YouTube Comment Analysis.ipynb

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     "name": "stdout",
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     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (1.5.3)\n",
      "Requirement already satisfied: nltk in c:\\programdata\\anaconda3\\lib\\site-packages (3.8.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2022.7)\n",
      "Requirement already satisfied: numpy>=1.21.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.24.3)\n",
      "Requirement already satisfied: click in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk) (8.0.4)\n",
      "Requirement already satisfied: joblib in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk) (1.2.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk) (2022.7.9)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
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   ],
   "source": [
    "pip install pandas nltk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6b414a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\91901\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\91901\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\stopwords.zip.\n"
     ]
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      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d8b2f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('Details _ comments YouTube video.csv', encoding='ISO-8859-1')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8844d770",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>View</th>\n",
       "      <th>Date</th>\n",
       "      <th>thumbs_up</th>\n",
       "      <th>Youtuber</th>\n",
       "      <th>Subscribers</th>\n",
       "      <th>Description</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Viewer_name</th>\n",
       "      <th>Comment_date</th>\n",
       "      <th>Comment_Datail</th>\n",
       "      <th>Comment_thumb_up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Rajat Agarwal</td>\n",
       "      <td>5 years ago</td>\n",
       "      <td>cross_validation is deprecated. You can use mo...</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Kavitha Rajni</td>\n",
       "      <td>1 year ago</td>\n",
       "      <td>youtubers like this guy, TechWithTim, Neural N...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Braun Brelin</td>\n",
       "      <td>5 years ago</td>\n",
       "      <td>Hello all.  I'm posting sentdex's code again w...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Tech Chak</td>\n",
       "      <td>3 years ago (edited)</td>\n",
       "      <td>I have tried many other courses and tutorial b...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Sleepless Swan</td>\n",
       "      <td>5 years ago (edited)</td>\n",
       "      <td>Interestingly enough, I'm getting almost the s...</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                                               Title           View  \\\n",
       "0  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "1  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "2  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "3  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "4  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "\n",
       "           Date thumbs_up Youtuber        Subscribers  \\\n",
       "0  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "1  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "2  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "3  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "4  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "\n",
       "                                         Description      Comments  \\\n",
       "0  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "1  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "2  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "3  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "4  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "\n",
       "      Viewer_name          Comment_date  \\\n",
       "0   Rajat Agarwal           5 years ago   \n",
       "1   Kavitha Rajni            1 year ago   \n",
       "2    Braun Brelin           5 years ago   \n",
       "3       Tech Chak  3 years ago (edited)   \n",
       "4  Sleepless Swan  5 years ago (edited)   \n",
       "\n",
       "                                      Comment_Datail Comment_thumb_up  \n",
       "0  cross_validation is deprecated. You can use mo...              577  \n",
       "1  youtubers like this guy, TechWithTim, Neural N...                1  \n",
       "2  Hello all.  I'm posting sentdex's code again w...               27  \n",
       "3  I have tried many other courses and tutorial b...                1  \n",
       "4  Interestingly enough, I'm getting almost the s...                0  "
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5432a77b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('Details _ comments YouTube video.csv', encoding='ISO-8859-1')\n",
    "\n",
    "# Check if \"Comment_Datail\" column exists\n",
    "if \"Comment_Datail\" in df.columns:\n",
    "    # Preprocessing function\n",
    "    def preprocess_text(text):\n",
    "        # Remove special characters and numbers\n",
    "        text = re.sub(r'[^A-Za-z\\s]', '', text)\n",
    "        \n",
    "        # Convert to lowercase\n",
    "        text = text.lower()\n",
    "        \n",
    "        # Tokenize the text\n",
    "        words = word_tokenize(text)\n",
    "        \n",
    "        # Remove stopwords\n",
    "        stop_words = set(stopwords.words('english'))\n",
    "        filtered_text = [word for word in words if word not in stop_words]\n",
    "        \n",
    "        return ' '.join(filtered_text)\n",
    "\n",
    "    # Apply preprocessing to the \"Comment_Datail\" column\n",
    "    df['Cleaned_Comment_Datail'] = df['Comment_Datail'].apply(preprocess_text)\n",
    "\n",
    "    # Save the cleaned data to a new CSV file\n",
    "    df.to_csv('Cleaned_Details_comments_YouTube_video.csv', index=False)\n",
    "else:\n",
    "    print('Column \"Comment_Datail\" does not exist in the CSV file.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dbaf31f9",
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   "outputs": [
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       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Rajat Agarwal</td>\n",
       "      <td>5 years ago</td>\n",
       "      <td>cross_validation is deprecated. You can use mo...</td>\n",
       "      <td>577</td>\n",
       "      <td>crossvalidation deprecated use modelselection ...</td>\n",
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       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Kavitha Rajni</td>\n",
       "      <td>1 year ago</td>\n",
       "      <td>youtubers like this guy, TechWithTim, Neural N...</td>\n",
       "      <td>1</td>\n",
       "      <td>youtubers like guy techwithtim neural nine mos...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Braun Brelin</td>\n",
       "      <td>5 years ago</td>\n",
       "      <td>Hello all.  I'm posting sentdex's code again w...</td>\n",
       "      <td>27</td>\n",
       "      <td>hello im posting sentdexs code significant com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Tech Chak</td>\n",
       "      <td>3 years ago (edited)</td>\n",
       "      <td>I have tried many other courses and tutorial b...</td>\n",
       "      <td>1</td>\n",
       "      <td>tried many courses tutorial explain theory cod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views  Apr 12, 2016\\n\\n\\n        443,...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Sleepless Swan</td>\n",
       "      <td>5 years ago (edited)</td>\n",
       "      <td>Interestingly enough, I'm getting almost the s...</td>\n",
       "      <td>0</td>\n",
       "      <td>interestingly enough im getting almost accurac...</td>\n",
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      "text/plain": [
       "                                               Title           View  \\\n",
       "0  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "1  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "2  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "3  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "4  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "\n",
       "           Date thumbs_up Youtuber        Subscribers  \\\n",
       "0  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "1  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "2  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "3  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "4  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "\n",
       "                                         Description      Comments  \\\n",
       "0  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "1  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "2  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "3  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "4  443,755 views  Apr 12, 2016\\n\\n\\n        443,...  760 Comments   \n",
       "\n",
       "      Viewer_name          Comment_date  \\\n",
       "0   Rajat Agarwal           5 years ago   \n",
       "1   Kavitha Rajni            1 year ago   \n",
       "2    Braun Brelin           5 years ago   \n",
       "3       Tech Chak  3 years ago (edited)   \n",
       "4  Sleepless Swan  5 years ago (edited)   \n",
       "\n",
       "                                      Comment_Datail Comment_thumb_up  \\\n",
       "0  cross_validation is deprecated. You can use mo...              577   \n",
       "1  youtubers like this guy, TechWithTim, Neural N...                1   \n",
       "2  Hello all.  I'm posting sentdex's code again w...               27   \n",
       "3  I have tried many other courses and tutorial b...                1   \n",
       "4  Interestingly enough, I'm getting almost the s...                0   \n",
       "\n",
       "                              Cleaned_Comment_Datail  \n",
       "0  crossvalidation deprecated use modelselection ...  \n",
       "1  youtubers like guy techwithtim neural nine mos...  \n",
       "2  hello im posting sentdexs code significant com...  \n",
       "3  tried many courses tutorial explain theory cod...  \n",
       "4  interestingly enough im getting almost accurac...  "
      ]
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     "execution_count": 10,
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   ]
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      "  Downloading wordcloud-1.9.3-cp311-cp311-win_amd64.whl.metadata (3.5 kB)\n",
      "Requirement already satisfied: numpy>=1.6.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from wordcloud) (1.24.3)\n",
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      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0)\n",
      "Downloading wordcloud-1.9.3-cp311-cp311-win_amd64.whl (300 kB)\n",
      "   ---------------------------------------- 0.0/300.2 kB ? eta -:--:--\n",
      "   ---------------------------------------- 0.0/300.2 kB ? eta -:--:--\n",
      "   - -------------------------------------- 10.2/300.2 kB ? eta -:--:--\n",
      "   ----- --------------------------------- 41.0/300.2 kB 326.8 kB/s eta 0:00:01\n",
      "   --------- ----------------------------- 71.7/300.2 kB 435.7 kB/s eta 0:00:01\n",
      "   ----------- --------------------------- 92.2/300.2 kB 476.3 kB/s eta 0:00:01\n",
      "   ------------------- ------------------ 153.6/300.2 kB 654.6 kB/s eta 0:00:01\n",
      "   ------------------------- ------------ 204.8/300.2 kB 692.4 kB/s eta 0:00:01\n",
      "   ------------------------------------ - 286.7/300.2 kB 886.2 kB/s eta 0:00:01\n",
      "   -------------------------------------- 300.2/300.2 kB 842.8 kB/s eta 0:00:00\n",
      "Installing collected packages: wordcloud\n",
      "Successfully installed wordcloud-1.9.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: The script wordcloud_cli.exe is installed in 'C:\\Users\\91901\\AppData\\Roaming\\Python\\Python311\\Scripts' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n"
     ]
    }
   ],
   "source": [
    "!pip install wordcloud\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa735fad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('thank', 153), ('thanks', 134), ('video', 124), ('much', 100), ('great', 94), ('sir', 94), ('sql', 84), ('data', 81), ('course', 78), ('videos', 69)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# Combine all text in the Cleaned_Comment_Datail column into one large text corpus\n",
    "all_text = ' '.join(df['Cleaned_Comment_Datail'].dropna())\n",
    "\n",
    "# Tokenize the combined text\n",
    "word_tokens = word_tokenize(all_text)\n",
    "\n",
    "# Count word frequencies\n",
    "word_freq = Counter(word_tokens)\n",
    "\n",
    "# Display the 10 most common words\n",
    "common_words = word_freq.most_common(10)\n",
    "print(common_words)\n",
    "\n",
    "# Plot the most common words\n",
    "words, counts = zip(*common_words)\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(words, counts)\n",
    "plt.title('Top 10 Most Frequent Words')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecf612cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Ghc7Trfffju9vb1YlsWKFStG/Js9ezZwVph8c2Sr//qv/7qgfp9P24P37YMPPjjs+EceeQTTNCds5z3veQ8LFy7kH//xHzl06NCoZZ577jkSiQQej4fVq1fz2GOPDVulsW2bn//851RWVo4wE7xc3HTTTaiqyunTp0cdvxUrVpx3nYPX980rVG+murqaP/mTP2HTpk25ZIc5crxLya1o5MjxLsfv97Ns2TKeeOIJZFke8s8YZMOGDXzve98DhufP2LRpEzfddBNf/vKXiUQirF+/ngMHDvC1r32NpUuX8rGPfWzCtr/whS/wox/9iNtuu41vfOMblJSU8OCDD3Ls2LFJ9X3t2rX853/+J3/0R3/E8uXL+fznP8/8+fMxDIO9e/fywAMPsGDBgjH9Iz7+8Y/zH//xH9x33300NDSwcOFCXn31Vb75zW9y6623csMNNwDZCfRHP/pRfvSjHzFjxgwWL17Mjh07RhWIvv71r3PzzTcP5aewLItvfetbeDyeYdm3RyMvL4+/+Zu/4S//8i/5+Mc/zoc//GF6e3u5//77cTqdfO1rX5vUuEz1OH3oQx/iwQcf5NZbb+V//a//xapVq9A0jZaWFrZs2cJ73vMe7rrrLtatW0cwGORzn/scX/va19A0jQcffHCEIHk+TLbtuXPn8tGPfpTvfe97aJrGDTfcwKFDh/jOd76D3++fsB1FUXj88ce58cYbWbt2LZ///OfZuHEjHo+HxsZGHn30UZ566ilCoRAA//iP/8imTZvYuHEjX/rSl9B1ne9///scOnSIhx56aMqzwF8o06ZN4+///u/5q7/6K86cOcPNN99MMBiks7OTHTt24PF4hkJNTxafz0dNTQ1PPvkk119/Pfn5+RQWFhIMBtm4cSP33nsvc+bMwefzsXPnTn73u9/xvve97xKdYY4cOa5oLqsreo4cOa4I/uIv/kIAYsWKFSP2PfHEEwIQuq6LeDw+bF8ymRRf/vKXRU1NjdA0TZSVlYnPf/7zIhQKDStXU1MjbrvttlHbPnLkiNi0aZNwOp0iPz9ffPrTnxZPPvnkhFGnzmXfvn3ivvvuE9XV1ULX9aEQu3/7t38rurq6hsq9OZqSEEL09vaKz33uc6KsrEyoqipqamrEV7/61RHRh8LhsPiDP/gDUVJSIjwej7jjjjtEQ0PDqNF3fvOb34hFixYJXddFdXW1+Kd/+ifxta99bVLhbYUQ4oc//OHQ8YFAQLznPe8ZFoZUiMlHnZqqcTIMQ3znO98RixcvFk6nU3i9XjFnzhzx2c9+Vpw8eXKo3Ouvvy7Wrl0r3G63KCoqEn/wB38g9uzZMyJq13333Sc8Hs+IPo42TpNtO51Oiy9+8YuiuLhYOJ1OsWbNGrFt2zZRU1MzYdSpQfr7+8XXv/51sWzZMuH1eoWmaaK6ulp89KMfFa+99tqwsq+88oq47rrrhMfjES6XS6xZs0Y89dRTw8qMdZ0Gz7O7u3vY9jePy2DUqW9/+9vDym3ZskUA4le/+tWk2nviiSfExo0bhd/vFw6HQ9TU1Ii7775bvPjii2O2/ea+nsuLL74oli5dKhwOx1BUr1QqJT73uc+JRYsWCb/fL1wul5g9e7b42te+NuLdkSNHjncHkhBCXAb5JkeOHDly5MiRI0eOHO9gcj4aOXLkyJEjR44cOXLkmHJygkaOHDly5MiRI0eOHDmmnJygkSNHjhw5cuTIkSNHjiknJ2jkyJEjR44cOXLkyJFjyskJGjly5MiRI0eOHDly5JhycoJGjhw5cuTIkSNHjhw5ppycoJEjR44cOXLkyJEjR44p54rNDG7YaUw7hQB02Y0qa5e7S1OKYacxRRohBLrsRpHUKyaT7DsN0zYwRQpb2GiyA1XSkaScjJ0jx6VACBNh95JJPU8mtRnLPIyw+xBCIMl5qNp8NMe16K47kOUgknTFfobehdik4g8RD3/5vI5yuO/F7fsSslJ8ifqV40IRQjCQbRFJkpBg6DeALElDvwf/BobKSUBjpB+HqlLk8qDKMpZtc7SvmwKXmyKXh+N9PVT4fAR057C6FVlGCIElbEBCkaTLNs+JxdO8tPUIP3/4DYQQ3HnrEu65ayVO59TNLQfPW5KkofNWZeW86vjxz1/lV0/sIp7IjLpfVWV+/t9/SFlJ4KL7+1Zxxb7h94We4rXun2IJk9srvsps/zWXu0tTyt7Qk+zq/TUJq587Kv6KGd41qJJ+ubv1juR07A1e6/4JfZkW1hTey/Lge3Gpb5+HNEeOtwtCGBjp10lEv4llHAXs4fvtTox0J0Z6C+nET/H4v47qWJcTNnLkuESYtk1LNIIhLJyKSpnHR386RV8qgSYrVPoC9CTj9KdTlLq92IAsgSYrGJaFrihEjQyaoiABKdPkTLiPvV1tLC0ux6vphDMpqiQ/kBVKDNvCsm1mBQuJZNKcDPWgKQqzg0W4tcujNO7uibD55WN09UQB2LW3kZXLpjF3dvmYx5wVkkCRZAQCIUAgUCQZSZIwbAtZkpCRsIRNzEzhUZ2kbYOj4RaWBmuRJAlFkrGFwBY2siQhkRXqbGEjDRwvSRK6ruJ2O7BtgS0EwhaYlo1tv31za1/yt7slTAw7hSwpaJIjp0nOkeMtJtQdJZVI4/Q48Oe5UdTz07C8mXQyQyQUR1ZkfHludMc7a7UxxwUiBGZmF4nI32OZxycqjGWeJhb+Mt7gv6HpK96SLuZ460iaGZyKNqYG+82TuCtpRT+dMUkkMsiyhNuto13kO/OtJBpLkcmYuF06LpdOzMjwTP0xpgfyaYtHua12Nvt72mmJhrm+uo7GSIgjvd0YtkVTpB+friOAWCZDvtPN/MJiGsIh4pkMBU4XL7c0IEsSzZEwCwpLUCWZA90dlLg9uP06j588zILCEvZ1tfPx+UvZ1dlKJJ2mP51ifkHJZRsXRVHwuB0oSnYO6nHrOCb4dpnCoineQ9o2meYpImwkSFoZklaGGd5SLGHRGO/GrTiodBfQlQ7zRPMONpUuIt/h43ikjaDuQZFkKt2F9Gdi9KSj5OteArqHiJEglIkT0FyUOPMAWL1iOsE8N6FQgkgsRSSW5PjJTpqaezEM61IP0yXhkgsafekmjkdeJqhXMNO3Hl1xX+omc+R4SxDCxLZDWHbvlNctoaIopciy96Lr+u9vPMErz+zj2juX8Ykv307BRSy5CiHYv+0UP/zGk+QX+/nIn9/MwtUzLrqPlwshBEbGJB5Lk0pmME0L28qaGKiqjMOp4fY4cLh0ZHlyE6FIf4JwKI5l2Xh9TvIKvKgDE5VUMkM8liKdNDBNO2uuIEtomoLTrePxOtH0C3st27YgncqQiGdIpzJYZlYLJssSiqrgdGl4vE50x6Ux0xQiTir+EyzzzOT7bHWQjP47av4PkCTnlPcpx+XjmbY93FmxAoFAQkKTlaH7zhI2oUyM+lgXli2Y7ium0OFDvkIUkYeOtvLUs/spzPdy+82LmFZdeLm7NGl+8eh2jh5r585bFnPdhrkD5ucqs4KFpC2TmJEmz+GkxO2lNhDkcE8nuqIwzZ9HKJ2k3BugIRLiVH8v11T6cSgquqJg2CYZyyaSTjO/sJi6vAI0WUEAblUlbmSGtPVXV9YSzqRIWyZBp4uD3R0sLCpFky/f9S0u9HHLpgVkMiaKIrNp47wJr2vESBIxkrzSfZQ7KlawN1RPnubhWKSFgOZmd99pKlwFbOs5wZ0VKxECMraJW82+ywzb4kysi950hNWFcKi/iYSZxqM6WRKcxqH+ZgxhsqZwVtZGDaibXkzd9OEmiA89uoNf/noH/eHEJRmbS80lFTSEEHSmTnEo/DyzfFdT612JTk7QyHH5mMrplW2HCUcfIBL79ymsNYssF1EY/Bfcrk1TXvfFIIQg3BOlq6UPgL6u8GXu0YVjGhbdHWGOH2ph7/bTnDjUSl9PlEQsjaYrBAu8TKsrYfGq6SxYPo3SiiAutz7hJP2lp/byq5+8Sl93lGtuWsBnvnQLeQVeejojHNh5hl2vnuTUsXb6+2KkUwYej5OisgCzF1Sy6prZzJxfQbDAO2nBBiART9PV3s/hPY3s33mG+hOdhHpjpJIZXC6dvEIvM+eWs2xtHXOXVFNSloeqTa2W1szswTJPAcZ5HGVgmccwjYNo+sop7U+Oy8sPTr5AtbuQUCaOKsvUeoupchegySrhTIKHGl7nt617cKkOFuVV8/mZmyhx5QFg28kBBY6NIhcgy56hem07gRBJZNmLJDmmvN+WZXPqdBfbtp9mwbwKksnzuZ8vL5mMyUtbj9IXirN21VkFUMI02NnZiiJBicdLyjTRlezzX+Lx0pmIsb+7nbq8Qqp8AdpiEcq9forcHiLpFKFUkg7TpNoXZH5hMYd6O2mO9bOgqIS+VIJwJs3R3m5q/EGK3V4UCYIOF7IkE8ukcWs6EhJJ08CtXR4TcadT4+p1s7h63axJH9OWDFEf76Q/E8cWNrYQLMirwhIWhm0RN1NUuPPpSUcIGwkqXPn4NTc+zYlhW3hUB8vya9kfaiRqJOlNx9BkBZ/mAqDQ4aPA4aPY+c425b6kgoYp0vRn2ombfZeymRw5Js3b18rxLIlYilBXBI/fRV6h7y1tW5ZlqmeVcs0dSwkUeJkxv/ItbX+qSKcMDu1p4NGfvsreN06PuDGMjEkilqa1sZc3fn+MaXUl3HbPKtZeO4e8Au+kVwQ6W/tJJjJ0trbwiwe2sH/HGUxzuN9CJJwgEk5w+lg7W393gGtvWcTd911FWVX+hO0IIQiH4ryx9Ri//dVOzhxvH1F/1EgSjSRpPtPN5mf2s2B5Dfd+ZiPzllTjdE3dR980j2Db5/+uFyKOmdl/wYJGf2+MnvZ+jIxJWU0h/qAbeRTNqRCCjuY++nuiKKpCzcxSHK7RTSdsyyYRSxEOxUnGM5iGCQJkRUZzqLjcDrwBFy7PWVOMsc9PEO6NEQ7FSScNbMtGUWWcbgf+oAd/nhvpPITKtxP3H/w1blVDQiKgu/nTWbewKFhNKBNnd98ZPlCzlnzdy4sdB3m95wR3Va0CwLTaiCR+hWX14vfcg8tx9t4wrTYyxnGcjuWoSumU97k/nKC1LUQq/fYRMAapb+gmFIoPe58pkkSZ18fdsxYM+AbAwqKz41bo8nBt1XQg6xgOsL6iZli9H5qzaNjvWcHCbF0D5f906dqhfffOXQzADTV1hNMp+tMpbq2dxen+PvrTqcsmaFwIAoFHdVLpzsehaBQ4vGiySkD34FBUlgSnsy9UTziTpNpdgAD8moujkVbm+CoocPjQJJWA5qbYGWBhXjVtyT7ydS8+zUXMTOFUL970WAhBys6gSSqqrCCEIGObJKw0Ac192VcKL6mgETG6CGVaEO+I6V2OHJcfIQQn9jXx6//ewtobF3LrR9a95X2YvaSG2UtqJi54hZLJmGzbcpQHvvMsfd1Zx0BNVwgW+vB4nSiqjG3ZpJIGkVCcWDTF6WPt/M/3X6KrrZ/bP7iKwkman3W2hTi6v5nfPPQGZ463o2oKxcV+PF4nqqpgWRaJWJq+niiZdFa42fz0fizT4tNfuAl/0DNu/aHeGL/91U6efXQnvd1RJEnCH3Djy3PhHDD3Mk2bRDxNaKCNQ7sb+Ze//jWf+dItrL9h/pStbNhWK0LEz/s4ITLYVsMFt7tj8xEe+vcX6Wju48+/dQ/X3L5kVAFK2IJf/dcWnn3oDfIKvHzroc9TXTfSZjydMmg53cXuV46z99WTNJ/uIhKKY9s2TrdOfpGfyhnFzF1Ww7obF1BWXTCqYDNYV/2xNl579iD7tp2kvamPVCKN2+ukckYxS9fPZO2m+VTXlaI73nkO8cvyp3F39RpsYfNs2z4ebX6DRcHqrBmObbK6sI4C3UdToof6WPfQcbo2A6/rNpLp14bVZ9n9CCx0bRaylH0GbTuOabUjMBHCQJJ0dLUOkLHsDkyrFwkJWQ6iqWM7/g7S0RWhpS00pePwVrHnQDO2LYYpKFyqxqrSSpRxlBbyeZpSKpM0gfJqOuvKazjT30ddXgHlXv95tXO5WZRXw4JAFdKAo3aluwCAVQV1AJS78pntLx9y5AZ4f9UaLCFQZZnrSxcCsKIgu7pU6sxjGbVD9eXp47/fJyJpZZAATVY5EWml0l2IX3MjIdGTDnMw3MCG4oW4FAembWEKC1VSUCQZwzaxhI2uZBUBtrAxhIkqKUgDju0CgS6rFy2oTOmbzbQzJKx+UlaMjJ2gPXmM7nQDAGGjk6b4PhzKyIEtcc7CpfjH1d4JYRO3QsSMXjJ2AluYyJKKLrvxqEE8aj6yNPYHUwibtB0nYfaTsmMYdgpbWEiSjCpp6LIbr1ow0I+Rg2oLi1CmhYjRjUvxU+qahSUM4maIuNlHxk4ihI0iqTgUD56Busbr00QIIUhYIbpTZxAINNlFkaN21DG8GPoz7fQb7fjUQoJ6JVGjm7DRgcDGrQQI6pUokoYp0oQyrSStCDIyHrWAgFaCck7o4ZQVJZRpJWVF8aoF5OnlaPLottemnaE9eQxTpHEqfkqcdWOOV9Zx0CRu9ZE0w2TsJLYwyYbMU1FlBw7Fi1sJoMuuMeuRBoynklaUmNFDyo5i2QYgoclO3Goefq0Y5QqNgmNkTFobumk41s7Sq2df7u687RBCcHBXPf/17d8S6okBkF/kY9HKWlasn0n19GLcHgfplEFnWz+H9zaw5/VTNNf3EOqJ8dzju9EdKnd8eA0+v2vC9sKhBA/+YAu93REKiv0sXDGNZWvrqJlRjMfrJJXM0FTfzRtbjrFn2yki/QlSyQx7t59h+8vH2fSeZWPWnYhnhZLfPbaL3u4oDqfGtLoSlq+rY+7iKgpLA+i6SjKRobm+h92vn2TPtlP0dUfp7Y7yo//zPIWlAeYvnRqhUYgYiAvQAgsL245MSR8uFtOwOH24lV/82wvsfe0kLnfW7CxQkLWZNtIm8UiSXVuPsf/1k+QX+SmuCI4qaGTSBjteOsJP/uVZ2ht7ySv0UlQWQFEVMmmD5lOdnNjfxJ6Xj/PRL9zE4rV1U27OdjlRJJkP1qxlfqAKgcChaHzz0OMIIbDJRtTRJBWXouNXXTTGuyes0zBbiCUewxZp8ryfQpdnYFjN9EX/DU0pB2FjWM0U5X0dWfIQjv8C204BFrLsJuj93Lh+b7Yt6Ojof1sKGpZls/dAI5YtUJWzcyldUagNBC9LnxRZpsoXoMr39jUNmmiSrbxpvyRJqGPMZaWBaFNTQcRIUB/vwKu6qHYX05EK4VR0+o041e4iXKoDWZIxbAsFg850P33pKHm6h1Jn1tyrMxWi1BUkT/PQkQplTcFcBcTNNHErRdxMM91bSp7muSi/vimdTYWNDvaHnqErfYZwpoO41TcwGYTTsW2cjm0b9bi7q79JjXspY1nQyyi0J49xIvoqTYn9hDPtGHYKTXYS0EqpdC9kpn89pc5Zo05qU1aMnnQ9bYkjtKWO05duImb2YtppJEnBpfgIaGVUeRYxw7uaYmfdiImmYafYF3qafaGnqXIv4j2Vf0tb8ggno6/TmjxM1OjCsg10xUNQr6Das5SZvrUU6DWo8vkvFQohiJk97A39hl29j6HKOnW+dawt/MiUCxrHIr9nR+/DzPFvYHn++9jd9xgno69i2RYlrpksz7+LavcSzsR3srfvCbpTZ1BlnWr3EpYE76DCPX9IOOtJN/Bq109pSR5kXuAG1hZ+hKA+uhYpaYV5rv279BvtVLuX8N7Kr40aLMAWFjGzl9bEEZoSe+lKnSZidJGxE0hI6LIbtxokX69ihm81tZ6VuMcIXyuj0JdppS15mDOxnfRlmkiaEWRJxasWUOaazSz/1VS7l+K4AgMXhHtjNBxrxzLfntEnLjf9vTEe/MEWwn1ZzXsg6Oauj67jjg+vGRFPfcacMpavq2Pp6hk89N+/58i+RkK9MV5+/hBV04tZf/3cMTXZgwgh6GwLESz08v6Pr+em9y3H6Rru51E7qzTbxgNb+c0vt2NbNn3dUfZsO821tywa1TlcCMHR/U288vxBejojKIrMopW1fOgPNjB7QeWICWvd3HLWXz+Px3/+Or/8760kExl6OiM88qOX+co/3YPLc/G27kJYXJhxogBhT1xsMlykk3skFOf15w+x++XjFJYGWHvjAhaunkF+sQ9JkoiEErTWd1N/tA3dqVE7pwxNG/0zenD7GX74j0/T3d7PrMVVXH3LYmYsqMDp0gn3xTi4/QyvP3eQEwdb+OV/vEhegZcZ8ysuqv9XEkUOH+3JfmZ4S7ERtCX6iBhJDodbaE32IoTAFBYSAxOwSVw7p74AIeIk028M227bUfy+D6Gp0+no+zyG2YwkaSRSr1AY+EtsO0Yk8TAZsxGnPh/IPkPpjEkkkiKeSBOPp4nGUuza20BPb1YJ0R9OsO9gM719sTH7tGh+JT6fc0T/Lctm975GMhkTWZYpKfYzo7ZownPs6Y3R2NxLMplBVWWmTyuiuGj4SoBl2cTiaWKxbN9j8TThSJLjJzuygpwQ1Df08Oq2k2O2kx/0MGdW6YTvMADTtOgPJ+kLxYjG0mQyJkIIVE3B49IJ5nkoKvShqpOPHtYXinPydCeGYVFaEqC2phBFyebdSCQzdPfECEcSpFIGti2y5oYOjYDPRX6+B4/bMaYfm2FYHD/ZMa7zdGGBlxm1xWgXKNwbhkWoP05/OEksniaTMbAsgSRLaKqMy6Xj9zkpyPficU+9L1F3OsyZWDv5up9qdxGWsDkSaSKoeZnuHW5S2JuJsrvvFBnbwKe6cSg6fZkIhyONRM0Ec3xVHIs049Pc1HhKOBE9gyortCZ78alOfKoL9SKU5lO8opEmboaQkQnq5TgtL/2ZNgyRwqcW4tdKR9UUO2UfY7vpCvoyLezoe4Su1Gl8ahF5ehm2sElZUbrT9fSkG+hInWBd4Uep8SwbcaNHzR529T7G6dgbKJKKU/EPaK01LGGQMPtpSx6hNXmI1sQhriv5PIWOaaOubAhsIkY3x6OvsKfvCcJGB161gKBegSXMgbqO0pk6SW+6kdUFH6LEWXfe0mDM7GVP3xPs7nscTXYyy38Naws/jF+7dOHhetKN7O57nObEAbxqMWGjnZbEQdJWHKvQYHvPLzFFCp9WRNTs5mTsNSRJJuioxKNeGo2JLSz6Ms3sDz3DkfBLpO04qqTjUgK4ND8CgWGnCBvt9KYbcSoeqt2Lx6wvbvaxo/cRmhMHcMhuPEo+XrWAjJUkbHTQb7TRnNjPVUWfYn7ghvGvm6SiKqVo2lyEyIBII0QGQQYx8DdcnEAgbEE8mqS1vptYJEnj8Q6O7q4nkzY5c7iV3/9mz7DyeUU+aueUj2pyI0kSwha0NXTT2dJHIprCtgVOl05hWR6VM4pHndSmkxmaTnXSVj9c4+jL8zBtThn5xWMvhx/d00BPez9zlk0jWOijs6WPrtbJtw1ZLXOoO0JHUy/RcBJz4CP3ZnSnxsyFVRSW5Y3Zn1dfOkL9yc4B8wK46ob5vO9j61HU0T+2DqfGolXTiceyztbdHWGaznSz+/WTzFtcRcE45z6IJMGmO5ey6b3LcI3ywRk0d7r5fSvY/vvjtLf0DTiq99PbHaW0YuSzFY+m2P36KepPdgJQVpXPHR9czZyFlWOGL9YdKnd9dB173zjNvu2nsSybo/ubOXG4lcWrpk94HhOfpxskFQaUS5M/UEGSrwyTinBfnDNHWwGYs7SGe//khlH9oNIpg1Qig8szuhIpFk7w6ANb6O0MU1qVz2f/+j3MWVo97H2ycNV0SiqC/Oifn6H+WDsvPbGb6pklFxx17EpjfdEcHm3aTl86hkDwSvcxKt0F/E/9y9kJj+biWLgNS9h0psIE9QuPsKcpZUiSK5uPQPIiSCNEGtvuHzK/cupLkeWzq5CZjMnR4+38/rUTdHaGae8M090TJRZPD5U5daaLU2e6xm373799L/PnVoyQcTMZk3/+P8/S3RPD6dS4bdMi/uzz1094LkeOtfHDn71MY3MfXq+DL3z+BjZtnD+sTH84wYtbj3LqTBcdnWE6usL09sawBnItWJbNsy8e5NkXD47ZzvrVdfzdV+9E18cWNIQQ9PTGOHGqg/2HWjh2op22jjCRaBLbErhcGsVFPuqml7B8SQ3z55RTXpY3qfnOsRPtfPv/PkdfKM6tNy7k85++FpdTp7m1j30HmthzoIn6hh5C/XEM08Lp0MnLc1FTVcB118xl7arpuJyjP3+JZIYf/c+r7N7fOGb7G6+ezZ//8Y0EtIlXps8lEk3S0hqisbmX4yc7qG/sob0jTDiaJJMxUZWskFFY4KW6Mp+F8ypZvLCKadUFQ9EHp4IC3Y+ExOlYG+sK5yIQRI0EVe5C0pZBzEwRykTpy0RRJYWklUYg8GpOokaChngXoUyMcmc+AIWOAFUDplcCQbW7CF1WQZKwL9L9YUrfaPmOKq4p/iS2yE6umhMH2Nn3a0KZFqo8S1gSvB2XPPKl7dUKx1RECQQHw8+RsWLMC1xPpXshXrUAW5iEMq2cjr1Bc/wAnamTHAm/RImzbkQyNq+ST5GzFsNOkueooECvJqCVoMkuDDtJb7qRk7HX6UydojlxgMPhF1lfdB/aGBEtYmY3O3t/hRA2C/Nuotw5F5cawLTTdKfrORXN1tUQ20WRo5agXn5eqxBRo2eYkDHXfx1rCj+MVyuYdB0XQm+6EdNOs7LgA/jUQk5GX+dA/2/pzTSys/dXOBQ3K/PuRpFUjkde5kT0FXozzXSnz+BRl095f4QQxM0+dvU+xuHw88iSSrFjBuXueRTo1biUAAJB0goTMTqImX3UeJbhVccep9Ox7ZgiQ5V7ITWeZQT1CmRJIW6GqI/t5ET0FWJmiF19j1LjWYJPG1sDJUtuXK7rUNVqhIhh23FsEXvT30mESGUFEDHw8RMRDOM0MHrmz3OxLIuG4+388t9foKslRG9nmGQ8jbAFW5/czavP7BtWftG6mXzkz28eVdCQFZkDb5xi32snOLT9DH2dYUzLJhD0MHNhFRvuXMq6WxbjeJNmPx5Nse35gzz/8HaMtEkqmSGTMpi5qIqPf+nWcQWNp376Cq88s48/+YcPEMj38vrvDnBox+TbTqcynDzQwstP7eXQjtP090SxLJtUItsHAE1X8fhdlNUU8PEv3TqmoGEaFq8+f4j0wHEuj4P3fGTtmELGILquMntBBcvW1vHc47uxLZuTh1s5fax9UoJGfpGPa29ZNKqQMYgkS/jz3MxeVEX7QESvZDJDb1dkVEGj8XQXp462kUlnJ/XL1tZRO6t0whwpukPl6hvns2/7aSA7Yd6z7dSUCBqyXIiEC0HqvI6T0FHUK0OTL0lnTSWMtEkyniYwivO/w6mNuFfP5fCueuqPtWNZNpvuXsnMhZUj6nB7nSy7ZhYvPbGb4/uaOHmgmZ72fspq3j6hVMfjrqpV9GfibO48jMBmureE2yuWc7C/if5MnDzdzSvdR9ndd4aomeSuyqwjuBA2ptVO2jiIYTYgSU5UpQRVKcUwm0kbhzHMRlLGvoFoVBYw8hnW1Rk4HStw6IsBGQkdTake2p/OmBw/0cFLW48MbZNlCYdDJT3wXCmKjNOhjuvwP9H741LQ2xfn968ep7n1bPAFt9tBNHb22XM4VBzjCK1u9/iWFrYtaGrp5fnNR9j8+6N0dEVGKHiMqEUkmuLUmW5+/9pxrr1qDu+9fSmz60rPa3ExkczQH05S39jDY0/tYceuehLJ4d/HTCZJJJqkqbmP2ppCVi6bNmZ9iiJTXZVPfySBYVhkMibpjEk8kSaTuTjl36GjbTzy+E4OHW7FeJNlgSRJZAyLjJEkHElyur6bbTvOsHxpDfe8dwWLFlSdVzTB8UjbBuWuApyKjoxEjbuY5cE66uOdZGwTy7bwKE4SZooqdzFLgtPpTkUoc+bjVV0UOwI4ZJVSVz5ORafQ4celZL9RZa5smSKHjVd1IV+kudeUChqa7CSglw39DhmtqFL2ZeyUveRpZbjVvPOuN5rpZEXB+1lV8MFhE/YasZQix3QSZpju9Bn6Ms10pxuoVodrtF2qn7n+65jt30BAK0WTh3/wZ/hWU+iYxgsd/0bU7OZ07A3WFt4LjD4xMEWGjJ1gVcE9LMy7Gf0cLUmNZxk+tYhtPT+n32gfMPPppEgZ+0M+kBMSyDrQ7+l7kr2hJ9FlFwvybmRlwQfxXMC4nS9pO84M31rmBzYho5CnV3A88gppO0rU7GFj8WeZE7gWW1jIqNTHdpKyooQznTC11lwAWMKgIb6HI+GXkFAodc5kZcEHqfEsHXENLWGStuLIkjJuUsio2c1s3zWsK/oYQb1ymP3lNM9yYmYvzYn9RI1uWhIHmRu4bsy6JElDU2vR1NoxywiRGQjHOCh4xMkYJ+kPfxPLHl9TNtAKTpeD2rnl1M4tJ9QVZd9rJ4iFk8xZWsPMxVXDSpdPKyJYNHokqraGbo7sqsfh0pi5sBLX2jrSKYPG4+1s33yYM0db8ed7Wb5hzrDj3F4nyzfMIZDvJRVPc/JgMzs2Hxm1jbHY8eJh2hp6UHVl0m3btk3rmW4e/o8XOLzzDDPmVbB8wxzcPifhnhhHdtVz5mgrBSV+1t60kHkraqmaMfaKX3dHmNamXmwra6ZTPaOYymmTm9TlF/upm1vOS0/vwzQsOlpCtDb2YFszkSeIOjR3cTV5+ROHq1U1haKSs4KLZdikkqMLo62NvbQ3ZycZiiIzra6EvILJaYRrZ2UnAUJkha8zJzomddxEKOosJDmAsM7Pvl2Svaja0inpw8XiD3qYPq+cva+f5OShFh7/0SusuWEe1TNLCRb6Jj2p3L/tVPbaSbBy45wxJ6pOt4Nps0o5vq+JaH+Clvrud4ygUerK48/m3EpTvAdLWEzzFOPVnCzIy76z+tIxbARnol0sy69lef7ge1Rg2f3YdgJFKUKIDJbdi6IUYdpdCGGgquXYdhjLDiNLflyOVchy1tTV5ViDKpegyEX43feQyuwFJGTZh4uz0ascusq8OeXcc9fZbbYtOHC4hV17G7LnUOJnxZJpFI0T3a+wwHexFnvnTX7Qw803LCAcSQ5tyxgWv/z1jgFTLYklC6tZOG9sAb6qIn9cAaq5tY9Hn9zNlleOEYulcegqVZX5VJQH8XkcSLJEKmXQ3RPl1JkuYvE0z75wkN5QjM9+YsOIXBDjkUhkOHComW07zrBjdz0+n4PptUXk+V3oujpkKtbdE6UvFGf+nApc4wj6TofKbTctYtXyWhLJDMmUQU9PlDd2neHYRb7vbMumuaUPw7QI5rkpLQlQEPTi9zmH+hqJpWhtC9HQ1EMqbbBzdz2qIlNWmkfJJJRTk6HEmUeB7htKdrkgbxoApa7sCoVfc1N7jgnVHLWK2T6GTBUHEwQOKkAC5zimzw9k/faKpijs7ttijTagl7Es/64RqwKypA74QyyhO32GlBUlYow+ect3jBeGU6LGuxy/VkLM7CVidGILCyHEqEuAMiolzpnMC1w/TMgAUGWdUtdMip119BvtxMxeEtb4uQZUSQdJIpzpYE/oSfaHnkGTHCwO3sbKgrtxKm+dWUGVexEy2RvXpfgJ6mV0pKI4ZDeVnmyIO1lScKk+3Goehp0ibZ9/pJnJYNhJjoRfwsbEqxawOHg7db41o5ZVJHVMv4xzcSt5LMy7mTy9fISTl0vxMcd/Lc2J/djCoifddNHnIEk6iqIDeedsm3z8d1VTqFtYSd3C7P17Yn8T7Y092FYPq26Yz/v+4NpJ9+XIrnrmLJ3GnZ+4moVr6vAHPSTiKQ5uO8UDf/8EPR39PPfLN0YIGk63zvwV05m/Iiss//6pvex//dSk2wXY/tLh8247nTQ4squeva8cp3Zu+VByQEVVSCUyvPL0Xh7+jxcxTYu5y2tZf8vicZfsG093YWTOmvXMnFcxaZNGXVcpKPKRF/TQ0xUhHkvR0xUhlczg9o6faK5mRvGkIgpJsjTMbMYWYkSoWsh+6Hq6wvQP2I1LssTR/U0YxuRMlvp6ogwqJm3bpq8nOqnjJkLVF6MoldhWEzBZnwsNRZ2Hqi2Ykj4wiknd+RDI97D6+rmcPNjM0T2NPPfIdg7vqmfBylrqFlZSO7uM8mmFE17zljPdmIaFhMSurcc4vLNh1HLJRJr2pmzCz3TaGPIdeqfgUR3MDYw+2c13ePlg9ToMYeGQtaHIR5Kk4NTnD/lSnIvbsRa3Y+2I7Zp6VuHi99w99LfLsQqXY9Wo7TscGosWVLJowdm5gWFYyIo0JGiUleRxy6aFzJ1dNmodl4vCAi933rpk2LZ4Is3jT+3OJqSTZZYvqeGD77uwkNHhSJKXth7llddPEIul8fucrF01gw3rZzF7Zhl5eW5kWSKRyNDU0sv2XWd49oVDdHZF2LWnAa/bwZ99/gaCgcn5Oba2h3j6uQN0dIZZMK+cNStnMGdWKcWFPlwufcAfIkFTax9tbSHqphejKGOv3qqqwswZJcw8R/HU2RWhpzd20YLG0sXVrFk5HSNjMWN6MTNqi6goD1IQ9KBpKrYtCPXHOXq8jec2H+bVbSfJGBYnTney90ATN98wNe+6/mSS1miU2mAQp6oSSibxORxD+VHeTMaySBgGXl1HU5RJf/umgreBoCFR4V4wpg+AJrvwa1nJ2RIGhp0ctdxEKJKKdyBylSUMrHFs63XFRZlrDq4xBACn4h9auTHsFKY9vomMLruIGb3s6nuMQ/3PockOluTdzoqCu6fc8XsismM58MJHxqUEAAldduFR8obKyWQjPaXtOPaFRJqZgGzErQgdyeNIyAS0UqZ7R/9gnA9FzhkE9NF9hUAi35H9YAls0vbUTMCuFDRd5Y5PXM2aTQvQB7RBvoCbNZsWsOeV4zzzP69x+nALpmGijuHg+la2nYilaBz4KFTOKGbBqhlDpkFOt86sxdXUzCrljRcO0dUawrLscW1g+7qzZleDlJSen7bG5dHx5bnp6cpGSIpFUyTi6QknnflFvklHExr27hdi1IlzJmMSj6QwBkwATMPihd/s5YXf7J3cibypiVQiM5RB/GJQ1Co0102Y5imEPZmPuYyiTsfp+egV46OhqAp18yu5989u5NVnD3B4Vz0tZ7qoP9ZGXqGPuvmVzF8xjUVr6qidU4pzDHO4SCiObQmEEPz0X343qbZtS2Ckz9O/5QrGEjbHwq00J3pJWpkRYe4LdB8bSuah8s6JtPVO4cjxNrbtPE1/OImiyKxbXcdH7llNZXn+sPeE1+Ng3uxy6qaX4PU4+OFPXyWVNti24zTLltRwx81j+0ueS1t7P909MVYsreFD71vFvDnlI5y0iwp9zKorGfKve6tXkQbxuB188K6V+P0u/D7XiPemokgUFnhZu7oOv9/F6TPdtHX00x9OcOJ055QIGinTZHtDGxJQ6fcjSxIt4TAzCwpQZZmkYWDaNoos49V10qZJY38/oVSKOYWFBMYR0i4FV7ygIQFFjmlj7pcleciXQiCwxxEQDDtFxOgkYnSTtMJk7BSWyGALcyB8bSv2QPST8XJ/qJKDPG1sDYciadlVCrITVjGOdk9CIiOS7Ot/isPh5zFEivn+G1le8H50+a2PenTuCo0EKHI2xrImu4ebJElZQQTBqI65F4vAJmp0YYgUquQgqFfgVC4+OV2eVoYmjT0xdAyMuUAM+Rq9UyivLaJuQeXQRH8ISRqKdpNKZkglDbxTLGhcUNsiq72XJAlVUUYs88uqjKzI2LbAMi2EPf59mEyksc8pc76RllRVGbYykUkZQz4S4+F0TpxN/HzIpE3SU5hMTNhi4Bm+2D7KOJy3I6weUomHEXYnY0ehUlC1RTg8H0dzXHOR7U4OAVjGxM+0w6WzYGUtVTOKObG/iaP7mjh1qIX6o23sfvkYh3aeYd/rp7jhfStYuXHOqP5Q2fEUaLrCquvmTSqyjT/fQ1n1pfXDeyvZ19fA/9S/TMRMoiCPuL3qvKVsKJl3eTqXY0ySyQyHjrTSNOArVlke5Lpr5lBRFhxTGaFrCrfduIhXt51i38Fm0hmT5148xPUb5uKeRFJQIaCyPI+brl/A/HkVqOOYdE2Vj8PFUF018XOqKjJlJQFmzyyhraOfVMqkry8+JUqdRCbDvvZ2bps9G7eukzIM6kMhaoJBME12tLSgqypp02RZWRkN/f009feTMAxm5OdfVNsXwhUvaIA05srB4P7BN9hYnzTTztCZOkljfC/d6TNEjW4SVhhjQNCwhDkQ23ty4RllScGpTDZChhi3TgmZw/0v0pTYR2ZgNca0U+MKJ5cKiax/w8hJkTR+jpJL0Jesk3dWcyxLygX59oyGU/FMOrfJOy3RZFl1wZgmPC7PgPAluCShcy+kbadHp2J6MbZl09HcR/2xNmrnliNJEqZh0XSyk7aGbgIFXoJF/gkdoSVpeATz8726I8pL0qTm5ooqT6n2TQgxbKHD5dZZtLKWklGcxidDIHhxMdLPRVaKcHo+jqyUY6RfxTKPY1sdCJENMynJPhRlGqq+DM2xEc2xHuktzFkTj07OUV2SJPIKvKy6bh4LV8+gtb6bU4daOby7nv3bTrF/20lCPVE8fifLr5k9IlKUx+dClmVkWeaez1+HdxI5V1RVmVS5twu/bt5O3Ezz3sqVBLSRSjP/KNtyXH66eqI0NfeRGgiaMX9u+YT+HJB1Rt+wfjb7DmaTBja39tHY3MvcWZMzO5szq4yFEwgZbzdUVSEwYD4mhMAwTCzLRpYvbkXBpWl4dZ0yrxdZklBlmd5kkqRhZJP69vayvLychlCIVo+HU729lPp8pCKXJ1/R20DQyPpiXChZh+Ld7As9RVvyKAKbAr2aStcC3GoeuuxCkXRkSeFoeDPd6foJJ/nSBBPv80Fgcyr2Onl6OXl6GZ3JUxyPvkxAK2VVwT0o0sWnp58s0jlC2/DtlwZLmOcx2ZuiidBA1st3I063PqHj8pXUtsvtYN6KWmYvqaHheDu/+D/Ps2DVdFxeB+HeGHtfPUFXa4iV181j1uKJo3m4vQ6kc2LGJ2LnFx3JNKyhSFeQjeqiX4ZQpLpDHSa06Q6NqzctYOXVsy6ovuyEeOqeCVkpweH+IKq+EttswLa7ESKrRJEkD4pSiaLNRVamxulZVs4qR9IDMfdHRYghf4jzweVxULegktq55SxeV0dZdQHPPbKDxhMdHNx+hpkLqygoGa4Mq6gt4sjuBjIZE0mSKK0quCI0sW8lx8KtfLBmHXdULH9L7cFzXBzdPVF6zskbUl2ZT17e5ITCRQsqhwJNGIbFiVMdkxI0nE6N8tI8gpNs50ohkcjQ0haipzdKf3+CZNognTYxTAvTtInH0xw51jZUXkyRBYhL03BrGgVuNzLQn0rRm0hwoqeHOUVFaLJMbTBIRyyGTTZxYlcshmm/9QpseJsIGhdDb7qZw+EXaYrvw63mMce/kWne5QS0EpyyD012IEsqEjLtyeP0Zhqxpip51CQQCKZ5ljEvcANOxcdr3T+jPXmMvaGn8KoFLAzexKWb6l8KztXyjr+ak7biiDFM3SSkoVUjW1gkzf4p6d07a43i/LicH/sLaVtWZGpmlvC+z2zk8R9uZfuLhziyqx6nW0cIG4/fzfXvX8mGO5dRPm3iRFiFxf5hWrnOtv7z6k8ykSbSfzYBlMfnxD0Fie7OF92h4fE6UVQZy7RJJAbio/tdE2od3yokSUXVZoF2YcLP+eDyOIZ8YHo6wmRSxqjXpb2pl/amngtuR1FkyqoLWHfjQo7sbqCrNUTPQLjpN7N4bR0vP7OfdMrgld/up3Z22UVrMd9uOBUNl3r+yWpzXF5isRTxgXtakiQCfhfOSQSzgGyUrkEsS9DVNTk/R7dLx+dzTip54JVAZ1eEnXsaOH6ynfbOMP3hBNFoirRhYmQsTMvGGvg3puLjIrmqpgZtwNfCoaqsraqi0OPBo+usqarCo+vMLy4m6HTi0TS643FcmoZHe+uU14NcYkHjYgwVpobu1Bm6Uqewsaj2LGFR8BaC2mjRZgRpK4p9CfwNJqLOt47p3lWosgMhbDZ3/id9mRZ29j2KRw0y3bf6Le/ThZIN1Zt9WZh21ixtLMJGB5Y9+n4JGb9Wgio5sIRBX6aVtBXDMWmTtXcBl+FefatRdRWXx4FlWsxZWsP6Wxbj9DjQHSp5hT6qZhSTXxKY1AR72sySYbkPThxuRdgCSZlYCMqkDXq7IoRDWUHD7XVQUOTHOQn746lGUWQKS/zk5Xvp7YpgpE3am/uIR5P4897a4BFXAsUVQTy+rPndvtdOctUti/AH3cMmLcl4mid+8grJ+PiBOWKRJP29MUqr8scMLBCPpobCDru9DjR9ZLmFq6dTO6eMQzvOsPXJPcyYV8FVtywa9T61TItwXxwjY1FSeWkSn14ONpUtYmvnERbl1TDNO7EiIMeVQdqwyAxEsFMVGU1TJy0AOBwaiqJgmha2EMQTI4Xw0dBUBW0Kk9ldSvYeaOKZ5w9w8FALXT3RIUEiEHCRn+fB7dZxOjQ0TRkyIWvvGD/y6IWwqPRs6Np8l4urp00b+j2vOBsgqTaYfZ8EnE5qg8HLpmy8pIKGhIw0YGJkiAzmJYhONBEpO0bayoYMDGoVeJX8UQe7L91K1OwZU8N+KdFl91AujWrPEtYVfYzNHd8nlGlhW89DuNQ8ylyz3/J+XQiarA/luIibIVLW6BoNIWyaE/vJ2IlR90sSuBQ/Jc46WpOHCRsdnI7tYN44eS3eDciKjKYrpFMGiVh6zBDM7xT6uiK88sw+YuEk7//sRm64e9UFZ04OFvqYNrOE3u4IlmnTUt9N45kuameWTnhsb1eUU0faMQd8SErKg5RXF1w2U7SKmkLKq/LpHYiAdWh3I6s3zHlXChoV04qonllK/bF2zhxr45f//iLrblxI+bQCJFmmp72fva+d4JXfHqC4IkhX69h5Plrru3n4P1+iqDxITV0JxZX5+PLcKIpMKp6mrbGHHVuO0niiA7c3a1Llzx855oF8L3d/5lq6WkN0NPfyi397gWP7mpg5vwJ/gQcJiVQiTV9XlLbGHiKhOIvX1nHjBy4+st6VgiRJHI+08S9Hn6bcHcSjOIb5KpW5gtxdPXq48nczlm1jWZdPiSRL0lCoYTFG5LsxOcc0SOI8HLelbJjuK53jJzt46NHt7NnfhGFY6LrC6hXTWTy/ipISPx63A13PCk2KIhOLpXjyt/suiaBxPlzuOcIlFTR02Y1Dzr6E+9LNRIwO/ONkWr4UaJKOIutgQ8zsI2XH0JXhdoBxo4/doceJmhe+rD5VyJJCnXcNicJ+ft/133SmTrCt5+dcV/JH5OlXVizv0XCr+XgGMnP3phtoTx6jyDFt2EqEQHAmtoPG+F4MMZbGIxtSd35gE+3JYyTMfvaFnsKhuJnmWT7Cd0UgSFlRTDuNS/GjyqObtFz5r7LxcXsdFJblcXD7aY7ubuDwzjPMXFQ1lFvCNm1cXscFT8avNBLRFE0nOzANCzOT/adqFxYDXFFkNt62mMN7GonHslrpJ37+On/61+8ZN/xsJmNy/HALeweyaUuyRN3cMmbMuXzP47S6EmbNr+DE4VbSKYOTR1p5Y+sxikoD5I+TXOydiNvr4Ib3L6e1oZtjexvZseUoDSc68AfdSJJEPJKis6WP5RtmM2NeOf/zr8+PWVc8kmT/66ewLJtAvhdvwIXDpSNLEkbGJNKfoK8rgqYpXP++FSxdN3PMVa3Fa+v41Jdv45EfbObUoVa6WkLsKPHjdOtIZG3Yk7E00f4EDrdOdd3YySbfjrTE+6hyFxAzU5yJdqLL6rD3r2GfVerFMxkiqTT5bhcO9Z3x7rpQjHNWFC4HjnN8wCzLJp3JOjBPZtU4Fk9jD/gBSLKE1zN+6O+3E7YtePbFgxw43IJhWGiawsc/tI51q+uoKMvD4VBHfJd6+2J4LoN57ZXGJX2iA1oJ+XoVzYkDdKdPs6v31/Slm/EN5L3I2EkMO8E07wq86qUJ6xd0VJKnlRE3e6mP7cAhu6n2LMWtBjDsFL3pJhrju2lJHsanFtCf6cDm8sYyV2UH8wLXETN72dH7ME3xfWzreZANxX84qaR0lxOX4qfYMQOPWkDc7GV/6BliZg+lztk4FS8ZO0Fn6hQNsd1ISKiSjjmGsKFIOtO8y5kbuI7D4RfoTJ3kla6fcMq5jULnNJyKD1tYpK0YUbObUKaNMudsFuTdhG8MQePtTn5JgIVr6ti99RjH9tTzX3/3OMFiP7KcjcQ0a3EVN9y9alI+C5MhlchwfF8jR3fXk0pkSCUzNJ3sJJXM0NnSx1M/eYU9Lx/H4dJwunQWrqlj5sIqtEna9E6EN+Bi2pxyTh1q4Tc/fYUdW45kTVqkrBOzy+OgoraI5RtmUzu3YkIBa8X6mcxbWs3u109hWzavbz5KeVUB77l3LU73yAljOm1wYGc9Tz20nZ7OrFaqorqApWvqyB8jA/tbgdfvZMVVsziyr4mjB5pJJjK88OQehBDc9N7llJTnjbnaIoSgtyvK0f1NpNMGN9xxZWTlvhhmL67mE1+8hVeePcDB7afpaO6js6UPl9tBxfQiPvDZjay/ZRHJWGpcQaNqRgn3fP46Du2op62xh47mvqyZlBA4XDr5RX5WXzeXpVfNYsn6mRSX5Y0p9Gq6yurr51FYGmD3qyc4sO0Uzae66G7rx7JsXG6d/BI/MxdWMn9FLSs3zr1Uw3NZuLt6zbj+ju4B/41ExuBETy+RVIrZRUX4HDqaLGMJga4oRNNpMpZFwOl82wkhQ0lhBUOh8yciFk8Ti03O5OhSEAy4CQxEPxNkJ8uJRAafb2Khobm1b+hvZSAT9juF7p4oR4+3k0pmLXOWLqrixuvnU1zoG/MdYFk2icT45prvBi7pU+tW85jhW0NX6iTtqeM0xHfTmTqNLmdvWFtYSJJMgWPaJRM0ih0zqPOtJWx0EDG7ORh+jtOx7aiyPjRJzdhJFgVvwaX42d7zS1J2bOKKLzEO2cvS4B3EzT4Oh1/gdHQbbiXAuqKPD5kmXYkoksp070r6Mk0cDr9Iv9HGof4XOKW8gSppWMIkaYVxKj7WFX6U/f3P0J48PmpdkiThVQtYWXA3muzkcP8L9KTr6c+04oh7USUdgRhI1JjCsJM4ZS/WZTDRe6twODVWXjcP07B45el91B9to/5YG6qmEMj3UjO7dEqXSVOJNPtfP8lvH3wdy7SwTBvDMDEzFtGQyd5XT3Bwx2kUVUFVZBRVoWZ26ZQIGrYtsCwbp0tH01VaTnfRcrprWBlVU/D4XOx5+Rjv+8xGVlw7d1xhw+d38dHPX0drUy9tjb1Ew0me+MU2zpzoYMX6mVTXFePxOMlkDLra+jm0u5Hdr5+iub4bIcAXcLH++nksX1d3WR2vJUlizsJKrr9jCf2hOO3NffR0Rvjdr3dxdF8TsxdVUTOjmECeG1mVMTMm0XCK7s4wLQ09tDf3EelPMGtBxWURNDJ2mtd6XmKBfylFzvNfGepLd3MovJuQ0Uuddy4zvHOYu2wapVX5XH/XchKxFLZlo6gKTRynx3uK7gI388tX8PUf/wGarlJUljei3vwSP7d8eA1rb1hAPJokkzaHzOUURcbh0vHnuckvHliZmOBZ03SVOUtrKJ9WyLpNC4hFkpgZE1sIVFXB4dLw+l0ECrwomkJHZ5hgnhuHY/iK7aCWeDRb+abmXgoKvHjGSB54uZgTKJ+wTCiZ5EB7J13xOIlMBqeqIUlQ7PXSn0ziVFU6ozE6YjFmFxUyv6R4yAH2UnFuVEIhxIX7bUrZaEqQvX6xUYIGvJloLEVnV5hk6kImp8OjRloXGGGotCRASdFZp+4zDT30hmKTEjT27GscsrTSVIU5kzBLfbvQ05sVuAbvhgVzK/D7nOO+A1JpcygfybuZSypoyJJCpWsBVxd/ihORV2hOHCBidBE3e1FlHZeSR4FWOSxJ3FSjK27mBa7HowY5EX2VjuQJ+o02JCRcSoBi5wxm+a+ixr2UtJ1gd9/jcAUIGoOT7FUFHyRu9tEQ38PRyBbcah7L899/VlNyBeLTilme/z7yHVWcie6gO32GmNmDjIJXK2Cmbz2z/ddQ4ZpPS/IQXanTY9YlSwr5ehWrCz5ItXsx9bFddKZOEjE7SVkRZFQcipcCvZpiVx113nVjZpF/JyBJEsFCH9e+ZzkLV88gHklimtmkdrpDHcgpMVzT/qE/2cQt964lr8A3anIxSYIl62fyrYf/GFVX8fjOPo8ev4sb7l7JkvWzOBvQQWJ4cIezv4sr84eZklxo27Zl03C8nZ9++xl6OsJcc8dSZi+uxuPPZmIVgJkx6e0Ms+35Qxzf38TrvztA9cxSKmrHX82pm1vOH3/1dv7zn56hpaGHUE+M1186wtH9Tbi9TlRVwbZtUkmDSChObCD3gj/PzXW3LeaW96/AfwWEYXR5HFxz0wJsIXjiwW20NfbS3xcn3N/A6ePtuL1OdE0FOZuUzzAs0kmDRDyNkTFRVJmauuLL0ndb2LSnmpnhmXNBxzclzmAIg3n+JRQ7ytBkHUWSKSzLo/BNAsQ0o4DtvRlCRg+ufAcrNozeZiZj0heKI8syVXXFxOMZ4ok0Ab8LSZKIxVNkMiY+rxNkib5Q1vfP53WiKDIdXWF0VSUYdJNKGaTS2VC7hQU+LEli2uxS0hkTYQtcbzK3SqYynD7dRSSaYu6cMpIpg3g8TTDPg6LKHD3WRjptMnd2GR6vg56erO9bMM/D6foukqkMhQW+rCZakmhrC+FxOwgGPUgStHeE0XSFogIfpmnR1R3F53Nmz+UtRghBQ7ybZ1r3cGfJGrrjcWYXFtAQCpE0DAzbwudwEEml6TRjNIb6s2NkGFi2fUkFDUWRUdWz39Z4Ik3qgib92Xd1ftBDY3MvhmnT0hYimcyMuPbn0tDUy/GTnRcU58PhOGtSKoSgry9+Qf0O5rmpm17M9l319IcTHDzcQn1DDxVlwXETT4YjCba8cgzI+mbMnFFMRXneBfXhSsS27WHXZaJrlEobHDvRnhM0eAvC2+qKiwrXfPL1KhZbt2OKDAIbCRlF0tBlJ151ZDz1uf6NVLoXIIQgqFeMWb8qaczwreFex78iSypebXhdEhIeNUidbx0Vrvmk7Bj2QCSkbPtuPGoQVXLgIsB7K+/HFgbuNyUJ1GQnK/Lfz1z/RhRZHzczuC67WBp8D7N8V6HKDvzayI/5XP91VLuXYAuToF6JMkquEEmSCeoVXF/6xyTNMJIk41ICU54HYn7gBmo8SxCIoczYkDXhuqboU6zKvwfHm/xaCh3TuLX8L7CEgVcdnmlSlmQCWinz/NczzbOCjJ0YGHMJVdJwKj7cah6ypLKm4MMsyrsFTXaN6VchSwpetZDp3tWUueaStuOYdnog30k2p4kq6ThkDw7FO5SVfZBq92KCFX+JaafxaoU4lLEdZoN6BffW/CtIEm7l/M3ULNsecIK7dIKgJEl4fE48vslpi6on0CpJkkReoY+8UWz7NV2lfFrRBZtiXWjb0XCCl5/ay5Fd9azetIB7/+zGrFOuqgw5lNq2wEibqKpCV0sf7Y29RPpiEwoaiiKzeOV0/r+/fx+//umrbNtyDMOw6GoPAyOd9mRFompaETe/fwVXb5pPfpH/sjvXDeILuLnu1sUUl+bx/BO72bf9DIl4mlgkRSwyfp6QYKGPuYurJ2zjTOw4oUwPxc5yTkYPU+uZRdQM41TcaJLGkeh+4maUUmc5i/NWY9oG+/q3k7BipKwkbsXLdSW341Rc7Oh9mYb4SVyKm6SZnQilrAQH+3fTkDiFLjuY5ZvPXP9iWhL1HI0cwKE46Ul3UuuZySzfAk7FjrKj7/cYwqAv083K/KsxhMGRyD7m+ZdS6Cjm5e7nqHRNo9o9g4AWJKAFCRtjO2QmkxmOnWinty/OjNoiBILTZ7pIJQ18PieqKnPoSCtOp0ZFWR7JlIEiy4QjSRbMq6Cw0EskkmLf/iY2XT+PvfubcLl0ztR38Z7blrJrdz3Lltaw/0Azy5bWDJtsRqJJDh1uzYbIjKWYXltExjA5drwdn8/F4gVVdPdEyWQsLFuQShlEIikOHGph+dIaUimD1rZ+Dh5qZf3aOlxOnXAkyebfH+NDH1hFfUMPjc291FTmE/C52Lu/kVg8TSKR4YaN88ad+F4KbCHoSUU42N/EhyqvRpNlXm9sRlNkyn1+Tvf1cayrhzK/jxn5+fTEE4RTaTy6fulXMyTwehx43A7iiTRt7f00t4RYOL8yK7SfB7IsMauuhL0HmhBC0NkVZvPLx7jtpkWjlu/qjvDya8c5eqL9gvquyDKFBR4i0SSWbbP/UDOxeBrvefoIqKrC0kXV7NzTwI7d9fT1x/nNs/soKfYzZ1bpqN+2dNrgR//zGi0DIcMdDpXbb1p8WfIMXSqCQc+wCHNHT7RjGBauUWR107Q4frKDX/56B5nM5TXFvxJ4S+4CRdbwygV4tcmbR3m1yZWXJBmPGpxQi63LLnTdxXhTRwWNUtfMUffJkkJALyWgTzy5y5YtIaCP7dzn0wrxaRMnrJIHhI3xhK2LIWq0kLGjFDmnI2VTEZ7TtkK+o2rU43TZRbFz+pj1SpKEQ/GMO6kHsmPKxGMqSVl/Dp9ciI/zS/TlUv241PGyy59tQ5OclLvnAVmhoS+aIGOYuHQNn8dJNJGiL5KgMODF5dBIGSbReApVkfG6HOw73YZpWiyaXkbA+87J8vtWk05kaDzRgSTLlFbmU1Q++vOtaSpIEkbGQndqqJOcDKiawuyFlXz2L27l+juWsPu1kxw90ExPZ4REPI3uUAkWeKmZUcKilbUsWFZDWWV+NunfFSJkwMDKp9/FsrUzmDGnlNPH2jm0p5FjB1robAsRDScxMiaaruD1uykpy6O6roh5i6upm1dOQdHEz0XGThMzI+gZBy3JBgJakIydJmL2kzDjlDorqHRNY3foNRrjpwhoQU7HjnJV0SYK9VJ+2/4IPekOPKqX/f3bubPiIxh2hsdafootLDpTbTQmTrGh6Ga60x2ciB6m1FlBykrSl+lmfmApc/2LccounIqLGd45dKfasbCZ619MsaOUsNFPX6Ybw86ap/RluinQswLDZEgkM/T0xpg/r4KCoIeW1hDptEl1VT49vTGisRQIyA96spGsemPMnlmGpikkEhmamnpp6wjT0NxDJmMR6k+wYF4lvb0xYvE0s2aV8saO0/SF4lxz1fDcIslkhr5QnLlzyjhT300slqKtvZ/G5l4qyoI4nerQqonP56S1tY/G5h4am3uYO6cMBFRV5COEIBJN0dkZoa2jn4amHmxLUF4a4PCRVk7Vd1NdVcCRY+34fE40Tcm+26ZI0DCFhW0Z6LI6FLVoNCxsYlYaS9jkuVxsmD6NhGGgyQoeXWdafpC0aQ4lJasJ5mHYFn6HY9x6pwJJkigpDlBZEeT4yQ6isRRPP7cfRZFZs2o6+XkeZFkmnTGIRFNEwgkqK/KHTKTORVVkrlk/i1//ZjemadMfTvLQr3cQjaXYsH4WJcV+hIBwOMHBo61sefk4u/c1YNs2uq6QyZxfBExJkli6qIbGpl4sW9DY3Mu/P7CZ99+5jOqqAnRNxbZt4ok0/eEkiixRMcZ7dVp1ITdsmEtHZ5imlj4OHmnl//7gJTZtnMuqFdMpLQ6gqgqRaJIjx9p45vkD7NnfhGlaKIrM9RvmsnpF7QVdgyuVkiI/FWV5NDX3Ypo2+w4288hju3j/ncvIy8sGnrBtQVd3hK2vHue3Lxykrb0ft0snkXx3+2m8c8TNtxm2ncSw6jGM45hmI6bZim33YYkQwk4CGYSwkSQnkuxCltxIkhtZzkNVKlDVKlSlGlWpQlEKh8IInw9NsZdoj2/HtOMsLfoCR0I/Y3HB59GkS2EWIjCtdtKZfRiZo5hWI6bVgRBxbDuJJGlIkhtFDqJqtWhKHQ7HMnRtJnB5zMTiqQwv7DpBvt9NJJ7ixpWzCcfTHGvuxm7sYv2Caew+0UI4nmLV7CpcDo3u/qzZ3SXK0fOuQdVV8ov9JCJJ9r12kgWrZzBveS36wAfdNCy6WkPseOkwLzy6g1g4wcyFlRRXTN5sTlFkikoDBAu8zF9aQzKRwTQthCWQZAlFldEdGm63jsOlTXqV6vo7lrLqmtlYZtZGuqDEnxWIJsDjcXDHh9aw4aaFQ2MQHCV06mjoDo2i0jzy8rPnkkoaGAPRYgZDICuKjKop6A4Nl1tHHyVKyqj9Ur3IkkLI6EWVNSJGPw7FmfWxA4ocJRQ7yyjQiwkbIZyKC5+WR5GjjEK9BKfiJmklSFhxPKqfEkc5KTtJnl6AJSy6052cih0jY6cxhIFTdpOyspnEvaqPMmcVBY6zq8Je1Y9H9WNjk68X4VTcRIz+YX0WYrIiRhaXU8PndfDyK8eZWVdCRXkQy7LZtbeBstI8An4XnV0RnA4dVZFBCE6d7iASSbFm9Qx6e2OEI8ns/YMAAR6PjtOpIYSgrCTACy8dpm5GyYj8HE6njtOh8trrJ1FUhZIi/8AKRtaXYzBze2NTL6WlAfpCCUL9CQzTQgiBoiocPtpKV1eEWXWlNDb1EIunsCwbEPT2xbGFoLUthKopTK8tor6hh9KBcJxTxbePPE1bSub+xfdQ6PDx6Tf+k6gx+qpawkxT6spDlWUCTicB51lbd9fAszL423HOeL0VQv7MGcUsW1TNmYZuDMPiTEMPP/zZyzz06Pas74wQQwnZdF3l/r98DzVVI5WikiRRW13IbTct5sln9mLbgtbWED9/5A2e/O0+9IHoeRnDJJk0iMXTFAQ9bLhqFi2t/ew90HTefb/zlsVsfvkoof4E6bTJ5pePsnd/Iw6HhqZm81uYA6vua1fN4I//cPRw8ZqmcNW6mUTjKR55fBedXRGOn+qgtT3Er57cja5n3x2maZFKGoSjSQwj+z5Yt3oGn/zIVW/5ShkwJGy/tv0UyWSGRDIz9H+oP8Hxkx1DZY8ca+d7338Bv9+F26Xjcum4XRpul87C+ZVUVQy31NA0hdtuXMTJU510dEVIpQwee3oPW189RuGAj1QklhpSTKRTBovmV7Ju1Qz+7YHNE/Y9Fk9x/GQnLa0hEuf2PZHmxOnOoZwklmXz7w9sJpjnHujz2X+lJX5WLrvyBLycoPEWYll9pNLbSKa3kM7swbZCCNIIYQAmQliAPfDvXHt4eeh/SZIBDUlSkdCQJB1FKUHXl+LQV+LQl6Iq5ZMSPLqTB5gRuJOT/Y9m47qbvXBen+fJnHOIVPoV4sknyRhHsO0IQmRAGAjMc8510JFNQUprSOhIshtNqcbtuh2P+3YUZfzwj7HEk0Si/4ltjxInX9IpDH4Hp2PyyQ9tW5AxLObVlHDgTDttPWFausM0dobwOHVsW+B2aJQEfZQW+JAlmTyvC7dDw3+FhbTrSLXym7YHubn0/VS6aod8fJ5pfxjDNnhP+UcwhcGrPS+yt/8NLNtkhncO1xTdRKGjhJSV4FjkIDv7XqEv04NTcbE4byXLg+vxaQGiRpjXel8kTysgZkY40L8TgeDWsg8w0zsfVT6/V40/38M1dy5l3+snOb6vkW/96f/g9jpwe7MTkmQiTSqRJhnPYBomV926hGvfsxxf8PyEZEmS0HSVgK4SGMWH5ELw57kvyIdDVmTy8j3kTVK4GA1NV9F0Fd8UBqfzqgEsYRIx+qlxT6c33Y2QBJWuaZyMHRkSCsJGH3laPpqko0kaMjKSJCFLMkIIvJqffqMXgcAWNhGjP/vMaEFKnRXcWnZP9i0gybgUD3EzmjWLnMS9o8oahp3BEhaWsOjLdI+bLPTNOF06SxbVMHd2BZqu4NBV1q6egWlY6Ho2YdnsmaVDEc9i8TT5QQ811QW4XTq10woxDIsbNs7D73Ny682LcLs0rl4/C11TECIr2K5cNm1E2z6vg3VrZpLOGKiKgq6r1NYWYZlZzTbAooVVzJ5VhtOpUVzoY8b0IizLxuN2UDutCNO0sS0br9dBQb4X07S4YeN8nE6dyoogeUE3iiyja9m4/0sWVaOq8pQGNJgXqKDU7cQxcL0a4z2sLKgjqA+/ny1h054MkTDPZp8+l4l+X2r8Phe33rgwa3728lFSaZP+cJL+cHJEWbdbHwoW8GYkScLt1vnoPasxTYvfPn8AWwii0RTR6EgBrLIiyD13rWDB3Ap++eudF9T3qsp8vvSnN/Gdf3tuSNjoGMi1cy6qKhMepQ/Dzs2lc+uNiyjI9/LI47s4cbIju4ozxnFul85dty/jA3ctJy9weXzYhID2jn7+84dbsG2Rdea3sw79g8FFBunqidK37SSynM0bIp3z/x//wcYRggbA8iU1fOIj6/nR/7xKV0+UeDxNPJ6mtb0fWc6uaNi2wKGrbLhqNp/86HrC4ST5Qc+QT9dY9PTGePp3+9m5p+Fs34f6bQ3lVhECtu88gywP77OqyMyfW54TNN6tGGYDscRjJJJPY5ltCJFCkGFyk3oB5yQRzDogpYY5IplWGxnjMPH4I0iSG02bgcu5CbfzRlS1ZkA4GYmNiUP2I0kKIvv5v4izfFPddoxE6gWisZ9iGMewRQIYLxqUGPhnI4SBIAFWP5bVSdo4RDT+UzyeD+L3fBRZHjmLEsLGMpvIGIdg1KSLOkKM/2IdjVgqw5a9p7Fsi/k1JXSGomQMC7cjewEURcahq6gDtsMScLy5m+I8L9UlV45TepGjBBWNU9EjFDnKcCluTGGwN7SdO8o/hC0sdode43B4D3eUfQhFUtnT/zpPtT3Eh6s/i4SMTwuwPH89xY4y2pJNHAjvxKG4WFuwERubvnQ3xyIHWBFcz91VnyBuRil3VaFcwGqbosjMW17LV79/Hy//Zi/7Xj9JR2MPXa0hJFnC7cnmE1m2oZLV181n7vJa/EHPJfWNebfiVf2ARMpKMsM7j87Ui1jCYppnJmk7xe7Q67zS/Tz5ehGL8lYhxgjjWewoo8o1nf9X/138ah4+NYAiqVS4aqh0T+Ox1p8hI1Huqua64tvPq48BLZ+AFuS5jsdwKW5sbBRJwRAZnmt7jDOxY2REhrgZ4bqS2wlow59NWZJwODQcjrPadLdLB9c5mvVzIqnNqC3C43YMhAGV0CVwDrwTsoE8sve8e0Cru+9AE9dcNRv3KCGUZVnG5dJwubShtgYFjMHfToeGQz/bN/3cvzmbJA0k3G7lbNI0SULTFIIB97DzOPc8p4qbyhaDXIhTOXuO99VeQ6VnuLbftC129p7m4cbXp6ztqUQeMCn6zCc3sHrFdF7ddpKjJ9rp6YthmhYOh0bA76KiLI9ZdaUEx0mSKUkShQU+PvvJDWxYP4vNLx/j2Ml2urqjGIaF1+OgojzIyqXTWLd6BjVVBdgC8i4w2ISiyKxeMZ3v/dOHeWnrEXbva6S5tY9EMoMsy3g9DooKvNROK2Ldqhnj1jX4DKxfU8f8ORXs2d/IGztPc/J0F719MSxb4PM6qKzIZ8nCKjasn01ZSWDYfXw5sEx7TGHoXITIBsgYjcwY23Vd5boNc5k1o4QXth5hz74mWtpDpFIGbpdOftDDgnmVrF89g4XzK/G4HViWoKaqYEJBw7IEiWQma6Y5AcYowq0sS8QnEdnsciAJcSHxDa5cdh9s4oe/eBVFlvnI+1axdvnYfgSXGssOEY//mmj855hWI0KkmeoVg9FRkCQdWQrgcKzA634/Dsc6FHm4PXZT7CVa4i/TmzqMWy2hwnM1M/3vyyY4vAgyxlHC0e+TTL6ALaIwJQKMhCS5cejLyPP/fzgdwzPKWlYf/dF/JRr74RjH65QU/gyXc8OkW+yPJXl62xHed/XC7AddU8iYFrZtZ7WDqopl28gDZimQfQFkTAtdzWYHHY2McYKuno9iWs1jti3LRRQG/wW3a9Ok+zsR23q2cCiym/dX3ke+XsTJ6GF+1fxj/nz2/cgofP/UP3BN8S0syVuNhERT4jSPNv+EOyvuZaZ3HjZZMxxZkgkbITZ3PYVTcXNr2QeIGP082fogLsXD9SW3k6cVIBDZfPcXGCFNCJGNlpQxMQwLe8AMCLIfQVmWUVQZTVdRVPmK8p14JyGEwBAGQthoso5hZwb8mXQsYWEKA4GNjIIm64DAsA00WUeWZNJWCkVSsxN/O4OFOeQLpsk6MjKGMIZWIGRkdNmBTXZ1QpW0EVH2DDtr86xKKpIkIxBDKxqDd4Eq6SiSQsZOYwkLEMiSgi47Ljpqn2XZA/fg5O45w7CQZemyhkSeHDap+EPEw18+r6Mc7ntx+76ErJw1cfvz3T/l/kX34FOHhwC1hM3evnp+cPIFfrjmc1PW86lGiKwGPGNkw3rbg1myBzJny4PmlZo64X0ghMAaeJeZlo1tZxVrEhKyIqOpMpqmIMvZ1b9U2iCdNpEkCZdTOy+naiEEQoBhmBjmgCZciGz27YG+Z6NrKeNGkXpznaZpZ+sbHAvE0Hs4238VSZq88GoYFslkZsA8UMLpOL/zHKufljW5UMLj4XLpQ4L9aGStHUxMIxugYdA8VZYlVFVGU5UhE0nLskmmDEzTQlMV3GOExbYsm1TKGFWIOJeD4dfYH9pKlXsO64vuQJV0LGGyo+85GhL7WFV+Hcvzr7/gc+9Jt+FSvHgm4dc6Wd5xKxqmaRGNZZ1zJ7pglwohBBnjCOHo/yWZemFAk/5WynMWQiSxRJJE8lkkyYWmzhohaFR6NhDUZxExGnCrxfi0GmRJHTaZmyzZY2ySqZfoj/wrGeMgo68sXCgCIeKk0q/RG2rD7/tjvO4PDJiISVh2D5bVeVEtmLaNIklntYi6xrJZlbgcGllBB5xadt9gGVke/qLWBvJJXIksyFvOaz0v0J3qIKDlczC8i4WBFWiSg7SdpClxhl82PcAjTVlhTSDwqD4iRghLmDQmTvNG7xbakk1k7AxxM8qagmuHxfnL14vwqXlTEn5ZkiQkRcLh0nHk/OovG5IkoZ8Tyc2hnA2zokoq6iifEcc50YHOLa8rDmCkWWG2/uEKDgV11Gh8wIBAc04fkdDHiFp3bvtTxfkKDJOd0A1yVv937ndjvG+INOLv7Cvq8gnf/7TkXvRRzN5kJKZ7S7hv+rVvfadGRZzzChs+3ooCLkUBRrt+0lC5c9W1o303JUlCVSTUSfgtZIULHZfzwhR+kpT9VmVX6EY6qV9onZo2ecFkkOF67OF/qyr4fPqbyg/OGUbez4P9mKifqqpcctMtWZZw6OrAyqMYUpxIkjR0zoMJGmVZwjO0kjlo/jRowXH2HpJkcLk13MPeg+KcUtm5yTr/Rrw+hbDRR8DvQpMdCCHY6LuNvSEXpjjreD7YF4EYqmN4zQzWPPT3kcg2KlwzqfUsyG6Xhu+/EN5xgsblRgibVHob/ZFvk87s4K0VMEZDxaEtRVUrR+zpTh7Ar9dQ6l6DjELUaKY3fZRS12qcSt55tCEAg3jyGfoj/4Jpjp0X4+KxMczT9Ee+jbDD+LyfQpK0AUGjY+LDx0AIwQce+SWPfOCDQyEUHZrC3OriYS+3yQhfV6pm3av4qPPN43jsEGWuKo5HD/KxaX+CIsnYQuCQndxX+2fUemYPHSORnUw2Jc7wbPujzPTN470VHyFjG7zY+ZsRbaiSMqaQkfXNyWqWJ40kA/oFr4pcCWRNidITB14fhgRSdmXyUpL9EKVhklmLp56R52kLG1tkTZ/Gepay9svZhK9Xck6hyTM4WbUAC2GHMY3jWMYRLPMMttWAbXdh2zEQ8azpreRAkrxIuFHUUhSlFkWdgaLNQdEWAE6yE+ThZljjM+gTePE4lLEnuUHdw/qi2WPuv9QMKsYGx9u2Q1jGUSzzNJbVhG02YtudCDuGEAmESAI2kuQAyYWEG1kpQlEqkJUqFLUWRV+MIpchUBkc9yv1W3DpGLyPB8ZWmNiiDzNzCNs8g201Y1nN2FYnQsQHxjYFwhoYWzeS7EGWC1CUamS1BkWdjqotQJaLEWhkx/byr2CHjRaeavpfJK1+7qz+v5Q452eVzHaKXX3PcyZ+CE3SqPMtZXHeNZyOHaA+foj1he+hPn6QvnQ7awtvJ2nF2RvaSmvyJLawWZG/iTn+lTTEDnEw/BpRM0SBXs66wtsJaEUokoosqcNFMUka2H5WEBRCEDND7A69RHPiBE7ZzVz/KqZ55nMk8gZnYgexB5TBS4PXUeGayeHw6+zvf5kTkV3sUvO5uvj9lLku3ucjJ2hMIULYpDM7CUe/Szqz/SJre3NStAvDoS9G1+eNOmE5EXmEhNlFlec6ZgbuYm/P/yXomEVHYgdriv9m0m0IYZJIPjcFQsbkz9my2gnHfgiSjs/zMWyrF8vqmvjANyGEIGEYtMdinOrtJZxKoSnZD4RH00gYBh49O3Yp00AIcGsalrAxLBtdURBA2jQxB8yoHKqCJl95HxlJklict5qn2h7iiKOUYkcFhXoJkiSjyzqFjhI6Us3M8s1HRkGQXSKXJJmoGcYSJgv8y3ArXsJGC1GzH8c4IZzfTCLyD6QSvwQx+URSirYQb953UbW5F3LKVwSWcYBo6LPYVuukj5EkHw73vXgCk38OLwRh9xDpvRvrkioHxkHy4HR/DE/gr4c2tSYbORo5yIbim3BIo69S9Ga62NX3OnXeOdT53r73xtCEV6Sw7Q4yqc1k0luwMvsRYuy8H9mDUwjCWc826yQGrwztkqQAmr4K1XktuvMmZDmAEI5JCOzSwCqxClxc/P/spMvMhr0V8OZ3uyLJuNW3LmjGWeHCQNhRTOMgmfQWzPROLPMkMLG5TVbg6B8Y89MjRkiWy1C1JWiuG9Ac1yJLPpB0pDFW5t5JCGGCyCCIY6RfxUi9gpnZi2WdZjLm00IkQfQjbLA5ickb5+yVkdVaNG0lmuNaNMdKkP1kV0Evk9AhwMZCYA0pkQQ2XelmWpIn+UDVnxPKdPJG7zPUeOZR7Z5DzOznydbvU+qsYVnwBnTZzb7Q71EkmXuqvohDyS7dR40QJ2J7mR9YR7V7Ni92PkRL8hRu1Y8uTW6F1hIGbal6YmY/H6j6c1oSJzgZ20tALyJhRilxVrOh+G72hrbSl+6gxj2H1QW3EMp0Mt27gDn+VVM2VO/8u/8tQgiBaTUSjT9EKj1ZJzcFSdLIamyzL3cJdSCkrRMh0gg7kb2RsUGY2UhNg/9PaJok43SsQlNnjbpXl7zMK7qP+sgzGHYCRXIwN/hR3uj8BsOX9cY7b4tUejuR2APnKWToSJIjG9YWB5LsRpIcWUd5O47AHIjGlRnTt8WyWonGf4GilGJZnRckaBiWxV+++AK729tIGAZ3/uLngESRx8P/ufU2/vzZ3/K9W25FliS+/dqrSBL8y0238EZLC8+fOsXd8+bTm0zw4IEDnOrrJeh08d65c7l91mzyXa4rTtiocc9Al3R29b3GyvyrUQc+gKqscXXRTWzp+i1BrYgSZwVRM0zMjLAwsByX4kGWZBoSp5EkmUPh3TTFT1N4HoKG5ryRTOpZbGvygoZtdWKkX0FR51xxYzkphCCTfhFhj4z8Mh6yUormuOYSderKxBY2GTtNvl7IVYXXo0lZjbglTDJ2BltYyJKCNqA0EQjSdnooOpUuOy4o8MDlYHDSK0QE0zhMJvEImdRmhOifovrDZNIvkEm/QCLybZzu9+Fwvx9FmZHVGo8rcOhIkgshohfVh6iR5KHG19jRe5pwJj7iDT7HX84/LPnwRbUxGbJjbSFEDNs8Qzr5DJn089hm/ZS3ZdvtZNLtZNLPIkk+NMdGHO73o+rLkCTPwPf+bfgeGwchMggRx8zsJ5N6ikzyuSm7j89iY5unSZunSSd/iSyXoTk34XC9B0WbBfi4ElaQbCHoS3fQnqznidb/AMCnBrGFhVPxUDCQh82j5pGnF2HYaWws3IofXc4KEAJB3AzTn+ni1Z4ncCleBnacl+7ZFAahdAf18UNDfSnUK7CFjUv1Dfi+Zd+bGTs54Mc22NCgudXUjOdFCRpCCNKZbAxowzSxbDGUFVnTFJwOFcdAaMA3k3WQyZBMm1gDvhSKIuN0anhc+oCd4egnadtZZ6lEMhv3ngFH3cEIHxPdbIPHJ1MZDDPrYKrIMk6HisftyIYNO+8bNk0q9SrJ5G8nUVZHln1o6gwc+goc+lI0rRZFrkCW/ed8BMSAY1cMyw5hmvUYxkkM8xQZ4+SAg3l26VGIDG/WGqhKFbq+GEUZPfqRSy0asonsT5/CxsCwE5M+96yDWCOxxK9IZ3ZN6hhJciHL+Tgd63A5NqDri9HU6qEXcPZ8DSy7lYxxlHR6B6n0K5hm24Bj+XDhyjCOEI78B4ocQDAy/OBE6KrK/7n1Ng50dPCRX/+K33/qD4ZMpzpiMaoCAZojYYrcHsKpFPOLi2mPRomm0xi2RUc8xm+OHWNJaSlfu3Yjx3t7+OXBAzhVlbvmzkO/xJlszxdZkpnrX8IrPc9R5503ZAOvSArLguuwhMXL3b8jaobxqXnMDyxFQqbcVc2yvHVs793Ktt7NzPcvY23BdUO28jIyXtWPUxnbmULTVyEr1dhWJ5P13xF2L0ZmBw73R5CkqQk/+1YiRAIj/QriPFZxQEFRqtH0lZesX1ciMTPCzr5XaYifRkLmrsp7CWhBGuNn2B16nZgRwasFWJq3Cr+WR8KKs7PvVXb1vYZTcXFN0Y2UOisu+2RjIobe6eYJ0vGfkk4+w2S06RfeYD+p+I9IxR/C4b4Hl+cTyEoFkjyGHbukg+SCixQ0flb/Mk+27GJxsIZ5gcoRk5Zy96WNyjckzNn9WOZJUon/IZP8HZd0rIe1HyWT+g2Z1G9R9RU4PR9B069CkvMGvndvb4TIIOwwRmYbqfjPMDM7mVq/zLGx7XbSiZ+RTjyI7rgep/eTKOo8kIOX1cxWlmSKnVVUumeyqeRj2XksMh7VR3+mi9bkaQodFaTtBM2J41S5Z6PLbmJmHyGjE01yoMtOfFqQIkclle5ZlLtmIBC4FR+qpJIwo6StBBk7RdyM4NOCSMgkzAhpOwkCEmYUTdYpclYxw7OIq4reC2TNq2xh05lqGDX5ZXbeLZO04sSMflyKF1W++Hv1ggSNwQgKnd0Rdu1v5OU3TnKmqYdoLIUkSwT9bqoqgly7dhbXrJlJwDd88pFMGZxp7Gbza8fZub9hKM5zcZGPVUumcfO185lWVYCmjpRQLdumqzvK5teP89Krx2ht70dRZGbUFLLpmrm4XdmESmMF0zJMi46uCFu3neDVHadobusjY1jk53lYuqCK265fyKzpxTj0ySWzOltvA4nU5oHJ8NhIkh+XYx1ez704HeuQ5fEmT9LAhfcjy340tQaX81oAhDAwrbbsRDyzg0zmIJbVgS2iA8u74HAsR1fHNivwazUc6/8FXq2C9uQbuJRimmMvoUgOJreaESOZeolE8qkJy4KMLBfgcd2Mz/spNLVu1OXk7PnqyHItmlqLx3ULth0lkXyWaPxBMsZRhIid2wsyxp5JtH/+OBSFqkCApv4wAYcTXVEo9ng43ddLNJPBoai0R7PX+7ZZs6kKBKjw+znU2cmhrk5WVVRSG7wyQtxmE5gJTGESt6IsyluJR/UMu8c1WWNd4XWsKxyZxEmVvawvuoH1RTeMWr9PC/C+yo+P2wdJ0tCdN2IZh89DW2phm41Yxl5kx1WTPObSkDFNwqlsRuOA04lLm/gFbGS2YVsdnE/kNUkOojrWjz0RfIfi1/K4vuR2GuKn2BfaAUDUCNOUOEOlaxprqjawu28bjYkz1LhnYNkWy4JrWRBYytNtj9CWbCJfL7wkzt9TweDE17baSCcfIxV/AGFPYB41pSRJJ36GkX4Jp/czOJx3Isn5IyZm2ZVm90Ub7r7afYz3Vq7kj2bdeJlMWxJY5hlS8f8mnXwauFzZmU3MzBvEMnvRHGtxej+Lqi1EkvyXdVJ8oQiRFd5MYyfJ2AOYmd1crJndhWORST9PJv0SuutWXJ4/QlGnI407r7p0yJJMoaOcOu9SXux8EEmSCGolrCm4la50MwLBDSUfoTF+mLbEKYoclcz0LeFQ+HU2d/4SCYkFgfXU+ZYw17+aw+FtHIm8gRCCq4veS1AvYU/oRVoSJzFEhm29T7G28A4csovXe39DX7oz+6yFYHnwesqctfSlO3ih40EkJCpcdcwLrMWleIeUjA7FhSVMZElBQmKaZz7HIztpShxlbcEdFDurLnpcLkjQsG3B6YZufviLV9mxrwGvx4Hf68TnzcuaChgWpxu6qSoLcs2amcOOTWdMXtt5ip89+gad3VGCATc1lQUIIYgnMjz1/EG27TrD//qD61mxqBpFOWt/J4SgpzfG//vla7zw8lH8XidlxQEcukJ/OMkvn9xFfp4Hy7JHDTdn2zbHT3fy/x56jSMn2vF7nZSX5CFLEvFkhi2vHWf7nnq+8P+z999hllz3eSf+OafyzZ1zmJ4cMRmZGABEJkCCEigKJEUrUDnZstbPai2Htb0/r70Oq/V6rUAFijJFiZkECQJEzoMJmMHkHDrnvvlWOr8/bk9P6DB9e3pmAIovnyG6761TVbe67qnzfsP7fuF+7ty2FF2bX+2fUiGef/qqUX0h4iRiP0c89gvoWtP8L/iM+zIw9A4MvYNY9CmCcIJSaReF4o/KZoAqi21uR9c7po0NwhKKkNbYPTRH70RM3mC+KjKQ38X66i9c9fhKhbjeIbL5b0wRm9khMfQlJOK/SdR5DCnjlXxSpEwQjXwKy7qTdPZ/kMt/Y2ZTvkWGqWl0JJMcHR6mOR5nSVUVVY7DoaEhoqZJteMwXihM/QxlPf6aSITeTIase7MebNNRDAuMuIMMFHo5kTnEEy1Pz6rScz1hWh+lmPtLVDD/aGkY9OOV3kI3b2c+RpTXC32ZLF8/cJD+TIYn167l9o65J2ClAtziS4TheEXHkbLhH1zZ1GxwwxJKldXPACJalAlvjFJYwNEjWNJCIIjqcbzQq8ik70aiTDI8fO8Qhcwf4ZWeu1lnQhh0k0//e3zvfZzYr6FpnZf18JWJxrUv1Aq+y4rEtT3nFgKlQsJwAK/4I/KZ/4oKr02NcPFQwiu9jO8dwIr8DHbkZ8uZpQ9RdkOpIoF/mlLubygVvoFSlZWEXj8EuIXv4pfewYn9JobzMFI23JTnhSEtNlbdw8aqy6X0VyW2sYpylnpZfBPL4pum3ruz9olp+2mJLKMlsmza63fVPTnjcR9p+oUZX99W8yDbah687LX1qYtBuxXxLZe9tzqxndWL2J8BCyAaSilGxnL81d+9xbvvnaG9pZqHd6zljm1d1NfGCQJF/+AEZ86NUF+XuCyboRQcPt7H339vDwNDae67cxVPPrKJJe01aFJy+vwwf/ed3bz05lH+ny++yP/vD56krfliRNh1fd7YdZLnXzlMXW2cTz+xlYfuXUvUMRkezfKDlw7yrWffY3g0y9KO2mnnPjiS5e++s5sDR3rZvL6Npx7fwvpVLZiGTk/fGP/zW+/y/KuH+G9/8TJd7bWXHXvua5LD908ThsNzbCWIOo8Siz59zSRjJmgyScS5n4hzL34wgOedQtdbZswajJaOUAhGUASEykcKg1B5SGHQGtuBNo+JLwzHKZZex/X2XXVbXesklfh9HOdBpFiYTqkQAkNvJRX/XTRRRTr3ZxUv4K62f7i8BNKczGh8//hxVtTWsq6+gaRt89Lp06yuq6MtmaQvkyk3hofllLFSCi8IyioQ89TYvxEYd0d5aeAZ8kGWu+oeoNnpuEyh4kZBM7rQjY24QR9zGzhehFJj+N4+VDiMmMMdfrxQIOu6lHwfU9NxA5+k7aBJgesH1EQcdE1jMJtFk5Iqx2Ewm50ihFHDpCkRxwsCBrM5Cr6HLsqa6DWRCB1VKT62ehWvnzk7v/MOB/G9vRU1v4ONbqxCN2buq/qHhqgex9EjDJcG6C/0MOT2Y0mLqJ6gGBQYdodIFHsY90ZpsJsxbgJ5nh9cvNKb5Cb+kDA4c7NPBlQeN/8tQv8ckfjvoZtbyqo/AFiLEhFem2rjaLqX22tXEDNuTJZJKY/AP0Eh80e4xWdYHA+nxYUKhynmvkjgHcWJ/Tq6se5Dkb0Mw3G80hsUMv+NwD/EjSqTqgRhOEgu/b9jlN4gkvidyd6+66vcN+u5KJ+sP0TBH0MXJjGjAUsrB0yKwQQZrw9LJogZ9fhhkUIwjhfmCVWIFBJDRnD0agwxe69noHwK/iilMEMQlp+nmjSwZBxHr7pkLacoBVkm3G5MLUbcaJx6Lwhdsv4gxSCNpcWI6nUY8uI6bdw9jxtkSZqtmDK2oOxkxUTDD0KOnOjnjV0nqa2K8dlP3spDO9Zctk0iZrOia/qCwPV8Xt95klNnh9i6oYNPPb6FJe0XCcHSjjp+/fP3cK53lEPH+nj2pYP8wqfvQNfLTqf5gsv3XziIaercumkJn3x041T/R31tnEfvW8v4RJ6vPTO9lEYpxTt7TnPoeC9N9Ql+9uPb2LCmdSrz0dpcxS89fScHjvZw5vwIL75xlM9+cvuU6cpcCMM0vn9uzm2kTOHYO9C19qvu79og0bWmOclMzu8j43Yz5h5DCoOY0UzRH8MLMzRH7rgq0ShncE6SL/yAq3UnSVlNIv7zOPa9CyYZl0LTaolFP02gRslm/wa1SPW2jlF2Mx3O52mIRgmUwpCS+miMsWKBoVyOj3YtJeu5jBULlHyfZdXVSCE4NDTE8ZERYoZJ1nU5n56g2nGmshzXG0PDGQoFl/q6BLY989+uyWnlc52/cUPO52owncfwSq9cXVXnEgTB+XKvhvP4rNu8fe48+/sH6E2nqY5ESBeLbGxuojkRZ29vHz+3aRO10Qh/vXcf6xsauHfpEv5057sUfJ+oYbK0pppPbVhPTzrNX+3eOzmhlu/vz27cSFdNdUWf0y29igqGKhojZRWGfT+LJTH6YcRkZT0CQUSP0hVdwfsTe3h9+AVSRjXrkpuIaFHaIksYKQ3yZrGblFFDi9OBsQj1xIsNpYp4pZfJjf9zwnDhEtyLDw/f3UUu/X8Qif8+hnVrWYhE2AgRq3hvp7IDlNTF+Xhz9RK+dOpVaqw4a5It6FcENiK6xZJY/ZW7WTBUmMdz91DI/Ht8bz8fRJIxBVXEK71IGPTjxH8dw7q3wkz/DYRShGE/pcJ3KGb/gjDsvtlndBX4eKUfkR3vJRL/pxjmdsQNvrah8hkuHmPv6Jfpzu2iObKZTdWfoTGyHoDu3C5eG/hPdMTuZF3VTzFYOMjpzKuMuqfxwjyGjFJtdbE0fi8dsTuJaNN7T0pBhoHCAU6kX6C/+D55fwxQRPVaGp31dMXvpcFZi62V/dMGCgd5se/f0uis58763yZulteIaa+XncN/ypns6zQ7G7m17leod8pr+kB5vDn4R/Tk9/Jw8/9BS3QzYgGFUBWPKJV83tl7GiEEHa017Lh9/pG3/sEJzvWMUnJ9ttzSQWN9cto2yYTD7Zu7OHFmiFfePsbPPXXblDlSOlvkxJlBqpIRbt3UOa3JvKYqyvKuemxr+sdyvYDjpwcYGsny8YduoakxOa28qjoVZUVXA9294+zef5anPzG/ZsxQ5QnCkTm30bUONK3xppZ+XEBn/GEA9g7/EcuSTxIzWgF1idrU3FAqj+vtx/UOX2VLnYj9II59P1Iuosuk3kws8tO47oFJr5JrR1MsxrLqGr564H02NDRgahp3d3Riahox06Qvm6E1meTM2Bi6kGTcEi2JBDHT4sjwMD84fpz+bJbeTIaxQpFPrF5CfbTyh/VC8OrrRzl+cpCffWo7He3TM3kfNBjW3UiticBPM18ZjTDow3ffxbQfmjNC1VGVYkl1FWOFAre2tXJ6bIxlNTW8fuYsw/kclq7RPTHBL2zdjBSCiWKJR1atoDEWozmRwA0CTo6MAvDP79vBru4evnHgYMWfUakSXun1Ckv8JFJrxjBvr/h4H3YEKiDrpRn3Rhkq9mFIfarXot5u4n77sWljbquZf3lZtuhiGxqavLwcNghCciWXRORixH0hhqWzQSkft/gKuYk/QIWVq+JdfwQE3vvkM/+RCL+HYd05qXpY+dz1lydf5lzx4jXTRVnm+69OvYKtGdMcwpfHm/jD9T+1KJ9Chbkpha0wOMfN96+aD0IC/wCFzH9FhUVMpyxD/IGCUgTBeYq5v6BU+DoqHL3ZZzRPBATeAXIT/xwn9tuYzqM37NqGymeweIQ9I1+iP7+f9thtbKr+HLX28mnbjrvneW/ky4yUTmHKKNXWUlCKQjDGYOEQQ4Uj+GGJlclHsLSL30k3zHN04gfsGfkSCkXSbCVhtEy+l+Vc7m2687vZWP2zrEg8jKXFsLQ4CbOFYpAm6w9OEY2M10/OH0YKg4w/QD4Ym3I6z/kjFIMJbC1B1KhDLlCmeQEZjYCz3aNYpk5Xew3WDIv62TA8liOdKWCZBnXVMexZXCuXtNega5Lu3nFKJR/L1AlDxcBwhiAIMQ2NlqbUtHFSSuJRi0R8eiR5bCLP2HiBMCz3guw/3INjTZ/484Vy+qlvYGLWhvLpCCZVn2aHEA6Cm5PCmx2C0dJRvCCHrwoEan7ZgSDop1B8matFjHStFcd54DpkcQSGvpxo5Alc7wBK5a95j1HT5PfuuINvHznCqdFRuqqqubujk5hpsqNzCaEKiZkmNZEI21tbsXSdlO1QZTs8uXoNzx4/xkunT5O0bZ5cvZptLa0zqjr8BCBlAtO+l0L2FPNu0FT5soGZf2bOsiJb19GlxA9CbL08b0ghuLuzkwMDg5wdG+e2tjYihoEUgt++83ZeOHGSXd29tCbjPLlmDUXfJ26ZCMrlcxd8VCpB4J8k8I4CxXmPESKKbm1HanUVH2/BECa6uQ0h60B5ZSO4qf+6l7+mXMpNn4sfKQ5UwECplyOZssnV8tgaHG1h5SSeH+AFAaFSREwDBZwdGKO9PkXMNgmVIlsoIYTADwL2n+5j+4r2sgO6rjGRL5IpFKlLxHCuxVlZhfjuu+TT/+4DSjIuoEw2Ctn/hpBxpKxDiMojwNtqlrHcn//frN5enOBTmWQ8Rz79f0561XwYSMZFBP4xCrn/DwgwnccWNSh3TVCKIDhHMftnlArfQqnr3xe5uFCEwXny2f+Mwsdynrh+ZGMy2xAqn4HCIXaP/CVDxSN0xu9mU81nqTKn98oCDBePktdHWBrfwfLEA6TMdkIVMFA8xP7Rv6WvsI8TmedpiW6+jGh053ayZ/TLhPisSj7GquTHSJqtoBTj3jmOTzzHkfSz7B/9exytiqXx+7C0BEmjhaHSMbJ+eT5SSpHx+nGDHEmjhVKYIecPEyofTRik3W7csECV2YkuF17+uIAeDcgXSmhSEI9VdmDX9fH8ANvS0XWN2dZh0YiFFIJQKQpFl0TcRilFoVBelEgpiNgzP/wNXcMyp3+sUqkswQvwwmuHefGNI3Oeq++HFUxX8qrppFBlCK/aNH1j0RS5jb78O4yJY3hBnubI7cirlk0F+EEfJfe9q+xdYNt3Yhrrr0sWR8oYlrER01i3aFmNO9s7uLP98gmhynH4wpatU783xeP8ytbLM10rampYUXNzo9C+H3Du/Ci9/ROoUNHYkKS5KYVtGyil6O0bp7dvnGLJIxa1WdpVRzxWjjC+8+4pupbUUVdbXlwUCi4HDvWwfGkD8bjN0HCG0bEcNTUxzp4bwXV9GhuSdLbXzKu0cCaY9mMU83+LCuffMB8GZ/HdXejGcmZTRZuN2t3a3sYfv7OTnOvx+c2bMCaNFs+PT7Cmvp7aXI7nT5zgU+vX0xSP89bZc7xzvpszY2Nk3BIKxXAux9GhIbonJjA0SVsqQUMsNiWHfCm80muEYWVlU0KmMK0HKhpzrRAiTjTxLwhVetIFOYsKM+X/quzka5nyf8Ns2c2XwiTxKE0adJVJiVIuYTg42ZNS2WLPlCYr4mtZEV97zZ/peO8ww+kcuibprK8mFbPpH8vQUpNAAX0jabpHJsgXXZa11HKqf5Rk1AEFK1vrONk3zOFzg9x3yzJarIUvTHz/2OTi98w1fyYARAQpU5NqRQ4Ig/IjXAH+5N+kUP4bheOTksrzD5b57j6K2T/Djn72kn6N+ePx1i1IbfFKoeYDpQq4xR+Rz/znRSIZJkImytdYOghsEBecvi+YK3qTkvI5VDgxqaB3bf0KoX+cYu7PETKKaT2AkDem7HYuBEH3ZCZjsUiGdvHaisjkPXzh2sIFJ/GL1zY9eW2v0Tgy6KOY/W/lwJHz8etC5HRhoQjpL7zPruG/YLR0mqXx+9lU8zRxY66eXEFrZAvrqn6KuNE49WpbdBtZr59xt5vR0mlKQXoqy+CFRQ6Ofws3yNIeu5Vttb+IfqGPQ0CNtQynuppimOH4xA85nn6e1shWLFnOaHTndpH1ykQjUCWy/gCaNKmzVzJUOELa7aEUponIGibcbrwwT5W5BGOeRoEzXp+FDNI0DaXK5UgVHUyTaFLiBSFhGKIUM5IN1/PLZiGCiwsZIdD1MmtUCrxg5qia4mLq+/JzllOlVmtWNNHRWjNVkjUTHNuY8/1LIYR51VRz4PcQBD0o5d605qQr0RjZRlRvJOf3EzNacPQ65FVuCaXyeN7hqzS+l2vNLXMzuta8mKd8GXS9A9vaTsl9lw9bFGux0TcwwZvvnCAMFdlskXjc4YnHNrJqRSM9feM88+x+BofSKFX+fqxf08IjD23AsQ3+7//+PL/8C/ew4+5VAIyO5/h//+RFfvfXH2Dlikb27jvH8y8e5O47V3Dy9BD5XIkN61tpa6laMNHQjfVo+ip8923mXz41iO+9Rxg+PmM9c1MiQTDpzl7teKQcBy8IiBgGEcNAF5L6WJSaiIMUAj8M2dvbh5Si7My6fj2GprG0ppotLS3s6emlFPjETQulYCSfpz+TwTF0MsUSvekMNZHINKKhwjSeuwtVUdmUjqYvQzc2VDDm2iGEBJFAI3HxeT8XVIiiNLkQyF4kJyqLCnMU8389KXc5v0b/64H9p/voH8vQUZ+i6Prcuqqd7uFxVrTUEnMsjvcOMzCeJZ0vsqa9AQVkCiV6hyfoaKhCComua0Sdhc/TYTBKIfvfr71XQDhoWitS70DTlqDpS5Ba86QsbQSBNWno6qLCPKEaRQVDhME5Av8cYdBLEJxDzYv0FvFKr6NUkcXwmjiR6ac1Uo0ljcvL1VRIxivghQF115DVUCrAK71OIftHhMFZFvoMECKK1Fqm/mlae/kaazVIkQRhTypDlT2elMqjwnHCcJjQP08QnCMMuif/9bFQ0hH4RyjmvoQUVejWbTdVjSoMhinlv0qp8O1rJBk6UmtC09qQWjNS7yhfZ1mHkFWT/UAmKFC4ZaIcThCGQ4RBT1naPOi95NouTMkxDHooZP8YhIPlfAxxDYvmGT+ltOkr7GPn0J+R9npYmXyYW6p/log+d1+frSWps1ddRjIuIGm2YWtxsn4/bphHESLQmHC7GS+dQxGwIvEQmrCmlXlaWoKu2D0cm/ghGa+P4dIJmiIbiBtN+KpEzh8mUB45f4SMN0BEr6beXk3WG2DC7aYQTBDRa5jwykSj2rrBGQ1NSupr45w6O0TfwDhBEM57QZ5KRohFLYpFl4lMEd8PMIzpT7fegQmCQJFKRKYmeynKPRRCCHw/YGQ0S/sVqlBKKYpFj2yuNC2rkYjZxCIWQsC2jZ381KObZiyxWgikiKHPoYYDEKoxCqWXMc1Nkx4SN7+sJu2eZbh4ADdMU2WtZCD/Lg2RbWjMPsGF4QSleShNGcbKyc95/XpSpKzCMFYjRZJw0Z1IP1xIpwu0t9bwwH1rGJvI86d/8QqHjvTS0V7Dj148xNBwhicf30x7Ww0HDnXzF3/9OiuWN7Jm1dWJoO8HDA1n0DXJP/rMnUhZNla0rqWsRGhYzsfx3V3Mf1HqEnjHCbwjSGt6/9QtTdMn69X1dfSlM7x2+gwTxSIf71qNqV9wQ5f85h23TRuTtG2eXFduhjs4MMAPjh5HCsGy2jpW1l29rMn39k+6Ds8/EidEDNO+H3ENk/kNgZAInHI0Uk7vB/Lc1/G990DdPKLhWAY18QhLGmoYTucoljw8P6BnJE19KkYqZrPz2HlWt9UjhMAxDFa11lMoeYShIh6xyuV3/sIIglIepcJ38IovsHDvBgtNX4Jubsew7kA3N1cs1xmGOQL/MH5pF763C9/dTxj2XuXcx/FKP1rgOV+Ovzj5Mr+87H7ao5ffJ27os3/8HN35EZ7uXLg3ju8doJD9UwL/FJWTubI/lW6sQTM2oJtb0I11ZYJRYR26UiUC/zi+uw/P3Ynv7SP0T1I58VH47m6K+b/B0erQjVUVjl8cqDCDW3y2TDIqzMpehI6mL0c3bkE3N6Obm9H0zooX+EoVCPzz+N57+O4efG8fgXecSkpSLyAMzlHM/QVS1mJYdy/q2mTcPct7I3/DUPEw9c6aeZEMAEdLEdFrZnzPkPZUT4RSAWryfppwzxNMPjNrrKWIGYRDNKET1eswZRQ3zJPxemkVW4ho1VhagkIwRjGYIOsPkPX6qbI6qbdX0Zffx0jpBEV/nMDwyHj9gCJhtqBdQ4C8YmkT09RYv6oZzw84eXaYE2fmfyM21idobkwhpeTw8T5GxrLTtikUPfYeOI/r+Wxe3z5ZYlU2rqutjlNfGyNf9Hj/SM+0zEW+4NE7MEEmNz0aE4vZtLVUEY/aHD7ez8BwpoIejLkhZQJdX3LVdHOh8CLZ3Ffxg3NTjtw3E+dzL1EMRhjM7yVQRU6lv0t4lV6TUGXm0QQOhr4cXb++CltCaGWFLb1rQeMHChle6D3Kj3qO0puvpCfng4e21mrWr2ultjbOsq566mrjjI/nGRvPc+hwD+vWtLB0SV1ZbGH7MpKJCPveP48/S2bwUigFVakI27YsobYmRnVVlJrqa290N+z7EbIyJacgOIPv7UGp+S8ssm6Jc+MTbGxpZmltDbqc/7RX7UTY2NxEwp5fKYlSIV7pgknffCGQWi2GtaOCMT/BbFjSUM2K1jpqkxFaa5MEoaKxOkGoFH4Q0jeaYUVLLUIISr7P8pZaHFOnrS6FbeokIzYNVTFypYWRBN87QCn/5QpMKS+HkDWYziM48d8jkvhnWM7H0LTmihdGUkYxzK3YsV8hkvzX2LFfx7DugUWO5s6Gd0aOk/amLwhDpegvjPP20PEF7zvwuynm/hLf20fl2TMT3diAHf1HRBJ/QCTxv2A5j6Lp7RWTDCj7jujGOuzoZ4gm/hWR+D/GtB9bkHIXeHilN3AL3ycMbnzjtVI+nvsupfzfEQanF7QPIZuwIk8RSfwe0eS/wo4+jW6sWlAWQQgH3ViBHfkU0cT/RiT++1iRTyO1DuaXgr0UIYF3mGLuzyfleRcP+0b/lnH3HEJISkGG4eKxeT2jNGmiz0OSW13y/74qlh/KgDGHLLIQEkM65XJ3Vf4e2lqShNFEKZgg7w+T9QbJ+SPEjAaSZhsxo55CME4+GCXnD1MMxonqddha8prk8Cv+VpmGxpb1HbQ3V9M3OMHffvtdPv7gLXS01RCLWAShIpcvMTqWQ0hBS2MKZ1JyMx612bKhnX2Hutn53plJ1arl1FTFkFIwOp7njXdPsu9QN1HH4uEda9A0MXnRBBHH4O7ty/nms3t5Y+dJNqxqYfXyJkxTI19w2XvgPG/vOU04w+JJCsHWDR3sef88h4718twrh/C8lbQ2V+HYBr4XUCh5DI9m6ekbZ+PaNlLJ2fWLL4UQJobeia4vxfNmv4FDNUE2/xVClSHqfAzL3ISQUcQ8XLivB/L+ICuTnybtnpuRFV+JshHSOL4/t4+AwMLQ29HkzEx9MaHJOgy9c0Hu4K/2n+BMdpQmJ0FXogbFfPzQP5iIRS2ikXLEQQiBpsmy8EGuRMn1ScSdqTInKQWplMP4RB4VzkCuVFmN51JYpk4qubh1w5rWhGHdiVv4xrzHqHAE3ztAGA6hXSWLeAHLa2tZXrswNa6mRJymxPwbY8NwAN/bX6GRlYFubEKbwVzzJ6gcG5ZMr4n+2PbVQFnMRArB0qYahtN5IqbB1uWtAKxuK/cXRG2TumR0QYEHFeYo5f+GIDjLQqLsUmvBcj6J5fwUmtHFYsxI5fmgBTv6OQxrG6Xc31Iqfhd1lfLX64VABYy5uQUXuypVxC18C6/0coUeNeWeJMO+H8v5JIZ1ezkzt4iQWhWW83F0YzOlXCelwtcJw76K9qHUGG7x+2jGSkz7wRtaQhX6Z3AL35gs+ascurEZK/LTmPYTSC21qOcmZBzTvg/d3Ixb3IBb+CZe6R0qyxq6+O67FPNfJRJrXDThjVB53FL9aQaKhziffYc9o1/G1KI0OuvnHCcm/1cJdGFP9Rz4YXFWvqUI8VUJTRhT2Ygy0WhmuHR8Um1qCFDE9HoM6ZAwmtGFTdbrRxc2bpgnZXZgiGvzeamYaEgpaW1O8dTjW/jKt9/ltZ0nGBjOsHxJPYmYTRCGZLIlBoczdLXX8slHN04RDYDN69vp7hvjW8/u45s/2Mvpc8M0NyQRUjAwlOHt3adQoeKJBzewYU3rZQt92zJ44COrOXC0hxNnh/izr7zBxrWtRByTiUyBU2eHyedLNDekZjz35V31PHLvWv7+e0V++PIhTp0bprO1hkjExPMC8gWXgaE0Z86P8K/+6eMkE86sDevTLqS+BMe6C887wlwPmDAcJZv7WzzvMI69A9u8DdNcjxALM0K5FsT0FgYKu8j5fZzLvkDEaJyTcChc/KDvqipPUqtFk403ZIKUsgpNr6wPJOuV2D/ay3M9R1iVaiBpOtTb5cXks92HaXDiDBTSSCG4t2kFfhhyeLyf7tw4pqazLFHLskQdY6U8RyYGGCpkiRomq5INtERT1+FTXh1SymlyzVDuNbJMg2y2iB+EmEAYKjLpIu0tNQgh0HUN3y9n2MLJQIF/Rf/VhaziYsNynsQtfJv51zWHBP5xAu/AvInGjYTv7ib0z1HJIlOICKbzGItNc8dHspw80sfqje1EohejZkopjuzvpvv0EB3L6lmxrnVRj1spFB5BMHNJj5hsgF4saFJy68p2ekYmWNlSR01iZmO6hd7vnvs2XulNqFj4o0wy7OjnsZynkNriy1QLoaHpa3Div4PQaijl/2ay7n3xcDY3xHtjZ3ADHzf0eXHgfY6kL/ouKGDCy7Nn9BRrkgu773x3F27x+xUTJSGqsCJPYUeeRjOmOy4vJqTWihP/TYRWTzH3J4RBZd4TgX8Mt/hDdH0VmrH0Op3l5QjDNG7pR7il16m8AVtgWHfjRH8F3bp7mufDYkLKFHbkp9H0LoT8c9zCc1RSSqVUBq/4Eq6xDsv5qUVZp6xOPsGK5IOMFE/iBXkGigd4b/QrbK8t+2EsJhJGM9rk0n3MPUvMaJpGVkLlU/DHcIMscbOJqF4OothagrjZTHd+N2PuGfL+GFG9ltjk+wmzBUdPMeF1EyhvshG88zIDv4VgQc3gtmVw312rME2dt3ad5MSZIZ596QAlN0CTgkjEorY6yvpVzRjG5YdIxh0e3rGWqGPx5q6TvL3nNNlcEaXKUdmO1hpu3dTJQzvW4tiXN5FpmmRpZx2ff+p2vv/iAY6c6Ofw8T4MQ6MmFWXjujY+cttydu49w/ne6WlH09C5+9ZlOI7JGztPcPTkAIeO9VEouOi6RsQxqauJsW5VM1XJyLxJBpQj67Z1N4Xiq3j+3IpW4FJy38X1DlIwXsGytmGZW7DMTWiy/rp+SS9FS/QuBgt7SZld+KpIe+yjyDnq8JQq4ftXnzA1WYemXf9sBpTL1jRZR3mRNv8YWYgiVCECCCdTnKFSfOfc+9xe30m1FUUBgVKcz43xYt8xlsZr6c2P050bI2k6HJsYZO9INy2RJIfHB8h6JVKmQ9T44LgTRxyTlSsbOXqsj5UrGmlrqebYyQFGxnKsXdOCrksaGxIcPtrHbduWUix5vPnWiRvWWq+bW5D6EkL/xLzHhP5ZfHffZJ3tB0NYAUCpcrQsqMiUTSL1dnRzfp49lWB0OMM7rxyhY1n9ZUQDIAgCDu8/Ry5bvPlEI8xQyH8HCAhVFlAIYaOUi2luwbbvX7RjCSFIxRxSscVX9QnDNKX81wiDgcrPS9ZiRX4Gy/kU8jrOnUIIkDXYkZ9DoFHI/smiypYGKqS/MM7J7AB+GLBr9CRR/fKSGU1IlsTqebDplor3Hwb9lArfxPdPUFEPhIhhRT6DHfv5GxKgEEKgiGJFPg0CCpn/UqE4RIhXehPPvA2ptyx68/I0KEXg7cctVE7gAAxrB078d9GNTTdo/SIxzK0IEUdgUCp8h0pK6MKgG7fwXXR9NbpZ+X14JVJmGwKNGnspm2o+xztD/4Oe3B72y79jc83nSZhzKU9Vfqyk2UahMM7J9Is0RzYhuNSjRk16abyDIiSm11FjlcmqLiPE9HqUChgpniLEJ2Y0EDXKRCNptOBoVaTdPtwgjx8WqbI6bg7REEIQj1p89K5VrFneyOlzI4yO53A9H01KHMekOhWls7WaeHT6oquuJs4j961j1fJGznaPks6Uoz/xmE1bcxXLl9RjmfqMESVr0hW8qSHJidODjKcLSCmprY6Wsypxh9rqGKPjOZa0TY8KRSMWd21fytKOWs6cH2F4NEux5KFrEts2qa2O0tZcTV1NZfWVQmhY5iaikU+Qzv7xvIy6lMpTcndScvdiGMswjfVY5kYscxOmsRowr2uWI2F24Oi1lILtGDKCQJs7o6FK+LNEHi+FJqsWNQo5F4TQ0WRZLk/NM40eMyzuaujixd5j3Fa3hG117RhSIwhDhBB0xWu5s6EchSj4HscmBjk41kdbtIqc7zFWynMuO8bh8X5OZYaps2OkvSI9+QkyXukDRTSEENy/Yw3P/OA9nnl2H6apk84UufO2Zaxa0YiUggfvX8czP9zPX3z5DQxdksuVqKmeOdK7+OcXxbIfpZD9o3mPUSpD4B8mDLrRFtifcz0Q+mfwvSMVlnMYmNZ9l+m7uyWf86cGOXNigDAsSxouX9NMx7IGzp4YoOfcCFJKxkeyLFvdRNfKJrrPDHPiSC+FnEtdU5I1G8v9UYW8y9svHQEBDc0pNt22DN3QWLW+jdPHBgj8i5mkYt7lyIFues8OY1oGS1Y0kqyOcuJQD7lMkXLUXRCL26y+pZ1YYpEW68JA15cQhIMEpWNY1h0IaeN7pwj884tzjBsA392N771PxYpNIopp3Ysd+ZnrSjKmDicEyHJ0PwyHKeb+imuVEL2AzmgdP9V+Kz35MQ6Od/NI06ZyM/jkY0wgsKRBo5OiJVI1986mQeGWXsErvQ0VeSeZWM7HcWI/j7yBWdDys9vBcj5JGPRRzP4JlShSqbAfr/RyuZn6OjeGh+EQbvFlfK9yc1Ld2IQT+y10Y+MNC5JePPZKnNhvEIZj5VK6eWeSA3z3Pdzis2j60gUZVM4EKXSanPVsqnmanUNf5HT2dWwtyS3Vn8bRK73fZ4ahRViVfIwx9wzncm9zaPw7LIl9hJhRh0KR8wY5k32dU5mXsbUU7dE7iOq1k+cncbQqLC3OuHsWKQzaotum3nf0aqJ6HX2F9ygG40hhENPrr6kRHBZINKY+sKHR0VpDR2vlk6NjG6xZ3sSa5ZUzPcPQWNpRx9KOmevrbt8y9+JDk5LWpipamxbnDz+1X62aiPMYfnCeXP7bKDW92X1meHjeYTzvMMXiKxjGCkxjNZa5GdO8BUNvZwF9+/OCIaMYsryoPJX+Hh2xB9Bma2pXHuFVHNABpEwixY0yHRJleUKZIAgqq9edCRJBg3OxJl9QjsBpUpI0HVKmQ9ywqbYiSCEwpU7SdNhc00aDHSd2E0jGhvVttLfXUFV1kRzsuHsVhqERiZpUV0d59OFbOHFygFzeJZWMsGFtK5FIWRbvjtuWYpo6o2NZbMuks72G0bEcLc1VGIbGujUtVFVFFixlOzckpv0ohdyfVlRuEnjH8L33P1BEw3PfnXQlnj+EjGI6j172mu/5nDraz/53T3HH/WvoPjPMrjeOU9eUoufsCO+9c5KV69uIJx1M22BiPM/uN48jpKC6Ns7BPWdwHBPTMshM5DFtHcs2eOn7+2ldUkdjy/R5LwhCes+PsOv1Y6xc18rYcIZdrx9j+doWjh3sAaCQc4lETWzHpKo2zrJFIhpSxLGdh/AmDRkj0acAQUm+jucubtPm9YJSHm7xhxV7p4BE15dhRz+HvI5S4FeiTDbqsCJP4XuH8N23FmW/UkhqrQS1VoK1yTa21HSxIt60KAGzwO/BK75MOI9g1yVnhG5uxIn9yg0lGRdQ/twJbOdnCNy9eO7bFY333J0Y3v7yYvh6lSIrhe8dxCu9QqUkWch6nNivoZsbr6vC5FzQjBU48d8h8E9X1MCu1ARe6U0M6y4M687FOx9p0Ba9jVKQZffIX3E8/Ty2nmRN6hOYczRvV4KO2J2kvR7eH/sG+0f/loHCwUmVK0XeH2WoeARfFVmZeISl8R2XEUBHTxHT6+kr7MfRq4gZDVPStZowSJjNdOd3UvDHqbaWYmrxa/7+XhPR+AmuhMDQO0lEfwGBTq7wvXktzC9FEA4QlAYolt7G0F/C0JdiGqsxre1Y5i1o8trJ0UjxEMVgesblbPZHtMV2oDHzYlnhE4YTV92/kFHEIn2h5gMhbKSIXqNl0qX7u/izoWmsTNWzdqKJ3vwEAmiNChqdBOurmsn67tTrtXYUR7vx2ufLl05/gG7eeHljcVdnHV2dMxPziGPxkTtnd9vuWlJH15Lr41YtoCzjaWzBd1+f97gg6CZwD6Cs+xctGnUtCMNM2eOjorIZiW7cgqYvn/aOkIKahgS33bua93edYc9bJxgfKQcu4skIaza20dRWDvAcP9RDLltk290rWbG2hbHhDN1nh2lurcGOWGzY1kVDc4pdrx+n79wIDU2pacfzXJ8zxwc4dqCb6to446NZCjmX+uYUsbhDXWOS0aEMiVSEQr5ENr345qNCmChVJJv9U4SIEPjn0fQli36c64HAP1pWQKoo0j5p0uh8DM2Yu2n0ekAIDU3rwo58ipx3CKWuPrdXgs8suYtGJ7VIe1N47hsVq0wJEceJ/fpNDUgIIZF6K1bkc3juPmD+3x0VDuO7uzDM29H0tutyfmE4hOe+SeCfrHCkxI7+LLp11031/AAwzE04sV8mN/GHVJKdC/yjuKWX0Yx1i+gaLtCFzZL4PZSCLPvGvsKR8WewZJwVyYfRFuFaWVqMNalPENFrOZt9g4HiAYrBBEIJbD1FtdVFe/RW2mN3EDUuf3Y7Woq42cT5/E4sGSOuN17W45Ey2zBkhIzqJ2W2Lgo5+gnRWGQIoWMYK0nEfwVd7yCX+xquP3eD+Mzw8PzjeP5xCqXXMYovYBjLsMxt2NYdGMayeSlFzYTe/JsopTAvMz1TuEGaOYVWVEAYXl1NRwjn+teUXnY8c0HHe6J9Pc2RBNok2xdC8JmlW6mzL14XTUhaIikea1vLQCENCKqtCKamsTrViKObjJXKi4taK/qB8Ef5UEEIwMJyPlER0YASvn+YwD+Fbt5Yk7uZEPhHCfzjVKaAYmA6j8/4kA6DEM/1UUoRThqcapMZJSdq4kQuBgMMUycIFMGk70Op5ONELYQUk/sIQUGp6GGY+ow950IITMvAiVi0d9XR3lWHE7XwSj7psTyGqU/+0yjkmHueWCCkbMB2Hsf3j4MKMcwNGMbGa9qnH4Ro8voIGVwKr/TGpKRxJRdGQ9OXYzofX5Cs6mJASAfd3IJh3Y5bfHZR9x3VLaLa4mR4g6Afr/Rmhc3rAtN+GMO6e1HO4dpgTV7n2/BKL1U00nPfxvQfRdNbuR66iL5/HK/0OpV6vujGJkz7iUkZ35v93JOYzuO4xWcnMzPzg1JZ/NJOfOsAZoVZjYhezZ31v02gXJLm5X1uQggsGWdl8mESZhNB6BI3m6fWbPX2au5q+CeYMkqVNXMwJW40sbnm8xSDcersFcgr5KUiejUrEw9Tb68m4w3gqTxCCQzNIabXkzTbZuytsLUUq5KPUm+vxtGqqbUvD3TV22vYXvsFisEEVWYHziKoh/2EaFwHCKFh6J3EIj+Dqa8iV3iGQvF5gnBwQftTKovr7cf1DlIsvUO+8CyWuQnHvh/LXH9V/44rYWvVJIxOIvrlTHekdPgqD+Rg0jV2bghhIrhxTboCY0HH21jTctnvUghuq++ctp2l6axI1rMiWX/Z6zHDYl3V4jV5/cOFhmHdgZSNhBU0UgfeEXz/ELq5npv7oFP47h6Cq8g+Xwmp1WJa9828RwW950b52l++zsRojtbOWqpqLumbueTjNjSnaGqpYuerR9jz5nGKBZdNty0FBbZt8NIz+wh8RTRm09JZi1vy+eE3drF/1xlUGBKN29x6zyq6VjaydFUTRw90I4Sgub2GptaqKw933SBlBMNYN9ngXy4pWkgAoWdonDf2neZUzwiZfAnb1GmqTbJj81KWttYuOulQKo/v7kGF4xWNEyKBaT+Apt3MOUQgZROm9QBu6ZUFqGXNjv9+7Dnq7ATbapayuXoJ1ebCM4+Bu5/AO0RFnhkiih37hYqfj1ciDEMQAnkN940QAqnVYDofm+wlqEC4xD9P4B9FhVsXPXurwhyBd2gySFIJdKzIp9C0thvelzEbhEhgR38Jr/QWlZAm3z+G7+7GMLdVJC5ialGWJWYXqhBC4OhVdMV3THsvYTaTMOculbS1JB2x2+fcRpMmtfbyaWRhLujSos5eRZ09c99PRK+mI3bHvPc3r2Mu6t4qwN7Rc/yg+30Gi5ebGj3QvIb7m1Zj34QSlMWFQNOqse270I2l2PZd5As/oFh6bV6N4jMjIAh6CIJeXHcfxdJrWOYWIs6jZU+OeX5JWqJ3Y8rENKOYlamfmb0/A1Ao1DwmeoF+Y+s1hYRFjgj6YcCh0SGOjQ3z08vXXfbeYD7Li+dPMlos8JGWJayr/eDJrH6YIIRAyhoM+15K+a/Me1wYDhF4BwmD4Tn10IMwRC5ArjRbLDGaztNeP3e5YhgM4XsHUGElBlsCw9oxa/OvYWo0tVWzbnMnQkB9UwrTMli5vrWsIhW7uAB3IhZb71rBUP8EbskjlnBobq/G90I++fk78bwAr+QTT0ZIVpX9IVbf0k7HsvJ9m0hFsB0DJ2Jy3+MbGR/OgiiXaFXXxoinIti2SYfroxsavhtgRxZ/fg6CEYqFZ/D9o5PNrxpBcB7nih6WuXD8/BBfeuZdTnYPU52MErEN8gWXQ6cHePfgWX79qbu4ZXnL1XdUAXzvGEFwjsqM4wRSq8e0H+VmR4OFsNGMVej6avwF+BHNhs3VnewaPc1fnnyJ73XvZkNVB9trlrEq0Ywu5/98UKqE77036U0yf5jWPWj67CWhAF4QkHVdvDDA0nSStk26VKLgeSRtGwHsG+hDAWtq64lbFkP5ci9gyrIxtEqec9ak+3hnhWZ4Hr53kDAcRFtkohEEPfjuLphHAPFS6OZGdHP7DTN/nB/EZNboLrzSi/MfpjL43n4C/wy6Mff98mGDFxbJ+ROkzGtbo4y5/cT0agy5sADyTSMaflg27TmXGyHjlxguZvFVwNJ4Lfc0rIAPPdEoQwijbF6n1WMZGyh5j1Io/IhC6aWK+zcuQhGqcUruHlzvGMXSTmz7TqKRJzGN1VctqbqQyTg28TVKwQRxo4WE0UnC6EDM6bapQM3nYSq5Xs3rsx5vkaMqoVIM5bMcG5su9RczLFpiSXqyaYYL196A/hMAwsJ0PkYp/1UqUg7xDhD4x2clGvmSy5uHz9JUnWB5cy2mrtE7msY2daqiDl4Q0D08QXU8Qirq4AchI+kcoVKESnHgbD+2aWAZOsnozA9V3ztI4J+gEkUZkFjOJ5ltkSmlIFUTm1KPuoCa+plFFqrr4lTXXW4saNmwdNXMUbPZJG1bO2pp7bhcrS+evDH9VmE4ih+cxDBuIQjOobTxirNEz719hL7hNJ99dCtLW2qxTB0/CBlP5/njb77J3z63Z9GJRuDtRQWVNoFb6MYmpLa457IgCIHUmtGt2xeVaDzRupVba5fTnRth9+hp3h4+zqsDh+mM1bG9dhlbq7uot69eFx/4p/G9wxX3v1iRp7jaEmcwn+Pdnm6klDi6zvLqGs5OTJBxSzRGY3RVVXFmYhxDaqyoruXYyDA9mTQ9mTS3t7TTmUqhyfk9e4SQ5YCKuZVSoTLXbd87QBj0L3KviSIMzuK5uyoeadoPIbWGD0w2Ay403kewIp+qjGgAgXeQwDvwY0c0Mt4oxzJvc1vtJ69pP4cmXmFd8l6SZv3VN54BN41orE428Rur7iPvu/hhwL/a/12OpyvXH/+wQAobaXSh6S1Y5iZi/pMUiq+QLz6H75+nskXKBSiUyuB6+/D905TcPcScTxKJPDavpvFGZxtumCHn93Mm+0PS7mnubPx3GGIuadP5R9/eO9XL4e5B+scy1CQijGby3NLZxK0r2jnaO8zbR8+SLZRY0lDNY9tW45gGX3n1PXpGJwhCxcYlTdy3fhmHzg/w+qEzhCg8P2BtWyMPblpxSdO2qOi8ZsKXD7/HmfQYXhiwuaGFRzqWEyjFyYkR/tPu1/FCn9ubOrindQkRw6A1luSYdTkJOTUxysvdp+nJTNAQjfNgxzI6E4urbPbjCw1NX4lmrCOowJU28I7ie0fQze0z1rmXvICTfSNELBOlFHtP9jCRK3J2aIyP37oWLwwZGM/y+qEzfPruWzjcPUjPSJolDdVELIOhiRy7T3SjScmO9V2YV/gCKRWUm8D9yhYOmr4SfZYGYMs2uWV7F667OJKjHx4ElBvkl05mCBSV9TzAifPD3LKihdvXdZKKO1NZLKUUj/Sv4YvfrUz15+pQ+N4hwgq9KISMYtj33jSlnishZBW6sQ5EpOIF/WxIGBHiusOSaD1rU20MFtOcyg6wd/Q0f3f2Lb7bvZsNVe3cU7+GlYnmqV65K1Eu7Zm/zw6A1FrQzS1X3a7o+ZSCgOWpFAPZLOfTExR9j+ZYnLRbwgtD4qZFyraJmSZvdZ+jN5vBDQLyvkeo1JyhuSshRAzduIVS4e8r+jyhf4Yg6EFX3qI1XodhhsA/jqpQKU3KRnRzK2LOdcLNgoZhbkVqXYTBqXmPCoM+fP8IRphByvjVB3yAoFTIgYlX6C+eRCCosVrZkLqfweIZ9o3/iO78IQpBljqrnbWpexgunuNo+i3yQZqInmB5bDsNThfHM++S9oYIlEfaG2ZN8m7ieg0nsrs5lH6dCW8IR0twW+2T2FqsIkfzm0Y0YoZNzLgYIYzqN8d74Nvn9tKdH+dTnZc3AV8vSGGVDbq0ZkxjHdHIJym5uyazHG9TicPlRShCNUGp9C6+fxbPP0I8+nkMY+66vdHSESbc04TKxdJSdMYfntOwrywlO59JLgRCxrIFXD8gHrHI5EvUJWOcH56goWqMl94/wW0rO6hNRPjyy3tZ2VrPmrZ6bl/VjueHpPNFvvijd7ln7VIGJ7Ic7hnktx+7k7NDY7x3uo+ty1upiS9epPXO5na2NrSQ81z+057XebhjeTmqHSo+1rWKMxOjvHT+JOtq6qlxpk+wWdflnb7zmFLjp1es54VzJ3hvsI8aO0Lc/OD4anxQIYRAigSm/RCFCoiGUhkC7wBh0Iumt097P+aYxByT1toEhqZx4Gw/upS4fkDB9Tg7OMa5wXFO9Y9S8gPODo6xvqOJhlSM4UyeqGPRXl9Fz/AERc+fRjTC4DyBd6QCKesyLOcxEDPLw2q6pKr2w/WwWwxIkUCKCNnsnxIGPYQqh1VhI2/UMUlELDQpLyuVE0Jg2zrxyOJ+F8NguCy3qiqVBY1jmLde07H3jB5HoViZaCOmO7wzcoQf9O1EQ/Lpjh10xWZfvE87H6EjtUY0rZPAXzxJYSEEutCotRKEKN4bO8Op7CDd+RFaIzXsGjnJzuET3NuwlifbthM3Lv9OKOUR+CcqlLRl0szz6uIcCoWpaVTZDmOFAnHTYrRQ4L2BPjpTVSRMC11KTo+N0ZFM0ZJI0pedVH8zrXlnMy5eEAfNWA6YVNZ87RIG51AqgxDVlR1zFqhwGN87RKVCNbq5BSkbPzAk+VIIIUAkMK2PUMzPn2iAT+ifJAy6kXL1dTu/6wGF4v3xF9hS/RhVZhOmdJBIUmYj7ZG1TLgDrEvuwNLK3624UcvKxB2EKuBEdie9xWM0OEvI+COMuwMsjW2mM3oLcaMWQ5h0RjdwaOIVlsW2UmU2Y8rKy+X+QTeD+2HASwNHGSlmebz1xirXCKGjafVIrQ5D78KxP4rvn6NQfJlC8Tk8/ySVRvPAJwh6yeT+Dj8YJhn/VSxz46xbF4MRcn4fGgYxo41qa/U0ZYPLIZnPLaOUj1LlDE0qamPqGhP5Ik1VcU72j3BuaJyj3UMMT+SI2ibDEzlGM3lcL+Cl/SfJlTyCMKR3dAKlFLqm0VxVLn1RwP4zfeSKpUuIRgBq4eK2XhDw/LkTpN0SSinOZybKkSopaIolWJGqQReCnf3d9OezMxKNsVKeI2NDnJ4Y4/DoIMPFPFHDoBT4xGeRC15svPT8QZ793ntks0UMU+Pzv3QPm7ZULg86MZ7ny3/xGrZt8Iu/NnOzcqX4qz97hb27TuN5AZZt8C//3U+TTF1BFIWNad9LMfs/UCoz846mQeF77xP4J2YkGoamIYVk/+l+auJRljXXcfBsH7qUxB2Ls0Pj5EouYRhi6hp1iRivHTzN0uYaWqoTGJokGbEZ1LOE4fTvY7l06xiVfVctTOfxCra/fgiCkOPH+vnzP3+VUtFj9ZpmfvXXFs+JuxJIrQ4n8jPo3iFUmEY3VqLryyrax/3bV/Dc20c52TPMuqVNGJNKXf0jab7/+iE+sWNxZWQD/xRhRb05ADqathwhr23BuGvsGA12FSvirfQXRvn6+VfpjDZSCEp8s/sNfn3ZE8SM+XudSFmLbqxaVKKhlOL98fO80P8+u0dPEaiQjVWd/NKy+2iP1lLwXd4aPs4bQ8dImlE+3rr1svFh0DeZ3apMEckw74B55BpaE0lqnAgRw6DGcTCkRnsySdH3iZkmtq6zvaW13LNh2VQ7EZpicUKlqHLsivPoQkiESCK1BsKgMjPK0D+LCtNwjffN1P6CockG+8qgm1sRi2DImy4VGS+VqItEcPRFLJUXJoZ9D8X8X1Y0LPBPEfpnwfhwEQ2BYEv1x+jJH+Jc/gDrkjtImQ3YMkrSqMfSItTZ5WdjqELG3F6OZ95FoegvnKAtsgY1KSOYNOqoszuI6qmp/SeNOkzpUGU2Te2nUvyDJhrncqP05ccJVeVL+sWCQCBkHCnj6FoLprGOeOyzuO775ArfoVB8CVVhKlupDIXic0hhIeK/iTlLZmNJ/FGaI3cyVjpGf+FdTqS/yf3N/x1Tm7nhTCAR4upZBKWKqMkInyYFmpToUiKlRKky+UhFbZ64dS2ttUnCUJGKORzpGeRk/wi/8egd9E9k+dG+YygUUgicSad4IcqmUJeu+RTBvJrUZ8P7IwMcGRvidzbeyXipwDdOHEQpCELFaDFPCJSCgIxXImnOzOajhkmNHaE1luTBjuWAImqYJK0b1yx3y+YO6hsSvPXGMV57+QjZzEKyY+D7AT3do0ScxVMOe+CRDdyyuYNnv/ceu3eewg+mR9HKTeFN6NYdeMUfznvfgX+ynFUwb0fMIOf3yNaVBEGIZehs6mpieVMNQgoilsmDm1YQhCHBJNHY2NXMsqZaTEPDsXSq4xEc0yAVs3HMyx+GShUJvPcr7iMwrDuQN1Vp6CKkFHR01vILv/ARXnrpEL09CxWquHYIoSNlDbrWRijzgCAMJ5By/iUaw+M5zvaN8m+++EOqE1ESMZtiyWNoLMtEroimSfYc7kZNzviNNQl+7zP3Lvicg+BUxWpTCBPd3HTN9e2jpTSd0QZszeS5/t3EdIeHmrZiCp3//eBf44YeMH+iIWQKuYi+Jc/37eeHffs4keknZUa5t3Etd9Wuos5OEDdsDKkTKkVUtxgupTk80T2NaATBGUK/MhPMsknfZubTJ2jrOrZeXgZZk/+98PuFbEjSsklMZqWFEFiXNIBXrmAmENJBai0VE43AP4eah4/VfKCUTxj0E1RY8ilEFN1YfdWyKT8MOTM+Tl0kQtyyKPg+WbdE0fdpSyTJuS4HhgYp+B4xo5mS75NxXaodB1NqFHyf0UKepG2TsuwKr7OGpq9GyLqKysKC4BxBcAal/JsmN70wCDqjt9DoLGXM7eX1oa/yVPs/n3o3UAEKhUBQCNL0FU5ga1GWx7fjhyXCSzJaujTQrvjsAkGoAkLCqf1Uig/T1Vx0HBjvYczNk6wg6nM9Uc5yVKFRha41Y1u34wd95ArfI5f/JkHQPe99KVUgV3gGTWtBi/0CmlY7bZuj41+l4A8TNZppiGxjWeJJ9BkWahdPUEdqyauKq5SJxuyL3IZUnPs2LOOHe4/i+gFKKf7xEx+hpSbJcDrHnzz3DhHLpLUmdenB5zigOy/Z3dnQEkswUizw3/a9RdK06Zjsq9CkRCnFH775HDnPZWtDK3WRKPuG+vjS4b2cnhij2nYYLxW5t62LO5o7+OHZY/yXvW+AUjy1Yj23NV4fk6WZUFUdJZmK0Nc3zq53KkkbX380NadoaEyy/71zvLf7zCxblUm3ZT9SEdEAF9/bRxCcQ5crp72bijpTERtdKzd2X3yvTAQvPMhs08A2Ly40zMlFhzGDK3rgnyg3qVYYbS03gZsfCM8VIQSOY9LaWk0qFaWvd/ymnYvvnyeb/e+E4cRUQMOybkXXPzXvfYxO5KivjuEHZaWxMAwxdElDdZzG2jhSClz/Yu+LH1ybzWfo96DC+WbfyhCY6MaaazougCF1gjAk7eXYOXKEO+rW0mRXE6iQUuBVHDwTIj7ZnC6p3PdpOv7kxAu0RKr4leUPsKmqk5hhE9Gsy2RipRBEdZukEWWoNH0RHfrdFZdNSa0dcQ3GtjN9L68sw7s2mEg5/Xl8NYRBf8UlmrNBqewClNJA6ksQsnrOa+CFAT88eQJL13mj+yyPLVvB0dERDgwNcFdrB4P5HHv7exkp5PHCkBonSn82w0AuS1syydJUNTt7u9Gl5M7WyiPoQgiEjKDrq/DcCvpPVIkw6EaFY4g5VAw/aAgJ+H7vHyGFjiKkxZl8BgpwtBi60PlW93+kM3oL65I7MKTNifS7jLl95PxxOs1bLtnbjCZLtERW8vrQV3C0JDvqP0dEn1mUZDZ8aImGHwacyg7z3fP72D/WzZibwxAaS+K13Ne4irsalpO4gkAUfJdXB47x1vBJTqSHOJ8bZdzLM1TM8PNv/DnaFZJ7T3Vs5QvL774pCwIhLKSsxZQ1GPoS4tGnyReeJ5v7Mp5/bF77UCpHLv91THMdUecRrryJVqY+Tdo9Tdo7h6NVkzKXzqlYJdDRxNUn8FBlUCrL7as6y8ZeopxCl1KyaUkztmnQVJ1g+4q2qcxEKlpOQ/+7zz2CoBxpDZXCNg1uXdHOpq6yes6S+mp+8YFtRCzzkuMVUeHC1Z/qnCj/6e5Hy8cVgkApbF3n7uYOttQ3o1S5DjKiG5hSY1V1HX+wbQeBChFCENENHN1gQ20jy1I1+GF58RIzzGvSXq8UQgg0TaBpcnEUM+XinfuFc5NSgJjr9Cx04xak1kFYgZyl7+4l8E+iG9OJxoXjz/Tz9O1gvhfPdw8SeEfmfY4AQtZjWHdyabR1bDTHv/233+bhRzbw/A8PMDKaYePGDp761K00NpZVec6dHea7393Lvn3ncRyDe+9dw457V5NKRQiCkLffOsGzP9hPd/coLS1V/PSntrNmTQumqfMv/vDrfOSeVbz95gmOn+inuTnFL//KfSyZp9v7XMdeTCiVAyTJ5L/hwt+gUg+Epx/eWhF5qLjG/gqEwcDkeVcAYVxVdnU+WJPs4KWB9/hu79vYmsG6ZCeOZnEi20tUrzQKDAINKasQsqbiBuGZ8K83PEWjnSKm25ja7EuNuGHzWMsmvPDKv1uICgcIw+nKf3NB05dNSqzffCI/E4QwFkSEwnD0mgJql0KFmQVkikDTll7VoC9UijMTY3x23UbeCAJ6Mmk0KVhVU8vy6hrOpyfIex5bG1s4PTHOUD7HsdFhHF1HICgFATVOhLbJsjYW8HcUmOjmWjz3tYrGhUE/YTi3XPq1IghCvJI3ZZIqrvE5K9F4oOmXpzIN+uScKRCkzEYeaPxlFAG6sNCFycrE7XTFNl2yvQkI1iXvAcC4Ys4VCLZVP4GvSoDEnqXiZS58KIlGzi/xg+73+e/HXibtFvDCgBCFAE5lh3hj8AR3Nyznl5bfzYpE49S4fODy9vAp3hws14qWQr+8iBTgqxB1RRBHXQ/72wpQnigFQsQRIkY89jmizsPk8t8mnf0iwTzMzfzgPMXiK1jGBnT9cknLgcIeenKvYWvV9OXfYcQ6zMrkU7N6aQhhzpgZuRJhmCZUGWxzhrrLyWiyrklsY/rtN1ODt23qU1FmQ9emRZeVKhBeQ6RHCkGtM8NxdQN7htpRS9OxnOnnbmoaZkW66jcepZLHKy8e5vWXj3Du7DBeyaeptYpP/PQ27rrnooGPEGUn6W9//V2e/d4+8vkSa9e38cQnt7BqzUVJzkLe5e03j/PDZ/bRc36ERDLC/Q+t596PrqGquvIJqRyNqsa076eY+/N5jwvDvrKnhnUHchHqh69+vHEC/yBhWIlTMVj2IwgZv+zZGYQhZ04P8dyz7/OZz96B74c88729/N1X3+Y3f+sBBgfTPPfc+wRByD/5vYcZHsrw3HMH8P2QJz6+ib17zvLss/vZcEs7n/u5O9mz5yxf/tIb/Mqv3ceyZQ2Mjeb48pfe4Od/4SM8/Znb+dY3d/Ff/tMP+C//92fLpHQO9PePz3ls01zMR4iOlCmkTC24dOHSZm/P98kVPSKWgaFri77wLM9zo1QaFS5nDuY265oP7qvfRMqI0VMYZmNqKe2ReoQQ5PwCjzRvx9EqLH0U5WeNlHUEi0A0ViWay6XBV7nuutBocqq4soA5DMcJwyGgMvU1TV+66HLniwsdsSBlozxKZRaltEepDEG4AKJhdF3VNFATkhXVtfz9kQOUfJ/bWlo5OTaKoxmYmkbCKme1vnX8MDVOhNtb2pgoFujLZokaBo6uY0iJresLDwQIA02vvNcimCQaiwmlFGODaV79zh7efu59uk8N4Xs+2+9fy+d+/zHqmsuks1T0mBjJ4hY9qusTROKzl13vOt3N8YERPrllLZahE9NnJq5SaNOyD6awZ2zoNmcRJwGwtAgWCw8sfeiIRinweWPwBP/xYLm04t7GVTy95FY6YjXk/RKvD57g78/u4vneQ2hC49dX7qA1UoUQgiozyj9b9wjBJKP461Nv8z9PvU2dHeffbXqStujlTVbGB0hVQQiBwEZorSTiX8C27mQ8839RKF5NL1pRKL6G4zyMprVcNukPFnazOvWzxI12FCFvDf5rQp5Em6V5WQgLbR6672E4QhDcmFpvpULCIE24SLWrP+7QNI19e87Q3JLirntWIjXJ9765m//6H79Pa1sNnV3lSI7vBZw+3U8QhDzxyS1k0kVefekwf/3nr/Jrv/0gre01uK7Ps997j+9+aw9r17dy3wNrOXtmmO9+czeZdIGP/9RWUlUz1/LOReGFTGJaH6WY+yvmL/us8NzdmP6DSDPJ9TVBUwT+sUnFlkqCEQIz8nHKijOXn5/jmNxx53LWb2gjDBWFosvX/v5dzpwZZnAwTX9fmk98cgvLlzeybFkDp08Pc+rkAL29Y7z77imamlLc/ZGV1Ncn6Ois5dDBbnbtPD2VEdm6bQmbt3QSjVp8/ufv5hd//oscPNjDhg2zl/YppThzZnjWY/f3T9DePrPh4ELhuXsYHnoSXe8AITHN24hGn573eKUUh04P8Dc/3MWew+cpuj7/6pcf4a5buvjq87tZ1dHAltWLU84YBoMVl02V68dbFkWxx5Q622tWEaoQTcipCOX61BLWJpegL2CxLUTkmsqOLsWXT79GS6SG+xrWXiWLeOHML9+mrOhVOeEp9z99kImGRLCw/jcVjpUVzq6VaIQZQr+yHhEAKZsRV+n70YTg3s4leEGIJgWm1Eg2XiTWNU6Ex5at5KFwOboUU8QkUCGG1JBC0BSLX2M1gI6cQRzkalBBHypYqL/ZFfuaVK088f55/vhffp0T+8/j+wFqsowjnyleJjCSHc/z5f/0DK999z0++08f5ZHP3HGZMeul+93Y3sSGtkYMTbssIC6EuGqA/GZk+j5UREMpxaib40+Pv0qgQj7atIZ/ufEJLKkjgCozwlOdW2mJpPivh3/EK/1HWZNs4umuW9EQSCEucxw3ZTnKJRE4mnnTJHYrQflGMjHNW6hK/kukiJIrfI+5Fjx+cAbfPweWB5dMcAqFENrkQ09cdc0khIU+jy9vEAwQhIMopa77Ta1UlkANsTAfkvkeo3xhLv0sV365PyzQNME//mePTTXWA6xe28Iv/9yfcGD/uSmiEYaKZDLC7/4vj1JbF0cpaO+s5Yv/4yXeev0YTz19O8eP9vHWG8fZdlsXT3/+LuJxmzAsl729/eZxNm7uJJmKzFzzPPdZliWgze347lvz/myB9x6BfwrNWHddpReVKnsnVKrYopub0LSlzLQI0nVJU1MKTZNIqUilIhi6ZHgoQyZd4LXXjvD228cRkxG+MAjZuLGdgYEJxsdzrFjRRDLpIKXANHWamlIMDaUplcrR4OaWFKapIaUgmYyQSjn09oxdhWgw57EL+cokXa8GTWskFv+9K16rrJb9wMk+/v1fvUAQhtyxoYsXdx0nCEJAMTKR56vP7100olFe9BUqHCWRsonFIMLh5ISti/JzLFAhQegDYkEkA8pEQy6SqtG3u3fxVPvtCx6vwtEFmdpKWQ3Km2r4/+DBY6F/fxXmUfjXdPcopcpVABVnrTSkVg9XKWcUQqAj0fWLEtP6FX05hpQYl0hQS6UwJufFstT5tX4/JFLWURZDmP93tFyeNr5oa5fe04P8jz/8Gkf3nkVqAidqoRsa6dHp5ZbVDQka22uRmuDQu6fYumM1HSuni4Y8d+A439h9kGUNNfzWR2/H1HW+tecQo9kcv/iRbYzni/zPt99jXUsDa1oa+PquA+w710fMNvno2mU8uHb5DV+zfKiIRqBCTmWGODLRT0skxRPtG7Hl5bWYGoINVa3cWb+MvzjxBgfGe+jJjdEeW9zI283EhZIqQ19OPPYFPP8Mrvf+HCMUnneUIBhC1y9mJGqt9RwY/SuiRh1Zr5dqezVyzlvCQNcakSJJqGbPIAThMEHQi8JFXGdp1yAcIVhAZCaczGpdSO2Xfy9H1sqPJzX1fkiIF/qY0kCKcoP4uJch7WWot2qwNWtONYYPFhEpn4vn+oShQilFLGZjGhrp9MX6X02X1NUnqKtPTEVJauvi1NbF6e8vT8QDfRNk0gUaG8sL5Hy+3BRdX58klykyNpabdcKeM6MhBELWYNoPVEQ0lMriuXvQrVvRFqE0ZTaUy7QOoFS6onGm/XGEnJl4KcpqX5e+oLgQWICNmzr4whd20HZJBkFKQS5b4kfPH5xcPFwcHih1WW1z4IWXvR+q8vi5oeY8trbIZYJCxDDNi6pDSqUr7n/4zmsHaW1I8ts/cw/NtQl2H7k4N6zsqOPFXccX7XzL51cp2ZKLUjYF8NLAewhga/VK4obDD/p28vfnXkETkl9d9jibq5ajV0q4hbNopYdu6NMwD+fv2aBUZgEZI8iO//aCj/lBh1Iu02q8K0ZYVrKskCRLWT0vbxK4+jPvyvcX+xlZ3p+BpjcT+CcrGOlN3nMuXOPaJZ8p8taz73Nkzxnqmqt48gv38uCnb2Pnjw7wH37rSzOec8uSOmoakpw/3s/YcGZGovHguuVoUnKwdwClyk/0HSuX8Dv/83t87o7NTBSKHO4Z5Ofu3Mzes72MZPP8Xz/zKPvO9/H6sbOsbW6gtXrh38uF4ENFNLww4Gi63JcQ0SxWJRpnvEGTZoQWJ4UldfoLEwwUMz9WROMChBCYxiqikY/jThxgruVbGI5Mk8ldkniIlLWUcfckzZE7qbZWzWnYV440xDHMVZRK78xxZgrPP00Q9C6qXOJMCIIhvArlRf0woLcwAAianTpCpTif68fWTBqdWgaLo/jKR6Foths4X+jj3dED3F6zkRannnEvw/HMWTzlEzdi5IICWS9PtZWcJCaKQAUYUieiOWjXtYynMhSLHq+8eIidbx6np3uMXLaI5wUUS5fXQQshsGxj6vslhEDXJYYuKRXLJKXk+gwOTPDnf/wSX/rzV6cdKwxCgkAxU5nt1a6IlDEMc1vFEoW+9y6h/xhSNl0XgqeUKruRu3MR++kQIolh7wBmrrv1vYDunjF8PyAMFSMjWcIgpK6urNlvGDr9A2k6l9QhpSAIykIEiaRDTU2M4aEM42M5LEunVPIZ6Btn7bpWLKucwT3fPUqp5GGaGiMjWTLpPC0tc5fISClJpiKzHnvxLm842eRqoC5Z/HjuPnz/NNHY5+e9p/7hNNvWtJOI2peU5JQRcyzyxbkVwsIwJAxC9Mn+sTBUBH6AEKBd0edRVgCqkGgIiZSL8yw6MHGajmgDmpScyvbxvZ63ebz5dvJBka+df5WV8TaSZmXuzUIYzGYkWSnWp9o5kxskUKsqJzyACrOLprL04wKFyzVn71UJVbH3CyCTIKaXfX5QUa7WqAYqIRoQqjRK5SsWorgS2XSBnS8cJBK3ufvxTTz5y/deOLFZx6TqEsSSEc4d7yc/izx92ZTw8t+jtsmmjmZeOHSCkhewtavcj3tudIKdJ8/zv6WfA2BJXRVBeO2KcpXiQ0U0QhRpt/wg0qUkYczeLGNrBhHdJB+4FIPK5Cc/TJAyjqEvR8qaOZuYQpWdFn1zgyyh8rC1agLlMlTcT4OzBTHHbSFkHFNfexWiAZ53BN8/ja51XreIvlIhftCD51cWpTyb7+VE9hwpI06DXcM7o+8TqIBT2fN8vOU+XhrcSWe0mdPZbp5ouY8gDPFDH0saDJfGOThxHFf5uKFHX2GI/uIw416GJruW5bEOjmXPcj7Xx7aadXTFbpy87Xzwna+/yzf//l0eeXwjn/7cHdTWJRACPvvUf7tsuzBU5LJFgiBE08pZnFLJp1jyaYpaaJrEsQ0am1Js2d7FrXcsnzZ/NrdUXbXZeHYIhFaPad1DqfC1eY8KvMNl9SlzE9cakZoZLoF/lKDCe86w75+MCM78XSgWPd556wRdS+oIQsUrrxxm2YpG2jtqiUQtOtprePGFQ5imRipZljCurYuzcmUTt966lO98Zy+vvXaUdevb2L/vHIWix5YtS4jFytdg754zvLuzjda2Gr733T0s6apn9ZoWwlCRyRQYG8uTyxYplXyGhtIYhk4sZrFkSd2cx9ZnkP2tFGE4TqHwDKa5lVzuLxGiPK8HQQ+arCz6H42YTGQLlFyP2CU+MGEI5wbGaaieuwl3tG+cMwe7Wb55CU7cJjee59juU5i2yYotS4gmLzZEqjCzgIyGAFmZNORsKAQl4noEU+q8PLiPFfFW7qnfgKWZvDj4Hv5CTEyFyXy8kuaDpzvv4j8c+jYbqjrojNZNK4fRhTbNCfxSKJVDhT8hGpdBuVyr9LDCJQwr76GUIoZgEY31rjskYgHfNRVOTGZSr61XyS24nDveT6omxqa7Z1ZDvBJOxMS0dAq5Ir47XQRBKUWmWCJXdCl6PhOFIromMaTGw+tW8P+++BZVEYdfu+82LF1nWX01ty1r55fu2YagLMKTcG6ct9cFfKiIBpQVDaDcXxAoNesHCFGESiGR12yM9EGHEBE0WTUn0VDKRV0RCTk8/jdkvW5MLY6YdFGtszcg52g0kzKBad4COY25IiuudxzXO4Jl3jG1cFhshOEYnnek4kmzxannXL6Ps/le1iaXMVwaQwioMVOEKDQh2VS1mlLo4oYeVWaChBEjYcQYcSdAQFeklcHSKPmgyIg7jiF0HM3G1iz80CeqO8T0yGT9y3X5+JfhQumMUqpck67KMnq+HyJlOfoshOD4sX7q6uN89KH11NbH8f2QA/vOIa5IhoVByMhwluNH+mjrrCUMQ86dHWFiPE9Le7mGu7mlivqGBJ7rU1sXJ56wEQg8r6zmFolaSCkmz63cGBdOumP6fkAQhFPlOzMtwKWswbDuplT4FvNXngnw3HcwrDvQrkM2LfTP4rv7qMw7Q8O0H5xzAReJWGza3Mm3vrWb0ZEct2xs55M/tRUpBfX1CR57fCPPP3eAv/7SG7gln6bmFI8+egtCCG7Z2IHnBTz7g/288MJBGhtTfOazd9DRWTt1Xe+9bw1vv32CM195m+bmFL/zuw9Nll4V+fJfv8HBgz0U8i4K+Bd/+HVWLG/kqZ+5ldbW6jmPvRgo934tnawbH8OxHwHAI0KlVqpbVrbxvTcO0tlczcYVLfhhyES2wPHzQ3z/jUPs2DK303j38X6++IdfZeWWLu751O3kx3O8+b09pOoS5NJ57vrEtqltlSqAqtQoVCAXpDg0HUkjypib4Xi6hwMTZ/h4yx0kjCj5oHgNiomLl9HYOXKCuO7we7v/mvZoDUnj8vu/K9bAP1792KzjlcpVLh38Yw/FNdsLKx8WkikSMRAfJqKhIWTlJUIqzEFYmo+x/JwIQkUhW6SqLk5yvgqMgkn1Nzlj4iNUir9/9wAHewbIFIr8+Wu7ePq2jXTUpGiq1jCMCWwroC4hMDWNFY1VHB1Q/Jvvfg0pNDa1d/H0bbeiRIAXZgnxEEgMGUMgCVQJTdpowsQLcygVossI8hp7Hj9UREMTknqnzFC9IGComKE1Op11+mFIxiuS913ihk3sQ9DkfW3wrhpZK6cBL/9zu0Ga9dW/TNxom/eiQQoH01iOrrXjB3O5iroUS29jW3djGusWPauhlMLzT1Eszb+G/wImvAyWNKc8QzanVnM4cwpTGiT1GEkjjkQS1SPoQptqujyRPUdHpJmYHmXf+FFSZpzOaDMoxYg7QcKIMeFlqbOqkEKS9QvUmOGCygYqRbHoMdA/wehIltMnB8nnSpw4NkA84RCPO7R11OA4JitXNXHq+AA/+uH7tLZVMzaW473dZ6iquXwi1I1y4/Df/OXrrN/YTi5XYu+u03QsqWXbrUsBWLK0ntvvWsEPn9nHl/7sFZYubwAhGB5MY1o6H314A61t1WSzJfp6RkmnC/ScH8Vzfd7bc4b6+iSJpMOSpfUzfiYhbDR9JZq+isA/MO9r4bvvEgbnkVrHogYZlArx/RP4c/ZDTYemd6EbG+ZMxeu6ZM2aZn7u83fN+H5zcxWf/0d38/l/dPeM72+/dSnbJ/8uM6GuLs5nPnvHVCnVBURjNr/xmw/Mef5XO/a1QogoprmRIBghEnkKy7oTAE1vxffPVLSvh29fxameYb74nXeIWAaZXImvvbCPoufTVp/iqfs3zjnesHQ237eOzfev4+yhbmqbq1i1fSmJmjjeFeWF5YbjyrIGAokQi0M0Nlev4Ae9O3lp8D3aIvWsSrRhaQancr00OTVTQbmKzk9oiDnKZytBd26EuOFwW+3yGd+355LfVWqSxP34ViPcPATlXo8KIYTDNa++bySE5GoO5jPDQ1UoqTwTpBTYEYvADynk5pf5TI/myGcKxJIOpj39+6FJyS9+ZOtlryml8FWBvuJ3ePrebhBwKp1leerT2GaOTSsPsWrpMKCod1IYmmS4eJAz6e/jqwKmTNAWuw9DxunPv0Wds5lqew3nsy8AipboR7C0a8vufKiIhiE1ViebcDSTjF9k7+g5miOpy1KyCsVgMc3JzBCBCmmNVNHozJw+0yhLArqq7MNxI1SSFhtKBYThBMFVai6lSCCvyCwYWpSMdw4m1acAYnrTVRV7NFmPbd1BNj8X0YBSaSel0rsY+tJFS8dfgFJZSu4eSu6+isc22LXUmCm0SbWWWCxCe7Sp3Mwt4OGm8mJve/X6qTGPNd0zle3YWrWWLVVrkJMP8ma7flLBS0w2hDcsymesBH0943zvW7s5erjsohtPOOzddZq9u06TTEb4wm/ez5Kueh55fBNBGLJvz1ne232altYafv6Xd/DyC4eoniQbuq6xbEUjH9mxGt3QePXFQxQKLms3tPHRh9bTNKn7bZg6Oz66ltq6BK+9fJg3XisbSdbXJ9h++zISyXJk9MTRPr7xdzsZHSlH0Rqbq/j213aVt21I8C/+7U/PmvWRWgOG9ZGKiEYYnMf3DqEbG0EsTpkKTCqc+UcIg8r05037oQWl8P+hQdNq0LQ7uBCxNYzVGMaquQddgXjU5rc/fQ+b3jvJ7sPdNNclcSyDtV2NPHrnmst8NmaC5Vg4cZvMaJb0cAav5CGlpJgrYdpXRnN9FlbGsjhR4VurV1FlxBgsjbMq3kaNVb7HJJKPt9xBRFtIgE1OZbevFf9iw08veKwiXJTF3k8wE0Iq9X4BEEJftMCNUj5emAYUpna9+mfFnGXgs0L5VOrdMhNMS6epo5aRgXHOHetj3W3L5hTgKBU9Th7oZrBnjKaOWhKzSMPPhIx7hoH8LrbU/T6lYJwTE18j450jpreyJP4EQkgGC7sZLR6gM/4YbpDG1BJ0Rh8jotdjyRRS6AwUdlIIhvDDPDmvh6bInZgLyApdiQ8V0dCEpDVSxT0Ny3mp/yjf697H6mQjzZEqbM0gVCETXoHXBo+xc/g0dXacDVWt1NszR5CSpoMpNYYKGc5nR2m0E9iagULhhQFSiCnpwNmgVAiEILRZVYeuJ4JwmJK7/6pNc5rWjLxisWPJJCcmvomt16JNRrE21vw6+lVS55pWh2PfTa7w7TmPG6oJcoXvYZqrscxt12wydAFK+ZTc/eTy32GhES9dXn4u8ioTqBBiqqn7IqFgxt9vBrqW1fPb//SRq24Xi9t8+rN38unP3nnZ60uXXzS2TKYi/OKv3jf1+wOPbJh1f6aps3nbEjZvm71MadPWJWzaurAyJiGrMaxtlPJJ1BxKZ1fCK72BYd2HvogL/NA/g+/uoaLSBRHDsD5C2U13ZhiGxvIVjcTmMGi6FnQtrae6OjYPlambC6VKBEFPuRRS+SA0NNmIdoXR6NXgWAYP3rqKj25fSRiESCmnSgevhnhVOSBy4M1j1DRXkR3LYUUsshN5Vm7tuvx88RemALRI86AQghXxVpqdWvJBkYHiOJbUWRprZoWQCwyaSVjkDKxSCnWhlFnIeUqX+pMLvp9gsaEIURWX/EE5m7E4c4gfZhjJv4jCozn+s4uyz+kQCyr1UniwkP6mKxCJO2y6ewXf+NOXeOMH+1h/x3KaOqbLdYdhSC5d5OC7J9n5wkEy43l2PNlJbXNq3scqBqMU/AFOTHwNEMSNNlCKCfcEZ9LPYOs1ZLxzk0EERZ2zCYGkN/cKAo2W6D1U22uottaQ9boZyO8kqjfj6HWLQi5vCtHIekWGS1kKgVdutFUhOb+cWuovpjkw3kNMt9GkRBcaXfHaqTRwwrB5esltnMmN8N7Yef79gWd5qHktjU6SYuCxf6ybF/sOk/GKPNG2kdvrumZdRK5KNFJvxxkopPnLk28wVMrQ6CQJVMCEW2RpvI7VyenyYpdCqQKefxrw0LQWNFlVVu64AQhVkZK7dx6mfSaG3oG8gpm2RO+mKXLH1APJDbLIeZy7ECaGsQrbuo1C8Udzblty3yGb/zqarEPXl1yzv4FSAZ5/mlz+G7jenmva109QhhdmKPiDJMzZS29uFoTQkFoHurkVr/TCvMf53l7C4BxK71oUgquUT7CAsinD3IbU2uc8h2Qywh/8b09c6ynOin/8Tx6+bvteTARBP9nM/0cQ9BAEA2haHZa1g1j8VyvaTzZfond4gtF0nnzRZW1XE3VVMfqGJog6JsnY7IGU+vZaPv1PH6eYdxECAj9k4NwQXsmnY80VhEctJKOxwCjrDPBCn/O5QfaOn6SnMIyvAuK6w/rkEtYmO4npzgLIhmQxy2PG3Rzd+VEmvDyBCrkl1UFENxkuZYjrDrHZBF2U/5OMxvWCUgskcRIQBGGeUtBPqNxJl3KDiNFFqEqU/F4ixnIgIO+dxtTq8MIxlPIJlYsmHZTy0EQMCPGCUbKlQwihYWoNGFoKP8zg+oMEqoAUDrbeiCajFLxyJjlUJUJVImJ0IcXc9/jC5v4AdY0N9wDRhMMdj27k7ecPcHj3Gf70X3+T+5/aTveJAaCsSnXqYA89pwY5vu8cr3xnD2eO9NK2tIGtO1ZT0zj/TELCXEK1vZbO+KOAQAqdiNFIf+4tpNBpityFXthL2jsNKPywgKklaY58hJ7cq0y4p6i211BlrWbCPcVY4QhtsY9i64uTbbopRONIup+vnd3NqcwQhcCl4HuMlMqR8ed6D/HO0Gkc3cDRTGK6zf9z689OmenpUmNNqpnfXf0Af33qLU5lhvgPB5/FDXykEER1i3o7wZNNq/lkx2aaIqlZz2N5ooFHWtbjq5Cj6X727D9HqMo19Y5u8Jur7r8q0QjDcbL5r1IsvoZj34dlbcPQO9G0RqRIXBfjMIVChXmK7k6yub/G84/Oub2hL51c5F9e81cIRkmZSzBlklIwykjxENXWynkFLXStjYj9CKXSu3N6akBILv9NpIgSj35m8jwWRsSU8vD8E2RyXyGb//qC9vETTEfWPc3p9N+xuf7f3uxTmRFSa8Sw7sArvcJ8U9oqHMd3d6EbmxCLkJpX4Si+dwBVkYGYxLDvQyySL8GPO5TKIYRFIvG/UnLfwtDX4AeVSVdnckV+tPMYz+08Sv/wBP0jGf7trz3GPZuX8swbh6ivivHxe9bPOj6fLnDq/XMM945NOfi2rWxizW0z9RksMLq7SImlU9k+vtn9OvmgRKNdTUSzGCml+crZl3ioaSv3N2zG0iqba8trtsU5wXE3xzfO7+TF/gOMu3nc0OM/b/k5mpwqvtezh+XxRnY0rJ1ltOJa1ZV+gtkgYEFR6vL3oej3cn7iTzC0WgSCot9NV/U/ww2G6Un/Fatq/yOBKtI98WfUxZ5grPA6oCgF/VhaEwqPlH07QVgk5x5F4eMHaRL2FmqcHUwU95Au7UEREKoSdZEHiVsbGch+k/Ii2sIP0zTHP4Olz119sTDCIBelQkHTJO3LG/mZ33qQv/2j59j72lHee/0YulleEx7ff44//pdfIztRIJcuIDVJU3stj//8R1i1uRM5ky78DBBCENEb6Yg/RHf2JUBg6VUsMz9J0lzKWOko/fk3EUJSZ28CBDmvh57cq4DA1muotsvfQ11a6MLClHFsrXqq0uVacVOIhiE0UkaE5jlIwAVoYvof3dJ0bqvrYkmsltcHj3MsPUDaK4LKUGVMsL1uDdvrbiM+h/wtlBtrPtW5lc5YLbtHztBbmMAPA2zNpM6OsaFqfil7pVw8/yhe9igy9xVM8xZs61YMYxW61owm65CyCiHsa+oBKaegi/j+OUrubrK5r1Byd11llIZt3Y6hd017Z6x0hJI/RsLsYLCwh2IwMu8vppQRbGsbtv0R8oVnmOuhoFSeTO7LhGGaaORJTPMWpIjPOyVX7kMZmSyX+ga5wjP8pElwYXCDNFnvDKHygYAqa8OkgluR0eJ+QuVhyDhJazl+WCTjnSIIi0ih4eiNWFo1BX+QUjA62QgriEymWIv+MMVgCD8sECoXR28kYrTgBRPk/XIEzNQSxIzOeWXOLkDKOLqxHqm1EgZn5j3OK72O6TxRdgq+pt4rRRCcw7/qd+1ySNmMbmxe9P6kH18YCBkvLzHDDH5wpqwAUwHePnCW77x6gM6WGnZsXsafffuiWISUgmffOjIn0eg5McCzf/EKTV31WJPyuNWNqVm21pnJ5X1uLDSaPB2vDu2nyozzj1oeosGumjIefXlwH9/ueZM7atdWTDQWE8/3vc/3enZzd91q6u0kf3XqZaCc08l6Rd4YOjo70RDaomV+foIrICSwkAXkxQxeoHK0xX6JiLGcI8O/T9HvmTanXygwlcImZq6mFPQh0DFkisJkGU/UXEV76lcZzv2QvHeavH6adGkPUlik7G0M539Ezj05mSUBQ0tSH30CY17NyRcEBSqEMBatvNGJWtz+0AYsx+RHf/cO3ScHGBvK4GmSfKZIIVvCckwa2mpo6apjxye2ctuD64glK3tmSKHRFLmDpsgdl72etJZyi/Vb07avdW6h1rnlstfKa4Me8n4/KWvFomUz4CYRjVuq27il+tr8BTQhaY6k+FRnWW5QKcVY8Q3OT/wpddGGq5KMCzCkzp31y7izfm7Zw/kiVOMUS69QLL2CFNUYxgpMYw2GsQxda0bKKqRMlhfaMooUDmBMIyBleUKfUOVRYZZQTRAEg3j+GYqlVykW3yJU41f/fHontn0Xmja9Qbkj9hBnMs8yUNhFRK9nVepptApManS9g2jkSTzvEN5V3DeVypPNfxXXO0zEeRTL3IiutSK1mrI+tyinZZlsyleqONnkPowfnKfk7iZfeA7fPzFt31LWIoRNEPRwzdJ/i4Ry746HUt5kzac/9bPvn5tHWYCPH/Th+WcQ6JNZIGPSUMuYfK2ymlmFoi/3Inm/GykslPJJmGV974I/yGjxPdwwjRuMsa7mnxAol/HSAdxgHC/M4uiNtMUeZSD/KuOlQ8TMLgp+H9XWepqjDzFYeJusdwYpdEYKu2mJPYwhE/TlXsYNRwFBKRhlafIzRPSWCmo/BVJrxbBuo5Q/M+/P6096amj60mtyqFfKJfBO4PtHKhpn2B9B0+p/7OW1FwtSpjDNreX5UcYJ/LMY5saK9vHaeydZ1lbLFz5xB3VVMb7yw91T7y1pquZbL89d+hYEAQ2ddTz9v37i6j0tQi/3M1Q45SxWSdBoKcO61BJSZmzq+SGFZH1yCX95+ocEC+gfWbAq7gz4Xs9u7m1Yy2+seIgJrzBFNEypU2cneGd4+lx+EToLW55o6Oati6ac9UGDpi+dNM1bOMrKZ5XvQ6kLPUkCU6ufKlvSRbSsYiVswkk1K6UC/DA9dTxNlNc6UlggJIoAKSwkZQPA8vlIApUjCLMUw+5ykaFMYhstUyVQltYyTdRmjjNeUC9KmeAuXiVKmWysZ+WmDg7tPMXpwz2M9E/glnx0XZKsidO+opG127to6qhD02/O88IN04yVjmBqSaqsVRhyIYpdM+PHJmQQqiIF7wx57xRhOLOj4o1GqEYpuW9Tct8Gyo6wut46meWoR9NqkTKJENHyzS10yiw8QOGjVJEgHCEMhsvGdN5x/OA883UGlSJJxHkcy9x8WQnXYGEvBb9cAiKEZLR0mJS5lL78Tlpj96DNUxVFCBPb3EY08hTp7J8QXtVtVOF6+3C99zH05ZjmOgy9C03Wl702hF7+bMolCMfw/T48/ySed5AgHJz5M8oUUedjgCCT+yuuJ9FQKiyTn6AXNUkiUO7kz+4lr5XlhpUqoigShoXyz6qAUgWCcIgwnLuxOQzz5PLfpOTunpzQncmM2MV/UtiTpMO4hIiYl5ESKWJoehNSOARhkf78S6ys+lWS5sqLC2CvHBHpSj6NF2Z4f/j/pOAPYuu1xI2lYMCEe4S0exTFIyh8YkYny5Ofpyf3PEV/AC/M4IdZDJkgYS4lDD2S1mp8lWWkuItaZxuO3sS5zLfJemdw9MYpeeH5QGoN6OZWSoXvwry19UvlpnBzG0JrvPrmsyAMBvG93aDy8x8kHAzrLoS8NlnAf0jQtDo07X6UCnGcpwjDUTStrqJ9TGSLbFzRim3p05JY5Yj/3PODpkvSoxne/M4uoskIQkBdaw0ty6bfP+XFz0IyGpWa/M2MejvF+fwgZ3L9tDi1aEIj5xfZOXqUBrsKXUj6CicpBjlKYQFFSKuzAkdLMFA6Q86fQBcGVWYjVeaFQFTIYijuAIy6WZbFm2YsQxEI1BxztUBbWH29MIgk/mDR3Nc/aBAyvgjyyNoCyUppsuJBm3HuNrUaFAGjhVdQKiCYmi8FMwbEVEDR72Ws8Bp59ziGTBIxuohZ6/DDCRx9CaCIGkvRFpQVVkBhAeMMFlvGV0pJbWOKjzyxmY88sbkcTA0VCPGBEemIGa3EkpUJb8wXPzZEwwtHybpHme8i/MYjJAyHcN0hXPZe8Z6cWhxeSK0rXK5l0SxEBMe+n6jzOPoV2Yys10vGO1/eDkljZDv5YJB8MESLuqsipQZNqyXqfIwg6Cab/9ZV1a/KCMulZlO9JRciLAZMLtrnU58rRATHuodY9FMUiq/Ma8y1waPkvUcm+5eTxOHSfwXCS35fiHzg5ShdQlJng4YQ1nQCgo2QNggbQ+8iEft5pN5VjkghrkhxKwQCR29CCn0yiuQQqBzp0hh9uRdIWCvJ+30EoQtKoYkomhYpN2oLY5KwhJhakrR7gpzXTY2zhajeSjEYIlBF8n4vgXKptjZgabVUWgcuhIWmL0c3VldUwuSV3iCM/CxCLiyzoJQiDM7jue9WNE7XV6HpK66bWeWPM4SQaFoVSuXw/VOYFWQ16qtjnO0fZXQid5kzuOsFvHe8h6Utcy9AI/EITtRm/+tHSFSXMwWrtoUzEw1MBFqFs7RaNBO6W2tW88P+d/lOz1vUWkl0oZH28gyWxniwYQuWZvDS4DO0OMvJ+COcyO7lEy2/QyHI8f74qySMGgLlM1A8y8aq+3C0GOAvyGNhJrQ41RwYP8fddRclihWQ9guczg7SFpnjbyHk5HNobmPYaVAemtaAlI2zlkvm/AKH0iewpcX61Ir57/vHBUJfkL+ECrOgPHRZRdLegi7LSnpJexuW1oilNVAbeYCCdxpNxmiIPYGtNaKsdZMN3VZZIhcdXSbQhIOUFgXvHIZWQ9LejKnVkrJvJV3cS8E7TbmHoAVTg7i1AUtrmj8BVQHhZFalEpSfo9c3IyaEQGgfDIJxI7BoRCNUPm4wQM49gRsMEaoCINGEg64lsbVmbKMVTVxM814KpYKpjIQbjpQVCoSNqTUSt9ZO01pWKsQLx8m5h3GDUfLecdKlXQRhgfHSuzBxRV+H3kTC2oylVxYhuzEIJw33FifSJUQMx76HeOzzGMbKae93JWZ3Y10IdH0JsejPEYY58sXn50k2LkU4uTiffyZKCBvbuo147OfQ9SWE6vsVHrNyKOXj+2coFJ+77seaHwKUyqPmiLQHeg+xyJMA6DJK0lrFcGEnGfcUAA2RO8oeIFdEqBQhXpjBC3OYMoklqwhkOTpULnC7otSPgCAsAgJDxvHDHG4whq3VUm3fgiFTmFoCpUIiRsuCtPo1rQPd3D4pMTs/UhkGZ/G9A2j6clhAVEypHL53hLBC4zjDugcpZzYiXCiKBZdj+89TLLis2dxJLHF1B+fuU0McP3CebTtWE41fW4/Y9YJSBVx3uoKc5x1ChdmKiMbdG5fy5e+/y9+/8B6rOhvIlzwOnOyjdzjNuwfP8fmPbZ9zfF1rNR/7wkVpZ7fk40RnKbsT0Yojwwp11WzmfLEq0YYUgl2jx+gvjOKrkLjh8EDjFrZVr8SUGqUwR8yowtIiFIM8VWYjh9NvYQiL22s/zkiplz1jzzPq9tHiLC/Lei4S0fhYy2a+dPpVksbr1FpxPBWwc/gEpdCnuzDC55fcM/cOhFP+V9HzJJjMJM8eykh7WZ7pfZUaK7WoRCNUIX2FIRqdulnNEpVSlEKXtJel3r45WReBiVxAplWpLAoPS6+nTn906vX62Memfm6MPTltXGQONcOkvXnaa7bejB1rnvZ6TeTeCs84RC3gu1bOGl19bp0PlFKUih6D3aNMjGQp5l3CIJhXieKyDW3Uztof9uHCohCNICyScfczmP0u6dI+SkEvQZgHUa7NM7VqHKOTptinqHLuQlxRmhOEOUYLrzKc/xGZ0gFKwQChKqFJB1trI+XcRkP0CeLWxcYxRUDeO865iT+m5A/gBiMEKguEjOZfYbzw5mXHSNq3YunNH1CisXiQso6I/VFi0c9gmbdcF9WrKyGExDRWk4j/MkLo5IvPE4bj1/F4UWzrThKxX8QybyMIBytuGv2HCCEk7fGPM1zYhRdmgBBFiK3X0xApuz5LYVHv3ImtNWLJOorBIH6YJaI3k7RWoUlnqq8DIGZ0YGlVuEFmspE8RqCKjJUOEIQFGqM7aIrcz7h7EDeYnPSVWpCwjZBV5aZwWU8Y9s9zVIhXegnD3oHGAohGOIDvvk0l5SRC1qKbmxfdpK+QK/HmcwcYG8rQuqRuXkTjxMEevvHFV1mxvo3odfLpuFaE4Rj53FfK9eeXIAjOlhv5K8C2Ne2MjOd4ec8JDpzsRynFzgNnicds7tu6nI9snFvC2XJMGjouPiPOHelluGeM2pbp51FekFTa+6MWtPiZCVJIVsbbqLWS9BZGcEOfOitJs1Mz5f8U06sYdweJ6kk2Vz1AOaegpiTfxeT/wknfgHL550LKTabj3sZ1DBQneGv4OMXARROSVwcPU2PFebBpA5ur5/bWkSKGlHHCoLLAVRiOIrV2brSL9Zib5gf9r/HZjsfRZjFL9FXAqVw3x9Jn+ETr/Tf0/KYgzMmSznLIaL5QYXrRSOiNgCJAhWMVjxMisUBH8csRBCED50d4/Zn3OPH+eUYHJijkSgTe/IjGL/2LT/yEaFyEohT0cH7iz5govkvEWE599Al0GZt0fxyn6PdQ8vvwZvijh8pnKP9Duif+nKLfQ8LaSJVzF7qM4IUTTBTfpTf9JUp+D11Vv49jdEyOFBiyhmqnHBUp+f0M51/AD8dI2beStLdddhxbb8aaoSH6WiGEha41IEQcpTKLvv95nwc2hrGaqPMIjvMIhrH0hprICaFjGhtIxH8NqdWTzz8zKU25uD0TUtYScR4iHvk0prkJISRKlQgrqZ//B4yI3kR7/PHLXjNkgkisLOOsS4fm2Een3us0npq2jyp73dTPCbMsopBxT+OrIlIY6DKCKRPoMoJAEjM7iJkd0/ZTKYTQ0PRl6OZG3OKz8x7nue8S+r1I2VgR8VYqJPC7y/0ZFUA3t6LpHYtmUHkt6FjRwCOfvo1Y6oOtfCVlAsu67bLXPC+BUpWVPjiWwWN3rWFZex1nekfJFkqYukZLfZJbljdjmzP/TfLpAoPnh6lqSHFy/0VJ3eN7zuDELFZtn05QhIxDpURDhahwuLIxsyDj5dk9epz9E6fI+gWUUmhC0h6t//+z997xcWX3eff33Dodg94LAZBg730LtxetVlp1WbIty3YkO65xkjfF8Rs78ZvEceI4ThwrcixFVq+r1WpX2/uyLnsnSAAkegemz9xy3j/uECSWaEOCu1x5n8+HH5LALefemXvPeX7ledhVvpYKXxE5N4MrPV+AwWwXEb2UCrORvvR5joy/SNZN41ODlBh5GXeZvY6M9MwIaiafXXIHq6MN9KbHyDoWQc2kKVjB8kgNhjr38+ERuQjQX9B5XWcAdK+XYPaDL5rK8BTOxDs5NnEWu+EDs26TdjIcmzhHX3pwkc++cAihegaiwl9Q35krJ/IZdDlrZlS6abDPIPRV19UHIt0E4HgLfaFNNZ8jBNIZAKVogdkGT3HKnaW3c3Yo+ef6xoMyyckUz3xzNz/92utk0jkCIR+hogCari5IBLFQYQYpJY4jGR2JMzGeJJ3KYdsOrjtXN9S1aG6toLx8cYNkNzwTSumSsXuZyOzFp9VRG/klSvx3oipBkDaWO0nW7sNyJwkarddIoCVyp+mPf4+U1Ull6DGqQh8jYHjNP7YbYzLzFhfG/gOj6Vfw6w00F/9zLhuSBI1WgvmFTix7jHjuONLOEPVto67o8zd6aQuCooQJ+B5EoJPJHSCXO4HjDvBOqR8J4UfXluEzd+L37cI0NlzjAP5OYSqzEfp1dG0pqfTPyOb2e06/N3psTAxjHQH/IwR896HrV+R6vcbr94nGuwm/Vk2Jbx0ZexAQFJltRM2VaIuoXAGgqvVo+gZymZdZaKmhdEexcgdQ9eUFNVJKGcexT+E6hUxWGrqxE6EsflDjerCkrZolbXN7Ab3bUJRizMAnrynzVJRy3AIX5dmcp+SypqWaNS3Tr1tKSf9ojJqya42wspkcw71jWFmb13+0n7pl3r79nUPUtMz8WXoS3YUuSFwcp7CF82w4MHaW14aOU+6L0BauQxUqCTvNqcmLTOaS3FVZT0QvJaRHAehKniCqV1Ljb2V5ZDsxa4SIVkKZWU9Q8+6JlBmkuzgBs4vJYeoCpWwtbUGIwlUdhYhcV1bQdfqYr7RSADnX5tjEWdrjF7GlQ7W/gpWRZsrMK2VFOdeiK9nLuXgXcSuJX/WxLNxEc6gOXz5rcWziLB3JHg6Nn2IsF+O7l36GruioQmFt0TLWRJeRdrIcGDtOZ6KXwxOnsF2Hv+/6CQBRPcz66HIagt53znJtLqb6OBfrYtKK41NNWkONtITqCWhXvm9vDh/Cr5nU+qs4FbvAQGYEgWBpqJGNxSvmKJMUCBHwnq9CfGpkJp8hsJiSx5UO0h0C4UeIIkAiraMIrRmE4fUFukOAhlCingCAG0coofwxcvkMmvDIjzsOSFADntCKfcbbTmtCWicRWiNSKQERQZDzSq6lA0rJVcqVeSsAN1Zw9lCIYJ7g3ng2LDaR4pUnDiKBLfesYvNdKwhFLxON+ZnGkhXXlo/NBtdxOXO6j4P7O+jtHmNiIkU6ncO5DqLxuV/bdesRjSsfrJd6VYSJpgS9aJ7QMBUfpjb7pDuaeomUdQG/3kh1+JOEjdVTH7KhllIWuI9Y9iA9sa8xknqR2sivYKqLW/t8IxDCxDBWomlN+Oy7sKwz5OxzWNY5bLsT2+lbtOa/KzDRtUZ0fTmGvhrTWIOhr0K9jibbmwFNrSIU+DCGvopsdg+Z7D6y1iEcZ4hCm/WFCGMa6/CZ2/GZOzCM9Shvq7X3iMbipPvfx/VBU3yU+tbf9PMIJYiqr0TVluAUIDdrZV/ADHwYKIBouCPYuQMUEjRQtSVo+kqECDI2FOPNZ4+zZEUNZw5fRErJ3R/ayMmDnXSdHaB5RQ23P7gGBCTjGU4d7OLS+UHik2lUVVBVX8rqrUuoaSib9Xy5rMXhN9vpvjDExtuX0bi0ClVT2PvCSc4cvUQu65V8ffof30vkqqyGbTm88uRhfAGDsuooR/ecJxXPEC0LsXxDI21r66cMozKpHBdO9XL68EUmx5M49pVF3IoNDazfuZRwgbrvV0MI/xTJkDKH644gZQ5FKZ5RlnsuvHjgHGuX1lBTVnSNmsvJzgEef/kYf/RrD16zXzDip3lNA7HRBLWtVWx5YO3Uz1135s9fKKXX4ZHi5BfCN44j4xdoDFbywZptlJpFKEJguw6Hx8/zv87/hG2lZWSdNLabQyIxlQA+NYiuGDQFV894TE/MYnFKu/7u/MvU+ItZU1zPyqJ6io3Cgg5CiSJE4b0Ejt3JfETDkS7dqQFeHX4LVSgkrBTHJ88xmBnhgarbKNJD2K7DkfEzvDq8H1u6hLQACSvJydh57q/cwbrockzVICdtcq5F0s7gShdbOggpkFLBzb87vN4Mi6ybI+3k0ISCnfdTcaQztZ0jHU5MtvPS0F6yjkVYC5J00pyKXWBX+RY2lazEr3pk48DYCWzpUKSHcXFxpUvKzhDS/MCKee5tCEWtLYxoAK47gJTpK83SMgVOP9I+D/4P5zMBKpel3bFOeRk8GQPjDqTT5akGCh9oy8DuQLqjCK0N1CC4I54/k1qOkDbSOgEiilBrwB1H2g64xxDmTuRlA1URQmgtSG3ZVQt4B9fpnfd7cO19Kb+u79yM98pxScbSREtDPPSZHWx/YM1N6ZGTUnLmdB9f/fIrnD7RQzZ7Y6pxscnFD9reMNEQQmCq1RT5NhPLHqE39jXSVhdFvo0EjWVoc0QkHDdFIncK241TFngAU62ZgUkKinzb6Yn9XyxnnFTuAqb/1iEaHgSKEpxa8PtlDMfuxXb6cZxBHHcQ2xnEdYdx3HFcN4brxr0IgUzntacdvIdCQl7aT+DLe22EUdUyVKUcVa1F0+rR1Fo0rQFNrUVZ5KjxYkAIH2ZevtbnuwPLOodln8eyzmM7PTjuEK47iXTTeYUtT0FJESFUtQJVrUbXWtH1pRhaK7rehqLMskiUl5WqbvY1GfjM7ZRE/8NNP9dsSKZynGjvo6N7hLrKYtYur6EoPHsqWVGK0dSFedY4jsuFSyOksxbrltcu1pABeOKFY9y2qZmy4tANHknky6c2FEQ0HOsErt2JolQuqKRJShfXGcTKvV0hbm5oxnYUtR4hBJPjSZ774QFaV9VimDoHXz/HQPcYUkrSiSwHXjlNy8oaqhtKSSYyvPLTI2iqguHXScYyHN59np7OIT7+j+4iEr32Gc9lLfa/fJoXfnSQhtYKjKskXYMRP+FogCO7z3Ns3wU+9Eu3TSMajuOy5/kTjA3HqaovIRDyIV3JhVN9nD50kU984W6WrqnDth1OHeri2e/vxx80Ka2I0H6uh3PHulmztZktu9pQ1cXRfXfdCTKZF/NeOSqKCGGad6IbKxd8jLdOX+LMxSE+8+AmKvOqUVLCm8c6+O7zh/HpM3/2hs+gtNrAH/Jxx0e3Upknd76gj2x65syZ599TqNSoi+v0I2XmhhXJNEWlwldEUPOj5D94TVGp8hfjVw2q/S2E9MvlK5Iys44yc+7nWsokrrM4pV1V/iKOT1zirbHz1AXKWFlUy9poIy3hKnRl/oixolSgXEdQ0bHPeKaIc6zpbNfBljbrom20BOvJujleGz7IW2MnaQzUsLV0DX3pIV4dPoBA8HDV7VT6ypiw4vyg+zleHtpPXaCKGn8FK8MtLAs1MpadJGEl+VDN3QTzjtWm4t1/n2qwvXQdzcE6BjLDGIrBx+se8K5TKFPbDWZGeX34IFnH4sGq26j2VxC3EjzR+xKvDh+g1l/BktAVCdJz8YvcWbGZLSWriOoRMk4WXZn//SZEGFVrxM7tnnfb6fe2Kx84zWfAnG6k24fM7UX4HnmbYqXrZUDs855wgsyA3YHQ2pBOD0IUId0JQAOlGFDznlPp/OdnAiZCKZ0qZRJKMdIZAncSaXd6REdfjnQuIrSlV04tLWy7vaBrA09GfbGkkf1Bk5Wbl3CpfRAra980IQ7Xlfz08YMcOdgFQDjiY0lLJcXFAQxTW7DD+GXU1hXWF7cQLEpGw6fVUh/5dfoT32Mys5+UdYHR9EsE9GbC5jqivq34tYZrSITlTORNXVzi2WN0jP8nz9BlGiS5qRefQ66gMoZ3HkIoqCKKakQxWIWULlKmcN1JXBnDdZP5OsfMlPcCeZfmKwkukdcR1/NSa36EEvYIh1KCokTfkSbv68XFZD+dyV4mcnECmo+0k6XO38ry8A7aswfoSJwgbY9QYfjZUrKUnlQfY1acTdG1aEqAt8a7KdXqWRnehqKWzOu3IBdR+30uCKFj6Csw9LmjRTcTI+MjXOw7R01FmOaGckqixQT9129GdzWEgHDQxJyljv1GsOdoJ2vaahaBaOQ9NfQ15MQzSLmwsjwpU+Syr6Pq6xa0OJQygW2dKKieXogIurEJ5SrvBytrU1Qc4q5H1zM6GGPviyf5gz/7FMVlYf7FL36JjtN9VDeUEi4KcO9jGykuCxMM+4hPpnnxxwc5uucCO+5dRWTjFaIhFIFlOex98RQv/+QwS5ZXc//HNlNVXzI1qaza1MSyNXUg4ezRSzOO13Fc+i+NsvOB1Wy/dxWarnLyrU5+9JXXOLLnPEvX1JGYTHNs3wVSiQyP/cod1C0p58yRi/zgb1+lcWkVy9Y2EAgtTpO564xg5Q5i+u5BYGLZp8nl9hdENO7d2sZ3njvE9144zGcf2kQk6OOZPaf50cvHaKwu5lP3bZhzf3/Ih/+q6ymfY9IVit9bBGFSiGKglAlcuxtVXzr/xm/DUGaC8ZxX2lRuFnFisgtXShoCFahCIWan2Dt6mi0lbRTpRVT7C8sISTeO6w4XPK6Z8KnGnVwqG6EzMcyJyUu8NHCCV4dO0xyqZF20gTXRBir90Vn3F0oRilpFoffXsc/lS2pmVrgEL0BaaZayrWQtpmogkUxaCU7HLtCV6mVr6Rq6kr0MZka5t3I7q4uWoikaVf4yVkaaeWFwL2O5Sap95VPlTKaiowhBWA8S0qZnuhShENYCpLQ0mtAwFI2Ifu27sCc1QE96kB2l61gbbUNXNKS/jBXxFp4ZeIPh7DiNwdopYikE7Chdx5Jg7VSD/0IglAiqWnjfnGNf8Poo8ssP6VwCZzjvDSPBHUI6F3GtEwh9q1c+5Q4i9I0eCRGmtw8uCNPLCCpRhBLxBF2cEaQ74GUw1GZve3cIcbkqRKkEddxbK+VN/lCqEO50s16JhWOdLvj6FLUKRZ09g1wIikpCPPbrd/OTr77Gs9/ZQ2w8SWNbNZGSIIapz+ufUVQawheYf26XUnLwgKciWVIa4lf+0S4al5QTDJoLLtO6GtHixe/nW5QVhaoEiPq2YmpVxHPHmcwcYDJ7kKHkacbTexg1XqI88CClgbunWce7XI7kQ9I6R9I6N8dZBEjFW5i/hyCEghAhFCUELG6U+FbFSHaCwcwYlmszYcXxqz4upQao8ZdTGdyIobdiuTbPD+xjU9XDVBvwUtfT7Kh+FFM12Hfx7/nF4i35UrCFQM5p/vTzAMt26OoZ5ZnXT9HecoNWmgABAABJREFUNcyKlirKSkK06uVc6hvj0KluxidTVJSG2bSqgaryCG8cvEAma5NMZxmfTHHfzuXUVBRx+sIAR8/0kM051FdHuXubJ/G471gXpy8MsLy5isa8ys54LMWew52Mx1LE4mlKoyE2rWlgbDzJhe4RbNtBKILNqxpYtqRy3ia30fEkL+w5w52bW6ksi3DkdA/HzvV6tcWN5Wxa3cDxc15pyebVDYxNJjnZ3k9dVZQldd73QQgDVVuBqi/Hzu1Z+D3MvIA/+CtAiPlKDKU7jpXdQ0FlU/oqVG3pNRrsLStrqGkqp76lgu4LgyxbW48/aOIPmEyOJxFCEAiabLrjSo9CWbXL6s1LOLK7nbGRt9XMS8nh189xbN8FWlfWcu9HN1FRUzxt4lJUBdNvYPiuNa67Gr6AwQMf30JRibfosXI2RSVBBns8881MKsv4SJxIcZD6lnJCkQDVjWVEigNkM14N8GJB4iJEBJ/vHkBAVsEqcLGwZUUDUkq+89whvvPcIYQQvHG0g60rG3jsrrU0Vs1dFtHfOUzXyW52ProJgM4T3SQnU6y5ffkMWwsUtQqhhJBuAdLk0sK2z10X0dg3epo3hk+AgJxjM5gdpz3eS0QPoiDIuDkGM+M0BSuxZWGfjRcUiy0a0Sg1w5QYIVZHG9hS2kJPapSOxCDn4v082XuIYxOX+IMVH5x1fyE0FKUKRSnBdRfe1yLdSWz7DLpxG7M955pQCekBTNV7VgWCoObHp5okrBSudInZCcZyk7wydIDTsQtT+/alhxnNTZByMri4qIuobhW3U4xmJ9g9cpjOZM/UzwcyI4xmx0k6aVzpouQDjaVGlLAWLIhkgNeLoGgNgI9CZOUduyMv0OAJCAttFWgtoK/KZx1chO+hPAG3wbmEMO/xei+khTA2gxvzGtHVKhDhK1kQoSP0FQjZAEqJd3xjPbgpwEAYW0EpRehrPKly8w5vHEoJyDKmmWfKHLZ9oqB7AqAo1fmx3zgMn87SdQ2s2NTE0994k+725ympjOALmGiaOm+V+yd/+37W7ljYO8J1JYoiaGgq494H12DchGDhjWDRRqMoJkFjKX69gSJzCxm7m0TuNKOpl5nM7CdjX0QIhbLAQ6iKFwFQhS9v9w4VwQ9R4r8NZQ5FASE0QkbbrL9/H7cOivQgftXHpJWgzIwykBkl5WTzJGQUBcGlVD9Z16LOX0GpGeVSaoCg5ieiBan2L05U4ecFihCUFAVorCllIpZmeXMldZVRkqksR8/2kslYrF1ex6nz/Zxo7yMcMunqHSWVzrG2rZb66mIiIZNYIs1Le8+yYWU9AZ/BT18+QWtjOQ3VJVSVRTjZ3k9Xzyi3bWxGSsnIWIKjZ3p56I4VHDvbR8/gBDs2LOFM5yCj40l2bWnl8OkeznQMUl9dTMA/s9KIqigMjsbZfaiD2qoowYDJRDzFs2+c4oHbV2JZNq/sa6ehphhNVXj1QDsbV9UzOp7k+Nk+VrRMN0xT9VY0fS12bh8LrcN17A5s6zS6UpY3yJwZngP8ELZ1rbfD7BDoxjaUGcrUfAEDRRFoukow7J/KOqiaiuvIKa31o3vOc/ZYN+NDMTJpi5GBCRzbndYTAdB+opeL5710/O0Pr6W8Knpd7rJCERSVhKZIBngERTd1rJyXIQyG/ZTnezjOHu1m6ep6LpzsZWI0yarNS/AFFtPYSuA43cRif4bAh213gRAk4l9GN9ZgmjvmPYKhq2xd2QASvvXsQTp6R/nkfev5wG0rqSwJzxvdi48luHi6l52PbvKaxzuGGOkbn4VogKo2IEQEyeiCr1KSw7GOg79wP6PVRU2UGAtr1PQpBX42MuX1jyxiv5sQAl2o1AVKqPBFEAjOJwY5H+8n48xf7qpqdShqTUFEAyRWdje6sZ3ZnNslEkdOf65cKXGlJ/17WfbXVA3KzCg1V5Vr1/gr2Fyyihpf+aIrOypCYKo6JTOcc2PxShqDNdO+w7qiXVdJjhAqilKFqjUVVIIq3TEcuwNNX+VVWmj1XHFVEl6TtrHZ21ZmEWqDV9aslIBSjFACoNYwtcpWr2QPhTBAmy55LNQ6UPLKU9rlrG7+b+VqUYcrAQQpHRynG9fuoRAIpRhVq18UaVvw5Mj3v3CC1396mNhYkmw6x3DfwoVx7vvktgVtJ4Rg7YZGXn/5DOlUrnC5qncAi057FGHi1+vxabWEzTVEzI30xr7GSOo5xjP7iJgb8SsNAOhqCbpamneKDFHsvx3jOtNWlx94TyX81rvRc2G4Z4xv/penGB+eXcrRMHXW37GcRz5/53WfZ3xwkh//7ct0ne6dc7tl65v4zD/7wA3VFCpCRRMquqKhChUkDKRHaU9cYnm4iSIjhDKgeFJ5CHaVb2L3yBH8mo9dFZsKjtD8vENVFUqLQyypL6V/eJLlLZVUlxdxsW+MeCLDkroyNq6sJ5ZIMzQaZzLuRakqyyKsaK2iKOQR+NMXBjh9YRDHkfh9OuOTSUbGEzTWlFBXFaWmvIiJ+JWFhq6rSCk5eqYXx3FZv6KOUMDE0FXqq6OsX1nH0FiCiXiaTM6alWg4jsuPnz9KbUWUD9y1inDQ5NjZXk6dH6A4EsR1XSbiaWKJDDWVRQT9JucvDjMRS1FWEqKkaPrLX4giNH1VvqGxe4F30SKXfQnN2DIP0Uhh544h3bEFHhcUtRZNX42YNvnlx6ooeTlNgfL2fgYJ2bTFz76zlzefPc7KTU0sW9eA32/Qebaf3S+cvOZ4hk9j2z0r6bkwxJ7nT1LfXE7r6rqCn1eBwDBnngIuv0EDYR9b71pOb+cwX/3zpymtKEKogtVblrDpjjb0RYycKUoxpnkHEhuBhqKWIjDwnIxnTuf3jUwSS1wbkS2JBLh781L6R2NoqsrIRJKJeBpFESxruLbuP5PMcmrPOZ7/1ht0n+mj78Ig0pU4tsPmfGP4TFC1FhSlGNfpXPiFyhzWlOlkYe+5JaFqloRujoqY607i2IsrSS6lpC89zr7R8xwc7aAvPU5AM3ioZj3bSudXolK1FlStqWCJaSv7AoR/B2+Jc+1zYUuH8VyMmJUgooeQUjJhxUg5aYoNj5CWmcVE9TAtoXrurtjqzWNXwa/6phENXdHnzSIJBKpQsWbZrliPUGIU0Rio4YGq29CuOaeJsijkRqCo5aj6qoKIBjjYuSMY5i6EejkoPEt5GgZSX4eQkyCCiOvtJS14LWDlA1CF9W0qaiOKem2J//VicjTBT7/2Op2neimtinLXY5uobirDHzSvnQdmwLK1DQs6j6IIPv7p7fR2j9HbPcZ3v7mHhx9dT9kiK0fdCBbFR8P7M/3GCaGgiTARcy0TxjJG0y9iO+M4VykwKcLIN5EfYiz9GuWBh9F8UZQZmjVl/jyz1+urKMLAddM4bpzLqb33AtLJDEdeP8PgpdmjYqbfIFpWaOPhdGTSOU4f6OD47rlK1Ljykd7w7Zt+gIBmknGyHJk4Q7HhNTFefpHWByrJSYt0NkdL9cKal99rsHOHySa/5jWzXQVFX4I/8m+vKblZCExDQ0pIZ72SwmQq5y0gNe++Bv0GunrlxRkKGAR8OtvWNVFcFOCOzS3UVFy7OAYvUqIIQdBvsHppNeUlYYqLAjiOi6FpGIZX/6moAiHykuezQEq4e9syTl8Y4GT7ANvWNREO+gj4DO7c0jIV5a+tLMLQNdavqOPlveeoKguzdU3jNRF7IRSvVElfWQDRACvzIoR+B2SI2WqKpDuJlSusbErT16NqLdc1SWXSOd549jiRaIAPfnYn4ah3j5OJzIzRqar6Um5/aC3xiSSPf+V1fvadfXzs133ULincjHQ+bqKqiqe6JATrdixly642T6mqKkpx2ew18NcDRQlhmDtRlMtSq14vm6KUzPpsPPnaSV4/0nHlesjPRgpIVzI2meIHLx3huX1nAIHf1PjbP/z0NcfRTY2GlbVsvm8toaIgt3/E82Hy+Q0qGmcPfqnaknzzaCHmZw6u04NjX0R9WwT33YR0R3Hss4t2vN3D53hz+AynY71knBwtoSoeq9vM0kg1FWaE6AJUqIRSjqq1FuxT5VjnsXPH0IxtM37HBYLBzCjf736WzSWrSNhpXhk6QEgLsDzseaYsDTfSEmrg8PhpDEWnOViPRDKYHcWVLpuKV1F8VXap1l9J2sny4uBeVkZasKVLiRGh8ioHcFPRKTeLOTxxmteHD1LlK0MIhXIzSpEeZkmwjuXhZk5MthPQfCwNNSKAoew4lrRZH22j3FycZl1FqUDTV5FL/7Cg/ezcXlz3l1HmU4QTIp8deGfFaqS0yGVeKng/VVuCqt2439Nl5DIW3e2DlFQW8fHfvI8dD67BH/Kh6cqC3puFBHGWtlXxu//0YV549jjP/PQIh9/qpKomSnl5EcGwiV5Ar8bGLUtomOOddz24YaJhuwnG0q+TzJ2jyLeJgN6KoZYiULCcCWLZw0xk9uJKC0OrQlem18iWBR5gIrOX8fRuOsb/jMrQYxSZWzDUMlyyWM44GbuHWPYIplZNXeRzM1+IEsan1TKR2c9EZj9j6TcoMjcjhI4jE0jpoCmhGZrN332YAZMlK+vQdI1kLEVyMj1VurCYMHw69UsrmRiOkYilSU6myGWtRbf8aAs3YkkbRSg40kETGi2hOgKqjypfKVnXQhMqW0tXTWmWa4pKys6yPtqGsQDVjPcmLtdA91yl8W2jyAkKleG7jNJokJaGMvYdu8jhk93ousrtm1uIzuIaXVkW4Y7Nrew/1oWUXrnAFz91G9mczTee2M+ZziFyORtdV7ln2zKyOZuR8SQv7T2HpqnUVxezdW1jwSRUCFjaVEFbcyXf/9khwkGTtiUV7NrayrOvn/b6FPw6v/LR7eiaSk1FEW9kctiOpG4Wd1RVa0TT12BlX1+w8ZTr9GLljmH47p4q27waMm+oZuf2F3B1BpqxCUWtm3/TGSCEwDR1xofjDPWNE5tI0n6il1eePIJhXpt5MQyNQMikvqWCxGSan35zNy8+fpBHPruT0soI0pVksxaZZI74RBrXlYwOThIImviCRsFZiJGBGBMjCXbct4q121sKVjFZKBy7l1TqewSCn0aIMJnMywggEPzMrPtsWlFHefHCFzKaOjMRVDWVspoSNt2/hoYVtbSuv7LgmGuC9mrdmxC5YEFGd9KNYWXfuGWIxmWVNccqJLo9N/53+/P4NIMdpcvYUtZCuRmhxAjh1xYeUBFCQ9WXoah1OHYh/ToW2fSP0IytzPSyCmtBGoqrEAi+felnpO0MFb4SHqy6naag518Q1cM8XHU7u0eP8NbYSV4a2odAENaCbCpeeU1mYVPJSs4lunh1+C1eHtpPSPXz4bp7pxGNoBbgtvINDGZH+WHP8yhCYUmwloer76BIDxPWg9xXuZ2g5ufoxFleG34LgSCkBVgfXY5SYAZsTgi/l5FT63CdhZcZOXYntnUSVW1EKAsxz3vn4JmsXsC2jhS4p5m/F4uXLTR8GrXN5aSTWZpX1VJxE9Sc4EpA0HEcerpHGRmOMzQY4+zpfkxTQ9XUgkpri0uCtx7RkNImleugL/5NhpJPooqAp5aEwJUWthvHckcIm2soC9yD/rbSKFOtpKnot0FKxjN7uTj5N+jKtxDogIuLjetmcGWasuDDs47DVCuI+nYyln6NeO447aN/jKZEEChIbCLmZmrDnyVgNM96jHcLJZVFfOHff5xsxsKxHRzLJZ3McOCFE/zwr59ftPMUlYb51O8/zId+/e587bdDJp3j1L7zfP9/PEcytji1uSF9dtWCqw2HLuPYxDneGD7iGRxFCzd2eq9A1Vbgj/xbT+YPm3Tsz7GzrxV0jJaGMipLw0Qj3j3WNZW1bbU0VBeTsxwMQ6O4KICmqTx0x0p0TcW8qjzG0DXu3r6MiVgKNx8t95k6CHjozpXcs6MNKaWnZCXgTMcgW9c2sqathr7BSc50DhJPZti1dSmX311b1zRiOy5Fc6gP/dZn76S8JISha3zm0c0Uhf34TJ0P7FrNRNwjCaqqoKlXIi9CKCxbUuE1zs0AIUw0fTWq1oxjLbTxz8XKvoBh3gYzydzKNLZ1LG9MtTCo+jJUvY3rdZMNhEwe+/wd/OBvX+Fv/v0TGKZG84oatt+3knOzKEYBmD6dzbuWMzmW5KUnDhEtC3PPYxvpuzjC9770Mv2XRpkcTZBKZPjLf/19DFMnXOTn3/3drxU0vmhpECklX/uvz/DD//MqqqoQLg6w6fZl3PbgGkorZ86IFQpFrUA31pCIfwkh/KhaLaZv9nc+wNrWGlYuqZpzm6sx33QbLg4RCPsXnqkRAk1fTU4pQToFEA2ZxMq8hC/4WQotn7oZkHIS2z5VsBP7XPjC0vuo8RdTYoQI61ckeAuFqq1G1ZYWSDQgl3kGX+g30LTpc36pGeVzTR/GVHQkMGklcHEJqn6KjchUg7giFKr95TxYdRvbS9eSdSwQYAidiB66Zp4L60E+Uf8gk1Yc23VQhUq5OT2wqioKzcF6Pr/kIyRtr7E7oPooMYumzlnhK+W+yh1sLllN1s2BBEPRCOshwtoVUv3phg9gS4eofn2VDkIoqFoTmrGJXLqQfgYLK/MCurkTlVuLaIBNLvNUwX1Gqtac9z9avJ6z4vIIn/zt+/nB/3qRV584SEVdCZU3gWxIKTlxrJu//M9PMTgYm/L9yeVsctcRsM5mFl9w6YaJhqoEKfHfTtYZIJE7Tsbux3YTgERTQvi1RipDj1IauJegvuyasighFIJGG62lf8R4+g1GUy+RyJ3CcsYRQkNXi/HrDUR9OygL3DfrOIQwKPHfiRAKg4kfE8+eIGsPeJKkahmq4l/UL9FiQjc0qpumlz04tsNo/407al8NTVevYdWu65LL5NDeRZWCllA95WYxhqIT0gI3TW/63YZQAqjKVZFSpZhCUwMBn0HAN/17HPAbM/ZGzCYlGwn5iMxACurepsiTzlpEQj5e3HOW9ovD2LZDQ00JDdUl0/afy8fjMuqrrxz76vNEI/5p2Zd0xuLVA+28dfwSLQ1lLGuaS0dfoOqrUbUVBRANyGVfIsC/QODn7fdfyjhW9g0KK5vakC/vmH6s2sYy/vCvf5nSigiqqvDwp7dx16Pr8QcMhCL4k7/9PEUlITRdZd2OVupbKsikcwghCIZ8+EMmdzy8dko+NhwN8PF/dBeO41CSr7/1B03u/tAG1u9sJRD24w8Y1DdX8Lk/eGjGrKjXlO4961/4ww/hutMzaRU1xfzm//th1Hzkv+NMH8//6C1KysPsuG8Vpl/HsV26Lwzx0o8PYZg6935k06L0agjhR1HKcNw+BDqa1og6j4+CoWsYs7fbFIzuc32c3neehz9/FwAn97QTG42z44MbZ91HMzblXZZnJ4XXwlOesnOH0YxNNzTmxYDr9GNld7OY6e2tpa1o+fp6SzpkbIuAaqAtwEPjaihqJZqxHiu3B1mAIpZ0x8kmv4JW9KfTfm4oOtX+K/NtqRmd/dxCIaKHZpSifTsEghKjiBJjduItEOiKRpVv9oixIhTCepCwPnemrmoRBFMUtQ7N2Eou/SyFqE9Z2VdxrPa8l8wiPoA3ACklrjtMLlVYKRjgmcDqq1nMcnvXlaiaSvOqWt56+RQn9l2guDxCcXkY02+ganMHGB749A6WrZu/T8N1Jd//9l66L3k9hf6Azup1DTQ2lROJ+Oc9z9uxbPnCHckXihs37EMnZK5kif4HuDKDlFbe10DmvSAMVBFEUwKzGmUJoeHTaqgIPkqJ/27vODhc9pNQhI6qBFHncGEVQqApEUr991JkbsGV6fw4vGNoSgh1NsO3WxBCURbUMHSjUBQFVV2c9rLrRVDzTxkcvY9bBz5DY+vaRpY3V04ton2mTjh488oPTUNj54ZmNqyoI+A35vX0UJRyNH01VvblBXteSGcQO3cU3bxzek+FlFf1ZywMQhSj6WtRlGvrlQ2fTn3zlYVycXmY4vIr76CG1iv7mD6d6oZrZRVDV5EwTVMpq7p2EROM+AletZ1fU6lvmd/orKr+2uiaYWpU13vjyOVszh3rYbBnnE/95j0sX9eAUARIuHR+kN7OYYb7J8hmrEUhGrbdSTr1OMHgF1CUCNnMS6RS3yYYnLlc9mYgm8oxkRflkFIyNjDBSO/cogCKWoeqt2LbJ/PZyoXBdYfJpn70rhMNKXP5cpNCVNbmh66oHBnv4ltdb9AZHyKo+/jXqz5Cpa+IV4dOsaKolqXh+UtVhNDQjG2o2gvYuUKkdyXZ9E8wfI+gL0Cx7B8mDDStDU1fVVDDvZQxsqnvo+krEGrhvWE3By6Z5DcKlmcWSimavmbGd/iNYKR/gv/5r75HLmORinslrN3nB1E1JV/KNPeqa91tyxZENKSUnDruZaSiJUH++b/+IC1LqzAMDeU61nazCYTcCBbFGVxgYKg3mhISqEoAVbl+sxAhBKrwTcnnvo/38T6uH0II/D4Dv++dywQqipg14zIThFDR9HWo2lLs3ELN9Vys7Ivo5m1wlQa+JINtHS2wbGo1qt52SxtoXi80TcEfMBgdmOTwG+cwfTqGoTM6NMnu504w2DvOrg+ux79IxFNVawiG/hGa1gAoKEqpZ7z2DiAVT3Pg2aM89bcv0d85xP5njuDaLsFogAc/t2vOfYXQ0PRtWNk3cJ25Ff2mQaaxcnuwckfRjXU3eAXXD9fpI5d+elFlbQGOjF3k3x7/HiHNR0u4ikNjHeRcC8u1OTPZR29qbEFEA0DTV6AZ67za+wX2Y4GX1UjF/hOR0r+fURHuHzqEEKj6cnTzDmzrMIX0CuYyz2D6H0b33XdLVIu4TgfZ1NcpNCunaSvRjc2L/g43TJ3WNdfXtwcUJP5j+nXUZIaly6rYuLkZTb+15qOf167b9/E+Fg2OfYHk+O+iKMX4i/4YIYLkUj/Czr6M6wwilBCqvgE98DE0ff1NX3S6zhBW5lmszEuerKa0EWoVurEN3f8hVG3p7JKA0sLO7cfKvOz1Ijj9SHIIEUHVWtF996L7P4iYo99AShvHPoOVehLbOoh0BgAHRAhFrUczNqCZd6PqSxFziC9Y2d1Y6R/jWEdw3UlvDPoqDN8H0H33wgLvo6avRNOXY+cOsFCH+FzmeQKRP5x+XW6qwLIpgWas9e73zyGEEKzd3sKHxm/jtaeP8tpTR7Esm2DYR31LJb/4u/ez8Y626/LwmPl8AVS1Ass6jRB+NK2J6xVJKBS+oI9tH9hAtDzC8TfPTpVOabqGPzQ/kTJ8d5FNf8vzoVjw90fiOJfIJr+Kpv+XWTP+NxNSZnGsE1jZFxf92N/oeo22SA3/cuWHSTsWn9/zvwAwVJ0yX5hTkwsnZULoGOYD2NkD2NZbBYxCYlsnSE7+O0LRP5u5L+sfOISIoJvbsbKvFNhEnSYV/wvC+hoUtXB57cWFQ3Ly3yHdiYL2EiKCZm5D1Vct+ojKaqL887/65eve35xFKv7tUBTBfQ+u5Ttff5OJ8eQtKbb6/lP3Pt7HfJA20h3BlSms9HPYuTexs/sAC3DBETjWWezsa5ih38IIfHTOBfZ1D0M6OLnDpOP/GSd3OH/+vB67042TO0wu8zS+0O+h+x+eYQwuyYnfx868ipRpvEW5t5CT9OPa57Gyr6BnniEQ/QuEcq0Ot5QpcqknyCT+Ki/Te+UYIHDtduzsa+jWIXzhf4GqX2t0JmWW9OSfYqUfR8pU/hhyagx25iV034P4i/7NwqKQwo+qr0NRX8Z1uubfHi+Ka1tn0fR1+QlSImWSXPaNBe0Pnu66pq9GiPnrt9+LEEIQLQ3x0Ke2ce9HNnnmgkgvCqoq6IaGqi1MqnEhcJ1+ksmvYllnMYyNGMZmbPsswVBhzevXA0UR+AImLeuaKK8rpeQqpbOFXJ9QytCN23Gs80g5sfATywxWbg+59I8xAx8vfOA3CMe+RDrxt/nncHFxZrKX3257iGIjhLCuHF8TCiHNR9wqJIMi0IwN6ObtOHaB95gsuczTpOIVBCL/DBbRyfvnAZ5U+Dp08y5s6zhTc8oC4NhnScX/C8Gif48Q755vQzr+N/keo8Kg6WswzLtvCslXVYVQ0fVX6CwUQgg++ZntJBNpXnj2BF/+ny/wqV/cSekN2iEsJhbt7kopPYMjx1t0KIpnTnX5JS2lREqQrpuXhpdXeTUIhCK8Mqz8O32hk9dCjqsI4RlmXceEKPPKPJevb9o5uOo8gquu4RaklDcRl++N67hTt0VRvTrEQu+FdCW2feVFJ4RY1MXMjcC1L5JNfAmhVuCL/GG+7lfHsQ6STX4TxzpMNvl3KGo5mnnvoo5ZSoljnSId/484uUMo+jIM/6emzOcc6xjZ1PdwcgfJxP8CoYTRzLsR0zIbAlVfh2udRzU2oZt3omgtIAxc+wLZ5Fews69hZ/eSTX4DX/gfXzMOJ3eMXOprSGcYzbcLM/AZVK0ZiYPr9OPkjmBlX0Izbp9RKlBKl3Ts/yOX/h5gYwR/Ed33ARS1Gun0kMs8TS71HXKZn4Iw8Bf9ybyTgBAC3dhETl+6YKIBEivzMpq+Lj8uC9s6jnSH5tnvCjRjbV6p5N3/bt4sePdWW1RjvtngyhgSi1D4t8hlX8cjf4WZbt0oghE/jm1zam874WiQ6uYKpPT6beaCEAqG/4PkMs/g2BMFndN1+kgnv4KitV5FfG8uvHf2MJnk3y96b8ZlmKpOyslek9+xXIfJXIoivbC+PCE0TP9j2Lm3sHILDwiAJ/KQSf5fpMwSCP8/IMxb8rn11hsScHGdHoTwoagLV1W7XggRQjfvxMrtwy6gRw0kufTjqGot/tBvgyhAsW0RIKVLLvMT0om/BrIF7StEMbp5+7QmcCklcSvLxdgEg6kE9tvEMrZX1xM1b24/qeu6SFd6fboLzBZLKbnznpUIRfDED9/imaeOUllVRGVVEeGwH8PUvF7cBRzuvofWsmJV7Q1exXQs2uyRiqX52dff4Cv/7nEA7v3Udn7zP3ySQNiP67ik4mmO72ln91NHOHe4i5H+CVzHJRwNUlJdxLL1Tay/cznrbm8jEJ6/PltKb2EbG0tycv953nrxJO2HLzI2OEkmlcMfNKmoL2H5piVsfWAtS9c3Egz7psjAQuA4LlbWJjaW4NArpzi1/wJdp3oZ7hsnncgCkkDYT0lFhKaVtay9vY11t7dRWlWEqi3cIOW9DsdxefYbb/J//viHZFM5EPCR37iPT//+Q4QL0LgHOHf0In/w8H9GuhJFFbSua+S/PPlPp5Ry3l1YCLUcf+RfoZn3TP1U0ZoRag2Z2J/jWIewsq+h6msR8yjmFALpjmJlfoaTO4BqbMYf+SNUff1VY2hD0VaQif8ZdvZVrMwzKNpSVO3qZjKBGfxlzODn8aJ6V76fitqAaqwnMfILuPZZ7OwbEP7NaduA18DqWO2o+irM4K+iGbfljwyK2opm3IEZ+u2p870ddvZNrPSTIDP4o/8Rw/8Jpl5DagN+fS2KUp2/jtewc2+gm3fNe38UtQlNW4Ut9izYzyCXfRl/+PcAgZQZrMyrC9rPu7QAmrYGRW1a+D7vYx6IvCGrkxcVyV2HK/CNoef8AD/5m+cZ6Bpi7Z0rqGmuZGxwkke/cO+8+6paG7p5F67TW5C5HEgc6ySp2H8iWPTHqNqytwUIFhceyRgjk/w22dTXWHQjpTx2li/je117WBNtJKz5kEDasbiQGGTvaDsPVM/uuD4bFK0FM/BRHKengKCCBynjZFJ/j+v2EQj/y3y5z/T34LsBj1y4gI2UCezcQbKp72Fbx/GHfx9f4Bdu+hiEEGjGRkz/h3Ds9gULa3hwSSf+ColLIPTbSILvwNpHesZ86Se8kqmCnjcABd3cma8+uPKsnRwb4q8O72bfYDeGoqK+zTPob+/9yE0nGi/+4ACvPXmIj37xHjbc3jbv9o7j8okP/gXuVCAc0qkcXR3DdHUU1hgP0Lay9tYlGuCZ2F7OAKQTGRKTaQyfwbnDXXzvr57l4Esnsa3pablMKsdw3zhnD3bx1Fdf5T/+6J+walsL6iza+d55JMlYmkOvnOYH//M52o9cvGabTCrL+HCMs4e6eOprr7HhzhV87LfuZ8WWZnRDm/dBcB2Xt148yVNffZXDr56+ZtyXkU1bjA/FuHCihxe/t4+qxjI+/IV7uOfj2wgX//xKtV4NLS/htmx9I8fePAcSTh+4QMfJHtbetqyg7NSL39uHm8+K6abBjofW3iIkA0BF1VrQzDuuuSbN2IRmbsOxjuDkTuDaXSiLRjQkrtOPnXnRqyk1dqAZG67ZSjNWo+rrsLP7sHOHMJwe0KarVszVe4H0oZu3kbVP4brDSCmvuU4hTIQSQrqjuHYX6Bvz0UGFKxP2bJ+3xEo/gXTjKNpyDN+j10ojihC67wEyiS8j3THszO4FEQ1votyMkn0Rxzo+7/YAjnUCKceBEpCpgqKkmtaGaqye1o/jupJc1iKXcwCJYWgYpj4VlbJth0zaIhg08665Cz7dvLj83n0vv28UpRRFKSMe/wukO4krJwgErnXxvpmIjyUIRQN87Hcf5vzRiwWptggh8AV/CSv7Oo59isIW8A52bg+p2B8TCP9rVH3FosuGXl7Quu4QmeTfkUl8mZvZA/P55rs5MdHNF/d9mWp/lLST4y/PPEXMSrMu2sgjNbNLBs8GIQSG/zFs6xTZ1HcLX2DKDLn00zjWOfzh30U370aIy4qY72QkXgI2SAspUzh2O7nMs+QyL+YJlPRKMm8OB5wRQmgYvvuxrZNkU99hof1ul5FJ/E+kM4w/8i9RlOKb1nMkpYOUCTLJr5NJ/PV1kAwvKGAGPoqqTl9Qv97bSdgw+c7Dn2Z5cfm78j7t7RzixN7z3PvxrQvex+czpnyxbhTaTVA7vWkruEwqR2wsQXd7P9/886c4faADIUAzVLR8tF9KcBwHO+cgpaR+WRUVdSVzkwxXMjka59lvvsm3/stT5LLew6CqCrpPzx/bm/RtyyaXtbFzDgdeOEFf5zCf/WePsOPhdZgBY84v0dhwjP/6O/+X+Fhy6meqpqAZmneO/OLBzWc9LmvWD1wc4Vt//lOSkyke++K902Qnf57R0FbDqm2tnNh7HtdxuXC8m44TPazc2rLgsotMKseepw5P/T8Y8bNzDv36dxqeC3DjjAobQvi9CJlSjHT7kO7ckpiFwCsPHMOxz6OoFShKMa7TP8sYNYQSQDp9SHdm8y0pHZAZL2IsHbyaXImUWeDy4saLsMH0a1XURjRjJ1bmWTLx/45jt2P4H/WyOiIAGLNGY6W08n4XOVStFdcdRsyQfZByEkUtxbW7CvIm0IwNqFoLjnWShS2gbKzsXgzfQzh2ZwHnUlD1lWjaimk/HR6K8fi393Jofyeu63L3g2t45CMbieazeu1n+vnSXzzLn/7lZwktIGtbCJKJLLquYpjzB1FuVahqGcHQr2L67kXKBKrahKpeK/l7U8egqSAEickU6USGTDKLUoAOvao1Yvo/RDrRlyexhcDByr5Jwv3n+EO/k5dfDt6wuMTlchwpk7j2eVLx/4aVfemGjrkQlJgh/vvmz/Oj7v0cGevEpxqUGiE+3tDGA9VrMBUdV8ppRn5SSizpgJToyszfZSEMfMFfw7E7sLKvUeiCGFwc+xyJ8X+Obm7HF/wlNH0DQgTzQZPF7eG4Ug5l5YlFDilj2NYJ7Oyb5HJ7cO0Orof02a6NKhavekJRqzD9j+LY7XlxjcLGlE1/F9s+QSDy/+b714KLRjikdEGmcOwOUom/zGegCyuXAhAiiuG7D/2qqoTLSNkWLdESGiPF79p7NJexZg1szwRFUfh//ujDLBYrbV22+KV6N41opBMZjr5xlsOvnubswU6CRX7KqqM0railuqmcQNhPLpNj4NIonSe7GR+KsXHXSvxzyFpKKUnG0zz7rd38/X/8Ca4rURRBuCTEkpW1rLu9jdqWCnRTJzGe5MKJHo7vPkfvhUEyqRy9Fwb59l88TSDsY9M9K+eMlJdVRbnzQ5t45htv4A/5CEb81DZX0LqugdrmSsLRAEIRjPZPcOpAB8d3n2NscBLHdolPpHjjyUMsWVXHjoffmZrbdxvBiJ9lGxqpa63k0tl+chmLU/svsPGuFTQu0ADm4IsnmBj1Fp6qptC2oYn61sXVtr4hCH3OhjchwggRQroTi9xcaSGdMSCH6/SQjv0J6difzLOPiuTtDp8S143h2hews69iW8dw7W6QCY9kyFy+SXx2KFqrV3ols9jWQXLJr5NLfRdN34DufwTN2Iai1uRrdacv0KQ7jpRJvP6In2BlfjLPNYj89guDohSh6Ruws3tx3YEF7CGxcvsxfPfk1aYWep5yT3ddnW6YdeDNdgb6Jvji799P89JKNE0lcJX0q2FoVFZHUdXFzWa4rss3v/Iaa9Y3suPOZYt34HcBQvjQ9fnLBW4WymqKCRUF+NlXXiadzJJN57jn0zsLOoYZ+CxWbjdW9k0KXwR7ZVTJiX+F4X8YM/ApVG2Jtwhm7uDYtYeSSLJIN4nrDpJN/5Bs8rsLaKS+PC8W7ioM4EgXV3oL1LDm43NL7uQzTbehCgVVKLhSErdTXEoMUusvJ6j5UBBIJLZ0ORvrJuVk2VDcgiF0HOkgpWdkd5mUqFo9/tBv4Lpj+QzmwhdmV5DGyr6Mld2Npq/E8H0AzdyKotZ4mVtMEDpwmfDMde9lnlA4eJkKGzmVscgiZSy/eD+GbR3FsU4vigv7qdgFWkONBLTFC1zoxg58gc+QdkZwnA4KXcA61knio7+M4XsYX/CXrnx/ha9gEielC+SQbgLX6Seb+THZ1A8LLO26Gj4M3z34gr8yY8awPhSlMzbG+YlRGsNRVGV6RtOnateUUy0mpJQe0bALIRqCnXfc2u/9m0Y0BrtHefYbbzLYPUr9smru+/R27vroVsqqo9dsm03nOH2gg6LS8Jz9GY7tcuatTr7/V89OkYzalkoe++I93PvJ7dfIgd2HV3P7w79+gdeeeItUPEN3+wDPfvNNqhrLaGirnvPF/cFf3UXP+UFu++B6tty3hqrGmZ04H/jMTo68fpav/9mTU2VcF8/2c/ZgJxt3rcB3Ew3ObiU0r6pj5ZZmutsHkK7kzFsddJ3upX5p1bzmg67j8srjb+HmHzDDZ7Dro1veiWEXAAHMcR1CBaEisfOGk4sF18s+AGAglFKEMl/vi7hGDcl1x8gmvkw2+S3AQlGKQQmjqK0egRI6rt0+Z+mREKo3GWut2NlXyaV/imufxraOYuf2oKjNGMFfwPA/AkrltInFy5jkS3yUEoQoZr4Vt6IUFl3Rza3kMk/j5hZGNJzcIaTMFWTSp+rL0Ywr9eWTEylikykudY1gGBqW5TA5nqKsIoIiBLbtMDwYQ9NUfvHX78S8qrFYSkkmbTExniRaEmR8NIFlOfj9BqXlYRRFkMvZTI6nyGQskBLTpxOJBvD5dGITKQb6J+m6MEy0OEh1RzECqKyJ4vPpixbkuFJH7nh/S+/fnimqm8+MuR5RLSiFn4+0O0OAmu/JUPK9Glf+T/7/NztoE62IcP8v3cGWB9aSTVtUNJRi+g2yqSyGz5jKZM8FRY3iD/0ujn0R17nI9UQapRwnm/oWuczzGOZd6L77PPd5JTy1ABaoePflSgPx9MVtEts6iZV9iVzmJVhAiYkQYVS9DemmceyTBY8boDc9QiyXBASNwQpcKenNjBDS/NT4Spm0klxI9DGUmSCo+UnYaYr1EAk7TZERIqCZpJyM14grLYayEyTsDJW+YqL6lfp/3dyJP/SbpOP/Dcc+x/WXgWWxrcOej0TCj6o2o+mrUfVlKGrenV6E8u+yq0tE8/ddut77XiZx3UlcZxDX7cd1+nCcblyrIx/4uL7xpZ0MSTuNI13UvFO5giBup3im/w0erdUoNYopNaMoQpC00yRtL2DkV01CWpCMkyXppKZ0bIKaj6A2iyKSUDD8H8BxB8kk/g7pDl7HqLPkMj8ml/kZurkZ3bwfzdiQdxI3QRiAnn/OL8+pbv59ctV32B3HsU+Ty7yMlX3zOrKEV0NHN7fiC34RRZ05gNlcVMzTXWc5MtzP+vJqik3/tIzbo80rqAxMn1fjE0mSk2lC0cA0lSnbcpgcvZyxn6YexFWKRdP+b1s2icn0O1oy907gphGN0f4JxoSgeXUdv/AHH2Dbg2tnNREx/Qbr77xWBvNqSCmJTyR5/H+/SDLmPUTRiggf/NVdPPzL19bMX0ZdaxWPffEeJkfj7Hv2GK4rOfTKaXY+soHqpvI51UQal9fw/33/9+a1cNdNnfV3LGe0f4L/0zFEMpZGupLB7lFG+ieou5Wi8jcR5bUltG1awr7njjM+FGOkf4Kzh7pYe9syiivmlikd7h3j+O52XFeCgJLKIjbds/ja1jcEac8d8ZdZkNl8NGwxDYxUhPBK8BStAV/4n8yY9p0OAVfL20pJLvktcslvAVkM/0cxAp9G1VfmIzsCKdNkEn+9oB4HRS3BCHwE3f8wjnUKK/McdvZNHPs8mfifId1xzODnp7nGelFZ7x1gBD6GGfytBcgAF/aKUrXlqFobdu4oML/Zm22f9/pfcgvr6wATVV8+zTvjwO52XnrmBBc7h8nlbLo6hjAMjc998W7Wb24ilcrxza+8zrlTvYwMx/naj36HcL6k0nUkp0/08JW/fpFP/vJtvPTMcUZH4rStqOXz//geDEPl1NEenn7iECNDMVxX0tBUxkMfWs/y1XXsfbOd5396lI72QXovjfL6i6cA+J1/8QjLVkxX/bocHUQ6eSJ8mSDkCcNUGd1l0nD1vy2QKaRMIt2k9/flP24Cmf+dnTsI12TS5oDMYufeJBlLeqWJIgBKMF9yEczXzwc9Yi0C+bLFyws+9arFn0fyxVX/ntoGJe+fMD/xGukd5+zBCyzbsITy+lISE0mOvXYa02+wbHMz0fKFSXjq5jYvKpz80g2VUUp3mGz6+2TTP0BRqlD1NhS1CUWtzmdQfSDcfDYyieuM4LqDXimgfW7eDOV0GOjm7fiCv0Yu89x1E41XB48Ss1LUBkqZtBIMZydoCFZxdLyD28tXcWy8k7DuZywXZzwXpz89xtpoMx2JPtZEl0w7VmdygAuJPsZycZaF61gfbcFQr8zZpv8RpJwgnfgyrt3JDfecSI9gOfZJmLp1AgigKAEQPkAHIfPzgeWVocoE15sBmg+Hxk9zePw0EkmRHuL+yp0Yis6bI4c5n7jEswO7CWp+PtPwAQzF4PmBPfSlh0BISvUoj9Ts4sRkO68OH6DcLCHlZGgNNXB/5Q40Zeb3qxB+fP5PIp3xfC/M9X6Hs1jZN/PZPQNVa0LVWlHUGhS1YqpcDRSQWaRM4bqjnoKh3YFjX4AFinvMDQ1NX4Mv+GtoxuzrivMTY1iuiyIUjo1cS7B21S25hmj8+P+8wt5nj3P7I+v5hd9/aOrnY4OTfPmPf1TQKB3HpeNkT0H7LAZc1yWdtrByNoGAueju4De1yzZcEuSuj25hy/2rb9ip0HUl3ecGOPr6WcCrpW1ZVc+9n9w+7+TR0FbN6h1LOX2gg4mRONl0jmNvnmX1jlaqGmbOUsBladWFRdB0U6OhrZolK2s5sfc8ALHxJInJxdcnv1UhFMHSdY0sXd/I/ue8hduJve1sf2gt0fLInJ/TG08eJpPy6i1VTWXHQ2sJLnId+41CkkG6Q0jpXJMCltLBdUeRbgyhViCUxfNWEOj5SFDYW+S5Y/mMxsKju1JmsLOvIOUkmrkTX/ifolxFAvIbIZ2JwsYmfGjGRjRjA47dQTb+1+QyT2Oln0A3b8+XF3njVJQiFLUE1+nCtS/lG8sLu475x6OjG9uwsq97ZobzQWbIpL7FVauKOaFqDXlTxiu9V/d9YB33fWAd3/rK64yOxHnsU1upvyr7WWRo/LM/+hBHDnTwX//0yWuO6bqSgb4Jzp/t59d/5z6CQZNEIkMwZDLYP8GBPeeJRPx88fcfACQT4ymixQFUVeHBD65n7YZG/uef/4x7HlrDPQ+unvU5c91B7NwBpBvzvkdcJg0eSUCm3kYiLhOLFF4t9M0Iszn5BUXHArYVeGThMhkJoOSJCEqekBBA5P/NFEkJoChV6ObtzOehMNA1xPf/4mmWb2lh+yMbyCSzvPX8MYrKI+SyFrc/tvAsqy/0eRznItn04xTiZj0zpBclz87cm3XjUNH01ZiBX0QzNmBbx/CIWuEL96Duw6caLI800JceJeNaVPuKGc1OMmklsaRNjb+UmJ3ySqakZDQbI+tYONIl4+RIWGnSTpa0k2Uil0ATKj515uCNL/BpkIJM8u9w7PNcXxnVXJBAEtddeBnnYiJmJWgIVLM80kyZWUREC6EpGo/U7OLIxGk+Wf8gNX5PeKQ7NcBb4yf4QvMnyLo5ftjzPL3pIRxcTMXkV5s/yvn4JV4Y3MNobpJK3+w9UIpahj/0a4DtlStdN9m4jByOfS6ffXonoaHqK/GFfgPDN7d63Kfb1vKpZWtI2haJXBZbuhiKSpHpw1Bmzqi+/KO36L80QjKenkY0UokMb/7s6KJfzc1AIp5h7+52Os4Pcfd9q2hbsbBy94XiphKN5tV1rLltGYZ54+oZVtbmwAsncPKlNaEiP2vvaFtQs7UQgsbl1ZTXlTAx4qWPO0/2EBtLzkk0CkUoGqCk6krk3spYWNkCons/B6hfWsXyTUs4/uY50sksnad66TjZw7L1TbOWkGXTOfb87Ch2vqHeHzC487HN7+SwFwaZwbG7kE4/Qqub/it3GNc6j5RxVG0bYjG1z4VAKOWoxvq8otTBvPfELKpW0jNW88qnLuuDJ/N9IxJFa82nrq/eRSLlOI51+NrjXbUNSIRnSvP2QaJqLei++/O9Hx3XyswKDdXYiW2dxs7tx7HPezLAMy2Mp66BgqU+dXMzaroxX7Yy30LJIpv8ygKPLFC1pWh64bKcc0JKTFPn7gdWU9fgTfzFpR5RvdznMTGepLtrhNr6EhqayjCuw9fCzh0gMfHPFmHR+25BAjnPW8OZ4HIx10Kg6msoMh9nPqKhGzrrdq1gwz2ruHSmj7LqYlo3NBEpDWNlC4tYC+EnEP4XSHeMXOYFCsr0vKPw1PTM4C9g+O4AFBSlFCEiC+jnuBblZhRHugQ0k2IjTJWvhOMTnTjSYUmwGsd1OTnZhRCCYiNMwk7TnuglogdwpEvSzjBpJRnLxaj1l5FxLGJWkhIjjD5jBF7FF/wMQgmTTvwNjnWKm5VdeDdwW9kGjk2cZffIYfyqyV0VW6j0lSG4ojYn8+/lmJVgMhfnxcE9IAQNgSo0oaIKhaDmBwm6ouFTTTLO/BlfRa3CH/oiANn0j/O9Ee+luh4NVV+DP/SbmP4PzLu15Tp0xyfZN9BN+8QoWccmrJusL69mU2UtpT4/ytvmo813r+D0wS6WrWuY8Zj+oEnT8mrEAno7pCvpvzgytU59pzA5mebNV8+y+/VzNLdUvHeIhhCCmqbyRSsbcmyHs4euRCj9IR+ta+oXvH+0LEKo6Aop6e8aIRUvJK08PzRNnUaqXEfiOu+lh/LGYfh0lm9aQuPyGs4c7MTOORx7s531dyynacXM2sznj13i0rl+r2wKWLKqjpa1C/9s30m4dhe59A8w/B9DKKUgFKQ7gZV5Bju33/NX0Degqos7fkWtQvd9ACd3HDu3j2zqGxi+R7x+DWHilbdkkW4c6Q4j1HIUtZ7LqlFeCUoYUHCtdqTTjxQBvFdAFtfpw0o/iWOdmWMUOVy7x8uoqOUIpSgf2Rf5MoIJz7XXjSGUYrj8u6tg+D+EnX0ZxzpJJv6X+EK/haLV5xWrRL4UIYnrjoPMoOgrr+k1mf9e1aLpa7FzR65roTQbhCjyarcX+bMFUHWFiqvcqC+juCTIttuX8tTjB/nht/ZQ31TGhs1LWLGmbqr86n0sHsyAgWHqDHePMTkcI5PIouoqqqrMKVQyGxS1hEDkj7w+oOzrwDtrQDg/VBS1CV/wc5j+j3G5Xl4oxShqZcHmgwB3lK+Z+ndDoBIpJcsj9VMLtLXRZtZEl0z9v9ZfhkRO/b/cjLK19EopdZlZNLWQnisrbvofRVGKSMe/jGUdgAKEJG5dSJJ2mjKzhKgR4cXBvYznYh7REAK/5qcr2UvaydAYqKXWX0FrqIGdZRu85nkUagMV9GeGGcqOcT5xid70EAJBsTF3OfNleGTjNxBKkGzqcVynh8XPGt0M+NDNLfiCvz5vJuMyumLjfPPMUS7GJ6gPFRHWTUYzKb566iB9yRgfbV11jY/Gr/7hY/RfHKG6cebsUMPSKv7fr3wBfQHlSHbO5st/8jgv/fDAgsa7WMhlbawClK4KxU0jGoZPI1oeIRhenMnQcVwunb3S4GnlbLrO9JFOzs/KAYb7JohPXInkpRIZchlrRq+At0NKOWXcl4pnSCezWDkb23JwbQfXdXFdyUj/BMO9N9Ks9POB5tV1LF3fyPljl7Ath9MHLnDp3AB1rVXXlNBJKXn9yUNk094ErCiCez6xDeUmKjtcN4QfIUxy6SdwnR5UfQ3gNVBbmZdwnV408w4083aEMr2W23Unce1LIJP5hk07L1HrIt04VuY1hOLHUzgx8jWsV2rsPafvXeiBc1jpJ8klvoKTPYRqrEUoUW+h747hOhdxrJMYwV/CCHx2SopXKAE0cweOfQY7t59s8quoxnaEEkQ6o9i53di5fWjmTuzsKzNevpQprMzPyKW+i2qszzenloPQPGUb+xxW9lWknMTwfzhPdKZD1dswgp8nm/gydvZlUk43mnmH1/QtFHATuE4vjn0WhEag+H8AhZeh6ebt5DLPX9dCaTao2hI0Y9NNMZETCNQZBBMUVWHp8mq+8Lv3c+50H6+/eJqffP8Atu2yc5enziREns5Jrw/7H4DI3U1DpDREMBqgv2OQ4qoomUQGAYwPxahoKJ93/5mgag0Ei/49qcl/Ty73MsiFzVk3H5qXyQh8FjPw6Wmy3UIpRlEqcTh7w2cRQnhZ0AX+f77954Ju3omiNpJOfBkr+zyu03f9A79F0JXq5Xy8GwGsiDRTkS93EgjuKNtIe5481PgriBoR7q3azlvjJ1EQ+FQf1f5y1HxW48jEGVzpsrl4FRF94e9VRa3EH/xNhFJBNvWdfEDqViPNVyBEMbrvbvyhL6LpC+/1fLPvIrbr8i823cmy4jIUIXCl5KXuC3zjzBF2VDdcQzR8AYMls2QAhBD4QybRsvCCzi9diT9ovuPv8FzuPUo0dFPHF1ichlgpJY7tkJi8EqEY7Z/gf//h967/mK4nI+Y67qy+HVJKxgYn6T43QH/XMF2n+xjuHWN0cJJU3NNY9zSPPdJhW86U2dw/ZBSVhlm+eQkHXzpJX+cwE8NxTu2/wMqtzZRVF0/bNjaa4OhrZ6bKEopKw2y9f81Mh33XIUQU3fcA4EUnc+mf5hcNOkItQfPdgxn4xTwBmQ7HOk028Tf5kqJ0Xp0nCThI5yKpid9DCL/3R4liBD6NGfylacdQtTp8wX+EIoqxsq/mScM+vBe+8OQDlSIUpRJFrb1Gvk/3fwLXGcTO7iaXehxS385L9gZQ1AbMwGfQzXuJZ/fOfP3ontu58GFn38BKP5M/t8QjSEGEWonuvw0j8IszEg0AM/BRhPCRSz2Oa7eTS30nfx9dPFWtEEIpQ9O35DMdhUPT16Jqzfma7cUoWdFRtWWoBUxai4Fs1iIRz2CaOivX1hMImPzgm3vo67lSL22YGpqhMjmRIp3Koesqmq7cmmT9Fkd5XSkf/e2HkFKiqAqO7dDfOUwqnqZuafX8B5gFilpPoOiPEYkirMwrC5Rfvpkw0Iz1+AKfwfA/eo0og6KUzKrMc6tD1RoJRv6IbHop2fST2NbJRWoofichEEoUoYTYUbqeHaXrZ9xqR9l6dpRN/92aomWsKboid5p1cihCodZfwSfqH7z+ESlBfIHPoirVZNPfx8rtywsd3EpVGyqqthzDdy++4OcK/g6PZtJUBILUhiJTalOKEKwrr+b/nHiLrFNYSZ6qKQV5qQlFYPh01BvsaS4UlwPnNws3jWioqjKn8V6hsHI2jr24i3jHcWdVYrRth1P7LrD3Z0fZ/+IJ+jqGkO70jYUi0HTPgFA3NQxTI5vv3P+HjuWbltCytp7+iyNIV3LktTPc9sh6SiqjU07JAEdeO8PowORUremme1dSvEBll8tQ1TL85t1oat2s2wh01BueOB2EWonh/yiqsdVzlnYnEcKHojWjGTtRtCUzaoULJYSqL0MoJfOeRQgDoczUOyRQtHrM0BfRfHfh5A7iOj1INwlC8UiGWouqrUTVl09rWIY8UQn/U2xjC451CulOekRDrUTTN6GbO5EyiRn8bL7safpCVYgAunk3ilLslUg5Q/mmYhchTBS1HEVfgaZvQlFLmb3JW8XwP4pmbMbO7saxzyHdCU/1SAl4RElrRTPWoCjXZ9gmlBCauQMr99Z1yjNOh6JWoBnrUZRowft2XRii59Io5073kU7neP3FU4QiflauqaO4ZO6oYmwyzcG9F0jEM/j8OmMjCfwBg4YlV74f4bCf1mVVnD/bj5SScNjH5p2tlJQuniDBPyQI5UoEXdVU6pbeeL+VEAJFrSEQ+SOy2lKy6SfepV4CgVBK0I3bMIOfxTB3MtNzKpRixHuUaAAIxYcZ+ByasYVs6kdYuTdxrHaux+DtnYWBolZ6vWDG5kUJbKhCodwsxnFv/LsmhIrhvx9VbyObfhwr8wK2dYpb4b4KpRLd3Irp/zi6ueu6jALL/UHaJ0Y4NTbEsuIyDEUlaeXYO9BN2DDxawvvNw5G/NzzsS20rW8saAymz7jG4811JbFYGsdxKCryhECu9F9K7BtcG6fTOXI3cd16U5vBFxOKEAhFTC32/UGTpesbb0jNqqQiMmOKSkrJgRdO8O3/+vSUL4YQgkhpiOrGcoorIgQjfnxBA9Pv1fRqukoyluboG2fpOPHOy5PdaqhsKKNt4xKOv9nOxEicnvMDnD96iebV9VMM37Yc9jxzlEzystqUwt0f21qwCJGuLSEa+Z3FvoQZ4Gl8K2oZhv9R8D+64D01fTWavnpRRiGUAJqxAc3YUPC+ilqOEfgY8LGZjy0i+Iv+eLYTe2RCvR+d+ws+97Vjqc6P5eZAN24jq/4Axx3iRqNuniP63IpDrW1VVNcWE3pbLf/IcJzzZwdIJXPcfvcKOi8M4fMZNDSVUVIWorwywl0PrJpRRts0NAJBk+6Lo+SyFqGwjzvuXcnKNVdItW5o3PXAat7ac57+3gnik+n3gx23IIQQCFGEL/h5VG05ufQTWLkDuE4X70xU2EAz1mGY92L4H0XVZl8ACRFEUcrxerxu3RKZuSCEQNNXokZasbK7sbIvYeeO4djti2KWt3hQEUppXvp1KZq+Ht3chqI2LIqrtqZotITqaQktXm+ZqjXgD/1jNH09VvZ5rNzBfDnVOy94oChVqMYajzz7PzS7SMoCsKWyjkvxCb577hg1wQg+TSOWzdKTnOSO2iYqAgsP3pRVR/m9//wLBXv/LF1bz12PbaKy7kpQ8sypXg7svUAqlWXdhkY2bW3GzPcDSyl58vG3CrMvehsudg0zNnrzsn7vCaIhhJdO8gUM0glvURqtiPCp33+o4Oj31aioK5nRSK6nfZBv/uef0nGiG/CiWmtvW8bGu1bQsraB6sYyikpD+ILmtC9R97l+xgYn3ycaeBmt1dtb2f/ccSZG4ji2y8FXTrPx7pVTRKOvc4j2o5emFkX1S6to27hkrsO+j0XAK+c66Z2YxHUluqry8Y2r0d72HMQyWV4+c4FYJosiBPUlUe5c2vTuDPg6oWpL0PSVnpzijSgtiSCavgJVa55zs623LZ3x55u3t7B5e8us+zU2V/D535zZFyUSDXDnvSu5896Vc567rqF0SrHqfdzaEMJAN+9A01d7ZnrZV3CsEzh2NzdlUS8CaNpKNGM9hu8BNGPbvA7NQqgoaimKWprvJXuvQiCE6ZkeGtuxrSNY2T3Y1jEcuyPfw/Fu9MyYKGolilqPqjWiam1oxlpUbRmKsrAm7XcbQugYvl3o5mas7F6s7KvY1okrGeqbenI/qtqAqi1DN7d6mXa1oWCFwrejrbiMj7eu5tXeTjomx8g5DmHD4P76VnbVNRM1Fi4IIYRAqIU3W2y+ZyXrbluG6b+SPfnZk4d5+YWTZDM2Rw9dpG1FzRTRcF3Jl/7q+RsiGjcb7wmiAaAoCpV1pXSd8Zq7hBCEogGWrJq9XOZ68dIP9nHpXL/XWKkIlm9awuf+9YdZtqFxTnbqOO5NrXN7r6GxrYbWdY20H7lIOpnl7MFOes4PUtNcgW5oHHjhJPHxK303uz6yGdNv3HT333/oGE0k6Rge462LvfRPxvnw+hXXEA3bceibjHN+aJSjvf2srql6zxENITQM8+68p8b1Ew1FrUYztk5rln0f7+NGIISCUEsx/B9FM3Zi597Cto7gWKdwnC5cZ5gbIh3C5y1i1SY0fQ26uR3N2HBNOeWch1BKEEoFvKeJRh5CIIQf3dyBZmzCsbtwrJPY9llc+wKO3Y3rDuQXyIudDRQIEUFRyxBKJYpanS9xXYKqLUXVmq6rJPNWgRBBDN+9aMZ2HOtE3mX9pGca6fQukiSu17OiKNUoWgOa1oaqr0Iz1qEoVfMS54VCEYLlJeUsKy4jaeXIuQ5BzcBUVYbSCXKug6ne3GWzYerXWEKkU7kpBdPx8STOe6wX+D1DNFRdZdmmpimikUlm6Tjh+TMsNo6+cXaKMGi6ygOf3UnbxvnPk0lmiU/8PEjqLQ58QZM1O1o5/MopOk/1kphMcXLfeVZsaSYQ9nPktTOk4l40KVQUYNuD62ZU3nkfi4sPr1vJgyuX8j9e3sOTx2aWsy0O+Pm12zZzYXiU//7S7nd4hIsHzdiCL/iF/GR3fVDUGnTjFvR1KRCqtgx/6B+DvHX8HBKTKZ79zl6yGYvSqiLu+chm9OvwCJkPXlPorTfdCaGiarWoag2G7x5sux3HPo9rd+E6PbjOMK47hpQT+X6oHMgcXhmnCsJA5AUUFKUEoZSgKOUoWhOq1oyqtXrqcKgFy5GpWgu+4C97annzQNPXeiaJ7wEIYaDpy9D0ZRgyg+v04Nhdntqd04/rDCDdMaSMId1JXDfuuX9j5Z8dG/I+RZ4vi6cU6KkS+vImkRGvZ05EEWqp95moFShKDYpWlzdgvfW+jzcCRQmimNvQjC247gCO1Y7rXMJxLuHafZ4qojv+tu/y5fsp8O6jjicIErxy/5RiFLUKRa1D1epRtGZUtSG/7c0JSipCEDamCyT8tOMM9zcupSEcvSnnnAt33rMSIQSJRIbVa+sJzuBJpiiC5tZKWpYW3ls1PBSj/ewA8djiWj5cxnvmm64bKhvvWskL396D60oSkykOv3qGOz60qaCu/vkgpWRsYHKqF0RRFZZtmL+Zx3FchvvG6e8cXrSx/DygbdMSmlbW0t0+gG05HHntDPd+cjvDveP0dQxOGTCu2bmUqsZShPJ+NuNmQ1MVQqqJT9dmfU8LITA0lZBpYKjvrALGYsJzt/38uz2MWwKavhxNXz7/hu8QXMflYvtFfvC/4+SyFm3rG3jgU/+YwD9EfxAhECKMbmxENzYiZSZPMoaR7jjSncibbubwTAvdfBTX8MQjRCgvSVuKUMtQlPL876//faqqtaiBTy3WFd6SEMI3RcZAImUW1x1FOh7RcN0YUuaJxuVFsbTyvh4KoIDQPNUu4ZtONEQRilLkNdb/A8qGCqGgqjWoak1e5CWL6wx4hNmdeNt32QKZJxpCQ6B75Fl4RMNTUSxGKBV5L6jFD0RezrXM96TsGehmc2Xdu0I0tt+2lIbGMrJZi5q6YgIzEA1NU9m2s5UPPrap4OMfPdzFxHjqfaKhairLNy6hZW0D7UcukstYnNp/gdeeOMi9n9xWkPu447ieCsgsi9ppfRsScum5I4BSSoa6Rzn40ilGByYXPI5/CIiWhVm1tZWTe88z1DNGz4VB+jqG6Dk/SGIy/6UWsOsjW9CNG3eQ/3nCSCLFT46e5o6lTSytKKV7fJKfnThLdSTMw6vbsByHfZ3d5ByHO5c2IRCcHhjiSM8A46k0IcNgfX01q2oqCbx/b9/HLQYrZ3Pu6CVy2Vsnw3KrwFsA16NyaxqX/nxCePddrQV1ZnPZ91EYvDJon9foTtO7PZwZcXxkgD39l9hSWUvCyrF/YOYe23PjIzgFNEJIKfNrTVBvMFhnGBpNzXP7+CiKoKKyiLLyhXl2XI2ysjB+/81bI7xniIYQguKKCI/8yp18+Y++TyqeYXxwkp/87UtkU1m2PbiWyoayWclDLmsx0jdO56leRvsn2HzvamqWXPvBCSGoWVLOYPcoruPi2A67nz5Cy5r6GeV6Xcelv2uYZ77xBvufP/6+j8bbIIRg3R1t7H7qMMO9Y1hZm3NHLnLmrY4pZ/ba5kqWb14yo+rO2zEykeDF/e10Dy6eMeKju1bTWleGOovvgFAr8IX/KUjnHfVRSGazvHy2g4jPpKWshI7hMf7v7kNsaapjW3M9tuPyWnsXQVPntpZG9nRc5CfHzgCSqN9H5/AYhy718bGNq7itpRH/+2TjfdxCyGVtTu7veLeHcQ2kK4lNpOg800d3xzBjw3Gy6RyKohAq8lPTWMrydQ2UVRdNeZVIKXn623vp7hhi1yPraV1di56XqLRth6/9xTNYWZvymigf+7VdU+dKp7Ic3XOBUwe7WLGpiR1va/qPjSdpP9FD59kBJkcTSAnhqJ/GpZW0rW8gWhKas6fNytmcOnSRjtN9jI/EsbI2qq4QCvspq47S0FpBQ2slPv/MEXfXdRnsneDc0Uv0XRwlEU+jqgolFRGWrq6jeUU1/sC10dXnfniAzjP9fOa372NkIMae50+QTedoaqtm+30rUYTg9JFLHN93AdeVtKysZf3OVkKzZLPGhmKcO9HDpfZBYuNJTwWyJMiStmqWr28gGPa9I719fSOTvHSgnYGRxVGtqi6LcM+WpVSXvTcawH8eYbkOadvCcl32D/RwcKiXpcXXystbbmH9t2ODk3z7r56lsq6ER39l14J85fovDvPWy6cRQrD1vtVU1BbPu89lCCEIX2c2WDe0G1JwnQ/vGaIBoJsaW+9fzaVz/fzkb1/Gthwune3nh3/9PMf3tNPYVk1FXSnBIj+qqmBZDql4hsnhOCP94wz3jTPUPYpmaDSvqpuRaABse3AtJ/a0k3VcbNvh5R/sx/QbrLu9jbLqKJqukUnnGO2foPNkD0ffPMup/RdAQlVjGQMXr78eHDzykk3nyCSvbgaUWDmbdCLjGbosgkeJ60qsrEU6mZ3ysQCwLZtUIoPpNxbly1fVWEbb5iW0H7tEfDzJ4VdPM3hpdKoPZvtDa4kUBxc0UUwmMrx8sJ1DZxZP2WvD8jpaamdX7FGUYszAJxftfAuFT9epK47QNTZO2rIYiieI+E1MTaN7bBKfrpG2LJrLS+idiPHT42fRVYWPb1xNXTTCWDLNl17fzxNHT9NaXkpT2cJfWu/jfdxMSCnJpHOcPtT1bg9lGqSUXGwf5Im/f4POs/2MDEySyMsFC0XgC5iUlodZuraeRz69nWVr66bexWeOXOK1p48SLQ3R0Fo5RTQGe8Z58hu7yaYtKmuLeeQXdkwtOlLxLLufP8Fbr50lWnZFOtN1XbrODfDi44c4fqCDwZ5xUvEMEs85uLw6yqpNTTz4ya0sWVY143wQn0jx+P99nSO7zzPQPUpiMo1tOyiqgs9vECkO0rSsioc+tY2td11bUpdJ5zi29wIv/+QwF071MTocI5vKoSieEEtNQymb72zjvo9uprQyMu39/dZrZ9n93Ak27FzKU9/aw4m3OrFyNrVN5cQmUjS2VvC1v3yOrjN9uFLS0FxJMp7m/o9tnmY06TouZ45c4qWfHOb04S6G+yZIJbIIIQiETSpqi1m7rYVHfmEHVfXFN92kcmwyxQv7znKq88a9eQBWNVexblnte4JonL04xL6TFxmeSBAN+bljfTNLakrRNZXh8QRPvH6CD962kmf3nmFwLE5ZNMSn7tuAqauc7xnl2IU+1rXW8Orh88SSWVY0VXLvlqWYusalgXH2nOiiZ2iScMBk26pGVjRVYhoak4k0Lx88z9L6Mrr6xzh7aRhNUfjo3WupKSuaNbi8UKwoLqcuVERIN9jTf4k7apr4cMu1Kn/nx0enTPwWgvhEiqe+9gbL1jXw0C/sXBDRiI0l2f3MMWKjCRqWVRVGNBRBOLJwVayrYRga+vtEw4MQgqLSMB/8/C6Q8Ny3d5OYSDHSP8Hoz45y5LUzhKNBT7lIEbiOSy5jkUpkSMUzU/0AtS0Vc2og7PzAenY/dZijb55DupKh3jF+/L9f4sALJwhFPbMUK+eQmEwx2j/O2OAkRaVh7v3UdjRd5fEvvUg2vTDFkD1PH+HiuX7SiQy5tEU2nSObsbBzNgOXRqe2s3IOR988y3/7/a+j6Z5BoOnL+3j4daobylh7exsVdTMbwh194yznDneRjKfJpXNkU5Z3HstmdGBiqikboPNUL3/5T76OrnuOwz6/gZE/V3FFhA27llPbsvCGI01X2XTXSt564QTx8SQXz/bj5EmGGTDY/tA6DN/70fa3w6dr1BUXcWZgmFgmS/9knJXVlUT8JhfHJigPB3FcSXVRmHODI/ROxPjohpVsbqxFV1XqiqNsrK/hB4dPMJRI0FgafV/R633cEnAdl57zg4wO3nqlppl0jp7OYVLxDGu3tVDTVEYw7CObtjh/oocTBzp5/emjmKZGtCxEdUMpQgjqmsvxB0x6OobJZS2CYW/SP3esG8d2iRQHScTS9HQO07rKK83JZnL0dg4TDPmou8qEsfv8ED/+6hvsefEk4aIAO+5bSU2j9/u+rhEO7znPiz8+RHwyzWd/5z7qlpRf82w//6O3eOpbe7GyNnc+spa6JeXopkYylmF0KEbH6T5GBibxz7AAsm2HY3sv8N0vvUTHmX7qWyp4+O6tlFYWYeVsOs8OcGR3O30XR8mkc3z4l2+neIaSjR995TWiZSF+4bfu5dAb7Rzde54nvvYGratqKa2MsPWu5Zw62MXhN9vZ/8oZNt2xjLKq6NT+Z49186OvvsbhN89TXh3lzkfWU1VfjGO5XLowxKE3zvHs9/aTSmT55d97gGjZ3Bme93F96Owb5endpzANjcaqEvpGJvn6z97i1z+8nfrKYmKpLE+8dgLLdigtCtBSW0bWstFUBceVdA9N8MOXj5JMZykOB4gEfZiGhqoo9AxN8vSe04zHUjTVlDAWS/HUmydxXJcNbXWkMhavHeng2Pk+WuvLaKwqJpbMoGtqofoGMyKgGwR07xnYVbsEv6ZTG7rWNmF9eTUh/eb22vgCJpqmMNw/fU02FxRF4bf/4CE0XaWhaSaj3/lhmNpUYORm4D1FNMDrn6isL+WxL95DdVMZrzz+FmcPdWLnvOzFfB9OMOJn6bpGiuZw5C2tjvLL//JDfOu/Pc1bL5wECbGxBLGxaw1NhCKobiznwV+8jbs+spmOkz2UVkfp6xha0PXsefYoB188SSaVw7YcHMuZUbrMdVwGukYY6PKyJYoiUPOu5Kru9a9ULymflWgc332O5769m1Qsg23Z2JY7RbzejvGhGG/85JB3fcJzP798ruqmMirqSwoiGgDNa+ppXl3HpXP9ZK/qeVmxuZmaJeUz+pm8G3ClpCceo9wfwK/PT35cKUHKgiJpvfEYQcMgYphzRkh8mkZTWTGvn++idyLGUCzBuroqJtIZLo1NoCkKrnSpioQ4dKmPoVicHx85zf7OK9meS+MTDMYSJDI5HFeiXYeu9/t4H4sN23Y4sb9jSnTjVoEQgtqmMn759x7EcV3Kq4qIFAfRTQ3Hdhjpn+SJr7/JK08e4fDu89z+0Bqq8/4l9S0V+IIGvV0j0wwTTx/xVJu23r2cN352nPYTPbSuqp3K6vR2jVBZW0xtk5dhj0+k2PfKGfa8cJKK2mIe/cWdrNvRQlGxp+oUG0/StLyaJ7/+JvtfPk3bunqKy8LXlB299vRR4hMp7v3IRj71G/cQLQ2hqAq5rEUqkWVsKEYua02RnqvR1znCy08epv14D+t2tPLoL+2kdVUtgZAP13EZG47TuLSSp761h+d+cIAlbdVsv3cl5ttKsAZ7xvnNf/sYFdVRmpfXcPKtLoZ6xykuD/Pbf/IRyqujVDeU0n6ih9GBSQa6x6aIxthwnDeePc6hN9ppWVHDBz+7gxUbGwkXBXBdl4nRJHXN5fzk62/y+tNHWbO1mdsfXF1Qv+b7WBjeONqJ7bo8vHkpTdWljE4m+Tdfeoqj7X1UlniL8mQ6S0nEzwPblhMwdZKZHKahkc3ZuK6LZTs0VpewZUUDqiJwXYmqKJzqHKCrf4wP3bGa9ctqmUyk+funD3Dg9CWaqr31TCZr4S8Ns2N1E9VlETJZm3DAXHRSubq0clby8omlq6kswLDveqCbGqqmkk5ksLILk1kWAh764HqE8BrCrweRiJ+NW5YQCvmoqZ15DXkjeM8RDfDIRllNMfd8YjtL1zdx4fglzhzs5OKZPkZ6vRSzZdlomoY/7KO4IkJVYxlNK2poXlVH3dIqKucxt2rbvIRf/aOPsOHOlRx+9TTd7f1MDHvqKJqmEi4JUt1UTtvGJjbcuYLWdQ1Ey8KMD8eoaihdMNFITKaIjSUL9t9wXYmbtae+jPHJJPYcbsCpRIbYaHLBmZbLkNIr2bo8ccbGk1jX0bzpD5ps2LWC47vb6btKmev2D24kGPHfElEoKSUZ22YklaQ8EMCRLrFslpzjoCkKpf4AadtiLJ0mpBv4dZ1zYyOMpFKsraikxB9gIpMm6zhETR+aojCYTKIIKPEHMFSVvniMo0MDLC0uJaQbcxINXVOpi0YQCC4MjzKaSvPJ+hpO9A1y8FIvuqpiahrVRWEUIfAbBnXFEVrLr3y3l1WWcd9yaCorXpToz/XCdV262wfpPN1L9/khxodjpJM5bNshEg1QVhNl7Y6lLFvXcFPkTeeCdCWTYwlOHuig83QfI/1eeYbruph+naLiEBV1JTQsraSprZriisgNf197O4c4c6iL7vZBRodiZFI5hIBg2E9RaZDaJRUsXddA7ZLyRb8fowMTdJzqpe/iCEPd4yRiKTKpHK7jThmjRsvCVDWU0rismoZlVbPW8F8PpJSk4lmO7WlftGMuJkJFflZtbkIo4prPORDysW57KycOdNJ/yStHklIihKC+uQJ/wKS3a4Rcxpr6+ZkjFwlG/Oy8fzWvPHmE9hM9PPypbTiOy/hwgmQ8Q6QkSHlNFID+7lEOvn4Wx3HZdPsy7nh4DcGwf9oY7n50A6cPdfHmsyc4+Po5tty5/Bqikc1YgEQ3NKrqS6auxfTphIsCVOTP9/ZrdF3JhdN9HN1znpKKCLc9sJr1O1qnLeADIR8PfmIL7Sd62P/yaV57+igrNzVR/rbvSWNbFQ3NFSCgqa2aitoow/0TNLRU0LSsCiEEVfUllFYWkUnnmBy/4nfTeaaP4/s6UDWFHQ+sYus9K6b1gwTDfh76xFYOvn6WU4cusuf5E2y6fdn7RGOR4bguF3pHOHGhn0sD45j5yHf30ARdfWPY+YClKyVbVjRSFPT6ZYyrIuQSCPoM1rbUEL7qM0xnLfpHYvgNnbaGCoI+g4CpU1cRpat/jLFYipDfxJWS5Y0V1JQVYRoavpvUb6gqyqzqU0uKFn8B/nZk01Z+rSVZqPeIEALjBueIcNjPrntWsHV7K9HiwA0dayYs2gxmBk3u+NBGluSjI5quUt14fWmchcCr0fSxbH0jjW3VbLx7JYmJFJlkFstykI6LUASaoWH6dAJhP+HiAMFIYEF9B4qi0LSilsr6Ujbfs5LEZIpsxjNNEUJgmBqBsJ9oeZhIaWiKSdYvq+bzf/QRPvKb9xEqClDTXDHneT7zTx/hkc/tmtYjcT0IRvzUtsx+rod/6Q4237Ma172xZnXTp1PXWnVd+5ZWRTGvesmU1xazcmsz+i00MbjS5fjwIA2RKJbq8nznBcKGSTyX5d6mZtrHRpnIZGgrLaNSVelLxOlPxFleVsalyUk6JsYYSMZZVlJGU1ExY5kUhwb6uLOhiYlMholMhhNDg1SHwsh5XiSKEER8JtGAjxN9gyhC0FJeylA8QTyT5cLwKHXFRRT5fVQVhSkNBlhRVcFj61egvU3lImjoBdWXLhaklJw+2MlrTx7m3NFLTIzESUymyWYsHMvBdSWGqeELmLzx1BGKSsPz1twGI37ufHQDtz28buq5mW3h/5OvvsaRN8+Ry3jk+EOfv5MNd7ShGxpSSnJZmxd/sJ83nj7KUO8Y8fEU6VQWx3KQUqJqqje+oI9wkZ+qxjIe+OQ2dj609rruR8epHl74wQHOHOpifChGIpYmk87hWC4I0HUVw6cTjPgpLo/QurqOux7bxPINjTfUlxWfSHL49XMc29POxXMDTI4lSMUzpBNZrJyNY3vXq6gKmuaNIRD2EY4GqW+tYMcDa9m0azmBcOE1wLGxBAPdY/RdHKG/a5j+rhEGe8Y4f7x72nbdF4b40y98ZUGiEAAPf3Yn2+9fvSj9aldjLkdfIQTF5WFCRX5sy/HmGulFFStqiwlHA3RfGGJsKEZ1QxkTYzGGesdpaK1k6eo6FEXh/MlepCuxsja9XSPoukp1fSmGqSOlZLhvgo5TfZSUh1myvHoaybiMSHGAyroSTL9O19kBkvFrZSnXbmuhp2OYfS+eIloa4vaH1rCkrXrqWZntmUnGM/R0DDMxmmDjbctoWVk74+I9Whpi+foGTh3s4sRbXcQmUpRVFU07blVdiadcKgSqqhAtCTIxmqCy9grx0TSvPHdyPDEVxJJS0tM5Qm/nMNWNpTS2Vs7YdB4tC1FVV0L78R7aj/eQy72vYFYoThzq4snv7MPnN7j3g+tZu2XJtN9LKcnmbJY3VXL72iUEfB6Z/NAdq6kpj+C7apEbCc3+ftBUheDbiKjjuuRsG1UVGPl12WWS4roS+6rqjqDfvMZYdrHxg/bjRE0/DzQunfbzeC7LN88c4QNL2m6avK1tOZw93EVf5zChoiDGIgZ35oOqKRSXhCi+SVxq0YiGpqlUNZZRdRPJxUwQisAXNKkKmtAw+3a27ZDNOQyNxwkHfYQCJiPjSYSAaNgPQtA7NEHQZ1BSFEBRFPqHJ9E0lYa2amzHpX84RnHEj8/UiSczWJaDki8puoxAyEfr2jkG8ja0rln4tjeC+qVV1C+9PoKwWLhwomda+dmme1ZRXHnjzVyLCV3xPkvLdVCEYCKTYUNlNUcG+7Fcl7BhcHpkiKCu0xAposgwsf0uJb4Ab/X3cnbUy9akbZve+CSdExOcHR1hY1UN7WOjrK2ooqGoCH2BpVZ+Xac0GOBk3xBbm+oIGBqlwQAg6J2IsaWpDlVRWFtbxZraSl5r70RXFVZWe31IPeOTOK7LXcuaKQ8HcV1JxrZI5Szi2RyulAxOJigJ+vEbOrqqerJ8riRl5RhNpkhbFqqiMhRLEDANfLqGtoDx57IWz39vHy/84ACXzvXnRQeu3S6b8fqFJmcoTZwJRSVB2tZ73jaH915A09SpyfHNF09S01DKkvx3/VL7AIdfP0sm5WXyVm5uZu2OVnRDIz6R4u/+9AkOvX6G0cHYjGU8tuVgW45XajI4iW27+UhxYbAtm2e+vZcXvr+P7gtDM5d4Sk+JKZe1SUymGewe4+LZfs4euciuD23kgU9tIxwtzBTNtmx+9s097HnuGP0XR5kYiU/di5ng2C5O/hrjEylvDOf6OXvkEu3Hu3n4Mzupbpw7G3wZo4OTfP2/PE13+yCpZIZ0MksmmSWdzJKboSwgFc9w5M1zC762TbuWe+UXC95j4chmLHo6hjhz5BJ9F0eYGE2SSeXI5WwmxxL05UtYXdcrnQQv+FTdUMq5Y930dI7Qtq6BCyf7yGYsWlbWEgz7qGksZWwoxsjAJLpPo6djCF/QpC4vXWnlbCbGEiTjaWzL4btfeomffXffjGMc7B3LR0AdrNy1GfEP/dJtDPWMs/+V0/z0G3vY//JpGpdWsfnONtbvXEpx2cxlIKl4mrHhGFJ6C/nZthNCUN1Qij9oMtA9yuRoAtdxpxG/YMi8anvQdA1FUfCHjGk/VxQBkqnAQTZjMTEaJ5XK0n9pjK/8+c/47pdennEcvZ3D2JbD+Ghiyj35fSwc46MJju7vIBj2sWF7yzW/VxWFopAfVRWsba2hqjTC5Wj728nqnMEscW2G0GdoFIX8dPWPMZlIEwn6cKVkMpFG11QCVxFcIcTN8uebQmdsnAr/te93RQj2DnSzo7phRqIhpaS3Y4i9zx2f+tnYUGzq75989TXMOeRjrZzDUM8YJw90MNw3waqtzRSXX9sn8l7Fe7J0CmBkJM6rr5xhx86l1ORTwG/HxYsjvPbqWaSUrFxbx0gyha6prF9ex7muYYZGY/QMTbJj/RIiIZNYPMNLe87yyYc30t0/TkfPKPVVxRSHA+w52omiCI6eSbNhZR1vnbiE3zSwbIf7drRhvsPlHu81pBMZTu2/wMRwHPB8UbY/sGZRzRYXA32JOO1joxSZPrZW16EKQYnfj0/TyNo28ZxF0rIYTCZRFQVNUehLxLg4OUFFIEhQN5jIZgnpBhPZDCPpJFnHixbXhSMcGeznzOgIbSVza2Jfht/QqY1G2NfZTUu5FwUsDvopDvgYT6aoKfJeRqXBAL+wZS3PnmrntfYufnzklLdtwM+uZUtQ82TuzQsX+dreQ4wl0wzGvMzI733vpxiqSmtFKf/xIw+Sylk8fuQUPz5yioxlMRBLIITgi9/8MSHT4OHVbXxm67o5x+3YDs99dy9PfOU1+rpGpsk+G6ZGXUslxRURNF0lOZmmu2OQyZGFEY2rMTGamCot8hSDhggETFg68/Yjg5M4jiSVyPC//s0P2PfCiTkX3m9HRW2U+jkyhzMhl7X4zv94nhd/uJ/h3omCspfpZJbzJ3oYH46TmEzxwc/dQWnlwhVqFFXl3NFLnDzQOZXVKRRW1qavc5hnv70HIeCxX91FccX8k2Aqnub43vNTi/L3AqSU9HaN8NS39nBk93liE0lyGRvdUAlG/BimTi5rzbigFULQ0FqB6Tfo6RjGshzO5hvBl62uRVEVWlfVsu+lU3Se6aNpeQ09ncP4gyb1eaJhWy7pZA4pvab0i+3zqxu5Unq9Ym9DdUMJX/jDR9l6zwpe+vFBTh26SPeFIY4f6KDyu/vYed8q7vnwRopKppNXK2dPkWnd1ObMOAcCJqqqIKWXCXEciXrVVHhN9YDIE4tZItOXr8LK2l4vn/TKfjtO9817H+x8FvJ9LC6EEGxf3ciPXjnG3hMXuXNDMwDne0Zoa6wgGrr+OVxVFFrryjh2vo9XD1/g/q1tnrLUxSG2rmqksiTM+AzZuvmQTuV48jt76Tg3wB/8yUcxzLnXZ5efoct/7KsqP6SUnB0fIWVbs5fMSujrHOYHf/PilWPm57zx4Rg/+eqrc5bbSinJ5QNugZCPHQ+upXYe34z3Et6zq+NczmZgYHLO6GJlZRHLllXR3j5APJ4h4Deoq4pSFPbz1olLjE4msG2XXM7mUl+KnoEJOnpHsR2Xmooox9v7aL84RGtjOZf6xnjwjpUcPdNL/9AkOcthzdIyzl8aIZ213ica8+DckYv0XrjiBN62sYm6pVU3Vbv5elAdCvEbG7bg13XChsljy1ZQZPq4s74Jn65R7PPTEIng17zJd0VZOXXhIoKG56Bd7Pdjuy5hw8SRLm0lZTywpJVSfwAJNBeXsLOugVK/H20BLqch0+ATm9Zw34pWKsLegqCm6P9n773j5LjPM89vha7OuSfnPANMQk4ECOYgkZJIKidbtmw5rO1d23u7t7u34TZ4b/d83j2fvbYsW7KyFUmRFHMAiJzTAJNznumcu8L90YMBBjMDDAJBUtajjz7EdFdXV1dX/+oNz/s8Ln7//p1kVJUCZ77aKIoCFV4Pn9jUzsMtDaTVfMXYLEt4rFZc1nxlsb28mH/+8J4lC+llWBY4tRaTzEMt9WysLF22jbiQvNwIZw/38fqPjjMxNLsYlEmSyH1PbeaBp7cQKPFgUmQEIV9FT8bTnD3Ux0+++iazE+HF/cgmiUCJh/K6QgpLvQRKPZRUBSgo9/PiD47x2nMnyWRUXvnpCTRVx+60sHFn/arHNT8ZRsupfPvPXl6SZJitCnXry6huKsXhsaGYZRKxNNOj8wx1T+YlqwUorgxQfhOJhmEY/OSrb/HK9w4TnIks6eiUVhew8d5mappLcHpsIAiE52IMnB/j7FUBuqEbzE2Fefn7R7DYzXzkV9emyQ7562LL/es4+vqFJYmGy+egeUMVFQ1FlFQFcDitmMwy8WiKicFZzh3uo/vU8BJhimgowb6fnaK6qYS9H910wzkVQRCw2s35z3btedEN4rHUEhqyKImLSk1rgWIx3fG5o+mxED/9+n7efO4UFpvCjgfXs3VvC4FiN7IiIYoivedG+dHX9tHftTz4zXtSmBgfylfZ+y6Moes6TZ2VSJJIY1sF77x8jv6LE5TWFDA2MIvLa6N8gWIrigLyAnWsqqGIxz65jdqW5b/DqyEIUNNUsuxxURQpqfTjDTjp2F7H+NAcR17v4sgbF+k6Psj4wAx9F8b50h89hr/4SvIqSuIifU3XjOt6Q6mqtpjkyIrEsiVtlS9IuEFpWpSExWSkeUMVjzyzhbIbKOoIgoDvFszKfokbY+u6ShKpDG+f6ufZ/eeQJZESv4uKQs9tJRqCILCupphoIs1Lhy7x2tEeLGaZ7a3V7O6oveW4KpPOceD1LlKJzIpJ+NUwDIOXhnr4xsWT9IXnkUWRH/adX7JNSs2xu6yaAusqHWUhL2zzT/7kk1w8OcTF44MMXMiLsmiqTjSYuOExm60KTZ1VPPDMVnY+1o7tOjS0Dxre0+j4nf09DA3NMjY6T2VVgOGhOe57YB0bN1bz1lsXOXNqGF032LiphocebiWVyvKz507R3z+D2SyTWxignp+P8/bbl+jtmcLlsrBjZwOdnVVYLCbcHtvioIxZkbFZFWRJpKrUSyiSBHScdjPDE0FiyfTijXU+nMAwYGwqjCSKlBZ5ePtoL9mcRn1lgJHJMA6bGZMsvu+UU95v0FSN46+fZ3LoqiHwJzbckaHaOwlBELDIJspdV266fls+SHJb8j96RZRwKsricdtMymLSIQgCPot1yf4uy+Fd3t4qy0v+vhEkUSTgsBFw5I+je2aOIyOjPNBQR01gKaFSFAVSao5DI6O0lhTRVrJUGeztvkF+fqmHhgI/n+xsx2FeOViVRJEil4Mi160pbGTSWQ68cIbBi+NLKr8f/+0HeeTT2yko8SBK4pJzYBgGJZUBKuuL+bM//g5zU5HFz9TQXsGv/osnsDksyCYJWZEX5UQ1TcubQm7NV9nMFhMe3+r0ovmpMK/96DhvPXuCdDKL2arw0Ce28eAzW/AWuDBbTPljW5DHzmVV0sks4wOzdJ8eoqqxZJmyzvVwcl83r//oGMGZ6GKSIZskPvE7D/LAU1uwu61X3hMhT/H8UCezEyFe/cFRXvrOofxwoAHh2Shv/Pg4JZUBdn+4c83X0Jb71lFWV0ju4gSb7m1m+0OtNHVWYXVYUCwyJkVe/D50TSebUXno41s58voFfvzXbzI/dUWCdmYsyJmDvazbUkvRKgp3l1FU4edf//WvLQ6LXo1ENMX/8cW/WnIDrmos5p/935/BYl/OxV8JHr/jjs5n6LrB+NAs+188iyRLPPixzTz1pd3YnVYk+cr1OjsRWlXdpbK+CLNVYWJ4nkwqy2jfDAUlHrwFTkRJoLGjHDWn0d81wabdTYTn8+pNvoUOkcks43DnJdRFUSCw4JdxIwjXoZ9abAollX4KS700tpXz2Ke38+ZzJ3n2Gwc48U4P5bUFfPq3H1jc3mY3LypcJaIp4tHUitK1AKHZGLlMDkkW8fgct+2AfBlmq4LDaUEUBURRoLTSf9vn4Ze4ddgsCvdtamBTcwXprIpAPp7yu/PXSUWhm6//m8/gti8Pjk0miXvaa2ivK1mcw7gaFkVmR2s1LdVFpLN5SVyX3YLdmr/PBjx2/u2vPYLLblnTnKGhG0RDCQZ6pihZow/FrtIqKpwevnbhOCZRYHfplTkVURDwW23Uurz4V0k0BEHA4bay9cFW2nc0kEpk6D03wn/+jb+lvL6I3/6PH8d6ncRBlAQUswmLVcHptWGx3nlFrfcS72miEU+kcbms1NQWkk7n2LiphoH+PPXhxPFBPv/5e1AUmb/+6zeorStAVXW6L03wld9+gInxMD/4h6Nksyq9vVNEwkl+7dfv5fy5Mc6dHaWi3Ic/cGVxLPA5qG8oWrxB1FUWUFbkQTcM7FYzuzZayeU0Ht7ZgtNmxmpWcDutiKKASRbZ0VlDJpMfWrKaTRR4HVjMJrZ31KAod2ZxnU8n+Ub3cfZPDBJTMzS6C/hSyxY2F5Sj6jonZsf476ff5ndbd3Jv2RUuZTCd5Pt9ZxiMBfmt9TuocflI5LK8Od7Ps0MXGImH8JltPFG9jkcrmvBZ8kHrX3cdZjaVYEtBBT8b7qI7PItLsfBgeT2/1rJ1cV7hdnH2QA9nD/SQjOd56ZVNJbTtaMS6wqL0fsdKP/6rH7v2+Rv9fbNIqyrBRIqctrJKmapphJIpUrnlnb4tlWXMJhLMxBPoxrvnYD/SO83QtTLGm6rZ+5GNFJb5VpzJEQQBq8NM86YqnvjVPfzdf/kZkO9cjvZNM9I7xbYHW5e8RjHLbN3TnA/IilxL9rUaRvun+f6fv0IslMDls/N7f/JJ2nc2YHdYVqdzGAZF5T5at9YiyuKav8PwXIyf/M1bTAzNLVI6REngt//jM+z+UOeKamsms4zVbsbltePyObDYFH74v97A0I184aN/hiOvnaeho4KSyrXNw1nsCr/xbz6KxWbGU+DAZrdgMsurfg6zVcHhsvLQx7ciAN/87y+SWuj86LrBSO8Uk8NzN0w0TIpM8SrqftFQYtn5VswyJVWB94xOqeZUgrOxvKFcYxHNHZUryqDPTUWIhFauUAaK3bh9dvrOjzPSN00ykaF9ez2yLCEIAkVlPhwuK4PdU0wMzyObZMprCpAWzoUoCvgKnRRX+JiZCDPaP8OOB9ff9hzbZZlyl9eO02PD+qntzEyEOfDyOXrOji4qZAE4PTZKKv0oZpmp0SCTI/Mr0gUNw6Cva5xYJEVFXSFOt+2OdZgkSaSgzIu/2M344CzT46H87++XicS7BmGFGYqrYbMoi4Pg10IxyYtStNdCFAQcNjOOFYb5L7+vxWyieBWKnkmWKAmsfV5BVTUunB65CXlYAbfZwnrFzHp/IR6zhUeql3JvJUFEusH5EQQBk5Iv3Dg8NrKZHIFSDxarQk1LKY7rzNdd3u0vUnJxNd5zvo/bbcVmU0gksvj9DiYmQgwPzVFY6CZQ4MRkkigudtPfN4PVplBQ6CIQcJLNagQKHKTTOYYH5zh8qI+x0SC5nEp5hZ/0NReZJIlYrrqQTbK0RMHAZlEwzFcGnBQRvAs3PEEQsCgyFuXKzflywmK9QyoI8VyGf37oBUbjYT5W24pPsfHGeB9/eOBn/OmuJ9gYKKPQmr/pvTbetyTRGEtEODIzQp3LT5ndRUZT+X7/GX7Qd5Z2fwl7S+voj8zx9UvHiWUzfLK+A4/ZSiST5o3xPl4f7+Xxihb2lNZybn6Sv7pwGKdi5rMNG2/7cw2cH+PZv36DgfNjizSJhz+9k+LqwD/q6tN3TpyhZ3aOyUiMUCrN7+7exvaqSr55/BSnxiexmGTub6jj8ZZGpmNxvnvyLAPzQWRRxHcD6lI0k+G7J8/y7eOnKfe6+dSGdio8bmyKgkNRmBOSS7Z//sIlXu3uRxBgW1U5n97YQSiZ4vunz9E1NYMiSWyrKufjnW0Mzgf5ybmLDAVD2BUTjzQ3srd+qUrJ8KVJgtPRJY9t2tuC7waD/4IgYLNb2Linme/9z1dIJTJgwNRIkFP7u5clGgCBIhfRUIKh3mk0NZ88FZV5cbpXPkfZtEo2rSJKAv/kv3ySrQ+sv6F87OUg7WZpfgd+fpahixNL/Goe/dSOVZOMqyFKIgWlHnY91kHP6RHOHMxLweqaTtfxQS6eGFpzoiEI+a6QIIoIwtpuZoIo4HDb6NjVyJlDfRx59QqVYGokyMx4aE3v/UHC1bQlNaeRSS+f3ZmZCHH6UB8zV9H7roYkiVTWFdJ3fpzTh/rJZlRaOisRF6RyTYpMbUspvefGOH9sAItNoaL+ShAvCALlNQV07qznhe8e5uhbeZ+MDTtXHjpSVQ1dM5BN0pLfVi6jIkh5padrCyGCIORNbRFWVNAUJZGaphKaOiq5eGqYUwd6aWgrxxtY2tU4e6SfrpPDpJNZdjzUittnv2OBkiAINLaW09RewYGXz7Hv52eoaiymsa185fOwoGBnUqRf2GDt3YYo5ju5/d2TvPnCGc6fGCIWTeHy2GjbVM1DH9lIWZX/up5Rhm5w/GAvb790joHuSZLxDA6XhbrmUnY/tJ4NO+oXk+qVkMtpdJ8b5cBrXXSfHyM0H8fQDRwuCyXlPpo7Ktm6u5GSCt+SrmIum+O57x1lsHuSob5pJkfz69P48Dxf+tCfLrsmtu9t5rf+xYeW7EMUBD7Z2IYoiJil2wuNBUFAsZgoqykkEU0hCMI/6iT5PU80BEFEEPSFhTj/WHGJmzOn8/xgWZYYHQmyfXs9hgHTCwFMLqsSDCYwm2UKi110bKjk85+/B8jfMCxrcJq+merzu714PTd4gTNzE/zprifYXFiOLIjcV17HZ1/7Ln9z8Sh/uecpvGYrO4qreHtigIlElFK7C03XGYwGCWVSbCwowyRKnJwd542xPnYUV/Gb67bhMVvRDB39FLw02s22oko6zflAbD6d5J927OYj1euxyQofqmrmXHCK10f7birRGO2dovvkEIpZRjJJxMIJek8Nc2Z/N1Mj84vB1qb71rH5wdb33RD43YSqaewfGOaP778Hsyzzh8/+nM6yUs5OTnFhaob/9KGHmU8k+NrhE9T4vMzE4oRSKf7DYw9ybHSM17r7rrv/VDbH/Q217K6t5i8PHKFraoYCuw3LCgaEE5EoL3b18F+eeJhkNsefvL6PLZXlyKLIybEJfn/PDio9nsWkfCQUIZxO8Xt7dlDkcKCsQCHJe2QsVVWqbChe21yBQF6dpzpA/4VxANLJDFMj8yTj6WW81WP7unnjhTPour448PfEp7bT0nF9NbdHPrWDzXtb3rUZoUw6y9HXzxOeiy0+ZrGbeeJX9mBzrs03RhRFKuqL2Hxfy2KiATA9GqT37Chb7lu34vzDSrgVipEgCBRX+mneWL0k0YhHkySjNz+g+X6HJEv4i9wUFLuZHg3y1vOnKasuoKa5hFxW5dLpEZ7/9kEunBhCksRVZxcqG4pQzDLH3rpENpOjqaMSYSE4k2WRhtYyzh7p59TBPixWhcr6pd2CQLGbex5po/fcON1nRvnL//As9zzSRvu2Wlw+O7msxtxkmL6uCU4f6uXDn93Jrodbl3idjA3O8j//zQ9pbKukY0cdVQ3FOFxWspkcU6NB3n7hTN513GOjZWP1sntdfWsZex5vZ6R/mld/fJxoJMlDT22moraQTDrLmcN9PP/twwz1TNHUXsHuR9qW+XjcLsqqA+x+rJ2RvhlOHeglNBdnz6NttGysxuGykknnmJkI0XtujJMHe/nSHz1Gx/b6993c3wcFkiRw/EAvP/jbfQz1zaCqGoZuMDEapO/iJEf39/Abf/QY7VtqVqQOZrM5/r///Dz7Xz5PdlEyG4QJgaG+GY7t72bPo2187rcewLHCLFYinuaF7x/lZ987QjgUR1P1vKob+X0M9k5zdH8P/Rcn+NIfPEzgKlGMZDLHT755MC9NrurkcvlCs67rJFZQ+Euv4ifmNOW7LsbCa6/Nw2/U1bgaZotCZWMxPWdGVp1V+seC9zTREBAWK2xX/ivQ0FDMyPA8/+U/PYemG3R2VrJuXRmJZJaCgJN/+S/+AY/Hht/nwGw2sW5dGWOjQf7kv/wMQYBNm2p47PF2Dh8a4ucvnmFuPsbZMyM8+lg7mzbXYL2L+sRrxeHpEYpsTpo8BZjF/NfiMCm0+oo4Oj1CVlNxKWbuLa3lheFL7Jsc4JN1HUyn4pycHaPE5mJzQTmCIDAUCxHNpmnyFOC32BEAWRBp9AR4Y7yXuXRicUDKLMnsLKrCrSyY7CBR6/TRFVqb4eBljHRP8O3/9jzzU+H8A0b+h6pp+mLVrLy+iGd+92HK6wr/UVedJFGkpaiAvzxwFMMweHJ9MxaTzOB8kPqAH5fFjK7rFDoddM/k51rK3S7cFjMBm40Cx/UlTn12KwG7DbfFTKnbRSyTIa2qKyYaA/Mheubm+d9+9jIAsiiSyGRpKizgC5s38K3jZzBJIh/vbKOtpIjOshJimQz/6+BRSl0uPta2jhr/Uh5scgVXU4fLet1K1mUIgoAki9hdSwPoTDpHIppalmhEQgm23NPInkfbFqUPb+SOKskiT/zKbswW07t2HZ4/0s/E0NySYeptD6wjUOK+qXuO1W6msqGYQLF7cW7FMAwmh+eYGQ+tOdG4VVjtZgpKPJityqLZZy6TVyXSdeMXqkonCAJVDcU8+sltfPcvXufY292cPti3MBgtoKkanoCTj//mfVw6Ncw7L51bcT9VDcUoFhNjgzM43FbKawOL37kkSzS0VaDmNCZH5qhuLKGybukslSiKtG6t5Vf+8FG+8+ev0XVqmB989S1+9LW8co0BYBh501bdIPNUdllnQtcNpkZD9J2f4KV/OJI3H0TAIE/B01Udk1lm2/3rePyTW5d9BpMic9+TG8hmVH78t/t4+/nTvPPSOUQhvw9dM9BUjcb2Cr78Lz5MZX3hHe9Qi5LIjofWIwgC3//LNxi8NMlwz9TCPBPXnAd9cWbzg4hr1bK0hb8vB7ZXP3/t332z87itFvx22+IMw62sa2PDc0yOh6hrLuF3/9UTNLWVo+s6Jw708eIPjzHcN823/vINfi/wEarql97DDcPgf/3JC7zx/GkkSeIjn9nBtnub8PodTI4Geee1C7zxwhle/9lpFMXEr/zeg8s6I6cO9fPqcyeJRpLsfqiV+z/cQXGZj2wmx+xUhK7TIxzd182mXQ3LklqX28pXn/19wCCbVvnB19/hh1/fT3l1gP/2d1/GbFka6kqSuOL9aCaV4FuXTvPsQBfBVHLxZ2UYYJJE/v7hj9NRsFx44fI50NERyXcRXT47v/avPpovgq2h8H2r+L2T/5nPVH6I7YHVlSBjuQT75k4QzEb4XNWHbyjGcKfxniYaDz28lAohCNC5oRJBEPjYU5v56Mc2LT4uiiIOh5mv/Pb9S5RbLt/oPvu5nYuPX05aduxsYPuO+qv2L7xvE8tQNkVfZI77n/urJY/rhkHAYiehZvEoVkpsTtZ7C9k3McDTte2MxcP0RObYU1K7SK1KaTlG4mH+7dGX+Q/HX13cl2GAjkFaU9EWOPpesxWTtJSvLQrC4vNrhWGwODzLNd+PKIvUd1TyxX/5EVp31K/Khf8gwDDyJkLqNdVMQQBZktZsKBTLZHimo5V1RQXIUp7/We3zcnBohIyqEs9mmYhEub+hltl4gtPjk6RVlWg6w3zy+tXkYDJFOJUmrapMRmJ0lpVglmVymoaq63mTJE1HNwyqfR4qPW7+rycfRRYENMPAtpCQdJYV01FazOHhUb594jT/5UMPY1NM7K2v5Z7aan505jyv9fTx5R1blh6AICyr4NyM9KShG2SvcZ+XJHFFipPZqmAYBoZuIC7QXm70G1+/pZaiMt+7qsnef36cWHgpRa19RwOK+eaSG0EQcHrtFJb7FhMNgOnReWYnQtQtGKS+WxAEAbPFhNV+JdEAyOXyFUvxF0xtz+W18eQXdlFWXcArPzrGUPckuZyGN+CkdXMNDz61iYa2cpKxNG7fwIrmgtVNxTjcVmLhJOs2VGMyXVlfJVmkrqUknzALUFLpXzFZlCSRzp31VDcVc3xfNwdfPs9Q7xTxSApJFgkUualdV8qWPc107qxfptFf1VDE7/z7j3H0zYsMXJxkfjpCJpPDJMv4Cpw0tlew+7F2Nu5qWHE9FgQBm8PCR75wD62bq3nzudOcOdzP/HQUkyJRWV/EzofXs/Ph1mWUKsgnKmaLskhFy+8UlAUD3avPmyAKmMwyisW0LPiTZYldj7TR3FnJ4de6OPxGF2MDMyRiaUyKTGGJh/rWcrbd10L79ro1mz2+n2BgEEqmmUsk8Ntt2Ewm3ukfJpHNck9dFV6blYlwFN0wKHI5UCSZ6ViMYCJFdcDL8HyYMk+e3eC2WrCuUFBaC3JZjXUdlXzhdx+kpaNycR2tri/C47fznb96i67TI5w5NkBhiQfbVR4pJw728dbPz6HrBn/0f36U3Q+3Liae5VUBGlvL8QWcfO9v3ub4gR427axfFPC4jKmJEJOjQTq21fKRz2ynsfUKTa6moZgtuxv5/O88sEhDvBqCIGBd6JiLoojpKvM/i01ZE8MF4NWRPnrDc/xO+3YOTAyzzldIhdPDj/susLe8hhr3ysPlhmGQ1jP8+/N/wX9q/wMkFqiSN5DVvRPIaNk1xWsCsLrv+buLu3qX0HWdXFbL80qvUWpSzDKKIpPNamiqhnVBnxvyX2IqmcUwDKw2BV3XSady+QVLuhxcCOi6QS6rLtAoLj/3Ps0sroFHsVLvDvDPOvbgUpZWbRVRxGnKqxB4zTb2lNbyd5eOc2xmhMFokJyusbWo4oqykWSi0uHhvrJ67imp5tpzUOvyIS3oEIrCCrntLZyygjIf7bsa6Ts3SiyUQNd0bE4rVU0lbHuknS0Ptf5CGNDMBOP8/YvH+PGbZ/LJ1ULwHPDY+cpTu3hyz/I5gmsRy+Ql9/7u6AkEQSCSSvNvHr6PjrISTo9P8ns//hlm2cQDDbW0lRQxGY1xZHiUf/qTF/FYLZR73Kuqb4iCQLnbxdv9gzx3/iIVXjfrigsQEfjakRMcHholmcsyEorw69s3U+f38UzHev75cy/lh+IsZv7dIw8wG0/w719+A1kUcVrMPNiYnwk6PjrO3x45iSQIFDodPLG+edkxuLx2zFaFxFX0mpnxENmMivUG3QbDMMikc8yOB5c8brYqKzoki6LAz753hFd+cmKRI/7kp69PnWrqrEIyrX2o+2ZhGAYjPZPLTPmqm0qQboHWYbEqy4LRaCixLJF5t5CXO73muA1W5Pd/0CEIAg6XlXs/3MG9H169Qvj533+Yz//+wys+5/E5+Mvn/9mq+y+u8POjU//hhscB4A04eeipzTz01OY1foI8ZJPEPY+0cc8jbTf1umuPQZIFGtsrabwJE1qAP/5vn1r2mMtj599/9UvLHq9tLuU/f/3Lqx6DIEBBiYcnPr+TJz6/86aO44OAYCLFxHCCVC5HR3kxFlkmmc2h6QaSKHJyZIJgMsVEJEpned7ZvWd6jiKXg6pAPvAdDoY5MhznU5vab5mmI0kiTe0VNLaWL+lUCoLAzvvX8c6rF5gcC3L6SD9b9zQtJhqGYfDqsyfJZHLUN5ey+5G2pZ1OAdxeG9v3NvP8PxxlbjrK+ZPDyxINi8WE2aowNx1ldjpKTaOKbMpLoF/uyK3R5/aWMZ2M0xEo4SO1LfSE5qh1+3iosp5WfyH/14n9bCoqY51vaXxmGAYpLc14aprR1BRxNYmEiCgIWKU8U0Q3dLK6imaogIAsSJhEGVEQ0Q0d1dDI6SoGBgICiigjC1cKFJqhk9NzqEa+ayciYJHMi50JHZ2kml7sqCiiCVlcMN818sqMewo2IwvSkm5GVs+hG3pe8dDQAANZkBePzTAMcoaKqqvoLJiTCvnjN4vKmu+hdy3RMAyDseF5Xnr2FKeODhAOJUjE8u7adoeFz/76Hh5+opO//6u3OH1sgD/5iy/gXVD8yGZV/vT/fJbpqQj/z1e/xPnTI/zZf/wZn/3yvTz0oSs3g9B8nB988wCT4yF+/fceouIuu5TfDnYUV3JmfgK3YqHFW4gsiovGMZIgIC8oQFllE+t9xTgVM88NdmFgUOfy0+a74vpd7fISsNrRDYMapw+Hkl8QNF3HwMAimdYkE3czaNpYTdPGX7mj+3w/wmI20VxdyM72GqKJDLOhGDOhmzOae2dwhHK3i1/btgmbovCNY6cYmA/SXBjgyzu2LOsQlLic/MsH713Tvss87uUdhgV8ZedWvrJzOU3i8XVNPL6uacljFV43f/Opjy3bdkd1JTuqrx90lNcV4gk4CE5fqcCfPdTDlvtasNhWX5wMwyCXURm4MM78VcPkDreVivrCFatD6zdUUVTmRdd0RCm/uBeXX1/SsLSm4LoDjbeLRDTF3FQk391bgCSJiJK4ZGZjrYhHU8uCh0wqR3aFYeW14HJnSc1peSdwLc/F1nVjQd3KWNzOMCAeSS4rDP0Sv8QvcWfgsVqQHTKHB0aYCMeo8fsW5cydZjOxTIYilwOfzUpO05iIxGgqClDt9y7IpRscHBjmkfWN+fv6QjB4s3B5bBQUuRa7AVfD7rBQWuHHalUYG5pfMuOgqjrd58fRVJ2q+kJCc7GripWX/2GQTmVxe21MT4SZnlguJlHTWExTazmnjw7wtf/nZfovTXLPg+spLPVgsZiQTe/+oL9JFNEMg5yuYzcphDIpItk0XouVYDpJRl2uZJUzVP605xv0x0dJqCn+4NSfIABl1kL+fevv5k1AUzM8P/EWF6J9iIh0eJp4uHgn5dZiwrkYb88c59D8aaK5OHbZyt7CLewObMajONENnYvRfl6eOkBvbBjN0PErbn6v8XOUWAowMBhIjPHK9EEmU7MEzB6eKN3LDv8GNEPjTPgSfzf4E6Jqgq2+Nv5Jw2cXj/3lyQP0xIZwmuxciPaR1XNs9q7nsZLdlFgKSGlpnp94m1Phi4RzUeYzEcyiwt7CLXyu6gnM0trGEO5aopGIZ3juB8cYGZjlc1/eS3mVn5eePcnBty7xO3/8OBu3196QWw351nNZhZ/axiIO7+tm70N51RjDMJgYCzLQO01rZyVlFSvLKr5f8WT1et6ZHOY/nHiVh8sbKbY5iWYzDMWC1Lv9fKHpSkUrYLHT6S/l2cELNHoCPFmzHuUqlYRWXzH3ldXxk4HzzKbji0nIaCKCIko8U9tGpXNt+tK/xFK4HRae2N3KE7vznYv9pwf4n99/m8Qqw2UrYVN5KX9z+Dh//s5hdMPAaTazu7bq3Trku47GjirKagoYujS5ODB75NUutty3nu0Pt66ouGQYBpqqMzYww0+/9taS5wpKvbRuXdmEz+GyEg0nmZ4IU1VXiNWuIJuuv6w5XJZ39YYVCyfJZpfekDRN559+5P+5Y++RTefIrlG+8TIMwyCTypJKZImFEoz0TTHSM83E8ByxYJxYJEkimiaTzpLLqGQz+fdQs+ovZKJhGAaqoWFamInTDZ2MnkMzdBzy8u6Zqmtk9RwmUV58zS86VF0jnsuS1VWskgmbrCCtkqQn1SzxXAazJOM0rc3z4JeAZDaHkNaRRIncwnqpyBJ9M/OUup3UBnycm5hmMhzl3sYayr0e3u4d5MLkNA+3NGAxmfji9o0cHBilNuCj2Hlr/kcWqwnrdTxsXF4bitlENJJEvWoeJhJMkE5mAHjlpyd55acnr/s+giCQTi2XX29uL+fJT29HU3X6uyf4h6/t47nvHqZ1UzX3PtJKS3slvgInynWkuW8XlU4Po7EI8+kk63yFvDjUzVwqSVrNIQnComfW1VBEE/+q5Tc5F+nh/7r0t/zNlv+wyBgBmM+EeX3mCAYG/7rlKyT1NC9MvM3zE2/z+aonMYsKzc4aWt31FJp9nAhd4K2ZY5Raitjoa2EqPce3h5+n3lHJ/77uN3DJdkaSkwTM3oWZLYPDc2f4nYZPU2ot4LWpw/zDyMusdzXgUZxs9rVS76jixcl9zGfDy47/fKSX3QWb+OfNX2I2E+L7Iz/nbLiHQKGXo8HzdMeH+LWap6myl/K3Az8mo2f5ZOVja04y4C4mGjOTYSbHQ3RurWHjthqsNjN7H2nl7Mlh5udiqDltTYkGgNNtZfueJn70rUNcujBO24YqclmNkcE5cjmN9R2Vd2RI0TAMYskMmezabuiyLGK3KCg3CHRWgsNk5r9uf5zv953mrYkBZlMJ3GYzTZ5COvxLnWG9Ziv3lNTw5ng/foudrYUVS543SzIfr+ugzO7mZ0NdfL/vTF420e7m4YpG3AvULL/FRoXDs8wvo9DqoNLhuenPsBouV0j/MQ+AX40ip4N/9dDe9/ow3jW4fXa2PdjKQNcE44MzYORVmL76H58lPB9n6wPrcLhtyAueFJpukE1lGeuf4Xt//grdp0cW92VzWmjbXk/HrpXlPc8dH+TQmxcJzsZ46KMbGRmYobmtgs5tdStuD2C2mt/VWa10Mov2Lg+mapp+Xcfma5FKZAjORDn2RhdHXzvPuSP9S4KFf4xIa1kuRAfZ7MvT/1JahsPzXcxnInyi8v5l289mwvTGRqmwF1FjX3kg9BcNg7EgPx0+y1gywj1Ftdxf0oDfsrIYxeGZYX4weIoGdwG/2bQTu2ltxotrhapn0A0VSVQQuf1gM39funxveu9mO5wWMy1VRWyuKkNeSOK2VJWzsaI0z6sXRSq97iU+F5/a3I5uGIjAPfXVANQV3F5xVRDF63Z6JUlEEPOzHFebFGczuUUWpctrw3lD9TEBr395MiRJEtv3NtOwrpTDb1/inVcvMDY4x5kjAxzb101tUwlPfnob2+5txuW2vSvy+DtKKmkPZAlY7ewuszMaD/PCYDeGAV9Yt4Fyx83Rvw3DIJiNcCk6wCPFO8kZKiZBpsxayLlIL+PpaRoc1VTZS0lqKZJamoDZh45BQkuBAceC53HINh4u3kWFLV809ihX+UYhsLtwE63uBgwM7inYyBszR5lOz+NRVjbavBpV9lK2+tsosxZRai1k3+wJ5rNhUlqauUwIn8mFQ7YhCxIVtmJOhC7ctA/X3SvLCAKiIJBJ58ikVUwmmVQii0B+PuNmLhqLxURLWzlOl4XD+7pZ117B7HSEi+dGKS330riu9MY7WQMMw+B7L5+ke3htCkwVRR4e29VCU1XRjTdeAU7FzK+v28avr9t23e1kUeSekmpefuLXV93GIsncX1bP/WUrV4IBvtSylS+1LKfS/HHn3hseq2EYZJJZ0skMdrdtxUHdVDxNeDZKKpZGFAVsbhueQhcm5d2rSPwS7w/seqyDoYsTvPqDI3lzMwOiwThf+0/P8vzf76e+tRx/sRuTSSYeTTHSN03vmZElwa/FqtC5q5Enf2X3ql4X87NRWjdWE4+mEEURURBvSBt4ty+9TCq7RG3qvYRhGITn4xz8+Rl++rW3GR+YXXEoX5RETIq0SPG6rKkvigK5rEoiml7zMP+dQlbLEVET5HQVAQGnyYZDtpJUM8TUJKqhYhZNuEx20lqWjJ4jq+UwSyYyWg6XyY5dtpJQU8TVJDpgk8x4TA4yepah5CQ/mzhImbUARZQXK3QpLcNkah4DA4dsxWmykdVySIJIk6sSlykfaOd0lWguf3w6BqIg4FNcmESZSDZORs+R0zU0Q8NvdmOX3t1O2mWktRzBTJIiixNJFJlIRPCabZglmfl0gqSWRSBfnS2wOMjpGuFsiqSaRRJE3IoFm6wQz2U4PjeKJIh8sX4rda4AFklmNh3HLivYZIW5dBxFlHGYFO4vbSClZhlNhN+VzzWZOsNcppcK2xZ85lqEWwxfdEMjrUXJaBFyehIDA0W0o0gOLKILSbz7qpQrdYmufuzaBEAgr0h1J6HmNNTc6kXVTDqHpupYrcoSAQGrzbxY2P3wJ7by9Bd333CNvd7Qvr/QxYc+vpX7P9RJ9/kxDr7exbkTg4wOzvLn/+l5koksD39kwzIFwjuBIpsjP0+haeR0nU82dvB0fSuSkC+KrdTRuBHSepb++AjfH4miiFdeX2Urya/PuQhH5s9xIdpHUk2R0XKMJicxFoL5YDaCX/FgWeW6FIBSS8HCv/P/k0WJrH5112j1tdtlcuCQbYuvlwUJ1dDQ0CmzFtEdG2QgMUpGzzCQGKPOUXnTHd27lmiUlHtpXF/GuRNDeDznKK30cWR/D16/g+q6QpTrqZcYLKu+ebx2Nmyt5cThfqYn8t2SqfEQex9uXUNGvTaksyo/eess85G1DV22VBexva36jrz3jWAYGqoeJqNOAAYmyY9ZvnkFGk1PktEmsMiViIKJtU6CZ1M5Tr95gZNvnGfP09to2Va/ZGA0EU1y+PmTPP/V1+k7NYRJkWnb3cyTX3mI1l1NmFdxCf3HAt0wiMRTROJp0pn84J8sidgsCj6XFbt1+flJpbNMzEXJ5FR8LhtFPteqC7qq6YzPRogl0nidVgp8zhU9L2LJDKFokmQ6i6brSKKI1WzC47Tist96YGSxKTz9lfvRDYODL51lbiKMuuClMj0aZHo0uOprRUnEW+CkY2cDH//tBymrXe5KvPg+VoVELE1oPj8no6raihzjuwnDWBiauwqySaK0pmDNXdsbQZRE3CtUBa9FcDrKP/zFa7zy/cOkk1fR+wRwum04PTbsLisunwN/kQuHy4rNZcViVVAsJswWE/0Xxnn1B0dIxTN35NjXiuHkNC9NHiarqzhNNrb6WljvruF0uIdToV5yhoZNMnNvQSf9iXHGk3OMpWYpsfiZzYTY4mthV6CN16aPM56cRUXHIVn4TNVDTKeDvD59gv7YGM+O76fY4uPBoi1k9Ry9sTFSWoZILkGLq4rHSrYzkw7x2vRxgtkYj5RspdVdy2wmzLPj75DW8gF6TE3yqcoHqLaX8NPx/eR0jVA2xsXoML/f+AwdnnzRRzcMook0ozPhFT+3IAj4XDZK/bcmnjEYC/KN3qP8i44H8ShW/u8Lb/KFui00uAv4X5cOklAz2GSFFk8RT1d3MJYI8+zwOULZFALQ6S9nb3E9Z4LjvDPdTzibIpRN8kh5C2U2N8+NnGdXUS2bAxX8w+Apqh1+7i2uu+MdjOWfaz8XI89BwMBpKsUs3TxFSDdUgplBLkaeZyj+Dkl1DhCwSl7K7Zupdz5AkbX1lvb9QUcyniYaTq4oW61pOvOzMTLpHCXlvkXPIgCXx4rbayc8H2diJIjVZrojM3BWm0Ln1lo6ttQw0D3F3//5a5w83M8L/3CETTvrsdrNq9+frnrYuAnaZ0rNMRgJ0h2eI5hOoV3z2o/UtVBku/61YRjGkvcXEKi0lfClmqdodFYveVwUBF6bPszR+XM8WLyDzd71zGaCfHXgh4vbyYJEwlgYxl4RAibh1uVz867nK9+XOjyNHAme4WcTb+GQbZRaC3m4eCcW8eZ+63ct0bBaFbbsqKfv4gSvvnAGX8BBSbmPz/zaHiqrA1cct00Sai5v1LI4tKhqzM5El2TRdoeFzi21HHmnl/2vdyFKIrIssX7DneO694/NEVxjknG3YaASz55jIvq3ZNRxfNYHqPb97ze9n1Sun6Hwf6XB/98wy8U3fsECgtNhDjx3nNe/s590Ik1teyW2BVUgTdU4/spZvv/ff8bIpXFsTguGrnP056eYnwzx23/6RZo2196SidgvArI5lf7xed483svRrhFGp0Ok0jkcNjMNFQXcu6GOnR01lBYsVZcan43wP76/j9PdYzy5p41/8sk9WFZJ0GeCMf7vb73B4fPDfPaRTXzu8c343VcoD7puMDodYv/pAfafHqB/bI5EKoPNolBd6mNHWw33baqnqsS3Zsnea+EJOPn8Hz5OUbmPH/3Vm8xOBBfmFAVESUDT9Dw1QBKRTRJWuxm7y0pxhZ9tD7Vy30c34XBf3yeitqmEQ29eZLB3mpGBGTq311FS7rul471TsNjNy9SlXF47f/xnn8N3lcnU7UAArI7rL/apRIaf/s1bvPaDo0uSDLNVoaallE33NtO2vZ7adWXX9ePY9/wp3nr2BCnubqKR1jJYZQt7fM0UmD34FBezmTCDiUk2+5rY6l/Hc+Pv0BcfI6GmqbIXUWYNEFWTdHjq6IuPM5Kc5sh8F5+vfgSzaOLvhn7OWGqGBkcFHyu/l7HkDF+p/ygACTWFbuiUWP18ue5JTgS76YoOEcklqLAXsaewk5OhniXHmFBT7C3cwEZvE3/d/yzT6RAllgDjyVk+XLYLs2girWdZ575iiqdqOke6hvnfv/rzFT+3SRZ5+t52/vhT992ZE7kQn+iGQSSb4smq9ZRY3RRZnWS0HF2haWbScT5fv4Wu0BQ9kRk2+cu5t6SelJZjIhnhicpWCiwORuOhlXZ9V5FUQyTVedJamKweRzNyC5VYCxbZg03yLZzra+fAdKK5CY7NfY2RxKGrnyGlBemNvsJsuptO36epdd6HSbzzFfP3MxLxDOPDQaLhBB7f0mB6aizE+PAc2YxKXXMJNvuVcyPJEht31DE+NMeZYwNMjAYpX0WI57LABLAkmbn8+GVrgqshCAJ1zSXsfGAdAz1TTI2HlsmfX4vLHXBdN0gm0ovStzfC6dlJvt51gqlkHJdiXjJrAXB/Re2qiYZFyq/H0Vwct+LAMAxkUcYl2/ApLgYSo9TYy1BEExk9lxfmEc1EczEcJhtVtlIEQWAsNU0kd0U0pNJWwpszo0ymZnGbHEiClF8bJfOqCcKdQkJNEc7G2BHopNpWiizIxNUUbpMD+Sboi3d1oq3v0gSSJPKVf/YIbRurVjzI4lIPiUSavkuTNLSUYug6vZcmmRwLUVZ1hYMoigJFxW4aW0rZ/3oXJeVeahqK7qjS1LGukfetgqMomPFa92IzNTIZ+/otr/iS6MCpdCAKFm5G1zYWjDN0YRR3wEV1a8VikgEwOzbPoedPMto9QWVTKRseaMXQDY6/cpb+M8Mcf+UMZQ3FuP035g/+okHTdI5fHOUvf3SAgfF5inwOakr8yLJIJqsyPBnkf/aMc7pnnC9/bAc1pVeu+UKfk80tFRzvGmFgfJ7BiXlaqlem6Z3uHWd8NoLDqtDZVIbbsbTL1z82x5//cD/HLowQ8NipKPKgmGQyWZWZYJyvP3+Ek92j/PbT97C+tviWOxuJWF7i9rLUtNWuUNdajtvvIJvOgQEWu4LdZaW0uoDqphJaNtXgcK+tK1lWFeBDH9/Ktj1NSLJEQbEb87tojrQW2ByWZe7EyUQGh8eGt8B512iDXccG2P/iaZLxKzK7JkVm94c6+NxCArgmvEdStvXOcnQMzob7UXWVTb5mnLIVSZAWb+p2yUpcTZI1VCySGVXIV/4U0YRmaMTUJNFcgn2zZzAJEnX20sXqn0DeV8gwjMXvxCKZCZjdSIKISZQwCRKasfosi09xYZeti1KWuqEjCgJltgKOzHdhlcw8Wrx1SUAgAG6HlebKQlRNJ6dpZLMqkWSG5C0qiV0NSRBQjbxRqm4YxNV8gmiVTfxWyy5en+jh4PQQTe5CHixtJJRNMhCd52cj5xGACrt3UeXwWogLXjvGwv9Tau6mPZduF+HsED2Rlwhlhwhm+klpUSRBxm4qpNTaSa3zXgotLYjCUs8a1cgwmjjKSOIwAiJW2YfLVIqISFqLEFOnCGeHuRR5EZeplGJb+103NstoOSbTc8xnwsueq7AVETB78vTQdwEGBhfPjHDwjYts29O0YIpnEA4mee25U4wNzmF3WujcVofzmvX5wSc2cHR/D+PD83zzz1/jmV/djb/AlV+LhTwjJZXM5jsmmr4g3HGlUJLN5JgcDZFOZfH4HdidFsxmGQQBNacRiyQZHZwlnc7h8TkWZG9XkXiXBApL8gWdZDzN6cP9bN3TjMVqyit55jREUcC6AqvixMw4Aaud/23zvdS4vDe1VheafRRZ/Lw6fZBaezkWyUybpxG/2cMmbysnQ13IgozL5CCuJvApbjo9LRRa/PTHRzkZ6sJvdnMpOkhcTXE5Huv0NHMm3M3+uRPMZoLYZCuhbJSd/g68yvULVxkty0R6lsnULFPpOWJqgrPhblwmB0WW68fKumEwlZ7DJls4On+O4/PnEYS8suOnKh6j3lmFtMbfx11NNDIZlWQyQ3fXBPF4BkkUUCwyxSUeCorcyCaJjk3VvPPGRX74rYOsa88POQ/3z1JZE1jWxnJ7bXRsrmbf6xdQLCbuf6x9qUHQbcAwDI5fHLvt/eS0EGl1GFWPIAoKFrkSk1SIgEQidzHPlTXVIgoWND1JLHMSm9KISQwQSR/EaqojrY6gGylk0YPN1IAkXt8ZGkDX02S0SbLaFLqRQRTMi+8tCiZ0I0siewFVD+OybFlINC5/dp1I+hBWUy1pdRTdSCAJDqymWkxSPvBNxlLMjs3jLfJQ0Vhy1fsadB3upft4P06fgye+8jAPfW43sknC4bHzs796lTNvd3Hfp3b+o0w0hiaD/M1PD9E3OktHQxkf3dtGR0MZDqtCMJrkyPlhfvTmGfafHkAxyfzTz+zFvcBFddrMtFQXUehzMjoT4nzfJE1VhcvUXdLZHOf7J5kJxdnQWE5FkWdJVyIcS/HVnx7i2IURasv8PH1/BxubynE7rASjSU5cHOVHb57hbO8E33zxGP/bFx/E57p5B+pYOMFL3znE89/YT3g+jtliYudj7Xzqdx+itLrgjhg3To0FESWRqvpbm4t6N+BwW7HY8gPnl6t3mWSWeCRJYdnN3bxuFbqmc+DnZ4nML5VebtlUzZf/j4/dlKO4rul3fXDcMAzSWhYRgXZPHWdCfQwlpri3sBObZGY4MYUkiEym56iyl0Bm5TJJqSVAo7OCbb4WLJIZA4NKW56KJyEiCxLnIwO4TQ785jxV6drg0gCCmSgjiWlm0iEcspUiiw91QX/+2vfVDJ2EmqbeUUaZrQDDyN/wrVKe6iFLIq01JfybLzxIPJ0lkcoyHYzxxqk+jl0ave1z51GsaLrOmeA4bsVKMJPvyuuGwVw6waZABaPxEIdmhvhw5Xoa3QVsClRwf2kDAuBRbIvmr9fCbjIjCQJ90TlkUWQyGaXOGUAzDPqic4wkQkynYlwMT9PiKcYm35xB5VownjzFWPIEV2e/qgGZTIxgpp+x5HF2FvwOZfYtS76dnJ5kOH4IMLBIHlo9H6PJ/TiSoDCX6eFi+DmG44eYS/cyljxOwNJ017saMTXBi5MHeHX6KKquohna4qf8St3TPFK8HctNqP3cDHwBJ7qu8+IPjjEyMENVXSGGAT3nxzm6r5tkIsOeh1tpbitfVsypbSrhqc/v4kdff4f9r15gbHiezm21+AqcCAgk4mlmJiMM9kzh8tr4g3/70SWJRiKW4aUfH+fU4X4aW8sory7A47MjiiLJeJr+7kmO7u9BzWnsfnIDbu/q65ckSTS1llNS4WNmIswPvv4Oc9NR/IUuNFUnmcxQXOZlx30ty16rGwYldicBq/3mjVVNdj5Z+TgH504xmpyi2l5Gm6cRu2xjZ6ATRZQ5F+klrWXxKS4CZi+iINLmbiSlZbgQ6WU4aWKTdx0O2UbA7AHArTh5puJhDs6d5kykG83QCSjexQWv3dOI76rhcItkps3dgNNkJ6mlORW6yEB8NK+YJ8i8On2IOnsFuwo2UGTxAwZW6cp3UW0vwyIqZPUsL0y+TaenhZ2BTqyShbSW4S/7vsex0Hmq7WVI7zd529npSL5yJIocevsSopT3KNR1g8raAp757A5Kyn1U1hTw+d/Yy+F93UxPhrE7LDz60Y1oqkZ318SSfZoUmeIyD4XFbjxeGy1XOUneLiLxNL1rHAJfDaoWIZR6g0j6ELqRrypa5BoKHB/FIlcSTu0jkT1Hmft3sJuaCKXeZiL6V9T4/g2y4qF37o8pcj5DVptF1YKAQMD+JH774zestKh6lHD6HWKZUxhGFt1IY1daKXQ8g0UuR9fThFMHiGWOEc920V7yUyTx8oyHTt/8P6fA/gRZbR5Nj2Kg4bbspNj5eUTBRC6jEg8nKG8owVd8RSo3EUnSe3KQ6aFZtj2+gdZdjVgWFpSWbQ3s/8lRxvumSCfuLg3j/QDdMHjhQBeDk0ECHju//cwu1teWLJpOOu0WSgvcuJ1W/tPfvsKRC8McOT/Ew9vzqjiCIFBa4GZjUzkvHbrIxaFpook0nmu6FUMTQQbG5lFVjc3rKgh4lgYNB88OcrJ7DLNJ5itP72JHW/Xi4KHHaaW0wIVZkfmTb7zGxaFpjl4Y5tEdyxfl68EwDI6/dYnXf3SM8EKw27ypmk//3sOUVhXcMcWQ8yeHUBQTxWXvH7lmu9NKUZkPxWIisyDjaBgGgxcnqGwoRjG/+wo3yXiawYsTS+cygI/+2l4cK8gLrwZd00klM0t08+8WgpkoJ0I9iIDLZKfNU4vbZKfdU8fpcB/Hg90UWby0uKoYT85ilkyouk5Wz+FVnFTZiwmYPTxUvJXToR50DGRBpNFZgSQIOEw2tvtbORa8RI29hEKLl0Kzd/Gm6zE5qbQXYRZNTKdDTKTnEQWRuUyEqdQ8AbOHansxzoVByip7EX6zm5lMCIukMJGeZyYTpj8+ztMVe2l11QALpoBWhearRENmQnGGp0N3JNEosDrZVVTD6eA4dlnh4dJm3IqVrKZxcHoASZRQRImPVLVhEiWa3IUEM0kOTA8iAE2eQiod+UCm2OpEEWWUheFPt2Jlo7+cs8EJjs4O0+wupMrhQxCgKzRJQs1iEiXOhCYod3iw3cLw7I1gkE96rZIXuxxAFq0Y6GS0GLHcJOHsMEfnvsoj5hrscsFVpmdZQpkhAOymQhrdj2KT8129UtsGRGQS6hxTqXMEMwPEcpP4zDV3/PivB6tkpt3dgCSIJLU0U6l5BhITxNV3n8K9rqOCHfe1cPxgH4fevMTPf3icXE7FpMh4/Q46t63jI5/dQaB45Sr6o09twqRIvPH8GSZG5nnxB8fIpHMYBphMEjaHGbfPTmVt4TIne5MiEShyoesGR97u5u2fnyO3UNyQZBGb3Yy/wMn6jVU8/swWXNcplIiiQGGJh4//6m5e/MFRpifC/P3/9zq6biCbRGwOCw9+uHPFRKPW5ePk7ASHJodp8AQwS/KSSCtgtS2xErgakiCx1dfKVt9y016XycH9Rdu5v2j7sue8iotHinfxSPGuVT9TqbWQZypWNgj9nYbPLNvfb9Z9YvHvp8ofXHW/BeblXe3HS3YDMJGaIaml8SourFJerjpn5E0Fb8asD+5SoqGqGgff6ubsyWH2PLCOpvWlyLKEmtM4e3KYH3/nEJu311FY4kYUZVo7K2ntXG4KtuuaC0PTdOKxdH42o6MSj+/Glf614nz/JMkb8ABvhFj2NNHMMTyWe/BY95BWRxkN/w+UdCGKvYhCxzMMhXoIJl9Ct6SYjn+HAvtT2JU2QMRAI5Htpsb7rxEEEzPxHzAV+xZuy/bFzsJqkEQ7bvNWPJZ7MIl+IpmDTMW+jcu8CYtchiy5qPD8LtH0UfqD/3rZ6w00Ypmz1Pr+LSYpwFzyRWbi/4Df9hhmuWTR90AySShXVTdGu8fpPzuMrEi0bKunpObKIK+3yI3ZqjARSrzr8p/vR8QSac70jpNMZXl4WxPVpf7FJOMyZEliy7pK6ssD9IzOsu90Pw9ubVrkswY8DjoaynjjeC+DE/P0DM+wdf2VuSTDMDjXP8nYTJgiv5OW6kKcV7WIDcNg36l+UpksrXUlbGgsX6Z4YjbJtDeU4nXZiCbSnO+fvOlEI5vOcez1C8xeZcx0z+Od+Ircd1SW0OWxkYxnmJ+JLrbpLRZlGXXpbqOmpRT769bFRAPg3KE+dj7SjmJ+96ldodko6dTSZF4xyzR2Vt7U+U/G0wSnozclpbsWCCxX/7q6Xy0IAnXOMuqcywUuqu0lVF8jL1uwUP27GrWOvPrgenc1693Vy563yxaeLFt6c2/zXHErrnGUUOPIv49XcdLsWn5PKrFeWYf3Fm4A4ESwG5Mg0+yqRBFNpNQMOf3mPE9uBwLwTE3nis/907blcx9uxcqj5S08Wr78N97pX1q8E4DdxXXsLl4uH/1k1a27kN8s/OZ6apz3UmxZj1X2ohsa0dwkw/GDDMbeYi7Ty0jiMC3uJxZfoxs6aT0CCJhFOzb5yncnIOAz11Bm28h06gKx3BSx3NRdTzTsspV7Cjq4pyBvRHwq1M03hp6nOzZyg1feOgqLPdzzUCsN60q556FW1m2o4szRAYb6pkklstgcZmoaitmwow5/gWtV6wBRFHnwiQ20bqzm9JEBxoZmiUdTaJqB1aYQKHRRVV9IfUsZbs/SWM3utLLnkTaKyryMDs4Rmo+RTuYwDAOzxYS/wEl9SynrNlRid9xYpMRsMfHgE52UVvg4f2KYuZkomqphtip4Aw7WrxBfAthMCufmpjg5M06DJ4BTMS9hDPxKy0bKnXdmzu79Dq/JRZWtlAuRXqK5OJIoMZcJkTNU2tyNNzUfclcSDU3VmZ4MY+g6BcUuPD4HApBIZBAEAbNFWeRw3wiGYZDNqETCSSKhJEff6cVqU9i8Y3UZ11vB8Ysj6Pqt31wNwyCZvUg6N4JFKieYfBUAzUiQzPagWiNY5DKKHZ9lLPLnxDPnscgVBOwfRsCEgYYgyHite7GYKjEMHa/1AeaTL5PM9uK2Xj/REAUzomAlpQ6S0LvIajPktHk0I4nBjc+0gIzPdh82pQkwcJu3MhX7NlltGrNcgmySsNjN5LIqqYXuhJpV6T87wnDXOOUNJdS2V2G5amhMViRESUTXtLsulfl+wNhMhFgigwG01BSjrBAMCwIoJomWmmLOD0wxOhUmnc1hs+RblBZFprbMT11ZgJHpEF2D02xsKl9UM4rE01wcnGY+kuSxnS2UBtxLFuV0JsfwVIicmhdc+PnBLkwrDOWHYnkFmsszGzeLSDBBcCa6hHJjtt55R3rZJDPYM0xoPr7AKYb2LTXv+UB4U2clnoCT4FUO5yf3dTM7EcLqsNwRn5/rIZXIoqlL1y+rw4LZcnNUlrmpCP0Xbp9CugyCgOkavyFDN34hvD0anOVMp4OMp+YAg3JbIS2uXxxDzvcaVslLq/dp6pz3YRKvdHMDlgaKrW0ktTnGEycYjh+gxf1hrnan1o0cIhKyaF3GClAkBy5TGYpoJ6WFSWnL3avfG7y7a0VTWzlNbVcSypJy3y2vn4Ig3NLrL3chCks8t/S+K0Exm+jcVnddT6VrYWDQFlgqiqP/I4xVAKyyhcdKdnMy1MVQYhwDcJucPFX+EPWOymWD8tfDXUk0FLNMS1s5U+Mh3nzpPKePDgKQSuWYmQzTsbmamoaiNc1X6HreAfylZ08Ri6SIhBPsfbiViuo7NwSuajqnusdv0w1XR9UT5LRZkmo/kjYJgM1Uj8PcirQwE+EwtyOJdkKpt6h3/AmS6Mi7PRoLmsaiB8gbCkmiFVEwoxrR1d50EclcD/PJl1H1CJJgRzPiaEaCtU51CoKAIl2uGgqAiICIYeQpFBa7hUCpj8hslPHeSeo7q5kcnOH8/ktEgzF2fWQzVc1Lq5GpWBo1p6JYlDsif/dBQySeQtXygZTXaVvVYVcSxEWFqExOJZpILyYaAGUFbjoaS7kwOEXPyAwzoTilBfkqS+/oLEMT8ygmiY6G0mW0qehVBpRneic407uUjngtBPIzH3cCR149T6DYQ3VzSd6wbyHRuh0Ot9NlpWiBNpVb+FzvBxfr6uZSGtormBicXaQvzU9HePUHR/nsHzyK1XEdacY7AJMiL0tm1JyKpupLhp+vh0w6x8CFMbpPDd/x4xMEsLusMH4lmMtlNSLz8TXJ9r6f4TLZebx0x3t9GL+wCJgbKLA0LUkyLsMme6l23MNU8izBzODKOxAERFYo8iBgllxYZS+J3CxZ7eYLLL/EBxsPVNTxQEVdXv9ihQTjH5v7V6m1kFLr6vLya8VdSTQEQWDT9jqcLit93ZN55QHDwF/goLWzgtYNVfgDjjXfeCVZxGIz4XRZuOeBFjZsqbmjFcLJuQjjM+HbFFoREAUzDnM7Za7fxGKqveoZARaywXjmNJoex2XeTDh9CJd5OyYpnzQZGKh6OP9vw0A3MuhkkYTrD3Iahk44fZBktocS1+dxmjeTyvWTzHbf1PELrE7x8BS6aNhYwzs/Pcab3z9IMppi8PwoZ/dfpKDcz/odjfhKlvLmZ8eCpOMZnF4HsvKPU9p28Zq63uV6LaXkmgXP47LSUlOM321jYHyeS8MzlBa4UTWdroEpRmfC1FcEqC3zY75G/la/yg13XU0RTVVFSNL1fzvVJTdf3XL77BSUejGZZXKZfAJw+NXzRIIJ6taX4Qk4VzRuFAQB2SRhtppwefP7KK7yY3eurELV1FZORW0ByXgau8OCcpMV+3cLisXE7g9toOvYIKP904tf/CvfP0xZTSH3P7UZs/XmjzWXVcllVExmeVUTQ8hLCyvXDGwmYxkmBmdxeu03NNTSVI2hixO8/dxJgjM3LmzcLERJpKDMy+DFK4luIpqk9+wIlY1rl9meiyQ40T3GbDiOIAjc21FLacB9w/uBYRjEU1leOnqJTFbFblHoqC+ltnR5p1g3DGKJNOPzUWaCcSKJFKlMntYhyxJ2i4Lfbae6yIvfbb9lOehbRTqr8tbpPubCCQDu7ayjNOBatZABEI6nePHwRXTdwOu0srWlkgLP9RO8ZDrL+FyE0Zlw3v8npyKJAg6rmSKvg7rSAC77u9etu/xbsZsKMF3nHugylSEgktZu/rqVRTMm0YZq5NCMO1NgeS9gGAaRXJz+xDiz6RAZPYsoiDhlG2XWAipsxasOlRsYJNQUU6l5ZjJBIrkEGT2Xl9SWzPgUF1X2EnyKe8Wqtm4YTKZmORrMqyjtKdiAqmvMZkIMJiYI5WJouoZZVPAqTirtxRRbrvzuBhMTXIoO4VfctLjystAjiSkm0nMk1BQCAg7ZSqm1kBpHKYpw5wyAE7ksF+Zn6AnPkcxll8SAoiDwZO3KPhq6YXB0boiUmqXC7qXU5sEm333jx/cr7towuM1upnNLDZ1bbo/zKEkildUF/OpvPXCHjmw5TnePk73NFr4giNiUBpK5HmLZM5ikAIKgkNNmkEUvsugmo00xHf8uDnM7HsteRiP/g/nUSxQ5PgmIYGhE04fxWu9HFExE08eQBAvWq5KW1aDrSURBwST60Y000cxxMtrUbX2mq+Er9rD54XbO7rvIiVfPcv6dbnI5FUEQePCJTazb0bjM/XO4a4xYME71+grMKxjS/aLD7bBikhYoTrHUqpV3XdcJx/PDfyZZwnGNDJ8kitSU+FhXU8zRC8N0D8+wo62aSDxF7+gssUSGDU3li12Oq+GwKos0q/W1JfzmUzsxm66/DNxK4GC2Kux8tI2BrnGGLk2g6/mZnvNH+jl/pH/V1wkCyIqM1abgCbgorvRT31ZO2/Z6mjdUYbnmXEyPhzh/cpjZqTAbd9STyeQoKHJT/B5TpwDWba5hx6NthL4ZJR7Jy/zGIym+9+evkIyl2Prgeirqi254k8xlVUKzUSaG5hi6NInVbmbTvc0ErkMzcHntFJX7GLo0udjpMQyDl757iIqGIuzXGQjPpLP0nhnlhW8d4NT+nhW3uV1IskhjeyVHX7uw+FgkmODQK+dp3lRDWU3Bmvajaho/P3qJA2cHMTCQRIEP71yH4wbri6YbXBqZ4c9+sI90VqW62LssodYNg5lQjFO9E3SPzDAwMc/4XJRQLEkinV1MNFw2M4VeJ43lAXa2VrNtXRV2y90LMlKZHN97/TTnBvJd84pCD8U+J9fLd+YjCf7HD/ejajoN5QEqCr2rJhqarjM4EeTghSHO9E0wMDlPMJoknVWRRBGnzUxZwE1rbTH3tNXQWV+KcoM15VYgYkJAQjdUDFanNetGbhk92DCMNSUOAiIiEgba4tD5Bw2GYdAVHWTf7Cm6ooNMpedIaVkkQcBtclBtL2Wzt4Xt/jYKLctFNAbjExyaP0d3bJjJ1CzBbIy0nneTt0sWCiweGp1VPFi4hQbncpdoA53BxARfHfgpDc4Ktvtb6YoM8vrMMS5Fh5jLRlB1FatkptxayIdLdy9JNC5Fh/jO8MvUOErRDZ2J9BxHgxcYSUwRU5OAgMtko8pWwq5AO/cXbcFyk8PJq+Hc3BQ/6e8insvSE5ojYLXhVMxcDM7S4PHzUOXKFH3N0Pmr7n3MZ+LUOgLUOANUOwJUO/xU2L14FduS4xuLRjg+MUGl283GktI1HVtfcJ7TU5PcX1OL17J2QY/3A+6qvO0HBScujaHegeFHp3kjGXWcWPo4yVwPIiYMdAK2DyGazMwmfoJu5PDbHsdmaqTI8UkmY1/HbmrGYc4Pg+moTMW+hW4kyahTBGxPoMjF5LR5wun9JLPdxDInAYGxyF9iNdXjMm/FrqwnlRtkMvYtTJIPw1ARBTOXl99Y5jTxzBkSuUvktCBT0W9gkgILMyHVN/xsFpuZjnvX8bHffZSDzx1nfiqEzWmlZVsDD31uN0WVS6lskbkoA+dGSESTNG+tw+FZPrhv5C6BXJvvpHyAfkRrRVmBG5c9L3vaMzzDfZvql3UcDAOyqk7P8CwmWaTY71oxaCktcNFaV8Lh80P0jc4yPhNmcj7G0GQIv9tOS3XRMjUqALtFoTTgZnQ6TO/IbJ4iZ5LelUWrc3cTM+MhfvI3bzEzFlrTXI5hQC6Tr9pHQ0lGeqc4faCH0+/08OAzW9n1eAcu75Vrp7drnKnxIJFggvnZGCP9MzS1V7wvEg2r3cxjn97J5NA8R147n/cNAWbHQ3z//3uVS6eHad5QTXGlH0/AgcWqIIoCuZxGNp0jHkkSno8zPxVhanSeicFZxvpnaNteT8um6uu+tySLbLy3mQvHBgjNXjF/eufnZ/AXu9nxSBvldUVY7eZ8t1TXiYWSjA/OcunkEMffusiFowOoOQ2H24Yki8ukcm8HskmmY2cDP/7qmyRjeUW+bDrH2cO9/OAvXmP7w21U1BficNkQJAEtp5PN5EgnMyTjaTLJLGW1hRQWe2ipLORs/wSReJpDF4a5p73mholGJqdy8Pwg6ayKSRapLvHRVLk8uekbn+ebLx+nb3xuUV7dqpjwOKzIokgik2U+mmQ2nODC4BTnBqbIqTp7Omqx3oWh/3cbqqZzrn+SH+07y6ELQ4TjaURBwGkz43fbyaka4XiKuUiC84OTnB+Y5JP3b2DvhrobFjBuFhbJiUm0Es6OktGiGJSuqL44m+5eSBJEgtlB/Oa6vLCKOpvfwGDVJMIwdHQ0xAWq8AcRPbERvjn8Iucj/RRb/GzytmCXLWT1HJOpec6G+xiIjxPJxflw6T14laUO9DOZIIfmzhLMRimzFdLgrMQmWVENlZl0iN74CIPxCYKZCF+pf5ois2/F+4eBQSSXoCsyyPdHX2UiNUulrYR6ZwWarhHMRhEFYdW5vYnULC9MHmA8NYtTtrHR24xZUoipCS5Ghzgd7mE0OYVXcbHVtx75DpjXnZ6bxCLLfKapg+92n2FjYSkbCks5MDHMTDKBaRV/mfFkmLOhMVJajr7YLOKUQKHFRbXDT43DT40jQI3TT52zgGKrm5FIhH+4cI7dlVVrTjS65+f49tmztBUW47FYP1A0rl8mGtcgmc5ycXAa7Q4kGopUQMD2IeJyJWk1L1toknz57gYiFrkap3kTFrkaQZDwWu9H02OIQj5AFAQJj2UPoiCh6lFc5i24LNsXFkABAQWTVIDP+lD+b0FBFPKZvcu8GVFQSOb6EQUTdqUVu7IOm6kxv28kRMGC1VRHmasKARlBMIMgISBR5vpNbErD4mcxSQFKXL+KWb4yNOYr9vDwF/bQsLGG0HQEq9NCZXMZhRX+FTwSBHY+uZmGjTVsfaQD+0rydLkTGNkjINchmDpA/MXy2XA7LLTVl9I/Ps/h88M8fs86mqoKl1AcNF3nXN8k3SMz2K1mtq2vXHGexWZRaKgsoLzQw8DEPN0js8yEYkzMhtnUUkFViXeZohXklUF2d9Zyri9fpT14ZpAHtzauSJ8yDANNNxAErkvDWAmz4yHOH+1npHd6YcD31omI2XSOiyeGiAYTCKLA/U9tXlRuSsQzlFb4cbltV3LT99HwXnGln6e+vJdsOsfJfZcWuwvxSIoDL57h5NuXKCzz4vI5MFtMVxKNTI5ENE00FCcWTi7Sz4A1f74t96/j8CvnOLmve3HIOhlL89OvvU3P2RFKqwtwuK2Iokg6lSUWSjA1Ms9wz9RiUlFaU8COh9uYGpnjwM/P3rHzIooCVU0l7Hi4jdd/dGzx8VgoyVvPnqD33Cil1QXYnRZESUTL5U3tMsksqUQaw4CP/cZ9FJR62dRUzttn+onE05wbmGR8Lkqxz7UqhckwDGLJDPvODADgtFnY2lyJzbw0oRcXZGgdC0F1Y3kBNSU+irxOHDYzkiiQzOQYngqy/+wg43MR+sbn+M5rJ2mpKqSi0POBqjquhL6xOb7/5mn2nx1A1XRaa4vZ1FhOWcCN1Wwip2rMhOMcuzTK+YEpzvRPEk9n8btsbGgoW3ENulU4TaVYJQ+h7BBD8f1YZR8OuQBhgb5jGDrT6QsMxQ8sdi+Oz/0dze4PYRKt9EVfB0BHI6sl0YwckrA0GczqSTJaFFm0IH8AncGTaprvjb7KuXAfre46nijdTbW9FJtsIafnmErPs2/2NK9OHeHNmeMUW/w8WLx1ScJWYy/jybJ70QyNcmshfrMHm2RGNTTmMmHemDnOmzMnOBG6RF98DL/ixiSsHEpGsjF+Mv4WOgafq3qManspdtmCamiEszGyeo5K28pUyZl0iGA2yjZfK3sLN1FlK8YsmYirKc5G+vju8MvMZSO8NHWIDZ7GVU0mbwapXI5im5MGbwCX2YLdpFDt8uK32Pij/T9nPp2kzOFa9rqDs/2ktCsdM90wmEpFmEpFODI7iMtkocVTzFNVG/lw+a2pszX7C/h8RycBm+2Wk4xkLkdSzeJSLCiSRCybIadrOBXzqknUncAvE41rMDA2TzCauGNGuIpchE9e2UwsYH98yd+SaKXI+SkAdEMFBBQpgM+2XAfZJPmWvf5aeKy78Vh3L/7tXOiSADjMbTjMq1/wxc7PXvN+XoocH1/ymCAIOH0O2vfcWPrU5Xdw/6d3gWHkPVRWugGb2hHUUdBDGJk3QHQjyE0glSzf9gMIQRB4bEcL5/om6Bqc5u9fOMYTu9fTVFWE1WIiGk9ztm+CH7x2mmxOpbOxjN0bVlbMEASBmhIf62tLeOnQRU5cHCWRzpJTNdrqSin1ry7Bt2dDHQfPDnLo3BDfeOEowViSjoZSvE4boiiQSueYjyQYmQ6RyuR4bEcL3jUa9umazoXjA7z10xOcPtDLzFgQNZeXFSyrLaCg1IPVbskHIEu4DVeM4ZLxNKGZKJOj86TieUUzwzAYH5rlzZ+eoLKhmHWb8xRMf6GTnvPjDPfPYOsx4ytw4HTfvLngu4mmDVV86p88hK/Qyb7nT5OIphafSyUyDPfcJKVxjcFroNjDR750L+G5OH3nxxYlalOJDCfeusQJLiHJIoKYd9+9etETRYGy2kKe+OJutj20nn3PneLIaxfumCqUIAjYHGY+9PldTI3Oc+HowOJzmVSOgQvjDFwYX/X1NqeFeDhPL2yuKKSm2MfARJBIIs3p3nGaKwtx21cOFDVd5+LwNCMzYQD8Lhs7rpKIvhq1JX6++MhmwvE0daU+SgNuHFbzIp3QMCAUS1JV5OXvfn6MqWCMi8Mz9I3PUex3oayg6PZBQTCa5M1TfRy+MExW1di5vppn9rbTUV+K03pFzCCdVdnSXMnXXjjC8Uuj9I/P853XT9FcVYjdcmcoLQCF1mbcSjmRxDg90VeIqdMEzA1YJDeGoRNXZ5hMnSaUGURAoNjaynD8IPHcFCbRxnymb2FPBiktRDDTT4GleXH/OT1FNDdOUp3HKvuwSB88GdPT4R5OhbqxShY+U/UIHZ7GJc8XmL1YJQsD8XF646OcDF1im389LpPjqm087C3ciCSIyyRMA2YPimjibLiX4eQUA/FxNnqaltGnLiOzkNx8pe5pOjz1S1zNV0swLiNnqNTbK3i0ZAfr3bWLHQuv4qLI7ONcuI+Dc2e5EBkgpWexGLcvsOE2Wwhn0kQyaUpsTs7NTxOw2lF1nVh2de+vQzMDqz6X7+ykmE5FMd9GMF/n81HnW1unPm94qhJMp/AsJEwpNUd/eJ5YLss6fyEZTaUnNAdAozeA6V2cm/1lonENTvWM3fZ8xi+xHIIg3HDoWDC1YcjrIHsI0q+AaMPI9YJpHYJ5dTObu4VkOkvX4BSXhmbI5FQyWZWhySDBaBJV1XntaDejM2HMJgmzSaa80ENHY9kSV+26igCfe2wzf/uzIxw4M8jEXJSyAjdmRSaZzjI8GWJ0OkRnYxmfe2wzRb7VuzoFXgct1YXsO9nHsa4RdMOgvMhLQ2UBVsvqtI0Cr4MvfmgrgiBw8Owg33zxGPtLfDhs+YU6m80rXc2G4/jcdvZurGetdnjnjw7w479+gzMHexfVltZvqWXHo+3UNJfg9i9U7leodF6e48ikssTDScb6Zzjw8lnOHuwF8vKng13jnDnUS/PGakRRoL6lDMPId2pkWaSprfw9l7a9FoIg0LyxGrffQd36cg6+co7zR/oXqVRr2oco4Ak4aWyvYOej7XgLllfVVkLHzgY+/4eP8+K3D3ByXzeZa4z3rpXABbDYzbRtq+OBp7ewcXcTDo+NkuoA/mI306PBNR/zjSDJEnWt5Xz2Dx7lpe8e4vibXSTjN2/k6bCZ6awv41TvONOhOAfODfLYthZcNsuKOVk2p/HmqT503cCiyKyvLqYssHJQ6bSZ2b6uClbp6gkC+Fw2Ht7SxDvnhpiLJFA1nYGJebatq/pAJxo9Y7McvDBELJWhpsTH4zta2NZSuWz+wqLIdNaX8vG9HfSMzjIXSXCka5jukVk2NJTdMRas01RCtWM34ewo0dw4/dE3GJOOo4g2DAzSWoScnk8+K+076PB9khPz32AieerKsUoeii2tjKdOcSH0U9q8H8djrkI3ckwkTzEcP4BqZHCainGaPngFrgNzZ8jpKjXOElrdy4tUoiDiU1zUO8u5FBtiJhNiMh1ckmiIgohyHenSclsRVimfxEdzcTRjdfaHIppoc9fT7qlbkmSsFR2eBqrtJctoUYpkot5RzrFgF0ktTSKXxGu6fQZER6CE2VQCSRDYUVLJty6d4i/OHCana6zzFxKwLC9iZTSVrvDkdfcrCgIVdh8bfFf8OwQEwukMP+/t5ez0FJquU+XxcE9lFRVu9yKl7JX+Po6Oj5HK5bvav799O4X26ws36IZBOJOiJzhLVtfZVlzOpeAcY7EIOV3Du5BQ9YeDuMxm6j3Xt0u4XdyxREPXDdSciq4bKGb5Aytferp7nJz63icaAiLV3n+FXVn3Xh/KXYOReQsjdwFB9IHlIRBdoM1iZF5/XyQaiXSWw+eGef6dC+Q0DVXVyana4jzPsYujnO4dxyRJyJLIxuZyygs9SxINSRTZ2V6D3WrmrRN9nOoZ49C5PFfcblWoLPLymUc3sWdDHetrr1/xMckSDRUF1FUEONWdr/7ubK+humRlzuzVaKsv4csf3UFbfQlHzg8zODFPNJHOmyuZTQS8dpqqiti2vhLPKopP12JmLMgbPz62JMlYt6WWZ37rATp2NmCxrb26aRgG67fW4i1yMTMWZGpkHshTjsb6ppkYnsPpseFwWmjdWE1DSymCKGB3WJaY9T3w9BbWba5Fu+o3Xd1c+p6sTyVVAR76xDYaO6sYvDhO9+kRRvummRkPEg0mFlx0DUyKjNmi4PLa8BW5KSr3UlFfRFlNISXVAYoqfNgca6N1SLLExnub8RW52Ly3hXNH+hnunmRuMkwqkUHTdBRFxu6y4i92U9lQTPOGKpo3VlPZWLxIUWveUM1X/t1TxBa6CLXry5aJPdwKFLOJtu31+IvdbLinka4TQ4z0TjE7HiIRS6NmVSRZxGxVsDosePwOCsu8lNUWUtNyhdu8paWCFw9fZDoUp298noGJeYr9zmWBvmEYhGIpDl/IS/Y6bRZ2d9SuSvFZS4EEwGW3UF7oxtwjo2pZgtHUbfkwvdfI5lT6xuYYmMj/7tbXFNNaXXzdIe9NTeUUeBzMRxOksypHL43QUV+yopTsrUASTFQ7dqEZWS5GnieUGSKthUlr4au2MVPt2EW79xMELI1sDXyZofgBwpkhZNFKqa0Tp6mYYHaQwfh+Yuo0djmAYehEcqMEM4NIgkLA3IBHWdnU7f0KA4Pu2DA6OvPZCH/W870Vt8vqOYYT+S5qWssSzS2fvdIMnblMmKHEBFPpeaK5BCktbzypGhpT6fx1oRrXj5XMookmZ9VNmbtdhkmQKbUEcMgrd6hdJvti8pLRlwsA3ApafAXU636ssgmvxconG9vpC88jCLDeX0TAuny2dCg+TyibuO5+XSYrbd4y/OYrr8/pGofHRplPJXGbzcQyWU53X2I6HufTbe2UOPOJU6HdTo3Hy/6RYd4ZGeZXOjfcMNHIaBoD4RADkRCxbIZGb4DxeJQyp4upRIzpZJyUqlJid6Lq7368e9uJRjqV5cLpEY6908vcbIyqmgIee3oT2azK9HiYqroCfIEPBtd+LpxgaDJ4RwbBbxeCIN6QGvULB20GQV4PUjmCXAmCgiGFQLt+teBuwWk1c//mBhpWGBpdCT6XjULf8gVBMclsbqmgosjD/VsaiMbTqJqOYpLwOm2UFboXfTRuhLryAF95ahfTwfzAb22pn8AKg/YAg7FZxlIhNvqqsMtmmqoKKStws3VdJfPRJOlMDt0w8kpXVvPC8TuXeHhcD13HB7lwbGAxyVAsJh54ejMdOxuw2m9OZUwQBMxWhfrWcpo6qxYTDcMwiIWTnD7UR0GZF6vdjCxLrFvF6bVlUw0tm+6uu+/1oFhMNLRXULOulM5djYTmYsQjSdLJ7CItSZJEJJOExapgc1pwemy4/Q5sdsstuaqLokDtujLK6wpp31FPcDZGIpoil1UxdANJElEsJuxOK54CJ/5iNxbr0u/cX+zGX/zuOD/LJglXgYs5AZRqP7/x6e3EIymymRyaqiOKIiZFyquR2c043FacHjvOq+a8ygJumioL6RufI5nJcbhrmI76UpRrkmRN1zl2aZT5aBJRECj1u9hQv9x9/FZgNyuLXY+sqr6fRoVuGqF4ipGZMOmsmq/GFnhuKH9rtygE3HYkUUTVdPrH5/PKenewqWOVvdS7HsSrVDGd7iKUGSKjRRFFGZvkp9C6jiLLetxKBaIgUmhZh8tUSlILIQkydrkAzcjR6HqEk/PfZCJ58pp3ECixtlPl2IX5AzYjmNGyRHP5gHc2E+a16aM3fI1m6Mtc6+czEQ7Pn+N46CLT6SDRXIKsnkMSJEyihCRIJNTUKntcCkkQ8Sq3dh7NkoJFMq9qDCcJ4mJicaciNptp6brXFihmvb8IARiPRxe6N0sv6O7o1KJQxGrwKTY6vOVLzXNVFZts4oGaWtoKi8hqGn9/9jTHJ8fZVVm5mGi0FxXTHCggq2ucmLi+79VlZHWVoUiISCaNbhhYJBlRgJ7QHJIgUOZ0E06nGUtFKLCtLda4HdxWopFOZTl2oJcf/v1BDN0gp2rEIin2PtZGLJLk8NvdpFJZdu5tvvHOrgNd1xmeCVNTfHuUiJlwHIsi47KtXA28ODRNLHnzrftf4vq4rDZ0w2q2shWkivyw++VtBSeC5ZF3+QjXBovZxLraYtat0mmIpzK8eayXYxdGEAWBpx7owL2C8hPkz0Wx30Wxf20UmMu49lw6bGY6G9cWKHnN+SDg6qEvh81MS83afQtWg6ZqDF6aYGbsCrWmtDpAbUsZFtutS31abAoFpZ4lj2UzKpOj88RiaWRFxmyR8fiuLJZev+OmE5u7DVmWKCz3UbgKzSueytAzNktWliitvnFiOx2KYbMoOK+jtqSYTZTXFVFet/LM2HuJVCZHV+8UiiKzbvON5buvhUmW2NpSwaELQyRnIxw8P8SnHujE47AsWXdUTeeVY3k/IavZxNaWClyrzHJcDVXTmZqP0j8xz8R8lFAsRSKVIZ1VyWkaOVWjd3SOZCafZBvG7cgfvPeIxNPMRfJBq2EYvHK8m0sj0zd83aWRabSFTs6dnHW8GhbJRaltA35LAxktiqpnEAQRk2jFJvkQhSv+NIIgYJW9WOUr5E/ZMNPgegjVyNAXfY24mv9ciuigzLaRZveHKLA0f+AG+XO6hrFwxtvdDewu6Lzha5yyjVrHlftHKBvjlanDvDR1iGguQb2zgl2BDkqtBdglC7IoISDwv/p/xEjyxteDIAi3PKQtC+JNuU+/W7hMYfpez1k+VreOumtoRkPxefQbXOk+s511nqVUPAGB5oIC7q2uXkxwmgMFnJ2eJpK5EoeKgoBFljFL8pppiDZZYWdZFVktXyjwW61sLa4gnElhNZnwW2yU2BxEshncZgs2+d1VyLutRCMUTPDWS+eprAnw2FObuHR+nCP78rrrNruZVDLDxEIl8naQ03R+uO8sf/yJvbe1n6OXRigv8NBZt7Kc2Nne8UXX5F/i+tA0nfGeSUa7J1i/swlP4fKAefD8KO/89CiD50aRFZmWbfXs/thW/KXeleXwUj9CcPwTuKrNKggySLfvTHk3YDbJtNaVkM6ovH60h9ngnXeW7Rme5dLQNE/saV3mbxHMJDgyN8BoIkhaz2EWZR4tbaXS4edsaJQDM334zQ4eL2tHWRje64/N8ML4WQzDQBREfqPhXnRD552ZXroiEyiSzAZvJdsLVh5Kv4xUIkN4Lkb2KnWkghIvtmsCvZuFrhuo18wRyLJIdUMRMzMxzh0bQBBFpq5ymH7gw53UNa9NMvD9CrNJprzAs6r047U41DVMfVmA1urbTxo/qNjQUEaJ38XEXJTJYJTzg1OU+F2LMquGYTA2E+Hsgt+Ew6pw34b66968szmVi8MzvHaih4vDM4TjKeKpLJmcSk7V0DQd3TDQdWPBDPMXA5msSjK9kDQB/ePz9I/f3L08nVXftWxLEEQskguLdHOFmsuvdZiKaPM+TYV9Cwl1HsPQsEhuXKZSHKYiZPH9XahYCRbJhLCgH+U3u3iwaOsNXyOwNBHojg2zb/YUc5kIews38njJLipsRdhlC6IgIiAseGDcDUWu91ei1xWcWdFHYywRWkzwVoJVMlHrDOBRlhYdzbKMz2pb0kUxSxIYxmKyfqtQJIlqlwe4UpQsdTgpdTgX/3YpZspZQwH4DuC2Eo1kPMPUeIgv/PZ9tLRXEJy7ElhZbWYkWSJxC8N9V2N8LsLPjnTxxpk+rGYZt93KZx/YSCKdZf+5QS6OTGOzmNjUUM7WpkpGZ8PsPzfAZDCG1Wxi1/pqqot8HOsZ5WdHunDbLLxzfoDHtjRTW+JfPMmXZUUzuV8mGmtBbD7GO88e49Vv7uPeZ7bziT96AttVNIW+00P8+H++yLGXzxAPJRFEgXPvXGSib4qP/+GHKawILN+pNsYHuQ5okiVqyvykMjmOd43c8f3nVI2B8TnOdI/zxJ71XLsQZ7QcZ0OjFFld3FNYz4vj5+iLzVBkdVNh81NkmWE8GSa70CrXDZ2/7H6TJys6CViceeV4QWAoHuTY/CAfrdzIdCrCwdk+ahwBiqyrq7DkstoyRSJZkRDWwG+/HtKJDNNjSwMcs1Whqb2SdquCxWpCkiXaN1+hRxVex8huNSTTWX6w/yxz4QSSJLC5sYKN9WWcHZjgzMAkqqYjSyI71lVRWejlePco/VPzpLMqbpuFvR11VBf7ePl4N8l0llgqw2wkwVP3tFFV6OVU3ziHLw6TyuaoK/XzxPb1CAJ0DU/zyvHuvIKb3cIXH9xMKpvlrTP9DE+H2dpcQWCBRtczNsvbZ/oJJ9L4nFb2tNdR4HFw9NIILxy5iN9lozzg5sM71lFV6OXMwCRHLg4TT2WoLvbxoW0tWJT3r7fD7d7vPA4rGxrK6BmdJZJIs+/0ALtaa65KNODtM/2kMjlkSaSpspCa67jeJ9NZ3j7dz/ffPM3AZJBEKossiRT7nDSUB/C7bDisZsyKjNkkc/TiCBeGpj4QYiIGXDcx0nR9kUIsiQIFHseq3dnVUF20ckHp/QBRkLDJfqySDx01r4YoyItSuR9EyIJModlHXE0xlprBvJB4rBW6oTOanGYsNUPA7GGzr4Vm1/L5ipiaQrvBbMYHCW+M9vOjvvM33O7s3NSKg++Tqch1PaJsskKNI7BsGF4WBUy3QIVdK6797d3o73cTtzmjkT+5JtNyC/hsVkXXdJTblMzyOW3saa3l5eM9PLa1BZMk5p1KJ4N0jUzzkR3rGZ4Ocqx7jOoiH0NTQUZmwty/oR6/y47facNmNtFWXcyhrmFqS3xsb6miyLuUbzo6HWYmGFvVrfmXWIrwbJQLh3oIz0bRdR3zVZzuZCzFweeOc/iFU2TTWapby9FyGpODs7z+3QM0ba3nno9uWebyLMgtkHkLQ27OL5CCDNLdq0wbRp7+95M3znKsa5R0Okehz8GeTXXs3Zz3FJkNxXn54CVOd48hyxJb11eyd3MDvjVKqh45N8yrRy4xE4xT4nfxsfvbaagqQBLz13VX/xQ/23eB8dkwiiyxrraYTz+6icm5KF9/7gh9o3OEYyl+6z//AIAHtjby8Yc6r1CpZDPV9gAt7lKOzg2S0rLkdA2f2U6R1c10+op5WySbYiYdXZzZEASBjKbSH5/hVHAESRAXtMENorn0dRMNxSJjusZ8MDwXvyllpWuhqhqTI/NcOjG0+Jgki/gKXRSV+zBbTOx8YD2iKFJ0Db3qZqGYZHa31qBpOmNzEV4/1Ut7TQnj81Hmowk+sbeTMwMTHOsexW5W6J+cxyRL3L+1gTdP93F+aIpCr4PJ+SjJTJYd66pxWBUKPQ7C8SSvnezhoU1NOG0Kf/fyMVqrSyjzu/jbl47y2Qc24rZbMUkisiRiMys0lhcwG0kwH00uHmOxz8n9GxrQdJ03TvdxcWSaqiIv7TUlHDg/yLqqIjY3VlDgdhCOp3jrdB/bWqoIuG18+/VTNFUU0lJ5xbclnc7xje8dRM1p/M6X7198n4mpMD994RRej51PP52visYTGU6dHeHQ0T5m5mJIokhJsZt7djTQ2lKG5bKvSSLDW+90c+zUEJFokoDfySP3r6ejtQLTwpC+rut0903z3IunmJ6NURBwsq65dNGx/lYhCAK72qp55VgPkUSao5dGmI8kcNstiKKApuu8cizfcbcoMvd21K063GwYBucGpvjWayfpGc0bvW1sKOPDO9fRWFGA3aKgmGRkSUQU8wl6OJ7K093e54mGYRioqnbde50sS4uD9BbFxId3ruOhzY2rbr8SzCYZ+Q4IBrybEAQBCdP7rXh+SxAEgXZPA0PJCSZSc/TERmhyrizbvBI0QyetZVANDYdsxSnbVxzi7ouPrHlG4+7h1uO2kViYUDrN9pKK6253bm5lqlg0l77uu1slhQr7WnUbfzFxW4mG1WbG63dw4I0u6pqvtOzVnMbwwAyhYJwN269PubgRLIpMRaEbs0miriTPjUtmsvRPznOyd4xsTiWVyWGzKMRTWRrKAsxGEuw7N0BZwM3e9jo8DisFbjtuu4Uir3NxP1fjQv8kiXR22eO/xMqIR5JM9E3hLXJTvb4C6aogoe/UEGf2XSSdSPPYl+7nkS/ei2yS+e5//SmHXzzFsZdO03ZPM5bKa9rT2jCo3SC8k//him4E5x/dtc9kAAfPDPLGsV4+/6EtKCaJcCyFY2GmJxRN8trhbgbG53hybxuJVIYj54ZJpLJ84uENy1y+r8WZnnGee/scnc3lPLy9mbO9E/zZd97i333lMYp8TsanI/y3b7zBtrYq7t28GV3XSWdUrGYTZYVuPvnIRl45dImRyRC/+cxORFHA51o6yKVIMoqYT/xFQVzgiq+8DFolhbSWI6FlsctmDAxEAfyKnVKrh09UbQHyXNmA5foDfRabGZfXjkmRF03phronGO2bpqK+aFG9aK3QdZ2xvhl++rW3CV/VKfUXeahpKSWZyBCPpvD6HbddmTEMg3gqw+unesmpOtFkiulQDAMDRZYW14xQLMVcJEkonsKimCjyOqgv9XNpZIZ4Oru4fpQHPNSV+hdnwQYm57k0Oksym8NikgnH0wRjSWxmE7FUho66UkzSld+PKEoUuB1LfCB0XWdkOsSBC0P5IHhoCodFQZZECj12XDYLxT4ndaX5tW14OkT32Cyz0QQOi8JcJEEwllxSedN0neGRuWWBcSaTY2hkjuSCFK6uGxw7OcRPnj9JdaWf7ZtrSaayjI2HiMfSi/SudDrHt/7hMCfPDtPaXEZzQzG9/dP8+Vff4Cu/upfNG6uRRIHZuTj//f99GYsis2N7PbmcytvvdDM5Faap4fYkRRvKC6gt9TE+FyGWzOTpsoVuLIqJ7tFZhqfyM0Ruu5Xd7avPgoTjKU70jNE3NoeuG2xqLOdXH99CZ30ZFmV5YQ3yNJT3oiF7s5e/pusE49cPFG1mE86FQlAmp2K3KNSU+G7auPOXuLt4pHgbr0wdJqWm+ebQz/lnjZ/GZ15eIDIwyGo5srqK05QvkkmCiEVSkASJSC5OTE1iYCzpioSyUV6dPsZ8NnrXPtO7DVmU2FBYyuebN1x3uxPT4yvOjCTV68eNVslEue3WEg3dMMioKolcjnA6jaYbTCfi+GxW7CYFsyS9b7uGV+O2Eg1fwMF9j7fz93/xBr0XJ5EkkemJMP/rv/+caDhF59ZaOjZX3/ZBCgjkVA3dMBAFAVkUCbhsVBf7+MKDmwABWRLxOK3IosD9nfVsTpbzyskeDnUN89Q9bSDkK1qapmMYxrIv51zfJKnbqL6+HxHMBJEECbeSX2jmM/McD52kzd1KqfX2buiZZJbQTITqdeUUXzWsqqkal473M3B2mNr2KnY8sYn6zmoEQWDbYxvoOTFA76khEpHk8p06fpel+hF3WYPegNlQglQ6S3N1IR6XDVXVFqtdk3NRzvZO8Ng969jeVo2u63mTvd4JxmbC1JWvQAe7Cq8c6qay2MueDXX4PXaaqgt55fAlTl4c46HtTTy//zwuu4XPPLYJx8JNPqdqSJKITVKoKfVR4HUQjqVoqSlas0SrYRj8cPg4B2Z6mU7HCGUTfLxqM7WOAr5Yt4v/0fUKsihjFmV+f92DNLiKafOW83f97yAgUOXw8/naHdd9D1EUqG4uoaDMy8RgvgKcimf40V+9icfvZP3W2mUdj9WQTmY5/lYXP/mbt+g9O3plAF4UqF1fRuc9jfR1jZOMZzBbTZhMMpt2Ndxgr6tD1XXOD04yH03yhYc20zU8zeBUEIx8kBVZcKJOZnJkcxo2i0IynSWeymIYEE2lkSVpkaJjVuQlAZnXYcXjsPD0Pe34XTY03cDntJJTdRLpLKl0FpPduqhStNJ9IxhL0TUyjddpZce6aqKpzBXqiyCg6jqabiyubR67BbfDwpPb11Ne4EbTDbwO6y0FiplsjvHJIKIosPeeJpoaitF1g/RCgedyp+LEmWGOnBjgsQfbuPeeJuxWhWQqy7//r8/xw+eO076+DIvFxMtvXGB2LsZ//XfPUFHuRdV06msK+e//78s3fWzXwmyS2bG+ijN9E8xHk7x5so/Ht+cpY/tO95PTdEyyyNZ1FXhdq1OBwvE0w1MhVE1HEkXW1xSzsaH8usWE+WiC7HsgjS7L4pJZnnQ2d10qR1bVGJ4Krfo8gN9tp3TBW0TVdCbmogSjyRsqT91N6IZOUp0jpYXI6UmMm9AeUkQHBZamd/Ho7gwMri4U3TiLLbcV8unKh/mbwWc5G+nlP178W3YHNlDjKMEsKqTUDNOZEIOJMeYyYXb423moeBuQ74iUWAoosfiZSM3xxvQxHLKVRmclmqHTFxvl5anDdEUHMQkSKr8YNPN7y6rJ6Tpey/WpgR0FJTiV5bM7ae36caNJlHArN0c7vIzzM9P8xbEj9AWDRNIZIpk0//K1VzHLEm6zma9/9Gmc5vf/PNFtJRpmi4ntexpxOC28+fOzDPROo5hlJFnisY9tZOd9Lbi9ty+dJUsS66qK+OdffYGGsgC/8fg2mioK6R6f5S9fOISAQFNFAZ+8t5MTveP85MB5RFHAYpJprcp3WgSgvjTA66f7OHRxmM/ct4HG8nyAnM7m6B6Z+YWbz+iKXcIu2digdAKgGiqRXIScfvudG03VyCSzmG1mnFfR0KaGZuk/PUQylqJ1VxN17VWL5myl9cXY3TYm+qfJZZb/OAWpBCPXA6TzMrd3eSETBNjVWcO+E338wX/7MXs21fPozhYqS7x5WdVEmkNnhzjfN4myEHAkUlkqi72Eo9evEKqazvBkkJ7hWV46eGlxkHtqLsroZAhdN+genqW1vmSJC7fpJugkBRYnn6jairIw3PfRyg1IiFhkEw+VrOOewoa8fK0o4TJZkASRB0rWsdlfDQgIAtgkBZsk8InqLaTU/HekiNIyw6Tl506gfWcDx968yNTI/KIL9UDXOH/6h99m64OtbHtgPbXry/AGnIvXhGEYqP8/e/8ZXtd5X3nDv11PLzjovYMEQbB3sYoSJVldliWXxHbs2E7iJJN5MpPJlCTT8k6ed1JnJpkUJ47j3mWrd4oUSbFXkCBA9N5Pb7s9Hw4IEiJAggRAUrLWdUkkz9nt7HLve/3LWprBSP8E/Z2jNJ/o5Ox7l+hqGSQajGNcJTVdWp3HrifWkl+SzcXz/fS0j6CoEqpdIZB3JeOSW+DH7Zl7s6IkiORleegcnODrrx7JKIJlZZpMRUGgY3Cc//6dN9ANg23Lqwh4HEiSyOHmLs52DCBLIo9sWobbPvOAXxDwsntNLT89cBbdMLFMi3/3qXtxO1Se2bmK//6dN1FliYDXxVce3sR4JM43XjtGx+A4NkUikUqzpaESu6Lw9uk2OgcnGAnFpsYvAagtzuGVoxfZf7aDX75vDWV5Wdy7qobXT1wkpRlYlsXvPLUdt+PmFcBsqkJRQRbB4AV+8Nwx9uxqYM2KMnKzp2e5LlwcQBQEli0pJHcy0+R02li2tIiXXjtDIqlhsyk0XeijtCRAXU0+kiRiWRblpTmUFC1MecE9jZV8/63TjIXjnO0YZGg8gsdpZ+/pNgBUWeaB9Uuu22if1nUSkwpSiizidqioyuzPQPfQBP2joTviweRQlWnH1jMcRDNMZmvHSSQ1Dp/vuu42PQ4blYUBAh4H45EEpy/109o7clcQjaQRoj28l7bo20S0QUzr+sRqJuQ6lvJg8f9vkY7w1jCRDrN/5CTHxptJGCkSRpKwHiOYzmR0v9v9Ki8NHMQp2XBINuySjc+UP0iFq2gq0i4JEg8WbsbE5Ntdr3Ax3EVPfAhVzPRrmJaJbhmkTR2v4mTFVc7hAgKNvmo2ZS/nhYEDnA620hbtRZWUTNDF1NAsnY+X3MtgYox3R0/didO04Chye+eUifxCwzpcyrUP1Y1UtSRRxCVPfzesLijkzx94iBOhE3y76wfcl7+TfHse91fXsLGkFN8keajLzuYPtu9Cm8HrQhQEnDMcz/sR1xOcmDjNSHqUJ4ofvqm+nYXCvIiGIAg4XTbWbKqmfkUJumZgWZk6artdwWZX5p3WEQQBmyLxb57eiWYYKJOpohyvi0/uWEVqMu2vyhKKJLKyqpCaomxAQBIFnJMlG4IgcN/qWu5pqADAfZUMZGv3CKFI4q7VPddNnYuRFo6MH8UCNFNjtX8VdZ4azoXP45HdrPSvoDvWTVusgwJ7PuPpcfYOv4OAwLGJE+zI3YZX8ZI0krwy+DqGpeOQnDxW/DABNcCB0YOcCzWhmzoN3mVsz9tGe7SDfaP7wYKkmaLSVc5DhQ9mlC0EAVESsUxralIJ0Hm+l/az3QTy/dSsqsCXe2Uy4vQ6kFWZZDw1bZ3LsOLfhtRhEGQs7x9B5M8RfP/ldpziKRRke/ijrzzImdZ+3jjSwn/86xf43KMbuHd9HYZpUprv58tPb6HiqiZSVZFu2ChpWRa6YfDx3SvYtb4Wx1WlRB6XHUkSSWvGdSczN7o95fdFTrzKlb/7VCczdVjYJQX7DL0XXsUxbf25IJDnZc8zG+nvHKXlVGYiYxomI/1BXv/BYfY/fxJZlbDZlMn+HItUSied1DANE10zSKev/PtqFJbn8NSX72Xj/cuRZJHqpYUM9o5z/OAlBKC9+YrXyiPPbqR+5dzNtgQBqgoD/PEXHkQUMvd2pvQsEyFfWV3EU1sbEQCHTSUcT+Kyq2xfUc09DRWIgoDLriKKAk9vX4EkitMi37IkZsaeZRVTY4zLpiII8NC6JWxfXsnl8cphUyhUPHz1sS0ZbXYB7IqMXVW4f20dW5dXZrw0LAu7qiCQGdseWr+UnSszZaoehw1ZEtneWM36JaVc7l/0zdHkz7KYNhYKgsDm9VV43Hbe2NvEP35zP9+2K9y3o577di2bIhyhcIK+gSD/6b8/h6xcyZzEYini8TTxRBqf18HYeJSCfN+USZ4gCCiyiGeOxpA3Qq7PzaqaIvpGQiTSGu9d6EbTTfpGQggClOT6WHkD7wxVlqae0VRaJxRNktIM7DNkNFKazo/3naVrKLggx3+zEAWB0jw/5zoGSaQ03jrZxqP3NOC0Xfv+TWs6xy/2cPgGYhWiKNBQWcDquhLePN5KW/8Yzx+8QH7AO1Wedz2EY0ncTtucVdPmilC6j+Nj36AneoiUGcPi1ohd0ggt6HEtBBJGikvRPk4Em7EsC+t9Ra8hLUZIiyFMinYICDxcuBXLaU3rMXFKdh4p2sYKXy1vDh/lXKiNoeQ4STONQ1TJtWVR4ylhbVY9q7OmZ3W8iounSnZR6Mjh7eFjdET7iekJPIqLpd5y7s/fSKOvhiPj5zg2cf72nJg54dbvM0kQp61uWhad4QlaJkaJ6xrZdieNOQVk2WZWUHRK1w/eSIKAXZpOCByKgkNRUGMWwUQY3coEVd2qilu9sj27rFDsnZ+Ah4VJwkgQ069vKriYmLdhnyAIKIqEMsdm2Fvdh+99WueiKOB22Hj//M6uKrMqqzhsyrQJ3mWcaxskmrh7+zMsLBJGgogW4YtVv0Jfop93Rw9Q7anEK3toj7Wzwt/ISHqUpJGkylVJpauSoeQwXsXDluzNqKLKWHqMiB5hS/Zmaj01/KjnJwwkhghrEZpCF3iq+AmcspNvdH6LClcFKTNFf6Kf36n9bSbSE7wy+DpjqXFybNmodgVPwEUyniI8KeOaiCRoPdFBX+sg6/asoKKhdFp5jzkZzZUkccb6ECt1EMH7hxD+I0AA8/a+DC5PMLP9Lratqaaxtoifv3OO594+w+4NdbgcNtwuO6ZpUZLvv6lty5JIbpaHZFon2++a5hYOmcGtONdLW+/odY9PFIRJxRi426qlRVGkcVMNn/93D/OtP3uZ5hOdUw2n6aQ2rTH88uW/EblXVJll6yp5+td3s2JzzVSvR3F5Dk/+8j3k5PtQVIl1W69E5uxzNBi8ciwCsiSR/b5+l0g8CYKATZEJeK5cr3A8c/xOmzLtc5gewLgas41LM30uShnjxvdDlsQZxy+YeWyzq/KME+OrIQjCNddA101isdRVy4DdrrB6RRnLlhYyNBzm7Xcv8vIbZ0kkNZ56dA1+nxObTaYw38eD9y+nMP9a8podcCEIAm63nURiekbTMC0SyfRUU/l8IIoCO1ZVZxSm0hpHzncTjacyymGiyO51ddc4hr8fAa9rKphgAe9d6KKxuoCdq2qmZRl7R0J867XjvH6shVjyzngwCYLAuiUlHDzXSSKl0do7wl98fx9ffXILxXkZiWTLsojEU/z8wDm+/vKxOZV4VRRkcf+6Otr6RukcnODtk60MT0R4/J4GtjRWEvA4pzKz8WSa3pEgZ9oHOHSuE1mS+KPP75mz2edcoJtJzk38mM7IPjRregZZQLqpSK04/6nPgiPfns1Xqp/kC5WPznkdl+y4pm9AEATsokqtp5QyVwG6qWNOEpfLJCVjvidfk6kWBAG/4uG+/A1sz12NYZqZ9QQBWZCwiSqSILItdzXrAstQBBm7NH3MExHZkN3Adzf990ww+ialcO/NW8+WnBUA15XR3Za7mnVZy7CwcMvOBYnTD8ej/FPTcV7oaEYQBFRJIpJO4VVt/OvV93BvaTWO93lOuGQVgdkDgZaVKfO7/ht78bIMTsnJjtx7MDHvSDYDFoBozAbDMHnjhdMk4ime+NSmxdrNvGFZFufaBqYaH+9WSIJEwJaNU3LiVTy4JCdhLUK2LZvh1AgXwy1opkaBPR9VVLGwUEQFVVRxyI6pGyxbzSZL9eOSXDhlF2kzTTgRIseWjUfxYJfsFNoL6E30kq1mU2gvxC27SZlpbJJKysi8TF0+J8XVhfRc7OPcgYvUb6zl3IGLHHn5JKIkUL+xlrKl06OGwZEw6WQah9s+Fc28GgImCJcnewYItzfFZFoWh8904nLaKCvIIp5MMzQWxutyIAgCRbk+llXl8bO9Z8nxuynI8dAzOIEgCCyrKkB+32+ysKal9B/cspSvPfcepQVZbFtdhWYYnLnYz5ZVlbidNh7f2cjv/vlz/PiNU+xYW4OmmwyMhamvyMNhV7GrMtk+F6MTUZouDVBRnGnOdDvvnhpNWZFYsbmWf/83+bz2/cO89v33GO6fwHqfws2NCIbdaaNhQyX3PrWejbsbsLts0+4ZSRKRHCrrt9VlJq+T0fCbLaG4HtwOG3vW1l3zAsnxuXh6+0oWUZnwtkAUBDxuO/2DQ6TTOooiYZoWY+NRevsnqKrMlGZdPqeSJOB0qFSU5fDpp/2EQnG6ukeZCMbx+5zUVOVx4nQXJUVZrF1VMfU8XD5/kpiZ8FaW5/DOgYuMB2Nk+ZxYFkSiSbp7xsnyLYxL7dolJRRkeRkNxTnXMcBoOIphmjhtKg9tuHFtvtdpY1VNEdXF2bT1jdHeP8b/+NZbfP+t05TnZyGKAr3DIdr6RwnFkuT53Ty0cQlvnWxjaDwy63YN06R3OET/aIhYKtPnE4unGQlGOX0p4/prGBZn2gb4++ffw+1QcdlVXJN/Fuf4KMj2XkOUtq2o5o3jlxgPx0nrBm+dbOXg+U4qCrLwOR1EEim6hyaIpzTsqswT25ZzoWuIi90jsx6rJIpsX1lFOJbkn18+Sv9YmDNtA5zvGkKVJVz2jLRvKq0RT2qZPiHDRDdNVtcUL3iFwGDiHEPJpimSUehYyVLfIxQ6G1FFz01Nou5GGVtJEHHJC5PVEyYzHg7JBtLNvR8EQUAVFFRxdtJ/ve+n1r9FKW2bpGCTbryuKt76PmbDK10ttIXG+ION97KxoARZlEhoGj9sPcu3m09R7ctmaWC6gWquLXPvzSa6YmKRMnVUaebpdtyI89LAq7RE2hAQuS9/B1tyNuCSXAylRnhz6B3OhJqIG3HybDk8UfwI9Z46ZFHGtEx+1v8iB0aPEDfiBNQAO3K3cH/+LnRT53ykma93fIeUmWKNfyW/WvXZqf1alsV4eoL9owd5b+wYET2KT/HyeNHDrMlagXKd63+zWDSioWkGI4Mh0ne5Ad5YKEb34MQdaeK7GRiWwWhqBM3SiBuJyZvCh0d2M26f4PD4EardVSz3LM+k9ybVhjRLRzf1KZm6y6Y7meh9Ztv59nzOhM6RNFLIgkxfoo9G33J0S0cUpMlBC7iKt+eVZrNmdwNNhy7y/T/9Oa/8814SkQThsSgrdtTTuG0pim367dXfOkgsFCe7MAvVPsNNbHsAK/T/gN4OwX+H4Hxq0c7njLBgeCLK97+/n4lwHKdNZU19Cb/+iXsAyPG7+PjuVfzkzTP85799mXgyTWGulyd2rqChqoADpzr43qsnaO8dJZ7UONXcx//94QGe3bOaB++pZ/OKSjTd5MdvnOJrPzmITZVZWpnPphUVCEB9ZT6//4X7+O7Lx/mHnxzCblfY2FBOXXnGsFCWRFYtLaa1Z4T//HcvI4kin7h/Nc8+cH21jNsNSRLJLvDx7G/dz+Nf3MGF4x2cP9pOx4V++rtGiYUTJKIpLMvC5lBxuGy4vXYKynMorSmgsr6Q+rWV+AJuROlKKdNMeH8P2NhIBIdTxTXHMqHrIVPSc23kWxSEG0bEPwiQFYl1qyvYf6iVv/ib19m0vor+wSBv7784rfRrdDzKO+9eZGg4TF1NPg67SnvXCOfO97FlYzVZ/kz2ZdumWk6e6eYfv/kurZeGqKzMJZXUaescwWFX+ORT61FVmScfWcMbey/wX/7k5zzy4AricY2391+4xoByPlBlie2rqmgfGCOaSHOpdwyBDAEpzL6xyZsgCKytK+WLH9vA3z3/Hr3DQcLxJKcu9WUIgcBU2VpNSQ6//dRWCrO9HG/puy7RSKV1/uGF9yZldjNjqTX5v8uEzrQsLnQO0dw1PLWfyaPiqe2NfP6h9RQEpvfHOGwyv/vMDizL5L3z3aTSOrFEmvMdQ5PbyIz3bofKp+9bw8OblvGXP9p3XaJx+Tw+sW05xbk+vvHKMc609ZPWDTTNmFYFcPlcCIKAIolUFgYW9HoCjKXaiGoZidEK9zY25PwqfrWMy2fog6C+8xHubgzHY6zIKWRrUTlOOVN66JIVnl2ygr19HVN9i1ej2pvDvuHWWaNnmmkQTCfwKDO/k7piPRRnb+C3a3+NtlgH744cwqt42RBYg020sdRTyz05G/HIbt4cfoef9j1PSc1X8Ks+jo6f4L2x4/yr2l/HozgZSo4gcKVfZ7l3GX+47Pd4a3gfQ8npz3rMiPP60Nt0xXt4pvRJSp3FjKXGybZlIwsLSw1uamuWZRGPzS09HIskiUWS10w27zacbx8kEr8zKe+bgYBIykjxTx3/jGbqrMtaQ5biB8Aju1BFFbvowCU5L69AubOMd0cP0hQ6z4MFe/ArfhRRmYrmKIKCJIiUOkuo9yzlm13fQjN1Grz1VLkruRRpQxUzqW9BEDINZZPregJuNn5sDU2HWjmz7zwjPaMIgkBeWTY7n9nM0vU10wZ+0zBpOdlBcDRCw5Yl08z9puD4GIJtLeidIFeBeHvdnQUBHtuxnMd2LJ/le4HCHC9ffXYrX3126zXf37OqkntWVc6w5pX1d6ytZsfamSWfJUlk17padq2bWUHp8v5/+1Pb+e1PbZ/DL7pzEAQhEwF321i7Yylrdyy9pW3MBsMwwcrEkK7u5zjw+jlqGoppWDV3/fhfVMiSyD2bahgPxnjljXMcOdFBRVkOD923nKGRMNpk/5vTruJx29l74CJv7c/Uj+fleLlv5zLu21mPb1K9yelU+e2v7Obl18/x9v5mnnvpFDabTHlpNo8+tHIqI1Vc6Oc//7tH+efvHuRvvraXogIfu7YtJRZLMTC8MOWSgiCwe20tLx46jzC5SVEUeXhz/ZwnpIoscv/6JdSV5vH60YscvtBN/2gYwzLxOO1UFgTY3FDO9pXV5EyWDhdn+xgcj2BT5Fnj65n+j1t7LyqyOKMqmSAI5Afc/I8vP8yBs528dfISzd3DBMNxREkkz+9mbV0JD25aQl1JLuF4iorCAG6HitOmIs1CCgQhM23ZWF/GquoiTl3q592zHVzoGmJoIkoipWWEDHxOyvL8rKgqYsvyCkpyfQtONOL6GCkziohMjedefGrpXZmZ+AgfXBS4PARTCSaSCRSnhEAmI9EbCZHvdKNK0lRjtiyICIJAtScPEWHWbqG0oTOWis7qpVHiLGZ11kpKHEUU2PNoi3TQEetkqacGv+Jjhb8BwzKwsFjlb+TQ2BE0UwMrI/AjkOkDcYhOat3VU5m9y1ktWZCuMQsE6Ih1MZQaYUvORlb7M6VqOWr21LoLiZsa7SKhBJ975C9RZ9ERvxqmZZFMpu/ysik43zFE9ANANCRBpNxVzifLnpn6zLRM0maaqB7DLbsod5VNXRcBgZX+FaycvIEu48nix6f+/kTxY1N/35G3jR1526Ytu8RbxxJvpvY9oAb4VNmzU98JgkD1ynJ+668+z9s/OETH2W4ChVnc89g6lm2uQ35fU/NAxzChkTAuj4N196/Amz2DeknyBVA3gLIaQbDP3yr4JjGXh2shxA3u5Pq3E5ZlArNnI+aDtuZ+UkmNVELj9JH2KR+XlnO9FJbO7vb8Ea5AEAQcdpVnnljPM0+sn3U5l8vGA7uX88DumQn41dtzOW08/fhann587azLSZLA2tUVrF1dcauHPieU52fx4//2+Vte/3Imt7IwwJcf28yXH7u+xDPAn371+vX1TrvKH35+D3/4+T23fFyz4XIGbufqanauvr5/lc9l59cf38KvP75lTtsFsNsUNjWUs6nhzpB4w0pjWjoepQCHnIV4AyW8j/ARbhYrcgr42zOHOTc2xJrcIuySzHAixtu97SwL5HF8uJ9zY5ms2sOVS3ErKg3+QmRRRDNmphoJQ6M7NsGqwMyGgE7JgUvOlGcrgoJHcRPT4yTNFJYW4sDoYZrCF4hoUTRLYzwdxJzMhq4PrKEp3MyfNP8FDd56tuVuodpVie0GDeoAMT2eKclX/Is+r7jpjIZpmmzf04Dbe/3ShHRK5/zpnnkd3GLDMEwudg0TS9zdRENAQBEVnNL0BtGwFua98SP0xntZF1hLru36Pg4LflyCQGFVPp/+/SduuGxhVR7/6bv/CiwLSZFm9oAwI1ih/4igLMey3Q9yOYL44XHUvNwUpuvmlKeLYWaeKdOyME3rShnF5EByebJzudRNEAQkMeNGLIkikiggTTpK320kxDB6EAQPouhf8MijP8uFphk0nehCkkVql2WyX8GxKPIsbs93CtbktdWNzHU3zcy/jcvXPeOqOKk0c6WERpismxEArroPLjtRX/2nJIqZvhVxcYjdR7g7kFEEM9ENc/J+sjCMK+OHaU32hc0yhiBkSv9EITNuXBlHxCmX87sVsmBDEhREpKnykI+wOEgmNeKxFE6niv0WJLE/qDg61Et7KGPq2Rma7jVzdnSQs6ODU//eUVyJW1Gp8eZR4sqiNTw84zZjeor2yMiM/m2QeU4veyJZloVpmVPZiJcGX2cwMcQTRQ9T7a6kJ97HX7b+36l1VVHlS5WfozPezb6RA/xD+z9zX/5OHi584Ia/VZjcuzlLb8lC4ibfyAJev5NPf2kHWTNFpK9CPJbih984ME0H/07DsjIOu5puZEyLBsYZGotkZCTvYsiiTINvGQ2+ZdM+96t+HixY+MjYYkAURUT1+i8HwfU5cH4akm9A7O8AB2T9+e05wEWAZVmkdYNUWp/6bzQUo2dogoGRMMMTUUYmogRjCSKxFNF4ilRan5xEGIiiiCqLqIqMKkuoqozLbiPgc5Ljc5Htc5Ltd1Gc66Mwx4vbacssp2TUhqQ7TD6ikb9CVpbjdD6NINy4Lv5mkDfpuZBKaixpLKG8Jh/I9Ib5shZPAe9GME2LtK6T1gw0PfNfWjMYDcXoHw4xGooyEUowHokzHooTjMaJJdKZ5TVjcj0dw7JQJAlJElEmpbtlWcQ+6djsczvwuux43Rljvly/m8IcLzl+N3ZVzqwjS9hUGVWRpmR7P8IHCxlZbJPkVWPIRCRO73CQ/pEQw+OZMWQimiAcSxKLp0imdDTDQJ9892bGBAlVllFVCYdNJeB1ku1zke3PjCUF2V5K8nx43Y6p5W2qMlmqdXfcN3bZjyI6SRrhjEGfZX5UOrVIOHyglR9//zCPPbWO+x5svNOHc9vw2aWr+Vz9GuQZAqFxTcMmSTOanu4uXDor0YhqKS6EBkmZ+jUytwAhLcx4eoISRxExI86EFiLXlo0qqgwnR6h0lVPkKMACehP9pM0rfSIZcR6Bcmcpnyz9OHm2PPaPHpoT0fApXkBgMDlEnbsaWZQxLAMREUlYWMfxmyIaiiKxdnPNnMywFEXC53eSvENqTpcH6JSmk07rpDSdZNpgIhynZyhI18A4Te2D9I0snIRqMq3TPxLiUs/1G+zuZthtCkW5vgXXP58LLHMMzAkgCWIOCJ4brnO3wTQtkmmNaDxFKJqgvX+cCx1DNHcO0d47OuUyPRcYhknCMEmkbiyoYFcVCnO8VBYHqCnJoaGqkMJcLx6nDZddRVXl235NLSuNJOZkyuAWGLpuoGsGRWWZmtLL48yG7UtQb1NfmGWBYRgk0zqJtEYypROKJugcGKe9d5TuwSB9IyH6R0IkZjCovBHSugG6cVPrqopEjs9FYa6X8oIANaU51JTmkuVx4LApOO0qTrs6lR37RYRpmgyNR+eVyfa67GR5nTdlqDlXmJZFOq0TTaQIx5L0DAY5PzmGXOodYTwUv6ngWDKtk0zrwJXf2zbDcooskR/wUFEUoLokh/rKfCoKA5kxxKFe43h/u5Fjq8Gj5DOSvMhYqp18x3Js0gfvHfGLBMuy0HST4YkIyVsYA+cKp12lINtzXeGQuaBpfBjdNFkWyMUx2QxuWCbjiQRv9raxs7iSAte199yDxQ18s+09Yvq1810Ti8F4iHMTfazLqbjm+4l0kKZwRk63J95HVI+yOXs9fsVHvi2XweQQbbEOQOBU8Awp88pzfD58kbiRINsWQDc1ehN9VLoyHlK6qRPUwoylxwlrYeJGnN54P3bJhlfxUuYsodRRzJlQE4qgkGfPJaJFKHDkU2wvXNCG8JvaktNt43f+4LEbL0hG0WTN5mr09OKrOV2OIqbSOsmUTnJSbm8kGKV7cCLz38A4XYNBJiLxKW3/hUZH/xh/8o03F2Xbtwur64r5X7/3cWy3WH5iWVZGylTg5h/6xHNY2hkEZSWC+ysgldzSMdwsMhPwWx+cLCvjJByOJhmeiHC+fYij57s509rPRCR+W4wgk2mNjv4xOvrHeOtoK5IoUpznY0VtEauXlFBXlkuO34XPbUcSb0+UUlFWYJoTGMYwkpQHvH9SduvHMdQXpLNlENPMlKKJYsZfRBAElq0sI7dwJnvC+cM0LVKaTjSeIppIMTgaobVnhJbuYS71jNI3HCKl3TmlvbRm0D8apn80zPELvUDmnBTneqkuzWVFTSEra4sI+Fx4nXZcTvWOTh4XB9Y1z9zV91kipfMX397L3hOXbnkPj25r4AuPbaQ4z3/L27galpUpgQrHUowGo7R0j3CkqZszrX0MjkWmSisWE5pu0DscpHc4yLun2hEFgdyAm8bqQlYtKaG+Io/8bC9+tx1FXtiI51yQa19Knr2BYKqbtsibBGyVFDnXIAu2X1jSfDcjQzIMTlzs439/7x1ae2b3iJoP3E4bD22p5zef2XZD36Ab4eRwP/v7O3m4cglbiypwygpd4Ql+dKmJixMjrM4tnJFoVHtyubdgKc/3nplxu0PJMO8MtbIyUIpylZO4V/GwNWczLsnBG4N7EQSBLdkbqXNnxHR25m3j7RtaUuoAAQAASURBVOF9PN//Kl7Fza7c7RgYKEImMyIKAscmThJMB1FEhSpXBffl7wIgasR4a3gfrdG2TOZDgG90fYdqdxW7creSb89jW+5m7JKNoxMnSBgJfIqX3Xk7wF44r/P4fixo6G+qtnhygllWmXuDNW5++6ZlkUxpJFIaiWTmz0g8xeB4hJ7BCXoGJ+geyqSVPwhqUh90WJZFOqkRHA4RHo+SiCbRkjo5JQGKqvJQJg24DMNET+tYloVqUxBn8NFAqkWwPw6CDQQ3WCbchtS4w6bcEs2wLItkWmc0GKOla4SDZ9o5cr6bobHZ5S1vFwzTnCLZL+xvojTPz5aVlWxurKC0wE9ewIO6yJMF1baOWPTrGOYgirIcQXBN07pXbVuAW9PqDk3EaG8ZYHggRCQUJ7fQj2mYhINxsvM8C0o0TNMkntQIRhKMhWJ0Doxzrm2Qs2399A4FSWt3tzS2ZVn0DofoHQ7xzvFL2BSZxppC1iwtYVVdMUW5PrJ9rmmSth9UpIwxkvowhpnEwkIS7HhttQi3eJ/NhqHxCOFYiuv7i98Ylydjo6EYnf3jvHe2i/fOdtA1OHFbAhTXg2lZDI1FGBqL8MaRFvKy3KxvKOeelZVUF2eTF/DgtF/rPr5YsEkelvgeJGFM0BN9j+Nj3yBphMi1L8UmeZAFG6IgM5egkYCILN5dvQeRSILgeAy7Q0XTDMLBOBYZMYacPA8Ohzp1rnXdYGwkQjAYR9cNbKpCbr4Xr88xtYxlWaRSOsODIWLRJBbgdtvJyfPgvMp7SdcMJiZijI9FMQ0Tl9tOIqnN67pezmScaO7lf31/H5cWiWT43HYe3dbArz6xGfscRIpuhCdrlqFKEi92XKQtOE7A7uDwYA85Dhd/sPFeqn3ZM64nIvDpqg0cGGljPHWtA3dYS3JstItL4WHq/Vcm8RsCa9kQyAhnPFh43zXrFTkK+Ez5M9M+W+FvmPr7Sn8jK/0zl7b5FR/PlD5x3d8bULN4oGA3DxTsvu5y88W83yyGbjAxHmN0KEwinsY0TewOFX/ARXaeF/tMfglzhKYbxJNpYok08WSaeFIjFEvSNxzMvDiHgvQOBRkcj5C+g1HEX1SYpkV4LMKZfRc4+PNjnDt4kbH+CUzD5JGv3Mdn/v2TBAr8AMRDcZoOtRAaCbNyZwN5pdnXkg2lCtKHAQ3L/hDoLQjK4teHuhzqTQlcWRakNZ2BsTDnLg3w5tEWTrX0EbuLTR97hoN8//WTPL/vHGvrS3lg81LqK/IpzPUuWoYjnTqIYQ6ipzpIJfdd8312zvcQhFsbH5atKmPZqjLeevEUiiKx9f7lYMFz3z40u0XrTcA0LRIpjbFQjOHxCBe7hjne3MuF9kHGwvH57+AOIqXpHLvQw7ELPbgcKpsaK9i2uoq60lzyszPldh9U9EVfJphqIpxuQRV9CEiszf+fqJJ/QfczPB4lHEve8vqWlQkGDI9HaO4a5u1jrbx3tpNQ9Na3udgYnojy4rtNvHHkIg2VBdy/cQkr64opyfNhW4BJ3o0Q08cQECl2rmE81c5Ispn9Q39Brr2ObFsNbiUfVXQiXJM5vRYOOUCF+8aKW7cTzU39vPDccQLZHizLortjhFRKx5/l5IGHV7JuUzVOpw3DMLlwro/XXjpDT/cYWlpHUSVWr6vk4cfXkJ2T6Z9NJTUO7GvhzdfOEQnHsUyLQLabrbvq2bp9CU6XDdM06e4a5aWfn+T82V5kRaKwOAtVkdBu0QPtctn6ieYe/up7+2jrXRySEfA6eXxnI5/92PpMsHAB7j+/zcFTNQ3Iosg/nz9BV2SChyuW8qsN66j0BWYtPxYEgXp/AZ+p2sDfX9xPyrz23HVER/lZz2mKXVl4Z/HU+LBiXkRD1ww624Z544XTnD3RSTKuYVkmNrtCaWUuOx9Yzsr1lXMyz0qkNGKJNLFEKuOWmkgxHorTNzJJKCZrnUeDsQV1//0ItwbLypCM17+5n+f++hVG+8ZnzlJMQtcMXvraWxx99RRf+pPP8MDnduDyTW/ateLfybwktBaw7YToP0LWXy7uDyFT3zlXpqEbJmOhGKdb+nn9yEWONnUTT969BOP9iKc09p/KZF62rapiz6alLKvMJzfLveATBYfzWeyO2eU+BWFhmraH+oO0nOvDMi0mRiO3PD5YloVmmIxORDO9Vr2jnGjupaltgJHgtVGqDwNiiTRvHmlh/4k2llUVsGtdDeuXlVGS58duW9gswO1ATOuixv8rDERfp8TzCB2h78EiKBSNBKOEoolZlWSuB9OyCEeTNLUP8tbRFvYev/SByr6n0jonLvZyrm2ANUtL2LNpKStriyjM8SIvopFle+QtLoZeJaYNkzIzWWPDSjGYOMtg4uxNbSvfsfyuIxoAo8MR+nrG2baznp33NRCNJHnxueO8/vJZCosD1C4pYKB/gm/9035kVeKJp9eRm+/j4oV+fvDtg6iqzCc+vQlRELjQ1Mc//e1bbNhSyyc+tRHTgkP7L/KT7x/G7bazZVsdkXCSfW9d4Oypbu5/aAV19YX0dI3x9utNjI7cfGb+Msk4dr6b//X9/YtGMnKz3Dy1awXP3r96qudsITCaiHF0qI+jQ30sC+SxIqeAsWScV7tb2VlcRbU/gG0Wh29FkHi2Yh1nJ/rYP9SK8b73UFhLsm+wlWW+QvYUL5uxMfzDinkRjdBEjBd+eJQLZ3pYf08txeXZSJLI6HCYcye6eP4HR7A7VNZsur6mt2Ga7DvZxsXOYfpHQvSPhugfCc8rYvQRFhdaSuPMvgv8/G9fIxqMUb2ynKLqAgY7h2k90XHN8ln5Psrqizl38CLnDlxky2NrryEa6G3g+3MI/S6LMTmYDXMpnbKsTIT7Ytcwbx1t5Y0jFxkLfXAj26m0zhtHWjh8rouHty7joS3LqC3LQZYWbqIgSblYloZpjmNZUSSpCEGwT/prwEJc46q6QkYGQux79SymYZJb6CevyH9T27Asi2giRd9wiI7+cY5d6OH4hR76F1Ao4m5HWjc41dLHhc4hVtV18sCmpaysK6IoxzdltPdBgCx6EJCxsIhpPSSNEWDhlQ9jiTTj4ThpzZhzyZllWRimRVvvCPtOtvP8vnMM3gVllreKtG7w3rkuzlwaYMeaah7aUk9jbRHOBYouvx8TqW7GUq0Lvt27CYZh0tBYymMfX0tWIJOZGOyf4J23LhCaDHYcOdTG8HCY3/idPaxeV4EsSyxbXsLZU928+uIpnvjEeiRR5LWXz+BwqHzuSzvw+51YFmQFXFy8MMB777aw6Z5aBvonaD7fR+OqMh55ci12u0JDYymRcJLB/uBNH79pWhw7381ffX8f7b1jC3lqplCQ7eHju1fy1K6VuBc4+3pwoJsXOpppzC7giepl5Dnd7Ovr4Gft5+kITfDbqzZT6vHPuK4gCPhUB7+5dBeRdJIT4z1TEtOX0R0b53udx8i2u1mXXT4rafmwYX5EIxin+VwvDz+9jkef2TCtNrBpbTff+6f9tJzvvyHR0HST7756gvPtg9dd7iPcPRgbCHLklVNMDAZZsb2eT/zuIzRsruMn/+vlGYkGQOmSQtx+J90XeknMUCIgiMWgHcUyQwjpIyAVLPbPQFEyUo7XezGapsVEJM7B0x38bN85zl7qv+P10wuFSDzF9147SUv3CM/ct4o19aX43TO4tt8CTDNKOn2YdPooht6Nx/tvkKRKDKMTwxhCVddzqz0al1FRm09eoZ/x0QiiKBDI9cxZ992yLIbGI3T0j9PcOcSRpm6a2gYmFXp+MZFK6xw+10Vz5xDbVlXxsXuWsayqIJP1+wAgYF+NInrw2ZYwnjyJSylFWED1lKsxNBYhEk9iU68v9Q5XAhVHz/fwk7dPc/hs121p8L4diCfTvHzwApd6RnliVyPbVlWRl+VZcE+OHHsNFcbWBdmWX70zpoM3gqpK5BX4pkgGgNtjz2RbJ3vBervGMA2TttZBIuHE1HK6bjA0ECKZSGN3qFy6OIgki5w40j61zMR4DF0zGBkOo2k6sWiKRFwjL983VeYuSSI5uR68vpt7DximydHz3fzV9/bR3rc4JKMkz8fTu1fxyLaGRSnxdCsqT9cuZ0thOW4lM+bdX1ZDjT/Aj1rPXZOleD8kQaTOm8e/WnYvf3b+Dc6M900jGyYW5yb6+Frru5iWyZrsMlzyB7dUda6Y1whsWaCqMhXVedMmaoIgEMhxk5vnxdDvHh+Nj7BwmBgK0nzkEoHCLLZ/fCOrd13fNRggkO/H5rQx2jtOOjmD1J3jEazUfpDysbQmBMfDi3Dk03Gj6JthmPSOBHnpwAVe3N/E8ER00Y/pTuBEcy89Q0Ge3r2Sj21ZRn72/GUj0+kjJOI/RBAcpFIHcBlfRJIq0PV+otG/JRD421vu0bgaTrcNp3vug7VhmvSPhGjtHuXo+W4ON3XROxSc93F8mBCKJnnlUDOdA+M8sXMFmxoryPW7F6xEYbHgVeuQRTf5zp14lBpsUjbyApXovR+D42Ei8RQ5/hsTjYlIgtcONfP9N07SN/zhzJS19ozwf390gK6BcZ7cuYLKosDMxqy3iFrvA1R77l2QbQl3qau4osjYZpLnvmqCq+sGiUSawwcv4XhfUGX1usqpHjVNM0jE07zxyvSysqyAi5q6ginjUMuyrsla3qz8tWGYHGnq4v/88N1FIxnlBVk8s2c1D2xaite1OD0OO0uq0E2TYCpBXzREvtONT7VT5vbzays24phDuZMsSqzIKuE3l+7kay0HODHejWZeEQzRLZNjo10kdY2nK9ZwT141eXbvHbEUuF2YF9FweWxU1ubT0TpE7bJiHM7MTa+ldfp7JjBNi5KKmbv0P8IHG4lIktG+MSoby6lYXjandWwuG5IskUykMI1rIwOCugbkCjBHEcRcuA2u4E67Ok0J6WroukFrzwg/fusMr77XTOpDHukemYjyLy8cJRxN8uSuFZQVzO/8J5NvoigrcDo/iaZfkRJVlCUYeidwe9WaNN2ge3CCC51DHL/Qw+FzXYx+SHsvFgK6YXKubZCxUJyB0TAPbamnNN9/V0uJBlNnMMwkDrkIj1qFLLoXzdRtaCxCJHbj3or+0RDP7T3LD147SXwRvQTuBkTjKX705mmCkQSffmAtSyvyFyyzoYh24BeriXYm5OZ7yQq4ePITG6iszruG/Ht8DgzdpKQsQCgY50tf3Y2iTCdWNruCzSbjcKioNpmJiRiaZqAoUkbkJZQgNod7GzIk49DZTv7uJwcWzUOsqjibZ/es5r71dXgWiWQAJHWNo0N9HBropjsS5JeWrmJTYRk90RDdkSArcgrx2268f1WS2ZBTiUu28c+XDvHu0CXixpVeTsMyOT3Ry2gqSkt4iO35dSz1FZClOj+UhGNeRMNmU3B7HOx95RzRSJLsXA+iKBIJJ2g61U00kmRoIMgbL5yaWmdpYwkl5TnzPe6PcIdhmha6ZiArEjbH3KLSelrHNEwUVZn15SOIARADC3mo14VzFsUp3TC50DnEN186xv6TbXe9e/xCIZZM85O3zxBPpnnm/tVUl9z6s2qZYUR1LYI4PaJsmtE74ujbNxLiX148ysEzHXe1us/dhoHRMD99+wyxZIqP37uSsvysu5Zs2KUCQsYFIlo7KWMCVfLilEtxyPkLfs8NjkVv2MTdMzTBd145wfP7z931MsgLBdO0eO29i8QTaT73yAZW1BbdtffLBxGr11Zy8mgHba2D5OZ58Adc6JpJcCKGJIsUlWSBLLJ9Vz3f/cYBWpsHWLKsCNUmk4iniUaS+AMuRFEkJ89DaXk2F8/3cexwG6Vl2YyPRbnQ1EcifmORE8MwOXCmg6/99BCt3aOLUlJcW5bDJ/esYeeamkUlGQDnxoZ4qfMiAtAWGmMgHsHCIpRK8oOWs+Q53HMiGgCKKLHcX8xXl+4kx+Zm72AL/YngtGX64kG+33GME2M9bMypZHlWEZXubIqcflyy7UNDOuZFNFJJjf6ecRKJNG+/fBa7U0WSRJKJNIZu4nTZOLK/ZVr02umyf0Q0PgRQVBmn10EqkSYanFtT9GDnCPFIguxCP8o8ZI8XEi67es1L0DBMmjuH+PrzR3j3VPssa354kUhpvHKoGd0w+ZXHNlJyi6ZkslKHpp1FkgqxrCSGOQzaGRKJn6Moq5hvf8ZNw4LzHUMfiUzcAsbDcV491IxlwdO7V02SjbmtOxqN0dQ3zJaaMpT3iQ1ohkHPeAjdMKkrmP29EE+naR+ZwKkqVOXOHojIsjfity8nlLpAf/Q1TCuNUynAo9SR41i/oCUzwUicYDSBbpjIMzTMdw9O8M2XjvHygfMZl/dfMLx7uoO0bvCvP71zXgGLjzAdS+oL2fPwSo4eauO5gWOoNjmj9qQZLFteQt3SQiRJZNM9tfR2j7Pv7WZOn+xCkkQMw8ThUNl4Ty1l5Tnk5Hq4Z/sSfvajYzz3w6Pk5l+RO78skzsbdMPk3dPt/NPPDtPSM7LgPUcCsKQij0/uWcO21VV4nIufzTo5MoDfZuez9Wv4sxP7pz4v8/gZTyVIGjdX1SCLIjWeXL68ZBuVnmxe7m3ibLBvWilV2jRoCvbTHBqkyOmjzptPpTuHIqePbJsbn+rAJavYJQVFEBGF22O4ez0U2L03ZfQ6v9Ipt43t9zfceMGrUF69sCZ+H+HOwOV3UlxTwHD3GG2nu1i2ue666jRjAxOc3d9MeDTChgdX4fQsTMPxfOG0TScapmnROTDOP/388C8kybiMREpj7/FLeF12fulj68j2uW56G3b7gyQSz5GI/wTTGCYe+w6i6AJEnK5fQhBubxNceWEWmxrLGRwN31H37g8qJsIJXnvvIg6bwtP3riQvMLc+npFwjFeaWlhXUXwN0YBMz4xhXb+XL5ZKc6K7jzyP+7pEI5RqJpxuxbJ0vGoNiuTDtJL0RH5GwLEGaQ4eC3OFbmR8MBLJ9DWR1qGxCN96+RivHLzwC0kyLuNoUzdff/4wv/nMNgqyvXf0WCzLJGVECGv9KKKDLFvFHT2e96OoJIs9H1tB6fsCsTW1BTz02GpKSjNl6Ioqc+/9yykuCdDeNkwknECRJfwBF/XLixEniYLH6+ATn97E6ZNd9PdOkE7rOJwqhUVZVNXkASDLEg2NpdjtKheb+0mndAqL/OQV+BgaCFFWMTNB1A2T/Sfb+OcXjtDaPYy5wBl/QYCGygI++cAatqyoXHB1qdmQ1DU8qg2vOn1/kXQKgblYQV6BZVmkTYOoniShp1nizWcgEKYrNsbYDKZ+hmXSE5ugJzaBAHgUOzk2N37VgVuxZ4iGKCIJ0i0ZDC8k/n3jg3jVuc/h5kU03F4HW3YtZXggSCScwDSsa+S8cvN9FBQvfq39R7i9yC0OsGpXAz/68xd597kjFFbm0bh16TXLaWmd3pYB9v7gEKf3nQdg7f0r8OXMv9l4IeB0KFyu4rIsi5GJCF9//ggHTv/ikozLiMRTvHLoAn6Pg6fvW4XrJpWHFKUWQXgGLX0CSa4AS0MUfSjqGhRl2aKpAc0GQRB4eGsDe49dYmj8gysreicxEY7z2nvN5PrdPLSlfs4TgERa49WmVjTDIMvp4P5ltWiGwamefrrGgtTkXenlG4vGOdXTTyiRIppMketxU5njRzNMmvqHiKfTiILI6rIiygL+aZmVmNaDaaVwyaV4bUtRRC+CIBBMnp+1F2s+GBgNE01MJxqhaIIfvHGS1967+AtPaC3g7WOXyPI4+fJTm29LVHo2mJbOcLKZMxM/IGCrZEveb96xY5kJxSUBikuuJdE1SwqoWTJdgVG1yTSuKqNx1ez9kZfJxtYd176Xr4bDqbJ8ZSnLV5ZO+3zZ8pIZl9cNk3dOXOKbLx6lpXtkwcuKRUGgsaaQT+5Zw6bGClxzVBFcCJR7szg61Mv+/k5CqSRjiTinRwZ4o/sShS7PrGVT54MDhLUEES1JREsR1ZJE9MyfMT1NVE8R01KMpWKk55AVscj4boS1uzP7/q+X7cbLbSIa0XCCt14+y+mjHWiaMWM92bb7l31END6E8GR72PDAKpoOtnDh8CW++d9/TP3GWrqb+wDoaurlxa+9STqRputCH81H24iMRdj65AZWbK/H7ro7JN2cV5VOpTSDb79ynLeOtnxo5Gvni/FwnBcPnKc4z8fu9UtuWnVIkvJBXY9sRcj4GYiAgK63Icu1t139paYkh/UNZbxy8AK68ZEi3q2gfyTMC+82UZDtYVNjBcocTNpGIjFUScImS7xwppm15cV4HZka5PaRcVK6weqyIizLoncixMG2btZXlNA8MMxgOEJNXoB4Kk1KM3CpKqd6BlAkiXyvG7ty5TXmty1DlbKQBAeCIJA2giiil1LP43NyjL5ZDI6FiSau9GlousEL+5t48cD5D5SR52JC0w1eOniB0gI/n9i9+o4pl5kYhLU++uLH0K27cwJ3t0M3TPYeb+VbLx/PkIwFHkNFUWBlbRGfemAtGxrKbrus9saCUkKpJHt722kNjTGSiHFsuA+XrPBoVT25zpnLyf66+W0m0nFiWpqYniKmp4kb6WklUr/ImBfRGB+L8tZLp6mqK2RJQ/GMpTPl1Xnz2cVHuEshSSKVy0v5+L/6GM/9n1c4u7+Z9jNdUw7brSc66DzfSzKWREvpqHaFzY+u5YmvPkBeafYdrzG8jKt7NF45eJ4X3z3/0QT0KlgW9A2HePHd81QW5VBdMncVOV3vJJF4Dl1rxkLj/Ylnv/8vEYQbS4NejVgkgZbS8d9iRkyWRB7fvpz9J9oIfdSrccto6R7hlUPNFOX65lR/77KpbKurwG1TebWplZFIjGy3k6WFeXSMTpC6qrxIEAR0wyQYT5DtdlKRk4XfaUeVJZYU5HB/Qy3BRJJIMkVS06YRDadSPG2/g/G3KHI9iFutWLDffjUGRsNEr2oIf/dUOz/bd46Jq/wNPgJE40l++vYZllUWsrx68f2RZoJlWWjmR9flVqHrJm8fb+XbLx+npXt4wd+TkiiwekkJn3pwLeuWluC4A949hS4PH6tcQoUvi1W5haR0HZeiUpeVy5KsHBzyzH2Fh0Y6SBofbkW5+WBeREMURDxeJ/UrSrj/0VV3zeTxI9we2F12Vu5Yhjfg5vgbZzl34CLdzf2ERkIk4yk0Tcef66VsaTErd9Sz4cFVlC4tRrHdHY3gAE67giDA+fZBvv3K8RuqyMwXboeK3+sk4HXiddlRFQlVllBkGUUW0Q0DzTBJpw2iiRThaJJgNMF4KH7HyjA03eDspQFeOnCeLzy2cc6p7GTiFTStGVVdhShm836icSseGt2XhhjtD7Lt4VU3ve5l1Ffms6a+hHeOt90x0zRVkfC67XidNtxOOx6HDbtNRpElZElEljN1uLphZu4HTScaTxGNpwlGEoyHY3fUWPCybn5jTSEF2d4b3hNZLgfyZPOgTZbRZ4n0CYKAIok4bSoBl5P6wjxKsnyYlolDUXDZVERBQJEkNMOYun7B1HkccgHRdEZt6jL6o6+R79yFzOJ4aQxPZJSnTMuio2+MH791hr7hINYi3lcOm4Lf48iMIW47dlVBVSQUWUKRJEzLRNNNNN0gnkwTiiYJRROMBmMk7pC8rmVB92CQ7756nH/3ud2L5oNw3WPARDPnJlzyEaZD0w3ePn6J77yyOCRDlkTWLC3hMw+tY3VdMfY7OEfIdbjYUVyJaWU8RkRB+GhuO0/Mi2hk5bjYuK2Ova+c5eK5Ptxe+zVZjeVrylmz8frO4JIosG1VFdXFt9dzY2g8QlP7ILHEwqS4fW47Syvyycu6uSjt3YSygiykm3io7E4bS9ZXU1xTwMaHVjE+FCIeSWBoBpIs4fQ4CBT4KajMw+VzLrhb7HzhtKskUxrfePEIPYPBBd22IksUZHuoKAxQURSgJN+Pz+XA6VBw2lUcqoIkiciSiCQKiKKIaZoYpoVhmqTSOsmUTjyVmSwMjoXpHpygpWuE7sGJ20o8wrEkh850sKK2kB1raua0jqadw6ZuwuH8OKLoZqZWundeOMXEcJjQeBTLstj1xFpyC/wcefs8/V1jOF02lq2roLyugPYL/bz+wyOMDAQZ6ptgyaoylq+vuumXgKrIPLVrJQdPd5BaZMlRURTI8bsozvWRF/CQl+UmL+Am4HXhsCnYVBm7KmNTMiRDFAUkSZx6uRmmmZGSNkxSmk4qrZNIakTiSUaDMXqHg7T1jtHeN0Y4lritJX+haJK3jrayrLKAxprC616Hmcpqo8kUPzp+jtO9gximSZbTzubqMpKazngszoWBYdpGxqnMyWJpQe51OzF1M5apwU8cQBIcKGIm46VbcWDxTkoiqTEajBGKJnjunbM0tQ+gLaBJrSSJ5PhcVBRlxpDS/CwCXgcuu4rTrmK3KSiyiCSKSFLmT9OyMuOIYZHWdRIpnURKIxpLMjQeoXsoSGv3CG29o7eVeGi6wbELPbx1tJUndjbetv1ehoWJZn2U0bhZaLrB28cu8Z1Xj9PStfAkQ5FE1i0r45ceWseK2iJs6szT0svk/XZN+sWMa+Ft2deHHfMiGvFoitPHO+nrHs80HkUciNL0C1Mxh9IpWRJ5dNty0vrtjdCduJBxQ14oopHjd/PApqWsWlJ844Vnga53I4pZiOJNloZYFmDBTWjF60Y/ouBEFL3oej+SmIXd5r2uetRMEEURb7YH76SbtGVZmXf7TbqL3gmcaO6ldzjI4XNdCxLdFkWBohwvq+qKWVFbRHGenyyPkyyvA6/LjizdmjSdYZrEkxrBSIKxyQnmmUv9HG3qpn80dFsmmD1DQfadaGNVXTE+940bwUTRB1M9GNf+ZkM3eesnR3nmN+5jpG+Cl757iEd++R7aL/Rx8VQXm+5vZHw4xPF3mvHneMjO95FT6Ced0qlfU0FOge+Wf8uK2iKWVxdyvLn3lrcxExRZojjPR2VRNuWFWZTlZ5Hjd+F12XE7bbjsKi6nik2R5/VsmKZFMq0RjiUZD8cZDcZo6RrmeHMvFzoGiSdvzwTyQucQR853U1aQhX8WJbniLC+f3rASmywjCgK/vHk1pQE/qiyxuqyIuvxM6VWO20lS0+kaC7KipIC6vBy6x4N0jk5QmuVjR10lNjnzytpYWYJpWbhtmUyKV61DEh045EJ8aj02ORO0CqUuLEpvxtV462gLFzqGeO9s54K8SwQBcnwuVtQWsbKumIrCAFleJ1keBz63A1WRbunesSyLREojFM2Q1MGxME3tgxw+10V73+J4ILwf4WiClw+eZ+uqymsc1S3LImmEiOtj2GU/TjkwrYHfsDRM69bnCLqZIGVEb3n9X0TousHbx1r57qsnFodkyBIbGsr45Y+tY3l1Iaoy+5T02xdPs6WwjErv3evj8xFmxvyawSNJ+rvHePIzm2hcUzHjBNWXdeOUtSAI5AVufxagq39iRrnFW4UqS+T4XTP6DphmDASZjBuyiGVpWFYSCx1ZzEEQFDS9i2S6DZu6CkXyohu9iKIHUfBjkcQy45hWHEUuxrTSGMYoouhCFNyktYvoxiA2dRWSGMAwx7CsFJKUDZaFacUAA1H0Iwp2NL2PlNaBIlejSLmkdQ1F9iAKbkwzhEWmhEiW8rGsNJrejSAoSGI+onj9tLcgCDenA3cHcaol07w+38ieIossKc9j59oaGqoKKcj2kO13zXtCeRmSKOJx2vA4bZTk+VlWlc/a+lJ2r6/j6Plu3j7WSs9QcN77uR5Sms65tgGOXehh9/q6Gy6v2u4hkfgZpjkxqTLl4uobQ1HW4vY7OXu4DUM32Li7AafbTl/HKIF8Hys2VdPbPkxv+zAj/UHq11RQWJ6DltJpWFc5r9/isCl8/N6VnGjunXe8O+B1UlOaw9KKfOpKc8nP9uDzZIilx2m7ZXJ5PYiigHMyql2Q7cU0LVbUFrF9TTVtvaO8fewSR893EUssLuFIpXUOnelgY0PZrETD67DTWHKlLn/FVX9fWVo4bdlwIokkCLQOjhKKJ4mn0xT5fRT6PQRcV94lxVnTSaYqZf5d4LwXRfIiCRkCUuX7ZWRxccqmLuN0az9nLw2QmmcpmyQKlOZnsWtdDauXlFCY4yXH754s75z//SMI0++Z+sp8Vi8pZseaas609vPGkRYudg3Pez/Xg2FadPSP8+bRVp69f/W078ZTHZyb+BHj6Q78ailLfY9Q6Fwx9f2F4M/pjr136/u2NIKpnlte/xcNum7w1rHWyXKpkQUnGaoisWl5BZ99eD31lfnXFZWIamne6mmj2heg0vuRuNAHDfMiGv6Ai807l5JMaCiqhNvjQHpfRsN2lxiz3Wkk06eRpTxMK4IgONG0NhAENL0Ht+NBNKMPy4yTTJ9GVepJpA5iWhF0vR+P6yk0vYNk+jR2dQ2SmUVKO4thjqPKSxBkO7oxQNroRGXZpDnaOMn0CVR5CVhpDHMU3RzBoW7AtJKYZpBk+iySmA+SRSrdhChkIcpuoomXkOU8UlorPuenJvcVwjCG8bk/fadP5YJivgRDkkRqSnLYs2kJ6+rLKM7z4XYurqOnIGTKfwpzvOQF3NSU5rC2vpRXDl3gwKl2wrHF6zPpHQ5x8HQHa5aUkOW9/gROS59FS59G15pJCq+DIE/jn4Hsf0GWRaqWFZFbmIU/240kiThcNvo6Rya3YZCIpXG4Jh3cTQtDN7Esa96Trw3Ly6kpy6W1e+Sm1832u2isLmT1khJqSnLI9rvwu+145pG1mg9EUcDvduB3OygvCNBQVUhTWw0/fPM0zZ1Diypw0NI9QnPnMNUlOfNWiXHZVLbUlFOVF8iUjyGQ5bTjd8xNStEu5wAWlmUCFh61iozS2eJhvgRDFAQKc7zs2bSULSsqKC3Iwu923HKZaVtwjFKPH/U6QTRByFQS5PgzZXzVJTmsrCtm7/FLvHqombHQtTr/C4VwLMmbR1rYtbZmmhdLWOujPfoOaTPKRLqTLFvlNKIxnuqkJ3Zk0Y7rI1zBZZLx7VeO07oIJMOmyGxqLOcLj22ktjQXeRaSoZsmP2o9y5u9bZwY6eN/HI3it2XGgj/e8gApQ+evTh3kDzfcS47DiSAIJHSNfz5/Ap/Nzo7iSv7vmffYVlzBmz1thNMp1uYV8Xj1MvIcmeB2e2icFzuaaRofxq2o7C6t5p6icrzqnZNi/rBhXkQjFk3x3r6LTIxGeefVc8iKeI1W+f2PreLRZzbM6yA/DDCtEKblxjBDiIKJbg5jV9dimhFMM0YqfTZDOPROBEQSqSNIkh/TSmGaiUx2QsxFlasRBBui4CNlNCEIDhS5ElH0IVu5yGIOujGEprWS1lqQxBxAR5QCSAKYVpK01oxdXY0qV2QacgUFECeVgSx0ox+XYxea3oVpRQCLtN6CLBUAmYlEIppkuHsUxaZQUJGLeJPlVh8G+D0Odq+v42P31FNeGFh0gjETJFEk2+diXb2d8oIsqotzeG7vGXqHQ4uyP003uNg1QlP7IFtXVV13WafzaeyO+2ffVkomldQ59No5bA4FWZF58gvbqW0soeVMD3/zRz9FkgQqlhRSXJEx+gzkeTn2TjN/91+fY8O9y1izbckt/xa3w8YTOxr5n998a07L+9x2VtQWs35ZKUvK88jNcuP3OHDa1Luq98imypQVZGVIaFkuP3/nHD/fd27R6vFTaZ3D57pYW19K1Tz77CRRJNvtJNt9a1mIofg+BmJvktSHsSwdh1xIQ/a/RZHuDt+e98OuymxeUckz962iqjgb3zwIxmX8oOUsX27cQLZjbudQFAW8LjuN1YWU5PmoLsnmh2+corlzcbIbpmnROxzkvbNdPLZj+dTnkiCjiHbSZhRJUJBuQSziI8wf2mS51LdePsalntEFJxl2VWbLikq++PgmKouzka8zdxAFgXuKyvGoNrrCQZ6obmB5dn7GudzhxDRNOsMTHBjo5NHKekQgpqX5eft5fnftNuK6xhs9bQTTSfaU1RJJp3i3vxPdNPlK40b6Y2F+3n6BUDrJ41X1DCdivNhxEd00eaC87rpk/SPMHfMiGl6/k0ef2YBpzF58UL3kzkjZ3W2QxBxS6TNoRgcOdStgIYneySZZC1WuIJ7aR0pvxcE92NQGUloTkuBDEj3ohogk+hFFN6aZwLAimGYc3RhEECQEQUE3BknrbViWhm4MYllJMt4FFqLgBkEHBGS5mKR2ilT6AopcjWEMkzYuYaVSyGLO5LEFEAQnlpVpljWMUVS5isteCCO94/zgT5+n80If935yC0/91kMId9FkazEhCAI1Jdk8u2cNm1dUEPA4b7qvZaGhyBKFOV6e2NlIfsDDv7x4lNaem4/UzwU9QxOcae1nU2PFdV8SslKNacYwjB5MM0Tm3rmCw282Ub2smI33NaClNN567jidFwdYuaWWxz+/jUQ8hSSJeLKc2CZVjepWlpFb5MfQTXzZ8yu3FATYubaGb79ynP6RmYmZIktUFgXYvqaatUtLKczx4nPbcdgURPHuJtd2VaGmJIfPP7KBohwv//jzw4QXSdL3VEsv/SNBKgoDd5R0jSaOkO/czkj8ILnOjYzE37vGRPZuQW6Wi089sJbd6+vIzXJf91l6P86ODvJmdxvDiRjlXj9PVC/Dsixe7LjIK52thNIpnLLC767dilNWODUywA9bzyEI4JFVfmfNVuzy9Ne/JGWCFrvX15Hjd/PdV47z3rmuhf7ZQMbU8MDpdh66p36qZCbXXs/G3F9jNNmKTy2m1LVxxnUDtmqq3DtwyTeWVb4ampmgLfoWQ4mmeR//hxWabrD3+CW+9dIxLvUuDsnYuqqKLz25OSM8c4MxVBQESj1+NNPEpagsycplfX7JtIzxfWU1vNLVykMVS5AROTUygCyKbCooYyAWwS7JrMgu4P6yGtKGgW6Z7O/rpCcS4uLECH3RME9WL2NtfjEpQ6cjNM7JkX7W5hVT5J6bm/3fb/6lO6ZgeCfgV28uEDQvouHx2tmxZ/l1l5GVjxghgE1ZjiKXY1lpRNGH3VqJKLoRxSwEQUWRyzGsCE7bDiQpF0Uqxa6uQEBBEFzYlOVcVk8RBBWbUo8qlSIImfSeKi9BlooQBBcCErJUOLmvTCRPQOFyfwhYmFYUp207kpgFgozf9asIgoIoevC5PwcouB0PggXxZCs+12dI6S3oRj+KXEZoNMy5gxdJp/RMNuMXg2MgSSIra4v41cc3sayq4LYbCl0PgpCJTG5fU42qSPzjz96j5RbKgm6EREqjtWeE7sFxqopnf9mn0+eIRf8aTbswqd4x/SYpqvhrDrzSxOhgCMMwURSRyvoiZFkit8g/4zYdLhsOV+6C/A5BEPB7HDyyrYG//8nBad953Xa2NFawfU0NS8py8bkduBzqHSeUNwtBEMj2u3h0+3I8Ljt/9u23F0z84mqEYylaukdorCmak1DAYsGyDFxKOSHpPH5bI8PxQyym6tStQBCgJM/Pb39yO2uWluKZo7v6ZQRTSfb1dlKblcMTNcv4TvNpzo4Msr6ghIcq6nixo5mnaxvIc7qxSzKaafKnx/fzu2u3EbA7kARx1kjt5T6OtUtLpvrLDp3tXIBfPR2abtI1OEFz5xCNNUUA2CUvFe5tlDjXIwkqyiy9gF6liHL3Fnxq6Yzfz4aUEWYs1fYR0ZgFmm7wzok2/uXFo1zqHV1wMz67KrN9TTVfeWoLxbm+WwrUzBQ0eKxyKd9rOc1ALEKh08O+vk52lVTjUjLvZocsU+PPxiEr2CWZUrcPzTQYjIcZTkR5vaeVY8O9U8R7OB5jXV4xYS1F0RyPa032zd2LCwnLgrbTndSsqpj2eTySpOlQC1iw/oFMCeJg5whvf/8g4bEo25/aSP2mjILkpVNdvPPDQyg2hXs/dQ8ltddPELy/culGmJ+PhigiiAIdLYN0XhomMcMLbElDMQ2ryuazmw8FRNGBYGUGzqvZ+OWmRQQQJrXeBQQQLMQp7XdhspF28l+ChIgHJM/UtkTRiWA5prYvYL+yrRkwbV+AKF95pGQpb/LYsrAsDbttDWm9DUUqnCyfglQiTXA4TGF1PkVV+b8QKhCqLLFheTlfeWozVcU5c3JEvhNw2BS2rKgklkzz9Z8fXpQm8Y7+cZraB69LNJKJnyEILnz+P0aSCng/0cjyl/Cr/7EQgcxUUFYkfLdZFEKWRB7ctJQfvXGSYCRJQbaHPZuWsmNtDYU5XtwOFUWWP/Aqh26njfs21JFIafzld/cuqATrZTS1D3Lvuto7SjTsci4iEqKgcCn4z8T1Xt6fSbuTyGRDc/i9z95LfWX+dVV2ZsNwPErzxDBHh3p5y+FkPBmnzp+NKAjkOd3YJJkil3cqGjsci5DUdVbkFEx5mdwIqiLTWFPIs3tWE02kOHtp4KaP80YYDcY4dLZrimgIgogi2GclGJdhE92oouuGy70fpqVhk64dXy6rJP6iZORngqYb7D/ZxtefP0x77yiGubDk3K7K7FxXy288fQ95Ac+ClhiXefysyC7g9Z5LfKJmOQcHuvibXY9PvW0EhKn9CYIw2fuV6f/QTZPlgXx+pWEtRa4r2Qu3opLrmPu7SLwJtc+FhmEY/OQvX+b3v/HVaZ87XXYaNy/B4srx5RZls3pnA6f3nicZS059Xr6kiMatS2k/3Y2e1Bb898xPdSqc4JXnTvCjbx7E0IwpR1cAWZYoLMkiMM/yhg8TbjQZn04KbrDsDNu6+rMbMc65M1IZm7IcVa5DEGSEy8TIsjAtC7tTxXMHFMNuN2RJZG19Kb/zqR0U5/lumPK907CpMvdtqGNwNMwP3zjFRGRh9eOHxsJc6hklremzTpYMoweb/X5UdS2CYGOmezq38M5nhHKz3PzqE5sBgS0rKvBOlkbd7df4ZmG3KTyyrYHWnhGe23t2wbff1D7IWChOeWH2bSNm7V2jnLnQy5rGMsqKA5R7n0ES7JR7nyGYPEuZ5ynkm5UKXyQIQGm+n//85QdvWJt+PfhtdnIdbvaUF7KxoBTTsnApKk5FQUBAM40pszFBEPCqNiJaikg6RZbdMRUTvtElUmSJdfWljAVjTIQT9A4Hb+l4Z0MknuJc2wCJlIbjJgzaFNGJeAv9GwIiygwKZJcu9NPa1M/HPrEegJPvtZGIp9hy77Kb3scHEZpusP9UO1977j3a+8cwF4Fk7F5fx1ef2Uq2z3VLQcnL5OAq5fwpSKLIk9UN/EPTUWp82ZR6fFT5AlPfp02DoXhG1lg3TcLpJLplErA78aoxXIqCV7GxNGthMuWLhf0/OcLR106TiCTILgrwy3/wFKGRMD//v29w/PUz/NmX/w6338WX/+QzxEJxXv/WPi4ea2fTw6vZ+cwWABSbjNvvmipFvgyb04bH70KxzYsSzIp5bXViLMr+N86zen0lz35hGxfP9XHonWY+8+WdHNrbTCScZEljyUId60e4A8gMCso1Ls6qXcEbcGHoJuk75DZ7uyAIAnXleXz1mW2U5Pnvqsbf68GuZiRcmzuHOHimc0HrbQ3Ton8kRP9ImIqiwIzLiFJRRirZSk6V+N1tEAQBVZF4dFumBPR6HgWvtLbyJ/v3UZudzVfWrWdd8c375fzL6VN87dgxUsYVo0CPqvK7W+7hobobSwbPFw6bwlee2sLJ5l66BiduvMJNYCIcp3c4SENVwW1z9r3YPshPXjpJbraHsuIAiugirg2imUEUyY85KXBxN8DvdfAHv/oA1SXZ8+rvyXG4uLe0ipe7WnmpowULi6+u3ERDdh4CsKmwjP906HVy7E7+6+b7cMgKv7FyE7+77yVskkyW3c5/WL8Lt3pjkq8qMjvX1tDeN8aP3jy1oAaXlmUxForR3jdGQ9XcezkV0XlLjeKCIKCI12bbtLRBbLJ3ybIsgmNRopHF6WW626DpBgdOd/D3PzlIZ//4gvcZ2FWZPZuX8puf2IbPbb/lyodsuxOnonJ8uI8afzayKJJlc0xl6HaUVPL/P7GPn7Y18XDl0mmZu4lUgpe6WliRU0BC13i9+xKV3gDVvkwW8OBAFz9tP4/P7sBvs3MpOEa23Um1Lxv5LkplH3rhOGvvb2TljgZsdgWH247daePZf/sIx147xZf/5DOZrJwATp+De57IiDAlonf+Xp4X0UildFJJjT2Pr6asKo/+3glUm0JhSRZbdy/j9edPceZYB4XFH+ke3wksppNmVr6funXVtJ3qYqB9iFU7PrzRn5I8H//qk9upKcn+wJCMy/B7HDy9exVdAxMLPrEcGA3TPTQxK9Gw2+8jEvlLdK0JRV2TMfC7SmrUbr/vSobsDkIQhFndaK9GUtcZicXIcbrQzFsjbUVuD/W5uQzFYozHE/SGQ6R0neRtNCvN8jj59ae38vv/5/kF3a5lQffgBPFk+rYRjXg8TTSemqonbwt+g7HkcSTBjjBpFrk8+z+gSndWwUiWRP7TF/ewvLpg3iICoiCwqbCMNXnFUxNDuywjkHGT/82Vm9HNTIWBQ8787ocrlnBfaTVMRoad8tzPh9tp4/6NS2jtGeFIU/e8jv39CEUTXOwamhPRWJ/zBVYGnsUmeVHFW8miT89oRMMJDr51gRd+cISRwRBvvXAKwzAJ5Hh47FObbmH7HyzousGhM5389Q/fpXtwfMENG22KxMNbl/Gbz2yftxeMW1H57NLV/NP5Y3y/5Qw5Did/s+sJSj0Z/xy7JPNQeR0/bWvijzfvmbau32anypvF7737CmPJOPcUlvG5+jUookiNP5tfXrqab108xRdf/zG6ZVLhzeLLyzdQ7Zufgt5C49l/+yhvf/cg7/70KLue3cKmh9eg2hVcPieiJE6rLBEEAbtTRXXc+fcrzJNoQKY5VlFkRFFAUSREUSAaSeIPZFJkw/2LI7P5Ea4Py7L4xvGT+B12HltWv+Cyq/nluWx7cgPnD7Zw4o2zrN+zkuyiD59jZ5bHwbN71rCytvgDRzIgM+BsaChj9dIShieiCypx2j8SpntgAmvVzJ4WifjP0LV2dK2DVOpd3l+sYbPdM2+iYU6W8ImCcNulhW8Fuyor2V5RgWVZRNNpNv7d3972YxAE2NRYzoaGco40LayqUNfABLFkmoDPdd3lrAWa1aTS+rRMXUIfZEnWb+BRq6c+ywhh3Fl86sG1bGmsXLByPFkUZ+23yDS1Tn+1K5J0y+a0giCwtCKfjcsraOkeIbiAZZihSJILHUNYO2/si2OX/Ngl/y3vSxIUCh0r2JT767jkXFweO7sfWUVJRQ4Xz/Xy4FNrAQFJFJA+xCI2lmWh6yaHznbyV997Z1F6+GyqzFO7VvDVT2xFkW/Nyf5qCILA7tJqdpRUTo4dAqokTY0jl0eTPWW1uJXp7xS7JLO7tJrfX7sDCwtRyDw7giAgIdCYU8AfB/ZgWubUvmRBvKveJ5ZlUVCRxyf/3ePEQnH+67N/QePWpah2H4IgoqV1tJSOKAlTVgOmYWIaJlgihmEiigKWNfm5aWEaFqZhIogClmlhGCaWaU5+b2Z6fRfoHMyLaKg2GZ/fSW/3GMvXlON0ZdQzWs71UVqZSzScwO25O0smbgdMy0IzDHTTzDTkAKokI4kCFpDSdRRJQjdMjMmb3C7LUze4ZppokyUWkiCgSvLkzWKhmyaaaU49aLIookjSVJPTRCJJ69gYpT4fsXQaAQGbLE09YLphkDavGJ+pkoR0EzeWYpNZ/8BKQsNhfvK/X+af/vD7PPP/PEJhZR7C5RfgdTYlK9JdLw+qKhLrG8p4ateK20oyrMnJ80JNSkRR5OGtDZxq6aOzf3xBtgkQTaToHw0RTaRnVM7x+f8HvhnWy8BgvnEOy7J4qaWFVy+18vFlDeysnJ9b+O2AJIpcnsLcalZkvricwfmlh9Zy9HzXgkYyuwfHiSXSWBbX7dOwLIuhkci89zc2HkPTDEw0DDOJLLqwLAPT0hEms2d3er6wtCKPLzy6ccYxxLIuv+AtlDlk1e4URDHTv3TsQjeHznQu2HZTmk7fcIhgJHFDA9B5T1YR8avl+NXyqe1JskB5TR65hT7sV0V/P2wBs8uwLIu0bvDemQ7+4rvv0D8SXvB9OGwKn9yzhq88tQVBWLhzmRk7p78Tk7pOJJ3iwsQQz3dc4Bv3f2KG/QlIgohNnvn5EifnP3AXk0sL/vE/fo/+tkEA1u1ZiX3ynSvJIhs/tpr/8OifULW8jC//6WcY7Bnm2//5OQY6hhBFkch4lN2f2cpAxzA/++vXGOkbx/t2ExNDITZ+bBUn3jzHW987SGgkTPORdh784i5Wbq+/ppfjVjFPeVsH9StLGerLlGTkFvjw+Z187S9fx+FSUVSZdVtqFuRAP2gwLYv2sXF+fO48e9s6iKRTBJwOfmvLJnZUVTIYifDf3nyHx5ctZX9HJ4e7e1Flib9+4lGqswOkdJ2/P3yUly+2ktR1GvPz+eKGtawoLCChaext7+A7J8/QFw5jl2W2V1bwmdUrKfF5eeHCRf7hyDF6QiFEBP752AkA/sue3eyprUEzDH5+vpkfnW2iLxyhyOPmV9atYUd1JTZpbtGHZCzFcM8YeeW5NNyzhLe/d5Cjr5yiakU5BRW5qHb1ui/4p377IQoq8hbqdC84BKA418evffyeW27avFWkDIOj/b1sKSmbVBDLRGwuk0pREKb+Lc6RHK6oKWJpeT79IyHSC1hnPRaKMx6KzUg0hOsoVyQSr2O37+KyAeStQDdNWkZHaRoe5mO1i9/f8GGCJIo0VBVQW5ZLS9fCSSD3jYQn5XPf37I5HeFokmd+7R8WbL+d4e/jHU2jmVGG4u/iVAqRhEw9/vLsf48qzU0Pf6HhsCn828/uxmGbuXQkmdA4dugSXW3D/NKXd9724zMtk5SRJm3pCIAqKtilmeV2q4pzaKwu4tylASLx1IIdQyyRpn8kdEOiMV/MNk7KsoR9nqU9twpREG6bZLZlWaQ1g0NnO/mL77zDwOjikIzPPbKeLzx2e0rPmsaH+PcHXkUQ4PfWbqfCO71MXxIEfKptzmprCwXLsjAsg5ie6Y+QRAmnZLtlNSdBFPiNP//sjN+Jksiv/c9fBkA3dZrC5zkrNPF7X//1a5bNLclmxbb6az7f9ewWdj275ZaObS6YF9HwZTl55vNbp7wycvO9PPTUWnTdZHw0wpZ761m/tXZBDvSDhvF4nG+dPM1QNMof3reL6uwA/eEwhV7PlIZ5MJHgR+ea+JW1q/m9ndvoCYUoz/IjCgI/PHOOwz19/J/HH8Fjs/GDM2f5b2/u5e+eegyPzUZVIMBvbN5AXW4Ol0bH+NbJ07zYfJFf37yRJ5cvY1NZKX+27wDL8nP5/Lo109KAe9s6eLWllS+uX8PqoiL2d3Ty/76znxK/l/q8vDnpUZ3Zd4E/fOpPp30WHoty6u25aZTvfGbLXU00nHaVR7Y1UJLnv+371gyDs8ND5Lvc2GSZIreHUCrFeCKOKAqUef2MJeJMJBPku9wE7I4bK5oJcM/KSo439zAyEV2wYx0PxxgLxSgvvNynMTdNm2jkT7HZNs2rdKo/EqErFMS4zZmBD0usU1VkHti4dEGJhqYbBKMJdMOck/yzIku4nLd+DyRTGqm0ToFzJ1W+TJ2/hYFl6RkjUyTkGRqAbxce3rqM2pKcqUx0PJZC0wwEpmd1NU0nFIyDZaHaZOwOFV03SSbSGLqJJIvYHQq6ZiCKYiarrRnYHSrJpIbdrtySZ9VYOsi3ul5g7/ARbKLC/flb+FL1J2ZcVhCgsaaQ/Sf9XOgcms9pmYZMZjRMQ3Xhgm3zZnDxXC9njnbwS79+723ftySJ2G9B4vhmYVkWKU3n8Lku/vzbbzM4Nv9s4vvhtKv8yqMb+OzD6xd827NhbV4xrz35hVm/r/IF+M5Dn7xtx3MZpmVyNtTG75/5vwAs9ZTzH5Z9jnz7zP2MCw3DMohoUSwsFFHGLtkxLZOkkcSwDERBwiaqSIJEykxhmMZUWZlNtCGLMpqpkTJTmJaJLGS2catEaV53uCAIqDZ5apIjCAIVNfn81n94BGDBZdI+SDg7OMxoLM4j9UvZXJ4xc8lzT69b1kyTB2pqWF9SjFNVyXZmIjqWZfG902d5duUKFEkiqeusKynh+QsXOdk/wH011VQFssh1OUnpBl67nVyXi+FojBvVLFiWxeuX2qgMBMhzu4mm06wuLkKVJN7r7qUmO3vWFOPVsDlU8spuzpn1aiyWjNpCQBCgOM/H49uvb0a5mNBNi/bgBF2hII/X1XN2eJCO4AS7K6vpDYc5MzyIZVl0BifYXlY5JwWZ9ctKyfI6FpZohOKMheJT/zbNIJYZQxADWFYMy4rPuF7GKfzmxgfLskjqOrF0mrRhcGKgn0vj4+imyUg8RmcwOG15l6Lgt9uvqU03TZO4phHXNdK6gWFZCGSi/DZZxq2q17gmX43LWaa4phFNpUgZxuSALuFSVVzK4knjWpZF2jCIptMkdR1jspZWlSTcqopDludckqjIEuuWlSFL4oIqkk2E42i6cUOiIUkiK5aV8KXPbL3lfb34xlneOtCMUy7Bb6tFM6PEtE4SxiiK6MGjVCPcoZKILI+Dp3evRJ0sidI0g+/84z6G+oPY7DLVSwp56Im16JpBy/l+vv5/XicWTVHfWMIjT6+n5UI/77x6jnAoji/LxdZ7l3GpeYCCIj/xWIqmMz187Km1vPPaOXY90Ej1ksWfqNdX5lOS76e5a3jB+mwuZzTuFGx2BZfHRiKeQp68Z0VJvC2ZBlkS5yREMR9YlkUyrXNkEUmG12XnC49t5Jn7V39oy84+KDAtk/5EPz/q/QlxPUaRs5hHCh+iPzHAu6MHCGlhXLKLVf6VFNkLeG/8CAOJgcneFJltuVupcJZzJniWc+Em4noCj+LhgYL7yFFzbun6zusOT6d1xoYj5BX4romm6LrB6FAYQRDIn8Xl98OMUDKJKkvkuK6fDi7xe6+Z2OumSW8ozD8dPcZ3T52e+jzgdKBPTpIOdfXwWuslRmNxkprOQCTC1oqyqZ6L2WCYJqOxGPvaO3m7rX1apuNmpO2Wba7lL/b+0ZyXfz98OXemlGEuuCwL610k47G4PkHKiONV85GEmR9BVRKpC2QT0zQi6RQem421hcVUZwW4ODaCIoqU+fyMxmPEtPSciEbA56KuNJeugQlS6YVROZqIJBgPx6fuu3jsR6RSb+L2/Dbx2LdIayenzCOvhmEMcrNEI2UYvNfbww/OnuPS+Dj9kTCJSbWm/7p3L6LwzrTlH6qt5d/cs5VS35VOEdOyOD8ywtsd7Rzq6eHS+DjBZBJJFMl2OFiel8+DtbXsrKjA75j5+osCjCcS/PT8eX5+8SIXR0fQDIMSn497K6t4ZMkSarOzZ3VfvlUYpslEIsF7vT281NrKmcFBxhIJbJJEVVaAB2pq2F1VRbnfP6fGX1EUyM/2UFEY4FLv6IId53g4TlozcN6gPU8SBYryfTTU3foE+XRTz9REHqAv8gITqTPYpGySxiiq6GNJ4DdRhNvv9fPEzkYKsr1TY6xpWvR2jvLUL22msCgLjy9zf11WOvqt33+E82d6ObSvmY5LQ7Re6KdmaSF7HlvN3lfP0nK+D00z0A2TcChBLJIkNB7D4VBvW3+H12WnqiQbb5ON0ALJZsaSaQbHIjd8dy0WHE4bybjGO6+cJa/QPzVnKSpbfNUhWRKxL+K1u0wyFjOTEfA5+dXHN/HY9sbbXmb8Ea6FiYlNVPlsxWcYSAzyxtCb9Cb66Ip1ka1m8+nyT3ImeI6OWAc20UbSSFLvXcrW3Ht4eeBVeuO92CUb58LnWe5roMxZxgv9L9Id68an+FBvoQphXnf4YO8EX/ur13n6s1uorS/C6bJlUnRJjbaLg7z6s5OUVuTwic/dM5/dfCAhTXb43yhSKAviNeZ5FmCXJf7TvbvYUVV+5QtBQBIEmkdG+dqRY9xbU8Uf7N5JStf52pHjxNJXObMLzFrjIYkiv7xmFc+sWI7Pbpv2+VyVFlS7Ss4ssqYfZAhAfiDjDr1YODXxcy6G9vLx8v8Xr3Jt+ZgkCvjsdo4O9GFaFgUuNyldn6ozzXW6GIhEOTnYT6U/QLZj7rXNDdWFvHuqY8GIRiyRIhxNZprXBQGH4yFsto1Ichlg4nJ9HkVZec16wYnf4GaLkEzLIpxKkdR1Srxe7IpM58QEumVRnZVFrnN6xnBJds41JF4zDP7gzTdpnxjHY7OR5XCQ53JhWhaRdJq3O9o50tdL16pV/PqGjTOShXAqxQ+aztExMYEqSZR4veimyXgiwT+eOM7Z4SF+d8s9NObnL1htsGVZDEaj/MupU/yw6RwAWQ4HlX4/pmXRFwnz5wcPcKy/j9/auIlleXlz2rciSyyryl9QonE5o3E9CIKAz+MgZ55mnzabMo1URbVOqv2fx6PWAhYnhn4Py7r9Pj8+t509m+qnyfyqqswXfus+9r56jn3BJhrXlLNx2xJsNpn8Qh+SLCErIqoqEw0nsUzw+DLPttvjYGwkgtfnIDQRIx5LkZXtprN9BK/POSXEcjtQVZRNts+1YEQjldYZD8cwTAtZmn1MSBkRdDNJRnHIjSzYboqYmJZBXB8jbcaQBRteNeNIrms6wYkYwYkYHa2ZkrA1m6pvC9GQJBGbujiqaJZlkUrrHD7bxZ99522GFoFk5GW5+dKTm3lwcz3qh1ip64MEWZDJs+UhCRKiIKJKNhJ6As3S8SgZ81KHZEcWZBJGHLtkxybZwQKX5ES3DILpMCEtxMHR9zgrnwMEFPHWS1znRTRsdgVRFPj6/36TPY+tYsO2OmRZ5OSRdl5/PhOJ33bfh9df4XoocHsAi7axMVYU5mOXZVK6kUmV3iDaqEoSjQUFHOvtY2NZpqxKMwySaQ2v3UYslSah66wtKcYhy3RNBOkKBslxXplwKqKEIorENI2Epk2qSonIkkRtdjY9wRBj8Tg+h31KatNns915iZY7DEkS2b2+Dtcd1J92KiqfXNaIyRVH1OV5+VPfBxxOdpRXZJTMbvJ6LavMx26TYYGqpywL4sk0yZSOy6EiySVIZEw6ZbkWVd2Iqq66Zj1JqgRubhLuVBQeX1rP40szzWxvtLXxpwcOkNDSfHXDxjkZ3tlkmcfr6+kLh9hUUsqyvDxyXS5i6TQnBgb4u2NHOdLby6uXLvGxukxm4v1oGRsjqes807CcR5YsodDjIZxK8mrrJb55+hTv9fTw3IULFHk85LsXJpIeTad5qaWF7587i8dm46n6eh6qq6PEm1GV29vZwbdOn2ZvRwe5Lhe/tXETBW73DSdiiiRSVXzrJZAzIRRL3jDAYlNlnnxoFY318zN09bjtBLJcmOI4Ua0LQZCJa/0IyJhWGll0c7P32UJg66oqcvzOqefTmnTq1jWDHfc3MNA3wRsvnGbjtiUgCNeUu3l8TtxeOwO943S2DdPfM4bDoVK3rJh3XjuH22Nn+aoyDu9vYfXGqttKNCqLAgS8Ttr7xhZsm8mUTiyewueZPYvcEd1Pf+wEgiBR4lpLsXMtdilrznXjaTPKqfFv0xF5lzz7Uh4o+WMAqpYU8qXffZBkPI1pWhn/gduUIZIlCdsiTNAv92S8d27xSEZhjpcvPbGZ+zbWLXr510e4Sbxv3HdIDvyKj75EP73xPoaSQ0iChF/x0xq9xHBymL5EP6PpMYocRRQ5CihxFFPmLKXAXoCFRa4tB+UWjDJhnkQjv8jPb/7+wzz/gyO88twJLjUP4MtycepIO7X1RXzs42upqMm/8YY+hFial8PS3FyO9PQBUODxEEomacjPozbnxpGSX1m/hv/17iF+2nSBEp+XWDpNPK3x5PJl+B2Znoz9HZ2Ek0mah0fpDYXJdV2J6HpsKuVZWbSMjvBayyUCTgf1ebnkud08XL+Ev33vCC82t9CQnwtATyjMU8uXEZilXOQXBW6njT2bltyGPd2oefv6leWX+wRuFmX5WTjtC0uiYok08WT6GnLmcn9pVkdwh/MTd8ys73OrVwPTr4DPbmdrWRmiACf6+4mm07SOjc1INBRR5P7qaj67ahUeW2aCl+N08cnGRiaSCb5x8iSvt13i0SVLyHW5FkSP/eLoKK+3XUI3TR5bspQvrl2Hd3LfblXl2eWNxNMafx8/xiutrXysto48lwvpBvuWZYmKooWN3KbTxg378+w2hV9+ev7KNLUVuTy+ZwWq/wRd4f3oZoTe6POoUhZpI4hNClxX/WwxoCoSu9bVXvOc6ZrBKz87gSRJKIrEfY+uQpQEfFnOqfJip8tOQXEW2bkenG4bh/dd5JWfHic7z8uqDVUUlWZz5ngneYU+6htLaTnfT0FxFnbH7fMKyQ948bkdCAILJo2c0nTC8eR1icZw4gKtkdcB6IodYJnvcWq9e/CpxYizlKBeDcsySRph4sYoCTN4Zd9Jje72ETpaBzE0g+LybKqWFOL2Lu67UBAy3mPyHEQTbgZT6lJnOvnzb+9laHxhSYYAlBZk8auPb2LH2hrsi5SR+Qg3D0EQcMku8m2ZSgmbqJJvy8OrePEqXoLpIO+M7MeneGn0Lccre1BFlaHkEO+OHsAje6hyVRBQA6zNWsPp0BlaopcAi/vzd2NTby2gMe9m8Jx8L098ejOCKPDcd95DkkS23dfAx395yy9kb8ZleGw2nlhej99h50hPLwe7esh2OanODiAgYJcV1hQVZurAZ5gLbCkvI6XpvH6pjQOd3fgddlYXFSIKAsU+L59c2cjLF1toH59gTVEhn1+7OuMuPDmxsMkyD9TVYFomb7W141AUAk4neW43Dfl5fHH9Wl5tucTPzjejSBLVgcANJyW/CFheXUhl8cJNvDQzSSg9QNwIAuCSs7Esc9olNy2DiDZMTB9HMzPlCKrkwqcU4JT9WJZJTJ8gpA2QpZbglP1T6+pmmmC6DwsTv1qMIl6/MN7ltJHjd9E1MI6xQGINsWSGaLwflhUFpGsIhWUZ2GxbMfQ+BDELUby9Ro+z7UmRJEq8PrIcmV6oaPra3wSQ53JTl50zRTIuQxJFNpWU8vqlNs4OD9EdCrE8Pw/7TbgwzwTDNOkKBjk/MkKF38/aosIpknE11hUX8fxFN6cGB2kZG2VlQcE1x/h+SKJAblaGDN1Mj9b1oOnGgm3rRqgsy6GyLIeEnotuxmZc5rLM7e1CTWkutaW505rhBUHAZlf46u89fM3yK9ZUwJrM38urcimvyp36ruSXrpWc/PhVn33xt+9fuAOfIxx2hWy/E5sik1ygEsy0phOOzb0UK2VEOD3xPaL6MPW+R8m1L0ES1WvKkOeC/p5x9r92Fo/PiaJKHH23hWRCY8P2xQ04yZKwKP0Zmm5w4EwHf7EYJEPIlM594fFNbF1VhcP2Ecm4myAJEtXuKqrdVQBk27LZnb9r6vv7C+6btvx4ahybaGN11mrWZK2a9l25q4xyV9mCHNe87nLTtIgE45w51kFX2wg1SwtxOG2MjUQ4ebiddVuqCeR6F8zsTDdNuseDTMQSFPg8FPvv3oZigHy3m2dXNvLsysZrvstzu/jD+3bNsFYGoiCwu7aa3bXV13ynSBL31VZz3wzfXY3KQBa/sXnjjN+tKipkVdHCqJTomkF4PMJ4f5BoKEYqkcYyLURJxOZQcWe5yC7MwpPlQlrg6M1CQhQEdq5dON8Xw9Lpjp2gKfQaMW0cVXLgU4pIGRFMrpSWmJbO6eDzjCW70KwkppWpb69yb2RV4AkkQWE42cqh0W+yMutRlvsfnFo3pA1wZOy72CQPG3M+fUOiAVCS5+d0az/GAk0S4sn0jI7j8fiPkeVKJLEABBuyXIkguNC0MyQTL2JZSUQpH6fzGSTp9mY+xxMJRmOxqZ4P3TQxLIuhaGRqqjKbqo7PbiPgnLkvpsjjwW3LEKv+SJikbsybaCR1neFYlLimYVoWl8ZnJokj8RhxLXMdBiNRUrp+Q6IhCAJOm4LLoS6YN0JaMxZMkWiucMh3Rhp1JmxsKJ+19NKwTPoSQwwkMpLCNlGhwlWCX/Vcd5vBdITexCAxPePKXWDPodCRiyrOfm+ZlsVYeoKxVJC4kUQ3DRRRxik7yFH9ZKleQLilyXlelhunXV0wopFKG0Ric7v/HFIAVXQS00dpDb9GVBumMevjFDvXoIiumw5aRENxbHaFpz+fUT/b9+o5RocW3mPi/ZAkccGzAZpmcPB0B3/x7bcZGl84dUHIvB9rS3P4lcc2snlF5awkw7QsRlITtEX7sEsqq/y16JZBf2KU0VQQAL/iodCRjUt2YFkWYS1Gf3KUiBZHEkSybT7y7QEc7/N00Uyd8+HOqeeg3luBT3FfN2sc1ROcDbZhYeGWHVS6ivAoN+5rTBkaQS1CMB0hbqTQTA1j0rtKFRUckopbdhFQPTikufULGZZBWIszmgoS0eNoZub5sYkKHsVFrs2PW3bcsozszUIRFfJtefiUxZ1Lz4toREJxXvzxMY4eaKVqSQGf/tIOHE6VV587wZsvnqa3e5T7H11FedXC+CUYhsmFgWH2XuxgY2UpT6+9c/KjHyEzEYtMxLh49BKn912g+UgbA+1DRMaj6JqBosp4czwUV+dTv7GWFdvrqVtbhdN7Y9+HOwG7TWH9soVh8ACh9ACnJ15AEmTWZX8ClxxgIHGBs8GX0c0rL1VRkLGJHhr8e/Aq+WhmkguhNzkbfIkS50qKnMvIspWSpZbQEztNrWcbNsmFZZmMpjqJamOUOdfgkubWnJ8bcC+o/Goyrc/YXK6lm0inDiCIWQio2O33YbPvJhr5G0QpB1muIpl4AwEZt+dac6HFQFLXOT04yHs9PZwZGqQ/EpmUuDUxLQvNNBmLx6eVIb4fiiihznL+7Ioy1YSd1HXMBfD4SBsGcS1zfptHR/mT/fuvOzUUgJShzzmrIIkifo9j4YiGrv/CSpurssTKuqLrTMJMWiKdfL3jp0T1OD7FwydK9/BgwVaUWUhDykhzePwMP+l9ncHkKH7Fw9Mle8i2+WclGgkjSXO4gyPjZzgfamckNU7SSOGUHeTZAzR4a1gXWE626kcRb34a4Pc6pzW6zxeaYcwYrJgJAVsVFe6tDCWa6IkdZiBxipg+QmPW01R6tuO8yXI5xSajaQbNZ3oQJZGJsQj+rNmf/4WCLIqZfrkFQlrTOXC6g7/63jsLTzJEgaUV+fzKIxvYuLz8utfetEyOjzfzV60/oMCezZ+t+i264oM817uP8+EOAKrcxewp2MiW7OXEjRR7h0+wd/gEffFhFFFhqbechwo3s9pfh0O+QjbiepK/b3uOS9FeAP648Sus8tchCrMHMAcSo/y381/HsAxq3aX8Zu3TLFXKZ11eNw0GkqNcCHdxIdxJW7SPkdQEMT2BZurIooxbdpCt+ih25rLEU84jRVtQkGed1wiCgG4ZXAh3cXT8AmdDbfQlRojrCbDAq7goceaz0l/D+kA95c4CVGnxs0UexcOawOpF38+87vLgeIyjB1rZunsZ935sBVnZmcbHpz97D4f2NrPv9Sayc9oor8qjayxIOJnEMC2iqRTVudnke92MRGJ0jwdJajp+p526/IxSTFP/EMF4Et00KfC6WVKQi02R2V5bSTC+MGoXH2F+CI1GeOeHh3jxa2/RdT7z4MuqjN2pYnOqGLrJ+MAEw92jnNzbxOFXTvLol+9n65Mb8NyGgfxmUVOaQ3729SOLN4P+RBNxfZwNOZ+iyrMZSZApcCyhP97EULJlajlRkNiY86mpf1uWiSI66IwdYyLdS5FzGS45QKlzJU2h1xhMNFPuXkvKjDGSbMMmuclz1MyZvGV5nEgLlGWETABgZtM8HVEqxm7fiWmOk0g8h6KuRtObCXj/AVmuQ1GWEw7999tGNPZ1dvJXhw7SPjFBY34BjXn55LpcuFUVmywTSiX5l1OnrrsN3TLRrZkJhGYYU9kGRZIWhFCLgjBFXoo8Hpbn5eO1Xb+/ZWVB4XW9QK6GIAoLWgKh6+YtZzSisRTdfeM3vV5JURZe942zeYuN0gI/xbm+WT1EZFFic/Yq2qI9vDp4gLAW5Y2h96h0lbDcd625rWmZtEV72D9ynMHkKKqosDF7BesCy3HLM0dlk0aK/SPH+WnfmwwkRrCwMuW6kopuGXRG++iI9nEm1MLO3PVIt9As73HaF1RlyDQtNOP6SmWXISBQ6Gik1LUel5xDW+RtItoAR0f/iZg+Qp13D361fM5kI7fAT0FxFkf2tyBJIm6vg+qlRfP5OXOCtIDStmlNZ9+JNv7mxwfoH1nYbIwkiSyvLuSzH1vH+mVlN0Uw06bGyYkWXh86wsVwN5IgETcSnAleIqYnUASJsBbjud59xIwEgiAQ1eMcGT9P0kgTUL0s8ZTdtsBk0khzIdzJq4OHOTZ+gYh+xQdKEkRkUUa3DMbTYcbTYVqjPbRH+3igYAOKNPu11E2DlkgPLw0c5FywHUEAh2TDIdlIGRqj6RCj6RBNoTZaI908U3oftZ5SZPHurQC5GczrLvcHXHzic/ewfmstylXulv6Ai3sfXkFBsX+qZ+B4dx8tQ6OUZvnQDINctwuv3cbbF9sJJ1PIokB/KMKz61ZQlZNFz0SIwXCUZFrn5XMX+R9PPviLLog0DbpmMNI/QWdzP9FQHCxweh0UV+ZSVJmLalMwDZPRwSC9bcMERyNoKR2bQ6W4Kpey2gJsDpVkIs3h189RWJZNVUPJlB+KZVoM941z9r1LrN62hOwC/7T9J2MpTrx5lv+Pvf+Ok+s877Px6zl1etnZ3hcLYNF7JQj23kSJkiyquKjYjhw7bnHy5pc4dmInb/JL4h7ZclGxVSiKoljEToAECwCi9162953Z6TOnvX/MYlF2F9hdzAJLihc++AAzc+acMzOnPPdz3/f3+8xfvMRgV5Tq5gqaltRTVhfBH/aiqDJG3iQ+mKSvbYBzh9o4f7idp/7XC4TKAqy+dylakZuSr5cNSxqK6vqcMgdRJB2PEh71y5CEQlCrYiB37pIlHYZyHcTyneTsFJZjkDKHwAHTKcwya5Kbctc8Tiffoy29n1rvMqL5DgZzrZTqTYS1yav3hHzuSZu6TQbTtsct5RHCjdv9MLp+B2CTzbxS6NtwcgjhBgSqugTb7pn2tgUFOejJDGtT+Tz/sGc3JwYH2VBby++OSNBekEd1HIez0SjfP3jwqutJ5nIMZ8ef/e9PXSxfKvN4iuKlUeivcqNKEk3hMF9ft45llZXXvd4LCFFwCS8WtuNM0SHlIp09Mf72u9um/L6vfn4TyxZdn4JVMVg2txqfZ+JyNYHALes8XHUb7eluDg2foivTx8vd71ChRyi7wjl4IBdl28BuTiTOIRC0+Ju4o2wdFa7xlcIcHPZGj/J0+6v05QoBW72ninm+Bkq0IJIQDBtJOjO9nE128HznVspc4Sl/Tr9HL+4xYzuY5tSyf0GtlhWRz+NTyzgx/DJDubMcjP6IpNHLwtCjVLqXIcaRj7+SSJmfW+5exJnj3Zh5i6b5FZRWBK/6nmIgS8UpncrlTd7ee5q/+8n7dPbFrn/HLkGRJZbPr+FLD65h1cK6KQdGaTPLlr49pMwsj9VsRhYSh2JnOBI/S0e6j592bMMla1S4wywNrsFxHPZGT3Im2cHxxHlOJzuY462+IbP7pm1xePgMT7W9yZH4WSzHJqB4qfNWUKGH8SseVEklbxskzBSDuTg92UHuqliDLmlXDYYGcjF+1vUepxLtzPXXMtdXQ0QPoQqZhJnmfKqbk4l2ho0kO4eOUqaHiehByqdxbs5GrutKEQx7ueXOgtSk4xQuFI7toKgyuq6yfG3T5cu7Xdw+v4nacOEkbh2Msqetk8VVFdSEAxzu6qN1MEZNKEDE68Gna9gOPLPv0E0z85mNOI5DX8cQL3x7G92tA2i6ijlSF71icwvlNeFCoGE7HPngLLvePEIua+DYNsODKUKlPj799XuYt6wey7R59QfvE4r4+PqffhbfiG67ZVm88+J+XvzONlpWNnJle3R/5yDvPbebgc4o81Y28uCX72LV3UuIVJcgKxcHsaZh0dc2wJ7XD/LSP27h/NEO3vnJTppXNFBeV1xZzetl2bzizmLZjj1SAX35cXvl4+7McQ5GXwQcFFGQGM5baWwun+ELqOVUuRfSlT7CUK6NwVwrhp2l0j1/Ur0ZF/C5taL1TcFIRmMcOVNZrsYwDo1IjSZxnPRIb0Yey+pAlmux7f6RoGPqyJJAlgQ50yRvWde8Rgym03TE49iOw31z57GssvIyrwnDtjkXjRLP5YhcRX2tP53mXDRK2jDwqBdvgLbjcKi3l/50ioCuUx8MTTqrcDU0WaY+GKI+FKJteJgj/X20lI71CLnAhWzCZK+XAoGqzA6jrVzOoKMnOuHrpmmTzuTJ5c2C501ZgPJS/6yR5Z7fUH7N7JAkJCpcER6rvove7BB9uUEOD5/i7f5dPFJ9B66RuvS0mWXX0GF2DR4ibxtUucq4q3w9zb66CWvSB3Mxfta9jcF8DIAmby2frruP1eHFuOWCz5XpmJxLdbKldydb+z7gROL8lD+nR1dRi2jOZjv2tNzpXXKAhaHH8CqlHBt+ka70fk4n3iRp9rEk/CkavJtQpKv3KSWG0xw/0E5Xe0GuN5POsWBZHeVVoel8lElTDLO+XN5k655T/ONzO+joGy6aChgUPHZWttTwxQfXsLKldloStlk7z7lUF1+b8xh3lq/GdCx2eI/QlR1gKB/nRKKV+f56nmy4lzUlC3Ecm6DqYyAXI2ok6Ez3kzDTROSZD/w6Mn282vMBR+PnsBybOnc5m8qWs7ZkIY3eKjyyC0kIHBwSRpquTD9nkl2sDM9Dvkb2LGokyCbzrAzP59HqW1kUbBrtP7Ecm/Z0L891bmNb/36SZoZdQ8dYH1lCmR76SIx7r/sumM+ZtJ3r5+SRTmJDKUorAqzf3IJlWUQHU5RXBfGPyMSFPW58lzQnWiPp0lgmi1tTWNdYS03YT288wUuHT7CstgrHccgYJoU5sg//F14MLNOm9UQ3u7ce5aEv3sqq2xdgGhYDXVF8YQ/aSCOiJEuU1YTZ+MCyQvDh0jh5oJWn/+YNDrx3ktrmcvwhL7c8sJyf/N2bdJ8foHlpLUIIchmD918+wIJVjdSM02MT6x3m5O4zlFSFuOvJTdz9hVvHdadVVJnq5grKau8gncjw7F+/wpHtJ0lEU7Mq0HBpKnOKqDYF4FFCmE6ejJXAdiwkIRcUpsx+LOdiT8OB6At0p49yd9VvUaLVo0ou+nNnaUvtvXwfZT9VroV0pA5yKvEuOSuFT4lQ7hpbcnE1VFUu6plkWTbmOKVTuutuspkXSZvPgpNDd92Bbcdxux8hk3kewziGaZxA1ycWRbgaAd1F0OXi9NAQh3p7WVVVRXWg0NSWM00cwK0oo/0ouqKgjvz/zNAQ8VyOsKsQoMWyWfZ0dfGDQwevWfaTzOd5t62VuZEI62tqCLndZE2TI729vHb6NP2pFLc3NlIbDBatF2ZeJMIdjU386PAhXjh+HLeisqyighKPBwFkTJN4LktnPIFp26ypriY8WalqQVFL6a6Hqoogv/zZjeO+5jiFZtd4IkNbV5TDJzqpLA/w2H3Laaq7cO46GFYSy8lyIc+lyxHEVWq4i4WuKtRXhic1IFMllUXBZu6vvIWn219j2EjwzsBeGr01rC5ZjO3YnEq2sq1/DwP5GD7Fw62lq1gRXoAuT5wJ3hM9SluqG8spuAM/UXsvGyMrRgdCQghUoTLX14BX9jCQj7Fr6NCUP6siS0WdrLBt55omjxMhC7XQm6GU4pFf5FzybXoyh0ibg6TNIeb678atTDwz3N0RZe/20zQ0lyMrMmeOdwPMeKAhy9J19blk8yZbdp3k2y9+QHtPrKgCDJoqs2ZhHZ9/YDUr5tdMO3slEJRpITZElhTM44REvbeCOd5qhvJxhBDM8VWzNNiMImQQMg3eSspcIaJGgqF8nLSVJcLMBhqGbbIvepKDsdOYjkWpHuKh6lu4u2INQfVyLySBIKB6CaheFgQaJ7V+gaDOU8ETtXewNHS54IwsJBq9VdxVvoa2VC+H42fpyQ7Rmx3CcCy0SUg3z3au6xPk8ybHDrbz3FM76emIkk7lqKgOs3BZHcl4hh3bTrJibRMr188Zfc+lwVlFwMequmokScKlFmbgK/x+hlJpoqkMOA66ohD2FJQJktkcW0+c5Wh3X0GKsiTAsppKPNrsKsGZaYQARZNRdZXBvmHSySx1cytoXnJ56YAkCZasu1yZqqqhlG3P76O3fYhcxsAfgvX3LuFn//wu2187SH1LFZqucPZIJx1ne/nsb9477g0ll8kT648zb2UTC9bNHTfIuBRVV1ly6wLeenoHHae6MXLFUSspFg1VYfweV1FnDypc8zkhvc3Z5HZ0yYtHCTGYa2Uo1z6qLAWQtRLIQsWjlODgMJhr41T8nTHa8EJIhLVaInoDZxLv41fLqfeuxKtMLUBSFbmoE8CW7Yzb/Ktpq5EkP6Z5FiFcaNrKkZuhRCbzNKZ5BiH58HieHLvSSdAQDLK4rJwDPT28dvo00UyGumAQBGQNg0Xl5dze2ERoJJiIeDxsqKuj9/hxXjl1ClkIKv0+TNumP5XiaH8/siSxsKyM/tT4UqkAc8IFOd5v7d3DjvZ2SkYCjQM9PRzu66UuGOTxhQupuKKh/HwsysmBAVKGSd4ySeRyOBSavd9tayNrmmiKjC4rlHu9rKquHs24VPp8PNLSQk8ywXttbfz1zh0sKiun1FsINNKGyXA2Q+vwMBU+H02h0OQDjVlEWcTPY/eNdZK/FMuy6e2P89LWw+zce45YPD1aqjWY2ctw/gSOc1Hqu8H/BIq4tsrM9VJZGiDsd086uPTKbjaXraE11c07A7vpyvTxRu8OqtxlCARv9+/mdLIVRcgsD7WwqWwVJdrEAy7bsdkXPUraKijyNPvqWBlaiDTOtIIkBKV6iM2lq9gbPYI1Qc/RRCiyVBR/mAvYjnOdctuCCvdiPEoJPrWcE8MvETe62Dv4XZJGHwuCD+GaYFbcyJuES3088Kk1IODdN46QThZHGOFqXE9GI5s3eGPnSf75pV209USLKietawrrFtfz+ftXs3Ru9XX14shCotxVgle5eC1yyzplrhAAHtlFuX756z7FjUcuXLPTVnZUmWkmGcwPcyrZTswoyAGvDrewMbJkTJAxXTyyzpLgHBYGmiZcptFbSbW7lMPxs9jYRI04OSuPNg2xhtnGdX2C4WiKN186QD5n8qkvbqSjdZBjBwtNwZqmMNQf5/yZXlaun8OiqnJs27ks3e/VNe5a0Myxnn6yhoGQBJKA2nCA+xfPw3bAo6n86ua1hYuaELhUheV1VSOOyYKfxyyHJEvMWVzLnZ9czbHd53jmG29S2RBh0dpmFq1pIlRaaGh2HIf+rhhnD7fT2zFEOpnFNCx62wcJlfpGB4dl1WFW376Qna8f5sEvbqKkPMi2F/ZSXlPCkvXjy71KsoSiKai6gnuSTZhur46iFsyqxCyZQb3AwqaKos7QAZTqTSwI3MmpxDb2DP0IlxREllQieiOGffzitoN3s2fwGT4Y+AGa5EYWKrLQCGljS7m8SgmV7gWcTryLXy2jyr1oylJ4qiJPS9JyQhxn3CYJIWQUZQ6gYtsDgIIs+3EcE6/3i1jWEJIURIjpXcwjHg8Pt7SQMU12tLfz2pnT5EwTRZIIulzoisItdRdVxBRJ4pdWrsStqrzf1sYzR49i2haaLFPp87Oxro475zTxflsbzx0/Pu42ZSFYU1PD/c1zee3Maba3t9GVSGA5DmGXi3W1tTwyfz4b6+pwq5fPVu7p6uLb+/YRy2bJW9Zo1iVrmrxy6iTbzp8fCTRkVldXs/SS0i5JCBaVlfFra9YyJxxmV2cnuzo7iWUz2I6DS1UJ6Tp1oRC31NWNBlcfRWRZoroyxAN3LOb4qR627TjFsoW1BHwuetPvoMsluJVKLtwbrm59WTyaqkumZIYphCCihbi/ahNd2T5OJ9s4Gj/Nz7q24Ve97B46jOlYzPXVc2f5Ouo9V+/LiRspujL9mCOTGCtCC9FkdcLJE01SqXFXEFYDDIyUWk0WucgZDZyJ5aSngl+tYmn4M/iUMo7EfspA7hRHYj8lZfbT5NvMeN1DsizR1z3My8/sRpIF50/1oqgyb79ykKb5ldQXSTVzzHal6QUahmHxxgcn+ZeXd9PaEy2qwptLU9i4tJEnH1jF4jlVE4oaTBZZyISvkG5WhIx7pJzNLesEVO8VryvIIxlI07GmHARPh57MIF2ZAQC8ipuFgUbK9OL1RwRULy3++qs2d3sVNx7l4nU7a+UxnZs3IftG1zFak4Pj9ts9OWctXmXy5n3XKW+b4dzJXp740i3cfv8S3ttybDTQ8PhdKKpMMl5QiFpQWTbuOmrDwdGejUt5cMlYsxyfrnHfoqmViXwUEUJQUh7gwS9uomVlI2cOdXD6UDs/++479HcOcdcT6/CHPLSf7uWNp3fS3xklUhHC7dMvNu1fcfTc8fga3n5uD4e2n2blrS3sefsYdz+xdkJ3VG/QQ3VzJZlUjuGByZkCDfXEyKVz1Myvwu2dnsPkTNFUEyl6LaQiacwLbCaglhPLdyEQlOj16LKPgew5XFJhgD3HtwFF6MSNXsDBr5ZR7prHQPY8PvXy8jJZUvEqJXiUEkJqDRFt6nK8haC9GJ/w6tj2EJn0c+Tze7CsboKh/4YQfgzjMKZ5Grf7MYSY/nEghGBJeTlhl4vNDQ30pZLkLAtFkvBrOi2lY031FpWV8+tr17Kprp7+dJqMYeBSFSp9fhaXlVHi8RDUXdQFgyy/ouF6cXk5v7fpVhaUlbG6upo5JSXc3thPXyqF7dgEdRdzIxGaS0ou6924wLxIhCcWLcKYRC16ld+PcsXxqMoyi8vLqQkE2NzQSPvwMPFcDhsHl6zg13Wq/X6awmGC1/DP+ChQVR4kEvby/u6zxOIX1GEcyjwb8auTV2ErFjXlwSk39yqSzBxvHfdXbmKgNUrMSPBO/25css6wkSCihdhctppFgbmjg6+J6MsNkrMvmkw2equvqiglREGJqtwVmXKgUexJmWKiyz7mBx/ArUQ4EnuW9tQHnEtsI5Zvx3TGKlYGQh4qa0LEY2kkWeAPutHdGrGhFLns5CR3p4MiS+hTPF4s2+ad/Wf44Wt7ae0eKmqQ4dZVNi1v4sn7V7GwqQKlCEIWkhCj2YmLiNHJMVVScF1RCijExWmwG+XHEzOSRPOFcUyZHqJMDxVV8ckt61S6rl55IAkJeUS8wMHBcqZnfGraFgOZNCHdPe0eQdO2eLnzMG/3nBr39U/UL79xgYZtO1iWQ6hkrEmObRUaw2fzBenDjBACX8DDso3zWLCqkc6z/bzw7W3s3nqMxeub8Yc8HN9zjgPvnuSWh5Zz26OrCJR4ySRz7N02dra2cUEVC1Y3su35vUiSIB3PsvnRVRNuv7wuwpr7lrHl++9yZPtJ5q+egz6BSRVAKp5m39bDDA8keOwzGwmVzS6zxbryIDNxqLrlAI2+tWOeL3ddzBSpkotm/9i69IA61sTOtPMkjQFUoVPtWYwmz3xJyHTJZreSzx9AVReQz+/GsZOAg+PYpFM/xOW6/7oCDSjMCtaHCo3Sk6XS56dy7sQyxssqK8dVdZoXiTAvcvFm0RAK0TCF7S6rqGRZxfWpRQkhCLvdrKmpYU1NzXWt68OO4zjYDqQzeQyjMIsvCYX2xHN4lFrkEUf6at+DKNLMl5FVlPjRtakPTtyKzurwYtrS3bzc/Q5xM0XcTOGSNFaXLGJjZMVlpSUTETdSWJeUZIa1wDWDLUVS8CuzT2r8epGEQr13A245hE8p51T8dQZz4w+avH4XdU3lDPQOY1s2LpdKbVMZDXPL8czghNhU5W0dx2HXkTaeen0f5zoHr7PU7HK8Lo1bV8zhc/evYkFDOXKRGv0F4qoDdglxjUbqGxNoZKwcWatQLudXPLjHBEfXxwXvjclwQUlxOpi2TWsixtnhIVaUVSMExHJZApqOIkmkTQPTttElGZ+mkzRyZC0Tv6qjXyLPm7NNUmaetJUfdztTDQCvK9BwuVQCITcHdp1j3qKLZR6WadPZFSU+nGHxyomNUT5memTTec4cbqenfYjqxlJcHo1of4JsOlfo3Ri5eNm2g2XaBZlUw6TrXD/7tp1gqDdOee3lMoqKKnPPp9fzl//uh1imzcI1TeM2gV8gEPFz6+Nr6Tnbx3s/3YUkCVbcsZjKpnK8ATeSLGHmTRLRFB2nutn75iF2/Gwfy25byC2PrcYbml03t5ry2avu4Dg2OTtFwugnlu/iTPJ9QlotNe7ZbViZz+1A01bg9nyKTPbV0ecVpRHL6gJmPiX+MR9dzncM0jcQR9fU0RKPkL6EpHEOhIPD9JqLp0tFiX9aTbMCQUgLsCq8iO2DBxjIFVS3SrQQiwNzKdcnZ8SZs/OXzYC6ZP2aJZISEvoNkA69GQghKHO14FJC+NRyjsSeI20OjFmuv2eY4wfbqWksHe1dC4Q8RGZ4MmyqzeAHT3fx1Ov7OHq2F2OKUsBXw+fWuG1VM5+7bxXz6suKauYKjNsjdIFLsxczybUEt61LvJEUIV9TRWqqSIhCs/sMYtgW+/q7iOYynIwO0BgMc2Z4kIxpIEsSdb4gJ6L9OIBf1ZkTKKE1GaM/k6LeH2JxSTnqSFCYMQ1Mu3jXz+vz0Yj4WH9bC6/+dC/9vXGy2Tx93TGe/f52BvoSVFQFWbyieE7LH1PAcRwGe4fZ+uwubNNGkiUkWeANeLjj8TVU1BZmXRetmcO5Y13s3Xaco7vP4vG5qJ1bSePC6nFLl5ZumEtZdYjje8/zW//zyavOasT64pzYfRbLsuk83cOzf/UKO1/eT6jUjzYin2qZNtlUjmjfMF2ne8hl80SqQvzkL18GxEhUfPkFQJIkfucbX7uhPRxet0bIP3sbZ23Hojd7kn1Dz2LaBn61lMWhe/FcRUllNuBgICQvQlx+M7WsHoRw8fPYX/UxVyeZytHaOXjVZfJ5i76BBNv3nOXUuX4a6koIjpR4RtxrCLuWc6nOp3ydWbPJ4NZVgj43yjRnglNmhnOpzoJT8AgJM0VnppekmcavXntiZvpDtg/Hedjsv4OwXo9PqcSjTE6xUAgJv1LJotDjeJVyTsZfIWsOE1AvTow6joORN7FMC9cFxcYiD7bHQ5lCj8aptj6efn0/e493kDOKV7fv8+jcuXouT96/ijk1kRn53Dfz6CrIOVvY1+jzUISEMhJcFPpCij9JMdMTmaZtc3Cgh9uqG+lMxkkZec7Gh1heWsW5+BCtcYfhfI5aX4B4LseZ+CDHo/3YjoNf1TBsazTQyFrGhKa00+G6Ag2PV+fWuxcBsOf90/R2xzBNi662IRYsrWXzvYupqA4VYz8/5hI0l8qitXPwBz2kk9lCk71LpaQySFVDIcMBUN1UxmO/chtd5wfIZfLobo26uRWk71uK4zgErnDn9vhdRCqCJGJpVtx69V6YjtPd/PjPXiQZS5NNZUkNpxnqiV31PZIscWznaQ6/fxLbtMedZVAUmd/+xleL26x8DcrD/qK5OM8EF9SmFgcfQBYKAbWCsFY7a/f3Apq6glxuO0K4cewkptWKnY2SzjyNpm8CPpozqcXCcRxMyyaXN8nmTfKGSd60ME0Lw7QwTRvDsjEuPL70/6Z1+Wujjy9d3sIwbVLpHGeuMbi/UbR3Rfmrf9p61WUsyyGZyjIwlEJRJG5bP4/qEYO14dxx+jLvkTcHsLFxyWXMC38NdZqCA5MlHPDg0pVpnZOGbXIicY43ereTtrLIQkISEgkzxfsD+6l1V3Jr2apr9mi4ZO0yYYiMlRt1BZ8IBxvDnrk+hGJS6VlGhXsxQkhIUxi6CCHQJT/N/jso0Zsw7AyadPHeZ+RN4sNpMun8aM+DUcTB/EQUSqeufQ3s6I3x4zcPsP3QeTK54v1Wfo/O3Wvn8+T9q2ioKvlIlrlbjn2Zu/dEuEZcumNGkmEjScoa28sz25GEIOxyc2Cwh+F8Fk2S8SgqJ6P9WI5DuduHO69S4faTtyw8ioZLVojnc3hVbTTIgJFAY7ZkNCRJUFrm575HV7BsdQOJeBbHtnF5dMoqAoQjvqLV+n3MRWRZorQyROkVbt1XoqgyNXPKr1oCdSlDvcOcP9HN5kdWjhr3TbgPikwg4icQmbjWfToUjpcbe8ErCbhn9aBdEjIBtWLcno3ZjMv9IOCQzfwMy+olmfgbJKkEVV2Ax/vF6+7P+LCSzRsMxNLEUxmGk1niySzxVOFvIpUlnTXIGia5vIlhWgWfEsvGsu1COeTIv7ZTkBUefW708YXlLixjX1xu5LFlOzijy9hFNfq6HtKZHMdP9151GUkS+Dw6C+dWsHn9PG7bMA+ft1BT3Z95H5/ayKDZR4lrBbHsIZwZmJ28koDXNS2FHsdx6Mn282LXW3Rn+nBJGstDCwhpAd7s3UF3tp+3+ndR6Sql5SrSmAAh1X9ZecZALkqzt+6qvWeGbRE3JpZynk3IQgUxvckJIQSKcFHmGisyI8sS/oCb0vLA6CSdd5JKiteDIotreq70R5M8+9ZBtu45TSJdPMndgNfFvetbePL+VdRVzN6y4StRJPmyUqy8bXC1joa8bdCVHlsudyVhzU+JFqA7O0h/LkZ/NoZpW0VtCJ9pVElmc1UjQ7k0SyMV1PqClLg8JPI5XLJCUHdR7wvh13SCuguXrFDp8ZExTSJuz2hGB2ZZRgNASAKv30VzS1Ux9udjbjD5rEHn2T5SySxbfvwB4HDPZ9Zd831Ni+v4rb/68ozs042+5gV87tliLPyRQpYrcLk/gaqtxO35LI5jIkleFLkeSa66ISZqN5NkOktXf5yewQS90QS9gwl6hxJE42kyOYO8YRX+miaGYZE3LQyjkJkwbbsgqHGzP8QNZm5TOf/zP37qqssIUZBo9ntdlJf68Xr1UU8Hy84Q0peQNtopd9/KcG58meJi43Nr0yqbGjaSvN6znaPxM4CgyVvLJ2vvQRISA7koe6JHOBo/wzsDe4joIUqvIrlZ4SoddRUHOJNsZ014McoE8r4ODlk7R0/22gOxjzKqriKrMp1tg6MToy63Rm3jzBrKFno0Jh6CxZMZXnjnMK+8f4xYIjPhclMl4HXx4C0L+dz9q6guvbZgwGxClzWUS3wlurODWI494UA2beU4NHz6muutcpVS7S7lSPwcGSvHoeEzLAvNpc4zM9LGM4EkBGVuL2Xui+JMXkWjwu0bfRzQCgG0Vy0E1D71ooDPpcdB1jLHNeGdLh9+J5Bx6IzHSeXyNEdKit7Y9FEjEUvzg794lZ72QVwejV/+d49R2XDtC6w36GHuisaZ38EbQNBXXKO+YtN+rp/v/OXrU3rPY5/fwLK1c6694Awjy0Ecx4ttDwImQuhIcimiyM12NxvDtOgdTHCua5CzXUOc6xykZyBOIp0jmzfI5gyyeZNs3ihqI+dHjaDfzbrruK7oSikCGSEUWhM/JmPeGNEBn0dHnaIcaM7KcyB2nK19O8nbBhV6hLsq1jPXV4+Dw21la+jK9NGd7ef9gf3UuCu4q3z9hM7gHtlFg6eazkwvedtg99BhPllzN5o0vpdG3jJoT/cQM+LT+swfFapqS3jwiTVc0PvJpPJ4fDOfbVVkGW0C+dF01uCV7cd5/u3DDMSKl3EKeF08unkxn7tvFeUlvll93xsPWUhUuEo4nWwnb5tsHzjM3eVrxz3G87bBsfg59kVPXnO9JVqAlkADe6InGMrH2T10jDpPOQ9V3TLGB2Q2c+V3cK3fd6LXZ0VGIzaQ4Pv/+yWCpT4aF1TTsKCairrINd2hbxTRdIYXj59AlWVubahnaWUlninqVf+8EAh7efLf3E8+Z+Dy6lTWRZBl6UN3Aboegj5XUV1ui43jjKiHjSDJEsPRFO1n+wiEvJRXhXCA/p4YiViGxasa8HhvvmGbZXWTSv4Tufz74AhAAjJIUjX+wO+gqks/tFkNx4FYMsPJ1j4Onurk6Lle+oaSpLP5kb8GecP8uctI3GzqfJ9AlfzU+R4lljtMmXsjijSz/RkwktFQJh88245NR6aXZzpeJ26mcMsuVoUXcUvpCtSRGduV4QV0pHt4vmsrQ/lhtvTupMpdxvJQy7h9F0II1pYsYX/sOHnboDPTy5a+nTxSfTvyFVkNx3EYyMXY0rvzhhiizWbcHg2356Js9aE95xkaSFBVOzm1r+kgS4WyqfH6IgzDYuvuU/x4ywF6BhNFu4ZoqsyaRXV84cHVRIK+D2UWXyBYFprL/thJ8vkEJxJtfL/tVT5bdzcRLYgQBZGZpJlhW/8+nu14e1L9Fookszq8gGPx87zVt5eEmeaFznfpzgxye/lKFgYa8MoXS6yTZprO9ACnku30ZAf5pcaHRs/bjwKZ2dCj4Qu6eewrd5CKpzl9qJ1t//NFIhVBfvW/fLpoO3Y9NEdK+NLKFQykU5weHOJATy/NJWFWVldT4pm96kKTJZPMMtgdpXbe5eVqg11RTu49R2l1mHmrrl7PewFVV2ha9POtx+/3uGb1RbeytoRf/38eHn3c0xFly4sHmL+4hnsfX4V7pK44m86z9aWDpBJZFPXmD+DT6acxrTZ8vl9FUeYCCrYdJ5N+mmTiLwiF/2raruA3A8dxGBxOsftYO7uOtnGmfYBoIkMykyOdNbAmYcT3MQUcx8EwbbQiH6eGnaQv8x6GFcV00khGOyF9EdI0a/sni8etTzp77uAQN5I80/Ea7ekeJARN3hoerr4dr3KxNy6g+NhUtpL2TA87Bg9wNtXOW30fUKqFqfWM36+1KryQub569sWOYToWz3S8juXY3Fd5Cz7Fg4ODaZuciJ/n+a6tHE+cHTUI+3ljsC9O65k+KmvCfPDOxVnv1tO9NM6tgFtnbtsTuYJbts37h87x1Ot7aeuJTsuwbSIM06KjN0Zn/zCloQ/PdfdKNkYW807/fuLGafK2wes9uzgQO02duwKv4iJlZehKDzBkxDFsk82lyzmf6qE9c/XerwpXCY9UbWI4n2Rv9CRRI8E7/fs5EDuFT3HjVdyoQiHnGGTMLFkrT9rKUaL7+VLDgzfo098YcrMho5FO5tj6kw9IDmfQXCorbm1h7rLpydjajsOZtgH2Hm3nbMcAsUQa2ynMENVWhFm9uI5l82umpIjgVlWqAwqWYxPLZukcHqY7HudgTw8PL2hhfunM1l7ONP2dQ7z99A6+dEUtc7DUz9JbW1CKlFnKJLP843/8YVHWdSWf/u2HqGycHfWPhYa82RtpaJpCRfXF2uzW031kklnueXQFjfMqRmdZHMdh/R0tPPe9HXS3D9E47+Y2j5vGMVz6ZnT9boTwjsw22chSmMHBLwAzr+xSDNLZPEfP9fD2ntPsO9HJQCxFKpsvZCx+/sZnRSGTNfibb7+Fadr82pc2UzLiq5NK5+jojhIKeKiYho9BW+IZvGo9Pq2ZC+e0uAEVwpoiTfoelbMM3urfxa6hw9jYlOslPFJ9O9Xu8ssyFUII6txV3Fa2mq5MH23pbnYMHqTOU0VI24RPGSvY4VZcfLb+AbqzfXRl+hk2EjzT8Rpb+nZQppUgC4moESeWj5M0M9S6K2n21fJ67/aifRc3AtPOEc23MpxvJ2sNYzvmlIIlr1JKRF6PpqsM9ic4c6yLxasKnl+qJs/47WA8xSnHcdh3opMfvLKXU+0DRXX9LqwfznUN8e0XPuB3P38HdZWzWx59IkKqn19peoS/O/Msx+LnSVtZzqe66Uz3IwmB7TgYjolHdvFg1UbuKF/Jv5x/9ZqBhiwk5vnr+MqcRynvepd3+g+QMNNkc3n6ctGRJvRCUH7psRachPT0h43MbAg0bMsmPpQilcgQLgsQLg9QNg0Z26HhFD94aQ9v7zpFNJ4ZVViBgrKIosg8++YBViyo5V9//jYqSyd34zne18+zR4+SNy1ubWzgoZb56LLMDw4coieRnPFA4/T+82z54fvEB5Nobo0Fa+bQtLSeQ+8eZ8mmFuavauL5v32dysYylm1eyOH3TvDe87tJxTPMXd7A479xH4qm8MLfvcGxnacRQjBnWT2Pf/0+Tuw5xwt/9wan9p6jv3OIuSsaefRrd9N1ro8tP3iPaN8wd3xmI8s2LwDg0HsneO+nuxjqGaZxcS13P3kLlmXz5g/eI9ozTGo4jb/Ez5f+0ycJXFGzmc8ZvPbdt2fkO7rn87fOmkBDlaVZndG4kkwqTyqRxeW+vC5VCIGmqaTiWdKp4imUTBdJKsNBBpzR/RRCwiGDLNdQKKWanTiOQzSRYcuuk7y+8wSt3VHS2Ty5j4OLomBaNrv2t6Iol5uWnT7fz//7169y720L+PLnNk15vV61nrwVQyAQo+VCM59pUmR5UuWXlmNzNtnOTzpeJ28beGQXGyPLWRNeMq5JmCLJrAwtoi3dzUBnlLSV5bWe96h1V7CmZOx7BIJmXx3/au6T/OPZZzif6iRppkmaaboyfYDAdmxkIbMkOJcn6x+iO9v/oQo0OlK7OBT9CYO501h2DnvUmHHyJ2a5axEPVt+J1++iryvGhjsWsGrjXAD8QTf5/MwqlcmSwH2FWd/x83384JU9HDzdNWPZUcO02Hu8g2+9sJPf+tzts9o/aiKEEDT7qvn3C3+R9wcO8cHgEc4mu4ibKQQS5XqQxcE5bC5bwaJgI4ZtUumaXBmcKik0eKv4ctOj3FG+in3RkxwePktXZpCEmcJyLHShEdYD1LrLWBBoYENkyUeqbApmibxtIOzlS//uUaI9w+x/7wQvfOtt3n9pP7/7F7846XWkMnn+/sfv88b2E6TSOcpKfMytryYSKsx8xlNZzrQN0NETZdue0wxEk/yP33uc4CRk5xRZ4vamRhaUleHVNLQRj4S1tTWUeq8u23q9pOIZ+g93UloV5rO/+wgvfWsrfe2D1MyvIhlNkc8WdLAL/hM5BrujvPfcLh75tXsorQ7zjd/7F84caGXuyia2/mgHX/mTX6C+pRpZkVE0hfmrGrn/FzdjGiZf/ZNfKGQvBFTUl7LhkVXse/MwuUzBNr6/Y5BD7x5n0cZ5LNu8kJf+aStHd56mqqmcU3vP8/jX72POsnr+7g++R8fJHhasnYN8qUSjw+j+XopAIMkSLq+Gqquk4hnymfGt6i/F7dWpa6mhbmE1wdLZ02ClzoIyo6ng8ekk4ll2vXOKmsay0cbFdCrHnvdOEY+l8PpvTo+GYRzDNDsRgCzXks08h2meRlMXg1CxrG6ymedxe54cMe2bXTiOQzSe5uXtx3j5vWN09g2TyRtFn138ecexHWLxNFXlQdyuiwMuw7AYiqVIT+J6Mh7x3ClUuXBtuTDreCN+ucn0tTmOQ8JI8k/nfsKwkURCoslby+M190zY4A3glnVuK1tDe7qH9wb20Zsd5NWedynTw8zx1Y1ZXhEyC/1z+E+Lfp13B/ayY/AAHZle0maGgOqjwVvNLZGVrCtZil/1kLcNNOnD0cPYmnyfvYPfZSB7CnuaGVGBBKLQ66bJElW1JZRXhdBGFKDWbJpf1JKl8bhScep81xBPvb6XHYfPY85wCWYmZ/DO/rOEgx5+9fGN6EXuX5WFxF0Vq1kXWYRA4JYvb6wPaz4+33Afn6y9HVnIeK54vcFTyR8s/BKGbaBKCl557H1CFjKlWpAHKtdzZ/mqEVM+BwFIQkKVFFyShiLJ2I7NLzU9xGfr70YRMl7l6sGVLCT8qoclgTnM99fzSdvEciwcp3BFubANRUgokoIuqWMSYJKQWBycw/c2/BEAilDwq9cee36h4X4+VXsHUDjvr/zubhSzohk82h/nr/7gB4TL/DQurOGzv3k/9fMqp7SOd/eeYc+RdoQQ/Mbnb+PuDS34L5EptB2wLJv9xzv4H//4BifO9/LsG/v55cc3XHPdTeEwfl2nbXiYMo+HMp8PCcGq6uoZn7lODacx8yaltSUEy/yU1ZSQjI01jHHsgnRlz/l+Wo918q0//BGaSyOTyJCIppAkwVf/9HO8+YP3yCSzPPq1u1l8y3xUXcXjd6NqymUeFooq4/bql5VNRXuHkWWJ8toIwVI/VY3lDPfHCZb6Ka+LUFpTQrDUjz/sJZPKYNvOZS2D/hIvT7V9Y8LPmhhK8up33uL5v3ud+aubuPNzm1i4bi6RqjCaW8M0TIa6YxzfdZo3v/8eXWd6uP2zG7jzF24hXB4sxtddFJQpqsXcbJoXVLFi/Rx+8t332PrSASprwwgh6O2MMdgX575PrmLewuprr2gGyKR/Sibzk5FHDo6TwzBOkBXPUXCDt4A8ycSf43Y/ghATD7BuJI5TKJF64Z3DPP3mfvqHkh9nL2YQBzDNgr/HhZt3MfCqNSiSH49aM2rqJt0AwQFlklnRgOrjjxZ/HYvCoKgw0Lp6wC2EoMpVxm/O+wK/1vwLhe0JGZc08bmjSDIlWpAHqzZzT8XGglfKiHmfLCRUSUURhQm4RYG5/OPa/wowqwOOjBnlVPwN+rMncUayGF6lnIBajSq56U7vx3AyhLVG3HII08kRN7rJWjEAdCnAqtIvUeVeTlC92JcoKxLyJY382lUkZ4vFpT0aPYMJntl6gNd3nrhhinTDySyvbT9BadDHL9y7sqhmfUIIXLJ+mdTypUhCwjvS8zAeiiQTlK5djiSEQJe1qwbpk9neRMiSjBt5WoN9IQSaUCnVQ1N633T2cybIWgbWzc5ohMuD/M6ffYnTB9sYHkgQjHinPEN99HQ3/dEEX3h4LQ9tXkxoAtO0W1bO4Xd/6U7+8K9+xvv7z00q0Dja18+PDx2mMx7nkYUt5E2LsNvNvfPmTmkfp4Pb5yIpINYfL5SYDSbJpLJouko+a5DP5jHyJj2tA5TWRmhaXEdpTQmf//efoKKhDMu0cXk0JFli3ooGmpfX03Gqm2/+wQ/4/7/2H0a3Y+RMLNNCkqTRu7RjO+A4hcjbdgiWBcjnDIYHE9iWTW/bAOGKIC6vjqzKSJJACIGQxLjTfpIkTfi75jJ5dr68j1e+/TYbHlrFr/yXz1JaU4J0xcxeSWWIOcsauOWxtfzt7/8zz/z5S9TMrWLNfcuQZomZoypLM12SW1TCpT4++7XbWLCinvdeP0JX2yA4Ds0LKvnSb9zFig3NNy2j4Q/8Lj7/b01qWSFmNrt4LZyRKMK0bA6d7ub//vhdjp3r+Vh+9gYgCQgG3KQzec61DjBnRFJ7NAvhXPx9JosQAll4SBttpI2O0ecD+gKkGQ5olUlkNIQoFHT5rlLTPZzv51RyD341wnz/GjrTJ+nMnKLRu4SMleBgbBtxY5AKVz0rw/cgkNgf3cKy0O2Uuep4s+dfaPAuotG7lJe7v0mpXktP9hxZK8Wn6n4PAewbep0zyf0oks58/2qWh+4ioM7+5uDe7FGi+fM4WOiSn1WRX2Ru4G60EVWxZ1t/naH8WZaGP02z/y4kIWM7Fp3pvXww8A8kjE560odoCTyAJk/8G9wIxUVZlnDrKtF4mhffOcyzWw/e8OtO71CCn751iNKQl3vWzf+5Upr8mKuTsUyMm53RSMUzvPitt+k810ekMsjebcdZd+8Sbnts9aTXEU9lsSyHxXOrJgwyoFDLuG5pI7IsMTRJPemeRILmSAmraqoLg26HGU+FXsAf9lK7di6vfPst/vSLf41t2SzaOJ+SyiDVzRU8/43XeeWf3gIBHr+L0powd39+Ez/4ny+QTWVxHPjdb3wVX8jDH//CX6CoCrIqcftn1o9uI1QewBvy8Ief+j+svX8Zn/hX93H4/RM8943X6W8fxBfyMtw3zIaHV7Hklhbe+tF2nv/bN2hYWMOtj6/FcRxUVSkEGICiKlMe9Hef7eXtp7cTrghy2xPrqWgoG/c3FEIgSRCpCnHbE+s5e7CV1777Nk1Laifs0SgMMC78lUbXM1MISRRlSvXCwGg6++qMBIjSJJRrhBB4fS423rGA9be3XAwSxchgRtyYm+X4+6Z/aBy/HQeG4mn+7pn3+Nl7R2e8ZOFjLqIoMktaqnnng9P8yV+8xD23LaQ84qe1YxDTsmnvirL1/Wvr319g6YJqyiJ+6gOfxHaMkcxZ4fqh3ICAVhTnEoJfjRBQS+nJnGWebxXRfC8CgSrpHI9/wDzfKuYH1rJz8EXa0sco0+uxHGM0QLMcA3vks5tOHklIPFr99dGsTlfmLO2ZkzxR//tE8z3sHPwZtZ4FlOqzX3lwON9BxhwCYHH4k8wL3ItLvuhqLUs6ILAxkSUNWag4jkOjbxMlehOvdv7/6Ejt4oP+f2BTxb+5IZmuiZAlQd60eGX7cf75Z7vJGzPvXj8e57oGeeq1fZQEPKxcUHvNPiPHsQFR1PuL4zjYWIWS7A+p3PlHjaxpYN1sw75sOkdP2wD/9q9/GYBTB9p494W9Uwo0fG4dXVMmWcrkIEmCwCT6M4BRmcG8ZWHaNpZtj6s7PlMsXD+XhesL2ZMtP3yfZCyFEIKHvnInD33lzjHLr39wJesfXDnm+f/2wh+MeU4IQWVjOb/7ja9e9vzSWxew9NYFY5ZfeediVt65eMzzX/1vnxv9/9f++5PX/lBXEOuLc3rfeeoX1lDdXDmp2byaeZV4gx6O7TxFIpqisnHsco7jYDtpsvmDOFh49fVYdhJFnv0KGUbOBCFQNXnKF2Ijb3J09zlWbJp/zWUdx8G2HYy8WWgadMYmpHSX+qHrPbmRGKbFqbZ+/vM3X6G1e+hm787PHbqm8MRDK9l3uJ1z7YP83Xe3XXYMb99zlu17zk56fX/67z5BWcRPe+I52pMvkDZaAYFbrmJd1d+g34jrRxFuMZKQCGsVJIxBzqYO4OBQqteStdLIQsGrhJCETFAtJWEOkbcvOEYXJiosrMu+xxr3fGShIISE5RgM5bvoypzi2Y4/A8CvlGA5Y/vwZiMZa4i8nUKVvFS4FuNWQlz6pStCQyAw7VxhFmF0wkXgUytYU/oVXu/6Q9rTH9Cd2U+NZ/LjlWKTyuR5bccJOnpjZPI39/s/dKaLf355NwGfi+ba0qsGG13Zs4TVCjxK8XosU2aMo/H3cck+loVuL9p6P2Z6OI4zO3o0CuU2EslYGkmWyKZzyFMc1MxvKuf9A+do7YqydF41Hrc27uDMcRyOnO4BB9YsbpjUupvCIQ52d7OjrQPLsbmlvp5NDZN7b7HR3RpG3kR8xBzKjbxBYiSAmmw25MLPm4imMCecwbHpi/85ebMNIWQ0pZG+4T+jNvK/i7PjM8h7rxzAthxufXgFmq5MKthwHIdMKsfrP9rJ9//8VZ46+N+u+R7TsDh2oJ3Xnt3D+ZM9ZNL5Mb0EX/m9+9l0z9gA82MKvRhbdp3iz77/Fon0zVfnKjajWS0uZreufHzpchf6U24ksiyxZEE1//0/PM6zL+/n1Lk+kqkcubxJMplF11W8nsmXO2kj9e7DueMsLf0P9CS3UON/gNb4M0h8uALusFZJLN/H8eGd1HsXUu2eh2FnAYdho5+IWc1QvgefEsavlGA4edJWgrQVJ5rvwfIsGl3XZXK5yJTpddS45/NA1VdGXhO4rlJGNJuw7Dy2Y+JTylEkN1dGdoooZDRyVmKM1K0sVMpc8wmo1WStOF3pAzc10Eikcxw7d3W51RuF48AHh1sp8Xv41U9upCLiH3Pvsh0Lw86zP/omi4ObqKABXfYghIRp5zHtPA4OslDRpMKEcM5Oj2TXBIpUeN5xbAzHKASDFH4XYMTfJUfaTCAAVXIhSwqWbWI6eWzHQhIymuRCEjJZK41AYGPhODYu2TdyfRv/nus4DpbjkLONwuTzyCBaEgJVktElBVn8fBkVT4SDQ842i2rkOa1Aw+XRqJtbwd//0TOEyvyk4lnW37d0SuvYvKqZfcc6eO39Y9RWBlk4p7LglDki12c7DqZpMTic4ptPv0dVeZCHb19MNjc2+pckgaZe/CgN4TBfXrOa++fPw3GgKuDHr9+cco5Nn1hzU7Y708iKgturEx9M0H22h7qW6qs2lNm2TcfJHlLDadxeHXnC4MQhZxynLvI3dEb/fUGi8kNy8nec7uOdn+1HCMGmB5ehudSrXrhs22Z4IMkP/+o1Xvzn93B7J3eMnjvZy4/+4W16O2PMX1KDP+QZM5laWjF7mu1nE4l0ju/+7AN+8Mpe8ubNKVeYDLIkkCTpin8LN1JJXPi3UPZ34QYrywJdUXBpCrqu4tYVXJqKrim4dBWXVnjs0hXcmoo+8lwqk+cvn9o27rV1JlEUmaULali6oFC2Y9sOu/af5z//7xd55J4l/OtfGZv9veY6JTcSCg4mtm2Qs4ZwboC8bTFRhIpL9qHJLtyyD5fswSV7aPAu5ujw+xxP7KBUq6Xes5ASvYpyvYE9Q6+hSjo+JYxL9gECjxwcHchBIVtSqtfQ7FvBy91/j0CiRKtkc9lnRu+7s5nLVcTGlkLrsh8hJNLW0LieGrLQCGl1dKT3EMufn9F9/bBhWjZbd58iHPDwpYfWEPS5Lrt3pcxhjgy/y7nUAYaNfrxykNvLfwG/GuFQ7G1a00cxHYOQWs6d5U9iY/NO/9MM5/uxsan1LGBj5DFSVpzDw+/QljqGQFDnaWFhYCOGneNc+iCt6aMYdp5loTuY51/FudRBTiV2k7bieJQg60oeIqJVs7X3e3iVIEkzRtKM8VD1r+KVx7/n2Y5D0sxxYriHrd0nODLczWAuieNARPeyOFTF5op5LA3X4FX0SclUX0lXOvaRsbzMWQYJ49pu6lNhms7gHh776h2cPdxBfChF3bwKKuoiU1qHadncuqqZU639/NdvvMLylhqa68tG5WuT6Rzt3VF2HDpPOpPn8w+vZe/R9kLd2BW/aGVZgFtXNY8+HkynyRoGLWVlGJbFyYEB/JpOY8nsL7/5sOAPe6lrqebMgVa2/eQDaltqKKsOo7m00d4PKDSo57N5+juGeOvp7fS1D9KyZg7uCZuVBZLwY1hd2E4Gw+pBuslNw5NF1RS6Wwd46m9eR5IFG+5diu4eP9gwDYvO833805++wJ63jiErEnMm6dDe3ztMLmfy5d+9n413LSz2x7ihOI6N7SRw7BQOeXBshHChKNWjrxeM/SSEkLme+pRYIsN3XvyAH285MCuCDCFAUxU0RUYd+asoBTWasN9DyO8m7PcQDrgJ+d34PDoeXcPjVkf+1XCPBAu6pqApyrTUYzr7Yvzfp29+MC9JApdLxeedfuO2T5uLLFy4lDLOx5/CcUxmsxnnlViOSdZKkTAG8cohyl2No6/VeRZQ5xlbHrs28gBrIw+Mef7+qi+PeU6VdFaE72JF+K6i7veNQJHcSEIlbyexnDyO41x2bXUpYSRkYrlWHMcc8zqAJFQcxyRnTa7f8+eJVDbPy+8fJRL08Inbl+J1XzwP/WoJ6yIP0505y9rIg9R6WgAYyvdwIvEBd5R/Hrfs56Xuv6Mne55qdzPrIg8jkBjKdfPOwI/ZEHmE/mw7XenTPFbzG6OZtKQRxbTz1HkWsrH0MfZF32DY6GMw18XZ1EHqPAup8yxkx+DzdGfP4lcKY01NdnN35FF0eWKVJsdxiObTPH1+D/946j1S5uUZ7HPJAXYPtvJ8+0F+oWkNn2tcS5nLN+XMxqe3fpOM9eEoQbw2DkYRFadgmoEGgO7SWLhmDlBoDu8630/d3MlL3H7jqXd59d2jWCP69Nv2nGHbnjMTLv+d53ZO+NotK5suCzRO9g9wYmCAu5qb6UnEefd8Gxvq6z4ONIpIWV2Etfev4OzBNt79yQdEe4a563O30Li4DpdXR0gCx3bIpnOcO9TGG997l2M7TxUG4I+sIlQ20Yy7IOB+gL74X2FZUYZS3yfk+eQN/WzTZd7yOhaubuT43lae+uvXkSSJdXcvQr+iLDCbznNifyv/8Cc/5eyRTnSPxqrNLXz9v356UtuxTAu3R6O8KjRDn+TG4Dh58sZZUtkXyebewzDbcOwMuraCqrLvA2A7w+Ty+5GEF01biiSmJ/0XS2T41gs7+elbh8jlb44juapIuPWR4EBXCHhdNFaVUF8ZprI0QGXET2VJgEjIizJLFNluNKoqE7wOE7F6/ycK/ypPENdP4parUKUb5NlThCnNtBnnWHw7Q7lu5gfWEVAi4w6YRzfp2JiOiSyUMVmJnJVBk1wfmXIQjxxGk7xkrChpcwjbMS/L2ATVGiShMJg7Q9ocJKhdLAkr9P4ZpM2BEZmRmz/RMBsZiKV47u3DhAMe7lwz9wr38ouu2BekkpNmjIQZZcfg8yhCw6+WFHzQjEG29T+NR/GTtVLk7SyWY2I4eXTZM1pedSHz5Fb8uGQfkpBRhY7lWKTMYZLGEIdzXZxPHQYc1EuERsr1ehRx9SFszjbZ0X+Wb558h+xVAoFoPs1T53YTVN18tnE1bmVqkx1Z2yBnf1QCjeIzpUDDtm2GeuNjnu843cux3Wd58ncenPS6ykt8tDRVTGXzE9JQdbnr47KqSqKZLN/ZsxdVlrmtqYGN9WONjW4Wlp0lbXajSj7y9jCOY+NRa8hbMQx7GEXy4VVqKZzYFjlrEMOOYzmFOmpFeHAppaM3UMNOkbMGkYWO49jk7RjgoAgPulKGIjw4WKSNToSQcSnlyJfIPZp2mqTRilupRJNCk7oxhcoCbHp8La1HO9j9+gH2vnmIvW8ewuXVCZT6R+V844MJsiMu1d6gh7X3L2fTY2vwhcbPUgghCHoewaOvxrB60JR6VLnsur7vG8WqzQvAgae/8SZH95zjqb9+HSFg7V0Xg414NMWuLUf5l//zMr3tg4RK/dz26Ep+8fcfxu2bXOlUMOzF63fR2TpA3ZxSNH32at9PhONYZHLbGYz9RwzzDEJ4EULBduI4zsXZRtPsJDr8P0BIREJ/gktbNeVtpTJ5fvjaXl7YdoTMDS4P8rg0Al4Xfo9OTXmABY2VtDSU01QdobLUP600/UcZv1dncUs1FaWB61qPJFRC+uzoUcpbWRwcVEmj4CVjY2GiCh0HB8sxkISMhIxHCbAifDeKUBEIMlYSyzFxyz6UKzwuHMcmZ2cYNgYJqWXoshvDziGQkIXC2dRBmrxLUYQ60hD+4T7W/GoVLjlIxhpiMHeKWu8avFLp6OsR1zxkoZG3kxwdfoEVJZ/HPSICYDk5BnNniObOIwkFl3x9x9fNJuB1IcsS8WRmdLK2WJzrGuQnWw4Q8rtZvaAObaT/VgiBImnEjH7cOT8htZwSrZIKVyMrQ/fiVrzYjk1Eq+Z86hCmnWd56E4Gcp0M5rqQhIJHDmA7Jl2Z0+iSZyQbIUb/XIpfLaHc1UBILafS1YSNTVAtGw1SClz9mB7OZ3ip4/BVg4wLRPNptvWeYkVJHctLaqf6tX3MVZhSoJHLGPzkb98gdIW3wlBvHNOY2izhr332Vn7ts7dO6T3XIprJ0BqNAeDXNUzbxqOqeFWNoXSGMt/saHpLmV0cGvwfhLVFJIyzJI12moNfYDh/nGj2EJocYlXZn6DLEQwrwenYd4gbpzHtJLZjoUkBanwPUud7GFnSiedOcGb4X9DlMEKoDOdOYDkZdLmEWt/DVHoLSg4nY3+P5RgsCP8r/Frj6P4MZvayf+CPWVb6H6jwbEZM8rBoWFjDl/7TE0Sqw+zbcphYf5x0IstAxxCO7SAkgaqrhCuChMoCrLp7KY/86t1UNVVcZYYOcuYpXGoLmlKD7eTJGefR1cZxl59NCEmw6vYFCEkUgo1dI8GGJFh9+wISsQxvPrOL57+1jeGhJJUNpTz2S5t56IubUCfZPA5QVhnE49V57dk9WJZNZU14TEN+ZW2YYHh2HO/jYVitDMb+I6bVha6tQdeWIQkPscTfXLacLEfQtdWksq+Qyx+ccqCRN0y27jnFC+8cJpm5MY3fLk2hJOChNORjQWM5qxbUsqS5itKQd1LyxT/P1FaF+e2v3X1j7LxvACkzzkCuk7QZp8m3BMPOETMGkJCpcNWTNGMMGwO4ZA8RrZrUSEO3TwkRVEtpSx8jYcRoCawmIF1enmw6BnFjiJyVxlFtUmacmNGH4ziUu+roy3bgU8I42NS65xX63T7ElOhz8KsVRPPn6ckcZq7Rj0eOjF43y/T5+NUqslaMI7Gfokg6dZ51CCGRMLo5OPRjDCeDLgWI6DPvqTUTaKpMdWmQu9bOw+9x8dzbh2jriRZdvv/AqS5+suUAQa+L+Q3lBZ8YBPP9qzmfOkxv5hzrI4/gV0tYHrqTE4kdo43fd1V8gQpXIy55FwdiW9GEizm+5QgEZXoNc/2rOBh7GyEkqlxzaPatwK+UoI2UQHmVEKrtwisHme9fw6nEHjozpwCH1eH70fUaSvRK3LLvmlWRaTPP0Vj3pD/3qXgfbamhjwONIjO1jIZlM9AdY/mtLZc97/K66O+4+RKR54aifH//AaAQfTsOpPJ5vrf/AJ9YtHDWBBoAlp0mZXZQ73+cs8M/5GT0mzQGP0tZaD37Bv6YoewBqrx3IQkVWbho8D+OW6kib8VoSzxPa/xpwvpignrht8haA8SNM1R6bmdB+NcwnQxtiZ/SnnwRr1pLiWs5pe51nIs/RdI4j1etRRIKjmPRnd6CSyknrC+b0s1ICEFdSzW//Eef4Y7PbOTw+yfoONnNcH8cwzBRVIVQWYC6+VUsubWFhkV16O5rpSRteuN/RkPkbwEHyx5iIPFNakqurcY0GxBCsHJz4Td5Rmzh8K6zPPXXr5OIpTmxr5V3XzpALpNn3tI6fuFf38vauxahalOrYIwNpejpiNJxrp9/+F+v4PHq6O7LZzu/9Bt3s/6OsfXcs4Vk6ilMsw2P+z4ioT9BkSswza4xgYYk/KhqE3Y6jmm2TWkbjuNw8FQ333t5DwOT9OC5HvwenYqInwWNFWxY0sCqBXWUBDxFdd39qFMwtePD1FZxVboyZ0iaUXqy52n2rWAgd5629DEWBjaQszOcTOwp9Clh4wp4SZhRujPnUITKivCdOIAsKajS2GznBRWe86mj+NUwHemTeJUQ3ZlzFxV+7BznUocp0SrxKaEb/vmLiV+tpESfQ3/2BIadwXAyONij9yxF0mkJPkDc6CBrDbNv8F84OPQ08khfB4BAwq9WUudddzM/ypQRAiJBL8vmVvHJO5exfH4NmiIjSfC9V/bQP5Qsemz+3oFzhPyF5vDa8hCSJFgQ2MCCwOWmyY3eJTR6l1z2nCrpPFT9a2PWqcseFgY2sjCw8bLnFwYvPm72rxj9v1vxUXFJn9IF1kcendRnsBybWD49qWUBYvl00RuhP2aKgYbmUnng85tYdcflDai97YOcOjC1QcBMsKqmmlU11Td7NyZNqXstZe71DOeOkzG7qPU9iFup4sjQn5OxClG4KvtYFLnotGw7JpJQODT4v0gZbaOBhuNYhLSFNAe/iFspGOHlrEHaEs+RNfsL23OtoTP5GoPZPYT1xbiUMjJWH0PZ/dT4HkSRPNNKr+senZa1zbSsbb72wlfBtKPkjFNY1gCJ7DtcCDQcbk5N/XQZDTZGVIEO7zzDN//4WfI5E92lsnJzC5/7rfuYv6weZRpeF16fzrK1TSxb2zThMuXVoev4BDNPJvc+DjYh/28hS+MbNwIIoSFJIRwnh+2MLdu8Gn3RJD94dQ9tPdEx8r/FxK2r1FaEWLuonttXNbOgsQL3h7Cc7WOuk3EunX4lzJnkfkJqOZIkIwmZKvccyl21pMxhciM+GH6lhLydpS/bRsIYwq+WoEoabtmHaefH9YGShYJfDY+WZNmOjS658CpBTCdfMIVV/HhkP6Z9Y+WLZwJJyNR51yMLlZDWQERvRlzRl9Lsv5v+7DHOJd4hZyewnByWU8hkCiSCWi3zAvdR6rq2X9Fswa2rNNWUcPfa+dy/YQFl4YvNyp+6czlD8TTPbj1IPFXcjK1p2by+8wQlAQ+fvns5pSHvh7L8birlqYWJjg/fZ5ztTCnQUDVlTJABEKkM4Z/BMg3bcRgYSpLO5mmsmZq61WxGFQEECrLkQZcjCBQEAlnSsUdMlBzHImV2kjX7MO00NibJ/DlwbCznYuQtCRW3UjkaZAAoUkHm0B7p7fCo1URcy+nLbCdtdqPLEfrS72E7FhWeTcg32dHZsgZJZt/BtPoZzvwMKGi/+11Tl7m82QghWHlrS0GaVJE5uP0UtmXTvLiWX/qDR2haUDVlN/YLNMytoGFucfqbbhaWNYAQMqrSfA31YgmBDljgTD7gNEyL13ec4Oi5HowZUpiShKAi4mfNojru37CApXOrPw4wioTjOOTzJoPRFNF4mmzWwLIdZEng0lVCQQ+RsHfE9HWWDAzGCWbjxiAhrQIJmYyVxKP40ezCdVaX3MzxLmUo30NAjeCSvXiUAOVCxiMXypM9sp+UGSNtJsb4XdiOPRpACKDWM4/+XCe2Y43UtTcylO9FESp+9fI+xg8rle6lVLmXjQkwLqDLXtaUfgWXHKI3c4SsNYyDhSxc+NRyGrybmBe4F8HsL2GUJEFFiZ+1i+p4+NbFLJpTia5ePmTTNYUvPriGoXia13ecIFtkoYt0Ns8L7xwm5Hfz0KaFBH3XFmpwHIe0lcCrTK4PJm9nSZlxNEnHq0xelr07cx6fEsSrBCeUZ1YlmXJXgNbU4KTWGVTd+NSbOw76KDJt1alLSQ2n6W4dYMHqiWdYrwfLsnlzxwlOtfbzh1+ffMP5bEcIaXQWTAhlTCTtYBPNHaQt8TyWnR0pB3PI29Ex2vCFEquJTpCLd8ByzyZ60u8Qyx3Fq9bQm36PkL4Qt1I14cX7RqGrcykP/DaS8FEaKDifF76T2X9TGA8hBCs2zUfTC54ju7YeRVZlZPn6B0ZG3mQ4miIZzxbcwa+gvCqIPzh7ZYGF5MGxHGwngXDcEwYbjpPDsgcQwoUkTf4mdLp9gK27TzEUn3zafCroqsK8+jIeunURd66eSyQ4uYkWy46SN07h0laPyPXeGPLGeQzzHLJcjq4uvOnn+tUwLZu+/jj7jrRz8Fgn59sHiQ2nMUwLVZEJBT001kZYsqCalUvqqCwLoCizs/8ga6dp9C6iP9uBZRuU6RclrBVJo9rdTJV7zuhMaouyBnBGf59yV91l77ls3VaKmNGHVwmgSS48SoCwVjnqq7IgsBbbsQvX0Elfci7cK2ZJ8HYF0iTOGa9SyrqyrzGUO08834ntGOhygBK9CY/y4Zio9Ht0WhrLuWfdfG5bOZfS0MTXl6DPzZcf28BwIst7B84VLACKyEAsxU+2HiDsd3PbqmY8rquXP1uOyZHhHayL3Dep9Rt2jli+H+9I0DBZ9kW30uxbRrNv2YSBhlfRWVNaP+lAo9lfRp33Y3XSYjOlQMMyLc4f6xrzfNf5fs4d7ZyxQMO2HQ6d6uLw6ck39XwUsB2DM8P/TNJoZUnJ7+HX5iALN4PZPRwevNIpWzCZm0NAm0dQX8BgZje6HCZptDI//FUUaWqDUiNvkoqlkRUJX8h7mXfG9SHhc23GsqIXnxIyijQ7Tv7+rih7t52Y1LJCEiiKhKIqNMyv5NShdo58cIaf/ct7NC2oRr5icKSoMnc/sfaa602nchzd28qud08y0BvHNCyckdogSZIIhNw89Nl1LFg2ewMNXV2EYZwklXmFgPeLME5vkOM4WFYv2dxOZKkEVZlcA2c2b/DajuOc6x6akZIpj0tl9YI6Pn3PCla21OKaQo9N3mwnlvwuFSXLb2hzrmn3EU+/gKrUoKvzma3Bu23bdHZH+fHP9rL1vZOkMjkCPjc+r46uq1iWRU9fnFNn+3h312lu3zCfJx5eSWNdBHkWNtrP86+kL9tGhaueoFo65vUrSzUK2RlxxTLjf66CK7JDtbu50BgLYwZcUzXis8xObHsAVVs+Zj8+TAgkIvocIvqcm70rU0KRJWrLQ9yyvIl717cwv74MdRJBdE1ZkK8+voFYMsOh011Fv+61dkd5+s39BP1uVi2oHZNZuUDaTNKRPsmuodcJa+WokkaDdyE5K0NX5iy65CZtFbJzNe455OwMg7luLMdEv0RNKmnGGMh1kbXSuGUv5a563LKXgVwXQ/leJARpK8G1VCMCqov7qhexe6CV1tTV+4jLdB+bK+Yyxzf2PP2Y62NqqlNZgxe/s42aOZeXbQz1DpOfQdlIx3FIZT78NaZTxyZrDqJJQbxqHSCRNM7Tn9k57UGKJFQq3LdyIvZNOpIvoctBwvpiJKamGx3rG+bdZz8gMZRi5V1LWHJrS5FKGBzS+T2Ag+NY2E4STakn5PlEEdZ9/Zw71sVf/MEPr72gKKS+VU1Bc6noLpVMKo9t2bz4nXfH/a7cPn1SgUZX6wAv/ugDBvvizF1YRevpfoy8Sd2cMjrO9eP2aOiu2V3C43U/Rjq7hXjyH5GEF11bMmKuVtC4t6xBTKuTVOZVsrn30dRFuPRrfzcAZzsHOXCqi2S6+CpTHpfGhiUNPHn/KpbOrbqqipRpDZIzjmA7aXAsPK7NgIPtZElnt+E4BrIUwuPaiG1nyeT3YNuJkZKyOahKHYbZjmG2wkgGU1PmoMiV5M2zmFY/BTNDGZe2GEWuwDC7yJtnsO0UslyCS12KJHnw6OswzFZMa+xE0Wwinsyx9f2TvPrWUUpCXjavn0tjXYSSkBdVlTEMi+hwmvPtg+w/0s6b7x4jFPTwxEMridxslbVxLn9u2UeDd9GMbM6nhIra4O04Jvn8Loz8PlRtKeMF/x8zc4T8bpbPq+HudfPZsKSB0BT9ZBY0VvDlxzbwZ99/i9bu4ovzHDrdzY/f2E/I52Jeffm4Xj+mkyea7yNlxhjK96DLHhooOIvvHnqdOk8LkpDwKSGq3U2YtkFvto3uTCsLAmsIaqUYdp5zySMM5LvQhIthYxDLsah0NXAo9h4ONl4lSNyIYjtXz95ossKKkjq+PG8Tz7cf5Eyij+F85rLwxKtoNHgj3FXVwj3VCwlo0/fxGQ+forMoVIU8i7PIl2I5NqfjfQxNoYn+Wky5dEoIWLT28lmC7tZ+2k/1jru8admY1vXVSOfyJqn0z1+gIQmFGu89tCd/xqnYt0eyDg6Wk8etTN4c8UpKXCtwyWUM5fbT6P80mhSecpAw2BXltX/eRn/7IJIqs+QKJbLrQRIucBwcYWFa/aRze2dNoKHqCpHKyad3L+A44PJouDwTB3RXe+1S+rqHiUdTfOILG9l83xKe/9520skcj35+A9u3HqOvKzrrlY48rs34PU8QT/2AoeH/iktfiySCgINpdRNL/BWGeYZsfh+yVILP8ziqMu+a67Vsmx2HWunsixV9nzVVZmVLDU8+sIqlzdXX+I4dkpmXsOwhJCkAjo0z0ndlWn2YVg+2kyGReRFdW4hAxbL6sexhLHuQbP4QYf9XyOS2k8ntxK2vxzBbMcx2vK47SGZexbaH0dR55PJHcTDx6OtJZbdg20mEcJPOvY/k9YyUSk18qXeg6PKY06V/IMG7H5wm4Hfz+AMreOCORYTGKQEcjqd5490TPP3CHrbvPsMtq+fc/EBjBMvqx8jvxba6cLCRpBC6636E8AJ58rldWOYZwEFWmlC1NUiSF8vswDAOIMmVmMYpcLLISgOqugxJLpT82PYwpnEE0zyL4+QQwoemb0SWaxBCxjROYBgHse0EkhRG0zcgSZWAQSb9NKq2BtM4hGPHkeQaNH09QgSxrS5yuS3ksu9gWx2kEv8ACFRtOZq+jtmU3XBwyFsJMlaMnJXEcoyCAZ9TeHUyaLKPMlfx7lnXg6bKNFVHuG1lM3esmcucmuln5zYubWTw4TX8zdPvMjRc/LLR9w+eI+R380uPrBtVoroUvxJmWWgzu6Kvs/4Kt/pCqeAcGrwX1RD9apgm72Ly9sVJobQZpz19ElmolLpr6M91MpTrQREqcWOQjaUPU6bXcj51jMkcl37VxSO1y2j2l7F/qJ2u9DBpM4cDuGSVcrefRcEqloVrCOvFv4ZUe0L89qK7UW9gqez1YDgWf3l0CzsGzhVtnVNTndJVHvjCrcxf0XDZ85X1EUqrxi9tOXSyk50HW6e/hxSCle6B+KRr26OZDDnTpNw7O3XrdTlEnf9R/GoTApkSfSma5EceKV9q8H+KoLYAgUqt/1FUOUjG7AYEPrWBgDaPeP7MSJYD3Eoltb4H8aiXK2751TnU+x/Fr11ecqLJAbxqDfH8CUrda6dcNgWQTmbpaxskVB6gdm5l0RoyhZAIez8DFDJZhtVNf+Ivi7LuYlDTVM7nf/uBay84DSarQGUYFh6fi6Z5FeguFUWVsWwbVZNZvLKeI3tb6Tg/MKsbxoVwEfR/HSG8pLOvk8q8BhQG4pbVxXDym0jCj66twOt5DI/7oUn1NPQMxDl0uovhZHElCiUhmFdXxqfuWs6S5qprBnK2nSaT20HI90uX9WMYVidCaAQ8nwIkunMfYFg9qHItkuRDkjw4Zo5M7gMuDJoUpY6g9wuksm+SzR/BcuKAg64tIeD5DDHnW1jWIHnjPJncLjS1CUWqwLIHMMzzaMrcqwcaDuSN2eGUnMrk6OiKsmRBNffetnDcIAMgGPBw16YW9h1uZ9f+8yRSs0OS0raTZFLfxTJbEVIA0LA4h+66G7DIZbeRSf8IWa4CbAzjEJbVg9vzGUzzLMn4n6HqqxHCi+Mkyed3YruiuNyPgmOSy75GPvseCBdCuAELVV0Ecg2meY50+gfgWCB0TOMwhnEQn/93AIlk/H/hcj+IEH4cJ4Gd245t9+H2fB4HA9sawrGHcJw8th2lYC6YuZlf5xiSRh89mYMM5E6RNPrIWgUjW8exRl2mJ0NEb6as8uYHGn6PzvolDTx4y0JWtNTi91xfI7IQcP+GhQxEU3znxQ9IF7nSxLId3vjgJCG/m8/fv5qS4FilSiHAHvk9Li0LLIgSXLsE2sHBdiwsxyJnp6lxz6XcVY8spJH1FXqOZCFPOvx1KyqrIvWsKKkjb5mkrTyO4+BSVNyyOuUSw8kigJDmYVm49kNjzmo7DhGXF0Hx7IymFGgoqjwaZJiGxUB3jEwiQ6g8wOL140ubHjzZxXee23n9ewqUlfgmtVxPIsnO9nZKPR4WlJVRFwqiK0Xpey8KulxCU+Czo48j7lVE3BeNyJqDX7hk2RD1/sfGrMOvXcwqedRqGtRPjlkmqM8nqI+V8TPsJBmzl7C+FK9aj3SVQchE2KZFPpvHG3BTUhma8vsnwnFshtMvUjjEHUx7kNlUT15eE+bBz2+89oIziKYpyIpEKlmYBfL4XHS3DzE0kESSZXKZPPns7JcEVuRKQv7fQNdWkTeOYlld2E4GEEiSH1VuQNdWoKlLkS5zg52Yw2d66OiNFX2GvjTs494NLaxdWDfp2cbCwEdc9gyAIleAUMCxkYQbx8mQN46Rym7Fo2/CsbM4Tr5woxYuZCmMEBICdSRgsZCED0kECs+LC5kwC7BHBl0WXtedaErzNYIMB8uyMMcRFLgZOE7hW/J5XZRcpQEWIBTw4PfqOI4z2qN0szHyH5DLbcPj/WU0/TaE0LGtHoQI4Dhp0qm/R9VW4/F+GQFksy+TzfwUTSuUBTpOBkkE8Pi+DI5NKvVN8vmd6PptWFYXuexWFKUZl+czSFIY244hSSUIIZPNPIdjp/H4fgVZrscyTxAb+nV01z2o6nIKx0YGr/+3AIls5lmy6efQXfegKI14fL8ImFhWB/7gv2W2lU6ljH6OD7/IqfjrJIyeMYIoU2G2iCFUlPh5+NZF3LKsqWiTdaoi8am7ljEUT/PMlgNFP7czOYMX3jlCScDDJ25fiu/S4EgUGvYDSoRdg68TUCMsCKy+9OVRHMchavRxMrmPrsxZUuYwXsVPWKugzjOfYWOwoA4mHLxKAI/iw6P4OZHYTXf2HBkrNeWBsCQELkXFpdyY0mJZSPhU/UMTZEDhO9KlQvBlXaM0bbJMa/Sdy+Q59P4pTh9uR5IEultj2S3zaVo0vjqGS1dY1FxF3TQHpJbl8M6e05NePuJx0xAKMZzNcqSvj9NDQ9QEAjSFw/j0qfUifFSwHZOcNYBhJxnI7CZltDM3+EvoUmha65NVGZdXByGK2AhewLQHuBhLSwTdjxR1/R92QhEvPr+Lno4hlq1toqI6xN73T/HSjz5AliUS8Qwe34dDok+SfHjd9+Bx3YVtD10SaHiRRGhKAwLbdjjZ1kd/kc35NEVm+dxq7lg1F9ck5WuFcOPW1pLOvU/OOAaAz/0QXDHLdwHHMbDsQWw7CUIgX3Jejq/rPvY5Ra7Ara/DttM4Tg6wkeUIDg6pzGtkcruw7CiJ9At4XXcjy0EcZk82A8DtUqksC5DNGaQzObxXmeFNpXNksgblET/eaxqB3gAcyOd2IMt1I+VMhXInSZqL4zjY9gCGcQR/8D8jy2WAg6atI5v5GYZxGEkKIcmlqPrakYyHgyzXYxj7sJ0EpnkScFD19ShK/ci6CzK4jmORz+0ETDKpH44Gn7YdxzQOFgINoaJpG5HlShzHRtPWk00/h2meHdne7KYrs5/T8S3EjS4koRJU6/Ap5aiSZ0SNavL3oZBWN3M7OgV0TSHocxdVolkIQcDr4sn7VxFLZnh1+/GirfsCsUSGp988QCTo5c4189BHBDEEAkWobCp7hKQ5jDIyyeFR/CwMrBsVLbiAhERAKUHzuVGEiiRkVEmnybeEnmwrGSuFwEESEi7Jy+LgLQzkOpGEzKrwnZS76ialRHazUCSJgDq5SbLZhEtWUSRpXEXL6TCtQCOdzLJryxFW3bEQt1en43Qv+94+NmGgEfS5uXPdPG5bPTnVmCvJ5U1OtfZNWqqy3OejzOulKx7nzTNnOTcUJehyURcMsrGhnuqAf1r78WHGtFN0JF8mmjuCYQ9T4bmViHs10jS9M/xhH/ULahgeSDDUEyvingqCnocxrX4sO46qVKMrDdd+288RVXUR7npkBYFQoaykaX4lcxdVs/3NY5imxYoNzdTPndgEb/YhEEJGlsuuaw51MJ6ivTdGJlvcfq6KiJ/Nq+ZQVTo5XXgozJj6PY+QzR/EcbIUJEsVVLmagOcJBAoIh4DnCVS5DmQZv/sRhNCR5TLc2hok4cGlrRgJGkBT5yJJIVS5CuFSkURhf9zamkJwIpfidd1D3jhRaEAHQEUgkIQfj76x4CkghWHk5uzYDslM8Zvmp0tZxM+G1U18sL+VPYfa2LS2edwMkmnZ7D3cRntXlNXLG6gsn3rf1EzgOHEkKcR4t1bHThayWKNBpABUhHDj2HGQQgUZZ3Hp6xfOCAvHSYPQEGK8cjIbx04iyRUj/RyFgNjr/zeo2uqRtQmEVMgSFaTVdRAqjl3cwHymGMqdJWX2IQmFef57afBtxKuUoUiuKQ82b7Zn1EwjRjx+vvjgGqLxNB8cKb6hclf/MP/y8m5Kgh5WtdSOSkxLQmaef+Vly7plH/Mucfy+sI8hrYyQVjZm3apUQmAc75cqdyNV7saifYaZRhYSfmVmAg3LspEkMSM+Qi5ZRRUyuSKZJU8r0DANi3Qiw7p7CrbzLo/Ouy/snXB5j0ulrMRHeWR6A/xsziDgd0860OgYHmZnezvxbI6Ix8O98+YiC8F7rW1UBwM/l4GGJHSC2kI0OYwq+QvO4HJk2gdpWV2ENfcv57m/eZVjO0+x5t5leIvi2+CQzu0jnd+LJNyQs/Hqt+Bz3dxypdlEIORhxfpmpJGepUDYwx0PLaNxXiWWadMwt5zyIpazfVg43zVE31CiaHWlALIk0VxbytpF9VM+VxS5DJ/77rHPuy8aUHrdd4z+3+95dMyyunqxAV5ValGV2pF1X+y/0bWLJqqSUo2qXN6rBeCZ4PyxHIfBGWganS6hgJs7b2mhpy/Oj57fQ0dXlDn1pQQDbmRZIm9YDMcznG0bYPues/g8Gi3NFcTiaeLj9OU01kZQJ9n7VAyEFMIyW7nQb3QpkhwGoWBZfchyDSNtzdhOsvBaYQ0wQRZPCC84uQkCAwkhBVHVRXg8X0Rc5jkjcJw0Dja2NQAUSlQdO4Pj5ApiBRe2jVTYL8e5hpHmjadQ92/jlkPM8d9OrXftrJ7NvtnIksScmgi/+PBa4sksx1v7ir6Nk239fOv5nYS/4KG5tnRWi5A4dhLTPAVCRVWXXHN52+oD4UGSJleyPx6ykPFPIqORM0yyeQPTcvC6VFyaSiyZwXEc/B6dbN5E1xQM00aRJEzL4oMT7cypKqG2LFT00iy3oqIUsbxwWoGGqil4gx5e+Ke38Ye9dLcOUF4/vhFOWdjHouaqSRtajYcQAu81TGIuJZXPA4JF5eU0hsOUej3IkkQ8lyPwc1o6pUguyj0birY+f8jLxodX03mym4NvH+ONOe9y+6c3ECqb/Kzv+DgMp58n5P0ksuTHMLuIZ175ONC4BEkScMkFXQhBWWWIsg9RcGFa3YCELJUXbUamoy/GULy4zavhgJsV82soCcxeT5LrwbZs+qPJm70bo3R0R3nmZ3tp7RyitX2Qts4hSkt8eNwakiQwLZtMJs/AUJJ4MktFWYCXtxxGlqRxA8w//J2HiISnP1CYKpq+mUTuXXLZLbhc94JwYVntKHLjiArULSPN4BWFUqvsFgQ6qroC0zxz1XUr6iJy2a3kc28jy9VIchm23V8oMZRC6K47yWW3oGorC1kMx8A0T414YgCOQS73FprrdgQK+dwWJCmMMhrM6ggpiGVEsczzSHLVSA9QceU+p0tIq8Mtl2A62ZH+jNk7qJ0tqIrM8nk1fOnhtfzNj96hayBe9G3sO9HJPz63g9/9wh2UhX0zMsNeFISKJJUw2d6jfO4dZGUekrZs2ptUhDSpQKO1N0rXYJxUNk9TZQkeXaUnmqAvlmTZnCo0RaazLU7/cJJbFjVi2Q77TncS8rmpLQ0V/VRwSSqyVLwgflqBhjfg5s5PreX0wTYSQynqmstpWTW+Wd+aJfW0NJZTMYWygyuRJMHCORWTjpYr/X5K3B5KvZcrIqyprUGTb94MyIWGxStPxH94fScLaspZO69uQiMcw7Q40tbL4bYevnD7qknPNh1r7+Vgaw+rm2uYW1U8IxrHcQhEfGx4eDWx/jg//etXOL7zFI1L6glXBFE15aoH/6q7lhIsnSizZON33wVIGEo3qfzuou33TNJxpo/9751ACMHyW+ZT1ViKPI7W+PXS1xWjv2eYuYuqx/hlJIbT9HXFKCkPEI7cuAHWVEmmnyGb243P80m87seKcnPqj6ZIpIurPlQa8rFsXvXsvXleJ5bt0DeLAo2BoSSvbTuGbReuldHhNNGrZFy6e4fp7h2e8PVc/gb2nwhQ1RW43Y+Tz71LPvsWCAUh3PgC/w+SKMHr+zXSyW8Rj/0xAgchefF4v4QkV8G1Ag1lDi73Q+Syb5CM/7+F8jfhwuP5Iqq2Apf7EWw7Rib9NOn0UwgUhORHURdRyFRISFKQVOKvcOwYIHB7Pz8y+AIhdFRtGYaxj0T8v4/K8rrck3N4nmlqvWsYyJ3ibPwtTsZfRZN8lLkWoEg/n5OHk0VTZTYubSSWyPD3z24nlizuZIztOLx74CzhgJvf/IXbcOvqjF8vLbOdfO5tHCeH48SQlWZ0V6GXM599BdM4hRAairYaTd+AbQ+Tz72NbbahauuQlVpsO4GZ34dpHgPHACRU/VZkpQEjv4ts5ifIchVGfg6662FkpZ6pjuhlSSKgXrtMbyiRBgdcmkIqm6etL8pQIk0mb5JM56gvD3O0tQeXpiJJApemoqsKNZHAjGQeXYqCerMyGpZp09M2QFVjGfXzKqmoi2DkTLxBNx7f+FFbeYmf8pLrK1WSZYn7Ni1k06rxla2u5MzgENFMhrvnXr582H1zZ2Z+tvs4TRVhFtVVXHYifnCqHZeqsqp5/B4XKPgDdEfjHDzfzRdun/w2+4ZTHDjfTWN5mLlF7Pc7vf883/0vPyaTzNHfMUhf+wB97YPs3XIYt9eFdI0Bdt386jGBxmDiO9hOFstJ0B39LyhyBMPqRuLD0Ux17ngXr/5wB5lUjkDYS3XjzDiMnjvZw+53T1JVXzIm0IgNpdjy4n5W3TKP1Zuu7Ttxs0hnt5LN7cDrGauoNh0M02JoOEUmWzw5R0kIysI+mmrGz9Z+FDAti/be6M3ejVHmNJTxX/5tcY4JgJKilHNOHkny4nY/gaquwLYLpmlC8o00bUuo6jK8/n+FZXYVJDqlchR1HkJIKOoifP7fRVEu3rd0152o2gokqRIhdDT9NmS5AdvuwXFMhPAgK42AhCxX4PF+Acs8i20nQMhIUrjQA+LkEEJF029Fkitx7DSSHEFRWrgweBJCQlEW4fX9OvaFjKMye5y1fUoFS8Ofxq9WcTbxFjv6/y8BtQa/WoFLDiJLOtIkZ6vdcgmN/k0zu8OzBCEEXrfG3WvnEUtk+O5Lu8jli6tKmDcsXtl+nLKwn19+ZN2Ml93ZzjD57Ju4vV8E4SaT+jaquhLHSZHPvoXu+TSOkyCffQ1ZrkOSI8hyFZZxAstqR2UdODlM4xC23Yfmeggjvx3TOIis1CArTUhSCFmZg6qtu6IUcfJMNqOBAz6PjiJJ2I5D2O9hMJ5GArwunZ6hOBXhAIPxFJmcgc+toygSJzr6KQl4kItdOiVrKEW0hphSoJHP5Xn9h9v51K/fzdaf7OITX73z2m8qApIQVE4hIzKczTKQnj11xwDpXJ4dJ9tQFYlFdVP3N1AVmdVza2mqKJkVtbOxvji7Xzt42XNm3iTWFyfGtdOz+XEGhLJcguQYBNwPA9aotKcqT9+c8EYy2DtMT9sQ4XI/ukcruhrXBdLJHAO9cSxzrCKEYzt0tQ3RvHB2N3gWSqdsdHVxUdY3nMwST2WLKmvrcWnUV4bxjChNpVM5ju5vY/d7JxnsjSMkifKqIGtunceCpXW4RpSP0qkcH2w7wf4PzhKPpSmJ+Nh072KWrGwoZPoYkXYcTPL6c/s4c7wb27ZpbC5n8wNLaZhz4xr5TdPmdPvADdvetQgHPdy+YfYGyFdl5NCT5DCavGaChRRUdVHB++IKZLkUWb58ckJRGoCLYhiS5EPSFgPjnzeyXIk8zvXygqCAECF0/dYJP4IkeZCkxVCk87KYCCEhCw3DTpM2B0mZ/QxkT6FKbmShTUl5qszV8nMTaMBI47Xfw0ObFjIUT/Ps1oNFlwBPZfL8+M39VJcFeGDjwmu/4ToRwoeiLkWSy8imf4Rlnse2BxByBE1fj20NYuQ/wDJPIys1SHI9knzFtVXoI6aZ67CtTmx7CMexUZRGJKkMWZmLqk10Ll8bWUxOdaqlvhyJwu/kALIkqC0NYlo25SEfOcOkLOwnmckR8LoQwANrWpDEzDWDKzerdMoybc4c7uDc0U5O7D1Hx5mLF0uXR5vQtO9GE/F46IzH2dvZRZnXA0IQcrnw6zdeacKybV7bf5LX9p3i4PkujnX08sz2wwgBv/PoZlpqCooLsVSG//vydlr7ovjcOo+vX8y6eXU4QFt/jL/62Xtk8gZN5SUsqL14shw8381bh8/QWB7mg1PtpLIGK5qquHfFfKpLxgZnHQMxfvDOfpY2VLF5UdOUel8upXlFA//u21+f1nsBasYxk/O7budKixjHsbCc2LS3cyPJJLJkUlnmVdYRLLk5taoDvXEyqTzyTSwRnAwF+U1xiQLP9RFPZUkXWW3K7VKpLgsghMC2bQ7tOcdPv7edsoogcxdWk80a9HbFiA4kR8s6c1mD576/nV3vnqJpbgXNLVW0ne3jW3/xGl/6+t2sXN+MosqkEjn+5k9fYLA/wYr1cxBCcPxwB2dO9PAr/+Ze6m9AsGHbDgOx5KwqnfqYj5mIhNHDgaEfci6xjYwVA8DGJGcnprwurzVW6eijjiQJKiMBPnnHUqLxNFt2nyr6NgZiKf7xuR1UlPhZ2VJb9PVfiu0M42ACDo4dRUgBBBbOBcEDLBx7GCFNXFEjhIZAH7lXXwhUL/gfmQXzyzF+SJNHkSaX0Qj7xlbbXDo2c49Mdl26XH15GNtxZqRTyS3fxGZwVVdZfecitjzzAeeOdfH8P741+lpDSxUP/9JtRdux6yGRy7Gvs5sjvX3ocuHgeaBlHhvqb7x2tiQEq+bUoCsKA4kUG1vq2dDSgABqIhfTcW8ePM1j6xaxdm4de8928hcvvMvf/qtP4XfrlAW9PLl5Be8cOcuhtt7L1h9LZXhl3wmWN1Zz68JG0jmD94+3osgyn75lKXDxFOmJJvj2lj2oikRzVWTCfpDJUFIR4tbH1037/Yo2diAsS2MDI9MaIpF5E90/e1L4EyErMrIi43KrozPXxWI4muLd14+w7/3T9PUMM9AzzF/+8U/RL/F1MC2bvq4o4YiPsqrZIfc5EZqyAMM4g2V1IUvXP6jO5U3yZnHr8d26SkW4cJPK50w6zg+Qyxrceu8SFiytxbZs0qkcHp8LZeRcOrKvlR1bj7PuthbueHAZvoCLZCLLn//Rs7z41E4WLK3FH/Tw9isHObTnPL//p08wb1ENQsDpo138/f95hS0/O8Av/+a9Rf0s42HZNifb+4umlf5zzyzINE+EEG5CJX8/Umb14aQjtYu25HbSVsHIrUSbQ8Q1D68SQRE6U/kBfOqHSf67eMiyRFNNhM/cs4JoIsO+Ex1F30ZbT4y/fuod/vBr99NQNVaitlgIJDKpf8CxU0hKLbLSjOyYGPntJGL/ERDIyhwUpQXLbCeT+jameQwhXICDoq6AC07j46xdVuaRy76Kkd+Ny/sFlGmUEcqTLZ0abw8mMVE5U0aALvkmNoNrmsLdn15Hx9lGJEnw+Nculk7p7ullCyzb5ujpbnYdbuN85yDxVBaXpvIffvV+Aj4XjuPQO5hAUNBYn8wXu7Sykprg5YPWkvH6M27AjUEIQUXIj2HaBNw6DeVhVjaNbS5tqijhnuXzqAz7WVRfwbM7DnO+L8qyxiq8usbiugrO9gyOCTQAfC6dlU3V3LV0Lpbj0BNLcLZ3kOGRxlhZkogmM7x95CyyJHh8/RKayktQrqNRWZIltCI3OsdSzxHwPEg09b0RV2Ww7STZ/GHwf62o25oJAiVe/CEP6VRu3NKw68Hj1Vm0op5c1iD59gkAHLvgpHwBVZVZtnYO629vYU7L7C4383s/Syb3Fon0j4kEV1z3+vKmhTlOKdn1oKky4UDhuqFqCuWVIdLJPK/+ZA/5rMHS1Y1UX6G2d+JQB6ZpsXhlPVV1JUiSIBDysGBpHa89t5d0Ko8v4GbH28epqA6xcn0zqqbgOA4NcyuoqA5z/EA7pmGhzLAsq2FafHC4+Pr6N5LWjkGOnuxm2cIaamZJRn02IoSMqq242btxXcTy7aNBxpLwE8wL3INHjiAJtaCONYV1iVnmen4jURWZJc1VPHn/KoYTGc52DRZ1/Y7jcOx8L3/xw238p6/eT9g/M72xklyK7noMIbwIyYcQfhAObu9XCp41QkISQcSIoaPb+6WREkKBkIJIUgBJepCCUAJortvBMRBSCBDo7sdQ9c0AyNL0Glwn3aNxDWzjOEKuRVyH1O5UcMk3sRlcSAJ/2Evz4loe+ZXbqW66vlmBWCLDD1/aw9u7TjE4nCKXN7EsG5/HhTEyO2nbDn/xL2/R3TfM//Or99HSeO3+hoBLx+/ScRxnQqUnGIkGixhs2I6DZU9vsDO/uoywz4Mqy5T6PWiKTCw1OQWdkNdFU0UJbl0tqEF5XPQNp8iONHyZts2P3z+I16XxxTtW0Vx5fUHGTHFhpiGeeZ3giKeAIwxmJjlYfBoXVFE/r4K2k730dUZpWdlQtPIpRZWpn1NGeVUIr8/FrndO8pkvb6ak7GJaWAiBqsl4vPqMD1KvF5d+C2H/7xFLfgNFrifg+8WCb8o0MQwTs8gz87Ik4dIKGSNJEqzaOBdNV9j22hH+5W+34PHq3HrPYm57YCml5YWJjeFoit6uKH/+xz/FdUnqOzaUJB5Lk05lcZwgfd3D9HQM8Zuf+8boMqZpMTSQoK6xjGzWwDeDv6HjOKSzeT440jpj27gRnDjTyzMv7SMU9HwcaHzEkYSMQMKrRKjzriOiz0cq4mDo5wlNVVi3uJ7hZIa/+8n7DMSK29NnWjZ7jrXzf59+l9//4p2jzuHFRUGWa5Eu62sShR6lKy6dQrjGzeYJ+eI958oyXkkKI0nXd01RJRnfJFSnroVtHkVyDJDCIFcghIptni9460gRsAcRUikOGXCcUWPO6eJStFngo6ErNC4Yawo1FfKGyfde3MXP3j7CcDJDXWWY2ooQ+461Y18yWBcC6itL2L7/HNv3n59UoHG8r58fHjjEwd4ecqZB2O3hK2tWc2fz5akvVZGKmnqybQfTml6DldeljVojiJEGn8k2a6myjGukdEMIMTosv5AV6B6K49JUemNJ2vqiLK6rQJmFNfx+12YECj7X7QTdhUDDtAYwzeIbDc0EcxbVsPbOxbSd6mX7qwdpWlhN3Ti9KNNBCIGsyHj9MrVNpcRjKcqqgpRMKBE8u8nnjyJJATRlPrH4n5NM/xhNXVCYgZrgnNS0Zfg9nxn3NcO0sKzilk7JkjR6gxRC4PHprNo4l5altfR0xti+9RhvvLCPVDLLY5/bQLDEi6opRMqD3HbfYqpqx5YNlJYHEQLcbo3axlKe+MWxDan+kAddn4kb80Vs2+HgqS5iieJKXd5oEqks0Vgaw7iBMrYfc1OocC2mTd1ByurHdswPyfTT7ESIQmnoHavnEk1k+M6LH5DKFLfHLZMzeGvPacrCXr76+MaijrUUZR5e/+8jphEIGIZF2/l+hmMZliyvQxsnCLIsGyHEpC0V7q9eRN6+/BokCUGTL1Kcz+3ksK0zOLlWZM8nsc3zYCexzOPIni9h57Yg6bdh53cg67cD1xdouGXl5jWDX+DCoOd62H24jZ0HzqPIEv/uy/ewdmkDbpfGr/3RDy7TTRdCsHxhDd978QOOnO6a1Lrbh4epDPhYVrWcjGli2864Ul2qIhfVydK0LPLG+LJxF6ziL5S7XHnsXfdeXOVgLg/6uH/lfOKZLK/uO0lVSYB18+qLntWwbZv4QJKBzkHyWQPdo1NWG8EX9k7qe5YkD47jUOL9IvJIRC4JN2XB3yjqfs4UqqZwz2fWks8bvPbDHfzTf3+eT/zKbSxc3YTuLp7W+/zFNTTNr8R1hbTth4mB6O9j2t04dhaHLHljGMM4NaErMoDXTkwYaAghrnoOTAchCsHGpdtQNYVQiQ9/0ENNQ4R0Msv5U70MDSQIlnhpmFvO3h2nqZ9Tzppb541pyr/gUr14dQPvvnaElRvm4vVfnloXEijXeX29FoZl8/L7x4vqon4zyOVNzCIHmNPmw/5lznJqvWvJ2nEODv2Qw9GfoEguKl1LkD/20ZgWQggCXhcPblxINJ7m6Tf2Fz0rHE9mePGdI1SWBHjs9mu7cU8WIXQGh3LEhweoq4+gqDKt5wYIhT0Egh5i0RTpdB5ZFpSWBUjEM+TzJh6Phj/gRlEUslkDw7DQNIVYLEU6lScc9iIE7Np5BkWRWbKsDn/ATW93DCEJwiW+0Wv4pfz7pQ+MOf0FhR4NUZSQ2EFSl2ID2Fns/KFCBYhwg5NH0tZjZ1/HcZIgrr8/03Uzm8GLycGTXfQMxHny4TXctmYuQb97wgiyssSH7Tj0D01eHcWnaQRcLry2TTKXIzfOzUhV5KIqA2XzJqlMbtzXgl4XPpfG0Y5eljcV+i58bh1thgcUUGgA87o0Ni38/9j77zg58rvOH39W7Jwn5zySRjmn1UorbfDmaO96nY0x+AwYzAEH3AHHwR3mB3d8jY2xccA4rO3NOUmr3VXOWaPR5By7p3Osqt8fPRpJq9FoJLVWktnX49E7q670qeqqT73j61XFQCDML7YdJs9ho74kLyfnH/JH2Pv6Id766ft0He8llUhjYCAIAqpJoXpuORufvIXldy3E4bm0p33ekAQRSbg5SiIGe/x0NvdjMivkFbs5vP00R3e24i104StyYbao015v1aLwJ//82UseR1HlnDebf9gwmRYh61OLfF50mykoQc9AlqWc8n5DtrzojBEbGIuw852TDA+MUz+7FItNpattmJOHe5i7uBKXN3tfL7+lkYO72vjlD95joDdATWMRqWSajtNDKIrEfY+vwGI1cf/jK9mx5QTf+G+/5I6HluL2WvGPRujrGqO8Oo8Ndy/I6bl88Lx6BgPsONJ+zY5xKei6QWSGpaHTIRROkM5xb06ukfE/AZluJN+vQLBhxH6KkXwXQx8EZAS5GsF0B6L5brhYDbaRwUgfQU+8jJE+BPoYCGaQZyGaH0BQV5xTLmGQGfsUaG2Inu8iyHMRBBHDSGMk30EP/XcQPYi230C0PDp5CD25DT305wjqKkTHH11RtPhaQkCkyDKXjPsBToy/wJt9f45VzsepFGGRPMiiBUmQJ+bY6d9rDrmIJs9DH87Ab2AIE1pBD66fhz8U442dzTndvwEM+cP85PV9FPocrJhbecltZoLWlkGaT/RTWOSkrNzH1s0nsFpNbH+vmTvvXshzv9qDIEBRiRuvz05/XwCX20oqmWHTXfOYfFUYBr09Y7SdHmJkKERZpY+GxmLGAzHsdhMIcOJoDyPDYbo6R1m7vpHKqvwLhHhd6oeg0SaYARUwENUl6Km9ICgIkhcQMLQhRHURgnD1NqX5eupo5BKjgQiptEZjVeGkk3ExuB1WDCObJp8JnCYTyUwGURB4u72DsViMh5ou5HVWFQkphxmNRDJNOJqcMmNhVRUeXNHET949wBf++Vd4bGb+6ok7mF1WiIAwRYYj+0U8meafXt7Ge8c7GI/GSWUy3PlX/8as0gJ+666V53AmnNOP8oFJ9sy/VFnmU+sW843ntvLUtsN86Y7lFHuuXLHdMAx6mvv52f95ju0v7ieTTE8q+p6L8eEgR7efYvV9S/jUnz9MWX3xNL+3Rvfo71CV/4Mpz+VGxjvP7eMX//wWhmGgZXT0iehQNJKgr3144oe4+PlYbTOr5YzHksQiSexOCyZzti9ndCjIiUPdqKrC7IUVuDzWG1rN2uf+m/M72WeA6SZQRRJzrsKu6QbJiZIc1SSjKBIHdrby2tP7MAwDX4GD5bfM4vYHFuHxZg1Eh8vCl75+F28+f5D33zrK0z96H1WVKa308bFHl01mOPKLXPyvb32GX3z/Pf79m28RCcdxeWzMml/OsrXXVkdCNwyefefwdTXQw5EEH/+t7131ftJpbbKf74aFPg76KEZqD0b0xxiZZpig5QQwtF6M1H6M9D5Ex5+dLw5mGBhGGCP2c/TYf2QdDLTJbcl0oie2IFgfR7R9CcR8BAEEKR8jfQDSJ0A+owyewUgdyO7DiGOkjoDlEUDAMBKg9YI2lDVochAVzTV2j/4rzeMvo6OhGWnAIJWKEkx1k31TzHy+K7Q0feRoTEAUBSqLvDy2cSGBUDznfVuGAd0DAf712e3kuW3UlPqu+t3U0T5C07wySsu9KIpEX88Yjzy+kmQyzcBAAItVxemyUl1bQE/3GIoi0zSvnNaWQULB+HnZB/9YlJ7OMUwWBXmCNdLtsVFQ6MRuN9Pb4ycwFsVsViYqUj781KVkeRSQkcwFQJb4QFQXkLUnTEAcBBPiRPP61eKGKJ3KFQRRQJQufcNFEykEAawzZLZaWlaKbhhkdJ0iux3N0KnxXlgv7bSZUXKYUQjHkowGo9lo/geNfUFgeUMFS+vK0I2JtNqEYfStLz+Yzeac8/C98t8/jySKCAL84YO38vUH1p33cIgCiKJIY0k+i+t0xqPfpWdsGK/9cT556xoMIyv6UuDys7xhPialGEEAu0XlLx7PUmdejZNlGAa9LQP89G+f471ndmXFyyryaFhSQ3F1AapFJRFN0Nc6RMv+dgJD47z79C503eDz//PjFFcXXGSyEZBEB4aR4QwbRPb63QRNf4aRbcQCZFkE+fLGLM2w+bf91CDP/Xg7C1fUcu/jKxjoHuMn336HHZtPgAB3PbyERz5/C/lFN56xcAaiYM4pEYMiSzkvBUylNYKRbA+D1WZi470L2XD3grMvmokM7JmyyOxXAi6PjUc+u4aHP736PDIKUTp3PSip8PF7/+OBbC/WBFV7Nqt7be/13qEgr2w/eU2PcSkYGMTi2Xn9ahzEqQIbNyZ09NBfZo0Bxx8jmG4H0YGROY0R+w+MxMsYyZ0Y8ksItk9NbmWQwog/hx79N0BDsDyKaHkIpErQx9ETz2PEn8GI/TuG6EGwPgmCE0FuwhC2YGSayUqAGWCks9kQwQWCAyNzCsPQEAQZ9DBkukB0I0jlN+R8qxvpCQeDGSuAf4SZQRQF5tYV8/idiwhF4zR35rYvUjcMmjuH+eYv3uNPP387+Z6r05maM7eUdzefwGxWuOOeBTTNL+fVFw4SDMZ45PGVHD/am30nyBKiIBAMRHlv8wksVhWzWeHk8T6OH+3BbJYpLvVSVOJmZCSEzWbCbFYRBDh+tAeny0Lj7BIO7u8kmczgdFkumK8yae0CEiBBEFCU3FXMZHWn4FybCLIlt4ZhoKcOIlseOGe9q4MqylTYvDQ4C6csCb1cJ+S6ORpetw1ZEuju9zOvvhireeqyEsMwONTchySK1JT5ptjThRgMR9je1UX3+DiZiRfRprpalpaVnree22FFzSGzS0bT8QdjhCJx3A7rect0IwXoiIKBIOiIQtZpMow0opAtNTKQwMiKx0hSBsPIYBggihICJiCDgYYomDEMDYMkgqBgVWuRxE8SSWzN7kUUMQwdgzSRxFsocgkmeRMGKoKQG4MsHkmw760jvPfsbnwlXu79zY3c/cXbcHgvTP0HR0I8/603eP1HW3n/2d0s3NCEp8CFxT4V7ZuAWZmFP/pTLBMeuyiYMSuNVz3ma40nfu9Onvi9O6/5cYL+KIlYmvLqfHTd4NDudkYGg3z9bx4mlcyw9bUjdJ4euqEdjVzDalFzzmySSmcm+8UEQUCQBGYyv2afX+EC5pMPrgMT2iu5GOwMoWk63/7V+ySSuaVfvhJIksj82aV88YkrV2h+bcsxtu5oyeGorhUMMDQk1/8C06azsmDKIgS7C03rh/ThrCNgPHk2JZ4+hp58E4wwgu2L2ayF4MpuL3oR7X+AjoYRewo99hMk0wYMwYmgNAEKRroZ0LPJQyMBmVMIygIMwQKZNsh0gFIPRhhD655gtam4TtdoejS67qHYuign+zJL/3nmxplCFARWz6siGE7w/Rd20Ts8ntP9ZzSdA829/Msz2/n9T67HYTVdsSFeUurh8U+vwTAMRFFg4eIq5i+snCy9/8STqybX9eU52L3zNB+7Z9FkC+At62dxy/pZk+sUFrmyQcIJJtI16xrRNQNBzM7V5ZV5E2xOF473O//4Om++eJBMRsPQDXTdoLjUwzd/8mUczmtZUnV2LKK6+oLvrmrPgsAfz82dLXPdHI2muiK2HXDw4jtHqS71MaeuCFWRORPc03SdZCqDPxjl56/sw2SSWbu4dkb7PjI4SPPIKLfV1kzWmX1QVwOypVN2qxlJFK+YlvaDGA1G6BsOXuBo+KM/I53pxTAypDN95Dm/hFVdRDD2GuHEFnQjjCpVku/8HSTRzUjo28RTRxAEGbMym3znV4gk3ieW3E2R+89JplsIxJ7Ba/skJqXmggyKpo8Tir9BMPYigqASir+Fz/5ZbKYlOTnPoc4Rdr96EIvdzMZPruXRr92DfBFDz13g4sk/e5hoMM5bP3mPnS/uY9H6pos4GgaJ9EkMDKLJfQAoUgnF7v+Wk3FfS3xYpUrpVAZRFHB5rfhHQrSe7KeqvpB5S6sZ90fZvvkE0ZucTehy4bKZsV6hyv3FEEum6B8N5nSf1xOGYbDzaCfbDndc76EA2YxqaZGbBXOuXEH4WHP/lKwx1wXTPv5CthfDfPs532SRdQzmYaT3gxECEoAlWzaVaYH0cZAbEdTlExz/528vmtajJbdApjWbwZCrEZRZIJgg0wJGBgQZI30c0BGUBYCIkT6KkTmFoNSDnnU0BNGDcIM6GvnmBvLNDdd7GL/WEEWR25c3MB6O8ZPX9jF2DjFPLpBIZdh2qJ08t50v3L9iUvH6cpFl5oRzHzrpItUxdruJufPKp62e+eC+ACRZmHb5GSxZUYsoCAQCEfq7/bQ2D3yovBDXyu7I5X6v2wy9fF4Vuw938saOZv7nd15n/bI6FjaWEU+k0Q2DE22DjAUiPPPWYXoGAyxpquDWZXUz2neRw45dVWkeHsGqZo0Pj8VCseNCKlCf04oii2ip3Dgag2NhOvr9zKkpuuCHEgQzXvtjmORsQ1Qq00c4sRmf/TOociX9439BIn0Mm2kVseRe8p2/h1mpRbwCTmRZ8uKxf5yU1o9JrsZjezgn53cGQX+EzuM9lNQWsmhD00WdjDNQVJlldy3g0NbjtB/pJnYRQ1gQRMp9/8QNLbN7naGoMpIsEg0nGegNMDYcYvXGJtw+O8FAlEw69+J1uYamB7lcmh4B5aLPgsNmxpZDZi+AaDxF9+A4uq5f83KmDwOD/jD//Mv3b4ieBgEBi0XF47JeeuVpYDbJ15yha8aY9nYWQFl4kUUKguA8I1EKRhoEC4YRBW0QjAhM9G0Y2oXMi1ka8+z9aWS6EYwUiF4QiyFzBLQBkCsw0gcBBUGdD0YKIxbL9osY92AYIdD6QK5CkEovOMZH+M8DVZW5b10T/lCM5945SuQiBDdXimAkwRs7T2ab0G+di6pcWzPUbFGprM6/ZvtftX4Wq9bPQtN0dr/fwl99/efX7Fg3K66bo2ExKXzm/uWkMhq7Dnfw3OYjPP3mocnl/+3/vjjRl6Eyv6GE//qFTTO+IYcjUYYiEUqcDlRJyvZDXKQfoTjPiVlVJsXtrhbD/gitvaMk05lJsa8zMMkVyOdEpDLaCGltgJHwdxBFGwLK5LuqxPM3+KNPEYj+HId5A07rXWSb9vSsECEahnGp8oczCXqYLALPEdKJNJHxKBWzS/EWuS+5PkB+mRez3cRA5zCZi1xvw4CMPoQiFU38O4OmB5GlmZXN/WeAJ8+BKIm8/IvdpFIZTGaVhrlZ42DcH8XQDdRrrMNwtQiE/m7C2Zg5zOpSXPbPT7lMlkScNjNmVc7Zs5xKa/SNBBkZj1LovTn1Ss4gEkvyzafeo2swcL2HAoCqStx/+3wWz7u66LnNasJpN2d7om5wXDaLk5HIOhsAqR3oqR0z2CYGWRJMBLUJI3NiIstRmm0OF0wgz0XQA4CQ7REhnm0CJwNiCVyl2NdHuPnhtFl4dONCxsNx3tp9Kmdz6hkMjoV5/p0j5LvtrF1YndNe2Y9w4+G6WiNF+S6+9ukNvNNYyju7W+gfCZFOa5N9Bg6biTWLanjkjkW4L0PG3muxUOF24TKbMcvZUzTJU59qRbEHi1lhPJKbUpN0RqOle5jWnlHm1n5Qtv582j1VLsOsNOK0fAxFLgVDy/5FAgy8tifIaIMMhf4Rl/VuRMGOZgRJaV2kMj1k9FHAQNOjpLUhNH08K3CnBZBEJ4IgIgoKmh4glelBFvMQxauLIE6eiSggSiKGbsxYKC2TytYwnts8eyF0+sf/kkrfdwCDjD7KSOifKfH8r5yM+9cBlbUFLL+lgZef2o3FbuLex1dQWVcAQGA0jCfPji//ytnEPgzE4lvR9KGLLDUmyADOONIqkuhEugQTToHXgcNmJpGaOQ32pTAWjHDkdD+3r5hZj1AqrSEIWcfn3Htc03Qi0SQ2mwlpivv/3KbxXCMaT/HLzYfYfrgDLcc8+VcKi1nlNz919QwpdVX53LtpHqUzDHZcVwiX+boVBCabPwUniPlwiWZPQcyf3EaQmzB4FiPTgmCshXQzyDUIUiEGaZCrsxmTTFe2P0NwI8i5oR+91jAMnYyRQjOS6MY5LFwzhCjIH/VpXALFeU4+fvsixiNxdh3tynkmtLV3lKfePIDbYWF+XXHOWQM/wo2D6x72tFtN3Ld+Hvesm8uwP4w/GEXTDOxWldJCD4osXvbL12EyYVUVBiNhIEtTmWezUu66cGKpKPJccZ3gxdDaM8qhlj4aKvInszCy6EMULJzbISpLebis9xOKv4luRMAwKHB9HVn0MRr5HoZhIAgiXvsnEQQZk1KDJHoZDX8HUXBgVRciCArJTCuh+JsTGZJRRMGCw7wOQbBhVZcSir/JaPh7eGyPY1GbcnKOZqsJb6Gb4GiY/rYhqprKp/2dDMOg51Q/kfEoeaVelCnE5rIOUx+6HiKeOknW0RhBM3JbJ/phQdN04pEEsUiCVDKDntERRAFZljBZVewuC4oqX/b9bbWbuP2hxay9cy6GYWCzmyf3MWtBOdWNxRQUu6/BGeUOduu96Hrogu+z5kIaTQ+QyfSgaUMIog2H9VGs59S3T4WyfBceh4WRQO4cjZFAlAPNvaxbVHvJZvNUOkNXrx+zSaEw3zmhzJllxEomM2zZ0cwty+vxeWwYhkEylUGSRGRJJJXWCARjuJ0WzCYFTdfJZPTJ5VeKaDzFaztO8LPX9hO/ARrAc4266gLqqguu9zCyyLWPKFhAdAIigmk1ov1rCPLMyocBBGVO1rnRWiHTA0Zsoj8DwIIgz8ZI7cRIH8lS24quLJvVDY6kFiaUHmAscZpgqpekHkIzztIFzwQutYzFvs9cu0H+mqCxsoCPb1pIOJrkWNtAzgX9jrT289Qb+3HaVlNd4rsqAWXDMEjE0wQDUeLxFJkJanJZllBNMja7GbvTPGWpZSajEQ0nCAXjJJNpMAxkRcJmN+Ny21DU3GquAYwHogwPBLE5TOTlOzFNYRONDocIBqJYrCr5ha5J/azerlHSKY2Kmnz8o2GCgRiyLOIrcGKzm4lFE4wOhchoOna7GV++4zztrfFAlJHBIB6fHY/PTiScIDQeI5nI0kYriozDZcHpsiLngDDpujsaZyCKAkV5Toryrj4SO6sgn1kFZ2vyOgMBVGnqi1Wa78JhMyMIl03rf1GMh+PsOdbF4sYyZlcXIggCbut9U65rMy3DZlp24bg8//uC7xSpgGL3n13wvUoFVnVqgS+7eQ1285WzulwM7nwndYuq2Pv6YXa/doi6hVXklXqnVIzPpDWGe0bZ8dI+xvoDbPjE6imF+9JaN/7Iz0im2xkO/V/AQBAkHJcwMG80GIZByB+lp22IE3vbaTnczUj/ONFIAlmRcHlslNcVMW9lLdWzSyipykO6TPFIURSxTdFMX1R6IY3zjQiv60+nXW4YOpo+TCT2DOHo0+h6CFkun3abkgIXHmduMnZnEE+mOdExyMnOIRY2XLx2PRpLMjAU5MTpAcpLvFjMCmPjUVLpDNVledisKqoik05nmeSC4Tg9AwEUWaS2Mp+B4SCb329mw5pGKko8jPgjjPojOOxmyos9VxTtC0cTbN7bwvdf2E0oB+J4H+HDhSBYEaRyDDEPI9ONkekEqSpLRzslzrzAJiiU5bqsQGCmAyNzHAQZQV04sYoFQZmNkdycbRLXBhBEN8IN7mjEM+O0ht7i+PgLhNJ9GFyZ4Vtsmf+RozFDLG+qJBCOE44l6egfyymltKbp7DrWhdth5XP3LafI57gig94wDIb6xzmwu40d7zTT0TqU1cswDBwuCwVFLpauquPuh5fiyz+/DDYRT9FxeogdW0+yf1cbg33jaJqGy2Nj9vxy1m1qYt7iShwOy5SsU1eK7VtO8s9/9wor1tbzuf+ykarawgvWefXZfbz4yz3MW1TJl//gLopKs+WX3/rGq3R3jPA33/wUT/3wfXa9ewqHy8p9jy1j48fms+X1o7z0qz2Eg3HmLqrg45+9hbmLKiav7fYtJ/mXv3+V+z+xgg13zWP7OyfZ/X4LA30BdE3Hl+dg0YoaNt6zgPpZJVddin3DOBq5QCyVIqlpyKLIWOxsFHxLWzsVbjclzgudGKtZpbrEy6mu4ZxSPh5tHWDr/laK85w5N35uBPhKPSy7YwGH3z3Bzpf2IQiw7pGV+Eo8mCwqoiigaTrJWIqR3jG2/HwbBzYfxeG1s/r+pbinKO0xK7Mpdv8lkuilwPW7wBnBvpsnparrOoPdY2x5dh+bn97LUK9/SoGfIztbee2n25m3qo6Hv7SBhWsaUEwzy27EoknCwRgOpwXrlMxdNz8EQUSWirBbP46mjxOLv4ZJXYTd+uBFtynyOsj32HPKIgfQPRhg854WasvycFin1vJpbhsiHk8RjSVJpzMcOdlHLJGif2gcq1ml6gPU3O/vOY0oivQPjlOU70IQskx7dquJYDjO7oOdpDMaiiyS77Vju8hxp4JhwOh4hLf3tEwwx0Sv6vw/wvWDIM9BUJdjJLdgJN/INnnLlSBMvFOMdJa2Vh8HNJCrEYSJOUGwIMi1WXap9GHAhCDPyy4STCDXgaFhZE6B7kdQ5iJIFxo7Nwo0I01beDNHAr8kksnqPEiCiiyYEASJpBbCQEcWLEiCgoFGxkiiG9n+AhEZp1qCWXJTaJl3PU/lpoIgCGxc1kAgFOepNw8wOBbKWVAWssGcd/afxuu08PHbF12RvTTYF+CH39rMe28dw+W2kVfopLgsa5SnkhnCoTjtp4cuyJikkmkO7+vkFz98n462IQqKXDQ2lSJKAtFwgkN72jl2sItHPrWaTfcswOGcXlz6w0RgLMJzP9vFcH+QhjmltJzo4xc/2kY6pbH1jaMUl3mxWqMc2tuB1WaifnYx5nMIUzTdoOVEHz2do/R0juDNc9A0v5xkMs1g/zivv3CA3q4xvvwHd1FdX3BVhCjX3dFIpTMEwwliidQl64clSaSy5OIR26FIhIFwBFEQ2NLWhtucnXBPDI/gsVy8x2NhQynbDrXn1NGIJlK8s+80pQUuNi1vzDkjzvWGxWZmwa1z2PCJNWx5ajtv/Ggru185ONkcrphkkvE0Y/1+uk/2EQpE8Ba5ufdLm5i1vA7louVqIh7bxxFuUkGmwR4/T39nC+++sJ9MRqew3IvbZ8dsMyHLErphZBvpQ3FGB8Y5suM0Pa3D/M7fPsaK2+fO6BidLYPs2HyStbc3MWvB9FH+mx2S6MWsLCIc+THJ1OFpHQ2zSaGuLA+P08LoeO6M62g8xe7jXcytK2bDkvoptXcymobJJON2WVEUmUQqSjAUx2YxocgSiWSacDTJaCBKntdOLJ5G03U8bhuSJOJyWLBaVCQp6yRFY0kQwOt2XdaLTdN0eoeDvPjeUV587xjByM2ZyTAMg0Qyw6g/QjCcLWcwgNIiN8UF2RLYVFpD0zQURZoQN70xDIBcIkuJey9ofRiJt7KsUuoKBKkku4IRxtCGMNInQHQjOf8UpHPogpW5kNqDkdoLUiGCPLFMkED0gZQPmXYQTAhSOQi5LSPOJUKpfroiO4lkhhGRcKkV5JnrcSiFSIKJY4FniGsBymxL8KjV6EaaqDZGMNVLINmJKEjUO2+nwfkx7Ere9T6dnGG0389A+zANS2pQzcrkc2DoBuMjITpO9FK/sBKH50KNq5lCkSXuXzeXYDjOc1uPEMgxffp4OM7rO5vxumzctXr2RQM6UyGVzPDzH7zPts0nKCrxcNeDi1mxrpHCYjeCIOAfDdPdOYIsSXh8Z6+BYRh0d47y6rP7aD89yIpbGrjv48upn12CqsoMDYyz5dUjvPb8AZ79yQ6KStwsXV1/49BpG9B+epA//dvHcDgt/P1fPMuu91p45Zm9PPrpNdz98BKOHuzi//c/nmOwb5zBvgBVdecHEo4f6qaytoBHnlzNmttm4/baiYTjvPvmMZ796U4O7+/gxJFuSso9WC7jN/kgrusVGxoLcbRlgGOtAwyPhUmk0tPKu7sdFv7iK3dffLnFgiJJtPsDuMxmlpZlJ9VkRsNykWZwgHl1xbhs5pwaJwBdgwGef/codquJlXOrfu2cjcKqfO7/7dtRVJm9bx5muHuUw++eOG8dQRCwua3UL6pm3cMr2PSpW6bMZpyLjDaEKmfLVHQjQSrdgVmdfc3OI1eIRRK8+8IBtr92GNWssnRVHctum0Pd3DJ8RS7MVhOaphMOROnvGOHQjtPsfusYnacG+OH/eZm6+eXkzaCpdXQ4RPupAVZvmnPtT+q6QwBBwTBS6PqlGZOaaooo8jly/ix3DwR44d1jFHodzK0tQv5AKWZVmY+2zhHiiTQWk8K8xlK6+sbIZHTcLiuJZBqTKuMfj5HO6KxYVE1r5zA2q4rdaiKeSFHgczAwHKSuKp+FTeUMDAcpyndgmaJ2dyqEowlOdg7zwrtH2XG4g2gildNr8GFB03RG/BH2H+liz6FO2rtGCQRjiILAZx5dwaP3ZrWA2jqHaekYpqGmkNrKvGtOk3lJXAvyfEFGNK1FR4D48xiZ5qyS+GTfmprt5ZB8CNI84HxjQFDmYiCA1otgeYTzssOCNZvxyDSDWA5S1TU4gdxhNNlCOD0AgNdcxxLvZyizLUUWswHFjvC7xLUAlbbV1LvuyGY1DJ2xZBvHx5+jPfwu3ZFdlFgW/Vo5GoffPcmv/ulV/uoXv09BxdnMqabpNO9r45u/9yP+7D++StOqq9MgsVlUHt20gEA4xpu7TxGN53Z+6RsJ8uJ7x3A7LKxdWDPj3tmO1iG2bzmBIMAjn17NfY8tP295aYWP0ooLWSvTaY2TR3o4sr+T2sYi7n10GXMXni0dLCrx8OATKxkaCLL19SNs23yCulklFNxAQrgr182aFOZdeUsju95rQTUpbLhzHmaLSmGxm9JKH7FIksBY5AJHw2xVWXd7E5vuXTDpSDhdVjbds4CjB7oYGhin/fQQ0Ujy5nQ0xkMxXtxylOe3HCEQimE2KZgv0RibdwmP3GOx4LFYkASRMpeTGm82+yGLIhbl4jdtSb6LqhIf3UPjOWdWON42yM9e3086rbFqfhVOm/nXJvImCAJl9cU8/kf3M++WWRx57yQDHcPEIwk0TUOWJWwuG+UNxSzeOJf6xdWYbZcq9dEZiXyXSlOWdUrTAoxF/p1S7//5ME7pqtDVMsiRHadJxtPc/vHlPPKbGygs9533eytkeb3zSzzMX1VPw4IK/uV/PENv+xDbXz3MA1+49ZLHkWUJs1VFyaGq/Y0Kw4iS0Tow0GYUba0ty6OswENz53BOGxd1w+Do6X5++vp+Pn33UmZVFaKe049UmOck3+tAEM4yR5UVuydJpQXBzEN3LZxcv6rMS2WpF4SsIq/NauL2dbPRdQNJEplTX8TsuqLz9ncxpNIZ+oaD7DrWxcvvH6e9byynpWMfJgzDYHg0zDOvHuS1d46RSKZx2s0YukEslSZ9jj5Ma8cIP3l6NyuXVPOph1dkG/BvQAim9SDPAflihp4CSiOC+T4yUgNDiRA6KcqsE8awYEY034ahzMZIHcg6BvoYYIBgQxB9jBulRKijEOd5rkbQqMFhvjd7D5o2fWBgjonvRJCKJ9TEb1yE04MktCACEg3O2ymxLpp0MgBEQQUE0kaCM16fIIj4THUsy/siKS1KV3Q7hwNP4VK/jkW+OfrZLoXCynyW37EA04cQyMxz23niriWMR+LsONxJMp1b2ttTXcM8t/UIboeFhQ2lM6K93bfjNKlkhrwCJ5vuWTjjYwUDUTrbhonHktQ0FFHTUHTBOlabiXmLKji0t50j+zsJjcfIL3TeMDZccal3ciye/Gx/i8NpxpOXtZUlWcRqMxEOxUlNQVFcXOqhrrH4AifCYjWRX+jEZFIIB+NoV6nLdd0cjYMne9m8u4V0WmPlgmpqyny47JZpWQdmqvxb6LCflxlZVPJBmtnzIYki6xbXcvBUL/5Q7hmOjrYOEI2nGA5EWLuwhrIC95TlFzcTzqXjdHjtrLxnMcvvWkhkPEZoLEw6nUE1KbjznVhn2ESV0UaIpY6QyfQzHnsJyCqc3yzifd2nB+ntGKa8toDVd86/wMn4IARRYNUd89i75QRv/mIXezafmJGjUVDipqDITefpISpqC6Zkq7jRkc70AtM59Tq6HiWVPkEk9hKi6EKegWKx3WpiQX0JB0/1MuQP52y8AMl0hl1HO9E0nUc2LmBBXQn2cyboD85dgiBc9M49q2x7/ndn1G2nWv5BZDIaA2MhjrYO8P6hdvYe777pm76jsRQ79rXx+jvH8TitLJxbTkWpl+On+tm+t+28dWsq88jPc9DSPsTYePSGdTQkxx9Pu1wQbQjmO8F8J5FUlEOjx8kY2llHI7sWglSCYCkB7r1gH7HYKMNxPz6LcZ6j8e7YGPeW/B8k8cL3jSDaESz3gWVqopIbDUktRNpIYJacuNUqVOn8Wn5ZVBEQyOjxbA/BxPMjCAJmyc0c94N0R3czkjhFf+wQtc7bPvyTuAaYu7qBuas/PMX0qmIvT961lFAkweHT/TlnojrY3IvLZsZhNVFfkY90id6Ans5RNF2npqEI82W8C8OhOP6RMIoq4/HasdqmjtgXlnqwWlU6WoeJhhMTbKA3hk1is5sm1dJUVUYQwXLOeQgIiBNMR1M18bs9NtzeqXVzTGYFSRbRMtq0lUYzwXVzNI63DTASiLBmYQ2ffWAFVaXenPEod4+PE02laCrMpolmclOsaKqkJN/FeDiOnstOpwm0943x41f2cqprmDULqmmqKaIkz3Xdo9LpjEYskSIaTxFNpKgq9l6gATAVRnrGOPzeSTwFTtwFLtwFTpxeO05f9nMl0I0oqUw7upEgkT4OgICE03LnFe3vw0ZwNEJoLErTshryJupDZ4L5K+t461e76Wm7mK7E+XB5bNidZnZvbSYeTZJX5LqAgq6msYi8whsnxftBhCL/dgnBPh1dD5BKt5DR+jCbVmAxrZ7RvpfPreTdA62MjEdyypACkExl2HmkE38oxl2rZrFibiVlBe4PVXAqlkjROzzO6e4R9p3sYc/x7qui9BUEAZtZIaPpORfmulyMjIV5f08rNqvKo/cu5o5b52C1qPzCMC5wNAryHDjtZppbB7M9LTcodo6eYJm3kZOhbgQE6h2lHB3vYL6nms7IEMPJcRRBpsTqwylb0QyN/vgYO0dPIAsSlbZCCsxuRhJBemLDxLQkJlGhzlGCR3UwGPfTGxvBIqmIQvYdGknHaY3080r/HnwmJyZRZZm3Ac3Q6YwOMZjwIwsSRRYvVbYbtwH8XGhGGsPQUEU70kT24lwoggUBkYQW4oN1bKIg4VJLcamlhNL9DCWO33CORiKWpONoD8mJkqSxgQA18yowDIOuk32YbSZmL6/DlZeNXB/bfoqeloFJGtfbn1yL+SLG8hnoms5In5+Tu1vJK/HQuKwWRZXJpDWGukfpONZD2B9BMSuU1xdTMasEyxSEI/PrS3jiriWEokna+kZzy0SlG2w/3IHLYeHJu5ZQVjD9uzQWTWIY4HBZLismqWV0UqkMsixNSzOfNbilLCV5Mo2uG1xFX/RlwTCYtiRTksTzdJkFhCntaGNyZ+dDUeXzaG+vFa6boxEIxUmlM9yypJaKkiujb7wY2sb8DEUik47GTOB1WVmzoJr2vjFi16i2ORRN8NbuUxxt7WdBQynz6oqpLvZRku8k32NHuUyK0+mgGwbptEYilSaeTBNPpIkl08QTKWKJNNFEilgiRSSWJBRNEIwkCEUT/LfPbZqROGLniV7+5es/pqDcR0FFHgXlPvLLffiKPXgL3XgKXXiK3NjdVuQZ1k6rchU++xcRUPE6PgWAgMjNktHQNA1N01FN8mVxT5ttJgQE0jMkIxgdCtLRMkhf1xhtJwdwem2o6vnH+8SXbr2hHY1I/EU0bTrHSkAQVETRg9W8HrvlYUzqzJhiSvNdLGos41TXcM6bFiHLDnWifZDB0Ww2YcXcSuZUF1Ja4MasKpfMRFwuDMMgkcow7A/TOzROa+8oR1sHONE+yFgwelWtAYIAJflONi1v5FTXELuOduVs3FeCcDRBe9cojbWF3LqqAes05SA2qwmTKhOJJkmnc1vyekW4yO/+Sv8e5rqqeGNgH7IoUWTx8NbQAQotHrYOHyHf5CKmJeiIDnJL/lySWgZ/MsRIIshgIoA/FWZ9wQKOBjs4Huwi3+TCpdgot2b1Q+JaiuPBLpJ6ilJrHhZJJW1o+FNhxlMRRpMhzJKKgYE/FebtoYMUmtwk9BSnwr34ytbgUG58ZsQz7wKd9JS0tiYpK1IbyQxPuVwUZOxyAYFk52Svx42EaCjOe8/toetkH2X1RbTs76CkthB3vpOBjmHGR0I8/NU7WX7XQkwWFf9QkPajPTTva6P9SBer7108raOhazpD3aO89sOttB3p5vZPraVBN9A0nfaj3bz1020MtA+hmBTSqTRWh4VbH1nB4tvmTulsrFlQzXgozvdf3JVzJqpEKsOWPafxOqw8tGE++dOUzSuKjAAk4mk4J5N1KUiSiKLIaJpOJq1dNFORTmbQNB1BAEWRshmCDwnpVAZ9ujJY4bw/l41sae4VbnwZuG6OhiQKSKKI3Wq6KkGqqeAymxmOROkMBHCYsqklq6JgnqZPA2DT8ka27DtNa8/oVaeKpsPgWJjBnc1sP9xOZZGXqhIvpfkufG4bHocVm0XFalZRZQlJEidvbN0w0I3sxJDRdDIZjfTEJ5nWSKUzJFMZEqlM1rmYcCyiExmLyAT9ZjieJBJLEo4lSaUy5xkqf/DJDTM6B1eeg8ZltYwPBWnZ187+t45kOau9DgrKvOSVZR2PvBIvvhIP3iI3vmI33iI3VocF8aK/uYDTeg/ZpkUDg6xLL9wEFLdmiwmzRSXkjxK7DLafwe4xDMPA5XNcemXA7rQwd2k1c5dWX3SdG12wz2X/Aro+XWmTgCCYkMQCTOpcVGU2wiVUkc9AFAXWLa5l74luDp7qRctxVuMM/KEYb+0+xaGWPppqiphVVUh1iZeSfBeFXgcOm+mSaf+poOsG8WSK0fEoY8Eow4EIvUPjdA4EaO8dpXckmDOGPI/DyiduX8Tda+aweW/LdXc0MhmdWCKFzWrCfQmaS1EUEEQB3TByauRcMS4yhhKLl97YKBlDQ9cNhhLj+ExOOiKDtIR7qXUUk0pm6IuN4k+FkQSBSlsh95auZNvIMbqiQ4TSUWyymQKTC7dqp9yaj3PCOai2F7EgVc3x8bO/nUe1c2vBfH7SuZm7i5dhklQyukZndJCTwW7qy0owUgZd0WFGk6GbwtFQRRuyYCKhhcjoMQxDRxDOPl9WOQ8BCX+yHd1IYxgX9kMa6OhopPTcByBygUxaI51MM3/tLGxOK1t/tZNV9y7hgd+6nV/+31doOdBB0+oGTBaVNfcvYent83jhX96it2U6xyl7DYa6R3n1B+/QeaKXj31+PSs+thBFlRnt87P16V10Hu/ljk+vpXZeJeOjIV75/jts/vl2CivyqFtYdcFeJVHkzlWzCIRj/OS1fTlnuBuPxHll+wk8Tit3rpqF8yL9nYUlbkRRoKdzhExGm3GE3ubI9jKkUhmC41ES8TQW64XvmJHhEIl4Cqfbis1hzpmWhiBky5oymaw46wehaTrjgSip5KWzzDfC9DcdrpujUZTnxGJWGAlESKYymHOozm0ArWNjDEciOM0mQGBFeRlzCqdXkK0o8nDXqln829CuD0VFNxJLcbx9kOPtgwgCOG1mvE4rdqsJm1lFVc44GtnJVDcMdF2fVA1OZzRSGY10OutoJFO/oY7SAAEAAElEQVRpEqkMiYm/uS4bORfljSV87i8fwz84jn9gPPv3nE/L/nb2vnEYAHeBk7wSD74SL3mlXvJKPGz85Fq8UzIsGSTTzYzHTmAYCUDEJFfjst5zzc4lV/AVu/AWOulsHqDjZD/ltYWol6gZHR8Ls/vtY+i6QePCS/cgAJRX51NenX/pFW9guB1fvab7ryrxsmFpPV0DfkZyzED1QYwEImzd38rOI50U5TkoK3BTnOciz23D47DgsJmwmFQsJgVZEiezt5p29llOpNNEY9lgQCSWIBCOMzoeYSQQZXAsxFgwel4jdC5gM6s8uH4e993ShNmkUF3iw2JWiCeun4K4JImYTQrJZJpYPDmtfsh4ME4slsLttGC6SkGpa4lGRzm7xk5SaskjY2js87dQby/BwEAzNFJaBqdipdTXiF22kC2AuLC/Z56rGodsoTs6zLaRY4iCyCzn9PTWuqFnQzUTnphunD2mVTaxKm/2TeFkQNaRUCU70cww4fQgGSOBIpwdu1utQBJkQqk+RhItlFoXc8bINgyDtB4nlO7P1q3fwIGroqp85qysB+DI+yepnV/Bgltns/VXu4iFE6QTWcNTkiWsDks2I34J4zfkj7Djpf30nB7k3t/YyJJN8yaz7n1tQ7Ts72DOijrWPbQCk1XFMAwGOoZ56V8309s6SM38yin7Z02qzEMb5jMeifP05sMkc1x6OTAa4rl3juBxWlkzv3pKBr4Fy6p59dl99HX5ObS3g2Vr6me0b7fHRmV1PmazQlfbCN0dIzQ2nS/KmoinaD7aSzAQzYr25VBHQzXJmC0KwfEYQf+F76j+Hj9D/eM3Rrb2KnHdZuelTRXsOtzBe/taqavIp6Eq/wLKyCtFgc3GktLzbxjrJbIZkE0h3bVqNu8fbOfI6f5r0qtxMRgGBCOJm4b33uqw0Li0dvLfmVSGcCBKYGgc/1CQwHCQ8eEQwZEQwdEQo31+Tu1tY+dL+1BMCvNvmX1RRyMQ/SVmZQ6RxDas6hKiyV03haNRM6eU6tklbH/tMJuf3otqVliwuh6nx3bB5KRlNLpaBnnnuX2c3N+JapLZ8OCSyz6mrhtk0hlAQFGknCqX3syQRJH1S+o40NzDtkMdOWdHmQrJdIaugQBdA1kaXlkScU40NVrMCmY162iIoohAtgQro+toGZ1kOpPtk5rolco1+90HoSoS9986l8c2LcRmyRrzHqeVyqIsY9f1gt1morLUS9/QOAeP9bB6ae2UBo6m6Rxt7qNvcJzq8rxLZj+uJxqdZTzVvZWvNjxAOB3jF11buaNoMWldY5azgoyhIRgCsiDhUrKNmf1xP68N7KU/NkapNQ+HYqU13E97ZAAdg3AmQUpPZ9nQxjs44G+lNzbCtpHjrPTNosDsRkSgzJrPS327yDO5WF8wn2p7EXNdVdljThjcbmXqZtAbDW61DKvsJZoZZiRxigrbKhT17O9eYJ6FLFpI6mEOB36BSXKQZ6oHBBJakPbwVsLpAURBwSrfuIEak1lFtajIqozNZUW1qFmiCEXEMIzLrraIRxJseWoHx3edZuXHFrL4tqbzSnvD/ggjfWOc2i/y0797fvL7nlMDjA0GiASiaBkN8SKZAqfNzON3LMYfjPHGruacBzhbe0f55VsHcdstLGgouaAfrml+ObPnl3NoTzs///57xOMpZs0tw+2xYhjZHo7R4RCxaJZdyuHMloabzAqz55czZ34Fp5v7efuVw6iqTFmlD1mRGPdH2bO9hQO7WpEkkdXrZ1+0cfpK4PHZKSxxM9Dj58DuNkoqfBQUubJOXm+A15/fT2/XaM6Odz1xzR2NsWCU0SkaFUVRYNHsMrbsPs2/v7CLpU2VVJZ4sVtNk8wrH4QiSdSUX5r/utLjpsBum1QH91mt09Lbnot8j50n7lxM91AAfzD3DFS/rpBVOduXUeiiPJkmMBxisHOYrhN9xELxbJ1jRsMwIBFNTiPOaJDRxvB6niSt9eO1P8lY+Acf6rlcKYrKvay8fS6dzQMc3dNGKBDl4PunKK0uwOWzo5pldM0gFo4z0hegvbmf47vbSSXT3P3kamYvrZnxsSLhOKeO9NJ8uIdxf5TGeWWsWN/I+FiUeCxJaWUeNsevp2r4TJHvsfPg+vl0D47T1jv6oQYOADKajj8UuyZMdlcDURS4Z20Tn7xzCT7X2RenzazQWFlwXR2NPK+dlUtq+Pnze3n65QMEQ3EaagsJR5PZXpVEmt6BACdPD/DK5mMEQ3Hu3TSPwryZlR1eU1zExy80e7i3ZAV19mISWpq7ipeRb3Kjo7O+YAH98TEEwCKZsEgqCzw1FJo9ZAyNJncl1bZiTKKCWVJxKNmMx/qC+VRaCxEAk6RQ5yil3FqAQ7EgTzBMCYLAY+XrGEmOY5FMCIKAT3WysWgRvbFRBAFssumGYdC5FDymahxyEaO0MBA/SiQzhEMpmiyfcqolFFhm0xX20xfdz17AZ6pDFBSi6WH6YgfQjDQWyUuh5cbVIBJEYbJcWpTEDzjal+gOngK6riOIAsvumE/H8V4OvnOcZXcuOO93FwQBLa0RPcfm8Ra5WPfwcsobSy5Zx1/odfDZe5czOh5l74nuyxrfTHCktZ9fvHkAl91MTZnvvJJUu9PC45+/hXgsxYkjPYRDcWoaCnG4rGBAPJYk4I9id5j5zG/dNuloAFTU5HPnA4sIBaNs23ycwb4ApRVeZDnraJw81ks4GGfjPQtYtKLmPGVtyGp49HSMkoinSCRStDYPAhCNJHj12X3YnRZUVcbhslBdV0jhOSXNFdV5LFxWzSvP7OO9t44zPBgkvzDraAz2jzM6FKS41Es6demg043+BF9zR+PA8R5efvfYlMvSGY1QJMH7+9s52T5EvseOxawiXSQq63ZY+KuvXjqy3RcM8V5nJ4PhMAICBXYbt1RVUe6eWXPs6vnVPLR+Pv/+8p6cU7f9usHQDeKRBEPdowx0DDPYMcxQ1whj/QECw0ECQ0HGR0IA+Eo81C+qoqi6EF+J5yJ7FJDlQsDAMFKE4q+hGdOxE904kBWZxetmEQnGefnH2+ho7qfzVD92pxWb04ysyOi6QTKWJBiIkk5mMJkV7vj4Ch7+zQ2oMyz/iIYT7Nx8krdeOEAiliLoj5JMpJi/vJrerlGO7+/k1o/Np/4DaeAbCYZxJsOQOwKEqbCosZRHbpvPv72wK+cifjcjBEHgnrVz+PTdSyn8QE+Q1awyq7KQF5h6vv4w4LCZWbOslvauUXbsa6N/OEhZsQf/eJSMprNtbystHUN09wXwj0dZvbSGlYursV+Cbed6QhFlHijLMqbZZAv3lK6YWCLR6Cyj0Vl23vo19mJq7BdSstc5SqhzlFzw/Sxn+UVLqBZ4zg9eyKJEvaOUeseNOzdcDBbJTYFlNoFUF06lBFU8P7osCQpzXPczGj9FODNIT3QPfdEDCIKEZmRZyURk8kx1lNuWT3WIX0uYrSbWPbScsvpifvVPr/LCv76Nw2Nj9opsiZHTZ6ewPI+aeRU88JXbLyAysTmtSDNg1asp9fEbD61iLBilvW8sp+eg6wY7j3Xiclj4wv0rKM47X8ti3uJKPveVjex45yQnDvewf2cb8VgSQRCw2k348hzU1BditZ3vKFgsKktW1qKaZLZvOcmJIz0cPdCJrhvY7CbKq/O56/5FrNk4B1/+hfoZ+3e28vYrh4nHUqRTGRITjGHRcIJnfrITVZWQFYnCYjf3f3zFeY6GL8/BxrsXAAIHdrWxd/tpNE3HajNRUZ3PpnsXIMsSz/50V06v5fXANXc0BkdD7L+EhyuK4A9G8QenNwTyPTOLWrX5/QyEwiwty06m+/v6OT02NmNHw6TKPLxhPqd7RnjvQNulN/hPiLGBAPveOsJA2xDD3aMEhoIERkKMDweJheJYXVZKqguYvbKektpCCsp9eArduAucePJduC7KeS/is30KQTDhst5NItOG03Ljl02dgctnZ919i/AWOtn91nGO7WljpD9AKHD+ve1wW5m3so5lG+awYlMTRZfQ3DgXvR0j7N7aTFlVHqs3zmHH5hOTFIc2u5mBHj8jg8Eb2tEYC/5PRMGKx/l1sjKGU8MwMiRTh4km3sBqWofFvPayjqMqMhuXN9AzPM7zW4/mXM32ZoIoCNy3rolP372M0vwL50KTKlNZ4sVqVohdpz4NURQoL/bwyYeWUZjvYPveNg4c7Z7MgLa0D9PaOUJpkZt7N83j9nWzKSvx3DRR+Y9w5RAEkUr7WjxqNVbZi0stv4Ayp8gyj4W+T3Jo7OeEMwPoZGAiqCEKCiWWhcz3Po5duTkofS8GwzBIJ9MkYyli4QSGbjA+EsJsM2XLrs5xFiRZxFPoorS+iLu/sIFn/uk1XvjO29hcNipmlVBWX8yclfWc2HOaA5uPUbugElES8Q+Ok0qkaVxSMyXr1FRYUFfClx5axT/85J2cB3ZSaY0t+07jdVr55F2LcTvOls2JosiCpVWUVfro6xqbbKIWBFDNCg6HmcISD073hSWWdqeFpavqqKjOp6/HTzgYQ9cNLBaVvEInZZV5Wb2KKeaYuYsqcbqt6Nr0WSaLVaWy9vxyPVESqaor5KFPrmTp6jrG/VG0jI7JLJNf6KK8Op9kIoXbY8NqM5039keeXMX6O+dRVVuQHZcAFdX5/N6f348v7yxDl9tr44EnVhCPpqhpPBu8mL+kiq/9+f3kFzrxXcQWW7V+FqUVPrw++5TX7XJwzR2NtYtrKczLjZCSeYZsArqhU+p0srYqKyc/FIlctlJuvsfObzywivFQnCOt/Zc91l939J0e5Of/+3lC/giCIFBQ4aO0tpCld8ynuLoAX7EHV55j4uPEYjdPwzR1FoIgYDUtxjAMbKZVmJRZSKL72p9QDuHy2Vm+sYnq2SUMdC2edDSSsRSSLGF3WfAVuigs91JWW4BpogZ3phgdytab3vfECuYvr6H15AD9E7WcTrcVLaNPRlZuVERjLyKKbtzOryFM42iAgKYPEY7+DAH5sh0NAJfdwmMbFxKJpXhj58nrrhVxPaAqEg+tn89jmxZSXjg1L70kingdFiquc5+GokjUVObhdS9m2YJK+gbHGfVHSabSKIqEz22ntNhNdbkPr9uWU2r0j3Bjw6WU4FJKLzpfyqKJOucm7HIhA/HDhFK9aEYGk+Qkz1RHoWUeeea6KVrtby50Hu/ltR9tZaTXT1/rIMl4ku/9+VNY7RacPju/9/99/oJtFFWmbkEld312Hc9/+01e/t5mPv71e/AWuln/8ZWIssju1w/x/vP7EDBQzSoNS6qpW1A543EJAqxZUMN4OM43f/F+zqUCIrEkL71/HJ/bxr1rm7CdU8okCAJ5BU7yCi7f3lRUmdIKH6UVvsvabtbcMmbNLbv0iheBJInkF7rIvwgVvd1hZs1tF5b5LV19YcN7XoGTO+9fdN53VpuJJSvrLli3vCqP8qrp2xDqZ5VQP+vCDOqV4Jo7GtVlPqrLLu/Hu1qIgsj+/n4GwmEMDDoDAXxWGz3BILdUVVGfN7PxNFTm85XH1vKPP9tKS9f1e/HeiIhHEgx0DCPJEvnlPsobS2hYUkPjkhqqmsqxT9EAfTnIbqugSNMzhd2oUFSZkqp8SqrySacypJJpMmkNURRRTTKqSbnixm1d1xFFAbP1QgcllcqAwK+N8SUIIgImDCOBpo9c8X5K8l185u6lpNMZ3t7TQuoaN1vfSLBZVD551xLuXdtEodcx7XNps5poqMi/ro4GZJ0en8eO121j3uxS4ok0mpa97y0TjHw3XBbjIkFN3TD4xv97nXQ6w8cfWkZjfdHkstGxCL98bi+DQ0Eqy308dN9ivJ6zJUH7D3XxxuZj1Fbns2HdLArynBgGjI6FaT49SHvHCMOjIWKxFJIk4nJaKC/zsnRRFcVFrvNq2ZPJNM++dIDmlkGKCl188dNrUacJ3rW0DvH21hMMj4bZcEsja1bUIX+IwpRT4Vw624tBFW2U21eQZ24gqYUw0JEEExbZgyreuKQBDreVOz+zDgDVolK/qAqn105BRdYg/Njn1mMA7nwXJmuCZXcsIBG9kDxGMSkgZCPmDYur+b1vfoGyhmw022RRmbd2Fg6PnXQqg9WepZovqy/m7s+vp799mHAgCoaB2WYiv8x3EdKWqSEIAiZFYtPyRvyhGD98cXfOy8/HglGeevMABW47axZWo85Qp+s/OwwMNF0nqWcQBQFVlJFm8DzlAr+Wv1CZy8nS0pJJbvVCux2TJCGJIqbLmCgFYEF9Cb/ziVv416e3c6x98NoM+CaEr8TDyrsX0d3ST2gswvEdLXQe72X7C/tw+RwUVeVTMbuUytmllDWW4PDYED8sOc3rhNajPQz3BSitzqegzDvpCORafdPhtmEYcGRvx3k0t+lUhubDPQiigPsK1dlvSAiAkcEwrk79ubzIwxceWImiSLyy7cR/iv6r0nwXT35sCRuW1uNxWi8pNmW3mGioKACOfzgDvAQEQUBV5BkbE/FMjO1j7zDPtRiX4mbz8Gus8K7Fp+ZfN8dEANo7RmjvGmF+U9mko2EYBoHxKK+/dYxgOE59bQHLllRPOhqZjEZr2xBvbz2JosiTNOfbd53m9beP0dU7RjAYJxZPkcloWSNPlXE4zLzzXjOf+vhKFi2snNSpEgQBRZHZuu0UxYUuVi2rYeH8qSm1NU2nuWWAV988is2qsnp57U01fwsIWGUvVtl7vYcyY6hm9bzsga/Yg6/4bC/juVoWZpuJZXfMn3Z/gnDhPgAsdjOzV5wf5ZYkccp1rwSCIOCwmbh/3VzGglGe3XLkqvf5QfQNB/m3F3fhc9toqim6YQJrhpHV/hI/JAN+OqR1jZ5ogGPj/bQEBxmMh4hmUpOMc2ZJxmuyUWHzMstVxGxXEW6T9Zpk+349HQ2ni7yGqSMXlxLtOxeCICCKsLixjK99cj0/fX0f7+xrzdUwb2qUNRTzG//7k0TGY/gHA3Q399N5vIeOYz10HO0GAewuG3a3FYfXTmFFPpVzSqlqKqOqqRxvkXtGDWY3E47sbOXVn2zH6jBjd1spKPVQVlNAaU0BZTUFFJZ5UUzyVRs8lXUFzFlUweYXD3HyUA9jIyGS8TTf/8c3GB0MsXrjHKrqb+4a5DPQ9QipdLZPShSunkWrosjDF+5fQaHXwY9f3UtiBmJINysWzyrjk3cuZvGscmwzLM+zmGQqijxYzWrOyx4+DCiiik8tYI9/Oxkjg0fxYpGubxRbEATqagto6xym4xy6ykxGY2AoSDSexGJWCEcS9PT6md+ULcWIRJOM+rOMjXk+Oy5Xli1H1w36B8dBhxXLaqgs9+FymIkn0pxuG2bnnjYOH+shkUjztzUFeN3WCSdDYvniKvK8NoKhOO/vPH1RR2N0LMLptiEikQQL5pVTW50/Jc3wR7ixkdY1Do3080b3aQQBCix2PjtrMUOxCC91nmQsEcOhmLi1tJpF+aW0jI/ybl87w7EIdtXEHeX1zPYWcGxsiPf7OxhLxKhwuLmnahY+88WfK1EQyHfb+PimRYyNR3n3GvS6tvaM8C9Pb+dPv7CJ0gL3h6rYPRUMw2A4EeYfT7zNqvwaVhfUUmD+8Nnw0rpGc3CQl3uOctDfTSAVI5SKE9fSZIyzwTUBAZMkY5dNuFQLZVY3q/Jr2FQym2KLK6eBmV9LR0OVJdQcGbGCIKDIEk01RXz5odUUep288O4R4r/GBspMYLaaKG8swTAMtIzG/HWziYbixIIxxkdC9J4epPtkL70tA/SeHqT1YCd73zyE1WnB5rTy9e9+meq504tN3WwQBAj6I/R1jiCQbUKz2ExYbGYsNhN2l5XiCh9ldYWU1xZQXl9IYakXURYv66F2eazc/sBinG4re99vIRpKoBsG6VSGTQ8sYuWGWVlqvxsIydRh0plODLLPjWEk0I0Q0djzIEzl/GdZx1LpFmLxNxBFN4o8c/rf6VCc5+LRjQsp9Dn4zjM7GJmCfvtmhs1i4r5b5nDX6tnUleWhyDMvMxJFcUJPw83J61g+lUxl6B0I0NY5QjiSQNP0aUk91yyrpazYgyzKzHbOozl8jEBqjDW+9Vgl63Uvs6qvKeS1t47S2X2WjSeRzNDeOYLdZqa+toCTpwbo6fNPLg+G4oyORXA5LOR57ZOZiXlNpfzeb29CVWS8HhtWi4qsiOiaQSSapKzEzVPP7qWlbYgTzf2sXlGLJAgIgoDXa2f1ijpeev0wR0/0MTQcpLDgwvrwnj4/zacHUVWZhtpCyi7KEvgRblQYhkEwmeBHzfv5dONiXCYzZklGFkW8ZiubyuvJ6Dq7h7rZOdjDwvxSjvuHGE1E2Vheh9dspchqJ5hM8G5fO5VOD+tLa3i67SjHxwZZUVSBSbq4CSmKIhVFHj5zzzL8wRhH26ZTL7+S84PDp/v41q+28d8+twmn7UIl+A8TGUNny+Ap3ug7wb7RLp7uOsBSXyUbihppchdP0k5fKxiGQTid4Cftu3m97ziD8RCRzMWrAAwMElqahJZmNBmhKzLG8fEB3htu5bHKxawpqMOu5IbN79fS0bgWkCSRqhIvn757KXNqCnnqjQOc6Bi63sO67hAEAVmRcXjsODzZch0tozFreR2xUJyRnjF6WgZoO9zJ4XdP0tvST39qiPhNIkx4OVj/4BKaltcyNhSkr2OEvvbhic8IQ71+DN3g1KEuzFZ14mPC7rJQWp1PZUMRlY0lLLl11iWPI4oieUUuNt63kKVr60nEswxBJrOCy2PDYjPdcNHHeOJdwrFn0I0wQPavFsUf+sbFNzIMDCOGbsSxmm7Bat6Us/G4HRY2LWugotDDz988wDv7TvMhy2zkHALQVFvMoxsXsGxOBV6n9YpKCpw2M3Xl+dfN0RgcCfHcawfZsa+dWCxFRtMu+dtUlnopmyj7GIj3IiJgkaz0J3rxqD6UD6OUYZpHrr6uAMMw6OsPkE5rKIpEIpmmrX0Yl9PC4oWVnGjuZ2BwnFQqg6rKk46G12vD57NPGlEup5W5sy0TGffzD2qzmbh9wxxefO0wsViKjs5RVi2r4YwQtsWssP6WWbz65lFGR8PsPdDJvXctOG8fqVSGru4xurrHKCv1UFdbMG0vx0e4MWEAY4kY8UyaJQWlKBOGrm4YDETDvN7VgmbotAbHKLLYMQyDhXnFhFJJ3uw+TYMnj9tKaxlPJjgZGOaYf4g8s5X+aIi53iIyuo7pErazLIk0VhbwxQdW8g8/fYeeofGcnmNG09l+uIN/e34Xv/OJdRNBlZweYuZj0TVe7DlMSs8wEA8yGA/RGhpm80Az9Y4CNhbPYmVBNT5T7suaDcOgPx7k/554mx3DbYyn4pc/fkNnNBlhfCRGd2SMroifhysXkWe++vF+NHtcBkRRJM9tY/2SOqpLfLyx6ySvbT/J2H9yYT/DMEhEkwx1jdLb0k9PywB9rQMMdowQ8kdIJdIkY0mioTiZtIbVafm1K5sC8OQ7cec5qE6XMH9VXfa8E2lSiTTjoxH6O7POR3/nKEM9fob7/PScHqT1aA8mi4rLZ+e7W/50RscSRQGr3Yx1hrSD1xt228dRldnEU3tJJvegaWcau8WL14QKErJcitm0CrvlQWS5KqdjMpsU5tYV83veW1k2p4Kn3jwwqep9s6E4z8m9a5tYv6SOskI3ZvXKS/ScNhN1ZZcWRr0WCIbjbH7/JC++cYRoPIndaiLPa8dsnr7k1WTKLh9P+dkT2MZCz3LciofXB1/Ep/ootVRe+7rpaZyh6opsZimeSDEwNE5FmY9kIkNbxwhOp5lZdUUU5DsZD8YZGg5RXuYlGIwxMhamvNRL3jmKxFnn4uK/bWGBC2WC3jQcSZw3LFEUqCjzMn9uGUeO97Jrbxt3bpw7uT7A0EiI5tMDJFMZaqsLqK8puO4ZoY9w+RAAq6wQSaeJpdO4TBIGEE4lOTI2gAA8VNPEK53NjCSiGBiU2lzcWzWLQCLO021H2a/2sSivBI/JytKCUhbkFaMbBu6J7MhMoMgSCxtL+eIDK/l/P9vKeI6DjMlUhpffP05JvovH71jE9ZCvMwyDzugYJ8bPZm0MDELpBKF0gp6onwP+bub2l/CFujUs9k1dsnilxx5OhPnvB1/k4Fg3Sf3qqm0yhk5vbJwfte0grqV4smbFVTsbHzkalwlBEDCrCnXleRT7VrBhcT1v7j7F23taGLuEDsivE8YGAux9/TDdzX30nOpnuGeUWDhBOpk1rNOpNJlkBsWkUFxdQOOyGmrmVlA1t5yiynyKa25ONqlL4dzmb5vjrAJpaY1Ow8IK0skM8WiCntNDdDQPcPpoD4e3txAci5C+SDleR8sg3/qbly5rHI9/6VaWrm24qnPJJWSpEMnsxWRajmH7LP0jD4GgUuT7AYJwsfSsgICEINoQBQeCkFvnVBAEJEGgyOfg7jVzWDyrjK37W3nx3aP0TYhM3ugo8jm4fUUjty2rp6LIi82sXHXTrsWkUlHswWJWiH/IehojY2He390KAty7aT4P3LkAl+PSJRGuCbVfu+zkzsIHsMl2ZEHmgZLHsMr2605najIplJd56R8M0tXjp7TEQ2A8ypg/Qm11Afl5DspLvfT2B+gbGKes1EMwFGfMH2HhvAp83vNf9JmMRkfXKEeP99HRNcJYIEokkiSZTJNKZxgZzWYOP0jrLggCDruZ9WsbOXC4m+7eACdODbDgHIrOvv7sdy6nhfragguO/RFuHvjMVu6vmsWf7XoTkyRRanfy+dlLcapmXhptZjQRJZRKUuPKNs2/09fGa12nwACrolJgsZNnsbGhrIZ3+zp4r78DA4PfalqB22S5xNGzEAQBi0lhzYJqAqEY33lmB8l0bkvPo4kUP3xpN+WFbtYuzE2J7eXAwOCdgVOk9anZDFO6xnAiTDiVwGuyTbnOlSKta/zNkdfYN9p5Xg/G1WI8Fedn7XuwKyYerVyCS53Z7z0VPnI0rhCSKOKwmZlTU0RViY+718xh++F2tu5vpa13FE2/eeow3A4L9RX5zKoowG5VL70B0Hm8h+/9t5+RSWVIpzNoGQ1Zkcgv9TF7RT218yuomV9JaX0Rdrcta3ybFBRVRroRqSmvEXRNZ3RgnPYT/bQe66X9eC897cPEwnEyKY10KkMynkKSRSrPob08F7Ii4T2j4jxBWxgYDdPRMojba6e43IsBDPb6CQViLFxZg+8KuMSvLQQEQUUSVBCdKHI9mj6ELFfkpMn7qkY28SKsLPbyidsXsWFJPdsOt7N5TwsnO4bQb7CaKkWWaKjI546VjSxvqqTQ68BiVs6jMr0aiKKAx2GlqsjLyc4Ptzw0Ek3S2TtGfXUBn7h/CeUl3ssqA5RFGbd6tp/Ard4YrEOCAHU1hfT2BejqHmPZoirau0aQZYnyMg92u4nyMg8nTvXT2+9nXryUUX8ETTPweW24z+m56uoZ4+nn97HnQCeRSIL0hFin1apisaiYzi1zmuLWNZlk5s8tp7jQxZg/wq69bZOORiyeLbfqHxhn7uxSGutvHEafj3B5EAQBi6zwcO1c7qhoQBBAFiRcqom1xZXMzytCEgQwQJFkRATWFlexKK8EBFAECZuiIosia4orWZhXjDZhyLpUy2W57oIg4LSZ2bSikbFgjJ++vh8jx/PqeDjOP/70HQp9DurL8y+9QQ6hG/DOQMu061gllQZXIWXW3PY7/XvbTt4basmpk3EG4UySH5zeQbnVy61F9ZikmZMpnYsPzdEIjYV55p9e5b1n9yArEl/+xqdYevv09Gw3OoQJvQK71URDZT5VJV4eXD+P092jvH+ojb3Hu+kbCd5QNJqqLFFW6KaxqpA51YXMriqkOM+JSZWRJRGLaWY3kq4ZqGaFxmU11C2oonZBFZVzSnHnO5FVGVmRkRUJSZauWC/iZkQ0HKfn9BCtR3s4faSH1uO9BIZDpJIZMmmNTFpDy2h48h3Uzy+nfn4FdfPKqGkqxWafOmJQUu7jd//ygcl/93WN8caz+2haVMm9n1ie5U0H0sk0bz5/gNHh0HWrU50ZBFS1kXhi9NKrfogQBWEymv9o3gLuWjWbtt5R3tl3mj0nuukbDub85ThTmFWZmlIfK+dVsWJeFVVF2YyDKsvXpB/HZbdQU+b70B0NTTNIpTQ8LhtlxZ4brtdoWkwzVEEQqK8tYPO7J+jp85NMZWhtH8FsVqiuzMdmNVFR5iUcTtDXP04onGBkNILLaSHfZ5+8Dn0DAX78sx28t/M0siSydlU9t65toLoyH4tZQRAEDMPgy1/7McMj4YuOxeO2smZlHc+8sJ9jJ/sYGQ2Tn+dgcCjIseY+DAOqq/Ko+zXNPP9ngSAIWBUVq3J+ANEsi5jlC9/1NkXFplwYbDRJ8rSN3zMdS77bPkl7+9qOk1e1v6nQPxLib37wFn//u/eT7/nwMnGjiTCnQtPLH+SbHSz2VSDnkCa6eXyQH7XuJHWRTEouEEjF+NeW96hz5lNtz7uiIPGH5mjYPXae+JMHWbC+iWf/6VXSNyF14sUgCCAJIhaTiFmV8TRZWTSrlHRaY2A0xKGWfo609tPSPcLASDDnacOpYDYpFPucVBS5qSz2UFnspaLIS0meE4tJQZJEJFFElrJsJJd78yza0MT3Dn4DSZEQJRFJlhAlAQThmtDMxRIp3tvXxtGWPv7haw9ettJ7Z5+f/3hxD4/duYhZ1YXnGQUWU26iwS/88D1+9e23iYWzLDlaRkfXdEwWhbLaQurnlVO/oJyGBRXkl3iy2R1JRJTFaaOGkixid551QgKjYUKBGLc/sBhP/lkBNsMws3h1HU//cBu9nWNUXSRDUlPm4xf/+3M5M5rFCf7+y4HT9jls5rsvoQr+4UMQsgVbZlXBpMh4HOUsqC8hkcrQMxRg74luDp/up7V7lLFg9JpkOwTAbjVRW5bHrMoCmmqLmF1dhM9lQ5ZEZFlEvIJn9nJQnOfkjz6zkd//5PrL3lYUhRkHLD4IRRaxWVUMw7hmzrLVrPCXX74rpwEgkyKjKhcv7RMEqK8tQNMMevr8pNMZOrtHsVhUaqryURSJPJ8Dk1lhdCzC4FCQMX8Yj9tKXt7ZZ3zXnnYOHOlG13Q+/eRqPnb7PBwOC9KEIyIIAql05pL3hsNuZu2qel587TDDwyH2H+rirk1zGRwKcqK5n6ICJ7Pqi7Baps5wl+a7+Mfff+iy5+GLX5+s0Nvl4rl/3cx7z+/j9//pM1RMiNJdS8yuKuRbf/zoeRUL//63L3B8dxt//K9fxFc0tcLzxSCJ2bnmPwtEUaCiyMOffG4TX//UhmtzDEHAap5ZZUausGe0czLbczEUWBws8c5cZf1S0A2DfzzxNsEraPy+XJwKDvFSzxE+V7f6ikqocuZoGIaBoRvoupHlHRNAEEVEUZhkxzBbTVgdZiT5fKNKy2gIE+tl96FPNgvrmj4RERfQNZ0z9COCKCBOGGeGbmAYBqJ0liZU13UM3Ti7X8NA14wLts/1y1oQBCRJQJJETIpMfUU+tWV5PLRhHrphEE+m6RsO0tnvZ2gsxGgwhj8YZSwYJRhJkEhnS5FSaW3SIVFlCVWRsy8zNftCM6syDqsZn8uK12XD57Lic9nwuWzku224nVZURZowSLLjEhAmXt7CVb/EJUXC5raeY+QavL7tJKFwnMfuWpz7SKQx8ZsaYDWrlz1+h9WEzazitJlw2MzXxIgJ+SOMDQZRVJnZS6pYtmEOjYurKK8tyDJBSSLixDOBwBXfe8lEmmgkOXn/nwsBgWg4QXKa2npJFHFYc0Nbd6WQpXJkqYyradybnHMMA4yJZ1rMnQF+3rOsyjjtxcyuLkLXswqr/mCU9r4xugcDDAcijEx8xiNxEskM8USajKaR0fWJOVBEFrNOmc1iwm5VcdrM+Jw2fG4bhR47JQUuygs85HlsSGLWaRfO/P0Q01RnnIUrdRiuFE6HhfqaAkYDEdq6Rqmryr3QnnAZhkhff4B4PE1tzfmlGOfOe+Fwgv6RAD6vHbvdPDnffhDVFfnIssjwSIhwJEl3j5+SIhcVZd5s74TDTHlJtjejtX0Yvz+K12OnIC9bBqlpOr39AfyBKHU1+cyZVYLbdSF1r98fRb+EAyCKAsWFLpYsrGTfgU72HexkxdIaTrcN4w/EWLmshjmzSi567UVRxHYRJ+TDRCqRJhpOZG2Dy4SuGwz3jDHYM8rCtZdm+4NsBYPNcv7caRJFZN3AYck+zx9helyvueVa4oC/e1r6bZusMstZSH4OGJwgO++81neMA/5upif+PguB7HtEPGfu0jEmRAanh47BrzoPcGdpEw7FfNnB5Jw5GtFgjJ0v7+e1H7zDUPcoVruZNQ8s5+7fuI2Cct+02/71E/+PjU+sZcXdi3jh22/y8nff5h82/w98JR7+4pF/4J7f2IjVZeEXf/8i3c39iJJA04oGPvHHD5BX4uGNf3+XE7ta+Pp3v4zVYcEwDPa9eYR3f7mTWx5ewbK7FrDz5QO88r236TrZh6LKLNzQxMe/fh+ldVNHfXOBM5O0JAmcidWosoSr2syc6rOCasYF/zOTnZ/3Z9rjXwqarpNIZzAAiyLPyKg590Wr6QahSILO3jFS6QyxeApBBFXJlmMZBqQzGpIooOkGmqaDkI0CimLWCUxltKwjSHYiUhUJURSzD8NE9OjWZXVsWNFwnpOQyWQNOVEQshFKI/syUGTp7L7TGgU+B//1Cxsxm5Tzto8n08iSmM1A6AaiAMrkuI3JsWsTTg6QXUeWkD/AnCXJEqpJRtMMju1pp6d1mMqGImrmlFLTVErdvDJ8RW4UNXtukiROlpVdjjHldFuJR5O8//pR8gqc2JzZl1s0nGDbW8eJhOK43DeWjsYHkT3fqzMgw8E4bzx/gLdfOkQsmmTFukYe+tQqSiumn2+uBFlHnWzWToJ0yqDAback/2wE89znuLN1iB9/ewtPfnk9tY1FTHmul3iGf537mAxjYh74AAryHNyxbg7/8uP3eOqFfXzxidXkeezZgNE0+xMl8ZpkUvcd6OR06xCPPLSUaCSJx5PNMKgTfWYnTvbznX97h9bWYTweG1//vTtZML/8grkBQDXJVJXnMTyaZXWKJ1JUVeYhTwTeHHYzFWVeTrUO0tYxQmA8RlVlHvl5WeMkk9Emr5mqKlMeQ9cNdu/ruGQTvyAIuN1Wbl3TwK69bfT0+dm5p41jJ/swmxVqqwuoKMv9c3QjIRqKsfP1w/SeHpyxo/ERPsIHYRgGh/29067jVW0s8JbnbE6Pa2l+cHo78cylK4NEBAotTm4vmc2dJU3UOfORBJHhRJiD/m5e7T3GIX8vsUxyWhPUn4rySu8xShvcOJTLc6hz5mjIqkzl7DI+/d8foXJ2GacPdvDq97ew48V9PPCVO6a9wMU1hQSGg6QSabqb+6iaW85wzyjuAhd9pwepnldBOpXh7i9uZPaKOsKBKL/4+xd5+h9f5uvf+zINi2s4+v5JTu46zZLb56OlNXpO9SObZGrmV9B+tJttz+1m2V0L+a8/+ArRYIzQaAjndWDTmOo6CBf8z4eLgz0DfO2plxmLxnj2t5+ksSh/xkMxDIOdhzr4t6d30Ds4jm7ovLHjJCDwpUdX8+DG+Qz7w/zrL7cxr6GUjt4xdh3uQBAE/uzLdzK3rpj23lF+8uJeTrYPks7oVBR7+M3HVtNUX4KmGxw/3c/ffvdNxiNxFjSU8o0/fHDy+Ft2t/D2zlNUlnjZdaSTWDzF8vmVPHrHImrKfISjCb77qx28t6+VRDLN3//hgyyYdZZh5b/8z19w59o57D7SyenuETxOK0/es4Tb18wGw6BrIMAPntlJW/cowUicWDxFdZmPT96zlE2rz3853f7YcsprC2g+2EXLoW76u0ZoOdzNif0daBOOlK/ISd28cmYtrKJhYQWVDUWYLCqSLCErIrJy6UeydnYJq26bzXM/3s67rx8lr8iFKAqMDgZJxNPc/+RK6ptKZ/gLXn9cTgnXuc/PiUPdNB/p4ZHPrGHZmnpEScBqu/aZGsMweOP5A2iazn2fWDGZvTv3OZZlEYfTPOlU5gppTSOZ0dANA1EQMMsSsiRlgwWZDLqejU5ZFRlJFNEMg0Q6TTbpY2BXTeiGQTKTwWZSs1nWVBqTIiMA8XQGSRDI6DqSKGJR5Il9nz2mSZZQJo6ZPOOEkw1SABP8+tnzTqQzCAKo0vkEEKm0xhtbj19wfoIgkMnoLJlfwZZtzRxt7mPxvHIqS71Yp1E3X7awiuIphOeuGobB21uO8/72FjIZDavVxEP3L+bejy3A6bTw3rZTOB0WvvY7t7P13WbeePsY5eVeCvLPJ2MQBAFJFKirzaen38+e/Z2YTQp11WepYx12M+VlXvYf7qK7Z4xINInPY59sBFdVBYfDjKpIDAyOMzIaJp3RkCUJMMhkdLp7x3j+lQPEYpc2QMwmhYa6Iqoq8vAHouzY00pb+zBlJR7mzi654fpjdE0nmUiTTmXAMFBMMhjGee8qwzAmWA8zExUQIMoiJrOaXX9iP4lYksHOUU7t70BWZYJj2X4WSZYwWVQUVcYwDDJpjXQyS3aSLecTUEwy6gfY3QRBIJPWiARjaBkdURIwW9TJ/rkz2ddUIk06ncGYCJ7JioRqVpHPKRvTdZ10MkM6mZnMTEmyhGpWkM8hUtF1g1Q8lT1X3chmXi0qsnr9yVYMwyCeSKOqZ4N26Uy2j0CdwTvuZkI0k6Q76p92HY/JxjxPbt7JhmHwUs8RuqOBS8amFUFioa+cP5iziQXesvOWVdl9VNl93Fkyh+e6D/Hj1l30xqbf55t9x3mieil22XRZ91jOfnGTRaV6bjnxSJJMOkN+mRdPgYvA8Pglty2rL8Y/ME48kiARTVI3v5KeUwPkl/nQdZ2CCh+GbuDOd5JOpTFZVBoWV7P9hb0A5Jf7KKsv5ui2ZhbdNpeh7lFGesaonF1GfrmP4FgYURTJpDJkkmnyy7yU1hXm1AD4zwpRFLllSS1zaov48Qt7UBWJ3378lgteUuFIkrd3NPPYnYv47AMrGA2EqSjxIkkiTruZ21Y28ttP3IKqSPzbr3byzz97n2//j08gSyILZpXx/b9+kpe2HuXgyQsjByfaBnE7LPzVf7mbYCTBT17cw/v72ygpcOG0W/jDz2/knnVN/H8/eXfKc/jpy3v5w89vZF5DCW9sO8k//+w9ls2tRJJFXt56DFWR+cHfPkln7xg/fnEvS5vKL3AyAPJLPOSXeLjl3kVAViW842Q/LYe7aTncTWdzP9FQnGO72ji0rYVMWkNWZKpnF9OwoJJZS6pYf//iS15zp9vKg59ezeyFFezccpL+7jHAoH5OCSvWz2bOonJMH3KN6pXAMDLoehiDBIaRBi5R7iHYkKT8idKxBIP944iSiN1pRtM0TGYzkiwRDsWz6XmLOlleNu6PYjIrmC0K4/4oFqtKIp4mk9aQZBGr3TSpxaDrBqlkmngshaZly55MZgWrzUQqmcY/GqarbRhZkRgdDiEKYHNYMFsUDMMgEk5gtZv57Fc34fKcT2VoGAaxaJJEPI1hGKiqjM1hRpJEMhmNcDCO2aKQiKfRNB1FlbHaVJSJl/PhvkFePNrMWDSKw2TikYVNLK0o5eTQCC8eOclQOEJa1/nttcuZU1TAvq4+nj9ynEQ6Q1LT+Ot7bqdnfJyf7zvCNx68i2A8wT9s2canli3Eosh8892d1OR56fKPU+X18IVVS2gZHuWZQ8cZjcSwqgr3z5vNmpoKTo+M8dLRZnrGg2i6zmeXL0bH4EBPHw/Mm0OJy8FP9x3Ca7XysTkNmM8xMOKJFH//L29d8h4ZGAryylDwkuv9zZ88cG0cDcDtsnLHxrksXFDOieYBjp/oo6Y6n5XLawmMx6irLWTFslpsNhNPP7fvoka+KArU1RTwxubjHDicdTRqq882W9usKiXFbiLRJNFoCotFIf+cHixBgMa6IsrLvLR1jPDL5/YSj6eorysilcpw9HgvL756iIym4fXa8I9dmm7d57WxZmUdv3puH4eP9hKOxFkwr5zZjSW5uXg5gq4bdJzs46Xvb+Xw+6eQZJEFaxv5YHQuGU/z0ve3su/towz1+MmkNfJK3Nz16VvY9ImVyIrE2GCQ//i7lzi+6zTDfQFEUWD/5qzTW91UxmO/eweL1s0mk9Y4+O5JtvxqNx0n+oiF4phtJpZtmsu9X1hPcWXeZEZalES2v3yQXW8cob9jBIfbyn1fvJU7nliDYpIxDBjuC/DCd7dwYncrgZEQoihSPaeUuz+3jsXr5yDJWYN8pDfAlqd3s+fNo/iHgkiySFFlPvd94VYWrZ+D2aqi6wb97cO89P2tHHq/mWgwhrfIzd2fWcutDy/HbL24U/5hIBZP8c0fbeXBOxYwq66IWDzFweM9GMAty+qu27iuBdrCI9P2Z8iCSJHFSanVnZPjxbU0z3YdvGQ2QxZEFvnK+fP5d1PnvDipg1U28UT1MhRR4nst2+iLjV903Z5YgOPj/eSbHSiXQTefM0cjHk5waOtx3n9uD2P9AVLxFCN9fm57fPVkJOBiKKsvpuNoD70tA7jyHNTMr+Tk3la8RW6q55Zj6AanD3bw+g+3MtA5QiqeIuwP4/DY0XUdX4mHmgWVbH9hL6P9AfrbhtAyGg1LqhEEgeqmcpbesYBXvr+Fg1uOseJji1l25wIKq/JmFEH+CFePjKazYn4VS+ZW4LSZyTvHAMvz2Fk4SyGRTJNIZlgwq5R3952+5H1zBuXFHtYtq6OmPA/DMNh1OJ+x8QjhaGJGdaDrl9WzfF4lJlXm3vVz+ffnd9M9GKC8yMOoP0JdZT4mRcZhM1PgsRMIzqz5yuW1s3BNAwvXZPUsUok0fe3DtJ3oo/14H+0n++g6NcipQ92c3N/Jm7/YNSNHA7Iq4POXVTN/WfWM1r/RYBgZEqm9jIe/RSK5G8OIXGILCbv1EQq8/8SRfR388ofbGOjxE4smaT7ai6rKPPjkKm6/byE//P/ewumycv/jK/DmZ2mB/9fXn+LWu+Zy+/2L+LPf/jEb713AkX2d9HWPYXdauPuRJWy6dyGCKBAaj/LOq0fYtuUkQX8Ui0XNlmV9ehVH93fyyx9uo6cjKzi4570speGnfnsD6++aRzSS5Gff3crebacJjcf5X9/6FI3naBQEx2P88gfvc3RfJ4lEiqr6Qh799Boa5pbS3+Pn7/7kV6zd1MShPe0ExiKUVeVx9yNLWbyqDkGA2YX5VHhcCILAj/ccpMMfYGF5CS8dbWZuSSFfn70Wk5yd09Kaxg927ecrt6xgXknhJOFBb/B8w/3chJIkiswrLuIrt6ycWGZQm+flK7esQBAEnjl0nNMjo6ysLufN5lbKPC5+59ZVk05ERtfZfKoNfyyOz2alZXiU3711NaYPlhmKIpVluaOevVjT8lVDEJg/t4z771tIns9BfX0RY2MRAoFoNuKd0bLN+aJAcZE7q2SemZoBRhQF6msK0XWD8fE4VRU2qqvyzlku4nZZ8bht9PUHKC50Ujhx/57BssVVdHSPEo0maW0f4Rv/9AZgIIrZPqI8n52vfvk29h3o5OU3jlzy9JwOC4sXVPLia4cJheJ43FYa64vw3GCll8HRMM9++226mvu5/zc2UFJTwKH3mtn95hES5zh2iiox1DPKstvnUTW7hExG55Ufvsu//+3z1M0vp25+Ba48O49/7S56Ti/gmW9vxpPv4Mk/uhcA1aTgystec0kSiQRjFJT5WHXXAmxuKwffPcnWZ/eSV+LhzifXYJsg6Rjp97PjtUN87DO34Mlzsv2VA/zgr54jr9jDijvnZ8t1DYN4JMGdT66hsMLHUK+fN366nee/u4XS2gJKqgvQMjrvvbCP7S8fYN2Dy2hYWEkkGOP0oS5sLiuKmn2OAsNB/uVPnyIajPPgb2bL0w+918z3/vIZJEVm0ydWXtb1TWc0orEU6XQm24diNWFSZSLRJJqmo0/c6w67GdM0YqChSIJEMk0qlZks89N0HU3TaawtwnqO8GYqlSESy+5fFLMkEGZTVrsnkUyRyWTLma1mBZvVRDSWLe3RtOz+3E4rsiwSiiRIpbMZJ7NJwW41EYrEs710mo6iSGQ0HafdfE2yKR3hMaZLytsVM7NcRTkr7Xxn8BQ9Mf+0vRkCUGHz8uWGddM6GWcgCiIfK51Le3iUpzsPENMu7sRsH25nVX7tpNL8TJCTq24YBm2Hu3jum6+x6r6lfPWfPk88Eufnf/fCjLYvqS0i7I/QfqSLktoiqudXsvmp7XgKXFTPqyAZT/Gt3/sRs1fU86f/8VVUs8KWn2/n7Z+8D4CiypQ3lOD0Ojjy7gni0QSKSaF6XlZ9UVZlbntiDQvWz2HvG4fZ/LNtnNjdwqf+7GEqZt08JSY3Owp8DqxTGP4dvWO8+u5xegYDJFMZIvHkRE+EwUzqyRxWE15n9sUoCAKKLKHr2VKCmaCs0H2WtEAQUBSJRDKNqkiUFblp6RymvWeUgZEQoWiCeY2XvmcMw0DLaKQmUuCpZDpLcZvRsdjM+IrdGBjYnBaO72kn5I+iXkL9+Nx9p5IZIqE4yUR6soflXHjy7NhuYNXwdKaNYf9/QdMGEUU3olgKGGjaAKLgQBQdGEYKXQ+BIKMqczCrSwBYtraBZWsb2PzyIQ7tbudjjyxlzsLLU1p96Re7+d3/fj+1jcW889pRfvKvW1m6ph6b3cz+HW28+cJBnvjSrcxdUkU8miSVzGCzm1l56ywa5pTy0+9uxe2z8eRvbjgve+dwWvit/3o36+/q5TvfePW8YxqGwdM/2kb7qUF+/68exOm28voz+/iXb7zKX//zpwEIjEU5dqCT3/j9OzFZFF7+xR7eeP4AjXNLUW0qOzq62dXRjVVVOdjbT6HdRjyVQhDAaTahSNnJ3wAiyRQmWcJtOdu8l32iBDRDn+hBMkhkztbzW1WFKt9Znve0prGvu4+tpzuwqgonBodZXllGPJUGDFxmE6p89piSKLK8soyOMT/+WIym4kKc5gvT7A67mZ988wuX9ZtdD5hMWZruRCJDJqORSKSJxpL4A1GGR8IkJ8pbDIxL6k0IgkBFuY/a6mxjeWN90aTQ4Bl4PTYWzi1DUSTq64ooLnKft9xiUXn84WXUVOWzbedpunv9aJqO02Ghsb6QOzc2UVrsIZPROXlqAJ/XNn1viyiQn+egqbGEnXvbKC32MG9O2TRbXB+c2NNKd8sAH/v0Wm5/YjWqWWHJhjmM9gc4ua99cj1JlvjqNz45+W9DNyiqzOMP7/17Wo90Uze/AtWkUFJTQDqdwWI3YXNbp2SsEiWR2x5dcd53sxZX031qgO5TA8TC8UlHI5VI8xt/8QgNi6uQJJEFaxto3tfByz94d8LRECiqzONr/+/Tk/tKJdNIkshL399Kf8cIJdUFpJNp4pEkngIXtfPKqVtQgcVmYu195wegtr98kP72Eb7690+wYO0sZEVi8frZtB3t5vl/3cxtj61AkmZm2Oq6TlvnCFt3teAfj6KqMuuW17N4XgVvvHeCoZEQkiwyFojy4B0LaKwtzOpvfADptMZPn9/D8GgYp92MfzwGQDye4q33T7L/WA8rF1dz/6asrEHPQIDXt54gEIwiSiLrltexZmktew51cOBYT1bTpWeM21Y3cPdtc3nm1YMYQCqdwT8e43OPrqSkyM1r7xyns3eMRDJNvtfOFz6xmp88t4d0WqN/aJzKMh+BYIwH71wwo3f35aI3Ps507dR2WaXWkRtdj6SW4ZWeo0TSyWnXs8kmNpbMYlXBzMULHYqZDUWNHPL3cCTQd9H19o52ktTS2OSZZ81y5mhExqNomk7T6gZMFoXu5j6Gu0epmsGk5Stxo2ka3c39rLpvCb5iN+lEmr7WQRZtaCKdzDDQOcJn//IxHG4bo/1+Oo+fX0JTUJFHaV0hu149SFVTGVVzy7FNGJ+pRIp0SsPhsbPpU7dQWJnHf/z1M4z2Bz5yNHKE82+3qR0EeQqWL8Mw+PbP38dpM/EHn7uNQp+TnYc6+OvvvDbjY0uicBmiUheOS1GkKZWDrRaVtYtr+bvvv8U//vs7+NxWls2tZO3iqR/eeDRJJBgjmUiTiKUYGwzS3zlCf8cIfROfscFxNE1HNSmYLSomi4LDbaW4Io/q2TMrV0jG0xze086Wlw/R3z1GKpm5IKLy+a/dzqrb5sxof9cD4ejP0bQxZLkCp+1zmJT5GEaKIf+XsFnuxmF7gnSmm1j8VTTdj8f5dcymVTk7/urb5jB/STWSLLLhrnn86ofvM9Djp6DEzb4dp1m8qo51d8zNruzLTS9XMpHmvTeP8+Wv30VlbQGiKHDv4yvYtuUkB3a2Ut1YhMNlYc3GOdTPKcEwDBYsq+aVp/fiH41gFq30jgep9HlYU1NJLJVCEkWsqorTbKY/GKZ1ZAxFkihw2HCYTTjMKq0jY+gT2cFStxOXxUwkmaLTH2A8nmQ0EjtnlMJ5z0IomaI7EKTY6WBjYy0CWWfCoiq4zGaGwhFOD4+iyjJ5dis2VWV1dQU/2nOAo/1DPLZoHtabmL6zIM9BJJrktTeOMK+plFMtg/T1BxgeCdHZOcrQcIj8PAd+f5S+vgAW89RN2nBWv+IH3/r8RY9XWe7jj772sWnHZDIp3LKqnltW1V90nXWrG1i3uuGS53cmIJJIplEUiYpy7w2pnTE6EJwQhPVOBmRkRaKsrpCOE2dtgawtEiM8HiWVSKNldCLjMURJJBZOXNYxDcMgGU8RDkQnSsI1dD1LW56Mp9DOCWS5fA7yStyT7yFREpm1tJr9W05k+zGEbG9IyB8lGoqTTqXRNYNoKI6hZ48DYLaZmLW0mpP72nn6n99gwdpGmlbUUVpbiNNrn+zl6Djeh2pSSERTdJw4axR6Cpwc391GMpHCOkMGrGgsxfGWARRF5tMPr2T7vjZOdw5TW5k1ji1mhbtubaK4cPrSxN7BAKfbh/n7P3+YSDTJn33jRQDsNjObbpl9Xh+KYRgUFTh58M4FCILA5u3NtHePsmpxDcNjYSpKvSyeW84b751gVl0R7glbzmZReeRji/BM9C3phsGtK+tZZ9SjaTp/9LfP8vmPr0aRJeY2lFBS6EYA5jaU0N49ek0cjcF4cNrsglVWqbLnhljh+HgfzaHBacX5RAQqbF4eq1xy2fuf7yml0VVEc3DwotocXVE/gVQcj2n6IMa5yImjIYoirjwHVoeFQ+8cJxKIcnJPK4Mdw1Q1lQNZwb6xgXG6TvQRCUTpbR2kZX87eaVeXHkO3AUuuk70UlDuQ1FlvEVuOo/18OBX7sRkUalqKmf/20dRTAq9LQO0HurkXPogp89Oxewy3nt2D74SN+sePhuJ6Gsbov1INyaLitlm4tTeVvJKvTi9uZWCv1khTP7nyiFJIooikUxlaT1NJnmiAXJ6B0DTdHoHA3zqvmX43DbiyTQn2gaubjDTYuaNx7pu4A/GkCWJzz64HKtZRZElgpE4eW77BX0oh3ecZvMze+nvHGGoe4xoJIEAKCYFizV775XVFmJzWiiq8FFRV0j5xKe4Mu8C2ueLobN1iBd/totIKM6cRZXYnReqtBaUeKbc9kZBMnUQyOBx/BF260PZZkptAEEwIYpezKblmE3LMKkLCYT+lvHIt/HJpajyzCM05+KDDefFE5SiwgR9rKxIJJPZno1IKM7cRbnjOz+DcX+UVDJNUZlnktlNkSV8BQ6GB4NUNxahqBJ5Ey/0LL1u1jlPpzMUWyxUej1sa+vCH4ujyjIVHjeSIHDv3EZeP3man+8/gmEYfGLxPBoK8vjciiW8cPQkOzq60Q2Dr65bSZnbSVNRAT/afRCX2cSKqnIsioxJlil3OyczFAAus4lqn4fNp9p44ehJdANqfV5EQWDTrDrePNnKU/uPYAAPzp/N3JIibCYVq6KgmQ3y7NacKZZfD9TXFbF4QSWvv3WUd99vxmxW2bhhDg67mWMnelm5vJbePj8vv3qY7p4xSoo92Kw3fn/UGWiaTu/AOM0tA+T7HMxvKsNkuvHKidPJNKIgICnn30uKST6v13JsYJy3f7GT9mO9RIIxUomJTHLq8gXNoqE4B7ae4MDWk/gHx0nEUmTSGr2nB1m84fwgjqLK59kjkO1bTSXTkyVEnSf72PL0bgY6R4iHk6RTGYJj4fMMcEEQWLqxiYIyHzteOcih906x7aWDzFley51PrqFqdgmSLJGMp/APBfnB/3wW+QMaRqU1BaQTaZiho5FIZfAHoxw71U8oHMfAoLGmaPJ0CvOdOOyXJtkYD8Xxuq0IZOct7zTld+mMxvFT/Wzf147LYaG5fYiGCSrrfK+DA8e6GRoJUVnmpaLkbIllWbEbyzmZ/1A4zi9f3o/VoiIKIrF4Cl3PavDY7SZCkQTyBAOlNsMKh8vFaCIyrVlhlU2UWK++f0w3dN7oO0koNb3DbJVVbi+ZTZnt8m0Ai6wy113CjuG2i/Zq6IZOS2iISrsXcYZ9GjmbUSpml7LxybVsf24vp/a1seDWOdz3W7ejmrKTbufxXt791S6GukeQVZmDW45xctdpbntiDUs2zadpVQPpZBqnz4Eoicy/dQ7hQISCyjwUk8Jn/uJRXv3eZn71f1+mZl4FT/zJAxx4++hk9E0QBPJKvZTUFGKxW86jrZUVicHOYU4f6EBLa+SVern7i7dRfpnerW4YhBNJhkIRIskUGU2b8v6SRZESt5Ni1/n1tRlNxx+LMRaJEU+l0QwDWRRxmk0UuhzYVOWiqSjDMIin0wyFIoTiSVKallUyVmS8Nis+u3WybGIqJDMZRsNR/NE4iUwGSRRwmEwUux2T1J1XA6tFpbzQze4jnWzZ1UKe105lqfc8+s+pIMsSs2qKONLST57HTiiapLl9aPI6pDMafUPjDI6G6BsKEozEOdTci91quuS+AcbGowz7w7R0DhOJJTndNYIgCBTnO/G6pnc0k6kMQ2MhAH747C4EQUCWRWZVF/HxuxaR9wHl0dajPezZfByLzYTTa6eowofNZaWg1ENZTQFltdlPcUUeiunida6XPKfhEOl0hie/chvL1zVe0T6uNzRtCBCwWtaf862AIKgYRnTy34pcjtX8Mfyh/0Ms/jaq4zcvuW9xQjfnjHOR/v+z99dhdqX5dS/+2XiYTzGjSszQLamZu2d6mD1jz9hjjB07TnLjOMnNzS9xkvtLYocMseN4xvZ4eHpmmlmtbrUYSypmpsO46f5xSiUolqrUatvreVotnbP3u+FseNcX1srrhRKX68iGqkoLK84KBX+d7FLyoMLsqtbVP1b2OyqqjCiJZDL52VULHiD5rIY6O7kTZkv3FoIqSzzUXM9DzfPJVnXAz9fv3Tvv8+biMP/44cPzPv/1B+5dcBtfvWfPjfssSRysr+Fg/XziVeHz8nMHbizpiGWyjCeSTKfSHKyvwfUhzmYAeDx2nnpyG7t31TA+HicYcFFREcBmU3jmqe0AHDnaziuvtSLJIo8+shn/XdbfsBgsy2JqJsV7x7vI5XWqK4Ps2Lq6EsQ7BbffiWGapGc9M0RJLPS6TCYKKlSzeO5/vcmb3z/O537zKbYf2oAv5CYymeC3nvwP88Ys+EoJcwpQN+PCux388A9fp6aljM/8+hOU1RUhSRL/6df/Yt6yqXiGbCo311doWRYTQzMEin3IikRkMs63f/8lRnon+MI/epqGrVU43Hbef/kCP/zD124YSxRFajeWU7uxnKd+9j5OvHqRH/7h69idNoIlDxMo9hIo9lJcGeRzv/UU5XXzPV5c3pWbqnlcNuqrwzjtKnu312ABwVkvrsJ5uvbnUigv8ZFM52jrGcM0LGKJQi9jLq8zODLD5HQSURQYGY+hKCJjk3F8HgeH9jYU1NNkqVDKmdMoDnvY2lJRkKMXheuMIW/cj9GJGNFYmkcOtWABrxy5PLfc7c9qVoZoPrNo6ZQsiARVJy759tUQJ7NJTk73Ldk/IQBlDh/PVG295e00eooJ29xLNoX3JqYwLRO4w0TD7Xfx4Gfu5cHPLPwC23bfRrbdt3HR9R/87L08+Nlr6z78+YM8/PmD19Y/1MK2m7Su9z2+Y9aIz0TL68yMRXEHXGw5uAHpupd1VXM5X/xnH7/VQwMKDU2DkRivtHbyVnsPQ5E4OcMgp2nkZpv/JFHAqaqUeFx85d7dfGr3lrn1U7k8l0cmeL2tm+O9gwxH42Q1HY9dpak4zCMbG7ivqY6KgHdeBNAwTcbiSd7t6uP1K920jU0Ry2SQRJEyn4e9tZU8sqmRreUleB3zoxjJbI7TAyO8eLGdU/3DTCZT2GSZpuIQT23dQKXftyoLj4WgyBL7ttWQyuQ5frEfRZF45v7NlBf5sKkyGxtKCPldCxrlfeVj+3nu9Qv86PULlIS9/MKnD/KtF08jCJDL6bx3tpfW7lFyOR23w8Z3XjpDXWWYJw9vIuR30VxbcoOJUmWJH7/XgV2V6R2e5u2TXYxPJygKujlxsZ8LHSM8fmgjezY72NpcTknIgzB7ykVJYPemKrwuO71D0zz/diu//dWHaagKY5oW59qG+PGbF7nUOcoD+24sXaisL+aBZ3dTUVdU+K++mJKq4JorgJimhcNpIxj2LL/wXQoLCwEFgeujvyKi4MIwp+c+EQQbklSCZWnoes/8gRaAy2MnEcuQTGRxex10XhkhlcyuKJlld6hU1YTpbB1majyON+DE0E0MoyBtKkoisiyhqDKZVJ5UMofdoSKuoHwvEHJRXRfmwsleauqLUG0Kgz2TxKMZGlvW39X4TmA4Fuetzh5KPG5aSormGtM/zFAVmarKEFWL+Ercd2gDe3bVFq4L5YOXFl0MlmWRzWoYZkESNp7McvxkD0ePdRIMuNi9s5bSZcpjPihUNpai2lSunOylqrkMb8DFzHiM/rZR8tcFBYa7xgkUedl5Xwu+kIdUPMO5I20LmpvKqoTDbSM6mWCsfwqHy4YgCthdNlSbQnQyjq4bNO2opXpDGYZu0Hm+n/h0Yt5EPpvOceqNVmRFwu60MT44TceZPg4/Wyhf0fM644PTVDSUsGl/A5IkMjkcoefi4A2Z8auZCl0zcHkdiJLIxj31vFd9di5DA7DtUDMX3usgNp2gYUslTq8DQzdJxzNIsjhneLwS2G0KW1sqyGQ1Xn+3HQTYu62GoM9JaZEXmyqvqDS5JOzlwXuaef2dNvw+J/t21M41cZ++OEgskUEUBC62DbFney21lWGGx6K8f6YXVZFoqismlsiQy+sFAnyqh8npBLu3VXPfviYqy/wE/c4bzldNRYjKsgDvne7B67bz4L3NSJJIVXkAr9uOFvIgSSIOu7KK8urVIaFlFn21KKJEkX1t3tNHJ7oK2ZMlYJMUDpU0UuG89YqGWneQoG3pYMlQOoKxCln6u/ItcP5oO6XVYYoqA8tK0Go5neGuUTrP9tFzvh9v0E3L3oY13R/LsohlsvzZ0VM8d+4KVQEv9zZUE3A5iGVytI1N0jk+hddh4576avbVVrKj6trEIatpvNPVx5++c5L+6Sjlfi/bK0tRJImMpjE4E+P/ffkdroxO8Iv37acy6Jtr3jQti4lEiv/97il+cLoVj8NGbSiAy1aMaZpMp9K8eKmDMwMjfPXgbh7Z2IjLdm3yphkG73YP8EdvH6dvKkJV0Me+2kpkUSSZy/O/j55if30VWU2fd9yrRVmRjy88s2fe5wGvk1/8zKFF12uoCvNbP/vQDZ/96199CgC307bgmFdRWeJn9+Ybo3BPHL6W1g76XfO+vx6/+ZUbt2tXFf7Vrz6Fphu8e7YHa/a4ZEkiZ+rohoksSQtGne9/dhf3P7tr3ScagbAbt9dBb8cYFbUhHB+wy/etQJLC6MYQhjmFLBUySwISohhG14cwzcRsQ7g1+52AZa2sxnrDlkpef/48775+meIyP93thVK8hSYbN8PttbP//g38zZ8d4affOUFlbbigtuJzsPveRuySitNlo6ouzLkTvbzx/HmCYQ+NG8soqwwyMRpldDhC95VRkvEsrWcHSMQyVNUVES7x8uwXDvDCd0/hcttxuFRazwywbU8tLVsrGR5cWov9w4BNpcVsKr37avxvFZZlkUhmGR+Pk0xmMYz51diNDcUE/Hd/Ga6um5w518/QSAQEgaHhGd470U1eMziwt54HDjUvGAi6G9C4rZo9D2/m2AvniM8kKaoIEJmIIykiquNa1mzH/S288I13eOEv3iFY4iUVzzLaN0lRxfyJlyfgomV3HW989wTf+a8vEyr1ESr1s/3wBspqi6hsLKWoPMDZt6+QiCSxrEJp1lW56+tR0VDC6TcuMz0SRbbJtJ/upbgqxBNfKrz37E4bWw40cun9Tn76Z29hd9qIzSQZ65+aU7kCSERSvPvTs/S1jRAs8aHYZGZGY+QyGpsPNOKd7RfbcbiF7guDvPfCOfrbRvAG3eiaQWwqQe2mCp79hQdXdX6LQx6eeXh+FPzgntXNpR6/fzOP37953udf+dR8FayQ38W2jTdWlXT1TZLN6WxuLqOsxMfljtE5M96FxnU6VL72uYPzPn/ygcKyTXdAlDFrLJ79lkUJn7ry7NJiyBk67050E9eWVrz0Kw6eqtyy5DLLwac68SlOJEFcVLZ3NBO7oUJgOdyVRONP/vm3eeZrD/LI5+5BVJeeHOiaznDXOBeOXKG6pZyDz+7FvcYPfcOyuDI6yXPnLuN3Ovil+/fzxJZmFEkip+kc7ern919/l3gmy46qMj69Z9sND+wro5N86/h5eiZnuK+5jk/t2sK2ylKcqspMKs07XX1889hZnjt/Bb/Twa89WJCLFAQBTTf46YU2vnPyIqU+N5/ft51HNzZR6nOj6QZt41P86OxlXr7cwV8dP0fI7eRgQ83cZHdgJspz5y7TOTHN/roqvnRgB3tqKrArMqPRBM9fbOdvTp4nnVve4OnvEiRRpKo0QMDj4IW3L1EU9JDJanQPThLyuWiqnq8icacimf6gG0WRePW5M6STWYrL/fMiWPUbSudq/e9GqMomcvmLZHMncDsLZTmCoGJTWkhlXiSdfQWH7UEsK0dea8W0sojijSZoxeV+NmytxHtTqcrOA/XkchqtZ/uZHIuxbU8dgZCbqtowkiSy+95GSisCc/eorEjsv28DgZAbWZZo3lzBp79yiBNHOzjzfjcOp8rWXTVz5F9WJHbf24SuGXS3jzHUN0W4xEtZZZCxkSin3+1kejJBQ0sp3e2jDPZNcf/jEsGwm/33bcAwLM4e6yKX06ltLOHhZ7YjyRJOl41dBxoIXNd8Hir2snV3LW7P7b+s/h6rRyye4ei7HRx9r5NYPDNnoHY9fvkXHvxwEA3D4PiZPl567SKaZiBLIgG/k0MHGvnYMzvnmQzeTbA7VR793AF8ITdtZ3qJTMZp2VPHoY/spPV491yG4eHPHECURLouDBKdTlBZX8IXfvtpTr/RSvlNTe5un5NDz+xCEEV6Lg0y1j+F/bqgzYbdtTzz1fs589YVhron8AZcHPrILrbc04Se17HP9uI0bKmiYWsVxZVBTr1xmcnBaeo2VXDoo7uo3lAIOLq8Dj7y8w/gCbgY6Z1EViSad9Zyz5Pb6bs8TLisQIRcPie1myqITiWYGYuBAP6Qh2d/4UE2H2jCMWtIanOofOJXHqGmpZzW412M9k1isysUVwXZcs+H16eivMTH6ISPvsFpxqfiKLJMS0MpXs9drKC4SNM0FEqn3MrtBwJ7kpN0xicWbdC+uq0tgQo2+koXXWYlEAWBoM2FXVJI6QurW83k00sqbd0MwVqNLe8dwq/e///MEQ1F/eC5UFbT+T/vnea/vv4eWypK+ObXPnNDScBINM6fHDnJd05f4LN7tvE7Tz0w1y+R13X++MgJvnHsLPVFQf7xY4fZVVMxT1P5WyfO8/uvvQsCfPOrn6axOIwAjMUT/MyffZdIOsNn927jtx45OOsEew0d41P8wevvcbSrj8/u2cqvPXjPXAnVd05d5I/ePo5hmvyzJx/g4Y0NN/RyZDWNf/L9l3irvQfNMOecwddK8/nDjHxe53z7MCcu9pNIZbHbFGrKg+zdUkPlTbKTdxKXz/bz13/0JmNDEXTdwOFUUW3KDeWrX/zlu7t/I5l+nsnIb+B2foKw/98jCCKWlSedfY3Jmd9Elitw2h/HsjKks29iWRn83t/A6/riB73rf4+/Qzh1upe//s5x3C4bmzdVFBpRb3o07t1dN0+G9m6EphkcO9nNhUtDpDN5bKpMbXWI3TtrqCxfO0+Tv8ff41YRjaYZGJpGQKCmOoR3Fb0m64me3smCgWbYM09V7uAL/5FIPr3gekHVxdebD/HlxttTTPxfHe/wf7qOLbodAJes8v/b+TEer7h9tck/7TjKX3QfYzq3sOlnkd3DTx76FbwrzNbc1iw+n9UY6Z0gOpkgGUtjd6qEywIMdIziD3to2lGDIAoMdY0z0jNONpXH5lSp21RBWW0Rik3BNE16W4cYaBvFMAyqmsowDXPuWW5ZFt0XBxhoG0XXDUKlfpp31eJZIIIU06boT10hZyz8YzhlD1XODXiV1UqNWeQ0HUEQUGUZ9aaJviyK2BUJyyqUKummOTeZn0ym6ZmcIZnLs7e2ktpwYMFJ/KObGvnzd08zGIlxoneI+nAIQYCO8WmGo3GKPS4e39Q0j2QAVAV97K2t5O32HronZxiYibGlwo5umPRPR5hKprinvpqakH9ew7hdUdhXV8X7PYNoxtLazH/XoKoye7fWsHfr2isQ3Q58Qfey0rXl1Wsjp7decNjuxe38BDZ1B3MzN0HBpu7BaX+UVPZFoonfL3wseHA7nsZhm9/UvJa4asAWi2WIxTMkUzmymTyZrIamGXNGURYgSYWeDFmSkGURm03B6VRxOVScThtutw2HQ123uuC1wNWG+WgsQySSIpnMkkrnyWbzaFqhL8W0Cr1nsixhs8lzx1YwlnOiLmHe9bcBk1MJFEXiUx/fw7atVR/07twWFEVaseztUjAMk3g8QySaJp7IkExmyeUKPiNXvYtkWZy7ZlwuGy6XDa/bQSDgxLYCE9U7Dcuy0DSDWDxDPJ4hmcyRzuTJzZrP6YY551dUuO9FZFlEkiRUVcLpVHG77LhdNlxu26yBpHDXlqItBsuyyOV0ItEUsVjht02n8+TyOrphYOgFRSdJFlFkCYddxelS8XoKv63P60QQbj27n87kudQ6RCSS5tGHN981ROPY8W7KSn149znmEQ1JWN9nfCyf4fT0AAlt8dLhQhO4nwNFtWuyTYesIC+hKJXSc6vq670topFN5Tj5ygWunOohUORlYmiGmpZyopMJJEVCtSmU1ITpvtBP1/kBAOIzKTrO9PHEzxyiqrmM4c5xfvpnb5FJZvEEXPS2DpGMpuYOYqBthB//yRsoNgWBArkZ6hzjqZ+9D+WmB9ZkdojXx/+amfzYgvtbaq/lybKvrppoSKJIfVEh4jOdTNE+NsmG0oIU29VG7c6JaVyqQtjtuoGITCVTRNOFC6TM58FrXziNFna7CLgcDEfjdE1MFyYBQM9koW5blWXqwgs3+DgUhSK3E4eqEElnmEoWWGhG04hlsmiGSbHXjWeRbVf6vch3QILyYqyb/tQYumUgCxKHi7bjU9bGo+BOIqVn6EoOIQkSm7y1iOv8oLkZFTUhKmrWnkiYlkUkX2g2C6huLMtaN2lSSQoQ9P0uouCZeykJCEhiEL/315HlcjS9j4LyVD0ux2Mo8tor4liWRTSaZmQ0yuhYjKmpBOOTcSYnE0RjaZLJLMlkluzsRErTCqnrqxMpRZFQFRmnU8XndeD1OvB5HQQCLorCHkIhN6Ggm1DITTDgmjOG/CBhGCajYzEGh2YYG4syMhZjdCxKJHJtcpHL6wXfAKtgRKcqMg6nMudcXVLipaIsQHmZn6qqIGWl/jlJ6zuJRCLDuQuDTE4mllyuuNjDPfsbV038BEHA6VBx3oJkbXfPBK2Xh5c0DrU7FLZsqqC6au3v58ttI3R0FhSAboaqSjQ1lbKhaWVlFpZlEYmkGByaYXA4wsDgNGNjMaZmXdLTmTz5nE5+NiCnKIV7w+lUCfhc+P1OQkEXZWV+iou9FIc9lJX5CQXdHxgZN02LZCrL8EiEsbEYY+NxJiZiTE4liUZTxBNZUukc2ayGpukYhoVlMXdsqiKhKDI2u1K45/1O/H4ngYCLoN9FUZGH4mIPRWEvXo/9A7/vF4NlWaTTeYZHIgwNRxgdizIyEmViMl4gk/E06UzhHGiaiSgWZLlVVcLttuPzOQmF3JSX+igrC1BVEaCyMkhR2DNPAn45lJf52bGtmos3+aSNT8QYGJgmk9Xw+Zw0N5XgsKu0Xh4mEk2haQYlxV42thTc4E+f7cPvcxKJpFAUmS2bK5icSjA0PINlWpgWVJT7qa0pIh5P0z8wTTSWxum0UVcbJhR0MzIaZWBwGtO0mJyMU1K8cGmhKi4+jTaxyJm31/96ITLEQGpmSe8MSRC5r7QJn7o2ineqKC1oyngVmrE6uejbrkvK5zREUeSRz9/LT/70TWJTCR79wr0ce/Ec/e0j1G2pZMs9zWw/3II36Kb1eDc/+qNXGeoep7KplHefP0N8JsnHf/lRalrKOfnqBV796/fmJOee//O3UG0Kn//Hz2CzK5x89SLf/x+vsP2+Fmo33hmzPVkU2VFVzvaqMtpGJ/nDt09wf3MdHrtKIpvnVP8Ql0cm2FRewoH66hsmZ5puoM3W9dpleckJvVNVEISCQtVV5cyCAy+IAjiWkIqUJQmbLKPpJvlZFay8YaAZy2/boSp3pFQqrqUYykzQFutjJDvFJm/th5JoxLQURybPYRMVNniq7zjRGO6f5tzx7iWX2b6vnsra8IrHNCyT85Fe2uLDOCSFh0q20Rof5N5wy/Ir3yIkcf6DWxBkVKWJgPe30Y1JQECSgojC2tboarpBf/8U7R1j9PRO0ts/Sf/ANJFIap754ULI5w3yy2jz2+2FVHtpiY/SUh/VlcGCelFVkJJi7x0nHZpm0NU9zuW2Edo7xujsGmdouFB+txRMs0CwUukcU1PXVE8kSaS4yENTQwnNzaVs2lhBU2MJrjVWWVsKM9E0P3zuNGdnA1mLYfeuWvbtqV/1pLaszI/DodDWPkppiR/3CvwEruL8xUH+7M+PkM4s3v8WDrn5ha/dvy5E4/iJbr71neNz5Ph6uF02Pv6x3csSDcuymJ5O0tYxxoWLg1xpG6FvYIpkcqnst4VhmGSzGolEoZH+KgRBwO9zUFERoK62iIa6ornr5k5BN0wmJ+NcaRuls2ucrp5x+gemmZ5OzmUtlkI+r5PP61xfVDJ40zKKIlFe5qeiIkBleYGQl5cHqKkOEQ6tfgK+HjBNi1gsTVvHKG3to7R3jtHdM8nMzNLnwTBmf9+cRjyRZWQ0OvedokjU1xbR2FjCppZyNraUU10VvC0ymc1qHD/ZQzajYbMrnL84iMOh0FBXTCKZJRpNk8vrHDnawb/4Zx8ln9d54aUL7NxeXTDFVWXyeZ3LV0Z4/0Q3e3bVMjYeY2hohlDQTVvHGO0do/h9Tjq6xkmnc2zbWsX7J7pJJDIFQ85ICn2B+wgKvhWLnmPLJGfcOtHQTYN3J7qXVZuySwqPl6+dQa8syEvOa3TLXFWPxm0TDVmVCZX6KaoIUlZbhK7pFFeFsDttZFM5JFksyMKd6yM+kyI6lWBqJFrQnDYtui8MUruxgqqmUjwBFwee2sk3/l3BUdI0TN5/6Twtexp4+ZvvAJCIpElE0vRdHr5jREMQBEq8bn7xvn386Tsneb2ti7ODI3jtNnTTREDgQH0VT23dwI6qGx/cqiyhzN5kOV1HNy0W62/P5DUsa5ZwAAjgmM3amBZkNH1RrwzdNMgbOookzm1PFsU50pM3jEXlyHRjdRfNreJAaDM7A808N3SEl8beX/ftrRfcsoO9wY1IgvSB9LKMD0d44yfnrn0wm/aPR9PkczrlNSGq6opWRTR00+CVsbM0e8q5FBtgb6iZdycvryvRWBwFPw1FXvv72zAM2jvHOXt+gMuXh2m9PEwsvrSSx60im9UKEeChQlbSbpOprAxRWxNm88Zynv3IznXZ7s0wTYuunnFOne7j7Pl+LrUOL+0TskJczYyMjsU4caqXxoZidu6oYf++ehrrS+5K47fVwulQyWQ0XnrlIkPDEcIh9w0GawD799RTVub/YHbwFqEbJpHIwvXXV5HLaXR0jfPO0Q5Onu5dESldDpZlEYmmiUTTXGodprjYy2c/te+OEA3TtIjG0pw83cuZs31cah1mbDy2InKxWmiaQf/ANP0DBalup0OlsjJIU2MJTY0lNDYUU1MVwu2+803OlgWpVI7WK0OcPN3HufMDDAxOL0hIVwtNM2jvHKO9c4z3jnWybWsVe3bVsXd3LSW3KE4yNZ2k9fIw5WUB/AHnXAa6sjyALEv4fAXJ2+9+/+QN5W21NWF27iiUPafTBWIcDrl58vGtnL84yJmz/YyORenoHGNiMkFpqY9sNs/oWJRg0M30dIK9e+rZvrWKvoFphEXIoV8tGOYudBUZlrlkydNyGExFuBQZWbQp+ypafKW03GYT+PUQhHmtaLeF234TiIKAPFunKyvSDfV5pmnRfXGQd350CoBQmf+amZZZSENmUlkcbvuc74XTbUe1ywhC4UWWiKRR7crcBeTyOfjIzz9IRcOdi4BAYdJe4fdiU2QqAz6e2NyMU1WwyTJht5PmkjB14cC8HoqQ24XfUagzHI0nSWRzhNzz01szqTQz6QymZVEbvupaDPXhQsmWZhj0TUfYVjH/YspqGtPJNOmchs9pnxvfqSp47TYkUWQqkSK1iLLUWDxxnSHO+kESJJyShF2y3TEznfWAV3FxIHR7EnK3g+r6Ij71c9fkgi0LdM0gMp3k7Pvd+IMufKt0vbewiORTPFiyjdbYYOHBedfJRNwehoZnOHa8mxMne7jYOkwud/uT7dUgm9Pp6h6nq3ucXE67I0QjGkvz/vFujhxt59yFQTJLRNdvB9mcxqXLw3R1T9B6ZZj7DjZz+GAzfr/rroje3ipi8UyhVj+vc/J0LzabjCxLNzy96muLPnxEQzeWJBqxWJoTp3p4+dVLXL4yQmYNiOlCUGSJcGj9s9q6btDROcYrr7dy8lTvDVH4O4F0Jk9HZyGL6PXaaWoo4b7DG3j6ie139P4wDJPhkShvH2nj3fc76e6ZWLK073YQiaY5crSD1svDtHeM8sB9LWzfVjWvx2EluDo9EQSB/XvrqakMMTmV4N1jnWzZXIkkCuRy+pwcuiQKhG/ymVIUCZ/PgTgbgJUkcbYczpozWtzQVEZpqQ+7Tb7h/bdUQDFocxXk1xegGpppMLlMNmIpFBy6I0uGgUUEnq7ciiKu/rwuBsMymS/kfQ2yIK5qDnf7ISfhuoYnYe4PACzL5MqJbkZ7J/nYLz3Mxn0NDLSPcvG9jqur4nDZySSzGLNsOpvKoeeN2YZLEW/QzfbDLTz06f3XbROEO1yuktU1XmntpG10kk/v2crXD+9dspTpKsJuJw1FQTw2lTP9wzy4oZ6Ay3HDhWtZ8EZbD7FMFpeqsKe2olBWATQXh6gK+JhJZ3jjSjebSouRr0tDWhYMReKcHhhBFAVqQwGqAn6g4OpbFfQRcjloH59icCZK/U1kSDMMTvcPr4mPxlrAsiwSepqTM1cYzkxiWial9hA7As2U2guky7RMIvkEV+J9DGcmSRs5FEGmxlXCZm8dQdu1yMmpmStkjTw1rlL6UqP0pcYwLZM6dzl7gxtxSDaOTJ7DtEyqnSWcibQT19K4FScbPNVs9dXPpRCnclHenjzHTC4GwAZPNfcV3zhZ7EwMMpiZoFj1M5WPMZgeR0Cg0lnMzkAzXtk1R8Snc3HORNoYy06Tvc7t0yapPFi8myrnwn4E4VIf4dL50SFDNwkWeTj+VhvR6SQ1DSv3MxARqHKGeWHkNCPpGY5MtFLrWn8/BMvSyWkX0bQ2DGMKy8ojyeV4XZ+f/V7DNOMIgoIoeriVOIumGZw518drb1zm5OleYrH1yWCsBg/ev/6ZosGhGV58+QJvvdPG+Hh8XSK3NyOb0zhztp/h4QgDgzN89JkdVFbcXunEB4mKcj8ffXrH0sss4NFwt8MwTGKxglzvzV5V0zNJ3nz7Cs+/eJ7+gel1DTgE/E42NK+vWaWmGRw73sWPf3qW8xcH121ivRJYlkUslqGja5xNd6gi4yryeZ229lGe++lZTp/pW7dM7vWwLIup6SSvvHaJ/sFpnpraxuGDzbhc80sQDcOkp3eSE6d6GRicJp83UBSJcNjDti2VpNN5Mpk8lmnh9dpJpzVisTTxeAanU8UfWL4/4ebJsdtto6W5DBDIpAsl63abPNc/dPZcP6OjUaZmktTXzZe0Byh3+Arv9AVuFM00mM4lyRoadml1IghT2SQnpvsWVX66iqDNxQOltyfwcDMKFTCL3yer7eld59y2UKhrE8DuspFOZLlwtJ3xganCt4JA444a2k/3MNAxSq0kcvqNVlLxDFgFg62Dz+zknedO0byrlpLKEKl4hshEjNpNFYhLNOGsNfK6wdnBUXTTxCZLmNex4KVgk2UONtZwqn+YS8NjfPf0RQzTZGNZEQ5FIZrOcrJviO+cukAql+djOzZRHw4iUDg/QZeTz+zZyn9/8xgvt3ZQ7vdyuLGWIo8LzTDomZrhJ+fbONk7RF04yD0N1fic19KxO6vKaCoOcbx3iOfOX8GpqmyrLMUmy0wlUxzp7ON0/wj5VTb3rBeyZp6/HniVrsQgFY5C6U9XYojOxCCfqX6IEnuhlnksO8N7UxeRRAlFkJjJJzgTaSdanOThkj045cI5aI310psaJWTzIlK4OdJGFrukopsGSHA60kZfcpQSexCXbEdAoCc1wrlIO3LN42z21QOFjIxbdjCameJMpJ2Enp5HNAbS47wydhxVVPCrblRBIa6nOB1pJ6lneKB4F27ZQdbI84OhNxnKTFDvrsACjkyeRTN1Pl/96JINZotBkkUqakKkklkiU6uLosiixGOlO7gY62ervxa/6mJHYH3djgwjQjz1l2RyR9D1fkwzioWBXd01RzR0Y5h48huIoheP6/PI0uoymalUjtfeaOWV1y7R0TX+gU4yrqK8zMf2beurYHT5yjA/fv4c7x7rXKaefn0wPhHnhZcvMDWd5DOf2suGpjJk+cNHNspK/XetdK1lGcykX8ClbsWu1K5yXchk86TSeTzXle9MTSd4+dVLvPDShXWP+ttsMlVVwXXNaOiGydH3Ovjrb79PV/fEum1ntSgKu9m/t+6OZTOyWY1TZ3r5wY9Oc7F16I4/B/OaUShTjWVIJLM8/ugWvDd5AwlCoa+tob6YyooAdruKzSbjsCsc2NdA38DUXMmnrEiUlDh49OEtmKaJ3a7wsz9zCEkSUFWZxx/div86fyVFkWluLKGi3A9AebkfRZEIBd34fU4cDpVorKBWarerOOwqB/Y3MDA4DRY88uAmKsoDqMr893K1O7ho+MsCYlqGoVSERu/qAnenpvvpjE8s2QQOcLikieI1ch+/ipSeW9IfZLXzk3WdqQuCQPPOWvquDPO9//Yy3qAbt9+JJ+Caq3c7+PROxvuneO6PX8cTcOENuQmV+ZBmX0pP/dz9fPcPXuI7v/8isiwhySLFVaGCEc4dVMlTJInmkhDHewf46YU2eqZmsMnyHCGwKzKlXg87qsrYVFaM7boLcmNZEZ/Zs5VkLsebbT30T0ep9HtRZYlUXqNzfIrBmRj3N9fxpQM758z6oJBafnpbC8PROD8+f4U/OXKCdzr78DkK/SHjsSQdE1N47DY+uWsz+2orb8iW1BeFeHLrBoZjCd7t6mcykaIq6EOVJOKZHD1TMxxsrOGn59MkVmnal9LjjGS6SOvXFF98ahFl9jps0q3J0p2Yvsyb46f4cu2TbPc3ISDQGu/hu4NvUjYZ4lNVDyEgUGIP8kjpXgKqB4doYzIX5UfDRzgf7WK7v5Fq+VqJWX9qlGKbn0NF2ym2BchbOhIidulaE9dAeoxt/kYeLtmDXbTRnx7lT7qf49j0pTmi4VfcPFS8m1pnGWPZ6UWPYTofp9we5mB4G9XOUtJ6lr/sf4nj05fY6W/GLTsYTI9zZPIcn6l+mMPh7ViAIsq8MnacA+EtlNhXr2tvGiZjQxGS8SyitLoXmIBAjauYMkeQjJHDozhwSOvnOG5aOaLJ/0Ei9TeYZhRFrkGW68hrl7Csa9ehZeloeieaMYSqbEZ2PLribUSjaX764jleeOkC4xN3JqK/Ehw+uAGvx7FuDdOtl4f5m+8e59SZvjXpxbhVZLMaR9/rIJnK8tUvH6ZlQ9mHNrOxEAzD5NU3WmluLF002rmeMK0cM+kXkcXAqokGgKaZRKPpOaIRi2d4+0g7P3n+HOMT8WXWvn243XY2bihft2vCsixaW4f55l+/R2/f1Lps41ZgU2Ua6oupr1v/jDFALqdz8lQP3/zWMTq7xu/INheCaVoMDs3wwx+fwTBMnn5y+w0kVxRFqiqDVFXOf/cVFXkoKpo/mb5n/3znclGEvXtuDJIpikTldeOGQx7CoWvjbd1SOW+civIAFeXLZyubvMWFOdcir5e4luVSdGRVRCOaT/POeBejmdiSy8mCyMerd6x43JUioeWWDD675NUJftwW0bC7bex9dCtaTsPtd3LgiR1YloUv5ObQR3ej2hSKKgLYHCpjA1NIkkhlUynbD7dQWlNUaHYu9XDgc/tIjcRJZ3M0b66iaV89VQ2lGFj4KgLs/9IBjIkU2VSOmJ6nrqEMeQFmuV6wLAvDNPE77NgVhe7JGbpnZWevQpFEfA47DUUhPrlrM49tbpoz9bMrCvc11+Kx23ijrZvT/cO83taNZhi4bCq1oQA/f3gvD29soLEodINqlSgIFHtcfPXgbmpCfo509HF2cIR4JossihR73BxsqOGBDfXsq6vE77xxgm9XZB7a0IBNlni5tZMLQ2O0jkxgkyXqwgE+sn0jT2xu5lTfEMmJxSfPC2E6N8zbE98jqV+7GZo9u/ArRbdMNN6ZPDc7od+DQ7ZhYSGLEm9OnOFcpJOPV96PJEiEbT7C15VIhW1+Lsa6uRDtIqHf6KMiCAI7Ak1s8tYhL1LHaAFPld1Lqb3QH+OQVUrsQYbSkzeMowgydmlpjWmw2OZvZKe/Gdssmal3V3BiupWsWZhET+ai5Mw8ze5qvIoLEGh0V/ATUyNnLE34uq+M8NaLF27cogW5TJ7uK6N4/E5KZiM3K4VmGXxn4Cg/U/cgHmX9tcsz2SMkU99FEFSCvn+BqjRjWVnGp3/+huUkMYCqbCGXP4emtcEKiUY8nuFHPz4zG1VP3Fb5hySJBS8Ap4qiyIhSIVObzWlkMhrZbB5jAQnRhWCzyTx4f8u8cpW1QnvHKN/5/olVkwxRFAgG3VSU+wkF3Hg8dhRFQhAENN0gnc4TiaQYG48xMREnl19ZqaVpWpw918+fGia/9PWHaGoo+VD3bFyPfF7n+IkePG77B0Q0MhhmEotbK3vVdYNoNE1VZZB0Os+Jkz0899OzKyYZsizhnvWMUFW54MGQ1+fUpgxj6Uis12Nn86b1Kx9KJLN846/eXTXJcLlslBR7CQXd+HwOHA4VRS745ei6SS6nkc1qpDJ5pqeTTE4miMUzrNT72O93snd3PXb7+kdKNc3g3IV+vvHX760qoyMI4PU6KCv1Ew678Xkd2G0KsizNKU4lElkmJxMMjURIJDIresZalsXYWJSfvnAeh13hyce3od4Fpsy3gyZPMXZJWdS1O5rPcHKqj6cqtqBKKzvWE1N9XIgMLatYtTNYddtO4DfDwiKaT5M1F39/uBTbqoqYb+sXVm3KDcpPdZuvscLGbdc07zfsrmPD7usY5uw6w5EYHePTOIud7NtRx7HuAUrqSphyC6ghFyOxBCd7h2gsDbF1dxPnh0YhnWU4r7HJMHDcgeiYZVlE0hn+4PX3aB2Z4J76ajaVFeN12OYyB7ppEk1nOd0/xJmBEVyqQmNxiI1lxXTHpzg63oMiSBwqrefnw3t4cssG4tkshmmiyjIBp4OakB+fw75g05EkilQGfHxq91b21lYylUyT1XREQcBtUyn1eSj3eW7IolyPgMvBwy2NbCgpYjSeIJPXkcVCWVZdOIjXYeP/evIBYpkMFX7vii4gzcwzmRthKNOJYV27GRJ63Q3/Xi2GM5NEtAT/peNv5j7LmRoD6XFCqo+coeGQRWL5JOdjXfQkh4lpSXKGRn9qDJdsx7zpiedX3XgV96IkA8AruwiqN3o62EWVjLn65lm7ZCOgeuZIBoBdVNEtE3M2DVruCGMTbZyYuUyx3Q/AmUg7IdVLUF1anSMRy9DVOjLvc0kWqWkq5sCDG6mqX120zLRMOhOjq1rndpDK/BjDnCHo+x28ri8iim50ff4xCYIDWSrDtFLo5spelJlsnhdevsCLr168JZLhdttoqC+mrraI8tlaXbtdQVUlJElEEARMw0TXTTTdIJnMMTWdYHIywfBolL6+KWYiC0tEbttSRUVFcF2MvMbGYzz3k7OcOr1ykuH12tmzu46tmyspL/Xj9TpwOgslC5JUaPgzTJN83iCTyZNIZpmcjNPeOc6Zs30MDUeWnVBa1qzU6/85wj/+zScJh9x3raeAaVoYhlEQN1mmaTWb08jmtDXNlCVzFxlPfGNFy5pWlrTWfsvb0nWDSDSFrhu0d4zyvR+emlNIWwhOp0ptTZiG+mKqKoMEgy6cDhuqUqg0sCwwdANNN8lkckxOJRkfj9HTN0lPzySJ5DX1HVkSKS31ryhifKt4/c0rXLh0s/DswnC5bGxqKWfb1iqqKoP4/U6cDhW7XUFRJCRRRBQFzFlTT10zyGsFAp5MZYnFMoxPxOjrm6KrZ4LhkeiCxEMUBUpLfOzasf4msKZpMjA4zTf+auUkw+2ysbGlnO3bqqiuChEIuHA5C+dBliVEUcAyLXSjQLiSyRyxWIa+gSnOnO3j/MWhZZXJLAtGx6I8/9IFAgE3hw82f+iMDa+HR7HT7C3h1HT/gt9nDY3L0VHOR4bYG65ddryRdJTXR9oYSkWWXfZTtbuxS8qaPk/Tep6oll6ydMqnOO5wM/htwGVTyeQ1RmNxGoqCjMYTaIbJZDxFiddNMptDEkXqwkEM0+RU3xAeuw3Lgqymr6gZ+3ahmyavXu7ihYvttJQW8asPHqDU60aV5bmbw7QKzuGVAS/D0TjD0Tgj0QQby4oJ2lz4FAd9yRnSRp6WQAkVgdXLvAmzpGJz+a2pbTlUhaaSME0lC0ue3tuwOjO0jJFkItd/W6RiIciChEd2Ueu6sUFwg6eakOpDFiWmcjFeGHmPjsQAzZ5qNnhqsIkKpmUxnZ+fapRXIENrW7RRa/WTCFmQlnULLXcU8YnK+3lh9Bht8T5skoplWXy55klc8tIZhfqWMn72Hz4273NJFnF7HQRCbmyrjJaJgkiNq4gT051UOUMICCiiRMi2sEnR7SKXbwUE3M5nEYTFFbIEQUUUfVhWHstc2pQNCoGBt99p56VXLjI1uXKSIYoCNdVh7tnfwJZNFYTDHnw+Bx63HZtNWTIKr+sG2axGOl2YiEdjGcbGonT3TNJ6ZZjevsk56ciHH9q0LsZ26XSOF16+wLHj3Uv6NlyFLIvcd2gDDz3QQk11mHDIs+IIq6YZ7NpZy+GDTZw83cebb19hbGzpFL9lwdlz/fzFN4/yG7/2KModzEivBr19k3z/R6fY1FLOM0/t4PjJHn7y/NkFl83nC74kTz2+bc22nzdGiGWP4FRaEISlTQJNK4dl3XppnKabRKJpxsbj/PinZ+nqXrispijsYe/uOnbuqKayIkgg4MTjKUS4F7svTNMim82TSuWJxtLMzCTpG5jm/IVBLrUOIggCG5vL1k0COZPJ85Pnzy3biyDLIs1NpTz1+DZaNpRRXOzF5VRXnXHUZ7N+0ViaaDTNxGScjs5xWq8M09E5Pjf5drls7NxRQ2AFjcu3A8uyiMez/MVfvUtb+/IBJJsqs3lTBQ89sPG682BbUfbRsmDb1kr27annStsIP/zxGXr7JpdcxzQt+vqnePX1S1RU+Gm4Q2Vk6wFBELi3uGFRogEwkJrh+/1nqXEHKbYv/k6N5zP8ePACxyZ7ljX6a/GWsj9cu+YS+1PZJElt6b6+kM29qu1+oE/7nFbobB+JJchqOqVeN0c6erkyNklLWSEVHXQ5CDjtaIZJddDPwEyUIrcb5x0gGVAgEReGxsgbBtUhP82LTNTtsozHZsM0C27KV5WhAjYHVW7/PImz14bbuRgZI6XlqPeEeKZ6M5F8hpcGr6BZBol8jhKHh5/bsJ+MnudH/ReZzKSYyaVwKTaerNxISs8RyWV4smojlyNjdMenuKekjtF0jCNjPcTyGYrtbh6uaKbOE+LYeC+XImNMZVPEtAyPlDdzb0kdlyKjHBvvI2NoNHqL+FjN1mVVBdJ6jNFM79qc5OvQ7K3mzExHoaH7ph4BUZBQBJnJbISz0Q6a3JU8XX4vLtlBzsjTmRhckGisHHcurKKKMhkjh19181T5vbgkOx7FRaWjaNkb2Ot34vWv7YvKsixGMjOMZ6OzDfFQbPfxuZr71nQ717aXQRAkRMG/TDTLAkwKv83y8n1X2kZ59fVWhoYj8zJbiyEc9vD049s4sL+BkmLvnC77SlEoISm45BYXe7EsyOfL2bc3SySSYngkwsVLQwyPRtmxreoG1bi1wvsnenjnaAexeHrZZd1uG1/50iHu2d9AaYlv1TXyiiIVXM+DbmqqQmxoKuUHPzrF5baRJSd2um7y1jvtbN5cwZOPrd3kfC0hSSJuV4FcAnOEsbGheF6JhwWLauvfOiwccgMVvl9HXsDQ8npoZpTB6L+/5S3pusHAwDTJRJYTp3rnZWacTpU9u+p49KFNNDaWEAy4VlzmIooCTqcNp9NGUZEHyypmy+ZKDuxrKLjRj0bXVXXpUusQIyNLR4RlWWTrlip+7mcO0dhQjMOxevf3a2NJeL0OvF4H1VUhdN1g5/YaZiIpRkajnL8wwLnzA5iWxaF7m9Y9o6frJi++cpHjJ3qWzbj5/U4eun8jjz+6harK4KrPgyAU+m0a3XYqKwLU1oT59vdO8O6xziXX0zSD8xcHeevtNspKfDidK+8JfHuwj9Njw2R1nbDTyZc370SVJN4Y6OHk6BCSILC9uIzH65oYTyX5QUcrJS43vbEIFW4vhypruDQ1QYnTzc6SMq5MT9IbnWFLUQk5w+CdoT7GUknKXR4eq2uk3O3lO20XkUWRyXSKqUyar23bTYmzkJ19qGwD/7PtrUUbtzOGxjvjnXgVO1+q30+1e34fylgmxo8GzvGD/nNM55YXc/l07W78qnPNr6WRdIyYtrQiWZF9dVnpD5Ro+Jx29tRWsrWilLDbxb7aKuLZHNsqSynzezBNi6qQv7Cjksihxlpi2Sx2WUa9BS3mW4GAQMjlRDdMLg6PcbSrjz01FdiVwovIME2mUxlO9w/zw7OtTCZTbK0snfO/WAwb/aVUuvxopskfXjnKodJ6UlqO10c6+CfbHsImKfz3y0d4vLIFRRQ5OtrDP9hyH23Rcd4e7SJsd9E7Oc14phDljeYzDKQi7DIqqXD5eaS8Gc0yeH7gMu2xSardAY5P9FPnDbErXMWfth/Dq9pJajleHGzj8coWvIqNP21/n62BMpp9RYteSJZlkdRjjGcXZ/ALrZMzNbJGjqSexpiVqI1pKRySiiIUorxPlt3D6Zl2/qr/ZQ6Ft+NRnMS1FBPZCCX2IPtCm5AECQGBmXyCyVyUqVyMi7EuLsS68cjrEymysDAtk4yRI6ql5o5lOhfDIdmwSeqyWYybcTHWTZk9xA5/E07J/oGWkyiixBduIhWLZ3luH5JUhG4MoRtDKELTomTDtJJoej+i4EKSlo56xeMZXnm9lStto8uW81zFtq1VfOWLB6mvL8LvW5trRxDAZlMoshWcwetqi9i6pZJMRiMU8qz57zwwOM1bR9oYGo4sm8HxeOz8g19+hHsONOJeQGJyNbja13FgXz2hoItv/NV7nDs/gLZE6UQqleNvvnOcbZur7kpZ2LIyP5/+5N4bIu0tG8r44ucOFCQ5rzu/8USWP/6zt9Z0+6LgxKE24lI3IYpLZzYVM4Ek3HrGMZ3Oc+RoOwjMy4IVF3l44rFtPPLQJkqKvbddRy8IAi5XgXhUVgTIZPMLKvisFU6f7Udf5hlQXubnM5/Yy6aNa9+QLssSoZCbYNBFXW2YzZvKeeKxreRy2rq4wF8Py7LoH5jiB8+dIr9ML1Uo6OLpJ7fzzFPbCQVv37HcZpPZvKmcr37lMIIAR99bmmwkEllOnu5l6+ZK9u2tX/F2XuhpZ3tRKZvDJTgVBUUUGU8leb67nZ/ftodkPscPOy+zMVSMaZm0z0xR5vbwdP0GHIqCz2Ynmc8xmIixraiE1qkJorkMO0rKeHOgF7di49nGKl7q7eD85Bg+m53eWASnrHCosgZVkvDbrjWy17iC7AhVcWpq8TlRJJ/mx4Pn6YxPsK+olo2+MtyyjbSepzMxzsmpflqjI8zk0svWUWz0lXK4pHFNvTOuYigdIZZfmmiUOX1IH5bSKbsiU+J136Dc5HUUXn43v4wFwOuwLfr9ekGWRJ7etoHX27rpnYzwb376BgGnE7ddRRIFsnmdRC5PLJMlls6wtaKUT+zcTKlvcck+3TQ4Oz1Eb2IaURDpiE2SN3UsCkxxg78Yp6Rik2Si+QzlTi9uReWnA60A3F/WiPsm23uLgnu4YVn0xKc4PtGPBVyKjFLnCSIgELS7uDgzSltsgk2BEqpdAUbTcS5Hx8gaGg5ZYSafntVtXry5MW9mmc6PkDFWLqF6Jd7Hc8PvMJSZmCUYSf5n1w9wSCohm5/f3vAFPIqTWmcZ/6Dp07wydpy/7H+ZvJnHIdmodBRT5SyUjZU7wjxWuo/Xxk7yPzq/h0Oys9Fbw+5ACzP59VFLSWoZ3pw4zStjJ8iaeaZzMSRB5F9c+hPsosrjZQd4vHT/8gNdhzpXOUenzvN/XfhDJEFEFWTKHGGeKDvARm/tuhzHUph/z63fPWa3HSCXP08s+ceE/b8HzI+iWZaJrveRyryMJBVjU5Y2STx9to9z5/tXbEp374FGvv61B6isCKyrGpKiSDconKwlTNPi2PEuLrUuXxstyxJf/+r9HLq3aU0bUW02hZYNZXz5i/eSzuS40ja6ZBR1eCTKt793gt/6jcfXbB/WCjZVpug6oy+/30lzUwnV1aF5E2OfL4fLeetR8IXgtu3AodQjCMuTQFFwEHQ+hk2ar5izEhiGydT0/Gd4eZmfjz+7m0ce3ITPt7bqaIJwLWu0nujsGl/yGlQViabGEnZsr17Xe/9qr084VMgAWpa1bkIQV2GaFn/zneNMLSNx7nbbuf9wC88+s5NAwLUmv3PBaFigtibEz3zhXoZHosuWUfX2TXH8VA8tLWXzJG8Xw8caN3JybJgfdLTyaG0jdb4A3dEZLkyO8Z22i+iWSTyfI5rN4LXZcCoKG0PFbAgWKlIsy6LC42U8neTk2DDxfJZKj4+cYdA2PclUJkXr1DhjqQRlLg/arAJTnS9AczCMS7nxvldEmc/W7lmSaEBBgerUdD/t8XHcsg1ZFDEsk5SWI6Hl0KzlbQYE4PN1eymyr33QCqAvOU0kt3RmvNoV/PCUTsF8x8WlTtwHEfEVgPqiIP/hk0/wUmsHx3sH6Z+OkNYKtbF2WSbkcrKprIj9dVUcqK+mMuBDkRZnmr2JGVojY9xX2kDY7uLloStzjWNOWUGcneDJgohpWQgIqJLMg+VNlDm8eFU7IgKiIJDW85iWRTyfZSaXYjKb5GJklCKHhz3hKoZS0bkSEsuyaPEXsz1YQcDmxK/ayZsGPsXOJ2q3U2R3YVgmRfaldc0zRpKRTM+CTpiLocZVxhdrHie/gJKBLEpzUqqSILLd30iVs5iUkcG0TCSh8L1PKeyXS3ZwX9FOtvoayJsaoiDikZ1IgkTe1PAo1yLTz5Qf5JHSvYTUxSN/X6x+nKyRQ7lOG9olO/iFhmcxKUTFnJKNg+FtbPLO95YQBIGAWpic7AtuZIOnem5fr+LR0n0cCG+h2FaI4v5o6AjdyWEeLdlHSPUiIJAxc5yNdPAn3c/xb7Z+HfdNvRqmmSKdeZF48o8XPRZZrsHr/jp2275Fl1kImmnwg8FjAORNnelcgm3+Wr5S/9CCy2dzJ4kn/wRd71t0TK/76zgdTyGK83swvK7Pk0r/kGT6JxhGHI/zWUTRD4Bl5clrXeTyp0mkv4Oud+N0PIbddu+i25qeSXL8RA/Dy5RLXMWeXbX88tcfoqLcf9c2Jq8EvX2TnD03QHQFJoQffXoH9x3agN2+ts2DUCAxGzaU8Yln9/CnM28zukTPhmGYvPd+F089sZWWDeVruh9rjd27atmumSgLZNBtdoVHH95MTfXaRahl0btsydRVCIJE0PU0orB2k/ZwyM3TT27niUe34HavLxlYT4yNx5ZUgXI4VGqqw2uu/KRpnWQy38fj+a15PTaWlSGfPwmWht3xyNznhjFFNvsS+fxpVGU7dscTSFIpuj6KIMhI0uoUzTq6xnnveNeSy0iSwKaN5Xz82V1rRjKuR4FshPn8Z/bzH/7T80uq8uXzOlfaRunoHGfPrtoVjb+jpIxaX4CZbJrfe/9ttheXEnY6CdodfHnLTkShECYrcrqYTKeQBBGnfO23FgSBBl+QnmiE73e00hIMsyVcjE2SCTgcbAoXc6iyBsuy8Ko23GphfuJUlAUrFwTg3qJ6tgYquBgZXnLfDcskmk8TzS9f5roQDhTVcW9xA+o6ZDNmcimG01EySyhfChQyOOIqKjg+cKJxt0MQBGRRZGNZEZVBH5/ftx1NNzAsC7AQBQFZlLApEi5VxaEqc+RJMw2OjHbx44FWRjNxOmITfLJuOzXuIFlD4/t95ymyu3ApNhRRIm+aC8SQLTKGRiSX5q+6TqGIEkV2N19p2keFy88rw+38zsmfYloWpU4vntmx3h7rojM2QTSfwaPa0UyDnKlzemyQE5MD6KbJ5xt2sTlQyhNVG/lh/wVMy8S0LH535/xG4+uRNhKMZnpWdR5dsh2XvPyLSxAEZEGi2B4AFi6tEAVhxeOFbMs33he2dSMkQaTMcW0CIYkSIZtv2fE8iguPMn9iHVA9c2QkoaV4dfwEB8PbeKLswGwvioBu6thEhW/2vUQ0n5hHNMDEMKfIa5cW3b5l5TFX0DR9MxRR5st1DwEWpmUxmp3hvcnFFW1MK4GmdxUkZxeBYU4BC0doZKmakP/fMhX5bdLZV8jlT4AgAyZ57QpjU1/EtNJYVhqbugef+xfniMhCOH9hkCvtoysyoqqqDPJLv/Ag5WV+7mRfzlrDsgolIu0dY8tKa1ZXBfnYR3fh8axfiZ4iS9x7oJELFwd59fXWJZvSY/E0P3juDL/zT+5uorFU5F0UBPbtqb9jRoSWpWNYGQQkRMGOIIgrJiUrgcOucM+BRp55cvuHmmRYFqTSSzezKop0g6Hb2m07g64PsZCIiCDYUdVd874zjAFMYxyn83PIcgOiWHjH5PPvIgo+pFV4BwH84EenyGSWFgkoK/Xz2MObKStbn0CLIAgoisT2rVXs2lHLydNL93P29k1y+cow27dVLUjqr4dpWfy308cYT6cwTIvGQBhJEKnzBXi8ron/efY4ogABu5N/tPfgouOEHE7CDidjqQS7S8opdXkQgEMVNbwx0MP5s6NgwRc2bSfguPouXvhcCYKAV3HwS8338Rsnvr2syd6twiWrfLnhnnXNZkxkE0uGkEsdPnyq487J2/5tgG7mEQV5SXYmCAKSIOB32PE7Vv4AlgWRA8W1bA2WY1gWsiDiVmyoosSvb74PzTSRBAHDMgnYnFiWxT/d/ggOWUUA/vmOx1AliecHLnO4tJ6HypuZyqZ4ebiNnsQUu0KV/IudjxXSsUJBJcgpq5Q7fTxWsQFRELEsC5eicn56GJso87XmA5Q5vfx04DI9iWmafUU8UdnC4dL6ucmKS168HMC0DJJ6hMnc0IrPw9/jRtglG7IgcTbSQa2rjBJ7kLyp0ZMa4acj71LmCM9lPu4UREGg2F54wVmWhWGZJPXlo+S3CkGQcNofoDT0DaKpPyKdeRXLLER4LHLoxiCyWIrb9VU8rs8hyzWLPlgzmTytl4cZGV0+myFJAl/72fuorgohCHyoZRUnJuO0d4yuqAH8E8/uprjIw3oTK7td4eknt3P+4gB9/Yv78ui6ybkLAwwOzlBVtXpzyg8CyWSWmUgKURAIhdw4ZuVP1xs5fYSp1I+IpF9BM6Ypdn+GYs8XSOUuYqHjse1Blvy3vZ2GhmI++vQOPJ4PL8m4iuXKoQzTvG0zy2zmJbK5NzHNBJJYjMf7DwGwzCjx2L/CMCLIch1e3z/FNJOk099F005jsz2E0/lJTDNDPn+KdOov0PUBNL0Tu/0RbLbD5HJHSaf+EhDJZl/C6fwsqm0vy92/E5Nx3j+xdBBQlkWaGku490DjDZ5daw1BEPD5nTz68OZliUY2q9HdM8noaHTZHhYB+Nq2PehmYTJvk2Tss+bJn2vZRkrLI1AwAFRFiQqPl9/cexCvemM5oiSK3FdZy87iMpyKOieAs7u0nJZgGG12fJ/NhojAL27fi02SsS1SrSIKAnvCNXy6djff6j25/Am6BXyhbh/bApXIq+wHXSnaY+OMpZcW1GnxlaKKq1NO/DtPNN6f+ks2+R4laKta87EFQcCl2HAp8+ttA7aFoymh6wxdQnYXumnS4A3zv9qPcXp6CMuyqHIF2eQvRZVkiqT5ZU6KKOG+aZuV7gBvj3Xz550nkAUBAYGfadqLa7ZO0LkEubgeOTPDaKYP/TZkFf+uQxYkfrnxE/x4+B3+T+8LJPUUIiIhu48d/maeqTh4QxnXnUDO0Pj9th8DYGCR1DNUORdWWFsrCIKKqm4lrPwHLO8/J693YRoRLEFElopRpDpE0YMgOBGWeLB2dU/Q3TOxomzGA/dtLCg/yeKHumQKoKNjjN6+yWUbwCsrAuzbU4+qyneEWNXXFbF9azXjE/ElI6vJRJa3j7bzpc/fs/47dRuYmIzz4ssXePPtNmLxAvn2+5w89sgWHntkM6Hg0qWmt4OcPsJo/H8RSb+GXanDsJLoZgwLE82cJpZ5B1UqvW2iEfA7ObCvgbraxUVAPiy4qoI0M5NadJlMWmNgcHHPkJUgm30NVd2LzX4YQbAjCIWMtWGM4vP/HiASi/4zdL0fSarG4XgSMLGs5Ox+2rDZ9oKVJZ9/H4fjE0hyLYJgx25/HF3vQhTDOB0fR1ig/HQhvH2kjewyPWrhkIf9+xruCElWFYnmphJKin2MTyw9gR0anmFwaGZ5oiEIhBwLz5/cqopbval/QpAI2hfu/XAoCg7lxvNgk2RsjvnvX/8iY1y/X27Zxs813Ut7fJwz0wNLLr9a3FfSxMdrdhSyCetwj+YMjY7EOJPLKF5tC1Ssumzr7zzRELgzae9bhSQI7AxV8J/2PTsXfpUFEfsKHSavotTh4Vc3HcKYTekJCNglBWmVF2zWSDGS6V7VOn+PGyEIAk2eKn6t6VPoljHb6yIgIqCIMqq49jX0y0EWJR4p21HYP8AjO6h0ra86ChQyG5LgxRK8OKQyrknZisDKyEB3z8SSRmNXIcsSH31mB273B6vwtRYwTYvungmGh5fP4hw+1IzXuz4vp4UgSSIH723i/ZPdSxKNbE7n3WOdfO7T++9Y+dFqMTOT5IfPneGddzvYvKmChvpiLNOirWOUH/3kDJqm89Fndq6ZYtnNSObOktW6qfT/BgHn4wxG/uPcdza5Es2cwrBuXwCjojzA/Yc2rKoxum1gnLaBCe7ZXEtJ4FoD/ZW+cRorwyiyhGVZXOwZ5eUT7fzjzz942/s5MB7B47QTWKZpOBx0Mzg4s2hJYTan0dU9Tv/A9C332LjcXyebeY5Y7P/Gbn8ah6NQcizLDbN9FRaC6ME0E7OBDSeCYOdqOWkheGJHEN0IggNR9M31swmCY/Y/F6K08ozfm2+3YZhLB1zCIRe7dyyeIV5LFNTG7GxsKVuWaIyMROeU8z60j2cByhw+fnfbU/zzM89xJbY2JrjbApX86oYHqHKurgl7NehKTNKfnJ6bIy6GveHaFTucX8UHSjSuPQSWbisWrv9zjU9yyF7DYPocOTOBKhZeFgG1ElG49VNz48PNuu7PpXH9cc59Ntuz4FFvr/FHFIQVZy0WwtVjyhhJhjNLN5p9WDH/pbSydndhob8tc50WmtPunhIFEYGtvpq5VPpC1+KtwrKsZc6HMPtiWf09l8nkGRyaJhJdPHp5Ffv31lNVGbxtCce7AROTcYZGIuS1pVVKBEHgwN47E728Htu3VhEKupmYiC+acbEsi+npJF3d47RsKFt4oQ8YV9pH6emd4IufO8DDD26au3ZM0+JHPz7DidO97Nxes25EQzMmkUQvbnUHsuhGEK79jpLowTLzmLeZXXY6VDY0l1J+k0v39c/Dq5O/QkikgMaKMPVlIeTZUhLLKjwv//K1M/yjz96P3+VAEEA3TFK5/DUVKOGqot3s8/XqxzfdloIg3LgPwOtnuthcW8KeDVVzyy/0bNnUUs75i4NLNiH39U/x3e+f5Fd/6aFbEEiwkKQaXO5fw7LiRGZ+BZu6d3aH1NmzZFEImFizx2HOfmZhWYWAyuLbFMAygDyWZbCSoMvwSITB4ZklM5x2u0J9XTGh0Ppl4RbaZlNjCW8dWbyfDwrkb2IiTiKRwetdmfrU3QZhNnTd5C3m93Z/jP948RWOTXbfgu1vAaIg8HBZC7/a8gANnqI5oaC1hmVZXIoM05dYvNwVoNFTRIXTv+r9uONEo/AwMjEtg6g2SV/qCkOZDqayg8S0abJmGt3UUEQFm+gioJZQZK+k1rmJWtdmHJIbUZAW7KkQBGHVGYqZ3CDTuX4GU+fnPnuk7DewS6uTo7x2XCaGpRHVphjN9DCRHWQ6P0JMmyalR2ePLw8ISIKEItpwSm6cso+AWkxQKaPMWUeFoxG76Cw0/q0wsrtWuPpwtzCxZh+SJgYpPUZP8iIxbXG5OtMy0a08eTO7BntSOEfSbZC+lcCyTMzZ3y5npBnJdjOW6WcqN0xEGyepRUkbcXQrjzGrgiULCjbJiUvy4lGCBNVSSuzVlDsa8CtFyIKKIIgIS75M7h7kTI3/ePkH/Mutn8OyLCayMb4/eIxfaX7ytsY1rTQiMmDj+pdl4eUpsPTLdnkMDUcYHomuyAH88MEmnE7bh+L3WA5DQzMr6kmpryuiqOj29fFXC0WR2LK5kr6+qSWbwjOZPOcuDNy1RCMSSaHaZDY0l87zkti2tYr3T3STSK7Fs25hCIIClolpZW68xi0LzZhGEFXEFUjhLoVAwMW2rVXzrpHXTneSyOQo8bt591IfD+5spGNwgvryEJIo8dzRi7gdNr78+B4qinyMTMX53pELHL/Sz7/5i1dRFYn/++cex7Ishqdi/Ms/f4lYKktzZZhf+dghJiIJ/uaNc/SOTBPwOHh4dzOb60p4+1w3qiLzzD2beO10JzPxNDubKjjTMcQLx6/wzoUeivwX+Nkn99JStbC3zs7t1XznBycwluDh2azGm0eugAA/+6WDBAIuRHGlzyOLROLfo2tdIIBq21cobzJnrlObEmb/LmKa48Tjv4eu9SIIAqYRwen6PJJUSsGIVOHGwI6Aomwhnf4u2ezbuN1fx2ZbWj793PkBtGUCD26Xjaam0jv6DFQViYqbSOximJpOMD2TuiWiYZrmnLzurcA0TXTDAgFkUZy7HwzTxDAKY8vS8nMxQRAQLWjyFPMf9nyCb/ee4lu9J4nm05iWuSzpECgEIksdXn6m4R6ertxCQHXOjb0eSOg5LkVHGc8unR19pLwFl7z6d+gdIxrWLIvPGCm6k+c5E3mdgXQbmrmwOoRu5MkYKaLaBL2pi5yYfhFFtLHBs5fdgYepdrUgC+oNhENEQhFXF7W/t+jLhb27BcpZmIAXiEVSj9GfukxX4hz96cvEtGmWy2PoVqHnIalHITdEX6r12rEIEjXOjWzyHmCT7wAOyYMsrH1JTYEgWZiWMUeUTAxi+SnGcv2MZ/oZz/YxkRskpk0tO97l+DEux4+tyb4pgo2D4Y/ycOkX1mS862FZJrqlY1g6Y9k+uhJn6Uqem5XtXTp1aFgaebKkjQQRxuGGnmmBoFpCk3sXW/wHKbHXYBPthcKou3SCq5sGaT1PztRIahksIKql1sSTJJr4H2Ry7xHy/g4O+2Gu+mVkckcRULDb9rMSx+/FMD4eY3JyeZUtn8/Bpo3lqMqdMfpcb4xPxFd03C0bSnHY1Tt+7QmCwMbmMl5bRn0ql9O53DaygqzXBwNRFLHMgovx9ftoWRa5XMEMbb1KGQDscg0R6yVm0i8hS0EsKw9IaOYkkfTLKFIRyirlT2+Gz+dgQ/N8omdTZJKZHJFkhkgiTTSZQZJEVEVmd3Mlkghnu0bm3nIVRT5+7eMHOd89wu9++RFCXtdcliOT0/hXX3mMmUSa3//eO3QNT9E7Mo0qS/zBr3+c890jHDnfjd9tx7SsuWCXaRVU8IJeJ595cDvD03H2bKjk/u0NSx7Ttm3VlJUF6O9f+p2VTud56ZWLdPdM8LlP7WP3rlrsdgVp2QmliM/3r+Z9qqo+VPU/z/07EPhP1/39DxYcyWbbh22eHLmA3fEo9lUoTl24OIixTJ+a02mjtmZ9e+9uhqJIFBWtLIMyM5MiGrs12deTbYPs21h9S+sCXOmf4AdHLmBa8JkHt7OxpuDbdfzyAD88cpGSkIefeWz3DWWCi+HqtROyufjFDYd5qnILPxo4x5tj7YykYxiWOTf3gtlMiCCgijK17hBPVmzhiYpNFDk86+pnBYVn2YWZIdpjY0vOWJ2ywgOlG26QCV4p7gjRsCwLzcrRl2zlvemf0J+6fEvNxJqZ41LsKO3xkzR7dnO46OOU2KuRZ8mFLCio4urKUTQzi2ZmMS0dCwsRCYfsX/THtbAK0SRLI2MkGEp3cjF2lJ7kRTLG6mVFF4NpGfSmLtGbusRbE99lb/AxdocexSMH1jTCnzPTjGX7Gc/2M5EdYDw7wHi2n6y5fCnKhw1XCVXezBHJj3Ml/j6XYu8xnRtdllysZisz+TGOz7zAqcirNLi3cU/oGSqdzQXCsU5qEbeDK/Ehvtn7Jpei/fzG6T/FwkIRZR4r23nbY+v6ALrWhSDauP5xMznzG4iin4qSlxBukWhYlsX0TGpFZVMtzWUfyIR7PWCaheNeyQu5tqbotl2dbxW1tWFU29Lb1nSD4eEommZ8YPu5FIqLPOQ1nZOn+ygKe7HZZCwgl83z3vud2O0KgcD6lE0BuG278DseZiz+Z0ynfoJpZRAEG9Hs20iCnarAP8Em3/rkShQFAn4XxUXzZXLDPhfjkQTRZAafy87odAynTSXgXibaPDtbuUoWREGgviyEIktIoojHoZJIZ0lkspSHC9t1O1RcdpXpWQU1y5pVvzPMm3oOrnlCLXUvy7LIxz+yk//2h68tWT4FBV+XtvZR/t3/+zxbNlfwkad2sHVzJW63DUWRPxSllqZp0ds/tWx/ht2uUFa6vOT7WkIQBFRFxulUSaeXblRPpnKk0zlyeZ2cpmNaFpIo4rApyJKIbhhkchrGbBmex2GbI7L/+4WTbKguRhQFPA4bmm6Q1w3cDhu6YZLNa9hVBbBI5zRMs6DWaVdlVEVmc10ppmVxtutGH4x7t9RimBbdI9dIa07TMQwTu61gZ5DO5hFFYXb8GyEJIrXuEP9w08P80ob7GEzN0BGfYCwdJ2PkkQQRr2Kn1OGjxVdCqcO3rmpgN8PEoi02xmgmhlNaPFD/RMXmQtnULcxh1v3JblkmST3KmcjrnJh+mbi+dA3YSqBZOVrj7zGS6eb+4k+z2XcPNsmBJCqo4upSbpdjr9KVeJeZ3CCSqOCS/Hy08l/jkOffjFdLiKZzo1yJn+BC9AgTubVVFlgIKSPG25Pfpz1xikdLv0StazOKeHvp8qs4PfM6b09+d1Uu3x9GWJZF2kjQn7rMuehbdCfPr1F51+IwLI2OxGkGUm3cE36GXYGH8SqhW7pR1xNb/TX8m21f5E+7X+GXmgqlUlcjLLcL00yCICLgXPNJfmHCnZxTAloKzY0l2O5wn8J64arM6ko9Q9Tb7O+6VVSUB3Cs4Jxns3mGRyLU1d5eZH49sKG5jB3bqvnJC+c4dryLivIAlmkxMDhDKp3jC587sKxKzu1AEp0Uez6PQ20mknqJjN6DgIhDbSHs+ggOpQVRuPXr+urEc6FbM+x3kczkSGby7Giq4GLPKFXFfvweB8lMjkQ6RyanEUtlKPK5UBWpUDYiCEzFUoAwR0pudsO2KzLFfg/nu0eYiiUZnU6Q1XQqi/2FDEoyw1QsxdhMgpxWyBwxO3Y8lWM6nsbrsqMu4bnw6MNbePHVS7S3r6whN5/XOXO2nwsXB2lsKOHRhzeza0cNoaAbh2MlWY4PDolElqnp5LKVGQ67QsC/MgWrtYQoitjtyrJEI5XOkU7nOdE2wJHzPUSTGbwuO597aCeNFSHOd43y0ok2Utk8mmbwm5+5H8M0+cE7F2kfnOAPvvcOTrvCr3/yMKfaBjl+eYDf+uz9DE1E+cGRi3zi/q0YpsUPjlxkKpZClSUe2tXIAzsaV9WAfqZjiLOdw3z58T247Cp//uIJNtaU8NCupiXXs0sKTd4SmrwlK9/YClCYm+qISAiztgbAdRnYQnl4oQz/xnsxZ+hs9JfyC82Hl9zGweIGfMqtBVXWlWiYlklMm+S9qZ9wNvIGOXNtdfkj2jivjn+TmDbJgdDTs3XzqyMaU7le7iv+Op2Jo7R4H+Ri9HkEYfGHVyQ/zrcH/v93hGBcDwuT0Wwvzw3/IY+WfIlNvntWXSb2dxkWFm9NfGddrsPlkDVTvDXxHVJ6jIPhZ/GrxXcd2VBEiSfLd6Ob12p8hdlU7m1BACwDi8yal8ckUzni8cy1JtMlUFkZ/FtTNhVPZIivwDtDUSSCAdc8JSHLssjndCLTSQJhN5HpJJpmYLMphGb7OTTNIB5Nk81oiKKAy2PH7bGTSuYwDROPz0E+pxGPZnB77ThdNmLRNJIo4nTbkCSxsP2gm4HBGQxjcVKUz+uMjkbvSqLh8dj5yFM7KCry8ObbV+jqHkcQBGqqQzz2yBa2b63C4Vjf57Ao2PDZ78Vnv3fNx1ZVmfAijcEBjwO3w4YkiuxoLKdnZAafy4HHYePV0x28f3mAWDJDMpPl6QObaKkpQZUlHt3TzF+8dAq/28E//NRh7KpMka8wuZVEgZDXhcdlpyzkpW9shv/6/XfwuR3cv72exvIw6Uye549f5o9+fAybIrOppgRJLJCMXU2VHLnQw4XuEb7wyE7qyhYneQ6Hwq/90sP8y//nB0QiKy/H0fVChqOtfZSSYi/799Zz8J4mqqtDeNz2udKquwmDw0vfY1BQg5NlaUUKfWuNsfGlFaeuIpfTyeY09jc00FxVhIDA//jhUSYiCWpLA3zv7fN87PBWdjdXIl/3G/zaxw/y7sVe/sWXHy08v/Qbe1Ws6/5SHvLy5cd2IwgCr5/uoH1gkgd2NLAa8ZO9LVX85L3LxNM5ZEmiY3CKX3jmwIrXXy1MywAsBESs2f8bVh5BkBCRsDCYyvUSUCpRRAeGpZEzk9hFN6KgkDNTpPQZvEoJMirmrDCBQMHa4EBRLfvDVXPjrTWhXjeiYVkWcW2aY1M/5XTktUV7MW4XKT3GiZmXANjo3Y9dXB3jUgUHoiBhWSZ5M01aj84qQsyHIAjYRAcl9po7TjSuIqZN8ebkt3HIbhrdO++6CevdClEQKbZXYROdd5xoQIHonJx5Gbvk4p7wM7gk310VHTMti5HMDCOZGUzLQrcMAqqbnYH62xpXEsMgyGRzx5Glill374ISC5iYZgJLWDrKdRUCCuJ1Gct0Ok9qmQgZFNRsSkt9KH9LiEY6nV82MggQDLhQlIVfGt0dY/yXf/tjvvZrj/Lyc2eYnIhTU1/ML/+jJ1BVmcsXBnnhh6cZG46gqDJbd9Xw8JPbOP1+N1OTcT71xXu5eLafH37rfZ751F7uf3Qz3/+rY4SLPDz4xFa8sypMRWEPkiQs2ZSrGyYzq5gI3kkUSrokHn1oM48+tHm2KfTGCL1hFJpFr3qzrOV9bZgZTCuLJDgQbyoL1s04lqUjiZ5bzmoosrRo460iSXz6ge1z//6Vj10jOk/t38hT+zcuuN5nH9rBZx/aMffvTbWlbKotBSDgcfKLH73mm/KVJ/bOW397YznbGxd2jL9/RwP371i6P+MqBEGgpbmUr33lPv78G+8wvYSvxmIYn4jz4+fP8dIrF2luLuXe/Y1s21pFcZEHj8eBzbY647L1wvR0EnMZomEYJqfP9vGzv/Cnd2ivVg9dN0ilc7x1touO4SkcqkLX8BQP7moknc0jiyJepw1JvBqpv7buzdM2QRAwzEIvhGma5HQdzTBo7R3jtdOduJ0q3cNT1JYGVx0EkyWJQ1vrON81gs9lZ9+m6hsczS1LxzRGsazUrGxx6W1p9ia1SXJmGqccIG+mcUp+ZvIDCECxfQOamSejxwiqVViYRLVhuhNHafTcR0CtIm9myJspBARyZoqkNolhadglL045QEKbIKFP4pGL8amlSKxt9n/diEbGSHIu+hbnom+tiGSoogOH5EIVHSiCMpfeKagYaWhmlqyZJmukubnJOqXHOBt5E8PSZ5naylFkb0ARHXiVEq7EXkMS5CXr6B2Sh63+Q7TFT6BZSx9XoTndhiraUUUbkqAgCiKiICEgzCodGehmnpyZJWskV9S7MpMb592p5wjbKgmqt5eCU0QVp7SyhiMLC8PSlyw5kgQZRbQhroE/iSLa1qxEDKDZs4eL0aMk9MgyPRkCiqCiivbZfVALSmcU1M4KvR4FEYC8mSVrpMmb2WX7PCws3p9+njJHPS2ePcjC3ZORMrG4GO0HQDN1RjMRyh3B2yYaNnUnufxpYok/Jq91oioNCDgwrTSWaZFI/TXCCh9DitKM67rmyHS6UM+7HBx2FZfTNq9848OKTGZlRMPjcSzqT2FZFlNjcc6f7OWrv/YIHp+DWCSFy22jv2eSF390huJSH1/9tUeITCd54YenefX585RXBhFFkVg0zcx0Eo/PiWGYJBNZZiYTNLWU3RDh93rss+d9caZh6OaK+mw+CPT2TRKPZ9i2rQrTsJBl8YbrKJPJ09c/xdh4jHDIQ31dEU7n2vUCpfIXSebO4HMcxqVuvuG7ePYYmXwnIddHsCs1tzS+JIk4nXfPc2itIcsSjz2yBVkW+dZ3jjM8EkXXl1ZmWgh5zeBS6zCXWofxeR1s21rFPQcaaKwvIRx24/U4VqFYtfaYmUmuKLN7t8MwTCajKWyqyYaqIjZWlzAwHkEUBFwOG16Xnb7RGRRJQhQFysM+bLPBFLsq0zU8hdOuUBr04nbYiKez9I7OMDARJZrIks5qDE1GCXgcPLKnmTdOd5LX9YIASjJT6ElKZBifSVAe9uGyq0QSaSZjSSKJDCNTcTwOGw6bwv3b6/njn7yPZcGXHtt13VFYWOYUyehvYWiXUO1P4PL93qyHyq1hNHuZaH4Er1KMaZkUO5rIm0naY2/yUGkDgiAwmeskZKvBJYdgth9VFR0IgGZmmMn141PKSWjjjGXbEAUZm+TGZ5YRzQ8TzQ8hOkX8LEzybwfrQjR0M09X8iznIm8tW/tvE50E1RKqXS3UODdRbK/Gp4RQRTsWFnkjS0KfYSo3wnCmi/70FSK5sdnJ4rUbK6pNcGrmVeyrLJ1q8T2EZmZp8NxDsb0RtxLGtkRWRBYVSu211Lm30JE4Pe97RbDhlD24ZC9eJUxYLSdkKyekluFWAthFBzbRiSjKaGaOnJEmqk0wkR2kP3WF8Ww/0/lRdGvxiYSFyVimnwvRtzlc9EmkJUq9lkO5o569wcfJm/FZKb7FH5SmZTCRHaR1CVWpsK2cBvd2HAs4lq8WkiBT6Wy+7XGuwqeEaPLsZCI7QMq4UVFJQMQhuXHJXtyyn6BaRthWTthWjl8txi66sIkOFNGGSUEGN6XHmMwNM5zpYjjTyVRumJQeX5Jw5M0sJ6ZfpNzRQEApviuiYQCqKPPLs/0ZlmUxkp3h+wPv3fa4TvsjaFoH6dzbpLMvksrkudbQmSQS//crHsvl+PgNRCOb08hklyfmXu/iE+6VIp3IMD44g8Nto7T6zqq23Iyr5QXLweVSl2wqFESBh5/eRlVt4Xj8AReGYTI+EmVqIsbnfu4QpeV+Ssp8DA9Oc/ydTrxeB5IkMjOdRMvrlJT6yGY0xoYjGIaJz+9Eua6p2+22L9tMaxgmicT69kvdKq60jXD6bD+pdI5UKo/f76ShvphwyI0kiZy/OMhff/t9otFCRuZnf+YQ9x5oXDPfkqzeSzJ3Fo9tz7zvRMFOPHcCj33vLRMNQRRuiMT+bYSiSDz84CbKywL8zfdO0NY2QiSauiWlSYBYPMM773bwzrsdVFcFObCvgT2766go8xMOexbNIq4norH03wqiYZoWIa+TQImfK/3jjM0kKAt5KQ54kESBTz+wnVdOtdPaN45pWQUVqKAHURD4yMFNfPet81SEfXzlib1Ul/ipCPv4wZGLuOwqO5vKCc1mWruGp3n1ZDumBc1VxZimRfvgJBe6R4mlspztGsHrttNcWcSZjmE6BydJZ/O8d6kPr9NGTWkQl8OGTZGxqTIhz409iJaVw9A7sKwMpjGCZaUQuHWiIQkKLjnARLaTWvd+YvkRkvoU+VnRHssyEJDIGnFccgiH5McmuZAF25yTm4VJ3kxhYeKWQ/iUMlJGBEWwoZmFAL5d9K2LifWaEw3LspjMDXMxepTp/MiSy/qUMBs8e9gTfIxie9WCakoO2Y1DdlNsr2ajdz9pI86l2HtciB5hNNN7Q1YhbcRJG6uT5ExoU0xkO8gYcRo89xLPj2IX3UhLZDVcso8tvoP0Ji+iWXkkQcYt+/ErRRTZq6hyNFPl3EBQLUVaosZdlmQckgu/WkStazN7go8xlO7g5MzLdCbPktIXr2tMG3G6kxfY5r+PoFq6qmO+HpXOZiqdzaSy7+Gw7V5Sk10zc7TGji1JNIpsVewLPknIdnfq4m/yHuBy/DjpdAILC7vkwqeECSjFVDqbqHZupMLZuKx6mSra8CgBSh21bPHdS1SbpDX2Hhdj7zCRHVwyM9WXusxopgevHES+jUbOtYRpmXQnx4BCOnoiFyVv6rc9riwVE/D9E2yZ7WTzZzHMabDyZHLvIwgKdnUXrPDBZlNuLNXQNXNF0UmnS73tbEZ/+yjf/I8/pWlHDT/3O8/e1liLwbIsdM0gncjgCy0uoagbxrL12AB2m4KwxCRflERKb9K313WDWCyNJEsEgoVgwVV3X0kSkBUJSRIZ7Jss9Co0FJNKZOnpHMfnd+F03fj8WIkRmmlZ5PK3f62tF06f6aOvfwq7XcG0LA7sbeAjT++gpNjLqdO9hEMePvvJfbzzbgdH3+1g08ZySu1ro+xjmlkEQUWW5vsQyGIAy8phLhGUWg4ChevgbztkWWLL5gp+u+JxXn2jlSNHOxgcnCZ+mwR3YHCGgcEZnn/pArt31XL43mYa6osoL/OjqneurCqTyS/qgv5hgypLPLm/hSf3t8z7rrYsyNc/cs8Ca8FnHtxxw7/9bge/9Oz8vqbKIv+CpXn7N1azfwF53Mf3beDxfRtu+GwmnmZsJkEinePBnQ03vV8EBNGP3fWzGFoHiu0+xAXu39WgQB58aGaWgFrNTK4PlxQkbC9kM7J6QfE0oU0QUAtzabvoIZIfotjeRH7Wvy2tR1AlFw45gCI6cWDMzqEFVNGFYeUxLG3Nqy3WnGjkzAzdyQv0pC4uuVxALeZA6Gm2+x/AJc+X1lsIgiDgkn3sCT5GtXMDRyZ/QEfi9G2pB3XGj5A2IkznBqhybuf0zA94sORXcIiLvyhU0UaVs5la9xbi+WmK7ZXUubfS4Np+W42+oiBS7Wqh1FHLO5M/4tjUT8iZi9cux/KT9CVbCQZXTjQ0YxxJ8GCYM5jWtV6FaOpb2JSNiNLalSrdjQjNZlw0M4dDdFPl2kCzexeVzubZsrnVvxgEQZi7nsO2So5O/ZChdAeGtfDkycKkLX6COtcWZPHuIBq6ZfLq6DmgEPvQLZMtvluLkt4MWSrF6/4KXr5SGN/SGRy7B0FwUxL+BuItppSvGiktB/XDIlFpmAx1j9N6optnvnLfossZhrUixanlVHIEQLop0yOKAjabgmVZZGezRZZloWk6pmlRVOojm83T3zNJbUMx9U0lnDrWRbQviT/oxOm+8fmhyNKyRZmWZaHdxUSjuNjLR5/ewcaN5XT3THDiVC9d3eOUFHuJxTNUVwXZvLkCWZH4q785RmYJ35DVQhBkLCuPYc6vDDDMBGCuSwTybyMEQSAQcPGJZ/ewb089r73Ryplz/QyPRImvQLluKaRSOY680857x7rYub2ah+5voaWlnMqKAPIdyBjlNeOW3af/HqvH8FSMoxd6aa4K01gRnvd+EUUfTs9vrtn2yhybAKhwbgOYK6Fq5D4EQcCnlrEn9Nm55WVsbPQ9joWJKEiUOTbNjXHTyIxl2vAqJYCAaRmYls5Vv6u1wpoSDcuymMoN0Z44ueTk3yX52Bt8YlUk43pIgkSpvZaHSj6LaRm0xU9iLlEDvBRi2hhbA09xfua5WSfnlcEjBzkc/jiGpVHpbMYurZ1knCraua/o4yS0GU5HXl10uaQRYyTbzQ7rwRWTm2y+FZvSTDz9XMG1eTaDkdO7WaqOej2R0EZJaKM4pAB+teaWvSZ0M4sFKMtkI7b5DlNmr6Xc0YBPKVqzhnpZVGj27CRvZsjoCSZyg4su25e6TN7M4LDcH3j5VCyfwqM4eLJ8N7JQmJw6JBtB2+2Xvy0EQZCRxNBtRWLhWhPucpCkld/XHySy6TznjrQtSzRM01pR9FISV3/csixRXOrDble5cmEQr89BNqMx1D+N3aFS11DMxFiMibEYzZvKKS33I4oCk+NxqmrCuFw33nuSJC4r5mJZhYbwuxGCKLChqZT7Dm0gFHJTWR6gs2ucWDwz5/MgSSKiIFAU9pDNaiu6JlcKVSrBJE8idwJVKkWWgoCFZkwRy76LKLiQxNW/Q/8uQ5ZFamvCfPmLBzl8sJl3j3Vy8dIQg0MzRKK3V4Kk6wYnT/dy9vwA+/bW8ciDm9nYUkZJ8cISwmuFq4aSf487g631ZWyt/2CrNpabtwiCsCKPqpCtlmh+GN3K4VcrUKW19wVaU6KhWTmGM90Mp7sWXUZEosW7j82+e26JZFyFIIiE1HLuCT1DJD/OaLb3lsZxyyGmsj0k9RmG0hewS74l5W2vwiY5qHNvuaVtrgSyoHJf0SfoTp4jqk0uuIxm5ojkJ8gZaRzyyiaFNqUBSfRhWTnsykZkqRiATP4iKy1hWWsktXE64y/iVsrYEfwy0i3uRyTfi2VZFC/I3K+h2F5Fsb3qlraxHERBYoNnN8OZLqLa5KKEO6ZNktAjeJXwujt/Loe3J1p5qnw3k7kY+0Jr1xOzFCSpGMtYurRyLbGaV7CuG0yNROhrGyGXzuMJuEgnsojSjb9TLptnrH+K0b4pspk8dqdKZX0JxVVBVFshU9V9aZBcJk+w2MdI7wTxSBrFJlNSFaJ+UwWiJKLldTrO9jHUPc7xVy+RTmZ543snAHB67NRtrqCk8pqM50qbTk3LWnWUUxAEyioC7NxXz8n3OolGUqRTOcZGouzaV09puR+Px16QvRVE3F47Pr+T6EwKl8eOw3VjJGw5A7HCNrmjBlWrgcOhYloWk1MJnE6VyakkMzMp+vunaL08QjyRRdONOTfrwu9yXa32bW7fqbbgkBuYSb1IXh/HrjQAJmmtnWT2NAHXE9jku7NMNR5Lc/pkL9t2VBMKL++mfKehqjIbmsuorytmaHiGk6d6udQ6TN/AFBOT8Tnn91uBrhu8d6yLi5eGeOj+jTz0wEY2bCjDdheaUv49/m5DEe0U2Vem5HarWNOrPqZN05dqXVKNKWwrZ7PvAH6l+La3JwoSJY4atvvvZ3J8eMkG6sVQ695Lf+o0NtHFZLaHes+BNVU6ulUIgoBXCbHFd5CjUz9adLmskSKmT62YaKhyoRzG7XgMVa6ei4Zp+ugtl7DcLsqcO0jqYyT1ibnPTMsgmu8nqY8jIuJWSvGrNRhmnoQ2SlwruHc65TB+tZqUPkFv4m1MSyNvJvGp1dgkD/H8EE65CKccZDrbiSq5cMnFTGbbkASVvJlEMzNUOHcjCTai+X4S+igCAi65mKBt9apLNslJvWsLPckLjGX7FlzGwmIqN0yZvf4Dlyh+e+ISe0KN/GjofZo9FXOfS4KIR1mduMJK4XQ8jmGM3bIrOMzqwq+gvlzXjRU3fpqmyeTQDD/84zfovDCA2+fE43ciSiKJ6LXyinxW4+J7nRz58Wmmx2IIglAguZVBHvrkPjbsrEW1K7zz4zN0nh+goqGE+EyCTDJHJpXF7rLzlX/6DA1bq9E1nSuneuk418dgZ6FP5t3nzwIQKvPjDbpuIBqrO+6FD9wfdPPQk9sWHMcXcHL/o5uxOxT6uidQbQoHH2hh2+5aREmkrqmE/YeaKK8KoCgyzZvKiUXTVFSH5rl7G7q57LkXhELvx92IijI/JyyLHz53mtraMINDEdLpPEMjEb79veNkMnkmJ+P0908xMDSD06nOySibKyBZy8EmVxB2fwJBkEjmzhPPFgQaZCmA3/kQYedHkaX1Mwy8HYyORvnP//55/tW/+9RdSTSuQlEk6mqLqKkO88D9LVy8NMSl1mF6eicYHI4Qu41G60Qiy09fPEd75xgf++guDuxrwLeInPDtYCUN6LIkUlrqo7zcv+bbXyvIskTpEs7lutaOoV3GsrIo9sOIYvmCFRCWlUfPn8bQ+xBEb6FPQlz4GrSsPKYxjmn0Y5ozWFaWgseEiiC6EcQQklSBIIYQFugjLoxhks/+BOvmUndBQBRLUO0PLnvshj6Erl0ETGR1F5JYgmGMYhg9WLP7JSAjiAEkuQ5Rql42KG5ZGqYxhmH0zo6Rm68DfB1kdReS3HTLVSWLjrtWA1mWRTQ/wWC6fdFlBEQaPTsptdeu2eTKLrqocW2i2F7FSKZ71esX2evxKMWk9RlU0YUi2hHX3zB9RRAFiQ3ePRydeo7FYmN5M0dyiabxm2GYUUwrhywVYVoZTKMweXLa9yEs0Qh+J2FZFlkjSlf8ZZxyCN3KM55tZYv/U+SMON2J15BE22wjtYBXKSdrxEjpE5iWTlwbxiEHsTDoTx6lwrUXpxxkMPUeXrUKu8tPd+I1HFIQu+RHNzOUObZjWBqd8RdxyiEMS2c0c45t8uexS6tv7Cxz1ONXixclGgBRbWq25O+D7dOodhXxzkQr49kYb45fnIvH+lUXD5RsXZdtel2fn/3brWdzZEmc12OwEHI5bVFvnHnLpvOceqOVk2+08uzXHmDz/gai00mOPHea8cHpueWGusZ5+a/fw9ANnvqZQxRXhZgYnOaFbx7lpb98l0Cxl8qGgvR0f/sIqkPh8S/cS0lliJG+Sf7od7/Lq98+TsPWamx2lYc+vY9t9zaTSf0EEPjlf/sZACRZwum5MQCw4uPO6wsSDUEQqKgK8sv/6IkF1xMEgVCRh6c+vnvB7xuaS2lovtYX1rypguZNFQsum8stvA83b++Dci9fDrU1RRw+2MyRo+2cOtOH22Xn8Ue34Hbb6Ombwu91cLlthB8/f46xsRibN1Xgmf29NM1gxQx3UQi41E3YpEqyei95YwIBAVUuxS7XIYl33uX5bytEUaC4yMvDD27inv0N9PZP0Xp5mM6ucfr6pxgeid5S/41hWLR3jPK//+IdotE0jz+yBb9/bctTVEVe9klqsyvs2V3Hxz+6a5klPzgIgoDft/i50XJHyST/O5Y5iUf634i2YhbqJ7CsDNn0t8hnfoAkNyEpGxckGqYZRcsdR8u9jp6/gGkMY1lJwALBjigGEaUqZGUjNueXkOS6RSb3JunEf8HUe+AG1UkRxXZoZURDayWT+M9YVhyX919jigHy2efRcscxjSEsKwWCiiSVIiu7UB3PoNjuR1hEUMYy02j54+SzL6Lnz8weW5qFS+QlBMGJ0/u7SHIDa13dsmYz6ryZZSo3TFybXnQZjxKg0tGES/av1WYLF6YSpsG97ZaIBoBdcmOflWPtSrxHjWs3yl0w6RYQCKnlOCQ3GSOx4DK6lSdrrFyDPp07TV7vx7KymFYGSXDP9mo4Cbi/hCB88C8uC5Novo+JzCU2BQrkYjxzgYQ2Rs6Mk9Inuaf4H97Qi1Fs38yUvQMLk03+jwMQ10ZuGvfGl75LDlPneWhunInsZUbTZ9ka/Dy6mWEq20ZSG78louGW/XjlIJKgYCyiQJXWYyueAK8nPlqxj4uxfmyijCJee4jejmzy8rj9B5msSCjy8o+wZDKHYaxswpeKZzj3TjtVjSU88cWD2F02TNPC0AyunOyZW67zwgBjA9N89Kv3c8+T2xFFkaZt1USnEvzkz48w1DVOWU1BNlbTDJ7+8n3sOLwBWZGo21TBK986Rsf5gm+JKIkEi30YuolqVxEECN+kBnU9bDYZu215cppK5da0X+BWkExll40GS6KA2/XBP28XgtOpcu+BRlo2lBGJpPF67ITDbhRF5p79jQBUVQY5frKH4mIvD92/Ee8s0cjnb79B9+rzQZa8uKXt8743rVwhyrmu9+rfPTidNjZvrGDjhnKmZ5J0do3T1j5KV/c4XT0TzMwkV/xMgVklv4k43/vBSSRJ4JmndqzoHl75/qpLKsxdhd2uUF11d2bA7jQsM1MgLon/hqG3I4pFSEozglAgJJaVxDTGZzMjnaj2R4G6RUYTsTu/hGmOY1kZLHMGLftmgRysEqYxRT73NqYxiK5dRJTKkNXdgIFpTGDoXRh6D7p2Gbc/iKTsmJeBsKw8unaGTOI/o2uXEKUqFPsjiKIXy8yg6+0YWhuQQ5QbUNQDiFIlsrKd9SihXzOikTbijGZ7503mrkepvY6QrXzNS0Ucsocyez2qaF+xAtVMbmDByfuV2GtUODbfNeVTsqjgVUKLEo2rhn8rhSi6kaUQyeybKFIFkhRAMBXS+TP4WV6b/07BtIxZQ7wkoiBR5boHm+Qla0QREK4zBLx2vVmzWtEW1uwyEiYGlmVgWSY5IzGrqFCASy5GvO4FbVkGJjp5M4mAQI370C2RDChko1yyD1W0kTEWPq95M7fk/XKnUOcuodIZRhZEniqfr9d/t8JhV3A4ln9ZxxNZ8rPR/eXKC7S8zsxEjPrNVdhnJ7+iKOD2OwkUF8oMTdMiNp1AVkSCpb4bpA3L64qRJJHpsSjabI23y+OgrCZ0Q3mQL+hifHBq1ccMhUnFSkzWEonsB040YvHM8kRDFtc8wruWkGWJ4iIvxUUL9xRu21pFc1MpsizeoPSVTOWwbtPbIKO1o5tRXOoOJPHGkhvNmCaaeQuPbc8t+2hcRS6n0dM1wcR4DI/XwUD/NPUNRbg9djqujCJKIlu3VVFa7sc0LYaHZujtnpzzU3G4VBqbSmloKplXPncVlmUxM5Pi/Xc78fkc7LunEVWVMQyTibEYl1uHmZlOIssSVTUhmjeU4fWtT+nmSiGKhSb/orCHPbtqGRqe4UrbKFfaR7h8ZZSh4ZlC5mqFmJ5J8pMXzlNc5OXwweY1EwIJ+J2Iy4xlGCap5PIGp5FchkvT40xmktR7gzT7wziVtVUhGk8n6YtH2BAI47d9ML+xaY6Qz76CobciKVuwOT6JrGxDEP0UTPfimMYwht6NINgR5fpFCb0giDjcPz+bvc1j6L3o+QtYqwgCX0OOfOanCFIQm+PjKLZDiFI5YGDqI+SyPyGfeQlDv0w29Ze4/Fu4OatjmVGy6W+ja2cRpToc7l9BtT+AIPqxzBS6dops6s/RckcRxWJszk+iqPtuYV9XhrUjGnqCyeziKjsAxbZK/Mram11JgoxXCRFUS5csVbkevcmT5MwUtptSzyl9+q6Y/F2FgIhzCfM7yyo4QK4ULtt+ALL58/icH8GmbAAsMtPnPxDVCsPSGUmfYiLTStaM0Z84QoVrLwG1lhLHNgREBAQkUS2UNJlZbJKX9vhPkQUbbqWcEscWREHGLvmYyLbSGXuREscWnHIYRXAwmjlLUp8gbUxzPTG5uQnbp1ZT5tg5t01RkHHIwVs+NlV0IC3hk7GY/O0HAVkQOVy0efkF7yI4nTaczuUDAvm8TiyWwTAsZHnpl7FFIfo475294K2xskmC3aki3NzsLAi3XFXjdNpwrSADEI2lC1H1FRCs9cLMTGpZsiNLEgH/B59JvR0sZNCXSGQwb/OZms5fYTr9AkHnk/x/7P11mF0Jft6Jfw5fhrq3mFElZm7G6Z7ugR7wgD3xGGMn6yQOPcnuL9ls8rOzySZZO06cOPbYnjEM8zQziFpMJamY8TLfQ/vHLZVUKpRUpe6x5+1H/UhV5x64h77vF9434HwQRQpjY5MrdhPJ/oRU4QQOpQkHd0c0igWDc2cGeefNK2zf2Uhv9yQnjvbQ0BgiGskwM50iky7wxNPbUFWZM6cGuHxhFEEEbJgcj3PsSA+/+CsP0tJaIts3w7ZtopE03//OSboujfLUMzvALs2xDA3O8OPvn2Z8LIbX60Qvmpw5NcDeAy08+MgmfOsw03AnUFWZluYKmpvK2b+3hSvXJrh4aYSz54bo7Z9elaePbcPYWIznXzpPW0sFNctULm8HoZB3gVjFrTAMk1h8abn867gam+bU1AgBzblukdB4Jsm74wNUuNwfHNEwI1hGqaosK7vRXD+HuIiCm20XsO0CgrByMqT0nNUQBO9dzTnYdhbN8XM43L+MJFUx965RtiLJzZj6ZUyjm2LhdVx2DrjhV2TbNpYVQc+/BThQ1N1ozo8hzBpRC5IDRXwQ0xjCKJ7DMnowjf4PP9GwsclbGSLFiSWXUQUHQbViTWVgb4ZL8lKmVq+aaEiCTIWjDd8tQ+kT+StrPghzNxBghTYu+46IkSQGSeVeIV/swrSTCMK9dzOFWcMoQabcsQkLA0nUSi7dcog23xMkikOl/RVKP/co1bR4HyFeHCwREEFGmP2v0rkFSVCxMBCQUEQnde79JPVhBCRavA8TUJsQBYVG93141dp5GvSa6KPd/xTxwsBN27zz70QRlGXbjz4MbVPXIQjCug1+rxfcbnXVLTdj43G2bK5FlpfPzCmqTLDCx/RYjHy2pCRlWTbpZJbYdMkMVBQFAuU+TNMkOpHAMq0507Ox/ikM3aSs0o+ilR6vq72GBEFAkkSKK3hK+LxOfN6Vz1U+r5NIlgZZpRWCkPWAbpjMRNIrDkUrikR5+Yd3WPhOEY1l7tqt2aVuIl28SCTzAwrGEEHXE+hmhEjmB+hmjKDzcTR5ocnY7cKmlPG2LJvOWdniH3znfcLlXj723G5e+sk5ensmScSzVFUHaG2rpLaujFDIg6xIXLk8xp/+0ZtcODtEfX0ZkvPGfSZJArFohu9+8wTd1yb42HO7OXi4A0WRiMcyvPvWVa52jfOx53azobOaXE7n1Zcu8PYbXVTXBNmz7/YFOZZD0TSJ5XP4NQeOW1ovrxPD5aoDgiAQDnu5L+xl25Y6du9s4tSZAd490s34eHxFcmkYFt09k7x3tIdPP7dnTd67lRW+FZXbDMMikcySz+uLEuO8YXAtPsPzg1eJF3LsCFfjkhU0SeZqbJrL0SmyRpFaj5/d5bXkDJ3T06PcX9OMW1H5ycAVtoWqsIH3J4dBECgYBtUeHw/VtpAq5jk+Ocx0LkM8nyNvfsCJNkGem0u1rGksa3J2jmP++RAE7Z7Pr0pyK6r2CKJYuWB/JKUdSe7ANPqxrRlsK4Yg3UyQTGwrhm1HEYSyUiVGnE+SBMGBKNUgiKHS/IcVxbatdYt914RoWLZJxkiSNxeaCl2HRwngkYPzWlXWEg7JRUAtX/XyzZ59OCQf2i3EZ0vgKZQPSH1pKayHKpHX+SS54mkMq5Tl97s+hbgKxr7WEAWZWtfi7TphRwdhR8cty0uEHRsIOzYsWN6rVONV5ks9LrYOgFr3wm0KgkBIayOktd3OISyND9gf4286nA4Vv9+Jokgrti8MDE5TLBo4ncsTDbfPyY77NvCDP36Dl//6CJv2tZKKZTjxykWyN7kIt29roLqxnGMvn8ftd1JZF2JqNMrRF8/R2FlNXWsl0m0adSmqTKjKz7kj1zjzVheh6pJHhT/sxXtTxt/lUikrc6OqEsXi8sc9NhZn88baFY97PTAzkyKbLaxYudE0mdrqtcnsflhgWTYzM6m7Vp5yKh1U+36JWPZVEvl3yBQvYtkFVKmaSu/P43XsRV5CTedOECxzs3FzLWOjMSoq/NTWlbF5ax1nTw0wPBShkNcRRYEt2+bLg5dX+Pjet04wMR5fYCZZLJp886+O0t87zXOf3ceefS1zJnbRaIYL54Zoag7zwMMbcTgUbBuikTRdl0bo65lk156mW5yXl0aqWMCtqOQNHVWSyRk6RdNEEUV8mgPdNBlIxBhNJ9karsK0LRKFAgFNQ5EkBhJx4vkcHWVh/JqDVKFA0TLxqhqqtPB+9vmc7N3TTHt7JRs7a3jx5Qu8f6p/xSpeIpHl9NkBHnqwk/I1UOSqrgrgcCgkVjAezGYKTE8nqV9kTkMUwCHLOCWFomzi15xokky0kOXtsX5USSbscHF0YgiPoqKJMm+N9bOnog63ovL6SC/lTjeWbfP1nvP8yqa96JbJS4NX2VxWwUgqwduj/RyoaiBZzJMs3p0r+91CkmqRlC3oxRMYxRPkUr+Hqj2CrO4pBeFLKEzdC8jKJkSpeonAX0CU6gAJ0LGsJKI0//dz5ERYLtF1/eel9vL1xJp8k7pVIKlHl82se+QAzmVagO4WqujEK6/+ZeVXSwGpaeuISLMn1Kba0Yn0AV5g9wqa0o4gODDMCQRBQxL93Gsfjd70GMOZaWaKSSKFJE9U7eZE5AopI8fHag8S0nwMZSY5EbnKTCGJJIi0eKrZF+okoLrpTo1yOTFIWPNzJTlMwdJp99ayK9hGSPuZidVPO+KxDL3dk3RuquXi+WGGBmfYvbeFlrZSFVKSREJlHvw+JzORpZMcAFe7J8gXdFaauHG4VPY+uoWhaxO88d33OfXGZTwBF063RvNNykq1rRU88fmDvP2DUzz/1XcQJQnTKFUyHvnUPirqbr/lzuVxsO+JrQx1T/DNP3gZp8dBTWM5D39q7zyiIUkioZAHv9/F9PTis1vX0T84syqCtR4YGJyhsEJ1RhIFQmWeO26PEWBVhP5eO49Ho+nSjMZd9p4Igogm1+LRdpLIv0MyfwRRcOHz7sOjbVtTkgGleRRNk5EkEadLxeEotWRcr9iV/EJgZDhC16VRJsbiZDIFDN1kZio1Nwt1HbZt8/Lz5zl7eoAt2+rZtbt5nlN2PldkYixOIp7lf/zXG+a0M9MppiaTpFJ5DN1C1Vb3bjoyOsQD9U2cnZqgLVDG8fERQk4XU9k0jzS0ciU6zUwuw1AyQXswRM7UuRKZRpMk9lbXMZCIMZ5OUe/zk9V1BhIxpnIZ2gIhNpSFkZcgPAG/i/sPd1Bd5UfTZN5579qy1SzTtBmfSNDTO7kmRMPt1qiu8jM9k1p2u9lckYGhyKJEQ5VkOgJhNpVVEC1keaCmmXKnm3Mz46SKRZ5sbGRTWSVjmSS9iSjNvvnx1s3xn1dReayujZl8hlNTo4xnUoxmkgQ1J083buDE1AgT2eWf2esNQQyjOp/CNAfRC+9QzD2PoZ9HljcgKVtQ1L1IytYlZXHXdd+kyrmh9EV/LzhLzz0b4NZnm4ggBhCEILadwzQGse0ignDjHVCS9J3AtqIIYgBRDK5rR8uaRNSGpZMx4ssu45Q8ONbBcfA6FFHFJV8ve63+6d6VeJ1G9068sy1UFxMvssX/kTVzR7Sx0a0CGSNJ1kiSMzMUrCy6VUC3i5iWjmkb8/4YtoE1+/eiVbhjM8LlkMq9Qq54DmCufajM++tIwvqRwVsxkYvxg9GjHAhvpD89wZ/0vcjWQDMDmUnenjrPJ+oOkzWL5K0iNc4QWTPP8UgXebPIx+oOMp6L8r2R99jkb6TJVYlgCrwzdQHLtnikcgeKeOeXt42NbVtkzRQZPUHOypA3MxStHLpVxLB1TFvHtM3558/SMSn9bDI/SNZYPhD8GZZGIpHl/NkhykIeLl8cQdNkLl8amSMaAOGwh7Iyz4pEY2AwwvRMinDIu6B//GaIokhFXRmf/s3H6L88Sj5XMuyrqAmSSeXn2qFUTWHrgXbCVQFG+6bm2qxqWyupbgyjzrYm3PfMTjp3NxO4JZh48guHOPDktnk/k1WJjbub+eI/fprp0RimYeEPeyhbRFe+qtJPRblvRaJxbZUEaz1wrXuCQn55gQlFlWlpqUBchWLOYrjuyr0SMquorKwlBoYitzUkvBQsu0CqcIqZ9Lexsajx/Sa6NUOycBwbi7DnOTSpbs1aHgSBuYBDEMSFwYcNV7tG+fEPzmDoJrX1ZVRVB1AUkWNHuhddpyyLPPOJXbzz5hXeePUSTzy9fR43lGURp1PF47nRSeDxOGhqLmfrtvpVqSldR38ixqHaBoZTCao9HnpiETaFK+iOzTCWTtIVmWZ3VQ2RXJasrjORSdMTi6BKModqG3ErCgGHA7/m4NzUBJciU+iWRcjhQrfMJYkGlFoqO9qr+NIXDzM6HqenZ3LZfU0kcvT0Ts2pl90NBAE6Oqq41DWGZS193aUzBbp7Jrn/8OqNWVVRwrStOfPNnGFQpolosxUjGxvTspjIpErzbQgENReSWJp1VEQJy7YRBYGCaWBTms1ZjZnnekIQJGRlKy7PP0JXdlIsvI6hX6Jo9CMUjqHLryIrW1EcH0FRdyPcQynpEpG4M1UyQRAQxTIUx4MUcz/A0E9SzP0AxfE4ouiflb09gV54BdtOoSj3ISkbuRup+ZWwNkTD1smtMF3vkNyo4vr1gAuCiCo6kAXltoz7pvO9VDk3UAoDBCZyV9joe/SO98O0DVJ6lKn8EDPFMWLFKdJGfDZILWBYhdkg1cC0TSxMLNuaVTyySn/Hwpr9t21b6zKcni0cR1M6UaT6uZeUKNz7rGfR0tlf1okiSHx3+F3+t46PIyJwKTnEc8L9NLoqCGs+/IqblJ7lh6PHOBfv49m6A0DpodbkruQj1XsxLJOvDbxKd2qUfaENBNXby0QUzByT+UFmCiNEihMk9Ah5M03BymNcJ4az5MKyS+etpGplzf594Xn8GwvbXgOPgOVhWTbpVI7p6SRuj0ZzawX9PVPzlqmuClBZ4eNa99LzYQC5XJFz54dpaa7AtUJ2X1YkqpvKqW5avhVTdSg0dtbQ2Fmz5DJt2xpo27awh37zvoVOrIIg4HBpbNrbCnuX3TR1NUGqq/xcujy67HL9A9NEoxnKw95Vt5+sBQzDpOvKOLkViIbDobBlCQ+O1UCWxFWRlEymyN37dK8e3T0TK87arAbJ/FEmkn+CLIWo9Pw8Hm03pp0iln2FeO418sYAtf6/j1O5u2D1dkKMc2cG6euZ5GPP7WHv/hbcbo1EPMv3v31y4XoFgf2H2ti+s5FC3uD73zlJoMzN/oOl/XW7NWrrynC5NZ795O4FilUOh4K8Cs+Y66hyeXhjqJ+r0Rn2VtViYtMdnSFVLODVNLyqyqWZKZKFAjnDYCgZJ1ksEnSIyGIpeI7l80xlMgQ0B4ooUjAN3Ko6T/p7ObQ0l/OZT+7hd//jT5ZdLpMtMDG5eg+slbBjWwM/+vHZZQluNlsiGul0AY9ndXMHDd4AdR4/Lw938/poLxldZ2NZObUePx5F40+7ThHUnIiCMJfmvZX8a5JEsy/IkfFB/uD8EXKGvixpu3vYsISs/M0QBAeyshlRrkPWDmHql2dbqY5j6OcwjCvo+hkcrs+hOj+GKN6bFs9S8vfOA39BDOJwfR7L6MPQu8im/ity/mUEwYNt5zHNAUyjF0luR3U9hywvbEVfS6wJ0TBtY0UvB0XUUMT1C2SvDwYrooZh3p6pTtHKYtkmupW/LQWn67Bsk6QepT9zieHsFSLFMTJ6gqyZIm9m0G2de/mSA0jGMsRnUvjLPLh9TkTxRvkbQEDCoWxFUzZ8YIo0AD7FhVdxUq75Cage/IqbgOohY5T6N3NmkSPTlxnKTpEzC4xkI3hkx1x5vkzz0eapwae4sG2boOplKh8jv8prQLeKjGS76c9cYDzfT0qP3lR5yv3NJgt3BYvFjX/WDqWBRYFzpwa5/+FO0qn8gkHLygoftTVBZFlc0Bt+K9565yqPP7oZp0P5QK/5tUBZmZv6ujKcToVcbukXai6nc/7iCE2N4XvaPtU/MMPYeHzFXnWXU2XzpqWJ2kpYbSAai2coFk1UVV73c2/bNhcujawJ0TCtLC51EyH3x3EqrYiChkKAcvdzOORGptPfxDDj99TzUxRFdN0kmymQSuYYG41x9N1uMpnCot+tz+8iWObh2U/sIpXM8c2/Ooo/4KJzYw3hci97D7Ty8gvnee3li2zdVo+iykQjaTKZPO0bqmlqXv3s5aG6BmL5PI2+AOUuNyICbWVhmgJBKlxuDtU2EM3nkASBSpcHn6pRMA202aHwlkAZPs2BT9OocLlL8x6mQYXLjXQb182B/a2EQ15mIktXHA3DJJ0pYFnWmiQBtmyqxed3LkvuSy1bcbqujrF39+KeEHsr69BNE59aIiJuReWRulbGM0kKlklAddDoDaLJMr+wYSeJYh5FlNhdUUuzN4gNfKmzZAro1xx8ccMO6j0BZFHkCx07yBhFVFHCJStUONerg8LGtldJ4gQBUQggqruQ5Y0o2mFMYwC98B7F/I8x9Yvk0v8TUWpA0Q7Pa0H6sEIQVGR1F07P3yeb+s+YxlWK5jAgIAgqolSF5vwYquMJZHXvuldr1mYYHGvFKoIiqOs++yAhowgKy49DzUejexeX46/QxasUrRzVzo3Iq/TQMG2TaGGCy8kj9KUvENNL1QvdWlmrej0xcGWM469cZGo0xuGnt5NN5aisD9F+c2ZVkIik/gBFbkCkVLIOen8JSbx3rVMA8qw4gCAIc61OAiVFpoyR5y8GX8OwTA6FNyEKAiciVxnLReZomybKOCVtbh2iIJRKsytk2wtmjp70GS4ljjJdGCWpz5A1Zx1Bf4ZVwMJeZ6IRCnl59rndGIZJfUOIXLZIde38jJKqKjQ2hAiHvUxMLP9i6R+Y4fSZQR55aOOSWv8/LZBlidaWCmqqg/T2TS277DvvXuOxhzfhmDUDvBc4dqKXeGJ5KU1FlujcUE1Z8M5fcm63hrYK47NCwWBkNEbnhqoVl71bDI9EGR6Orkh8VwOfYz9ebSeKVDmvPUqWAvid96PK1ahS9TJrWB1u56m370ArY6Mx3nnzCu+9fRWf38mWbfV0bqpB1Ra/r0RRoKomwCc+vYevfuVtvv61I/za33uUquoAh+7vwLbh9Ml+Th7vxbJsnE6V9g1VtHfc3rFVuDyEne65zPq28ipa/UGYzbZXujxUujxzhMivld591/8ddDgJ3PSzOvnGrN/tEFS3S6OuLrgs0bDt0uxQobA2M1Qej4Md2xp45bVLy85pTE+nOHqshx3bGlCUhVWaGvfC+cbr39ut2BBcnASWOUqt55oks7mscu7nm0OViy6/WpQGtGdlXK0US165toE5K117W+sXnUhiM6LUMDuQXU4+86dY5hB68XjJZ0P66TA8tK0shn6x1B7leAKH81MgOGbVttyIYgWiVHFPFLXWRt7WtjBW8AQQBQmR9XUvLQWat7eNOvc23EqIrBFHFhTKtAbkVTDWrJHiYuI9zsXfJlIYI2smPzT+G8O9k6gOhXB1gGwqz/RoHKdnvpKWW7sPXR6evcikkk7BBzIELyz6VxvIGDlORq7x620fZV9oA1mjwOXE4C2fFlbVo30zBjNdvB99mZHsVeL69IfKz+KnBbZdxLbXl1CrmkxNTZCpqSQjQ1HKK7zU3EI0BAE62qtoagitSDQMw+S7PzjFju0NVFb4fuqrGp0dVbQ0henrn1q2i627d5ILl0Y4fLB90cBirTE5leTk6X4ymeWvD82hcP/hjrvK5kqSiNfrWJXy2NXu8XtCNI4e711R/We1UG4KamzbxLLzJZ8fQUMUVFxKJ2vRW+1yqTz6xBYOHm7ncvc4x0/388hTm+loK31fjzy+mXxOp6LKz49fPY+z3M1nDrbhdqpoDpmq6gD7DrYhigLOWTPJ+voQ/+4//hzNrTfEG5pbK/iV33iUWDTN1f5J3jzRzec+vpdHn9jC1u31pNMFbNtGVWX8AReh0O0nvubeB7bN/pq6eff5rff8Ys+A5Za/HazUonljG3e8iVvWI/CRx7fw+ptdy85p5PJFLl4e5eKlEXbuuDv/lXsNQfAhCEpJjtkYLLVH3RIo27aBYXRhmWN3sR0JQapE0R6kmH+hJAFrzmBze90yHxRsW8fQu8hn/xJRDOF0fxlZPfCBKWmt0VbtFeWxBIR1f7GXjNZu76Wlii4qHG1YtjFLhJbfT9u2iOlTvDP1Pa6mTpI2YmtOMCRBRhZVFFQkUSFjxDFW0W94HUbBwBtw4Zh94Nu2vYD4O9VdONh+4zPmBMLa+TeuCSRBxMYmbeTQLZOu5BDHZrooW1FRavHzYVg6Z2Kvczr2GpP5QfTbmOVZDQREZFFBEVRkUUO38uTN7N/I9ivbLmJbK5s/3Q1i0TSvvHiBocEImiYjyxL3PbiBrdvnzzzU1QZpb6vi/IURsrnlz2lf/zTf++FpfulL960qE/5hRiDgZmNnDWfODS07DF8sGvzgx2fYsa0Bv9+57s/hN9/qYmg4umxWVRCgotzL7l1Nd7UtQSg5N7tcKonE8sH9e0e6+dhHd65rVScSTfPeke4VSdbtoGhMEs2+TCL/FoYZpcz9DGH3J8kWrwAmbm0bsnB3KnuyLFFVHQCg563LTEZSVFYHKK8orff67wDGJhNoikzbhioqbhI5CNxSmXK5NbbvnB/IyrJEY1OY8kofbxy5yvBYDEEQ8HgdeLxrKysvCAJe9d76H9yMlSp6UPKQUZS1e+9u7Kyhva2Sy11LB9m2DcPDUV5+7RItLRX4PySGiKuBpLTNGecV8z9Gc30cUXDNVftKZnVT5FJ/CCwfM1nmBJaVQJJbEJYYvLbMIWxr1j9JCiPw4W+bKsHAtsaxrQi24ME0J5HsHILgYT2HvpfCGl3hN+n2fsC4k5doab5j5aDDti0ixQleHP8z+jMXKFqr14GWkPGpJfdyvxLGKwdxyT4ckhtNdKCIDhRBQxZLJm/X3al1u8APRv+Qyfzqy4BVDWGOvHSO7nPDSJJIx44Gwje9KADEWwbzY+nnCXq+jLSMpNq9hAB4ZCfP1hzgR6NHeXH8fepd5ewLddKfWV7NYzHoVpGjkR9zKvoKseLUbQT/Ak7JTUirIaCU41NCuGU/TsmDJjpRRUdp/khQkUT5JldxkVPRVzkbf5PcMv4yP52wscli2cvPZd0tEokcUxMJPvuFA0iSyOULo1ztGl9ANFRVZtvWet4/1c+Vq+PLrtM0LV548TzNjWEef3TzsgpUH3ZIksie3c28f6p/RdWtS5dHefm1S3zi2Z3r2jZ25eoYb717leQKGX1VlXni8S2rNlxcDlWVfjxux4pE41LXGAND07Q0VSy73N3g5VcuMjgcuWujvusoGpNMpr5KNPsyilSObkUpGmPYGOjmOMn8cRQphKyurZy3MPe/9YFDkzm8t5VdWxv+RtoNZbNFhkeiyy6jqjJ+v+uOFdcWg6YpPPfx3csSDYB8QefkqX5eaAjxqU/suSeVzrWAJLcjKZswjSFMo5d07B+jOT+OpLSBbWMYVynkf4KpdyFKTVjmwJLr0otnyaV/H1GqRla2IMltCGIZAhK2ncDQL1LMv4ppDCCIZcjqPoRFnMNvwMa2DbCz2NZ06e+UknKmOYkguBAE5z2qKqhIUhOCWI5ljpFL/t/k038Ecx0/s7MaYjmyshPV8TiS0sJ6WRysyRGXAqvlL1T7Dh2sPyywbZuUkeDlia/Rkz6LuYoKg1v20+jaSLNnC9WOFtyyH1lQSmQCudROJoizpXBhLki92WQlb2ZWcAZfiJYtdXj8Tjq2NyJJIk0bayivKbWcpPNvo8lt5Iqn0c0bQVkq9xoB9xeAe0c0dgRbafPW4Ffc7Ay20eqpRhUVDoQ2stnfhCapPFN7gMPlm7GwcEoaDlElZxYREdhd1k6Ht46geqO8/tGa/RiWSUC9kV2zbIuT0Vc4GX2ZeHFqxetQQKTG2UKrdwf1zg7K1CoUUUUSFGTh+nmTEBFLEpClO2CBOY5bDiDeY2+SewHLzmFacVbKGN0tRFHA53eVNN+FkrZ+Jrt4pnjzplq2balncHBmRaWjVDrP//rTt3G5VO4/3PFT3UJVXR1g964munsnmZlZmmzousk3vnWc9tZKtm+rX9Pg5jqisTTf+f4p+vqnVwy0Kyv9PPnYljX57psbw/j9TkbHYssul88X+c73TvFP/uFT6xLcnjk7yGtvdpFKrZ0RWbp4lkzxMpXeLxJ0PcZo4g/mfqdIVRTNMQxr7ZSLriObK/Lym5f5b/1vYts2jxzu5MFDHfhnHemT6Rx/8Z3j9A1NI0siTzy0mQcPtON2aQyORPnhS2e52juJbpi0NJTzhef2UV8TxDQtrvVO8t///E1yeZ0tnbX8w18tqTwmUzleevMymVyBL3xyH+pspv/Y6T7OXhzhgQNthMu8vPZOF6fOD5HO5CkPefn4R3awbWPth2ru6uiJ3hWrWm6Xui4tnAf2tbJ1Sx0XLo4su1wkmuGFl87j8zp54rEtt6Xu9UFBEBw43b+BZYxg6Gcx9DOYZh+C4CgJTZHDtgtors8jihXkUr+79MrsLKbRi6l3YRSOgaDdqGzYJradxbYzCGIQp+fvIas7WTxkNknOfAnbjmHbecCa9akYBywM/RzJmc8gCBIggaChqodweL6MKN25EMbyEBCkSlTHoxSy38CyxsC6lXwKgIJeOEKx8AZOz2+iag/AOhhErw3REATkFVjadTnX9YSFtW799kUrz8noS3SnTq1IMvxKmG2B+9nkO0hALZ+V3VVnycT6BzWaQ6G2pYLK2QCtkC1izaq/lMqMMtnCcWSpEmm2B/jmIat7BbfswC2XyuWyKM393aM48SilF5pXceJV5ldf/LgXfP46biYd19GbPse5+JurIhntnp3sD3+UKkfTbKVJQxSke3LeVofl98PGYL3VoCwrhWlOr+s2rqO3e5J/+6++i9OlMj2VxOXSGBuJsn1nI4cfuCHJ59Bk7jvczqXLo1zqWl7yFUqGar//314lHs/y7Ed33FOyYVn2PL+Cu4Esidx/uIMLF0d4573uZZ2oo7EM/+W/vsT/7198jLbWijU95mQqx199/RjHT/RRKCz/DFYUic88t2fNWjbq60OEQ15EUViW4Nh2aTB+184mHn1o45ps+zp6+6b42l8fZXBoZp5h3d1CNyeRRCc+xwE0uRZRuOHvJIt+LLuAtcYtoACDo1GqK/188ZP7GBiJ8M6JHlxOlUfu6wTg/bODPPHQRn7p84fp6Zvie8+fpqEmyMaOamRJpLEuxP37SzNB3/nJaf7kr97l//wnzyKKAs2NYf7BrzzK6+9doW8oMrdNt0vD73Ny6eoYA0MROlorMU2LK90TFAo6Qb8bSRSoqvDzqY/uxOdz8vbRbr7941NUV/jm3OX/+hvH6GivYsf2BkRRvOcVk+mZFH/19aMrkm2/z0lr89pX11wulV/4wiH+5b/6Doax9LvAtm1GRmN841vHKRYNnnx8yz1VpjNNi7GxOF1Xx6mpCaxa5lpSOvEE/jOF3PfQC69jGv1YVhJBDCKre9Ccn0ZRD2AaF5cVBlK0w7h9/wa98BamcRXLnMSySlUoQfAiyk0o6j5Ux1PIyiYQ3Es8M20M/RS2nWHRtm07h2X23fQDAUMMlVqP16GQZNtF9OJRssn/gGWOIskbUbS9CEKQGxs0sK0khnEZQ7+IUTxBMVeDJNcjyQul1+8Wa0I0REFe0SNDt0rmdOsJyzZXVWm4/fVaRIvjHI88v+yshIBAg2sjD1Z8igZXJ4roQFwHdrgaiJKIOtsWcuSFc1Q1hOnc1YRD3YaAhFPbjVPdgSyWHnT5wsVZxv03Czkzzfn420zkB5YlGSISj1d9kZ3BR3BI7g+pO7y4YtnVtvLY63AP3AzLjGIay2fL1gLBoJunP7YDywZRKAXooiggiAJVVYF5ywqCQGdHNfcdbmdiMkEkunK7WiSa5o//7G0udY3xpS8eorZm/TTSLcumu2eSo8d7OHGyj//yH76AtoRCz+0iHPLyxGObGR6J0te/PAEcGY3yf/3OD/kn//AjbN9Wvybbn5xK8pdfP8qbb18hlV45m797ZxOPPbx5zYiOqkps3lTDxcsjRKPLt/Ol0nm+8mdvUxZwrdkg7PkLQ/zp197j0qXRNVGamo+Snn7p2TX/+zLtFALKuszWVYZ97N/VzI4t9Wxoq2JkPE5X9zg7tpSumZbGMPfta6ejtZLO1ireeO8qQyNR2porqKrw8fiDm1CUksjII4c38F/+12slMzdBwKEpVFX48HnnxwySJNJYV8a5S8N09UzQ0VrJ+FSCqUiKDS2z8yACHNrTgiiKiFJJCOQ//veXyeb02fXDe8d6+OZ336euJshDD3Ry3+EOKivWtrVsKUxNJ/nPv/cSI6PLt00BhMNeOjvvXjFsMWzZVMunP7mbr3/rxLLLWVaJbHz1r44wMDTDcx/fTcMiruFrBdu2SSRynD47yHtHu7nUNYbX4+DnPr1vVUSjROIlRLkZp+fv4nD/ndnkmoWABII6O4egIKsHCFQcRxBkBHHhMQliOZrrE6jOJ8HWZ1UUr9+/Yum+ErRSyxPSMlP7Ev6K12/LV0oQNARxvo2q4ngAn7oT29YRRB+CsLQan8Pzq2juz4NtI0o3lL9s28Yyh8kk/jW2OYnqfBqX95+C4LrFm8PGxsK2kuQyf0gh85cY+gVMY+jDSzQkQcYhLS9RWLTy6OusUmPaOsV1kJY17CKnY6+TNZd34W3xbOPxyp+nytn0gQSqfZdHOfvutQU/7zrVzyOfKjmAXTfl8zqfKr2kZolQReCfIX5I5jPWEn3pC4zlepetdAmIfKLu77HZfxBF0D68rTSCBCsMo9nk110Zw7Sm0M21d6u/FR6vg917W+b+nc0VS5nNgHtRt2BFkXjqia309k7x1jtX0ZfJ5l1HKpXn9Te7OHtukEce3sRHHt9KY8PavGjj8SzXeia4cHGEE6f6mZhIUMjrGKa1pllvURTYu7uFa92TRCLpZRWPbBtGx6L8n///7/HxZ3bxqU/swXuHQ7i6bnDq9ADf+PYJLl8ZX5VvhM/n4Nd/5aFZj5S1gSAI7NnVzBtvXVmRaACMT8T59//pJ3z5F+6/qzmdRCLLCy9d4CcvnmN8PI65RnMZN0OVajDMOIncm2jyjUDMsrLEcq8jS4F5ylRrBZ/XQXnIiyxLeNwioaCb0Yk46dl2oOpKP0G/C1kSkRwKmqaQyRexLJtMrsgPXjzLyXODpNJ5CkWdQtHAsqwVv+v6mjKqK/309E+SSufpH5pBVWSaG0OIokguX+TtY9d4+1gPU5EkxaLB2EQC0zRhlowViwaJRJZkMkdP3xR/+fWjdG6o5sD+VvbsaqbmlnnFtYBhmBw51sOfffVdhkejmOby10Iw4GLn9oZ1GcQWBAGHQ+HTn9zLhUsjXLq8/LyGZdvEYhmef/E8584P8/CDnTz68CZqqgNr8i40DJOR0RjnLwxz6swAV66Nk04VKOoGhmEhScKqXcL/oPu7PF1zgBZ3DYLoQWA5VTINSV6avJRiH0ep7equICCK1VyJzHB2Yoyn2joIOG7/vAqCE0Fa3edE0QcsRp4L6IUTWEYvotSA5vwMolTJYp0QAmALXmS5naLox7ZS2PbaqOXdijWJhmVBxiktL0OXM9PkzfVTqbFtG93Sb2tAe7XrLZoFzsffXna5cq2OPcHHqXa2rGkVw8ZetdP5aP80/V2jbLhF6UOSxAWXmYCCbo7M9tqXbnKHspn1GgZab1wP3G5+MFq2RX/6ItHi8q7Re8ueZJNv/5qTDNPWsdZQcUpAQRSXJ4OWlcCyEti2uS4VKts2McwRdL13zdd9Yxs3XtLSTX3DI0MRhgZm+MgzO5b8rM/n5HOf3c/0TIrzF4dXlWQyDJOp6RTf/u77/OT5czQ3hdm/r5VNG2tobgovyLzeCtO0iMWzjI7FGB2LMTg4w+Ur44yMRMkXdEzTwjStuX1ZDx4ryxKf+sQeJqeSvPFW17LtS7YN8XiOv/rGMV597RJPPbmNBx/opLYmsKptpdN5zp4f5oWXznPx8iiZTGFVw8+iKPCPfutJGurLVntYq0ZTY4jt2+oZHomu2Btv2zA1leL3//urvPLaJT793F5272xc1UCsZdkMj0R590g3b7zVxchIFF03F62Vej0O0pnCXZFKr2MPWf0SE8mvMJ3+DqadRhQ04rk3sW2d+uA/waE03fH6Vwth1pji+rFoqjJHGgRBmP29Ddj83h+/Rjqd5+9/+WGqK/2cuzTM7/7XF1e1HadDoaWxnLGJBFd7J+kfmsHj1miuDwM233/xLGcuDPHM49vYvrmesYkYv/v7Ly76/VuWTT6vk8/rHH+/j1OnB1BVmaoqP9u3NbB5Uw0drZVUVvrviGxals3wcJTTZwd5+dWLDA1HyBf0FZ85ggD1dWU89sjaVfUW2QrBoJvf/q0n+Vf/1/dXnF+C0hzX4NAMX/uro/zgR2fo3FDNnt1NbO6spb6+bFXJgUymwMRkkrHxGH390/T1T9PTO0ksnsUyLQzTum2hhOvXXNLI0pseJWsUSuf7pi9aEIRF77Olfn47EASBgmGQ1XUmMinqfQE0SeJqZIaAw0GN10ckm+UvL5zluY2bcSoqk5k05U4XWUNHQMCt3qOWNNuclfW154z5lmy3tm3AwrJS2HYGUSpbA9K1ONaEaCiihk9ZPquSNZMruoffDYpWnowRX/P12liM5ruXrWbIgkKTezMb/fvXvlXKhsIqZUTrmssJfHY/m/fNL32pmoI34Jr3s0jqD8gWTiEK7rmAtCr475CE+eW8Dwq2faPR6Uaxb/4tc/O/DasUyCmSOPfwjheniOoTy7a7eaQA95V/AlVce9lPwy6u8cyQODtj48K2l7omTAxzCMuKIUnhNdx2CYY5SqFwmvUcBE8mcrx/vJet2+r58Q/OzA0uT00m5+Q2l4IgCLQ0l/PLv3g//+1/vs617olVV7QNwyKVznP+4ggXL48iCAKSJOB2OwiVuXG7NVRVxrZLAUwuVySX00mm8uTzeumanZWSXskwcq0hCODxaPziLxwmkcjy/qn+Fdt4dN1kdDzOV776Dl/76yPUVAfY2FlDXW2QUMiLy6kgiiKFgk4ymWd8Ik5v/xS9vVMkU3ksy1r1dyuKAl/+0v0cPtC+LsGVKIo8/eQ2zp0fXlF57DpyuSJnzg1y/uIwXq+TTRtraG+tpCzoxuNxoKkyumGSTueZmUkxPBqlu2eKyakkum4sGzCVl3v5nX/zaf7t7/6AoeGV22iWgiR4qfL+Mi5lI5HMj8jp3SCIuNXNhN2fxqNtWxfJzVQmXwoOLZuibhBPZNE0Gbdr1hx1ic/pusnFK6P88ufuo6k+hCgKTM4s3wlwMwRBoKUhzPlLI7x9/Boel4OWxjAet4ZpWvQNTtPWXMGWzlp8HgddyRxFY+VnrGXZFC2Tom7S0ztFX9803//hKURRxOPWqK0JUlMTpKLcSyDgwud1llznJRHLsikUjdn7IMfkVIrxiTj9AzPE4hkMw7yt4Lmyws8zH91Befn6dRBcv8UaG8L8s3/8FP/ud3/E9CrOg22XEi/RWIajx3s4dqK3ZKarSAT8LoJBFy6nhqxI2JaFbpgUCgapVJ5kMkc2W5x79l1/Ht7to9C0Lf584AXenDrDdCHO/37hj5BmY5b/vvu3qXKUYdoW3x15ixfGjpGzCnR6G/n5xsdp8dRSsHRORq/w/dF3GMvN4BAV9oU284m6+9EtgyMzF+hNjzGSm2J7oB1JEHk/cplHK3fzZPUB/IqbkVSSF3uu0VpWRr0vQCyfI6vr/PWl8/zL+x7EtC1AwKOoFAyDtwb62VFVw/nJCR5tbmF9fbdvgiDNVjBKSUe98B6iVAvz2qYALLBN9MKbFHPPg51HkjcgSnXrsltrQzQEDZ9ShoiEtcQgakqPkjGT2La9Li+aopUnZazM2m8Xlm0xmu1ZdhmfEqbBtXHNDQlt28bEJKWv7riaNpYUDG79fh/8+G4kaf7PDHOaisC/QJM38GGDbdvopsnQTALDsmivCpHOFxicjlMR8BBwOphOZSkaBm5NJeBycLx3mOlEhvs7m6gMlB7g0eIEGWN5RZYO3+4Vq3F3egxZI7mmLvGCICCKHiSpEsNYunVJ13tKutlSiLUc8LdtC13vJl94d83WuRgUVaK6OkAmW2RiPD439C3JEtIq1JIEQWDL5jp+7Zcf4n/8rzfo7Zu67SxaaXkb04RiMUMstr5SvmsBQRCoKPfx93/jMX7/v73C6bODK5rYQelYCwWD/oEZ+gdm1ny/FEXic5/Zz2ee24Msi+uWxa2vK+OZp7YTjWaYmk6u6jOlwMoiFsvw3pFu3jvSfdf7oakyf/dXHqahvozmpnJGx+KY5p1VNgVBQMRJwPkoAeejSy6z1hgbT/De+z0E/C76h2boHZzhyYc2U76CgZ6iSJSHvFy6Nsb2LXWMTyb44Utn5x5Dtl0K2rO5IsWigWlYpLMFJFFEVSQkSaSy3EdtTZAfvHiOvdsbaWsqnyX9IsGAm/HJBGOTcWaiaZ5//SLRO7g3LdsGE0zTJBbPEotnuXh5ZRGJu4XP6+CRhzfy2MOb1t9XTBAQRdjUWcP//s+f5T/93ouMjMZWneG35ypYNqZpMZFPMDG59gpnK0ESRH6p+aM8XrmX3+36Gr/Z9hyb/c3z3mwvT5zg7alz/O+b/w5+xcXz48f4f65+g9/d9uu4ZAfVzhCfrX+YVk8tI9lpvj/6Di9PvM/DFTsYyk7iU1x8NHCQF8aPszPYztM1B+lJjTKancbvd2Pb0BkuZ1d1DW5F4ezEOH3xKNFcDtuGcpebsMtFe1kIGzhQ18BPeq7iVhQCjvWpEiwODVk7NCvtO0g29e8x9QvI2mFEsTTLYVtJTLMfvXAUo3gC204jSg2ojo+sy3wGrFGfjCAIOCQ3fnXpDGraiJPUI+umPJUz08SKt++vsBJs7BVbb7xygGpn85o/OGxsIoWxVZv1lUrYC/chEUmRTZcCXsvKYlpJRNGFZSWxrDiWncSyUyxldHevYVo2x7qHOdU/imGamJZNJJ1jKBLnRye7GI+nePViNyf7Rnnv6gA53aCom1jYKPINspc24iu26zV7tiILypqfu7QRI2Ok1lzSWRR8yNLyQ7yF4kl0oxvbXru2Ldu2Mc1JcoU3McyhNVvvYnC5NDZvqycYdPPE09t46NFNPPToJvYfbKWyZnUVN0EQ2LWjkd/+rSfZsrkOVf2bJ3SwGARBoKY6wG/9vcfZt6dlTWch7gQul8oXPneQz392P5q29vfZzRAEgScf38KD92/A67mXL/cb0DSZX/jiIfbtbUFVZZqbwqsix8vh+nN9qT9rDVWROLinhZqqAP/5f77Cd35ymoO7Wziwu/SOczlVnE5lrtJoGmbJIVxTAIHf+NIDDI/H+Hv/4q/5828d5bd+5RHCZSWCkkzl+R9ffYt/9K+/xfdfOEt3/xS/9k++xn/6Hy8zOBKZO96muhChoAu3W6VuVqRBEASeeXQrbrfG7/zeC/yXP3qVBw91sHlDzbzn/ocVPq+TRx/ZzJe+cOiezQEKgoAsS2zZXMu//GfP0Lmh6qfGM+M65l/nwtz/b/75j8be47Gq3QRVD5IgcTi8layZ52KiD0WQaHRVsdnfjCxKhDQftc4wkUIcG3DLTupdFWzwNtLorqLBVUmnt7HUMmWV2tZlUcAhy4iz24wX8oizhpDXPWdcijKrLAoeVaFgGGwMl9/Tmc8SIa/D4/8dRLkd2y6Qz36DdOw3SUY+TTLyaVKxXyab/B30wjuAjazswu37P1CdT6+bINCaTSy7JA/lWt2Swf71oDllRAmqayvpZts2WTPFTOHOLeeXWTs5Y/mSoyyquKS1L4PaWAxnr656eb1oYFs2CFC8yUvg6EsXaO6sYduhduKZr5MvXsS0k2TyR5GlGiTBCQhUBv/1Cq1T9+aGsW2b8XiKg+0NhL0uCrrBldEphmbipUFaIOxxs62hiv7pGIIAZd7SYKLXccNzpGjlV1Qh88nBuYH4tUSkME56HVr5RLEMRW4lX1h6ZsiyYuQLR1DVXchS/Ro86GwgT774Hpnsd+9yXatHIOhiz74bw+AdndW0b6i6rXV0bqjmX/zTj/JnX3uX4yf6SCSzd13K/2lATXWAf/rbT/Enf/Y27753jXji3h63JImEyjx8+Uv38fCDnffMhV2WJX7xFw6TyRZ4+92rpNPrK0ByM9wujec+sZtnnt4+Z0TY3FiOKImwisrShwG2bXNgRxN7NtXj9Gh84ZP7ZpVsLARBxLZtfunnDgGlc2zbNqM9k/z2lx/GH/YiAJ2tVfyHf/kc0k3Vqz/7f38RKEm6/sNffWzJbdu2jWnZ5PI6NVVBNrRWIUs3gp/G+hD/9DeemPe5x+5bW6nitYYgCIRCHp56Yitf/NyBe3Yv3AxJEtnQUcX/8c8/xl98/QjvvHuNTLa4pqIUd4O7eUOZtsVkPsrXBl7iG0Ovz/3cIzvJm0WKlsGlRD+vTp1kIhehYOnEiyl2BjuwbRtFkFAEGUkQcUoqiigjCiKWbc+1gTUFgjQFbqgSPtO+AdOy+OKWG2Tn13aVBHdsoGCYVLjc7KpaL5+MpSEIKrJ2H77QX1PMPY9RPIZpDmJbqdnfuxGlciS5HVk9gKLuRVwHQYmbsWZEwyl5qXA0cC11asllJvIDxIoTBJS1ZXkWJgl9hkhxPYgG6CsMY8uCgrZKtYDVwrZtDKu47Pd5K4a7J0gnchiGyem3r6LOymf2nB+ifFZpw+N8DJe2f9HP36zRvhRWMma0bBPrLqtWkiiys6mGl85foyEUYF9rPQXdxOvU5uYw3A4VRZJwqQqiIKBKEpOJNAMzMdqrSpU10zZW3Ben5GHhqPzdwbItxvJ9JPS195qQpBCqsonSrbt0b3Im+0MUZTMe16eApfS/V0bpRVQgXzhCMvU/5nTG7wUM3SSbKeAPljpci0UDXTfx3Ga2uqrSzz/+Bx/hhZfO88OfnGV8Ik42u77KXMvhXmVf/T4n/9tvPEZrSznf/+EZxsbjq1KGuhsIQing3rChml/4wkE2ddbe8wyqy6Xx9//uo7hdKm+8fYVIJLOuAdV1UvXZT+3lice2zFPxamwM/1S5z+sFnUtHuxntmWD/UzsIlPuIzyRJzqQIVvrxh7yM9U+RSxeoa6tCdSik4hncficCMDk0QyFfJJ8u0Lq9EVmRVv3sMQyLWCLLyFiMI+/34PE46Gy7vcQCgNfrQJalVbUNrjecTpW62iCf+8x+Hn5w4wfqgi4IAjU1AX7jVx9hU2cNP/zJWUZHY2RzH8yzUBQEHE6FUJkHt3sVpsRLfHk2NhISv9n+HIfCW24sDoiCyEh2iq8OvsiuYAe/0foJTNvieyNvEykm5pa8uWKyWs8sSVz8vjYsk7F0kkebW5dcZr0hCCKSVInT82Xgyx/IPtyMtatoyF6qHc3IgrqkStJUfojJ/DD1rk4UYe0G2NJ6nOHMlXUz61tJqtbGxlrDNpXrmCwMM5i5vOrlfSEPDpfGtfNDqJrMhlmd+HQ8gzJLOlS54Y73R2BlY0bdKty1jLEoCrRXhWmvCmHbpX9/Yu8mbNtGnL1x60MBAGrKSsPBWxuq2FxXMa9RSRSkFasVpm0tolB/d0gbcYazV9dlZkgQNBS5BUXuQDeWvjYsO0Eq/RUkMYDT8QjguW2yYdsWlp0gXzhKIvn/UtQv3eXe3x7GxmK8/vIlvvxrD2HbNlcuj9HfO8UnPr33ttelKBIfe2YnB/e38f0fn+a9o93EohlS6fw9yfQLgoDbpeL1OmhuKl8XZ+7FoCgSH39mF9u31vP1b53g/IVhItHMmhMOQRDwuDUqKrw8+vAmnnpiK36/6wOTinY6VX79Vx6mrbWS7/7gFKNjsTWvboiigMfjoL21gk9/ci87dzQsyFbXVAdwOpQVlbA+TDBnZaFVh0oqluHsG5eRZJFQdZB0IksqmuHES+e47+N7aN3awPDVMWRZwhfycuz5M1Q1lXPxyDXCtWWEbkNKNpMt8Pq7V3jrWDfbNtbyxIOb7qgF7rPP7cOhKfT1T5cGlD+AQNrt1igLujl4oI1PfWI3FeX3xsdjNfB6HTz9ke3s2N7A8y+e5+jxXiKRNJns6tTj7gaCAJqm4HFrlJd72bWjkQfu37Aq40JFkBAFgayZx7BNBFtAEkQUUabNW8eV5CB7yzpxSCqmbVIwDZyyRtYskDHy7ApuwCU7GMpMMp6fQRXXp7KkSjK7q1dnPvi3BWtGNCRBJqhWUuloZDS3+ECdbhfpS5+jyb2JKkfTmryELNsiUhynL3Phrte1OAQc0vKZfsMqkjPTKy53OyhaeY5O/+C25FHDsyZmpmHSvLGGxo6SGZBt25RV3L2alChIaJILYc5AaiGyZorsCq1mq4Ew2/h4/RJZTT+yeEv2QBG1FUlixohjY7FWsr6mbdCdOs1Ybv3kX2W5CYd2EN24ynIu4LpxjVjidzCtGE7HA0hiVUmre5nvsZT5NbCsJIY5Si7/Cqn01zCtW1sihdk/a0+wLcsinSowPhYnFsvQ1zOJbcPYSGxF1+mVUF7u5Ve//CDPPLWdN96+wpEj3URjGdLpwpq/aDVNxulUcTlVgkE3O3c0cvhAG+1tlavKci8m2XwdhVmlHU1e3SO8qbGcf/6PP8q5C0P85IVzXL02QSKZI53O3/ExXw8afF4H4bCX/XtaePSRTVRX+Rfcix8EJEnkice2sHNHIz9+/ixHjvUQnSWXd5PtVlUZr9dBVaWf+w+18+jDmwiFFifyiiJRX1dGJJr+qWjZUx0q/rCXTCKLw6WSSeYI15ZR2RCirCpA9+l+Bq+MEp9JYhomlm2jaAq5dB6jaGCaFpsP3zbG4AAAW/1JREFUdpBJZClkCwulApdBwO/ic5/Yy+c+cfuJhJuxf18Le3Y3ceHiCO8d7ebCpRGSyTzZbIFcXl+Xqp4oCjgdCm6Pg6Dfxe5dTTz+6GaaGtde+W8tIIoCdbVl/PIvPsCTj2/lrXeucOr0AFPTKVLpPLlccc2ehYoi4XKquFwqgYCLttZK9uxqYtuWegKB1cdMQdVLjSPE6ehVREFAEWU2epvQJIVP1T/IV/p+wutTp6h3lpMx8sT1NI9V7sUtOQiqHs7ErmFaJtfSQwxkJujwro1h6c+wMtbUVS6glNPs3sxYrmfJQLQ/c4mBzCXK1Eq0uwzMbdsmYyTpSZ1hurA+TsUCAn6lfNllSoPoE2s2e2LaJldTJ7mcXN7V81Zcn9GobZ2/H/se27ImGXtRENFEF5roJm8t7ryc1CPEipNYtrlim9V6wyV50VZwrJ/ID9Ds2bImBouWbTGZH+Ry8uiKAgJ3A0mqwum4n1z+NQxzYNllDXOIWPxfk3Ucxu18FkXuQBIDCIIGgoyAWLpXbQPbLmDZGUxzgnzxKLnca+jGQgNIUJClWkDAWAfjPl036b46zmsvXaS/d4pUModt23h9Lh56ZG36saurAnz+M/v55LO7uNQ1yvmLI1y9Nk4sliWXK5LP6xR1E33WVMqybCzLnlXNK5FaUSw5E4tSSTFHVeXZPxJul0ZtbZC2lgra26poa61A1iRUScKybQzLQhIEsoaOKkrIoohhWciiiGnbCEBW10kVCpS5XCiiiM2sQ7oNfbFStawlGEQWRSRRpGAY2IAqlTJ/t0IQYMe2BrZvrae3f4rjJ/o4f3FkLptZyJeM1XTdnPX9sOcqiqIoosgiqirPESif10lTU5hdOxrZtrWeYOCDq2Ash/Kwly9/6X6e/ehO3jvazbnzw4yORslki+TzRQoFA90w587zzedYkkQURSods0PB63FQV1fG7p1N7NndRKhs5Urh3j3NmJaNtYgpWcDvIuhfH/HLigofWzbVLmtcWR72LqgaONwaesFgYnCGsko/3oAbzVnqQLABt9dJTXMFTo+DTCJLPp1nfGCKuo5qQtUBZEUiUO5Ddaj3aqxvASRJZMf2BrZvqyeXK9J1dYJr18bpH5xhfCJOLlc678ViSUjEmD3/pnnjXocb0qyCcH3QVkSWRWRZQlNlVE3G6VAJ+J20tlSwZXMt27c24PU6PpT3wq2QJJHGhhC/8IXDfOyZnVy5Os6580P09E0Ri2VLJql5naJ+47lw4/u55XuRROSbnoMOTcHlUqms8NHaUkFHWyXtbVX4/XfWZu6QVD5Z9wAvT7zPd4bfwq+6aXJVo0kKu4MbKDbqvD51mremzuKVXWz2NyMKAhWOAB+vuY9XJk9yJTXIFl8zn6p7kLieQRNVqhxlBFUvmqRQ7QzhV9w4JJVaZxiPvPaGin8bIdhr2Lxq2xZXU6f48dgfkdCXlkmsdbbzSMXP0eLZiizeeQtV0SpwJXmCF8f/jJSxcu94laOJp6p/iRbP1lVvw7RNzsXf5Hsjf7DkMh45wMHQsxwu//icvvOdwrQNhrJX+N7IfyVWnFpx+aBSwYMVn2F32WMM90ySS+cJVvjAhvLa4Iqfv11M5of47sjvL5mxFxDYHXyMhyp/Dv8K3irrjbFcHy+Mf4WBzNItP83uLXym/rfxyHfngmrZJtHiJO/NfJ/z8XdWZRy5zX8/z9b+Og7p9gMNw5wgmf5jUuk/XcZTYyFEsRxV6USSyhEFL4LgwLZ1LDuFZU6jmwMYxjBLV0oEFLkTn+eXMcwhEqnfX3JbQf+/wuv+4qyL6e1jciLB6ZP9PPHUtrkX2t2+vKfjacajKTY2VCBL86VWTdNiJpJiaDjKxEScaCxDPJ4jlc5RKBoYRRPDspFEofQyVSQUtRR0B/xOwiEPoTIPobCH6soALpc6b/1dM9M0+P1MpdP4HQ7yhsl4OoUmSzT5gwwl4rQEyxhPpyhzOumOzHB6fJwnW9txqypZvYhX1cgZOldmZkgXi9T7fFR7vThlhdFUkoxepCVQRtDpXJRs3ArTtBgbj9PXP83ERILpmRTxRIZ8wSglLuyS3LBDU/B6HYTKPFSU+6irC9JQH8LjXluTy3sBy7KZiaQYGJhhdDzG9HSKeCJLOl2gOBtMiaKAdv3cBlxUlHuprQnS0lxOqMzzUzV3cSewLAtsSoPsi/xOQEC4y/a/rFGkNz1J3tQp17w0eZZP6K0FbLsk5Tw5lWRqOkkkkiY+6yKeyZQqHvmCPi+oBuaCaKej1Pbj8zkJhbyUl3upqwlSVelHlqUlZzAMy2Qin2A0G8Ula7R7q3BIH6wi3HIoFA0mJxMMDUeZnEoQjWaIJbLkssVSMqJYSmrc/L24XaXvpazMTTjkoaoqQHWlf3XzFz/D32isaUVDEEQqHQ20e3dxKvrqbEvKQozmunk/9hKK5KDO2Y5ym2TDtm10u0Bf+jxHZn60KpJxpxARqHG2Ljt7kjYS9KXPscG3mwqt4Y5fvDkzzUDmMq9N/hWx4u0PEo/0TDI2OI034Ma2bO776I6536kOBUW9+9PtEF1Uag1LEg0bm/7MJepTZ9kSOIwqfjAykwBBtQKfHJrN2i9+LQ5muujLXGCz7yAS8h2dO90qMl0Y5v3oy1yMH1lzd/rFIEtVuB0fRdevkMu/zWoN9CxrmnzhTofUBSSpGo/7U7hdHyed/eYsUVmf4w2WuTlwqH1Ng7pjXYN89dVT/OFvPUfIN5/gSZJIZYWfyjVoM1wMY8kked3g/bERPtm5iZf7ethaUcHxkRFkQeTYyDCVbg8XJyfZXVOLQKli4lYVxlJJJtJp6n1+prMZ8oaBYVlcjcyQ1XUKpslUJs1kJo0siPg0DVFaOekhSSL1dWXU1629W/eHFaJY8hv5MPXNf9iwXOvbWrXFjeVi/M6FHzKUneHjdbv5Z5ufWZP1LgdBEHA4FBobQjQ23LtEWM7U+eHIaf645002+Kr5f3Z9gVrX2icC1wqaKtNQH6Kh/oNNFv40wLYtdLuILCgfeBfHncK27VmFTgFJuLM4aDmsKdEA8CvldHh305++uKwK1JXkSSzbZHfwcRrdG3FK3hVdta8PXceLUwxkLnEi8gJj+b61PoR5EASRgFJOtbN5GalZm7F8LyciL3Eo/CxBtfK2HMJ1q0isOEF3+ixHpn9A8g6Jk8OjkYpnmRiKgM08YrFxTzPVa9Av6pDc1Lk6OBd/e0lzxkhxjDPxN9AkFy2erTjEO1c9uhs4JQ+VjkZ60+fImIsbDVmYvDP1HdySnyb3ptsiG6ZtkjHiDGevcSb2Or3pc6v2PFkLqOpWvO5fxLJSFIpngfUcehSQpCo87s/hcX2uZBwohpHEcgxzeF22qKoyatnaPqKqynzs72xAXuVsw1riQF0937h0Acu28Gkatm3hUTT8mkbBMFAkkf54jEQhjyKK+B0OnLKCgIAiSRRMg+FknFkFa5yyjFMpabvnDZ1EIY9Dllc9t/G3DbZdavkYG4kyM5UkPyvvKSsyHq+D6togwWUM6WzbJpnIMjEWJxnPohdNBFFA02TCFT6qaoJzSn8/w8/wM/ztQFKP0J16n1bvToJq9Qe9O3eEvJVhONOFImrUONvueqzhVqz5U1EUROqc7WzxH+JY5HkK1lJtHTbXUqeJFibZ7D9Ig7uToFKJS/ahiQ4kQcam1JKi2wXyZoa0kSBanKA3dY6rqffJmjeGjgVKpoGSoJBeY7UfWVDZFniQkWz3kpnxnJnhYuI9TFtni/8Q5Y56PHJgyd5/3SqSNZOk9BgT+X6uJk/Rmzm3wElaFhTKtTrG8yv3wrdsrKWQK3L0hfOYpkV85sb3UyysTQCsig6qnS2UaVXMFJZ2Uh3IXKJg5YgWJ6h3bcCvhHBKXhRBRRQk7BJtxLJNTNvAsHUMS0e3iuh2gaKVx7JNKh0NuOU7zzA3ezZzNXWSTHZpR9PJwhAvT36Vg6GPUutsJ6BWoIqLl3st2yRvZkkbMSLFcfrTl7iWOkWkOM7NhocCAkG1kpyZIWfe/XD8YhAEGafjfsAimf4TCsVT2PZ6OFjLKHITbten8bo/P+s4ToloSFXrRjTWA3s31LN3wwczBOhSFDK6zoG6OmRRZH9tPQPxGC5FobUsRN40GEkm8DscaLKMx1YJOh3MZLOUu1zMZDNMpNPUeL14VY2JdJqcoRNyuWgOBvGoGnnDoNztRr6Hw9gT00l6h6ZpbQhTGfZ9qFupzr7fx0s/PMOVC6NEI2ks08LpUmlur+Rzv3gfew61L/nZybE4b792iWNvXWV4YIZMuoAsi3h8Lp54Zjsf+7l9lGlr76f0M/wMP8OHFz3p07w88RU+qfzjn1qiMZMf5ujM9yjTagiqVR9+ogHgVYJs8h9kujDC1dTJZWVnZ4qjvDX9bUKJ6lIAq1bhlv1zgZ5h6WTNFEk9wkxhlIn84KLkxSMH6PTtR7fynI2/uabHIwkyHd5dnHE0LVtByZpJzsbfZCR7jSbPllKQLJWORRQkLNvCtHV0u0jaiDNTGGMyP8hYrnfRdhtRkOj07WOr/z6+PfxfVvTz8Ic8HHh8K4GQF9uy2bin+a6P/VYIgkBQrWCT7wDvznx/WZ+K8VwfM/kRKhwNVDmbCSjlOCQ3sqDMVqdMjJuIRd7Mkjcz5Mz0LIm0ear6l+6KaFQ5mmlyb2K6MEzOXHyA/fq+Pj/2J7R6tlPv3oBPDqFJrlIZEeaIUN7KEitOMpUfYjzXR0xffI6mwtHAvrKPcDV1ku7UmSUJ6t1CEDScjocRRT/p7LfJ59/BMEdZzmPjNtaOKPrR1D24nc/icn4UUbzRbiRKISSp8rbXmsoVuDY8zUwyQ1E30VSZcr+bDfXluLRSG6Vl2cTSOa6NTBNNZRFFgaqgl9bqED73jXa8/okosVSW6jIfsXSO4emSqWNl0ENnfQUeZ+k5cq53jOHpOObsQO5TeztRlfmPP8u2SWbyXBmeIprMIghQEfDSWhMi4LkxFFjQDUZnEgxNxcnkiyWHWJdGS3UZFQEv8hKtXlOZNPV+H1vKKxEFgY3hcjpD4blWlH01dbMD36VA3adpPNXWMSfrfLi+9PC//vutFSXJjetutBVuz+xZW1ypar1w/Gw/X/3ucf7uFx+gvMyLJH04icb0ZJI//r1XGOidormtkk3b60t+C0UDp1sjULZ0NSObKfD2q5f4zl8cRRAF2jfWEAx5sG2bXLZIVV1wTVpTf4af4Wf46cJA5vy6vd/vFSLFURL6FGXa+hgMrtuTsdLRwJ6yx8kYCYazV1eUaY0Ux2ezwrcPh+Rmk+8A+8Mf5VrqFEL8rSVVr+4EgiDglYMcCD/Di+N/RtZMLrmsaRtMFoaYLAwhCTIuyTsXXJu2iW7lKVg58mZ2ydYjAAGRds8uHqn4HE7ZS1irXVVVA6B5Y80aHv1CuCQfnb69DGQuMZS9suyyul1kNNfDaK7ntrfjlLx33YqkiCpb/IeZyA/Qmz6/rFN43spyKXmUS8mjOEQ3btk/Oz8koFv5EhmysguqTrcirNVxIPRRNvsPkTJiDGevLkty7haCoODQ9iNL9eTUXeTz71LUL2IYw9jk7mCNIpJYgaJ04tD24HJ8BFXdyK0SwKWKxu0RDdOy+Mmxy5y4MowsiehmSSLT69T4R596AJemYtk204k033/vEuf7xpBlEcuyURWZQ5saeWxXB/5ZsvH+1WGOdQ3SVhMmWygyHc+QK+i01oSoLvPNEY2RmQRneka5PDhJz9gMD21vnUc0bNsmnsrxvfcucqp7BGV2m7Iksa+znqf2dhLwOLFsm6vDU/zoWBeRRAZRFDBMC0GAZw9smnOoXwxZw+BwXSOafKM972ZCcJ0w3IybB+BvHe6+dfnVDH+vNQzDpH84Qjy5ekGCDwqnjvbQ3zNJoMzDl/7uw2zf04zmkMnnimTSRfzBpbN44yNRLpwZIp3O8/gzO/jk5w9QW1+GjU0ynkNzyLg+pEOvZ98vvTd27F37xNPP8DP8bUbezDCWu7YuPmr3CrpVIFIcW/NOoJuxbkRDEmQa3Zs5GM5jzViM5nru2jF6MThEF5t8B9gbepKwWsO43Icmushba9tCIgsKG7x7mMoPczL6EvklW8JuwLQNUkbsto3bREQ2+PbycMVnKXfUkTez1Ls6V000rksRrhdEQaRCa2Bv2UfIGMl1c2RfK1Q5mtgdLJHe8Vz/sgTvOvJWhnzx9q4hAYEKRz37yp5io28/TslNhdaAS/KtK9G4DlmuwSP9HE7tfgrFMxT1i+jGAKY5gWlFsK0klp3BtouUVKUEQEYQVETRjygGkMUKZLkBRdmApu5BVToRhMXVUUTRh1N7GGEJDxJV2Qa3GHNm8zrfeOsc21tr+Mz923BoCtFklvFoEu8sKcgXdN69OMDLp67y8UOb2dNRT143eO10N8+f6KLc7+GBbS1z6xyejiOJAg9ua+WZA2EMoxT4+2+qfHxkzwYe3NbCX7x2mqGp+IJ9LRomR7sG+eHRS3z84GYObGpANyzeOt/HCye6KPe7eWxXB7phcqp7lGsj03zq/q1sbqwkVyxVOBoqg8u6fjcHPrzDn3eKmViGsck4+bv0N7kX6LowgiAINLZUsP/+jjkC53I7cLmXF62YnkwyPZHAH3CxfXcTDc3huc+Hyj+87VKmafEXf/QmwZBn3YlGpJBiID3DZD5Bxihi2CaaKOOSNco1L9WuADXOxe6B0vdYMHVGslH609PEi1lM28Ipq1Q7A7R6Kgiqi8/62dhEC2mGM1Gm8kmSRo6iZSILIl7FQZXDT5u3Eq+yUKp0Jp/ieKQXURDYGWwkoLoYzkQZyJT2wbJtXLJKtTNIm7cSv7K0D5Fl2/Slp+hLTRHXS/GBX3HR7CmnyulfVSIgrecZzMwwko2S1HPY2LgkrfQdrLD96xjLxuhJTzKTT1G0DFRRJqi6afaUU+8OId3G/Oh6QrcKxIoTRIvjZI0khl1EREQRNZySF58SXraNJ6nPMF0YJqVHKFp5REHCJfkIa7WEtYZF52RTepT+zDkERBpcm3DJPqLFcWYKI+SMFBYmmujEr1RQ4WjEKXnnfd+2bTNVGCSlR8gYCSLFMVJ6DBuba6kTJG5RClVEB3WuDiodi997hlUkVpxkpjBC1kxg2iayoOBVgpRrjXiVskXb73tSp4gWx6hzdlLlbCVtxJjI95HWoxh2EUlQ8MgBwloDZWr1gmPIGHFmCiNkzASJ4hQD6QsYts50YZAzsVdwSvOru2Gtngb3RtQV7AKWwrrWelVRo927C1GQOBF5iaFs15oq8rglP1sD97G77HEqHSUXbJfkxaeEyBfWlmgIgoBT8nAg9DQ2FhcS75LUI2u6DQCH6Gajfx+Hwx+nQiu5eMuCQr2rgxPRF9Z8e3cKTXLS7t1JwcpyIvICU4UPb5++IAh0eHdh2EVORF5kNNe95kPbsqBS62xlT9kTdHh345JLwUelox637L9nZEwQRGS5Dlmuw2V/BNMcRzdHMc0JLCuGZaWw7QI2BgICCAqi4EAUgkhSGFmqQZabEcWl20hubEvB5XwUl/PR29pHn0sjkysSSWbpbKigpaps3oMwmS3wzoU+akI+PnXfVtxODdsuBU0X+sc51zfGfVua5lqOCrrBjtZaHt/VgbZE+4okiXicGi6HuqgEZb6o8/qZbsr9bj770HY8s9uURIHTPSOcvDbCIzvaANAUGUGAWCqHZdu0VofY0lS15PHats0Lb14imsjywL426qqCiKJQarvJ63z/5XMAVFX4eOTghrnPZbIFLnWPMzQaZWNbFZs7SmVty7JJpHIMj0WZmEmSSJUM6CRJxOPSqK7w0dZUgXcJ6dkX3rpEPJnjkYMdhAJu+oZn6BuKkEzlMC0Lt0ujripAa2M5fu/8F4tl2UxMJxgcixKNZ+kdnKZvuCRl/u7JHsanEtyqevrsY9vweT54X4HoTBpBEKis9t/2vuSyRXLZAl6/E7fnp0fSd3oiQe+1CTZvW7+5JN0yOB0d5L3pa1yMjzCSjZLSc+i2iVNScMsO6lxlHC7v4MutDyz4vCgI5E2dd6ev8dLYBboSo0QKKQzbwiM7aHCHOFjezkdqttHgCi347o/P9PLe9DWuJsYZzUWJFbMUTANZFAkoLurdIfaFWnimdic1tyg9jefi/HnvOyBAsfkQmqjwyvhFribHiBTSmLaNR9FodIc5XN7BUzXbqHEGF+yDbpm8PXWFl8cucDExQqSQBtumzOFhs7+Ow+UdSMuYwlq2zUg2ypuTXZyI9NKXmiJWzJSqvYqDBneYPWXNPF69hSZPOYq4MKFhWibvzXTzxkQXF+PDTOQT5E0dTZSpcPjY5K/lgcpODpd34JY/uOqbZVsk9Wmupd5nIHOB6fwQaSOGbhcQEVFFJ245QLWzjf2hZ6l2tt7yeZPBzEWuJI8zkrtCvDhBwcwhCjJeOUCVs4V27146fQcXSMcn9GmOzHwXbDDDH0cWVS4n3mM810vGiGNh4hDdhLRa2jy72RJ4kIBSMXe+bUxORp5nJHeVlB6d7W4p9Y+ci7224FjdcoAHKz63KNHIGHF602foTp1kPNdLyohi2gaKoOJXyql1bWCj7xANro2o0vzn8PnEm3Ql3mN/6GMgwNnYawxlLxEvTqFbBWRRxS+HqXN1siXwAM3u7TddszYjuascmf4uKSNKxojPKaqOZK8ysojo0dbAQ1Q6Gj+cRANKw8Ptnl24JB8XEu9yLXmKuD59Vz1tkiBT5Whmi/8QW/yHCNxklOeUvfiVEFOFobXY/XkQBAGfEuJw+OP4lTDn4m8znutbVYZ8JYiIVDub2eg7wPbAg/iVGxkzSZDmTnLRupNWmPWBW/ax1X8fmujkXPwthrJXP1T7dzNkUWGz7yAuycfZ+BtcS51ekyFtAQGfUkarZwfbAvdT79owT9I3qFbiU8oQkdbkOrmtfRMUZLkBWW64p9tdDh6nxhcf2cVrZ3v4zrvnKfd72NRYyb4N9dSVBwAoGgaDUzEcqsxfvH567rPT8QzRVJZYKotuWGhq6cUdcDupDHqWJBmrgWFa9E9EEUWRv7xpm9Fklmiq9KdomGiKwr4N9cwk0pzpGeXqyBRNlWXsaq+dNxNyK1597wonLwyViEC5H1GUsG0YGovyh3/xNgCb2qu5b3cr6uxxRBNZfvLGRS5eHeNLz+1nc0cNtm1zpXeCV9+7wrX+KcYm4yRSOYq6iSSKeD0Oaiv97N7awEcf2UpV2Id4S+T/nRfP0Dc4QzjoBhtefreLnoFp4skspmXjcWs0VAc5tLuVx+/vpLr8xoyUYZqcODfIT964SCSWJp4seYwAvH7kKm8cXWjw+MC+dnyeD07mGkreD8WiDgLIyu1JUNq2PWfoJonih8L1fLU4f2pgzvNgPWBaFm9NXuGvB45yMTGCIsi0eSuoCDagiBJZo8hMIcVgZoZdZU2LrsOyLa4lJ+hNTxEtpNngr8avtJI3i/SkpuhKjjGYKZHZTzfsI6TNT4J0JcZ4fvQcTkml0V3OnjIvDkkhaxYZSE9zNjrI1cQ4OVPnNzseWzRIjxbSvDx2gbieI6Pn2eSvxas4yRlFrqUmuBwfZTAzgyJKfLxuFwF1fgB7dLqbP+p+g97UJNXOAI9UbsKjOMgYeXrTU3yl9y0a3UurPo5ko3xz8Bgvjp3HtG06vJXsD7ciCiKRQooryXH+emCC4WyEL7c+QKu3ckFl4o3JLv6k9y2uJSdo8ZTzQMUG3LKDnFFkIDPNaxOX6EqOkdLzPFO7A+0D8PGwbZt4cZITkR9xOfkuOTONTwlT79qIJrmwbJOcmSJenCJjxBbtgulLn+W9me8wkr2CXymnyb0dl+zDtHQixTGupU4ymu0mayTZG/ooyiLCLhkjwcXEO+TMFAUzS42zDYfkQbfyTOYHGMt1EymMIgkyO4KP4ZKvS2ELlDsacM4mEpN6hEuJtzFtk42+g4S02nnbUUUn1Y62BdvPGgkuJ97jROTHZIw4Ya2eOucGRFGmYKaZyPdzPv460/kh7q/4LE3urYsex3D2CjOFEQazF6lzdlLr3IBtWyT0aUayV4kl3iJjxPHIQSpmk/EAbtlP06yfXN7MMJi5wHRhmCpHy9y5uBlVjuY7JhlwD4gGlIK8BncnfiVMrbONvvR5hrJXiBenbyv4KhGMRprcW2jzbKfB1bmA6bkkL37l7mVcl4IgCHiVILuCjxLWaulJnWUwe5nJ/NCSPhvLQRUdVMyWpVo922lybUIR52fMBEHELfuo0OoZyS3m1PzBwSV72ew/SEiroTt1moHMZSby/XfdKqSJTsJaDc5VZNZXC0mUafNux6+EqHO205e5wGCma9mZm6UgIuFXw9Q5O2jxbKHZvXVRWWNF1CjX6tAk17qpT/00QRDgsV0dNFeX0TU0RdfQJM8f7+J83xi/8ewhKoNebGa9cgyLqdj862h/ZwPbWmrmOQ6rsoS8Cs+IlWBTCpxu3ebejno21FcAAoIArTUhPv/ITq4MTdE1PMXV4SkuDU7wsYObObSpCac2/wUuCAIt9WHOdY3SPzyDaVkoSFiWRVfvBDY2bqdGLJllfDpBY21J1SuTLTA+mcDncVB9k7fHxHSSkxcGMQyLztYqKkJeNFUml9e51j9F98AUfUMzODSFTzy+fYHzM5SI1SvvXGF8OoEgwMFdLXjdGulsgZ6BaS73TDA+lUSSBD779G6U2eBcFAQaaoI8fKADgOHxGEfP9DEdSfPY4U7aGssXZHsDvrtz17Usm8h0ku6uccZGomQzBSRJpCzspb2zmvqmMJpjYdB0/N1rTI3HyaQLZDIFxoajWKbNlYsjfOUPXp1bTlEkWjdUc+ihzrmfTY7FOXuyn/SskVt31xiZdB7DMHn5R2c4f3rgxvEF3dz36CYqqhaKVuSyBboujDDUP0MqMetw73fS0Bymc0sd7iUI2MxUktPHetF1k10HWgmXexkejNDdNUZkOoVpWrhcKtV1QbbsaMQXKAUGxYLO5QsjTI7FiUynOHmkG9O0GB2KzDtmAKdLZcfeFjZurbut83EzupKjfH3gGBfiw9S6gjxds4PtwQbKHV4UQSZnlojGRD7B9uDiCY+CadCfmaLVU8kvtz3ItkADPsVJ3tS5mhznm0PHORXp563JLvaUNS8gGofK25EEgbDmo8EdIqR5cEgKOaNIf2aarw8c473pa7w+cYln63bS4qlYsA8JPce52BAbfNV8vu0hNgfq8MoOcmaRy4kxvj5wlPPxYV4bv8TusuZ5RGOmkOIbg8foT09R6wryy20PsTPYOEs0CnQlxvj20AlORBb3nkoUs7w12cXzY+fQRJlnqrfxcNVGqpwBJESixTQno/18d+gkb052EdI8/J2W+yl33PCB6U1N8md979CdnGB3WROfbzpIu7cKt6KRM4r0pad4afwCL41d4NtDx6ly+Dlc0bHo/uTMHJZt4ZAccwbEaSOFiIhDct6WdP/CdSe5lHiHc/HXERDY4DtAp3c/ZVotDtGFhUnOSJPQp3FK3gXDyZHCKCciP2Yke4VKRwt7y56i1rUBp+TFtHWihXEuJ9/ldOxl3o/+BL9awWb/fYvsR4qRbBdVjhb2lT9DrbMdbZZojOd6OB4tbaMreYRG95Y5oiEKEnvKnppbz3D2CleSR7Fsi82B++n0HliwrVsn70zbYDh7hZPRF8hbWbYEHmSj7xBBtRJJkMlbWcay3bwf/QmjuWucir5ImVpNmVazYF0TuV5EQeJQ+JO0enbhVcLYWCSKU1xKvMPJ6AuM5bq5ljoxRzQEQaTOuYFaZ+n8x4uT5MwU04Vhqp2t7A9/jIBy6z0iLNj27eCeyWQICATUcrYp99Pg2sB4foCp/CDThVFixSnSRpy8mUa3ithYiIKMLCi4ZR9eOUiZVk2FVk+Vo4kqZzOeJZSI3LKPbYEHCN/CLEu/8xNSl25xuB1okpM2z3ZqnK2M53Yynh8gUhgjWpwgqUcoWFmKVgHDKmBTqkrIgoomOXFJXrxyGUG1gpBWQ4WjnipHMy7Jg7DETeyQPDxY8SkihfFbfu6izrW0JOO9gCJq1LnaCWs1tHl2MJEfYKYwSrQ4SVKPkDETFMwshlWcEwUQkZAEGVVyoIlOHJIHt+zDIwfwyWX41BB+JUzZGp2vm1HuqKNMraLJvZmxfC/T+RGixQmixQmyRmpOBcu0DSRBQkJBkTRckhe35MevlhNSqyh31FHpaCSoVCKLS2eHNvr241XKKJrzqz3lWh2ysL7zNB9GiKJAe2057bXlHN7cxLHLg/z7b7zBA1tbqQx60WSZpqoyLMvm7zyxF/WWDLRTVZadhbgTyJJIS3WIZCbP33lizwJFKocio85uUxAEyv0eyrd62N1eR/foDP/zJ0c51jXIxobKBUQDoKWxHFWRZolGKb9sWjaXro2jKjI7NtXR1TtBz+A0jbUhbBsy2SJjUwnaGsupmSUagiDQ2VbFL37qIJIsUlcVIBRwoyoy+YLOwEiEr373OGcvj/D6kWs8dKBjUaJhmhanLw3R1ljOLzy3n47mStxOlWyuyOWeCb79/GlOXxri5Pkh7t/bRkNNydBPliV2bWlg15ZS0Hji3ABX+yaZjqQ5tLuVRw52rKnBYrFocPXiKK+/cJ6uiyNMTyTI5YpIoog/6KKxtYIHHt3EgQc34A/MzzK/9fJFrl0eI5spkM/r5LIFbNtmsG+aidEbc3MOp8qDj2+ZRzRGhyP86Fvvk4xnyeeK5HM6xaJOIa9z7J1ryPKNY6xrCLNlZ8MCojE6FOGlH57h/KkBxoajZNKl7bu9Dmrqgmzb3cxjH91OY8tCR+xoJM1br1wimcjiC7jouTLO6y+cp+fqOPFoBsuycTgUNm2vp7qubI5opFN5XvrBafp7pojHMqQSOSzLZnIizo+//f68bQTKPARDnjsmGoZl8fL4Ra6lxnFKKp9rPMjTtTvwyPMTZe1ULSvOYmHjU1x8pGYbT9Vsn5dpr3T6mSwk6E9PMZCeYSKfwLKtecFuq6eCJncYRZTnzUEEVTeVTj+GZfLe9DXSRoGe1OSiRMO0Lco0D0/X7uCJ6q2o0o37v8rpZyQbZSgboTc9yXQhhWXbc9s6FennWnICw7Z4rmEvT85+viRx7qbS4UcRRC7FR9AXSawOZSO8MdlFRi9wX00Hn2ncP8/ML+zwUusKkijm+PbQCV4Zv8ih8g6Cqht5tjrz49Gz9KWmcMoqv9z2IPtCrXPnIKi6qXD48asu+tPT9KZKpGN7sAGPMv/ZYNkWaSMN2GiiBkKJZAxlh/DKXqocVWSMAoIg4JrNesf0GJqo4ZaW98uybZtocYKLibcx7CItnp3cF/40lY6m+XGPBvVshEWumavJ44zlepAEmf2hZ9noO4h8k+GzVw7hU8NM5QcZyV7lTOwVWtw7cMrzyamFiVsuZ2vgITb775u3Dp8SJlocJ1oYLc1jGFFs25rbx5tN+W6+DgXEVRn2pfUYvenTRAqjbPDtZ1/o2XnxqpcQZWo1aSNGUp9hIHOeqcIgfqViQZyh2wW2+B5kX+jZeVUIrxxEETX6M+eJFEaZyPVh2uYccRQEcY42CPP2WUBEWnPjwXuuxycJMiGthjKtmjbPDpJ6hLQRJ2emKFp5DEvHxkYURCRBwSG6cMqluQu/HEISl99lVXTQ7NlCs2fLuh/L9UpDq3c7je5NZIwEydm+vaKVx7CLmJYxezyzgbWooYkuXLIXjxzELfsQBWlFtqiKGp2+fet+THcKgdIMS4O7k1pXOzkzRUqPkTES5K0MRasw+12Ys0uLJfIlqqiihio6cEhunJIXl+RFk+4uC7oSJFGmytlEpbORnJEmpUdJGtESIbJ1DLuIZZsIgoREaT9LhMiNRw7gkQOokmNVLL/a2Uy182eKL1Aa3D7bO0Z10EvQ68S0bDIFHVEQ5lqffG6Nh7a18s23zvHOhT62t9agKTLxdI5UrkBDeWDeoPdKsG2bomGSLxpk80VsG6KpHJIo4lBlZEnCqSo8trOdr7z0Pm+c7WVXey1OVSGRyZPI5KgN+wl4nCQzeS4PTZLJF6ku86HKEolsHsO0UGUJ6dYBhVm0NoRRFImB0Simac2ax1lc7h4nFHBzeHcrZy+P0DM4zaOHOjFMk2giQzpTIOh3UX6T9GpV2Edl2It4kyIVgMupEvS7uNI3Se/gNEOjEfIFHdu2lwwAPvfsHg7tap2bW3E5VfZua2AmmubUxSGi8QzD47E5onEvYZkW3ZfH+Mafv8vlc0PU1Id44mM7KQt5SgTk0iiXzw0zPhJDlEQOP7xxnvLTg09sYee+G6IB3/izdxkdirB5ewOPP7N97ueSJFJ9izN6bUOIT37hAJZZSoxcOjfMu69dxulSuf+xzTS33QhW3R4HldWBeZ+PTCf56z99h3dfu4zX7+ShJ7fMbsNmcizBqWO9/Ojb7xOPpvnCrzxIde3iQgGpRI4jb3QRjWYwdJOD92/AF3BRLBhMjMUIlXvnVXMcTpXDD29k1/5SX/vLPzrLxTODNDSX8+mfPzRv3aom09J+54mc6UKSrsQYGaPAfRUbuK+iYwHJuI6VnpO1riCHytsXtPMookSDK0RY8zFTGCOp59AtE+0mMiuLEjKLB0YiAht81QgImLZFbBmBj2ZPOfvCLfNIRmkfZJo8YYKqm750hpSew7BN1Nkh3dPRAbJmAZ/i5MGKTlRRnne8siCy0V9Li6eCi4mRees2LJOhTISryXHCDi87go3UOAML9s0tOzhc3s5709e4nBjlYnyEzf5a/KqLrFHk2EwPBctgf1kr24ONCz4viyINrjAPVHRyOTFKb2qSvvQ024LzZ3dsbBJ6icz55QBThSkihRmGsoPUuxqw8zZpI028GKPTtxFN1IgVo4zkRjgcug9lCeEQKAXF0/khIoURvEqYDu/ehSRjHm6dgykwmLlI3kxT59pAvWsjkrCweuyTw2z2P8BQ9jIzhREm8n00e7YtWHtYq6PZvX0eySh9VwphrQ637Ge6MEzeTM8Oaa9NAiWuTzGSvYomOalxtlO2iPeGJMhUO1txywHS+RiTuQEaXVsWTWjuDD62oK1JEERcso9KRyPThSFyZhrdyiPdMrNyr3BPiIZtl5jpvHYgBBySC4fkooI7H1RbbN13im/83ots3NvCpr0tyMrqvxoBAUVUCajlBNSF2am/bZAEaS4Y/yCw1DXxV5fOUen2cKC2HrdSergICLhkLy7ZSyULH9CrRW8swqnJMTrLytlWsbZVGN00uTgzyQ+6r2BaFruravhEx6bbXs9a3it3ioJucOzyIPFMDgFQZAnTsvnMg9vY3FiSynWoCoe3NJHKFjjdPcKJK0PYlKoOdeEAFf7be1j2jkX4/pGLzCQy9I1HKRoGv/fdd3BpCmG/m9/+9IMossSBTY3E0jlOXRvmdM8ItlXaZlWZl6C3lC0yrNIsx3sXB7Cx56Rsq0M+HtzWStCzOEGurw7idqqMTiaIxjN43BqRWIbJmRS7tzbQ0VKBYVr0Ds7MDYmPTyXQVJnKch/aTVWS0szF4udQEARqKwM4HSrRRBbdWLw1VRCgIuRl3/amBcPxDk2hptKPQ1PIFw3SmeXlnNcLM9Mp3n71EudPDbBhcy3PfeEgnVtqcXsdGIbJ+HCM7/zFEY68eYUXv3+alvZKWjqq5q7v/ffNbw159cfnGBuJUtcY4vFndiy77crqwDzyIAgCJ4904/U52bWvhT2HFvZd34zXnj/PkTev4HCq/OJvPML2Pc1zruOJWIaNW+v42v96k6NvXaW8ys/P/+pDC2ZpAKIzKS6eHWLPoTYeemILdQ0hnC4NwzCJx0ryyqHwDdUrl1vjvkduPBsunx/m0tkhwuW+FY/5djGciZDQs9jAzmAjPsV1R88WURAIa94Fg9rX4ZRUHLMERLcMzEWkRE3bYiA9Q296kslcgpSRJ2cU0S2TjFGYNYi1MazFZ0MlQaRc81LlCCz6e7ekoc0mOYuWMU/OdCAzQ8EyaPZU4FcXfgeCIKBKMs2LEI2sWWQ8FydnFmnTKqlzly35HTZ7ygmqpedQd2qCjFHAr7oYz8WIFtLY2GwL1iML4qLrcMsqG3yloDah5+hPTy0gGiIilm1SsAqYtslkfhxFUPApPiRBYig7SMEskjWzZI0sCStOpDDDtdRVDpQdRGFpolG08swURrCw8MpBqp1ty5CMhUjpEdJGDAuTKmcrqri4wIQkSNS5SqIaupVflGgIiHjlEP4l4jVVcs51Gxi2vmY+GTY2WSNBtDiObVtcTR0nUlzc+DhrJEjNCg6ljeiiAjaKoC2paCUKEg7JO7tda7a1/28w0Tj9ZheSLLH9cAfCEhm/O8VY/zQXj/bw+OcPLvqgvh0cf+UCnqCbDbuakBU4caqfC5dH2NxZy4G9NzJj+bxOb/8UNrBl48IWrZ9hPiYmE4yMxWhuDBNaxhRrrfDGUD9uRWFvdd28UvrRsWE6ykLsrKzBvcZzcFPZDO+PjeBR1DUnGqIgEHa66SgL8dZQP6cnx+6IaPz5xTM80dRGtcf7gZGNmpCPzzywjcjscLUiifhcDlqqb5jiiYJAyOfmY4c2s6W5ikgyg2nZOFSZioCH+tmhcSjNbDRUBGiuWjrj7vc42N1eR66oz5PFhVIbFpSCgYDbwbMHN7GpsYJIMothWDhUmbDfTX1FaZtep8bhzU3UhwNkCqWZLJemUB3yURf2L2i5mtuOQ6W2KsDoZIKBkQh11UGu9k1iWRbtTRX4fU5CQTdTkRSxRJaibjI6Ecfr0aitXKT3P68zNBald2iaqZkUyXSeQtEoBeBTSWKzvha2tXjLynVC4lpEClsQBGRZRFNlbMvCND8YjfjRoQgnj/bg9jg48MAGdh9oRdVK36+qyrRuqOLwoxvpuTbBta4x+numqGsKzyNlHwSS8Szvvt5FNlPg0ae2cvChTpxOde6eKwt72Xu4nYmxGH/6317n1NFeHn16G7X1oQXrKhQMquvKeOzp7WzcVjdvEP16u9QHhVgxQ8EsBT9VTj/qIkPWq4EsSHhkDXWJbgVRELne6LHY1TyYmeEno2e5EBsuydvOKl6poowsLqf1dAOKKOGWHYsOipf2QZh7l9j2jUYwwzJJ6Xks2yakepaUjpUEkYC6MAmRN3XixdK96pJUfPLSlXy37MAlaYgIRAppilZJhGGmkJojX5Waf8nqkSSI+BUniiBRMPVFqzsFq0BCjxMtRilTy3CIDqLFGJFCBJ/sxyf7GDfGkQUJh+RgPD9G0khiWAaLn50bMC2djBkHSkPSHvn25L4zZmIu2PbIgWXaewRckg9JUGYtBqILlpAEZc6QdzGIiDeRIHvNfNls2yJvZedEc0ayVxhZwYsMZsnOIgTbIbkXzPTegDDX2lWae7ybPb87rDvRKOSLXDzeg8fvYtvhjrsYJ1kI0zAZ7p7g/dcv8djnDrBUlu9OUV3l5/S5QQaHI/OIhiyLlIe962qK9zcJbrdGdZUf5zr7ewDkDJ3jY8PUeLzsrV6k9/in8KRJoki9z88jDS0MJRJkjdsXHUgVC7zQe43dVbVUez443X+XprKjbWVyLgoCfreDnSss21gZxONUkZZRAir3e3h4x/IZaN0w6RqaJF802Ne5cGi1fyLK8StD7GyrpaEiSEPF7b0kBQHaGis4c2mY/uEIh3a3cPHaOLIk0tFcgabItDaE6RmcZmA0SsDrZHQygdftoLYyMLce27bpHZzmpXe6uHh1jEg8QzZXRJZFHJqCIknk8kV0fXmRDUEQ8HmXbz8ThNkX1G0d6dpA1w0mx+NMjMZp7ayiua1ijmTcjPrGMIGgi8HeKUaHIuSzxQ+caAwNTBOdSWFbNvvv60DTlAWBgMOpsH13M4oiEYum6bs2uSjREEWB1o5KWjoqP3RqV4ZtYc1GL+ot8xG3A0kQlgzwV8JwJsIf97zJu1NX0S2Tg+XtbAvUU+n045QUJEEiaxT4Z2e+vux6RASUO/h+Ldueq24oywhSlLoeFl6/tm3PkYRSu/jS+3Az2TEscy74NSxr7h5VRGnJMEiY/bwoCFjYGIsErrIg0+RupspRjU/2E1SCeBU/FY4KgkoQWVSo0Cpn52r8NLtb0a0ize7WWXPbpWFjY9rG7L6IyMu0WS0Gy75xzKIgL0moSianIiIiYGPZCz1+RGF18xRrDQtrbn88cpA2z26qnC0rfArCWi0OaWGS9ta2rw8r7opo2LZNfDrFm997n+5zQ5iGSVVjmANPbmPDzibOH7nGq988RtfJfiRZ4sKxHgTg47/yENvv20B0IsGxl89z7ewguUyBUJWfRz+1n6bNtciyxORwhO/84as89+uP8qM/fYvJ4QjVjeX83D94klQ8y7f+4GX6Lo4wORzh3375fyIIAjvu38DHfukhBFFgpGeS954/w0DXGC6vg32PbWXroXZcHgeWaXH8lQuceOUChZzOzgc6Keb1eZdubXWQqko/hZvMqLLZAkfe72VgMMK2LXVUlvvIZotcujJK17UJzNlWhV07Gtm2uZ6R0SgnzwwwPZMiHPZyaF8rVZV+jhzv4VrPJNlckYa6Mh66vxOPW+Ob33ufynIfI2Mxcvkiv/C5Q0vKdg6PRjn2fh9ej4PxiThNjWHuP9jO1HSKk2cGmJhMUFbm4cCeZupqyzhzfoju3klisSypdJ6D+1vZvb2RF1+9iN/nZGIqQTyZ40ufO4hDUzh+so+LXWPIksjGDdUc3NdKIpHl3WM9jI7HURWJjZ3V7N/dwuhYjBOn+pmOpHE6FPbtbmZDexV9A9McP9mHLEs8cKhjLoN68swAFy+PUigatDVXcN+hdqanU7z53lVs2yabLVJV6edjT+1Y1PfgVpi2xbsjg3z/WhenJkZxSDLvDA8iCPAr2/ewb5Z0JAp5vnL+JIPJOA5J4aNtGzhU24AqSfTFony3+zKDiTgAnWVhntuwmWqPl754lG9duci28ireGx0kUSiwOVzBR1raafIvDDrH0ym+deUitV4fjzW24tU03hke4PWhPqYyGZyywt7qWj7RsRGnfHeBkWXbXJqZ5MW+bgYScUJOF0+3drCjohpZFPl613neGxmiKzLNv33vDXyahgD83w99hKDjg/c3uFuc6RnD49Q4sPHOZXxFUaA65Fsye5/I5Ogbj9JZv3CQdLVoaypHliT6R2awLJuunnFkWaKztRJVlWlrLOfC1TH6h2bY0FLJ2GSc+uogtVWBuXX0j0T41vNneOv4NVwujYM7m9m2oZag34WiSkiiyPmuUb79wmmmo8srv33YAtebUSwYJGIZDMNkbDjK1/7oTb7/9eMLlivkdQZ6SiZZyUR2RYJ1LzAzlcSYfQ9U1QYXrbSLoojb58Drd1IsGMxMLa58p6gyPr8Lxz1I0twuVFGeC4xzpo5p28s0ziyPO1W0eXn8Akemr5ExCvx6+6M8VNlJlTOAU1Ln1jidX1lVULhDVR1ZFJGv+/mY+lx76q2wsdGthQGvLIo4ZmdCDNucq1IsBtO2KM4SDIekzGWrnbI6t+8Zs1jah0UOxbZtDNtEt0wkQURbpN9fFmXKtfnPOOfskPH194T7ep+/AGEtvOqW3BK5KF3Hlm2iW7fXkqkI2ix5KLVELVVlKJE3A8PWUQUHirC45PjdqCjdKUSkOYKlCNqcz8VqPrd49eWn4919V0Qjl85z4tULdJ3s575ndgJQyBWRZAlBFKhvr+LQ0ztIRNKUVQZ48BO7EYTSz6EkWyirMlsPduBwq5x+s4vv/dHr/Nq/+RT+sJdcusB7Pz6Dqim0bK5j455mcpkCmlNFlETuf3bXnOzis79U6nEtq/SDABNDEd78/kmyyRwHntxGZCLB6985gW1Z7Hl0M5ff7+PVbxyjeVMd9R1VXD3VT2QygbVEq8F1qKpMU32I0dEYU7MvB8M0GRyKMBNJ8fTjW7lweZSr3RNUV/o5e2EYSRJ54PAGjp7ooevaOP9fe+cdJdd53ufntrnT287M9o7dxS7KohKNBItYwCZKIkXbUmjLVGL5RLGT4+MS2yflJMdJznFOEseO4mPHcWSLskQ1kmIVKVaARO9YANuwvc6W2dmddlv+uIsBFtgFQRRSybnPX9jF3jJ37v3u937v+/5+waCH+roYsRI/um7yDz88yKYNtfi8Kl299ktz6+Z6LPNSDfhyzC/k6e2bZOumOnbtaCLgUykUdE51DKPpBrt3tXDkeB9nzo0QDHo4dnKAyvIILU1lPP/jQ/g8LmRFordvkvKyEBvX2xM1lyIzMTnH+/s6+cJjm0jP59j7USf1tTEy2QLHTg5w3+7VxGIBwiE71XuhP8ng8DTbtzYSiXiJl9ir5vGSAIl4kAv9STJZeyV+dCzFwSMX2Li+hkjYxys/O0lFRRjLtHh/Xye/880HyOU0fvLKMXbe0Ug89vEr8CICLZEYD9U3MZlZoDYYZk9DE4IgUB++VFbz3mAfexqaeKi+iTPJCf7iyEfUh8LUBMMYlolfcfFwQxMFw+DNvh4WTh/j97fvZi6f56dd5+hPzbK7pg7dNNk/PEje0PnGhq2Le7dr5yczCzx35gTTuQy7qmrwKAodyQl+dP4M6xNl7KyoIZXP4VGUWzLYnZua5AfnziCLIvfXNdKfmuV7HScRBdhcWsn2imo8ssLpyXEea2yhIRwBQcCnfDorv++f6qV7JEkylSGdzfHIHa1saari228dpjoepm9smoJu8E8e3o5lWXxw+gKn+8ZQXRKbVlWyo7WOwclZ3jzayXQ6S9CrsnNNHbWJCEe7hnnxozPIksiRrkHu27CK1dWJZV96R7qGGJ2Z4/4NTbhdCj/d30E85GNTUxXvnujhVN8oGxoqKIvaUobJ1AJvHutidHqOgqbjXiyLGk6m2NfRx9DkLCVBH3etraehvIQTvSMcPDfAXCZPfVmUe9obiQYulbesqo0jyyL9w9MUCgZ9w9OUxYPEIn5M06KpvpRcXqNncJKK0hCpdJb21ipKFtWUTNOio3OUvYe7UWSJJ+5fz56724iGfMjypbrsyan0LVfl+rQxDQt9MWhIp7KcPTl0zb8XBAHDsFac6H2aGLpZLFNQrtHrJyAgSSKGoRcXqK5EFAUkefma+8+amBoo9k4MLkyRN7Tiz58GumlwbKafVCHLqmAp95W1UuuLL8msmJbFzGJp0u1AFERKXAFkQWQsmypmeK7EsEyS+asDf6+sknDbpZFzWpapwsqLA1P5+cV+EyjzhIuBQpU3WswIDSwkV8xAFkyd8dwcJrbbecJzfZnt5XpOrvXzSiiCi6BiZ+1y5gIpbXLFHonlCLriKIseVbOFCYwVTHctTFLaOBYmkqAQdpVe9zFuN3Zvsh+vFCRrzpM15nCJKv+vBAw3yk0FGlrBYGJoBgtYv6sZX8BDIa/ZgYYgUFIWpmVDHQd+doqKujgb7lq9ZHUnHA+w46H1uNwKkizhC3j4yz9+nly2QHDxgc1m8jSuq2bbg+tQXDKFvIbiklFcMi2b6hjsGmW0P8mGu1qWyCp2newnOTLDvU/eQevmenLZAkM943Qc7qWpvZbDb58hEPFx5+MbqaiLU1Zbwr5Xj39snYAsS8RjQSJXSCmqqkxFWZiWpjKSU/MMDE0xNjFHd+8Ek8k0/YNTTCbThMNestkCZ8+PMjQ8gygKXOhPUihcqnGsr42zqj5+XSuOfp/KqvpSaqrtyfT4xBw9FyboH5hiZHSWqekFFEVC0wxCQQ9dPeP0DyZpaiylsiJSDGRqq2M01idQFAnLsugbmKLj/Ag+n914mJ7Pk0pnKUuE2LyhlpNnhgkF3dy5w5bWbaiLk5ye5/DxfqorI4S32BOsQMBNIh5gdGy2eM7DozO4XBKNDQniJQH2H+6hrz9JdWWEkoiPpsZScjkNRRaZS2evK9AQBIEyf4B24I0LPurDEXZV1V6Vzq8MBHmwfhXN0Rhby6t4uec8PbPTVAZCVAdDPNWyhqCqUjAM0oUCP+nsKN4SqiyzJp7g0cYWBCCraRyfGGUqa7/IJFFgLp/jex0nSWYzfKmljbXxUlySRDKbYSIzz6pICdsrqjEsE8M0cd2k/4NpmRweGyaVz/GrazewJlbKXD7Hnx7cy0fDg9SHoqyK2HKpXkWhvbSM9fGyT23iYhgme8/0ceeaOjY3ufmfL39EIuRDEkW6h5N4VRf3b2rGsiwkUaB3dJojXUM8eec6xmbS7D87QH1ZCdGAl7vXNaKbJgfPDXCsa5iWyjitNQlqSyOEvG7u29BIPLxyD1BVPMR33znGztV1KLLEqwfP8q+/+gCyKLK6OsHg5Cxj07bXSUEz6ByeZHByhid2rOX0hVHODk6QzWt0jyTRdIPHtrWx/9wAJ3pHMS04eH6QqniYhvISfnakk46BcbY0VeFe7AOpKA0R8LkZGZtlcHSaXE5jdUMpkiQiihYVCbvHo7c/SXNdKS5FpqI0hLwYNOQLGhNTaVLpLOtaKlnXUkFpLHjV5xybnCObX/4lfKuxyxTsf19U07oVyIpYXMVfvbaKx7+8lbKK8DW3iZT4CUc+m2bHy/H53cWxOz2XJV4WWjYra5omC/N53B5liVrWEoSVG/8/a+r8MSKqDyENH0528XBFO0HF86mNLZppkNELmFjE1AA+2X3VeG9YJkemL9zW82gMlnJ0po/BzDRjuRQh19VeE/lFT5ArUUWFSk+EhBpkLDtLb3rCdhFfpoSqIzVMMm+PT62hCvyKfc+UqH4a/QmS+TQHkj0UTH3ZhvC0nuPYdD8AYcVLk//WS8dfC5foIa7WIgoSc4VJBjJnqPa2Xvf94pUCxNQqxnN9DGbOkjMW8Eqhq7Y3LJ2e9PHFY7qp8Nw++X/psim0fh0ZGkEQ8MsR4mo1A5kOxnP9zBYml5hOf9qIiMVMkWnpy5ok3iw3FWh4A27W7ljFqf1d/Jd//ndsf2g9dzywjtgVUn8roWs6xz44z/H3z5FKplmYzzExPI2hX1a+YMGabY14F7Xgletw/zVNk6mRWfa/cZLzR/tweewX/eTQDOt2NDGfyjDWP0VNSzmReBCXW6FxTRW+gOeGx3RJElFV2a6DFG3ZSbcqEwq6qawIs21zA6ZlEQi4mU1l6OweZ8vGOiIRH+98cH7JC9rvu/4IV5Yl3O5L18TtVggFPaxdU8nuHfYEzudTCQY9YEFdbYzW5nLCIQ+hy4y0fD7XkpdhNOwlHPbx+MPti5MJgViJ7b68c9sq2lZnOHl6iDff7uDZZ+4kHg+we1czs7NZ3t17nqMnBnjoc2uWPedQwEN6Pl9csZyamqepodTW5va67GBUsK/pSs2sN0pztIRSnx9Vkkl4fXgkhZlcDsuyyOk6Pz7fwcnkGAuaxnQ2Q07XMReVSoIuldUlcQIuFcuyiHq86KbFgmZP6nTT5JXeTlyixNOr19KeKENdTIuvjZXSHI3x3w9/yLuJMj6/qpV18dIbrmu+SEbTGEmnCbvd1IejuGUZVfJRHQwxNj9PKp8j7v3sJl+iKBAP+Th4fgBJENjSVEUiHCguOLRUxWkos5VW8ppOz9g0x3tGAMhpOqIgMLeQYz6b5+3j3ZimRfdIkppEGEkSiQW9RPweSoJeGiuubdQZD/opjwbpGBxHlWXKS4IkIn77HMM+okFvMeuW13Sm5jIkQn6aK2PMLeQYnkoxl8nRNZLk7MAEAxOzzKSztNWWMjBh+zLUl0Vproxz6sIoo1NzZGu1YqDhUmTqq0sYnUhx+FQ/giCwptk2pBIEAZ/XRU1FhMnpec52j+H3qVSVXSrLuziptyw7u7Hc6unI+CxHTg8wN5+7yW/u+vCoSnHVfnIqTTanEfDffDZFdSvEEkE8Xpf9rMUCrN1446pwnyY1DXHUxX6SrnMj1DYmEK/oQdA0o2g+GIn6qKy5uj/jVnBxeLkdiZ6Q4mFLtJ7OuTE606P8cOAgzzbeTXgZ5SW7bMe84V6M5XBJMh7JzgqPZmfJG/oSKWfLshhamOIHA1eX3N1KdsZW8drwccZzczw/cIDfa3tksXTLPo+8obE/2cPgwtRV24qCQJ0/zpaSBl4bOcGBZA+bonWsv8LcMJlP8/ZYB8OZGep8MdaEKvFKdiAuCSKPVG7g5OwgAwtJXhg8wlfqdizZvmDqdKRGeGe8A5+ssjZcRZXv05WslgSZmFpFladl0ejuAKXuBlYFNi2b2Tctwy5ou8y/YnVwO/0Lp5ktjHNy9h12xr60xD/Cskwm8v2cTL2DJChUepuJqzeuavpx+OQQAiIWFqPZXtaF7/nYbaJqBfX+dgYz5+hfOEXH3D42Rx66yo3bxiKjp3GJnmt6dd0MLtFTlMedLUyQMeaIcrXk7s1wU4GGrEi0bWngG//uy5w+0MWht05z9N2zfOEb97F227WbLwFef+5DzhzsYdcjG6htKWesP8m3/uj7V62K+QKfzFPBskDXDVatq+aRX7uLkstqnP0hL9FECF03kGWpOOGRFRnJJRUH5nxe47nn93OqYxjDsCgUdB7b045hmHzvRwfpvTCJKArohsnG9TUst+4UDHjY2F7L/kO9PP/CISwL9ty/lqqKCPm8zlvvdBCN+vF51eKqJSyzo4/l0gZ+n0r72mr2Hejm+Z/YBk337l5NIOAmr+mcPjdCx9lhdMPisT3raW1e/oaqri5h+9YGvv+Tw0gCRMI+nvmVHQyPzvLc9/cjSnbwsX5tFZZlceLUIG++04Eo2Kn+9WsqMU2Lffu7eXfvecbGUwyPzrLn/rU01sWpr43x3R8cQDcMwmEvrS3lTEzO3fa1O5/iKuphX2yOMxdVRP7DR++R0TR+bd0mfIrCh8MDPH/2VDGjIYviJVlc4dLQePF+ncgsUDAMMlqB/rlZCrpRDDSiHg/f3LSd05PjvD/Yx7/64C2+2NzGV9va8dxECZOx+AKXBXHJ51JEEeOyZs3PCmHx+q6rK6elOkHIp+K7TPff675Uey6JArGgl8pYkK/etxEQkEQBWRI5cG4ARRL53JZmXj10llxBsx90QcAw7ezQtfwiwA56HtzczCsHzpLXNB7d1rZioCdLIi5FYnYhi4UdeCzkCrhdClG/l02rKtmzZTUWFl7VRWohS0f/GJnFTML0XIaKWOiqBtGm+gT7j13g/YPdiKJA22U+Bm5VobE2Tnf/JCfPDeP3qlRdtmjjcsnEowFCAQ+9A0n2HuqhLB6kPBEim9M4fX6EF948QXff5KfWwV1RGiIasl+Qb3zQQTTsZfP6WtwuhWyuQCqdpaEmtqIa10qIou1t0bKmks6OEY4f7KW5rQL/Cg3spmkWF0Q+axJlIVrWVpKcmOONF4+x4+7VhMLeJRPg+bksb71yAlEUKKuM0LS64mP2emNczApNJeewTOuWqj6KgshjlRs5OTvI/mQ3Lw4d5cL8JHclWmgIJHAJErNalrHcLB2zw0Rcfv5F60O38PgCa8KVnEkNM7gwzXcu7OOpmq1UeCOktRyHpnr5Xv9HJPPzeOUVMka3gLXharaWNPCz0dO8OXIK0YJHKzeScAeZKSzwUbKbl4aO4pFdaFr2qu3LPWEeKF9DZ3qU4zMD/I/ON9lT0c6aUBWKKNI3n+SN0VPsT3YDFl+q3kq9P7Eka3J36Wr2J7t4feQkf9vzHpP5Oe4pbaPE5SOlZTmQ7OGV4WPMFjKsC1fzxeotK6p83S4EQSCqlrMx8iCT+SHGc728Nfa39M4fp8bbhk8Oolsa8/osk7l+FowUGyMPUu29ZKRZ729ndXA7x2be5PD0ayzoKVqDOwm7ytDMHAOZMxyZfoN5bZqIq5ydsS/e1oZpvxIlplYxnO3kVOo9omoZNd41KKJK3syimXlCSrxYMgbgFr00B7Yymu2hM32Q/ckXmcj10eDfSEiJIyCQNdJMF0YZznbiEtzsij9FiXp7xghV8hJxleGW/Ixkuzg6/QYFM0dIiWNhkDHm8Uh+IkrZDQc7N3WnCYKA6nFR31ZBeV2M1k31vP7dD9n/+slioCEsru5b1sX62UXVBN2g41AvNavKaN/VTCQRZGo8teJxlv09IIgilsWS4EQUBfwhLx6/m0giSHN77dKNgEDYR3o2Q2FxYjCfypBbyC+prX3i0Y08/OB6sCxUVSEQcINl8UtPbkXXTBDA41bwel2URP3Fc9i4voa1rZV4vSrRiI/a6hJbz96CYNCN6pJ55pd3oOsGkiTyxCMbiEbtVeff/PW78fvd19UA3VAXpzwRwu+/NIhKkkhzUymVFWEKixmDgE/lXOcYLkXmyc9vIh4L8M775xgcmqa+NsbXvroLr9dVLD0TBAGPW+HxPe1kMgUQ7ImXLEuUJYI8+8wuQECUBHxe+9htLRVUV150Dxbx+1QEATa2V9PcVIphmMiSSMDvRlUV7r+njZ3bVmGZFqoqEwx48PlUfvPr9wK2HOhvfO3uZV2Nr4VgJ0Mu3W9XXEhx2ZDQTsO/3dfDv9/9ABtLy9EMgw+HBq7e/zWOHXV72F5RjSrLvNrTSVUgyP11q3BJEqIgUOrzE3F72FBazlt9PfzNiSM80dR6U4GGT3ERcbvpS80ym88SVFUM02Qyk8ErK3gXG80vVuF92nFHXtPRNIOfn+hm75k+NN3gaw9uoanSzj5cfj0lUaSpIsbaujL++rUDCAjUlUZ4+u52/G4Xbx3rYiK1QGohR0tVvLh9XWmUt493c6pvjKfuXM/aupVLAlqrE3z37aPMZfK0LjZ2z2fz/OXLH9E5nEQ3DQq6waPbWllVHuNw5yD/5u/eQJYkIn4PAa/KpqZKfn6sm79+bT8g8OCmJra21NBcFefVg2f50QcnKQl6WVtbildd+pJrqksgSSLd/ZNEw74lRnhuVWZVXZwXf6YxPDbLhrYqqssvZTREQaC9rZLtG+t5c+9ZXn3nNHsPd6MqMoZpkc0V8HpUfvXJbbz01il6+idv7su7DsJBD3dtXUXPwCT9w9N86zvv43W7EEQB07SQJJE//7dPkyj55EpnNfUxdt+/hr6eCd746TFmZxa463NtVNfFEEWB9FyWibEUZ08Nkc/pPPbUFqrrrp3V+jSQZYkvfWUH504P0XVulD/7k5f48jN3sqrVXtTp6x7npecPcXBvF9F4gEe+tAmP7/ZMhhqbyxFEgdGhGX703Ec89PkN+AJuclmNzHwOt9eF/xMu5F1O3B3kt1seQhEk9k12cnCqhzOpIVyigijYpUuaaaCZJjvjt7aERUDg81WbOTkzyNHpPl4ZPsYHE+dRRAnTMskYBURB4A/aHuOVkeN0pJb3K7hZ3JLCN5ruYyI3x5HpPl4bPcm+ZBeyIC02cOs0Bcq4u3Q1f9rxylXbK6LEHSWN5Bo1/nfPexybHqArPY5btNXKCqbOvJbDJcr8euNuHqpYh++KwMknq/xWy4NYwGsjJ/hh/0FeHzmJLIgYlkVGz5MzNNaGq/jtlgep8302fl+KoNIc2Ipm5dk3+UOS+UFS2gSnU+8hIgEWhmWgWwWCSoy24J1LtneJbnbFnsTC5NjMW5yafZeu9CEkQcbComDmyBkLlKiV7Cn/DRIreEzcKiRBZnfil/jx4H9mQZ/hnfHnbH8PRCxMfHKIu+K/tCTQEASRuLuWXfGnEAWJzvRBOlL76E4fLTZ8274XGpqZp9LTVFTruh2IgsiqwBaGsuc4P3eQM3N76Zk/unhNwbIMNkTu546Sxz+bQCOTztJ1YoBgiZ9YWRhdN8nO5wld5pXgDbgJhL0M9Ywz1D1OJBFEdSu43AqqW2F6IkU+W2C0b5I3vruP9Mz1N24pqkwkESA9M8/5I33UtlYgAL6gh6b2Gk592MX7LxzBH/LiD3roPz9KSVmYivoE63as4s3v7+fMwV7atjTw8x/uZ276kq60KAorej7El3lpXr5i5/G48BTHbpHoMuVe0RVqiT+Jz4TqkpdVpHIpMq7w0t+Xl4Y4eKSXF14+VgwovvDoRjxuZdnJvCAIBPzuq/7P5ZIpTVyt6+/1uvB6r35Z+n1u/Mu4OPt96mKJ2BWfZ9HUShA+2bW4SMClElLddM1McSE1Q9TtxasouOVr3+qKKOJWFC6kZtBNgxOTY/zg/OlPdGxFFAmqKvfWNjCZXeDFrnPEvX62lFVwJjlJTteoDoZwSRIzuSwuSbzpDI4kCGxIlNORnOSN3m4eaWzm5OQY56cmebp1HaU++xrGPD48ssLB0SEqAyHAIur2XFMW9lZwuHOISNDDnq0thP0eXth3mq7hJHWlUf7g6Xvxey7dA4IgEPJ5eHp3OznNHlgVScLrdrFzTR3rGyqKGUjXYh+YIAjsXlfP5qYqwFqyv+VQZInff/peu6RwMZviVV18fc8d6IYtE6kqMj63i2jAyzcf34VumoiCnVnxqnYpT1UsjGbYgbxPdaEqErvXNbCluRrTtFBkCZ/bdZXiUHNdAkkUyekaLQ2lS5q2VZdMQ1UMURQRRYFY1EfwCgPAikSYX39qB9XlEd7d38nQ2CwWFrGIn20b63nknrU0NyQ4dnqQ/uGrSzVuNaIocu+OZoIBN6+/18HJc8NMTKWRZZFw0EttZfSGG9NVt8Kd97WRy2n8+Lsf8c4bp9j//nnkRQEQ07QwDJNCXqeptXyxz+0Xg8aWMv7p7z3MX/ynVzn0YTcdJ4dwLY7VmmawMJ/DH3Dzla/vZuvOptuWidm+u5mGpjI6O4Z57n+9xwvf248gClimRTjq51eevYtd97be8P5FQaDeH+OP1z3BwWQv702c5dTMEFOFNIZpElQ8lHsibIrW8bmy5Utpb4YKT5g/XvsELwwd4b3xcwxnphGAqBrgrngzv1y3gwpvhL75ydsWaACUeyL8u/VP8fLwcd4aO0X/QhIBgRpfCQ+Ur+PRyg0k8+kV5WvdksI9pa3U+GK8PdbB3snzDGWmMS2TmBpgR6yJhyvWszZctWwvCtjN+b/b+gh3xlt4efgYZ1PDzOgL+CWV5mAZuxOt3F++hrgauO3j/koIgt0MvT58LxXuVZxLH+DC/AmmCyPkzAVkwYVPDhJXV9MU2EKpu+6qfXjlEHcnfoU6XzunU+8xmDlLWp9GFlRiagVNga2sC91D0BVD+hQkbOt97Txd80ccmn6Focw55rQpJFHBJwUJKaX4ljEuvuj8/VD5P6YluI1zcx8xlu0lrc/YWXIpQEKtpdq7mpbgdqKu29tPE1Mrua/0GRLuOs7PHWC6MIph6aiil7ASJ+iKr+g5cj0I1k10781Nz/Pad/by3guHyaTz+EIeNt61moefuZPKBnu10LIsOg718tLfvEv3iQG8QQ9f+8PPs/neNs4c6OZHf/lzLpwZJhIPsOeru/iHP3udP/neb1FeF6P/3Ci/89if8p3j/xHvMilzy7KYGJrmhb96m0M/P4OsSHzu6W18+ZsPYugG545c4LXn9nH+yAVM06K8NsZT33yAdTubyWcLvPjX7/D+S0cwNJOdj7Zz/mgf93xxC/d8aSuq+xdPTvBmME2TbE4ryndezFpI0i+mosmNYlkWB0eH+PapY3RMTRBwufmX23ezq6qG33rzZZqjJXy1bQPRxUhwz/e/zdfbN/NEUytv9nXz54c/Yl7TWF0S43O1jfz9meO89OQ/4vTkOH/y4bv87rY72VZh13z++PwZ3rjQze9s3cl0LsuPz5/h3toGHm5sIZXL8d8Of8iCVuAbG7YyOp/m704fo3PanvxVBYI8u34zd1XXfWxD+OBciv96aB9Hx0dJZhfAgjJ/gLuqa3l23WYSPh/vDvTxDx0n6J2ZJurx8uXVa3i4oZmI21MsX/pp9zm+c+Y44wsLJLw+/mrPF4rX4XYxPJXi7986wtxCrti/9PWH7qAmsbzs5//vmKbJzFwWy7JQFXlJIG9ZFrphkkrbrukul0xgmSDdNC3yBZ18QcMwFnXlRQGXIuFWFURRYH4hT0HTCQY8yFc847NzGXTDRF1h/2A3w88vfmcet4L7Y7wpNN0gn9co6MZiX5X9XcuSiM+r3vB3bVkW+ZzGha5xPni7g2MHLzA2MoOhm/gDbhJlIdraa9h5z2pWrS7H7V75PP/gN7/NqeP97HliE7/9h499ovN465UT/J9v/ZxgyMuz/+z+j3UGB9A1g7GRWV5/8SgH93YyNjILFiTKQ2y8o4EHH2+ntiGBa5lr23l2hG9/623OnBzkK8/u5ulf2/WJzvcilmUxMZbiR9/5iEP7ukhOzGFhEQx5aW6r5MvP7GTNhhuXhS4eBwvNNMgbOpppYGKCZb9nbJ8MGXXRQO8iummQ1nMYlolbVPAry9+LmqmzoBfQTB2frNr9D1coS+UMjbyp2Z4Ulh0AuUQZ76L0a8YokDMKeCUVj+y6bN8G81oOAxOP5LoqU3CRgqmzoOfRTQO/4i5mG5ZcA8siZ2rkDa3oUSELIqqk4BYVDMtktpBBEkWCimfZoMOwTPKGTsG8tA8JEUWUcEsK0gqu31eea87Q0EyjWE568TxUUf4Fed/b/iO6paGbBUyMYrmnIIhIgoQkuJAFeUX3cMPS0cw8hqVhWiYCAqIgIYsuFGF5EzvD0skZC1iWiSKqK/RFgG5qFMysPdmWvCvu7yKmZVAws+iWjrV4Lva9L6OI7hUn6ZekePMYll40gxQEAWlR0lYWXQgs/d5zxjyaWUAUxGUb4u1zMimYOTQzhyTIuCX/VSIFV/69bhXs72PRs+SiH4kiqsiC64bvnZsKNCzTolDQ0PK6PaiIArIioajyEsUkQzco5G0JP7vcSkFWZAzdpJAvYOh2fa3LrVDIaXh8ajH1nk3n8IVWVrIwTRMtr6MtrmYpqlwMEgzdRCtoxaZjURQXFa4WtZgXt7MsC8UlYxomsiIju6RfkIfR4UbQTXuw1k37gffIMookkdHsB/NiKRPYRnaqJKOIIrplktV0LCykxb/LGzp+xYVhWeR0DbesFHXTC4atSe6WFtO2hoks2ttZlkXO0DEtC7ckY2KR142iOZMkCLhlBek66soN0yS3+Hms4mBsp9xVWUJEQLfM4v5FQSh+psv3rRlG8ZxEQcCnuG66Gf3jME2LvHb5AApuRbFL3JxnzOE6sCwLy7TQdANDNzEXM08XA1dJFov9dte6pzILeQzDRFGkT+xLoRV08nkdQbAzLfJ1Zmksy+7v0zWjKJ0uigKyLKG4pBWVBQ3DJJ/XMHUTlyovG4xcL5ZlUcjbrvEXz0EQbGldl0suvg8dHBwcbgc3FWg4ODg4ODg4ODg4ODgsh7OU4eDg4ODg4ODg4OBwy3ECDQcHBwcHBwcHBweHW44TaDg4ODg4ODg4ODg43HKcQMPBwcHBwcHBwcHB4ZbjBBoODg4ODg4ODg4ODrccJ9BwcHBwcHBwcHBwcLjlOIGGg4ODg4ODg4ODg8Mtxwk0HBwcHBwcHBwcHBxuOU6g4eDg4ODg4ODg4OBwy3ECDQcHBwcHBwcHBweHW87/BQUFkLZWQI8dAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate word cloud\n",
    "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(all_text)\n",
    "\n",
    "# Display word cloud\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.title('Word Cloud of Cleaned Comments')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5e6b116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(('thank', 'much'), 49), (('thanks', 'lot'), 18), (('linear', 'regression'), 15), (('great', 'video'), 13), (('god', 'bless'), 13), (('thank', 'sir'), 13), (('aman', 'bhaiya'), 12), (('company', 'database'), 11), (('great', 'job'), 10), (('foreign', 'key'), 10)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nltk import ngrams\n",
    "\n",
    "# Function to get the most common n-grams (in this case, bigrams)\n",
    "def get_ngrams(text, n=2):\n",
    "    tokenized = word_tokenize(text)\n",
    "    n_grams = ngrams(tokenized, n)\n",
    "    return Counter(n_grams)\n",
    "\n",
    "# Get the most common bigrams\n",
    "bigram_freq = get_ngrams(all_text, n=2)\n",
    "common_bigrams = bigram_freq.most_common(10)\n",
    "print(common_bigrams)\n",
    "\n",
    "# Plot the most common bigrams\n",
    "bigrams, counts = zip(*common_bigrams)\n",
    "bigrams = [' '.join(bigram) for bigram in bigrams]  # Join bigrams into readable format\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(bigrams, counts)\n",
    "plt.title('Top 10 Most Frequent Bigrams')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a25f6f3",
   "metadata": {},
   "source": [
    "**Sentiment Analysis**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "881d950e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting vaderSentiment\n",
      "  Obtaining dependency information for vaderSentiment from https://files.pythonhosted.org/packages/76/fc/310e16254683c1ed35eeb97386986d6c00bc29df17ce280aed64d55537e9/vaderSentiment-3.3.2-py2.py3-none-any.whl.metadata\n",
      "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl.metadata (572 bytes)\n",
      "Requirement already satisfied: requests in c:\\programdata\\anaconda3\\lib\\site-packages (from vaderSentiment) (2.31.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (1.26.16)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2023.11.17)\n",
      "Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
      "   ---------------------------------------- 0.0/126.0 kB ? eta -:--:--\n",
      "   ---------------------------------------- 0.0/126.0 kB ? eta -:--:--\n",
      "   --- ------------------------------------ 10.2/126.0 kB ? eta -:--:--\n",
      "   --------- ------------------------------ 30.7/126.0 kB 1.3 MB/s eta 0:00:01\n",
      "   ------------------- ------------------- 61.4/126.0 kB 544.7 kB/s eta 0:00:01\n",
      "   ---------------------------- ---------- 92.2/126.0 kB 581.0 kB/s eta 0:00:01\n",
      "   -------------------------------------- 126.0/126.0 kB 615.5 kB/s eta 0:00:00\n",
      "Installing collected packages: vaderSentiment\n",
      "Successfully installed vaderSentiment-3.3.2\n"
     ]
    }
   ],
   "source": [
    "!pip install vaderSentiment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2f8ecba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "\n",
    "# Initialize the VADER sentiment analyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Function to analyze sentiment using VADER\n",
    "def vader_sentiment_analysis(text):\n",
    "    sentiment_dict = analyzer.polarity_scores(text)\n",
    "    \n",
    "    # Classify as positive, negative, or neutral based on compound score\n",
    "    if sentiment_dict['compound'] >= 0.05:\n",
    "        return 'Positive'\n",
    "    elif sentiment_dict['compound'] <= -0.05:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'\n",
    "\n",
    "# Apply VADER sentiment analysis to the \"Cleaned_Comment_Datail\" column\n",
    "df['VADER_Sentiment'] = df['Cleaned_Comment_Datail'].apply(lambda x: vader_sentiment_analysis(str(x)))\n",
    "\n",
    "# View the results\n",
    "df[['Cleaned_Comment_Datail', 'VADER_Sentiment']].head()\n",
    "\n",
    "# Save the results to a new CSV\n",
    "df.to_csv('Sentiment_Analysis_VADER_Results.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3feb1914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting textblob\n",
      "  Obtaining dependency information for textblob from https://files.pythonhosted.org/packages/02/07/5fd2945356dd839974d3a25de8a142dc37293c21315729a41e775b5f3569/textblob-0.18.0.post0-py3-none-any.whl.metadata\n",
      "  Downloading textblob-0.18.0.post0-py3-none-any.whl.metadata (4.5 kB)\n",
      "Requirement already satisfied: nltk>=3.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from textblob) (3.8.1)\n",
      "Requirement already satisfied: click in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk>=3.8->textblob) (8.0.4)\n",
      "Requirement already satisfied: joblib in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk>=3.8->textblob) (1.2.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk>=3.8->textblob) (2022.7.9)\n",
      "Requirement already satisfied: tqdm in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk>=3.8->textblob) (4.65.0)\n",
      "Requirement already satisfied: colorama in c:\\programdata\\anaconda3\\lib\\site-packages (from click->nltk>=3.8->textblob) (0.4.6)\n",
      "Downloading textblob-0.18.0.post0-py3-none-any.whl (626 kB)\n",
      "   ---------------------------------------- 0.0/626.3 kB ? eta -:--:--\n",
      "    --------------------------------------- 10.2/626.3 kB ? eta -:--:--\n",
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      "   ----- --------------------------------- 81.9/626.3 kB 508.4 kB/s eta 0:00:02\n",
      "   ------ ------------------------------- 112.6/626.3 kB 435.7 kB/s eta 0:00:02\n",
      "   -------- ----------------------------- 143.4/626.3 kB 532.5 kB/s eta 0:00:01\n",
      "   -------- ----------------------------- 143.4/626.3 kB 532.5 kB/s eta 0:00:01\n",
      "   ------------- ------------------------ 225.3/626.3 kB 625.1 kB/s eta 0:00:01\n",
      "   ------------- ------------------------ 225.3/626.3 kB 625.1 kB/s eta 0:00:01\n",
      "   ------------------ ------------------- 297.0/626.3 kB 679.4 kB/s eta 0:00:01\n",
      "   ------------------ ------------------- 307.2/626.3 kB 655.5 kB/s eta 0:00:01\n",
      "   ---------------------- --------------- 378.9/626.3 kB 737.3 kB/s eta 0:00:01\n",
      "   --------------------------- ---------- 460.8/626.3 kB 823.7 kB/s eta 0:00:01\n",
      "   ------------------------------ ------- 501.8/626.3 kB 827.5 kB/s eta 0:00:01\n",
      "   -------------------------------- ----- 542.7/626.3 kB 896.4 kB/s eta 0:00:01\n",
      "   -------------------------------------- 626.3/626.3 kB 916.8 kB/s eta 0:00:00\n",
      "Installing collected packages: textblob\n",
      "Successfully installed textblob-0.18.0.post0\n"
     ]
    }
   ],
   "source": [
    "!pip install textblob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a5b9a28",
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "\n",
    "# Function to analyze sentiment using TextBlob\n",
    "def textblob_sentiment_analysis(text):\n",
    "    # Get the polarity score (-1 to 1)\n",
    "    polarity = TextBlob(text).sentiment.polarity\n",
    "    \n",
    "    # Classify as positive, negative, or neutral\n",
    "    if polarity > 0:\n",
    "        return 'Positive'\n",
    "    elif polarity < 0:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'\n",
    "\n",
    "# Apply TextBlob sentiment analysis to the \"Cleaned_Comment_Datail\" column\n",
    "df['TextBlob_Sentiment'] = df['Cleaned_Comment_Datail'].apply(lambda x: textblob_sentiment_analysis(str(x)))\n",
    "\n",
    "# View the results\n",
    "df[['Cleaned_Comment_Datail', 'TextBlob_Sentiment']].head()\n",
    "\n",
    "# Save the results to a new CSV\n",
    "df.to_csv('Sentiment_Analysis_TextBlob_Results.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a38d5a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot VADER sentiment distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='VADER_Sentiment', data=df, palette='Set2')\n",
    "plt.title('Sentiment Distribution (VADER)')\n",
    "plt.show()\n",
    "\n",
    "# Plot TextBlob sentiment distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(x='TextBlob_Sentiment', data=df, palette='Set1')\n",
    "plt.title('Sentiment Distribution (TextBlob)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95a8142d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (1.3.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.24.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5c0493f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Title', 'View', 'Date', 'thumbs_up', 'Youtuber', 'Subscribers',\n",
      "       'Description', 'Comments', 'Viewer_name', 'Comment_date',\n",
      "       'Comment_Datail', 'Comment_thumb_up', 'Cleaned_Comment_Datail',\n",
      "       'VADER_Sentiment', 'TextBlob_Sentiment'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "abbe591f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (1.3.0)\n",
      "Requirement already satisfied: vaderSentiment in c:\\users\\91901\\appdata\\roaming\\python\\python311\\site-packages (3.3.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.24.3)\n",
      "Requirement already satisfied: scipy>=1.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: requests in c:\\programdata\\anaconda3\\lib\\site-packages (from vaderSentiment) (2.31.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (1.26.16)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2023.11.17)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn vaderSentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bd51d843",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f42bd52d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 476, Test set size: 119\n"
     ]
    }
   ],
   "source": [
    "# Assuming you already have the 'df' DataFrame loaded\n",
    "# We will use 'Cleaned_Comment_Datail' as the features and 'VADER_Sentiment' as the labels\n",
    "\n",
    "# Split the dataset into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(df['Cleaned_Comment_Datail'], df['VADER_Sentiment'], test_size=0.2, random_state=42)\n",
    "\n",
    "# Check the data split\n",
    "print(f\"Training set size: {len(X_train)}, Test set size: {len(X_test)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6a3b86e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF matrix shape (train): (476, 2489)\n",
      "TF-IDF matrix shape (test): (119, 2489)\n"
     ]
    }
   ],
   "source": [
    "# Convert the text data into numerical form using TF-IDF\n",
    "vectorizer = TfidfVectorizer(max_features=5000)\n",
    "X_train_tfidf = vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = vectorizer.transform(X_test)\n",
    "\n",
    "# Check the shape of the resulting TF-IDF matrices\n",
    "print(f\"TF-IDF matrix shape (train): {X_train_tfidf.shape}\")\n",
    "print(f\"TF-IDF matrix shape (test): {X_test_tfidf.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d8020f90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.8403361344537815\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.00      0.00      0.00         4\n",
      "     Neutral       0.00      0.00      0.00        15\n",
      "    Positive       0.84      1.00      0.91       100\n",
      "\n",
      "    accuracy                           0.84       119\n",
      "   macro avg       0.28      0.33      0.30       119\n",
      "weighted avg       0.71      0.84      0.77       119\n",
      "\n",
      "Naive Bayes Accuracy: 0.8403361344537815\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.00      0.00      0.00         4\n",
      "     Neutral       0.00      0.00      0.00        15\n",
      "    Positive       0.84      1.00      0.91       100\n",
      "\n",
      "    accuracy                           0.84       119\n",
      "   macro avg       0.28      0.33      0.30       119\n",
      "weighted avg       0.71      0.84      0.77       119\n",
      "\n",
      "SVM Accuracy: 0.8319327731092437\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.00      0.00      0.00         4\n",
      "     Neutral       0.00      0.00      0.00        15\n",
      "    Positive       0.84      0.99      0.91       100\n",
      "\n",
      "    accuracy                           0.83       119\n",
      "   macro avg       0.28      0.33      0.30       119\n",
      "weighted avg       0.71      0.83      0.76       119\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "# Load the labeled dataset\n",
    "# Assuming that 'Cleaned_Comment_Datail' contains the preprocessed text\n",
    "# and 'Sentiment_Label' contains the sentiment (Positive/Negative/Neutral)\n",
    "\n",
    "# Example: df['Sentiment_Label'] should be filled with sentiment labels\n",
    "# You can use VADER or TextBlob sentiment results for initial labeling if needed.\n",
    "\n",
    "# Split the dataset into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(df['Cleaned_Comment_Datail'], df['VADER_Sentiment'], test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert the text data into numerical form using TF-IDF\n",
    "vectorizer = TfidfVectorizer(max_features=5000)\n",
    "X_train_tfidf = vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = vectorizer.transform(X_test)\n",
    "\n",
    "### 1. Logistic Regression\n",
    "lr_model = LogisticRegression()\n",
    "lr_model.fit(X_train_tfidf, y_train)\n",
    "y_pred_lr = lr_model.predict(X_test_tfidf)\n",
    "print(\"Logistic Regression Accuracy:\", accuracy_score(y_test, y_pred_lr))\n",
    "print(classification_report(y_test, y_pred_lr))\n",
    "\n",
    "### 2. Naive Bayes\n",
    "nb_model = MultinomialNB()\n",
    "nb_model.fit(X_train_tfidf, y_train)\n",
    "y_pred_nb = nb_model.predict(X_test_tfidf)\n",
    "print(\"Naive Bayes Accuracy:\", accuracy_score(y_test, y_pred_nb))\n",
    "print(classification_report(y_test, y_pred_nb))\n",
    "\n",
    "### 3. Support Vector Machine (SVM)\n",
    "svm_model = SVC()\n",
    "svm_model.fit(X_train_tfidf, y_train)\n",
    "y_pred_svm = svm_model.predict(X_test_tfidf)\n",
    "print(\"SVM Accuracy:\", accuracy_score(y_test, y_pred_svm))\n",
    "print(classification_report(y_test, y_pred_svm))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0a385fb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install tensorflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "20be8c36",
   "metadata": {},
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      "   ---------------------------------- ----- 860.2/991.5 kB 1.2 MB/s eta 0:00:01\n",
      "   ------------------------------------ --- 901.1/991.5 kB 1.2 MB/s eta 0:00:01\n",
      "   ---------------------------------------  972.8/991.5 kB 1.2 MB/s eta 0:00:01\n",
      "   ---------------------------------------- 991.5/991.5 kB 1.2 MB/s eta 0:00:00\n",
      "Installing collected packages: sentencepiece\n",
      "Successfully installed sentencepiece-0.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9092f9aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 231508/231508 [00:03<00:00, 61526.85B/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 433/433 [00:00<00:00, 27660.09B/s]\n",
      "100%|████████████████████████████████████████████████████████████████| 536063208/536063208 [07:22<00:00, 1211762.60B/s]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'BertTokenizer' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[37], line 10\u001b[0m\n\u001b[0;32m      7\u001b[0m model \u001b[38;5;241m=\u001b[39m TFBertForSequenceClassification\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbert-base-uncased\u001b[39m\u001b[38;5;124m'\u001b[39m, num_labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Tokenize and encode the sequences\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m X_train_encoded \u001b[38;5;241m=\u001b[39m tokenizer(\u001b[38;5;28mlist\u001b[39m(X_train), return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m'\u001b[39m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, truncation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m     11\u001b[0m X_test_encoded \u001b[38;5;241m=\u001b[39m tokenizer(\u001b[38;5;28mlist\u001b[39m(X_test), return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m'\u001b[39m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, truncation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# Convert labels to categorical if needed\u001b[39;00m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;66;03m# y_train = pd.get_dummies(y_train).values\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'BertTokenizer' object is not callable"
     ]
    }
   ],
   "source": [
    "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
    "from transformers import BertConfig\n",
    "import tensorflow as tf\n",
    "\n",
    "# Load the BERT tokenizer and model\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
    "model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)\n",
    "\n",
    "# Tokenize and encode the sequences\n",
    "X_train_encoded = tokenizer(list(X_train), return_tensors='tf', padding=True, truncation=True, max_length=100)\n",
    "X_test_encoded = tokenizer(list(X_test), return_tensors='tf', padding=True, truncation=True, max_length=100)\n",
    "\n",
    "# Convert labels to categorical if needed\n",
    "# y_train = pd.get_dummies(y_train).values\n",
    "\n",
    "# Train the model\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.fit(X_train_encoded['input_ids'], y_train, epochs=2, batch_size=16)\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test_encoded['input_ids'], y_test)\n",
    "print(f'BERT Test Accuracy: {accuracy}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb4a913a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: nltk in c:\\programdata\\anaconda3\\lib\\site-packages (3.8.1)\n",
      "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (1.5.3)\n",
      "Requirement already satisfied: matplotlib in c:\\programdata\\anaconda3\\lib\\site-packages (3.7.1)\n",
      "Requirement already satisfied: seaborn in c:\\programdata\\anaconda3\\lib\\site-packages (0.12.2)\n",
      "Requirement already satisfied: click in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk) (8.0.4)\n",
      "Requirement already satisfied: joblib in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk) (1.2.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk) (2022.7.9)\n",
      "Requirement already satisfied: tqdm in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk) (4.65.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2022.7)\n",
      "Requirement already satisfied: numpy>=1.21.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.24.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (10.0.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Requirement already satisfied: colorama in c:\\programdata\\anaconda3\\lib\\site-packages (from click->nltk) (0.4.6)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: nrclex in c:\\users\\91901\\appdata\\roaming\\python\\python311\\site-packages (3.0.0)\n",
      "Requirement already satisfied: textblob in c:\\users\\91901\\appdata\\roaming\\python\\python311\\site-packages (from nrclex) (0.18.0.post0)\n",
      "Requirement already satisfied: nltk>=3.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from textblob->nrclex) (3.8.1)\n",
      "Requirement already satisfied: click in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk>=3.8->textblob->nrclex) (8.0.4)\n",
      "Requirement already satisfied: joblib in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk>=3.8->textblob->nrclex) (1.2.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk>=3.8->textblob->nrclex) (2022.7.9)\n",
      "Requirement already satisfied: tqdm in c:\\programdata\\anaconda3\\lib\\site-packages (from nltk>=3.8->textblob->nrclex) (4.65.0)\n",
      "Requirement already satisfied: colorama in c:\\programdata\\anaconda3\\lib\\site-packages (from click->nltk>=3.8->textblob->nrclex) (0.4.6)\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk pandas matplotlib seaborn\n",
    "!pip install nrclex\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69bd9412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               Title           View  \\\n",
      "0  Regression Training and Testing - Practical Ma...  443,755 views   \n",
      "1  Regression Training and Testing - Practical Ma...  443,755 views   \n",
      "2  Regression Training and Testing - Practical Ma...  443,755 views   \n",
      "3  Regression Training and Testing - Practical Ma...  443,755 views   \n",
      "4  Regression Training and Testing - Practical Ma...  443,755 views   \n",
      "\n",
      "           Date thumbs_up Youtuber        Subscribers  \\\n",
      "0  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
      "1  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
      "2  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
      "3  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
      "4  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
      "\n",
      "                                         Description      Comments  \\\n",
      "0  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
      "1  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
      "2  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
      "3  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
      "4  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
      "\n",
      "      Viewer_name          Comment_date  \\\n",
      "0   Rajat Agarwal           5 years ago   \n",
      "1   Kavitha Rajni            1 year ago   \n",
      "2    Braun Brelin           5 years ago   \n",
      "3       Tech Chak  3 years ago (edited)   \n",
      "4  Sleepless Swan  5 years ago (edited)   \n",
      "\n",
      "                                      Comment_Datail Comment_thumb_up  \\\n",
      "0  cross_validation is deprecated. You can use mo...              577   \n",
      "1  youtubers like this guy, TechWithTim, Neural N...                1   \n",
      "2  Hello all.  I'm posting sentdex's code again w...               27   \n",
      "3  I have tried many other courses and tutorial b...                1   \n",
      "4  Interestingly enough, I'm getting almost the s...                0   \n",
      "\n",
      "                              Cleaned_Comment_Datail  \n",
      "0  crossvalidation deprecated use modelselection ...  \n",
      "1  youtubers like guy techwithtim neural nine mos...  \n",
      "2  hello im posting sentdexs code significant com...  \n",
      "3  tried many courses tutorial explain theory cod...  \n",
      "4  interestingly enough im getting almost accurac...  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Try reading the CSV file with a different encoding\n",
    "df = pd.read_csv(\"Cleaned_Details_comments_YouTube_video.csv\", encoding='latin1')\n",
    "\n",
    "# Assuming 'Cleaned_Comment_Datail' contains the preprocessed text\n",
    "comments = df['Cleaned_Comment_Datail'].dropna()\n",
    "\n",
    "# Check the first few rows\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f3db1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Title</th>\n",
       "      <th>View</th>\n",
       "      <th>Date</th>\n",
       "      <th>thumbs_up</th>\n",
       "      <th>Youtuber</th>\n",
       "      <th>Subscribers</th>\n",
       "      <th>Description</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Viewer_name</th>\n",
       "      <th>Comment_date</th>\n",
       "      <th>Comment_Datail</th>\n",
       "      <th>Comment_thumb_up</th>\n",
       "      <th>Cleaned_Comment_Datail</th>\n",
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       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views Â Apr 12, 2016\\n\\n\\n        443...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Rajat Agarwal</td>\n",
       "      <td>5 years ago</td>\n",
       "      <td>cross_validation is deprecated. You can use mo...</td>\n",
       "      <td>577</td>\n",
       "      <td>crossvalidation deprecated use modelselection ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views Â Apr 12, 2016\\n\\n\\n        443...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Kavitha Rajni</td>\n",
       "      <td>1 year ago</td>\n",
       "      <td>youtubers like this guy, TechWithTim, Neural N...</td>\n",
       "      <td>1</td>\n",
       "      <td>youtubers like guy techwithtim neural nine mos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views Â Apr 12, 2016\\n\\n\\n        443...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Braun Brelin</td>\n",
       "      <td>5 years ago</td>\n",
       "      <td>Hello all.  I'm posting sentdex's code again w...</td>\n",
       "      <td>27</td>\n",
       "      <td>hello im posting sentdexs code significant com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views Â Apr 12, 2016\\n\\n\\n        443...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Tech Chak</td>\n",
       "      <td>3 years ago (edited)</td>\n",
       "      <td>I have tried many other courses and tutorial b...</td>\n",
       "      <td>1</td>\n",
       "      <td>tried many courses tutorial explain theory cod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Regression Training and Testing - Practical Ma...</td>\n",
       "      <td>443,755 views</td>\n",
       "      <td>Apr 12, 2016</td>\n",
       "      <td>3.4K</td>\n",
       "      <td>sentdex</td>\n",
       "      <td>1.16M subscribers</td>\n",
       "      <td>443,755 views Â Apr 12, 2016\\n\\n\\n        443...</td>\n",
       "      <td>760 Comments</td>\n",
       "      <td>Sleepless Swan</td>\n",
       "      <td>5 years ago (edited)</td>\n",
       "      <td>Interestingly enough, I'm getting almost the s...</td>\n",
       "      <td>0</td>\n",
       "      <td>interestingly enough im getting almost accurac...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title           View  \\\n",
       "0  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "1  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "2  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "3  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "4  Regression Training and Testing - Practical Ma...  443,755 views   \n",
       "\n",
       "           Date thumbs_up Youtuber        Subscribers  \\\n",
       "0  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "1  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "2  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "3  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "4  Apr 12, 2016      3.4K  sentdex  1.16M subscribers   \n",
       "\n",
       "                                         Description      Comments  \\\n",
       "0  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
       "1  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
       "2  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
       "3  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
       "4  443,755 views Â Apr 12, 2016\\n\\n\\n        443...  760 Comments   \n",
       "\n",
       "      Viewer_name          Comment_date  \\\n",
       "0   Rajat Agarwal           5 years ago   \n",
       "1   Kavitha Rajni            1 year ago   \n",
       "2    Braun Brelin           5 years ago   \n",
       "3       Tech Chak  3 years ago (edited)   \n",
       "4  Sleepless Swan  5 years ago (edited)   \n",
       "\n",
       "                                      Comment_Datail Comment_thumb_up  \\\n",
       "0  cross_validation is deprecated. You can use mo...              577   \n",
       "1  youtubers like this guy, TechWithTim, Neural N...                1   \n",
       "2  Hello all.  I'm posting sentdex's code again w...               27   \n",
       "3  I have tried many other courses and tutorial b...                1   \n",
       "4  Interestingly enough, I'm getting almost the s...                0   \n",
       "\n",
       "                              Cleaned_Comment_Datail  \n",
       "0  crossvalidation deprecated use modelselection ...  \n",
       "1  youtubers like guy techwithtim neural nine mos...  \n",
       "2  hello im posting sentdexs code significant com...  \n",
       "3  tried many courses tutorial explain theory cod...  \n",
       "4  interestingly enough im getting almost accurac...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "256e9fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows where 'Cleaned_Comment_Datail' is NaN\n",
    "df = df.dropna(subset=['Cleaned_Comment_Datail'])\n",
    "\n",
    "# Alternatively, you can fill NaN values with an empty string\n",
    "# df['Cleaned_Comment_Datail'] = df['Cleaned_Comment_Datail'].fillna('')\n",
    "\n",
    "# Vectorize the comments (make sure NaN values are handled before this step)\n",
    "vectorizer = CountVectorizer(max_df=0.9, min_df=2, stop_words='english', max_features=1000)\n",
    "X_aspects = vectorizer.fit_transform(df['Cleaned_Comment_Datail'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c18e918c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:  ['xd', 'key', 'sql', 'boss', 'table', 'values', 'use', 'videos', 'primary', 'foreign']\n",
      "Topic 1:  ['data', 'thanks', 'using', 'series', 'regression', 'features', 'high', 'training', 'label', 'video']\n",
      "Topic 2:  ['thank', 'sir', 'wordpress', 'course', 'xd', 'video', 'bhaiya', 'website', 'content', 'aman']\n",
      "Topic 3:  ['er', 'database', 'company', 'mysql', 'course', 'work', 'diagrams', 'creating', 'data', 'basic']\n",
      "Topic 4:  ['thanks', 'thank', 'video', 'great', 'really', 'sql', 'lot', 'im', 'course', 'videos']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Vectorize the comments\n",
    "vectorizer = CountVectorizer(max_df=0.9, min_df=2, stop_words='english', max_features=1000)\n",
    "X_aspects = vectorizer.fit_transform(df['Cleaned_Comment_Datail'])\n",
    "\n",
    "# Apply LDA to extract topics\n",
    "lda = LatentDirichletAllocation(n_components=5, random_state=42)\n",
    "lda_aspects = lda.fit_transform(X_aspects)\n",
    "\n",
    "# Function to display the topics and their top words\n",
    "def display_topics(model, feature_names, no_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(f\"Topic {topic_idx}: \", [feature_names[i] for i in topic.argsort()[:-no_top_words - 1:-1]])\n",
    "\n",
    "# Display the top 10 words in each topic (aspect)\n",
    "display_topics(lda, vectorizer.get_feature_names_out(), 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "716ff94a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Aspect'] = lda_aspects.argmax(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b522e135",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Title', 'View', 'Date', 'thumbs_up', 'Youtuber', 'Subscribers',\n",
      "       'Description', 'Comments', 'Viewer_name', 'Comment_date',\n",
      "       'Comment_Datail', 'Comment_thumb_up', 'Cleaned_Comment_Datail',\n",
      "       'Aspect'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d57662b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Title', 'View', 'Date', 'thumbs_up', 'Youtuber', 'Subscribers',\n",
      "       'Description', 'Comments', 'Viewer_name', 'Comment_date',\n",
      "       'Comment_Datail', 'Comment_thumb_up', 'Cleaned_Comment_Datail',\n",
      "       'Aspect'],\n",
      "      dtype='object')\n",
      "Sentiment column not found. Ensure sentiment analysis has been performed and the column exists.\n"
     ]
    }
   ],
   "source": [
    "# Checking the exact name of the sentiment column\n",
    "print(df.columns)\n",
    "\n",
    "# Adjust column reference as needed\n",
    "if 'VADER_Sentiment' in df.columns:\n",
    "    emotion_aspect_cooccurrence = pd.crosstab(df['Aspect'], df['VADER_Sentiment'])\n",
    "else:\n",
    "    print(\"Sentiment column not found. Ensure sentiment analysis has been performed and the column exists.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "90de4d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: vaderSentiment in c:\\users\\91901\\appdata\\roaming\\python\\python311\\site-packages (3.3.2)\n",
      "Requirement already satisfied: requests in c:\\programdata\\anaconda3\\lib\\site-packages (from vaderSentiment) (2.31.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (1.26.16)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2023.11.17)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install vaderSentiment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f3abe7c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              Cleaned_Comment_Datail VADER_Sentiment\n",
      "0  crossvalidation deprecated use modelselection ...         Neutral\n",
      "1  youtubers like guy techwithtim neural nine mos...        Positive\n",
      "2  hello im posting sentdexs code significant com...        Positive\n",
      "3  tried many courses tutorial explain theory cod...        Positive\n",
      "4  interestingly enough im getting almost accurac...        Positive\n"
     ]
    }
   ],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "\n",
    "# Initialize VADER sentiment analyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Apply sentiment analysis to the 'Cleaned_Comment_Datail' column\n",
    "df['VADER_Sentiment'] = df['Cleaned_Comment_Datail'].apply(lambda x: analyzer.polarity_scores(x)['compound'])\n",
    "\n",
    "# Classify the sentiment into Positive, Neutral, or Negative based on compound score\n",
    "def classify_sentiment(score):\n",
    "    if score >= 0.05:\n",
    "        return 'Positive'\n",
    "    elif score <= -0.05:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'\n",
    "\n",
    "df['VADER_Sentiment'] = df['VADER_Sentiment'].apply(classify_sentiment)\n",
    "\n",
    "print(df[['Cleaned_Comment_Datail', 'VADER_Sentiment']].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e18fff04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Title', 'View', 'Date', 'thumbs_up', 'Youtuber', 'Subscribers',\n",
      "       'Description', 'Comments', 'Viewer_name', 'Comment_date',\n",
      "       'Comment_Datail', 'Comment_thumb_up', 'Cleaned_Comment_Datail',\n",
      "       'Aspect', 'VADER_Sentiment'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a70192b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VADER_Sentiment  Negative  Neutral  Positive\n",
      "Aspect                                      \n",
      "0                       2        9        62\n",
      "1                       6       16        74\n",
      "2                       7       12       131\n",
      "3                       0       10        38\n",
      "4                       4        8       214\n"
     ]
    }
   ],
   "source": [
    "# Create a cross-tabulation between topics (aspects) and sentiment (emotions)\n",
    "emotion_aspect_cooccurrence = pd.crosstab(df['Aspect'], df['VADER_Sentiment'])\n",
    "\n",
    "# Display the co-occurrence table\n",
    "print(emotion_aspect_cooccurrence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8cffb3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VADER_Sentiment  Negative  Neutral  Positive\n",
      "Aspect                                      \n",
      "0                       2        9        62\n",
      "1                       6       16        74\n",
      "2                       7       12       131\n",
      "3                       0       10        38\n",
      "4                       4        8       214\n"
     ]
    }
   ],
   "source": [
    "# Create a cross-tabulation between topics (aspects) and sentiment (emotions)\n",
    "emotion_aspect_cooccurrence = pd.crosstab(df['Aspect'], df['VADER_Sentiment'])\n",
    "\n",
    "# Display the co-occurrence table\n",
    "print(emotion_aspect_cooccurrence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7ad57240",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot a heatmap of emotion-aspect co-occurrence\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(emotion_aspect_cooccurrence, annot=True, cmap=\"YlGnBu\", fmt=\"d\")\n",
    "plt.title('Emotion-Aspect Co-occurrence')\n",
    "plt.ylabel('Aspect (Topic)')\n",
    "plt.xlabel('Emotion (Sentiment)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f2b1308",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}