAAaKyOJE9TEMwzAMw/g1I3vBv70VW5wbhrFCRGJ7Q18E0K+8yCGOBPA4yekA3gVQi2TNSJwOABaLSOxAzldCYf0AvAAAIvIFgHoka0fuPzh0zwvllOUZAJeQTAVwNoCXEbx06ALgU1fGvwOIHQbbheTXJGcBOB9A51Bab4iovZL/E5F8EdkI4EsAfVzZXxGRUhFZB2A0gN7lXAeAjgCGAjhJRPSeOQfJK0hOJjn5/fzvyqmuYRiGYRiG8WvDbM4Nw4i+QCzPHzZ0TAHQV0SUER3JTwA0AjAZwBPl5JvoII1ELzO/d43kXQi+ZkNEugN4C8A/AHwBYIqIbHJfuOeISN8EaQ4HcIqIzCB5MYABobCdkbiJ2iPZISDlHQ6yBkH79QCwOlEEERmKYAGPnf+6IJ7v2ld1Eer31tnILv8uocEmbZddGjHsYuiV7OIybUPaYoy2j92+znd3Sal+l/uttn5Ata+9HXF2hrYprt1Q21mOXeTt9zIaaNvfxk10XCnx+TbvqW2e10zPVv7Z93jbv32b6zaK3tv2fD8slvxF2403PVB5MXGrt0U88QBt95d1eGflX/fMwrj7sOf1e671f3tH+VPTfefsWLFep1Os7eCLHvV2rieKrltmPd3Jh6zy9qZbNlVHu+vjmzdQeK9uh1Vb9fu0zKnezrFnuraznFKs3521WuFtHr+6Q9vHnt1A20+vfdGX+ezIMe3v/UfbtjeV6XF3nwM3qLCNC7SN/IGn+7qWbtVaDNsX6HaqsU9J3F2wTo/nmqd3Uv4d9/h00zK0XfO2L3WZtpR5e/vWossA6mf3sHzfVz1TtB1uzab6+Vsy09u1Tqe2OT8wU/dNtbq+blvn6j/t3ljfRPnPbu6nn1kP5KiwudQG6kfs8Pa+zRrr5zrKmtHennZL5NNPi6tbKH/RRG/bnlpT99M3H+kxemjIprtzXW3XfNtOPR5u2OzbqUGafi767Kttl9/7j9cVOOnvuo3CNuYAcMScu+PuWT2uV2HN6ujyLnzez2mdW2v79OpNdZmK1/vx0ed3un2LFpUof4MNOXF3t9Z6I1nRLm3LvHWtb5eax+q6dS5aqtOd4B/I/evrsd3w4oj1VUnbuLPRO/oFcssm2mZeinzdBhyg9RVemZ6DZLR8/Bjlr3aztp/uebYfoy0fzVNh7U/SP9/bZ/s23LJO/2Y166n7Jreu10xIa62fzeJ52v67znr/DGUdsI8Kk3ytB7Fr4sa4u2iH7qdel3fX+Uzy8+g+x+mHqOlWbduefqCfs8qW6vYtXa/ngKMW2LfYXxLWW4ZhtCAZW6mcC2BMJHwdyY4kUwCcGro+EsA1MU9sW7mIHCMi3UXk9wDmAWhNMtdFC9tgf4XgqzVIDgCwUUT0rzswFsA5zn1+7KKI3OryiOVZAOATAE8CeNZFmw+gQaxuJNNJxlZSNQGscVvf4+kmYSDJLJL1ECziJ7myn00ylWQDAP0BTCznOgDkIXihcLerr2H85IQX5oZhGIaxN1AGqfJ/eyu2ODcMYy6Ai0jOBFAXwQI3zM0ItHe/QPD1N8YgAL2coNu3AK6MJuy+qv8RwMckxwBYByD2yWdw7H4A9wK4KEHZ/gTgapKTAES3vEd5CcFX7ZEu7yIAZwC4j+QMANMBHOTi3gbgGwCfIniBUB4TAXwAYAKAO0VkNYC3AcwEMANBu9wkImvLuR5rj3UATgLwBMkDKsjXMAzDMAzD+A1h29oN4zeMiCwF0ClB0IBQnDcBvJng3o3QX8KT8aWIdCBJBNvcJ7v7NwMYmCDd4Qi2ncPZqoe3pd9bTj79AAwL24uLyHQEX6+jeTyJ77+EgIhcHPEPTpSRiAiAv7h/lbk+CsAo514ObeNuGIZhGIZhGLY4NwzjJ+dykhcByAAwDYF6+x6F5NsA2gA4fE+nbRiGYRiGYew57Jzz5Nji3DCMnxQReQjAQz9xHqdWHOuXxaZ3vAWBlOmpevHnWhyqXkMvbLOhVAviTJfGyn/2Rblx90kFBTrT9K7KmzlmTtw9aXR9FdaiVIsX7RIvgrRStEjTvr30z3CHFV4UJ31fLZCzY6wWFkvN9II+NU7ooMIabNMWCWkrvXDX+rVa6Kx6Ay2mM+OayXF3q3a6zZaO0Ypl20JFnD1eC7V13DFH+evu79thxXXvq7B6rXWbVT/XC8YVfjRehTVdr4Wv6oSEsEZtaKTCMEkLqvX4m++rNU8vUWH9Ous+/998LQDWfR8virR4bV0VNrFakfJ3pa9P/8ZaMKlgpxb6k5CInZRpAbAdqdrfqplPa9Q3zVRYYYqO2/Zd33ebI4J1O6gt99pu9MJdaWla5C3/KS1uNTPD131ALV3vnZszlL9eqheBezsjU4V1iBgPTs/yz/IkaCGxljN1e7fsEGrTiPFNaaQN58/wY6BxfZ1u0xJtWzl6hRe7OixXC0kNW62fxxPr++dv0Yx6KqzDIfqZKivy7bTveP3ML3tcH1BRP9e3f2rkjI8TSvSFtFRfn/mbdBvtl6XL2/5YL0S44RNd78lztf7CjnTfhlPu1AJrretqsbCwCFzXafrn7PVutyt/8zI/HjYt1nNhreV6LG0v8c9JsxW63xp21X0cnttXfFdHhWVX10KEK7b5Oazh9KUqbNZo3Y8b0vLi7qlr9fyWeY+WgenWwgtXZrfQ7btonC5TeugZW1+gxdg2pG9EMmZf8qny55VEyvuYH3ejU7UoaPH7+oGrV9vPmw1b6fYtWKPjLvzat1mh6Pm3U2s9nrdt9L/BSybruaRdPT12Vm/2bZqRouMuulULjDbM9vNDVnU9VtZuzFH+ml8tjbsZEZ5MS9NzVNfGydvb2PuwxblhGIZhGIZhGIbxs7D3yrFVPSYIZxiGYRiGYRiGYRhVjC3ODcMwDMMwDMMwDKOKsW3thmH8aEguBdDLKbj/4Dg/MO/BAHaIyAMJwsaJyEHfv2vvp8F1B8bd390+Q4XVb6Rt2spKvW1itzRtn3dI9Q3KP/MOb0u3BNoeslmZtnGrnuZteDtnaNvDph21f8WcnLi7dmGJCpv3hv6p6ZTm7Q+3f71ahdW+YD/lXz/k27h769ClKmzLVm1/uqzMp3vCM71VmMzWbdi1d37cPXWotqfv9Wg35d85aGbcXT1D243XOEXb6c+6y9en4wmRftql7WPn3zor7t6nk7ZFLK+PS7UpKvb/7/7KP/6q6SFfbbTL9Y/cp3O1jXnvdN2PGxZ6W8rt1OU9NV/bXbbs5m0/583Sdu950DbndeHbbavo8VCmo2L+Up/Wca9qjcft/3pB+WteGjqMITtivJyl7Vyxcmnc+eUtK1XQoYO0jeZBj/kxvHy5tqXNqa7t9tsd4HUS/rBI25wvnqufx3BdryrQcTtdpdt77JPeRnpXxH4+o0T3RVZIXqnxWXo8V/uv3kB6zLP+FMeZl41SYV1SdZl2rPHtkhqxa104JgfJmJqlB+kx/9InRxaPHBN3M1vn+W6aHpMNCvyYPPwJfdBF3iBtjP/ZB37sdMvSz9Th97ZW/hdvXhZ373+tHivfPKrbu1kdb68ctTE/a+Ydyj++y1/j7oOG9FBhKNZzLIq8rbis0nMhc3OVv9skPxe27qxt5Gvc9kflb/HxOz6dpt1VWK9ztY5Dt0GT4u7j7m+rwmRrni5Tur935+uTVFinP+g2TOni+6p9NW0b3u2qsUhG+6O0lkH6Aa10hFo5cWf9u79WQfUP179pBXP9793kqVpzoP9f9XzR+Glv/9387/r3A0Utlbduih8fuWURabMM3YbNckJzQFFE56UgX3nzXx8Vd2d20vb/TQ46WPll8YK4e8OzC1RYw1sGKP8Df56r/Leh6jFBuOTYl3PDMH61/FIX5oaxpwgvzA3DMAzD2LuxxblhGLsFyXdITiE5h+QVkbBckvNIPkdyJsk3SYZfp19LcirJWSQ7uHv6kBxHcpr7v32SfEeRfNjFmU2yTyi4kwtfTHJQ6J4dCZICybquHjNJTiDZzV0f7Mo+kuRSkqeR/Lcr78ck0128niRHu3b4hGQTd723S3M8yftJzg61y9eu7lNJ2ksDwzAMwzB+k5Sx6v/trdji3DCM3eVSEekJoBeAQSTrRcLbAxgqIt0AbAMQ3m+3UUT2B/AkgBvdtXkA+otIDwC3A7i7nLxruK/hfwQwLHS9A4BjAPQB8I/YIroc/glgmivjLQCeD4W1AXACgIEAXgTwpYh0BZAP4ASX9mMAznDtMAzAXe7eZwFcKSJ9AYT3VK4HcJSr+9kAHk1UKJJXkJxMcvL/fTGtgioYhmEYhmEYvybM5twwjN1lEMnYueLNAbSNhK8QkZhB2YsABgGI2YOPcP9PAXCac9cG8BzJtghO1yhvYf0KAIjIVyRrkcxx1z8QkUIAhSTXA2gEYGWSNACgH4DTXVpfkKxHMmZg/ZGIFJOcBSAVwMfu+iwAuQhePnQB8ClJuDhrXFlqisg4F/9lACc6dzqAx0l2R7Bob5eoUCIyFMBQAMh/8VY7acQwDMMwDOM3hC3ODcOoNCQHADgSQF8R2UVyFICsSLToojLsj6nflMLPP3ci+Dp9KslcAKNcXs8C6AFgtYgcX0HahaFr4bRj5b4LwddwiEh3AIk2NKm0RKSMZLGIxK6XuXQJYI77Oh7OQ6tGaa4HsA7Afgh2LBWUExcAMO0vXuioVZudKiy7ixZQKtvpxbZWjN6lwtZt1qI31dJ93HpFWjCpx5k6n6Jl3p91gBYS2/CmrkLjFl7EqWSJ3pTV7kgt8PTKaF/+rCa6S2fdsUL5O53iu3Lhe1q0q8vd2gKi5l1euG3VXz5SYSUlWmxr9VYvUHTAH/RP4Y6nP9dlONgPl/SWtXQ6TyxU/mbNffuu/EKLEzXprsWgmrTeGndXH6DFqlY8sVn5257vy5j1nArCmn+MUv6VaV5IqE2x7oucUt3nhZE/A1p092VaPUGLOGWl6HtrnOaF81ptm6PLtLK28ne+rUXcvfxB3WaHHK28YE3fbuPO+VSFtWmix8CMG7wQUr0MPSZXFUcE4UJ0abxJ+Qsn677ZmeLHe5fj9PhlNf3+cMVHvp1Ky3R75+6r+7HrfD/2p2XqujR4bqvyd27tn7+UVP2cFGzX/VarhS//1P9qcT5C5zPt0tFxd7eL9VRY/z0tINngJD+t1Vmp67Jlmr63Xj/fLm1W6LhL/65Fvuo288/JphVaQPLGIv2T0v2vvn3fv1YLW3VK15ZLLbrmxd0vzNLP6hd/Xqz855zp23vxM3p+OOAK/ewufN7XrXlZoQoLC8ABQN/Z98Xd73X5uwrrUEP3cZPuvo+njdECYI2zJyr/itDY2rVe9//8U19X/ro1vdBY3eZaaG71ou90uuLH1qTrv1VhGyPvyg8/wgvRLZmtBTnb77Ne+XeO9u/HR3+7j84zXf9OhVk1Stet3uIpyj95thd2G1tNj/WeL+u0Dmqnn90w3z2mRfV2Ffr5Yl1EQK1RHT3Olm7O8fdFxBr7dlyl/HO/9f26MzLfZkVk0fbv75+p4qV6jpr4zATlTw39OdSynh6/Wx/9TPnXsRH2NsrspPOk2LZ2wzB2h9oAtriFeQcAByaI04JkbOF6LoAxCeJE04z9ml0cuygil4hI99DCHAi2hINkPwBbRUT/pZMEEbnVpdXdXfoKwPkurQEIttsn/xXXzAfQIFZHkukkO4vIFgDbScba5JxIHdeISBmACxF8bTcMwzAMwzCMOLY4Nwxjd/gYQBrJmQi+eE9IEGcugItcnLoI7MvL498A7iE5FhUvWreQHAdgCIDLdqvkmsEAerky3gvgosreKCJFAM4AcB/JGQCmA4gJvF0GYCjJ8Qi+sMdeHvwXQZtMQLClXX+iNgzDMAzD+I0ge8G/vRXb1m4YRqVxdt3HJQjKBQCS2QDKROTKBPfmhtyTAQxw7vHQNtjlHcH5loj8LZLu4Ii/S8it9+X665sRCL5Fr0fTyk4UJiLTAfTH95njROZA8mYAk138hQDCh2f/LcG9hmEYhvH/7J13fB3F9faf5151ybZsS+5Fbrg3bGMMBkxvoXfSIAkkIY03IaRACCE/EgikEZKAIZQkQAiEFnq1AQOuuPfeu2X1du95/9jVnT1jXckQwDKcLx9/mLNndnZ2dnZWc3fOs4ZhfIaxyblhGMZHx+kkf4JgbF2HyDL9D8qhz34xlV5x/t+Ub6cOaUObdi4WOBSpS9HvEP2d6w2rXAzprrheqPDeYzo+fUhfF8O74i86RrAqoUPss+li5xJeSP/0F3Vc4I4cFx+7cZY+5sj/XKDsLd9wovyduulYzyXX67jA7XUuxvSYx05RPmxZp8zeSVffqVcvU75JL/xA2W+d+lAqnTNNx12P/bs+zvQvvpFKj/uujltddY8X/13k2nTLX3R8oYhuw3fvc4/rhPfk7nbf15Xd7oz/pNJzN3fC0Ue5mNPqLboObfN1nHb1dtcnSjqVKt/uPTqG++0bXXxyrRfT6Petsp+7+M5M5ivfg8/quNYxte7cz3jlS8q344pblX3M9B+m0lKnz2VYno57T65zmgQrv/iI8nX5/DHKLnl3bir99rP6gxTDe+m47BWV7twLRPePrct0m03Pde2f9OJN+98wWNmTf+6uW/9aXW6Wt2/hTndPjf3jcOV79urlyh794PGp9HNfnqZ8K7N0nz36Xhe7XJnU7dCzXbmyZz/mruvzubrtb3roc8pOTHkplW7TUY8lX/21vldP+q2rw+dnXKt89x5xm7Kr57ibg1n1ynfCFPXlT9x46j2p9A2/G6Z8b31zvrKH9nUxx7tW6zY64q7Ryo7GmZ+x8P+UL7FGf4VD5rn2n3CJjuFmb90feL4Tmyg8UveropN1/8WubS5dqK/bkOGTdLlH35hKH/bS5bqcCq0VINvctRnU7i3li/fWceVtv+bEJM7qqnVjHznmJqSj12VFyo4dca6yjy0vTaWPelHrg8T76zrUzY6Md3qYR//7z1F29e9d++b95CqdOe5pczRENCq8mHMp02P5hH6HOiOp71vZox/miaeddgCLdX845ka9wC+5Zm4qXffIE8qXfbW+T/LO+geMgwebnBuG8ZEhImsRKJl/HGVP+jjK/SgRkUcBPHqg62EYjUQn5oZhGIbRGki2nOUzi8WcG4ZhGIZhGIZhGMYBxt6cG4ZhGIZhGIZhGJ8I9im19Nibc8MwWj0kC0leFaYnkXz2Ez7+AyTPb2L7J14XwzAMwzAM49OJvTk3DONgoBDAVQg+SfbZICMrlYwKhwFAu0O9oTvpBLTqX9KRXDk99W+w/YudwA+n61+u4xnaLjyxkzvEC1oEK56pj7NrixPGL6/JUr728QZll4oTaup5hD43qdFfmSs60p1r/ZY65cvZqcutqY+c657tyof2WpQOO7akkiXtvE/cV2uhq8El7txzCrUwF7JylNkh14mSsVCLpPU+Sov/1G529e/Zda/yZXTQ1zjxqju3NqKFrtCg22VAvisrs68WFcp9U1+3Tofo9pZIk2bm6XPNa6+PU1TmhNz27NYCVT2p+1JWjiurpk6fW7bo/pIpbl9p0CKA7U/VbZrcviZyEC3UJRW7tb19Qypd3KsCzdG9i2vDDpVasK5NX92Gh5SWunL76/bcvlx/MOL8hLuOC+q0aBrytFBepwbXDm2o+/roi7X4Xc2iSB+uqVa+PE+EKioWVhLX9X2f+jr2H+IEJeurtMhfTpHuH8nFbhwqqdMib1Kn68RCJ97ITvqaduRWZeclXTv4gmptvduxpI+75vUbdXtKuRbHzI6KLjbo9i3K8tqwm2vDtuv1fYB6bQ+K3H9+feN9tHhc/QwnIElfLMwTFqsX52em7pO+YBn7OekXKdVjd3Ln+rTlytqFzZaL9u6ZEOvZSfsKdHuzbeT5sWtj2mP6sED3Qb99lWuLfn7EumibGe4ajz10i/KhUo/7mV3d+CGVejyOddOCdrLXPV/26dttvL4f7Xfe880/N/bs5tLZ2bqcMu+ZFnNjSbyLHktkp27vPLF3sQcTdrUMwzgYuAVAP5JzAdwGoIDk4ySXknyIoUQ5yTEkp5KcTfIlkl2bKozkz8J9XyH5CMlrwu2jSL5Hcj7JJ0m2b2LfU8J93wZwbmR7Psn7SM4k+T7Js8Ltl5F8guSLJFeQ/M1H3jqGYRiGYRgHCQf6G+eteVG9Tc4NwzgY+DGAVSIyCsAPAYwGcDWAIQD6AjiSZCaAPwE4X0TGALgPwM1+QSTHAjgvLONcAGMj7r8D+FH4rfIFAH7u7ZsD4B4AZwA4CkCXiPs6AK+LyDgAxwK4jUx9M2oUgIsADAdwEcmeTZ0kyStJziI562+Pv9ByqxiGYRiGYRifGmxZu2EYByMzRGQjAIRv00sAlCL4jNsr4Yv0OIAtTew7EcDTIlId7v/f8P/tABSKyNQw34MAHvP2HQRgjYisCPf5J4DGj+eeBODMxrfwAHIA9ArTr4nI3nCfxQB6A3BrbENEZDKAyQBQM+/51vzDrmEYhmEYhvERY5NzwzAORqKBqAkEYxkBLBKRCdGM4Vvq/4bmXWG+/4V0k2YCOE9ElnnHH5+mvs0fZPH0VDq7rY6HZKaORavf5OLYOsTaKN/u+Tqmbe9eF1c3Dzoe9pwx+veCxX9zi6sK8vQx91To+N6hJ5Wm0skKHbNdX6qbbPdSF7saK9Cxk7JwjrKZ4/zbF+mYxh6T9LnVvRqJI/ZiwZOzpyubkXi9rkfqcqRexzlP3eDiAI/P1rF8skjXt9tAF8coZbodojHmALB5pYsT7H+pboc37tdx2Eef6uJG//5qF+UbRb0IrrzaXatF/wAOOdLF4XbK0JEay+YUKbuo0PWlZXt03rWZOub4vJ4uhr5uu/ZtSer2P/4c1xYzH9F96Zqv6PjpWCQeWRa8o8v9t44j7957JtLixXDLbhf7uWO97vttc7U9c5uLlz3hCK0V4A8hW8rcvoll+lr0PFLHoz4w1UXaHBXX/SMxa66yJ/UtTaWnrO6mfPWP6OOMGuv6XXLVWuV7N1fnnbTW+Qeerfv+dxeWKjv/CBcPvuO/e5Qvo0IHfPe+3PWX+F3QJHTfR7W75g1vvqtccejr9rnvuuFSFs1Wvle82PAjyt191CGm+1ly5lRlX3Opq4Os13HYfY/2xrDtbgwrb/Divev0eNF1lLuHZN40XU4kxhwAMi/6vivmrhuULzZK98no+dQu1vHzeYNW6yq9N98d48SJyiderLVqpx063h/e2IKIPED9fN1mWUcMVnZyxzpnrF+R/pg+9bqvJN95S/sj8fYNnlxIVpnWkkiUubJWzeuofMOXLVL27hkub6dTS3Ud5ryuD9QQ0f3w9AqkyIvFr4rEmcf1OCml+p6qn+7qlHFId+WL5Wj9Ail1Y2HdUt0fck/QVahg6/uqeOurUevBlrUbhnEwUA6gTQt5lgEoJjkBAEhmkhwqIhtEZFT47y4AbwM4g2QOyQIApwNA+GZ7D8mjwvK+CGCqd4ylAPqQ7Bfal0R8LwH4TiT+fTQM4wATnZgbhmEYhtG6sTfnhmG0ekRkF8lpJBcCqAawrYk8deHnzu4Il6hnAPgDgEVevpkknwEwD8A6ALMANEqzfhnAXSTzAKwGcLm3bw3JKwE8R3Ingol+oyzuL8PjzQ8n6GsBfO5/PHXDMAzDMIxPFfad8/TY5NwwjIMCEbk0zfZvR9JzARy9H8XdLiI3hpPwNwH8NrL/4U0c47JI+kUEsed+nmoAX29i+wMAHojYNmE3DMMwDMMw9sEm54ZhfBaZTHIIAtG2B0VkTks7GIZhGIZhGMbHiU3ODcP4zJHuLXyropMTg4lleb4GLaWS0cmJbxVDZ87vWJfWLq7WokfVW7VYTXGxE9dpqNMSJSJaFGvXHLdvRXme8vUar0V6umY4oaO69XuVL+tLRyo7uebRVLr7CXoZ3OKntAxBdSJS/81arCjWXQtqIdu12d7HdN7ibVoYrz+qUuk9W/S5FY5S+oNYdfPmVHrEd0YqX+I5fZwuvZ2aUaXWxMLgDlowaf0UJ8DXXutwQVYtVHa37q5NMzrr+mbEdd/p0adU2Zn5zt+zSvelnBotArhklRM+6t5O96UuBVqpaeUTTuQrJ6ZP4J6HdLkTGlz9Rz2tF5oU9vYaqlsvl/aEmdCg+z47OzG2NoVLdF7R7dKTTmhswbRi5Rs4eLuyqxHp+zW6zSpWVik7O9KF36Duv4M6axG9mnJ33/SFFj6LZeh7Yfty1769P6frO7Zml943ci9M+4MWM3snR4sAfhNODG/b9g7K16tAi1lt/Kc7zu64Ft9CjW4HxNz4ER9Yolx1XtTSW5E6HvPbtsp3iGhNhbURYcLiXD2esVCf2z23lqbS3/hVZ+Wb86ruv4d9yQmAdd+g+7ps2qzs999298WES3SbMan7WVQELusbNylfw3tPKTs6tmf11PeML34Y7+v6OnL0GIC2uk7F0civTO9hk69F6aICcfHO2ie7dH/gpjUuPehQ75hrkQ62L9QbMrzr2H+gc81Yp3yx3lpEjWvcWKKeDwA4eLiyO/1mjDNKtcAacr323h0ReevZV5fboauyZVukjt71Z18tHpdVMsAZcT1Fi3UboOzkCieOmHt5b12/mH5eX9VGjwGtAVvUnh4ThDMMwzAMwzAMwzCMA4y9OTcMwzAMwzAMwzA+EexTaumxN+eGYRiGYRiGYRiGcYCxN+efAUheBmBsVNX6A+7/AIBnReTxj7JerQWSUwBcIyKzDnRdmoLkJAB1IvJOaH8DQJWI/P0TrMO9AH4nIoubyTMFYTuSfB7ApSJSmibv1QAmi0hVU/4PWLcLANwEYKuIHPu/lvchjt8NwB0icv5HWm77Lql0bakXK5eh408zOrihfBd0nG12Zx0bnqxzv1fv3abjC7tM1L9lVyxycZble3V8Iakjxgr7urhQWaWPKUmdty9dfGR2n3rlQ347vW+diyNmvo7J7TNYx9Ht2eRiK2PjTlS+5MrZSEdmjhcH2F/Hiq+X5an0MNSkLQcA+g13dZLNOnY9b4C+jmiItIv3U3nlLt1m3Ya5GNi1M/W1iA2fqOz8bm+m0kkdHovyOh2b2rCmUNkDT3FxzhkrdLvs9GIgB2W4vO066T65cqWOOe7X17XLhrU69nd0rY577to9Eu9b57W397qFXSLxnnW6DszW7ZQs3ZJK57bX/Y7tdJz2roSL+e/XTusiZOqwZxTSlbVOdGzqoF46DrcsEp5cRq/fddSxwLH42lS6Iak7SDym981v59qQ3XX8aaZ48bPFLib2kK76sVe7tUjv2971Qz9GNLujrkP7bDec16/0Yq176g9cRO+NqBYAANCLOS9p5/oDh2tNip4NOua4d7G7VrsqvHjpbrpdRtcujRxUt2//nnpsqVvpxqFOw/X4xpISZXcpmOF8vQcrn5R58f+jXB39GPOMw89W9i5MQ1o6ebHWpS4Wn137aJ93X6hnRvcSXa7XLtGY9HgPHaePbP084YARzsjS94X/nFJ48dLs00/7c1394229aUy21upglrtWPYu1jkCs+0BlV/3i1+4QP/xms3WSooiGSb0+F6nwYu8jfZ/Uz4Dk7k3afv1ld8g+vZRPCnV8elQ7oOGJJ5Qr68rvKXvJdj0e6yh5o7Vhk3PDaAWQzBCRhjTuSQAqALwDACJy1ydVr0ZE5GsfMP9pLWS5GsA/AfzPk3MAXwVwlYi88RGU9YEIr9tmAB/pxNwwDMMwDOPTipgkXFpsWftBDMkvkZxPch7Jf5A8g+R0ku+TfJVk5xb2/yXJ70Xsm0l+lwF3klxM8jkAnSJ5bgm3zyd5e7itxeOS/CrJ30fsK0j+Lkx/n+TC8N/V4bYSkgsj+a8heWOYnkLyVpIzSC4neVS4PY/kv8O6PRrWaWwTdckl+a/GfAByI76TSL5Lcg7Jx0gWhNvXkvxV6JtF8lCSL5FcFb7JRthut4XnsYDkRZFyrw23zSN5S+Q8fkVyKoDvNdWOJEsAfAPA/yM5l+RRJG8keU1YRv8w77ywzt5PzMGb5bBO80i+GW7LIXl/WKf3SR4bbo+TvD3cPp/kdyJ1HRum/xq2wSKSv9i3Z6Xaq4hkPsnnwmMvJHkRye8C6AbgDZJvhMd8INJu/y9NmZeE/oUkbw233QBgIoC7SN7m5S8g+VrYLgtInhXpW0tJ3huW9RDJE0hOI7mC5GFhvnyS95GcGbZR4/6XhX3jvwBeZqSvNtN+N4TlLCQ5maR+9WIYhmEYhmF85rE35wcpJIcCuA7AkSKyk2QHBKvODhcRIfk1ANcC+EEzxfwNwBMA/kgyBuBiAIcBOAfAQADDAXQGsBjAfeExzgEwKDxGYVjO2/tx3H8BmE/yWhGpB3A5gK+THBOmxwMggOnhZHUPmidDRA4jeRqAnwM4AcBVAPaIyAiSwwDMTbPvNxEsCx9BcgSAOQBAsgjA9QBOEJFKkj8C8H0ES6YBYIOITGDwI8MDAI5E8J3sRQDuAnAugFEARgIoAjAznAyPAnA2gPEiUhW2YyOFInJMePz2fjuKyA9I3gWgQkQafww5PrL/QwBuEZEnSeag6R/cbgBwsohsilyzbwGAiAwnOQjBJPMQBNeiD4DRItLg1bWR60RkN4P1Wa+RHCEi89O09SkANovI6WHd24nIXpLfB3Bs2HfHAOguIsPCPIV+IQyWjt8KYAyCvvEyybNF5CaSx6HpsIQaAOeISFl4bd8j+Uzo6w/gAgBXApgJ4FIEk/wzAfwUwfW6DsDrIvKVsE4zSL4a7j8BwIiwHUoix7wyTfvdKSI3hefyDwCfA/DfJs7zyrAM3HndVfjquSenaVbDMAzDMIyDExOES49Nzg9ejgPwuEgQTBZOEoYDeJRkVwBZANY0V4CIrCW5i+RoBJPw90VkF8mjATwiIgkAm0m+Hu5ShmDCcy+DN+rPhtt7tHTccLL7OoDPkVwCIFNEFjB4c/+kiFQCAMknABwF4Bm/DI/GAJvZAErC9EQAfwyPt5Bkugnj0QDuCPPNj+Q7HMAQANPCF5tZAKIf1W2s0wIABSJSDqCcZE04eZsI127bwh8ZxgE4BsD9jfHVIurDrI9G0i22YxSSbRBMap8My00XDDsNwAMk/w3XbhMB/CncbynJdQAOQfAjx12NS+y9ujZyYTiJzADQFUGbpWvrBQBuD990PysibzWRZzWAviT/BOA5AC83kWccgCkisiM894cQXMen0hwXCH7s+VXYn5MAuiPo5wCwRkQWhGUtAvBa+KPIArj+dBKAMxmuUkDwQ0xjENgradomXfsdS/JaAHkAOiD4QWefybmITAYwGQAu7X2OTPvH/QCAfOp4sYYt+rG2JvJd6FfmqgUE+PrYa5X9fs2WiFWOftmu7MRLepnZu2U6ZnpYGxcD1yZT173XPB03WgX3Leut03UscLeYi+C45vkCrE242OXuzz+g8pZL9EBJtKGzF9fpBQgJRL49e8rNmJDl6rSoQf/e1zUejbvUMdA1596n7C2xyHfaq4CiuItt7/j5J1XeMonEzN+8CvmR+q7zAldqkvp73z0iwczbGyqVb1S50yBAThKz63ekzGfOvFflTUDHT9dFvuG9Pq67bY8MHUBd9krkW8pevOmqpB6SekbarWKtjqUs41ZlD9/sFlRNqV2hfMe3OUTvu8u1b/fP69tkbVJ/G7zjWe5a1YhuzxwvvjMO119y0UX5yr/4lLIzct25P79DxzV3Lde/WXbJcv07Ifq63fOOjsPNomv/Yuo43O/+Qrfv9qTTWKjP0hFAexL6npqw07Xvlu+8rXx7s3VM7LPfdP61Cf1nYE5WhbL/NMvVsZo69n6cd8+/WeviZ0uytNjB5WdOVvbWhGunjvFVyuf/0X59ratj8kwd1VUf17mv3bg+ld6w8jnlO37kFcrun+v676PXLVO+JQ16bCneUZhK70joazxippZi2RDRB+D5D+r6iq5vh5iLkY5+xxzYN8Z88iw3tg8fcpHynfDia1793XjXIb5e+bKh74v7Z9+eSl8z9qfKVwatzVAVucc2N+hr3CGu+3Mm/5FKd6f+1nr0XHy+NOb7yi6gvi8qxNWpr7dQs+qtLcp+psL12Y5ZeuwY6vXJYrj+vO3ifyvf7qTWxciIjCUdYzrOfUdS/ynWlu66VkM/BHK9aVheZMzaLbpPFnGtslck3LkNj+tn2N5n7lb29mw9XpwOozVjy9oPXoh99Vn+hOAN3XAAX0cwmXA7BEtu54b/Gt8G3wvgMgRvTKN/ke4TDBJOOA4D8B8EbxZfbO64DJZ9z2UgJuYf6/7IeTRFA3T/zPH8jSNlAu5HpibLInlO5Lwbl7k3FexCBJOuUeG/ISLy1SaOmYykG+2MZs6lqWvVSPQp3+z1S1PuvhuD8IS5JOcCgIh8A8GKgJ4A5pLs+CHrCpJ9AFwD4HgRGYFgMp22niKyHMHb7gUAfs1gGbqfZw+C1QZTELzRv5dkz8g1+0Yz9Y3WbXxknzMBfB5AMYAxIjIKwLZIXf3rF7220f50XqQ/9BKRJaFP/3UWqQa89gtXNPwFwPnhtb0HLV/bT4ToxLwlohPzlohOzFsiOjFviejEvCWiE/OPkujEvCXyP0B9e/gqY80QnZi3RJ34Ux3DMAzDMForNjk/eHkNwRvMjgAQLp9tB6Dxp+sv+zuISCIy0WicJD2JYOnxOAAvhdveBHBxOJnvCqAxHrkAQDsReR6BoNeoMH+TxxWRk8NjfS20pyOYIF4K4JHIsc5mEC+ej2DZ/FsIJlKdSHYkmY1gGXBLvA3gwrCuQxAsy4eIPBk571nhMT8f5hsGoFFS9D0AR5LsH/rywqXe+8ubAC4K260YwZvdGQjeBH+FDH46TrNUHEh//coBtPEzi0gZgI0kzw7LzSaZJyLXNZ5vuL2fiEwPr/lOBNcg2gaHIHgjvCys6zdIZqSpa1sEE9O9DLQFTm2uQcLl6FUi8k8AtwM41D+ncMl5TET+A+BnAA4VkQ2Ra3YXgOkAjmEQxx4HcAmAqV57TI/s80zYnttFpJ5BTL2W6m2ZlwB8h+EyinCFSUs01X6NE/Gd4T1k4nGGYRiGYXxmSUIO+L/Wii1rP0gRkUUkbwYwlWQCwPsAbgTwGMlNCCaafZoporGcOpJvACgNl2MDwYT9OARvO5fDTYLaAHg6fBNIAI3CXR/kuP8GMCp8WwoRmcPgU22N3x65V0TeB4Dw7f50BMu7lzZRls9fADwYLlN/H8FS671N5PsrgPvDfHMbjy0iOxh8du6R8AcBIHjjvLyJMpriSQSxyPMQvD29VkS2AniR5CgAs0jWAXgeQVyzz41ouh3/C+BxBoJk3/H2+SKAu8O2qkcQR73ay3MbyQEIrtlrYf2WIhBRW4BglcJlIlIbrnI4BIE+QD2Ct7x3NhYkIvNIvo9gWfZqoLlvuwAIfiC5jWQyrF/j90kmA3iB5BYEP/TcT6bW0P7EL0REtpD8CYA3wvN4XkSebuHYDwH4L8lZCK7z/vShKL8E8AcEbUEAa9Hyj0T7tJ+I3EnyHgT301oEMe6GYRiGYRiGobDJ+UGMiDwI4EFv8z4TFhF5AIGA2T6EE6LDEUzqGvMLgHTfRD+sifKfbuq4aZgI4PfRDSLyOwC/a6LcOxDGhnvbJ0XSO+FihGsAfEFEahiolr8GYF0T+1cjEL/bBxF5HcEqAn97SST9ACLtGfUB+GH4z9//FgC3pDuP0G6yHcOl4ZEPhuKtiG8Fgh9S0iIi5zaxuQZBiIGftwGBCN73ve2TIul99msiT0mYfAluRUY0758QxryHHOrnaWKfhwE83Nxxve07EfxY0hTDIvkui6TXNvrCfvL1Jsp9APr6R/dJ137XI/ihxzAMwzAM4zNN631vfeCxyflnmHDp97MIBNlWtJT/fzxWIYI31PNE5LUWsn9Y8hB8nisTwdvVb4pIXQv7GEar5J4vRYTEHtYh7t2P1uI0mcPdYhVfKOipQi0O1hBzwjGzanWc8yFJLRoz8lup3zCw/t+6DtU1OioqUe/qlJutRYQq67TQ0a8y3HF+n6XL6X2ptjc97sr1b+fCPvrxPntxt1T68JFaGCh7WCdlS50T5nnz71rQZ9JPdd5Fv9mWSg86W59bfLCObV/0fxtT6SGX6XOpmqH3jeqtbVmuz2VHg9YD2CbucX1aQtdvwkk7lf328+6aTzh8s/Kte79Q2Xtr9J8B476Tn0ovu1sLPm0XrTsw4Xh33Nde0HUaVazrtGmHk4246/dajujmG/RvqJfUOFGkI76m9Qvq12phtIwerm9Jpb4vkPT+/Mtw5ZbN1OUUHqfb++EHXZ/47bndlC/ziJHKfvU6J4TWKabrUJHQugNb4s5OekoaZ07S1+rBt9xxj0xqbYbsLC3qVd/g7ql+k3Te+u1ahCp7gEvPfzxf+aZk63vhrAy3+Kz3mfpcdk3ZpeyfdnYdes1CXb9BZ2mRrIyzz3H1e1SLKv7lVS3yNabG3TeHX6D75L3P6MirO3r3SKX9sXDOz8cr+1e3OXG+n3xN36vT/lSk7BF9ncDhhlVafKvvUC1+WLXd3VOFR2qNCmbqNqxd7O6TrJ5aUM0nej4LFj+qfBXf/Era/TJ7aomTWB8thhgtd8ZZ+tz8e4htImXV6HOJ99HPmsSayBicofugf22ivHuU7pNZg3S59atdn1wzTdev9+hSZd986lGp9IKbtMDpkFP0M61mlbsWOf30tWB7XafkTtcPY4V6DI2P0hFwibkLnSH6ppc6/UxLlrv7JNZGXzfm6jZkobuOdbP0GJp9wnBlX3vrdhgHDzY5/wwjIosB9P2EjlWKYLnvx3mMcgD7fNfcMAzDMAzDMAyjtWOTc8MwDMMwDMMwDOMToTULsh1oTK3dMAzDMAzDMAzDMA4w9ubcMIxPPeF3z4eIyC0kbwRQISK3f4zHOxvA8jB05EMRGzY0le5+9FvKV79Vxy43bHcfFDghT0eqFPXR8ZDrFxem0if13qR8a9fo+M3S111MaXaOfly0K9bx6bWVzl9doWPM83N0XF1ewuXt0L1U+WInXqDs4qUPpNLJKv1L+9xZOnayPObiXHO/eobyIaHj9ZILFqTSfdruUT720dEx3Xq59i2bCSxa7eKrj7pDx6ZWNrj2nnkvMOacSGyibhaUbXQbCtrWoG0v107Fe3Vs7bBMfe53r3Sxtcecd5ryDZ71YipdujobHQ91cdsZcf3d87Ff0u1SM93FJsZi7ZRvSE/9ffW9rgnRM677w5u7dQz64W1dfO8TP9axn/2o3xO0z3Tx4PHTdH9omHyfsuNHHO6MHO8b9LkF2i5zddjz7Ov6mD10Xzqt56JU+r1n9bmMXK0/uDDe3apoqNbn4t9TcyMaCyUJHZedfbKOiR72qvvwRo+Bpcq3fb3+umZNg7unsibpmNcnb9BjwEUXu1j25OP64x5x6JjYNoUuBnbGIzouf3A/Hdu+faVr70cydez6zceMUnbilVfcMfvrmP7Zr5Yq+3C4+yTjuInK1+M/i5S9cZvrsyfk6bZntx7K7lfvjhPrqX1dcnW5dVXuWhXkaV2B/J9dpexl5/w7lS46+RjlQ1yPo3mDIu2fp+Oa0am7Mk940cn1+DHmBX/V90XDW64OqNRjyT7l5rlxPvNsraEqG9Yom0Uu/jvxnr4Pkjt2Kzvj0ojurjf+nvDsK0hHRk+tBxEb2F/Z2RG7b8YM5cscpp9/ex50g9QuT6sj49TDld1w2wvOd5SnC5zhxZXXR55pNXrsg6crED898iyqLNN5K/QHhRJz5qXSsa461p4jdZ1k/qxUulYPzcjpqT+a1Cepn3GtgWTLWT6z2JtzwzA+9YjIM6Fi/ifF2QCGfILHMz4hohPzlohOzFsiOjFviejEvCWiE3PDMAzDMFo3Njk3DKPVQ/ILJGeQnEvybpJxkhUkf0tyDsnXSBaHeb9LcjHJ+ST/FW67jOSdTZQ7iuR7Yd4nSbYPt08heWt4zOUkj/L3DfP1I/kiydkk3yI5iOQRAM5E8H33uWGeK0jOJDmP5H9I5jVVnmEYhmEYhvHZxSbnhmG0akgOBnARgCNFZBSABIDPA8gHMEdEDgUwFcDPw11+DGC0iIwA8I0Wiv87gB+FeRdEygCADBE5DMDV3vYokwF8R0TGALgGwF9E5B0AzwD4oYiMEpFVAJ4QkXEiMhLAEgBfTXOuV5KcRXLW316d1VQWwzAMwzCMgxppBf+1Vizm3DCM1s7xAMYAmEkSAHIBbEcQstT4sdd/AngiTM8H8BDJpwA8la5Qku0AFIrI1HDTgwAei2RpLG82gJIm9i8AcASAx8J6AUC2ny9kGMn/A1AIoADAS01lEpHJCCb8qH70F633yWEYhmEYhmF85Njk3DCM1g4BPCgiP1EbyZ95+Rons6cDOBrB0vKfkRyKD0ej6k8C4VhJ8n4AowFsBnAxgNLwbX5LPADgbBGZR/IyAJNa2kF270ylkxVaTMcXFmOWE3Fa0qCFX3LHd1V2h21O/CdRrxdPVSb1I6H9l1zTVT4xX/myuui8dRFxsIaELndrtRY6Yo4WVFJs0gJE8Q7u947abXq/uCcpky3u94zkogXKFyspUXb9si2pdE6eFuZCmW7DwpM7p9IZf9Ux3LJ9m7Jr4MqqXaeFgjK7aKGgvCoXZ+5poqFgghaz2vrfylS6rWjRLl+0acuOtql05wE5ylf5iie21K+nsuMbS1PpHoO0WFHe2SOUXf286xN9O2sxqJnz9TVv18m1RX6Zvm5LdBVRVJObSg/dtk75sk73BLZ2RMTOirWoG3bqayNb3DUvLNbtIJ6YVbsJTtwsvlr/TpY3MFfZtWvcuRUM1zdn9Sp9nxSIu9Dr454eQI4ud0WW6y+j2ug69LtMi90tudfVQWpqlK9Xg9YzSCxdmUrnZuhrsYe6Tll5zs6N6XGooc4bP2rcuXfzhM+SK1cpGzHXh5mrO8DRSS12tz5S1GE7tfLV6PY7ld2uuzv3JUu830qrKpW5KtOdu3i+zn20cNfere7abChrq3y9XnxK2R3aRO77XboPst8wZde95+6heF89VrNU90l/bI+iBOAAZBx1YSpd/4jWPtWjhy43OdsTedut2yE+xF1zqdN9JVmu+50aVVcvTXtMHyn3fpdOeMdZ6ca7uq26TwLrlVVf7WrRK18LGGLndmVmF7v+kFiotVzjY7TIomx39ZcK3XdiXt/HFjeGSbknCLdXj7HxASUub5mnWbJ+pTKTW929kOmND9i6UZmbYn47Ga0ZW9ZuGEZr5zUA55PsBAAkO5DsjWD8Oj/McymAt0nGAPQUkTcAXAv3pnofRGQvgD2RePIvIlgenxYRuTxcqn6aiJQBWEPygrBeJDkyzFoOIPoXZhsAW0hmIliSbxiGYRiG8Zkk2Qr+tVbszblhGK0aEVlM8noAL4eT73oA3wJQCWAoydkA9iKIS48D+Ge4ZJ0Afi8ipZFl5z5fBnBXKNC2GsDlH7B6nwfw17B+mQD+BWBe+P97SH4XwQ8IPwMwHcA6BLHtbZouzjAMwzAMw/isYpNzwzBaPSLyKFx8OQCAJETkZwgmvlEmejZE5AEES8shIjdGts8FcHgT+SdF0jvRRMx56FsD4JQmtk+D/pTaX8N/hmEYhmEYn2lasyDbgcYm54ZhGK2Q2NhJqXR8zjLli3dqp2x2KU6lO8zYpHxSoWNrO05y8Z0739Axgkf+UL/Qb5jl4gQrd+hY2oZqHcPWfqRL17yrYwQnnKbj6p563h2n7Rl9lY999OfhM5Nu8Vm7SToSa/Tzbyu7fqerU2zMRcqHah1vmP2Fs1Ppov++oOvQX8dWb7jp/VR65GFefOHh+jgTv/TPVDp+zFnKl5g2TdltRhVDZ3DtVvXScuXqfLSLef0KdmLZs+46ctAklXfQOBfHWjMdyD26n6sTdf13TF6k7KJzu6fSFf/drHwrf6n7Vs/eLp4zt5O+5md20/sWjHD1PbRGx+GW7NDx6SXHR64VRypf7ZNvKDvne+6DDLJXxyMjQ/fZWK9DUuk2Czxtg7N0tMnsk+9LpQf307GpsR7dlV2/0LXL+ud1/PRh5+i+X/GU0xJYlu1pHXhcOHZDKr16Znvl67RN12ng51x8Onv0Ur6iXB23H+vm6t933BLlO+YdXf82h7l7tXdCxwm3Ha+vW2Gl61sLn9e+jLO+qOzknNddfQcdqnwL/6Djp3/Rx11XtumnfDNLi5R9bJG7Fh3iOoYfbfS4eb64fsY83WY5PfSfx21OcfHgneauVT52G6XsDj0jfb+wo/JJqe6jmSdGfkvO0V/YZNc+uty4i6fO7OkJNVTq+ORonHnmJdcoX2KJHoc6xBem0rGeum/HDj9S16lziTNqfQ0Qj0TkGdGlh3fMFX5uV98LT9Qbykv1cc44P5VOTtO/eWddcKqyO7Zz/azh9XpdbgfddzJ7ur4eP/UMnbde6zYw5p5F9MYZtCnUdqQd2EGP+Wyr+0dy3nvOV6Trh56678d7uGdnTq6OyONIfd2+kPg7jIMHizk3DOOgRESajCU3jIMST/SoOaIT85aITswNwzAMw2jd2JtzwzAMwzAMwzAM4xOhNQuyHWjszblhGIZhGIZhGIZhHGDszblhGAYAkiUAnhWRYS3lNQzDMAzDMD4cSTFBuHTY5NwwDKMVwty2qfSOd/Wn4NoP0GJQmZVO2C0bmcqX3KuFgja96fzryrQYTc3dWsSrQw8nglNRpsXiKsqylb13u1uklp2rhXc2vqLrVBdZ0Fb239XK1/5znZRd+/I7qbTU6IVwuxZpIZ4NO5zY1uGFWnhHPEE4RAR91r6gH4WH/KC3squqXf0rt+i8+TEtg7T6MSf+M2CsbvuKabuUnah3Aluk/kOlYo8Ws6pZ5uqwvkELRw3r0E3ZK2a76zq05xbl25TQwmLFSd0uiQ1OsEqSut9lxHT7793u6rh2jSe+5dF2uxOPKmirxQTfS7ZVdtXr7lzH/Nz/+MJLyor1cAKCUqzF7hjX1yq53YnA7dU6eOhUq/dtn1+dSlfs0X29zfZSZS9e4fpsbkyf25apWjRrSbbrd/ssXfQEwWa940TIMrxFoLUb9XWsr3F9rZvWxMLWai3Odshhx6TSS2/RAmXbM3Sb7ZriBCWTCd3Xq+aVKbt6j7sf67yvV0qDbgcUuPFE1mvByyrR+guLlnVOpScepk+uKnansjevcaJv2d69GR92tLLfS85NpQf20MKUC19ZqeyhdWtT6QVT9bg59hItorZ55apUesjwScqX3Lle2VIZacO2HZSP2VpWJTsiuRbr00X50EnXIdr8vgBcfLAWC8vGk67cw0726rdbH6fMjWGySwsEsoseu9nWCZrF+mrRv2y8ibRs0M8EDhuv61Tn7s2Co/QxUa4FGGNFrj/sLNV9sEevQfo4We+6dFtPjC3pLcLOj4gLZumxj56NbH3/RZGqUm9D5Dje+BXvM0pXaYcWeowS69hT2W9QP7/11TBaG7as3TAMw5FB8kGS80k+TjKP5BiSU0nOJvkSya4AQPIKkjNJziP5n/Bb6SDZmeST4fZ5JI8gWUJyCcl7SC4i+TLJ5mczhmEYhmEYxmcKm5wbhmE4BgKYLCIjAJQB+BaAPwE4X0TGALgPwM1h3idEZJyIjASwBMBXw+13AJgabj8UQOM7ugEA/iwiQwGUAjjvEzgfwzAMwzCMVoW0gn+tFZucG4ZhODaISOMawH8COBnAMACvkJwL4HoAjR9sHUbyLZILAHwewNBw+3EA/goAIpIQkcZ1dmtEZG6Yng2gxD84yStJziI5697HnvtIT8wwDMMwDMNo3VAsIN8wDKNREG6qiPQO7eMAfAdAFxGZ0ET+NQDOFpF5JC8DMElELiO5A0APEan1yk6JzZG8BkCBiNyYrj6X9j4nNTjnU8eeNXjxp2saXJzdK3MnK9/Xx16r7PdrXAxyv2wdO5nwngfvlrm4y2FteilfG+p4714xF1dXBR0zujVZrexusUisckLHPHeP6/i8cnHx622oY9cX1+3U9Y+0y4Ssrsq3qEHHR3aN63jOKDXw4oYbXB2L4jouuGNMf3O8TFycfr5X33UNOh6yJumO0yNTx11vb9Ax0KMydQzk7HoXKzwk07uO3juBukgc43qvDj0y9HGj9c+ijtldVavbu2eWi3uuSOp4zrJEjbKHZ7u44SnlK5Tv+DaHpK1Dd+r2XuvFyEfbv8aLVc7x6h+PROLmeu8myr1rnhHxP793sfJ1zdGxwV0i1y4h+t6s9+7VaJsWx3RkS5ZXp+2R+8YvZ09C31MTMl37bhHd9nsj7QkAvWOu769NaF2EHG+sqYzcf9VJrSUxLlPrOrxZuymVLsnSMfGZ3rltTbj+3TGu28H/xFKsGV+9194zy1y894aV+gfO40deoez+ma6O2V79ljToWOviyLi0I6HvzREZ+v7bkHRx+qQOvvfr2yHm9AyKocfUXdDXbfKs21Lp4UMuUr4T8nTM/JLIeNfBa99o7DoA3D/79lT6mrE/Vb4y6Gse1QPY3KD7jn+cTLo29e/j22f9Cun40pjvK7vAG0crIn2yr1dulddDnqlYnkp3zNJx10Oz9Jgabf9tXtvv9sa3jMhY0jGmNSl2JPX91zbyrKz2xplcT/orLzI+7Pbu2yLq46xIuLF8eFzfb3u942z3nsHPrn/OU4X45In+jXOgeHjdkwe8HZrC3pwbhmE4epFsnIhfAuA9AMWN20hmkmx8Q94GwBaSmQjenDfyGoBvhvnjJPXsxzD+R6IT85aoE386YxiGYRhGa8Um54ZhGI4lAL5Mcj6ADgjjzQHcSnIegLkAjgjz/gzAdACvAFgaKeN7AI4Nl7vPhlvubhiGYRiGYRhpsU+pGYZhABCRtQCGNOGaC+Bof6OI/BVhbLm3fRuAs5ooZ1gkz+1N+A3DMAzDMD71SKuWZDuw2JtzwzAMwzAMwzAMwzjAmCCcYRhGK6T6hTtSg/PCr72hfLnZWqSnYzcnUHTLxs7K95NeW5U9d1mXVHpXXC+eyvKeB93hhG32JLVYUVlc/7bbIyKYE6cuZ4doIZsnclzeb9bpvCMfOk3Zm7/zGNKxY7cWj9soTpDo9EdPVD6Z866y2d4J6Lzwo/XKd+q/T1L2ks8/mUrnZGmhnT63HansJ77txMPOvlILlC2Y7IumuTbNpy43Aa1TE4+8ZXg9W7fnNf/Q5zrzQieENXj4duV7eFlPZR8NLerUc3BpKr1qgRa62pLU4neZkf6yI0OLTG331uX1iVznsrg+tz3ea4K+dS5O/rT/nK58m7/xoLK7PXCVMxL6vmBeobKTK2an0u9fNVP5Rv9xpLLf/968VLouqSvYq5MW1Xtib6cm6w4A7TyRusdzXTt0Ey109YPva8Gqf9zmxO+kBdmiEQ1O8Gn0//XR9fuZHgPO/YMT4Jv13fnKNy9TX+MhdU6Uqm9XLaqYaNDtsmKHu6fyqM973N+P1fu+9moqzSzdWU6/X4ux/bDeCc8d/+z5yverC55Qdp9611BzM/U9dcvfT1H2n7/g6vCtX/ZQPn9MOLSL03mYs1UL4Z36+wHKnvn/3Bhw2EuXK5+sXahs7Ihcm0w9xqJ7iTJ//LUpqfQvT9RtlHm2HjeTs13/jvXsrnyxw05W9rWn35NK+0JtDfNeVTbq3DOh9u9PKle8WI/HGSef4I7ZW0d3Xfu5vyEdv/q87juxk/UYgC3rUsnEzDk6b2c9ZtXNWpNKv/pmN+U7/b7xyi671fWldr/+uvLJri1Ii3/d9uprw/6jnNGgnwFSqvVD6h97KpWOdylUvvg5X9D7rng/la7+1+vKl/f9ryn7J1/U4oi/X/uvAy6EdlHvsw/4BPTRdU8d8HZoCntzbhiGYRiGYRiGYRgHGJucG4ZhGIZhGIZhGMYBxgThDMMwDMMwDMMwjE+EpAnCpcUm54ZhGK2QWM/BqXRu9svK17ZDtbIZi8TzQsfd7t2eq+we+S6OdWFDofIdjwpl1za4OOK49yDd68UNjy6oSqVLK3Tcas+MSmWXJV1cbtcu+lxQWabMvEIX8xo9TwDYU6rPLbshEu9bpuP+ENcx0fFJF7pjyG903khcJQBMpYsFPqfNNuVjDx1v2g+z3DFOvkD5iv79gLJzKl1MrHhBxTtrdRtGY9DH1+iYTJ/2ua7+W1e2RddBrk3be7suTxbofbe565gZ15mXZ+rFdr0jceS1XuTe4bU6tjKD7tp0adCZ53pxztmRWHbx4sjLynTe7jnu2kj5TuWTCq8PRGJDu3XW/Yzd+yl7QXx5Kj1MdH9IJnT9o5avxTA4r1TZkypcXPaODF2OlOv7r0ukPy/M1nmP9tq3pK87V6nQMdF1flRlIn3/yfT+Xm6b6e6/nTt1THH7Qn3vto+7vM9k6es0vt+hyk6+81YqzfbtlK8ortu7IdJfpFprJKyDzntUg/uz9k0v5hzVun2zI+cqVXqMqo7p69jpsr5uv1/rviN7S5W9ExEtgQodpw9P5wOMHCe/IL0P3tie1BdKNqxRdnK3q2PscK2LIZX6voiW68eYZ4w8QdkNC6e46mXo+sU6d1A2at1YIrXeM8B7TkWJX/hVXd+5byqbo9zHU+of13osOSP8D664dunJ5p812V1dP4uXjFK+hrJdXiXddYz3H6tciYW6voicu1Tp/hsfcpQ+TuZ/nZGj7yFfUyM2wrVD1qy5yidebHv3pE33DibsahmGYbQASSIQ0Ey2mNkwWhHRiblhGIZhtAbsU2rpsZhzwzCMJiBZQnIJyb8AmAPgbyRnkVxE8hfN7Pd9kgvDf1d7Zd0T7v8yydx0ZRiGYRiGYRifPWxybhiGkZ6BAP4uIqMB/EBExgIYAeAYkiP8zCTHALgcwHgAhwO4guTo0D0AwJ9FZCiAUgDnNbH/lR68lk4AAQAASURBVOEPALPufew5320YhmEYhmF8irFl7YZhGOlZJyLvhekLSV6JYNzsCmAIgPle/okAnhSRSgAg+QSAowA8A2CNiMwN880GUOIfTEQmA5gMALULX7E1X4ZhGIZhfOqwGMH02OTcMAwjPY2T7D4ArgEwTkT2kHwAQA7J8QDuDvPeAK0N5RNVaEkAaHZZe1TcqvsYT8hobB+dOdsJXVX9XguWFQ+uUnZWfyfac/4rOm/RqW11HfY4IZu975cq3yFV+vGRX+ROL2unFmIqPjZL2blPO8Gk4iuHKh9y8pRZ+I0jUunk8lXK17+7rn/HxRHRoVwtrsQevZSdWD0nlZ74FS20w7Ydlf2FgRtS6bin2SQ1WuiozyERYa5yLQbV5Sy9c8NqlzdrnBYk6zNTnytz3EK3J1/tqn35hcruVOLizHOP7Kl9c/S1GT14q7LzBjkRojYbSpXvlEX6z6nuh7rjVG7T/aGqLFvZXSe4/rH6Dd0OpxZokaS2XSMiX+W6Dv3O0nVIbneCT8zQx5S9O5SN3c7Oaetd83adlH1sG3f/FQ/TomOZJe2VfeJje1Pp/LZ1ytflfC2S9fJ9bojI8/46jfXR9/XRRzrRtLy3Oytfz657lV0wPHKPeWJmXRv0NZedrh1Gnq7Lkef0EDb4O25MaFi6Seet0yfQMebO/YuzveHNE3KL9S1xRqG+33KxXdnHHLvFGWuXKd8RDfo4o0a4e/Xe5fo6+X3p1DauHZiv75MTB3u/uzY44ccRvXT9mNld2ccd7+4p2bZOl9Ne9zNEL6snABcVMASAKnFCfmyjxcJYpEUA40NcWexcosv1xM2i5fpimFEBOADIGDYplZYx05VPqvW+7DfK+bauTn9MD9m2Vm/o1E3b0b7k3UOyV/fnzP5FqXT/Cn3d/PskXuwEDxNbVigfO+gxV3a6eyGx5B2dt0j3B4m2tydbk9y4WNkZYyLPw3aFupxK716NiA3Guut+5Y+Fk5L6bwijdWPL2g3DMFqmLYKJ+l6SnQGcCgAiMl1ERoX/ngHwJoCzSeaRzAdwDoC30pZqGIZhGIZhtEpInkJyGcmVJH/chJ8k7wj980ke2lQ5HwR7c24YhtECIjKP5PsAFgFYDWBamnxzwrfqM8JN94rI+yRLPpGKGoZhGIZhtHJEWn/kHsk4gD8DOBHARgAzST4jItElD6ci0BQagEBv6K/h/z80Njk3DMNoAhFZC2BYxL5sP/f7HYDftVDW7R9FHQ3DMAzDMIyPhcMArBSR1QBA8l8AzgIQnZyfhUA4WAC8R7KQZFcR2bJvcfuHTc4NwzBaIcnpU1LpWE5c+ape1/F7+ReMSaU3N3ixnTk6eqlhY2kqHc/UvsTmUmVH4/lKd+hY8JwcHbOb3c3Vcc0yHVPcbpWOcdySiMRS1tYqnyyaq6uwbnMqHeun48YrXtIxhCu3uvje4ueeVT5/34YX34gcRP+Cn3j638retqpNKp2Xr2OKs195Xtk5PV071D/2lPIxWz9y411dO0lVta5fmY5NrFrlrlVJg26z5Bv6XCXh4obLX96I3N6uTkWZbVTe6tJMZeflODt7mL7mdfN0nUrXubhXUrfhhr36OMW7XWx+p+66j05f00XZg2pdbGXOE/qrBQ27dfvHpr6WSkut9kU1EwAg1tHVKX+U7qOJqU8re2epiz/tFNPtjZiOy87MdPGzm3fq8+6wSP99FpMeqfTaDN2eskP35+2L0stS1NZ4fanInU/D/JXKtzBbx6OOeWZRKh3t2wCwPqZjVUetdfHTtZt17HqGp7+Q2cfFpy8r0+X2eu5hZUuDazM/vjLTi73eMsu1Q/e8WdoX92K4I/hjIfaWKnP+bhfrXvzaDOXbvLKdsjs/5TQgCnrpvl7575nKXrPQjUOD2umoplhPXd/6+etT6Xhn3aDxHlpnQJ1Pjb5vE+/pOkhdJKbbH2N3aS2MaLm1f39S+Zihr0U0zjzzCz9SvrLLL1d2brYrK7FW3webG/R4F6Xuof8oO2OAF++922ld7Fqi+2unDrrvJyvcc4o6hB/J+QuUXTHHjRcFgS5riviwAcpGvSu3YY7WQYh301oHUu3aPz5Ia4s0vPqazlvu2oV5usLiPafiJS4WP7lRa4dI5SvKXpbUfWssDjzJg+M7590BbIjYG7HvW/Gm8nQH8KEn5xZzbhiGYRifUqITc8MwDMMwAqKfrw3/XelnaWI3/1eF/cnzgbA354ZhGIZhGIZhGMZnhujna9OwEUD0Uw49AGz+EHk+EPbm3DAMwzAMwzAMw/hESLaCf/vBTAADSPYhmQXgYgDPeHmeAfClULX9cAB7/5d4c8Am54bxqYDkAyTPbyHPWpJFzeXx8l9G8s7/vXYtHidVd5L3khzyIcqYRPLZNL6rSeY15WuhzJtIntBCnjOb+rSGYRiGYRiGcfAiIg0Avg3gJQBLAPxbRBaR/AbJb4TZnkfwFZ+VAO4BcNX/elxb1m4YRqtBRL72MRR7NYB/AqjyHSTjIpLYZ4+gLje0VHD4bXP/V9SPBHZ2Ilkr3timfH3HaEGfyv/MTqU7xLUQ04o3tXBQQZ4Tp5lT2UH5xlXsVHZugRO96XfTSOVb+bP5yt4608U2Vyb0MbN6699GMue7cKyKZ5YoX/4RWgRpz1tOpCfxuhbe6fQlLa5TelvkEmfqx5ts0+cW7+rOfcm9WpxoyDW6XRZVV6TSn/u8FsKLlWihufKX3XHan9pR+arf1SvdGqpdqBpjWrxqyRItHNWjoxNBapulhc8g+h3AupURQao+e5Uv5gm3Tdmh2/uIl1z955W2Vb5dGTq07qxCV/aSFcXKN6iXbu8oeT10fXds0O8J+iecnTF2kM775+XK7nqkE26L9e+rfLJXn3tUGGv3C/oeKirS9lPZToyp7xbdZskNOm99vRNbapejxbfWzdBtOC7h+miiXp939StLlb1pj7s2O+O6P/f2ruOWJ13/KB6v27dnvc6bd5QTpatfVqF8W72/CiuXuP5evVeLb8V2eeVWuDpsydSCamirBamiPSkxf7Hy/ThXt+HG3a4New/S5ex9TQ/ru9a5/tAh7omZef1hR0TsLHNYb+Xb/qIut3dXd24r39GCX0O+rse3gT2csF+8dw/lQ0G+MrOOGOzq5wm1IVsLgnWIO2G8eB99vyV37NZ2eY3Lq0sFu+ixpUPcjT1xXSxinfVYKNWuXF8Aru399yu77p5fuHL7dveOuQnpiHf0rlulHp9Z6J5xa3Zp0cSiqh26rIjI3rJ/6+fSsDG6ZeLZ7r6Jl2gROvhjSZa7NhnjhykXi7xGLHd9R/boaxzrqt+X1K5dkUrnXHyh8smiObrcuKt/3YpS5cq9QNdpe8Y6tDbk4BCEg4g8j2ACHt12VyQtAL71UR7T3pwbxkEGyZ+RXEryFZKPkLzG8x9P8n2SC0jeRzL6F9UPSc4I//UP859Bcnq4z6sk9V/rTdehguStJGeH+xxGcgrJ1STPDPPESd5GcibJ+SS/Hm4nyTtJLib5HIBOkXKnkBwbpk8hOYfkPJKvhdsOI/lOWNd3SA5soZ7fBdANwBsk34jU/SaS0wFMIHlDWMeFJCeTZJgv+kZ/LclfhPVZQHJQuD21uiDMf0dYr9WRfWMk/0JyEclnST7f0ioHwzAMwzAM47OHTc4N4yAinLieB2A0gHPhfRGDZA6ABwBcJCLDEayO+WYkS5mIHAbgTgB/CLe9DeBwERkN4F8Art2PquQDmCIiYwCUA/g/ACcCOAfATWGeryKIvRkHYByAK0j2CfMMBDAcwBUAjmjiPIsRLA86T0RGArggdC0FcHRY1xsA/Kq5SorIHQiEOY4VkWMjdV8oIuNF5G0Ad4rIOBEZBiAXwOfSFLdTRA4F8FcA16TJ0xXAxLCMW8Jt5wIoCc/3awAmpKtvVDn0b6/NTpfNMAzDMAzD+BRiy9oN4+BiIoCnRaQaAEj+1/MPBLBGRBrXfj6IYLnNH0L7kcj/fx+mewB4lGRXAFkA1uxHPeoAvBimFwCoFZF6kgsQTEQB4CQAIyJvidsBGADgaACPhMvJN5N8vYnyDwfwpoisAQAR2R0p40GSAxB8qiKziX1bIgEg+iHVY0leCyAPQAcAiwD47QoAT4T/n41gwt0UT4lIEsDiyAqEiQAeC7dvbXyD3xRR5dDqR35+cKz5MgzDMAzD+AAcJN85PyDY5NwwDi6a+p7iB/FLE+k/AfidiDxDchKAG1WBZBzBhBQAngljsevDOBsgEL2sBQARSZJsHFcI4Dsi8pJX3mlo+RuQTJPnlwDeEJFzSJYAmLLPjuRLADoDmJUmhr2mMc48XGnwFwBjRWQDyRsB5KSpU2MgZALpx85osCS9/38g2OuQVLr/kW8qX7y9jrNDNHZysV4QNWBiqbKTdS6ubshs/dtGh146ti/3MBfhUHrXO8pXpEMIEctwlyszS4fx73pHx8C2i7kYzfwjdExu7LiTdJ1y3LnXvKfj5qpe0fHH/TMLU+mMC3U8pOzZqiu8bmUqecjpC5Qrfsqlyp701M/dbg/rmNE+d+qYc8ZcXCC9+NLswfpckwtcLGL+cTrmdXQf70ssDa59X5iidQWGnfcNZef/9aZUesNbuehzmYv/3fNig8p79gn6OMkKd+2OqdiofJLQXTkj39WppFOp8r26WcdsnjfKxeHOfEbH7J5QrONEC4pdXGts9ETl63KO1l+ITTjWGQW6XDbo2HxZs9Clk2t1OZNOVPb3X30gklefd9uj9HGq/ut0EXLaaE2CghI9jN0+3bVLYVLfq4d+9WRld1ri7rn2DTo+trCbvlczIqHYWUePV76cF/R1jPVzWg0l495Svonv6uO0OdKda9VzlcpX0FXHhscLXDt1bNDnHRtxuLJl+Ty332FjlO+vj+sY9OvHOuFjjj5O+Y6s1fXvMt61S+Z7+j7haN0uJxQ+5nz9jlG+cUe/rOtbFxnfMvT4Fhs2VNmVU117t/2aHs/YVsd7J3e4MY2b9O/iHDBC2Zn8RyqdWKPFoDMuvVjZ6iom9D3PtjrOOZPuuBkn6/ZFrY69Z79RqXRu9pPKF40xB4CsK9y4mVgx3TvmI0hHrI+O0+chnkbsbjdejP+cfi7Fe+txp36RG98GjNPPodhw3SezV7lnBA/VC9xi3bX2RXJL5NlTqvU1WOw9HEvcc4AN+p6RUj325RRFrs0W/byLHXqk3ne9q0P2SH3eHDxO2Vtjq2EcPNiydsM4uHgbwBkkc0gWADjd8y8FUNIYTw7giwCmRvwXRf7/bphuB6BRneXL/gFFJCEio8J/LYqkRXgJwDdJZgIAyUNI5gN4E8DFYUx6VwDHNrHvuwCOCZfBg2SjKk20rpc1dVAROTmsa+PEvBxAm6bywk3Ed4bt+XHEgr8N4Lww9rwzgEkfwzEMo0miE3PDMAzDaA2IyAH/11qxN+eGcRAhIjNJPgNgHoB1AGYB2Bvx15C8HMBj4RvsmQDuihSRHQqhxQBcEm67Mcy/CcB7APp8RNW9F8ES9zmhyNoOAGcDeBLAcQiWwy+H/vGg8Tx2kLwSwBMkYwC2I4hp/w2CZe3fB9DUcvimmAzgBZJbInHnjccpJXlPWJe1CNrro+Y/AI4HsBDB+U5H5JoZhmEYhmEYBmCTc8M4GLldRG5k8O3uNwH8VkTuaXSKyGsIBOMUIlISJn/hbX8awNNN5H8AgbjcPohIQSR9Y1O+MMb6p+E/n2+nKXdSJP0CgBc8/7sADols+lm4fQqaWOIe+v6EYOn+PnUP7esBXN/EfpdF0iWR9CyEb7+jbRTNHz1OuNT/GhGpINkRwAwEPwYYhmEYhmEYRgqbnBvGwcdkkkMQLMl+UETmtLSDccB5lmQhAsG9X4rI1hbyG4ZhGIZhfCpJtpzlM4tNzg3jIENELm05l9GaiK4I2F9Y2CWVXjlNxw137Vmm7Hime8x1Zxflq9upV9CvX+YEnrbVa2G53XO1Fl73NeWpdGmFJ4KV0I+P/LgTHapNalEpn8LIYTY8WqF8JedowaS9z21IpWMZWphr+fJiZW9hVio9OKlFm7BZi+ugR0kqOf2nG5TrqK9uV/b7S1ybds7WQlzspAXhVqzumEqPL9bCS5vv3aTs2honEJexdpcuN5al7Iqq7FS6vRcqlyzVv/VU1rh965dp4aiVmd2UXTxNi9Z16OxEv3Zs0VINO2t1/6iLyNbEPf3GNjFtv/dMh1Ray1MB/yjX7TRwt0uf5wkxVczQgnBtLx+QSosnAMc8fd8kq1x/Lt+rz6Vzte6Hq5a7Ou2Avhajq3T/mL/b9dni3boO8fW6HQozXZvleNdRdmpxqHk1hal0vScruXOhrn9RthPRG3RhNpqDfZ3A1mvvaKGoFVn6QDn/cmNNWa0WNIxn6D+vV2xx13hOrj65M7J0fWVnRESrVotkbRR9jz01r2cq/YXeWiRtXebbyp4y1Qljdc/R9WMH3fej/e5HGfoav/GWzjtpvLt3t9fkKd/AXLUYC1MXO0Gzs7oOUL7kLi3Oh/UrXP0GHap9WXp87s7IcTO8u8gTfcPqpS7dRQusxfrq40TLjfXW4nZSq0UAZavrL4m1emyJ99VCaFERuPgALcbXnfss1nNkeh9hoSeP1dWJZzJ7hs5a3EHZiSrX3nNn6GfjkT/RIpwZfZwAKtvrvLJHj92BTm6Y7qcXKkq5JxDXLvJMS3p9Mq9Q13eFE0Nkge5XMa/vJxsi13zlKl1uth7XO0rzz2SjdWGTc8MwDMMwDMMwDOMTQexTamkxtXbDMAzDMAzDMAzDOMDY5NwwDMMwDMMwDMMwDjBszd95MwyjZUiWAHhWRIZ526cAuCZUF/9fyr8MwFgRaVJh/bMKyXdE5IgPsd/ZAJaLyOLm8l3a+5zU4JxPHYHU4EmprGlwceWvzJ2sfF8fe62y369xcYL9sjsqX8J7HrxbtjKVHtZGx1a3oY7R7BVzMW5V0PHeW5M6hrRbzMVSrk3oWN/ucR0rVy71kWPqWMTFdTq2LxFplwlZXZVvUcMeZXeN63i+KDVeVPSWBlfHoriON+0Y07G0ZeJijvO9+q5r0PH/NUl3nB6ZOp53e4OO9RyV6eJjZ9fr2OQhmd519JYL1olrl/VeHXpk6ONG659FHae4qla3d88sp0NQkdRxw2WJGmUPz3bxnFPKVyjf8W0OUXa0DirOFsDapO4v0favEd3vcrz6x+HiqXO9dxPl3jXPiPif36tv1a45Oq61S+TaJUTfm/XevRpt0+KYjinO8uq0PXLf+OXsSeh7akKma98tott+r+g4+N4x1/fXJsqVL8cbayoj9191sl75xmVqzYc3a11cbkmW1qjI9M5ta8L1745x3Q6+UFSsGV+9194zy1zs7YaVzynf8SOvUHb/TFfHbK9+Sxp2K7s4Mi7tSOh7c0SGvv82JKtS6eArounr2yHm9AGKPW2DXdDXbfKs21Lp4UMuUr4T8vp69XfjXQevfbOh74v7Z9+eSl8zVn9cpQz6mldF7rHNDbrv+MfJjMSK+/fx7bN+hXR8acz3lV3gjaMVkT7Z1yu3yushz1QsT6U7ZmkNjaFZWusi2v7bvLbf7Y1vGZGxpGNMazzsSOr7r23kWVntjTO5XnRxXmR82O3dt0XUx1mRcGP58Li+3/Z6x9nuPYOfXf+cp2LxyXNCz5MP+AT01Q0vHfB2aAp7c24YxsdK+L31T+pYH6nqSXPlfZiJecjZAIa0lMkwPgrqxDRxDcMwDONgwSbnhvHpIIPkgyTnk3w8/AZ6CpKXkFxAciHJW/dj++Ukl5OcCuDIdAclWUHytyTnkHyNZHG4fQrJX4X7f4/kGJJTSc4m+RLJrmG+75JcHNb7X+G2Y0jODf+9T7INyUkkn40c987wjT5IriV5A8m3AVxA8iSS74Z1eozkPq9Iw/LeJPlkePy7yOBn/vCcbiI5HcAEkt8P22chyauj5x5J/5DkzPA8fhHZ/qVw2zyS/yB5BIAzAdwWnl+/5i+rYRiGYRiG8VnBJueG8elgIIDJIjICQBmAqxodJLsBuBXAcQBGARhH8uxmtncF8AsEk/IT0fxb3nwAc0TkUABTAfw84isUkWMA3AHgTwDOF5ExAO4DcHOY58cARof1/ka47RoA3xKRUQCOAqDXYzVNjYhMBPAqgOsBnBDWaRaA76fZ5zAAPwAwHEA/AOdGzmmhiIwPj305gPEADgdwBUn13RSSJwEYEJY3CsAYkkeTHArgOgDHichIAN8TkXcAPAPghyIySkRWeWVdSXIWyVkrK9bux2kbhmEYhmEcXIjIAf/XWrHJuWF8OtggItPC9D8BTIz4xgGYIiI7RKQBwEMAjm5m+/jI9joAjzZz3GTE7x+3cftAAMMAvEJyLoLJc+OHV+cDeIjkF+A+fzwNwO9IfhfBBN//LHJTNB7rcAQ/JkwLj/VlAL3T7DNDRFaLSALAI5G6JwD8J0xPBPCkiFSKSAWAJxD8YBDlpPDf+wDmABiEYLJ+HIDHRWQnAIjIbrSAiEwWkbEiMrZ/QUlqe+V+NUHAiaOu3O+8q2p3tZwpZGH5+v3Om4f9jy4oaSb22ycaf94S79ZtaTnTh2BnoqrlTCGVH6C+G+vLWs4UMsaL9W2OLP8bwYZhGB8B3TLatJwpZJPs/7j599m/2++8qz9AubvqylvOFNLZi/9vtlwvHr05/Bjz5ujA/a/DgsSeljMZBw32nXPD+HTg/wQYtdMJXjQnhLHPT4ph/PXs0HxGRG5oYb9G1RwCWCQiE5rIfzqCHwTOBPAzkkNF5BaSzwE4DcB7JE9AMHGPzjJyvHKix3pFRC7x6j4ewN2heQOC1QXp2qwmnLA3ltcSBPBrEblbbQx+XPhIfpr1BeGawxeEaw5fEK45fEG45vAF4ZrDF4RrDl8Qrjl8QbiPCl8Qrjl8Qbjm8AXhmsMXhGsOizk3DOPjwBeEaw5fEK45fEG45vAF4ZrDF4RrDl8QrtlyPUG45vAF4ZrDF4RrDl8Q7mAgad85T4v9pG4Ynw56kWyc/F4C4O2IbzqAY0gWhRPsSxAsQW9u+ySSHUlmArgAAEQkES7FHhWZmMcAnB+mL/WO28gyAMWN9SOZSXJoGOPdU0TeAHAtgEIABST7icgCEbkVwbL0QQDWARhCMptkOwDHp2mH9wAcSbJ/eKw8koeIyPRI3Z8J8x5Gsk9Yj4vS1P1NAGeH5eQDOAfAW16elwB8pTG2nWR3kp0AvAbgQpIdw+2NEs/lAPb/rwTDMAzDMAzjM4G9OTeMTwdLAHyZ5N0AVgD4K4AzAEBEtpD8CYA3ELzlfV5EngaAZrbfCOBdAFsQLNVOt065EsBQkrMB7EUwyVWISB3J8wHcEU6sMwD8AcByAP8MtxHA70WklOQvSR6LYHn5YgAviEgtyX8jWAa/AsES8n0QkR2hUNwjZOq7I9eHx/J5F8AtCGLO3wTwZBPlzSH5AIAZ4aZ7RaTx2BLmeZnkYADvMvhsTgWAL4jIIpI3A5hKMhHW+TIA/wJwT/hm/Xw/7twwDMMwDMP4bGKTc8M4yBGRtWhatG1SJM/DAB5uYt902+8HcP9+Hv9nAH7mbZvk2XMRLF/3mehvEJHvpDnOtQjesPvbSzz7dQTx9C1RJSJN/ZhQ4Nm/A6CC4MK34bsjef4I4I9NlPUggAe9bdNgn1IzDMMwDOMzitiy9rTY5NwwDOMDEKrcTwFw+8d5nL/dc2IqXfZrrcmX29cXinGh8X683m86+XGBLlx/6iYd53zauA3K3rGsJJUuLctVvtXQ9ilHbkqldy3SMXi57bQw2lc2upj0fx+n4+oyx/ZRdmKVq1Niixaw2z5fSw+UlTt7yLd15IBU6CiujC9+z5Vz8XXKV3TVGGU/dv3mVPq0obqN8n90rrJ3/eifqXTxfbrcqht+qezydW5BSl7xXuVbOU/rARTllbr9KvS5Dfq5brNtf3QLSwqH6vj/aa92Vna8Qf+BdOgwp1u4dZXuH+9m9FX2yAYnxkToa96xvV5sU9h7Zyq9fXlP5aus0XXs0N4JLHX9tj63nffqePviaw9LpaXUE0XaU6rtuKtT7fSVypX7/a8r+5Hzn0ulf9S1h/K171Oj7NfnFKbSmZ4C8NHjNyl7+Ux3XefG9T30hYv0vbr1eXffvFamRQBP7aoFDwvHuHOPFeYr3xP/7KDscz/vPoCx6yV9b670xoQRI7a6vOt0uYVdtir7h0n3J+XdG3Qf/fGT+nfQxH/+kUqzUOf97p+1OOJPCly7dLmwSPmuvk+PH1M6uf7ij4UvXNVd2Yv/4sod/AWtzbDxST3G9r7z5FR64eWvKN/AE3W/2zTFtUOvy3R9WeDFSNe7GGS2L9S+mB6zoufz7lH6WmT01MJoUu76YeaFJyofNqxOW+69X9A6GfELv6rL3bY2la576D86b0dd31ifyH2TqcttLq7cF4Sr+b/vKTvjjDNS6S1XP658Xb6mx6jrXnR9a/tyLT5acrUeC5Pr3T2VceEXtO99L+qtzPUdHua9Y9igF8PJNnefSK3ur/HTvPvi6Yec4cWyx7p2UTaHuncQyXde1b7OOu9j166BcfBgk3PDMD40/lvmgwURmYJggv1h9t0M4JCPsj6GYRiGYRifFZKt+FNmBxoThDMMwzAMwzAMwzCMA4xNzg3DMAzDMAzDMAzjAGPL2o1PFSRLADwrIsP2M/8DYf7HSU4BcI2IzGom/9UAJotIVRr/IwCGArhfRH6/H8dcC2CsiOz08lR80CXjJN8RkSM+yD7/CyQvAHATgK0AfoGg7T7XTP6xAL4kIt/9iOvxgdvq44Dk8wAuFZHSj6TAGtfFGmp0/G7ZQh2jW1DilocVeN/WbjdC/wa7/V1nFyX0N1d3r9Ax3F3GReLjZurqjU7quNvyNa6O63e1U76u9fpb5ifSxcA27NQx3Fm9dcxg9VOLUumc/rp+hd2qlb12YWEqzcF6CODWjcpOPP23VLrjJX2x+d71ztm9ROU9/dDpyq7Y5No4f7eOu92xxcU47jj1Dgy4xLWLePHdbftF41xjSFQ5e8BoHV+/aZGLBY7HkpjZ4Np4UE/dZrt3LXPpN4G+E10M74Ys3ZdO6bhN2XkjXbl9RyoX2rym8xYe4uKVl7ytY+Tf36NjbYc3uLhcxnQ7vJih43AH7XL253rqmPPCkYt1pbr0cuUWd9M+eu8fVi5MJf1rIat1ueNzXH0TSV0Ovb+cuomLkW+fr/vk6tn6+8NPZrk40kJvVWf8cK1jOfPhJan0Ubk6rnnLZn2P5Xd1/SV3cIny9UvovhQ/zB1n90PvKt/cbD1+9Nnq6tu+e6XySYLKXr3S9YFzc3TcuGxYqmxkukZkBx0TXyX6XJftcG3Y7chjlO97Dzyj7OhKWX8sjI06VNkvZrhBbWiJrsOOMh3Tn/vjJ1Lp0gbd1zPH6z7acfVsd8wjtCYF6nXMcfKdyJc5M/S9yT79lF1AFzecNUhrEMQG9tfHSUSeEeWlutxh471y3RgcO/l05ZO5b+pyO7l7LGNAV523Uvd9HhLRPfXuxQJuRjr8GPOc67XWasPz96TShX1rlS+5wbtuY11cecMSr35eXHbN8+5Zkz9+ic47YKiuZDIydnsx5sjTegAc4TRM6D1zZcUcnTeqSVDvfRO9Wy9lyiLXf5PbvHt8oK7vkszW91EYW9SeHpucG8YH42oA/wSwz+ScZBcAR4hI70+yQiTj4TfIP/KJeWPZadxfBXCViLxBclJLZYU/eqT94eNgR0ROO9B1MD4camLeAtGJeUtEJ+YtEZ2Yt0R0Yt4S0Ym5YRiGYRitG1vWbnwaySD5IMn5JB8nmUfyBpIzSS4kOZnhB6nTQfIkku+SnEPyMZIF4XepuwF4g+QbTez2MoBOJOeSPIrkKJLvhfV4kmT7JvZpPF4uyRdJXuFtJ8nbwnovIHlRuH0SyTdIPgxgQbitIrLftWH+eSRvCbftV31IVpC8ieR0ABNIfoHkjPC87iYZJ3kDgs+g3UXyNm//fJL3he39PsmzInV+Nkx3IPlUWJf3SI4Itx8THmduuG8br3og2Se8NjNJ/tLz/TDcPp/kL9Kc341h/3iZ5FqS55L8TdheL5LB6xaSY0hOJTmb5Esku5JsR3IZyYFhnkcar1lYVlGY/lJYh3kk/xFu603ytXD7ayR7NVU/wzAMwzCMTzNJyAH/11qxybnxaWQggqXnIwCUAbgKwJ0iMi5c7p4LoLnl10UArgdwgogciuBt7/dF5A4AmwEcKyLHNrHrmQBWicgoEXkLwN8B/CisxwIAP09zyAIA/wXwsIjc4/nOBTAKwEgAJwC4jWTjWrLDAFwnIuqb2SRPBXA2gPEiMhLAb0LX/tYnH8BCERkPYBeAiwAcKSKjACQAfF5Ebgrb5fMi8kNv/+sAvC4i4wAcG9Y538vzCwDvh3X5aVg3ALgGwLfCYx0FoBr78kcAfw3LT60pJnkSgAFhu4wCMIZkU99WB4B+AE4HcBaClRBviMjw8HinhxP0PwE4X0TGALgPwM0ishfAtwE8QPJiAO39a0ZyaNgGx4Xt37hG704Afw/P+SEAd/iVInklyVkkZ/3tpffSVN0wDMMwDMP4NGLL2o1PIxtEZFqY/ieA7wJYQ/JaAHkAOgBYhGBC3BSHAxgCYFr4gj0LwLtp8jYJyXYACkVkarjpQQCPpcn+NIDfiMhDTfgmAngkXFq+jeRUAOMQ/OgwQ0Sa+njlCQhi3qsAQER2f8D6JAA0fsD0eABjAMwM2yIXwPY0+zVyEoAzSV4T2jkA/LfEEwGcF9bvdZIdwzpOA/A7kg8BeEJENmJfjmzcF8A/ANwaOe5JABo/8lyAYLLuBc0BAF4QkXqSCwDEAbwYbl8AoATBDzzDALwSnnccwJawvq8wiLf/M4IfTXyOA/B4o46AiDR+OHoCgh9bGuv9G39HEZkMYDIAVD9ze+v9WdcwDMMwDMP4yLHJufFpxJ/UCIC/IBBe20DyRgQTxnQQwCsicklzByF5Dtzb568B2NlM9uaYBuBUkg+L7PPhx+aW31em2U7sp9YGyTiARvWaZ0TkBgA1kThzAnhQRH6yP+VF9jlPRJapjWRnL4+PiMgtJJ8DcBqA90ieAOCLCN5yI3yjDjR9fgTwaxG52zvutwA0hgs0xoXXhuUlSdZH2j2JYFwkgEUiMmGfg5AxAIMRvGXvAMD/AWF/27/ZPOzoxHb2bNOCWd3HaIG1eHvXnSukXvnqNmoBojadXNN3rtALEwqKtLjOpnfcgoek6EsWj+sY6d07XN4ab1FW5yG6qzbMdeJL8UIvLjtHn2ve6MJUmnm5yrfuWV3f4uymFlqEiK5v7LiznHHvn3Teat2+i2Z2SqW7F3kx3EVahKy63j1W48cfr3wZO59Uduk8V6fc9l79svRh8vLcdRxZpusXFQ8EgMJC1w5JL5Zd9w5g/dZCZbdd5X57K12drXxLt2shrDbbnGBR1w7lyldSuFvZa9dErjl11495YfyZ0WEwUzdE7Xrdn7OyIn3Cu8ZSuVcX3NFdx/J1+qC53bWo1/JKJ6BU6N1T+Vt1v0tEhrO1FToSZ/xYLVA14n2nXbnF+wtMdmtRp2NKnGjWExu6K9/ghK5T7S53z+Xm6MfbPmoGda7+O2t13myv3+V3dO29aqkW+evWWd8LGTF3pMfRVvmu66N/x0zOm+eMdoXKVyb6Gu/MiCy88vr69Dq9b6961y4VOfo6wRPjUqNUrT7mNuq+P+ainqn0jj9pwTq01XWYtdCN3cd6Ymz7kOn6IfsP1L5crXMaHdvrV+u+ne0JwiVXut/t42ecr3xSp8dJ9czYsk75OMpbfFbt7nPZra8/C70otN07XLqrluLxn1NRMs44Q9lRATgAyDjNRf81PPiO8uWO06J/dS86wb2KWk+rI0+3b7zA3cexQ7Q4Y3LlbGUjw90oHDBCuWTHJp03KoaX9Mb5YUfp+r7txEfjA0t0Ofn6nuKYSe6YC5bpvA26P1/R5sP+efrx0ZqXlR9obFm78WmkF8nGSdUlAN4O0ztJFgA4v+ndUrwH4EiS/QGAQcz6IaGvHEAbABCRJ8Ml7KN8hfdw+fMeko0j7xcBTEXT3IBg+fhfmvC9CeCiMM67GMDRAGa0UP+XAXyFZF5Y/w7p6hMKyTWeww1NlPUagPNJdmosi2RLgncvAfgOw1fOJEenOa/Ph/5JAHaKSBnJfiKyQERuRbBsfpCIXNdYx3DfaQAuDtOf9477lfAag2R3kp1E5M+Rc0wvEatZBqC4sR+RzAyXqwPA/wOwBEHfuq8xRj3CawAuJNkx3LdxVvKOV++3YRiGYRiGYRgh9ubc+DSyBMCXSd4NYAWAvwJoj2DJ8lrs81EojYjsIHkZgEfI1M/n1wNYjmDJ8Qskt6SJO4/yZQSCaXkAVgO4vJm8VyOY6P1GRK6NbH8SwXLoeQjetF4rIltJDmqm/i+SHAVgFsk6AM8jiOv+IPVpLGsxyesBvBy+Ma4H8C0A65rZ7ZcA/gBgfjhBXwsX49/4U+mNAO4nOR+B8v2Xw+1XkzwWwdL6xQBeaKL87wF4mOT34JbfQ0ReJjkYwLvh7wIVAL6Alpfh74OI1JE8H8Ad4XL7DAB/IFmPYJXEYSJSTvJNBH3j55F9F5G8GcBUkgkEy+wvQxBecR/JHwLYgf1of8MwDMMwjE8b+y4UNRqxybnxqUJE1iKIF/e5Pvzn578skp4USb+OILbbz/8nBEJh6Y49LGLPRRC/3twxSyKuyyPbC8L/C4Afhv+iZUwBMMXbVhBJ3wLgFs/fZH2aqF+BZz8K4NEm8k1qqj4iUg3g600U3RHA7jDPbgRibH6Z39mP+q1B8INFI7dEfH9EIBjX3P43enZBU76wvZoSlBscyfP9SLokkn4QQVx/9DhrEcSjG4ZhGIZhGMY+2OTcMIyPHZJnArgZwFcOdF0OFmon35dKr68sVr6CFTqWMr/UxRD2ZQflyx2vY0rvu98N+6Ux75frdToGb3it87+bo/PmJ3RU1LBI3lN/q+N3/3CdftTMhYuZfuGNrsp31gi9sGX3FJd3+1Yd9/5eTNe3MuLu8/AzyocGXf+av89JpR+r7KF833lrmrJXx10sZXKHjvtr9+v7lT0nw8WmDvqr/k3r4Vk9la1aZRsQDcUu8AKF6zyVhrmZLn72pkefU76ZO7s4YydwzAAXA9nVa4fR52s9gNLprlY5bXRcaGKHrsQrOS4ut7ZKR3dsrdIxjyMyXWz4Xr/feezIcH2r7pEnlI9eMF7iVafrKXv1uUhljbZrXZst3KTvqWNeeFbZJ9/cN5V+9nodNz68U0LZz25zbdZBtIZC3WwdK35/povD7UitocB8fd/8brO7jqP0IbE4S7f3yvXuOJcuXa18j+foj2UMvu/1VHpnXPd9/4/Cp1Y6/5G5pcrXfrS+jgtecP3Bv8TVN6vfihFr6+pfN0/H6G6o030nFnNf/ax/Qvf15Rk6Dj4n6crtS30tGl5+Xdm1ka+JSqmOn16WpTta7ztKU+mpcT3uFP3qLWVPy3V966gXX1O++i06Zr4hctiMGXpBWrytvhp9I7Ita6bpBu6boaPd6ra6vp6c9lflKziqk7L70ul8JGbOUb76x72vxkbGpV1LdFz+ml26P4//nIsHZ7auX9/0X5bFlqsfV3ZhX/28i8aZt39Uj7+1t/5A2UtecuP1ypiu36ili3Xet52mxpBbb1U+JNOPWckaT+tCSxsgs1vkGeyVkyjV49uWOe5ezXlNx5EXHbZUFxxz4/Gy5/U9fsiel5T98zIdb/8PGK0Zm5wbhvGxIyLPAHimxYyG8RmFH2CFX3Ri3hLRiblhGIZhtAZMEC49JghnGIZhGIZhGIZhGAcYe3NuGIZhGIZhGIZhfCKIvTlPi705NwzjoIdkIcmrwvQkks+2tI+3/2Uku7Wcs+ljGoZhGIZhGMb/ir05Nwzj00AhgKvQ9Lfi94fLACwEsL/fQf8ojtksWScdkUpPzHhP+apXaaGugovdhwWqfrnRK0gP80eiPJVe2NBG+ca32aXsPfVOQGdijRb/Gdxhp7Lbj3DiW9WP6TjnC/K1KF1WRUQwqaduch52qbI71rtz7VCuxZTkn1oAbEnSieJkTRyqfOzeW9dhw9pUesJN25QvPqifstsmtqbSfXvuVr7888Yo+3NbFqTSOZMGK98py9You00nJ3RUulWLFWVkaJGheMTuvKWj8mWO0Oc2+C0nOpbdWbkwEfraVMzVIm8dvzAwlV75e513baYW2BoaEQHsJrpPlkmWsnvm7nXHrNW+uRn63E/v5Y6bOUSLpNVtXKXs2JjDUmlZvRzaqc8tftSZqfSRVb/SvqP1xyPmX/FmKt0duhwl8ATgyJnOv9P7q6p3lu6jZ8D1/e30VP8K9P14Zo3TFijK14Jluyp1m3Uvdv6MY/RHIT73yPvKzurh2n/kylLle75eC3UdFxkv6hv09a/frK/5xKOccN6iGVpoLvuMI5SdWOTErTLPPkr5+s3Swm1965zgZcYgLarY/yUtHtc14exZ3vunjHEjlN3laTdWsqM+76G1pcoeeIa7jvXP6nKLjtPXYszDLh3vr9sh1kWPYVllTvAy1luLByJbj7lVb7n27T1a1y9zWF9o1rtjXHCqdpXvVWYVVrg6dNZjS84I/fEb2ev27dRhpfIVVe1Qdry3E/tksRYqrXpe543S5Wv6XJIbtCBj7rhDU2lfAC77R79Vds+3v5ZK16zW/ReZWtivV689rpwTxuo6bNXPCGZGBBmz9XgmFfqej/V0fSC5QT+fM7voAbpT2XRXbB/dr+KH6ucJI+Nb79WzlS/rlEnKHv+6HjeN1o29OTcM49PALQD6kZwL4DYABSQfJ7mU5EPh99ZBcgzJqSRnk3yJZNfwe+ZjATxEci7JXJI3kJxJciHJyY37pzsmydtIFpB8jeQckgtInhUes4TkwsadSF5D8saPuT0MwzAMwzBaJSJywP+1VmxybhjGp4EfA1glIqMQfBN+NICrEXzzvi+AI0lmIvhG/fkiMgbAfQBuFpHHAcwC8HkRGRV+p/1OERknIsMA5AL4XHPHFJEfAqgBcI6IHArgWAC/TTOpNwzDMAzDMIx9sMm5YRifRmaIyEYRSQKYC6AEwEAAwwC8Er5hvx5AjzT7H0tyOskFAI4DMDRNvigE8CuS8wG8CqA7gM7N7+IVQF5JchbJWX97fU7LOxiGYRiGYRifGizm3DCMTyO1kXQCwVhHAItEZEJzO5LMQRBHPlZENoRL0HNI9gTw3zDbXQBe9Hb9PIBiAGNEpJ7kWgA5ABqgfwjNQRpEZDKAyQBQecPFklwWxAJumaaH6sxM/UI+f4mLGXymYoPynX+njt8bGIlVnL1Ax7gWdqlWdvFAFx9ZvEpXe9POtsquqnJxd+2LdVxl0UgdF/r8W9tT6SvH6XJrJz+k7Mq17lxzuySUb/BF7ZSd+Jc7bvmjOrYvf5y2G9aXptLtsvW5JLduV3bPuGuX9hPzdDlzlii7psrFIsrecuXz24E57rq2bahRvnWrdQzsckaOmwEcIu5c6+fr+E1G8m6c1QbtO7kYyNIduv5zaguVffZ8F5vYY7Sub7sZur1Pmuhiw59+R//OdcE3lYl197v46tH36hjjN66aqeyVq4tS6eHvrVa+1Yt0TOyQp59Ppf1livUbdZvi+fmpZEO5vodiz7yg7KmZLjb8O3/R8acNz72k7Fxx59apQdehc18dK952nbvn2ib0+5G6J3S5hOsDi6oKlW9Atu5bBRH9gr2/10PT/Gx9bQ5rcPXNb1erfAVeKHCnbu44HS4ZoHwb79b7djnanfsFbXRBDTNKlZ0xZlgqnXjjDeUbQh0LnJPh+m/dLK3bsCRD//55wVHuuFe8omOr/2+djjneE2n+6hcXKV9lTMe2ly908f8d2+nxrWaJto84xF3zutn6GjND97tEmSuXa3R9maXzPlPh/Defqu+hPQ8uUHZ9tTvXju10DH+sSN/Hz1Q4rYafz9I+QLd3Zn93byYrtOZAvLO+bvWLnJ5IokqPv89U6P4b5boX9XMpd6y+xnUvvpVKL3lJj93RGHMA6PTfe10dTrlC12+W1g+prXRjd93rOoY7o0SPOxIZRhvm6TbKOkXrK9Q+P83tV+NpiRSml7lh+3xl1781X9mZY/qn0tk99N8Iibm6P7RN6nG/NWDfOU+PvTk3DOPTQDmANi3kWQagmOQEACCZSbLxjXh0/8bZ4k6SBQDOBwAR2RAuYR8lInc1ccx2ALaHE/NjATSqdG0D0IlkR5LZaHqJvGHsN9GJeUtEJ+aGYRiGYbRu7M25YRgHPSKyi+S0UHitGsGE2M9TF4q/3UGyHYLx7w8AFgF4AMBdJKsBTABwD4AFANYCmOmX1cQxXwBwK4D/kpyFYCn90jBfPcmbAExH8BpiaVPlGYZhGIZhfBZozYJsBxqbnBuG8alARC5Ns/3bkfRcAEc3kec/AP4T2XR9+O+DHrPJJfMicgeAO1oqzzAMwzAMw/jsYsvaDcMwDMMwDMMwDOMAY2/ODcMwWiH1y9zK/O2lnhgNPEG42U7ArGOWDr3fmcxS9qHfPieVrvrmLOWLZeplZtmH9U6l58zQYmw58IRtarJT6XUbtDDQ4EotvFMvTgQp40i92GDpNVr0pmsPJ4JUujJb+TrkVSj7jcxuqXT+Oi1W1GecMrFhhqtjeZ1uo63/0mJWy8SJg3WZtkX5kgl9Lbod437zrnh9k/Llj9ViS7XLSlPpnM66nCFDdaz4yv86QZ+l1OI+3ZdqIaneJzkht3LdnIhRX+My7yf6hc87gaW8LN2GyzL1NR89y/W1rd5fEw/fpe2xMZdh2VVaoKpj0hMXFCdmNe/tYuXrWawF1lY+7wQF8/K0gN3y0m7KbhPpdx1ytVhc21JtxyLNJEsWKt+6F31hMWc3eF9PLBisG2bzJldwx6TOu2aKFoAqhROo2uYJiZ1ylr4WO95w98aq7VpMcF2uvo6zXnSiXoddpPvZ1qd1/yjo6+yaN3REToxazDFZ5to/M0d3rD3vNCg7b8W7qXTOYH1fxLzxLTfH1X/alC7KV5mry806dlQq3XGq/uLFwru04GXHuLuPVryvx9gK777Ys83l7dRHi5nNmtMVaVmmzbGH6vFj1Tx33OqE7ld+X4+O7Qtu0sKfuxKdlN0r342NDa/r67+zVAv5Rct99U19z/SkbrP+Fe5ZQz1sYtm/M5U9YJzro3Nn6OvW0ds3yvbl+vnRsETXoaLW9ZeVsVzlq1mt2zAqAtf1xXuUb8awa5WdEXP1rZ6mx5I+g3Rfb1jqBO5WTdH17bbuLWVvWevqW1Gn26hdjr4WGXHXMG12lyrfmg0dlN17kRPvzOuo74N1r+nx4ZUMrVPyJRx4TBAuPfbm3DAMwzAMwzAMwzAOMPbm3DAMwzAMwzAMw/hEEHtznhZ7c24YhmEYhmEYhmEYBxh7c24YhvEBITkFwDUiMovkWgBjRWQnyXdE5Igwz20ATgPwvIj88IMeI/uKi1LprgueVL7i8Tr+O5br4iGHrtHxyCd+U8dpb/3pc6n0OZ10XFrVLh0PV/OUi5ke20n/lrtxm44T7XfIzlR6SGflQq33YbvRVS7+sOzuqco3/LlrlF1/nwteZrau37YXdBzdqXAxmv3vPlUfdLuO/+4/0qXX3fS+8nX70wXKHvTFZ5GOTr+9WNkLv+Dad9hNI5Sv9uXZyq7b49qUMf0WoX69jiE8oqOL9dyxS8c4Fv38dGWX3f5MKp2RDeQOdH1g3lNak+C8Abpd2kwoTKXL39Wxteet07HAZfWu3K8M1TGwm1fq/iHi9q2p1396NOgwUeTTnfvYf56g8z7xtLK7X/YFpKNXla4/trs40eqHXlOu3G9dpOyLf/Z4Kr34DzomfsDxOgZ2+UvO3yap783KZfo6doloFCzL0Hm/fOckZed8/9VUulOFjnld9oRuw7Zt3HEm3qd1HEq/pr8GecQ9o1Pp6VdMV76B3rWo3eL65S5PS6LnGXpM2Pyisx+p1vGxP3rgWGXLsgWpNPP0mLXliZXKXlPm2vf4Z89Xvoozn1D26ltWpNJDs4qUb8S9Jyp75uVvp9Ijfz9S+fJ+pPU4uo9xMdw1W/R5H/0jfU+t+tPWVLr//ecoHyp1HPnwZYtSaQ4ernyx7gOVPfTMyan0kFO0VkDGqYfr4+x04wU66Hbo0WuQLve8f6TSp/91fLP1Rcyde3L+AuUaNkZ3nthwV6cjf9Jb+YZe/G+ko+Rq/QBhZx2vjjzXD0ctXax9mbqP1s9yeid+jPlhC3+j7JqbvptKZxzvfdQlQwfJZw12bTj4An1vIlPnbRfd1/P57StLIvdFJ60jUNxvmM5b4/pA8tWXlG/oz/Qz4cILtd9o3djk3DAM4yOicWIe8nUAxSJSmy6/YXzcRCfmhmEYhtEaSNp3ztNiy9oNw/hMQLKE5FKS95JcSPIhkieQnEZyBcnDSOaTvI/kTJLvkzwr3DeX5L9Izif5KIDcNMeoCP//DIB8ANNJXkSymOR/wnJnkjzyEztxwzAMwzAM46DA3pwbhvFZoj+ACwBcCWAmgEsBTARwJoCfAlgM4HUR+QrJQgAzSL6K4C14lYiMIDkCwJymCm9ERM4kWSEiowCA5MMAfi8ib5PsBeAlAIP9/UheGdYNf7r6Unz19KM+glM2DMMwDMNoPZggXHpscm4YxmeJNSKyAABILgLwmogIyQUASgD0AHAmycbA5xwAvQAcDeAOABCR+STn71Ny85wAYAjdN5DbkmwjIiooVkQmA5gMANWv3mVPLsMwDMMwjM8QNjk3DOOzRDT+OxmxkwjGwwSA80RkWXSncFL9v0yWYwAmiEh1izkbK/SeEyvavkeL3HQo3ans+q31qXQxCpVv/d88NTY4UbUd27QQU/u8GmUX9XAiSJs2aYGvhGhxsPnLnGhP7XLt6xTTYfez6MSKGmp1dFXDP+9Rdt0a9/tFQuu/Ye0OLRS0IcOdW9dfacGhrI5634yeTsSpoUHXoe7v/1J2TpYT/Kmv8RSz1i5XZpv8yLmKFtwrXaYfuYnIcXPb1inf3l06cmJnpbMrvUd39T1aJG350uJUelix7itrs/S+PTfpvlX6lKt/WWV75WtI6nbKiLnzm7Owq/K1j+vzyc9xdnamFklL1Ot+uJUuTl4WarG+yoX6Fsr/54OpdHKP9tVv18dhRItp5WzdIYa9N03Z69Y5QbPoeQJA7UZdbreYvm+ibNugxcIyI6NIgej2LP+9vo7by5wg1IaY1g4YW7BL16nG9f2dN/5X+Spi+tokprzpjsl85VuhNRcxodRtqPWE/Ha8ptu7tMz1pa4xPQb491QUZul7KhN6rKmKOX/pD/6kfIuyuyu7b62rb7EnvlVzt65Dgj1S6fqX31a+dTVaRK2kg+u/K97SAoFd7tFiiFW1rj9X//5B5cvsqu/r3TNcX+r0mzG6nF/8WtnFcNexZpW+rxtue0HZ2cWuz2b21H2QWe965br6lt2qBfayu+rrGC92/aVijhali2fr+yR7lRPGy+ijRd6K4QmjRUiu36LsmucXKTte4Oq05G19H/fqtUfZtZWuP/j3cVQADgBybrgjlS6/4nLly+qvBQ6r5jihubzRhbp+w/WiuMp/ufaO5+n2zBqu782Mcy9LpRNT9Xggb76s7MSmHa6cK7+nfA3//puyd8ZtuncwYTHnhmEYjpcAfIfhbJxko6zxmwA+H24bBmBE07un5WUA3240SI76n2tqGIZhGIZxEJIUOeD/Wis2OTcMw3D8EsGr5fkkF4Y2APwVQEG4nP1aADM+YLnfBTA2FJRbDOAbH1WFDcMwDMMwjE8Hts7BMIzPBCKyFsCwiH1ZGt/Xm9i3GsDF/vbQVxJJF6RJ7wSgP6RsGIZhGIbxGcQE4dJjk3PDMIxWSMZ5X02lO/z9NuWr36Pj1jLy3UNuG3Ssb2ZWIu0xFmTmKPvkPKVPhx2ReNl6L954U9yLgW3rYmBLy3RcZbsCHZPbrdaVm922VPniJ56obK54LJX2QrjRs8NeZWfuceW2++VXlU+W6djl2LEXptINT/5K+bK/foWyp7/iYnhPKNyqfBw6Ttk1tUudcYiOfohnzlN2RZlrwwzvOlXU6JjMDrmuDUtrdAxp/s3XK7vgtDtT6bXT26H3mNKUnen9PbS6Wpc1prc7v0RCX/Mp9YXK7l/n4mXLYzpuOJ7Qwct54nQRtlfq/lFCfWG7RGK4Y+M/p3xld69Tdvtv/iCVlmrdf7O9OEvZ6PQBOv30OV3f07+k7DV3PZ5Kj8jS/ay+Sp/r3LiL2W3foBv4yC46fjax0cUrd0jq+7jg9EOUzUUufna9FwveaY+Oex49yl23vHNGKV/OTTo+OT7x8FS65Ekdf5zw+kMy4eroawUwps+1bb67bhW1Wkcg98bfKLvh366Poo0+5voXViq7b4a7joW36t9Od1zypLJrEu7abMvUY2HONy5VdslbU1LpzCNGKl/+s+uVndG3WypdK1r8ouf1egzY9oMlqXTeT65SPqnUfanTqaXO8LREcn/4TV3uxU5HI6efvocyjtJ1SCxcnErHTz1D+dhWx9Nve/z+VLrdr3X7xktG6XK3rEilCwLt0kheHT/NQye4dHutD7LtPh0THSXjwi8oO3/8EmXHDnHnOuTWW5Uv+4Sxyq57fXYqXT1N94eM449WdjTOvM099ytfYpm+T9qe6cYsdumnfMl1Wi+24NcTnVGj4/RjXQcou/b3P0+l4710m8VOPEfZ8dzI8/m+O5Qv49wLlb37AT3eGa0bW9ZuGIZhGJ9SohNzwzAMwzBaN/bm3DAMwzAMwzAMw/hEaM2CbAcae3NuGIZhGIZhGIZhGAcYm5x/xiD5AMnz/4f9S0IV6/3NfyPJaz7s8SLlTCL5bBrfWpJFTfk+5LE+0Dl+RMe8jOSdLef8WOswiuRpH2K/D32Nw7a+tOWcnwwkv0HySy3kOZvkkIh9E8kTPv7aGYZhGIZhHPxIK/ivtWLL2o39hmS85VzGQcwoAGMBPP8JHrMEwKUAHv4Ej5kWEblrP7KdDeBZAIvDfW74WCoTdwpQxQO0iIxoXSZk9clPpXcvqFW+dj21GFvuhB6p9KA/a4Gc9gN03q5H9kyltzywQfmKarUoUkF7d9z6Oj1UtC2q1vXf5B49eSdrESxkaZG6zN5O9Cajtl75cnpoe+/rrmFEtMAaR0xQdvL9V1Pprn3KdN68QmUfkbvb1SHbU6Wr123YY1BE8GnDKuVrf1w7ZRfujbShd97tqyuUvfVt91t6Ul8mwBM+69LLnU/2cC3SNHqmLrdDsXcd+7t0m0P0dTtiir6unfs6AbbN6/W57UpqwcBOJa5Oudt0m+3e3VHZUWExJD2xuInaltrIveG1AzyBOET6bNseug5Sul3ZQ+Ju314TdJvF2mqxvuFrXH3zsnSfLCjRfwCWRfThvNsY7NFT2XG6fjehRt/XIw7X9Y23defOPC3GVuC1YZTuI3Tfnz9TX8eiw1z9a9bqdojrw6BdhmvTUe94wnLb9L3Abt2d0a238vWLbdPlFjhhPDToVhuZ0Ndi1MXu/tv9hG57ZmkRNTVC5OUrX6+OngjgUncthvT12rNO179z+0g7eX0y1k0LgCXnvO6MXF0/xPT7s91J1wfYXtcXGfo48TGjneGNUf49FS1XdmkBw4ayXcpmBzeexIfpc8Fe3Wax7oNcuXs2pT2mT/L9t/UxBwzV/pWzI4a+xsmtuu9klLixpc8gPcYiQ/edrP4dUmlfAC4+0Ht+7HTPw+TGxcrni98pPFXT5PpFukqHDnNGjtcfEnpsSW524nzxId61oO47e2LpxwCj9WFvzg9C/De7JK8J3172J/kqyXkk55Dsl6aIE0i+RXI5yc+FZcRJ3kZyZvgt5q+H2yeRfIPkwwAWePXoS/J9kuNI9iP5IsnZYdmD/IM2lYdkG5JrSGaGedqGb8Iz/f0RfGf6cZJLST5Ekl75uWH5V5DMJ/lc2BYLSV4U5rkhPMeFJCf7ZUSIk7yH5CKSL5PMDfcfRfK9sI2eJNk+3D6F5K0kZ4TtelS4PY/kv8P8j5KcTnJsmmM2nsfpJN8lWUTyEpILwvreGslTQfLm8PzeI9k50sbvhed4E8mKNMe4ICxzHsk3SWYBuAnARSTnkrzIfyMe5i8J09eRXEbyVQADm7vG4fYHSN5B8h2Sq+lWb9wC4KjwmP+viXpeEZ7LPJL/IZnXVP3DbUPD9p8btveAcPv3w7wLSV4dKftLYb55JP8Rbkudc1PHJnkEgDMB3BYepx8jq1FIHs/gnlhA8j6S2eH2tSR/weC+XMAm7g/DMAzDMAzjs41Nzj9dPATgzyIyEsARALakyVcC4BgApwO4i2QOgK8C2Csi4wCMA3AFyT5h/sMAXCci0aW8AwH8B8DlIjITwGQA3xGRMQCuAfCXJo67Tx4RKQcwJawLEHxL+j8iUt/E/qMBXA1gCIC+AI6M+AoA/BfAwyJyD4BTAGwWkZEiMgzAi2G+O0VkXLgtF4D+To9jAIK2HAqgFMB54fa/A/iRiIxA8GPFzyP7ZIjIYWEdG7dfBWBPmP+XAMakOR4AgOQ5AH4M4DQAWQBuBXAcgrfa40ieHWbNB/BeeK3fBND47ac/AvhjeB03N3OoGwCcHO5/pojUhdseFZFRIvJoM3Ucg+A6jQZwLoL+0khz/aArgIkI2vyWcNuPAbwVHvP3TRzuifB6jQSwBEE/3af+4bZvhOc+CsEKgI1hXS8HMB7A4Qj69WiSQwFcB+C4sIzv7c+xReQdAM8A+GFY59TroPA+egDARSIyHMHKpOi3cHaKyKEA/hq2zT6QvJLkLJKz7n30maayGIZhGIZhHNQkRQ74v9aKLWv/9NAGQHcReRIARMRf+Bjl3yKSBLCC5GoAgwCcBGBE5I1mOwQT1DoAM0RkTWT/YgBPAzhPRBaRLEDwY8BjdC+i1ZrGFvLcC+BaAE8hmEjpjww7ZojIxrC8uQh+ZGhc//Q0gN+IyEOhvQDA7eHb5mdF5K1w+7EkrwWQB6ADgEUIJvU+a0RkbpieDaCEZDsAhSIyNdz+IIDHIvs8Ec0fpicimDBDRBaS1B/A1ByLYFJ5koiUkTwawBQR2RGe80MAjkbQTnUIllY3Hq/x49ATECy7BoKl4renOdY0AA+Q/Hek3vvLUQCeFAk+9krymfD/LfWDp8J+t7jxTf9+MIzk/wEoRPADzEvN1P9dANeR7IFgYr2C5MSwrpVhHZ8I6y8AHheRnQAgIm7NYsvHTsdABP2m8WPKDwL4FoA/hHa0f5zbVAEiMhnBDxyoXf52631yGIZhGIZhGB85Njk/OGmAXvWQA6DJ5dkkb0b4Vjp8owhgHxUECff/joioCQjJSQAqvfx7AWxA8OZ6UViX0kj5TZE2j4hMY7BU/xgA8XASOx7A3WGWGwCUAYgGKSWg++80AKeSfFgClodvTU8D8GuSLwP4DYI3uWNFZAPJGwHkkOwJN0G/C8Fbdv9YXvBPkzTuE61buutyDtzb9a+F/1+NYEXAIQBmpds3pF4k9bOf3xZNHU/1AxH5RtjGpwOYS3JUE7s11c8aaWri2FI/iLZpuna5H8Eb+c0ichqCN9Fni8g8kpcBmBSewz71F5GHSU4Pt71E8mvpjhNub2ny2+Sxm6G56wU03T/2i4ZKXfTuTTrYs3Oeu0Uz0Fb56vbqBVKZa1xM3pa4jo0bsFFXi++uTKWrKnUMaXmNjtfLb+cub02dF//ox2lHkD06TpF1+nfF2hUu9je7t74N67br+NPtDZEuWqZ/bxEvhhu1rr7b1+lza7tbx0duKHNtOrhQx0r6MZq71rlrk1+th87EVn2uiTJX//pSXT2IvuYbdrvfs5Ix7RMvtrq2wrX/jmd2od2A6EKkQpV360597oVVO1PpjCIdN76qoUDZsc3uFlJtDyAH+prv2eRiZP0XFouy9fl0LndtWOKdW/lCfc07JiJ2oqkFVxEi8b+xHO923aNjuLfWub7Wu6JU+Wo26T6wAy6ulXX6vmi/TMf0R1ulhs0PQXuSrqw9cR3v33OVbu+OIyPn7jXw1gxPLibS9ys26Xt1babed9csl47FdbSZ7NJtmF3grsUWL68f9yxlpak0OxQrX53Xd5btaJ9Kd/fisDfHdX2r5rr+kgHdt32i/e6UUj1eLNvVQdntt7uFaGU7dbx3B69Oa3cXptK9Grzz3qv7GRoi1223py1S1E3ZGZFHTHKnvi/ifvtud3H69OqHfK0rkNHco8uLmZedkbGx3rvfsnTfT25Znkr7kkXNHrPM04rwNRO8WPEozPT6aORR2bB0o/JlDdbRZVVzXB9oe6YXGx6JMQeAWJHTh0huXKKP6elXsKO7jlLvxdp71y0x38Wvx4cOVD5/nI9qKCTWefXr0kPZ2dLSnydGa8KWtR+cbAPQiWTHMKb1cwgmrxsblz2TzCaZJyLXhZOxUZH9LyAZYxCT3hfAMgRvBb9JF/t9CElPcSRFHYK3s18ieamIlAFYQ/KCcF+SHBndYT/y/B3AIwDuD/NPb6y3iOzP+t4bAOxCuIyaZDcAVSLyTwRvjw+Fm1zuDN/ynh8ea0PkWGkFwURkL4A9DOPJAXwRwNR0+UPeBnBhWKchAIaHZT0ZOWbjnz/rELxR/Xu47Ho6gGMYxJ7HAVyyH8d7D24J/sWRuqt+QLJf2MY3ANgJoCeAckD9NbMWQbuB5KEAGsMc3gRwDoMY/zYAzgiP0WI/aAJ1TBG5PKxno2p8GwBbwn75+cZ8TdWfZF8Aq0XkDgRLz0eEdT07jBfPB3AOgLcAvAbgQpIdw/L0X2LNHLuJdmpkKYIVFo2SWvvTPwzjY0VPzA3DMAzjwHOgldpbs1q7Tc4PQsJ47JsQTN6eRTApAILJwHfDpdPvAEgnGbkMwaThBQDfCJfA34tAfXoOA7G5u9HM271wmfDnAPw/kmchmLx8leQ8BG/Tz2pit+byPASgPYIJ+oflagRvwn+DYBI8I1z+fh2A/xORUgD3IFjy/hSAmR/iGF9GIAY2H0Ec+E0t5P8LgOIw/48AzEew8qBJRGQZgnZ6DMHS+58AeAPAPABzROTpFo53NYDvk5yBIMY73bFuC4XJFiKYwM4LjzOEoSAcAk2BDmEbfhPA8rCOcwA8CmBumOetSLn70w+izAfQwEB0bR9BOAA/Q9DPX4Hr5+nqfxGAhWF9BwH4e1jXBwDMCMu5V0TeF5FFAG4GMDWs6+8+wLH/BeCHDITfUqKL4X10OYJl/QsQvCTbH/V3wzAMwzAMw7Bl7Qcr4dvBO5pwHdfCfpel2Z4E8NPwX5Qp4b/GfGsBDAvTpdBiYKc0Ue6NkfSapvKETEQQA1yapn5+Pb4dSZdEsl4eSe8TIywi1wO4Pk0dGvOsRXiOoX17JD0XgbCYv8+kSHonXMx5DYAviEhNOJF7DcEbcn//BxBMIiEi7yMQvQOAVWjiM2MiUhBJPw7g8dDcBOBwERGSFyNYHt/UOTYV87wb+noCgRZBU/vfjGBy629v8hr7/a6x/uEPTcc3dYzQ/1cEAmr+9qbq/+vwn5/3d2hi8i0iDyKIC49uu3E/jj0N7voAwGUR32sIluX7+5RE0rPQ8hJ5wzAMwzCMTyUi6cPdPuvY5Nw44JD8E4BTEcSHf9rIA/BGuDSaAL4pgTL6x8UYAHcyUGQrBfCVj/FYhmEYhmEYhmF8RNjk3DjgiMh3DnQdPi4k+FRcs981/4iP9xaAluK8jYOBiHDM5tVawKdbv1Jl1+5wEUodY1rEq6ZcC9DsmuL8CU8jZu2G9spuv8uJs9U36CioiqQW3qnc68oVT3xm7bKOyq7KcOVue6pU+bqO179dVe/MiKR1/PTWzVoqIDMaQ+YLJG3Wgjnxo85MpbdXLVe+/gktOrYu09VhZH5C+djjEGXv2vtuKt2rjxYcKrt7ns673V3XjLh+i7C3Sgt+7Ym5OvTP0sJAUrZT2eV73b7toNtsY4MWE/RlgoqWOpGhPXvz0Bxlte6aV3miU53j1creUebKKszVfVK3KLCFrlwW99J563WNmenqK554FbzrGBW32rNci0p1LSxS9rYMl3fpTC1Y1rlDuZfXnfuQBk/QsEbX6ZBad7b0Yh6TS5cpuyLSplu9U6ur1QJbexa6/tN53B7ly/BDK4u7ppILNnRSrm4Z6YWj9u7WgowFbfR13LTW9efNnh4c2+j2RbVrJ1m2SLk6i955e7RO3jXeAn2fL1/qjtNR30KQhvT9jm0LvWPqNswZ7wS21szSPbbEEyyrYuReoL4vpE7fF2iI9NGefbXPG8OiY3us0OsQNbpcqXDicvQF1LL0dVTPjEydN95f//mSWPKOq/oc3V8zxg9TNkrduMR+ekGZ/5yKwsMm6g0bVmn/gBGpdLLGe/uarevfMM99aGjVFC1oOfgC3UHyRhe6Y3Tpp3zJjYs924nAZYzSCwwTS6bpOkWe5czU5x3rMUTZybzXIwXp8SvWobsuN9q3PNFHFuoxK7NFvVqjNWGTc8MwDMMwDMMwDOMTIdmKBdkONCYIZxiGYRiGYRiGYRgHGHtzbhiGYRiGYRiGYXwiiNib83TY5NwwjI8EklcDmCwiVQe6Lh8HJNci0A9oAHCpiPwl3N4NwB0icv5HeTypdzGE3frqL+K1OUXHJkqDi4Hccfdu5WtXouMsO7R1cWx8Vz8cuw7VsbTZA1wMafWCUuXrXKHzRmOB2xXr+Me8PjreTd50cXedz9Tx9Cgo1PU9w30RMrFJx1YXlOi40OXTIjHonhIsu+h4veSK2an0yEO36rwduyn72LY7UumcPt5j04uB7dG91Ble/Gb7Y3TMY8E6d10zumpf1Xx9rp23uzZbvUtrAwzUNUL7Ti7etHJ9DO3PcOfT490Klbe4k7bbj3EL6vIXlipf1iYda9tnQlkqvXFmvvJ1H63Lrdvl0nmDdKzn6Kd0PxxWFMnsXcf2E3UcvOzd7gwv7l2qdR9FlatTQSd9XyBL12k4Xd6B5+q8sW46nvPI+0pT6aK++ryzdVYsmuauXaHo9ox10/1ubEcXi917t27fDv0qlZ0RbZa4jj/t3uDF3kdimUf02q5cb2zuquyiY127tN+iz81fe9kxooWw/iXvOlXqcSk2fpLzbdIxxUnqWObD892+snOT8g1L6hjewSOdtsSOJW10Bb17dXRtpN+10/fU+Jw5ypZq1w6HdPTaIWuAMicMdnWUsl3Kxzb6OFLkYv7ZQbe9VOjxbUfSxenHR3kfBcnUcfqx6Lm2KdR18GLOo+Vir75OiYVv6n2L3Dga79be8+nOzmKXV8r1eKaO6ePFmCNP933ZEWlfr2tHY+0BIOuUI1Lpbuve0pn9+Prhg1Pp5Lr5ysf2+svEUuruGz/GPD74SGU3LJwSMbSOQHLLCmXHJrj6Ir+tPmZC64cgMr7FBml9E+Tqvj+01lf2MFoztqzdMIyPiqsRqNN/2ikEcFWjISKbP+qJuWF8VEQn5oZhGIZhtG5scm4Yn3FIlpBcQvIekotIvkwyl2Q/ki+SnE3yLZKDSGaQnElyUrjvr0neTPK7ALoh+GzcG6HvqySXk5wSln1nuL2Y5H/CcmaSPDLcfiPJ+8L8q8Mym6pvAcn7SS4gOZ/keeH2S8JtC0neGslfEdZxHsn3SHYOt18Q5p1H8s1w22WN9QztZxvPNcItAPqRnEvytrD9Fkb2fyJstxUkfxMpq8n2MAzDMAzD+CyRhBzwf60Vm5wbhgEAAwD8WUSGIvg++nkAJgP4joiMAXANgL+ISAOAywD8leSJAE4B8AsRuQPAZgDHisix4VLvnwE4HMCJAKJrrv4I4PciMi48zr0R3yAAJwM4DMDPw+/D+/wMwF4RGS4iIwC8Hh7vVgDHARgFYBzJs8P8+QDeE5GRAN4EcEW4/QYAJ4fbz8T+82MAq0RklIj8sAn/KAAXARgO4CKSPVtojxQkryQ5i+Ssv/3nxQ9QJcMwDMMwDONgx2LODcMAgDUiMjdMzwZQAuAIAI+RqXjhbAAQkUUk/wHgvwAmiIgOogo4DMBUEdkNACQfA9D4QegTAAyJlNuWZGOA1HMiUgugluR2AJ0BbPTKPgHAxY2GiOwheTSAKSKyIzzeQwCOBvAUgDoAz0bO7cQwPQ3AAyT/DeCJ5hrnA/KaiOwN67EYQG8ARUjfHilEZDKCH0VQM/dZYcz9NpGo03HbG+53cdI9vliMmnfWhVYbtKWOpUtEVADibYG9S9zvsiurXFxbV5Rj5Wz3TfKhAxpQMSuIK45nAzWl7pERyxBs2eD27dSlHFVlQfxn26JqrFkdKafPbmydGY3pFZTE26TSO54rTXk6n6i/55oEUDk16AI5/XNQtcTF/2Z3BspW6cdYbdLF27KHiwWVreuAvEhcd1UFUOfiHpe87+IlxwGItj0ArNtVCAAoxi6setXFQA65GpA9OmZ9xYaiMC8gM95WvtK3XJxgfi9X/w5dgc0vu7jAwi5A1R5dh/IK14ars9x5HwUgVtRT5d24IahvewCJNdsinnxUN+g2q65w/aVtaVQyIoZdW3S8545aF6/aB2WYOSWIme2aX4mKGt3vnn/XfRv6hEM2YsPKoE4DB9XgzScj8aoElmS7PjkMwM4wxro7AGbrOlTNdW3Y7jyAnfsAAGTHOsS6umueWD0HGUOPSdkNbz0G9h8VWq+hOtK+bQCgTMfb7q1vjGeuxdwnXB0O/TZQNXVDJGcbzK0Jzu0EVGDOMhefOqF4K1a+p2NzF2YH9/LEGuD1XHctJgBAvo4VnbMr6Es9WY3yhO4Pa5e5e6z/6F3YEsZY9z0F4PDo96lfRj694NzIt7l3bNV6B5u8vwzXPRO8YeoxDlj6ljuXQcfswe6Frk4dD01g8zsuBnx55JqeBnjf/E4iOfddAACLOwH1Op424b3VWlpWCCAYTGODI3G5mIO49wJs1YKwXTLq9hkL/W9FN/a7k/apH1Dp9eeqGY1x0znYvNuNfd0BoLCjyrtkcXBfjAcQ63doartsX7NPPDCqXJy0bFvntue3BTvosJTo+STmLkyl4+NGA53c/YY1S4GcSGy5981sePdUtFx3jwCyeSXY0YuFj8TRS7Wn3VDudCjQNXKcumqwuHfaY5aJvlayzY2p7NNPx83X1gA5LoIus5vWi4j17KHs2uddTPiWtU7jpB0AeN+Ar/xX0Cfzf1ACFkXKSTTso1mQ0iYp36PqA3hx5gAyhk0Kts99GbHuTiVEdm/eRysj8eZUAED81DOALZH+0K4TWOB0VaS6HFLpdEvq/vtGKp39/0bBZ3bY18fUJvfxHQhMEC49Njk3DAMAok/YBIJJcamIjEqTfziCN+yd0/iZZjsQrNiZICJKNSycrPv1yCD5Lbi33aeFZfujenPHqxf3FEggHPdE5BskxwM4HcBckqMQiL1Fn5T6qb9/7HMOLdSvSfZ3Yg4gMjFHsxNzIP3EHICamANITcwBPTEHoCbmAFITcwBqYg7Am5gjMjGHmpgD+/7x3DgxB6Am5sD+T8wB6Ik5kHZiDqSfmANQE3Mg/cQcaH5iDuj6RyfmwP5PzIH0E3PAn5ij2Ym5T3MTcwCpiTmAZifmAFITcwB6Yg49MQfcxBxofmIOuIk5ADUxB6Am5oCedFR77Zt+Yg41MQf8iTlSE3MAamIOIO3EHNATcwBpJ+YAmp2YA0hNzAF/Yo6PZGIO6Ik5ADUxB5B2Yh5UStuNE3MA+z0xB/yJOdJPzLHvWJhuYt5U/dJPzKEm5gDSTswBPTEHsP8Tc2C/J+YA9MQc+Egm5gA+/MTcO05zE3Pfjk7MAewjaPdRTMwBpJ2YA9ATcyD9xNyrD5B+Yg5ATcyDDU1PzIMKe/0hMjEHkHZi3hSzI319dnYMZzSb2zjQ2LJ2wzCaogzAGpIXAAADRobpcwF0RPBm+g6SheE+5QhfQAGYAeAYku1JZiBYvt7IywC+3WiEk+K0iMifwyXko0RkcxP7twcwPTxeEck4gEsATG26xNR+/URkuojcAGAngJ4A1gIYRTJGsieCFQA+0fPcX5prD8MwDMMwDMOwyblhGGn5PICvkpwHYBGAs0gWIRBE+6qILAdwJ4IYciBYjv0CyTdEZBOAXyGYNL8KYDGAxp95vwtgbCjmthjANz5gvf4PQPtGMTcEce5bAPwEwBsA5gGYIyJPt1DObY0Ccghi0echWOq+BsACALcDmOPvJCK7AEwLj3/b/lS4hfYwDMMwDMP4zJAUOeD/Wiu2rN0wPuOIyFoEYZ6N9u0R9ylN7HJIJO8dkfSfAPwpku9hEZkcvil+EsEbb4jITgSCaX49bvTsYX6ecHsFgC83sf1hAA83sb0gkn4cwONh+tymykfwo0RTxy2JpC/13MPC7Q8AeCCS73ORPE22h2EYhmEYhmEANjk3DOPj40aSJyCI234ZgTjbZ5kP1B5S4+L3tnrx3Z17eHG3kdC5auj4wgwv1LpyrYsL7UQdM5idqfct2+Hi+Sors5UvkUy/8MqXm6mvjyu7Qtxx4pk6t1Tq2N8osUz9S3flXl2nBonE5nsxpNiwWtt5Lh6yuK0OzI+2PQBU0NW/rX/elTpvcW6krIQXi1ir26EuYrctqlG+XZt1XGhG3LVTGy/UUxI6Zjcr7uLXkzU6ln3d/2fvvOPtqKq3/33uTScklNBbAOm9V2kKKAqCgIqgCChgoYgNKwgqgoCiKIgoIoJKk6b0EnoLSeiIht5bILTU9f6x9tyZPXfmnDk3Cbn83v3cz/ncs2fW7NmzZ8+cvfZa61ldcYzm8HfjY996Mr++mRZzHXSXYoGLd27YoLiewVNi2emFfhtYqmf+uIm8MyPvF5vyVrxTJWvHu4WY3e5Sx5RgL+f8BW+8FsfPLzwwjj/tLlxdeaTPLIXwDp9ZLzv/yHhsdb+Vj4mhpf61V1+JyvMVTvT4wDi+e+Hp8VhangIv5xP/ifY92RU/J8Uo6Jenxv2wWGlWOHLhnBrkzWfiPhoyb3zPi895r7fDwLgNdBfu8aTXol2vlt5hA61wrdPie/x6Vzwe3pya99M7AyNak14xxtG4eyNuw7szSs/qm3l5UFdpwE6Nn923ilPrmaW34bul8Vzoh7KsFLchereXxk75PWSF+G8tEHNqlBHVOz3uX3s7/q3B8jZ2r7x8vOu1uA9VrKt0beXfqaieKTHHrMox88W6ZpbeSU/F/LH2bi5bHBsAlJ/5YYU+Ld8nK/1OFcahymN7etz+mS/lseNdpdj7GS/H7WV6fh57rfRbOLU0ngvt16B4rJR5Uxad2THtzRyH9eNUZnMbSTlPSEiYIzCzb87tNvQnpP5ISEhISEhISEhohRRznpCQkJCQkJCQkJCQkJAwl5Es5wkJCQkJCQkJCQkJCQnvCVKe83q875RzSUcCb5ZIq2ZHvbea2abtJec8JL1ZJLGaA/UfBdxoZtdI+iBwKjANz/d8kpnt1rCexYFft5KfXdciaTRwWR1JWH9CNpZCmzcNRGVz8nxz9DyS/oz3/fl9PH40s9g+SYcCp5nZ26H8b+CzZjapr3XOToR0cp81s9/Ntjrnz3PyzjNPHMM2ZFQc86iuPJ5saOm1/u5LsYPU4KF5/N5Tb8fxx2sPjeNjBw3Oz/Pmm3H73poRn2dYKa68iO6uOF7vjRn59QxbOI4nLOZuBRi4QH5tGhRfy7v/KeX7LsSw3f7xv7PRHzepb9PGeabX5a7ekXs3OypvQynOcmAh3vCl6YNZe/k8d3jX6DUi2cFDruz5/tQZz7PM2Qf0lIde8cu4vZPz9k97t4uuQtLmrlJs9eRC3uUP8HbUp/b6i+XL68HEG+dl+Q/n8ZNTSv5y02fEG4ptGDlfHOM46fk4tnJIoY3/fTfOLrj8oHjAFNs7cmAco/vQlDh/7+sqxEuW4k1tRil2ckgem29vT4ply/wFhfs436h4rGuBOJ/zVPLxPHxA3N5yvO+ChXjv7lIM9MAh8bO6yqv5/lcGtHZeNPLzlCWXHhTHxC60TD5my7G/Myj12YDmU7/pU/MzDx8Sv4emvRs/82+9lY/RN7rjZ75r/jhn98zXCzHRS8T7Js74X3we5fnVZz4f73u79JwMUd7f5XfhzBcfi8rvFjt1WnxtrxLHI6//pbV7vv/3+3Gee96Nn5MhFOOGn4llS+cpxttruYWjfTNfjY8tXo9NjevhzVLyj9fzskbEedjLz0lU76SXon3dq34wbtPTD/Z8n37NtdG+rsVGxecp1KVh89Wes4zuHWK+WHs0TprStXrephmTLoz2DVx0kbiu+Z7t+T5ySImTohSnP2iN/B3QtdgK0b6ZTz4QH1u4j11LrhrLPvdoVLZJed72cox59yqbxcfek+dlV4lnojx2uhbr4eZlwOrLxecsvfvKVB0J/RvvmVt7yJPcb93o+4ti/l7AzH5kZteE4p7A8SGH9DNNFfNQz7OdyL+XUJlJJd43S2MxsG3XojCWRgNlVu85gffqPLVo1d/MnvYdCvQwKZnZDv1FMQ+YD/hKJwf093fi+x2tFPMyiop5OxQV83YoKubtUFSK26G82NEKRcU8ISEhISGhP2AmNtc//RVzdGIoabSkhyT9Ds8X/ENJd4X8xj8uyF0kaaykByTtX9j+EUn3SJogqbhEt6qkGyRNlHRwzbk3lHSrpHHh/0ph+2qS7pQ0PrRjhbD9zfB/uKRrw3nvk/SJ0rX8IbTzKklDa85ddz1vSvppuJ7bJS0Sti8r6bbQN0e36M8/SzpV0k2S/iPp44W23RTafI+kTQvHfDtcxwRJPy/Us5ukLwKfAn4k6exQz/1BplvS8eHYeyUdVHN/M/nKfi3IdtyvktYL7b4N+Gqhrm5JvyiMpQPC9q0kXS/pHDxPdbmtxbG4lKRv1YzHz4dtEySdVeizEyVdDxwr6UhJ3ywcc7/cQtwzlvB84B8MffJ1SV8IY+NSSY9J+pqkw8IYvV3SAuH45SVdEcbQTZJWLrTh1/LxPFHSblXnqbhPXwrXOUHSBZKGtapPjpMlPSjpX8DC5Tqr+rvuvlT0Q6v7d4Ok8yU9HMak5M/44sD1of+R9Lg85zqhD+8Pn0PbjauK66i63wuFvrorfDYL24+U9Cf1fv/8HFg+XOMvgmyv8VU1DqvalJCQkJCQkJCQ8P8f3gurzUrAX4DvAEsAGwJrA+tJ2iLI7Gtm6wHrAwdLWlDSQsAfgF3NbC1g90KdKwPbh7qOkFTy/QDgYWALM1sH+BHws7D9QNx1e+1wvlIeA94FdjGzdYGtgRMkZT5hKwC/NbPVgEnArjXX3Ot6wvZ5gNvD9dwIfClsPwk4xcw2AJ7vVVuM0cCWuAv6qZKGAC8C24Y2fxr4NYCkjwI7AxuFcx5XrMjMTgcuAb5lZuXczvsDywLrmNmawNlt2jUn+vUM4GAzK5vA9gNeD/21AfAlScuGfRsC3zezVemNlYC/hDGxUjhvNB4lrQZ8H9gm9NkhheNXBD5sZt9o0xcZDgduCl4JmT/r6rgVeUPgp8DboT23AZ8PMqcBB4Ux9E2g6Cq9GLA58HFcIaw7TxEXmtkG4XoewvuvVX274P2zBj5GW3mVFPu77r6U29fq/q2DW8lXBZYDNgu51J8FtjazrYsnl7QesA+wEbBxqGudsLvt89rifp8E/DK0cVfg9MJhVe+fw4H/hWv8lqTtqBhf4fiecWhmTxTqRdL+ku6WdPfpf7+4rs8TEhISEhISEhLmEiQtIOlqSY+G//NXyCwVjFgPBUPRIVV1lfFexJw/YWa3Szoe2A4YF7YPxyevN+IK7C5h+1Jh+0J4XPRjAGZWDKD4l5lNAaZIehFYhN7K4EjgTLkF14BMgb8N+L6kJXGl5dHScQJ+FibSM/EFhSyI5TEzGx++j8UV5SpUXc8rwFTgssLx24bvm5ErDmcBx9bUC3Cumc0EHpU0EVcUHgNOlrQ2MANXIgE+DJyRxemW+rAdPgycauYJiRscO1v7VdJIYD4zGxO2nwV8NHzfDlhTueV4JN7HU4E7szFTgSfM7PZCHVXjcS3gfDN7ueK6zzOzUoLTjnG9mU0GJkt6Hbg0bL8vXNNwXBk+L1+7oBjoeVG4/w8qeF40wOqSfoK7Xg8Hrizsq6pvC+Bv4VqflXRdi7qL/d3qvhTR7v49DSBpPP6M3dzi/JsD/zSzt8IxFwIfxBedmjyv21B9vz+Me+hkciMkZUG1Ve+fMurG15PE4zCCmZ2GL84w5dFb+6/PVUJCQkJCQkJCH/F/gBDucOBaM/u5pMND+TslmenAN8zsnjCHHCvpajN7sFxZEe+Fcp4FvAk4xsx+X9wpaSt8IryJmb0t6QZgSJCvu3NFVocZwABJXyW3RO8AHI0rQrvI3Y1vADCzcyTdgVuer5T0RTMrKh974gsD65nZNEmPh/ZUnXeopKXIFaxTcYt91fUATLN8NM4g7v9e1yrpp6GdBIt0lZwBXwdewBXLLtxKDa37sB16HStpIyC7fz8C7u1pxGzu1zZtF25ZvjLa6GMpU9LK9+UK8rGY1VE1Hg9ucd7i8dOJPU+G0AzFa51ZKM/Ex0MXMKlwv1sdryoBSWfgFuhnzWwH4M/AzmY2QdIXgK0a1Fc1Hsv3/w1692ndfaGhXK9nu9yOirrq0OR5rRtnXfgzHDH9BGW9SRvrxtdo4j6rxYyrcv68hVeLCYcGLjtfVLY38yYNK4X/D5o3jk8eNDInt9p83ueifUMXjdeehmyck8wMvCgy8rNYd0ymM+mFPGrASrdlnpExEc+QV3KOyIGLxyRjPP6fuL2rLNrzfeaLk6J9IwvXAtA1Ob+VM8bcFO3TyHmi8szTjun5vtJOcfvsP1FUDB/cJT/vM9fF0RHz/29cVLaZ+bXPvDMmTBr6gRKh2tS8v7sXjxfdhz8er4e+81ROYHbFY0tE+1a55fqo/Oa0nMyqe+F4PAyfEQ/3lXYoMf115Y5oQyfF62rzrRiTRRWx7DPxPX/8sZjkbdWt8ut5fmzch6tMiUnflh6Zjy176K5o3/DNYnKrGZedmctOKZFtvRqP0ZmT8kdv0PAS0dxDd0fltVbNn41Jzw2L9g3fKiaPW/K1l3u+D5k3rnfKm/Hr4ZmB+c/GU93x82ZvxESESw7Py6ssEI/1oQvE5+kqNHHGxJhIbKWSc+HMu+/s+b7cgjF53BNvLBSVF1y/cNwd06J9Q0bEbVhgZj5G134h5oGd9peSU9fMwjh8MSY0XKN7vqj8gYH5fZx+7nnRvp2mxmNpmRXzcTbsyfhaZl75r6i8ypT82Zj5ZEzy9sFN4j6cdtcrPd8XHh7f03fOvSEqr7tF/izMuPjcaJ+Wisnvpt2RE40NGl0iIbvuqqhcfLfPnByPhxn3TIjK3SuMzmUnlNaCLf5NKNY77byLon3TB14alQest1pezeT4d2nK47FNZsionCBuxqOxLlL+nSpixsWxo6aGx8/f1Jvv6Pn+3D3xe33hN+6gDgO6Y5I/eyh+zw/45Bd6vk/55RHxvnVjLuIZ9+bXM3NYbL/o2iR2Npxx45i8MD3u+yIBHMDAPb+di159Ziw7Nv49mfGfnBxxwO57R/vsnhtImOP4BPl8+kxcz4yUczN7DngufJ8s6SHcONlSOX8vyYiuBPYNlkEkLSFpYdxq9lpQZFfGXVPBLbFbZu6uCvG4dTCz3waX0rXN7NlQb/Z2/UImJ2k5YGJwlb0EWLNU1UjgxaBAbg0s0+a8TxXOe2qL62mFW4DPhO897uVm9v2s7oLs7pK6JC2Pu/0+Es75XLCAfg56aGavwvs8izFu2YclXAUcqEB+JmkBM7ujcK2XFIXnQL9OAl6XtHnYVHS7vxL4chbOIGlFSfOUji/flzLqxuO1wKeyUIQWffY4sG6QWRcPAShjMjBvxfZamNkbwGOSdg91S9JabQ6LzmNm+4Tr3iFsmhd4LvRXOXyhCjcCn5HHhi+GhyHQ6v4H1N2Xcj+0vX/trrHU1p0lDQt17ALcVCFHuIbyuKi731cBX8uOC14pnbSvbnwlJCQkJCQkJCTMZRRDCcNn//ZH9WCRoHxnSnjLOV4wzqwD1K8gBbxnqdTM7CpJqwC3BevTm8BeuEXzQEn34orm7UH+pdBJF8oZjV8kdwNvguNwt/bDgOKy1qeBvSRNw+O7yzS9ZwOXSrobGI9bwjtB5fW0wSHAOSEW4YI2so8AY3BX2gPN7F05udQFQaG7nmCZM7MrglJxt6SpwL+B7zW8jtNx9/h7Q1/9ATi5hfyc6Nd9gD9JepvYFft03EX5HvlgegmPrW+MuvFoZg8Ej4UxkmbgbslfqKjiAuDzwfX6LuA/FTL3AtMlTcCt169VyFRhT+AUST/AwzH+DkxoIR+dx3rHnf8Qfxk8gbvPt1sw+Cfu7n0ffl1jWov3oO6+lPvhpBq5VjgNuFzSc8W48+Aq9GcgMwWdbmbjwkuwLVrc74OB34bneAC+CHBgi3pekXSLnCDx8hB3XvW+m9WwiISEhISEhISE9zVm9gO39mIoYRUkXQMsWrHr+52cJxhqLgAODUa4lpijyrmZPY4TYGXlk/CJeRkfrdiGmV0OXF7admSpXJn32sxuI4+9BldQMLNjgGMq5IeH/y8DdTl4itdSmWc9xKLWXc/wwvfzgfPD98dK5/w59bjFzCJG7hDfXbRUf7ew7+fl+szsCzXfHydco3ms+WHhU4mS/GzvVzMbi7vqZzgybJ+JLzKUFxpuCJ+WbS1sqxyPZnYm7qJS3PaFUvkdPKa46lzZNU8DPlTa/eeC3OjC9z9n+8J4+EhFveU2tDpPUe4U4JQO6jMKVuMW9d5Aob9b3Bcq2tf2/pnZ1wrffwP8plAeXfh+InBiqW2P0+B5Dfuq7vfL+IJTWfbIUrl4js+W9tW97yrfWQkJCQkJCQkJCf0DZvbhun2SXpC0mJk9F7xMX6yRG4gr5meb2YVNzvueWc4TEhISEppDiy/Z892mx7FxU/83KSoPXCSPu3zV4rjbt1+JX/MjV8m/v/F0HPc3ZHps2J/2cB6HucC34t+op75/Y1QeMSqPgXx3ShzjutB+K0flqcfm8etd85UiGgbGcYEzC3HDMybH17bonnEmuhl/yXOQdy0cx3ZqsThO2J55tuf7lHvi39R5No4pJF66JY8TXGiFOM6Sd2IKgUGD8zjc53/3CIt+Ne9wDY7vxcw38xje6Y+/SteI/NqnvRzHJg4s+LvsuOZT3HFPfn2aP47DHr1kzo/66rWw4C55jPomS8SxtG/cH1svBo/Ix8BLE+O44QGDSpwE8+TtL8baAyw6Ko6f1qA8im7w0Dh2+bXJ8Tj8QKEPGRLHFE998OWoPOQjeT90rbletM8efSAqd72Tj9EZD/w32sfUmHfg/gdzY8k6G8dJVKbeF4+Bya/nN+f1SXE/LLvN21H5jevy/p7f4uu2N+L4/+Hz5+2d/Go8JmdMi5+FmTPy8y6yatxnE2fGz9Rq843o+T54nng8zP9aPB40NH+Wp0+P982YGl/rfKvk923A87Fs17KlSLbpuaxNjsfK7qV70TU0r6t72fg5Htcd98Po7jw2vPwu1OLx++K1rtyZTQvH8ekv3B9zPiz50Xz8DhkW1zt41dibddrjeRu0UBwZp8Ex78SAFQv8Ed3x+6Fr2aWj8qv2SL5v3ng8dC0Wt7/IX6BC7HfVeV61J/Ndi84Xyw4p0emMzPdrWDyuhnzmU7Hsc/l7XsPjd8mrFsf4R+gq8ZBMi7kNulcanZ/z2keifYOXjce+5s9/X+Z9dVK8b+H4vs0Yk2dI6V66ZCwtvYe6V1upcGDcPuYZEcuundvO7LV4XGlg/FtZjDMfsG0cRz79xr/H9a6ccxSU+U00Kh4Pq09/kv4G6weW81nEJcDeuAF0b6BXip3gHfpH4KFgSGqE9zLmPGEWYWZfCBb3hISEhIR+jqJi3g5Fxbwdiop5OxQV84SEhISEhITZgp8D20p6FA+7/jmApMUl/TvIbIZzgW0jaXz47FBdXY5kOU9ISEhISEhISEhISEhIaAAze4WKkNJASr5D+H4zrbMKVSIp5wkJCQkJCQkJCQkJCQnvCWb2OdPz/30kt/aEhITZCkk7STp8Fo4/XdKq4XvT7ALF478gqVVmgSZ1PC5pVHtJz88u6bJZOV9CQkJCQkJCQkJCspwnJCTMVoQc6FV50Jse/8VC8XvAz2a5UR1AUnd7qTmPrhVycqu3nr062jds0ZiAZupTOUHVKMVkNENGTorKr92Xxza/+EpM0vPm5JiIZ75ROZnVG2Nujfa9/nZ87NQpBbKtEjnYs799NCoPUE4y9Or1cVaRRT63dVSedtFtPd+txLvz6MnxsW9Nz4m5ltpoi1j45WejotZcp+f7kyfHJD2rrrZRVH7mpbz9774dk4Ett1JMQvb4izkJ2WLzx2RQr94aE0lBPtRmTIutCFPejYnyXnszJyR6ZUA8RLvW3Tw+z0uP9Xyf/5mXon0PPxUTBa2w6CtRuUgIN3V6fJ4X3xwWlWe8mq/vD++OSd7KNpGnr8zrmlnaOXFIPF4WenG+nu9LrhsnxZj5x/hZ6P5QIanCjLgNWm7d+Njn8vs45czH40Ys/YGoOEQ5gdK422NyqNVWeiEq/2dK/sytNCQek0/eEBNJzV94NkaUEit2f2DZqPzIn+pJs+Z5O34Y5huWP39dG24Q7Rt++riorJXyhBEPPBkT7D1U4v9a76a8T995J76WeWbExG0Tb86fv/sGx/f0I2tsGpVn3nJF3p554+yeJw+Mx+xOb8/X8335HfaM9k06+YyoPO7BnDBu1NCYVLFrk3gsTRx0bt6GpeK+f3ly3PeLv56Txz3/8nzRvsU23Swq33l6nkF3yyNjUi97Iyaf7BqS35uuxVeIZeeLCctG6fG8vUPj+6+14nvOkwXCw6WWj3Z1L7t2qd6cVK17l73iekrPlL31ev699CDbA/dE5a51837pWmbNaN+oI35MHboWK5GxLR4T4xUJ10ZtGGfl7V435vmYdtO9Pd8feyp+Hy+0fJw4xW68Km/DtrvE5yz3wzs54V7XAjGvh5XfQ0sUCFGnlghFy8SOY3NitzIB3IAtPhM36ZHCb+Nt10f7ujb/eFR+Wg/R3/B/gBBujiFZzhMS3oeQ9G1JB4fvv5R0Xfj+IUl/lbSHpPsk3S/p2MJxb0o6VtJYSddI2lDSDZImStopyBwm6U/h+xqhjmGSjpT0p4L8wTVt67FcS1pe0u2S7pJ0lKQ3w/atQj3nS3pY0tmB1ZKwfX1JPweGBgKNs8O+vSTdGbb9voUivZSkKyQ9IumIQtsqjw/9cpSkOyik/JM0NNTzJUnzhOu/S9I4SZ+ouPZKGUk3SVq7IHeLpDXLxyckJCQkJCQkJPz/i6ScJyS8P3Ej8MHwfX1geMiluDnwKHAssA2wNrCBpJ2D7DzADWa2HjAZ+AnOMrkLcFSQ+RXwAUm7AGcAB5hZZkJdGdge2BA4IpyzFU4CTjKzDYBnS/vWAQ4FVgWWw1kte2BmhwPvmNnaZranpFXw3OObmdnawAwgNqPk2DDsWxvYPSj7rY6fB7jfzDYKBB4Aw4FLgXPM7A/A94HrwrVsDfxCUikPWK3M6cAXACStCAw2s3tJSEhISEhISEhICEjKeULC+xNjgfUkzQtMAW7DlfQPApNwBfwlM5sOnA1kPr5Tgcyf8D5gjJlNC99HA5jZTFyRPCvsv6Vw3n+Z2RQzexl4EVikTTs3Ac4L388p7bvTzJ4O5xufnb8FPgSsB9wlaXwoL1cje7WZvWJm7wAX4osWrY6fAVxQquNi4Awz+0sobwccHo69ARgClPztamXOAz4eFjP2Bf5c1WhJ+0u6W9Ldp5/3r5pLS0hISEhISEh4/2Km2Vz/9FekmPOEhPchzGyapMeBfYBbgXtxS+3ywJO4ElqFaZYH+szEFXvMbKak4vtgBeBNoJxQuRhkOAMYIOmrwJfCtrb5G1vV1UZewJlm9t1oo1v4M9f1LF69/Na1uuMD3jWzUgQotwAflXRO6DMBu5rZI0UhScUFikqZIHc18AngU/hCSi+Y2WnAaQBT/nNzzzVMejmO9R0+enJUHrhcHkv36L2vR/u6SzGk8yyUx7g99mIcQ7rZqPjYmdPz9dup0+IIgucUx6cvNk8eazvvfO9G+wYOibv2zcl5vSNLqcBnPv+/qNw1rLvwPR4io0a9GZW7Xy3001NxPbwbx/p1rbJJvmv6mGifTYrjQh8ekHfip7eK+6gcQ7jMQvn+rlU2jvbNu9R18aGvxXG5776RO6K8827slPLmzLy86MzprLhYHiturzwXyc43f96mV26F+dfKY2+nlbK6PPr8glF5gw883/N9/gXfjvY98HzMZ7DgjDzu9c0ZcXvXWvf5qPzfCfl5PrBWHOd++4NLReWpBbvBjP/eHe2bNjm2KdibBb6Ad+LnYuZbpXv15qSeryOWjGM9Ne/8Ufklcm6GD67/TLSve0SpDQVKhf+9E8dPb75OfOx6E/J+ur8rfq5tahzDPUT5c3PnoPhB3mFwfK0LLfdWXs+zcbz0iyWOAt7I+2z+7rgflp0Wt6mrO3+VLrV6qT9LWGxg/g4Y8FTcDxoex/tq4cJrc3ocP/2mxTG7Qyx/18x8Jn61/lfxu2aHoXkfPjqjxPFQelaLT4I99Vi075XC/QcYuPGqPd/nvfHxaJ9N/E9U7i78/Mx8bHzchq7S2JmU34uZj46NZUfEffbojLz/NV8cl233xs/JzOfzuP3uJeM17JkvPVFbrz0a8xN0rRlzd9ibeex99+jS9KA7Hmf2ZN4vM0v3uHjOMrRaHD9vD9wV719vq0ID4/eZSuWB6+VcEss8EP8m2LtvReUZBX6O7qHx+J35bMybokGF306V7Jzl99DLTxcaFI+rrsVWjNvwn7yN3SvHHATFGHOA7pXy37CZN18Tn7N0j/sFkU5CYyTLeULC+xc3At8M/28CDsQt0LcDW0oaFWKq9wDG1FVShqSRuDv6FsCCknZrJW9mvw2u52uH/I5F3A7sGr5/hs4xreA6fy2wm6SFQzsXkLSMmf2zcP5shrJt2D8U2BlXtCuPb3HuHwGvAL8L5SuBgwqx8etUHNNK5nTg18BdZvZqxbEJ/x+jqJi3Q1Exb4eiYp6QkJCQkNAfYP3gr78iKecJCe9f3AQsBtxmZi8A7wI3mdlzwHeB64EJwD1mdnEH9f4S+J2Z/QfYD/h5ptD2AYcCh0m6M7S1temlN04D7pV0tpk9CPwAuErSvcDVoc4q3Iy75Y8HLjCzuzs8vtj+IZKOA44GBob23B/KZdTKmNlY4A08jj8hISEhISEhISEhQnJrT0h4n8LMrsUVway8YuH7OfSO8cbMhhe+H1m1z8z2LWx7Csj8wsrycR6SfPufyWOqnwE2NjOT9Bng7iBzAx6TnR3ztcL3rQrfvwN8p1D+B/CPqvPWnL+8r/L4Yr+E8uhCcZ/C9wMqjr2BcC0hxr2XDICkxfEF0auq9ickJCQkJCQkJPz/jaScJyQkzEmsB5wc3Lwn4WRo/99B0ueBnwKHBQK8hISEhISEhIT/L9GfCdnmNpJynpCQMMdgZjcBa83tdsxtBMb3v7QVLGDmjRf2fF9g0bdaSMKM5/NogTW6F4r2vfBwTE6zyKo5idqCM2KithErxT+WNjUnZpr0Wrxv/pkxwU/3wHzN4YlnY3KtlVZ+KSq/NTMnahqwbNzeGddfG5W7huex2Boak+kMGBQTlj03NSez+sB1t8f1zF8i33r1rJ7vq24RUwDMHBMTt61DTjr15kPxdc//n/FReci8eZ/N/F9MrtS9QBxXPqDQvwuuGlP2dN0dk93xQv514nNx/y409s6o/PbkvJ9GvBHX81KJHGyp6TH51rsv5IRKC2wYTxGGXxSvK61ciH1/9LmYvOql/8ZZBldYJ5edHt821n437tMRXXmb7P64D2dMiwmfZlyWO8LYpHisT38mLtv0whguBfWVSaeiet4qkU4NivthSGG9bWBpwtkVD1kmkhNJPd8dy864779RefFReb3DJ8WEcIMGx8/u0A1zgrU3/nZftG9K15JReebYvE/nHRqT0HW/E5NEjlwpP8+kh+KLUVfpWgvXs+a7cfumn3tqVLZXc/K4rkViUsKFu+JrXWFE/n579w/nRvvmK0UmLb5S/q5Z45F439Qz/hSV1343f46m33F/tG8B5ovKMx/Pif2k+LpfOiMmhFtmwfwZm/q3C6N93YuOjNv08Ms934fuE1OgTL8wPnaN7ry9U++OCb+mxK9YBs6bt3HI0NaUM8V63/l7/O4bdPf4qNy1RB7hNvPpmPRx6qOTovLgtQr9/9+YjK14zjJm3loiN3sh5tiw+3JSwEf+Hb9nlpkYk+oNXjJ/hw1bMH7PzLzmyqg8aP9Der5P+9Ovo33dq5bI2Z4okC6W3qldK68cladeen3Pdw2KZQesHpP1Ddh977x9d8a/hXbb9VG5SAI3cL8fRvumnfGTqPxmiSgvoX8jKecJCQkJCQkJCQkJCQkJ7wksWc5rkQjhEhISEhISEhISEhISEhLmMpJynpCQkJCQkJCQkJCQkJAwl5Hc2hMSGkLSn4HLzOz8FjKPA+ub2ct1MiX5LwT5r7WTnduQNBq//kqW9tl8rjfLDOpzE1l7Qh9sGtjwkbQ+8HkzO3i2n3PdrXq+D7vynmhf1/D41T1g8Tx+73XiuLrFN34nKr94Tx5TOoA4dnbGpDhOdEYeas0CC8Vx78PfjmNV51sjd1EbNCwe/tOnxOvASw/I4y6nPfJCtG/I1w+MytPOyWPDZ74WBysPWzi+1uVfz2NTBx2wf7TPno9jNKP2XRfHQw79XHw77Y+/yOsdGfeZVlw7btPit+SFd+O+t6nxsd3DC3GApZjAt98YHJWXWXNSXu09o6J9A3b6XFSectrver4/MXY+PvDJ/LzrT5gUyQ5bYGpUHr5uHsP51L9it8MFiGUHDcv7f8Ul4rjQp5+dLyovNiLvi6mlhIqLD43H1pJrFeKRt9kn2jfPbcdH5QGf/ELPd5sS1zNgQCnge0be3snfOSHapbU2i8ojLI9lnzk9vjeDVo5jpEePz+P6X3snjpee+W5U5LXuvK4hJa/OARuvHZWnXHlvz/f1iLkDJk+Ox8fwm/PnaOQ3PhbtW3n/OJ6+a4stc9nr4rjmFZ6KeRE0KH92yzHmwxaM+QoGj87bNOWpuM+6d/hUVLbHCzHe0+PneEl7JCrPv3geUD302wdF+5bY9Z9R+Y1n8zaU34WDvhi/Exa/8s893wfutmO0b8VH436Z8WL+zi2Pq4W/t1VUfv3XeSzw4EO/He2zl5+OykM/XCh0xe/JYgw0wOuX/D6v98NrRPuGLLVsVOb5/Dzlsd214FJxvX86ruf7sMO+GLd3evye14C8f+2tq6N9Q3ePpwZaZYP8++A4Nvz1P/yeOmiRRaNy90qrxQLT8/fQiq+V4sY/slVUnjE+51944tr4/bvaD+PnZPq5f+z5PuCT8XhF8b3pWjTncdB8MW8KQ+eNioO/vnahmvj5srdivhO754ZcdlRcb9fmH4/KM1/Kf9PKMeYD9/lBVH7m5DgmvT+gP+cZn9tIlvOEhPcBJHW3l5pt50qLdvUYDXw2K4T86bNdMU9ImF0oKuYJCQkJCQkJ/RtJOU9IqICkH0p6WNLVkv4m6Zul/R+SNE7SfZL+JKloxviWpDvD5wNBfkdJd4RjrpG0CG0g6U1JR0m6A9hE0l6hzvGSfi+pO3z+LOn+0Javh2OXl3SFpLGSbpK0cqt2SDpS0mmSrgL+ImkRSf+UNCF8Ng3N6pb0B0kPSLpK0tCatl8Uzv2ApP0L29+U9NNQ5+2F8y8r6TZJd0k6ukWfVLZL0mGhD+6XdGjYNlrSQ1XtlXSwpAcl3Svp74U++GbhXPcHS3kRPwc+GO7B1yVtJemywvF/knSDpImSDi7U1XI8JSQkJCQkJCQkJCTlPCGhhOCqvCuwDvBJYP3S/iHAn4FPm9kaeHjIlwsib5jZhsDJwK/CtpuBjc1sHeDvQOzrVo15gPvNbCPgFeDTwGZmtjYwA9gTWBtYwsxWD205Ixx7GnCQma0HfBPI/FxbtWM94BNm9lng18AYM1sLWBd4IMisAPzWzFbD85bvWtP2fcO51wcOlpT5gc4D3B7qvRH4Uth+EnCKmW0APN+rthy92iVpPWAfYCNgY+BLktZp097DgXXMbE0g9qNujcOBm8xsbTP7ZcX+lYHtgQ2BIyQNbDeeipC0v6S7Jd39xwuvrBNLSEhISEhISHjfwszm+qe/IrmvJiT0xubAxWb2DoCkS0v7VwIeM7MsuemZwFfJFfG/Ff5nCtySwD8kLQYMAh5r0I4ZwAXh+4dw5fkuSQBDgReBS4HlJP0G+BdwlaThwKbAeUEWILPst2rHJdk1A9sAnwcwsxnA65LmD9c9PsiMxd28q3CwpF3C96VwJfkVYCpwWeH4bcP3zcgV57OAY2vqrWrX5sA/zewtAEkXAh8ELmnR3nuBsyVdBFxUc66+4F9mNgWYIulFYBHaj6cemNlp+MIK795zSf/95UhISEhISEhISJjtSMp5QkJvaBb3W8X33wAnmtklkrYCjowq9JjysaF4iZn9CHg3KKDZOc80s+/2aoy0Fm6t/SrwKeBQYFKwsJfRqh1vVciXUWSHmQEMlbQUvkgAcCrwMPBhYBMze1vSDUDGkjTN8uXKGcTvoF7KqKSfAh8DqLkeaH0/erU3fP8YsAWwE/BDSasB04m9iWJmp2Yon29Am/bV4vc7n9vz/aWuxaN9nx8ak8j899WckuDFwZOjfb+6ebGovO9CuWPChjvHBHBT7o+K/PR/+bFLWOufi2fGFMi2SkRMJ385JshZ+JScHOypO2Pev4k7XxyVN1oxJ8IaXOLdOfaBuF8+WCDfOm/3i6J9A0q3YeHCpX9w3jg64+fb/iY+0cB8KMx4KO7PVT8Zt/fBATmZ0fY3x8RWb3bFxy4wMyfUmlpyZHtkcNzfg+9doOf7bYNjornFdzozKu8/f06cd+YFcQTNw90xidewyfG9GXVxPpamlR7JewfHr4gFn81vyGHDYpa3tT4bk8d9/8K8HUNK9+J/Fte71IS8nxb4xN+ifQNYMirzsfzah5XeIM93xfH2Iyzv4ymlelb4REwsduPQvLJpT8cEcMP/FlOAzFd4Vbw5JH6mlnkpJoCa2J3vf5X4XvzuB09F5f13ytvwzcvieiZZfOwqE/PrGXLwvdG+14fGx158wM0931+y+aJ9j8yMyRxPvHLhnu9nlN6IG74Ub/jvo/m1PTU4Jm+c8bF/ROV3CgSIi5YI4e4dEo/vbz6Wj/3Fd4/5WKeVSOp+XBjPr8yM6/lB6djHLW/DegfeGe17rhR5tu1/8rGzxqJxHx3/jYei8guFY4d94qxo3zCLn/M3lY/Rr8wbkyo+9GI87l4sPPffPvbFaN+yM1+Lys905X2614y/RPuuV/zMv0he73c/969o3xIz4/fQVjPz9/EjM+N394sDYtLN57sm9nxf0OJn5kWL700R5307tl08NDAm7PzSvHn/H/HGyGjfRiVyzxEzh/V8v3pAPCY/9anYO+3l7vxaX/1z3A+vld4lgwtjZ2DpfbbalPgdMHZwfs8XnRnLqsUS/OrTn4zKTyseZ8UefbNEKFomgPv22NpowbmG/my5nttIbu0JCb1xM7CjpCHBCv2x0v6HgdEK8eTA54Axhf2fLvy/LXwfCTwTvu9dPqGZzQiu0msHxbyMa4HdJC0MIGkBSctIGgV0mdkFwA+Bdc3sDeAxSbsHWQUFvm07Suf7cji+W9KIOkEze6rQ9lPDOV4LivnKuKt5O9wCfCZ837NQ9/ezulu060ZgZ0nDJM0D7ALcVHciSV3AUmZ2Pe7WPx8wHHgcd5VH0rrAshWHTwbmrdjeCu3GU0JCQkJCQkJCQkJSzhMSyjCzu3CX6AnAhcDdwOuF/e/iMc7nSboPmIlbjDMMlpO4HQJ8PWw7MsjfBDRKs1Zq04PAD3C39XuBq4HFgCWAGySNx+PgM8v6nsB+kibg8eKf6LAdhwBbh+sbC6zWQraMK4ABoZ1HA7c3OOYQ4KuS7sKV+8btMrN78Gu/E7gDON3MxrWooxv4a6hjHPBLM5uEhxAsEPryy8B/Ko69F5geyOi+XrG/F9qNp4SEhISEhISEhARIbu0JCXU43syOlDQMt8yeYGZ/yHaa2bU4wVcEMxsdvv64tP1i4OIK+T/jimUvlPN8m9k/gH9UiK5bcexjwEcqtte148hS+QVyhb6I1Qsyx1fsJ8Rcf7Rm3/DC9/OB8wvt3aQg+vOa4yvbZWYnAieWtj3eor2bV9TxDrBdq3ab2TQ8/r+IG8K+I0vHFJO+9hpPVedJSEhISEhISPi/juTUXo+knCckVOM0SaviccdnButsQkJfkcZTQkJCQkJCQkJCa8xtGvv0SZ/0SZ/0qf8A+yfZJJtkk2ySTbJJNsnOKdn06T+fFHOekJCQ0L+xf5JNskk2ySbZJJtkk+wclE3oJ0jKeUJCQkJCQkJCQkJCQkLCXEZSzhMSEhISEhISEhISEhIS5jKScp6QkJDQv3Fakk2ySTbJJtkkm2ST7ByUTegnUCAMSEhISEhISEhISEhISEhImEtIlvOEhISEhISEhISEhISEhLmMpJwnJCQkJCQkJCQkJCQkJMxlJOU8ISEhISEhISEhISEhIWEuIynnCQkJCQnve0haoAPZj0tq/PsnaaiklRrIfU3S/E3rnROQ1D03zz+nMCevS9Lgim2Nx9N7jTnVF5LmKT4XkrokDZsN9Xb0XEgaJGlNSWtIGjSr5+8UkkZXbNvgvW7H/1VIWrbJtveoLfPMpfNK0lIdyK8+J9uT0L8wYG43ICEhISHB0U4hMLNXK47ZGPgNsAowCOgG3jKzERWyZwC9WEDNbN+CzGFt2nBiRb3HA2eY2QOtjg2yywEnAZsAM4HbgK+b2cQK2UOAM4DJwOnAOsDhZnZVRdV3SBof5C+31mynnwFOknRBaPdDLdq7I3A83rfLSlobOMrMdqoQXxS4S9I9wJ+AK+vaIemTwLHAwoDCx2ru2wWhvsvNbGaL6wL4r6Tzw3U92EpQ0hDgK8Dm+Li4GTjFzN4tyXVyf3cHrjCzyZJ+AKwL/MTM7qmR7wY+BoymMCepGGeNryvUuwJwDLAqMKRQ73IV4hdK2tnMpoVjFwMuA9arqLeT8dv42Swdd5mZfbyFSCf3+G78mTjHzF5rJQtcC3wYeDOUhwFXAZtW1NvJmOzkufgYcCrwP/yZWFbSAWZ2eY38JymMXzP7Z2n/uWb2KUn3Eb/7sudtzYpqL5S0o5k9E+rYEjgZWKPi/B3dY0lLAMsQj/Uba2Q/BqxGPH6PqpAbAuxXIbtvhexgYFd6P29V9XYi+8mKS3gduM/MXixtvwB/LxRxPtXP25J4/26OP283A4eY2dMVsj+qaENdezfFf1OGA0tLWgs4wMy+UiG7IvAtet+3bSpkf13RhNeBu83s4sKxJukiKq65BqeGhao/48/ypDrBmt/w14GxZja+4fkS5iKS5TwhISGh/2AscHf4X/7cXXPMycAewKPAUOCL+GSmCpcB/wqfa4ER5BPxDPOGz/rAl4ElwudAXNGpwsPAaZLukHSgpJEtrvEc4Fx8wr44cB7wtxrZfc3sDWA7YCFgH+DnNbIr4mljPocrLz8Lk6peMLO9cEX/f8AZkm6TtL+keSvEjwQ2BCaFY8fjk9Wqen8ArAD8EfgC8Ghox/IV4scBO5nZSDMbYWbztlDaTgE+G+r7uaSVa+QA1gT+A5wu6fZwXXX1/gWf0P8GH0erAGdVyHVyf38YFPPNge2BM0P763Ap3lcLko+9qvvQyXWBK6SnANOBrfFrrbo2gIuA8yR1B6vplcB3a2Q7Gb+dPJtFLNFmfyd98ZnQzrsk/V3S9pJUIzvEzHreB+F7neW88Zjs8Lk4AdjazLYysy3xe/fLqnol/Q5/L90H3A8cIOm3JbFDwv+PAzsWPlm5CgcAF0laVNIO+GLMDjWyje+xpGOBW4Af4Mret4Bv1sieCnwaOAhfSNgdVw6rcBY+HrcHxgBL4guaVbgY+AT+XLxV+Myq7H64srtn+PwBOAy4RdLnwjWtLGlXYKSkTxY+X6CwqFDCGcAlwGL4c3Fp2FaFYhtnAB+l5l2Nj6ntgVcAzGwCsEWN7HnAPcT37Vs1skOAtfHx8Cj+rC4A7CfpVyXZ25t6ZJjZ5ni/LgXcLekcSdvWiK+PPxfZb/f+wFbAHyR9u8n5EuYyzCx90id90id93qcffEUe4N7CtlsbHtsFXFez7ypg3kJ5Xtwi2qq+lXDl+Qlcidm6QuaOim2319R3b/h/ErBL+D6uwXVtDTyDK9RjgE1q5EYBhwKPA5fjk6mDqtpbPG+xr2vqXQv4Fa7UngKMA44rydzSh3s9Ep90PQXcii9WDGwhv0Xoh7dwJfkDpf0TKo7pta3D+zsu/D8G+Gy7e9auL/tyXUFmbPh/X2HbTS3q/Co+8b8P2LSFXCfjt0/PJvCn2dkXQa4L2CnIPgX8GFigPCaBdQvl9YDbZteYbPhc3Fgqq7ytsO8BQkrgwjU+UCM7D9AVvq8Y+qLVs7MJcC9wJ7BQC7nG9xh4BBjc8L7eW/o/HLiqRnZcSXYg9e/1+zsYW53IXgosUigvAlyIK6b3h22fwBXrV8L/7PPrumcOGN9kW82xg3EvjdrnmPi9XvnuI7xLGp7zOmBAoTwgbOsGHizJPogvIvwvjLX7aP/b0o17MzwDPBSepU+WZK4EhhfKw4Er8MWjB5teS/rMvU9ya09ISEjoZwiWrT2BZc3saElLA4ua2Z0V4m8Hd7fxko4DnsMnok2wArB0zb6lgamF8lTqrRCZe/LK4fMyMAE4LLikfqYger2kw4G/426mnwb+lbn0W+y6P1bSVcCywHeDZbvShVbSgsBeuOX8BdzidAluxTgv1JHJ7gjsCyyPW502NLMX5fG1DxFbvu6X9FmgO7hKH4wrIVVtOBjYO1z/6cC3zGyaPI73UaBotbhb0j9wq+2UbKOZXdjg+sYBZ+OunnvjVpFMLnMT3we/XycE2Q8C/8YVkwzjJG1sZreHYzfCFbSq8ze9v89I+j3uHn1scI1t5aV3uaTtrDpUoXz+ptcF8G7W75K+hk9mFy7VWXT/FG6VGg9sHPrlxIJsFnJSOX5rmt2nZ9Mq3JFL7e6oLyStGWR3wF2Ks7FzHf58ZDgE9yB4NpQXC9dX146mY7KT5+IBSf/GvRMMtxjflblNl56PR/D31BOhvBSu5FThRuCD8tj3a3FPpE/j79msnZcSu74Pw92B/ygJqw5l6eQeT8QV5yk1+4t4p1D/4rhCWxeXPS38nySPTX6e+nf1rZLWMLP7GrShE9nRZvZCofwisKKZvSppGoC5W/fFkjYxs9sa1AnwsqS9yL1T9iBYuxtgGFAVxgLwVHBtt3D/Dsbf/VW4VNJXgH8Sv6t7hZnhlup58HFD+L64mc2QVL7vH212GdEz/DHgamBHM7snjI3b8IWQDOXf7mnAMmb2TkUbEvohknKekJCQ0P/wO1wJ3QY4GndRvACocoH7HL6a/jXg6/gEddeqSiVNxiefCv+fB75T04azgDsl/TPI7oK7BlfVeyJuiboW+FlhEeFYSY+UxLPJ/gGl7fuG8xQnU/vhysNEM3s7KAP71LT3ttDmnS2OR7w7uIgWsTvwSyvFeoZzlBWjg4Dv45Oyc3CrxE9q2rAgbsV4orjRzGZKKscQjwDexl32e0SJJ1kASLoQV4rPwidlz4Vd/5DHFBfxKHA98AszKy4inC9pi1BfFn87EPi8pCdDeRncmlM+fyf391PAR4DjzWySPH67zgUU4Hbgn0FRm0Z97H3b6yrhUHxyfjD+DG2DK4hFlN3n/1mzHTy0JHt2IB6/Fs5RxufwhYnaZ1O9Y6EjWHVMdOO+kDQW9yD5I87XkE3O75C0WUGuG1fuV8Y9JAQ8bCEOv4wOx+Qomj8XQ/DFtS1D+SXc+rojvZ+PBYGHJGXjcQPgNkmXhPqLyrTC870f8BszO07SuNK5j6+61jZo/P7Fn/fxkq4lVvIOrpC9TNJ8wC9wl2rDFzaqcFpYdPgBviA5HPhhjezmwBckPRba0Cr2vhPZmyRdhi+EgvfBjXLCtUkl2QMlPWQhbjq0/YSaRal98dCBX+J9cGvY1gulZ6kbD4XqFW+etQH3yFoCeBr3FPtqjWz23ii+x8q/VRmOw+/xDXh/bQH8LPTDNUVBM3tCHv6zgpmdIWkh/N5V4WQ8VOB7ZvZOoY5n5dweRZyDu8xnMe47An8LbWjL15Ew9yGz2t+EhISEhIS5AEn3mNm6ksaZ2Tph2wQzW+s9bsd6+AQN3LW0PJnN5PYF/m5mb1fsG2lmr1cc1uT815rZh9ptC9tlZiZpHjOri4ssyi+DT4qukTQUd0Wsi9Ns0tYu3CVxtrPqStrGzK5rKDvcCnHDNTLLtNpfVqI6vb9B0VuEmDzpyZq2TAR2xt3PayckTa6rLwht/bmZtVpA6GvduwD/LijEVTLZvcgUgywufk/gbasms2rcF5KWswqyuhrZG8xsq4aynYzJs8zsc+22dQo5UVstzGxMQXYcTn74S2A/M3tA0n1m1ovkLcgvQr4Yeqf1JjXrS3vLi0NZO89sc9xgnA+g8j0qaVkze6zdtrC98tkvP/N9kBWukG+GK6U3AxdUPdPF37VW2zpFqb3TgRfMbHpJ5lgz+46k3c3sPOYAwoLkhng/3Glmz9bIHYHHh69kZisGK/h5ZrZZjfxQYGkzKy+IVsmuT+FemFkdZ01CP0RSzhMSEhL6GSTdgTMk3xWU9IXweMN1CjJ9sbh1xBYc5BcmZgDupWQFxfSzwHJmdpQq3PCzybyqWX0jd1U5+/Aw3Dq4Fbm1cgTODr1KRRs2wa2Dw82sHfvul3CSnAXMbHm5u/qpNUr/1cDuJSvP381s+wrZs4Hv1imiJdmMhXgzcqb0Q6yahXgYTq60tJntH9q7kpldViG7Ih7Pu4iZrR7cIXcys58UZDrKCtDk/hZkDwKOwK2fWQhCnbUNSVcCH7U2jN9NrqtCvinDcuWCT029n6/abma9vErk2RG2wV2q/47Hv04vywXZW8qT8qptYXvjvghK5s9w19qPSloV52D4Y4XsT/EY8n9QIP6yCqb9DsfkPWa2bqHcjS/GrFrY9u1gzf4N1RklqqzLjREU+W/gXA/Hyln3D62qV9KncIv1Dfi754O4K/75FbJV7+HXcbf5n5jZKyX5RgqWpK8CZ5feO3uY2e8qZKP+DdvGmlkV+/nGeFz+5FCeF1jVzO6oacda+PWDczZMaNXuJpA0AdjKQvaA8D4aU7VQIulM/L04KZRbWdmzY2p/s8L9WhePOS8zxtfVNxAnR828Um4Afm/1XiWNfmPl2UXWAe4pLMLfW/WuVCFriJktq9ZZQzpaIE3of0hu7QkJCQn9D7/GXWwXDhPm3XCXxSIyd9BKi1tVpXK24E+TE9GATyyrJg474bGsi+Pxg0vj5DOrVVT9W3I3/KOodsPfEo9xrWJILrurHoC7JS+OuxNnyvkb4VxV+BXOvpu5tE5QtbszeJ9tCNwRZB8NE7oqjLJC2hoze62F7GJ4zOydxMpN1QTqDNz9cPdQ3itsq2LgPQPvhyyl1dO4+2gvRQh3ffwW8Ptw7nslnUPsil900V4aeC18nw94kt6xrU3ub4ZDcCWtaVzoc8ANki4ndvUtp1Jrcl1FnIen5PoD+Vivw3i5K/R5xPetKv6/eM1DgA/hbse9lHMz2ydM7D+KL278TtLVZvbFinrnkbS5md0MII+HrYtd7qQv/oyPn++H8n9w5buXck4+vorWesPvexltx6Sk7wLfA4ZKeiPbjMfDnlaqL4v3bWvhk3SzmW2uPEynZxc16QiDFX2MpBGS5g3eBHUK//eBDTJreVgcvQZP91XG5fj4OieUPxPa8Tre9z3vO3WWlvFLZtbzrgvvnS/hIU9ZfSvj7+ORpUXPEdSzn59CnMbsrYptWf2HAF8ifzf/VdJpZtaLjV4dpIbEf1dulacDNDwU5qc17V2z4v1baWGv+M1aBh9Xxd+sK3Dug3nCmMxCvFq19xQ8BCjr+8+Fbb2e48Jv7AMUFiep+I0FppqZSbJwbCs+iiPx36wb8IaOl2eW6IXSAumMwjVWLpAm9D8k5TwhISGhHyFYKR/DSZI+hP+w7mylXNyZa6GkzUrWtcMl3UJ1rN3OuOLUhBTmaGBj4BozW0fS1jgZTxU2Chb+caFtr8lJdortPSL8r4sZL8qehOchP6hqItjiuKcUZ4mqU8qmmNnUTFbSAOq9EGZKWjqzOshdJ+tkf9y0rTgD9BmF8p8lHVoju7yZfVrSHgDmxD516bCGmdmdpd2RtdbMlgWQx+JfYmb/DuWP4kRuZbS9vwU8RU6G1ASPhc+g8KlD2+sqYbqZtUrhVsQCOMlUUQmtjP83s4OKZXlauboUbZgTn10e6huKM1ZXKef7AX9SnqZuEjWxtXTWF6PM7NygKGNm0yX1ei6Cpe0SM/tl3bWU0HZMmtkxwDGSjjGzutR0meyl4X9LF+8gs3n4X8UNUAm5m+8ZOJ+AJE3CUzWOrRDvstiN/RXqSQ3L79/7Mo8HOZFZEUfSW8GqI3nrkjxUJ7S/m97Px0r4Iu18xIuek3Glugo9dYY2zAzvvyrshz/7b4U2HItze1S9k4/DuQfqSNV6YGZ/kfMSbIP/vn3SzOpiobskzV+yste1t+1vlnn4yrckXWxmn2jX1oANLA4puy5Y/6uwM81/Y8+Vk2fOFxZe9qWeV2C6mb1e/9qP0OkCaUI/Q1LOExISEvoRwmTpBDPbBLdUt0MnFrdO2IKnmdkrkrokdZnZ9WFyVikbJo/ZRHIh6lnVB+OxiaOJXe6qFhOeD1auyXLSm3VxV9FebrZ0xr47RlJm0dsWj0W9tEb2+8DNkrL41S1wl/heMLMxah6r2gkL8VS5O2zWv8tTfw9fDvsz2d1w63QVNjCzAwvtv1xSFbFZ4/uLj7EbJP2L1pbwbHvTBY1Orgs6YFhusmDUAm/jWQ96QdJHcEvq1rhCdjpuJeyFoCSuJc9XLmvN09BJX7wlJ1LMZDemYvHEnE16J2pyileg7ZiUtLKZPYwzwPeyzFY9x3KX/W/S+/1QZb3vxH33T8BXzOymcNzmuLJeZU28Qh5ukT2bn8ZZ8KswXNJGFtzCJW1ITupVXjCpUrDqFvquxJW3U4PMgbjVNz+wb+znE+Xs+dnC1VfwZ7YKIl7gzKywVXihiWJewALAWxaI0FQTI09sZQf3NKqzsjf+zepAMQeYIWl5M/sfgDwkom7ht/FvrJkdH35/3sAXWn5kZlfXiDfOGkLnC6QJ/QxJOU9ISEjof7hK0q7AhUUrRw06sbh1whY8SdJw4CbgbEkvUm+da+KGn+FifOIwlvYTmB+a2XlhIr097hJ6CrBRhWwn7LuH4/12H+5C/29qLBZmdkVQLDbGJ6ZfN7OXq2TVO1b1N5IqY1XpgIUYd1G8AlhKHte+GfCFGtmv4i7DK0t6BrdKly14GV4Oix5/DW3Yi+oFgk7u75Ph084SDvQo+t/GXU+LcaJlZayT64IOGJbVWfx/Md1WF7AqnvarCl/AY80PaGdJKy9aZQpczaJVJ31xGB7qsbzco2Yh/P5V4VZJJ9Mg5pxmY/IbuAX3hIrj69zls3CE02kTjqAafgOqFe7JmWIOYGY3y93iezfM7FtyN+3N8ef4NDP7Z00zvoi/fzOFfDLwRbmL8jEl2U4UrO/g76YvhzZcRb1V9ZXwTm/Cx3Ag/jz/AO+ra6lZbMQXL+6QZ+wQ7vVRFQ4BHaSGVIEILZxjIP4O6sWv0KGVPfvNupGa3yz1Dolo4tb+LTyF4sQgtwz1WUMa/8YqkNPhqdHK28ooZg35G754U7WQCh0ukCb0PyRCuISEhIR+hjBxmAefWLxL64lDdkxbi5s6YAsOk8vs3HviRFFn17nKyeMfMzf8a+usKJLut4aM5goMvpKOwQmkztFsYPXtFGpO8DMB2NZKsao2G1j2g/UzWyC4vW6BoCA/D+6eW8tAL3cRPYKc8OlG4MeZC2lJttH9LcjPi4/ZdszxV+HK4DdxxWFv4KWaCWqj6+oUctK/c8jd0/cC9jSzXvH/ihnCpwNPVCnxBflGnhSSriBftOpRSs2sSrHNjmnUF3K35Sw92iNWT2R1fcVma2G17mhMNoFqiMxqZP+Lu123dd+V9EucZPJv0JOf/jWcO6HXAkS4bxsG2bZs7WFxVFaIj66QGYYrWNvhfXYlcLSZvduu/W3OPYbAQWA5sVjj92ybutclX6RolbHjjIrNZhXEbWpAhCZphJm9oRryyioPmPA8vIMvmmW/WX+tku0UYfGsmGKwcrGtw9/YKiK/SkK4Dtt6RE0bOgm7SpiLSMp5QkJCwvsYYYJ8BD6Byqx+R7WbsEpat8YiVpRZlHyCepeZPV/a3xHrdzjmNDzP8H2tjg2ylwHP4HHQ6+ETrzuLyq5q2J0LbTi4INsxw71qCH6sgsRJpdRMcv6ACaVtjVmpVeEKXJK9pyB7WBvZXlYTeRzu94ldiC3rhz7e39VxJTc79mXg82b2QFUdmTJWnJRKGmNmW/blulSTDaAgX2XJG29ma7fb1ikk7Y57e9wAbVm/2ypTnfRFX/qhCTock43bUBhrB+NkXm3DEcJiwrZWw4BfIduiKfkCRIUHTKv71pgNvwkknWtmn6p7V9W8o+4ysw0Up96sHL/yTBj70dtTpZb9PBz3catg4u8LJN1pZhsqTxk6D3BbSTm/zMw+Ls+xXkX6V+UB08vqXN7WyTtNHWQY6QSSvoyHEywH/K+wa148m8BeBdmip05VGyrZ2hPe30hu7QkJCQn9EPKUMSsQT6CqGF//jls8dw3lPXFLZBWxVxGnU8HQWzj/F4Ef4QzrmYv2UWb2p4JYkfW7p5nkroI9E6jCZHMAsE9wEZxCPtmqshZ8CvgIcLyZTZLnjy3no+4kf+vH24v0ws40J/ipilW9vCTTmJWaanfgDGW34MbkWAWcjVus76c6hrzx/S3gNOAwM7seQNJWOLv4phWyAJkV9zlJHwOeBZYs7O/0ujJirIXDObNc3Fncd9WEum38v3ozg/fsot6r5Qc0Z/2+VdIabRatOumLqqwIGSrJ7orIlKOKXZ2MyU7aUB5rteEIhUWKxu67ZrZ1i7aU0Qlb+59pw4bfoYJ1SPjfybuqEw6Cs3Auk+1x0tA9qefmKOIoKrJDdLLYWEAVEdofSsd9PPyvI8yrwrZ4OEARHy1tq3qn9ZyW+J22JQ0zjHS4qHIO/rtwDB5ilWFyxSLU8RXnroSkX5nZoXXjLSny7x8ky3lCQkJCP0NQjA/BlZTxuOvobVXupVVuoJLuNrP125yjx8pSs/8RYNPMAh8s9Lea2UqdXk84fplW+y2wzwfZjl0aC8fOY4FduEF7VjCza+TEVgOq3IPlTNu7Wxv37IJ8MVb1RquPVZ3rUIi/nM11TrCSG3/VtsK+j+O8Bkvhcd8jcNf6S2axHZfh6aieC+XFgN+aWS8rmDxv+8nAJuTx/4cUx2Qf29DWk6Kw70HgA3j8eLtFqzmOdu+HuYk6t90M7dx3Wyw8ZPs7uW9trdbKQyE+CSyKx1eDLwI9bmbfK9XZDVxpZu0WWDP55fBFsU1xV/3H8LCMXuNXeajQvWa2pjzV35VVvy1Vx1Vs39HMLlUH7tzhuG0puPdbDRGapP2KXgihb35QvMedWKL7AlWQ1ZW3SVrMzJ6r+52re5eog3zk4XdqaTN7pGb/emY2VnHoTbENY6q2J/Q/JMt5QkJCQv/DIXic6u1mtrU83rduwnm9pM+Qk1LtBvyrwTnaxZ89jZMbZZiMs8D2gqQsLn1ZMzs6KDuLmtmdmYzlqd+WB542synBqromvXNEn4Nbjuost1UujZvg1qrhwNKS1sKJuL5SIfslnARpAWB5fBHkVDymuoy+EPxcWLGtLNuYlVoeq3oYPjHbX04mtVKVm2mo9xSakUMdIel0nBSqlsSpyf0tYKKkHxLHb1exMGcT0xXCdbyOW7cr0eF1AYzOFPOAF4AVa2RfbGpVkrOdP5At5MgJqFazwNZdQies3x9tcv5wzsZ9ob67XVfGFhfqbTsmJe1lZn9VjTt+lYVbHgpwhcUZGo62QqxzlfIdFOjhZvZGeV8Flmizv5P71pYNP1OKJB1tZlsUdl0qqZc3lDlz/tuSRlpr1v5MfiLwYTXjIMg8VSbJQ1Cex99B7XBAzbkbp8ArHXc1BSK0FviQnBx1P2AUzrpfVjIbW6LVQVhGARfQ28vsfDzUKjsue9d8pfy+l4dGVf0GfA1Pr9eW0FDSjrgVfRCwrKS18fC1nveW5WkB1zZPR1o8/hB691tCP0WynCckJCT0MxSsMeNxwqMpFdaYItvsPOQ/7l3Am1ZDHqeG7vKS/gKsgbOrG87UeyfutlmObz0lnH8bM1slnOMqM9ugot7xOFPvaJwQ6RJ8Ur9D245pAUl34AsTl1gbUqTQhg2BOwqykbWsIDtHCH7k5HGn0psArFfeZTkL8lg8dnv1YEG5zapjShuTQ0n6K7AyvePp9y3JdXJ/58cXfnq8B4AjrYJkLshfbw1cjju5rrDvZHycZwRgnwH+a6U85UH2v/gE+abQ3lvqlCJ5rvd1zXryT3cBd5fve0F+V5yFuq0nhTwrwQoW0kvhymavhY0O7/HlBLdrM1tLTg43rmqsl45ryUnRZExKOsDMfq8OCKoKFt3NcWXreOB7ZtYrQ4Okc3ASwRmhLSOBE83sF22u7U/lMV4h08gDJih7vwFWx8NDFgJ2M7N7K2QfAj4WlGnkOc7/bWarVMiei3tMXU3MnF/lJl48rp1XwBdxZXNNfFwMx1N4nVojvzqekaD4e1FeTG202KjeTOllvAL8wsx+V6r708Bv8YXSPczslrrrKxyzv5mdVrE94x4Ygv8OTcDv8Zr478HmBdmV8dj844jDLEbgHASrVdTfyW9AJ4SGY/GQkRushkSvTRvGWT/1hEnojWQ5T0hISOh/eFrSfHhamqslvYbH4vbAzDqOMVaNuzzVKY3+R+wieHH4X3XejcyJfcaFtr0mzzVehZlmNj1Mfn9lZr/Jjqto77Vm9qF22zKY2VOKcwjXpWKaYmZTM9mgsFSuVDexCKngVimpOCmfF6ibSE43s1Nq9pWxvJl9WtIeoU3vqHShBQwzsztLu+sIs9Zqp6QFNL6/QQlvqUCU0DR9VyfXhZl9TdIueF56aJEOy8w+IPcG+CDusfE7SZOqFj9wo4YVjp0Zxk9dOy4gMIK3gjpIL0VnfTHKzM6V9N3QnumSWqYoC2jJSUGDMWlmvw//O2GJztr2MeAUM7tY0pE1squah7/siVu2v4Mr6S2V83aKecCtoS0zgbta1HWP3I24LRs+8HU8Rj7LKz6aGos07v3UxAOqjJZeAWaWpWMbQzVnRA/CmNwKV87/jXt33ExvTydokAIvU3zrfruCB8KtwO8K21bAf7MuAFYBPhcUzbdbtR1ftOmlnGcLgZL+DuxvgeMhLEJ8syS+Ev4+mI847nwyniKw2Pa+/AZ0ko98upm9Xv/ah/Asfha3rBfDgualOkVmQj9FUs4TEhIS+hnMbJfw9ciw0j8SzylcCblb62hii0UV4VNjd/kOJ9TT5C7KmTVxIaoJxjLZPYDPk094BhYF5IzCw4BRwRKbzUhGAIvX1PuUpE0BC4rjwdQTHY2R9D1gqDz28SvApVWCYXJ4DL2tR8WJbScEPxkulfQVGrBSA1ODZTLr3+WpzxHfCTnU7ZJWtfq8wRna3l/1nYwoI4or5vMuE4tBZ9eVWSUvzxRySUMljTazxytkl8SV4A8Ca+GeBDfXVD1R0sG4Wzn42JlYJRgWoI7FyekUPmbVXi27ENJL4ULPytPRVaGTvmjrdl2Dei3A0XhMSjoTj+GfFMrzAyfUKMnPyMnCPgwcK09h1VXThoHymOmdgZPNbJqkykW2hs9xJtuEDDOT7QZ2IH//biepjpTuitCOlcOm2pRcZnam2sQY16BdOEInYQ674c/DODPbJxxbl2u9k8VG5GFHPSkczexeM3tFHupUxKXA18y5QYSHUtyFW7RbnqLN/pWtQL5oZvfLXcUpbLsYuFjSJmZ2W5v6+vIb0Ek+8vslfRboDmPoYHwho4hb8ffAKGLixslAL0+OhP6L5NaekJCQ0A+hPL+s4W62lS6mkv6Eu+S1dE0Osm3d5QuyncRE74nHZa4LnIlP6n5gZudVyK6KWzVuM7O/BSXq02b284LMIcChuCL+DPlE6w3gD2Z2ckW9o4CT8Em9gKuAg6smRnJX5P2ICYn+UJYLsjfjqep+iS8m7IP/dtaSUklamFgB6EXwI08RVIbVKAvb4szfq4br2gz4gpndUCHbCTnUQ3jMfUsSsib3V3OYjKiT6wryd+OEhlNDeRD+HFW54mfW0Z+FCXmrdiwM/BpfPDA8Xv9Qq8iDLXdb3dHa5IQPsm3TSxVkO7nHjd2uS8ftbGYXtdjfyZgcZyWX2qptYfswPEPDfWb2qJzIbw0zu6pC9mDcWj4Bt7Qvjee1/mCFbOPnWB2QYUr6N/AucB+FBSuLCcv6kt6vJ8bYzJZVRYxxX6AOwhwKvxdjga1xJe9+q3bnPpLmKfAOwS3P2XXvgnu2/KZCdoSVeAQkrWBmj7a5ziXN7OkW+/+Ge+r8FX+O98LDSPaokG2bfk59SzvZSbjHMDwjwHZh01X4eGiSRSThfYaknCckJCT0M0j6EbA7+eRlZ+A8qyZ8etDMVm1Y7z/xSemhuHLxGjDQKuK91VlM9GBgWZxQTbjC8kLNhGQb3HLfzi0RSQdVTdhqZD9qZpeXth1oFbGUwQr2o0K5G/iLme1ZIZvl4e6JSZd0U40CsCNwIr6o8CKwDPBQ1WS2E4SJn/AwBAG3A/NadTxypiT3kEMpMCpXyDZiFu7w/h5iFWRE5W2FfVVkYa8DY81sfF+uK8hX5S6vZI0PVrzNcRf4pYFHgTE11sTGkHSLmVW5pVfJfhOPkd8Wt77tC5xTo7B0co9H4+SOPW7XOGFUrat2OO5IMzuyxf5OxuQEYCsLvAPh2DFlhTAsmt1rbfK9t2iTgG6ryHve4XN8LfDR0sLOv62CPV01cb8lmTNa7K5bSK2KMa7jxdgMJxZbBl9IzRbZqhb6GuVED315OvANnK/hG8CbwHgz26ei3k4WG+/FrfVvhXKrhajM0r+EmX1EbQgN5ekYy0r0URVyQ4Avk4e93IiHUbxbIXsenn7usxTSz5nZIQWZx8g9hspW+8p+KBzbNsOISqz1YdvPzezwQrkv6R4T+iPMLH3SJ33SJ3360Qd3xx5SKA/FJwNVsn/EYy87PceWwE64ZaZq/9gO6voXruRn5cXqjsfjFf+Dx7ofh1ux5m9R96b4pOjz2adG7lacsCwrfxt3a66S/TPw3fB9EB5Pf2SN7C24W+2FwNdwK88jNbITgAVxSxS4tem0GtnPV31atGFEobwKbsGqkr0HtzRm5c/gREezMh47ub/3VGwb16Luc8J4OCF8HsaZ3u8Cvt3X68KJtHYqlD8BXNtCfjhusf0p8ASe4qpdv/S61tL+k/BY+j3wNFqfBD7ZQn5bPF76eGDbVudt2hf44toShfIWuFV6Vq+tkzH5efyddnT4PAx8rkb2bNyVu8m4/F+QP5A278AmzzHuMn0Y/o4ahyu8R4T+PrWm3mOB7Zq0t5NPdj+Lzw6+cFEl+zAeD74w/v5ZEFiwRvaGsP+eUN4YXyipHDuF76OBNWfTtd1H/Ps2pG5M4q7in8JT2YEvPtTJnhru3VPhvt0H/LFFO4biZKTt2juu2P94GNZ1s6EfNgEeBJ4M5bWA37Xohz0L5d+2urb0eX9/Usx5QkJCQv/D4/iEJVvFH0xMzlbEmcBtkp6nQX5k9XaXn1ran7nndRITfRFwnpyZeimcgb1MrpMd//lwnsVx9+jf4pbmXr9Hks7C3a7Hk1vvjWpCop2AyyR9C1eyVg7bqrAPcLacJGtrXIn/ZY3soXj8+8G4YrENsHeN7DTzuMkuSV1mdr08jU4Viu7VQ3Cr9D011/Yz/H7sEK7rL7j1pgq7AecHV/TNccVouxrZpriINvdXfScjWhBnP38z1HMEnqZoC1yxPC7IdXpdB+L3+Leh/FQ4phfkLvCD8QWem4EtrFmO83ZxrSNwhuliO41Cqr1CG5YFbrKQ71ktYuTprC8OBC4KXh3r4mOpSWaEdtfWeEya2V8KrtHCFyjqeA4WAx6QdCcxQWDVs7wqsBEeu3y8nENjguWcHUUcSvvnOIvxryPDrMLtwD+D1X8aLayUkkbiSmNmrR2DuyZXcQA0iTHO8LqVvIZa4DD8+V1O0i2EMIca2dslbWBmd9WMQyRtY2bX1bnuWzX3yZ+AO4InF7hnWJ2XSieEhpuaM/3fa2Y/lnQCFc9aaPdO+EJYbWqyAhqnn5O0RdV2q8iIAvwK2B6/H5jZhLrj8YW9S+QhOB8FXjWzr9a0YemaNlTmT0/of0jKeUJCQkL/wxR8gno1PpnfFrhZ0q+hVzqdPwGfoxTzWIUKd/kzJJXd5cu5xYspZIwKhl8z+0Nw/byIwEBsZpUTSUl74ZPpNYCXgZPxFFZVWB+3iLWNvzKzl8OE65pwDbuVj1Oc4/Yk4Pe4RW2MalJHWe7++yau1LfCJHne6xtxxfBFali0rZTSK0zcz6qR/Zec+OpqXIHY2WpiLs1sojzv/UW4Qrqdmb3Tpt0t0fD+9pWMaGmguEA0DVjGnP27uCjU0XWZ2f+AjcP9kLXO/fwVK+Vsl7SsVbhol9CSTdsq3H9b4DxycjzwxajziBdxsnob94WZ3SWPzb4KX+zb1sxeatCe9Vrt7GRMBvkHJL1EcDeWtHSNstAps/s0clb1F/Bwkqrzt32Og0LXDfzczL5VJVOBE3AL6H0N3lN/wuP+PxXKn8Pjv6sU24PwGOMpeDrAK/FFhSpcL+kX+Hu9+MxU8ZQ8iC+4vo0/mxcR0mNWYGvgAElP4AslVQu/W+LEeTtWHN9rISosYtyBL0xkqer2sUIe+xI6ITTMnoG3w+LvK3g4ThWOwNNp3oBf1PgQAlKF0+Qkhj/EFenhOGFgFYrjZkg4Rxai0AvWJsOI4lj2L+L36xbgKEkL1CyWF99LQ/A+eIT2JHoJ/QQp5jwhISGhn0E1ubUzWCG9l6TrrIKkrabeh4B1LMTVydmA77GKPLsN6yvGC4t8kWBcaGcv1llJL+NWqVOB6+ssMkH2PJzUrRUrdznObhCuEBslC5byHLdVMItz8layjheEe1lYQuzkO7j77J44y/7Z1iyP7UDcbXKVwrbflNqwDc7w+3how8EF2ftKsgvjk9gpQbZlXGxNmzq+v304xw9xF+OLwzk+jk+AT8BJz9akD9cVJvRHkHuJ3IxbxnrdC1XnBR5rZr0UVEnHmtl32m0L29sSSRVkx1ubGPlO7nHF+F0VXzx5LchWjd+FcKKu0cQkkEXiq8ZjsnDMTvj9bMTFII8zzhYl7rQKsr0g9zY+Hk8Ermn1nMkJLr9FHpedXVsVwWVtusYK2Svx+PSWC6NBtuoe99rWKWrea1ZzbefixJpnh0174GFFu1fILlN1voZeJbWQdJuZbdJQtpM88j8Msh/CPbIMON3Mflghe4eZbaQ49r4tf0CnkLQUcJxVE82dj4/dk/HwgoOB9c3sMwWZYiw7xB4tZi1i2Qt1rIsvqNal7UvoZ0iW84SEhIR+BvM0OoNwd1HDYyOn1og/LOkcPOVM0WpS5c73OA3d5SV9FVcsJ4Xy/MAeZva7glg51dM/a7b3wMxGSVoNd+38aXDZfMTMPlchPgp4MLi4Fq9tp8L3eeWmh6Xaue2Zp4/rAnY3s3+0ksXjfsGtWovirL7gk9nHy8LB4naxOWnUTDzcoBYl5akbj9k9tyR2d6nci4yvgI+3Ol8f0fH9LS2WDMLjM9+yGjIiMztaznidWdEONLPsuvesUxAa4O+4B8OuWV14/HcPqVdwg14NGFlyyx1BQZkuYVucIbyIj1ZsA/eEeBh3Xe0hkqqp9yVJO5nZJaFtn8A9S4ro5B4f316kFy7GvViuoSZXNZ2NyQxH48rHNWa2jqSt8eeoFyR9Cnc3vgF60ph9y8zOrxDfAx83XwG+KOlWPC3XtRWyWR7uP1B/bRnGy0MzziN2ra96pz6Hp8O6nPbpsN6RtLmZ3RyudTNya28ESesD36P3Qkkv5dFC7u6GWMliUsTr5YR9vdCpEq6GZGzAVfIQmQvbeRtYZ3nkjzNnL79A0mXEv3VltA0bkLSXmf1V1aSVTRcnn8YXFqpwIO7BtUSQuwqIXNXNmfq7cBK8unzpLRH6sJcHTkL/RbKcJyQkJPQzyOM4f48rzsLd0g6wirhCVTMBW4117iLcIhW5yxNcQUuW2CorT4+Voa+QNAJPu7Ql7t4+Cmdv7+UtoA7SctVZOmvacKOZ1cX2tZWtOz5M6D9n1TGkZdnitU0HnrCK1D9B6T/TzPZqUOcssV3X1Nmpm2/5+J2BDc3sey1k1sIXawyPu55Q2t/xdVWNB0l3m9n6hfIn8HjXnQhxnwGTgb9bwXVf0pdxJXA54gWteXHuhl73J3teMotc8I64ssaiuTxuzVwcf+afwgkC/1uSa9wX4d5daRUs4zXyjay4nYzJIH+3ma0flMB1zGymQuq4CtkJuOv9i6G8EK7Ur1WWLRyzMr5AciiwsJkNrZDp5P3QyTv1iKo6rDod1tr4ot1I/B6/iqef66Ucy9O5fYveKdqq0uU1jmWX9Gec3O72UN4I2NvMvlJ1HU0h6VQ8pn9rnOV9N9zrYb8K2cnAPPgiSaY8W9UCXhhrH6P3IkWVV1aVB0yvbWF7OTXZlcDRVkhNJukAM/t9h/e46FnSBayNk0s2elbq0KG3QXExoQsPUVnAzLaflTYkvHdIlvOEhISE/ocTga2ziXmYuP8LZ2yNYJ3Ftf6T3PoJId6uBl2SlFk2wiRpUJWgPDZ+95KV/e81k4GbC5+TqxTSDGY2JlhOVzCza8KEqrtGvIe8qMU1ZbhanrrqH8SWsar4vYUkLWdmE8O1LYu7VlbhXeC+0B/Fenu5+hYXGCR9vM4qYmYzJC0kaVAL74lMdqakCaqP5+0Y4fy9JrcdHH+RpMPr9ivPeXwBrrD8VVKU87iP13W9PC4780bYjVKMuHlO84slbWJmt7Wp7xz8+TsGKF7P5JpxAx0QSVnDGPlO+iLcu7cljWyyYIQTKu5gZv9uUG+jMRkwSQ25GPDUcEU39ldwBaMXJF2AKz//xd8nn8fjmYsyHRNcdvJOzRQ0SfN60YkNa2THA2uFBUqslL+7hJcseFE0QNtYduUhEQOBz0t6MpSXwePQZxWNydjMrNbzpgKXUpFHvghJi+LW56GS1iF3/R6BLxhUYdXwGRA+n8AX6Xo8E8zs9+F/JzwIRc+S6cDf6t7tks4EDin9bp5QtQhEB94GxJ5N04HL8PdrwvsEyXKekJCQ0M9QtsxKEp7upspa2ziuNcg3cpeXEwyNxl1BDXfBe8rMvlEh27GVvclkVtKXgP3xVf/l5e6Hp1pFPKikB3HXx8epJy/KZKuIvsyqc/J+BI99nhg2jQb2N7OrKmQruQKswBFQhTrrTmH/73Gm7UuIlf4q69F1uHdEE7brRggT7RVo4Oar2D28Cyf127LO6qOGOY+bXpdyt3qRW+fAF3XerLHOrQicAixiZqtLWhNPw/aTsmyQ7wYWIbbk9VKUJX0RnxSvgafvGw78MJv0l2QPwRWqybjr9brA4TXjrPE9lscYb4x7y7RcMCpYNKeSLyzUWTTbjklJg81sSrin7+L3pCUXQ3jvrImToAF8Gidb+3aF7AY4Z0atm7rymN0q9vm6Z74TroDV8fCFbBHgZdzj4YEK2Sr36NfxlGXjS7Ifwt32r6VNuFLN+zfapjbhIVUW+U6QeUJIuh1fFHgFT623Qo38J8n5IG4ys4tq5Jrkkd8b+AL+rikqx2/gHh5VffYInnHiftp7JnSiRBePmx8Pt6okxKz6jaz73Sx5G7wDzXKXyz1thrdZCEroZ0iW84SEhIT+hwfkcbjn4pOX3YG7MsWnNNloHNeqCnf54LpXlYbnO8ABwJeD7FW4u2IVZhQteWEiWLnyW5rMSs7gvLeZ3V8h/lWc7faOcN2PSlq4pg0frdneC2ZWx+BbJXtFWBRYOWx62AqujyXZM8Pix4phU6v4yCLapa16Nny6aBHvHdCJlacpFsAn20V37MqUYMSszdPxxZJPtKhbxDHAM6juj0bX1aFVLsMfcBfizFJ2r5zHoZdyLulreP7rF8gn9UbB4lZoS/a83EhFloMS9jWzkyRtjxO97YMr672Uczq7x/+iDat8hg77rsmYvA1X4E+1nFOi5UKVmX2roLgJOM3M/lkjPh74qvL0U2PCuXqeuU6e9QI64Qo4DTjMzK4HkLQVPp42rZBdP3wuDeWPAXcBB8qzZhxXkN0Hf+cMJB5nVc9c21j2WVW+G+BSSfPhfAH3hLb+oUpQ0u+AD5AvwBwoaVurTg12uaTtqhapMoTFzzMl7WpmTS3EL5nZpe3FAM/vPqlwvteChb4XJN2AW+AH4OPzJUljzKxqYaZL0vxm9lo4dgFq9LJOns3w7joQf5eOxTk1TjSzXzStI2Euw/pBsvX0SZ/0SZ/0yT/4pLzu86eS7Ljw/97wfyBwXU29DwMfKJSXx5XNWW3vR4An8UntWcATwPY1srfiLvtZeSvg1hrZO0rXOCC7zhr5zfG0POCu58vWyA0DfoBP/MGtwh9vcJ2ntdm/Vbj2MbhC9hieM7tKdnDh+4blbTXHzNPwfiwDfLhwrfPOzfHcpq1fBybgCu+R+IT20Fm9LpwBfmShPB+e7qtK9q7iOAvfx9fI/hdYsA/XeVmb/dnzexKwS7k9s9gXQ3EisCbt3Aknkzu+4TNROyZxq+Te+GLgJ8ufmmOObbItbD8dV/a3CZ8zcHbuKtkheI7vC3FvhkOBITWy40r3pNU7dUKTbWH7lbgVMysPB64I9+fBkux9HYyttcMz9Dj+/hkHrNXpGO3rB1+g2bRQHlx89irkHyB47haOf6BGdhfcM+Md3BI+GXijRnZRPF/65aG8KrBfjeyHwvjZo8GYnIAz2mflBeruT2HsfBH4cXEcVch+Hl/0OTp8HsY5S6pkBeyFe94ALEX43aiQHR/+74mHyA2sa0P69M9PspwnJCQk9DNYZ3HkjeNagRctJpiaSE1e4GB9OZI89VDmRleV5/wKeVzyxmHT182szDSdYR4LVqZw7A3B7bUKYyR9D48l3BYn5Kq0dshJe9bHXdvPwCckf8XJ58o4A7coZNatp3GX7ctq2pFh/Tb7T8BzTj8S2rQibh2qIqLKrIpYnmO7Z1sRkjbBJ53DgaXlBGoHWAWJUzEUAF98WQIPTWiUGqoKTdy+1TvFVgSrdqNunPO4D9d1hBUsrmY2KYyRiypkX5bzOlg41244C3cVnqI+z3IrLNFm/1hJV+Hkj98NYR91MbaN+0LSjriiPQj3lFkbJwurcoH/Oe4un6XZOiRYZHtxBjQckwfiCsJ89M6DXWcF7oQNfwOLieKuUw3zOPAXXLHLuAz2wBcSe6UQo7N36kR5Cq+zQnkvfFGuCkvjIQPF8yxjZu9IKnvj3C5pVTNrGw9uncWyz3aY8yBk+d4x9yyq9C4KeATvi8yavxRQ6fpNZ3nkswXs74fyf3BekT9WyHbimXACcKs89Rn4mPlpTRsGSFoMj///fo2Mn8zsL5LuxheWhC8O1N3v34V2boMr8m/i6eKqWNgHysknd8Z5XaZJatd3Cf0ISTlPSEhI6GfoJOYROC3Etv0Qj/8cDvyopupO3OX/iFs1x9I+9RC4oluMia9TdDuZzB6O98N9uIv9v6l3rd8FWAd3qcTMng0KThWWN7NPS9ojyL4jqZ1rOdQsZBQwMFPMQ73/CZOkHqhv5EW/wl1sLwn1Tii48pbRSShAUzRx+y6n2GqLbFJvHo9+TxvxTq+rikSsbs7zVdw9eWVJz+DjsY5deSKeOutftE+dVUSvBYcS9sMtoAPxRaBReJx6XXub9sWRQfaGIDteTmpYhR2AtS3k7A6xtuOICfAy/Io2Y9LczfpmOVt7lYLUAxXY8OU8BBnmBepSSM2QtLw5mR6SlqP+XdU4hRidvVP3xcMMLsSf5TG44leFc3Cl++JQ3hH4W1icLCtlmwN7y2Pmp1DBoaGaFF/Zq6zBmJyd6ISwbEHgIXmKTHAF8zZ5tgtKC0eP4rHrTZTLUWZ2rqTvhnqmS6obD2uZ2RoN6iwr0dBaiT4K95C42czuCmPy0RZ1P4inC92/zULMRma2rqRx4bjXQghVFX6Pe1FMAG4MYWYp5vx9hKScJyQkJPQ/NI55tDyudQzt41qH4LGyW4byS7j1bUd6Ww1et+pY9F6osbhtZmbfrRAvT2ZvpGYyG5SEP1ATu1jCVDOzzELQwhoPMFXSUHJL6fLUWHokDTGzd0N7PhK2jarxDLhb0h/JFx72pHce6O1x8qIlcZfDDJPxvMaVMLOnSusHdZPOKWY2NZOVNIAWFu2GGGZmd5bOH7FtW4n0Tg0I/wKaTuo7va67JZ2IW5cMOIianNzmTPwfDmOmy2qY0gOeDJ9B1GQvyCCPTz/bzF6rWVgrYl/gEHxcjMe9UG4jt/QW0UlfTDez10v3rlW/zYen+AInbqtF0zFZVMzlTPz7V4j1hQ3/W7iSPRF/lyxDvWI8TtLGFqcQq8uQ0Pidah4v3MsrpEb26LA4mnmJHGhm2aLWniXxjzSosi/8CnMKh+GEZdMlZeR/ZtWEZXULHVXoJI/8W5IWJH+vb0y9l0tjz4SAgYRrCt8rYWbn4V5YWXkisGuD+g/EFwjrME1ORJld20LUeNaY2a+BXxc2PSFp6wZtSOgnSMp5QkJCQv/DB8xsd0mfMCcZOwdfje9BndUkQ9XkpUN3+evlzMkXEk+KqiycdRa3Xsp5k8mspHPN7FPK0/+U66hi7z1XziA9X3D73Zd6pf4IPNZzKUln467vX6iRvUvSlwqT+l1xBWLFCtkv41bNg8kXHn5XantfyIuekrQpYMFacjD1BFVj1DAUoAM0dvtWNeFfJXt1QDapnxEm9VA9qe/0ug7CLZ9/Jyc0rHQzlTQYn0CPxt1Ss0YcVZa1PHXWPBYY5ltgUXz83IOnu7qyxQLEIfgC1+1mtrU8d3cd8VsnfXG/pM8C3XJiw4Nx3ocqHIMrsdfjfbYFFc9wQCdjsojK0BDzVG+vA3soZsMfLmm4VbDhm9m14ZpWCu2tJWsENiJPIQbuVv1Q9o4pWaQXAX4GLG5mH5W0Kp5RoJ31v2XWhdDmsXgIw/4FxbxK7olQ58IUvKdKMnOC/LFPsA4Iy6x3GslW4USPhU/bxTD8XXIJsLykW3Dekd1qZNt6JhTaeAht0j0WZBcKsqOJszm0W5xr57n1azwN4CKSfhqu6weVFZXeZ4Vdvd5nCf0TKZVaQkJCQj+D8rQ0N+IT7+eBO60Q7y2Pn61F1cRNnaUIur68zUVtmwrZe4GtMguXnHX2htKE91JaxyTvVJBdzMyeU036H6thHg6KynaheJWZXV13vmBh2RifFN1eYwlH0hq4YnUDsDjukvlFq8nPHhSVVXCrRqtUdZUTqCqFUNIonCjsw7i79pV4ap+qVFRd+D3eLlzblThJVp9/7INr5ml46MJr+GR5z6r7IOlW4PsWs1f/zMyq2Ks7aUNH1yVpfVwZH03ev3WT7ysIKa0oWH/N7IQK2Z5YazNrGf8f5BXavA+umJ4L/NGCK3ZB7i4z20DSeNyFdYoqUmR12heShoV+KMoenXmDVMgvhi8SCCdkfL5GrvGYLB13ReaBUrO/kg2/9C75ZMWhPbDq1FmV75LCMU8UZC8nxC6b2VrBM2GctXGDVpv0kSXZdukTd8JjnRfHw2mWAR4ys9UKMt82s+NUw/dgFTwPcwqSrrVSisuqbRXHtV3QKMguWjceCzIDyBdrarNldPLboobpHsO+W4Gb6P0uabkQK2nJut+UgszK5LwS15lZXVaWxu+zhP6JZDlPSEhI6H/IYh5/QB7z+MOiQB+tJp24y3fiBtfE4nZ808rMLLPKdgHPZYqE3BV9kRaH3oczH1v43gpbkufZHYhbJaracl+wVJyFu55v0UIx/xhOzNUkVd3F5BOoVuRJhIWDsttrnezM4LlwR7i2R2ZFMQ91duL23QnhH9CjbLXMedyH6zqbijzGNViylcJYwq9oHv+PmZmk5/EFtunA/MD5kq62OHf30/JUVBcBV0t6DU9VVlVn474ws7eB70s6NjSn172TtLKZPSwndQQnSARYXNLiVuEt08mYLB3Xrp8PxePDWyn5ZXK56BQUwnPCQiH4s1vVniqX+U5il4tolLIua1qb/Ufji4fXmNk6crfkPUoy2bu7Y76H2YWw4DsMGBV+s4ocGos3qaKD0/2bCsLMEjYkX5BbVxJm9peyUN0Cbw1Es3SP4CFAVeSFvSvtwGMnqxvIXNuHtqi6k/dZQj9EUs4TEhIS+hmss/zIPWhghWjrLl+oqzIusGriYGZ/k+d3zSxu3ylbOIqujB3gPOJ8wTPCtl4MtZK+iMcyXhfa8BtJR5nZnypky3l2D5D0YavIsyuPIV8ez2O9Ip7P92Qz+21Fe0/A08T9Nxy7PD5hr1LOG0+gguX6JHyybngs8teD0lyW7WSBoBGCl8ERBAVa0s0443eVAtUJ4V/VvajMedyH6+okj/GtktYws3YLOkDzWGtJB+OpxF7GiQy/Zc6c3IWTRPUo52a2S/h6ZFjkGomHXlTV27gvJG2Ae37MG8qv4znVi/H3h+Hs71WWNSPOb5/V28mYrPKaeR1XKn9fsuK3ZcO3zsJzxoZzVylTRvX7tZPY5WK7Kt2Ma9BqgQFgmpm9IqlLUpeZXR8WWIrnuzT87+F7CGNruL13jO0H4Asqi+N9nfXzGzjfQ5Pjm6KlIi/pLPxdPZ78mTScqX9WcAZwh6R/hjZ8gmoGeIDLJO1gZv9uUG/jBdrwe7w7uWv9GZLOs0LGjAI6ep8l9D8kt/aEhISEfgxJl5nZxxvKtnSrVAN3+YLsNwrFIcDHcbfKfQsy7eIr7ynIVsaP0zrWr5dbr6QJFrMuZ9sfwXPtvhLKC+L501eqkH0AWD2zNoYJ7X1Fl9GC7NeBXxVkRwInmtl+FbI3mtkWhbKAMcVthX2nAb9pMoGSdDs+0c0U2M8AB5nZRhWyD+P5qaMFAjNbud15Wpz/anyh6K9h0554GMOHK2Tnx2OlM9KrMXi+39dq6m50Lzq9Lkkfwi2N1xJzJlS5PD+ILxA0iT89HyfyOxlXTA8G1jezz1TIHoW7sFe5yq5S55baDp30RXDJ/aqZ3RTKmwO/q7m2HvLDVtvC9k7G5El4/G8m+2n83TMUGGFmnyvI/hF3S65lw1cf+DY6gaT18Bjf1XHPi4WA3czs3oLMzWa2uaTJxO+1WiI0dRDLLukaPBXWMThz/4t46rhe4SFhkfVAXCEdiy/snGhmv+jL9fcFkg6yihjsGtlhwDeApc3sSwq8AdY69hxJXzGz37XY/xCwahuPmj4h/NZl77QbrSLdY5CbjHNoTCVPyVc3Hu43s9Ubnv8hYB2LvcjuMbNVKmQbv88S+ieS5TwhISGhf6NdfuQi2rlVtnWXz2Cl+DRJx4djiijK9JqgElvcGi0wlPCSpJ3M7JLQhk/gVsgqPE3sujoZt8JVoW2eXeXxkosWJ3vmxFX7lWSzGNjKVHU1bdgc+IIaEBLhC+lnFcp/lcfmVqFxLvsOsICZHV0o/0TSzlWCViD8kxN7zdPGitc053Gn17UPzfMYf7RFPWUciFuMl8DH3FU4CWAvmNmPJG0uaRszO0NOFjXczB7rq2Ie0ElfTM4U89Cmm4MCUYVb6e02XLUNOhuT65QWqC7NFrLC4kwRTdjw+8RSHt59KxDzbdxYljOzsZK2pEXsspltHv530pY/0zwP9yeAd/B0lnviCnedu/OqZvaGpD1x1+/v4Er6e6acFxVz1TPyZzgDb98mofw07hFVqZyHBaUVzOx3xWeoQvR+nISxkqyyrwiLXw+Y2T1yDo0PSnrMzCaVZTscD51YuB/Hx222UDYY95ypQifvs4R+iKScJyQkJPRvtMuP3IMGbpUXBuWpx11e9TmPyxhGyQXUQlx6WMX/CoW4YeCUkmyRcGkZfLJ1TTi27rfoQOBsSSfjk+SngM8XBQpWtGdw18OLQxs+AdxZks3ca0cS59ndkN4M1ouFCfpOkv5RbpjFcbhFF9Vyqrr5a66t7QRKebzs9ZIOx5nHDbc8/qsk25cFgqa4XtJnQp3gTMGVC0FVVjxJrax4LXMek+f67vS61rLmeYyLY3N/M6tNaWQdxFrLSRvXx5W8M/CFgr/i2QE6Rif3uODVcqc8i8HfyMfODSXZRfHFhqGS1iGOGx5Wkm08JgtYSNLSFhjXJS2NW4PBLYw9sAZs+NYHvg152EtVqroql/278VCAv9V5fJTki+zyWRt7scvTQSx74dpnSnrFSqkKSxgoaSBuaT/ZPHRibrrFVjLyF7C8mX1a0h4AZvZO8DLqhSbPUOG9Pi+eM/xOYq+LYt70vuACYH1JH8DDUy7FU//tUNPmnXDeFXBi1DqPgLYLtMrJ/qbgz/3VobwtcHNVpWb2hJyo8oNh001mNqHpxSbMfSTlPCEhIaEfw9qkYJHHQ/4GZwgfhBPGvFXlRodbrD6aWTIlrYJbLHq51il2Q+/GXTvrLDdn4jGGWW7VPfA4v09V1PslPL51ATw+cEk8frYXq685o/XGkobjlroqi19mqfgfsSXh4grZxqR0ePz64aF95VjcyCvAOouBzY55omAR6rGqlsTK8bLF+EzDSaMy9GWBoCUKLrvC45LPCt+7gDfxOPQyOrXitct5/IXC906uq9M8xhla5hsOi1kH0Ztlv0oB2AVYB7gnyDwrz//eV3Ryj8tjtnivyorb9ng/L4m77GeYDHyvJNvJmMzwDeBmST0x8sBX5ESBkdKpAhs+0JINX9KK+CLgIma2uqQ1gZ2sOg63k1R1n8E9L+4KivoZePaHKjb8g/C+jdjlcY6KMvoUy46/d1u5fP8et6xOAG4Mi5/vVcx5Fdp56kwNi7JZPyxPfcx1k2foeHxcHYsvUGTIts0qZoaFlE8CJ5nZbyTVubX/HB9nZ4dNh0ja3MwOrxBvYuHOyP7GEpOW3lB3gPLUb5mXUG3qt4R+CjNLn/RJn/RJn37wwRnG76343AfcW3PM3Xh82Thcid4H+GmN7MfwGODhwHrAA3h+8irZZQqfJYABLdo9ocm2sH08vogwrnjdNbKH4NY74RaLe4DtGvTjoh30+cfb7P8hrvhshCtDW+CM7VWyQ3AX59/hlrc/AX+qkT0Ct8D8J5QXB26Z22NwNozhB3Dr1nnAlq3GQqf3osN2PIRbZR9p9wyVjhvXZv8E3G1/6zAetsyus0L2zvD/nvB/niZtmIv3btc5WPdgYC1gbWBIC7k78NCGcYVt99fIjsG9XprI3hX+jwcGZ9/btLkL2An3ynkKV+YXKMn8F1iwYR+sC9yCK+S34G7ta87qmKw5pvZ9Pbc/uNV3DL6odDa+sLBVjWzjZyiTKW2b5ectjMk9cLf5ZduMs3vxjBZZubtVG8Iz8bXwWavpOGqz/148nCgr9+v3Tvr0/iTLeUJCQkL/QV/isjGz/0rqNrMZOItr2UU7k/tXcH+8Crc472xmj9bIPgEgaWFc6Vxcnpamyl1znKSNzez2cMxG+OSzClPMbGrmxSjPS1vngrmvmZ0kaXtgYXzh4YzQ/lZoknInQzur1PN4GEBbd1g6SFVHH62qDeI5i7KNcwg3haQjzezIFiKzYsWrvRdykq7P46EVLwJ/tULKtgo0ZcLvwieumfdIOxbtd83s121kMpwbXMrnCx4j+wJ/aHhsI3Ryj1VDLilpLzP7KzBaFWRr1oZgreGYXI/c22BN1aS4CudrxIaPp626syQ7vUa2cao6gGCF3wd3Xb4AVyI3x7NBrF0Qbcsun8E8ZrllLLsC14WkYy1PydWS0VylbAq4u/NRQMuc87MDqmbi74GVPErC8zY/8En8XSrgEPNwkSq0fYYkfRkPq1pOToCYYV7qf4c6wT64R81Pzeyx4D3z1xby8wFZir6RdUKzYOE+nda/b52kfkvoh0jKeUJCQkI/gXWWezXD25IGAeMlHYeT4UR5pQtxaxlG4CRSB4VJ8sHlSkPc3Am4RfdF3IL+ELBaQSZzfR8IfF7Sk6G8DFDnTjxG0vfw+NZt8UlVXcqrbEKxA3CGeU7pJpOMTiYi7WQPprk7bONUdcBUM7MsNlRtcoEX0C6es4g5MSHbCTiybmdQXHuU1zAmtm5Yd13c6Yl4bPoJuLVxYeAHkhYDLjGzNyva0ehZMs8ZPkEhJtpqctgXcFKIg72KOK61Khf48WGMv4ErZD8ys6ubtKsDdHKP68gls7FXDqtoipZjUp2luHpK0qZ4yr5B+PNXt8D1cnCJzp6h3aghA7POUtWNBSbh7vWHm1l2n++QtFmQyRYxJgI3SKplly+hXR7uItfF3/H7O12BQ6BqnOFx/zfiObPBFwX/AfTKpjAH0EmoUPa8fc3MzqVBXviGz9A5eLrKY/BQpAyTrTqPfUcwD43JSC7nB+Y1s5/XiB+DL1Zfj9+7LYDv1sjuB2xkgV9AnirvNjxMrRXaPfNnkKd+A3f1r0v9ltAPkZTzhISEhH4GdRZH/jnc/fJrOLPvUrhVooi7S+WxtMfRuGXjGjNbR9LWuGtfEX2x9B+OT0ruwy1C/8YtAVUYK+kqPEb1u8GyPLNGtoiW1klJgwsT7gMqthXxrpm9KymTeVhSr/RsAZkVbJKk1XGr++ga2b5aVTthXm87+e0DGiuDBUttnTWzjF4WQkmfw91E95an99s57HoBX9gZIul1M7ugabsqsBhOtnQn0ENCVrb6BayBP3PbEMcYV3lSEBSJ2aKQSzokeJJsZmaZRbCTe1wZJ2tmvw//OyZaC2g3JteneYqrxmz4YftpwMqSnsFTRzUh61vJWpD+AbtbRb52ADPL3q2Zl0sTdnmg8SJFkeuirODXjbPG2RRmN8xsTB8Ou1rSN/EFhOLzVqlIt3uGzDNovE7v36fZAkk34IuSA/B795KkMWZW5WXytyC/Af6u/I6ZPV9XNX2zcLd8Ts3sxNCGLPXbPlaT+i2hfyLlOU9ISEjoZ5CTEH0Gj9tdH3fn/YCZfb9C9hAzO6ndtorj1q2xwvS0wczWlzQBT4U0UyFPeh+vqcpds90xXbgL6UQzmxTcN5ewQr7hPrallytwnXtwsD7sAxyKT4xfAwaaWS+mXjkj9AU4GdQZuCXyR2Z2ak07tgW2wydQV9ZZVSXtbmbntdsWtvfq3076vOb8PcqgpK4wFooKYt1x48xsnTYyQ4iZ/m8GTrE8n+8NePjFJEk/wZWiy/HQgck40dz5Zrb9LFzfllXbqxQPeY7xNc1sasUhmUw593XPLmpyHjds53gzW7turPYVklq66Vd51nRY/3nAwWY2W1JclRcpgtdJl1UTRlYd30koQCNZSSPwe1vbBnWQh1vSD0sKdyvZ4/EF2GI2hdXMrIqwcY5Anqv8GGBV4lR1y1XIPlZRhRVlWzxDmXCfnqG+IHuPhff7UmZ2hKR7rSLtpaRdgOvCggHyUIqtzOyiCtnDgL3Jid52Bv5sZr+qkBW+8LScmR0lz3iwqJndWSG7MZ76bXIoz4uPuzs6v/qEuYGknCckJCT0MxQU454JgKRbzWzTCtkqRbOJUtRy0inpGnyycAye9uhFYIOqNjSBpAeBL+PM7J+lZCFotVAQjm8X61yWz1xhf2tmJ4dtWcqov5baMAI41cxWblPnlgR32FbK2exGh4sJVbKVE8k5cf6SzJ+sfbaBc3ElO4vh3AOY38x2D/snmNla4fvtZrZx4djbzWxjSWPNbL3OryxqxzLk6f2GAd1VipY8rd5BZjarueP70sa/4bmhFyLOTNArBVOQH4iTKmas0IbH4B5tZtMLcnu3Oq9VpPGSu5DvhS+WvIsvkJxR0+7r8UW2timu1IANf1YXKZq8H5vKSlofX4jLLOmv41wZvbyTOl2kUMOUXEGRHUbuydFNbpHu82JQJ5B0Mx73/kuct2EfXMeYpQUCSUfhHkhZpog9cbfy42atxR214T58EfVM4PtmdlcL5Xy8ma1d2lY7huThCpmF+8Y6C7ekU/D7u42ZrSJ3r7/KzDaokB2Hk8Zl4R5dwN2zc0EvYc4iubUnJCQk9D80iSPfA1cwl1WeExpc0WxCBNTOfe4T+KT76/iEaCT1qdSaoC/umkW0jHUuI0xgRuEs6xk6SRlVVWelC6cqSLRKx51YkG1sEZL0UTzefomSdXMEJVdxzQFSJHlaq03xPNXFaxyBKwBVx3wNONvMXmunmAeslCnfAdcHb40ML0paysyeAiZKOhiPFd4eeFyed3uW0kapd3q/JahJ74fns35Y0l00yKWsOF3eKFyxqLIctoWZ7REWmK7En4daBI+Ey3Ais+3MySKR9HngR2FR5BEzm1alfLeotwuPcX4I2NvMXg/vqu9IOhQ4z8yeKR12ZNP6ccK2P+I8FHUhLA9Jehwfl8WxXrlIUYF2pH9FtAsb+BPwFTO7CXru9xkUUqmpD3m4JR2Dx6cXU3JtZmZV8csj8Xf0sgWr6mLvsaV0qJldK0nmfA9HSrqJinSLYQz2glUTBG5vZsV3+CmS7gDeM+Uc/927Erg5KObLAZVEqniIWRmRriVphHm6yQVw8szHC/sWsGr3/o3MbN2geGNmr4XnrgoqemeYezolfe99hHSzEhISEvofquLIdy3J3Ior7aOIcxpPxlOptEO7uLW3CsXGk/cW9Z0PnN+Ju2YJHZObmTMA/6tQPhM4U9KuNmsxymU0zl1tZvNCvUWoJP4s7q66EzFPwGR8XBQxJ0iRBuGu+QNKbXsDd52twqJ4fuh7cMXlyjZuvO2Y/k8FfoY/E/vh7MaH4qz4++Ju7XWcBU3xVVwRugPAzB6VZymoQmNLoJw4bn2cyOoMvD//CmzW14aax6+uFSbmK4bNvVi/gW8D/wiLAqcHhQLy5+hOfOGnR8mRtBCel77smlxcOPsantrrGEm/krtzg7+vVgVekDTczP5QOL6TuOS2bPidLFIUIeljOKHlEAVeSTNrueBoZj9oU+3kTDEP8jeHBbgi+pKH+2N4msuZoe1n4pwBVcr5bwlWVVyRnIyH1/Syqs5BvBsWbh4NC3TP4MSNVSi2awi+CHYP1QSBMyTtiS8IGe5ZU8feP0dgHj50XqE8kd6/xxnulhNY/hZv70H05ng5B+drGUu8UKtQ7hUKAEyT1J3Jh2e1bvEqW8Q8JZS/ghMXJrxPkNzaExISEvoZ5DGU7xQmZt14bt63a+SLLrlD8Ry3lbGPwR1uBeLJ942F/XMkXrZQ/8CyIiFplNWn0slkurL+qNn/SXyiu3Boa8v2Fifq2bZ2E/XZCUl3lCxCldvC9l591qD+LAUeQF0KvKZ1LWMdZBKQaz7b4a6t6+OxsH80s/9VyD6EK69Z+5bGrbIzCVbQYEVcGjjCzP4bjlsOV3qeMrND+nptoa47zGwj5bGlA/CcyX0OBQj1jieky8vcWuvcYTusd0tckXkcH+dL4Vbs4nN8B7CZmU0P3je34Ys3H8Fd478PXGtmWxaOuQon6fomTsy2N/CSFfgKJN0CfNjM3gmLD2+QcwCMxJX9f5vZNpJuNrPNK94ptc+mpM/i76e2bPil49pxaJyKu35vjS/m7Ibn0N6vINNxnLOkX4Z6/xaO/TTOS3FBud3qIOQkeARslS2sBSvrDTWy92RW1cI46wkHeS8gaQP8uZ0PJxMdAfwiW3Rrc+xI4KwaD4LROEFgtqB1M3ComT0+WxreAMELZT96/1708gwKv90/JGfKvwpPwfZWWbbDNuyJj6118cXy3YAfmrPel2UXxjNmbIOPyWvxPnvPQ3ES+oZkOU9ISEjof7gW/3HPUkQNxX/kq2LOyy65S1LjkisntDmEFjm7M8vu7Iac7f0sYHBwzTug4N57FYW8reqd+i3bnrWxiqDqOGBHM6tLu1Ssp3Ki3sn1NDhHu3jYTixCo4OC2oRsaUfcZb82BV4f8LakX9B7clrHUG6Snsc9A6bjeY3Pl3S1mX27JN42H7mZfTcopD+WtASuuL+AE8fNDib0MWqY3q/DRaC+pstrhxNxV/VHQr0r4sphMe5+qOVx5dtk/S7pYuAHZvYdOVFUEQua2R/lhGtj8H4pW73nNbN3wvePWx7z+rCku8zdqucDMLPNw/9O3ikdseEX0C7386ZhoedeM/uxpBPI80tTbGdDr5YMa4f/ZY+KTbN2q28hJ52k5OrEqjpHYGZ3ha9v4otyneBtfEGmqt7H8RCruYmzcE+d7XHPhD2pSe8XlPDDASQtZi34BRRIUtttC/WeLU/x9yF8POxc91sXlPDPNLmwhH4KM0uf9Emf9EmffvQBxjfZlm3H3WXHFbbdVyN7H65cjQ/llXHX1/fimu7CGYTBleFHgY1DeVxJdu/wOQ23lBwUPjcCv6yp/5YO2nJv6f9wnFxndl7vuDb7RwMXAy8DL+GxtqNrZG/GJ2X34sr2kcCPa2Qn4DnBx4Xy1sBps3gtV+GWo4eALXF39WNrZA/G3TWvBHbHme3B3Z7/V5AbEf4vUPV5L8ZkoS1duLv8ecD54btqZP8LrNKw3m8Cv8ddSr+EL4QdPBvae2+7bfiiz4bh+w/Dte2PezEcgXsr/LN0zO3h/5W4W/U6xXsW9l2SXT9wMmGhAA+t+R2+8HdF6ZizKtrba1vY/jAwqA99Mq7N/juya8QXrgYDj7aSbbetg7aNDM/738Lzm31ajnM8xd9OuHK6aAu5PcN9eRr4KfAIng7uvXyGrgbmK5Tnx0NaqmQvDe29BOdFmAj8vME57nkvr6k8tsh/LwbijOx9ai/+G7wA/q6ev/DeGw08VHNM42eoP/RZ+szaJ1nOExISEvof3iq6aUpaD3inRnaKmU3NrMrBJbfONbOTnN2zG4PM7AHw+PPgznyhpMPL7bVAUCXpC8DWFly6g8X7qpr675YzaV9E7A57YYVs1pdvS1ocJ9Bbto/XVYeWRFLWmUWoMdkSMM3MXpHUFUIBrpdUF9faFE0sqhlGAZ+0khu8OSnRxwubynGXRU6BXnGXnbiWNkXBSnWMuet2kzzzL1gD74zQtuODJf4NXBn+Eb7ANKu4W9IfcYseuHJWjmv9OfAbSduZ2dGS1sQX436CM71fQm9L7E+Ci/E3gN/grsllboNfAieEsJCDcHK1tYExwL9xBfRXpWMir43wjqpj15+Au0Z36oLbLkf7ZcGi/ws8vtmo5yroU5yzpMvM7OPl7dbHPNzmVtdLJO1v9bmysQ6sqnMQo8xsUqFNr6met+H4wvfpwBNm9nSDc3TMOzKbkIUUTZK0Ou5VMbrBcXXtPQDnzVgcf24zuTfwWPUqlJ+hbuqfoSZtSOjHSMp5QkJCQv/DocB5kp4N5cXweLMqNHbJBZ4OE9SLgKslvYaTjr0XmCZp0WySaWYPSPoQbjlZvuaYxXHXz4zQbHjYVoURuHvkdoVtRsl1NaBqot5EMWsL5fH/P1CL+H9JZ1CxiFKjbHZCtjRJ0nBcCTxb0ouUmN37gGxy+lxQyp7FLaRV+BX0xMhmmGzOCt6jMGRKjJk1XRRp7FraARYL7vI7Sfo7NErv13gRSHkquatDeTiuwFaxwHeCL+MkdgeHNt+IW62LbR8fQhFuDItat+OeF5viCyM/tlKOZMtTdb2Oe1z0QljsWRm4Bifqu5Y85OYmnK39inC938WzIAyVlDHqC5iKe8VUoVM2/CVwK/SrkrYIslULIMeZ2RTgAkmX4Qs879a04bN4nPNJ5OnnPlsjW8QSDWT6ggOp7y8AzOxh/PmYW5gpaWkL3BbhPVi3SHw3gVMlhGSsK+kFa8+r0Y45f07hNDlXyw/wRa3huDdKO1T+ppjZScBJkg4ys9+0qqDiGcreUa2eoSLmVp8lzAISIVxCQkJCP4Q8R/FK+I/xw3UTl6C07YcrpcJdUk+3Ni935Tm7L28wKZplSPowTi41obR9JPA1M/tpxTH74C7c14dNWwJHWgepnxq0azAwJFi3ZrWunvh/M1te0gp4/vSq+P8i2+8QYBfgWauIp1cHZEshrvkd3FU7S4F3tpk1Sa9Xd10fxxWvpcgtqj82s0sqZB8Pcq/h43E+PKvAi8CXrDr/c6ZgFfNa31iSGWdO1naveezwQNxttl0scqvr2g1/djbHFYYirKrusKhShlUtqkg6GrcofjlM7v8F/MFq8oH3BQ3iWkcCnwIycrD7cAW6F4O/pCXx+7s5HrN8M3BIlVVT0gfw2OJivX+psthKOsaqU4BVtXfLqu1WwfgePEI+DTxIbtm2KkVe1WRsfcqTXofCYsxshTrIyz63IOkjuLKY3actgP3N7MoK2bHAB3GX7tvxZ+9tM9uzQraHHDUo8ivzHv1m9RXBqr0I8fuskpBT0qa4Fb4o24u1vpNnKOH9j6ScJyQkJPQTSNrGzK6Tk071Qo2LNvK0SivjlopHzGxqjdxZZva5dtveKwSFZSkzq039Jk+ZlDGY31Hn3llQLDbD+6FWsSgdd5qZ7d+X9lfUNZ6Qksty1uT7zGyNBsd2Ade0UzaL3gcN6vx4wRr6niBYaf+ZTcolbYeTvp0LnGS9GeobKViS7jSzDSXdiHuHPI+zbVelHeq0zX1N79ek7mPxBZL18Lja2ZnCryMFU+0Zza/GreqZu/xewJ5mtm2betstEGxRtb3Gwl08ruX4lfQIsGawiNfJLIpbtP+KW78zy+MIfOFs5YJsJRFlob1VRJRzHJKWbOj2PVchaRROMirgNqvJwKGcXf4gPGTnuLoFiE4U+fcCdaELhf0H4eFGL+Dvs4wwsopl/yzca2w88bvv4ILMyubhZ5XPuMXZADrONpDQP5Hc2hMSEhL6D7YErsPjOMuodNEObsan4nGkApaVdICZXV5RR1/j1mYbJN2AkxwNwCclL0kaY2aH1RzSjROmDQBWlLRizaT+DFyx2D2U9wrbWioWeKqv2YVO4v/LWAFPF9YO/6Y1K3URR+FhA7MNDZTB9c3swKxgZldJ+pmZHRa8FMrYGViplYIVkLmW/pDOXEub4DpJ85jZW5L2wvv3JCvEzUv6dlAiKhW40oS6uLh2Z2jnnYBJ+mTdIlsf0UlMaTtG84VKVv0/Szq0Qb3/alPvtwrfh+ALWGNpz8DebvxOxMm5Wo2d7YEv4GEYJxa2v4G7CxdR9p5oi+Ad0yiTQof1HoK/vybjWQrWAQ43szrOjbmCCuUxC5NaOri5Vy0GSdImuGdPlsquTh+Rmb0taT/gN5kiP/uuoGO0C104BH+fNfFUWh9YtY2X22G4N9YJFfuMikwr6izbQEI/RFLOExISEvoJzOyI8L+TVDQn4KRpWf7n5fHJco9yXhG3Bu1jP+cURprZG/K0bmeY2RGK0wv1oGBVfYA4rVKVct5XxWJ25n4do+YpucpWjueB71TJlg/toD1zggyoXZ2vSvoOTqYFIe9zWAiqSu/URMHCzDLyrjGUyOJmA04B1pK0FvBt4I94HvGii3Xmrt1EgSsvro3Dr3FH6nkQ+opOuBLa3buXw+LE30J5D5wscZbqNbOoPyQthac+nKV6cY6J8ZKuJY5PP7jw/UzgTEm7tvNaKIfLyFPNmZm9WXMIuAJ9BE6UtzXu6j87nrt9zewkSdsDC4V6z6CeEHNu4Rt4JoK2ymMBh+JkhP805x5Zjjx0qYxOFPn3Au0WBp7CORua4H5gUTzspxKZV5eZVXJA1GD7kofSKZLuoNkzl9APkJTzhISEhH6GYGHcld6xaEdViL+YKeYBEykpnGZ2DHCMpOPw2NDlzHP9Lo1PDt5LDJC0GB4H+/02sjvTzKoKfVQszKxtnu0OcDg+gbwPZ+T9NzWM0Nb3fPItlTFJ3WaWuUge0MdztEI7gqHP4srKRaF8c9jWjd9zIHIhbqtgBfkFcf6BLGzhJuDohhaqdphuZibpE7jF/I+S9i4KmNml4X9bvgMz2ycsRhxsZr+cDe0DepHsZfh7tt0q4shLaMdovi+eHu2XeB/fGra1Q6dkik8Dq5c3hj670sw+HDa1G79ZOq4muEXOcL+4mX1U0qrAJmb2x4p2rI5bHRfwol4CPm8h20QJnWRS6ASZgr8Dvog5QZlLTj+CmX0p/G+sPFrI+AA94TwvtwgZOJTmivwcgTxLxUmQE3YWt5UwEbhB0r+I32cnVsiOAh6UdCfNyA8bxafTx2wDCf0HKeY8ISEhoZ9B0hX46vtYCj+qZnZCQSZznd0WJ9M6F/8h3h2PO/9GRb2nhvq2MbNVgpvwVWa2wZy6loo27IanlLrZzL4SJlu/MLNdK2Qvx/P1trJcZbJL44rFJuSKxSFWSukVZBfCrdRlV9Q+k4uFeufB09XNCOVuYLCZvV0hm6XxarktbL8Azy1+uZlVWZ+Lso/hubrPMLMH+341swZJw1vdt7LyW0aFFfNq3GPir2HTnsBWBUVuVto6BrgCt05ugYdRjLcKroBOxo6k6zu0eLVr52NUp53L4lp7eRQEhW5PfEHuqGxBzkpM7X1oS2P+ilIoQBeeeu1xM9urQvYS4HM2GwgaS/Vejluev29ma4WQk3E19/jWIHd9KG8F/MzMNq2QvQWPiT4fD0l6BucWmKUUlXLiwSXwFI9r4YtbN5jZexqG1A6q4UfJUBXCIekcnIF+Bv4bNxI40cx+0eI8TbwY5giqQnlUHyNfuShjZr0WxtQZ+WHb+PSC7Gg800C2kHkLcKh5+s6E9wGScp6QkJDQzyDpfjPrZVkqybRifDarZo/OiHh6JhaSJpjZWr2rmDOQtJmZ3dJuW9h+AT4xbWlV7UMbrgL+AXwTnyTujTPJN3Erb1Xv7cCHswmkPHXWVcVJvTxf9zDc+rMVMUHV5Wa2SkW9H8YVx42B84A/m6dOqmrDvMBngnwXrtT/3czeqJJveF2fBI7F07eJXBnsRTAUrDunA8PNbOngKn6AmX2lwXlqCQIljS0rJpLuNrNZ5gyQk4Z9FrjLzG4KCuxWVVapTsaOpJ/iisc/gLey7daClG12Q9IpeDhB2wU5SWfiC1qTQnl+4IRW75JCuRu4z8xWrZAtLsRMxxXzXs97kD0XH+dXE/dZlRLSON5b0l1mtkHp3TfezNaukO31Tqx7T6p3JoWReNq2XpkUOkGwKK8NTDSzScFzZImqZ2NuovA7tDCeTu+6UN4aX0zopbxn/R6su+vhi11jrZo0bQ08xGQB/L3TyothtkLSHvh7YXPcUyfDvMCMVguDChwWDc6Rpd68RtIwoNuqU28+RPv49IT/I0hu7QkJCQn9D7dKWsPM7qsTsM7i0jNMC5Nogx4rYEtL7BzAb+hNHlW1DRq4raoDoq4CFjR3XT4kc7EM1tNZxZCiZcfM3gwTriIOwF01F8etRply/gbw26pKzewa4Bp5Wqw98Bz1T+HuxH+1QlqhMLH7A/AHOUv234BfSjofdwP/b+8ztMVxwI5WkSarAr/ESbguCe2ZoBq2bqATgsDrJX0G9xAB2I3ZlMPXnP3+xEL5SVwhqEInYydblCmGo9TF4XaEoDivQKyUVnExbJQtyAWZ1+TZHaqwZqaYF2Qj66D6wF9hZmeqlFGixaX9i+b3tZN477eCgpu9+zamPjZ4oqQfErPWP1YlaGZ3ha9vhvPPLlxd9KIxs1fCwkUvz5q5iex3SJ47flULrP3y0KXK9xkwUJ4KcWfgZDObJqlO6fw9cFjJi+EP5M/WnMSteDz4KOKY+slAHU/KJjhnxXCcFK92cVKF1Ju4VXwJnNy16h63jU8v1LsQzgMwmtgFfran+UuYM0jKeUJCQkI/gaT78MnjAGAfSRNxi3FtOpbS8e2YtH8N/BNYOFj1dgN+MFsa3wZh0rIpsJCkouI1AnfZ7AVrls+8E6KuDJky+5yc7f5ZnM15VvGWCumqJK2H5xzvgXmc4kmSDjKz3zStOCgWewGfw0mJzsYtOnvjFvhMrhv4GK4ojMYnlWfjrrf/Blbsw3W90FAxB8DMnlIcHtsq3nGkNSMIPABnLs7c2rvw/j6MGit+U3TiGUAHY8dmo0t7EaGvDgnnHY9bmm+jWunvZEGuS9L8ZvZakF2A0jzRcv6KTnKX74ArWW0zShQU+WycPmL1Oa07ifc+DF8wWk7uir4Q/v6rwr54fP6Fob03UqN4y3NvfwsPLSoqQn1agCl41owKCzBFz5rF+1Lne4TRFqfTe4H6d83vgceBCcCNwXpc59kzT6aYA5jZDfLwoTmOMKaewEOlmuJXNF+c/Coh9WaQfVTSwjWyncSnX4xb+q8hxZq/L5GU84SEhIT+g9r8qQ3RjjX5bHne2A8F2Z07UbpmEYNwa8IA4rQub1AzSW7itmqBqAvPfXte6fjdqcZPghX6G7jVfgRuzZ5VHAqcJylLJ7QYzlZeheclzWtmkyX9APcc+EmVy7OkC3Gr41m4BTubBP9DUnlR4lHcZf4XZnZrYfv5rSzYbXC3pH/gJG/FiWEV6/hTctd2C0rWweQLKFVoRBBofSfQa4JOPAMajx1JiwA/owEJWYc4BNgAuN3Mtpa0MvVkb50syJ2Ae+2cH8q7Az+tEjSz70pagt5KaZX1/kRonVEiQ7CMnokrbwKWkrR3Tb3vBvfvRyV9DY/3rlNuHsT74W3c8nkR8J+aa3sNH7dNcB5u7fwDs0cR6tizpp/gBklX4p46hofWVBK3mdmv8XGZ4QlJdQtZjb0Y5hQ6XLzrZHGyk9SbR3bQ5GFVYTYJ7x+kmPOEhISEfgZ1QLZUkvmJmb0nlvC+QtIyVkHSViN7M7nb6o4Et1ULKedKslWkPZWeBOog7r1TBHfNlfAJ3MN1Vj9J95rZmpI2xxcgjge+Z3EKnEx2BzP7d2nbYKtgsZe0uZndXNo2S9eman4Dq3KTlDQKJyP6MN4HV+Gs5ZVM4uqMIPCTuLeAATeZ2UV9vKRyvbeY2WYNZTvhTGhMQtZhe7P46fG42/oU1cRPB/mVyRfkrm21CBEWELYpyFaSCkr6Oa6APUhMUNXLkifpRjPbolAWMKa4rbBvLPBZM3sklFcE/mYVRGjqIN47uIS/gXuRgIeHzG9mvRbwJF1KbyXpddw75/dm9m6xvVVtm1V06lnTHyBpF5xQEeBGM/tnjVzjRavgPfBj/LnPvBiOzLw73gtI+i8NF+/CwtaJODnpxvgiz/pm9pkK2eOAScDngYPw1JsPmlm7LCbt2vAT4Nbyb0bC+wdJOU9ISEjoZygrlWFSf69VkC293yBn3d7dYtKpv5vZ9hWyY81sPUn3ZQqNpJvM7IMFmY/i6YY+hRNvZRiBx0BuWFFvY0W+D9fXKN2NAjGVpGNwIq1zVM8A3MnCwxy7tiboUHltnG5M0u+AD5Cnyvs08D8z++psaPNJeDznRbTxDOjwXjQmIeuwvf/EF6oOxRXp14CBZrZDQaYq7VoP6hZLCsfvb2aVMeRh/yN4jHrbNIdyUrpeGSVwFumon7NFq9LxvbZ1CnVG8nYS7vZeHGvPA0OBEWb2uUL/Hoynrvwn8dhpl9auXXt3B65o4lnTX6Dm5GZzZNFqTqHDxbuqxclDrCLlY/D62A/YLsheaWaVaQklTaZ+wegbZjaxJDsPPh6n0cbSn9D/kNzaExISEvoJ1AHZkqSbzWzzih/t/v5DPMp6k07VuaI2cVt9Fp+g7IS7gWaYDHy9KKg+xL13AtWku6GaXOwZSb/HJ3HHynPbd5XqWxQnCRoqJ+Yqxp8OK8nOsWuTtCTuwp2l5rkZn3A+XSHemPDPzGZI2gn3jGiHLYHVLVgU5MzitYSJHWIE7u68XbF5eMwx4Xx96d9OSMgaw8x2CV+PlHQ9bjG+oiQ2ljzN2tK4Ai/cyvwknqKrFQ6khuAtYCIwkIJC2gJD8BjkLHXUSzgJ1o6U+hkPofgjuRvznsTPNZJ+ZWaH1li46+Jwx0naOLOqS9qIsDhQgXVKVv1LM+u/pIwlvNi/4HHnPU0AejHGd4gfmtl5wbNme9yz5hSgl2dNf4A6IzcbZWbnht87zGy6pMj1u809NuBV3IthlljxG6JxWI+ZvYyP2SY4yJyDpEchV33+9BPx37pz8DH3GXxB8RE8I8dWhTbMyRCghPcASTlPSEhI6CewnGzpOFzxWM7MfqyQm7gku3n4/377IZ4paWlzRmzkOVnrXLgOxZXQg3G31a1xArQemNkEYIKkc6yeOCpDx3HvHWJ9mqe7+RTwEeB481RJixFP8MEn5V/Aib9OLGyfjC/iFDEnr+0MfFKYuQDvFbZtmwnMwuLArZJOpn26sUdwJTMLiViKGsbkTmHNMh/0pX87ISHrCMHrYBHy+NtFcaUbADNbNsidClySubgGT5MmueFb8lfgixnjJbVNc9iwfzN8GSfKOpjcjfl3JZlMcT++XWXKSTYHAp+X9GQoL4O75FdhodI7ammckAt8obSnf+cgMmX1Y8ApZnaxpCPn8DlnBZ2QmzVZtGp3j0fhSul74U3WdvEug6Rfl7cRLNxmdnFp+964lb2IL1RsA/iIxSFPp0m63cyOklT+Lcg80ppkc0joh0hu7QkJCQn9DGFCPYMGuYmDfDZRL7pSP1klO7ch6SO4RS5LP7UFsL+ZXdnmuEXNU17V7e8k5/Ey1jDuvRNIOg93026b7qZ0XDsX4l3N7IKGdc32a6tyxS5vk7Qlbr05ELeYZZgMXGpmj9bUXUUaZVZiu5anK9sAuDNs2gBnKH87HFBlLW0EOUP2fsBqxGNn35JcN/APM2ukYId6v4YvskwO7f2NFWKW+9jeg3AuhhfImdetyvVbfcwPL2nJGs+IbP/eVdutIsOCpGXxmNrRxO+oynsmJxJciZB2rW7RrcrKWN4WXK1rUfWsyNnlT6XALo/HA98AfMnMflWQHRL29XAhAKfOhnt8Ge4p9GE8F/g7wJ1Vbvj9AZLuMLONlIfrDADuqRmT6+LeNKvjKcIWAnazmhzukoYCS1vgIShs39FyQtB+AUmn4eSdGTnprsAD+GLixOAN0HH+dEm34R5GGVnjbniKuY0r3sWV2RzK79SE/ouknCckJCT0MyjEsCqOVa2Lj2w8Ue8vCBaV/fGJwxDgxXar+moTN63OyOMWAr5Nb2VsliYvQdFcG1cg26W7KR5XF7O8l5n9VdI3qHbfPbEg2xdX30aQdA3wZ/IY3D2AfayQh7lSmKTBAACBeUlEQVQgO6cWPrZstd8853hf6z4PeBifMB+Fu6U+ZGaHVMhe13ScqAMSsg7b+1+cCK5XHGuF7JW4AvBXfFzsBWxh1RwPHbHL1ylNFXIT8NzP91FI41Z1z1TB1g7sXfV+qHpuVMPb0CnkYSYrhzY8XKdsh3s8mTzF3+y6x8Nwz5r7ghV6MWANM7tqVuqdU1BDcrOwwHUwrpxnxJmtFmB2xK3ng8xsWUlrA0fNyvusU8hJCU8BFjGz1SWtCexkZj+pkL0O2M7MpofyADzufFv8Xq4aFoyWxReTDy8cPhnnlpleUe9yuEV9E/w5vh0P23oGWM8KJKDBWyTL5rC2QjYHs//X3n3HOVaVfxz/fJfelo4UpYog0otUC6goCAgKKKIioICVotj9gYAFFBVQKVIEpQgoCkgVYZEOC0sHC4giWFDKSl/4/v44Jzt3MjeTZJLJTXae9+u1r9ncuXPzzEySyTnnOc/jRp1DQp+JtPYQQug/7fQm3hdYpZU36v2g0aw+5T2ah31pk8+30/P4dFIa9Takld7dSPtgO3XwGL+u0fdW6+c7fwvXaDnVdwz2IFUf/h7pMXldPlbmGUnfpsWJD6W2ZAcxVOV5CunN97A0V9tT8uCxlj1yk+1/je3bGeHVtneS9C6nPttnAI0yOW6TdD5pZayYhl/WVm6Vugm1K/NAtVN/o/W967uQfr61ytlX52NlfkIu1JVv/4H0PCmroj1z0ETqW742jQdNzzm1z2rFkaTBzbBq7aTV49p911YeV8i/i5rJQFdeB50K3d0u6QTbe41yald/x5Im236K9Ny5Kh9bhDTZV982sZ98gZR9ciepHdxFwIn1JznVmXiXUxHIu+s/X+JgUrr8VfnrpyltheqlH5O2HB2fY7gjv0aMGJyT9trPx9Dzcz7SZNdLkp7PXz+zf7qGF9Gbh1R0cEQRPaeCb9s2iO+autvP2X5OEkpdPe6TtEo733CoVgzOQwih/7TTm7idN+r9oJ0ezUWlVWwL2ul5vKjtk3IK7BRgSk6b7kgeQI6oWNzCl5a+6bJdezPY9Odje2othjZCbonTFolWV6ranfg4mZTaunO+/UHSAPHdxZMk7Qx8m/QmXcAxkg60fS6dq63aPSFpdVJl7uUbnLsIaQBYnGwo3X9Ke0XI2vEAqa/0bxieofHd+hOdqobvK2ky8LLt/41y3aaFugoOZuSgqdE+7KMkHURaQSzGW1Z5fI7iSrztPyi1Jyy6DniUtO/4yMLx6XSpDkHBqOn/dP93fAbpuVNfcA66U2huXNh+mfQa3ex1GuBatVZnAmCG7SelZnOz42pe2zfVxTBidTs7glSL4SrS7+6NwDckzQf8tniiRhbReyV1RfQkfc72EZKOoTwjakSNB+BhSQuRCthdLulxUjG5MCBicB5CCH3G9ulK/X5rvYm3d12PVQ0V3Wr5jXqfaHlWX9JKwMN5FeseSZ8GTnOh2nvBfjQpHldQG4w9KumdpDcurxzzdzQUb9OKxYVU9QPqvhYo/73llNHDSPtOLwHWAvaz/bPCObXCV6XGss1hjG8M2534WMnDe5p/Tal/d70vAxvUVstzNslvGdqD2YkTlOo6fIVUwG1+4KtlJ7qF4mYaWxGydvw1/5sz/xstljVI3QIWybcfI6WJ31VyejvV5csGTY0ef2uQJl22oLD1hvJsmabV2msrj5LeCjxr++W8wr4q3avgX9MsO2NDhn7HkIoW3lt7DLT7vLO9Tf443gXnukrSNqTX3eVIY4vRuoZskj8eUjjW6PFwl6T3A7Mp1RX5NGlyppcey3+Las+LHUmTQyPk176LSBNXAr5kuzYwri/42UoRvdrf/ZazJtxaN4fQx2JwHkIIfcj2faR9sI3UKka3/Ea9T7Qzq/8LYH1Jryal1p5PWlnauv5E2zfn//6PtN98NIfldOrPkPY+Tqau7doYtfJmq5aq3k6V/S1tf07SDsDDpKrpVzK0zxXSalu3tf3GkPYnPp6VtFltz6SkTUmTEPUm1aWx/4e61nNjkbMtnrL9OCnle9SVSbW2/3Q8fhe1+5+NlJnxgRa/5HhS4agr89e/mVSQcZOScz9Deo6tpObV5dsZNO1A6jzxQgvxtlKtveZq4A15YuUK0uP0vbTeympUkuaz/Y4mpzX7fLv32bCuBjRcXe4H3ydlu9xpj17MyvbmbVz3U6SJuedJ2xsuJU0C9NInSM+ZVSX9ndQhofT5J+kKp1ocvy45Vu952y/UJriU9qcP+9k5F7xzodCimhRHrbOKRyk2GvpTFIQLIYRZQE5bte0R+9X6lVKRrwWBS8reuGuoMN6BpBX3Y9Sg4JOky4Gdaqvq+Q37WS4pfDVe1EbF4jave7ft10n6MfAL25eoQYHA8SBpJ9vnNDuWj29DKkD2KoYmPr5m+/z6c/P5a5FWdhfMhx4nrezeUXfeEaSMgVpRuveSiid9fszf2NC1r/bwvtajnTuFvP/UQ8Ua77K9eqdxtEqpyNu2rQx2yx4noz128mO2lUJd85IGTVvmcy8FDnVJ4TSlHtGfcvdqBNSuW3t9+BSp5sQRjV4f2rzuJqT90vPbXjY/Rve2/fHCOYuMdo28nWAs913WvaBw2f6suJ3jfktOb2927qKkOgi1CvfXkOoV9HXdlJyaPqnsb6xS1f55SZOmb2ZoO8Jk4GLbry35mpaK6JV83ajFUcd6bugfsXIeQggDTNL6pD26C+TbTwJ71PYg97MW9ke/qFT8aTeG9mXX7z+tWayY7m778fpV60bp2YWvKUvTbscUpZ6z80h6G+nNVmmrnxZXYGvOl3QfaUX54zmlu1H16I1Ig+LXkjIpZgOebpBe2qovMtQaqOGxwqruhaR06FZWyJ6yvVaeXML2Uyrfu2zSKvBmpDe+J5CKCXbD5ZI+y8g9sGUDrHb2n46Xv5D27Z7P8HjLtrI8IOmrDKWJf4Ch3ujDKBUy+zmpXdyfRwvA9jOkwfmoA4nsFcB9km6mQReDMW7LkKSNSSvle+Zj3Xhf+z1S+7vz833fLql+8qZ+T3gtdtHB3vA2V5X7yeeAi/LkVbPtVWeRsh5q21l2JT3uZrYQU4OuE4Xrjnu1dtVtPSocr8VQ/N72Jm2tWpr02Kg9Lp4CftjgLj4PfIQmRfTKQmjhnLGcG/pEDM5DCGGwnQx83PbvASRtRhqs920rtTbsTioq9nXbD+ZB288anPuypGWd+7srFWarf3NXS8/elNQP/ef59k7U7Wsdo5YqFmctVQDOadcXkAoNPeVU9fcZ4F0NrvsD4H2kgfP6pFWZV4/lm5G0FWkLwTKSitW2J1MyIM2xbUca3LTqF8C6ThWqa86lUJ07e1teJZ9ZeE3S10hvcDtVqzz/icKxRgOslvefjqNH8r9JNN8esQep4GLt53Y1jbd9bEfKSDhb0suk58fZtedUUZ4U/BIje5eXve6UdUyoV9sKUPsdFPecP9Pga/YjTRKdZ/tupXZTo608t8z23+omYF6q+/zMCaS8ir4yhe4E3aBUnHA1hnc9OK2b99FFXydtKZqb5turFrFdTE0/TNL2deeMR9eJdtWeW6uQipjWsn+2JT2PZrJ9FKnw4adsH9Pswvl1/Y6ccdNKEb2ids5vVOE99LFIaw8hhAEm6VrbmzY7NmjyKuypre6tlfQO0mpqbTX+jcBetke0xMopmFvWUnaVqkFf1stVK0k3295Aw3vZT7O9dsm519veuMXr3mJ7fUl31AZKkq6zXbbHuNm11iL1bT8E+L/Cp6YDV+Z92vVf83VSivqolZiVqvS/jjTpUCyUNBk40Pbr8nkfI2UgrAgUV3MXAK5tY+91Q5Lmrk/HLjuWj6/I0J7tx0mr0Lt6HHq7VynvI/8q6Xsb0XFA0v2k31t97/LSn4NabIPXD69nks4Fvkua6NqItP99fdvvKzm3rDXkdQ32GLcTw0Gk9OjVSJN8WwHX2G5UA6BStdedFs/9Dmmi9Ox8aEfgdbYPKpxzhe23SDq8G1tXOiHpMuA9tXR2SQsA57ikHoGkQ4GDbb+Ub08GjnJJIUlJpwNfLJv8ahDHbKQslOJkWNnE2VykrITl6849pP7c0J9i5TyEEAbbTZKOJ+3FNWnl6yrlwkL1g6JBkVdhF5c0Zyt7a/M+7HVJb44F7G/7sQanL00a3NXSlufPx8ZE0tm2d26UmttgNbGdFdjLJL0H+KWbz6g/I2lOUjufI/I152vyNaVs307q9XxGo73HJVqtxLwKabV0IYav7kwHPlq4fQZwMfBNUmbCzPPGuq+3xHVA/b7MsmOQ9v2+tbj/tEEa/rjJk0tlj7MR+5HVZi0GpR7SO5NeR14ipSuX+bcb1BEouWY7bfDm0/ACgZtQ9/iV9H3b+zVKfe5CyvM+wFGkbgsPk1rAfaLBuWNtDdnMjqQaC7fZ3j1PbrSS8lyV30ra0vZlLZy7N3AAKTtCpAyQp3MaufMWnKWUapJsJ+ks6tKze/x3bVmg+DfoBRq3WpyN9Dd5d2BJ0hajRivpSwF3S7qJ4ROZIx6/SnUVDgL+SXpe1rZPlP1t+TVpW9FUClsMwuCIlfMQQhhgGtACQq3Ikw7rktIJR91bW7IntHbu1SXn7k7q01z72b2JtNpxav25Lca5lO1Hcyp9WQwjVhPbWYGVNJ00QHmJtO+8YZuiHMM/Saml+5NWsX9k+09j+d7yNVcmDY7rU2xHpH1LWtH2A82OFT63se3rxxpbJyQtSRqA/Qx4P8OLOB1ne9WSrxlRYEnSVNv1afjjRlLxvuYmrZLNsD1iIK2SAmllx/LxG0k1Hc4h7Tsv/Z3lc98C7EKqkl7cYzyi37vSXva3ua4NnkuK0uXv7WSGCgQ+QaqhcWvxHNtT8+BtBDevZdE1hQyYacCGtp9vlAHT5nVvsv16pZaam5Mmre6qZZT0m8Jr1PMMbXkpfY1q8Xo7krYIbcbIbhE9/bsm6cukCavzSAPiHUjbPb7R4Py3krYiPQ68sdFrbzuPX0l/Ij2+mhbNU48LVIbui5XzEEIYYL1Mxa5AO3tri6nRc5Namk2lpHeu7VMkXUzqUQzwBbfemmYE27UV70nAo7V0aEnzkNIQyyxcsgK7LTBicG675bZrth/K97uU7W6s4EGqYXAQaS/55qQ9y40KDZ3LyBXncxi5h7zmT0pF9JZneArmHg3O76a3Ax8mpSQfyfAiTl8qnlhIw19Q0rsLn5pMl/caN+ORxR6vVeNe8q3UYqjZzamFYyt2J/UVn4PhvctHDM5pow1e/t5qBQJle0Sf9Twwnw34aDe2NdSTdArlK/Jlj8l2WkO245Z83R+TXsf+B9zUheuOi3Zeo4okHWz74JLrnQucK+mrHr4/vedsfz3/vXhDPrS77dvKzs2TxEeRMofWAH4gaQ8P9TovXndK4eu2cSqk2cjfSKvhrbhO0hq272zx/NBnYuU8hBBmEZIutD1uPZarotRv+OnmZw77mlcBR9jepcl5pW8Ox0LSLcAmtTT8nF5+re0NSs69lTQYujPffh8pFX/DknNFKoy1gu1D8/e2lO0Rb9bzAP87wJy2V5C0NqlN0ZhTfWsrw5LutL1GPvZ7228onNPSHvKSa19Har02lULRLdu/GGu87ZL0nmb3J+ldwPakomnFdO7ppDTxRj2+u07D23hNIk18HG17lZJzm9ZikPQB2z9Tg+rUDTJVZj4WWoj326T022IbvDvLVvrrvm7U1zO10VKuHXkLSc3cpJXSR9ykm4OatIbsIJ7lgcmuay/Yr9p5TS3LRKn7/CRSVsuKtg+RtCywZNlr33jJ9zmCy/d73wR82PY9+fa7gW+UZeHUfV2zn8NJpK1Av6FBNXwNbauanVSg8IF8bi3TalYoEjshxMp5CCHMOpapOoBuUmqTdBJpT3hpv+FRPAy0ktq3HSnFvRtmL74pt/1CHqCX2ZG0MrQrKXXzQ6Se0WV+RFqd3AI4lLSK9kOGCmwVHUzKGrgqxzAtv7nvxHP5TfIfJX0S+DuwRN05re4hrzevKy74VByYNxoQ2v418Osq0/ALim28ZpC2ROxZdqJbq8VQ29NdtvrZaAXnBkmr1QYho7F9YB6kzGyDZ/u8Zl9H89ezv9B6S7mW1U/USDoT+G0LX9dxOr2kVW3fl39n9Z9b14NRQ6Sd19Rmrb5+yNBr3yGk15NfUP7aN15+w9DzYB5gBeB+0mRkvY2di8FB2uYxSlZLUbOfw1/zvzlpXA1/lpuYn6hicB5CCLOO0lS7AfZ9mvcbBkDDe5hPIlUZv72F++hmH9h/S9rOuVBWXm0tLUpn+4G8Wv4rUsrilrafbXDdDW2vK+m2/LWPjzLon2H7Samb3xb7AfOSqlYfSnqjvFvxhA4GrxdK2tr2Rd0KtkPNBoRVpuHX7qutAnR5MH5hXtG8oOTzx+f//tb2tcXPSWpUJX0zYDdJD9JkdU6pYN5FzvvRJc0jaXnbf2kSerPXs3a2vXRiZVJRsF44ANiLtM2iXllhxX7UzotPs1oN7bz2jYv6DJE8cbJ3g9NXknQs8Arbq0takzRZcViD82saXa/mcI/sKLFYXZwP5eMbAXd7eHX51SjZMhX6U6S1hxDCAMsrmae7pK3VoJN0o+0NNbzd2O0uLyRVHCzOAP5SP9BocB+TbL/c7LwW410JOJ1U+V2kQfeHXCgIpJEV3Zcg7SV8HsoruysV6toEuDm/UV2c1PqtrKjXSaQiXV8gFQr7NDCH7X268T02o1Qh/jBS4bpLSBWn97Nd2p9eQ4WkXsj/Gha76wVJJ4820O6TNPw5gI+RUtQhZUkc7yYV9VtInS0rdlf6NWqv+GHL2z36QX5M1jITDPyD1PKqZ7/jQdboNbVuAnWEsm0D7bz29dIoz4sppG09xxf+Zg0r0KbhNStGcHlRxTtI21FuyLffA3zT9mtKzr0NWNd5gJeznm4Z7bkf+kusnIcQwmBbErg572E+Gbi09kd5FvA3pVZKzm/oPw3cW3ai26i0LunoutuQBsi35BXgMbH9Z2AjSfOTJr+nl5w2ltTDo0mVgpdQ6iO+I/CVBud+CvgyabB/BnApzVdtSmlsLau2tP05STuQthbsRKqKXzo49xgLSY2jz0hac5T9vZWn4QPHkgqx/Sjf/mA+9pEmX1e6opm3j2wCLF6373wyqTXUCE6FB9clraCbNNhulHLd8naPvFJ/MLAc6T1qbbKmrDPAa4DPMjKLoaPV5X54TEr6BGnS9Yl8e2FgF9s/GvULe6zRgLuWuVM34K5VXd+UtJL783x7J9JkV5l2XvvGRd1zYhKp4OW/G5w+r+2b6jKXZtSdU9v2swTpefe7fHtz0kRbWVHFXYGTJV1FmvxdlMZZFCq+B7D9sqQY7w2Q+GWFEMIAs/0VSV8l7VfenVQd9mzgpDxYHGRN+w2XrEQP06AIztykStPn5NvvAe4G9pS0ue39xhKspH1Jlc2nAz/Og5cvuND7t5B6uKftk+q+/lsM7+Vd+5rTlVoqvYU0WNne9ohJCqUK1ufbfitpgN6pn+aP32nja+bIH7cGzrT939FS7KXWi92Nl/yGdzvSe6JppO0JU2yXFUjrhzT8DeqyR36n1K6smUYrZ3OS6jrMzvD08KdIg6ERJP0faVBVG0icIukc22UTQS1v9yDVmNifusyEBs4BjiP1/252blNl+7yLerzf+6O2f1i478clfZShCZl+Ud/mrKHaBKqkDwOb1zI9JB1Hem0fJq/4Pgh8jiavfeOs+JyYQdqD3iiL4rGcQVVbtd4ReLR4gu3d8+cuBFZz7vYhaSnSHvsRbN+ZJyd+Svr78kbbDzeI4QFJnyZN2AF8nFQcLgyIGJyHEMKAs21J/yClX84AFiYVG7vcTSoi97O8V3bXJqfVVqJrg/bagHJX4JkGX/NqYAvbMwDyHsHLgLcBnbSf2cP2UZLeTloV2Z00WB/xxhPYUdJztk/PMfwImKvsopKOIvWdLn3jVmP7JUnPSFrQJS2o2uWhll23AM/WUlXzJEBprMAFku4jpbV/PKehPtfgXGiv2N14WdD2U5I+Apxi+6CcRlpmX+BLkqpMw39J0kq1yTdJK9JgcCppQdJK9Bvy7Smk6v0zHx9OhcymSPpJWVp6A7sA63iobeC3gFspz9LYBzhd0g8Y2u7xwQbXfdL2xS3GMMP2sc1Pa1lxn3dxwq+W3t7L/d6TJM1cAc3PuZ7utW5FOxlLBUuTBrz/zbfnz8fqr/2ypCNtbwy02uJvPNxj+5ziAUk7MTS5W/QJUneEVSX9nTS50Ohv2PIeasMJ8E9gRJp6vr+TgJVIXQ9eQ3qd/UGDvwn7kDIOvkJ63F5BqmMQBkQMzkMIYYDlGfLdSCtRJ5LaVr2YVx3+SFp1GEg5bXVEcZ3i6lxhJXpT28XiVV+QdC2pwm+9ZUj7nGsDlPmApfPg9vmS81sOOX/cmjTIu12Nl43fDZwv6WVgK+C/blyF/lbgK/nncR5poN5oxeo54E5JlzO8gvWobaCauAJ4K2ngDKli8WWklMxhbH9B0uHAU7XJAuBdo1y78oJPwOx51WpnmmQc9EPKM2lP65WSHiA95pYjTQSVORm4i/S9QRoUn0J6/NV7Rqnt2eso9G5vkCb+l3xObeJlLqA0U6fF7R41V+YYfsnwllEzV6011EruAkkfJz0niufWBn1tsb15vv48pNXGWsr+7xlaheyVS4Gz86qySQOuS3ocQ8vyJNznSenqzR473wJuk3Rlvv0mGld3vyzvr/5lhdu1vsjIgXjZMUidLE4hbeVZhJR9shvlf4euUmoHeCbpd/y+/HVl7gI+kn8GDyoVfStrcTgb8F3b72v2TYX+FYPzEEIYbIsB765f8cqrDoPeWuXH5OI6ALbvkHQG5atz80nazPY1AHmv+nwl50HqxT0tpzOLVFjrG5Lmo4WWSaOYKukyUqudLypVyR1WGEnDe1R/hFSt/VrgEEmLlA0s8urUqflr3wMcLmlZ2yuXxPCb/K+b5rZdG5hj+3+S5m10snNxQkkn2N6LwiRBiRfzG8raCuHi1P3MeuAQ0mDoGts355XoP5ad2A9p+LavkLQyqX2dgPtsN5pUWsl2sW/31yRNa3Du6aR9wNuQBoO70Xhv7fPA3XkSyKSsk2uU6zmUTQblx82FjF53YcP8cf3ilzJ81brYSg7Sa0Tx3BH709t0KmlQVatNsQtwGkMTHL3weVIF74+Rvs/LSJOv/ar22HknTR47tk+RdDHpd23S1p9/NLjuAaTX8RmSnqOHmSqStiJNtC6j4XVKJjNyH3nNr4EnSBOqj4x2fdufVCoO94Z8qGGLQdvfq7v9JCXtE/OE6OKS5nShzkMYLFGtPYQQBpykzYCV85uexYH5bT9YdVydknSz7Q00vFr7NNtrl5y7HmmVcEHSG74nSWnmpftE80rp60lv9m6yPeobqRbjrbVwe8D2E5IWBZZxobiYUuupYiXo4sq6XVL4qvC1rwfeC2xPSrXcttG5+fyu9EXOGQifql0r/6x/kNNNR/u6UauD53N2JX1P65IGRTsCX6lPI+0XeQvEy6RtEa9VKtR1mXtQeVzSFrZ/p/JqzyalCV/jQp9lSdeTsmlqk1abAt8p+91Jmmp7PUl3ONdqUNp7/6aSc3erPzYsmAbpzsXncr9SSUeIsmM9iGNO0gSMgfvdpBp/ldp57OTPbcdQt4EpLmnxVzVJa5Fezw8B/q/wqenAlS7pkKK6yuxdjGUb0raf+kKJIyYpJB1Pej09n+HZUyNW2kN/ipXzEEIYYJIOIq0yrUJKp5uDVBm7UX/iQdK0uE5N3h+9lqTJpInnEXuuJa1q+z4NFX76W/64pKQlOx3I5j3ZtQHswbYPBv5Td05bParztQ4npSH/GTgbONS5inMTJ9K4CFg79gPOkVSbwFiKNKBu5l/NTnCLxe7Gk9pr/1ZlGv6bSJWdG03KLEraZ/q2wrF9gNOU9p4DPE5dj/qC2uDvUUnvJK38vbLsRNun5u+7tke21cHjqL3LJb0C+AZpm8lWklYDNnZd8cR87nhVNL9N0kYealu1ISm7pWckvZk0WfUX0vPiVZJ2s311L+NoQ8uPHaX6BBuQVtsBPi1pE9tfHO0OCq+pPWH7duB2SWd4qHjdwsCrygbm2XWS1rDdtHZJnmQ7nFSfRIyeFfB90t+AO1tI738k/5vE8GJ2YUDEynkIIQywnKK6DnBrYXV55urFIMvpxSeQ9jY/Tiqu8wHbfyk5t+mb+lqadd7rOKLgU4P9kWONvVEf3NFWPxv1uN0HONepQF47MXRtlVKpt3YxjbpZT+35bI+Wzl48d2HgVQxvh9Wzyti1bAyl9m/bk6qFX1m2Uqo+7btcI+kk23sWbq9g+8E8aYVT4bsVyjJr8urc70m/i2NI6btfc66yXnfum6kbPAIdDx5zuvMpwJdtr6XUAuo222uUnDsii6aTx7yGOj/UHut/zbeXI2WqdH1FdJRYpgLvt31/vv0aUveD9XoVQzvafOzcAazt4QUmb2v2N6uVTJzxoJJuDqTV/hHdHCTdQyo4+iBp60ftb8uI703Sn4BtW5mMzH+z3uKS/vFh1hMr5yGEMNhesG1JtdXlRvusB47tB4C35u9pkkcvJPUT8pv6fPsPpD2QMwfnTvufIe0jHO+CT40KwdWvftYmCWpp7jMH57WVfuAmYFlJyxYv1MIA9mvtBl0mD8w/xlAa6lWSji8boCvt9T+RVIF52ZwaurcbFLuTdCjwYVJWQO1n0evK2O20f6u873KRpAttz9zDXRyYZ78A1rX9VOHYucCIQZ7tC/N/nwQ2b3LXR5J62g8bPJZdV230LgcWs322pC/mmGZIatQmrdsVzfupRscctZ8tgO0/5OdhX2rzsQOwEEPV2hcc5byixj0Zx9eCbr2bw1ZtXPefbWQJfQ64SKnbQrH4YVlRuMXz+a0Udgx9KAbnIYQw2M7Oe8wWUuqDuwepkNrAkzQXqQDa8qSK2gDYLqt8286b+l4UfCpd4bJ9UP7vxyh8b7VP151+AKkFzpGMNGwAq/IezX+tHe9wJfpY0gC2li78wXzsIyXnfg94O2m/I04V699Ycl7NzqSiZVUWL2q5/Vs/pOHXWabsoKRVSW/OF6zL0phM4Q173dcsDnyU4Y9JbO9Rcno7g8d2epc/rVSroTbg3oihrgr1ulrR3K23keuFW5TaZ9VaQ36A9PPrSznL6ShgY1JNhuuB/fMEa71vMlStvVaQc9SU9qznq+ZZO90c2nkM3SLp56SioMUB94jsKeDrpG4Zc9N8Aqqdwo6hD8XgPIQQBpjt70h6G2mwuQrwf7Yvrzisbvk16Y35VApvXhpo5039KnUpy1dKun2sQUo6hpEDawqTCWVtzH7FUFXf2kBw2DVqK/3OLZ6aqA3g5ybVILid9MZ3TeBGUpbAWG1Q9/P63Wg/L9t/q1t5Hm1AdhdpFa3p/vTx4jbbv+Vshvsk7VXxwBwa7+FehfTmfCGG71GfThqAl/k1KYvktzQfRNcPHnel8eCxnd7lB5AmdlZSKkS4OCk7ocznSZNXg1LRvB0fI/XM/jTpe7uaocmxfnQG8ENgh3z7faRMig3rT7R9Zk4V34D0vX3eDaq151oJB5MrmueV40NcUlNkHLXczaFNk4FngC0Lx4ZlTxUsYnvLkuNlFrV9kqR9bU8BpuSfWxgQsec8hBBCX1IblW/zCvExwOqkAd/iwI4uVEovnPsT4DgPL/i0W6PU6xbuu+3K1e18b/n8TRi5onlayXlnAV93LkgkaXXgs7Y/3Op9lVzzVmAnp37VtVWycxvsqT+X1H/3B8BGpMHF+m7Qd1fS+qRB4V0MXz3abqzxtkupLdwBwLK5JsHKpAmcC5t8XSV7YNshaWPb17d4bmknhAbnzkUaPG5GYfDokpZuSgXAZmOU3uV158/OUH2DlgrNqUudCfqRUgvFV5a9lvULSTfa3rDu2A22N2pwfkvV2iX9gvTaUHsN/SCwlu3Smh2zqvwc+p3ty1o49wbbGyn1UD+aVBzuXNsrjXecoTticB5CCANI0nRKVmsZveLrQJF0AnCMW6h8m88f9U19nxV8avl7k/RTYCVSMaLaiqbLVuQbFMlqedDV4P7fQtrP/wDpZ7scsLvtK0vOXYyU3vpWhlYz97X9n/pz8/l3k/rY30mhv3le8emJnFo6FfiQ7dUlzQNc3+xn1knxsbEoPH5HfIoGRafqvn7UyQRJhwHX2b6os0hHXHfE44QGBRglzc3IehDH2S7dZlD4ur6fKGlHO0XIqpQnDiDtcX4COIv0e3svMJftQ0u+pr5a+y7ALS6p1j4er2ed6ObjLD/W92Tk3vAR20jy3/v5gBcYqoxf+ndebRTnC/0pBuchhBD6ktqofFv3dSd4qPhb8fhyo31dp3tO857dzwOr0aQQTzvfm6R7gdXcwh9sSWeSetv+jPQm+QOkvve7jOV7Klx3LoZXay9bIZ0NONX2B9q4bsNeyL0i6Rbb6xcH22qhr7WkV9p+uDdRdv74bTaZUBgAPA/MGLrs0ABglAmC2skddYmQdDYp9b7Wxm4XYGHbOzX5up5OlIy32vejVITsVc5FyDr9+XabpAdJj4eyYm12SdE/tVGtXdL1wIG2r8m3NwW+Y3vjLn4bLevm40zSOcB9wPtJqfO7Avfa3rcb1w+DK/achxDCgJO0GbCy7VPyyuUCLmmVNIDaqXxbtH7ZwU4H3y2oFeJ5J80L8bTzvd0FLEmDHu91diftV629wbuaDivRS9oJuMT2HZK+Ahwk6bD6NGKn/dqLS5rTrRd4myrpm6R9xk1TnsfJC3m1vFavYCUa1DhQe0UKu6oLj9/fNLl+Kz2R265orjZ6lzP2ehBd6UzQR1ouQlYl2yuM8UsXorVq7R8DTs17zyG11PzwGO+zG0Z9DrXp1bZ3kvQu26dKOoO0t71U3VaAqxptu1HqnHAs8IqcCbQmsJ3tw7oYexhHMTgPIYQBJukg0mB0FVLq8ZykVadNq4yrE5ImO7V+Gq11WvH8ScBGtq/Lh6oqLtZyIZ42B1qLAfdIuokm+7JtP6dUvfoiF6ppd+irts/Jk0BvB75DevM3otgTqe/1tZLOJ63g1+Ia0fInq61CFfem9rqV2kGkKt+vknQ66bnz4QbntlOkcFwoFTs8Bngt6fk+G/B0gxTX+YBn8yrlafkN/sXN9nFLOtj2wfXHi4/bvJK/su3f5smNRu8pf0KTNocFt0nayMPrQVzbIMZNgWm2nwbml/Rd4KgeTML1wngVIRsXKmm3CJS2W6SNau22pwFrSZqcbz9Vdl6v2O5m28Taz+YJpdog/yBN+o1QshVgX0mb2f5Cyek/Bg4kbRciT6qeAcTgfEBEWnsIIQwwSdNIA5xbCym5fZf+2A7l3s0NUiYbpUpeX1WqYyGGcSnEI6k07btsX3YefH0bmNP2CpLWJlU3HnOBtUKK7TeBO22f0Si9M08WlcXa1yubSpX+NyI91m6w/ViD89oq5DceJN1CqoZ9Dmli7kOkVbgRK6xKbd/eACwM3ADcAjxje9cm99Fsf/pHSZXSF7G9klIRveNsv6Xk3Jttb1C3baB033DewlFfD+JeUj2CYds+cnr0WqSOBKcBJwPvrnqbxEQk6URSLY9i4baXbJe1WyRnBdSqtd/oxtXavwEcYfuJfHth4DNdHiSPSuX1XZ4kPZc+4/J2ca1c9yPAL4A1SBNY85MmQo8vObedrQAtP99Cf4qV8xBCGGwv2LakWkrufFUH1Cnb2+SP7aRMXibpPcAvXd2s82E5/fIzDBXi2b/Ti5YNwkdxEPB60soVtqdJWr7DEP4u6XhSkbfDc2r3pLITOxmE1yZlxvr1HZqblDI7O7CaJGxfXXLedZLWcItFCseL7T9Jms32S8Apkq5rcKpsPyNpT1IBwiMkNWq/Nuzrmnz+E6TH2Y05nj9KWqLBue20OXwHaSLhDfn21aRCY2Vm5Ne+dwFH56yVUTsnDKJmEyV9omm7RaWOGkW1eg1LS1q6wVaWrWx/qXbD9uOStgZ6NjgndZ94hNQuTqSJsSWB+0kTQm8ey0Vt19r+XQ2MmHAusRCtbQV4LG/NqT3fdqS1LVGhT8TgPIQQBtvZeeC0UF7N2oNZpNdvyZs5SG/qH7I9o+74AaRiVi9Jmtk3vCzVd7wU9gA+CbTSm7wldSs3c5JWqErTmEkDlielZmOrtuxMGjR9x/YTedXrwAaxXsnIVabSongllukoyjFS6nH+XuBuhirGm/Smud5mwIdzVkfLRQq77BlJcwLTJB1BeuPdaFJOkjYmFZvaMx9r5b3fek0+/7ztF2qPM6VOCY0mxdrpXb498BFS2zWR+qj/2PYxJedOl/RFUtHDN+bVxDmaxD2IuvpkHicvSVrJw9stvlR3zpGF/xcfK6LxVpbZJM3lXIAyb5+Yq3tht+QdHt4m7oScJXWIpC81/Ko2tDAx+Q3gVqUq/qNuBSBNnJ0ArCrp76Sioy0X6QzVi8F5CCEMMNvfkfQ24ClSOuj/UT6oGEQ/AtYF7iC9IVkDuB1YVNI+LvR8bbGY1biQ9Lm8InkM5QPTES3P2lH/vUnanrRqWeYuSe8nvaldmdRnvNGqaquWAn5j+3lJb2YojbjMZwv/n5tUPK1+IqWRVlZ0x8P2pEJkrewhH2uRwm76IClz4ZOkzIxXAY36Pu9HehN/nu2786BpWGuzRo9bDRW7K3v8TskDk3ny68/HgdJe1bZvzVszWuldviepfsTTOYbDgetJmSj13kuqdL2n7X9IWpa0pWNW080iZOPlQFLxvmHtFosn2N4cZg6w69vlNSpa+TPgCkmn5HP3YCh1vldelrQzcG6+XZxY6laWVrOJyXeSVukfJ235+HyjrQA5zf6tOYtuku2WareE/hF7zkMIYYBJOtmFvqiS5gd+Xbb3c9BIOgs41Pbd+fZqpDeBh5LS19euO7+larbjEOe2ti9olFJru+tvJmv720uOz0sqvLUl6U3ypaSf4ah9opvc1zTS3ubl8/XOJw1mt27x6xu2S1MqnndUs2PjSdLFwE62/zfKOZNtP6Whvs7D2P5v2fHx0O2fWeFxuympDeDP8+2dgKm2R2zNUCrCuCfDH2cnlm0pURu9y5VatW1Q+1z+2pttrzGW721QSVoBeLTwc5iHVH37L5UGNgq10G4xn3c2aTK52Od8Ids7Nzh/K+At+bqX2W5Y0Xw85Amto4CNSY/fG0iTYn8H1nNu89bhfQz7O17y+S1Iz583kFLgpwFXlz3nJe1LKsA4nVQcbl3gC8XJ7NDfYnAeQggDTNKhwGK2P5aL5fyGlAZ6SsWhdaysiE3tWP3nNLKa7S6kgUVZNduBIqm4KjqJNFB+k3tUAK+251XS50iVv49R44JwxcHrJFJ69NG2Vxnt2nXHSq89XiT9glRY7AqGV8P/dOGctosUjmO8TX9mki5g9H7kIwoE5i0JW9ZWtZUqcF9WW/GsO3cHUkeAptkGaqN3uaQDSC0Iz8uHtgd+Yvv7hXOusb2ZRhbqqm0x6NlWlvGiVPRvE+eWhHkbw7W2N6g2suHqXptGsP3Lkq+5vW5/eumxWV27k2x528YGpC1T+5Bei1ctOe9222tJejspxf2rwCkDULcgZJHWHkIIA8z2VyUdrtQ+az3gW7Z/UXVcXXK/pGOBs/Lt9wJ/yCs09WmxWzO8mu2ppDTpng3OJV1OWoF9It9eGDjL9ts7vPS2hf/PILUre1fdfbc9GGvDi5J2IVUFr8XSaG/vVIYGrzNI+x33rD8pX+/9wApKbddqFgD+00GsY3F+/teQx1aksKva/Jl9Zwx3sXS+Vi0LYP58rMx2wPclXU16fl5aUgeipuXe5ba/m/fVbkZ6DO1u+7a6czbLHyvbytIDs9cG5gB5f/+cVQbUwLZ1t2uvQbV95CMG57TQLq+fJmAkLQ58lJQ5NHPcNNpKd4t2I63IF3245BiSriDVlbielHmyge1GLUNrE4dbkwblt0vdLUISxlcMzkMIYQDVrVjcRJodvwmwpHeXrVgMoA+T0mH3I73huIa0p/lFyguuLURr1WzHy+K1gTnMrCzcqIJ1y2zv3vysmYOxd5MqCRdXKf/SYQi7k1Zqvm77wZxy+7MG5762Pl05T6bUu45UyGwxhheKmk6qMdAzY912oAa9wMdRyz8z21PyStuptlstBvUthvpPA7wJOLjsRNu755X1rUgTBj+SdLnLW2e13Ls8X/tWoKxy9zBKFakfdl0thOJzcID9W9J2ts8HUKpIX9rer0q11yZJn2F4RomBJyWt7dSrvLZlwaSJvQ9JKrbLu6fuuv00AfNr0oD4t4wscte2MU5M3kGafF+dVHD0CaX2oc+WnDtV0mXACsAXJS3AUKHLMAAirT2EEAZQLpDTiLswqz9Q8hueb5GKXc2sZmv7rFG/sLsxTAV2sP3XfHs5UiGurqUTlqU0133+attvbHZsvDRIue7LVlCSzra9c2HQMPNTtFCBvV+/ryJJlwLbFldhm5y/JFCrTN2w/3Th/DlIlfx3B95ge/GSc1ruXd4OdVgLoZ/liYfTSZkLAv4GfMj2nyoNrAFJZ5B+F+eT4n0ncDOwKnCOU8HM5Ua7hu2H6q45CbjD9urjE3VryrZXdXi95UgD528yPLNrOun7bVhAU6mmzO6kSeolbY+Y+Mw/t7WBB5y6aywKLGO7p5OeYexi5TyEEAZQXrmaDfi07e9VHc94kLQpaeVuOYanE65YPMf2taT0yatIe/LEKNVsx9GXgWsk1fqSvxHYq8v30Sw9cXFJKzpV7K0VlhoxYGrrDlPV92+SioXNXTte93tYklRxeB5J6xTinAzMO8q13w0cDiyRv6aXaav75o9j7a1eSapomz+zvwDX5hW6p2sHbX+3weVnA/5Ner69RtJrXNLvXdI7SP2eNyc9704ktdwr007v8na8bHtG3v/+/VothC5ct3JOLck2yoMxuf8rbi8KrOtcVFHSQaTq5m8kbXU5on7w3YztlyXdLmnZ2oRnRS6UtLXti7pxsfxzeIhUYK4lkj5Jev6sl7/2ZNJqfuldkF6rtwEOIaXDz93g3NCHYnAeQggDyvZLShXKZ8nBOXASqSruVBqnEx5NesNyfV7FHHXv8HiyfYlSb/ZaFfX9bXc7FbVZW6X9gauUWhpBWlXcu8P7PAU4iPQ425y0clM/MH07aRvCK4HiwG86MFov4CNIK7v3dhhj22w/mv/7GKm40suSXkNa7bu4hUs06wU+Xtr5mT2S/00ipc02pPb6vX+YtNd87xaKwm1P673L21GrhbAbzWshDARJH7D9s1wYr3gcGHVSpWrLAsXsjBeB5Ww/K6mVFoWNLAXcLekmhk8udVJDo137Al+S9AJDtU46nkBsc5JtHtLr6tTRVtazH5Gev1uQBufTgV+QJq7DAIi09hBCGGCSvk7aX/1zhr95abpns99JutH2hk3OuYGUIrs1Qy2gZnKHPcbbpYraudXFMBdpgAmjtDRq43pTba8n6U7nllaSfm/7DSXnvqedgoSSrrW9aSfxdSpvR3gDaXX3BuAW4Bnbu5acewRwGPAscAmpyvt+thvtwR+PeMflZybpfmDNVh8vOT13Zdu/VWr1NXvZCq+kO4CNPdS7fD7SZNqY0tkL112NVAvhettn5iyR99r+VifXrZKkvW0fn1ee69n2IT0PqgWSvgrsQNqfDWmy5HxSbYQTyp5LLV63tAWj7SllxweJpD8xDhOTGuqucZtzBwdNwGr4gyxWzkMIYbBtkj8W37SZNGs+6K6U9G3SiluxxVVx4mEb4K2k73dqb8MbTiPbue2b0+6/2OF12039Xo+hysJrScL2aR2E8Fzex/jHnF759xxLmQslvZ+RlY0bDSpukfRz4FcM/x33sqChbD8jaU/gmLw/tlF69Ja2P5dTqR8m9QK/ksYF8sZD05+ZpO/b3k8Nqvg3WHl8gLTy3Ep7tI+StmwsAqxEypg4jtSPesTpDM98eYkubAmwfY+kz5NWbbH9IKnuxMCyfXz+72/zdp2Z8jafvmT7UEkXMVRlfx/bt+RPj2lgnq87RdIrGFr1vcmNq5SPm3GadP3nOGUMvZi3vBlmVpuPgnADJAbnIYQwwFzSg3gWUls1X79wbNjEQ04bP0vSvbZL2zP1UKN2bh0NzmkjjVnST0mDpWkMDYgMdDI434+0b/zTwKGk1PbdGpz7a1I14am0MMgj7Ul/BtiycKxRC6bxIkkbkwYRtbZvjd4f1dKmtwbOtP1f9b5LUSs/s5/mj+20VHsGmKbUtqm033vBJ4DXAzfmc/6oxp0JTgFulFTsXX5SG3GVkrQt6fubk1T5em3gkB6nPI+XY4D6YoNlx/qG7al0eYJU0s7At0l1DQQcI+lA2+d2836axFA26bqZ7U7bdI7XxOTRwHnAEjmzbkfgKx1eM/RQDM5DCGGA5VWFbwBL294qp3pubLvjN79Va2fioTgwr7iK9kJ0v51bOyss6wOruYt71mzfnP/7P0lfbFJo75W239HGtVtpEzfe9iNNoJxn+25JK5JWw8ucL+k+Ulr7x/Oq1HMNzh0XrfzM8kCp3fTfpv3eC5536r0NgKTZKVmhzzE07V0+RgeTJgiuyvczLae2D6w8SbQJqbBjcd/5ZFKxvonmyxR6eufn229JxeZ6pdGka6eD865PTOYMpweBz5GyWARsX0VNjzB2MTgPIYTB9hPSytSX8+0/kPZeD/zgvIOJh0qqaJMqmtf6RM9s59aF67azwnIXqc/5oyWf64aLGH317jpJa9i+s5WL5QJsxwKvsL26pDWB7Wwf1oVYW5IHsFMKtx8gZQnUxzoJuICUyfBULsj4DPCuXsWa42j5Z6aRbeIgZTbcAhxme2ZfZbfX732KpC+RqvO/Dfg46WdTyi32Lm/TDNtP1mUuDHohpTmB+Unvz4sF/J4irYBONJPq0tj/Qypu2GsL0eVJ1/GYmMxFLY+0vTFwX7evH3ojCsKFEMIAk3Sz7Q3qir90tS9rVSRdTJ54sL1WXp27rVaUbJSvO8x2JWl8kpZiqJ1b0z7RLV6zrKe9XdLLPk8MrA3cxPCBfFdSfYuPswafvwd4NWn15nma9AxXajt3IHB84fF7l3vY2zj/zMr2ZY+o2yDp+vzGtzLt/MxyAbuXgDPyofeRfidPApvZ3rZwbtOWeYVzJ5G2AGyZr3cpcGI3MzaakXQScAVpBfM9pAmVOWzv06sYxouk5dxm67FZUa45siZwZj70XlIv8M/3MIZdSLUMhk262j6rw+uOy8SkpK8BdwC/7OXzMXRPrJyHEMJge1rSogwVf9mI9MZ7VrCY7bMlfRHAqadxo5ZqM1U4MN8UmGb7fEkfAD4n6ahO32S3ucJycCf31YIfN/n8Vm1eb17bN9WtfjZrFdRtny38f27SQK9RDJdJeg/VvvFt52e2qYdXdr9Tudp7fowWtdIyD0grdKTHQrPHw3j6FClj6HnS4O1SUk2EWcEzeWD6OoZPlMwKhT5bZvvA/HzblPRYPMH2eU2+rNsuJW2dqE26fp7U2qxTPyZPsgHYvkPSGaRuEJ04gNTbfIak52heQDT0mRichxDCYDuAtE90RUnXAosz66Q/tjzxkD93DPBaUmrobMDTPX5DciypOvpapDddJ5MKsZW2A2qVpFeSvrdNST+La4B9bT9cf26ublxscTUvY9yrKmmRksNn1Y7b/m/9J20/JGmzfP+n5D2i849yN49JWomh3/GOjF9Kfqna/uyCa/PqdJnaG9+XJD1LNW982/mZzS9pQ9s35nNfz9Dvo35AP4/tKyQpTygdLOn3pAE7+evL0uRnapQhMR5sP0ManH+52bkD6HTS9qRtSO3idgP+XWlEFXFqzdhye8ZxcAGwle3zASS9FjgH6DS7Z1wmJm0v0Pys0M9icB5CCIPtHlJl1meA6aR9yX+oMqAuqk08rNTCxMMPSCm755CKon2IlF7dSzNsW9K7gKNtnySpUVXzdpxCSkveKd/+QD72tvoTNbLF1TI0bnHVzFTSQEykdlWP5/8vBPwVGFF8S6k/8/rAKjnGOUhtxhq1gfoEcAKwqqS/k9Lh61d0x1XdJMQkUvxLlp3bJ2982/mZfQQ4WdL8pN/dU8CeSr3Gv1l3bist87YpxABDVeF3Jb0GjTs1aA9XM4tUa180v37sW6uJMMqE0SxH0nSGXnuKv+sqJsO+AVwgaWtgVdKE65jbwxWM+8SkpINtH9zNa4bxF3vOQwhhgEk6m/SGu9bmZRdgYds7Nf6qwZH3ma9CelN2v+0XG5x3i+31Jd1RW72TdJ3tTcrOH6dYpwCXkNKB30ha6ZrWbI98C9cdUUOgUV0BSdPILa4K+5Hv7CQGSccB59u+KN/eCnir7c80uP91gFsL939HsxXVPFicZHv6WOMcK0kPMjQAmAH8hdSS65qSc0V6Y76CU2/nVwFL2b6pV/EWYmn5ZyZpQdJ7vidGOWcD4F7S5MuhpGrS37Z9Q8m519aly5ceGw+Sapko65MK2xVNtt2wMN2gkHSD7Y0kXUpqjfUIcK7tlSoObUKStD2pAvoCwLtt/7EL11yRNMm2CWni80HgA7b/0um1C/dRZeeSMEaxch5CCINtFdtrFW5fKanqft9dY3sGcLekE2zvNcqpz0iak9Sn+QjSCsR8PQlyyHuB9wN72v6HpGVJPXo79VjeH1wrirQLqWpxmZZbXLVhg2KRLdsXS2q0t/eFnD1QWw0q/R1oeJuo4vHafXy3s5Dbshqp2vhmpJ/V7xk56Kv5EfAysAVpAPs/4Iek/ag9IWkhUmbI8sDshZ9ZWT/y2ueelHQhQyvfZee00zJvPqVez9fkmDahR8+3vJKMpO8Blzl3BsiFu/ZjlKrxA+SwPKHyGdKWlsnA/tWG1HuS9nRddw5J33LnPcZbue9jGP7aORl4APiUpFGfb61w6grx1nGemKyqc0noQAzOQwhhsN0maaPa6pakDYFrK45pPKzf5PMfJKUkf5L0JvZVpMJePZMHM98FkLSN7QtJKZCd2oOUtv+9fPvafKzMFLXR4qpFj0n6Cik93aQU6kaTA2dLOh5YKKfY70F50bBaevgqpIFtrb/2tsDVHcbbrlNJ2SdH59u7kNK1y7JPNrS9rqTbAGw/nieFeuki4AbgTtJEQauWafM+Rltx25OULl9rK/UEjR+T42VH4FxJu5ImVj7E8J7RA0nSbKSaDReSamxsXnFIVdpR0nO2TweQ9CMKBfLGWf0EXX1tio6MZZJtDNbr4rVCj0RaewghDKBCYaY5SAOcv+bbywH3lLVVGmSSLrH9jqrjaFVV6YQqaXFlu6OK2nlP9kGkVH1Ig+evlRWEyyvi/wJq2RyX2b58lGtfBryntmokaQHgnF7+riXdXpd9UnosH7+RlIZ6cx6kL076Hhu2l+u2sT62JJ3skvZ7Dc69rZXvSdJk0nvJSjpEKLWj+hXwN2B7289WEUe3SbrS9kQelAMgaR7SxN3JpE4Q/7W9X4XxrGv71i5d6zpKJtlsnzrG69Wv9A/T5UF/GEexch5CCIOpYXrqrKjZYE2pjdnBpMmJmX/bXNKjuUe6lk6oNqq1A5+yfRSF1epcVOqosd5/HoTvmwdiL9v+3yinL0CaHPgvcBap3+5olgVeKNx+gbSS1EvtZJ8cTSrA+ApJXyet3va6dd9Pc1bChQzvZT9isqSo1YF51tKEju2nmqXLd5tGVoxfhNSR4MacbtyzivHj6DpJPyBVbH+6drBbA8N+V1ek8SOkCZhrgUMkLdLssT6OTmT0jJJ2zG27dHvPGDXaihMGTKychxBC6EuSLgd2qhWxkrQwcJbtt5ecex8pnX0qMLMXuu1G6dfjStLru1UkLP8czmCoMvYHgF1tl1VrH7Gq2uoq6Cj3vwYpPb/2hvkxYDfbd43yNWuS9uC/B3jY9lsbnPdlYGfSgNfADsDZtr8x1nhbNdbsE0mrkqrfC7jC9r3jHWvd/X8C+Doplbz2Js5lE1ElA1lIqdK3AIcVnx9KlaMftv28pDcDawKnjVZELn9dR4+vdim1CmzIqQ3cQJN0Zclhe4L0OS8UaVThY03pY71HcXXtsS5pf1LNirYm2cKsLwbnIYQQ+lLZG6FGb44k3Wh7w95FN+y+3z3a523/ssPrN63WnothvZ+09/b3hVMXAF5qNDhu8f6vA75s+8p8+83ANzxKJXxJS5L2bL8PWGC01UxJ6wJvyDevtn3bWGNtx1gHeSrp4277wfGIscH9/5m09/2xFs49gjRZdUY+9L788SlgM9vbFs6dRqrtsDxwKSmdeBXbWze5j5bT5UMYZJK2t/2rLl2r5Um2Nq+7OPB5UqHLmfvzJ8rEzqwg0tpDCCH0q5clLWv7rzBzMDVsRjkP7CBVqf828EuGr0L0Ig1021E+Z1JMnWilWvt1pAr1iwFHFo5Pp3lqeTPz1QbmALavGqUK+8dIK+aLA+cCH7V9T6ML54r2j5FWzmceq/3Ox9NYVljVfh/38XA3rfcU39TD25vdqdzyLD+mil62PUPSDsD3bR9TK3w3mhiYd5+kbwBH1GUNfcZ2r7dQVGK8JzzbjKWYifTXfPtJ4CGnbiJjdQDw6lYm2dp0Omk7xDuBfYDdSG09w4CIwXkIIYR+9WXgGqX+4ZAKktW3Uzuy7naxqrtJLa/Gle3dx/kumlZrzwPNh3LroWGD4bzSfVUH9/+ApK8yPK2+0UrxcsB+tqe1eO3fMDThMg+wAnA/8LqxhTrudiD3cQew/UguYtdLL5FaBl7J8ImosoJP80va0PaNkLZbAPPnz9UPLF7MGRi7MTThNEfxhAZp8jPNIvu9+8FWtr9Uu5G7AmxN7+sbVGW8Jzzb8SPSPvM7SOn1q+f/LyppH9uXjfG67UyytWNR2yflWiNTSB08pjT9qtA3YnAeQgihL9m+JK9SbER6U7R//SpDv1U0lvRO0sCymE54SCfXzKvI27V4+tmSTiP1V58bOII0YbFxByHsAXyN9IZYpGrtpRMSbrP/sO01irfz73vvsYXZEy31cR9nv8r/WvERUsuz+Um/u6eAj+S4v1l37u6klbav235Q0gqkrICiWuG3T+SPtQmbXRmfgcZENZukuWw/DzOrls9VcUw904MJz3b8BdjT9t0AklYDDgQOJb0mjnVw3s4kWztezB8fzX+PHgFe2eE1Qw/FnvMQQgh9RdKqtu+rSyecqSxVXdK+pDTj6aRK0+sCX+hgVaNtko4D5iX1JT6RVMn7Jtt7dnjdFYGjSJMUBq4nTVQ8UHLufMDhpP62C5BSHA+33U4/7EZxtFKtvWNjbRXWC5I+C6wMvI00uN0DOMP2MZUG1oRSP3I1K+7WxvWurUuXLz0WxkbS50gTcqeQnvN7AOfbPqLSwHosP26LbRynAIe4h637Rqv5Ufa5Nq67W8lh2z5tLNcrXHcbUt2RV5G6fEwmtb48v5Prht6JlfMQQgj95gBS+np9yjo0TlXfw/ZRkt4OLEFaBTyFsa9qjMUmtteUdIftr0k6ku6kX54B/JCUUg2pqNeZQFkBvBeBZ0kp4nMDD3Y6MK+v1i6pabX2Nq5dbCU0iTSp0pf7IyWJtJdzVdIK9CrA/3mUPu7jFEd920DRuFr7XKSK+csDs6dvoTybo1Ahe5gGBarmk7SZ7Wvy124CVJFFMEuyfYSkO4BaIcdDbV9aZUwVORm4i9TRAeCDpNf1Ufekd9n9ko4ltYaEVFPjD/m59WLjL2tqIde1uMyTzB2xfWH+75OkieIwYGLlPIQQwsDLA+I1JR0FXGX7vApaPN1oe0NJN5DePP4HuMv2yt24bt2xG2xvVHLu7cCvgUNIxeGOB160vWMH9992tfY2rn1Q4eYMUgrpL2w/1+m1x4OkqbbXqziGltsGSrqE9Ca9/twRE1+SFi3cnJtUbX8R2/9Xcu56pIHTgvnQE6QJsgnRh7sXJL0CeD1pwuQm2/+qOKSea6VTRQ9imAf4OKkThoBrSPvQnwPmHWsmUVmGUDf+ZhUyrTYGXmaUTKvQn2LlPIQQQt/KK3LLU/h71SDtb6qky0gFxb6Yi3R1nMrdpgslLUTa730r6U31iV247pWSvkBauTFp5eY3khaBEX1xP0pa0f2S7UMkfQr4UIf333K19jG4x/Y5xQOSdgLOaXB+1W6QtIHtmyuM4UnbF7d47ittv6OVE0sG99+XdA0wYnBueyqwVt7qoF6mGU8EknYmvY5cRRoQHiPpQNvnVhpY7z1bl6GxKSkzqGdsP0vK4irL5Gp7YK6htpcrSCqmmi/AyC4cY9FOplXoQ7FyHkIIoS9J+imwEjCNoVU/lxXMkTQJWJtUXXou0qrxMr3cC1xXwGku0urjc7VjHVy3WBm99kdbtdvFtOOcfvkysIXt1+YWTJfZ3qCD+z+PNNlQrNa+vu3tx3rNwrXLVo/6ec/5PcBrgIeApxlKKe9ZlXJJ3wJmo4W2gZJOAI6xfWcL1y3+zCeRCgl+zPZaJecOS5cvxNBR8cOQ5AyYt9VWy5V6V/+27HcxK5O0FmlLTS1D43HSlppO20O2E0P9NhKg4XaPVq63HGkS+ZtAsYDmdOAOd9aera1Mq9CfYuU8hBBCv1ofWM2tzSLvAexLqko7jVQ87XpSQZxeuZ60Z5o8IH9e0q21Yx34PHCJ7aeUWpqtS9qDWpZCvKHtdZX7Uzu1YJqj5LymJP3U9gdJxYWWZ6ha+xQaVGtv49pbAVsDy0g6uvCpyYxs8dVPtqo6AIZWwFppG7gZ8OE8wfM8o08mFFcGa1sMdi45D9LWiVq6fEeTT6HUpLo09v+QJkwmhNwG7Chgftu1DA1sP1VBOCdRso1krJzbXtJZB40RaplUNMi06uZ9hfEVg/MQQgj96i5gSeDRFs7dF9gAuMH25pJWJbX/GneSlgSWAeaRtA5Dq9qTSdXbO/UV22dL2oxUJfxI4FgaFISTNBt5hT2vuI01RW69vMqzG6mwkBi5cj9WjwC3kCpSTy0cn056I9yX8htrJC1BoV1ej2Nop8hTy5MJbV635XT5MCaXSLqUlI4MaYDV6laGWcHupH3TxwDrVjQor2lnG0lTkq6xvZmk6Qx/ba5NnE0e46Wn5uvVXpuLLSlNav0WBkAMzkMIIfSrxYB7JN3E8PTdsp7fz9l+TlItvfw+Sav0KM63Ax8mrdp/t3B8OvClLly/tlrzTuA427+WdHCDc48GzgOWkPR1Uju3r4zxfo8DLgFWJA2ka2qD9DGldQLYvh24XdIZtl8EyCn4r7L9+FivO94kbUeaHFka+Bcp1fVeUm/7XsXQtL2UpMl5QDO9m9ctuE7SGq2ky4f22T5Q0nuATUnPtxNsn1dxWL10r6S/AIvnqvU1Pd9GQlqJ/jYtbCNphe3N8scFuhPezOuu0M3rherEnvMQQgh9SdKbyo7bnlJy7nmk1Zb9SOm9jwNz2N56PGOsi+E9tn8xDte9EPg7qa3SeqSCSDc12n+aswbeQnoje4Xtezu8/2Ntf6yTa4xy7atIq+ezk7Yj/BuYYvuAUb6sMnkv8Bak/b/rSNoc2MX2Xj2M4RekrJJT86EPAmvZfnfhnAttb1Noj1bMdBhWp6Cd6xbOvQd4NdBKunwYo5zOXdzn/N9RTp+l5IykS0mvD8PUMlh6FMeVJYdtu2wbSTvXXbbsuO2/dnjdOYCPMTTJdhVwfG0SNPS/GJyHEEKYpeRB/YKkfdov9PB+x6VIlqR5gXcAd9r+o6SlgDVs97KH+7iotQ6S9BHSqvlBym3xqo6tjKRbbK+fB+nr2H5Z0k22X9/DGMalvVQ7183bHUbo5aBpViZpb1I7xGdJBR4b9rKfKCStO9bV6n4kqZh1MjepSNz9tjvKwpF0IqkwanGS7SXbH+nkuqF3Iq09hBBCX5K0EWnP4WuBOUkVqp9utievbGW9R8alSJbtZ0gplbXbj9LaPvxBMHuebNgZ+HLVwbTgCUnzA1cDp0v6F70vYNdWeylJyzCy0vTVnVy3H/bez+I+C7zO9mNVB9JHTqTz4ppta3O7R8tsr1F3P+syfJ/4WG1Ql1X1uzyZGAZEDM5DCCH0qx+QerSeQ6pM/SFg5UojGl0UyWrfIaTU1Wts3yxpReCPFcc0mneRBqz7A7uSMjR63T5sH+C0PGiA3F6q7ERJh5OKid1DoR0haXJhtOsK+C+plkLZdSvfez+L+zPwTNVB9JlOi1CO1cmk7R61zgUfBE4BRmz36ITtWyWNueVlwUuSVrL9Z4D8mtpxlfnQO5HWHkIIoS8VUohnpjlLus72JlXHVqadntJhMElaAXjU9nP59jzAK2z/pUf3PxvwrVwwrGl7KUn3A2vm1n6t3kcr16187/2sLHd9OAW4keFFyD5dWVAVk7S97V9VcL/jtY2kWFdjEikrYFHbb+/wum8hPXYeIE1oLAfsbrts73zoQ7FyHkIIoV89I2lOYJqkI0ip3PNVHNNo2ukpHepIutV2z9NW23QOUJwceikf68aKV1O2X5K0Xv5/K+2lHiDtP206OK+vmSCpdp9lmQEv2v6PpEmSJtm+Mq/Sh+44HvgdcCdpz/mEJOkK228BqA3Mi8d6pK1tJG0oVmufQepF3nFBUdtXSFoZWIX0N+i+dibnQvVicB5CCKFffZC0ovBJUhrxq0iDh37Vck/pUKqqtNV2zF4sMmj7hTyB1Eu3STqfNCnwdCGWmXUJJB1DSl9/hjS5dQXNV2DbqZnQD3vvZ2Uz+rVjQS9ImhuYF1gst1isvTZMJm2l6KWWt3u0KmfAzG/7wM7Dm3nNRmn2K0ka9voQ+lsMzkMIIfQl2w/lgc/ypIJo9/ey+nq7crybASvbPkXS4sD8Vcc1QH5TdQAt+Lek7WyfDyDpXUCvi3YtAvyHlFZeYwpFAxnqSz8VOL/F67ZTM6Ef9t7Pyq6UtBdwAcMnVSZKK7W9SW0xlwaKFdqfAn7Yy0Bs3w6s1cp2jzau+VIuANdN29bfTf4oRr4+hD4We85DCCH0JUnvBI4jFUcSqdXM3rYvrjSwBiQdRCpct4rt10haGjjH9qYVhxa6RNJKwOmkQYOAvwEfrBVf6jeS5gOes/1Svj0bMFfuAFB/7phqJkjaxvaFXQk4AJC3xtSbcK3UJH3K9jEVxzBeLTKPJBU4bZgBM8brfoY0GK9lG5icEWN7WifXDr0Rg/MQQgh9SdJ9wDa2/5RvrwT8xvaq1UZWTtI0YB3gVtvr5GN927O7SpKmM7SyM+xTpEHIqO3yqpZTumV7eg/v83O2jyikrA9Tlqou6Qbgrbb/l2/PD1xWVlRR0j3Aq4G2aiYMSK2AMIDy5NL+wLK296rtpe7lZJCkSxja7jGz6rntIzu87iklh217jw6vewZpkvh80nP4ncDNwKqkyeIjOrl+GH+R1h5CCKFf/as2MM8eILVt6lcv2LYkw8w3lqGE7QWan9W/bP9P0oXANj2823vzx1tGPWu4uWsDc5gZ97wNzh1rzYRBqBUwUPLv6AAqHJT2iZNJg+LaZNLDpJXmXv4cxqVFpu3du33NbFFg3cKE3EHAuaQ+7VOBGJz3uRichxBC6Fd3S7oIOJu0UrgTcHOt8E0fFrg5W9LxwEKSPgrsAfy44pgGgqQlgLlrt23/tcJwWrVML+/M9gX5v3fYvq3FL3ta0rq2bwXIld5LK013UDNh7xZjCa07heoHpf1gJdvvlbQLgO1nVWsj0DvXSVqj2y0yJZ0K7Gv7iXx7YeDITlfOgWWBYm2WF4Hl8s8uqrYPgBichxBC6FdzA/8E3pRv/5tUDGtb+rDAje3vSHobqWjRKsD/2b684rD6mqTtgCNJe7j/RerJey/wuirjalGrA+Ru+66kpUiDtbNs3z3KufsB50h6JN9eCnhv2YnFmgmkweEcwM+A0poJkjZhqO3aqgC2T2v3mwml+mFQ2g9ekDQPeRtH3trUkwGmpDvz/c4O7C7pAbrbInPN2sCcdMHHc3/7Tp0B3CDp1/n2tsCZOZPrni5cP4yz2HMeQgghdFGu6lssHDRRKiy3TdLtpKrjv7W9jqTNgV1s71VxaE3lla5X2b6jgvteEtiZNNCeDPzc9mENzp2D4T2PX2xw3jRarJkg6afASsA0hvbhukGLttAmSdcBbwGutb1uHpSeafv1FYfWU3my8yvAasBlpImiD9u+qgf3vdxon7f9UIfXvx14s+3H8+1FgCm21+jkuvla6wGbkZ7z19huZytMqFgMzkMIIfQlSUcAh5HScC8B1gL2s/2zSgNrQNLepHZSzwIvM7TCMqEqLLdD0i22189vVNex/bKkm/p1ECLpKmA70uTLNFI2x5SqelJLWgP4HPBe26X91iWtThrcFLcNjFjhrv3cawXe8krb9Q0G5/cCqzneRI6LKgel/UbSosBGpNfTG2z3unVhMZa9bJ/QpWt9CPgiaT+4SZNtX7f9025cPwyuGJyHEELoS5Km2V5b0g7A9qSqvVfaXqvayMpJ+iOwcZVvHgeNpN+SfrffBBYjpbZvUFZNvB9Iui2v8H+EtGp+UK8r8kt6LWnFfEdSv/OzgF/YHlEsMaeqv5k0yLuIVPTtGts7lpz7WVJrp7eRfh97AGeUtbKSdA7waduPdunbCnX6aVDaa816gNdqKPRatzsTSFqNlDkk4ArbkXYeYs95CCGEvjVH/rg1KaXzv32+7fLPwIj+0WFU7wKeI0287AosSMo+6Fez5/3eOwNfriiGU4AzgS1tP9Lk3B1JGSe32d5d0iuAE8tObLNmwmLAPZJuorAH2PZ27X0roahkUFqb/FhW0rJVDUorMFqbMpMGtFXo9h+gRYCnawUYJa1gu6zHfZhAYnAeQgihX12Qe50/C3w8V49+ruKYRvNFUmXfGxk+YIl9uA3Yfrpw89TKAmndIcClpNXnmyWtCPyxlwHY3qiN05/NWwVm5FoI/wIabrPIg/FWihge3EYMoXW1QencpOJ8t5MGhGsCN5L2Ec/ybG9edQwNbNutC7VbgDFMHJHWHkIIoW/loltP2X4p9/6dbPsfVcdVJq8iXgPcSdpzDoDtQRh0ViK3xTscWII0CKnt059caWB9TNKmpMHxcqRFloa1DST9CPgS8D7gM8D/gGllPZYlTSdXxS54ktRX/TO2H+jitxFGIeks0v7jO/Pt1YHP2v5wpYH1SK1dZiO9bqMp6Z2kDhLFug0dZfi0U4AxTCyxch5CCKGvSNrC9u+Kb9Dq0tn7qoVawYyqCoMNsCOAbW3fW3UgreiTIoUnkbYBTGWoUnop2x/P/z1O0iWkya1G1eW/CzxCasUk0oB+SeB+4GTS3nUgBvI9sGqxr7btuyStXWE8vTbaCnVP22hKOg6YF9ictCVkR+CmLlz6BduWVGsTN18XrhlmAbFyHkIIoa9I+loutHVKPlT7Q1VbIdyjotBGJenrwEPABQxPa49Wag1Iutb2wKRx9kORQkk32t6wxXNF2su/ou1DJC0LLGl7xOCi7LqSbrC9kaTbi9+jpK/ReCD/MdtvHuO3FwBJZwJPk9KcDXwAmN/2LpUGNgHVVrMLH+cHfml7yw6v23IBxjCxxMp5CCGEvmL7oPzfjwHvAZZn6O9VP88ovz9//GLhmBllj2/gFkk/B37F8AmNfs2O6IcihVdK+jZp9bD4MysrFvYj0haLLUj75acDvwA2KDn3ZUk7k1o7QVohnHn5unPfUTeQPyEP5A+R9KW2vptQZnfS69+++fbVwLHVhVOd8Ugpb9Oz+eMzkpYmdUhYoZML5kmznwOr0loBxjCBxOA8hBBCv/oV8ARwK0OF4Pp2cG67ozdsE9RkUoX74ipUT9NW29QPRQprg+L1C8caVbDeMPcsvw3A9uOSSvuhk1bYjyIN6A3cAHxA0jzAJ+vObWcgH9pk+znge8D3JK07gaq0DzOOKeXtuFDSQsC3SX+LTIOOB63K6ey/sr0erRVgDBNIpLWHEELoS5Lusr161XE0U7ZHvqiPV4HDGNQVKZwPWKCPixTeCGwC3JwH6YsDl9UKUHVw3RVJA/mNGRrI7w/8HVjP9jWdRR5qut1be5CMV0p5B/HMBcxt+8kuXOuHwE9s39x5ZGFWEivnIYQQ+tV1ktYoFkbqU28Cfkd5EaN+XgWunKRXAseQ2geZVO1+X9sPVxpYA7ljwCeAZYG9gKVJKakX9jCGVwDfAJa2vZWk1YCNbZ9UcvrRwHnAErkmwo7AV+uu9znbR0g6hpJV77JWgLngW6OiXTEw766e75voI11PKW+XpJ2AS2xPBw4E1pV0qO3bOrz05sDekh4i1Reo1VSJau0TXAzOQwgh9BVJd5IGCbMDu0t6gLS3ti/fvOTidZOAi22fXXU8A+YUUlGxnfLtD+Rjb6ssotGdQqqSvkm+/TBwDj0cnAM/yXF8Od/+A2n/6ojBue3TJU0F3kJ6/mxfUhm/dvuWZnc8loF86NjXqg6gQrWU8iNIzzvoMKV8DL5q+xxJmwFvB74DHMfQ9pKx2qrjyMIsKdLaQwgh9BVJy432edsP9SqWdki62vYbq45jkNSqnzc71i8k3WJ7fUm3FXoTD6tk3oMYbra9QV0MpT8zST+1/cFmx9q4721tXyBpt7LP2z51LNcNw0kqS2N/EnjI9oxex1OVXO/gY8AbSJNBvweOzXvyexXDbbbXkfRN4E7bZxSfe126j71sn9Ct64XBFivnIYQQ+kq/Dr5bcHluj/NzUpoiEK3UmnhM0geAM/PtXUipq/3qhTxgqPUmXolCxfQeeVrSooUYNiIN3Mq8rnhD0mzAenXHLmCUIm62tyv8/4L8ceYgPGeNzG/7qfa+jTCKHwHrAneQMh5Wz/9fVNI+ti+rMrgeOpXUYeDofHsX4DRg5x7G8HdJxwNvBQ7P+84ndfk+9gFicB6AGJyHEEII3VLrv/6JwrFopTa6PYAfkCpTG7iOoZ9jPzoIuAR4laTTSXvlP9zjGA4AzgdWknQtsDjDq6Uj6YvAl4B5JNUGzQJeYOQg4Dv547tJvcp/lm/vAvylLABJZ5AGFC+R0o0XlPRd298e+7cVCv4C7Gn7boBcV+BA4FBSDYuJMjhfpS4r5UpJt/c4hp2BdwDfsf2EpKVIv4tumsh1BUKdSGsPIYQQQmhRXrXeiPSG+gbbj1UQw+ykQnQC7rf9YoPzvmn7iy1ec8S2jEZbNWpp9JJ2Ja3Efx6Y2m/1IAbVaNs9+nnbR7dJ+glwnO0b8u0Ngd1sf7yHMexZX2xR0rdsf6GDa04C7qh1I5H0yn4tghl6L1bOQwghhC7IlbwPAJa1vZeklUkrP70sFjYQBryw2NzA46T3UKtJwvbVvQwg7zu+W9IJtvca5byZA3NJB9s+eJTLLi5pxVyJHUkrkFbly8whaQ5ge+AHtl+UFKs93XO/pGOBs/Lt9wJ/yCnVpRMxs5JCUdA5gA9J+mu+vRxwT4/D2VHSc7ZPz7H9CJirkwvaflnS7ZKWtf3XGJiHohichxBCCN3RD5W8B0XLFcL7iaTDSQOlu4GX82EDPR2cF6zfxrnbAQeP8vn9gatydwSA5YG9G5x7PCn1+nbg6lzEMfacd8+HgY8D+5GyI64BPksamG9eWVS9s03VARS8Gzhf0sukCuv/7dLK/VKkCbabGF6jZLvGXxImgkhrDyGEELqgHyp5DxpJO9k+p9mxfiHpfmBN270uAldK0iW239HiuU0rTOeV2VXzzfva+T4lzT6RKomHWZukRQo3FwB+BVwL/B90XuhT0pvKjtue0sl1w+CLwXkIIYTQBZKuI/WTvtb2urmS95m2X19xaH1L0q221212rF9IuhjYyfb/+iCW+Ww/3fzMmedPsv1yk3M2Ia2Yz8ystH1ayXmvAL4BLG17q1ywbOP6vblhbCRtSspyWI7hv4soLtkjkh4kZcWo8LHG3fhd5IyTlW3/Nm+Lms329E6vGwZbpLWHEEII3dEPlbwHgqStgK2BZSQdXfjUZKCfV1+fAaZJuoJCC7Ve7pHPA+gTgfmBZSWtBexdlmor6QjgMOBZ4JJ87n62f1Zy7k+BlYBppCrskAYlIwbnwE9I2zi+nG//gdRCMAbn3XESaZvBVIZ+F6GHbK8wnteX9FFgL2AR0vNuGeA40gRvmMBicB5CCCF0ge3LJd3KUCXvfauo5D0gHiHtN9+ONACpmU4alPSr8/O/Kn0PeHstDtu3SxpRUT3b0vbnJO1AqoGwE3AlQ+3SitYHVnNrKZWL2T47t2zD9gxJMYjsnidtX1x1ECFpNaOkTZ8AXg/cmK/3R0lLdHjNMAuIwXkIIYTQPW8CNmOo0vB51YbTn2zfDtwu6YxGbcD6ke1Tq44BwPbfpGGtkRsNjOfIH7cmbbH4b93XFd1F6nP+aAshPJ1byhlA0kbAky18XWjNlZK+TeppXszQuLW6kCamNjNK2vG87Rdqz8fcHjH2GocYnIcQQgjdkFvsvBo4Mx/aW9JbbX+iwrD63fKSvgmsRmpRBvTf3lpJZ9veudDiaeanSPtPe9nf+295Jc+S5gQ+zVD1+3oXSLqPlNb+cUmLA881OHcx4J5cPbo4ICyrHn0AaeV+JUnXklqu7Tim7yaU2TB/LFbjN7BFBbFMdO1klLRjiqQvAfNIehupOv8FXb6PMICiIFwIIYTQBZLuBlavvYmTNAm40/brqo2sf0m6hrRX/3vAtsDupPcmB1UaWB1JS9l+NBdwGsH2Qz2MZTHgKOCtpMmBy4BPN6oeLWlh4CnbL+WiU5Nt/6PkvLaqR+eVvlVyDPcPUgZECK2SdA7p+dVKRkk7150E7AlsSXoOXQqcOA6TAGHAxOA8hBBC6AJJvwT2rw3U8kDuW7Z3qTay/iVpqu31JN1pe4187Pe231B1bGUkzQc8a/tlSa8htR27uJcDU0mb2r52tGOS3j3aNWz/coz3PS7XDcNJ+r+y47YP6XUsE5WkC0jZCgsAawOtZJSE0LFIaw8hhBC6Y1Hg3pwWDLABcIOkWuGueDM30nN5BemPkj4J/B3o56JIVwNvyKvRV5CK2r0X2LWHMRwD1Leaqz+27Shfb9JeZiBlL9jeTNJ0ylP2J5dcdwlgE+B3+fbmwFXF64aOFFvkzQ1sQ+OtC2F8fGc8LlqyNWaYHm+RCX0oVs5DCCGELmiUFlzTKD14IpO0AWnQsRBwKLAgcITtG6qMq5FaD3ZJnwLmsX2EpNtsr9OD+96YNCDej7QNoGYysIPtterOnwTsaPvscYjlQuCjtVRfSUsBP7Q96sp6GBtJcwHn23571bFMZJK2sX1hh9eobY2p1SL5af64K/BMZEeEWDkPIYQQuuPftu8pHpD0ZttXVRRP37N9c/7v/0j7zfud8iB5V9J+Uejde6k5Sb3NZyel2tY8RUkxtpx6/0mg64NzYPm6Pbj/BF4zDvcTknmBviqSOEEdAnQ0OC9se9rU9qaFT30hF1eMwfkEF4PzEEIIoTvOlnQa8G1SKuoRpEq/G1caVR/L+7YPBJZjeA/hfq1KvR/wReA823dLWpHUN3zc5cyLKZJ+0kYBusslfRb4OYVU6UbF49pwlaRLSZ0JDLyPHv0cJoK61OfZSNXwY9BWvYZ9CMdgPkmb2b4GZvZSn6+L1w8DKtLaQwghhC7IxcIOB9YjrWyeDhxu++VKA+tjkm4HjgOmUujVbXtqZUH1udwO7XPA6xjefm7EhIakB0su4W60qpO0A/DGfPNq2+d1es2Q1HUFmAH80/aMquKZyCTNZvul/P/X276p2de0eN31gJNJW3kAngD2iF72IVbOQwghhO54kdRPeh7SoOnBGJg3NcP2sVUH0SpJV1JSzKnHK/2nk1bCtwH2AXYD/l12ou0VxiuIPBg/rxv7cMNwth+StBZQ61pwNXBHhSFNZH+SdC5wSrcG5jBzAnItSZNJi6VPduvaYbDFynkIIYTQBXkV+Nek9NPFgOOBF22P2A8cEkkHA/8CzmN4m6JO067HRV7tqpkbeA9pguFzPYyh1n7ujlplZ0lTbL+pcM4Wtn/XqPVZN1ue1Yrkdet6ASTtC3yUoer3OwAn2D6muqgmJkkLkLZt7A5MIq12n2X7qQ6vOxfp9WN5hm/pie0LE1wMzkMIIYQukPR6YBVgBduHSFoW+JDtwyoOrW+NZ9p1r9QPjHtwfzfY3ijv+T4aeAQ41/ZKhXO+ZvsgSaeQVvpV/Gh7jy7G05Nq9ROJpDuAjW0/nW/PB1wfbbaqJemNpDoLCwHnAofa/tMYr3UJ8CQjt/Qc2XmkYZBFWnsIIYTQHbsDLwNbkFbPpwPvAmJwXiK3+vqC7Z9XHUurJC1SuDmJVPBvyR6HcZikBYHPkPqbTwb2L55g+6D837sYGpST//+kpLVtT+tSPHt36TphiCgM2PL/u1mMLLRI0mzAO0mv78sDR5K2lrwBuIixdyl4pe13dCPGMGuJwXkIIYTQHRvmHti3Adh+XNIcVQfVr3Krr0+Q9k8PiqkM7TmfAfyFoZZqPVHY3/0ksHmT09cjTSCcTxrcvRO4GdhH0jm2jxhLDJLmJU0OLGv7o5JWBlaJveddcwpwo6Rakb3tgZOqC2dC+yOpE8G3bV9XOH5uXkkfq+skrWH7zs7CC7OaSGsPIYQQukDSjcAmwM15kL44cFmk/DYm6aukInrdbvU1LiTNA3wc2Iw0SP89cKzt53oYQy1VfZiyVPWc+v4e2//Lt+cnpePuAEy1vdoYY/g5aaLiQ7ZXzz+X622vPZbrhZEkrUt6nIlUDf+2ikOakCTNX3v+dPm69wCvBh4k1duobTmJrQsTXKychxBCCN1xNKmw2RKSvg7sCHyl2pD6Xm1A+YnCMQP9uuf8VOAp0u8aYBfgp8BOPYyhuDo9N2mg/UiDc5cFXijcfhFYzvazkp5v8DWtWMn2eyXtApCvF2nXXSLpO6Tq4Ec3PTmMt6UlHQu8Ik9ErQls14VaIlt1IbYwC4rBeQghhNAFtk+XNBV4C2kVZHvb91YcVl8bz1Zf42QV22sVbl+Zq/T3jO1fFG9LOhP4bYPTzwBukPTrfHtb4MxcYOyeDsJ4Ia+WO8ewEoVq+6Fj9wEnSJqdlOJ+ZrTaqsyPgQNJ3TewfYekM+iwlojthwAkLUGaZAsBiMF5CCGE0DW27yO9sQ4tyHuXDyDtXd5rAPYu3yZpI9s3AEjaELi24phWJq2Qj2D7UEkXMZQevY/tW/Knd+3gPg8CLgFeJel0YFPgwx1cLxTYPhE4UdIqpEJkd0i6Fvix7SurjW7Cmdf2TXWJITM6vaik7UjF5ZYmtZNcDrgXeF2n1w6DLQbnIYQQQqjKKaS9y5vk2w8D5zA8dbtyku4krRLPAXxI0l/z7eXobAV6LLFMZ3h7tH8An290vu2ppJ9x19i+XNKtwEY5jn1tP9bN+5jocpXwVfO/x4DbgQMk7W37fZUGN7E8ljNDalkiOwKPduG6h5KeP7+1vY6kzUnbZMIEF4PzEEIIIVRlUPYub1N1ADW2F6g6hmwZYDbSe8k3SsL2LyuOaZYg6bukLQi/A75h+6b8qcMl3V9dZBPSJ4ATgFUl/Z1UwO0DXbjui7b/I2mSpEm2r5R0eBeuGwZcDM5DCCGEUJWB2Ltc2x9apVy9uyHbt/YwlpOBNYG7gZdrIQAxOO+Ou4Cv2H6m5HOv73UwE5ntB4C35joNk2xP79Kln8jdE64GTpf0L7qQLh8GX7RSCyGEEEIlJG0JfBlYDbiMtHd599hXO5Kk2s9kblLv8ttJKeVrAjfa3qyHsdwz1jZsoT2STrC9V9VxTDSSDhjt87a/2+H15wOeIz2HdwUWBE63/Z9OrhsGX6ychxBCCKESti/LFe5j73ITtjcHkHQWsJftO/Pt1YHP9jic6yWtZrun++0nqPWrDmCCGtftI7afLtw8dTzvKwyWWDkPIYQQQiUkXWH7Lc2OhSGSptleu9mxcY7hjcAFpGJ0z5OL09les1cxTBSSLrH9jqrjCN1RUtBx5qdIz6HJlQQW+kasnIcQQgihpyTNDcwLLCZpYdIbU4DJpNZCobF7JZ0I/Iz05v4DpBZMvXQy8EHgTob2nIdxEAPzakl6DXAs8Arbq0taE9jO9pj6nPdRQcfQp2LlPIQQQgg9JWlfYD/SQPzvDA3OnyL1cv5BRaH1vTyx8THgjfnQ1cCxtp/rYQy/s71Fr+5vopG0OKk93mqkGgMAxM+89yRNAQ4Ejre9Tj52l+3VO7zuSsDDtp+X9GZS7YjTbD/RWcRh0MXgPIQQQgiVkPQp28dUHUdoj6QfAQuRUttnVtePVmrdIeky4OekWgL7ALsB/7bdsJ99GB+Sbra9gaTbCoPzjreRSJpGqiewPHApcD6wiu2tO4s4DLpIaw8hhBBCJWwfI2kT0hvU2QvHT6ssqD4naWXgm4xcVV2xh2HMQxqUb1k4Fq3UumdR2ydJ2tf2FGBKXsENvfdYXuWutXvcEXi0C9d92fYMSTsA38+vhbd14bphwMXgPIQQQgiVkPRTYCVgGvBSPmwgBueNnQIcBHwP2BzYnaFtAT1he/de3t8E9GL++KikdwKPAK+sMJ6J7BPACcCqkv4OPEhqfdapFyXtQsqK2DYfm6ML1w0DLgbnIYQQQqjK+sBqjj127ZjH9hWSZPsh4GBJvycN2Hsi73vfE3gdw1fv9+hVDLO4wyQtCHwGOIZUKHH/akOasBa2/dbcl3yS7emStgUe6vC6u5O2LHzd9oOSViAVeQwTXOw5DyGEEEIlJJ0DfNp2N9JEJwRJ1wJvAM4FfkcqqPct26v0MIZzgPuA9wOHkFYS77W9b69iCKEXJN0K7Gb7znz7fcD+tjesNrIwq4rBeQghhBAqIelKYG3gJoYXFtuuqpj6naQNSK3TFgIOJa2qftv2DT2M4Tbb60i6w/aakuYALo1q4t0h6QjgMOBZ4BJgLWA/27Gy2mOSViRNhO0KbAZ8CNjG9pNjvN7ZtneWdCfD+5wDYHvNTuINgy/S2kMIIYRQlYOrDmCQSJoN2Nn2gcD/SKmxVajtiX5C0urAP0hF/UJ3bGn7c7lY2MPATsCVRNpzz9l+IK+W/wr4G+l382wHl6xll2zTaWxh1hSD8xBCCCFUIleiDi2y/ZKk9fJ+8ypTH0+QtDDwFVILqPmBr1YYz6ymVhhsa+BM2/+Velrzb8IrWdleBJgNuFHSmFe4a1t4cr2I2n1tY/vCTuINs45Iaw8hhBBCT0m6xvZmkqYz/A2wANueXFFofU/SkcDKwDnA07XjvewxLukzDP3eaqPGJ4Cptqf1Ko5ZlaRvAduT0tpfT9rCcGHsc+4dScuN9vni4LoL93Wr7XW7db0w2GJwHkIIIYQwICSdUnLYvayULukMUqX9C/KhdwI3A6sC59g+olexzKpyZsJTOVtiPmAB2/+oOq6JRNIk4A7bq4/z/dxme53xvI8wOGJwHkIIIYQQWibpUuA9tv+Xb89PKpq1A2n1fLUq4xtUkt492ud7mR0REkmnA1+0/ddxvI/X275pvK4fBkvsOQ8hhBBCGBCSji45/CRwi+1f9yiMZYEXCrdfBJaz/ayk5xt8TWhu2/xxCWATUqs8gM2Bq4AYnPfeUsDdkm5i+DaSjjpKSPoEcLrtJ2zflDMldrH9o87CDYMuBuchhBBCCINjbnL6eL79HuBuYE9Jm9verwcxnAHcIKk2GbAtcGZOv76nB/c/S7K9O4CkC4HVasXDJC0F/LDK2Cawr43TdT9qe+bv1Pbjkj4KxOB8gou09hBCCCGEASHpd6R2TjPy7dmBy4C3AXf2KqVc0nqkvs8CrrF9Sy/udyKQdFdxn3Ov9j6Hcrk43Mq2fytpXmA229M7vOYdwFq1rgu5TeIdtl/XecRhkMXKeQghhBDC4FgGmI+Uyk7+/9K5cFjPUsptTwWm9ur+Jpir8r7+M0lV8d9H6nMeeiyvZu9FaqW2Eun5dxzwlg4vfSlwtqTjSL/jfYBLOrxmmAXE4DyEEEIIYXAcAUyTdBVp1fqNwDdySvlvqwwsdIftT0ragfS7BTjB9nlVxjSBfYLUzu5GANt/lLREF677eWBv4GOk5/FlwIlduG4YcJHWHkIIIYQwAHJ680bAg6QBg4CbbD9SaWChqyR9klQs7PGqY5noJN1oe8Nau7O8jeRW22tWHVuYNcXKeQghhBDCALD9sqQjbW8M9Koye+i9JYGbJd0KnAxc6lhNq8oUSV8C5pH0NuDjwAVjvZiks23vLOlOUjr7MDHoD7FyHkIIIYQwICR9DbgD+GUM2GZdkgRsCewOrA+cDZxk+8+VBjbB5GyVPUm/C5H2ip841ueepKVtP5KLzI1g+6ExBxtmCTE4DyGEEEIYEJKmk4rAvQQ8Sxow2PbkSgMLXSdpLdLg/B2kgnAbAZfb/lylgU0wkuYktS80cL/tFzq41q2215X0U9sf7FqQYZYRae0hhBBCCAPC9gJVxxDGl6RPA7sBj5GKhB1o+8W8ivtHIAbnPSLpnaTq7H8mTYStIGlv2xeP8ZJzStoN2ETSu+s/afuXY482zApicB5CCCGEMCByuvOuwAq2D5X0KmAp2zdVHFronsWAd9enOOeaA9tUFNNEdSSwue0/AUhaCfgNMNbB+T6k5+9CwLZ1nzMQg/MJLtLaQwghhBAGhKRjgZeBLWy/VtLCwGW2N6g4tDAOJB1s++Cq45ioJF1t+42F2wKmFI+N8bp72j6p4wDDLCdWzkMIIYQQBseGec/qbQC2H897YsOsaTvg4KqDmGgKKed3S7qIVJDPwE7AzR1cdwvbvwMej7T2UCYG5yGEEEIIg+NFSbOR2zBJWpy0kh5mTao6gAmqmHL+T+BN+f//Bhbu4LpvAn7HyJR2iLT2QKS1hxBCCCEMDEm7Au8F1gVOBXYEvmL7nEoDC+NC0iTbMfkSwgQRg/MQQgghhAEiaVXgLaRV1Sts31txSKELJB1DzogoY/vTPQwn1Km1QevStb4BHGH7iXx7YeAztr/SjeuHwRWD8xBCCCGEPidpkdE+b/u/vYoljI/cYqsh26f2KpYwkqTbbK8zXtfq5uA/DK7Ycx5CCCGE0P+mklZVBSwLPJ7/vxDwV2CFyiILXRGD7773my5eazZJc9l+HkDSPMBcXbx+GFAxOA8hhBBC6HO2VwCQdBxwvu2L8u2tgLdWGVvorlzk7/PAasDcteO2t6gsqAlK0grAo7afs/2VPIh+he2/dHjpnwFXSDqFNOm2B6mGRJjgIq09hBBCCGFASJpqe726Y7fYXr+qmEJ3SboM+DnwWWAfYDfg37Y/X2lgE5CkW4BNbL+Qb88JXGt7gy5ceyuGakdcZvvSTq8ZBl+snIcQQgghDI7HJH2FtPJm4APAf6oNKXTZorZPkrSv7SnAFElTqg5qgpq9NjAHsP1CHqB3zPbFwMXduFaYdUyqOoAQQgghhNCyXYDFgfOAXwFL5GNh1vFi/viopHdKWgd4ZZUBTWD/lrRd7YakdwGPdXpRSRtJulnS/yS9IOklSU91et0w+CKtPYQQQgghhD4haRvg98CrgGOAycDBti+oNLAJSNJKwOnA0vnQw8AHbf+5w+veArwPOAdYH/gQ8GrbX+7kumHwxeA8hBBCCGFASHoNaS/y8hS2J0axsFmHpFOBfQs9sBcBvmN7j0oDm4Ak/R9pT/gC+eNTgG0f0uF1b7G9vqQ7bK+Zj11ne5OOgw4DLfachxBCCCEMjnOA44ATgZcqjiWMjzVrA3NIPexzanvovafzx/+RKudvA9zbhes+k/euT5N0BPAoMF8XrhsGXKychxBCCCEMiLJq7WHWIul24M22H8+3FwGm2F6j2siCpLlIrQzf3uF1lgP+CcwJ7A8sCPzI9p86jzIMslg5DyGEEEIYHBdI+jipINzztYO2/1tdSKHLjgSuk3QuqSL/zsDXqw0pZPMCK3Z6EdsP5ZXz5YFfAvcXq8KHiStWzkMIIYQQBoSkB0sO23bHA4bQPyStBmxB2ud8he17Kg5pQpJ0J2mCBGA2UqeEQ2z/oMPrvpO0PeXPpN/xCsDeub1amMBicB5CCCGEEEIIdXL6ec0M4J+2Z3ThuvcB29TS2HNV+N/YXrXTa4fBFmntIYQQQggDQtKHyo7bPq3XsYQwq7P90Dhd+l91+8sfAP41TvcVBkgMzkMIIYQQBscGhf/PDbwFuBWIwXkIg+NuSRcBZ5PS5ncCbpb0bgDbv6wyuFCdSGsPIYQQQhhQkhYEfmp7u6pjCSG0RtIpo3za0dN+4orBeQghhBDCgJI0B3CH7ddWHUsIIYTORFp7CCGEEMKAkHQBw6tHv5aUGhtCGECSbrW9btVxhP4Qg/MQQgghhMHxncL/ZwAP2X64qmBCCB1T1QGE/hGD8xBCCCGEAWF7Su3/kraxfW2V8YQQOvabqgMI/SP2nIcQQgghDKBIhw0hhFlLrJyHEEIIIQymSIcNYYBIms5QzYgRbE/uYTihD8XgPIQQQghhMO1ddQAhhNbZXgBA0iHAP4CfkibZdgUWqDC00CcirT2EEEIIYUBImhf4DLCs7Y9KWhlYxfaFFYcWQmiRpBttb9jsWJh4JlUdQAghhBBCaNkpwPPAxvn2w8Bh1YUTQhiDlyTtKmk2SZMk7Qq8VHVQoXoxOA8hhBBCGBwr2T4CeBHA9rPE3vMQBs37gZ2Bf+Z/O+VjYYKLPechhBBCCIPjBUnzkItKSVqJtJIeQhgQtv8CvKvqOEL/icF5CCGEEMLgOAi4BHiVpNOBTYEPVxpRCKEtkhYHPgosT2E8ZnuPqmIK/SEKwoUQQgghDBBJiwIbkdLZb7D9WMUhhRDaIOk64PfAVAp7zW3/orKgQl+IwXkIIYQQwgCRtAywHMNX3K6uLqIQQjskTbO9dtVxhP4Tae0hhBBCCANC0uHAe4G7gZfzYQMxOA9hcFwoaWvbF1UdSOgvsXIeQgghhDAgJN0PrGk7isCFMKAkTQfmIxVzfJG0RcW2J1caWKhcrJyHEEIIIQyOB4A5iArtIQws2wtUHUPoTzE4DyGEEELoc5KOIaWvPwNMk3QFhQG67U9XFVsIoX2SFgZWBuauHYvaESEG5yGEEEII/e+W/HEqcH7d52KPYggDRNJHgH2BVwLTSN0Xrge2qDCs0AdicB5CCCGE0OdsnwogaV/bRxU/J2nfaqIKIYzRvsAGpFaIm0taFfhaxTGFPjCp6gBCCCGEEELLdis59uFeBxFC6Mhztp8DkDSX7fuAVSqOKfSBWDkPIYQQQuhzknYB3g+sIKmY1r4A8J9qogohjNHDkhYCfgVcLulx4JFKIwp9IVqphRBCCCH0OUnLASsA3wS+UPjUdOAO2zMqCSyE0BFJbwIWBC6x/ULV8YRqxeA8hBBCCGEASdrG9oVVxxFCGDtJe9k+oeo4Qn+IwXkIIYQQwgCSdKvtdauOI4QwdvE8DkVREC6EEEIIYTCp6gBCCB2L53GYKVbOQwghhBAGkKTX276p6jhCCGMn6ZW2H646jtAfYnAeQgghhDBAJC1p+x+NbocQ+pukuYD3AMtT6J5l+5CqYgr9IdLaQwghhBAGy0lNbocQ+tuvgXcBM4CnC//CBBcr5yGEEEIIIYTQI5Lusr161XGE/jN781NCCCGEEEKVJC0y2udt/7dXsYQQOnadpDVs31l1IKG/xMp5CCGEEEKfk/QgYMorO9v2ij0OKYQwRpLuAV4NPAg8T3pe2/aalQYWKheD8xBCCCGEEELoEUnLlR23/VCvYwn9JdLaQwghhBAGiKSFgZWBuWvHbF9dXUQhhHbUBuGSlqDwPA4hBuchhBBCCANC0keAfYFXAtOAjYDrgS0qDCuE0AZJ2wFHAksD/wKWA+4FXldlXKF60UothBBCCGFw7AtsADxke3NgHeDf1YYUQmjToaSJtT/YXgF4C3BttSGFfhCD8xBCCCGEwfGc7ecAJM1l+z5glYpjCiG050Xb/wEmSZpk+0pg7YpjCn0g0tpDCCGEEAbHw5IWAn4FXC7pceCRSiMKIbTrCUnzA1cDp0v6FzCj4phCH4hq7SGEEEIIA0jSm4AFgUtsv1B1PCGE1kiaD3iO1EJtV9Lz+PS8mh4msBichxBCCCH0OUmTbT8laZGyz9v+b69jCiGE0F0xOA8hhBBC6HOSLrS9jaQHAZNW3Gpse8WKQgshtEjSNbY3kzSd9Dye+SnS83hyRaGFPhGD8xBCCCGEEEIIoWJREC6EEEIIYYBIWobUF3nm+zjbV1cXUQihHZJWAh62/bykNwNrAqfZfqLKuEL1YuU8hBBCCGFASDoceC9wD/BSPmzb21UXVQihHZKmAesDywOXAucDq9jeusKwQh+IlfMQQgghhMGxPelN/PNVBxJCGLOXbc+QtAPwfdvHSLqt6qBC9SZVHUAIIYQQQmjZA8AcVQcRQujIi5J2AXYDLszH4nkdYuU8hBBCCGGAPANMk3QFMHP13PanqwsphNCm3YF9gK/bflDSCsDPKo4p9IHYcx5CCCGEMCAk7VZ23PapvY4lhBBCd8XgPIQQQgghhBB6RNLKwDeB1YC5a8dtr1hZUKEvxJ7zEEIIIYQBIWlTSZdL+oOkByQ9KOmBquMKIbTlFOBYYAawOXAa8NNKIwp9IVbOQwghhBAGhKT7gP2BqQy1UsP2fyoLKoTQFklTba8n6U7ba+Rjv7f9hqpjC9WKgnAhhBBCCIPjSdsXVx1ECKEjz0maBPxR0ieBvwNLVBxT6AOxch5CCCGEMCAkfQuYDfglw6u131pZUCGEtkjaALgXWAg4FJgMfNv2DVXGFaoXg/MQQgghhAEh6cqSw7a9Rc+DCSF0TNKStv9RdRyhP8TgPIQQQgghhBAqIOlW2+tWHUfoD1GtPYQQQghhQEh6haSTJF2cb68mac+q4wohjJmqDiD0jxichxBCCCEMjp8AlwJL59t/AParKpgQQsd+XHUAoX/E4DyEEEIIYXAsZvts4GUA2zMotFQLIQwGSbNJWhq4UNKykpatOqZQvWilFkIIIYQwOJ6WtChgAEkbAU9WG1IIoR2SPgUcBPyTNLkm0nN6zSrjCtWLgnAhhBBCCANC0rrAMcDqwF3A4sCOtu+oNLAQQssk/QnY0PZ/qo4l9JdYOQ8hhBBCGACSZgPelP+tQlptu9/2i5UGFkJo19+IjJdQIlbOQwghhBAGhKSrbL+56jhCCO2TdED+7+tIE2y/AZ6vfd72d6uIK/SPWDkPIYQQQhgc10r6AfBz4OnaQdu3VhdSCKFFC+SPf83/5sz/QgBi5TyEEEIIYWBIurLksG1v0fNgQgghdFUMzkMIIYQQQgihRyRdDuxk+4l8e2HgLNtvrzSwULnocx5CCCGEMCAkvULSSZIuzrdXk7Rn1XGFENqyeG1gDmD7cWCJ6sIJ/SIG5yGEEEIIg+MnwKXA0vn2H4D9qgomhDAmL0latnZD0nKkPudhgouCcCGEEEIIg2Mx22dL+iKA7RmSXqo6qBBCW74MXCNpSr79RmCvCuMJfSIG5yGEEEIIg+NpSYuSV9kkbUT0Sw5hoNi+RNK6wEaAgP1tP1ZxWKEPREG4EEIIIYQBkd/QH0Pqk3w3sDiwo+07Kg0shNCWXARuZWDu2jHbV1cXUegHsXIeQgghhDA47gHOA54BpgO/Iu07DyEMCEkfAfYFXglMI62gXw9ES8QJLgrChRBCCCEMjtOAVYFvkFbQVwZ+WmlEIYR27QtsADxke3NgHeDf1YYU+kGsnIcQQgghDI5VbK9VuH2lpNsriyaEMBbP2X5OEpLmsn2fpFWqDipULwbnIYQQQgiD4zZJG9m+AUDShsC1FccUQmjPw5IWIm1LuVzS48AjlUYU+kIUhAshhBBCGBCS7gVWAf6aDy0L3Au8DNj2mlXFFkJon6Q3AQsCl9h+oep4QrVicB5CCCGEMCAkLTfa520/1KtYQghjJ2kzYGXbp0haHJjf9oNVxxWqFYPzEEIIIYQQQugRSQcB65NqSLxG0tLAObY3rTi0ULGo1h5CCCGEEEIIvbMDsB3wNIDtR4AFKo0o9IUYnIcQQgghhBBC77zglL5sAEnzVRxP6BMxOA8hhBBCCCGEHpAk4EJJxwMLSfoo8Fvgx9VGFvpB7DkPIYQQQgghhB6RdCvweWBLQMClti+vNqrQD6LPeQghhBBCCCH0zvXAE7YPrDqQ0F9i5TyEEEIIIYQQekTSPcBrgIfIReEAbK9ZWVChL8TgPIQQQgghhBB6RNJyZcdtP9TrWEJ/icF5CCGEEEIIIYRQsajWHkIIIYQQQgghVCwG5yGEEEIIIYQQQsVicB5CCCGEEEIIIVQsBuchhBBCCCGEEELF/h82of0FtBzwpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(distance_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance-based distribution plot from all clubs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tejashah88/extra-software/anaconda3/envs/sc-notebook/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(distance_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Recommend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(table, club_db_df, club_name, k):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "    Recommends club based off of k-nearest neighbors, prioritizing matching tags.\n",
    "    \n",
    "    Input:\n",
    "    table - table containing all distances between each club\n",
    "    club_db_df - table containing club tags\n",
    "    club_name - string of club name we want to create recommendations for\n",
    "    k - represents how many neighbors\n",
    "    \n",
    "    Output:\n",
    "    recommendations - k recommendations based off of closest distances\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    def filter_by_tag(club_link_name, k):\n",
    "        \"\"\"\n",
    "        Description:\n",
    "        Return boolean list that contains max amount of matching tags that satisfies matching k amount of clubs.\n",
    "\n",
    "        Output:\n",
    "        filtered_clubs - list of booleans based off of clubs filtered by tags\n",
    "\n",
    "        \"\"\"\n",
    "        \n",
    "        def count_tags(a, b, num_tags):\n",
    "            \"\"\"\n",
    "            Description:\n",
    "            Returns if club A and B contain at least k tags in common.\n",
    "\n",
    "            Input:\n",
    "            a - list of club tags for club A\n",
    "            b - list of club tags for club B\n",
    "            num_tags - minimum number of tags required to match\n",
    "\n",
    "            Output:\n",
    "            matching_tags - # of matching tags\n",
    "\n",
    "            \"\"\"\n",
    "            matching_tags = len(set(a).intersection(set(b))) >= num_tags\n",
    "            return matching_tags\n",
    "\n",
    "        club_tags = club_db_df[club_db_df['link_name'] == club_link_name]['tags'].iloc[0]\n",
    "        filtered_clubs = []\n",
    "\n",
    "        num_of_tags = len(club_tags)\n",
    "        while (np.count_nonzero(filtered_clubs) - 1 < k):\n",
    "            if num_of_tags == 0:\n",
    "                print(\"Matched {} tags\".format(num_of_tags))\n",
    "                return [True] * len(club_db_df)\n",
    "\n",
    "            filtered_clubs = []\n",
    "            for other_club_tags in club_db_df['tags']:\n",
    "                if count_tags(club_tags, other_club_tags, num_of_tags):\n",
    "                    filtered_clubs += [True]\n",
    "                else:\n",
    "                    filtered_clubs += [False]\n",
    "            num_of_tags -= 1\n",
    "\n",
    "        else:\n",
    "            # print(\"Matched {} tags\".format(num_of_tags + 1))\n",
    "            # print(\"{} matches\".format(np.count_nonzero(filtered_clubs)))\n",
    "            return filtered_clubs\n",
    "    \n",
    "    filtered_clubs = filter_by_tag(club_name, k)\n",
    "        \n",
    "    filtered_distances = table[filtered_clubs]\n",
    "    club_distances = filtered_distances[club_name] \n",
    "    sorted_club_distances = club_distances.sort_values(ascending = True, na_position = 'last')\n",
    "    recommendations = sorted_club_distances.keys()[1: k + 1]\n",
    "    \n",
    "    return list(recommendations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['girls-who-code-at-berkeley',\n",
       " 'girls-teaching-girls-to-code-@-berkeley',\n",
       " 'plextech']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations = recommend(distance_table, club_db_df, 'big-data-at-berkeley', 3)\n",
    "recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_description(club_name):\n",
    "    return club_db_df[club_db_df['name'] == club_name]['description'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Extended Reality @ Berkeley is a registered student organization at Cal for people interested in experiencing or developing virtual and augmented reality technology and applications.'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_description('Extended Reality at Berkeley')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_by_club(club_index = -1):\n",
    "    if club_index == -1:\n",
    "        club_index = random.randint(0, len(club_db_df))\n",
    "    print(\"Club index: {}\".format(club_index))\n",
    "    club_name = club_db_df.iloc[club_index]['link_name']\n",
    "    recommended = recommend(distance_table, club_db_df, club_name, 3)\n",
    "    return club_name, recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Club index: 46\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('5&2-berkeley',\n",
       " ['blood-pressure-project',\n",
       "  'engineers-without-borders',\n",
       "  'creative-arts-to-reinspire-and-empower-(c.a.r.e.)'])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend_by_club(46)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Club index: 23\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('giant-filmmakers',\n",
       " ['acts2fellowship',\n",
       "  'indigenous-and-native-coalition---rrc',\n",
       "  'open-door-mentorship'])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend_by_club()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing/Evaluating Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "normed_df = vector_table['vector_sum'].apply(lambda arr: arr / np.linalg.norm(arr))\n",
    "normed_vector_table = np.array([arr for arr in normed_df.to_numpy()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest mean: -0.12684513132585443\n",
      "Highest mean: 0.1510109993342106\n",
      "Lowest Standard Dev: 0.052833305437679105\n",
      "Highest Standard Dev: 0.10233847430482201\n"
     ]
    }
   ],
   "source": [
    "means = np.mean(normed_vector_table.T, axis=1)\n",
    "stds = np.std(normed_vector_table.T, axis=1)\n",
    "\n",
    "print('Lowest mean:', np.min(means))\n",
    "print('Highest mean:', np.max(means))\n",
    "\n",
    "print('Lowest Standard Dev:', np.min(stds))\n",
    "print('Highest Standard Dev:', np.max(stds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tejashah88/extra-software/anaconda3/envs/sc-notebook/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/tejashah88/extra-software/anaconda3/envs/sc-notebook/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dims = normed_vector_table.shape[1]\n",
    "fig, axs = plt.subplots(nrows=2)\n",
    "\n",
    "NUM_BINS = 50\n",
    "\n",
    "sns.distplot(means, bins=NUM_BINS, ax=axs[0])\n",
    "sns.distplot(stds, bins=NUM_BINS, ax=axs[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Beta) Student Profile Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tejashah88/extra-software/anaconda3/envs/sc-notebook/lib/python3.6/site-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Net Impact Berkeley',\n",
       " 'Valley Consulting Group',\n",
       " 'Seed Base Consulting',\n",
       " 'Beta Alpha Psi',\n",
       " 'Ascend']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def favorite_recommendations(favorite_list, interest_list, k):\n",
    "    \"\"\"\n",
    "    Description:\n",
    "    Returns k amount of recommendations based off of favorite_list and interest_list specific to user.\n",
    "\n",
    "    Input:\n",
    "    favorite_list - array that contains names of clubs favorited\n",
    "    interest_list - array that contains tags (aka interests) student has chosen\n",
    "    k - integer amount of recommendations\n",
    "\n",
    "    Output:\n",
    "    recommendations - array of club names recommended\n",
    "\n",
    "    \"\"\"\n",
    "    #condition that favorite list doesn't exist -- use interest list\n",
    "    if len(favorite_list) ==0:\n",
    "        new_club_df = club_db_df[['name', 'tags']]\n",
    "        intersections = [set.intersection(set(interest_list), set(i)) for i in new_club_df['tags']]\n",
    "        new_club_df['intersection_with_interests'] = [list(i) for i in intersections]\n",
    "        new_club_df['num_intersection'] = [len(i) for i in new_club_df['intersection_with_interests']]\n",
    "        new_club_df = new_club_df.sort_values('num_intersection', ascending = False)\n",
    "        final_recommendation = list(new_club_df['name'][:k])\n",
    "        \n",
    "    else:\n",
    "        club_list_pool = []\n",
    "        for club in favorite_list:\n",
    "            club_list = recommend(distance_table, club_db_df, club, 6)\n",
    "            for recommended_club in club_list:\n",
    "                club_list_pool += [recommended_club]\n",
    "    \n",
    "        not_in_favorite = [club for club in club_list_pool if club not in favorite_list]\n",
    "\n",
    "        unique_recommended = list(dict.fromkeys(not_in_favorite))\n",
    "        sample_len = len(unique_recommended)\n",
    "        recommended_indices = random.sample(range(1, sample_len), k)\n",
    "        recommended = [not_in_favorite[recommended_index] for recommended_index in recommended_indices]\n",
    "\n",
    "        #print(recommended)\n",
    "        #print(len(recommended))\n",
    "        final_recommendation = recommended[:k]\n",
    "\n",
    "    return final_recommendation\n",
    "        \n",
    "    \n",
    "favorite_recommendations([], [2,4,6], 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generic Club Recommender Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GenericRecommender:\n",
    "    def __init__(self, table, list_of_club_tags):\n",
    "        self.dist_table = table\n",
    "        self.list_of_club_tags = list_of_club_tags\n",
    "        \n",
    "    def _count_tags(self, a, b, num_tags):\n",
    "        \"\"\"\n",
    "        Description:\n",
    "        Returns if club A and B contain at least k tags in common.\n",
    "\n",
    "        Input:\n",
    "        a - list of club tags for club A\n",
    "        b - list of club tags for club B\n",
    "        num_tags - minimum number of tags required to match\n",
    "\n",
    "        Output:\n",
    "        matching_tags - # of matching tags\n",
    "\n",
    "        \"\"\"\n",
    "        matching_tags = len(set(a).intersection(set(b))) >= num_tags\n",
    "        return matching_tags\n",
    "    \n",
    "    \n",
    "    def _filter_by_tag(self, club_tags, k):\n",
    "        \"\"\"\n",
    "        Description:\n",
    "        Return boolean list that contains max amount of matching tags that satisfies matching k amount of clubs.\n",
    "\n",
    "        Output:\n",
    "        filtered_clubs - list of booleans based off of clubs filtered by tags\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        filtered_clubs = []\n",
    "\n",
    "        num_of_tags = len(club_tags)\n",
    "        while (np.count_nonzero(filtered_clubs) - 1 < k):\n",
    "            if num_of_tags == 0:\n",
    "                print(f'Matched {num_of_tags} tags')\n",
    "                return [True] * len(self.list_of_club_tags)\n",
    "\n",
    "            filtered_clubs = []\n",
    "            for other_club_tags in self.list_of_club_tags:\n",
    "                if self._count_tags(club_tags, other_club_tags, num_of_tags):\n",
    "                    filtered_clubs += [True]\n",
    "                else:\n",
    "                    filtered_clubs += [False]\n",
    "            num_of_tags -= 1\n",
    "\n",
    "        else:\n",
    "            #print(\"Matched {} tags\".format(num_of_tags + 1))\n",
    "            #print(\"{} matches\".format(np.count_nonzero(filtered_clubs)))\n",
    "            return filtered_clubs\n",
    "        \n",
    "    def recommend(self, club_link_name, k):\n",
    "        \"\"\"\n",
    "        Description:\n",
    "        Recommends club based off of k-nearest neighbors, prioritizing matching tags.\n",
    "\n",
    "        Input:\n",
    "        table - table containing all distances between each club\n",
    "        club_db_df - table containing club tags\n",
    "        club_name - string of club name we want to create recommendations for\n",
    "        k - represents how many neighbors\n",
    "\n",
    "        Output:\n",
    "        recommendations - k recommendations based off of closest distances\n",
    "\n",
    "        \"\"\"\n",
    "        # First, fetch required data\n",
    "        try:\n",
    "            club_info = client['production-db']['new_base_user'].find({\n",
    "                'role': 'officer',\n",
    "                'club.link_name': club_link_name\n",
    "            })[0]['club']\n",
    "        except IndexError:\n",
    "            return None\n",
    "        \n",
    "        target_club_link_name = club_info['link_name']\n",
    "        target_club_tags = club_info['tags']\n",
    "        \n",
    "        # Next, filter by tags\n",
    "        filtered_clubs = self._filter_by_tag(target_club_tags, k)\n",
    "        \n",
    "        # Finally, sort by distances as pre-calculated from Word2Vec\n",
    "        filtered_distances = self.dist_table[filtered_clubs]\n",
    "        club_distances = filtered_distances[target_club_link_name]\n",
    "        sorted_club_distances = club_distances.sort_values(ascending = True, na_position = 'last')\n",
    "        recommendations = sorted_club_distances.keys()[1: k + 1]\n",
    "\n",
    "        return list(recommendations)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIST_OF_TAGS = [club['tags'] for club in LIST_CLUB_INFO]\n",
    "g_recommender = GenericRecommender(distance_table, LIST_OF_TAGS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Club name: \"Brown Issues\"\n",
      "Club link name: \"brown-issues-\"\n",
      "Club recommendations:\n",
      "  \"in-between\"\n",
      "  \"bone-health-initiative\"\n",
      "  \"volunteers-around-the-world\"\n"
     ]
    }
   ],
   "source": [
    "RANDOM_CLUB = True\n",
    "SPECIFIC_CLUB = 'big-data-at-berkeley'\n",
    "\n",
    "if not RANDOM_CLUB:\n",
    "    target_club = [club for club in LIST_CLUB_INFO if club['link_name'] == SPECIFIC_CLUB][0]\n",
    "else:\n",
    "    target_club = random.choice(LIST_CLUB_INFO)\n",
    "\n",
    "print(f'Club name: \"{target_club[\"name\"]}\"')\n",
    "print(f'Club link name: \"{target_club[\"link_name\"]}\"')\n",
    "\n",
    "recommendations = g_recommender.recommend(target_club['link_name'], 3)\n",
    "\n",
    "print('Club recommendations:')\n",
    "for club_name in recommendations:\n",
    "    print(f'  \"{club_name}\"' + ('' if club_name != target_club['link_name'] else ' (SELF)'))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph network recommendation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define needed variables and helper functions\n",
    "NUM_RECOMMENDED_CLUBS = 3\n",
    "LIST_OF_CLUB_LINK_NAMES = [club['link_name'] for club in LIST_CLUB_INFO if club]\n",
    "\n",
    "def random_club():\n",
    "    return random.choice(LIST_OF_CLUB_LINK_NAMES)\n",
    "\n",
    "def sorted_dict(d):\n",
    "    return dict(sorted(d.items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 146/146 [00:13<00:00, 11.19it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x7f72e547ae48>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct graph of recommendations\n",
    "r_graph = nx.DiGraph()\n",
    "\n",
    "# Add nodes\n",
    "r_graph.add_nodes_from(LIST_OF_CLUB_LINK_NAMES)\n",
    "\n",
    "# Add edges\n",
    "for club_link_name in tqdm(LIST_OF_CLUB_LINK_NAMES):\n",
    "    recommendations = g_recommender.recommend(club_link_name, 3)\n",
    "    \n",
    "    for recommendation in recommendations:\n",
    "        r_graph.add_edge(club_link_name, recommendation)\n",
    "\n",
    "nx.freeze(r_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes:\t 146\n",
      "Number of edges:\t 438\n"
     ]
    }
   ],
   "source": [
    "# Look at basic atributes of graph itself\n",
    "print('Number of nodes:\\t', r_graph.number_of_nodes())\n",
    "print('Number of edges:\\t', r_graph.number_of_edges()) # Should be number of nodes * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of self loops: 8\n",
      "['codebase',\n",
      " 'launchpad',\n",
      " 'blockeley',\n",
      " 'afx-dance',\n",
      " 'the-berkeley-forum',\n",
      " 'innovative-design-',\n",
      " 'codeology',\n",
      " 'greening-the-greeks']\n"
     ]
    }
   ],
   "source": [
    "print('Number of self loops:', nx.number_of_selfloops(r_graph)) # Should ideally be 0\n",
    "pprint([edge[0] for edge in nx.selfloop_edges(r_graph)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen club name: \"creative-arts-to-reinspire-and-empower-(c.a.r.e.)\"\n",
      "\n",
      "[\"people's-test-preparation-service\",\n",
      " 'project-rishi',\n",
      " 'camp-kesem-berkeley',\n",
      " '5&2-berkeley',\n",
      " 'berkeley-anova',\n",
      " 'cub-support',\n",
      " 'stempathy-resources-at-berkeley',\n",
      " 'gifts-at-berkeley',\n",
      " 'alpha-phi-omega',\n",
      " 'kidney-disease-screening-and-awareness-program',\n",
      " 'the-golden',\n",
      " 'splash-at-berkeley',\n",
      " 'cal-ydsa-young-democratic-socialists-of-america',\n",
      " 'peers-espousing-amiability-cooperation-and-education-peace',\n",
      " 'medlife',\n",
      " 'eggster-organization']\n"
     ]
    }
   ],
   "source": [
    "chosen_club_name = random_club()\n",
    "print(f'Chosen club name: \"{chosen_club_name}\"\\n')\n",
    "\n",
    "in_connecting_clubs = [two_pair[0] for two_pair in r_graph.in_edges(chosen_club_name)]\n",
    "pprint(in_connecting_clubs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In-degree centrality: https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.in_degree_centrality.html\n",
    "n_minus_1 = r_graph.number_of_nodes() - 1\n",
    "all_in_degs = [round(in_deg_frac * n_minus_1) for in_deg_frac in nx.in_degree_centrality(r_graph).values()]\n",
    "\n",
    "# histplot docs: https://seaborn.pydata.org/generated/seaborn.histplot.html\n",
    "sns.histplot(all_in_degs, stat=\"count\", discrete=True, bins=1)\n",
    "\n",
    "# Force x-axis to show integer values for labels\n",
    "plt.xticks(range(max(all_in_degs) + 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 33,\n",
       " 1: 22,\n",
       " 2: 24,\n",
       " 3: 16,\n",
       " 4: 10,\n",
       " 5: 14,\n",
       " 6: 15,\n",
       " 7: 5,\n",
       " 8: 3,\n",
       " 9: 1,\n",
       " 16: 1,\n",
       " 17: 1,\n",
       " 19: 1}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "sorted_dict(Counter(all_in_degs))"
   ]
  }
 ],
 "metadata": {
  "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",
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