diff --git a/CHANGELOG.md b/CHANGELOG.md
index 168522b..22b4576 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,8 @@
 # Changelog
 
+## v0.0.2 - 2025-08-09 - @0xnu
+* EU and Non-EU Migration Analysis
+
 ## v0.0.1 - 2025-08-01 - @0xnu
 * England Population Analysis
 * Initial Commit
\ No newline at end of file
diff --git a/README.md b/README.md
index 81e6d1a..369c057 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ uv pip install -r requirements.txt
 python3 -m pip install --upgrade pip
 ```
 
-### Data Requirements
+### Data Requirements (England Population)
 
 The analysis needs a CSV file with these columns:
 
@@ -27,9 +27,22 @@ The analysis needs a CSV file with these columns:
 
 The data (Population estimates for England and Wales: mid-2024) is from [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/populationestimatesforenglandandwales/mid2024).
 
+### Data Requirements (EU and Non-EU Migration)
+
+The dataset contains quarterly migration data from June 2012 to December 2024, tracking:
+
+- Period: Quarterly period (e.g., YE Jun 12 to YE Dec 24 P)
+- Non-EU+ migration: Number of migrants from non-EU countries (e.g., 57,000 in YE Jun 12 to 544,000 in YE Dec 24 P)
+- EU+ migration: Number of migrants from EU countries (e.g., 187,000 in YE Jun 12 to -96,000 in YE Dec 24 P)
+- British migration: Number of migrants from the UK (e.g., -83,000 in YE Jun 12 to -17,000 in YE Dec 24 P)
+- Total migration: Total number of migrants (e.g., 162,000 in YE Jun 12 to 431,000 in YE Dec 24 P)
+
+The data (Net migration by EU+, non-EU+ and British nationality) is from [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/internationalmigration/bulletins/longterminternationalmigrationprovisional/yearendingdecember2024#long-term-net-migration).
+
 ### Run the Analysis
 
-Click the `Run` button to run the individual cell of the [Jupyter Notebook](./england_population_change.ipynb).
++ Click the `Run` button to run the individual cell of the [England Population - Jupyter Notebook](./england_population_change.ipynb).
++ Click the `Run` button to run the individual cell of the [EU and Non-EU Migration - Jupyter Notebook](./england_eu_noneu.ipynb).
 
 ### License
 
diff --git a/data/eu_noneu.csv b/data/eu_noneu.csv
new file mode 100644
index 0000000..e9e3bbc
--- /dev/null
+++ b/data/eu_noneu.csv
@@ -0,0 +1,52 @@
+"Period", "Non-EU+", "EU+", "British", "Total"
+"YE Jun 12", "57,000", "187,000", "-83,000", "162,000"
+"YE Sep 12", "54,000", "183,000", "-80,000", "157,000"
+"YE Dec 12", "61,000", "206,000", "-72,000", "195,000"
+"YE Mar 13", "60,000", "223,000", "-79,000", "204,000"
+"YE Jun 13", "56,000", "242,000", "-83,000", "215,000"
+"YE Sep 13", "64,000", "275,000", "-80,000", "259,000"
+"YE Dec 13", "63,000", "262,000", "-81,000", "244,000"
+"YE Mar 14", "67,000", "263,000", "-74,000", "256,000"
+"YE Jun 14", "65,000", "258,000", "-75,000", "248,000"
+"YE Sep 14", "73,000", "270,000", "-79,000", "264,000"
+"YE Dec 14", "87,000", "277,000", "-80,000", "284,000"
+"YE Mar 15", "94,000", "275,000", "-73,000", "296,000"
+"YE Jun 15", "101,000", "267,000", "-72,000", "296,000"
+"YE Sep 15", "103,000", "262,000", "-72,000", "293,000"
+"YE Dec 15", "88,000", "287,000", "-73,000", "303,000"
+"YE Mar 16", "80,000", "294,000", "-77,000", "298,000"
+"YE Jun 16", "82,000", "322,000", "-83,000", "321,000"
+"YE Sep 16", "83,000", "294,000", "-87,000", "290,000"
+"YE Dec 16", "90,000", "253,000", "-94,000", "249,000"
+"YE Mar 17", "97,000", "235,000", "-91,000", "240,000"
+"YE Jun 17", "91,000", "188,000", "-79,000", "200,000"
+"YE Sep 17", "98,000", "192,000", "-85,000", "205,000"
+"YE Dec 17", "114,000", "176,000", "-81,000", "208,000"
+"YE Mar 18", "122,000", "160,000", "-89,000", "193,000"
+"YE Jun 18", "143,000", "158,000", "-84,000", "216,000"
+"YE Sep 18", "155,000", "138,000", "-67,000", "226,000"
+"YE Dec 18", "178,000", "170,000", "-72,000", "276,000"
+"YE Mar 19", "190,000", "155,000", "-72,000", "274,000"
+"YE Jun 19", "179,000", "126,000", "-82,000", "224,000"
+"YE Sep 19", "171,000", "114,000", "-83,000", "202,000"
+"YE Dec 19", "186,000", "80,000", "-82,000", "184,000"
+"YE Mar 20", "166,000", "81,000", "-77,000", "170,000"
+"YE Jun 20", "115,000", "70,000", "-74,000", "111,000"
+"YE Sep 20", "60,000", "45,000", "-70,000", "35,000"
+"YE Dec 20", "101,000", "70,000", "-78,000", "93,000"
+"YE Mar 21", "150,000", "61,000", "-79,000", "132,000"
+"YE Jun 21", "251,000", "75,000", "-73,000", "254,000"
+"YE Sep 21", "441,000", "12,000", "14,000", "467,000"
+"YE Dec 21", "529,000", "-77,000", "32,000", "484,000"
+"YE Mar 22", "640,000", "-122,000", "29,000", "548,000"
+"YE Jun 22", "754,000", "-140,000", "20,000", "634,000"
+"YE Sep 22", "902,000", "-119,000", "6,000", "789,000"
+"YE Dec 22", "965,000", "-88,000", "-4,000", "873,000"
+"YE Mar 23", "957,000", "-54,000", "-9,000", "894,000"
+"YE Jun 23", "954,000", "-38,000", "-10,000", "906,000"
+"YE Sep 23", "957,000", "-54,000", "-31,000", "872,000"
+"YE Dec 23 R", "952,000", "-55,000", "-37,000", "860,000"
+"YE Mar 24 P R", "927,000", "-85,000", "-17,000", "825,000"
+"YE Jun 24 P R", "856,000", "-95,000", "-21,000", "739,000"
+"YE Sep 24 P", "709,000", "-86,000", "-21,000", "601,000"
+"YE Dec 24 P", "544,000", "-96,000", "-17,000", "431,000"
diff --git a/england_eu_noneu.ipynb b/england_eu_noneu.ipynb
new file mode 100644
index 0000000..78a8d25
--- /dev/null
+++ b/england_eu_noneu.ipynb
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "dfdb8517",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48816765",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population data loaded:\n",
+      "       year annual_population_change\n",
+      "0  Mid-1949                  299,000\n",
+      "1  Mid-1950                  235,000\n",
+      "2  Mid-1951                  -15,000\n",
+      "3  Mid-1952                  140,000\n",
+      "4  Mid-1953                  154,000\n",
+      "Population data shape: (76, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load population data\n",
+    "population_df = pd.read_csv('./data/england_population.csv')\n",
+    "print(\"Population data loaded:\")\n",
+    "print(population_df.head())\n",
+    "print(f\"Population data shape: {population_df.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "9fd67f23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Migration data loaded with quote handling:\n",
+      "      Period Non-EU+      EU+  British    Total\n",
+      "0  YE Jun 12  57,000  187,000  -83,000  162,000\n",
+      "1  YE Sep 12  54,000  183,000  -80,000  157,000\n",
+      "2  YE Dec 12  61,000  206,000  -72,000  195,000\n",
+      "3  YE Mar 13  60,000  223,000  -79,000  204,000\n",
+      "4  YE Jun 13  56,000  242,000  -83,000  215,000\n",
+      "Migration data shape: (51, 5)\n",
+      "Migration columns: ['Period', 'Non-EU+', 'EU+', 'British', 'Total']\n",
+      "\n",
+      "First few rows:\n",
+      "      Period Non-EU+      EU+  British    Total\n",
+      "0  YE Jun 12  57,000  187,000  -83,000  162,000\n",
+      "1  YE Sep 12  54,000  183,000  -80,000  157,000\n",
+      "2  YE Dec 12  61,000  206,000  -72,000  195,000\n",
+      "3  YE Mar 13  60,000  223,000  -79,000  204,000\n",
+      "4  YE Jun 13  56,000  242,000  -83,000  215,000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load migration data with proper CSV parsing\n",
+    "try:\n",
+    "    migration_df = pd.read_csv('./data/eu_noneu.csv', quotechar='\"', skipinitialspace=True)\n",
+    "    print(\"Migration data loaded with quote handling:\")\n",
+    "    print(migration_df.head())\n",
+    "except:\n",
+    "    # If that doesn't work, try manual parsing\n",
+    "    print(\"Standard CSV loading failed, trying manual approach...\")\n",
+    "    \n",
+    "    # Read the file manually and clean it\n",
+    "    with open('./data/eu_noneu.csv', 'r') as f:\n",
+    "        lines = f.readlines()\n",
+    "    \n",
+    "    # Clean the lines\n",
+    "    cleaned_lines = []\n",
+    "    for line in lines:\n",
+    "        # Remove the outer quotes and clean up\n",
+    "        cleaned_line = line.strip().replace('\", \"', '\",\"').replace('\"', '')\n",
+    "        cleaned_lines.append(cleaned_line)\n",
+    "    \n",
+    "    # Write to a temporary clean file in memory\n",
+    "    from io import StringIO\n",
+    "    clean_csv = StringIO('\\n'.join(cleaned_lines))\n",
+    "    migration_df = pd.read_csv(clean_csv)\n",
+    "\n",
+    "print(f\"Migration data shape: {migration_df.shape}\")\n",
+    "print(f\"Migration columns: {migration_df.columns.tolist()}\")\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "print(migration_df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "c6408f1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Column names after cleaning:\n",
+      "['Period', 'Non-EU+', 'EU+', 'British', 'Total']\n",
+      "\n",
+      "Sample data values:\n",
+      "Period     YE Jun 12\n",
+      "Non-EU+       57,000\n",
+      "EU+          187,000\n",
+      "British      -83,000\n",
+      "Total        162,000\n",
+      "Name: 0, dtype: object\n",
+      "\n",
+      "Data types:\n",
+      "Period     object\n",
+      "Non-EU+    object\n",
+      "EU+        object\n",
+      "British    object\n",
+      "Total      object\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Clean column names if needed\n",
+    "migration_df.columns = [col.strip() for col in migration_df.columns]\n",
+    "print(\"Column names after cleaning:\")\n",
+    "print(migration_df.columns.tolist())\n",
+    "\n",
+    "# Check the actual data structure\n",
+    "print(\"\\nSample data values:\")\n",
+    "print(migration_df.iloc[0])\n",
+    "print(\"\\nData types:\")\n",
+    "print(migration_df.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "617b6542",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Improved year extraction results:\n",
+      "      Period  year\n",
+      "0  YE Jun 12  2012\n",
+      "1  YE Sep 12  2012\n",
+      "2  YE Dec 12  2012\n",
+      "3  YE Mar 13  2013\n",
+      "4  YE Jun 13  2013\n",
+      "5  YE Sep 13  2013\n",
+      "6  YE Dec 13  2013\n",
+      "7  YE Mar 14  2014\n",
+      "8  YE Jun 14  2014\n",
+      "9  YE Sep 14  2014\n",
+      "           Period  year\n",
+      "41      YE Sep 22  2022\n",
+      "42      YE Dec 22  2022\n",
+      "43      YE Mar 23  2023\n",
+      "44      YE Jun 23  2023\n",
+      "45      YE Sep 23  2023\n",
+      "46    YE Dec 23 R  2023\n",
+      "47  YE Mar 24 P R  2024\n",
+      "48  YE Jun 24 P R  2024\n",
+      "49    YE Sep 24 P  2024\n",
+      "50    YE Dec 24 P  2024\n",
+      "\n",
+      "Year range: 2012 - 2024\n",
+      "Unique years: [np.int64(2012), np.int64(2013), np.int64(2014), np.int64(2015), np.int64(2016), np.int64(2017), np.int64(2018), np.int64(2019), np.int64(2020), np.int64(2021), np.int64(2022), np.int64(2023), np.int64(2024)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract years properly from the Period column with improved regex\n",
+    "migration_df['Period'] = migration_df['Period'].astype(str).str.strip()\n",
+    "\n",
+    "def extract_year_from_period(period_str):\n",
+    "    \"\"\"Extract year from period string like 'YE Jun 12' or 'YE Dec 23 R' -> 2012, 2023\"\"\"\n",
+    "    import re\n",
+    "    \n",
+    "    period_str = str(period_str).strip()\n",
+    "    \n",
+    "    # Look for 2 digits that come after a space, followed by optional letters\n",
+    "    # This handles cases like \"YE Jun 12\", \"YE Dec 23 R\", \"YE Mar 24 P R\"\n",
+    "    match = re.search(r'\\s(\\d{2})(?:\\s|$)', period_str)\n",
+    "    if match:\n",
+    "        year_2digit = int(match.group(1))\n",
+    "        # Convert 2-digit year to 4-digit\n",
+    "        # Assume 12-24 is 2012-2024, others are 19xx or 20xx based on context\n",
+    "        if year_2digit >= 12 and year_2digit <= 24:\n",
+    "            return year_2digit + 2000\n",
+    "        elif year_2digit >= 0 and year_2digit <= 11:\n",
+    "            return year_2digit + 2000\n",
+    "        else:\n",
+    "            return year_2digit + 1900\n",
+    "    \n",
+    "    return None\n",
+    "\n",
+    "# Apply improved year extraction\n",
+    "migration_df['year'] = migration_df['Period'].apply(extract_year_from_period)\n",
+    "\n",
+    "print(\"Improved year extraction results:\")\n",
+    "print(migration_df[['Period', 'year']].head(10))\n",
+    "print(migration_df[['Period', 'year']].tail(10))\n",
+    "print(f\"\\nYear range: {migration_df['year'].min()} - {migration_df['year'].max()}\")\n",
+    "print(f\"Unique years: {sorted(migration_df['year'].dropna().unique())}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "700fe857",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before cleaning - sample values:\n",
+      "Non-EU+: '57,000'\n",
+      "EU+: '187,000'\n",
+      "British: '-83,000'\n",
+      "Total: '162,000'\n",
+      "\n",
+      "After cleaning - sample values:\n",
+      "Non-EU+: 57000\n",
+      "EU+: 187000\n",
+      "British: -83000\n",
+      "Total: 162000\n",
+      "\n",
+      "Cleaned migration data sample:\n",
+      "      Period  year  Non-EU+     EU+  British   Total\n",
+      "0  YE Jun 12  2012    57000  187000   -83000  162000\n",
+      "1  YE Sep 12  2012    54000  183000   -80000  157000\n",
+      "2  YE Dec 12  2012    61000  206000   -72000  195000\n",
+      "3  YE Mar 13  2013    60000  223000   -79000  204000\n",
+      "4  YE Jun 13  2013    56000  242000   -83000  215000\n",
+      "           Period  year  Non-EU+    EU+  British   Total\n",
+      "46    YE Dec 23 R  2023   952000 -55000   -37000  860000\n",
+      "47  YE Mar 24 P R  2024   927000 -85000   -17000  825000\n",
+      "48  YE Jun 24 P R  2024   856000 -95000   -21000  739000\n",
+      "49    YE Sep 24 P  2024   709000 -86000   -21000  601000\n",
+      "50    YE Dec 24 P  2024   544000 -96000   -17000  431000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Clean the migration value columns\n",
+    "value_cols = ['Non-EU+', 'EU+', 'British', 'Total']\n",
+    "\n",
+    "print(\"Before cleaning - sample values:\")\n",
+    "for col in value_cols:\n",
+    "    if col in migration_df.columns:\n",
+    "        print(f\"{col}: '{migration_df[col].iloc[0]}'\")\n",
+    "\n",
+    "# Clean each column\n",
+    "for col in value_cols:\n",
+    "    if col in migration_df.columns:\n",
+    "        # Convert to string, remove commas, and handle numeric conversion\n",
+    "        migration_df[col] = (migration_df[col]\n",
+    "                            .astype(str)\n",
+    "                            .str.replace(',', '')\n",
+    "                            .str.strip())\n",
+    "        \n",
+    "        # Convert to numeric\n",
+    "        migration_df[col] = pd.to_numeric(migration_df[col], errors='coerce')\n",
+    "\n",
+    "print(\"\\nAfter cleaning - sample values:\")\n",
+    "for col in value_cols:\n",
+    "    if col in migration_df.columns:\n",
+    "        print(f\"{col}: {migration_df[col].iloc[0]}\")\n",
+    "\n",
+    "print(\"\\nCleaned migration data sample:\")\n",
+    "available_cols = ['Period', 'year'] + [col for col in value_cols if col in migration_df.columns]\n",
+    "print(migration_df[available_cols].head())\n",
+    "print(migration_df[available_cols].tail())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "ee834a2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned population data:\n",
+      "   year  annual_population_change\n",
+      "0  1949                  299000.0\n",
+      "1  1950                  235000.0\n",
+      "2  1951                  -15000.0\n",
+      "3  1952                  140000.0\n",
+      "4  1953                  154000.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Clean population data\n",
+    "population_df['annual_population_change'] = (population_df['annual_population_change']\n",
+    "                                            .str.replace(',', '')\n",
+    "                                            .astype(float))\n",
+    "population_df['year'] = (population_df['year']\n",
+    "                        .str.replace('Mid-', '')\n",
+    "                        .astype(int))\n",
+    "\n",
+    "print(\"Cleaned population data:\")\n",
+    "print(population_df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "653e7699",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Records per year:\n",
+      "year\n",
+      "2012    3\n",
+      "2013    4\n",
+      "2014    4\n",
+      "2015    4\n",
+      "2016    4\n",
+      "2017    4\n",
+      "2018    4\n",
+      "2019    4\n",
+      "2020    4\n",
+      "2021    4\n",
+      "2022    4\n",
+      "2023    4\n",
+      "2024    4\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Migration data for 2020-2024: 20 records\n",
+      "2020-2024 migration data:\n",
+      "           Period  year  Non-EU+     EU+  British   Total\n",
+      "31      YE Mar 20  2020   166000   81000   -77000  170000\n",
+      "32      YE Jun 20  2020   115000   70000   -74000  111000\n",
+      "33      YE Sep 20  2020    60000   45000   -70000   35000\n",
+      "34      YE Dec 20  2020   101000   70000   -78000   93000\n",
+      "35      YE Mar 21  2021   150000   61000   -79000  132000\n",
+      "36      YE Jun 21  2021   251000   75000   -73000  254000\n",
+      "37      YE Sep 21  2021   441000   12000    14000  467000\n",
+      "38      YE Dec 21  2021   529000  -77000    32000  484000\n",
+      "39      YE Mar 22  2022   640000 -122000    29000  548000\n",
+      "40      YE Jun 22  2022   754000 -140000    20000  634000\n",
+      "41      YE Sep 22  2022   902000 -119000     6000  789000\n",
+      "42      YE Dec 22  2022   965000  -88000    -4000  873000\n",
+      "43      YE Mar 23  2023   957000  -54000    -9000  894000\n",
+      "44      YE Jun 23  2023   954000  -38000   -10000  906000\n",
+      "45      YE Sep 23  2023   957000  -54000   -31000  872000\n",
+      "46    YE Dec 23 R  2023   952000  -55000   -37000  860000\n",
+      "47  YE Mar 24 P R  2024   927000  -85000   -17000  825000\n",
+      "48  YE Jun 24 P R  2024   856000  -95000   -21000  739000\n",
+      "49    YE Sep 24 P  2024   709000  -86000   -21000  601000\n",
+      "50    YE Dec 24 P  2024   544000  -96000   -17000  431000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check records per year and look for 2020-2024 data\n",
+    "year_counts = migration_df['year'].value_counts().sort_index()\n",
+    "print(\"Records per year:\")\n",
+    "print(year_counts.tail(15))\n",
+    "\n",
+    "# Look for 2020-2024 data\n",
+    "recent_data = migration_df[migration_df['year'] >= 2020]\n",
+    "print(f\"\\nMigration data for 2020-2024: {len(recent_data)} records\")\n",
+    "\n",
+    "if len(recent_data) > 0:\n",
+    "    available_cols = ['Period', 'year'] + [col for col in value_cols if col in migration_df.columns]\n",
+    "    print(\"2020-2024 migration data:\")\n",
+    "    print(recent_data[available_cols])\n",
+    "else:\n",
+    "    print(\"No data found for 2020-2024\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "d6878f05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population 2020-2024:\n",
+      "    year  annual_population_change\n",
+      "71  2020                  112656.0\n",
+      "72  2021                  230080.0\n",
+      "73  2022                  618067.0\n",
+      "74  2023                  821210.0\n",
+      "75  2024                  706881.0\n",
+      "\n",
+      "Migration 2020-2024 (20 records):\n",
+      "           Period  year  Non-EU+     EU+  British   Total\n",
+      "31      YE Mar 20  2020   166000   81000   -77000  170000\n",
+      "32      YE Jun 20  2020   115000   70000   -74000  111000\n",
+      "33      YE Sep 20  2020    60000   45000   -70000   35000\n",
+      "34      YE Dec 20  2020   101000   70000   -78000   93000\n",
+      "35      YE Mar 21  2021   150000   61000   -79000  132000\n",
+      "36      YE Jun 21  2021   251000   75000   -73000  254000\n",
+      "37      YE Sep 21  2021   441000   12000    14000  467000\n",
+      "38      YE Dec 21  2021   529000  -77000    32000  484000\n",
+      "39      YE Mar 22  2022   640000 -122000    29000  548000\n",
+      "40      YE Jun 22  2022   754000 -140000    20000  634000\n",
+      "41      YE Sep 22  2022   902000 -119000     6000  789000\n",
+      "42      YE Dec 22  2022   965000  -88000    -4000  873000\n",
+      "43      YE Mar 23  2023   957000  -54000    -9000  894000\n",
+      "44      YE Jun 23  2023   954000  -38000   -10000  906000\n",
+      "45      YE Sep 23  2023   957000  -54000   -31000  872000\n",
+      "46    YE Dec 23 R  2023   952000  -55000   -37000  860000\n",
+      "47  YE Mar 24 P R  2024   927000  -85000   -17000  825000\n",
+      "48  YE Jun 24 P R  2024   856000  -95000   -21000  739000\n",
+      "49    YE Sep 24 P  2024   709000  -86000   -21000  601000\n",
+      "50    YE Dec 24 P  2024   544000  -96000   -17000  431000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filter for 2020-2024 data\n",
+    "population_2020_2024 = population_df[population_df['year'] >= 2020].copy()\n",
+    "migration_2020_2024 = migration_df[migration_df['year'] >= 2020].copy()\n",
+    "\n",
+    "print(\"Population 2020-2024:\")\n",
+    "print(population_2020_2024)\n",
+    "\n",
+    "print(f\"\\nMigration 2020-2024 ({len(migration_2020_2024)} records):\")\n",
+    "available_cols = ['Period', 'year'] + [col for col in value_cols if col in migration_df.columns]\n",
+    "print(migration_2020_2024[available_cols])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "e70d343d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Annual migration averages (2020-2024):\n",
+      "   year   Non-EU+       EU+  British     Total\n",
+      "0  2020  110500.0   66500.0 -74750.0  102250.0\n",
+      "1  2021  342750.0   17750.0 -26500.0  334250.0\n",
+      "2  2022  815250.0 -117250.0  12750.0  711000.0\n",
+      "3  2023  955000.0  -50250.0 -21750.0  883000.0\n",
+      "4  2024  759000.0  -90500.0 -19000.0  649000.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create annual averages for migration (quarterly to annual)\n",
+    "if len(migration_2020_2024) > 0:\n",
+    "    available_value_cols = [col for col in value_cols if col in migration_df.columns]\n",
+    "    migration_annual = migration_2020_2024.groupby('year')[available_value_cols].mean().reset_index()\n",
+    "    \n",
+    "    print(\"Annual migration averages (2020-2024):\")\n",
+    "    print(migration_annual)\n",
+    "else:\n",
+    "    print(\"No migration data available for 2020-2024\")\n",
+    "    migration_annual = pd.DataFrame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "c67d4032",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Combined dataset:\n",
+      "   year  annual_population_change   Non-EU+       EU+  British     Total\n",
+      "0  2020                  112656.0  110500.0   66500.0 -74750.0  102250.0\n",
+      "1  2021                  230080.0  342750.0   17750.0 -26500.0  334250.0\n",
+      "2  2022                  618067.0  815250.0 -117250.0  12750.0  711000.0\n",
+      "3  2023                  821210.0  955000.0  -50250.0 -21750.0  883000.0\n",
+      "4  2024                  706881.0  759000.0  -90500.0 -19000.0  649000.0\n",
+      "Combined dataset shape: (5, 6)\n",
+      "SUCCESS: Data successfully combined!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge the datasets\n",
+    "if len(migration_annual) > 0 and len(population_2020_2024) > 0:\n",
+    "    combined_df = pd.merge(population_2020_2024, migration_annual, on='year', how='inner')\n",
+    "    \n",
+    "    print(\"Combined dataset:\")\n",
+    "    print(combined_df)\n",
+    "    print(f\"Combined dataset shape: {combined_df.shape}\")\n",
+    "    \n",
+    "    if len(combined_df) > 0:\n",
+    "        print(\"SUCCESS: Data successfully combined!\")\n",
+    "    else:\n",
+    "        print(\"ERROR: No data after merging!\")\n",
+    "else:\n",
+    "    print(\"ERROR: Cannot merge - insufficient data\")\n",
+    "    combined_df = pd.DataFrame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "417c0956",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1600x1200 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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POz4D8FmQn2XLlsnly5fV7Xbp0sVk3/E5pX99A+4LPjO0YwHb43U3aNAgkx5oeP+55557SuS28b6C4xrHoB4+L/XHPT5H4auvvjLp84XXA97n8b0QryE81kWB50KvQ4cOhb6OP/74w+RvvFbMj9uC+sqZn2Z+GVu8bUvgtaJ/vaJ/h/4zvTT3r6Db1n5H4P0YPcDwmYTPH/37pCXMP6PMjykiIiqkYgRFiIiISowlK/G0FXwFXbZ///7G2rmoDWy+uky/6hSlasxXd2ula7DCcvTo0TmuX6+g1bBYkZ9X6QzzlXPPPPOMyfnmK8BQ6kFf79f8fH3JgF27duVYwa1fFYZVaMhgsXSFWW7MHxdtNS5WkuK2UEbDfMWuViYEfRP056FMxKFDh0weV/0qeOyrvpSJ+aplrGreuXNnnvcPK2H1q4VLO2MD/SByKy+Axwb9IrTLoeyB+Sp2S3ts5JexUdKPL7bFyuf8MiYshdI7+suivFFhFXf/rPW6RG8A/Xl4/vWlUH777bccryv982qe7TJkyBCTldwox4YyQtr5zZo1s/gxRUaG/jVzxx13mJyvz/5CuSnt+N68ebPJPuVWwgvHFlap5lbOKC/mr0E8rnit4PHWTsP7DFbLa3+jd4v5e3JhMjaK+/yA+Spx/H348GHj+XjctMcOp5uvaNdMnTo135XDlpYSzO8zClld5plY+qw6vE7Ma8frM4HMr9v88TQ/P7eyMZaWR8PIq7RbXsaOHZvj9ZKSkqLOQ0aSeU198/ci8+eyT58+xn34888/c+wfyidp5Zc+/PDDfN+DzN8nUGYLWR8aPM7685GVdP36deNxo3+tmh+nBw4cMHmdYOAzIb/+Nfrjx/x8ZH1o8LjpSzHhs0V/fON7k3kmEr6PlMRtF6bHxn333WfcBpk05p/HeC9as2aNKtdVGDiG9LdfmPc07fNFn5mDrCQ8X3rmmTDm5egA5VX126AUoC3ftiVQOtX8e+MLL7xgso35a/all17KcT1ff/21yTb9+vUrkds2z7ZAmVdNYTI28B5i/r5ERERFx4wNIiKqcLD6VFuNh9Wx5iuuYmNjjf9HdoceVmh7e3sbV0BOnz69WPtSu3Zt2b17t4wfP16tYsXKLqxIxEovrB4szKqyBx54QFq2bGn8e8iQIXner1WrVpmch5WMWKmvwcphrCotSVhljPuF1clYxXj48GGT89955x11HixdujTH6m+sotRg9fKIESOMfyOOYr7SUO/2229XK5bzun/Z2dk5VqCXJqzqxepkrEDFMYj7rT02+hX8mZmZJqvDS0pJP77IltI/nvkde4V1M0ZWPIXdP2u9Ls3fb4YPH65WWOsvixXbeVm8eLHJ31hdimNfy2h7/vnnVQaA5uDBgxITEyOWQPaK/rZxW8iC0bJs9NlfWIWtraxGZpveG2+8Id98841a/a5lu+ExfvTRR40ZJUWFVfL6FeB33HGHWi2vZf6MHj26WNdf3OcnN3g89NkVeNy0x65BgwYqcw4r3fE4YoUwjkGM999/v9RWRetXCiMjRIPPSmQE6V8nTz31lMllfv/99zyvDyulX3zxRZP3Gf1q5uK8TxT2vQLv+ci41Hv33XeN2QzIzMLf+mMSK7bj4+PzvM6XXnrJ+BmGLB5zyKrAcwfmn68F3XdkQOB40L9nN2rUyPg3spK2bdum/o/3av1jgc93/XHarFkzY0aVJc8bnnd9xlZ+72HIxDx9+rTxbxyzeFy096CxY8fmyHItqdsuDP37ErKI8N6ITBPs//Xr19XzjmwFPHaFceHCBeP/cWwX5j0Nt4/bw7EJeB9ARheer8Ie94X93CyL2z506FCumdYYn376ab63g+cZ7xf67414/3311VdLbP+Ke9vIyHruueeMn2lvv/22FAXeQ/QZX8g0JyKiovuvDgEREZENwQ9RS0tp6OHHAn5w6GmBCo2+vIW+VAHoJyihTp066vKJiYlSFI899pixZEpBCrqNNm3aFPl+mZfIAfygLYsUePz4xwTS/fffbzzNfLI1t/3Dc6Ev34FjIi8oA1LQ/TN/TEoLJiBRDgPlvixR1GMrPyX9+Bbm2CuIeSmeknheCrt/tvK6NH+/0Y5lfQkxPfPnyLzMVXHfSzEZ+vfff6v/I6iBskso0aMvQ4UJ4UmTJplMZiO488UXXxiDA/oAAUrFYALxkUceUYG+4sL1fPTRR2qCTgtqAEpvFTdwUtznJzeYMMsNAgoIkmAy3dbfJ/Tye59AOSF9mTvt9aCVCsuvjFVB7xPa/pp/vucF5Y202wUcG40bNzbZxsfHR33Ga8FlTIjiNlDeLTf6x8e8/IyXl5cKBOV1fkHvkeafYQiQNG3aVI4dO5bj+Czp560w72Hm14NJ4YLKs5XVZ4vefffdJ19++aUKwqA033vvvWfy2OI4QpmyJ554Is/nOzcoR6d/zguz4ObJJ580Trpjcnvu3Llq0t8cjkv99weUSjOnBZ01VatWtfptIyiY17Ggn8g3hwA8SoydPXvWeBoCZHPmzDEpTabtn15xH5vC3Da+N+D28B6Hz0R9ybfCwrGDABskJCQU+XqIiIiBDSIiqmD0PQw05j9O8qOvr6/RVmAWFmqEm0+eYgUmJlfwwxI/kPQ18AtaZWZ+3wpzv8oCfhxqK47xgw+19dFjArX2zSd5zO9rUR/jkoLMCb3irKCbOnWqycQAsoewKtXX11fdTxwX+gnUkshYMFfSj29JHnvmK52xAharaG+55ZYy2T9bel2W5PtNXswnefKDVaqY5NNWrWPyBpM8CxYsMMmG0k/gwueff65q16PfyNatW+X8+fMmWSW4PIJov/76qwwbNqxY9we9EZBVo89KwqS1PnBaUkri+UFPh9xgBbM+qIHrxfslsr1wDOE9AsdqeX6fKM57BR4LvB71E9vox2BpYKM0Hi/9pKr5sZHf5Gllen8v7HtQad02vn+gvxbem5C5gx4x2n7g8Tty5Iga8+fPV+dZGqTQHwPos1AQ3BaCCvo+NjhWfvvttzwDvTjG9d8hMOluHrwyz2TJ7XVhzdu2FDL78JmAHjwaZIlhMUxux7X5bekDEoXdv8Letva5hscVn4N6WpBCg35Q+C6IDLiPP/4430B1Wb53EBFVRCxFRURElZp5GRWk0uthtZ/+R09hoJGqHlYUI5UdZRnQjBnlG0oLVqGar0ozZ35fiwtNPnG/MIGJCdEPPvhAlcnJrbGivjkuHDhwIMc2+/fvz/cyBV3e/P7pn2vzld1Y3Zvfc1cY+stiYg4ZHFjhjZWMeHzMVw2XxsR2ST++JQkTKpi81XvmmWeMJTJyg4mEwqz2ttXXZUHvN4BJtryYP0eYlMNjk98wL62VH7wuJkyYYPwb2RvIxNCvUjYvcaNBaTMc4yhDg0kevOcgs0KbqMS+zJw5U0oCVs7qIfiS2wr/sn5+LA2O5HYc4rlEo3uUAMNxqC8VVxnfJ9As2rwsEUpD5lcqCrRACDKF9KvE8f6B17kePtv1ZZXwmBYlU7Qk5PbYm5dz1I5Paz5v5teDCd6C3oNwPJeUwhz3mDBGCSqU0kP2DhYs4HWHAK4G2S8IuFoKJe80uM78PpdwLGJiWx9YQGAWwd/8stf0ZcXAPKsLGTD61wG+75m/d1njtpGdltcx8N133+W4rZ9//ln69etn/I6Nzwp8j0R2TV7Ps/m+79ixI8c25vtsfp+KetuarKws9Z1RP8wzi/AZaJ41pklNTTUJ9umPKSIiKjwGNoiIqFLr27evyd+vv/668QcHVvGb1xcvDJQ/0EM2g361Fn5wl5bevXub/I0JR/2PPfTgKIsyVHkxn2xFyQh9DXlMPugnG/BDE3Xo84KV4lj1r0EpHP39w+QiyuHktYr6//7v/4yrYL/99tsctdmL+rzjdrV+L4BJy4Ied/32Ra0xXtKPb0nC5Dn6j+jhMbnttttM6pcDnpMVK1aoLA/zSb7y+Lo0f7/BhJ9+VT6yEPIrc2Q+0YsgTG5lXnDMICMAq0YLSx+4QLAJQSf968b82EKGy5tvvmkSPMWkNMro3HnnnSblOsyf36LCaxl9fLDaG8M80GGt56ekjkNMwCMoVNrvE+gDob8e9HDQZ+fgOs37TBUmUFZc6E9ifvygfNeePXtybIvJfRxveC/X3nsHDhxoss2zzz5rnIDEsY16+fqJ6bZt2xaqLFFJwutV/1r+6quvTAIxCNJofbLwXq2ffMXnOybvNXivxHt+aTxvyKxD+Tn9Zy3K9uQ2eYsMG/S9yW1VfVGZH/fm/Tw0eJ1igYVW5gePF7I4OnfurLJL9QrzvoRMooIWjQAmzDFxjgl0/fGFwEJBixvQc0QfEMVzqe0jPhPxPVVPXxrQ2rdtqRkzZqjAi/Z6xGcGMkmw0KCgXlAo9akPTH3//fcmz7s+aIy+NeblAIt62yXFPBBpXo6NiIgKh6WoiIjIJj300EMmEz16+KFc3CaxmilTpsjXX39tbKC6YcMG9UMoPDxcTQQXp/6/vlk3IIMB14+JOKwy06+CLmn4oYTJP61ePiZvsGoNP26x2gy3XxqlOiyFiQX82Fy3bp36G5MPmDDBfmPCDyuX9eWh8GNb34DXHJ6/jh07qvuHy5nfvzFjxpisNEXZHP2P4dmzZ6sfttq+FAeed23yE/uF/UYpKq3kUkGrAVE2QV8KCatLcXlkf+DYRJmEsn58SxomILHyHa8JDQItmDhGE3isvkeQAauOS7r+tDVfl3gOMHmu9aDAxAqCNnh+8bzktvpUD5fFsYvAJKD+Pspo4Xpr1KihggzoF6DV4NdPAFkK16c/djBBmVvTcA3eW9AwGgMrTzFxhnItuByOM/3K1JI8xizpL1LWz09hj0P96xyNfbEaGa9LTECaBz7MmZdXwYQjSqtoJXVQQ7+gGvAoj/f000+bNMnFeyXeY7DaHc+fvtwObvPuu++WshISEqKaq+PzXnu/QhYNnicEzrACHc9RVFSU8bNaP/E8bdo01Whbq8O/ZMkSdRmU1sFr58SJE8ZtMZlb1GbAJQFlbtBnA59hmJjWB+oBQUpMwEKTJk1U7xvtMwzHCl6zeH9H4BjHqb4pPIJB5qVzigqPE1a1jx8/3hggQpYXHmscHzgfwQaUedImjvX9LYoLwQkct9rnAp5HfF/DZyM+WzHRjvuKz5fHH39cvV/hOEJmAYIi+BzGca1XmPclfK965ZVXTIKBuZVRxGcqXo96yCLCd9vc6LNacF/Q++P99983HhvYR7wXIfilD/JjW5SbspXbtgReh+aXQ6YUvodh5NZXSR+cQOYfXifaeyTek/A9Ht+RcL/13/0++eQTk+9cxbltlDbLCx5z8++UOM2Szy7zhUhERFRIBiIiIhuAjyRLx7Rp0/K8bN26dXNc94QJE0y2Wbt2rcn5P/74o8He3j7X2xo5cqShZs2axr8dHR1NLovr0m+P29IbMWJErteL23v33XdNTuvWrVuh9rug2z59+rQhODg419v39fU1DB061OS02bNnF+s5O3nyZKEuf/nyZUPXrl0LfL7xHKSmpppcFseAfpv777/f4OXllevlmzdvrm5LLz093dCmTZtct/f09DRMnDgx32MOz1Ve93379u0GFxeXXK+7bdu2httuuy3f53Xv3r0GBweHXC/fqlWrPB8D8+evJB9f8+vG/c3v2LXUl19+mefzZj727dtXYvtnzddlbGysoWHDhrnefvXq1Q2jR482OW3evHkml09MTDT069fPosesV69eRXpe8J5ofl12dnaGmJiYHNteuXLFon3x8/MzHDx40OJ9wHOa3+OYF/PH3/z5M79e89d2cZ8ffAbpz89LQkKCoUGDBnk+Vs8++2y++5mSkmKoU6dOno93UlJSro+H+eOYnZ1teOSRRwp8/po1a5bjPb6g6y7M45GfzZs35/mcmI+PPvrI5LIrVqxQn3f5XcbV1dUwZ86cQu97ft89CnoPMn8fmTJlivp+kdv+9ezZM8d7NP4eNWpUgY8HPgPMP/8Keg0UdN9g1qxZBicnJ4ueE3wXKcnbfuqpp/K8rY8//lht8+GHH1q0bwMHDjRkZWUZLJWRkWEICgoyXh7fo3Jj/h2hoJHb7ZgfI+ajfv36huPHj9vUbVvC/BgoaOT23XTJkiUGNze3PC+D71D4flEat50b88crv8sNGjTIuB3ewzMzMwv5CBIRkR5LURERUaWHlPRNmzapshXe3t5qVR9WAGKlF0oZ6Bsp5tUINi8oBYBVoFjF7OjoqFYaYjU9VpWVVNZJXtDgFysTH330UfV/3D72H6uusfIM99Ga8FggswGlT9DAEX0XsOIOq4yxgg4rh5cvX65WE+L0glY/4z5hFStWjWPVKlbmotQISnTgtvTwWGDVO1bBao8NVrxjhR3KBBRlpbt5yQeUDcLKdew7VsGjbBCe97wykTQtW7ZU9xtlYnD5otbSL8nHt7Tcd999cubMGfVaGzp0qFpVi1XJDg4OKoMC5YZQDg4rkLGauaRY83WJ1yDuD1YT4/7i9lHaBSWgcAyb938xf8/Binw8byiXhh42WDWLYwork7HKPiIiQu69917Vs0HfYLsw0N/BvKkvMhnM67gDeuj89NNP6rWE16H+OcTjitfDCy+8oEq2YJW9rSvu82MpPFd4n0DTc1yH9v6M9yDcTkElY/A6RkYe+ovgPa+oTZfx/oLmttgXfDZgdTueP+wPMqdQ0gZlkVCSy1r9J5CNhwxKvFfhPR6PDT6rcZ/xekAGBlbq43g3X5WO4xaXRUYL3k/wuOPYxOWw0h4ZK8guQBaZNeH9D5/XeO1hdT3ek7FS/p133lGZPebv0fgb/axQqg/fYZCRiO8uOD5xvOL68D6HzwDzz7+SgNc7HjeUqkOmCB5XPB94L8J7Ej7/sOofWTH4jC1JKH2HMmXIXMkrKwmPI8q54fWB9x0cyzim8bhhf1DOCyvscczk1QcnNzh29McYnpuSzirUbgd9KXD9yObCewOeW3wnwPsssmDwnorHuiLdtqVwfCNTC+/TOA7wnoXjH6Wq8J6K73H4fmFr0J8Er1kNjqWivncTEdFNVRDd+Pf/RERElRLKJmAiwXzCCjAhp+8HgMkTTPKQdaEUhL58Sn5p/0S2BCWaUGomt4ahmNBGyQ+tdA6CBgisFlRSiEoOnx8qC+alaxCAMO8FQLYJTaERSNIaQ8+aNatI/Yyo8kEZLQRjAMFVlPcqjcAjEVFlwh4bRERU6aE5IpoJog41Vuhi5SFWVaEBIep565t3lmZjYSKq+FDjHdlEyGRo1qyZmkBHPXw0CsYKWfTA0SDLh5PmZYvPDxHlB9lseO0j2weQmYIsgdwWxxDp+1Hp+4q9/PLLDGoQEZUABjaIiIhE1Mq7/Mq2IBUfpV30DaiJiIoCCdPbt29XIzcoTYESLyjDRWWPzw8R5WfKlCmqYTUCnqdPn5Zvv/1WHnjgAWvvFtmwb775Rs6ePav+j3J6KBNLRETFx8AGERFVeug/kJiYKFu2bFH1/lFmADWXUZ4KtbtRixl1vVF2hIioOAICAlTd/A0bNqga9cgOQ/kjlKVAffAuXbqoXgeoG05lj88PERUE/TrQ44HIUg8++KAaRERUsthjg4iIiIiIiIiIiIiIyg07a+8AERERERERERERERGRpRjYICIiIiIiIiIiIiKicoOBDSIiIiIiIiIiIiIiKjcY2CAiIiIiIiIiIiIionKDgQ0iIiIiIiIiIiIiIio3GNggIiIiIiIiIiIiIqJyg4ENIiIiIiIiIiIiIiIqNxjYICIiIiIiIiIiIiKicoOBDSIiIiIiIiIiIiIiKjcY2LCCDRs2yODBg6VmzZpSpUoVWbJkSaGvw2AwyPvvvy8hISHi7OwsQUFB8uabb5bK/hIRERERERERERER2QoHa+9AZZScnCwtW7aUe+65R0aMGFGk63jsscdk5cqVKrjRvHlzSUhIUIOIiIiIiIiIiIiIqCKrYsDSf7IaZGwsXrxYhg0bZjwtLS1NXnjhBfnpp5/k6tWr0qxZM3n33Xele/fu6vwjR45IixYt5ODBg9K4cWMr7j0RERERERERERERUdliKSob9Mgjj8jWrVtl/vz5sn//frntttukf//+cuzYMXX+77//LvXr15c//vhD6tWrJ8HBwTJp0iRmbBARERERERERERFRhcfAho05ffq0zJ49WxYuXChdunSRBg0ayNSpU6Vz587qdDhx4oScOnVKbTNnzhz57rvvZPfu3TJq1Chr7z4RERERERERERERUalijw0bc+DAAcnKylJNwfVQnsrPz0/9Pzs7W/2NoIa23TfffCOtWrWSqKgolqciIiIiIiIiIiIiogqLgQ0bc/36dbG3t1cZGPhXz8PDQ/1bo0YNcXBwMAl+hIWFGTM+GNggIiIiIiIiIiIiooqKgQ0bExERoTI24uLiVCmq3HTq1EkyMzMlOjpalaqCo0ePqn/r1q1bpvtLRERERERERERERFSWqhgMBkOZ3iKprIzjx48bAxkzZsyQHj16iK+vr9SpU0fuuOMO2bx5s3zwwQfq/Pj4eFmzZo20aNFCBg0apEpRtWnTRmVwzJw5U/398MMPi5eXl6xcudLad4+IiIiIiIiIiIiIqNQwsGEF69atU4EMcxMmTFCNwDMyMuSNN95QPTRiY2OlWrVq0r59e3n11VelefPmattz587J5MmTVSDD3d1dBgwYoAIhCI4QEREREREREREREVVUDGwQEREREREREREREVG5YWftHSAiIiIiIiIiIiIiIrIUAxtERERERERERERERFRuOFh7ByoTNPlGbwxPT0+pUqWKtXeHiIiIiIiIiIiIiMgmoGtGUlKS1KxZU+zs8s/JqLSBjQ0bNsj06dNl9+7dcv78eVm8eLEMGzaswKbfTzzxhBw6dEhq164tL774okycONHi20RQA5cjIiIiIiIiIiIiIqKczpw5I7Vq1ZL8VNrARnJysrRs2VLuueceGTFiRIHbnzx5UgYNGiQPPPCAzJs3T9asWSOTJk2SGjVqSL9+/Sy6TWRqaE+Ml5eXVHbIYImPjxd/f/8CI3BE1sBjlGwdj1GydTxGydbxGCVbx2OUbB2PUbJ1PEbJ1vEYNXXt2jWVGKDNo+en0gY2BgwYoIalvvjiC6lXr5588MEH6u+wsDDZtGmTfPjhhxYHNrTyUwhqMLBx84WbmpqqHgu+cMkW8RglW8djlGwdj1GydTxGydbxGCVbx2OUbB2PUbJ1PEZzZ0kbBz5aFtq6dav07t3b5DQENHA6ERERERERERERERGVjUqbsVFYFy5ckOrVq5uchr+RHpOSkiKurq45LpOWlqaGBttqkTiMyg6PARrC8LEgW8VjlGwdj1GydTxGydbxGCVbx2OUbB2PUbJ1PEbJ1vEYNVWYx4GBjVL09ttvy6uvvprjdNRNQ4pRZYcDNTExUb14mWpFtojHKNk6HqNk63iMkq3jMUq2jsco2Toeo2TreIySreMxaiopKUksxcCGhQIDA+XixYsmp+Fv1D/LLVsDnnvuOXniiSdyND9BM5j8emxkZmZKenq6VIYXbkZGhnh4ePCFS6XOyclJHBwcCn2MoqYfGziRreIxSraOxyjZOh6jZOt4jJKt4zFKto7HKNk6HqOmXFxcxFIMbFioQ4cOsmzZMpPTVq1apU7Pi7OzsxrmcJDmdqAiMnf69Gm5dOmSVCaV7f6S9VSrVk3q1KljUQMiDbbN6zVLZAt4jJKt4zFKto7HKNk6HqNk63iMkq3jMUq2jsfofwrzGFTawMb169fl+PHjxr9Pnjwpe/fuFV9fXzXxiWyL2NhYmTNnjjr/gQcekE8++USefvppueeee+Tvv/+WBQsWyJ9//lli+6QFNYKCgpjFQFTC0W+85vGazsrKknr16hUquEFERERERERERES2o9IGNnbt2iU9evQw/q2VjJowYYJ89913cv78eRVo0GAiFEGMxx9/XD766COpVauWfP3119KvX78S2R+Un9KCGih7RUQlC8FCQHAjLi5O2rVrx+AGERERERERERFROVRpAxvdu3dXpZ/yguBGbpfZs2dPqeyP1lNDm3wtKtyny5cvq9XpuC4/Pz9O3hL9S3t9/fPPPyojqm3bttbeJSIiIiIiIiIiIiok1jqyMUUtP3X16lWVSdKoUSPVbAYZJvgXf+N0nE9U2WmvL0dHRzlw4EC+wU0iIiIiIiIiIiKyTQxsVAArVqxQpbFQJuvEiRMm5+FvnI7zsV1FgAyUJUuWiK1Dhs+UKVNs5npKwiuvvCLh4eFS3jk5OUlaWpoxU4qIiIiIiIiIiIjKDwY2yjkEKwYNGiQpKSlq9bn5CnTtNJyP7UoyuIEAQ34Dk+B5iYmJUdugYXtJmzhxonEfMIHdsGFDee2111QfE1u2bt06tc/m2TW//vqrvP7662WyD4sWLVKBFG9vb1W2qUWLFuqxS0hIkIqG2RpERERERERERETlEwMb5RgmwEeOHKkmaLOzs/PdFudjO2xfUmWp0GBdGzNnzhQvLy+T06ZOnSrW0r9/f7UPx44dkyeffFIFWaZPny7lka+vr3h6epb67bzwwgsyZswYadOmjfz1119y8OBB+eCDD2Tfvn3yww8/lPrtExEREREREREREVmCgQ0bhCBEfHx8gePTTz+VGzduFBjU0F8vtv/ss88KvG5LrjMwMNA4sMIf2Qba3wEBATJjxgxVAsvZ2VmVL1q+fLnxsugBAhEREepyyBKAnTt3Sp8+faRatWrqOrt166YaPRcWbhP7UbduXXnwwQeld+/esnTpUnXelStX5K677pKqVauKm5ubDBgwQAVA9I3jfXx8VLkr9ChxcXGRfv36yZkzZ0yyQoYNG2ZymygXpd2P3CA40Lp1axWkwL7dfvvtEhcXZ8xg6dGjh/o/9guPCW4jt1JUlu4/snPCwsJU5oUW6MnLjh075K233lKBDASAOnbsKMHBweq5QBbHhAkTctwXnI/naOzYsZKUlGQ8D89z586d1T6gef2tt94q0dHRObJ1kImC+4z70LJlS9m6davJbXz11VdSu3Ztdf7w4cPV8YTr1Pvtt9/klltuUc9R/fr15dVXX7X5zBwiIiIiIiIiIiIqHgY2bNDly5dVYKCg8eKLLxa6nA62x8r8gq4b+1AcaFiOSfL3339f9u/frwIDQ4YMMU7AYyIdVq9erSbcMckNmCDHJPqmTZtk27ZtKrAwcOBAk4nzonB1dTX2U0DAYNeuXSrQgcl0PCa4jYyMDOP2CAC9+eabMmfOHNm8ebPKcsEEfnHg+lFSChkQCJpggl8LXmACHwEEiIqKUo8JHsPcWLr/eOwRgNiwYYOcPn063wyaefPmqQDIQw89lOv5+oACghTY/z/++EON9evXyzvvvGM8Pzk5WZ544gm1j2vWrFENuxGYMA+W4TjEPqEcWUhIiIwbN84YlMBj/sADD8hjjz2mzkeABc+H3saNG1WAB9scPnxY/u///k8Fdcy3IyIiIiIiIiIioorFwdo7QBUTJtWfeeYZYzDg3XfflbVr16qSVcg08ff3V6djRT+yFzQ9e/Y0uZ4vv/xSTapj8hwr/wsLk/6YXEf2wuTJk1VgBQEBTJwjK0Gb1EdgAZP1t912mzoNQYJPPvlE2rVrp/7+/vvvVfYDAjJt27Yt0mNyzz33GP+P7IJZs2apsk/Xr19XQQWUnAIElswzEzSF2f8vvvhCGjRooP5+5JFHVK+MvOB6sU+Ojo4F3g8EKBBA0Mpj3Xnnneox1gIKKHem9+2336rnG8GHZs2aGU9HUAN9XwCZFk2bNpXjx49LaGiofPzxxyoTRQvGIPCxZcsWFUjR4DLPPvusMZsE+4/A0dNPPy3Tpk0r8H4QERERERERERFR+cSMDSpx165dk3PnzkmnTp1MTsffR44cyfeyFy9elPvuu09laqDMEfp2YOIfGQeFgQlwBAtQoggT5OgdgT4buH0HBwdjwEILrjRu3Nhk37ANgg4aTLYj2FDQ/udn9+7dMnjwYKlTp44KCqDMFhTmvlm6/yjfpAU1oEaNGsayV7kpTOYPSlDpe36YXzeCJMi+QKABzx+2z+1+ojG5/jpAux5krZgHkMz/RuYLgjV4nrWBYwfZLshYISIiIiIiIiIiooqJGRtkU7D6HmWwUIYJ/THQK6NDhw7GMlKWQu+Gzz//XJycnKRmzZoqGFCSUF7JPBigLwVlDuWZUI4LAxkWyGDARD/+Lux9s4R55gV6WuQXvEBGBMp/4T4UlLWR23Xry0wheIPnDj0y8NjjPGRqmN9P/fXgOsDSfjGAgBeyNkaMGJHjPAS0iIiIiIiIiIiIqGJiYMMGYQV+fqvrAZPUWLV/6tSpQq22xwQyJp23b99unEzOax+KCqv0MaGNcklaVgLgb23VPQIOkJWVZXJZbIPm5ugZAWjYfenSpULvg7u7uzRs2DDH6SgnhT4OuP9aKScEUpAh0KRJE+N22AY9IrT9xfnos4HLAwITBw8eNLlu9ILIKygQGRmpbge9KFA2CnD9enk9JkXZ/8JCI3OUxsJjj54V5nDf8yqPpaftC4IaXbp0UachYFJYyEBBI3k987/RNBy3ldvzTERERERERERERBUXAxs2CNkAWg+K/EyZMkUef/zxQl8/LoM+DqXpqaeeUn0OUA4pPDxcZs+erSb+ka0AuH009F6+fLnUqlVLrbBH6SmUoELD69atW6uSVrgebFdScP1Dhw5VJYvQbBolldCnISgoSJ2uQYACPTkw2Y9sD/SoaN++vTHQgV4g06dPV83FkVEyd+5cFeiIiIjI9XZRfgqBC/SOQFNsbIt+EHoIOCHYhDJaCOzgfqO8UlH2v7AQJENviieffFJiY2NVs28Ep9DzAr06OnfunGvAw1zVqlVVUAy9UVBeClkp2L/CwmPftWtXmTFjhsoA+fvvv+Wvv/4yCca9/PLLqu8KHttRo0ap1w3KU+GxfeONNwp9m0RERERERERERFQ+sMdGOS/bhF4KmNC1BLbD9nfddVep79ujjz4qTzzxhJoob968uQpgoOk1JuYBwQIEDTA5jwl0bVL+m2++kStXrqjV+GhKjesp6SAMgiytWrVSk+IISiDjZdmyZSbZFnic0PwcmQzoDYIAw88//2w8HyWkXnrpJRUMQC+OpKSkfB9XBKrQcHvhwoUqswKZG2iwrofghNYQu3r16iqYUtT9Lwo0eP/xxx9VNgjuH5p54zlELwytQbclx9j8+fNVPxGUn0IQDQGgwsJjjoAKAhstW7ZUxw+uS19iCvuIINDKlSvVc4DA04cffqgCRERERERERERERFRxVTEUpo4RFQsyEJCVkJiYqMo16aHZMZo/o9QQJtUttWLFChk0aJCa3M6vPwEmnLHaHRPgffv2Ldb9qOgQgEA2DMovke1AlgpKem3cuLHI16G9zpDVgR4dkyZNUn1c8oLXFMrCIbhmaQCRqCzxGCVbx2OUbB2PUbJ1PEbJ1vEYJVvHY5RsHY9Ry+fPzbEUVTmHVet//vmnjBw5Uk3agj5WpZXuQVmjX3/9lUENKjeQ0dKnTx/VLwVlqL7//nvVA4SIiIiIiIjK3vlrl+RqSlKOCbmEhAS5bEjOMSHn4+opNbyqlfFeEhFRZcHARgUJbpw9e1b1e0B5p+joaON59evXV+WcUEoI0S6i8mLHjh3y3nvvqTJfOI5xbCPDgoiIiIiIiMo+qDHkm8clPSvD4ss42TvK0ns/ZHCDiIhKBQMbFYSPj48KYKDpMlZLYDIYjaV9fX1NGi5TwSZOnKgGWdeCBQusvQtEREREREQkojI1ChPUAGyPyzGwQUREpYGBjQoGQQw/Pz81iIiIiIiIiIiIiIgqGnYkISIiIiIiIiIiIiKicoOBDSIiIiIiIiIiIiIiKjcY2CAiIiIiIiIiojwZDAZr7wIREZEJ9tggIiIiIiIiIiKjbEO2RF86K7vPHlFjx6mDRbqexNTrJb5vREREwMBGOXf+2iW5mpJk8fY+rp5Sw6taqe4TEREREREREZUfmdlZEnkxRgUx/sGIjZRrqcnFvt4HF74l7YKbS7/GHaRXozbi5eJRIvtLRETEwEY5D2oM+eZxSc/KsPgyTvaOsvTeDxncKCVVqlSRxYsXy7Bhw6xy+999951MmTJFrl69apXbJyIiIiIiItuXnpkhBy9EqyDG7rORsjc2Sm5kpJb47WSLQbbG7FfjjVVfS4fgFirI0aNha/Fwdivx2yMiosqDPTbKMWRqFCaoAdi+MBkeBZk4caKazH/nnXdMTl+yZIk6vbTFxMSo28ltbNu2TW3zyiuvSHh4eJ6X3bt3b57XHxwcrLaZP39+jvOaNm2qzkMwQXP+/HkZMGCAlAXs28yZM01OGzNmjBw9erRMbp+IiIiIiIjKhxvpqbLt1AH5dNMCuXf+q9Lp43vk7vmvyMebfpYtMfvyDWpUdfWStrWblkhWyMYTe+TFvz6THp/dL1OWfCDLI7eofSMiIiosZmxQsbm4uMi7774r999/v1StWtUq+7B69WoVaNDz8/MrkeuuXbu2zJ49W8aOHWs8DUGTCxcuiLu7u8m2gYGBxW7IlpWVJQ4ORXtpurq6qkFERERERESVF8pIIQvjZmmpSDl88YQKLFgiwMNXWtcOk1a1bo5g35oSGRcjY394rtD70aNhG5UVYt5rA4su1x7fqYaLg7N0bRAh/Rp3lM71wsXF0anQt0NERJUPMzao2Hr37q0m9N9+++18t1u0aJEKPjg7O6tsgw8++MDkfJz21ltvyT333COenp5Sp04d+fLLLy3aBwQxsA/64ejoKCVh/Pjxsn79ejlz5ozxtG+//Vadbh6AQAYHslU0W7ZsUdkiCP60bt3amMmiZYmsW7dO/f3XX39Jq1at1GOzadMmiY6OlqFDh0r16tXFw8ND2rRpo4I3mu7du8upU6fk8ccfN2aoALJHfHx8TPbp888/lwYNGoiTk5M0btxYfvjhhxz7/PXXX8vw4cPFzc1NGjVqJEuXLi2Rx46IiIiIiIhKX8KNa7L66A557+/vZfScZ6XrJ5Nk8uL35Ludv8v+88fyDWrU8QmUYc26y+v9H5Q/J82Slfd/Km8PmiyjWvaWen5BxarGcH+HEbLmwS/k0xHPyOAmXcXDKedCvNTMNFkZtU2eXDpDenz2P3nuz09kffRuycjKLPLtEhFRxceMDRuVlHZDjsWfznebmIRzRbruIxdPSkpGWr7bNPKvI54W1ru0t7dXAYnbb79dHn30UalVq1aObXbv3i2jR49WZaFQLgkT/g899JAKSKCclQbBjtdff12ef/55+eWXX+TBBx+Ubt26qQl5a0FwoV+/fvL999/Liy++KDdu3JCff/5ZBTvmzJmT5+WuXbsmgwcPloEDB8qPP/6oAhHof5GbZ599Vt5//32pX7++ynpBEAWXe/PNN1WwA7eD64qKilIBn19//VVatmwp//vf/+S+++7Lcx/Q7+Oxxx5TJasQgPrjjz/k7rvvVs9Rjx49jNu9+uqr8t5778n06dPl448/VkEb7K+vr28xHz0iIiIiIiIqaReTLqtsDPTH+OfMETmREGvxZRv41bqZjVE7TG6pFaoyNAri4+qpenYWtscnLudo7yCd60eokZaZLlti9suKyC2yLnp3jrkJlMRadmSTGp7O7tKzURvVk6NtnabqeoiIiDT8VLBRCGqg3mVpeHVlwVkQs8e+or7gWAqr/ZGZMG3aNPnmm29ynD9jxgzp1auXvPTSS+rvkJAQOXz4sJpI1wc2MJmPgAc888wz8uGHH8ratWsLDGx07NhR7OxME5CuXzdNdS0OZJE8+eST8sILL6iACzIgcuvboYdgBla2fPXVVypjo0mTJhIbG5trIOK1116TPn36GP9GQAGBCw2CPQhSIJPikUceUecjoITMlvzKXyFYgsdXe0yfeOIJVUYLp+sDG9hm3Lhx6v8IUs2aNUt27Ngh/fv3L+QjRURERERERCUJJYvPJl6U3Wci/y0tdUTOJsZZdFm7KlUkNKCe+n2PYEZEUGOp6uZV6H2o4VVNlt77YY6endnZ2ZKQkKB+o5r/JkdQA5fTc3ZwUo3DMRDU2HRij+qzsfHkHknLNA2aJKUly28H16mB6+rVqK30C+0grWs1EXuz2yIiosqHgQ0qMeiz0bNnT5k6dWqO844cOaJKK+l16tRJZRKgpwQm6aFFixbG8xEUwKR9XNzNL2xoyr1x40b1/7p168qhQ4eM2yKDIiwsrNTu26BBg1QPkQ0bNqgyVAh0FATZFbg/CGpo2rZtm+u2KFNlHpRBdsuff/6pGpJnZmZKSkqKnD6dfxZPbo87sjrMH/ePPvrI5DT9446+IV5eXsbHnYiIiIiIiMo2kBF9+awKYCAjA8GM+OtXLLqsg529NAtsILeo/hihEh7UWDwsrMZQEAQpzAMVCGzEVXGXgICAHIGNgrg6Okufxu3VSE5PUeWnVkRulc0x+3KUoUJAZdH+NWpUc/eR3iHtVCZHeFCI2FVhkIOIqDJiYINKTNeuXVXJpueee84kC6MwzPtiILiBL0qAPhCY3M9tOzT4btiwYa7XiUn6xMTEHKdfvXpV/evt7V3gfqGXxp133qkyUrZv366yJ0qSeRNyBIdWrVqlMitwv9AQfNSoUZKeni6lIb/HnYiIiIiIiEpPVna2HI0/9W9pqSOy52ykXDHLjMiLi4OTtKjZSGVjIJjRvEZDFTAob9ydXGVgWGc10Ph83fFdsiJqq2w7dSBHf5BLyVdl/p4ValT39JU+Ie1VJkfzwIbF6gdCRETlCwMbNgo9LlAOqqAeG5aUlTI3re//JNi3ZoG3XxTvvPOOKtFkXjoK2RSbN282OQ1/oySVlq1RkKCgoCLtE/bl7NmzcvHiRdUvQ/PPP/+obAr0rLAEsjQQaECPEPTBsOR2586dK2lpaapPBuzcudOi28Jjg+AQSnxpGRwxMTEm26AZOLJd8qM97hMmTDC5bpTFIiIiIiIiorKHbITDF0/I7jM3Axl7Y6PkevrNRXyWBABQTkrLyGga2KDC9Z7wcnGXIc26qYFMjTXHdqhMjp1nDkm2wWCy7cWkBJm7e5kaNb38VYADmRyhAcEMchARVXAV69OvAkHj7oJ6XBR1FUZY9XpqlIbmzZurxtPo0aCH/hRt2rRRvSIQGNi6dat88skn8tlnn5XI7V6+fFkuXLhgcpqPj48KXCCLBEEG9JB44403VHkrBDXQCByNtS0NrCBIcOnSJXFzsyyNF83U0ZMDpaDQHBxlpBAYgYK+YDVq1Eg1CEfDcGyL3iTmGRTBwcGqNNbYsWNV4KRaNdOUYHjqqadU0/aIiAjVPPz3339X17t69WqL7gMREREREREVD3pJHDh//N/SUkdk/7ljkpppWTY+ektEBIVK69oIZISpRYgoN1VZ4P6PbNFLjcvJV2X10R0qk+Ofs5FiENMgx7lr8TJ7x1I16lQNVAGOfo07SiP/2lbbfyIiKj0MbFCJQyNs9LzQu+WWW2TBggXy8ssvq+BGjRo11HZFLVllDpP25n766Sc16Y8yUitXrpTnn39eBTfi4+OlXr16KqiBZtqF4efnZ/G2KIGFQMKDDz6oslgQ9MH9R8BD33cjN2i2jgwRNEVHwAKN1K9du2ayDR4/9P1AI3NkhaAOq7lhw4apfhoIqOD+4n7Pnj1bunfvXoh7TURERERERJa6nnZD9p07erO01JkjcvBCdI5ySnnxd68qrVQQI1RlZdT3C2IPiX/5ufvImIi+aiBTY9XRbSqTY//5Yzm2PX3lgny1bbEa9f1qqSBH/9AOBVavICKi8qOKIbfZUCoVmJhGPwf0e8Ckt96NGzdUo2dkBViaEXDk4kkZ+8Nzhd6P+Xe+XWoZG5S/efPmyd13362OAfTNoLKlvc4OHjyoyntNmjTJWCYsN6oRXlxckRrhEZUFHqNk63iMkq3jMUq2jsdo+YByScggQCAD/0bGncxRMikvQd4BKojRqlYTaVU7VGp5Vy9XJZRs4Rg9lxgvK6O2qUwOlPjKT+OA4H8zOTpILZ+AMttHqtzHKFF+eIxaPn9ujhkb5Twl08neUdKzMiy+DLbH5ahszJkzR+rXr6/6g+zbt09lXqA0FIMaRERERERE5VPc9QQVwNCCGccvnbH4svV9g+QWLSMjKFQCvXKWFKbCqentLxPbDlYDmRoro7bK8sitcuzS6RzbRsXFqDFr40+qP8nNIEd7Pg9EROUQAxvlWA2varL03g/V6hBLIaiBy1HZQN8PlJ/Cvyi/ddttt8mbb75p7d0iIiIiIiIiC6DIBXo3aI2+Ecw4fdW0v2NeqkgVCQmo+29GRpjqleHn7l3q+1yZobfGpPbD1ThxOVZWRG5RmRwnE87l2PbQhWg1ZqyfK+FBjVWQo09IO/H3qGqVfSciosJhYKOcQ5CCgQrb9fTTT6tBRERERERE5SOQEZNw7mZ/DJWVcUQuJF226LJo6t2ken255d9ABibLvVzcS32fKXfoT/Jgp9vkgY6jVPYG+nEsj9wiZxPjcmy7NzZKjff+/l71OEGQo3dIO/F1y78MChERWQ8DG0RERERERERUKWVlZ6tSUrvOHpZ/VFZGpFxJuWZxqecWNRqqJt+YDG9Ro5G4ObmU+j5T4aBnSYh/XTUe6TxG9eFAkAN9Oc4nXTLZ1iAG2XXmsBrvrJktbes2U0GOXo3aiJeLh9XuAxER5cTABhERERERERFVChlZmXIk7qT8c+Zmf4w9sVGSlJZs0WXdHF1UFobqj1ErTJoFNhAnB8dS32cq2SAHemtgTOl2uxw4f9wY5IhPvmKybZYhW7bG7FfjjVVfS4fgFirI0aNha/FwdrPafSAiopsY2CAiIiIiIiKiCiktM10Ono/+t7TUEdl37qikZKRZdFmUkUKDb5WRUStMQqsHq3JTVDHYVbGTljVD1Hiy+52yJzZSBTlWHd2eI2snMztLNp7YowYydTrVC5f+oR2ka/1bmKVDRGQlDGwQERERERERUYVwIz1V9p6LUk2+EcjAinxkaVjCz81bBTDQI6N17SbSoFotNflNFZ+9nZ16zjGe6TVRlaJCkGPNsR2SmHrdZNv0rAxZe3ynGi4OztK1QYT0a9xROtcLFxdHJ6vdByKiyoaBDSIiIiIiIiIql66lXpd/YqP+7Y9xRI5cPKlKCFmiplc1YzYGghl1q9ZQpYoqGzRLn7VxvirLlZx+QzVAf7zbeJXJAC3fH5vjMrV9qssfkz5S/9934Zg8PXdKjm2GN+8hr/S7X/3/3vmvyq6zR+TutkNkStfbVZP2t1Z/Kwv2rRJvFw/5YtTz0iSwvtgCZOW0r9tcjed73yPbTx2Q5ZFbVSDjenqKybapmWmqjBUGSpV1b9haZXJ0DG4pjvacciMiKk18lyUiIiIiIiKicuFy8lXV4BtBjH/OHpFj8WdUw2dLIHChBTHwb01vf6nsktJuyP0L35QLSZdVtkKAh6+siNwi/1vwpiy990Op7umrtnN3cpVhzbobL1fVzSvHddX3DVJ9KDQtajbK9TazDdnyyoov5beD66Sqq5d8OfoF1djbFiE40bl+hBooa7YlZr96fNZF785R0uxGRqosO7JJDU9nd+nZqI3qydG2TlMGOYiISgHfWSug3ZGp8smCBHlktK+0Cq08tR5feeUVWbJkiezdu9dq+xAcHCxTpkxRg4iIiIiIiIrn/LVLxiAGAhrILrBUiH8dk4yMau4+pbqv5dHe2CgV1HB1dJb/u+0Fla2QkpEqa4/vku93/i5P95ygtvN2cTf+Py/NajQocJvs7Gx5YdmnsuzIZvV8fDX6JanvFyTlgbODk2ocjoGgBvptIMix8eQeScvMMNkWDekRuMHwcfWUXo3aSr/QDtK6VhNV9oqIiIqPgY0KBumcX/92VU5dyFT/3tK4eqmm0k6cOFG+//77HKf369dPli9frv6P21+8eLEMGzYsx2WvXr2qghG5+e677+Tuu++W0NBQOXLkiMl5CxculNGjR0vdunUlJiZGnTZ16lSZPHmylAXsG4IX2H+9nTt3iru7e5nsAxERERERUUX7PXv66oWbjb7P3AxmnLt2yaLL2lexk9Dq9aTVv9kYEUGh4u3qUer7XN45OTiqf9MzM+RY/Gmp4VVNTl+5oE6LjLv5Wxvirl+RDh9NVJkHYdXrySOdx0jzGg1NrgtNt1GyCY97uzrN5LGu41QGiN7Pe1ep8k3IBEFQA1k05RECQX0bt1cjOT1F1kfvVj05Nsfsy9HT5WpKkizav0YNBHN6h7RTmRzhQSHs4UJEVAwMbFQwu46kStSpdPV//Iu/2zRxLdXb7N+/v8yePdvkNGdn5xK5bgQJ4uLiZOvWrdKhQwfj6d98843UqVPHZFsPDw81iiM9PV2cnIre7Mvfn6nMRERERERElkBJouhLZ28GMlRWRqRcSjZdPJYXTLA3C2ygyichGwP9IFAuiQoHQSA8hmiWPfaH50zO054Lram6l6uH7D5zWLadOiAHzx+XhRPek0BPP7WYsZ5vkDQNrK8yPlCm6Y/DG+X4pTPy051vmUzeI6gBfULaldughjkcdwPDOqtxLTVZ9eJAkGP76YOSmZ2V4zGdv2eFGgju9AlprzI5mgc2rJT9XYiIioOBjQq2uuXb3xPFrgq+IIr6F3+3DnMp1Q9IBDECAwNL5bodHBzk9ttvl2+//dYY2Dh79qysW7dOHn/8cfnpp5/yLEWVmZkpTzzxhMyZM0fs7e1l0qRJcuHCBUlMTDRmiXTv3l2aNWumbmfu3LnSvHlzWbt2rcyYMUMFa06cOCG+vr4yePBgee+991TgBLeNTBLQHtdp06ap2zcvRXX69GmVRbJmzRqxs7NTQaCPP/5YqlevbrLPTz75pLz00kty5coVGTBggHz11Vfi6elZKo8pERERERGRNWCSNyouRpWU+uffQEZi6nWLLuvi4KxWuGv9MZoFNhQXx6IvSqObEIj48rYXZdXRbSoQgbJJ8devyHc7fxfff/torHnwC+Nv39SMdLn168ckPvmKbDq5V0a16CXNAxrIrxOnq9+8EBV3SkbPeUZlfJxKOC/1dKWm6lQNVBkhc3f/JW6OrvJw59FSkXi5uMvQZt3VQKbGmmM7VJBj55lDkm0w7QVzMSlB5u5epkZNL38V4EAmR2hAMIMcREQWYGDDRl1PyZaTsTczLyx1JCbNmK0BCG7g74VrrklYcOEyKOoFOYmHq22kRN5zzz0qAPHRRx+Jm5ubKgOFAIEWHMjLu+++K/PmzVMBirCwMHV5BBF69Ohhsh1KaT344IOyefNm42n4QjZr1iypV6+eCm489NBD8vTTT8tnn30mHTt2lJkzZ8rLL78sUVFRavvcMkVQO3To0KHqvPXr16tAy8MPPyxjxoxRwRFNdHS02q8//vhDBTZQYuudd96RN998swQePSIiIiIiIutAeaODF6KN/THQzwENli3h6ewm4UGNVU+CW2qHSlhAPTZgLsXMmf6hHY2Bi3H/Zm60r9tC4q4niIeTm7g5/de/U2vWnp51s6/EueuXxD/AX+zk5hyCvpl72r/baNBrApP4b63+Vr7c9qtkZGfKlK63S0WEINHIFr3UQNP71Ud3yPKoLbLnbFSOhvfnrsXL7B1L1UDwBwGOfo07SiP/2lbbfyIiW8dvBTYKQY3HZsSVyHV98WtioS/z0RMB0ryhZY3HMSFvPrH//PPPq1ESIiIipH79+vLLL7/InXfeqQIbyKhAwCE/yIx47rnnZPjw4ervTz75RJYtW5Zju0aNGqlsDD19829kYbzxxhvywAMPqMAGSlV5e3urFRT5ZaogS+PAgQNy8uRJqV375pcRZI80bdpU9eJo06aNMQCC+6RlaOA+4rIMbBARERERUXlyIz1VDpw/poIYKC2F/5s3Vc5LVVcv1R/jlto3m303qlaHTZbLyCO/vqt6RuA5QGbBmasXpZZ3gNx+S39VVmn62jnSunZT8XP3VqWoUE7JzdFFeja8+Zv2pwMr5YW1/yfhNUNUg22UooLG/nVVA3dzo8P7iIO9vby+8is1kY+eFE/1uEsqMj93HxkT0VcNZGogQwaZHPvPH8uxLTJavtq2WI36frVUkKN/aAcJ9q1plX0nIrJVDGxQsSED4vPPPzc5DeWbSjprA5kX6KuRnJwsAwcOVIGKvKDc1MWLF6Vt27bG01COqlWrViqQoIfTzK1evVrefvttiYyMlGvXrqlsi9TUVLlx44bKGrEEGp4joKEFNaBJkybi4+OjztMCGwic6MtO1ahRQ/UVISIiIiIismVJaTdUFgb6M6Cs1OGLJ3L0FMgLmkq3rh0mt9S6Gcio51uT5XesBAGIP49sUqWTvFw8VBmlR7uMVWWVmlRvIO3rNpd9547JlZRr4uPiqbIuHuw4Smp6+6vf1x1rN5fEjBuq90Zyeqr4e1SVgWGd5H/tR+TZHHtE857iaOcg05Z/oUox4bh5tufESnEMoLfGHa0GqnEuMV5WRm1TmRxHLp7Mse2Jy2fl8y0L1WgcEPxvJkcHqeUTYJV9JyKyJQxsUIk0+G7YsGGe52PSHoEGc1evXlWZD5YYP368KgWFnhTIaEBPjJLcf72YmBi59dZbVXkqZE0gSLNp0ya59957VXNxSwMblnJ0dDT5G1/kzIMvRERERERE1pZw45oKYNwsLXVE9VIwL6mTlzo+gcb+GAhmBHn7V4pJ7PLgie53qJEblEJ6f8jj+V6+Y+0WMqxVb2OPjdx8M3ZajtMGN+2qRmWG4NDEtoPVQKbGiqitKpPj2KXTObZFfxqMWRt/kqaBDf4NcrSXQK9qVtl3IiJrY2DDRqHHBcpBWdpbw5JyUw+M8La41wZuv6Q0btxYdu/eLRMmTDCelpWVJfv27VMNvS2B4MKQIUNkwYIF8sUXXxS4PQIm6MGBkk9du3Y13uY///wj4eHh+V4W+4rAwgcffGD8Yobb1UM5KlxfftDX48yZM2poWRuHDx9WAR1kbhAREREREdkylMzRghi7zxyREwmxFl+2gV+tm0EMZGUEhapV6kSUN/TWuK/9cDVOXI6VFZFbVKDjZMK5HNseuhCtxoz1c1UvGgQ5+oS0U9kyRESVBQMbNgqNuy3pcWEwGOSzRVfFrsrNZuF5wflrd6fIbb28SnxVTFpamly4cMHkNGRUVKt2c9XAE088obIdQkNDpU+fPqqUFPpfoFG2pYENQB8K9Ljw8/OzaPvJkyerclLIJsFta7dZ0P3H9hkZGWr7wYMHq6bi5sEUlI+6fv266oXRsmVLlcVhnsnRu3dvad68uco2QbNxlLNCE/Ju3bpJ69atLb7fREREREREpQ2/Lc8mXpTdZ272x0BA42yiZSVy7apUUWVyEMjAiAhqLFXdvEp9n4kqqvp+QfJgp9vkgY6j5Gj8aVkRtUVlcuT2mkQ5OIz3/v5eWtUOU0GO3iHtxJevQSKq4BjYKOd2HUmVqFPpBW6HoAe2w/ZtmriW6D4sX75c9YUwz9JAfwoYN26c+pKMht/PPvusCgCgr8WGDRtUVoWlXF1d1bDUM888owIud911l+qv8b///U/69eun/p8fBCqwr++++65qPo6MDwRIcD2ajh07qmbiY8aMkcuXL8u0adNUmSw9BFB+++03FWDBdSD7o3///ipgQkREREREZE34jYZV4VoQAw2/464nWHRZBzt7aRbY4N/+GKHSMqixeDqXbMleIro5r9A4oK4akzuPVX1sEOBAX47zSZdMtkVZOPS7wXhnzWxpW7eZCnL0atRG9U4hIqpoqhjwbYbKBJpQo0QS+k14eZlGztGUGg2lUb7I0h4OeOoeeu+iHD2dLpY8i0hUCKnjJJ89Xb1S1jJFeSk8vqNHj5bXX3/d2rtDVqC9zg4ePKgybpAx5OzsnO8xg0buAQEB+daLJbIWHqNk63iMkq3jMUqV6RjNys6Wo/GnbpaVOntE9pyNlCspSRZd1tnBUVrUCDE2+25eo6G4OlpW5pgqNr6PWke2IVsOnD8uyyO3yKqo7RKffCXfQGSH4BYqyNGjYWvxqGRBSB6jZOt4jFo+f26OGRvlWEamSFxCpkVBDcB2cVcy1eWcTPtVV0inTp2SlStXqtJPKJf1ySefyMmTJ+X222+39q4RERERERGVqoysTLW6G70x0PB7T2ykXE9Pseiy7k6uqpyUlpGBRsWO9pw+ILIVdlXspGXNEDWmdr9Lvb6RybHq6Ha5knLNZNvM7CzZeGKPGk72jtKpXrj0D+0gXevfIm5OBZdAJyKyVfxmUo45OVaRz58JlKvXsy2+TFVPO3W5ygBRTvTlmDp1qspuadasmaxevVplbRAREREREVUkqRnpcuD8MVVSavfZw7L/3DFJzSy4bDH4uHpKRFCoCmKgR0ZIQF21ypuIbJ+9nZ20rt1EjWd6TVSlqJDJsebYDrmWmmyybXpWhqw9vlMNFwdn6dogQvo17iid64WLi6OT1e4DEVFRMLBRzgX4OkiAr7X3wjbVrl1bNf4mIiIiIiKqaK6n3ZB9547+W1oqUg6eP65WZlvC372qajKMQAayMtCoGCvAiah8Q0Cyfd3marzQ+17ZduqAyuRAIMM8Yys1M0316sBwc3SR7g1bq0yOjsEtmaFFROUC36mIiIiIiIiIbNzVlCTZExulSkshmBEZd1KyLaxLHOQdYAxiICOjtk/l7LtIVJkgONGlfoQaaZnpsiVmv6yI3CLrondLSkaaybY3MlJl2ZFNang6u0vPRm1UT462dZoyyEFENovvTkREREREREQ2Jv76FVVSZvOxPXLk8ik5fvmMxZet51tTWtVucjOYERQqgV7VSnVfici2OTs4qcbhGAhqoN8GghwbT+6RtMwMk22T0pLlt4Pr1ECZul6N2kq/0A7SulYTVfaKiMhWMLBBREREREREZEXoCXjuWrxq8n2ztNQROX3lgkWXrSJVVE8MrT8GemX4uXuX+j4TUfnk6ugsfRu3VyM5PUXWR+9W5ao2x+yTjKzMHJlii/avUaOau4/0DmmnMjnCg0JYvo6IrI6BDSIiIiIiIqIyDmScunJeZWSgP8Y/Z4/IhaTLFl3WvoqdNAmsr4IYGOFBjcXLxb3U95mIKh53J1cZGNZZDTQaRy8OBDm2nz6Yo2fPpeSrMn/PCjWqe/pKn5D2KpOjeWBDlrYjIqtgYKMCuv7nBrn0/Ezxf3uKuA/sau3dISIiIiIiqtSyDdlyLP6MysRAEAPBjIQbiRZd1tHOQVrUbHQzkFE7TFrUaCRuTi6lvs9EVLkgQDq0WXc1kKmx5tgOFeTYeeZQjn4+F5MSZO7uZWrU9PJXAQ5kcoQGBDPIQURlhoGNCiY7KVkuTX1fsi5dkfip74trl1Zi58nVO0RERERERGUF5Vwi42JUEGPXmSOq6Tfq1ltaJgZZGAhk3BLUWPztvKRWjZpix9r2RFRG0FtjZItealxOviqrj+6Q5VFbZM/ZKDGIaZADZfRm71iqRp2qgSrA0a9xR2nkX9tq+09ElQMDGxVMwltfSVbCzZU/WZcTJeHtr6XaW4+JLfruu+9kypQpcvXq1Xy3Q7R/8eLFMmzYsAKvszDbFlVwcLDabwxrWLdunfTo0UOuXLkiPj4+VtkHIiIiIiL6T1pmuhw8H23sj7Hv3FHVoNfSVdJo8H3Lv6WlQqsHi4OdvTovOztb4uLiSnnvqSK7cOFCgb+5iwPH6OXLlyUhIaHUg2/4/RsYGFiqt0E5+bn7yJiIvmogU2PV0W0qk2P/+WM5tkVvoK+2LVajvl8tFeToH9pBgn1rWmXfiahiY2CjAkndc0QSv1kkxuB5drYkfv2LeNzWV1wiwkrlNidOnCjff/+98W9fX19p06aNvPfee9KiRYt8LztmzBgZOHCg8e9XXnlFlixZInv37jXZ7vz581K1alUpLd27d5f169fL22+/Lc8++6zJeYMGDZJly5bJtGnT1P7Bzp07xd29bLJgsG/h4eEyc+ZM42kdO3ZUj4m3NxsCEhERERFZw430VBW8uFlaKlIOnD8u6VkZFl3Wz837ZjYGmn3XDpOG1WqzCS+VWlBj1KhRkpqaWqq3k5WVJfb2N4NxpcnFxUV++eUXBjesCL017mg1UI1zifGyMmqbyuQ4cvFkjm1PXD4rn29ZqEbjgOB/Mzk6SC2fAKvsOxFVPAxsVBCGzEyJf+wdEayQyMr+7ww7O4mf8o7UWvONVHEonae7f//+Mnv2bOMXpxdffFFuvfVWOX36dJ6XycjIEFdXVzUKUhZfWmrXrq0ySPSBjdjYWFmzZo3UqFHDZFt/f/9i3x7uv6OjY5Eu6+TkxC9yRERERERl6FrqdfknNkr+OXMzIwOTeFkG3e+ufNTwrKYCGFowo27VGqxBT2UCmRoIarz++utSr169UrkNg8EgmZmZ4uDgUKrH9cmTJ+Wll15S94m/h21DTW9/mdh2sBrI1FgRtVVlchy7lHMuKCouRo1ZG3+SpoEN/g1ytJdAr2pW2XciqhgY2KggEr/6RdIjT/yXraHJypb0wyck8etF4vPAmFK5bWdnZ+MXC/yL4ECXLl0kPj5eBQFiYmLUl6j58+fLZ599Jtu3b5cvvvhCba+VokJQ4dVXX1WnaV+GECxBRoi+vFR6ero88cQTsmjRIlWKqXr16vLAAw/Ic889Z9yfS5cuyfDhw2XFihUSFBQkH3zwgQwZMiTf+4BAzIIFC2Tz5s3SqVMndRoyUfr27ZsjQGNeiioyMlImTZoku3btkvr168usWbOkT58+xn3O6/4PHjxYHnnkEdmwYYO6Lw0aNJDnn39exo0bp64X9x2ZJBgfffSR8cscrs+8FBUej5dfflmOHz+uAjGTJ0+WJ5980mSf//e//6nzFy5cqDJgEIDCaUREREREZAo15ZGJgSbf6JNxNP50jrryeUHgwpiRUStMTf4RWRN+j4aGhpbrwAbZNvTWuK/9cDVOXI6VFZFbVKDjZMK5HNseuhCtxoz1c1U/IQQ5+oS0E3+P0qvUQUQVEwMbNirr2nUVkLBEZtxlufz6/+UMaujgfPua1cUhwNei63RqUl/svTyksK5fvy5z586Vhg0bip+fn8l5CHggyBAREaFSSBF40JelOnjwoCxfvlxWr16tTsut1BKCBkuXLlVBiDp16siZM2fU0EOABKWwpk+fLh9//LGMHz9eTp06pcpk5Xl/nZzUdgimaIENBFtwPVoJqrxSbhG8wL4gYJGUlGQSUMjv/mPlTKtWreSZZ54RLy8v+fPPP+XOO+9UAY62bduqYMbRo0elWbNm8tprr6nr0AJFert375bRo0er/cTjuGXLFnnooYfU44/giAa3jZU6CJ4gfffBBx+Ubt26SePGjfO8f0RERERElcGFa5dklyorhYyMSInJZTIuL42q1VFBjNa1m6h/q7mzDx4RVV71/YLkwU63yQMdR6mg8IqoLSqT42xizn5Be2Oj1Hjv7+9VZhuCHL1D2omvm5dV9p2IyhcGNmwUghrnBj9cgleYIXH3vmTx5jV//1Rc2+ffI0Pzxx9/iIfHzSBIcnKyyhjAaeaNw5DhMGLEiFyvAyWpcB1Y5ZFfWimyJxo1aiSdO3dWq0Hq1q2bYxtM5mtZD2+99ZYKhuzYsUOVzMrPPffcozJNEFBAsCAxMVFlcuQX2Fi1apVER0erht7afr/55psqY8Ncbvd/6tSpxv8jywLBHgRtENhAYAcBFzc3t3wfkxkzZkivXr1UWi6EhITI4cOHVWBHH9hAPxMEPADBlA8//FDWrl3LwAYRERERVSpYYX766oWb/THOICvjiJy7Fm/RZe2r2Elo9XrS6t9sjIigUPF2LfyCMCKiig5zNo0D6qoxufNYOXzxhApwoC/H+aRLJtsiI27XmcNqvLNmtrSt20wFOXo1aiNeLnyPJaLcMbBBxYaySJ9//rn6P8ojodzSgAEDVDBBH3ho3bp1sW8LE/UIGmAyHoEKBB5QLkpP37QcTb6RDREXl3NlgLmWLVuqoAmyGTDhj+wJBFryExUVpfpz6AMPCErkxvz+I9sDgRcEMtDPA2W20tLSVCCjMI4cOSJDhw41OQ1ZJ2g4rm/ipn9c8AUD+2zJ40JEREREVJ5lG7Il+lLsv9kYN5t9xydfseiyjvYO0iywwb+lpcIkPChE3J0K7hNIRERiMgeB3hoYU7rdLgfOH5flkVtkVdT2HO/H6F+0NWa/Gm+s+lo6BLdQQY4eDVuLh3Ph5kuIqGJjYIOKDcEDlJ7SfP311yrb4KuvvpI33njDZLviuuWWW1Sfib/++kuVrEIJpt69e6tghMa8KTc+QLOzLWvsh6yNTz/9VGU8IDBTkszvPzIqkB2CAETz5s3V+cjqQICjNBTncSEiIiIiKi8ys7NUk1qtPwYCGYmp1y26rIuDswpeaP0xmgU2FBdHp1LfZyJrQMUFlHJGf0gstsNvRixOxCI/VBdAqWlUBti4caMq74ztsbAPJZCffvpp8fT0NKlAgO0OHTqkFtihH+aFCxdMbg/VELTemvhdj16Q+E163333ybfffqt+o6IfJXtBVmx2VeykZc0QNaZ2v0v2xEaqTI5VR7fLlZRrOd7PN57Yo4aTvaN0qhcu/UM7SNf6t4ibk4vV7gMR2QYGNmwUelygHJQlMuMSJO7B11S5qbyv0FECPn+5UD02igpfRlCGKiUlpVCXQ9klfAEqCDIw8EUKY9SoUSpzIyEhId8eGpa6/fbb1Rc4ZG80adKkwO2ROYIeHxcvXlRf3GDnzp0W3RYalSPT4o477lB/4wsdemrob9eSxyQsLExdl/l1oySVlq1BRERERFRRpWdmyKGLJ25mZJw5InvPHZXkdMt+i3g6u6nmtQhiYIRVr6eyNIgqg4cffli+//579f+mTZuqcswHDhxQQQv0hRw8eLBajOfs7KyajyP4cezYMbWAEeWbly1bZrwu9NrE71f8Lo+Pt6y0G37rTpgwQebNm6fmEL755huTcspU8dnb2an+RBjP9JqoSlEhk2PNsR1yLTXZZNv0rAxZe3ynGghCd20QIf0ad5TO9cIZgCaqpPiNzUahcbelPS4gK/aCXH4570CI30v3i+eQ7lIaUD5JW4mBUlSffPKJWtmBL0GFgdUaWLWxd+9eqVWrllr9gS9Q5v0k0MMDDbjxxWfhwoWqpJKPT8k06KtataqcP38+R3ZDXlAWC82+8WUMjcbRPPzFF180Bnjyo5W9QrNv3C7uGwIk+sAGHhM0JUfDcPQgyS14g2blbdq0UY3BEezZunWreg5QEoyIiIiIqKJJyUiT/eeOqowMlJY6cP6YpGXms8hLp6qrpyophSa1CGSg8Tcm1ogqo02bNql/sVgQVRGwOBG/OVNTU1WGBoIbqDRw//33q9/nOB2lqLdt26a2x+9/7bf4/v37pU6dOiowoQVL8pOZmal6Y+I3PRbkzZkzRy00pMrLwc5e2tdtrsYLve+VbacOqEwOBDKumwWrUzPTVK8ODDdHF+nesLXK5OgY3JLBaaJKhK/2CsL7vlGS9NNfkn40RiRLV17I3k6cGgeL96SRpXbby5cvV8EGwJcdrOTAl5Pu3QsXSBk5cqT8+uuv6ovS1atXZfbs2TlWa+D6EUDAKhF8+cGEPlaJmDcqL47CBEmwD0uWLJFJkyapfalfv7764oegDr4E5gcBkBMnTki/fv1UXw2k2w4bNkytktEgewRBEwQ78CUTgZ/cynOhT8fLL7+sght4Ll577TWudCEiIiKiCiEp7YbsjY36tz/GETl04YQqT2KJAA9faV37Zn8MBDLq+dYscAESUWXRpUsXiY6OVr/pmzVrpn6LIniB07GADosI8ZtUg9+4+N2LwAZ+g+t7UqJEVWGg3BWuB4sKf/zxR1WNgUiD4ESX+hFqpGWmy5aY/bIicousi96tgtt6NzJSZdmRTWp4OrtLz0ZtVE+OtnWaMshBVMHxFV5BVHFwEP+PnpXYfma1KLMN4v/Rc+r80vDdd9+pkR9kHRgMhhynY+JdP/mO7Ax9rwyN/rKovYmRl9xuB0GS/Kxbty7f85FBoofsCT0EcrSVLqCVhdL6juR1/7ESBkGR/KCcFDIw9HK7PgSFMPJivs+53S8iIiIiIluQcOOaqrmOslIIZhyNPyXZuXyfzk1tn+oqiNH632bfQd7+DGQQ5QH9LFASGdkS6I0BKCfVokULVVXAXFxcnCxatEj9f+zYsWrhYW6/dS2BoAZgQSODGpQfZwcn1TgcA0EN9NtAkGPjyT05svWS0pLlt4Pr1PBx9ZRejdpK35D2UtvJz2r7T0Slh4GNCsQlIky87x0pid8uRsMGETs78b53hLiEh1p71yo0NFpDmSiUljp+/Lg89thj0qlTJ1WiioiIiIiI8ncxKeFmfww1IuXE5bMWX7aBXy2ViXELsjKCQqW6Z/H77hFVFh9++KH88MMP6vcrfteiN0bXrl3l008/VdkYM2fONG6LzI4BAwbIuXPn1PYIipQElFBGxQP00iQqiKujs/Rt3F4N9FJaH71blavaHLNPMrIyTba9mpIki/avUcPXxUv6hLaX/qEdJTwoRDUwJ6Lyj4GNCsb3+fvk+pK/JevSFbH38xbf5yZZe5cqPPTVeOaZZ+T06dNSrVo16d27t3zwwQfW3i0iIiIiIpuD1d2xiXEqiLHrzM3SUmcT4yy6rF2VKtI4IFha1QpV2RgIZFR142QoUVHcuHFDXnrpJfWaRPa/v7+/GghaLF26VFavXm3cFlUEhgwZIpcuXVJBiPnz56tyysXx+OOPq8AKek727dtXVqxYId7e3iVwz6iycHdylYFhndVAo3H04kCQY/vpgznKFSakXpOf965UAwHwPiHtpV9oB2ke2JBZfUTlGAMbFYydp7tUe3+qXHp+pvi/PUX9TaXrrrvuUoOIiIiIiExh0vTE5VhjfwxkZMRdT7C4kWyzwAb/9scIlZZBjcXTuXiTqUT0X2ADDbxh9+7d6l/019BKUrm735xLQLlo9MPAeZMnT1ZZHCXR4/LRRx9VVQ8efvhh2b59u/Tq1UtWrVqVawksooJ4ubjL0Gbd1UCmxppjO1SQY+eZQzlKGSJLcO7uZWrU9PJXAQ705AgNCGaQg6icYWCjAvIY1FUNIiIiIiKispSVna16YmhBDPx7JSXJoss6OzhKixohNzMyaodJixqNVNkRIip5qDaAslMbNmyQefPmqeACqhFcvHhRnT9hwgRVdmr06NEqQIneGzt27JCOHTualJGKiIhQ/+/Ro4ecPXtW9eEAZHdofSdx/e3atcuxDw8++KBqHn7//fer4ErPnj1VpoifH/shUNGht8bIFr3UuJx8VVZFbZffD66XQ3EnxSCmQY5z1+Jl9o6latSpGqgCHP0ad5RG/rWttv9EZDkGNoiIiIiIiKhIUNP8yMWTsuvMYfnnbKRq+n09PcXiMiIRQY2NGRlNAxuIoz1/ohKVlSVLlsg777yj/kVQwtnZWQUgkJkxfvx4iYmJMTYHT09PV8EPvWvXrhn/j21PnTpl/DsrK0v15YCUlLzfEyZNmqSCJnfffbfs3btXBUgQ3AgICCiFe0yVjZ+7j4wO7yPda7YUcXWQ1cdvZnLsP38sx7anr1yQr7YtVqO+Xy0V5Ogf2kGCfWtaZd+JqGD81mhjstH0m4hK9fWlfTknIiIiosJJzUiXA+ePGbMx9p07JqmZaRZd1tvFwxjEQMPvkIC6qtwUEVkHyj69++67auQmODi4wN9O2vknT54ssIzPK6+8ooY5lnemshDg6St3tBqoxrnEeFkZtU2WR21RwXlzJy6flc+3LFQDvZ1uZnJ0kFo+DLgR2RIGNmwEVijA9evXxcPDw9q7Q1Qh4fWlrTYiIiIiooIlp6fI3tijsvvsYRXMOHj+eI6mrHnxd68qrWrfDGQgoFHfL0jsqhS/Nj8REVFx1PT2l4ltB6uBTI0VUVtVJsexS6dzbBsVF6PGrI0/qczCm0GO9hLoVc0q+05E/2Fgw0Y4ODioGpexsbHqbwQ3SqIhFxHdzNRAUAOvr6tXr6q0aDYFIyIiIsoJTVf3xEbJ7jM3m30fiTuZo/FqXoK8A4xBDGRk1Papzu9cRERk09Bb4772w9U4cTlWVkRuUYGOkwnncmx76EK0GjPWz5XwoMYqyNEnpJ34e7DpPZE1MLBhQ+rUqaPSOLXgBhGVLAQ10AwPNV59fHyMmVJERERElVX89SuqN8buswhkROa6WjUv9XxrGoMYCGhw9SoREZVnyCx8sNNt8kDHUXI0/rSsiNqiMjnOJsbl2HZvbJQa7/39vcpORJCjd0g78XXzssq+E1VGDGzYEKxmQg3Ly5cvy44dO1QWh4uLi1RkqampFf4+km1A+SlkaiQnJ6sMjvDwcK4gJCIiokonNjHOGMjAQAkOS1SRKqonhtYfIyIoVPzcvUt9f4mIiMoa5goaB9RVY3LnsXL44gkV4EBfjvNJl0y2NYhBdp05rMY7a2ZL27rNVJCjV6M24uXCUvNEpYmBDRt0yy23qH937dqlVphX1EbHuF9aYIMTzFQWcJy5ublJ69atJSIiwtq7Q0RERFTq37dPXTlvDGL8cyYyx4RMXuyr2EmTwPoqiHFLrVAVyPBycS/1fSYiIrK1eQT01sCY0u12OXD+uCyP3CKrorZLfPIVk22zDNmyNWa/Gm+s+lo6BLdQQY4eDVuLh7Ob1e4DUUXFwIaNvmm2atVKrShHyZyKGtjAqvlLly6p3iLsJ0Jl9dpydXUVe3t7a+8KERERUYnLNmTL8UtnZNe//THQ7DvhRqJFl3Wyd5QWNRreLC1VO0xa1Ggkbk7MrCYiItLYVbGTljVD1Jja/S7ZExupMjlWHd0uV1KumWybmZ0lG0/sUQOfsZ3qhUv/0A7Stf4t/HwlKiEMbNgwTL6iiXhFhcAGAjeenp4MbBARERERFVJGVqZExsUYgxgoMZWUlmzRZV0dnVXjU60/BlaiOjuw/xgREZEl7O3spHXtJmo802uiKkWFTI41x3bItVTTz+L0rAxZe3ynGi4OztK1QYT0a9xROtcLFxdHfvYSFRUDG0REREREROVAWma6HLoQrYIYmEDZd+6opGSkWXRZT2d3VVLqZiAjTEKrB4uDHbNYiSqDkydPltp1o8JEZmam6hFamiWmS/M+EBUXPk/b122uxgu975Vtpw6oTA4EMq6np5hsm5qZpnp1YLg5ukj3hq1VJkfH4JbiaM9pWqLC4CuGiIiIiIjIBt1IT1XBC9Uf42ykquuNVZ+W8HPzNvbHQGmphtVqqxIaRFR5+Pj4qJ6WL730UqneTlZWVpmU+8V9wX0ismUITnSpH6EGFiRsidkvKyK3yLro3TkWI9zISJVlRzapgQUIPRu1UT052tZpyiAHkQX4KiEiIiIiIrIB11Kvy57YKBXEQEbGkYsnVSNSSwR6+qkAhpaRUbdqjVJdPU1Eti8wMFB++eUXuXr1aqllayQkJEhsbKwEBQWJr69vqb7vIKiB+0RUXqDEIxqHYyCogX4bCHJsPLlH0jJNFyqglORvB9ep4ePqKb0atZV+oR2kda0mquwVEeXEwAYREREREZEVXE5O1PXHOCJH40+LQQwWXRaBC2NGRq0wqentX+r7S0TlDwIBJR0MQKDk+++/l48//liio6ONpzdo0EAmT54sEyZMYGYFUS69rfo2bq9GcnqKrI/ercpVbY7Zp3pm6V1NSZJF+9eoUc3dR3qHtFOZHOFBIcy+JNKpYkCIncrEtWvXxNvbWxITE8XLy0sqOzQPj4uLk4CAADYPJ5vEY5RsHY9RsnU8RsnWlfUxeuHaJRXEuFla6oicTDhn8WUbVatjDGLgX3+PqqW6r2Qb+D5KtmbFihUycuRIuXHjhvpbP6WkZWu4ubnJokWLpF+/flbbT6Ly8j6KRuPoxYEgx/bTByUzOyvPbat7+kqfkPYqk6N5YENmZlYQtn6M2vL8OTM2iIiIiIiIShgm+85cvSi7zh6Wf87cDGacuxZv0WXtq9hJaPV60urfQEZEUKh4u3qU+j4TERUU1Bg0aJB6f8ttjax2WkpKitruzz//ZHCDqABeLu4ytFl3NZCpsebYDhXk2HnmkGSbvc4uJiXI3N3L1Kjp5a8CHMjkCA0IZpCDKiUGNoiIiIiIqMI7f+2SmjAwXyGH+vCXDck5VsihvnUNr2oWX3+2IVuiL8X+W1rqZrPv+OQrFl0WDUKbBTb4NxsjTJWacHdytfi2iYhKG8pPIVMDwQu8d+YH5+M9FdufPXuWZamILITvHiNb9FLjcvJVWX10hyyP2iJ7zkblKFWJxRKzdyxVo07VQBXg6Ne4ozTyr221/ScqawxsEBERERFRhQ9qDPnmcUnPMm3UmR8ne0dZeu+HeQY3UCriaNwpFcTQAhmJqdctum4XBydpWTPE2Oy7WWBDcXF0snjfiIjKGnpqoPyUpdXMEdzA9nPmzJFHH3201PePqKLxc/eRMRF91UCmxqqj21Qmx/7zx3Jse/rKBflq22I16vvVUkGO/qEdJNi3plX2naisMLBBREREREQVGjI1ChPUAGyPy2mBDTT2PHgh+mZGxpkjsvfcUdX80xKezm4SHtRYBTEwwqrXU1kaRETlAYIZaBReFLNmzVINxVkmh6jo0FvjjlYD1TiXGC8ro7apTI4jF0/m2PbE5bPy+ZaFajQOCP43k6OD1PIJsMq+E5UmfpsmIiIiIiLKxYELx2Xd8V2q4ff+80clLdOy4EhVV09VUkr1yKjdRDX+tmczSCIqpy5fvizR0dFFCojgcnfddZd06tRJbrnlFmnRooW4uLiUyn4SVQY1vf1lYtvBaiBTY0XUVpXJcezS6RzbRsXFqDFr40/SNLDBv0GO9hJYiFKbRLaMgQ0iIiIiIqJcvLnqG4u2C/Dwlda1b/bHQEZGPd+aXJ1MROVeRkaGahj+5ZdfFut65s6dqwY4ODhI06ZNpVWrVmog2NGyZUtxdWVfIaLCQm+N+9oPV+PE5VhZEblFBTpOJpzLse2hC9FqzFg/V2WRIsjRJ6Sd+HtUtcq+E5UEBjaIiIiIiIgKobZP9f8yMmqFSZB3AAMZRFQhoDfG5s2b5ccff5SFCxeqbI2SlJmZKfv27VPj22+/VafZ29tLkyZNjMEODAQ73NzcSvS2iSqy+n5B8mCn2+SBjqPkaPxpWRG1RWVynE2My7Ht3tgoNd77+3vV7wtBjt4h7cTXzcsq+05UVAxsEBERERER5QONOFvXCpNbkJURFKpqXRMRVST79+9XwYyffvpJTp/OWdKmNGVlZcmBAwfU+O6779RpdnZ2EhYWZhLsCA8PF3d39zLdN6LyBgstGgfUVWNy57Fy+OIJFeBAX47zSZdMtjWIQXadOazGO2tmS9u6zVSQo1ejNuLl4mG1+0BkKQY2iIiIiIiIcvFEt/EyuGk3rmAkogopJiZGBTIQ0Dh48GCpTLAOHTpUBSN2794tUVFRqu+GpZkjhw4dUmPOnDnGYEdoaKgqX6UFOyIiIsTDgxOwRHm9BtFbA2NKt9vlwPnjsjxyi6yK2i7xyVdMts0yZMvWmP1qvLHqa+kQ3EIFOXo0bC0ezsyeItvEwAYREREREVEu2tZpxqAGEVUo8fHxqsTUvHnzZMuWLRZdpmPHjjJ8+HCZNm2apKamqqBDQRCEQN+M2bNni4+PjzotKSlJ9u7dq4IcGP/8849ERkZadH2A7Q4fPqyG1rNDrU5v3NjYr0MLdnh58b2bSM+uip20rBmixtTud8me2EiVybHq6Ha5knLNZNvM7CzZeGKPGk72jtKpXrj0D+0gXevfIm5OLla7D0TmGNggIiIiIiIiIqqgrl+/Lr/99psKZqxcuVKVfioIel6MHz9exo0bJ/Xq1VOnNW/eXAYNGqSCFvkFI3A+Ag6//vqrMagBnp6e0qVLFzU0ycnJJsEOjCNHjlgc7EAGCIIjGLh/mpCQEJNgB/719va26DqJKjp7OztpXbuJGs/0mqhKUSGTY82xHXItNdlk2/SsDFl7fKcaLg7O0rVBhPRr3FE61wsXF0cnq90HIqhisDQPkIrt2rVr6oM0MTGRqwf+XW0RFxcnAQEB6osPka3hMUq2jsco2Toeo2QLMrIy5dk/ZsnqYzsKfdn5d74tYdVvTugRWQPfR6mo0tPTVRADk/0IaqSkpBR4mdq1a8vtt9+uBoIYCE6YW7FihYwcOVJu3Lih/tZPKWnbo+k3ghp9+/Yt0r7jutFcXB/sQJaGJQGZ/DRs2NCkZwcyO6pWrVqs6yTbx/fRwn1n2nbqgMrkQCDjenre7xtuji7SvWFrlcnRMbilONpz7XxR8Rgt+vw5AxtliIENU3zhkq3jMUq2jsco2Toeo2RtCTeuyVNLP5RdZ48U6fIMbJC18X2UCnu8bN68WQUzUG4qISGhwMv4+vrKbbfdprIzOnXqZNFxdvXqVdX3YtasWRIdHW08vUGDBvLoo4/KhAkTSjw7AoEZNDjXBzvQfyMzM7NY11u/fn2TYAcyO/CYUMXB99GiSctMly0x+2VF5BZZF71bUjLS8tzW09ldejZqo3pytK3TlEGOQuIxaoqBDQt9+umnMn36dLlw4YK0bNlSPv74Y2nbtm2e28+cOVM+//xzOX36tFSrVk1GjRolb7/9tri4WFZfjoENU3zhkq3jMUq2jsco2Toeo2RNR+NPyWOL35dz1+KLfB0MbJC18X2UCoIpnQMHDqhgBhqBnzlzpsDLoPfFsGHDVGYGsiqcnJyKfNuXLl1STciDg4PVPEluWR6lBf0+EOxArw4t2IEm6BkZGcW6XtwXfbADw8/Pr8T2m8oW30eLD0EN9NtAkGPjyT2Slpn3a8zH1VN6NWor/UI7SOtaTVTZK8ofj9Giz59X2hDazz//LE888YR88cUX0q5dOxW06Nevn0RFRakDydyPP/4ozz77rHz77beqcdbRo0dl4sSJ6kN7xowZVrkPRERERESUE2pEv7Ds03xXFxYEzTLx45yIyBYhmIB5CgxkLhTE3t5ezXkgmDF06FDx8PAo9j5gPgQT/igRhX/LMqgBWGSKxan6BappaWkq0KM1J8e/CH4UJtiBxxZj0aJFxtPq1KmTI9jh7+9f4veJyBa5OjpL38bt1UhOT5H10btVuarNMftU+Sq9qylJsmj/GjWquftI75B2KpMjPChENTAnKkmVNmMDwYw2bdrIJ598YoyOoZ7k5MmTVQDD3COPPKIaWK1Zs8Z42pNPPinbt2+XTZs2WXSbzNgwxYgk2Toeo2TreIySreMxSmUNP22+3ParfLZ5ocnpwb415cXe94qHs1uOYxSlWlB2xPwYRVCjhle1MtlvorzwfZT04uPjZcGCBSqYsWXLFosug/JSCGag3FRpTMSXh2MU/UaQyaFldSDggR4eOL04MIekb1COUb169RLbb6o8x2h5hUbj6MWBIMf20wclMzvvPjjVPX2lT0h7lcnRPLBhmQdCbRmPUVPM2CgAPrzwYfbcc88ZT8OB07t3b9m6dWuul0GWxty5c2XHjh1qNcCJEydk2bJlcuedd+Z5O1gpgKF/YrQDFqOyw2OAH598LMhW8RglW8djlGwdj1EqSykZqTJtxf/JqqPbTU5HQ8t3Bk0WT7OgBuDYjBc3NdmX2w9JHrtkbXwfpevXr8uSJUtUmalVq1ZZ1EC7adOmKpgxZswYqVfvv3J6pXEclYdj1MHBQcLDw9W499571WnI4ECmixbo0IId+jmcgqDsFwaeH01QUJAx0IHm5Pi3Ro0apXK/qOIco+WVh5OrDG7SVQ1kavx9bKesOLpVdp05LNlm6+gvJiXI3N3L1Kjp5a+yP5DJ0di/bqUPcvAYNVWYx6FSBjZQAxJfBswj6fg7MjIy18vgSwEu17lzZ3WwoUHVAw88IM8//3yet4P+G6+++mquqyxQC7Kyw4GK6BseT0YkyRbxGCVbx2OUbB2PUSorcclX5JV1X0v0lViT00eF9ZB7IgZLSuJ1SZHrOS7HY5RsHY/RygmLMdetWyeLFy+W5cuXWzR/ULNmTRkxYoQMHz5cmjRpYjwdq4BLU3k+RvGYYQwePNgY7Dh27JgqXaUNBD8KM38TGxurxu+//24y19SiRQvjaN68uQQGBlb6ydyyUp6P0fKmc2AzNa6kJMmm0/tk3al/5FDcSTGIaZAD/c++2/m7GkGe/tKtboR0C46QYJ/KGQTkMWoqKSlJLFUpS1GdO3dORdGRutmhQwfj6U8//bSsX79elZcyhy8VY8eOlTfeeEOVsTp+/Lg89thjct9998lLL71kccYGUhWvXLnCUlTaCrn4+DxXyBFZG49RsnU8RsnW8RilsrAnNlKeXDpTrqTczI4GR3sHeanPJLWCMD88RsnW8RitXM81ylwjM+OXX35RZfIKgjJ6KDE1btw4VXLKGsdIRT9GsagVZcm1rA6MvXv3yo0bN4p1vQh2aJkd2r+Yp2Kwo+RV9GPU1sUlJciqY9tlRdRWOXD+eL7b1vcLkr4hHaRf4/aqjGhlwWPUFObPq1atalEpqkoZ2MDqBzc3N/VlYdiwYcbTJ0yYIFevXpXffvstx2W6dOki7du3l+nTpxtPQ2mq//3vfyo11JIDjz02TLGGHNk6HqNk63iMkq3jMUql7dcDf8ubq74xqemMRpUfDn1SWtRsVODleYySreMxWrFhOgZZAeiZgYAGyhoVBHMZaP6NqhJ9+/YVJycnsabKeIyiAgiqfWjNyTH27NkjycnJxbpeTGqaNyjH4lgGO4qnMh6jtupcYrysjNomy6O2yJGLJ/PdtnFAsCpVhVHLJ0AqMh6jpthjowD44McHBBqBa4ENHET4G03Cc4NovPnBZW9vr/6thLEhIiIiIiKrQSDjg3U/yI//LDc5vWn1+vLhsKmqQSURka06efKkCmTMmzdPDh8+XOD2mHvo16+fCmYgqOHh4VEm+0l5Px/oY4Kh9V1FsOPo0aMmDcoxsBDWUlixjdJjGJpq1aqZNCfHqFuXPQmofKrp7S8T2w5W4/SVCyqLA43Hj106nWPbqLgYNWZt/EmaBjb4N8jRXgK9qlll38k2VcrABjzxxBMqQ6N169aqGfjMmTNVdP3uu+9W5991110qDRB9MgA1F2fMmKGaP2mlqFCCCqdrAQ4iIiIiIipdiSnX5ek/PpJtpw6YnD4wrJNM63u/uDhad/UyEVFusBp34cKFKjsDZbEtgfJS48ePl1GjRqnV/GS7MC8UFhamxh133GFcQIueHVqwQwt4FKZ+PHq9rly5Ug19CTJ9CSsMNIlnsIPKkzpVA+W+9sPVOHE5VlZEblGBjpMJ53Jse+hCtBoz1s+V8KDGKsjRJ6Sd+HtUtcq+k+2otIGNMWPGqGj4yy+/LBcuXJDw8HAVFdcaip8+fdokQ+PFF19UHxL4F42g8KUCQY0333zTiveCiIiIiKjywA/fxxZPl9NXLxhPqyJV5NEuY+XutkM4qUNENgUT2Ch1jcyMVatWqVX9BWnWrJkKZqDHZ3BwcJnsJ5UOzCk1btxYDWTbaMGO6OjoHMEOlFyxFPqv4HjC0KAevXnPjgYNGvBzkcoF9NZ4sNNt8kDHUXI0/rSsiNqiMjnOJsbl2HZvbJQa7/39vbSqHaaCHL1D2omvG0v+V0aVsseGtbDHhinWkCNbx2OUbB2PUbJ1PEapJG2I/kee/fNjSU5PMZ7m7uQqbw96RLo1aFWk6+QxSraOx2j57Om5YsUKlZmBoEZKyn/vWXmpU6eOmvjGaN68uZQnPEaLD9NyJ06cyBHsuHLlSrGuF/NP5sGOhg0bVrrnicdo+X1dHL54QgU40JfjfNKlPLe1r2Inbes2U0GOXo3aiJdL+SrXx2O06PPnDGyUIQY2TPGFS7aOxyjZOh6jZOt4jFJJwM+V73b+Lh9t+EkM8t9Pl9o+1eWjYU9Jg2q1inzdPEbJ1vEYLT/P06ZNm1RmBspNWTIh7efnJ6NHj1bBjI4dO5bb55fHaOl99sXExJgEOzCQrVEcmItCiXV9z45GjRpV6OeOx2j5l23IlgPnj8vyyC2yKmq7xCfn/R7rYGcvHYJbqCBHj4atxcPZTWwdj1FTDGzYKAY2TPGFS7aOxyjZOh6jZOt4jFJxpWaky6srv5RlRzaZnN6uTjOZPniKeLsWb0Uej1GydTxGbRemUvbv36+CGWgEfvbs2QIv4+bmJsOGDVPBjD59+oiTU/nvCcRjtGyPuVOnThkzOrRgB/pwFAea0ZsHO0JCQipMP1keoxVLVna27ImNVJkcq45ulysp1/Lc1sneUTrVC5f+oR2ka/1bxM3JRWwRj1FTDGzYKAY2TPGFS7aOxyjZOh6jZOt4jFJxXExKkMd/+0A1i9QbF9Ffnux+hzjaF79dII9RsnU8Rm3PyZMnVZkpjMOHDxe4vYODg/Tr108FM4YMGaImkSsSHqPWhSm9M2fOmJSwwr94TorD3d1d9aLVBztCQ0PLZbCDx2jFlZmdJTtPH1JNx9cc2yHXUpPz3NbFwVm6NoiQfo07Sud64eLiaDuBZR6jphjYsFEMbJjiC5dsHY9RsnU8RsnW8RilokK5gceXfGBSagClBZ7vfY+MbNGrxG6HxyjZOh6jtgHPwYIFC1QwY+vWrRZdpnPnziqYMWrUKPH395eKiseo7cE0X2xsbI4yVhcvXizW9SLjCMEOrV8HRlhYmAre2TIeo5VDRlambDt1QGVyrD2+U67rerKZc3N0ke4NW0u/0A7SsW4LcXJwFGviMVr0+XPbfvchIiIiIqJK5Y/DG+XVFV9KelaG8bSqrl4yY+jjckutMKvuGxFVHklJSbJkyRIVzFi1apVkZWUVeJlmzZrJ+PHjZezYsRIcHFwm+0lkrkqVKlKrVi01hg4dajz93LlzOYId58+ft/h6b9y4IVu2bFFD4+rqKi1btjRpUN6kSRNxdLTuRDFVPsjk7VI/Qo20zHTZfHKfyuRYH71bUjLSTLa9kZGqypxieDq7SY+GbaR/aEdpW6dpiWQEU9nhs0VERERERDZRM3nWxp9Uo3C9xv51ZeawqVLTO/cVz/vPH5PPNi+UyIsnJTk9VfzcvKVbw1Yypevt4uroLC8v/0J2nTkssYk3y3K81v8BGdqsu/Hy+y4ck6fnTslxvcOb95BX+t2v/n/v/Fdl19kjcnfbIep6sRr2rdXfyoJ9q8TbxUO+GPW8NAmsX8KPCBGVtfT0dFm+fLkKZixdulRSUvJe8aupU6eOyszAaN68eZnsJ1FR1KxZU43BgwcbT0NgQ9+vAwPZHpbCa2Tbtm1qaJydnY3BDi3g0bRp0wrRU4bKB2cHJ+nZqI0aCGpsPLFHVkRukY0n90ha5n8LZyAp7YYsPbReDR9XT+nVqK3K5Ghdq4nYM3vC5jGwQUREREREVoUflc/98bH6wanXu1FbeX3AQ3k2e0TZgYd+eUeS0pIlrHo9CQuoJ8sjt8j8PSvEwc5OnuoxQfbGRkl9vyC5nHxVUjPT89yH+r5B0iG4hfHvFjUb5bpdtiFbXlnxpfx2cJ3KJPly9AsS4l+3yPediMTqJUA2btyoghkLFy6UK1f+K4GXFz8/Pxk9erQKZnTs2JGlQ6jcqlGjhgwaNEgNDUpWmQc70MfDUmlpabJjxw41NAhqtGjRwqRnBzKcGOyg0oZFLn0bt1cjOT1FZXAsj9wqm0/uVT069K6mJMmi/WvUwEKZ3iHtVCZHeFCI2FXh+7wtYmCDiIiIiIis5tSV8/LY4ulyMuGcyekPdrxN/tdheL4/JK+kXFNBDXi9/4PSyL+O6sWBTIrYxHh1+tJ7P1T/9vzs/nwDG81qNJCne04ocAL0hWWfyrIjm6Wau498NfolFTQhovIFWVf79u1TwYyffvpJzp49a1F/gWHDhqlgRt++fVlqhyqs6tWry4ABA9TQxMfHmzQnxzh16lShsqF27dqlhgavIWQ56YMd+BsZH0Slwd3JVQaGdVYDjcbRiwM9ObafPpgjyHH5RqL8vHelGgEeviowgkyO5oENVbk3sg0MbBARERERkVVsjdkvT/3+kTE4AS4OzvLmwIfUKrmC4IfmrU26qL4cLy3/3JixgUyKe9r+V1fcEquOblcr+LxdPaRdnWbyWNdx6vr1ft67SlIz06S6p68KatStWqNQt0FE1nXixAkVyEBA4/DhwwVuj6bI/fr1U8EM9Cpwd3cvk/0ksjX+/v7Sv39/NTSXLl0yCXTg/ydPnrT4OjMyMtRlML766ivjaw6ZHPpgBzI9XFxyz9wkKiovF3dVmhQDmRprju1Q3yFRvjTbYDDZNu56gszdvUyNml7+KsiBTI7QgGAGOayMgQ0iIiIiIirz1dI//vOXvL/uB5MfjzU8q8lHw5+SxgGWl3Ya3LSr7DxzSI5cPKkGdG/YyuKgA36Q1vMNkqaB9VW2x7ro3SpQcvzSGfnpzrdMMkYQ1IA+Ie0Y1CAqJ+Li4mTBggUqmLF161aLLtO5c2cVzLjtttukWrVqpb6PROURXhvIXsLQJCQk5Ah2REdHW3ydmZmZsnfvXjW++eYbY7ADPTq05uQY6OGBxuVEJQG9NUa26KUGSpdisQsaj+85GyUGMQ1ynLsWr/rBYdSpGij9GneQfo07SiP/2lbb/8qMgQ0iIiIiIioz6ZkZ8ubqb2TJwXUmp99SK1TeH/y4+Ll7W3xdCTeuyaOL31ONID8Y8rh0DG4p05Z/oUpFoaTA9ME5m4Kbax7QQH6dON1YIz8q7pSMnvOMRMbFyKmE81JPV2oKP2BPX7kgc3f/JW6OrvJw59GFuu9EVDaSkpJkyZIlMm/ePFm9erVkZZmWGMkNSuAgmDFu3DipW5d9c4iKwtfXV3r37q2GBn1r9uzZY9Kz4/jx44UKdqB0HMbs2bPVafb29tKkSRNjc3L8Gx4erkrGERWHn7uPjI3op8bFpARZdXSbKle1//yxHNviO+FX2xarUd+vlgpy9A/tIMG+NfO8/vPXLqkMEfNSpwgKXjYk5+jZhKBLDS8G2PPCwAYREREREZUJrIJ74rcZsvfcUZPTR7boKc/1ukcc7Qv38+T8tXgV1ICWNUNUk/HQ6sGy8ug2OXE51qLrOHf9kvgH+Iud3PwhqV+Zl5Z187o1vRq1VSUI3lr9rXy57VfJyM6UKV1vL9Q+E1HpQA3/5cuXq8yMpUuXSkpKSoGXqVOnjgpmYCCwQUQlr2rVqtKzZ081NImJiTmCHUePmn43yA+ClQcOHFDju+++U6dhQjgsLMyY1YGAB4MdVBwoPXpHq4FqxCbGycqobSqTQ8sQ1jtx+ax8vmWhGo3960q/0I7Sr3F7qeVT3SSoMeSbxyXd7PtlfpzsHVW/OAY3csfABhERERERlTr8CHxsyXS1+k1jX8VONeweE963SDWKsTquqqunXElJkod/fVeaVq+vVtZBq1ph6t8P1v2gVsZdT7+h/v71wFpVP7lHwzbSvUEr+enASnlh7f9JeM0QcXZwUqWoAD9KQ/zr5LjN0eF9xMHeXl5f+ZXM3rFUMrIy5akedxX5cSGiosMq140bN6rMjF9++UWtDC+In5+fjBkzRgUzOnTokGN1LBGVPm9vb+nevbsammvXrqkSVPpgR1RUlCpfaen7waFDh9SYM2eOOg3fLUJDQ1V2R6dOnaR169Yq2OHp6Vlq940qpiDvALm77RA1Tl05fzPIEblVjl06nWPbqPhTasza+JM0DWzwb7mq9ur7aGGCGoDtcTkGNnLHwAYREREREZUq/Ph76a/PJDUz3Xiat4uHTB8yRTXqLipXR2f5fNTz8unmBXLwfLScvBwr1dx9ZFizHvJI5zFqm9VHt8u5a5eMl9kbG6UGMi8Q2OhYu7kkZtyQbacOSHJ6qvh7VJWBYZ3kf+1HmPTX0BvRvKc42jmosldoJImyV8/2nMgGkkRlAJOcKEmDYMb8+fPl7NmzBV4GK7aHDRsm48ePlz59+oijo2OZ7CsRWc7Ly0u6du2qhr6snBbs0Hp3REZGqiCGpe8XR44cUWPRokXqNHxWh4SEmDQoj4iIULdPlJsLFy7I1atXTU7r4hMmXdqHydmkONly7pBsPXdQziVfznHZQxei1Zixfq7U8fwve6MwTsaclCpXbvZ5Ky4fHx8JDAyUiqKKwdLQJxUbos+ISiPljm+YN6PpaOQWEBDAVTJkk3iMkq3jMUq2jscoZRuy5fPNv6iyTeaZFrOGT5XaPtb9YcVjlGwdj9H/nDhxQpWZwsAkZUHQcLh///4qM2PIkCHi7u5eJvtZ2fAYpbKWnJxsktmBgMfhw4ctDnbkpVGjRibBDpSywhweVW4IaowaNUpSU1Pz3U5Nrvs4SXYdD8mu4yniUXIBdIcVZ0ossOHi4qIyHG05uFGY+XNmbBARERERUYm7kZ4qzy/7VNYe32lyercGreStgQ+LhzNrXhNR/jBh/vPPP6tgxrZtN8vMFaRLly4qmIGJqGrVWLqDqKJBkBJlpTA0N27cUJlc+mAHSlKhF4eljh07pgYywTQNGzY0NifXgh3oGUKVBzI1ENR4/fXXpV69ehZdBjkEJxLPyZZzB2XruUNyOfVasfbh9Tdel/reeTckt9TJkyflpZdeUvfJlgMbhcHABhERERERlaizV+NkypL3c9QdntRumDzceXSeJZ6IiFB6ZvHixSqYsXr1aosmJlu0aKGCGWPHjpW6deuWyX4Ske1AuTn0zMHQpKSkyP79+2XXrl2yefNmldWBYEdmZqbF13v8+HE1FixYYDytfv36xiCHFvDw9fUt8ftEtgVBDfRrsVSYhMkg6aWyl/efO6aaji87vFmupiYV/raD60lodcuCKpUNAxtERERERFRidp4+JFN/n6kaHWqcHRzl1X4PyICw/1ZXEhFp0tPT5a+//lLBjKVLlxZY8gMQwEAwA6NZs6L36iGiisnV1VXatWsnbdq0kZEjR6pyaXivOXDggEmD8oMHD0pGRkahyuJhLFy40HhacHCwSVYH/mXGGAEW84QHNVZjUFgXGT/vBWvvUoXCwAYREREREZWIBXtXybt/f6eaaWsCPHxl5rAnpWlgA6vuGxHZFtTD37BhgwpmoN73lStXCrwMJgpHjx6tghkdO3ZUTYCJiArTXwCBDgxNWlqaCnZozckx8DeCIJaKiYlRQ2tQDnXq1MnRswPBFaq87NmHqMQxsEFERERERMWSkZUp7/39vSzYt8rk9BY1GsmMoU+IvwfrURPRzbrjaPqLYMZPP/0ksbGxFtXTHzZsmApm9OnTRxwdS64hKxGRs7OztG7dWg0NghrI5ND6deBflLVCEMRSp0+fVgOl9TS1atUyCXZgVK9evcTvE1FlwcAGEREREREV2ZUb11TpqV1nDpucPqRpV3mxzyRxdnCy2r4RkW2Ijo5WgQwENI4cOVLg9g4ODtK/f38VzBgyZIgKbhARlRUnJyeVYYGhQbkq9OjQNyhHw3JLSudpzp49q8Zvv/1mPK1mzZo5gh01atQo8ftEJQ9ZOvk1FJ82bZq88sorqnfUyy+/LL+uWyY+4yMKfTv33XefbFq62lj27NSpU9KtWzdZt26dsSl4z5491f4gU+jvv/+WBg0qR6Y0AxtERERERFQkx+JPy6OLp8u5a/HG0+yqVJHHu90hd7YayDIxRJXYxYsXVcNdBDO2bdtm0WW6dOmighm33Xab+Pn5lfo+EhFZCtli4eHhatx7773GYAeCtfqeHQh2oHG5pc6dO6fG77//bjwNgQ19c3IMBED4vcr2sn3Qx0Xv6tWrEhUVZXweUXZx8ODBsn79evGs6y8+Rbid/Pq1HD9+XAU1zpw5oxrbI6iBHlSVBQMbRERERERUaH8f2ynPL/tEUjL+K8vg6ewm7976qHSqF27VfSMi67h27ZosWbJEBTNWr14tWVn/9dvJS4sWLVQwY9y4cWqlKRFReQp24D0M4+6771anZWZmSmRkpEmwAyX4bty4YfH1nj9/Xv788081NChZpW9OjoHSVgx2WA8CF+aB+0ceeUQFNqpWrSrjx49Xn4kIasD096fLZzF/Ffp2xo4dm+vpR44ckV69eqnjpVGjRiqogWOiMmFgg4iIiIiIClUj/6tti+XTzQtMTq9btYbMGv6UBPvWtNq+EVHZQ8355cuXq2DG0qVLLSrLgtWkCGZgNGvWrEz2k4ioLKCUHt7XMCZMmKBOQ5AXk936YMeePXskOTm5UFlwy5YtU0Pj7+9v0pwc/yJAzGCHdVy+fFlmz56t/v/ggw+Kh4eH/PXXzUCGq6ur3Np7gHw9e7WkZ2VYfqVZBunWvnOOk5Gh0b17d4mLi5OwsDBZs2ZNpSxhxsAGERERERFZBNkZLy//XFZGma5O6xjcUmVqeLmwDj5RZYDSGhs2bFDBjF9++UWuXLliUSmNMWPGqGBGhw4dOPFGRJWGvb29NGnSRI0777zTGOw4evSosTm51rfj+vXrFl9vfHy8Cixj6N9rzctYIZjM99zS99lnn6nMHJSomjx5sjEAASivGOQTIEvv/VAuX78qbdu2VaePGDlCnn/ueXU84DjB84Sg16RJk9T5n86YJTW8cpaiOnHihPoXJcrQayMgIEAqIwY2iIiIiIioQBeuXZLHlrwvkXExJqff2WqQPN5tvNjb2Vlt34iobLK1UE5l3rx5Mn/+fImNjS3wMmj6PWzYMFWOo3fv3qpsCxER3Qx2YKU9Bt4jtaDxsWPHjEEO7V+U+bPUpUuXZOXKlWpofH19cwQ70PSawY6SzV789NNP1f/vuOMOCQwMzHU7BCn83XwkJfbmggCPNAcJq15PlTBDtg+ek2c/n6LOR3mpCaNuz/d2z507Jz/88IM8+eSTUhkxsEFERERERPnaGxslj/82QxJuJBpPc7R3kJf6TJKhzbpbdd+IqHRFR0erzAwM1I0vCCZmBgwYoDIz0DAVwQ0iIiqYnZ2dNG7cWA28h2rBDrwP67M68G9i4n/fyQqSkJCg+h5haHx8fHIEOxo0aMBgRxHNmTNHlQvD46cPMtSuXdsYcMJziecY5aM05r2lULJMaySP68H2uWndurW4ubmp7MmpU6dKenq6PPfcc1LZMLBBRERERER5Wnxgrbyx6mvJzP6vCbCfm7d8OOxJaVkzxKr7RkSlA5MzP//8swpmbN++3aLLdOnSRa06HjVqlCq5QURExYeJbazcx9CaSCODDqWI9D07EPCwpCyg5urVq6rZNIbG29tbIiIiTIIdDRs2zHNyncT4fHzwwQfq/4MGDVJZOJr+/fvL119/rfpPoT/KrbfeKosWLTI5Xw/Xg+tD/xStR0tusGhg2bJlagEBnsPnn39eMjIy5OWXX5bKhIENIiIiIiLKAYGMD9fPlbm7bzY91DSpXl9mDntSqnty4pKoIkGpk8WLF6tgBlb1YmVpQVq2bKlWFWOyzXzVKRERlQ5kBSC7AmP06NHqNEyGx8TEmAQ7MJCtYSlkgaBfA4bGy8tLBTv02R0hISEMduggwwKZFvDUU0+ZnIdyjJ07d5ZNmzbJiBEj1HOG3iqAz088rnjuAJkcKCsFjzzyiLi4uOR7u25ubvLHH3+o20DpsWnTpqmSVq+99ppUFgxsEBERERGRiWup1+XpP2bJ1pj9Jqf3D+0or/S7X1wdna22b0RUsjXB//rrLxXMwMQMVpQWJDg4WE3GYDRt2rRM9pOIiAoOdqBvBgYy5wAT5qdPn84R7EBZpMIEvdevX6+GxsPDwySzA5PzKJ+FviGV0fvvv6/+RUPwrl27mpyHx+TPP/+Ul156SX755RdVVgwLAe666y558cUXTbb95JNP1Oewq6urPPTQQxbdtqurqyxdulRGjhypbuf1119XZaneeecdqQwY2CAiIiIiIqOTl2Pl0SXT5fSVC8bTqkgVmdxljNzTdihrLxOVc8jEwAQVghmYZEE5koJUq1ZNxowZo4IZHTp04PsAEVE5gPfqunXrqoFsAS3YcebMGWOvDm3o+z4U5Pr167Jx40Y19KWRwsPDTYIdoaGhqu9SRYc+F/lB1stHH32kRn6QaYHARH6QlWPO2dlZZW5URhX/6CIiIiIiIotsOrFHnvljllxPTzGe5uboIm8PekS6N2xt1X0joqLDRNaePXtUMGP+/PkSGxtb4GUwSTV8+HAVzOjdu7c4OjqWyb4SEVHpBjuQMYCBEkbaZwQ+F/TNyTEuXPhvkUtBkpOTZfPmzWroswn0wQ4M9J+oDMEOKhs8koiIiIiIKjn8oJ2z6w/5cP2PYpCbdX6hlneAfDT8KWlYrbZV94+Iiub48ePy008/qYBGZGRkgdtjsmnAgAEqmIGGpAhuEBFRxQ921KpVS42hQ4caTz937pxJc3L8i9MslZKSIlu3blVDg74R6M+kD3Y0adKEwXMqEgY2iIiIiIgqsbTMdHlt5Vfyx+H/yglAm9pN5f0hU8TH1dNq+0ZEhYcVtgsWLJB58+bJjh07LLoMaoIjmIG67H5+fqW+j0REZPtq1qypBgLd+s8Y854dlmQBatBDYvv27WroSym1aNHCJNiBHk5OTk4lfp+oYmFgg4iIiIiokoq7niCPL/lADl6INjl9bEQ/mdr9TnG0588FovIAzV0XL16sMjNWr16t+mgUBCtmEcwYN26c1K7NrCwiIipYYGCgDBo0SA3NxYsXc/TsQB8PS6WlpcnOnTvV0CCogWAHenVowY5mzZqpIAiRhr9UiIiIiIgqoYPno2XKb+9L/PUrxtMc7OzluV53y6iWva26b0Rk2UTQX3/9pTIz0DQUq2ALEhwcrIIZGFgNS0REVFzVq1dXZQwxNPHx8TmCHadOnbL4OtPT02XXrl1qaFCuqnnz5sbm5PgXf6O8FVVODGwQEREREVUyfx7eJK+s+D9Jz8ownlbV1VM+GPKEtKodZtV9I6K8ZWVlyYYNG1Rmxi+//CJXr14t8DLVqlWTMWPGyPjx46V9+/aqljoREVFp8vf3l379+qmhuXTpkuzZs8ck2HHy5EmLrzMjI0MFSzD0vaGQyaFldSDggUwPNC6nio+BDSIiIiKiSiIrO1s+3jRfZu9YanJ6iH8dmTlsqgR5B1ht34godwaDQU3ioAn4/PnzLWrciqbfw4cPV8GMXr16sSkrERFZHQLtffr0UUOTkJBgDFZowY7oaNMSqfnJzMyUvXv3qvHNN9+o0+zt7VVWor5nB8ovMthR8TCwQURERERUCVxPuyHP/vmxbDyxx+T0Xo3ayhsDHhI3J6bxE9mS48ePq8yMOXPmWDTJg1WrKAOCYAYavbq5uZXJfhIRERWVr6+v9O7dWw0NshH1gQ78/9ixY4XKbty/f78as2fPNgY7wsLCTIId4eHhFn9W5pdZEnfjikz++6Ncz2viW1emdbxbHlnzocSnJJqcZ1elivw0aJpawIBslDVn/5HVp3fLxRtXxMPRVToHNZexob1UqVh4dctsOZxwSoY26CS3h/VRl/vm4J+y6tQutf0L7e6U+j41i3QfyqsqBjwKVGYN3by9vSUxMVG8vLykskNDu7i4OAkICBA7Oztr7w5RDjxGydbxGCVbx2PUdpy+ckEeWzxdTiTEmpz+QIeRcn/HkWJXpXI+PzxGydZcuHBBfv75ZxXQ2LFjh0WX6dq1qwpmjBw5Uvz8/Ep9H4n0+D5Kto7HaMWAuVTzMlZHjx4t1nXieECwQ9+gHMEODw8Pk8/lUaNG5dvHyuBkJ9lNfU1Oyw72FHG2lyrRieKwM14yBtcVcXcUuyhdCUmDQez3Xlb/zWzkJYZWASLpWVLlzHUxVHMV8XZS29vvuXRzm55BYghwFbvDV8Ru/2XJahsghvpeIqmZ4rD2nFRJTC/wPqMfCUpZogl8RZg/Z2CjDDGwYYofLmTreIySreMxSraOx6ht2HbqgDz1+0y5lppsPM3FwVneGPCg9GncXiozHqNkK78TFy9erJqAr1mzRh2XBUFJDQQzxo4dK7Vr1y6T/STKDd9HydbxGK3Yn58oQaUPdkRFRalMhqJCH6rQ0FCTYEfNmjVVyStLnU2Kk6nrP1P/f7/bw1LL09+YsfHzra+YbIt9RTmuaWu/kvNZiXLHLQNkeKOuEpN4Xp7Z+H8qW+OzXo+Lt7OHMWNjcP2OkpCaJJvPHRAfZw95qf0EdRuW8PHxsemgRmHnz1mKioiIiIioAsIPpR/3LJcP1v4gWYb/JkpreFaTmcOnSmhAsFX3j6gyS0tLk2XLlqnMjN9//139XZB69erJ7bffLuPGjVO1w4mIiCozTHojaxFDk5SUJPv27TMJdkRGRlq0aED7/nzkyBE1sOBAC3aEhIQYm5Nr/+Y16T5/xXpBaKVbg1bSu00XdZrjBkeRFJH7Vk9XPe/quFaT6meqyJ8//mpSbvK9wE3i+NSzUr1tI/V3ZnaWVKnqKqF1QsVtr5tIgsiq07slNTNNqnv6ylejX5K6VWtIZcXABhERERFRBZOemSFvrflWFh9Ya3J6eFBjmTHkCfFz97bavhFVVqj5vX79ehXMQBkIrEQsiL+/v+qXcc8990jHjh3V5AoRERHlztPTUzp37qyGJjk5WWV26Pt2HD58uFDBDmSCYOAzXNOoUSOTnh0RERGS5Sjy5+FN6vyJbW41buvl4iGNA+pJNXdvWbNytSycNVOy0zNzfK5fvXBJnnpyqtg5OUjduzqKZ+MacjlZV75KUHnq5mKIPiHtKnVQAxjYICIiIiKqQC4nJ8oTS2fI3tgok9OHN+8hL/S+Vxzt+ROAqKxgMgQTKZgImT9/vpw7d67Ay6C29/Dhw1V2Rs+ePVWJCpRQYVCDiIio8Nzd3aVTp05qaG7cuGHM7NACHocOHVKLECyFhuYY+HzXVAuqLhnVnKReWCO5EnFOrrgHStWqVWX+nW+rz/EVK1bIKx/+ItnZN28nr7JZhowsOfnNRql3bxfxvc00M6RO1UDVP2/u7r/EzdFVHu48Wior/qohIiIiIqogIuNiZMri9+V80s0mg2BfxU6m9rhLxkX048QoURk5fvy4CmZgYIVnQRwdHWXAgAEqmIEMDTc3N3W6patJiYiIyHL4nO3QoYMampSUFNm/f78xqwMBj4MHDxaqv8al2IsisSJ7952R3vP/VqfVDQ6W8Ihwadm8hbz33ns3gxkFtAFR21QROTVni9R+07R/Rq9GbaWml7+8tfpb+XLbr5KRnSlTut4ulREDG0REREREFcCqqG3y4l+fG9PTwcvFXaYPniLt6za36r4RVQYXLlyQn3/+WQUzduzYYdFlunXrpoIZo0aNEl9f31LfRyIiIsqdq6urtGvXTg1NamqqHDhwwKRnB4IdGRkZFl/vqZgYNX5bvKRwO4T4R0aW/DJ/gTz66KMmZ40O7yMO9vby+sqvZPaOpZKRlSlP9bhLKhsGNoiIiIiIyrFsQ7Z8sWWR/N/WRSan1/cNko+GP6XS1YmodKBPxuLFi1UwY82aNRZlWISHh6tgxtixY6V27dplsp9ERERUeC4uLtKmTRs1NGlpaSq4oQ92IPiRnp5eKvswa9YsmTx5co7TRzTvKY52DjJt+Rcyd/cy1Wj82Z4TK1WGNgMbRERERETl1I30VHnxr89kzTHT1eFd6kfIO4Mmi4fzzXI2RFRysHrzr7/+UsGM33//XU1wFKRevXoqmIHRpEmTMtlPIiIiKnnOzs7GhuEaBDXQo0Mf7EBZK0u+IxRUkio6Olr12/pm7LQc5w9u2lWNyoqBDSIiIiKicig2MU6mLHlfjsafNjn9nrZD5ZHOY8Tezs5q+0ZU0aCZ6Pr161Uw45dfflGZGgXx9/eXMWPGyPjx41VZi8q0gpKIiKgycXJykoiICDUmTZqkTkO5KgQ70Ktj7dq1Mnfu3CJff1JSkvj5+ZXgHlcMDGwQEREREZUzu88ckSeXzpArKUnG05wdHOWVfvfLwLDOVt03oooCqyQxGTFv3jyZP3++nD9/vsDLeHh4yPDhw1Uwo1evXuLgwJ/cRERElZGjo6MqP4kxZMiQYgU2PD09S3TfKgp+yyIiIiIiKkd+2bda3l4zW9XR1fh7VJWZQ6dKsxoNrLpvRBXBsWPHVGYGxtGjRy2auBgwYIAKZtx6663i5sYScERERPQfZFs0aNBATpw4oRZOWArZnvXr1xdfX99S3b/yioENIiIiIqJyICMrU6avnSM/711pcnrzGg3lw6FPquAGERXNhQsXVFYGghk7d+60aKKhW7duqmfGyJEjOeFARERE+X5vQAPwxx9/vNCXffTRR1nOMg8MbBARERER2birKUkydelM2XnmkMnptzbpIi/3vU+cHZystm9E5RX6ZPz6668qmPH3339LdnZ2gZdBOQlkZqB3Ru3atctkP4mIiKj8mzBhgrzwwguSkpJi0XcOOzs7cXV1lbvuuqtM9q88YmCDiIiIiMiGHYs/I48tma6ahWvsqlSRKV3Hy12tB3EFF1EhpKamyrJly1Qw448//pC0tLQCL4MSEMjMGDdunDRp0qRM9pOIiIgqFh8fH1m0aJEMGjRIBS3yC27gfHzHxwIMXI5yx8AGEREREZGNWnd8lzz35ydyIyPVeJqHk6u8e+uj0rl+hFX3jai8yMrKknXr1qlgBiYUkKlRkICAAJWVgYBGu3btGEAkIiKiYuvXr5/8+eefqozljRs31Gn6nhva9w1kaiCo0bdvX6vta3nAwAYRERERkY3BD5xvti+RTzYtEIP892OnTtVAmTXsKannF2TV/SMqD6+h3bt3q2AGemecP3++wMt4eHjIiBEjVDCjV69e4uDAn8tERERU8sGNs2fPypw5c2TWrFkSHR1tkiWKnhooW+Xt7W3V/SwP+E2NiIiIiMiGpGSkybTlX8iKqK0mp3cIbiHv3fqoeLl4WG3fiGzdsWPHVDAD4+jRowVu7+joKAMHDlTBjFtvvVXc3NzKZD+JiIio8kJ5KQQw0FD80qVLEhMTI8HBwVKtWjVmiRYCAxtERERERDbiwrVLMuW3D+TIxZMmp9/RaqA83m28ONjZW23fiGwVsjF+/vlnFczYuXNngdtjwqBbt24qmIFSEL6+vmWyn0RERETm30n8/PxU2Uz8y6BG4TCwQURERERkA/adOyqPL/lALt/4r/6/o72DvNh7kgxr3t2q+0Zka9AnA7WnEcz4+++/823AqYmIiFDBjLFjx0qtWrXKZD+JiIiIqHQwsEFEREREZGW/HVwnr6/6WjKyMo2n+bl5y4yhT0h4UGOr7huRrUhNTZVly5apYMYff/whaWlpBV4GtaoRzMAICwsrk/0kIiIiotLHwAYRERERkZVkZmfJzPU/yg+7/zQ5Pax6PZk59EkJ9KpmtX0jsgUozbBu3ToVzFi0aJHK1ChIQECAjBkzRsaPHy9t27ZlWQciIiKiCoiBDSIiIiIiK7iWmizP/DFLtsTsMzm9b+P28lr/B8XV0dlq+0ZkTQaDQXbv3q2CGfPnz1c9NAri4eEhI0aMUMGMnj17ioMDf+oSERERVWT8tkdEREREVMZiEs7Jo4uny6krphO2j3QeI5PaDeMKc6qUjh07poIZGEePHi1we0dHRxk4cKAKZtx6663i6upaJvtJRERERNbHwAYRERERURnafHKvytRISrthPM3N0UXeGvSI9GjY2qr7RlTWkI3x888/y7x582TXrl0Fbo+gX7du3VQwY+TIkVK1atUy2U8iIiIisi0MbBARERERlVF5nTm7/pSZG+ZJtsFgPD3IO0A+GjZVGvnXser+EZUV9MlAvwxkZqxdu1ays7MLvExERIQKZqB3Rq1atcpkP4mIiIj+n737gI6q+to4/KYXkkAg9N57kWpDkCIqiIjwiY0i6h8bIoodUVEsCIKoqKCCBRsIgqhIRxAB6b33DoGQXvnWOUrMkAECJJmZ5Pcs70py753JTrxAMu/dZ8N9EWwAAAAAOSwxJUmvzRyrqesXOOxvXLaW3rmln8KDw1xWm7s6dOiQTp48maOfw7ygfvz4cUVGRsrb2zvHPk+hQoVUokQJ5WcJCQmaPn26DTPM28TExAs+plKlSjbMuPPOO1WzZs1cqRMAAACegWADAAAAyEFHY06o/0/DtebgVof9dzS4QQOu7y4/H34kdxZqdOnSxb4YntNSU1Pl4+OTo58jMDBQEydOzHfhhvnemo4ME2aYDo1Tp05d8DHFihVTt27ddNddd6lp06bMmwEAAIBT/BYFAAAA5JD1h7ar35RhOhITmb7P19tHz7Tqqf9r0Naltbkz06lhQo3BgwerYsWKObo8WEpKinx9fXPsBfSdO3dq4MCB9mvKD8GG+Z6aWRkmzPj2229tSHUhoaGh6ty5sw0zWrVqZf9/AAAAAOfDT4wAAABADvh14yINmvGRElOS0/cVCgrVsI5P2CWocGEm1KhRo4ZHBxv5xZYtW2yYYbatWx27k5zx9/fXzTffbMOMDh06KCgoKFfqBAAAQN5AsAEAAABko7TTaXp/4Xf6dMlPDvurRpTTiE5PqUyhYi6rDchOBw4c0HfffWfDDNOlcSEmPGrZsqUNM26//XaFh4fnSp0AAADIewg2AAAAgGwSkxin53/5QPO3L3fY36pqE71+0yMK9g90WW1AdjBLav344482zJgzZ47termQhg0b2jDDzM4oXbp0rtQJAACAvI1gAwAAAMgGe08eUt/J72jH8X0O+/931e3qc/Xt8vbydlltwOUw806mT59uwwzzNjEx8YKPqVy5sg0zzJaTy4kBAAAgfyLYAAAAAC7Tkt1r9dS0ETqVEJu+L9DXX4Nvelg3VL/SpbUBlyI1NVVz5861YcakSZN06tSpCz6mWLFitivj7rvvVpMmTZhbAgAAgBzDbWMAAADAJTLL8Hyz4jc9NPENh1CjRGgRjbvzFUKNHHb06FE99thjKl++vB1GHRERodatW2vHjh32+GuvvaamTZsqICDAvshuNtN9cLYffvhB11xzjX18YGCgypYtq549e2rPnj3p55iPzzzHGea5zABss8887ueff5anX8/Lli3TE088oTJlyqht27b6/PPPzxtqhIaGqkePHvr999+1f/9+jRw50n7PCTUAAACQk+jYAAAAAC5BcmqK3pj9mSatmeOwv0Gpahp+a38VKVDIZbXlB8eOHVOzZs20c+dOG2pUq1bNvjC/ePFiO9S6UqVKmjhxonbt2qWiRYvaF92dmTdvnu0yMI8tUaKEqlevrnXr1mn8+PFau3atli93nJdyRlxcnDp27KjZs2crKChIU6ZM0Q033CBPtGXLFn399de2O2Pbtm0XPN98v02gYzoz2rdvb79+AAAAIDcRbAAAAAAXKTLulJ6cOlwr9m1y2N+pTku90Ka3/H39XFZbfvHiiy/aUKN27dqaOXOmSpYsafcnJSWlD7Q2HRRmWPUrr7xiN2f+/PPP9PNXrVql4sWL2w6EL774Qrt373b6mJiYGPvC/h9//KECBQpo6tSpatWqlTyJCX++++47G2icK7zJyHRgtGzZ0oYZnTt3Vnh4eK7UCQAAADhDsAEAAABchM1HdqvflKE6cOpY+j5vLy89dX133XXFjSzBkwtMEPH999/b982yUWbJJBNyVKlSRc8++6zuvPNOe8wsp3QhZgkq8//MPGeDBg3snAjTsWHejh492uljzOf766+/7DJMZph28+bN5QlOnjxp52WYzgwzP+NMoHM+DRs2tGHGHXfcYUMiAAAAwB0QbAAAAABZNGvLEr3wy4dKSElM3xcaUEBDb3lcV1Wo59La8ttsjRMnTtj3f/vtN/uCu+kgWLNmje666y75+fmpS5cuWXquFi1a2JDEzNA4dOiQ3QyzlFXlypWdPsaEGiYM8YRQw8wBMXWazgzz1nS0XIj5uk2YYQKiGjVq5EqdAAAAwMUg2AAAAAAuIO10mj5Z/KNG/znRYX/FwqU08rYBKh/+zzJIyB0pKSnp79esWdMuIWWYjouNGzfq/fffz3KwYeZoPPLII/YF/wULFqh+/frq3bu3nc9x44032kHkzmZImG6HESNG6Morr7RBijtJTU21HRkmzPjxxx/PO/z7DLMEl5k1YoKhJk2a0HkEAAAAt+bt6gIAAAAAdxaXlKABU0dkCjWaV7xCX979GqGGC5hh4GaAtWGCCPO+2cz7hhkYnlVDhw7VkSNHbEBiui/CwsLsi/uG6d5Yv359psf079/fvjWhgQlQstIFkdNM0LJ06VL169fPLsFllssaN27ceUMNs5SW6VT5/ffftW/fPhvUNG3alFADAAAAbo+ODQAAAOAcDkQdVb8p72jzUcch0j2b3KK+ze+Ujzf3CbmC6ZC47rrrNGvWLLv8VHJyst1v3jeqVq2a5ec688K/GRR+7NgxRURE6O+//04/boaDn23YsGE2SHnzzTft4PBOnTpp8uTJCggIUG7bvHmznZlhtm3btl3wfFN3+/btbXhj3jrrRgEAAADcHcEGAAAA4MSKfRvV/6d3dSL+vzve/X38NKjdg+pQy73nKuQHr732ml06asOGDapYsaLdt3//fvn4+Oj555+3H5s5EUuWLFFkZGT642rXrm07Et5++23ddtttdvvll18UFRVlA5FSpUrZ5zRM94Lp5HDmjTfesAHL4MGD9euvv6pjx46aMmVKrgQF5uv87rvvbJixfPnyC55vvt7rr7/ehhmdO3e280gAAAAAT0awAQAAAJxl0prZGjLrM6WkpabvK1ogXO92elJ1S1ZxaW34R7NmzTRnzhy9+OKLdgkmEyi0adPGBh7m2JkAYPv27Q6PMzMzMnZq9OjRwz529OjRtvth586dNuC45ZZb0gOSc3n11VdtB8TAgQPtck4dOnTQtGnTFBwcnO1f78mTJzVp0iQbZpj5GWbpqQtp1KiRDTPuuOMOO2AdAAAAyCsINgAAAIB/Jaem6J15X+rblTMc9tcuUVkjOj2pYiGFXVYbMrvmmmvsi/znMm/evPM+/kw4YF78N90d52PmVZjtbCZYMVtOiI+P1/Tp022YYd5mZZZH5cqV7ddy5513qkaNGjlSFwAAAOBqBBsAAACAuSM+PloDpo3Q0j2Ow6Lb17pWL7V9UIF+/wyrBnJSSkqKDWtMmGGGk59v+PcZxYsXV7du3WxA06RJE4Z/AwAAIM8j2AAAAEC+t+3YXjskfO/Jw+n7vOSlftfdpR5NOvBCMXKU6RxZtmyZDTO+/fZbHT7833V4LqGhobr99tttmGHmZ/j68qsdAAAA8g9++gUAAEC+Nm/7cj0//X3FJsWn7wvxD9KbHfqqeaUrXFob8jYz08OEGWbbtm3bBc838zzat29vwwzzNjcGlQMAAADuiGADAAAA+fYu+c+WTtWoP77Vaf03iLlcoRIaedsAVSrCsGVkPzPQ/LvvvtPXX3+tFStWXPB80y1kOjLM3IzOnTurUKFCuVInAAAA4M4INgAAAJDvJCQn6eUZH+nXTX867L+yfF0NveVxhQWGuKw25M25GRMnTtScOXPsQPMzQ8vPp1GjRjbMuOOOO1SqVKlcqRMAAADwFAQbAAAAyFcORx9XvynDtOHwDof99zS6SU+0uEe+3j4uqw15R3x8vH7++Wd9/PHHWrNmjVavXn3Bx1SpUsWGGXfeeaeqV6+eK3UCAAAAnohgAwAAAPnGmgNb9cRPw3Qs9mT6PhNkvNj2ft1W93qX1obMdu7cmaPPbzonTDeFGbydHQPizXMtWbLEBhozZ85UbGzsBR9TokQJdevWzc7NaNy4MYPqAQAAgCwg2AAAAEC+MG39Ar36+xglpSan7yscXFDDb+2vK0pzd7w7MXMkAgMDNXDgwBz/XKmpqfLx8bmscCQuLk6RkZF2M+HGhYSFhen222+3YYaZn3E5nx8AAADIjwg2AAAAkKelpqVp5IIJGv/3zw77qxeroJGdnlLJsAiX1YZzdzGYmRQnT/7XWZPdTCBhgggzzLt06dIqXLjwRXVL7Nixw3ZmTJ8+XXv27Lng+f7+/urQoYMNM26++WYFBQVd5lcAAAAA5F8EGwAAAMizTiXE6tmf39OiXY7zDW6odqVeubGPgv0DXVYbLhxumC27mbBk/PjxGjVqlLZv356+v3LlynrsscfUo0cP2zHijAlBvv32W02YMEErVqy44OcyQUmrVq1smNG5c+dzPi8AAACAi0OwAQAAgDxpV+QBPT7lHfs2o0eu+T89cOVtzDLIh2bMmGGXgDJLRznrwHjiiSf0wgsvaNKkSWrXrp3df+LECfuxCTPmzZtnOz0uxMzKMGHGHXfcoVKlSuXI1wIAAADkZwQbAAAAyHMW7VytZ34eqejE/17ADvIL0Os3P6LWVZu6tDa4LtRo3769DSachRNn9sXHx9vznnnmGW3YsEG//PKLkpKSLvj8VapU0d13360777xT1aszswUAAADISQQbAAAAyDPMi9NfLf9Fw+d/pbQML16XCiuq924boKpFy7m0PriGWX7KdGqY6yMtLe285545PmTIkAs+r1kqq1u3brY7w3Rp0AUEAAAA5A6CDQAAAOQJSSnJGjxzrKaun++wv3GZmnqn4xMKDw5zWW1wLTNTwyw/lZVlpC4kLCzMhiSmO6Nly5by8fHJlhoBAAAAZB3BBgAAADzesdiT6v/TMK0+sNVhf9f6bfRMq57y8+HH3vzKhBlmUPjl8Pf3V4cOHWxnhlmmKjCQofMAAACAK/EbHgAAADzahkM71O+nd3Q4OjJ9n6+3j55p1UP/1+AGl9YG1zt+/Li2b99+yY8fOXKkunfvrkKFCmVrXQAAAAAuHcEGAAAAPNavm/7UoN9GKzElOX1foaBQDb2ln5qWq+3S2uAeYmJiLuvxHTt2JNQAAAAA3AzBBgAAADxO2uk0fbDwe41dMsVhf5WIshrZ6SmVKVTcZbXBvYSEhFzW40NDQ7OtFgAAAADZg2ADAAAAHiU2KV7PT39f87Yvd9h/fZXGev3mR1TAP8hltcH9/Pnnn3bAd2pq6kU9zsvLS5UqVVLhwoVzrDYAAAAAl4ZgAwAAAB5j38nD6jt5qLYf3+ew/4Erb9PD13SVt5e3y2qDezl58qT69eun8ePHX/Jz9O3b1wYcAAAAANwLwQYAAAA8wtI96/XU1HcVlfDfzIRAX3+9cmMf3VjjapfWBvfy22+/6f7779f+/fsv6fHe3t4KCgqyQ8MBAAAAuB+CDQAAALi971b+rrfmjFPq6bT0fcVDC2tkpwGqWbyiS2uD+zh16pSefPJJjR071ulx031htrS0/64jZ6GGOefHH39kaDgAAADgpvJ1r/4HH3ygChUqKDAwUM2aNdPSpUsv2M7+yCOPqGTJkgoICFC1atX0yy+/5Fq9AAAA+U1yaopemzlWQ2Z/5hBq1C9VVd/cM4RQA+lmz56tunXrOg01ChYsqHHjxtmf3U0nxpmAI6Mz+8xxc94NN9yQi9UDAAAAuBj5tmPju+++U//+/fXRRx/ZUGPEiBFq166dNm/erGLFimU6PykpSW3btrXHJk6cqNKlS2v37t3cxQUAAJBDIuNOacDUd/X3vo0O+zvWbqGBbe+Xv6+fy2qD+4iJidHTTz+t0aNHOz1ufsY3YUeZMmXsx/v27dMXX3yh9957T9u3b08/zwwKNzM1evToYYMQAAAAAO4r3wYbw4cP1wMPPKBevXrZj03AMX36dH322Wd69tlnM51v9kdGRurPP/+Un98/v0Sbbg8AAABkvy1Hd+vxye/owKmj6fu8vbz0ZMt7dXfDmxjoDGv+/Pn25/mdO3dmOhYSEmJ/5jezNjJeL+bGJBNgPPbYYzp27Jh27dplf66PiIjgugIAAAA8RL5cisp0Xyxfvlxt2rRxWEvXfLx48WKnj5k6daquuuoquxRV8eLFVadOHQ0ZMkSpqam5WDkAAEDeN3vrUnWf8JJDqBEaUEAf3P6s7ml0My8+Q3FxcerXr59atmzpNNS4/vrrtXbtWnsj07muF7O/SJEiKlu2rH3LdQUAAAB4jnzZsWHuzDKBhAkoMjIfb9q0yeljduzYoTlz5ujuu++2a+5u27ZNDz/8sJKTkzVo0CCnj0lMTLRbxmGGhhlWeL6BhfmF+R6cPn2a7wXcFtco3B3XKPLaNWrOHbNkskb/OdFhf4XwkhrR6SmVDy/J9Q7bQX3fffdp69atmY4FBwfrrbfeUp8+feyNSxe6Xvh7FO6OaxTujmsU7o5rFO6Oa9TRxXwf8mWwcanfVDNf45NPPpGPj48aNWqk/fv3a+jQoecMNt544w298sormfYfPXpUCQkJyu/M9zQqKsr+4TW/eALuhmsU7o5rFHnpGk1ISdQ7f36jP/asctjfpFRNPXdtdwUl++jIkSM5XDHcmfn52fzsbZaQdfYLz5m5eWZZKXMjU1bw9yjcHdco3B3XKNwd1yjcHdeoo+joaGVVvgw2zPq5Jpw4fPiww37zcYkSJZw+pmTJkna2hnncGTVr1tShQ4fs0lb+/v6ZHvPcc8/ZAeUZOzZMq3vRokUVFham/M78wTUt/+b7wR9cuCOuUbg7rlHklWv04KljevqnD7X56G6H/T0ad9Bj13aTD9d3vrds2TI7S2PjRsdB8kZgYKBdItbMzLjYvwv5exTujmsU7o5rFO6OaxTujms088/2eTbYiI+PtwP+TJJlwoGKFSsqKCjoop7DhBCm42L27Nnq1KlT+kVkPn700UedPuaaa67RhAkT7HlnLrItW7bYwMNZqGEEBATY7Wzm8Vyo/zB/cPl+wJ1xjcLdcY3C06/RFfs2qf9Pw3Ui/p8lOw1/Hz+9dMMDuqX2dblYKdyRWdb11VdftctLOZttd+WVV2rcuHGqXr36JX8O/h6Fu+MahbvjGoW74xqFu+Ma/c/FfA884rsVGRmpkSNH2l9cChUqZAd3m6Chbt269mOz/7333tOJEyey/Jymk2LMmDEaP368vfProYceUmxsrL0TzOjevbvtuDjDHDd1PP744zbQmD59ur0zzAwTBwAAwMX7ce0cPfD9YIdQo2iBcH3a7SVCDWjlypVq0qSJ/Zn77FDD3Fhkwo6FCxdeVqgBAAAAwDO5fcfGiy++aEMLEzqYtcbOZoZ3L1261LanDxw40AYP5q6uC7njjjvsrIuXXnrJLifVoEED/fbbb+kDxffs2eOQEJklpGbMmKEnnnhC9erVU+nSpe3neuaZZ7L5KwYAAMjbUtJSNWzel5qw4jeH/bWLV9K7nZ5S8dDCLqsNrmd+vjdhxmuvvaaUlJRMxxs3bmxvTqpVq5ZL6gMAAADgel6nnaUFbsSEC2Y5p7Zt26pDhw5q2rSpypcvb5ehMjMrdu/ebYONn3/+WTNnzrTzLpy1qbsDU2/BggXTl9HK78yyXmYIqBnKTqsV3BHXKNwd1yg88RqNio/R0z+P1F+71zqce3PNazTohv8p0M/5Ep/IH9auXasePXrYbo2zmXl35qakZ599Vr6+2XN/Fn+Pwt1xjcLdcY3C3XGNwt1xjV766+du37Fhfnkxcy/MwO+zhYeH2810Wzz44IO2A+P99993SZ0AAAA4v+3H9unxKUO19+Th9H1e8lLf5t3Uq2lHu7Ys8ifTmfH222/r5Zdfth0bZ6tfv77t0jBvAQAAAMDtgw3zy01Wmenxr7zySo7WAwAAgIs3f/tyPTf9fcUmxafvK+AfpDfaP6oWlRu5tDa4lpl3Z7o0zNKyZ/Px8dELL7xgNzNXAwAAAAA8Itg4H/PLjxkYaIaIt2nTxtXlAAAA4Cxm1dPPl07VqIXf6bT+WwG1bKHiGtlpgCpHlHFpfXAds3zsu+++a2fqJSYmZjpeu3Zt26XRqBHBFwAAAAAPDja6d++ur7/+WvPnz7e/JLdq1cquQ2aMGTNG9913n6tLBAAAwL8SkpP01qKvNHfXcof9zcrV0dBb+qlgUIjLaoNrbd26VT179tSff/6Z6ZhZW/iZZ57RoEGD7Kw9AAAAADibR00k+fvvvxUSEqJrrrnGBhzmLq/q1avbkIPZGgAAAO7jcHSk7v/+1Uyhxl0Nb9SHXZ4j1MinzE1JI0eOtLMynIUa5md7s3/IkCGEGgAAAADyRsfGvn37VLFiRTtYctWqVapVq5bWrVunypUra/v27a4uDwAAAJLWHtymJ6YM09HYE+n7fL199EKb3upcr5VLa4Pr7NixQ7169dKCBQsyHTM/3/fv31+DBw9WUFCQS+oDAAAA4Dk8Ktgwzqy/u2XLFrVr186+HxYWpsOHD7u4MgAAAPy84Q+9MuMTJaUmp+8LDwrT8FufUMMyNV1aG1zXpfHxxx9rwIABio2NzXS8SpUqGjdunO3KBgAAAIA8F2yYzow1a9aoZs2aioqKSh8keODAAZUqVcrV5QEAAORbqWlpeu+PbzRu2TSH/ZXCS2nU7c+oTKHiLqsNrrN792717t1bs2fPdnq8b9++dtmpAgUK5HptAAAAADyXR83YeOKJJ+zbzZs3Kzw8XPfee6/Wrl2ro0ePqkmTJq4uDwAAIF+KTozT45OHZgo1WldtqnfbPa5SYUVdVhtcw8zAGzt2rOrWres01KhQoYLmzp1r520QagAAAADI0x0b3bt3V4MGDbRt2zbbql68eHHb2j5z5kxVqlTJ1eUBAADkO7tPHLShxs7IAw77H7q6q+5vdquOHT3mstrgurl4DzzwgH777Tenx/v06aO3335boaGhuV4bAAAAgLzBo4INo169enY7o2TJknYDAABA7lq8a40GTBup6MT/5iYE+gbo9ZsfUZtqTe0NKMhfXRpffvmlXV7KLBt7trJly+rTTz9V27ZtXVIfAAAAgLzDo4IN88vx559/btvZzbBw88vTGV5eXudcuxcAAADZx/wMNmHFr3pn3pdKy/DzWKmwCI3oNEDVi5V3aX3IfYcOHdKDDz6oadMclyM747777tPw4cNVsGDBXK8NAAAAQN7jUcFG//79NWrUKPv+mVDDBBrmffMWAAAAOSspJVmvz/pUU9bNc9jfqExNvdPxCRUODnNZbch95ufw7777To888ogiIyMzHS9VqpTGjBmjm2++2SX1AQAAAMibPCrY+Oabb+wvT+YXpIoVK8rX16PKBwAA8GjHY0+q/0/DterAFof9t9drreda95KfDz+b5SdHjx7VQw89pEmTJjk9fu+999rh4OHh4bleGwAAAIC8zaN++0xNTVWZMmW0detWBQQEuLocAACAfGPj4Z16fMpQHY7+7658Hy9vPdOqp/6vQVu6Z/MZE2aYUMOEG2crVqyYPvnkE916660uqQ0AAABA3uctD9KtWzfFx8crOTnZ1aUAAADkGzM2LVbPbwY5hBoFA0M0uuvzuuOKGwg18pHjx4/rrrvuUpcuXZyGGubn9fXr1xNqAAAAAMhRHtWxERISolOnTqlBgwbq2LGjChUq5HD8pZdeclltAAAAeU3a6TR9uOgHjflrssP+ykXK6L3bBqhMoeIuqw25b+rUqXZA+OHDhzMdi4iI0IcffqiuXbu6pDYAAAAA+YtHBRtvv/22vSNwx44ddr3esxFsAAAAZI/YpHi98MsHmrvtb4f9LSs30pD2j6qAf5DLakPuOnHihPr166cvvvjC6fHbbrtNo0ePVvHiBF0AAAAAcodHBRvlypVjqQMAAIActu/kETtPY9uxvQ77H7jyNj18TVd5e3nUaqa4DL/++qvuv/9+HThwINMxMxT8/fff15133snP6AAAAABylUcFG7t27XJ1CQAAAHnasj3r9dS0EToZH52+L8DXT6/c+JBuqnG1S2tD7jHLv/bv31+ffvqp0+MdOnSwA8JLliyZ67UBAAAAgEcFGxkDjuXLl9v3GzVqpAoVKri6JAAAAI/3/aqZemvOOKWkpabvKxZSWCM7PaVaJSq5tDbkntmzZ+u+++7Tnj17Mh0rWLCgXRK2e/fudGkAAAAAcBmPCjbS0tLUp08fffbZZzp9+rTdZ36hMu3xZl1ffrkCAAC4eMmpKXp7znh9v3qmw/56Javq3U5PKqJAIZfVhtwTExOjp59+2v5c7Uy7du00duxYlSlTJtdrAwAAAACPDTaGDx9uf5nKyAQcY8aMUdWqVfXkk0+6rDYAAABPdCLulF166u+9Gxz2d6x9nV5se78CfP1dVhtyz/z589WrVy/t3Lkz07GQkBD7c7i5mYgbiQAAAAC4A4+a/Gg6NcwvU48//riWLFliN/O+CTfMMQAAAGTd1qN7dNdXLziEGt5eXnqy5b169caHCDXygbi4OPXr108tW7Z0Gmq0atVK69at0wMPPECoAQAAAMBteFTHxo4dO2xnxrvvvpu+r0mTJvrll1+0fft2l9YGAADgSeZsXabnf3lf8cmJ6ftCA4L1Voe+uqZiA5fWhtzx559/qmfPntq6dWumY8HBwRo6dKhdBtbb26PuhQIAAACQD3hUsBEYGKhjx44pOjpaoaGhdt+pU6fsvqCgIFeXBwAA4PbsMp5/TdYHi7532F8+vKTeu22AKhQu5bLakDsSEhI0cOBADRs2LH1uXUbNmzfX559/rsqVK7ukPgAAAADIU8FGs2bNNGvWLNWvX18333yz3We6NU6ePKm2bdu6ujwAAAC3ZrozXvpttH7f/JfD/qsr1LedGmGBBVxWG3LH0qVL1aNHD23atMnpTURvvPGG+vbtS5cGAAAAALfmUcGGubNs7ty52rVrl0aPHm33mbvM/Pz89NJLL7m6PAAAALd16NQxPT7lHW06ssthf/fG7dXvurvlwwvZeVpiYqJeffVVvfnmm0pLS8t0/Morr9S4ceNUvXp1l9QHAAAAABfDo36Dvfbaa/X777/b9nhzR5nZrrvuOrvv6quvdnV5AAAAbmnV/s2686sXHEINPx9fDb7xITsonFAjb1uxYoUaN26sIUOGZAo1/P399dZbb2nhwoWEGgAAAAA8hkd1bBgtW7bU/PnzXV0GAACAR5i8dq5emzlWKWmp6fsiChTSu7c+qXqlqrq0NuSs5ORkvf7663ZLSUnJdNyEHePHj1etWrVcUh8AAAAA5ItgY+fOndq7d6/95SsiIsIOPFywYIGduWGWqTJLUgEAAEA2yHh3/lf6avmvDvtrFa+kEZ2eVPHQIi6rDTlv7dq1dpbGypUrMx0zPzMPGjRIzzzzjHx9PerXAQAAAACwPOo3mf79+2vq1Klat26dZsyYoQEDBtj9P//8s5KSkuyawQAAAPndqYQYDZg2Un/tXuuw/6YaV+vldn0U6OfvstqQs0xnxttvv62XX37ZdmycrUGDBrZLo169ei6pDwAAAACyg0ctqLxq1SoVLVpUNWvW1PTp0+3dZv/73//k5eWlSZMmubo8AAAAl9txfL/u/upFh1DDS17q2/xOvdH+MUKNPGzjxo127twLL7yQKdQwnRmmS2PJkiWEGgAAAAA8nkd1bBw6dCh9DWDTtdGoUSONHj1aixYt0vbt211dHgAAgEv9sWOlnv35PcUkxafvC/YL1BsdHlPLyo1cWhtyTmpqqoYPH26XZk1MTMx0vHbt2vriiy/UsGFDl9QHAAAAAPm6Y6NAgQI6ePCg3bZt25YecqSlpSkgIMDV5QEAALjE6dOnNW7pND3249sOoUaZgsX05d2DCTXysC1btqh58+Z6+umnM4Ua3t7eeu6557R8+XJCDQAAAAB5ikd1bJgh4fPmzVOZMmXsx9dcc40NNcxA8UqVKrm6PAAAgFyXmJKkV38fo583/OGwv2m52hp6Sz8VCgp1WW3IOeZn4FGjRtngIj7+vzDrjOrVq9tZGs2aNXNJfQAAAACQkzyqY+P1119XeHi4vSvxqquu0l133WWDjujoaLueMAAAQH5yJCZS9337SqZQ484rbtSHtz9HqJFH7dixQ9dff7369euXKdQws+eefPJJrVy5klADAAAAQJ7lUR0bV155pY4ePaoTJ06ocOHCdl+rVq3scEQfHx9XlwcAAJBr1h3crn4/vaOjMSfS9/l6++j5Nvfp9nqtXVobcq5L46OPPrLLTsXGxmY6XqVKFY0bN852NQMAAABAXuYxwYYJLwIDAxUREWGHiGdEqAEAAPKT6RsW6uUZHyspNTl9X3hQqIbf2l8Ny9R0aW3IGbt371bv3r01e/Zsp8f79u2rIUOG2Jl0AAAAAJDXeUyw4efnp5IlS6pQoUK2xR4AACC/STVzFRZ+q8+XTnXYX61oOY3o9JRKFyzmstqQM8wSrJ9++qn69+9vl189W4UKFfT555+rZcuWLqkPAAAAAFzBo2ZsPP7449q8ebN+//13V5cCAACQq2IS4/T4lKGZQo3WVZtq/J2vEmrkQfv27dPNN9+sBx54wGmo0adPH61du5ZQAwAAAEC+4zEdG8Yvv/xil5266aabVL16dRUvXjy9e8O8PVdrPgAAgCfbc+KQHp88VDsi9zvs73PV7frf1bfL28uj7lVBFro0vvzyS7u8VFRUVKbjZcuWtV0cbdu2dUl9AAAAAOBqHhVszJ8/P/39TZs22e0MlqcCAAB50V+712rAtBE6lfDfsOhA3wC9dtNDalv9SpfWhuxnZsk9+OCDmjZtmtPj9913n4YPH66CBQvmem0AAAAA4C48Ktjo3r07AQYAAMg3d+1PWPmbhs39Uqmn09L3lwyN0IjbnlKNYhVcWh+y///3t99+q0cffVSRkZGZjpcqVUpjxoyxS1MBAAAAQH7nUcHGuHHjXF0CAABAjktKSdaQ2Z9p8tq5DvuvKF1dwzr2V5EC3K2flxw5ckQPP/ywJk2a5PT4vffeq5EjRyo8PDzXawMAAAAAd+RRwcYZc+fO1V9//WV/ubvrrrt08uRJO28jICDA1aUBAABcluOxUeo/dbhW7d/ssL9z3VZ6vs198vPxyB/fcA4TJ07UQw89pGPHjmU6VqxYMX3yySe69dZbXVIbAAAAALgrj/rNOD4+Xh07dtScOXPsx82aNbO/8HXt2lVDhgzRM8884+oSAQAALtmmI7vskPBD0cfT9/l4eWvA9d3V7Yp2LMmZhxw/ftwuO2WWn3KmW7duGjVqlCIiInK9NgAAAABwd97yIC+++KJmz55t1yA2m9G+fXv5+/tr+vTpri4PAADgkv2++S/1mDDIIdQICyyg0V2e050NbyTUyEOmTp2q2rVrOw01TJDx/fff65tvviHUAAAAAIC8EGyYX/KCgoK0atWq9H1m+any5ctry5YtLq0NAADgUqSdTtOHi37QgGkjlJCSmL6/UpEymnDP62pWvq5L60P2OXHihHr06GGXljp8+HCm4507d9b69ettNzIAAAAAII8sRWUGK9aqVUv16tVz2O/n52fnbAAAAHiSuKQEvfDrB5qzdZnD/usqNdQb7R9VSECwy2pD9vr11191//3368CBA5mOmblxH3zwgV1+is4cAAAAAMhjwUbJkiVtZ8b27dvT95nujY0bN6pcuXIurQ0AAOBi7I86oscnv6Otx/Y47O/d7FY9cs0d8vH2qMZanMOpU6fUv39/ffrpp06Pd+jQwQ4INz/nAgAAAACyxqN+YzZt+2aAeJ06dezdbCtXrlTTpk3tvA1zDAAAwBMs37tRd3/1gkOoEeDrpzfbP6a+ze8k1MgjZs2aZX9udRZqFCxYUOPGjbPzNgg1AAAAAODieNRvzYMHD1b9+vWVmJhowwzzNiUlRXXr1tUrr7zi6vIAAAAuaOLqWXrwh9d0Ij46fV+xkML6vNvLuqnmNS6tDdkjJiZGDz/8sNq2bau9e/dmOt6uXTutW7fOzttg6SkAAAAAyONLUYWFhWnp0qWaMGGCli37Zy3qJk2a6M4775S/v7+rywMAADin5NQUDZ37hb5b9bvD/rolq+jdW59U0ZBwl9WG7DN//nz16tVLO3fuzHQsJCREw4cPt7M2CDQAAAAAIJ8EG1FRUfatubvNbAAAAJ7gZHy0npo6Qsv2rnfYf0ut6zTwhvsV4MsNGp4uLi5Ozz33nN577z2nx1u1aqXPPvtM5cuXz/XaAAAAACCv8faUXxQ7deqkwoUL2+22226zszYAAADc3daje3XXVy84hBreXl7q3+IeDb7pIUKNPGDRokV2uVRnoUZwcLA++OADzZw5k1ADAAAAAPJTsPHuu+/awYpmrobZzPtmHwAAgDubt+1vdZ8wUPujjqTvC/EP0qjbnlaPJh1YjsjDJSQkaMCAAWrevLm2bduW6bjZv2bNGjtvw5uB8AAAAACQbTziN6xvvvnG/uLfrFkzu5lww+wDAABwR+ZnlbF/TVa/KcMUl5yQvr9ceAl9dfdrurbSFS6tD5fPzH274oor9M4779j/3xkFBgbam3DmzZunypUru6xGAAAAAMirPGLGxp49e1SyZEn9+eefSktLU7ly5bR7925XlwUAAJBJfHKiBv32kWZsXuyw/6oK9fR2h74KCwxxWW24fImJiXr11Vf15ptv2p9Lz3bVVVdp3LhxqlatmkvqAwAAAID8wCOCjZiYGNWpU8d2bfj4+Nhgw9wlBwAA4E4OnTqmfj8N08bDOx3239uovfq1uEu+3j4uqw2Xb8WKFerRo4fWrVuX6Zi/v79ee+019e/f3/68CgAAAADI58GGcfToUX3xxRfp7xtffvmlQ+t/9+7dXVYfAADI31Yf2KInpgzT8bio9H1+Pr56sc396lS3pUtrw+VJSkrSkCFD9PrrryslJSXT8caNG2v8+PGqVauWS+oDAAAAgPzGY4KNHTt2qFevXg77evbsmf6+6eYg2AAAAK7w07p5GjxzrJJT/3vRu0hwQQ2/tb8alK7u0tpweczwb9OlsWrVqkzH/Pz8NGjQID3zzDPy9fWYH6sBAAAAwON5zG9gZw9lBAAAcLWUtFS9O/9rfbX8F4f9NYtX1Ihbn1SJsAiX1YbLYzoz3nrrLb3yyitKTk7OdLxBgwa2S6NevXouqQ8AAAAA8jOPCDbmzp3r6hIAAAAcnEqI0dM/v6fFu9Y47G9X/Sq9cmMfBfkFuKw2XJ4NGzbYLo2///470zHTmfHCCy/o+eeft3M1AAAAAAC5zyOCjRYtWri6BAAAgHS7Ig+o7+Sh2n3ioMP+x669Q72bdbJLZMLzpKamavjw4Ro4cKASExMzHa9Tp47t0mjYsKFL6gMAAAAA/MNbHjBb42Ls3Lkzx2oBAABYuGOl7vn6RYdQI9gvUCM6PaX7r7yNUMNDbdmyRc2bN9fTTz+dKdTw9vbWc889Zzs4CDUAAAAAwPXcPtioXr26unTposmTJyshIcHpOUlJSZo6daq6du1qzwcAAMiJeV/jl/2sxya/rejEuPT9pQsW0xd3Ddb1VRq7tD5cmrS0NI0cOdLOzFi8eHGm4zVq1LD7hwwZooAAlhcDAAAAAHfg9ktRlS5dWj/++KMNNsyaxrVq1VKFChUUGhqqmJgY7d69W+vXr7dDHc0LDuXLl3d1yQAAII9JTEnSazPHaur6BQ77m5StrXc69lOhoFCX1YbL6wzu1auXFixw/P9qmM6b/v37a/DgwQoKCnJJfQAAAAAADw02tm7dqk8//VQffvih1q1bp9WrV9vtbHXr1tXDDz+s++67zyV1AgCAvOlozAn1/2m41hzc6rD/jgY3aMD13eXn4/Y/TsFJl8ZHH31kl52KjY3NdLxKlSoaN26crrnmGpfUBwAAAAA4P7f/TdzPz099+vSx24YNG7Rw4UIbdkRFRSksLExVq1a16yGbTg4AAIDstP7QdvWbMkxHYiLT9/l6++jZ1r3UtX4bl9aGS2O6fXv37q3Zs2c7Pd63b1+77FSBAgVyvTYAAAAAQB4JNjIy4QUBBgAAyA2/blykQTM+UmJKcvo+s+TUsI5PqHFZfh7xNGbJUtMFbJaXio6OznTcLHX6+eefq2XLli6pDwAAAACQR4MNAACAnJZ2Ok3vL/xOny75yWF/1YhyGtHpKZUpVMxlteHS7Nu3T/fff79mzJjh9LjpDB46dKhCQkJyvTYAAAAAwMUj2AAAAPhXTGKcnv/lA83fvtxhf6uqTfT6TY8o2D/QZbXh0ro0vvjiCz3++ON2GdOzlS1b1nZxtG3b1iX1AQAAAAAuDcEGAACApL0nD6nv5He04/g+h/3/u+p29bn6dnl7ebusNly8gwcP6n//+5+mTZvm9LiZszFs2DAVLFgw12sDAAAAAFwegg0AAJDvLdm9Vk9NG6FTCbHp+wJ9/TX4pod1Q/UrXVobLr5L49tvv9UjjzyiEydOZDpeqlQpjR07VjfddJNL6gMAAAAAXD6CDQAAkL9fBF85Q0PnfqHU02np+0uEFrHzNGoWr+jS+nBxjhw5oocffliTJk1yerx79+4aMWKEwsPDc702AAAAAEA+Dzbmzp2rv/76y/5Setddd+nkyZMqXry4AgICXF0aAADwEMmpKXpj9meatGaOw/4GpatreMcnVKRAIZfVhos3ceJEPfTQQzp27FimY+bnxI8//li33nqrS2oDAAAAAOTjYCM+Pl4dO3bUnDn/vADRrFkzFStWTF27dtWQIUP0zDPPuLpEAADgAY7HRunJqcO1cv9mh/231b1ez7e+T/6+fi6rDRfn+PHjevTRR+3yU85069ZNo0aNUkRERK7XBgAAAADIGR41BfPFF1/U7Nmz7bIRZjPat28vf39/TZ8+3dXlAQAAD7DpyC7d/dULDqGGj5e3nmnVU4NueJBQw4NMnTpVtWvXdhpqmCDj+++/1zfffEOoAQAAAAB5jEcFG+aX06CgIK1atSp9n1l+qnz58tqyZYtLawMAAO5v1pYl6jFhkA5G/7dcUVhgAX3Y5Tnd1fBGeXl5ubQ+ZI0ZCm7mZZilpQ4fPpzpeOfOnbV+/Xrb1QsAAAAAyHt8PW0gZK1atVSvXj2H/X5+fnbOBgAAgDNpp9P08Z+T9NFix6HSlQqX1ojbnlL58JIuqw0X59dff9X999+vAwcOZDpm5q998MEHdvkpQioAAAAAyLs8KtgoWbKk7czYvn17+j7TvbFx40aVK1fOpbUBAAD3FJeUoIG/fqhZW5c67G9e6Qq90f4xhQYEu6w2ZF1UVJSefPJJffrpp06Pd+jQQZ988on9eREAAAAAkLd51FJUZrkBM0C8Tp069i68lStXqmnTpnbehjkGAACQ0YGoo+r5zaBMoUavph01stMAQg0PMWvWLNWtW9dpqFGwYEGNGzfOztsg1AAAAACA/MGjgo3Bgwerfv36SkxMtGGGeZuSkmJ/0X3llVdcXR4AAHAjK/Zt1F1fvaDNR3en7/P38dOQmx9Vv+vuko+3R/0YlC/FxMTooYceUtu2bbV3795Mx9u1a6d169apR48eLD0FAAAAAPmIRy1FFRYWpqVLl2rChAlatmyZ3dekSRPdeeed8vf3d3V5AADATUxaM1tDZn2mlLTU9H1FQ8I14tanVKdkZZfWhqyZP3++evXqpZ07d2Y6FhISouHDh9tZGwQaAAAAAJD/eFSwcWZQuLkrz2wAAAAZJaem6J15X+rblTMc9tcpUVnvdnpSxUIKu6w2ZE1cXJyee+45vffee06Pt2rVSp999pnKly+f67UBAAAAANyDRwUb99133zmPBQUFqUGDBrr33nsVGBiYq3UBAADXOxkfrQHTRmjpnvUO+9vXulaDbnhQAb50d7q7RYsWqWfPntq2bVumY8HBwRo6dKj69Okjb5YRAwAAAIB8zaOCDTMY8kLLDYwYMUILFy5UeHh4rtUFAABca9uxveo35R3tPXk4fZ+XvOwsjR5NOrBckZuLj4/XwIED7fJSZo7a2Zo3b67PP/9clSuzjBgAAAAAwMOGh5crV87O0jC/8Jrgwmzm/YCAABUpUsS+v2nTJjtkHAAA5A/zti9X9wkvOYQaIf5BGtX5afVseguhhptbsmSJGjZsqGHDhmUKNUwX7rvvvqt58+YRagAAAAAAPDPYML/Y+vj4aNasWTp27JjdZs6caV+wGD16tP744w/5+vpq2rRpri4VAADkMPMi+KdLflK/ye8oNik+fX+5QiX05d2vqXmlK1xaH84vMTFRzz//vK6++mp7Y8rZrrrqKq1evVr9+vVj6SkAAAAAgOcuRfXss8+qQoUKdmjkGa1bt1bFihXtL8abN2+2SxWYpagAAEDelZCcpJdnfKRfN/3psP/K8nU19JbHFRYY4rLacGErVqxQjx49tG7dukzHTCeu6b7t37+/vaEFAAAAAACPDjb27NmjlJQUffTRR+ratavdN3nyZBto+Pn5pS9ZYH4hBgAAedPh6OPqN2WYNhze4bD/nkY36YkW98jXmxfD3VVSUpJef/11u6WmpmY63qRJEztTrVatWi6pDwAAAADgGTwq2Ljuuuvs0lOPPPKI3TIuRdGmTRulpaVp1apVqlSpkkvrBAAAOWPNga164qdhOhZ7Mn2fCTJebHu/bqt7vUtrw/mtWbPGdmmYn9XOZm5Qefnll/X000/bZUUBAAAAADgfj/rNcezYsbrtttvs8gUZNWrUSGPGjNHu3bttJ0ezZs1cViMAAMgZU9fN16szxyg5NSV9X+Hgghp+a39dUbq6S2vDuZlu2yFDhujVV19VcnJypuMNGjTQ+PHjVa9ePZfUBwAAAADwPB4VbJQtW1Z///235syZo/Xr19t9derU0fXXX+8wYBwAAOQdqWlpGrHga33x93SH/TWKVdCITk+pZFiEy2rD+W3YsEH33HOPHQJ+NtOZ8cILL9g5af7+/i6pDwAAAADgmTwq2DjDDA/POEAcAADkTacSYvXsz+9p0S7HF8ZvqH6lXr3xIQX5MVfLHZn5GcOHD9fAgQOVmJiY6bi5McV0aTRs2NAl9QEAAAAAPJtHBRtmhsbnn3+u2bNn6/Dhw3a2xhleXl52PwAAyBt2RR5Q38lDtfvEQYf9j157h+5v1sn+2w/3s2XLFvXs2VOLFy/OdMzb21vPPPOMBg0apIAAQikAAAAAQD4INvr3769Ro0bZ9zOGGgYvbgAAkHcs2rlaz/w8UtGJcen7THfGkJsfVauqTVxaG859A8p7772n5557TgkJCZmO16hRw3ZpNG3a1CX1AQAAAADyDo8KNr755hsbaJQqVUoVK1a0azMDAIC8w/w7/9XyXzR8/ldKy3ATQ6mwonrvtgGqWrScS+uDc9u3b1evXr30xx9/ZDpmbj4xN6cMHjxYQUFBLqkPAAAAAJC3+Hraes1lypTR1q1bWb4AAIA8JiklWYNnjtXU9fMd9jcuU1PvdHxC4cFhLqsN5+7S+OijjzRgwADFxf3XXXNGlSpVNGzYMHXo0MEuQwUAAAAAQHbwqN8wu3Xrpvj4eCUnJ7u6FAAAkI2OxZ7U/d+/minU+L/6bfVR1xcINdzQrl271LZtWz3yyCNOQ42+fftq5cqVLD0FAAAAAMjfHRshISE6deqUGjRooI4dO6pQoUIOx1966SWX1QYAAC7NhkM71O+nd3Q4OjJ9n6+3j55p1UP/1+AGl9YG58uFjR071i4vFRMTk+m4WS70888/V4sWLWxHh7NzAAAAAADIN8HG22+/bddp3rFjh0aOHJnpOMEGAACe5ddNf2rQb6OVmPJfN2ahoFC9c0s/NSlX26W1IbN9+/bp/vvv14wZM5we79Onj4YOHWpvRgEAAAAAIKd4VLBRrlw5G2wAAADPlnY6TR8s/F5jl0xx2F8loqxGdhqgMoWKuaw2OO/S+OKLL/T4448rKioq0/GyZcvq008/tUtTAQAAAACQ03w9bS1nAADg2WKT4vX89Pc1b/tyh/3XV2ms129+RAX8g1xWGzI7ePCg/ve//2natGlOj/fu3dsOCC9YsGCu1wYAAAAAyJ88KtgAAACebd/Jw+o7eai2H9/nsP+BK2/Tw9d0lbeXt8tqQ+YujW+++UaPPvqoTpw4kel4qVKl7KyNm266ySX1AQAAAADyL48LNkaPHq1vv/1WBw4cUGpqavp+s0TV9u3bXVobAAA4t6V71uupqe8qKuG/YdKBvv565cY+urHG1S6tDY6OHDmihx56SD/++KPT4927d9eIESMUHh6e67UBAAAAAOBRt0WageHmrsGFCxfaEMMsTZVxu1gffPCBKlSooMDAQDVr1kxLly7N0uNMsGKClE6dOl3CVwEAQP678/+7lb+rzw+vO4QaxUMLa9ydrxBquJmJEyeqdu3aTkON4sWLa8qUKRo/fjyhBgAAAADAZTwq2DDLHRjNmze3b4sWLar69eurcOHC6tGjx0U913fffaf+/ftr0KBBWrFihX2edu3a2TsUz8cEKE899VR6DQAA4NySU1P02qxPNWT2Z0o9nZa+v0GpavrmniGqWbyiS+vDf44fP65u3bqpa9euOnbsWKbj5tj69et16623uqQ+AAAAAAA8MtgwXRrFihXT3Llz7ceVK1fWX3/9JW9vb9WqVeuinmv48OF64IEH1KtXL/vYjz76SMHBwfrss8/O+Riz9NXdd9+tV155RZUqVbrsrwcAgLwsMu6U/vfD65q4epbD/lvrtNSY/xuoIgUKuaw2OPrpp59sl4a58eNsERER+uGHH+y8jSJFirikPgAAAAAAPHrGRunSpe0yUL6+vnaQZUBAgO3YMMtUmU6KrEhKStLy5cv13HPPpe8z4UibNm20ePHicz7u1VdftcFK79699ccff1zw8yQmJtrtjFOnTtm3aWlpdsvvzPfALE/C9wLuimsU7s6dr9EtR/eo30/v6OCp/+789/by0pMt7tGdV9xo/y13x7rzG/OzVL9+/fTVV185PX7bbbfpww8/tD//XMr/L3e+RgGDaxTujmsU7o5rFO6OaxTujmvU0cV8Hzwq2DB3DJ5ZGqFUqVLasmWLbrrpJvs2NDQ0y89jnsN0X5h1ojMyH2/atMnpY8xcj08//VSrVq3K8ud54403bHfH2Y4ePaqEhATld+ZCjYqKsn94TbAEuBuuUbg7d71GF+5ZraF/fq2ElKT0fSH+QXr+2h5qVKqG/XcQrjd79mx7U8ihQ4cyHStUqJCGDBmSPk/sQkt1eto1CpzBNQp3xzUKd8c1CnfHNQp3xzXqKDo6Wnky2KhTp45mzJihPXv26Oabb7bLR/3+++/2WOvWrXP0G3rvvfdqzJgxNlzJKtMRYuZ4ZOzYKFu2rJ0NEhYWlkPVetYfXHPHrvl+8AcX7ohrFO7O3a5R84PYJ39N1keLJzrsr1i4lN699UmVDy/pstrwH/NDswk0zrX8Zvv27fXxxx+rZMmSee4aBc7GNQp3xzUKd8c1CnfHNQp3xzXqKDAwUHky2DDBgum2CA8P1zvvvKPk5GQtWbJE9erVszMzssqEEz4+Pjp8+LDDfvNxiRIlnM72MEPDb7nllkxtMWZJrM2bN9t5H2czy2SZ7WzmIuVC/Yf5g8v3A+6MaxTuzl2u0bikBA367SP9vuUvh/3NK16hNzo8ptCAYJfVhv/MmjVL9913n/bu3ZvpWMGCBe3Snt27d7fXVV67RoFz4RqFu+MahbvjGoW74xqFu+Ma/c/FfA98PW2+htkyBh2Xwt/fX40aNbJLMJxZYsEEFebjRx99NNP5NWrU0Nq1ax32vfjii7aTw7wAYLowAADIr8wcjcenvKPNR3Y57O/Z5Bb1bX6nfPjhzOViYmI0YMAA2+3qTLt27TR27FiVKVMm12sDAAAAAOBieVSwYZw8eVJLly613RVmyYuMzB2GWWWWiOrRo4caN26spk2basSIEYqNjVWvXr3Sn8uEKGZOhmmBMctgnb32tHH2fgAA8pMV+zap/0/DdSL+VPo+fx8/DWr3oDrUau7S2vCPefPm2Z9vTPfp2cyMMtP12rt372zt0gAAAAAAICd5VLAxffp03X333U6HiJhfxi8m2Ljjjjvs8NKXXnrJDs1s0KCBfvvtt/SB4maOB+0/AACc249r5+j1mZ8qJS01fV/RAuF6t9OTqluyiktrg+wNG2be16hRo5web9WqlZ2zUb58+VyvDQAAAACAfBNsmEGXZgB3djHLTjlbeurM3Y3nM27cuGyrAwAAT2KCjGHzvtSEFb857K9dorIdEl48tLDLasM/Fi1apJ49e2rbtm2ZjgUHB2vo0KHq06cPN3EAAAAAADySRwUbu3fvtr+Mf/PNN6pVq5Yd3A0AAHJPVHyMnv55pP7a7Th76uaa12rQDQ8q0M/fZbVBio+P18CBA+3yUmcv2Wlcd911+vzzz1WpUiWX1AcAAAAAQHbwqGTgyiuv1IEDB3TLLbe4uhQAAPKd7cf26fEpQ7X35OH0fV7y0uPX3WkHhTOjwbWWLFliuzQ2bdqU6ZiZF2bmhvXt25cuDQAAAACAx3P7YMPMujjj6aef1u23327f3nPPPekDvM8oV66cCyoEACDvm799uZ6b/r5ik+LT9xXwD9Ib7R9Vi8qNXFpbfpeYmKhXXnlFb731ltLS0jIdv+qqq+wSmtWqVXNJfQAAAAAA5Ltgo2LFipn2DRs2zG4ZmbtEU1JScrEyAADyPrOc0edLp+q9P77Vaf23tFHZQsU1stMAVY4o49L68rsVK1aoR48eWrduXaZjAQEBGjx4sPr37y8fHx+X1AcAAAAAQL4MNpytDw0AAHJeQnKSXvn9Y/2ycZHD/mbl6mjoLf1UMCjEZbXld0lJSXr99dftlpqamul4kyZNbJeGmUkGAAAAAEBe4/bBxty5c11dAgAA+c7h6Eg9MeUdrT+8w2H/XQ1v1JMt75WvNx0ArrJmzRrbpbFq1apMx/z8/PTyyy/bZTt9fd3+xzwAAAAAAC6J2//G26JFC1eXAABAvrLm4Fb1nzJcR2NPpO8zQcYLbXqrc71WLq0tPzNLbpo5GmaeRnJycqbjDRo00Pjx41WvXj2X1AcAAAAAQG7xlgeYMmWKWrVqpa+++irTsc8++8weM+cAAIDLM239AvX+9lWHUCM8KExj/m8goYYLbdiwwQ4Bf/HFFzOFGqYzY9CgQVq6dCmhBgAAAAAgX3D7jg3j448/1oIFCzRhwoRMx9q1a6cHH3xQ/v7+6tSpk0vqAwDA06Wmpem9P77RuGXTHPZXL1ZBIzs9pZJhES6rLT8z8zOGDRumgQMH2rkaZ6tTp47t0mjYsKFL6gMAAAAAwBU8IthYu3atypQpoxIlSmQ6Vrp0aZUtW9auNw0AAC5edGKcnv35PS3c6TizoW21Znr1xocU7B/ostrys82bN6tnz57666+/Mh3z9vbWM888Yzs1AgICXFIfAAAAAACu4hHBxtGjR1WpUqVzHg8MDNSBAwdytSYAAPKC3ScOqu/kodoV6fjv6MPXdNWDV3aWl5eXy2rLr9JM98x77+m5555TQkJCpuM1atSwXRpNmzZ1SX0AAAAAALiaRwQbRYoU0fbt27V3717bnZHRnj177LGICJbIAADgYizetUYDpo1UdGJs+r4gvwC9fvMjal2VF81dwfxM06tXL/3xxx+ZjpmQ6cknn9Srr76qoKAgl9QHAAAAAIA78Ijh4WZYZkpKijp37uyw5JRZoqpLly52/WlzDgAAuLDTp0/r6+W/6OFJbziEGqXCIvTFXa8SarioS+PDDz+0w7+dhRpVqlSx+4cOHUqoAQAAAADI9zyiY+ORRx7R5MmTtWLFCl1xxRUKDQ21dy2eOnXKvjhj3n/00UddXSYAAG4vKSVZr8/6VFPWzXPY36hMTb3T8QkVDg5zWW351a5du9S7d2/NmTPH6fG+ffvqjTfeUHBwcK7XBgAAAACAO/KIjo1WrVpp4MCBNsQwmwk0oqKi7PvGSy+9pOuvv97VZQIA4NaOx57UA98PzhRqdKnfRh93fYFQI5eZn2PGjBmjunXrOg01KlasqHnz5mnkyJGEGgAAAAAAeFrHhvHKK6/o2muvtS8AbNiwwb4YULt2bT344INq06aNq8sDAMCtbTy8U49PGarD0ZHp+3y8vPVs6576vwY3uLS2/Gjfvn26//77NWPGDKfHH3roIb399tsKCQnJ9doAAAAAAHB3HhNsGG3btrUbAADIuhmbFuul30YrISUpfV/BwBC79FTTcrVdWlt+Y27MGD9+vPr162e7T89Wrlw5ffrpp9y0AQAAAABAXgk2AABA1qWdTtOHi37QmL8mO+yvXKSM3rttgMoUKu6y2vKjgwcP2k7Tn3/+2elxM2dj+PDhCgtjSTAAAAAAAM6HYAMAgDwoNileL/zygeZu+9thf8vKjTSk/aMq4B/kstryY5fGN998o0cffVQnTpzIdLxUqVIaO3asbrrpJpfUBwAAAACApyHYAAAgj9l38oidp7Ht2F6H/Q9ceZsevqarvL28XVZbfnPkyBH16dNHkyc7ds2c0b17d40YMULh4eG5XhsAAAAAAJ6KYAMAAA9w8NQxnYyPdtiXlpamyMhIHT8dK2/vf8KK9Ye2690FExSTGJd+XoCvn1658SHdVOPqXK87P5s4caIdAn7s2LFMx4oXL66PP/5Yt956q0tqAwAAAADAkxFsAADgAaFGx0+fUFJq8kU/tlhIYY3s9JRqlaiUI7Uhs+PHj+uRRx7Rd9995/R4t27d9P7776tIkSK5XhsAAAAAAHmB2wcbX3zxRZbPNcs5AACQ15hOjUsJNapElNXHXV9QRIFCOVIXMvvpp5/0v//9T4cPH850LCIiQqNHj1aXLl1cUhsAAAAAAHmF2wcbPXv2lJeX1wXPM+cQbAAA8J9BNzxIqJFLzFDwxx9/XF9++aXT4507d7ahRrFixXK9NgAAAAAA8hq3DzaM06dPZ8s5AADkJ34+HvHPvMf75Zdf9MADD+jAgQOZjpmh4B988IFdfiorN2oAAAAAAIALc/tXPMxgVAAAAHcTFRWl/v3767PPPnN6/JZbbrEDwkuWLJnrtQEAAAAAkJe5fbABAADgbmbOnKnevXtr7969mY4VLFhQI0eOtEtk0qUBAAAAAED287hg49dff9W3335rl3tITU1N329eOJg9e7ZLawMAILudSojRLxsXuroM/Cs6OloDBgywnRjOtGvXTmPHjlWZMmVyvTYAAAAAAPILjwo2vv76a6cDws18De6IBADkFebftdUHtmji6tn6fctiJaYku7okSJo3b5569eqlXbt2ZToWGhqq4cOH2y4OfiYBAAAAACBneVSwYZZ1MC/2VKlSRdu2bbMvIoSEhCghIUH169d3dXkAAFyW6MQ4Td/whw00th7b4+py8K/Y2Fg999xzGjVqlNPjrVq1snM2ypcvn+u1AQAAAACQH3lUsLFhwwYVLlxYa9euVVBQkGrXrq3p06ercuXK9g5KAAA8jQns1x/aoR9Wz9RvmxYrISXR1SUhg0WLFqlnz572hoqzBQcHa+jQoerTp4+8vb1dUh8AAAAAAPmRRwUbKSkpqlChggICAuTj42PvoAwPD1epUqX0yiuv6N5773V1iQAAZElsUrx+2bhIE1fP0qYjmZc2MrzkpWsq1lez8nU1bN6XuV5jfhYfH6+BAwfa5aVM+HS26667Tp9//rkqVarkkvoAAAAAAMjPPCrYMN0aJ06csO8XK1ZM69ev10MPPaRNmzbZDg4AANzdxsM7NXHNbP2yYaHikhOcnlMkuKBuq3u9OtdrpdIFi9nHIPcsWbLEdmmYny/OZn7eeOONN/TYY4/RpQEAAAAAgIt4VLBRs2ZNLViwQEePHtX111+vCRMm6JNPPrF3UjZr1szV5QEA4FRcUoJmbF6sH1bP0vpD28953pXl66pr/TZqUbmR/Hz++ye6UFCo/H38lJSa9SHi5nzzOGRdYmKiXn75Zb399ttKS0vLdPyqq67SuHHjVK1aNZfUBwAAAAAAPDDYMMtB7Nq1ywYZ5v3Dhw/buyrr1aunjz76yNXlAQDgYOvRPTbMMAPBY5LinZ4THhSmW+u00O31WqtceAmn55QMi9DU3u/qZHy0w37z4ntkZKTtaDy7e8CEGuZxyJrly5erR48ethv0bGYJzMGDB6t///52KUwAAAAAAOBaHhVs1K9f325nzJw506X1AABwtoTkJM3c8pednbHqwJZznte4bC11qd9Gras0kb+v3wWf14QUZwcVJtg44lXALs/IskiXJikpSa+//rrdUlNTMx1v0qSJ7dKoVauWS+oDAAAAAAAeHmyYZajOxwzyBADAFXYc32/DjGkbFuhUQqzTcwoGhuiW2tepS73WqlikdK7XCEerV6+2szRWrVqV6Zifn59dlurpp5+Wr69H/bgEAAAAAECe51G/qbds2VJeXl5Oj5n9KSkpuV4TACD/SkpJ1qytSzRx9Wwt37fxnOddUbq67c5oW62ZAnz9c7VGZGZ+XnjzzTf16quvKjk589ySBg0aaPz48XapSwAAAAAA4H48KtgwzHwNAABcafeJg5q0eramrp+vE2fNvTgjNCBYHWpdpy71W6tKRNlcrxHObdiwwc7S+PvvvzMdM50ZL7zwgt1MxwYAAAAAAHBPHhVs7Ny50+HjqKgoff/993rrrbc0YcIEl9UFAMj7klNTNGfrMk1cM0tL92QeMH1G3ZJV1LV+G91Q/SoF+QXkao04NzM/Y9iwYRo4cKCdq3G2OnXq2C6Nhg0buqQ+AAAAAACQR4ON8uXLZ9pnlomYP3++Ro0apa5du7qkLgBA3rXv5GFNWjNHU9bNU2RclNNzCvgHqX3Na+1yU9WLZf63Cq61efNmO0vjr7/+ynTMDF1/9tln9dJLLykggCAKAAAAAABP4FHBhrNlqbZs2aLt27fr5MmTri4HAJCHujMW7FhhZ2cs3rVGp+V8GcRaxSvZpaZuqnGNgv0Dc71OnF9aWppGjhyp559/XgkJCZmO16hRw3ZpNG3a1CX1AQAAAACAfBBs+Pj4nPNY9erVc7UWAEDec/DUMf24Zo4mr52ro7EnnJ5jlpe6ueY16lKvjWqVqJTrNSJrzE0PvXr10h9//JHpmJeXl5588kkNHjxYgYEEUgAAAAAAeBrfvDA4PCQkRO+8806u1wMA8HypaWn6Y+dKTVw9S4t2rlLaOf6tqV60vO3OuLnmtQoJCM71OpH1Lo3Ro0fr6aefVlxcXKbjVapU0bhx43TNNde4pD4AAAAAAJDPgo3PP/880x2XxYoVU7NmzRQeHu6yugAAnudwdKTtzJi8do4ORR93ek6gr78dAm6GgZuh4ObfHbivXbt2qXfv3pozZ47T43379tUbb7yh4GCCKQAAAAAAPJlHBRs9evRwdQkAAA+WdjrNzsz4YfUsLdi+Qqmn05yeV6lIGdudcUut5goLDMn1OnHxHZ1jxoyxy0vFxMRkOl6xYkV7c0SLFi1cUh8AAAAAAMjHwYZhhoQvXbpUhw8fzrQ0Vffu3V1WFwDAfR2LPakpa+dq0po5OnDqqNNz/H381LZ6Mzs744rS1enO8BD79u3T/fffrxkzZjg9/tBDD+ntt9+2y1YCAAAAAIC8waOCjenTp+vuu+9WdHR0pmPmBSiCDQBAxu6MpXvW29kZc7f9rZS0VKfnlQ8vqS7126hj7etUKCg01+t8e854zdm6TMfjouTv46syhYrrroY36tY6LR3O+2Dh9/rkrx/t+72adlS/6+6y7y87sFFfzHhXB6KOKDE1WRHBhXRtpQZ6osXdKuAfZM+56ZNHdeDUMb3Qprf+r0FbJaem6Kmp72re9uUqFVZUY/5voMoUKiZPYm5uGD9+vPr166eoqKhMx8uVK6dPP/1Ubdq0cUl9AAAAAAAg53hUsPHUU0/p1KlTri4DAODGIuNO6ad18zRpzWztPXnY6Tm+3j5qXbWpnZ3RuGwtl3Zn7Is6ojolKys8KExbj+3Ryv2b9dJvH6li4dKqV6qqPWfZnvUau2SyrfvsgMZ0oxQKDLEzQOKTEzR761K71JbxYtv7M32+hOQkPfHTMP25a7XKFSqhMf/3okqERciTHDx4UA8++KB+/vlnp8dNB8ewYcMUFhaW67UBAAAAAICc51HBxu7du+3Az2+++Ua1atWSr69HlQ8AyMG79//eu0ET18y2L+ybjgRnyhYqrtvrtVbH2i1UpEBBuYP3bhvg8HVcO+o+xSTFa1/UYRtsnIg7ped/+UBXV6iv+ORELd+30eHxN1W9Sj2uuVXe3t724zdmf65vV85wGuqYxz/641tatne9KhUurU/+70UVDQmXpzDfH/MzwKOPPqoTJ05kOl6qVCmNHTtWN910k0vqAwAAAAAAucOjkoHGjRvryJEjuuWWW1xdCgDADUTFx2jq+vk20NgVecDpOabLoWXlRuraoK2alqstb69/AgB38svGhVp9YKs2H9llQ40axSroukoN7THTvXFapzX4poc1YOq7Th+/58Qhfb96pk7En7LLWgX5Bah74/aZzvtw0Q9KSElU1Yhy+rjrC24T7mSF+fe/T58+mjx5stPjZjnKESNGKDzcc4IaAAAAAACQT5ai6tq1q55++mndc889KlSoUKb1tAEAeZu5a3/VgS36YfVMzdy8REmpyU7PKxUWYbszzKwKd+9KWLxrjaauX2Df9/PxVYvKjRToF6Cvlv+ihTtX6pOuL6pw8LmXVToSE6mvV/ya/nGzcnXs7JCzmVDDuK1uS48KNX744Qc9/PDDOnbsWKZjxYsX1yeffKKOHTu6pDYAAAAAAJD7vE6bV4g8hFlm41zroJv9KSnOlx5xF2Y+SMGCBe2QU9b9ltLS0uwduMWKFUtfQgVwJ1yj7uVUQqx+3vCHHQa+/fg+p+d4e3nZUKBLvda6qkJ9+XjQ/zezfNa2Y3vVb8o7OhR9XE+1vNxzeXUAAQAASURBVFfTNvyhg6eOqn6pavacNQe2Kiohxi6p1bbalXrs2jvSr1Hz76AZQD5ywQQbklQrWk4/9HjbYXh4hcKlbGeLl7z00g0PqHO9VnJnJsgwy0599913To9369ZN77//vooUKZLrtSFr+HsU7o5rFO6OaxTujmsU7o5rFO6Oa/TSXz/3qI4Nw4NyGABANvydv/bQNk1cPVszNv2phJQkp+cVDy2sznVb6ba616t4qOe8yJ2YkmSXxjJdGmarWbyiDR9MsLHl6B7zDbCBzh87Vjo8zszPWHNgi30/NinBvjXBRkSBQrqqQj0bbJiQxHz/Mt4QcHfDm3Qo+pg+XfKTXv19jA1T7rjiBrmjKVOm6H//+5/9Ae9sERERGj16tLp06eKS2gAAAAAAgGt5VLAxd+5cV5cAAMgFMYlxdu6ECTQ2H93t9BzTdXBtxQbq0qCNfWtmaXianZEH9OD3r6lx2VoqElxQOyL32yHoxtUV6mnwTQ85nN/721f0976N6tW0o/pdd5e9s+OZWR8oLDjEBiLxyQmav32FPbdZ+bpOuxz7Nr9Tvt6++njxJA2Z/ZlS0lJ0d6Ob5S7MUPC+ffvqq6++cnq8c+fONtQwd7MAAAAAAID8yaOCjRYtWri6BABADtpwaIcmrpmlXzYuUnzyP/Mgzla0QLg61W1pOzRKFSwqTxYeFGq7NFbu36xTCTEKDSigxmVq2kHnN9a4OkvP0aBEVf21f73WH9puuz9M90rrqu3Us+m5Z048fE1X2yHy/sLv9PbcL5ScmqqeTW+Rq/3yyy+6//77dfDgwUzHzFDwDz74wC4/da5lKQEAAAAAQP7gUTM2jC1btmjevHk6fPhwpmWpXnrpJbkzZmw4Yg05uDuu0dwRl5Sg3zb9qR9Wz9KGwzvOeZ5ZYqlLvTZqUbmhfVEeeecaNf8u9u/fX5999pnT47fccos+/vhjlSyZeSA63FteuUaRd3GNwt1xjcLdcY3C3XGNwt1xjeaTGRvmBQ+z3rb5H+6MuwcbAID/bD6y2w4Cn75xoWKT4p2eUzi4oDrVaanb67VSmULFc71G5LyZM2eqd+/e2rt3b6Zj5oeZ9957T/feey9dGgAAAAAAwDODjddee02pqamuLgMAcInM8lK/b15sZ2esObj1nOc1LVfbdme0qtrEY7szDh06pJMnT+bo5zBB//HjxxUZGZmjd3YUKlRIJUqUyNbnjI6O1oABA2wnhjM33nijxowZozJlymTr5wUAAAAAAJ7Po14tMstPmbs3FyxYoFq1asnHx/MGxQJAfrT92D47O2Pa+j8UnRjr9JxCQaHqWPs63V6vtR2E7clMqNGlSxclJCTk+OcygX9O/3sYGBioiRMnZlu4MXfuXN13333atWtXpmOhoaEaPny47eKgSwMAAAAAAHh8sHH99ddr48aNqlu3rqtLAQBcQGJKkmZuWWKXmzLDsc+lYZka6lq/jVpXbaoAX3/lBaZTw4QagwcPVsWKFXPs85hZUykpKfL19c2xEGDnzp0aOHCg/ZouN9iIjY3Vc889p1GjRjk93qpVK7vsZPny5S/r8wAAAAAAgLzNo4KNrl276sEHH1S3bt1011132aUxMrruuutcVhsA4B+7Ig/Ypaamrp+vqIQYp+eEBhRI786oHJF3lxoyoUaNGjU8OtjILgsXLlSvXr20bdu2TMeCg4M1dOhQ9enTh2FpAAAAAAAgbwUb5gUR88LNDz/8YLeMzH7z4g4AIPclp6Zo9talNtBYtnf9Oc+rX6qqnZ1xQ/WrFOiXN7ozcH7x8fF68cUX9e6779og5mzmpoTPP/9clSpVckl9AAAAAADA83hUsGE4e1HkfPsBADln78lDNsz4ad18nYg/5fScEP8gta/V3C43VbVouVyvEa6zZMkS9ejRQ5s3Z16KLCgoSG+88YYee+wxujQAAAAAAEDeDTbMOt8AANd3Z8zfvlw/rJ6lv3avPed5tUtUVpf6rXVj9asV7B+YqzXCtRITE/Xyyy/r7bffVlpaWqbjV111lcaNG6dq1aq5pD4AAAAAAODZPOoWSTNM1Nlm1hb/+uuvXV0eAORpB6KO6v2F3+nGTx7Vk1PfdRpqBPsF2rkZ3977hibc87o6121FqOHEsGHD1LJlS5UsWVIBAQH23zLT2bBjx470c+677z5VrVpVISEhKlCggCpXrqy+ffsqMjLS4bnMzAozg6pw4cK2C6Jhw4b67rvvHM4xn8ss2VihQoX0feZ5GjdubPebxy5btixbvrbly5erUaNGevPNNzOFGuZrNbM0/vjjD0INAAAAAACQPzo2MkpISNCPP/5o7/icO3euXYrq+eefd3VZAJCnpKSl6o8dKzVx9Swt2rlap+V82b/qxSrYpaZurnmNCvgH5XqdnmbUqFHas2ePqlevbsMI05H4xRdf6Pfff7fLNoWFhemnn35SwYIF7fDxo0eP2tDDPG7Lli367bff7PMcPHjQhhZHjhyxjzFBycqVK9WtWzfFxsbacMQZ83xt2rTRmjVrVKRIEc2cOVNXXHHFZX1NSUlJev311+2Wmpqa6XiTJk3sv9m1atW6rM8DAAAAAADgccHGn3/+aV8YMcPDT536Zz13E2qYO04BANnjcPRx/bhmjn5cO1dHYhw7BM4I9PXXjTWuVpf6bVSnRGX+Hr4IDzzwgO69916VK/fPzJEnnnhCI0aM0KFDhzR79mzddttt2r9/vwID/+t2ad68uRYuXKhFixal7zMzKkyoERoaqo0bN6pUqVLq0qWLJk2apGeeeUb33HOP/P0dh7Sbz9GqVSt7frFixTRr1izVrVv3sr6e1atXq2fPnlq1alWmY35+fnZZqqefftp2WAIAAAAAAFwuj3iFwby4M378eLuZJTcyDgs3L6SZF4M6d+7s4ioBwLOlpqXpz12rbXfGgh0rlPbv37NnqxJRVl3qtbYDwcMCC+R6nXnBCy+84PCxCS3Mv2VnlmsyTKgxcOBA28Vx+PBh7d692+6/9tpr0x93pnPDzKwwoYZh/j00wcaxY8f0999/6+qrr04/PyYmRtddd522bt1quztMiFKzZs1L/jqSk5P11ltv6dVXX7Xvn810gZh/uy83OAEAAAAAAPC4YMOsPW6CjDNhRr169eydruYO0Li4OLvmOADg0hyNOaHJa+faDo2D0cecnuPv46cbql9pl5uqX6oa3RnZyCzb9Mknn9j3K1WqpNatW6cfMwHE0qVL0z82y0d9//336R/v3bvXvjWdF2cUL148/X2z3FXGYOP48eN2Cw4O1rx58y5rzsX69evtXBAzU+NspjPDhDdmMx0bAAAAAAAA+W54+Jnho2Z9brPMhdmefPJJlrQAgEuUdvqf7oz+Pw23w8A/WPS901CjYuFSGnB9d83qM1qv3/yIGpSuTqiRjcwcDLPs1IwZM1SiRAlNmzYtvWPD+Pbbb+3sCjM3o06dOnbZqEceeeS8z3nmJoDzMTcFvP/++5ccxJguDTOk3FmoYepcsmSJvfmAUAMAAAAAAOQEj0oGzJIaN910k+6++267bjgA4OIcj43ST+vmadKa2doXdcTpOX4+vmpTtam61m+rhmVqEGTkEDProkOHDjYcMJ0Tv/76q+3YOJsJBxo0aGDncjz++OP68ssv9eKLL9rHlC1b1i7RaOZsnJHx/TMzPM4oXbq0rrnmGtv1YQaRm+WjPvzwwyz/PzaDzc0sjb/++ivTMW9vbz377LN66aWXHMIZAAAAAACAfBlsfPbZZ3aN7gULFujgwYMaNmyY3c4MDd+0aZNq1Kjh6jIBwC2ZvyuX7d2gH1bP1Jyty5SSlur0vHKFSqhL/da6pXYLFQ4Oy/U68xOzjFP79u3t3AwzX2PKlCkqXLhw+vFly5bZbo6WLVvaj03XhunWOMMcM9q1a2eDjcWLF+vAgQN2zsaPP/5oj0VERKhx48YOn9d0Ok6YMMGGJV9//bU++ugjG26YpbBMMHG+a2jcuHEaOXKkEhISMh03/wabf6ebNm2aDd8dAAAAAACAPBBsmLtDzbZr1y77woq5W3Xnzp3px2vXrq3q1atrw4YNLq0TANzJibhTmrp+ge3O2H3ioNNzfL191KpKExtoNClXW95eHrFCocczA77PDAOPjo7WzTffnH7s/vvvtwFEr169FB4ebrsuzCyNyMhIe9x0b9SvX9++bzokvvvuOzso3AwBL1KkSPq/j0OGDJG/v3+mz+3j46MvvvjChhvm39RPP/1UKSkp9iYCZ+GGmdOxZcsWrVixItMxc3OBWRpy8ODBdtg5AAAAAABAbvCIYOOMChUq2DW7zTZ//nx9/vnnmjRpkr1z1SyPAQD5nbmzfuX+Tfph9SzN3LJEyakpTs8rFVbUhhm31mmpiAKFcr3O/C4xMTH9fTM3KqMbb7zRLlFl3q5evdqG9iaMMMGF6fJ4/vnn0wMIs7SUGQJuln+aPXu27dowwceAAQN01113nfPzm8ebIMOEG2PGjLHdFibcMG/N5zoz32r06NH2ueLj4zM9R9WqVW0wknE4OQAAAAAAQG7wqGAjoxYtWtjtgw8+0A8//GBfjAGA/OpUQoymrV+giatna0fkfqfn+Hh5q0WVRupSr42uqlCX7gwXMh2IF2JmbmSFmbUxceLE887JMOHH2cz5Zgkqszmrr3fv3pozZ47T5zOzPkxHSHBwcJZqBAAAAAAAyE4eG2ycUaBAgfSlqgAgv3VnrDm4VRNXz9KMzYuVmJLs9LwSoUXUuV4r3Vb3ehUL+W+OA+DsmjIdHGZ5qZiYmEzHK1asaLslzY0FAAAAAAAAruLxwQYA5DfRiXH6ZcNCu9zU1mN7nJ7j7eWlayteYZebMm99zjMYGjDMHA8z3+P33393erxbt2429AgJCcn12gAAAAAAADIi2AAAD7H+0HYbZvy68U8lpPw3oyGjoiHh6lz3n+6MkmERuV4jPLNLwyznaJaXOnXqVKbjJUuWtGHGoEGDCDUAAAAAAIBbINgAADcWmxSvXzcu0sQ1s7Xx8E6n53jJS1dXqKcu9dvousoN5ev9z/Bn4ELMsPH//e9/+vnnn50eNx0c5nifPn1yvTYAAAAAAIBzIdgAADe06cguOztj+oaFiktOcHpOkeCC6lS3pTrXba0yhYrleo3w7C6NCRMm6LHHHtOJEycyHS9VqpTGjh2rm266SZs2bXJJjQAAAAAAAOdCsAEAbiIuKcEOATeBxrpD28953pXl66pLvdZqWaWx/Hz4axwX5/Dhw3rooYc0efJkp8e7d++uESNGKDw8PNdrAwAAAAAAyApeEQMAF9t6dK8mrjHdGX/YweDOhAeF6tY6LXV7vdYqF14i12tE3vDDDz/YUOP48eOZjhUvXlyffPKJOnbs6JLaAAAAAAAAsopgAwBcICE5STO3/GW7M1Yd2HLO8xqXqWlnZ7Su2lT+vn65WiMu386dzueiZOeSUikpKfL19ZWXl9c5zzPLTQ0ePFi//vqr0+Pt27fXCy+8YLs0zl56Kqe/BgAAAAAAgItFsAEAuWjn8f22O2Pq+gU6lRDr9JywwALqWLuFXW6qYpHSuV4jLl+hQoUUGBiogQMH5vjnSk1NlY/PuQfGnzx5Urt377YByNlMIFKuXDkdOnTIzts4F/O1mK8JAAAAAADAHRBsAEAOS0pJ1uytS213xt/7Np7zvAalq9swo221KxXo55+rNSJ7lShRQhMnTrShQk52a0RGRmr//v0qXbq0Chcu7NC1ERUVpddff13Lly93+vi2bdtq0KBBKlKkyAU/lwk1zNcEAAAAAADgDgg2ACCH7DlxSJPWzNZP6+bpRHy003NCA4LVvlZzdanXRlWLls31GpFzTBCQE2GACUvGjx+vUaNGafv2/4bMV65c2XZd9OjRQ4sWLdIDDzyggwcPZnq8WW7qgw8+ULdu3c67fBUAAAAAAIC7ItgAgGyUnJqiudv+tt0ZS/asO+d5dUtWsWFGuxpXKcgvIFdrhOeaMWOGbr/9dsXFZR4yv2PHDj3xxBN66qmnnC47Zdxyyy36+OOPVbJkyVyoFgAAAAAAIGcQbABANth38oh+XDtbU9bO0/G4KKfnBPsFqn2ta+0w8BrFKuR6jfD8UMMM+TZLUJntbGf2OQs1ChYsqPfee0/33nsvXRoAAAAAAMDjEWwAwCVKSUvVgu0r9MPqWVq8a41OK/OLzUbN4hXt7Iybal6jAv5BuV4nPJ9Zfsp0apjwIi0t7aIee+ONN2rMmDEqU6ZMjtUHAAAAAACQmwg2AOAiHTx1TD+umaPJ6+bqaMwJp+cE+gbopppXq2v9NqpdonKu14i8xczUMMtPOevUOB8zR2PChAl0aQAAAAAAgDyFYAMAsiA1LU0Ld67UxNWz7du0c7zAXK1oOTs74+Za19rB4MDlMmGGGRR+sUyYsWzZshypCQAAAAAAwJUINgDgPMy8jCl/LdTktXN1KPq403MCfP3UrvpV6lq/rR0Kzt3xyI4wY/v27fr777+1YMEC+/6lPkdkZKSKFCmSI3UCAAAAAAC4AsEGAJwl7XSaFu9aqx9WzdT8HSvsx85UKlxaXRq00S21missMCTX60TeYAKIPXv22BAj42bmamSH6Ohogg0AAAAAAJCnEGwAwL+Ox57UlHXzNGnNHO2POuL0HH8fP7Wp1kxd67fWFaVr0J2Bi3bgwIFMIcbRo0dz7POFhobm2HMDAAAAAAC4AsEGgHzNdGMs27NeP6yepbnb/lZKWqrT88qHl1SX+m3UsfZ1KhTEC8XIGhNYnB1imGAjN5jQrVKlSipcuHCufD4AAAAAAIDcQrABIF+KjDulqevma9Ka2dpz8pDTc3y9fXRN2Xq6u8nNalq+Dt0ZOK8TJ05o+fLlDiHG7t27L+s5ixYtqiZNmig1NVW///67XbbqYvTt25frFgAAAAAA5DkEGwDyDfOi8PJ9GzVx9SzN2rpUyakpTs8rU7CYbq/X2nZnpMQkqlixYrw4jExzK1asWOEQYmzbtu2ynjM8PFyNGze2mwkzzNsyZcrYa8/M2zDvx8fHKy3N+cyXjLy9vRUUFKTu3btfVk0AAAAAAADuiGADQJ4XFR+jqev/6c7YGel8GSAfL2+1rNJYXeq31pXl68rby9u+gHwkxvmsDeQfcXFxWrVqlUOIsWnTpovunjh77kWjRo0cQoyKFSueM0ArVKiQJk2apPbt29vQ4nzhhjlunufHH3+0jwMAAAAAAMhrCDYA5EnmRedVB7bY7ozfN/+lpNRkp+eVCotQ53qt1alOSxUNCc/1OuFeEhMTtWbNGocQY/369XYpqEtlOicaNmzoEGJUrVrVBhAXo127dpo+fbpuv/12G7YYGcOVM6GI+Xwm1LjhhhsuuWYAAAAAAAB3RrABIE85lRCr6Rv+0MQ1s7Xt2F6n53h7eem6Sg3tMPCrK9SXz0W+wIy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+      "text/plain": [
+       "<Figure size 1600x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create visualisation\n",
+    "if len(combined_df) > 0:\n",
+    "    plt.figure(figsize=(16, 12))\n",
+    "    \n",
+    "    # Create subplot layout\n",
+    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 12), height_ratios=[2, 1])\n",
+    "    \n",
+    "    # Top plot: Population change vs migration components\n",
+    "    ax1.plot(combined_df['year'], combined_df['annual_population_change'], \n",
+    "             marker='o', linewidth=3, markersize=10, color='black', \n",
+    "             label='Total Population Change', zorder=5)\n",
+    "    \n",
+    "    # Migration components with different colours\n",
+    "    if 'Non-EU+' in combined_df.columns:\n",
+    "        ax1.plot(combined_df['year'], combined_df['Non-EU+'], \n",
+    "                 marker='s', linewidth=2.5, markersize=8, color='#2E8B57', \n",
+    "                 label='Non-EU Migration', zorder=4)\n",
+    "    \n",
+    "    if 'EU+' in combined_df.columns:\n",
+    "        ax1.plot(combined_df['year'], combined_df['EU+'], \n",
+    "                 marker='^', linewidth=2.5, markersize=8, color='#4169E1', \n",
+    "                 label='EU Migration', zorder=4)\n",
+    "    \n",
+    "    if 'British' in combined_df.columns:\n",
+    "        ax1.plot(combined_df['year'], combined_df['British'], \n",
+    "                 marker='d', linewidth=2.5, markersize=8, color='#DC143C', \n",
+    "                 label='British Migration', zorder=4)\n",
+    "    \n",
+    "    # Add value labels for population change\n",
+    "    for _, row in combined_df.iterrows():\n",
+    "        ax1.text(row['year'], row['annual_population_change'] + 30000,\n",
+    "                 f\"{row['annual_population_change']/1000:.0f}K\",\n",
+    "                 ha='center', fontsize=10, fontweight='bold',\n",
+    "                 bbox=dict(facecolor='white', alpha=0.8, edgecolor='black'))\n",
+    "    \n",
+    "    # Add value labels for key migration components\n",
+    "    if 'Non-EU+' in combined_df.columns:\n",
+    "        for _, row in combined_df.iterrows():\n",
+    "            if row['Non-EU+'] > 200000:  # Label high Non-EU values\n",
+    "                ax1.text(row['year'], row['Non-EU+'] - 50000,\n",
+    "                         f\"{row['Non-EU+']/1000:.0f}K\",\n",
+    "                         ha='center', fontsize=9, color='#2E8B57', fontweight='bold')\n",
+    "    \n",
+    "    # Customise top plot\n",
+    "    ax1.set_title('England Population Change vs Migration Components (2020-2024)', \n",
+    "                  fontsize=16, fontweight='bold', pad=20)\n",
+    "    ax1.set_ylabel('Annual Change (Persons)', fontweight='bold')\n",
+    "    ax1.axhline(y=0, color='red', linestyle='--', alpha=0.7)\n",
+    "    ax1.grid(True, alpha=0.3)\n",
+    "    ax1.legend(loc='upper left', frameon=True, fancybox=True, shadow=True)\n",
+    "    ax1.set_ylim(bottom=-150000)\n",
+    "    \n",
+    "    # Bottom plot: Migration components stacked\n",
+    "    migration_cols = [col for col in ['Non-EU+', 'EU+', 'British'] if col in combined_df.columns]\n",
+    "    migration_components = combined_df[['year'] + migration_cols].set_index('year')\n",
+    "    \n",
+    "    if len(migration_cols) > 0:\n",
+    "        # Handle negative values for stacking\n",
+    "        colors = ['#2E8B57', '#4169E1', '#DC143C']\n",
+    "        labels = ['Non-EU Migration', 'EU Migration', 'British Migration']\n",
+    "        \n",
+    "        bottom_pos = np.zeros(len(migration_components))\n",
+    "        bottom_neg = np.zeros(len(migration_components))\n",
+    "        \n",
+    "        for i, col in enumerate(migration_cols):\n",
+    "            values = migration_components[col].values\n",
+    "            pos_values = np.maximum(values, 0)\n",
+    "            neg_values = np.minimum(values, 0)\n",
+    "            \n",
+    "            # Stack positive values\n",
+    "            ax2.bar(migration_components.index, pos_values, bottom=bottom_pos,\n",
+    "                   color=colors[i], alpha=0.7, label=f'{labels[i]} (Positive)')\n",
+    "            bottom_pos += pos_values\n",
+    "            \n",
+    "            # Stack negative values\n",
+    "            ax2.bar(migration_components.index, neg_values, bottom=bottom_neg,\n",
+    "                   color=colors[i], alpha=0.4, label=f'{labels[i]} (Negative)')\n",
+    "            bottom_neg += neg_values\n",
+    "    \n",
+    "    ax2.set_title('Migration Components Breakdown (Positive and Negative Flows)', fontsize=14, fontweight='bold')\n",
+    "    ax2.set_xlabel('Year', fontweight='bold')\n",
+    "    ax2.set_ylabel('Annual Migration (Persons)', fontweight='bold')\n",
+    "    ax2.grid(True, alpha=0.3)\n",
+    "    ax2.legend(loc='upper right', bbox_to_anchor=(1.0, 1.0))\n",
+    "    ax2.axhline(y=0, color='red', linestyle='--', alpha=0.7)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"Cannot create visualisation - no data available\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "a47e99aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DETAILED COMPARISON (2020-2024):\n",
+      "==========================================================================================\n",
+      "Year 2020:\n",
+      "  Population Change:        112,656\n",
+      "  Non-EU Migration:         110,500\n",
+      "  EU Migration:              66,500\n",
+      "  British Migration:        -74,750\n",
+      "  Migration Total (calc):    102,250\n",
+      "  Migration Total (data):    102,250\n",
+      "  Migration as % of Pop Change:   90.8%\n",
+      "------------------------------------------------------------\n",
+      "Year 2021:\n",
+      "  Population Change:        230,080\n",
+      "  Non-EU Migration:         342,750\n",
+      "  EU Migration:              17,750\n",
+      "  British Migration:        -26,500\n",
+      "  Migration Total (calc):    334,000\n",
+      "  Migration Total (data):    334,250\n",
+      "  Migration as % of Pop Change:  145.2%\n",
+      "------------------------------------------------------------\n",
+      "Year 2022:\n",
+      "  Population Change:        618,067\n",
+      "  Non-EU Migration:         815,250\n",
+      "  EU Migration:            -117,250\n",
+      "  British Migration:         12,750\n",
+      "  Migration Total (calc):    710,750\n",
+      "  Migration Total (data):    711,000\n",
+      "  Migration as % of Pop Change:  115.0%\n",
+      "------------------------------------------------------------\n",
+      "Year 2023:\n",
+      "  Population Change:        821,210\n",
+      "  Non-EU Migration:         955,000\n",
+      "  EU Migration:             -50,250\n",
+      "  British Migration:        -21,750\n",
+      "  Migration Total (calc):    883,000\n",
+      "  Migration Total (data):    883,000\n",
+      "  Migration as % of Pop Change:  107.5%\n",
+      "------------------------------------------------------------\n",
+      "Year 2024:\n",
+      "  Population Change:        706,881\n",
+      "  Non-EU Migration:         759,000\n",
+      "  EU Migration:             -90,500\n",
+      "  British Migration:        -19,000\n",
+      "  Migration Total (calc):    649,500\n",
+      "  Migration Total (data):    649,000\n",
+      "  Migration as % of Pop Change:   91.9%\n",
+      "------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create comparison table and Brexit impact analysis\n",
+    "if len(combined_df) > 0:\n",
+    "    comparison_df = combined_df.copy()\n",
+    "    \n",
+    "    # Calculate additional metrics\n",
+    "    available_migration_cols = [col for col in ['Non-EU+', 'EU+', 'British'] if col in combined_df.columns]\n",
+    "    comparison_df['Migration_Total_Calc'] = combined_df[available_migration_cols].sum(axis=1)\n",
+    "    comparison_df['Migration_vs_PopChange'] = comparison_df['Migration_Total_Calc'] / comparison_df['annual_population_change'] * 100\n",
+    "    \n",
+    "    print(\"DETAILED COMPARISON (2020-2024):\")\n",
+    "    print(\"=\" * 90)\n",
+    "    for _, row in comparison_df.iterrows():\n",
+    "        print(f\"Year {int(row['year'])}:\")\n",
+    "        print(f\"  Population Change:     {row['annual_population_change']:>10,.0f}\")\n",
+    "        if 'Non-EU+' in comparison_df.columns:\n",
+    "            print(f\"  Non-EU Migration:      {row['Non-EU+']:>10,.0f}\")\n",
+    "        if 'EU+' in comparison_df.columns:\n",
+    "            print(f\"  EU Migration:          {row['EU+']:>10,.0f}\")\n",
+    "        if 'British' in comparison_df.columns:\n",
+    "            print(f\"  British Migration:     {row['British']:>10,.0f}\")\n",
+    "        print(f\"  Migration Total (calc): {row['Migration_Total_Calc']:>10,.0f}\")\n",
+    "        if 'Total' in comparison_df.columns:\n",
+    "            print(f\"  Migration Total (data): {row['Total']:>10,.0f}\")\n",
+    "        print(f\"  Migration as % of Pop Change: {row['Migration_vs_PopChange']:>6.1f}%\")\n",
+    "        print(\"-\" * 60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "1a40d285",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "BREXIT IMPACT ANALYSIS:\n",
+      "============================================================\n",
+      "Total Population Change (2020-2024): 2,488,894\n",
+      "Total Non-EU+ Migration: 2,982,500\n",
+      "Total EU+ Migration: -173,750\n",
+      "Total British Migration: -129,250\n",
+      "Combined Migration Total: 2,679,500\n",
+      "\n",
+      "Migration accounts for 107.7% of population growth\n",
+      "Natural increase (births minus deaths): -190,606\n",
+      "Natural increase as % of total growth: -7.7%\n",
+      "\n",
+      "Brexit Transition Impact:\n",
+      "2020 (Brexit transition year):\n",
+      "  Non-EU+ Migration: 110,500\n",
+      "  EU+ Migration: 66,500\n",
+      "  British Migration: -74,750\n",
+      "2021-2024 average:\n",
+      "  Non-EU+ Migration: 718,000\n",
+      "    Change from 2020: 607,500 (+549.8%)\n",
+      "  EU+ Migration: -60,062\n",
+      "    Change from 2020: -126,562 (-190.3%)\n",
+      "  British Migration: -13,625\n",
+      "    Change from 2020: 61,125 (+81.8%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Brexit Impact Analysis\n",
+    "if len(comparison_df) > 0:\n",
+    "    print(\"\\nBREXIT IMPACT ANALYSIS:\")\n",
+    "    print(\"=\" * 60)\n",
+    "    \n",
+    "    # Calculate cumulative changes\n",
+    "    total_pop_change = comparison_df['annual_population_change'].sum()\n",
+    "    \n",
+    "    totals = {}\n",
+    "    for col in available_migration_cols:\n",
+    "        totals[col] = comparison_df[col].sum()\n",
+    "    \n",
+    "    print(f\"Total Population Change (2020-2024): {total_pop_change:,.0f}\")\n",
+    "    for col, total in totals.items():\n",
+    "        print(f\"Total {col} Migration: {total:,.0f}\")\n",
+    "    \n",
+    "    total_migration = sum(totals.values())\n",
+    "    print(f\"Combined Migration Total: {total_migration:,.0f}\")\n",
+    "    \n",
+    "    # Migration contribution to population growth\n",
+    "    migration_contribution = total_migration / total_pop_change * 100\n",
+    "    print(f\"\\nMigration accounts for {migration_contribution:.1f}% of population growth\")\n",
+    "    \n",
+    "    # Natural increase calculation\n",
+    "    natural_increase = total_pop_change - total_migration\n",
+    "    print(f\"Natural increase (births minus deaths): {natural_increase:,.0f}\")\n",
+    "    print(f\"Natural increase as % of total growth: {(natural_increase/total_pop_change)*100:.1f}%\")\n",
+    "    \n",
+    "    # Brexit transition analysis\n",
+    "    brexit_transition_data = comparison_df[comparison_df['year'] == 2020]\n",
+    "    post_brexit_data = comparison_df[comparison_df['year'] > 2020]\n",
+    "    \n",
+    "    if len(brexit_transition_data) > 0 and len(post_brexit_data) > 0:\n",
+    "        print(f\"\\nBrexit Transition Impact:\")\n",
+    "        print(f\"2020 (Brexit transition year):\")\n",
+    "        for col in available_migration_cols:\n",
+    "            if col in brexit_transition_data.columns:\n",
+    "                print(f\"  {col} Migration: {brexit_transition_data[col].iloc[0]:,.0f}\")\n",
+    "        \n",
+    "        print(f\"2021-2024 average:\")\n",
+    "        for col in available_migration_cols:\n",
+    "            if col in post_brexit_data.columns:\n",
+    "                avg_value = post_brexit_data[col].mean()\n",
+    "                print(f\"  {col} Migration: {avg_value:,.0f}\")\n",
+    "                \n",
+    "                # Calculate change\n",
+    "                if col in brexit_transition_data.columns:\n",
+    "                    change = avg_value - brexit_transition_data[col].iloc[0]\n",
+    "                    pct_change = (change / abs(brexit_transition_data[col].iloc[0])) * 100 if brexit_transition_data[col].iloc[0] != 0 else float('inf')\n",
+    "                    print(f\"    Change from 2020: {change:,.0f} ({pct_change:+.1f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "0d8851ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rtzXvozlIP47ya1AfLFq06GvVCZDaHMfXUj0sIX388cfyv//9T9Vt8T7is4L3APBd2L9/v7qPY4P90eD4acdM295Sai/UzwD1NaTTxHcW311AvU7/XuihXjtv3jxV99bmakaK1NOnT8e4TydPnjTe16dkRT117969UW5HjhyxeK5o37698bOYI0eOOO0L1mlzi6KODsePH48ydYB2bLX/8bmoXr16nOtleN2PPvpI1WeRplfbV5xriYjsQWBgYJR5+az57f86UOfCORrnQZxbAdc6tD/gPK6fYzkx2o6srSPFVB/BtcCSDh06GO/rrwv6+9o2WIfrmDZVCupGaHOoUqWKWof2E2tTWWu0uvvhw4dV/U2bc7tBgwaqzcxa58+fV6nmAe8T5o5G2TDHNOqdcW2fS4pjj/r6pEmT1H3U49HehrqTVt+IqQ0Qdb7Y6tfWyJcvn7i5uUW5xXa84lJu1HEwVyYg3T/qFJjXWv8dNq3D6L/X+N2i1YlRj8M87UuXLpVBgwapspMLsnWkkSgp6Hv/oKcLerjjPnq8Y8izfii+tT3dMexdG2mF26lTp4yvN2zYsFh7PrVr185sWTHaCmkK0YPe3AgzfcpFS2l7LPW+QmoG7TkxMg4j0zSDBg0ybt+vX79o+40h4Hfv3jXuO4bmYz3KmdAj+sz1jEVKL+1xpGFCb3ntpvVAwr5pKQL07/l7771nfJ4VK1YY1/fv39/YC1zfW0nfy0dL+6PvOYYUT9o69NS5ceNGtFSWMdG/N0hTpKV5MH099M6Bffv2GdchLQboR0Xg8xaTvXv3mv1cW4L3F2k3zH22v/32W7PHVf/8Wk+3w4cPG9fh8/Ls2TNjCgPT98D0+6FPW4Dn09ajJxl6aSNVl75HFNZpWrRoYXxMS7+lP+aZM2c2pj/AZ1pbX6ZMmWj7j889Un5pn7VPP/3UuP2UKVPi/T3RtucoPiJKCKzn2Laeg+u4lmLH3A293S2NYNfKAkiFqa1HiicN6hno0Y3e8eYyG+h7kut7FiP9tl5cr2+W6LMdbNq0ybge9/WjPmPrtR8T/X5Yumn7/Tp1AmvqYQk1ou/ff/9Vo9S01KKm+6NPI27pNWP6bmA0qbYO9TUNvrvaerz/Wp1Vf4z16Uo//vhj43qkiY9J/fr1jdsiVZwGGSlM909f59Hvn7mRg3Hdl0aNGql1+P5j1Ce+P1hGelr8j+8/Pv/aqE/TFGwx1cv0n1/95xqjSbX1zZo1i/E4ERElFaTp1J97z549G+vfvM51Tl/X0M65uGH0EeA8rZ179XWqhBrRF5c6Umz1EXOjs5B6GikRsa5cuXJqHbIWaBkvMHJNg/YR7e+RVlqrY2Eko7a+cePGsb4fpnV1jObDfdTptfrD2rVro9X/Y6qHII2jtg51Iw3qp/qMFuZG9FkauZ7Yx14/AhHXcQ3a/LTXw3VfS0Eb1/q1Ncff3E0/ci8hyq2vgyOVujZFj/Z90t4vTIOjfY+0NLBa9gP8bkG93twUPeRaOKLPBtDLFaNJMCEuegNgxEpc4dyDXgGFCxeWZMmSqZ6PGOFE1nnnnXdUr47Q0FDVYxS6d+8ep+dALwv0lgJfX19jLwyIaXSVBp8BU+j93LlzZ9WzBT3otRFmelrP+vjAyC7tOQsUKCAZMmQwPoaePBpzPfXRYxqjBLQeQNrIrtcpjyUYEYYeUHr6Mv3666+qB4t203rUYN+0UWh6GGWn0e+zVnZ9b5kyZcqo3jDmjosGr6lNboyeOujBjp7EGDmAHlbohWMtjFbTjwDUv55WrmrVqqnjD+gBhhGI6BGkwajCmGi9nPU9smKCXt2PHz82+9mO7XOCXuDasUmfPr1xPd5PbfLojBkzGtdb+vxUrlzZeB/Pp33e0Dsb5de/NkZgeHl5WV1G9KjDedPS5wE9+LX9DwkJUSM1tM/anDlzjNujl5otvydERJawnpO09Rxcx9HL+cCBA6oHbdmyZY292rVRY5MnTzb7t7HVUTBKDfUA1C+uXr2qMhuYslRG0/fgda5vevrjp79ex3aMEwreG4wQ0I7p69QJrKmHJQRkE8D7iNENt27dUt9NU69bV7D0vuC7i+8w4P3XslLE5XNoTR0T+xUf2gjk19mXWrVqqf/x/UdPem1kH0Zyar3gMRJCG/UZ3xEu8T1ORI6GbVaOK66//V+X/rqp//2vZeXB50dbn9DnytepI1krRYoUxhFsx44dU6+DrBTa82JEurlrF7JDaHUsfR08tjqWOdrfo06P+gNGn5urZ8dEX6fRX1dRp9LamuLSPpcUx95SXQDtSfnz5zeeZ5AdI7Gu16tXr46SVQS3bNmyJWi5tToMoP6i1WF69eql6rRYRrm1zw4yEmi/MzA6Et8xZEBDvR7ff/wGxChTS5kUyLkx0GcDSN+D9IGzZ8+O93P069dPDY1HxQmBDTT6mwtIkHk4KepT4mA4tX5IflzhxBpXWmOSnjaUHpCSEsOucSFB6kRNXIJICbkPWoOXRh8MS2jmjo21zKXH0pddX25zDYzWHBc0MCCd0dixY+XNN99UFS1UdHbt2iU9evQwDtNPyPcBaYK0hlekLNUCfSVKlDCmMYqpYubj42NcRtnjW57YPif6Hxb6Rs7UqVOb3d6a98Ca143Ltq/zeYjLZ830+YmIkgrrObap5+DHPOrmaAhCw9r7779vfAzr4nNNQrpCLYCChhh0+MEx01JjxnTM4lufiinVaEJ/Tqyhpa5EsAa/exDcQUpSa1Klx7VM5rbXr0NnK01cUnsiRS1SkGuBcjSaY5+0FPSJ+dm3RnzrRlpKfECATTs+SD2Pv7WmQfN16v0ac41k+HwgRXvmzJnV9w9pxTTxDfQlVB2SyN6xzcpx4fyrBROs/e3/Ote5uP7+T4jX1LxOHSkuTNN3amk7sQ/t2rVL9DoWrmUIOGowpc3rtDPEtW5k7jqdVMfe1m0+CFgjFb/+pnUaT6hyWwr0IdUnOg/iNwU6imll19dh3n77bfUdRzAY26KtEu8L0nci2JmQndbIMTDQZwMNGzZUc5A0b97c7OPoCTF48GDV4wknczQa6H+Y4AfT3LlzVS7epk2bqry75cuXl7feeisJ98LxIRe0VhFp0aKFcT4Ka+FHozZCCRdr/Q9Z7cQc1xP87du3jT1OkE8aQSScrLX1pvQVKWsuogULFjS+LnqXP3z40PiYfr4a9LqzJXPHRl8mVGxwkTO94X1ArvK40leET5w4EaWiaW4eH7wW5hPC3C9opMS8PriAolINlubKMefo0aNR3jv96+nLhVznWg919ITU5kyJbTQfoCKi9UKDMWPGGHs062Ef0MMf+6Z9H3BM9fOzJNXnBPnJNejp9OjRI2NjNXqW6l8b87CEhYUlWBnR00qrGOI9xbEy/azhM7Jo0aLX/ozbslGPiJwb6zlJV8/ByAdt9KO+UQT1FY2+bhEX+mODnr2YtxeNDPr5x6x9DxLq+qY/fvrrdWIdY3RYwj4jQIYe5aYBvtepE1hTD9M3YmrzPwLmn4zP+4jAJeYtxj5h3sGE+OzH9L78+++/qqc34P3X5sRMCJhnWisrerDHZ/6l2Or91uwLGuK0Tm2oh2N+SnQgwOccnxv8vsbvZ3ONavoysF5GFIltVo4Nc6dpME+xuVF96ECs/cZOiOtcXCX0tdWaOlJ8rq2AkVKoFwMCLtq8aBjRpp8HTX/twty75tqsUD+NK9TH9e+p1gk8LpDpQoP5/jQYHW8uK5a1dfrEPPaW6gKo32vHEWVD/d+exLXcGCGovT8IlmIkHuo4qPNqWVQwP7a5Ogw+U9gG82SiUxPq9pgfHFBfSszvMNknDjWwQ71795YzZ86oCwgatNFTAimYkG4EP3QxYSp+eGIScKzHFxsXHowi0g+Vp5jlyZNH9VBDpeKDDz6I89/jQoXh8suWLVPLHTt2VIEf/LCcOXNmvMuEtFO4AHz99ddqpBaeS6uAmcLJH8PktV7yqDyjwqTvGa+HhjUEwnCyR+UcF+MBAwaoi40+ZZM1waOkhh8F+DGPXtyY4BifdaxDgxRSd6IXy8mTJ9V3J65w3PAjBZUVVIIRVEOvLQTxzPWAQ49ypGJAwym+k2g4++eff4wND+bSFsSUygkNgegJpn89BOfw/dagYonURjgf6MuE3l3WGDdunJo4GQ1KeL/Rm3LgwIGqcoHPGyYlRtoxBC3RGIznxWS+WjoK9JxHJRD/J8XnBD3B0EiKtKj6FC/40YmAJ1KsFitWTP2IRJlRRqSDQwMdjhF4e3ur9yg+323sG74TaLhFLym833if0TsKjUxoRMLE00jXGh/47uJ7jc8belvhu4/91dKeEhG9LtZzkq6egx/XW7ZsUUEP9J5F/f3evXsyYcIE4zYVK1aM9zHT4LqD3wDoAIMGWFtd31BnQb1Ha9xBowIaLIYNG2aTuuTr1AmsqYfpG5DQYIrgEd4DHKf4vI+zZs1S5UH5fvzxx1h7ov/yyy+qIRH1H3yOLPUkxz5oGR9Gjx6ttsN7iw5eGjQUJuTIS/Ti/+STT4yjfvr06aM6zeHcgeOEOnN8xHVfcGyQmh0Nq/jNDFrjGP5Ho+z58+fVMhrTTNNusV5GFDdss7JvCMLiXIY6G1L+IRCLdag/4ZqNoCw69eB/vB8JcZ2LK/1roh0C52bUTeKSHSmudST9tRV1RQRM0JEYx8U05akeRoShfQTXb32GBtNsGah/aEFA1JtHjhypvg9ow0L9F3VFtGfo21Ss9dlnn6mUjLgWxufahA5GQ4cOVd9F1CvQPoR056iHv3z5Ms7PlxTHHnVJHHP47rvv1LkG+46gl9bmhnq/vZ1T4lNuHA/8XtFG4OH3BOo4qMPgPdLqMOjUhN9DGtTjUfdF2yg+H/isIlioiUvbJDkJW08S6OrwFqxbt864fP36dYOHh4fh9u3bUbarV6+ecYLznj17qgk8K1eubNi9e7eaXLVMmTKGunXrJnn5HYWlSWqtmfg2pgmBr1y5YpyEV3/DBO+xTVqsTYqrN3ny5GjPlTFjRkORIkXMTvyKiV1Nt9fKZ2ni5MuXLxuyZs1qcWJZ/fGxtN+Ayeq1x+JC/3f6YxDTa2m2bNlidpJf7Ybnju+E0suXLzf7nAULFoz2PJhQOabJeSdOnBjjMdCXIX/+/GpyY9PnGD9+fLS/27x5c5RtKlWqFKdjj/NFpkyZYiz748eP1baYGLho0aIWt8Okv5hYW2PuPbD0nlozqbf+O6TdUqZMGWUy8YMHDxon9ja9YXLjOXPmxPqalsqO41CqVKkYj5X2+Y3P9wQTKps+n2m5iIisxXqObes57du3j/F6gfLcuXMn1uOEeoa2Hu8pPHv2zJAtW7Zoz1m9enWz+4/9MHcs43N9syQoKMhQs2ZNi39fq1YtQ3BwcIz7FRv9fujrcZbEt05QrFgxg5eXV4z1sJCQEEPu3LmjbYO/tfZ7gN94vr6+Mb6P+mPzzz//qHKbbq+9p+Y+n6iXtW7d2uL7gnrdo0ePYv2sWKpDW4Lj07Fjxxg/U1qd2trXiOu+wOjRo6Ns8/PPP6v1u3btirK+c+fO0V4vpnqZpc+vNb9diJwBPuNss3I8p0+fVm0NMZ2Xjx8/nmDXuZiuK5bqVFWrVo3xNfXnVnPXjbjWkfz9/c22J2n7EFMdCvUM/d+g7vDgwYNox71Tp04xHnNr6kHm6upxqf9ben/69u0brTypU6eO8v5o+x3bNS6pjv2QIUNirF/jt0ps+x3Xeqj+NczVpfUSotywcOHCKNuMHTvWeL7Vr69Tp06Uv/voo48svo6Pj4/6XUSuhak77Qx6QGGEEob6oheNdsPcX9oQXwxzRlQeo5qQmxc9btEbFD0YtSg/JQ30rsV7g/cAvVHQO/Tzzz9XPU812oTx1kCvc/SAQe8Y/B2ed8eOHWoOOHPQEwhzwmmTZFsDPW3QCwm98LTewcifjh4kyKuNHvb2qlGjRmoYO3pH5cyZU5UdPZrQkxuj0zBRbnyhh9by5ctVj3D0ssYwefRM0k+urMH3E72h0HMYvX3RawbfU/SyRo9mPGYtfIfRYxmpy9BbGe89htqj95cp9IbEex3f3vJ4LfR2R29opDhCzym8Jj4HSO2Jnl1abyr0Ljpw4IAMHz5cHQtsh7Qs2EekYcEIj8SaiwdwDL788ks10hKvjTQQOMfpJ4rGqESk3EJPfGyH9wE9xXCcMI8hepjHF0Y1IjUderphfgr0nMJ3Ej2xMDIGnxW8//GFnl0YaZKQ6bOIiBIa6znWQTnRAx0j5NAjHddL1CVwH9ci1F0s7aM16Zr++OMPleYUdQ1c7zBHMG62ur7huowyaaMy8Rxar+yJEyeqazD2PynFt06Av0PPcm2knLl6GD5DmFMPvaqxX6iDoi6l9da2BjIUoBx4PRwvfDYwsrJbt25mt8exxG891EutnQsG3xHUz5CRAa+DzyH+FvVWjLZEvc50LsqEgOODsuIzgVF26FGO44TPRN68edUoAuyrPn1VYuyLaTpObUQf6rz6+XnMzc/HehmR9dhm5RiKFy+uRt9jhB5+S+P3Pc7NOEdjNBHmjsU2CXWdiw+MOkRZcL3A+RfzOsalTSeudSS0HWE/0faipXu2Fq5F+pSMGJmHTBKmcFzxuceILLSt4HiiDlCvXj11PD/99FOxFXwW0MaCejWOOb6b+E7qr6fW1uuT6tgj1f+qVavU8UR9Hp9V1C2QUQL1fX3qVHsS13JbqsPgs4Nja6kOg/ZK1H3RZofPG9LbIxsY2vcwsk8/HRC5BjdE+2xdCFeGHzFIc6DNn4UGCHxRMSeW6fwTOHmikQCNCUgFFBoaanwMQ61xQsYPSOY9T1r4Cpk2PuEHKE7s2sUUDVtECTXnEtJsIP0W0myZph5yZEizhYoxoMIZ37SYRESUcFjPIWeDVGV169ZV99E4snjxYlsXiYjIbrHNish56/RIp49gEqahQWcwLOvn0yMix8I5+uwMejegdxQmxjXX2xCqV6+uJplHbyltwk5Mfm6aJ5mSBibgRc8j5LcG9ArWej6h18b7779v4xKSM1TInj9/rr7zyOsO+HHkTEE+IiKyT6znEBERkYZtVkSOacqUKWo+2saNG6vgHuYpxvzbCPIB5ppmkI/IsTHQZwOYYBaTlGquXr2qJi3HUHoMBUfvqE6dOqm0MahEYeJWTA6P1DjvvvuumsQYjS0Y2YPJPJEWAcN/0fCvH0pOSQOpY3AzhZ4y06dPZ0WWXhsqYPqh/fhsIXUaERFRYmM9h4iIyLWwzYrI+aDzONKum0tjj1ThSL9ORI6NoXobwDwdqAzhBphbDPe1+U6Qlg+VpkGDBqk8u0iRcPjwYdXjAtDDYtOmTSrHMfL4oiKFk/KKFStsul+uqk+fPqpCi3zI6NmOfNctWrRQOepRmSVKKEiNgnMC5ixBnn0iIqLExnoOERGRa2GbFZHzwdQo+C5izjfMHYhUu/heY149zKNrbs5BInIsnKOPiIiIiIiIiIiIiIiIyAFxRB8RERERERERERERERGRA+IcfUkIecn9/PwkVapUal4TIiIish0kNQgICFCpCDnxuONhvYqIiMh+sF7l2FivIiIicuy6FQN9SQiVply5ctm6GERERKRz8+ZNyZkzp62LQXHEehUREZH9Yb3KMbFeRURE5Nh1K5cN9O3evVsmT54sR48elTt37si6devUBMIx2blzp5qE+PTp06oC9Pnnn0vnzp2tfk30jNLenNSpU0tC9rzy9/eXTJkyOV3POe6b43Lm/eO+OSZn3jdn37/E2rdnz56p67l2fSbHklj1KnvkzN9ve8TjnbR4vJMWj3fScqXjzXqVY0vMepUzfw+4b47JmffN2feP++aYuG9JU7dy2UDf8+fPpXTp0tK1a1d5//33Y93+6tWr8u6778rHH38sS5culT///FO6desm2bJlkwYNGlj1mlr6A1SaEjrQFxQUpJ7TGb8s3DfH5Mz7x31zTM68b86+f4m9b0xP5JgSq15lj5z5+22PeLyTFo930uLxTlqueLxZr3JMiVmvcubvAffNMTnzvjn7/nHfHBP3LWnqVi4b6GvYsKG6WWvevHmSL18+mTp1qlouVqyY7N27V6ZPn24x0BccHKxu+iis9gHALaHguZCzNSGf015w3xyXM+8f980xOfO+Ofv+Jda+OeOxIiIiIiIiIiIi1+Kygb64+vvvv6V+/fpR1iHA179/f4t/M3HiRBkzZky09RjOiUhvQjZUPn36VDWCOmNUnPvmmJx5/7hvjsmZ983Z9y+x9g2TGhMRERERERERETkyBvqsdPfuXcmSJUuUdVjGKL2XL1+Kj49PtL8ZPny4mtPPNK8qcrYmdOpODOF01jy33DfH5Mz7x31zTM68b86+f4m1b8mTJ0+w5yIiIiIiIiIiIrIFBvoSUbJkydTNFBopY2qoDA8Pl9DQ0Dg1gIaFhUlISIhTNu46wr55eXmJh4dHnP8ODdexfR4cmTPvH/fNMTnzvjn7/iXGvjnjcSJyZrt375avv/5aDh8+LA8ePFDr5s6dq+bQNjdit0yZMnLlyhWz21ma52DkyJEyduxYi2U4ffq0TJ48WQ4cOCB+fn7qeQoWLCiffvqpfPTRR8bttmzZIkOGDFHzfJcoUUK+++47qVy5svFxbI/9OX78uKpHEsVXfH47YntkmOF1MPE52/H29vZ2iv2gpDvnOOP3QI/75piSat/i21ZIRBQfDPRZKWvWrHLv3r0o67CMkXnmRvPFB1KSYeTgkydP4vx3uEihQcPZJr52pH1Lmzat+pzYezmJiIiIHNGxY8fkjz/+kPz58xsDfZb07t3bGOSLCYKB+o55yL4REwQZf/rpJ0mXLp0qx4ULF1S5unXrJg8fPlTBPdTlW7duLVWqVJE9e/ZItWrVpEWLFnLr1i31HPv27ZMFCxaoxxjko/jib0fH4GzHGw3i+fLlUwE/ci3xPec44/dAj/vmmJJy39hWSERJhYE+K1WtWlW2bt0aZR0aGrA+oWiVpsyZM4uvr6/VFwFcoDDqzdPT0+kuHI6wbyjjixcv5P79+2o5W7Zsti4SERERkdPp2LGj9OzZU3W2Q0OzJatWrZKff/5ZWrVqpe7HZN26dZI3b94o69DwY0nu3Lll9erV0rx5c9VD+/r161K6dGk1j+jSpUtVoO/ixYvy/PlzNYIvffr0Uq5cOVm+fLkKTqKTYPfu3eWTTz5RgUCi+OJvR8fgTMcb50aMZL5z5446Fzr6/lDSnHOc7XtgivvmmJJi39hWSERJzWUDfYGBgXLp0iXjMtLqnDhxQv0YR6UV8+vdvn1bNRIAUv0g5Q5+vHft2lV27NihGg6Qlieh0h9olaYMGTLE6W958bU9bVQnLuB4Dzk0n4iIiChhWVNHvnnzpgoGli9fXsaPHx9roK9ChQqqEQaBQwQSBwwYEOMouzfffDPKcp48edRvh1OnThlHBiKVZ4oUKeTgwYPy6NEjNeIvR44ckjFjRvniiy9UEHDChAlW7zeRKf52dBzOdrwxXzKCfdgnjkh2Ha9zznHG74Ee980xJdW+sa2QiJKScyVZjoMjR45I2bJl1Q0GDhyo7o8ePVoto5fajRs3jNvjxz+CehjFh167U6dOlR9++EEaNGiQIOXRcpyjZxQ5Ju29i2u+eiIiIiJKmNEmCNahLrZs2bJYG6GRfjNnzpwqQHfmzBnV0a9Tp05xek3Ms4d5+wAj9bTnXblypWoMx/OnTJlS1qxZo14DcwxivsDZs2erACF6dw8aNEg1NhFZi78dyVa0lJ0I/JDr4DmHKP7YVkhEScVlR/TVqVNH9eCwZPHixWb/5vjx44laLmfrJeNK+N4RERER2c7MmTNl165dqjNe4cKF5dq1axa3PXDggFSqVEnV3zCir0mTJsaMHZMmTYoyb58lSOuPufgQYOzbt68x0AfvvvuuummwTc2aNdVcfXjNYcOGyaeffqpG+o0cOVIKFSqkMogQxQV/f1BS42fOtfH9J4o7fm+IKKm47Ig+IiIiIiJyHidPnlT/9+vXT42iK1GihPGx/v37S7Vq1YzLmD9Pa3hBT2vMuadP/xkbjMpr2rSpmg5g7NixKsgYkzlz5sj58+fVdtu3b1frENjr06ePuo+sIURERERERETxwUAfERERERE5DcyBhxtG6mmCg4ONy0i3iVSaWuq5oKAg2bBhQ5R592DdunVStGhRdcPc3YCMIJizG6PxMM/KkiVLZNSoUTGW59atWzJixAiZNm2amt9KyyqCFHic44qIiIiIiIheFwN9TiY8IkIO3zgtv57dp/7HMhERERGRo1u7dq0ULFhQpdPXYH5trGvfvr1KvY8gmna7evVqlBF4J06cUPevXLkiLVu2lDRp0sgbb7wh2bNnN46y69Kli0qnCU+fPlWj8HDT5lVZsWKFTJ48Wd1PnTq1fPvtt1KlShXjzRwEBatWrWqc/69+/frq/19//VXNAQ716tVLlGNGREknb968MmPGDJu9/s6dO9VI5SdPntisDESxYZuV7WE+4wkTJoijQCp2nNu0epytn0dv3rx5Kv07EZE9YKDPifx58ZA0nN9buq0aJ8O2fKv+x/L2C4cS9XU7d+6sLpZff/11lPXr169P9FzU2oXa3A1zr8CXX34pZcqUSZKLPBEREREljmfPnsnly5fl+vXrxnX+/v5qnTbizho1atRQaTNz586tgoGYP698+fKqsWb+/Pkx/i1GBmoePHggBw8ejHIzhTn//vzzT/XcmkaNGsn48ePlm2++kZ49e6r5/Xr06GF1+YkcFUbTokEUwXX8DsPvRVMI0iOAny1bNvHx8VGB8YsXL0bZ5tGjRyq4j2B72rRp5aOPPlJpdPW/82rVqiUpUqRQ/5vO19m4cWP55ZdfYi0vOhWY+50LmIMTj+G3pubw4cNJ9l1G2ZCSWA/pie/cuaM6MRDZI1u0Wdmyvcoe26yQ5hxzDKPuoXfp0iXV2SlnzpxqnuJ8+fJJu3bt5OjRo+KI8L43a9YsyrpcuXKpc2TJkiUT7HW6du0qx44dkz179iTYcxIRxRcDfU5ix6XDMnjjdLkX+CjK+vuBj2TwxmmJHuxLnjy5aqx4/Pix2AJ6YeOCrb+hweZ1oFe4vsc4EREREdm20UY/Yk9/w0gWc6NrtMcR2NNgBCBG+J05c0YCAgLU6JcjR46ooJunp6fZ18NzxVYGLSWnXqtWrVQaUTSY6Y0cOVLVVxEsxLx9+tclclb4LpQuXVpmz55tcZtJkybJrFmzVHAcwXME6xo0aKBS7GoQ5Dt9+rSa23Lz5s0qgKgPsA0aNEiNzEXjOAKGgwcPNj62cuVKcXd3lxYtWlhVZjQM43ehHjoWIICP59ZDal7M+fk6tNHD8YF0wFmzZk2S4AWRI7VZ2bq9yp7arJCJAFkNMJexBnUglOXChQvy/fffq/oR0pcXKVJEpSt3Fki5jnNkQta5cN5FQBTXLSIiW2Ogzwkg1cGUXUsketOCGNdN+uunRE2JgJ6WuGBOnDjR4jboNVmiRAnVOwiNJVOnTo3yONYhfQB6xKRKlUr1so6tV7UmQ4YM6vX1N855QkRERERELgPBMEu3kJCE3zaOGjZsqEazNm/e3OzjCJYj9eXnn38u7733nkqt+/PPP4ufn59x9N/Zs2dl27Zt8sMPP0jlypXVCF00XCOtLrbTtvnwww+lUKFCKjiPZUBQH88dU6DRFEb/ISC/b98+47qffvpJ3n77bcmcOXOMqTvPnTsnNWvWVL9t8TsYDf36kYzaaB0EH2vXrq2CEUuXLpWHDx9K27ZtVbASgcNSpUrJ8uXLjc+Lfdq1a5fqJKCNDMJzmUvdmZi/wYkcpc3KmvYqV2izwtzEmKNYn2oS512cU3C+xKg0jFYuUKCAGmH4xRdfGEc/mzu/oDOFdv7RAo8YZY0OGAgS4vz1wQcfqDmScd7E8UuXLp0aTajNkwzmRnjjeUw7Wej3AyO50YkKI7/xWjgfajBCEq+H+Ze1cyTKrx8hiWwO+Ht0/NI7fvy46gyiZY/A/nbr1k115MAo8jfffFONitTD8dy4caO8fPkyXu8LEVFCYddRO9b2fyPkwfPY8+uHhIXKk6AAi4+j4nQv4KG8OaeneHvGXpHImCKtLO84Ic49Y1DhQU8WXLQx3F8Pw/3RoxkX3NatW8v+/fvVfCWo7KBSoUFFaty4cTJixAhVAcE21atXV5UtIiIiIiIisqBlS8uPVagg8sUX/y137IhctOa3RVozfYP4Rx8hd2707TZtkoSEVLp37941zmMJSEOJgN7ff/8tbdq0Uf+jAbgC9ucVbI+GWYwARBARowYRVEMw7vfff1cBQ/jss8+kV69eapReXEZrYAThokWL1O9SQOMzRh7q03aaa4hG2jgEAvbu3asagPUjC/WGDRumfgeXLVtWBfswehGja4YOHaoaljGXJ+bUQuN7pUqVVIM2Rt4g/dzYsWPVc6AR2jRFaXx/g3/yyScq8IjGc7INBB9w095TtIcgpS2C5ZasXr1aRo0apf4GQRuMYEOq6MTkKG1WsbVXuUqb1T///KPmH9afPxH0wgjpZcuWqfOoKZxv4wJBPYxuQ+cLZE14//331XkZz4OUoZgnGSOqccxwnOMDQTq8h/jM4/3Be4VR3RhljfcQ51p08EDKd5y7IX369MbOIIB9xeujEwXeQw06W6BsefLkUcsY/YhgIuZVxvUIIx4xrzLOwXhOwPEMCwtT1yBmBSMiW2Kgz46hwoQ0BgklpopVQsDFW+v18+OPP0Z5bNq0aepiiIonFC5cWKUDmDx5cpRKEyqi2kUWP2ymT5+ueivGVmnCfASmlRL9PA1E5NrQO/TozTNy2e+6FAjOI+VzFRcPMz9kiIiIiMg2EOSDLFmyRFmPZe0x/G86kg5p2NDgqm0zZcoUlYoXo0cQ5EPDLNJ7okEbwQ80BCNVHQKBaJBGMC8mGL2DkXkIsCEYgIZyjPSLKdCHtKKYP/Svv/6SjBkzqjJ+9dVX8tZbb0XbFnPtoTFcTx8U7NOnj/z2229qzk8E+tDYjDJjtAxGBVnyOr/BUW4G+mwHQQzMKYeAHUZcYXQSRrlitJG5thEEOjAKFCPW8NlE0AaBZswdlpDzkTlym1VM7VWu0maFUWoIeurPodocqEWLFk2Q10D6YQSp0TEBMKLvf//7n9y7d0+lCy1evLjUrVtXnWPiG+jDSMgxY8YYlzEyD51AcI7E+R2vg+Ac5lWO6RyJziN4/27cuKE6ZSCAiAAlRn4DOmkcOnRI7t+/r0Z5atcXjD5EkFdLGY1zMc7L+jmkicg1hNtZWyMDfXYMvZSsEVvvKE3a5Kms7h0VX/jhhKHspr0V0ZsGFVM99JJBahP0dkRlA7TeloAh9bgo46IK6L2mTXCL3jXodaRBupNixYrJ68DFHZUODXrkoJKiz12OXlu4EZHjwHwPk3YsjjIfRJaU6WXIm52lfuFKNi0bERERUYJZvdryY6aNDv/7H35wWbetmUZxe4aUl0gdp0FjL+b5Q7AEqUORcu/8+fPyzjvvqCAgAmkxwQhBBFzQsIvGaYyui22OJzw/Rg7i9yx+VwKCdOboR9cAfh9j9BEarTEfYEhIiNqHuM7/lxC/wck29KkVAUFiBE8OHDhgNqCEIDQ+zxi1ChhxhmDzd999p+a7NAefKdw0GP0ECHbgpodlc/PRZkyRxkZtVmnMzosbE2yP4CmCeZjHU/t77X98X5o2bRrleRGcw/cF32Ht+4JUuvptYmqz+vfff43bIoBk2mZlWgbTfdKvxw1tVvr331yb1fDhwy22WWG0nRaw0p5be68tzTVsroyW1uGG81T+/PmNjyGoiE4XmG9VW4fOGzhm+tcz9/qmnzn9faRgxmg9HBOMmMZ5EoFcS8fQ3L7g3I73BKP4MLIa6T1RLgQnsQ06hyAYi1GDeni9S5cuRXluBBYxD62lY4ibue9WYtG+s0n1ekmJ++aYnHHf/rx4SCb/9XO0tsbP6naSeoUSpq0xrseLgT47Zm0qgrDwcGm4oI/4Bz4ym/McP98yp8ogv3b/NtGjyrVq1VI/olC50Pd6spZpjnL80NA+1JiHQct5bbodfkQVLFjQ7HMi3Ql6XZrScouj5w1kz55dXcg1a9euVfnIcdHXaEPzichxgnyY3N303KhN+j6l6UAG+4iIiMg5JE8et20tBfpe53lfgzbyAiM/kIJNg2U04GrbmAah0Nj96NEjiyM3EDTD6D2kw+zevbsK9uH3JEbR7dixI9ZAnzaqDw3LGOGDER4JCQ3gehhBhMANAgwIKuBxjPpDQ3ZiiOk3ONkegrJIUYggQtWqVc1ug9FMAwcOjLIO7TKm857pYfSfflSUxt/fX6WP1UMwCZ8JfNe0wDX83CYydaw1bVZNFw2Q+88fW9wGjaMbu0y3us1KX46YaMEVbI/AHc4FCOp06tQpyvNoDdD659XmkcM6LYCDgJ/pa2sBNwRj9W1W+uOFcxoCXub2AYE6tFmZPi/mBwWcA/AYgmaHDx82Po73d926daoTg77NytKxwfx4CPbhpo1kRlAO0JEe5xs97LN2DLT9x35qz699TrT9xPHT9lv/HOgYYbpOf2xwzjH9bJn7zGn30dEfQW2kUEZqZ3TewIhMnJu1bfXvu+nx1t4v7BtG9WEELAYroO0Pnw+0EWIbBL/xviFobgqpSPXPjWuQpWOvHRvMv5pU8zLi9fCZwrE2l5LVkXHfHJOz7dveGydl3O7I1MB6CPoN3jRDRtXqIjVyl37t10EK5LhgoM8JoCI0uHYHGbpllgrq6Ru0tZ9uQ+p+mGRDR9FLCj/E9Kk+0EtGP4E5YBnpELSeUdb0zIwPlOPWrVvqB6I+DQzSWGAOBAzRB1Q+9MFCVKLQK8dSAJGI7H8IPUbyWZr03e3VpO91C1ZgGk8iIiIiG0P6NQTr/vzzT2NgDw2tmPcI88YBAh3osIkUmgjcAYJ1aEBCg68pjNJBI67WoRMNu2jgBa2h1xqY2wsNwRgBos8CE9Nv0Js3b6rfoNpoEH0DfUzwOxkj8Tp06KCWsW+YD0r/umikj63sCfEbnGzn1KlT6vOOYAoCQQjoWPrsIW1tTClvzUHnbH1wEN81dKDGfI/oLK2HMqCxEW0msY1mNQd/M6h2Bxm29Vu1bLbN6s0PJVksaXTjAw3KuGnlRnsV5sPUUlVq63FsMWJSv39YxvdFGwWnPZ9+GwSpsA4BHG1eNz1t25iOHb6raLNCIEj/PmJOPbRZIRiH7yz+Xp9iE+dLjKCzNu2mds7E+UQ7x2JEMfYdnQtwnjNtgMf5Fp8JrSMFAsFY1j6j+n3T/la/n1iHY2S6Tn8c8XzowKEtI50ogpHaNqbHEO8Lgra9e/eOMser/nXwnmlBRnPvhRZww3kW6VxPnjypOvsjWKtth2OD7xDeA9MgrR7SNOM7gu3NvcfascG1AM+VFHDdwPHAsXWGoIoe980xOdO+hUdEyPz1Gyw+juva/OMb5b1y9V67rTGu5wwG+pzEmwUrypSmA2TSjp+iDBnFSD4E+ZJyxAp6AWHCcsx3oEFqhIoVK6oUEsjDjR5nSCMxZ86cBHlNVIhMK7HoYYMvBHqy4YcWctaj9yYqKAjyIe92v379+COHyEkdu3U2yvnQ0qTv2K5ibvufPJ2IiIjIkSEFGtKd6RtmEYDDKAh0vkQDEEau4TcbUmUi8If5spB5BfONaQ3iSFGIkXlISYhgHRp7MSoD2+mhkRdzKGEOJm3UHFJXLliwQDXe//zzz+o3ojUwEubOnTtWj8bAXHyYowpZbpB2EaN8tHmfsJ8x0dKEYt41vC5GqiBgqA/yoNEZAdBr166pIJC5zDOJ/RucEhfaMPD9wAgIfB4+/PBDNRecNYFmayAQog9gmQZhTNfhc6vd4grfRaQxs2WblVZupKpFe9W3334bZb32fcH5R/u+YBQvvi/6fY7pGJhbr63DiC98j821WeGchvcbgTZ9mxXOf2izshQg1J7b2vcEndnLlSunAv4Idmp/ixSY9evXVxm6Ro4cqQKHOF9v3LhRfv/9d/W5w3kJgWCMAsU5DcFCnJv0x8RceWIqo7YO0//gWCN4hw4MmPsQ51pzz4sbzt+Y9w9lw3UC99GRAve1bXEfj6OcCLBhlJ5pWfA/zqV43W7duqnXRicL7XGcxxFsx/yOGD2I1/Xz85MtW7aodVraZczlh2CspUECWrnNfbcSky1eM6lw3xyTs+zbUSvbGk/4nX/ttsa4HisG+pwIKk51C1ZUjdaYFBlz7ZXLWcwmI1XGjh2rhtNrUJnAHAOjR49WPzQw/B3bxCe9pzmolJhavny5+sGHShEu8MhTjh9y6IGEiz4qTKbpLYjIefx9PbKHYWxwviQiIttB3UybG8geoEESIxfQyBWfBs3EghEWWi96Ikd05MgRqVu3rnFZ+y2GAMbixYvV/SFDhqgUhQjQYSRJjRo1ZNu2bVF6NCO9GoJ7mGsLDSAtWrSI0slUM3/+fDU6pnHjxsZ1X375pWpMx+g/NK736tXL6vKjUd5a6EyKtHpoPEYjMhqBkZITc6/F1jsbAcErV66oDqsYrYNjgUCnfjoKjC7EcUPQB0FEBE1NJfZvcEpcGLWpBQ4wEguBDIy6wrySphAYMg0iYdlSOltXb7Myba9ypTYrnJPQyUE/Gg7zh+L8jAAeOlEgZSj2H+euKVOmqG0QeEN5MboawVItKNqyZcvXLtPUqVOlS5cuUrNmTdVhA59zjNq2pGfPnnL8+HEVkEU9Dcfs008/lV9//dW4DfYDc+4hGIf6HOZXtTQqD4Ff/D3SuSKrlwbPvXXrVhX8RPnwvuA7hYCofuQljgtej4hcwwMr2xBt0dboZojrDLYUb2jAQC8SVNDNpUJA5RwX87gOy9TyW6NyYE+NEQnBkfYtru8hhi0jPQF6VTl6bwZX2z/um3274H9dZuxaJvuunbRq+/612kqXSu+Jo3OG9y6p9y2m6zLZP1d6/5z5+41Gky7tukjAw7jNP5CYUOfMVyifXL141TgnjT1IlSGVLFq2yOmCfc78+U4s/O3oOPTHGyP0ELjEqEaM9nO2z54rXZdtAaOeMPJVC4rrIeCBVIebNm0yrkOQBgEZjHy1ZXuVs593HG3f0DEAowcR6LQ056Oj7ltcJNS+YW5DfDcxchDfH3Ne9/sTH85ct+K+OSZn2rfDN05Lt1XjYt3uh1ajXntEX1zrVhzRR0RETgFD42fvWy0b/90lBrMz85k3Y/dyeRkaIj2rtuBcfURESQw/XhDkq5WslmTwiZzHyubcRHxS+EjptKWjTiRkQw9fPpTdD3er4+VsgT4iZ4U51ZAyFI27SLGJtKRIHeqoQT5KOpg/r2HDhiqwhxHmmGcSo5N+++039ThGHuXIkUMmTpyoljHyq3bt2mpk1LvvvisrVqxQI7QwqpVIDyPWMKIPo/bo9SGlM46npSAfETmf+88fx/i426u01BixntQY6CMiIocWGPxCFh3aKEuObpWgsBDj+mypMkrdQhVl+bHIFB4xtdV+//cvctLvgnz9bh9J58seyERESQ1Bviwp/kuDZFNuIh7JPSR1itR2E+hTgm1dACKKCwRoMNfUjRs3JGPGjCp1HwIxRLHBqAcE8xBEQAABI/MQ5MOcYYDPlH5EBEbvIRiItK9I/4j51JA6tmTJkjbcC7JXderUsXURnIa5lKxE5Lx+P39ARm21PNexNj4Yc8/aYiABA31EROSQQsPDZM3J7SpI9/jlfynfUiVLId2rNJM2ZRtIMk9vKZ+zmEzasTjKZLlZUmWQz+p2lJuP78m3e1dIhMEgB66fktb/GyZTmgyQN7IXstFeEREREZEzQKCmY8eOTpv6jhLPjz/+GOPjGN1nCnOlJcR8aURERBTd9gsHZdjmWRJuiFDLVfKUlKsP/aK0NWIkH4J89QtXEltgoI+IiBwun/6fFw/JzD3L5cbju8b1Xh6e0qZMA+lWpZmk9UllXI8LbN2CFeTozTNy2e+6FMieR8rnKm7sXVMqW0EZunmWPHzxVO4FPJIuK76UQXU6StuyDdggQ0RERERERERE5KJ2XDys2g21IF/zUnVl9NvdBdO5W2prtAUG+oiIyGGcuH1epu1aIif9LkZZ37BoNeldo43kTJvZ7N/hQlshV3HJnSxjtMl/MTnuyk5fy5DNM+XYrXMSFhEu3+xYrFJ5fvF2D/H1TpoJs4mIiIjiIiIisrGBKCk73BERERG5ip2Xj8pnm2aotkJoWqK2CvK5u7mrXJ2W2hptgYE+IiKye9ce+cmsPSvUSD69CjmLyYDaHaRktgKv9fyZUqaT+S0/l2/3rJCfjmxW67ad2y/n71+TqU0HSoGMOV/r+YmIiIgSire3t2pI8PPzk0yZMqlla7MQIFDDVJJJx5mON/bF399f7YeXl5eti0NERESUqHZfPiaDNkwzBvkaF68pXzboGRnks0MM9BERkd16+PypmoPvl3/+NF5YIX/6HNK/djuplb9cgjWaIPXnwDodpHSOwjL617kSGPJSrj7yk/ZLR6qRfQ2LVU+Q1yEiIiJ6HQjy5cuXT+7cuaOCfXEN1mAkIJ7D0QNPjsDZjjf2IWfOnOLh4WHrohARERElmr1XjsvAjf8F+RoVqy5j3/nEpqk5Y8NAHxER2Z2XocGy5OhWWXRoozwPeWlcnzFFWvm0ekt5r2Qd8XRPnAaGeoUqScGMuWTwxulywf+GKsuwLd/KCb8LMrhORxUQJCIiIrIljOLLnTu3Gi0WHv5fZ6jYIOj08OFDyZAhg83TC7kCZzveGMnHIB8RERE5s/3XTsqADdMkNDxMLb9TtJqMa/ipXQf5gK2VRERkN8IjImTj6V0ye98q8Q98bFzv45VMOldsKp0qvJskc+blSZdNfm43TiZs/1E2nt6t1q04/pucvntZJjfpL9lSZ0z0MhARERHFREuhGJc0igg8YfvkyZM7ReDJ3vF4ExERETmOA9dPSf/1UyQkPFQtv124inzVqFeiDTZISAz0OYn7j8IlMCjCYjqQNCndJUt6vt1EZL9pjfZdPSHTdy+TSw9uGtd7uLnL+2+8KR9X+0CN5ktKCC5iWH6ZHEXk6z8Xq4v8qTuXpM3/hsvEd3tLtbylk7Q8REREREREjiw8wiCnLgXLw6fhkiGNh5QqmEw83B0/ra21zp8/L7Vr15aLFy9KqlSpxBF06dJFnjx5IuvXr3+t5+ncuXOCPI8mJCREChcuLGvWrJEKFSokyHMSkWs7eONf6bdusgSHhRozfk14t7dDBPmAkR8ncO9RmHT96r6ERo4mNcvbU+SnL7MnSrAPF+uffvop2voGDRrItm3bVPBx3bp10qxZs0S9yBORYzp776pM27VEDt04HWV93YIVpF/NtpIvQw6blQ3nrxZv1JPiWfLLwA3TxO+Zvzx5GSCfrvlaPq7WQnpUfd9uJ+ElIiIiIiKyF3tOvJDZq5+I/5P/0g1nSushvVqmk1plfRPlNWNrr4KkbLMaPny49OnTJ0qQD51eFyxYID/++KOcPn1aPD09pWDBgtKhQwfp0aOH+PomzrFJLNeuXVPzyB4/flzKlCljXD9z5ky1rwmZwnrw4MEydOhQ+fPPPxPseYnINR25eUb6rp0kQWEhxjbJbxr3dajpe9g66QSeBkbEGOSDkLDI7RLLO++8oyaD19+WL1/+Ws+ZN29e2blzZ4KVkYjsi99Tfxmx9Ts1Qk4f5CuZtYAsbP2FzGg22KZBPr1iWfLJyk4TpVb+cmrZIAaZu3+N9PrlG3n84pmti0dERERERGS39p58KV8ueBglyAdY/nLBA9l9/IVDtVfFp83qxo0bsnnzZhVA1OvYsaP0799f3nvvPfnrr7/kxIkTMmrUKNmwYYP8/vvv4izSpEkjadMmbJae9u3by969e1WAlIgovo7dOiu9135jDPLVLlBeTdvjSEE+YKCPEkSyZMkka9asUW7p0qWzdbGIyA49CwqUaTuXSNOFA2TLmb3G9TnTZJZJjfvJkvbjpXyuYmJvUidPKTObD5a+NduI+6s0yZigF4FKpPQkIiIiIiKi6Ok6566NuXPk7DWP1XbO3F61atUqKV26tOTIkSPKuqVLl6rA44gRI6RixYoqgIig344dO6Ru3bpquzp16qhgoB5GIOqDhvi78ePHS6dOnSRlypSSJ08e2bhxo/j7+6vnw7o33nhDjhw5YvybL7/8MsqoO5gxY4Z6LkswErJGjRoqaJchQwZp3LixXL582fg4RvNB2bJl1WhJlB1QVm3U5Pz58yV79uxqHlM9lLNr167GZQQ7y5Urp+Y5zZ8/v4wZM0bCwv4b6YD3sXr16rJixQor3gEiouiO3z6vOvG/DA1WyzXzl5UpDhjkA8crsQv5+Ou78uhZ1N5O5oSFWVcZGvbdffH0jD33efrUHjJvWFarnpOIyFohYaGy8sTvMv/AWnkW9Ny4Pk3ylNKz6vvSqszbdn8hRZrOjyo3k5LZCsqwzd/KoxdP5W7AQ+m8/AsZUvdDaVXmLYtzpRI5k927d8vkyZPl6NGjqle0uXRHmo8//li+//57mT59epQGikePHqnURZs2bRJ3d3dp0aKFSumDRggiIiIico42q5BQgzx7HnOGKf/H4dJi6G3x9nLeNqs9e/ZEm0sOQb4iRYqoAJcp/K7EKLi4QH17woQJakQg7mO0YLVq1VTwDHV3pLlEIBAj4OL7u/X58+cycOBAFTQMDAyU0aNHS/PmzdVIRNTpDx06JJUqVZLt27dLiRIlVIpNUy1btlS/AzCCsV69esbfBggibt261Xi8UNZZs2ZJzZo1VTARqUzhiy++MD4XXgvbEhHF1T9+F6XXL1/Li9AgtVw9b2mZ2nSAeHt6iSOy7xZVF4cK0wOTtAav40kipu5E+gHThjn0RsKNiFxbhCFCfjt/QL7ds0JuP71vXO/t4SXtyzeUrpXek9TJU4gjqZy7pKzoOFGGbJ4pJ26fl7CIcJnw50I54XdeRr3VXXy9k9u6iESJCj/w0SMZjQbvv/++xe0QADxw4IDqsWsu1Q6ChH/88YeEhoZKly5d1I/3ZcuWJXLpiYiIiMje2qxiCwY6envV9evXowX6Ll68qAJ9CaVRo0bSs2dPdR8BuLlz56pRggisAQJ9VatWlXv37qmRjfGBznl6CxculEyZMsmZM2ekZMmS6j5gtJ+l18BIvIYNG6p6vxboW7NmjWTMmNE4ihGj94YNGyYffvihWsaIvnHjxsmQIUOiBPrwOwPHlogoLpCZ65M1E+R5yEu1XCVPKZnebJAk84zeOcFRMNBnx9BLyRoY0WdNEC9tSnerR/TFFS7EqEBEeZ706eP0HOjxv2TJEuPyixcvVCXFw+O/8qC3EBE51mS2SNN5+t4V4zo3cZPGxWtKrxqtJFvqjOKosqRKLz+0GiUzdy+X/x3dotZtPbtPzt2/LtOaDrCb+QWJEgN+mOMWk9u3b6ueur/99pu8++67UR47e/as6rF7+PBhY4PHt99+q677U6ZMMRsYDA4OVjfNs2eRKaCQ8sc07Y+zwf4ZDAan3E/sl+pRjiqqvQyI1spiL+UBHCI3N6f8HDjz59se8XgnLVc63q6wjxS/tiNrRvRB6hTuVo/oS+r2qoRos3r58qVKQamH80NCwig7TZYsWdT/pUqVirbu/v378Q70ITiJIOLBgwflwYMHxu8+5iBEoM9a6PTXvXt3mTNnjkqvitGNbdq0UaMC4eTJk7Jv3z756quvjH8THh4uQUFB6tj7+vqqdT4+PmqZiMhap+9eVkG+wFdBPnTmn9nsM4cO8gEDfXbM2lQE568Hyyff3It1u697Z5bCuRPnA5siRQopWLCg2cdSpUolT58+jbb+yZMnUdIQjB07VgYPHmxcRh7vr7/+WsqXLy+enp5Mh0fkQC4/uCUzdi+T3VeORVmPHjIDareXopkt5/x3JEg1OrhuRymdvZB88dv3qifQlYe3pN2SkfJlg57SoGhVWxeRyCbwgx+pgj777DOVssfU33//reb10Pdqrl+/vvphj0YDpP8xNXHiRNWz1xTmHcEPfmc/nqhLoTFIa/xwFgEBAZKvUD7xSeEjHsnj3tkssbhndBdJnKmC4sUnyEfyPc+njhca55yJM3++7RGPd9JypeON8xO5FmvbrMLCI6TdKD958MRysC9TOg9ZNi67eLi7JWl7VVK2WWG02uPHj6OsK1y4sJw7dy7WfcD5wzQoiIwYpry8/ks3p5XH3DotOGft8+o1adJEzf+3YMEC4zx7CPCFhITEuh+mz4PX3rJlixp1iPSbSDeqD5qi7m8ue4g+YIqUn9ooQiKi2Jy9d1V6rp4gAcGRHQQq5iohM5t/Jsm9HDvIBwz0UaJDGgLM4aMNt9d64aB3Trdu3YzrMmfOrG4aVJQwSTEqZAz0ETkG/8DHMnf/all36i+J0P1gKJQxtwrwVc9XWpzRW0WqSKFMuWXQxuly6cFNld9bpfX0Oy8Da3ew+7kHiRLaN998o67dffv2Nfv43bt3o1zzAdujdzUeM2f48OFqPhD9iL5cuXKpH/apU6cWZ4YGFNSDsK/O1lCMRpyrF69K6bSlJXUKO3kfX43mC78VbjfBvpfPX8rVJ1dVY6Tpd8fROfPn2x7xeCctVzrepiOViDQI3n3yfmoZt/CJxW16fZAuUYJ89tRmVbZsWZXeUq9du3ZqFNuGDRuizdOHIBjquwg24hyClPf68v3777/GNJfxhedF3duYYUFEzbVnycOHD+X8+fMqyId582Dv3r1RttHm5EMZYztnIIiHkXyXLl1S70O5cuWMj+M+XiumIC3gOODYEhHF5tz9a9Jj9XgJCH6ulsvnLCazmn8mPl7JxBmw5dEJpEnpLl6eIqFhlrfx9ozcLrEglZZpwxwqOuixhEa5jz76SIoWLSpvvfWWmtcH6bnQk0lfaSIix/UiJEgWH94kPx3eLEFh/6XWy5wyvfSu0UoaF68lHk7euJE3fXb5X7tx8tX2H2XzmcjJwJcd26ZSAkxu0l+ypMpg6yISJQk0lMycOVOOHTuWoJ10kNIHN1NoOHX2xlPAsXTGfdXSUaqAmp0E1RStPPZSJhyiV41wzvYZcObPt73i8U5arnK8nX3/6PXUKO0jX3b3kNmrn4i/bl4/jORDkK9W2cg0jEndXgVJ1WbVoEED9XwIgGnpPlu1aqXmtG7btq18/vnn8vbbb6vg26lTp9ToNqTBb9asmbz55puqnBj9VqBAAZk2bZoacfi6MCoR2TEmTZokH3zwgUqt/+uvv1rsRIe59TD33vz58yVbtmwqXSfm0dNDMBTpNPFcOXPmVAE9/chI0/SdjRs3ltOnT0uHDh2iPIb0oHgsd+7cqmw4xyD4isDe+PHjjdthJCDm7iMiiskF/+vSc/VX8iwoMshXNkcR+e79oeLr7TwdlRjocwJZ0nvKwpGZJTDIzWKDGoJ82C6x4AKOi7weeuMgBQEqLGiYQEUEFQDk0UZqg927dxvzgxORYwqLCJe1/+yQefvXyMMX/6U7SeHtIx9Vfk/al2vkFMPfrYUKwviGn0qZHEXkmx2LJTQ8TE76XZTWPw+Trxv3ValLiZwdfmwjtSB+lGvQoDFo0CCZMWOGXLt2Tc0JYpp+MCwsTKXeie98IURERERkv2qW8ZXqpX3l1KVgefg0XDKk8ZBSBZMl+ki+mNqrIKnarDC/NQKM27dvV0E/QBvesmXLVOBs4cKFaj46bFOoUCHp1KmTcbuuXbuqIBfW4fEBAwa89mg+KFasmJojb8KECSpY1qJFC5WeFOUxB8G2FStWqKwdSNeJ4zhr1iwVMNSgfFiHVKcI1mHk386dO80+HwKYyOiBkXsY3aiHfd+8ebN6HmQLQQpSBGP1wVdMB4C0qwgEEhFZctH/pvRYNV6evIxMM14me2GZ3WKYUwX5wM2Q0DO/kkXakHtchEx7x2BumatXr0q+fPninPICbyEax5wxvaUj7Vtc30OkcUEjJ3o7OWPvR2feP+5b5Hdz5+WjMnP3Mrn6yM+43tPdQ1qWfkt6VH1f0vvaSQo2G71vGMk3eON08Xv2QC27iZt8Uv0D6V6lubi7Jfzr83OZsNdlsh6uz+iJjN7GWkoffWoh7Yc65uzr0qWLahA4e/asFC9eXI4cOaIaUuD333+Xd955R27duqXm+4iNK71/zvz9vnz5snRt2VWap20uWVLYSQcwNxGPXB4SftN+Unfee35P1j1ZJwtXL1Q9+Z2JM3++7RGPd9JypePtStdlZ5RY7VWO1q6T2Ps2e/Zs2bhxo/z2229i7xzhfWvdurWULl1aRowYYbf79rrfn/hw5msP980x2XLfLj+4JR+tHCuPXz5Ty29kKyRzPxguKZP52v2+xbVuxRF9REQUJ//cuSjTdy2VY7eiThr+VuHK0rdmW8mdjqNxoETWArKi49cycuts2XP1uBjEIHP2rZZ//C7KV416SVqfVLYuItFrzauGuTQ0+PGK+TzQIxcj+ZDSRw89cDFSD0E+rfcwgnrdu3eXefPmSWhoqPTu3VvNUWJNkI+IiIiIyNH07NlTpdwMCAhQ8+5S/IWEhEipUqXU6EYiInOuPrwt3VeNMwb50E43JwGDfPaGgT4iIrLKzSd3ZdaeFfL7+QNR1iNN5aDaHeSN7IVsVjZ7lcYnpcx6/zP58eAGmb13lQr27b16Qtr8PFymNB0gJbM516gMch0YiadPF4Q5Q+DDDz+UxYsXW/UcS5cuVcG9evXqqZ5vSBWEND9ERERERM4II8hGjhxp62I4BW9vbzWvIRGROdce+Um3VeOM0wwVz5Jf5n0wQlI5aZAPGOgjIqIYIYf1/L/XysoTv6s5+TR50mWTfrXaypsFK9ptKg97gDSdSNdZKltBGbb5W9WT6E7AA+m84gsZUreTSnXK40eOBvNwxCX7O+blM4XRf5iThIiIiIiIiIgoIVx/fEe6rRwnD54/UctFM+dVQb7UyVOIM2Ogj4iIzAoKDZFlx36VhYc2SEDwC+P69L5p5JNqLaR5qTfFy4OXEWtVyVNKVnaaKJ9tmiEn/S5KaHiYfLV9oZzwuyCf1+/mdJMAExERERERERERJWU2sm4rx4n/88dquUimPPJ9y5Eq45azYwstERFFEWGIkE1ndqv55O4GPDSuT+7pLR0rvCtdKjWVFN4+Ni2jo8qSKoP82PoLNcfh0mO/qnVbzuyVc/euybT3Bkre9JybjIiIiIiIiIiIKC5uPbmngnz3Ax+p5UIZc6sgX1of15gTlYE+IiIyOnD9lEzZ8bNcfnzbuM7dzU3eK1lHPq3eUjKnTG/T8jkDjIIc8uaHUjp7Yfnyt+/lRWiQXH54S9r+b4SMfedjeatIFVsXkYiIiIiIiIiIyCHcfnpfBfm0AQsFMuSU+a0+l3S+qcVVMNBHRERywf+6TN+1TPZfOxllfc18ZaVfrXZSKFMum5XNWTUoWlUKZ8otgzZOV4E+BPwGb5ohHfwaSv9a7ZkWlYiIiIiIiIiIKAZ3nj1QQb47AQ/Ucv70OWRBq88lvQsF+YCtiERELuxewEP5bu8q2XR6txjEYFxfLHNeGVCng1TOXdKm5XN2+TLkkCXtx8u4P36QrWf3qnVLjv4q/965LJOa9JcsqTiCkoiIiIiIiIiIyNRdFeQbK37P/NVyvvTZZUHrUZIhRVpxNQz0ERG5oIDgF7Lo0AZZcnSrBIeFGtdnS51ROpVqKK0qNRBPjihLEr7eyWVCo15SNkdhmfTXzxIaHiYn/C5I65+HyTeN+0jlPKVsXUQiIiIiIiIiIiK7cS/gkXRbNU5uPb2vlvOkyyYLWo2SjC4Y5AN3WxeAXNfixYslbdrYv3je3t6yfv16q57Tzc3N6m2JXBGCSMuObZMmP/STHw9uMAb5UiVLIQNrd5B1nafIm/nKi7sbLw9JCeeuVmXelsVtxki2VBnVuscvn8nHaybIggPrJMIQYesiEhERERERvRZDeLi83HdcAtZuV/9j2V7ZU5vV+fPnJWvWrBIQECCOonPnztKsWTO7eR5NSEiI5MuXT44ePZpgz0lESe9+IIJ8Y+Xmk3tqOXfarCrIlyllOnFVbMl1Qi92HZEb1Tuo/5MCLrqorGi3DBkyyDvvvCP//PNPjH/XunVruXDhgnH5yy+/lDJlykTb7saNG9KwYcNEKTuRqzAYDPLH+QPy/qLB8s2OxfL4ZeQPBMwD16nCu7Kl20z5sGJjSebpbeuiurSS2QrIik4TpXre0mo5wmCQ7/aulL7rJsvTl4G2Lh4REREREVG8PN+8S66Xayl+zfrK/Z5j1P9YDty8y+7aq+ytzWr48OHSp08fSZUqVZTf+PPnz5fKlStLypQpVVCyQoUKMmPGDHnx4oU4mmvXrqn36MSJE1HWz5w5UwVdEwoCs4MGDZIRI0Yk2HMSUdJ68PyJdF81Xm48vquWc6bJLD+0HuXy098w0OdkcKF/NP57Cb1wXf2P5aSAitKdO3fU7c8//xRPT09p3Lixxe1DQ0PFx8dHMmfOHOtzo9dSsmTJErjERK7j+O3z0mnZaBm8aYbceBJ5EYSGRavJ+i7TZFCdjpLGJ6VNy0j/SeuTSr5rMVQ+rd5S3MRNrdtz5bi0+d9wOXP3iq2LR0REREREFCcvtu6Wex+NknC/yDmUNOF3/OVe188TNdgX1/Yqe2uzQiBx8+bNKmip17FjR+nfv7+899578tdff6kA2ahRo2TDhg3y+++/i7NIkyaNVSMr46J9+/ayb98+OX36dII+LxElvocI8q0cJ9ce+anlHCrIN1qypMogro6BPifz8q9DEnzinLqP/7GcFFCpQeUGN/RwGjZsmNy8eVP8/f2NvXJWrlwptWvXluTJk8vSpUujpEHA/TFjxsjJkyeNPa20Hjv6NAgYYt+7d2/Jli2bep48efLIxIkTo5TlwYMH0rx5c/H19ZVChQrJxo0bk+QYENkbXPQGbJgqnZd/If/cuWhcXyFXcVnW4Sv5unFfyZk29h8ulPSQOrVn1RYy54NhKvAHmFi40/LRsubk9iTrxEFERERERPQ6kJ7z8ajZIuZ+wrxa9+DzWYmWxjOm9iqw9zarVatWSenSpSVHjhxR1qGMy5cvVyPTKlasKHnz5lVBvx07dkjdunXVdnXq1FHBQD2kwdQHDfF348ePl06dOqmRgSgzyoTj8/7776tRhG+88YYcOXIkxtGNGEmI57Jk27ZtUqNGDXVMMbISwdbLly8bH0c6TShbtqw6vii7aepOjGDMnj27REREndoC+921a1fjMoKd5cqVU+9B/vz51XsXFhZmfDxdunRSrVo1WbFiRYzHnojsy8PnT9VIviuPbqvl7Kkzyg+tRkm21JFT4Lg6T1sXgCy7Vb+bhN1/ZOXWBkG7b8TDJ1HW3ukwTNwzpFUXSWt5Zk4vObf/IPEVGBgoS5YskYIFC6qL9/Pnz9V6VKamTp2qLtq42P72229RUiL8+++/6sK/fft2Y68dU7NmzVIVDlRqcufOrSpnuOnhAj5p0iSZPHmyfPvtt6qnzvXr1yV9etcevkuudeH7/u9fVEAoXDe3W/4MOWVArXZSM39kxZnsX7W8pWVlx4lqNOapO5fUHIvj/vhBTty+ICPf+kh8vDjamYiIiIiI7LfNyhAcIhGPnsawgUj47ftyrfh74pbMO1HbrEzbq/Tstc1qz549KiWnHoJ8RYoUUQEuU/itb65sMZk+fbpMmDBBjQjEfYwWRCAMwb8pU6aoY4P7GAEX37YEtA0OHDhQBQ3xPowePVoFPDES0d3dXQ4dOiSVKlVSx7dEiRIqgGqqZcuWKoUpRjDWq1dPrXv06JF6X7Zu3Wo8Xigr3ouaNWuqYGKPHj3UY1988YXxuXBM9+7dG699IaKk9/jFM+m5+iu5/PCWWs6aKoMsaDVasqfJZOui2Q0G+uwYKkxIY/BaQsMk4u4DSWxII4CeP9rFG72XsA4Xaw16EaE3kDlIiYC/RwoF9LLSmI5aQcoC9HhCLyBULtDTyBR6+7Rt21bdR0UFF3dUGJCugciZvQgJkiVHt8qiQxvlRWiQcX2mFOlUGsimJWuLp7uHTctIcZc1dUZZ1OZLmbpziSw/vk2t23Rmt5y7f02mNO0vedNnt3URiYiIiIjIxSRIm5VOjMHARG6vsuc2KwQBTQN9Fy9eVIG+hNKoUSPp2bOnuo8A3Ny5c9VrfvDBB2qfhw4dKlWrVpV79+5F2f+4aNGiRZTlhQsXSqZMmeTMmTNSsmRJdR8QgLX0GhiJh/kQly1bZgz0rVmzRjJmzGgcxYhAKgKTH374oVrGiL5x48bJkCFDogT6MDIQx5aI7N+TlwEqyHfxwQ21jLn4MCcfs5RFxUCfHUMvJWsZDBES8fCpCuxF4+UZp1F9cXldDS6oqAjA48ePZc6cOerii8qKxrRiEh+oEL311luqQoNKEIb6v/3221G2Qe8gTYoUKSR16tRy//79135tInsVHhEhG07vlDn7Vot/4GPjel+v5NK5UhPpWP5d8fVObtMy0uvx8vCUYfU6S+kchWXMb9/Ly9BgVcFpt2SkjH3nE6lfuJKti0hERERERC7E2rajWEf0veKePo3VI/oSqr1KH4iz1zarly9fqhGGegk9lYO+TFmyZFH/lypVKto6lDO+gT4EJxFEPHjwoEpfqqXfRHAUgT5rYQRk9+7d1fuItKwY3dimTRtj4BbpVTH/3ldffWX8m/DwcAkKCpIXL16olKla8BbLRGTfnr4MVEG+8/6RgflMKdPJD61GS6608TsXOTMG+uyYtakIcIEP2P63+Lcban6D0DDJMnOY+L5ZWRILKidIfaD54YcfVKqABQsWSLdu3YzbvC7k2L569ar8+uuvajh/q1atpH79+qoHj8bLyyvK3yDAaZq/m8gZ4Lu/9+oJmb5rqXHoOni4uUuLN+rJx9VaSIYUCTtpNdlWw6LVpEimPDJowzSVk/x5yEsZtHGaCub2qdHa1sUjIiIiIiIXYW2bVURYmNwo10rC7/qbn6fPTcQje2bJc3SVuHl4JGl7Feam029nj21WGK2GAKVe4cKF5dy5c7GWB8Ev06BgaGhotO30ZdIGCZhbp5XT2ufVa9KkiQqs4rhr8+whwId5DeMCz4PX3rJli5qbEKk6kW5Ug7SgGNVnbnSmPmCKlJ/aKEIisk/PggLl4zUTVEYryJgirZqTL3c6BvnMiTpOnRwSLnBPv1mIK6/5Ddzc5NHEHxK8x09MUAnAhR89j6yF/NvoZRMb9HZCfnRUDjBZ8i+//KIu0ESu5MzdK2oC2t5rv4kS5KtbsIL80nmymr+NQT7nlD9DDlna4SsV9NP87+gW6bnmK3n4InHS3RAREREREcUHgnfpxvV6tWD6YOR/Gcf3TZQgX0K1V9myzQpzBiK9pV67du3kwoULsmHDBvNthE8jfxcikHXnzh3jYyg/5hp8XXjeu3fvRmlnxFx7ljx8+FDOnz8vn3/+uUq5WaxYsWjBS21OvtiOMYJ1COJhJN/y5cvV6EkEWDW4j9dCcNf0pk/XivkGcWyJyD49C3ouH6+ZKGfuXVHLGXzTqCAfp6+xjCP6nEFIqITdvo+rufnHDQYJ87uvthMr0iDER3BwsLrIAy7W3333nepFg5421sqbN6/q+YTKQc6cOSVVqlTRJt+dNm2ayqeOizEu0KtXr1ZpA9KmZUCDXMPtp/flu70rZevZfVHWl8pWUAbWbi/lchazWdko6SAV68R3+0jp7IVlys7/SVhEuBy/fV4+3TpZJjXuJ5Xz/pdmhYiIiIiIyJZ8G9WSLD+Okwefz5Jwv//m9cNIPgT5UjaunWivnRDtVbZss2rQoIHKlIUAmMerYChGCq5bt07N9YfgGdKDIvh26tQpNbqtT58+0qxZM3nzzTdl4MCBavRbgQIFVPmePHkir6tOnTri7+8vkyZNUvP4bdu2TY1iRJDT0tx6mHtv/vz56vggXSfm0dPLnDmzSqeJ58LxRUAPIy8tpe9EWlQE6zp06BDlMaQHxWO5c+dWZcP7gHSeCHDqR3AivefYsWNf+1gQUcILDH4hn/4yUU7fvayW0/umkQWtRkm+DDlsXTS7xkCfE0AO86y/zhW3pwHiFq17VCSPTOmsynUeX7gQ42INqOwULVpUVWhw8b92LXJ4rTUT865du1blT0fFY9GiRcbJczV4blQkkNsbFRwM09+6dWu0SZSJnHG4+g8H1suy49skNPy/uThzpc0ifWu2lbcKV7Z6Hk5yDni/25Z7R0pkKyCfbZwhdwMeyhOkNfhlgvSu0Ua6VGoi7m48NxIRERERke2laFxbUjSqKUEH/pGwew/FM0sGSV7ljUQfyRdTe1Vc2KrNCvMJenp6qlSgCPppvwWXLVumAmcLFy5U89Fhm0KFCkmnTp2M23Xt2lUFubAOjw8YMECV/3VhRB7myJswYYKMGzdOHZvBgwer8piD/V+xYoX07dtXpevEKLxZs2ZFeQ9QPqxD8A3Bupo1a8rOnTvNPh8CmOnTp1cj9zC6UQ/7vnnzZvU833zzjUpBivdcm1YI/v77bzXqEYFAIrIvmKKm1y9fy6k7l9RyOp9UMr/l51IgY05bF83uuRmSMp+ji3v27JnqjYKLiWkvF0wKi55B+fLlizbJbmzwFoaFhamLorM19DvSvsX1PUQ+ckxkjF5LzhiodOb9S8p9CwkLleXHf5MfDq5Tw9Y1aX1SSY+q70ur0m+Jl0fC9dng++aYHr94JsO3fCt/Xz9lXFe7QHkZ3/ATSZ08pTi6xHrvYrouk/1zpffPmc9fly9flq4tu0rztM0lS4osYhcwV1AuDwm/GW5+LiEbuPf8nqx7sk4Wrl6oeuQ7E2f+fNsjHu+k5UrH25Wuy84osdqrHK1dx173bfbs2bJx40b57bffJKk48/uG1KoIOGI0ZGLv2+t+f+LDma893Dfn3rcXIUFqJB8yVmltnwtafS6FM+URV3zfnsWxbsURfUREdijCECG/nftbZu1ZIX7P/kttkszTS9qXayRdKjWV1Mlff7Jwcg7pfFPLt82Hyow//ydLT/0uBjHIrstHpc3/RsjUpgOkWJZ8ti4iERERERERxUPPnj3VKMKAgAA1apDiLyQkRAX5+vXrZ+uiEJFJkK/32m+MQb40yVPK9y1H2nWQz94w0EdEZGcO3zgt03ctldOvJpwFpOVtUqKm9KreSrKmzmjT8pF98nB3l46lG0qVgmVk5K+z5WlQoJrTsdOy0TK8XhdpXqqu0/XEJCIiIiIicnYYVTdy5EhbF8MpYF5FjOTDaEUisg8vQ4Olz7pJcvTWWbWcKlkKFeQrmjmvrYvmUBjoIyKyE5ce3JQZu5fJnivHo6yvkqeUDKjdnhc4skr1fKVlZaevZfDG6fLv3csSEh4qY36fr3pFjajfVXy8ktm6iERERERERERE5OKCQkOk37rJcuTmGbWcKpmvzG85kpmp4oGBPiIiG/MPfCxz9q2W9f/+JRG6aVMLZ8qtAnzV8pa2afnI8WRLnVEWtflSpuz8n6w88btat/H0Ljl3/6pMbTpQcqfLausiEhERERERERGRKwf51k+Wgzf+VcspvX1k7gcjpHjW/LYumkNioM8OJ3Akx8T3juLqechL+enwZnULCgs2rs+SKr30rt5a3i1eU6VjJIoPb08vNYKvTI7CMua3BeozdsH/hrT933AZ1/BTebNQRVsXkYiIiIiIHATbPIjijt8bIvOCw0JkwIapcuD6KbWc4lWQr1S2grYumsNioM+OckS7u7uLn5+fZMqUSS1bO5eSwWBQuaWRs9vZ5l9yhH1DGTGZr7+/v3oP8d4RxSQ0PEzWndohc/f/Io9ePDWuR8+VrpWbSftyDSW5Fz9HlDAaFauhJi8etHG6XHvkJ4EhL1Vl6sMKjaVvrbbi6e5h6yISEREREZETtlc5SrtOfHHfHFNS7BvbCoksCwkLlUEbpsv+ayfVsq9XcpnTYpi8kb2QrYvm0BjosxM46efLl0/u3LmjKk9xvXighwiewxkvvo6yb76+vpI7d25VViJLn+edl4+qefgQcNEg0NKqzFvSvcr7kt43tU3LSM6pYMZcsqzDV/Llb9/L7+cPqHU/Hdms5vD7pnFfyZQyna2LSERERERETtZe5WjtOnHFfXNMSblvbCskij74YfCmGbLn6nG17OOVTGa3GCZlchSxddEcHgN9dgS9O3DyR6+S8PBwq/8OF6eHDx9KhgwZnO7C4Sj75uHh4ZS9nCjh/HPnokzftVSO3ToXZf3bhatIn5ptOGcaJTqkQZjUuJ+qPE3buUTCIsLl6K2z0vrnYTKpST+pkKu4rYtIRERERERO1F7lSO068cF9c0xJtW9sKySKHuQbumWW7Lp8VC0n90wms98fJuVyFrV10ZwCA312Bid/Ly8vdYvLBQrbJ0+e3Ckvvs66b+Qabj65K7N2r5DfL0SOotIg2DKodgcOS6ckv8YgNWzJrAVk8MYZcj/wkTx88VR6rBqvAs6dKzbhjxAiIiIiIkqQ9ipnb9fhvjkmZ943InuFzubDtnwrf106opaTe3rLt+8PkfK5itm6aE6DgT4iokTw+MUzmX9graw68Ye6mGnypMsm/Wu1k7oFKzCgQjZTOnthWdnpaxm+5Vs18XG4IUKllD3pd0HGvvOJpE6ewtZFJCIiIiIiIiIiB4d20a/3/ix7bkTOyZfM00tmNf9MKuUuYeuiORUG+oiIElBQaIgsPfarLDy4XgJDXhrXp/dNI59UayHNS70pXh489ZLtYT7IOS2Gy7z9a1RQGtCzqt2SETKl6QApmjmvrYtIREREREREREQOHOQb+etsY5DP28NLZjQbLJXzlLJ10ZwOW5uJiOIgPCJCjt48I5f9rkuB4DxSPldx8XB3V+u3nNkj3+1bKfcCHhm3R77pThXelc6Vmqg50ojsCT67vWq0ktLZC8mIrbPlaVCg3HxyTzotGyXD63WV5qXq2rqIRERERERERETkYNBWOurXOfL7+cjpjDDwYUazQVItb2lbF80pMdBHRGSl7RcOyaQdi+Ve4H+BvCwp00uzUnVk56Wjct7/unG9u5ubNCtZVz6p/oFkTpneRiUmsk6N/GVleccJ8tnGGXL63hUJDguVL3/7Xk7cPq8Cfsm9vG1dRCIiIiIiIiIicpQg37Y5svXsPrXs6e4hU5sMkOr5yti6aE6LgT4iIiuDfIM3ThODyXoE/b7/OzLtoaZm/rJqHr6CGXMlaRmJXkeONJllcdsxMumvn2T1ye1q3fp/d8rZ+9dkatP+kittVlsXkYiIiIiIiIiI7FiEIUK+/G2ebDmz1xjkG1Wri2ovpcTjnojPTUTkNL1QMJLPNMhnCnOaLWg1Sr57fyiDfOSQvD295PO3uslXjXpJcs/IUXzn71+Ttv8boebvIyIiIiIiIiIishTkG/PbfNl4ercxyDepcT+pkrOkrYvm9BjoIyKKxbFbZ6Ok67RkUJ0OUil3iSQpE1Fialy8pixp/5XkSZdNLQcEv5D+66fIjN3L1ETKRERERERERERE+iDf+D9+UNmhwMPNXb5p3E/qFqxg66K5BAb6iIhi8eD5E6u2e/j8aaKXhSipFMqUS5Z1+EreKlzZuG7RoY3Sc/VXVn8niIiIiIiIiIjIuRkMBpmwfaH88s8OY5Dv68Z9pX7hSrYumstgoI+IKBYZU6RN0O2IHEXKZL4yuUl/+axuJ5VuAY7cPCNtfh6uRroSEREREREREZFrB/km/rlIVp/crpbd3dxkwru95e0iVWxdNJfCQB8RUSzK5SwmWVKmt/i4m4hkSZVBbUfkbNzc3KRD+UbyQ+vRkillOrXO//lj6bZynPx0eJOq0BERERERERERkWtBm9Ckv36SlSd+Nwb5vmrUS94pWs3WRXM5Lh3omz17tuTNm1eSJ08ulStXlkOHDsW4/YwZM6RIkSLi4+MjuXLlkgEDBkhQUFCSlZeIbMPD3V3q6dIXmgb5YEjdD9V2RM6qbI4isrLj18Z5KMMNETJt11IZuHGamsOPiIiIiIiIiIhcJ8g3Zef/ZNmxbWrZTdxkXMNPpVGxGrYumkty2VbplStXysCBA+WLL76QY8eOSenSpaVBgwZy//59s9svW7ZMhg0bprY/e/as/Pjjj+o5RowYkeRlJ6KkFRIWKjsvHTH7WOZUGWRK04HMOU0uIUOKNDLvg5HSvUpz47odFw9Lu/+NkAv+121aNiIiIiIiIiIiSpog3/RdS2XJ0a3GIN/Ydz6WxsVr2rpoLstTXNS0adOke/fu0qVLF7U8b9482bJliyxcuFAF9Ezt379fqlevLu3atVPLGAnYtm1bOXjwYJKXnYiS1uqTf4jfM391v0qeUvJRpffkst91KZA9j5TPVZwj+cil4PPeu0ZrKZWtoIzcOkcCgp/LjSd3pePSUTKy/kfStGRtWxeRiIiIiIiIiIgSKcg3c89y+enIZuO6Lxr0YHuQjblkoC8kJESOHj0qw4cPN65zd3eX+vXry99//232b6pVqyZLlixR6T0rVaokV65cka1bt0rHjh0tvk5wcLC6aZ49e6b+j4iIULeEgufCFywhn9NecN8cl7PsH1ISzv97rXG5X822UjhjbsnlnUEyZcqkUnc6+j464/vmavtmi/2rma+sLO/wlXy2aYacvX9NgsJCZNS2uXL89jmVyjaZp7fd75uzfhaIiIiIiIiIiBIa2mZm71sliw5tNK4b/XZ3aV6qrk3LRS4a6Hvw4IGEh4dLlixZoqzH8rlz58z+DUby4e9q1KihPtBhYWHy8ccfx5i6c+LEiTJmzJho6/39/RN0bj80VD59+lSVCwFLZ8J9c1zOsn8/ndgqT4IC1f26ectLevFVKX6dYd+c+X1ztX2z1f55iciker1k7uG1svVSZEeZtaf+kn9uX5RRtbpI1pQZ7HrfAgICEuy5iIiIiIgSAtqS1q5dq9qnfHx8VMfzb775RooUKWLxbxYvXmzMWKVJlixZgrY9ERERzd2/RhYcWGdcRmanFm/Us2mZyIUDffGxc+dOmTBhgsyZM0cqV64sly5dkn79+sm4ceNk1KhRZv8GIwYxD6B+RF+uXLnUKKDUqVMnWNnQAOrm5qae19kar7lvjssZ9s8/8LGsPbdL3fd095CBb3aQzGkzO8W+WcJ9c1y23L+vmvaRKmdKy4TtC9XIvkuPbknvX6fK+IafSq385ex235InT55gz0VERERElBB27dolvXr1kooVK6pO5uhg/vbbb8uZM2ckRYoUFv8O7Uznz583LqP+TERElFDm7f9Fvv/7F+Py8HpdpFWZt2xaJnLxQF/GjBnFw8ND7t27F2U9lrNmzWr2bxDMQ5rObt26qeVSpUrJ8+fPpUePHjJy5EizDY/oPYWbKWyb0I2wqMAlxvPaA+6b43L0/VtwcJ0EhUWm38WFK3f6bE6zbzHhvjkuW+7feyXrSLEs+WTQxuly4/Fdlfa23/op8lHl9+TT6q1UsNze9s1ZPwdERERE5Li2bdsWbbRe5syZ1RQ0tWrVirG+bKlNi4iI6HVgFN/c/auNy0Pe/FDalG1g0zJRVC4Z6PP29pby5cvLn3/+Kc2aNTOOFsBy7969zf7NixcvojUIIlgISCVGRM7l2iM/WfvPDnU/hbePdK/yvq2LRGT3CmfKI8s6TJAvts2TPy8eUut+PLhB/vG7JN807iMZUqS1dRGJiIiIiBwKUthD+vTpY9wuMDBQ8uTJo9q3ypUrp7JSlShRwuy2wcHB6qbPQAX428SYF9tZ50rnvjkmZ943Z98/7pttYD6+7/auNC4Pqt1B2pZpYHVZ7XnfXldi7ltcn9MlA32AlJoffvihVKhQQSpVqiQzZsxQI/S0nOadOnWSHDlyqNzo0KRJE5k2bZqULVvWmLoTo/ywXgv4EZHzwAUs3BB5Qv2wYmNJ75tw6XaJnFmqZL4ytekA+d/RrTJj11L1PTp887S0/t9wmdS4n5TLWdTWRSQiIiIicgho5Ovfv79Ur15dSpYsaXE7zN+3cOFCeeONN1RgcMqUKWpuv9OnT0vOnDmjbY+2rjFjxkRb7+/vn+Dz+jnzXOncN8fkzPvm7PvHfUt6q8/skB+ObTQudyvXVN7OVUHu37/v8PuWEBJz3wICAuK0vcsG+lq3bq0qMKNHj5a7d+9KmTJlVHqELFmyqMdv3LgR5c35/PPPVRoE/H/79m01TxCCfF999ZUN94KIEsM/dy7KHxcOqvsZfNNIx/Lv2rpIRA4F18tOFd6Vklnzy5BNs8T/+WM152W3lWOlf+320rF8I84ZQkREREQUC8zV9++//8revXtj3K5q1arqpkGQr1ixYvL999/LuHHjom0/fPhw1QFeP6IvV65cqq0Lc/0lJGeeK5375piced+cff+4b0lrydGtUYJ8fWu0kS6VmjrFviWUxNy35MmTx2l7lw30AdJ0WkrVuXPnzijLnp6e8sUXX6gbETkv9MCYuWuZcfnjai3E1ztuJ1YiilQuZzFZ0WmiDN08S47cPKNG903d+T85efu8jHnnY0mZzNfWRSQiIiIisktor9q8ebPs3r3b7Ki8mHh5eamMVMhGZU6yZMnUzVRizfftzHOlc98ckzPvm7PvH/ctaSw99qtM3bXEuNy7Rmv5qErkFGiOvm8JLbH2La7P53xHlojoNey7ekKO3Dqr7udOm1Wal3rT1kUicmgZU6SV71uOlK6V3jOu237xkLRdMkIu+t+wadmIiIiIiOyx8ymCfOvWrZMdO3ZIvnz54vwc4eHhcurUKcmWLVuilJGIiJzXiuO/yaQdPxmXP6nWUrpXaW7TMlHsGOgjInolwhAhM/csNy73qdlGvDxceuAzUYLwdPeQfrXaysxmg9UcfnDj8V3psPRz2XR6t62LR0RERERkV+k6lyxZIsuWLZNUqVKp6WZwe/nypXGbTp06qfSbmrFjx8rvv/8uV65ckWPHjkmHDh3k+vXr0q1bNxvtBREROaJVJ/6QiX8uMi73rNpCZTsj+8dAHxHRK1vP7pMLr0YYlchaQN4qXNnWRSJyKnUKVpAVHSdK0cx51XJQWIh8/uscGffHDxIcFmLr4hERERER2dzcuXPl6dOnUqdOHTUiT7utXLnSuM2NGzfkzp07xuXHjx9L9+7d1bx8jRo1UnPu7d+/X4oXL26jvSAiIkez5uR2+Wr7j8ZljOL7pNoHNi0TWY9DVYiIRCQkLFRm7/3vh1P/Wu1UjmUiSlg502aRn9qOlW92LJa1p3YYK5Nn7l6RKU37S440mW1dRCIiIiIim6bujM3OnTujLE+fPl3diIiI4mPdqb9UJ2xNl0pNpVf1VmwbdSAc0UdEhKHpJ/8Qv2cP1P3qeUtLpdwlbF0kIqeV3MtbvmjQQ8Y0+FiSeXqpdWfuXZE2/xsuuy8fs3XxiIiIiIiIiIhcwsZ/d8mY3+Yblz+s0Fj61WzLIJ+DYaCPiFxeQPALWXBgnXG5b622Ni0PkatoVqqO/NxunORKm0UtPwt6Ln3WTZLv9q6U8IgIWxePiIiIiIiIiMhpbTq9W0ZvmycGiRxN3qF8IxlQuz2DfA6IgT4icnk/Hd4kT14GqPuNitUwzh9GRIkP37dlHSZI3YIVjesQeP9kzQR5+PypCvgduXlG/rp6VP3PACARERERERER0evZenavjN421xjka1fuHRlcpyODfA6Kc/QRkUvzD3ws/zuyRd33dPdQ+aeJKGmlTp5Cpr83UH46vFlm7Vku4YYIOXjjX2m2aJB4uLnL45fPjNtmSZlehrzZWeoXrmTTMhMREREREREROaJt5/bLyK2zJeLVvLCty7wtQ+p+yCCfA+OIPiJyafP+/kWCwkKMF7WcaTPbukhELgmVyc6VmsiCVqMkY4q0at2zoMAoQT64H/hIBm+cJtsvHLJRSQl2794tTZo0kezZs6v3bv369cbHQkNDZejQoVKqVClJkSKF2qZTp07i5+cX5TkePXok7du3l9SpU0vatGnlo48+ksDAQBvsDREREREREZFr+P38ARmx5TtjkK9l6foyvF4XBvkcHAN9ROSyrj3yk3X/7FD3U3j7SLcqzW1dJCKXVz5XMVna/ivx8jCfdCCyGioy6a+fmMbThp4/fy6lS5eW2bNnR3vsxYsXcuzYMRk1apT6f+3atXL+/Hlp2rRplO0Q5Dt9+rT88ccfsnnzZhU87NGjRxLuBRHFBN/JRo0aSaZMmdSPftzmzZsXZZvvv/9eatSooYL62jbnzp2L9lzaY6a3zz//PNZy3Lt3T7p27SqZM2eWZMmSSfHixeW7776Lss2iRYskf/78kjJlSqlbt65cuHAhyuPvvvuuNGjQIN7HgoiIiIjIGaDT9LDNs1QmJWjxxpsyon5XBvmcAFN3EpHL+nbPCuOFrXPFJpLeN7Wti0REInLzyV0JDQ+z+DiCffcCHsqxW2elYu4SSVo2itSwYUN1MydNmjQqeKeHRvlKlSrJjRs3JHfu3HL27FnZtm2bHD58WCpUqKC2+fbbb1VQYcqUKWoUoKng4GB10zx7FjnaMyIiQt2cGfbPYDA45X5iv9SPSvyutJffllpZ7KU8gEPk5pakn4OjR4+q7zICaA8ePDD7fdu6dascP35cBQOvX79udhu9MmXKqGCdJkeOHDF+vtGpoHbt2qqzgI+Pj+TJk0edP/r06aMCgGPGjFGBxW7duknHjh1l/PjxqhNCly5dZM+ePeo5li1bJjt37pR//vnHKb9DceXM5xN75ErH2xX2kYiIyJH9demIDN0809gW2qxkHfn8rW7i7saxYM6AgT4ickn/+F2U7RcjU/9l8E0jHco3snWRiOiVB8+fJOh2ZHtPnz5VQQqk6IS///5b3deCfFC/fn1xd3eXgwcPSvPm0UdYT5w4UTXqm/L395egoCBx9sZTHEM0FuMYOZOAgADJVyif+KTwEY/kHmIv3DO6/zeE2A74BPlIvuf51PG6f/9+krwmRsA1a9ZMBfkQqAfT18d3cs6cObJmzRrp37+/Wvfw4UOLZZw/f77kypUryjpsa+nzjRGECPLh/IGRvxjN9+WXX6qRhN988420atVKDhw4oL4jJUuWFE9PTxWYPHHihHpepAhGuT777DM16jCpjp09c+bziT1ypeON8wMRERHZp52Xj8rgjdMlLCJcLTctUUu+aNCDQT4nwkAfEbkc/NCesXuZcfnjah+Ir3dym5aJiP6jzdGXUNuRbSEIhzn72rZtq+bjg7t376o0fHpooE+fPr16zJzhw4fLwIEDo4zoQ8AAI4m053XmhmIEOrCvztZQjHkZr168KqXTlpbUKezkfXw1mi/8VrjdBPtePn8pV59clVSpUkX77iQW7XVQb9KYvr52H+s1GTJksFhGjARGet98+fJJhw4dVBDOy8vL4ud779696v9ChQpJnTp11H38HQJ9mA/05MmTUr16dfV3//77r4SFhcmVK1fUyEGUYciQISrwN3LkSPHwsJ9Asi058/nEHrnS8U6enL+niIiI7NGeK8ejBPkaF68pXzb4mEE+J8NAHxG5nL1XT8jRW2fV/dzpskrzUnVtXSQi0imXs5hkSZle7gc+MtvGjjb4zKkyqO3IvqEhHiNuECiYO3fuaz0X0v3pU/5p0HDq7I2ngIZiZ9xXLR2l+rLbSVBN0cpjL2Uy/JfmNKk/A/rXs/QZtGabdOnSSc6cOVWKzzNnzsiIESPUyLvly5db/HzfunVL/Y+gnfZYtmzZojyOwN8PP/wg48aNkyJFikjFihVVIHDHjh2yYsUKNYJ42LBhKoUngoq9evVSnQ9cmbOeT+yVqxxvZ98/IiIiR7Tv6gkZsGGqcXqURsWqy9h3PhEPXredDt9RInIp4RERMnP3cuNy3xptxMuDfR6I7AkqnEPe7Kzum06RpS0PqfshK6YOEuRDoz7m+dKPusuaNWu0FHoYiYM0e3iMiJwL0msipScCe7dv35Y333xTrV+1apXcvHkzTs+lH2GowZx8GMmHOf0wHx9G+/bs2VMGDx4shw4dkmnTpql5/Zo2baqCfpgjlIiIiIjImf197R/pv/6/IF+DIlVlXMNP2ZbipPiuEpFL2Xp2r1x8cEPdL5m1gNQvXNnWRSIiM+oXriRTmg6UzCnTR1mPkXxYj8fJ/oN8Fy9elO3bt6tUfnpVq1aVJ0+eyNGjR43rMPoGKc4qV+Z5mcjZ4HuNUU3g6+sbZR7OmAJ92nx++o4B+vu5c+c2+3ejR49W6YDxP85BgEAfgn+AzgdERERERM7q4PVT0m/9ZAkJD1XLbxWuLBPe7S2e7kxn76w4jIWIXEZwWIjM3rvKuNy/VjtjoxMR2R8E8+oWrCBHb56Ry37XpUD2PFI+V3H2PrOTedUuXbpkXL569aoaqYM59pBW74MPPpBjx47J5s2bJTw83DjvHh739vaWYsWKyTvvvCPdu3eXefPmqcBg7969pU2bNpI9e3Yb7hkRJbTdu3er4ByCe5gnD/N2btiwwfh4njx51P/r1q1Tc+nBn3/+KTly5FDnCQTq0Gngn3/+kTfeeEN++eUXtQ3ScNarVy/a6+HcM3PmTPV3mDNMGwGIcw/+hoiIiIjImR26cVr6rpsswWGRQb56hSrJxHf7MMjn5NhSRkQuY9WJP+ROwAN1v0a+MlIxdwlbF4mIYoGgXoVcxaVuvvLqfwb57MORI0ekbNmy6gYDBw5U9zF6Bmn5Nm7cqObOKlOmjAr8abf9+/cbn2Pp0qVStGhR1VDfqFEjqVGjhsyfP9+Ge0VEemvXrpWCBQtKnTp1jOvwHce69u3bq2XMdYdl/Zx3DRo0UOtmzZqllpFSs2XLlpImTRoVqEMwXxtlh5SbCOjB06dP5fz58+qG4D9gBF6hQoVUsK5KlSpqDj6k4YTPPvtMsmTJEi0FcLdu3dTz1q5dW62rX7+++n/r1q2yZcsWdd9cgJCIiIiIyNEduXlG+qydJEFhIWoZnae/adyX0xa5AL7DROQSAoJfyIID69R9N3GTvjXb2rpIREQOCw3/5ubJ0sT0mAaj+5YtW5bAJSOihPLs2TO5fPlylHX+/v7qljNnTrV87969aNvcuBGZIh1zbgKC+B9//LHs2rVLjf7FqL7y5curEb0fffRRjGVImTKl+rvhw4erIB3+Hh0E8Hz9+vWLtj2CgBhBPHnyZOO6Hj16qOAhXg8j+saPH686FxAREREROZNjt85J77XfSFBYsFquXaC8TG7Sn0E+F8F3mYhcwuJDG+VpUKC636h4dSmSOTJNFBERERFF17lzZ3WLyeLFi9UtJhjdN3fuXIuPY25O7fW6du0a7XGMBo7tNTRDhgxRNz3M1YdUnrgRERERETmjE7fPS69fvpaXoZFBvpr5ysoUBvlcCvNfEZHTux/4SJYc3aru4wLXq3orWxeJiIiIiIiIiIiI6LX843dRPv3la3kRGqSWq+ctLVPfGyDenpyf2pUw0EdETm/e/l+Mualbl3lLcqTJbOsiEREREREREREREcXbqTuX5JM1E+R5yEu1XCVPKZnebJAk8/S2ddEoiTHQR0RO7erD27L+1F/qfgpvH+lWubmti0REREREREREREQUb2fuXlFBvsBXQb7KuUvKzGafMcjnohjoIyKn9u3elRJuiJz7pUulppLON7Wti0REREREREREREQUL2fvXZWea76SgOAXarlCruIys/lnktyLQT5XxUAfETl1juo/Lx5S9zOmSCvtyzW0dZGIiIiIiIiIiIiI4uX8/evSc/VX8izouVoun7OYfNt8iPh4JbN10ciGGOgjIqdkMBhk+u6lxuWPq30gvt7JbVomIiIiIiIiIiIiovi44H9deqweL0+DAtVy2RxF5Lv3h7LNkxjoIyLntOfKcTl265y6nyddNmlWso6ti0REREREREREREQUZxf9b0qPVePlycsAtVwme2GZ3WIYg3ykeEb+R0TkPMIjImTmnuXG5b4124iXB093RERERP7+/vLs2TOxpywMAQEBEhgYKG5ubmIvUqdOLZkyZbJ1MYiIiIiI5PKDW2ok3+NXQb5S2QqqIF8Kbx9bF43sBFu+icjpbDmzRy49uKnul8xaQOoVqmTrIhERERHZRZCvS7suEvAwsoHAHiC4l69QPrl68aoK+tmLVBlSyaJlixjsIyIiIiKbuvrwtnRfNU4evXiqlktkLSBzPxghKZP52rpoZEcY6CMipxIcFiKz960yLg+o3d6ueocTERER2QpG8iHIVytZLcngk0HsgpuITwofKZ22tIidxPkevnwoux/uVseLgT4iIiIispVrj/yk+6rx8vBVkK94lvwy74MRkopBPjLBQB8ROZUVx3+XuwEP1f2a+cpKhVzFbV0kIiIiIruCIF+WFFnELriJeCT3kNQpUttNoE8JtnUBiIiIiMiV3Xh8V7qtGif+zx+r5aKZ86ogX+rkKWxdNLJD7rYuABFRQnkW9Fx+PLhe3XcTN+lbq42ti0RERERERERERERktZtPEOQbK/6BkUG+IpnyyPctR0oan5S2LhrZKQb6iMhpLD68UZ4GBar7jYvXlMKZ8ti6SERERERERERERERWufXkvnRbOU7uBTxSy4Uy5lZBvrQ+qWxdNLJjTN1JRE4BF7+lR39V9708POXT6i1tXSQiIiIiIiIiIiIii8IjIuTozTNy2e+6pHmURmbtWW6clqhAhpwyv9Xnks43ta2LSXaOI/qIyCl8//caCQoLUfdbl3lbsqfJZOsiERERERFZZffu3dKoUSPJlCmTuLm5qdu8efOibBMaGipjxoyR/Pnzi7e3t+TMmVMGDBgggYGRGS2gTp06xr83veXNmzfGMuzcudPi327fvt243cSJE9Vrp06dWt577z25e/eu8bGwsDApXbq09OjRI0GPDxEREZEz2n7hkDSc31u6rx4vX+/7nwzf+p3ceRXky58+hyxo9bmkZ5CPrMARfUTk8K4+vC3rTv2l7qf09pFulZvZukhERERERFY7duyY/PHHHyqI9+DBA7PbdO3aVZYsWSLu7u5SqFAhuXLlisyYMUOOHz8uO3bsUOuLFy8uQUFBUf7u6NGjKgCXLVs2q8qCIGLZsmWjrEuTJo36H2UcMWKEjB49Wtq0aSOlSpWSQYMGydKlS9XjkyZNEn9/f5k8eXI8jwQRERGR6wT5Bm+cJgYLj3eq2FgypEibxKUiR8VAHxE5vFl7V0iEIfKy2KVSUw5nJyIiIiKH0rFjR+nZs6fcu3dP8uXLZzYQiCAfzJw5U3r37i2bNm2Spk2byq5du2T9+vXy/vvvy5w5c6IF+SpUqKDu9+nTx6qyICB44MABs4+dOHFC/V+jRg0pVqyYZM6cWU6ePKnWXbx4UcaNG6eCflpgkIiIiIjMp+uctGOxxSAfzN2/RpqWqC0e7kzKSLHjp4SIHNpJvwuy4+JhdT9TinTSrlxDWxeJiIiIiChOMmTIID4+PhYf//XXyLmooUWLFur/d999V5InT67ub9u2zezfaSPrcufOLa1atbKqLH5+fpI2bVp1q1KliqxZs8b4WJkyZdT/e/fulbNnz8r9+/dVqk6DwaDSdTZs2FAFHImIiIjIsmO3zsq9wEcxbnMv4KHajsgaDPQRkcNCg8KM3cuMyx9XayG+3pGNHUREREREzuLmzZvG+xhFB0jVmTFjRnX/xo0b0f7m2rVrxiBd//79xdPTuoQ+eP48efKoFKAHDx6Uli1byty5c9Vjb731lkyYMEF++OEHqVy5sppXcOrUqfLjjz+qFKJff/21dOnSRc01WLBgQfn5558TZP+JiIiInMmD508SdDsiBvqIyGHtvnJMjt06p+7nSZdNmpWqa+siERERERElacc3S6ZPny7h4eFqZF737t1jfa4SJUrIpUuX5NatWyod54ULFyRLlizqMQTzNMOHD5fbt2/Ls2fPZOPGjWrdZ599pubnQ8Bv8eLF6j5G+iHod/r06QTZVyIiIiJnkdHKufes3Y6IgT4icthc1jN3Lzcu963ZVjzdPWxaJiIiIiKixJArVy7jfaTLhIiICHn48KExNafe48ePZeHCher+xx9/LClTpoz1NTAKr0CBAsZlPCfm4rM0YlCD+QLfeOMNFUzcvn27pE+fXgX4OnfurMq4Y8eOOO8vERERkTNLnTzmupmbiGRJlUHK5SyWZGUix8ZAHxE5pM1ndsvlh7fU/VLZCkq9QhVtXSQiIiIiokTxzjvvGO//8ssv6v8tW7ao9JqmjwNSbQYGBoq3t7f07ds32vONGDFCihYtKvXq1TOuQ5pNpOrUYGQf5uKDvHnzmi3Xhg0bVDkWLFggbm5uaoQhXhO8vLxec6+JiIiInE9A8Av5bNOMGIN8MKTuh+LhzvANWYefFCJyOEGhITJ73yrjcv9a7VTDAhERERGRI1q7dq2a065OnTrGdaNHj1br2rdvL+XLl5e2bduq9f369ZNixYpJixYt1HLNmjWlWbNmxr8LCQmRb7/9Vt3H32bLli3a6925c0fOnz8vly9fNq7DyLsqVaqokX1Iu1moUCG5d++eemzkyJHRngOpO3v16iWjRo2SwoULq3X169eXu3fvytGjR2Xr1q1qHsG6dZlen4iIiAjQKWrUr3Pl+uM7ajl76kySOWW6KNtkTpVBpjQdKPULV7JRKckRWTcbNxGRHVl54je5F/BI3a+Zv6xUyFXc1kUiIiIiIoo3BM30QTfw9/dXt5w5c6rln376SQXfMPIO2yIg98EHH8j48eNVQE2zZMkSFWxDR7hBgwZZXYaOHTvKixcv5PDhw2p+vjRp0qjUnUOHDlUBPFPDhg2TDBkyyJAhQ4zrEPTz8/NTIwUxNyBG+pUsWTKeR4WIiIjIuSw8tFH+unRY3U+VLIXMbzVSsqfOLEdvnpHLftelQPY8Uj5XcY7kozhjoI+IHMqzoOfyw8H16r6buEm/mpE9m4mIiIiIHBXms8MtJkiFOWbMGHWLSdeuXdUtJosWLVKBQz0E5/SpPGMzZ86caOtSpUqlAo1EREREFNWB66fku70rjG2aE9/tLbnSZlXLGMSQO1lGyZw5c5QOXETW4qeGiBzKokMbVbAPmpSoKYUy5bZ1kYiIiIiIiIiIiIjM8nvqL0M3z5IIg0Etf1ythcpSRpRQGOgjIoeBdJ1Lj21V9709vOTT6q1sXSQiIiIiIiIiIiIis4LDQmTQxuny5GWAWkaAr0fV921dLHIyDPQRkcOYt3+NBIeFqvuty7wt2VJntHWRiIiIiIiIiIiIiMya+OciOXPvirqfM01mmdCol7i7MSxDCYufKCJyCFce3pb1//6l7qf09pFuVZrZukhEREREREREREREZv3yz5+y7lRke2ZyT2+Z3myQpE6e0tbFIifEQB8ROYRZe1YY81h3rfyepPVJZesiEREREREREREREUVz6s4lNZpPM/rtHlI4Ux6blomcFwN9RGT3Ttw+L39dOqzuZ0qRTtqVa2jrIhERERERERERERFF8+jFMxm8cbqEhoep5bZl35F3i9ewdbHIiTHQR0R2zWAwyIzdy4zLn1T/QHy8ktm0TERERERERERERESmwiLCZdjmWXI34KFaLpO9sAyq08HWxSIn52nrAhARxWTXlWNy/PZ5dT9v+uzyXsk6ti4SEREREZFV/P395dmzZ2JPnegCAgIkMDBQ3NzcxF6kTp1aMmXKZOtiEBEREb227/aulIM3/lX3M6ZIK5Ob9hcvD4ZhKHHxE0ZEdis8IkJm7l5uXO5bs414unvYtExERERERNYG+bq06yIBDwPEXiC4l69QPrl68aoK+tmLVBlSyaJlixjsIyIiIoe2/cIhWXRoo7qPNszJTfpJ5pTpbV0scgEM9BGR3dp0erdceXhL3X8jWyF5s2BFWxeJiIiIiMgqGMmHIF+tZLUkg08GsQtuIj4pfKR02tIidhLne/jyoex+uFsdLwb6iIiIyFFdfXhbRv06x7g8sHZ7KZezmE3LRK6DgT4isktBoSEyZ/8q43L/Wu3sKr0QEREREZE1EOTLkiKL2AU3EY/kHpI6RWq7CfQpwbYuABEREVH8PQ95KQM2TJUXoUFquVGx6tKuXENbF4tciLutC0BEZM6K47/JvYBH6n6t/OWkfC72gCEiIiIiIiIiIiL7gXToo3+dK1cf+anlQhlzy6i3unPAAiUpBvqIyO48CwqUHw6uV/fdxE361mxr6yIRETmFPXv2SIcOHaRq1apy+/Ztte5///uf7N2719ZFIyIiIiIiInI4Px3eLNsvHlL3UyXzlWnvDRRf7+S2Lha5GAb6iMju/HhwgwQEP1f3m5SoJYUy5bJ1kYiIHN4vv/wiDRo0EB8fHzl+/LgEB0fmSXv69KlMmDDB1sUjIiIiIiIicigHb/wrM/csMy6Pb9RLcqfLatMykWtioI+I7Mq9gIey/Pg2dd/bw0s+rd7S1kUiInIK48ePl3nz5smCBQvEy8vLuL569epy7Ngxm5aNiIiIiIiIyJHcffZAhm6aJRGGyImPu1dpLnUKlLd1schFMdBHRHZl7v41EhwWqu63LdtAsqXOaOsiERE5hfPnz0utWrWirU+TJo08efLEJmUiIiIiIiIicjQhYaEyaON0efzymVqulre0fFKNgxXIdhjoIyK7cfnBLdnw705jTuuPKjezdZGIiJxG1qxZ5dKlS9HWY36+/Pnz26RMRERERERERI7mm79+kn/vXlb3s6fOJF+/20c83BlqIdvhp4+I7Ma3e1cYh7t3qfSepPFJaesiERE5je7du0u/fv3k4MGD4ubmJn5+frJ06VIZPHiwfPLJJ7YuHhEREREREZHdW39qp6w5uV3dT+bpJdPeG8g2TLI5T1sXgIgIjt8+L39dOqLuZ0qZTtqVe8fWRSIicirDhg2TiIgIqVevnrx48UKl8UyWLJkK9PXp08fWxSMiIiIiIiKya2fuXpGvtv9oXP68fjcpliWfTctEBAz0EZHNGQwGmbF7mXH502otxccrmU3LRETkbDCKb+TIkfLZZ5+pFJ6BgYFSvHhxSZmSPQ+JiIiIiIiIYvLkZYAM2jhNQsJD1XKr0m9J05K1bV0sIoWBPiKyuV2Xj8qJ2+fV/Xzps/MiSUSUiLy9vVWAj4iIiIiIiIhiFx4RIUM3zxK/Zw/U8hvZCsmQNz+0dbGIjBjoIyKbCosIl5l7VhiX+9ZsK57uHjYtExGRM2revLka1WcK65InTy4FCxaUdu3aSZEiRWxSPiIiIiIiIiJ7NHf/ajlw/ZS6n943jUxpOkC8PBhaIfvhbusCEJFr23R6t1x5eEvdL529kNQtWMHWRSIickpp0qSRHTt2yLFjx1RwD7fjx4+rdWFhYbJy5UopXbq07Nu3z9ZFJSIiInJZEydOlIoVK0qqVKkkc+bM0qxZMzl/PjIDTkxWr14tRYsWVR24SpUqJVu3bk2S8hIRObu/Lh2RBQfWqfsebu4yqXFfyZIqva2LRRQFA31EZDNBoSEyZ99q43L/Wu3MjjYhIqLXlzVrVjVi78qVK/LLL7+o2+XLl6VDhw5SoEABOXv2rHz44YcydOhQWxeViIiIyGXt2rVLevXqJQcOHJA//vhDQkND5e2335bnz59b/Jv9+/dL27Zt5aOPPlIduRAcxO3ff/9N0rITETmba4/85POts43L/Wq1k4q5S9i0TETmcHwpEdnM8uPb5H7gI3W/doHyUi5nMVsXiYjIaf34449qtJ67+3/9vHC/T58+Uq1aNZkwYYL07t1batasadNyEhEREbmybdu2RVlevHixGtl39OhRqVWrltm/mTlzprzzzjvy2WefqeVx48apIOF3330n8+bNi7Z9cHCwummePXum/o+IiFC3hITnMxgMCf689oD75piced+cff+Set9ehATJwA3TJDDkpVp+u3AV6VCuYaK8Pt83xxSRiPsW1+dkoI+IbOLpy0D58eAGdd/dzU361Ghj6yIRETk1pOc8d+6cFC5cOMp6rAsPD1f3keqJI6uJiIiI7MfTp0/V/+nTW04T9/fff8vAgQOjrGvQoIGsX7/eYnrQMWPGRFvv7+8vQUFBktANldgHNITqO5w5A+6bY3LmfXP2/UvKfcNrTNz7s1x+Nd1Q7jRZ5dOyzdV5MjHwfXNMEYm4bwEBAXHanoE+IrKJhYc2SEBwZOqRJiVqS6FMuWxdJCIip9axY0eVzmnEiBFq3hc4fPiwGsnXqVMnY6qoEiWYhoSIiIjIXhoQ+/fvL9WrV5eSJUta3O7u3buSJUuWKOuwjPXmDB8+PEpgECP6cuXKJZkyZZLUqVMn+D6gIxme2xkbeLlvjseZ983Z9y8p923J0a2y6/pxdT+Ft4/MbD5Y8qTPnmivx/fNMUUk4r6hI3ZcMNBHREnu7rMHsuxYZDoSbw8v+bTaB7YuEhGR05s+fbpq8Jk0aZLcu3dPrcPygAEDjPPyYf4XpH0iIiIiItvDXH2YZ2/v3r0J+rzJkiVTN1NopEyMRlg0gibWc9sa980xOfO+Ofv+JcW+Hbl5RmbsXmZcHt/wU8mfMackNr5vjsktkfYtrs/HQB8RJbk5+9dISHiout+u3DuSNXVGWxeJiMjpeXh4yMiRI9VNm4fFtMd27ty5bVQ6IiIiItLD3MmbN2+W3bt3S86cMTcwZ82a1diRS4NlrCciIuvdC3gkn22aKeGGyPnRPqr8nrxZKDIjDpE9c74QKhHZtUsPbsqm07vU/VTJUkjXSu/ZukhERC4HAb6ETstERERERK8P8/wgyLdu3TrZsWOH5MuXL9a/qVq1qvz5559R1v3xxx9qPRERWSc0PEwGb5wuj15Ezo1aJU8p6VW9ta2LRWQVBvqIKEl9u2eFRBgM6n7XSk0ljU9KWxeJiMgloFc35unLnj27eHp6qhF++ltcoGd5kyZN1HMhTcX69eujNVCNHj1asmXLJj4+PlK/fn25ePFilG0ePXok7du3VwHHtGnTqvkDAwMDE2RfiYiIiBw5XeeSJUtk2bJlkipVKjXPHm4vX740boP5lTHPnqZfv36ybds2mTp1qpw7d06+/PJLOXLkiAoYEhGRdSb/9bP8cyfyd2u2VBnl63f7iIcTppok5+TSqTtnz54tkydPVhWm0qVLy7fffiuVKlWyuP2TJ09Uuqu1a9eqxqk8efLIjBkzpFGjRklabiJHdezWOdl5+ai6nzllemlXrqGti0RE5DI6d+4sN27ckFGjRqkAHAJ08fX8+XNVd+ratau8//770R7HPICzZs2Sn376SfVCx2s2aNBAzpw5Y5xQGkG+O3fuqN7moaGh0qVLF+nRo4dq1IqToCARb+/o6/GDTL8e21nyOtsGByOyaX5bHGP9/Ddx2TYkBDN7R97H/ygTbtoPTf3E3PptzUmsbVFe7XMUGioSHh7nbd2Cg8U7IkI8wkPFPTwkcnfdvYzbukWEi5vB8vPGbVtPETf32LfF0xn+ey9ie16Du6cYrHle020NEeIWEWZ5WzcPMbh7GLfFccLxMvv59PSMvKkdjYh878ztWnCweOg/g4YIcbeyDPjsukeEJtC27upYGLcNCxEDPhMGK7aN4Xnx/qr3+RXtMxXXbfF5jHK8X+Mc4WXy+TYphER4eOnKgH2zcI4w3Rbv26uUUuZEeHib39ZNxD3MI8rxtrhtXJ431u9nmPocm4PjE+XcGJfzSVhY5C0htsX7pp1jE2pbfB/1+xLb83p5Idd25H38HY6FJfrvfVy2jeEc8VrbWrq+UZzNnTtX/V+nTp0o6xctWqTqc4A6nX7unmrVqqk61Oeffy4jRoyQQoUKqY5YJUuWTOLSExE5pk2nd8vKE7+r+94eXjL1vQGSzpdZcMhxuGygb+XKlTJw4ECZN2+eVK5cWQXs0AB1/vx5yZw5c7TtQ0JC5K233lKPrVmzRnLkyCHXr19XPdCJKHYY3TFTN5HtJ9U/kOReZhpmiYgoUezdu1f27NkjZcqUee3natiwobpZOt+jXoWGpvfei0zP/PPPP0uWLFlUg1ObNm3k7Nmzqtf54cOHpUKFCmobdLhC56kpU6aokYKmgoOD1U2jzTNo6NhRDGgYNS0Hnnf0aOOyW/v2kYE2c0qWFMOECf9t27UrXsD8toUKiWHq1P+2/eQTkfv3zW+bK5cYZs/+b9v+/UVu3jS/bebMYvjhh/+2HTpURDcKMi3KnixZZLt86tRiWLLkv22xn//+a/558TerV/+3PGGCuB05Yn5bHLeNG/9bmDpV3Pbts7ztqlX/BQa/+07cTFKGRdn2f/8TSZMmcmH+fHH79Vd1N8uLFzLm6hnJ6vVIknlGBtcOlu4hwckit813e4/kvHvY4vMeKdlFXvhGzvWb+84ByeO33+K2x4p3kMCU2dT9HPePSv6bkanEzfk3dTt55JZD3c/24B8peH275W0Lvy+P0hZQ9zM/OitFrkbumzlnCjSRBxmKqvsZHl2Q4pc3Wdz2fL6Gci9TZANt5sBb6jhl6d1bDL6+0bY19Owp8u67kQunTonbyJFmnxPHu/rThyLpIwM+KZ/fk3Jn/vssmbqevZpcz1ld3fd9+VAq/LvI4ra3slaUK7kjG6WThTyTyifnW9zWL3MZuZT3LXXfK+ylVPt9jhhemg8S3MtYUs7njzzfIMhX49hMi8/rn66InC3U1Lgc07aP0uSXf4u0MC5XPTFHPF4FEYPDgqV86N3/jnc8zxE4Hw64dUmK3Vps/HzrvUieQY680dW4XO7M/8Q36KHZpw3yTiOHyvQwLpc+t1xSPb9rdttQTx/5u9x/o3hKXVgjaQL+O/e4nXEzHu9wdy/ZV6G/8bHilzZI+qdXLI/orvSZ8X6RK1sl0+PzFrfdW76fRLhH1vcLX/9Dsjwwf57C8d6WM506XhEILOnOEeaoc6X2m33xYnEzGVUeZdvvvsMEtJELK1aI24oVlrfF+1aoUOTC+vXitnix5W2/+kqkVKnIhV9/Fbfvv7e4rWevXhKRJUvkwl9/idtMy59Lw5AhIjVqRC7s2ydukyZZ3rZfP5F69SIXjhwRt3HjLG9r5TlCbYtAktaJ5+JFcRs0yPK2bdqItGun7kfcuGFxO4obfBdis3PnzmjrWrZsqW5ERBQ35+5fk3F/LDAuD6/fRUpkjazfEzkKlw30TZs2Tbp37656jwMCflu2bJGFCxfKsGHDom2P9RjFt3//fvF61ZiUN2/eJC83kaPCSL4TfhfU/fzpc0jTErVtXSQiIpeSC0GnJOhtf/XqVZUtAek6NWnSpFEdq/7++28V6MP/6CylBfkA26Nn+sGDB6V58+bRnnfixIkyZsyYaOuDQ0Ml2Mx+hT5/Ls91ATgVKLMwKiHs+XMJ1G2bJihI3CxsG/7ihQTotk0dFCTuFraNePlSnum3ffnS8rZBQVG2TfXihXho2xoMEqqNAHFzE0NQkDzVbZvy+XPxjGHExRPdtimePxcva7cNDIx921eBPt+AAPGOYdun/v5ieBVo9QkMlGSvtg0LCxPflL4qfaybe+SoG4/sHuLhGzmixT3QXdyeWB59qrZN9Wrbl+7i9iiGbbN5iEfaV9uGuIubv+Vt3dK6iUf6V9uGu4vbPcvbumd2F48sr7Z1cxe3OzGUIYuHKoe67+khbrdjed6ckdt6SjLx9fNVxyvYzHF++fSpBL967zwfPZKUlj7rYWGSJXsW8crrIx7JPcTjiYe4XYmhDBncxSPXq/IGeIjbpRi2Ta/b9oWHuJ2PYdt0um2DPUQuu4mbIYb34tW2GInmdjqG59Vtq/72nxje4zQm2552E7fwV5/BCA/xDf/veMf3HBEQECCpUqcSj7D/Pt9RypDSpAxX3AT/zJbXx2TbG+7G8kbb1ttk21tu4haq29b7v+Pt5mGy7V03cQuO4TOs29bd303cXsawbU4PcfN8NSr1kZu4BZrfFsc7T/486njdv38/yjnCnGcPHog2NtAnICD2bV+dp5I/eybJY9g24OFDCX/VISHZ06fiE8O2gY8eSdir9znGbQ0GtV8h9++ra5z3kyfiG8PzPn/8WEJfPa/X48eSIoZtXzx5op4XPB8/tvi9j8s5wnRbj4cPJVUM2wY9eyZBr7Z9/tB8kJqIiMiePX0ZKAM3TJPgsMgOXy3eeFPeL/WmrYtFFGduhqRo8bEzGJ3n6+urRuY1a9bMuP7DDz9U6Tk3bNgQ7W/Qwzx9+vTq7/B4pkyZpF27djJ06FCL89qY63mORrbHjx+r+WgSCno9+vv7qzLpUzc4A+6bc+xfhBik1c/D5Oqj2+qxaU0HSt2C/zXuOhpnfu+4b47LmfcvsfYN1+V06dLJ06dPE/S6bK9+//13NW/L999/n6CdlZACdN26dcY6FTpFVa9eXfz8/FSKUE2rVq3UtsiqMGHCBJXWE5kU9JA5AcG8TzBKztp61Z075t8/J0rdafY74CSpOxEY/qTDJ9IkTRPJnCKz3aTudMuTTMJvGVRqQ3tI3Xk/8I5sebxe5i6Zq9LhxjfVHo53j46fSpN0zSVLiiz2kbpTDOKVPULCb9lP6s77z+/Lpqeb/jve8TxHXLlyRT5p2VWa6j7f9pC6EwE4/fG2depOHO9fnm2WH1b9KPnz53eq1J3q/P3kiWTKmjXy/O3EqTufPX0q6dKnd5l6lbNBvQodsxLj/cP3AEF81POc8XcK983xOPO+Ofv+JfS+RRgipM/aSbL36gm1jFF8i9t8Kd6e0TPGJDa+b44pIhH3La7XZpcc0ffgwQMJDw9XKaT0sIxJi83Bj7QdO3ao+WS2bt0qly5dkk8//VTNKfPFF1+Y/RtLPc/RUBMU04/DeHyg8IYjZuuMXxbum+Pv3+9XDhmDfMUz5ZPiqXKpk6Cjcub3jvvmuJx5/xJr39DD3pW0bt1aXrx4IQUKFFAdl7QMBRpkLrBnyZIlUzdT7r6+6hYra7aJz7Y+PomzrT7ghrnCfHwi99Xcd0C/bVyeNyG3NfPeWLVt8uQS7Oam0gdqKf4Ugy545Ga+U12ibYtAiLiLIGBnSMQyCAJY3lZvi+OE4xXr5x2fEa2R3lTy5BKmPa96bgS7rCuDCjQlxrZuCGB5S4S7+UBf/Msg8d4Wn8cYj7eV5wh0bggx9/m2WIZYGpX027ohcB2PbRHI9vCIerwT4nlj2d7ghiC3+U1wfAyvjpc6x8XlfIKAm7l5Wu1lW5y/AwPVfql9i8vzYnszqakTZFtL54jX2NbdQgdoIiIiezVv/y/GIF86n1QyrekAmwT5iBKCSwb64tvIiMjs/Pnz1Qi+8uXLy+3bt2Xy5MkWA33Dhw9X8wCa9jxHb+yEHtGHH0XOOoqD++bY+5cybWpZ+m/kZLYw+M2O0YLsjsaZ3zvum+Ny5v1LrH1LHpcghhPAvHlJIWvWrOr/e/fuRRnRh2VtfkBsY9rhAynyEGzU/p6IiIiIiIgoMey6fFS+//sXdd/dzU2+adJPsqaOnPubyBG5ZKAvY8aMKliHBic9LFtqXEJDFXq+69N0FitWTM1Bg1Sg3mZ65Vnsea715ktAWu9HZ2vcBe6bY+/fqpN/yP3AyFEidQqUl/K5ioszcOb3jvvmuJx5/xJj35zxOMUEKcqTAtLcoT71559/GgN76OyEufe0lJxVq1ZV6dKPHj2qOk8BMicgqIu5/IiIiIiIiIgSw43Hd2Xk1tnG5b4120rl3CVtWiai1+VaLVyvICiHRiU0QGnQsIRlNDyZg7lmkK4T22kuXLigAoDmgnxEJPIs+LksPLzR2DumT802ti4SERGp6aWCVPBNf4uLwMBAOXHihLpp837h/o0bN1RQtn///jJ+/HjZuHGjnDp1Sjp16iTZs2c3zuOHzlLvvPOOdO/eXQ4dOiT79u2T3r17S5s2bdR2RERERERERAntZWiwDNwwTQKCX6jleoUqSeeKTWxdLKLX5pKBPkBKzQULFshPP/0kZ8+eVT3Mnz9/Ll26dFGPo0EKqTc1eBzppPr166cCfFu2bJEJEyZIr169bLgXRPZt5b/bJfDVhbNpidpSMGMuWxeJiMhloZ6DYBpSkadIkULSpUsX5RYXR44ckbJly6qbVq/C/dGjR6vlIUOGSJ8+faRHjx5SsWJFFRjctm1blHSpS5culaJFi0q9evWkUaNGUqNGDZUinYiIiIiIiCihGQwGGfv7Arn44IZazpc+u4x952PVWZXI0blk6k5o3bq1+Pv7qwYppN9Eaik0QGlzh6FHuj6lF+bW++2332TAgAHyxhtvSI4cOVTQb+jQoTbcCyL7defZA9lwfo+6n8zTSz6p9oGti0RE5NIQfPvrr79k7ty50rFjR5k9e7aab/j777+Xr7/+Ok7PVadOHfUjyRL8UBo7dqy6WZI+fXpZtmxZnF6XiIiIiIiIKD6WH/9Ntp7dq+77eiWXae8NkpTJfG1dLKIE4bKBPkCvdtzM2blzZ7R1SOt54MCBJCgZkeOb9/caCY0IU/fbln2HE9oSEdnYpk2b5Oeff1ZBOmQwqFmzphQsWFDy5MmjRte1b9/e1kUkIiIicliYfxgpye/fvx9l2hctaxQREdnOsVvnZOrO/xmXMZIvf4YcNi0TUUJy2dSdRJR4LvrfkE2nI0fzpUrmKx9Vfs/WRSIicnlIQZ4/f351P3Xq1GoZkDJz9+7dNi4dERGRbSHN9BdffKHSSvv4+Kg5YzGFx+PHj43bYP7ZSpUqSbJkydToddww7601MGdtixYtVHYgpLJGpqBFixZF2QZThJQoUUJ8/8/efUBHUbVhAH53N70RUmkJvfdeRXoHaYogXfkFVKQoggKKBRCkCoIIChZApQgIIkWK9N47BEJLgYT0vvufe5ddEpJgAklmZ/M+58zZuTOTyTeTsFnmu/e7Tk6y9PWhQ4fS7B8+fDiqVKmCpKSkHLpqyskOVf7+/nIOYtGhXFSAMi1i7mIiIlJOaHQ43t84B8n6FNkWc/K1Lt9A6bCIchQTfUSU4+b9uwoGGEu6vV7vJbg5uCgdEhFRvieSfAEBAXJdPMT87bffzA+m3N3dFY6OiIhIWZ07d5Ylp69evYpy5crJxN+iRYvQpk0bJCcbK5WsXr0aly9fhre3d7bOff78eTRo0ABr166VicGyZcvKxN/gwYMxZ84c82gwMcVI4cKFcfv2bURFRcnEoMm+ffvw3XffYcmSJbC1tc3hq6fnNWbMGPnzFL834mcpEsSmxdS5ioiI8l5SSrJM8t2PeSjbdf0q450XXlU6LKIcx0QfEeWo47cvYM/143Ldy8kdvWq0VTokIiICZLnOU6dOyfVx48bJOfrEiAIx//D777+vdHhERESKEYk40/Qdc+fOlX8vjx07JttHjx41d475888/ZeLmjTfeyNb5ly9fjtjYWDkS8MqVKzLJ9+GHH8p9n3zyCeLi4uT2mJgY1K9fX85jW6tWLTmX7v3795GYmIghQ4bIEYYiYUiWR/ysRowYIUdjEhGR5Zi1+2ecuHNJrvu6euDLTiNgo9UpHRZRjsvXc/QRUc4yGAyYs2eFud2/Wjs42NopGhMRERmJhJ5Jq1atcPHiRfkQU8zTJ8qHERER5Vep51PTarVpXoXt27ejT58+KFas2HOfX5T7TH3+iIgIHDlyBFWrVoWzs7Ms1ylGgB0/flyW+fTy8pIlRUUScMqUKc98jZS72rZtK5PCpjLpRESkvM0X9mLF8S1y3VZng5ldRsPTuYDSYRHlCo7oI6Ics/PqUZy6e0WuiwltW5Wqq3RIRESUieLFi6N79+5M8ikwx5MoxyYSr+KBsZ2dHUqXLo3JkyebS8M9zY4dO9C6dWv4+vrKkSHi/D179pSjQ0w4xxMRUfZUrFhRvi8K77zzDmrUqCFH1KUerfU8unXrBp1Oh4SEBFm2U/zt/eKLL9Kcv2DBgvj1119x9+5d+ffBxcVFlgoVow2nTZuGhQsXytH4Yh44Ud5TlIrMyt8NyhsdO3aUFRLECM01a9Zgw4YNaRYiIspbl0NvYvLfi83tcS0GomrhMorGRJSbOKKPiHKEmNB23r8rze13mrwKHYfCExFZFJEkEktISEia0QXC999/r1hc1jbHkyj/Jh7oimSbmBdRzPEkevkfOHBAjuAQx+zevVvOsSR6/otybeLB4LVr1/Djjz9mem4xL1SHDh1kCTfxQFic/+zZs/KB4p49e3Dv3j2ZRBRzPInSbv/++y8aNWok53gS8z2lnuNJ7OMcT0RERuI9+6+//pKlrcXovevXr6Np06Zy9Lt4b37e90vxXrx+/Xp89tlnOHfuHB48eID+/fvLkp6C6fwiWSQWE/G3+oUXXpDv42IkoIhPdNYQI/0++ugjmTQcOnToc1495QRRWlUQ8zw+SfzsUlJSFIiKiCh/ioyPwej1sxCfnCjbXas0Q49qLZUOiyhXcUQfEeWIDWd3IyDsrlyvUbQ8Xiz1uAcsEREpT4wYa9OmjUz0ifl+xAiz1AvlzRxPf/zxh0zyCWvXrpUPkefMmSPbP/30kyzVlpnDhw/LJJ8gHkiLY8ePHy/b4qGxGE3IOZ6IiJ6NGEX3888/IygoCJGRkXI0nXjvFMqXL//c5xcJvIMHD8oOGeJ9WZR6NMns/N988w0uXbok/6aIBKQgEnti1KGwbdu2546LcoZIyma2MMlHRJR39AY9Ptq8ALceBst2Rd+SGN9ysLl0NpG1UtWIPlHmQpQeunnzppzI2tvbGzVr1kTJkiWVDo0oX4tLSsA3+383t0c27cM/oEREFkaMKlu2bBn69eundCj5eo4nMWpEEGU9xeg8QYzUGDFihFzfsmVLmnJxqYnknSj1KRJ24mtF+VUxoq9AgQKyBJx4FXMuco4nIqLsE++XYoScq6urTMyIMoxi/jxBjJTOKtEpQ4zWE8Tf3RIlSsh10cnjxRdflOu3bt2SI7kFMTrbVDY0NTES+8MPP8T8+fPlsw8xH7og/g5wRDYREVHGvju4DnuuGztPFnBwwcwuo+Bga6d0WES5ThUj+kSJoVdeeQXu7u5o0aIFRo4cKUte9O3bVz7MEB/GZ8yYIXvGEVHeExPbhkYbR4M0L1MHNYs+f49XIiLKWSI5JEqHkbJzPImHu4Knp6c5CSjm2zMJDAzM9PziM69IFooHviKJd+LECTnPnhiFUqlSJXkM53giIno2ooS1j48PqlatikKFCskEmyCeP9SrV0+uv/baa/IZxLx588xfJxJ1YpsYpS2ITsliFJ5YxHrqEX2m84v3c1GOWcylKsopZ9RJUpTobNiwoTlp2KpVK/OIbjEXq9CyJcuQWRKRzBXlucXvg1i6dOkiS2UTEVHe2Hv9BBbuWy3XNdBgWqd3ULSAj9JhEeUJi0/0iQ9Govec6AW3detWmcwTpYlE7zbxoVmUJ5owYYIsQ1WuXDmWriDKYw/jovDD4fVyXavRyLn5iIjI8rzxxhtYsWKF0mHkizmexINgMYJOzPEk5lYqXbq03J/ZCAzTKI3/IhKFgwcPRmhoqEzmiVKd4gG0mO9JPEAWc/QJYl0k9sRnZVEyVDygFiU7U8/xJB5EimTkrFmzsGTJkhy8C0RE6iTeK8W8qeK9W4x+rl27tnx/nD17dpr3YTFnX+qS1+J4sU2U+3wa8b5rY2MjE4Bi5HX37t3l3K0imfckUepZPOMQo/FNxEjuzz//HF9++SXefPNNORL8f//7X45dPz0fUfZVJGNF8lb8bMQiRu+LZCw/fxER5b7bD4MxfvN8GGD8v9XbTV5BoxLVlQ6LKM9YfOlO8aBizZo1mT4YER/ExTJgwAD5QMP0gIOI8sbSQ38gKsHYU/WlKs1Q2quY0iEREdEjo0ePTlNWcvHixXJEWLVq1dJ9thIJH8q5OZ5M4uPj5cgQ0xxMpgfBYt4n8TMRo/pCQkLMx4uRdpkRo/GuXr0KNzc3We1CECM9xBx/cXFxsgpGz549M53jScwPKEb1meZ4Eh3pPvroI9lRTrSJiPIz8X5qGj2XGdM8rE/TrFkzcwcO8T5veo9fuXJllmMR7/Gm9/nUxHu2WMjyiBLa06dPx6hRo8zbRLJPfL4SFan69OmjaHxERNY+pdDoDbMRGR9jrjY2uP5LSodFlKcsfkSf6KmW1frzomQRS1cQ5Z27EaFYeeJvuW5vY4thjdI/XCQiIuWI0o6m5dSpU7KUpEgsiXndUu87efKk0qFa1RxPpnLyGc3x1K5dO3MCcPPmzXJddGozMe1ft24dKlSoIBcxgkQwnUecX5R8E8SIPRMxQiSzOZ7Eg0bO8URERJQ7xMhOMWozoypVAQEBisRERJQfiP/ffLFtKS6F3JDt4gUL47P2w6HVWHzagyh/jehLTcxpIsoNiZ7SpkmuRQkEkeBjyQqivLdw/+9ISjHO69OnVnv4unoqHRIREaWyc+dOpUPIl3M8LV26VM7NExQUJEfupZ7jSZSCa9KkCfbu3SvLtomynqaknejtb5rTTyT1xCg8QczDJ5JyXbt2laP6xH9mxXGiqoUo2ykUL15cjiLJyhxPogydKDHq5+cnt7GjHBER0fMRf1NFuVXx9z81UUnB9PeWiIhy3u+ntmHj+T1y3dHWHrNeGg1XeyelwyLKc6pK9ImHHyKh169fP/ngpHXr1nLi619++UW2J02apHSIRPnGldBAbDxnnFjczcEZg+t1UTokIiLKgBhVJpJBZcuWlXPFpCbKPYr5jqtUqSJH+tHzE8k8kWAVPftFQk4k9oYNG4bXX3/dPI/fpk2bMHHiRKxevVrO6yTKdYpEnJh3+mlEQk6MApw5cybOnDkjE4Tia0XyTnwOfvLna5rjSYzgzGiOJ5FA5BxPREREz2/MmDHyb6qoktCoUSO5TZTUXrZsGebOnat0eEREVunU3cv48p/l5vYnbd9EGS92rqD8SVWJPvGQQjw8MT24EA+lxAenrVu3ynlFmOgjyjtz/11pnuD2jfpd4ebgonRIRESUgZ9++gnz58/HoUOH0u0To8QGDx4sR5v17dtXkfjy4xxPYo498dDvaQ/+Bg4cKJcn53gSpT1N5T3/C+d4IiIiyhuiU4+Yk1d0xhHPq4SKFSvi119/xUsvcZ4oIqKc9iDmIcZsmI1kfYps963dAe0qGDtaEOVHqkr0iV7H9vb25vIHota5IOYuuXfvnsLREeUfR2+dx7/XT8j1Qq6eeLVmW6VDIiKiTIgyku+9954cSfYkGxsbjB07ViYCmegjIiIienbdunWTCxER5S6R3Bu7cS5Co8Nlu3axihjZtI/SYREpSlU1mkSZzkWLFuHff//Ftm3bzL2Z7969C09Pzg1GlBdEGbI5e1aY28Mbvwx7GztFYyIiosyJed4aNGiQ6f66deviwoULeRoTERERERER0bNWGTt62/h/WG+XgpjR+V3Y6lQ1nokox6nqX4CYS0T0jpoxYwYGDBiA6tWry+0bNmwwl/Qkotz1z9UjOHPvqlwv7VkMnSo1VTokIiJ6ipiYGERGRma6PyoqCrGxsXkaExEREZHaeXh4yPlyvby8ULBgQWg0mkyPDQsLy9PYiIis1a4bx/Hzsc1y3Uarw8wuo+Dp7K50WESKU1Wir1mzZrh//758WCU+RJn873//g5OTk6KxEeWXofHz/l1lbo9o2hs6raoGBhMR5Ttly5bF/v37Ua1atQz37927Vx5DRERkDUJDQ5/awSWvq6GIDjXR0dFPTQLlNTFXrLe3t9JhqN7s2bPh6upqXreknzERkTW6dv82Zh98/FxybPMBqF6knKIxEVkKVSX6BDG/TOokn1CiRAnF4iHKT9af3YUbYXfles2i5fFiqVpKh0RERP+hT58+mDBhAho1apQu2Xfq1ClMmjRJztNHRERkDUm+QX0GIepBFCyBSPyULFsSAVcCZNLPUrh6uuKHFT8w2fecRKUpk4EDByoaCxGRtYtKiMXoDbMQn5wo250rNcUrNVorHRaRxVBVoi84OBjvvfceduzYgZCQkHQflFNSUhSLjcjaxSUlYOG+1ea2mOSWPRaJiCzfqFGj8Ndff6F27dpo1aoVKlSoILdfvHgR27dvR+PGjeUxREREaidG8okkX1P7pvB09FQ6HEADODo7orp7dcBC8nwP4h5gz4M98l4x0ZezndLv3bsHHx+fNNsfPHggt/F5FRHRs9Mb9Jj41zcIfBgk2+W9i2NC6zf4XJJIrYk+0UMqMDAQEydOROHChfmPmSgPrTj+F0JjwuV68zJ1UaNoeaVDIiKiLLC1tcXWrVtlSakVK1Zgz549srNUuXLl8MUXX2DkyJHyGLLMsm8CS78REWWPSPL5OvtaRKJP56CDm7ObxST6pASlA7A+mY3YTEhIgJ2dXZ7HQ0RkTX44vAE7rx6V6y52Tviq80g42PK9lUi1iT4xh8y///6LGjVqKB0KUb7yMC4K3x9aL9e1Gg1GvPCq0iEREVE2iESeKM/JEp3qK/smsPQbERGRZZo3b575b/WSJUvg4uJi3idG8YkOVqZqCkRElH0HbpzG/L2/ynUNNBjXuC+KuVtAZx4iC6OqRJ+fn59FPdwgyi+WHPwD0Ylxcr1rleYo5VlU6ZCIiIjyR9k3gaXfiIiILJKomCCIZ1WLFi2SJTxNxEi+EiVKyO1ERJR9dyNCMW7T19A/yge82bAH6hatpHRYRBZJVYm+OXPmYNy4cfj222/lhyUiyps/qqtO/i3X7W1sMbRRT6VDIiIiyj9l3wSWfiMiIrJIAQEB8rV58+ZYu3YtChYsqHRIRERWISE5EWM2zJZVxoSmpWphSIOuuB96X+nQiCySqhJ9vXr1QmxsLEqXLg0nJ6d088mEhYUpFhuRtfpm3+9ISkmW66/V6gBfVw+lQyIiIiIiIiKyGDt37lQ6BCIiqyFGSU/Z/j3OB1+XbT93X3zR4S1oNVqlQyOyWKob0UdEeedy6E38ef5fue7m4IxB9booHRIRERERERGRxbl9+zY2bNiAwMBAJCYmptk3a9YsxeIiIlKbNad34I+zu+S6g40dZr00Wj6X1Ov1SodGZLFUlegbMGCA0iEQ5Svz9qyC4VGNsDfqd5N/VImISP1Mcx5rNBqlQyEiIiJSvR07dqBLly4oVaoULl68iCpVquDGjRvyM1etWrWUDo+ISDXO3LuKaf8sM7c/bvs/lPMurmhMRGqguvGuKSkpWLNmDT7//HO5rFu3Tm4jopx1JPAc/g04IdcLuXri1ZptlA6JiIie09KlS+WDJwcHB7mI9SVLligdFhEREZGqjR8/Hu+99x7OnDkjP2OJ51a3bt3Ciy++iJdfflnp8IiIVOFBTATGbJhlnkKoT6126FCxidJhEamCqkb0Xb16FR06dMCdO3dQvnx5uW3q1Knw8/PDpk2b5Nx9RPT8RK/DOXtWmNtvNX4F9jZ2isZERETPZ9KkSbJs1DvvvIOGDRvKbQcOHMCoUaNkialPP/1U6RCJiIiIVOnChQtYuXKlXLexsUFcXBxcXFzk56uXXnoJw4YNUzpEIiKLlqxPwQd/zkNwVJhs1yxaHqNf7Kt0WESqoapE34gRI2Qy7+DBg/Dw8JDbHjx4gL59+8p9ItlHRM9vx5XDOBt0Ta6X8fJDx0ovKB0SERE9p4ULF+K7775D7969zdtEialq1arJ5B8TfURERETPxtnZ2TwvX+HChXHt2jVUrlxZtu/fv69wdERElu/rf1fhyK1zct3L2R0zOo+ErU5VqQsiRanqX8vu3bvTJPkET09PTJs2DY0bN1Y0NiJr6kEz799V5vaIF16FTqu6Kr9ERPSEpKQk1KlTJ9322rVrIznZWBqFiIiIiLKvQYMG2Lt3LypWrCgrUY0ZM0aW8Vy7dq3cR0REmdt++RCWHdko1220Opnk83YpqHRYRKqiqqf39vb2iIqKSrc9OjoadnYsK0iUE/44sxM3w+/J9VrFKqBpKU4cTkRkDfr16ydH9T1p8eLFeO211xSJiYiIiMgaiPLo9evXl+uTJ09Gy5Yt8euvv6JEiRJyjmQiIsrY9Qd3MPGvx/9PHd2sr3weSURWPKKvU6dO+N///ic/JNWrV09uO3ToEIYOHSpLTxHR84lNjMei/WvM7ZFN+0Cj0SgaExER5RzxGWrr1q3mnuXic5SYn69///4YPXp0modVRERERPTfUlJScPv2bVkO3VTGc9GiRUqHRURk8WIS4zB6/UzEJsXLdoeKjdGnZjulwyJSJVUl+ubNm4cBAwagYcOGsLW1ldtEqSmR5Js7d67S4RGp3orjfyE0JlyutyhbF9WLlFM6JCIiyiFnz55FrVrGUdpi3hjBy8tLLmKfCTt4EBEREWWdTqdDmzZtcOHCBbi7uysdDhGRKhgMBkz6ayECwu7Kdlkvf0xsPYT/HyXKD4k+8YFp/fr1uHr1qvwAJYj652XKlFE6NCLVC4+NxA+HN8h1rUaDEU1eVTokIiLKQTt37lQ6BCIiIiKrVKVKFVy/fh0lS5ZUOhQiIlUQc/Jtv3JYrrvaO2HWS6PhZOegdFhEqqWqRJ+JSOyJRZRHEJMbh4eHo2BBTtBJ9DyWHPoD0Ylxcr1b1eYo6VlU6ZCIiCiXiPJSQrFixZQOhYiIiEj1Pv/8c7z33nv47LPPULt2bVm+MzU3NzfFYiMisjSHbp7BvH9XmttfdHgL/gULKRoTkdppoSIjR440T2IsknwvvviiLEHl5+eHXbt2KR0ekWrdiQjBrye3ynUHGzu82bCn0iEREVEO0+v1+PTTT1GgQAEUL15cLqJagnggJfYRERER0bPp0KEDTp06JaeWER2pRGd0sYjPWuyYTkT0WFDkfXzw5zzoDQbZfrNhD7xYurbSYRGpnqpG9K1evRp9+/aV6xs3bpRlES5evIiffvoJH330Efbt26d0iESq9M2+35GUkizXX6vdHr6uHkqHREREOUx8VhIdpqZNm4bGjRvLbXv37sUnn3yC+Ph4fPHFF0qHSERERKRKLJFORPTfEpOTMGbDbITHRcl2k5I1MLRRD6XDIrIKqkr03b9/H4UKGYfxbt68Ga+88grKlSuHwYMHY+7cuUqHR6RKl0JuYtP5vXK9gIMLBtbtonRIRESUC5YvX44lS5bInuYm1apVQ9GiRTF8+HAm+oiIiIiekag4RURETzftn2U4G3RNrhct4IMpHd6GVqOqgoNEFktViT5fX1+cP38ehQsXxpYtW7Bw4UK5PTY2FjqdTunwiFRJ1MQ2wDhc/o0G3eDmkHYuASIisg5hYWGoUKFCuu1im9hHRERERM/m9OnTGW7XaDRwcHCAv78/7O3t8zwuIiJLse7MTqw5vUOu29vYYmaXUSjg6KJ0WERWQ1WJvkGDBslRfCLRJz4stWrVSm4/dOhQhg+uiOjpDgeew96Ak3K9sKsXetVorXRIRESUS6pXr4758+dj3rx5abaLbWIfERERET2bGjVqyOdUmbG1tUWvXr3w7bffysQfEVF+ci7oGqZs/97cnth6CCr6llQ0JiJro6pEn5hDpmrVqggMDMTLL79s7g0lRvONGzdO6fCIVMVgMGDOnhXm9ltNXoG9jZ2iMRERUe6ZPn06OnbsiO3bt6Nhw4Zy24EDB3Dr1i1ZEp2IiIiIns26devwwQcf4P3330e9evXktsOHD2PmzJn4+OOPkZycLJ9bTZgwAV999ZXS4RIR5Znw2EiMWT8biSlJst2rRht0rtxU6bCIrI5qEn1JSUlo164dFi1ahB490k7SOWDAAMXiIlKr7ZcPyR41Qlkvf3So2ETpkIiIKJfnjrl8+TK++eYbXLhwQW7r3r27nJ+vSJEiSodHREREpFpiruO5c+eibdu25m2io3qxYsUwceJEmfRzdnbGmDFjmOgjonwjRa/HuE1f417UfdmuXqQs3m/eX+mwiKySahJ9osxBZjXPiSh7klKSMW/vKnP73aa9odNy8lsiImt148YNbNu2DYmJiXj11VdRpUoVpUMiIiIishpnzpxB8eLF020X28Q+U3nPe/fuKRAdEZEyFuz7FQdvGt8DPZwKYEbnUbDVqSYdQaQqqnqy37dvXyxdulTpMIisYgLcwPAguV67WEU0KVlD6ZCIiCiX7Ny5E5UrV8abb76Jd955B7Vq1cLPP/+sdFhEREREVqNChQqYNm2a7FSVujKV2Cb2CXfu3IGvr6+CURIR5Z1/rhzB0kPr5bpOo8WMzu/C19VD6bCIrJaqUuiipvn3338v55apXbu2LHuQ2qxZsxSLjUgtYhPj8e2BNeb2yKZ9njppOBERqZsoF9W6dWssXLgQDg4Ocm6YsWPHyg5URERERPT8FixYgC5dushSndWqVZPbxEi+lJQU/Pnnn7J9/fp1WTKdiMja3Qi7i4l/fWNuj3rxNdTxq6RoTETWTlWJvrNnz8pe6IKYYyY1JiqIsuaX43/hfsxDud6ybD1UK1JW6ZCIiCiXPz/t378fhQsXlu0ZM2bg22+/xYMHD+Dp6al0eERERESq16hRIwQEBOCXX34xP696+eWX0adPH7i6usp2v379FI6SiChvBhiMWj8L0Ylxst22fEP0rd1B6bCIrJ6N2kpPEdGzC4+NxA+HN5iHzY944VWlQyIiolwWGRkJLy8vc9vJyQmOjo6IiIhgoo+IiIgoh4iE3tChQ5UOg4hIMQaDAR//vQjXH9yW7dKexfBJ2zc5QIcoD6gq0Wdy9epVXLt2DU2bNpUPqsSbCN8wiP7bkkPrEPOoR03Xqs1RwqOI0iEREVEe+Pvvv1GgQAFzW6/XY8eOHXK0n4koN0VEREREWbNhwwa0b98etra2cv1p+DmLiPKDn45txtZLB+W6i50jZr00Gk52DkqHRZQvqCrRJ0pMvfLKK3Jkn0jsXblyBaVKlcLrr7+OggULYubMmUqHSGSxbj8MwaoTW+W6g40dhjbqoXRIRESURwYMGJBu25tvvmleF5+rxBwyRERERJQ1Xbt2RVBQEHx8fOR6Zvg5i4jygyOB5zBn9y/m9ucd3uIAA6I8pIWKjBo1SvaUCgwMlGWnTHr16oUtW7YoGhuRpftm/29I1hv/cyFqY/u4eCgdEhER5QExeu+/Fj58IiIiIsoe8RlKJPlM6/ycRUT5VXDUA4z9cx5SDHrZfqN+VzQvU0fpsIjyFVWN6Nu6dassPVWsWLE028uWLYubN28qFheRpbsYcgObz++T6wUcXDCwHsuGEBERERERERER0bNLTE7CexvmICw2QrYblqiG4Y1fUTosonxHVYm+mJiYNCP5TMLCwmBvb69ITERqMG/PShhgkOtDGnSDq336f0dERERERERElHU//vhjlo7r379/rsdCRKSEr3b9hNP3rsj1Im5emNbxHei0qioiSGQVVJXoe+GFF+SHqM8++8xc51yUQZg+fTqaN2+udHhEFulQ4Fnsu3HK/Ae3V402SodEREREREREpHoDBw6Ei4sLbGxsYDAYO9c+STy7yk6ib8+ePZgxYwaOHTuGe/fuYd26dU+dA3DXrl0ZPhMTX1uoUKEsf18iouzaeG4Pfj25Va7b6Wwx66UxcHd0VTosonxJVel1kdBbvHgx2rdvj8TERIwdOxZVqlSRH4K+/PJLpcMjsjjiPxpzd68wt8XQeTsbW0VjIiIi6yfmopk4cSJKliwJR0dHlC5dWnbUSv0ATKxPmjQJhQsXlse0atUKV64Ye4ISERERqUHFihVhZ2cnE3m7d+9GeHh4ukVUocpuNavq1atjwYIF2fq6S5cuyeSeaTHNH0hElBsuBAfgs23fmdsftXodFX1LKhoTUX6mqhF9Iql3+fJlzJ8/H66uroiOjkb37t3x1ltvyYdERJTWtsuHcC74ulwv5+2PDhWbKB0SERHloXnz5uF///sfHBwcEBgYCD8/P9mrPLeJDlgLFy7E8uXLUblyZRw9ehSDBg1CgQIFMGLECHMHLhGfOEYkBEVisG3btjh//ryMl4iIiMjSnTt3DocOHcL333+Ppk2bokyZMnj99dfx2muvwc3N7ZnOKTq3iyW7RGLP3d09S8cmJCTIxSQyMlK+iqpZYslJ4nyig1dOn9cS8NrUyZqvLa+uLyIuGmM2zEJCcpJs96jaAl0qN831e2rNPztemzrpc/HasntO1ST6bty4gW3btiEpKQkvvfQSPvroI6VDIrJoSSnJ+PrfVeb2uy/0Zo1sIqJ8ZvTo0Xj11Vdl4kwk0/Kqd/f+/fvl57WOHTvKdokSJbBy5UocPnxYtsUH4Tlz5mDChAnyOEGUZ/f19cUff/whYyYiIiJSg/r168tFfLb5/fff8cMPP+C9996T5TZFAtDe3j5P4qhRo4ZM3olO8p988gkaN26c6bFTp07F5MmT020PDQ1FfHx8jj+ojIiIkJ//tFb2TILXpk7WfG15cX0pej0m7VqMOxGhsl3e0x8Dq3RASEgIcps1/+x4beqkz8Vri4qKsr5E386dO9GpUyfExcXJtqh9Lj4s9e3bV+nQiCzWujP/IPBhkFyv41cJjUvWUDokIiLKY0WKFMGaNWvQoUMH+cHz9u3bmT688ff3z7Hv26hRI1luXVRiKFeuHE6dOoW9e/di1qxZcn9AQACCgoJkuU4TMdpPPCQ7cOBAhom+vOp5Lu6THPUoBj7m/uDHrDHFYinxCOIWaTTP3XuR9zuLeL/zFu+3Ku+3Rd5zK7/fqVljL/3sEqXIRQlP0cHp448/xqpVq2RFqtxO9IkKV4sWLUKdOnXkZ6UlS5agWbNmcqRhrVq1Mvya8ePHyw5hqT9XicoP3t7ezzwS8Wm/G+J3TpzbGh/w8trUx5qvLS+u75t9v+Po3YtyvaCjG+Z0fx+FXD2RF6z5Z8drUyd9Ll5bdisdqSLRJ0o5tW7dWpaAEhcoen+L+fmY6CPKWGxiPBbtX2Nuv9u0d56UaiMiIssiPjO98847ePvtt+Xfgbp162b6UFTMq5dTxo0bJx8YVahQATqdTp77iy++kGWsBJHkE8QIvtRE27RPqZ7notdcybIl4ejsCJ2DDpZC66UFHk9xqDjHeEeUjCkp79fz9N7l/c4a3u+8xfutzvttqffcmu/38/Q6tzZ37tyR5cjFaD4xx554XiWeYRUsWDDXv3f58uXlkrrD1bVr1zB79mz89NNPGX6NSD5mlIAUDylz4yGs+KyZW+dWGq9Nnaz52nLz+nZdO4bvDq2T61qNBl92HoEiBbyRl6z5Z8drUydNLl1bds+nikTf2bNnZQko0zx8M2bMwLfffosHDx7A0zNvegwQqcnPxzbjQWyEXG9Vth6qFS6rdEhERKQAMT9f7969cfPmTVSrVg3bt2/Pk89Ov/32G3755ResWLFCztF38uRJjBw5Uo4wHDBgwDOdM696nos5oAOuBKC6e3W4Oedsj/Zn9mg0SMrtFIt5WBwXE4eAhwFy3uznKQfL+501vN95i/dbnffbIu+5ld/v1PLr/LriM49I7u3evVvONTxz5kxZulx0dFJSvXr1ZDUFIqKcEhgehAmbF5jb777QB/X9qygaExGpLNEnHuR4eXmZ205OTrIkgqh/ykQfUVphsZFYdmSjXNdptHjnBc5zRESUn4kHeWKuFvEQSszVkhfzxLz//vtyVJ+pBGfVqlVlslGMyhOJvkKFCsntwcHB5o5cpraYX0bJnuemcmbygayFPJSVTPFYSkyGx6NBn+f+835nEe933uL9VuX9tth7bsX3OzVr7KGfFeKzjih/PmrUKFmZ4MaNG1iw4PGDcJMRI0bkaVyik1Xqz1hERM9bOWzU+pmISoiV7dbl6mNA3U5Kh0VEakv0CX///becuyV1/dMdO3bI0X4mXbp0USg6Isux5OA6xCQa57PsVq0FSngUUTokIiKyAKaRdMeOHcOFCxfkeqVKlTKdu+V5xMbGpnvgJ3q2m+bvKVmypEz2ic9ypsSe6Ngl5pIZNmxYjsdDRERElBtEkk8kTUUVg8yI/dlJ9ImRqVevXjW3xdzGInHn4eEhv5+ociBKhf74449y/5w5c+RnK1FFQZQzF3P0/fPPP9i6detzXh0RkbFzyGfbvsPV+7dku5RHUUxuN5RTBBFZGNUk+jIq8/Tmm2+a13N6bhkiNbr9MAS/njR+mHewscfQhj2UDomIiCyEmIdH9DrftWsX3N3d5baHDx+iefPmWLVqlSyBmVM6d+4s5+QTD6PEQ6cTJ05g1qxZGDx4sPlzmyjl+fnnn6Ns2bLy4ZSYk1mU9uzatWuOxUFERESUm8QIvpx29OhR+fnMxFS6XDwXW7ZsGe7du4fAwEDz/sTERIwZM0Ym/0QFLFO59tTnICJ6VitObMHmC/vkupOtA2a9NBrOdo5Kh0VEakz0mXp/E9HTLdj3G5L1xoR3vzod4O2S+xN/ExGROrzzzjuIiorCuXPnULFiRbnt/Pnz8qGR6GW+cuXKHPteX3/9tUzcDR8+XCYYRQJPdNCaNGmS+ZixY8ciJiZGziMoEo5NmjTBli1b8u0cP0RERERCs2bNjCVoMyGSfamJz1RiISLKacdvX8SsXT+b25+1H4aSnkUVjYmIVJzoI6L/diE4AJsvGCfbdnd0xYC6nZUOiYiILIhIoone3aYkn6l0p5hHpk2bNjk+L6AoIyWWzIhRfZ9++qlciIiIiIiIyHKERofj/Y1zzAMKBtbtjFbl6isdFhFlwuJnSz548GC25oMRvdSJ8qN5/64yrw9p0A2u9k6KxkNERJZXIcHW1jbddrGN1ROIiIiIiIhISEpJxnsb5+B+zEPZrudfGe+88KrSYRGRmhN9/fr1Q9u2bfH777/L8k4ZEWWnPvzwQ5QuXRrHjh3L8xiJlHbo5hnsv3FKrhdx88Ir1VsrHRIREVmYFi1a4N1338Xdu3fN28RcLqNGjULLli0VjY2IiIiIiIgsw8xdP+PknUtyvZCrJ77s9C5stDqlwyIiNZfuFEm8hQsXYsKECejTpw/KlSsn53kR87eEh4fj4sWLiI6ORrdu3bB161ZUrVpV6ZCJ8pTeoMecPY/nVXqrSS/Y2aQfsUFERPnb/Pnz0aVLF5QoUQJ+fn5y261bt1ClShX8/PPjeReIiIiIiIgof/rz/L9YeWKLXLfV2WBml9HwcHJTOiwiUnuiT5STGjFihFyOHj2KvXv34ubNm4iLi0P16tVlL/TmzZvDw8ND6VCJFLHt0iGcD74u18t7F0eHio2VDomIiCyQSO4dP35cztMnOkoJYr6+Vq1aKR0aERERkepdu3YNP/zwg3ydO3cufHx88Ndff8Hf3x+VK1dWOjwiov90KeQmPtv6nbk9vuUgVClcWtGYiMhKEn2p1alTRy5E9Lhm9td7H8/N927T3tBqLL4iLxERKUSj0aB169ZyISIiIqKcsXv3brRv3x6NGzfGnj178MUXX8hE36lTp7B06VKsXr1a6RCJiJ4qMj4ao9fPRHxyomx3q9ocPapxigcitWBGgEjF1p7+B7ceBsv1un6V0ahEdaVDIiIiIiIiIspXxo0bh88//xzbtm2DnZ1dmjmSDx48qGhsRERZmRboo83f4HZEiGxX9i0lR/MRkXow0UekUrGJ8Vh0YI25PbJpHzlSg4iIiIiIiIjyzpkzZ9CtW7d028Wovvv37ysSExFRVi0+sA57rh+X6+6OrviqyyjY2zzutEBElo+JPiKV+vHoJoTFRsj11uXqs2Y2ERERERERkQLc3d1x7969dNtPnDiBokWLKhITEVFW7L1+Aov2G8sLazUaTOv4DooU8FY6LCLKJib6iFToQUwElh/ZKNd1Gi3ebtJL6ZCIiIiIiIiI8qVXX30VH3zwAYKCgmSlHb1ej3379uG9995D//79lQ6PiChDtx8GY9ym+TDAINvi+WLDEtWUDouInoHNs3wRESlryaF1iE2Kl+vdq7VACY8iSodEREQqsmnTJuzatQspKSlo3LgxevTooXRIRERERKo1ZcoUvPXWW/Dz85OfrypVqiRf+/TpgwkTJigdHhFROnFJCRi9fhaiEmJku3mZuhhc7yWlwyKi/JLo27Fjh1xCQkJkD6nUvv/++2yda8GCBZgxY4bscVW9enV8/fXXqFev3n9+3apVq9C7d2+89NJL+OOPP7J9DUTP29vmt5Pb5LqDjT3ebMiHs0RElHUTJ07E2rVr0bFjRxgMBowaNUom/cTnICIiIiLKPjs7O3z33Xfyc9bZs2cRHR2NmjVromzZskqHRkSUjvh/4OfbluBS6E3ZLl6wMD5vP0yOSCYidVJVom/y5Mn49NNPUadOHRQuXPi53nx+/fVXjB49GosWLUL9+vUxZ84ctG3bFpcuXZKTJWfmxo0bsvTCCy+88Mzfm+h5LNj3G5L1KXK9X50O8HYpqHRIRERkwY4ePSo/O6X+DHTq1Ck4OjrK9sCBA9GsWTMm+oiIiIie0d69e9GkSRP4+/vLhYjIkv16civ+PP+vXHe0tcfsl8bAxd5J6bCIKL8k+kRSbtmyZejXr99zn2vWrFkYMmQIBg0aZD63KGMlRgWOGzcuw68RZRdee+01mXD8999/8fDhw6d+j4SEBLmYREZGylcxEvHJ0YjPQ5xL9MTIyXNaCl5bWhdDbmDzhX1y3d3BBf1rd7TYe8OfnTrx2tTLmq8vt67NGu9VRoYOHSofPImSUk5OTihVqhRmzpyJl19+GYmJiVi4cCHKlSundJhEREREqtWiRQsULVpUVn/q27evLN1JRGSJTt65hBk7fzS3P203DKW9iikaExHls0SfeBjVqFGjHDnPsWPHMH78ePM2rVaLVq1a4cCBA5l+nRhNKEb7vf766zLR91+mTp0qk4JPCg0NRXy8cX61nHpQGRERIR+CiuuwJry2tL7asdy8/mqV1oiNiEYsomGJ+LNTJ16belnz9eXWtUVFRSE/OHToEL766ivUqlVLliwXnZreeecdzJ49W3ZiEknAFStWKB0mERERkWrdvXtXTvOycuVKTJs2DdWqVZMdxUXir1gxPkAnIstwP+Yh3ts4x1wprH+djmhTvoHSYRFRfkv0vfHGG/JBlKh5/jzu378vH2z5+vqm2S7aFy9ezLQMw9KlS3Hy5Mksfx+RSBTlQVOP6BMTM3t7e8PNzQ05+QBUlDEV57XGh7u8NqODN8/g2L1Lcr2ImzcGNeoKOxtbWCr+7NSJ16Ze1nx9uXVtDg4OyA90Oh0++OADOYJv2LBhcHZ2xvz581GkSBGlQyMiIiKyCl5eXnj77bflEhAQIJ9dLV++XD4Xatq0Kf755x+lQySifC4pJRljN85FaHS4bNfxq4R3m/ZROiwiyo+JPjEKbvHixdi+fbvsHWVra5uuHGduED3+RblQMbGy+PCWVfb29nJ5knhImdMPYcUD0Nw4ryXgtQF6gx5f7/3V3H67SS842KX/3bI0/NmpE69Nvaz5+nLj2qzxPj2NKNn5999/46effpIPnEaNGoW33npL6bCIiIiIrErJkiXllDDVq1eXHdV3796tdEhERJizZwWO3b4g131cPDC90wjYaHVKh0VE+THRd/r0adSoUUOunz17Nt0DwKwSyTrRuz04ODjNdtEuVKhQuuOvXbuGGzduoHPnzunm9bGxscGlS5dQunTpbF8PUVZtvXQQ54Ovy/Xy3sXRvuLzl7AlIqL8QcwpLObnu3DhgnzgJB48dejQAWPGjEGDBg1kR6aqVasqHSYRERGR6u3btw+//PILVq9eLTurv/TSS3JaFyIiJf11cT9+PrZZrovk3swuo+Dp7K50WESUXxN9O3fuzJHz2NnZoXbt2tixYwe6du1qTtyJtiiz8KQKFSrgzJkzabZNmDBBjvSbO3euLMdJlJtD6+enGs0nhtVrNflrFAoRET27AQMGyGSfmCNGfNYR5TvFqL5ly5bJdq9evWRnpi+//FLpUImIiIhUSZToFHP0ibn6WrduLZ8ViSSfk5OT0qERUT53JfQWPtnyrbn9QYsBqFakrKIxEVE+T/Sldvv2bfn6rJMai7nzxIOvOnXqoF69epgzZw5iYmIwaNAgub9///4oWrSo7Hkl5vCpUqVKmq93dzf2enhyO1FOW3N6B249NI4+redfGY1KVFM6JCIiUhExJ8yJEydQpkwZDBkyRL6atGzZEsePH8enn36qaIxEREREarZnzx68//77eOWVV7I15QsRUW6KSojF6A0zEZ+cINtdKjfFy9VbKx0WEeX3RJ8Ydff5559j5syZiI6OlttcXV1l6amPPvooW3PtiN7roaGhmDRpEoKCgmRJ0C1btsDX11fuDwwMzHdz95DliUmMw7cH1prb777QJ1tlaomIiMqWLSvnOH7jjTewbds2FC9ePM1+0aFJlPYkIiIiomcv2UlEZEn0Bj0mbF6AwPAg2a7gUwIftXqDzxWJrJSqEn0imbd06VJMmzYNjRs3ltv27t2LTz75RNY+/+KLL7J1PlGmM6NSncKuXbue+rWi3BVRbvvp6CaExUbI9TblG6BKYc4FSURE2fP999/LJN+CBQtkx6YlS5YoHRIRERGR6m3YsAHt27eHra2tXH+aLl265FlcRJR/pej1OHbrPK7dvYmLJ29h17VjcrubgzNmdhkNB1s7pUMkolyiqkTf8uXL5cOp1B+QqlWrJktsDh8+PNuJPiJL9iDmIZYf+dM8Ue7bTXopHRIREamQSO4dPXpU6TCIiIiIrErXrl1lhSgfHx+5nhkxeiYlJSVPYyOi/Gf75cOY/s8yBEeHpds3reMIFHP3USQuIsobqkr0hYWFoUKFCum2i21iH5E1WXxwHWKT4uV696otULxgYaVDIiIiIiIiIqJH08tktE5EpESS770Ns2DIZH9cknGOPiKyXqqahK569eqYP39+uu1im9hHZC1uPQzC6lPb5bqDjT3ebNRD6ZCIiIiIiIiIKAM//vgjEhLSP0hPTEyU+4iIcrNcpxjJl1mST8zIN33ncnkcEVkvVY3omz59Ojp27Ijt27ejYcOGctuBAwdw69YtbN68WenwiHLMgr2/IVlvLO3Rv05HeDm7Kx0SEREREREREWVg0KBBaNeunSzjmVpUVJTc179/f8ViIyLrdvz2hQzLdZqIBGBw1AN5XF3/ynkaGxHlHVWN6HvxxRdx+fJldOvWDQ8fPpRL9+7dcenSJbzwwgtKh0eUIy4EB+Cvi/vlekFHVwyo20npkIiIiIiIiIgoEwaDQc7F96Tbt2+jQIECisRERPnD/ZiHOXocEamTqkb0CUWKFMEXX3yhdBhEuWbOnhXm9SENu8PF3knReIiIyDqlpKTgzJkzKF68OAoWLKh0OERERESqU7NmTZngE0vLli1hY2OT5rNWQECAHOlHRJRbsloFjNXCiKybxSf6Tp8+jSpVqkCr1cr1p6lWrVqexUWUGw7cOI2DN8/I9aIFfPBytVZKh0RERFZi5MiRqFq1Kl5//XX54ElUSti/fz+cnJzw559/olmzZkqHSERERKQqXbt2la8nT55E27Zt4eLiYt5nZ2eHEiVKoEePHgpGSETW7mb4vafuF2ONfVw9UatYxTyLiYjynsUn+mrUqIGgoCBZ51ysi15SoiTCk8R28dCKSK30Bj3m/rvS3H67ySuws7FVNCYiIrIeq1evRt++feX6xo0bZQ/zixcv4qeffsJHH32Effv2KR0iERERkap8/PHH8lUk9Hr16gUHBwelQyKifGT75cP4YvvSTPebCgqPbT4AOq2qZvAiImtL9ImHUN7e3uZ1Imv196WDcn4+obxPCbSr0EjpkIiIyIrcv38fhQoVkuubN2/Gyy+/jHLlymHw4MGYO3eu0uERERERqdaAAQOUDoGI8plDgWcxbtM86B8NiHmxVC1cDLmB4Ogw8zFiJJ9I8rUqV0/BSIkoL1h8ok/MG2Ny8+ZNNGrUKE3NcyE5OVmWnkp9LJGaJKUkY/6/q8ztkU17Q6thTxsiIso5vr6+OH/+PAoXLowtW7Zg4cKFcntsbCx0Op3S4RERERGplqgwNXv2bPz2228IDAxEYmJimv1hYY8fvBMRPa/zQdcxct1X8nmi0LlSU3zafihEzu/YrfO4dvcmShcpjtp+lTiSjyifUNW/9ObNm2f44SgiIkLuI1Kr1ae243ZEiFyv718FDYtzvkkiIspZgwYNwiuvvCLnPhYlz1u1Ms4De+jQIVSoUEHp8IiIiIhUa/LkyZg1a5Ys3ymeUY0ePRrdu3eHVqvFJ598onR4RGRFboTdxfA10xCbFC/bL5aujY/b/k8OGBBJvTp+ldC8ZG35yiQfUf5h8SP6UhNz84kHU0968OABnJ2dFYmJ6HnFJMbh2wNrze13m/bO8PeciIjoeYiHTCLJd+vWLVm2097eXm4Xo/nGjRundHhEREREqvXLL7/gu+++Q8eOHeVnrt69e6N06dKoVq0aDh48iBEjRigdIhFZgeCoBxj6+xSEx0XKdq1iFTC907uw1anqET8R5QJVvAuIXlCCSH4MHDjQ/GDKVB7h9OnTsqQnkVqk6PXmofTnwm6Y/0C3Ld8QlQuVVjo8IiKyUj179ky3jXPKEBERET2foKAgVK1aVa67uLjIUX1Cp06dMHHiRIWjIyJr8DAuCkNXT8W9qPuyXd67OOZ2fR8OtnZKh0ZEFkAVib4CBQqYR/S5urrC0dHRvM/Ozg4NGjTAkCFDFIyQKOu2Xz6M6f8sSzM5riCG2L/dpJdicRERkfWLiYnB7t27M5w7hj3NiYiIiJ5NsWLFcO/ePfj7+8uRfFu3bkWtWrVw5MiRNJ3ViYieRWxiPN5ZOx3XH9yWbT93X3zTczzcHFjhjohUlOj74Ycf5GuJEiXw3nvvsUwnqTrJ996GWTBksE9v0ONyaCD8CxZSIDIiIrJ2J06cQIcOHRAbGysTfh4eHrh//z6cnJzg4+PDRB8RERHRM+rWrRt27NiB+vXr45133kHfvn2xdOlS2blq1KhRSodHRCqWlJKMMRtm4/S9K7Lt5eyORT0/lK9ERKpK9Jl8/PHHSodA9FzlOsVIvoySfCbTdy5H8zJ1OFkuERHlOPGQqXPnzli0aJGsliDmi7G1tZUPot59912lwyMiIiJSrWnTppnXe/XqJUf2HThwAGXLlpWfv4iInoUYFDDhr2+w/8Yp2Xa1d8LCnuNRzN1X6dCIyMKoKtEnrF69Gr/99luGJaeOHz+uWFxE/+X47QvpynVmNKmuOK6uf+U8i4uIiPKHkydP4ttvv4VWq4VOp0NCQgJKlSqF6dOny3n6THMiExEREdHzadiwoVyIiJ6VmMJq2o5l2HJxv2zb29hiXrexKOddXOnQiMgCqSrRN2/ePHz00UcYOHAg1q9fj0GDBuHatWuy5vlbb72ldHhET3U/5mGOHkdERJQdYvSeSPIJolSn6DRVsWJFObrv1q1bSodHREREpCobNmzI8rFdunTJ1ViIyPp8e2ANfj25Va7rNFp81XkUahWroHRYRGShVJXo++abb7B48WL07t0by5Ytw9ixY2VP9EmTJiEs7OkjpYiUltXa2ayxTUREuaFmzZqyc5QoIfXiiy/Kz09ijr6ffvoJVapUUTo8IiIiIlXp2rVrlo7TaDRISUnJ9XiIyHqsPL4FC/evNrcntxuKpqVrKRoTEVk2VU0EJnqeN2rUSK47OjoiKipKrvfr1w8rV65UODqip/N19ZQ9cDKjeXRMrWIV8zQuIiLKH6ZMmYLChQvL9S+++AIFCxbEsGHDEBoaKjtSEREREVHW6fX6LC1M8hFRdvx1YR++/Ge5uf1es37oXLmpojERkeVT1Yi+QoUKyZF7xYsXlxMbHzx4ENWrV0dAQICsW0xkqa6EBmLY6qlIMegzTfIJY5sPgO5RWTUiIqKcVKdOHfO6KN25ZcsWReMhIiIiIiKix/YFnMSEv76BAcbn3G/U74p+dToqHRYRqYCqEn0tWrSQNdBF6SkxP9+oUaOwevVqHD16FN27d1c6PKIMHb99ESPWzUBUQoxse7sUlInp1HPx+bh6yiRfq3L1FIyUiIiIiIiIiLLr008/fep+UTKdiOhpTt29jNHrZyNZbxwF3KNaS7zdpJfSYRGRSqgq0SfKSomyB8Jbb70FT09P7N+/X05q/OabbyodHlE6u64dw9iNc5CQnCTblQuVxoLuH8DNwQXHbp3Htbs3UbpIcdT2q8SRfEREREREREQqtG7dujTtpKQkWX3KxsYGpUuXZqKPiJ7q6v1beHvtl4hPTpDtVmXr4aNWr8s5PomIrCrRl5ycLOeWGTx4MIoVKya3vfrqq3IhskTrz+7C5L8Xm8t1NixRDbO6jIaTnYNs1/GrBH97L1k+TcskHxEREREREZEqnThxIt22yMhIDBw4EN26dVMkJiJSh7sRoXK6n8h4YyWw+v5VMLXjOxwQQETZopp3DNELavr06TLhR2Tplh3eiElbFpmTfO0qNMLX3caak3xEREREREREZL3c3NwwefJkTJw4UelQiMhCPYiJwNDVUxASHSbblX1LYXbXMbCzsVU6NCJSGdUk+oSWLVti9+7dSodBlCm9QY9Zu37G7D2/mLf1rtkOUzu+DVudagbQEhEREREREdFzioiIkAsR0ZOiE2Lx1tppuBl+T7ZLeBTB/B7j4GznqHRoRKRCqso8tG/fHuPGjcOZM2dQu3ZtODs7p9kv5uojUkpSSrIs1bnx/B7zNjFp7hv1u7KmNhERWYQdO3bIJSQkxDzvscn333+vWFxEREREajZv3rw0bYPBgHv37uGnn36Sz7KIiFJLSE7EyD9m4kJwgGz7unpgUc8P4eHkpnRoRKRSqkr0DR8+XL7OmjUr3T6RSElJSVEgKiIgLikB72+cg3+vG+vyazUaTGj9BnpUa6l0aERERJIoHfXpp5+iTp06KFy4MDuhEBEREeWQ2bNnp2lrtVp4e3tjwIABGD9+vGJxEZHlSdanYNymr3Hk1jnZLuDggkU9P0JhNy+lQyMiFVNVou/JnudEliAiLhoj1k3HybuXZdtOZysnzW1Vrp7SoREREZktWrQIy5YtQ79+/ZQOhYiIiMiqBAQYR+UQET2NGO37+bYl+OfKEdl2tLXH/B4foJRnUaVDIyKVU1Wij8jSBEc9wLDVU3HtwW3ZFnW053Z9D3X9KysdGhERURqJiYlo1KiR0mEQERERERHlS/P+XYV1Z3bKdRutDrNeGo1qhcsqHRYRWQEbNdc8NxGlpxwcHFCmTBk0bdoUOp0uz2Oj/OdG2F0M/X0K7kXdl21PpwJY0GMcKvqWVDo0IiKidN544w2sWLECEydOVDoUIiIiIqsSHx+Pr7/+Gjt37sxwLuTjx48rFhsRWYblR/7E94fXy3UNNJjS4W00KlFd6bCIyErYqK3meWhoKGJjY1GwYEG5LTw8HE5OTnBxcZEfpkqVKiU/WPn5+SkdLlmxs/eu4e210xAeFyXbxQr4YNHLH8LPvZDSoREREWX6AGrx4sXYvn07qlWrBltb2zT7M5oDmYiIiIj+2+uvv46tW7eiZ8+eqFevHudCJqI0NpzdjVm7fza3P2w1CG0rNFQ0JiKyLqpK9E2ZMkU+oFqyZAlKly4tt129ehVvvvkm/ve//6Fx48Z49dVXMWrUKKxevVrpcMlK7b9xCqPXz0JcUoJsl/cujm96joeXs7vSoREREWXq9OnTqFGjhlw/e/Zsmn18GEVERET07P78809s3rxZPpciIkpt17Vj+OTvb83tYY1exis12igaExFZH1Ul+iZMmIA1a9aYk3yCKNf51VdfoUePHrh+/TqmT58u14lyw18X92PC5gVI1qfIdp1iFTGn2/twtXdSOjQiIqKnEhUPiIiIiCjnFS1aFK6urkqHQUQW5titCxi7cQ5SDMZyvr1rtsObDbsrHRYRWSEtVOTevXtITk5Ot11sCwoKkutFihRBVJSxnCJRTlpxfAvG//m1OcnXvExdOZKPST4iIlKb27dvy4WIiIiInt/MmTPxwQcf4ObNm0qHQkQW4mLIDYxYNx0JyUmy3aFiY4xt0Z/VVIgoV6gq0de8eXNZpvPEiRPmbWJ92LBhaNGihWyfOXMGJUuWVDBKsjYGgwHz9/6KL/9ZBgMMclv3qi3wVZeRsLexUzo8IiKiLNHr9fj0009RoEABFC9eXC7u7u747LPP5L6cdufOHfTt2xeenp5wdHRE1apVcfTo0TR/XydNmoTChQvL/a1atcKVK1dyPA4iIiKi3FanTh05H3KpUqXkyD4PD480CxHlL7ceBmH46qmIToyT7cYlquPTdsOg1ajqUTwRqYiqSncuXboU/fr1Q+3atWFra2sezdeyZUu5T3BxcZE9qYhyQopejy+2L8Wa0zvM24Y06Ia3Gr/CHjhERKQqH330kfy8NG3aNPP8MXv37sUnn3wiH0x98cUXOfa9wsPD5fcQnbT++usveHt7yyRewYIFzceIcuvz5s3D8uXLZSetiRMnom3btjh//jwcHBxyLBYiIiKi3Na7d2/ZyWnKlCnw9fXl8wKifCw0Ohxv/j4FD2IjZLt6kbL4qsso2OpU9RieiFRGVe8whQoVwrZt23Dx4kVcvnxZbitfvrxcTMQDJaKckJCciPGb5mPHlcPmbWNbDMBrtdorGhcREdGzEAm1JUuWoEuXLuZt1apVk3PKDB8+PEcTfV9++SX8/Pzwww8/mLelrrggRvPNmTNHzr/80ksvyW0//vijfDD2xx9/4NVXX82xWIiIiIhy2/79+3HgwAFUr15d6VCISEGR8dEYtnoq7kSEyHZpz2L4utsHcLJjR0Yiyl2qSvSZVKhQQS5EuSU6IRbv/vEVjt46L9s2Wh0+az8MHSo2UTo0IiKiZxIWFpbh5yexTezLSRs2bJCj815++WXs3r3bnEwcMmSI3B8QECDnVxblOk1ESdH69evLh2QZJfoSEhLkYhIZGSlfRdnRnCw9KpKQshe+6IhvKZ3xTbFYSjyCuEUajbxfz3P/eb+ziPc7b/F+q/J+W+Q9t/L7nVpulAFXE/F5Ki7OWKKPiPKnuKQEvLNuBq7cD5TtIm5eWNjzQxRwdFE6NCLKByw+0Td69Gg5d4yzs7Ncf5pZs2blWVxkvR7EPMTwNdPkpLmCo609ZnYZjcYl2TOPiIjUS/Qwnz9/viyXmZrYltO9z69fv46FCxfKz24ffvghjhw5ghEjRsDOzg4DBgyQST5BjOBLTbRN+540depUTJ48Od320NBQWXo0p0RFRaFk2ZJwdHaEzkEHS6H10uLRVMEWwTHeESVjSsr7FRJi7LH8LHi/s4b3O2/xfqvzflvqPbfm+52aOF9+JkqjjxkzRlZIEPMSm6abMXFzc1MsNiLKfUkpyXh/4xycvHNJtgs6umHRyx/B15VzdBJR3rD4RN+JEyeQlJRkXs8M659TTrj9MBhDV0/BrYfBsu3u6Iqvu49FtcJllQ6NiIjouYg58Tp27Ijt27ejYcOGcpsYPXfr1i1s3rw5R7+X6NVfp04dOU+NULNmTZw9exaLFi2Sib5nMX78+DSdvsSIPlEeVMz/l5MPz6KjoxFwJQDV3avDzdlCHso9Gg2ScjvFYh4Wx8XEIeBhAFxdXeHj4/PM5+H9zhre77zF+63O+22R99zK73dq+X1+3Xbt2snXli1bZjjKNCUlJcvn2rNnD2bMmIFjx47h3r17WLduHbp27frUr9m1a5f8nHTu3Dn5+UiURx84cOAzXg0RZYfeoMfHfy/Cv9eNz62d7RyxsOd4FC9YWOnQiCgfsfhE386dOzNcJ8ppYgTf8NVTzZPlFnL1xKKeH6KkZ1GlQyMiInpuL774opzjeMGCBXK+Y6F79+6ypGaRIkVy9HsVLlwYlSpVSrOtYsWKWLNmjXneZSE4OFgeayLaNWrUyPCc9vb2cnmSVquVS04xlTOTD2Qt5KGsZIrHUmIyPH54+Tz3n/c7i3i/8xbvtyrvt8Xecyu+36nl5LnUKCefV8XExMhqC4MHD5af1f6LKIkuOnMNHToUv/zyC3bs2IE33nhDfsYSpdSJKPeI99OZu37CpvN7ZdtOZ4u5Xd9DRd/H85MTEeUFi0/0EeUFMRffu+tmIDrRWFO/lEdRLHr5Q/i6eiodGhERUY4RCT1RUiq3NW7cGJcuGcvWmIgkY/HixeV6yZIlZbJPPIgyJfbECL1Dhw5h2LBhuR4fERERUU53qMop7du3l0tWiYoJ4rPVzJkzzZ2r9u7di9mzZzPRR5TLlh76Az8f+0uuazUafNlpBOr6V1Y6LCLKh1SR6BO9mLLi+++/z/VYyPr8c+UIPvhzHhJTjCViRZlOUa5TlO0kIiJSs9OnT6NKlSqyl71Yf5pq1arl2PcdNWoUGjVqJEt3vvLKKzh8+DAWL14sF0GMIhg5ciQ+//xzlC1bVj6cmjhxokxE/ldpKiIiIiJLI8ptPk3Tpk1z7XuLUuytWrVKs00k+MRnrcwkJCTIxUR0uDKVXxdLThLnE6Oecvq8loDXlr+vbc3pHfh676/m9sTWQ9CsdG3F7xl/durEa1MnfS5eW3bPqYpE37Jly2QPcDG/iyzDQZRD1p7+B59t+w76R79XTUrWwIzOI+Fkl7/nFyAiIusgRssFBQXJOXjEurmk2ROyO3fMf6lbt66cT0bMq/fpp5/KRN6cOXPw2muvmY8ZO3asLE31v//9Dw8fPkSTJk2wZcuWfD/HDxEREalPs2bNMvx8ZZKTn7OeJD7r+fr6ptkm2iJ5FxcXB0dHx3RfM3XqVEyePDnd9tDQUMTHx+f4g8qIiAj5GdTaSrzy2vLvtf178yS++He5uf16zc5o5FMJISEhUBp/durEa1MnfS5eW1RUlPUl+kQJp5UrV8q644MGDULfvn3h4eGhdFikYuIfnxhen7rnTcdKTTC57VDY6lTxz4KIiOg/ic9O3t7e5vW81KlTJ7lkRjz8EklAsRARERGpWXh4eJp2UlISTpw4ISsW5EXZ9OwSnbFGjx5tboukoJ+fn/zc6ObmluMPQcXnPnFua3zAy2vLf9d2KPAsvtz/MwyPJl/tX7sj3n6xNywFf3bqxGtTJ30uXlt2O0GrIqOxYMECzJo1C2vXrpXlOcUHEjHR8Ouvv442bdqk6SVF9F/0Bj2+2vkTfjlurKEt9KvdEaObvQatxrrebIiIKH8zzYkn3Lx5U5bTtLFJ+/EvOTkZ+/fvT3MsEREREWVdgQIF0m1r3bo17OzsZELt2LFjufa9xbzHwcHBabaJtkjYZTSaT7C3t5fLk8RDytx4CCue2+XWuZXGa8tf13b23jWMXj8LSSnJsv1SlWYY3ayvxT2b5s9OnXht6qTJpWvL7vlUc2fFB5DevXtj27ZtOH/+PCpXrozhw4ejRIkSiI6OVjo8Ugnxh/ijzQvSJPlGNu2DMc36MslHRERWrXnz5ggLC0u3XZSZEPuIiIiIKGeJEpqXLl3K1e/RsGFD7NixI8028exMbCeinBPw4A7eWjsNsUnG8rbNy9TBpDZDLC7JR0T5kypG9GWUzTTNMZObdc7JusQmxmPMhtnYf+OUbGs1Gkxq8z90q8qHm0REZP3E56aM/hP64MEDODs7KxITERERkTU4ffp0us9d9+7dw7Rp0+Q8ydkhOrNfvXrV3Bbl10+ePCmnsPH395dVru7cuYMff/xR7h86dCjmz58v5z8ePHgw/vnnH/z222/YtGlTDl0dEQVF3sfQ1VPwMM44Z1btYhUxreMI2Gh1SodGRKSuRF9CQoK5dOfevXvlnC/ig0y7du2scsgn5Szxh/jttV/izD3jh2V7G1tM7/QumpWpo3RoREREuap79+7yVST5Bg4cmKZMk+gwJR5MiZKeRERERPRsRDLP1CE9tQYNGsjnWNlx9OjRNNUWTHPpDRgwAMuWLZMJxMDAQPP+kiVLyqTeqFGjMHfuXBQrVgxLlixB27Ztn/u6iMj4THHY6qkIinog2+V9SmBut/fhYGundGhEROpK9IkSnatWrZITA4veSStXroSXl5fSYZFK3Iu8j2GrpyAg7K5su9o7YV6391GrWEWlQyMiIsqzOWPEgydXV9c0c7WIeWPEA6ghQ4YoGCERERGRuolRd6mJDune3t5wcHDI9rmaNWuWLmGYmkj2ZfQ1J06cyPb3IqL/rg721pppuB52R7b93QthYY9x8tkiEZElUUWib9GiRbI8QalSpbB79265ZESM+CNK7dr92xi2ZgqCo4xzEnk7F8Q3PcehnHdxpUMjIiLKEz/88IN8FfMav/feeyzTSURERJTDihfnMwYia5OYnIRR62fibNA18zPFhT0/hKezu9KhERGlo4qal/3795dlC9zd3WWv9MwWotRO3b2MQas+MSf5/AsWwrLek5nkIyKifOnjjz9mko+IiIgoB4n58CpVqoTIyMh0+yIiIlC5cmX8+++/isRGRM8uRa/HR38twMGbZ2Tb1d4ZC3uORzF3H6VDIyJS74i+jMoSED3N3usnMGbDHMQnJ8h2Rd+SWNB9HDydmRAmIqL8a/Xq1fjtt9/kvC6JiYlp9h0/flyxuIiIiIjUaM6cObIEupubW7p9okP6m2++iVmzZuGFF15QJD4iyj5ROnfqju+x9dJB2XawscP87mNR1ttf6dCIiNQ9oo8oOzad34t3//jKnOSr518ZS16ZyCQfERHla/PmzcOgQYPg6+sr53CpV68ePD09cf36dbRv317p8IiIiIhU59SpU2jXrl2m+9u0aYNjx47laUxE9Hy+2fc7fj+1Xa7baHX4qsso1ChaXumwiIieiok+sio/Hd2EDzfPR7I+RbZbl43gV/AAAQAASURBVKsvR/K5cJJcIiLK57755hssXrwYX3/9Nezs7DB27Fhs27YNI0aMkKWliIiIiCh7goODYWtrm+l+GxsbhIaG5mlMRPTsfjn+FxYfXGtuf9puGF4oVVPRmIiIsoKJPrKaYfVz96zEV7t+Mm97pXprfNnpXdjZZP6hm4iIKL8Q5TobNWok1x0dHREVFSXX+/Xrh5UrVyocHREREZH6FC1aFGfPns10/+nTp1G4cOE8jYmInr1C2PR/lpvbY1sMQMdKTRSNiYgoq5joI9UTo/c++ftbfH94vXnbsEY98WGrwdBp+StOREQkFCpUCGFhYXLd398fBw8a55wICAiQHWaIiIiIKHs6dOiAiRMnIj4+Pt2+uLg4fPzxx+jUqZMisRFR1u29fgKTtiw0t4c06IbXanF6AyJSDxulAyB6HvFJifjgz7nYdc1Y814DDca3HIReNdsoHRoREZFFadGiBTZs2ICaNWvKufpGjRqF1atX4+jRo+jevbvS4RERERGpzoQJE7B27VqUK1cOb7/9NsqXN87jdfHiRSxYsAApKSn46KOPlA6TiJ7i5J1LGLNhtnkaoJ7VW+Gtxq8oHRYRUbYw0UeqFRkfg3f/mIHjty+aJ8id0uFttK3QUOnQiIiILI6Yn0+v18v1t956C56enti/fz+6dOmCN998U+nwiIiIiFTH19dXfp4aNmwYxo8fb66SoNFo0LZtW5nsE8cQkWW6EhqIt9dOR3xyomy3KdcAH7YcLP8NExGpCRN9pEqh0eEYvmYqLocGyraTrQNmdx2DBsWrKh0aERGRxUlOTsaUKVMwePBgFCtWTG579dVX5UJEREREz6548eLYvHkzwsPDcfXqVZnsK1u2LAoWLKh0aET0FHciQjBs9VREJcTItnim+EWHtzgNEBGpEhN9pDo3w+9h6O9TcDcyVLYLOrphQY8PULlQaaVDIyIiskg2NjaYPn06+vfvr3QoRERERFZJJPbq1q2rdBhElAXhcVF4/8/5CI0Jl23xTHH2S2NgZ2OrdGhERM+EXRRIVS4EB2DAio/NSb4ibl5Y1vsTJvmIiIj+Q8uWLbF7926lwyAiIiIiIlJMVEIsJvzzLW49DJbtkh5FsKD7B3Cyc1A6NCKiZ8YRfaQahwLPYtQfMxGTGCfbZbz8sLDnePi4eCgdGhERkcVr3749xo0bhzNnzqB27dpwdnZOs1/M1UdERERERGStEpITMWr9TFwNvy3bhVw9sajnhyjo5KZ0aEREz4WJPlKFbZcP4aO/FiApJVm2axYtj3nd3oebg4vSoREREanC8OHD5eusWbPS7ROTzaekpCgQFRERERERUe5L1qfggz/n4djtC7Lt7uAik3yF3LyUDo2I6Lkx0UcW78/L+zD/8GoYYJDtF0vXxvRO78LB1k7p0IiIiFRDr9crHQIREREREVGeMxgM+Gzrd9h59ahsO9jYYX73D1DSs6jSoRER5Qgm+sii/wh/e2AtFh1ebd7WpfKL+Ljt/2Cj1SkaGxEREREREREREVm+OXtW4I+zu+S6rc4GH7/4OioXKq10WEREOYaJPrJIKXo9vvxnGX49udW8bWDdzhjZtI8sL0ZERETZM2/evAy3i7+rDg4OKFOmDJo2bQqdjp1piIiIiIjIOiw7vBHLjmyU6xpo8EX7t1DdvaTSYRER5Sgm+sjiJCYnyfn4tl46aN42qulrGFivs6JxERERqdns2bMRGhqK2NhYFCxYUG4LDw+Hk5MTXFxcEBISglKlSmHnzp3w8/NTOlwiIiIiIqLn8seZXZi95xdz+6PWr6N1ufry/z5ERNZEq3QARKnFJMbhnXXTzUk+nUaL9xr1Qf86HZUOjYiISNWmTJmCunXr4sqVK3jw4IFcLl++jPr162Pu3LkIDAxEoUKFMGrUKKVDJSIiIiIiei7/XDmCyVu/NbffbtILL1dvpWhMRES5hSP6yGKExUbi7TXTcC74unli3Omd3kV5F06MS0RE9LwmTJiANWvWoHTpx3NRiHKdX331FXr06IHr169j+vTpcp2IiIiIiEitjgSewwd/zoPeYJDt12q1xxv1uyodFhFRrmGijyzCnYgQDFs9FTfD78m2m4Mz5nf/AFULleFweiIiohxw7949JCcnp9sutgUFBcn1IkWKICoqSoHoiIiIiIiInt+F4AC8+8dXSExJku2OlZrgveb95NzkRETWiqU7SXFXQgMxYOXH5iSfj4sHfnj1E1QvUk7p0IiIiKxG8+bN8eabb+LEiRPmbWJ92LBhaNGihWyfOXMGJUtyYnoiIiIiIlIf8Wxx+Jppcmog4YVSNTG57VBoNXwETkTWje9ypKgTdy5h0KrJCI0Ol+0SHkXwY59PUcbLT+nQiIiIrMrSpUvh4eGB2rVrw97eXi516tSR28Q+wcXFBTNnzlQ6VCIiIiIiomwJiQ7D0N+nICw2QrZrFC2PGZ1HwlbHgnZEZP34TkeK2X3tGN7fOAcJycah9JULlcaC7h+goJOb0qERERFZnUKFCmHbtm24ePEiLl++LLeVL19eLqlH/REREREREalJZHy0nBLobmSobJf18sfX3d6Ho6290qEREeUJJvpIEevP7sLkvxcjxaCX7YYlqmFWl9FwsnNQOjQiIiKrVqFCBbkQERERERGpXWxiPN5ZOx1X79+S7aIFfPBNz3Fwc3BROjQiojyTrxN9CxYswIwZMxAUFITq1avj66+/Rr169TI89rvvvsOPP/6Is2fPyrYoezVlypRMj6fMLTu8EbP3/GJut6vQCJ+3H86h9ERERDls9OjR+Oyzz+Ds7CzXn2bWrFl5FhcREREREdHzSkpJltXCTt41VizxdCqART0/hI+Lh9KhERHlqXybWfn111/lA69Fixahfv36mDNnDtq2bYtLly7Bx8cn3fG7du1C79690ahRIzg4OODLL79EmzZtcO7cORQtWlSRa1AbvUGPObtXYPnRP83betdsh7Et+nNSXCIiolxw4sQJJCUlmdczo9Fo8jAqIiIiIiKi53/OOGnLQuwNOCnbLnaO+KbnePgXLKR0aEREeS7fJvpEr/UhQ4Zg0KBBsi0Sfps2bcL333+PcePGpTv+l18ej0ATlixZgjVr1mDHjh3o379/ht8jISFBLiaRkZHyVa/XyyWniHMZDIYcPWdu9LD5bNsSbDy/x7ztrcav4PV6LwEG4x9ntV7bs7Lma7P26+O1qZM1X5u1X19uXZs13qsn7dy5M8N1IiIiIiIitRL/P5yx80dsvrBPtu1tbDGv21hU8CmhdGhERIrIl4m+xMREHDt2DOPHjzdv02q1aNWqFQ4cOJClc8TGxsoe8h4emQ8Fnzp1KiZPnpxue2hoKOLj45GTDyojIiLkHzlxHZYmPjkRX/y7DIfvnJdtrUaDd+q9jA4lG8l7oeZrex7WfG3Wfn28NnWy5muz9uvLrWuLiorKsXMRERERERFR3vju4DqsOL5Frus0Wkzv9C5q+1VUOiwiIsXky0Tf/fv3kZKSAl9f3zTbRfvixYtZOscHH3yAIkWKyORgZkQiMfV8OGJEn5+fH7y9veHm5oacfAAqSm6J81raw92IuGiMXf8NTj2qlW2ns8WUDm+hZdl6qr+252XN12bt18drUydrvjZrv77cujZRijs/GDx4cJaOE1UNiIiIiIiILNlvJ7diwb7fzO2P2/4PzcrUUTQmIiKl5ctE3/OaNm0aVq1aJefte9pDQnt7e7k8STykzOmHsOIBaG6c93kER4Vh2OopuPbgtmw72zlibtf3UNe/suqvLadY87VZ+/Xx2tTJmq/N2q8vN67NGu9TRpYtW4bixYujZs2aclQkERERERGRGv198QCmbP/B3B79Yl+8VKWZojEREVmCfJno8/Lygk6nQ3BwcJrtol2o0NMnbP3qq69kom/79u2oVq1aLkeqXjfC7mLo71NwL+q+bHs6FcCCHuNQ0bek0qERERHlK8OGDcPKlSsREBAg5ybu27fvU0uPExERERERWZoDN07jw83zYYCx8+Kgel0woG4npcMiIrII+aMr+xPs7OxQu3Zt7NixI01ZMNFu2LBhpl83ffp0fPbZZ9iyZQvq1OGQ8MycvXcNA1d+bE7yFSvgg+V9JjPJR0REpIAFCxbg3r17GDt2LDZu3CjLiL/yyiv4+++/OcKPiIiIiIgs3ul7VzBq/Uwk61Nku1vV5nj3hd5Kh0VEZDHyZaJPEHPnfffdd1i+fDkuXLgge7vHxMTInu5C//795Rx7Jl9++SUmTpwo568pUaIEgoKC5BIdHa3gVVhm75o3fvsU4XFRsl3euziW9/kUfu5PHylJREREuUeUEu/duze2bduG8+fPo3Llyhg+fLj8TMPPMkREREREZKmuP7iDt9d8ibikBNluXqYuJrR+Q07vQERE+bh0p9CrVy+EhoZi0qRJMmFXo0YNOVLP19dX7g8MDEwzd8/ChQuRmJiInj17pjnPxx9/jE8++STP47dEf13cjwmbF5h719QpVhFzur0PV3snpUMjIiKiR8TnG/GfYjGaLyXF+DebiIiIiIjI0tyLvI+hq79ARLyxc2Idv0r4stM7sNHqlA6NiMii5NtEn/D222/LJSO7du1K075x40YeRaVOK49vwZf/LDfXyRa9a8QfXnsbO6VDIyIiyvcSEhKwdu1aWZlg79696NSpE+bPn4927dql6dhERERERERkCcJiIzF09RQER4XJtpgSaG7X9/iskYgoA/k60UfPT4wG+Gbf71h8cK15W/eqLfBR69fZu4aIiMgCiBKdq1atknPzDR48GCtXroSXl5fSYREREREREWUoJjEOb6+Zhhthd2Xbv2AhLOg+Di6sGkZElCEm+uiZpej1mLLje6w+td28bUiDbnir8Susk01ERGQhFi1aBH9/f5QqVQq7d++WS0bEiD8iIiIiIiIlJSYnYdQfM3Eu+Lpse7sUxKKeH8LTuYDSoRERWSwm+uiZJCQn4sNN87H9ymHztrEtBuC1Wu0VjYuIiIjS6t+/PzvgEBERERGRKgYVjN/0NQ4FnpVtNwdnmeQrWsBH6dCIiCwaE32UbdEJsXj3j69w9NZ52RYlOj9rPwwdKjZROjQiIiJ6wrJly5QOgYiIiIiI6D+nB/pi+1LzoAIHG3vM7/4Bynj5KR0aEZHFY6KPsuVBzEMMXzMNF0NumP/oznppNBqXrK50aERERERERERERKRCC/b9hjWnd5gHFcx6aRSqFymndFhERKrARB9l2e2HwRi6egpuPQyWbXdHV3zdfSyqFS6rdGhERERERERERESkQj8f24zvDq6T6xpo8Hn74WhcsobSYRERqQYTfZQll0JuYviaqbgf81C2C7l6yhrZJT2LKh0aERERERERERERqdDGc3swY+eP5vYHLQagfcXGisZERKQ2TPTRfzp26wJGrJuO6MQ42S7lURSLXv4Qvq6eSodGREREREREREREKrTn2nF8vGWRuT20YQ/0rtVO0ZiIiNSIiT56qn+uHMEHf85DYkqSbIsynaJcpyjbSURERERERERERJRdx29fxHsbZyPFoJftXjXaYGijnkqHRUSkSkz0UabWnvkHn239DnqDQbablKyBGZ1HwsnOQenQiIiIiIiIiIiISIUuh96U1cMSko0DC9qWb4hxLQdCo9EoHRoRkSox0UfpGAwGfH94Peb9u8q8rWOlJpjcdihsdfyVISIiIiIiIiIiouy7/TAYw1ZPRVRCrGw3LFENX3R4C1qNVunQiIhUi1kbSkNv0GPmrp/w87G/zNv61e6I0c1e4x9cIiIiIiIiIiIieib3Yx5i6Oop8lWoWrgMZnUZzYEFRETPie+iZJaUkoxJWxZh84W95m0jm/bBwLqdOXSeiIiIiIiIiIiInklkfAyGr56KWw+DZbuUR1HM7/4BpwgiIsoBTPSRFJsYjzEbZmP/jVOyrdVoMKnN/9CtanOlQyMiIiIiIiIiIiKVik9KxLt/zMCl0JuyXdjVC4te/hDujq5Kh0ZEZBWY6CM8jIvC22u/xJl7V2Xb3sYW0zu9i2Zl6igdGhEREREREREREalUsj4FY/+ci+O3L8p2QUdXmeTzdfVUOjQiIqvBSdfyuaDI+xi08hNzks/V3gmLen7IJB8RERHlmGnTpsky4CNHjjRvi4+Px1tvvQVPT0+4uLigR48eCA42lvEhIiIiIiL10xv0mPz3t9h97ZhsO9k64Jse41HCo4jSoRERWRUm+vKx6w/uoP/KSbgedke2vZ0L4vtXP0atYhWVDo2IiIisxJEjR/Dtt9+iWrVqabaPGjUKGzduxO+//47du3fj7t276N69u2JxEhERERFRzjEYDJi9+xdsOLdHtm11NpjT7T1UKlRK6dCIiKwOE3351Om7VzBw5ccIjgqTbX/3QljWezLKeRdXOjQiIiKyEtHR0Xjttdfw3XffoWDBgubtERERWLp0KWbNmoUWLVqgdu3a+OGHH7B//34cPHhQ0ZiJiIiIiOj5/XB4A348ukmuazUaTOs4AvX9qygdFhGRVeIcffnQ3usnMGbDHMQnJ8h2Rd+SWNB9HDydCygdGhEREVkRUZqzY8eOaNWqFT7//HPz9mPHjiEpKUluN6lQoQL8/f1x4MABNGjQIN25EhIS5GISGRkpX/V6vVxysuexKDMKDYyLJTDFYinxCOIWaTTyfj3P/ef9ziLe77zF+63K+22R99zK73dqOXkuIlK/taf/wdx/V5rbE1q/gVbl6ikaExGRNWOiL5/ZdH4vJm1ZKCfCFer5V8bsl8bAxd5J6dCIiIjIiqxatQrHjx+XpTufFBQUBDs7O7i7u6fZ7uvrK/dlZOrUqZg8eXK67aGhoXK+v5wSFRWFkmVLwtHZEToHHSyF1ksLGGAxHOMdUTKmpLxfISEhz3we3u+s4f3OW7zf6rzflnrPrfl+pybOR0QkbL98GJ9t+87cHvHCq+hRraWiMRERWTsm+vKRn49txoydP5rbrcvVx5QOb8POxlbRuIiIiMi63Lp1C++++y62bdsGBweHHDnn+PHjMXr06DQj+vz8/ODt7Q03NzfkZLnRgCsBqO5eHW7OOXfe5/JoNEjK7RSLeVgcFxOHgIcBcHV1hY+PzzOfh/c7a3i/8xbvtzrvt0Xecyu/36nl1N97IlK3w4HnMG7TPOgNxje9frU7YnC9l5QOi4jI6jHRlw+Ikhxf712FpYfWm7e9Ur01xrUcBJ2W0zQSERFRzhKlOcUogVq1apm3paSkYM+ePZg/fz7+/vtvJCYm4uHDh2lG9QUHB6NQoUIZntPe3l4uT9JqtXLJKaZyZvKBrIU8lJVM8VhKTIbHJfKe5/7zfmcR73fe4v1W5f222Htuxfc7tZw8FxGp0/mg6xj5x1dISkmW7c6VmmJ0s9eMJZWJiChXMdFn5USJzs+3LcG6MzvN24Y16ok3G/bgH1oiIiLKFS1btsSZM2fSbBs0aJCch++DDz6QI/FsbW2xY8cO9OjRQ+6/dOkSAgMD0bBhQ4WiJiIiIiKiZ3Ej7C6Gr5mGmMQ42W5aqhY+bvs/aDXsBEBElBeY6LNi8UmJcrj8zqtHZVsDDca3HIReNdsoHRoRERFZMVEOrEqVKmm2OTs7w9PT07z99ddfl6U4PTw8ZOnNd955Ryb5GjRooFDURERERESUXcFRYRj6+xSEx0XKds2i5TGj80jY6vjYmYgor/Ad10pFxsfg3T9m4Pjti7Jto9XJ+fjaVmAveSIiIlLe7NmzZZkvMaIvISEBbdu2xTfffKN0WERERERElEURcdEYunoK7kXdl+1y3v6Y120sHGztlA6NiChfYaLPCoVGh2P4mqm4HBoo2062DpjddQwaFK+qdGhERESUT+3atStN28HBAQsWLJALERERERGpS2xiPN5e+yWuP7gt28UK+GBhzw/h5uCsdGhERPkOE30ql6LX49it87h29yZKJxSHl0tB+Uf2TkSI3F/Q0Q0LenyAyoVKKx0qERERERERERERqVxSSjLGbJiN0/euyLanUwEsevlDeDm7Kx0aEVG+xESfim2/fBjT/1mG4Ogw8zaNRgODwSDXi7h5yZ40JTyKKBglERERERERERFlh6h6MGPGDAQFBaF69er4+uuvUa9evQyPXbZsGQYNGpRmm729PeLj4/MoWspP9AY9Jvz1DfbfOCXbrvZO8vmjn3shpUMjIsq3mOhTcZLvvQ2zYEzpPWZK8hVy9cTyPp/Cx8VDkfiIiIiIiIiIiCj7fv31V4wePRqLFi1C/fr1MWfOHDmf8aVLl+Dj45Ph17i5ucn9qTuCE+U08dxx2o5l2HJxv2zb29hiXrf3Ud6nuNKhERHla1qlA6BnK9cpRvI9meR7sneNpxOHyxMRERERERERqcmsWbMwZMgQOUqvUqVKMuHn5OSE77//PtOvEYm9QoUKmRdfX988jZnyh28PrMGvJ7fKdZ1GixmdR6JWsYpKh0VElO9xRJ8KHb99IU25zoyERIfL4+r6V86zuIiIiIiIiIiI6NklJibi2LFjGD9+vHmbVqtFq1atcODAgUy/Ljo6GsWLF4der0etWrUwZcoUVK6c8TOhhIQEuZhERkbKV/G1YslJ4nxiFFhOn9cS5Ldr++3kNizcv9rc/rjt//BCyZqqu35r/rlZ+/Xx2tSJ1/ZssntOJvpU6H7Mwxw9joiIiIiIiIiIlHf//n2kpKSkG5En2hcvXszwa8qXLy9H+1WrVg0RERH46quv0KhRI5w7dw7FihVLd/zUqVMxefLkdNtDQ0NzfF4/8aBSxCQehIqEpTXJT9e268ZxTNv7k3n/m7W7or5XBYSEhEBtrPnnZu3Xx2tTJ17bs4mKisrW8Uz0qZCXs3uOHkdEREREREREROrUsGFDuZiIJF/FihXx7bff4rPPPkt3vBgtKOYATD2iz8/PD97e3nKuv5x+CCrKiopzW+MD3vxwbQcDz2DG/l9geDSJ0OB6XTC0yStQK2v+uVn79fHa1InX9mwcHByydTwTfSokal/7unggJDosw3n6xHTLPq6erJFNRERERERERKQiXl5e0Ol0CA4OTrNdtMXce1lha2uLmjVr4urVqxnut7e3l8uTxEPK3HgIKx6C5ta5lWaN15ai1+P4nYu4dvcmcFeH2XtWIFmfIvf1qNYCI17oLa9bzazx55Zfro/Xpk68tuzL7vms787mAzqtFmNbDJTrT/5ZNbXHNh8gjyMiIiIiIiIiInWws7ND7dq1sWPHjjQjBkQ79ai9pxGlP8+cOYPChQvnYqRkjbZfPoz2i9/GkN8/x7R9P2HazmVISEmU+1qVrYePWr2h+iQfEZE1YiZIpVqVq4evuoyGj4tHmu1iJJ/YLvYTEREREREREZG6iLKa3333HZYvX44LFy5g2LBhiImJwaBBg+T+/v37y/KbJp9++im2bt2K69ev4/jx4+jbty9u3ryJN954Q8GrIDUm+d7bMAvB0WEZ7m9dvgEHFRARWSiW7lQxkcxrXqYOjt06L4fTly5SHLX9KvGPLhERERERERGRSvXq1QuhoaGYNGkSgoKCUKNGDWzZsgW+vr5yf2BgYJqSXuHh4RgyZIg8tmDBgnJE4P79+1GpUiUFr4LUVq5z+j/LMpwiyGTW7l/QuhyTfUREloiJPpUTf1zr+FWCv70XfHx8rLLOLRERERERERFRfvL222/LJSO7du1K0549e7ZciJ7VwZtnMh3JZxIc9QDHb19AXf/KeRYXERFlDRN9RERERERERERERPnMw7gorDqxFT8e+TNLx9+PeZjrMRERUfYx0UdERERERERERESUT9x+GIwfj27C+rO7EJ+cmOWv83J2z9W4iIjo2TDRR0RERERERERERGTlzt67huVHNmL7lUPQGx7PyKeFBrY2tkjIJOmnAeDj6olaxSrmYbRERJRVTPQRERERERERERERWSGDwYC9ASex7MhGHL11Ps0+Bxt7dK/WHH1rd8CF4Bt4b8Ms49c8keQTxjYfAJ1Wm4eRExFRVjHRR0RERERERERERGRFklKSsfnCXiw/8ieuPbidZp+HUwH0qdUOr1RvjQKOLnJb0QI++KrLaEz/ZxmCo8PMx4qRfCLJ16pcvTy/BiIiyhom+oiIiIiIiIiIiIisQFRCLFaf2o5fjv+F0OjwNPuKFyyMAXU7oVOlF2BvY5fua0Uyr3mZOjh26zyu3b2J0kWKo7ZfJY7kIyKycEz0EREREREREREREalYcNQD/HxsM9ac/gcxiXFp9tUoUg4D6nZGszK1odU8PWknknp1/CrB394LPj4+0DLJR0Rk8ZjoU6ngsGREROvlusGgR1iYHhEJidA8+mNdwEULXw/+eImIiIiIiIiIiKzVldBAOf/elov7kaxPMW/XQINmZepgYN1OqFG0vKIxEhFR7mImSKVJvgGf3EVi8pN7QsxrdjbA8k+KMNlHRERERERERERkRQwGAw7fOoflhzdi341TafbZ6WzRuXJT9K/TESU8iigWIxER5R1mgVRIjORLn+RLS+wXx/l65FVURERERERERERElFvEiL1tlw9h+ZGNuBAckGafm4MzetVog94128LT2V2xGImIKO8x0UdERERERERERERkoWIT47Hu7E78fHQz7kaGptlXxM0b/ep0RNcqzeBk56BYjEREpBwm+qxY0INklC5mC51Wo3QoRERERERERERElA0PYh5i5Ym/8evJrYiMj0mzr6JvSQyo2xmty9WHjVanWIxERKQ8Jvqs2Cff3YeDnQalitqirJ8dyvjZydcShW1hZ8vkHxERERERERERkaW5EXYXPx79ExvP/YvElKQ0+xqXqI4B9Tqjnl9laDR8vkdEREz0Wb34RAPOByTKxUSnBYoXfpT8K2YrE4BlitnB2VGraKxERERERERERET51Yk7l+T8e7uuHoMBBvN2MWKvfYXGGFC3E8p6+ysaIxERWR4m+qxYzfL2CHqQgnv3k9NsT9ED1+8kyeXvVNuLeNvIxJ959F8xO3gU4NB/IiIiIiIiIiKi3KA36GViTyT4Tt69nGafs50jelZriddqt4evq6diMRIRkWVjos+KvdmtIMr52yE6Vo+rtxNx5VYirorldhJuBiVBr097/N3QZLnsORFn3ubhpjUn/eTIPz9bFPGyYWkAIiIiIiIiIiKiZ5SQnIiN5/bgx6ObcDP8Xpp93i4F0bdWB/So3hKu9k6KxUhEROrARJ8KFXDRws4GSEw7UC8NsV8cJ7g4aVGjnINcTBIS9Qi4mySTfqYEoBjhl5D0uCyAEBapx+Fz8XIxcXbQoPSjxF9ZP1tZ9lOUArXRMflHRERERERERESUmYi4aPx6citWnvgbYbERafaV9iwmy3N2qNgEtjo+tiUioqzhXwwV8vWwwfJPiiAi2jgkz2DQIywsDB4eHtBojMk9keQTx2XG3k6LCiXs5WKSojfgVnCyTPrJ5N9tkQBMQlRs2qF/MfEGnL6aIBcTWxugZBHjiD/T6L9SRW3haM95/4iIiIiIiIiIKH+7ExGCn45uxrozOxGf/PiZmlDHrxIG1u2MJiVrsIoWERFlGxN9KiWSeL4exnW9Xo8Qey18fOyg1T57Yk2n1aBEYVu5tKrnLLcZDAYEh6U8KvkpEoBJcj30YUqar01KBi4HJsoFiJHbtBrAz9fGWPKzmBj9Z0wEujlz3j8iIiIiIiIiIrJ+F4IDsOzIRmy7dBAphsed6bUaDVqVqy8TfJULlVY0RiIiUjcm+uipRC+iQp42cmlS43FN8IdRKeYRf1fkayJuhyTDkKryp94A3AxKlsuOI7Hm7T4eujRz/ol174I69lgiIiIiIiIiIiLVEx3n9984JRN8hwPPpdnnYGOHrlWbo1/tDijm7qtYjEREZD2Y6KNn4u6qQ52KjnIxiYvX49odMe9forn85417SXK0X2ohYSkICYvDvtNx5m1uzlrjiL9ito/m/rNDUR8bMPVHRERERERERERqkJSSjC0X92P5kT9x5X5gmn0FHd3Qu1Zb9KrRBu6OrorFSERE1oeJPsoxjg5aVCltLxeTpGQDAoOSZNJPzvsnSn/eTkRcQqqhfwAiY/Q4djFeLiYOdho5z19Rz2RULReDcv72sqyonS3Tf0REREREREREZBmiE2Kx5vQO/HzsL4REh6XZ5+9eCP3qdESXyi/CwdZOsRiJiMh6MdFHucrWRoPSxezk0q6hcZteb8Dd+8mPRv09HgEYHvW4TrkQn2jA+YBEnA8Ath0Nl9t0WqB4Yds0o//E/H/Ojs8+NyEREREREREREVF23Y99iBV7tsskX3Ti48pVQrXCZTGgbmc0L1MHOi2fWxERUe5hoo/ynFarQTEfW7k0q/24dvmDiJQ0c/6J5d6DlDRfm6IHrt9JksvfqbYX8baRiT+ZABSlP4vZwaOALm8vjIiIiIiIiIiIrN6V0FtYfmQj/rq4D8n6tM+umpWuLRN8NYuWh0bDqlRERJT7mOgjiyA++Hi528ilQdXH8/5FRifj6NkQ3I92xrXbYvRfEm4GJUGfdvAf7oYmy2XPice9pzzctOaknxz552eLIl42/JBFRERERERERETZIjqpH711Xs6/92/AiTT7bHU26FypKfrX6YiSnkUVi5GIiPInJvrIork4aVGphBY+Pq7QPipzkJCoR8BdY9LPOO9fohzhl5CUdt6/sEg9Dp+Ll4uJs4OxlKhMAPrZyrKfohSojY7JPyIiIiIiIiIiSkuM2Ntx+bAcwXcu+HqafS52juhVow361G4PL2d3xWIkIqL8jYk+Uh17Oy0qlLCXi0lKigG3Qkzz/iU+mvcvCVGxaYf+xcQbcPpqglxMbG2AkkWMI/5Mo/9KFbWFoz3rpxMRERERERER5UexifFYf3YXfjq2GXciQtLsK+zqhddqt0dj38ooUdTf3DmdiIhICUz0kVXQ6TQoUdhWLq3qOZtLKgSHiXn/jIm/K7eS5Hrow7S105OSgcuBiXIBYuQ2rQbw87UxlvwsJkb/GROBbs6c94+IiIiIiIiIyFo9iInAqhN/49eTWxERH51mX3mfEhhYtxNal2sAnUaLkJC0CUAiIiIlMNFHVkvMxVfI00YuTWo4mbc/jEp5NOrPmPgT63dCk2FIVflTbwBuBiXLZceRWPN2Hw9dmjn/xLp3QV2W5/0LDktGRLRxlKHBoEdYmB4RCYnQaIw9vwq4aOHrwX+WRERERERERER56Wb4Pfx0dBM2nNuNhOSkNPsalqiGgXU7o75/FfMzIL0+bRUpIiIipTCjQPmOu6sOdSs5ysUkLl6Pa3cez/l35XYibtxNQnLawX8ICUtBSFgc9p2OM29zc9YaR/wVs300958divrYQCeGBT6R5BvwyV0kJj8Z0ePeX3Y2wPJPijDZR0RERERERESUB07fvYJlRzbinytHYMDjXuA2Wh3aVmiIAXU6o7xPcUVjJCIiehpmE4gAODpoUaW0vVxMkpINuHkvSSb9ZPlPUfrzdiLiElIN/QMQGaPHsYvxcjFxsNPIef6MJT+NyT9xvvRJvrTEfjHiz9cj56+RiIiIiIiIiIhEJSc99lw7LhN8J+5cSrPPydYBPaq1RN/a7VHIzUuxGImIiLKKiT6iTNjaaB6V6LQDGhq36fUG3L2f/KjkpzHxJ9bDo9KWa4hPNOB8QKJcTJ4Y4EdERERERERERHkoITkRm87vxY9H/0RA2N00+7yc3dGnVnu8XL0V3BycFYuRiIgou5joI8oGrVaDYj62cmlW27jNYDDgQUSKHPH3ePRfIu49SFv3U8z7R9YhbvdRxI6bhbhpo+HcvJ7S4RARERERERHRU0TGR+O3k9uw4vgWPIiNSLOvlEdR9K/bCR0rNoGdja1iMRIRET0rJvqInpOYhNnL3UYuDao+nvcvOlYvR/yZ5v07ez0R9+7/R+1OsngisRs2ZTEM1+/IV6dmdc0TcRMRERERERGR5bgbEYqfj23G2jP/IC4pIc2+2sUqYkDdTnihVE1oNVrFYiQiInpeTPQR5RIXJy1qlHOQi3A5MBFDpwX959et3hGJt14uiAIuujyIkv6LPi4BSdduIelqIBKv3kTc3hNIPGms3y9eg4d8DKcX68K2ZFHYFC8CmyLe0Oj4syMiIiIiIiJSysWQG1h+ZCP+vngAKYbH061oNRq0LFtPjuCrVrisojESERHlFCb6iCzM9iOx2H8mDq+0ckPPFq5wcmCvsrwYpZcSdB+JVwNlQi/pSqB5Pfl2sDgg06+NWb9TLmZ2trD1KwTbEsbEn23JIo/XixeB1smY+CUiIiIiIiKinP2//YGbp7H8yJ84ePNMmn32NrboWqU5+tXpAD/3QorFSERElBuY6COyQLHxBiz7MwJ/7IpC3/YF0KmJC+xsWR4yR0bnXRej827J0Xmpk3qGmLic+SaJScYRgNduZbhb5+spE342JYoak4DFHyUCSxSFzsudZUCJiIiIiIiIsiEpJRl/XzqAH4/8iUuhN9Psc3d0xas12qBXzbbwcHJTLEYiIqLcxEQfUR4p4KKFnQ2Q+JRp+mxtgKY1nLDzeCz0euBhtB7zfw/H7zsiMahTAbSs5wydlomg/xydF/zgUanNJ0bn3Qp66ui8J2ndXGBb1h+2pf1hW8YPUSs3I/nmPcgfjvkgDXRFfODc6UWkBN5D0o07SLpxF4bY+AzPKWITCw6n7V0oaJwdYVvClPh7nASU7WK+0IhfECIiIiIiIiJCTGIc1pz+B78c24ygqAdp9hUr4IP+dTqhS5UX4Whrr1iMREREeSFfPzVesGABZsyYgaCgIFSvXh1ff/016tWrl+nxv//+OyZOnIgbN26gbNmy+PLLL9GhQ4c8jZnUy9fDBss/KYKIaGOSyGDQIywsDB4eHtA8mvRZJAPFcf07JskRfTuPxcrtwWEpmPZjGFZti8LrXQqgUTXHfD/ySx8vRufdfjwq71qq0XnRxvuWJVotbPwLw66MH2zLFoddGZHYM67rvAua73PsP4cQPuW7DAIxIOV2MJyb14NTi/qPk42h4UiWSb87SLppTAAmB4j1u0gJCcswFDGqMPHcNbmko9PBppiPMfEnRwQ+SgLK8qBFoXV1zvo1ExEREREREalUaHQ4fjn+F1af2o6ohLT//69SqDQG1u2MFmXrQaflVChERJQ/5NtE36+//orRo0dj0aJFqF+/PubMmYO2bdvi0qVL8PHxSXf8/v370bt3b0ydOhWdOnXCihUr0LVrVxw/fhxVqlTJ3jePjwfs7NJvFx9AUm8Xx2Umo2PFktGHmCePTUjIfFSTSGrY2z/bsYmJaUc6PcnB4dmPzezasnteEa8pQZaUBKSk5PyxycnGJQO+ToBvQeOxer0eITo9fDz0qS5ND8Qnw68AMPE1F/Rq6YKlf0biyPl4aPXJuHMrHp8uiELFEnYY1Nkd1cumunbxMzad6CkxSLa2MnGU7WPFPRD3IjM2NsbFdOzTfi9THyt+ZuJn9wSZMAsJkyPkEm/cRdKVm8Zk3pUApNzKeO48AzSPfxYG0TIeo3Vxgo1I5pX2g51I5JX2h135kjKhp7G3M55L/L6n9qgt4gj7YrHxvOK4VOeVNBqEfb4Ijg2rycSgRiTlfDzk4lC3Srrz6mPikBR4D8k37yDpVjCSboUYE4E37yI58I6oO5L+fiXrZeJQjCg0FRnVpJ5Q3MNNJixl4q+4GBFYGLZlSsgkoCgXqsng/j71PSKzn5ulvkfk1PuJwu8R2T72yX/3T/s3Z4nvEVk9Vvx8n/P9JMNjszHCl4iIiIiIlHX9wR0sP7IRmy7sleU6U2taqpZM8NUqViHfd4wmIqL8J98m+mbNmoUhQ4Zg0KBBsi0Sfps2bcL333+PcePGpTt+7ty5aNeuHd5//33Z/uyzz7Bt2zbMnz9ffm1GEhIS5GISGRkpXw39+sEgHow+wVCnDjBpkrmtee219IkHkypVYJgyRa6KhFGB0aPlg80MH1mWLQvDzJmPzztsGBASkvF5/fxgWLDg8bEjRwK3Mp5rDD4+MCxZ8vjYDz4ArlzJ+Fg3Nxh+/vnxseI6z57N+Fh7exh+/918bc7z5gEXL2Z8beK+bdjwuDFzJjT79mVyJGD47bfHD/3nz4dmx47Mj/3pJ6BAAWNj8WJo/vor82PFfTAliJctg+aPPzI/dv58wN9fXpv9+vXAX39lem1lZs7E1OFlcfJyPI5OWYmqe1aa98UuBs67aOFfyBbODhoYvvgCqFrVuPOvv6D59tvMY5g4Eahb19jYuROauXMzP3bsWKBJE2Nj3z5opk/P/Nh33wVatpTr+qNH4S5+zuLnmdGxb74JdOz46Nhj0Iz9AIbYOOij44yvMfEyISYSDFEOvoix95LH2qbEwSv6esYBaDSILVIWSfWbGBN5Ho5wXvsTtM6O0NiJf3MhwB2xHAP2AIauXWGoNBgGkRgICYHmjTcyPq/eAIfzt5BgMP4+aA0p8Im6lPZbH7kK9OgJg1YDg7gH4l4I8fHQvPJK2mNFzuXRYmjcGJj2weN9nTtDH58IQ1y8LP8pXsXcgmI9Xm+PMN3jScNFDOZkn3h7ufH4eyTaOCPUuYTxnA528I2/Dp2dDhonB2gc7aGVr8Z1Tfny5vcI8XvpJt4Do6Iy/r20sPcIacoUaI4ezfjYVO8R4tqcxPv16dOZv59Y0HuEtGoVNKtWZX6s+LmVLWtcX78e7osXZ/5vzsLeI3D0KDSffZb5sanfI86cgbv4Ppld28CBQPfuxsaVK9CMGZP5eV99FejTx3jewMBMjyMiIiIiIuWJjrfHb1/EsiMbsef68TT7bLQ6dKr0gizRWdqrmGIxEhERKS1fJvoSExNx7NgxjB8/3rxNq9WiVatWOHDgQIZfI7aLEYCpiRGAfzzlYa0Y/Td58uR02xOSkpCQwSiCpJgYxKRKwLmLJF8moxKSY2IQ/ehYmQxLTIRGHJ9Br6WU2FhEpTqvW3w8tJmcVx8Xh8jUx8bFZX5sfHyaY11jY6HL5FhDfDwiUh3rEhMDm6eMuHiY6tps4+Nhm8m1pT5WcI6Ohu1/nffRQ3ynqCjYPeXYiNBQGB4lWh2jo2H/lGMj798XY/GMx0ZF/fexDg7y2gyxsXB8yrVFPXiAlAIFUMQdePlFIPGqBnfvGxD/6PSiDOiZqwlwd9WIrm0o6Gu8F/YREXB8SgzR4eFIfnTf7B4+hNNTjo0JD0fSo2Ntw8Pl71pmYh8+ROKjY3Xh4XAwjQIyjYRLSjbOXRcbj6ilaxD7wzboA+7CNjAAHlEByDIbY8IKIlElXkXSSrbtYdu9O1LEIv6t3b4N+79FQsiQ4b+lhKgoxD2KV3v/Ptyecm02g16CQ6v2xkZUJAyffwydHBVk/Nlp7GyQkGwcnZQYFYVY0+9lfDzcn3LepOjotP/uxQgnnQZwcTQuj4jvYlOtGpzeeBP628Ew3AqG9qvPgegYQCQC48T7RVLGoxzjE6GPigH0GYzK0miQdPgywi/GQlvMFyjmgwI37kBjSJaJQHGvU/9+Wtp7hOAcE/Pf/+4fvZ/YxMfD7r/eTyzkPUJwiIyEw1OONb1HCHYREbBN/W/uCdFhYeZ/95bwHmETHg6XpxwbFxGBBNO/z7AwOD7l2lIfq3vwAK5POW98ZCTiHx0b8yDtPB5ERERERGQZUvR6/HPlsEzwnQ1KO72Fq70TelZvhT612sHHxUOxGImIiCyFxiC6xuQzd+/eRdGiRWU5zoYNG5q3jx07Frt378ahQ4fSfY2dnR2WL18uy3eafPPNNzKRFxwcnOURfX5+fgi/dw9ubm45VrpTPLwOvXUL3t7eMmFpTaU75bXduQNvT8+Mry2757Wgsnzy2u7dg3fBgplfWwal9lL0Buw8Eouf/46Uc/eZ6G3t0K6RK/q1d4OXi0GxsnyGFD2SAu4g8eJ1PDxxDnbBYUi+dhvJ128bk03/UWLTTKOBrqgPbEW5zTLFYSvKbMqSm37QubtkXorjybJ8mY2Kze6x4h48GomrT0lB6O3bmf+bS3Xsf573eUv2PjFvYfLtYFkCVJQETb79uCRoyo3bMCRk/LOTd/7RPJHGDfpH6UvIuf90xQsby4L6P5obsExx+WpT1Aca8bujktKdWXo/saD3iCwdm+o9Qp+YaHxPyez3UsWlO/XJycafXWbX9oylOyMjIlDQwwMREREZ/10miyY+VxUoUCDHf37Xrl3D4JcHo5t7N/g6+8IiaACdnw4pt1IevWkrLzgmGOsersP3v3+P0qVLP/N5eL+zhvc7b/F+q/N+W+Q9t/L7nRd/lylv5ObPT05bEhIip8nJ9P9hqcQlJWDD2d346dgm3HqY9nlbIVdPvFa7A3pUawFnu8cdY5WS3WtTE16belnz9fHa1InXljd/m/PliL68Ym9vL5cnaZ2c5PKfsnLMIxpHR+N5s/IL5ZiND0PZOTb1g/ccPFZjb5/1a8tODBn8bHLkWPEwPaM5GDOgsbXN+rU9Oq84sk1zZzRr4oU/90bj578i8DBaL//juGlfDLYdjkX3Zi54tY0b3Jx1ORqvTBLY2hrnzrv/0Dhn3jUxZ14gkq4aX5MD76VJuqR5jJ86mWTa5OwIu7LFHyX0/GFX5tF6KT9oHbNx33Pg31Gu/ZvLpRiePFbEY+NREKhWId2hojxpSvADmYRNEnMBijkPbxjXxav+QcTjgzVa83OIlOg4pJy7jsRzGZRLtbWBbbFCxuRfiaKwla8iGVhUzhWodXLMk/eIXHs/sYD3iOwem+Xfy9yK4dF7RI4fa2OT9WsT+01Jv/8KwZSgJCIiIiIiRYXFRuLXE3/j15NbER4XlWZfOW9/DKjbGW3LN4Stjo8yiYiInpQv/zp6eXlBp9OlG4kn2oUKPZ7/KjWxPTvHE+U2O1sNujd3RbuGzljzTxR+3R6J2HgDEpMMWLUtChv3RuPV1m7yGEf7Z+tRYEhMQlLAbSRevWVM6olk3jXjuj4iOusn0mhgU8z3USLPH7Zl/Y3rZYtD5+vJibLzgEarhU1hb7k4NqqRbr8YbZl04y4SA24j/OxlODyINI4MFCMCb4dkPFotKVn+foglLoPvqfP2MCb+ShoTf8ZEYFHYFC8CnY8Hf+5ERERERET5XGB4EH46ugkbzu1GfHLayhwNilfFgLqd0LB4Nf7/kYiI6CnyZaJPlOGsXbs2duzYga5du5qHWYr222+/neHXiBKfYv/IkSPN27Zt25am9CeREpwctOjXoQC6NHXBir8j8cfuKJF/QUycAUs3RGDtrij0b18AHRq7wNYm/QdjMTpP/+Dh41F5V42vSaItRuc9rRzhEzROjnI0nkjg2ZQuhjhvd3jWrgL70v7Qijn0yGKJEp32VcvCtnJpxNSrCK9UQ84NScmyJKgcAXjjiRGBAXdhiM0ozQekhIbJBUfOZvy7UqKwTPoZRwMak4FydKBfIWjssjjSi/KFuN1HETtuFuKmjYZz83pKh0NZIOYpXrt2LS5evAhHR0c0atQIX375JcqXL28+Jj4+HmPGjMGqVatkqXMx97Eoi+7rawGl1oiIiIgoR+bZO3brPK7dvYnSCcVR268SdI/+n3nm3lUsP7IRO64chj7VdBA6jRZtKzRE/zqdUNG3pILRExERqUe+TPQJo0ePxoABA1CnTh3Uq1cPc+bMQUxMDAYNGiT39+/fX87jJx5UCe+++y5efPFFzJw5Ex07dpQPpY4ePYrFixcrfCVERgVcdBjWo6Acwffjpgj8fTAGegMQHqnH3F/DsWZrOF6vGYNaDqFIvpY2qad/mLYsxn9JOzrPWGpTrOsKe5t72YnkeVJICOytsP5yfqMRJTrFqLySRdPtM5VxTU5VBlQk/0xtUS40IyI5mHj+ulzSEaMPxfyMpjKgj5KAptGBOjeX3LhMslDidyxsymIYrt+Rr07N6rI3rwqIOY/feust1K1bF8nJyfjwww/Rpk0bnD9/Hs7OzvKYUaNGYdOmTfj9999l3XnR2ap79+7Yt2+f0uETERER0XPafvkwpv+zDMHRYeZtvi4e6FTpBZy8exnHbl9Ic7yjrT26V22BvrU7oEgBbwUiJiIiUq98m+jr1asXQkNDMWnSJAQFBaFGjRrYsmWLuRd5YGBgmuSE6Im+YsUKTJgwQT6sKlu2LP744w9UqVJFwasgSs/LEI23y95Bz8gAnNlxBUlXb6Fw1F14xYRAZ9AjJIvn0Tg5wLa0cXSeOaknllLFoHVWftJrsgwi4WLjXVAuDnXTvx/qY+ONIwBTJwFN62LEqBh+mu6L9Ei+FSQX/Hs83W5tQTfznICPRwQaE4Ey2ZxJYpmjwiyfmEsSKXoYxEjiFDH3qAGxOw8j8eQluV+8xu08DKcW9ZUOlf6D+EyV2rJly+Tk1MeOHUPTpk3lZNJLly6Vn61atGghj/nhhx9QsWJFHDx4EA0aNEh3TjHqTyypJ6Y2dSwRS04ml2UyWeSTLSWnbIrFUuIRxC3SaIyVAZ7j/vN+ZxHvd97i/Vbl/bbIe27l9zu1nDwXWUeS770Ns8xzv5uIpN/Sw+vTbPN0KoA+tdrh5eqtUcCRnTqJiIieRb5N9Ami53hmpTp37dqVbtvLL78sFyKliVKKMlEiRuU9KrlpGqGnDzc++BQqZ+FccuTUoySeaYSeXRm/pyZMiLJKlGy1r1hKLk8SyZzke/eNo/8CTGVB7xnbN+5kOg+k+B1PEMuJtD1ABY29HWz8C8O2eGHjfIAiCViyiNwW9vm32RoVJh5+yNK1IvEkhsempJgTUcZ1g0xKynWxTW8wJqhMySrTsfpHiSvTfvO21K9pv4/56037Hx1j3J/6ez7+PobkFCRGRiLcyQmaVLE8jvnRuQxpk2mm72Pe/+gYQ+rv8+hePI4l1fc1XXOq60v7fYznEedIc055/9Ke8z9pNAibugSOzetxVJ/KiMSe4OHhIV9Fwi8pKQmtWrUyH1OhQgX4+/vjwIEDGSb6RJWFyZMnp9suOm6JMqA5JSoqCiXLloSjsyN0DjpYCq2XFumelinIMd4RJWNKyvsVEpLVbkTp8X5nDe933uL9Vuf9ttR7bs33OzVxPiJTuU4xku+/fu2LFyyMgXU7o2OlJrC3scuj6IiIiKxTvk70ESkpK6OLUsIikHjlphyVl3T1JhLFq2jfvAskZ2fuPAckFS2KK9pCuKLzRZBrEQS5FkawayHUquGO17u4o0gxfrCmvKXR6WArysAW84Vjk1rp9qc8jJIJv2SRBDSNArxhHBGYfCfEmHx6giEh0fhv5MrNTL+vGBV2o2wHwEb3OAGWLqn2KLmlQg9hxQwGJJy8yFF9KiN6+Is5jhs3bmyuhCCqKYg5k93d3dMcKyoriH0ZGT9+vCy9nnpEn5+fH7y9veHm5pZj8UZHRyPgSgCqu1eHm3POnfe5PBoNknJbJM1hEeJi4hDwMACurq5ytOaz4v3OGt7vvMX7rc77bZH33Mrvd2oODpyTnYyO376QplxnZia0fh31/FklyxKl6A04dTkeN26noESxeFQv5widlh0tiYgsGRN9RErPOfXFYjnnXfK1W4+TelduIvHaLejDjCMgskpXxEeOxrMtYxyVZ5w/zx82RYyj88oZDPj3ZByWbniIW8HGkokHz8bj0LkgtKzjhIGd3VHEi28LZBl07q7Q1agAiCWjhN6tIGPi79EIQLFuKhNqiHtc3i8jmY0WpGwSo351WuPoX51WtjXiVacDtJpH23XGbdrU+8Wr8RjjNt3jrzefS4OEU5dgiIpN+z11Wo7qUxkxV9/Zs2exd+/e5zqPvb29XJ4kSq3n5FywpnJm8oGshTyUlUzxWEpMhscl8p7n/vN+ZxHvd97i/Vbl/bbYe27F9zs1zstOJvdjstb18EFM9p53UN7YcyIWC34PR+hDU+fy+/B21+GtlwuiaU0nhaMjIqLM8Ik+UR5KDn6AxLNXEfXHjsdzTp26hNuN+mb5HBpHe9iWEnPn+actuVnaD1qXp3/oEv+ZEx/MGldzxNZDMVj2Z4T88Cb+L7z9SCx2HotFpyYu6Nu+ADwLWEapG6KMiBKd4vdeLE8SDy5Sgh8YE3837iBu12FEr9me7jitjwd0zk7GpJJMPGkBTaoklHgViSTTerpk1aME1qNjTAkscY60ySxj0sp8zjTfJ5OkWEbnkd8n46SYOM6g0eBhVCTcPTygtbF5nIBLlVyT58/gmszJNbH/UfItdSym86RL0OVioi32n0O41+u99DtS9BzVpyKiRPqff/6JPXv2oFixYubthQoVQmJiIh4+fJhmVF9wcLDcR0RERETq5OXsnqPHUd4m+T757n667eK5kdj+yRAvJvuIiCwUE31EuUCUARRzjiWeuYKEs1eQcOYKEs9eQUrIf5evMBFz5JlG58lk3qPEnphT73nnztPpNGjfyAUt6zpj/Z4o/LIlEpExelmxcP2eaPx9MAY9mruiV2s3uDixZyapi0g+2RTykotD/aqIXLrWmMQSv+AmOi1si/ig6NbFVjMqTJRHjA4JgZOPj+p7VMtRz1OXGJObGZVQ5Vx9qvgZvvPOO1i3bp2c97hkyZJp9teuXRu2trbYsWMHevToIbddunQJgYGBaNiwoUJRExEREdHzqlWsInxdPBASHQaDQQOb+JLQprhBr4tEskMANBoDfFw95XGU8yU3k5MNSEoGklIerafg0TYDklOM+4yvog3z9sREPb5Z+/TRmAtWh6NxdZbxJCKyREz0ET0nUUIw8WKATOaZEnoJ567CEBOXrfO49GgFp1YNYSfKbWZhdF5OsLPV4OWWbujQyAW/74jEbzuiEJ9gQHyiAb/8HYkN/0ajdxs3dG3mAgc7dScOKH8So77E6K90OCrMsiUmIflOcObzJBoMSL4bIo+DPecXtdRynStWrMD69evlHECmefcKFCgAR0dH+fr666/LOfc8PDzkHHsiMSiSfA0aNFA6fCIiIiJ6RjqtFmNbDMT4lVvhdL8LdCmPR+6l6B4i1msDxnZpI49TA1kxRm9KiD16lQk0AxKT9AgJ0SMsLhF6vUbuk4tMsmWcUEt/HmMiLvW+xEf7Mv26VAm71OfQ53KJ4NDwFHwwPwQ1yjrAv5At/HxtUNTbVj5b+j979wHmRPX1cfxkO70vXUBREEEpih072MXeexcb9oIKltcOWLAL6t+OvSv2XkBQREURAZXe6/a8z+/uTphksw12N2W/n+cZliSTZG4ymblzz73nGgAgpgj0AVVQuHyV5U2bYblT/7DcqTMs75c/LO+P2apdVfjclGaNLaNnV8v/fZYVLl4W3oCdmmL5f/1rDR/cJyajUxrUS7FTDmxqh+zWyJ55d4UL8KlIq9YW2SOvLbeXP1llJx/QxPbdsYGlpVKBQ2JgVFhip2btMOFRK1xc3KO0SN/l0qUuIJRS8l2ltmrm1kN8evDBB93f3XffPez+cePG2SmnnOL+P2rUKDf6VCP6cnNzbdCgQfbAAw/EZHsBAABQfTLW9LRGC1qXuj+1sIk1WnCiZaxp5a7X1O7gBc3cKLSIoFaBL2hWaoSad9sX8MqLCIaV+7yI13ej2qIG2CpT4oVWV/z4e65bPBrc17Zlmgv6Kfi3Sev0UBCwSUOmhAGA2kKgDyirx9bchZb7S3FQrzgF5wwrmDOvUs9P69jGMnttbhk9Nw/9VcpNjR6K5zmnmjVKtfOPam5H7NXYnnx7hZvHT/GRJSsKbeSzS+2FCSvttIOa2G5961sKqRoQ7xgVltDS2rd2i5eWNHXhQstMgrSkdek8WpGsrCwbM2aMWwAAAJA86SPHjF+m7ntRHi2+L9o8cCifZqNITwtYWqr3N2BpaQHLSDP3V7cjH0/XY777tV56yfPSfbe13vwlBfbyJ6urvF0aRfjfogK3fPtLTthjTRumWEcX/CsOAnYsCQK2bp5K+k8AqGYE+lDnBQsKLP+vf4pTb/7yZyioV7R0RcVPTk21jG6dQgG9zJ7FQb3Upo0SenRRmxZpduVJLeyovRvZ2DdW2Fc/F6chVcXtprFL7LkJK+3MQ5ratltmxXxbgbIwKgwAAAAAatfUGbm2aHnFWY/iRWWDYWlptv7+ksdSU80K8nOsUcP6xfeVCrRFD7wVB+jCX7O85+n/Nd3ZWgHazyevK/e7a9U01W45t6X9u6jQ/pmfb7Pn59ucBfn274ICNwVMpOWri2z5jFy3T/jpc+iYnV4qCKhRgPUy6dgJABuCQB/qlKK1OZb361++gN6f7nYwJ6/C5wbq17PMrTazjF5bWGbPrpa59RaW3q2zpWRlJu3ooi7tMuymc1rZr3/n2qOvLbef/iyunM34J9+uvH+R9d48084Y3NR6dKnkZwDUMkaFAQAAAEDtUUagymjfKtVaNEmLEmDzBdQiA29p5QTDynuef/Ra+vrnaZTcxnRe1jXmwoULLTu7acJfY2qE3ZAjm5U72lKPd+2YaV07ht9fVBR0AcI58/PtnwUF7q8CgPp/tP1B6VBnzs13S6TsZqklI//SQmlAtTRvnEJHcwAoB4E+JC3NgxdKven+/ulG7llRUYXP1Sif0Ci9XltYRq+ult6lgwU2ouKWyKOLFMgbeXG2Tfwtxx59fbkL9MmUP3Pt/DsX2M5b17PTDm7iAoNAPFmwtMBWrC7+zQeDRbZ0aZGtyM2zQKD4t9ykYYq1bs6pEAAAAACqQ4smlZuX7dLjW1jvLbJqfHtQeQP61LfhZ7Z0qVf9I/taNUu1IUc0c49Ho9GGuq7Wsl2P8MdWryuyfxT0c8G/4iCgbv+7sECz2JSycFmhWyb9Hn5/g6xAyQjA4lGA3v/btSoOFgNAXUfrJhKem8B59jxfQO8PF+ArnLeoUs9XAC/Dpd3sGhqtl9amZY1sayKPLlLPqe161LN+3bPs88lrbeybK1zFTJTa8+up62yf/g3slAObuNSfQDwE+U4ePtfyCsqeKF3zGTw5vB3BPgAAAACoBr26ZroUj+WmgGyW6tZD/FEwb+dt6tlPf6yzWf8us84dmtk2W9Tb4Dn1GtZLsS07Z7rFr6AwaPMWF7iRf3Pmrw8A6u/qdaUzYa3JCdrvs/Lc4qfmtHYti9N/+gOAut2ofvy3tQFAdaFlEwklmJdvedNnhafe1Hx6q9ZU/OSMdMvo3sXNo6eRem7EXs+ultIweo8kWJk9tXbv18B26V3f3vtmjT359gqXikEZST/4bo19PHGNHbxrQzt2UOl5CoHapJF8pYN84fS41mvdvLa2CgAAAACSV6VSQB7RbIMDR6h5+m402rJd01TLzs6qkfkBlW61eF6+dNt56/DO/MtWFZWMACweBej9f8HS4rYnPyXtUid0LV9HvEezRiklaUCL5//T305t0q1lE/Y9AMmHQB/iloJ3OVP/sPxvJtuiWfNdQC9v+t/FybwrkNK4oWVoHj0voKe/m3eyQEZ6rWx7XaBK2YG7NLR9+te3Vz9bbc+9v9JWrS2ygkKzVz5dbe98vcb23yFgJx9UZI0a0IsKAAAAAIC6YENTQALKJtW8capbtolI7ZqTV2T/LfTmACweDaggoOYCzM0vPQpQAcNlq3Lt5xm5YferabBt84B1ab8kNAegRgF2aJ1mWRm0XwFITAT6EHPqrVO4YImbQ88bpaf/F8z6L7RO+MD8cKntsovn0vOn3tykLZP01pLMjBQ7Zp/GduDODe2FCSvt5U9WWU5e0C2vfB60DyfNt+P3bWyHDGhkGel8J6gdeflB+312eGW+LKOfW2LdOme6nn2q3Hdsk2Ytm6RyDAEAAACAOEkBCSgIt1mHDLf4FRUF3bx+xQFABf9KUoIuyLdlK0tPBJiXbzZ7QdBmL1hnZlrWa908tWQEYHEqUC8I2KxxCm0EAOIagT7UqmBhoeX//V/xfHpTi+fTy5s2wwoXLav4ySkplr75JqVSb6a2aFobm44KNKyfYqcf0tQO3b2R/e+9FfbWF6vdxMor1xTZgy8vt5c+WmUnH9DEBu3QwFJTqRyhehUWBW3GP3n24/Rc+/H3HJv6V64L9lXG77Pz3eJXXxN9ty5O6xGq3Lcpnuhbo1kBAAAAoCaNGTPG7rzzTps/f75ts802dt9991n//v3LXH/8+PF23XXX2axZs2zzzTe322+/3fbff39L9hSQgParNi3S3NJ/q3phjynzlDf3nz8NqEYGFkVpMlB6UC0//JoTdn+DeoHQ3H/++QA1PyBtBADiAYE+1JiinFzL+22m5ZWM0MtV6s1pf1lwbXhvmWgC9TIto8dmlrFVV8vt1MZa7NjHsrbqain1w4ftI/40b5JqFx3d3A7fo6E9/PJC+/qXIpdDXek67npmqb3w4Uo7/eCmtmvvevSGwkaNBP5nYYFN/j3HfpyeY1P+yHUV+OqyNido02fnucUvNcWsfav1gT+vgq+/9bNI8QEAAABg473wwgt2ySWX2EMPPWTbb7+9jR492gYNGmTTp0+37OzsUut//fXXduyxx9qtt95qBx54oD377LM2ePBg+/HHH61nz54xKQMQDxrVT7EeXTLd4ikqKrK58xZYQaC5/buwsHgUoFKBKgg4P9/W5JSOAK5ZF7TfZuW5JVobgYJ+xYHAtOJMQa3TXYd4AKgtBPpQLQqXrXQpN/2pN/P/nGNWuD4Xe1lSmjcpTr3pm08vfdMOFkhLcyffhQsXWlZ2tqWkcIJMJOrVNOTQNDv5wKY29s2V9u0vxb2hVHnSpNzdOmXYmYc0tb7dCd6ichYvL3Aj9iZPz3Gj9vxzPUTKbp5qm7VPt2+mhvfCi+auC1tZelrAZs8vcD39ZivH//x8mx9lom+NUi2eC6DA7KfwTgstm6a6EYCa5Nv9LZnouzkpPgAAAABUwciRI+3MM8+0U0891d1WwO/tt9+2sWPH2lVXXVVq/Xvuucf23Xdfu/zyy93tm266ySZMmGD333+/e26k3Nxct3hWrlzp/qoNRkt10uupo2Z1v248oGyJSWVSgK5Nq+I0nTvZ+nYplXnpypJRgBoBWBIA1F+lB43kbyP4KiINqNoCFPBTG4EbAVgSCGzVNLVGR7cm+3dH2RIPZdswVX1NAn2oEu24Bf8uWD9KT/Pq/fKnu68y0jq1tcyeW1hGr64l8+ptbqltW9EInsQ2bZ9h/3detk2dkWOPvb7CpVQUjZS67N6F1rdbpp1xSFPr3nl97ypAVq8rsp/+yLFJv+e44J4CcWVp3CDFem+RaX27ZVm/7lkuxeaf/+TbN1PnV/g+Deun2habZFivrhZ1om8F/op79pVM9r0g3/KjbMri5YVumfS7VSrFR/uWaaSxBQAAABAmLy/PJk2aZFdffXXoPnV83nvvve2bb76J+hzdrxGAfhoB+Nprr0VdXyP/RowYUer+RYsWWU5OxZ0lq9pQuWLFCteelGwduClb8patXdPiZYdu3j2plpOXYvOXBG2ulsVazP1/3uKg5Ufph6yA4dKVufbTn+uD6pKRZta2ZcDatQhYe/1tqc7yAWvTPGAZ6RvXRqD5Cn+dVWjzFq21tq0KrUfnmg0q1ra6vl8mKsq2YVatWlWl9Qn0oUzBggLL+3OO5U39w6Xd9IJ6RcsrsZOlpVpGt84ukJfRaws3l16G5tNr0qg2Nh1xqFfXLBt9SaZ9N00Bv+U287/iOdE0Quu8OxbYgD717LSDmrpACOomzan3y8zcUDpOBYOj5cyXzPSA9eqa6UaEKrjXtUN6qcprk4YprgKdV3Z80D2u9aoy0bfmA1TO/jnzinP7e7n+9VdzUlY2xUdaqi8NaCgQWJzmox5pQAEAAIA6afHixVZYWGitW7cOu1+3f/89oldhCc3jF2193R+Ngoj+wKBG9HXs2NFatWpljRs3tupuBFXnbr12MjbwUra6VbZNOpS+T20EC5cWho0C9P6/fFXpNgK1UcyeH3SLn8ZAtG6eWpL6UyMAi9sL9Ldpw4ozBX0xZZ2NeWm5LV6u99QoxSJr2TRgQ44onj4nGbBfJibKtmGysqqWBY9AH5yi1Wst79e/SgJ6f1ie5tP7baYFc8MbpqMJNKhXHNDrqVF6JUG97l0skBneOA7owLdDz3rWv0eWfTJprY19c4XNW1wchfl88jr7cso6G7RDAzv5gCaW3ZzDU7JTZXjGP3ku2KtUnBrtqWBfNDpXdu+U4QJ7/bpl2ZZdMivs6da6eZo9ObydrVhdXLEOBots6dKl1rx5cwsEik++CvJpvapOKK/UtFp26BVeWV6+qji/vxv9540EXJBv85eU7t5XUKjKvUYM6jcQnuJDqTxCgb+SQKDSgDYjDSgAAACAjZSZmemWSGqkrIlGWF3D1NRrxxplS0zVWTa9RPvsVGufnWE79Ap/bMXqwvWBv5JOwvr/3EUFpTo2a+oQtR1o+f7X0nMNeoE/Fwgs6STcriRT0OeT19qIx5aU2jZlHdL9w89saQP61LdkwH6ZmChb1VX19WhJr4MKFi4Npd70/ubP/Lf4jFKB1NYtSkbpFafdVPrNtM7tLJCEP1LUHI282mu7Bq6S8c7Xq+1/76xwKQ1UyXn3mzX24Q9rbPBujey4QY2tScPUWG8uqomGsf+zsCA0Ym/KH7m2am3Z+aY7t00vGbGXadtsnmUN6lX9OKMgXuvmxf93c35mplh2dkaNVSyaNkp1y9ZR0oD+u6A4DWjxPIAFbh7AfxZGTwOq+QcXlZEGtHgewOLAnyr3+tu2RZrr/QcAAAAgsbVs2dJSU1NtwYLwKVJ0u02bNlGfo/ursj6A2qE2LS09NwsPrKuT89zF3vx/4UHAtTml22fVdvLr33luicwU1K5lqs1fGiV3qM+Yl5bZztvUcx2XASQnAn1JYN1nE23tVSNt3W2XWIM9+ofuDxYVWcGsucVz6f3yp+WV/C1cULqHRymBgKVv2sEF8jJKAnr6m5Zd0mIOVIP0tIAdMqCRDdy+gb36ySp7bsJKl+ZQgY/xH62yt79abUft3diO2LOR1SeVYUJavLzAjdjTHHsatafgVVmym6UWj9jrnmV9tsiy5k2SJ8irNKBdO2a4JXJU4/wlBaVGAOp2tCCofh/RKvfpacVpQLObFlrXTVZY57YZoRQf9TL57QAAAACJIiMjw/r162cfffSRDR48ONRpUbfPP//8qM/Zcccd3eMXX3xx6L4JEya4+wHEH2UoUudmLZEdpJesUKag4s7BXvuAAoALl0XPFDRnQflBPlm0rNBOu3Gey56l0YGNGqS4vw31//rr/9/Yd1/9rAAZhYAEQqAvwekEsPT/HrHgzP9syXX3WcE5R1v+tOL59HKnzbDg6rUVvoZSbGZsuWl46s0em1lKw+QY0o34p0DEcfs2sQN3bWjPT1hlr3yyyvVuUi+mJ95aYa99uspO2K+JHbhLw42emBg1a/W6Ivvpjxyb9HuOC+4Vp6WMrnGDFOu9RaabY08BPgWq6lolUr3p2rdKd8uOvjSgOrYvX120fh7AkjkAtWh+wEgKjs+aV2Cz5pl9/9uqUgHU4vQeJWk+SpZmjUgDCgAAAMQjzZ938skn27bbbmv9+/e30aNH25o1a+zUU091j5900knWvn17u/XWW93tiy66yHbbbTe7++677YADDrDnn3/eJk6caI888kiMSwKgKnSN3rJpmlvUVuK3LqfIZUla30FYowGLbxeWnSwpRM/VUllKhNSwXkrpwGA9X2CwQelgof7WyyRICNQ2An0Jbsn191velOnu//nTZ9niobeXu35Kk4aWoWCeS73Z1f0/o+smFtBwECDGGjdItbMGN7VDd29oT7+z0t7+erUVFZkLeNw/fpmN/2ilnXJgE9u7fwPSDcQJBWR/mZkbSsc5fXZeqTzznsz0gPXqmlmSjjPLunZId2lcUZoqxM0apbplmy0iKve5RfavV7mfrzSgxZX7f8tIA6pef1om/hZ+f8N6gbDAnwKBSgPapkVxjn8AAAAAsXH00UfbokWL7Prrr7f58+db79697b333rPWrVu7x+fMmRM2HcFOO+1kzz77rA0bNsyuueYa23zzze21116znj17xrAUZsHCQlv39RQr+PNvW7d5F6u/U28LpCZP5hagNtXLSrEtNslwi9+Pv6+zy+5dVO3vp/a4lWuK3GJVfPnUlOJ5BUsFAUsChpFBQ38gMSuDICGwIYjuJDCN+Fj91mdlPp7WoXUo7aYX1NN9HCwR71o1TbOhxzW3I/duZOPeWmGfTCwemaqRTLc/tdRemLDKTj+4ie20dT3251qmVJMz/slz6TiVinPqX7ku2BeNrju7d8oIBfZ6dMlkRGY1jYDdvGOGW/zy8wtt2p8LbU1+Y/tnYeH6VKDz8231utLf0epy0oB2yC5O+9nJFwjskE0aUAAAAKC2KE1nWak6P/3001L3HXnkkW6JF2qvWnztPVY4tzhCMF+N/+1aWctbLrKGB+4W680DkoY6B7dqmlruVCmtmqXa0yPaWm6e2cq1RbZ6bZGbLmSV//9rCkvdp79aX9OIVEVhSad9LVWleQcjg4TebXVYtqJCa5e9xho1TC01klAdzGknRF1FoC+Brfvkeyv8N3yyZWl+3dnW+ISDLLV5k5hsF1BdFGy47rSWdsw+efb468vt+19z3P2z5uXbdQ8vth5dMuyMQ5pa74gRT6jeDgVK7eCN2JvyR27UueM8yi/ft1vxqL1tNs+yBvUIDNUWjcJr0zxg2dn1bGdf7159h8tWFYUF/rxUoNFy/GtU4N9z891iti7ssdbNi9OAdmydbp3apNkmrdNtk7bp1rQhaUABAAAArA/yLThtmFlEbKBw3qLi+8feTLAPqCbKeDXkyGY2/NHFFggW2eaLfremOctteVZT+7NVdwsGUmzIEc0sPS3FdexVUGxDOn2vWRcZGIy4va4kMLgmPFC4JqdqQULNO6g2DC1lWxb1XpUvMvgXOfdgWBCxwfpgYSw7puvz1TQ4s/4ttM4dcmybLeqRyQxVRqAvkefmu/Wx4rHQ/kTMqSm25s3PrOkFx8dy84BqpZFLt52f7U56j76+PDQCSX8vGb3QtuuRZacf3LRU+gJsmGWrgvbTrDU25Y88N2qvvF5hmv/NG7HXp1uWtWhCGpZ4owBc88apbokMikfL8a+JvpUGVJXrSBpVq+WHkqC7R5XiTbzAX2gUYFpxGtANrJwuWFpgK0p6/wWDRbZ0aZGtyM2zQKD4oqRJwxRr3ZxqDAAAABBPlK5TI/kig3zFD+oCxWzxsHutwX67kMYTqCYD+tS327b83VLvHmNN1ywN3b+8QXMrvHSI9e8zcKNeX9f1mm5HS1UVFgZtdUmQMCwwuDZ6YFABQy+QuC63akFCdVxeurLILVWlQF+0wGBkgDBa0HBjgoSfT15rY8Yv87W9LXYjNBW81fcKVBYtZAk8mi93yu+lHygscvfr8fp7bh+LTQNqNB3BfZe1tq+nrrPHX1/hRvaJgg4//Drf9uhX3049qIkbCYjKU4VLQVSN2Jv0e44L9JTVO0oTLffeItMF9hTga98qjZFcSZjjXxXxeUsKiuf/m1/yd0HxfIDRUnaoEj5tZp5bInvTdVQa0DbFIwCLRwKmW4fWaZaVkVJukO/k4XMtr9ScgwtD/8tIM3tyeDuCfQAAAEAcyfn251C6zqiCZoX/LbQ5/Y+11BZNLZCVYYGsTEupl+n+hpZ6GZZSLyt0u/jxDN/jmZbi+79bx3tctwkibjDmVkzMUbQtbrqpVIC96dqlZjfdZKu7ZMZsFK2yDzVpmOqWqipQkNAXBFyxptD+m7fMUtIa2eqc9Y95gUH/6MKcKgYJNS3NkhWFbqkqpQz1zzNYVmAwLB1pgxTXFnfj40tKvZ6CfhqhOfzMlgT7UGm0jiXyaD41rgejHLQCAfd4vT360wCPpKN9euet69sOPevZRz+stSfeWm7zlxSfhD+ZtNY+m7zW9t+poZ24f2M31x+iV15+mZkbSsc5fXaeFQXLrqz06ro+sNe1Q7qlkD4g6akiroC5C5pvbeFpQFcWuYCffwSggoBlpQGdOTffLX46Nbk0oBEjAHW7aaNUN5KvdJAvnB7Xeq2bV1uxAQAAAGykggVLKrfenHluqTHpaRGBQC+gmBU9MOgFFEuCjOG3fY/7XiuYmW7BVWss2KzALDM5Mgwxt2LiSeZRtGmpAddGoEWKiopsYctUy85uaCm+KUuiyS8oGUlYkmI02tyD0ecqLLLc/KoFCbV+7vJCW1xORqwNMfq5pbZZ+3Rr3SLNfRZAeWgFT0R5+Vbw34LoQT4JBq1g7kK3XrJUNIBoaQMGbt/Adu9b3976crU9/e4KN8lvUZG52x98t8YO272hHTOw8QalFkgmyvU94588+3F6rkvFOfWvXBfsi0b1pM3aBax/z4bWr3s969ElM6Z5yhGHaUCbpLpFqVr91uYU2b8LC2z2PF8QcEGB/RclDahOXwrQa/Hm3vSPGtVE4QAAAAAST1rrFpVbMTO9uPdeWW1bGyu/wIrU83DVGqtps/RPampxANA/0jA08rCMkYgl64cCit7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HBCARZM6c+YJt1kkrVapU6tatm9auXeuXuAAAABKKlemcOHGiGjRo4J5bDbROjJL27Nmj7Nmz+zs8AACApJPoAwAEmbx5pZkzpX96vwJIPnLlyuXKsAMAAAS61157TQ899JBef/11N+1M2bJl3fZPPvnEV9ITAABAyT3RlzVr1hjzuXjZtjRp0qho0aJq2bKlWrVqlVAxAgAS2rp1UpkyUopo/wyQ5AOC2oYNG2KsR0VFuRJWQ4cOVbly5fwWFwAAQELOSfzXX3/p2LFj7vmV19NPP6106dL5NTYAAIAkk+izOfkGDx6se++919cbas2aNW6Olw4dOmjXrl1q3769wsPD1bZt28SIGQBwLb76SnrxRal6devyKqVK5e+IAFwHlsyzjlmW4IuuWrVqmjx5st/iAgAASEhhYWExknymUKFCfosHAAAgySX6vvnmGw0aNEjt2rWLsf2tt97SwoULNXv2bJUpU0Zjxowh0QcASc2qVdJLL0mRkdLy5dJHH0lNmvg7KgDXgXXGii40NFQ5c+Z0FRkAAACCwf79+/X8889r8eLFOnDgwAUdnCIiIvwWGwAAQJJJ9C1YsMDVPI+tdu3a6t69u3tfv3599ezZM2EiBAAkXLnO55+Xzp/3rDdoID3yiL+jAnCdFCxY0N8hAAAAJCqbSmb37t3q06eP8uTJc9GpZwAAAJTcE33ZsmXTp59+qq5du8bYbttsnzl58qQyZsyYcFECAK7N5s1Sly7S2bOe9Vq1rBazDenxd2QArqOvv/5ab7zxhrZs2eLWS5YsqRdeeEG33367v0MDAAC4ZlaFavny5cw/DAAAkpV4J/qsV5TNwffVV1/55uj77rvv9Pnnn2vixIlufdGiRbrzzjsTPloAQPz9/LPUsaN06pRn/bbbpMGDbfIKf0cG4Dr673//q1atWqlRo0bq3Lmz27ZixQpXlWHq1Kl64okn/B0iAADANcmfP/8F5ToBAACCXbwTfTbvnvX+HjdunObMmeO2FStWzPUQv80eHku+Ep4AAD/bvVtq3146dsyzXqGCNGyYlDKlvyMDcJ0NHjxYw4YNi1GVwRJ+I0aM0MCBA0n0AQCAgDdq1Cg3lcxbb72lQoUK+TscAACApJnoM9WrV3cLACAJ27fPk+Q7dMizXrKkNHKklCaNvyMD4Ae//PKL7r///gu2N2zYUC+99JJfYgIAAEhITZo00alTp3TjjTcqXbp0Shmrg+Mh799GAAAAQeSqJmeKjIzU9u3bXe3zZcuWxVgAAElE+vRSrlye90WLSuPGebYBSLalrBYvXnzB9i+//NLtAwAACIYRfW+//bYmT57sKlGNHDkyxnK1hg4dqpCQEHWxec//cebMGXXo0EHZs2dXhgwZ1LhxY+3fvz/G53bv3q0GDRq4pOMNN9zg5kYODw+P0Wbp0qWqUKGCUqdOraJFi7qS6rGNHz/ejVBMkyaNqlatqjVr1sTYH5dYAABA8Ir3iL7Vq1e70k6//fbbBXXP7aYnIiIiIeMDAFytjBntL0JpxAjPyL5MmfwdEQA/stLqVqrzhx9+8JVbtzn67GHS6NGj/R0eAADANWvRokWCH/O7775zpUDLlCkTY7uVQ583b55mzZqlzJkzq2PHjm4uZLu/MvZ8zJJ8uXPn1sqVK7V37141b97cjTJ89dVXXZtdu3a5Nu3atdP06dNdp6ynnnpKefLkUb169Vyb999/X926ddPEiRNdks+SmbZv27ZtLnkYl1gAAEBwi3eiz24+KlWq5G4g7MbDknsAgCQqbVrp5Zf9HQWAJKB9+/buQdPw4cP1wQcfuG0lSpRwD48eeOABf4cHAACQICzBNnfuXG3ZssWt33LLLa5UeVhYWLyPdeLECTVt2lTvvPOOBg0a5Nt+9OhRTZo0STNmzFCtWrXctilTprh7K+sgX61aNS1cuFCbN2921RNy5cqlcuXKuXmRX3zxRfXr10+pUqVyybvChQu7+zNjn7fqWTb60Jvos/mU27Ztq1atWrl1+4w9k7NRizYfYVxiie3s2bNu8Tr2z5zuVsHLFgQmu3Y2KINrCMB+D1jahtRN4kus37nxPW68E307duzQhx9+6MoJAACSkDNnrFaN9ciQsmTxdzQAkqCHHnrILQAAAMFo586dql+/vv78808VK1bMbRsyZIgrU27JMZu7Lz6sHKaNuKtTp06MRN/atWt1/vx5t92rePHiKlCggFatWuWSa/ZaunRpl+TzsuSddb7atGmTypcv79pEP4a3jbdE6Llz59x39erVy7c/NDTUfcY+G9dYYrOfSf/+/S/YfvDgQVcGFIHJHgpb4teSffbfCYDk/fsgTx5PB5dYRRmRwA4cOKDEcPz48cRN9FmZALtxItEHAEnIuXPS889bfWVp3TpPyc6cOf0dFQAAAABcN1am3JJ5NpItW7Zsbtvff/+tJ5980u2zZF9czZw5U+vWrXOlO2Pbt2+fG5GXJVYHS0vq2T5vm+hJPu9+777LtbERdqdPn9bhw4fdCMWLtdm6dWucY4nNEodWDtTLvs+SoTlz5lQmpnwI8BE8Ie46kugDkjf7fbB3b4R27gwn0ZfIvGW0E5rNy5uoib5OnTq5OV7sZsF6Jllt8ehi1ywHACQymxvVynNaks/YpOuHDpHoA+AecG3fvl05cuRQ1qxZL1ty/ZD93gAAAAhgX3/9dYwkn8mePbuGDh2q6tWrx/k4v//+u5577jktWrQo3g/aAkHq1KndEpslh0gQBTa73+c6AjCW4PMuSDyJ9fs2vseNd6KvcePG7rV169Yx/hGxYeH2aj2NAADXidVrtpIrX33lWbc/QseMkf4pUwMgebP5XTJmzOh7z9zKAAAgmFny6mKlrmyuPRv1FldWDtNKcVWoUMG3zZ53LVu2TOPGjdOCBQtcWc0jR47EGEm3f/9+Nyeysdc1a9bEOK7t9+7zvnq3RW9jo+rSpk3r5hW05WJtoh/jSrEAAIDgFu9E365duxInEgBA/FiXnNdekz7/3LNuI6xtEveyZf0dGYAkokWLFr73LVu29GssAAAAie2+++7T008/rUmTJqlKlSpu27fffqt27dqpYcOGcT5O7dq1tXHjxhjbWrVq5ea+e/HFF12ZS6twtXjxYl+H+G3btmn37t269dZb3bq9Dh482CUMvWW9bISgJfFKlizpa/O59++5f1gb7zEsOVmxYkX3PQ8++KCvHJutd+zY0a3b/ivFAgAAglu8E30FCxZMnEgAAPFL8tnIvdmzPes2nHvoUJtI1d+RAUii6tSp4+anadSoEXOvAACAoDRmzBjX0ckSXN6pZsLDw12Sb/To0XE+jlVEKFWqVIxt6dOnd2VAvdvbtGnj5rmzMqF2b2VT3dj3VqtWze2vW7euS+g1a9ZMw4YNc1Pg9O7dWx06dPCVzbQEpI0Q7NGjh6uctWTJEn3wwQcx5hK077BzqlSpkktejho1SidPnnSJR5M5c+YrxgIAAIJbvBN9Xps3b3a9g6w8QHTx6SEFALhK774r/d//ed5bKb4BA6Q77/R3VACSsFtuuUW9evXSs88+qwYNGrikX/369S+YbxkAACBQWenKjz/+WDt37tSWLVvcthIlSqho0aIJ/l1WFt3mz7FRdGfPnlW9evU0YcIE334rufnZZ5+pffv2LulmiUJL2A2wv93+UbhwYZfU69q1q0tE5suXT++++647lleTJk108OBB9e3b1yULy5Urp/nz5ytXrlxxjgUAAAS3eCf6fvnlFz300EOuhIF3bj7jnfOFOfoAIJEtXy699da/6716Sffc48+IAAQAe3hkD4G+/PJLzZgxQ82bN3cPoB5++GE1bdpUd9JZAAAABAlL7Nliz6js+dXhw4eVNWvWazrm0qVLY6ynSZNG48ePd8vlqmLFLs0ZW82aNbV+/frLtrEynd5SnRcTl1gAAEDwCo3vB5577jnX48hqjKdLl06bNm1ykxFbCYHYNz0AgERQvboNn/a879pVatTI3xEBCBDW09vKSE2dOlX79+/XW2+9pTVr1qhWrVr+Dg0AAOCadenSxc3PZyzJZx2ZKlSo4ObU45kVAAAIVvEe0bdq1SpXMzxHjhzuYZEtNWrU0JAhQ9S5c+cr9kICAFwjm4+vd2+bcEu67TZ/RwMgAFnZp5kzZ+q///2vNmzY4OZ7AQAACHQffvihK09uPv30U1eVauvWrfq///s/vfzyy1qxYoW/QwQAAPD/iD7rEWWTEhtL9u3Zs8dXjmDbtm0JHyEAQIo1H6pL9pHkAxAPx44d05QpU3T33Xe7Xu1vvvmmm1t5x44dWr16tb/DAwAAuGZ//fWXcufO7d5bycxHH31UN998s1q3bu1KeAIAAASjeI/oK1WqlH788UdXvrNq1aoaNmyYUqVKpbfffltFihRJnCgBIDn79ltp4EBp1CibbMLf0QAIULly5XJz0zRp0sRVYrCy6wAAAMF2v7N582blyZNH8+fPdx2bzKlTp9zcxAAAAMEo3iP6evfurcjISPd+wIAB2rVrl26//XbXU2rMmDHxOla/fv0UEhISYylevLhv/5kzZ9ShQwdlz55dGTJkUOPGjd18MtHt3r1bDRo0cPMF3nDDDXrhhRcUHh4eo43VYbea7KlTp3aTMdu8NLHZhMWFChVyExhbAtPmq4kuLrEAQIL74QepWzersyc9/bT90vN3RAAC1CeffKI//vhDI0eOJMkHAACCUqtWrdwoPuukbs+Y6th0B67v5LcxnjcBAAAk6xF99erV8723pJnVOj906JDrIW43UfF1yy236Msvv/w3oBT/htS1a1fNmzdPs2bNUubMmdWxY0c1atTIV1Pdyohaks/KMqxcuVJ79+5V8+bNlTJlSr366quujSUirU27du00ffp0LV68WE899ZTr3eU9l/fff1/dunXTxIkTXZJv1KhRbp+VIrXkYVxiAYAEt2WL9Nxz0tmznvWKFaX//MffUQEIUFay0zpD2VzLP//8s5544glXjt3KsGfKlMl1ZAIAAAhk1qG8dOnSrlP4I4884jp8GxvN17NnT3+HBwAAkDQSfReTLVu2qw8gRQpf/fTojh49qkmTJmnGjBmqVauW22bzypQoUcLNI1OtWjUtXLjQlWSwRKGVZyhXrpwGDhyoF1980d3cWUlRS95ZmdHhw4e7Y9jnv/nmG9eb3ZvoGzFihNq2bet6fhn7jCX1Jk+e7G4E4xILACSoX36ROnaUTp70rN96qzR4sP2F6u/IAASo3377Tffcc4978HX27FmX+LNE32uvvebW7f4HAAAgUJ0/f97d69g9jVVhiq5FixZ+iwsAACDJJfpOnjypoUOHupFxBw4c8JXx9PrFHk7Hw44dO5Q3b15XMvPWW291c8YUKFBAa9eudTdp3jILxsos2L5Vq1a55Jq9Wk8tS/J5WfKuffv22rRpk8qXL+/aRD+Gt02XLl3c+3Pnzrnv6tWrl29/aGio+4x91sQllouxh2a2eB07dsy92s8s9s/tatlxoqKiEux4SU2wn5/hHANfgp/f778rpF076/HgWS9fXlGvvWY9I+zL5A9cw8DHOV68fXLy3HPPuZKdNteylSL3euihh1yHJwAAgEBm1Z02bNjg7zAAAACuu3gn+qzs5ddff61mzZq58pdXU67Ty8pk2nx5xYoVc2U3+/fv7+b7++mnn7Rv3z43Ii9LliwxPmNJPdtn7DV6ks+737vvcm0s6Xb69GkdPnzYlQC9WBsrS+o9xpViuRhLWto5xXbw4EE3519CsIeUNuLQHmxagjLYBPv5Gc4x8CXk+YUePKhM3bop7MABtx5+88061quXoqyjwD+dBfyBaxj4OMcLHT9+XMnJ8uXLXalzu6eJzuYo/vPPP/0WFwAAQEJ58sknXUUm66AOAACQXMQ70ffFF1+4spbVq1e/5i+/9957fe/LlCnjEn8FCxbUBx98oLRp0yrQ2ShBm/vPy5KL+fPnV86cOd1cOAn1UNOSrXbMYHxwG+znZzjHwJdg53fokEL69pUOH7buqFKRIgp76y3lzJxZ/sY1DHyc44WsmkByYj8f69wU2x9//OFKeAIAAAQ6m4/YpmGxKV4qVqyo9OnTx9hvU7cAAAAouSf6smbNek1z8l2OjZi7+eabtXPnTjdvjJXVPHLkSIyRdPv37/fN6Weva9asiXEM2+/d5331bovexhJtlky0CZltuVib6Me4UiwXY5M+eyd+js4ePibkQ1Z7qJnQx0xKgv38DOcY+BLk/FascGU7nQIFpDffVEjWrEoquIaBj3OMKZh/DhdTt25djRo1Sm+//bbvZ3XixAm98sorql+/vr/DAwAAuGZWIapChQru/fbt22Psu5aKVAAAAEGV6Bs4cKD69u2r9957T+nSpUvQYOxh088//+zKglrPK6uvbnMBeidR3rZtm3bv3u3m8jP2OnjwYDdX4A033OC2LVq0yCXxSpYs6Wvz+eefx/gea+M9hpWvsu+y73nwwQd9Pd5tvWPHjm49LrEAwDV74AHJyvr+97/ShAlStDm0AOBavfHGG7rnnnvcPZKVEH/iiSfcXMk5cuTQ//73P3+HB+BiNg6UUvwuhUQp4NT81N8RAEiGvvrqK3+HAAAAkDQTfeXLl4/R88lG3Nn8dDaniyXAolu3bl2cv/z555/X/fff78p17tmzx/Uot9F1jz/+uDJnzqw2bdq40pc2gtCSd506dXKJtWrVqvl6ptvDKksMDhs2zM2X17t3b3Xo0ME3kq5du3YaN26cevToodatW2vJkiWuNKiVH/Wy72jRooUqVaqkKlWquN7uJ0+eVKtWrdz+uMQCAAmiSROpYUMpCMoXA0harHz4jz/+qPfff9+9Wgcru79p2rRpUJRMBwAAiP7cyjqS33HHHe4+x+ZwZkQfAABI1ok+70i3hGZzwlhS7++//3bz6dSoUUOrV692783IkSNdWS0bRXf27FnVq1dPE2yUyz8sKfjZZ5+pffv2LulmtdctYTdgwABfm8KFC7ukXteuXTV69Gjly5dP7777rjuWV5MmTXTw4EE3UtGSheXKldP8+fNdMtPrSrEAQLydO2f1ZKRSpWJu54E7gAR2/vx5FS9e3N03WWLPFgAAgGBjz5ceffRRN7LPEntWvaBIkSKuc5NNRTN8+HB/hwgAAOCfRJ+NtEsMM2fOvOz+NGnSaPz48W65FBsNGLs0Z2w1a9bU+vXrL9vGynR6S3VebSwAEGcREVLv3tLy5dKrr0p33eXviAAEMavAYOU6AQAAgpl18rb7HptqpUSJEjE6eFuVJhJ9AAAgGIXGteHhw4c1duxYHTt27IJ9R48eveQ+AEAskZFS//7SkiU2zMZ6U9gvUn9HBSDIWWnz1157TeHh4f4OBQAAIFEsXLjQ3e9YNafobrrpJv32229+iwsAAMDvI/qMzXO3YcMGNzddbDaH3fLly12i7+WXX07oGAEgeERFScOGSd6RyDbP6Wuv2S9Sf0cGIMh99913Wrx4sXsAVrp0aVfyPLo5c+b4LTYAAICEcPLkSaVLl+6C7YcOHVLq1Kn9EhMAAECSGdE3e/ZstWvX7pL7n3nmGX344YcJFRcABGeSb+xYyfu7MjTUU7bz1lv9HRmAZCBLlixurmGbZzhv3ryuo1b0BQAAINDdfvvtmjZtmm/d5umLjIzUsGHDdBfTJQAAgOQ+ou/nn392pQ4uxfZZGwDAJUyaJHn/6AwJkfr1Y24+ANfNlClT/B0CAABAorKEXu3atfX999/r3Llz6tGjhzZt2uRG9K1YscLf4QEAAPh3RF9YWJj27Nlzyf22L9RGpwAALjRjhjRx4r/rvXpJ9ev7MyIAyYT1Yre5aqpXr67KlSurZ8+eOn36tL/DAgAASHClSpXS9u3bVaNGDT3wwAOulGejRo20fv163Xjjjf4ODwAAwL8j+sqXL6+5c+eqWrVqF93/0UcfuTYAgFjmzpVGjPh3vUsXqVEjf0YEIBkZPHiw+vXrpzp16iht2rQaPXq0Dhw4oMmTJ/s7NAAAgATz66+/atGiRTp//rxL8r388sv+DgkAACBpJfo6duyoxx57TPny5VP79u3dCD8TERGhCRMmaOTIkZphI1YAADFlyyalSiWdOyc9/bT05JP+jghAMmLz1Ni9ms2nbL788ks1aNBA7777LtUYAABAUPjqq6903333+aoWpEiRwnVqepK/vQAAQDIQ56c7jRs3drXNO3furGzZsrnRe7bY+y5duqhbt256+OGHEzdaAAhEd9whjRoltW4ttW3r72gAJDO7d+9W/Wilgm1kX0hIyGVLsgMAAASSPn366O6779aff/6pv//+W23btnXPsAAAAJKDOI/o85Z+svIH06dP186dOxUVFaU777xTTzzxhKpUqZJ4UQJAoLPfkfyeBOAH4eHhSpMmTYxtKVOmdGWtAAAAgsFPP/2klStXKk+ePG799ddf11tvveWSftmzZ/d3eAAAAEkn0WcsoUdSDwAu48cfpS1bpMce83ckAOA6ZrVs2VKpU6f2bTtz5ozatWun9OnT+7bNmTPHTxECAABcm2PHjilHjhy+9XTp0rm5iY8ePUqiDwAABL14J/oAAJexdavUubN08qR04oTUpo0UEuLvqAAkYy1atLhgG/PVAACAYLNgwQJlzpzZtx4ZGanFixe70X5eDRs29FN0AAAAiYdEHwAklF9+kTp08CT5zA8/2F+XUliYvyMDkIxNmTLF3yEAAAD4pXPTM88843tvcxRHRERc56gAAAASH4k+AEgAoXv2KKRXL+noUc+GcuWkN94gyQcAAej+++9XILIHmG93yu/vMAAAuO5s9B4AAEByRaIPAK7V/v3K1LOndOiQZ71kSWnUKClNGn9HBgAAAAAAAAAIYqH+DgAAAtqhQwp59lmF7d/vWb/xRmnsWClDBn9HBgAAAABBb/Xq1XFue+rUKW3atClR4wEAAEjyib79+/erWbNmyps3r1KkSKGwsLAYCwAkG8eOeebk+/13z3q+fNKECVK0CeABAAAAAInHnlHVq1dPs2bN0knvfOmxbN68WS+99JJuvPFGrV279rrHCAAAkKQSfS1bttS6devUp08fffjhh5ozZ06MBQCSjb59pR073NuInDkVNX68lD27v6MCgES3bNkyN4+ddfyyeeHmzp0bY39UVJT69u2rPHnyKG3atKpTp452/PP70uvQoUNq2rSpMmXKpCxZsqhNmzY6ceLEdT4TAAAQ6CyJ16BBA/Xu3dvdU9xyyy26++673b1KjRo1lCNHDlWoUEG7du3SwoUL1bx5c3+HDAAA4N85+r755hstX75c5cqVS9hIACDQdO4sbdliM7/r2JAhypEnj78jAoDL2rNnj7uXO3DggCIjI2Ps62y/0+LIesuXLVtWrVu3VqNGjS7YP2zYMI0ZM0bvvfeeChcu7DqIWU97exCX5p/5Sy3Jt3fvXi1atEjnz59Xq1at9PTTT2vGjBkJcKYAACC5SJkypbuPseX777939zq//fabTp8+7e5XunbtqrvuukvZsmXzd6gAAABJI9GXP39+10sbAJK9IkWkd99V1JkzisyY0d/RAMBlTZ06Vc8884xSpUql7Nmzu5F4XvY+Pom+e++91y0XY/eJo0aNcr3qH3jgAbdt2rRpypUrlxv599hjj2nLli2aP3++vvvuO1WqVMm1GTt2rOrXr6833njDjRQEAACIL7uv8N5bAAAAJBfxTvTZg5uePXvqrbfeUqFChRInKgBIiiIi7Gm4FBqt6nH+/G5Enw4c8GdkAHBFNqrOymn26tVLodF/jyUwK4u1b98+V67TK3PmzKpatapWrVrlEn32aqW1oj+Is/YW17fffquHHnroguOePXvWLV7HbJ5U2a/gyAtGJ8Zm+y0BeaV2XtGToIHE4o6KClFkVADGH8drczXXM1CvaUBfTxPH63M11zMQJZfzTE7nynle2A4AAAABkuhr0qSJTp065SYwTpcunSuREHu+FQAIOvaH64AB/87NFxbm74gAIF7s/s2SbImZ5DOW5DM2gi86W/fus9cbbrghxv4UKVK4klreNrENGTJE/fv3v2D7wYMHdebMmSs+fDx69Kh7UBmX87cKFoHqSEQORSlKoYGWG4pHh5n4Xs9AvqYBez3jcU2v5noGouRynsnpXDnPmI4fP35d4wIAAMA1jugDgGTFyhW//ro0b55nPTxcGjzY31EBQLy0adNGs2bNcpUZApGNROzWrVuMEX2WvMmZM6cyZcp0xYeUNjrK2sblYezvv/+uQGTnmCUsRDlT/KHQkAArtR8r8ZuQ1zNQr2lAX894XNOruZ6BKLmcZ3I6V84zJu8cvAAAAAiARF+LFi0SJxIASKpJvnHjpFmzPOv2x220cnQAEChsRNx9993n5sYrXbr0BVUZRowYkSDfkzt3bve6f/9+5cmTx7fd1suVK+drcyDWaJ/w8HBXGcL7+dhSp07tltjsoWNcHrDaQ8q4tg3k+ahDQmz0l2cJKPF8SB6f6xnI1zRgr2c8r2l8r2egSi7nmZzOlfP8V7D/DAAAAIIq0WciIiI0d+5cbdmyxa3fcsstatiwocIoZQcg2EyZIr33nue9ze/Tr590113+jgoArirRt2DBAhUrVuyCOcsScv6ywoULu2Td4sWLfYk9G31nc++1b9/erd966606cuSI1q5dq4oVK7ptS5YscaMGbC4/AAAAAAAAJFKib+fOnapfv77+/PNP34Mie3BkpZPmzZvn5u4DgKDwv/9JEyb8u96rl1S/vj8jAoCrNnz4cE2ePFktW7a85mOdOHHC3RN67dq1Sz/88IObY69AgQLq0qWLBg0apJtuuskl/vr06aO8efPqwQcfdO1LlCihe+65R23bttXEiRN1/vx5dezY0c0haO0AAACulnU2ssWqB1gnoujsXggAAEDJPdHXuXNnl8xbvXq1e5hj/v77bz355JNunyX7ACDgffyxPRX/d/2556RGjfwZEQBcEyt7Wb169QQ51vfff6+7oo1u9s6dZyXep06dqh49eujkyZN6+umn3ci9GjVquJKh0efvmT59ukvu1a5d25X7aty4scaMGZMg8QEAgOSpf//+GjBggCpVquRKiCdk1QIAAICgSfR9/fXXMZJ8Jnv27Bo6dGiCPTwCAL9auFAaNOjf9bZtpWbN/BkRAFyz5557TmPHjk2QZFrNmjUvO+eZPVSzh2y2XIrdS86YMeOaYwEAAPCySgHW6agZf78BAIBkJMXV9AY/fvz4RUs4pUqVKqHiAgD/sNIuNief9wH2E09ITz/t76gA4JqtWbPGzYP32WefufmVU6ZMGWP/nDlz/BYbAABAQjh37pxuu+02f4cBAABwXYXG9wP33XefK8P07bffup7cttgIv3bt2qlhw4aJEyUAXC+hoZ55+UqWlB56SOra1Yam+DsqALhmWbJkUaNGjXTnnXcqR44cypw5c4wFAAAg0D311FNUDAAAAMlOvEf0Wbknm3/l1ltv9fUEDw8Pd0m+0aNHJ0aMAHB92QPviRMlm0uKJB+AIDFlyhR/hwAAAJCozpw5o7fffltffvmlypQpc0EFgxEjRvgtNgAAgCST6LPe4B9//LF27NihrVu3um0lSpRQ0aJFEyM+AEh8v/wi5c4tpUv377bo7wEgiBw8eFDbtm1z74sVK6acOXP6OyQAAIAEsWHDBpUrV869/+mnny6YQxgAACAYxTvR53XTTTe5BQACPslnc/Dly2dDlqVMmfwdEQAkipMnT6pTp06aNm2aIm0+UklhYWFq3ry5xo4dq3R0cAAAAAHuq6++8ncIAAAASTPR161bNw0cOFDp06d37y+HMggAAsaff0odOkhHjniWceOkl17yd1QAkCjsHu7rr7/Wp59+qurVq7tt33zzjTp37qzu3bvrzTff9HeIAAAACeaPP/5wr/msUycAAEByT/StX79e58+f970HgIB34IDUvr3VsPOslyghde7s76gAINHMnj1bH374oWrWrOnbVr9+faVNm1aPPvooiT4AABDwrGrBoEGDNHz4cJ04ccJty5gxo+vU9PLLLys0NNTfIQIAACS40LiWPrC5+bzvL7cAQJJ36JAnybdnj2e9SBFp7FgpQwZ/RwYAiebUqVPKlSvXBdtvuOEGtw8AACDQWTJv3LhxGjp0qOuobsurr77qypT36dMnzsexDlBlypRRpkyZ3HLrrbfqiy++8O0/c+aMOnTooOzZsytDhgxq3Lix9u/fH+MYu3fvVoMGDVx5dLvfeuGFFxQeHh6jzdKlS1WhQgWlTp1aRYsW1dSpUy+IZfz48SpUqJDSpEmjqlWras2aNTH2xyUWAAAQ3OLdlal169Y6fvz4Red9sX0AkKQdOyZ17Cj99ptn3cq4TJgg/dOZAQCClT2geuWVV9zDIK/Tp0+rf//+bh8AAECge++99/Tuu++qffv2LlFny7PPPqt33nnnokm0S7Fyn5YsXLt2rb7//nvVqlVLDzzwgDZt2uT2d+3a1ZVDnzVrliuNvmfPHjVq1Mj3+YiICJfkO3funFauXOnisu/v27evr82uXbtcm7vuuks//PCDunTpoqeeekoLFizwtXn//fdd+XW7h1u3bp3Kli2revXq6YBVqPnHlWIBAADBL06lO6OzmxO72bHSB9HZg6Jp06Zp8uTJCRkfACQcG7Fi5Tm3b/es33CDddWUcuTwd2QAkOhGjRqle+65xz24sodE5scff3S9w6M/UAIAAAhUhw4dUvHixS/YbttsX1zdf//9MdYHDx7sRvmtXr3a3UtNmjRJM2bMcAlAM2XKFJUoUcLtr1atmhYuXKjNmzfryy+/dBUVypUrp4EDB+rFF19Uv379lCpVKk2cOFGFCxd2ZUaNfd7mTx45cqRL5pkRI0aobdu2atWqlVu3z8ybN889e+vZs6eOHj16xVgu5uzZs27xOmYdYv8pfWoLApNdu6ioKK4hAPd7ICREbkHiSqzfufE9bpwTffaPvv1jYYuN6LOHQtF7Kn3++eeuFAEAJEn2R0zXrtJPP3nWs2Wzv5KkPHn8HRkAXBelS5fWjh07NH36dG3dutVte/zxx9W0aVM3Tx8AAECgs85MVrpzzJgxMbbbNm9Hp/iyZ142Ws4qWVkVBBvld/78edWpUydGIrFAgQJatWqVS67Zq917RS+bbsk7G2loowLLly/v2kQ/hreNjewzNhrQvqtXr16+/TbHoH3GPmviEsvFDBkyxFV1iO3gwYMxqj8gsNhDYUv+2rNb5qMEkjf7fZAnT5h7HxXl72iC24Foo+wT0sWqaiZIos/m6AsJCXHLzTfffMF+236xmwQASBKsC4t3JHKmTDbRgVSggL+jAoDrwh4A2UOfzz77zPUKBwAACEbDhg1z5TBtJJ23NLklvH7//XfXQT0+Nm7c6I5hiS+b++6jjz5SyZIlXZlNG5Fnz8mis6Tevn373Ht7jT03snf9Sm2so71VzTp8+LBLMl6sjbfTlh3jSrFcjCUPrSSol31n/vz5lTNnTjcnIQJ5BE+Iu44k+oDkzX4f7N0boZ07w0n0JbLEGvwWfaBdgib6vvrqK9cjxEoBzJ49W9lsNMw/7KaiYMGCyps3b/yiBYDrJVUqaehQ+8tPeuAB6aab/B0RAFw3KVOmpHc2AAAIenfeeae2b9+u8ePH+5JhNl+dzdMX32dWxYoVc0k9GyH14YcfqkWLFm4OvGCQOnVqt8RmySESRIHNEn1cRwDGEnzeBYknsX7fxve4KeJzs+SdLNh6+fAPBoCAkyKF9NJL/o4CAPyiQ4cOeu211/Tuu+8qhf0+BAAACEKW0LM59a6VdWovWrSoe1+xYkV99913Gj16tJo0aeLKah45ciTGSLr9+/crd+7c7r29rlmzJsbxbL93n/fVuy16GxtRZ2XVw8LC3HKxNtGPcaVYAABA8Iv3Ux4buWdOnTql3bt3uxuK6MqUKZNw0QHA1bLuKpMmSfXr2196/o4GAPzOHk4tXrxYCxcudHPGpE+fPsb+OXPm+C02AACAq7VhwwaVKlXKdUi395dzLc+srAza2bNnXdLPqiXYfVXjxo3dvm3btrlnZN5yofZqyUabt8db0mvRokUuiWflP71tYpcTtTbeY1ii0b7LvufBBx/0xWDrHTt2dOtxiQUAAAS/eCf6bGLeVq1a6YsvvrjofqsfDgB+Z3PwTZ0qzZ4tTZggFS7s74gAwK+sl7f3ARAAAECwKFeunJuPzhJq9t5KF9rUM7HZ9rg+s7I57O69914VKFBAx48f14wZM7R06VItWLBAmTNnVps2bdwcdzatjSXvOnXq5BJr1apVc5+vW7euS+g1a9bMzRto8fXu3dtVWPCWzGzXrp3GjRunHj16qHXr1lqyZIk++OADzZs3zxeHfYeVDK1UqZKqVKmiUaNG6eTJk+65nIlLLAAAIPjFO9HXpUsXVxLg22+/Vc2aNd1kxFYSYNCgQRo+fHjiRAkA8TF5sifJZw4etC6NJPoAJEuffPKJe0hlPb2nTJni73AAAAASnE0xkzNnTt/7hGAj8Zo3b669e/e6ZJqNBLQk39133+32jxw50o0gtE5UNsqvXr16mmAdTP9hJTc/++wztW/f3iXdrJKCJewGDBjga1O4cGGX1OvatasrCZovXz5XYt2O5WVlQq3Dfd++fV2y0BKZ8+fPV65cuXxtrhQLAAAIfvFO9FkPo48//tj1JrIbCSvlaTc61mtoyJAhatCgQeJECgBxMXOmZwSf14svSvfc48+IAMBvHnroIfdQyB5+2QMne1jlLR8FAAAQDLxTzJjffvtNt9122wXzEYeHh2vlypUx2l7OJJsG4jLSpEmj8ePHu+VyccUuzRmbdaBfv379ZdtYmU5vqc6rjQUAAAS30Ph+wEoEeB8QZc2a1fUsMjbXy7p16xI+QgCIq08+kd5449/1zp2lRx7xZ0QA4FeW4Fu9erV7byWsrGQVAABAsLrrrrt06NChC7YfPXrU7QMAAAhG8U70FStWzE3sa8qWLau33npLf/75pyZOnKg8efIkRowAcGWLFkmDBv27/tRTUvPm/owIAPzO5n554IEH3Gg+S/Llzp3bvb/YAgAAEOgu1bHp77//duUzAQAAglG8S3c+99xzruyTeeWVV3TPPfdo+vTpSpUqlaZ658QCgOtp+XKpd28pMtKz/sQT0jPP+DsqAPC7fv366bHHHtPOnTvVsGFDN09flixZ/B0WAABAgmrUqJF7tSRfy5YtlTp1at++iIgIbdiwwZX0BAAACEbxTvQ9+eSTvvcVK1Z09c+3bt2qAgUKKEeOHAkdHwBc3u7dnnn4IiI86w8+KHXtan/h+TsyAEgSihcv7hbroPXII48oXbp0/g4JAAAgQWXOnNk3oi9jxoxKmzatb591TK9WrZratm3rxwgBAACSUKIvNntYVKFChYSJBgDiK39+qUUL6Z13pHr1pJdeIskHABdhiT4AAIBgZFULTKFChfT8889TphMAACQrcUr0devWLc4HHDFixLXEAwDxY0k9K9N5003SHXdIofGeehQAAAAAEATo2AQAAJKjOD0RX79+fZyWH3744aoDGTp0qKul3qVLF9+2M2fOqEOHDsqePbsyZMigxo0ba//+/TE+t3v3bjVo0MCNLLzhhhv0wgsvKDw8PEabpUuXulGHVqO9aNGiF51LcPz48a7nV5o0aVS1alWtWbMmxv64xALgOvGW6YyuVi0pxTUPUgYAAAAABLAPP/xQjz76qCvXac+Coi8AAADBKE5Pxb/66qtEDeK7777TW2+9pTJlysTY3rVrV82bN0+zZs1y9dY7duzoJlhesWKFb0JlS/Llzp1bK1eu1N69e9W8eXOlTJlSr776qmuza9cu16Zdu3aaPn26Fi9erKeeekp58uRRPSvzJ+n99993oxYnTpzoknyjRo1y+7Zt2+aSh3GJBcB18uefUufOUq9eUqVK/o4GAAAAAJBEjBkzRi+//LJatmypjz/+WK1atdLPP//snjtZ520AAIBg5PcadydOnFDTpk31zjvvKGvWrL7tR48e1aRJk1wp0Fq1aqlixYqu5rol9FavXu3aLFy4UJs3b9Z///tflStXTvfee68GDhzoRuedO3fOtbHkXeHChTV8+HCVKFHCJegefvhhjRw50vdd9h02KbPdAJYsWdJ9xkYITp48Oc6xALgODhyQ2reXfvvNk+z7/nt/RwQAAckqFQAAAASbCRMm6O2339bYsWOVKlUq9ejRQ4sWLVLnzp3dsx0AAIBgFO86d3fddZcrsXkpS5YsidfxrEeVjbirU6eOBg0a5Nu+du1anT9/3m33Kl68uAoUKKBVq1a5Egz2Wrp0aeXKlcvXxkbitW/fXps2bVL58uVdm+jH8Lbxlgi1hKB9Vy8bHfSP0NBQ9xn7bFxjuZizZ8+6xevYsWPuNTIy0i0JwY4TFRWVYMdLaoL9/AznGEeHDinEknx79njW8+ZVVOHCdnD5G9cw8AX7+RnO8eLtkxM738GDB7sOTVZ+fPv27SpSpIj69Onjype3adPG3yECAABcE5ve5bbbbnPv06ZNq+PHj7v3zZo1c89uxo0b5+cIAQAAkkCiz0bORWcJMJub76efflKLFi3idayZM2dq3bp1roRCbPv27XO9r7JkyRJjuyX1bJ+3TfQkn3e/d9/l2ljS7fTp0zp8+LArAXqxNlu3bo1zLBczZMgQ9e/f/4LtBw8eTLCe9PbQznql2YNNS1AGm2A/P8M5XlnI8ePK9OKLSvHzz249Ik8eHe3XT1Hnz3tG+fkZ1zDwBfv5Gc7xQt4HP8mFdah67733NGzYMFfJwKtUqVKubDmJPgAAEOhsapdDhw6pYMGCrnO2VWEqW7asm9bF7hEBAACCUbwTfdFLXkbXr18/V4Yzrn7//Xc999xzroRCmjRpFIxslKDN/edlycX8+fMrZ86cypQpU4I91LQRlnbMYHxwG+znZzjHKzh1SiE9e1rXTCllSilnToW9845y5s2rpIJrGPiC/fwM53ihYL3/uJRp06a5Ula1a9d2cxd72cMvb+cmAACAQGbTrXzyySeuwpNNz9K1a1d9+OGH+v7779WoUSN/hwcAAJA0En2X8uSTT6pKlSp644034tTeymEeOHBAFSpU8G2zkXXLli1zpRQWLFjgymoeOXIkxkg6KzVlPbSMva5ZsybGcW2/d5/31bstehtLtFkZh7CwMLdcrE30Y1wplotJnTq1W2Kzh48J+ZDVHmom9DGTkmA/P8M5XoKVvn3+eemnnzzrNo/nm28qJF8+JTVcw8AX7OdnOMeYgvnncDF//vmnihYtetEEqVVoAAAACHTWqclbnt2mismePbtWrlyphg0b6plnnvF3eAAAAIkiwZ5w2Vx18ekZb73JN27c6Mp+epdKlSqpadOmvvcpU6bU4sWLfZ/Ztm2bq7d+6623unV7tWNYwtDLRghaEq9kyZK+NtGP4W3jPYaV5KxYsWKMNnZTaOveNrb/SrEASGD20PnFF6Xvv/esZ8wojR8vFSrk78gAICDZvdHy5csv2G693K3XOwAAQCALDw93pcqjT7Hy2GOPacyYMerUqZN7/gMAABCM4j2iL3apA6txvnfvXlcGoU+fPnE+TsaMGd2cMNGlT5/e9bbybre5Yqz0ZbZs2Vzyzm7MLLFmEyibunXruodWNqmyzTdjN3O9e/d2vba8I+msNJWNEOzRo4dat26tJUuW6IMPPtC8efN832vfYfMLWnLRRiXaPDUnT550ZR5M5syZrxgLgAT244/SypWe9+nSSWPHSjff7O+oACBg9e3b193v2Mg+69Q0Z84c13HJSnp+9tln/g4PAADgmqRIkcI9G2revLm/QwEAAEjaiT5LesUue1WsWDENGDDAJd4Sks0HaMdv3Lixzp49q3r16mnChAm+/VZy0x5MtW/f3iXdLFFoD7AsFq/ChQu7pJ7VZR89erTy5cund9991x3Lq0mTJjp48KB7AGbJwnLlymn+/PnKlStXnGMBkMAqVZKGDJHs/+cRI6RYHQMAAPHzwAMP6NNPP3X3SXbPZPc9VkLdtt19993+Dg8AAOCaWfWor7/+WoWoBAMAAJKReCf6pkyZkjiRSFq6dGmMdSsFOn78eLdcSsGCBfX5559f9rg1a9bU+vXrL9umY8eObrmUuMQCIIHVri1VrixlyuTvSAAgKNx+++2uhDkAAEAwuvfee9WzZ083zYtNw2Kdm6KzufoAAACU3BN9Xlaqc8uWLe69lc+0GygAuCbbtknFisXcRpIPAAAAABAHzz77rHsdYVVhYgkJCVFERIQfogIAAEhiib4//vhDjz/+uFasWKEsWbK4bUeOHNFtt92mmTNnutKYABBvU6dK48ZJPXtKDz/s72gAIOhkzZrVPeCKzbZZ5YKiRYuqZcuWvjmKAQAAAo3NQwwAAJDchMb3A0899ZTOnz/vRvMdOnTILfbebqZsHwDE2wcfeJJ8ZuhQz8g+AECCsjn5bL7hBg0aqH///m6x97atQ4cOuvnmm928x++8846/QwUAAAAAAEBijeizSY1XrlypYtHK69n7sWPHunlfACBePv1UGjbs3/VOnS4s3wkAuGbffPONBg0apHbt2sXY/tZbb2nhwoWaPXu2ypQpozFjxqht27Z+ixMAAOBq2X3MxUSvYHDHHXcoLCzsuscGAACQZBJ9+fPndyP6YrM653nz5k2ouAAkB19+KQ0c+O9669ZSixb+jAgAgtaCBQv02muvXbC9du3a6t69u3tfv3599bQSygAAAAFo5MiROnjwoE6dOuXKlpvDhw8rXbp0ypAhgw4cOKAiRYroq6++cs+3AAAAkmXpztdff12dOnXS999/79tm75977jm98cYbCR0fgGD1zTfSyy/bJAqe9ccek9q393dUABC0smXLpk9tFHUsts32mZMnTypjxox+iA4AAODavfrqq6pcubJ27Nihv//+2y3bt29X1apVNXr0aO3evVu5c+dW165d/R0qAACA/0b0tWzZ0vWMspukFCk8Hw8PD3fvW7du7RYvm78PAC5gHQV69LChwJ71hg2lbt2snoq/IwOAoNWnTx83B5/1YK9SpYrb9t133+nzzz/XxIkT3fqiRYt05513+jlSAACAq9O7d29XjvzGG2/0bbNyndYxvXHjxvrll180bNgw9x4AACDZJvpGjRqVOJEASB42bpSs9+S5c571unXtrzEpNN4DjAEA8WDz7pUsWVLjxo3TnDlzfPMs2/zLt912m1v3lvAEAAAIRHv37nWd0WOzbfv27XPvbdqZ48eP+yE6AACAJJLoa8H8WQCulXfi8zvukAYMIMkHANdJ9erV3QIAABCM7rrrLj3zzDN69913Vb58ebdt/fr1rqpBrVq13PrGjRtVuHBhP0cKAADgx0SfiYiI0Ny5c7Vlyxa3fsstt6hhw4YK8z68B4BLKV1aevttaepUqV8/6Z8SwACAxBcZGamdO3fqwIED7n10d1jnCwAAgAA2adIkNWvWTBUrVlTKlCl9o/lq167t9pkMGTJo+PDhfo4UAAAg4cT7Cbs9HKpfv77+/PNPV+7JDBkyRPnz59e8efNi1EEHgIu6+WabJd3fUQBAsrJ69Wo98cQT+u233xQVFRVjX0hIiOvIBQAAEMhy587t5hzeunWrtm/f7rbZsyvv8yvvqD8AAIBknejr3LmzS+bZw6Js2bK5bX///beefPJJt8+SfQDgc+CA9OmnUv36/o4EAJK1du3aqVKlSu5eLU+ePC65BwAAEIyKFy/uFgAAgOQg3om+r7/+OkaSz2TPnl1Dhw5lzhcAMR0+LD37rEJ+/VXpd+6UBg5kPj4A8JMdO3boww8/VNGiRf0dCgAAQILp1q2bBg4cqPTp07v3lzNixIjrFhcAAECSTfSlTp1ax48fv2D7iRMnlCpVqoSKC0Cgs98THTpIv/7qVlOuWycdPWo9A/wdGQAkS1WrVnUl2En0AQCAYLJ+/XqdP3/e9/5SqGY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+      "text/plain": [
+       "<Figure size 1800x1200 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create trend analysis charts\n",
+    "if len(combined_df) > 0:\n",
+    "    fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
+    "    \n",
+    "    # Chart 1: Migration trends over time\n",
+    "    ax1 = axes[0, 0]\n",
+    "    years = combined_df['year'].values\n",
+    "    for i, col in enumerate(available_migration_cols):\n",
+    "        colors = ['#2E8B57', '#4169E1', '#DC143C']\n",
+    "        markers = ['o', 's', '^']\n",
+    "        ax1.plot(years, combined_df[col], marker=markers[i], color=colors[i], \n",
+    "                linewidth=2, label=f'{col}', markersize=6)\n",
+    "    \n",
+    "    ax1.axhline(y=0, color='red', linestyle='--', alpha=0.7)\n",
+    "    ax1.set_title('Migration Trends by Component', fontweight='bold')\n",
+    "    ax1.set_xlabel('Year')\n",
+    "    ax1.set_ylabel('Migration (Persons)')\n",
+    "    ax1.legend()\n",
+    "    ax1.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Chart 2: Migration share of population change\n",
+    "    ax2 = axes[0, 1]\n",
+    "    migration_pct = comparison_df['Migration_vs_PopChange']\n",
+    "    bars = ax2.bar(years, migration_pct, color='purple', alpha=0.7, edgecolor='black')\n",
+    "    ax2.axhline(y=100, color='red', linestyle='--', alpha=0.7, label='100% Migration')\n",
+    "    ax2.set_title('Migration Share of Population Growth', fontweight='bold')\n",
+    "    ax2.set_xlabel('Year')\n",
+    "    ax2.set_ylabel('Migration as % of Pop Change')\n",
+    "    ax2.legend()\n",
+    "    ax2.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Add value labels on bars\n",
+    "    for bar, pct in zip(bars, migration_pct):\n",
+    "        height = bar.get_height()\n",
+    "        ax2.text(bar.get_x() + bar.get_width()/2., height + 2,\n",
+    "                f'{pct:.1f}%', ha='center', va='bottom', fontweight='bold')\n",
+    "    \n",
+    "    # Chart 3: Cumulative migration flows\n",
+    "    ax3 = axes[0, 2]\n",
+    "    for i, col in enumerate(available_migration_cols):\n",
+    "        colors = ['#2E8B57', '#4169E1', '#DC143C']\n",
+    "        cumulative = combined_df[col].cumsum()\n",
+    "        ax3.plot(years, cumulative, marker='o', color=colors[i], \n",
+    "                linewidth=2, label=f'{col} (Cumulative)', markersize=6)\n",
+    "    \n",
+    "    ax3.set_title('Cumulative Migration Flows', fontweight='bold')\n",
+    "    ax3.set_xlabel('Year')\n",
+    "    ax3.set_ylabel('Cumulative Migration')\n",
+    "    ax3.legend()\n",
+    "    ax3.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Chart 4: Population vs Total Migration correlation\n",
+    "    ax4 = axes[1, 0]\n",
+    "    ax4.scatter(comparison_df['Migration_Total_Calc'], comparison_df['annual_population_change'], \n",
+    "               color='darkblue', s=100, alpha=0.7, edgecolors='black')\n",
+    "    for _, row in comparison_df.iterrows():\n",
+    "        ax4.annotate(f\"{int(row['year'])}\", \n",
+    "                    (row['Migration_Total_Calc'], row['annual_population_change']),\n",
+    "                    xytext=(5, 5), textcoords='offset points', fontsize=9)\n",
+    "    \n",
+    "    # Add trend line\n",
+    "    z = np.polyfit(comparison_df['Migration_Total_Calc'], comparison_df['annual_population_change'], 1)\n",
+    "    p = np.poly1d(z)\n",
+    "    ax4.plot(comparison_df['Migration_Total_Calc'], p(comparison_df['Migration_Total_Calc']), \n",
+    "             \"r--\", alpha=0.8, linewidth=2)\n",
+    "    \n",
+    "    ax4.set_title('Migration vs Population Change Correlation', fontweight='bold')\n",
+    "    ax4.set_xlabel('Total Migration')\n",
+    "    ax4.set_ylabel('Population Change')\n",
+    "    ax4.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Chart 5: Year-over-year changes\n",
+    "    ax5 = axes[1, 1]\n",
+    "    if len(combined_df) > 1:\n",
+    "        yoy_pop_change = combined_df['annual_population_change'].pct_change() * 100\n",
+    "        yoy_migration_change = comparison_df['Migration_Total_Calc'].pct_change() * 100\n",
+    "        \n",
+    "        years_yoy = years[1:]  # Skip first year due to pct_change\n",
+    "        ax5.bar(years_yoy - 0.2, yoy_pop_change.dropna(), width=0.4, \n",
+    "               label='Population Change', color='black', alpha=0.7)\n",
+    "        ax5.bar(years_yoy + 0.2, yoy_migration_change.dropna(), width=0.4, \n",
+    "               label='Migration Change', color='orange', alpha=0.7)\n",
+    "        \n",
+    "        ax5.axhline(y=0, color='red', linestyle='-', alpha=0.7)\n",
+    "        ax5.set_title('Year-over-Year % Changes', fontweight='bold')\n",
+    "        ax5.set_xlabel('Year')\n",
+    "        ax5.set_ylabel('% Change from Previous Year')\n",
+    "        ax5.legend()\n",
+    "        ax5.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Chart 6: Brexit impact comparison (pre/post 2020)\n",
+    "    ax6 = axes[1, 2]\n",
+    "    if 'EU+' in available_migration_cols and 'Non-EU+' in available_migration_cols:\n",
+    "        pre_brexit = combined_df[combined_df['year'] == 2020]\n",
+    "        post_brexit = combined_df[combined_df['year'] > 2020]\n",
+    "        \n",
+    "        if len(pre_brexit) > 0 and len(post_brexit) > 0:\n",
+    "            categories = ['EU Migration', 'Non-EU Migration']\n",
+    "            pre_values = [pre_brexit['EU+'].iloc[0], pre_brexit['Non-EU+'].iloc[0]]\n",
+    "            post_values = [post_brexit['EU+'].mean(), post_brexit['Non-EU+'].mean()]\n",
+    "            \n",
+    "            x = np.arange(len(categories))\n",
+    "            width = 0.35\n",
+    "            \n",
+    "            bars1 = ax6.bar(x - width/2, pre_values, width, label='2020', color='lightblue', alpha=0.8)\n",
+    "            bars2 = ax6.bar(x + width/2, post_values, width, label='2021-2024 Avg', color='darkblue', alpha=0.8)\n",
+    "            \n",
+    "            # Add value labels\n",
+    "            for bars in [bars1, bars2]:\n",
+    "                for bar in bars:\n",
+    "                    height = bar.get_height()\n",
+    "                    ax6.text(bar.get_x() + bar.get_width()/2., height + 5000,\n",
+    "                            f'{height/1000:.0f}K', ha='center', va='bottom', fontweight='bold')\n",
+    "            \n",
+    "            ax6.set_title('Brexit Impact: Pre vs Post Comparison', fontweight='bold')\n",
+    "            ax6.set_ylabel('Migration (Persons)')\n",
+    "            ax6.set_xticks(x)\n",
+    "            ax6.set_xticklabels(categories)\n",
+    "            ax6.legend()\n",
+    "            ax6.grid(True, alpha=0.3)\n",
+    "            ax6.axhline(y=0, color='red', linestyle='--', alpha=0.7)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "e3777f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "STATISTICAL ANALYSIS:\n",
+      "==================================================\n",
+      "Correlation Analysis:\n",
+      "  Non-EU+ vs Pop Change: 0.983 (Strong Positive)\n",
+      "  EU+ vs Pop Change: -0.846 (Strong Negative)\n",
+      "  British vs Pop Change: 0.680 (Moderate Positive)\n",
+      "\n",
+      "Volatility Analysis (Standard Deviation):\n",
+      "  Population Change: 309,347\n",
+      "  Non-EU+ Migration: 354,741\n",
+      "  EU+ Migration: 76,096\n",
+      "  British Migration: 31,406\n",
+      "\n",
+      "Trend Analysis (Average Annual Change):\n",
+      "  Population Change Trend: 148,556 per year\n",
+      "  Non-EU+ Migration Trend: 162,125 per year\n",
+      "  EU+ Migration Trend: -39,250 per year\n",
+      "  British Migration Trend: 13,938 per year\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Advanced statistical analysis\n",
+    "if len(combined_df) > 0:\n",
+    "    print(\"\\nSTATISTICAL ANALYSIS:\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    # Calculate correlation coefficients\n",
+    "    correlations = {}\n",
+    "    for col in available_migration_cols:\n",
+    "        corr = combined_df[col].corr(combined_df['annual_population_change'])\n",
+    "        correlations[f'{col} vs Pop Change'] = corr\n",
+    "    \n",
+    "    if 'Migration_Total_Calc' in combined_df.columns:\n",
+    "        correlations['Total Migration vs Pop Change'] = combined_df['Migration_Total_Calc'].corr(combined_df['annual_population_change'])\n",
+    "    \n",
+    "    print(\"Correlation Analysis:\")\n",
+    "    for relationship, corr in correlations.items():\n",
+    "        if pd.notna(corr):\n",
+    "            strength = \"Strong\" if abs(corr) > 0.7 else \"Moderate\" if abs(corr) > 0.4 else \"Weak\"\n",
+    "            direction = \"Positive\" if corr > 0 else \"Negative\"\n",
+    "            print(f\"  {relationship}: {corr:.3f} ({strength} {direction})\")\n",
+    "        else:\n",
+    "            print(f\"  {relationship}: Unable to calculate\")\n",
+    "    \n",
+    "    # Volatility analysis\n",
+    "    print(f\"\\nVolatility Analysis (Standard Deviation):\")\n",
+    "    pop_volatility = combined_df['annual_population_change'].std()\n",
+    "    print(f\"  Population Change: {pop_volatility:,.0f}\")\n",
+    "    \n",
+    "    for col in available_migration_cols:\n",
+    "        migration_volatility = combined_df[col].std()\n",
+    "        print(f\"  {col} Migration: {migration_volatility:,.0f}\")\n",
+    "    \n",
+    "    # Trend analysis\n",
+    "    print(f\"\\nTrend Analysis (Average Annual Change):\")\n",
+    "    if len(combined_df) > 1:\n",
+    "        years_span = combined_df['year'].max() - combined_df['year'].min()\n",
+    "        pop_trend = (combined_df['annual_population_change'].iloc[-1] - combined_df['annual_population_change'].iloc[0]) / years_span\n",
+    "        print(f\"  Population Change Trend: {pop_trend:,.0f} per year\")\n",
+    "        \n",
+    "        for col in available_migration_cols:\n",
+    "            migration_trend = (combined_df[col].iloc[-1] - combined_df[col].iloc[0]) / years_span\n",
+    "            print(f\"  {col} Migration Trend: {migration_trend:,.0f} per year\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "58d821f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create Brexit impact heatmap and detailed analysis\n",
+    "if len(combined_df) > 0 and len(available_migration_cols) >= 2:\n",
+    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "    \n",
+    "    # Heatmap of migration changes\n",
+    "    migration_data = combined_df[available_migration_cols].T\n",
+    "    migration_data.columns = [f'Year {int(year)}' for year in combined_df['year']]\n",
+    "    \n",
+    "    im = ax1.imshow(migration_data.values, cmap='RdBu_r', aspect='auto')\n",
+    "    ax1.set_xticks(range(len(migration_data.columns)))\n",
+    "    ax1.set_xticklabels(migration_data.columns)\n",
+    "    ax1.set_yticks(range(len(migration_data.index)))\n",
+    "    ax1.set_yticklabels(migration_data.index)\n",
+    "    ax1.set_title('Migration Patterns Heatmap', fontweight='bold')\n",
+    "    \n",
+    "    # Add text annotations\n",
+    "    for i in range(len(migration_data.index)):\n",
+    "        for j in range(len(migration_data.columns)):\n",
+    "            value = migration_data.iloc[i, j]\n",
+    "            ax1.text(j, i, f'{value/1000:.0f}K', ha='center', va='center', \n",
+    "                    color='white' if abs(value) > migration_data.values.std() else 'black',\n",
+    "                    fontweight='bold')\n",
+    "    \n",
+    "    plt.colorbar(im, ax=ax1, label='Migration (Persons)')\n",
+    "    \n",
+    "    # Brexit impact timeline\n",
+    "    if 'EU+' in available_migration_cols and 'Non-EU+' in available_migration_cols:\n",
+    "        ax2.plot(combined_df['year'], combined_df['EU+'], marker='o', \n",
+    "                linewidth=3, color='#4169E1', label='EU Migration', markersize=8)\n",
+    "        ax2.plot(combined_df['year'], combined_df['Non-EU+'], marker='s', \n",
+    "                linewidth=3, color='#2E8B57', label='Non-EU Migration', markersize=8)\n",
+    "        \n",
+    "        # Add Brexit timeline markers\n",
+    "        ax2.axvline(x=2016, color='orange', linestyle='--', alpha=0.7, linewidth=2, label='Brexit Referendum')\n",
+    "        ax2.axvline(x=2020, color='red', linestyle='--', alpha=0.7, linewidth=2, label='Brexit Transition End')\n",
+    "        \n",
+    "        # Add annotations\n",
+    "        ax2.annotate('Brexit\\nReferendum\\n(June 2016)', xy=(2016, ax2.get_ylim()[1]*0.8), \n",
+    "                    xytext=(2017, ax2.get_ylim()[1]*0.9), fontsize=9,\n",
+    "                    arrowprops=dict(arrowstyle='->', color='orange'))\n",
+    "        ax2.annotate('Brexit\\nTransition\\nEnds', xy=(2020, ax2.get_ylim()[1]*0.6), \n",
+    "                    xytext=(2021, ax2.get_ylim()[1]*0.7), fontsize=9,\n",
+    "                    arrowprops=dict(arrowstyle='->', color='red'))\n",
+    "        \n",
+    "        ax2.set_title('Brexit Timeline Impact on Migration', fontweight='bold')\n",
+    "        ax2.set_xlabel('Year')\n",
+    "        ax2.set_ylabel('Migration (Persons)')\n",
+    "        ax2.legend()\n",
+    "        ax2.grid(True, alpha=0.3)\n",
+    "        ax2.axhline(y=0, color='black', linestyle='-', alpha=0.3)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "b939ddff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "ENGLAND MIGRATION PATTERNS 2020-2024\n",
+      "================================================================================\n",
+      "\n",
+      "1. FUNDAMENTAL DRIVERS:\n",
+      "   • Primary cause: Brexit policy shift fundamentally altered migration rules\n",
+      "   • Economic principle: Labour demand remained constant whilst supply sources changed\n",
+      "   • Regulatory principle: Points-based system favoured skilled over EU workers\n",
+      "\n",
+      "2. EFFECTS:\n",
+      "   • EU migration change: -157,000 (substitution effect)\n",
+      "   • Non-EU migration change: 648,500 (compensation effect)\n",
+      "   • Skills composition shift: Higher qualifications, different sectors\n",
+      "   • Geographic redistribution: Different settlement patterns\n",
+      "\n",
+      "3. CONSEQUENCES:\n",
+      "   • Economic: Potential wage pressures in EU-dependent sectors\n",
+      "   • Social: Integration challenges with diverse migration sources\n",
+      "   • Infrastructure: Uneven regional pressures on services\n",
+      "   • Political: Immigration remains contentious policy issue\n",
+      "\n",
+      "4. POLICY IMPLICATIONS:\n",
+      "   • Migration drives 110% of population growth on average\n",
+      "   • Infrastructure capacity must align with migration policy\n",
+      "   • Skills matching becomes critical for economic productivity\n",
+      "   • Regional planning needs migration flow forecasting\n",
+      "\n",
+      "5. STRATEGIC RECOMMENDATIONS:\n",
+      "   • Monitor labour market mismatches in real-time\n",
+      "   • Develop flexible visa categories for demand-responsive migration\n",
+      "   • Invest in integration programmes for non-EU migrants\n",
+      "   • Create migration impact assessment framework\n",
+      "   • Build cross-departmental migration coordination mechanism\n",
+      "\n",
+      "6. QUANTITATIVE CONTEXT:\n",
+      "   • Average annual population growth: 497,779\n",
+      "   • Total 2020-2024 growth: 2,488,894\n",
+      "   • Migration dependency ratio: 110.1%\n",
+      "   • WARNING: High migration dependency. Policy changes have major impact\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Policy analysis and recommendations\n",
+    "if len(combined_df) > 0:\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"ENGLAND MIGRATION PATTERNS 2020-2024\")\n",
+    "    print(\"=\"*80)\n",
+    "    \n",
+    "    print(\"\\n1. FUNDAMENTAL DRIVERS:\")\n",
+    "    print(\"   • Primary cause: Brexit policy shift fundamentally altered migration rules\")\n",
+    "    print(\"   • Economic principle: Labour demand remained constant whilst supply sources changed\")\n",
+    "    print(\"   • Regulatory principle: Points-based system favoured skilled over EU workers\")\n",
+    "    \n",
+    "    print(\"\\n2. EFFECTS:\")\n",
+    "    if 'EU+' in available_migration_cols and 'Non-EU+' in available_migration_cols:\n",
+    "        eu_decline = combined_df['EU+'].iloc[-1] - combined_df['EU+'].iloc[0] if len(combined_df) > 1 else 0\n",
+    "        noneu_increase = combined_df['Non-EU+'].iloc[-1] - combined_df['Non-EU+'].iloc[0] if len(combined_df) > 1 else 0\n",
+    "        print(f\"   • EU migration change: {eu_decline:,.0f} (substitution effect)\")\n",
+    "        print(f\"   • Non-EU migration change: {noneu_increase:,.0f} (compensation effect)\")\n",
+    "        print(\"   • Skills composition shift: Higher qualifications, different sectors\")\n",
+    "        print(\"   • Geographic redistribution: Different settlement patterns\")\n",
+    "    \n",
+    "    print(\"\\n3. CONSEQUENCES:\")\n",
+    "    print(\"   • Economic: Potential wage pressures in EU-dependent sectors\")\n",
+    "    print(\"   • Social: Integration challenges with diverse migration sources\")\n",
+    "    print(\"   • Infrastructure: Uneven regional pressures on services\")\n",
+    "    print(\"   • Political: Immigration remains contentious policy issue\")\n",
+    "    \n",
+    "    print(\"\\n4. POLICY IMPLICATIONS:\")\n",
+    "    migration_share = comparison_df['Migration_vs_PopChange'].mean()\n",
+    "    print(f\"   • Migration drives {migration_share:.0f}% of population growth on average\")\n",
+    "    print(\"   • Infrastructure capacity must align with migration policy\")\n",
+    "    print(\"   • Skills matching becomes critical for economic productivity\")\n",
+    "    print(\"   • Regional planning needs migration flow forecasting\")\n",
+    "    \n",
+    "    print(\"\\n5. STRATEGIC RECOMMENDATIONS:\")\n",
+    "    print(\"   • Monitor labour market mismatches in real-time\")\n",
+    "    print(\"   • Develop flexible visa categories for demand-responsive migration\")\n",
+    "    print(\"   • Invest in integration programmes for non-EU migrants\")\n",
+    "    print(\"   • Create migration impact assessment framework\")\n",
+    "    print(\"   • Build cross-departmental migration coordination mechanism\")\n",
+    "    \n",
+    "    # Calculate key metrics for policy context\n",
+    "    if len(combined_df) > 1:\n",
+    "        total_change = total_pop_change\n",
+    "        avg_annual_change = total_change / len(combined_df)\n",
+    "        print(f\"\\n6. QUANTITATIVE CONTEXT:\")\n",
+    "        print(f\"   • Average annual population growth: {avg_annual_change:,.0f}\")\n",
+    "        print(f\"   • Total 2020-2024 growth: {total_change:,.0f}\")\n",
+    "        print(f\"   • Migration dependency ratio: {migration_share:.1f}%\")\n",
+    "        \n",
+    "        if migration_share > 80:\n",
+    "            print(\"   • WARNING: High migration dependency. Policy changes have major impact\")\n",
+    "        elif migration_share > 60:\n",
+    "            print(\"   • CAUTION: Moderate migration dependency. Requires careful management\")\n",
+    "        else:\n",
+    "            print(\"   • NOTE: Balanced growth pattern. Multiple contributing factors\")"
+   ]
+  }
+ ],
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+   "language": "python",
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+    "name": "ipython",
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