5. Handling Missing values.ipynb

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   "source": [
    "# Lecture 5: Handling Missing values\n",
    "\n",
    "Instructor: Md Shahidullah Kawsar\n",
    "<br>Data Scientist, IDARE, Houston, TX, USA\n",
    "\n",
    "#### Objectives:\n",
    "- performing data cleaning\n",
    "- data visualization of missing values\n",
    "- string to datetime conversion\n",
    "- removing missing values\n",
    "- replacing missing values by: 1. mean, 2. median, 3. constant, 4. interpolation, 5. forward imputation, 6. backward imputation\n",
    "\n",
    "#### References:\n",
    "[1] time-series / date functionality: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n",
    "<br>[2] dataframe slicing: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html\n",
    "<br>[3] Offset aliases: https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases\n",
    "<br>[4] removing missing values: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html\n",
    "<br>[5] Working with missing data: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context('talk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/01/2021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/02/2021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09/03/2021</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09/04/2021</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09/05/2021</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>09/06/2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>09/07/2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>09/08/2021</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09/09/2021</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>09/10/2021</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  verbal_score  quant_score\n",
       "0  09/01/2021           0.0          1.0\n",
       "1  09/02/2021           1.0          2.0\n",
       "2  09/03/2021           2.0          3.0\n",
       "3  09/04/2021           3.0          NaN\n",
       "4  09/05/2021           4.0          NaN\n",
       "5  09/06/2021           NaN          NaN\n",
       "6  09/07/2021           NaN          7.0\n",
       "7  09/08/2021           7.0          8.0\n",
       "8  09/09/2021           8.0          9.0\n",
       "9  09/10/2021           9.0         10.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 3)\n"
     ]
    }
   ],
   "source": [
    "# reading csv file containing GRE verbal and quant practice test scores\n",
    "df = pd.read_csv(\"gre.csv\")\n",
    "display(df)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checcking for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Date          10 non-null     object \n",
      " 1   verbal_score  8 non-null      float64\n",
      " 2   quant_score   7 non-null      float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 368.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# method 1\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    Date  verbal_score  quant_score\n",
       "0  False         False        False\n",
       "1  False         False        False\n",
       "2  False         False        False\n",
       "3  False         False         True\n",
       "4  False         False         True\n",
       "5  False          True         True\n",
       "6  False          True        False\n",
       "7  False         False        False\n",
       "8  False         False        False\n",
       "9  False         False        False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Date            0\n",
       "verbal_score    2\n",
       "quant_score     3\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# method 2\n",
    "display(df.isna())\n",
    "display(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>verbal_score</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Date  verbal_score  quant_score\n",
       "0  True          True         True\n",
       "1  True          True         True\n",
       "2  True          True         True\n",
       "3  True          True        False\n",
       "4  True          True        False\n",
       "5  True         False        False\n",
       "6  True         False         True\n",
       "7  True          True         True\n",
       "8  True          True         True\n",
       "9  True          True         True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date            10\n",
      "verbal_score     8\n",
      "quant_score      7\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# method 3\n",
    "display(df.notna())\n",
    "print(df.notna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How to convert from string column to datetime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   Date          10 non-null     datetime64[ns]\n",
      " 1   verbal_score  8 non-null      float64       \n",
      " 2   quant_score   7 non-null      float64       \n",
      "dtypes: datetime64[ns](1), float64(2)\n",
      "memory usage: 368.0 bytes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "display(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How to set index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score  quant_score\n",
       "Date                                 \n",
       "2021-09-01           0.0          1.0\n",
       "2021-09-02           1.0          2.0\n",
       "2021-09-03           2.0          3.0\n",
       "2021-09-04           3.0          NaN\n",
       "2021-09-05           4.0          NaN\n",
       "2021-09-06           NaN          NaN\n",
       "2021-09-07           NaN          7.0\n",
       "2021-09-08           7.0          8.0\n",
       "2021-09-09           8.0          9.0\n",
       "2021-09-10           9.0         10.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 2)\n"
     ]
    }
   ],
   "source": [
    "df = df.set_index('Date')\n",
    "\n",
    "display(df)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df.dropna()\n",
    "# display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score\n",
       "Date                    \n",
       "2021-09-01           0.0\n",
       "2021-09-02           1.0\n",
       "2021-09-03           2.0\n",
       "2021-09-04           3.0\n",
       "2021-09-05           4.0\n",
       "2021-09-06           NaN\n",
       "2021-09-07           NaN\n",
       "2021-09-08           7.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['09-01-2021':'09-08-2021', ['verbal_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score\n",
       "Date                    \n",
       "2021-09-01           0.0\n",
       "2021-09-02           1.0\n",
       "2021-09-03           2.0\n",
       "2021-09-04           3.0\n",
       "2021-09-05           4.0\n",
       "2021-09-06           NaN\n",
       "2021-09-07           NaN\n",
       "2021-09-08           7.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:8, [0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['verbal_score', 'quant_score'], dtype='object')\n",
      "DatetimeIndex(['2021-09-01', '2021-09-02', '2021-09-03', '2021-09-04',\n",
      "               '2021-09-05', '2021-09-06', '2021-09-07', '2021-09-08',\n",
      "               '2021-09-09', '2021-09-10'],\n",
      "              dtype='datetime64[ns]', name='Date', freq=None)\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(df.verbal_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(df['verbal_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(df.verbal_score.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(df['verbal_score'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a function for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_series_plot(data, title):    \n",
    "    plt.plot(data.index, data.verbal_score.values, label='verbal', marker='o')\n",
    "    plt.plot(data.index, data.quant_score.values, label='quant', marker='o')\n",
    "\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.ylabel(\"score\")\n",
    "    plt.legend()\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score  quant_score\n",
       "Date                                 \n",
       "2021-09-01           0.0          1.0\n",
       "2021-09-02           1.0          2.0\n",
       "2021-09-03           2.0          3.0\n",
       "2021-09-08           7.0          8.0\n",
       "2021-09-09           8.0          9.0\n",
       "2021-09-10           9.0         10.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 6 entries, 2021-09-01 to 2021-09-10\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   verbal_score  6 non-null      float64\n",
      " 1   quant_score   6 non-null      float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 144.0 bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df_drop_all_nan = df.dropna()\n",
    "\n",
    "display(df_drop_all_nan)\n",
    "print(df_drop_all_nan.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(df['verbal_score'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(df_drop_all_nan['verbal_score'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacing missing values by mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.00</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.00</td>\n",
       "      <td>5.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.00</td>\n",
       "      <td>5.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>4.25</td>\n",
       "      <td>5.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>4.25</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.00</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>8.00</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>9.00</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score  quant_score\n",
       "Date                                 \n",
       "2021-09-01          0.00     1.000000\n",
       "2021-09-02          1.00     2.000000\n",
       "2021-09-03          2.00     3.000000\n",
       "2021-09-04          3.00     5.714286\n",
       "2021-09-05          4.00     5.714286\n",
       "2021-09-06          4.25     5.714286\n",
       "2021-09-07          4.25     7.000000\n",
       "2021-09-08          7.00     8.000000\n",
       "2021-09-09          8.00     9.000000\n",
       "2021-09-10          9.00    10.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 10 entries, 2021-09-01 to 2021-09-10\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   verbal_score  10 non-null     float64\n",
      " 1   quant_score   10 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 540.0 bytes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_replaced_by_mean = df.fillna(df.mean())\n",
    "\n",
    "display(df_replaced_by_mean)\n",
    "display(df_replaced_by_mean.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_replaced_by_mean.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacing missing values by median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>3.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>3.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score  quant_score\n",
       "Date                                 \n",
       "2021-09-01           0.0          1.0\n",
       "2021-09-02           1.0          2.0\n",
       "2021-09-03           2.0          3.0\n",
       "2021-09-04           3.0          7.0\n",
       "2021-09-05           4.0          7.0\n",
       "2021-09-06           3.5          7.0\n",
       "2021-09-07           3.5          7.0\n",
       "2021-09-08           7.0          8.0\n",
       "2021-09-09           8.0          9.0\n",
       "2021-09-10           9.0         10.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 10 entries, 2021-09-01 to 2021-09-10\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   verbal_score  10 non-null     float64\n",
      " 1   quant_score   10 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 540.0 bytes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_replaced_by_median = df.fillna(df.median())\n",
    "\n",
    "display(df_replaced_by_median)\n",
    "display(df_replaced_by_median.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacing missing values by constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score  quant_score\n",
       "Date                                 \n",
       "2021-09-01           0.0          1.0\n",
       "2021-09-02           1.0          2.0\n",
       "2021-09-03           2.0          3.0\n",
       "2021-09-04           3.0          0.0\n",
       "2021-09-05           4.0          0.0\n",
       "2021-09-06           0.0          0.0\n",
       "2021-09-07           0.0          7.0\n",
       "2021-09-08           7.0          8.0\n",
       "2021-09-09           8.0          9.0\n",
       "2021-09-10           9.0         10.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 10 entries, 2021-09-01 to 2021-09-10\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   verbal_score  10 non-null     float64\n",
      " 1   quant_score   10 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 540.0 bytes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_replaced_by_zero = df.fillna(0)\n",
    "\n",
    "display(df_replaced_by_zero)\n",
    "display(df_replaced_by_zero.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacing missing values by interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score  quant_score\n",
       "Date                                 \n",
       "2021-09-01           0.0          1.0\n",
       "2021-09-02           1.0          2.0\n",
       "2021-09-03           2.0          3.0\n",
       "2021-09-04           3.0          4.0\n",
       "2021-09-05           4.0          5.0\n",
       "2021-09-06           5.0          6.0\n",
       "2021-09-07           6.0          7.0\n",
       "2021-09-08           7.0          8.0\n",
       "2021-09-09           8.0          9.0\n",
       "2021-09-10           9.0         10.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 10 entries, 2021-09-01 to 2021-09-10\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   verbal_score  10 non-null     float64\n",
      " 1   quant_score   10 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 540.0 bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df_interpolate = df.interpolate()\n",
    "\n",
    "display(df_interpolate)\n",
    "print(df_interpolate.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacing missing values by forward imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score  quant_score\n",
       "Date                                 \n",
       "2021-09-01           0.0          1.0\n",
       "2021-09-02           1.0          2.0\n",
       "2021-09-03           2.0          3.0\n",
       "2021-09-04           3.0          3.0\n",
       "2021-09-05           4.0          3.0\n",
       "2021-09-06           4.0          3.0\n",
       "2021-09-07           4.0          7.0\n",
       "2021-09-08           7.0          8.0\n",
       "2021-09-09           8.0          9.0\n",
       "2021-09-10           9.0         10.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_forward = df.fillna(df.ffill())\n",
    "\n",
    "display(df_forward)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replacing missing values by backward imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>verbal_score</th>\n",
       "      <th>quant_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-05</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-08</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-09</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            verbal_score  quant_score\n",
       "Date                                 \n",
       "2021-09-01           0.0          1.0\n",
       "2021-09-02           1.0          2.0\n",
       "2021-09-03           2.0          3.0\n",
       "2021-09-04           3.0          7.0\n",
       "2021-09-05           4.0          7.0\n",
       "2021-09-06           7.0          7.0\n",
       "2021-09-07           7.0          7.0\n",
       "2021-09-08           7.0          8.0\n",
       "2021-09-09           8.0          9.0\n",
       "2021-09-10           9.0         10.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_backward = df.fillna(df.bfill())\n",
    "\n",
    "display(df_backward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yXYAHqfi8uqS1b0WDw5Z8pldEFprZD4DrKom9qvVW5L/B77+bWWmrZTNLwXe7kwy85ZxbUM31NgR/DH4/aWZ7DABqZi3N7OxyA/VuxFeIdwlaa9eKoJ/uuUAH4LbgO1oSx0Dg98G//41YZhf+ewe+dXdaxDLdgH9Hey0zOy0YUNjKladSNvBvZX3ki4iIiDQb6iZFREREwtQOX/lzt5l9gR84rgW+FWN/fFchf4yY/0bgX2a2AF+JmI/vhqCkIve3QWVtrXPOrTCzq/F9dT9nZp/gW9kOAAbhuye4mrKWptVZ9ywz+46yltjLgY+izPe5mb2O755mnpm9D2zHV8R3iIgh1tddZmYPAhOAj8wsI4h/GL7V/0v4gT3LL/ffoCL1auBzM/sS33d5Mr67kAPwleUHUNYFREPzCX5f+z7YjgYcjb8BkEFZpXiJDPxArccCXwTbKgG/730JzCL6DZEXgPH4/fZYYENQ/i/n3Lf4GwtPA38zszPwA3b2Dtb1D3z3P9G8gB909r0g/h0AzrkJVbzvu/EVpGcA3wTL7gzKuuE/x0uqWEeD5Jx7wcx+Dfwd+DA4TpS07t8H/11the9+aWGwTIGZvYYfo+ALM5uBv+m1yTlX0baP1Tn4G06XAD8ws1n4Y97R+H1nsnPuqXLL3IgfC2E4sCQYpLgFcEwQc7T9bCxwLbDBzD7Hf3fb4cdTaAvMYM8ul0RERESaJbUMFxERkTAtAX6J73alM741+HFAIb6l80Dn3KcR818FPIavuDwGOBXohO9mZJRz7p66DNY59yC+IjoDGAj8CN8S/TigJM5NNVx9ZEvwx8q1So50OvAHIBM/UN44fAXZ4cDnNXjdy/E3HFYH6zoUeBZf4ZZV0ULOuWvwlXrP4z+7HwOj8JV8TwZxLqlBPPUlDx//VPy2OxHf/ctfgeOdc7vd1Ag+j9Pw++U2fKV4f3zl8g+p4CaIc+5lfKvvRcAP8BWjlxD0be+ceyZY13T8jZ0f4c/RL3TO/baS+H8P3IqvBD89Yr2VCvrcPwt/A2QeviL1lOA9/QMY6pxbXeEKGjjn3C3AUPzTFi3xFd9H4/t6fx04j7Iuj0pcim/dH4/vBukSatjtUblYvsN/n24HivCf0wj8mANnO+cui7LMdvxn8m/8Z3tysI77g/cRbT+bCtyC/74Nwt/oOAzfbcwk4Ji6ukkoIiIi0thYxddZIiIiIhIrM7sfX8H4K+dc1O4MJHxmNg7f5clHzrlxoQYjIiIiIiL1Si3DRURERGJkZn3MrGOU8guAi/GtNp+s98BERERERESkSuozXERERCR2P8IPhvcFvl/vkn6x98UPRHltY+5iQkREREREpClTZbiIiIhI7DKAJ/B9Y++PHzByE34ww9uccxkhxiYiIiIiIiKVUJ/hIiIiIiIiIiIiItLkqc9wEREREREREREREWnyVBkuIiIiIiIiIiIiIk2eKsNFREREREREREREpMlTZbiIiIiIiIiIiIiINHmqDBcRERERERERERGRJk+V4SIiIiIiIiIiIiLS5KkyXERERERERERERESaPFWGi4iIiIiIiIiIiEiTp8pwEREREREREREREWnyVBkuIiIiIiIiIiIiIk2eKsNFREREREREREREpMlTZbiIiIiIiIiIiIiINHmqDBcRERERERERERGRJk+V4SIiIiIiIiIiIiLS5KkyXERERERERERERESaPFWG1wEz+9DMltXRup2ZTa2LdYs0dmY2PviOjKuL+euSmU01M1eH66+z41LYGtLnKNWjfCkSjsacL+ubmd0UvPc+dbT+Os3/YTKzccG2Gx92LFK7lL9Fas7M+gT7+U1hx9KQVGe71HVulqavyVaGm9mzwZdjcCXzmJktNbMsM0upx/DqjZl1NLN7zWy5meWY2RIze8DMutdwfZ3N7BYz+8bMdprZdjP7wsxuNLM2tR1/bQlOxm8ys7RqLDPezH5ed1Ht9lp9gvgG18frNSdmNjjYtn3CjkWkIVK+9JQvPeXL5kv5UqRxUf72lL895W+pa/W5v4jUtSZbGQ48GPy+qJJ5jgL6AE8553LqPKJ6ZmYGvARMBF4HrgGeAo4E+tdgfSOABcDPgU+BXwK/BhYDfwLmmdm+tRF7HRgH/BFIq8Yy4/HvtT70wcc3uJ5er6l6DEgBMiLKBuO3bZ8Q4qmOS/Gx15XjgP3rcP3SeClfKl9GGofyZXPQmPNlffsrflstr6P113X+l6ZL+Vv5O9I4lL8bi+X44/5fww6kmsZTf/uLSJ1qEXYAdehtYCVwrpn9yjmXH2WekhOHB6NMq5YgEbdyzu3Y23XVov2AkcBk59zlEeW/N7PE6qzIzLriTzRaAKOcc3MiJt9jZicCLwIvm9mQpniyJRVrKPu/c64IKAozhppyzhUABXW4/mjHQBFQvgTlS6knDWX/b8z5sr455wqBwjpcf53mf2nSlL+Vv6We1Ob+75xzQO7eRyUiNeaca7I/wJ8BB/w0yrS2wE7g64iyJOB3wDf4g1MW8ApwaLllxwXrHQ9cib97nAfcFEz/EFgGpOMTajawDXgBSC+3rjjg9/iWOeuAfGAFcC/QIUrcDpga4/vfL5h/ci1sy/8E67q8knluCea5MqLspqCsT5T5lwEflis7E3g52AZ5wCb8ScchFS0PDABeA7YH2/o5oGvEfFODGMr/3FTJe1lWwTLjym3fx4C1wee2DPgXPklGrmsf4CH8HeA8YAMwE7gwmD6+gtf6sKL4guXaA/8FlgT762ZgLvCrKPP+BPgAv0/vAr4F7gASI+ZpBfw9WF9esD8+CvSuzv4f8Tl+HHwmu4DZRPkeRokzGcih3D4OTAle87Zy5U/jv1stym3LceX2v/I/U8vNfzTwfxHv/buSzyeGmCO3xxXBts0FvgZOCuYZCLwZxLo52PYJ5dYzleDcKNZ9J5jH8Hfovwq297YghgcjX4PguFRu/R/i99vuwJPAVvxx8S2gf5T32gd4PniNbPzxrS9RvstRlj0g2E63VjD9Sfz3qFPw/wDgHvzxuGQ/mgtcGmXZ3T73mhx7gvIf4C8ss4LP8CvgsijzjQTewH9HcoHV+NZIw2PZZxriD8qXypdO+TKYV/myfvLlRfjjR17weV9fwTKnAjOAHcHPDOCUGF+vT/B6NwE/A+YF22wxcFEwTy/892BL8Bk8DrQpt56SbdMnoiymfQq4AN+6NAt/HM0EniDIdZHfu3LLTQ1eMxV/jNsQvM4M4Igo77UD/ruzOdhO7wOHEiX3R1k2LVj3/yqY/vcglsHB/93xx7l5+POGXPy+/WsgvqLPPKKsZF8aF+W1osYLHI7PC5uCfeZbfD5oUW6+g4Bn8Xm55Lv5AcH5WFP8Qflb+dspfwfzNor8HSzbp/znQ+3lrJJ9oVPw3jbjjwPvUcn3PEqMU4nITbW5vwTzHonPaTnAeuAu4ODy26WSbXhTMO9Bwee8LljXbOCYiPkSgY3AxxWs5/pgPaOreL2S7doh+HtT8Bm8SPBdxD+hshC/ry6igvOVWPcd6uBYoZ+yn6bcMhzgYeAG/An3c+WmnQW0JLhLbmYJ+Mqqkfgv8F34E9BLgRlmNsY591m5dfwc/2W4H//lWxkxrRX+YPwp8Fv8geEKYLiZHeqcWxfMlwj8Cl/B9BL+QDUUuAQ4MrjrXKMWnc65783sPWC8mU1zzn1Uk/UEfoI/oD1SyTz349/LT4C7a/g6V+EP7lPw27Qf/qAyw8wOc859X27+Hvgv/QvBaw8CJuFP/o4L5pkc/H8a8Av8QQR8RVdFfo5PlB2DZUosBDCzIfgLjaxg/auD174GGGVmY51zBWbWAngniPMe/EVjKnAIMBq/PTOAv+FPTKcA04PXWl9JfOBP9scEr/8lfn8egE9q/yqZycxuDta9AH8ysRa/XX8C/AHID+J8CxiF/678B7/PXg4cZ2aHO+dWRdlGe+z/ZvZX/Anvm8CNQDF+2z9rZlc55yrcN5xzuWY2C3+xHenoYD3HRLwvC95rhvOttqL5H9ANvw/9jeDzw58ARfob/lG1yfhEczkw1cwWO+dmVBRvOVcC7YAH8AnwGuBFMzsDv42exCev44Cr8SeJFT4aF+O+A/4Y92f8hcx9+JZ+fYEf4y94qmpt1gq/D36C30/6AtcCL5nZwc63HsTMOuD3zS7B6ywM4vggWEelnHMLzWwOcE7Qeqm0RaKZtQVOAd5wzm0Misfh9+9XgaXBa5wBTDGzjs65v1f1mtVhZhPx7+sT4Gb8sfhY4F4z6+ec+1Uw3/74z2UdcDv+e9oV/90ZFCzfGClfKl+C8qXyZf3ky8vwueRB/H5xHvBPM1vlnJsWEfcV+O/HIny+LKmYeNHMJjnnpsT4ej8KXvMe/HfmEuAhM8sP3s/7+M99KHAxPodPqGKdVe5TZnYefr+djt9/cvAVGScAnfEX51V5K5jvz/h96JfA62bWxzm3PXidROBdfNcDU/HH0kOCsi1VvYBzLsvMXgZOMbP2zrnSZcwsDjgX+Mo5Ny8oPgQ4HX8sWQIkBO/pH/iK0UkxvK+YBa1xX8BXCP0H/55G4LfJYPy5Qcl5yvvBYvfhK+U64ivSj8BXDjRFyt/K36D83WjydwxqK2e9GSx/E/5a5Sogw8xGOOfm1yCun1ML+0sw7xH4HLUd+GewzFn4yvvqehR//ftPoA3+u/GmmZ3gnHvXOZdvZo8A15nZAOfconLLXwR855ybTmzeBFbh96t9g/f3gpn9D/89fpCyuoDnzKy/c25pycLV3Hfq4lghJcKuja/rH/wdsEKge7nyWfiT+I7B/7/An2T/sNx8bfF3Yj6MKBsXzLsF6BzlNT8kequc04Ly+yLKDEiJso5Lgnl/Vq68OnfKu+JPTnLxB5oxNdyGbYLX/SqGebcBmyL+v4nq3SmPdtfwgOCzuifK8tG20d1B+YBY4qjkvXxIBa1p8Ml4EXveiS35jMcH/x8S/B+1xVOUfWp8jLGlBvPfU8V8w4L53geSy00zwIK/Lw3mu6XcPCcF5Y/Fsv8DhwXT/hYllheD/aNNFTHfEKxjv+D/fUpiCH53CcoHBv//MmLZ8ex5h3qPsijTvmD3VgM9gn3uyRg+i5LtsRpIjSgv+eyLgdPLLTMXWFuubCq7332Pdd/5HFhQk/2ZsmPV9eXKf0W54yFlLWHOLTdvSfmHMcRwZTDvieXKS453p0eURTsWxAUxZ7N7q/don/tNxHjswVcA5QLTosx7O/4Eq1/w/zXBeodV9X4b2w/Kl8qXVcRRyXv5EOVL5cuqP4uS7bEGSIsob4mv8J0VUdYO38J5MdA2orwtvgJ2e+Q6Kni9PsHr7SSi1R++tVwuPj//stwy/8NXhrWu6DtRjX3qf0S0xq9kvqlU3DK8/Hf5jKB8UkTZFUHZ78vNW1Ie9btZwf57RbnyY6LsOykE34dy8z6Gz5fdKvu+VrGffRgZL/4JiHX4irTyrcB/EbkefCOAPY5zzeEH5W/l7yriqOS97PadKzdN+bvu8ncfKm4Zvrc5a2qwnv8RcawGhgTreDOWz4TouWmv95egbGYQd/+IskT8d3m37VLJNizZ32ez+3lJT/z5w8KIsv4VfPajYtl3y23Xu8uV3xqUr2D385WS78Xfa7rvUEfHCv34n6Y8gGaJB4F44PySAjMbAAwHXnbOldw1PQ//5Z1rfkTqjmbWEf+lfAd/17r84DaPOuc2VPLa/4j8xzn3Av5xnVMjypwL+hszs3gzSwtet6RlwxHVerdl77El/s5nIv6O1TfAG2Z2dLn53jazFVWsrm3wOzuGl96GP5moEefcziAuM7O2wbbYiN9u0bbFGufcM+XKSrZdnQxuYmYD8Qe3aUBSuf3lY3wCK7nzVrLNjjKzzrUYRg7+IHiEmfWpZL5zg9+/dc7t1i9ZsO+54N/T8Mnx7+XmeQ3/iNYpQeugSNH2/3PxB9tHIrdLsG1exu8bI6p4byWfX8m+egz+4uqmYN0l5UeVm39v3OMiWqQ451bjWzXsV411THXOlX5HnHNf4b8Pa5xz/ys378dAVzNrXcn6Yt13soEeZnZkNWKNVIx/tCxSyTaNfP8n41tZPFlu3n9X47VKukK5oFz5BfiTzVdLCkqOBQBmlhy0+GqP78akLb5VSG35Kb4V/YNR9ttX8JXwJa0sSz6XU8wsuRZjaAiUL5Uva5XyZSnly9097JzLiljHLvxTNZHrOBbf6vQO59y2iHm3AXcCrfFdW8XiRefc8oh1lHxPitmzZed0fEvnPpWsL9Z9Khtf0X9S0Dq/Jv5b7v+K8nMR/uZtpPuJ7VgE/hi4nuj5uQjftQsAzrmcku+DmSWaWftgv30Lny8Pj/E1Y3Es/imCh4G0ct+T14N5yh9DTjD/xFlzovyt/F2rlL9L1VX+rkxt5axbIt47zrm5+O/5D6q4Bq226uwvwT4yAnjJOfddRHz57JnzYvHfcuclq/A5a4CZHRCUfQd8BFxg/gmBEpfgbyRW9jRIebeV+7+kRfmj5c5XSuoCIvN1tfadxnCsaMyaQ2X4//CPXVwUUXZx8PuhiLID8JUrG6P8XIw/wehYbt3fUbEsV/ZoWKSFQBczK+1WwMx+Zmaz8Qf8rcFrZgaT21XyGpWZhD8gXRkcEH6If2zoVTMrORAZvl+mj6tYV8mXOjWG121L1Y87VcjMDjWzVynr46jkMxhI9G2RGaVsc/C7Q03jqMIBwe8/see+sgF/8dYFIEhkN+MP/mvNbK6Z3WJmQ/cmgOCA/3P857fUzL4xszvN7Jhys5b0o/dlFavsiz94bo0y7Rv8gTmW/f8A/B34Rey5bUoG7ulSRSyf4u/mlpzIHg185pxbgu+HO7J8M1W/t1hUtB9VZx+Kto6t+C4+opVT2fqrse/8Dt9aYLqZrTazJ8zsHIt90KA15U8cif4d6gssds4Vl4tzA/4YWyXnH71+DX+ymQoQnNyOxrcqLD2RMbPWZvbv4OIlB/+450b8NoGaHxujKflOv8ue++07wbSS/fapYL7fAVvM7H0z+7WZ9a7FeMKifKl8WduULz3ly+qvo2/w+5so85Y83p2+F6+3Ff+EVl6Ucqg8P8e6T/0N31XHi8BGM3vezCaYWXUq0XaL3TlXUX5e48oN6ub84+jRzkH24Hz3OdPwlU79AYJj7+n4VoSlxyoza2FmN5jZd5T14bsR3zIc6iY/P8Se35OSR91LjiEf4R+XHw9sMrMZZvYnMzuwFuNpqJS/lb9rm/K3V1f5uzK1lbMWRilbgP+e1/Z1S8z7C2W5u3x3JSXxVVdF7zPytcB3NdIF3w0NwQ2BnwGvRua4GJT/fEo+g4qu+SM/m2rtO43kWNFoNfU+w3G+T8VpwBVmNhL/GMX5+H5+3o6Y1fAXDr+sZHXl+/bbVdlLV1C+W8sQMzsdP6jRp/h+elfiTyzj8f0I1fSGxTh8S46ZAM657OCk4D38CNin41urdMP3cVzxG3Fuu5ktB/Y3s5ZBC54935jZvvgkEtnXXEXbAcrtf2bWC/8Y5DbgL/g7XjuDddyGbwVUXlGUstJVVjJtb5Ss9z/4zyia0iTrnLvBzB7CP4I1Gt+n16/M7Bbn3K9rGoRz7j4zeylY71h869arzOxp59xZEbFW9hmUqMm2irYflLzeCVT82US7sC3lnCs0s+n41gWGb9FWcoH1PmV37ccA70fe8d4LFcVane1S0TpqvI/Gsu8452aZWT/8BcBRwc85wA1mdqSL6PuztuOroUfwLTPOwB97zg9ep3wfcdPwJytT8MeFLfg79yfiH/Ot6tgY87GHsvd5Ab71ezSZAMFJ6LFmNgy/zcfg+y29yczOCVpENUrKl8qXdUD50lO+jG0dNV1fTV9vb/JzlfuU830ZH4hvsX9MMN/9wJ/M981cvj/2aK8Ty/aurW31CD6/XoDvgud0/LGkfH6+FT/2ydP4CrAN+PFJDsP32VoX+flX+Jab0awpXbFzF5rZv/DnCkcC1wG/N7OfO+fuqiKuRkv5W/m7Dih/e3WSv6tQ6zmrkvmqczyOZb2x7C8l80Z77Zp8RrGu53n809CX4G9Sn4WvpK/02LDHi1Wcl2PN1zHtO43oWNFoNfnK8MCD+H7zLsI/Zt8VuLncjvw9vj+m98u3fKyhdmbWNcrd8gHABlfWBcD5+JOBoyKTrvlH2/ZGMf4EoyfBQA/Oua1mdiz+AukF/Mnrm865WB6b/R9lJ8j3VTBPycANj0eUlVTEtcf3YwT4bg/wJyaLI+Y9Df+l/rFz7oPIFZvvIqH8ndDqqMkFYEXLlAxUUOScezemFTmXiX+s987gvb8FXG9m/wla1tboAtU5txZ/AH/AzOLxF8FnB+udgz9oHo9vNfFpJataAhxvZmku4tHlwIEEfePFENL3weutcM5Fu0sbq/fxSeKn+H34vaD8PXwLgdPxd0Nj2Xdr4+I/NDHsOwStwZ4PfiIHHbuEiMFl9tIyYF8zi4s8Rpp/1C2tGut5HX+hdQFlleGLnHOl+6eZpeErwh9zzl0WubCZxfpYfHWOPSXf6U3V+E5/SvCdMrN98P3o/hV/bG3MlC9RvqzFZZQvK6Z8WbmSiuKDKHtPJUpa+kZrAVVvYtinSm6gvh78lAwG+Rq+MvLKWgplKcFj75Gtw80PltiX2J/e+tLMvgTOM7Mb8cewLPyj25HOxw/GelZkYVBJGIvIY115fdl94O+SY8jOahxD5uOfHrglOJ+YDfzDzO6upRtCDZXyN8rftbiM8nfFait/17UD8F2QlS8rwj+1BJUfj6M9fVUb+0tJfj8gyrRoZVU5kD0Hii1ZT+l5gnMuz8weBa4xs+746+TVVFx5Xxeqs+/U5bFCaB7dpOCc+xzfmuBM/IisDt/3XKRH8ScNUe+Um1lNHnX5Tbl1nAbsj78TVaIoiCcuYj7Dt8jYGyX96N1iEX1fBY9X/gx/Z6gn/pH/WPwLnxz+bmaHlZ9oZj/Eb7tv2L1P4ZJHi8pXYEVr2Vlysla+NcGl+M9mb5RcHEQ70Fe2TLvg84j0Bf4k+zIz2yNJmH98tH3wd2pwMVLK+S4pSg5+JY+3VCs+M2tpvp+8yPUWUZYIStYzLfj9NzNLirKekvf2Iv7zKL/PngAciu9vMJaT5pIWaX8LTlbKv16s/ciVnLD+CX+gnxH8/xG+dfCfys1XmZp89qGLdd8x33dYeZ8Hv2vzPb+CP6E/u1z5/1VnJcFj20/i+6U8B/9o4yPlZqvoWNCN6KOlR1OdY88z+P3sT7ZnX5kln0VS8He07b0KX8HfqPaxaJQvlS9RvlS+bBjHsnfwLaCutohuRYK/r8bH+k4Fy9apWPepes7P8fgWt5EuJbYuHyI9gn98/hx89zpPuz27Uitiz2NPK/yxKhZRj3VmdjbQvdy8b+ErM39Tcqwot0xKyf5hvu/y3Y6VQYXXUnzr4KY2zsdulL+Vv1H+bmz5u65dH/m5Bvv0D4D3Im6cLsWfK5Q/Ho/EjzlQ3l7vL8GNkU/wT6/1j5gnkdjzSKRfWET3oGbWE5/Dvo1S4Xw/Pl/+E//+plbS0rsuVGffqctjhdB8WoaDv1t+J/6x9g+jPJp4O36Qln+ZH3TjffzdwV74RxtzKRuAKBabgNODu04f4it8rsD3L3ZTxHzPAT8B3g/uVCXgBxzZ7cBfAw/jWwOdhR884Bn8yeRB+Lvd6/EXGfea2RLnXKX9qDnn1prZj/En3LPM7An8QSwe/4jaT/EH05NdMEBK4F18n0h/Du5gLcU/tjicPe+8voF/FOkxM7sL/yjNKPyjjkvYu/215K7oP4PYc4H5QeuRypb5EXCXmc3EH5Ded85tMLPz8fvIV+YfCfsG/5nti9/uv8WPOHwUMMXMnsfftd6BH8l5AjDbOfdt8FoL8H1BXWFmu/AtcTZU0oqhP/CRmb2ATzxb8XdAL8dv4+ngW6+a2T+BX+MHy3kaWIdvefNT/OjbWUGsFwK/Nt+Hc0bwXkr22d9Vsp1KOefmmNkf8Rff88zsWfzjq92C930ifpCbqszD36k+AP99zQ3Wv93M5uIHjFjrnIvW11h5c/AtR35vZu3w+/1S59zsWN5TiGLddxaa2Sf4lk8l23oifqDKWE/+Y/FP/InFw+a7CFmE/y6Pwn+Xq9Pa4xHgGuBe/GcT2bqm5HN+G986LQf/GfbG9w25lNj6PIv52OOcW2Vml+NbnSw0s8fwLSY64ftkOxXf6mAZvvuZ4/CDfS7Fn6CcjG8FdUs1tkFDpnypfAnKl8qXIeZL51yWmV2Pf8pptplNDSaNx2/vSS5i0Op6FtM+BbxtZtn4fWQl/imq8fh8+Ri15wF8fvyr+dbZn+JbSP4M3yK1OseDJ/C57B58pVH5m9Xgj8WTgu/Iu/j+TS+mrF/SSjnnvjWzd4N1GH4fHoxvBbcYf2wvmXenmV2Ar8T6NjiGLMZvywH4Y8hp+NxxAb5S5IVgngJ8two/BJ4pd7xtqpS/lb9B+bux5O+61ht4y8xexsd2Fb7P/l+VzOCc2xHk1wlm9iRl3+OL8DcdBpVbZ23sL+BvKn0IzDCzu/Gfz1nUbP9vgR8760l8F0aXASn4a83dOOcWmtnH+MGEHbuPqVDnqrnv1OWxQgCcc83iB39HMge/059fwTwt8F+aOfjEuRP/KMMTwHER840L1jO+gvV8iK80SQdewp9kbA/+3jfK/Jfik0Muvr/aKfg7nQ5/typy3j3KKnnP8fjWM3PxX6QcfD9xN+FPIvviTxiygMExrrML/iR5QbBOF/y8CyRXsEx//OMnJUnvGaBHsI0+LDfvGPwAJ9uDeV/DD5rxIbCs3Lx7LF/Z5wNcj39UpiCYflMV77UV/qRyPWUtGsZFTO+Nf4RuGb7icXOwrf8O7BPM0zeYZ2GwH+wM/v4zkFru9U7EtxjKDV5rj/cWMW8H/GjL84LtlIM/8b8N6BZl/rPxrcW2BzEsCuZNLPd+/x5so/xg33gM6B3L9i03z0n41jxb8C3VVuIP6JdX4zv7fPA6N5YrvzkofzzKMuPLf05B+YX4fTafiO9QRfNHfo9jiLPC7VHJPnpTsEyfiLKpgIv4P6Z9B9+6ISP4vEq29bPAYVW9n4reI35E8j2+I0FM/wv2o234Y1pf/In+67F+tsG6vg5e450KpnfEX+CvwX8nvsYfK/f4zCr53GM+9gTzj6Lskdr84LU/wPc5mhzxeT8drCMHv4/Pxp/wW3W2QUP9QflS+VL5Uvmy/vPlVCJyYET5afj+gEuOMzOBU2PcLn0q2n8rijna+6Rczo51n8Ifr97BV8rk449Zr+O7iqj0vVe0PYJp0Y53nYJltgTb6X18BfNnwIJY96VgXa8Er/FdBdNb4lvQLsd/B7/Hn4scU/7zregzx7dqexb/Xd+B3+cPqORzORh/43x1sC3XB/vCjUD7YJ7B+Mr7xcE22IYfUO86IKk626Cx/qD8rfyt/N0o8jdR8lO0svLftyjl46N8ZlODsk7Be9uM3y/fB4ZEWUdrfIvpkvk+BkYSPTft9f5S7nswM9gXNuBvfh9c0TaIEvdNwbwH4W8CrgvW9SlwbCXLnR8s914sx4Py2zXW72IV39+Y9h3q8FihH+cv3EVqyvyjiW8DQ4GznXPPhhySiNSjoAXMJmCyK9e/t4iUUb4UkfoUPIK9Cd8y8/iw4xFprJS/pTEJWnpf6Jwr35WJAGb2M3zDpnOcc09WNb80Xc2iz3CpO8657fhBAOYBT5jZyeFGJCJ1xaL0p41/JBFC6rtVpLFQvhSRulJBfr4M3zJW+VlkLyh/izQpV+JvFP8v7EAkXOpnRvaa8302Hh52HCJS594ws+X4x67j8Y9E/wj/iNuLIcYl0igoX4pIHbnfzJLx+TgPGIEf52MxvjsKEdkLyt8ijVcwMOUxwGh81yO/dc7lhRuVhE2V4SIiEqtX8AMSnYofmGQV8B/gT65+R+IWERGRMm/jW7vdiO//dT1+3I0bg1atIiIizdWBwDR8v9v34a9fpZlrtn2Gm1khvpuYbWHHIiIijUJboNg5pxvJVVCOFRGRGlCejZHyrIiIVJNybITmXBleDFhqamrYoYiISCOQnZ0NfhRxjbdRBeVYERGpLuXZ2CnPiohIdSjH7q453xHYlpqampqVlRV2HCIi0gikpaWRnZ2tFlixUY4VEZFqUZ6tFuVZERGJmXLs7nRHQERERERERERERESaPFWGi4iIiIiIiIiIiEiTF2pluJn1NLPbzexjM9thZs7MxlUw77Fm9omZ5ZjZBjObbGZp9RqwiIg0LjlbYe5U+OgW/ztna9gR1RvlWBERqXPKs8qzIiJSN5pxjq1rYfcZvi9wNvA58B7w42gzBScVrwMvAjcA3YF/Ageb2WjnXHE9xCoiIo2Fc/DBzTDzTrA4KMiBhBR449cw8mo46vdgFnaUdU05VkRE6obyLCjPiohIXVCOrXNhV4ZnOOc6A5jZqVRwAgHcAswHziw5WTCztcDbwBnA03UfqoiINBof3Ayz7obC3LKygl3+96y7/e+jb6j/uOqXcqyIiNQN5VlQnhURkbqgHFvnQq0Mj+UuuJn1AIYC10XO75x7x8xWAz+hjk4giouLyc7OZseOHRQX64Z9QxcXF0fr1q1JTU0lLk7d4Ys0Wzlb/V30yJOHSAW7/PQRV0FKWr2GVp8aeo4FyMnJYdu2beTl5eGcq6uXkVpgZiQlJdG2bVtSUlLCDkdEwqQ8CzT8PKtr2cZF17IiAijH1pPGcJQ9OPg9P8q0ryOm78bMsir7AVKreuE1a9awbt068vPzaxy81J/8/HzWrVvH2rVrww5FRMK04CX/OFllLA4WvFgv4TRwoeXYLVu2sGzZMrZu3UpRUVGN34DUj6KiIrZu3cqyZcvYsmVL2OGISJiUZ6tD17ISE13LigigHFtPwu4mJRYdgt/Rrry2AIfVxYvu3LmT7du307FjRzp27IipP54GzznHxo0b2bx5M+3ataNly5ZhhyQiYdixoewxsooU5Pj5JJQcm5OTw/r162nTpg3dunUjPj6+Ll5GallRURFr165l/fr1pKSkqIW4SHOVtVJ5Nna6lpWY6FpWRABdy9aTxlAZXqKi56ejljvn0ipbWVV31Ldt20ZcXBwdOnTQyUMjYWZ07NiRrVu3kp2drRMIkeamuBi+ewPmTat63oQUaN257mNqPOo9x5qZKsIbmfj4eLp168aOHTvYvn27KsNFmpvt6+CTe2H25KrnVZ4tT9eyUildy4o0c8XFsPgd+DKGnrOUY/daY6gM3xz87hBlWnui32Xfa4WFhSQkJKi/rkYmLi6OhIQECgsLww5FROpLQS589RTMvAs2fx/bMq4YDjy1TsNqJELJsXl5eSQlJakivBGKj48nKSmJ3NwK+jEUkaZn47cw8w746hkoirHLDeXZErqWlZjpWlakGSrMh6+f9f2Ab1wY2zLKsXutMVSGfxP8Phg/4nakgcDMunjR4uJiXaQ3UvHx8RokRqQ5yNkKcx70LdR2Rjwmtu+xkNIeFr0S/RGzhJYw4koNOOKFkmOdc7pAb8Ti4uI04KlIU+ccrJgFM26H794sK2/ZAYZNgtxtMPch5dmq6VpWqkXXsiLNRG42fPYwzL4PtkeMFZA+Dlp2gm9fU46tQw2+Mtw5t8rMPgPONbPbSkbhNrNjgB7A/0INUERE6lfWCph1D3z+KBTs9GVxCTDwDBh5NXQ50F/Ef9DL32G3ON+vWkKKv4s+4ko46vfhvocGQjlWRER2U1wEi16FGXfA6s/Kytv19Tl28DlBPnWQmKI8WwXlWRER2U32aph9L3w2FfK3+zKLh4NP93m226DgWvZm5dg6FHpluJn9NPhzaPB7rJl1BHY6594Iyn6Nv5P+pJlNAboD/wRmA8/WZ7wiIhKStV/5x7Tn/w9ckS9LbAOHj4cjLofUHmXzmsHRN/iThQUv+QFGWnf2j5M1o7voyrEiIhKTghw/5sasu2BLZll5j8Nh1DUw4EcQF9HSWHkWUJ4VEZEYrf/GV25//SwUB10hJbSCwy6AEVdAWq+yeZVj61zoleHseQJwU/B7OdAHwDn3vpn9CPgT8BqwHXgRuN65khoRERFpcpyDzA/8Y9qZH5aVt+kGwy+HIeMhucLxoyClnZ+n+VKOFRGRiu3aAp/eD59OgV2bysr7n+ArwXuN8BflFVGeVZ4VEZHonINl0/3TVovfKStv1RmOmASHXwwt21e8vHJsnQm9Mtw5F9Pw1s65N4E3q5yxgcveVcDr89eycXsendokceLB3UhtmRB2WHVi3LhxZGVlMW/evFpZ37Jly+jbty8PP/ww48ePr5V1ikgDVVQA37zgW4Kv+7qsvNMB/vGxgWdAi8Tw4mskmluOBeXZvaE8K9KMbF0Gs+6Gzx+DwhxfFp8Ih/wMRlwNnQeEGl5j0dzyrHJszSnHijQjRYWw8CVfCb52Xll5h339tewhZ0FCcmjhSQOoDG8unHPc+s53TMnIJC7OyM0vIjkxnpte/oaJY9L55bH9scpaXYiINAd5O3xf4J/cA9kry8r7jIaR18B+x1beQk2aLeVZEZEYrP7c32he8JLvexQgKRWGXgxHXAZtuoYbnzRIyrEiIjHI3wlfPOG7HMtaXla+z3D/tFX/EyAuLrz4pJQqw+vJre98xwPTl5JXWDYydE6+fyrugelLAbjuuP1Dia225eXlkZSUFHYYItKYbF/vR9L+7EE/sjb4wUIO+LE/cegxJNz4pMFTnhURqYBzsPhd3+XYsull5W17wPArYMiFkNQmvPikwVOOFRGpxI6NvruxOfdDztag0GDASb5BV68jQg1P9qRbEtVUWFTMyi27qvWzYE02kz/KJKcgepdwOQVFTM7IZMGa7Gqvu7CoOOo6K/LCCy9gZnz00Ud7TPvrX/9KixYtWLNmDQCvv/46Y8eOpU2bNrRq1YpjjjmGOXPm7LbM+PHjSUtL4/PPP2fcuHG0atWKSZMm7TbPu+++y5AhQ0hOTqZPnz7ceuutu03Pzc3ll7/8JYcccght27alY8eOjBkzhvfee69a701EGqGN38HLV8NtB8PHt/qK8BYpMPRSuHou/OwRVYQ3IzXJsXWZZ6ubY0F5VkQakMJ8PyjmvSPhiZ+WVYR3PghOmwLXfgkjr1JFeDOia1nlWBGpRZuXwKu/8NeyGbf4ivD4JN/P91Vz4KwnVBHeQKlleDWtzc5l9C0f1Pp68wuLOfGOj6u93PTrj2Kf9i1jnv9HP/oRHTt2ZOrUqYwdO3a3aY8++ijHHnss3bt356GHHmLChAmcddZZ/N///R9FRUXceuutjB07ltmzZzNw4MDS5XJzczn99NO55ppr+MMf/kBCQlm/catXr2b8+PHceOON9O7dm6eeeorrrruOwsJCrr/+esDffc/KyuI3v/kN3bt3Z9euXbz00ksce+yxvP322/zgBz+o9nYRkQZuxSe+hdq3r5eVtewAwyb6ivBWHcKLTUJTVzkWapZnq5tjQXlWRBqA3G0wdyp8ci9sX1NW3nesf9qq3zHqcqyZ0rWscqyI1IJVn8GM22Dhq4DzZclpMHSCHxizdecQg5NYqDK8mUlISOCcc87hoYce4q677qJVq1YAzJgxg++//56//vWv7Ny5k+uuu46f/vSnTJs2rXTZ4447jv79+/PnP/+ZZ58tGzg9Ly+Pm2++mXPPPXeP19u0aRNvvvkmP/zhDwE4/vjjWb9+PX/729+45pprSE5OJjU1lYceeqh0maKiIo477jgyMzO5++67dQIh0lQUF/nK7xl3wKpPy8rb9fUt0wadA4nVq3gUaWiUZ0UkNNvW+ArwuVMhb5svs3g46FT/mHb3wSEGJ7L3lGNFJDTFxfD9W/5adsXMsvLUXjDiSjj0PEhqHV58Ui2qDK+mbqnJTL/+qGot8/KXa7jz/e/JLaj4MbDkhDiuPno/fjyoe7Xjqa6LLrqIO+64g+eee44LL7wQgEceeYR27dpxyimnMH36dLKysjjvvPMoLCwsXS4xMZFx48bx9ttv77HO0047LeprtWvXrvTkocRZZ53Fm2++ybx58xg+fDgAzz//PP/9739ZuHAhW7ZsKZ13wACNZC/S6BXkwJdPwsy7YMuSsvIeQ/zF+QEnQ1x8ePFJg1GTHAt1l2drkmNBeVZE6tmGhTDzTvjqGSgu8GUJLeGwC3yf4O16hxufNBi6llWOFZFqKszz+XXmnbDp27LyrofAqGvhwFMhXlWrjY0+sWpqER9X7UemzzuiN3e8932l8zgH5w3vTWpKQqXz1YbBgwczaNAgpk6dyoUXXkhOTg7PPPMM5557LklJSaxfvx6AU045Jery5UcKb9u2LS1bRt8mXbp0qbBs8+bNADz77LP87Gc/46yzzuL666+nS5cuxMfHc+ONN7Jw4cIav08RqQc5W2HBS7Bjg38c7MBTIKWdn7ZrC8x5ED6dDDs3li3T/3hfCd57ZJ0/pp29q4DX569l4/Y8OrVJ4sSDu5Hasu6Ps1IzNcmxoDxbUZnyrEgjV1mOdQ6WfQwz74DvIyr3WnWCYZNg6CXQsn2dh6g827joWlY5VkQiVJZnc7Lgs4dg9mTYsa5smX7H+C7H+o7VtWwjpsrwepDaMoGJY9J5YPrSqAOPpCTEM2F033o5eShx4YUXct1117Fs2TJmzZpFdnY248ePB6Bjx44A3HPPPQwdOrTKdZU/oYhUcjISraxDB98n8LRp00hPT2fatGm7rWvHjh0xvx8RqWfOwQc3+zvkFudbfyekwBu/9i3RHDDvcSjY5eePS4BDzoSRV0Pnum8l45zj1ne+Y0pGJnFxRm5+EcmJ8dz08jdMHJPOL4/tX+mxSxoX5dnoZcqzIo1UZTl2xJXQ5WA/bc3nZct02BdGXAWDzoaEmj3VUr0QlWebC+XY6GXKsSKNWGV5dsiFYC3g80cgP/gex7WAg3/ir2W7Dqx83bUSnnJsXVNleD355bH9AfbYmYuLHRNG9y2dXl/OO+88fv3rX/PII48wa9YsDjrooNKThVGjRtG2bVsWLVrE5Zdfvlevs3XrVt56663dHi976qmnSE1NZfDgwYA/AUlISNjty/zNN98wa9YsevbsuVevLyJ15IObYdbdUJhbVlZS8f3plLKypLZw+MVwxGXQtlu9hXfrO9/xwPSl5BWWPdKbk+8v4B6YvhSA647bv97ikbqnPKs8K9JkVJZjp99K6WBdAD2H+ce09z8R4uLqLUTl2eZFOVY5VqRJqSzPzp5cVpbYGoaMh+GXQ2r9fZ+VY+ueKsPriZlx3XH7M+HI9N0fcxjYrV7vopfo1KkTJ5xwApMnT2bDhg384x//KJ3WunVrbrvtNiZMmMCWLVs47bTT6NChA+vXr+ezzz4jKSmJv/zlLzG9TseOHZkwYQI33HBD6Qjcb775JrfccgvJyb7VykknncQLL7zAlVdeyemnn05mZiY33XQTvXv3pqhoz9YHIhKynK3+LnrkyUM0R/3eV4Int62fuALZuwqYkpG528lDpJyCIqZkZDJhdHoox1+pG8qzyrMiTUKVOTaoCN/3OBhzHfQaXm+hlVCebX6UY5VjRZqMWK9lx/waRlwBKWn1ElYJ5dj6ocrwepbaMoGzh/UKOwzADz7y8ssv06JFC84777w9pvXq1Yt//etfXHLJJeTk5NClSxcOP/xwLrvssphfo0ePHvznP//hV7/6Fd988w1du3blP//5D7/85S9L57n44otZt24dU6ZM4cEHH2TAgAHccccdvPLKK3z44Ye19XZFpLYseMk/TlaZhJa+37V6rggHeH3+WuKqeGwsLs54/eu1DeZ4LLVHeVZ5VqRRiyXHtkiGA04KpSIclGebM+VY5ViRRi/Wa9nU7vVeEQ5Bjo1Tjq1r5pyreq4myMyyUlNTU7OysqJOX758OQC9e2v09cZGn51IHcrbDs9cCEveq2JGg6N+B2Ovr5ewShQVO6596gte/WptpfMZ8Itj+3PNMfvFvO60tDSys7OznXNpexdl06cc27Tp8xOpQ6/8HOY+XMVM4eRYgDVZOVw97XPmrsiqdD7l2bqlPNt06bMTqUN5O+DZ8bD4nSpmDCfPFhc7fv70PF7+ck2l8ynH7j21DBcRkaptWwuz74PPHoa87KrnT0jxLcPrSU5+Ec/NXckDHy9l+eZdVc6fnBhPpzZJ9RCZiIhIFYqL4fu3YeYdsHxG1fPXc44FWLh2G/dnZPLyl2soLK66MZXyrIiINBg7Nvi+wOc8ALlZVc9fz3k2t6CIF75Yzf3TM8ncuLPK+ZVj954qw0VEpGIbFvk+1b56GooLfFmLFCjKB1dJP4iuGA48tc7D27Izn0dnLePRWcvZsjO/tDzOoLJr9eJix4kD629ATxERkT0U5sHXz/o8u3FRxARjt0Eyy6unHOucY9aSzdyXkUnGdxtLyzu0TiRrVwFFlSRa5VkREQndpsUw606Y9yQU5fmy+CQoLmwQ17JZu/J5/JPlTJ25nE078krLdS1b91QZLiIiu3MOls/0LdS+e7OsvGVHPyDm0Evgk3v8CNwFUVphJ7SEEVfWaR9ryzfv5IHpS3l27kpyC/zgIonxcZx+WA8mjE7npXmreWD6UnIK9jzJSUmIZ8LovhpwREREwpGT5btC+eQ+2LGurDz9KBh1DSyb4fNsSDm2sKiY1+evY0rGEuav3lZa3q9TKyaOSefUQ3tw1/uLlWdFRKRhWjHbX8sueo3Sm8sp7WDYRBh6KXw6OdRr2VVbd/Hgx0t5es5KduX7PJoQb/x4UA8mjknn1a/WKMfWMVWGi4iIV1wEC1/xJw6r55aVt+8HI6+CQWf7R8YAjvq9/z3zTj8ASUGOn+aK/clDyfRa9uXKLKZkZPLG/LWld8vbJrfgvOG9GT+yD53bJgPwy2P7AzAlI5O4OCM3v4jkxHiKix0TRvctnS4iIlJvslf7Su65j0D+dl9m8XDwT2Dk1dDtEF+WfhSY1XuO3ZVfyDNzfJdjq7bmlJYP7dOOiWP6ccyAzqWDeinPiohIg1JcDN+9ATPugJWflJWn9YYRV8Gh50JiK18W0rXs/NXZTMnI5LWv15Y+XdU6qQXnHNGLi0b1oVuqv9bu30U5tq5pAE0NOtLk6LMTqaaCHJj3BMy8C7YuLSvvORRGXQv7nwhx8dGXzdnqR+TescH3q3bgqbV+F7242PHhdxuY/FEms5duKS3vkZbCxUf25cyh+9A6Kfq93exdBbw+fy0bt+fRqU0SJw7sVuO76Bp0JHbKsU2bPj+Ralr/jb84n/+cfzQbIKEVDLkQhl8Oab2iL1cPORZg0448Hpm5jMc+WU7WLt8lmhn88MCuTBybzmG92lW4rPJsOJRnmy59diLVVJALXz3lr2U3f19W3m2wv5Y94McQX0E74HrIs845pn+/iSkZmXy8eFNpeZe2SVw8qi9nH9GLtsnR86ZybN1Ry3ARkeZq52aYcz98OgV2bS4r3/9EGHkN9Brur4Yrk9IOhoyvk/DyC4t5aZ4fSOS79TtKyw/o1pZJY9I56ZBuJMTHVbqO1JYJnD2sgkoGERGRuuIcLM3wT1stfresvFVnGH4ZHH6xz6GVqcMcC7B0007un57Jc3NXkV8YdDnWIo6fDunJhCP7kt6pdZXrUJ4VEZFQ5GyFOQ/6gTF3bigr3/dY3+VYn9GhXssWFBXz6ldrmJKxlIVry7oc69+lNZeOTueUwT1IbKFr2bCoMlxEpLnZstT3kfbF41AYPAYdnwiHnOkf0+60f6jhbcstYNrsFTw8Yynrt5UNJDJ6v45MHJPOkft2xKo6sREREQlDUSEsfAlm3A5rvywr77Cfz7GHnAkJyeHFB8xdvpUpGUt4e8F6Sh4STmuZwAXDe3PByD50bJ0UanwiIiIVyloBs+6Bzx+Fgp2+LK4FDDzD59kuB4Ua3o68Qp76dAUPfbyUNdm5peXD09szaUw/xu3fSdeyDYAqw0VEmovVc/1j2gtf9v2hASSnwuGXwBGToE3XUMNbm53DwzOWMW32Cnbk+cfI4+OMHx3SjUtHp3Nwj9RQ4xMREalQ/k5/k3nWXf5CvUSvEf5pq/7HQ1zlLcDqUnGx471FG5j80RI+W761tLxnuxQmHNmXnw3dh5aJujQUEZEGau1X/mmr+f8DFwwsmdgGDh8PR1wOqT1CDW/DtlwenrmMJz5ZzrZcfy0bZ3DCwd2YOCadQfukhRqf7E5nPCIiTZlz8P07/sRh2fSy8rY9YcQVcNgFkNQmvPiAReu2MSUjk5fnraEwGEikZWI8Zw3txcVH9qFnu5ahxiciIlKhHRt9d2Nz7vePbANgMOAk31fpPsNCDS+3oIgXv/Bdji3ZuLO0/OAebZk0ph8nHNyVFlV0OSYiIhIK52DJ+/5aNvPDsvI23eCIy+Dwi3zjrhAt3rCD+zMyeeGL1eQX+QZnyQlx/OzwfbjkyL707tAq1PgkOlWGi4g0RYX58PWzfoTsjQvLyrsc7C/ODzoN4ms2+EZtcM4xK3MzUzIy+fDbjaXlHVsncdGoPpx3RG9SW4YXn4iISKU2L/E59ssnoTB4DDo+CQafAyOugo77hhpe9q4CHp+9nIdnLGPTjrIux8b278SkMemM6NdBj2mLiEjDVFQA37zgn2pe/3VZeacDfFcoA8+AFomhheec47PlW5n80RLeXVjWX3n7VolcMKI3F4zoQ/tW4cUnVVNleH3bY7TaU6oePKeZuu+++0hOTmb8+PFhhyLSeORmw9yp8Ml9sH1NWXn6OP+Ydr+jqx5IpA4VFhXzxvx1TMnI5OvV2WXhdWrFxNHpnHpoD5IT4kOLT5oA5dmYKMeK1NDKOTDjNlj0GhB0uJ2cBsMuhWET/XEnRKuzcnhw+lKemrOCXfn+MfIWccaPB3Xn0jHpHNCtbajxSSOnHBsz5VmRGsjb7vsC/+ReyF5ZVt77SD8o5r7HhtrlWFGx450F65ickckXK7LKwuvQkgmj0/npYT1JSdS1bGOgyvD64hx8cLNvQWJxUJADCSnwxq/9na2jfh9qBVVDdN9995GWlqYTCJFYZK+G2ffCZ1Mhf7svs3jfAnzUNdBtUKjh7cov5NnPVvHAx5ms3JJTWn5473ZMHJPODw7oQlycjoGyF5Rnq0U5VqQaiovhuzf9Y9orZpWVp/XyrcAPPQ8Sw30MesGabUzJWMIrX62lKOhyrHVSC84etg8XjepL97SUUOOTRk45ttqUZ0WqYft6mH0ffPagb9wF/lhzwI/9tWyPIaGGl1tQxPOfr+KB6UtZuqmsy7FB+6QxaUw6PzyoK/G6lm1UGk1luJkdCvwRGAakAsuBR4H/OufyKlu2QfjgZph1d9ljlAAFu/zvWXf730ffUP9xiUjDVlULnPUL/IXJ189CcYEvS2jl+wIffjm0612n4WXvKuD1+WvZuD2PTm2SOPHgbrt1b7JpRx6PzlzGo58sJ2uXj88MjjuwCxPH9GNIb7UmaggafY4F5VkRqb6qcmxhHnz1tM+zm74rK+82yD9tdeCpEF93l1NV5VjnHB8v3sSUjEymf7+ptLxzmyQuPrIvZw/rRWqKuhxrCBp9nlWOFZGaqCrPbvzO32j+6mkoyvdlLVLg0HNhxJXQPr1Ow6sqz27dmc9jnyznkZnL2Lwzv7T8mAGdmTgmnWF926vLsUaqUVSGm9kAYCbwLfBzYBNwNHAzcCBwQb0FU1QI21ZXb5ncbb6vo6IKznMKdvnpB54CSdV8dLFtjxqfhL/yyivccMMNLFq0iG7dunHZZZeRk5PDn//8Z5xzLFu2jL59+/Lwww/vcUfbzPjjH//ITTfdBMDixYv561//yvTp01mzZg0dO3bkiCOO4B//+Af77lvWZ+LUqVO56KKL+Oijj3jiiSd4/vnncc4xduxY7rrrLrp37w5Anz59WL58eelrAYwdO5YPP/ywRu9VpNGprAXOiKug7xg/bfE7Zcu06gRHTILDL4GW7es4PMet73zHlIxM4uKM3PwikhPjuenlb5g4Jp3TD+3BAx8v5bm5q8gr9AOJJLaI4yeH9eTS0X1J79S6TuOT2DX6HAt1l2eVY0WapqpauQ6/AuY+DLMnw471Zcv1O8aPu9F3TJ22gq0qx1599L68MX8dkz/KZMHabaXL7de5NZeOSeeUwd1JaqHHtBuKRp9ndS1bupzyrEiMqrqW7XcMzLoTvn29bJmWHXx3Y0MvhVYd6ji8yvPsGUN68uDHS3nms1XkFPguxxLijVMH92DimHT269KmTuOTutcoKsOBs4Bk4CfOuSVB2ftm1hs428wucc4V1Esk21bD7YfU/nqL8uC+I6u/3LVf1ajl59tvv82pp57KkUceyVNPPUVhYSH//Oc/2bBhQ9ULR7FmzRo6d+7Mv//9bzp06MD69eu59957OeKII1i4cCGdO+/ef+JFF13EySefzLRp01ixYgXXX3895513Hu+//z4AL7zwAmeeeSatW7fmnnvuAaBtW/VxKM1IZS1wpv8Hpv+7rLzDvv7i/ZCzICG5XsK79Z3veGD60tKKboCcoG/Suz9YzJ3vLy4tT01JKB1IpFObpHqJT6ql6edYqFmeVY4VaZoqy7Ef/xc+vq3saau4FnDwT32e7XpwvYRXWY6976MlPDxjGTvyCkunDevbnsvGpjOuf2d1OdYwNf08q2tZ5VmRSJVey966+7Vsuz6+gnzwuZDYsl7CqyzP3lPuWrZNUgvOHd6bi0b1oUvb+rnWlrrXWCrDS04OssuVZwfTiuo3nMbvxhtvpHv37rz99tskJfnKqeOOO46+ffvWaH1jxoxhzJgxpf8XFRVx0kkn0blzZ5588kmuvfba3eb/0Y9+xG233Vb6/9atW7n++utZu3Yt3bp149BDD6Vly5a0bduW4cOH1ygmkUYrZ6u/ix558rCbYMCuHkPgyF/C/ifW60Ai2bsKmJKRudvJQ6Sgq1K6tU1m4th0fnb4PrRKaizppllSjq1lyrEiDVhVObY4qGROaAWHX+S7HEvtWW/hVZVjC4ocBUWFGHDCwK5MHNOPwfuk1Vt8UiPKs7VMeVakAavyWjbIb10PgdHXwQEnQ1z9Pc1UVZ4tCq5lu7RNYsKR6Zw1bB/aJKvLsaamsdROPAb8ErjXzK4HNgNHARcC/3HO7bEXm1lWFetMrVEkbXv4O9jVMf85+OhfUJhT8TwtUmDsr3zLk+rGU007d+5kzpw5XHvttaUnDwCpqamcfPLJPProo9VeZ35+PrfffjuPPPIIy5YtY+fOskEFFi1atMf8P/7xj3f7/5BDfAuF5cuX061bt2q/vkiTsuAl/zhZZVok+37BD/hR/cQU4fX5a6tseZYYH8eVR+/LecPrts9yqRWNO8dC3eVZ5ViRpieWHBufAEffCCMur5+YIsSSY1vEGT//wX5cdfR+9RSV7KXGnWd1LbvH/MqzIpWI6Vo2yXftedCp9RJSpFivZa86aj/OH6Fr2aaqUVSGO+dWmNlw4EUgM2LS35xzN9ZrMPEtqv8o1+EXw0e3VDGT8weDlLSaRhazrVu34pyja9eue0yrafL+xS9+weTJk/ntb3/LmDFjSEtLw8w48cQTycnZ88SpQ4fd+4AqOZHJza3o7qFIM7JhUdljZBUpzPMDkYRg5ZZdpY+RVaSgqJgtEYOMSMPV6HMsNKg8qxwr0sBtW1d1ji0qhPzt9RNPORu351WZY4uKXelTWNLwNfo824ByLCjPijR4G7+N4Vo2H3aGcy27amts17Jbd+latilrFJXhQX9qrwDrgNOALGAs8FszK452EuGcS6tinVnU9I56daW08/0Mzro7+kEhoaUfKbceTh4A2rVrh5mxbt26PaatXbu29O/kZN8fUl7e7oOlbN68eY/lpk2bxgUXXMBf/vKX0rL8/Hy2bNlSW2GLNH2rPoMZt8PCl6ueNyHFj8hdj9Zk5fDQx0t5dNayKudNToxX/+CNRKPPsdCg8qxyrEgDlbcDvngMPp1c9bwh5NjiYsfbC9bz3NyVVc6rHNu4NPo824ByLCjPijRYa77wg+l+80LV84aQZ9dvy+WhGUuZOmNZlfMqzzZ9jaIyHPgH0AY41DlXcmv2w2Bk5j+Y2YPOuWVhBReTo37vf5cfTdcV+5OHkun1oFWrVgwbNoznn3+ef/zjH6V3srdt28Yrr7xSOl+XLl1ITk7mq692f5TupZde2mOdZkZiYuJuZQ899BBFRTXvAi8pKSnqnXiRJqW4GL5/21eCr5gZ+3KuGA48tc7CirRw7TamZGTyypdrKIyxKVpxsePEgXpMtJFo/DkWGkyeVY4VaWC2r/cV4HMehNys2JapxxybW1DE/z5fzQPTM8nctLPqBVCObYQaf55tIDkWlGdFGhTnYPF7MPN2WJpRjeXqL89+v347UzIyeXHeagqKdC0rXmOpDD8UWBBx8lDiMyAOGAAsq++gqsUMjr7BnywseMl3b9C6sz8A1NNd9Eh/+ctfOP744znuuOP4xS9+QWFhIf/4xz9o3bo1W7duDUI2zj33XB566CH69evHoEGD+PTTT5k2bdoe6zvppJOYOnUqAwYMYODAgXz88cdMnjyZtLS0Gsd48MEH88QTT/Dss8/St29f2rRpw/7771/j9Yk0KIV58NUz/qJi07dl5V0PgVHXwvoFMPve0FrgOOeYuWQz9320hOnfbyot79QmiYtH9WXLzjwe/2QFOQV7XiSkJMQzYXRfUlM00Egj0fhzLDSoPKscK9IAbPre59gvn4KioGVoi2QYfK4/XsybFmor16xd+Tw2azmPzFrGph1lj2IfPaAzqSkJvDl/LTkFew7upRzbKDX+PNuAciwoz4qErqgA5j/vW4Jv+KasvPOBMPIa2PQdzL4v1GvZ2Uu3MCUjk/cXlXXH0qFVIuNH9mFbTgGPz9a1bHPWWCrD1wAHm1lL51zkt2lE8Ht1CDHVTEo7GDI+7Cg49thjefHFF7nhhhs488wz6dq1K1dccQU5OTn86U9/Kp3v1ltvxcy45ZZb2LFjB0cffTSvvvoqffr02W19d9xxB/Hx8dx8883k5OQwYsQI3nrrLU4++eQax/jHP/6RFStWcNFFF7Fz507Gjh3Lhx9+WOP1iTQIOVkw92H45D7YEfF4Z7+j/YlD+jh/wXGwg7i4em+BU1hUzGtfr2VKRibfrNlWWr5v59ZMHJ3OKYd2J6lFPM45khPimZKRSVyckZtfRHJiPMXFjgmj+/LLY/vXSXxSJ5pOjoUGkWeVY0VCtOITf3H+7etA0AIspT0MuxSGTYRWHX1LtpR2obRyXbllFw9+vJRnPlvJrqDP0oR445TBPZg4Jp3+XdrgnKNnuxTl2Kaj6eTZBpBjQXlWJDS52+DzR+CTe2FbxKGr7xgYeS3se4y/lnUO4uLrPc8WFTve+mYdkzMy+XJlVll4HVsxYXRffnJYT5ITgmvZRF3LNmfmXMMffcXMTgVeAD4GbgOygXHA9cBHzrlja7DOrNTU1NSsrKyo05cvXw5A797Na/TYm266iT/96U80hv2iIs31s5MGLnuVP2mYOxXyd/gyi4eBP/X9MHYdGH25nK310gJnZ14hz3y2kgemL2V1VlnDpWF92jNpbDpH7d856qjb2bsKeH3+WjZuz6NTmyROHNityd5FT0tLIzs7O7uqfjwbG+XY+tMUciw0389PGrDiYl/5PfMOWDm7rLxdHxhxlW8Nnthyz+XqKccCzF+dzeSMTF7/ei1FQZdjbZJacM7wXlw0si9dU5P3WKY55VhQnq3mOpVno2gKeba5fnbSwG1b61t6f/Yw5GX7MovzeXPUNdD90OjL1VOezckv4rm5K3ng46Us31x2z/HQXmlMGtOPYw/sQryuZZtkjq2pRtEy3Dn3opkdC/wGuAdojX+U7C/ArSGGJiJSuXXz/cX5/OehuNCXJbaGwy6E4ZdD2j6VL1/HLXA2bs/jkZnLeOyT5WTnFAD+Zv7xB3Vl4ph0Du3VrtLlU1smcPawXnUWn9Q95VgRabQKcuHLJ2HWXbB5cVl590P901YH/BjiK7ncqeMc65wj4/tNTMlYwozFZYP2dW2bzMVH9uHsYb1ok1zxRbdybNOgPCsijdaGRb5191dPQ7G/ViShJRx6Poy4wt90rkwd59ktO/N5dNYyHp21nC07y7oc+8EBXZg0Np3De/sBdyuiPNt8NYrKcADn3LvAu2HHISJSJedg6Uf+Me0l75WVt+4CR1wGh1/kTwxClLlxB/dPX8rzn68iv9D3SZrUIo6fDunJhNHp9O3YKtT4pH4px4pIo7JrC3z2IMyeAjvL+gJlv+N8JXifI/2d3ZAUFBXzypdrmJKRyaJ120vL9+/Sholj0jl5UHcSW8SFFp/UP+VZEWk0nIPlM32Dru/eLCtv2RGOmARDJ0DL9uHFByzfvJMHpi/l2bkryQ3G10iMj+P0w3owYXQ6+3ZuHWp80vA1im5S6oIeLWu69NlJaIoKYcGL/sRh7Zdl5R37+65QDjkTWiSFFh7A3OVbmPxRJu8sXE/J4b9dywTOH9GHC0b0pmPrcONryPRoWeyUY5s2fX4Smq3L4ZN74PPHoGCnL4tLgIFn+Dzb5cBQw9uRV8hTn67gwY+XsjY7t7R8RHoHJo5NZ1z/TpW2UGvulGdjpzzbdOmzk9AUF8HCV/y17Oq5ZeXt032OHXS27/c7RF+uzGJKRiZvzF9L0OMYbZNbcN7w3owf2YfObffsckw85djdNZqW4SIiDVb+Tn9h/sndkLWirLzXCBh1Lez3Qz8YZkiKix3vLlzPlIxMPlu+tbR8n/YpTDgynTMO70nLRKUDERFpoNZ+6Z+2+uYFcH7QSZLa+kevh18ObbuHGt76bbk8PGMZT8xezvZc3yVanMGJA7sxcUw6h/RMCzU+ERGRChXkwLwnYOZdsHVpWXnPof5pqwEn+cEwQ1Jc7Pjwuw1M/iiT2Uu3lJb3SEvh4iP7cubQfWidpGtZqR7tMSIiNbVjA3w6BT69H3KzgkKDA37kR9PeZ2iY0ZFbUMQLX6zm/umZZG7cWVp+SM9UJo5J5/iDutIiXo9pi4hIA+QcLHkfZtzuux4r0aabrwAfMh6SU0MLD+D79duZkpHJi/NWU1Dkm6glJ8Rx5uH7cMmR6fTqEGXQThERkYZg52aYc7+/nt1VNq4F/U/wDbp6DQ+1y7H8wmJemuevZb9bv6O0/IBubZk0Jp2TDulGgq5lpYZUGV6BuLg4CgoKwg5DaqCoqIiEhKY5ArA0EJsWw6w7Yd6TUJTny+KT4NBzYcRV0KFfqOFl7crn8U+WM3XmcjbtyCstH7d/JyaN6cfw9PZ6TFtCFRcXR35+Ps457YuNjHOOwsJCEhMTww5FmqqiApj/Pz9g1/qvy8o7HQCjroGDfwotwtv/nHN8unQLUzIyeW9RWX/lHVolcuHIPpw/vDftWun7IeHStWzjpWtZqXNblsKsu+GLx6Ewx5fFJ/ouPUdeDZ32DzW8bbkFTJu9godnLGX9trJr2dH7dWTimHSO3Lejrh9kr6kyvALJycns2LGDLVu20L59uIMDSOy2bNlCXl4ebdq0CTsUaYpWfupbqC16DQg6KUtpB0MvhWEToXWnUMNbtXUXD368lKfnrGRXvn+MPCHe+PGgHkwck87+XfW9kIahdevWrFu3jo0bN9KxY0fiQuxGSGJXXFzMpk2byM/P17mR1L687TD3EfjkXti2qqy8z2j/mPZ+x4baQq2o2PH2N+u4LyOTL1dmlYXXoSUTRqfz0yE9SU4I7zFykUi6lm2cdC0rdWr1XN/l2MKXwflBJ0lKhaEXwxGXQZuuoYa3NjuHh2csY9rsFezI812OxccZPzqkG5eOTufgHuE+DSZNiyrDK9CxY0fy8vJYv349WVlZxMfr5LahKyoqKj156NixY9jhSFNRXAzfveFPHFZ+Ulae1gtGXO1bgye2Ci8+YP7qbKZkZPLa12spCkYSaZ3UgnOO6MVFo/rQLTXcgU5EyktNTWXXrl1s3ryZrVu3kpCQoDzbwBUVFVFQUEBxcTFt27YlNVUXJFJLtq+D2ffBnIcgL9uXWRwceIqvBO9xWKjh5RYU8ezcVTw4PZNlm3eVlg/eJ43LxqZz7IFdiY9TCzVpWHQt2/joWlbqhHPw/Tt+UMxl08vK2/aEEVfAYRdAUrg3Xxat28aUjExenreGwuBatmViPGcN7cXFR/ahZzt1OSa1T5XhFTAzevTowaZNm8jNzaW4uDjskKQKCQkJpScPemxGYpKzFRa85Pv+bt3ZX3intPPTCnLhq6f9Y9qbvy9bpttg/5j2AadAfN0fQrN3FfD6/LVs3J5HpzZJnHhwN1JbJuCcY/r3m5iSkcnHizeVzt+lbRIXj+rL2Uf0om2yHrGUhikuLo4ePXqQlpbGtm3bKCwsVJ5t4BISEkhJSSE1NZWWLXVRIrVg47f+4vyrZ6Ao35e1SIFDz4MRV0L7vnUeQkU5FmDLznwem7WcR2YtY8vO/NJlfnBAZyaO6cfQPu10vikNlq5lGx9dy0q1VXYtW5gPXz/rr2U3LixbpsvBvj/wg06D+Lq/VqzsWnZW5mamZGTy4bcbS+fv2DqJi0b14bwjepfmY5G6YM65sGMIhZllpaampmZlZYUdiojUN+fgg5v9yYHF+RG0E1L842JDL/UDcn06BXaW9QXKvj/wJw59RtfLY9rOOW595zumZGQSF2fk5heRnBhPcbFjTP+OrNySw6J120vn79+lNZeOTueUwT1IbKEuJ+pCWloa2dnZ2c65tLBjaeiUY0UkKudgxSz/tNV3b5SVt+wAwybB0AnQqkM9hFFxjj3z8H1wOJ6du4rcAl+BmBgfx2mH9uDSMX3Zt7O6L6gryrOxU54VacYqu5YdNtHn1NmTYfuasmXSx/mnrfodHfq17Lj9O7F6aw7z12wrC69TKyaOTufUQ3uoy7E6ohy7O1WG6wRCpPl5/69+0JCCXZXPF9cCBp7hBxLpclD9xBb4z9vf8sD0peQUFFU63/D09kwa049x+3dSK5I6phOI2CnHishuiov8eBszbofVn5WVt+sLI6+CQedAYv09cRBrjm2T3ILzhvfmopF96Nw2uZ6ia76UZ2OnPCvSjMV6LWvxvgX4qGug26D6iS0Qa549vHc7Jo5J5wcHdCFOXY7VKeXY3ambFBFpXnK2+rvohbmVzzf0Ujjy55Das17CipS9q4ApGZnkFVb8SGucwWOXHMGofdWnoIiINFAFOTBvGsy6C7ZklpX3GOKfthrwI4ir3xZgseRYgP87rj/jR/WldZIul0REpIGI9Vp2yMX+WrZd73oJK1Ks17JTLxrGmP6d6jEykTI6uxOR5mXBS/5xssokpEDXg0OpCAd4ff7aKu+MJyXEs2LLLkbVU0wiIiIx27UF5jzgH9PeVTauBf2P949p9x5ZL49pR/P6/LXEVfHaKQnxdGidpIpwERFpWGK9lu0+KJSKcIj9WnZ1Vk49RSSyJ53hiUjzsmODb6lWmYJcP19INm7PIze/8kfKcvOL2Lg9r54iEhERicHWZf7R7S8eL3t8Oy4BBp0JI66GzgNCDW9bbgGvf722yse2cwuUY0VEpAHavr7q7lF0LStSJVWGi0jz0rqzv1te2UlEQoqfLySd2iSRnBhPTiUnEcmJ8XRqk1SPUYmIiFRgzRd+UMwFL/oBvACSUuHwi+CIy6Btt1DDW5edy8MzljJt9gq25xVWOb9yrIiINDjrvoavnq56vgZwLZvYIq7SblKUZyVsqgwXkeblwFPgjV9XPo8rhgNPrZdwojnx4G7c9PI3lc5TXOw4cWC4lQsiItKMOQeL3/WDYi6bXlbetgcMvwIOuwCS24YXH/Dtuu1Mycjk5S9XU1DkAEhJiCO/0FHkXIXLKceKiEiDsWsLfHAzfPZQ2Q3nyoR8LXtYr7Qqx+VQnpWwqTJcRJqXlHYw8uqKR+BOaAkjroSUtHoPrURqywQmjkmvcATulIR4JozuS2pKQgjRiYhIs1aYD/Ofh5l3wIYFZeWdD4JR18DBP4H48PKTc45PMrcwJWMJH3y7sbS8Y+tExo/sw3nDe/Pgx0uVY0VEpGErLoLPH4H3/gI5W3xZx/2h6yHw7WsN8lo2r7CI65//utJ5lGelIVBluIg0P0f93v+eeacfgKQgxz9O5or9yUPJ9BD98tj+AEzJyCQuzsjNLyI5MZ7iYseE0X1Lp4uIiNSL3G0wdyp8ci9sX1NW3ncMjLoW+h0T2qCYAIVFxbz5zTqmZGTy1ars0vL0jq24dEw6px3ag+SEeEA5VkREGrgVn8Drv4J1X/n/k9rCuN/AsIkQ18K3FG9g17LOOf7w4jd8uTILgB8P6s5b36xTnpUGyVwljwg2ZWaWlZqampqVlRV2KCISlpytfkTuHRt8v2oHnhpqi/BosncV8Pr8tWzcnkenNkmcOLCb7qKHJC0tjezs7GznXFrYsTR0yrEiTci2tTD7XvjsYcjb5sssDg46DUZeA90HhxpeTn4Rz85dyQPTl7JiS1kruSG92zFxTDrHHtCFuLjolfTKsQ2L8mzslGdFmqjt6+CdP+zeN/jgc+GYP0KbLrvP28CuZZ+YvZzfvzAfgF/9cH+uPGpf5dkGRDl2d6oM1wmEiIjEQCcQsVOOFWkCNiz0rc6+egaKC3xZQks49HwYcQW06xNqeJt35PHIrOU8NmsZW3f5+Mzg2AO6MGlsOkN6tw81Pqk+5dnYKc+KNDGF+f7G80e3QP4OX9b9UDjhX7DP0HBji8Hc5Vs5a8osCoocxx/UlXvPOwwL8Wkx2ZNy7O7UTYqIiIiIiPhBMZfPgBl3wPdvlZW36gTDJsHQS6BluJXMyzbt5IGPM3n2s1WlA3QltojjJ4f1YMLodPp1ah1qfCIiItXy/bvw5q9h82L/f8uO8IM/wuDzIC4u3NhisGF7Llc8MZeCIse+nVvz758NUkW4NHiqDBcRERERac6Ki2Dhy74SfM3nZeXt+/lBpwed5fsjDdEXK7YyJSOTN79ZR8mDrakpCZw/vDcXjuxDpzZJocYnIiJSLVsy4a3fw7ev+/8tHoZdCuN+2+C67qxIfmExVz7xOeu35dEmqQWTzx9C6yRVM0rDp71URERERKQ5yt8F856AWXfD1qVl5T2HwahrYP8TIS4+tPCKix0ffLuByRmZfLp0S2l5j7QULjmyL2cO3YdWuugWEZHGJH8nTL/Vd0VWlOfL+oyGE26BLgeGG1s1/fW1BcxZthWAW88crKezpNHQ2aOIiIiISHOycxN8ej/MuR92bS4r3/9EGHUt9BoeXmxAXmERL81bw5SMTBZv2FFaflD3tkwck85JA7vRIr7hPzouIiJSyjn45gV4+0bYtsqXte0JP/yrH/yykXUt8tzcVTw6azkA1xyzH8ce2KWKJUQaDlWGi4iIiIg0B1syfSvwL56AwhxfFp/ou0EZcTV06h9qeNk5BUybvYKHZyxlw/a80vLR+3Vk0ph+jNq3g/ohFRGRxmf9N/DGr2HZdP9/fJK/+XzkLyCxZbix1cDXq7L53QtfA3DMgM78/Jj9Qo5IpHpUGS4iIiIi0pStmgszb4eFr4Dzg06SnAqHXwJHTII2XUMNb212Dg99vJQnP13JjrxCAOLjjB8P6s6lo9M5sHvbUOMTERGpkZyt8MHfYc4D4Ip82f4nwQ9vhvZ9w42thjbvyGPSY5+RX1hM346tuPXMwcTF6Ua1NC6NqjLczMYBvwOGAYnAMuA259yU8KISERFp/JRjRZqY4mJY/I4fFHP5x2XlqfvA8CvgsPMhqU148QGL1m1jykeZvPzlGgqL/aiYrRLjOWtYLy4+si890sIdtFOkNinPijQjxUXwxWPw3p/LuiPrsB+c8A/Y9wfhxrYXCouKufrJL1iTnUvLxHgmnz+E1JSEsMMSqbZGUxluZhcCDwL3A7cCBcAA/ImEiIiI1JByrEgTUpgHXz/rB+bauKisvMtAPyjmQadBfHgXrs45Zi3ZzOSMTD76bmNpeac2SVw0qg/nDutNaktdWEvTojwr0oysnAOv/x+snef/T2wNY38NR1wGLRr3V/6fby5i5hJfuf+vnw6if5dwb6qL1FSjqAw3s32Ae4HfOeduiZj0XkghiYiINAnKsSJNRG42fPYwzL4Ptq8tK08/yleCpx8V6uBchUXFvDF/HZMzljB/9bbS8n6dWjFxTDqnHtqDpBbxocUnUleUZ0Waie3r4d2b4MtpZWWDzoYf3BR6d2S14eUv13D/9KUAXDa2Hycd0i3kiERqrkaV4WbWFzgG6AI84ZxbZmaJQFdgnXMuvxZjBLgk+H1nLa9XRESkwannPKscK9KYZa+GT+6BuY9A/nZfZvFw8Okw8mroNijU8HblF/LMnJU88PFSVm3NKS0f2qcdk8b04+gBndXXqNQrXcuKSK0qzIdPJ8OH/yzLw90GwQn/gl5HhBtbLVm4dhvXP/cl4Ae1/tUP9w85IpG9U+3KcDP7J/BLIB5wwCx8f2fJwALgBuC2WovQGwMsBE43sz8A+wJrgceBP0Q7YTGzrCrWmVrLMYqIiOy1EPKscqxIY7T+G98VytfPQrEfdJKEVjDkQhh+OaT1CjW8TTvyeHTmMh79ZDlZuwoA3zD9hwd2ZeLYdA7r1S7U+KR50rWsiNSqxe/Bm7+BTd/5/1PawzF/gMMugLim8bRT1q58Jj02l9yCYnq2S+GOsw4lXjexpZGrVmW4mU0CfgXcAbwKvF0yzTm3zcxeBk6m9k8gugc/dwI3At8ARwO/BfYBzq3l1xMREal3IeVZ5ViRxsI5WDYdZtwOi98tK2/VGY6YBEMvgZRwK5mXbtrJ/dMzeX7uKvIKiwFIbBHHGUN6MmF0On07tgo1Pmm+dC0rzU7OVljwEuzYAK07w4GnhJ4jdtOY49u6DN76PSx61f9vcTB0Ahz1u4b1HvZSUbHj2qfmsWLLLpJaxHHfeUNo16px93suAtVvGX4F8IJz7udm1iHK9K+Aq/Y+rD3EAW2As51zTwVlH5pZCvB/ZvZH59ziyAWcc2mVrTC426476iIi0pCEkWeVY0UauqJCWPgSzLijbEAugA77+a5QDjkTEpJDCw/g8xVbmfzREt5esB7nfFlaywQuGN6bC0b2oWPrpFDjE0HXstJcOAcf3OyfHrI4KMiBhBR449c+Zxz1+1DHkGjU8R1xGcQnwsw7oDDXz9/7SDjhn9D14PBiriP/fee70sGu//GTgRzcQ4cdaRqqWxneHz/4R0U2Ah1rHk6FNgP7AW+VK38D+D/gMGBx+YVEREQamTDyrHKsSEOVvxO+eBxm3Q1Zy8vK9xnuB8XsfwLExYUWXnGx471FG5iSsYQ5y7aWlvdsl8Klo9M54/CetEys0RBFInVB17LSPHxws88bJZW1AAW7/O9Zd/vfR99Q/3GVaMzxzbgd38MS0LYHHPcXOOj0cCvv68ib89dx1wf+0HTRqD6cdmjPkCMSqT3VPTvNBSp7trE3kFXjaCr2NTA8SnnJEae4Dl5TRESkvoWRZ5VjRRqaHRvh0ykw537/mDYABgNOglHXwj7DQg0vr7CIF79YzZSMTJZs3FlaPrBHKhPHpHPCwV1pER9eJb1IBXQtK01fzlbfojmyIjdSwS4/fcRVkJJWr6EBjT++korwEVf6FuyJTbPrr8UbtnPdM/MAOKJve3534gHhBiRSy6pbGf4pcBrwn/ITzCwZOB+YUQtxlfc/4FLgROCJiPIT8UejOXXwmiIiIvUtjDyrHCvSUGxeArPugnnTyi7E45Ng8Nkw4mrouG+o4WXvKuDx2cuZOnMZG7fnlZaP7d+JSWPTGZHeAWuCreOkydC1rDR9C17yXXtUpigfpoyD1l3qJaTd7FjvX78yDT2+FinQsX+TrQjfllvAxMfmsjO/iG6pydx1zmEk6Aa3NDHVrQz/F/CWmT0GPBSUdTWzHwJ/AnoC59RifAA45940szeAu82sI2WDjlwL3OecW17pCkRERBqHes+zyrEiDcCqz2DGbbDwVUpbnSWnwbBLYdhEP3BXiFZn5fDQx0t56tMV7MwvAqBFnPHjwd2ZOCadAV3bhhqfSIx0LStN344Nvo/ryrhi2LrU/zREDT2+wly/nZug4mLHL5/+ksyNO0mMj+Pe84bQqY3G/JCmp1qV4c65d83scuB2yk4UHgt+5wOXOudm1WJ8kc7An6RcD3QCVgA3ALfU0euJiIjUqxDzrHKsSH0rLobv3/KDYq6YWVae2ss/fn3oeZDUOrz4gAVrtjElYwmvfLWWomJfSd86qQVnD9uHi0b1pXtaSqjxiVSHrmWlWWjd2Q/2WNLHdTRxCbDfsdD9sPqLq8Saz+H7d6C4oOJ5Gnp8CSmh36SuK3d9sJh3F64H4C+nHsTgfdLCDUikjpgrGe69OguZdcUn9AH4vs6+B55xzq2u3fDqjpllpaampmZlZYUdioiINAJpaWlkZ2dnO+fS6vq1GnueVY4VqURhHnz1jO+TdNO3ZeVdD/H9gR94KsSHN+ikc44ZizczOWMJ07/fVFrepW0SF43qyzlH9KJtckJo8UnTVV95trHnWFCelUrkbIVb+oErqnieFslw3bfh9cn9nwGV9MmN4gvJB4s2cPEjc3AOzjmiF387bWDYIUktqs9r2cYg5jNtM2sN3AG84Zx7FrizzqISERFpZpRnRZq4nCz47CGYPRl2rCsr73cMjLoG+o6FEPvbLiwq5rWv1zL5o0wWrN1WWr5f59ZcOiadUwZ3J6lFfGjxiewN5VhpNpa8X3lFeEJL//RRWBW5Ke1g5NUw6+7ordcVXyiWbdrJNU99gXNwWK80/njygWGHJFKnYq4Md87tMLOzqJtBRURERJo15VmRJip7FXxyL8ydCvk7fFlcCzj4p/6Cu+vBoYa3M6+Qp+es5MGPl7I6q6yf2SP6tmfS2HTG9e9MXJwGxZTGTTlWmoV1X8OLV/q/U3vBzg1+MM2CHN+1hyv2FblH/T7cOEtef+adiq8B2JlXyMTHPmN7biGd2iRx73lDdPNbmrzqPoO5AOhTB3GIiIiI8qxI07FuPsy8A+Y/D8WFviyxNQwZD8Mvh9SeoYa3YXsuj8xcxuOfrCA7x/eNGmdw/MFdmTimn/oJlaZIOVaarp2b4alzoDAH0nrBpR9CXBwseMkP9ti6s++GqyG0aDaDo2/wFcuKL1TOOa5//iu+W7+DFnHGPeceRpe2yWGHJVLnqlsZfgtwj5k95pz7ri4CEhERacaUZ0UaM+dg6Ucw43b/qHqJ1l18BfiQi0K/kF6ycQcPTM/k+c9Xk19YDEByQhxnDNmHCaP70rtDq1DjE6lDyrHSNBUVwnPjIWsFtEiBs6ZBqw5+2pDxYUZWuZR2ii9kUzIyee2rtQD88eQDGdqnfcgRidSP6laGDwBWAl+b2av4wUbKd6TknHN/qY3gREREmhnlWZHGqKgQFrzoK8HXfVVW3nF/3xXKIT+DFkmhhQfw2bItTM7I5N2F63HOl7VrmcAFI/pwwYjedGgdbnwi9UA5Vpqmd/4ASzP836fcBV018KFU7ePvN/HPNxcB8NMhPTlveO+QIxKpP+ZKzoZjmdmsOIbZnHOuwXcwpBG4RUSkOupjBO6mkmeVY6XZyNsBXzzuB9rKXlFW3mskjLoW9jvOP6YekuJixzsL1zMlI5O5y7eWhde+JZeO7stPh+xDSmKDPpxIM1LXebap5FhQnpUIXz4FL0zyf4+6Fo79c7jxSKOwcssuTr7rY7J2FTCwRyrPXjaC5IQGf+iTvVAf17KNSXVbhvetkyhEREQElGdFGocdG2D2ZJjzAORmBYUGB5zsKyN6Hh5mdOQWFPG/z1fzwPRMMjftLC0f1DOViWP6cfzBXYnXoJjS/CjHStOy5gt45Vr/d7+j4Zg/hhuPNAq5BUVc9vhcsnYV0L5VIvedP0QV4dLsVKsy3Dm3vK4CERERae6UZ0UauE2LYdadMO9JKMrzZS2SYfA5MOIq6NAv1PCyduXz+CfLmTpzOZt25JWWHz2gMxPHpHNE3/aYqRJcmiflWGlSdmyEp86Dwlxo1xd+8iDEqUJTKuec47f/+5pv1mwjPs6465xD6ZGWEnZYIvWuui3DS5lZB8ruri91zm2unZBEREREeVakAVkxG2beAYteA4IuBlPaw7BLYeil0LpTqOGt3LKLBz9eyjOfrWRXfhEACfHGKYN7MHFMOv27tAk1PpGGRjlWGrWiAnj2Qti2ChJa+QEzW2rgQ6na1JnLeOGL1QD89oQBjOzXMeSIRMJR7cpwMxsE3AEcWa58OnCNc+6rqAuKiIhIlZRnRRqI4mL47g2YcQes/KSsPK23bwV+6LmQ2Cq8+ID5q7OZkpHJa1+vpajYV9K3SWrBOcN7cdHIvnRNTQ41PpGGRjlWmoS3fgfLZ/i/T7sPuhwYbjzSKMzO3MxfX1sIwMmDunPJkeo5SpqvalWGm9nBwMdAMvAyMD+YdBBwMjDdzEY6576p1ShFRESaAeVZkQagIBe+egpm3gWbvy8r734ojLwGDvgxxNf44cq95pxj+vebmJyxhBmLyxqzdm2bzMVH9uHsYb1ok5wQWnwiDZVyrDQJnz8Gn07xf4/5FRz443DjkUZhbXYOV077nKJix4CubfjnTwaq2zRp1qp7Jv9noAAY6Zz7OnJCcHKREczzk9oJT0REpFlRnhUJS85WmPOgHxhz54ay8n2P9YNi9jkSQrxwLCgq5tWv1jAlYykL124rLd+/Sxsmjknn5EHdSWwRF1p8Io2Acqw0bqs+g9d+6f/ufzyM+1248UijkFdYxGWPf86mHfmkpiQw+fwhtEwM76a+SENQ3W/AGODu8icPAM65+WZ2D3BZrUQmIiLS/CjPitS3rBUw6x74/FEo2OnL4hJg4Bkw8urQHz/fkVfIU5+u4KGPl7ImO7e0fER6ByaOTWdc/05q3SUSG+VYaby2r4Onz4OifOiwH5w+BeJ0A1Sq9seXvuHLlVmYwe1nDaZ3h3C7eBNpCKpbGd4KWFfJ9LXBPCIiIlJ9yrMi9WXtV35QzPn/A+cHnSSxDRw+Ho64HFJ7hBrehm25PDxzGY9/spztuYUAxBmcMLAbk8akc0jPtFDjE2mElGOlcSrMg6fPh+1rfZ46axokp4YdlTQC02av4Kk5KwH4v+P2Z9z+nUOOSKRhqG5leCbwI+DuCqb/KJhHREREqk95VqQuOQdL3veV4JkflpW36QbDL4ch40OvYFi8YTtTMjJ58Ys15BcVA5CcEMeZh+/DJUem06tDy1DjE2nElGOlcXrjelj1qf/7J/dDp/7hxiONwucrtvLHl/3QCMcf1JUrxvULOSKRhqO6leGPAn83s2nAzcCioPwA4LfAccBvai88ERGRZkV5VqQuFBXANy/AjDtgfUQPCZ0O8F2hDDwDWiSGFp5zjjnLtjIlYwnvLizrr7x9q0QuHNGH80f0pn2r8OITaSKUY6Xx+ewhmDvV/33U72H/E0INRxqHDdtzufzxuRQUOfbt3Jp//2yQulQTiVDdyvB/A4cBZwFnAsVBeRxgwDPAf2otOhERkeZFeVakNuVt932Bf3IvZK8sK+8zGkZeA/sdG+qgmEXFjncWrOO+jzKZtzKrtLx3h5ZcOjqdnw7pSXJCfGjxiTQxyrHSuCyfBa9f7/8e8CMY/X/hxiONQn5hMVc+8Tnrt+XRJqkFk88fQuskDZgpEqla3wjnXBFwppk9AJwK9MWfOCwBXnTOvVvrEYqIiDQTyrMitWT7eph9H3z2IORm+zKLgwN+DKOugR5DQg0vt6CI5+au4oHpmSzbvKu0fPA+aUwak85xB3UlPk4tuERqk3KsNCrZq+GZC6C4ADoNgNPu04CZEpObX1vAnGVbAbj1zMH069Q65IhEGp4a3R5yzr0DvFPLsYiIiAjKsyI1tvE73x/4V09DUb4va5ECh54HI66A9umhhrd1Zz6PfbKcR2YuY/PO/NLyHxzQmYlj+jG0Tzs9xixSx5RjpcEryIWnz4OdG/w4FmdNg6Q2YUcljcBzc1fxyKzlAFxzzH4ce2CXkCMSaZiqVRluZu2Bns65ryqYfgiw0jm3tTaCExERaU6UZ0VqwDlY8YmvBP/29bLylh1g2EQYeim06hBefMDKLbt4YHomz3y2ipyCIgAS4+M49dDuXDo6nf26qJJDpK4px0qj4By8+gtY8zlg8JOHoIMGPgxD9q4CXp+/lo3b8+jUJokTD+5GasuEsMMqVT6+Pu1b8bsX/LgoRw/ozM+P2S/kCEUaruq2DL8F38/aYRVMfxiYA1y2N0GJiIg0U8qzIrEqLvKV3zNuh1Vzysrb9YWRV8GgcyCxZXjxAV+vymZyxhJe/3otxc6XtUluwXnDe3PRyD50bpscanwizYxyrDR8n06BL6f5v3/wR9jvB+HG0ww557j1ne+YkpFJXJyRm19EcmI8N738DRPHpPPLY/uH+hRX1PgS4ktvtvdun8J/zxxMnLpbE6lQdSvDjwIer2T6y8D5NQ9HRESkWVOeFalKQQ58+STMvAu2LCkr7zHED4p5wMkQF96gk845PvxuI1M+ymRW5ubS8m6pyVxyZF/OGtZLA1mJhEM5Vhq2pdPhzd/6vw86DUb9PNRwmqtb3/mOB6YvJa+wuLQsJ99XND8wfSkA1x23fyixQQXxBRXhAKP27URqSsNpwS7SEFX3TLw7sKKS6auCeURERKT6lGdFKrJrC8x5AGZPhl2bysr7H+8rwXuPhBBbauUXFvPKl2uYkpHJt+u3l5YP6NqGSWPT+dEh3UmI1+BnIiFSjpWGK2sFPHshuCLocjCccneoOa25yt5VwJSMzN0qmiPlFBQxJSOTCaPTQ6lwrio+gOc/X8WvTxigCnGRSlS3Mnwn0LuS6b2BvJqHIyIi0qwpz4qUt3UZzLoHvngMCnb5srgEOORMGHk1dB4Qanjbcwt48tMVPPTxMtZtyy0tH7VvByaO6ceY/TpqUEyRhkE5Vhqm/F3w1LmwazOktIOznoDEVmFH1azkFxazLjuXxz5ZhnOVz1tQVMzJd35MpzZJ9RNchI3b8ygoqrgiHCAuznj967WcPaxXPUUl0vhUtzJ8NnChmf3LObc9coKZtQEuAD6treAqY2Y3AX8EvnTODa6P1xRpaBr6oB4iUm0NIs8qx0qDsOYLmHEHLHgRXHDhl9QWDr8YjrgM2nYLNbz123J5aMZSpn2ygu15hQDExxknDuzGpDHpHNwjNdT4RGQPDSLHBq93E8qzAn7AzFeuhXVfgcXBGVOhXZ+wo6oX9XUtW1zs2LgjjzVZOazJymVttv+9JivH/52dy8btsd8HK3awYssuVmzZVeux1obc/KJqvR+R5qi6leH/Bt4FZprZn4B5gAMOxSfznsCE2gwwGjM7CPg1sL6uX0ukIWrog3qISI2FnmeVYyVUzsHi92Dm7bA0o6y8bQ8YfjkcdiEktw0vPuC79duZkpHJS/NWU1Dkm4+lJMRz5tB9uOTIvuzTPtxBO0WkQqHnWFCelXJm3Q1fP+P/Pu6vkD4u1HDqQ21eyzrnyM4p2KNye01WDmuzclmTncO67FwKi6to7l0NCfHGUft34pCeabW2zlh9tSqLD77dWHr+EU1yYnwordZFGpNqVYY75z4wsyuA24GnIyYZkA9c5Zx7txbj24OZxQEPAg8AA4G0unw9kYaooQ/qISI1E3aeVY6V0BQVwPznfUvwDd+UlXc+CEZdAwedDi0SQwvPOcfspVuYkpHJ+4s2lJZ3bJ3IhSP6cN7w3rRrFV58IlK1sHMsKM9KOUs+gHdu9H8fciYMvyLceOpJda5ld+UXlrbmXpuVy+qgwnttdvB3Vu5ug0dWpU1yC3qkpdAtNZluaSllf6em0D0tmZTEeEb/84NK++SOM+NfZwwOrc/wYX97F38fL7riYseJA8N9ek6koav2UPbOuclm9irwM2Bf/MnDt8BzzrnVtRxfNL/A37U/Dj/it0iz0tAH9RCRvRNynlWOlfqVuw0+fwQ+uRe2RezefcfAyGth32NCHUCsqNjx5vx1TMlYwperskvL0zu2YsLodE4/rAfJCfGhxSci1aNrWWkwtiyF5y7y3YB1GwQn394sBsyM5Vr27g8W89Y369mwPZesXQUxrzuxRdxulds90nyFd7fUZF+elkLrpKqrwCaOSeeB6UujVrKnJMQzYXTf0K6zU1smNOj4RBqLaleGAwQnCv8FMLME4MfAiWb2inNuXS3GtxszSwf+DJzrnNtW2aMzZpZVxerUkaM0Sq/PX0tcXOUnSho0Q6RxCyPPKsdKvdq2FmbfB589DHlBJbPFwUGn+UExux8aang5+UU8N3clD3y8lOWby/oEPaxXGpPG9uPYA7pUmYtFpGHStayELn+nHzAzZyu07AhnPgEJKWFHVS9iuZYtdr5LskjxcUaXNkl0S0uhe1oK3VOT92jd3b5VYq10FfrLY/sD7NGNS3GxY8LovqXTw9LQ4xNpDKpVGW5mtwBHOeeGBv8b8A4wGn9X/W9mNtw5t6S2Aw1e637gLefci7W9fpHGwDnHnGVbSh8jq4gGzRBpnMLKs8qxUm82LIKZd8JXT0Nx0NoroSUcej6MuCL0QcO27Mzn0VnLeHTWcrbszC8tP/bALkwak87hfdqHGJ2I7A1dy0qD4By8eIXvEiyuBfzsUUjbJ+yo6oVzjrnLt1Z5LQswer+O/Ozwfeie5lt5d26TRIv4uHqIEsyM647bnwlHpu8+wOfAbg2ixXVDj0+kMahuy/Dj8YOOlDgZGAPcgh+A5E7gN8CltRFcOZcChwMHxjKzcy6tsunB3XbdUZdGoaComFe/WsPkjzJZtG57lfNr0AyRRiusPKscK3XHOVg+E2beAd+9WVbesiMccRkMvQRahlvJvHzzTh6YvpRn564kt8A/up0YH8fph/Vgwuh09u3cOtT4RKRW6FpWwvfxf2HBi/7v4/8BfUaFGk59KCwq5vWgy7H5q7dVOX9KYjwnDuzGyYO610N0FUttmdCgn7Ru6PGJNGTVrQzfB/g+4v+TgaXOud9A6cjY59ZSbKXMrCP+JOXvwE4zSwsmtQDig/9znXO5tf3aImHakVfIU5+u4KGPl7Imu2z3NvN1CxXRoBkijVa951nlWKkzxUWw8BVfCb56bll5+34w8ioYdHboj4V/uTKLKRmZvDF/LcVBXm2b3ILzR/TmwpF96NwmOdT4RKRW6VpWwvX9O/Den/3fh54HQyeEG08d25VfyDNzfJdjq7bmlJbrWlZEwlbdyvBEIPKZlqPY/e56JlAXR62e+Dvffw9+ytsK/BN/J1+k0Vu/LZeHZyzjidnL2Z5bCECcwYkDuzFpTD/eXrBOg2aINE1h5FnlWKldBTkw7wmYeRdsXVpW3nMojLoW9j8R4sIbdLK42PHhdxuY/FEms5duKS3vkZbCxUf25cyh+8Q0wJaINDq6lpXwbF4Cz10COOhxOJz4nyY7YOamHXk8MnMZj32yvHQATDP44YFdmTg2nQ8WbdC1rIiEqrpn+iuB4cCU4M55OvCHiOmdgR21FFukxfiTlfJuA1oDE4AVdfC6IvXq+/XbmZKRyYvzVlNQ5G+XpyTEc+bQfbjkyL7s074lAAf3aAto0AyRJiiMPKscK7Vj52aYcz98OgV2bS4r3/9EGHkN9Boe6oV/fmExL81bzf3TM/lufdnX6MBubZk0Np0TB3YjoZ76IxWRUOhaVsKRtx2ePNsPGN26C5z5GCQ0vSePlm7ayf3TM3lu7iryC32XY0kt4vjpkJ5MGJ1O346tADh0nzRA17IiEp7qVoY/BdxoZp2Bg4BtwOsR0w8Fan3AEefcDuDD8uUlo2w75/aYJtJYOOf4dOkWpmRk8t6iDaXlHVolcuHIPpw/vDftWiXutowGzRBpsuo9zyrHyl7bshRm3Q1fPA6FwWPQ8YlwyJkw8mrotH+o4W3LLWDa7BU8PGMp67eVDS49er+OTByTzpH7dsSaaOs8EdmNrmWl/hUXwwuXwaZvIS7BD5jZNty+sGvb3OVbmZKxhLcXrC/t/iStZQIXjOjDBSN607H17mNZ6VpWRMJW3crwv+P7WjsVyAYucM5lAZhZKvBj4L+1GJ9Ik1VU7Hj7m3Xcl5HJlyuzSsv7dGjJpWPS+clhPUlOqPwxcg2aIdLkKM9K47F6Lsy4Axa+DM63ACM5FQ6/BI6YBG26hhre2uwcHp6xjGmzV7Ajz3c5Fh9nnHxINy4dk85B3TX2nEgzoxwr9S/jX7DoVf/3if/yT0k1AcXFjvcWbWDyR0v4bPnW0vJ92qcw4ch0zji8Jy0TK69u0rWsiITFXGUjF1RnRWZxQBtgl3OuoFZWWofMLCs1NTU1Kysr7FCkmcktKOLZuat4cHomyzbvKi0fvE8al41N59gDuxIfpxZqIg1NWloa2dnZ2c65tDBevzHlWeXYJsw5PwDYzDtg2fSy8rY9YcQVcNgFkNQmvPiAReu2MSUjk5fnraEwGBWzZWI8Zw3txcVH9qFnu5ahxici0YWZZxtTjgXl2UZj0evw1Nn+7yEXwcm3hRpObcgtKOLFL3yXY0s27iwtH9gjlUlj0zn+oK60UJdjIg1O2NeyDU2tjQ7knCvG32EXkSi27MznsVnLeWTWMrbszC8t/8EBnZk4ph9D+7TTY9oiUiHlWQlVYT7Mfw5m3gn/z959h8dR3fsffx8VS66Se8NNBtNMNeAumxCSAKGGBBKaAWNDCknIDcn9pXHvTW46NyEFbDok1BBaIAQIRe5gU22MAcu9N8my1aXz++OMiuWVtPJKO2dHn9fzzCP77Ozqszuj/c6cmTmz/YOG9oHHweQb4dgLIT28S5uttSwq3MXcgkJeW7Wjvr1fjyyunjySy8ePIKebLr0WkdhUY6Xd7VgFf5/l/j1sApz1q3DzJKi4tIq/LFnHvQvWsnNfw5Bj04/sz6z8PCbm9dW+rIikjHbrDBeR2NbvKuWu+YU8tnQD5VXuMvIu6WlccNIQZuXncfiAcM+gExERaVZ5MSy7DxbfASWbG9rzpsPkb0Le6aHeFLO6ppZ/Lt/K3IJC3t/U0I+V1787s6bmccFJQ1sdckxERKRdlRW5G2ZWlkDPwW6c8IwurT7NR5uKyrh73hoeeXM9pZU1AGSkGc470e3LHjWoV8gJRUTaTp3hIh3kvY1FzCko5J/vbyG4Spue2RlcPmEEV08ayYBe0buDuIiIRMTezbD4dtcRXrHXtZl0GHuRuynm4BNCjVdaWc3jSzdy1/xCNuwuq28/dWRvZuWP5oyjBpCmIcdERCTZamvg79fB7tXuZtKX/BV6Dgw7VZt9sHkvcwtW8+x7W6gJdmZ7ZGXwlfHDuXrySAbndA05oYjIoVNnuEg7stby2qodzClYzeLC3fXtQ3KyuWbKKC49bTg9svRnJyIintr2gRsK5f3HoTYYNjezuxsLfOJXITfcG13t3FfBAwvX8sDidRSVunzGwGeOGcis/NGMG9E71HwiItLJvfq/8PGL7t+f/z84bFy4edrAWsv8T3Yyt6CQeR/vrG8f2CuLqyeP4ivjh9MrW0OOiUjqU6+cSDuorK7lmXc3c2dBIau2ldS3HzWoJ7On5fH544eQqRuJiIiIj6yFtfPdTTHrduABug+A8bPhlGugW5/w8gFrd+7nznmF/G3ZRiqqgyHHMtK4eNxhzJwyirz+PULNJyIiwgdPw7zfuH+fNhtOujzcPHGqqqnl+fe3MOf1Qj7Ysre+fczAHlw3NY/zTxxKlwzty4pIdKgzXMJTtsdtMOzbDj0GwDHnQ1e/zugqLq3i+eVb2FFSQf+eWZw9dvABN+DaW17Fw0vWc++CtWzdW17fPvnwvszOH83UI/rpRiIiIhKO1upsTTWsfMZ1gm9+u6G97+FuKJTjL4XMcIf0env9Hua8Xsi/PtiKDYYcy+mayZUTR3DlxJH075kVaj4REemkmtbY/kfBkze4x0ZMgc/+LNx8tL4vu7+imkfe3MA989ewqahhyLHxo/owe1oe08doyDERiSZj6/YsOhljTFFOTk5OUVFR2FE6H2vh1Z+5y7BNGlSVQWZXsLVu5/v0H4R6My4X0XLrSx8xt6CQtDRDeWUN2V3Sqa21zMrP4yunDee+hWt5aMl6SiqqAUhPM5xz3GBm5ecxdmhOqPlFpP3l5uZSXFxcbK3NDTuL71RjQ9ZanZ38bXj3IVj0R9iztuF5wybA5BthzFmQFt4ZYLW1llc+3M7cgkLeWNsw5Nhhvbsyc8oovnTqMLp10fkcIlGjOhs/1dkQxayx2VBVDljodRjMfh269wsxYsv7spdPGM4Di9bxl8XrKS5zQ46lGThrrNuXPWFYbmjZRaRjqMYeSJ3h2oBIvld+Cov+BFWlBz+W2Q0mfg0+9cPk52rkty+u4q55ayirqjnosXQDFupvitk1M51LTh3GtVNGMaxPt+QGFZGk0QZE/FRjQ9ZSnU3LcFN13dVMBo46BybdCMPHJzVmUxXVNTz99mbmzivkk+376tvHDu3FrPzRnD12EBkackwkslRn46c6G6KWaizASVfA+X9MbqYmWtyXTTNYa+v3ZbMz0/jiuGHMnDqKEX27JzmpiCSLauyBdFqNJFfZHncUvbo89uNVpe7xiV+HrrlJjVanuLSKuQWF9WOSNlUTbDj06Z7JNZNHcfmEEeR265LEhCIiIs1orc7WVrsprQuc9BVXb/sdkdyMTRSXVfHXJeu4b8FatpdU1Lfnj+nP9fl5TBzdV0OOiYhI+FqrseBuQP2Zn/q7Lxv0gud0zeDqyaO4cuJI+nTXvqyIdC7qDJfk+uBpdzlZS0wafPAUjJuRjEQHeX75llbHRstMN3zr02O4cuLI5IQSERGJRzx1Ni0TPv1jN2RKiDYXlXHP/DU8/MZ69le6s9cy0gznnTCE6/LzOHpwr1DziYiIHCBC+7I3nXkkV00amZxQIiKeUWe4JNe+7W5ctZZUlbn5QrK5qIyyyoMvKWususZSVFqVpEQiIiJxKtnW/KXbdWqrW6/FHWjllr3MLSjk2Xc3Ux2coda9SzpfGT+cqyePYkhu19CyiYiINCsF9mW3xLkvWzdWuIhIZ6TOcEme7Svhk5dxI263ILOruyN3ku3aV8H9i9Zx9/w1rc6b3SWd/j2zkpBKREQkDtUV8N5jsPTu1ucNoc5aa1m4ehd3vL6aeR/vrG8f0DOLqyeP4ivjh5PTNTOpmUREROK282NY/Qq+7ssWlVby4KJ13LNA+7IiIq1RZ7h0LGth3QJYcBt8/K84n1MLx1zQobEaW7tzP3fNL+TxpRubHVutqdpay9nHDe7gZCIiIq0oK4Jl98LiO2Df1viek8Q6W11Ty3Pvb2FuQSErNu+tbz98QA9m5edx/olDyMpIT0oWERGRNlu/2O3LrnqeVjvCIen7sht2l3L3/DU8tnQDpa2cEV5H+7Ii0tmpM1w6Rm0NrHzGbThsfquhve/h0HsUrJsf+xKzzG4w8WtJueHI2+v3MLegkBdWbMUG2zW9sjO4YuIIyiprePiNDTHvwN01M52ZU0fpDDYREQlP8UZYfDssux8qS1xbWgaMvRgysuH9x2IPl5KkOru/oprHlm7grnlr2FTUUO9PG9WH2fl5nH7kgFbHNBUREQlFba3r/F54G2xY0tDeeyT0GQ3rF4a+L7t8UzFzCgp5/v0t9TfF7JmVwVcmDKeyqpZH3tS+rIhIc9QZLu2rshTe+Sss+hPsaXSJ1rDxMOlGOPJsMAZe/Zm7E7dJcxsSmV3dUfSJX4PTf9Bh8WprLa+u2s6cgkLeWLO7vn1obleunTKKS04dRvesDKy1dM/KYG5BIWlphvLKGrK7pFNba5k5dRQ3nTmmwzKKiIg0a+tyVz+X/82N/Q3QpYe7UdeEGyDnMHdVVo/+odTZHSUV3L9wLQ8uXlc/Hqkx8LljBzErP4+ThvfusN8tIiKSkKpyePdhWPRH2PVJQ/uQk2HyjXD0ea6uhrQva62l4OOdzC1YzYJPdtW3D+qVzbVTRnHpacPomZ2JtZYe2dqXFRFpjrE2jkt9IsgYU5STk5NTVFQUdpRo2L8T3rgT3rwTSusKs3Gd35NvhOETDn5O2R53R+592924asdc0GFH0Suqa3j67c3MnVfIJ9v31bcfM7gXs6flcfZxg8lMP/jO4MWlVTy/fAs7Siro3zOLs48brKPoIp1Ubm4uxcXFxdba3LCz+E41tp1ZC2sKYMHvYfW/G9p7DHQd4OOujl0/k1hnC3fs4855a3jirY1UBkOOZWWk8cVTDmPmlDxG9uveIb9XRKJDdTZ+qrPtrHS3u+fGkrmwv9HNL4/4rNuXHTHZHdltLIk1tqqmln+8t5k5rxfy4daS+vYjB/ZkVn4e554whC4Z2pcVkeapxh5IneHagEjM7kJ3Fvjbf4Xq4FKx9Cw44VKY9A3od0So8YrLqnhoyXruXbCG7SUV9e1Tj+jH7PzRTD68L6bpho2ISAzagIifamw7qamGD55yl2lvebehvd+RrsYe/yXICPcGWMvW7WbO64W8tHJb/ZBjvbtlcsXEkVw1cQR9e+gGXSISH9XZ+KnOtpM962Dxn+GtB6Fqv2tLy3T1ddI3YMDRocbbV1HNI2+s5575a9hcXF7fPml0X2bl5zFtTH/ty4pIXFRjD6RhUuTQbFwGC38PK591l4QBZOfAqTPhtNnQc2Co8TYXlXHP/DU88uYG9lW4y8jT0wznHj+Y6/LzOHZITqj5REREmlW53+2YL/4TFK1vaB8+CSZ/E474DKQdfAZYstTWWl5euY25BYUsXbenIV6fbsycOoovjhtG1y66KaaIiHhqy7vu3lYrngQbjKud1QtOuRrGXw+9hoQab/vecu5ZsJa/LllHSbnbl00zcPZxg5mdP5rjDtO+rIhIItQZLvGrrYVPXnIbDuvmN7TnDHPjo510BWT1CC8fsHLLXu4sKOSZdzdTHdxIpFuXdL582nCumTKKobldQ80nIiLSrH3b4Y25btix8qKg0cDR57pO8MNOCTMd5VU1PPn2Ju6cV0jhjv317ccflsOs/Dw+d+wgMmIMOSYiIhI6a2H1K27IsTWvN7T3HBIMOTYDsnuFFg/gk+0lzC0o5Km3N1NZ404465qZziWnDuPaKaMY1qdbqPlERKJCneHSuuoKeP9xd5OQHR82tA86DiZ9E469ANLDG3vMWsui1bu4o6CQgo921Lf375nFjEkjuXz8CHK6aWw0ERHx1M5PYNEf4J2HoSYY0isjG078Ckz8OvQdHWq84tIq/rJkHfcuWMvOfQ1Djp1+ZH9m5Y9mQl4fXaYtIiJ+qqmC5X93+7Lb3m9oH3AMTLoRxn4BMrqEFs9ay5tr9zDn9dX8+8OG8cr7du/CVZNGcsWEEfTuHl4+EZEoUme4NK+8GJbeC0vugJItDe2jP+U2HPKmH3wjkSSqrqnl+eVbmVuwmuWb9jbE69+dWfl5XHDSULIydJm2iIh4asMb7gy1D58DggG3u/aB066DU6+DHv1DjbdxTyl3z1/Do29uoLTSXUaemW4474ShzMrP48hBPUPNJyIi0qyKElh2Pyy+HfZubGgfOdVdbXX4p0Pdl62ptby4YitzCgp5Z0NRQ7y+3bguP48vnHwY2ZnalxUR6QjqDJeDFW9yNxJZdj9UBnerNunuqPmkb8Dg40ONV1pZzWNvbuCu+WvYuKesvv3Ukb2ZlT+aM44aQFqazlATEREP1dbCR/90Q45tWNzQnjvCnQV+0mXQpXt4+YAVm4uZW1DIP97bQk0w5FjPrAy+Mn44V08exaCc7FDziYiINKtkqzuZ6817oKLYtZk0OOYCty879ORQ45VX1fC3ZRu5a14ha3eV1refNDyX2fl5nHnMINK1Lysi0qHUGS4Ntq1wl4+9/zjUuht1kNndjZ824QbIHRZqvJ37Knhg4VoeWLyOotIqwB3M/+wxg5g1LY+Th/cONZ+IiEizqsrhvUddnd31cUP7kJPc1VZHnwfp4W2WWWuZ9/FO5hYUMv+TnfXtA3tlcc3kUXx5/HB6ZWvIMRER8dSOVbDwNnjvMaipdG0ZXeHkK2DCV6HPqFDj7dlfyQOL1vHAorXs2l9Z3/7powcwe9poThnRW0OOiYgkiTrDOztrYe08d5n2Jy83tPcYCONnwynXQNdwO5nX7NzPnfMKeWLZRiqq3Y1EumSkcfG4w5g5ZRR5/cO9aaeIiEizyvbAm3fDkjmwv2EsUA4/012mPXJKqJdpV9XU8tx7W5hTUMjKLQ1Djo0Z2INZ+aM574QhdMnQTTFFRMRD1sL6Re5qq4/+2dDerZ/blz11JnTrE14+YMPuUu6aV8ijSzdQXhXsy6anceFJQ7kufxSHD9CQYyIiyZYSneHGmDOAK4CJwDBgN/AG8BNr7fstPbdTK9sDHzwN+7ZDjwFwzPkNHds11bDyabfhsOWdhuf0G+MuHzv+EsjI6tB4xaVVPL98CztKKujfM4uzxw4+4EaXb613NxJ58YNt2GAo1dxumVw5YQRXThpJvx4dm09EpDNQjU1AS3W2aL0bp3TZ/VC137WlZcJxX3R1duAxHR6vpTq7r6KaR95Yz70L1rKpqGHIsQl5fZidP5rpR/bXGWoiIu1AdfYQtVRja2vc/TYW/B42LW14Tp88N+TYiV+BzK4dGq+1fdn3NhYxp6CQf76/hWDEMXpmZ3D5hBFcPWkkA3ppyDERkbAYW9fL6DFjzONAX+AxYCUwELgZGAtMt9YubuHpzb1mUU5OTk5RUVF7RvWDtfDqz9yl2CYNqsrcxoCthfHXQ8/BbkzwonUNzxk+0V2mPeZzkNaxZ4BZa7n1pY+YW1BIWpqhvLKG7C7p1NZarps6iuMPy+XOeYW8uXZP/XMO692VmVNG8aVTh9GtS0ocwxGRiMnNzaW4uLjYWpsbdpb2pBp7CFqqs8d/CSpLYcWTYN1NJ+nSE06ZAeNvgJyhSYjXfJ29bPxwsjLS+OuS9ewtd0OipRk467jBzM7P4/jDcjs8n4hILKqzbXrN6NbZ1vZlc4bB4j/B7sKG5ww9xV1tddQ5kNaxN51sbV/25BG9ubNgDYsKd9U/Z0hONtdMGcWlpw2nR5b2ZUUk+aJaYw9VqnSGD7DWbm/SlgusAV6x1n7hEF4zuhsQr/wUFv0JqkpbmdHA0Z+HSd+EYacmJRrAb19cxV3z1lBWVRMrEY3XyOOG5jArP4+zxg4iI12XaYtIeKK6AaEaewjirbM9B7t7boybAdk5SYkGLdfZxrIz0/jSKcOYOSWP4X27JSmdiEhsqrNtes3o1tm492WBMWfB5BvdiV1JupqpLfuyRw3qyfXTRnPO8YPJ1L6siIQoqjX2UKXEYcmmGw9BW5Ex5mPgsBAi+atsjzuKXl3e8nwnfAWmfgf6HZ6cXIHi0irmFhTWj/3dVN3Gw+TRffnapw5nYl5fXaYtItKBVGPbKK46a+DsX8PJV0FGl6RFg9brbJ0bpo3muvw8+nRPbj4Rkc5GdbYN4t2XPe5LkP8f0P/I5OQKxLsvO2FUH756+uFMPaKf9mVFRDyUEp3hsRhj+uMuLXu4mceLWnmJ5J2ilUwfPO0uJ2tJZlcYPj7pHeEAzy/fQlpayxsE2RlpfP6EIUwa3S9JqUREpDHV2BbEW2fTM5PeEQ5BnW1lx7trZjrD+3ZTR7iISEhUZ5sRb40dOTnpHeEQ375sVkYa5580lPwx/ZOUSkRE2iolr9Ux7vDqXFz+34Qcxx+1tbB2fuuXlFWVuxuRhGDF5r2UVbZ82XZFdS07SiqSlEhERBpTjW1ByTZ499E46mxZKHW2vKqGl1dua3V4lPKqGtVZEZGQqM42w1pYt9DrfdmVW1rfl63UvqyIiPdS9czwXwMXAFdba1fGmqG1cXCCo+3ROKJeXQHvPeouKdv5UevzZ3Z1d+ROEmstCz7ZxZyC1cz7eGer82d3Sad/z6wkJBMRkRhUY5va8REs+gO8+wjUVLY+f5Lr7J79lTy4eB33L1zLrv2t51OdFREJlepsY9WVsPwJWHgbbP+g9flD2JddXLibuQWreXXVjlbnV40VEfFfynWGG2N+BnwH+Ka19r6Q44SrrAiW3gNL7oB92xraTZq723ZzbC0cc0FHp6O6ppbn3t/CnNcL+WDL3oZ4HHhjkaZqay1nHze4w/OJiMiBVGObWL8YFvweVj3f0Na1N1SUQG11889LUp3dsLuUu+ev4dE3N9SfDZ6RBrXWTc1RnRURCYfqbCPle2HZfbD4dijZ3NDuyb5sTa3lheVbmVuwmnc3FjfEQ/uyIiKpLqU6w40x/w38P+Bma+1tYecJTdEGt9Hw1v1Quc+1pWXA2Ith0jfgg6eavwN3ZjeY+DXomtth8fZXVPPomxu4e/4aNhWV1bePH9WH2dPyeGvdHu6evzbmZdxdM9OZOXUUOV0zOyyfiIgcTDU2UFvjOr8X3AYb32ho7z0KJn3d3YB6/q2h1tn3NxYzp2A1z7+/pb7Tu2d2BpeNH8HVk0fyl8XruGveGtVZERGPqM4G9m6BJbfD0nuhIjhhyqTDsRe6fdkP/xFqjS2rrOHxZRu4a94a1u9uyDBuRG9m5efx/sYi7cuKiKS4lOkMN8b8BPgR8CNr7a/DzhOKre+7oVCWP9FwRlqXnjDuKphwA+QENyMfeKz7ufAP7sh6VZm7nMzWuo2H03/QIfG2l5Rz/8K1/GXxeorLqgBIM/C5sYOYlT+aE4flAnD6kQMwxjC3oJC0NEN5ZQ3ZXdKprbXMnDqKm84c0yH5REQkNtVYXK1892FY+EfYvbqhfeg4mHQjHH0upKW7tro6msQ6a63l9Y92MLegkIWrd9W3D87J5topo7jk1GH0zHY733V1VHVWRMQPqrPA9pWubr73GNS6fUUyu8HJV8KEr0LvEa5t8AnuZ5L3ZXftq+CBRet4YNFa9pS6fMbAmUcPZPa0PMaN6APAZ44ZqH1ZEZEUZ6xt6SIfPxhjvoO7ucg/gJ81ebjCWvv2IbxmUU5OTk5RUVE7JOxA1kLha24MtdWvNLT3GOQ6wMfNaP7IeNked0fufdvduGrHXNAhR9FX79jHXfMKeeKtTVRWu0vasjPT+OK4YcycOooRfbvHfF5xaRXPL9/CjpIK+vfM4uzjBusouoh4Kzc3l+Li4uLWxvFMNZ26xgKU7oY374Y35sD+RmOBjvmc6wQfMcntDceShDpbVVPLs+9uZm5BIR9uLalvP2pQT2bl53HuCUPITI99P3TVWRFJJaqzbXrN1Kiz1sK6Be5qq4//1dDevT+Mnw2nXAvd+sR+bpL2Zdfu3M9d8wt5fOlGKoJ92S4ZaXzh5KHMnJrH6P49Yj5PNVZEUklUa+yhSpXO8NeAac08vM5aO/IQXtPvDYiaajfcyYLfw9b3Gtr7H+UuHzvui5AR7o05lq7dzZyCQl5euY261ah3t0yunDiSKyeOoG8P3ThERKIjqhsQnbLGAuxZ5y7DfvvBhkux0zLh+EtcnR1wVKjxSsqreOSNDdyzYA1bisvr2yeN7svsaaPJP6IfprlOehGRFKQ626bX9LvO1tbAymdcJ/jmtxra+x4OE78OJ3wZMrPDywe8s6GIuQWreWH51vohx3K6ZnLFhBFcNWmkboIpIpES1Rp7qFJimBRr7fSwMyRNxT63Y77oz1C8vqF9xGSY/E04/ExIi30GWDLU1lpeWrmNuQWFLFu3p759eJ9uXDd1FBePG0bXLumh5RMRkbbpVDUWYPM77mqrFU+BDcb7zOoFp1wD46+HXuHe9Grb3nLuXbCWvy5ZR0m5GxItzcA5xw9hdn4eY4fmhJpPRETaplPV2cpSeOev7mDznjUN7cPGu6utjjw79H3ZV1dtZ05BIW+s2V3fPjS3a/2QY92zUqKLREREEqBvel/s2w5L5sCbd0F5kWszaW6M0knfhMPGhRqvvKqGv7+1ibvmFVK4c399+wmH5TArfzSfGzuI9DSdoSYiIh6yFlb/211ttaagob3XUDfk2MlXQXav8PIBH28rYW5BIU+9s4mqGneKWtfMdC45dRjXThnFsD7dQs0nIiLSrP074Y074c07obTuvhbGdX5PvhGGTwg1XkV1DU+/s5k7Cwr5ePu++vZjh/RiVn4e5xw3mIxmhhwTEZHoUWd42HZ+7G4O8u4jUFPh2jKy4cTL3A1C+o4ONV5RaSV/WbyO+xauY+e+ivr2Tx01gFn5eYwf1UeXaYuIiJ9qqtxNpxf+AbYtb2gfcKzbOT/2IsjoElo8ay1vrNnN3IJC/v3h9vr2fj26cNXEkVw+YQS9u4eXT0REpEW7C4Mhx/4K1WWuLT0LTrjUDTnW74hQ4xWXVfHQkvXcu2AN20sa9mWnHtGP2fmjmXx4X+3Lioh0QuoMD8v6Je4y7Q+fA4JByrr2gdNmwWnXQfd+ocbbsLuUu+ev4bGlGyitdJeRZ6Ybzj9xKLPy8xgzsGeo+URERJpVUQLL7ofFf4a9mxraR+W7q60OP6P5m2ImQU2t5V8rtjKnoJB3NxQ1xOvXneum5nHRyUPJztSQYyIi4qlNy9zVViufBetuOkl2Dpw6E06bDT0HhhpvS3EZ98xfw8NvbGBfhRtyLD3NcN4JQ7huah7HDAn3ajAREQmXOsOTqbYWVj3vOsE3LGlo7z3S3UjkxMugS7iXQS/fVMzcgkKee38LNcGdRHpmZfCVCcO5etIoBuWEe6MTERGRZu3dAkvugKX3QkWxazNpcOyF7gy1ISeFGq+8qobHl23krnmFrNtVWt9+8vBcZuWP5sxjBmrIMRER8VNtLXzykrsp5rr5De05w9wVzSddAVk9wssHfLh1L3NfL+SZdzdTHezLdu+SzqWnDeeaKaMYmts11HwiIuIHdYYnQ1U5vPcILPwj7Pq4oX3Iye4y7aPPg7TwzgCz1jLv453MKVjNgk921bcP6pXNNVNG8uXThtMzOzO0fCIiIi3ascodaH73Uaitcm2Z3dyO+cSvuoPOIdq9v5IHFq3lgUXr2L2/sr7900cP5PppeZwysk+I6URERFpQXQnvP+6GHNuxsqF90HHuaqtjL4D08PYVrbUsWr2LOQWFvP7Rjvr2/j2zuHrySC47bQQ53bQvKyIiDdQZnoiyPfDB0+7mlz0GwDHnQ9feDY+X7oald8OSubC/YSxQjviMu5v2yCkdepl2cWkVzy/fwo6SCvr3zOLssYMP2BCoqqnlH+9tZm7BGlZu2VvffuTAnszKz+PcE4bQJUM3EhERkRC0VmOthfWL3GXaH73Q0N6tH4y/Hk69Frp1bCdza3V2/a5S7ppfyGNLN1Be5S4j75KexkUnD2Xm1DwOHxDuGXQiItKJtVZny4vdlVZL7oCSLQ3toz/l9mXzpoe6L1tdU8s/l29lTsFqlm9q2Jcd3b87s/LzuOCkoWRlaMgxERE5mLHWhp0hFMaYopycnJyioqK2P9laePVn7ui4SYOqMsjs6sZLm/QNdyba4tvhrQegar97TlomHP8l9/iAo9v1vRwcz3LrSx8xt6CQtDRDeWUN2V3Sqa21zMrPY1Z+Ho++uYF75q9hc3F5/fMm5vVl1rQ8po/prxuJiIg0kZubS3FxcbG1NjfsLL7r0Bo77fuw6jl3mfampQ3P6zMaJn0dTviym78DtVZnzzhqAHfOW8M/l28huEqbXtkZXD5hBDMmjWRALw05JiLSlOps/Dq0zp48A964A5beB5UlwS9Mh7FfcI8PPr4d30mseC3X2Oun5fH40o3cNX8NG/eU1T/v1JG9mZ0/mk8dNYA0DTkmInIA1dgDqTP8UDYgXvmpu2t2VenBj5n04CYiweea1QvGzYAJN0CvIQkkjt9vX1zFXfPWUFZVc9BjGWmGNAOVNS5fmoGzjxvMrPw8jj8sNyn5RERSkTYg4tdhNTY9EzK7Q3mj1z3sVJj8TTjy7KQNOdZSnU0z1HeAAwzN7co1U0ZxyanD6JGlC/JERJqjOhu/pO3LZnZv2JfNHZZA4vi1ti+bnmaoqHZXWxkDnz1mELOm5XHy8N4HzS8iIo5q7IG0V9ZWZXvcUfTq8tiP26Bo9xjkbiQy7ip3Z+0kKS6tYm5BYf0GQlN1NxLJyjBceupwrp2Sx/C+4d60U0REBGi9xtZUQU2R+/eRZ7vLtIdP6NDLtJtqrc7WdYSPGdiTr04fzTnHDyYzXUOOiYiIB+Ldl+3Wz91z45RrDhw6pYPFsy9bXWvJTDd86ZRhzJyax6h+3ZOWT0REokGd4W31wdPucrKWpHeB/JvhtGuTk6mR55dvafWysMx0w82fPYprp+YlKZWIiEgc4qmxaRmuxk7/XnIyNRFPnc3KSGPGpBFccNLQJKUSERGJQ7z7stO/D6ddl5xMjcRTYzPS3L7sdfnalxURkUOjU5Xaat92N65aS2qqoGxXcvI0sW1vOWWVB19S1lh1jWV/K/OIiIgkXTw1trYmqWeCN1W4Y1+rdbayupad+yqTlEhERCROce/L7klOnia2x7EvW1NrYw6hIiIiEi+dGd5WPQa4G4zEGmOtTmZXN18SlVfV8LdlG3lw0bpW583ukk7/nllJSCUiItIGntZYgFVbS5hbUMhTb29sdV7VWRER8ZKndbaiuoan3t7EXxZrX1ZERDqeOsPb6pjz4Z+tXJpta+GYC5ISZ8/+Sh5cvI77F65l1/74zkKrrbWcfdzgDk4mIiLSRp7VWGstiwp3MbegkNdW7Yj7eaqzIiLiJc/qbHFpFX9Zso77Fq5lR0lFXM9RjRURkUSpM7ytuvaGSd9o/g7cmd3cjTO75nZojA27S7lrXiGPLd1Yf5lYl/Q0LjhpCJnpafz9rU0xLx/rmpnOzKmjyOma2aH5RERE2syTGltdU8sLK7Yyt6CQ9zYW17fn9e/OrKl5rNu1n/sWrlOdFRGR1OJJnd1UVMY989fwyBvr64fvzEgznHfiELIy0njq7c2qsSIi0mHUGX4oTv+B+7nwD+4GJFVl7nIyW+s2Huoe7wDvbSxiTkEh/3x/C7XWtfXMzuDyCSO4etJIBvTKxlpLn+5dmFtQSFqaobyyhuwu6dTWWmZOHcVNZ47psHwiIiIJCbHGllZW8/jSjdw1v5ANuxvGVD1lRG9m5efx6aMHkpZmsNaSkZ6mOisiIqknxDr7wea9zC1YzbPvbaEm2JntkZXBl08bxtWTRzEktyvWWvr1yFKNFRGRDmOstWFnCIUxpignJyenqKjo0F+kbI+7I/e+7W5ctWMu6JCj6NZaXvtoB3NfL2RRYcONOYfkZHPNlFFcetpwemQdfFyjuLSK55dvYUdJBf17ZnH2cYN1FF1E5BDl5uZSXFxcbK3NDTuL71KpxgLs2lfB/YvW8eCitewprQLcPTo/c8xAZuWPZtyI3jGfpzorItJ+VGfjl0p11lrLgk92MadgNfM+3lnfPrBXFldPHsVXxg+nV/bBtVM1VkSk/ajGHkid4YlsQHSwyupannl3M3cWFLJqW0l9+1GDejJ7Wh6fP94NiSIiIh1PGxDxS4UaC7B2537unFfI35ZtpKK6FoAuGWl84eTDuG7qKPL69wg5oYhI56E6G79UqLPVNbU89/4W5rxeyAdb9ta3HzGgB9fl53H+iUPIykgPMaGISOehGnsgDZPioZLyKh5+Yz33zF/L1r3l9e2TD+/L7PzRTD2iH8aYEBOKiIikrrfX72FuQSEvrNhK3TkBOV0zuXLiCK6cOJL+PbPCDSgiIpKi9ldU8+ibG7h7/ho2FTUMOTZ+VB9mT8tj+pgBpKVpX1ZERMKjznCPbC0u594Fa3hoyXpKKqoBSE8znHPcYGbl5zF2aE7ICUVERFJTba3l1VXbmfN6IW+s3V3fPjS3KzOnjuJLpwyje4whx0RERKR120vKuX/hWv6yeD3FZW7IsTQDnxs7iFn5ozlxWG64AUVERALa6/PAR9tKmFtQyNPvbKKqxp2i1jUznUtOHca1U0YxrE+3kBOKiIikporqGp5+ezNz5xXyyfZ99e3HDunF7GmjOXvsIDI05JiIiMghWb1jH3fNK+SJtzZRGQw5lpWRxhdPOYyZU/IY2a97yAlFREQOpM7wkFhrWVy4m7kFq3l11Y769n49ujBj0kgunzCC3G5dQkwoIiKSuorLqnhoyXruXbCG7SUV9e35Y/ozOz+PSaP7asgxERGRQ7R07W7mFBTy8spt9UOO9e6WyZUTR3LlxBH07aEhx0RExE/qDE+ymlrLC8u3MrdgNe9uLK5vz+vXnZlT87jo5KFkZ+pGIiIiIodic1EZ98xfw8NvrGd/ZQ0AGWmGc08YwnVT8zhmSK+QE4qIiKSm2lrLSyu3MbegkGXr9tS3D+/TjZlTR/HFccPo2kX7siIi4jd1hidJWWUNjy/bwF3z1rB+d2l9+8nDc5k9bTRnHj1QNxIRERE5RCu37GVuQSHPvruZ6lp3ilr3Lul8+bThXDNlFENyu4acUEREJDWVV9Xw97c2cde8Qgp37q9vP+GwHGblj+ZzYweRrn1ZERFJEeoM72C79lXwwKJ1PLBoLXtKq+rbzzxmILPz8zhlZJ8Q04mIiKQuay0LV+9iTkEhBR81DDnWv2cWV08eyWXjR5DTNTPEhCIiIqmrqLSSvyxex30L17FzX8OQY6cf2Z9Z+aOZkNdHQ46JiEjKSZnOcGNMD+B/gS8CucAK4L+ttc+Elam4tIrnl29hR0kF/XtmcfbYweR0czvda3fu5675hTy+dCMVwY1EuqSncdHJQ5k5NY/DB/QIK7aIiMgBUq3GVtfU8nww5NjyTXvrnzO6f3dm54/m/JOGkJWhy7RFRMQPqVZnN+wu5e75a3hs6QZKgyHHMtMN5584lFn5eYwZ2DOs2CIiIglLmc5w4EngZOBmYA0wA3jSGHOutfb5ZAax1nLrSx8xt6CQtDRDeWUN2V3SueWZFZx34hD2lVfxrxXbCK7Spld2BldMHMFVk0YyoGd2MqOKiIjEIyVq7NWTRzKgZxb3LFjLxj1l9c85bWQfZuXn8amjBmjIMRER8VFK1NkLThpKaUU1zy/fSk2wM9szK4OvjB/O1ZNHMShH+7IiIpL6jK279bPHjDFnA88BF1lrnwzaDDAP6GutPfoQXrMoJycnp6ioqM15fvviKu6at4ayqpoW5xua25VrpoziklOH0SMrlY47iIhIU7m5uRQXFxdba3PDztKeUrXGGgOfPWYQs6blcfLw3m3+PSIi4hfV2Ta9ZofX2YG9srhm8ii+PH44vbI15JiISCqLao09VKnSQ3shUAw8XddgrbXGmPuBucaYY6y1HyQjSHFpFXMLCuuHPonFAP970VguHjeMzPS0ZMQSERE5VClVYwG+OO4wvnr64Yzq1z0ZsURERBKRUnXWAP99/lguOXUYXTK0LysiItGTKp3hY4EPrLVNq/Z7jR9v/IAxpqiV18w5lCDPL9/S6iXY2ZnpgFFHuIiIpIKUqrFdM9M5eURvdYSLiEiqSKk6m52ZTka6UUe4iIhEVqpUuL7A7hjtuxs9nhQ7Sioor2z5krLyqhp2lFS0OI+IiIgnVGNFREQ6juqsiIiIR1LlzHCAlgY3P+ix1sbBCY62t/mIev+eWWR3SaeshY2I7C7p9O+Z1daXFhERCYtqrIiISMdRnRUREfFEqpwZvovYR8z7BD9jHWnvEGePHUxtbcs3Ha2ttZx93OAkJRIREUmIaqyIiEjHUZ0VERHxSKp0hq8AjjbGNM17XPBzebKC5HTLZFZ+Hl0z02M+3jUznVn5eeR01R23RUQkJajGioiIdBzVWREREY+kSmf4k0AucG6T9iuBVcm6+3adm84cw8ypo8jKSKNrl3QM0LVLOlkZacycOoqbzhyTzDgiIiKJUI0VERHpOKqzIiIiHjHWtnyZlA+MMQb4N3A8cDOwBrgKtwFxvrX22UN4zaKcnJycoqKiQ85VXFrF88u3sKOkgv49szj7uME6ii4iElG5ubkUFxcXtzaOZ6pRjRURER+ozrbpNVVnRUQkblGtsYcqJTrDAYwxvYD/BS7GHVn/APhva+1Th/h6CW9AiIhI5xHlDQjVWBERCZvqbJteT3VWRETiFuUaeygywg4QL2vtXuDrwSQiIiLtRDVWRESk46jOioiI+CNlzgxvb8aYWsDk5OSEHUVERFJAcXExgLXWpsr9NkKjGisiIm2lOhs/1VkREWkL1dgDdebO8GrcDUT3JvhSdVsgxQm+TkdRvsT5nlH5EuN7PvA/Y2fJ1wuotdamzFVVYelENRb8z6h8ifE9H/ifUfkS43s+UJ1Nuk5UZ5Uvcb5nVL7E+J4P/M/YWfKpxjbSaTvD24sxpgjA13F3lC9xvmdUvsT4ng/8z6h80lFSYdn5nlH5EuN7PvA/o/Ilxvd8kBoZJTbfl53yJc73jMqXGN/zgf8Zla9z0unxIiIiIiIiIiIiIhJ56gwXERERERERERERkchTZ7iIiIiIiIiIiIiIRJ46w0VEREREREREREQk8tQZLiIiIiIiIiIiIiKRp85wEREREREREREREYk8dYaLiIiIiIiIiIiISOQZa23YGUREREREREREREREOpTODBcRERERERERERGRyFNnuIiIiIiIiIiIiIhEnjrDRURERERERERERCTy1BkuIiIiIiIiIiIiIpGnznARERERERERERERiTx1houIiIiIiIiIiIhI5KkzXEREREREREREREQiLyPsAFFjjMkGBlhr14eYIctaW9HMY5nA4LDyGWPSrLW1TdpOAMYAn1hr3w4jV0uMMd2Bl4CvWmvfCTnOQYwxWcBYoBZYYa2tDDlSveDv4SvAybh8i4HHrLXVIWT5EvCCtXZvsn93vIK/z4lAhbV2SdCWDnwZOAbYCDxsrd0TXkrHGDMet971wy3bzcB8a+26UIMBxpghwHm4fH0BC+wGlgPPWms3hRhPEqAa2zrV2falGtumPKqz7UQ1VsKiOtsy1dj2pzobdxbV2HakOitYazW14wR8AagJ6XffCGwHaoCPgStjzDM+jHy4qxD+DJQB+4GfBO23BXlrg5+PAmkh5OvVwjQ8yHdWXVtIy3cW0K9J2zeA4uCzqwGKgNkh5fsj8JtG/x8MrAo+u6pgqgWWAbkh5KsN1r0HgOlhfEat5Kv7vOqW5StAN+DlIHvdtAk4LMScFwFrG+Wsm2qBauBpYGhI2Qzwv0BFo+W9MZj2B20VwC/CXt6aDnkZq8Y2n091NrF8qrGJZ1SdTTyjaqymUCfV2WazqcYmnlF1NrF8qrHtk1N1VpP7vMMOELUprA0I4Nzgj+MV4JfAm8Ef9V2AaTRfWBsQXwvyPRwUmhLgv4MNipuAKcCPgyLz9RDyNf0yjPXlWP//kNatGuC0Rv//YpDrdeA6YDYwL5jv7BDyrQUua/T/h3AbtGfjrkLJCNbTHcDtIeSrO5pfEXxGq4EfhFmMm+S7Pfi8rgEuBt4HnguKX36wMfFpYAswN6SMFzTauPkWcAPwV6Ac+CruzIklwbowIIR83wUqgZ8Ch8d4fDTwP8E6cHPYy1zTIS1j1djmM6rOJp5PNTaxjKqzieVTjdUU+qQ622w+1dj2yag6e+j5VGMTz6g6q6l+MsGHKq0wxvw4zlmPAb5orU3vyDxNGWMKgA3W2ssatX0L+DXwN+Bya21NcDnIwhDyvQW8bq39dvD/L+EKzC+stT9sNN+fgFOsteOTnK/u0ph7cUcEG+sK3Azcj/tixFr7X8nMB/UZJ1hr3wj+vxgotdZ+qtE8BngNqLTWnpnkfOXAmdbaecH/dwPftdbe3WS+G4AfW2sHJzlfLTABWANcBczA/b3W4I5Y3w08ba2tSmauRvkKgV9aa+cE/z8FeAOYZa29q9F8XwO+ba09PISMbwIfWGuvatL+DeA7wCigCy73fGvt15Kc7xPgLmvtL1qZ7/vAddba0clJJq1RjW2XjKqziedTjU0so+psYvlUY6XDqM4mnE81NkGqswnnU41NPKPqrDQIuzc+VSYajqbWxjGFcbR6J/C5GO0X4I50PYE7mhnW0fRi4DON/p8bfFZnNJnvPKA4hHzTcZfjLQPGNXksJ8ia78E62PhoehlwcYz5LgV2h5BvC3BRo/9XANNizHcGUBb25xe0TQDm4i7Jq8Ed6f8dcEII+Uobr2NA9yDz5CbzTQf2Jztfo4yfidHeJ8h6ZPD/a4H1IeQrj+fvFHd2QtLXQU0tLhPV2MQzqs4mvg6qxrbjZxi0qc62LZ9qrKaOWn6qs4nlU41tn3VQdbadPr+gTTW27RlVZzVhrSUNidcO4E6gdyvTjJDyZeAuyzqAtfYp3OVu5wB/B7KSG6teGgcepS4Jfu5pMl8xIWS01r4GHAe8Csw3xvzWGNMt2TnayOKKdlNbcMUn2V7GjQVX5w3gczHmOwt3WVforLWLrbWzcGOcXQ2swI1d91YIcbYARzX6f92/mx7xHQ1sS0qig5UCA2O0D8Ktj3V/458AA5IVqpF1wOlxzHc6ENqNoSQm1djEqc62L9XYdqA62yaqsdKRVGcToxrb/lRnE6Qa22aqs1IvI+wAKWQZcKy1trilmYwx+5OUp6lC4BTg300fsNY+Z4y5ELcBcXSygwW2AiMaZaoJLkdperfeocCuZAarY60tB/7DGPMobny6LwSXQS0MI08zvmWM2Rr8uxQYFmOewbijw8n2Y2CpMeZp3CWNPwKeMMb0Al4M5jkbN45YUi85ao21tgx3M5IHjDF5hLMj8ALwP8aYSmAvcAvwFPBjY8xb1trlwd3qfwAsCCEfwL+Anxpj3rXWvgdgjBmMu6HQBmtt3YZhH9wZPsn2J+BWY0wObnmusMGlgsHdzY8FrsBtJP5HCPmkeaqxiVOdTZxqbAdRnY2Laqx0JNXZxKjGtg/V2Q6gGhs31VlpEPap6akyAT8DSuKYLx94NYR8t+L+WFqa57O4ohPGpWWPAw/EMd9dwPMeLO8M4Ce4S1WewV125MOlZU2ngz5T3KVSr4eU8XjgPQ6+FLPu3/sJ6WYPxLi0zKcJd5R6VaPPbBHurIi69a88+LmHGDfUSFLGobiN/mrc0eiPcTf5KAM+22i+3+PGrAsj4w/rvueCnHtxZ+lUB21luHH+Ql/mmg5YbqqxiWdUnU0sj2ps+3yGqrOHnk81VlNHLjvV2cTyqcYmnkl1NvHPTzU2sYyqs5rqJ91AMyKMMUcAnwEettbubmG+KbixzZJ9Y6q+QLa1dlMr830HeMdae9BZAWEwxhyLK8jHAOdYa306sh5T8Bmustb+I8QM04GpwBDcZYW7cHeU/qe1tiikTFcB/7DWhnK2RjyCI76TcJeJLrbW1hpjMnDjlp2IuzHOfdbaDSFmzMXdbXsC7gYjH+DuCP5ho3kMgA2pwBhj+uAuYTwW6Bs078JdOvjPlr4jRWLxvcYGv1t1NglUY1vMpTqbeL5cVGOlE/K9zqrGJo/qbLOZVGPbJ2MuqrMC6gwXERERERERERERkejTmOGHyBjTD3eUxuLudhzGmELNUr7E+J4P/M+ofInxPR+kRkZJTb6vW77nA/8zKl9ifM8H/mdUPunMfF+/lC8xvucD/zMqX2J8zwepkVE6UNjjtKTShLtM5k+4O+XWNJm2Bo8dpnzK11kzKl+086VKxjjewzjgnrBzaDpouXi9bvmeLxUyKl+086VCRuVLyntQjfV08n39Ur5o50uFjMoX7XypkjGO96A62w6ThkmJkzHmaOB1IAt4FndjhbqxevoAxwHn4sZHyrfWrlQ+5etMGZUv2vlSJWM8jDFfAB6z1qaHnUUc39ct3/OlQkbli3a+VMiofMmhGusn39cv5Yt2vlTIqHzRzpcqGeOhOts+1BkeJ2PMc7g/kHNsMwPWBwPdPwfsstZ+XvmUrz35nlH5op0v+P3eZ4yHNiD84/u65Xu+4Pd7nVH5op0v+P1eZ1S+5FCN9ZPv65fyRTtf8Pu9zqh80c4X/H7vM8ZDdbadhH1qeqpMwF7g7DjmOwfYq3zK19kyKl+086VCRg6+1K3FKYzPUFOzy873dcvrfKmQUfminS8VMipfwvlUY1N4SoH1S/kinC8VMipftPOlQkbV2eROuoFm/GqBeI68pAfzJpvyJcb3fOB/RuVLjO/5wP+MNcA7wIJW5jscOLvD00hb+L5u+Z4P/M+ofInxPR/4n1H5EqMam9p8X7+ULzG+5wP/MypfYnzPB/5nVJ1NIg2TEidjzFPAcOAsa+22ZuYZAPwTWG+tvTCJ8ZQvQb7nC37/U3icUfkS43u+4Pc/hccZjTFvAx9ba7/Uyny6tMwzKbBuPYXH+YLf/xQeZ1S+xPieL/j9T+FxRuVLjGpsakuB9esplO+Q+Z4v+P1P4XFG5UuM7/mC3/8UHmdUnU0udYbHyRiTB8wHcoCXgOW4wfYt0BcYC5wJFANTrbWrlU/5OlNG5Yt2vlTIaIy5G5hmrT28lfm+ADxurU1LTjJpTQqsW17nS4WMyhftfKmQUfkSzqcam8JSYP1SvgjnS4WMyhftfKmQUXU2ycIanyUVJ6A/8CvgI9wlDLXBVAN8HDw2QPmUr7NmVL5o5/M9IzAZ+HEc8/XDbWiE9jlqirlcvF23UiFfKmRUvmjnS4WMypdQNtXYFJ98Xr+UL/r5UiGj8kU7n+8ZVWeTO+nM8ENkjMkGegf/3WOtLQ8zT1PKlxjf84H/GZUvMb7ng9TIKKnJ93XL93zgf0blS4zv+cD/jMonnZnv65fyJcb3fOB/RuVLjO/5IDUySsdRZ7iIiIiIiIiIiIiIRJ7GmEmQMSbdGFNjjDk57CyxKF9ifM8H/mdUvsT4ng/8z+h7Pmme78vO93zgf0blS4zv+cD/jMqXGN/zSct8X37Klxjf84H/GZUvMb7nA/8z+p4vVakzvH2YsAO0QvkS43s+8D+j8iXG93zgf0bf80nzfF92vucD/zMqX2J8zwf+Z1S+xPieT1rm+/JTvsT4ng/8z6h8ifE9H/if0fd8KUed4SIiIiIiIiIiIiISeeoMFxEREREREREREZHIU2d4gqy1NcDVwJqws8SifInxPR/4n1H5EuN7PvA/o+/5pHm+Lzvf84H/GZUvMb7nA/8zKl9ifM8nLfN9+SlfYnzPB/5nVL7E+J4P/M/oe75UZay1YWcQEREREREREREREelQGWEHSDXGmF7AWcBYoB9QC2wGCqy188LMBsqXKN/zgf8ZlS8xvucD/zP6nk+a5/uy8z0f+J9R+RLjez7wP6PyJcb3fNIy35ef8iXG93zgf0blS4zv+cD/jL7niwxrraY4J+DbQAlQg1sha5v8+23gOOVTvs6aUfminS8VMvqeT1PqLjvf86VCRuWLdr5UyKh80c6nKbWXn/JFO18qZFS+aOdLhYy+54vSpDHD42SMuQ74DfA48AXgHODnuBX1GmAysBZ4zRiTp3zK19kyKl+086VCRt/zSfN8X3a+50uFjMoX7XypkFH5op1PWub78lO+aOdLhYzKF+18qZDR93yRE3ZvfKpMwHLg1hjtXwG244acMcDrwAPKp3ydLaPyRTtfKmT0PZ+m1F12vudLhYzKF+18qZBR+aKdT1NqLz/li3a+VMiofNHOlwoZfc8XtSn0AKkyAeXAGTHac3CXKxwd/P8KYIvyKV9ny6h80c6XChl9z6cpdZed7/lSIaPyRTtfKmRUvmjn05Tay0/5op0vFTIqX7TzpUJG3/NFbdIwKfHbDRwRo30MYIH9wf/XAb2TFaoR5UuM7/nA/4zKlxjf84H/GX3PJ83zfdn5ng/8z6h8ifE9H/ifUfkS43s+aZnvy0/5EuN7PvA/o/Ilxvd84H9G3/NFijrD4/c08FNjzLnGmAzjnArcDay01q4P5hsMbFU+5euEGZUv2vlSIaPv+aR5vi873/OlQkbli3a+VMiofNHOJy3zffkpX7TzpUJG5Yt2vlTI6Hu+SMkIO0AK+T5wGvAUDXd0zQT2AJ9tNN8JwL+SHQ7lS5Tv+cD/jMqXGN/zgf8Zfc8nzfN92fmeD/zPqHyJ8T0f+J9R+RLjez5pme/LT/kS43s+8D+j8iXG93zgf0bf80WKCcackTgYYzKALwETgC7AB8BfrbW7Qg0WUL7E+J4P/M+ofInxPR/4n9H3fNI835ed7/nA/4zKlxjf84H/GZUvMb7nk5b5vvyULzG+5wP/MypfYnzPB/5n9D1flKgzXEREREREREREREQiT8OkHAJjTHfgaKAvbiD73cCH1tp9oQYLKF9ifM8H/mdUvsT4ng/8z+h7Pmme78vO93zgf0blS4zv+cD/jMqXGN/zSct8X37Klxjf84H/GZUvMb7nA/8z+p4vEqy1muKccGPzPAdU4sbvqRvHpyZoew44UfmUr7NmVL5o50uFjL7n05S6y873fKmQUfminS8VMipftPNpSu3lp3zRzpcKGZUv2vlSIaPv+aI0aZiUOBljJgL/BrYADwPv4Y7OAPQBjgcuxd3Z9dPW2kXKp3ydKaPyRTtfKmT0PZ80z/dl53u+VMiofNHOlwoZlS/a+aRlvi8/5Yt2vlTIqHzRzpcKGX3PFzXqDI+TMaYAKAEutNZWNjNPJvAk0Mtam698yteefM+ofNHOF/x+rzP6nk+a5/uy8z1f8Pu9zqh80c4X/H6vMypftPNJy3xffsoX7XzB7/c6o/JFO1/w+73O6Hu+yAn71PRUmYB9uKMvrc13JrBP+ZSvs2VUvmjnS4WMvufTlLrLzvd8qZBR+aKdLxUyKl+082lK7eWnfNHOlwoZlS/a+VIho+/5ojalIfEqw12a0JreQHkHZ4lF+RLjez7wP6PyJcb3fOB/Rt/zSfN8X3a+5wP/MypfYnzPB/5nVL7E+J5PWub78lO+xPieD/zPqHyJ8T0f+J/R93zREnZvfKpMwP1AIXBCC/McD3wC3K98ytfZMipftPOlQkbf82lK3WXne75UyKh80c6XChmVL9r5NKX28lO+aOdLhYzKF+18qZDR93xRmzRmeJyMMQOAV4CjgfeB5bjB7C3QFxgLHAd8CJxurd2ufMrXmTIqX7TzpUJG3/NJ83xfdr7nS4WMyhftfKmQUfminU9a5vvyU75o50uFjMoX7XypkNH3fFGjzvA2MMZkAdcCF+BWxL7BQ7uAFbiB7O+21lYon/J1BN8zKl+084H/GX3PJ83zfdn5ng/8z6h80c4H/mdUvmjnk5b5vvyUL9r5wP+MyhftfOB/Rt/zRYk6w0VEREREREREREQk8nQDzXZgjBlujMkIO0dzlC8xvucD/zMqX2J8zwf+Z/Q9nzTP92Xnez7wP6PyJcb3fOB/RuVLjO/5pGW+Lz/lS4zv+cD/jMqXGN/zgf8Zfc+XitQZniBjTDqwBjeQvXeULzG+5wP/MypfYnzPB/5n9D2fNM/3Zed7PvA/o/Ilxvd84H9G5UuM7/mkZb4vP+VLjO/5wP+MypcY3/OB/xl9z5eq1BnePkzYAVqhfInxPR/4n1H5EuN7PvA/o+/5pHm+Lzvf84H/GZUvMb7nA/8zKl9ifM8nLfN9+SlfYnzPB/5nVL7E+J4P/M/oe76Uo85wEREREREREREREYk8dYaLiIiIiIiIiIiISOSpMzxB1toa4HRgVdhZYlG+xPieD/zPqHyJ8T0f+J/R93zSPN+Xne/5wP+MypcY3/OB/xmVLzG+55OW+b78lC8xvucD/zMqX2J8zwf+Z/Q9X6oy1tqwM4iIiIiIiIiIiIiIdCidGd7OjDHDjTFXhp2jOcqXGN/zgf8ZlS8xvucD/zP6nk+a5/uy8z0f+J9R+RLjez7wP6PyJcb3fNIy35ef8iXG93zgf0blS4zv+cD/jL7nSxXqDG9/pwL3hh2iBcqXGN/zgf8ZlS8xvucD/zP6nk+a5/uy8z0f+J9R+RLjez7wP6PyJcb3fNIy35ef8iXG93zgf0blS4zv+cD/jL7nSwnqDBcRERERERERERGRyMsIO0CqMMa8Eues/Ts0SDOULzG+5wP/MypfYnzPB/5n9D2fNM/3Zed7PvA/o/Ilxvd84H9G5UuM7/mkZb4vP+VLjO/5wP+MypcY3/OB/xl9zxc1uoFmnIwxtcBWYFsrs+YAI6y16R2fqoHyJcb3fOB/RuVLjO/5wP+MvueT5vm+7HzPB/5nVL7E+J4P/M+ofInxPZ+0zPflp3yJ8T0f+J9R+RLjez7wP6Pv+SLHWqspjgn4BLg7jvkuBmqUT/k6W0bli3a+VMjoez5NqbvsfM+XChmVL9r5UiGj8kU7n6bUXn7KF+18qZBR+aKdLxUy+p4vapPGDI/fMuDkOOYL61R75UuM7/nA/4zKlxjf84H/GX3PJ83zfdn5ng/8z6h8ifE9H/ifUfkS43s+aZnvy0/5EuN7PvA/o/Ilxvd84H9G3/NFijrD4/cPYEcc830A/HcHZ4lF+RLjez7wP6PyJcb3fOB/Rt/zSfN8X3a+5wP/MypfYnzPB/5nVL7E+J5PWub78lO+xPieD/zPqHyJ8T0f+J/R93yRojHDRURERERERERERCTydGa4iIiIiIiIiIiIiESeOsNFREREREREREREJPLUGd4GxpjTjDGPG2MWGWPmGmOOjDHPicaYQuVTvo7ge0bli3a+4Pd7ndH3fNI835ed7/mC3+91RuWLdr7g93udUfminU9a5vvyU75o5wt+v9cZlS/a+YLf73VG3/NFirVWUxwTMBYoA3YDS4D9QClwRZP5xgM1yqd8nS2j8kU7Xypk9D2fptRddr7nS4WMyhftfKmQUfminU9Tai8/5Yt2vlTIqHzRzpcKGX3PF7Up9ACpMgFPAG8COcH/+wNPAjXA1xrNF9YfjvJFOF8qZFS+aOdLhYy+59OUusvO93ypkFH5op0vFTIqX7TzaUrt5ad80c6XChmVL9r5UiGj7/miNoUeIFUmYANwcYz23wQr503B/8P6w1G+COdLhYzKF+18qZDR93yaUnfZ+Z4vFTIqX7TzpUJG5Yt2Pk2pvfyUL9r5UiGj8kU7Xypk9D1f1KYMJF59gG1NG621/2GMqQB+bYzJAl5JejJH+RLjez7wP6PyJcb3fOB/Rt/zSfN8X3a+5wP/MypfYnzPB/5nVL7E+J5PWub78lO+xPieD/zPqHyJ8T0f+J/R93yRos7w+G0AjgbmNX3AWvsDY0wN8FPgH8kOFlC+xPieD/zPqHyJ8T0f+J/R93zSPN+Xne/5wP+MypcY3/OB/xmVLzG+55OW+b78lC8xvucD/zMqX2J8zwf+Z/Q9X7SEfWp6qkzAvcDLrczzE6CWcC6pUL4I50uFjMoX7XypkNH3fJpSd9n5ni8VMipftPOlQkbli3Y+Tam9/JQv2vlSIaPyRTtfKmT0PV/UpjQkXg8Cu40x/ZqbwVr7X8B3gYKkpWqgfInxPR/4n1H5EuN7PvA/o+/5pHm+Lzvf84H/GZUvMb7nA/8zKl9ifM8nLfN9+SlfYnzPB/5nVL7E+J4P/M/oe75IMcHRBRERERERERERERGRyNKZ4SIiIiIiIiIiIiISebqBZhsZY74IfAEYC/QFLLAbWA48AfzNhni6vfJFOx/4n1H5op0P/M/oez5pnu/Lzvd84H9G5Yt2PvA/o/JFO5+0zPflp3zRzgf+Z1S+aOcD/zP6ni8qNExKnIwxObi7tk4GNgHv41ZIgD7AccBQYBFwtrW2WPmUrzNlVL5o50uFjL7nk+b5vux8z5cKGZUv2vlSIaPyRTuftMz35ad80c6XChmVL9r5UiGj7/kip7k7a2o6cALuADYDn25hnjNwK+0dyqd8nS2j8kU7Xypk9D2fptRddr7nS4WMyhftfKmQUfminU9Tai8/5Yt2vlTIqHzRzpcKGX3PF7Up9ACpMgHbgMvjmO8KYJvyKV9ny6h80c6XChl9z6cpdZed7/lSIaPyRTtfKmRUvmjn05Tay0/5op0vFTIqX7TzpUJG3/NFbdINNOPXE3eUpjWbgnmTTfkS43s+8D+j8iXG93zgf0bf80nzfF92vucD/zMqX2J8zwf+Z1S+xPieT1rm+/JTvsT4ng/8z6h8ifE9H/if0fd8kaLO8Pi9C8yKY75ZwbzJpnyJ8T0f+J9R+RLjez7wP6Pv+aR5vi873/OB/xmVLzG+5wP/MypfYnzPJy3zffkpX2J8zwf+Z1S+xPieD/zP6Hu+SMkIO0AK+THwnDFmGfAAsAI3mL3F3eF1LHAZcALweeVTvk6YUfminS8VMvqeT5rn+7LzPV8qZFS+aOdLhYzKF+180jLfl5/yRTtfKmRUvmjnS4WMvueLlrDHaUmlCZgOvAHUAjWNptpgegM4XfmUr7NmVL5o50uFjL7n05S6y873fKmQUfminS8VMipftPNpSu3lp3zRzpcKGZUv2vlSIaPv+aI0meADlzYwxgzBHZXpGzTtAlZYazeFl6qB8iXG93zgf0blS4zv+cD/jL7nk+b5vux8zwf+Z1S+xPieD/zPqHyJ8T2ftMz35ad8ifE9H/ifUfkS43s+8D+j7/miQJ3hIiIiIiIiIiIiIhJ5uoFmGxhjTjPGPG6MWWSMmWuMGRNjnhONMYXKp3wdwfeMyhftfMHv9zqj7/mkeb4vO9/zBb/f64zKF+18we/3OqPyRTuftMz35ad80c4X/H6vMypftPMFv9/rjL7ni5Swx2lJlQl3iUIZbgD7JcB+oBS4osl844Ea5VO+zpZR+aKdLxUy+p5PU+ouO9/zpUJG5Yt2vlTIqHzRzqcptZef8kU7XypkVL5o50uFjL7ni9oUeoBUmYAngDeBnOD//YEncYPZf63RfGH94ShfhPOlQkbli3a+VMjoez5NqbvsfM+XChmVL9r5UiGj8kU7n6bUXn7KF+18qZBR+aKdLxUy+p4valPoAVJlAjYAF8do/02wct4U/D+sPxzli3C+VMiofNHOlwoZfc+nKXWXne/5UiGj8kU7XypkVL5o59OU2stP+aKdLxUyKl+086VCRt/zRW3KQOLVB9jWtNFa+x/GmArg18aYLOCVpCdzlC8xvucD/zMqX2J8zwf+Z/Q9nzTP92Xnez7wP6PyJcb3fOB/RuVLjO/5pGW+Lz/lS4zv+cD/jMqXGN/zgf8Zfc8XKeoMj98G4GhgXtMHrLU/MMbUAD8F/pHsYAHlS4zv+cD/jMqXGN/zgf8Zfc8nzfN92fmeD/zPqHyJ8T0f+J9R+RLjez5pme/LT/kS43s+8D+j8iXG93zgf0bf80VL2Kemp8oE3Au83Mo8PwFqCeeSCuWLcL5UyKh80c6XChl9z6cpdZed7/lSIaPyRTtfKmRUvmjn05Tay0/5op0vFTIqX7TzpUJG3/NFbUpD4vUgsNsY06+5Gay1/wV8FyhIWqoGypcY3/OB/xmVLzG+5wP/M/qeT5rn+7LzPR/4n1H5EuN7PvA/o/Ilxvd80jLfl5/yJcb3fOB/RuVLjO/5wP+MvueLFBMcXRARERERERERERERiSydGS4iIiIiIiIiIiIikafOcBERERERERERERGJPHWGi4iIiIiIiIiIiEjkqTNcRERERERERERERCJPneEiIiIiIiIiIiIiEnnqDBcRERERERERERGRyFNnuIiIiIiIiIiIiIhEnjrDRURERERERERERCTy1BkuIiIiIiIiIiIiIpGnznARERERERERERERiTx1houIiIiIiIiIiIhI5KkzXEREREREREREREQiT53hIiIiIiIiIiIiIhJ56gwXERERERERERERkchTZ7iIiIiIiIiIiIiIRJ46w0VEREREREREREQk8tQZLiIiIiIiIiIiIiKRp85wEREREREREREREYk8dYaLiIiIiIiIiIiISOSpM1xEREREREREREREIk+d4SIiIiIiIiIiIiISeeoMFxEREREREREREZHIU2e4iIiIiIiIiIiIiESeOsNFREREREREREREJPLUGS4iIiIiIiIiIiIikafOcBERERERERERERGJPHWGi4iIiIiIiIiIiEjkqTNcRERERERERERERCJPneEiIiIiIiIiIiIiEnnqDBcRERERERERERGRyFNnuIiIiIiIiIiIiIhEnjrDRURERERERERERCTy1BkuIiIiIiIiIiIiIpGnznARERERERERERERiTx1houIiIiIiIiIiIhI5KkzXEREREREREREREQiT53hIiIiIiIiIiIiIhJ56gwXERERERERERERkchTZ3gHMca8ZoxZ20GvbY0x93XEa4v4wBgzMljPbwk7i0/a8rkYY24J5h3Z8cn8Euu9d+bPQw6k+ixy6FSfY1N97txUV0QOnepKbD7XlWb2tWYEbdOTkaGjRe39HIoOrm2hbwtFujPcGPN48AGf2MI8xhizxhhTZIzpmsR4SWOM6WeMud0Ys84YU2aMWW2MucsYM+QQX2+AMeZXxpgVxpj9xpgSY8zbxpgfGWN6tnf+9mKMmR780eW24TkzjDHf6rhUB/yukUG+E5Px+6T9JXN9EUllqs+O6rOj+iwdTfU5+lRXHNUVR3VFOprqirQm+Bu/IOwcYTHGXGA8PsgV6c5w4O7g59UtzHM6MBJ4xFpb1uGJkswYY4CngVnA88CNwCPAFGDMIbzeROAD4FvAG8BNwPeAT4D/At4xxhzeHtk7wHTgJ0BuG54zA/dek2EkLt+JSfp9PlsHdAV+GnaQNppB8tYXaZuf4tapdWEHEUD1WfX5QNNRfU4Vqs/iK9UV1ZXGpqO6kipUV6LhQdxyLAg7SDtpj/fzE+CCdkmTmi7AfQaxhL5vnhHWL06SF4ENwGXGmO9aaytjzFO3wXR3jMfaJNgA6W6t3Zfoa7WjI4BJwBxr7Q2N2n9gjOnSlhcyxgzCbWBlAJOttW82evjPxpizgaeAZ4wx46K4kSnNa8/131prgfLEU4k41tpqoDrsHFJP9Vn1WZJE9VmSwRjT01pbEmIE1RXVFUkS1RVpylpbA9SEnSNerdUs39+PBzU3IV7sm1trIz0B/w1Y4OIYj/UC9gPvN2rLAv4fsAL3pVwEPAuc1OS504PXnQF8DXfUvAK4JXj8NWAtkIfbkCgG9gJPAnlNXisN+AHuqNNWoBJYD9wO9I2R2wL3xfn+jwjmn9MOn+Vvg9e6oYV5fhXM87VGbbcEbSNjzL8WeK1J2yXAM8FnUAHsxG1sHd/c84GjgOeAkuCz/hswqNF89wUZmk63tPBe1jbznOlNPt8HgS3BclsL/Bq3cdD4tYYB9+COfFUA24GFwFXB4zOa+V2vNZcveF4f4P+A1cH6ugtYBnw3xrxfAF7FrdOlwCrgNqBLo3m6Az8PXq8iWB8fAEa0Zf1vtBznB8ukFFhCjL/DZt7XyKbLp3Eb8CXgHaAMd3bK1cE8w4Nlvzv4vX8BejZ57bp1oX/w3nbhvgf+TQt/5zEy3kew/dbe60sw7xRgQfAetwF/BMY2/Vxa+AxvCeY9NljOW4PXWgKc0Wi+LsAOYH4zr3Nz8DpTW/l9dZ9r3+DfO4Nl8BTB3yLuTKWVuHX1Q+D8Zl4rrnUH9935n8Ca4DXfBy4jxndOM21DcN9r7wB7gtf4AHfWU3qT3zUjeP6ngP+g4W/kI4K/Y01tm1B9Vn22qs/BvKrPB64Lqs+2fepzo8+puWlGo3kNcANuPS0NltOrwOnNrQPBurQsyH9fo3lmAm8F7cW4juopiX7XxfHZqq6oroDqCqiuNF0XVFds+9SV5v7OaVivp8doi3v/Cfg0rmYU4dbz94DrY8z3GeBRoDB4r0XB86bFmPc1Gr6j69YZ28p7POT3Q8P6e9B0iO91bfAeTgL+hfveW9Mk06eD5VL3vfMecGkz7+2CYH3bF0wLiLFfXve5NWk7Dff38BEN2woLgAtjPLfZ7Y5Y61Cjz+5B3N9BRfAZ/y/QrZl18Mjg8Y3B/O8CZ8f13RPPTKk8AaOAWuC5GI/NCj7AbwX/z8QVjQrgLtwG4feDBVAKnNLoudOD576DOwvhx8HrndVo4e8IVsbHga/iilcF7guxccHODv4A7ga+A1wf/LsS17nTpUnuuDeKgvlfDn7vtAQ/y7XB63RrYZ66jbBXYqyoI5t5zdeatM3DbUj+ELcx/XNc4SoBjojx/I9xX/i3B5/d7cEyf7HRfBOBv9ctb+DyYDpoQ6vRcy7AddztaDT/5cDA4PFxuC+idcF7vA5XuCpwGzyZwXwZuI6/EuCXwLW4ywzvA+4K5skDfhbkm9Pod53ZyjL5N1AV/N7rgG8G7/+5JvPVvfYK3KUqs4IsnwC5jXLOD+ZrvM6WB5/vYW1Y/38aPP7P4PO+Efe3ZWm0wdzC+xpJ8xtFS4M8Pwa+DrwdtF8WLIt7afgbsnWfcaPXuS9oXwa8BHwj+HyKg2U0Nsb7nBEj430cuFHULutLMO/44HPfEcz7LWAxbseyrRtFy3CXtn47+Mw2BevMpxvN+5tg3qNivM5KYFUcv6/uc30Tt/P3VeBW3BHfRcB3cX+r3w/eT2GQY1ST14l73QF+F7S/Hsz3U9x3ad3nNDLG59G47XO4jvRbcRv338JtYFia7EjSsKGxGFdkb8b9vX0YtE9O5Pu1M06oPoPqM6g+qz43vM59qD63a30GBjZ5z3XTO8HrntVo3r/gzoJ7NFh+3wneVzVwXozl/Q6uQ+FnwWd2SfD4L4PHlzR6bxuD9xbXDmoC34WqK6oroLqiutLwOvehutIR+311v29ko7YZNN95HNf+U7Be1Qaf0Xdx6+WTwby/bjLvQ8EyvQX33fET3PpZTZPOfNx39M7g8Udw3/c/aeU9HvL7wR1oujxoK2i8jhzie12L23feA8wNnntTk0zLghzfx53YVZdpRpPX+mrQvrLRvCuDtlkxPre1Tdp+Hrz/n+LW6+83ev5XGs13ZvDeLQf+jeS1sA6NwB08rMDt538VeIyG/f2MGOvgYlwd+VawPDbj6ulBNeigZdzaDFGYcIWjGhjSpH1R8EH3C/7/7eAD/WyT+Xrhjla/1qhtejDvbmBAjN/5WvD475q0Xxi039GozQBdY7zGtcG8X2rSHvdGETAI94VYjvvCzz/Ez7Bn8Hvfi2PevcDOGCvqQSsksTeKYh0tPTpYVn+O8fxYn9GfaPIl31KOFt7LazT5Amj02Lu4L5mmR6DrlvGM4P/HB/+/uZXfNZ0YX1gtzJ8TzP/nVuY7LZjvFSC7yWMGMMG/rwvm+1WTec4J2h+MZ/0HTg4e+98YWZ4K1o+erWQeSfMbRftpdMYC7kh/Oa6Y3NTkdf6O+zLs0ajtvuB1/l733oP2ccFrvBDPMqHJRlF7rS9B28Ig95hGbV1wf8tt3ShawoFngRyGOwK8slHbmGaW/eR41t0mn+ufmrTfGrSvB3o1aq/7u/j5oaw7uKPAtbjv9/Qmr1FLfJ3hXRuvA43aH8R1CAxu1DYjeP7bTT7Pobjvpofj+bvVdNBnrfqs+txijhbey2uoPqs+qz63Wp9j/P4baPId2Oi9Nt0RzsB1Rq1ptD7WLe8q4Ogm89fV5vlN3tsQXAfwWppcedXeE6orqiut5GjhvbyG6orqiupKPPt9db9vZKO2GTTfefw2rew/AYODZftQjN/3e9y+2ehGbbG+OwbiOr2fj7GsLPDTeP7e2uP9BO0xv78P4b2uDV5rZgs51wE5jdpzgrbdBDUH6B2sD59w4H55L9yB4BKCA2bNrePNfO7dcFeffNDa300r69Bfg7azm8z766D92hjP/wcH/m2fSpM+huamqN9As87dQDpwRV2DMeYoYALwjLV2Z9B8Oe5La1lwJ+5+xph+uC+jl4ApMe48/oC1dnsLv/sXjf9jrX0St6Jc0KjN2mCcNWNMujEmN/i9rwSzjG/Tu214j91wZzl2AQ7HHR3+pzHmU03me9EYs76Vl+sV/CyO41fvxW1EHRJr7f4glzHG9Ao+ix24zy3WZ7HZWvtYk7a6z65DbupijDkOt7HzEJDVZH2Zjyvcnwlmr/vMTjfGDGjHGGW4L93xxpiRLcx3WfDzP621B4zHFqx7NvjvhbiNgp83mec53JkA5xtjmn5nxFr/L8N9Ad3f+HMJPptncOvGxHjeYDOestaua5Svbt2oxW0MNzYPd+bPyBiv86tG7x1rbd0ZA582xvRIIN9B2rK+BOvIROBpa+1HjfJV4s7YaKv/s43GzbTWbsQVmqOMMUcHbR/hjrZeaYxpfC+Ja3E7lPe34ff9rsn/5wU/H7DW7m2U4z3cd8URjeZty7pzPm6j/lbrxnSre923cMuxVdbasrp1wBjTxRjTJ/hd/8JdxnxKjKf9ucnnuQl3mdgRMeaV1qk+qz63K9XneqrPcehk9RljzGeBP+CGmLip0UOXEwxt1uQzyMUNGzKSg+vcc9balU3a6mrzr5q8t824neIRuMu8O5LqiupKu1Jdqae6EofOVlfiFM/+08W4oavujrEuPYvbNzuj0Wvsr/u3MaaHMaYvrhN5Cc1/j/4mie+nJW16r4HduCshmnO7tbb+Ozv49x24DvDpQfOZuLPWb2uyX74Xt23QAzfcSrOafO7dgs+9G+57+GhjTK9mn9yC4PvmPOBta+3zTR7+Oe5v/sIYT/19k7/tNwmuLGrtd3aWzvC/485GuLpR2zXBz3satR2NG4NsR4zpGtyGVb8mr/0RzSuy1m6N0b4SGGiM6V7XYIz5kjFmCa7Q7Ql+Z2HwcO8WfkdLZuO+iL8WfBF+FjfG1z+MMXVfwAY3HtX8Vl6r7o8lJ47f2ws3xs8hMcacZIz5Bw3jwNUtg+OI/VkUxmjbFfzse6g5WnF08PO/OHhd2Y77khkIEBTwn+GK3hZjzDJjzK+MMacmEiD4Av4WbvmtMcasMMb8wRjT9Iuz7hLGd1t5yVG4Dcw9MR5bgduYiWf9Pxq3I/QhB382dTcsGthKlpbEWt57gC3W2ooY7RB7PWi6Awfu7yMdt7PWnuJeX3CXToL7/GLla6vm3mfj3wXukquBwOfBbVTgxuj7h7W2LX/PTZdP3TJYE2PePRy4bNqy7iT8ORljMowxPzTGfETD2Is7cGeGQ9u+bzrquybqVJ9Vn9ub6rOj+hyfTlOfgw6ax4DluHFEaxs9fDRuPdrGwZ/DLcE8TdeNWOvYqODnihiPLQ9+5sV4rD2prqiutDfVFUd1JT6dpq60QTz7T3Wf28sc/LnVnehUvy4ZY0YbYx4xxuzBfX/sDOY9m9jfHTustUUJvIfGEt0fbNN7DaxufAJYDPEs+4RrtDFmgDFmrjFmG+7ATt3nfn0wS25Lz29Bf1xn/EHZrLW7cUOOxcoWa1nsJo5lkdHaDFFgrS03xjwEfNUYMwl3tOgK3Ph1Lzaa1eDGarvp4Fept6PJ/0tb+tXNtJsD/mPMRbjx+d7AjTm0Adcxkw68wKEftJiOOzq2ENzRoWBj6N+4O39fhDuKMxg3Vl7zb8TaEmPMOuBIY0w3a23M922MORxXPJc2fnoLL33AOmiMGY4bW2gv8D+4I7/7g9f4He4PpKmWvhRMC48lou51f4tbRrHUb1xYa39ojLkHd+nZVNy4Vt81xvzKWvu9Qw1hrb3DGPN08LrTcEcZv26MedRae2mjrC0tgzqH8lnFWg/qft9ZNL9sYn0Bx6u512yP9aDpfHGvu3G+bjzrS928sX73oSyjeF/nCdwNV67FXdZ4KW5jrcXvhoN+WfNFurl20+Tf8a477fE53YobO/BR3I7Ldtxl3yfjxlaM9d0bz/uQOKk+qz53ANVnR/W5ba8b6fpsjBmEu5R4P3CutXZfjN+7A/hKCy+zvMn/m1vHQqW6orrSAVRXHNWVtr1upOtKG8W7HwhwJa7jM5ZCqO+8L8Bl/h3uu7wEd/bwf+JucNlUS9/fbZXo/mDc77WR1vLHs+wT+o4MDqi+iOvMvw13r7Bi3OdxNW4b4lBr2KFmO+Rl0Sk6wwN34wZgvxp3J+ZBwM+adNx8jDsi8UqTsyUOVW9jzKAYZwkcBWxvdInBFbiNoNMbb2wYd0lfImpxG1aH4Ta0sNbuMcacibuM4Ulc588L1tpXmn2VBn/Hja93Je6Si1hmBj//0qhtd/CzD268IwCMMdm4DbJPGs17IW7D5zxr7auNXzi4BKPpEeC2iGfDIN7nfBz8rLHWvhzXC1lbiLv85A/Be/8XcLMx5rfBJWeHkg9r7RZc4brLGJOOO6v1y8HrvonbsPwc7myRN1p4qdXA54wxuTGOmh5DMCZgHJE+Dn7f+hiXz/rkaNwNF5q21eDG14ID192mYh2ZbI/1ZXWjLE3FamvNMbi7Scd6nfoia62tMMY8ANxojBmC2zjaRPMbcR2hLetO48+p6cZCvJ/TFUBBox0IoH7nTpJH9RnV53Z8jupz81SfDxb5+mzc8BnP4M7ynGat3RBjto9x48gujtFR3hZ1n9Gxjf5d55jgZ6yzuNqb6gqqK+34HNWV5qmuHCzydaWD1H1uO+P43M7A3YviGmvtAUOHGGN+2hHh2llb3mu8jsHV+saaLvvGNfrfMZ7feN5YjgdOAP7bWvuTxg8YY2bGmL8t33PbcQc0jm36gDGmN65+vNOG12tVZxkmpW4c2XeAS3B3IrYcPObOA7iNpZhnCBhjDuUSn+83eY0LcTeXeapRc02QJ63RfAZ3V+1E1I218yvTaMwva+0u3GUwBrfB9Eicr/drXFH8uTHm5KYPGjcO4U24o78PN3qo7pKqpuMPfZuD18G6jdSmZ1Fch1s2iajbuI9V4Fp6Tu9geTT2Nu4MmeuNMQcVR+OGX+gT/DvHGJPZ+HHrxnCr22Cou4ynTfmMG6OpW5PXraGhANa9zkPBz/81xmTFeJ269/YUbnk0XWfPwo3v+EycOwt1Q0z8b7CR1vT3tef4eYm4ufFyDdbpTwP/brQjuAY3dtoB625wptGEGK+Z8PoSbCAvxo3VN6bRPF1wfzNt9e3guXWvcxjuqO2qGButd+J2pH6Je3/3tXI5Vntry7rzDO5786bG8zZajvGo4eDvmu4c2ucsh0j1WfUZ1WfV5wOpPrdTfQ7e74O4e2BcYa1d2sysD+DWsZ/HerAN37F1tfm7jf+2jDGDcR3T63CfeYdSXVFdQXVFdeVAqiv+7fc19RjuANh/mYPv11D3t1W3Tjf33fEZDvG+Cx1kH7H/xtvyXuN1gzGmfmir4N/X44YNez1ofgl39c03jDE9G83bE3e19D5avvdWc5/7WGKP570veLzV77ng++ZZ4CRjzOeaPPx93PfVk629Tlt0pjPDwZ0l8AfcGGqvWWubnrHwe9yg8r827mYjr+COig7HHX0qB05vw+/bCVxk3NG213BjeH0VN67aLY3m+xvwBeAV447QZeJutHJAwTsE9wIX4S57OcoY8xjuiMuxuKP8deP83G6MWW2tbXH8OGvtFmPMebiVdJEx5q+4L+903KV5F+OKyLk2uDFM4GXcOFj/bdxR/jXAFNyXbtMjzv/EXQLyoDHmj7hLiCbjxn5aTWLrbN3R4F8G2cuB5dbappd9Nn3O54E/GmMW4r4AXrHWbjfGXIFbR94z7lK4Fbhldjjuc/9P3M2CTgfmGmOewB2t34e7g/VMYIm1dlXwuz7AHQ37qjGmFPfFtb2FszfGAK8bY57EFdw9uKN/N+A+43kA1to3jDG/BL6Hu0nQo8BW3JhRF+PuOl4UZL0K+J5xN2YpCN5L3Tr7/1r4nOpZa980xvwEN07aO8aYx4HNuKN543DLsksLL5EsI4B/GWOewWX7Om7sxu/WzWCt3WeMuQ+YaYx5mIa/46txG58nNHnN9lhfwO1cvAYsMMb8Cbd8LuXQ1v8MYF6QvyeuKHYFbmw6o7V2pTFmPu6mUpYDx9bscG1Zd6y1Hwafzddx351PAAOC/79LfDfo+hswO/ibeBk3Nts1NIw9Kcmj+qz6DKrPqs+O6nMTCdTn64P38DrQzRhzeZPHF1prC621fzPG3IsbcuFk3JAqO3GdpxNxn0WsMyOb5lxljPk1cDNQEKzTPYFZuDOAL0tiZ4vqiuoKqK6orjiqK02Evd8XI89GY8wNuCsfVhpjHsQdQO2Pu4fABbizl9fi7nuwFfhtsP5uBE7EXXnzfjC/DxbjbtL6PWA9YK21j7TxvcZrJ7AkWN8Mbr0dDsysuwrJWltkjLkZd/PZJcH6DjADt27Oto1uwhnDSty6fLNxB+dW4b6bZuO+l5oeOF2M+1v7szHmOdxwpEustWuaef3/h6vLTxlj/oy7kigfd2C7gPa+uau1ttNMuCOxZbg/9iuamScD92XxJm6DYT/uMoa/Ap9pNN/04HVmNPM6r+FW3jzgadzGVUnw78NjzH8driiW48YNmos7imRxR+kaz3tQWwvvOR13lGcZbmOjDPcFcQtucPtRuA2lIuDEOF9zIPCrIG9pkMfiNn6ym3nOGNxlN3XF/jFgaPAZvdZk3nzcF1xJMO9zuJuFvAasbTLvQc9vafngNs4LcX+IFrillffaHbcxvY2GMzmmN3p8BO7SwbVAJa4TbRnuzJphwTyjgnlWBuvB/uDf/w3kNPl9ZwNvBeuBjfXeGs3bF3eX6XeCz6kM94XxO2BwjPm/DCwIPtf9uA3V3wFdmrzfnwefUWWwbjwIjIjn820yzzm4SwJ34458bsBt9N4Qxzo2sunyidXW9O8tRvuMGMvsvqCtf/DeduHWy1eAcTFeowfuyHndfPOBSXWv097rS5O/g4XBurAdV7TGNvcZxMh9SzDvsbidwa3Ba70BnNnC864InvfveL4Pmn6u8f4ttvL3G9e6gztC/APcxkMFrghf1ui9j4zxeTRu64Y782ld8Nl8jDvyfEbTzLHWpdbWP01tWn9Un1WfVZ9Vn+9D9bld63Oj39Xc1PTv8Apcp9reINNa3HAZl8SzvBvNcx3u7Mjy4LVeAqbGm7s9JlRXVFdUV1RXVFc6ar+v7veNbOXzP6gtjuU4mYZhnSpxB1deBb5Do+8c3JAdL+AODJUErze1mWUV83e18h4Tfj+4gykv4r4LbIxc8b7XtTTz/dAo06dxB6XW07Bf/JVmnnNhsL7V1b2FwAVxvqcRwOO4e2qUBuvYhc2sE2nAb3AHK+r+RmY0tw4F7aNwf6t1n0kh8L9At9bWwXg+r8aTCWYWOWTBZRUvAqcCX7bWPh5yJJFmBUdAr7LWJnQDiagyxnwJd2Onr1hrH25tfhHxl+qzpBLV55apPosPVFcklaiutEx1JfUZY2bgrgw63Vr7WrhpUkvTcbtE2sxaW4K7ccY7wF+NMeeGm0hEEvA13GVWfw87iIgkRvVZJFJUnyV0qisikaK6Ip1WZxszXDqIdWMLnRJ2DhFpO+NubnMG7tKyfOA/rbUV4aYSkfag+iySulSfxUeqKyKpS3VFxFFnuIiIHIO7+3wRbmy734aaRkRERED1WURE2pfqigh03jHDjTHVuGFi9oadRUREUkIvoNZaqwPJrVCNFRGRQ6A6GyfVWRERaSPV2EY6c2d4LWBycnLCjiIiIimguLgY3F3Adb+NVqjGiohIW6nOxk91VkRE2kI19kCd+YjA3pycnJyioqKwc4iISArIzc2luLhYZ2DFRzVWRETaRHW2TVRnRUQkbqqxB9IRARERERERERERERGJPHWGi4iIiIiIiIiIiEjkqTNcRERERERERERERCIv1M5wY8xhxpjfG2PmG2P2GWOsMWZ6M/OeaYxZbIwpM8ZsN8bMMcbkJjWwiIiklrI9sOw+eP1X7mfZnrATJY1qrIiIdDjVWdVZERHpGJ24xna0sG+geTjwZeAt4N/AebFmCjYqngeeAn4IDAF+CYw1xky11tYmIauIiKQKa+HVn8HCP4BJg6oyyOwK//weTPoGnP4DMCbslB1NNVZERDqG6iyozoqISEdQje1wYXeGF1hrBwAYYy6gmQ0I4FfAcuCSuo0FY8wW4EXgi8CjHR9VRERSxqs/g0V/guryhraqUvdz0Z/cz0/9MPm5kks1VkREOobqLKjOiohIR1CN7XChdobHcxTcGDMUOBX4TuP5rbUvGWM2AV+ggzYgrLXs3LmT8vJyamt1wN53aWlpZGdn069fP4yOkol0XmV73FH0xhsPjVWVuscnfh265iY1WjL5XmMB9u/fz969e6murlad9VxaWhoZGRn06tWL7t27hx1HRMKkOgv4X2e1L5tatC8rIoBqbJKEfWZ4PMYGP5fHeOz9Ro8fwBhT1Mrr5rT0oLWWTZs2UVJSQlZWFunp6a0GlXBVVVWxb98+KioqGDp0qDYiRDqrD552l5O1xKTBB0/BuBnJSOSzUGpsbW0tmzdvpqSkhLS0NDIzM1VnPVdVVUVpaSlFRUX07NmTIUOGkJam+7CLdEofPA20sp2tOltH+7ISF+3LigigfdkkSYXO8L7Bz90xHtsNnNwRv3Tnzp2UlJQwcOBA+vTp0xG/QjrA7t272bZtGzt37qR///5hxxGRZCsvdhsQdZeRNaeqDPZtT04mv4VSY4uLiykpKaFfv3707dtXnaopora2ll27drFz506Ki4vp3bt32JFEJNk2vBlcul3W8nyqs3W0Lytx076sSCdXUQIrn9W+bBKkQmd4HduWdmttbksvFhxtb/aIenl5OVlZWdp4SDF9+vShqKiI8vJmLikRkWgq3gRLboel90FlSevzZ3aFHgM6PFYKSWqN3bdvH126dNGlwCkmLS2Nfv36sXfvXvbt26fOcJHOorYWPnoBFt4G6xfF9xzV2aa0Lyut0r6sSCdVsg2W3AFL73Ynd7VGNTZhqdAZviv42TfGY32IfZQ9YbW1tbqcLEWlp6drXDyRzmLbCjdm2vuPQ221a8voBjUVYGuaf56thWMuSEpEz4VWYzMyMtQRnoKMMWRkZKjOinQG1RXw3qOuzu78qKF94FjYsQpqq5p/rupsHe3LSptoX1akE9nxkTvQ/N6jUFPp2tKzXX3VvmyHSoXO8BXBz7G4O243dhywMLlxREQkVNbC2nmw4Db45KWG9u4DYML1cMo17hLuRX+KfYlZZjeY+DXdcMRRjRURkQOV7YGl98CSObBvW0P74Z+Gyd+EkVPh1Z+pzsZHdVZERBpYC+sXu07wVc83tHfrC6fNhlNnuiueVWM7lPed4dbajcaYpcBlxpjf1d2F2xhzBjAU+HuoAUVEJDlqqmHl064TfMs7De19j4BJ34DjL4HMbNd2+g/cz4V/cDcYqSpzl5PZWrfxUPd4J6caKyIi9Yo2wOLb4a37oXKfa0vLgOO+6OrswGMb5lWdjYvqrIiIAFBb4zq/F/weNr7Z0N57FEz6OpzwFejSzbWpxna40DvDjTEXB/88Nfg5zRjTD9hvrf1n0PY93JH0h40xc4EhwC+BJcDjycwrIiJJVrkf3v4rLPojFK1raB8+ESbdCGM+B01vwGgMfOqHbmPhg6fdDUZ6DHCXk3Wio+iqsSIi0qqt77sDzcufaLgsu0tPGHcVTLgBcg47+Dmqs4DqrIiItKKqDN59GBb+EXavbmgfOs5dbXXU5yGtybBWqrEdLvTOcA7eALgl+LkOGAlgrX3FGPN54L+A54AS4CngZmtbGkhHwjR9+nSKiop455132uX11q5dy6hRo7j33nuZMWNGu7ymiHhs3w54Yy68eae7ZBsAA0ed4zYchp3W+mt07Q3jZnRkSt+pxkaY6qyIHDJrofA1d5n26lca2nsOhvHXwylXQ3az92dsoDqrOhtRqrEikpDS3fDmXW7IsdKdDe1jPudO6BoxyXV6t0Q1tsOE3hlurY3r7lnW2heAFzo4TocrLq3i+eVb2FFSQf+eWZw9djA53TLDjiUi4o9dq91Z4O88BNXlri09C078Mkz8BvQ7PNx8KaSz1VhQnRURaVFNNax4Ehb+3p0RXqf/UW4olOO+CBlZ4eVLMZ2tzqrGioi0Ys9aWPRnePvBhjG/0zLhhEvcvuyAo0KNJ07oneGdhbWWW1/6iLkFhaSlGcora8juks4tz6xgVn4eN505BtPaUaEUUVFRQVaWNqJFpI02LoUFv4OV/wCsa8vOhdOug9NmuUvDRJqhOisi0oKKfW7HfNGfoXh9Q/uIKTD5Rjj8zIOHHBMJqMaKiLRi89tuyLEPnnJjewNk5bgrrcZfD70GhxpPDqQtniS59aWPuGveGiqqaymrrMECZZU1VFTXcte8Ndz60kdJyfHkk09ijOH1118/6LGf/vSnZGRksHnzZgCef/55pk2bRs+ePenevTtnnHEGb7755gHPmTFjBrm5ubz11ltMnz6d7t27M3v27APmefnllxk3bhzZ2dmMHDmSW2+99YDHy8vLuemmmzj++OPp1asX/fr1Iz8/n3//+9/t/O5FxDu1tbDqn3DPWXDXGbDyWcBCznD43C/h2yvceGnqCJdWqM6qzopIDCXb4N//Df93LLzwfdcRbtLgmPNh5itw9XMw5rPqCJcWqcaqxopIDNbCxy/D/efC3Omw4u+uI7zXUPjMz+Dby+HM/1JHuId0ZngbVdfUsqW4vE3PKSmvYs7rhVTW1MZ8vKyqhjkFhZw1dhA9s9t2mdngnGwy0uPfeP385z9Pv379uO+++5g2bdoBjz3wwAOceeaZDBkyhHvuuYeZM2dy6aWX8h//8R/U1NRw6623Mm3aNJYsWcJxxx1X/7zy8nIuuugibrzxRn784x+TmdnwHjZt2sSMGTP40Y9+xIgRI3jkkUf4zne+Q3V1NTfffDPgjr4XFRXx/e9/nyFDhlBaWsrTw2OHLwABAABJREFUTz/NmWeeyYsvvsinP/3pNn0mIpICqivgvcfcHbJ3rmpoH3S8Gw/8mAsgXSWqszmUGgsdV2fbWmNBdVZEPLHzY1dj330EaipcW0ZXOOkyd0OuPnnh5pNQaF9WNVZE2kFNlbvp9ILbYPuKhvYBx7qrrcZ+AdI1hJTPjLU27AyhMMYU5eTk5BQVFcV8fN26dQCMGDHigPYNu0uZ+qtXOzpe3ObdfDrD+nRr03O++c1vcs8997B161a6d+8OwIIFC5gyZQqPPvoo55xzDocddhhnnnkmjz32WP3zSktLGTNmDBMnTuTxx929YmbMmMH999/PX/7yFy677LIDfs/06dN5/fXXeeGFF/jsZz9b337WWWexaNEitm7dSnZ29kH5ampqsNZy1lln0aNHD5588kkg/puONLfsRMQDZUWw9B53I5F9WxvaR5/hNhxGTWv9RiIhyc3Npbi4uNhamxt2Ft915hoLqrMiEqL1i93O+arnqR9yrGsfN9zYaddB936hxmuJ6mz8OnOdVY0VkdCU74W37ofFt8PeTQ3to/LdCV2jz9C+bIrQ9XCd0NVXX82+ffv429/+Vt92//3307t3b84//3wWLVpEUVERl19+OdXV1fVTly5d6jcKmrrwwgtj/q7evXsfsPEAcOmll1JcXHzAnbmfeOIJpkyZQt++fcnIyCAzM5OXX36ZDz/8sH3etIiEq2gDvPD/3GXa//4v1xGelgHHXwrXL4Ar/g55073deBBpC9VZEUmq2lp3v427PwP3fBZWPQdY6D0Szv6NG3Ls9P/0uiNcJF6qsSKSdHu3wEs/gf8bCy/+0HWEmzR3Bvis1+GqZ+HwT2tfNoXoGvQ2GpyTzbybT2/Tc555dzN/eOVjyqtiX1oGkJ2Zxjc+dQTnnTCkzXna6sQTT+SEE07gvvvu46qrrqKsrIzHHnuMyy67jKysLLZt2wbA+eefH/P5TW+O0qtXL7p1i31Ef+DAgc227dq1C4DHH3+cL33pS1x66aXcfPPNDBw4kPT0dH70ox+xcuXKNr8/EUmisj3wwdOwb7sb1/uY86Fr74bHt77vLtNe/gTUVru2Lj1g3AyYcAPkHNah8YpLq3h++RZ2lFTQv2cWZ48dTE43XbLmq0OpsdBxdfZQaiyozopIO2mtxlaVw7sPw6I/wq5PGtqHnOzOUDv6XEhL79CIqrOpRfuyqrEi0khrdXb7h25f9r1HobbKtWV2g5OugIlfdQedO5BqbMdRZ3gbZaSntflSrsvHj+C2f3/c4jzWwuUTRpDTNTkr9lVXXcV3vvMd1q5dy6JFiyguLq6/XKtfP3fWyJ///GdOPfXUVl+rpTuH122MxGrr27cvAA899BB5eXk89NBDB7zWvn374n4/IpJk1sKrP3MbByYNqsogsyv883sw8eswYjIs+gOsfqXhOT0Gug7wcVdD19wOjme59aWPmFtQSFqaobyyhuwu6dzyzApm5edx05ljWvzuknAcSo0F1dnm2lRnRVJUSzV20jdg/A2w7B5YMhf2b2943hGfdUOOjZjc4Wenqc6mJu3LHkw1VqQTam1fNm+6e+zjfzU8p3t/OG02nHotdOvTwfFUYzuaOsOTIKdbJrPy87hr3hrKqmoOerxrZjozp45K2sYDwOWXX873vvc97r//fhYtWsSxxx5bv7EwefJkevXqxYcffsgNN9yQ0O/Zs2cP//rXvw64vOyRRx4hJyeHE088EXAbIJmZmQf8Ma9YsYJFixZx2GEde9aoiByiV38Gi/4E1Y1uwlRV6n7O+y3M+01De78j3c778V+CjKykxLv1pY+4a94aKqobzmIqq3Tfv3fNWwPAdz5zZFKySMdTnVWdFYmUlmrs/P+D+b9rOEMtLROOvwQmfR0GHJ20iKqznYdqrGqsSOS0ZV+2z2i3L3vCpa7DPAlUYzueOsOT5KYzxwAcdGSnttYyc+qo+seTpX///px11lnMmTOH7du384tf/KL+sR49evC73/2OmTNnsnv3bi688EL69u3Ltm3bWLp0KVlZWfzP//xPXL+nX79+zJw5kx/+8If1d+B+4YUX+NWvflV/w5FzzjmHJ598kq997WtcdNFFFBYWcssttzBixAhqag7e4BKRkJXtcUfKG288HCC4Yddhp8HUm9yZamnJu0VFcWkVcwsKD9h4aKysqoa5BYXMnJqX1B036Viqs6qzIpHQWo2tH3KsJ5x6DYy/Hnq1bWiKRKnOdj6qsaqxIpER777skJPdvuyRZ3f4kGONqcYmhzrDk8QYw3c+cyQzp+QdOObPcYNDW4GvvvpqnnnmGTIyMrj88ssPemz48OH8+te/5tprr6WsrIyBAwdyyimncP3118f9O4YOHcpvf/tbvvvd77JixQoGDRrEb3/7W2666ab6ea655hq2bt3K3LlzufvuuznqqKO47bbbePbZZ3nttdfa6+2KSHv54Gl3OVlLMrLhpMvgyLOSk6mR55dvIS2t5cvG0tIMz7+/hS+fNjxJqaSjqc6qzopEQjw1Nj0TPvVDmBD/d0V7Up3tfFRjVWNFIiPefdlxV7l7bySZamxyGGtt2BlCYYwpysnJySkqKor5+Lp16wAYMWJEElNJe9CyE+lg//weLLmjlZkMnP7/YNrNSYnU2K9e+JA/v7a6xXkM8O0zx3DjGUfE/bq5ubkUFxcXW2tzE0sYfaqx0ablJ9KBXv05vP6LVmYKr8Zaa/neE+/x2NKNLc6nOtuxVGejS8tOpIO98H1YfHsrM4VXZ3/9rw/506val+1oOjNcRETis34JLLwNPvxH6/NmdnV35E6ijXtKuXv+Gv66eF2r82Z3Sad/z+SMXy4iItKqihJYdn8cB5sJpcbW1FpeXLGVOwoKeXdDUavzq86KiIhXNi2DBbe5M8NbE0Kd3VJcxj3z1/DAIu3LJoM6w0VEpHm1tbDqedcJvmFJ/M+ztXDMBR0Wq7Hlm4qZW1DIc+9voaY2vqudamstZx83uIOTiYiItKJkq+sAf/MeqCiO7zlJrLHlVTU8vmwjd88rZO2u0vp2Q/2oqjGpzoqISOishY9fcvuya+e14XnJq7Mfbt3L3IJCnnlnM9Xal00adYaLiMjBqsrhvUdg4R9h18cN7YNPhMnfhK3LYcntDXfdbiyzG0z8GnTN7bB41lrmfbyTuQWFzP9kZ337oF7ZXDNlJDv3VfDgovWUVR1846KumenMnDpKNxwREZHw7Fjlds7fewxqKl1bZjc46QqgFt7+a2g1FmD3/koeXLSO+xetZff+yvr2Tx89kNnT8nh91Xbunr9WdVZERPxTXQnvP+5ulLljZUP7oONg0jdh+wfuQHSI+7KLCncx5/VCXv9oR317/55ZzJg0kqLSSv6yWPuyHUmd4SIi0qB0Nyy9G5bMhf3bG9oPPxMm3wgjp4IxcOyFkJbmNjBMGlSVucvJbK3beDj9Bx0Sr6qmln+8t5m5BWtYuWVvffuRA3tyXX4e550whC4ZaVhrycpIZ25BIWlphvLKGrK7pFNba5k5dRQ3nTmmQ/KJiIg0y1pYv8hdpv3RPxvau/WD8bPh1JnQrY+bLzs36TUWYP2uUu6aX8hjSzdQXlULQJf0NC48aSjX5Y/i8AE9AThlRG+MMaqzIiLij/JiWHYfLL4DSjY3tOed7vZl8053+7LWQlp60utsdU0t/1y+lbkFhby/qeFqsNH9uzMrP48LThpKVkY61lqyM7Uv25F0A03ddCRytOxEDkHRelj0Z3jrAaja79rSMuG4L8Kkb8DAY2I/r2yPG3dt33Y3rtoxF3TIUfR9FdU88sZ67pm/hs3F5fXtE/P6MmtaHtPH9MeYg++6XVxaxfPLt7CjpIL+PbM4+7jBh3wUXTcdiZ9qbLRp+Ym0UW2Nu9/Ggttg09KG9j55rsae8GW3E95UkmoswHsbi5hTUMg/399C3VXavbIzuHzCCGZMGsmAXtkxn6c6Gw7V2ejSshM5BMWb3FXLS++DyhLXZtJh7BdcnR18fOznJanOllZW89ibG7h7wRo27C6rbz91ZG9m5Y/mjKMGkJamfdlk0pnhIiKd2ZZ33c75iifBBpdhdekJp8yA8TdAztCWn9+1N4yb0WHxtu8t596Fa/nr4nXsLa8GIM3AWccNZnZ+Hscfltvi83O6ZfLl04Z3WD4REZFmVZXBOw/Boj/C7sKG9sNOhUk3wlHnuDPTmtPBNdZay2urdjCnYDWLC3fXtw/JyeaaKaO49LTh9MhqeXdRdVZEREKz7QN3dvf7j0NtlWvL7A7jroIJN0BuK/Wpg+vszn0VPLBwLQ8sXkdRqctnDHz2mEHMmpbHycN7t/h81diOo85wEZHOxlpY/Yobq7TwtYb2noPdRsO4GZCdE1Y6AD7Zvo87Cwp58u1NVNa4y7SzM9O45JRhXDslj+F9u4WaT0REpFmlu+GNO+GNuVDacF8Lxpzl7rsxfILbGw5JZXUtz7y7mTsLClm1raS+/ejBvZidn8c5xw8mMz0ttHwiIiLNstbdDHPBbfDJSw3t3QfAhOvhlGtcJ3eI1uzcz53zCnli2UYqqoMhxzLSuHjcYcycMoq8/j1CzSfqDBcR6TxqqtwZ4Atug23vN7T3P9pdPnbcFyGjS2jxrLW8uXYPcwtW8/LKhvHK+3TvwlUTR3LFxBH06R5ePhERkRbtWQuL/gRvPQjVwWXQ6V3g+Etcne1/ZKjx9pZX8fCS9dy7YC1b9zYMOTbl8H7MnpbHlMP7xRxyTEREJHQ11bDyGXdC1+a3G9r7HuFq7PGXQGbsIb2S5a31e5j7eiH/+mArdSNS53bL5MoJI7hy0kj69cgKNZ80UGe4iEjUVZS4scAX3w7FGxraR051l2kfcWaoZ6jV1Fpe+mArd7xeyDsbiurbR/TtxnVT87h43GFkZ7ZwGbmIiEiYNr3lds4/eNrdfAsgKwdOvQbGXw89B4Uab2txOfcuWMNDS9ZTUuGGHEtPM3z++MFcNzWPsUPDvRpMRESkWZX74e2/uiHHitY1tA+f6PZlx3wO0sK7mqm21vLKh9uZU7CaN9fuqW8/rHdXZk4ZxZdOHUa3Lup69Y2WiIhIVJVsgyV3wNK73Z21wd0t++jz3N20h44LNV55VQ1/W7aRu+YVsnZXaX37CcNyuT4/j88cO4j0GDcSERERCZ218MnLsOD37nLtOr0Og4lfhZOvhKye4eUDVm0tYW5BIc+8u4mqGneKWrcu6Vxy6jCunTKKw3pryDEREfHU/p1uuLE37oSyuvtaGHe/jcnfhGGnhRqvorqGp97exNyCQlbv2F/fPnZoL2bnj+assYPI0JBj3lJnuHjrjjvuIDs7mxkzZoQdRSS17PjInaH23qNQU+naMrrCSZe7HfQ+eaHG27O/kgcXr+P+hWvZtb+yvv2MowYwKz+P00b10WXaIh1MNVbkEFVXwvK/uRt2bf+goX3gWLdzfuyFkJ4ZWjxrLYsLdzO3YDWvrtpR396vRxZXTx7JZeOHk9tNQ46JdDTVWZFDtGu1Owv8nYegOhjSKz0LTvwKTPw69Ds81HjFZVX8dck67l2wlh0lFfXt08b0Z3Z+HhNH99W+bApQZ3iyle1xl1Du2w49BsAx54c+uL+v7rjjDnJzc7UBIRIPa2H9YtcJvur5hvZufeG0WXDqddC9b3j5gA27S7lrXiGPLd1IWVUNAJnphgtPGsp1U/M4YmC4Z9BJRKjOxkU1VqSNyvfCsvvckGMlmxva86a7y7RHfyrUIceqa2p5YcVW5hYU8t7G4oZ4/bpzXX4eF540VEOOSeJUY+OmOivSRhuXuqutVj4LBANuZ+fCade5/dkeA8JMx6aiMu6Zv4ZH3ljP/kq3L5uRZjjvhCFcl5/H0YN7hZpP2iZlOsONMScBPwFOA3KAdcADwP9Zaytaeq4XrIVXf+bOIjFpUFUGmV3hn99zg/2f/oNQN6BFJEXV1rjO7wW/h41vNrT3HgWTvg4nfAW6hHsZ9Psbi5lTsJrn399CbbBd0zM7g8vGj+DqySMZ2CvcG51IBGosqM6KSMfYu9l1gC+7Dyr2ujaT7s4An3wjDD4h1HhllTU8vmwDd81bw/rdDUOOnTKiN7Py8/j00QNJ05BjoUv5OqsaKyIdobYWPn7R7cuuX9jQnjvcnQV+0uXQpXt4+YAPNu/lznmFPPvuZqqDndkeWRl8+bRhXD15FENyu4aaTw5NSgxgY4w5ClgIjAS+BZwL/B34GXBnaMHa4tWfubvLV5dDVSlg3c/qctf+6s+SHunZZ5/lhBNOICsri5EjR/KLX/yCn/zkJ/WXdKxduxZjDPfdd99BzzXGcMstt9T//5NPPmHGjBmMHj2arl27MmzYMC6++GI++eSTA5533333YYyhoKCA2bNn069fP/r27ctFF13E5s0NZ9mMHDmSd999l9dffx1jDMYYpk+f3hEfg4jfyva4HfDXf+V+lgU35agqg6X3wB9PhUcvb+gIHzoOvng/fGMZnDqzwzvCi0urePiN9dz27495+I31FJdWAe4y7VdXbefLcxdz7h/n84/3XEf44JxsfnjO0Sz8/qf4/llHqSPcA5GoseBdnVWNFUkBzdVYgO0r4amvwu+Od1ddVeyFzO4w/ga48W24+O4O7whvrsYC7NpXwa0vfcSkX/ybHz+9gvW7SzEGPnPMQJ64YSJ/u2ESnzl2kDrCPRCJOutZjQXVWZGU0Fydra6Atx6EP0+Ahy9p6AgffAJ84W74xtswfnaHd4S3tC87/+OdXHH3Es6+bR5Pvr2J6lrLgJ5ZfP+so1jw/U/xg3OOUUd4CkuVM8MvBbKBL1hrVwdtrxhjRgBfNsZca62tav7p7aimGvZuattzyvfCgtugppmD/lWl7vFjzoesNl5a0WsopLd9Mb744otccMEFTJkyhUceeYTq6mp++ctfsn379ja/FsDmzZsZMGAAv/nNb+jbty/btm3j9ttvZ/z48axcuZIBAw68pOXqq6/m3HPP5aGHHmL9+vXcfPPNXH755bzyyisAPPnkk1xyySX06NGDP//5z+6t9tJlJ9KJNHcGzvM3w2GnwPYPoWxXw/xjPucu0x4xKSln5lhrufWlj5hbUEhamqG8sobsLunc8swKph7Rj/W7Svlo+776+Y8a1JNZ+Xmce8IQMnUjEd+kdo2FjquzqrEi0dTSWa5Hn+9u1PXJSw3zd+/vdspPuRa69UlCvOZr7JdOGUatreVvyzZRUV0LQJeMNL5w8mHMnDqK0f17dHg+abPUrrPal1WdFWmrZuvszTBsPGxfBfu3Ncw/+gx3341R+aHvy04b04+Ne8r4YEtJ/fxHDOjBdfl5nH/iELIyNORYFKRKZ3jdxkFxk/bi4LGapCXZuwl+f3z7v25NBdwxpe3P++Z70HtEm5/2ox/9iCFDhvDiiy+SlZUFwGc+8xlGjRrV9gxAfn4++fn59f+vqanhnHPOYcCAATz88MN885vfPGD+z3/+8/zud7+r//+ePXu4+eab2bJlC4MHD+akk06iW7du9OrViwkTJhxSJpGU1vgMnDpVweXP6xa4n2mZcMIlMPEbMOCopMa79aWPuGvemvodcXCXagO8vLJhR2TS6L7Mnjaa/CP66UYi/op+jYVDq7OqsSLR1FKNff/Rhra+h7shII6/FDKTdyVTSzX2wcXr6ttyumZyxYQRXDVpJP17ZiUtn7RZ9Ous9mVVZ0Uaa6nOrilwP9MyYOzFrs4OGpvUeC3V2Rc/aNiXPW1UH66flsf0MQN0pVXEpMrpeQ8Cu4HbjTGjjDG9jDHnA1cBv7XW1rb8dGls//79vPnmm1x88cX1Gw8AOTk5nHvuuYf0mpWVlfz6179m7Nix9OjRg4yMDLp3787+/fv58MMPD5r/vPPOO+D/xx/vNsrWrVt30LwinU7ZHncUvaq0+XlMOly/AM7/U9I7wotLq5hbUFh/E8xY0gw8MmsCD103gWlj+qsj3G+qse1INVbEc/HUWAxcOBe+9iaMm5HUjvB4aizA9z53JAu//yn+47NHqiPcf6qz7Uh1VsRz8e7Lzp4HF81Jekd4vPuyf5k5nsdmT+RTR+neG1GUEmeGW2vXG2MmAE8BhY0e+l9r7Y9iPccYU9TKy+YcUpheQ90R7LZY/jd4/ddQXdb8PBldYdp33ZGxtuZpoz179mCtZdCgQQc9Nnjw4Da/HsC3v/1t5syZw3/+53+Sn59Pbm4uxhjOPvtsysoOft99+/Y94P91GzLl5eUHzSvS6ax4mvo7aDcnIws2LIIBRyYlUmPPL99Ca5sDWZnprNm5nwl5fVuZU8KW8jUWOq7OqsaKRM8HT7d+CXZmV/d9kpb884aeX76FtFbydc1MJ7dbF7pnpcSuXKeX8nVW+7IHza86K9KCePdlN74BA49JSqTGnl++pdXNgKzMdDbsbumguaS6lNiCCsZTexbYClwIFAHTgP80xtQ2txHRIdIz2n4p1ynXuBsGtMi6cQi75h5qsrj17t0bYwxbt2496LEtW7bU/zs7250FU1Fx4Phwu3btoqmHHnqIK6+8kv/5n/+pb6usrGT37t3tFVsk+mqqYPkT8OpP3U1FWlJVBvsObVzEQ2WtZcma3cwtKKS8uuWTmMora9hR0sp7EC+kfI0Fr+qsaqyIx3athmX3uxrakhBqLEBxWRXPvbel1bPCy6tUY1NJytdZj2osqM6KeKumGj54Cl77uZf7sgDL1u3mrnmFlFdpX7azS4nOcOAXQE/gJGtt3dbra8Fl9z82xtxtrV3b+AnW2tyWXjA42n5oR9TbqmtvNw7Soj/FvlQksxtM/FpSNh4AunfvzmmnncYTTzzBL37xi/oj2Xv37uXZZ5+tn2/gwIFkZ2fz3nsHnj3w9NNPH/Saxhi6dOlyQNs999xDTc2hD4GXlZUV80i8SOSU74W37ofFt8d/U6PMrtBjQOvztYOaWssLy7cyt2A1725sOtxlbNld0nXZdupI7RoLXtVZ1VgRD21cBgt/DyufhXhGpEhijQXYXFTGPfPX8MibG9hXUd3q/KqxKSe166xHNRZUZ0W8U7kf3noQFv8JitbH95wk1tnaWstLK7f9f/buOzyu6tr7+PfMqFuyii25SpZG7hX3KplmAyYJPUDo4EIgQEJuknuTvAkppN5wEwgJFqaHQGixCRiwaZbcC8a23O2R5CbbslWsrtHMfv/Yo4KtLs2cM9L6PI8ejfcU/WzJWnP22WdtMjKdbMsratNzpM52f4EyGT4R2NPozUOdrei+5yOBXH+HapdLfqI/n7+brvLoNw919/vJr371K6688krmz5/P9773PWpra/nd735HZGQkRUX6F4RhGNx22208//zzpKamMmHCBDZv3sw///nPC17v6quv5sUXX2TkyJGMGzeOtWvXsnTpUmJiYjqccezYsbz66qu8+eabpKSkEBUVxYgR/m8JIYTPnMuHTc/A1heg2jvJbNhg5NVw4CNw1zT/XOWB0df6NF5ljZu3th1l2doc8s42HPyMH9ybvfmluNzNX/7m8SgWjOvYparC7wK/xoKl6qzUWCEswOOBg6tg/ZMNG08D9B4MZSfB08Kksx9qLMDe/HM8m+nk3R0nqPXomhoRbKO6VuFWUmO7kcCvsxaqsSB1VghLKDsNmzNg87NQVewdNGD4FXD4U9OPZatcbv69/TjPZjlxFpTXj48Z2JsDp+RYtqcLlMnwE8BYwzAilFKNT0fP9H5u41JKExkGXPpT/WZhzwr9iyMyQf8C8NNZ9MbmzZvH8uXL+elPf8rNN99M//79eeCBB6isrOQXv/hF/eOeeOIJDMPgD3/4A2VlZVx66aW89957JCcnf+X1nnzySex2O48//jiVlZXMnDmTjz76qMObmAD8/Oc/58iRI9xzzz2Ul5czd+5cPv/88w6/nhCWcXqfPpjY+S/wuPRYcARMvANmPgCxyfDpr01bgXO2rJqXN+TxysY8Cssb3sRcPqof9891MCU5jj+t2s+yrJwmL+UOD7azMC2F6PBgn+QTXS7wayxYqs5KjRXCRLXVsOtNXWcLGm181388zH5E/05Y8zvTaqxSig2Hz/JMppPMAwX14/FRodwzO5nbpg9hWZZTamz3Evh11kI1FqTOCmGqM4dgw1Pw5Wvg9rYSCQqDi27TvyP6pJp6LFtcUcM/Nubx4vo8zpQ1tDq5ZEQ8i9NTmeGI44nVB6TO9nCGamHVgVUYhnEt8G9gLfBnoAS4GPghsEYpNa8Dr1kcHR0dXVxc3OT9dTtBDxnSgd6lAeyxxx7jF7/4BYHwc9Gcnvq9ExanFOSt1yvUDnzYMN4rHqYtgan3QUTcVx//2eNNr8CZ9ZBegdPazh/tlHe2nGVZOby57Wh9H7UQu43rJw1iYZqDoQmRjeIpnlh9gIxMJzabQVWNm7AQOx6PYnG6g0fnDcfo4nxmi4mJoaSkpKS1S5cDjdRY/+kONRZ67vdPWFxlMWx7ATY+o1d+10m9FGY9DI6LG+qmCTW21u1hpbflWPbxcw3x4nuxJD2VayYOJDTI7o3X82osSJ1t52tKnW1Cd6izPfV7JwLA0c2w7i+w733qN8gMj4Npi2DaYujVt+GxJtTZY0UVPLc2h39tOUpFjZ7kDrYbXHPRIBanOxjeL6pRvJ5XZ7trje2ogFgZrpRabhjGPOC/gb8BkehLyX4FPGFiNCGEaJnHrXuUrn8Sjm9rGI9L1W8EJtyi3xicz48rcL48WkxG5mE+zD6J9ypteocFcfuMIdw9K5mE3mFNxDP4/vwRLJzjYGV2PgWl1cRHhbJg3AA5ix5gpMYKIQJayXHY+De9MWZNqR4z7DDuRl1n+4+78Dl+rLEVNbW8sUW3HDtW1NAlY1pyHIvTHVw6MgGb7asH3FJjuxeps0KIgOXxwIEPYN2TcHRjw3hsMsz8jl4NHhJx4fP8WGezj5eQkenk/V35uL0Hs1GhQXxrehL3zE6hf7Qcy4oLBcRkOIBS6mPgY7NzCCFEm7gq4ctXYf1foSinYXzwNJj9MIxYADZ7668THguT7+7yeB6P4vMDp1m6xsmmnML68UEx4dw7J4WbpyYSGdp6iYiOCObWaUldnk/4l9RYIUTAObVbH5xnv9XQ/zskEibdBTO+DTGJrb+Gj2oswJmyal5an8srG/MortAt0QwDrhjdn8VzHUxKim31NaTGdh9SZ4UQAcVVpVt6rn8Kzh5sGB84UV9tNeobYG/DdKKP6qxSiqyDZ8jIdLL20Jn68f69w7h3TjK3TEuid1jrk9pSZ3uugGiT4gtyaVn3Jd87Yarys7DlWb2ZSMXZhvERC3Sv0qQZ5mUDqmvdrPjyBM9mOjl4uqx+fNSA3ixJd3D1+AEE220mJrQuubSs7aTGdm/y/ROmUQpyMvXVVocazStG9oPp98OUe/SBt4lyzpTzbJaTt7Ydo6ZWtxwLDbJx4+TBLExzkNK3l6n5rEzqbNtJne2+5HsnTFVZBFueg01Lofx0w/iw+XoSPHlOl7c3aQ+X28N7O0+QkZnD3vyGlmMj+kWxKN3BNyYMJCRIjmWbIjX2qwJmZbgQQlhaYY7eJGT7P6DWexm0PUS3QZn5EMQPNzXeuSoX/9x0hBfW5XDqXMNGImnD+rI43cGcoX27XV80IYQQ3YS7Fvau0L1K83c0jPcdrluhjL8ZgkLNywdsyysiI/Mwq/acom6tUUxEMHfOTObOmUPoG2luPiGEEKJZxUdg4991yzFXuR6zBcO4m3Sd7Tfa1Hhl1bW8vvkIz6/N4URJVf34TEcfFs91cPHweDmWFe0ik+FCCNEZx7fpy7T3vqs3BAEIi4Yp98H0JRDV39R4+SWVPL82h9c2H6WsWl9GbrcZfG38ABalORg7KNrUfEIIIUSzasr1SeYNf9UH6nWSZuqrrYZdATbzVoB5PIpP9p1m6ZrDbM0rqh9PjAtn4RwHN00ZTESIHG4JIYSwqPyd+mqr7HdA6U0nCe2tW5vM+Db0HmhqvNPnqnhhfS6vbszjXJU+lrUZcNW4ASxJdzB+cIyp+UTgkndnzbDZbLhcLrNjiA5wu90EB8umB8KHlIKDq/Ubh9yshvHoRJjxAEy6A0Kjmn++H+w7eY6MTCfvfnmCWu9GIhEhdm6ZmsS9c5IZHNvERidC+InNZqOmpgallKziCDBKKWprawkJCTE7iujOygp0u7Etz+pLtgEwYNTXYNYjkDjV1HhVLjfLtx/n2SwnhwvK68fHDYpmyVwHV47pT5C0HBMmkmPZwCXHssLnlALnZ3pBl/OzhvGoAXoCfPLdenGXiQ6dLuPZTCf/3n6cGrdecBYWbOPmKYncN8dBUh85lhWdI5PhzQgLC6OsrIzCwkLi4uLMjiPaqLCwkOrqaqKizJ2IFN1UbY3eqGv9U3B6T8N4v3F6U8wx14HdvDevSik2OM+ydI2TNQcK6sf7RoZyz+xkbp8+hOgIeXMtzBcZGcnJkycpKCigb9++2Exc2SnazuPxcObMGWpqauS9kfCNs4d1jd3xGtR6L4O2h8LE22Dmd6BPqqnxSipc/GNTHi+sy+VMWUPLsYtHxLM43cFMRx85wScsQY5lA5Mcywqfcrtg97/1gq6TuxrG40fpY9mxN0KQeYsdlFJszSti6ZrDfLy3oV95XK8Q7pqZzB0zhxDXSxZjiK4hk+HN6Nu3L9XV1Zw6dYri4mLsdrvZkUQr3G53/ZuHvn37mh1HdCdVJbDtRdj4DJSeaBh3XKLfODguMXUjkVq3hw+yT5KR6WTX8ZKGePG9WJzm4NqJgwgLlt9hwjqio6OpqKjg7NmzFBUVERwcLHXW4txuNy6XC4/HQ+/evYmOlhZLogsd3QLr/gz73ge8DbfDY2HqIpi2GCLjzUzH8eJKnsvK4fUtR6io0ZeRB9kMvnHRQBanOxjZv7ep+YQ4nxzLBh45lhU+U10GX7wMG/8GJUcbxpPT9KaYw+aZeizr9ihW7znJ0kwn248U148P6RPBojQHN04eLMeyosvJZHgzDMNg0KBBnDlzhqqqKjwej9mRRCuCg4Pr3zx02aqcyiLYswLKTkNkAoy+Rh+cWYXk65zW8p074d1I5EWo9u5Wbdhh7PV6I5EBE3wesaTCxcrsfApKq4mPCmXB2AH1q7sramp5Y8tRnluXw9HCyvrnTE2OZXF6KpeNTMBmkxVqwnpsNhuDBg0iJiaGc+fOUVtbK3XW4oKDgwkPDyc6OpqICLk0VbRRS3XW44GDH+lNMY9saHhOTJJeBT7xdgjp5dN4LdVYgD0nzpGReZj/7MzH7W05FhkaxK3TErlndgoDY8J9mk+IjpJj2cDjk2NZ0b21dixbego2PQNbn9OLuwAMm37crIdh0CSfR2ypzla53Lz9xTGWZeWQc6ah5dhFiTEsSXcwf0x/7HIsK3zEUHXbnfcwhmEUR0dHRxcXF5sdRViRUvDZ4/pSXcMGrkoIDtcbJM56CC75ialnTyWfj/ONvk5v1rXrTfB4+y0G94LJd+k+ajFJfoioeGL1ATIyndhsBlU1bsJC7Hg8ittnDCEi2MYrm45QXKHzGQbMH92PxempTB5ioRMO3UhMTAwlJSUlSqkYs7NYndRYIXq4lursjG9DbIqus2cONDxnwAS9Keaoa8Du2/U6LdXYRWkpTEuJ49msHLIOnql/Tr/eodwzO4VvTU+id5i0HPMFqbNtJ3VWiB6stWPZsTfBxr/CjtfBXaOfExSuTzLPfBDiUvwQsfk6e+eMIUSGBfHyhjzOltfUP+fyUQksTk9lanKsnBDyAamxXyWT4fIGQjTl01/DhqfBVXHhfcERuohc+lP/56oj+TqnpXyGTb+RqNMrAaYvgan3+XVV+59W7WdZVg6VLneLjwsJsnHj5MEsnJOCIz7ST+l6JnkD0XZSY4Xo4Vqqs+cberleoZaS7rcT5S3VWIP6Ri0ADEuIZFG6g2suGkhokFym7UtSZ9tO6qwQPVh7jmUj+sC0JTB1IfTq47eIbT6Wtdu4duJAFqU5GNZPeuX7ktTYr5I2KUKcr7JIn2Wt27jpfK4KfUnvsCsg1IRf2FXn9Nd31zR9v+RrWWv56t48xDpgzndh/M0QHOa3eKAvJ8vIdFJd2/IlrYvTUliUnkp8VKifkgkhhBCtaO19VJ0x10Pa96H/WP/k8mqtxtZNhE9OiuHBS4dy8XBpOSaEEMIiWquxdcey0Ukw5xGY8C0I8W97u7Yey947O5n756aS0Nu/x9pCgEyGC3GhPSv0GdWWuGvgucv9k6cjJF/n2EP1xpiT7zLly6/Mzm/1wDs82E5KfKRMhAshhLCWtryPCgoHx1y/T4RD22psaJCNG6ckcunIfn5KJYQQQrRBW2qsPRTSvgdT7vVPpvOszM7H1sqVXuHBdob1i5KJcGGaVv4XCdEDlZ3WfbdEz+WugfIC0758QWk1VTUtX1JW5XJTUFrtp0RCCCFEG7XlfVRtlX6cCdpSY2tqPVJjhRBCWE9RbustyNw1UH6m5cf4iMvtYc3+glbbo8ixrDCbrAwX4nyRCXoDipaKTHC43gRy/C3+y1Vn5+t6w4yWDjQlX/Pami8ywX+ZzhMfFUpYiJ3KFg7Ww0LssipcCCGE9bT1fZRJdVZqrBBCiIBTlKf7hG97ofXHmlBjS6tcvL75KM+vyyG/pJU2aUidFeaTyXAhzjf6Glj5w5YfoxRMvAPCY/wS6Ssm3q43zWiJ5Gtem/J5YPS1fonTlAVjB/CzFdktPsbjUSwYN8BPiYQQQog2Gn0NfPCjlh9jYp1dMHYAj727u8XHSI0VQghhCSe+hPVPwu7loFpebV3PjzX21Lkqnl+Xwz83HaG0qhaAuk5kHtX886TOCrNJmxQhzhceC32GNX9/cATMesiciVzQ+WY9pHM0RfK1zOr5gNBgG+HB9mbvDw+2szjdQXR4sB9TCSGEEG1QV2ftIU3fb3KdjY4IZnG6g7Cgpg+DpMYKIYQwlVJw6GN46euQMRey39YT4b0Hwfxfw6yHTT+WPXiqlB+8uYM5v/+UpWuclFbVEh5s5+5Zyaz5wSU8eMnQZo9npc4KK5CV4UKc79RuOO1dMWQL0gdzrkp9uZHywMwHdYsPM9V9/fVP6Q00JF+3yvfUpwc5V1WLzYAgmw273aCqxk1YiB2PR7EwLYVH5w03NaMQQgjRrEt+AtnvQOFhMOy6vlqozj46bzjbjxSz9pDuqWqA1FghhBDmcrv0xPf6p+BUo6uEE8bA7IdhzPUQFKIny4NC/X4sq5RiU04hGZlOPt3XsO9H38gQ7pqZzO0zhhDbS58Ir6ujGZlObDY5lhXWYyjVwrUL3ZhhGMXR0dHRxcXFZkcRVvPqTXBwFfQdDnd/APvf05s8RSboy41MXDF8gcoivaO05OsYC+Y7cKqUBX/JotajePjSodw3x8HK7HwKSquJjwplwbgBchbdJDExMZSUlJQopWLMzmJ1UmOF6OGqy+APDnBXw6Q7ITrRMnW2zs1LN7App5ApQ2JJHx4vNdYCpM62ndRZIbqR6lLY9hJs/BucO94wnpIOsx6BoZeBYVz4PD8dy7o9io92n2RpppMdR4vrxx19e7EwzcH1kwYR1swq8JIKlxzLWoTU2K+SleFCNOZcoyfCAS5/DCL7wuS7zUzUsvBYydcZFsvn8Sh+8u9d1HoUyX0ieOCSoYQF27l1WpLZ0YQQQoi2O/ypngi3BetLusOizU70FYXlNWzJLQTg2xenctmofiYnEkII0eOcy4dNz8DWF6C6RI8ZNhhznW51MnBiy8/38bFsZY2bt7YdZdnaHPLONmyKPSkphiVzU5k3qh82WxOT9I1ERwTLsaywJJkMF6KOxwOrf6ZvJ82EEQvMzSN6nDe2HmVLbhEAv752XLNn2IUQQghL279Sf05Js9xEOMCn+07jURARYmf20L5mxxFCCNGTnN6nW5zs/Bd4XHosOAIm3gEzH4DYZFPjFZbX8PKGXF7ekEdheU39+LzR/ViS7mBKcpyJ6YToGjIZLkSd3e9A/pf69rxfNX0pkhA+UlBazW9W7gXguomDmDNMDs6FEEIEIHct7P9A37bowoJVu08CkD4sXk48CyGE8D2lIG89rH8SDnzYMB7RF6bfD1PvgwhzJ5nzzpazLCuHN7cdpcrlASDEbuP6SYNYmOZgaEKkqfmE6EoyGS4EQG01fPJLfXvUNyBxqrl5RI/z+Pt7OFdVS3R4MD+5epTZcYQQQoiOObIeqor1bQtOhlfWuMk8WADA/DHSHkUIIYQPedyw9z96Evz4tobxuFSY9R2YcKve/NJEO44Wk5Hp5IPsfDzeLQV7hwVx+4wh3D0rmYTeYabmE8IXZDJcCIAtz0FxHtiC4LKfm51G9DBrD55h+ZcnAPifq0bSNzLU5ERCCCFEB+17X38eOAmiB5mbpQlZBwuocnmw2wwuHZlgdhwhhBDdkasSvnwV1v8VinIaxgdPhdmP6JPFNvOuTPJ4FJ8fOM3SNU425RTWjw+KCefeOSncPDWRyFCZLhTdl/x0C1FZDJl/0Lcn3wN9h5oaR/QsVS43P12+C4CpybF8c0qiyYmEEEKIDlKqYTJ85NXmZmnGqj2nAJieEkdMRIjJaYQQQnQr5Wdhy7OwOQMqzjaMj1gAsx6GpBmmtmOtqfWw4svjPJvl5MCpsvrxUQN6syTdwdXjBxBst5mWTwh/kclwIdb9GSqLICQS5v7I7DSih3n6s0Pknq0g2G7wm+vGtbojtxBCCGFZJ3dCyVF9e+TXzM3ShFq3h0/26snw+aOlRYoQQoguUpgDG56G7f+A2ko9Zg+B8TfDrIcgfoSp8c5VufjnpiO8sC6HU+eq68fThvVlcbqDOUP7YsieaaIHCajJcMMwLgZ+DEwDQoBc4M9KqQzzUomAVnIMNv5d3579CETGm5tH9CiHTpfyzJrDACxOdzCsX5TJiURPJjVWCNFpdavC41JNP/Bvyta8IooqXADMG9Pf5DSip5E6K0SAqSyCPSug7DREJsDoayA89quPOb4N1j0Je98FpTedJDQapt6rN8aM8l2tKalwsTI7n4LSauKjQlkwdgDREcFfeUx+SSUvrMvln5uOUFZdC4DdZvC18QNYlOZg7KBon+UTwsoCZjLcMIy7gOeAZ4EnABcwEv1GQoiO+ew3UFsFkf1g5oNmpxE9iMej+PE72bjciqS4CB66dJjZkUQPJjVWCNEl9q3Un0debepl4M1Z7W2RMnZQbwbFmLthmehZpM4KEUCUgs8eh/VPgWHT/b+Dw+GDH+lV3hf/GA59rDfFzM1qeF7vwTDzAZh0J4T6bpGTUoonVh8gI9OJzWZQVeMmLMTOY+/uZnG6g0fnDWf/qVIyMp28++UJar27YkaE2LllahL3zklmcGyEz/IJEQgCYjLcMIxE4O/Aj5VSf2h01ycmRRLdwcls+PKf+vYlP4aQXubmET3KW9uOsTlXb1by62vHEhZs3gYqomeTGiuE6BJFuXBK74FhxX7hSilW7TkJwLxRsipc+I/UWSECzGeP65YntVUNY64K/XndX2Dr81/tB95vrL7KfMx1YP/qymxfeGL1AZZl5VBd66kfq6xxA7DUOwGeV1hRf1/fyFDumZ3MbdOTZK8MIbw6NBluGEYKcBnQD3hVKZVrGEYI0B84qZSq6cKMAPd5Pz/Vxa8rerKPHwMU9B0BF91udhrRg5wtq+Y3H+wF4JqLBpI+XNrziK/yc52VGiuE6Ly6VeG94mHwVHOzNGHfyVKOFuo+rvPHSL/wnkyOZYUQzaos0ivCG0+EN+auaZgId1ysN8VMvdRvV0OVVLjIyHR+ZSK8sZpaT/1EuKNvLxalO7hu4iBZeCXEedq9TaxhGL8HDgAZwC8Bh/euMGAP8ECXpWuQDuwFrjcMY79hGG7DMI4ZhvE77xuXpnIWt/QBSHOknsz5ORxarW9f/hjYA+IiCdFNPL5yL8UVLnqHBfHTq0ebHUdYjAl1VmqsEKLz6vqFj7gKbNY76F61W7dISYwLZ2R/2aOjp5JjWSFEi/as0K1RWmLYIf0HcOcKGHqZX9uCrczOx2Zr+evZDLhjRhIfPzqXW6clyUS4EE1o12S4YRhLgB8ATwPzgfr/hUqpc8C7wNe7MqDXQGAY+mz6k+gz+c8D3wde8MHXE92ZxwOrf6ZvJ83SB21C+Mn6Q2d454vjAPz3VaOIjwo1OZGwEpPqrNRYIUTnlJ+FI+v17ZFfMzdLM+papMwf3R/Dgv3Mhe/JsawQolVlp3WP8JYoD9jNaTdSUFpNlbclSnOUgviosFYnzYXoydq7HPYB4N9Kqe8ahtGnift3At/pfKwL2IAo4Fal1Ovesc8NwwgH/sswjJ8rpQ41foJSKqalF5Qz6j1Y9tuQv0Pfnv8rS27wJLqnKpebnyzPBmDykFhumZpociJhQWbUWamxQojOOfChnhwI7gUpc81Oc4FjRRXsPnEOgPmjpUVKDybHskKIpimlN8PMfgdQLT82OBwiE/wSq7HcM+WsO3SmtXSEhdhlwZUQrWhvm5ThwOoW7i8A+nY8TrPqdif46LzxD7yfJ/nga4ruqLYaPv2lvj36Whg8xdQ4omf5++eHyTlTTpDN4DfXjZOz9aIpZtRZqbFCiM6pa5Ey7HIIDjM3SxNW79EtUuJ6hTB5SKzJaYSJ5FhWCPFV7lo9Af7sJfDS16Fgb+vPUR49l+An248Ucf8r27jkT5+zKaew1cd7PIoF4wb4IZkQgau9K8OrgF4t3D8EKO5wmubtAmY0MV43k9T07gFCnG/LMig+ArYguOxnZqcRPcih02X8/fPDACxKdzBC+pWKpplRZ6XGCiE6rqYCDn+qb1u0RUrdZPhlIxMIsrd7yyTRfcixrBBCqymH7a/Chr9CcV7DeOJ0iBoAB1eBq+LC5wVHwMwHITzGp/E8HsWn+06Tkelkc27DBPigmHAc8b3YkltIlevCXx3hwXYWpqUQHR7s03xCBLr2vhvcDFzX1B2GYYQBdwDrOhuqCe94Py84b3wB+hqWLT74mqK7qSyCNX/Qt6fcC31Szc0jegylFD/59y5q3B4S48J5+NJhZkcS1mVGnZUaK4ToOOdnUFupNxQbNs/sNBcorqipX0k3T1qk9HRyLCtET1d+Bj77DfzfWPjgB96JcEOfzL13Fdy3Cm56UU94B4XpyW8M/TkoTI9f8hOfxauudfPGlqPM/3MmC1/eWj8RPmZgb/5yy0Ws+cHFvHzvNBalOQgNshEeYscAwkPshAbZWJiWwqPzhvssnxDdRXtXhv8R+MgwjFfQm34A9DcM4wrgF8Bg4FtdmA8ApdSHhmF8ADxtGEZfYDdwKfAI8IxSKq/FFxACYO3/QVUxhERB+g/NTiN6kLe/OF5/IP6ra8YSHiI7eotm+b3OSo0VQnRKXYuU5DkQbr0WJJ/uO43bowgLtpE2LN7sOMJcciwrRE919rBeBf7lP6G2So/ZQ2HCLTDrIejbaLGSYcClP9UT33tW6E01IxN0axQfrQgvqXTx6qY8XlyXy+nS6vrx9OHxLEl3MCu1z1c2f/7+/BEsnONgZXY+BaXVxEeFsmDcAFkRLkQbtWsyXCn1sWEY3wb+QsMbhVe8n2uARUqpDV2Yr7Gb0G9SfgjEA0eAnwJ/8NHXE91J8VHY+Iy+PecRiJSDIeEfheU1PP7+HgC+Nn4AF4/w/2YrInCYWGelxgoh2s9dC/u9bY8t2iJl1W7dIiV9WLycjO7h5FhWiB7o2FZY9xfY+x/qN8YMi4GpC2H6kpY3wgyPhcl3+zTeieJKnl+bw2ubj1Be4wYgyGbw9QkDWZTmYPTA3s0+NzoimFunJfk0nxDdVXtXhqOUyjAM4110QR+J7nV2EHhDKXW8i/M1/rrlwH95P4Ron89+A+5qiOwPMx4wO43oQX6zci9FFS6iwoL42ddGmx1HBAAz6qzUWCFEhxzdCJXeXqYjz+8AYb4ql5s1BwoAmD+mv8lphBXIsawQPYDHo3t+r/sLHFnfMB6dBDMfgIl3QGikefmAvfnnyMh08p8dJ6j16En6XiF2bp2WxL1zUhgYE25qPiG6uzZPhhuGEQk8CXyglHoTeMpnqYToSid3wY7X9O1LfgwhLe2bI0TX2XD4LG9tOwbAj64cSULvMJMTCSuTOiuECDh1LVIGXATRg02N0pS1B89Q6XJjM/TmmaLnkhorRA9QWw0734D1T8GZ/Q3j/cfD7Ed0mxN7u9eDdhmlFOsPn+WZNYfJOnimfjw+KpR7Zidz2/Qh0uZECD9p828CpVSZYRi34JtNRYTwndU/BxTEj4SLbjM7jeghqmvd/GT5LgAmJsXwLbmETbRC6qwQIqAoBfve07ct2iJl9R7dImVaShyxvUJMTiPMJDVWiG6sshi2vaDbopadbBhPvRRmPQyOi3UfcJPUuj28vyufjEwnu0+ca4gX34sl6alcM3EgoUHSxksIf2rvabE9QLIPcgjhG4c/g8Of6NuX/8LUM8GiZ3nmcyfOgnLsNoPfXDcOm828N2AioEidFUIEhlPZUHxE37ZgixS3R/HxXj0ZPm+0tEgRgNRYIbqXkmOw8e+w7UWoKdNjhh3G3ag3xew/ztR45dW1vLH1KMuycjheXFk/Pi05jsXpDi4dmSDHiEKYpL0zg38A/mYYxitKqQO+CCREl/F4YPXP9O0hs2H4FebmET2Gs6CMpz87BMDCtBRGDWh+4xMhziN1VggRGPat1J9jkyHBentifHGkiLPlNQDMH93P5DTCIqTGCtEdnMyG9U9C9tvgqdVjIZEw6S6Y8W2ISTQ1XkFpNS+tz+WVjXmUVLoAvTD9itH9WTzXwaSkWFPzCSHaPxk+EjgK7DIM4z30ZiMV5z1GKaV+1RXhhOiU7Lfg5E59e96vTL00SvQcSil+ujybGreHQTHhPHLZMLMjicAidVYIERgat0ix4HusVbv1pfKjBvQmMS7C5DTCIqTGChGolIKcNbDuyYYrvwEi+8H0+2HKPRBu7iSzs6CMZ7NyePuLY9TUegAIDbJx4+TBLExzkNJX9i4TwiraOxn+WKPb1zXzGAXIGwhhLlcVfOL9MRxzHQyebG4e0WP8e/tx1h8+C8Cvrx1LRIi05hHt8lij21JnhRDWVHykYcHByKvNzdIEpRSrvP3CZVW4aOSxRrelxgoRCNy1sGe5Xgmev6NhvO9w3Qpl/M0QFGpaPIBteYUsXeNk9d5TKKXHYiKCuXNmMnfOHELfSHPzCSEu1N5ZmhSfpBCiq21ZBiVHwBYMl/3M7DSihygqr+HX7+8F4OpxA7hkZILJiUQAkjorhLC+uhYpEX0gcbq5WZpw4FQZeWf1gt/5Y2QyXNSTGiuElVQWwZ4VUHYaIhNg9DUNq7tryuGLV2Dj0w37UwAkzYTZj8CwK8Bm82m8kgoXK7PzKSitJj4qlAVjBxAdEQyAx7svRUamk615RfXPSYwLZ+EcBzdNGSyLooSwsHb971RK5fkqiBBdprIIMv+ob0+5F+Ic5uYRPcZvP9hLYXkNUaFB/Ozr1uufKqxP6qwQIiDUtUgZcRXY7OZmaUJdi5RBMeGMln07hJfUWCEsQin47HFY/xQYNnBVQnA4fPAjffweHKEXt1UVe59gwKivwaxHIHGqH+Ipnlh9gIxMJzabQVWNm7AQO4+9u5t7ZyeTGBfBsrU5OAvK658zblA0S+Y6uHJMf4Lsvp2kF0J0XodPVRmG0YeGs+s5SqmzXRNJiE7KekIXzpAomPtDs9OIHmKT8yxvbD0GwA+uHEG/3mEmJxKBTuqsEMKSKgohb72+PfJr5mZpxuq93hYpY/phWLCfuTCf1FghTPTZ47Dhaaitahhzedv3b/xbw5g9FCbeBjO/A31S/RbvidUHWJaVQ7W37zdAZY0bgL+vcX7lsRePiGdxuoOZjj5Sb4QIIO2eDDcMYwLwJDDnvPEs4GGl1M4uyiZE+xUfhU1L9e0534VefU2NI3qG6lo3P/73LgAmJMZw2/QhJicSgUzqrBDC0g58BMqtV+45LjY7zQVOFFey81gJAPOkX7g4j9RYIUxWWaRXhDeeCG/KrIf1R2S8f3J5lVS4yMh0fmUivClfGz+A71w6lJH95eojIQJRuybDDcMYC6wFwoB3gWzvXWOArwNZhmHMUkrt7tKUQrTVZ4+DuxqiBsCMB8xOI3qIjDVODheUY7cZ/Oa6sdhtsipAdIzUWSGE5e1/X39OvVRf1m4xH3tXhUeHBzMtOc7kNMJKpMYKYQF7VujWKC0JjtArwf08EQ6wMjsfWyvHcmHBNmYP7SsT4UIEsPauDP8l4AJmKaV2Nb7D++Yi0/uYG7omnhDtkL8Tdryub1/yYwiJMDeP6BFyz5Tz1GeHALh3djJjBkabnEgEOKmzQgjrclXCoU/0bYu2SFm1W0+GXzYqQfq2ivNJjRXCTK4qfXVRXUuUZh9XqTfV9DOlFFtyC+tbojSn2uWhoLTaT6mEEL7Q3snwdODp8988ACilsg3D+Btwf5ckE6K9Pv45oCB+FEz4ltlpRA+glOKny7OpqfUwKCac714+3OxIIvBJnRVCWJfzcz2JYdhh+BVmp7lASYWLjU7d+nn+6P4mpxEWJDVWCDNUFsGW53Q70/I2THIHh0Nkgu9zebncHt7fmc/STCd788+1+viwEDvxUaF+SCaE8JX2Tob3Ak62cH++9zFC+NfhT/UHwLxfgL3De8MK0WYrvjzB2kNnAPjlNWPoFSo/d6LTpM4KIaxr33v685BZEGG9FiSf7T9NrUcRGmQjfbjsGyMuIDVWCH8qPgIb/w7bXgJXuR4z7IAC1UJPbuWB0df6PF5ZdS2vbz7CC+tyOV5cWT9uGKBU88/zeBQLxg3weT4hhO+0d+bGCXwNeLqZ+7/mfYwQ/uPxwOqf6dvJaTBsvrl5RI9QXFHDr97bA8BVY/tz2SjZpEt0CamzQghr8rhh/wf6tlVbpOzR85xpw+KJCJET1OICUmOF8If8nbD+Sch+R2+4DBASBZPvghnfhm0vwoanm26XEhwBMx+E8BifxTtdWsWL63L5x8Y8zlXVAmAz4KqxA1ic7uDjvadYlpVDpevCdinhwXYWpqUQHR7ss3xCCN9r77vEl4HfGobxT+BxYJ93fBTwP8B84L+7Lp4QbbDrTTjpvdpx3i/0qVwhfOz3H+7jbHkNkaFB/PzrY8yOI7oPqbNCCGs6ugkqdAsSRi4wN0sTqlxu1uwvAGD+GDlBLZokNVYIX1EKnJ/Buif15zpRA2D6/TDlHgjz7q10yU/05/VP6c00XZW6NYry6Inwuvu72KHTZTyb6eTf249T49Yr08OCbdw0OZGFaSkM6aMvDBk/WOfMyHRisxlU1bgJC7Hj8SgWpqXw6DxpjSlEoGvvZPj/ApOAW4CbgbprW2yAAbwB/KnL0gnRGlcVfPorfXvM9TBosrl5RI+wJbeQ1zYfBeC/5g+nf3SYyYlENyJ1VghhTfve15/7j4OYJHOzNGHD4bOU17ixGXDZSP/1mhUBRWqsEF3N7YLd/9YrwU82ascfPxJmPQTjboKg8/prGwZc+lM98b1nhd4sMzJBt0bp4hXhSim25hWxdI2Tj/eeqh+P6xXCnTOHcOfMZOJ6hZwXz+D780ewcI6Dldn5FJRWEx8VyoJxA2RFuBDdRLsmw5VSbuBmwzCWAdcCKeg3DoeB5Uqpj7s8oRAt2ZwBJUfBFgyX/T+z04geoKbWw4/f0W/0xg+O5o6ZyeYGEt2K1FkhhCUp1TAZbvEWKVOGxNEnUjY2ExeSGitEF6ougy9eho1/08fjdYbMgdkPw9B5YLO1/BrhsTD5bp/Ec3sUq/ecYmnmYbYfKa4fT4qLYFFaCjdOTiQ8xN7ia0RHBHPrNOud/BVCdF6HmukppVYDq7s4ixDtU1EIWf+rb09dCHEOc/OIHuHZLCcHT5dhM+A3143DbpO2PKLrSZ0VQljK6b1QlKNvj7za3CxNqJv0AGmRIlonNVaITig9BZuega3PQVWJHjNsMOrrMOsRGGzuldpVLjdvf3GMZVk55Jwprx+fMDiaJXNTuWJMfzl+E0K0bzLcMIw4YLBSamcz948HjiqliroinBAtWvuELsChvSH9B2anET1A3tlynvzkIAD3zE5h7KBokxOJ7kbqrBDCkupWhcckQb+x5mZpwpdHizhTVgPAvNEyGS6aJjVWiE4oOAAbnoIdr4Nb/74lKBwm3qbbnZi8MK2ovIZXNubx0vpczpbX1I9fOjKBxekOpqfEYcjeYkIIr/auDP8Dus/apGbufwHYAtzfmVBCtKooDzYt1bfnfBd69TE1juj+lFL8dHk21bUeBkaHycYpwlekzgohrGffe/rzyK9ZcqPyVbv1qvCR/aPqN0AToglSY4VoryMbYd1fYP/KhrHwOJi2GKYtgl59zcsGHC2sYFmWkze2HqPS5QYg2G5w7UWDWJTuYHi/KFPzCSGsqb2T4ZcA/2jh/neBOzoeR4g2+uxxfUY6aiBM/7bZaUQP8O6OE2QdPAPAL64ZS6/QDnWZEqI1UmeFENZScgzyv9S3LdgiRSnFqroWKbIqXLRMaqwQbeFx68nvdU/Csc0N47HJMPM7cNFtEBJhWjyAXcdKWJp5mJW78vEoPRYVGsS3ZiRxz6wU+keHmZpPCGFt7Z3NGQgcaeH+Y97HCOE7+Ttg5xv69qU/Mb0Qi+6vpMLFr97bC+gDbbkEW/iQ1FkhhLXs864GDI+DxBnmZmnC4YKy+r6w88f0NzmNsDipsUK0xFUJO16D9X+FwsMN4wMn6U0xR30DbC1vOulLSinWHChg6RonG5xn68f79w7jvjkp3DItkaiwYNPyCSECR3snw8uBIS3cPwSo7ngcIdpg9c8BBQmjYcKtZqcRPcDvP9rHmbJqeoXYeewbY8yOI7o3qbNCCGupa5Ey/EqwW++qqI+8LVIGRocxZmBvk9MIi5MaK0RTKgphy3OweSmUFzSMD7tCT4IPmW1qi6yaWg//2XGCZ7Oc7DtZWj8+ol8Ui9MdfH3CQEKCbKblE0IEnva+o90E3GUYxh+VUqWN7zAMIwq4E9jc5DO7mGEYjwE/B3YopS7yx9cUFnDoE3B+pm9f/gtTz0xbQUmFi5XZ+RSUVhMfFcqCsQOIjpCz4V1pW14h/9ykFxF9f/4IBsaEm5xIdHOWqLNSY4WQGgtAZRHkrdO3LdgiBahvkTJvdD/ZHE20xhI11vv1HkPqrPC1yiLYswLKTkNkAoy+BsJjG+4vyoMNT8P2V8BVocdswTD+mzDrIUgY5dN4rdXZ0ioXr28+ynNrczh5rqp+fFZqHxanO5g7PF5+7wshOqS9k+H/C3wMrDcM4xfAl4ACJqKL+WBgYVcGbIphGGOAHwGnfP21hIV43N5V4UByGgybZ24eEymleGL1ATIyndhsBlU1bsJC7Dz27m4Wpzt4dN5weWPQBVxuDz9+JxuAsYN6c9esZHMDiZ7A9DorNVb0dFJjGzm4Gjy1EBQOqZeaneYCJ0uq2HG0GJAWKaJNTK+xIHVW+IFSeo+t9U+BYdPtT4LD4YMf6UnuEVfDhqdg93JQetNJQnvDlHtg+v3Q27fdglqrs7dNT+KF9bn8c+MRSqtrAbAZsGDcAJakpzJucLRP8wkhur92TYYrpT4zDOMB4C/AvxrdZQA1wHeUUh93Yb4LGIZhA54DlgHjgBhffj1hITvfgFO79O15vzT1Ui2zPbH6AMuycqiu9dSPVdboNzLLsnIAvYpZdM6yrBz2nyrFZsBvrxuP3dZzf+aEf5hdZ6XGCiE19ivqWqSkXmrJPVpW79Vzib3DgpiWEmdyGmF1ZtdYkDor/OSzx/WK79qG1dT1K7+z/gSZf2wYjxoIM74Nk++GMP+0mmqpzv7ts0M8/dmh+k0xw4Pt3Dw1kfvmpJAYZ706JIQITO1u/KeUWmoYxnvAN4Gh6DcP+4G3lFLHuzhfU76HPms/H73jt+gJXFXw6a/17bE3wKBJ5uYxUUmFi4xM51fePDRW6XKTkelkYZqD6PAedjl3FzpaWMFfPjkAwF2zkmUFgvAbk+us1FjRo0mNbcRVBQe984JWbZGy+yQAl43qR7Bd+sWK1smxrOj2Kov0ivDGE+GNKW996zsC5nxPH1sHhfgtXmt11u2dBI+NCOae2SncMWMIsb38l08I0TN0aBcc7xuF/wMwDCMY+AawwDCM/yilTnZhvq8wDMMB/BK4TSl1rqVLVA3DKG7l5WRmK5BsXgrnjukeZpf+P7PTmGpldj62VlYo22wGK3flc+u0JD+l6l6UUvx0eTZVLg/9e4f1nBWAwjLMqLNSY4WQGvsVOWvAVa4vsR9+pdlpLnCuysVG51kA5o/uZ3IaEUjkWFZ0a3tW6N/bLQkKhRkPwEW3+idTI22ps8F2g+/NG86dM5P9E0oI0eO0awmFYRh/MAxjS6M/G8Bq4A1gKbDLMIzUro34la/1LPCRUmq5L76GsKiKQsj8k749bRHEpZibx2THiyrrLyNrTmWNm39uPsI7XxzjyNkKlFJ+Stc9vL8rnzUH9E7qj31jDJGhHTpvKES7mVVnpcYKoRWUVrdaY6tq3BSUVvspkYnqWqQkzYJefczN0oTP9xfgcitCgmykD483O44IAHIsK3qEs4cbWqI0p7YGyk/7J895ThS3fixb61YUV7j8lEgI0RO1d4bnSvSmI3W+DqQDf0BvQPIU8N/Aoq4Id55FwBRgdFserJSKael+79l2OaMeCLL+BNUlEBoN6T8wO41pCkqreWl9Ls+tzWnT43cdK+HRN3YA0DcylClDYpk8JJbJybGMHRhNSJBcTtyUkkoXv/jPHgAuH9WPK8bIajPhV2bVWamxokdTSpF58Awrvmy9S0JYiJ34qFA/pDKRxw37P9C3Ry4wN0sz6lqkzBnal15y0lq0jRzLiu7r9D7dHmXHa60/NjgcIhN8n6mRwvIaXt7QtmPZHlFnhRCmau87x0TgYKM/fx3IUUr9N9TvjH1bF2WrZxhGX/SblN8C5YZhxHjvCgLs3j9XKaWaaYwlAlZRHmzO0LfnfBciet7mSM6CMp7NyuHtL45R00xvtfPZbQbTU+LYdayE0upazpRV8+Huk3zoPXAMCbIxYXA0k4fE6QnyIbHESS82AP740T4KSquJCLHzi2vG0NIlrEL4gN/rrNRY0ZO53B7e23mCpWuc7DtZ2qbneDyKBeMG+DiZyY5tgXJ9hRQjrDcZXl3r5vP9Op+0SBHtIMeyontRCvLWw/on4cCH7XieB0Zf67NYjeWdLWdZVg5vbjtKlattx7I9os4KIUzV3snwEKDxNS2X8NWz607AF7+1BqPPfP/W+3G+IuD36DP5ojv59NfgroHeg/Qu1z3ItrxClq5xsnrvKVSjjUTumJlMeXUt/9x0hErXhZeYhQfbWZiWwvfnj8DtURw8XcrW3CK+yCtia14RRworqKn1sCW3iC25RfXPc8T3YnJSLFOSY5k8JI7U+F49biL4iyNFvLrpCACPzhvOoJhwkxOJHsiMOis1VvQ4ZdW1vL75CM+vzeFEScP800xHH/pGhvDx3lNUNnHQHmw3WJzeAzbP3Pe+/txvrCXb0204fJay6loMQ2+eKUQbybGs6B48bt3Kat1f4Pi2hvE4B8z8DhQf0QvKmmqXEhwBMx+E8BifRtxxtJiMTCcfZOfj8R7LRoUFcfuMIVS73Ly2+WiLx7Ldvs4KIUzV3snwo8AMIMN75twB/KzR/QlAWRdla+wQ+s3K+f4MRAILgSM++LrCTCe+hF1v6NuX/ERfztXNeTyKj/eeIiPTyda8honqxLhwFs5xcNOUwUSEBKGUIiLETkamE5vNoKrGTViIHY9HsTAthUfnDQf0CvGR/Xszsn9vbp8xBIDTpVV6Yjy3iG1Hisg+XoLLrXAWlOMsKOfNbccAiIkIZnKSbqsyOSmWCYkxhAXb2/X3KalwsTI7n4LSauKjQlkwdgDREdZ5Y9M4X1yvEF5an4tSMHpAb+6elWx2PNEzmVFnpcaKHuP0uSqeX5fLq5vyKK2qBcBmwIJxA1ic7mD84BiUUjyx+sBXaqzNZuD2KOw2g0Vp1psc7lJKNfQLH3m1uVmasWrPKQAmJ8XKpfSiPeRYVgQ2VyV8+SpseBoKnQ3jg6bA7Ef072ybXf8etwfrtimGTT8vOFyvCJ/5oD629gGPR/H5gdMsXeNkU05h/fjA6DDunZPCLdOSiAzVx7K9QoNaPZYVQghfMdqzsZ5hGI8B/w/4ABgDxALJSqli7/2ve/88o8uTNp3ncyBGKXVRB55bHB0dHV1cXNzVsURXUApevgZy1kDCGLg/Sxf2bqrK5ebf24/zbJYTZ0F5/fj4wdEsTndw5Zj+BNkv7PF9wWTzuAHtPote5XKz81gJ2/KK2JZXyLa8Ioqa2LAk2G4wZmA0k4fE1vcfT+gd1uRrNjWRUPcGZ3G6g0fnDTd11XlT+YLsBi63/n24/IFZXJQUa1o+YU0xMTGUlJSUtNbHszOsVGelxoru5NDpUjIynSzffoIat17xHRZs4+Ypidw3x0FSn4gLntO4xoYF2Xhi9QGqaj08dOlQvj9/hL//Cv5zeh/8bbq+vSQTBkwwN895PB7F9N9+QkFpNT9eMJLF6T7Z71CYwNd11ko11vv1PkfqrGiL8rOwZRlsXgoVZxvGh18Fsx+GpJnQ1LFVZRHsWQFlp3WP8NHX+mRFeE2thxVf6mPZA6cazieN7B/F/XNTuXr8AIJ9dCwrhGgbfxzLBpL2rgz/LbrX2rVACXBnozcP0cA3gP/rwnyipzr8iZ4IB5j3y247EV5S4eIfm/J4YV0uZ8qq68cvHhHPkvRUZjjiWpw0jo4I5tZpSZ3KEBZsZ1pKHNNS4oBUlFI4z5SzLbeIbXlFbM0r5HBBOS634sujxXx5tLh+45PEuHCmDIljkneCfHi/KOw2gydWH2BZVg7VjXqc1+0avixLP9fMiYSm8tVNhNttBp/sOy2T4cIsUmeF6CJKKbbkFrF0zWE+2Xe6frxPrxDumpXMHTOGENvCfhnn19iyGjdPfnKQZ7Oc3D5jCP2aOSEc8OpWhUcnQv/x5mZpwo5jxRSU6vdM80b3NzmNCDBSY0VgKczRq8C3/wNqK/WYPQTGfxNmPQzxrRxPhcfC5Lt9Fu9clYt/bjrCC+tyOHWu4Vh2ztC+LE53kDasr8+PZYUQoiPatTK8xRcyDBsQBVQopS5cVmoxcjbdwjxuWJoOp7IhJR3ufLfpM90B7FhRBc+tzeFfW45S4Z0kDrYbfGPCIBanOxjRP8rkhF9VVF7DF0d0z/FtuUXsOFb8lYnkOlGhQYwZ1JstuUW4Pc3/bgkNsrH5J5ebcua/pMLFtN983GT+OmbmE9Zl9tn0QKqzUmOFmdwexardJ1ma6eTLo8X148l9IliY5uDGyYPb3fYLdJ/xi//4GWfKarhlaiK/u8F6E8VdIuMSOPEFTFsCC/5gdpoL/P7Dffz988MMS4hk9aNzzY4jupCZdTaQaixIne32jn+hN8Xcs0K3NgEIjYap98L0+yHK3BOB+SWVvLAul39uOkJZtW45ZrcZXO1tOTZ2ULSp+YQQFzL7WNZq2rsyvFlKKQ/6DLsQnbPzX3oiHPSq8G40Eb77RAkZmU7e25lfP1kcGRrEt6Yncc/sZAZEW7MvemyvEC4b1a9+k6qaWg+7T9S1VtGT5AWl1ZRW17LRWdjKq0GtR3H385tJ6dvL19EvkHOmnNoWJuoBbDaDlbvyZaWCsBSps0K0rMrl5q1tx1iW5ST3bMOmYRclxrAk3cH8Mf2x2zr+niIyNIhHLhvG/1uxmze2HuW+OSkM62etk9edVnJcT4SDdfuF7z4JwPwxsnGm6DpSY4XplIKDq/UkeG5Ww3jvwTDzAZh0J4SaW3P2n9Qtx97dcbz+qtqIEDs3T03k3tkpJMZd2HJMCCGsqMsmw4XoEq5K+PTX+va4m2DgRHPzdAGlFGsPnSEj00nWwTP14/16h3Lv7BRunZ5E77DAWoEcEmRjYlIsE5NiWZim/47HiirZmlfIC+ty2Xms5WMJt0ex/Wgx2xut2LOSqhp3/SXYQgghrK2ovIaXN+Tx8oZczpbX1I9fPiqBxempTE2O7bJ9Km6ZlsTz63LJOVPO7z/cx7K7pnbJ61rG/pX6c1gMDJllapSmHDpdxmHv3irzpUWKEKI7qK2B7Lf0Zpen9zSM9xurW6GMvV5vhmkSpRQbnGfJyHTy+f6C+vG+kSHcPSuZ22cMISai+ZZjQghhRTIZLqxl01I4d1z3Qrv0p2an6RSX28P7O/PJyHSyJ/9c/fjwfpEsSnNwzUWDCAm6cCORQGQYBolxESTGRVDl8nDw9J76HuFNsdsMLhocjSM+0o8pNWdBGV8eK2mxjUtYiJ34qFA/phJCCNFeRwsrWJbl5F9bj1Ll0peRh9htXDtxIIvSHD5ZtR1st/GjK0dw/z++4OO9p9noPMsMR58u/zqmqZsMH36lqZMvzVm95xQA/XuHMU4uwxdCBLKqEtj2Imx8BkpPNIynzIXZj0DqpaZeIV3r9vDh7pNkZDq/stDJ0bcXi9IdXDdxUIdajgkhhBXIZLiwjopCyHpC3566CGKTTY3TUeXVtby+5SjPr83heHFl/fj0lDjun5vKxSPiu2yFmhUtGDuAx97d3eJjgmwGz98zzdSe4S1Nhns8igXjBvgxlRBCiLbaeayYpZlOPtiVT92v8qiwIG6fMYR7ZiWT4OONLa8Y059JSTF8caSY367cy/IHZ3ePul5ZDDmZ+rZVW6Ts0S1S5o3uh60TLW+EEMI0507Axr/rifBq74Ipww5jroNZD8HAi8xMR0VNLW9uPcaytU6OFjYcy04eEsvidAfzRsnvXyFE4JPJcGEdmf8L1SV6c5D0/zI7TbudLq3ixXW5/GNjHueq9EYiNgOuGqs3EpmQGGNuQD+JjghmcbqDZVk5VLouXB0eHmxnYVqKaZtTWj2fEEKICyml+PxAARlrnGxwnq0fHxAdxn1zUrhlWhKRof55W2sYBj9eMIobn9nAjmMlvL8rn6+NH+iXr+1Thz4GTy0EhcHQy8xOc4HT56rYfqQYkH7hQogAdGqPboWy603wePdoDY7QvcBnPACxQ0yNd7asmpc25PHKhlyKKnQ+w4B5o/qxZK6DyUPiTM0nhBBdSSbDhTUU5cLmDH077VGICJxie+h0GcuynLzzxXFq3Poy7bBgGzdNTmRhWgpD+vh/k0izPTpvOAAZmU5sNoOqGjdhIXY8HsXCtJT6+yWfEEKIltTUenh3xwmezXSy/1Rp/fjI/lEsmevga+MHEmz3f8uxKclxzB/dj1V7TvHHj/Yzf3T/wG99tu89/dlxCYRY773Lx3tPA/oqgOkp3ag1jRCi+1IKctfqTTEPrmoY7xUP05fAlPtMP+7NPVPOs1lO3tp2jOpab8uxIBs3TBrEwjQHqSa0tRRCCF+TyXBhnsoi2LMCyk7DgQ/1GfLeg/UbA4soqXCxMjufgtJq4qNCWTB2ANEResXw1txCnlnj5OO9p+ofH9crhDtnDuHOmcnE9eq5G4kYhsH3549g4RzHV//9xg2wxIprq+cTQogu0bjORibA6GsgPNbsVPVaqrGlVS5e23yE59fmcvJcVf1zZg/tw+L0VNKH9TW9NckPrxzJJ/tOk3e2gn9uyuPu2Smm5umU2mo4uFrfHrnA3CzNqGuRcsmIhMA/8SCECHwt1Vh3Lex9V0+Cn9je8Jw+Q2Hmd2DCrRDs25Ze0HKd3X6kiIxMJx/uPonythyLDg/mjhlDuGtWsuyfJITo1mQyXPifUvDZ4/oyMcMGroqG+/qP05fnmkwpxROrD1ywcvixd3dz2cgE8ksq2X60YSORpLgIFqWlcOPkRMJDZCOROtERwdw6LcnsGM2yej4hhOiQC+psJQSHwwc/0v1IL/mJqZtytVRjvzUtiWC7wWubj1JarVuO2W0GC8YNYEm6g7EW2jRxaEIkt0xN5NVNR3jy00NcP3kwvcMC9IRqTibUlAEGDL/K7DQXKK1ysf6Qbo8jLVKEEKZqqcZOvx96D4KNT+srn+skTodZD8OIBWDz/cm8lursvFH9OHWuii15RfWPHxQTzn1zUrh5aiK9/NRyTAghzCS/6YT/ffY4bHgaaqsuvC/nc33/pT/1e6zGnlh9gGVZOfWXigFU1uj+0iuzT9aPTRgczZK5qVwxpj922UhECCGEFTRVZ+tOPG94Wn82sc62VGNfWJ9bPxYebOfmqYncNyeFxLgIf8dsk0cuH8a/tx+nsLyGpWsO84MrRpodqWPqWqQkzYDIeHOzNGHNgQJq3B5C7DbmDrdePiFED9JSjV3350YPNPTk9+yH9e9WP2qpzr63K79+bMzA3ixOd3D1uAEEmdByTAghzCKT4cK/Kov0WfSmJsJBn1lf/5S+fCw8xq/R6pRUuMjIdH7lzcP5bAY8e+cULh2ZYPpl2kIIIUS9Vutshal1ti01FuDBi1NZmOYg1uItxxKiwliU5uAvnxzkubU53DEjmf7R5l/h1i4eD+z/QN8eebW5WZqxarduSTdraB+iAnX1vRAi8LVWY+uMvxXSvw99h/knVyNtPZb9+22TmT+mnxzLCiF6JDn9J/xrzwp9OVlLDBvsWe6XOE1ZmZ3f6tXjocF2TpdWy5sHIYQQ1mLxOrsyOx9bK7UzPNjO4LgIy0+E11mU7qBvZChVLg//t/qA2XHa7/g2KPPufzLCev3Ca2o9fLZPb545f3R/k9MIIXq0ttTY4HAYMsOUiXBo+7FsYUWNHMsKIXosmQwX/uPx6J6UjXuEN8VVqTciMUH28RJeXJ9LlavlFWtVNW4KSqv9lEoIIYRog3P5sOM1y9bZyho3q/ecpNLlbvFxVa7AqrGRoUF893I96fHmtqPsP1lqcqJ2qmuRkjAa+qSam6UJG51nKa2uxTDg8tEJZscRQvRUHg/krmtDja0y7Vh2b/45Xt4gx7JCCNEaaZMifM9VpQ/ON/wVzh5q/fHB4XpHbj9RSpF58AxL1xxm/eGzbXpOWIhddtgWQghhDaf36cu2d/4LPK7WH+/nOnu2rJqXN+Tx8oZciipazxeINfbmqYk8vzYH55lyfv/hPp6/e6rZkdpu3/v6s0VbpKzeo1etT0yMISEqwFrQCCECX2017HxD19kz+1t/vAnHsusPn2VpppPMAwVtek4g1lkhhOhKMhkufKeiELY8B5uXQnmjwmzYQLVwtlp5YPS1Po/ncnv4z44TZGQ62ddoFdfQ+Ehyz5ZT61HNPtfjUSwYN8DnGYUQQogmKQV562Ddk3Dwo4bx8DioPgee2hae6586m3umnGVrnby59Vh979Jgm4FbKVoosQFZY4PtNn545Uju/8c2Pt13mg2HzzIztY/ZsVpXcADOHtS3LdgixeNR9ZPh86RFihDCnyqLYdsLsPEZKDvZMG6RY9lat4eV2SfJyDxM9vFz9ePJfSI4VlQpx7JCCNECmQwXXa8oDzb+Db54BVzleswWDOO/CbMeguy39Q7cTV1iFhwBMx/06aZeZdW1vL75CM+tzSG/pGHzk1mpfVic7mDu8Pj6HbibupQ7PNjOwrQUosNlAychhBB+5nHD3v/A+id1r+c6cakw6zsw4VbI+lPzdRZgyj0+rbNfHi0mI/MwH2afrJ/07h0WxO0zhnD3rGRe2ZjXLWvsFWP6MXlILNvyivjtB3tZ/sBsbDaL92Ota5ESNRAGTjQ3SxN2HS/h5Dn9Xm3+mH4mpxFC9Aglx2Dj32HbS1DjXTBl2GHsDfpYdu+7ph7LVtTU8q8tR3lubQ7Hiirrx6cmx7IkPZVLRybwfx/LsawQQrREJsNF1znxpT44370clLfwhvaGyXfDjG9D74F67JKf6M/rn9Jn1l2V+nIy5dFvHuru72KnzlXxwrpcXt2UR2mVXjFnM2DBuAEsSU9l3ODo+sc+Om84ABmZTmw2g6oaN2EhdjwexcK0lPr7hRBCCL+oqYAvX9UH4EU5DeODp8LsR/SqXptdjzVVZ4PCoNZ70Hxqj+59auu6rWM8HsVn+0+zNNPJ5pzC+vFBMeHcOyeFm6cmEhmq33Z21xprGAY/XjCSG/6+gZ3HSnh/Vz5fnzDQ7Fgta9wixYIbqa3ao1djpsb3IjU+0uQ0Qohu7WS2rpvZbzVcXRXcq+FYNiZRj/Ufpz/7+Vi2oLSalzfk8vKGPEoqdcsxw4ArRvdn8VwHk5Ji6x/bXeusEEJ0FUOpFq5T7cYMwyiOjo6OLi4uNjtKYFMKDn+iL9POWdMwHjVQv2mYfBeERTf93MoivSN32WndV230tT45i37wVCkZmU6Wf3kcl1v/vIcH27l5aiL3zUkhMS6i2eeWVLhYmZ1PQWk18VGhLBg3QM6iC9FDxcTEUFJSUqKUijE7i9VJje1C5Wdhy7OwOQMqGu1rMWIBzHoYkmY0P4l5fp21h8Dyb+v7Ln8M5nyv0/Gqa92s+PIEz2Y6OXi6rH581IDeLEl3cPX4AQTbm5507641dskrW/lo9ymS4iL4+NG5hARZdL/60pPwpxH69h3LIfUSU+M0Zd4Tazh4uoxvX5zKj64caXYc4WNSZ9tO6mwXUQpyMmHdX/QxbZ3IfjB9CUy5F8Jjm36un45lnQVlPJuVw9tfHKPG23IsJMjGTZMHszDNQUrfXs0+t7vWWSFE+0mN/SpZGS46xu2C7Hf0SvBT2Q3jCaP15WNjb4SgkJZfIzxWn2n3AaUUm3MKych08sm+ht28+/QK4a5ZydwxYwixvVrJB0RHBHPrtCSfZBRCCCGaVejUq8C3v9qwotseAuNv1nU2fkTrr9FUnT26WfdA/fTXkJwGg6d0KF5JpYt/bjrCC+tyOF1aXT+eNqwvi9MdzBnaF6OVlcbdtcb+8MqRfLz3NEcKK3h1Ux73zE4xO1LT9q/Un0OjIXmOuVma4Cwoqz/BMn+0tEgRQnQhdy3sWa6PZfN3NIz3Ha5r7PibIaiVDSZ9eCwLsC2viIzMw6zac4q69YsxEcHcOWMId85Kpm9k6xtgdtc6K4QQnSWT4aJ9qkt1/7SNf4dzxxrGk9P0ZdpDLzf1Mlu3R7Fq90meyXSy42hxQ7w+ESxKd3DDpMGEBdtNyyeEEEK06Pg2fbXV3ncbNugKi4Yp9+lValGd3ETwyt/CkY1QsBfeuhfuz2r+Cq4m5JdU8vzaHF7bfJSyan0Zud1m8LXxA1iU5mDsoLa/VneVGh/JrdMS+cfGIzz5yUFumDyY3mEWXIlX1yJl+BVgt16+uo0zE6JCmTA4xtwwQojuoaYctv8DNvwVio80jCfN1FdbDb+yS1uItZfHo/h47ykyMp1szSuqHx8cG86iNAc3TRlMRIhM4QghRGfJb1LRNqUn9QT41hegukSPGTYYfY1+4zBokqnxqlxu3tx2jOeynOSebdjMZGJSDEvSHcwb3R+71TexEkII0TN5PHBotZ4Ez1vbMN57MMx8ACbdCaFRXfO1gsPhxufh2UugOA/e+x7c8FyrJ7L3nTxHxhon7+44Qa13V8yIEDu3TE3i3jnJDI5tvuVYT/TIZcN554vjFFW4eObzw/zQai0+qs6B09vebuTV5mZpRt1k+LzR/ay/EakQwtrKCmDzUtiyTLc3AcCAUV+DWY9A4lRT41W53CzffpyMLCfOgvL68XGDolmc7uCqsf0JaqblmBBCiPaTyXDRsoL9+vKxnW+Au0aPBYXDpDtgxgMQZ+6lv4XlNbyyIY+XNuRSWF5TP375qASWzE1lypDYVi/TFkIIIUxRWwO73tSbcBXsbRjvNw5mPwxjrvPNit1+o+GKx+H970P225B6GUy87YKHKaXYcPgsSzOdrDlQUD/eNzKUe2Ync/v0IURHWG9FsRXER4WyON3Bnz8+yHNrc7hj5hAGRIebHavBodXgcenWO0MvMzvNBQpKq9l2RE9YzZMWKUKIjjp7WNfYL/8Jbm9LL3soXPQtmPkd6DvU1HglFS7+sSmPF9blcqasoeXY3OHxLJnrYKajjxzLCiGED8hkuLiQUnBkg95I5MCHDeMRfWDaEpi6EHr1MS8fcORsBcvWOnlj61GqXN6NROw2rps4iEXpKQxN6KIVdEIIIURXqyrRV1ptegZK8xvGHRfrlmOOS3zfcmzKfXD4M9j3Hqz8L0icBn2HAVDr9vBB9kmWZh4m+/i5hnjxvVic5uDaiYOk5VgbLEpz8I+NRzhTVs0Tqw7wx5smmB2pQV2LFMfFXXfVQRf6ZK/ukRsZGsTMVHPfcwohAtDRzfpYdt/7gLfhdngsTF0E0xZDZLyp8Y4VVfD82lxe33KEiho3AEE2g29cNJDF6Q5G9u9taj4hhOjuZDJcNPC49UHxuifh+NaG8dgUvZHIRd/Sl1ebaMfRYjIynXyQnY/3Km2iwoK4fcYQ7pmVTELvMFPzCSGEEM0qOQ6b/g5bX4SaUj1m2GHs9brODvDjZKlhwDeeghPb4dxxeOseKu78iDe2n2bZ2hyOFVXWP3TKkFgWpzu4fJS0q2iPXqFBfG/eMH7y72ze/uIY96WlWGOCo7YGDq7Wty3aImWVt0XKxSPiCQ2SEy9CiDbwePRCrnV/gaMbG8ZjkvQq8Im3Q0gv8/IBu0+UkJHp5L2d+bi9B7ORoUHcOi2Re2anMDDGQlcQCSFENyaT4QJclfrSsQ1/hUJnw/igKfoy7ZFfA5t5ByJKKT7fX8DSzMNsdBbWjw+MDuPeOSncMi2JyFD5URZCCGFRp3bry7R3vQkevekkwb10L/CZD+gDdTNExMENy1AvXo1xchfv/GERj1XpdimGAfNH92NxeiqTh8Sak68buHlKIs+tzcFZUM7vP9jHC/dMMzsS5GZB9TnAgOFXmZ3mAmXVtaw9dAaA+WM6uWGsEKL7c1XBzn/pY9kzBxrGB0zQV1uNugbs5h0rKqXIOniGjExn/e82gH69Q7lndgrfmp5kzU2WhRCiGwuIGUTDMC4D7gBmAolAIbAZ+LlSapeZ2Sytsgj2rICy0xCZoDe7DG90QFtRCJufhc0ZUNFQmBl+lZ4ET5rp08u0SypcrMzOp6C0mvioUBaMHfCV3qM1tR5WfHmcZ7OcHDhVVj8+sn8U989N5erxAwiWjUSEEKJTpMZ2Qkt1Vik96bjuL3Do44bn9EqA6Utgyr16MtqHWquzOWfKefaLKPrXXs/D9re4nffJDB5Dn4nfYFFaCo74SJ/m6wmC7DZ+dOVIlryyjc/2F7D+0BlmDe1rbqi6FimJ0yDKev24Mw8UUFPrIdhucPEIc1sZCNEVpM52UGvHspVFsPV52PgMlJ9uGB96Ocx6GFLSTT2Wdbk9vL8zn6WZTvbmN7QcG5YQyaJ0B9dcNFCufBFCCJMYSimzM7TKMIw3gT7AG8BeoB/wQ2AscLFSamMLT2/uNYujo6Oji4uLuzKqNSgFnz2uV6EZNr3yOzgclEdfhj3hW7Dxb7D9H1DrvQzaHgLjvwkzH4KEkT6Op3hi9QEyMp3YbAZVNW7CQux4PIrF6Q4WpqXw+uajvLAul5PnquqfN2doXxanO0gb1lc2EhFC+F1MTAwlJSUlSqkYs7N0JamxHdBSnZ3xoN6gcv1TkP9lw3P6DNU1ePwtEOzbll6t1dmLR8STkelk1R7dl9mOmzfCfsNk9uIJ74Pt2+ug9wCfZuxJlFLc9MwGtuYVMW5QNCsenG1euxmPB/5vtO5VP++XetWkxXzvX1/y7+3HSR8ez8v3WmAlvfAbqbPtes3uW2dbO5adeKduObbtJXCV6+fYgmDsjfr+/mN9HK/lGrs43cG/tuhj2ePFDS3HpqfEsWSug4uHJ0jLMSGE33XXGttRgTIZnqCUOn3eWAyQA3yqlLqhA6/Zfd9AfPpr2PA0uCouvM+wg3I3/Dk0GqbeC9Pvhyj/XIr6p1X7WZaVQ6XLfcF9QTYDwwCXW/9c2m0GV48bwOJ0B2MHRfslnxBCNKW7voGQGtsBLdVZDOo36wJInKGvthp+Fdj8czVTS3XWZlC/5wbAoJhwFqalcPNwGxHPpUNVsV5Nd8dyU1ukdTfb8oq44e/rAXjy1ol8Y8JAc4Ic2wbLLtW3H/oC+qSak6MZLreHyb9azbmqWn597VhunzHE7EjCj6TOtus1u2+dbfVY1kN9nQ2Jgsl3wYxvQ/Rgv8Rr7VjWZkCN91jWZsCVY/uzOD2VixJj/JJPCCGa0l1rbEcFRJuU8988eMeKDcM4CPin6gWKyiJ9Fr22qun76ybCowbojUQm3wWhUX6LV1LhIiPTSXWtp8n7a71H6GFBNm6dnsS9s1NIjIvwWz4hhOhppMa2U2t1tu4AfdgVkPZ9SJrut2jQep2tmwgf2T+KBy4ZyoKx/Qmqazl2zV/hX7dDTias+7POL7rE5CGxXDW2Px9kn+SPH+3jijH9zLk8ft97+nPfEZabCAfYnFPIuSrdV3/eaOu1cBGiI6TOtkNbj2V7JcDMB2Hy3RAe4690bT6WDQ0y+OaUJBampTCkj7mbdgohhLhQQEyGN8UwjHj0pWWvNXN/cSsv0T2XGe9ZoS8na4k9BNJ/AFPv80+mRlZm57d6WViw3eAHV47gvjkOP6USQgjRmNTYFrSlzgaFw8gFfp8Ih7bV2dAgG3fOHHLh6uRRX4cp98HW5+DTxyE5HRKn+jBtz/KDK0awas8pjhZW8o+NR7hvTor/Q9T1Cx95tf+/dhus2n0SgAmJMfTr7dt2QkKYSepsM9p6LDv3RzBtoX8yNdKWGhtkM/iv+SNZlC7HskIIYVUBufugoRtGZ6Dz/6/Jcayl+Egzl2034nZBxVn/5DnPqZIqKmsuvKSssVq3ory65ccIIYTwDamxrTi+vfU6W1ulN/wyweGCslbrbE2thzNlNU3fecXjkDBar757+16oKvFByp7JER/Jt6YlAfDUpwcpqXT5N8CZQ3Bmv7498mv+/dptoJRi1Z5TAMyXVeGiG5M624Lio207lq0s9E+e85w61/qxrNujmmyhIoQQwjoCcjIc+CNwLXC/UmpvUw9QSsW09AF0r6O7cydg1f/T/dVaExyud+T2o4qaWl5an8tLG3JbfWxYiJ34qFDfhxJCCNEUqbHn83hg/wfw/JXwxYutP96EOrvnxDm++/p2nlub0+pjW6yzweFw4/MQFKZPsP/nu3ozM9ElHr5sGL1C7BRXuHhmzWH/fvH93lXhUQNg4ET/fu02yD5+jvwS3RrhijEyGS66Namz5ys9BZ/80rLHslUuN69uyuOVjXmtPlaOZYUQwvoCrk2KYRiPA98HHlFKvWhyHPOd2qP7qu16EzxtXGGkPDD6Wp/GqnO2rJqXNuTxyoZciirals/jUSwYN8DHyYQQQpxPaux5aqth5790nT1zoNEd522SeT4/1VmlFOsOnWVp5mGyDp5p8/NarbMJo+DK38J734Pd70DqpTDpji5ILOKjQlkyN5UnVh/g+bU53DFjCANjwv3zxetapIxY4LfNXNtj1R7dIsXRtxep8ZEmpxHCN6TOnufMQVj/JOx4HdzNXLF0Pj8eyxaV1/DKxjxeWp/L2fK25ZNjWSGEsL6Amgw3DOOXwI+BHyqlnjQ7j2mUgty1+o3DwVUN473iYdoSqCqGrc83fYlZcITebMTHG43kninn2Swnb207Vr/BSEiQjRsmDcJmM3hn2/EmLx8LD7azMC2F6PBgn+YTQgjxVVJjG6ks1nV00zNQdqphPPUymP0w5GTBxr+ZVmdr3R7e35XP0jVO9uSfqx8fmhDJ4jQHOWfLeHFdXufq7OR74PBnsPdd+OCHkDgd4od39V+lR1qYlsIrG/MoKK3midUH+N+bJvj+i5aegqOb9W2L9gtf7W2RMm9MP3QXCSG6F6mzjRzZCOuebLhiBSA8DqYthupS2PaCqceyRwsreG5tDv/acrS+lgbbDa65aBAhQTb+/YUcywohRCALmMlwwzB+Dvw/4P8ppf5odh5TuGv1Qen6J+HE9obxPkNh5ndgwq0QHKYny4PD9Uo2wwauSv1n5dFvHi75ic8ibj9SREamkw93n6y/qjo6PJjbZyRx16xkEqLCUEoRFxFCRqYTm82gqsZNWIgdj0exMC2FR+fJwbYQQviT1FivkmOw8e+w7UWoKdNjtiAYewPMegj6j9NjKXPBMPxeZ8ura/nXlqM8tzaH48WV9ePTUuJYku7gkhEJ2GwGSimCbLbO1VnDgG88Cce/gHPH4K17YeHH+n2G6JSIkCC+d/lwfvzvXbz9xTHum5PCqAG9fftFD3wAKAjtDclpvv1aHZB3tpx9J0sB6Rcuuieps3hbjr2vJ8GPbW4Yj03Wx7IX3QYhEfpYNiTClGPZXcdKWJp5mJW78vF4j2WjQoP41owk7pmVQv9ofSzbp5ccywohRCAzVAD0gTQM4/vozUXeAx4/7+5qpdT2C5/V6msWR0dHRxcXF3dBQh+rqYAvX4UNf4Wi3IbxxOkw6+HmL3etLNI7cped1n3VRl/rk7PoHo/is/2nWbrGyebchs1MBsWEc9+cFG6emkiv0AvPu5RUuFiZnU9BaTXxUaEsGDdAzqILISwrJiaGkpKSEm+vzm6jx9dYgJPZ+kRz9tvgqdVjIZEw+W6Y8W2IHtz08/xUZ0+XVvHS+lz+sfFI/aaLhgFXjunP4nQHE5Nim3xel9TZvA3w4gI9CTH9frjq95396wj06v4r/pzJ4YJyLh4Rz4v3TPPtF3z1Jn014dgbdE94i1mW5eTX7++lb2Qom358GXabrAzviaTOtus1A6fOuqpgx2v6WPbsoYbxgZP01VajvgE2+4XP81ONVUqx5kABGZlO1h8+Wz/ev3cY981J4ZZpiUSFXVg75VhWCBFIumuN7ahAmQz/HJjbzN15SqnkDrym9d9AlJ+BzRmw+dlGO2YbevJ79sOQNMPUeNW1blZsP0FGlpNDp8vqx8cM7M3idAdXjxtAkN16PSmFEKIjuusbiB5bY5WCnDWw7i9w+NOG8ch+egJ88j0+vwy7NYcLyliW5eTtL45T4205Fhpk46Ypg1k4x0Fy317+CfL57+Hz3+jbt74OI67yz9ft5lbvOcWil7cC8OrC6cwe2tc3X6i6FP7g0P14b3xeT4hbzDef2cDm3EJunZbIb68fb3YcYRKps+16TevX2YpC2PIcbF4K5QUN48Pm6wVdyXP0mV2TuNwe/rPjBBmZzvorUwBG9IticbqDr08YSEiQHMsKIbqH7lpjOyog2qQopS42O4NfnT2sd9L+8lWordJj9lCYcIu+TLvvMFPjlVS6+OemI7ywLofTpdX142nD+rIkPZXZQ/tIr0chhAgQPa7Gumthz3I9CX5yZ8N43xG6xo7/JgSFmhYPYGtuIUsznXy891R9y7HYiGDumJnMXTOH0CfSz/nS/0ufOMhbB8sfgG+vh96yOVhnXT4qganJsWzJLeK3H+zl3QfnYPPFiuhDH+uJcFswDJ3X9a/fSWfKqtmapxd9zB/d3+Q0QnS9Hldni/L0vhpfvAKucj1mC9b1ddZDepNmE5VWuXh981GeX5dDfklV/fis1D4sTncwd3i8HMsKIUQ3FxCT4T3Gsa364HzvfwDv0W9YNExdqDfGjDK3h+KJ4kqeX5vDa5uPUF6jNwyx2wy+MWEgi9IcjB7o436XQgghREdVl8H2f+iTzSVHGsaTZsHsR/RKtaZajvmJx6NYvfcUGZlOtuUVNcSLi2BhWgo3TU4kPKSJy8j9wWaH65+FZ2brK9XeWQR3rmj6snbRZoZh8D8LRnH939aTffwc/9l5gmsuGtT1X2ifd4M6x1wIs957tU/3nsajoFeInZmpfcyOI4ToqBNf6pZju5eD8m4uGdq7oeVY74EmhoNT56p4YV0ur27Ko7RKt0SzGbBg3ACWpKcybnC0qfmEEEL4j0yGm83j0T0c1z+pV1zViU7UG4RMvANCI83LB+zNP0dGppP/7DhBrXcnkV4hdm6ZlsS9c1IYFBNuaj4hhBCiWWWnYdNS2LIMqoq9gwaM+rqeBB88xcx0VLncvPPFcZZlOXGeKa8fHz84msXpDq4c098aLceiB8E1T8Pr34LcLFj7f3rFuOiUSUmxLBjXn5W7TvLHj/Zz5dj+hAZ14UkGtwsOrNK3R17dda/bhVbtOQnAxSMSCAuWEyxCBBSl4PAnelPMnDUN41EDvS3H7tKLu0x08FQpGZlOln95HJdbH8uGB9u5eWoi981JITEuwtR8Qggh/E8mw81SWw0739C7ZJ/Z3zDefxzMegTGXAt28zbgUEqx/vBZlmY6yTzQ0OMtPiqUe2Ync9u0IURHyAYhQgghLOrMIdjwFHz5Gri9Lb2CwuCib8HM70CfVFPjFVfU8I+Neby4Po8zZQ0txy4ZEc/i9FRmOOKsd5n2yKth6iLY8ix89htISYdEH2/82AP84IqRrNp9imNFlbyyIY+FaY6ue/HctVBdom+PWNB1r9tFKmpqyTp4BoD5Y8y9AlII0Q5uF2S/oxd0ncpuGE8YrVuhjL0RgkJMi6eUYnNOIRmZTj7Zd7p+vE+vEO6alcwdM4YQ28u8fEIIIcwlk+H+VlkM216Ajc9A2cmG8dRL9UYijotN3Uik1u1hZfZJMjIPk338XEO8+F4sTndw7cRBXbtiSQghhOhKRzbpg/N971Pfciw8DqYt0hO5kfGmxjtaWMFza3N4Y+tRKrwtx4LtBt+YMIjF6Q5G9I8yNV+r5v8K8tbD6d3w1n1wf5bpG40GupS+vfjW9CRe3pDHU58e4qbJiV234KCuRcqgKRBlvX7cmQfOUF3rIchmcPGIBLPjCCFaU10K216CjX+Hc8caxpPT9NVWQy839VjW7VF8tPskSzOd7Dha3BCvTwSL0h3cMGmwXIEihBBCJsP9puSYftOw7SWo8e5Wbdhh7A367PmA8abGq6ip5V9bjvLc2hyOFVXWj09NjmVJeiqXjkzwzaZOQgghRGd5PHDgA32Z9tGNDeMxQ/Qq8Im3QUgv8/IB2cdLyMh08v6ufNzelmNRoUF8a3oS98xOoX90mKn52iw4HG58HjIu1r3X//MI3PSiqZMf3cHDlw3j7W3HKKl08bc1h/ifq7pggzmlGibDLd4iZYajD9HhcsWhEJZVelIfy259oeFqE8MGo6/RC7oGTTI1XpXLzZvbjrEsy0ne2Yr68YlJMSxJdzBvdH/sciwrhBDCSybDfe1ktm6Fkv0WePRGHQT3athIJCbR1HgFpdW8vCGXlzfkUVLpAvTx7BWj+7N4roNJSbGm5hNCCCGa5aqCna/D+r/C2YMN4wMn6oPzUd8Au3lvdZRSZB08w9LMw6w7dLZ+vF/vUO6dncKt05PoHRaAE4AJI+Gq3+mJ8D3LYfsrMOlOs1MFtL6Rodw/N5U/rT7AC+tyuXNmcuf3ZDmxHUpP6Nsjv9b5kF2s1u3hk726fYG0SBHCogr266utdr4B7ho9FhQOk+6AGQ9AXIqp8QrLa+qPZQvLa+rHLx+VwJK5qUwZEmu9lmNCCCFMJ5PhnVFZBHtW6M25IhP0mfHwWL0SJycT1v1FbyhSJ7IfTF8CU+7Vj/OxkgoXK7PzKSitJj4qlAVjB9RfdussKOPZrBze/uIYNbUeAEKCbNw0eTAL0xyk9DV3BZ0QQogerrkaW3ffluf0xpjlDb1AGTpPX6adPMcvK5Wbq7Mut4f3dp4gIzOHvfkNLceG94tkcXoq35gwkJAgC2yK2RmT7oLDn+rv0cofQuJ0iB9hdqqAdl9aCq9szON0aTVPrDrAn745oXMvWLcqvM8wiB/e+YBdbHNuYf1CjMtHyWS4EH7X0rHskQ36WPbAhw2Pj+gD05bA1IXQq4/P47V0LHvkbAXL1jp5Y+tRqlzeY1m7jesmDmJRegpDEyzeckwIIYSpDKWU2RlMYRhGcXR0dHRxcXH7n6wUfPa4XvFt2MBVqS8bVh7dJ634CJzc2fD4vsN1K5TxN0NQaJf9HZqPp3hi9QEyMp3YbAZVNW7CQux4PIqvTxjIucoaVu89Td23PiYimDtnDOHOWcn0jfR9PiGECEQxMTGUlJSUKKVizM5idT6rsZPuBGx6JbKrXD/eFgzjbtJ1tt/orvxrtBCx+To7NTmWw6fLyD/XsCnmDEccS9JTuXhEfPdaoVZZBM+kQclR6DcWFn4CwQHS7sWiXtt8hP95ZxeGAe8/lMbogb07/mJPz4CCvTD7uzDvF12Wsas89u5uXlyfy/jB0bz7nTlmxxEWIHW27XxWZ4ddodt7ntjW8PjYFF1jL/qWfpyPtVRjr7loIGVVtXy4+yTejmNEhQVx+4wh3DMrmYTeUoOEEKIpUmO/SlaGd8Rnj8OGp6G2qmHM5e1Ntu+9hrGkmfoy7eFXgs1/K8CeWH2AZVk5VHtXfANUejfpemtbw0Yng2PDWZTm4KYpg4kIkR8FIYQQFtBSjd2c0TAWEgVT7obp34boQX6N2FKdXetth2Iz4KpxA1iS7mD84Bi/5vOb8Fi44Tl44So4lQ2rfwYL/mB2qoB20+TBPLc2h0Ony/jdh/t4+d5pHXuhs4f1RDhYskWKUorVe04BMH+0rAoXwq9aqrN7VzSMDZoCsx/Wv0Ns/tt0sqUa+8bWhmPZgdFh3DsnhVumJREZKseyQggh2k6qRntVFumz6I3fPJzPsMFt78DQS/yXy6ukwkVGpvMrbx7OZwC/u2EcN0waTJA9wC/TFkII0X20pcYCXPw/et+NsGj/5GqkLXXWbhi8+53ZjBnk/3x+lzRdfz8++zVsXgqOi2HkArNTBawgu43/vnIkC1/eSuaBAtYePMOcYX3b/0L7V+rPkf1g0OSuDdkF9uSf43ix3rB9/pj+JqcRogdp67Hsrf+CYfP8vjlyW49lH79uLDdNSSRYjmWFEEJ0gFSP9tqzQr9BaElQGJTk+SfPeVZm52Nr5U1LWLAdj0ImwoUQQlhLW2pscARE9TdlIhzg/V0nWn1MSLCNncdL/JDGItIehSHeNhcrHoBzrf8bieZdNiqBaclxAPz2g714PB1oaVjXL3zEVX69OrGtVu3Wq8KH9IlgWEKkyWmE6EHaeixbesLvE+HQ9mNZwzBkIlwIIUSHSQVpr7LTuq9aS1yV+nF+dqyogje3HqXS5W7xcVUuNwWl1S0+RgghhPA7C9dYt0fxYXY+T35yqMUVawBVNT2sztrscH2GbptSWQTvLAZPy+9FRPMMw+B/FowEYPeJc7y7o50nF8oK4MhGfduCLVIAVjVqkdKt+ugLYXUWrrP5JZW8ve2YHMsKIYTwOWmT0l6RCXrjkLq+ak0JDteP85PdJ0rIyHTy3s583G1YPRQWYic+SjbKFEIIYTEWrLFVLjdvbTvGsiwnuWdbyNVIj6yz0YPgmr/B67dCbhZkPQFzf2B2qoA1MSmWq8cN4P1d+fzxo/1cObY/YcFt7Nl74ANAQUgkpKT7NGdHHC2sYG/+OUBapAjhdxass/tPlpKR6eTdHcdxueVYVgghhO/JyvD2Gn2N3mm7JcoDo6/1aQylFFkHC7jjuU1c/eRaVnx5ArdHER8VQpCt5RU2Ho9iwbgBPs0nhBBCtJtFaixAUXkNT35ykNm/+5SfLs+unwhPH9aXYLvU2SaNXADTFuvbn/+2YXWy6JAfXDGCIJvB8eJK/rGxHe336lqkDJsHQdabMKpbFd6nVwiTkmJNTiNED2OROquUYv3hM9z9wmau+HMmb39xDJdbEdcrGLscywohhPAxmQxvr/BYmPWQ7lnalOAIfX94jE++vMvtYfn241z95FrueG4zWQfPADAsIZI/3DietT+6lG9fnEp4M6uHwoPtLE53EB0e7JN8QgghRIeZXGNBr1r9+YpsZv3uU55YfYCz5TUE2w2+OWUwq7+Xzsv3Tef+uVJnmzXvV9BvLCg3vL0QKovNThSwkvv24rbpSQA89ekhSipcrT+pugwOf6ZvW7VFyu6TAFw+ql+rk15CiC5mcp2tdXt4b+cJrnl6Hd96dhOf7y8AwNG3F7+9fhzr//syHpBjWSGEED4mbVI64pKf6M/rn9IbkLgq9eVkygMzH2y4vwuVV9fy+pajPL82h+PFDX3epqfEsWSug4uHJ2DzHlA8Om84ABmZTmw2g6oaN2EhdjwexcK0lPr7hRBCCMsxocYC7DpWwtLMw6zclU9dx7GosCBumz6Ee2Yn0693WP1jpc62IDgMbnwels6FkqPwn4fhppdM2YitO3josmG8/cVxSipd/O3zQ/zPglEtP+Hwp+CuBluwXhluMYXlNWzJLQRg/ph+JqcRoocyoc5W1NTy5tZjLFvr5Ghhw7Hs5CGxLE53MG9UPzmWFUII4TeGUh3Yob4bMAyjODo6Orq4uLjjL1JZpHfkLjut+6qNvrbLz6KfLq3ixXW5/GNjHueqagGwGXDl2P4sTk/losTmv15JhYuV2fkUlFYTHxXKgnED5Cy6EEJ0UExMDCUlJSVKqRizs1hdoNRYpRSfHyggY42TDc6z9eMDosO4b04KN09NJCqs+bopdbYF217SE+EAX/8LTL7b1DiB7K+fHuR/Vx0gJMjGZ/91MYNiwpt/8DtLYOfrkHop3PFv/4Vso7e2HeO/3txBRIidL/7fvLb3QRc9gtTZtguUOnu2rJqXNuTxyoZcirxXtxgGzBvVjyVzHUweEtfsc6XGCiFE15Ea+1WyMrwzwmN9dnB36HQZy7KcvPPFcWrcuq9bWLCNmyYnsjAthSF9erX6GtERwdw6Lckn+YQQQgif8mGNran18J8dJ8jIdLL/VGn9+Mj+USxOd/D1CQMJtrfeSU7qbAsm3alXKe9ZDh/8NyTOgISRZqcKSPfNcfDKxjxOnavmT6v288Q3L2r6gW4XHPhQ3x6xwG/52qOuRUr6sHiZCBfCbD6ss7lnynk2y8lb245RXauPZUOCbNwwaRAL0xykxke2+hpSY4UQQviKTIZbzNbcQp5Z4+Tjvafqx2IjgrlzZjJ3zhxCn0jrbYQkhBBCBILSKhevbT7C82tzOXmuqn58VmoflsxNJX1YXwxp59E1DEOvCD/+BZQcgbfuhUWf6EvxRbuEh9j53uXD+e93dvHv7ce5b04KYwZGX/jAvPVQVaxvW3AyvLLGTeZB3R9YWqQI0T1tP1JERqaTD3efpO4C9OjwYO6YMYS7ZiUTHyXHskIIIcwnk+EW4PYoVu85RUbmYb44Ulw/nhQXwaK0FG6cnEh4iKyeEUIIITri1Lkqnl+Xwz83HqG0uqHl2NXjB7Ik3cHYQU1MLIrOC4+BG5bBC1fB6d2w6v/B1f9rdqqAdOPkwTy3NoeDp8v43Qf7eOW+6Rc+aN/7+vPASRA9yL8B2yDrYAFVLg92m8GlIxPMjiOE6CIej+Kz/adZusbJZu+eAACDYsLrW471CpVpByGEENYhVclEVS43b39xjGVZOeScKa8fnzA4msXpqVw5tj92m6xQE0IIITriwKlSMjKdrPjyOC63XqIWHmzn5qmJ3DcnhcS4CJMT9gBJ0+GS/4FPfw1bnoXUS2Dk1WanCjhBdhv/fdVI7ntpK1kHz5B1sIC0YfEND1CqYTLcov++q/boqx6np8QRExFichohRGdV17pZsf0EGVlODp0uqx8fM7A3i9MdXD1uAEFtaDkmhBBC+JtMhpuguKKGVzbk8dKGXM6U1dSPXzoygcXpDqanxMll2kIIIUQHKKXYlFNIRqaTT/edrh/vGxnCXTOTuX3GEGJ7yUScX815FJxrIDcLVjwIAy6y5Mplq7t0ZALTU+LYlFPIb1fuY/ZDfbHVLZrI3wHnjunbI79mXshm1Lo9fOJtATh/tLRIESKQlVS6+OemI7ywLofTpdX142nD+rIkPZXZQ/vIsawQQghLk8lwPzpaWMFza3P415ajVLrcAATbDa65aBCL0x0M7xdlckIhhBAiMLk9ig+zT5KReZgdx0rqx1P69mJRmoPrJw2SDfvMYrPD9Rnw99lQWQjvLIa73tXjos0Mw+B/Fozi2qfXsSf/HCt2HOe6iYP1nftX6s9xqRA/wryQzdiWV0RRhQuAeWP6m5xGCNERJ4oreX5tDq9tPkJ5jT6WtdsMvjFhIIvSHIwe2NvkhEIIIUTbyGS4H2QfL2FpppOVu/Jxe/Rl2lGhQXxrRhL3zEqhf3SYyQmFEEKIwFRZ4+atbUdZtjaHvLMV9eOTkmJYnJ7KvNH9pOWYFfQeCNf+DV67BfLWQtafYO4PzU4VcC5KjOHq8QN4f2c+//vRAa4aO0Cf5GncIsWCKzLrWqSMHdSbQTGyiaoQgWRv/jkyMp38Z8cJar3Hsr1C7NwyLYl756TI/2khhBABJ2Amww3DiAR+A9wExAC7gV8qpd41K1NJhYuV2fkUlFYTHxXKgrEDiI4IBvRl2pkHz7B0zWHWHz5b/5z+vcO4d04yt05LIios2KzoQgghRL1Aq7EAZ8uqeXlDHq9szKOwvKHl2OWj+nH/XAdTkuPMiC1aMuIqmH4/bHoGPv8tDJwI545D2WmITIDR10B4rNkpG1QWwZ4Vlsv3wytGsGr3SY4XV/LiJ9uZUbiCi05lA1A2cBaRJudrrKTCxfu7TvDmtqMApA2Nb+UZQnRPgVZnlVKsP3yWpZlOMg8U1D8nPiqUe2Ync9u0IV+pyUIIIUQgMZRSZmdoE8MwVgOTgB8COcDdwG3A15VSKzvwesXR0dHRxcXF7c6ilOKJ1QfIyHRisxlU1bgJC7Hj8Sjum5NCanwvns3KYd/J0vrnjOgXxeJ0B1+fMJCQINlIRAghAk1MTAwlJSUlSqkYs7N0tUCpsYvTHdwwaRDPrc3lzW1HqXJ5AAix27h+0iAWpjkYmmClqUBxAVcVPHsZnNaTtwSH67HgcFAemPUQXPITc1c3KwWfPQ7rnwLDBq5Ka+UDfr58F322/C+Lg97HhiLEqMWjoIZgtifewYx7/4RhM+/9ZuP/xwZQVdvwf3XJXAePzhsuPYXFBaTOtuv1fFJnF85JYXj/KJ7NcpJ9/Fz9c1Lje7E43cG1EwcRGiQtroQQItB05xrbEQExGW4YxgLgfeB6pdS/vWMGkAX0UUqN6sBrdvgNxJ9W7WdZVk593++WzHT0YfFcBxcPj5c3/UIIEcC66xuIQKqxNgM8jd629A4L4vYZQ7h7VjIJvaXlWMB471HY+lzT9wVHwMwH4dKf+jdTY5/+GjY8Da6KC++zQj4ga+l3mXziVSKMmgvuq1Ch7Ei8jZkL/8+EZFpL/4/Dg+0sTEvh+/Ot19tcmEvqbLte0y/HslOTY1mcnsplIxMaNuwVQggRcLprje2oQGmTch1QAqyoG1BKKcMwXgIyDMMYrZTa448gJRUuMjKdVHtXuDTnijH9ePCSoYwfHOOPWEIIIURHBUyNrZsIH9A7jIXpDm6emkhkaKC8lRGAbj3y5avN3++qgHVPwtibIMyEzdiqSvTXd1c3fb/Z+YBzxWeYduIfhBquJu+PMKqZePQVnEe/T6/ovn5OB+cqXSzNdFLTzP/jSpebjEwnC9McRIdLmwXRIwRMna1z2cgEHrx0KJOSzG8NJYQQQnS1QDmCHAvsUUqdX7V3Nr6/8R2GYRS38prRHQmyMju/1bPiYcE2Lh6RIBPhQgghAkFA1dgQu40HLx3K7TOGdORLCLPtWaFbj7TEXQ1/m+afPB1hcr7eAK0s0HRjI+OZ/+N196X+iNRuNpvByl353DotyewoQvhDQNXZsGAbl4/uJxPhQgghuq1AaV7dByhsYryw0f1+UVBaTVVNy5eUVbs8FJQ2s6JICCGEsJaAqrEut+crG2aKAFN2WvfgFj4VRg3xFJsdo1lVNW55ryx6koCqs3IsK4QQorsLlJXhAC01N7/gvtb64HjPtrf7jHp8VChhIXYqW3gTERZiJz4qtL0vLYQQQphFaqzwj8gE76aZTfTjrhMUDmmPwpjr/Zerzu53IOsJqG1hwt7MfMDOj15g2IEMwpvoF16nmhAuGjWCT6+Y68dk2ns78/nb54fqN7htivw/Fj2Q1FkhhBDCIgJlMvwsTZ8xj/N+bupMu08sGDuAx97d3eJjPB7FgnED/JRICCGE6BSpscJ/Rl8DH/yolQcpmLYYwmP8keirpi2CrD+18iAT8wFDrvouxoGlLT7GhocpV99LdGykn1I1uGtmMk9/dqjFx8j/Y9HDSJ0VQgghLCRQ2qTsBkYZxgVNJsd5P2f7K0h0RDCL0x2EB9ubvD882M7idNkQSAghRMCQGiv8JzwWZj0EwRFN3x8coe83aaLZ8vmA6Lh4tifeQYVqeuVmhQple+IdRMf6f/NMkP/HQjRB6qwQQghhIYEyGf5vIAb4+nnjdwL7/bX7dp1H5w1nYVoKoUE2wkPsGEB4iJ3QIBsL01J4dN5wf8YRQgghOkNqrPCvS34CMx+EoDDvpLOhPweF6fFLfiL5WjHj3j+xI/E2qlQwFSoUtzKoUKFUqWB2JN7GjHtbW93uW/L/WIivkDorhBBCWIihVEvty6zBMAwD+AQYD/wQyAHuQr+BuEYp9Z8OvGZxdHR0dHFxcYdzlVS4WJmdT0FpNfFRoSwYN0DOogshRDcVExNDSUlJSWt9PAON1Fhhmsoi2LNCb6oZmQCjrzV1xfUFrJ4PKCksYP+nr+AuPYU9qh8jLrvTtBXhTZH/x6I9pM626zWlzgohhGiz7lpjOyogJsMBDMPoDfwGuBF9Zn0P8Eul1PIOvl6n30AIIYToObrzGwipsUIIIcwmdbZdryd1VgghRJt15xrbEYGygSZKqXPAd7wfQgghhOgiUmOFEEII35E6K4QQQlhHwKwM72qGYXgAIzo62uwoQgghAkBJSQmAUkoFyn4bppEaK4QQor2kzrad1FkhhBDtITX2q3ryZHgtegPRc518qbp3ICWdfB1fkXydZ/WMkq9zrJ4PrJ+xp+TrDXiUUgFzVZVZelCNBetnlHydY/V8YP2Mkq9zrJ4PpM76XQ+qs5Kv86yeUfJ1jtXzgfUz9pR8UmMb6bGT4V3FMIxiAKv23ZF8nWf1jJKvc6yeD6yfUfIJXwmE753VM0q+zrF6PrB+RsnXOVbPB4GRUTTN6t87ydd5Vs8o+TrH6vnA+hklX88ky+OFEEIIIYQQQgghhBBCdHsyGS6EEEIIIYQQQgghhBCi25PJcCGEEEIIIYQQQgghhBDdnkyGCyGEEEIIIYQQQgghhOj2ZDJcCCGEEEIIIYQQQgghRLcnk+FCCCGEEEIIIYQQQgghuj2ZDBdCCCGEEEIIIYQQQgjR7RlKKbMzCCGEEEIIIYQQQgghhBA+JSvDhRBCCCGEEEIIIYQQQnR7MhkuhBBCCCGEEEIIIYQQotuTyXAhhBBCCCGEEEIIIYQQ3Z5MhgshhBBCCCGEEEIIIYTo9mQyXAghhBBCCCGEEEIIIUS3J5PhQgghhBBCCCGEEEIIIbo9mQwXQgghhBBCCCGEEEII0e0FmR2guzEMIwxIUEodMTFDqFKqupn7goEBZuUzDMOmlPKcNzYBGA4cUkptNyNXSwzD6AWsBh5QSn1pcpwLGIYRCowFPMBupVSNyZHqef8/fAuYhM63EXhDKVVrQpZvAh8qpc75+2u3lff/50ygWim1yTtmB24FRgPHgNeUUkXmpdQMw5iO/rnri/7engDWKqXyTA0GGIYxEPgGOl8fQAGFQDbwH6XUcRPjiU6QGts6qbNdS2psu/JIne0iUmOFWaTOtkxqbNeTOtvmLFJju5DUWYFSSj668AO4AXCb9LUfBk4DbuAgcGcTj5luRj70VQh/AyqBcuDn3vEnvXk93s//Amwm5OvdwkeSN99VdWMmfX8XA33PG3sIKPH+27mBYmCJSfn+Cvxvoz8PAPZ7/+1c3g8PsA2IMSGfx/uz9zJwsRn/Rq3kq/v3qvtefgpEAB97s9d9HAcGm5jzeiC3Uc66Dw9QC6wABpmUzQB+A1Q3+n4f836Ue8eqgd+Z/f2Wjw5/j6XGNp9P6mzn8kmN7XxGqbOdzyg1Vj5M/ZA622w2qbGdzyh1tnP5pMZ2TU6ps/Kh/73NDtDdPsx6AwF83fuf41Pg98AW73/qZYDR6HFmvYF40JvvNW+hKQV+6X1D8SgwB/iZt8h8x4R85/8ybOqXY/2fTfrZcgPTGv35Jm+uNcAiYAmQ5X3cAhPy5QK3NfrzP9FvaBegr0IJ8v6cFgB/NyFf3dn8au+/0WHgJ2YW4/Py/d3773UvcCOwC3jfW/zSvW8mLgfygQyTMl7b6M3Nd4FvA68CVcAD6JUTm7w/Cwkm5PsBUAP8GhjaxP2pwK+8PwM/NPt7Lh8d+h5LjW0+o9TZzueTGtu5jFJnO5dPaqx8mP4hdbbZfFJjuyaj1NmO55Ma2/mMUmflo/7D8P6jilYYhvGzNj50NHCTUsruyzznMwwjEziqlLqt0dh3gT8CbwG3K6Xc3stB1puQ7wtgjVLqe94/fxNdYH6nlPppo8c9DUxRSk33c766S2NeQJ8RbCwc+CHwEvoXI0qpX/gzH9RnnKGU2uz980agQil1aaPHGMDnQI1Sap6f81UB85RSWd4/FwI/UEo9d97jvg38TCk1wM/5PMAMIAe4C7gb/f/VjT5j/RywQinl8meuRvmcwO+VUku9f54CbAYWK6WWNXrcg8D3lFJDTci4BdijlLrrvPGHgO8DKUAIOvdapdSDfs53CFimlPpdK4/7b2CRUirVP8lEa6TGdklGqbOdzyc1tnMZpc52Lp/UWOEzUmc7nU9qbCdJne10Pqmxnc8odVY0MHs2PlA+aDib6mnDhxlnq88AVzYxfi36TNfb6LOZZp1NLwHmN/pzjPff6rLzHvcNoMSEfBejL8fbBkw+775ob9Z0C/wMNj6bXgnc2MTjbgEKTciXD1zf6M/VwNwmHncZUGn2v593bAaQgb4kz40+0/9nYIIJ+Soa/4wBvbyZZ5/3uIuBcn/na5RxfhPjcd6sI7x/vg84YkK+qrb8P0WvTvD7z6B8tPg9kRrb+YxSZzv/Myg1tgv/Db1jUmfbl09qrHz46vsndbZz+aTGds3PoNTZLvr3845JjW1/Rqmz8oFSChuirQqAZ4HYVj7uNilfEPqyrK9QSi1HX+52NfAOEOrfWPVsfPUsdan3c9F5jyvBhIxKqc+BccBnwFrDMP5kGEaEv3O0k0IX7fPlo4uPv32M7gVXZzNwZROPuwp9WZfplFIblVKL0T3O7gF2o3vXfWFCnHxgZKM/190+/4xvKnDKL4kuVAH0a2K8P/rnse7/+CEgwV+hGskDLmnD4y4BTNsYSjRJamznSZ3tWlJju4DU2XaRGit8Seps50iN7XpSZztJamy7SZ0V9YLMDhBAtgFjlFIlLT3IMIxyP+U5nxOYAnxy/h1KqfcNw7gO/QZilL+DeZ0EhjTK5PZejnL+br2DgLP+DFZHKVUF/JdhGP9C96e7wXsZ1Hoz8jTju4ZhnPTergASm3jMAPTZYX/7GbDVMIwV6Esa/x/wtmEYvYFV3scsQPcR8+slR61RSlWiNyN52TAMB+YcCHwI/MowjBrgHPAYsBz4mWEYXyilsr271f8EWGdCPoCPgF8bhrFDKbUTwDCMAegNhY4qpereGMahV/j429PAE4ZhRKO/n7uV91JB7+7mY4A70G8S/8uEfKJ5UmM7T+ps50mN9RGps20iNVb4ktTZzpEa2zWkzvqA1Ng2kzorGpi9ND1QPoDHgdI2PC4d+MyEfE+g/7O09Jgr0EXHjEvL3gRebsPjlgErLfD9DgJ+jr5U5V30ZUdWuLTs/I8L/k3Rl0qtMSnjeGAnF16KWXe7HJM2e6CJS8us9IE+S72/0b/ZBvSqiLqfvyrv5yKa2FDDTxkHod/016LPRh9Eb/JRCVzR6HF/QfesMyPjT+t+z3lznkOv0qn1jlWi+/yZ/j2Xj69836TGdj6j1NnO5ZEa2zX/hlJnO55Paqx8+PJ7J3W2c/mkxnY+k9TZzv/7SY3tXEaps/JR/yEbaHYThmEMA+YDrymlClt43Bx0bzN/b0zVBwhTSh1v5XHfB75USl2wKsAMhmGMQRfk0cDVSikrnVlvkvffcL9S6j0TM1wMpAED0ZcVnkXvKP2BUqrYpEx3Ae8ppUxZrdEW3jO+s9CXiW5USnkMwwhC9y27CL0xzotKqaMmZoxB77Y9A73ByB70juD7Gj3GAFAmFRjDMOLQlzCOAfp4h8+iLx38oKXfkUI0xeo11vu1pc76gdTYFnNJne18vhikxooeyOp1Vmqs/0idbTaT1NiuyRiD1FkBMhkuhBBCCCGEEEIIIYQQovuTnuEdZBhGX/RZGoXe7diMnkLNknydY/V8YP2Mkq9zrJ4PAiOjCExW/9myej6wfkbJ1zlWzwfWzyj5RE9m9Z8vydc5Vs8H1s8o+TrH6vkgMDIKHzK7T0sgfaAvk3kavVOu+7yPk977Bks+yddTM0q+7p0vUDK24e8wGXje7BzyccH3xdI/W1bPFwgZJV/3zhcIGSWfX/4OUmMt+mH1ny/J173zBUJGyde98wVKxjb8HaTOdsGHtElpI8MwRgFrgFDgP+iNFep69cQB44Cvo/sjpSul9ko+ydeTMkq+7p0vUDK2hWEYNwBvKKXsZmcRmtV/tqyeLxAySr7unS8QMko+/5Aaa01W//mSfN07XyBklHzdO1+gZGwLqbNdQybD28gwjPfR/0GuVs00rPc2un8fOKuU+prkk3xdyeoZJV/3zuf9+pbP2BbyBsJ6rP6zZfV83q9v6YySr3vn8359S2eUfP4hNdaarP7zJfm6dz7v17d0RsnXvfN5v77lM7aF1NkuYvbS9ED5AM4BC9rwuKuBc5JP8vW0jJKve+cLhIxceKlbix9m/BvKR7PfO6v/bFk6XyBklHzdO18gZJR8nc4nNTaAPwLg50vydeN8gZBR8nXvfIGQUeqsfz9kA8228wBtOfNi9z7W3yRf51g9H1g/o+TrHKvnA+tndANfAutaedxQYIHP04j2sPrPltXzgfUzSr7OsXo+sH5Gydc5UmMDm9V/viRf51g9H1g/o+TrHKvnA+tnlDrrR9ImpY0Mw1gOJAFXKaVONfOYBOAD4IhS6jo/xpN8nWT1fN6vvxwLZ5R8nWP1fN6vvxwLZzQMYztwUCn1zVYeJ5eWWUwA/Gwtx8L5vF9/ORbOKPk6x+r5vF9/ORbOKPk6R2psYAuAn6/lSL4Os3o+79dfjoUzSr7OsXo+79dfjoUzSp31L5kMbyPDMBzAWiAaWA1ko5vtK6APMBaYB5QAaUqpw5JP8vWkjJKve+cLhIyGYTwHzFVKDW3lcTcAbyqlbP5JJloTAD9bls4XCBklX/fOFwgZJV+n80mNDWAB8PMl+bpxvkDIKPm6d75AyCh11s/M6s8SiB9APPAH4AD6EgaP98MNHPTelyD5JF9PzSj5unc+q2cEZgM/a8Pj+qLfaJj27ygfTX5fLPuzFQj5AiGj5Ove+QIho+TrVDapsQH+YeWfL8nX/fMFQkbJ173zWT2j1Fn/fsjK8A4yDCMMiPX+sUgpVWVmnvNJvs6xej6wfkbJ1zlWzweBkVEEJqv/bFk9H1g/o+TrHKvnA+tnlHyiJ7P6z5fk6xyr5wPrZ5R8nWP1fBAYGYXvyGS4EEIIIYQQQgghhBBCiG5Pesx0kmEYdsMw3IZhTDI7S1MkX+dYPR9YP6Pk6xyr5wPrZ7R6PtE8q3/vrJ4PrJ9R8nWO1fPB/2fvvuPkqur/j78+W7K7abvpJIE0ILTQW0JJ6AqKgqIiRYqhiIqIX9GfjaCiiIoCghCRKogiUkWkhoQ0egmhZtMI6clu2vY9vz/O3d3JZrZldueemX0/H4/72Nkzd+68d+7sfPaePffc8DMqX2pCzyetC33/KV9qQs8H4WdUvtSEng/Czxh6vkylzvDOYXEHaIPypSb0fBB+RuVLTej5IPyMoeeTloW+70LPB+FnVL7UhJ4Pws+ofKkJPZ+0LvT9p3ypCT0fhJ9R+VITej4IP2Po+TKOOsNFREREREREREREJOupM1xEREREREREREREsp46w1PknKsDzgMWxp0lGeVLTej5IPyMypea0PNB+BlDzyctC33fhZ4Pws+ofKkJPR+En1H5UhN6Pmld6PtP+VITej4IP6PypSb0fBB+xtDzZSpzzsWdQURERERERERERESkS+XFHSDTmFlf4ERgHDAQqAc+AaY752bEmQ2UL1Wh54PwMypfakLPB+FnDD2ftCz0fRd6Pgg/o/KlJvR8EH5G5UtN6PmkdaHvP+VLTej5IPyMypea0PNB+BlDz5c1nHNa2rkA3wU2AnX4N2R9s9uvA3srn/J114zKl935MiFj6Pm0ZO6+Cz1fJmRUvuzOlwkZlS+782nJ7P2nfNmdLxMyKl9258uEjKHny6ZFc4a3k5ldAPwOeAD4IvAZ4Nf4N+r5wOHAImCamY1RPuXrbhmVL7vzZULG0PNJy0Lfd6Hny4SMypfd+TIho/Jldz5pXej7T/myO18mZFS+7M6XCRlDz5d14u6Nz5QFmAdcl6T9DGAVfsoZA14A7lY+5etuGZUvu/NlQsbQ82nJ3H0Xer5MyKh82Z0vEzIqX3bn05LZ+0/5sjtfJmRUvuzOlwkZQ8+XbUvsATJlASqBY5O0F+NPV9gj+v5sYLnyKV93y6h82Z0vEzKGnk9L5u670PNlQkbly+58mZBR+bI7n5bM3n/Kl935MiGj8mV3vkzIGHq+bFs0TUr7rQN2TdI+FnDA5uj7xUC/dIVKoHypCT0fhJ9R+VITej4IP2Po+aRloe+70PNB+BmVLzWh54PwMypfakLPJ60Lff8pX2pCzwfhZ1S+1ISeD8LPGHq+rKLO8PZ7BPilmZ1sZnnmHQz8FXjXObckWm8osEL5lK8bZlS+7M6XCRlDzyctC33fhZ4vEzIqX3bny4SMypfd+aR1oe8/5cvufJmQUfmyO18mZAw9X1bJiztABvkhcAjwME1XdM0H1gOfSlhvX+B/6Q6H8qUq9HwQfkblS03o+SD8jKHnk5aFvu9CzwfhZ1S+1ISeD8LPqHypCT2ftC70/ad8qQk9H4SfUflSE3o+CD9j6PmyikVzzkg7mFke8GVgPNADmA/c65xbG2uwiPKlJvR8EH5G5UtN6Pkg/Iyh55OWhb7vQs8H4WdUvtSEng/Cz6h8qQk9n7Qu9P2nfKkJPR+En1H5UhN6Pgg/Y+j5sok6w0VEREREREREREQk62malO1gZr2APYAB+Ins1wHvOec2xRosonypCT0fhJ9R+VITej4IP2Po+aRloe+70PNB+BmVLzWh54PwMypfakLPJ60Lff8pX2pCzwfhZ1S+1ISeD8LPGHq+rOCc09LOBT83z3+Aavz8PQ3z+NRFbf8B9lM+5euuGZUvu/NlQsbQ82nJ3H0Xer5MyKh82Z0vEzIqX3bn05LZ+0/5sjtfJmRUvuzOlwkZQ8+XTYumSWknM5sAPAssB/4OvIX/7wxAf2Af4HT8lV2Pc87NVj7l604ZlS+782VCxtDzSctC33eh58uEjMqX3fkyIaPyZXc+aV3o+0/5sjtfJmRUvuzOlwkZQ8+XbdQZ3k5mNh3YCJzqnKtuYZ184CGgr3NuovIpX2cKPaPyZXe+6PmDzhh6PmlZ6Psu9HzR8wedUfmyO1/0/EFnVL7szietC33/KV9254ueP+iMypfd+aLnDzpj6PmyTtxD0zNlATbh//vS1nrHA5uUT/m6W0bly+58mZAx9HxaMnffhZ4vEzIqX3bny4SMypfd+bRk9v5TvuzOlwkZlS+782VCxtDzZduSg7RXBf7UhLb0Ayq7OEsyypea0PNB+BmVLzWh54PwM4aeT1oW+r4LPR+En1H5UhN6Pgg/o/KlJvR80rrQ95/ypSb0fBB+RuVLTej5IPyMoefLLnH3xmfKAtwFlAL7trLOPsBHwF3Kp3zdLaPyZXe+TMgYej4tmbvvQs+XCRmVL7vzZUJG5cvufFoye/8pX3bny4SMypfd+TIhY+j5sm3RnOHtZGaDgeeAPYC3gXn4yewdMAAYB+wNvAcc7ZxbpXzK150yKl9258uEjKHnk5aFvu9Cz5cJGZUvu/NlQkbly+580rrQ95/yZXe+TMiofNmdLxMyhp4v26gzvAPMrAD4OnAK/o04ILprLfAOfiL7vzrnqpRP+bpC6BmVL7vzQfgZQ88nLQt934WeD8LPqHzZnQ/Cz6h82Z1PWhf6/lO+7M4H4WdUvuzOB+FnDD1fNlFnuIiIiIiIiIiIiIhkPV1AsxOY2Qgzy4s7R0uULzWh54PwMypfakLPB+FnDD2ftCz0fRd6Pgg/o/KlJvR8EH5G5UtN6PmkdaHvP+VLTej5IPyMypea0PNB+BlDz5eJ1BmeIjPLBRbiJ7IPjvKlJvR8EH5G5UtN6Pkg/Iyh55OWhb7vQs8H4WdUvtSEng/Cz6h8qQk9n7Qu9P2nfKkJPR+En1H5UhN6Pgg/Y+j5MpU6wzuHxR2gDcqXmtDzQfgZlS81oeeD8DOGnk9aFvq+Cz0fhJ9R+VITej4IP6PypSb0fNK60Pef8qUm9HwQfkblS03o+SD8jKHnyzjqDBcRERERERERERGRrKfOcBERERERERERERHJeuoMT5Fzrg44Gng/7izJKF9qQs8H4WdUvtSEng/Czxh6PmlZ6Psu9HwQfkblS03o+SD8jMqXmtDzSetC33/Kl5rQ80H4GZUvNaHng/Azhp4vU5lzLu4MIiIiIiIiIiIiIiJdSiPDO5mZjTCzr8WdoyXKl5rQ80H4GZUvNaHng/Azhp5PWhb6vgs9H4SfUflSE3o+CD+j8qUm9HzSutD3n/KlJvR8EH5G5UtN6Pkg/Iyh58sU6gzvfAcDd8QdohXKl5rQ80H4GZUvNaHng/Azhp5PWhb6vgs9H4SfUflSE3o+CD+j8qUm9HzSutD3n/KlJvR8EH5G5UtN6Pkg/Iyh58sI6gwXERERERERERERkayXF3eATGFmz7Vz1UFdGqQFypea0PNB+BmVLzWh54PwM4aeT1oW+r4LPR+En1H5UhN6Pgg/o/KlJvR80rrQ95/ypSb0fBB+RuVLTej5IPyMoefLNrqAZjuZWT2wAljZxqrFwEjnXG7Xp2qifKkJPR+En1H5UhN6Pgg/Y+j5pGWh77vQ80H4GZUvNaHng/AzKl9qQs8nrQt9/ylfakLPB+FnVL7UhJ4Pws8Yer6s45zT0o4F+Aj4azvWOw2oUz7l624ZlS+782VCxtDzacncfRd6vkzIqHzZnS8TMipfdufTktn7T/myO18mZFS+7M6XCRlDz5dti+YMb79XgQPasV5cQ+2VLzWh54PwMypfakLPB+FnDD2ftCz0fRd6Pgg/o/KlJvR8EH5G5UtN6PmkdaHvP+VLTej5IPyMypea0PNB+BlDz5dV1Bnefo8Dq9ux3nzg512cJRnlS03o+SD8jMqXmtDzQfgZQ88nLQt934WeD8LPqHypCT0fhJ9R+VITej5pXej7T/lSE3o+CD+j8qUm9HwQfsbQ82UVzRkuIiIiIiIiIiIiIllPI8NFREREREREREREJOupM1xEREREREREREREsp46wzvAzA4xswfMbLaZTTWz3ZKss5+ZlSqf8nWF0DMqX3bni54/6Iyh55OWhb7vQs8XPX/QGZUvu/NFzx90RuXL7nzSutD3n/Jld77o+YPOqHzZnS96/qAzhp4vqzjntLRjAcYBFcA6YC6wGdgCnN1svUOBOuVTvu6WUfmyO18mZAw9n5bM3Xeh58uEjMqX3fkyIaPyZXc+LZm9/5Qvu/NlQkbly+58mZAx9HzZtsQeIFMW4EHgZaA4+n4Q8BBQB3wzYb24fnGUL4vzZUJG5cvufJmQMfR8WjJ334WeLxMyKl9258uEjMqX3fm0ZPb+U77szpcJGZUvu/NlQsbQ82XbEnuATFmApcBpSdp/F705L4++j+sXR/myOF8mZFS+7M6XCRlDz6clc/dd6PkyIaPyZXe+TMiofNmdT0tm7z/ly+58mZBR+bI7XyZkDD1fti15SHv1B1Y2b3TO/Z+ZVQG/NbMC4Lm0J/OULzWh54PwMypfakLPB+FnDD2ftCz0fRd6Pgg/o/KlJvR8EH5G5UtN6PmkdaHvP+VLTej5IPyMypea0PNB+BlDz5dV1BnefkuBPYAZze9wzv3YzOqAXwKPpztYRPlSE3o+CD+j8qUm9HwQfsbQ80nLQt93oeeD8DMqX2pCzwfhZ1S+1ISeT1oX+v5TvtSEng/Cz6h8qQk9H4SfMfR82SXuoemZsgB3AM+0sc6VQD3xnFKhfFmcLxMyKl9258uEjKHn05K5+y70fJmQUfmyO18mZFS+7M6nJbP3n/Jld75MyKh82Z0vEzKGni/blhykve4B1pnZwJZWcM5dBXwfmJ62VE2ULzWh54PwMypfakLPB+FnDD2ftCz0fRd6Pgg/o/KlJvR8EH5G5UtN6PmkdaHvP+VLTej5IPyMypea0PNB+BlDz5dVLPrvgoiIiIiIiIiIiIhI1tLIcBERERERERERERHJerqAZgeZ2ZeALwLjgAGAA9YB84AHgX+5GIfbK19254PwMypfdueD8DOGnk9aFvq+Cz0fhJ9R+bI7H4SfUfmyO5+0LvT9p3zZnQ/Cz6h82Z0Pws8Yer5soWlS2snMivFXbT0cWAa8jX9DAvQH9gaGA7OBk5xz5cqnfN0po/Jld75MyBh6PmlZ6Psu9HyZkFH5sjtfJmRUvuzOJ60Lff8pX3bny4SMypfd+TIhY+j5sk5LV9bUsvUC3AJ8AhzXyjrH4t+0tyif8nW3jMqX3fkyIWPo+bRk7r4LPV8mZFS+7M6XCRmVL7vzacns/ad82Z0vEzIqX3bny4SMoefLtiX2AJmyACuBs9qx3tnASuVTvu6WUfmyO18mZAw9n5bM3Xeh58uEjMqX3fkyIaPyZXc+LZm9/5Qvu/NlQkbly+58mZAx9HzZtugCmu3XB/9fmrYsi9ZNN+VLTej5IPyMypea0PNB+BlDzyctC33fhZ4Pws+ofKkJPR+En1H5UhN6Pmld6PtP+VITej4IP6PypSb0fBB+xtDzZRV1hrffm8CF7VjvwmjddFO+1ISeD8LPqHypCT0fhJ8x9HzSstD3Xej5IPyMypea0PNB+BmVLzWh55PWhb7/lC81oeeD8DMqX2pCzwfhZww9X1bJiztABvkZ8B8zexW4G3gHP5m9w1/hdRxwJrAv8FnlU75umFH5sjtfJmQMPZ+0LPR9F3q+TMiofNmdLxMyKl9255PWhb7/lC+782VCRuXL7nyZkDH0fNkl7nlaMmkBjgJeAuqBuoSlPlpeAo5WPuXrrhmVL7vzZULG0PNpydx9F3q+TMiofNmdLxMyKl9259OS2ftP+bI7XyZkVL7szpcJGUPPl02LRS+4dICZDcP/V2ZA1LQWeMc5tyy+VE2ULzWh54PwMypfakLPB+FnDD2ftCz0fRd6Pgg/o/KlJvR8EH5G5UtN6PmkdaHvP+VLTej5IPyMypea0PNB+BlDz5cN1BkuIiIiIiIiIiIiIllPF9DsADM7xMweMLPZZjbVzMYmWWc/MytVPuXrCqFnVL7szhc9f9AZQ88nLQt934WeL3r+oDMqX3bni54/6IzKl935pHWh7z/ly+580fMHnVH5sjtf9PxBZww9X1aJe56WTFnwpyhU4CewnwtsBrYAZzdb71CgTvmUr7tlVL7szpcJGUPPpyVz913o+TIho/Jld75MyKh82Z1PS2bvP+XL7nyZkFH5sjtfJmQMPV+2LbEHyJQFeBB4GSiOvh8EPISfzP6bCevF9YujfFmcLxMyKl9258uEjKHn05K5+y70fJmQUfmyO18mZFS+7M6nJbP3n/Jld75MyKh82Z0vEzKGni/bltgDZMoCLAVOS9L+u+jNeXn0fVy/OMqXxfkyIaPyZXe+TMgYej4tmbvvQs+XCRmVL7vzZUJG5cvufFoye/8pX3bny4SMypfd+TIhY+j5sm3JQ9qrP7CyeaNz7v/MrAr4rZkVAM+lPZmnfKkJPR+En1H5UhN6Pgg/Y+j5pGWh77vQ80H4GZUvNaHng/AzKl9qQs8nrQt9/ylfakLPB+FnVL7UhJ4Pws8Yer6sos7w9lsK7AHMaH6Hc+7HZlYH/BJ4PN3BIsqXmtDzQfgZlS81oeeD8DOGnk9aFvq+Cz0fhJ9R+VITej4IP6PypSb0fNK60Pef8qUm9HwQfkblS03o+SD8jKHnyy5xD03PlAW4A3imjXWuBOqJ55QK5cvifJmQUfmyO18mZAw9n5bM3Xeh58uEjMqX3fkyIaPyZXc+LZm9/5Qvu/NlQkbly+58mZAx9HzZtuQg7XUPsM7MBra0gnPuKuD7wPS0pWqifKkJPR+En1H5UhN6Pgg/Y+j5pGWh77vQ80H4GZUvNaHng/AzKl9qQs8nrQt9/ylfakLPB+FnVL7UhJ4Pws8Yer6sYtF/F0REREREREREREREspZGhouIiIiIiIiIiIhI1lNnuIiIiIiIiIiIiIhkPXWGi4iIiIiIiIiIiEjWU2e4iIiIiIiIiIiIiGQ9dYaLiIiIiIiIiIiISNZTZ7iIiIiIiIiIiIiIZD11houIiIiIiIiIiIhI1lNnuIiIiIiIiIiIiIhkPXWGi4iIiIiIiIiIiEjWU2e4iIiIiIiIiIiIiGQ9dYaLiIiIiIiIiIiISNZTZ7iIiIiIiIiIiIiIZD11houIiIiIiIiIiIhI1lNnuIiIiIiIiIiIiIhkPXWGi4iIiIiIiIiIiEjWU2e4iIiIiIiIiIiIiGQ9dYaLiIiIiIiIiIiISNZTZ7iIiIiIiIiIiIiIZD11hmc5M5tmZou6aNvOzO7sim2LhMDMRkXv8ylxZwlJR14XM5sSrTuq65OJpJdqrMj2U41NTjVWREREpGupMzwNzOyB6A/V/VpZx8xsoZmVmVlRGuOljZkNNLM/m9liM6swswVmdpuZDdvO7Q02s2vN7B0z22xmG83sdTP7qZn16ez8ncXMjooOXko68Jhzzeyyrku11XONivLtl47nk86XzveLSNxUYz3VWE81VrqaaqyIiIhIZlNneHr8Nfp6XivrHA2MAu53zlV0eaI0MzMDHgEuBJ4ALgXuB44Axm7H9iYA84HLgJeAy4EfAB8BVwFvmNkunZG9CxwFXAmUdOAx5+J/1nQYhc+3X5qeL2SLgSLgl3EH6aBzSd/7RSRuqrGqsYmOQjU2U6jGioiIiEja5cUdoJt4ClgKnGlm33fOVSdZp+Eg/q9J7uuQ6KC4l3NuU6rb6kS7AocBtzrnvpHQ/mMz69GRDZnZDviD/jzgcOfcywl332xmJwEPA4+a2YHZ2PEhLevM979zzgGVqacSkS6kGqsaK2miGisiIiIimU4jw9PAOVcP3AkMAD7X/H4z6wt8AZjXcNBpZgVm9qPo9OTK6NTux8xs/2aPPSo6PfxcM/ummc3HH1j8X7P1xpjZI2ZWbmYbzOwhMxvTbJ0cM/uxmU03sxVmVm1mS6LTrgek+jI0PM02dyTvuGjN94FBwP9rdpDesL0ngD8CewDnN7RbK/MqmtkiM5vWrO0rZvZo9BpUmdkaM3vYzPZp6fFmtruZ/Sc6nbzczP4VdSw0rHcnfkQYwMIoT6vzQpqfj3YSMDJhfWdmRyWss6uZ3WNmy6P9tsjMfmtmvZptayczuz06jb7KzFaZ2SwzOye6/1zg+Wj1OxKea6vXJknG/mb2h+i0/EozW2tmr5rZ95Os+0Uzez56T28xs/fN7IbEDhsz62Vmv462VxW9H+82s5HNttXm+z/ajy9G+2SLmc01s9Na+3kSHrvNvJ2JbWb2ZTN7w/yUBB+Z2XnROiOifb8uet6/WbNpBczszmg7g6Kfba35qQiebe33PEnGO83MJXy/iE56v0TrHmFmM6OfcaWZ/Qno3Z7Xr5le0X5eEW1rrpkdm/A8PcxstZm9mOzBZnZF9HMcuR3PLVlMNda/DA1Ps80dqrGqsaqxqrGqsSIiIiKNNDI8fe4AfoIfnfavZvedDvQkGrFmZvnAk/hRXvcAfwKKgQuAmWY20Tn3SrNtXIbvCPgLsAI/Sq5BL/zB10vA/8OPILsEGG9m+zvnVkTr9cAfBD+IHxW2GTgY+DpwhPkRYB09qAbAOfehmT0LnGtm9znnXtie7US+CFQDd7Wyzl/wP8sXgZu283m+BawDpuJf053xp6DPNLMDnHMfNlt/ODANeCh67n2Bi4C+wAnROrdG358KfBdYE7W/1UqOy4BfAwOjxzR4F8DMDgSeA8qi7S+LnvtS4HAzm+ScqzGzPODpKOfNwAf499U+wJH413M68CvgR9HPPSN6rpWt5AN4AJgYPf+b+Pfz7vjT1X/bsJKZXR1tez7wB2A5/nX9IvAzoDrK+T/gcPzvyu/x79lvACeY2UHOuY+TvEbbvP/N7JfAj/G/Tz8F6vGv/QNm9i3n3Pa+NwA+C1yMfy3X4X9Pbjezavxr+Fz0sx6M7zCqBCYn2c6T0eOnADvg33fTzWyCc27eduS6jE54v0TrHgo8A2wEfhM95nTg7u3IdTdQF22nD/5340kzO9E594xzrtrM7gK+Z2a7O+fea/b484APnHMzENmWaqxqLKjGqsZuSzVWNVZERERka845LWlagGeBWmBYs/bZQBUwMPr+u/hRXp9qtl5fYAkwLaHtqGjddcDgJM85Lbr/j83aT43ab0loM6AoyTa+Hq375WbtDriznT/7DviOgkr8H/0Tt/M17BM971vtWHcDsCbh+ynRY0clWXdR4usatfVKst4e0b66Ocnjk71GN0Xtu7cnRys/yzRgUQv3vQm8B/RpYR+fG32/T/T9FW0811GJj2tHtuJo/ZvbWO+QaL3ngMJm9xlg0e0LovWubbbOZ6L2e9rz/gcOiO77VZIsD0fvjz5tZB4VbWNKkrbNwMiE9kHR+7seuLzZdv6N71zqndB2Z7Sdfzf87FH7gdE2nmzPPmnYTme/X6K2WVHusQltPfC/y1u9Lq28hg3v97lAj4T2HYFNwLsJbWNb2PeHt+e9q6V7L6jGqsa2kaOVnyXlz0xUYxPvexjVWNVYLVq0aNGiRYuWQBdNk5JefwVygbMbGsxsd2A88KhzrmEE01n4P6RfNbOBDQv+D+Sn8SPIippt+27n3KpWnvuaxG+ccw8B7wOnJLQ5F839aWa5ZlYSPe9z0SqHduinbfoZe+JHIfUAdgHeAf5rZsc0W+8pM1vSxub6Rl/L2/HUG/AH9tvFObc5ymVm1jd6LVbjX7dkr8Unzrl/NmtreO265EJjZrY3/gD8PqCg2fvlRfzBZMOIuYbX7GgzG9yJMSrwnReHWpLT4xOcGX39f865reYIjd57Lvr2VPyB6q+brfMf4A3g82bW/LMr2fv/TPzB3V2Jr0v02jyKf29MaM8P2IKHnXOLE/I1vDfq2Xak5AwgH3+Q39y1CT87zrlX8b/nx5nZ9pwq3aKOvF+i98gE4BHn3AcJ+arxIw476g8uYdSr8yMP7wV2N7M9orYPgBeAr0WjFxt8Hd/J2dpIVRHVWNXYTqUa20g1th1UY0VEREQygzrD0+vf+FMgz0toa5hv8/aEtj3wp7+uTrKcjz/YH9hs2x/QsjLXdJp2oneBIZYwh6H5+Rnn4g++1kfPWRrd3a+V52jNRfiDg29Gf5x/Cn8K7+Nm1nBQYMA4/MFCazZEX4vb8bx9afvU4xaZ2f5m9jh+lF05Tftgb5K/FqVJ2tZGX1OdD7Yle0Rfr2Lb98oq/On7QwCig8qr8Qdiy83PN3qtmR2cSoDo4Osy/P5baH4O3hstYa7KyK74A+c329jkaHynx/ok972DP8Buz/t/D/xouPfY9rVpuIjekDaytCbZ/l4PLHfOVSVph+Tvg3eTtM3H/56P3P54SbX7/QI0zHfc/FTqhnwd1dLPmfhc4KcOGII/RZ6os+LLwOPOue3+fZZuQTVWNbazqcZ6qrHtoxorIiIikgE0Z3gaOecqzew+4BIzOwx/SuPZwMfAUwmrGvA2cHkrm1vd7PstrT11C+1bXWjLzL4A/AN/euZ38HNCVuIPGJ5k+/95chR+HsNZAM658ugA/Vng0eh5ewJDgdta25BzbqOZLQZ2M7OezrmkP7eZ7YI/oEuc97Wl1wGa/S6Y2Qj83J4bgF/gRyNtjrbxR5Jf3Kiule1vc1GzTtKw3d/j91EyjQe8zrmfmNnt+NOhj8TPr/l9M7vWOfeD7Q3hnLvFzB6JtjsJOA34lpn9wzl3ekLW1vZBg+15rZK9Dxqe70Ra3jfvbMdzNWhpm53xPmi+Xrvfu+3cbnveLw3rJnvu7dlH7d3Og8AN+JFqD+PnT+1FG58NIqqxqrFdQDXWU43t2HZVY0VEREQCps7w9Psr/sJa5wH98fN8Xu2cS/zj/kP83IjPOefqO+E5+5nZDklGru0OrGo4VRnfaVAJHJ14ABydZp6KevzB/o5EF11yzq03s+Pxpzg/hB8x86Rz7rkWt9Lk3/g5X78G3NLCOg0XUfpbQtu66Gt//PyjAJhZIb6T4KOEdU/FH4x/zjn3fOKGzWwA/pTl7dWeg9X2PqbhAmN1zrln2rUh50qBG4Ebo5/9f8AVZvb76DTo7cmHc245/mDqNjPLxV+Y7qvRdl/Gd3Z8Gj+C8aVWNrUA+LSZlTjnyprdtyfRPLXtiPRh9HxLnHPJRkyFYg9gTpK2OqDhFPHE925zY5K0dcb7ZUFCluaStbVlT7a9iF3DdhpHADrnqszsbuBSMxuGP2BfRssdCyKJVGNRje3Ex6jGtkw1dluqsSIiIiIZQNOkpJlz7jX8nIxfwV/R3gF3NFvtbvwBfNJRa2a2Paed/rDZNk4FdsOPCmlQF+XJSVjPgJ9sx/MleiL6em3iPJTOubX4UzMNfxB/fzu391v8gdqvzeyA5nea2afwr907wN8T7mo4zfe4Zg/5Ltv+LjR0nDQf2XcBft+kYlP0NdlBV2uP6Rftj0SvA/OAi81smwM2M8szs/7R7WIzy0+8P5pXtOEgtuG09A7lM7Oe5uesTdxuHU0HZQ3buS/6+iszK0iynYaf7WH8/mj+nj0R2B8/9297OrDuSXi+3CTP15lzuqbiisT9Gr2njwOedc417IuF+Pk8t3rvRqNfxyfZZsrvl6jTZg5+/tixCev0wP/OdNR3o8c2bGdH4Azg/SQdKX/Bd+79Bv/z3dmsM1MkKdVY1VhUY1Vjt6YaqxorIiIishWNDI/HX/Gjhj4FTHPOLWh2//XA8cBvzV8A6zn8SJ0RwLFEI8s68HxrgC9EI0Cm4eeVvAQ/1+eUhPX+BXwReC4aNZKPv/jXVgdh2+EO4Av4UzF3N7N/4kep7YUfebYSf3r0n81sgXOu1TlNnXPLzexzwGPAbDO7F39AkYs/Xfw0/IHNyQ0XK4s8g5+b8efRyLOFwBH4A4Hmo6D+iz8t+B4z+xP+tNbDgZPwo3lS+d1pGKH0myh7JTDPOTevjcd8FviTmc3CdyQ855xbZWZn498jb0WnZ7+D32e74F/3/wfciX/PTDWzB/EjyDYBB+JH+M11zr0fPdd8/Byul5jZFvwcvKtaGVE4FnjBzB7CHwSux49G+gb+NZ4B4Jx7ycx+A/wAf+G6fwAr8POXngYcEj3XncA5wA/MXyxsevSzNLxnf9TK69TIOfeymV2Jn7vzDTN7APgEP0LxQPy+7NHKJtJlJPA/M3sUn+1b+PmEv9+wgnNuk5ndCUw2s7/T9Ht8Hr5DZN9m2+yM9wv4Dq9pwEwzuwm/f05n+97/ecCMKH8f4GKgCLi0+YrOuXfN7EX8hQ4dW8/3LNIW1VjVWFCNVY31VGObUY0VERGRbs85pyXNC350UAX+D9CzW1gnD/8H7Mv4g9jN+NMv7wVOSFjvqGg757awnWn405XHAI/gD/g3Rrd3SbL+BfgDtUpgOf5CO/2j57iz2brbtLXyM+cC3wZexR8AV+DnbJ0ClOAP1lbhDwT2a+c2hwDXRnm3RHkc/oC8sIXHjMWfCtpwAPpPYHj0Gk1rtu5E/MXGNkbr/gd/AatpwKJm627z+Nb2D3AF/rTVmuj+KW38rL3wHTwraRpdeFTC/SPxp7MvAqrxFxV7Ffg1sFO0zuhonXej98Hm6PbPgeJmz3cS8Fr0PnDJfraEdQcAf8CPxiyL9u1H+HlfhyZZ/6vAzOh13YzvPPkj0KPZz/vr6DWqjt4b9wAj2/P6NlvnM/jT1NfhT71fiu+I+UY73mOjmu+fZG3Nf9+StJ+bZJ/dGbUNin62tfj35XPAgUm20Rs/mqthvReBwxq209nvl2a/B7Oi98Iq4Cb870Gb79vo8VOidffCd1CuiLb1EnB8K487O3rcs+35PNCipWFBNVY1VjVWNVY1VjVWixYtWrRo0aKlhcWcc4hkAzPrg79I2sHAV51zD8QcSaRF0Si0c5xzXXXht4xmZl/GX2zwDOfc39taX0S6lmqsZBLV2NapxoqIiEh3pjnDJWs45zbiL+b0BnCvmZ0cbyIRScE38VMr/DvuICKiGiuSZVRjRUREpNvSnOGSVZxz5cBBcecQkY6LLrh2LHAk/tTx/+ecq4o3lYg0UI0VyVyqsSIiIiKeOsNFRCQUewL34efFvQX4faxpREREsodqrIiIiAhoznARERERERERERERyX6aM1xEREREREREREREsp46w0VEREREREREREQk66kzXLKOmR1tZnPMbKOZOTM7N+5M6WJm50Y/81HtWPdOM9M8SUlEr+GdcecQEUmXuGuCmY2KPnunxJWhM5jZUe3928PMpkTrjur6ZJnFzBaZ2bS4c4iIiIhI9lFnuGQVM+sH/BvoBXwPOBuYHmso6TRRZ8kUM9svxe2URNs5qlOCiYiIdAOdWT+j7ZyScigRERERkQ7IizuASCc7GCgBvu6c+3fMWUJ3AXBx3CE6aBRwJbAIeCOF7ZRE2wGYluT+IqAuhe2LiIi05ZfANUBV3EE6oITW62dHXAncBTyc5L7dAJ29JiIiIiKdTiPDJdvsEH1d15kbNbNcM+vZmdvczhx9Omtbzrka51xlZ20vmzjnKp1zNXHnEBGRsHRyHa6N6o06fZtxzlU556rjziEiIiIi2Ued4ZI1zGwRfoQRwPPRPJwu4f6BZnaTmS01s+ro601mNqDZdhrm3T7OzH5qZguASuDL0ZyqlWZWmLD+YdH668wsJ6H9xKj9ywltl5jZU2a2LMqw3Mz+lmy+0IZ5q83sWDN70cw2AY8l3D/ZzN4zsyoz+8jMvgNYB16vbeaHbWgzswHR7TXR3OsPm9kO0ToXmtm70evwnpl9vtk2Gud9NbOvmtlb0bpLora8ZutPi/Zd83xbzR8bzb/6fHT3HQ37t2FOUTPLMbMfm9l0M1sRvb5LzOzPifs4OrV7YfTtlQnbWZSwTtI5w6PX/DUzqzCz8mhfHpFkvYZ9N8HMXjCzzdFreZuZ9U62P0REQmBmg8zsbjNbG312PWtm+ydZr931LFr/aDP7T7TdSjMrNbO/mtnANvJ8KqpDM8ysnzXNsz06YZ2hUVudmfVPaN8jar8ioe0rZvZoVB+qos/mh81snyTPvSiqUfub2f/MrBx4K+H+z5vZ69HPs9TMfg7kt/ESJ25/mznDE9r2NLM/Rq9rw37YLVrnCwm1aJGZXZhk2w116Djz11HZEtXG682sV7N1W5wvPrEetrN+tvm+sKi+R9+ek7CdxL/Zks4ZbmanmNlMM9sULTOt2d8hiY83s92j993GqG7/y6K/Z0RERESke9I0KZJNLgNOBC4EfgW823CHmRUDs4BdgNuB14D9gW8Ax5jZIc65jc229zv8Qe1fgA3A+0A9cA5wOPBstN4xUXu/aJuvJrQ7mjpwAf4PmAPcgB+9Pg6YHGXY2zm3tlmGg4AvRhkaOvoxs8uAPwBvAj8CegLfB1a18Rq115PAx8DP8K/ZpcBDZvZv/Ov7V/w/CC4F/mVmY51zC5tt42T8PrkJWAF8Dn9K9EjgvO3INB2/X38ETAVmRO0ro6898K/Bg8AjwGb8tDlfB44wswOjUWbvAt/Fv34P4eeYB9jU2pOb2W+AK4CXogx98K/F82b2eefcE80esh/wOHAHcB9wVJSlPnqciEiInsTXpyn4s62+BUw3swnOuXkJ67W7npnZRcCfgWXR18XACHyd2BFYkyyImZ0D3Ib/R/AZzrlKM3sOX0uOwdcigGPxn605wNH4OkC0DsBzCZv9VpR3Kr427Yz/TJ5pZgc45z5sFmNE9PgHou32jrKdGn2/CPg5UIuvbZ9N9rNsh7vwdelXwCD8dVD+Z2Y/Ba7Fv4634+vKrWY23zn3YrNtHACchv8b4m78a3MpMM7MjnfO1XcwU3vqZ3veF6vx13S5B1/Lp7bnyc3sEvzfFO/hp5hxwLnAw2Z2kXOu+XaG46dyeQj/98G+wEVAX+CEdv7MIiIiIpJtnHNatGTNgj8ocsBRzdqvjtovadb+zaj9F0m28T7Qs9n6w6P7rk5oew4/32U5cEVC+6vAW80e3ytJ5mOjbV7RrN1Fy3HN2kvwHb3zE/PhOxQ2Jfv5W3it7vQfAdu2ATc1a78ual8C9E1o3ydq/3VC26iorQ44IKHd8AekDhif0D4NWJQkX8N2piS0HRW1nZtkfQOKkrR/PXrMl1vbdpLX/s6E73fDd7S8CPRIaB8GlOE7Q3KbPb4+8eeM2v8D1AC94/5d0aJFi5bEJeHz/9+AJbQfGH2ePdls/XbVs6g2VUU1qyTJY3Kir1t9LgM/jL6/uWGdqL1HVAPvTWi7HV9z5wM3J7Q/CKxv9vhkufeIMt7crH1RlGFys/bcqB6uAQYmtBfjO/qT1qkkzzslWndUkrbHmu2HS6P2jcCIhPZB+H9O/73Zthv+hjilWfv1Ufvpzfd9Cxmb18Ot9lOS9Tv6d86dLWxnETAt4ft++L9xPmLrv0P6Agui16Wk2eO3qv1R+01R++5x/85p0aJFixYtWrRoiWfRNCnSXZyKH4nUfNTQrfiD2VOTPObPzrktiQ3OuWXAh0SjzcxPlzIBeAo/cvnYqL0EPzL4uWaP3xzdn2NmxdHp4W/iO9IPTZLhTefcM83aTsCPBL8pMZ9z7mPg3iTb2B5/bPZ9wyjsu51zGxKe8y38qPldk2zjaefcawnrOvxoNkj+eqfEeRXQOMd7SfT6NuyDZK9ve30e39l+rUuYw9Q59wm+E2Ek/qyARLOdc3OatT2HPyNnVApZRES60rXR5zUAzrlXgaeB4yxhmqcO1LMv4Tuwr3LOlTV/Mrft6OQcM/sT8Gvgp865SxLXiT6DZ+JHOTc4Gn+21rM01WEDJgEvNHt8Q24zs75R7tX4f4AnqxPr8Gf4JDoQ2Am4wznXOKrdOVcO3JJkG9vjhsT9QFMdfsQ5tyThORuyJ6vD7zvnHm7Wdk30tdPrcJSno3/ntNfxQC/865L4d8gG4Eb8iP3jmj3mE+fcP5u1NfxNsEsKWUREREQkg6kzXLqL0fiDwtrExuj794ExSR7zQQvbeg44yPxFtA4DCqO25/DTcfTAj2DOoVlnuJkdE82BuRk/onh1tBTjRz21J0ND1veS3De/hcwdVdrs+/XR1+ZToTTcNyBJ+7tJ2hryJXu9U2ZmXzazuUBFlGs1TT9Lste3vRrmpn0nyX0N0wY0/5mav4YADdMGJHu9RERC0NJndy7+H39Ah+pZQyft6+18/svwZ2392Dn3yxbWeQ4Yan5O8DH4fzA21OGxZjYcPyXGALatw/ub2eP4kcTlCbn3JnmdWOCcq2vWlrF12Dm3HL+/uqoOd/TvnPZSHRYRERGRTqE5w0VatqWF9ufwc05OxI8K/8Q5956ZFeBHbI/HjxyvA15oeJCZHYwfQf4R/vTvhfhOWwfcT/J/TiXL0HCRTNfKfSlJcuDfoKX2ZM+bLF/Sp2uhvUOfT2b2BeAf+Dm9vwMsxZ86noufAzeVf/5tz+va0mu1vdsTEYnLVp9ZHaxnrdWsZJ7G19eLzOx+51yyDs2GDu5j8NOb1OBHTvfAT+lyLDCw2bqY2Qj8WVwbgF/g/xm+Ocr2R6L5wJuJpQ7Tcg3pjDrcfN2k61mzC163ZTv/zmn35rfjMarDIiIiIrINdYZLd1EK7GZmeYmjw6MDvbEkHz3UkufwB3bH4jvDGw6038KPfjoWf8r2a9Ep0w3OwHfMnugSLjZpZr3o2GipBdHXPWg24i1qC8WerbQlvt7r8KecN5ds1FprnSln4zu/j06cPsbMdu/gdpJpeM33SrjdINnPJCKSqfbAXwCxeVsdfj5s6Fg9ez/6uj9+mrG2vI2/ePOzwAtmdozb9qKWr+JHdR+L7wyfG03PsdnMXo/a++MvKp04kvhUfIf355xzzydu0MwGRNtqj8Q63FzQddjMhuJHaTevw5hZf+fcuoT2jtbhzvo7J5nEOvxss/tUh0VERESk3TRNinQXD+MvMjW5WfsFUftD7d1QND/oPOCzwEFEHdLR3J7T8POj7sW2HdUNI5Saj0b6ER37XXwaP9Lqm2bWs6HRzHbEH4iG4ngzO6Dhm2j+1iuibx9OWO8DoI+ZHZKwbg7w3STb3BR97Z/kvjr8QXrjaxk95086uJ1kHo22/X0zy0/Y/lDgPHwHUXunABARCdkV0WcnANHn+HHAs865hs/OjtSzfwHVwJVm1rf5kyU+VwPn3Dv46cZy8R3iuze7vw4/wnsS/p/PifX2OXxn+ETg+WbzbifNbWYXADs0z9GKV4GPgfOiObEbttMXuLgD2+lqu5nZKc3afhB9fTihrWFKtuZzbn8vyTbbqsPQ/r9zNrWwnWSexo/i/3Y0TZ1/In/729G2nm7ntkRERESkG9PIcOkursV3Ut8UHdi/jh+l9nX8qLVrW3lsMs/hp+JouJ3Y/qUk7eA73L8LPGFmU/GdA8cD++Av4tkuzrn1ZvZT4HfALDO7Gz89y8X4UXfNL+QYlzeB58zsJmA5/iKUxwH3OOdmJ6w3FX/A/ZCZXY9/XU4j+efTfPw8r5eY2Rb8fKSrnHPP4Ttcvhg9591APnAK/rXZinNurZl9BJxuZguAlcBm59xjyX4Q59z7ZvZbfGf+dDP7B9AHuBA/yvDMVqaWERHJJCOB/5nZo8BQ4Fv4f8B+P2Gddtcz59zHZnYZcBPwdvT5vBgYjq8L5wNvNA8RTT82CV9Lp5nZsVEneYPngJMTbie2fz9JO8B/8dOe3BNdpHM9cDhwEn7kcbv+LnbO1ZnZd4F/Ai+Z2V+A2uhnWQuMaM920uBt4G9Rvg/x/zg4DT+F2z8S1vs78CtgavSPh7XAiTRNNdOojfrZ0b9z5uAvzPoDYInfvLs/2Q/inCszsyvw76O5ZnZndNe5+IthXtTsbDwRERERkaQ0Mly6hegA6XDgVvxB7w3R11uAI5xzGzu4yYZTdEudc4uTtNcALzbLMBPfWbsZP1fpFHwHw6Sord2cc7/Hd34XAb/GHwz+DrixI9vpYo8C38B3Vvwe2B3/c389caXoVOpT8FPM/ALf4TwTOKf5Bp1zFcDp+Ple/4g/gP9ZdN/9NHVO/y7azvvAp1rIdya+c+BX0XZafe2ccz+Itl8IXIPvbHkPOMY590RrjxURySCfxndwXoXv2HwVmOSce6thhY7WM+fcn6PtfgBcCvwJ/xn/Kv76DklF06NMwk+B9byZ7ZNwd0O9rQAS/8E6A1+DoVlnuHNuAb6TdyF+tPI1+JHJk/AjvdvNOfcvfMfyBvzPfyn+n7I/aOVh6fYavr4ehq/DE/Gv/cnOufqGlZxzG/B/E72Df12uAj7B77NkktbP7fg75xL830o/Bu6LttUi59zNwBfw/wi/MlrKgFOdc1Nbe6yIiIiISAPb+uxREZHUmNkofEfDVc65KfGmERER6X7MzAF3OefOjTuLiIiIiEhINDJcRERERERERERERLKeOsNFREREREREREREJOupM1xEREREREREREREsl63nTPczGrx/wzYEHcWERHJCH2BeudcXtxBQqcaKyIi20F1VkRERLpcd+4MrwesuLg47igiIpIBysvLAZxzTmdVtUE1VkREOkp1VkRERNKhO//XfUNxcXFxWVlZ3DlERCQDlJSUUF5erpHO7aMaKyIiHaI6KyIiIumg/7qLiIiIiIiIiIiISNZTZ7iIiIiIiIiIiIiIZL1YO8PNbEczu97MXjSzTWbmzOyoFtY93szmmFmFma0ys1vNrCStgUVEJLNUrIdX74QXrvVfK9bHnShtVGNFRKTLdeM6KyIiIpkp7jnDdwG+CrwGPAt8LtlK0cH7E8DDwE+AYcBvgHFmdqRzrj4NWUVEJFM4B89fDbNuBMuBmgrIL4L//gAO+zYc/WMwiztlV1ONFRGRrqE6KyIiIhkq7s7w6c65wQBmdgotHKgD1wLzgK80HJSb2XLgKeBLwD+6PqqIiGSM56+G2TdBbWVTW80W/3X2Tf7rMT9Jf670Uo0VEZGuoTorIiIiGSrWzvD2jDYzs+HAwcD3Etd3zj1tZsuAL9JFB+rOOdasWUNlZSX19RoYF7qcnBwKCwsZOHAgppEoIt1XxXo/Ui3xAD1RzRZ//4RvQVFJWqOlU+g1FmDz5s1s2LCB2tpa1dnA5eTkkJeXR9++fenVq1fccUQkTqqzIiIiksHiHhneHuOir/OS3Pd2wv1bMbOyNrZb3NqdzjmWLVvGxo0bKSgoIDc3t82gEq+amho2bdpEVVUVw4cPV4e4SHc1/xF/ynZrLAfmPwwHnpuORCGLpcbW19fzySefsHHjRnJycsjPz1edDVxNTQ1btmyhrKyMPn36MGzYMHJydB12kW5JdVZEREQyWCZ0hg+Ivq5Lct864ICueNI1a9awceNGhgwZQv/+/bviKaQLrFu3jpUrV7JmzRoGDRoUdxwRicPK+U2narekpgI2rUpPnrDFUmPLy8vZuHEjAwcOZMCAAepUzRD19fWsXbuWNWvWUF5eTr9+/eKOJCLpVlsNHz6tOisiIiIZKxM6wxu4jrQ750pa21g0qq3FkWuVlZUUFBSoIzzD9O/fn7KyMiorWzhtU0Syk3OwZDbMvAE++G/b6+cXQe/BXZ8rc6S1xm7atIkePXpoWqsMk5OTw8CBA9mwYQObNm1SZ7hId1JZDq/eCXNugY2ftL2+6qyIiIgEKhM6w9dGXwckua8/yUezpay+vl6nbGeo3NxczT0r0l3U18F7/4GZ18OyVxLuMFru3wVcPex5SheHywix1di8vDx1hGcgMyMvL091VqS7KF8Gc/8Mr9wJ1RujxpyozLbyOaA6KyIiIoHKhM7wd6Kv44Cnmt23NzArvXFERCR2NRXwxn0w+0+wrrSpffhBcPilsPxNmPPn5Kdx5/eECd/URb081VgREdnWyvn+IphvPwD1Nb4tvxcc8DUY/w14/R6YfZPqrIiIiGSc4DvDnXMfm9krwJlm9kfn/BAEMzsWGA78O9aAIiKSPlvWwcu3wdxbYcuapvaxJ/pO8BETwAz2+Jy/eNesG/3Xmgp/yrar9wfoR/84vp8hIKqxIiLSyDlYNMNPOfbR003tvQbBoRfBQV+HntEUkg11VHVWREREMkzsneFmdlp08+Do6yQzGwhsds41TPz6A/yItb+b2VRgGPAbYC7wQDrziohIDNYv8iPQXv9b0yi03B6wz5dhwrdh8O5br28Gx/zEH5DPf8RfxKv3YH/KdjcaqaYaKyIibaqrhXcfhVk3wCevN7UP2AUO+zbsczrkF279GNVZERERyVCxd4az7YH2lOjrYmAUgHPuOTP7LHAV8B9gI/AwcIVzri4tKUVEJP0+ed2PUJv/cNPcpAXFcPD5cOjF0GeH1h9f1A8OPLerU4ZMNVZERJKr3gyv3+unHCtb3NS+06Fw2KWw20mQk9P6NlRnRUREJMPE3hnunGvX1bOcc08CT3ZxnC5XvqWGJ+YtZ/XGKgb1KeCkcUMp7pkfd6wucdRRR1FWVsYbb7zRKdtbtGgRo0eP5o477uDcc8/tlG2KSICcg4+e8RfFXDSjqb3vcBh/CRx4DhT0iS9fBuluNRZUZ1OhOivSTWxeAy9NhZf+AhUN10k22P0zvhN8xKGxxhMRERHpSrF3hncXzjmue/oDpk4vJSfHqKyuo7BHLlMefYcLJ47h8uPHYtauPgsRkexUWw3zHvSnaa+a39Q+eC84/Dsw7guQm52dmpI61VkRkTasXeBHgb9xH9RW+rbcAtj3dD8dysBd480nIiIikgbqDE+T657+gNtmLKSqtr6xraLan31+24yFAHzvhN1iydbZqqqqKCgoiDuGiGSKyg3w6p0w58+w8ZOm9tGT/EUxdz7Wz00q0grVWRGRFnz8ij/b6t3HAOfbCkvg4Mn+wpi9B8eZTkRERCSt2pgETpqrratn6botHVrmf1LOrS+UUlGTfOrVipo6bp1eyvxPyju87dq6+qTbbMlDDz2EmfHCCy9sc98vf/lL8vLy+OQT3xn1xBNPMGnSJPr06UOvXr049thjefnll7d6zLnnnktJSQmvvfYaRx11FL169eKiiy7aap1nnnmGAw88kMLCQkaNGsV111231f2VlZVcfvnl7LPPPvTt25eBAwcyceJEnn322Q79bCKSYTYsh6d/Bn/YC57+qe8It1wY90W48AU451HY5Th1hHcj21Nju7LOdrTGguqsiASivh7efxJuPxFuO9ZfIBMHxSPg09fAd9+BY3+qjnARERHpdjQyvIOWl1dy5LXPd/p2q2vrOemGFzv8uBlXHM1O/Xu2e/3PfvazDBw4kDvvvJNJkyZtdd/dd9/N8ccfz7Bhw7j99tuZPHkyp59+Ov/3f/9HXV0d1113HZMmTWLu3LnsvffejY+rrKzkC1/4Apdeeik/+9nPyM9vmsZg2bJlnHvuufz0pz9l5MiR3H///Xzve9+jtraWK664AvAj3MrKyvjhD3/IsGHD2LJlC4888gjHH388Tz31FMcdd1yHXxcRCdiqd2HWjfDWP6G+xrfl94QDvubnBO83Mt58EpuuqrGwfXW2ozUWVGdFJGa1Vb6+zroR1rzf1L7DPn7KsT1PgVwdAoqIiEj3pb+Eupn8/HzOOOMMbr/9dv70pz/Rq1cvAGbOnMmHH37IL3/5SzZv3sz3vvc9TjvtNO67777Gx55wwgmMHTuWn//85zzwwAON7VVVVVx99dWceeaZ2zzfmjVrePLJJ/nUpz4FwKc//WlWrlzJr371Ky699FIKCwspLi7m9ttvb3xMXV0dJ5xwAqWlpdx00006SBfJBs7B4pkw8wb48H9N7b0GwSEXwcFfh57948sn0klUZ0UkFhVl8OodMOcW2LSiqX3nY/xFMcccpTOtRERERFBneIcNLS5kxhVHd+gxj775CTc+9yGVNS2fbl2Yn8O3j9mVz+07rMN5Ouq8887jhhtu4F//+hfnnHMOAHfddRf9+vXj85//PDNmzKCsrIyzzjqL2traxsf16NGDo446iqeeemqbbZ566qlJn6tfv36NB+gNTj/9dJ588kneeOMNxo8fD8CDDz7IH/7wB959913WrVvXuO7uu+/e4Z9PRNKoYj3MfwQ2rfKnWu/5eSjq13R/fZ0/NXvmDfDJa03tA3aBCd+Cfb8K+R3/HGuv8i01PDFvOas3VjGoTwEnjRtKcU9dhDNU21Njoevq7PbUWFCdFZFO0laNBSj/2F9z49U7oXqTb7Nc2Ps0f1HMHfbeZrOdSXVWREREMo06wzsoLzenw6dMn3XoSG549sNW13EOzho/kuKirv/jcb/99mPfffflzjvv5JxzzqGiooJ//vOfnHnmmRQUFLBy5UoAPv/5zyd9vDUbVdK3b1969kz+mgwZMqTFtrVr1wLwwAMP8OUvf5nTTz+dK664giFDhpCbm8tPf/pT3n333e3+OUWkCzkHz1/tT8O2HKipgPwi+O8P/MH34d+FN++D2TfB+oVNj9vxEH+a9m4nQU7XXbbCOcd1T3/A1Oml5OQYldV1FPbIZcqj73DhxDFcfvzYbT7LJH7bU2NBdbalNtVZkQzVVo09+sew8h2YdQPMexDqo3+q9egNB5wD478BJTt1cUTVWREREclM6gxPg+Ke+Vw4cQy3zViY9OJeRfm5TD5ydFoO0Bucc845fO9732PRokXMnj2b8vJyzj33XAAGDhwIwM0338zBBx/c5rZa+0O34YA/WduAAQMAuO+++xgzZgz33XffVtvatGlTu38eEUmz56/2Hd21lU1tNVv81xf/ALP+BLUVTfft9hk4/FIYMT4t8a57+gNum7GQqtqmkcIV1f7z97YZvnP+eyfslpYs0vVUZ5O3qc6KZKjWauzMG+CtB6BsUdN9vYfAoRfDQedtO3K8i6jOioiISKZSZ3iaXH78WIBtRk/U1zsmHzm68f50Oeuss/jBD37AXXfdxezZs9lrr70aD8gPP/xw+vbty3vvvcc3vvGNlJ5n/fr1/O9//9vqFO7777+f4uJi9ttvP8Af5Ofn5291gP7OO+8we/Zsdtxxx5SeX0S6QMV6P1ot8SA9UX2tX3LyYb+vwoRvw6D0fcaVb6lh6vTSrQ7QE1XU1DF1eimTjxyT1s5R6Vqqs6qzIlmhrRpbV9XUET5wrB8pvs9XIK8gbRFVZ0VERCSTqTM8TcyM752wG5OPGLP1vHp7D43lj8RBgwZx4okncuutt7Jq1Squueaaxvt69+7NH//4RyZPnsy6des49dRTGTBgACtXruSVV16hoKCAX/ziF+16noEDBzJ58mR+8pOfMHLkSO6//36efPJJrr32WgoL/Vysn/nMZ3jooYf45je/yRe+8AVKS0uZMmUKI0eOpK5u2xF+IhKz+Y/407Zbk5MPx/7MjwZPsyfmLScnp/VTs3NyjCfeXs5XDxmRplTS1VRnVWdFskJ7aqzlwEFfhxOv7dIpx1qiOisiIiKZTJ3haVbcMz+YPwrPO+88Hn30UfLy8jjrrLO2uW/EiBH89re/5etf/zoVFRUMGTKEgw46iIsvvrjdzzF8+HB+//vf8/3vf5933nmHHXbYgd///vdcfvnljeucf/75rFixgqlTp/LXv/6V3XffnRtuuIHHHnuMadOmddaPKyKdZePKptO1W1Jf2/Koti62emNV46naLamsrmP1xqo0JZJ0Up1VnRXJaJtW+TnCW+Ocv6BmDB3hAMvLKlRnRUREJGOZcy7uDLEws7Li4uLisrKypPcvXrwYgJEjR6YxlXQG7TuRLlJbBW8/AM/+AjataH3d/J7w6V/DgeemJRr4i3nNXrCWKY++wwerWp8LuahHLj/77J4d6jQtKSmhvLy83DlXkmLUrKcam920/0S6yJqP4NFvw5JZra8XQ40FPz3K3+Yu5s/TFrCpqrbVdVVnRUREJFQaGS4iIq2rLIdX7oC5t8DG5e17jKuHPU/p0lgNauvq+e+8Fdw6fQHzlm1o12Pq6x0n7T20i5OJiIi0w9KXYOb18N5/gHYMVEpjjQX4eP0W/vriQv7x8lK2tDEivIHqrIiIiIRKneEiIpJc+TKYczO8ehdUb/Rtlgvjvgj5hfD2v5JPl5LfEyZ8E4pKujTelupa/vnyUm57cSEfr286pfzgUf0Y3KeQ595bSUXNthf3KsrPZfKRo3VRLxERiU99PXzwX5h5Ayyd09ReMgIG7OpHhyebLiVNNRbgnU/KmTq9lMffWk5dve+k712QxxmHjqCmtp77X15KRc22neOqsyIiIhIydYaLiMjWVr4Ds270U6LUR6dB5/eCA8+B8d/wB+rOQe8hfj3L8Qfs+UV+tNqEb8LRP+6yeGs2VXH3rEXcPWcxZVtqADCDT+25AxdOGsMBI/rhnOO6pz9g6vRScnKMyuo6CnvkUl/vmHzkaC4/fmyX5RMREWlRTSW89Q9fP9d+2NQ+dD9/0ek9Pg85ufD81bHUWOccMz5cw9Tppbz40ZrG9iF9Czjv8NGccegI+hbm45yjd2Ge6qyIiIhkHM0ZrvlMs472nch2cA4WzfCnaX/0TFN7r8Ew/mI46Hwo6rft4yrWw/xH/AW/eg/2p2130Wi1hWs285cZpTz46sdU1foR3z3ycjjtwB2ZfMRoxgzqvc1jyrfU8MS85azeWMWgPgWctPfQ7R6pprlM2081Nrtp/4lsh4r18PJfYe6tsHlVU/sux8Hh34FRR/r/7DZ/TJpqbE1dPf95azm3Ti/l3eVNU46NHdKbC44cw+f3G06PvG0v2Kk6KyIiIplGI8NFRLqzulp49xF/mvbyN5raB+wKh30b9vmKnxKlJUX9uvwCXq8tWc+tLyzgqfkrafj/bUnPfL42fiRfO2wUA3sXtPjY4p75Hbp4l4iISKcqWwJz/uynHKvZ7Nty8mDvL/k6O2Svlh+bhhq7qaqW+19awh0zF7GsrGlalkNH9+eiSWM4auxgcnKsxcerzoqIiEimUWe4iEh3VL0ZXv8bzL4JyhY3tY+YAIddCmM/DTnbjgBLl/p6x7PvrWLq9AW8vGh9Y/uO/YqYfMRovnzwTvTsoRImIiKBWv4WzLoB5v0bXDSvdo8+TVOOFe8Ya7xVGyu5c+Yi/jZnMRsq/ZRoOQYnjhvKhRPHsO9OJbHmExEREekq6kkQEelONq2Gl6bCy3/xp18DYLD7Z/xp2jsdEmu8qto6Hn59GVOnl7Jg9ebG9nHD+3LRxJ05cdwO5OXG10kvIiLSIueg9Hl/tlXp803tfYbCoRfDQedBYXF8+YCPVm3iL9NLeej1ZVTX+SnHCvNz+NKBOzH5yNGMHNAr1nwiIiIiXU2d4SIi3cHaBf5CXG/+HWorfVtuAex3Bkz4FgzcJdZ45Vtq+Nvcxdw5axGrN1Y1tk8aO4iLJo5hws4DsOZzqYqIiISgrgbeeciPBF/xdlP7oN39VCh7fwnyWp7Sq6s553hl8XpufaGUZ95d2djev1cPvjZhJF+bMIr+vXrElk9EREQkndQZLiKSzZa+DLOuh3cfB6IJtwtL4JAL4JAL/QW5YrSsrILbX1zI/S8tYXO1P408L8f43L7DuGDiGPYY2jfWfCIiIi2q2gSv3Q1zbobypU3tI4+Awy+FXY6PdcqxunrH0/NXcuv0Bby+pKyxfUT/nlxw5GhOO3AninrkxpZPREREJA7qDBcRyTb19fDh/2Dm9bBkdlN7yQg/Cnz/s6BHvKdBz/9kA1OnL+Cxt5ZTV+876XsX5PHVQ3bivMNHM6ykKNZ8IiIiLdq4EubeAq/8FSrLfZvlwB4nw2HfgR0PjDVeZU0dD772MbfNWMjCNU1Tju27YzEXTdqZT+21A7mtXBRTREREJJupMzzdKtbD/Edg0yo/InPPz/srxcs2brnlFgoLCzn33HPjjiKSGWqr4K1/+OlQ1nzQ1D50X39RzD1Pgdz4Pvadc7z40RqmTi9lxodrGtsH9yng/CNG89VDRlBclB9bPskSqrPtohorsh1WfwCzb4Q374e6at+WVwT7nwkTvgn9x8Qab/3mau6Zs5i7Zi1i7ebqxvZjdh/MhRPHcOjo/ppyTERERLo9dYani3Pw/NW+k8pyoKYC8ovgvz/wcwke/WPQH6dbueWWWygpKdGBukhbKsrgldv9KLVNTXOBsvOx/qKYoyfG+vlSU1fPE28v59YXSpm/fENj+66De3PBxDF8fr9hFOTpNG1Jkepsh6jGinTAkjn+bKv3n2hqK+rvpxs75ALoNTC+bMDSdVu4bUYp/3zlYypq/JRj+bnGKfsN54KJYxg7pE+s+URERERCkjGd4Wa2P3AlcAhQDCwG7gb+4Jyrau2xQXj+aph9U9OF6wBqtvivs2/yX4/5SfpziUjYWhvlWrYU5vwZXrsLqjf5tpw8GHea7/zbYVyXxyvfUsMT85azemMVg/oUcNK4oRT39KO7N1fVcv/LS7n9xYUsK6tofMwho/tz8aQxHDV2MDk6TTsIGV9jQXVWRDqutRpbX+c7v2feAB+/1PSYfqP8lGP7nQk9enZpvNZqLMDbH5dz6/QFPPH2cqIZx+hTkMcZ40dw3mGj2aG4sEvziYiIiGSijOgMN7PdgVnA+8BlwBrgGOBqYE/ga2kLU1cLG5Z17DGVG/wf0nUt9CfUbPH37/l5KOjgxeL6Dt/uaQ8ee+wxfvKTn/Dee+8xdOhQLr74YioqKvj5z3+Oc45FixYxevRo7rjjjm1GjpkZV155JVOmTAHgo48+4pe//CUzZszgk08+YeDAgRx66KFcc8017LLLLo2Pu/POOznvvPN44YUXuPfee3nwwQdxzjFp0iT+9Kc/MWzYMABGjRrF4sWLG58LYNKkSUybNm27flaRjNPaKNd9vgLVW+Cdf4PzI8Do0RsOPBfGfwOKd0xDPMd1T3/A1Oml5OQYldV1FPbIZcqj73DmoSMozM/h3rlLKa+oASDH4NPjduDCiTuz304lXZ5P2i/jayx0XZ1VjRXJTq3V2EMv9tfYmH0TrFvQ9JhhB/iLYu7xOcjp2rOZWquxFxw5mgNH9mPq9IXMLl3b+Jgd+hby9SNGc/ohO9GnUFOOiYiIiLQkIzrDgdOBQuCLzrmGv0qfM7ORwFfN7OvOuZq0JNmwDK7fp/O3W1cFtxzR8cd95y3oN7LDD3vqqac45ZRTOOKII7j//vupra3lN7/5DatWrep4BuCTTz5h8ODB/O53v2PAgAGsXLmSP//5zxx66KG8++67DB48eKv1zzvvPE4++WTuu+8+lixZwhVXXMFZZ53Fc889B8BDDz3EV77yFXr37s3NN98MQN++HfxHgUgma22U62t3NbX13gHGXwwHngdFJWmLd93TH3DbjIVU1dY3tlVU+47522cuamwryMvhSwftyOQjxjBqYLwX7ZQWZX+Nhe2rs6qxItmptRo7849br7vrp3wn+MjD0zbVUms19qbnF+AS1t19hz5ccOQYTt53GD3yctKST0RERCSTZUpneMNBeHmz9vLovrr0xsl8P/3pTxk2bBhPPfUUBQUFAJxwwgmMHj16u7Y3ceJEJk6c2Ph9XV0dn/nMZxg8eDB///vf+c53vrPV+p/97Gf54x//2Pj9+vXrueKKK1i+fDlDhw5l//33p2fPnvTt25fx48dvVyaRjFWx3o9WSzxI34bBSdfCAedAXkHaooE/bXvq9NKtDtKTuXjiGC6YOIYBvdObTzpMNbaTqcaKBKxdNRYY92WYeDkM3iM9uSJt1diGjvBDRvXjkqN3YdLYQboopoiIiEgHZEpn+D3A5cCfzewKYC1wNHAO8Hvn3DZ/LZpZWRvbLN6uJH2H+5FiHTHvX/DCb6G2ouV18opg0vf9XL8dzdNBmzdv5uWXX+Y73/lO40E6QHFxMSeffDJ33313h7dZXV3N9ddfz1133cWiRYvYvHlz433vvffeNut/7nOf2+r7ffbxIwEXL17M0KFDO/z8Illl/iP+tO3W5BdBbo+0d4QDPDFvOTltHHgX5ecycmAvdYRnhsyusdB1dVY1ViT7tLfGjj487R3hENXYNq6nUZCXw6kH7MhRuw1udT0RERER2VZGdIY755aY2XjgYaA04a5fOed+mtYwuXkdP2X6oPPhhWvbWMnBQV9PyzQH69evxznHDjvssM1923uQ/N3vfpdbb72V//f//h8TJ06kpKQEM+Okk06iomLbzokBAwZs9X1Dh0FlZRujdESy3caV8NY/mk7XbklNhb/gV5pV1tTx7LsrqahpfbBwZU0dqzdmxnUXu7uMr7EQVJ1VjRUJmHOweHY7amxlLDUW4L3lGxqnRGlJdW29aqyIiIjIdsqIzvBo3tLHgBXAqUAZMAn4f2ZWn+xg3TlX0sY2y9jekWsdVdQPDvu2n5sw2R/f+T1hwjfTNt9vv379MDNWrFixzX3Lly9vvF1Y6K9AX1W19R/ba9eupbn77ruPr33ta/ziF79obKuurmbdunWdFVsku6350J+2/eb9LV8EMFF+EfRO34iwsi3V3DN7MXfNXsSaTdVtrl/YI5dBfTQqPBNkfI2FoOqsaqxIgOpqYN6Dvs6unNf2+mmusc45Xlq4jqnTS3n2vbY74VVjRURERLZfRnSGA9cAfYD9nXMNQ6CmRfPj/czM/uqcWxRXuHY5+sf+a/Or1rt6f4DecH8a9OrVi0MOOYQHH3yQa665pnHE2IYNG3jsscca1xsyZAiFhYW89dbWp6w/8sgj22zTzOjRo8dWbbfffjt1dds/1WxBQUHSEW8iWWXJHJh5A7z/BI0zgRaWQPUmqK9t+XGuHvY8pcvjLV23hb++uJB/vrKULdFItbwcqHd+aUl9veOkvTUdQ4bI/BoLwdRZ1ViRgFRthFfvgjk3+wv0NrAc/9nQkjTV2Lp6x//eWcGt00t5c2lZUzyglRKrGisiIiKSgkzpDN8fmJ9wkN7gFSAH2B1YlO5QHWIGx/zEH5DPf8Sfetl7sP9DO00jwhP94he/4NOf/jQnnHAC3/3ud6mtreWaa66hd+/erF+/PopsnHnmmdx+++3svPPO7Lvvvrz00kvcd99922zvM5/5DHfeeSe77747e++9Ny+++CK33norJSUl251x3Lhx3HvvvTzwwAOMHj2aPn36sNtuu2339kSCUV/vO79n3QBL5za19xsFE74F+50JL14X6yjXecvKuXV6KU+8vZy6qNe7T0EeZxw6gvMOH829cxdz24yFSadLKcrPZfKRoykuyu+yfNKpMr/GQlB1VjVWJGYblsPcW+CVO6Aqujaw5fjPg8O+7WtwjDW2sqaOB179mNtmlLJ4bVOG/UeUcNHEnXl7WRm3v7hINVZERESkC2RKZ/gnwDgz6+mcS/yrdUL0dVmSx4SpqB8ceG7cKTj++ON5+OGH+clPfsJXvvIVdthhBy655BIqKiq46qqrGte77rrrMDOuvfZaNm3axDHHHMPjjz/OqFGjttreDTfcQG5uLldffTUVFRVMmDCB//3vf5x88snbnfHKK69kyZIlnHfeeWzevJlJkyYxbdq07d6eSOxqKuGt+/3I1bUfNbUP2x8OuxT2+JyfMxliGeXqnGP6h2uYOn0BMz9qmqphSN8Czj98NF89dAR9C/3B9+XHjwVg6vRScnKMyuo6CnvkUl/vmHzk6Mb7JSNkT42FIOqsaqxITFa95+vmW/+A+hrfllcEB5wN4y+B/qN927D9/dc0n0mybnM1d89exN2zF7Nuc9OUY8ftMYSLJo3hoJF+mqVP7TWEHDPVWBEREZEuYM61dhJeGMzsFOAh4EXgj0A5cBRwBfCCc+747dhmWXFxcXFZWVnS+xcvXgzAyJHbcSGvDDZlyhSuuuoqMuF90ZLuuu8kYFvWwSt/hblTYXPCXKC7nuA7wUcd4Ue1JlOxvstHudbU1fP4W59w6wulvLdiY2P72CG9uXDiznxu32H0yMtJ+tjyLTU8MW85qzdWMahPASftPTRrR6uVlJRQXl5e3tZ82ZlGNTZ9sqHGQvfdfxIo52DxLH+21QdPNrX3HAiHXgQHT4ae/ZM/Ng01FmDJ2i3c9mIp/3xlKZU1fnqWHrk5nLr/cC6YOJpdBvdJ+rjuVGMhe+usiIiIhCUjRoY75x42s+OBHwI3A73xp2z/ArguxmgiIi0rWwKzb4bX7oaazb4tJx/2/pI/TXvInm1vowtHuW6qquX+l5Zw+4sL+aS8srF9/Jj+XDRxZ47abRDWUid9pLhnPl89ZESX5JP0UI0VkYxUXwfvPQ4zr4dlrza19x8TTTl2hh/t3ZouPpPkzaVlTJ1eyn/nLW+8zkbfwjzOGj+Scw8bxeC+ha0+XjVWREREpPNlRGc4gHPuGeCZuHOIiLRp+Zv+opjvPAQumu+zoK8/4B7/Deg7LNZ4qzZUcvvMRdw7dzEbK/1FOnMMTtx7KBdNHMM+O5bEmk/STzVWRDJGTQW8ca+f83tdaVP78IPg8O/A7p+BnNzY4jnnmPb+am6dvoA5pesa24cVF3L+EaM5/ZAR9C7ImEMwERERkayTEdOkdAWdwp29tO8kFs7Bguf8adql05ra+wz1HeAHnguFxXGlA+CjVRuZOr2Uh1//hOo6f5p2YX4OXz5oJyYfMYYRA3rGmi90On27/VRjs5v2n8Ri81p4+TZ46VbY0nRdC8aeCIdfCiMmtDzlWBpU19bzyBvL+MuMUj5YuamxfY+hfblo4hg+s89Q8nOTTzkmnuqsiIiIpIOGJYiIpKKuBub921+Ea+XbTe2D9vAH5+NOg7wescVzzvHyovXc+sICnn2vab7y/r16cM6EUZw9YST9e8WXT0REpFXrFvpR4K//DWorfFtuD9jny/66G4N2izXehsoa/j53CXfMXMSKDU1Tjh2xy0AumjSGI3YZ2OaUYyIiIiKSPuoMb0FOTg41NTVxx5DtUFdXR35+9l5cSAJRtdHPBT77ZtjwcVP7qCP9wfmux8c6Qq2u3vHUOyu4dXopbywta2wfOaAnk48cw2kH7EhRj/hOI5fuLScnh+rqapxz6iTKMM45amtr6dFD/0STLrbsNX+21fxHwPmzmSgohoPPh0Mvhj47xBpveXkFd8xcxH1zl7Cpyk85lptjfHafoVxw5BjGDY/3bDARERERSU6d4S0oLCxk06ZNrFu3jv79W7gCvQRn3bp1VFVV0adPn7ijSLbauALm3gKv3A6V5b7NcmDPz/tO8OEHxBqvsqaOf736MbfNKGXR2i2N7fvuVMLFE8dwwl47kJujzkeJV+/evVmxYgWrV69m4MCB5ORo6oBMUF9fz5o1a6iurtbfRtI1nIMPn/ad4ItmNLX33REmXAIHfA0K4v0b7/0VfsqxR99cRk2dn26yZ49cvnLwTnz9iNHs2E9TjomIiIiETJ3hLRg4cCBVVVWsXLmSsrIycnM1gjJ0dXV1jR3hAwcOjDuOZJvVH/iD87f+AXXVvi2vCPY/CyZ8E/qPjjXe+s3V3D17MXfPXsTazdWN7cfuPpgLJ47hkNH9NQJXglFcXMyWLVtYu3Yt69evJz8/X3U2cHV1ddTU1FBfX0/fvn0pLtaoV+lEtdUw719+yrFV85vah4zz/2ge9wXIje+sP+ccs0vXMnV6KdPeX93YPrB3AecdPoozDx1BSU+dLSEiIiKSCdQZ3gIzY/jw4axZs4bKykrq6+vjjiRtyM/Pb+wIV6eftEvFen/69aZV0HuwH91d1K/pfudgyRyYeT188N+m9p4D4JCL4ODJ0GtAl0Ys31LDE/OWs3pjFYP6FHDSuKEU92zqEFi6bgu3zSjlH68spbLGf07l5xqn7j+cC44cw65DdJaEhCcnJ4fhw4dTUlLChg0bqK2tVZ0NXH5+PkVFRRQXF9OzZyeNfG3rMzhuoecDytet5v3n7qFu40py+wxht2POprj/oLhjNWozX2U5vHonzLkFNn7S1D56Ehz+Hdj5mC6dcqytGltbV8+T76xg6vRS3vq4vLF9zMBeXDBxDKfuP5zCfP0jT0RERCSTmHMu7gyxMLOy4uLi4rKysrijiEi6OQfPX+1HoFkO1FRAfpGfk/Swb8OkH8L7T/iR4B+/3PS4fjk9+D8AAFyoSURBVKPhsG/BvmdAj649Ddo5x3VPf8DU6aXk5BiV1XUU9silvt5x4cQxHLfHYKbOWMh/315OffQx3qcwjzMPHcl5h49iSN/CLs3XHZWUlFBeXl7unCuJO0voVGOlVW19Bh/941ivuRB8PsDV1zPn9u+x/9J7qCeHQqqopIAc6nl9p7MZf/7vsRinH2oz32nfw1661XeEV23wD7Jc2OtU/xoP269r87VRYy+eNIZ/vbqM214sZem6isbHHTSyX1SDh5CjKcc6neqsiIiIpIM6w3WgLtL9PPdLmH0T1GzZ9r7cfMjvBZVlTW3DD/Qj1Hb/LOSkZwTY7596n9tmLKSipm6b+3KMxg5wgKHFhXz9iNF85eCd6FOoi8d2FR2kt59qrLSqtc/g/J5+6qljfpL+XA1CzwfMvu277Lv0Xnpa1Tb3bXEFvLnTmUyY/IcYknmt5at1OeSYI4eokOX39HOBj78E+o1MS77WamxejpGXY1TW+rNVzOD4PYZw0aQxHDhSc+V3JdVZERERSQd1hutAXaR7qVgPv98daivbXnfsp/1cpSMPS+sowPItNRzyq2eoqm192ohdB/fmG0ftzMn7DiM/Vxcg7Go6SG8/1VhpUXs+g/MK4XvvQ1FJ2mI1Cj0ffuqRguv3oNBqWlyn0uVTddl7FPdL/zVU2pMPoL5oADkTvgEHfR16pq+Tub01Nj/XOO3AnZh85Gh2HtQ7Tem6N9VZERERSQfNGS4i3cv8R/xp763JyYOJ34ejfpieTM08MW95m6dfF+TlcN7ho/jCATumKZWISCdoz2dwbRXceAAU9E1PpkRVG/zztybOfEDB5jJ6UNvqOj2oZcv1+/Oxde2UXsn0dFvazFfl8riu8lSenHsQzH0zTcm8jZW1VLfREZ6XY3z/U7tz4cQxaUolIiIiIumiznAR6V42rfLzv7amvq7tzpoutHpjFZXV2566nai6tp41m6rTlEhEpJO05zMYB1vW+iVI8eYrBGjjZKUcc/RnA/3dhnRE2lYb+fKoo0d1GYsrkkxFE4C6ekdlkilURERERCTzqTNcRLqX3oP9hdCSzQXbIL/IrxeTQX0KKMzPTTqXaYPCHrkM6lOQxlQiIp2gPZ/BuT1gry/4KarSbfEseOffUNfKPxvjzAeUvvY0wz/+LwXW8ujrKpfHW8XH4GLIaItnsU/5c63nowc77TSKaw7YO43JvJcWruPxtz6huq7lqSJVY0VERESylzrDRaR72fPz8N8ftL6Oq4c9T0lLnGROGjeUnz48r9V16usdJ+09NE2JREQ6SXs+gy0HTvxNPHNy7/k5mP9w6+vEmQ8YMPok3PVPtrqOwxh73p9jmjP8LNz1e7S6Tg71fOor34gl34njhvKft5cDLXeGq8aKiIiIZC9dcU1EupeifnDYt8Fyk9+f39PfH1MnB8Caza3PV1uUn8uFE8dQXJSfpkQiIp2kqB8cenHL98f9GdxQI/JbmGs77nzAmroibqv7LFtcj6T3b3EFvL7T2bF0NAMU9x/E6zudzRaXfGR17Pl65nPhxDEU5Sf/O0A1VkRERCS7qTNcRLqfXU4AF01BktsDMN/BkVcIE74JR/84tmhVtXV8+77Xqa139C7IpSAvh6IeuRhQ1MN/P/nI0Vx+/NjYMoqIpKRH76bbeUWE9BkM+Oef8E2fJ79nUPkaasTvak/jbj5LpctniyugzhlbXAGVLp83dzqT8ef/PraMAOPP/z1v7nRmsPkuP34sk48crRorIiIi0g2Zcy2fIpjNzKysuLi4uKysLO4oIpJOzsEdJ8GSWTBkHBw8GTav9vPY7nlKrKP9AK567B3umLmIHIP7L5zAbkP68MS85azeWMWgPgWctPdQjVaLSUlJCeXl5eXOuZK4s4RONVZatHkNXL8fVG+EQy6EIXv5i2oG8hm8lYr1MP+RoPJtUyOK63j/uXuo27iS3D5D2O3Yr8U24jqZ8nWrw863pUY1NiCqsyIiIpIO6gzXgbpI9/LeE3D/V/3trz0KYybFmyfBs++u5Ot3vQLAZcftymXHaWRaSHSQ3n6qsdKi//4A5t4ChSXwnTf8tCTSLqoRku1UZ0VERCQdNE2KiHQfdbXwzJX+9i7HB9URvnJDJd//11sAHDKqP986epeYE4mIdLK1C+Dl2/ztif+njvAO2KpGjO7Pt4/ZNeZEIiIiIiKZSZ3hItJ9vH4PrPkAMDj+qrjTNKqrd1x2/xus21xNcVE+fzx9P/Jy9fEsIlnmuV9AfS0Uj4CDL4g7TcbYpkZ8ZT9ycyzuWCIiIiIiGUm9LSLSPVRvhmm/9rf3O9PPUxuIW15YwOzStQD85ov7MKykKOZEIiKd7ONX4Z2H/O1jfwr5hfHmySCJNeLa01QjRERERERSoc5wEekeZt8Em1ZCXiEc/aO40zR6dfF6rnv6AwDOGj+CT4/bIeZEIiKdzDl4+mf+9g77wLjT4s2TQRJrxNnjR/KpvVQjRERERERSoc5wEcl+m1bBzOv97fHfgOLh8eaJlFfUcOnfX6eu3rHbkD785DN7xh1JRKTzffA/WPyiv338zyFHf362R/Ma8ePP7BF3JBERERGRjKejERHJfi/8Bqo3QVF/OOK7cacBwDnHjx96m2VlFRTk5XDjGftTmJ8bdywRkc6VeOHinY+FnY+ON0+GSKwRhfmqESIiIiIinUWd4SKS3dZ8BK/c4W9PugIKi+PNE/nnK0t5/K3lAPzs5D0ZO6RPzIlERLrAG/fC6vcI7cLFoduqRnx2L9UIEREREZFOklGd4WZ2lJk9ZWZlZrbFzOab2YVx5xKRgD17Fbg6KBkJB50fdxoAPlq1kSmPzgfgxHE7cMYhI2JOJKIaK10g8cLF+54OO+wdb54M0bxGfPWQnWJOJCIiIiKSPfLiDtBeZnYO8FfgL8B1QA2wO9AjzlwiErClL8G7j/rbx/4M8grizQNU1tTx7b+/QUVNHcOKC7nmC/tgZnHHkm5ONVa6xJybYeNyyC2Ao38cd5qMkFgjhpcUqUaIiIiIiHSy7eoMN7PRwLHAEOBe59wiM+sB7ACscM5Vd2JGzGwn4M/Aj5xz1ybc9WxnPo+IZBHn4Kmf+tvD9oe9vhBvnsg1/32Pd5dvIMfg+q/uT3HP/LgjSYDSWWdVY6VLbFoNLzZcuPhiKNHo5vbYqkacvp9qhIiIiIhIJ+vwNClm9hvgA2Aq8HNgTHRXITAfuKTT0jX5evT1xi7Ytohko/f+A0vn+NvH/xxy4p8V6pn5K7lz1iIALjtuLAeP6h9vIAlSDHVWNVY63/RroXojFPWDIy6PO01GaF4jDlKNEBERERHpdB3qHTKzi4DvAzcBJwCN52065zYAjwInd2bAyETgXeALZva+mdWZ2cdmdk00Ui5Z1rLWFiCMq+iJSOerq4Vnpvjbu54AoyfGGgdgRXkl3//XmwAcMro/3zx6l5gTSYhiqrOqsdK51i6AV273t4/8PygqiTVOJkisEYeqRoiIiIiIdJmODpW8BHjIOXcZ8HqS+98Cdks1VBLDgF3xo9ZuwJ86fjvwPeCOLng+Eclkr98Naz8Ey4Hjroo7DXX1jsv+8Trrt9RQ0jOf60/fj9wczQErScVRZ1VjpXM9+3Oor4WSEXDIBXGnCV7zGvFH1QgRERERkS7T0TnDx+LnFW3JamDg9sdpUQ7QB/iqc+7+qG2amRUB/2dmVzrnPkp8gHOupLUNauSaSJaq2gTP/9rf3u8MGLJnvHmAP0/7iDml6wC49ov7MLS4KOZEErA46qxqrHSej1+B+Q/728eEceHi0KlGiIiIiIikT0dHhlcCvVq5fyRQtt1pWrY2+vq/Zu3/jb4e0AXPKSKZaPafYPMqyCuEo34UdxpeXbyOPzzzIQBfmzCSE/baIeZEErg46qxqrHSOxAsXD90Xxn0x3jwZQDVCRERERCS9OtoZ/hJwarI7zKwQOBuYmWqoJN5uob3hHNL6LnhOEck0m1bBzBv87fGXQPHwWOOUV9Rw6d/foK7esfsOffjRSXvEmkcyQhx1VjVWOscHT8KSWf52IBcuDplqhIiIiIhI+nX0KOW3wAQzuwfYJ2rbwcw+BUwDdgR+13nxGv07+npSs/aTAAe83AXPKSKZZto1ULMZivrDEZfFGsU5x4/+/TbLyioozM/hxq/uT2F+bqyZJCPEUWdVYyV1dbXw9JX+9i7HwZijYo0TuuY14k9nqEaIiIiIiKRDh+YMd849Y2bfAK4Hzoia74m+VgMXOOdmd2K+hud90sz+C9xkZgOBd4BjgO8AtzjnFnf2c4pIhlnzIbx6p7896QdQGO90xf94eSn/eXs5AFeevBe7DukTax7JDHHUWdVY6RRv/A3WvA9YEBcuDl1ijZhy8l7sMlg1QkREREQkHTp6AU2cc1PN7FHgS8Du+NOoPwT+6Zxb1sn5En0JuAq4AhgELAF+Alzbhc8pIpnimSng6qDfKDjo/FijfLhyI1MeeweAz+w9lNMP3inWPJJZYqqzqrGy/ao3w/O/8rf3/SrsMC7ePIFrXiO+ohohIiIiIpI27e4MN7PewA3Af51zDwA3dlmqJJxzm4H/ixYRkSZL5sJ7j/vbx/4M8nrEFqWypo5v//11KmvqGV5SxK++sDdm1vYDpduLs86qxkpKZt8Mm1ZCbgEc8+O40wRNNUJEREREJF7tnjPcObcJOB3o23VxREQ6yDl4+qf+9rADYM+k1x5Mm18/8S7vrdhIbo5xw1f3o7goP9Y8kjlUZyUjbVoNM//ob4//BhTvGGuc0KlGiIiIiIjEq6MX0JwPjOqCHCIi2+e9x2HpXH/7+J9DTkc/1jrPU++s4K7Zfnrl7x63KweO7B9bFslYqrOSWV74DVRvgqJ+cMR3404TNNUIEREREZH4dbTX6FrgG2Y2tivCiIh0SF2NnyscYNdPwegjY4uyvLyCKx58C4DxY/rzjaN2iS2LZDTVWckcaxfAq3f42xO/D0UlscYJmWqEiIiIiEgYOnoBzd2BpcDbZvY4/oJeW5qt45xzv+iMcCIirXrtblj7EVgOHDclthh19Y7L7n+Dsi019OuZzx+/sj+5OZoDVraL6qxkjmevgvpaKBkBB0+OO02wVCNERERERMLR0c7wKQm3W5qY1wE6SBeRrlW1Eab92t/e70wYsmdsUW56/iPmLlwHwG9P25cdigtjyyIZb0rCbdVZCdfSl2H+I/72sVdCXkG8eQKmGiEiIiIiEo6OdoaP7pIUIiIdNetPsHk15BXB0T+KLcYri9bxx2c+AODcw0Zx3J5DYssiWUF1VsKXeOHiofvBXl+INU7IVCNERERERMLSoc5w59zirgoiItJuG1fCrBv97QmXQN9hscQo31LDd+5/g3oHewztyw9P3D2WHJI9VGclI7z/BCyZ7W/HfOHikKlGiIiIiIiEp6MjwxuZ2QCaRrAtdM6t7ZxIIiJteOEaqNkMPQfA4d+JJYJzjh/++y2WlVVQlJ/LjV/dn8L83FiySHZSnZUg1dU2Xbh4l+NhzKRY44RKNUJEREREJEwdHspjZvua2QvAKmButKwys2lmtk9nBxQR2crqD+DVu/ztST+AwuJYYvz9paX8d94KAKZ8bk92Gdw7lhySfVRnJWiv3wNrPgAMjr8q7jTBUo0QEREREQlTh0aGm9k44EWgEHgUmBfdtRdwMjDDzA5zzr3TqSlFRBo8exW4Oug3Gg48L5YIH6zcyFWP+Y+5z+wzlC8ftFMsOST7qM5K0Ko2Nbtw8V7x5glUYo34rGqEiIiIiEhQOjpNys+BGuAw59zbiXdEB/DTo3W+2DnxREQSLJkD7z3ubx/7M8jrkfYIlTV1fPu+16mqrWfHfkX86tS9MbO055CspTor4Zp9E2xaCXmFsV64OGTNa8TVqhEiIiIiIkHp6DQpE4Gbmh+gAzjn5gE3A5o8UkQ6n3Pw1E/97WEHwF6nxhLj6v+8y/srN5KbY1x/+v4UF+XHkkOyluqshGnTKph1g789/htQPDzePIFSjRARERERCVtHO8N7AStauX95tI6ISOd69zH4+CV/+4RfQAwj7f73zgrumbMYgMuPH8uBI/ulPYNkPdVZCdMLv4HqTVDUH474btxpgqQaISIiIiISvo52hpcCn23l/s9G64iIdJ66Gnhmir899kQYdUTaI3xSVsEV/3oLgMN2HsDFk3ZOewbpFlRnJTxrPoRX7vC3J10R24WLQ6YaISIiIiKSGTraGX438Ckzu8/M9jKz3GgZZ2b3AicAd3Z6ShHp3l69E9YtAMuB46ak/enr6h2X3f8G5RU19OuZzx++sh+5OZoDVrqE6qyEp+HCxSUj4aDz404TnMQa0b9XD9UIEREREZGAdfQCmr8DDgBOB74C1EftOYAB/wR+32npRESqNvrT8wH2PwsG7572CH967iNeWrQOgN99aV+G9C1MewbpNlRnJSxLX/LTVEF04eKCePMEaOsasY9qhIiIiIhIwDrUGe6cqwO+Yma3AacAo/EH5wuAh51zz3R6QhHp3mbdCJtXQ14RHPWjtD/9SwvXcf2zHwBw3uGjOHaPIWnPIN2H6qwEZasLF+8Pe30h3jwBal4jjtldNUJEREREJGQdHRkOgHPuaeDpTs4iIrK1jSt8ZzjAYd+CvkPT+vRlW6q57P7XqXew59C+/PDE9I9Kl+5JdVaC8N5/YOkcf/v4n0NOR2fXy26qESIiIiIimadDRzVm1t/M9mnl/n3MrF/qsUREgGm/hpot0HMAHHZpWp/aOccPH3ybT8orKcrP5cYz9qcgLzetGaT7UZ2VYNTVNl24eNcTYPTEWOOERjVCRERERCQzdXSIz7W0fuGuO4Bfb3caEZEGq9+H1+7xtyf9EAr7pvXp7527hCffWQHAVZ/fi50H9U7r80u3pTorYXj9blj7YXTh4qviThMc1QgRERERkczU0c7wo4HHWrn/UeC47Y8jIhJ55ipwddB/DBx4blqf+v0VG/nF4/MBOHnfYXzpwB3T+vzSranOSvyqNsHz0f9c9jsDhuwZb57AqEaIiIiIiGSujnaGDwOWtHL/x9E6IiLbb/EseP8//vaxP4O8Hml76sqaOr7999eoqq1np/5FXH3qOMwsbc8v3Z7qrMRv9p9g8yrIK4zlwsUhU40QEREREclsHe0M3wyMbOX+kUDV9scRkW7POXj6Z/728ANhz1PS+vS/eHw+H6zcRF6OccPp+9O3MD+tzy/dnuqsxGvjSph5g789/hIoHh5vnsCoRoiIiIiIZLaOdobPBc4xsz7N74javga81BnB2mJmU8zMmdkb6Xg+EUmTdx+Fj1/2t4//BaRxxN2T85Zz71w/KPfyE8ay/whdp1DSLog6qxrbjb3wG6jZDEX94YjL4k4TFNUIEREREZHMl9fB9X8HPAPMMrOrgDcAB+wPXAnsCEzuzIDJmNlewA+AlV39XCIhK99SwxPzlrN6YxWD+hRw0rihFPcMZ5Rah/PV1cAzU/zt3U6CUYenLV9+rnHz8x8BcMQuA7l44s5d+twiLYi9zqrGdmNrPoRX7/S3J/0ACotjjRM31QgRERERkezToc5w59zzZnYJcD3wj4S7DKgGvuWce6YT823DzHKAvwK3AXsDJV35fCIhcs5x3dMfMHV6KTk5RmV1HYU9cpny6DtcOHEMlx8/NtY5TLc736t3wrpSsBw49sq05jODegeF+Tn8/kv7kJOjOWAl/eKus6qx3dwzU/yFi/uNgoPOjztNbFQjRERERESyV0dHhuOcu9XMHge+DOyCP0B/H/iXc25ZJ+dL5rv4kXEnAI+m4flEgnPd0x9w24yFVNXWN7ZVVNcBcNuMhQB874TdYskG25mvcgNMu8bf3v9sGLx7WvM557/W18Pf5i6J9fWT7i3mOqsa210tmQPvPe5vp/nCxaFRjRARERERyV4d7gwHiA7G/wBgZvnA54CTzOwx59yKTsy3FTMbA/wcONM5tyHOka8icSnfUsPU6aVbHaQnqqip4+ZpCyivqKYgLzfN6aCqpo57X1pKXb1Len9L+SYum8qRW9ZQnVPIn+u+yKb/zI8lX3VdPVOnlzL5yDEUF4Uz5Yx0L3HUWdXYbizxwsXDDoA9T403T4zaqrGqESIiIiIima1DneFmdi1wtHPu4Oh7A54GjsSPXPuVmY13zi3o7KDRc/0F+J9z7uF2rF/WxirdeyJMyVhPzFve5jUl6+odd89ekp5A26F5vsGs57sF94HBn6tP5A9zNwGbYsuXk2M88fZyvnrIiNgySPcUV51Vje3m3nscls71t4//OeR09Prq2eOJecvJaaPIqkaIiIiIiGSujo4M/zT+wl4NTgYmAtfiL/J1I/BD4ILOCNfMBcBBwJ5dsG2RjPDm0jLueHEhlTXJR6wlGj2wF6MH9kpDqq0tXLOZhWs2t7leYr5z1vyNnpuq2JBTwvtjzuOYnJ6x5qusrmP1xqouyyDSirjqrGpsd5V44eJdPwWjj4w1Tpy2VNfy1DsrqKipa3U91QgRERERkczV0c7wnYAPE74/GVjonPshgJntBZzZSdkamdlAfEfAr4HNZlYS3ZUH5EbfVzrnKhse45wroRXRqDaNXJPg1dc7pn2wiltfKGXuwnXtekxRj1wunDgmllFrf39pCT9/fH7jHOHJbJVv1Xvw5ycB6Pvpn3DzIZNiz1fYI5dBfQq6NIdIC9JeZ1Vju7nX7oK1H/kLFx83Je40sVizqYq7Zi3injmLKdtS0+b6qhEiIiIiIpmro+fB9gASe5COZusRbKXA0FRDJbEj/qD618D6hOVwYFx0e0oXPK9IbKpr63nglaV8+vrpnH/nK40d4WOH9CEvp/VTuOvrHSft3RW/im07adxQ6luYj7vBVvmevQpcPfTfGQ48N7x8IukVR51Vje2uqjY2Xbh4vzNhSPc6MWDhms386KG3Oeya57jxuY8o21JDfq6R28ZUZKoRIiIiIiKZq6Mjw5cC44Gp0ei0McDPEu4fTNdM9PsRvkOguT8CvYHJQLgTJIt0wIbKGu6bu4Q7Zi5k5Yam07CP3HUgF04cwxG7DOS6pz/gthkLk57KXZSfy+QjR8d2Ya/invlcOHFM+/ItmgnvP+HvOO5KyO36zB3KJ5J+cdRZ1djuatafYPNqyCuCo38Ud5q0eXXxeqZOX8BT81fiov+NlvTM52vjR/K1w0Zx16xFqhEiIiIiIlmqo53h9wM/NbPBwF7ABuCJhPv3Bzr94pnOuU3AtObtDRfwcs5tc59IplleXsEdMxdx39wlbKqqBSA3x/jsPkO54MgxjBveNOPA5cePBWDq9FJycozK6joKe+RSX++YfOToxvvj0q58zsHTP/UP2PFg2ONzYeUTiUfa66xqbDe1cQXMutHfnnAJ9B0Wb54uVl/vePa9Vdz6wgJeWby+sX3HfkVccOQYvnTQjvTs4f8sVo0QEREREcle5lzr0wVstbJZAXAzcApQDlzmnHs0uq8YWA78wTn3486PmjTPNKDEObffdjy2rLi4uLisrKyzY4l0yHsrNjB1eimPvvEJtdH0HT175HL6wSM4/4hR7Niv5YtJlm+p4Yl5y1m9sYpBfQo4ae+hQY1WazXfOw/BA+f62+f9F0YeFlY+kWZKSkooLy8vb2u+7FSEVGdVY7PcY5fBq3dAzwFw6etQmJ1TvFfW1PHw68v4y4xSFqxuunjy3sOLuXDiGE4ctwN5uclnDVSNEEmvdNRZERERkQ51hre6IbMcoA+wxTnX9tWHYqYDdYmTc47ZpWuZOr2Uae+vbmwf2LuA8w4fxVmHjqS4ZxYfcNdWw82HwrpS2O0z8NX74k4k0qa4D9Izqc6qxgZu9Qdw83hwdXDitXDoRXEn6nTlW2r429zF3DFzEWs2NU05NmnsIC6aNIYJYwZg1sbk4CKSVnHXWREREekeOjpNSoucc/X4UWwi0oLaunr+O28FU6eX8vaypl+XMYN6ceGRYzhl/+EU5ufGmDBNXr3Td4RbLhw3Je40IhlBdVY6zbNX+Y7wfqPhwPPiTtOplpVV8NcZC7n/5SVsqfZzfuflGJ/bbxgXThzD7jv0jTmhiIiIiIjEqdM6w0WkZVuqa3nglY+57cVSlq6raGw/aGQ/Lpw4huP2GEJOTjcZoVa5AV64xt8+4GwYpLlXRUTSZvFseO9xf/vYn0Fej3jzdJL5n2xg6vQFPPbWcuqiKcd6F+Tx1UN24rzDRzOspCjmhCIiIiIiEgJ1hot0oTWbqrh71iLunrOYsi1+VgMzOH6PIVw0aQwHjuwfc8IYzLwetqyF/J5w1P+LO42ISPeReOHiYQfAXqfGmydFzjle/GgNU6eXMuPDNY3tQ/oWcN7hoznj0BH0LcziKcdERERERKTD1Bku0gUWrdnMX2aU8q9XP6aqth6AHnk5fPGAHZl85Gh2HtQ75oQx2fAJzL7J3z7s29Bnh3jziIh0J+8+Bh+/7G+f8Av/39kMVFNXzxNvL+fWF0qZv3xDY/vYIb254MgxfH6/4fTIS35RTBERERER6d7UGS7SiV5fsp5bXyjlf/NX0HBt2uKifM4eP5JzDhvFoD4F8QaM27RfQ20F9BrkO8NFRCQ96mrgmSn+9tgTYdQRscbZHpurarn/5aXc/uJClpU1TTl26Oj+XDRpDEeNHdx9phwTEREREZHtos5wkRTV1zuee28VU6eX8tKidY3tw0uKmHzkaL580E70KtCvGqvehdf/5m9P+gEU9Ik3j4hId/LqnbBuAVhOxl24eNXGSu6atYi/zVlCeYWfcizH4MRxQ7lw4hj23akk3oAiIiIiIpIx1EMn8alYD/MfgU2roPdg2PPzUNQv7lRbKV+3mvefu4e6jSvJ7TOE3Y45m+L+gwCoqq3j4deXMXV6KQtWb258zF7D+nLhxDF8Zu+h5OV28Wnaob+GifneeRhcPfTfGQ48N+5kIiLZr+EzuGwpzL3Ft+1/FgzePd5ckfItNTwxbzmrN1YxqE8BJ40bSnHPpjm+F6zexF+ml/Lv15ZRXeenHCvMz+FLB+7E5CNHM3JAr7iii4iIiIhIhlJnuKSfc/D81TDrRj9CraYC8ovgvz/wU2cc/ePY5zF19fXMuf177L/0HsaRQyFVVFJAztu/Ysaws3hr10u4c/YSVm+sanzMxLGDuGjiGA7beQDW1flDfw23ybel6b4h4yBHHz0iIl2mtc/g/F7+/hhrhHOO657+gKnTS8nJMSqr6yjskcuUR9/hwoljmLjrQKbOWMjT81c2PqZ/rx58bcJIvjZhFP179Ygtu4iIiIiIZDb1SEn6PX+1v4hibWVTW8OBesPFFY/5SfpzJZhz+/fYd+m9FFpNY1tPfMf3gZ/cy+tL1rO67svk5Rgn7zuMC44cw57D+qYvYOivYbJ8DT562t8f8z4WEclarX0Gv3YXFPSO9TP4uqc/4LYZCxsvMA1QUV0HwJ+e/4gbn/uosX1E/55ccORoTjtwJ4p65KY9q4iIiIiIZBdzDVf562bMrKy4uLi4rKws7ijdS8V6+P3uyQ/QG+QWwIUvQGEaO5cTbChfR8Ffj6YgoSO8uSqXz627386Xj9iLHfoWpjEdUFkOU4+CuqqW14nzNWxPvrxC+N77UFSSrlQiKSspKaG8vLzcOVcSd5bQqcbGqD11NsbP4PItNRzyq2e26ghPZtywvlxy9C58aq8dyNVFMUW6BdVZERERSQeNDJf0mv+IP2W7NXVV8Ofx6cmTRF+ANo67C6yGS98/G95PR6LtEPNr2CbLgfkPa+5wEZHO1p46G+Nn8BPzlpPTRud2QV4OZxw6gpP2HpqmVCIiIiIi0l108dX9RJpZv3jruUule6qp8BfVFBGRzrXs1bbrbIyfwR+t2tQ4JUpLqmvrWbOpOk2JRERERESkO9HIcEmP8o9hzp/hpb+0vW5eERz5PRj3ha7PFdlSXcd/3l7OA698zMGbn+dbeY9QZC0fiFe4Hnyw20Xs+6nz0pax0bx/w4zfQ21Fy+vE8Bo2ak++/CLoPTh9mUREsll9Pbz/BMy6AZbObXv9GD6D5y0r59bppTz+1idtrlvYI5dBfQrSkEpERERERLobdYZL11oxD2bdCPP+BfW17XyQg0MuSMtcpqs3VnHXrEXcM2cx5RU1QAnvcTzfznu41ccZjlEnXgb9BnZ5xm0cMhlm/K6NldL3Gm6jPflcPex5SlriiIhkrZpKePPvMPtPsPajhDsMaOWaMGn6DHbOMf3DNUydvoCZH61t9+Pq652mSBERERERkS6hznDpfM7Bwukw83pY8GxTe+8hMP4bsHktvPLX5Kdx5/eECd/s8k7cBas3cduMUh58bRnV0UW8CvJyOO3AHZl85Bhef3gO+y69l5627UUgt7gC3tzpTCbE0REOUNQPDvs2zL4p1tewRaHnExHJdFvW+To6dypsTpjuZNcT4LBLoXQazLk5ts/gmrp6Hn/rE6ZOX8i7yzc0tu82pA8XThzDgtWbuGPmIipqtp0upSg/l8lHjqa4KL/L8omIiIiISPelznDpPHW1/oJcs26A5W82tQ/czXeO7vNlyCvwneX5hX7EuOX4uUvzi/xItQnfhKN/3GURX128jltfKOXpd1fiokFzJT3z+dqEUXxtwkgG9vanZY86//fMuR32X3oP9eRQQDVV9CCHet7c6UzGn//7LsvYLg2vUQyvYVbkExHJROsX+07u1+6Bms2+LSff19fDvg2D9/Bto44As7R/Bm+qquX+l5Zw+4sL+aS8srH9sJ0HcOHEMUwaOwgzwzlHbo4xdXopOTlGZXUdhT1yqa93TD5yNJcfP7ZL8omIiIiIiJhzrZxGm8XMrKy4uLi4rKws7iiZr3ozvP43f5p22ZKm9hGHweHf8SPVcpJcq7ViPcx/xF/Eq/dgf8p2F4xUq693PPPuSqZOL+WVxesb23fqX8TkI8bwpYN2pGeP5P8XKl+3mvefu4e6jSvJ7TOE3Y79GsVxjQhPJk2v4XYLPZ9IB5SUlFBeXl7unCuJO0voVGM72fI3YeYN8M5D4KLR1AV94cBz/RlXfYclf1yaPoNXbajkjlmL+NucxWys9FOi5RictPdQLpq4M3vvWJz0ceVbanhi3nJWb6xiUJ8CTtp7qEaEi3RjqrMiIiKSDuoM14H69tu0Gl66FV6+zR9wA2Cwx8m+E3zHg2KNV1lTx0OvL+MvM0opXb25sX3v4cVcNGkMn95rB/Jyk3TSi4gkoYP09lON7QTOwYLn/JRjC19oau8zzHeAH3gOFCbvZE6Xj1ZtZOr0Uh5+/ROq6/yUY0X5uXzl4J34+hGj2al/z1jziUhmUZ0VERGRdNA0KdJxaxf4U6/fuA/qojm18wphvzNgwrdgwM6xxivfUsPf5i7mjpmLWLOpac7vo3YbxIUTxzBhzADMLMaEIiIiLairgXn/9nV25dtN7YP39FOhjDsN8nrEFs85x8uL1jN1+gKeebdpvvIBvXpwzmGjOHv8SPr1ii+fiIiIiIhIa9QZLu239CU/Qu29/wDRGQVF/eGQC+DgC6D3oFjjfbx+C399cSH/eHkpW6r9aeR5Ocbn9hvGhRPHsPsOfWPNJyIi0qKqjfDqXTDnz7Dh46b2UUf6s612Oc7PAx6TunrH0/NXcMsLpbyxtKwp3oCeXDBxDF88YEcK83NjyyciIiIiItIe6gyX1tXXwwdP+k7wpXOa2ktG+lHg+58JPXrFlw9455Nypk4v5fG3llNX7zvpexfkccahIzjv8FEMLS6KNZ+IiEiLNq6AubfAy7dDVblvsxzY8/Nw2KUw/IBY41XW1PGvVz/mthmlLFq7pbF9v51KuHjSGI7fcwdyc3S2lYiIiIiIZAZ1hktyNZXw1j/8RTHXfNDUPnQ/P0Jtj89BbnxvH+ccMz5cw9Tppbz40ZrG9iF9Czjv8NGccegI+hbqIlwiIhKo1e/DrBvgrX9CXbVvyyuCA86G8ZdA/9Gxxlu/uZp75izmrlmLWLu5urH9uD0Gc+HEnTl4VD9NOSYiIiIiIhlHneGytYr18MrtMOcW2Nw0Fyi7HA+HX+pP147x4Lemrp7/vLWcW6eX8u7yDY3tY4f05oIjx/D5/YbTI08XxRQRkQA5B0tmw8wb4IP/NrX3HACHXAQHT4ZeA+LLByxdt4XbZpTyz1c+pqLGTznWIzeHU/b3U47tMrhPrPlERERERERSoc5w8cqWwpyb/XylNZt9W04e7P0lf8GuIXvFGm9TVS33v7SEO2YuYllZRWP7oaP7c9GkMRw1djA5Ok1bRERCVF/nr7cx6wb4+OWm9n6jfY3d7wzIj3dKr7c/LufW6Qt44u3lRDOO0acwj7PGj+S8w0YxuG9hrPlEREREREQ6Q0Z0hpvZscDZwARgJ2Ad8BJwpXPu7TizBa1iPcx/BDatgt6D/fyjRf22XmfF236E2rwHwfkRYPToAwedC4d+A4qHd1m88i01PDFvOas3VjGoTwEnjRtKcc+tpzZZtbGSO2cu4m9zFrOhshaAHIMTxw3lwolj2Henki7LJyLSHajGpqCtOltTAW/+HWb9CdYtaGoffpA/22r3z0JO1110sq0665xj2germfpCKbNL1za2Dysu5PwjRnP6ISPoXZARfyqKiIiIiIi0iznn4s7QJjN7ABgA/BN4FxgCXAGMA45yzs1p5eEtbbOsuLi4uKysrDOjhsE5eP5qmHWjvwhXTYUfcebq/Qi0o34EC6f5TvDS55se12coHHoxHHQeFBZ3YTzHdU9/wNTppeTkGJXVdRT2yKW+3nHhxDFcfvxYFqzezG0zSv9/e3cepmddH/r//Z3JZCWZQEgkYUky7HtF2UmQg7iACxa3VoEqGNytW8/iqR492vZ3XK7TnlpLhOLS1iptBQVcqFbCjiIIAURhQgghQEzIELJn5vv7434yiWGWJ7kzz/15nnm/rut7wdzPPXne19yD3zxf74V//+VyNvf2ATC+o403v/RALjljLrOnVfvQTkmjz9SpU+np6enJOU+tumVPco7dDcPNsye/B35xJdx5Oazf/lwLDnt1sQh+0Kkjesux4ebZ9591CNfdt4KFi7p5+Om1/d93xH6TuezMLl5z3Cw62r3lmKTGatV5VpIkxdIsi+Ezcs7P7LRtKrAE+GnO+YLd+DNb94P6Tz8Lt38Ztqx/4WvtY4uF7nUrt2+bfmTx4f3YN8GYsSOe98UfP8wVNy/pvxfpjsaNaWNW5wSWrFrXv22fSWO56NTZXHTqHPaZNPJ9kjSQVv2Q7hy7G4aaZ9tqZ1L3FVcz0T4WjnsznPoBmHFEQ/KGmmc72hJjx7SxbvP2104/ZBqXzT+YeYfu60MxJVWmVedZSZIUS1Nc+7rzh/TatjUppd8CB1SQFNeGZ4sz1bZuHPj13s3bF8Jnn1GcoXbIOdDWmDPAetZvYeGibjZt7Rvw9U1b+/oXwmdPm8il87p44wkHMGHsyF1GLkmjmXPsLhpunt22CD52Cpx0SXHF1eT9GpY33Dy7pS+zZXMvbQlec1zxUMxj9h+5q8EkSZIkKZKmWAwfSEppOsUl3N8a5PU1w/wRrfnJ78Fri0u2h5La4YwPwdmfakzTDm5YvGLYB122JXjrSQfxv19/DO0+FFOSGs45dgj1zLPtHXD2J4qF8AarZ54d05b48DmH8b6zDmlQlSRJkiTF0JQ3hEzFNbwLKfq/UHFOLM8/U9y7dCi5D8ZMaEzPTlau3cTGzS+8bHtHOcN+U8a7EC5JFXCOHcZzKwa+PcqOerfCxuca07OTZ57byIZh5tnevkxvX/zb5EmSJEnSntasZ4Z/HjgfeEfO+aGBdhjuXnO1s9pa68y1x++AB78HDPMBt2MC7DWjIUk7WrZ6PXd0rxqujvFj25k+eVxDmiRJL+AcO5CNz8Evvw53fGX4fSuYZ3v7Mj9c/BRX/+KJYfd1npUkSZI0WjXdYnhK6XPAR4EP5Zy/VnFO9fr64OHr4da/gSfuqu97ch8cdf6IZu1o8fIeLl/UzQ33r6jrTLS+vsy5x85sQJkkaUfOsQN4bgXc+RX4xVWwqc6zvRs4z27Y3Mu/3r2MK25ZwtJVw5yxXuM8K0mSJGm0aqrF8JTSZ4D/AfxZzvlvqu6p1JaN8Ktvwe1/C6se2b591gnQeQA8cuPAt0vpmAinvg8mTB3RvJwzi377Oy6/6VFue3RV//aZnePp2ncSdz/+LBu3vPDhXhM62rl03lw6J3SMaJ8k6fc5x+7kmV8XD8q879vQt6XY1jERTrioWOy+5x8Hvl1Kg+bZVc9v4hu3L+Wbdyxl9brNAKQELz/yRUwZP4Yb7n+KDVteeLsU51lJkiRJo1nTLIanlD4F/Dnw5znnz1fdU5n1q+HnV8Jdl8O6ldu3H/pKOP2DMPv04uv//FzxIT61FYviHROKD++nvg/O+sSI5W3p7eP7v3qShYu6+fVTa/u3H7HfZBbM7+I1x82ioz3xpRt/w8JF3bS1JTZu7mX82Hb6+jKXzpvLR845bMT6JEkv5BxbkzMsvbW42uq3P9q+fdJ0OOkyOPESmLhPsd/4zkrm2aWr1nHFzUu4+u5l/f+n8tgxbVxwwv5cOq+Lg6fvRc6ZWVMnOM9KkiRJ0k5SzvEfoJRS+ijFQ7yuAz6308ubcs737Mafuaazs7NzzZo1e6CwAZ5dCnf8Hfzym7BlXbGtrQOOewuc9n6YceQLv2fDs/DgtcVDNfeaUVyyPUJnqj2/aSv/ctfjXHnLElb0bOzfftrB01gwv4szD5tO8Uy27XrWb+GGxStYuXYT0yeP49xjZ3qmmqSwpk6dSk9PT89w98tuNs6xQF8vPPR9uO1vYPnd27dPOwROfT8c/0fQMf6F39fAefbeZWtYuOhRfrj4KbbdcWzK+DFceOpsLj5tDjMmv7DPeVZSM2nVeVaSJMXSLIvhPwPOHOTlpTnnObvxZzbHB/Un7y0+nD9wDeTa5c7jpsBL3wEnvxumzKqyjqef28hVtz7GP925lLUbtwLQluC842axYF4Xxx7QWs9PkzR6teqH9FE9x25eD/f+E9z+ZXh2yfbtB5wEp38IDj8X2toqy+vry/zsN89w+U3d3Llkdf/2/adO4JIz5vKWEw9k0rimuchPkobUqvOsJEmKpSk+QeWcX1Z1Q0PlDI/+pLhMe8lN27dPngWnvhdOuBjGT6muD/jt02tZuKiba+5dzpbe4v9QmdDRzltOPJBLzpjLgftMrLRPklSfUTfHAqxbBT//Kty1ENZvf64Fh59X3HLsoFOqawM2be3l2nuf5KuLuvntM8/3bz9q5hQuO7OLc4+dSUd7dYv0kiRJktSsmmIxfNTo3QKL/704E/zpxdu3zzgKTvsgHHMBjBlbWV7OmbuWrGbhom5+8utn+rdPmzSWi0+bw4WnzGbvSdX1SZI0pNXdxVng9/wTbK09ZLp9LBz/Vjj1AzC92ntpP7dxC/985+NcdesSnn5uU//2eYfuy4L5XZxxyL4vuOWYJEmSJKl+LoZHsGkt3P11uOMr8NwT27fPnQ+nfQgOORsq/PDb25f58QNP8feLuvnVsjX92+dMm8i75ndxwQkHML6jvbI+SZKGtPzu4mqrh75XPOQSigdgnnhp8WDMyS+qNG9Fzwb+4ZYlfOuuZTy/qbjlWHtb4rXHzeRd87s4epa3HJMkSZKkPcHF8CqtfapYAP/FVbCpp9iW2ooHcJ3+QZj14krzNm7p5eq7n+DKm7t5bNX6/u0vPmgql83v4pyj9qO9zTPUJEkB9fXBIzcWi+BLb9m+vfNAOOW9cMKFMG5ydX3Ar596joWLuvnevU+ytfZUzIlj23nriQfxzjPmcMDe3nJMkiRJkvYkF8OrsPLh4lYo930HejcX2zomwosvLO4JvvecSvNWr9vMN29fytdvf4zV6zb3b3/5kTO47MyDeensvb1MW5IU09bNcP/VcNv/g5UPbd++37HF1VZHnw/tHZXl5Zy5vXsVl9/UzU2/Wdm/fd+9xvGO0+fw9pNn0zmxuj5JkiRJamUuhjdKzvD47XDrX8Nvfrh9+8R94eTLiku1J+5TXR/w+Kr1XHFLN9/5xTI2bikuIx/b3sYbXrw/75o/l0NmVHsGnSRJg9rYU1xpdeffw9oV27d3nVVcbdV1VqW3HNva28cPFj/FwkXd3L+8Z3ve9EksmNfF+S/e31uOSZIkSdIIczF8pPX1wq+vKy7TXv6L7dv36YLTPgDH/xF0TKiuD7jviTVcvqibH9y/gtpV2kweP4a3nzKbd5w2hxlTxlfaJ0nSoHqWw51fgV98DTavLbal9uKh06d9AGYeV2ne+s1b+c7Pl3HlrUtYtnpD//YT5+zNgvkHc/YRM2jzlmOSJEmS1BAuhpex4Vl48Fp4/hnYawYc9XqYsHfx2pYNcO8/w+1/C6u7t3/PASfCaR+EI86DtpE9A6xn/RZuWLyClWs3MX3yOM49Zmb/pdc5Z3728EouX/Qod3Sv7v+eWZ3jeecZc3nrSQex1zh/PSRJFRlqjgV4+oHiVij3Xw19xUMn6ZgEL7kYTnkPTD1oxBOHmmd/9/wmvnHbY3zjjqWsWb8FKE5Mf8VRL2LB/IN5yey9h/qjJUmSJEkjIOWcq26oREppTWdnZ+eaNWt2/Ztzhv/8XPEhPLUVC98dEyD3Fbc7GTsZfv5VWP+77d9z2Kvh9A/BQaeM+GXaOWe+dONvWLiom7a2xMbNvYwf205fX+aSM+YyZ9pErrhlCb95+vn+7zliv8m8+8yDOe+4mXS0t41onyQ1o6lTp9LT09OTc55adUt0IzbHnvp+mDu/eO2RG7d/z6QZcMq74aXv/P0F8xEy1Dz7ppceSG9fH//+y+Vs2lq75diYNt74kgO49Iy5dE3fa8T7JKkZOc9KkqRGcDF8dz6o//SzcPuXYcv6ofdrHwvHvaW4THv64bvVuTu++OOHueLmJWzY0jvsvmccsi8L5ncx79B9fSimJA3BD+n1G7k5NgE7/L1l2qHFHHvcW6Cjcbf0qnee7ZzQwUWnzuaiU+cwffK4BtVJUnNynpUkSY3gfTB21YZnizPStm4cer9T3gOn/ylM3q8hWdv0rN/CwkXd/WejDebVx+zH+846hGP272xQmSRJwxh2jq0thB9wIpzxETjsVdDW2KuZ6p1n/+urDufi0+Ywcax/1ZIkSZKkKPyEtqsevLa4bHsoHRNh+hENXwgHuGHximEfxDW+o435h013IVySFEs9c+yY8fDit8MR5zamaSc3LF5B2zBXUk3oaGfqxLEuhEuSJElSMN4celc9/0xx/9KhbNlQ7NdgOWfuXvosGzYPfdn2pi19rFy7qUFVkiTVqZ45duumSuZYKM4Kv+6+J4e9PcrGLb3Os5IkSZIUkKcs7aq9ZhQP8hrqfuEdE4r9GmRrbx8/fOApFi7q5r4neobdf/zYdu9dKkmKJ+AcC7B8zQb+4ZYl/Mtdj7NumP/DGZxnJUmSJCkqF8N31VGvhx/816H3yX1w1PkjnrJ+81au/sUTXHFLN8tWbz+TbqfHi71AX1/m3GNnjnifJEm7JNAcC/Dgk8/x1Zu7+f6vnmRrXzGzTuxoY9PWTO8QDyB3npUkSZKkmFwM31UT9obTPgC3f3ngM9c6JsKp74MJU0csYdXzm/j67Uv55u2P8ez6LQCkBC8/8kVcNr+Lm36zkituXjLgZdwTOtq5dN5cOid0jFifJEm7JcAcm3Pm1kdWcfmiR7n5t7/r3z598jjeefpc/vjkg7ji5m7nWUmSJElqQi6G746zPlH887b/Vzzoa8uG4rLt3Fd8SN/2+h722O/W8dWbu/nXu59g09Y+AMaOaeOCE/bn0nldHDx9LwBeMntvABYu6qatLbFxcy/jx7bT15e5dN5cPnLOYSPSJ0lSaRXNsVt7+7j+/hUsXNTNA08+17/9kBl7sWBeF69/8SzGjWkH6J9HnWclSZIkqbmkPMRlvq0spbSms7Ozc82aNbv/h2x4Fh68tniQ114zisu2R+BstXsef5aFi7r54QNPse1wTRk/hgtPnc3Fp81hxuTxA35fz/ot3LB4BSvXbmL65HGce+xMz1STpN00depUenp6enLOU6tuia6Z5th1m7by7Z8v48pblrB8zfZbjp00Zx8uO7OLsw6fQVtbGvB7nWclac9xnpUkSY3gYniZD+ojqK8v858PP8PlN3Vz12Or+7fvP3UCl5wxl7eceCCTxnlivyQ1ih/S6xd9jgVYuXYTX7/tMb55x1J6Nmy/5dirjt6PBfO7ePFBe1dcKEmji/OsJElqBFdTg9m0tZdr73mShTd388gzz/dvP2rmFC47s4tzj51JR3tbhYWSJDWvR1c+zxU3d/Nvv1zO5totx8aNaeNNLz2AS8/oYs6+kyoulCRJkiSNFBfDg+jZsIV/vvNxrrp1Cc+s3dS/fd6h+3LZ/IM5/ZBppDTwZdqSJGlody9dzeU3dXPjQ0/333Js74kdXHjqHC4+dTbT9hpXbaAkSZIkacS5GF6xJ9ds4B9uWcK37nqcdZt7AWhvS7zu+Fm8a14XR82aUnGhJEnNqa8vc+NDT7NwUTd3L322f/tB+0zk0nlzedNLDmTC2PYKCyVJkiRJjeRieEUeWvEcCxd18/1fPcnWvuIUtUlj23nrSQfxzjPmsv/UCRUXSpLUnDZu6eW79yznqzd3071yXf/24w7oZMH8Ll519H6M8ZZjkiRJkjTquBjeQDlnbnt0FZcv6mbRb1b2b58+eRzvOH0ObztpNp0TOyoslCSpea1Zv5l/vGMpX7ttKb97fvstx846fDoL5h/MKV37eMsxSZIkSRrFXAxvgK29fdyw+CkWLnqUxcuf699+8PRJLJjfxfkv3p9xY7xMW5Kk3fHEs+u58pYlfPvny1hfu+VYR3vidcfvz4L5XRy+3+SKCyVJkiRJETTNYnhKaS/gL4A3AVOBB4DP5Jy/V1VTz/ot3LB4BSvXbmL65HGce8zM3zuze/3mrXz758u48pYlPPHshv7tJ87Zm8vmH8x/OWIGbW2eoSZJqlYzzrEAi5f3sHBRN9ffv4Le2i3HJo8bwx+ffBDvOH0u+3WOryJdkiRJkhRU0yyGA98FTgD+DFgC/Anw3ZTSa3PONzQyJOfMl278DQsXddPWlti4uZfxY9v5X997gAXzu7jwlNl8846lfOP2pfRs2AJASvDKo/ZjwZldnHDQ3o3MlSRpOE0zx3745YdyyyOrWLiom1se+V3/971oyjjeefpc/ujkg5gy3luOSZIkSZJeKOWcq24YVkrpXOB64A9zzt+tbUvAzcC0nPORu/Fnruns7Oxcs2bNLvd88ccPc8XNS9iwpfcFr7W3JXLO1E5QY+yYNt70kgO4dF4Xc/edtMvvJUmKYerUqfT09PTknKdW3bInNdMc29GemDK+g1XrNvdvO+xFe7Fg/sG87vhZjB3jQzElqVm16jwrSZJiaZYzw98A9ADXbtuQc84ppa8DC1NKR+WcH2xESM/6LSxc1M2mrX0Dvr7tMu0pE8bwJ6fO4aLT5rDvXuMakSZJ0u5omjl2S2/uXwg/pWsfLpt/MC87fLoPxZQkSZIk1aVZFsOPAR7MOe/86fi+HV/f8YWU0pph/szO3Qm5YfGKYe/z3dGe+Og5h3HxaXN35y0kSWqkpppj29sS7z6zi4+/8ojdeQtJkiRJ0ijWLNcTTwNWD7B99Q6vN8TKtZvYuPmFl27vaGtvpmfD1gYVSZJUSlPNsX19mXFj2htUJEmSJElqJc1yZjjAUDc3f8Frw91rrnZW2y6fuTZ98jjGj21nwxAf1sePbWf6ZG+NIklqGs6xkiRJkqSW1yxnhq9i4DPT9qn9c6Az2kbEucfMpK9v6IeO9vVlzj12ZoOKJEkqxTlWkiRJkjQqNMti+APAkSmlnXuPrf1zcaNCOid2sGB+FxM6Br5Ee0JHOwvmd9E5oaNRSZIkleEcK0mSJEkaFZplMfy7wFTgtTttvwh4OOf84Au+YwR95JzDuHTeXMaNaWPC2HYSMGFsO+PGtHHpvLl85JzDGpkjSVIZzrGSJEmSpFEh5Tz05cgRpJQS8BPgOODPgCXAxRQf1F+fc/7+bvyZazo7OzvXrFmz210967dww+IVrFy7iemTx3HusTM9W02SWtTUqVPp6enpGe5+2c3GOVaSFEGrzrOSJCmWplgMB0gpTQH+AngjxRlsDwKfyTlfs5t/XukP6pKk0aOVP6Q7x0qSqtbK86wkSYpjTNUB9co5Pwe8vzYkSdIe4hwrSZIkSRoNmuWe4ZIkSZIkSZIk7bamuU3KnpZS6gNSZ2dn1SmSpCbQ09MDkHPO/h/Jw3COlSTtKudZSZLUCKN5MXwrxZnxz5X8o7Z90u8p+eeMFPvKi95oXznR+yB+42jpmwL05Zyb5hZjVRlFcyzEb7SvnOh9EL/RvnKi94HzrCRJaiKjdjF8T0kprQGI+qAX+8qL3mhfOdH7IH6jfRopzXDsojfaV070PojfaF850fugORolSZK28RI0SZIkSZIkSVLLczFckiRJkiRJktTyXAyXJEmSJEmSJLU8F8MlSZIkSZIkSS3PxXBJkiRJkiRJUstzMVySJEmSJEmS1PJcDJckSZIkSZIktbyUc666QZIkSZIkSZKkEeWZ4ZIkSZIkSZKkludiuCRJkiRJkiSp5bkYLkmSJEmSJElqeS6GS5IkSZIkSZJanovhkiRJkiRJkqSW52K4JEmSJEmSJKnluRguSZIkSZIkSWp5Y6oOaDUppfHAjJzz4xU2jMs5bxrktQ5gZlV9KaW2nHPfTtuOBw4DHsk531NF11BSSpOAG4H35pzvrTjnBVJK44BjgD7ggZzz5oqT+tX+e/hj4ASKvjuA7+Sct1bQ8mbghznn5xr93vWq/fd5KrAp53xnbVs78EfAUcATwLdyzs9WV1lIKZ1M8Xu3L8WxfRK4Jee8tNIwIKU0C3gdRd80IAOrgcXA93POyyvMUwnOscNznt2znGN3qcd5dg9xjpUkSRpBOWfHHhzABUBvRe/9QeAZoBf4LXDRAPucXEUfxVUIfwdsANYBn6pt/5tab1/tn98G2iromzLEOKjW9+pt2yo6vguAfXfa9gGgp/az6wXWAJdV1Pe3wBd2+Hom8HDtZ7elNvqAu4GpFfT11X73vgG8rIqf0TB9235e247lT4GJwH/U2reN5cABFXb+IfDYDp3bRh+wFbgW2L+itgT8BbBph+P9RG2sq23bBPxV1cfbsdvH2Dl28D7n2XJ9zrHlG51nyzc6xzocDofD4XCM8PA2KS0ipfRa4P9SnJXxBYoPbFellK5IKaUK07Z5D/Bu4BrgKuBjKaXPAO8CPg7MBz5N8SHgvRX0PTvEWFLb57odtlXhK0DXti9SSm8C/hq4l+Jn+17gfuDvUkrnVtD3GmDHMw6/COxd2z6hNl5Psejxlw2vK9wPvAX4SUrp0ZTSJ1JKB1TUsrNPUvy83kXROB24GjgCeBmwF/AKigWvT1YRmFI6v9bUDXwUeD/wLxSLMO8HLgL2A25NKc2oIPFjtfF54LCc86Sc8wG1MQk4FPg/wIdTSn9WQZ+aVBPMseA8W5Zz7J7hPLubnGMlSZIaI+Wcq25oCimlev9SfBTwppxz+0j27CyltAhYlnN+2w7b/pTiL6z/Crw959xbu+zytgr6fgnclHP+cO3rNwP/THH2yP/cYb8vAy/NOZ/c4L5tl6BeRXHmzY4mAH8GfJ3ibB1yzp9uZB/0N56Sc76r9vUdwPqc83/ZYZ8E/AzYnHM+p8F9G4Fzcs43175eDXw853zlTvu9B/hkznlmg/v6gFMoFl0uBv6E4r/XXoqzwq4Ers05b2lk1w593cD/l3O+vPb1S4G7gAU55yt22O99wIdzzodU0Phz4MGc88U7bf8AxQf3ucBYiu5bcs7va3DfI8AVOee/Gma//wa8K+d8cGPKNBzn2D3S6Dxbvs85tlyj82y5PudYSZKkRqj61PRmGWy/vLivjlHFbUh+B7xqgO3nAxuBf6O4R3xVt0npAV6xw9dTaz+rs3fa73VATwV9L6O47P1u4CU7vdZZa50f4HfwpB2+3gC8cYD93gqsrqBvBfCHO3y9CThzgP3OBjZU/fOrbTsFWEhxlmcvsJLi7M/jK+hbv+PvGDCp1nz6Tvu9DFjX6L4dGl8xwPZ9aq2H176+BHi8gr6N9fx3SnGGbMN/Bx1DHhPn2PKNzrPlfwedY/fgz7C2zXl21/qcYx0Oh8PhcDhGeHiblPqtBL5KcXnlUONPKuobQ3EZ5e/JOV9DcY/V84B/B8Y1NqtfG79/Jtja2j93vhS6hwoac84/A44F/hO4JaX0xZTSxEZ37KJM8eF4ZysoPuA12n9Q3HN1m7uAVw2w36uBRxtSNIyc8x055wUU9xF9B/AAxT1if1lBzgqKS7W32fbvO59ZdTDwdEOKXmg98KIBtu9H8fu47b/xR4AqLuFeCpxVx35nAZU94FADco4tz3l2z3KO3QOcZ3eJc6wkSVIDjKk6oIncDRydc+4ZaqeU0roG9eysG3gp8JOdX8g5X59SegPFB/UjGx1W8xQwe4em3tpln0t32m9/YFUjw7bJOW+kuMfqt4ErgAtqlxvfVkXPIP40pfRU7d/XAwcOsM9MijOwGu2TwC9SStdS3Drgz4F/SylNAX5c2+dc4J1AQy/tHU7OeQPFA7++kVLqopoFtx8C/zultBl4DvhfFPf+/WRK6Zc558UppeOBTwC3VtAH8CPgsymlX+Wc7wNIKc2keGjfspzztgWYfSjOpG20LwNfSil1UhzPB3LtcvyUUgdwNHAhxULMxyro0+CcY8tzni3POXaEOM/WxTlWkiSpAbxneJ1SSp8DPphznjzMfvOBT+ec6zlzYo9JKX0JeGXO+egh9nkl8F1gXG78/Vavprhk8qJh9rsCmJVzruLhVDt2jKH4MPTfKT5kngeclXNeVGFT3wCb/3Hnn2lKaSHFpbRnNqbs9977OOAfgWMozmLa9mC5bf++geK/j/9TQdvv3Q82mpTSi4BFFA+gArgTeDnwLYrfvy1AB8UH+BNzzo9U0Lg/xaLV/hT3/t1EsfjWC5yfc/5Rbb+/BubknF9fQeP/BP4HxZmvmWJBK1OcyZmAzcBf5pw/0+g2Dc45tjzn2dI9zrHl+5xny/U5x0qSJDWAi+EtIqV0KPAK4Fs559VD7HcGxf1DG/1gqmnA+Jzz8mH2+yhwb875BWffVSGldDTFvS6PAs7LOUc5e21QtZ/hwznn6ypseBkwD5hFcen+KuB+4Ac55zUVNV0MXJdzruSMyHrUzqw6jeID+R05577agtElwB9QfDj+Ws55WYWNU4H3UtwHdizwILAw5/zrHfZJALmiCSaltA/FrQKOBqbVNq+iuDz/B0P9b6Q0kOhzbO29nWcbwDl2yC7n2fJ9U3GOlSRJGlEuhkuSJEmSJEmSWp73DN9NKaV9Kc6GyMDqnHMV9+4blH3lRO+D+I32lRO9D5qjUc0p+u9W9D6I32hfOdH7IH6jfZIkSaNTW9UBzSSlNCul9OWU0gqKp8w/CDwEPJ1Seqr22gH22TdaG+1r7b5maRxOSuklKaV/qLpDvy/671b0vmZotK+1+5qh0b6R5xwrSZKi8zYpdUopHQncRPHAmO8D9wHb7om3D3As8FqKexDOzzk/ZJ99o6nRvtbua5bGeqSULgC+U8VDDjWw6L9b0fuaodG+1u5rhkb7GsM5VpIkRedieJ1SStdT/EX0vMEeDJOKB8pcD6zKOb/GPvv2pOiN9rV2X+39wzfWww/q8UT/3YreV3v/0I32tXZf7f1DN9rXGM6xkiQpOhfD65RSeg54a875hmH2Ow/4Vs55SmPK+t/XvhKi99XeO3SjfeVE76u9d+jGlFLvruzvB/U4muB3K3Rf7b1DN9pXTvS+2nuHbrSvHOdYSZLUKnyAZv36gHr+Utde27fR7Csneh/Eb7SvnOh9EL+xF7gXuHWY/Q4Bzh3xGu2K6L9b0fsgfqN95UTvg/iN9pXjHCtJklqCZ4bXKaV0DXAQ8Oqc89OD7DMD+AHweM75DQ3Ms6+k6H2197+GwI32lRO9r/b+1xC4MaV0D/DbnPObh9nPS7iDaYLfrWsI3Fd7/2sI3GhfOdH7au9/DYEb7SvHOVaSJLUKF8PrlFLqAm4BOoEbgcUUD7XJwDTgGOAcoAeYl3N+1D77RlOjfa3d1wyNKaUrgTNzzocMs98FwNU557bGlGk4TfC7FbqvGRrta+2+Zmi0r3Sfc6wkSWoJLobvgpTSdODjwPnAwUCqvZSBbuC7wBdyzs/YZ99IiN5oX2v3QezGlNLpwNk5588Ms9++wNE555saU6Z6RP7daoY+iN9oX2v3QfxG+0q1OcdKkqSW4GL4bkopjQf2rn35bM55Y5U9O7OvnOh9EL/RvnKi90FzNKo5Rf/dit4H8RvtKyd6H8RvtE+SJGl0cjFckiRJkiRJktTyvJdbSSml9pRSb0rphKpbBmJfOdH7IH6jfeVE74P4jdH7NLjoxy56H8RvtK+c6H0Qv9G+cqL3SZIk7czF8D0jDb9LpewrJ3ofxG+0r5zofRC/MXqfBhf92EXvg/iN9pUTvQ/iN9pXTvQ+SZKkfi6GS5IkSZIkSZJanovhkiRJkiRJkqSW52J4STnnXuAdwJKqWwZiXznR+yB+o33lRO+D+I3R+zS46Mcueh/Eb7SvnOh9EL/RvnKi90mSJO0s5ZyrbpAkSZIkSZIkaUSNqTqg2aSUpgCvBo4B9gX6gCeBRTnnm6tsA/vKit4H8RvtKyd6H8RvjN6nwUU/dtH7IH6jfeVE74P4jfaVE71PkiRpWDlnR50D+DCwFuil+Itf307/fg9wrH32jdZG+1q7rxkao/c5mvfYRe9rhkb7WruvGRrta+0+h8PhcDgcjnqG9wyvU0rpXcAXgKuBC4DzgL+k+AvhO4HTgceAn6WUuuyzb7Q12tfafc3QGL1Pg4t+7KL3NUOjfa3d1wyN9rV2nyRJUt2qXo1vlgEsBr40wPY/Bp6huOVMAm4CvmGffaOt0b7W7muGxuh9juY9dtH7mqHRvtbua4ZG+1q7z+FwOBwOh6Pe4Znh9TsEuH6A7ddT3C/v0JxzBq4AzmlkWI195UTvg/iN9pUTvQ/iN0bv0+CiH7vofRC/0b5yovdB/Eb7yoneJ0mSVBcXw+u3Gjh0gO2HARlYV/t6KbB3o6J2YF850fsgfqN95UTvg/iN0fs0uOjHLnofxG+0r5zofRC/0b5yovdJkiTVxcXw+l0LfDal9NqU0phUOBG4Engo5/x4bb+ZwFP22TcKG+1r7b5maIzep8FFP3bR+5qh0b7W7muGRvtau0+SJKkuY6oOaCL/DTgJuIbtT07vAJ4FXrnDfscDP2p0HPaVFb0P4jfaV070PojfGL1Pg4t+7KL3QfxG+8qJ3gfxG+0rJ3qfJElSXVJxazfVI6U0BngzcAowFngQ+Kec86pKw2rsKyd6H8RvtK+c6H0QvzF6nwYX/dhF74P4jfaVE70P4jfaV070PkmSpHq4GC5JkiRJkiRJanneJmU3pJQmAUcC0ygeGLMa+HXO+flKw2rsKyd6H8RvtK+c6H0QvzF6nwYX/dhF74P4jfaVE70P4jfaV070PkmSpCHlnB11Dop74F0PbKa4T962++X11rZdD/yBffaN1kb7WruvGRqj9zma99hF72uGRvtau68ZGu1r7T6Hw+FwOByOeoa3SalTSulU4CfACuBbwH0UZ0EA7AMcB7yV4gnqL885326ffaOp0b7W7muGxuh9Glz0Yxe9rxka7WvtvmZotK+1+yRJkurlYnidUkqLgLXAG3LOmwfZpwP4LjAl5zzfPvv2pOiN9rV2X+39QzdG79Pgoh+76H219w/daF9r99XeP3Sjfa3dJ0mSVC8Xw+uUUnoeOD/n/B/D7HcO8N2c816NKet/X/tKiN5Xe+/QjfaVE72v9t6hG6P3aXDRj130vtp7h260r5zofbX3Dt1oXznR+yRJkurVVnVAE9lAcQngcPYGNo5wy0DsKyd6H8RvtK+c6H0QvzF6nwYX/dhF74P4jfaVE70P4jfaV070PkmSpLq4GF6/G4C/SikdP9gOKaXjgL+geHhMo9lXTvQ+iN9oXznR+yB+Y/Q+DS76sYveB/Eb7Ssneh/Eb7SvnOh9kiRJdfE2KXVKKc0AfgocCdwPLKZ4aEwGpgHHAMcCvwbOyjk/Y599o6nRvtbua4bG6H0aXPRjF72vGRrta+2+Zmi0r7X7JEmS6uVi+C5IKY0DLgHOp/gL37TaS6uABygeGHNlznmTffaNhOiN9rV2H8RvjN6nwUU/dtH7IH6jfa3dB/Eb7WvtPkmSpHq4GC5JkiRJkiRJanneM3wPSCkdlFIaU3XHYOwrJ3ofxG+0r5zofRC/MXqfBhf92EXvg/iN9pUTvQ/iN9pXTvQ+SZKkHbkYXlJKqR1YAhxXdctA7Csneh/Eb7SvnOh9EL8xep8GF/3YRe+D+I32lRO9D+I32ldO9D5JkqSduRi+Z6SqA4ZhXznR+yB+o33lRO+D+I3R+zS46Mcueh/Eb7SvnOh9EL/RvnKi90mSJPVzMVySJEmSJEmS1PJcDJckSZIkSZIktTwXw0vKOfcCZwEPV90yEPvKid4H8RvtKyd6H8RvjN6nwUU/dtH7IH6jfeVE74P4jfaVE71PkiRpZynnXHWDJEmSJEmSJEkjyjPD97CU0kEppYuq7hiMfeVE74P4jfaVE70P4jdG79Pgoh+76H0Qv9G+cqL3QfxG+8qJ3idJkuRi+J53InBV1RFDsK+c6H0Qv9G+cqL3QfzG6H0aXPRjF70P4jfaV070PojfaF850fskSdIo52K4JEmSJEmSJKnljak6oFmklH5a567TRzRkEPaVE70P4jfaV070PojfGL1Pg4t+7KL3QfxG+8qJ3gfxG+0rJ3qfJElSvXyAZp1SSn3AU8DTw+zaCczOObePfNV29pUTvQ/iN9pXTvQ+iN8YvU+Di37sovdB/Eb7yoneB/Eb7Ssnep8kSVK9PDO8ft3ATTnnS4baKaX0RuDbjUn6PfaVE70P4jfaV070PojfGL1Pg4t+7KL3QfxG+8qJ3gfxG+0rJ3qfJElSXbxneP3uBk6oY7+qTrW3r5zofRC/0b5yovdB/MbofRpc9GMXvQ/iN9pXTvQ+iN9oXznR+yRJkuriYnj9rgNW1rHfg8BnRrhlIPaVE70P4jfaV070PojfGL1Pg4t+7KL3QfxG+8qJ3gfxG+0rJ3qfJElSXbxnuCRJkiRJkiSp5XlmuCRJkiRJkiSp5bkYLkmSJEmSJElqeS6G74KU0kkppatTSrenlBamlA4fYJ8/SCl122ffSIjeaF9r99XeP3Rj9D4NLvqxi95Xe//Qjfa1dl/t/UM32tfafZIkSfVwMbxOKaVjgJuAsyl+bm8D7kkpXbjTruOA2Q3Os6+k6H0Qv9G+cqL3QfzG6H0aXPRjF70P4jfaV070PojfaF850fskSZLq5WJ4/T4NLAbm5pxPBuYAPwK+llJ6X5VhNfaVE70P4jfaV070PojfGL1Pg4t+7KL3QfxG+8qJ3gfxG+0rJ3qfJElSfXLOjjoGsAx44wDbvwD0Ah+pfX0y0GuffaOt0b7W7muGxuh9juY9dtH7mqHRvtbua4ZG+1q7z+FwOBwOh6PeMQbVax/g6Z035pw/llLaBHw+pTQO+GnDywr2lRO9D+I32ldO9D6I3xi9T4OLfuyi90H8RvvKid4H8RvtKyd6nyRJUl1cDK/fMuBI4OadX8g5fyKl1At8Friu0WE19pUTvQ/iN9pXTvQ+iN8YvU+Di37sovdB/Eb7yoneB/Eb7Ssnep8kSVJdUs656oamkFK6Cjgw5/zyIfb5FPApIOec2xsWh31lRe+rvX/oRvvKid5Xe//QjdH7NLjoxy56X+39QzfaV070vtr7h260r5zofZIkSfXyAZr1+yawOqW072A75Jw/DXwcWNSwqu3sKyd6H8RvtK+c6H0QvzF6nwYX/dhF74P4jfaVE70P4jfaV070PkmSpLp4ZrgkSZIkSZIkqeV5ZrgkSZIkSZIkqeX5AM1dlFJ6E3ABcAwwDcjAamAx8G/Av+YKT7e3r7X7IH6jfa3dB/Ebo/dpcNGPXfQ+iN9oX2v3QfxG+1q7T5IkaTjeJqVOKaVOiqejnw4sB+6n+IsfwD7AscD+wO3AuTnnHvvsG02N9rV2XzM0Ru/T4KIfu+h9zdBoX2v3NUOjfa3dJ0mSVLecs6OOAfw98CTw8iH2OZviL4d/b599o63Rvtbua4bG6H2O5j120fuaodG+1u5rhkb7WrvP4XA4HA6Ho95ReUCzDOBp4O117Hch8LR99o22Rvtau68ZGqP3OZr32EXva4ZG+1q7rxka7WvtPofD4XA4HI56hw/QrN9kirMhhrO8tm+j2VdO9D6I32hfOdH7IH5j9D4NLvqxi94H8RvtKyd6H8RvtK+c6H2SJEl1cTG8fr8CFtSx34Lavo1mXznR+yB+o33lRO+D+I3R+zS46Mcueh/Eb7SvnOh9EL/RvnKi90mSJNVlTNUBTeSTwPUppbuBbwAPUDw0JlM8Sf0Y4G3A8cBr7LNvFDba19p9zdAYvU+Di37sovc1Q6N9rd3XDI32tXafJElSfaq+T0szDeBlwF1AH9C7w+irjbuAs+yzb7Q22tfafc3QGL3P0bzHLnpfMzTa19p9zdBoX2v3ORwOh8PhcNQzUs4Z7ZqU0iyKsx+m1TatAh7IOS+vrmo7+8qJ3gfxG+0rJ3ofxG+M3qfBRT920fsgfqN95UTvg/iN9pUTvU+SJGkoLoZLkiRJkiRJklqeD9DcBSmlk1JKV6eUbk8pLUwpHTbAPn+QUuq2z76REL3Rvtbuq71/6MbofRpc9GMXva/2/qEb7Wvtvtr7h260r7X7JEmS6uFieJ1SSscANwFnU/zc3gbcm1K6cKddxwGzG5xnX0nR+yB+o33lRO+D+I3R+zS46Mcueh/Eb7SvnOh9EL/RvnKi90mSJNXLxfD6fRpYDMzNOZ8MzAF+BHwtpfS+KsNq7Csneh/Eb7SvnOh9EL8xep8GF/3YRe+D+I32lRO9D+I32ldO9D5JkqT6VP0Ez2YZwDLgjQNs/wLFU9Q/Uvv6ZKDXPvtGW6N9rd3XDI3R+xzNe+yi9zVDo32t3dcMjfa1dp/D4XA4HA5HvWMMqtc+wNM7b8w5fyyltAn4fEppHPDThpcV7Csneh/Eb7SvnOh9EL8xep8GF/3YRe+D+I32lRO9D+I32ldO9D5JkqS6uBhev2XAkcDNO7+Qc/5ESqkX+CxwXaPDauwrJ3ofxG+0r5zofRC/MXqfBhf92EXvg/iN9pUTvQ/iN9pXTvQ+SZKkuqScc9UNTSGldBVwYM755UPs8yngU0DOObc3LA77yoreV3v/0I32lRO9r/b+oRuj92lw0Y9d9L7a+4dutK+c6H219w/daF850fskSZLq5QM06/dNYHVKad/Bdsg5fxr4OLCoYVXb2VdO9D6I32hfOdH7IH5j9D4NLvqxi94H8RvtKyd6H8RvtK+c6H2SJEl18cxwSZIkSZIkSVLL88xwSZIkSZIkSVLLczFckiRJkiRJktTyXAyXJEmSJEmSJLU8F8MlSZIkSZIkSS3v/wdUoGW47029PQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1152 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,16))\n",
    "\n",
    "plt.subplot(3, 3, 1)\n",
    "time_series_plot(df, \"Verbal & Quant test score with missing values\")\n",
    "\n",
    "plt.subplot(3, 3, 2)\n",
    "time_series_plot(df_drop_all_nan, \"Verbal & Quant test score with no missing values\")\n",
    "\n",
    "plt.subplot(3, 3, 3)\n",
    "time_series_plot(df_replaced_by_mean, \"Verbal & Quant test score imputed by mean\")\n",
    "\n",
    "plt.subplot(3, 3, 4)\n",
    "time_series_plot(df_replaced_by_median, \"Verbal & Quant test score imputed by median\")\n",
    "\n",
    "plt.subplot(3, 3, 5)\n",
    "time_series_plot(df_replaced_by_zero, \"Verbal & Quant test score imputed by zero\")\n",
    "\n",
    "plt.subplot(3, 3, 6)\n",
    "time_series_plot(df_interpolate, \"Verbal & Quant test score imputed by linear interpolation\")\n",
    "\n",
    "plt.subplot(3, 3, 7)\n",
    "time_series_plot(df_forward, \"Verbal & Quant test score imputed by \\n forward imputation\")\n",
    "\n",
    "plt.subplot(3, 3, 8)\n",
    "time_series_plot(df_backward, \"Verbal & Quant test score imputed by \\n backward imputation\")\n",
    "\n",
    "\n",
    "plt.suptitle(\"Missing value imputation methods\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}