DocumentedCode/Part 4 - Data Processing.ipynb

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    "## Data 512A Project Part4 - Project(Data Pre-Processing and Preliminary Analysis)\n",
    "\n",
    "This part of project focused only on the Honolulu, Hwaii Data and completed the following tasks:\n",
    "- Data acquisition and Data cleaning\n",
    "- Detect missing values, and unchanged varialbes\n",
    "- Multicollinearity Testing"
   ]
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   "source": [
    "# import all required packages\n",
    "from matplotlib import dates as mdates\n",
    "from matplotlib.colors import ListedColormap\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Step 1: Data acquisition and Data Cleaning\n",
    "\n",
    "- extract general information and cases by date for Honolulu, Hawaii Data\n",
    "- clean and format goverment response data, daily passenger count data, and Social Distance Index Data\n",
    "- detect missing values and feature selection"
   ]
  },
  {
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   "id": "f10a1eff-d3b8-4de3-884e-c73b27a23c0d",
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   "source": [
    "# laod all input data\n",
    "raw_us_cases = pd.read_csv(\"RAW_us_confirmed_cases.csv\")\n",
    "raw_SDI = pd.read_csv(\"Social distancing index_county.csv\")\n",
    "raw_goverment_response = pd.read_csv(\"oxford-government-response.csv\")\n",
    "tourist = pd.read_csv(\"tourist.csv\", header = None)"
   ]
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Daily_Cases</th>\n",
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       "      <td>72.0</td>\n",
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       "      <th>368</th>\n",
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       "      <td>24981</td>\n",
       "      <td>112.0</td>\n",
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      "text/plain": [
       "          Date  Cases  Daily_Cases\n",
       "0   2020-04-20    385          1.0\n",
       "1   2020-04-21    385          0.0\n",
       "2   2020-04-22    388          3.0\n",
       "3   2020-04-23    389          1.0\n",
       "4   2020-04-24    392          3.0\n",
       "..         ...    ...          ...\n",
       "365 2021-04-20  24754         24.0\n",
       "366 2021-04-21  24797         43.0\n",
       "367 2021-04-22  24869         72.0\n",
       "368 2021-04-23  24981        112.0\n",
       "369 2021-04-24  25021         40.0\n",
       "\n",
       "[370 rows x 3 columns]"
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   "source": [
    "## Clean confirmed cases Dataset\n",
    "# Focus only on Honolulu, Hwaii Data\n",
    "raw_Honolulu_cases = raw_us_cases[(raw_us_cases[\"Province_State\"] == \"Hawaii\") & (raw_us_cases[\"Admin2\"] == \"Honolulu\")]\n",
    "# Save general information as DataFrame, first 12 columns\n",
    "Honolulu_info = raw_Honolulu_cases[raw_Honolulu_cases.columns[0:11]]\n",
    "# Save cases by Date Data as DataFrame\n",
    "Honolulu_cases_by_date = raw_Honolulu_cases[raw_Honolulu_cases.columns[11:]]\n",
    "# Pivot the DataFrame for better readinng\n",
    "Honolulu_cases_by_date = pd.DataFrame(Honolulu_cases_by_date.iloc[0])\n",
    "Honolulu_cases_by_date = Honolulu_cases_by_date.reset_index()\n",
    "# rename the columns\n",
    "Honolulu_cases_by_date.columns = [\"Date\", \"Cases\"]\n",
    "# convert date to pandas datetime\n",
    "Honolulu_cases_by_date[\"Date\"] = pd.to_datetime(Honolulu_cases_by_date[\"Date\"])\n",
    "# since the SDI data is only availble before 2021-4-20, take observation from 2020-4-20 to 2021-4-20\n",
    "# subset the data to match the mask policy data\n",
    "Honolulu_cases_by_date_part = Honolulu_cases_by_date.loc[89:458,].reset_index(drop=True) # also calculate 1-day, 7-days, 14-days delayed confirm cases for modeling\n",
    "# calculate daily cases from case\n",
    "Honolulu_cases_by_date_part['Daily_Cases'] = Honolulu_cases_by_date_part['Cases'].diff()\n",
    "# replace first row from NAN to 1\n",
    "Honolulu_cases_by_date_part.iloc[0,2]=1\n",
    "# change negetive values to 0\n",
    "Honolulu_cases_by_date_part['Daily_Cases'] = Honolulu_cases_by_date_part['Daily_Cases'].clip(lower=0)\n",
    "Honolulu_cases_by_date_part"
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       "      <th>362</th>\n",
       "      <td>2021-04-17</td>\n",
       "      <td>26.0</td>\n",
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       "<p>366 rows × 2 columns</p>\n",
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      "text/plain": [
       "          Date   SDI\n",
       "0   2020-04-20  67.0\n",
       "1   2020-04-21  67.0\n",
       "2   2020-04-22  63.0\n",
       "3   2020-04-23  66.0\n",
       "4   2020-04-24  64.0\n",
       "..         ...   ...\n",
       "361 2021-04-16  23.0\n",
       "362 2021-04-17  26.0\n",
       "363 2021-04-18  31.0\n",
       "364 2021-04-19  20.0\n",
       "365 2021-04-20  24.0\n",
       "\n",
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   "source": [
    "## Clean Social Distance Index Dataset\n",
    "# Focus only on Honolulu, Hwaii Data\n",
    "raw_Honolulu_SDI = raw_SDI[raw_SDI[\"NAME\"] == \"Honolulu County\"]\n",
    "# Save general information as DataFrame, first 2 columns\n",
    "Honolulu_SDI_by_date = raw_Honolulu_SDI[raw_Honolulu_SDI.columns[2:]]\n",
    "Honolulu_SDI_by_date = pd.DataFrame(Honolulu_SDI_by_date.iloc[0])\n",
    "# reset index, change column names\n",
    "Honolulu_SDI_by_date = Honolulu_SDI_by_date.reset_index()\n",
    "Honolulu_SDI_by_date.columns = [\"Date\", \"SDI\"]\n",
    "Honolulu_SDI_by_date[\"Date\"] = pd.to_datetime(Honolulu_SDI_by_date[\"Date\"])\n",
    "# since the SDI data is only availble before 2021-4-20, take observation from 2020-4-20 to 2021-4-20\n",
    "# subset the data to match the mask policy data\n",
    "Honolulu_SDI_by_date_part = Honolulu_SDI_by_date.loc[110:,].reset_index(drop=True)\n",
    "Honolulu_SDI_by_date_part"
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2021-04-17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2021-04-19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>69.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>69.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  school_closing  workplace_closing  cancel_public_events  \\\n",
       "0   2020-04-20             3.0                3.0                   1.0   \n",
       "1   2020-04-21             3.0                3.0                   1.0   \n",
       "2   2020-04-22             3.0                3.0                   1.0   \n",
       "3   2020-04-23             3.0                3.0                   1.0   \n",
       "4   2020-04-24             3.0                3.0                   1.0   \n",
       "..         ...             ...                ...                   ...   \n",
       "361 2021-04-16             2.0                2.0                   2.0   \n",
       "362 2021-04-17             2.0                2.0                   2.0   \n",
       "363 2021-04-18             2.0                2.0                   2.0   \n",
       "364 2021-04-19             2.0                2.0                   2.0   \n",
       "365 2021-04-20             2.0                2.0                   2.0   \n",
       "\n",
       "     restrictions_on_gatherings  public_transport_closing  \\\n",
       "0                           4.0                       0.0   \n",
       "1                           4.0                       0.0   \n",
       "2                           4.0                       0.0   \n",
       "3                           4.0                       0.0   \n",
       "4                           4.0                       0.0   \n",
       "..                          ...                       ...   \n",
       "361                         4.0                       0.0   \n",
       "362                         4.0                       0.0   \n",
       "363                         4.0                       0.0   \n",
       "364                         4.0                       0.0   \n",
       "365                         4.0                       0.0   \n",
       "\n",
       "     stay_at_home_requirements  restrictions_on_internal_movement  \\\n",
       "0                          2.0                                2.0   \n",
       "1                          2.0                                2.0   \n",
       "2                          2.0                                2.0   \n",
       "3                          2.0                                2.0   \n",
       "4                          2.0                                2.0   \n",
       "..                         ...                                ...   \n",
       "361                        2.0                                2.0   \n",
       "362                        2.0                                2.0   \n",
       "363                        2.0                                2.0   \n",
       "364                        2.0                                2.0   \n",
       "365                        2.0                                2.0   \n",
       "\n",
       "     international_travel_controls  income_support  ...  fiscal_measures  \\\n",
       "0                              3.0             2.0  ...              0.0   \n",
       "1                              3.0             2.0  ...              0.0   \n",
       "2                              3.0             2.0  ...              0.0   \n",
       "3                              3.0             2.0  ...              0.0   \n",
       "4                              3.0             2.0  ...              0.0   \n",
       "..                             ...             ...  ...              ...   \n",
       "361                            3.0             1.0  ...              0.0   \n",
       "362                            3.0             1.0  ...              0.0   \n",
       "363                            3.0             1.0  ...              0.0   \n",
       "364                            3.0             1.0  ...              0.0   \n",
       "365                            3.0             1.0  ...              0.0   \n",
       "\n",
       "     international_support  public_information_campaigns  testing_policy  \\\n",
       "0                      0.0                           2.0             1.0   \n",
       "1                      0.0                           2.0             1.0   \n",
       "2                      0.0                           2.0             1.0   \n",
       "3                      0.0                           2.0             1.0   \n",
       "4                      0.0                           2.0             1.0   \n",
       "..                     ...                           ...             ...   \n",
       "361                    0.0                           2.0             3.0   \n",
       "362                    0.0                           2.0             3.0   \n",
       "363                    0.0                           2.0             3.0   \n",
       "364                    0.0                           2.0             3.0   \n",
       "365                    0.0                           2.0             3.0   \n",
       "\n",
       "     contact_tracing  emergency_investment_in_healthcare  \\\n",
       "0                2.0                                 0.0   \n",
       "1                2.0                                 0.0   \n",
       "2                2.0                                 0.0   \n",
       "3                2.0                                 0.0   \n",
       "4                2.0                                 0.0   \n",
       "..               ...                                 ...   \n",
       "361              2.0                                 NaN   \n",
       "362              2.0                                 NaN   \n",
       "363              2.0                                 NaN   \n",
       "364              2.0                                 0.0   \n",
       "365              2.0                                 0.0   \n",
       "\n",
       "     investment_in_vaccines  facial_coverings  vaccination_policy  \\\n",
       "0                       0.0               2.0                 0.0   \n",
       "1                       0.0               2.0                 0.0   \n",
       "2                       0.0               2.0                 0.0   \n",
       "3                       0.0               2.0                 0.0   \n",
       "4                       0.0               2.0                 0.0   \n",
       "..                      ...               ...                 ...   \n",
       "361                     NaN               3.0                 3.0   \n",
       "362                     NaN               3.0                 3.0   \n",
       "363                     NaN               3.0                 3.0   \n",
       "364                     0.0               3.0                 5.0   \n",
       "365                     0.0               3.0                 5.0   \n",
       "\n",
       "     stringency_index  \n",
       "0               76.85  \n",
       "1               76.85  \n",
       "2               76.85  \n",
       "3               76.85  \n",
       "4               76.85  \n",
       "..                ...  \n",
       "361             69.44  \n",
       "362             69.44  \n",
       "363             69.44  \n",
       "364             69.44  \n",
       "365             69.44  \n",
       "\n",
       "[366 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Clean goverment responses Dataset\n",
    "# Focus only on Honolulu, Hwaii Data\n",
    "raw_Honolulu_response = raw_goverment_response[(raw_goverment_response[\"location_key\"] == \"US_HI\")]\n",
    "Honolulu_response_by_date = raw_Honolulu_response.drop('location_key', axis=1).reset_index(drop=True)\n",
    "Honolulu_response_by_date [\"date\"] = pd.to_datetime(Honolulu_response_by_date[\"date\"])\n",
    "# since the SDI data is only availble before 2021-4-20, take observation from 2020-4-20 to 2021-4-20\n",
    "# subset the data to match the mask policy data\n",
    "Honolulu_response_by_date_part = Honolulu_response_by_date.loc[110:475,].reset_index(drop=True)\n",
    "Honolulu_response_by_date_part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "677e2df2-a2a6-44fc-a43b-2e6182204eb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge goverment responses with SDI data\n",
    "Honolulu_dataset = Honolulu_response_by_date_part.copy()\n",
    "Honolulu_dataset['SDI'] = Honolulu_SDI_by_date_part['SDI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d46aea13-23b0-4688-83ce-b843202813f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values in date : 0\n",
      "Number of missing values in international_travel_controls : 1\n",
      "Number of missing values in contact_tracing : 0\n",
      "Number of missing values in stringency_index : 0\n",
      "Number of missing values in restrictions_on_gatherings : 0\n",
      "Number of missing values in income_support : 0\n",
      "Number of missing values in fiscal_measures : 58\n",
      "Number of missing values in emergency_investment_in_healthcare : 35\n",
      "Number of missing values in vaccination_policy : 0\n",
      "Number of missing values in debt_relief : 0\n",
      "Number of missing values in international_support : 0\n",
      "Number of missing values in facial_coverings : 0\n",
      "Number of missing values in investment_in_vaccines : 35\n",
      "Number of missing values in workplace_closing : 0\n",
      "Number of missing values in restrictions_on_internal_movement : 0\n",
      "Number of missing values in public_transport_closing : 0\n",
      "Number of missing values in stay_at_home_requirements : 0\n",
      "Number of missing values in public_information_campaigns : 0\n",
      "Number of missing values in SDI : 0\n",
      "Number of missing values in school_closing : 0\n",
      "Number of missing values in testing_policy : 0\n",
      "Number of missing values in cancel_public_events : 0\n"
     ]
    }
   ],
   "source": [
    "# Find and Print missing values\n",
    "dbLength = len(Honolulu_dataset)\n",
    "columnNames = list(set(Honolulu_dataset))\n",
    "\n",
    "# loop over the column names and print missing values in each column\n",
    "for column in columnNames:\n",
    "    numMissing = Honolulu_dataset[column].isnull().sum()\n",
    "    print(\"Number of missing values in\", column, \":\", numMissing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4eabf98d-c257-4006-93b4-43d8e63a1595",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>date</th>\n",
       "      <th>school_closing</th>\n",
       "      <th>workplace_closing</th>\n",
       "      <th>cancel_public_events</th>\n",
       "      <th>restrictions_on_gatherings</th>\n",
       "      <th>public_transport_closing</th>\n",
       "      <th>stay_at_home_requirements</th>\n",
       "      <th>restrictions_on_internal_movement</th>\n",
       "      <th>international_travel_controls</th>\n",
       "      <th>income_support</th>\n",
       "      <th>debt_relief</th>\n",
       "      <th>international_support</th>\n",
       "      <th>public_information_campaigns</th>\n",
       "      <th>testing_policy</th>\n",
       "      <th>contact_tracing</th>\n",
       "      <th>facial_coverings</th>\n",
       "      <th>vaccination_policy</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>SDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2021-04-17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2021-04-19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  school_closing  workplace_closing  cancel_public_events  \\\n",
       "0   2020-04-20             3.0                3.0                   1.0   \n",
       "1   2020-04-21             3.0                3.0                   1.0   \n",
       "2   2020-04-22             3.0                3.0                   1.0   \n",
       "3   2020-04-23             3.0                3.0                   1.0   \n",
       "4   2020-04-24             3.0                3.0                   1.0   \n",
       "..         ...             ...                ...                   ...   \n",
       "361 2021-04-16             2.0                2.0                   2.0   \n",
       "362 2021-04-17             2.0                2.0                   2.0   \n",
       "363 2021-04-18             2.0                2.0                   2.0   \n",
       "364 2021-04-19             2.0                2.0                   2.0   \n",
       "365 2021-04-20             2.0                2.0                   2.0   \n",
       "\n",
       "     restrictions_on_gatherings  public_transport_closing  \\\n",
       "0                           4.0                       0.0   \n",
       "1                           4.0                       0.0   \n",
       "2                           4.0                       0.0   \n",
       "3                           4.0                       0.0   \n",
       "4                           4.0                       0.0   \n",
       "..                          ...                       ...   \n",
       "361                         4.0                       0.0   \n",
       "362                         4.0                       0.0   \n",
       "363                         4.0                       0.0   \n",
       "364                         4.0                       0.0   \n",
       "365                         4.0                       0.0   \n",
       "\n",
       "     stay_at_home_requirements  restrictions_on_internal_movement  \\\n",
       "0                          2.0                                2.0   \n",
       "1                          2.0                                2.0   \n",
       "2                          2.0                                2.0   \n",
       "3                          2.0                                2.0   \n",
       "4                          2.0                                2.0   \n",
       "..                         ...                                ...   \n",
       "361                        2.0                                2.0   \n",
       "362                        2.0                                2.0   \n",
       "363                        2.0                                2.0   \n",
       "364                        2.0                                2.0   \n",
       "365                        2.0                                2.0   \n",
       "\n",
       "     international_travel_controls  income_support  debt_relief  \\\n",
       "0                              3.0             2.0          1.0   \n",
       "1                              3.0             2.0          1.0   \n",
       "2                              3.0             2.0          1.0   \n",
       "3                              3.0             2.0          1.0   \n",
       "4                              3.0             2.0          1.0   \n",
       "..                             ...             ...          ...   \n",
       "361                            3.0             1.0          2.0   \n",
       "362                            3.0             1.0          2.0   \n",
       "363                            3.0             1.0          2.0   \n",
       "364                            3.0             1.0          2.0   \n",
       "365                            3.0             1.0          2.0   \n",
       "\n",
       "     international_support  public_information_campaigns  testing_policy  \\\n",
       "0                      0.0                           2.0             1.0   \n",
       "1                      0.0                           2.0             1.0   \n",
       "2                      0.0                           2.0             1.0   \n",
       "3                      0.0                           2.0             1.0   \n",
       "4                      0.0                           2.0             1.0   \n",
       "..                     ...                           ...             ...   \n",
       "361                    0.0                           2.0             3.0   \n",
       "362                    0.0                           2.0             3.0   \n",
       "363                    0.0                           2.0             3.0   \n",
       "364                    0.0                           2.0             3.0   \n",
       "365                    0.0                           2.0             3.0   \n",
       "\n",
       "     contact_tracing  facial_coverings  vaccination_policy  stringency_index  \\\n",
       "0                2.0               2.0                 0.0             76.85   \n",
       "1                2.0               2.0                 0.0             76.85   \n",
       "2                2.0               2.0                 0.0             76.85   \n",
       "3                2.0               2.0                 0.0             76.85   \n",
       "4                2.0               2.0                 0.0             76.85   \n",
       "..               ...               ...                 ...               ...   \n",
       "361              2.0               3.0                 3.0             69.44   \n",
       "362              2.0               3.0                 3.0             69.44   \n",
       "363              2.0               3.0                 3.0             69.44   \n",
       "364              2.0               3.0                 5.0             69.44   \n",
       "365              2.0               3.0                 5.0             69.44   \n",
       "\n",
       "      SDI  \n",
       "0    67.0  \n",
       "1    67.0  \n",
       "2    63.0  \n",
       "3    66.0  \n",
       "4    64.0  \n",
       "..    ...  \n",
       "361  23.0  \n",
       "362  26.0  \n",
       "363  31.0  \n",
       "364  20.0  \n",
       "365  24.0  \n",
       "\n",
       "[366 rows x 19 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop variables with more than 10% missing values, and fill NAs for variables with less than 10% missing.\n",
    "Honolulu_dataset.drop(['investment_in_vaccines','emergency_investment_in_healthcare','fiscal_measures'], axis=1, inplace=True)\n",
    "Honolulu_dataset[\"international_travel_controls\"].fillna(0, inplace = True)\n",
    "Honolulu_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3da00cd-d473-4c47-9997-960aafe20010",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of 0 values in date : 0\n",
      "Number of 0 values in international_travel_controls : 0\n",
      "Number of 0 values in contact_tracing : 330\n",
      "Number of 0 values in stringency_index : 0\n",
      "Number of 0 values in restrictions_on_gatherings : 0\n",
      "Number of 0 values in income_support : 102\n",
      "Number of 0 values in vaccination_policy : 84\n",
      "Number of 0 values in debt_relief : 240\n",
      "Number of 0 values in international_support : 0\n",
      "Number of 0 values in facial_coverings : 73\n",
      "Number of 0 values in workplace_closing : 351\n",
      "Number of 0 values in restrictions_on_internal_movement : 366\n",
      "Number of 0 values in public_transport_closing : 0\n",
      "Number of 0 values in stay_at_home_requirements : 344\n",
      "Number of 0 values in public_information_campaigns : 366\n",
      "Number of 0 values in SDI : 0\n",
      "Number of 0 values in school_closing : 240\n",
      "Number of 0 values in testing_policy : 129\n",
      "Number of 0 values in cancel_public_events : 278\n"
     ]
    }
   ],
   "source": [
    "# Find and Print number of 0 values in each variable\n",
    "dbLength = len(Honolulu_dataset)\n",
    "columnNames = list(set(Honolulu_dataset))\n",
    "\n",
    "# loop over the column names and print number of zero values in each column\n",
    "for column in columnNames:\n",
    "    numMissing = (Honolulu_dataset[column]==2).sum()\n",
    "    print(\"Number of 0 values in\", column, \":\", numMissing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f488837-ffa2-4d79-af4a-726ae342aa44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop variables with only 0 values\n",
    "Honolulu_dataset.drop(['vaccination_policy','international_support'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b411a6b-1296-4a3a-ae51-ffa0bb1227a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# repeat the above process and drop variables that stay unchanged (i.e., have identical values for all rows)\n",
    "Honolulu_dataset.drop(['debt_relief','restrictions_on_internal_movement','stay_at_home_requirements','international_travel_controls','public_information_campaigns','restrictions_on_gatherings','income_support','public_information_campaigns'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f15de893-ccb1-4e6c-9861-004633641874",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 366 entries, 0 to 365\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   date                      366 non-null    datetime64[ns]\n",
      " 1   school_closing            366 non-null    float64       \n",
      " 2   workplace_closing         366 non-null    float64       \n",
      " 3   cancel_public_events      366 non-null    float64       \n",
      " 4   public_transport_closing  366 non-null    float64       \n",
      " 5   testing_policy            366 non-null    float64       \n",
      " 6   contact_tracing           366 non-null    float64       \n",
      " 7   facial_coverings          366 non-null    float64       \n",
      " 8   stringency_index          366 non-null    float64       \n",
      " 9   SDI                       366 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(9)\n",
      "memory usage: 28.7 KB\n"
     ]
    }
   ],
   "source": [
    "# report the data info after preprocessing\n",
    "Honolulu_dataset.info()\n",
    "Honolulu_dataset_numeric = Honolulu_dataset[list(Honolulu_dataset._get_numeric_data().columns)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6abe943e-fac9-43bd-aba4-00359ae94281",
   "metadata": {},
   "source": [
    "#### Step 2: Muticollinearity Tests\n",
    "\n",
    "- Calculated Pearson Correlation Coefficient for each pear of variables, and plot a heat map\n",
    "- Calculated Variance Inflation Factor\n",
    "- Drop variables with high VIF and high correlations with other variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e9b7913-5d43-4ffc-92e8-696cab4937c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ISkrC3r170bJlS1n648ePC+VVdHL9ui93794tdO/OnTswMzMrk9dj1KxZE7Vq1cKBAwewYsWKEg9tet2umzdvQiqVyv2i/3oZ8+t2V69eHSdPnkSzZs1K/KLgQ5mbm8PQ0LDUX3J8yM+7JIr+fIFXf/Z37NiBLVu2YNq0aYXup6Wl4cCBA6hdu3ahVQPlQdExMjc3h46Ojizi+qai/vwW9Zx3/fyKG9c3/5589dVXhZ5dmn8TnJ2d4ezsjGnTpuHvv/9Gs2bNsGbNGsydO1fhOoiIyhr3oBIRlbPJkydDT08PQ4YMQVxcXKH7Dx8+xIoVKwAAPj4+AIDly5fL5Vm6dCmAV/vO3perqyuqV6+OxYsXIyMjo9D9hISEYsu+jqy9GUnLzc3FL7/8Uiivnp6eQkt+ra2t4eLigs2bNyMlJUWW/s8//+D48eOysSgLs2bNQlJSEoYMGYL8/PxC948fP45Dhw4BePUziI2Nxa5du2T38/PzsXLlSujr66NVq1YAXp14W1BQgDlz5hSqLz8/X65PH0JNTQ2dO3fGn3/+iStXrhS6X1x080N+3iXR09NTuG/du3dHnTp1sGDBgkJtl0qlGDlyJJKTkxEUFPRebflQio6Ruro6vLy8sH//fkRHR8vuR0ZG4tixY+98Trdu3XDjxo1Cp3AD//38Xn8Z8/bYurm5wcLCAmvWrJGL5P7111+IjIxU6N+EtLS0Qn/unZ2doaam9lFFh4no08QIKhFROatevTq2b9+OXr16wcnJCf3798cXX3yB3Nxc/P3337JXmABA/fr14efnh7Vr18qW1V6+fBmbN29G586d0bp16/duh5qaGtavX4927dqhbt26GDhwIGxsbPD8+XOcPn0ahoaGsteCvK1p06YwMTGBn58fxo0bB5FIhK1btxY5OXJ1dcWuXbvg7++PRo0aQV9fHx07diyy3h9//BHt2rWDu7s7Bg8eLHvNjJGREWbOnPnefX1br169cOvWLcybNw/Xr19H7969YWdnh6SkJBw9ehQhISHYvn07AGDYsGH49ddfMWDAAFy9ehX29vbYs2cPwsLCsHz5ctleyVatWmH48OGYP38+IiIi0LZtW2hqauL+/fvYvXs3VqxYge7du5dJ+3/44QccP34crVq1wrBhw+Dk5ISYmBjs3r0b58+fl3vNyGsf8vMuiaurK06ePImlS5eicuXKcHBwQJMmTYrMq6WlhT179qBNmzZo3rw5Bg4cCDc3N6SkpGD79u24du0aJk6cCF9f31K3oyyUZoxmzZqFo0ePokWLFhg1apTsS4u6devKlikXJyAgAHv27EGPHj0waNAguLq64uXLlzh48CDWrFmD+vXro3r16jA2NsaaNWtgYGAAPT09NGnSBA4ODli4cCEGDhyIVq1aoXfv3rLXzNjb22PChAnv7OepU6cwZswY9OjRAzVr1kR+fj62bt0KdXX1994bS0RUZiryCGEios/ZvXv3hKFDhwr29vaClpaWYGBgIDRr1kxYuXKl3Gta8vLyhFmzZgkODg6CpqamYGtrKwQGBsrlEYRXr7Jo3759oee8fqVGca+UuH79utC1a1ehUqVKglgsFuzs7ISePXsKISEhsjxFvWYmLCxM+PLLLwUdHR2hcuXKwuTJk4Vjx47JvdpEEAQhIyND6NOnj2BsbCwAkL1ypqjXzAiCIJw8eVJo1qyZoKOjIxgaGgodO3YUbt++LZfn9WtQEhIS5NKLamdJQkJChE6dOgkWFhaChoaGYG5uLnTs2FE4cOCAXL64uDhh4MCBgpmZmaClpSU4OzsXavdra9euFVxdXQUdHR3BwMBAcHZ2FiZPniy8ePFCludDXzMjCILw5MkToX///oK5ubkgFouFatWqCaNHjxYkEokgCIVfM/OaIj/v0ozvnTt3hJYtWwo6OjoCAIVeORMfHy/4+/sLNWrUEMRisWBsbCx4enrKvVrm7f6X5jUz78pbFn8nBEEQzpw5I7i6ugpaWlpCtWrVhDVr1sjG7k1vv2ZGEAQhKSlJGDNmjGBjYyNoaWkJVapUEfz8/ITExERZngMHDgh16tQRNDQ0Cv1d2bVrl9CgQQNBLBYLpqamQt++fYVnz57JPaO4P2ePHj0SBg0aJFSvXl3Q1tYWTE1NhdatWwsnT54scdyIiMqDSBDe86QDIiIiIiIiojLEPahERERERESkEjhBJSIiIiIiIpXACSoRERERERGpBE5QiYiIiIiIPnJnz55Fx44dUblyZYhEIuzfv/+dZUJDQ9GwYUOIxWLUqFEDwcHBhfL8/PPPsLe3h7a2Npo0aYLLly+XfePfwAkqERERERHRRy4zMxP169fHzz//rFD+x48fo3379mjdujUiIiIwfvx4DBkyRO59zq9fExcUFIRr166hfv368PLyQnx8vLK6AZ7iS0REREREpGIkEgkkEolcmlgshlgsfmdZkUiEffv2oXPnzsXmmTJlCg4fPox//vlHlubr64uUlBQcPXoUANCkSRM0atQIq1atAgBIpVLY2tpi7NixmDp16nv06t00lFIrERERERHRJyYv8VG5PWv+qi2YNWuWXFpQUBBmzpxZJvVfuHABnp6ecmleXl4YP348ACA3NxdXr15FYGCg7L6amho8PT1x4cKFMmlDUThBJSIiIiIiUjGBgYHw9/eXS1Mkeqqo2NhYWFpayqVZWloiLS0N2dnZSE5ORkFBQZF57ty5U2bteBsnqERERERERCpG0eW8nxpOUImIiIiIiBQhLajoFpQZKysrxMXFyaXFxcXB0NAQOjo6UFdXh7q6epF5rKyslNYunuJLRERERET0mXF3d0dISIhc2okTJ+Du7g4A0NLSgqurq1weqVSKkJAQWR5lYASViIiIiIhIEYK0oltQrIyMDDx48ED2+fHjx4iIiICpqSmqVq2KwMBAPH/+HFu2bAEAjBgxAqtWrcLkyZMxaNAgnDp1Cr///jsOHz4sq8Pf3x9+fn5wc3ND48aNsXz5cmRmZmLgwIFK6wcnqERERERERB+5K1euoHXr1rLPrw9Y8vPzQ3BwMGJiYhAdHS277+DggMOHD2PChAlYsWIFqlSpgvXr18PLy0uWp1evXkhISMCMGTMQGxsLFxcXHD16tNDBSWWJ70ElIiIiIiJSQF5MZLk9S9PaqdyepUq4B5WIiIiIiIhUApf4EhERERERKUBQ4T2onwpGUImIiIiIiEglMIJKRERERESkCCkjqMrGCCoRERERERGpBEZQiYiIiIiIFME9qErHCCoRERERERGpBEZQiYiIiIiIFCEtqOgWfPIYQSUiIiIiIiKVwAgqERERERGRIrgHVekYQSUiIiIiIiKVwAgqERERERGRIvgeVKVjBJWIiIiIiIhUAiOoREREREREChC4B1XpGEElIiIiIiIilcAJKhEREREREakETlArkEgkwv79+5X6jNDQUIhEIqSkpHxwXR4eHhg/fvwH1wOUbbuIiIiIiMqFVFp+12eKE1SqEE2bNkVMTAyMjIwquilERERERKQieEgSVQgtLS1YWVlVdDOIiIiIiBTHQ5KUjhHUMrBnzx44OztDR0cHlSpVgqenJzIzMwEAGzduRN26dSEWi2FtbY0xY8bIlU1MTESXLl2gq6sLR0dHHDx4UO7+mTNn0LhxY1n5qVOnIj8/X3ZfIpFg3LhxsLCwgLa2Npo3b47w8PD37ktYWBg8PDygq6sLExMTeHl5ITk5uci8ycnJ6N+/P0xMTKCrq4t27drh/v37svtPnjxBx44dYWJiAj09PdStWxdHjhwBUHiJb3BwMIyNjXHs2DE4OTlBX18f3t7eiImJkdWXn5+PcePGwdjYGJUqVcKUKVPg5+eHzp07v3d/iYiIiIhIdXCC+oFiYmLQu3dvDBo0CJGRkQgNDUXXrl0hCAJWr16N0aNHY9iwYbh16xYOHjyIGjVqyJWfNWsWevbsiZs3b8LHxwd9+/bFy5cvAQDPnz+Hj48PGjVqhBs3bmD16tXYsGED5s6dKys/efJk/PHHH9i8eTOuXbuGGjVqwMvLS1ZHaURERKBNmzaoU6cOLly4gPPnz6Njx44oKCgoMv+AAQNw5coVHDx4EBcuXIAgCPDx8UFeXh4AYPTo0ZBIJDh79ixu3bqFhQsXQl9fv9jnZ2VlYfHixdi6dSvOnj2L6OhoTJo0SXZ/4cKF2LZtGzZt2oSwsDCkpaUpfQ8vEREREZGMtKD8rs+USBAEoaIb8TG7du0aXF1dERUVBTs7O7l7NjY2GDhwoNyE8k0ikQjTpk3DnDlzAACZmZnQ19fHX3/9BW9vb3z//ff4448/EBkZCZFIBAD45ZdfMGXKFKSmpiI7OxsmJiYIDg5Gnz59AAB5eXmwt7fH+PHjERAQgNDQULRu3RrJyckwNjYusS99+vRBdHQ0zp8/X+R9Dw8PuLi4YPny5bh//z5q1qyJsLAwNG3aFACQlJQEW1tbbN68GT169EC9evXQrVs3BAUFFarr7XYFBwdj4MCBePDgAapXry7r6+zZsxEbGwsAsLKywqRJk2ST1oKCAlSrVg0NGjQodqIqkUggkUjk0sRiMcRicYljQURERET0NsmdM+X2LHHtVuX2LFXCCOoHql+/Ptq0aQNnZ2f06NED69atQ3JyMuLj4/HixQu0adOmxPL16tWT/beenh4MDQ0RHx8PAIiMjIS7u7tscgoAzZo1Q0ZGBp49e4aHDx8iLy8PzZo1k93X1NRE48aNERkZWeq+vI6gKiIyMhIaGhpo0qSJLK1SpUqoVauW7Nnjxo3D3Llz0axZMwQFBeHmzZsl1qmrqyubnAKAtbW1bCxSU1MRFxeHxo0by+6rq6vD1dW1xDrnz58PIyMjuWv+/PkK9ZGIiIiISI4gLb/rM8UJ6gdSV1fHiRMn8Ndff6FOnTpYuXIlatWqhbi4OIXKa2pqyn0WiUSQVtCx0jo6OmVa35AhQ/Do0SP069cPt27dgpubG1auXFls/qLG4kMD/IGBgUhNTZW7AgMDP6hOIiIiIiJSDk5Qy4BIJEKzZs0wa9YsXL9+HVpaWjhx4gTs7e0REhLy3vU6OTnJ9na+FhYWBgMDA1SpUgXVq1eHlpYWwsLCZPfz8vIQHh6OOnXqlPp59erVU7i9Tk5OyM/Px6VLl2RpSUlJuHv3rtyzbW1tMWLECOzduxcTJ07EunXrSt0uADAyMoKlpaXcAVAFBQW4du1aieXEYjEMDQ3lLi7vJSIiIqL3wvegKh1fM/OBLl26hJCQELRt2xYWFha4dOkSEhIS4OTkhJkzZ2LEiBGwsLBAu3btkJ6ejrCwMIwdO1ahukeNGoXly5dj7NixGDNmDO7evYugoCD4+/tDTU0Nenp6GDlyJAICAmBqaoqqVati0aJFyMrKwuDBg0vdl8DAQDg7O2PUqFEYMWIEtLS0cPr0afTo0QNmZmZyeR0dHdGpUycMHToUv/76KwwMDDB16lTY2NigU6dOAIDx48ejXbt2qFmzJpKTk3H69Gk4OTmVul2vjR07FvPnz0eNGjVQu3ZtrFy5EsnJyXJLoImIiIiI6OPFCeoHMjQ0xNmzZ7F8+XKkpaXBzs4OS5YsQbt27QAAOTk5WLZsGSZNmgQzMzN0795d4bptbGxw5MgRBAQEoH79+jA1NcXgwYMxbdo0WZ4FCxZAKpWiX79+SE9Ph5ubG44dOwYTE5NS96VmzZo4fvw4/ve//6Fx48bQ0dFBkyZN0Lt37yLzb9q0Cd999x06dOiA3NxctGzZEkeOHJEt1S0oKMDo0aPx7NkzGBoawtvbG8uWLSt1u16bMmUKYmNj0b9/f6irq2PYsGHw8vKCurr6e9dJRERERKSwz3hvaHnhKb700ZJKpXByckLPnj1lJyETERERESmL5J8T5fYs8Rdfl9uzVAkjqPTRePLkCY4fP45WrVpBIpFg1apVePz4sewVO0RERERESvUZ7w0tLzwk6TPSrl076OvrF3n98MMPFd28d1JTU0NwcDAaNWqEZs2a4datWzh58uQH7WslIiIiIiLVwSW+n5Hnz58jOzu7yHumpqYwNTUt5xYREREREX08cm4cKbdnadf3KbdnqRIu8f2M2NjYVHQTiIiIiIiIisUJKhERERERkSJ4iq/ScQ8qERERERERqQROUImIiIiIiEglcIkvERERERGRIviaGaVjBJWIiIiIiIhUAiOoREREREREiuAhSUrHCCoRERERERGpBEZQiYiIiIiIFCEtqOgWfPIYQSUiIiIiIiKVwAgqERERERGRIrgHVekYQSUiIiIiIiKVwAgqERERERGRIvgeVKVjBJWIiIiIiIhUAiOoREREREREiuAeVKVjBJWIiIiIiIhUAiOoREREREREiuAeVKVjBJWIiIiIiIhUAiOoREREREREimAEVekYQSUiIiIiIiKVwAgqERERERGRAgShoKKb8MnjBJU+O8k9PCq6CSrFZHdoRTeBiIiIiAgAJ6hERERERESK4R5UpeMeVCIiIiIiIlIJnKASERERERGRSuAElYiIiIiISBGCtPyu9/Dzzz/D3t4e2traaNKkCS5fvlxsXg8PD4hEokJX+/btZXkGDBhQ6L63t/d7tU1R3INKRERERET0kdu1axf8/f2xZs0aNGnSBMuXL4eXlxfu3r0LCwuLQvn37t2L3Nxc2eekpCTUr18fPXr0kMvn7e2NTZs2yT6LxWLldQKcoBIRERERESlGhQ9JWrp0KYYOHYqBAwcCANasWYPDhw9j48aNmDp1aqH8pqamcp937twJXV3dQhNUsVgMKysr5TX8LVziS0REREREpGIkEgnS0tLkLolEUmTe3NxcXL16FZ6enrI0NTU1eHp64sKFCwo9b8OGDfD19YWenp5cemhoKCwsLFCrVi2MHDkSSUlJ798pBXCCSkREREREpIhy3IM6f/58GBkZyV3z588vslmJiYkoKCiApaWlXLqlpSViY2Pf2a3Lly/jn3/+wZAhQ+TSvb29sWXLFoSEhGDhwoU4c+YM2rVrh4KCgvcfw3fgEl8iIiIiIiIVExgYCH9/f7k0Ze3/3LBhA5ydndG4cWO5dF9fX9l/Ozs7o169eqhevTpCQ0PRpk0bpbSFEVQiIiIiIiJFSKXldonFYhgaGspdxU1QzczMoK6ujri4OLn0uLi4d+4fzczMxM6dOzF48OB3dr9atWowMzPDgwcPFB+zUuIElYiIiIiI6COmpaUFV1dXhISEyNKkUilCQkLg7u5eYtndu3dDIpHg22+/fedznj17hqSkJFhbW39wm4vDCSoREREREZEiVPg9qP7+/li3bh02b96MyMhIjBw5EpmZmbJTffv374/AwMBC5TZs2IDOnTujUqVKcukZGRkICAjAxYsXERUVhZCQEHTq1Ak1atSAl5fX+42fArgHlYiIiIiI6CPXq1cvJCQkYMaMGYiNjYWLiwuOHj0qOzgpOjoaamry8cm7d+/i/PnzOH78eKH61NXVcfPmTWzevBkpKSmoXLky2rZtizlz5ij1XagiQRAEpdVOpIKSe3hUdBNUisnu0IpuAhEREdFHIfuvn8rtWTrtxpXbs1QJl/gSERERERGRSuASXyIiIiIiIkVIS783lEqHEVQiIiIiIiJSCYygEhERERERKeI9Ttel0mEElYiIiIiIiFQCI6hERERERESK4B5UpWMElYiIiIiIiFQCJ6hERERERESkErjEl4iIiIiISBE8JEnpGEF9T1FRURCJRIiIiKjQOsqTvb09li9fXiZ1BQcHw9jYuEzqIiIiIiKiTwMjqFQhevXqBR8fn4puRpkSe3WG+BtfqBmbouDJA2Rt/AkFD+4UmVerTXtotfKCuq0DAKDg0T1k71gnl19kZAKdb4dDs54bRHr6yI+8iawNKyCNfV4u/SEiIiKit/CQJKVjBPU95ObmVnQTPno6OjqwsLCo6GaUGc2mraHjNwo5u4ORNmUoCp48hP73P0JkaFxkfo26Lsg7H4KMWROQ/v1oSJPioT9tMUSmZrI8+pPnQs3CGhmLvkfa5KGQJsRCf8YSQKxdTr0iIiIiIipfn+QE9dChQzA2NkZBQQEAICIiAiKRCFOnTpXlGTJkCL799lsAwB9//IG6detCLBbD3t4eS5YskavP3t4ec+bMQf/+/WFoaIhhw4YVemZBQQEGDRqE2rVrIzo6GgAgEomwevVqtGvXDjo6OqhWrRr27NlTbLsLCgowePBgODg4QEdHB7Vq1cKKFSsK5du4caOsvdbW1hgzZozsXkpKCoYMGQJzc3MYGhriq6++wo0bNxQeuz///BONGjWCtrY2zMzM0KVLl2LzRkdHo1OnTtDX14ehoSF69uyJuLg42f0bN26gdevWMDAwgKGhIVxdXXHlyhUAhZf4zpw5Ey4uLti6dSvs7e1hZGQEX19fpKeny/Kkp6ejb9++0NPTg7W1NZYtWwYPDw+MHz9e4f4pi3aHHpCEHEZu6FFInz1B1tqlQG4OtL4qOkqc9dM8SI4fQEHUA0hfRCNrzY8QiUTQ/KIhAEDNugo0atZF1rplKHh4F9IXT5G1bhlEWmJoNWtTnl0jIiIiotcEafldn6lPcoLaokULpKen4/r16wCAM2fOwMzMDKGhobI8Z86cgYeHB65evYqePXvC19cXt27dwsyZMzF9+nQEBwfL1bl48WLUr18f169fx/Tp0+XuSSQS9OjRAxERETh37hyqVq0quzd9+nR069YNN27cQN++feHr64vIyMgi2y2VSlGlShXs3r0bt2/fxowZM/C///0Pv//+uyzP6tWrMXr0aAwbNgy3bt3CwYMHUaNGDdn9Hj16ID4+Hn/99ReuXr2Khg0bok2bNnj58uU7x+3w4cPo0qULfHx8cP36dYSEhKBx48bFtrVTp054+fIlzpw5gxMnTuDRo0fo1auXLE/fvn1RpUoVhIeH4+rVq5g6dSo0NTWLff7Dhw+xf/9+HDp0CIcOHcKZM2ewYMEC2X1/f3+EhYXh4MGDOHHiBM6dO4dr1669s19Kp6EB9Wq1kH/z6n9pgoC8m1ehUbOOYnVoiQENDQgZ/z8hfz1OeW9E6wUByMuDhpNz2bSbiIiIiEjFfJJ7UI2MjODi4oLQ0FC4ubkhNDQUEyZMwKxZs5CRkYHU1FQ8ePAArVq1wsyZM9GmTRvZpLNmzZq4ffs2fvzxRwwYMEBW51dffYWJEyfKPkdFRQEAMjIy0L59e0gkEpw+fRpGRkZybenRoweGDBkCAJgzZw5OnDiBlStX4pdffinUbk1NTcyaNUv22cHBARcuXMDvv/+Onj17AgDmzp2LiRMn4rvvvpPla9SoEQDg/PnzuHz5MuLj4yEWiwG8mljv378fe/bsKTLy+6Z58+bB19dXrg3169cvMm9ISAhu3bqFx48fw9bWFgCwZcsW1K1bF+Hh4WjUqBGio6MREBCA2rVrAwAcHR1LfL5UKkVwcDAMDAwAAP369UNISAjmzZuH9PR0bN68Gdu3b0ebNq8iiJs2bULlypVLrFMikUAikcinFUghVi+772ZEBkYQqatDmir/JYCQmgx1m6rFlJKn8+1wSF8mIu/Wq0mu9Hk0ChJiodNnKLLWLoEgyYG4fQ+omVlAzdi0zNpORERERKXAPahK90lGUAGgVatWCA0NhSAIOHfuHLp27QonJyecP38eZ86cQeXKleHo6IjIyEg0a9ZMrmyzZs1w//592RJhAHBzcyvyOb1790ZmZiaOHz9eaHIKAO7u7oU+FxdBBYCff/4Zrq6uMDc3h76+PtauXStbMhwfH48XL17IJmhvu3HjBjIyMlCpUiXo6+vLrsePH+Phw4fFPvO1iIiIYut+W2RkJGxtbWWTUwCoU6cOjI2NZf3z9/fHkCFD4OnpiQULFryzDfb29rLJKQBYW1sjPj4eAPDo0SPk5eXJRXSNjIxQq1atEuucP38+jIyM5K5ld6IV6mN5EXfuA61mXyHzx+n/RUwLCpC5eAbUKtvCOPgQjH87Bs0vGiDv2sVXkVQiIiIiok/QJztB9fDwwPnz53Hjxg1oamqidu3a8PDwQGhoKM6cOYNWrVqVqj49Pb0i0318fHDz5k1cuHDhg9u8c+dOTJo0CYMHD8bx48cRERGBgQMHyg5l0tHRKbF8RkYGrK2tERERIXfdvXsXAQEB73z+u+ovrZkzZ+Lff/9F+/btcerUKdSpUwf79u0rNv/by39FIhGkH/gtVWBgIFJTU+WuCbUVi2oqSkhPhVBQADUj+cimyMgE0pSSl1aLO/aCduc+yJgTgILoR3L3Ch7dQ3rAECT7tUfqsK7ImDcZIgNDFMS9KNP2ExEREZGCpNLyuz5Tn+wE9fU+1GXLlskmo68nqKGhofDw8AAAODk5ISwsTK5sWFgYatasCXV19Xc+Z+TIkViwYAG++eYbnDlzptD9ixcvFvrs5ORUZF1hYWFo2rQpRo0ahQYNGqBGjRpyUUcDAwPY29sjJCSkyPINGzZEbGwsNDQ0UKNGDbnLzMysyDJvqlevXrF1v83JyQlPnz7F06dPZWm3b99GSkoK6tT5b99lzZo1MWHCBBw/fhxdu3bFpk2bFKr/bdWqVYOmpibCw8Nlaampqbh3716J5cRiMQwNDeWuslzeCwDIz0fBo7vQcG74X5pIBE1nV+Tfu118277xhU73fsiYNxkFj+4WX39WJoS0VKhZ2UC9ei3khYcVn5eIiIiI6CP2Se5BBQATExPUq1cP27Ztw6pVqwAALVu2RM+ePZGXlyebtE6cOBGNGjXCnDlz0KtXL1y4cAGrVq0qco9occaOHYuCggJ06NABf/31F5o3by67t3v3bri5uaF58+bYtm0bLl++jA0bNhRZj6OjI7Zs2YJjx47BwcEBW7duRXh4OBwcHGR5Zs6ciREjRsDCwgLt2rVDeno6wsLCMHbsWHh6esLd3R2dO3fGokWLULNmTbx48UJ2+FFxy5RfCwoKQps2bVC9enX4+voiPz8fR44cwZQpUwrl9fT0hLOzM/r27Yvly5cjPz8fo0aNQqtWreDm5obs7GwEBASge/fucHBwwLNnzxAeHo5u3bopPK5vMjAwgJ+fHwICAmBqagoLCwsEBQVBTU0NIpHoveosSzmHdkNvdCAKHt5F/oNIaLfvDoi1kXv6LwCA7phASF8mImf7OgCAuFNv6PQaiMwVcyFNiIXo//eVCjnZQE42AEDzy1YQ0lIhTYyDetVq0Bk4FnmXzyP/5pWK6SQRERHR545brZTuk52gAq/2oUZERMiipaampqhTpw7i4uJkexcbNmyI33//HTNmzMCcOXNgbW2N2bNnyx2QpIjx48dDKpXCx8cHR48eRdOmTQEAs2bNws6dOzFq1ChYW1tjx44dchHGNw0fPhzXr19Hr169IBKJ0Lt3b4waNQp//fWXLI+fnx9ycnKwbNkyTJo0CWZmZujevTuAV0tijxw5gu+//x4DBw5EQkICrKys0LJlS1haWr6zDx4eHti9ezfmzJmDBQsWwNDQEC1btiwyr0gkwoEDBzB27Fi0bNkSampq8Pb2xsqVKwEA6urqSEpKQv/+/REXFwczMzN07dpV7gCm0lq6dClGjBiBDh06wNDQEJMnT8bTp0+hrV3x7wXN+/s0sg2Nod1rINSMTVEQ9QAZ8yZDSE0GAKiZWcr9gyZu2wkiTS3oT5otV0/278HI2R38qoxJJWj7jYbI2ATS5CTknjmOnD+2lFufiIiIiIjKm0gQ+DWAsohEIuzbtw+dO3eu6KZ8kjIzM2FjY4MlS5Zg8ODBCpdL7uGhvEZ9hEx2h1Z0E4iIiIg+Ctk7gsrtWTq93z+w8zH7pCOo9Gm5fv067ty5g8aNGyM1NRWzZ7+KPnbq1KmCW0ZERERERGWBE9TPSN26dfHkyZMi7/3666/o27dvObeo9BYvXoy7d+9CS0sLrq6uOHfunEIHQBERERERfbDP+HTd8sIJqhKp2urpI0eOIC8vr8h7iuxRrWgNGjTA1atXK7oZRERERESkJJygfkbs7OwquglERERERB8vgRFUZftk34NKREREREREHxdGUImIiIiIiBTBPahKxwgqERERERERqQROUImIiIiIiEglcIkvERERERGRIlTsLR2fIkZQiYiIiIiISCUwgkpERERERKQIHpKkdIygEhERERERkUpgBJWIiIiIiEgRjKAqHSOoREREREREpBIYQSUiIiIiIlKEwAiqsjGCSkRERERERCqBEVQiIiIiIiIFCFK+B1XZGEElIiIiIiIilcAIKhERERERkSJ4iq/SMYJKREREREREKoERVCIiIiIiIkXwFF+lYwSViIiIiIiIVAIjqERERERERIrgKb5KxwgqERERERERqQRGUOmz0/xsdkU3QaV0se9T0U1QOXOjtld0E4iIiEgV8RRfpWMElYiIiIiIiFQCJ6hERERERESkErjEl4iIiIiISBFc4qt0jKASERERERGRSmAElYiIiIiISBECXzOjbIygEhERERERkUpgBJWIiIiIiEgR3IOqdIygEhERERERkUpgBJWIiIiIiEgRUu5BVTZGUImIiIiIiEglMIJKRERERESkCIF7UJWNEVQiIiIiIqJPwM8//wx7e3toa2ujSZMmuHz5crF5g4ODIRKJ5C5tbW25PIIgYMaMGbC2toaOjg48PT1x//59pfaBE1QiIiIiIiJFSIXyu0pp165d8Pf3R1BQEK5du4b69evDy8sL8fHxxZYxNDRETEyM7Hry5Inc/UWLFuGnn37CmjVrcOnSJejp6cHLyws5OTmlbp+iOEElIiIiIiJSMRKJBGlpaXKXRCIpNv/SpUsxdOhQDBw4EHXq1MGaNWugq6uLjRs3FltGJBLByspKdllaWsruCYKA5cuXY9q0aejUqRPq1auHLVu24MWLF9i/f39ZdlUOJ6hEREREREQKEKTScrvmz58PIyMjuWv+/PlFtis3NxdXr16Fp6enLE1NTQ2enp64cOFCsf3JyMiAnZ0dbG1t0alTJ/z777+ye48fP0ZsbKxcnUZGRmjSpEmJdX4oTlCJiIiIiIhUTGBgIFJTU+WuwMDAIvMmJiaioKBALgIKAJaWloiNjS2yTK1atbBx40YcOHAAv/32G6RSKZo2bYpnz54BgKxcaeosCzzFl4iIiIiISBHl+B5UsVgMsVistPrd3d3h7u4u+9y0aVM4OTnh119/xZw5c5T23HdhBJWIiIiIiOgjZmZmBnV1dcTFxcmlx8XFwcrKSqE6NDU10aBBAzx48AAAZOU+pM73wQkqERERERGRIgRp+V2loKWlBVdXV4SEhMjSpFIpQkJC5KKkJSkoKMCtW7dgbW0NAHBwcICVlZVcnWlpabh06ZLCdb4PLvElIiIiIiL6yPn7+8PPzw9ubm5o3Lgxli9fjszMTAwcOBAA0L9/f9jY2MgOWpo9eza+/PJL1KhRAykpKfjxxx/x5MkTDBkyBMCrE37Hjx+PuXPnwtHREQ4ODpg+fToqV66Mzp07K60fnKASEREREREpohz3oJZWr169kJCQgBkzZiA2NhYuLi44evSo7JCj6OhoqKn9t4A2OTkZQ4cORWxsLExMTODq6oq///4bderUkeWZPHkyMjMzMWzYMKSkpKB58+Y4evQotLW1ldYPkSAIqjvKREpQ17JJRTdBpXTRqV7RTVA5c6O2V3QTiIiISAVlzu5bbs/Sm7Gt3J6lSrgHlYiIiIiIiFQCJ6gKGDBggFLXWb8tKioKIpEIERERxeYJDQ2FSCRCSkoKACA4OBjGxsbl0j4iIiIios+SVFp+12eKe1A/Eb169YKPj09FN6NM2dvbY/z48Rg/fnxFN0VhYyYPQ/dvO8HAUB/Xw29i9uRFiH78tNj8x8P3waZq5ULpOzbuwdzAHwEAZuammBg0Dk1bNYauvi6iHjzB2uXBOHH4tNL6UZbaTOgOt96toW2oh+gr93Bw2kYkRRX/cufG33qicV9PGFcxAwDE33+O0z/txf3QGwAA4ypmmHT+pyLL7hi1Av8euVT2nSAiIiKicsEJ6idCR0cHOjo6Fd2Mz9rgMf3Qd0hP/G/cbDyPfoGxU4Zj7a4V+KaFL3IluUWW6eU9EOpvbFav4VQdG3avwrE//zvO+4dVM2FopI8x/Sch+WUK2nf1wpJ189Cz7QDc+eee0vv1IVqM6IgvB3rhj4lrkPw0Hp4Te8Bvy1T89HUA8iV5RZZJjXmJ4wt3vprEioAG3Vqi79qJ+KV9IOLvP0fqiyQsaDRSrkyj3l+h+bAOuB8aUQ69IiIios+WCh+S9KlQiSW+UqkUixYtQo0aNSAWi1G1alXMmzcPADBlyhTUrFkTurq6qFatGqZPn468vP9+sZ05cyZcXFywdetW2Nvbw8jICL6+vkhPT1eofgB4+vQpevbsCWNjY5iamqJTp06Iiop6r754eHhgzJgxGDNmDIyMjGBmZobp06fjzbOoRCIR9u/fL1fO2NgYwcHBcml37txB06ZNoa2tjS+++AJnzpwp9rlFLfH9888/0ahRI2hra8PMzAxdunRRqA8SiQSTJk2CjY0N9PT00KRJE4SGhgJ49e4jHR0d/PXXX3Jl9u3bBwMDA2RlZQF495i+Xja9ePFiWFtbo1KlShg9erTsZ+vh4YEnT55gwoQJEIlEEIlEAIAnT56gY8eOMDExgZ6eHurWrYsjR44o1C9l6zfMF78u24TTR8/i3u0HCBwzExaWZmjTrlWxZZKTUpCY8FJ2eXzdHNGPnyL872uyPA0aOWPb+t24df02nj15gV+XbUJ6agbq1q9dHt36IE0HeSN05X7cOXEVcXeeYo//ahhYGsOprVuxZe6GXMO90AgkRcUi6XEsTi7+HblZObBt4AgAEKQCMhJS5S4nr0b45/BF5GZJyqtrRERERKQEKjFBDQwMxIIFCzB9+nTcvn0b27dvlx2HbGBggODgYNy+fRsrVqzAunXrsGzZMrnyDx8+xP79+3Ho0CEcOnQIZ86cwYIFCxSqPy8vD15eXjAwMMC5c+cQFhYGfX19eHt7Ize36KjXu2zevBkaGhq4fPkyVqxYgaVLl2L9+vWlricgIAATJ07E9evX4e7ujo4dOyIpKUmhsocPH0aXLl3g4+OD69evIyQkBI0bN1ao7JgxY3DhwgXs3LkTN2/eRI8ePeDt7Y379+/D0NAQHTp0wPbt8qecbtu2DZ07d4aurq7CY3r69Gk8fPgQp0+fxubNmxEcHCybpO/duxdVqlTB7NmzERMTg5iYGADA6NGjIZFIcPbsWdy6dQsLFy6Evr6+Qv1Spip2lWFuaYaLZy/L0jLSM3Hz2r+o7+asUB2amhro0M0be3f8KZd+PfwWvDt7wsjYECKRCO06fw0tbS2Eh10rpibVYGJrAQMLEzwM+0eWJknPxrOIh7Bt6KhQHSI1EZw7ukNLR4zoa/eLzFP5CwdUrmuPK7tCy6LZRERERMUTpOV3faYqfIlveno6VqxYgVWrVsHPzw8AUL16dTRv3hwAMG3aNFlee3t7TJo0CTt37sTkyZNl6VKpFMHBwTAwMAAA9OvXDyEhIZg3b94769+1axekUinWr18vi9Jt2rQJxsbGCA0NRdu2bUvdJ1tbWyxbtgwikQi1atXCrVu3sGzZMgwdOrRU9YwZMwbdunUDAKxevRpHjx7Fhg0b5PpenHnz5sHX1xezZs2SpdWvX/+d5aKjo7Fp0yZER0ejcuVXeyMnTZqEo0ePYtOmTfjhhx/Qt29f9OvXD1lZWdDV1UVaWhoOHz6Mffv2AVB8TE1MTLBq1Sqoq6ujdu3aaN++PUJCQjB06FCYmppCXV0dBgYGsLKykmtft27d4Oz8atJXrVq1EvsjkUggkchH1aSCFGqisv1uxsy8EgAgMeGlXHpSwkuYWZgqVMdX7VrBwEgf+3celkufOPR/WLJ2Hv6+ewJ5efnIyc7BdwOmIDrqWdk0Xkn0zY0AABkJqXLpGQmpMPj/e8WxrGWLYXtnQUOsidysHGwfvgwJD54Xmde1lwfi7z/D02ImsERERET08ajwCGpkZCQkEgnatGlT5P1du3ahWbNmsLKygr6+PqZNm4bo6Gi5PPb29rLJKQBYW1sjPj5eofpv3LiBBw8ewMDAAPr6+tDX14epqSlycnLw8OHD9+rTl19+KZuYAYC7uzvu37+PgoKCUtXj7u4u+28NDQ24ubkhMjJSobIRERHF9rkkt27dQkFBAWrWrCkbD319fZw5c0Y2Hj4+PtDU1MTBgwcBAH/88QcMDQ3h6ekJQPExrVu3LtTV1WWf3/y5FWfcuHGYO3cumjVrhqCgINy8ebPE/PPnz4eRkZHclZj5otTj8rb23bwQ/ui07NLQ/PDverr1+QbnT11AQlyiXPrYqcNhYKSPQd1Ho1fbAdi8ZjuWrJsHRyfVen9p/U7NMP3fjbJLXVP93YWKkfjoBX72CcSvnWfg8m8n0W3JCJjXsCmUT0OsiXqdmuIqo6dERERUHqRC+V2fqQqPoJZ0sM+FCxfQt29fzJo1C15eXjAyMsLOnTuxZMkSuXyamppyn0UiEaT/fzTzuw4OysjIgKurK7ZtK/wiXHNzc0W7USoikUhuTyoAuX21ZeF9D0zKyMiAuro6rl69Kjd5BCBbSqulpYXu3btj+/bt8PX1xfbt29GrVy9oaGjI6lBkTEv6uRVnyJAh8PLywuHDh3H8+HHMnz8fS5YswdixY4vMHxgYCH9/f7m0JjVKP3F/2+mj53Dr6r+yz5riV30xMzdFYvx/y7ArmZvizr/vjuxZV7HCly0b4btBU+XSbe1s0HdwT3zT0hcP7z4GANy9fR+uX7qg98DumD154Qf3paxEnryKpxEPZJ81tF79edA3N0JGQoosXd/cCDG3n5RYV0FeAV4+iQMAvPjnMarUq46mg7xx4H8b5PJ94dMEmtpiXN97rox6QUREREQVqcIjqI6OjtDR0UFISEihe3///Tfs7Ozw/fffw83NDY6OjnjypORfbEtTPwA0bNgQ9+/fh4WFBWrUqCF3GRmVvAyxOJcuyb/m4uLFi3B0dJRN+MzNzWV7KgHg/v37ssOF3i73Wn5+Pq5evQonJyeF2lCvXr1i+1ySBg0aoKCgAPHx8YXG482ltn379sXRo0fx77//4tSpU+jbt6/sXlmNqZaWVpFRZ1tbW4wYMQJ79+7FxIkTsW7dumLrEIvFMDQ0lLvKYnlvVmYWoqOeya6Hdx8jIS4RTVo0kuXR09dDvYZ1cePKrXfW18W3A14mJuPsiTC5dG1dbQCvDgZ6k7RACjW1Cv/rKyc3Mwcvn8TJrvj7z5Een4zqTevK8oj1dVDFpXqpl+OK1ERQ1yr8fZprLw/cOXkVWS/TiyhFREREVLYEqbTcrs9Vhf+Gq62tjSlTpmDy5MnYsmULHj58iIsXL2LDhg1wdHREdHQ0du7ciYcPH+Knn36S7XMsi/qBVxMtMzMzdOrUCefOncPjx48RGhqKcePG4dmz99vjFx0dDX9/f9y9exc7duzAypUr8d1338nuf/XVV1i1ahWuX7+OK1euYMSIEYWiiQDw888/Y9++fbhz5w5Gjx6N5ORkDBo0SKE2BAUFYceOHQgKCkJkZKTsQKF3qVmzJvr27Yv+/ftj7969ePz4MS5fvoz58+fj8OH/9ka2bNkSVlZW6Nu3LxwcHNCkSRPZvbIaU3t7e5w9exbPnz9HYuKrZa/jx4/HsWPH8PjxY1y7dg2nT59WeNKubFvX7sTwCQPR2qsFHJ2qY/6qIMTHJSLkr/9OX96wZxX6DOouV04kEqGLbwcc+P1woQn54/tRePLoKYJ+nArnBnVga2cDvxF94N6qsVy9qurvjUfhMbYLans2hGUtW3RbOhLpcSmIPH5Flmfgtv+hSf//9np/PbkX7BvXhnEVM1jWsn31+Usn3NgvP3k3tbOEXePauLrr43gfLBERERG9W4Uv8QWA6dOnQ0NDAzNmzMCLFy9gbW2NESNGYPDgwZgwYQLGjBkDiUSC9u3bY/r06Zg5c2aZ1A8Aurq6OHv2LKZMmYKuXbsiPT0dNjY2aNOmDQwNDd+rP/3790d2djYaN24MdXV1fPfddxg2bJjs/pIlSzBw4EC0aNEClStXxooVK3D16tVC9SxYsAALFixAREQEatSogYMHD8LMzEyhNnh4eGD37t2YM2cOFixYAENDQ7Rs2VKhsps2bcLcuXMxceJEPH/+HGZmZvjyyy/RoUMHWR6RSITevXtj0aJFmDFjhlz5shrT2bNnY/jw4ahevTokEgkEQUBBQQFGjx6NZ8+ewdDQEN7e3oVOda4oG1ZthY6uDmYuDoSBoT6uXb6B4b7fyb0D1dbOBsamxnLl3Fs2RmVba+zd/ifelp9fgBF9JsB/2mis2roEuno6ePr4Gf43djbOhfyt7C59sHNr/oSWjhid5g+BtqEuosPvYbPfArl3oJraWULP9L895PqVDNFt6UgYmBsjJz0LcXeeYnP/BXh4/h+5ul17eiAt5iUenH13hJqIiIioTHzGe0PLi0h4ezMkfRAPDw+4uLhg+fLlFd0UKkZdyybvzvQZ6aKjWoctqYK5UdvfnYmIiIg+OxlTupbbs/QX7i23Z6kSlYigEhERERERqTxGUJWOE9RSiI6ORp06dYq9f/v27XJszfs5d+4c2rVrV+z9jIyMcmwNERERERHRfzhBLYXKlSsjIiKixPuhoaHl1p734ebmVmIfiIiIiIioGMLne7pueeEEtRQ0NDRQo0aNim7GB9HR0fno+0BERERERJ8mTlCJiIiIiIgUwT2oSlfh70ElIiIiIiIiAjhBJSIiIiIiIhXBJb5EREREREQKELjEV+kYQSUiIiIiIiKVwAgqERERERGRIhhBVTpGUImIiIiIiEglMIJKRERERESkCKm0olvwyWMElYiIiIiIiFQCI6hERERERESK4B5UpWMElYiIiIiIiFQCI6hERERERESKYARV6RhBJSIiIiIiIpXACCoREREREZECBIERVGVjBJWIiIiIiIhUAiOoREREREREiuAeVKVjBJWIiIiIiIhUAiOoREREREREimAEVekYQSUiIiIiIiKVwAgqERERERGRAgRGUJWOE1T67HyhbV3RTVAplaRcSPG2nHNbK7oJKke7Rb+KbgIRERF9BjhBJSIiIiIiUgQjqErH0AkRERERERGpBE5QiYiIiIiISCVwiS8REREREZEipBXdgE8fI6hERERERESkEhhBJSIiIiIiUgBfM6N8jKASERERERGRSmAElYiIiIiISBGMoCodI6hERERERESkEhhBJSIiIiIiUgRP8VU6RlCJiIiIiIhIJTCCSkREREREpACe4qt8jKASERERERGRSmAElYiIiIiISBHcg6p0jKASERERERGRSmAElYiIiIiISAHcg6p8jKASERERERGRSmAElYiIiIiISBHcg6p0jKASERERERF9An7++WfY29tDW1sbTZo0weXLl4vNu27dOrRo0QImJiYwMTGBp6dnofwDBgyASCSSu7y9vZXaB05QiYiIiIiIFCBIy+8qrV27dsHf3x9BQUG4du0a6tevDy8vL8THxxeZPzQ0FL1798bp06dx4cIF2Nraom3btnj+/LlcPm9vb8TExMiuHTt2vM/QKYwTVCIiIiIioo/c0qVLMXToUAwcOBB16tTBmjVroKuri40bNxaZf9u2bRg1ahRcXFxQu3ZtrF+/HlKpFCEhIXL5xGIxrKysZJeJiYlS+8EJKhERERERkYqRSCRIS0uTuyQSSZF5c3NzcfXqVXh6esrS1NTU4OnpiQsXLij0vKysLOTl5cHU1FQuPTQ0FBYWFqhVqxZGjhyJpKSk9++UAjhBJSIiIiIiUoS0/K758+fDyMhI7po/f36RzUpMTERBQQEsLS3l0i0tLREbG6tQ16ZMmYLKlSvLTXK9vb2xZcsWhISEYOHChThz5gzatWuHgoIChep8H+U6QY2KioJIJEJERESxeUJDQyESiZCSkgIACA4OhrGxcbm0j4pnb2+P5cuXl0ld/JkSEREREZUsMDAQqampcldgYKBSnrVgwQLs3LkT+/btg7a2tizd19cX33zzDZydndG5c2ccOnQI4eHhCA0NVUo7gI/gNTO9evWCj49PmdRlb2+P8ePHY/z48WVSnyoTiUTYt28fOnfuXNFNKaQsf6aqpod/b7Tp/TX0DPVw98odrP9+DWKjYorN3328L3pM8JVLe/7gGfzbjJF9nrFzLuq6fyGX58RvR7H++zVl23glcffvBuc+rSE21MWLK/cQ8r9NSImKKzZ/o9EdUcO7EUyrWyM/Jxcvrt7H+fm7kPzov3E0srNAy+/7oHKjmlDX0sSTMzdxesZmZCWmlUeX3tvOU1ew+dgFJKZmoKatJab29oJzNZsi8w5etAVX7kUXSm/hXAOrvnv1ZyYpNQPL/ziFC/8+Qnp2Dho6VsXUPt6wszQtVI6IiIg+3PscXvS+xGIxxGKxQnnNzMygrq6OuDj537Hi4uJgZWVVYtnFixdjwYIFOHnyJOrVq1di3mrVqsHMzAwPHjxAmzZtFGpbaan8BFVHRwc6Ojrl9ryCggKIRCKoqX2cq59zc3OhpaVV0c0oUXn/TMvLNyO6oN2ADvhl4grEP41Dz4l98L+tQZjoORZ5krxiyz29+wRz+gbJPkvzCy+ZOLn9OH5ful32OTe76P0HqsZtZAe4DGyLY/6/Iu1pAppO6o6uv03B5jZTUFDMmFRp4oQbm08g7uYjiNTV0WxyT1mZ/GwJNHTE6PrbFCTcjsYe3x8AAE0ndUenjROxo9NMQBDKsYeKO3r5Xyz+/QSmfdsOztVssO3kZYxcvgMH5o5EJUO9QvmXjuqBvDeWz6RkZKPnrLX42s0JACAIAsb/vBsa6mpYPqYn9HXE2HL8IoYv+Q1754yArli1/x0gIiKisqOlpQVXV1eEhITIAlSvDzwaM2ZMseUWLVqEefPm4dixY3Bzc3vnc549e4akpCRYW1uXVdMLKdUszMPDA2PGjMGYMWNgZGQEMzMzTJ8+HcL//0IoEomwf/9+uTLGxsYIDg6WS7tz5w6aNm0KbW1tfPHFFzhz5kyxzyxqOeiff/6JRo0aQVtbG2ZmZujSpYtCbX/y5AkmTJgge4fPm/UfPHgQderUgVgsRnR0NMLDw/H111/DzMwMRkZGaNWqFa5duyZXp0gkwvr169GlSxfo6urC0dERBw8elN1PTk5G3759YW5uDh0dHTg6OmLTpk0A/lvuvHPnzhLH4syZM2jcuDHEYjGsra0xdepU5Ofny/VrzJgxGD9+PMzMzODl5QV7e3sAQJcuXSASiWSf36U04xodHY1OnTpBX18fhoaG6Nmzp9w3Njdu3EDr1q1hYGAAQ0NDuLq64sqVK3Jj/trMmTPh4uKCrVu3wt7eHkZGRvD19UV6erosT3p6Ovr27Qs9PT1YW1tj2bJl8PDwUKlouM/gjti76ndcOXEZ0Xee4Gf/FTCxMEWjtk1KLFeQL0VqQorsSk9OL5QnN1silyc7I1tZ3ShTDQd74/LKA3h04hoS7zzF0QlroGdhjOptXYsts6//Itzecw5J954jMTIaxyf+CsMqZrB0tgcAVHZzhGEVcxyfuBZJd58h6e4zHPP/FZb1HFC1WZ1y6lnpbT1xCV1bNEDn5i6oXtkc0771gbaWJvafjygyv5G+DsyM9GXXxduPoK2lKZugPol7iZuPnuP7b33whUNl2FtVwrRvfZCTl4+jl/4tx54RERF9RspxD2pp+fv7Y926ddi8eTMiIyMxcuRIZGZmYuDAgQCA/v37yy0RXrhwIaZPn46NGzfC3t4esbGxiI2NRUZGBgAgIyMDAQEBuHjxIqKiohASEoJOnTqhRo0a8PLyKn0DFVTqMOHmzZuhoaGBy5cvY8WKFVi6dCnWr19fqjoCAgIwceJEXL9+He7u7ujYsaPCp0EdPnwYXbp0gY+PD65fv46QkBA0btz4neX27t2LKlWqYPbs2bJ3+LyWlZWFhQsXYv369fj3339hYWGB9PR0+Pn54fz587h48SIcHR3h4+MjN2kCgFmzZqFnz564efMmfHx80LdvX7x8+RIAMH36dNy+fRt//fUXIiMjsXr1apiZmSk8Fs+fP4ePjw8aNWqEGzduYPXq1diwYQPmzp0rV8fmzZuhpaWFsLAwrFmzBuHh4QCATZs2ISYmRva5rMZVKpWiU6dOePnyJc6cOYMTJ07g0aNH6NWrlyxP3759UaVKFYSHh+Pq1auYOnUqNDU1i33+w4cPsX//fhw6dAiHDh3CmTNnsGDBAtl9f39/hIWF4eDBgzhx4gTOnTtX6AuDimRhawkTC1PcOn9TlpadnoUHEffg2LBWiWWtHKyx+vJG/HRuDcaumIBKlc0K5WneuSXWXd+CxcdXoPfkb6GlrfrRMaOq5tCzMEb0+X9kabnp2YiNeIjKro4K16NloAsAyEnJBABoiDUBQUBB7n8R2AJJHgSpgMqNSh7ripKXX4DIJzH4so6DLE1NTYQvnexx89HzEkr+Z9/5CHg3riuLjOb9f6RdrKkuV6eWhjquP3hahq0nIiKij0GvXr2wePFizJgxAy4uLoiIiMDRo0dlBydFR0fLzYFWr16N3NxcdO/eHdbW1rJr8eLFAAB1dXXcvHkT33zzDWrWrInBgwfD1dUV586dU3jp8fso9RJfW1tbLFu2DCKRCLVq1cKtW7ewbNkyDB06VOE6xowZg27dugF4NTBHjx7Fhg0bMHny5HeWnTdvHnx9fTFr1ixZWv369d9ZztTUFOrq6jAwMCi0DjsvLw+//PKLXD1fffWVXJ61a9fC2NgYZ86cQYcOHWTpAwYMQO/evQEAP/zwA3766SdcvnwZ3t7eiI6ORoMGDWTh8qIimSWNxS+//AJbW1usWrUKIpEItWvXxosXLzBlyhTMmDFDtgzZ0dERixYtKlS3sbHxO9ecv1aacQ0JCcGtW7fw+PFj2NraAgC2bNmCunXrIjw8HI0aNUJ0dDQCAgJQu3ZtWRtLIpVKERwcDAMDAwBAv379EBISgnnz5iE9PR2bN2/G9u3bZWvdN23ahMqVK7+zXxKJpNBx3AVCAdRF6sWUeD/GFsYAgNTEFLn01MRUGJsX/66oBxH3sHriT3jx6DlMLEzQbbwvZu3+AZPajkNOZg4AIOzAWSQ+j8fLuGTYOdmhz9T+qFzdBkuGLyzTPpQ1XXNjACi0LzQrMQ265kaKVSISwWPmt3gefhdJ954BAGKuPUBelgTNA30RtvB3QCRC86m9oKahDr3//zmomuSMLBRIhUJLeSsZ6uNx7Lu/nLv16DkePE/ATL///u2xt6oEa1ND/LT3NKb384GOWAtbT1xCXHI6ElIzyrwPREREVL57UN/H69WuRXn7YKOoqKgS69LR0cGxY8fKqGWKK3UE9csvv5QtjwUAd3d33L9/v1RHDbu7u8v+W0NDA25uboiMjFSobERERJlvyNXS0iq0ITguLg5Dhw6Fo6MjjIyMYGhoiIyMDERHyx9a8mY5PT09GBoaIj4+HgAwcuRI7Ny5Ey4uLpg8eTL+/vvvQs8uaSwiIyPh7u4uN97NmjVDRkYGnj17JktzdS1+uaSiSjOukZGRsLW1lU1OAaBOnTowNjaWtd3f3x9DhgyBp6cnFixYgIcPH5ZYp729vWxyCgDW1taycXz06BHy8vLkIrpGRkaoVevd0bKijueOTL2vUD9L0rxzS2y+vUN2qWu833buiNBruHjkb0TfeYIbZyOwYMAc6Bnqwb1Dc1mekB3HceNsBJ7efYLz+8/iZ/8VaOztDsuqin35UF5qd26K0ZHrZZeaxod/CfDVXD9UqlkFR0b/LEvLfpmOQyN/QjXPBhhzZz1G/7sW2ka6iLv1GIJUxf9f4z3tOx8BRxsLuQOVNDXUsXRUDzyJe4kW3y1Bk1ELEH4nCs2/qA61N/7NICIiIvqYlOkhSSKRSLYf9bW8vOIPh3kfyjhcR0dHR24SCAB+fn5ISkrCihUrYGdnB7FYDHd3d+Tm5srle3vZqkgkgvT/f0lu164dnjx5giNHjuDEiRNo06YNRo8eLQublxU9vcIHrJRWWY/rzJkz0adPHxw+fBh//fUXgoKCsHPnzmL3tZY0jh8iMDAQ/v7+cmmDvuj7wfVeOXEZ96/fk33W1HrVfiMzY6TEJ8vSjcyMEHX7scL1ZqVlIubxC1jZFT/5fPD/z7Wyt0JctGLvtSoPD09cQ8z1/76I0BC/+udF18wQmfEpsnRdM0Mk3C58Ou3bWs/uj2ptGuD3HnOREftS7l70uX+wqcVEaJvoQyiQQpKWhWFXViE1OqFsOlPGTPR1oa4mQlJaplx6UloGzIz0SyybJcnFsfDbGNWpVaF7deyt8XvQUKRn5SCvoACmBnroO28j6tor7+ACIiKiz5mqR1A/BaWOoF66dEnu8+v9merq6jA3N5db13z//n1kZWUVquPixYuy/87Pz8fVq1fh5OSk0PPr1auHkJCQ0jYbwKtIqaKR3rCwMIwbNw4+Pj6oW7cuxGIxEhMTS/1Mc3Nz+Pn54bfffsPy5cuxdu1aufsljYWTkxMuXLggN+kPCwuDgYEBqlSpUuJzNTU1SxXVLs24Ojk54enTp3j69L99brdv30ZKSgrq1PnvkJqaNWtiwoQJOH78OLp27So7IKq0qlWrBk1NTbm9tKmpqbh3714JpV4Ri8UwNDSUu8pieW9OZg7insTKrmf3nyI5/iWcm/0XUdfR10ENl5q4f+2uwvWKdbVhaWeF5DcmuW+zr/tqH2NJeSpCXmYOUp/Eya6ke8+RGZ8C22Z1ZXm09HVg5VIdL66WHMVuPbs/ani7YY/vD0h7WvykMyc5A5K0LNg2rQNdM0M8OqE6+5LfpKmhDic7a1yK/O/LCqlUwKU7UahXzGtmXjtxJRK5eflo/+UXxeYx0NWGqYEensS9xO2oGHi41CyzthMRERGVp1JHUKOjo+Hv74/hw4fj2rVrWLlyJZYsWQLg1b7NVatWwd3dHQUFBZgyZUqRB+P8/PPPcHR0hJOTE5YtW4bk5GQMGjRIoecHBQWhTZs2qF69Onx9fZGfn48jR45gypQp7yxrb2+Ps2fPwtfXF2KxuNCBRW9ydHTE1q1b4ebmhrS0NAQEBJQ6yjhjxgy4urqibt26kEgkOHToUKGJeEljMWrUKCxfvhxjx47FmDFjcPfuXQQFBcHf3/+dr8Gxt7dHSEgImjVrBrFYDBOT4vdBAqUbV09PTzg7O6Nv375Yvnw58vPzMWrUKLRq1Qpubm7Izs5GQEAAunfvDgcHBzx79gzh4eGyvbalZWBgAD8/PwQEBMDU1BQWFhYICgqCmppaoch3RTqy4U90GdsDMY9fIP5pPHpN7IPk+JcIP/7flzrTts9G+LGLOLb5CADg2+8H4OrJcCQ+T4CJpQl6TOgNaYEUYQfPAQAsq1qhWeeWuH7qKjJS0lG1th36zxiM2xf/QfSdJxXSz9K4tuEomozrjJSoOKRGx6PppO7IjE/Bw+NXZXm67QjEg6NXcGPzCQDAV3MHoFYndxwcsgy5mTmy/aqStCzZq2nq9GiJlw+eI/tlOqwbOsJj5re4tv6o3LtSVU2/r5tg+saDqGtnjS8cbPDbyUvIluShc7NXe72/33AAFsYG+K6b/P73fecj0LpBLRjr6xaq8/iV2zDR14V1JSPcfxaPRTuPo3WDWmhat3q59ImIiOhzwwiq8pV6gtq/f39kZ2ejcePGUFdXx3fffYdhw4YBAJYsWYKBAweiRYsWqFy5MlasWIGrV68WqmPBggVYsGABIiIiUKNGDRw8eLDEyeKbPDw8sHv3bsyZMwcLFiyAoaEhWrZsqVDZ2bNnY/jw4ahevTokEkmh5chv2rBhA4YNG4aGDRvC1tYWP/zwAyZNmqTQc17T0tJCYGAgoqKioKOjgxYtWmDnzp1yeUoaCxsbGxw5cgQBAQGoX78+TE1NMXjwYEybNu2dz16yZInsqGkbG5t3boIuzbiKRCIcOHAAY8eORcuWLaGmpgZvb2+sXLkSwKsTv5KSktC/f3/ExcXBzMwMXbt2lTuAqbSWLl2KESNGoEOHDjA0NMTkyZPx9OlTaGtrv3edZe3gmn0Q62pj2PxR0DXUw90rkZjff7bcO1Atq1rBwMRQ9rmSVSWMWzkRBsYGSHuZirvhkZjWeQrSX746WCg/Lx/OzerBZ1AHiHW0kRSTiMt/XcDelb+Xe//ex5XVh6CpI4bn/EEQG+rixZV72Ntvkdw7UI2qWkDH9L/9x/X7ewIAeu6W/3N+zP9X3N7zauJuWt0azaf0hLaxPtKeJeDyyoO4tv6vcujR+/NuXBfJGVn45cAZJKZlopatJX4Z3xuV/n+Jb2xSaqG9o1GxSbh+/ynWTOhTZJ0JKRlYvOsEktIyYW6kjw5N62F4hxZK7wsRERGRsoiEkmZpb/Hw8ICLiwuWL1+uxCZ9HqKiouDg4IDr16/DxcWlopvz0cnMzISNjQ2WLFmCwYMHl6psL7vOymnUR+pLoeQ9kJ+jkduU926vj5V2i34V3QQiIqIKF+fhUW7Psnzr1N3PRZkekkSkLNevX8edO3fQuHFjpKamYvbs2QCATp06VXDLiIiIiIiorJT6kCRVde7cOejr6xd7fe7q1q1b7Nhs27atopunkMWLF6N+/frw9PREZmYmzp07p/DScCIiIiKiDyVIy+/6XJUqgvr2y11ViZubGyIiIiq6GQqzt7cvcQ9sWTty5Eixr/yxtLQst3a8rwYNGhS5n5mIiIiIiD4dn8wSXx0dHdSoUaOim6Gy7OzsKroJREREREQfNUGqOm+Q+FR9Mkt8iYiIiIiI6OP2yURQiYiIiIiIlOlz3htaXhhBJSIiIiIiIpXACSoRERERERGpBC7xJSIiIiIiUoAg8JAkZWMElYiIiIiIiFQCI6hEREREREQK4CFJyscIKhEREREREakERlCJiIiIiIgUIEi5B1XZGEElIiIiIiIilcAIKhERERERkQIEoaJb8OljBJWIiIiIiIhUAiOoRERERERECuAeVOVjBJWIiIiIiIhUAiOoRERERERECmAEVfkYQSUiIiIiIiKVwAgqERERERGRAniKr/IxgkpEREREREQqgRFUIiIiIiIiBXAPqvIxgkpEREREREQqgRFU+uzcyH5e0U1QKZa61Sq6CSpHyEqt6CaonMx5/Su6CSpH7/stFd0EIiIqZ4LACKqyMYJKREREREREKoETVCIiIiIiIlIJXOJLRERERESkAEFa0S349DGCSkRERERERCqBEVQiIiIiIiIFSHlIktIxgkpEREREREQqgRFUIiIiIiIiBfA1M8rHCCoRERERERGpBEZQiYiIiIiIFCBIGUFVNkZQiYiIiIiISCUwgkpERERERKQAQajoFnz6GEElIiIiIiIilcAIKhERERERkQK4B1X5GEElIiIiIiIilcAIKhERERERkQKkfA+q0jGCSkRERERERCqBEVQiIiIiIiIFCIygKh0jqERERERERKQSGEElIiIiIiJSAN+DqnyMoBIREREREZFKYASViIiIiIhIATzFV/kYQSUiIiIiIiKVwAkqERERERERqQROUD9CUVFREIlEiIiIqOimlEpoaChEIhFSUlIAAMHBwTA2Nq7QNhERERERKUoQROV2fa64B7WMeXh4wMXFBcuXLy+T+gYMGICUlBTs379flmZra4uYmBiYmZmVyTMqSq9eveDj41PRzVCqsVOGo8e3nWFoqI9r4TcxK2ABnjx+Wmx+NTU1jAkYhm+6e8PMohLi4xKxb+chrF66oRxbXbZ8JvSAe+820DHUw+Mrd/H7tPVIiIpVqKznyE74ZkofhG48gr2zNxeZZ0TwVNTxaIB1w37EreNXyrLpZW7n2ZvYfOoaktKyUNPGDFO6t4SznVWReQf/tBdXHzwvlN68jh1WjfgGALD6yCUcu3YPsSkZ0FRXRx1bc4zp4A5n+6LrVEUarm2g+aUPRPpGkMY9Re7xrZC+ePTOcup1mkC7y2jk370KyZ4V/93QFEPrq55Qr+kKkY4+hJQE5F05jvxrp5XYCyIiIiorjKB+hNTV1WFlZQUNjY/7+wUdHR1YWFhUdDOUZsjY/ug3pBdmBsxHz3YDkZ2ZjfW/r4SWWKvYMkPH9kfvAd0wJ/BHtG/eE0tmr8SQMf3Qb0ivcmx52fEc8Q1aDmyH379fj6Wdv0dudg5GbvkfNMSa7yxbtV51NOvjieeRT4rN4zHY56M57v3YtXtYsu8chns3xo4AX9S0McOoXw7iZXpWkfmXDvbBybmDZNeewD5QVxPh6waOsjx2FsaY2qMV9kztg03ju6GyqSFG/nIAL9Ozy6tbH0TdqQm0PPsg79x+ZG+YAWl8NLR9AwBdgxLLiYzMoNWmNwqi7xS6p/V1H6hXqwfJgTXI/nUq8sKPQcurP9QdGyirG0RE9BkRhPK73sfPP/8Me3t7aGtro0mTJrh8+XKJ+Xfv3o3atWtDW1sbzs7OOHLkyFv9FTBjxgxYW1tDR0cHnp6euH///vs1TkGcoJahAQMG4MyZM1ixYgVEIhFEIhGioqLwzz//oF27dtDX14elpSX69euHxMREWbk9e/bA2dkZOjo6qFSpEjw9PZGZmYmZM2di8+bNOHDggKy+0NDQQkt8Xy+dDQkJgZubG3R1ddG0aVPcvXtXrn1z586FhYUFDAwMMGTIEEydOhUuLi4K961z586YNWsWzM3NYWhoiBEjRiA3N1eWRyKRYNy4cbCwsIC2tjaaN2+O8PDwYussaonvn3/+iUaNGkFbWxtmZmbo0qULAGD27Nn44osvCtXh4uKC6dOnK9SH8tZ/WG+sWbYRp46exb3bDzBlTBAsLM3g2a5VsWUaNKqHkKNncOZkGJ4/jcGxQ6cQFnoJzg3rlmPLy06rQT44vnIvbp24ghd3orHV/2cYWZqgXttGJZbT0hWj//Ix2DF1LbJSM4rMY1PHDl8N6YDtk1cro+llbuvpCHRtWhedv6yD6tammNazNbS1NLD/4u0i8xvpacPMUE92XbzzFNqaGmjrUkOWx8etFr6sVRVVzIxQw7oSJnZpgYycXNx/kVhknapGs4k38iNCkX/zHITEF8g9EgwhXwLN+sX/HYFIBHGnEcg7uxfS5IRCt9VtHJF/6zyk0XcgpCYi/3oopHHRUKtcTYk9ISIiqni7du2Cv78/goKCcO3aNdSvXx9eXl6Ij48vMv/ff/+N3r17Y/Dgwbh+/To6d+6Mzp07459//pHlWbRoEX766SesWbMGly5dgp6eHry8vJCTk6O0fnCCWoZWrFgBd3d3DB06FDExMYiJiYGBgQG++uorNGjQAFeuXMHRo0cRFxeHnj17AgBiYmLQu3dvDBo0CJGRkQgNDUXXrl0hCAImTZqEnj17wtvbW1Zf06ZNi33+999/jyVLluDKlSvQ0NDAoEGDZPe2bduGefPmYeHChbh69SqqVq2K1atL94t9SEiIrI07duzA3r17MWvWLNn9yZMn448//sDmzZtx7do11KhRA15eXnj58qVC9R8+fBhdunSBj48Prl+/jpCQEDRu3BgAZOPz5oT3+vXruHnzJgYOHFiqfpSHKnY2sLA0w99n//vWKiM9Ezev/QsXt3rFlrsefhPuLRrBvlpVAECtuo5o2KQ+zob8rfQ2l7VKthYwsjDB3bBbsrSc9Gw8iXgA+4aOJZQEeswZjH9PX8e9N8q+SVNbC34rxmH3jI1IT0gt03YrQ15+ASKfxqNJLVtZmpqaCE1q2eLmY8WWO++/eBterjWhU0z0OS+/AH/8/Q/0dbRQ0+YjWP6vpg41a3sUPP73jUQBBY9vQ61KjWKLabboDCErDfk3zhZ5v+D5fag7NoDIwOTVY+ycoGZqhYJH/xSZn4iIqDSkgqjcrtJaunQphg4dioEDB6JOnTpYs2YNdHV1sXHjxiLzr1ixAt7e3ggICICTkxPmzJmDhg0bYtWqVQBeRU+XL1+OadOmoVOnTqhXrx62bNmCFy9eyG0/LGsf9xpRFWNkZAQtLS3o6urCyurVHrC5c+eiQYMG+OGHH2T5Nm7cCFtbW9y7dw8ZGRnIz89H165dYWdnBwBwdnaW5dXR0YFEIpHVV5J58+ahVatXkYepU6eiffv2yMnJgba2NlauXInBgwfLJnMzZszA8ePHkZFRdHSqKFpaWti4cSN0dXVRt25dzJ49GwEBAZgzZw6ys7OxevVqBAcHo127dgCAdevW4cSJE9iwYQMCAgIUar+vr6/cpLd+/foAgCpVqsDLywubNm1Co0avom+bNm1Cq1atUK1a8ZERiUQCiUQilyYVpFATKfe7GXOLSgCApPgkufTEhCSY/f+9oqz9aTP0DPRx5O/dKCiQQl1dDct/WI1DfxxVanuVwdDcGAAKTSDTE1Jl94rSsGNT2NZ1wOJO/ys2T9cZfnh89R5unVDtPaevJWdmo0AqoJKBrlx6JQNdRMUlv7P8rSexeBCThKA+XxW6d/afx5gSfAw5eXkwM9TDmlGdYaKvU2ZtVxaRrgFEauoQMtPk0oXMVKhVsi6yjFqVmtCo3wrZ66cVW2/usa3Q8hkE3XErIBTkA4KA3CMbIX16t9gyREREqqio32PFYjHEYnGhvLm5ubh69SoCAwNlaWpqavD09MSFCxeKrP/ChQvw9/eXS/Py8pJNPh8/fozY2Fh4enrK7hsZGaFJkya4cOECfH1937drJWIEVclu3LiB06dPQ19fX3bVrl0bAPDw4UPUr18fbdq0gbOzM3r06IF169YhOfndv7AWpV69/yJz1tavfsF7HdK/e/euLBr52tuf36V+/frQ1f3vF2x3d3dkZGTg6dOnePjwIfLy8tCsWTPZfU1NTTRu3BiRkZEK1R8REYE2bdoUe3/o0KHYsWMHcnJykJubi+3bt8tFiYsyf/58GBkZyV0vs2IUak9pdOjmjauPz8iu990f3K6TJzp288akEdPQzfNbTB07E4NG9UXnXu3LuMVlz61Tc/z472bZpa6pXuo6jK0roesMP2wZvxL5krwi83zh6QpH97r4Y3bwB7b447H/wm04Vq5U5IFKjRyrYNcUX2we3wPNnOwwedPRYve1ftS0tCHuNBy5RzYC2cV/sabh9jXUbaoj5/elyN4YhNyQHdDy6g81+49zmTwREamW8jzFt6jfY+fPn19kuxITE1FQUABLS0u5dEtLS8TGFr1aKzY2tsT8r/+3NHWWBUZQlSwjIwMdO3bEwoULC92ztraGuro6Tpw4gb///hvHjx/HypUr8f333+PSpUtwcHAo1bM0Nf9b+icSvVoWIJVKP6wD5UhHp+SoT8eOHSEWi7Fv3z5oaWkhLy8P3bt3L7FMYGBgoW+G3Kq3/uC2vu300bO4ee2/JYRaWq8OQqpkUQkJb0RRzcwrIfKfe8XWExD0Hdat3Iwj+08AAO5FPkTlKtYYNm4A9u86XObtLku3Tl5BVMR/m+Y1tF79eTQwN0JaQoos3cDcCM9uRxVZh62zAwzNjRFwaIEsTV1DHdUbO6FFfy/41+yLmk2/gJmdJRbe3CRXdvDqiXgYHomVvrPLrlNlxERPB+pqIiS9NXFMSs+C2VtR1bdlS/Jw7Np9jPRpUuR9HbEmqpobo6o5UM/BCh3nbMG+C7cxuK1bmbVfGYSsdAjSAoj0DOXSRXpGEDILL9tWM7GAmrE5xD0nvJH51b9zuoGbkL16CoSMZGi17gHJnhUoeHADAJAf/xRqllWh+WU7SKL+LVQvERGRqirq99iioqefGk5Qy5iWlhYKCgpknxs2bIg//vgD9vb2xUbVRCIRmjVrhmbNmmHGjBmws7PDvn374O/vX6i+91WrVi2Eh4ejf//+srSSDjAqyo0bN5CdnS2bSF68eBH6+vqwtbWFmZkZtLS0EBYWJluqnJeXh/DwcIwfP16h+uvVq4eQkJBi95RqaGjAz88PmzZtgpaWFnx9fd85qS1qGYQylvdmZmYh87H85CM+LhHuLRrhzv9PSPX09VCvYV3sCN5TbD06OuJCXypIC6RQU1P9d2FJMnMgyZTfMJ8an4yaTZ3x/Park3i19XVg51ID5387UWQd98L+wfy2k+TS+vw4EvEPn+PkmoMQpAJOrN6PCztPyeUJPL4Ye+dsxj8nr5Zhj8qOpoY6nGwtcPneM3xVrzoAQCoVcPnuU/i2LH5PMgAcj3iA3PwCtG9US6FnCVIBufkf/m+G0kkLII2Jgrp9XRTcu/b/iSKo29dB/pWThbMnxiBrbaBcmlar7hBpaUNy4jcIaUmAhiZE6hqFjz6USmVf2hEREX2I99kb+r6KW85bFDMzM6irqyMuLk4uPS4urtitglZWViXmf/2/cXFxstWZrz8retDq++AEtYzZ29vj0qVLiIqKgr6+PkaPHo1169ahd+/emDx5MkxNTfHgwQPs3LkT69evx5UrVxASEoK2bdvCwsICly5dQkJCApycnGT1HTt2DHfv3kWlSpVgZGT0Xu0aO3Yshg4dCjc3NzRt2hS7du3CzZs3S9y/+bbc3FwMHjwY06ZNQ1RUFIKCgjBmzBioqalBT08PI0eOREBAAExNTVG1alUsWrQIWVlZGDx4sEL1BwUFoU2bNqhevTp8fX2Rn5+PI0eOYMqUKbI8Q4YMkY1NWFhY6QahnG1ZuwMjJgxC1KOneB79HOOmjkB8XCJO/nVGlmfTnl9w8shpbNu4GwBw+vh5jBg/EDHPYvHg7iM4OdfCgBF98MeOgxXVjQ9yZuMReI3tgoSoGCQ9jUf7ib2QGpeMm8f/+3Jk9LZpuHksHOe2HIMkMwcx9+TfE5ubnYPMlAxZenpCapEHIyW/SMTLZ4VPdVUV/Vq7YPpvJ1HH1gJf2FliW2gEsnPz0alJHQDAtK3HYWGkj3HfyB+Etv/Cv2hdrxqM9eS/jMmW5GHd8XB4fFENZka6SMnIwa5zNxGfmomvGxR/yJAqybt0FOJvhkIa8xgFLx5Bs3FbiDTFyLv56gAkrY7DIKQnIy90N1CQByFB/r2wQs6rL4Vk6bkFKHgSCa2vfCHJy4WQmgh1u9rQcG6O3JPby7VvRERE5UlLSwuurq4ICQlB586dAbxaSRkSEoIxY8YUWcbd3R0hISFywaQTJ07A3d0dAODg4AArKyuEhITIJqRpaWm4dOkSRo4cqbS+cIJaxiZNmgQ/Pz/UqVMH2dnZePz4McLCwjBlyhS0bdsWEokEdnZ28Pb2hpqaGgwNDXH27FksX74caWlpsLOzw5IlS2QHDQ0dOhShoaFwc3NDRkYGTp8+DXt7+1K3q2/fvnj06BEmTZqEnJwc9OzZEwMGDHjnu5He1KZNGzg6OqJly5aQSCTo3bs3Zs6cKbu/YMECSKVS9OvXD+np6XBzc8OxY8dgYmKiUP0eHh7YvXs35syZgwULFsDQ0BAtW7aUy+Po6IimTZvi5cuXaNKk6CWPqmL9yi3Q0dXB7CX/g6GhPq5evoGhvcYhV/Lfq3mq2tvApJKx7PPcwB8xbuoIzFg4BZXMTBAfl4hdW/bilyXrK6AHH+7kmoPQ0hHDd/4w6Bjq4lH4Xaz2my+3v9TMzhL6piW/9/JT4NWwJpIzsrH6yCUkpmWiVhVz/DLyG1QyfLXENyY5o1CULyouGdcfxWD1qE6F6lNTEyEqLhkTLx9BSkY2jPV0ULeqBTZ+1w01rIs/iEuVFEReQq6eATRbdYWWnhGkcdHI2fkj8P8HJ6kZVYK0lC+Ck+z7BZqte0DceQRE2voQUhORG7oH+ddOvbswERHRO6jy69f9/f3h5+cHNzc3NG7cGMuXL0dmZqZsdWL//v1hY2Mj28f63XffoVWrVliyZAnat2+PnTt34sqVK1i7di2AV6s8x48fj7lz58LR0REODg6YPn06KleuLJsEK4NIED6W19xTWfv6669hZWWFrVu3vjPvgAEDkJKSotQjpRUhCAIcHR0xatSoQmvyFVXbouR3cH5u2ury/ZBvW/hri4pugsqRXlH8y6zPhd73Wyq6CUREVM4uVu5abs/68sXeUpdZtWoVfvzxR8TGxsLFxQU//fSTLKjj4eEBe3t7BAcHy/Lv3r1btjrS0dERixYtgo+Pj+y+IAgICgrC2rVrkZKSgubNm+OXX35BzZo1P7h/xWEE9TORlZWFNWvWwMvLC+rq6tixYwdOnjyJEyeK3guoihISErBz507Exsaq5LtPiYiIiOjTVp57UN/HmDFjil3SGxoaWiitR48e6NGjR7H1iUQizJ49G7Nnl98hlJygfiZEIhGOHDmCefPmIScnB7Vq1cIff/whe6+Rvr5+sWX/+uuv8mpmiSwsLGBmZoa1a9cqvGyYiIiIiIg+HpygfiZ0dHRw8mThkzFfi4iIKPaejY0NWrSo+CWPXI1ORERERBVJUPEI6qeAE1QCANSo8XGc+klERERERJ8uTlCJiIiIiIgUIH13FvpAahXdACIiIiIiIiKAE1QiIiIiIiJSEVziS0REREREpAABPCRJ2RhBJSIiIiIiIpXACCoREREREZECpHzrodIxgkpEREREREQqgRFUIiIiIiIiBUi5B1XpGEElIiIiIiIilcAIKhERERERkQJ4iq/yMYJKREREREREKoERVCIiIiIiIgVIK7oBnwFGUImIiIiIiEglMIJKRERERESkAO5BVT5GUImIiIiIiEglMIJKRERERESkAO5BVT5GUImIiIiIiEglMIJKRERERESkAEZQlY8RVCIiIiIiIlIJjKASEREREREpgKf4Kh8nqPTZOe+qW9FNUCkJD9IrugkqZ+/AixXdBJWTwfU2heT8+m1FN0HlfBf9W0U3gYiIPnKcoBIRERERESlAygCq0vE7cSIiIiIiIlIJnKASERERERGRSuASXyIiIiIiIgVIeUiS0jGCSkRERERERCqBEVQiIiIiIiIFCBXdgM8AI6hERERERESkEhhBJSIiIiIiUoC0ohvwGWAElYiIiIiIiFQCI6hEREREREQKkIp4iq+yMYJKREREREREKoERVCIiIiIiIgXwFF/lYwSViIiIiIiIVAIjqERERERERArgKb7KxwgqERERERERqQRGUImIiIiIiBQg5SG+SscIKhEREREREakERlCJiIiIiIgUIAVDqMrGCCoRERERERGpBEZQiYiIiIiIFMD3oCofI6hERERERESkEjhBJSIiIiIiIpXAJb5EREREREQK4GtmlI8RVKoQUVFREIlEiIiIqOimEBERERGRimAEVQXY29tj/PjxGD9+fJnVGRUVBQcHB1y/fh0uLi4KlRkwYABSUlKwf//+MmtHcWxtbRETEwMzMzOlP6u8aHfoDJ3uvlAzMUX+o4fIXL0C+ffuFJlX7N0B2m28oG7nAADIf3AXWcHris2vN8YfOu07IePXlcjZv0dpffgQJn07wHRIN2iYm0By5zFiZ69Gzs17xeY38G4O8/H9oFnFErlRLxD/40Zknrkil0erui0sAgZCt7EzROrqkDyIxrMx85AfkwAAUDczgeWUwdBr5gI1PV3kPn6GxNW7kH4sTKl9/VD1ArqhRp/W0DTURcKVewifugnpj+OKzW/RpBacRrWHqbMDdK1McGbQMjw7elUuj7aZIVy+94V1K2doGeki/uJdXJm2ucR6VYnbpG6o3bs1xEa6iA2/h3P/24S0EtruMrojHNo1gnENaxTk5CL2yn1c+mEXUh/FFJm/3dYAVG1dH8cGL0PUsatF5lElX/p3wxd9WkNsqIsXV+7h9P82ISWq+PFwG90RNbwbwaS6NfJzchFz9T7Oz9+FlDfGQ9fcCM2/742qzb+Alr42kh/GInzVATz4K7w8ukRE9EmQVnQDPgOMoFKp5eXlfXAd6urqsLKygobGp/EdiVbL1tAbNhpZ2zYjZexQFDx+CMO5iyEyMi4yv2Y9F0hCQ5A6dTxS/UdBmpAAw3mLoVap8IRdq2kLaNaug4LEBCX34v0Z+LSExf+GInHVdjzuPBY5kY9QdeMcqJsaFZlfp4ETbJZNQcqe43jcaSwyTl6A7S/TIXa0k+XRrGoFux0/IvfRMzz5dgoedRyFxJ93QJDkyvJU/nEitBxs8HTEbDzqMAppx/+GzYqpENeppvQ+v686ozug1qC2uDx1I451CEJ+lgStt0+Bmliz2DIaumKk/BuN8P9tLjZPy40TYGBngTMDl+FI22nIfJaINrsCoa4jVkY3ylT9UR3wxcC2OBe4Efs6vhqT9r9NgXoJY1LZ3Qn/bj6B/d/MxKHeC6GmqYH226dAo4j+Og/xBoSP59xF15Ed4DKwLU4FbsSub4KQlyVB53eMh00TJ9zYfAK7Os/Evr4LoaahgS6/yY9H22UjYFLNGn8OXorf2gbiwdFwtPtlLMzr2hVbLxERUXnjBFUBUqkUixYtQo0aNSAWi1G1alXMmzcPAHDr1i189dVX0NHRQaVKlTBs2DBkZGTIyg4YMACdO3fG4sWLYW1tjUqVKmH06NGySZ6HhweePHmCCRMmQCQSQSR6tbA9KSkJvXv3ho2NDXR1deHs7IwdO3Yo3C4Hh1eRuQYNGkAkEsHDw6PEPs6cORObN2/GgQMHZO0IDQ2VLcXdtWsXWrVqBW1tbWzbtu2D2/f2Et/Q0FCIRCKEhITAzc0Nurq6aNq0Ke7evStX59y5c2FhYQEDAwMMGTIEU6dOVThCrEw6XXoi569DkJz4CwXRT5CxcgkESQ602/oUmT9j0VzkHN6PgkcPUPAsGhkrFgFqatB0cZXLp1bJDHojxyF90VygIL88uvJeKg3qgpRdR5H6xwnkPniK2BmrIM2WwLh72yLzm/p1Qsa5q3i5/g/kPnyKhOVbkXP7IUz6dZTlMZ/gh8wzVxC/aCMktx8hLzoWGacuoeBlqiyPbgMnvNz6J3Ju3kPe01gk/bITBWmZ0KnrqPQ+v6/aQ7zxz4oDeHbsGlIin+LCuDXQtTSGrbdrsWVenL6JG4v24NnRK0XeN6hmBXM3R1yeugkvbzxC+sMYXJ66CeramrDv4q6srpQZ58HeuPbTATw5fg0vI5/i9PhXY2LvVfyYHPl2Ee7tPofke8/xMjIaoRN+hUEVM5jXs5fLV6lOVdQb7oPQieuU3Iuy02CwNy6vPIBHJ64h8c5THJ+wBnoWxqjetvjxONB/ESL3nMPLe8+RGBmNExN/hWEVM1g428vyWLs64kbwccTdeIS06ASErzwASVomLJwdyqFXRESfBqEcr88VJ6gKCAwMxIIFCzB9+nTcvn0b27dvh6WlJTIzM+Hl5QUTExOEh4dj9+7dOHnyJMaMGSNX/vTp03j48CFOnz6NzZs3Izg4GMHBwQCAvXv3okqVKpg9ezZiYmIQE/NqOVZOTg5cXV1x+PBh/PPPPxg2bBj69euHy5cvv7NdAGT5Tp48iZiYGOzdu7fEPk6aNAk9e/aEt7e3rB1NmzaV3Z86dSq+++47REZGwsvL64PbV5zvv/8eS5YswZUrV6ChoYFBgwbJ7m3btg3z5s3DwoULcfXqVVStWhWrV68usb5yoaEBDceayIt4Y9mgICAv4io0nOoqVIVILIZIXQPS9LQ3EkXQn/Q9svfsREF0VNm2uSxpakC7bg1k/h3xX5ogIPPvCOg0qF1kEZ0GtZH593W5tIxzV6Hj8v/5RSLoezRCbtRz2G6cA8eL22G/Zxn0PeUnW1nXI2HYviXUjPQBkejVf4u1kHnpZln2sMzoVzWHjqUxYs/9I0vLS89G4vWHMHN9/0m1utarlQgFkjdWNwgCCnLzYd6o5nvXWx4MqppDz9IYz98Yk9z0bMRHPIRlKcZEy1AXAJCTkilL09DWQptVo3H++2BkJ6QWV1SlGFY1h56FMaLPy49HbMRDWJVmPAxejYfkjfGIuXofNTt+CbGRHiASoWbHL6Eh1sSzC5Fl1wEiIqIP9Gmsr1Si9PR0rFixAqtWrYKfnx8AoHr16mjevDnWrVuHnJwcbNmyBXp6egCAVatWoWPHjli4cKFsMmZiYoJVq1ZBXV0dtWvXRvv27RESEoKhQ4fC1NQU6urqMDAwgJWVley5NjY2mDRpkuzz2LFjcezYMfz+++9o3Lhxie0CAHNzcwBApUqV5Ootjr6+PnR0dCCRSIrMP378eHTt2lUu7UPaV5x58+ahVatWAF5Nitu3b4+cnBxoa2tj5cqVGDx4MAYOHAgAmDFjBo4fPy4XsX6bRCKBRCKRT5NKIVYru+9m1AyNXk0uk5Pl0qXJydCsUlWhOnQHjYD0ZSLyrv83ydXp0QeQFiDnwB9l1lZl0DAxhEhDHQWJ8v0vSEqBuLpt0WXMTFCQmCKfPzEFGuYmAAD1SsZQ19dFpWE9kLBsC+J/3AT9Fq6o8vP3iO43FVmXX/3y/nzcfNismIpaV36HkJcPaY4Ez0bPQV500fsQK5q2hTEAIDshTS49JyENOhZFL4dWROqDGGQ+S4RLYC9cnrIB+VkS1B7WDnqVK0HH0vgDWqx8uubGAIDsRPkxyU5Ig665gmMiEqHpzG8Rc/kuku8+kyW7z/wWsVfv48nxa2XVXKXT+//xyHprPLIS06BXivFoNfNbvAi/i6R7/43HkVEr4fPzGIy49SsK8vKRn52LQ0OXI/XJx7FPmYhIFfAUX+VjBPUdIiMjIZFI0KZNmyLv1a9fXzY5BYBmzZpBKpXKLU2tW7cu1NXVZZ+tra0RHx9f4nMLCgowZ84cODs7w9TUFPr6+jh27Biio6Pf2S5lcHNzK5f21atXT/bf1tbWACAbq7t376Jx48Zy+d/+/Lb58+fDyMhI7lrxMLpUbVI2nR59IG71FdJmTwPyXu2vVK9REzqduiFjyfwKbl3FEKm9+tc/PeQiXgbvhyTyEZLW7kbG6csw7v3fsmnz8f2gbqiPJ/0D8bjrd3i5cR9sVgRCXNO+glouz75LU/S8v152qWmov7vQexDyC3B28HIYVLdCj8i16PVwIyyb1sHzkAhAqlqLhGp0aYpBd9fLLjXNDx+T5vP8YFqrCkJG/yxLs/u6IWya1cHfQVs/uH5lqtW5KUZGrpddZfFnpPVcP1SqWQV/vTEeAOA+sfv/sXff8TWebQDHfyd7L5kISewRe9QotRU1a++tSm2lrVGKUkpLa+8abRVFa+9VM3YkxIiRIYnsecb7R+pwJCJIck7fXN/3k88r97mf51z30+TJuZ/rHpjbWbG16yw2t5qM34rdtPh5OAVKFX7n9xRCCCFyimRQX8PS0vKdz2FqqruwhUKhQK3Oeg2w7777jh9++IEFCxbg6+uLtbU1I0eOJDU1NcfiehMvdsJzM74Xr9Wz+bivu1ZZmThxIqNHj9Ypi+vY8q3Plxl1bAwalRIjR0edciNHR9RPo7I81rJDZyw7dSP2izGo7t3RlpuWr4DCwRHHdb9pyxTGJlgPGIpl24952qdLjrbhXSifxqJRqjB21m2/cQEHlE8yb78y4inGzg669Z0dUD55+vycaUpSbus+TEgJeoBV1fRh06ZF3HHq1ZqgD4eQ+m+9lJt3sapWDscerQidvCgnmvdOHu67SIRfkPb7Z0NxLV3sSA6P1pZbuNjx9Pq7PTiJunqP3U2+xNTWEiNTE1Ki4mi2aypRV+6+03lz2v19F9mS2TVxtiPxhWti6WJHZDauSZ1velG0cWV2dPiGhJDnP2+F6pTFrqgrfW8s06nfZNkIQs8GsLPjjHdsSc64s/8ioS9eD/P062H10vWwcrbjyY3XX48PpvXCu1FltnT8hvjQ59fDvqgrlfo2ZX3jz4kKfARAhH8wBWuUomLvJhz6YnUOtUgIIf6/ySq+uU86qK9RokQJLC0tOXjwIAMGDNB5rUyZMqxZs4aEhARtB+7kyZMYGRlRqlSpbL+HmZkZKpVKp+zkyZO0adOGHj16AOmdtMDAQMqWLfvauJ6dE8hw3jeN41XeNb63UapUKc6dO0evXr20ZefOZb09grm5Oebmuqt6pubg8F4AlEqUtwIxrVSV1NMn0ssUCkwrVSF5x7ZXHmb5cVcsu/Qg9qtxKG/pLgaVcnCfznBfALtvviPl0D5S9u3O2fjfVZqS5Ou3sa5VkfgDp9PLFAqsa1fi6fqdmR6S5HcT61qVeLrmT22ZdZ3KJF26qT1n0tVAzL11MzvmXoVIe5yeUTeysEgvfGl1Vo1aDQrDGH+jTEgmPiFZpywpLBq3uuW0HVITG0ucKxfj1rqDOfKeaXFJANh6u+FU0YfL3xnWtkRpCcmkvXRNEsKiKVS3HJH/dsBMbSxxrVSMG6+5JnW+6YV382rs6DiDuAe6q1z7/bQT/01HdMo6HfyW01//wv39uvOf9SktIZmYl69HeDSedcoR8e/1MLOxxL1SMa6uz/p6fDCtF8WaV+OPTjOIfel6mFik/03QvJRR16jUYGQYvy9CCCEEyBDf17KwsODzzz9n/PjxrFu3jqCgIP755x9WrlxJ9+7dsbCwoHfv3ly7do3Dhw8zfPhwevbs+drFgF7k5eXFsWPHePToEREREUB6B2///v2cOnUKf39/Bg8eTFjY83lCWcUF4OrqiqWlJXv27CEsLIyYmNcvEOLl5cWVK1cICAggIiIiy+1k3jW+tzF8+HBWrlzJ2rVruXXrFt988w1XrlzRZlr1KWnbb1g0b4l542YYexbFethoFOaWJO9P70zajPkCqz4DtfUtO3bFqlc/4ufPRhUWisLRCYWjE1ikZ541cbGo7t/V+UKlRP00CtWjB3ppY1YiV23DoXNz7Ns1wqyYJ+7TPsXI0pzoP/YD4DFnDC5j+mjrR639E5v3q+LUrx1mPoVxHt4dy/IldDq0USv+wK7F+zh0aoZpEQ8ce7TCpmFNnm7YBUDKnQek3nuEx/ThWFQomZ5R7dcO6zqViXvWUTZAN1fsofyIthRqWgWH0oWp/eNgEsOiefDCvqaNfp1Iyb5NtN+bWJnjWK4IjuXS5zTbeLrgWK4IVoUKaOsUaVUD11plsCniQuFmVWi4eQIP95wn9OjzxXYM1dWVe6jyWVuKNqmCU+nCNFiQfk1e3K+01eaJlOvz/JrUndGHEu3qcHDYz6TFJ2PpYo+liz3GFumjMJKexPA04KHOF0D8o8gMnVlD47dyDzU+a4t3kyoUKFWYpvMHkxAeTdC+59ej/aaJVOj9/Ho0+KYPpdvVYc/wn0lNSMbKxR4rF3vt1jRPg0KIvhtKo1n9cKvog31RVyoP/JAi75fnzn9gX1ghhDAU6jz8yq8kg5oNkyZNwsTEhMmTJ/P48WM8PDwYMmQIVlZW7N27lxEjRlC9enWsrKzo0KED33///Rudf9q0aQwePJhixYqRkpKCRqPhq6++4s6dOzRr1gwrKysGDRpE27ZtdTqar4oLwMTEhB9//JFp06YxefJk3n//fY4cOZJlHAMHDuTIkSNUq1aN+Ph4Dh8+jJeXV6Z13zW+t9G9e3fu3LnD2LFjSU5OplOnTvTp00dn5WB9ST12mAR7B6x69MPIyQll0G1iJ41DE50+ZNXY1RU0z281Fi3boDA1w+6r6TrnSfxlNYkb1uRl6Dki7u9jhDvZ4TKiJ8YujqT43yG4/2RUkdEAmBZ00Wl/kp8/j0bPwWVUL1zG9CH13iMeDJ1Oyq37z8+5/zQhUxbhPLgTbpOGkHr3IQ+HzSDpwo30CkoVwQOm4DquL55Lp2BkZUnq/cc8Hv89CUcz347FENz4aRcmVubUnNMPMzsrws8Fcrj7HNQvrMBr4+WKuZOt9nunij40+eNL7fdVv04fuRD06zH+GZU+hNXSzYEqU7tj4WxPcng0d34/wbUFr87gG5LLP+/C1MqcerPTr0nouUD+7jFHZ1Viu6KuWLxwTcr1bgxA6y1f6Zzr8KilBP5+PG8CzyUXFu/C1NKcRrP6YW5nxePzgWzvqXs97Iu4YvnC9ajQK/16fPy77vXYN3op/luOo1aq+LP3d9SZ0JnWq8Zgam1O9L0w9o1eyr3Dl/OmYUIIIQxCVFQUw4cPZ+fOnRgZGdGhQwd++OEHbGxsXll/ypQp7Nu3j+DgYFxcXGjbti3Tp0/H3v75An6ZJY02bdpEly5vNjVNodH8h3YvF+IlTZo0wd3dnfXrs78QSsSH9XMxov+eJ7etX18pn7mY4KTvEAxOvIy3ySBZrkkGI4J/0XcIQgiRq5Z49siz9xryIHfuqR9++CEhISEsXbqUtLQ0+vbtS/Xq1dm4cWOm9a9du8aUKVPo06cPZcuW5f79+wwZMoQKFSqwZcvzqUQKhYLVq1fTvHlzbZmDgwMWz6ZlZZNkUMV/RmJiIkuWLKFZs2YYGxuzadMmDhw4wP79+/UdmhBCCCGEEDkqs+0SM1tf5U34+/uzZ88ezp07p92lY+HChbRo0YK5c+dSsGDBDMeUL1+eP/54vu1hsWLFmDFjBj169ECpVGJi8rxL6eDgkK0tLrMiz3/zERsbm1d+HT9u+EPiFAoFf//9N/Xq1aNq1ars3LmTP/74g8aNG+s7NCGEEEIIkQ/k5RzUzLZLnDXr3bYgPH36NA4ODjpbSDZu3BgjIyPOnDmT7fPExMRgZ2en0zkF+PTTT3F2dqZGjRqsWrWKtxmsKxnUfOTSpUuvfK1QoUJ5F8hbsrS05MCBA/oOQwghhBBCiFyX2XaJ75I9BQgNDcXV1VWnzMTEBCcnJ0JDQ7N1joiICKZPn86gQYN0yqdNm0bDhg2xsrJi3759DB06lPj4eD777LM3ilE6qPlI8eLF9R2CEEIIIYQQ/1l5ubrumwznnTBhArNnz86yjr+//zvHFBsbS8uWLSlbtixTp07VeW3SpEnaf1euXJmEhAS+++476aAKIYQQQgghRH4yZswY+vTpk2UdHx8f3N3dCQ8P1ylXKpVERUW9du5oXFwczZs3x9bWlm3btmFqappl/Zo1azJ9+nRSUlLeKPMrHVQhhBBCCCGEyAZD3f7ExcUFFxeX19arVasW0dHRXLhwgapVqwJw6NAh1Go1NWvWfOVxsbGxNGvWDHNzc3bs2JGtlXkvXbqEo6PjGw9Llg6qEEIIIYQQQuQDZcqUoXnz5gwcOJAlS5aQlpbGsGHD6NKli3YF30ePHtGoUSPWrVtHjRo1iI2NpWnTpiQmJvLLL78QGxtLbGwskN4xNjY2ZufOnYSFhfHee+9hYWHB/v37mTlzJmPHjn3jGKWDKoQQQgghhBD5xIYNGxg2bBiNGjXCyMiIDh068OOPP2pfT0tLIyAggMTERAAuXryoXeH35TVt7t69i5eXF6ampvz000+MGjUKjUZD8eLF+f777xk4cOAbx6fQvM3av0L8h0V8WF/fIRiUJ7et9R2CwbmY4KTvEAxOvGxKlkGyXJMMRgTnzqbyQghhKH4o0iPP3iu/3lPlz6sQQgghhBBCCIMgQ3yFEEIIIYQQIhvycpuZ/EoyqEIIIYQQQgghDIJkUIUQQgghhBAiGySDmvskgyqEEEIIIYQQwiBIBlUIIYQQQgghskG2P8l9kkEVQgghhBBCCGEQJIMqhBBCCCGEENmgVug7gv9/kkEVQgghhBBCCGEQJIMqhBBCCCGEENkgq/jmPsmgCiGEEEIIIYQwCJJBFUIIIYQQQohskFV8c59kUIUQQgghhBBCGATJoAohhBBCCCFENqglh5rrJIMqhBBCCCGEEMIgSAZV5DttrhjrOwSDUtPUTt8hGJxZB/vrOwSDo751Qd8hGB5j+RP6sl89uus7BIPTOWSDvkMQQuQgWcU390kGVQghhBBCCCGEQZAOqhBCCCGEEEIIgyDjk4QQQgghhBAiG2SJpNwnGVQhhBBCCCGEEAZBMqhCCCGEEEIIkQ2ySFLukwyqEEIIIYQQQgiDIBlUIYQQQgghhMgGtULfEfz/kwyqEEIIIYQQQgiDIBlUIYQQQgghhMgGtazjm+skgyqEEEIIIYQQwiBIBlUIIYQQQgghskHyp7lPMqhCCCGEEEIIIQyCZFCFEEIIIYQQIhtkH9TcJxlUIYQQQgghhBAGQTKoQgghhBBCCJENsopv7pMMqhBCCCGEEEIIgyAZVCGEEEIIIYTIBsmf5j7JoAohhBBCCCGEMAiSQRVCCCGEEEKIbJBVfHOfZFCFEEIIIYQQQhgEyaAKIYQQQgghRDbIKr65TzKoQgghhBBCCCEMgnRQhRBCCCGEEEIYBOmgvkSj0TBo0CCcnJxQKBRcunTpnc43depUKlWq9EbHKBQKtm/f/k7vq29v024hhBBCCCEMmSYPv/IrmYP6kj179rBmzRqOHDmCj48Pzs7O73S+sWPHMnz48ByK7r8jv7Z7wNg+fNStJbZ2Nlw5f425Exfw8O6jLI9xdndm6BcDea9hDSwsLHh47xEzR8/h5pVAABydHRn65UBq1KuGjb0Nl/65wvxJC197XkPRfFRH3uvaEEs7a+6eD2DLVyuJuBearWMbftKaVp9349iqv9k+bZ22/L2ujajSpg6Fy3lhYWvFFxX6kRybmFtNyDGb/z7Cmm37iIiOpaRXYSYO7IxvSe9X1l+/4yC/7TlGaEQUDrY2NKldmRE922FuZgpAQlIyizbs4NCZS0TFxFHa25PPB3SifAmvPGpR7th88jprj14hMi6Jkh5OfN62Nr5FXDOt23/xLi7cCclQXre0J4v6N8/tUHPF5hPXWHv4EpFxiZQsWIDP29XFt6hbpnX7//QnF4IeZyivW6YIiwa2BODglTv8fuo6/g+fEJOYwuYxHSld6N3+tulD+XEd8OneAFM7ayLOBXJhwiri74a9sr7Le6Up9UlLnCp4Y+nuyIm+3/Noz4UM9WxLFKTil11wqVUGIxMjYgMfcXLADyQ+iszN5gghhHgF6aC+JCgoCA8PD2rXrp0j57OxscHGxiZHzvVfoNFoUKlU+a7dAN2HduHjfu35ZuS3hDwIZeC4vny/YTY9GvQlNSUt02Ns7W1Ysv1HLp66xJgeE4mOjMbTuzBxMfHaOt+umoYyTcXn/SaRGJ9I50Ef88PmuXT/oC/JScl51by30nBIa97v25yNY34m6sETPhzTicHrJjK7yViUr7gmz3hW8KFWt8Y89r+f4TUzSzNuHr3EzaOXaPV5t9wKP0ftOXGe71ZtYdIn3fAt6cUvOw4x5OuF7PhpKgUc7DLU/+voWX5Yv42vh/WiUmkf7j8OZ9KPa1EoFIzr1xGAqYvWczv4MTNG9sXVyZ5dR84waMoCti2cglsBx7xuYo7YeymIeTv/4csOdfEt4sqG49cYumI3f47vhJONZYb63/duTJry+aL/0YnJdJ6/lSYVfPIy7Byz1+828/48yZcd66e3/9gVhi7bxZ8TuuJka5Wh/vd9mpGmeqn9c3+jScVi2rKk1DQqe3vQtFIxpv12NE/akdNKf9qKEv2bcWbEUhKCw/Ed35H6myawu/541K+4lxhbmRN9I5i7m49Sd9WoTOtYF3Wl0fbJ3Nl0lGtz/yAtLgn7UoVRJWd9fxJC5F+yzUzukyG+L+jTpw/Dhw8nODgYhUKBl5cXe/bsoW7dujg4OFCgQAFatWpFUFCQznEPHz6ka9euODk5YW1tTbVq1Thz5gyQcajruXPnaNKkCc7Oztjb21O/fn0uXrz41jFn9d4AixcvplixYpiZmVGqVCnWr1+vfa1bt2507txZ53xpaWk4Ozuzbl16tkqtVjNr1iy8vb2xtLSkYsWKbNmyRVv/yJEjKBQKdu/eTdWqVTE3N+fEiRMZ2t2nTx/atm3L3Llz8fDwoECBAnz66aekpT3/EBASEkLLli2xtLTE29ubjRs34uXlxYIFC4D0zu/UqVMpUqQI5ubmFCxYkM8+++ytr11O6zSgA2t/+IUT+04R5H+H6SO+xdnNmfeb1X3lMd2HdiX8cTgzR8/B/9JNQh6EcvbYeR7dT8+IePoUpnzVcsyduICblwMIDnrA3AkLMLcwo0nbhnnVtLdWr9+H7F+4jev7LxByM5iNo3/Czs2R8k2rZXmcmZU53RcM57cJy0iMScjw+rFVuzm0eAf3/W7nVug5bt2fB+jQtA5tG9WmmGdBJn3SDUtzU7YfPJVp/csBQVQqXYyW9WtQyM2Z2pXL8uH71bl26x4AySmpHDjtx6je7alWrgRFPFwZ2vUjPN1d+W3PsTxsWc5af+wq7WuWpm31UhRzc+Sr9nWxMDVh+9mATOvbW1ngbGel/frn1iMsTE1oWvHVmWlDtv7oZdq/V5a2NUpTzN2Jrz6uj4WpKdvP3sy0vr31S+0PePBv+593UFtVK8XgZtWoWbJwXjUjx5Uc2JwbC7bzeO8FYvwfcOazxVi6OVCoedVXHhN66DLXZv/Oo93nX1mnwoROhBy6zJVvNhF97T4J98N5vO8iKZGxudEMIYQQ2SAd1Bf88MMPTJs2jcKFCxMSEsK5c+dISEhg9OjRnD9/noMHD2JkZES7du1Qq9Ofn8THx1O/fn0ePXrEjh07uHz5MuPHj9e+/rK4uDh69+7NiRMn+OeffyhRogQtWrQgLi7ujeN93Xtv27aNESNGMGbMGK5du8bgwYPp27cvhw8fBqB79+7s3LmT+Pjn2bq9e/eSmJhIu3btAJg1axbr1q1jyZIlXL9+nVGjRtGjRw+OHtV9Cj9hwgS+/fZb/P39qVChQqbxHj58mKCgIA4fPszatWtZs2YNa9as0b7eq1cvHj9+zJEjR/jjjz9YtmwZ4eHh2tf/+OMP5s+fz9KlS7l16xbbt2/H19f3ja9bbihYxANntwKcP/F8+FhCXAI3/PwpX7XsK4+r27QWN68EMn3pFHZd/oPVe5fyUbeW2tdN/x3KmZqSqi3TaDSkpqZRoUb5XGhJznHydMXO1ZHAk1e1ZclxSQRfuo1XlZJZHtthej/8D/tx6+S13A4zT6SlKfEPCua9CmW0ZUZGRtSsWIbLAXcyPaZiqWL4BwVzNfAuAA9Dn3D84jXqVkn/765Sq1Gp1ZiZmuocZ2Fuit+N/07H/UVpShX+jyKoWaKQtszISEHNEoW4cj88iyOf2342gGaVimFpZvr6ygYmTanC/+ETnY6kkZGCmiULceXeq4eyvmj7mZs0q1wcS/P/XvtfxbqIC5ZujoQdv64tS4tLItIvCOdqJd7+xAoFHo0rEXcnhHqbPqfN1Z9p/NfXWXZ6hRBCk4f/y69kiO8L7O3tsbW1xdjYGHd3dwA6dOigU2fVqlW4uLhw48YNypcvz8aNG3ny5Annzp3DyckJgOLFi7/yPRo21M16LVu2DAcHB44ePUqrVq3eKN7XvffcuXPp06cPQ4cOBWD06NH8888/zJ07lwYNGtCsWTOsra3Ztm0bPXv21J6zdevW2NrakpKSwsyZMzlw4AC1atUCwMfHhxMnTrB06VLq16+vfa9p06bRpEmTLON1dHRk0aJFGBsbU7p0aVq2bMnBgwcZOHAgN2/e5MCBA5w7d45q1dKzaytWrKBEiecfPoKDg3F3d6dx48aYmppSpEgRatSokeV7pqSkkJKSolOm1qgxUuTssxkn1/TrH/XkqU55VMRTCvz7WmYKFilI256t+XX576z7cQNlKpVi1LRhKNPS2P37Pu7fDib0YRiDJw7gu8+/Jykxmc4DP8atoCsFXAvkaBtymp2LAwBxT2J0yuOexGD772uZqfRRLQqX82Z+my9zMbq89TQuHpVanWEobwF7W+4+zHw+bsv6NYiOi6f3F3NBo0GpUtOxeT0GdvwQAGtLCyqW8mHZb3/h4+lOAXs7dh8/x+WAO3i6Zz5f09A9TUhGpdZQ4KWhvAVsLLkXHv3a468Gh3M79ClTOtbLpQhzl7b9ti+139Yqe+2/H8bt0CimdP4gdwLUEwtXBwCSX7qXJD+JwSKLe8lrz+tsh6mNJWWGfcTV2b9z5ZvNuDeoQJ2VIzn88QyenM48ay2EECJ3SQb1NW7dukXXrl3x8fHBzs4OLy8vIL2zBHDp0iUqV66s7SC+TlhYGAMHDqREiRLY29tjZ2dHfHy89nxv4nXv7e/vT506dXTK6tSpg7+/PwAmJiZ06tSJDRs2AJCQkMCff/5J9+7dAbh9+zaJiYk0adJEO6fUxsaGdevWZRjm/KxTmZVy5cphbGys/d7Dw0ObIQ0ICMDExIQqVapoXy9evDiOjs/n0XXs2JGkpCR8fHwYOHAg27ZtQ6lUZvmes2bNwt7eXufrYVzGOY1vqmm7RuwP/Ev7ZWJi/PqDMmFkpCDw2i2WfruSW9dvs2PDX+zY+Bdte34EgEqp4osBkyniU5g9N3Zw8PZuqtSuxOmDZ16ZpdeXKm3qMOv6Gu2XsembXxMHjwK0m9ybX0Yueu0c1f93564GsGLLHr4c3JXN875k/oTBHD9/laW//qWtM3NkXzRA434TqNZxGBv/OsSH71fHyEihv8D1aPvZAEq4O71yQaX/d9vP3KSEh9MrF1T6ryjavjbtb6/Ufhm9xb0kW/79PXm05yKBy/YQff0+Nxft5PF+P4r1bJQ77ymE+M9T5+FXfiUZ1Nf46KOPKFq0KMuXL6dgwYKo1WrKly9Pamr6kEtLy4yLdmSld+/eREZG8sMPP1C0aFHMzc2pVauW9nxv4k3fOzPdu3enfv36hIeHs3//fiwtLWnePH3ly2dDf//66y8KFSqkc5y5ubnO99bW1q99L9OXhiIqFIo36mR5enoSEBDAgQMH2L9/P0OHDuW7777j6NGjGc79zMSJExk9erROWbPSrbP9nq9yYt8prvv5a783MzMDwMnFkcjwKG25k7Mjt66/erhlZHgU9wLv6ZTdux3MBy2eZ4ACrt6iT9NBWNtaY2pqQnRUDMt2/sTNK5nPydOX6wcuEHzpeVuN/x1iaetiT9yTaG25rYs9j25k/pCgsK83ti4OjN416/l5TIzxqVGaOr2aMb5kDzTq/96QF0dbG4yNjIiM1p3XFhkTh7NjxgWSABZt3EmrD2rSoUn6HOaSXoVISk5l2s+/MLDjhxgZGeHp4cLqGWNITE4hITEZFyd7xn23nMJu/70VWgEcrS0wNlIQGZ+kUx4Zn4RzJgsEvSgpNY29l4P45DXzmw2Ztv1xL7U/LvH17U9JY++l23zSvHpuhpgnHu29SOTF5w9BjczSP6pYuNiT/EIm2cLFnujrb//AMTUqDnWakthbuiuix956jEuNUm99XiGEEO9GOqhZiIyMJCAggOXLl/P+++8DcOLECZ06FSpUYMWKFURFRWUri3ry5El+/vlnWrRoAcCDBw+IiIh4q/he995lypTh5MmT9O7dW+f9y5Z9Pieydu3aeHp68uuvv7J79246duyo7eyVLVsWc3NzgoODdYbz5oZSpUqhVCrx8/OjatX0+T+3b9/m6VPdIbOWlpZ89NFHfPTRR3z66aeULl2aq1ev6mReX2Rubp6hM50Tw3sTE5JITND9EBkRFknVulW4dT39g5WVjRVlK5dh27odrzzPlXPXKFLMU6esiE9hQh9lnG+WEJe+WFBh70KUrliSFd+tftdm5KiUhGRSEnRXFY4Nf0qJ2uV5/G+H1NzGkiKVinPyl/2ZnuPWyWvMaTpWp6zLd58QHvSYQ0v+/E92TgFMTU0oU6wIZ67cpOF7lYD0BcjOXLlJ1xYfZHpMckoqRgrdTKiRUfrPrualy2BlYY6VhTmx8Qmc8rvBqN7tc7oJecLUxJgyhZw5e/sRDct7AaBWazh7+zFdar96LjfAvst3SVWqaVnl1VMsDJ2piTFlCrtw9tZDGvqmL/KkVms4e+sRXepmPed83+UgUpUqWlbNen73f4EyIZn4l+4lSWFPcatbTtshNbGxpEDlYtxee+Ct30edpiLq0h1si3nolNsWcyfh4dv9XRZC/P9T5+O5oXlFOqhZcHR0pECBAixbtgwPDw+Cg4OZMGGCTp2uXbsyc+ZM2rZty6xZs/Dw8MDPz4+CBQtq522+qESJEqxfv55q1aoRGxvLuHHj3joT+rr3HjduHJ06daJy5co0btyYnTt3snXrVg4c0P2D3q1bN5YsWUJgYKB2ASUAW1tbxo4dy6hRo1Cr1dStW5eYmBhOnjyJnZ2dTsf3XZUuXZrGjRszaNAgFi9ejKmpKWPGjMHS0hLFvx/S16xZg0qlombNmlhZWfHLL79gaWlJ0aJFcyyOd/Hbij/o/VkPHt55xOMHIQwc15eIsAiO733+UOOHX+dybPcJ/lizHYBfl29h6Z8L6TW8Gwd3HqFspdK07t6SOeO/1x7ToFV9oiOjCXsUjk9pb0ZOG8bxPSc5e+zVK1MaimOrdtNkeDsi7oUS9SCc5mM6ERv2lGv7nsc+ZMNXXNt7jhPr9pKSkExo4EOdc6QmpZAYHadTbutij62LA87/DmX0KFWElIQkoh9FZLrqryHo1aYxX/2whrLFi+Jbwotfdh4iKTmVto3St7T6YsFq3Ao4MKJn+gJl9av7sn7HQUr7eOJb0psHIeH8tHEH9atXwNg4vaN60u86Gg14FXLjQUg436/Zildhd9o0ypltsvShZz1fJv16lLKFXSjv6cKG49dISk2jTfX0jtdXmw7jam/NZy10559vP3eTBuWK4mBtoY+wc0zP+hWZtOkQZT1dKF/EjQ1Hr6S3v0ZpAL7aeBBXO2s+a/WeznHbz/jToLx3pu2PSUgmJDqeJ//+btz/NwvpbJu+8u9/QeDyPZQd2Za4u6EkBD+h/OcfkxQWrbOv6Qe/TeTh7vPcXp3+AMzEyhwbb3ft69ZFXHAoV5TU6HjtHqc3F/9FrSXDefLPTcJP3sC9QQUKNqnC4Q7f5G0DhRBCaEkHNQtGRkZs3ryZzz77jPLly1OqVCl+/PFHPvjgA20dMzMz9u3bx5gxY2jRogVKpZKyZcvy008/ZXrOlStXMmjQIKpUqYKnpyczZ85k7NixmdZ9nde9d9u2bfnhhx+YO3cuI0aMwNvbm9WrV+vED+nDfGfMmEHRokUzzFmdPn06Li4uzJo1izt37uDg4ECVKlX44osv3irmrKxbt47+/ftTr1493N3dmTVrFtevX8fCIv0Dl4ODA99++y2jR49GpVLh6+vLzp07KVDAMBYL2vDzZiytLBg/ZzQ2djZcOXeVMT0m6OyBWqhoQeyd7LXf37wcwMQBkxkyYQB9RvYi5EEIP0z5mX3bDmrrFHB1YviUT3ByTh8+vGfLPlYvWM9/waElOzCzNKfjrIFY2llx91wAy3p/qzO/1LmoG9ZOtm903trdm9Bs5Mfa74f/PhWATWMXc26LYe7z2LxuNZ7GxPHzpp1EPI2llHdhFk8Zrl04KfRJlE7GdFCnFigUChZt2EF4VDSOdjbUr16B4d3baOvEJyTxw/rthEVGY29rReNalRnevS2mbzkn2hA0q1SMpwnJLN57gYi4REoVLMDPAz6kwL9DXEOiE7QPrZ65Fx6N390wFg/8UB8h56hmlYvzND6JxXvOERGbSKlCzvw8qNXz9j+Nz6T9T/G7G8riwZkvtHfk+j2mbH7+8PHz9ekduMFNq/1nhgTf/GkXJlbmVPuuP2Z2Vjw5G8jRbrN19kC18XLD/IV7iWNFHxpu/Ur7feWv0xcDvPvrMc6OXArAo93nufD5KsoMb03l6b2ICwrh5IAfiDgbmEctE0L810j+NPcpNJqXB4sJYRgePnyIp6cnBw4coFGjnFuwok4hw98/NC/VNM2fC8pkZdbuwfoOweCob114faX8xlie8b5sx4Bz+g7B4HQO2aDvEIQQOegTr0559l6L7/2WZ+9lSOSvqzAYhw4dIj4+Hl9fX0JCQhg/fjxeXl7Uq/ff3DJCCCGEEEL8f5E5qLlPtpkxYDNnztTZ3uXFrw8//O8PZXtZWloaX3zxBeXKlaNdu3a4uLhw5MiRV67QK4QQQgghhPj/IhlUAzZkyBA6dcp8GEFObDFjaJo1a0azZs30HYYQQgghhBCZys/7k+YV6aAaMCcnp2xtXSOEEEIIIYQQ/w+kgyqEEEIIIYQQ2aCROai5TuagCiGEEEIIIUQ+ERUVRffu3bGzs8PBwYH+/fsTHx+f5TEffPABCoVC52vIkCE6dYKDg2nZsiVWVla4uroybtw4lErlG8cnGVQhhBBCCCGEyCe6d+9OSEgI+/fvJy0tjb59+zJo0CA2btyY5XEDBw5k2rRp2u+trKy0/1apVLRs2RJ3d3dOnTpFSEgIvXr1wtTUlJkzZ75RfNJBFUIIIYQQQohs+K8vkuTv78+ePXs4d+4c1apVA2DhwoW0aNGCuXPnUrBgwVcea2Vlhbu7e6av7du3jxs3bnDgwAHc3NyoVKkS06dP5/PPP2fq1KmYmZllO0YZ4iuEEEIIIYQQBiYlJYXY2Fidr5SUlHc65+nTp3FwcNB2TgEaN26MkZERZ86cyfLYDRs24OzsTPny5Zk4cSKJiYk65/X19cXNzU1b1qxZM2JjY7l+/fobxSgdVCGEEEIIIYTIBk0e/m/WrFnY29vrfM2aNeud4g8NDcXV1VWnzMTEBCcnJ0JDQ195XLdu3fjll184fPgwEydOZP369fTo0UPnvC92TgHt91mdNzMyxFcIIYQQQgghDMzEiRMZPXq0Tpm5uXmmdSdMmMDs2bOzPJ+/v/9bxzJo0CDtv319ffHw8KBRo0YEBQVRrFixtz5vZqSDKoQQQgghhBDZkJdzUM3NzV/ZIX3ZmDFj6NOnT5Z1fHx8cHd3Jzw8XKdcqVQSFRX1yvmlmalZsyYAt2/fplixYri7u3P27FmdOmFhYQBvdF6QDqoQQgghhBBC/Ke5uLjg4uLy2nq1atUiOjqaCxcuULVqVQAOHTqEWq3Wdjqz49KlSwB4eHhozztjxgzCw8O1Q4j379+PnZ0dZcuWfaO2yBxUIYQQQgghhMgGtUaTZ1+5oUyZMjRv3pyBAwdy9uxZTp48ybBhw+jSpYt2Bd9Hjx5RunRpbUY0KCiI6dOnc+HCBe7du8eOHTvo1asX9erVo0KFCgA0bdqUsmXL0rNnTy5fvszevXv56quv+PTTT7OdBX5GOqhCCCGEEEIIkU9s2LCB0qVL06hRI1q0aEHdunVZtmyZ9vW0tDQCAgK0q/SamZlx4MABmjZtSunSpRkzZgwdOnRg586d2mOMjY3ZtWsXxsbG1KpVix49etCrVy+dfVOzS4b4CiGEEEIIIUQ25E5eM285OTmxcePGV77u5eWF5oUMrqenJ0ePHn3teYsWLcrff//9zvFJBlUIIYQQQgghhEGQDKoQQgghhBBCZIP6/yKHatgkgyqEEEIIIYQQwiBIBlUIIYQQQgghskEjGdRcJxlUIYQQQgghhBAGQTKoQgghhBBCCJENan0HkA9IBlUIIYQQQgghhEGQDKoQQgghhBBCZIOs4pv7pIMq8p0ZygL6DsGgFDBL0HcIBke5dbW+QzA4Ru/V1XcIBuf2gJ36DsHgOKut9B2CwYkb+ZG+QzA4tgvkd0cI8WrSQRVCCCGEEEKIbJBVfHOfzEEVQgghhBBCCGEQpIMqhBBCCCGEEMIgyBBfIYQQQgghhMgG2WYm90kGVQghhBBCCCGEQZAMqhBCCCGEEEJkg0YjiyTlNsmgCiGEEEIIIYQwCJJBFUIIIYQQQohsUMs2M7lOMqhCCCGEEEIIIQyCZFCFEEIIIYQQIhtkFd/cJxlUIYQQQgghhBAGQTKoQgghhBBCCJENGpmDmuskgyqEEEIIIYQQwiBIBlUIIYQQQgghskFW8c19kkEVQgghhBBCCGEQJIMqhBBCCCGEENmg0UgGNbdJBlUIIYQQQgghhEGQDKoQQgghhBBCZIPsg5r7JIMqhBBCCCGEEMIgSAZVCCGEEEIIIbJB9kHNfZJBFUIIIYQQQghhEKSDKoQQQgghhBDCIMgQXyGEEEIIIYTIBrUM8c110kF9R2vWrGHkyJFER0frO5Q89cEHH1CpUiUWLFjw1ue4d+8e3t7e+Pn5UalSpRyLLa94je+MR49GmNhZE3vuJoHjl5N0NzTLYwr2bUaRoa0xc3Ug/sZ9bn2xiji/2zp17KqVxHtiV+yqFEejUhN/7R5XusxAnZyKhacLRUd/jEPd8pi5OJAaFkXYluPcX7AVTZoyN5v7Wk49W+AyqD0mLo4k+9/l8dSlJF2+9cr6di3q4Da6B2aFXUm9+5jQ2WuIO3Ih07oFvxlKge4f8njaciJX79B5zbZBNVw/64JFaS80KWnEn7lG8OAZOdq2nGJStRGm77VAYWOPOuwBqfvWo35857XHGZetiUW7T1EGXCBlyw/PX7C2w6xBZ4x9yqOwsEIVHEDq3vVonoblYity1uajfqzdf57I2ARKFnbh804N8fXyyLRu//m/cuHWwwzldct5s+jT9trv74RE8sP2Y1y49RClWo2PewHmDWqNh5NdrrXjXejjd8fl007YNqiGZVkfNGlp3KjYNcfb9a58xnek4L/32JhzAdwcv+K199jCfZtSZOhH2nts4BerifULAsDC04U65xdletzVAfMJ3/mPTpmJow01D83BomABjpboizI2MWcalkNM67bArGF7FLaOqB/fJfmPpaiDM/+5MalQC7PGHTFy8QAjE9QRj0k9vB3l+cPaOgobB8xb98G4VCUUljaogq6R/MdSNBEhedUkIYQApIOaqT59+hAdHc327dtfW7dz5860aNEi94MyMFu3bsXU1FTfYeiN57A2FB7wIf6fLSI5OBzvz7tQ4devOPf+KNQpaZke49KmNsW/7k3g+GXEXrxN4UEtqbD5S87WGUFaRCyQ3jmtsOlLgn/cxq0vVqJRqrEpVxSNOn1Rc6vihUChIHDsUpLuhWJdugil5g3G2MqcoK/X51n7X2bfsi4eXw7g8Vc/kXgpEOd+rfFeO42ARkNQRcZkqG9VpTRFfhhH6HdriTt4Doc29Smy9EtufzSSlMBgnbp2Td/DqnIp0kIjM5zHrnltCs0aRth364g/fQWFsTEWpYrmWjvfhXGZmpg17kbq7jWoHgdhWqMZFl3GkbhkPCTGvfI4hb0zZo26ogq+meE1i49HglpJyu8L0KQkYVqzORbdPydp6QRIS83F1uSMvedvMu+Po3zZtTG+Xh5sOHSBoQv/4M+p/XCytcpQ//tBrUlTPl/gPzohic4z19GkSklt2YMn0fT9fjNta5Xnk1a1sbYwJygkAnNTw/xzp6/fHYWpCTF/nyTR7yZOnZrkWvveVtFhrSk84ENufPYzycHh+Hzeicq/fsE/74955T3WtU0tSnzdi5vjVxB78Raeg1pQafMXnK4zirSIWJIfRXC8/CCdYwr1bEyRTz8i8qBfhvOVnT+E+BvBWBQskCttfBcmleti3nYAyb/9hPp+IKb1W2M1ZBoJM4egic/4c6NJjCN1/2+owx+iUSoxKVcdi64jSIqPRnUzve2WA75Eo1KStGIGmpREzD5oi9XQb0j4diikpuR1E4UwWBqNZFBzm8xBfQdpaWlYWlri6uqq71DynJOTE7a2tvoOQ28KD2rJ/fl/ELnnPAk3gvEftghzN0ecP6z+ymM8h7Qi5JeDhG4+QmLgQwLHLUOdlIpH14baOsWn9ebhir8JXridxICHJAU95smO02hS07OjUYcvETDyZ54evULy/XAi957nwc87cW5ZM9fbnBXnAW15+utenm45SMrtBzz68mfUSSk4dcz8g2+Bvq2JO3qRiGXbSAl6SNj3G0i+HkSBXq106pm4OVFw6mAejJyHRvlShtjYiIKTBxI6azVRG/eQevcxKbcfEPPXidxq5jsxrdkc5aUjKK8cRxPxmNS/16BRpmBasf6rD1IoMG8zhLRjW1E/faL7kpM7xoWLk7J7LeqQu2iiQkndvRaFiRkm5WrlcmtyxvpDF2hfx5e2tcpTzKMAX3VtgoWZKdtPXc20vr21Jc721tqvf27ex8LMlKZVSmnrLNpxgrrlvBnVvj6lPd3wdHHggwrFM+3wGgK9/O4A4Qs2ErnqT5Jv3s+Vdr0rz0EtuDd/KxF7zhN/I5jrw37CzM0RlyzusUWGtOTRLwcJ2XyEhMBH3By3AlVSKgW7NkivoNaQ+iRG58ulRXXCd5xGlajbASvUuwkmdlYEL96Zm818a2YftCXt9F6UZw+iDntAyu8/o0lNwbRm5j83qtvXUF79B3XYQzSRoaQd24n68T2MvcsCoHApiLFXaVJ+X4z6wS004Y9I+f1nMDXDtEoW9yghhMgF+bqDumXLFnx9fbG0tKRAgQI0btyYcePGsXbtWv78808UCgUKhYIjR45w7949FAoFv/76K/Xr18fCwoINGzawZs0aHBwctOecOnUqlSpVYv369Xh5eWFvb0+XLl2Ii3ueIYmLi6N79+5YW1vj4eHB/Pnz+eCDDxg5cqS2TkpKCmPHjqVQoUJYW1tTs2ZNjhw5on392fvu3buXMmXKYGNjQ/PmzQkJ0R2Ks2rVKsqVK4e5uTkeHh4MGzYMgH79+tGqle4HmrS0NFxdXVm5cuVrr93L8Xp5eTFz5kz69euHra0tRYoUYdmyZTrHnD17lsqVK2NhYUG1atXw88v4xPratWt8+OGH2NjY4ObmRs+ePYmIiADgyJEjmJmZcfz4cW39OXPm4OrqSlhY3g1ptCjqirmbI0+PPf8QrYpLJPbibeyqlcr0GIWpCbYVfHh6/MrzQo2Gp8euYFctPftj6myHXdWSpEXEUHnXN9S+tpxK277GvkbpLOMxsbNC+TT+3Rv2lhSmJliWL078icvPCzUa4k9ewqpK5tfDqnJp4k9e0imLO+aHVZUX2qpQ4Pn9aJ4s20rKLd3MEIBl+WKYejiDWk3xXQsofWYtXqunYl6ySE40K2cZGWPk4YXq7vUXCjWo7t7AqHDxVx5m+n5bNImxKC8fy/ii8b8ZQeWL2SQNGlUaRoVLZqxvYNKUKvyDw6hZ6vl/LyMjBTVLF+HK3ewNKdx+6hrNqpbC0jx9NIdareH4tTsUdXXkk4VbaDD+Z3rM2cChS68eLqtP+vrdMXTP7rFROvfYJGIv3sa+WolMj1GYGmNbwYeo4y883NBoeHrs6iuPsa3gja2vN483HNYpty5ZCO8xHbg+/Cc0agPMlBibYFS4OKpA3Z8bVeAljLwy/7nJcIoSFTByLYQqKP2epDBJ/x3SvDjyQqMBZRrGPmVzLHQh/h+o0eTZV36VbzuoISEhdO3alX79+uHv78+RI0do3749U6ZMoVOnTtrOXkhICLVr19YeN2HCBEaMGIG/vz/NmjXL9NxBQUFs376dXbt2sWvXLo4ePcq3336rfX306NGcPHmSHTt2sH//fo4fP87Fixd1zjFs2DBOnz7N5s2buXLlCh07dqR58+bcuvX8g1ZiYiJz585l/fr1HDt2jODgYMaOHat9ffHixXz66acMGjSIq1evsmPHDooXT/8wPGDAAPbs2aPTod21axeJiYl07tz5ra7pvHnztB3PoUOH8sknnxAQEABAfHw8rVq1omzZsly4cIGpU6fqxAoQHR1Nw4YNqVy5MufPn2fPnj2EhYXRqVMn4HmnuGfPnsTExODn58ekSZNYsWIFbm5ubxXz2zBzcQAg9Um0Tnnqk2jMXB0yPcbUyRaFiTGpT2JeOiZGe4xl0fQ2eI3tRMgvB7jSZQZxV+9QcctkLL3dMz2vpZc7hfp/yOP1+9+6Pe/K2NEOhYkxyoinOuXKiGhMXBwzPcbExQFlRHQm9R2037sM6YBGpSZyTeYZDDPP9GviOrIb4Yt+417/aahi4vHZNAtje5u3b1AuUFjZojAyRpMQq1OuSYhBYW2f6TFGhUtiUrE+KX+tyvR1TWQI6pgIzBp0BAsrMDLGtFZLjOwKoLBxyOkm5Lin8Umo1BoK2FnrlBewtSIiNuG1x1+9F8LtxxG0q+OrLYuKSyQxJY1V+85Su6w3i4d/TMOKxRmzfAfnAx/keBvelb5+dwydufYe++r75ctMnewwes099mUFuzUkIeAhMecDtWUKMxPKLRnB7a9/IeVRxqHRhkBhbYfC2Bh1nO7PjSYuGiO7zH9uALCwwmb2b9jM24bloCkkb12KKvASAOqwh6ijwjFv1RssrcHYBLNGHTBydEGR1TmFECIXGOaknDwQEhKCUqmkffv2FC2aPmfN1zf9g46lpSUpKSm4u2fsFIwcOZL27dtnKH+RWq1mzZo12iGwPXv25ODBg8yYMYO4uDjWrl3Lxo0badSoEQCrV6+mYMGC2uODg4NZvXo1wcHB2vKxY8eyZ88eVq9ezcyZM4H0jOeSJUsoVqwYkN6pnTZtmvY833zzDWPGjGHEiBHasurV04dH1a5dm1KlSrF+/XrGjx+vjaNjx47Y2Lzdh/sWLVowdOhQAD7//HPmz5/P4cOHKVWqFBs3bkStVrNy5UosLCwoV64cDx8+5JNPPtEev2jRIipXrqxtH6RngD09PQkMDKRkyZJ888037N+/n0GDBnHt2jV69+5N69atXxlTSkoKKSm6Q7dSNSrMFMbZbpdrh7qU+m6w9vsr3Wdl+9g3olAA8Hj9fkI3HwEg/to9HN/3xb1bQ+7O2KhT3czdiQqbv+TJztOE/HIwd2LSE4vyxSjQtzW3W418dSWj9Odr4T/9RuyeUwA8HL+A0qfWYN+iLlGb9uRBpLnEzALzNoNJ/XsVJL0iO65WkbLlR8xa9cd6zBI0ahWqu9dR3r4MirwNVx+2n7pGiYLOOgsqqf+dF/RBheL0bFQVgNKerly+85gtJy5TraSnXmLNS9n63TEwbh3qUvq7gdrvL3f/NovaOcPIwhS39nW49/1WnfLiX3Yl8dYjQv8wzKkC7yQliYTvRqAwt8C4REUs2vYnKTIU1e1roFaRtGomFl0/w3bWZjQqFarASyhvnNf+bRJCpNPk48xmXsm3HdSKFSvSqFEjfH19adasGU2bNuXjjz/G0THrJ4XVqlV77bm9vLx05md6eHgQHh4OwJ07d0hLS6NGjRra1+3t7SlV6vmwnKtXr6JSqShZUneYXkpKCgUKPF+swcrKSts5ffl9wsPDefz4sbYTnJkBAwawbNkyxo8fT1hYGLt37+bQoUOvbd+rVKhQQftvhUKBu7u7Nh5/f38qVKiAhYWFtk6tWrrz5C5fvszhw4cz7SAHBQVRsmRJzMzM2LBhAxUqVKBo0aLMnz8/y5hmzZrF119/rVPW26oMfWzKZbtdkXvOc/7C85V2FebpvzZmLg6khkdry81cHIi/fi/Tc6RFxaFRqjBz0c2WmbnYa8/x7P8TA3RXKU289QiLQs66x7k5UmnrFGLOBRAwZmm225IbVE9j0ShVmDjr/u6YODugfPI002OUT6IxcXbIpH40ANbVy2FSwJ7SJ59nDxUmxnh82Q/nfq0JeH8AyvAoAFJuPc+MaVKVpD4IxbSQSw60LOdoEuPQqFUorHVXkVVY26NJyLigiZGjK0YOLph3GvVC5fQPiVYTV5O0+HM00eGoQ++RvGISmFumD/lNjMOizxTUIXdztT05wdHGEmMjBZEvZUsj4xJxfimr+rKklDT2nr/JJ63qZDiniZERxTx0F7Xxdi+AX9CjnAk8B+nrd8fQROw5z9kLz0cHGf07ZPvF++Oz7199j41F/Zp77ItcW72HsaU5Ib8f1Sl3rFsemzJFaNAqfV6/4t/fu/f9V3BvwTbufvf7mzYvx2kSYtGoVBjZOqJ+oVxh64A6NvOfm/QDNWgiQtAA6kd3MXLzxKxxR5JuXwNA/TCIxO9GgIUVCmMTNAmxWI2aiyr49qvPKYQQuSDfdlCNjY3Zv38/p06dYt++fSxcuJAvv/ySM2fOZHmctXXWH5yADKvbKhQK1Gr1K2pnFB8fj7GxMRcuXMDYWDfT92LnLbP3ebaymKWl5Wvfp1evXkyYMIHTp09z6tQpvL29ef/997Md58tyot0fffQRs2fPzvCah8fzLMmpU+nZsqioKKKiorL8bzJx4kRGjx6tU/ZP8T7ZjglAlZBMUoLu1gYpYU9xeL+89sOSsY0ldlWK83jt3kzPoUlTEnflDg7v+xKx+1x6oUKB4/u+PFqVnulLDg4nJSQKy+IFdY619PEg6tDz+bpm7k5U2jqFuCt3uDni5/R5QnqkSVOSdO021nUqELv/320aFApsalckct1fmR6T6HcTmzoVdba9sKlbicSL6SvVRm87nGGenffaaTzddpinWw4AkHTtNuqUVMx9CpF4/kZ6JRNjTAu7kvooPGcb+a7UKtQh9zD2Kocq8NlwfgXGXmVRnj+QsXpECInLJuqUmdX/GIWZBSn7f0ET+9LQw5Sk9DM6umHk4U3a0T9yoxU5ytTEmDJF3DgbEEzDSulzBNVqDWcDgulSv1KWx+67GECqUkXLGmUynLNsUTfuhUXplN8Pf2qQW8zo63fH0KTfY5N1ylLCnuL0vi/x19MXcXp2j320NvPpDJo0FXFX7uD0vi8Ru8+nFyoUOL5fnoerMt6XC3ZrQMTe86RF6q6gfbXf9xhZmmm/t6tUjLI/fMKFNlNIumcg2zeplKgf3sa4RAWUV5//3BiXrEja8cx/bjKjMFKASSar8ScnogEUzh4YeRYn5e8NORO3EP8n1LKKb67Ltx1USO9A1alThzp16jB58mSKFi3Ktm3bMDMzQ6VS5cp7+vj4YGpqyrlz5yhSJH1xkJiYGAIDA6lXrx4AlStXRqVSER4e/tYdRltbW7y8vDh48CANGjTItE6BAgVo27Ytq1ev5vTp0/Tt2/ftGpUNZcqUYf369SQnJ2uzqP/8o7vnXJUqVfjjjz/w8vLCxCTzH82goCBGjRrF8uXL+fXXX+nduzcHDhzAyCjz6dTm5uaYm5vrlL3J8N5XebjsL4qO6kDS3dB/t5npTErY0+edT6DilslE/H1W2wF9sGQXZX78lLhLQcT5pW8zY2RlTsjm5wt0PPj5T7zGdSbh+n3ir93DrXN9rIoX4nr/eemxuztRadtUUh4+IWjqeswKPP/Q/fKc2LwUsWI7heeNIunKbZIuB1KgXxuMrCy0H4gLzxtFWmgkYd+tAyBy9Q58Ns/CeUBb4g6dx/6j97H0Lc6jL9L3KFRFx6GK1v3gqFEqUT55Suqd9EyYOj6JqA27cRvZjbSQCFIfheMyKH34vSGu5Jt2Zg/mrQeiDrmL6vEdTGs0RWFqTtqV9AWQzD4ahCbuKWlHfgdVGponuhk/TXL6HowvlhuXrp6enY2NxMjVE7Mm3VEFXkB191reNewd9GxYlUnr9lC2qDvli7qz4fBFklLSaFOrPABfrdmNq4MNn7XVvQ9uP3WNBhWL42CT8UFcnybVGb9yF1WKF6Z6SU9O3bjHsatBrBjZKU/a9Kb08bsDYFrQBWN7G8wKuoCRERZlvAFIvR+COlG3s6gPD5b9jdeodiTeDSEpOJxin3cmNewpT164x1be8hVP/j6n7YAGL/mLsj8OJfZSELF+QRQZ1AJjK3NC/p0y8YyllxsOtcpwqVvGocRJ93U7oaZO6aOhEgMfGdQ+qKlHtmPRbRSqB7dRBwdiWr8NCjML0s6k/9xYdB+FOiaS1F3pPzdmjT9GFXwbdWQIChNTjMtUw6RaA1J+X6w9p0nFOmgSYlA/fYKxhxfm7QeivHoGVUDGBQ2FECI35dsO6pkzZzh48CBNmzbF1dWVM2fO8OTJE8qUKUNycjJ79+4lICCAAgUKYG+f+SImb8PW1pbevXszbtw4nJyccHV1ZcqUKRgZGWmHEpUsWZLu3bvTq1cv5s2bR+XKlXny5AkHDx6kQoUKtGzZMlvvNXXqVIYMGYKrqysffvghcXFxnDx5kuHDh2vrDBgwgFatWqFSqejdu3eOtfNl3bp148svv2TgwIFMnDiRe/fuMXfuXJ06n376KcuXL6dr166MHz8eJycnbt++zebNm1mxYgUAPXr0oFmzZvTt25fmzZvj6+vLvHnzGDduXK7FnpkHi/7E2MqCUnMHY2JnRczZm1zpMkNnfz7Lom7aDzcAT/48hVkBO7zHd07fRP76Pa50nUHaC4t6PFz2N0bmZhSb1htTRxvir9/nSqfpJP/7ocmpfgWsfDyw8vGg9mXdob1H3DrmcqtfLeavE5gUsMdtdHdMnB1J9r/D3T5TtIu5mBZ0gRdWw0y8eJPgkXNxH9MDt7G9SL33mODBMzLs4/g6IbNWo1GpKfz9KIzMzUm8HMDdbl+hzsYiO3lN5X+GVGtbTOu3x8zaHnVYMMmbv4N/F04ysi/wxk9lFTYOmDXplj5UOD4a5dWTpB3fngvR545m1UrzND6JxbtOEhGbSKnCLvw8rIN24aSQp7HpWZ4X3AuLwi/oEYuHd8j0nA0rleCrro1Zufcsc34/TFE3R+YObE3l4oVzvT1vQ1+/O26juuP48fMpICX+/hGAO10mknBG/w847i/agbGVOaXnDvr3HhuAX5dZWd5jw/88jVkBO3zGd8Lc1YG46/e41HVWhoWTCnZrQMrjKKKOXOG/Sul3ghRre8w/7I7CzhH1ozskLp2CJj4aAIWjC0Yv3k/MLLDo+AkK+wKQloo6/CHJv8xD6ff8YZ7C3gnztv1R2DqgiX1K2rlDpO77NY9bJoThk/xp7lNo8ulus/7+/owaNYqLFy8SGxtL0aJFGT58OMOGDePJkyd0796d06dPEx8fz+HDh/Hy8sLb2xs/Pz8qVaqkPc+aNWsYOXIk0dHRQHqncPv27Vy6dElbZ8GCBSxYsIB79+4B6dvMDBkyhO3bt2NnZ8f48ePZvHkzDRs2ZNas9AV40tLS+Oabb1i3bh2PHj3C2dmZ9957j6+//hpfX98M7wuwfft22rVrp7OB8NKlS5k/fz537tzB2dmZjz/+mB9//FH7ukajwdvbm3LlyvHXX9kfGvTBBx9QqVIlFixYAKTPux05cqTO1jOVKlWibdu2TJ06FUjPmA4ZMgR/f3/Kli3LpEmT6NChg841vXXrFp9//jmHDx8mJSWFokWL0rx5c77//numT5/OkiVLuHr1qnYu7tatW+natStnz56lYsWK2Ypdnx05Q1TASv/ZEkPjM0BWrXyZ0Xt19R2Cwbk94L+5Sm5uCk80zP1m9alGV8PJvBoK2wXyuyP+u94v9Or1XXLa8Uf/X4tgZle+7aAakoSEBAoVKsS8efPo379/nr53fHw8hQoVYvXq1a9dnfj/hXRQdUkHNSPpoGYkHdSMpIOakXRQM5IOakbSQRX/ZXUKNcyz9zr56O0XL/0vy7dDfPXJz8+PmzdvUqNGDWJiYrRbw7Rp0ybPYlCr1URERDBv3jwcHByy3KpFCCGEEEIIIfKCdFD1ZO7cuQQEBGBmZkbVqlU5fvw4zs7Orz8whwQHB+Pt7U3hwoVZs2aNzqJEwcHBlC1b9pXH3rhxQ7vAkxBCCCGEEPmFWmah5jrpoOpB5cqVuXDhgl5j8PLy4lWjuwsWLKgzhzaz14UQQgghhBAip0kHVWRgYmJC8eLF9R2GEEIIIYQQBkWW78l9mW8eKYQQQgghhBBC5DHpoAohhBBCCCGEMAgyxFcIIYQQQgghskEWScp9kkEVQgghhBBCCGEQJIMqhBBCCCGEENmgkQxqrpMMqhBCCCGEEEIIgyAZVCGEEEIIIYTIBtlmJvdJBlUIIYQQQgghhEGQDqoQQgghhBBCZIMaTZ595ZaoqCi6d++OnZ0dDg4O9O/fn/j4+FfWv3fvHgqFItOv33//XVsvs9c3b978xvHJEF8hhBBCCCGEyCe6d+9OSEgI+/fvJy0tjb59+zJo0CA2btyYaX1PT09CQkJ0ypYtW8Z3333Hhx9+qFO+evVqmjdvrv3ewcHhjeOTDqoQQgghhBBCZMN/fQ6qv78/e/bs4dy5c1SrVg2AhQsX0qJFC+bOnUvBggUzHGNsbIy7u7tO2bZt2+jUqRM2NjY65Q4ODhnqvikZ4iuEEEIIIYQQBiYlJYXY2Fidr5SUlHc65+nTp3FwcNB2TgEaN26MkZERZ86cydY5Lly4wKVLl+jfv3+G1z799FOcnZ2pUaMGq1ateqsOvXRQhRBCCCGEECIb8nIO6qxZs7C3t9f5mjVr1jvFHxoaiqurq06ZiYkJTk5OhIaGZuscK1eupEyZMtSuXVunfNq0afz222/s37+fDh06MHToUBYuXPjGMcoQXyGEEEIIIYQwMBMnTmT06NE6Zebm5pnWnTBhArNnz87yfP7+/u8cU1JSEhs3bmTSpEkZXnuxrHLlyiQkJPDdd9/x2WefvdF7SAdVCCGEEEIIIbJBk4ur677M3Nz8lR3Sl40ZM4Y+ffpkWcfHxwd3d3fCw8N1ypVKJVFRUdmaO7plyxYSExPp1avXa+vWrFmT6dOnk5KSku12gHRQhRBCCCGEEOI/zcXFBRcXl9fWq1WrFtHR0Vy4cIGqVasCcOjQIdRqNTVr1nzt8StXrqR169bZeq9Lly7h6Oj4Rp1TkA6qEEIIIYQQQmSL+j++im+ZMmVo3rw5AwcOZMmSJaSlpTFs2DC6dOmiXcH30aNHNGrUiHXr1lGjRg3tsbdv3+bYsWP8/fffGc67c+dOwsLCeO+997CwsGD//v3MnDmTsWPHvnGM0kEVQgghhBBCiHxiw4YNDBs2jEaNGmFkZESHDh348ccfta+npaUREBBAYmKiznGrVq2icOHCNG3aNMM5TU1N+emnnxg1ahQajYbixYvz/fffM3DgwDeOT6H5r2/mI8QbOuLWUd8hGJQCVsn6DsHg+Axw1HcIBsfovbr6DsHg3B6wU98hGJzwRCt9h2BwanRNfH2lfMZ2gfzuiP+ucm6vHwabU66HZW/bl/83ss2MEEIIIYQQQgiDIBlUke/E9G6k7xAMipG9pb5DMDgzd9jpOwSD82Fymr5DMDjzzJL0HYLBWeUbp+8QDM7s6wX1HYLB+cwtTN8hGJzCZw7pOwSRTWVca7y+Ug7xDz+bZ+9lSCSDKoQQQgghhBDCIEgHVQghhBBCCCGEQZBVfIUQQgghhBAiGzTI7MjcJhlUIYQQQgghhBAGQTKoQgghhBBCCJENallfNtdJBlUIIYQQQgghhEGQDKoQQgghhBBCZIPMQc19kkEVQgghhBBCCGEQJIMqhBBCCCGEENkgc1Bzn2RQhRBCCCGEEEIYBMmgCiGEEEIIIUQ2yBzU3CcZVCGEEEIIIYQQBkEyqEIIIYQQQgiRDRqNWt8h/N+TDKoQQgghhBBCCIMgGVQhhBBCCCGEyAa1zEHNdZJBFUIIIYQQQghhECSDKoQQQgghhBDZoJF9UHOdZFCFEEIIIYQQQhgEyaAKIYQQQgghRDbIHNTcJxlUIYQQQgghhBAGQTqoQgghhBBCCCEMggzxFUIIIYQQQohskEWScp9kUIUQQgghhBBCGATJoIo89+TJEyZPnsxff/1FWFgYjo6OVKxYkcmTJ1OnTh28vLy4f/8+ABYWFri5uVGjRg2GDBlCw4YNtee5d+8e3t7e+Pn5UalSJT215jmzRm0w/7ATCnsnVA+CSP5lIao7AZnWNalaF/OPumHsWghMjFGHPiJlz++knTqQXsHYGIsO/TCpUAMjVw80iQkob1wk+bcVaKIj87BVb8/0/ZaYNWyPws4R9aO7JG9Zijo4MNO6JhVqYda0E0bOHmBsgvrJY1IPb0N57vDzSmYWmLfug0mF91BY2aKOCiPt6E7STu7OoxblnMajPqZ61wZY2llz/3wg279aReS90FfWr9mjMTW7N8axsDMA4bcecfDHrQQeuaytY+NiT4uJ3Sj+vi/m1hY8uRPC4UXbub7nXK63500VHd8Z9+6NMLGzJvbcTW59vpzku69uP4BH32Z4Dm2NmYsD8TfuE/TlKuL8bmtfr7B1Kg61y+kc83jtPm5/vjzDuUwcbah6cC7mBQtwsmRvVLGJOdOwHNRtdHeadGuGtZ01N8/7s/iLnwm59/iV9buM6kbXUd10yh7efsCnDT/Rfu9e1J2+X/anTPWymJqZcvHoBZZNXkpMRHRuNSPHmLdsi2WHLhg5OqG8G0Tikh9QBt7MvG6zVpg3bIaxlzcAytsBJK1d/sr61p+OxqJFGxKWLST5zy251obc0HTUx9To2hBLO2vunQ9g21eriMjiXvKiDz5pTYvPu3J81W52TlunLa/ZtSGV2tShUDkvLGytmFyhP8kG+DuSGeuP22DbvTPGBZxIuxXE03kLSbuR+X93E28v7Ab3waxUSUwKuhM9/yfiN/+RoZ6RizP2nw7EonYNjMwtUD58RNT0OaTdzPzvmfj/p5YMaq6TDKrIcx06dMDPz4+1a9cSGBjIjh07+OCDD4iMfN7xmjZtGiEhIQQEBLBu3TocHBxo3LgxM2bM0GPkr2Za4wMsug4h+c91xE8ZgvpBENZjZ6Owdci0viYhjpSdG4ifPpz4rwaSenwvlgPGY1K+WnoFMwuMipYgZccvxE8eQuLCqRi5e2I1cnreNeodmFR+H/N2A0jZs4nE70agenQXq6HTUNjYZ1pfkxhP6r7fSJw/loTZw0g7cwCLbiMxLl1FW8e83QBMylQhed08EmZ+QtqRPzH/eAjG5WvkVbNyRL0hH1G7bzO2f7mKn9tOIjUpmX7rJmBibvrKY2JCotg7ezOLPvqKn1p/RdCp6/RcNgbXEoW0dTrN+wRnn4KsGzCPBc0mcH3PObr9NAKPckXzolnZVnhYGwr1/5Db45dxqcVEVIkp+G7+CkUW7XdpU5tiU3tzf97vXGz6OQnX71N+05eYOtvp1AtZf4DTvgO1X3en/5Lp+Up+/wkJ/vdztF05qf0nHWjZ9yMWT/yJca3HkJyYzNRfpmGaxTUCuB9wn95Ve2i/JnT4XPuauaU5U3+ZjkajYVKXL5jQfhwmpiZ8tWoyCoUit5v0Tszeb4D1wE9J2riWmM8GorobhO30uSjsHTKtb+pbiZRjB4mdOJKYMUNRP3mC7fS5GBVwznjuWu9jUros6ognudyKnPfBkI+o07c5W79cycK2k0hNSqH/a+4lzxSu4MN73RrxOJPfA1NLcwKOXubQz3/mRti5xrLxBziM+ITYlesI6z2Y1NtBuPwwGyNHh0zrKyzMUT0KIebn5agiMn/wq7C1wXXZj6BSETFyIqFd+hL94xLUcfG52BIhhHRQRZ6Kjo7m+PHjzJ49mwYNGlC0aFFq1KjBxIkTad26tbaera0t7u7uFClShHr16rFs2TImTZrE5MmTCQjIPCupT2bNPyb16N+kHd+L+vF9ktYsQJOaglm95pnWV928jPLCSdQhwajDQ0jdvxX1gzsYlyyfXiEpgcTvxpN29ijq0IeogvxJXr8QE+9SKJxc87Blb8esQVvSTu1FeeYA6tAHpPz2E5rUFEzfa5JpfdXtqyivnEYd9hBNRChpR3egfnwXY5+y2jrG3mVIO3sI1e2raKLCSTu1N71O0ZJ51awcUadfcw4v3I7//guE3nzAb6MXY+vmQNmm1V55zM2DFwk4conIe6FE3A1l39zfSE1MpkjlEto6RaqW5PTavTy8HMTTB+EcXrSd5NgECpX3zotmZVuhgS0JXvAHkXvPk+AfTMDwRZi7OeLcvPqrjxncipANBwnbfITEwIfcGr8MdVIq7l0a6tRTJaWQ9iRa+6WKT8pwLo/eTTGxt+bhzztzvG055aP+bfh94a+c3X+G+zfvsWDU9zi5OvFe01pZHqdSqoh+Eq39insaq32tTLWyuBZ25Ycx87kfcJ/7Aff5YfR8ilcoToU6FXK7Se/Eol0nUvbsIuXAblQP7pOwaB4kJ2PetEWm9ePnfkPKX9tR3bmN+mEwCT/OASMjTCpW1alnVMAZqyGfEf/dN2hUyrxoSo6q2+9DDi7cxo39Fwi9Gcyvo3/Gzs2RclncSwDMrMzpumAYWyYsJykmIcPrJ1bt5sjiHQT73cqt0HOFbdeOJPz5N4m79qC8e5/ob+ejSU7B+qMPM62f5h9AzMKlJO0/jCY1LfNz9uyKKjycp9PnkHbjJqqQUFLOnEf16NWjGcT/P00e/i+/kg6qyFM2NjbY2Niwfft2UlJS3ujYESNGoNFo+PNPA3uqa2yCsVdJlNcvPi/TaFBev4hx8bKvPu7FU5StjJFHYVQBV19dydIajVqNJtHAn9wam2DkWRxVwKXnZRoNqoBLGHmXzt4pSlbEyLUwqqBr2jLVXX9MytdAYV8gvU4JX4xcCqK66ZeT0ecqR09X7FwduX3yebtS4pJ4cCmIIlVKZHHkcwojBRU+qoWZpTnBF59/gAy+EEiFVu9haW+NQpFex8TclLv/+Od4O96WRRFXzN0ceXrs+c+5Ki6RWL/b2FUrlekxClMTbCv4EH3syvNCjYbo41ewrab7cMK1w/vUur6Sqkfm4fVFN4wszXRetypZmCKjPyZg+CI0GnXONSwHuRVxw8nVicsnLmnLEuMSCbwUQKmqWf/+FPQuyOpza1l6YgWjfxiLc0EX7Wum5qaggbQXPoinpqSiUWsoU71cZqczDCYmmBQvSeqlC8/LNBpSL13AtHQ24zY3R2FsgibueYcdhQKbMV+S/MdmVMH3cjTkvOD0773k1gv3kuR/7yVFX3MvaTu9HzcP++nch/7zTEwwLV2S5LO6PyfJ5y5g5pu9v8OZsaxXi1T/QJxmTsFj9x+4rluKdZuWORCwECIrMgdV5CkTExPWrFnDwIEDWbJkCVWqVKF+/fp06dKFChWyforv5OSEq6sr9+7dy/b7paSkZOgIp6jUmBvn3LMZha09CmNjNDFPdco1MU8x8vB89YGW1tgt+BVMTEGtJmndDyivX8i8rqkplp0HkvbPIUg27LlACms7FMbGqOOidco1cdEYuxV+9YEWVthMX6u9Hsm/L9bp5Kb8sQSLzsOxmb42Pduh0ZC8aSGqoOu505BcYOuSPsQ5/kmMTnn8kxjta6/iVsqTT7Z+jYm5KamJyfwyeD7htx9pX9847Ee6LvqMyZeXo0pTkpaUyi+D5xN5PyznG/KWzFwdAEh7Eq1TnvYkGtN/X3uZqZMtChNjUl+6ZqlPYrAv/nyIc/jWE6Q8fEJK6FNsyhbB+6seWBUryI3+cwFQmJlQevEI7k5bT8qjCCyKGuZIBEcXRwCiX5oXGh0RjaOLwyuPC/QL4Icx83kU9AgnVye6jOzKrC2z+azJpyQlJBFw8SbJicn0ntiX9bPXoVBArwl9MDYxxtHVMRdb9G4Udvbpncvol+6v0U9ReBbJ1jms+w5BHRVB2gudXIuPu6FRqUjekXHO4X/Bq+4lcU9isM3i56TiR7UoVM6LhW2+ys3w8pyRgz0KE2PUUbo/J+qop5gWzd7PSWZMChbEpn1r4jb9TtyaDZiVLYXD6GFo0tJI/Hvfu4Yt/qNkFd/cJx1Ukec6dOhAy5YtOX78OP/88w+7d+9mzpw5rFixgj59+mR5rEajeaP5UrNmzeLrr7/WKfu8ghcTK/m8Teg5KzmR+EmDwMISk7JVsOz6CeonIahuXtatZ2yM1aeTAQVJa3/QS6h5IiWJhNmfoTC3wLhkJSza9icpIhTV7fRsm2m9jzD2KkXismloosIxLlYei45DSIqJRBV4+TUn149KberQdmZ/7fdr+81563NF3HnMwhYTMbe1wrdFDT6eN4TlnadrO6lNRnfE0s6KFd1mkPA0jnJNq9H1p89Y2nEaYQEP3rktb8O1fV1KfDdY+/21HrNy7b1Cfzmg/XfizWBSw6Kp8McULIq6kXw/DO8vupN46xHhfxzPtRjeRv22H/DJrE+130/v83UWtV/t4pHnna/7N+8ReCmA5adWUadVXQ78up/YqFjmfPItQ2YOpVXfj9CoNRzbcZTbV2+jUf//ftiy6NgNs3oNiZ0wAtJSATAuXhLLNh2I/mygnqPLvspt6tB+5gDt96vf4l5i7+FE68m9Wd5zJsqUzIe0ipcYKUj1DyR28UoA0gJvY+LjjXX7j6SDKkQukg6q0AsLCwuaNGlCkyZNmDRpEgMGDGDKlClZdlAjIyN58uQJ3t7Zn1M3ceJERo8erVOWPLTN24adKU1cDBqVCoW9bhZCYe+IJiYqiwM1qMPT57GkBgdhXLAI5q26kvhiB/XfzqlRATcSvh1r8NlTAE1CLBqVCiNbB14cRKmwdUAd9/SVx6HRoIkIQQOoH93FyL0wZk06knT7KpiaYd6qF0krZqC6cR4A9eN7GBX2xqxRe5IMtIN648AFHlx6vtKssVn6LdfGxZ64F7KINi72hNzIetEeVZpKmw19fO0uhSsUo3a/5mz/YiVORVyp3acZ85uMI/xWeoc11D8Yr+qlqdWrCdu/XJXDLcueyL3nib34vP1G5untN3VxIDU8Wltu6uJAwrV7mZ4jLSoOjVKF2UsZZjMXe51zvCz23/lzlt7uJN8Pw6FueazLFMGl1XvpFf590FX7xiqCf9jK/e9+e8PW5Yyz+88Q4Pd8Xv2zhZAcnB14Gv7898XB2YG7N+5m+7wJsQk8vvsID6+C2rJLx/0Y8v5AbB3tUKtUJMQmsOb8ek4EZ2/VV33QxMagUSlROLx0f3VwRPM0i/srYNG+M5YfdyP2yzGo7t3RlpuWq4DC3hHHNc//myuMTbDqPxSLNh8T3a9LzjYiB9w4cIHgF+4lJmbpPycv30tsXex5fONepuco7OuDrYs9I3bN1JYZmxjjXaM0tXs15YuSPf+zDyvU0TFolCqMnHR/ToycHFFFZf1zkhVVRBTKu/d0ypT3grFqUO+tzyn++9T5eG5oXpEOqjAIZcuWZfv27VnW+eGHHzAyMqJt27bZPq+5uTnm5uY6ZZocHN4LgEqJ6l4gJmUro7x4Mr1MocCkbGVSD2zP/nkUChQmL6y++Kxz6laIhG/HoEmIffWxhkSlRP3gNsYlK6K8+k96mUKBcamKpB3ble3TKBRG6cN9AYyN06/Ny8Nq1GptR8MQpSYkE5mQrFMWG/6UYrXLaTuk5jaWeFYqxpkXMoDZoTBSYPJvh9fUMv1n/OUPl2q1Ov066okqIRlVgm7nJyXsKQ7vlyfh+j0AjG0ssatcnJA1ezM9hyZNSdyVOzi870vksy1zFAoc6vryeNWeV763TTkvAFLD0jt5N/rP1ZmTalupGKUWfMqlNpNJvq+/DlpSQhJJCbqLOUWFR1GhTiVth9TSxpKSlUqxZ332t1SysLLAvagHR7YezvDas8WTfGtXwN7ZnrP7z7xDC3KZUonydiCmlaqS9s+J9DKFAtNKVUjete2Vh1l06Ipl5x7ETRqH6rbuwnoph/bpDPcFsJv2HSmH95G83zC3rUpJSCYlk3tJidrlM9xLTv+yP9Nz3D55jXlNx+mUdfpuCOFBjzmyZMd/tnMKgFJJ2s1ALKpXIfnY87/D5tWrkPD79rc+beqVa5gU1Z2qY1KkMMpQw5k6IcT/I+mgijwVGRlJx44d6devHxUqVMDW1pbz588zZ84c2rR5ntmMi4sjNDSUtLQ07t69yy+//MKKFSuYNWsWxYsX12MLMpe6ZwuWAz9HdTcQ1Z2bmDXrgMLcgtTj6R+6LQd9jvppBCm/pw8TMm/VNb1u+GMUJqaYVKyJae0mJK37dwivsTFWw6ZgXLQECfO/BCMjbYZWEx8HBr7iZOrh7Vj0GIXqwS3U9wMx/aANCjML0s6kd8IseoxGHRNJ6s61AJg16Ygq+BbqiBAUJqYYl62OSfUGpPz2c/oJk5NQ3rqKeZt+pKSloo4Kx7h4eUyrNyRl+wp9NfOtnFy1h4bD2xF5L5SoB09oMqYjcWHR3Nh3Xlun/4YvuLH3PKfXpQ8haza+MwFHLhP9OAJza0sqtamN93tlWN3rWwCeBD0m4m4o7Wb25++ZG0l8GkfZptUoXrc86/rN1Us7X+XR8r8oMrIDSXdCSQ4Ox+vzzqSEPSXihf1afX+fTOTus9oO6KOluyj1w6fEXw4i1u82hQe2xMjKnNDN6Z0vi6JuuLavS9RBP9KexmFdpijFpvUm+vQNEvyDAUh+aS6uqZMtAIm3HhrcPqg7V/5Jp886E3LvEWHBYXQb24Oo8Cj+2XdaW2faphn8s+c0f69Nf+jT58t+nDtwliePwnFyc6Lr6O6oVWqO/XlUe0yjjo15cPsBsVExlKpSmgFTB7FjxZ88uvMoQwyGJHnbb9iMnojq1k2UgTexaPMxCgtLUv7tTNqM/gJ15BMS16bveWvxcVesevQjfs50VOGhKBydANAkJUFyEpq4WFRxug/8NCol6qdRqB/pZzj82zixajcNh7cl4l4oUQ/CaTqmI7FhT7n+wr1k4IYvub73HKfW7SMlIZmwwIc650hNSiExOl6n3MbFHlsXB5yLugPgXsqTlIRkoh9FZLrqr6GI2/Q7TpMnkOofQOqNm9h06YCRhQUJu9LvI45TJqB6EkHsz//+zTAxwdQ7fRsuhakJxi7OmJYohjopCdXDx/+ecwuuKxZi27sbiQePYFa2NNZtW/J01vd6aaMwDDIHNfdJB1XkKRsbG2rWrMn8+fMJCgoiLS0NT09PBg4cyBdffKGtN3nyZCZPnoyZmRnu7u689957HDx4kAYNGugx+ldLO3sEhZ09Fu37oLB3RBUcRMLcCWhi07M3Rk6u8OLTaXMLLHp9hpGTC5rUFNQhD0haOou0s0cAUDg6Y1qlDgC23yzXea/4WaMzzlM1MEq/46TY2GPeogcKO0fUD++QuHgymn8XTlI4umD04iqqZuZYdByKwqEApKWiDn9I8rp5KP2ezxdMXjMb8496Y9FrLAorG9RPw0n5az1pJwwz4/Eqx5bsxMzSnHazBmBhZ8X9c4Gs7v2tzpywAkXdsPq3AwVgXcCOTt9/gq2LA8lxiYTefMDqXt9y+0T6KpxqpYo1fefQ/PMu9FoxFnNrcyLvh7FlzBICjlzK6yZm6eGiPzG2sqDk3MGY2FkRc/Ym17rOQPNC+y293LQdSIAnf57CtIAdRcd3xszFgfjr97jWdQZpEekLxGjSlDjUq0ChgS0xtjIn5XEkEX+dIXj+f3MBnK2L/8DC0oKhs4ZjbWeN//kbfN1zMmkvXCP3Iu7YOT3fB9bZw5mxi8Zh62BHTFQM/uduML7tGGKjnnfEChUrRM/Pe2PjYEP4w3B+X/gbO1Zsz8umvZXU44dJtHfAskc/jBydUN65TdzkcdqFk4xcXHVWZbZo0QaFqRm2X+ruG524YTVJG9fkZei56si/95IO/95L7p0LYGUm9xLrF36XsqNW98Y0Gfmx9vuhv08F4Nexi7mw5ViOxJ4bkg4cIdrBAbtBfTEu4EhaYBARIz/XLpxk4uaaPurmX8YuBXD75fnfV9senbHt0ZmUC5d4MjR9alCafwCR4ydjP3QAdv17oXwcQsz8n0naezBvGydEPqPQyGMAkc/E9G6k7xAMipG9pb5DMDgzd9i9vlI+82GyLKrysnlmGfdZze9W+cbpOwSDM/t6wddXymc+c5Mhsi8rfOaQvkMQ2eRkm71t4XJCVNx/az/inCL7oAohhBBCCCGEMAgyxFcIIYQQQgghskEGn+Y+yaAKIYQQQgghhDAIkkEVQgghhBBCiGyQfVBzn2RQhRBCCCGEEEIYBMmgCiGEEEIIIUQ2yBzU3CcZVCGEEEIIIYQQBkE6qEIIIYQQQgghDIIM8RVCCCGEEEKIbFDLEN9cJxlUIYQQQgghhBAGQTKoQgghhBBCCJENGtlmJtdJBlUIIYQQQgghhEGQDKoQQgghhBBCZIPMQc19kkEVQgghhBBCCGEQJIMqhBBCCCGEENmgkQxqrpMMqhBCCCGEEEIIgyAZVCGEEEIIIYTIBlnFN/dJBlUIIYQQQgghhEGQDKoQQgghhBBCZIPMQc19kkEVQgghhBBCCGEQJIMqhBBCCCGEENkgGdTcJxlUIYQQQgghhMgnZsyYQe3atbGyssLBwSFbx2g0GiZPnoyHhweWlpY0btyYW7du6dSJioqie/fu2NnZ4eDgQP/+/YmPj3/j+KSDKoQQQgghhBDZoMnDr9ySmppKx44d+eSTT7J9zJw5c/jxxx9ZsmQJZ86cwdrammbNmpGcnKyt0717d65fv87+/fvZtWsXx44dY9CgQW8cnwzxFUIIIYQQQggDk5KSQkpKik6Zubk55ubm73Ter7/+GoA1a9Zkq75Go2HBggV89dVXtGnTBoB169bh5ubG9u3b6dKlC/7+/uzZs4dz585RrVo1ABYuXEiLFi2YO3cuBQsWzH6AGiFEnktOTtZMmTJFk5ycrO9QDIZck4zkmmQk1yQjuSa65HpkJNckI7kmGck1MTxTpkzJkFidMmVKjp1/9erVGnt7+9fWCwoK0gAaPz8/nfJ69eppPvvsM41Go9GsXLlS4+DgoPN6WlqaxtjYWLN169Y3ikuh0chMXyHyWmxsLPb29sTExGBnZ6fvcAyCXJOM5JpkJNckI7kmuuR6ZCTXJCO5JhnJNTE8uZVBfWbNmjWMHDmS6OjoLOudOnWKOnXq8PjxYzw8PLTlnTp1QqFQ8OuvvzJz5kzWrl1LQECAzrGurq58/fXXbzScWOagCiGEEEIIIYSBMTc3x87OTufrVZ3TCRMmoFAosvy6efNmHrfg7cgcVCGEEEIIIYT4DxszZgx9+vTJso6Pj89bndvd3R2AsLAwnQxqWFgYlSpV0tYJDw/XOU6pVBIVFaU9PrukgyqEEEIIIYQQ/2EuLi64uLjkyrm9vb1xd3fn4MGD2g5pbGwsZ86c0Q7drVWrFtHR0Vy4cIGqVasCcOjQIdRqNTVr1nyj95MhvkLogbm5OVOmTMmxOQT/D+SaZCTXJCO5JhnJNdEl1yMjuSYZyTXJSK5J/hEcHMylS5cIDg5GpVJx6dIlLl26pLNnaenSpdm2bRsACoWCkSNH8s0337Bjxw6uXr1Kr169KFiwIG3btgWgTJkyNG/enIEDB3L27FlOnjzJsGHD6NKly5ut4AvIIklCCCGEEEIIkU/06dOHtWvXZig/fPgwH3zwAZDeKV29erV22LBGo2HKlCksW7aM6Oho6taty88//0zJkiW1x0dFRTFs2DB27tyJkZERHTp04Mcff8TGxuaN4pMOqhBCCCGEEEIIgyBDfIUQQgghhBBCGATpoAohhBBCCCGEMAjSQRVCCCGEEEIIYRCkgyqEEEIIIYQQwiBIB1UIIYQQQgghhEGQDqoQQgjxHzJlyhTu37+v7zDEf8yzvQ6fPn2q71D05smTJ6987erVq3kYiRAiK7LNjBB5pF27digUigzlCoUCCwsLihcvTrdu3ShVqpQeotOP0aNHZ1r+4jVp06YNTk5OeRyZ/uzZswcbGxvq1q0LwE8//cTy5cspW7YsP/30E46OjnqOUOhbpUqVuHbtGvXr16d///506NABc3NzfYelV5UrV37t/bVPnz40aNBAD9Hpx8iRI/H19aV///6oVCrq16/PqVOnsLKyYteuXdq9DvMTd3d3Vq5cScuWLXXK586dy6RJk0hKStJTZHnvypUr2apXoUKFXI5EiIykgypEHunTpw/bt2/HwcGBqlWrAnDx4kWio6Np2rQply9f5t69exw8eJA6deroOdq80aBBAy5evIhKpdJ2zAMDAzE2NqZ06dIEBASgUCg4ceIEZcuW1XO0ecPX15fZs2fTokULrl69SvXq1Rk9ejSHDx+mdOnSrF69Wt8h5jl5kJGRn58fq1evZtOmTSiVSrp06UK/fv2oXr26vkPTi4kTJ7J48WJ8fX2pUaMGAOfOnePKlSv06dOHGzducPDgQbZu3UqbNm30HG3eKFy4MNu3b6datWps376dTz/9lMOHD7N+/XoOHTrEyZMn9R1inpszZw6TJ0+mb9++fP/990RFRdGrVy+uXr3K0qVLadeunb5DzDNGRkYoFAoy6wY8K1coFKhUKj1EJ/I76aAKkUcmTJhAbGwsixYtwsgofXS9Wq1mxIgR2NraMmPGDIYMGcL169c5ceKEnqPNGwsWLOD48eOsXr0aOzs7AGJiYhgwYAB169Zl4MCBdOvWjaSkJPbu3avnaPOGjY0N165dw8vLi6lTp3Lt2jW2bNnCxYsXadGiBaGhofoOMc/Jg4xXS0tLY+fOnaxevZq9e/dSunRp+vfvT58+fbC3t9d3eHlm4MCBFClShEmTJumUf/PNN9y/f5/ly5czZcoU/vrrL86fP6+nKPOWhYUFt2/fpnDhwgwaNAgrKysWLFjA3bt3qVixIrGxsfoOUS/8/Pzo2bMnKSkpREVFUbNmTVatWoW7u7u+Q8tT2Z0mULRo0VyORIhMaIQQecLZ2VkTEBCQoTwgIEBToEABjUaj0Vy5ckVjb2+fx5HpT8GCBTXXr1/PUH7t2jVNwYIFNRqNRnPhwgXt9ckPHB0dtdekTp06mqVLl2o0Go3m7t27GktLS32Gpjfz58/XtG/fXhMTE6Mti46O1nz88ceaBQsWaBISEjRt2rTRNG3aVI9R6kdKSopm8+bNmqZNm2pMTEw09erV0xQvXlxja2ur2bx5s77DyzN2dnaaW7duZSi/deuWxs7OTqPRaDT+/v4aGxubvA5Nb4oUKaLZu3evRqlUajw9PTW7du3SaDTp91cHBwc9R6c/sbGxms6dO2tMTEw0JiYmmjVr1ug7JCHES0z03UEWIr9QKpXcvHmTkiVL6pTfvHlTO4TGwsIi03lU/69iYmIIDw/PkPV68uSJ9um+g4MDqamp+ghPL+rWrcvo0aOpU6cOZ8+e5ddffwXSM4aFCxfWc3T68d1337F//35tlh3A3t6eqVOn0rRpU0aMGMHkyZNp2rSpHqPMWxcuXNAO8TU3N6dXr1789NNPFC9eHICFCxfy2Wef0blzZz1HmjcsLCw4deqUtv3PnDp1CgsLCyB9xMqzf+cHffv2pVOnTnh4eKBQKGjcuDEAZ86coXTp0nqOTj9OnjxJjx49cHJy4sqVK5w8eZLhw4fz999/s2TJknw5x//WrVv8+eef3Lt3D4VCgbe3N23btsXHx0ffoYl8TDqoQuSRnj170r9/f7744gvtPLFz584xc+ZMevXqBcDRo0cpV66cPsPMU23atKFfv37MmzdP55qMHTuWtm3bAnD27NkMnfr/Z4sWLWLo0KFs2bKFxYsXU6hQIQB2795N8+bN9RydfsiDDF2+vr7cvHmTpk2bsnLlSj766COMjY116nTt2pURI0boKcK8N3z4cIYMGcKFCxd07iUrVqzgiy++AGDv3r1UqlRJj1HmralTp1K+fHkePHhAx44dtQtpGRsbM2HCBD1Hpx8NGzZk1KhRTJ8+HVNTU8qUKUODBg3o0aMHvr6+PHz4UN8h5qlZs2YxefJk1Go1rq6uaDQanjx5woQJE5g5cyZjx47Vd4giv9J3CleI/EKpVGq++eYbjbu7u0ahUGgUCoXG3d1dM2PGDI1SqdRoNBrN/fv3NQ8ePNBzpHknLi5OM2DAAI2ZmZnGyMhIY2RkpDEzM9MMHDhQEx8fr9FoNBo/Pz+Nn5+ffgMVetWtWzeNt7e3ZuvWrZoHDx5oHjx4oNm6davGx8dH06NHD41Go9Fs2rRJU7VqVT1HmjemTZumefjwob7DMDi//PKL5r333tM4OjpqHB0dNe+9955mw4YN2tcTExM1SUlJeoxQ6NuRI0cyLVepVJpp06blcTT6dejQIY2RkZFmypQpmqioKG15ZGSkZtKkSRpjY2PN0aNH9RihyM9kkSQh9OBZ1ufFIYv5WXx8PHfu3AHAx8cHGxsbPUekP8bGxoSEhODq6qpTHhkZiaura75cUTE+Pp5Ro0axbt06lEolACYmJvTu3Zv58+djbW3NpUuXAPJVhkyIrPz444+Zlr+4+nW9evUyZN/zg9u3bxMUFES9evWwtLTUrlibn3Tu3BkHBweWLl2a6euDBg0iLi6OTZs25XFkQsgqvkIIYVCMjIwIDQ3N0EF9/PgxxYoVy1f79L1MHmSk69ChAzVq1ODzzz/XKZ8zZw7nzp3j999/11Nk+peamkp4eDhqtVqnvEiRInqKSH+8vb158uQJiYmJ2rmVT58+xcrKChsbG8LDw/Hx8eHw4cN4enrqOdq8ERkZSadOnTh8+DAKhYJbt27h4+NDv379cHJyYu7cufoOMc94e3uzfv167Z7bLzt+/Di9evXi7t27eRyZENJBFSLPhIWFMXbsWA4ePEh4eHiGvcfyY2YsISGBb7/9VntNXv5Q+awzkh88y3Y8mx/1YudLpVJx7Ngx7t27h5+fn75CFAbCxcWFQ4cO4evrq1N+9epVGjduTFhYmJ4i059bt27Rr18/Tp06pVOuycd7OW7atIlly5axYsUKihUrBqRnDgcPHsygQYOoU6cOXbp0wd3dnS1btug52rzRq1cvwsPDWbFiBWXKlOHy5cv4+Piwd+9eRo8ezfXr1/UdYp6xsrLKcvG9hw8fUqJEiXz9UFTojyySJEQe6dOnD8HBwUyaNEm7qmJ+N2DAAI4ePUrPnj3z/TWZP38+kP6BesmSJTrD7szMzPDy8mLJkiX6Ck+v5EGGrvj4eMzMzDKUm5qa5tu9Lfv06YOJiQm7du3K9/eSZ7766iv++OMPbecUoHjx4sydO5cOHTpw584d5syZQ4cOHfQYZd7at28fe/fuzdApK1GiRLb3Bf1/kZycnOl95BlTU9N8s/CcMDzSQRUij5w4cYLjx4/LHLkX7N69m7/++os6deroOxS9ezaMqkGDBmzdujVfbnfwKvIgQ5evry+//vorkydP1infvHlzhpWO84tLly5x4cKFfLt9SmZCQkK0c7ZfpFQqCQ0NBaBgwYLExcXldWh6k5CQgJWVVYbyqKgo7SrH+cmKFSteOVUiP/1cCMMjHVQh8oinp2eGYb35naOjI05OTvoOw6AcPnxY3yEYHHmQoWvSpEm0b9+eoKAgGjZsCMDBgwfZtGlTvp1/WrZsWSIiIvQdhkFp0KABgwcPZsWKFVSuXBkAPz8/PvnkE+3PzdWrV/H29tZnmHnq/fffZ926dUyfPh1IXzBKrVYzZ84cGjRooOfo8laRIkVYvnz5a+sIoQ8yB1WIPLJv3z7mzZvH0qVL8fLy0nc4BuGXX37hzz//ZO3atZk+1c6PVCoVa9aseeVw1kOHDukpMv3x9vbm77//pkyZMvoOxWD89ddfzJw5k0uXLmFpaUmFChWYMmUK9evX13doenHo0CG++uorZs6cia+vL6ampjqv58cV00NDQ+nZsycHDx7UXg+lUkmjRo1Yv349bm5uHD58mLS0NJo2barnaPPGtWvXaNSoEVWqVOHQoUO0bt2a69evExUVxcmTJ3WGQwsh9Ec6qELkEUdHRxITE1EqlVhZWWX4ABUVFaWnyPSncuXKBAUFodFo8PLyynBNLl68qKfI9GfYsGGsWbOGli1bZjqc9dlc1fxEHmSI1zEyMgLI8PuSnxdJeubmzZsEBgYCUKpUKUqVKqXniPQrJiaGRYsWcfnyZeLj46lSpQqffvopHh4e+g4tT50+fZrIyEhatWqlLVu3bh1TpkwhISGBtm3bsnDhwnw59Fnon3RQhcgja9euzfL13r1751EkhuPrr7/O8vUpU6bkUSSGw9nZmXXr1tGiRQt9h2Iw5EGGeJ2jR49m+Xp+zSwL8SrNmzenQYMG2u2qrl69SpUqVejTpw9lypThu+++Y/DgwUydOlW/gYp8STqoQghhQAoWLMiRI0coWbKkvkMxGPIgA5ycnAgMDMTZ2RlHR8csF4rKj6MxREYyXSDdlStXsl23QoUKuRiJYfHw8GDnzp1Uq1YNgC+//JKjR49y4sQJAH7//XemTJnCjRs39BmmyKdkkSQhclFsbKx27tPrtn/Ij3OkREZjxozhhx9+YNGiRfl+tdpn8kMH9HXmz5+Pra0tAAsWLNBvMAbiypUrlC9fHiMjo9d2QvJTx+OZESNGaKcLlC9fPt/eTypVqoRCodAO937mWX7mxbL8NBT86dOnuLm5ab8/evQoH374ofb76tWr8+DBA32EJoRkUIXITcbGxoSEhODq6oqRkVGmHxDy2xwpyQRlrV27dhw+fBgnJyfKlSuXYTjr1q1b9RSZEIbFyMiI0NBQnftrZh9p8tP99UUyXSDdi/ub+vn5MXbsWMaNG0etWrWA9LmY8+bNY86cObRt21ZPUea9okWLsn79eurVq0dqaioODg7s3LmTRo0aAelDfuvXr58v/w4L/ZMMqhC56NChQ9ptVGT7kHQvZoLmz5+fb5/qv4qDgwPt2rXTdxh6Jw8ydL1uBMaL8stojLt37+Li4qL9t9BlZmZG8eLF9R2G3hUtWlT7744dO/Ljjz/qdNorVKiAp6cnkyZNylcd1BYtWjBhwgRmz57N9u3bsbKy4v3339e+fuXKFVnVWOiNZFCFEEIYnLVr19KlSxfMzc1Zs2ZNlh3U/LDA2KtGYLwov43GEFmbN28ed+7ckekCL7C0tOTixYsZtqzy9/enSpUqJCUl6SmyvBcREUH79u05ceIENjY2rF27VufhaKNGjXjvvfeYMWOGHqMU+ZV0UIXII3v27MHGxoa6desC8NNPP7F8+XLKli3LTz/9hKOjo54jzHsXL17E1NQUX19fAP78809Wr15N2bJlmTp1KmZmZnqOUD+USiVHjhwhKCiIbt26YWtry+PHj7Gzs8PGxkbf4Qk9eN0qtS/KjyvWzpo1Czc3N/r166dTvmrVKp48eaJdqTQ/kekCGVWpUoXy5cuzYsUK7d+X1NRUBgwYwLVr1/LliuAxMTHY2NhgbGysUx4VFYWNjU2+/Tss9Es6qELkEV9fX2bPnk2LFi24evUq1apVY8yYMRw+fJjSpUuzevVqfYeY56pXr86ECRPo0KEDd+7coWzZsrRv355z587RsmXLfLkYzP3792nevDnBwcGkpKQQGBiIj48PI0aMICUlhSVLlug7xDwnDzLE63h5ebFx40Zq166tU37mzBm6dOmSL4cA9+3bN8vX8+PfnLNnz/LRRx+h0Wi0C2dduXIFhULBzp07qVGjhp4jFEKAdFCFyDM2NjZcu3YNLy8vpk6dyrVr19iyZQsXL16kRYsWhIaG6jvEPGdvb8/FixcpVqwYs2fP5tChQ+zdu5eTJ0/SpUuXfLmCYNu2bbG1tWXlypUUKFCAy5cv4+Pjw5EjRxg4cCC3bt3Sd4h5Th5kZBQdHc3KlSvx9/cHoFy5cvTr1w97e3s9R6YfFhYW+Pv74+3trVP+7OclOTlZT5EJQ5OQkMCGDRu4efMmAGXKlKFbt25YW1vrOTIhxDOySJIQecTMzIzExEQADhw4QK9evYD0xWDeZAGU/ycajUa7N9+BAwdo1aoVAJ6enkREROgzNL05fvw4p06dypAV9PLy4tGjR3qKSr8CAwOpVKkSkL43X/369dm4caP2QUZ+66CeP3+eZs2aYWlpqc34fP/998yYMYN9+/ZRpUoVPUeY9zw9PTl58mSGDurJkycpWLCgnqIShsja2ppBgwbpOwwhRBakgypEHqlbty6jR4+mTp06nD17ll9//RVI//BduHBhPUenH9WqVeObb76hcePGHD16lMWLFwPpK3K+uD9bfqJWqzNd5Obhw4fa1Y/zG3mQoWvUqFG0bt2a5cuXY2KS/mdcqVQyYMAARo4cybFjx/QcYd4bOHAgI0eOJC0tjYYNGwJw8OBBxo8fz5gxY/QcXd6pUqUKBw8exNHRkcqVK2e5OFJ+nG8JcOvWLQ4fPkx4eLj2vvLM5MmT9RSVEOJF0kEVIo8sWrSIoUOHsmXLFhYvXkyhQoUA2L17N82bN9dzdPqxYMECunfvzvbt2/nyyy+1WyJs2bIlw1yy/KJp06YsWLCAZcuWAel7OMbHxzNlypR8u5+hPMjQdf78eZ3OKYCJiQnjx4+nWrVqeoxMf8aNG0dkZCRDhw4lNTUVSB/2+/nnnzNx4kQ9R5d32rRpg7m5OUC+2jIlu5YvX84nn3yCs7Mz7u7uOh14hUIhHVQhDITMQRVCGJzk5GSMjY0zrDqZHzx8+JBmzZqh0Wi4desW1apV49atWzg7O3Ps2DFcXV31HWKeu3LlCt27dyc4OJjRo0czZcoUAIYPH05kZCQbN27Uc4R5y83NjfXr19O0aVOd8r1799KrVy/CwsL0FJn+xcfH4+/vj6WlJSVKlNB21vIblUrFyZMnqVChAg4ODvoOx2AULVqUoUOH5stVnYX4L5EOqhB5SKVSsX37dp2FTVq3bp1heff85sKFC9prUrZs2Xw5h+5FSqWSzZs3c+XKFeLj46lSpQrdu3fH0tJS36EZlPz6IOOzzz5j27ZtzJ07VzvS4OTJk4wbN44OHTrkuzm5InOvWjgqP7Ozs+PSpUv4+PjoOxQhRBakgypEHrl9+zYtWrTg0aNHlCpVCoCAgAA8PT3566+/KFasmJ4jzHvh4eF07tyZo0ePap/yR0dH06BBAzZv3oyLi4t+A9SD5ORkLCws9B2GQZIHGelSU1MZN24cS5YsQalUAmBqasonn3zCt99+m2+zhufPn+e3334jODhYO8z3mfy452e1atWYPXs2jRo10ncoBqN///5Ur16dIUOG6DsUIUQWpIMqRB5p0aIFGo2GDRs24OTkBEBkZCQ9evTAyMiIv/76S88R5r3OnTtz584d1q1bR5kyZQC4ceMGvXv3pnjx4mzatEnPEeY9Ozs72rVrR48ePWjUqBFGRkb6Dknv5EFG5hITEwkKCgKgWLFiWFlZ6Tki/dm8eTO9evWiWbNm7Nu3j6ZNmxIYGEhYtaAQDgAAOblJREFUWBjt2rXLl3t+7tmzh4kTJzJ9+nSqVq2aYRsVOzs7PUWmP7NmzeL777+nZcuW+Pr6Zhh98dlnn+kpMiHEi6SDKkQesba25p9//sHX11en/PLly9SpU4f4+Hg9RaY/9vb2HDhwgOrVq+uUnz17lqZNmxIdHa2fwPRo27ZtbNy4kb/++gt7e3s6d+5Mjx498u3iNyAPMrLybK9gT09PPUeiXxUqVGDw4MF8+umn2NracvnyZby9vRk8eDAeHh58/fXX+g4xz734cOvFxYA0Gg0KhSLT1cL/32U13FmhUHDnzp08jEYI8Sqyiq8QecTc3Jy4uLgM5fHx8Rn2vMwv1Gp1pvMHTU1NMyz/n1+0a9eOdu3aERcXx5YtW9i0aRPvvfcePj4+9OjRI1+uMrlnzx4OHDig7ZxC+hDfn376KcNCQfmBUqnk66+/5scff9Q+2LKxsWH48OFMmTIl383JBQgKCqJly5ZA+p7TCQkJKBQKRo0aRcOGDfNlB/Xw4cP6DsHg3L17V98hCCGyQTqoQuSRVq1aMWjQIFauXEmNGjUAOHPmDEOGDKF169Z6jk4/GjZsyIgRI9i0aRMFCxYE4NGjR4waNSrfz5uytbWlb9++9O3blxs3btC9e3e+/vrrfNlBlQcZuoYPH87WrVuZM2cOtWrVAuD06dNMnTqVyMhI7TY8+Ymjo6P2AWChQoW4du0avr6+REdHk5iYqOfo9KN+/fr6DkEIId6KDPEVIo9ER0fTu3dvdu7cqf2wrVQqad26NWvWrMHe3l7PEea9Bw8e0Lp1a65fv64dovjgwQPKly/Pjh07KFy4sJ4j1J/k5GR27NjBxo0b2bNnD25ubnTt2pVvv/1W36HluTZt2hAdHZ3hQUb37t1xdHRk27Zteo4wb9nb27N582Y+/PBDnfK///6brl27EhMTo6fI9Kdbt25Uq1aN0aNHM336dBYuXEibNm3Yv38/VapUyZeLJAEcP36cpUuXcufOHX7//XcKFSrE+vXr8fb2pm7duvoOL088+5mwtrZm9OjRWdb9/vvv8ygqIURWJIMqRB5xcHDgzz//5NatW9y8eROAMmXKULx4cT1Hpj+enp5cvHiRAwcO6FyTxo0b6zky/dm7dy8bN25k+/btmJiY8PHHH7Nv3z7q1aun79D0ZtGiRbRu3RovL68MDzJ++eUXPUeX98zNzfHy8spQ7u3tnW+nCyxatIjk5GQAvvzyS0xNTTl16hQdOnTgq6++0nN0+vHHH3/Qs2dPunfvzsWLF0lJSQEgJiaGmTNn8vfff+s5wrzh5+dHWlqa9t+v8uI8XSGEfkkGVQghDIiVlRWtWrWie/futGjRIl/OJ8yMRqORBxn/mjZtGjdv3mT16tXaLWVSUlLo378/JUqUYMqUKXqOMG8plUo2btxIs2bNcHNz03c4BqNy5cqMGjWKXr16aReO8vHxwc/Pjw8//JDQ0FB9h2iwHj58SMGCBWUVdSH0RDqoQuSi1w0nelF+GVr0448/ZrtuflzyPy4uDltbW32HIQxYu3btOHjwIObm5lSsWBFIXw08NTU1w9zt/DK01crKCn9/f4oWLarvUAyGlZUVN27cwMvLS6eDeufOHcqWLavNOIuM7OzsuHTpEj4+PvoORYh8SYb4CpGLshpO9KL8NLTof+3dd1SU19o28GtAUJCujIJRiiCKWFAklkQEPTaCUUwUG5bIGxsooOJJIhiNNUGiMUvjGyzEHuKxJMGGChEUURAsWEAUVFCKqJRYBr4/fJnjOEQ954uzR5/rtxZrwX7mjwvWzDD3s/e+d1RU1Cs9TiaTSbJANTY2Rk5ODtavX4+cnBysWLECcrkccXFxaNGiBdq2bSs6okbwRsZfMzMzw9ChQ1XGpH7MjLu7O86cOcMC9RlNmzZFdna22nLwY8eOsfB6Cc7dEInFApXoNWKbf3Vs8/9iCQkJGDBgAHr06IHExEQsXLgQcrkcGRkZiI6ORmxsrOiIGsEbGX9t/fr1r/S4pKQkPHz4ULkM+G02ZcoUhISEID8/H507d0bDhg1Vrrdv315QMnECAgIwffp0rFu3DjKZDLdu3cLx48cxc+ZMzJ07V3Q8IqK/xCW+RBpy7949KBQKWFhYqIyXlpaiXr16MDExEZSMtEm3bt3w8ccfIyQkRGVZ3smTJ+Hr64sbN26IjkhvCCktU6xrr6BMJkNNTQ1kMhkUCoWAVGLV1NRg0aJFWLx4sfKonfr162PmzJlYsGCB4HTa7dn3XiLSPM6gEmmIn58ffHx8MGXKFJXxHTt2YM+ePZLpqPisoUOHwt3dHWFhYSrjy5YtQ2pqKn7++WdBycQ5e/YstmzZojYul8tRXFwsIBG9qaR0/5krM9TJZDJ8/vnnmDVrFrKzs1FeXg5nZ2cYGRmJjkZE9EJsT0akISkpKfD09FQb79WrF1JSUgQkEi8xMREDBw5UGx8wYAASExMFJBLPzMwMBQUFauPp6elo1qyZgETiDR06FEuXLlUbX7ZsGT7++GMBiUjbXL9+Hc2aNYONjY3KV7NmzXD9+nXR8YTYtGkTKisroa+vD2dnZ7i7u7M4fUVS6gtBpI1YoBJpyMOHD/HkyRO18cePH6OqqkpAIvHKy8vrPLdRT08P9+/fF5BIPD8/P4SFhaGwsBAymQzV1dVISkrCzJkz4e/vLzqeELyRQS/j6emJ0tJStfF79+7VeWNQCoKDgyGXyzFy5Ej8/vvvklzm/N+S0uoDIm3EApVIQ9zd3bF27Vq18TVr1qBz584CEonXrl07bN++XW1827ZtcHZ2FpBIvEWLFqF169Zo3ry5cklez5490b17d3zxxRei4wnBGxn0MrV7TZ9XUlKi1jBJKgoKCrBt2zbIZDIMGzYMVlZWmDp1KpKTk0VHE2b9+vXK/bgvcuHCBXaEJhKITZKINCQpKQl9+vRBly5dlGcVxsfHIzU1FQcOHMD7778vOKHm7d27F76+vhg5ciS8vLwAPP2bbN26FT///DMGDx4sNqBA+fn5OHv2LMrLy+Hq6gpHR0fRkYRxd3fHBx98gPDwcJXxefPmYe/evTh9+rSgZNpNCk2SfH19AQC7d+9G//79VToWKxQKZGZmwsnJCfv27RMVUStUVlbiX//6F7Zs2YJDhw7hnXfeQU5OjuhYGtekSRNUVVXh448/xieffILu3buLjkREdWCTJCIN6dGjB44fP46vv/4aO3bsgIGBAdq3b4/o6GjJFh8+Pj7YtWsXFi1ahNjYWOXf5NChQ/Dw8BAdT6jmzZu/8GxLKRQftebOnQtfX1/k5OTUeSOD6iaF+8+mpqYAnv6uxsbGMDAwUF7T19dH165dERAQICqe1jA0NES/fv1w9+5dXL9+HVlZWaIjCXHz5k3s3bsXGzZsQK9evWBvb4/x48dj7NixaNq0qeh4RPR/OINKpGWWLFmCSZMmwczMTHQUrbF161YMGjRIskv16iK1YxB+++03LFq0CGfOnFHeyIiIiJD8jQx66ssvv8TMmTNf+h6RlJQENzc3SZwNC/x75nTz5s2Ij49H8+bNMWLECIwaNQqtW7cWHU+o27dvY9OmTdi4cSMuXryI/v3745NPPoGPj0+dxxYRkeawQCXSMlKaGXtV/Juok1qB+iqkciPD1dW1zv2WMpkMDRo0gIODA8aNGyfZ5kAvIqX3Ej8/P/z6668wNDTEsGHDMGrUKHTr1k10LK2SkpKCdevWYePGjbCyssLdu3dhbm6O9evXo1evXqLjEUkWbxERaRneM1LHvwm9ik8//RS3b98WHeO169+/P65evYqGDRvC09MTnp6eMDIyQk5ODrp06YKCggL06dMHu3fvFh1V60jpvURXVxc7duxAQUEBVq1axeL0/9y+fRvffPMN2rZti169euH+/fv49ddfkZubi5s3b2LYsGEYO3as6JhEksY9qERE9FaQSvFRXFyM0NBQzJ07V2X8q6++wvXr13HgwAFERERgwYIF+PDDDwWlJNE2b94sOoLW8fHxwf79+9GqVSsEBATA398fFhYWyusNGzZEaGgovv76a4EpiYgzqEREbyAeJC9dO3bswIgRI9TG/fz8sGPHDgDAiBEjcOnSJU1HIy2TkJAAHx8fODg4wMHBAYMGDcIff/whOpYwcrkcCQkJOHfuHGbMmKFSnNaytLREbm6ugHREVIsFKhHRG0gqs4WkrkGDBnWeZZmcnIwGDRoAAKqrq5XfkzRt2rQJffr0gaGhIYKCghAUFAQDAwP07t0bW7ZsER1PiOjo6JcudZbJZDwDlUgwLvElInoDxcXFoVmzZqJjkACBgYGYNGkSTp8+jS5dugAAUlNT8eOPP+Kzzz4DAOzfvx8dO3YUmFI7SWnlwcKFC7Fs2TIEBwcrx4KCgrB8+XIsWLAAI0eOFJhOjKCgIDg4OCAoKEhlfNWqVcjOzsa3334rJhgRqWAXXyItM3DgQERHR8PKykp0FK3h4uKCuLi4F54L+rYYOnQo3N3dERYWpjK+bNkypKam8tzPF5BSZ+PNmzdj1apVymW8Tk5OCAwMVBYdVVVVyq6+9G9Seo7Ur18f58+fh4ODg8p4dnY2XFxc8OeffwpKJk6zZs2wZ88edO7cWWU8LS0NgwYNwo0bNwQlI6JncQaV6DW6f//+Kz/WxMQEAPD777+/rjhaqaysDLGxscjJycGsWbNgYWGBtLQ0NGnSRDlDeO7cOcEpNScxMRHz5s1TGx8wYAAiIyM1H+gNYmNjAz09PdExNGLUqFEYNWrUX143MDDQYBrxvLy8sHPnTrXzo+/fv4/Bgwfj8OHDAIAHDx4ISCdG8+bNER8fr1agHjp0SBI3++pSUlICU1NTtXETExMUFxcLSEREdWGBSvQamZmZvXRJWU1NDWQyGRQKhYZSaY/MzEz06dMHpqamuHbtGgICAmBhYYGdO3ciLy8PMTExoiNqXHl5OfT19dXG9fT0/qMbHm8Te3t7pKamolGjRirjZWVl6NSpE65evQpAWjcyAODRo0e4c+cOqqurVcZbtGghKJE4R48exaNHj9TG//zzT8k2BQoNDUVQUBDOnDmD7t27AwCSkpKwYcMGrFixQnA6MRwcHLBv3z5MmzZNZTwuLk4Ss+pEbwoWqESv0ZEjR0RH0GohISEYN24cli1bBmNjY+X4wIEDJbk/CgDatWuH7du3Izw8XGV827ZtcHZ2FpRKrGvXrtV5A+fhw4e4efOmgERiXblyBRMmTFBrlCTFm12ZmZnK7y9cuIDCwkLlzwqFAvv27ZPsXu3JkyejadOmiIyMVHZ3btOmDbZv3y7Z44dCQkIwbdo0FBUVwcvLCwAQHx+PyMhI7j8l0iIsUIleIw8PD9ERtFpqaip++OEHtfFmzZqpfNCUkrlz58LX1xc5OTkqH6C2bt0quf2ne/bsUX6/f/9+laV5CoUC8fHxsLW1FZBMrHHjxqFevXr49ddfYWVlJanGP8/r2LEjZDIZZDKZ8vXyLAMDA3z33XcCkmmHIUOGYMiQIaJjaI0JEybg4cOHWLhwIRYsWAAAsLW1xerVq+Hv7y84HRHVYpMkIg0qKytDdHQ0srKyAABt27bFhAkT6twTIwVyuRz79++Hq6urSvOSgwcPYsKECcjPzxcdUYjffvsNixYtwpkzZ2BgYID27dsjIiJCcjc8dHT++iQ0PT092NraIjIyEh988IEGU4nXsGFDnD59Gq1btxYdRbjr16+jpqYG9vb2OHnyJCwtLZXX9PX1IZfLoaurKzChOKmpqaiursa7776rMp6SkgJdXV24ubkJSqYdioqKYGBgACMjI9FRiOg5LFCJNOTUqVPo168fDAwM4O7uDuDpB4iqqiocOHAAnTp1EpxQ8yZOnIiSkhLs2LEDFhYWyMzMhK6uLgYPHoyePXtyyRUBAOzs7HDq1Cm1PahS1aVLF0RFReG9994THYW0mLu7O2bPno2PPvpIZXznzp1YunQpUlJSBCUjInqxv749TUR/q+DgYAwaNAjXrl3Dzp07sXPnTuTm5uKDDz7AjBkzRMcTIjIyEuXl5ZDL5aiqqoKHhwccHBxgbGyMhQsXio5HWuDx48ewt7dHaWmp6ChaY+nSpZg9ezaOHj2KkpIS3L9/X+VLihYvXox169apja9btw5Lly4VkEi8Cxcu1Hnj09XVFRcuXBCQSLzbt29jzJgxsLa2Rr169aCrq6vyRUTagTOoRBpiYGCA9PR0tWV5Fy5cgJubGyorKwUlEy8pKQkZGRkoLy9Hp06d0KdPH9GRNMrCwgKXL19G48aNYW5u/sI9hVIs1CwtLZGcnAxHR0fRUbRC7dLn558nUmySVMvW1hZbtmxRdqutlZKSAj8/P+Tm5gpKJk6jRo3w66+/olu3birjycnJ8Pb2xt27dwUlE2fAgAHIy8vDtGnT6ty/LdXmUUTahk2SiDTExMQEeXl5agVqfn6+SgdbKerRowd69OghOoYwUVFRyucAlzWrGz16NKKjo7FkyRLRUbQCu4OrKywshJWVldq4paUlCgoKBCQSr2/fvvjnP/+J3bt3K/sclJWV4bPPPsM//vEPwenEOHbsGP744w907NhRdBQiegEWqEQaMnz4cHzyySf45ptvVM6kmzVrFkaMGCE4nRhBQUFwcHBAUFCQyviqVauQnZ0tmWJt7NixdX5PTz158gTr1q3DoUOH0LlzZzRs2FDl+vLlywUlE0NqzbJeRfPmzZGUlAQ7OzuV8aSkJFhbWwtKJdY333yDnj17wsbGBq6urgCAM2fOoEmTJvjpp58EpxOjefPm4MJBIu3HJb5EGvLo0SPMmjULa9aswZMnTwA87UQ6efJkLFmyBPXr1xecUPOaNWuGPXv2oHPnzirjaWlpGDRoEG7cuCEomWb9J/sGTUxMXmMS7eTp6fmX12QyGQ4fPqzBNGJkZmbCxcUFOjo6Kmd/1qV9+/YaSqU9li1bhmXLluHrr79WOZ5p9uzZCA0NxT//+U/BCcWoqKjA5s2bkZGRoewIPmLECOjp6YmOJsSBAwcQGRmJH374QZJHVBG9KVigEmlYZWUlcnJyAAAtW7aEoaGh4ETiNGjQAOfOnYODg4PKeHZ2NlxcXPDnn38KSqZZOjo6Lz3LUsr7C+npc6SwsBByuVz5fKnr37dUnyM1NTWYM2cOVq5ciUePHgF4+v4SFhaG8PBwwem0m7e3N3788cc6l0i/bczNzVFZWYknT57A0NBQrVCX4h5/Im3EJb5EGmZoaAhzc3Pl91Lm4OCAffv2Ydq0aSrjcXFxsLe3F5RK87in8NXVzqq/8847gpNoVm5urvKMTyk2/HkZmUyGpUuXYu7cucjKyoKBgQEcHR0luTLlP5WYmIiqqirRMTRCKttGiN50LFCJNKS6uhpfffWV8mgVADA2NkZoaCg+//xzZWdOKQkJCcG0adNQVFSksiwvMjJSUh8kuKfwxfjaAWxsbJTfX79+Hd27d0e9eqr/wp88eYLk5GSVx0qNkZERunTpIjoGaSnu8Sd6M7BAJdKQzz//XNmJtLZj7bFjxzBv3jz8+eefkjz3c8KECXj48CEWLlyIBQsWAHh6XMTq1avh7+8vOJ04d+/eRXR0NLKysgAAzs7OGD9+PCwsLAQnE4OvHVWenp4oKCiAXC5XGb937x48PT0lucQXAE6dOoUdO3YgLy9Pucy31s6dOwWlIm2Tk5OD9evXIycnBytWrIBcLkdcXBxatGiBtm3bio5HROAeVCKNsba2xpo1azBo0CCV8d27d2PKlCm4efOmoGTaoaioCAYGBjAyMhIdRajExET4+PjA1NQUbm5uAIDTp0+jrKwMe/fuRc+ePQUn1Dy+dlTp6Ojg9u3byiW/tS5fvgw3N7f/qOnW22Lbtm3w9/dHv379cODAAfTt2xeXL1/G7du3MWTIEKxfv150RK1lbGyMjIwMSWyrSEhIwIABA9CjRw8kJiYiKysL9vb2WLJkCU6dOoXY2FjREYkInEEl0pjS0lK1M1ABoHXr1mzMAKh92JaqqVOnYvjw4Vi9ejV0dXUBAAqFAlOmTMHUqVNx9uxZwQk1j6+dp3x9fQE83W85btw4lf2VCoUCmZmZyiOspGbRokWIiorC1KlTYWxsjBUrVsDOzg6ffvqpJJr/0KuZM2cOvvrqK4SEhKicP+7l5YVVq1YJTEZEz3r7N+4QaYkOHTrU+Q9w1apV6NChg4BE2iE2NhbDhg1D165d0alTJ5UvKcrOzkZoaKiyOAUAXV1dhISEIDs7W2AycfjaecrU1BSmpqaoqamBsbGx8mdTU1M0bdoU//M//4NNmzaJjilETk4OvL29AQD6+vqoqKiATCZDcHAw1q5dKzgdaYuzZ89iyJAhauNyuRzFxcUCEhFRXTiDSqQhy5Ytg7e3Nw4dOoRu3boBAI4fP478/Hz8/vvvgtOJsXLlSnz++ecYN24cdu/ejfHjxyMnJwepqamYOnWq6HhCdOrUCVlZWXByclIZz8rKklQx9iy+dp6qXaZqa2uLmTNnomHDhoITaQ9zc3M8ePAAwNPzlc+dO4d27dqhrKwMlZWVgtNpt88++0wy+9vNzMxQUFAAOzs7lfH09HQ0a9ZMUCoieh73oBJp0K1bt/D999/j4sWLAIA2bdpgypQpsLa2FpxMjNatWyMiIgIjRoxQ2QcVHh6O0tJSySy5yszMVH6flZWF2bNnIzAwEF27dgUAnDhxAt9//z2WLFmC4cOHi4opFF87/1ZVVYWamhrlMVXXr1/Hv/71Lzg7O6Nv376C04kxcuRIuLm5ISQkBAsWLMB3332HDz/8EAcPHkSnTp0k0yRpz549r/zY5/d0S8HMmTORkpKCn3/+Ga1atUJaWhpu374Nf39/+Pv7IyIiQnREIgILVCISyNDQEFlZWbCxsYFcLsfBgwfRoUMHXLlyBV27dkVJSYnoiBqho6MDmUyGl70dy2QyyXZopX/r27cvfH19MWnSJJSVlcHJyQn6+vooLi7G8uXLMXnyZNERNa60tBR//vknrK2tUV1djWXLliE5ORmOjo744osvlGdPv+1e9cglqb6XPHr0CFOnTsWGDRugUChQr149KBQKjBw5Ehs2bFDZWkFE4nCJL5EGlZWV4eTJk7hz5w6qq6tVrknxWJWmTZuitLQUNjY2aNGiBU6cOIEOHTogNzf3pcXa2yQ3N1d0BK3Ho3f+LS0tDVFRUQCe7uFu2rQp0tPT8csvvyA8PFySBeqzzwMdHR3MmTNHYBpxnv+/Qqr09fXxv//7v5g7dy7OnTuH8vJyuLq6wtHRUXQ0InoGZ1CJNGTv3r0YNWoUysvLYWJiAplMprwmk8kk1Y201sSJE9G8eXNERETg+++/x6xZs9CjRw+cOnUKvr6+iI6OFh2RtACP3lFlaGiIixcvokWLFhg2bBjatm2LiIgI5Ofnw8nJSZJ7LnV1des8G7akpARyuVySs4VERG8qFqhEGtKqVSsMHDgQixYtUu4dk7rq6mpUV1ejXr2nizm2bdumXJb36aefQl9fX3BCzYuJiXnhdSnOtLdr1w7dunWr8+id5ORkyR290759e0ycOBFDhgyBi4sL9u3bh27duuH06dPw9vZGYWGh6Igap6Ojg8LCQrUC9datW2jZsiWqqqoEJROroqICCQkJyMvLw6NHj1SuBQUFCUolTkhISJ3jMpkMDRo0gIODAz788ENJrswg0iYsUIk0pGHDhjh79qwkDkOn/97ze+UeP36MyspK6Ovrw9DQUJIz7QYGBjhz5oxaZ+NLly6hY8eOkis+YmNjMXLkSCgUCnh5eeHgwYMAgMWLFyMxMRFxcXGCE2rOypUrAQDBwcFYsGABjIyMlNcUCgUSExNx7do1pKeni4ooTHp6OgYOHIjKykpUVFTAwsICxcXFMDQ0hFwux9WrV0VH1DhPT0+kpaVBoVAo308uX74MXV1dtG7dGpcuXYJMJsOxY8fg7OwsOC2RdHEPKpGG9OvXD6dOnZJ8gfpsx9qXad++/WtMop3u3r2rNnblyhVMnjwZs2bNEpBIPB69o+qjjz7Ce++9h4KCApXfv3fv3nWe8fg2q92LW1NTgzVr1qg0udHX14etrS3WrFkjKp5QwcHB8PHxwZo1a2BqaooTJ05AT08Po0ePxvTp00XHE6J2dnT9+vUwMTEBANy7dw8TJ07Ee++9h4CAAIwcORLBwcHYv3+/4LRE0sUZVKLX6NmW/0VFRZg/fz7Gjx+Pdu3aQU9PT+WxUmn5z461/51Tp05h9OjRymNWpGT79u0vPHqnTZs2ysdK6aZGdnY2cnJy0LNnTxgYGKCmpkZlb7uUeHp6YufOnZLp1vsqzMzMkJKSAicnJ5iZmeH48eNo06YNUlJSMHbsWEm+lzRr1gwHDx5Umx09f/48+vbti5s3byItLQ19+/ZFcXGxoJRExBlUotdo8ODBamPz589XG5NSMcaOtf+devXq4datW6JjCDFixAgAwOzZs+u8VnvDQyqvo5KSEgwbNgxHjhyBTCbDlStXYG9vj08++QTm5uaIjIwUHVHjjhw5IjqC1tHT01MeOyOXy5GXl4c2bdrA1NQU+fn5gtOJce/ePdy5c0etQC0qKsL9+/cBPC3sn9+vS0SaxQKV6DViy391NjY2oiNotWdn3YGnSxcLCgqwatUq9OjRQ1AqsXhTQ1VwcDD09PSUBUet4cOHIyQkRJIFqkKhwIYNGxAfH1/nMV6HDx8WlEwcV1dXpKamwtHRER4eHggPD0dxcTF++uknuLi4iI4nxIcffogJEyYgMjISXbp0AQCkpqZi5syZyhvKJ0+eRKtWrQSmJCIu8SUioS5duoTvvvtOeb5lmzZtEBgYqLbfUCpqZzxqyWQyWFpawsvLC5GRkbCyshKUjLRF06ZNsX//fnTo0AHGxsbIyMiAvb09rl69ivbt26O8vFx0RI2bNm0aNmzYAG9vb1hZWaktda7dqyolp06dwoMHD+Dp6Yk7d+7A399f2SV93bp1kty/XV5ejuDgYMTExODJkycAnq5OGTt2LKKiotCwYUOcOXMGANCxY0dxQYkkjgUqkYYEBQXBwcFBrbX/qlWrkJ2djW+//VZMMIF++eUX+Pn5wc3NDd26dQPwdG9hamoqtm3bhqFDhwpOSNpg48aNaNy4Mby9vQE8Xeq7du1aODs7Y+vWrZKblTc2NkZaWhocHR1VCtRTp06hX79+KCkpER1R4xo3boyYmBgMHDhQdBR6A5SXlyu7GNvb26t0fyYi8VigEmlIs2bNsGfPHnTu3FllPC0tDYMGDcKNGzcEJROnZcuWGDVqlNq+3IiICGzatAk5OTmCkmmH2rdnqTa+qeXk5ITVq1fDy8sLx48fR+/evfHtt9/i119/Rb169bBz507RETVq4MCB6Ny5MxYsWABjY2NkZmbCxsYGfn5+qK6uRmxsrOiIGmdtbY2jR49yaSYR0VuABSqRhjRo0ADnzp2Dg4ODynh2djZcXFzw559/CkomjqGhITIzM9X+JleuXEGHDh1QWVkpKJlY0dHRiIqKwpUrVwAAjo6OmDFjBiZOnCg4mRiGhoa4ePEiWrRogbCwMBQUFCAmJgbnz59Hr169UFRUJDqiRp07dw69e/dGp06dcPjwYQwaNAjnz59HaWkpkpKS0LJlS9ERNS4yMhJXr17FqlWrJH1Dp1OnToiPj4e5uTlcXV1f+LdIS0vTYDLtUFFRgSVLlvzlXmUpng1LpI3YJIlIQxwcHLBv3z5MmzZNZTwuLk6yZ6P26tULf/zxh1qBeuzYMbz//vuCUokVHh6O5cuXIzAwULns+fjx4wgODkZeXl6dXaDfdkZGRigpKUGLFi1w4MABhISEAHh606eqqkpwOs0zMTFBVlYWVq9eDWNjY5SXl8PX1xdTp07F48ePRccT4tixYzhy5Aji4uLQtm1btWO8pDLL/uGHH6J+/foA6u4iL3UTJ05EQkICxowZU+deZSLSDpxBJdKQdevWYdq0aZg1axa8vLwAAPHx8fjmm2+wYsUKBAQECE6oeWvWrEF4eDiGDRumcr7lzz//jC+//BLW1tbKx0rlnFhLS0usXLlSebRKra1btyIwMFCSZ/ONGjUKFy9ehKurK7Zu3Yq8vDw0atQIe/bswWeffYZz586JjqhRurq6KCgogFwuVxkvKSmBXC6XxFE7zxs/fvwLr69fv15DSUibmZmZ4bfffpNsR3SiNwULVCINWr16NRYuXKg8z9LOzg4RERHw9/cXnEyM5zvW/hWpnG8JPP0AVXs0xLMuX74Md3d3lJWViQkmUFlZGb744gvk5+dj8uTJ6N+/P4Cne5X19fXx+eefC06oWTo6OigsLFQrUK9fvw5nZ2dUVFQISkbaJDU1FdXV1Xj33XdVxlNSUqCrqws3NzdBycSxs7PD77//rnI8ExFpHxaoRBpSVVWFmpoaGBoaoqioCLdv38bBgwfh7OyMfv36iY5HWiIwMBB6enpYvny5yvjMmTNRVVWF77//XlAyEq12aXPtigtDQ0PlNYVCoSw8kpKSREUUrqioCJcuXQLwtLmWpaWl4ETiuLu7Y/bs2fjoo49Uxnfu3ImlS5ciJSVFUDJxNm3ahN27d2Pjxo0qrx8i0i4sUIk0pG/fvvD19cWkSZNQVlaG1q1bQ09PD8XFxVi+fDkmT54sOqLG3bhxA++8806d106cOKFc9islgYGBiImJQfPmzZW/f0pKCvLy8uDv76+yt+75IvZtVlZWhpMnT6o1NpHJZBgzZozAZJrj6ekJAEhISEC3bt2gr6+vvKavrw9bW1vMnDlTbfZdCioqKpSvndrnh66uLvz9/fHdd99JshgxMjJCZmamWo+D3NxctG/fHg8ePBCUTBxXV1fk5OSgpqYGtra2anuVpdg4ikgbsUkSkYakpaUpD4uPjY1FkyZNkJ6ejl9++QXh4eGSLFD79u2LY8eOwcLCQmU8KSkJ3t7eklzOeu7cOXTq1AkAlMfsNG7cGI0bN1bZayml5h579+7FqFGjUF5eDhMTE5XfXUoF6pEjRwA83W+5YsUKmJiYCE6kPUJCQpCQkIC9e/cq9xceO3YMQUFBCA0NxerVqwUn1Lz69evj9u3bagVqQUEB6tWT5sc/No4iejNwBpVIQ549KmPYsGFo27YtIiIikJ+fDycnJ0keqTJhwgRkZmbiyJEjMDY2BgAkJibCx8cH8+bNQ3BwsOCE2uvGjRuwtrZ+5X28b7JWrVph4MCBWLRokSRnwujlGjdujNjYWPTq1Utl/MiRIxg2bJjkjiICgBEjRqCgoAC7d++GqakpgKcrEQYPHgy5XI4dO3YITkhEVLe3/5MNkZZwcHDArl27kJ+fj/3796Nv374AgDt37kh2JuTHH39EixYt4OPjg4cPH+LIkSPw9vbG/PnzWZy+hLOzM65duyY6hkbcvHkTQUFBLE7pL1VWVqJJkyZq43K5XJI3/wDgm2++QX5+PmxsbODp6QlPT0/Y2dmhsLAQkZGRouMREf0lzqASaUhsbCxGjhwJhUKB3r1748CBAwCAxYsXIzExEXFxcYITivHo0SN4e3ujsrISmZmZWLx4sdpZsaTO2NgYGRkZkjhD19fXF35+fhg2bJjoKKSlevfujUaNGiEmJgYNGjQA8LQx3dixY1FaWopDhw4JTihGRUUFNm/ejIyMDBgYGKB9+/YYMWKE2t7Lt5mFhQUuX76Mxo0bw9zc/IXbI0pLSzWYjIj+CgtUIg0qLCxEQUEBOnTooFyaefLkSZiYmKB169aC02lGZmam2tiDBw8wYsQIeHt7q+zFbd++vSajvVGkVKBGR0dj/vz5GD9+PNq1a6f24VoqZ+TSXzt79iz69++Phw8fokOHDgCAjIwM1K9fHwcOHEDbtm0FJyRRNm7cCD8/P9SvXx8bN2584WPHjh2roVRE9CIsUIlIo3R0dCCTyfDsW8+zP9d+L6WzT/8bUipQX7TPls8TqlVZWYnNmzfj4sWLAIA2bdpg1KhRMDAwEJxMrAsXLiAvLw+PHj1SGZfajZ0nT55gy5Yt6NevX53LwYlIe0izjRsRCZObmys6Ar1hnj1WhqguixcvRpMmTRAQEKAyvm7dOhQVFSEsLExQMnGuXr2KIUOG4OzZs2o3AQFI7sZOvXr1MGnSJGRlZYmOQkQvwSZJRKRRNjY2sLGxgbW1Nb788ktUV1crx57/or8mpWNmiF7mhx9+qHObRNu2bbFmzRoBicSbPn067OzscOfOHRgaGuL8+fNITEyEm5sbjh49KjqeEO7u7khPTxcdg4hegjOoRCSEnp4efvnlF8ydO1d0lDeS1HZnVFRUICEhoc6likFBQYJSkbYoLCyElZWV2rilpSUKCgoEJBLv+PHjOHz4MBo3bgwdHR3o6Ojgvffew+LFixEUFCTJQm3KlCkIDQ3FjRs30LlzZzRs2FDlOvseEGkHFqhEJMzgwYOxa9cuHinzX7hw4QKsra1Fx9CI9PR0DBw4EJWVlaioqICFhQWKi4thaGgIuVzOApXQvHlzJCUlwc7OTmU8KSlJMq+T5ykUCuX50o0bN8atW7fg5OQEGxsbXLp0SXA6Mfz8/ACo3tRi3wMi7cMClYiEcXR0xPz585GUlFTn3WypFB6+vr6v/NidO3cCePqBXCqCg4Ph4+ODNWvWwNTUFCdOnICenh5Gjx6N6dOni45HWiAgIAAzZszA48eP4eXlBQCIj4/H7NmzERoaKjidGC4uLsjIyICdnR3effddLFu2DPr6+li7dq0kmqvVhT0QiN4M7OJLRMI8P9vxLJlMhqtXr2owjTjjx49/5ceuX7/+NSbRTmZmZkhJSYGTkxPMzMxw/PhxtGnTBikpKRg7dqyyaytJV01NDebMmYOVK1cql4A3aNAAYWFhCA8PF5xOczIzM+Hi4gIdHR3s378flZWVGDJkCLKzs/HBBx/g8uXLaNSoEbZv364s5KUkMTER3bt3R716qvMzT548QXJyMnr27CkoGRE9iwUqERFpNUtLSyQnJ8PR0RGtWrXCd999h379+uHixYvo3LkzKioqREckLVFeXo6srCwYGBjA0dER9evXFx1Jo3R1dVFQUAC5XA57e3ukpqaiUaNGyuulpaUwNzeXbJO1Z/8+zyopKYFcLucSXyItwSW+RKQVnj8CQcqePHmCo0ePIicnByNHjoSxsTFu3boFExMTGBkZiY6nca6urkhNTYWjoyM8PDwQHh6O4uJi/PTTT3BxcREdj7SIkZERunTpIjqGMGZmZsjNzYVcLse1a9fUjmiysLAQlEw71O41fV5JSYnaFhMiEocFKhEJFRMTg6+//hpXrlwBALRq1QqzZs3CmDFjBCcT4/r16+jfvz/y8vLw8OFD/OMf/4CxsTGWLl2Khw8fSvLIjEWLFuHBgwcAgIULF8Lf3x+TJ0+Go6Mj1q1bJzgdkfYYOnQoPDw8YGVlBZlMBjc3N+jq6tb5WKlsoQD+vc9fJpNh3LhxKjPrCoUCmZmZ6N69u6h4RPQcFqhEJMzy5csxd+5cTJs2DT169AAAHDt2DJMmTUJxcbEku/tOnz4dbm5uyMjIUFmaN2TIEAQEBAhMJkZNTQ3kcrlyplQul2Pfvn2CUxFpp7Vr18LX1xfZ2dkICgpCQECAspOvlJmamgJ4+n5ibGwMAwMD5TV9fX107dpVku+vRNqKe1CJSBg7Ozt8+eWX8Pf3VxnfuHEj5s2bJ8mOi40aNUJycjKcnJxgbGyMjIwM2Nvb49q1a3B2dkZlZaXoiBpVXV2NBg0a4Pz583B0dBQdh+iNMX78eKxcuZIF6jNmz56NefPmwdDQEABw7do17Nq1C23atEG/fv0EpyOiWjqiAxCRdBUUFNS5rKp79+4oKCgQkEi86urqOht13LhxQ5IfNHV0dODo6IiSkhLRUYjeKOvXr5fke8aLpKenIyYmBgBQVlaGrl27IjIyEoMHD8bq1asFpyOiWixQiUgYBwcH7NixQ218+/btkp0t69u3L7799lvlzzKZDOXl5YiIiMDAgQPFBRNoyZIlmDVrFs6dOyc6ChG9wdLT0/H+++8DAGJjY9GkSRNcv34dMTExWLlypeB0RFSLS3yJSJhffvkFw4cPR58+fZR7UJOSkhAfH48dO3ZgyJAhghNq3o0bN9CvXz/U1NTgypUrcHNzw5UrV9C4cWMkJiaqHY8gBebm5qisrMSTJ0+gr6+vsn8MeHp0BhHRyxgaGuLixYto0aIFhg0bhrZt2yIiIgL5+flwcnKS3BYKIm3FJklEJMzQoUNx8uRJLF++HLt27QIAtGnTBidPnoSrq6vYcIK88847yMjIwPbt25GRkYHy8nJ88sknGDVqlFphJhVRUVE8foiI/r85ODhg165dGDJkCPbv369sxHfnzh2YmJgITkdEtTiDSkTC+Pv7w9PTEz179kTLli1FxyEiordYbGwsRo4cCYVCgd69e+PAgQMAgMWLFyMxMRFxcXGCExIRwAKViASaOHEiEhMTkZOTA2tra3h4eKBXr17w8PCQ7B7UxYsXo0mTJpgwYYLK+Lp161BUVISwsDBBycTR1dVFQUGB2vLmkpISyOXyOptKERHVpbCwEAUFBejQoQN0dJ62Yjl58iRMTEzQunVrwemICGCBSkRa4ObNm0hMTERCQgISEhJw+fJlWFlZ4caNG6KjaZytrS22bNmi1t04JSUFfn5+kjx6R0dHB4WFhWoF6q1bt9CyZUtUVVUJSkZERER/N+5BJSLhzM3N0ahRI5ibm8PMzAz16tWDpaWl6FhCFBYWwsrKSm3c0tJSckfv1HbVlMlk+PHHH2FkZKS8plAokJiYyBkPIiKitwwLVCIS5rPPPsPRo0eRnp6ONm3awMPDA3PmzEHPnj1hbm4uOp4QzZs3R1JSEuzs7FTGk5KSYG1tLSiVGFFRUQCAmpoarFmzBrq6uspr+vr6sLW1xZo1a0TFIyIioteABSoRCbNkyRJYWloiIiICvr6+aNWqlehIwgUEBGDGjBl4/PgxvLy8AADx8fGYPXs2QkNDBafTrNrlzJ6enti5c6dkb1oQERFJCfegEpEwGRkZSEhIwNGjR/HHH39AX19f2SipV69ekixYa2pqMGfOHKxcuRKPHj0CADRo0ABhYWEIDw8XnE67mZiY4MyZM7C3txcdhYiIiP5LLFCJSGtkZGQgKioKmzdvRnV1taS7s5aXlyMrKwsGBgZwdHRE/fr1RUfSesbGxsjIyGCBSkRE9AbjEl8iEqampgbp6ek4evQojh49imPHjuH+/fto3749PDw8RMcTysjICF26dBEdg4iIiEijWKASkTAWFhYoLy9Hhw4d4OHhgYCAALz//vswMzMTHU2YiooKLFmyBPHx8bhz5w6qq6tVrl+9elVQMiIiIqLXjwUqEQmzadMmvP/++zAxMREdRWtMnDgRCQkJGDNmDKysrCCTyURHIiIiItIY7kElItIiZmZm+O2339CjRw/RUd44bJJERET05tMRHYCIiP7N3NwcFhYWomO8kXi/lYiI6M3HApWISIssWLAA4eHhqKysFB3ljRMXF4dmzZqJjkFERET/H7jEl4hIi7i6uiInJwc1NTWwtbWFnp6eyvW0tDRBycQZOnQo3N3dERYWpjK+bNkypKam4ueffxaUjIiIiP5ubJJERKRFBg8eLDqC1klMTMS8efPUxgcMGIDIyEjNByIiIqLXhgUqEZEWiYiIEB1B65SXl0NfX19tXE9PD/fv3xeQiIiIiF4X7kElIiKt1q5dO2zfvl1tfNu2bXB2dhaQiIiIiF4XzqASEWkRhUKBqKgo7NixA3l5eXj06JHK9dLSUkHJxJk7dy58fX2Rk5MDLy8vAEB8fDy2bt3K/adERERvGc6gEhFpkS+//BLLly/H8OHDce/ePYSEhMDX1xc6Ojp17sOUAh8fH+zatQvZ2dmYMmUKQkNDcePGDRw6dIh7domIiN4y7OJLRKRFWrZsiZUrV8Lb2xvGxsY4c+aMcuzEiRPYsmWL6IhERERErw1nUImItEhhYSHatWsHADAyMsK9e/cAAB988AF+++03kdGIiIiIXjsWqEREWuSdd95BQUEBgKezqQcOHAAApKamon79+iKjaZSFhQWKi4sBAObm5rCwsPjLLyIiInp7sEkSEZEWGTJkCOLj4/Huu+8iMDAQo0ePRnR0NPLy8hAcHCw6nsZERUXB2NgYAPDtt9+KDUNEREQawz2oRERa7MSJE0hOToajoyN8fHxExyEiIiJ6rVigEhFpkcWLF6NJkyaYMGGCyvi6detQVFSEsLAwQck06/79+6/8WBMTk9eYhIiIiDSJBSoRkRaxtbXFli1b0L17d5XxlJQU+Pn5ITc3V1AyzdLR0YFMJnvhY2pqaiCTyaBQKDSUioiIiF437kElItIihYWFsLKyUhu3tLRUNk+SgiNHjoiOQERERAKwQCUi0iLNmzdHUlIS7OzsVMaTkpJgbW0tKJXmeXh4iI5AREREArBAJSLSIgEBAZgxYwYeP34MLy8vAEB8fDxmz56N0NBQwenEuXv3LqKjo5GVlQUAcHZ2xvjx43nMDBER0VuGe1CJiLRITU0N5syZg5UrV+LRo0cAgAYNGiAsLAzh4eGC04mRmJgIHx8fmJqaws3NDQBw+vRplJWVYe/evejZs6fghERERPR3YYFKRKSFysvLkZWVBQMDAzg6OqJ+/fqiIwnTrl07dOvWDatXr4auri4AQKFQYMqUKUhOTsbZs2cFJyQiIqK/CwtUIiLSagYGBjhz5gycnJxUxi9duoSOHTuiqqpKUDIiIiL6u+mIDkBERPQinTp1Uu49fVZWVhY6dOggIBERERG9LmySREREWiczM1P5fVBQEKZPn47s7Gx07doVAHDixAl8//33WLJkiaiIRERE9BpwiS8REWkdHR0dyGQyvOxflEwmg0Kh0FAqIiIiet04g0pERFonNzdXdAQiIiISgDOoREREREREpBU4g0pERFotJibmhdf9/f01lISIiIheN86gEhGRVjM3N1f5+fHjx6isrIS+vj4MDQ1RWloqKBkRERH93XjMDBERabW7d++qfJWXl+PSpUt47733sHXrVtHxiIiI6G/EGVQiInojnTp1CqNHj8bFixdFRyEiIqK/CWdQiYjojVSvXj3cunVLdAwiIiL6G7FJEhERabU9e/ao/FxTU4OCggKsWrUKPXr0EJSKiIiIXgcu8SUiIq2mo6O62Ecmk8HS0hJeXl6IjIyElZWVoGRERET0d2OBSkREb4zq6moA6kUrERERvR34H56IiLRedHQ0XFxcYGBgAAMDA7i4uODHH38UHYuIiIj+ZtyDSkREWi08PBzLly9HYGAgunXrBgA4fvw4goODkZeXh/nz5wtOSERERH8XLvElIiKtZmlpiZUrV2LEiBEq41u3bkVgYCCKi4sFJSMiIqK/G5f4EhGRVnv8+DHc3NzUxjt37ownT54ISERERESvCwtUIiLSamPGjMHq1avVxteuXYtRo0YJSERERESvC5f4EhGRVgsMDERMTAyaN2+Orl27AgBSUlKQl5cHf39/6OnpKR+7fPlyUTGJiIjob8AClYiItJqnp+crPU4mk+Hw4cOvOQ0RERG9TixQiYiIiIiISCtwDyoRERERERFpBRaoREREREREpBVYoBIREREREZFWYIFKREREREREWoEFKhEREREREWkFFqhERERERESkFVigEhERERERkVb4f/2I9tYR8wthAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot heat map of Pearson Correlation Coefficient\n",
    "# set figure size\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "# Generate a mask to onlyshow the bottom triangle\n",
    "mask = np.triu(np.ones_like(Honolulu_dataset_numeric.corr(), dtype=bool))\n",
    "\n",
    "# generate heatmap\n",
    "sns.heatmap(Honolulu_dataset_numeric.corr(), annot=True, mask=mask, vmin=-1, vmax=1)\n",
    "plt.title('Correlation Coefficient Of Predictors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02ac0c6d-520f-426c-8b6c-720ccf544625",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Calculated VIF score\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.tools import add_constant\n",
    "\n",
    "# compute the vif for all given features\n",
    "def compute_vif(considered_features):\n",
    "    \n",
    "    X = Honolulu_dataset_numeric[considered_features].copy()\n",
    "    # the calculation of variance inflation requires a constant\n",
    "    X['intercept'] = 1\n",
    "    \n",
    "    # create dataframe to store vif values\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"Variable\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "    vif = vif[vif['Variable']!='intercept']\n",
    "    return vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "491d42c3-0494-4e28-a2d6-9e8e0ceb0058",
   "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>Variable</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cancel_public_events</td>\n",
       "      <td>11.340655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>public_transport_closing</td>\n",
       "      <td>8.042267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>school_closing</td>\n",
       "      <td>6.611896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>stringency_index</td>\n",
       "      <td>6.392624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>facial_coverings</td>\n",
       "      <td>5.151146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>testing_policy</td>\n",
       "      <td>4.699721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SDI</td>\n",
       "      <td>2.283208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>workplace_closing</td>\n",
       "      <td>1.686978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>contact_tracing</td>\n",
       "      <td>1.217730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Variable        VIF\n",
       "2      cancel_public_events  11.340655\n",
       "3  public_transport_closing   8.042267\n",
       "0            school_closing   6.611896\n",
       "7          stringency_index   6.392624\n",
       "6          facial_coverings   5.151146\n",
       "4            testing_policy   4.699721\n",
       "8                       SDI   2.283208\n",
       "1         workplace_closing   1.686978\n",
       "5           contact_tracing   1.217730"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute VIF use the defiend fucntion\n",
    "considered_features = list(Honolulu_dataset_numeric.columns)\n",
    "compute_vif(considered_features).sort_values('VIF', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0b1de16-901c-4ba2-95bf-d1fe040cf367",
   "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>Variable</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>facial_coverings</td>\n",
       "      <td>3.373013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>school_closing</td>\n",
       "      <td>2.222538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>stringency_index</td>\n",
       "      <td>1.625625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SDI</td>\n",
       "      <td>1.537526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>workplace_closing</td>\n",
       "      <td>1.340548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>contact_tracing</td>\n",
       "      <td>1.127542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Variable       VIF\n",
       "3   facial_coverings  3.373013\n",
       "0     school_closing  2.222538\n",
       "4   stringency_index  1.625625\n",
       "5                SDI  1.537526\n",
       "1  workplace_closing  1.340548\n",
       "2    contact_tracing  1.127542"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove variables with VIF score > 5\n",
    "considered_features.remove('cancel_public_events')\n",
    "considered_features.remove('public_transport_closing')\n",
    "considered_features.remove('testing_policy')\n",
    "compute_vif(considered_features).sort_values('VIF', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "83e7b90f-f712-461d-ad54-95590869af3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Re-plot the heat map after multicolinearity test\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "# Generate a mask to onlyshow the bottom triangle\n",
    "mask = np.triu(np.ones_like(Honolulu_dataset_numeric[considered_features].corr(), dtype=bool))\n",
    "\n",
    "# generate heatmap\n",
    "sns.heatmap(Honolulu_dataset_numeric[considered_features].corr(), annot=True, mask=mask, vmin=-1, vmax=1)\n",
    "plt.title('Correlation Coefficient Of Predictors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7fbb7a1f-1732-46bc-a2c3-781470c0bd16",
   "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>date</th>\n",
       "      <th>school_closing</th>\n",
       "      <th>workplace_closing</th>\n",
       "      <th>contact_tracing</th>\n",
       "      <th>facial_coverings</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>SDI</th>\n",
       "      <th>daily_confirm_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76.85</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>23.0</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2021-04-17</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>31.0</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2021-04-19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>20.0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>69.44</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  school_closing  workplace_closing  contact_tracing  \\\n",
       "0   2020-04-20             3.0                3.0              2.0   \n",
       "1   2020-04-21             3.0                3.0              2.0   \n",
       "2   2020-04-22             3.0                3.0              2.0   \n",
       "3   2020-04-23             3.0                3.0              2.0   \n",
       "4   2020-04-24             3.0                3.0              2.0   \n",
       "..         ...             ...                ...              ...   \n",
       "361 2021-04-16             2.0                2.0              2.0   \n",
       "362 2021-04-17             2.0                2.0              2.0   \n",
       "363 2021-04-18             2.0                2.0              2.0   \n",
       "364 2021-04-19             2.0                2.0              2.0   \n",
       "365 2021-04-20             2.0                2.0              2.0   \n",
       "\n",
       "     facial_coverings  stringency_index   SDI  daily_confirm_cases  \n",
       "0                 2.0             76.85  67.0                    1  \n",
       "1                 2.0             76.85  67.0                    0  \n",
       "2                 2.0             76.85  63.0                    3  \n",
       "3                 2.0             76.85  66.0                    1  \n",
       "4                 2.0             76.85  64.0                    3  \n",
       "..                ...               ...   ...                  ...  \n",
       "361               3.0             69.44  23.0                  115  \n",
       "362               3.0             69.44  26.0                   48  \n",
       "363               3.0             69.44  31.0                   93  \n",
       "364               3.0             69.44  20.0                   52  \n",
       "365               3.0             69.44  24.0                   24  \n",
       "\n",
       "[366 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modify data types and add response variables\n",
    "considered_features.insert(0,'date')\n",
    "Honolulu_dataset_selected = Honolulu_dataset[considered_features].copy()\n",
    "Honolulu_dataset_selected['daily_confirm_cases'] = Honolulu_cases_by_date_part['Daily_Cases']\n",
    "Honolulu_dataset_selected['daily_confirm_cases'] = Honolulu_dataset_selected['daily_confirm_cases'].astype(\"int\")\n",
    "Honolulu_dataset_selected"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bfb9e7-96b4-43d5-aa1c-18283934421a",
   "metadata": {},
   "source": [
    "#### Step 3: Generate outputs dataset\n",
    "\n",
    "- Generate \"Honolulu_dataset_tourist.csv\" with daily passenger count as response variable\n",
    "- Generate \"Honolulu_dataset.csv\" with daily confirmed cases as response variable\n",
    "- Generate \"Honolulu_dataset_delayn.csv\" with daily confirmed cases delay by n days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a28d7e3-708d-4ed4-b39c-46059da6f857",
   "metadata": {},
   "outputs": [],
   "source": [
    "# output dataset\n",
    "Honolulu_dataset_selected.to_csv(\"Honolulu_dataset.csv\", encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b5952be2-0018-4394-ad4d-bfb019c07325",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add daily passenger arrivals data to df\n",
    "Honolulu_dataset_selected['tourist'] = tourist.loc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ff4931f-41a2-4291-840d-b882473ddb68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# output dataset\n",
    "Honolulu_dataset_selected.to_csv(\"Honolulu_dataset_tourist.csv\", encoding='utf-8', index=False)"
   ]
  }
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