{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ERA5-Land Stündliche Daten\n", "\n", "ERA5-Land ist ein hochauflösendes Reanalyse-Datensatz, der eine konsistente und detaillierte Ansicht von Landvariablen über mehrere Jahrzehnte hinweg bietet. Durch die Kombination von Modelldaten mit atmosphärischer Antriebskraft aus ERA5 wird eine hohe Genauigkeit sichergestellt. Durch die Korrektur von Eingangsvariablen für Höhenunterschiede und die Nutzung indirekter Beobachtungseinflüsse bietet ERA5-Land eine verbesserte Präzision für Anwendungen im Bereich der Landoberflächenanalyse, wie z. B. Hochwasser- und Dürrevorhersagen. Trotz gewisser Unsicherheiten macht die umfangreiche zeitliche und räumliche Auflösung ERA5-Land zu einer wertvollen Ressource für Entscheidungsfindung und Umweltanalysen.\n", "\n", "**Informationen zum Datensatz:**\n", "* Quelle: [ERA5-Land Hourly Data](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview')\n", "* Author: str.ucture GmbH\n", "* Notebook-Version: 1.2 (Aktualisiert: März 05, 2025)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Festlegen der Pfade und Arbeitsverzeichnisse" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "''' ---- Verzeichnisse hier angeben ---- '''\n", "download_folder = r\".\\data\\era5-land-hourly-data\\download\"\n", "working_folder = r\".\\data\\era5-land-hourly-data\\working\"\n", "geotiff_folder = r\".\\data\\era5-land-hourly-data\\geotiff\"\n", "csv_folder = r\".\\data\\era5-land-hourly-data\\csv\"\n", "output_folder = r\".\\data\\era5-land-hourly-data\\output\"\n", "''' ----- Ende der Angaben ---- '''\n", "\n", "os.makedirs(download_folder, exist_ok=True)\n", "os.makedirs(working_folder, exist_ok=True)\n", "os.makedirs(geotiff_folder, exist_ok=True)\n", "os.makedirs(csv_folder, exist_ok=True)\n", "os.makedirs(output_folder, exist_ok=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Herunterladen und Entpacken des Datensatzes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 Authentifizierung" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import cdsapi\n", "\n", "def main():\n", " # API-Key für die Authentifizierung\n", " api_key = \"fdae60fd-35d4-436f-825c-c63fedab94a4\"\n", " api_url = \"https://cds.climate.copernicus.eu/api\"\n", "\n", " # Erstellung des CDS-API-Clients\n", " client = cdsapi.Client(url=api_url, key=api_key)\n", " return client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Definieren Sie die „request“ und laden Sie den Datensatz herunter" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "27814f47066445f8abd3fc208f368a57", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dropdown(description='Wähle eine Variablengruppe', layout=Layout(width='50%'), options=('var_group_temperature…" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ipywidgets as widgets\n", "import _utils.extra_era5_land_hourly as utils\n", "\n", "var_group_name_list = utils.var_group_name_list\n", "var_group_dict = utils.var_group_dict\n", "\n", "selected_variable_group = widgets.Dropdown(\n", " options = var_group_name_list,\n", " value = var_group_name_list[0],\n", " description = \"Wähle eine Variablengruppe\",\n", " style = dict(description_width='initial'),\n", " layout = widgets.Layout(width='50%'),\n", ")\n", "\n", "selected_variable_group" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2f457a4f32b445c0926b0afeee71dfa7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dropdown(description='Wähle die gewünschte Variable', index=1, layout=Layout(width='50%'), options=('2m_dewpoi…" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "current_variable_group = var_group_dict[selected_variable_group.value]\n", "\n", "selected_variable = widgets.Dropdown(\n", " options=current_variable_group,\n", " value=current_variable_group[1],\n", " description=\"Wähle die gewünschte Variable\",\n", " style=dict(description_width='initial'),\n", " layout=widgets.Layout(width='50%'),\n", ")\n", "\n", "selected_variable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Definiere das \"Jahr\" zum Herunterladen\n", "\n", "> Hinweis: Für das ausgewählte **Jahr** sind alle Monate (Januar bis Dezember), Tage (1 bis 30/31) und Stunden (00:00 bis 23:00) im \"request\"-Parameter angegeben. Ändere diese Einstellung, um die Dateigröße zu reduzieren oder einen spezifischen Datensatz herunterzuladen." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bd9c9699ea0f46ffb14fdd9f97f666cd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dropdown(description='Wähle das Jahr zum Herunterladen der Daten:', index=74, layout=Layout(width='50%'), opti…" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from datetime import datetime\n", "\n", "selected_year = widgets.Dropdown(\n", " options=[str(year) for year in range(1950, 2024+1)],\n", " value=str(2024),\n", " description=\"Wähle das Jahr zum Herunterladen der Daten:\",\n", " disabled=False,\n", " style=dict(description_width='initial'),\n", " layout=widgets.Layout(width='50%'),\n", ")\n", "\n", "selected_year" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Define Bounding Box Extents (Bbox)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Definieren der Begrenzungsrahmen-Koordinaten (WGS84-Format)\n", "# Das Koordinatenformat lautet: [Norden, Westen, Süden, Osten]\n", "bbox_wgs84_deutschland = [56.0, 5.8, 47.2, 15.0]\n", "bbox_wgs84_de_standard = [5.7, 47.1, 15.2, 55.2]\n", "bbox_wgs84_konstanz = [47.9, 8.9, 47.6, 9.3]\n", "bbox_wgs84_konstanz_standard = [9.0, 47.6, 9.3, 47.8] # [West, South, East, North]\n", "\n", "# Alternativ können Sie ein Shapefile für eine präzise geografische Filterung verwenden\n", "import geopandas as gpd\n", "import math\n", "\n", "# Beispiel: Shapefile von Konstanz laden (WGS84-Projektion)\n", "de_shapefile = r\"./shapefiles/de_boundary.shp\"\n", "de_gdf = gpd.read_file(de_shapefile)\n", "\n", "# Extrahieren Sie den Begrenzungsrahmen des Shapefiles\n", "de_bounds = de_gdf.total_bounds\n", "\n", "# Passen Sie den Begrenzungsrahmen an und puffern Sie ihn, um einen etwas größeren\n", "de_bounds_adjusted = [(math.floor(de_bounds[0]* 10)/10)-0.1,\n", " (math.floor(de_bounds[1]* 10)/10)-0.1,\n", " (math.ceil(de_bounds[2]* 10)/10)+0.1,\n", " (math.ceil(de_bounds[3]* 10)/10)+0.1]\n", "\n", "# Ordnen Sie die Koordinaten in das Format: [Nord, West, Süd, Ost] um.\n", "bbox_de_bounds_adjusted = [de_bounds_adjusted[3], de_bounds_adjusted[0],\n", " de_bounds_adjusted[1], de_bounds_adjusted[2]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 Definiere \"Datensatz\" und \"Anfrage\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Definition des Datensatzes und der Request-Parameter\n", "dataset = \"reanalysis-era5-land\"\n", "request = {\n", " \"variable\": selected_variable.value,\n", " \"year\": selected_year.value,\n", " \"month\": [str(month) for month in range(13)],\n", " \"day\": [str(day) for day in range(32)],\n", " \"time\": [f\"{hour:02d}:00\" for hour in range(24)],\n", " \"data_format\": \"netcdf\",\n", " \"download_format\": \"unarchived\",\n", " \"area\": bbox_de_bounds_adjusted\n", "}" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datensatz bereits heruntergeladen.\n" ] } ], "source": [ "download_folder_subset = os.path.join(download_folder, f\"{selected_variable.value}\")\n", "os.makedirs(download_folder_subset, exist_ok=True)\n", "\n", "# Führen Sie es aus, um den Datensatz herunterzuladen:\n", "def main_retrieve():\n", " dataset_filename = f\"{dataset}-{selected_variable.value}-{selected_year.value}.nc\"\n", " dataset_filepath = os.path.join(download_folder_subset, dataset_filename)\n", "\n", " # Den Datensatz nur herunterladen, wenn er noch nicht heruntergeladen wurde\n", " if not os.path.isfile(dataset_filepath):\n", " # Rufen Sie den CDS-Client nur auf, wenn der Datensatz noch nicht heruntergeladen wurde.\n", " client = main()\n", " # Den Datensatz mit den definierten Anforderungsparametern herunterladen\n", " client.retrieve(dataset, request, dataset_filepath)\n", " else:\n", " print(\"Datensatz bereits heruntergeladen.\")\n", "\n", "if __name__ == \"__main__\":\n", " main_retrieve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Entpacke die ZIP-Datei im Ordner\n", "\n", "> Hinweis: Da der Datensatz für eine einzelne Variable heruntergeladen wird, wird nur eine NetCDF-Datei heruntergeladen, und das CDS erstellt keine ZIP-Datei für eine einzelne Variable im NetCDF-Format." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# import zipfile\n", "\n", "# Definieren Sie einen Extraktionsordner für die ZIP-Datei, der dem Arbeitsordner entspricht\n", "# extract_folder = working_folder\n", "\n", "# # Extract the ZIP file\n", "# try: \n", "# if not os.listdir(extract_folder):\n", "# dataset_filename = f\"{dataset}.zip\"\n", "# dataset_filepath = os.path.join(download_folder, dataset_filename)\n", "\n", "# with zipfile.ZipFile(dataset_filepath, 'r') as zip_ref:\n", "# zip_ref.extractall(extract_folder)\n", "# print(f\"Dateien erfolgreich extrahiert nach: {extract_folder}\")\n", "# else:\n", "# print(\"Ordner ist nicht leer. Entpacken überspringen.\")\n", "# except FileNotFoundError:\n", "# print(f\"Fehler: Die Datei {dataset_filepath} wurde nicht gefunden.\")\n", "# except zipfile.BadZipFile:\n", "# print(f\"Fehler: Die Datei {dataset_filepath} ist keine gültige ZIP-Datei.\")\n", "# except Exception as e:\n", "# print(f\"Ein unerwarteter Fehler ist aufgetreten: {e}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Untersuchen der Metadaten der NetCDF4-Datei" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Erstellen eines DataFrame mit verfügbaren NetCDF-Dateien" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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filenamedatasetds_variablevariable_nameyear
0reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1950
1reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1951
2reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1952
3reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1953
4reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1954
5reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1955
6reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1956
7reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1957
8reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1958
9reanalysis-era5-land...reanalysis-era5-land2m_temperaturet2m1959
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" ], "text/plain": [ " filename dataset ds_variable \\\n", "0 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "1 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "2 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "3 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "4 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "5 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "6 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "7 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "8 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "9 reanalysis-era5-land... reanalysis-era5-land 2m_temperature \n", "\n", " variable_name year \n", "0 t2m 1950 \n", "1 t2m 1951 \n", "2 t2m 1952 \n", "3 t2m 1953 \n", "4 t2m 1954 \n", "5 t2m 1955 \n", "6 t2m 1956 \n", "7 t2m 1957 \n", "8 t2m 1958 \n", "9 t2m 1959 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "import pandas as pd\n", "import netCDF4 as nc\n", "\n", "def meta(filename):\n", " # Überprüfen, ob der Dateiname dem erwarteten Muster entspricht\n", " match = re.search(r\"(?Preanalysis-era5-land)-(?P\\d+m_[a-z_]+)-(?P\\d{4})\",filename)\n", "\n", " # Fehler ausgeben, wenn der Dateiname nicht dem erwarteten Schema entspricht\n", " if not match:\n", " match = re.search(\"Der angegebene Dateiname entspricht nicht dem erwarteten Benennungsschema.\")\n", " \n", " # Funktion zum Extrahieren des Variablennamens aus der NetCDF-Datei\n", " def get_nc_variable():\n", " with nc.Dataset(os.path.join(download_folder_subset, filename), 'r') as nc_dataset:\n", " nc_variable_name = nc_dataset.variables.keys()\n", " return [*nc_variable_name][5]\n", "\n", " # Metadaten als Dictionary zurückgeben\n", " return dict(\n", " filename=filename,\n", " path=os.path.join(download_folder_subset, filename),\n", " # index=match.group('index'),\n", " dataset=match.group('dataset'),\n", " ds_variable=match.group('ds_variable'),\n", " variable_name=get_nc_variable(),\n", " year=match.group('year')\n", " )\n", "\n", "# Metadaten für alle NetCDF-Dateien im Ordner extrahieren\n", "# Das Dictionary 'nc_files' enthält alle relevanten Metadaten der verfügbaren NetCDF4-Dateien\n", "# Dieses Dictionary wird später verwendet, um die Dateien in GeoTiff zu konvertieren\n", "nc_files = [meta(f) for f in os.listdir(download_folder_subset) if f.endswith('.nc')]\n", "nc_files = sorted(nc_files, key=lambda x: x['year']) # Nach Jahr sortieren\n", "df_nc_files = pd.DataFrame.from_dict(nc_files)\n", "\n", "# Pandas-Anzeigeoptionen anpassen\n", "pd.options.display.max_colwidth = 24\n", "\n", "# DataFrame anzeigen, ohne die Spalte 'path' darzustellen\n", "df_nc_files.head(10).loc[:, df_nc_files.columns != 'path']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Einzigartige Variablennamen und verfügbare Variablen ausgeben" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 t2m : Verfügbare Variablen: ['number', 'valid_time', 'latitude', 'longitude', 'expver', 't2m']\n" ] } ], "source": [ "# Variable definieren, um bereits verarbeitete Variablennamen zu speichern und Duplikate zu vermeiden \n", "seen_variables = set()\n", "\n", "# Alle Variablen in jeder NetCDF-Datei auflisten\n", "for i, nc_file in enumerate(nc_files):\n", " variable_name = nc_file['variable_name']\n", " \n", " # Überspringen, wenn die Variable bereits verarbeitet wurde\n", " if variable_name in seen_variables:\n", " continue\n", "\n", " # NetCDF-Datei im Lesemodus öffnen\n", " with nc.Dataset(nc_file['path'], mode='r') as nc_dataset:\n", " # Alle Variablen im aktuellen Datensatz auflisten\n", " variables_list = list(nc_dataset.variables.keys())\n", " \n", " # Details der Datei und ihrer Variablen ausgeben\n", " print(f\"{i + 1:<2} {variable_name:<18}: Verfügbare Variablen: {variables_list}\") \n", " \n", " # Diese Variable als verarbeitet markieren\n", " seen_variables.add(variable_name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Zusammenfassung der Variable 't2m':\n" ] }, { "data": { "text/html": [ "
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BeschreibungBemerkungen
0Variablennamet2m
1Datentypfloat32
2Form(8759, 82, 96)
3Variableninfo('valid_time', 'lati...
4EinheitenK
5Langer Name2 metre temperature
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" ], "text/plain": [ " Beschreibung Bemerkungen\n", "0 Variablenname t2m\n", "1 Datentyp float32\n", "2 Form (8759, 82, 96)\n", "3 Variableninfo ('valid_time', 'lati...\n", "4 Einheiten K\n", "5 Langer Name 2 metre temperature" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Alle Variableninformationen in jeder NetCDF-Datei auflisten\n", "seen_variables = set()\n", "\n", "# Alle variablen Informationen in jeder NetCDF-Datei auflisten\n", "for i, nc_file in enumerate(nc_files):\n", " variable_name = nc_file['variable_name']\n", " \n", " # Überspringen, wenn die Variable bereits verarbeitet wurde\n", " if variable_name in seen_variables:\n", " continue\n", " \n", " # NetCDF-Datei im Lesemodus öffnen\n", " with nc.Dataset(nc_file['path'], mode='r') as nc_dataset:\n", " # Primärvariable-Daten abrufen\n", " variable_data = nc_dataset[variable_name]\n", "\n", " # Zusammenfassung der Primärvariable erstellen\n", " summary = {\n", " \"Variablenname\": variable_name,\n", " \"Datentyp\": variable_data.dtype,\n", " \"Form\": variable_data.shape,\n", " \"Variableninfo\": f\"{variable_data.dimensions}\",\n", " \"Einheiten\": getattr(variable_data, \"units\", \"N/A\"),\n", " \"Langer Name\": getattr(variable_data, \"long_name\", \"N/A\"),\n", " } \n", "\n", " # Datensatz-Zusammenfassung als DataFrame zur besseren Visualisierung anzeigen\n", " nc_summary = pd.DataFrame(list(summary.items()), columns=['Beschreibung', 'Bemerkungen'])\n", " print(f\"{i + 1}. Zusammenfassung der Variable '{variable_name}':\")\n", " display(nc_summary)\n", "\n", " # Variablenname zur Liste der bereits verarbeiteten Variablen hinzufügen\n", " seen_variables.add(variable_name)\n", "\n", " # Ausgabe begrenzen\n", " output_limit = 2\n", " if len(seen_variables) >= output_limit:\n", " print(f\".... (Ausgabe auf die ersten {output_limit} Variablen gekürzt)\")\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Exportieren der NetCDF4-Dateien im CSV-Format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Definieren eine Funktion zum Konvertieren von NetCDF-Daten in DataFrame" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "\n", "# Funktion zur Konvertierung von NetCDF-Daten in ein Pandas DataFrame\n", "def netcdf_to_dataframe(\n", " nc_file,\n", " bounding_box=None):\n", "\n", " # Öffne das NetCDF-Dataset im Lesemodus\n", " with xr.open_dataset(nc_file['path']) as nc_dataset:\n", " # Zugriff auf die Variablendaten aus dem Datensatz\n", " variable_data = nc_dataset[nc_file['variable_name']]\n", " \n", " # Sicherstellen, dass die Namen für Breiten- und Längengrad korrekt sind\n", " latitude_name = 'latitude' if 'latitude' in nc_dataset.coords else 'lat'\n", " longitude_name = 'longitude' if 'longitude' in nc_dataset.coords else 'lon'\n", " \n", " # Falls eine Begrenzungsbox angegeben ist, die Daten filtern\n", " if bounding_box:\n", " filtered_data = variable_data.where(\n", " (nc_dataset[latitude_name] >= bounding_box[1]) & (nc_dataset[latitude_name] <= bounding_box[3]) &\n", " (nc_dataset[longitude_name] >= bounding_box[0]) & (nc_dataset[longitude_name] <= bounding_box[2]),\n", " drop=True\n", " )\n", " else:\n", " filtered_data = variable_data\n", "\n", " # Umwandlung des xarray-Datensatzes in ein Pandas DataFrame\n", " df = filtered_data.to_dataframe().reset_index()\n", "\n", " # Entfernen nicht benötigter Spalten (variiert je nach Datensatz)\n", " if 'height' in df.columns:\n", " df = df.drop(columns=['number'])\n", " if 'quantile' in df.columns:\n", " df = df.drop(columns=['expver'])\n", " \n", " # Valid_time in Datum und Uhrzeit aufteilen\n", " df['valid_time'] = pd.to_datetime(df['valid_time'])\n", " df['date'] = df['valid_time'].dt.date\n", " df['time'] = df['valid_time'].dt.time\n", " df = df.set_index(['date', 'time', latitude_name, longitude_name])\n", " \n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Nach Begrenzungsrahmen filtern, DataFrame erstellen und als CSV-Datei exportieren" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datei existiert bereits unter .\\data\\era5-land-hourly-data\\csv\\2m_temperature\\t2m-1950.csv.\n", "Überspringen den Export.\n", "Letzte vorhandene CSV-Datei lesen...\n" ] }, { "data": { "text/html": [ "
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valid_timenumberexpvert2m
datetimelatitudelongitude
1950-01-0101:00:0047.89.11950-01-01 01:00:0001270.76500
9.21950-01-01 01:00:0001270.69080
9.31950-01-01 01:00:0001270.63416
47.79.11950-01-01 01:00:0001271.12048
9.21950-01-01 01:00:0001271.46814
........................
1950-12-3123:00:0047.89.21950-12-31 23:00:0001267.41724
9.31950-12-31 23:00:0001267.42114
47.79.11950-12-31 23:00:0001267.54810
9.21950-12-31 23:00:0001267.38208
9.31950-12-31 23:00:0001267.46216
\n", "

52554 rows × 4 columns

\n", "
" ], "text/plain": [ " valid_time number expver \\\n", "date time latitude longitude \n", "1950-01-01 01:00:00 47.8 9.1 1950-01-01 01:00:00 0 1 \n", " 9.2 1950-01-01 01:00:00 0 1 \n", " 9.3 1950-01-01 01:00:00 0 1 \n", " 47.7 9.1 1950-01-01 01:00:00 0 1 \n", " 9.2 1950-01-01 01:00:00 0 1 \n", "... ... ... ... \n", "1950-12-31 23:00:00 47.8 9.2 1950-12-31 23:00:00 0 1 \n", " 9.3 1950-12-31 23:00:00 0 1 \n", " 47.7 9.1 1950-12-31 23:00:00 0 1 \n", " 9.2 1950-12-31 23:00:00 0 1 \n", " 9.3 1950-12-31 23:00:00 0 1 \n", "\n", " t2m \n", "date time latitude longitude \n", "1950-01-01 01:00:00 47.8 9.1 270.76500 \n", " 9.2 270.69080 \n", " 9.3 270.63416 \n", " 47.7 9.1 271.12048 \n", " 9.2 271.46814 \n", "... ... \n", "1950-12-31 23:00:00 47.8 9.2 267.41724 \n", " 9.3 267.42114 \n", " 47.7 9.1 267.54810 \n", " 9.2 267.38208 \n", " 9.3 267.46216 \n", "\n", "[52554 rows x 4 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Erstelle einen Ordner zum Speichern der Teilmengen-CSV-Dateien basierend auf der ausgewählten Variable\n", "subset_csv_folder = os.path.join(csv_folder, f\"{selected_variable.value}\")\n", "os.makedirs(subset_csv_folder, exist_ok=True)\n", "\n", "# Exportiere alle netCDF4-Dateien als einzelne CSV-Dateien\n", "for nc_file in nc_files:\n", " # CSV-Dateiname und Pfad für die Ausgabe definieren\n", " csv_filename = f\"{nc_file['variable_name']}-{nc_file['year']}.csv\"\n", " csv_filepath = os.path.join(subset_csv_folder, csv_filename)\n", "\n", " # Exportiere das DataFrame als CSV, falls es noch nicht existiert\n", " if not os.path.isfile(csv_filepath):\n", " dataframe = netcdf_to_dataframe(nc_file, bounding_box=bbox_wgs84_konstanz_standard)\n", " dataframe.to_csv(csv_filepath, sep=',', encoding='utf8')\n", " else:\n", " print(f\"Datei existiert bereits unter {csv_filepath}.\\nÜberspringen den Export.\")\n", " break\n", "\n", "print(\"Letzte vorhandene CSV-Datei lesen...\")\n", "dataframe = pd.read_csv(csv_filepath).set_index(['date', 'time', 'latitude', 'longitude'])\n", "\n", "# Zeige das DataFrame an\n", "dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Exportieren der NetCDF4-Datei nach GeoTIFF" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.1 Define a Function to export the NetCDF4 file as GeoTIFF File(s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from rasterio.transform import from_origin\n", "import rasterio\n", "import sys\n", "\n", "from tqdm.notebook import tqdm\n", "\n", "def main_export_geotiff(\n", " nc_file,\n", " bounding_box=None,\n", " start_year=None,\n", " end_year=None,\n", " merged=None,\n", " output_directory=None):\n", " \n", " \"\"\"\n", " Parameter:\n", " nc_file (dict): Ein Dictionary mit den Schlüsseln 'path' (Dateipfad), 'variable'...\n", " bounding_box (list): [lon_min, lat_min, lon_max, lat_max] (optional).\n", " start_year (int): Startjahr für das Dataset (optional).\n", " end_year (int): Endjahr für das Dataset (optional).\n", " merged (bool): Gibt an, ob ein zusammengeführtes GeoTIFF oder einzelne GeoTIFFs erstellt werden sollen (optional).\n", " output_directory (str): Verzeichnis zum Speichern der Ausgabe-GeoTIFF-Dateien (optional).\n", " \"\"\"\n", " \n", " # Öffnet die NetCDF-Datei\n", " with nc.Dataset(nc_file['path'], 'r') as nc_dataset:\n", " nc_dataset = nc.Dataset(nc_file['path'], 'r')\n", " lon = nc_dataset['longitude'][:]\n", " lat = nc_dataset['latitude'][:]\n", " \n", " # Falls eine Begrenzungsbox angegeben wurde, filtere die Daten entsprechend\n", " if bounding_box:\n", " lon_min, lat_min, lon_max, lat_max = bounding_box\n", " \n", " indices_lat = np.where((lat >= lat_min) & (lat <= lat_max))[0]\n", " indices_lon = np.where((lon >= lon_min) & (lon <= lon_max))[0]\n", " start_lat, end_lat = indices_lat[0], indices_lat[-1] + 1\n", " start_lon, end_lon = indices_lon[0], indices_lon[-1] + 1\n", " else:\n", " start_lat, end_lat = 0, len(lat)\n", " start_lon, end_lon = 0, len(lon)\n", " \n", " lat = lat[start_lat:end_lat]\n", " lon = lon[start_lon:end_lon]\n", " \n", " # Extrahiere die Zeitvariable und konvertiere sie in lesbare Datumsangaben\n", " time_var = nc_dataset.variables['valid_time']\n", " time_units = time_var.units\n", " time_calendar = getattr(time_var, \"calendar\", \"standard\")\n", " cftime = nc.num2date(time_var[:], units=time_units, calendar=time_calendar)\n", " \n", " # Berechnet die räumliche Auflösung und die Rastertransformation\n", " dx = abs(lon[1] - lon[0])\n", " dy = abs(lat[1] - lat[0])\n", " transform = from_origin(lon.min() - dx / 2, lat.max() + dy / 2, dx, dy)\n", " # Hinweis: Die in diesem Code verwendete Transformation unterscheidet sich von anderen Datensätzen\n", "\n", " # Extrahiere Variablen-Daten\n", " variable_data = nc_dataset.variables[nc_file['variable_name']]\n", " variable_data_subset = variable_data[..., start_lat:end_lat, start_lon:end_lon]\n", " \n", " if merged:\n", " # Erstellt ein zusammengeführtes GeoTIFF mit allen Zeitscheiben als separate Bänder\n", " if output_directory:\n", " subset_directory_path = output_directory\n", " else:\n", " subset_directory_path = os.path.join(geotiff_folder, f\"{selected_variable.value}-merged\")\n", " os.makedirs(subset_directory_path, exist_ok=True)\n", "\n", " # Pfad der Ausgabedatei festlegen\n", " output_filename = f\"{nc_file['filename'].replace('.nc','')}.tif\"\n", " output_filepath = os.path.join(subset_directory_path, output_filename)\n", "\n", " # Erstellt eine GeoTIFF-Datei mit mehreren Bändern für jede Zeitscheibe\n", " with rasterio.open(\n", " output_filepath,\n", " \"w\",\n", " driver = \"GTiff\",\n", " dtype = str(variable_data_subset.dtype),\n", " width = variable_data_subset.shape[2],\n", " height = variable_data_subset.shape[1],\n", " count = variable_data_subset.shape[0],\n", " crs = \"EPSG:4326\",\n", " nodata = -9999,\n", " transform=transform,\n", " ) as dst:\n", " for time_index in tqdm(range(variable_data_subset.shape[0]),\n", " desc=f\"Exportiere zusammengeführte GeoTIFF-Datei für {nc_file['year']}\"): \n", " band_data = variable_data_subset[time_index,:,:]\n", " band_desc = str(cftime[time_index])\n", " \n", " # Schreibe jede Zeitscheibe als Band\n", " dst.write(band_data, time_index + 1)\n", " dst.set_band_description(time_index + 1, band_desc)\n", " \n", " else:\n", " # Export als einzelne GeoTIFF-Dateien\n", " if output_directory:\n", " subset_directory_path = output_directory\n", " else:\n", " subset_directory_path = os.path.join(geotiff_folder,\n", " f\"{selected_variable.value}-individual\",\n", " f\"{nc_file['year']}\")\n", " os.makedirs(subset_directory_path, exist_ok=True)\n", "\n", " for time_index in tqdm(range(variable_data_subset.shape[0]),\n", " desc=\"Exportieren einzelner GeoTIFF-Dateien\"):\n", " # Bestimmt das Datum für die aktuelle Zeitscheibe\n", " band_desc = str(cftime[time_index])\n", "\n", " # Definiert den Speicherort der Ausgabe-GeoTIFF-Datei\n", " output_filename = f\"{nc_file['filename'].replace('.nc','')}-{band_desc.replace(' ','').replace(':','-')}.tif\"\n", " output_filepath = os.path.join(subset_directory_path, output_filename)\n", "\n", " # Exportiert die aktuelle Zeitscheibe als GeoTIFF\n", " with rasterio.open(\n", " output_filepath,\n", " \"w\",\n", " driver=\"GTiff\",\n", " dtype=str(variable_data_subset.dtype),\n", " width=variable_data_subset.shape[2],\n", " height=variable_data_subset.shape[1],\n", " count=1,\n", " crs=\"EPSG:4326\",\n", " nodata=-9999,\n", " transform=transform,\n", " ) as dst:\n", " band_data = variable_data_subset[time_index,:,:]\n", " \n", " dst.write(band_data, 1)\n", " dst.set_band_description(1, band_desc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Der Ordner ist nicht leer. Überspringe die Konvertierung.\n" ] } ], "source": [ "if __name__ == \"__main__\":\n", " # Exportiere alle NetCDF-Dateien als zusammengeführte GeoTIFF-Datei\n", " # Auf True setzen, um alle NetCDF-Dateien zu konvertieren, oder auf False, um nur zwei Dateien zum Testen zu konvertieren\n", " convert_all = False\n", "\n", " for i, nc_file in enumerate(nc_files):\n", " main_export_geotiff(nc_file=nc_file, bounding_box=None, merged=True)\n", "\n", " if not convert_all and i >= 1:\n", " print(\"Testkonvertierung abgeschlossen: 2 Dateien erfolgreich konvertiert.\\nBeende Konvertierung.\")\n", " break\n", "\n", " # # Zusätzlicher Fall: Exportiere alle NetCDF-Dateien als einzelne GeoTIFF-Dateien\n", " # # Hinweis: Aufgrund der großen Zeitschrittanzahl (In den meisten Fällen sind 365*24 Zeitschritte pro Datensatz verfügbar),\n", " # # wird empfohlen, einzelne GeoTIFF-Dateien nur bei Bedarf zu exportieren.\n", " # # Der folgende Code exportiert die NetCDF-Datei als GeoTIFF für die erste verfügbare Datenreihe, d.h. Jahr=1950\n", " # for nc_file in nc_files[:1]:\n", " # continue_conversion = main_export_geotiff(nc_file=nc_file, bounding_box=None, merged=False)\n", " # if not continue_conversion:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Analyse und Visualisierung Optionen" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.1 Definieren eine Funktion zur Erstellung einer Heatmap" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.feature as cfeature\n", "import cartopy.crs as ccrs\n", "import numpy as np\n", "import cftime\n", "\n", "def main_plt_plot(\n", " year=None,\n", " month=None,\n", " day=None,\n", " hour_of_day=None,\n", " bounding_box=None):\n", " \n", " # Definiere den Dateipfad basierend auf der ausgewählten Variable und dem Jahr\n", " filename = f\"{dataset}-{selected_variable.value}-{year}.nc\"\n", " filepath = os.path.join(download_folder_subset, filename)\n", "\n", " # Öffnet die NetCDF-Datei\n", " with nc.Dataset(filepath, mode='r') as nc_dataset:\n", " latitudes = nc_dataset.variables['latitude'][:]\n", " longitudes = nc_dataset.variables['longitude'][:]\n", "\n", " # Falls eine Begrenzungsbox angegeben wurde, filtere die Daten entsprechend\n", " if bounding_box:\n", " lat_indices = np.where((latitudes >= bounding_box[1]) & (latitudes <= bounding_box[3]))[0]\n", " lon_indices = np.where((longitudes >= bounding_box[0]) & (longitudes <= bounding_box[2]))[0]\n", "\n", " lat_subset = latitudes[lat_indices]\n", " lon_subset = longitudes[lon_indices]\n", " else:\n", " lat_indices = slice(None)\n", " lon_indices = slice(None)\n", "\n", " lat_subset = latitudes\n", " lon_subset = longitudes\n", "\n", " # Konvertiere die Variable valid_time in cftime-Objekte\n", " time_var = nc_dataset.variables['valid_time']\n", " time_units = time_var.units\n", " time_calendar = getattr(time_var, \"calendar\", \"standard\")\n", " cftime_values = nc.num2date(time_var[:], units=time_units, calendar=time_calendar)\n", "\n", " selected_time = cftime.DatetimeProlepticGregorian(year, month, day, hour_of_day, 0, 0, 0, has_year_zero=True)\n", " time_index = np.where(cftime_values == selected_time)[0]\n", "\n", " # Extrahiere Variablen-Daten\n", " nc_variable_name = nc_dataset.variables.keys()\n", " variable_name = [*nc_variable_name][5]\n", " variable_data = nc_dataset[variable_name][..., lat_indices, lon_indices]-273.15\n", " var_units = getattr(nc_dataset.variables[variable_name], \"units\", \"N/A\")\n", " var_longname = getattr(nc_dataset.variables[variable_name], \"long_name\", \"N/A\")\n", "\n", " # NaN-Werte für Perzentilberechnungen entfernen\n", " band_data_nonan = variable_data[~np.isnan(variable_data)]\n", " vmin = np.nanpercentile(band_data_nonan, 1)\n", " vmax = np.nanpercentile(band_data_nonan, 99)\n", " \n", " def dynamic_round(value):\n", " # Bestimmen Sie die Größe des Wertes.\n", " order_of_magnitude = np.floor(np.log10(abs(value)))\n", " \n", " # Verwenden Sie diese Größe, um die Genauigkeit dynamisch zu wählen.\n", " if order_of_magnitude < -2: # Werte kleiner als 0,01\n", " return round(value, 3)\n", " elif order_of_magnitude < -1: # Werte zwischen 0,01 und 1\n", " return round(value, 2)\n", " elif order_of_magnitude < 0: # Werte zwischen 1 und 10\n", " return round(value, 1)\n", " else: # Werte 10 oder größer\n", " return round(value)\n", " \n", " # Dynamische Rundung auf vmin und vmax anwenden\n", " vmin = dynamic_round(vmin)\n", " vmax = dynamic_round(vmax)\n", "\n", " bins = 10\n", " interval = (vmax - vmin) / bins\n", "\n", " # Erstellen Sie ein 2D-Netzgitter für die grafische Darstellung.\n", " lon_grid, lat_grid = np.meshgrid(lon_subset, lat_subset)\n", "\n", " # Erstelle die Figur\n", " fig, ax = plt.subplots(\n", " figsize=(12, 8),\n", " facecolor='#f1f1f1',\n", " edgecolor='k',\n", " subplot_kw={'projection': ccrs.PlateCarree()}\n", " )\n", "\n", " # Kartenmerkmale hinzufügen\n", " ax.coastlines(edgecolor='black', linewidth=0.5)\n", " ax.add_feature(cfeature.BORDERS, edgecolor='black', linewidth=0.5)\n", "\n", " # Erstelle ein Colormesh-Plot\n", " cmap = plt.get_cmap(\"viridis\", bins)\n", " pcm = ax.pcolormesh(\n", " lon_grid, lat_grid, variable_data[0, :, :],\n", " transform=ccrs.PlateCarree(),\n", " cmap=cmap,\n", " shading='auto',\n", " vmin=vmin,\n", " vmax=vmax\n", " )\n", "\n", " # Passe die Kartenausdehnung an die Daten an\n", " ax.set_extent([lon_subset.min(), lon_subset.max(), lat_subset.min(), lat_subset.max()], crs=ccrs.PlateCarree())\n", "\n", " # Einen Farbbalken hinzufügen\n", " ticks = np.linspace(vmin, vmax, num=bins + 1)\n", " cbar = plt.colorbar(pcm, ax=ax, orientation='vertical', pad=0.02, ticks=ticks)\n", " cbar.set_label(f\"{var_longname}, ({variable_name})\", fontsize=12)\n", " cbar.ax.tick_params(labelsize=12)\n", " \n", " # Gitterlinien hinzufügen\n", " gl = ax.gridlines(draw_labels=True,\n", " crs=ccrs.PlateCarree(),\n", " linewidth=0.8,\n", " color='gray',\n", " alpha=0.7,\n", " linestyle='--')\n", " gl.top_labels = False \n", " gl.right_labels = False\n", " gl.xlabel_style = {'size': 10, 'color': 'black'}\n", " gl.ylabel_style = {'size': 10, 'color': 'black'}\n", " \n", " # Titel und Beschriftungen hinzufügen\n", " fig.text(0.5, 0.0, 'Longitude', ha='center', fontsize=14)\n", " fig.text(0.04, 0.5, 'Latitude', va='center', rotation='vertical', fontsize=14)\n", " ax.set_aspect(\"equal\")\n", "\n", " # Einen Titel hinzufügen\n", " ax.set_title(f\"{var_longname} ({variable_name}), {str(selected_time)}\", fontsize=14)\n", "\n", " # Layout anpassen und das Diagramm anzeigen\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\ShaileshShrestha\\anaconda3\\envs\\cds_env\\lib\\site-packages\\numpy\\lib\\_function_base_impl.py:4842: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n", " arr.partition(\n" ] }, { "data": { "image/png": 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UpRgFYjpTaRwDc6VRe7KPKWIxbYopKBz80OeC687ZYQ46uXQUSGyBufUztpWh+1x6094Mk8na2tp+OxctnVNdvWapjXCwSjoKKb2B6WI0kWTaCKMSmCrQ1ey6KWw3pu/rCPc3vV4oYjAlyFwkmCFSWfsbbJMdz1WmpFEQ6cn6Mh2P+4FikKlnD1Nn6OXB9DBGShgljxmx1VGQoWDYU0HEEjzObGOM3qE4Y2tBxJpj1Rncp5b2u7mIDGuOlbXpOgP524qiKI6ICiKKovQbHIDR6JKO9wypZ567qSlkf2IM8mk0Su+DvqKjkaLp7/E1owqELbeJgw1bGlZ2BU1NaR5pCqNxGNlgKhYY65uVlWVWdOnuPn7xxRdFFKI5KQdfpnDWvLvGgsbvcFDf0ZyzsxB0c+tnbB9Td8xV++kORvqSIbIMxnORx4n01Qw5K7fwGLO0akc/GgNGaDGqhdV42B47pgZ15sVAAYcDbPpi0FuI3inmYLSM6Xd1hM8zgsyItqHPiyWvl56sr1FNy1w0BCNPODA2jdKyVD7XFhjHurfCb1dYe6w6g/uUBrl5eXl7eHmY2++m3h4d/Zn4HZWVlZ1GttnLbyuKojgi6iGiKEq/YDoAY5UKDlq76xvSl7CDyIGrkbZhDcaA0doKEabQd4OCgDWGjZ1h7DNz68BB98iRIyVE3BhwDgQ0aO0IO/gUFkxnnrlPSHeOQWcYYhM9PExh1AxnpbuzL4kxq8qIm45ted26dd1aNx4XzuQyXcBS+WFrMXwxWK3IUjvtrI3aw7nIFKLo6GizwkVPxRDuY6ZkmPrddIQmwByQm4sKo/GxuZQv0wE2DX7NeVEYsEwsBQ++l+3OFKamcLtZ4tvaqlrdXV+jbZkrrcvoKA6O+9rEtjvmpsSWKWvdOVadYZhFG/vYFOM5U0Np429TI1kDpgQSayo9DfRvK4qiOCIqiCiKYnOM0HwOwFhthMafAymGEM68nXjiiTJQptFpx8ELoUeGaQqD4XvAiIbuQmM/wv1geEB0nMlj5Zyu4CCdQo5hrNkRmhZynWkKa24mlh4rnHG2JazmY7p+9Gl54IEH5G+j1C1heWAODOltQAPIjlDUWbNmjdW/a8y6dxw80lNi48aNe7zfOJ6W9qUxq2pqBEtYqae7+5DbyRQGDoppQGpOFOE6WmMyypQQrrslDxe+Rm8Ocyk19nAu8lizvZvzmmEUA9NOrN2/TA+hGDJixAgRQ+hh0RksKU0YFWZa0pmRFUyvYMqV6YDdSL3gYJPRZEz56AyKXieffLKcZ6wcZcDrCw1rTdfBGrq7vhRbyBNPPLFHehi9kihK9tb3pDN4vpq7ljJqh+cRr19M37EF3T1WhOcJBWQ+mkLTZp6zTOczjHIJo2to9EzxjWXITX1SaOjM9DPTCBx+lseCIllHf56B/G1FURTlXzRlRlEUm8N8dnaGmdOfkpKCxx57bI/39MbIr6ews8gZW5aFZQUEzu5yMMYBMkuCMpqDHVbD54CmgMy9P+ecc6RTz/KhNFGlV0VX0MyQA38aHTKlhf/mAJ7iCCMbGFXBgTI7u53BfciBOk0wWZ2A4dKMCGCKCAdGHHRzoMz9TbGHnWWG3XOgzW3ha6+//joSEhJgK7gfOWvJ6APuu++++072MytcmJYVZqQDjwFz/Bktwn3KKiycxeaAkttIAcVanxluO00ieXwodjH6gNEnHKQdccQR+P7779u9n4NompzSgJKDBlbGodjE/cp2wIEJxTIOJBkRwkEHIzwoXHDGtbspOKwuw3WhYMR14T5iNAONR/md/A2W7O1qUM915PnCbWVbNa3oY7RTtl+mwnDgxG2j+MDfs4dz0ZjJNkogdxRsiDURFEwLY0oEB+DcPhqqdqSjQSn3DctP00+Dfx9++OEizrDiFIWkjvvjuuuuk/U1ypeaM2BmGzU9n1g1imIF/UxYpYppNGzLbIu8VlAwsZburi9FRopd3Dc8p3idoecMRUJGaLC9dSwJ3hm8ZrBUrqnvC5+jEa8B060M+N28nvEawHbJSCW2eZbtZmQK32utGTCFAl4TDSgi8jnT36boYaTi9ORYMTqKHi633npru9Qlnht8jt/Pc52pbvTi4H4nLNVummLG9spqPrzmMUKNj4zYoxBEEZTf0/GaO5C/rSiKovyLCiKKotgco4oBB7qWDDRtYeTXFRwwc6aVHVN2NjnDxgEZfS44kKKAYepxwNlabgvfy0E6Z1vZwbZGECGsvsCBG0thcsDCGTyuAzur7BCfeuqpVn0P15fvp9BALwsOCFkil/uQg2UOOigucPac72GkCAfZFE/YOe5YErav4UDkyy+/lEEco2koyHB9KXx0hPuU+/qFF14QUYjry1l2Vh5hWodpRElXsBzvF198IdvIaAEOGjhjTvFhwYIFewgijIxg1R96DFAUMUwqeRw4aGM7mDdvnhw3hqpz4MHZdYoW3TWCJRwQ8ne4Xz7++GMZsHAWn79DIeyCCy5oVya2K/GH6842ywgJU9huGV3D7eU+5aCUAywKIvZwLs6ZM0faY8cSsFxnpiexLXcUeczBtmVEI7CEszkMg1JTKHIxyoapNhSn6NXB97ASieEBYmDsL4oQloyKOWg1HXCyvTNVgVFRP/zwg7Rpip8c3FPAseSBY4nurC/PHbZPXp/mz58v4hePP9OT2GZoLmrNvjXgdneMkKJHkOlzpoIIzx22awqvbH+8nlJ0pKjDFBaKkNbCNtrxt3ktM32O7dq4RvfkWHXGTTfdJO+lNxHFNgqLFBh5PTBnCkvBitv88MMPy3WIAg7PZwpk3RHBBvq3FUVRHA2nsrKyPWMbFUVRFKWbcOaWgxWGbeuMpO1hOWh6RdCfZaDMibsLo65Y8pUC2S233NLuNQoHjHSiyMNICEVRFEVRFFszOHpQiqIoiqK0gxEITEVi1MlggSk7jKCgx0NHGM3CaCEVQxRFURRF6S9UEFEURVGUQQh9Gpga0ZOqRwMBw/hZjtlI++gIK4J015dFURRFUZTOoYcUUyaZRsw0RqZhnnfeeXtUqwsICLC4mHrAdQbTNZlezHRg/g5T+Zhibs+oh4iiKIqiDFLoOzJYcHNzE28ERVEURVH6D06e0Gif1eUoUtALin509B/66aef2vzL+FxHaNJOryhWNesK+vLRu4t+TSwiQMN4mn/T2N8w6LZH1ENEURRFURRFURRFUYYgFEMmTZokBs2mnl40a2bkx2uvvWbxs0xxpYn7hg0bujTlZgQKJz9YYc2oFsf0XlYUpN9Zd4y1+xNNmVEURVEURVEURVGUIQiFClMxhLDyYGpqKrZs2WLxc6yEx8phjPjoSgzZvHmzLEzFMcQQoxw8K8J99dVXsFdUEFEURVEURVEURVEUB4EiBVNZQv4pXW4Olo4vKyvD7Nmzu/w+VhgkkydPbvc8PUsopqxZswb2inqI2Am1tbWor68f6NVQFEVRFEVRFEUZVDACwtPT02HGaRQ0nJyc2j3n4eEhizXMmTMHu3fvxu23397pe/h91hiq5ubmymNERMQer/E543V7RAURO4An2YQJE+y6oSiKoiiKoiiKotgjHHQzSqGvRREZp42LRm6+fVV0Y7W2qqqqds/deuutuO2227r87NatW6XqzN577y0mqOYoLy+XCJHDDjsMgYGBVu0nYk6Q4TGpqKiAvaKCiB1AxZFiSGZmJvz9/W1e9vDLL78Ul2Ga3igK0XZhWwX/lltuaStx1pHm5mZkZWWJCk+n7xdeeEHyOjuybds2cey2ZHx11MlP22T9nZ1bMDqpHhvT3dHc3H4mYrDQ0tKE7Zu/hbu7D4JCRqClpRl19RVoqC1DXV0F6uvK0dzchBOO2Q/jxo3D0UcfjcjISJuv1yWXXIL//e9/OO2008SwjO2A/546dSr23Xdf3Hz3xygrzkB1ZZ683wlOCAobicjYvTDQ2LJdLJj7f3s8xxxkluWNjo6GPWKcfy1oQUN9NVqaG9DS3AQnZxd4eHbdkewuTU31qK0uRk1NEWoqC1BZls2DAv+AOLh7BaK0cBucXdwwbMQRcHH+95re0FCDrWvnYMyUPa9FtmgXTc31KMhZh4pdG+DtHogw3xQEerYew12lq+VcTAhqDa+eu/xem6yTYh9oP2NwwfvSJ5980u1+hrnrd2fQV2Lx4sUy4D733HMRGxuLnsDBe1xcnIyp+loQkXFafhN2rhgGfz/7cJsor2hGwpQMqeLi5+fX9rw10SF5eXmSAsMx57vvvgsXFxez76N3CEWOU0891ap1MvY7fUc6wu+xVfROX6CCiB3BhmlrQYSDs1NOOUV+p2OYleK4aLuwHT///LPUb7/66qs77RD89ddfOPLII+WGYe46MGXKFAQHB4v51V577TkgdnWz1Y2mBTtyPODs4iTLYKKxsQ5FeeuRl70SUXH7ICxqfKfvf/PNm9GfUPiiCELxhR2agw8+GAcddBC+++47bNq0CfW1FYiMmQIf/2g5Lzl43Jm2ENk7FyMh5RAMLLZrFx3b/8qVK+W8oPmbvWJ6/rm5efXL73l4+iMAw9qJJOUlu0TgY/vw9g1v95mK0kzs2PodkkYdbfPrRX19OXIzl6C8dBciYqZgQvRxqGksx9b8X+EediA83fxRVJ2BqbGntt1zbN3/UQYW7WcMLihadTwnrbludPc8Pvvss3HssceKv8R///tf1NTU4MILL8QRRxwBZ2f7EB8MKIb4+5kXDwYK9h26s8/pB3LyySfLI/sa9PewBCfq2H9l39QajIkkCi4dhS0+19FbxJ5QQcQB8fb2HuhVUOwQbRe2gQNbDnItce2114rQwRuTJZXe4O6778YVV1yBL774Av1JQ6PToOlwF+VtQFlJBmqqC+Hk5IyQ8DEYPekcGw4Aew6PPTuDTz75pISsvvjiizLoP+qoo2T56qfGdu/n9gwbfhjSNn6Nwtz1CI0ci4GkP9oFZ/0Y1vvpp59a/RmGTJeUlEiUjbW51EMBFxd3BIWmWHw9b/dK+AXEwtc/Bs1NjSKgcGluqoebuy/c3L37rF1s3/gVYhMPRMLww6Tduu4ogZ9LGBKCp6KgMg1xQZPh5uyhA2MHQ/sZGDT3Ui79mfpx4IEHYsaMGcjPz8dbb70lZVoPPfRQEUfCw9uLu0rPYJTG6aefLuV2ORHX2SRDbm4ufv/9d+mbWHsfZYStMYnBSTyDnJwcZGdn4/zzz4e9Yl/Sm2JzGhsb8dlnn8mjohhou7AdjOjorO46b0yhoaGYO3eu1G1n+GNhYaGk0nSESj6PUX92VDhBMzalXh7tnary3cjPWY2o+H0wetLZGDP5XETGTrFLMcTId6Yg8ttvv+HDDz/ENddcIx2QrkhKnYXcrOUoLtiMgaK/2sXzzz+PG264oVMXfFO4L5k/zRBszkbt3LnTtis4iEgedZyk72xY8Q42r/kI6ZvnITP9V2lLW9d9iu0bv0ZDfft89J62Cx+/UBFgaqqK2r2eX7EN4X4j4OzkgpqG8l5ukTKY0H7G4KG0tBRBQUED0i4ofjDNmN4VEydOlIkDprEyxZSTBn///be2oR7Q1NSECy64AEuXLsU777wj3iGdMXfuXOmHWkqXYQocfUhM/S9HjRol/V1+P3/P4I033hDx2xpj1oFCI0QURVFsCG8YSUlJFl9/5plnkJJieVaXeHoFIyA4Ec7OrhL9cMCRD8lssNIed09/1NWUIn3zt/APTEB88sFtry2a37/pMNbC2bDi4mIJNf38889x+OGH4/vvv5cw2M7Wubb2esnxvuSS0TjmmGN6vR4zZj2K/qC7x4EC4ZVXXmnVe3/55ReJtmGYL1Ns+O/4+Hj0B/bavqw95k2NdcjJXIqsHb8hceRRvf5uinYVZXnI3PErGuqq4O7pBxcXDxQ3ZGLXjpXynqj4fZHpnQMPzwB4++1ZlUBRlIGBs/ksk9qT61x37yX0HaKI+uoHT1rwo5okk0Drtlfgr1XLUPHU5ygv2SnRZ6v/erFbv+XI3HHHHZg/f75EnzKCsqM/zGmnndbu37yPchLugAMOMPt9rE7D9G1GkLz00kttzz/wwAMShUKvIKbmMEr61VdfFX+YkSNHwl5RQURRFMVGcKDLdJjOwsIZSvjQQw+1K3tGZZ214dkp4Qz3tTc+g7LiHagoy8SoSWerGNKB3KwVyN+9Am5uPvDyCUNjY434bQwGli1bhmHDhuH1118XUYShqb6+vl1+bsWKFaiursb06dMxVGEnmLNPpoZxlqAh8XPPPSedPIblc3Zx5syZmpLRYdb3o48+kusKl/UrltDyQ3BxdRNhIipuvz77PR+/SIwcNxtNTQ1obKiR1Jzw6EkivjQ11cljfX0Fyst2oTrtJ8ya9YfMWt57r5qrKoo9CiIDBa/j9EziEhI+CjXVRf+IISqIWMu6devkccGCBbJ05DQTQYQm/qtXr8ZVV13VbR8X+o3QJJ5+MDfffLNEQDPKk1E/9owKIoqiKDaClaM6iw4xRBPeMEyhlwgHx1yYh/nM62mtMyTL3kBImP0aSw4ULi5uMvvMgReaG9rSZyiURMRMEg8De4WhpIyC4MCfBqucxbE0iGeYMP1jmF/N0FQObq0phTcYYXm+//znP2LC2FWHbPPmzXjqqack7NrLq9XQ9O2338bjjz/eT2s7OGD488svvywd0//7v//D9tzWMOb+OT+7rioyf/7NMou4a9eufovsURRlT+hpZm1lkYFgy5pPbFYpa6jy7bffWv3e4cOHi+lqZyQkJFh8D6NW+yJytT9RQcTBcHV1lQ4mHxXFQNuFbeDNoiv3b2tzdcuK08UQkaU0+xNamaxPYwlN2C2sHtOxgkxTYz1ys5eLX8KIcafAXmH0wz777NPpexjeyggSdlIZhsooCGuiJgZru2BUFI307rvvvi4jYCgScXDP/WOIIfTgYZlEey3RO1AwWo2RNGeccYaUAbeFGNLbdsFBGA10OaOoDC20nzF4Igl4Dd1vv76LFuvrawbv97U1JbZcLcXBsN9pM8VmMMxaUTqi7aJ3PP3002KIaWp4SnMppkR0NrPNsrydlT0jTP/Ytf1nqdowELi59p+Ja1/h4uqOmIRpSEo9GptWf4S0tDQMRkGN/hlnnXUWEhMTxW+GM+eMhHjvvfckssQUtr3XXntNfDT4uGjRIrPmvPbcLihkXHrppSJwWJMO9Oijj0oOs2mJP4br0qzWkWDqHWcA169fL9E1lggLCxNB7X//+x82r/lEyuLaS7tgmP67777bbwMxpf/Rfob9w0kaivBM2+0vunvNYOpdUV77+5+i9AaVaR0MzqbRVIcqPWuMKwrRdtF7aB7FcHQaSjEXk4M0prww+oMD19GjR7e9lwOW6667TmZoTzrppC4HfrlZyxAaOa7PymJ2B2YrjBzWYPdRIpbw9g1HyujjcMQRR0hebHfyYXfs2CEz6qwElJeXh6KiIpndZG41B+AUKRix4e5uG08Xtg9GOTBigukxTL+iyMaKK3R4f+WVV8S095FHHhE3fhqXcT1pglZZWSkVVx588EFMnToV55xzTrs2aK/tgsIixQzuW2vK6zLPmVVlTEUhCgO8njnaNZz7ge2b6VQ8/hTDGNZ82GGH4eCDD25Lr2L7YVWjfQ66Fbt3/YWd235EePRkhEWNE+Pm/mwXDQ3VKMrdgKKCTbj11nWSsz5t2rRerYNin2g/Y3DA+0dAQICkr02aNEmqvLAimq3oyb2kIGcNAoISbLZOiuOhgoiiKEofEBcXJ+aorPPOGdgTTzxRDAJvvPFG8ULgjCxrtP/55594+OGHcdddd2HGjBldfq8YrOaswbipF/XLdgw1KsqykL55Pr6f/2GXYkh5ebmUPqYhJ8UTDso5mOQAjR1CphywU093dfp9MLSY5QAZkcFj29cw3YqeIpag8EYBhB1WCiWc1bvoootEHCHHHnusCAQss8dyhdwmOswzZcKWHdyeRoYwKoQRDjfddFOX7+d28dxipIxp6gcjstiJd7QBF68/LEfJaxDbJaOKWLGIoe+MQrvssstEiGUp4kMPPVSiMDy9gyWCiullLJG7fvmbEoUW3E8+RZXlu7Fy8TMYOf5UMV997717+uV3FUXpnPHjx4t4RVH98ssvl8kdXm+78kTrDwpz10tkG68bitJXqCCiKIrSB3DG/v7778cff/wh+fnXX3+9hJxyQMswcA6aKYSwTBn/ba2/AWe7g0JHwsnZxebbMJSor6tA5vZfUF9fhdGTzrbo00Fx480335TBJD0oOJNOT4rk5GSzHguMBmFVGC6M7KHwdfXVV4v5LQfyNMTtTyhsMHqEndcXXnhhD4NebgO3nQtFB/qQcPso/lAg4usUFzasSEdIxBhExEzuExPayrJsmfXno6SR/bMrjzvuD/m3qThF0Y9C0+zZs8Vk1hpvC6ZAsYRfR2GHx5Lb54iwug4FEV53vvrqK2mXVVVVbWlT3OfcP3wPPWjcveMREDQM/kHDEBoxFpExU7FpzYdwcnZFUEjnpcD7Ah+/KIwYdyqqKnIRETOly/f/+OOPePbZZ6V9cJuMykyKovQ9PM84acNlzZo1uOeee9qEaEN0729olF5anIYR42bbtVm6MvhQQcQBUUMrxRzaLnoOUyuYlsDOOVNg2HmgwzYHKPvuu6/M0FMQ6Qn0i7BmsGBLmprtt3Rpc1MjKityUFdTgrraMtTVlqKmqhAurp6Ijt8XAcGJbX4cHGwzxWTChAkyS04ndYoJFEOY6sTBI1MwWJ2kpqZGxCxGXnQGj/mcOXPwzjvvyDHngI3f298dV0aqcOkMijnHHXecLB054IiHkJu1HOuWvS4DVW/fCCnDyhLPLq4e7R6d/yn7bK5dMCKHKRhePqEyyI5LOhjOJmLe11/f3CfbTH+Ugw46qN1zPH6s7JSa6tiVmOgTcvHFF8tiDqbTcFDz6eeLkL97NXbv+hte3iFwcnJBc3MDNq58F3sdeBPcPfxser1gu42MnYL0zfMkCq4r6KHD6kEU/ei/xCiYjz/+WLZXGRxoP2Nwwnsmo/HS09PxxBNPSBonn+PkD1Mxec319PTs8fdbc80oKUpDWfF2iSZTMUTpa5zKysoGn1veEIMzdQx3taYihaIo9gXTY1gG9fvvv5eOOme7WX+dnhXsIHC2ljOZ9EboCUx7+Prrr7ucNZ8x61E4Glk7fkdxwWaZ5fbwCoanV6AM4pubG1FdmS9RNfREqKkukjDb2GHT4eMfharyHBTkrYOrqxfiUw5FZekulBZvh6ubt/i0lBalo6G+CmOnnNetqj50vd++8WtJRaAY4B+UYLHjtmh+3wgDfQ3bLz1TmHbBUsAcPHN56/1f0dRUj6bGOtk3FE1CI8fCPzBetpH7PDtjMcpLd2LE2FMs+t30xXZT1OJgn+eFadlhilKMHLjgggt6/RuOAPscLHFLLxZGX9CYdezYsZg5c6YsRtWerujttaeluQkbVr6HRT/Pkd+3xMKFC3HaWdciaeTR8PIJkciStI1fw9snDIGhKfAPTICrm6ec810NmOz1/FOUwQAnDLZs2YINGzaIRxoFSqYLs8/Dc5g+MZ2lkfbkmsHU15CI0XK/N3cecyxF7xOK4n09ljLGaSVbk+DvZx/RuuUVTQgakW6T7XVEVKp1MNhZZM45w4y7Yy6oDG20XfSM5cuX48svv8Q333zT5llA402WjmSlDNOZ695gi/KY1tMCX+8WVFZzHewrUoTRIBExU+HpHYSK0l3YXbBJBu1e3qHw9Y8GGltkxpsiybi9LmwzjPT0CpKOVUlhGjateh+xiQeIlwJZt+wNxCUdhIDgpG7vd37v6MnnykCtKG8DdqX/IgMzFxc3uLn7yYx7YHAS/ALjYa8w5YdRMR0jY35b8W8HlmHTVRU5aGnIwMaVv4IZMdzPkbFTMXrSOTZtrzS5feyxxyQywFQM4Tp98MEH+Pzzz23220MNDh44aOHCaAtJndqwQTx0WKWI1a8o5Hp4eNj0ekHhkqWxr7nmGjl+psfVlEMOOQTDhh8mFbcaG2sQGbsXxu99CWprilFalIad235AU1OdRI21ronJfB//dIKcgzxP338/WgxnWbWJJskauWB7tJ8xdKBYyrSZjqkzFEVWrVol0ZgUShiJSc8qigm9vWZQCOG91VQQUZS+Qu8ADgZn/2gaSPVWb0iKgbaLnu2z22+/XQZhFEOMwQRTY0xD1X19fWV2oafU1dXJb/W3N4UBm0NSrH1WmUlIOQzZGX9IuoxfYBwi4/aBq6v1g7cgzioHxWNX2kLkZS1HbNIMRMfvh8z0X5GV8Tv8A+Il5YaiRmNjHZoaayVCovGfx6amWgSFpbbzW6AY4OsfJYsBTSvr6ytQX1uOooLNyNj2A+68s0LSFiwN/uwZbqN/YBTGpoQgIGw/NPdTShWNYWlK/MUXX0g6mik8DxmVxfNN6flx5ewuF3ogzZs3T65lDJW39fXC3cMXpxxzityHWLnJEoxMGjl+Nqor87B22RvY64AbJd2HS1SceZ8g0/Ll9BZiJBcFELYlpszRR4iiNVPJKGR3TwBSrEX7GUMfRojQsJkLfaEY1UUDeU4U8fyiqNmxgpi11wzed2uri22/EYpDooKIoihKDwdnjAThrBeFEVa3YD7thRdeiP3337/tfT4+PlL9o7q6eo9BnDXQJ4FVIljtRGkPw+MThh/aq++gJ0biyKPEfyRrx2/iP8IZaw/PQNTXlSMveyXcPf3h5uYtviSubl7w8AqSqI9d23+Bt29Z17/h6g4v19ZBGwUWimczZ+4lfjM333wzjjzyyF5tgyMYFrNMdXh4+B5VZQhTemgWSsNYpe+gJ87vv/+OBQsWSHUiQuGAohOvS3ykX059nU+P/UZMYfSAtf4vGdt+xKgJZ8i5ZS0UNhktxuX000/fY7BOUY3bSVGk4+uKonQPio4UqbnwukGDeE4Y0Ydk46osSbf0C4iDh6cvmpu80FDfBGdnjz0M5BnxtX3TN3BxdUPC8MMHbHuUoY0KIoqiKN2EM4ocJLCULsNBacjJyg2WUgVuu+02mRVjRABnTqzNzSfnnnuuVItg6OnAps4MbThISh51bNtMMmeRayoLUFWZh5LCbYhJmCZpNgY1VUVoqK+UKJPuwuNIjwYa7t56661iEMoKRHp89xRCmJL26quvyiCVVWg6QnGJohIXmsYqfcvdd98tPkasNFFaWir+LKzExDRBCrxMG3zn/fPgGxAjJXutPQb0oeHS3NSAmupCMUwcFT9JxGReXym2dJYX7wQneHqH9Nl2MgKP11qWsj7zzDOlGhgrTSmK0ns4McQJJC5k/8MfkDTXspIdaGmuQ03yaGRsWY7auirxFKIoQm8gCq1F+RsRM+wAhISPGujNUIYwKog4GBLq7O+vHW+lHdouugfLq3IAxpx3VhjpqsoBBxDMx6cJ5HPPPSflT/kcP9/VACI2NlYq2NAzgeJLv9MC1NY7tebgOwicSTbC8IPDUxGTsD/WLX8TQWEjUZy/STxAaOqYPPp4MXpDSzMi4/ZCYEhKt9zvOaBkVZqXX35ZIos48De8aLoLhYGO5WwHc7ug4Pjoo49KFA0rLdHvwhxPPfWU5KdrlI3tBjIURVhemlE4LNfL42FA4WDMlPNQUrBFqtOER41Dctwks+2ioaEauZlLxe/Dzd1XBjvOzm5iQhyTMB0vv/yEzCSzegWvj/QjoLcJjy2PPytbMGKLgyh+V0NdBTw8+9ZMkOky3F5WmnrppZf69LsdHe1nKAZMbQ0KHS6Ls1MLXDwaMHzsSWhucWqLCqFQyomJMZPP71YkmKL0BBVEHDCEravSjIrjoe2iewM1ihQM/7zvvvusLvnIUHAjHJy5tRxUMKeW39dVB9FIqzj++ON7lHbTG9hB2Zrh2J0RZxdXBIeOQFnxDgnd5cx2SMQYSbUZNfEM1NWWIzdrGTav+QQT97sSXt7B3fr+//znP2I0ePbZZ0vYfk8MHlkthFEnzz//vIQo21oYsVW7YOUTRlJx4M1zrLP2PnfuXKl0QEFJ6R5M9du0aRNCQkKkdHRHmELCNsRrU1BQkFSVYMUscwbRfA+Fw6CwEcjLXoHP534gqWgMiff2DUd9bQUqyrNRV1OKqLi9JZKko3DISlD333+/XBtHjhwpJXYpxtDj47LLLpPfpfkpY0Pik2eKMaqtoCDD9B1WWTK3b5Seof0Mxdp7Ce+5Pn6RsihKf6CCiAN2gnbs2CGmRmpqpRhou7AODgo4c8hID4bv0yyspx1D5qivXLkSa9as2cOpvSOMGuDvzZ8/X1Jv+hMntCAooBklZc5osbMqM/0JB3y5WcsRl3wwPDz84eUThm3r54owIoO8YdPF4NXNzfp0KFM4A89B6FVXXdWjAT4rZlCoY/lUVgZhuD+jK5jq0NOok/5uF6xuwpLVFBoPOOCATt/7999/4/3335cIrf6aceb5T+GJC6MV+FhYWCipT/T4YRSYvcNtYHniTz/9VK47xcXFkgpDIY2CA6OMWOmFkUtMW2F6H2f1ua8pVN15550Wv5siRxQrDY2ZjIKiBpSV7EJNVYGkozHljOJIRxgen5XxB8pLMjDzwVfk/kNhz9iXFEO4dFWuk2WfyxmCX5wu5sX19ZVoaW6WtuHuGSACiizeLM8dJNvZWbuZMmWKlHdWQaTv0H6GYg7tYyj2gAoiDgY73EuXLpVSc3pDUgy0XVjHgw8+KOaOnLlkLv1bb72Fa6+9tsffx0EfB3ZdCSKEg1v+dr8LIs5AbEQjSivcmRnisHj7RqC6Mhcurh7wC4iFt28Yxk69ADm7lmDdstcRHj0FPr4R2LLuU4yZfG6PfoPHlpUvfvnlF0kZ6C4U62haR3PRbdu2STWWJ554QgaXFFw4aGcVAHtrF6ykxLbN9DF6U/D86ozt27eLGMkIkb4QeyoqKuS7mJrGdTEVPLgwdYNQHOA1kgsFqMmTJ0v0xF9//SVRXMZ20CfIHqHo8dVXX+G8886TNmLsu82bN0vKFiPY2G722WcffP/99xIlQTGE28iINm6XqWE0B7jr1q0ToTAscryEtf/bLjwQHDYC4NIJ5WWZ2LFlPvadeSemT5/ere2hqFFatB05mX+LIOIfmICgkOFieuzm7i1ltim41NWVS3UKht8X5W+SqlTHHbdSPk9xmseT5wi3h+bXaWlp8jcr7Sh9h/YzFHNoH0OxB1QQURRFsYKNGzfKQIzmqSQpKQlr167t1XcyXH39+vVdvo+zoqXF6SjOz7Y4Q6p0zqL5N+/xHL1cTD1cOtu3nE329ArBaSdOkoGwaRUKDpiZ6uLkdATGjx+PG+7+ucfryYE+jecOPPDAbpdapp8D142z+Pfccw9uueUWWfY5+Db8dsvrKLnoZhksxjPKxTOgy/3T2es0POUg+cnHTumWKNFxH9fXVWLL2k8QTdPa8GGYNfuFTj/f2FCLjavexbrVv3Zqumnut8xRVpKBjK0/IDx6Imqq3oSzi7tUGGIUgYdXIDw8J0lFIVJRBeRvApZvqgOw9Z/FYJxUKjr1rFtQXVkg5ZvpOdPd6JWujoO1mNt2CncsW/vki0vw2oe7273W4jwTf64qgLvnfvh5iQd+PunZdq9nZywWz48vf2z93m3rP0dtTTF8/KLh5RMqx2TM5PNEhOjOdlF4uO02Pzz66IM48CifLj14jO9htBz9mGYcMRXXXvszQkND0RPYjrOyssTIlecbRS9GMHTVthRF6Tmm14Oe3ksUpS9RQURRFKULjEoWpiZ7zL9nmd3ewI43O/XWkL3jdzEdU3oHU5ToVcDZfA6AKGawhChn9q1Jm2EI/dtvv429995bRDHCqIuLLrrI5J09F0Q4G3/CCSdIWgNn7bsL0xpowDtr1iypfsRZfQ7uY4btL0tFWRbSNn4FX/9oxCcfMqAGh83NTdi85iOp7mNtrnhWxu/iQcH0oN79diN2bvsJ9fUVGDP5HCmn3FsoMiWlHiOGnzk7/0b2zlYRgaa8rMIy0IydeiGqKnKxa/vPiIiZ0q5qA9uBuZQWg4iYydiw8l2pwMQIqfq6CoRFThAzYUJzU/rrjBx/YrfWiYJddHS0RGYceXLX10KKmDxXmc7Ddm7JbNdaOADjdZiLoiiK4phozJqDwU4PO/Tq8q2You2ic959910ccsghUs3CYMOGDRgxovNw8K5gmDYHAzfccIP4iRih+R2h4zpnn2lU2O+0cGbceUhUmWFqxP/93//hlVdewbx58yR9gI/0UXj99de7/HxgcDJ+++23tqowf/zxh8X3cuBYXrITBTlrxduA5UWthZ4JjDjh+nYXnsOsRkRvDaZ00ZOksaGm7XUOZpnS4+bmjW0bPpf1NGXJkiU4//zzLbbFvrxm5Oz6C+HRk6wWQ2hmW1mWhaDQkegNTU0N2LjyPREpRo6b3SdiiCnct/EpMzFur4uQkHIocrKWYseWBegvsrOzJUWkI4y+oBCWOv505GQu6dZ3ch8xRYxtafeuv6Q6RFjUhLbXg8NS4e7hj81r5qC4tMaq68WyZcvw008/Salba0qR8xpIY+nDDz9cSp73VgxR+hftZyjm0Hah2AMaIeJgMF+2J7npytBG24VlWPWAgghz6k1hJQ/OxnNgfOaZZ/Y4J5oeD/Qg+PLLL/Hwww/LQJTh2uPGjZP0Cy5Ozi4y+8xZbRd4dPp9HHizogMHjrU1pWior4Szi5vMVHPAQkNQd09/+dvd3Ue+u9Pva3HCjuyhEcZ6++23S0oKw+sZpkuxIDMzU7wTGPHx6fxf9/iMqfkiPRJ4nAMDA/Hhhx9KJQweM7YBekpwUMdoEfoU7N65GL4BcXLcigo2oamxTo4f/x0SNqpTU0eej4xIosGokaLVXYKDg6WM76JFi/DByRchOn6aGMAav8kUlfzdq7Bp9YcYMfbkts8x5ebnn3+WyjeMhDFNbTBt431xzaD/SmzSQdi98y9UlGVixLjZnXaKM9N/QVzSQb3uOKdt+BIxw6bLoN7W0MRz+JgTRRDJy16FiJhJNv9NXlO2bfhWUllYkYXnfscKDmjpvsJJQTY6wbI/SsLwQ6VazI8/LBAPD4pBbu6W/WBee+01SVVZvHgxjj322E5/mymDu9J+wpI/5/daiFYGBu1nKObQdqHYAyqIOKCpFb0QRo8e3e38dGXoou3CMjSmZApDx1KoTLNgOgKjBSiOcEA9fPjwtoX+IEzLoMBh+mjuOT6yk89BNf9m1YeMjAyJQOCAYdOWnXB2csHOtJ9kNt3LO0xEDmeKGS0tqKrMR0XZLhmMMIefs8B+AXHiYcABCSNMGOJeX1cuS2V59j//rkBzS5MM0gOChsE/KFHSK0wHnE5OLQgPbkJ+sQtaWgbvDM5jjz0mQhMrWBC2c86k02uDJpKttBdEGOGRtulrqRzj5RMOV1dP1IZUSGWRlJQUmaXmsWJpUEYM8dhxYWWN2MQZyNn1N6oqcmT/NtRVwtXNG15eISJYsYzyNddcI6kt5gb4bFOMZKG5J9tFT2GVmbFTLsSu9F+Qn7MayanHiDcGYXQGPUU2rnofl16aLaV/WVqVZr/cDtOyo1wHRjR98sknkl7QF9eMCftejuKCLTJgp1ltVflui6kljA6oqS5GQHDvUxsaGqr6RQwxZdjww2U/+/hFyPlpS1gdhekxFJm2rp8LH99IxCYdKJErtsbbJxgHHTIb27ZnYeu6z1rFXA9/nHvuehQVFbWJany89957Jf2FpccZJbJh5XYxRQ0MSRYfFwqQLNVL09bqqgLxKFExZPCi/QzFHNouFHtABREHg7N8NHHkjKheeBQDbReW4Qw7I0FM99WqVaskzYJVKVhl5tJLL8WWLVukQgNTKt544w15ndECHh4eMuBlBEhZWVmnv0WDT0YfGAtTJthJyMisRsqYE8BxMz0AONBhtEELFxmEhItPgad3iPnZc7fWHH/A/GCTkSTlJTuQtWORhNpz4B4QlABf/xh4+4YgPMQNBSUURDBooAjEVBUOtMuKtyN7RAqeeuqptmPAcHumpTA95Nxzz5UqLB0pzNsgIkhw+GipYME9+8uPj8Db+9+BJY/T5Zdf3u5zK7c8ioLcdWhqqhdzTb/AOBn0M+WjMG89SnPTMGzYwZKmwygTRq1QhGC0CoUVim8cMIaHh0tb640gYkQEDBt+GKor87BtwxcICh0hM/1MoaAQNn7vS3DXXWegpKREtoepYSzfyxl8o7Q014NCCCst5efn44477pASmj29ZpQUbkP65vnwD4xHC5pRXZmP+rBKi++n7wWjHfoCRlGx4gjFv/6CwsCIcadg46oPkDjiCPgFxts4RLxF9u3YKeejpDANm1d/hIDgJLlOUGjgwvSXvk4X4iZFhDShoCQB/oHnyW+w3T/99K0StWRAoZcRWjQnZjUXLvsf/gBKi9KQk7kU9XVlaGpsgKu7l1SwiU8ZWL8bpfdoP0Mxh7YLxR5QQURRFKUTcnJyxOeDJSjffPNNLFy4UCIKwsLCJF2CFT04a8mIEC777ruvfI4pEfws388UBJZE5Syon1/78HXTToERLSJRBjU1koJRVVWF/Q86Dd4+IfI+L+/Wx77E04tVNSZJxABpqK9GeUmGlLQs2L0cw2MmYOOqv9Dc1JoCQOHF659HT8/ALtNu+puy4h3I2PYjgkJSRDjienIm+qabbpKOV2VlpQz4KYpQCKGXiDlBJCl1lggrRfkbkL55HoaPOamdGNIZYZHjRGyghwiFFaYucbTI2frhY0/BxRefKNFHbB+sKMNIEUZjTJo0SWbMuNC3hpEkfVk6eMyU85CbtQzrl7+JxJGz2qIVKIKYeuQceuih+Pzzz2W9WAaXZpZcF4p9nOmniEQ/h56aqTL9ZeJ+l0sEAFMx3D0DLA54a6qKpLoM/U/6ApoTb103V0Sh0IjeGSN3B0ZrjRx3igz4M7b9IIIM2yaFR0ZFSMRXH0Bx7fX3liA6vvVaFBSaIt9flLcB61e8Jb4pFPjYNqPijego20DBhYupGEJ4TaXPzdVXXy2ljemj5OrqIcejP4+JoiiKoqggoiiK0gkcCDJKhNEFTHFgVQQj7JuDwqeffloGjubgAJfiya233tqlxwhfpwDS0VyQaRmRMVPRnzD/PyRitCzOzi3wC6zH6EnJaGxolFKbTF3gjD69MjigpfjD8AmmhlCwaRNMvEJkJrq/YATEru2/SkSEUTmEETVZO34XQYRCCNNkKGJRzGIVFqa7UORgegqFC0YPMLKjualeBqpM0aB5JNOJtq7/jMk3Vq+PVBgZtn/bv1uam6TKC8WV//53i0RjsKrMk08+iYsvvtiiWNaXMCokKm4fhISPRvrmb+WYcfbdXHtkOhi9VijOsBqHMXtH4Y/tury8vPXYdwMKVEyliGakgot7p5VNDHZtX4iElL6JDiFso4ycyNj2nUSqUPjqL8NiCoqJI4+U/caUNZ5PFB+zM34XX59580bjsMMOk8gya6D/0J9//il/Uzxdvny5CEsN9e1NcflcaORYEZ4y039F8qjjsH3zN5JSE5c4Q/xG+htGQr344ouSzsZraUN9UqeeI4qiKIpiC1QQcTDYyWWpyJ4aQCpDE20XncMIgueff76tzKoBZz1ZwtWWMFXB1380BgqOd4vLWtNl6FvCKAMue76vWXwemHJDL5OK0l3yN8UFwhQBhuu7unn2+TpS9OAgz8nJRap7eHmHSpQIjU1d3DwRGJKC2toySfegSSkjdSiInHTSSW0RPhxYMrRfohXcfWVbGUnB1AqWHGXKEdNnKALQi6QnMJLGPygBo4MS8O67V0qKSm/Lx/YUijWpE04XUWv98rfxww97S/WOjlAI4cLopbS0NGzfvh3p6elSdpopM0yd4WvWQD8UVncRQSZitNXHlpE15tpcb6BoxhK59DDZsOJtJKUeC1//KPQXFCjYprgwkohmsWxr3Mcs781ry8knnyxeMuYqsDBijdWIkpOTcdxxx7V+n4eHCLZ8nDHrUbO/6x8YJ+lxW9Z9iuFjTpBzloavvgGxiEuaIYJZX10vrN0PNBCmkHPYkacjJmG61W1DGVxoP0Mxh7YLxR5wKisrG0RZ4UMTdrAZqszc9p52tBVFsU2FmdmzZ8uAkCVMmTrQ33CwU1K/X5+F0w8EnA0vLtgsosX4vS4WsaGvyMteKakATP/w9A6SKI/czKViPtvQUI3G+mrx8AgJHyOGllvWzkFS6tEyE11XW47sjN9QXVkgERNMKzD1YaHIs2HFu1KRhd+dtuEL7M5cZ1WJ0MEEI2doLstUmEcffVS8SwxYFpVRSrw30UOEHVcuHIjT24S+N5agYMLvra6ulugFRsOkZcd2GY2waP7NbX9TvOLvDxs2rNvbZUkU6Eh9XSW2b/wKbh6+kjbm6xfdWollAKFQwbZcUrBVfHCYRsLStkbEVUVZtgh+XVXmsURZSQYytv4g30tPmZKibaivLZfoFVtgekwtQf8cikCbN2+2yTooijK0x1JMg6UXV1+PpYxxWsnWJPj72UdfrLyiCUEj0m2yvY6IRog4GAx95kwMzSDVvEgx0HZhHqPKBg1TrfWO6GtYfnfBr7nws1B9w9awykxMeBOy83teZYYDtpDwUairKUFx4dY+8whgikxR/kaMGH+qlJAt3LAWfgHxCAhKkgEeZ7yDw0a2+wy9Ezav+Qg+ftEilHBGmgNO+qVk71yMmqpCSbVhlQtGUXC2Oj9nDZ04EZ80U8QQ+r1QPOAsPdsIH03/ZuSHkUrC9/r6+mLs2LFSlYi+IPZmDsn1Y6nWFStWiEEwBcApU6bINYGdQHqd8D3dvWbEx8dLhAPNY4866ijxizjy5OesXq+lS5eKONMTMaQ7uHv4YtSks0RkoLcJBbvU8adhIGEqU0zCNFmYWkNxhO2WVaRC/hFHGDXD9e2J2ayY6e51EXIyl2D5748jLulgqYg0ENeLNWvWiMcSZ4g/+4xpacpQRPsZijm0XSj2gAoiDgY75wx3njx5sl54lDa0XZiHfhMxMTHyOFCDWBokvj/n8QEURIDggCbsLuh9lZmA4GSZ1bZWEGlsrBN/BUZ8MPWGwgUHf4YRaM6upRKjv3bJy3Bz54DdCY2NtTLYi0mcDhczkSg0kYyM20vElPLSTCllzAgBKSHzT1pLQ0ONzJa7uHq2Gs56BqCpsRa7M//Gscce2+rHEBoqZWq5GGaofOS/GQlh2l5YLYiz3zRyZQTGf/7zHynlbG8hwhRBWA2Js2ErV67EhRde2C5apLvXDD5/1lln4YwzzhCfnWOOOQaVtWMtltbtCNOb6GPS19DUtbIsC85Mj/LwEzPPpqYGaQKsijJq7EmwJ7iONEjlwrSuoryN2LJuDpzgJJWUImKmiIDSXdjWQyPHiRBI8ZBlgblvehON1p3rBcuK05uJ58x99923R0qiMrTQfoZiDm0Xij2ggoiiKIoFsrKyRBAZSDjDXlt9h0QwMKVjMMOUFZpqpm38Cn4BcSYDr/ZiU1NjHUqL01uNTQNiMXzsyWL0SgNXll+tqysXIYSCCUWP0MjxEoHCGXNrhCv6JPj4RckSFbe3xffRPJZlQMuK02UgSnPVjz/ufuoUzVJZfYgL01M4yKdfx0UXXYRTTz3V7jqBDL896KCD+uz7KPyccsopmDlzJpJH7IuU0cd26QnCykyjRo1CVFTf+XpQ9Ejf9I14dbAkbXNLk0RfNDXUitjGhaVx3dwGJhrMGpjqRUGPC8UbesAYPj09FVtGjj8dG1a+J/uE52Zy6jE2M0Nm5NSCBQvwwgsvSNQUvZn68hgriqIoSndRQURRFMUCjAzhoGwg4QCf5ocbV70nEQsDFSnSV9DzoKa6EJVl2TIb3UrrVLIxo8yUlYSUQ/bwmvDwCpSSqf0FK+V4ee8togmrz+TvXi3VMFgWmVVWGEHRlSnqr7/+KuWXOQvG6iGMuLjxxhtx5ZVX4rXXXhNhhN955plnSqrKUIZGocPHnohdaQsxcvypnQ6aWXnkgw8+6NPfL8xdBx//6H5tQ7aE5wkNf3sLjVZHTzoL2RmLUVmWidV/vYD4lEOlKk1fRsatW7dOSl9Pnz4d77//vkRSKYqiKMpAY1/xuorN4UwdZ2XsLVRbGVi0XZiHA1nOXnK/MM91oODMderEM7Fjy3xUVeT1629TpMgr6n26jAEHWN4+YQiPniiDudZliiyRsa0LB2IDUQa0M+grwVn5uXPn4uuvv5ZUJlb0oC9GZ6Vnb7/9dnnke6699lqpbMPv8PT0lDLO8+fPR01NjUQCUSBhmeehfM1gyVtGNNDQ1hLz5s3D/vvvLwJKX8KKNWx7ivnIk2EjDsf4vS9DSOQ4udasXfqqeOr0xfVi4cKFci68/fbbUtlJxRDHQ/sZijm0XSj2gLY+B4Oh2TRptLcQbWVg0XZhHg5ieZMeaEGEMIyfZVLTN3+DlrbICttDY8S8ItceG6oORXieHHzwwRLBEBISIr4bHWFECH1D2IZoKHr22Wfjo48+wptvvikla/nv0tJSKZFKT5Hvv/9evpfv/fDDDzGUrxlR8fsiJ/Nvs6+xss8zzzwj4lFfw9K29Iyhd4xiHpbFHjb8UEza/2pJb2N5XlZh6s31gma1Tz75JObMmSM+O4pjov0MxRzaLhR7QFNmHLCM6O+//y6zm66ueviVVrRdmIez+Jy9r6ur67S8qCU4GGaUCSuS0DuC38X9y0olrFrDR5aJ46DaGlqrnoxFbvaKTr0v+hJnpxYkRDdi525XNKsosgeMEmGpUMN4l74zGzdulHOK6VZMj6GHiEFgYKDMkP/111/yuR9++EE6gkyXYQoO02cyMjIwlK8ZgcHJyNz+C5qbGvcob1uQsxaXnX9Mu33WV9CHJnXCGVKZZdf2heIhw4gVLiy3zPPL3ioADaQAGxE9WSKj0jZ+gdCIcYiM3avLcsQdrxc1VUXYseVbLNy2bMiVq1a6h/YzFHNou1DsAW15DgYHaCwN2VmIt+J4aLswz4QJE8QAk6VHuwMHuW+99RbKysrElDUsLEwGeBRYePOnMGIshYWFKC4uxnHHHSclT7sKG+UMOgdu/YYT4OfT3Op7qs1jDzjIY4QI02AodtAfITU1VSI/OoMleOkpwrbCqBADdghTUlIwlK8ZFB1oUJufsxqRsVPbnm+or5YysP/5z5uwFR6e/kgZfbwY8tZWF0lKSHnJTuTtXom62jK4uLgjMCQFQaHDRShxZILCRmLnth8xevJ5WPP3i5Lm1pUgYnq9qK+twNb1c5E6/lRNkVG0n6GYRduFYg+oIKIoimKB448/XmYu6BNhDZmZmRLqzwExqygcesc7yOULJf8sbZddChr/iBp+CWj2bsSD736Hu55/H7HTT9pjlnrl/Jvb/j799NNx441XS4nU7s5mz5j1aLfer3R3HzIVYx2A7636rvq6KjzxzC0YPYmf2ZNFJsd9qGBsE8XA0047DdOmNYuQxCiq775biJ9//FSEw778re7ANKZffvlFSiSnp68RMfO3v3Pg6RUET69g8bbpSYnbwUhVRY5E1ZQUbEZEzFQxcbUWiltb181FyujjxAxZURRFUewVFUQURVEswGgN5r5bQ15eHs477zyJKBkxYkT3fsfFFeETZiB/7SIUbliMsLHTLb731ltvFWPCO+64QyIJWK1k9OjR3fo9xT5gpI+zs6sMPJm+4WiRNZ9//rl4p5AxY8ZIBZKepKb1JRRnTjzxRFkIo7cOPfZe1FYXo6IsSyJJ6mvLpcwzo1xoRjqU4Cwt22NBzhoxvmUZ4vraMmTv/ETKVbNcsq9/lMXt5udra0qxdd0CxCYeAB8/9QxRFEVR7BsVRBwM5qrvvffeal6ktEPbhfWww79+/XqJHGHJVaa68Lmjjz4ad911V7fFENPvrSnOQWDiuE7fN3HiRDz99NPyN70qWJ6UBp70pRg+fHiPfrvT9WoGsvJc5VHpe5JSj8Gm1R9KhY+AoGFwpGsG04N43tgzrHTjFxAriwG9T4oKNmHbhi/gBCeERU9EcNhIEbcGM7wGbV7zMTw8/RAcNhqBIUnyvKd3MMbvdQlKirZh24bPEZc0A1Fx++zx+dqaEmRsWYDGqlEYNfEcwEnvJ8q/aD9DMYe2C8UeGNx3b6VHM97JyckDvRqKnaHtwjponHnddddhxYoVSE9Px4svvijPM3Xl2WefxbvvvisRIhzkMR2gO5Smr4WHXzACEsZY/RlGhtCrZPPmzbj//vtldp2RI0lJrQOZvqAFTigu046KLSgryYCrqxdGTz4bOzbPl1n55NRj4ORs//vbka8ZjOgKixwnS31dJQpyVmPDinfg5RMmZaR9/WMGpTkrPVWqK/MlTaaxoVrSXvi34OSEwrz1iEs0L4Zkpi9CZVkW4pJnwsXbsaKdFOtw5GuGYhltF4o9oIKIg0FDR4YoH3HEEermrLSh7aJrWPmDVUD22msv1NbWSpSIadWEadOmycKKNPPnzxffkfQlG+AVEi2Lm28gaop2o7ogE76RifCLGwk3r1YfkdqSPEmVSZ51SY/WjZ4l7733nqzTvffei4qKCqlgctJJJ0k1m97AqhEpCQ1I2+mmVWb6AJZMLi/dhd27/kRDfZWkytDYk2a5vv7RaBEJyv7Ra0Yr7h6+iBk2XRammuRlr8SOLQskYoTCgYtr5+a69gT9USbvf40II6VF27F26WuYMv1aiRxhZEhw6EiERY3f43O5WcvR2FiD1IlnwsUZSEmo1+uFA5Gfny/m4IxUpJk0UznNCYJ6zVDMoe1CsQe05TkY7NiUl5erm7PSDm0XXXf4KIYwGoM37HPOOUfMF82VkWSH0PAgmHjx/0TsqCnejZqdu0UYiZg4E5W707D7r2/QWFfDnQ9Xb18kzDwTzq5uvVrPsWPHSpQKq9t89tlnOOGEE+TR39+/51/qBHi6t2iVmT6gIHcdsjP+QEBwEuKSDkZTUz2aGuulDG1ASDJcB9HgWa8Ze0JxKyn1aBG9ivI3YcPKdxEUOgLRCftJ9ZrBAAeyFEaYJsNtIPm7V4mhrDkxhNRUFSA8enLrINipRa8XQxQaH2/YsEGEdz7u3LlTnqfxcEREhMz0U4zfsmULxo0bhwcffLDdPVKvGYo5tF0o9oAKIoqiKJ1QWVmJs88+G8899xwSEhLkOabFUBSh2EATRks4u7nDOzxOFlM8/EMQkrpn2HlfERAQgIsuukhSavg4Z86cQRnCP9RoaqyDi4sbqsp3o66mBN6+4XBxba2okr3yHQQEJYpxp49/NBJHHDHQq6v0EKY8hUaORUjEaBTmrpd0muCwVETF7yvHvy/hIIKRRvV1FVJS2NXNu9fnOtOAtm/6RtaflBRuQ/KoYztZhyY4dVEuXBk8cHC6bt26NvFjx44d0s7op0PzYy4sFc77Yccy8Q0NDViwYIH4aV1//fVSdl5RFMXeUUFEURSlE1gelKHAubm50hEkNE7973//izPPPBNz5841GyliD+y3336YPHkyPvroI1lXZWCJjJ0qiznoR1FZvhuxSTOwfvnb/b5uSt/DqiyMqqA4UpCzFhtWvAX/oESER02Et29Yt7+PkScUzChQMD2nublJwjBY8YUViyiKUByR33Z2kfLAjOxgtEdr2eCgTkvnMlqpKH8jcrOWIXHkLPgFtA5mmxpr4dypkOMs66YM/khIChmsmEaTS97vZs6cicTExC4NL5lS+uijj4q31mGHHYZFixZ1OlmgKIpiT6gg4mDwpnbQQQepm7PSDm0XlmE48Ndff43LLrsMu3fvlsgQMnXqVNxwww0499xzRXCw19zXG2+8EbNmzcLhhx/eo883NwPpWW7yqNgODmjpO1FanD4oqs3oNaN7wkh49EQRR8pLdiJ75x/i0xEYkiKleylUWIJCQ37OahTnb5YUK9+AWASFDpdKL52JFM3NjairKZPKL7U1LBmcidrqEhE3CCvieHgFwc3NG1WVeW2ih39gAsZOvaBdxZzw6EnISl+EhOGHmv0tluLld7DErl4vBiclJSViBP74449jypQpknbJ1Je///5bzLrj4+MtfvbDDz+UiYF77rkH48ebT6sies1QzKHtQrEHnMrKyjRpyw7CE+Pi4uQG1Ktcf0VRbEZTUxMuuOAC8eWgWanBp59+itdff10iMM444wzpPNoby5cvx/PPP4+33+468mDGrEf7ZZ0cnUXzbzb7PEPO6T8zb968fl8npf/gcf7555/x8ccfy4w8RcvZs2eLFwNhisIXX3wh5+2pp56K008/vU9n3Bn5Rg8I+kIwtS4oqBNRpqUFF198sZQYP/744/d4nWkV9C5ihIAyOGEZebY1Nzc3FBcXS1sbOZKlpJ3FRPyhhx4y+7n6+npJn6Eppr1OCiiOM5ZiunBmZmafj6WMcVrJ1iT4+9mHcFNe0YSgEek22V5HRJM+HbATxgEcHxXFQNtF13D24o033sAPP/wggwMOJtLS0mSfhYSE4J133pGOpD3CaJbQ0FB899133f6ss3MLxqTUy6Niezgg6ZiXb4/oNaP3x5lVFWjU/Pnnn4vXAkt6UxhhhSi+xhl6CmP/+c9/+jz9gGl+rE61//77dyqGEHqSvPTSS3jzzTexZMmSPV7n97D0N9F2MThh9TQKXkwFZZU0Rn0w4oOCx6GHmo8MMu6LPj4+Vokh2jYUc2i7UOwBlXMdtMSVonRE24V1gxgaqq5duxZ33nmnmMyxI3nfffdh+PDhsEc4u/vbb79h165d+PXXX3HkkUd2+ztcVAzpV+hZMxjQa0bf4Onp2VaZirB0NyPSelsyuy9h5BujQCjWvPLKK0hOTm57jYNhrq9RJULbxeBsg2eddVbbv1kthn4iNBVnOoM5OCHANsrjzskA3g+7QtuGYg5tF8pAo4KIoihKN2Ge9HvvvYfBAGeAWSKYxngMbecsIGdiaHzHKgCK/YkhgyFCRLEdLN1tjzAcnREt559/Ph544AFMmzat7bXY2FhkZ2e3pfwo9gH9reiBRWGDwgWjGZ999lk5lpbg+y699FI5zoxU6gjFj5tvvhmFhYVyb7niiitw1VVXSZQkJw0URVEGGyqIKIqiDGHuv/9+bN26Fcccc4zkwDL83fAEYPj7PvvYrvyv0n04qNRSlYq9wmsIvU2OPfZY/Pjjj22eSRRZGWWg2JcvyMKFC/Hwww+LkEURKzw8XP7+v//7P4ufo9jPNEtzYsjq1atFSGdqDSvRMEqE4gl9bii2M1qSz1sD70P0ZqAHQlZWljzSS4/iGgV8LjQ115LxiqLYGjVVdTBTVeMGxN/Rm4xioO3C8aCZIo1g2TGmKZ5lU9UWeLi3oK6e7ULbhq1NVX/55ResWLFCqgPZM3rNcGxefPFFEe8ouNJHguk+9AHg39ou7IPbbrtNhApeS26//XZ8++23eOqpp7Bp0ybxfbEEfWxMy8kznYH+MV9++aWIFTT4pe/Nn3/+Kd/H91FsYYleGgUzyo3pMyz5PnHiRPEhodjBJScnRxYjvYqRKuz/cuF3G6aYLOO7Y8cOWW666SZZJ2Vo0lf3EjVVVXqDRog4IPaUl6zYD9ouHAuGTrNTS+M8hlU/9thjFsPdGxp1YNOfFYEmTZqEwYBeMxyXyy+/XCINWHWLEQE0maaXCAc32i4GHh4HCqsULyZMmIBDDjlEBk30kOpMDOHnKIAw2od/03fqkUceEWGF4sfdd98twteFF14oRrr77befRAnx/sGIQ95XDIPgVatWyfWM38XBpJ+fH7Zt2yYlfF944QWrtqO6uhr33nuvVGN6+umnrfIpUQYfes1QBhpNVHYweKP77LPP1MBIaYe2i8EBZylYAeDJJ5/sk+9jdYDHH39cOq/nnnuulA9uaWlv6Ek7i7FSZaZPflLpAkaIHHDAAbB39Jrh2HAml4NilmplagV9Koi2C/uAQoYhVI0aNUqeowH44YcfblZ0YETG0qVLsWDBAkRGRkq0B9/L7/nggw8k8oeRJowEee655yRCiINYpta8//77IrKcd955UtqdBrtMqeFvR0VFSdrLhg0bcNFFF4k/Dr/LWvgbLOfMktT8XWXoodcMxR7QCBFFURQ7hU7/ixYtkk7pxo0bZYaNJqnstHIw0lelOBmRQKGFg5sNK95BUuosePuqOWJ/wxB0Dizs1VRTUTqSkJAgi2JfPPPMM3jttdfwySefSCoKyc/Pl+foKWWUiKewRdGB3iJcGOHBimkMw2dUCKsKLVu2TITar776SgyfGRXEv/lZ+oew4lptba0IHnyOESW8ljEtk88zZYYGvLxnMVKkM/8SSzCyhdWNFEVRbIEKIoqiKHYEw5RphMeOK8tvshoM3f5Hjx7dVn3km2++6bOcUeZ78zc5a3fttdfiwy/zkL55Hrx9wxGfdDCcnfU2YQs6erXU1ZZjy9qPMWriWW2vWfIZUfoOY1/X1ZSirq4c9XUVqK8tR0NDFXz8ohAQnAg3t67DufVYKfYCIz1oSEpxgwa4FLvJX3/9hbvvuR+BwUkYO/WCtveXlQM55ayjy39VsM4RGlxm4KkXF+GmWx5BYurRyNj6Nw4+5vF/PjENX311IebNmyfVZb777jusWbMGV155paTCUBShcN8RRqwwRZOpNIbx6p6eVeaprSnB5jUbLb5fzz9FUXqD9nQVRVHsiIceekgiQxgezBm7jrDjyRDo7pZmZboNZ/lYGWL79u2S900hxDAxY5gzSyZu37QNsYkz0FBXgfUr3saoiacC0OoRtqSsJAMZW3/AiLEnwc3dZ6BXx6GorixAxtbv4OLqAU/vELh7+MHTKwi+/tGorMhB2oav0NhQLQJhYHCyCCSubq1mk4pijzzxxBNiokqfF3qIGBFnxx9/PKYf/oBc11uam+DkbDl1xc3dG2FR4+HlE4qsHb+JubZBc1ODVJqhAMKqMoz44MJyvkznvOGGG8x+J+85Y8eOxYMPPtiWYmUNVRV5SNv4FVLH816kKIrS96gg4mDQ9OyUU06RR0Ux0HZhPzA6hE795tzWaWJHvw/maVtLTU2NdFIXL14sZRHZcU1OTjb7/QxtDgyOgaurBwKCEuDjF4mNqz5BU+MJ8PQO6/W2KXsOxnem/QA3d1+MnnTWoBJDBvs1o6SkRCqk7Ez7BYmps+DlHbLHe/yDEhAdv68Ih9WV+SgrTse2DZ+jsaEWfoHxiEmYNqiOWX8w2NvFYGf9+vVYt26d+H3QANWcH1FY5DisW/4WkkcdDVdXL4m+oChbUZaJlNEnwMPz3+hDCoNc/IMS255zcnKWSBCWWqa3h5GSw3QYepFYgm1i3LhxeOmll6zaFkbN5WWvQFnxdoyaeIaIlcrQQ68Zij2grc8BoYGWlmhSOqLtwj5gagxDkE1L4fLYME2GpqesCGOpGkxHfvvtN5kp5IwdHzsraVdVVYWHH34YPr6RMhtOODvIjmhm2pdIHnN2H2yd0thQg6L8TSjK3yjpSMOGHyH7eTAyWK8ZHMiddtppck6s3BLV5ft53vj4RcgSnbCfGA+XFqVjy9o58PWPQXzKzH5Z78HCYG0XQwH6QNG3g6kyvr6+Zt8TETMFAcFJyExfxBxNeHgFiGdUbtYytLQ07fH+2MQD2/2bkSU00u0ITTEZwchUT3M+SPQloUjTWXQIz63i/M0ihDg5u8q6xiXNEBFGGbroNUMZaFQQcTB4w2I+KdVYhscrCtF2YT+wfCHzsl9++WUxTWVHgR3MWbNmYe7cud3qNAwbNkxKHk6ePLlTMaSsrAxnnHGGhED/sSqg3WueXr44dOZ+2FlAv5FebZrDwciCutpSVJbvRmV5NqrKc6RjHxw+CsPHMD3Gu9OBQVZWFtLS0mRhmhMXtgd+r5eXl5gb8rh1p2pDXzGYrxmslEEDyIMOOgh4dGm3P89jGBSagsCQZBTlbcDGVe+jpuY6OSaOzmBuF4RGoNwGa0Vne2P16tUSFWJJDDFgWtjwMSe0/bu5uRE5u/5GYe46hISPgad3cKf3DHMwWoQGrbxXUdBnVZuAgADZn1wYGcJ1s1Q6t6x4B3amLURQSApSxpwId4/Ot0EZGgz2a4YyNFBBRFEUxY5gh/LNN9+UiA0KIewgsLpMT2B1AHqHdDZQ27RpkwgwrAxAo7tHX9iwx3ta/k0fVzqhIHcd8rKWc8Tc+kQL4OEZICHnIWGjEJd0MFxc/u3wMY+/tqYUNdVFqK0uQk11oYSv83l+x0MP7ZT0ppSUFOy7775ISkqS9kEqKyvxzjvvyEztJZdcIoOR7vrKOCJ///23hPUzXaa3cMAYGjkWLq7uUnL0ww8/1LDvQQqFhLvuukuMQs855xy8++67GIzs2rWrXXShtTBabeyU81FcuBXZGb/LtSw4bCRGdtO34+ijjxbRY/ny5XKu8T7Gc4ILox8p0FsSQ7Iz/pCIRE1DUxSlv9E7t6Ioih3i4+MjS2845JBD8Oqrr+LRRx/FlClTJJKAA2kuhYWF0mmlcet7772H6Ohos99RXZEP5wi9VViDi4s7mpub4OUdjJDw0RJBQGprilFTXYyyXX+L8FFXWyYRIIw08PAKEv8KLoGhKTJza1T2eekly5UTOAPMiB6WsmQ5SgojNFFk1IgKI5ZnIpkmw0irviQodARmz06U/c/zrbsz68rAQm+mW265Be+//76Uo+VxpGjGCLvBBn2ggoKCevTZpqYGabu8Jnl6BWLYiD3TYqyBUYwzZ86UpWOq2meffWb2M5npvyJ1wulqWKwoyoCgvVwHRGewFHNouxh6MOWGoahbtmzBypUrpZIMw5VpfsfXOCNqLkS1qbEOpUXbUZi3AU5owMSxxw7I+g82OKPKpaaqSDxCcjKXiOjB8HNWMKFJbWjEGHh4BfZZTjyjf2iUyygRiiGsJMQKRf3BYLtmcLBHk2GW/mRYPwXD8tJMqSLD48HBGD10nF26v12M0MnLy8Odd94plaIcmcHULph+dvPNN4uQFRYWJstbb70lYiNFR5aPZcQDzUAHIjWtu/z6669tJqddQZG2pqpAltLidDjBSbxFvH3CMHbqhTaJ1LDUNlxcPeVYKI7JYLpmKEMTp7KyMr0CDTAMaecAhXn8aiqkKMpA0NzcjDlz5uCqax+QdA3mcQeFpUq0gzIwLJpvOUKkIxRFQkNDxUC3Ky8LziJTJOMAhOlYvO9wcYT87S+//BKpqam44oorRCBJ2+kk1StoJtlQX42Som0YNfEsmSHvybGiIEL/iauvvtpGW6D0JRQ/cnJyJHKoI6Wlpfjzzz/x+++/Y8OGPVMJe3u9pUC93377SYoLU0k+/fRTiVZhmtzIkSOlZK6lFJOeMmPWoxKdtm3DF/Lo6xctps4BQcOk9LQtrk1drQ/Jz1kjpd5jhk3v0ff01foog3ssRc8amvf29VjKGKeVbE2Cv599CKPlFU0IGpFu9fauWLFCTPl5PWNqHSfHpk6dKhNjKSkpbe9jFUP2Bbdu3Srj0sjISBGFGUWXkJDQ5e8wEoylv5lCymtrVFSUpCGyQqI9C18qiDiYIMKbMGex2GHTsGrFQNuFfWN0Gm0BB8WMZti9808Eh6UiKn4fSf0weQd8vVtQWc00AE0FsFdo7MmSmV0ZERbkrEVO5t/wC4gT4YvRQFwaGqrFLCYwJAWRsVP3CF3vOOAY7NeMiooK2QZ2oE154403pKN44okn9vh8uuyyy3DooYfi1FO7578wFBhM7YKpU6zGwgGAuY56d667rB7FKAtnZxe4ufmIyGCcQ5YG60zhos8Gq4rRLJmmkuPHj0d6ejqWLl0q68eBCUVLY134OyyPW166S8yaXV09JfIsPHoy/lrYtS8Ov2fHlu/g6R2EwJDhEh3C6z1LTNu+kovlewnFmXXL3hAxsqPZtIodQ5u+umaoINI5FCWWLFkikaRjxoxBfn6+RMbR5+enn34Sjx9y/fXXSyQl/81I4p07d4pfGQXcxYsXi8DRGeeff75MPJx99tmYNGmSpGZTHKHP1rPPPgt7xX6lGsUmsEEzpJI3XnvvrCj9h7YLx6OxsU4qChTkrEZAcDLGTD7X7Awhm0NSbAPWp9EfY0BWVbECGrZmbPseI8ae3O55GrQ2Ntaivq4c+bvXoKG+EuP2utjs4Ke5qRHFhVuwec3HMsiKTtgfHp7+Q/KaYcmouKCgQDq+5qJqWPqappuMpGGn0HShBw879HztxRdflOo/jNjp6KMw1Bks7YIddgoONE/tzqxldVUBKkoz4e7hDw9PPzg5uYon0M60nxAWNV5ExYr6LFRn/IHGxhq4u/vhlVcCJI2Fbc5YmI7DR6bkTJ8+vd3+4/pwFtdIfaFYkpe9EgW5a+Hi7Aa/wHgEhQxHXOIM5GavQHH+JhFErKGqIhf5Oavl/ObfXj5haKivQtaO3xCfPFPS+2xlatrZvYTXo8SRR2Hb+rkYNfFMKe2rOAaD5Zox2KF5PgV/d/d/J7xOOukkiVJ76qmn8Nprr8lzTz75pFmzZFZlY4QJBZPOolAoMjMN8Y477pDnWNGNkwwvvPACLr30UowdOxb2iAoiiqIogxDOqNVWF6O6Ml+Wuroyqz7HtICmxlrpcLK84qhJ58C1G6HSin3iFxCDndsqxBiRxzdt41dopkmis4vMInOQwwggegRYMv2kdwY9Triw6sP2jV/J52ITD4SjwDQFRnhwRoxCB2ccOVtGD57vv/8eO3bskDSG3bt3Szgwl7Vr18pjbm6uzPoTfoYzcaziFBMTM9CbpZhAYevjjz8WM2lrxBBG/ZQUbpWy2RQMo+L2RlVFjjzHVCueTyHho1BTmY+Ghho5Z1gthdTVlkvIOWdxaWbNyCTThWKH6e/QpyQ+Pl7C0zmIuO2222SGtqXFE6kTztjjWs00k7ikg6wuUUshZJ+DbjUvlOxejarKXKk2MxD4BcQiLHoiNqx8F1Fx+4jJdPtoRUVReso+++yzx3NMz2MK6ZYtWzr9rJEqw0yGzvjrr7/k8eST20/M8N/PP/88Pv/8cxVEFEVRlO7PnDC3fd2yL+DtGynlPWsqC9DUVN9q1skKJb7h8AuKR6gnjTq7Tmnh4Fid/IcmPP6MCEnb8CUShh8mg5+eEhCcKAsHfru2/ywzRMw3ZsQDSwAP1Zk8VhZhCgNDpClwsAPIUscXX3yxVH1iFAjhILerMPCSkhIZ1Cr2A0PGmSLDmU5rPXPWLXtdPDZa0IKk1GNEfDRl6aJHEZ90MNwC4rBjy7fw9g1ve40RVscff3y31pFth7O0P//8Mx544AGpEGYpfYeRIds3fdOaAtcLeK1g2gzvDwNJWOQ42RamcW5Z84lU7Tr++MUStUVfFQ6sLFVEUxSle1CEZVRkamrqHq+x4hb7oBRzWamQzJgxo9Pvq6urk8eO3ke8hxrlze0VFUQcsMPMXDMtC6iYou3CPqFRJnMwx069CNWVeWhuboR3Yli3zO96TQtQW+8kj4r9dmqydiyCj38U6usq4Oru3SsxxBQfvyiMHH8qvvrqegmH5SCNhmkURCiQ8Lm99tprUFTgIIzgoOmbOeNZGlp+8MEHyM7OFtGHocTctp5cF7l/QkJC4GjY872EkTyM2mFuO011DTNhRgIdfvjhSExMlPfV1tZK2Hf65nmSWlhZlo3SojQkpBy2hxhSU1Uo4jSjqzK2/SgeH6GR4+W11qopLW1/08D14IMPbvsdSzAqhFFF3377bZf70csnRHyf1q94C++9FyfVk3ra7hihkZm+SAR2L+9Qi+lytr6X0NA4JmGaLOTzz2+QQdn69evbTKMZft+VebQyOLDna8ZggJFmpnh4eMhiDfQoYrTj7WZMpSmSGAIHhX2KIl2lgA4fPrxNeDYtW05zasJISntFBREHg+GhnOlTFFO0XdgfDJPmje60007DEy9cAV//aPh49c0gtzs0tzhha4aGLdtr2hTNFZn/7x8YLwM2zlAz3NwW1wiG3Bphtxw0MjyWnhr333+/zAAdeOCBUsqWZmwD0bnloJOixqJFi7Bt2zYUFRVJlMbXX3/d7n1cP6NqCM38mD6xYMECJCUlyWCZA9bHH39ctouGcF1Fgyj2fy9h+XGuF/06jjnmGIk24PWVkUCsuPDf//4XhYWFIkKzUhAjEXwD4tBCEdonTNISYxIPaBNBCnLXoax4Ozy8gsTMuDBvPepryzFl+nVSbnvzmk8knYb+ItdemyOpVoxs4MDg9ddft7ieLJvNNJprrrnG6m1jug7Pf8MngNt12GGHSb6+qWhAMZBeJKykVF9bAW/fMETG7iXnDc9W34AYDBt+GNYueUXeP+3Q++Dq5jng9xKKrRxcceGx+/HHH3HuueeKUaMjVMYa6tjrNcMcP9W4w9vVPsT/6pomeTTMUA1uvfVWEVW7glVkbrzxRuy9994488wz93j9s88+E0GE6TSffPKJmK92BYVlpvzxGsprz8SJE+Ueykg3HmemktorWmXGAavM8MbMDt9QDXlWuo+2C/uDqj1dudlZ3ZHthcrybDg5uUgVEKYydLcsaE9xQguCAppRUuaM1m6zYg8U5W/ChhVvIy75YETHT5PZXPoVbF3/GcZNvbDPf89clRnTawbvYzSCXLhwITZu3Ciz1JwN54w8zUVtBdfjt99+E3NMzj5xoMuwXnYS+bt0yadnBDtodNUfN26cDIC5bhwAcyaNBqhHHnmkmM3R54GzZTRR/d///rdHFRplcN1LeP187rnnpA1wYM2oC0vRTIxAoHfHhAkTpB3nFPkgN3OpiNHOzm4iQPKK6OEZgNDIcQgMTmwz/1y//G3EJx8saSekprpIxBSalr729ImSasW2+OCDD4pg8dBDD+3hX8LzhoIGDV879gWtqXhjnKMcxDCCgkaVhhhIkYX3k9UbG2TdWWqa6XC5WctRX1cp6ZgUSTy8AkUk6RgJM5D3EnNVZuhF8NVXX0nUjT20M2Xgrxn9UWXm0zWp8LaTKjPVFU2YPWGzXDdMTcKtiRDhRADFC4qkrDAT1UXlGFa+YsQkhQ0KrZ1B3yxWmuHkhLE+nFzgJAMnF/744w/YIxoh4mAwH4zl3Kjg6U1EMdB2YX9wNpHu3Nu3b0fa279Kx7uidBfyspcjImaKpDH0ByxGEhvRiNIKd8h4QLEbgsNHIXHEkfI3vUNYoSE59dgBuWawA8pZPmOmj3nJnO1mqT9GjnDmvS9D3BmhwmounPnn9999993tQnRNoSjDxRRGkISFhckglekRHEBOnjxZOm6cXWPZXGVw30vY2b/22muls3/cccdJGkpnqV00+6NgwggEmuo2u8Riv0Pubv2uhlpJVbQU+TR2ansjUi/vf9NWKNIZUJhjZAPXh79HUY7nBQcojHZgylZvB3OlpaWyHUbVCB4TRhqyOsS9jy1rlw6XPKp/rhfW3kskWsVkH/Pf9PLh7DQ9fLhvGI3G6hicbWZpa25nUFBQv22HMnSvGYMRiiHduWZw8p0RcHykX1ZXYghh9CRLgjPFpitBZNSoUVJOnIIIr0W8/vEax/sqq2rZKyqIKIqi2NlsCWu1GyH8nO3+7KMjpLPAPE7O/rHjbFo6zdoZRHvDe0dJn3xPdWKQw227R3MISmqdUbN+NUJ8ErC1YBFiPUYgtMAVKCjp833YsX05O7dgbEo9Xv3gSTQ3W57tbcFBeO/T9Xj0iXEi5IVHTzJb8tdaOEvP8sG5Wcuk2kdY1KFY+LczFv49p8ffWVGWjS1rPkZg6HCpqPPAkytl6WyGWrFf2FabGuukfDTNOYNCR4iRNKOonnk9zWybYhsoKdgsKWh8b1DINASF/Stk9CZ1xNy1ua5mIn7+ixVpYtHc3AA3N5pme+D0i97r8fXiqFGtYfI7ipbAyz0QVx7dKohkFC+Fq7MHHr3iB7RaG/bP9dT0vKF4wSgdht/v61Uqho1cKGx2vP8xcuaxxx4TkYgz0nfcsVUGfYwi4Gw2/QkoJp111lky2GK4PweEFDEvueSSPll3RRmK8HxjJTVOtDHCypyZqiV4DjNy0hooalIYMfjhhx/k3GbpXntFBRFFURQ74t5775VZayr35mYjrSkTqQx9XJzd4OMegsbmWmzKWwgXZ1dE+7fPJbYH2IZZOYI+Bzm7lmDdsjekTGhQaKv5WncoKUxDZvqvCA4bKWlBNLLsC5ge4BcYj9jEA/otFU2xHYyW2rJ2DmKGTZcy1CPGzRazYRcXtz2EkJzMpSjMXQv/wAQRQOKTZ7alwdgSpqZ4wDZtLdg7HrtKVyLCd4T8e2fJCoyOOHyP6Iv+gBEwNGNkOhKjYhjJceyxxyI8PFz+Nhc1RlNjpi5R5GA0DdOXmPbESYCrr75aJgVo5syILho7c8aakwimaQOKouwZiXPBBRdINA6rbNE7xFxUXUVFxR4RVzzfmJoze/bsPXxIeA4znagzIYWpgkyXOeWUU2CvaM/aweDNkI1S3ZwVU7Rd2A9U4I844gj7ORYtQEWVs1aZsTPyK7ZhW+FvCPVJRHzgZIT7dV9g6M924ezsiphh+yMiZjJ2bf8Fu3f9hWHDD++yGg5TFQpz14lpJd87etJZNikbHRiSjPKSDCllrQzeewkH/Wkbv0Zo1HgEBCfBZediODu7mPXEKC3ajprKfIybelG/iCD9RYBXFPxrI7Ex7wcRQvYbdj4yS1ZJpEiU/2hE+Y8RAdWWsNrOPffcg2XLlom4QVGEgy36DDG6ozNhn6lNV155JaZNm4aXXnpJnmPpYob2s0IUn1u8eLGILDR+ZNTI999/v0fUpDI4GOhrhqNwxx13SIrpUUcdJWbjNEo15bTTThMPrTFjxuDEE0+UCA+mp1EIMVL5br65fbQkq7DRqJopegb0KuK5ahhXv//++8jIyJB0G3sWLVUQcTB4E6KZnKKYou3CfoiNjUVaWprclPoLlvOluV5x/mbU15UjPHqipA7Iay1O2JGtTv72RqhvEg4fcaMIDQNBT9sFxYyk1FmorSnBzm0/oqG+Cm7uPmI+6ekdLKkOrNZRV1cmJo/sJNMEctTEM21abppmmFk7fpeUHmXw3kseeeQRODk7SzvauOo9BIWkSPvqSHNzE8pLdopJ9VASQwwSgqYgu2wd1uV8i3FRRyM5dBoSm/fG7vKNWJ75CUJ8hiEpZF84O/XttjPqJiv9N5QUpeGuG56ViEdjoGtt22AECMUQfvauu+4SXwl6EVBMYeQkU2loqkpx5JVXXpFwfI2cHLwM9DXDUVi3bp08Mh2bS0dOO+00EUBYwYnnGg2ZGd1BcYOeIzfddBMSElpNoztj0qRJIqDQ8NjT01POZVbWogeJPaNXEAcMmaLaRwf+zszFFMdC24X9wJsSczx586Drel90UDn4rKrIlaoCHGQ2NFTJDN7YqRciL3sFCnJWIzx6MkaMOxmbVn8kVQYMnJxaEB7chPxiF7S06AyOvSADmQE8HL1tF4zEoDEwZ/QpitRUF6K2ukgGr75+UXD3DIC7h2+v/Ea6tz7BqK0u7JffGsoM1L2Ev8soBFYSCgxORv7uVWIY2jHih5WYdqb9KKKbX2CcpF8NVWICxqGyrghltTkI9IpGZX0hdpdvgJuLB9IKf4enqy88XP3kNTcXzz651zBViZE54/a6SCo39aRtsCoFrwuGOMLoEKaR0jyZ6TE0bGTUCStZ0DdExZDBjfY/+wfTKA5LuLu7Swlya6Exa0dooM5lsKF2vg4GTW0YZshHRTHQdmE/sPLF22+/LRVm1qxZ02kngu9jTidzsjeseAdb18/Fji0LsG39F9i06gOsX/6WPJ+d8Qca6isRFDIc8SkzERY1AaVFadiw8h32YjF26kUICR8tJoTJo45pl5LAyb2IkCZ5VJS+bhecPabwERA0TExXQyPGyECVBpj9JYYY6+HtG4mKsqx++82hSH/fSzhwZtQAw8BZ2vapp55CSMRoiXqjl4jp+zLTF0klpqi4fTF26gVISDl0SEaHmOLnEYqahtZBS2V9MVyd3eHs5IZQnyTUNlYgv3Ib0ov+7vXvcP/u3PaTRNzQ7Nhc+kNP2gYHyEyhYSg/j7MRcXLIIYfgvffe69S7QBkcaP9TsQdUVlUURbEz2Mlj3iU7f/QUYe7n8OH/ekSwLOT//vc/Mah68803JS/zwCMfQX09Iz+q4erqCVd3b7i4mM+pDg4diWmH3tsmfFRX5mHbhq+QnHo0fP2j+207FcWeCAxOQknhNvgFxA70qihdQEGYYd/PP/+8hGQzP51RBIRCWvLo4yQlK3XC6fIchWIPzwCMmXK+Q3kV0Hh5e9FiMWF2c/ZEmG+KiCIBnlHwdg+UtJqymhw0NtfL851Ff+Rlr5Ky7ymjj4e3bwSKCzajKG+DGNYyXM0/KB6RsVP7dP0Z/fHGG2+0/Zslsllyl1AE4wSCoihKb1FBRFEUxQ6Jjo7Gq6++ik2bNuGJJ57Arl27JB+zqqoKEydOxNy5cxEQEND2fs50cladS2ezeOy8VlcyfSYPdbWl8m92dkdNPAPuHvZreKUotiY3ezlGjD15oFdDsQDF4V9++UUGxTTpo+8AhRCa/XVk+8Zv2gx7a2tKUVq0DRP2udyhxBDDYDU5dDrKanaL6LGrZCVqG8sR4BmNvePPkLQaFyc3rNs9D80tTZI6E+o/HYEhSfJ5plbW11dKWWoPz0AR0SmGlJfuRE7mEkTF7QMnJxfxnuK9pKx4BwKCe5/qaQmaPTKSgGVDi4qKxNRRURSlt6gg4mDQnCopKUkeFcVA24X9Qqfvl19+ua2GPPOru9upb25qRPqW+aipypdOLTu0Pn4RUgrV1c0bbu7eFj/LVO7iMvpE9HpTlCHEUGwXFAbNGXAqA3cvqa6ulgoiFEFYwnXmzJli7pecnNzp55JGHY2cXX9LmWea8sYMO0BSBBn9Q3NeesbQPNoRCPCMlIWGqnWNlVJpxtPVH6t3fyVRISzPOz76WIkiqWuswta8ldi9c3FrXlxLi5QIZgQIq/JMmna13H9qqgpQnL9JHqPjp8Hd0x8B3iHI2PYjxu99sdl0t75qG/z9lJQUWZTBj/Y/FXvAqaysbAh1ZwYn5eXlEiJPcxpzMx2Koii9YefOnZJ2k1lsXy7f3jtK+u23qhMHXznV/tw/Q3EfL5rfvkSgOWbMerTt71YPnWP3EEWs+R6l9xjHglEJ2Tv/lCgEVlGi91FIxJhOo98s0dTUgMLctSjM2yC+IpWlWWhqrsfIcbPFS8nRz2MKIHkVW1BQlS5GzUyx8XYLhLurD/w9I+Hp2nouFFbtwMa8H5EYvDei/Ebh5OtHYPny5Xj44YcRHh7e9n2sCnP00Udj3333HcCtUhx1LMWo2czMzD4fSxnjtE/XpMLbzz58h6ormjB7wmabbK8johEiDph3y5vY1KlT1c1ZaUPbxdCGpdJYd76xoRaubp7driYSE96E7HytMqP8CyeAI5JdkLe9CS1DxAvPPzBBSrHSlFMZmHtJQ301Nq3+ELHDpiM++eBeG+u6uLiJWS8XZU88XH0QHzRZFqbUVNeXyFJem4NdJSukEs3wsAMR6pOI/YddgLyKrViT8w38fxyPyZMnY/Xq1eJvRUNbHm+mZTKSxxzaz1DMoe1CsQc0PsnBYO5lenq6ujkr7dB2MbQpLi6WdBvOtnYXRk0HB2iVGaU9bA8Bkc5Dql2wuk1FWeZAr4bD3kvq6yqxafUHSBp5FILDU/u1ypACSZ/x94xApH8qUkKnY2rcqYgPmoJ1Od+iqblRUmqiA8ZgSuwpePfddzFjxgxs2LABTz/9NC644AIpGc8ZelaAMYf2MxRzaLtQ7AGNEFEURRnCNDY24tJLL8UjjzyCm+79daBXR1HslprqInh6hwz0ajgcFGtZLnfL2k+k7LePX9RAr5LyDxRIEoL2wobc7zAu6ug2/yofHx9Mnz5dFkVRlMGOyu+KoihDmOuvvx6nnnoq9t5774FeFUWxazEkN3MJgsNGDvSqOBScGT7uuOMk7WLs1AtVDLFDwnyTRBjZUbxkoFdFURTFJqgg4mDQxXns2LHq5qzYZbuoq6uTGSiWnP3555/lby5r164d0PUarNBsi+kyFER6CquI5BUNrWoiSu+hb0jhrqHhH1Jemom0DV9ixLjZWnraRvcShsPzmn7eeefhzjvvlOfmzZuHyy+/HK+99hpOOeUUhyuJO5hICJqK+sYqiRSpaSgf1P0Mxb7QdqHYA5oy42DQsGjcuHEDvRqKnWEv7YIlZbOzs3HAAQdIisfnn38OLy8vmT1Uus+cOXMwe/bsXn0HjVTzivRWobSHAlnRzsGthmzevBnpm+ejsjwbqRNOVzGkD+CgJiYmRsw2MzIyZGEUyJYtW8Rz4v7778cVV1yBe++9F/fdd59Eh1x99dW46qqrBnrVlU6gWJUacQhKqjPxd8Y7mDt3Jk444QQsWbIE69atE4PVSZMmwdXVtV/6GTTiNGfAWVRUhD/++EP6Efvvv7/8ng607Rt76X8qjo32ch3QT+D333+XAWdnNy7FsbCndsHokBUrVsjfgYGBA7ougx12WDnQOOqoo+Du7t6j73B2akFCdCN27nZFs1aZUf6Bfpcxo12RvbFxUEWJsHwihcK5c+di2LBhCIsch8SRR2l0QjcoKyvDjh072gQP/r1r1y40NDTIfkxKShJzzcTERBkon3TSSVLpyhiY8pECyQ8//ABvb28RRzhDDKwe6E1TOiGnfBPW7P4K/h4Rcv4wsofldSdOnIgrr7wSd999N4455hib9DP4WUYUffnllygoKEBeXh7uueceHHvssW0CyRNPPIFff/1VnouNjcU777wjbfX111/X89uOsaf+p+K4aMtzMFgSLTc3Vx4VxV7bhQohfUNycrKEpF933XV44YUXevYlToCfT7M8wj6ah2IHcHzhE8SUNvtvFryucdb4rbfekpKgrIbBAR0H4zNmPYqhDAWgL774Ajt37pSBZH5+vjzPKA4uFKBN/2ZEXmVlZZvQYTzy8/X19fJZih0Uk7hwZpcD0Li4OBFdKYp89tlnkgLj5uZmcUZ45syZeOyxxzB+/Hg8+eST8tuKfRPoFY2pcafD2ckZjz32qLQZQiGC4hcHsxS6Ro4c2Wf9DLY7Chp//fWXiC3//e9/cfvtt4uoZvo7d911F6KiovDtt9+2iR+MPuL7KYycf/75vd5+xTH6n4pjooKIoijKEIadwvXr1+OBBx7Aovl3tXtt+/bt+O6776SzyZk08ueSNFFB/IMSEJ98sE3XrToxqE++x3tHSZ+8pz/XZyjSn9ttzbFi2DzLgy5cuFDC5zlo4sDNlEXzb8ZQZOoBNyB75x+or6tAWOR4ePuEwc3dB27uraHpO3LLUbd4K+pql6O+rhz1tWWoqytHc1MjXFzd4ekVBE+vYHjw0TsInp6T4ezSKnCUFwOZxcDvK4tZ1BtAa0QfcXZuwdiUerz6wZNobjY/K//H999iyYrdGD72ZCzb6IRltywAwKVvcNTzz9Z4uQXIQi4+9Hl5bGlpxrLMT0QvX/1LvqTUJIXshyDv2D0+7+wCDJ/mhvcfuhvNTeZ/g4PiqvoiFFZloLg6A67OHggdNR0BQYfhix/q8cUP72L3rlIU5W3FATPPkLYdHJaKLWu/R1DocHy2oKb99zW3YN3yh/Dq+zvw50/32WK3KAOIqaDd2FA7oOuiDG5UEFEURRnicEaNg8H//e9/uOWWW2TG+MUXX0RKSoqk05x55pkICgpq62Cwk7t++ZuAjQURRbEFbL+Z6Ytw1VV/iV/FzTffbNZvYKiybNkypG+eh+RRx8LbN9zse7xcQ+HlE4qBwN2zdVCtaQyDHycnZ+wdf0bbvxsCJ2JV9heYGjsbzs57DjFaQMGjBNW15ahtLEdtQ7mYtNY3VbVGmrW0wNs9CKE+iYgNHA9XZ3dUB7cXP6Pj95WlrrYcednLkZ2xGFFxe6OyfDc2rf4ISamz4GG0MWcXDBtxJHZsoeCmgoiiKOZRQcTBYKeQ5TcdqXOodI22i6EPzQzvuOMOydOluSHzsWlia5aWFpltYQfTzz8KWXmug8onQrE9zc1A7rYmebQnGuqrsG395wgMHY7PP5/rcIPuDRs2iAA6auKZcHXz6vff53Wiq+sFRRpGrihDDzcXT8QHTsK2wj8wMvwgea6gMh07S5ZLqk2eUwyKiqrh4eoHL1d/hPgkwtPNHx4uPt0+Vz08/RGfPBMxCftj966/UFGWCf+gYdi67jOJGolO2E8EG//AOBQXbMKzzz6La665xkZbrvQU7X8q9oAKIg4G8y7pK6Aopmi7GPqws/nwww9b915nF4yZcj62b/oGrm6eSEg5VCtwKO1pAcpy7UcNYah9adF27Nr+M5JTj4ZvQIzDiCHc9ttuu03EzqeeegoffvghZp//9sCsC5xQXNbFwKalBbU1pWhsqBkQ0UaxLZH+qSityUZW6RrEBk6AsxPLtjdjVOSR8Gr0R5j5oKUe4+LqgbikgxAdPw05mX+jpaUJRfkbpXrUyPGtJecTUg4Tn4pzzz1XfEXUs2Zo9D/rastQWpSOspJ01FYV9fm6KY6D1qJyQDdnmk7xUVEMtF0oHXH38MWoiWcgOm4qogK3o6YqZ6BXSbGzKjPDprjKY39Q21CB4updaG5uvUZxgFVdWYC87FUi3K1b9jpKi9MwZvI5IoYMdXit/vPPP9sMJpkO99BDD+H9999HeHgfjzi7WZVqxLB6ebTEiHGniG/Jnz/d26/rpvQfI8MPRkVdAbbk/4oAz0iMiTwSG/MXIGpCnc2uGfS+iU08UMR8RojkZa9AVUVOuwmBG264ARdffLGk0a1atUqNPAdZ/zMzMxPvvfeeHEOm++7c9qPIsIwUGj353H5ZX2VoohEiDgYv/nSd15uAYoq2C8USfoGxiIgKwIrVv2HYSMslFRXHgsEXHt79U2VmR9ESlNbulnKfaYV/wAnOaCnygpdPCPwCYhEVvy+8vEMkPH4ow1KjCxYskHK1paWlUjVm8+bNePnll3HppZdKtRZGh1x//fUDt5JOgKd7S6dVqRhttt8hd6Mwb31/r53ST/BcHBVxKPIqtmJNzjfyHIW7ZrcKODl52PSa4ezsJpEDLi7ue1wTJkyYIIPvlStXyrlCT61p06bh9NNPR2pqqg3XSulJ/5NVhhYtWiTllHNycqSaFVN+WaqbpZXVVFXpK1QQURRFUbrEyXloDzYV+4Md5C0Fv8DN2QMTo0+QWd7Eln3k+brkMDgSFD9OPfVUXHbZZTjrrLPw0ksvSRnSX375Bb6+vvI6xZIPPvgAgwEey7DI1oo3ytAlwm+ELM0tzXB2bkaAlxcK0GDT3yzIWQ1XV08EBCfByyfMbNubMmWKLM3NzVJl7fnnn8e2bdswefJkEUj2228/hIYOjOmwo8LrOkt8UwDhwhSnhIQEEUAefPBBTXNSbIoKIoqiKEqnMNffPzBhoFdDcSA4gNqQ+x0CPKMQHzSp7Xn6EUj0gYNRU1OD4cOHw9XVVWa233jjDUREROCPP/7AE088IYaEfORzimJv0FCVESJO/TCozsteifDoSWhsrOkyaozrNGLECBxyyCFISkqSKIRvvvkGc+bMQX5+PmbOnCkCJKMRlL6DEW5btmyRhVFudXV1ku4XHx+Pgw46CI888ggiIyMHejUVB8IhBBGeWDRRMoUdi+XLl+9xIT3llFPw008/ySwLZ18M5s+fjzvvvFOUZebpHnnkkW3hXOPHjxclefXq1fDz+9d4cPr06Tj66KPF7MxeYKeJFxt1c1ZM0XahWIJVRBYvXorAyEMGelUUO2sXmesbbVJlpr6xWsSQKP/RYtCoQAYK27dvl77J3LlzRRjJzs7GAw88gI8//ritbPZAw/aQnuVmd9WHlKF9zTDI371SjMALctZi9ORzunw/B+OMujrnnHMwbtw4iUJg2Wp6VbAyGwfut956q5xvjz32GMLCHCsyrTc0NTUhIyOjnfDBaxYJCAjAyJEjJU2JkW/8N6NBKFApykDgEIIIGTVqFL766qu2f/Pi1pEXX3zRrCs9lcsbb7wRL7zwgogmV111lajG7u7ube+prKzEc889JwZn9gwvNlFRUQO9Goqdoe1i6FNVVYV9Z1yNksKtqKkuluecXVylBGZQ6AiJADF3/WM1iKKSKoTFew/AWgPeO0q6fE91on0MBruzPtZsl13TAlSX2MYJwDs1HfM++gBTp06FrTlqVNcTFgs2PQJ7gDPXLJXN/gv7IldffbVUlTEnhgxk++J4d2CuFspQvGZY05brm2qQW74JOWVr4eLkikkxJ8F9Z7m8Vl1fiuLqnTjttBWShnHllVfKIJxQAGH/hx4iTD0j/Lu4uFh8efg+Ri5QJDnjjDNw0UUXyeuOUsGqI6aeHaYRpNVVhaipKkB1VT5qqgrR3NQg0TkeXoHw9gmX1CVvn1C4eybJvqsoAbL+Bhb+nQGAi6IMLA4jiLAD0Vko6dq1ayWHkMY9DJ/rKIhw5pyRIIR/8zlTQYQXTgoml1xyiV0ryA0NDfjyyy9xwgknwM3NbaBXR7ETtF0MbTiLvHDhQtRU+yMqfh94+YRLp6SpsR7VlXkozNsgbu0h4aMQHj0Zbu6twxl2ajK3/4jjjzsS2zJb0NzsmJ1AZU+cXYDkfdywfUkDmpv69rsPO+wwGYD0hyBib5SVlWHjxo0SxWp4GHDCZeLEiVIy9MADDxS/kLfffluu16NHj4ajtAtlcGOrtpFbvhnrcxcgNfwQBHnFINIvFe6u3iKEbMz7AV5u/gjxHiZ9dE9PT5x22mltgsj69evl/GIkiCGIkODgYHzyySfyPqbS7LXXXhIp/uijj2L27Nl4+umnHTKNpqG+SsqblxWno7amVahycfUUscPLNxxhkePh5R0qFX+sxdm5BaOSGrApnZFl2sdQBgaHEUQYasrwLM6u7L333rjnnnvErZhUV1dLCafHH3/crGji7+8vOYQUSjiIYOqMaWoMYaoNzc1Y+o7fY89oaVXFHNouhi68fv3222+IjN0LLq4ebc+z0+IXGCdLc3MTivM3IW3jFyKUEM7wRMZNgoeH1wCuvWLPAxxbwChMDvZpbEghYKBhNAb7CfTx6GujRYaVc+D19ddfo6KiQvobFDk4QcNZas5KcwDH9/n4+GDp0qXS/6CPCMPMHaldKIMfW7SNCL+RcHZ2RUbxUinNHeqTjF0lK5FfmYYxkUfAy631POH5c+aZZ8rEJWHkB42IuZg7rzn5+corr+D8888XE2P69Vx44YUinvA7eI3i41BO82Bk6e+//y4TKuvWrUPaxjwEhiQhZtgB8PQO7rNIGRdnrXCoDCwOIYhwlonpMJxxYbgcRQvWr6azNDsW9PigSEK/D0swh/Dyyy+XC19HMYTwosAyUAylu+KKK0RRVhRFsQd4PbrrrrtwymlXI3XiGWaN5pydXRAaOVYW0tLSLO/j7A3QKpAoSn/A+ywjIHg/ZRoq/Y1sFRlXUp2F2sYKNDTVoL6pWvxLGH7f1Pxvmz/uuPUymGJUaFFRkQykeuvZweoWn376KV577TUcf/zxePXVV0UMMeWZZ56Rqgt8/dprr5UUGfV5UpQ9+9/hviliwPxXxjvYWvArEoKmYkrsKe3udfQSpKDBFPp3331X/HgY9dHZOUWhhH6C9BNhlMh5550n0bTz5s2TiJPjjjtO0tY4vhgK8JpI0ZUCCB95zTvggAPEY2Xs2LE4+Bj7nvBVlJ7iEIIIw28NeEJTIKF50hdffCEXO86cUgHtiq5mYw499FDsu+++YrpKB/ieXIi4GB1CXqQ5K8SOk4HxPGfzTWt28zm+Zul543v5yO/je7h0jAowvFU6Ps80Cn6O62N6E+L7LT1vad37epu6Wnfdpq63yWgXfBwq2zQUj1Nvtokmz4Ghw5GZ9iOGjTy87fnWjzr9I3z8S2voass/zxuPxvv5O7DwfpMnW1gtxAlOaIGTueedWmA6wcTVbenwPGcUW5pbX+N3mL5f1qWFFQz4IqzYJkvr3r/bJGvn1Pk2WdzWDn13I/S8W887td9WWcdmy89zPUy31aRZtfv+ro6TtdvEdkvBgQMWTmBwwEHfLkaL0HiPM7Tl5eUSUcHBDQU/a88nRnn8/PPPMqgpLCxEWX2lzCB7uwYiwCkK7m5e8HDzhouTm3wHt+mbbx5pO59+/PFHESruvvvuHl8j+H6mv+yzzz4SGeLl5SXbw5Rd9iH4Hdy2N998U8xUKZqw4oLxO9ZcI+S8MY5fh/3eV8fJXBuz9Hdftr3+3qYun9dtsmqbTH/HFtvk5eKDGcn/wZaCX9GASjQ71cPNxbPt/VlZWViyZAlSUlLk3HvnnXfkWsLIcd6TjUiPjucTfUO48HVGkdMv8IYbbpBJ0lmzZonH4P7777+HYDkY+hGEKXosJvHnn3/KOrEcMavucLJYroH/rDs/y/teC++tfXjPNe1j9Oae2/G3FaU7OIQg0pHAwEAkJycjPT1dLgScgWGnwxSqoaxF/u2333bru++77z4RRq655ppurxc7aN7erbn77ODxgs1KOFxPU0GHYg4FHEa7GDDChdv0/fffS8fKgDNrNMvkdxsXZV7UGP7LGa/PPvtsj9QfvkbV3IAXUeZM5uXlSYfNgLNZjKrh/qOSbMCO28EHHyz7lvmZBrbaJgPemLj/dJu6v00MhWS74O9z/YbCNg3F49TbbYqK2xuRAdsQEFAAD89WgZcVISqrnSSH1zRsdUuGGxoanTA6iSJZ6yNZn+YON9cWjBz2b2euqdkJG9Lc4evdgqTYf5+vrXfC1gx3BAU0Izbi3/1VUeWMHdluCA9uQkTIv5224jIXZOW5Iia8CcEBrc+7RLuhcFcTinY2I2a0K3yC/u2F5W5rQlluM1ISGuDp/u+6d7VNY1PaR7z09zaRkkbnTrcpYZIrPLz/fZ7VGWhIyBx800HAjhUNaKwDhk9r7/2z7c8GMDsqccq/z3NQwOe9A50QN/bf239ddQsyVjQiIMIZkcP//fKqkhZkrW9EcLwzQuP/fZ7rx/WsLG6W9THo6jhZu00853g+sZPOzjnbMwcu7LAzqoL5/RwMcGDBcHfO2jKf39z5xAkPRoPyO2tra0UQ4fddcMEF2L17N5ZEb2q3Tdz+gEjndttEjPOJ91CKFfTloUDDkHJihI5bc41gBYsxY8bIuc3t4LpR+OH6rVixQvojDM9nhAx/j3939xrB9mBsU0Tynttky7aXsdr2ba+/t6k/zidH2KbS3Cb5jdixttumcQkzET2xDnWNlWhpqYBTiysy/nbFgw8+2O4aQcGV1w+ebxwXGAN/egQyFYb36o73XN5feR0yvdczemTVqlWS6scJV2Py1F77ERSFKAYz2o2P9D1kmh5TgAzxhdvAaxOtAEz7Ebzv8Z7W1/dc9jH494Y0jx7fc+vr6tF6pVSU7uNUVlbmcJIaDZTYGaH6eeKJJ8pFwRTmLbNzwtK6w4YN6/S7jLK77CwZpquc+WGHicZn1pTd5YWSfia8MBkhs7aa0TaiQmgsxQ6cPc9oDwZ1fahsExd+F//NdRkK2zQUj1Nvt4nhrk5owpa1nyAmYX/4BcVbMbPTOltjvK+/oym8M0q7nCmtTQocdBEiXumlg37218W1fbRIX81oz1v3sNXnE8WFhx9+WFJOTM8b3lfpFcZ78UknnYQJEybIoIDfYXo+nTDp7i5n6edv/DdChNTX14twwVKSHGDQlJ2RHgwvt+YawT4GK13QG4TRIYSpthR12AfgwOW6666Tcz8xMVH8RLp7jeB2DVTkgfGaRlPoNnVcR/7d1Nh/28RztrQ2B2l5i5FbvrXdecPrBkUIiq6mz/M5psUxgrzjvTU/Px933HGHeIuYPs/znhEot9xyiwgnfKS4YQ/9CIomixYtEnGDZW85ruE1hgbNFEO604847Pgn+zxCxLjH8rnmZuce33MbG2rxxw/3yj2hY/phbzHGaZ+uSYW3n32kLVZXNGH2hM022V5HxCEiRHjxomcIGzOVTl4EeaJTdeUFz5yRKjsmXYkhlmAoLWduzZX27Qxe3DpW+DAGpx2x9N2Wnje+lxdglh/mtpv7vY7vN4UXSHPmUZaet7Tufb1NvXlet6l13XkjNNqFsR2DfZuG4nHqi21qgQuSRp2EjavexYhxs+Hp1eqF0Jm7++ikepnRMX1Ph6jbf/hXMGn/m62pB3s838Jw3M6fN61GIAMEM7/KTpG5Fyxtk/l1779tav1359tkcVstVGfo1vMt3XteBjcdnueAI2U/N5mR7fiZ3m6TEW1hzfnEFBrOhmZkZMg9nh17DmY++ugjuRfPmDHD7HcY54216256PvH3OZjgQugxQGHD9Lw1XUcOKEy3iREfTKvlhAxnpvkaRRaKKzRv5Ow03/f5559LNExPrhHWnDe2aHtsF5y1N9cu+qrt9WTd7f18coRt6njN6J9tckKAezT8PMMlkoKpdwaMFmNVJ9PzyRAGwsPDJVLEEBV4DjOqmmk2xx57rNlrE8cNjDrheUvfH0aRMdK8v/sRRlQZbQE2bdok28LtNi0m0dN+BO97fX3P5eobfYze3HO1Qo3SGxxCEGFYLPP/6NhOAYQ5ugzF7Wu3eAPmJ5599tliCqcoimJvuLp5YvjYU7B13WcYM/ncdpVnFGUwwU48Z2OffPJJuddzMECjQ4bCM2LD1hizp+YECkan0G/gu+++E+GGfQKuLyM0GSHChRGrnIWlMMJIEA6iaP7Iz5kTQxRF6T40XGVxhc2bN+OII46QVBZGb5988smSMsMqkoRjA0Z4UPTg9YP+Ioz8oMCwePFiibLg9aYzGJHGCAy+7+OPP5ZJWNOSvraCKXcUgrlwUpbCKj2W+qoSjKIMZRwyZcbeMEKxqFTbOuyJESLMTzQiRBSFaLtwTNjJe/bZZ6XTZql0YE/bxoxZj6K/8N5R0uV7qhN7VxVkINbZnukyEsCBWLLzfSSF7AdPN38xY+VcZm7FFhRVZSAhaArCfFOQXbYWlXWFiAoY0/Y5V2cP+Ljv2S5Z6WZj3g+YGHMCBhvaLhR7bBtNzQ1yPjK3Iq9iK+obq+Du6oNo/9HYXvQnJkQfDw9XH2QUL0dm6UrsFXcGPN1aK0qyhG9uxWYEecUhr2ILJh70f3B2se5eWFK4Dbu2/4Lw6IkIj54EFys/1x3qasuRs+tvlJfuRHgUf2ei1etnDzDNhh4jHaNQuwtTZv786R5NmVF6hENEiCiKoih7wsozNGSlKSVnz8yVFFcUpXPGRx8nA6WSmiwZeFEQCfZOQGLwPm2zszEB4+U9BZVpbZ8rrNqBuMBJiAloLXVtkF22DtH+/woniqL0DhdnN4T7tZbGZYleUlKdidyKrRgdcTjW7v4Gk2JORIh3PCL8hreJISQ+aDKi/Eejoi4f7q7eaG5utFpwCAodjoCgRGRl/IbVf70AL+8QhMdMRkhYKpzMme9YSVNTA0oLt6Egdx2amxsQHb8fEoYfptEgitJDVBBxMJgPyJne7vqbKEMbbReOC02gGSJMh3maSdMl3xRtG4o5jGoUGgUAeLn5Y1jwXp2+hwOVSP/Uds8lhUzD9sI/sD5nPkZFHCaDNlbGoFDS1ffZK9oulMHQNpjqllO+SUQSX49Qecyv2i4RI+Zg+d5g73hZqt1ajZCtxcnZGeUlOzFmyvlwdfVATuZSbMh8T17z8YuAK8t8u3rAxcVDXnd2dYeriwdcXGnS7CHCSV1tGepqSlFbU4KK0l0ivAaFDMewEUfA0ysQgxn6hbRGhwz0miiOjPZwHRCW79LwKqUj2i4cO1KE5UxZ6YJeB9deey2io6PbtQ065jM0k6+bM19THA9az9RXD/RaDF6cnZwxPOxAFFZlYEXWZxgeegAKqrYjKWRfOLUrpzC40Hah2HPbYOratsLfEeQVi1CfxH+eK0Bi8L59/lvVlXnIzVqOoNARbcJFXNIMWVqam1BdlY+G+ho0NdWhqbEO9fWVaKqu++ff9fLI97l7+sPTMwg+/lGIiJkMN3cfNDc3wbkXUSb2BEvw1tVrdIsycKgg4mDQvI21zNUrQjFF24VCA0easbGEOAURps9ceOGFGDdunDjrs6wojedoMEdfkdGjR+Pwww+XShtG6VDFcWA/PHGKekX0BaE+w+DnESpeBY1NdQjw+leMHGxou1DstW2U1+ZhR/ESODu5YkTYDPi4t5oWNzTVoqq+BN7ufRtpQSFj+e9PIip+P0QnTNvjdUZ++PhFWfVd9XWVKMxbj7qaEri7+yE7YzGyM/7AvjPvlEiSwQzty0YOa9AoEWVAUUFEURTFzmC5P1bJmDdvnpQKZ7g9Fxqf0iH/0ksvtUmUBn/DKCe6ZcsWzJkzR0xXExMTpYSf4THCcOMNGzbghx9+kDKhLFNoVNswcpjXr0iz8BvOUurXyzsUXj4hkl89mAzgFMUWeLj6SrSIoih9S1lNDtKL/4a7ixeGhx4owkdLS7OYrOZUbEZtQ7lEZ/U1FDymH/4gtq7/HKVF2yRKpCdUVeQgbeM3iEmYJuudm7UUbm4+8A2IHTIRIooy0KggoiiKYifU19dL6U2KISwdeu+99yImJqZNZKBQ8t5770lJwGeeeQbDh7eaxNmCkSNH4q677mqrMsNSoQZcn7Fjx8py/fXXd6vKDA3pmAddW12Eqopcmely9/BDePRkBAQPG9SpAoqiKIp9UN9Ugw05C0RsZBWooqodSCv8XSJCSKBXjBgfm6v0ZOnexQiTxmZfRMROgZu7r9wLORlAY9PGhho0NlSjQR5b/+Yj01u2b5onkSLBoSNkIsBaamtKseqvFxEWOR71deXwC4xHaORYpG+ej7DIsb0yZlUU5V9UEHFA1BxRMYe2i4Fl586duOiiiyRN5fvvvzdbBtfDwwMXX3yxpKrceOONiIiIwK233iqiyUC0DXYECwoKsG3bNlmqqqpkvdlJzNm1REocGpQWbZcOnZdPKHz9Y+Dq6glXNx8Eh49CMx3zi1iecCGi4vdFaMQYFUYGAZoSoZhD24ViD22DYoifZziq6ouRXvgn4oImSVUnGqR2B97nSmoysb1wsXy+0S8I6Vvmi9gBBkY6tVaxoTmqq5tX2+Lm5g1Pr2AEhnDiwgn1NaXYtf1nSXFJTj3GKjGDviP7HnwH6mpbDVULc9djx5YFGD7mRGTv/BPrl78l91lnJ5d/ftcTLq5e/9xf/3309g0XYcZeaepFuV1F6QucysrKWuOclQHDqG9dVlamppaK4oDk5eXh9NNPx1tvvYVhw4ZZ/bmlS5dKZRgaoLJ07pQpU2xadu/HH3/EH3/8ga1bt6KiokKeCw8Pl0iVlJQUSalpnS1rxi33fMaeJPgfoQjCzl1NVSGqK/PFLM7J2RXOzq6oqSoQwSR51HGSJ11SuE3CgymWWLM93jtKunxPdaJ1s4D9hTXrrCiKovSMNbu/EaGgur4YE2NOkEgRa2BaSk1DGSrri1BSnYXy2lzx9RkWNFXK7vbkXkLD1A0r38W4vS5CQc5aFOauw4jxp0pVme6Sse3Hf8r5/ttXaG5qRGNjLRobGZlSi6Z/Hlv/XYPK8mx5T8yw6QgMSW77XE1VEdw9fKXKzWCH2/vnT/eI+Xtfj6WMcdqna1Lh7WcfUTnVFU2YPWGzTbbXEVFBxMEEEQ5UOPjizLK5GWjFMdF2MXAwtWTT6o/Edd7Xv2dmihVl2SjMXSudHk/vEAQGJ0skBn06umO4RjGjvq5CBArOSFGsYEcpOMgbWzcvgaurD8Kixsv3ciaqr934t234Ckmps+DlE4bdGYtRVrJDOnDs/A21iJFBL4g4Ad6BTqgubWmdJVUUou1CsZO2QYPijJLlyCpdg9rGcnnOxz0EYb7/CAL/CPYi2vPe11SNqTNSpA/EiYlRo0Zhr732wuTJk/tkouGpp56SyYvTTjsNf/75J+6++26ZBGH/vzNycnLw4YcfIigoCLt378aqVavk34wY7Q6cxOBvP/HEE7JtTNHld7DCHA31LaW52vze1kftgsf7p21PqSCi9AiNkXcwmpqa8Ouvv8rFTwe+ioG2i4GjKH+TRE70VAwhfgExspDqqgKUl+xEQe5aicZgOgph2T4KGcx7Rkszmlua5LFVAClEU1O99EzcPf3kfVwnlvVrrivF8GH+qKzaC74B1kevdBdv3wiMmXwONq56H8mjjkV8ykyZ2WJY8O6df4og4hcQJyk1bu7eNlsPxTp4mYgb66rVRJR2aLtQ7KVtuLp4ICV0f1ko9lc3lGBdzreI8h8NFye3VrNyjsb5fycnuLl44ZtvnrDZ+vznP//BMcccg5NPPhnTpk3DK6+8gvPPPx933HEHDj744DbRJTs7W3zE+Lhy5Ur4+vrizDPPRFFREfbdd1/cdtttPTJV9/b2Fm+wDz74QMSYm2++GSEhIRgzZgwGEr1mKPaACiKKoigDBDtpNBUdM+XcPvtOb58wWTr+Dv07KHw01FdJ7jIFBi7+QcPg5R1iMWTW2bkFXt718A+yfUk85jqnTjgDm9d8iOFjTpIol4SUQ+Q1CjvZOxejuGAzImIm23ZFFEVRlCEDxQaW2R0ZdhC2FSzCuKijrU6h6StYnp6TTu+//74IIcnJyWJY/thjj+GRRx6RCJAZM2YgPz8f999/vxinf/TRRyJk9AUUYyjE7LfffjjqqKNwySWXYP369UhNTe2T71eUwYwKIoqiKAPE77//Dr/AOLi4WJ/W0tPOoIdngCz2DvOZR44/HZvXfIyR42dLiV7C0ry+/rGSFqQoiqIo3SXIOw6pLodgXc58+HmEw83FA1H+Y+Dl1j8pBzROnzVrlpS2T0pKEhHk4YcfbktpWbhwoUSGXH755RI10ldiCHnuuedwww03SHTI5s2bJeLkgAMOsKnvmKIMFlQQcTB44WOumV4AFXtsF+wQMB+ytLRUKpvQsDMhIWHA18tWvPDCC4iO3w92TQtQW+/Ur34AHp7+GDnuFGxZ+ylSJ5wu/yb+QQnITP8ZsYnsxGlq10DS0gLUVbfIo6IYaLtQ7L1t+HqEYkrsbJTV7kZjcwO25P8s6TJJIfva/Lfd3d3x5ptv4oILLsDnn3+OgIB/JyloSn7CCSfIYgtofP7VV19JZbjg4GAxSd97770x0NhLu1AcGxVEHAwOMo8++uiBXg1liLYLmrPm5uYiPT0du3btEj8SznD4+PjIo+nfoaGh0jkgnBFhuVmahfFmTThzwvdRDBk3bpzk0HIZKh4n27dvlxBaY7BvrzS3OGFrhm0jWMzh6R2M4WNPxJa1H2PUxLOkZKCLixuCw1LFoT8sakK/r5PyLy3NQMaKxoFeDcXO0HahDIa2wX5FoFer71aozzCpJLMl/xfx+WAp++5Ue+su8fHxeOihh0QUmTNnjsWy9rYiLCysbQKqLyNQhkK7UByXoTGyULo1YOVAjI+K0lftori4GGeddRaOP/543HvvvVKa1bR6DfNUadpKM69DDjkEMTExuOuuu9o+b0SEUAxZu3atrMvcuXNxzz33yPc2NjbinHPOEZFlqMDw1WuuuQb2jhNaEBzQJI/9Db1QaLC6afWHYrBKouL2xe5dS9rMYpUBwgkIiHSWR0VpQ9uFMgjbhr9npJTmZaoKTUsvu+wyLFmyRPo29ODqa2iOykiQJ598EgPFoYceip9++gkDjh23C8Vx0AgRB6wmsnTpUlGoh8pMu9I76urqkJGRgUWLFmHKlCkyc8IOAMUMPpr+zdcmTpwokQ2mPhgULv7v//5PQkEZAnriiSeKYVhNTQ0KCwvlPWvWrJGScXQ2nzBhgrxuQJfzb7/9VkpPGyGkzK9lDu1QhPt7586dUs4PsIMOSScwMyU2ohGlFe4yk9Pf+PhFIXHEkSKKjJ50tpi/xiUdiLRNX2PE2JP7f4UUgbePyOEuqCho1soAShvaLpTB3DbYN6GRKSdmvvjiC7z44osiikRGRop4wbSW3sKIWKYoc7KHkbnsE/UH7OOx9C/NVX/77TfZrtjYWAw0g6FdKEMfFUQUxYFhuOYbb7whNel5g2YEBsu5MSIjJydHRDOmtRgLBZE777wTo0ePlggHQoGNwsfbb7+NRx99VEQNuqPz8wzHDAwMxP77748HHnhAIkM6wzSfdqhCcYmRIQM5MzTYoPFsfPJMbFrzEUZPPEvSZspLM5GbuQyRcXsN9OopiqIoQ4jx48fLYkABgRGwnMiprKyU1F+KGTRI9fAwX6GtI5xUYuRJfX299Jlqa2vle7KysmwuTHAy9MEHH8TTTz8thqqsatNVf0xRHAkVRBRlCDJj1qNWvW/t0tcxbuoFWJPmjLEp9fjii3moqSmXsqxGRZKW5ia0tDSjuaUJLRIp0oRtOzdirclvuAediKJa4JL/+/KfZ5h/OwzFdUBWCbB+RxZeef+DXm3Tovl9M4vCtBxPT88+menpCS+99BIOP/xwMThTrCcgOBFNTQ3/VJ85Vcrxbl03F1WVuSKW0GPEXvDeUTLQq6AoiqJ0k6NG3WbxNdfmydieWQ8XZ3/kNtXijnkv4dLKG+Hm4gkf9xC4OLvBxckNrs7urX/L4i59poq6AhRX70KwdzwSgqbI91XXl2Lt7m9wxOKLUFqzG7WN5YgNGI8w3xT5TrJg0yN9sl0TY49Fc4szbjzlffn361i+x3sG3k1EUQYOFUQcDM7wM/RvqFbtGIrRBBzAM9WEPhuM2GAEB/02+Ggspv82DLNys5Zh986/4OZOY1Ke6i1SwjQm8UB4eQfLe1xdPVBdVQAPD18UlzaK6DF26gUYqhVdvv76a0REREgEDPcr9ye9TKZPn94v65CWlobvvvtOnN4HDS1ARZVzv1aZsURw2Ag4O7tg/Yq3JY2GZXlLi9OxZe0cSaUJCE5CaMQYuHsMjNjlSDCtvqpEKwMo7dF2oQzVtmGIHISiB4UNLvVNNaipL0Vjcz2aWhrQ1NyABj7XUC7/doIT/DzCEBE+HJ4m5X1ZKY2TT8XVmfB09UVR9U60oEVEkgi/EYgN7BvjcJqn7i7fiKlxp8EeGeztQhkaqCDiYHDgPFR9GYYCNBNl/upff/0lg3UuFDiioqKk6goFEoY+cuGg3vibIZx//vknli1bJr4UvhGnIS9rBbx9w9HUVC9RHq5uXqgoy8KutIUykCSJqUcja8dvYlC5Y5srwqJaZy4GCkai0Dyzvq4SDfVVaKj/9/G663KkBO/YsWNlobBhrbD3zjvv4NNPP5X0IIaJMo924cKFsg9fffXVfhFEjFQZ5iQPJv8eVpnZkd3aCbQHAkOS4eMfhe0bv0ZxwRbEpxwiIl5DQzVKC9OwfdM8NDXWIThsJEIjx6o4YiPoJ5O1XisDKO3RdqE4Wttwd/GCu4mvWleU1+ZhV8lK1DdVI8J3OJydXJFdtg5uzh4I8R6GaP+xWJn1GUJ9Evtk/djvSQiaDGc7LVU/VNuFMrhwKisrU01ugCkvL0dcXJx4L9DHwZZw8Lxx40bxgGA0gWIfXH/99WLgRbGK1VQOPPDAtuPDPFMak3JhVIPxt/FvmnMyh5X15A844AAxPZ01+4U9foPCAsURD8/APYQEJ6cWhAc3Ib/YBS0tTn0qcDTUV3cQN1r/rv/nsbm5sS36gOtF4cbN3VdSIPjozkcPX8x55xoxI123bp1UrWF5X74/MTFRjNBYws5Sm77jjjtkloS5u8zXPf/883HyySf3a6TU+++/LyZmHSvLWJveNFDYqm30Bfm7VyM3a6lUnqH4wRk3wnZeUrAVedkrEBw+ClFxe/frejlCygxPneB4ZxTvYhrdQK+NYi9ou1As4ehto6gqAxnFy+DlFoD4oCnw9QjByqy5GBd1tESVbMj9DilhByDAM1JSbDbm/oCHnrhNvEuio6N7/LvHHXccareMhIuz65BuF41Ndfhp21PIzMzs87GUMU77dE0qvP3sY+xUXdGE2RM222R7HRH7PDsUm85SczCZmpqqgkg/0dDQIBfT4OBgiwNwRi3Q+ZsXtieeeKLNcJPvp99FaGhou2XkyJFiVMq/+VlrjiUFBtcWb5k9b2ysET8QT+/WdeJqRYQ0oaCEg96ujcEaG6pRW1MiS0MdhYZ/hY7mpsbW8mkthsDh/Y+44QN3D194egeJSaYheLi4WBd9QM8NLiwVZ9qeKZJcd9118vywYfQu2ZOHHnoI9mBgy+ifwUZ32kZ/Ex49ESHho5GTuQTrlr2BhOGHISBoGFxc3EUgCYkYjQ0r3ul3QcQRoPYUGu+CkqxmtGhlAOUftF0oltC24SSeItUNpUgr/EMiNirrCrFm91dwc/FCSuh0EUMIU2z2ij8dISEhuP3222UCiFVpzj777G5P5HCy1R0DUCLOSrRdKPaACiKK0k04CN+yZQvy8/PbIjWKiora/qbXhyGEMCKA6SxMzeB7mOrBCA5GhLD0scENN9zQ45K5y5cvF4+RkpISWfib2zb8LKknTY317T/gBLi4eMDNzUuEjbraUqSMOQHePoFtb+HzFDfq/hE8uPDvutoyeY3Ql8TDKwieXoHw9A6BX2B8twWOvoCpJyzPy/1L9d6e4b6jo7zSt7i4uiM28QBExk7FtvWft6XLkNaIkdYy0uqbpCiKogwUIT4JshD6jVTU5osnyeiII8zen5ydXHDqqafKwj4OU31PPPFE3HvvvRIVa+09jZUBLz71VoyOPLzPt0lRhgoqiCiKFXBAxbQLelFQfGCKCiM3TE1NWTKWaj7LzHKgzpKzDHNkZAj9P0wXvq8nUOygV8jixYsldYS/PWnSJBFX+L0UB/j4058tIno4u7SWyrVETXUxdmyeDyenZiSE7oeNK/9EY2OziBs0YKXo4RcQh7Co8fDw8BcDMHuDopCbm5tdRzyx/RhikmIbmGo1fNwp2LT6gzZBhLAtU+BjdJKiKIqiDDQ0ZfV2D5K0YmuEDfYxb7rpJsyePVsiiHfs2NH2OU60UCDZZ599MHXq1D0q6B122GFwcroNBZXbEeabbLNtUpTBjAoiDoYxoz6YTB3tAd54vv/+e/H3YG16U3GDggdFEA7KbQFFEKZ80PuFvzVt2jScdtppUlPekgjg6fW9Vd/NajOjJp0lPhENTk0YNSnZ7nwiuoKROAwJpUfIQJXStab9UBCprq4WoWwwQR2nuMz+0mXMwapJPr6RKCvJkNQZwvLRjHBSQaRvYXsoy3VMLwDFMtouFEto22gPo0RoqNodmBb87LPPtnuOKdmrVq3C0qVL8fLLL0tfiAb0V111FcaMGSPvGRF2EFZkfYYAzyi4u9pXH0TbhWIPqCDiYHAATRVZ6T4rVqzo19/jAJpGnIxKue+++8RbxHa/5YSsvMF5OfD19ZX9c8UVV+Ddd9+129SIyy+/HA888AAeeeQRDCYGW9uIGTYdW9Z+jBHjZkuUE6NFCvM3iG+N0reVAXK3acK30h5tF4oltG20p7w2Bz4ePYsWNoWGmjNmzJDFgGnd7DOyP/Tcc89Jak5q+EysyfkGrk5ucGXqtLMngn0SEO6bgoFE24ViDwyeXq7SZ1Vm6DnBsDp7TjFwVFhxhEJIadF2ZGf8gYDgRMQMOwS3P7QYABfbwAiRmPAmZOfbXyURa6uxZO0owfAxJyBm2P6dvm/R/JsxENA094MPPsDWrVsxYsQIDBYGQ9swxcPTH8PHnoKt6z5F0sijxd8mY9sP/4Qm9z4yzhEqyFgDd2VEsgvytjdJh1ZRiLYLxRLaNtoT7jtcyuuy9K6nm/kqIUeNuq0XvxCC/IptiPtwDCZEnwB/zwjsFXeaVPZjdEp9Uw025f044IKItov+m9Slsf/vv/+OXbt2ScQ5x4J33XWXFCwwhYLabbfdhr///lui34844gg8/PDDUsihK1gZ84UXXsAnn3wiv8MIelbB5PeNGjUK9ormTTigIWh6ero8KvZHeclOrFv2OspLdmD42JMQlzQDzv3g2yFlzwKa5HGwEjPsAFSWZ2P3rr/t1q+DZrqvv/46BhODsW1IKtjEs0UIKSnciqCQ4chMXySiiNI3sD0ERDoPqnah2B5tF4oltG20RwxVI4/ApryfbPYb4X7DERc4Getz57f1i5ydXVHTWI7N+QvtwlNE20X/8PTTT+Prr7+WSKL//ve/OP/888WT8MADD5SUfIPs7GwcddRRMla8++67cfXVV4tlwAknnID6+g6FGsxw8cUXi3gyffp0/O9//8MFF1wgv0MvGwok9opGiCgOSV5enpiSsipMVVWVVII54IADMHny5AFZn5qaGrnw5GQuFU8PNzf7yvEcDDA0dMS4U5CZ/qtEB6SMPkEqkPQGdiB4AS8oKBDzVi78nb322ktSdboL1Xia8zLnV+vG2xZWQho96WxsXT8XAcFJkqjMErzJo46Dl0/vw5QVRVEUpTf4uAfD2z0YhVUZCPVp9b3qa8J8k1DXWIEtBb9I2kxdYxW25P+CiTEnwN3Fyya/qdgf9JR544034O7+b7/4pJNOwn777ScVjF577TV5jqlW9LtbtGhRW/XGKVOmiCDCKGcKHJZg0YlvvvlGRBT6HBrwN4499lh57corr4Q9ooKIMqRhqbJNmzZhzZo1sjBdgc+Fh4eLOWpERATCwsLEgOrmm2/G888/j9TU1H5bP4aWMayMPiHXXXcdlm4I67ffHoowJSI+eSZKitKwYeXbIop4+4b3KLXspZdewpdffimhhFFRUW2VhWjiynbCMEDm5nbHJJViCtvZPffcIzcgxbY4u7hh5PhTsWPLdzIrljz6eKRv+gae3sGIjN0LPn6RA72KiqIoigOTFLIvVu/+CiHeCTbzQIsNnIBtBb8jo3gZPFx9EBMwVsUQB8Ocf2RycrKMebZs2dL2HKNImCJjiCHk4IMPlr7wF1980akgwsllwjGWKZGRrX0t9qHtFRVEHAxWlxk7dqxDVJlhaVrmxnE2nyXJGB42cuRIKV/GAS9DxH7++WfMnTtXhAlWKlm5cmW/CCJUX2mwyZw+VoyZN2+eDKwff2kTBgJGUuYVDY5KItYQFJICb59wpG/+Vv4dFb8PAoISre5s3H///VJG+YcffrB4rrDtUF2noMX3Wsuhhx6Kr776StrdySefDHtnsLcNimRJqbOwe+efyNj6PVLGnICGukrkZC5BTVUhQiLGIDxqgpTtVayH2UeFuzTnW2mPtgvFEto2zOPm4olQn0TkVmxGlL/tPBZSQqdjW+FvyCxdjbGRR8Je0HYxcDAKmhHQqf+MexjhwX9PmjRpj/cySoR94s5ITExETEyMTBoOHz5cJp5zc3MlAp4Tz/bc51VBxMGgkeq4cePgCDC9gWIIc9hMOeWUU5Cfny9q6cyZM3HRRRf1KP2hp1B44UXhhhtukDw7e6iKQrPMvKKhdTmgueaoiWegtqYUuZlLkJe1XKIFrClzzBJ2FC06OzZsOz4+Pjj77LPx4Ycfdqvk7+OPPy55loNBmBwqbSM6YRr8AxOwfdM3ovLQbJURIuUlu1CYuxYenoEIj54I/6BhcHHpXaqVI0CBrGin9mCV9mi7UCyhbcMy0f5jsLXgV5sKIpJWHDYDw4L3tqvoEG0XvYNllk1hNDMXa5gzZ46IILfffrv8m+KFaUSHKYyoLykpkbGVpe+nAet7770n46rTTz+97fmJEyfixx9/lMhqe2Xw93KVbsF0EToM0y+DkRJDGYZ4cRafeXOXXnqpOCp/++23+OOPP8RDhErmQQcdBC+v/r0x0LOECipNi+wFZ6cWJEQ3YuduVzQPgkoi3cHTKxC+AbFWG60uWbJEzJ+sEaooqt166604/vjjceaZZ0qOJG8aXcGbCXM5Z82ahQbsDzd3H9grQ6lt+AbEiK9IY2MdKsuy0FBfKaJIY0M1aqqLkb3zTzHlpQs/I4woqrm6eUv0iNs/j84N9XBz8YKL89C+flpTGSBmtCuyNzbqzJ7ShrYLxRLaNizjhP67t9qTGDLY2sVPZWPg0eQGe6CusgHAZowePbrd8+yTsqJLV9BC4MYbb5QKMGeeeaY8x2h5YuozYmCku9DzsDPBhaIHJ97pOcJJaZqzPvnkkzjvvPMkDd1e02Ycu0fngHBQSAXQXqtw9CUc0NLUh+7G7777LkpLS3HkkUdKGSlGADDd4a+//pKLQn9CPwqqr1RPzzjjDEmhYKTBgOIE+Pk0yyOGYNNo4gC4PAsbV72Pe+6pwllnnWWx9O3SpUulnVjL/vvvLxf5BQsWSNRPUVGR3GCSkpIQHx8vS2xs7B43EN5wHnnkERx74uVWRa4MGEOwbbi6eiAw5F93/djEA1BbUyJpNBWlmZJG4xcQi+amBjQ0VKOhvgo1VQVobKhBXnEpGppq0NTcAFdnD0T6jUSYb4rDCSTUC32CnORxiDQLpQ/QdqFYQtuGZVgCl0K7I6Ltoncw/d80Qtma6BBOCs+ePVvM/Tk+cnFprWZpiBXmqskYYklnk8iMgGf/+ZprrhFjVQOm4Bx99NF4//33JTraHnGsHpxi11CkYY4axQKe3IzeoFhAI5/epJVMmzZNFlOGDRsmlT44+O1vuC1M48nMzBTHZQ7OaV5EZ2fFNkTETJalqbEes2YdJi7XzHGkp0xHKJjdcsst3fp+3lToBcOFpqurV6/Gzp0721JveKyNGwxvOKxmxOgQVp1xc/dFUd5GhES0V/mV/sXTKwiJI46U6JDCvA3YlfaznKsengFiwurlEwZPr2AEuzjB1aW1w0G3fuZ9r8qeiwi/kYgLnDjQm6EoiqIMMhocWBBRegfHS92pWmik7fPxu+++k0laAyNVxkid6SiiBAUFdSq40JCVlgTs35rCErxcR0ZgqyCiKF3AwceJJ54oKT1MO1i2bJnknNHV2Eh/6euLyCGHHNIn3zVj1qNdvmfR/Jvb/ZsiyBVXXCELBZL58+f3yboolmEZXqa5UKmmSNFREGE6FcMPrQnps+aYtxL9z9LK/7N3H/BxVOfawJ/tq1XvvUuWbbliA8YFTO+9BZIQIEBCEkI6JTcXcpMQEr4EUoCEJEBIKCH03pvpuOBeZBWrWb1rVbZ9v3OEZMmatcqWmd19/rlz19o2Z9Gr2dl3z3nfzp5hPPZMDe752/Xo790va1nUVr4VUgkRW3WnX57HXpgILb6uGOShIDkPbo8bg44eDPR3wd7Vii5HBRqHu+ByjyS3RGIkypSAjNi5qGz/CFGmeFkcj4iIaLrcHhf0upFv6YkCRczyELPTKysr5Zd1BzeRyMrKQkpKivwy72CiCcRUNShFMkQQjSsO/sJbXCfKNmgVEyIRRkyLEtP5R6dHaY3oCiO+YRctcsUf3/r16/HjH/8YP/3pTxGuxIFCLOcRxYhUG4MbqG82an79pr86/IiuPmJmzsHE0hdRDGp89W3RtlnUfBEzlfxBFOwUyzVi4rJQX70O9r5mOQtBqyIpNg6m1+lhMyfILVnhdodrEAOObtgdXViQcSoSbTmIFG430FThkpdEoxgX5A1jwzux5FLMToxEjIvgEAkJ0TJXzIx/9NFH5WdBJWeddZa8vb6+Xi73Ft555x3s3btXfoE7SnxWq66uljM/RmeWjJ4niy6K4+uYiC98Rf1E0XVGq5gQiTCiq4XoO61lIjEglhKI7Rvf+AbCgfhw/dvf/lYmekTtCDE7RNSYEEt3RB0J0bFEzFr47d3b1RkfdOjo1maSzF9E/Yem+vU488xXZeItNTV10n1Edlwk4EaXaIle6iKDLmrNiFlKokCvPzrDNDdsQnPDeuQUHgNLVDx0Ou0eiiMhNnxplyi2OOvUhXTDjgfobuIZLB2EcUHeMDa8spkSUTO0HhGJcREU4otlkZgQDR1Et5j//Oc/E26/+OKL5aWohSe+HDzjjDNw7bXXoq+vD3/84x9RXl4uuyqOEt1pRNFUUZD13nvvldeJ5543b95YWQDxOU4UVf3b3/4mkyaXXXYZtEq7Z+EUEGK60quvvoqTTz55Vl1m3G43Kioq5Lfm4lJ8gy5qcYi2T2ITH/zH1/sQ9xeb+GB5/fXXK/a2jgSirWdfXzH+8pe/yClr4kAhDhJiWdAtt9wyqcaJGp1ESvId2LvPFPKdREQHkbambRi0t8l/u5yDcAzboTcYkZa5BK+99ozXGVI/+MEP5HYwEdf33HOPbNksMt+zrWnjdrtQsf0pOSNkzsILULv3TXl98byzoFXhFBvk384A+UuN2LdJ+50BKHgYF+QNY8M7s9EGj8eFQWcfrMYYRBLGRXBs3bpVXooGAGI72MVfJETErBCROBGteG+99Vb5JbX4zPirX/1qyoKt4gtf8dx33HGH/Kz5xBNPICYmRn7h+7//+79ITlaaa6sNTIhEGPHBTiQwZtJlpqOjQ641e/PNN2UHDfGNuaizIDKDotaHmC4lNtEpRXx7LhIjYiaEWIO2ceNGuaZMdHIRlZAjNSHS2rQV3/ven2TdErGJ2QmisKZm6ACr2RPynUREV5CN79+F/NITkZKxEAajVXYUEZd6/UgSZDbLxUQCRBRiFQWofFGz51VYrQmw97ehevdLcoaI6GaiaWESG+RfIidosbEzAE3EuCBvGBuHVpp6DLY0Pi+XX4plmpGCcREcL7744rTvO2/ePFm/8VDy8/NlYdaDicKrt912m9xCCRMi5JWYJvWHP/xBFpoU/aPvvPPOScsM2traZOJjdBPJFpENXLx4sfzAf9FFF8mkSSTr6qiCyRyNpKQktYcS9vQGExJT5sAWnSprdPjTaBJxtrNDOlr3yPatfYOdmLPwQtnVhIiIiCjGkoyFmadh6/4Xv0iK8ByBKFiYENEo8eFLtOkU29DQ0Iz+LaY7iSUYvhZOffjhh2Xr0NHuJw0NDbKlkkh8bNmyBQMDA7IasZj1IYrzXHPNNUhIiJys9nRnLOyreAPlh31V7aFEjPzSE7Bn6xMoX3a5T+2aD7Z//37FuiPT1bp/M9yuYeQWrWUyhIiIiCYQncpEMqSibR0WZ2l3KS1RuGFCREPE+q3RTiPig5xYizW6iXVb0/3322+/Ldd6ic4YF154IU455ZSxfYgkydq1a6eVLBFdOEQhUPF8v/jFL1BaWipnfYjnE4UnbTYbQo1IGIle2qKOh+i9LWazBFLDvg+RlbcCRpO2+8uL6t5V9aawqPJttsQiNiFXJiDSspb47Xn/7//+D1deeeWsH+8Y7pPrT8QMllASTrFB/iPioW6bk3FBEzAuyBvGxvSImSEGnQmdAw1IjMpGuGNckBYwIaIhouKvqMUxk1kkoj6H6CctNtESSVyKFq6iYKqYwSHa1p500kljnTHEpUgETIdoqSTqgjz44IN4/vnnA5488KWVlFi609TUJJMd4nL8Jpb+jBIJI7GERySPxt8m/lvGx8fLLiOjm6ioLGql+DLLoLujGtmHrYL26dBnD5+CmblFx2LHxoeg0xtgi05BZ1sFBgc6Zcvbt98+XCYFZ/J7feutt2SycvXq1bMaj8fjhk6nhyckV8iGV2yQn3gAe2coxjMFFOOCvGFsTFtZ2rH4vPFZzEk9BvHWkZamYYtxQRrAhIiGiLZEIoEhipKKD2vT+cAmpvCLNrpiO/PMM+WlKGgz6pjTfovXz/h/Yz/r9R7MK3JgZ5X4xnfk+d996SeKzy2e55NPPoHVap3WWMS+/EkkKYYGuzBo78DwUK/cxLfs8tJhH6m+pBP/p5M1OkyWWDk7wGyOGbmU2zwYjOOqIjuB1urRHzIm7KujeQi7a3swNLQDw4Mfo6/nz+jvbUJicgmyC9eMFeWcEZ34b24MyH8ff1KKi1Amflfzl34VddXvoqdzn5yVkZK+AE7nIL521f9hcOAHKJl/loybqYhONds3/BPly77mw+9QN5IMmUExY60IhdiwVXeqPYSIIw6HxUeaUPmJA26X2qMhrWBckDeMjekT7dyXZJ2NTQ1Py6UzFuPU5yqhinFBWsCEiIaIWQmil3Og63AY9NP/UBYVFZylHiLBIZIP9t4meTk0OFK52GJNQFR0skxuRMdlwmweSXSIJSj+rA8hnstossrNFpMmr0vPPkwmSlr3f44dm/6F8sO+5td9as1M4iIUiDa7+SXHT7peFFvt7a7H7i2PIzo2Q3akGU1aKSVDdn7+MArniDbVh243diijcSNmiYjWu7NKrqko3GKD/CPEwpiChHFB3jA2ZpYUyYybh66BBqTHhtZS25liXJDamBDRENGneSZLZkK1yGh/XwvsfSOJj4H+NrmcQCQ4omMy5AfUpLT5sFjjNZF8EGNIy1oqx9rbVYu4xPyZPV72KiWtEa1uFyy/QtYZqdj2FOYsOF8urxlvcKALFdueRF7J8TP+vXsjknuD9jbYYiK78xIREREdWpw1Ay29e8I+IUKkNiZEIsSAvR323mbodC4M5aSjqX6frBHicTvxq185ZJHR0U414nqn0ykvD94O1Xp024a9I//weGQXjdziY2ViQyQ82pq2oalhPQx6E6Ji0mTiIz17OaKiU0Li2/KM3CNQu/dNv30wJm1IzVwsV15t2/AAUtIXyhlCTscgervr5KylorlnIDrWf8kLW0wG+nubmRAhIiKiQ4q1pKKy7QO1h0EU9pgQCVNutxsbNmxA9Z5X0dddB0tUImLjcqA3mLBxSysMhmgYTQbo9SaccMIJssjoaJcaUTxSbKIw6+i/R38eLc6qZHx9hb7uBlTueA5ut6gc7URy2jzMX/IVGIxmhKIoW7KsP+EYtsNkDr3uOlMR1b1310RmJ5G0zMVISpmDrvZKuFzDcklWXvFxsNqS/LsjjwfRMelob92F2TfvDb5Ijg3yTqz1rt7ANd80EeOCvGFszJxeZ5BfKoovI7UwazoQGBekBUyIhIldu3bhs88+kwVZt2zZgqqqKixfvlwmIgpKT5S1C0Z4IHIa0fLDzcjB9cgjj/T7eGLiszH/sK+OddcIB+nZy9DcsAE5hWum/ZhQ6iricIbnm+10iCVbKRkLAroPUadEJCbtfc0INZEcG+Sdc0jtEZAWMS7IG8bG7Nrw9g23ydki4YpxQWoLj0+qETwL5IUXXsA555yD3//+93J2R2FhIX70ox/htddew2233Ya4hLwJCQmRDFlQMiwvgyFckiFCcupcdLTuGls2NB0et2tG91dLsOMiEonlVj1d++B2ORFKGBukRKx0LF1pYjE8moBxQd4wNmYnM24+9vfsQLhiXJAWcIZIiCVAdu7ciffeew/r1q1DR0cHjjvuOPzjH/9AcnKy2sMLe6LoZnxioVxakZhSMq3HxCXkyySKmKlDkS0j90hs++wfiIoO3295iIiIyH/irZmoaH03rJfNEKmNCRENE4VNN2/eLBMgH374Ifr7+zF//nwcffTR+POf/4ykJD/XOKApZeQejj1bn5DFYK1RU7dHzi5cgx0bH5I1Kg7uYkKRRbTtLSk/B4P2DrWHQkRERCFAJEESbbnoHKhDki1P7eEQhSUmRDREdHl5//33ZQLk9398BB63GzFxmYhPKkRcwiIYjBZ8tgP4bMcu/O4vu6b1nLbqzgk/i8/khiwTbDX2sQJGp867yS/jD79So8qvsSRqGeo2Pit/Lko+Sq7rfHnnr70+5tlnF+G+++5DYmKibK28bNkyubxptIDtwYVsp/oGwF+/L3thol+eh6YvJi5LbqFuOrFz8LFntvdhnBIRUSTLjCtHdfvHTIgQBQgTIhpy+eWXY+XKlXIGyMvvemRHGH8TSZCKD1nN2RfJ0flyG3B0Y0fTa0iLLZUtiUVCQ8nZZ58tt5aWFrz88sv461//Ku8/2uJ4/KWYFTSVDXVfrCX9InGig04mZRKishEflQWzIWrGr0l0ENm218xOIjQJY4OU8L2ElDAuyBvGxuxFmxMx6OyFy+2EQR9eH90YF6QF4fVXFeIeffRRxMXFyX/rDR8FbD9GCzBsD9jTR4woUzwOyzkf+zo34Pzzz5fJjKKiIlnkdu3atXK2x3hpaWn42te+5tM+a2pqsGrxGXC4Bkeu8Hjg8jjR2l+F/uEONPZslwmS8oxTZ/ymaTJ6MDTM9ak0GWODlPC9hJQwLsgbxsbspcYUo7W/EhmxZQg3jAtSG/sGRBixZKZwGas5+7OLTkHS4Xjuuefw4osv4vrrr8f69evl0phvfetbuPvuu+XlO++8IxMm27Ztw1VXXYVPP/10xvsS7ZSvvPJKlKSsxrLcC7Es5wJ5uTz3YizOOhvR5iQMO/thd3Rjfd1jGHYNTPu5RQeRsgIHO4nQJIwNUsL3ElLCuCBvGBu+SY+Zg9a+SoQbxgVpAWeIEPmJqP1RWlqKG2+8UW67d+9GRUUFTjjhBDz44IO48847ZVJEXC+SIjOxY8cO2U758ccfx1fX/G7SfsV0ytLUo+HxuNE5UI8Oey26BxrlNwpEREREFLosxhj5pRcR+R8TIkQBUlZWJjfh178eKbr62muvyaK5K1asmNFzxcbGwmKxICUlZcoZK6LoFgtvEREREYUH8eWXBx61h0EUljgJOgKxcJF6TjzxRGzcuHHGjxP1R1yuwP7iXG7WiCBljA1SwvcSUsK4IG8YG76xGKPR1l+NcMO4ILUxIRJhWM1ZA/3kExNx8803y5ogHs/U2X6xzOaaa67BddddF7Bxud06bJedRPjBlyZibJASvpeQEsYFecPY8N289BNlHZHPG56By+1AOGBckBZwyUyk0QG2BB3sXR5w5p06/v3vf+Ozzz6TdUV27twJq9Uqa4/MnTtXLrERl8nJyfK+ogbJ97//fXz1q1/FqaeeGsBReRBj86DPLj708oMvjcfYIAV8LyEljAvyhrHhM6PejHnpJ6C9fx821D+BuWnHIs6agZDGuCANYEJEQ049/y4YTVb5b1t1Z0D2ITpF5C4wMhvrZ6fOu2kWj7IAWAK724mPNnfi9eEX0D/8EPqH2uFwDWDYZZetfeekHoMHb/1cbv40PsZEde/SlSZU7Jp5XNgLE/06LtIWccwoynFgm5wlEvz9B+pYSL7hewkpYVyQN4wN/4m3ZsivJ4z6kc8MoYxxQVrAhAiRyvR6I2IsKXIbr6bjM8RaUmEzM+FAREREFOlEN8Gt+19EScoa2MwJag+HKCwwIUKk4RZrQ84+tYdBRERERCpze9zY2fwa0mPLkGjLUXs4RGGDCZEII2p4Dtk98pK0zWywoWeoOSj7YlyQVx5gcFjHtb00AY8ZpIRxQd4wNnzT1LMLtV2bkJuwBJlx8xAuGBekBUyIRBiPG6jZ4FR7GDQNep0BbndwfleMC/LG7dFhT41Z7WGQxvCYQUoYF+QNY2P2ugYa0NS7G8tzL4ZeF14NQhkXpAXh9VdFU9MB8Rl6NosIAb1DrZPqigQM44K80MGDpHiXvCQaw2MGKWFckDeMjVmrbv8E89NPDLtkiMS4IA0Iw78smqqac0apQV6StrX27UWyLT8o+2JckDfi/Csn3SkviUbxmEFKGBfkDWNj9lweJ8xGG8IR44K0gOFHpEFdA42wmuJgNIjWvEREREQUiXRijqZYW0JEAcGECJHGuD0u7Gl9F6WpR6s9FCIiIiJSUUJUNtrt+9QeBlHYYlFVDbHt6wr4jABRxbm/k9Wctayxexuy4ubDbIgK2j4ZF9pmq+4M2r7shYkTr/AAvf36GXeZmfQ8Kr8u8i8eM0gJ44K8YWzMXnb8QvlFWUp0IcIN44K0gDNEIoyYcVe/zSkvSZv6htuREBXc/vKMCzpUl5nqBpO8JBrFYwYpYVyQN4yN2RP1Q4ZddoQjxgVpARMiEUanA5Lz9fKStCnemoHm3l1B3SfjgrzR6TxIT3bKS6JRPGaQEsYFecPYmL36ri3IiC1DOGJckBYwIRJhRKeIlDwDO0ZoWEbsPNly1z7cFbR9Mi7IG3GSkp7s4skKTcBjBilhXJA3jI3ZcbkdaOrdhaz4hQhHjAvSAoYfkcbodDpkxM5F50Cd2kMhIiIiIpXs69yAvISl0DNjQBQw/Osi0qAoc0JQZ4gQERERkXY4XIOyu0x6mC6XIdIKJkQijKji3N3kZjVnjYsyxWPAEbyECOOCvBEx0dFtYGzQBDxmkBLGBXnD2Ji5mo7PUJh0hJw5HK4YF6QFTIhEGFHFuanCxWrOGmfSW+U3A8HCuCBvPB4d6puN8pJoFI8ZpIRxQd4wNmZm2DWAroF6JNsKEM4YF6QFTIhEGLEEMaOUxYu0LtjfBjAuyBvRXSYnnV1maCIeM0gJ44K8YWxMn9vjxo6mVzEndW1Yzw4RGBekBUa1B0DBJY6r8Rl6tFS5wI832qbXG+FyO2HQGzUdF7bqTmiFvTBR7SGEHREbSfEuNLZqd9nMdH7vWorTcMD3ElLCuCBvGBvT0z3YhD0t7yA3YQniozIR7hgXpAVMiBBplMUQjWFXP6L08WoPhYiIiIgCZMjZh6r2j+BwDWFx1lkwG21qD4koYjAhQqRRbo8Leh3/RImIiIjC0YCjG9Xtn8iESEHS4Ui05ao9JKKIw09bEUYULWqrZfGiUODyOGDQm4KyL8YFeSOWyTS3a3e5DKmDxwxSwrggbxgbE/UPd8hEiNM9jMLkIxFvzUAkYlyQFjAhEmHEh5r2fTzqhAKnawgGXZASIowL8kJ0l2lu51sFTcRjBilhXJA3jA3APtyFlr49aO/fJ5fEFCQdgVhLKiIZ44K0gDV9I4yo4pyzwMhqziGwXEYIVnVxxgV5o9d5UJjtkJdEo3jMICWMC/ImUmOjb6gde9vex/q6/8gaIdHmZCzNPhcLM0+P+GRIJMcFaQu/9osw4vN1dKJOXvLjjXZ12uuRYMsJ2v4YF+SVDoiNdstLBgeN4jGDlDAuyJtIig2Px4MOey1qOzfAZIhCdvwCFCevhI6f+iM6Lki7mBAh0qDGnm1yKiURERERhYbm3j2o7dqEBGsm5mecDIsxWu0hEdEUmBAh0pj2/hpZO4RTKYmIiIhCR03Hp1iWcyGMBovaQyGiaWJCJMK43UBThUtekva43A5Utn+Iw7LPD+p+wyUubNWdfnkee2EitGQ64/HXa5/0PDqgtV8Pa7Pd7/NZtfbfmSLvmEH+xbigSI+NrPiFaOrdhZyExWoPJSRESlyQtnExW6TxAN1Nbi7U0yC3x43tTa+gMGlF8L9ZYFyQN4wNUsK4ICWMC4rw2MiMm4fmvgq1hxE6IiQuSNuYEIkwop5TwTJWc9ZiV5ktjc8jNaYEqTFFQd8/44K8YWyQEsYFKWFcUKTHhlFvltugs0/toYSESIkL0jaGX4QRVZwttpFqzqSdZTKbG55FVny5/GZBDYwL8oaxQUoYF6SEcUHeRFJsZMSWoaV3j9rDCAmRFBekXUyIEKnI6RrC5w3PIC/xMKTFlKg9HCIiIiLyQUJUNroH96s9DCKaJhZVJVKJwzWIzY3Pojh5FRJtOWoPh4iIiIh8NOTsh8UYo/YwiGiamBCJMKKKc902J6s5a+DNUtQMmZO2FvHWDLWHw7ggrxgbpIRxQUoYF+RNJMVGY/c2pMQUqz2MkBBJcUHaxYRIpPEA9k6WclbToKMXW/a/gHnpJyDWkgpNYFyQN4wNUsK4ICWMC4rg2Bh22lHZ/iH0OiNSogvUHk5oiIC4IO1jDZEIozcApStN8pKCz+12ymRIecYp2kmGMC7oEBgbpIRxQUoYFxSpsVHftUWe36XHzkFZ2lq1hxMywj0uKDRwhkgE4kFHPTWdnyE3YQmizYnQGsbFAbbqzqDty16YqKnxKGFskBLGBSlhXFAkxobVFAezIUoWVKWZCee4oNDAGSJEQVwq02mvR0bsXLWHQkRERER+IpbIpMWUYn3d49jb9j5a+yrRN9SOQUcP3B6X2sOjCNfX14fbbrsN5513HvLz8xEfH4+HH3540v3i4+O9bmefffYh97Fu3bpDPv6OO+6AVnGGCFGQ1HVtQlHyCujYbJ2IiIgorGTEzZVLZroGGtEz1Ix2+z643A4MOnuhgw75icuRHJ2v9jApArW3t+M3v/kNcnNzsXDhQpm8UHLfffdNum7Tpk249957cdxxxx1yH2VlZYqPf+yxx/DWW29N+Xg1MSESYdwuoHqDQ15ScPUMNqM4ZTW0iHFB3jA2SAnjgpQwLijSY0On0yPRliO38ZyuIexqeQtt/dUoTV0DvY7rRCIpLtSWkZGBPXv2ID09HRs3bsSxxx6reL+LL7540nUieSK+zL3gggsOuY+0tDTFx99+++0oLi7GsmXLoFVcMhOBnENqjyDyON3D8s1Pr9PunxzjgrxhbJASxgUpYVyQN5EcG0aDBQsyT0W8NQMb6p9AS28FXG6n2sPShEiOi2CxWCwyGTJTQ0NDeO6557B69WpkZ8+8Ps6GDRtQVVWFiy66CFqm3U9nFBCs5qyO3qFWxFlnfiAKFsYFecPYICWMC1LCuCBvGBsHltUsyjwddkcXPm94Glv3v4iewSZEKsaFtr322mvo7u7GhRdeOKvHP/744/Jyto8PFi6ZIQqCYWc/LMYYtYdBRERERCoS54MFSYfLzT7ciZrO9Rhoe192IUyNLmatOZq23t7eSTNBxOYvjz/+uHy+qQqqKnG5XHjqqafkUhmxZEbLmBAhCoJhlx0WQ7TawyAiIiIijbCZEzE//UQ4XIOy+P6+js+QETcPWXHlMOhNag+PxvmsJR+Gfv8lG3zh6h9ZZzR//vwJ199444246aab/LKPnp4eOUPkxBNPREJCwowf/84776ClpQU//OEPoXVMiBAFaYZIrCVN7WEQERERkcaYDFYUJR+FgqQjsL9nJzbWP4mEqGzkJR4Gi5FfqJGyHTt2IDY2duxnf84Oee655zA4ODjr+h///e9/YTAYZKtfrWNCREPs+Qkwmqzy37bqzoDsQ1RxrviQ1ZyDbchlh9lgg1YxLrTNXpio4t492NwIuPPEv6c/jXc6x7BAHeco8HjMICWMC/KGsTE9ogB/dvwCOUOkw74PO5pehckQhZLUNbCG4dJrxoVvRDIkLi4uIM/9+OOPIz4+HqeccsqMHzswMIAXXngBa9euld1ntI5FVSOQURuzvSLKsNMOs1G7CRGBcUHemIwetYdAGsRjBilhXJA3jI3pE3VEkqMLsDTnPDlLZGvjC7JlbzhiXGhPU1OTbLd75plnzmrWyUsvvSTrm2i9u8woJkQijKjiXLiM1ZyDzeUehlFvhlYxLsgbvR4oK3DIS6JRPGaQEsYFecPYmD3RpXBZzgXY17EePYPNCCeMC2168skn4Xa7vSY0HA4H9uzZIxMnSp544gnYbDacccYZCAVcMkNERERERKRRer0RCzNPx+eNz2Bx1tmsK0Izdt9998kWuvv375c/v/zyy2hsbJT/vuaaa+TymPHLZTIzM7FmzRrF5xKPO/zww3HppZfi3nvvnXBbR0cHXn/9dZx11lmIiQmNZV5MiBAREREREWmYWHo9P/0kbN3/IpZmnweDnh/jaPr+9Kc/oba2duzn559/Xm6CmAkymhCpqKjA559/ju985zvQz2J68DPPPCNnkFx44YUIFfxLikAsXBRcbo87JHrKMy7IG5db+/FLwcdjBilhXJA3jA3fxVhSkJ+4XM4UmZN6DGLMKSFxjnkojIvg2Lp167TuV1paKmeSHEp+fr7X+1x55ZVyCyVMiESY0WrOFDz9w+2wmZOgZYwL8sbt1mH7Xu3WvyF18JhBShgX5A1jw39SY4oQZYpHXddG9A21IydhMTLj5iEUMS5IC1gmL9LoAFuibibdM8lH3QP7EW/NhKYxLsgrD2JsbnlJNIbHDFLCuCBvGBt+FWNJxrz0E7Es9yJ0DdSjonUdPJ4QfJ9mXJAGMCESYcRSsNwFRnaMCKLuQe0nRBgX5I2IiaIcdpmhiXjMICWMC/KGsREYep1eJkYsxhhsangSDd3bMOA49HIHLWFckBZwyYyG2PZ1wWhgM+5gGHL2o2ugQbYv6xtug9szxQJGj0cWs4q1pMlNtEATbXSHXQMYdvZjyGWH2+NEnCUdVlPs2MM67fXydjG1kSKPvTARkcpfr91W3emX5yFSWyQcD/R6D1y2YdgLouVyO9IWHk/DV17iUqTFlKDDXouq9o8w4OiB2RCFRFsuMmPn8fMF0SEwIUIRRSRA9nWuh9M9jJToQvnmUWRZAYPeNOVjh5x96BlskTM+6ru3wOUegskQJbPyZoMNOp0ezb17ZAJErzPAaoqT+xO940O94BURERERaZf4Qi4rvlxuwrDTjnZ7DTY2PImSlNVIsuWpPUQiTWJCJMKI5YVDdo+8jDR7295H31AbSlOPRvQsipyKxEdqjNiKpryvy+2Q+xIVwKeTbFFbJMcFTcEDDA7rWEKEJuAxgxTxeEFe8JgRfGJmc2bcfKRGF2NXy1to6duL0pSjNdWul3FBWsAVWxHG4wZqNjjlZaTJilsgL2eTDJkpkQSJj8oMiWRIpMcFHZrbo8OeGrO8JBrFYwYp4fGCvOExQz1iucyCzFORFJWLjfX/RfdgE7SCcUFawIRIpNEB8Rn6iKzmbHd0yv7tpCCC44IOTQcPkuJd8pJoDI8ZpIDHC/KKxwzVpcWWYnHW2ajp+FR2pRHLx1XHuCANYEIkwogqzhmlhois5iwKnCZE5ag9DE2K5LigQ9PpgZx0p7wkGsVjBinh8YK84TFDO8toFmWeiThrGjbVP4Xq9k9UTYwwLkgLGH4UMfISD8O+js9kLRGna0jt4RARERERBZUo9J8eW4bluRdjyNWPqvaP1R4SkapCLiHy61//GvHx8RO25cuXy9s6Ojrw4x//GMuWLUN6ejrKy8vxk5/8BN3dh+7Hfe211056zvPOO2/CfT799FOsXr0aCxcuxEMPPTThNnH/tLQ01NbWTrj+0ksvlc9N2mAxRuOwnAsQZ83Ax7X/Rn3XFrWHREREREQUdIPOXvQOtqA4+Si1h0KkKu2UGZ6BefPm4dlnnx372WgceRlNTU3Yv38/fvnLX6KsrAx1dXX4/ve/L6/717/+dcjnPOGEE3DPPfeM/Ww2myfc/p3vfAc//elPkZGRgW984xs47rjjkJOTMyHbetttt+Evf/kLtExUce7vjLxqzm6PS/Zmb+mtQP9wB1KjixBvzVB7WJoRqXFB0+ABevv17BpBE/CYQYp4vCAveMzQFrfbie1NL2N+xsmqNgBgXJAWhGRCRCRAxAyQg82fPx///ve/x34uKirCz372M1xzzTVwOp1jiRMlFotF8TlH2e12LF68GKmpqUhISEBfX9+E26+++mrcfffd+O53vyvHoVWiinP9NiciRddAA7Y3vQKrKR6JUTnIS1yGGEuy2sPSnEiLi2CxVXdOeR97YSK0PubmKsA6w+eZzuuazn+fYNLa72I61PpvyGNGYITiMWM80V2muiE0uqtFIjWPyzxmaEtl+0fITViKaLO6xxPGBWlByC2ZESorK+UMkEWLFuGqq66SM0G86enpQWxs7CGTIcL777+P4uJiudxGzCoRy2/GE0tvjjjiCOTm5uLwww/H3LlzJ9y+YsUKnHLKKbj11luhZTodkJyvl5eRIN6aBZs5CSUpq1GUvILJEC8iLS5o+hgbpIRxQUp0Og/Sk53ykmg8HjO0w+PxoGugHmkxpWoPhXFBmhByCRFRL0QsbXnyySfx+9//Hvv27cOpp56K3t7eSfdtb2/HHXfcgcsvv/yQz3n88cfLpS7PPfccfv7zn+ODDz7A+eefD5fLNXafyy67DNXV1TIZI55TyS233II33ngDH374IbRKVH5PyTNETAV4sZSpJHkVmnp2qj0UTYu0uKDpY2yQEsYFKREfatKTXfxwQ5PwmKEdA45uuD1uON3qNxhgXJAWhNySmRNPPHHs3wsWLJAJElHo9Omnn5ZJi/EzQy688EI5k+Smm2465HNecMEFY/8WhVjFtmTJEqxbtw5r164duy06Olpu3ohZI5dccomcJfLaa6/N+LXpDIDeMPJvsZZOTCMTB4jxJxbiOnHbwde73SNrd71dP/q88nLc7aPXj93fdYjrdSPtscbG8sUYvV0vxjH+ABeo1zTV2KMtKegfbg+r1+Tv39NoXIjLcHlNofJ70uu/+CbVMzLdXAfPxBOD0et1noljF6/pENfrxTe0B79WTL5eviZx/RfjGB3rhLGPxsgMfk/y+aZ8TRr7PY3+LsaeRzypZ2I7QI38ng4W7L8npf1q4e/Jl9cU+Ng7KJbGfq/j/m7G/515G7tGYu/gsY/8W9zH4+W1av/vSek1Hfr68HtNUgD+nsb/TfAYoe5riolKwJz01fi88WmUp5+KaGuCaq9p/DmGL6+JOViKqITIwUQ9D7HUpaqqauw6MVtEzPCIiYnBww8/DJNpZutZCwsLkZycLJ9zfEJkOkTyRSy7eeGFFzBTxUeaYDGPjLW7yY2mChfSiw2IzzhwBGirdaF9nxvZ842ITjzw5y/uKx6Tv9QIi+3A9XXbnLB3euRzjx50ohP1MEcBwwNA6cqJ/20qPnTAaAEKlx24XhygxPW2BB1yFxwImSG7BzUbnIhP18se4qNEcSSxHjApTy+zvqMC9pq+UL3BAeeQ8muKjrVNuD4cXpNff0/5BhkXxStM6N4fJq8pRH5PLtuwvH5wWIc9NWYkxruRk35gPa0oUCjW5KclueQ3r6M6ug2obzYiO82FpPgD1ze3G9DcbkR+lhOx0V+c8QLyvuIxJfkOWM0HTpir6k3os+swr8gBg94DQ5ZpwmsSMTEaG+KEZ7q/J/G6pnpNWvs9lZSM/C7k+N06bN9rRozNg6Icx9j1Wvk9jardD1X+nlqqXLBE68biIpi/p1A9RgzqgQXjYkzYttcMk9GDsoKRGBN/f1O9Jq3E3u4aExxO3cTXNJIfgMUMlOaF3t+T4mtS+D2F82uqrkRg/p50gMk68rfCY4T6r6kUJciqsWHn5j1YUX6Ueq/pi88lhYebUPHB7F/T0LAbr++asBuiadN1d3eH9EJPUdxUzOgQiYhvfvObcmaIaJkriqT+97//hc1mm/FzNjQ0yOd85JFHcNppp015f9F2VyRezjjjDPnz//zP/8gZIiKxkpSUhHvvvfeQjxdjFrVJTpz7fZgMloBmosXtaYUGNO11yeeKlOz6poansTDrZJiNtrB5TX6dIWIciYuWapd8jnB4TaHye7IXJGjqW0VbTdeEsRtMB2JDPMd0f0/ydU3xmqJrOjX1exos/uJ3EULf/loru1SbIZJeYkDrF3ExNnYeI7y+JnthwpTf0k/4+/My9oHCBE3EntIMA7GvzBQXGloMcslqqP09cYbIF8eUAPw9iX+nFhjQXDnyh8xjhLqvyekawsb6p7E48xyYTVbVXtPo5xJxjuFyzP41OVxDeH3XnbKuZFxcHPxp9HPaYU/8AIbokc9panP1D2HjBb8PyOuNRCE3Q0S0vhU1Q0Rgija7otWtwWCQy15EwJ577rkYGBjAfffdJ2eKjNYWSUlJkfcTxDIbUe/jzDPPlAmV22+/HWeffTbS0tJknZD//d//lR1qRG2R2fjBD36Ahx56SNY3EcmZ6fK4APfB18k3PoX7zvD60YMPXMD+Pa7J13u7/4Qnn9n18mCldL2/X9M0rs9LOAxbG15BecYpE5IiofyavF4/i9ck3oTGx0U4vKZQ+T2NnFCPv1o39gFzwvUenWJbOm/XixNvzOT6L8Zx8FiVYmOq1zT++Q79mjT2ezrodzFCN/bBQUu/Jy38PTUpxIW8P48RXsauHEvy/m6Fx3kbu9Zib8JYdKhvHvnUotxGM0T+nqb4PYX9awrE35NrZIbBuKc69P0j8hgRnNck6odsaXwFRUkrYTJY1X1NB30ume1rUvpdEU1XyJWwaWxsxNe//nWZ1BDFUsUMDFHIVCQ8Nm/ejPXr12P79u1YunQp5syZM7bV19ePPUdFRYVMnggiSSLu/6UvfUkudbnuuutk/ZBXXnlFzjKZDTGm66+/HoODg9AakUUVU84irXhRcnQ+ilNW4vPGZ1HZ9iF6B1tklW2K7LigqTE2SAnjgpSIWQ5imQi7zNDBeMzQBrfHha37X0B67Bwk2fLUHg7jgjQh5GaIPPDAA15vW7NmDbq7u6d8jvH3iYqKkgVZfaG0zx/+8Idy0xoxtUysExTrvyPtdCXOmoHluReh016Php5t6B9qH7tNrzfCbLDJzWKMhtUUh4SobJgNUdN6bqd7GNubXkVqTDGy4uYj1ERyXNChMTZICeOCvMWFqIPR2GrwMkOEIhWPGeqzD3dhV8ubyI5fgPTYMmgB44K0IOQSIkS+0OsMcraI2MZzu50Ydtkx5LJj2NkPu6ML+3t2wuEakMmRxKhsxJhTYDHGyCSJyLA7PQ7YhzvRO9SClt49KEpeiabeXXC5HchNWKzaayQKFfbCRLWHEPLU+m8o6hSIwrn2guixKfq26k5VxhJO+DdBRP427BqQs6MHnT0oTT0asZZUtYdEpClMiBB9MUPEqo+TyY9R+YnLxvq1dw40oLW/EkPOPvnGIhIrBp0JNnMCYi1pWJp9HowGi5x+uKP5NdR1eZCbsETFV0REREREkUp8QVfTuR6d9joUJR+FJFuu2kMi0iQmRCKMKIQk2mkpFfoiZVGmeLlNh6iqPz/9pC+SIp+HTFKEcUHeMDZIiVgOIdqCclkEjce4IG/4XhJ8Yqaz+GNclnPhpK5PWsG4oEOx2+14++238fHHH2P37t1ob2+XsZycnCxrhK5YsQJr165FdHQ0fMGESIQRJymitzgFzmhSZGfz66jt3IS8xKXQOsYFecPYICWiI0ZzO08haCLGBXnD95LgE7Oene4hzSZDBMYFKRENT/785z/j+eeflx1hRc3P7OxsJCQkyKYYe/fuxbvvvos//elPMhly1lln4Tvf+Q7Ky8sxG6zpG2FEFeecBUZWcw4w8eYzL/1E9A21yqSI1jEuyBvGBinR6zwozHbIS6JRjAvyhu8lwWczJchad1rGuKCDiS6yolHKnj17cOONN+KDDz6Q3WJFJ1nRWfbNN9/Ehg0b0NDQIG8T9xEdZMVjrrzyylntk2n8CCOSxNGJOnnJ05XgJEU2NTyFlOhCWW9EqxgX5A1jgxTpgNhot7xkYNAYxgV5wfeS4IsyxckmAVrGuKCD6fV6vPPOO1i0aBEOxWAwyBkhYrvuuuuwZcsW3HXXXZgN5uOIApwUERW997a9r/ZQiIiIiChC6HR6WI2x6B5sUnsoRNN2//33T5kMUSIeIx47G0yIEAWYaG8mutOINW9ERERERMFQkrIalW0fqD0MIk3jkpkI43YDTRUueUnBIRIhok2vlotaMS7IG8YGKREdAeqbjewMQBMwLsgbvpeow2qKRYwlFW391XL5ttYwLmi66urqUFNTg66uLsUvmUVh1dliQiTSeIDuJh51gql/uB3R5iRoGuNCNbbqqQue2QsTgzIWb/uyi/9XELQhUIjE6aBYo47wF8y/v1DngQ4d3Qa1h0EajXetv5f46/14Os8TTEVJR8p6dkm2PPkFnabw/JOmkQj59re/jXXr1smflZIh4kvnzs7Z/90xIRJhRBXn/KVG7Nvk5Dc4QdLSVyHfhLSMcUHeiG4RJfkO7N1ngtuj3VlOFFw8ZpASHi/IG8aGeowGC3ITlqKi9T2UpR0LLeF7CU3l2muvxaefforvf//7WL58OeLi4uBvTIhEGLFqw2JjNedg6RtqQ9dAIwqTVkDLGBfklQ6wmj3sGkET8JhBini8IG8YG6rKiJuLdnsN2vprkBKtnWk6fC+hqXz22Wf43ve+h5tvvhmBwqKqRAHi9riws/kNzE8/SdP1Q4iIiIgovJWkrEFd50a1h0E0I1lZWUhISEAgMSFCFCD1XZuRGTdfFrQiIiIiIlJLp70OiRpfwk10sOuuuw7/+te/YLfLKkQBwSUzEUZUca7b5mQ15yDVDjks50KEAsYFeSNioqrexNigCXjMICU8XpA3jA31xUdlor7pFZgMVmTFlWti9jLfS2gqV155JVwuFw477DCcffbZcsaIwTCxOLCIZVF4dbaYEIk0HsDeyVV6waDTGaAX1aJCAeOCvNKhz67+SRNpDI8ZpIjHC/KGsaG2KFO8/KKuuuMTrK/7D+KsGUiPnYN4awZ0ap2v8r2EprBjxw788Y9/RFNTE/76178q3ocJEZoRvQEoPtKEyk8ccLvUHk1483jccLkdMOhN0DrGBXmj13swr8iBnVXimz2ezNIIHjNICY8X5A1jQxvEF3XFyUehKGkFegab0Ny3B5XtH4qTVuj1RiRG5SAluhDR5uSgzCDhewlN5frrr0d3dzfuuusudpkh/x58KPAyYudif88O5CQsRihgXJA3Bj2/vaHJeMwgJTxekDeMDe0QyQ6xhEZso5zuYVlnpLZzIzzwBK0pAN9L6FC2bt2Km266CZdffjkChQkRogARBVU31D+O5OgCOU2RaLZs1Z1T3sdemIhIfe3TEa7/fSjwIvnvjyiS+OvvOJjHA3+9RwpGvRmpMcVyq27/BDUdn6Iw+Ui/PT/RbOTn5yPQQqTAAVFo6Rtqw962dfLfOv6ZEREREVGIKEg6Ah0DdWoPg0jODvn73/+O+vr6gO2DM0QijFifV72B6/QCwePxyCUyjT3bYDMlIjt+IeakrtVEFe+pMC7IG1H5fXcNOwPQRDxmkBIeL8gbxkZoGXT2QB+EL/T4XkJT+fDDDxEfHy/rh6xduxbZ2dmKXWZ+85vfYLaYEIlAziG1RxCeBVS3N70ql8YszT4fBn3o/WkxLsgbh1P7ST0KPh4zSAmPF+QNYyM0iDoie9veR3nGqUHZH99L6FDuu+++sX+/8sorivfxNSHCufwRRhQuKl1pYgEjPydDtu5/CUm2PBSnrAzJZAjjgrzR64EFJcPykmgUjxmkhMcL8oaxEToq2z/CgszTYDMnBHxffC+hqXR1dU25dXb6VkuHhyUiHww4uvF547OyRVlWfLnawyEiIiIimrWy1LXYtv8lOFyDag+FKChC76tsIpUNO+1os9egvb8aTtcQSlLXINaSqvawiIiIiIh8EmtNg9UUD5fbAZPBqvZwiAKOCRGiaRZMbejeiqbenTAbbEiy5aM0ZQ2spji1h0ZERERE5Lcv/oad/bCaYtUeCkWw1tZWWT9k9+7dSE5Oxvnnn4/Vq1dPut+LL74oO9Fs2bJl1vtiQiTCiCrOFR+ymvNMiFkgW/Y/j0RbHpblXAidLvxWmjEuyBvREWDbXjM7A9AEPGaQEh4vyBvGRugQBVWLU1YFZV98LyElzc3NOProo+VlYmIi+vv78eCDD+K8887DH//4R0RHR4/dV9xWV+dbi2gmRCKQ0QIM29UeRejMDNnW9DIKk1Yg0ZaDcMa4IG9MRg+GhtXpDmAvTESosVX7VtxrJq/dX/uajUg5ZoRiDEbq8YK0jbGh/eOTOO/tbeuDeX45gnN498BiHo2LmcWGmu9/FFi//OUvYbfb8dJLL2HlypUy6XHPPffITjJ79+7Ff//7X6Slpfltf+H3VTcdkqjiXLiM1Zynq6r9Q7k8JtyTIYwL8kZ0BCgrcLAzAE3AYwYp4fGCvGFshIaB/lbYYvz3QXMqjAtS8t577+Gaa66RyRBBzAj58Y9/jGeffRa1tbU4+eSTUV1dDX9h+FHEc7udGHT0oneoFR32OjT37kF91xZsaXwBLo8TuQlL1B4iEREREVGA6TA02C1nihCppaWlBQUFBZOuX7VqFV599VUMDw/jlFNO8aluyHhcMkMRq7WvEnVdm+TB32yMhklvhdkQJStqGw1WFKesRLQ5Se1hEhEREREFnC0mFXEJ+di1+THkFh2DmLgstYdEESgrKwuVlZWKt82ZM0cmRc4991ycccYZOPvss33eHxMiESjSCxeJGSHbml6BxRiDRZlnwmiwqD0kTYj0uCDvXG6u+abJeMwgJTxekDeMjdCQU7gGA/3tqK9Zh+GhHmTlr0RCUjF0usD8/hgXdLAVK1bgmWeewa233golOTk5MilywQUX4F//+pfPscklMxGG1ZyB3a3vID12DsrS1jIZ8gXGBXnjduuwXXYG4AkLHcBjBinh8YK8YWyElqjoZJSWn4PS8vPQ3VGFbev/gdb9W/y+lIZxQUouuugiJCQk4KOPPoI3SUlJeOGFF3DaaafJBIkvOEMk0ugAW4IO9i6PKOwcURyuQdR3bYbH45YJERonguOCpuJBjM2DPvvMK8BTGOMxgxTxeEHeMDZCkdkSg4LSk9DSuAn11e8hOX0+dDp/fnxkXNBkxxxzDN555x1MxWaz4ZFHHoGvOEMkwogqzrkLjBFVzXl3y9tYX/c4tje9KpfJzEs/Ue0haU4kxgVNj4iJohxWgKeJeMwgJTxekDeMjdDVuO8jdHdUY/GR34Re79/v0hkXNBXRanfHjh1eb9+5c6e8jy8YfhTWegab4HI7sDz3IizJPhtZ8eUBWwNJRERERBQuHMP96GjdhZLyc6E3cGEBBd+vf/1rbN++3evtIlly++23+7QPRraG2PMTYDRZA7oPvd4Dl20Y9oJov6/Xs1V3QmsMejMc7kG4PS7odQa1h0MUMNP5+7MXJgZlLBS+x9RIxL8tIopUXe2VSE6br8kvE8cfd52OQaBC1eGQSjo7O2E2m316DiZEIo0HGBzWRcyab5spEUm2PGyofwImvQWxljSkxZYi1pKq9tA0RdTIGrJ75CVRJB8zaHp4zCBFPF6QN4yNkONxu9BU/xnKFl0cwJ0wLmiyDz74AO+///7Yz8899xyqqqom3a+7uxtPPfUU5s+fD18wIRJh3B4d9tT4lkULJSKjnZuwRG5O1xB6hpplYdW+oTaZKMmIm4doM7/Z87iBmg1OtYdBGhRpxwyaHh4zSAmPF+QNYyO0iG4yFTueQUbO4bKwaqAwLoKjr68Pf/zjH7F+/Xps2LABXV1duOeee/DlL3/Z62McDgdWrVqF3bt34xe/+AW++93vTmtfvb29+O1vfyvb5jY1NSE5ORmHH344/vrXv8oiqNOxbt26sWUw4rPc888/Lzclc+fOxR133AFfMCESYXTwIDHejc5uPTwRVs1ZtNgVSRCxiU4zHfY6VHd8gkFHD1Kji5CbuDRyl9XogPh0Pbqb3czS0wSRfMygQ+AxgxTweEHeMDZCS13l24iOyUBq5qKA7odxERzt7e2y8Ghubi4WLlwoEw5T+etf/4r6+voZ7UfM2Dj99NPR0NCAyy+/HEVFRWhra5Ptc4eGhqadELn++utxzTXXyMRccXEx7rzzTpx99tmT7ieez2r1vdwEEyIRRqcHctKd6Oo1y2/4IpVOp0dydL7cRH2Rpp6d2FD3OOakHYt4awYijajunVFqQG+rG26X2qMhLeExg5TwmEFKeLwgbxgboWN4qBd9PQ2Yf9hXA74vxkVwZGRkYM+ePUhPT8fGjRtx7LHHHvL+ra2tcpbH9773PfzqV7+a9n5+/vOfo7a2Fu+99x4KCgrGrv/+978/o/FGRUXJTdiyZQtSUlKmnUyZDXaZoYgnZoVkxS/A4qyzUdPxKRq6t6k9JCIiIiKioBu0dyAmLlvtYZAfWSwWmQyZrltvvRUlJSW46KKLpv0YsQzn4YcfljNDRDJkeHhYzgrxVV5eXkCTIQITIkRfMBttWJR5Jvb3eO91TUREREQUrpzOQRgC3PWStGvDhg145JFHZA2PmXQX+vjjjzE4OCiXyXz1q1+Vs1JEEuakk06Sszym64gjjsCjjz4qEyrTJRIv//73v+VjZ4NLZiKNB+jt13PNt4Jh1wAaurZEZB0R0Smiv5MdI0gBjxmkgMcMUsTjBXnD2AgZLucQjMYgJUQYFz4RBUwPngkittnyeDz48Y9/jPPOO08mF/bt2zftx1ZWVo4tmyksLJQ1SERNEVG75Mwzz8Qnn3wikyRTufTSS3HzzTfjhhtuwGmnnYa1a9di8eLFyM/PH5sp0t/fL8e2adMmvPPOO3jllVdgMpmmXfj1YEyIRBhRzbm6waT2MDRnwNGDrftfQEHSEbK4aqQR6zbrt7FjBE3GYwYp4TGDlPB4Qd4wNkJrhojJHB2UfYVSXLQ2x0MfpY2ZM+6BQXl5cLvZG2+8ETfddNOsn/fhhx/Gjh078NBDD834sSJJIYhZJaJNbkzMSHcikcw44YQT8Le//Q0/+9nPpnweUbfk61//Ov71r3/JmSqPPfbY2EwVo3EkdeF0OscSOOK/gXjNX/nKVxAXFzfjccvnndWjKGTpdB6kJbnQ0mGAx+Pfas72wtBsX+t0DGDHpidQuvxLiIpOhpigNf1JWtNnq+6EVonjTFKeHh21bn7jG8amE4MH/x0H8pgRrqZzLNTy8WA6eMzw/W8rHPF4Qd4wNkKHyzmIKFtyUPbFuPCNSF7ExsaO/ezL7JCenh45u0PMssjJyZnx40e7vZx66qljyRBBtNwVszvEDJHpEq/pW9/6ltzETJBPP/1UFoXt6OiQtyclJWHOnDnyuccXb50tJkQijDiJTU92obVTHHjUHo36hof6sHvLf1BYdqpMhkQqUeU7Jc+Azno3POwYQePwmEFKeMwgJTxekDeMjdDR292A1MzFQdkX48I3InEw21kRB/vTn/4k63aI5TKjS2UaGxvHCqaK6zIzM2E2mxUfL24TUlNTJ90mrhPPMRsimSK2QGJChCJWV3slaivfRNHcMxETN/JHTEREREQUqR1mBIs1Xu2hUJDV19fLpMWRRx456bbf/e53clu3bh0WLVqk+PglS5bIy/3790+6rampCaWlpdAqJkTIK7EuS1Q50omvAkOEy+XA8GAPPB6X3NxuNzxuJzweN9ziOrcLfT2N6O6ohC0mHeWHXQ6DUTnTSUREREQUCVzOYezZ9hRKy89Reyikgm984xs4/fTTJ1zX2toqa3p8+ctflgVOR2dqOBwOVFdXy9kpo4VSRcJj4cKFeOmll9De3o7k5JGZ92+++aZMtlxzzTXQKiZEIozIcXR0e5+WJpIg/b370dq0Bb1ddbKIjcFoRWLKHKSklwetyNJ0iCSHva9FjrO3uw6Dg10w6E0yq63TG6HT66HTGWTXmNF/6/QGxMbnIrfwaPlvGiHiobuJtQBo5scMikw8ZpASHi/IG8aG9uvp7dryH+QVH4eo6JSg7ZdxETz33Xef7PoyOoPj5ZdfHlsSc80118gZHqOzPEaNLp2ZO3cuzjjjjLHrxeNE/Q7REebee+8du/62227DOeecg5NPPhlXXHGFrEty9913o6SkRBZK1SomRCKMKFhU3+z91+4Y7seOjf9CQkoJFh7+dTk7xOGwo7OtAru3PI7o2AzkFB6tSmJEtAHraNsjEyAiESLYYtIQG5+D3OJjYbEmzKhfNk3sGNFUwUIANPNjBkUmHjNICY8X5A1jQ7v6e5tRufNZFMw5BXEJeUHdN+MieESNkNra2rGfn3/+ebkJF110EeLjfV8mdfTRR+PJJ5/Er371K/zf//0foqKi5KwT8e/xhVa1hhEYYUQ15+w0FxpalKs5my0xOPzoH6Gx9iNs3/AQiuedKYuNpmUuRmrGInR3VMnEiNFkQ0JyERKSimEwWjBg78DQQKf8d2JKqV+X2TiG7Wjc9wG6O/chNWMh0rOXwhadxhkefiR+XenFBjRXimVFao+GQumYQZGJxwxSwuMFecPY0B63y4Hayrdh729B2aKLVakbwrgInq1bt874Mfn5+XJWyXSvF4499li5hRImRCKMbJUY70Jj6+TpaSLx4HTYYYlKQHbBaiSlzUfVzucRn1QkfxazLxKSi+U2PNSLro4q1FW9A5drWLbnEo8b6G9DS+MmlC266JBJkdrKt9DTWYv4pEJZ0DQ6NksmY8YbGuhCw74PYO9rRVb+SuSVnMAZIAEi/rPGZ+jRUuUCZy3SdI8ZFLl4zCAlPF6QN4wNbTYWyMpbiYI5J6k2DsYFaQETIhFK1AoRCYm25m0y4SCKpxpNUXLmx9Bglyw+qjeYZOHR5oYN6GjdhYWHXzWWkDBbYuWsEbEdrLlhI3ZuehhxiQXyfmKLicuSzz/KGpUkDoOIic9Gf89+tDR+LpfrSDqd3L/JEoPM3CNRNNf3/tJaYC9MnPI+turOoIyFiIiCazrH9+m8TxARzZZYBl+96yV5jj9/6VcnnJsThaK6ujq53MeX9sNMiEQYh2MAQ4OiTsgLsNoykZa1VNYFUZrNIWZ+iBkf4nZRtHS6szPSsw+TyRDRukvMJOntrpczPWzRqSgsO1XeJym1DLu3/hd5xcciMbnE76+TiIiIiIhGiC8bd2/+D3KL1soZ2kThYOHChbDZbLj66qtx/fXXIylJfOk+M0yIhDGRiOho2YWhoW44hvpkVxaT2QKbYRnKFl0CvcF6yMcbDGY5s0NsaVkTqw5PJcqWJLfxNrx/F8yWOPT3NclkSXr2slm9LvI/UQOgrZa1AGgyMYW1uZ1TWWkiHjNICY8X5A1jQ33iy8mUjIWaSoYwLshXl1xyCfr7+/HII4/ggQcemFA4drqYEAlTe7Y9KRMaohCqJSpR1ucYnQUyBECNeqTzllyCwYEupGQskIkR1gPRDvFG1L6Pn2xoMlHkrLmdbxU0EY8ZpITHC/KGsaE+t8sJW0IqtIRxQb4a3/Z3165ds3oO/7UCIU0RS11Ea1zRltZiFcmHkV+1XudBYbZDXgabqEcilsqIKtZMhmiLCI+cBUZ5STSemscM0i4eM0gJjxfkDWNDfbbYdHS07oaWMC7In+bOnTurx/FUJkwtOuIaRMdmYvvGh+B0ijkhX9ABsdFueUk0SuSnohN18pJoAh4zSAGPGaSIxwvyhlNWgIgAAOClSURBVLGhuuS0+XC7naipeF223NUExgVNU2NjI5544gk5I6ShoUFe53K50NHRIS99wYRImBIzMJLT5iG/5ARU73pR7eEQEREREZGKnw1Ec4MoWzK2bfgnGvd9KLtOEmmZiNGbb74ZixYtwlVXXSX/vXfvXnlbX1+fvP6vf/2rT/tgQiTMJSQXY2iwW+1hEBERERGRykkR0Q1y4eFflzPI91W8pvaQiA7pD3/4g5wVct111+GZZ56ZkMQT7XbPPPNMPPfcc/AFEyJhzjFsl91lRol/1jcb2RmAJnC7gaYKl7wkGo/HDFLCYwYp4fGCvGFsaC8xkld8LPp6GlUdB+OCpvLPf/5TdpK55ZZb5GyQg5WXl4/NGJktlvUNYy2Nn6Op/jMUzDl57DoPdOjoVqHFDMFW3QnN8gDdTXw3IuU4HRSttMf9bC9MDOqYwtF0/hvymBF5/PU7V+tvlOcY5A1jQ6N0OvnF6WjzhWBjXNBURL2QI444wuvtNpsNvb298AVniIQpMZ2ovvo9lC26GNaoJAwP9ckDnqjiPKdgmNWcaQLxPliwjB0jaDLGBilhXJASnmOQN4wNbYqNy0F3R7Vq+2dc0FRSU1PHiqgq+fzzz5GTkwNf8FQmjKXnLEfNnldQvedlVO9+GVs/ux/Ve16F2ehgNWeaQHSKsNjYMYImY2yQEsYFKdIBVrOH5xg0GWNDkzJyD8f+uk/VGwDjgqYgaoTcf//9qK6unrDkS3jzzTfxyCOP4JxzzvFpH1wyE6ZEoGTnr5x0fV9XNfp7m7BvbzVyi05QZWxERERERKQuizUeRpMVDTXvIyt/1dgHTSKtuOmmm7Bu3TqsWbMGRx11lIzRu+66C7/61a/w6aefyroiP/zhD33aB2eIRJi4pALExOegu33vhGKrREREREQUWUrmj3y7vnPTvzFgb1d7OEQTiE4yb7zxBq6//nrs378fVqsVH3zwAbq7u3HjjTfilVdekXVEfMEZIhFGdATYtHkfElLmqVZAibQZF3XbnOwYQZMwNkgJ44KUiHioqjcxLmgSxobGZ5UXrEZCcilq9rwKozFKdqCxRCUEfN+MCzqUwcFBPPjgg1i4cCF+/OMfyy0Q+Ik44uiwZ+cniE8qVnsgpCUewN7pkZdEEzA2SAnjghTp0GcXp5acdk8HY2xoXXRsOuYtuVTWFanc+bzcHA57gPfKuCDvxGwQ0W63oqICgcSESITR6z04//xzUV/1Btqat8tuNER6A1C60iQvicZjbJASxgV5O8coLxmWl0TjMTZCR2x8DuYf9lUkp83Hni3/DegSe8YFTWXevHmora1FIDEhEoFMJhPmLfky+rrrsW39/diz9Qm07t8Mx3C/2kMjFfGDDXnD2CAljAtSYuAHG/KCsRFaEpKLZVJEFFwNJMYFHcrPfvYzPPDAA3j77bcRKKwhEqH0BiMK5pws/z040IWutgrs3fEcnA474hLykF96otpDDCm26k61h0BEIYTHDAqF+LIXJvrleYgoNKXnLMPWz/4ha4yw9iCp4W9/+xsSExNx3nnnIT8/X25RUVGT6uA8+uijs94HEyIEa1SCXC8otqHBHuzZ+l+1h0RERERERCoSSZD4pEJ0d1TLGSNEwbZ9+3Z5mZOTA5fLhaqqqkn38bVdNBMiEUZUcd5d472ac331u8gvOSHYwyKVuV1A9QaHvCQaj7FBShgXNJtzDIpcjI3QFZeQj/7epoAkRBgXNJWtW7ci0Dj3KQI5nMpZNJdrGPb+VsQl5gd9TKQ+55DaIyCtYmyQEsYFzeQcg4ixEZqMpig4HQMBe37GBamNM0QijF4PLCgZxra95knZWHGwM5mi1RoaaaBjRMWH/MaXJmJskBLGBc30HIMiG2MjdA30t8qkSCAwLmgqdXV1mI7c3FzMFhMiNMZijZdrsJobNiE9e6nawyEiIiIiIpW4nENoqv8MC5ZdqfZQKEItXLhwWjVCOjtnX0ycCRGaYM7CC1C58wUM2tuRV3K8z0VqiIiIiIgotLicw9j5+aPILzlRdqckUsPdd9896fOoKK5aW1uLxx57DCkpKbj66qt92odfo7ujowN2u11WgaXQrSZdMv8sNNR8gD1bn0Bp+bk8CBIRERERRQiPx42dmx9BTtHRsssMkVq+/OUve73te9/7Ho4//nj09PSoW1S1u7sbN9xwA0pKSlBcXIxFixaN3bZ+/XpccMEF2LRpk6+7IT8R6/Oms04vu2AVUjIWYMemf8ExbA/W8EglogYAawGQEsYGKWFckC/nGBR5GBuhpbNtL+Li85CQVBTQ/TAuyBfR0dEyYSJmkfjC6OuMkJNOOgl79+7F4sWL5ZSV3bt3j91eXl6OTz75BP/973+xdClrUmiFyejB0PDUS2GS0+bBYonDzs//jdLy8xEVnYxIZKue/Zq0UGK0AMx90XRiI5h/E/bCRGhJpBwPpoPHDPLlHIMiD2MjdLQ1bUFu0bFB2RfjgnzhdrvR0tKi3gyR22+/XSZD7r//frz77rs455xzJtweFRWFVatW4b333vNpkOTfas5lBQ55OR0x8dkoW3gR9u54Gm3N2wM9PFKxY0ThMpO8JBqPsUFKGBfkj3MMihyMjdASE5+Dvp76gO+HcUGzJZbJvPLKK/jjH/84YYVK0GeIvPzyyzjllFNw3nnneb1PXl6enCVCocsSlYDywy5HTcWr6GqrQOHc02EwmNQeFhERERER+VlqxiLs3vIfuWw+M+9IWWOQSA0JCQlem3x4PB7Zbvd3v/udegmRpqamQyZDBLPZLAutUmgThVWL5p6Ojtbd2L7hQZSUnwNbdKrawyIiIiIiIj8ymW0oX/Y17K/9GNvW3w+jKRqx8TmITchFTFwWDAaz2kOkCHHDDTdMSoiIn0WipLCwEMcddxyMRt8agPj06KSkJDQ0NBzyPhUVFUhPT/dlN+RnLvfs1+klpZbBYo1H474PUTL/bL+Oi9TF4ojkDWODlDAuyN/nGBTeGBuhRcwKycpfKTeHw46+7np0d1ShoXodXK5hxMbnIiv/KJgtsT7th3FBh3LTTTch0Hya/7Ry5Uq89NJLXpMiu3btwhtvvIG1a9f6shvyI7dbh+2ymvPsDz62mHQM2Nv9Oi5SFztGkDeMDVLCuKBAnWNQeGJshDaTyYbElDnIKz4O8w/7KhYsvxIJycXYu/0Z7Pz8UbS37ITL5Zjx8zIuaCpnnHEG3nnnHa+3i1ql4j6qJUR+9KMfweVy4eSTT8bjjz+O9vaRD8mi08xDDz2EM888ExaLBddff71PgyR/8iDGJnpbeWb9DGKakl5vhHsWBz7SKB1gS9TJS6IJGBukhHFBATrHoHDF2AgncslCcrFMjogl9QP9rdi9+TFsXX+/TJDUVb6DrvYqeKbMmjMu6NDef/99tLa2er1d3PbBBx9AtSUzoq3uAw88gG984xtyGy1usmLFCnkZGxuLBx98EMXFxT4NkvxHVHEuynH43PNbTJPr6aqVB0MKj7jIXWDkN740CWODlDAuKJDnGBR+GBvhy2KNQ07h0UDhyM9ieU1/TxO6O6tRV/0OrNYE5BQdgyhb8qTHMi5oOrwVVRWqqqpkzsEXvlUgAXDaaadh8+bNePTRR7F+/Xp0dnbKQS1fvhxf+cpXkJw8Ofgp9MUnFsgDHRMiREREREQ0urwmIblIbkJ/735U734Z0THpyC0+Vs4yJzqURx55RG6j7rjjDjnJ4mDd3d3Yvn07TjrpJPjCLxEpiqt++9vf9sdTUYgYGuyGxZqASGAvTJzyPrbqzqCMhSjS8G+LiIgodEXHZmLeki+jrWkLdn7+COYv/Qrb+NIhiQ61o6U4hL6+PujFdKKDZo3YbDZceeWVshONL5iiizQeYHBY5/NSve7OGmQXrPLXqEhlHg8wZPfIS6LxGBukhHFBgTzHoDDE2Iho4sNrauZiDPS3o3X/ZqRlLR25gXFBCq666iq5CQsXLsRvfvMbuSolUGaUEBHLYmbrkksumfVjyX/cHh321PjWO9ztdmHQ3g5bdKrfxkXq8riBmg1OtYdBGsTYICWMCwrUOQaFJ8YGCV0dlZiX9+WxnxkXNJWtW7ci0GaUELn22msnFDURhVMPVeRk/H2YENEGHTxIjHejs1sPzyzbA/T3NiE2PsfvYyMV6YD4dD26m93M0tNEjA1SwrigAJ1jUHhibJAg6oeYzLaxnxkXNBO9vb3o6emBW6ECb25uLoKSELnnnnsmXffss8/i1VdfxTHHHIOVK1ciNTV1rP2N6At8yimn4Kyzzpr1AMm/xJK9nHQnunrN8hu+2XA6+qHXm/w9NFKRWJaXUWpAb6ubHSNoAsYGKWFcUKDOMSg8MTZI0ung8bjHaogwLmg6/v73v+Puu+9GTU2N1/uIxi5BSYhceumlE35+4YUX8Pbbb+Opp57CcccdN+n+b775ppwZctlll816gKQ9cQkFaKrfgMbaj5GVt0Lt4RARERERkcaJFrxDA12w2pLUHgqFiH/84x/40Y9+hOOPP152sP3FL34hm7lYLBbZiUZMxvjmN7/p0z58KvH7u9/9Dueee65iMkQQAz/nnHNkqxwKHwajGUmpZfDwa0EiIiIiIpoGqy0ZA/YD3UOIpnLffffJnMKTTz6JK664Ql4n2uz+7//+Lz755BPZgaajowOqJUR27dqF7OzsQ95H3C7uRxrhAXr79T6v+e7p2ofk9Pn+GhWpTHSK6O9kxwiajLFBShgXFMhzDApDjA0CEBWdMjEhwrigKVRXV8sSHILROLK4xeFwyMv4+Hi5EkUsqVEtIRITE4MPP/zwkPcRt4v7kTaIas7VDSZ56YtBewcs1ni/jYvUJdZt1m9zcv0mTcLYICWMCwrkOQaFH8YGCVG2ZAza28Z+ZlzQVOLi4uByucb+bbPZUF9fP3a7yDO0tLRAtYTI6aefLqeqfP/735eFVMcTP3/ve9/Dp59+ijPOOMOnQZL/6HQepCc75eVs9XTVymTIaEGkcGer7pxyC3WiWVRyvl5eEo3H2CAljIvIMNP3P3+cY1B4YmyQIGqH2PsPJEQYFzSVefPmTWi9u3z5cllXpLGxUSZGHnzwQRQXF8MXMyqqerBbbrlFJjweeOABWdSkqKgIKSkpaGtrQ1VVFYaGhjB//nx5P9IGcfKanuxCa6dhxlOdh4d60Vj7Efp6GlG26KJADZFUIHJbKXkGdNa74WFpGBqHsUFKGBfk73MMCm+MDRptu6uDDi7nsKxJyLigqVx88cW4//77ZV5BFFK9+eabcfbZZ6O8vFzebjKZ8K9//QuqJUQSExNlJ5m77roLjz32GHbu3Dl2W35+Pr70pS/h+uuvl1NbKHQ5nUOo2fOKTIhk5R2F/JIToePXgkRERERENAOZeUdi785nMWfBBWoPhULAV77yFbmNWrFiBT7++GO88sorMBgMsrlLSUmJegkRISoqCjfddJPcent75RYbGys3Cn1utxO7Pn8U2QWrkZjiW7AREREREVHkSkqdi0F7J6p3v4TieaeqPRzSsMHBQbkkZuHChVi1atXY9YWFhbj22mv9th+/FoEQSZCsrCwmQzRMTEfr6J7etDSPx4OK7U8jM/cIJkPCnIiH7iY3pyvSJIwNUsK4IF/PMSiyMDZovKz8o2CNSsTOzx9Fa4eLcUGKrFarLL1RUVGBQIqMqpg0xuPRob7ZKC+nUrv3TcTEZrG9bgQQnSKaKlzsGEGTMDZICeOCfD3HoMjC2KCDZeWvRHbBMXjlxX9haLBP7eGQhouq1tbWajchkpCQIOuITLUlJSX5b8TkE1HFOSd96mrOzQ0b4XTYkV1wYHoShXeBxIxSg7wkGo+xQUoYF+TLOQZFHsYGKYlLyMYJJ12Ayh3PwMMMOyn42c9+Jhu4vP322wgUn2qIiLU8SsU1u7u7ZZeZ/v5+LFiwQCZOSBvErysp3oXGVu/TFrvaq9DesgPzllwa7OGRinERn6FHS5ULPFWh8RgbpIRxQbM9x6DIxNggb3GRmR4Dqy0B9r5mRMdmqj0k0pi//e1vcoLFeeedJ5u2iE3UMB1P5CMeffRRdRIiL774otfb7HY7br31Vrzxxht49tlnfdkNBVF7y0407vsQ85d+FTp+9UdERERERAHkGOqF1Zai9jBIg7Zv3y4vc3Jy4HK55KSLg/na/dTnLjPeiFa7v/3tb7F27Vo51eWee+4J1K7ID0QB1X17X4djuB/lh10GvcGESGCr7lR7CERERBTE92x7YWLYnUP46zURqcHpHIBeb1B7GKRBW7duDfg+Aj4FYOXKlXj11VcDvRuaJjFNsbl98nTFuqq3YTBYUFp+bsQkQ+gAsWyzrZYFEmkyxgYpYVzQTM4xiBgbdKi4AAxob96h9nAoQgU8IdLW1iZriZA2iOreze0Tq3w7HHb0dNUit+gYVcdG6r4hte9jC02ajLFBShgXNN1zDCKBsUFKRDzsqaiDTmdiV0vySiyVeeKJJ3D99dfjy1/+8tgyGlG39LnnnkNLS4s2EyJutxuPPfYYnnrqKSxcuDBQu6EZ0us8KMx2yMtRLQ2bkJFzuKrjInWJcjE5C4zsGEGTMDZICeOCpnuOQSQwNkiR24HMpBaULTqftQsDqK+vD7fddttYYdL4+Hg8/PDDk+63YcMG/OAHP8DRRx+N5ORkeb+ZOP300+VjDt7Efmerq6sLJ510Eq666io8+eSTeOmll+SECyEmJgY33HAD7r33XqhWQ2TRokVeszitra1wOBwwmUy45ZZbfNkN+ZMOiI12y8vR1gD9vfuRmqn8u6TIIGoRRSfq5CVPVWg8xgYpYVzQdM8xiCTGBilobtyAo1cWoaLeAjeXYAZMe3s7fvOb3yA3N1dOVFi3bp3i/V577TU89NBDKC8vR0FBAfbu3TvjfWVnZ0/67J+RkTHrsYsmLbt27ZKTLBYvXozi4uKx2wwGA84++2y8/vrrPuUbjL4W4lR8UqMR8+bNw2GHHYZrrrlG/pum1ttVB5MlGtGxGUHNkg4P9cFksnm9TVTuNZmjgzYeIiIiIiIKXx63C23N22G2LFF7KGFPJCT27NmD9PR0bNy4Eccee6zi/b7+9a/je9/7nmxr+6Mf/WhWCZG4uDhcfPHF8BfR1VbkE4477jh0dHRMul0kSB555BGf9mHUetXXSNLdUQ0PXKja9SJSMxcjM/eIoOw3PqkAnW0VSEqbO5bo6mqvxP7aj+TPfT2NGBzowMoTfg6jyRqUMRERERERUXjq6a5DfGKBzy1TaWoWi0UmQ6aSlpbml/05nU4MDg7KJS2+6unpkct8DrUvsWmy7S7NXE7R0TLhMNoCt2rXCygsO82vs0VER4D6ZuOEzgDp2cuxe+vjaGveBmtUEro6KhGXWID80hNRvfsVWeQop/AYJkPCmJim2FTh4nRFmoSxQUoYFzTdcwwigbFBB+to3YXUjAWMizCzd+9eZGZmYnh4WCZYvva1r8k6H6KMxmwUFhZi8+bNXm9/6623UFZWpl5CJDExETfddBN+8pOfeL3PHXfcgV//+teKU1xImciUFpSehKb6Ddi2/gHEJeZjeKgXQwNdcuG2WN5SuuA86PUz//V5oENH98Q+32ZLDBYuvxID/e0YHGhHTuHR0BuMcLuciI5Nx4C9nYWOwp0H6G7iuxEpYGyQEsYFTfMcg0hgbNDB+nub5Oedjm5+xpit3t7eSTNBxKaWgoICrFmzBvPnz4fdbsezzz4rcwEiSfLggw/O6jkvu+wyWR9k9erVWLt27dhn5aGhIVkX5Y033sAf/vAHn8btcw0Rb3VEDr4fzVxGzjIkp82Dva8ZlqgEWKzxMjHRun8zaivfRkHpiTN+TlHduyTfgb37THAf1PosKjpZbqN0egPikwrR293gl9dD2iXyXflLjdi3yRn2WXrn7pmvh1SbsaxEtX1HUmzQ9DEuaJStujPk4mL8mMNFMF+TvTDRr+efFLkMeh1K8odDIi7MDSYYrLOb5eBvrkGXvBSJh/FuvPFGOVlBLXffffeEn7/0pS/hu9/9Lv75z3/i29/+Ng4/fOZdTa+99lrs3LlT1jcZ7XojOs6IyRZiqcwVV1whkyaaXjIjqtqKwiw0OyazTSYlRrndTnS27519kVMdYDV7vFb5Fsmr/p5GtOzfjL6eBsQl5KF82WUwGMw+vArSOrF802JjxwiajLFBShgXpIRxQbM9/6QIJOKAceGTHTt2IDY2duxnNWeHeHPdddfJhMg777wzq4SImA3ypz/9CZdeeimeeeYZVFVVwe12y6U05557LlatWgVfzTgh8uijj074ecuWLZOuG22929DQIG9jlxn/EcVOLZZ45Jee4NfndQz3o3Hfh+jurEFMXCZSM5egsOxUFjoiIiIiIiK/Ecvx9XouofKVSIaIri5alp2dLS87O32bxXbUUUfJLRCMs5m2MvohWVy+9NJLcvO2TEbMDlFz6k64iY5JR0PN+2iqX4/07GU+JyxEW9366vdg72tBdsEq5JUcz3ohRERERETkd51te+Rnj9IFF6g9FAqCmpoaeZmcfKAsw2yIJTJilkltba38WXSeOeaYY5CUlBT8hMg999wzlvAQa4FOP/10uR3MYDDIoqtiaoy4JP8cQBzDdllkdV/Fa4hLyIUtZuoWSuOJjgBV9SYMDohEyLuw97chp+BoFM09LWDjJu0TcVG3zcmOETQJY4OUMC5ICeOCpjr/ZGyQSIaUH/Y16A0iHjyMixC1Z88eOfEhNzd3rD3uwUVdRb5AFFUVjj/++FnvSzRoueuuu2Qh1fHMZjOuv/56/PSnP0VQEyJi/c6oDz74AGeccQZOO40fpgNNBNS29Q/K7jLxSUXILVwrO8HM1NBgLyp3votBe4fsJlM0rj4JRTAPYO/k4k1SwNggJYwLUsK4IK906LNzGTaJFQZGmQz54ifGRZDcd9996O7uxv79++XPL7/8MhobG+W/r7nmGlmwVMy++M9//iOv27Rpk7wcTWiIxIcokjpKTHoQnV9efPFF+bNojSsKn15wwQWyvsfg4CBeeOEFfPzxx7j88suxZMmSWY37t7/9rewmc/LJJ+Pqq69GSclIowHRuUa8pv/3//6fbOl7qK63AS2qOjpbhAJPLI1JyViA+MTCCZ1gpsvhsGNfxetwOftw1hmnoaI+AW43D0A0QizjLD7ShMpPHHCPFK4mkhgbpIRxQUoYF+SNXu/BvCIHdlaJ2QA8/6QRjIvgEYVJR5ebCM8//7zchIsuukgmRPbt24df/vKXGG/0Z5H8GJ8QOZhImIgaH+I5W1paoNfrMWfOHNx5552yE8xs3X///Tj11FMn1SwVLX5POOEEXHzxxfjHP/6hXkKEAs/pHJI1PUTHF5FNnU0yRPT53rvjORTMOQmJyfmwWIcDMlYKbaxtRd4wNkgJ44KUMC7IG4Oes4cinSimarYe6IoiMC6CY+vWrVPeZ82aNXIWyXQcfD+RoBDdZPxNLMU51HKbE088Ee+//75P+5hR9cyEhARZuERMURn9WdQHmWrzR7ETX4h1RyLrNX5bvnz52O0PPPCArIOSk5Mjb+vq6pr0HJ9++qnMjC1cuBAPPfTQhNvEY9LS0iZk3UaXF4kitLNl72/F1k//hq2f/R21e99A8dwzFJfS9HbVyc3jmbwAz+kYlMmQeUsuRXxiwazHQkRERERENFtitnpWXmA6hVB4OvLII7Fhwwavt4vbxH18MaMZIqLPr1i6IQqojP85FIjWv88+++zYz0bjgZc+MDAgM09i+/nPf674+O985zuyYEtGRga+8Y1v4LjjjpMJlFHiv8Ntt92Gv/zlL34bc1PdZygpPxcxcVmK/50b932EtuZtiI0fGUf1nldQWn4uoqJT5M8iQVKx/Wk5M8RsifHbuIgCwbl7JNFKgftvYywbWXdJREQ0W7bqzlnNHDJkmWCrsY8tp7IXsumC4HINY3iwF8NDPXJWeHRsBgzGA4Upw0VH626YLXHyc42/49Dpmlhsk8LHnXfeifPPP192rb3qqqtkfRKhuroaf/vb3/DZZ5/hySefDF5CZLRoireftUwkQNLTlTuyfOtb35KX69at8/p4u92OxYsXIzU1Vc6M6evrm3C7KPJy991347vf/S7mz5/vlzE7nQNwDPd7TTr1dtdh7uIvwWwZmXo2NNCFiu3PyISIxRqPrva9SM1cPGFmiKjivLuG1ZxpInFyUr2Ba75pMsYGKWFckBLGBXnD2JjM5RxCzZ5X5TKSKFsyzNY4uFwO1Nesk0kSqzUBucXHwRqVcMjnEV+Arn/vdyiaezqS0/3zGcTfxOsR3S3LD7t8wvX8XEJTERMw3G63nHQgNlGbRBDXCaKrjbjPeOKzc11dHaYrYmqIVFZWoqysTP5HO+KII3DLLbeMtQmaDlGoRTzO6XTKCrpz586dcPuKFSvkPm699VY8/vjjfhlz8bwzsfnje+UMEJPZNul2UWC1q70SaVkjVXstUQkoX/Y19HbXw+UakskQizVu0uMcztCY1UPB5WRynbxgbJASxgUpYVyQN4yNAwbsHdiz9QnkFR+LxJRSrzUAd295HEVzT5OfBdwuB7o6qtHdUSUTDAaDSc4oEffLLliNhn0fICltrrxOa2r3vomcgqNhMJon3VZfsxHxyWVjX/ASjXfWWWcFfEWKTwmRRYsWydkV3/zmN73eR0xlEVVtt2zZArWIeiGiI05paSmamppk6x5Rrfajjz5CbOz0/vguu+wyOV1neHhY1kVRIpIsK1euxIcffigvfdXf24zo2EzFZIiQnFGOXZ8/KhMfo4EiLuMSvCd6RFJtQckwtu01MxtLE6aylq40oeJDfntDEzE2SAnjgpQwLsgbxsZEdZVvoWT+WXJ5jDfiNlEDsLbyTVl7Q6c3yFnfqRkLYTBZZYLE43Yhu2CN/KxgNNuw8/NHUFh2GqJs6tZvHK+/dz8GB7pQWDZ30syWjpatWL44Fq+8/hRM5kTYYtLkbBmxiS96D07uDA/1oaHmfQwNdqHUchhiLalBfjUUbPfee2/A9+FTQkQUEZ2qEq0oUDqTKSuBIKrPjlqwYIFMkIjiqE8//bRMdExXdHS03LwRs0YuueQSOUvktddem1XrKbEJjuFBVO9+GeWHfXnsOsHjEZsOep0HFksUklJL0Nm2Hcmp5fBg5HqMS6KNJD10Y88xcjm6jSRIxhtNkky+XjypGN+4Kz2A26ODTuxZ6XqdB+MTeqNj93b9wWMXNWKn85qmHvshXpNu4vVyLOL+Xq4X49YpXa8fuW3C2D2Tr5djOcT1B1fnHz1pmNH1s3hN8nm+uFTrNY3SGydmgd3Okd+/3nDgejlGl/fr5Vj0uoljd4vrdRN/f27PyGs1iLgcP0bxRN6vVxyjt7Fr6TVhlr+n0RgJQOyF499TJLymUeOfP9RfUzj+noL9mrz9O5Rf05TX8zVN6zWN38/o9aPncUE539PQOeygvRM6nQux8WIpv+eQYzdbomXiZOrX5EFG1iLYbCky2eJ2DcgvSWMT8pGZczh0Bqsq5+Ui6VG791WUlJ83dvuAvUfOGHEMdyM5tRRxCXOwYFkJervbMNDfjkF7C7rad30x82UNElPmyt8TdG5U7XwK2fmr5fKiqo0vYkn2WSPxNPE/D9GMBHzJjGiVI5apaImoAVJcXIyqqiq/P7co+LJs2TK88MILM37s/KJhmC16efDYvm09ispORUGOBUnxB9rkNrcb0NxuRH6WE7HRbpQXH4G+7gZ0D7nR0W1ASb4DVvOBA1JVvQl9dp3s8S3bWumAOJsHFjMwNDwyW2Q8MXPEZPSgrMAxdp3LrcP2vWbE2Dwoyjlw/eCwDntqzEiMdyMn3Tl2fW+/HtUNJqQluZCefOBrADG++mYjstNcSIp3eX1No8R9p/WaviDWIIrlQDN5TbYEHXIXHPgzGLJ7ULPBifh0PTJKD7x793d6UL/NiaQ8PVLyDlzf3eRGU4UL6cUGxGcceLdqq3WhfZ8b2fONiE48cJgW9xWPyV9qhMV24Pq6bU7YOz0oPtI04aRBrLcVU0zFtyrjiW9YRL2twmWmCScx4vpZvaZ8A6IT9SheYUL3fnVe0849OphsRhSflnng9+T0YPcTdYhOtyJ/bdqB19TjQOVL+5FQGIOsIw58C9LXNIjad1qQMj8eqQvix67vrOrD/k87kLE8EYlFBwoMt27rllvumlTEZFjHrm/8tANdVX0oOjkDlrgD49z3Tgv6mwZRek4ODOOSHGIsDrsTcy+YODtr1xN1mnpNovLRTH9PIiZGY0Ocj/g99sLx7ykCXlNLlQuWaN1YXITDawrH31PQX9MXDzNHiWNNmLymcPw9qfGadIDJOjLe0dfksg0H7XxPK+ewe6pd2Lr9aVx44QUwm4f9/5o82Vix+lz5mkQnSsdwH2r3fY7m7hyUFGUE/bx8X9WnOPusU2CNEp8FR17bE0++hNyCw3HkstwvPpe4Ma/IiW0V6UhLSx17TWL8Tfsr8enGV7Bg4ZHyNZVknyhnv4jfU80GF2KyBpFVFIuhYTde3zXhPx2FmQ8++AA1NTVysoWIjfFE8u/b3/72rJ9b193d7ZnpYEaJVrVf/vKXZXvZg7lcLjQ0NMiuLSkpKT73B/YnURC1vLxcJi/GL/cRRVXPOOMM7Nu3TyZNpku03X344YflY4X/+Z//kTNERBVc0XJ4qqk+Imkk6pmsPulW+fPuLf9BTtHxiI3PnVbWdsemf2Pe4q/A4RxGd/se9HTVYHiwGyaTDVExGYiOzYbZbIXD0Q+PaxArVxRgR6VVZpdDNbvur28MbDWd/GZHBxhMIx98Kz92wOVU5zUN79irrdkUYThDxDinZMa/J7HcdzQ2xHX8ppSvSY5bN/IBq/KTA9PfQ/01hePvSY0ZIuIDufhQfbBQfU1TXs/XNO0ZIqOxMXoMsRckRNwMke6OelTtfglRtjh5nm40xyA6JhUx8UVyNkggXpNjeAA7Pn8MxXNPRUxcRtDOy/v7OlC543ksPPyrE5a+dHXUy1nwuUUrkZY5X34hvKPKDKdTP+k1iXOajrYqtDdvg0GvQ17pCSOlBMRr3VWNrU0vwGqOgdvjQnP3XrkqIS5ucv1EX4x+Tiu++TYYrAe+aFKTa3AQlbfdHJDXqzWi7MYVV1whJzIcnAgZnxDp7Jx596tZJ0REomC6hU3EoMV9RULgS1/6EtQi2uWKmiEimEUNEdEed+vWrfj0009lsqa5uVlumzZtkl1iXn75ZcTExMi2uiKhMdOESEdHB5YsWYKhoSGcd955006IHL72Rmz+6G5ZEyQqOhUJSUVyLd1UNrz/B1k8VawtTEyZg8TkUlmReni4H309DXIGidvtgNFkg72vWSZHCueeeeCrnAg2m9Zx4UqcrKi5rpdtdwNvtm131Y4N0ibGBSlhXNB0YyOS2+6KbjJi9oboJtlU9yn2132C1Sf9CnpDYCbvi+UnTfWfyYYNwSBnu294CMXzz1KsZyJe/76K16A3mFE453iZgJnp55KD2+6+UXEnEyJh6JhjjpEtdv/v//5Plr3w9nrz8vJmvY8Z/9XdcMMNMskhkh2iOOnq1avldjCDwSCTJ0cffbTs7qKmxsZG2RlGJCpEAkR0hHnjjTfkv4X7778ft99++9j9RfJEEIVYxQyYmRJJlOuvv17+4mbC7RySRVRj4rLlQXLXlv/gsJXXTfm4pSu/DZ3OMClRJZIkYktOmzd23bb1D2D+4tPxRTkBojFi6uywXe1RkBYxNkgJ44KUMC7IG8bGAaJDjCEqEdaoRLQ1bcWC5VcELBkySq8PXnPR/bUfIym1zGtxV/H6RZtg0WnHOdQCg1nUUyGabNeuXbj55ptx+eUTWzb704z/MsQyk/HLZ0TCQBQS1bIHHnhgytc0/nXNlFJh2R/+8IdymwmLNQELD/86nI4BbN/4L5Qf9jW/H+CMRgPKS43YtvfANDwi8a2NWEfM6u90MMYGKWFckBLGBc0kNrQ2S1etGSuiZW7lzhfQ3VGN3KK1MJqi/L6PxtqPkJa5BMEwYG9HR+selC9Tblwx/vee6IrF4nwjaj7p4jGDFIm6n5puuzubwqF0aGLmTcX2Z1A452Q5u8PfxBo+N484RERERESqM1tiMW/JJeho2YWKbU/B5RqGwWBGTHwOUjMXyVkkvmhp3CRnZMQnFSIYS2VE3RCxVObglrlK9DoDPBDf0HppPUgR78Ybb5TlLy644AJkZWUFZB/BmztFU/LAg317X0dsfA7iEvMDso+8omNlXZGaPdVIzVyGqOjkgOyHiIiIiIimJyltrtwEl3MI3Z37sK/idTlzfN7SL89qycvQYA+aGzdhwRezzuXzdlSjq6NK1i/JLTpmWvUKp6tx30dISp3jdanMeB32OrT2VcKoP0KMzG9joPBy1llnybqcon6IqCcikiKiNMd4YgaJKOWhWkKkvr4ed9xxB9555x1ZsHR4eGK7qNFBivoddGh7tvwXcYl5yClcE7B9RMdnITpuGMmpZtRVv4OhgS45NU8UYs3IPTxg+yXt48Qh8oaxQUoYF6SEcUHeMDamz2C0yMSC2Lauv39asy2UdLXvlW1j9lW+KZssiKRKfFIR0rOXylnptVVvY+6ii30er9vlQPWeV+Bxu+TskOmobPsAy/LPA9zitTE4SJnoVPuDH/wAdrtdNj5RompCRFR8Pf7442U/4Hnz5snsjajCa7VaZZ9gh8OBBQsWzKiFbSQrmHMyomMDW1RItOnavteC6Ph8zIkfmYWyv+5T9HTXMiES4ScpSm0SiRgbpIRxQUoYF+QNY2N2xBeXBr1p1gmR9OzDEBOXJWeG5BcfLztSjmpr3o6Y2Eyfx9jXsx9Vu16QtVDGN3KYinhNeo+ZcUGH9JOf/ASxsbH45z//ecguM76Y3V/XF0QmRrQieu6552SBVeErX/kKPvvsM9nW9rTTTpPZnIceeshf4w1rFmt8EPbiQYzti2b1XxAZ45yCo4Owb9IsHWBL1LETM03G2CAljAtSwrggbxgbY8QSmN7uBrQ375B1Q8QSFnEuPtDfhuGhXllDRNynes+rqNjxDPJLT/Rpf9GxGXIp/vhkiNM5hIaaD5CRe6RPz93Zvhc1e17B3MWXzCgZIuigk+UCGBd0KFVVVfjud7+L4447LmAthn1KiIhlMieddNKEtrti+pWQkZEx1t3l5z//ua/jJD/R64GiHIe8HJWWvRTNDRvUHBapTMRD7gLjhLggEhgbpIRxQUoYF+RNpMeG+HxUX70OWz69D3t3PIv2lu0YGurB4EAnerr2oa15Gxr3fYiaPa/KJfSiHW18YqHsOCkSGv5Wvfsl5JUcB6PohTxLQ4PdqK96R9Y3MVtiZvx4WRNF54rouKCpzZ07V07ACCSflsy0t7ejtLT0wJMZjXJGyCiLxYJjjz0WL774om+jpICKS8hH7d431R4GEREREVFYEZ1XKrY/jeiYdCw8/OqAtxCdiph9ImaiJCaX+PQ8w0N9shOOSGyIAq39vU1yE4kScZ3BaJbdcvQGy9i/RW0U5+AgjHoznO7hCTPWiZT88pe/xNVXXy3LdCxbtgyaS4gkJydPSICIn2trayfuwGhEd3e3L7uhANtf9wkSkorVHgYRERERUdgYHOhC1c7nkZw+H+nZgfkwN1MiaeGf2iGN6O2ux46N/4LBaJUzWcSWmFICt9sll/6I2iUjl8NwiOVAzmH09XbLZEiCNUvWRyE6lD//+c+IiYnBCSecIGeL5OTkKHaZefTRR6FKQqS4uFgWVh0lsjZvvfWWvK6wsBBtbW149tln5b9JIzxAZ2cHtm94HS63W1aFjonLRuGckxGJ7IVT93a3VXci3ImVbkN2j7yk8OXcvXfK+xjLJn5jxNggJYwLUsK4oFCODX+c74mlMb1DzWjtq0LXQAOMBivKklcgdjgVmOHzT+ccdTZEkdW6qnewfeNDsEWnIjouC9n9aTAZrNN+DqdrCO0Nn+GI3Iug1437cNr3xSZFTX6gWBqTGlpxQeravn27vBSJkL6+PuzatWvSfXyddeVTQkRkam6//XbZZUZ0krn22mtlO5xVq1Zhzpw5sgiKWPNz4403+jRI8p+Wph3YvW0P5iy6BAYDs7I0wuMGajY41R4GaRBjg5QwLkgJ44IiMTZkcsBeg9a+Sgw6exFrSUdqTBEKk4+cmCzQCLFsZcHyK+B2OWHvb5EFXbfufxFmg02OOdqcNOVzVLS9h6LkFT6/vnCOC/IP0agl0HxKiHz961/HmjVrxqatiH/ff//9Mkmyc+dO2YL3Zz/7GS6//HJ/jZd8NDTQjvnly+GEkav26AAdEJ+uR3ezm8s5aSLGBilhXJASxgVFQGyIWSD9wx1o7a9Eh71WJgWSbQUoTlmFKFMwOkb6h95glLNFxFbkLEHvUCsq20a6huYmLEWMJWXSrBHx2vd1boBOZ0BKtB9WAIRRXFDo8ikhIlrfiH7A45177rlyG3XnnXfizDPPxPPPP+/LrshPLNYYFOVZUFE/kpUlEkR174xSA3pbxTIqtUdDWsLYICWMC1LCuKBQjw2HaxCDjh75gd9stMGkt8rp+C63Ax32OrT27ZXJkGhLMlKji5GXcBgMoltKGIi1pGJR1pmwD3eisWc7ars2wekaHLlRLEnwiCa5HiTb8lGWemxExQWpy+Vy4emnn8a6detkSY6bb74Z5eXlsk7pu+++ixUrViAtLW3Wzx/wv+CKigq8//77gd4NHYLI5nZ3VsvCRk3162FeM7M+4URERERE4UrU/Kjp+BRmYzSsxlh44Maw0w6ne1BOXBCzQBKjspGXuEwuKVG7U0wg2cyJKElZrfYwiCRRmuP888/Hhg0bZHHV/v5+XHPNNfI28fMNN9yAL33pS7jlllswW+GR0qRD6u2uw9bP/oH84uMxf+lXodOx2TcRERERReYXhS6PAwadSV6KRMiAoweH5ZzPridEGnPrrbfKQqpPPfUUFi9eLJu6jBJlO84++2y8/vrrTIiEG1HkqLO9AhZrvFzX56vY+BwkJBUhNkG0KbKgt9/FdXo0gaju3d/JKt80GWODlDAuSAnjgrQeG2LWx5b9z8Oot8hlMOJLwqy4chQnrwrrWR9apZW4IO168cUX5YyQ4447Dh0dHZNuFwmSRx55xKd9MCGiMcNDfdi1+TEkppSivWUH9HojCstOhcFgnvVzioP9vCWXyhZb7S274MFZfh0zhT5RT6Z+G6t802SMDVLCuCAljAvSemzUdK5HUfJRSLLlqT0U0lBckHaJjrX5+fleb3c6nXLzBRMiGlKx/RkMDXahtPwcRMdmyOs6Wndj2/oHEROXKZMjHo8LHrcbHs+Bze12wWyJlbNJrFGJ0OkNMgmil5cG+bOQlDoPtZWvIz3ZiZYOAzweZsJphPhSJClPj45aEVNqj4a0hLFBShgXpIRxQVqPDXEeLWaHkDZoJS5IuwoLC7F582avt7/11lsoKyvzaR9MiGhIfukJiPoioTEqKbUMicklGLC3yzWPOr1eJjtGtpHEh5jiNzzUi76eBnR1VMLjdsEtkiVu1xcJFJdcISPu63E7kBTbi9bOhLA/8NiqO9UeQsgQZWVS8gzorHfDwyrfEc25e++En/VGHZKX5KL1rTq4ncE/aBjLSoK+T5rdMePg2AkFjC//4nsJaTk2xHm0UW+WXVTirOnQukg4j9VCXJC2XXbZZbI+yOrVq7F27Vp5nfjsOzQ0hN/85jd444038Ic//CG4CZELLrhgRvffsWPHTHcRsczmmAnJkFHiOlvMoVsJmczRY7NKDmVooA0D9jYMDephModOr3QiIiIiIiXii0Ax7/ngxgHi+rb+KjR0b5U1QxKispAeO0e1cRLRzFx77bXYuXMnvv71ryM+fuSz61VXXSXriYilMldccYVMmgQ1ISKyMDPFIkXaERWdDJ2uG0bjgemCYtmNqF0iltgYTVHsQkNEREREmiNmeQw5e9E10IjOgXr0D3dAJ/73xbmrZ3zXgC+mQot6IfPTT4LFGK3WsIlolkQe4U9/+hMuvfRSPPvss6isrITb7ZZLac4991ysWrXK533MOCGyZcsWn3dK6hHvDfUNrajZuwPDjkG51Ea8kYgaJCIxMjTYg8KyU2RnGoqsuOhu4vpNUi541lnVJy+JRvGYQUoYFzTd2HC5nWjt24ueoWYMOLrhdA1N+SXqaFhZjDGIt2YiN2Epos1J/OI1hPGYQdN11FFHyS0QZpwQyctjVeZQJgqpDumWISO3GwajBSaxTGfcG4mYKVK581kkpsxBRs7hqo6Vgkd82G2q4OJNmszj9mD/p5PbnFFk4zGDlDAuaKrY6BlsQn3XFtgdnUiLmYPM2HmwmuJgMljVHiKpgMcMmkpiYiLuu+8+XHjhhYq3P/nkk3IJTWfn7GvucG1EhNHpPMjL0sEWkyxnhRycVTdbYjB38SXo7apHY+3Hqo2TgkvMNM0oFR2J1B4JaY1Or0PmEUnykmgUjxmkhHFBhyyeWezCpsankZuwBMtzL0Ze4lLEWtOYDIlgPGbQdJbJHYpYPuPrLDGGXyS2t4p3yUvv99GjpPwcDPS1oKHmg2AOj1Qi4iE+Q3QsUnskpDXiJCWxSBR8VnskpCU8ZpASxgV5I2IiOcuK4pQj0T24X+3hkEbwmEHT4S3h0dPTgzfffBPJycnwBU9xyWvgFc07E0OD3airelft4RARERFRiHN7XDAZotQeBhFp2O233y6XyohNfCa9+uqrx34ev+Xn5+Oxxx7D+eefH9waIhQ5RAAWlp2KfRWvYd/eN5BfcoLaQyIiIiKiEON0D6N/uBd9wx3ISTlM7eEQkYYtW7ZM1gURy2X+/ve/49hjj0VJScmkz6k2mw1LlizBWWed5dP+mBCJMGIZVnO7YdrVnEWw5RUfh+3r/oLOPjOy4xcGeoikUlGrtlqXqp1EjGUTD3QHc+7eG7Sx0MSiqq3buuWlGoL5e58qBv05nunsS8v/DcUSqhZ9PBy7RGz4/ekpRGnhvYS0Z9hpx/r6x2BOOQ3zU08SRxCEM3thIiKVrXpmhS15zCAlJ554otwEu92OK6+8EsuXL0egMCESgV1mmttn9msfHOiCwzWI9NiygI2L1CUSZO37+G5Ek4mTFJEQIRqPcUFK+F4SOfqHO+SWFJULo8FyyPuOFE3Vw9GSzha5NAGPGTSVe+65B4HGGiIRRq/zoDDbIS+nyxaTiqKUldhU/xT2daxHe38NOuy16LTXo2ugEd2DTegdbIHL7Qjo2ClwxLe9OQuMLJxJk+gMOuStTZOXRKMYF6SE7yWRUQOksXs7tjQ+j/6hdmxseBIDDu/J0aaeXfis7j8oSjkCuQtNjA2agMcM0gLOEIk0OiA22j0yW3EGM+Cz4uYjPWYO2vqr0DvUCo/HDQ/c8tLtcWPQ2Su/AZibdlwgR08BIr6wiU7UyUt1FkaQVomYiMmwMjZoAsYFKeF7SfhyuoZQ1/U5WvurkBFbhiPyLoVBb5Kzh7fufwkLM09HlCnuwP3dw9jb9r48T1yeexGMRgNjgybhMYO0gAkRmjaD3oj02DmTrh9w9GDb/hdRkHS4KuMiIiIiosAQM4A3NjyF/MRlODzpYujGfZ1vMydgQcYp2Nn8OvQ6A+KsGRh22dEz2CzPC9NiglcviYhoNpgQIZ+mTbb2VaKi9T0szTkf0ebILSJFREREFKqGXQNyCYzFGCOTHOPt69yAvISlil+KCTZzIg7LOV8+R+9g88hzpCZBz3UQRBQCmBCJwEJ49c1Gn6o5i0RIfdcWNPXuQlpMqZw2aTba/DlMCjK3G2iqcMlLovHcLg8aP+2Ql0SjGBekhO8locntdmJ97WNy+cuAswdDjl757xhLCjrtdeiw70NhzpFTPo/ZEIXk6AIv+2Bs0GSMC9ICJkQijAc6dHQbZv341r4qVHd8ImuKLM+9mNn/cOEBupv4bkQKPEBXVZ/aoyCtYVyQEr6XhKTqzs9QmLwCmXHz5M+is6CoGdfSV4HEqFzkJy33vTsMY4OUMC5oGlwuF55++mmsW7cObW1tuPnmm1FeXo7u7m68++67WLFiBdLS0jBb/DQbYUR3mTkFwzPqMjOquXcPGrq3YlnOhchJWMxkSBgRv8qCZazyTZOJLiLFp2WymwhNwLggJXwvCT2Djh502euRETt37DpRJD8zbj7mpB6D1JgiWRvEV4wNUsK4oKl0dXXhpJNOwlVXXYUnn3wSL730kkyKCDExMbjhhhtw7733+rQPzhAJc7bqzgk/6w1AdLYJtn39cLum/zzi24KdzW9gVeGVsrgqhRfxxY/Fxirf5CU24kwRERvO3XvDcl+BEC5xEeq/By0wlpUE7b2Evy///a5G7Wl9VyY+fJ4BMoVIOs84+Nxbib0wPOvuzfR16fUeGFOGMVAYA7dbN6P/hhQZbr31VuzatQtPPfUUFi9ejOLi4rHbDAYDzj77bLz++uu45ZZbZr0P5uNoWsS3BQszT8P6usfh8YT7WxkRERFReOuw18GotyDWOvup5kREgfTiiy/immuuwXHHHaeYuBUJktraWp/2wYQIzaiYqug539pfKf9NRERERKFn0NmHvW3rUJp6tNpDISLyqqenB/n5+V5vdzqdcvMFEyIRRlRxrtvmnFU15+ToQuTEL8S2/S/JiuQUPnyJCwpvoovIvnda2E2EJmBckBK+l2ifmOXb1l+DzQ3PYn76yXIGcDAwNkiJiIeqehPjgrwqLCzE5s2bvd7+1ltvoaysDL5gMYhI4wHsnbM7gRVFVDPi5qN/uAP7ujaiOPmokaf0eDDo7EHXQCNiLWmIsST7edCk5bigMOcB+psG1R4FaQ3jgpTwvSRoBjz9qEUFhjCARKQiCekwwoRhDKIHnehBBwYxAB0OTDE31G+B0z2EhKgcLM0+F2ajLXgDZmyQIh367CzOTd5ddtllsj7I6tWrsXbtWnmdWDozNDSE3/zmN3jjjTfwhz/8Ab5gQiTCiKKqxUeaUPmJY0ZFVccer9OjJGUN9rS+I+uJjL7RWk1xiLWkorZzA+yOLlmR3GZKgM2chChTvHxcYlQO9CzIGpZxQeFLb9Sh9JwcVDxTD7eTJ7M0gnFBSvheEjzV2IksFCIW8WhDE+pRCRdcMMOMWCSiEPNg1U1MeOizC+V5m06Flh6MDfJWVHVekQM7q8QsESZGaLJrr70WO3fuxNe//nXEx8fL60THmY6ODrlU5oorrpBJE1/w02kEEm9KvhBZubK0Y+HxiPlt4o118gFM1BixD3fBPtyBxp7t2N+zHWuLvwWrPs63nZNm44LCl8HIkxSajHFBSvheEhxiJkg8kuQ5WDpy5DYVf7TP9QVjg5QY9Eyqk3fiGPenP/0Jl156KZ599llUVlbC7XbLpTTnnnsuVq1aBV8xIUKzdqhvGMSbrlg6I7bOgXqsLLhcziIhIiIiotkb7fYX6Fa5RERqstvtuPrqq2Vr3YsuughHHTVSrsHfWFSVAi4luhBNvbvVHgYRERFRWMwOMSE4xVCJiNRis9nw7rvvysRIIHGGSAizVXfO+DFi3Wb1huCu30yy5aOheytqOzchK74cRr05eDsnzcYFTZ+xrGTK+zh37w3IvkV9iMqX9rNOBE3AuCBvx5699SYM9zhUG4+WOD1ODGMAHnhgghkDsKMR1bIQqhlWZCIfCUiZ8UyPFjQiBRkzH8803iem834zGzzPICWiu8zumsldZuyFiTN6HqdjEKjw79hIG1asWIHPPvsMl19+ecD2wRkiEcg5FNz9iTf6BRmnwuVxYG/b+8HdOWk2Lih0OOxss02TMS5ICeMC6PS0YpPnfezAZ6hHFRpQjQpsRSsakI85WKJbLYuedqAFG/Eeajy7MOSZXtemLk87mlGHFGQi1PA8g5Q4nFz6Rd7dcccd+PDDD/GLX/wCDQ0NCAQmRCKMKGhVutIU9MJWortManRxcHdKmo8LCo1uInMvyJWXRKMYF6SEcQHUe6rQiBosxJFYpDsKc3SL5VauOxwluoWw6WLl/aJ00SjWleMwHI0YJGAPNmOL52O0ehrhlkXrJ3J5nKjwbJXdZJZgFQwqF0idKZ5nkBK9HlhQMiwviZSIdruNjY34/e9/jwULFiA1NRU5OTkTttzcXPiCS2YoaLoHGxFnSVd7GEREREQBSYb0oRvzsXzay2DE/cTyF7ENewaxH7Wow/uI9SQgCtHQwwAnnHJ2SR5KkarLCvjrICLSirPOOivgBaSZEKGgae2rRHnGKWoPg4iIiGhSvY8+dKEXXTKp4cAw3Jg8U2M8C6JgRRQMMKIHnfLfZVgy65N3s84ql9TkeUrlOETxVBdcMMKMxVgFo46n7UQ0c6JV7S9/+Ut8/PHH6OzslLMqLrzwQlx33XWycOl0iW4v77zzjuz88v/+3/9DMNx7770B3wePrBQU9uFO2abXZGBVdCIiItKGfk8PKrFDNLNFLBLkloosWfRUr9MfsvXtEAYxCDtccMqaHjZdjF/GJBIqcZhZUUkiIiX19fU49thjER8fLxMZiYmJskjpbbfdhs8//xyPPvrotJ7nueeek48Ltt/85jc488wzMX/+fMXbd+7cKcd2ww03zHofTIhEGFHdu+LD4Ff5brfXIj1mTnB3SpqPC9I+0UVk1xN17CZCEzAuKNTjYtgzhCpsl8tRSrBgxskMkbQQM0LERlPjeQYpEd1ltu01T+oyQ/7z2GOPobu7G6+++irmzZsnr7viiivgdrtlMqSzs1MmSQ5lcHAQP/3pT/G9730Pv/rVrxBMv/71r1FUVOQ1IbJjxw7cfvvtPiVEWMImAhktwd9nQlQWGnu2w+3hO6FWqREXFBpMNubOaTLGBYVyXOzERmQgHwt0R/htZgcdGs8zSInJqP0Eaijr7e2Vl2lpaROuT09Ph16vh9lsnvI5/vCHP8hZcWKJjdaIhM50XsOhhMa7FvmNqO5duMwU9Cx9rCUV2fELsL3pFSzIOC3gxXEoNOKCtE90iyg+LTNkvvWl4GBcUCjHxaBnAHrokKBLVnsoEYPnGaREdJcpK3BwlkgArVmzBnfddRe+853v4KabbkJSUhI++eQT3H///fjmN7+J6OjoQz6+rq4Od955J/785z8jKio4M+I++OADvP/++2M/iyUxVVVVk+4nZr489dRTXmePTBcTIhpi29cFoyF80+fpsWUYctqxp/VdlKWtVXs4REREIcdYVuKX53Hu3otI4/K40IAqdKFN1gihwMWFv+I0XNmqO4O2L3thol/GM53nIXVmf4yyWCxyG++EE07A//zP/+B3v/sdXnrppbHrf/SjH+FnP/vZlPsQS2UWLVqECy64AMGybt06uQxGEF+iP//883JTMnfuXNxxxx0+7Y8JEQqqvMSl2NP6Hhq7tyMrvlzt4RAREVGY6/N0Yz/2yc4tWSjEPCyDESa1h0VEISS6CTD4tjLDb1zDI5cHz4y48cYb5SyQg+Xl5WHlypWyha2YISLqiYgEiVg2c80113jdz3vvvSdnZ7z55psIpuuvv16OSyzTKS4uljNURIebg4kOOVar78ltJkQikNpTFXMTFmNv2/tMiGiM2nFB2uXS8NR3Ug/jgmYaF42eGphglm1qxf/E5ci/TTDq/J+gGPIMYBc2wQobMpCHEizkkl0V8TyDlLjc/JucLVFQNDY2duzng2eHCE888YRMMGzYsAHZ2dnyOpEYEcmGW265Rc78EEmSgzmdTlmo9Etf+hKWLVuGYBJLc0aX52zZsgUpKSkzag88U0yIRGiV70DyeNyo796C1r69EKdFMeZkRJkS4PG4MODsQc9gE0pSVgd0DKS9uKDQJOoA7H6iTu1hkMYwLmimcSFOvitlVxcH8lEm29yKdrUj/xuWy1nyUIok3cTCf7Pl9DiwHZ+hDEsQrYvzy3PS7PE8g5S43Tps36uRaRchSCRD4uIOfXz7xz/+IZe8jCZDRp166ql4+OGHsXnzZtmW92CiA01FRYWsP7Jv374Jt/X19cnrUlNTA5qoGJ3dIjQ2NuLDDz9Ea2urTOiI1+NyuWQdEdFS2GAwzHofTIhEGh1gS9DB3uUR5yIBSYZ83vgskm35WJx1NvQ6A/qG2jDg6IZOZ0BydAGiUo6GQc/Qi6S4oBCmA6LTrehvHmRs0AGMC5phXIiZGWtwOlo9jahHJRbhKBh0xgltcHdgPayeKNh0B77xnA23xy2TIUUoZzJEK3ieQYo8iLF50GcXs0Q4UyQQWlpakJCQMOl6h2MkQelyKU/dqq+vl/c56aSTFJMlYhMJlTPOOAOBJJLpoo7JfffdJ2etiPcSsVRIJEREYkYke26++WZ861vfmvU+2HY3Aqs55y4wystAaOjeimRbAfISD4NBb4JOp0esNQ1psaVIjSlCjCWFyZAIjAsKXXqDDvlr0+Ql0SjGBc02LlJ1WchEvqzpMZ5ZZ0EastGHHp9PnvdgM9KRyy4yGsLzDFIi4qEox8G4CKCSkhK57GTv3okFk5988knZdre8vHysm8yePXvGbj///PNlwuPgTRBJEvHv5cuXB3z8ouXvvffeK1v+PvPMM/IYP0rMDDnzzDNlnRNf8JMp+VXfcDsyY+epPQwiIiLSqASkoAo7J11vgAHDGJz18/Z7elGF7fL5M3S5Po6SiCj0ffe738Xrr7+OU045RRYqTUxMlEVVxXWXXXYZMjMz5f1EC17R6lYsQRHmzJkjNyX5+fkBnxky6p///CcuueQSWe+ko6Nj0u0ioSNeiy+YECG/ENm67sFG9A62ICW6UO3hEBERkQa5PS40oRY2xEy6LRVZ2IEN6PK0Ix5JstiqDjpZFDUeyYcsiFrnqUQXWlGE+VwmQ0T0hVWrVsmEwa9//Wv8/e9/l0kFkdAQLXe/973vQesaGhpwxBFHeL1d1DA5uP3wTDEhEmHELKMhu0de+mLQ0YPW/ip0DzbJf4sTlmhLMhZlnQGLcfJJDkVGXFCYxkaPg7FBEzAuaDZxYff0yTohYslMFgom3S7qji3AERj0DKAPXXDBBQ/caEQN7OhHFvK97NeDVjRgKdawi4xG8TyDFHmAwWEd68oEmOgSI7rNHMqLL744recanUESLKJwq0iKePP5558jJyfHp30wIRJhPG6gZoNz1o93uZ3Y1vSSPGlJjS5GSfJKWIyxPAGJ8LgIBmNZyZT3ce6euD6SfOdxeVD50n61hxF2Qj2eGRfqCeW42IWNmINFiNNNbvE4nlUXBStGWi52e9rRj54vOtMoa0IdYhDPcxENC4XzjHBlq+7U1PMcrL4KsB50nb0wMSD7otBz5pln4v7778ell14qa4YIo8f6N998E4888ohsK+wLlrCJNDogPkM/40LOLrcDdV2fY0P9f5EdvxALM09HRtxcWE1xPAGJ4LigCKADEopiGBs0EeOCZhEXc7AEe7AFDZ5qOD3T/3CshwFuKHdCqPRsRy86UYKFsx01BQPPM0gJ44KmcNNNNyEjIwNr1qzBN77xDfm5U7QCPvnkk3HBBRfIGiI//OEP4QsmRCKMqOKcUWqYUTVnsSRmY/0TclbI8tyLWCMkDM0mLigyiG4RWUcksZsITcC4oNnERYwuTi5r0UOPrfgYOz0b0evpOuRzxuuSsQBHohq70OSpm3Bbt6cDDgxjjm4x9Dq+gWkZzzNICeOCpiJmhbzxxhtyFsj+/fthtVrxwQcfyKU7N954I1555RVZR8QXXDJDU3J5nPDAg2hzkkyKEBEREc2GQWeQNUTE1ufpQT0q5XlGGZbAqDMpPsais2Kh50hsxHtI9+SMzUztQAvS4dvacSIi0raoqCj8+Mc/llsgMB9HUxKJkMOyz0dF2/tqD4WIiIjChJgxMle3FNkoxGZ8KGd8eCOSIKLzTDcO3Ee06DVPqj5AREQ0fZwhEmFEde/+zplX+TYaLHKKK4Wn2cYFhT8RE31Ng4wNmoBxQf6MiwRdChZ7VsplNMWeBYjTKRdUzEYRKrEdCUgeeRxS0I5mRCPWH8OnAOJ5BilhXNB01NbW4tFHH0VNTQ26urpkZ7GDE+bi9tliQiQCiKBxe5zoHGjAvo7P0NF3FBJt05ti2jfUjiFnH/qH22E2+rY+i7Rd/b1+G6u/k3LXiNp3WtQeBmkM44L8HRdiucwCz5HYgo+w1LNGsSZIlC5antP0e3oQrYtDKrLwOT5AHqbu2kTq4nkGKWFc0FREu+BvfvObcDqdsp5IXFzcpPv42uCDCZEw195fg4q2dbAa4xBjSUZ55kkYit8F9GePlHYep8NeK080kmx5MrB2Nb8Jt8eFKFM8rKZYzE8/WbXXQYEljiNJeXp01LqZpacJxGeSlPnxaNvRLU9ciATGBQUiLkw6M+I9SbLNbiwSFO8j2vZuw6eweWLlkplYjLRhJG3jeQYpYVzQVH7+859jzpw5eOihh1BSEpjkNxMiYUwkNyrbP8LhuV+CQT9SqExvAMoWLcYzTz2JGGO6vH7YZUf/UDtiLCkySVLfvQULMk5F33C77CpDEXISm2dAZ70bHuXOhhShdHodUhfEo31XDzxunq3QCMYFBSoujDDJzjHeWHRRcgbJMIZggEEmUUj7eJ5BShgXNJX29nZ897vfDVgyRGBCJIx1DTQg3poxlgwZZTXGYmnWOei2t8rK7maDDVGmuLH77Wh+Hb2DzfLfzb17kB47R5XxE82UsWzqg6Vz916EmlAccySbThwG83mmgzFGWtGFNuSh9JD3EctprIgK2pjI92OK3qiDZ1EunHvq4HZ6gn6Mo9Biq+6c0f2drqGAjYXUtWzZMtTX1wd0H6ySGaZE3Y+KtvdQkHSE4u16vRHxUZlIsuXKpTTjkyZGvRkO9xCKk1eitY8nyURERBQc4qOyXmdQexhERKQBt99+Ox5//HE888wzAdsHZ4iE2RKZ7sH9clZHz2AT5qWdAIsx+qD7AN1Nh16nl5uwBHtb38ewawALM08L/MBJddOJC4pMog5AZ1Uf60TQBIwLClRceMCgCkc8ZpASnn/SVMrLy/Gzn/0MV155Ja677jpkZWXBYJiYNBe1Lz/44APMFhMiYUBME6vq+Bjdg02It2YiI24u5qQeo1hxV7wRNVUcepGeKKIqZoyUJi1nZ5kIMZ24oMgk6gDs/7RD7WGQxjAuKFBxEYckNHvqka6bXjc8Cg08ZpASnn/SVP72t7/hJz/5CaxWKwoKCmSnGX9jQiTEDTvt2NTwNIqSj5JJkOkUL0ovNqC50uU1Sy86y9gdXYizpvt/wKRJ04kLitwiiRnLE9G0vpPFM2kM44ICFReFmIs92Iw2z35koxDxSPa5pSKpj8cMUsLzT5rK73//exx55JH4z3/+E5BkiMAaIiGutb8SeYlLkRpTNK37i3OK+Ay9vPSme2A/EqJEW16KFNOJC4rck5XEohh5STSKcUGBiguDzoh5umUowFy0YT8+xwfo9XT5c5ikAh4zSAnPP2kqPT09uPDCCwOWDBF4WApx/cMdsJmT/N6dhgkRIiIiUku0LhYluoWYj+WowBbYPX1qD4mIiIJs1apV2LFjR0D3wYRIiPJ43Kjv2ozeoVbEWfy7tEUsl9nc+Kxfn5OIiIhopiw6q5wt0ooGtYdCREQqLJkRBVPvuusudHQEpg4Ra4iEILfHjc8bnkJydCGWZp87o7W1Yn1eW+2h1+kNOnuxMIPdZSLJdOKCIpNY6926rZtrvmkCxgUFMy4siEIz6v36nBRcPGaQEp5/0lRE/RC3242f//znchPFVZW6zNTV1c16H0yIhCCXexgGvRn5ictm/FjR1qp936GPOgWJh6Oy/QPEWtMRZYrzYaQUKqYTF6HAuXuv2kMIO+IkRZzEUngzlpXM+DGdDsBQmjqjx/BvNLwF6nhhQwyMMGGj5z0kIQ3pyEWULtrv+6HgxsZ0jgezOTZR6AiX808KnLPOOivghbWZEAlBRr1ZJkVmQxSzyp5vRMMOp9dsbHJ0PnQ6PRq6t6AkZbVvg6WQMJ24oMikM+iQuyYVdeta4XHxmz0awWMGBfN4IU6GS7FQzpDtRCuqsRNDngEkIwPpyIFFF+W3fVFg8L2ElPC9hKZy7733ItCYEAkxQ85+1Hd9jnhr1qweLxJs0Yk6eXmot6PEqGxUtX8063FSaJluXFDkETERk2FlbNAEPGaQGscLvU6PZKTLze1xoR3NqMBWOD0OpCATaciGWWcJwJ7JV3wvISV8LyEtYEIkRDhcg9i2/yXo9SakxZQgI3ZuQPfngQc6sAcWERERaY9eZ0AqsuTm8jjRiv3YhU2y6LxIjIjrjTqT2sMkIiKNY0IkRIhWuIm2PBQkLQ/K/nSiAZFOh57BZsRZ/dvFhoiIiMhfDDojMpArNzFbpAUN2I7PoPfokYVCJOt4HkNERMrYdjdEWI2xaOuvhHOWtUNGud1AU4VLXk61Xndh5unY3fI2egabfNonad9044Iij9vlQeOnHfKSaBSPGaTV44WYFZKlK8Bi3UqUYSna0YTNng9R4dmCGs8uNHiqMOwZUm18kUoLsUHaw/cS0gImREJEjCUVBp0Jg44e357IA3Q3uae1UM9siMLi7LNlUkRMQaUwNoO4oAjjAbqq+hgbNBGPGRQCxwtRT2SObjHmYRkyUYAEpMAIM7bhE7R4GtQeXmTRWGyQRvC9hDSACRGNG3L2oal3N7bufwHJ0QWIsaT4XM25YJlRXk6HSIqkxc5BQ/c2eDweJkbC1EzjgiKrM0DxaZnykmgUjxkUSscLkRiJ0cUhQZeCdF0OlmA1GlGt9rAiilZjg9TF9xLSAtYQ0bDG7m3Y37MT6bFzUJR8lM/JEEFUcbbYZlbNOTdhCdbX/Qe9Qy1o6N6KFfmXIcaS7PNYSDtmExcUQbERZ2Js0AQ8ZlAoHy+ccMhqaRQ8oRIbFFx8LyEtYEJEo/qHO9DcuweH5Vwg63moXcl9fvrJqO/ejDVFV2Nz43NYlHkmrKZYVcdFdDBjWYnaQ9A05+69ag8hIkznv3Okxup0XjfjlAKtCbXIRqHaw6Bp4PGUKDLt2rULd955J3bv3o3k5GScf/75uOSSSyZ9Ln788cfxjW98A52dnbPeFycoaZDL7cTO5jcwJ+1Y1ZMho8SMkLlpx8FijMG89BOxq+UttYdERERENGMmmFGPKjR5auHyuNQeDhERjVNZWYnjjz8ezzzzjCzZsHPnTnzrW9/CqaeeiubmZvgbEyIaI37p25peQn7ickSbE/3+/KKKc902p0/VnGMtqXJsorYJhQd/xAWFJ9ERYN87LewMQBPwmEGhfLzI1OVjIY6EE05swUfY6dmAZk+9PAejyI4NCi6+l5CSX/ziF4iOjsaHH36Id999Fzt27MBf//pXeXnCCSegoqIC/sSEiIaIN+JdLW8iJboQqTFFAdoJYO/0+LxQrzh5JWo7N8LhGvTXyEhNfooLCkMeoL9pkLFBE/GYQSF+vBDteXN0RViqW418lKEfPajBLrWHFb5CKDYoiPheQgrWr1+Pa665BsXFxWPXXXzxxXjjjTeg1+tx8sknY8OGDfAXJkQ0pLZrI8wGG7LjFwZsH3oDULrSJC99eh69EaUpa2RLXgp9/ooLCj96ow5lF+TKS6JRPGZQOB0vbLoYpCH7i2KrFAihGhsUWHwvISUdHR1IT0+fdP2cOXPw+uuvIysrC2eddZZMkPgDEyIaIjq4ZMTNC/h+/HXQSbTlyJoiO5peg9M15J8nJdXwzYi8MfAElhTwmEHhdLzoQjtikaD2MMJaqMYGBRbfS+hgeXl52L59u+JtaWlpeOmll7Bo0SJZZFXUGfEVEyIaMjfteFS1f4gtjc+jb6gdoaA0dQ3SYudgy/4XsLH+Sezr3IABR4/awyIiIiKaFpfHiWbUIQ05ag+FiCjirV69WiY6nE6n4u1xcXHydlFPRCRHfMWEiIYk2XKRGTcfTvcQ3q/+G3oHWxAKUqILcFjO+ViUdSaijHGoaH0XWxpfgNutHMREREREWqnftgPrUYT50Ot4WkxEpLZLL70URxxxBDZt2uT1PhaLBQ8//DC++c1vYtWqVT7tz+jTo8nvKlrXyfa2eYnLEG1O8vvzu11A9QaHvPQ3o96MtNhSuTX17EJVx8coSVnt/x1RSMUFhTa304PKl/bLS6JRPGZQuBwvKrENSUhHoi5V7aGEtVCMDQo8vpeQksMOOwwPPfQQpiIKrN5+++3wFRMiGuJyOzDo6MaQqx8ZsWUB248zCOU+0mPL0NCwTS6h6R5oxLDLjjhrBnITliDKFB/4AZAm44JCk8PO2V7+5ty91y/PYywrQbgdM6bzmvz1348i+3jR5WmDAw6U6ArVHkpECFRsBPN4oOYxN1z9//buAzyuq8wb+P9OUe+9WrYlWe41seM0khAICSF9Axv4lrIfLBB6z1ICPAsL37LA0gILWVgILQSSEEgIBAgkcRI7jnuRJRdJtnrv0pT7Pe+RR1a56jNz7537/+WZjHU1MzpX8+rMve895z1+Hn+SyTg20ELqu/ejPOdy5Kessn01Z03TsCbvWnhdCViVexW2ldyJ3ORyHGt5Cifbd3E6jcWwyjfNRFYEWM2VAWgK9hlk5/7iiL4HL+t/xxE1VSbyxezJPrFB0cXPErICjhCxkBF/H4rT16tkQixIistQt4mr0mQkFqO57zheOvsrlKRvVCNhZAlfIiIiokjz6aMIwI+t2pWqfkisHHMREdHi8EzUQrKTytDUdwzl2TsRq+TAozBtDXJTytHYcwT7Gh+RimbTDkjcrnikxechLSEfqfF5iPMkmdZmIiIiig1++BCPRPVvJkOIiIgJEQvJSV6Jw82Po33gNLKTlsf0B7UUYF2WuUXd5ArNVLLSTu9wC3pHWrCn4Ze4rupjMf37ICIiougkRNzg+HwiIhrDhIiFyAn/hqIb1UozdZ0vqakkUoQ0Jzl8xb6kinPNLmtVczZKdHjdCchOLkNW0jK09p1gMiTCrBgXZA2yIsDxhxq4MgBNwj6D7Npf9KITyWBx92izQ2xQ9PGzhKyACRELjpxYk/9K9e/RwBCOt/wZ/uBoWFed8cQDo4OwhSFft5oyQ5Fnp7ig6PImeTDS6zO7GWQx7DPILv2FjETtQzeaUY9RjGA1tpjdJEeyYmyQ+fhZQmbjKjMWFudOxPrC63Gu+yD6RzrC8ppSxXnFNvtUc+4baUNqQr7ZzYh5dosLih5ZEaD8hkKuDECTsM8gO/QXXXobDuu7sR/PogVnkYdirNe2w6N5zW6a41gtNsga+FlCVsARIhbn0txYV3g9DjY+hq3Ft8HjjoeTBIKjGBgNTzKIiIiInKFf70EdqrEGFyFeSzC7OUREZFFMiNhAgicFy7MuRl33yzG9As1UQT2Auq6Xsa3kDrObQmQqf3XtnI/xVFWE5THz+VkUe7FhN+HaJ8Z77JIRIaWoYDKEwsqpfS5RLOOUGZuQ4qK9Q01heS37FC7S1IgYr3tseTyKLPvEBUVbgEXwyAD7DLJyf1GClajDCQR0v9lNIYvFBlkLP0vIbEyI2KjYqteThJcaHsSxlqdwsn0XzvUciulqzjLLdHC0C/vO/cbspsQ8O8UFRZesCFDNlQFoCvYZZPX+Il5LxHKsxmHsZlLEAqwUG2Qd/CwhK2BCxEbWF7wG20r+ASUZm9SIka6hc+gabFjYi2hAUqY2lm2wuIHRToz6B1CWuc3spsQ+G8UFRZkGJBckMDZoMvYZZIP+IkvLU9NmDuIFjOojZjfH2SwWG2QR/CwhC2BCxGY0TUNqfC5S4nMwONqJeE/qgp7vcgGl6z3q3qqCQT8aew7jeOtfUJi+DhmJxWY3KebZIS7IHC63hrKr8tQ9UQj7DLJLfyFJkXKsw2G8iHq9Bn6OFjGFFWODzMfPErICFlW1qc7BeuSlrEJSXAZixZCvFw3d+9Az1ISCtNXYXHyLmipEREREtFhpWia26FeoQquH8QI8ehxyUYRs5HMJXiIih2NCxKZa+2tiZirJsK9PjQbRNBeWZWxGZc6VaiQMERERUTjIcUUBStVtRB9CG5pwBHug6S6swkYkaElmN5GIiEzAhIhNrcjagZr2Z9TStBo0VOVdjeS4rDmfp+vAyKCu7q1AiqYebn4C6wpeM6/2U2RYLS7IYrHR62Ns0CTsM8jO/YUUXJVVaOQ2qPfhOPZhMy4zu1kxzS6xQdHFzxKyAiZEbEpqiGwpvnU8qVDb/hw2Ft045/P0IHBmr3Xmzw77+xAI+lXdEDKP1eKCrEMP6Dj5eHiW/KbYwT6DYqW/SNJSofNsLOLsGBsUefwsIStgQiQG+IIjCM63SJgGpOe70NMSBCzw+S+r5VTmXoGmvuNITcgzuznOZbG4oMk8VRXm/SyD2PBX10atPbR04Xi/5hMXTv7b4t/EeRqQsSIF3af7bRMXvXoXdLs01s5sGBtO6g+ieZwRi58lZG+s6WtzpzpewOmOF7Em/1XzerxUcS6odFummvOQrwenOp6PmXoodmW1uCDrYGyQEcYFGZEVRIq2Z9lqJRE33OhDN/y6z+ymxDQ7xgZFHj9LyAoYfjZfaWbY14tNRTch3pMMu/EFhnGk+Q9YX3C9LdtPRERE9paEVCQhGd1o59QZIiIHYkLEpuRD+2T7LlTkXmHLFVn6Rzqw79zDqMi5HElxmWY3h4iIiBxIjqG24Ar0ohv78AxO6kdUwXoiInIGJkRsmgw50fY3FKatRZw7cYHPBQa6zK3m3DFwBsdb/4yNRa9DRmKxeQ0hS8UFWRNjg4wwLsiIxEN/87Dt4sKjebFSW6MSI6nIxEG8wNEiYWbX2KDI4mdJdDzzzDNIT083vO3Zs2fW5z722GO49dZbUVVVhdzcXKxZswb/5//8Hxw9ehSxgkVVbehoyx/RO9yC8uydi6rmfPaw39RpPnVdL6kVctwur2ntIGvFBVkXY4OMMC5oppVE6p9uhZ1Hi+ShSC3F244m5KLI7CbFDLvHBkUGP0ui653vfCe2bt06advKlStnfY4kPjIyMtRzs7Oz0dLSggceeADXXHMN/vSnP2HDhg2wOyZEbGh13iux79yv0TfSisyk0gU9V2bXZC1zobM+GPVsrAxBrW1/FttK/oHJEIsxMy7I2hgbZIRxQUY0F5CzNh3tR3vUiY5dZSMfzahnQiSMYiU2KLz4WRJdO3fuxC233LKg53z84x+ftu3Nb36zGily//334+tf/zrszvZTZr761a+q4T6f+MQnxredOnUKb3zjG1XGq6SkRL1pra2zZ6X//d//fdoQoosuumjSY2pqavDqV79aBcB//Md/TPqeZMeMhh1Ju1772tciXGQIZ1PvUcR7UhacDBn/QFrmVvfRNuTrRWp8HpMhFmRmXJC1MTbICOOCjGguDbnr09W9nSUjDf3oMbsZMSVWYoPCi58l0dfX1we/f2mjcnJzc5GUlISentjoJ20dfnv37sUPf/hDrF+/fnzbwMCAmucUmvP05JNPwufz4fWvfz2CwdlT0pLoOHHixPhNnjvRRz7yEdx555342c9+ht///vd48cUXJ30/ISEB995775KXoT3a/EfsbfiVGk0hq8iEEiF9w6040PhbjAYG1cos9sQPQiIiIrIul+ZCPJIwoI8dgxERxYK7775bDRbIy8vDjTfeiJdffnnez+3u7kZ7ezuOHDmC97znPejt7cUrXvEKxALbTpnp7+/H29/+dnzjG9/AV77ylfHtL7zwAurr61XxmLS0NLXtvvvuQ1lZGf72t7/h6quvnvE1PR4P8vPzZw2ELVu2qARMYWHhtKzYW97yFvzP//wP/vjHP6qRJAvV0ncC53oPYU3etUiJz1H1NqrbnsaobxCjwSFkJ5ehPOdSpMbnwp44Fo4oFniqKsxugqX5q2sR6/vk8mjQN5bCf6IBQf9Y3+7kuAjXvsdi7NjVclShBgexUd9py9X8iMLd9zi5j7fyaI+J4uPj1W2quLg43HTTTer8VOqAHD9+HN/85jdx/fXXq/PWTZs2zfmzrr32WjVbQqSkpOCjH/0o/umf/gmxwLYJERmtcd1116kEx8SEyOjoqPrgmhgMMnLD5XKpZMlsCZGTJ0+qCrry3O3bt6vRHqWlF6alfPKTn8TNN9+MoaEhvOY1r8ErX/nKSc+XpMvb3vY2fO5zn1NBIz9zIUYDQ9hSfBu87gT1dXbycnWT0SG17c+oGhwpcTlYCpmf19Nszjw9Gf2S6E2N/g8mS8cFWRtjg4xIDYCuU/2sBUAxGxdJWgqy9DzU4hAq9A1MiixRLMUGOfMYI6XeB4/XDSvw+3zqfu3atdNKNdxzzz3THr9jxw51C7nhhhvUOe1ll12mzlt/85vfzPkzv/Od76hRIWfOnMFPf/pTdT4cCAQWfL5rRbZMiDz00EM4cOAA/vrXv0773sUXX4zk5GSVzPjMZz6jkgmf/exn1RvW3Nw842tKvRB5oysrK9XjvvzlL6us2fPPP4/U1LGTeMmqSdJEsnE5OcaJCcmWSZA8+OCDeMMb3rCg/SrN2ASPe3pWTz6EK3OvxNnuAzjY9BjWFbwGHlccFkM+iJprAjBDz3AzMhNLTPnZZN24IGtjbJARPaijaXen2c0gi4m1uCjVKtCon8E+PINUPRMeeFCEFYjXxi5ckXNjg8KDxxhLIyvAhM5ThdHokJmUl5erxMhjjz2mzpPd7tmTPTJYIOT2228f//oLX/gC7M52CZGzZ8+q7NcjjzyiRn5MJYmKH/3oR/jQhz6E7373uyprdccdd6ihQLNlsF71qleN/1umxEiCRAqlPvzww5OGA800FGniz3/ve9+rguO2225b0L5pbsB1PhYlUyqdhBQZCl2UWJa9CUlxGXj57K9Rlr0ZeSkV4wVKVXkUffLjJ24Pva58P2+FG821AfX6oe3jjz/fJ03d7vcH4Q8Ow68PwxcYUi8qSRk3EhHvTYHbfeGHjrddG/t5IQOjbSjL3DatjfJYfZa2z7VPc7V91u0aMDEsQm2fafvUfTJ6n+y4Ty7PWFy0ng6o14iFfYrF98mMfXJ7L8SGvHYs7FPk/560yW0P6qqQ4KR9Depjj3drk9sekBeaefvE11bbz09XWdB2aePUPjsw83b1+51QCDF0hbfg4ky07utW+yKkvXZ6n6wYe6H3K1zvU7RjT35W3uYMNL/UNa1Iol33qQQrkO8rwaDWj2HPAM7hJCqw3tb7NOf2COyTio0tGWje06VeJxb2KRbfp9n2SZUBDHO/FzovkWOMgG/xfblTx29JMiRUImIxiouL1ewKqcG5kNfJzMzElVdeiV/96ldMiJhh//79aGtrU29CiGS1nnvuOfz3f/+3+p5MZZERJB0dHSrbJWsny8iP5cuXz/vnyHMkcyYr1iymYM0PfvADdVuI8h1exMeNJThk+JhkTPPL3UgvuNADZNavQPrpImSs6kZSZg80aCoxUXe0BwNtXmy4tBAJyRd6nobDfgx26eq1VYekAcmZLnQ1BjA6BFReOnnFl5pdPnjigRXbxrb7AsPo7D+Hvzy+BwWFBbj4svXQkK46nv6+QTzz52eQW5KKzVvXqZVv4jzJ6ufJmuKyjJZUjg4ZOb4Cnra4afvUXh9AR10QxWs9SM680KXJ/svvoWyLB/FJ2sz7dN7pvT74R+bep1BnLNuTMjSUrr/wZzAyqOPMXj/S810oqLzw4gMz7NNM75Pt9qnMreIircCFnqYY2adYfJ9M2KeKnd7x2JADnljYp0i/T9mbL0y1lCHiclW04KJMZK5MGd/edrhH3UqvyEVKwYXkfuPuTnSf6sfK6woQn3ahPXVPt2KgeRiVt5TAPeHA+OTjTfAN+rH6jsmrjh1/qAHeJA/Kbygc3xbw66h+qAHJ+Qkouyrvwj71+tTrZKxIQdH2rPHt/c3DqH+6VS2VKatDTNynlpe7ULAlC5nlqbKmutre0emy1ftkxdiTuizhfJ+iHntysqXr6Krtx8pXF4xvtvU+nf97yknKxYobNmAAPUhFRkzsUyT7iGn75NIQl+xGy94ulFweI/sUi+/TLPvU0uQKf793/rwkJdeFmucW35ePjAbxp+OTfgzNg0x/SUhIUDVBFmp4eFhNoYkFWk9Pjw1mbV0g01UaGhombXv3u9+NVatW4QMf+MC0uVRCiqnKPClZElcSI/Mt2rpu3To1D+ud73znnI+X0STvete7VFvE97//fbWUr0y7kWCTVWlmIsEktUpetfqD8J6fMjPfq1X+wAgGRrvQN9yO/pEODAe6oesBeNyJqMy5DHHutElZW7kvv8SrOh05kDTK2g77+lDd/if1taa5kByfg/LMy2fN2vqC/WjsPYrOgToUpW9AXtIqNSJnYsZ5b/2vsaX4dtOvwMXiVcWl7pOMApC4OPmCDwF/bOxTLL5PZuyTJ+5CbMi2WNinSL9PgdqTMX9VUbbJgfuJR86O/yx3Rbmt3icrxp7/xElbX9GWf6+6pUSdxE1l132auF3zAIfwIjbikpjZp2iNPJgYG+pvKgb2KRbfp9n2yVVRHvZ+L3ReIscY/tHF9+W+wAj+dPxr6hxxKSMmZjtPu/Taz8HjtcZ0Ob9vGLueunfe+yurw0wt93Do0CFVW/NVr3oVfv7zn6tt8npSG0TOq0NksIEssztRXV2dqj8i579PPPEE7M5jx6FBU5MeUjMkKytrfPsDDzygiqNKFV1Jgnz84x9XozYmJkNe97rXqds73vGO8YKpkryQgJcaIl/84hfV6BKZbrMYsuLMt7/9bVXvRKbfzIcuJxtTt0lnZfTY89tlYbjUuAJ1m6h/pB0Hzz2BFVmXqNVpQp3P2JMv/HPS9vMae4+gMHWjmpIz+YcaP162e7UUlKVvR3HqZpzt3o89Hb9Ecfp6FKSuvjCtR9dVTRcENQT0AHqGmhDQfUhPKFSFZOfa16kM27LQ7frCto99qMy/jXbZJ7X9/H1oOLzd9ykW3yfT9ul8myY+xvb7FMm/p/MHsJPbOHaAOW17QDfepxm2G732grfrC9seOmieKHRQLY8fP2DX7fU+WTH2Jv3+w/A+xWLsmblPuh/w634ENb5Pk9vOfXLCPs3Vxy+6L5/YTy+yLzfqz2nyeWliYqKq+yHJjerqalViIikpSdXaDJFBAM8+++yklVR37typlteV5IfMoJB6mj/5yU/g8/kmPdfObJcQmQ9ZEkgq5nZ1dWHZsmVqRRpJiEwkozZkSk1IY2Mj/vmf/xmdnZ0qg3bJJZfgqaeemrF46ly8Xi8+9alPqdc0gyzbu7noFuxvfARZScvGq6NLJyZDgI06vlH/IA41/R7x3lRV62MxZPrO8qztKM3YqhIr+889oqbRSNLD7x9Bc+9xDPq70TFwBllJpfC44tHQvV/1aLkp5chLqUScJ2mpu08LNFtckLMxNsiIHEDLkGqjg2tyLifEhZwiysUdrjqzME6IDVo4HmNEx4033qgW/JCL9aHFQWRggAwaKC8vn/W5ci775JNPqvNimUEhCZVrrrkGH/7wh9VsilhguykzsSg0FOvayg8arjKzFDVtzyDRmwa3Kw4j/gEEdR8CQR8Cun/sPuhD8Py/ZdzZqpwrkZpwYT5iuJbblRErUo9EDiLivcnITipT03FCZOpP28BJtPTVqDbmJJcjP7VS1SUhIrITf3UtnMhTNWVUIS2YU2PHTmr0g8hHKdK0TLObQhRVVu7j5TziqRpOmaHFickRInRBWdZFONn+HFLj89SoEa/Hi4pN2ag/PAhN96hEiVvzQNPcEbvakehNV7fZSCKoMG2tuvmDo2jvP4XjrX+FPzCMovR1KEhdw6sxESS5KSkaeO6on1l6moSxYf+DxkicZMvccim41/BM29icdQuezEfzfZjPvs+nPfN5jNV+z3PFRawpxkpUYx826ZfBNXUpHXJ0bNj579iszy0eY5AVMCES4+LciViTf+3411K8KCvXi474hBnn9JlNpt0UpK1WNxm5cqbrJew/9zA2Fr1uvB4JhZfkmmQFBbVsptmNIUthbJARiQdZfYBxQU6LiyQtBSV6OfbjOazQVyNTm1xskJwbG7RwPMYgK2BqmyxNEiDl2TtRkrEJJzueN7s5RERE5HC5WhE2YAfa0YyX9b+raTTdevtY4XgiIrIVJkTIFrKTV+BczyGzm0FEREQErxaHSm0DtuAKVVOkE63Yh2dQp59gYoSIyEaYEHEYWSu8uSYwtma4jcg83ZL0DTjc9IQqnEThZde4oMhjbJCRYEBH4+5OdU/k5LiQ+mZSYHWltlYlRzzwquk0ft1ndtMsxYmxQXPjMQZZARMiTqMDPc0zLCBucZW5V6qiqy+f+zW6Bs+a3ZzYYuO4oAhjbJARHeg+1c+4oMkcHheSHCnWVmA5qnACB81ujrU4PDZoBjzGIAtgQsRhpJrz8m0edW9H2cll2FJ8G872HMCJtr8hqFu0MqzN2D0uKHIYGzTTihHlNxSqe6IQxsUYKbTqgQdteqPZTbEMxgYZ4TEGWQHDz2GkinN80lg1Z7vyuhOwofC1SIvPx+Gmx81uTkyIhbigyGBs0IxxkeZlXNAkjIsLyrEe9ajBqM5pvoKxQUZ4jEFWwGV3yXakWFlt+zMYGO1US/QSEdHMPFUVS34Nf3Ut7MZqbbZaeyiy3JoblfpGVGM/1uvb1XQacoZw/a2Ho+8morkxIUK20z/ajiFfrxol4tIYwkRERGQ9Umw1Ry9QRVZX6KuRhixVJN7IYf1F+DBWiDUeCUhBOjKQg1RkMJlCRBRBPJt0GKni3HDYb+tqzilxORgNDKB/pB3piYVmNycmxEJcUGQwNsiIrBRR93QrV4ygSRgX0xVqZcjQc9CEOtThwpK8UmMkDgkq+QFo6EEnduBV8GgejOjD6Ec3WtCAWhxCkb5cLe1r58QIY4OM8BiDrIAJEafRgcEue38YyQHBpqKbVf2QrOQyLMvYauuDBEuIgbigCGFskBEdGGgeNrsVZDWMC0OJWjJWYu3415IUCcCPEQxjFMPQoWMTLoUbbvX9eE0SJQXIRgECegBncRIHsAur9S1I0JJgS4wNMsJjDLIAFlV1GJcbqLzUq+7tTAqrbi6+FRo07D37K/SNtJndJFuLlbig8GNskBGXR0PVHaXqniiEcTE/chHHo3mRrKWqFWmytDykaOmGF3ekFkmZtgqV2Ihj2Iuz+klbFmplbJARHmOQFTAh4kCx0unIgcOyzK1YX3A9Trbvwom2vyMQ9JvdLNuKlbig8GNskBE3T2zIAOMiMiR5sgmXwQ3PeGLEbhgbZITHGGQ2JkTI9hK8qdhcfDMyEgrVaJGOgTNmN4mIiIgorKQgq9Qk2YidaEOj2c0hIooJTIhQzMhLrcTW4tvQ3FeNhu4DZjeHiIiIKCIjZBORjE691eymEBHZHhMiDhMMAKf3+tR9LPK441GYthYj/n6zm2IrsR4XtHiMDTIS9Os4+XiTuicKYVxETwU24CxOqakzoZVrrIyxQUZ4jEFWwFVmHMhvv1pcC5ISl426zj14+exDWJ61HVlJy8xuki3EelzQ4jE2yIhvkDWbaDrGRXRIUdYN+g6cQTWO4iVU6hsRp8XDyhgbC+Ovro3az/JUVcAsPMYgs3GEiMM4oZpznCcJW0puU0vznmx/zhZXTszmhLigxWFskBFZKWI1V4ygKRgX0Z86s0JbjSIsxzG8jFP6Mcse8zA2yAiPMcgKmBChmOV2eZGVVIaW/hNmN4WIiIgoImTp3k3aTnjgwXHsQ1APmt0kIiLbYEKEYppMmanrfAnNvcdxsPEx+ALD835ux0AdznYfQPvAaQz5eiZddQnqActehSEiIiLnWaZVIgu5OIwXEdA5PYWIaD5YQ4RimtvlwbLMrRj29yMzqRSNvUdQlrltzudJAuR054soSd+I/pF2tPSdwLCvd+ybmgYXXOpgw+OKR1H6OuQkr4BL43g/IiIiMk++VgqvHo+DeB7r9O2WrytCRGQ2JkQcRqo41+xyVjXnwrQ14yM+uoYa5vWcvuFWZCeVoSBt9ayPk9VsGnuPoq5rL1LiclCcvh5pCfmwGyfGBc0PY4OMyEoRxx9q4IoRNAnjwhqytDzE6fE4hBewRt+GJC3F7CYxNsgQjzHICjhlxoE8Dr1Y0NJXrZbknY+clJVoG5h7Kbt4TwpWZG3HxaWvV8mQcz2HsLfhV3ip4UE1VcdOnBoXNDfGBhnxJvGaCk3HuLCGFC0d63AxjmEvevUuWAFjg4zwGIPMxoSIw0gV5xXbnFnNWaa4yIiO+ZDpLzLiY9DXPe/Xl5Eha/KvxbbSf8Da/Fct6LnhNuofVPVPjjQ/qRI0e88+hOa+6hkf7+S4oNkxNsiIrBRRfkMhV4ygSRgX1pKgJWEjduIkjqBdbza1LYwNMsJjDLICpmrJMSRZceDco6oeiIzs0DD2oaxpLqTE5yAjsRgeV5zaNuIfwMBo56J/llR4l9c1Q3XrX1UNlPzUKqzI2oFEbzp0BLG7/mdo66+FW/PC7YpDnDsR6YlFyEgsggv8JCIiIoo1Xi0OG/WdOIqX4NNHUKiVmd0kIiJLYUKEHEOSHVtL7sDAaAdGA0MAdDUlRv7rH2lFQ/d+BII+9bjRwCDW5r8ayXGZi/55+gzL3kmiJc6dBK87AeHWNdigVsDZXHzLpO0a3Ni+7C74AyMI6D74g6NqFEn7wCm09Z/EmsKrwt4WIoo8f3UtrMJTVWGr9hI5hVtzY72+XRVaTdMzkaylmd0kIiLLYELEgZxcuEjTNDUaZKqc5OVYrpIYOgLBUXjcS5vQmByXhb6RNjVSxDVhpMjpjhfRO9KiEhPSjrKsi5HgWXyxM2nvsL9XjQgZ9vXhbM8BbCq6ecZpQHGepAsb4se2HW35I4CrHB0XNDvGBhkJsDgiGWBcWPf4p0rfrEaKbNIvhVuL/ikAY4OM8BiDzMaEiEOrOdPMBwxLTYaEXic3pRyt/TUoSK0a3y6jN/JTVqnVa2Q0x7HmP8LrSUJx2jo1ZWch02zktQ42PgaPOwHJ3kzEeZKxsegmxHuS5/0ajb2HkZtSgYA/yLggQ+wzyIisFFH90PxW7SLnYFxYv6bIcn01DmM31us71MiRaGFskBEeY5AVMCHiNBqQlKFhsFuXGSMUQSXpG3G4+Ql0DdYjNT4PLf01iHcnozRjs/p+ZlKpuvWPtKOp9xhOdbww9kRt5oJjCZ5U5KVUIjtpGU60/13VCQktK7wYa/OvU1OFjrb+ERdXvQaDPWomEdEF7DPIiAYk5ydgoGWYcUEXMC5ssSSvTOk9gOeQoqcjH6WIQ7yqJeZS/43dy4WdsGJskBEeY5AFMCHiMC4XULrewzW/o0BqhGwpvhXdQ+fQP9KBjYU3GtYNkakzlblXzOs1B0a71KiTuq6X1AiUpSRDhBzwLMvcgnN9+5FZ1YOhPWnQdVaApwvYZ5ARl1tD2VV5OP5Qg7rySyQYF/aQrRUgGwVqOd42nIMffgQRUP8F1X8BlTRJQDKWoRLJWuqSfyZjg4zwGIOsgAkRogiTqTByCwcp8roia7u6hZOMWnFhAMdb/4LK7Ksn1T0hIiKi2JOmZSINMxePH9B7UYvDyNbzUaKtjGrbiIiihWc9RKQkxWUgK7EELzX8Eqc7d6tRLbLqDhERETmPrEazEZdgGAOo1Q+pQu5ERLGGI0QcRj7LRgZluVmzW0JWjIu81CpkJ1Wirb8WZ7v3Y9jfN54USfSmqyk6GYkl4Z9bTJbFPoNmjIteH+OCJmFcxB75vK/ABpzVT+II9mC1vgUezbvg12FskBEeY5AVMCHiMHoQOLPXb3YzyMJxIdNl8lNXqdtEUvy1sfcI6rpexsai13FajUOwzyAjekDHycebzG4GWQzjInaVaOVI1tNwALvUSjXZWv6Cns/YICM8xiArYELEaTQgPd+FnpYgqznTguJCir+uyn0FGnuO4ETrX1GVd7VaJliG0PaPtmNotBua5kZaQv740r/yvc7BejT3HcOwrw8uzY2U+Fz1GLkleNI42sTq2GdYmqeqYs7H+Ktrw/+DNSBjRQq6T/ePx0VEfg7Zi0FcUOzI1HKxSb8MtTiENv2cGjky79EijA1Lm0//PZ/PmwXjMQZZABMiDqzmXFDpRl9bkNWcaVFxUZS+DgHdj71nfwUNLujQkRKXjaS4LAT1AJp6j2A0MAQNmvqeFJRdkbUDSXGZavpN30gbeodbcKrjeZUkEcnx2WpJYn9wBL7AMHzBYfiDo2Nt09zqNQpT1yDBu/RK97Qw7DNophUjirZnobd+gCtG0DjGRezzaB6sxhZ06q04hBdUgmQ+I0YZG2SExxhkBUyIENGClWZsUreFcru8yEgsUreQoB7EwGgHfIEheFzxamlirysBblecGj0SCPrROViHE21PwxccQV5KJQpSqwyXMCYiIqLIy9LyMKwP4ixOqqV5iYjsigkRIjKVXFlKjc+d8ftulwe5KeXqJqNGpODr4eYn1PcKUlcjL6VCJVqIiIgoegqwDIfxIhMitCi9w80YDfai1F+G3uEeJHty1FRsomhjQsRhpIrzQBerOZM948LjikNh2lp1k2k5LX3V2N/4qBpRIivgZCcvV1NsyHmxQdEl8dDfPMy4oEkYF84iU2ODCM7rsYwNmkimTp9o+zuKMtagu30ATb3H0TvYIssaIS0+H+mJhYhzJ6rRwjJ6WI7/5MaECUUCEyIOrOZ89jCrOZP940I+KEszNqvbsK8XTb3HUNf1EpK8mSphInVHWLDVmbFBkScrRtQ/3Wp2M8hiGBfOIp+xfn0UPn0UXi1u1scyNmiiIV8PspPKUJS6AT01QGX2lUC2TKMOqGSJjB7pG25VI4MDwVFVY07+retBVZ9ua8kdXO2QwoYJEYeR88OsZS501geZpaeYiYsEbxpWZO9Qt/6RDjT1HkVt+7NI9KYjKS5DJUkS1X0Ga484LDYoMuQ4NGdtOtqP9qikGZFgXDjPKmzGIbyItfo2JGhJMz6OsUEhUmBfLmBtLb592jHGWCH9ybXmpmro3q8K+Benb4hquyl2MSHiMOoDaZkbXWeD0FnNmWIwLlLis1GZe4Va8nfE348hXzcGfd1o7Tuh7v2BEfU4jzteJUhkNIksKUyxHxsUPppLQ+76dHQc74UeZKaMxjAunCdNy0SVvgnH8TIy9TyUosLwyn2vqxvF69Nw8PgLarRIKjJQhOWI1xJNaTeZp7mvGkVp69Vx2GKOMeS47WDjY0yIUNgwIUJEMTuUV5bplVsmSqd9XxIjfSOtePb0D/CK8nep0SRERES0MMlamlp+twl12IdnUKiXqYKrocTIoN6P0ziKi7Eem7ATPvjRjXacwEG4dTdWYA0StWSzd4OiJKj7Ee9JWfTzpZaITJ0hChcmRCzEX3MK0Oa/WoanqiKi7SGKRf7qWnUvywXW4CVswqXwnmqDH22Idewz7BmrVoiNaLaFiOx5EUJGfBTopeOJERkxkodinMQRVGEzXBhLkLg1N7KRr259erf6vqpDgjh44MVqbYvZu0MRJMkMKci7lPojnvPTn32BYfSNtKF7qDGMLSSnYULEYWR+Xk8zawGQs+MioPtxFC9hNbYiSVv8VQoncFps0PzIxbmuU/2sBUCTMC5IakAUYyWK9BXoQhsaUItM5CIxmGwYG6laBtZjOwJ6AEEEcBi71ZRXFkWPXVLLzRcYWvQxhhRdHfUPYG/Dr9S0m5T4XDUFmmixmBBxGPkgaq5hIQByblzIgdZx7EMZqpgMmQcnxQbNn9SHaNrdaXYzyGIYFxQiCY0s5KnbfGJDRo3If0l6CnrRhXRkRbG1FE1S6L5t4OSijzHyU1ep20Sh+nBEi8H1ihxGpnMWVLrVPZET46IeNUhBOrK1fLObYgtOig1aWPHMwu1Z6p4ohHFBS42NFViNUziCIIcZxSyp2SbTXgSPMcgKGH4OIyMQ0wtc6p7IaXEh69rLladlqDS7KbbhlNighZGD18yVKTyIpUkYF7TU2IjTEtSUm6PYw6RIDE+rCp5fUobHGGQF/MgiIsdMlalpfxarsJFzk4mIiCwqTytGLopxALvUCjUUi3gcRtbBGiJE5AjtA6eQFp+H+IFEs5tCREREs8jXSpCmZ6IWh9VIkUzkIAFJiEfC2L3Gz3K7GhjtRIIn1exmEI1jQsRhZPRhe32AFeDJUXEhB1NnOndjS/FtQGeD2c2xlViPDVocKZDYdrhH3ROFMC4onLGRqCVjA3bAr/vQgw6MYBgD6MUQBjCiD2El1iFdY/FVu2nqO4bCtLXq3zzGICtgQsRhZFmrjjr2OuSsuGjsOYT81NVqeTa/2Y2xmViPDVocOXiVkxuiiRgXFInY8GheZKNg0jZJkhzEC6jQ1yNNywxTKyka05f7hltRlXv1+a95jEHmY0LEQjyVK9UJm/BX10bkZ0gxq+K1Hpw76mc21oIi9b7PRXNrKL0iFw3PtEEPxNaVvYDuRyP2YjMuh7/VnN+vnePLDrHhqaqA3Zj1tx6u9hjFxXzeB6vtd6zGl1lmOsaw4/tO4RXuzxJJkmzQd+AQXkCVvhnJWlpY2kkzm8/f8Vz9Zc9wI9ITi8ZrufG8hKyARVUdRvqf5EyN1ZxpEomHlIKEmIyLOpxAKSrg4rIHixLLsUGLx7ggIzzGoGj2GV4tDuuwHcexD36d4z/toKn3GApTV49/zT6DrIBnCEQUs4b1QfSiEzkoNLspREREFGbxWgJWYA2qsU9NxyDrkqV2+0fakRKfa3ZTiCZhQoSIYtKoPoIj2INV2MxldomIiGJUlpaHJKTiHE6b3RSaRX3XyyhO38BjMrIcJkQcJhgEmmsC6p4oJBjQ0bi7U93bfTWZDr0ZR/Q9OIqXUIXNSNJSzG6WrcVKbFB4MS7ICI8xyKw+Yzmq0IEW9OpdEXl9WrrOwXrkp66atI19BlkBi6o6jQ70NLPXoSl0oPtUP+xqQO9DDQ5CRxCZyEU51iFBSzK7WbHB5rFBEcK4ICM8xiCT+gwZdbBG36qKrG7Ud6r6ImQteSmVONt9AGVZF13YyD6DLIAjRBxG6kou3+ZR90QTq7+X31Co7u1G5gwfw16sxlZs0a7Acm01kyFhZOfYoMhhXJARHmOQmX1GnBaPCqzHYbyIYX0oYj+HxpY9rtb3Y5/+LPbrz+Gg/jyO6/tQ2/4sGnsOY9jXN+05Ml2mruulSbVe2GdEx969e/GRj3wEO3bsQGFhIdatW4c3v/nNqK2de+Wgp59+GnfffTe2bt2KgoICbNy4Ee95z3vQ3NyMWMERIg4j0/bik8aqOXOgM02KizSvbeJCPkxD/7WjCenIRoKWaHazYpLdYoOig3FBRniMQWb3GelaNlbpm9RIkXQ9C2nIQgrSVI0RrjYXPqdxDFnIwypsUqNzAnoAPowgkJyLgdFO1LT/HSP+fiTHZSM7qQzxnhR0DzeiIG3tpBoi7DOi4+tf/zpefPFF3HLLLSoZ0traiv/+7//GlVdeiaeeegpr166d8bn33nsvurq61HPLy8tx5swZfP/738eTTz6JZ599Fvn5+bA7JkSIyFarxkhtEBfc0M7/l4w0NXeYiIiIKA4J0OBCLooxjAE0oQ4D6IVb96AQZchGAQt7LoEscdyHblRqG8e3uTU33EiCJ7EQ6YmFKEpfpy5eDYx2oGOwDr0jLfC6E1GRfampbXcqGdFx//33Iy7uwlSy2267DTt37sTXvvY1leCYyRe/+EX1OJfrQkLx2muvxQ033KCSKp/+9Kdhd0yIWIi/5hSgeef/+Oq5hzl5qipgN07eL5rdWZzCSqxDhpZtdlPIQvi3RYtlx88SJ/+enfq3brU4tfL7ICfh1divps6MHSvkjH9vRB9WK9E0oBZlepVanYYW7jj2qqWO5yJJp5T4HHUjc8lUmalktMfq1atRXV0963Mvu+wyw22ZmZlzPtcuOHbMYaSKc8NhP6s50yRS9b3u6VbLrxghwzETwfog0WSX2KDoYlyQER5jkNl9RhPqkYQUwwsn8VoCVmprsB470IFmHNB3oUtvm1TTgmY3qo+o+0wtNyyvxz7DPBL3bW1tyM5e+EXG/v5+DAwMLOq5VsQRIk6jA4Nd7PhpCh0YaB6GlcmVnUH0w4t4s5viLDaIDTIB44KM8BiDTOwzpI7FOZzCRbhq1sfJCjSV2Kim4cqIEamHIfVGClCGZC01om20M58+ilocRg6Kwvei7DOWpK9vcvHa+Ph4dZuPBx98EI2NjfjXf/3XBf/c++67D6Ojo2raTSxgQsRhXG6gfIcXJ1/0IRgwuzVkFS6PhspbSlDzyFkE/db7YOrW23ESR7AaW1gULcqsHhtkDsYFGeExBpnZZ0ih9SH0oxMtqk7IXGRFunKM1broQQfqUYNhfQA5KFS1RjwLmMYey+T304jTaMFZLMfqsE41slOfkVTXDY/bGhfl/IGxkTpTi6F+4hOfwD333DPn80+cOKFWndm+fTvuuuuuBf3s5557Dl/60pdw66234hWveAViARMiDiSdD9FUbo/1CowN6L04iaOIQzw2Yqe6qkPRZ8XYIPMxLsgIjzHIrD7Do3lwmX4DDuJ59OrdKMJyNU1mPrUuMpCjbjLKRFavO4BdWK1vdfyIEbkgdQpHVYHazbg8Ihel2Gcs3tGjR5GaeiFG5zM6pKWlBf/wD/+AtLQ0/PjHP4bbPf83QBIpb3zjG7FmzRp885vfRKxgQoSILKlVP6eGsq7BVnUVh4iIiGg2stpJub4OR7AH2chHPBIW/Px8lCBDz8Zh7MYafSuSHJoUadTPoBOt2IBLeEHKoiQZIomN+erp6cHtt9+u7v/whz+gsLBw3s89e/asGhUiP++hhx6alIixOyZEiMhyy7mdxlEEEcQmXMopMkRERDRvzahXJ/Ep2vxPFKeK1xKxTt+OI9iNdfrFjrswIxelutCGdbiYSxTHiOHhYbzhDW/AyZMn8eijj6oVZuars7NTJUNGRkbw5JNPoqBg7ilpdsKEiMPI/LzTe60/T4+iS+bznny8ybRaADI/tRXn1LzfIQxgGSqRo80/a02xGxtkTYwLMsJjDLJKn6Fh6SfxCVoiVutbVCFRt+7BGm0rnEJG6MpFqUgnQ9hnREcgEMBb3/pW7N69Gz//+c9V7RAjzc3N6O3txYoVK+D1jtXQkdVk7rjjDjQ1NeGxxx5Ty/XGGiZEHMg/VoeHaBLfoN+Un+vXfTiEF9XQVlnXPh6JvBphMWbFBlkb44KM8BiDzO4zkpGGAfQiGUsf0p+EVKQgTRUUdRI5CovWCF32GZH3yU9+Eo8//jiuv/56dHV14Ze//OWk77/+9a9X95/73Ofws5/9DAcPHkRZWZna9va3vx179+7Fm970JlVDRG4hycnJuPHGG2F3TIjEOH917bQq36vuKMXxhxp4ZS/KPFUVC36/okXiYrUJcRHQ/SoZIomQDC021jKPtfgyKzbI2oziwqz+i6xDiiNWXupFza7IXPG18ueok8znfZhPbETqvZJEiEz3AIqXfIxyDC8jFRnYiithNbJscBsaUYBlYa3xIReq3FjcCjvzeU8nxk+k+wwac+jQIXX/xBNPqNtUrz+fEJntuQ888IC6TbRs2TImRIiIFksquUsyZDmqmAwhIiKisHDBreqQLdUoRtTrlGmrYDV9ejdqcFAVgJVVdSr1jUjTMsP2+hynG1t+//vfz+tx9913n7oZJURiGRMiRBR1QT2oCpWVohyZWq7ZzSEiIqIYMYphxGHu5UfnkqglI6gHVPIhVcuAVUjdtZM4rAqeSvHXPL0Ex7EPmq6hAKXIQv6k6S5yzNWNdrViTAB+NaIkXcua8fU1uKCDI0LJOZgQIaKoO4tTyEEhsrXYqlJNRERE5pKT/9wlTpcJWYuL1BK+xfoK5Gnhec2lkmkyachSyRAh02U2YIeaQiO1ThpQizg9QS05PIxB+DCKDOQgD8Vww40TOIgtuHzG15ekiYyyIXIKJkQcRuZ6sxYAmR0XoaXcyPrYZ5ARxgUZkRoArAVAZsaGTx9FL7pQjvVheb04LR6b9EvViIxz+mlkIQ/ZKFB1SswoAC/7JwmPzbhs2vdkaeAyrFK3EX1YjZRJQNK0+iKyYs5+/Tk1CkRHEKuxFUlayvj3+9CNFKRHZX/YZ5AVMCHiQN4kD0Z6fWY3gxwaFzJ0U0cAHo3dj12wzyAjjAsy4okHRgfNbgVZSTDox0hgAJprGAMDQ0jwpiHJG756FxOdwAGsxNqwJitk+kklNqraZ91qfMZpDKIfE2eVeBE35RY/7Wu3tvhRF5LgaEcTmlGv2uKe4xgqXhsbIWJko3aJmnYjvyMZVSIjYDbrl6nX9Ot+teTuMlQiWthnkNl4RuLAlQHKbyjklT0yLS5k+GYiLlyJIGtjn0FGGBdkRFaMWLGNK0YQ0DV4Fq39tegbaYVLcyM5IQOvfO12PP+nBrV9cLQLQX1AjWCQVVxSka5GJcx1oj+bZr0B8UhEhpaDSJCEhowOkdtEklzwwwcfRtT0FLnJf8PoHv9avier1khiJAPZagqLLA88U+JGXrMHHWhHM/rQpZ6XjXxsxuVLSqyEhH6ujCop19dhP55Dkp6CAfRFdeU/9hlkBUyIEFGU6dBYv5yIiCgm1bT9Hb7gCIrTN2BV/JXQNJc68U2O86Is8yIE08Ye5zteoy6S9KtT/xbUoUYlDTzwqMKgUvNituVkZcSpJBtGMKgSB3Iyb8Z0XEkuhEaCzGVUH1F724Q69KMXLt2l6oFIkkTuXXCh8/zvIg2ZyEUhyrEuotNzJIEkI0TkdxmHhEkFWYmcgAkRIiIiIiIKi47BOmxf9sY5T6zlJD8RyeqWi6Lx7X7dp6aHyGp0SXqqmgLj0byTniuP2Ydn1CgLGRWSjmw1ssGMuh4LrUki+xraX5mG04tOdKMD9ahRVT0kMSJFUmdLBoWbjMxx87SQHIqR70CBGBje7K+unfMxnqqKqP2saIrUfi02LhbangT/IEaaT8NTUmHp3zPFVp9B4RcLcRHNzxKniOSwd6d+ToRrv+cTy+H4WRl6Krqr9yJNy5w0zc6/tgT+E2fnnGYnyQ9ZGlZunXorDuEFbNAvmZQUkeSBJErsvlqdTH+RcSByc4qJMbaQuJj1NXXWs6LFY0LEYaSzqX6owexmkIPjIs6TpAqqdQ02IDOpNCo/kxaPfQYZYVzQbCtGkLPJSixSeDRVzxgfsbHYPiNLy1OrohzALvV6OShU9TdkaV0ZEUL2xs8SsgJOEnMaDUguSFD3RGbFRUHqajT2HonOD6OlYZ9BRhgXZEQDkjI1xoXDqSQGPKoWRjj6jHQtC1txJYqwXE0v2Yu/zVqQlGyEnyVkAUyIOIzLraHsqjx1TxTtuBjx9+NI85Oo734ZK7J2RPRnUXiwzyAjjAsy4nIBpes96p6cTRIWsu5KuPoMSX6kaOlYrq3GxdrVqNI2h7G1ZBZ+lpAVcMoMEUVF52A9Trbvwqq8q5CeYO85v0RERDRzPQdZRWUTLjW7KUREc2JChIii4lTHC9hSchs8ruhVTSciIqLoqsFBtVTs1JVhiIisiIMaHUbXgZFen7onilZc6LoOTXMxGWJD7DPICOOCZoyLQZ1x4WDD+iACCCBDy5m0nX0GGWFckBVwhIjD6AEdJx9vMrsZ5LC46BluQrL3wvJ7ZB/sM8gI44KM6EHgzF6/2c0gE/WjF2nImradfQYZYVyQFXCEiNNoQMbKFFZzpqjFxYh/ACfansaKbBZRtSX2GWSEcUFGNCC9wMW4cDAPPAjCICnGPoOMMC7IAjhCxGGkinPR9iz01g+otb/DyVNVEZbX8VfXIhaF6/cTifa43EDxpV4M7vIhGAjvzzne+mesybsW8Z6U8L4wReXvJpJ9BtkX44KMyOoyBZVu9LUFw/5ZInicYY39nu19SBjJQKBnCJ68yY8xOs5w6vtAF/CzhKyAI0SIKGIGR7vh0txITcgzuylEREQUYUO+XsS5k8xuBhHRvDEhQkQR09Zfi/yUVWY3g4iIiCIsEPThdOeLKEpfb3ZTiIjmjQkRh5Eqzv3Nw6zmTJNIPAx0hX9lgM7BemQll4X3RSmq2GeQEcYFRfOzhOyhtb8GhWlrEe9JnvY9xgYZ4WcJWQETIg6s5lz/dKu6J5q4MsDZw351H05BPcCldm2OfQYZYVxQND9LyB7aB04jN3ml4fcYG2SEnyVkBUyIOIzmAnLXp6t7ohBNA7LLXOo+nHT5j2l/W2OfQUYYFxTNzxKyvt7hFvgCw0jwphp+n7FBRvhZQlbA8HMYzaWd73j4iUQXyAdRzjJ32D+QUuNz0T10NrwvSlHFPoOMMC4omp8lZP2RIcdb/4L1hTfM+BjGBhnhZwlZAbslIoqY5VnbcaZzj9nNICIiojAbHO3C/nOPoGPgDLYW34Y4d6LZTSIiWjDPwp9CRDQ/ocJq/sAIPO54s5tDRERES+QPjuJUx/MYGO3EqtxXIDkuy+wmEREtGhMiDiPFrLpO9UekqJW/uhZO5amqgJ1JmY+e5mBEqnwHdB9cLnY1dhXJPoPsy0lxMZ/PNrt/BtjhsyScovl+xVL8SE2wpt6jOHv211iRtV0lQ5YSG3bZbysf69rxdziRTKHq7XfDXVkO11I+TwIjQE0YG0aOwrMUh9GDOpp2d5rdDLIYOalprgmE9zV1XV1Bykgshktzh/W1KXrYZ5ARxgVF67OErKFnuBk1bX9HdlIZLiq9c8Gf64wNMsK4ICtgDRGHkaJFhduzWLyIpmXoCyrDU+xMpsfUtj+Llxp+AbfLi/Lsy8LRRDIJ+wwywrigSH+WkPnkwkbPUBMONT2Ohq592FD4WqzI3rGoixyMDTLCuCAr4AgRh5EOJ3NlClpe7nLEUGeaH1kGL73AhdZTASxlpLMsuffy2YewMnunSoRoXF/P9thnkBHGBUXys4TMSX6MBgYw5OvFsK8XA6Md6BxsQFpCAVZk7UBKfPaSXp+xQUYYF2QFTIgQUdi09tegJGMzclPKzW4KERERTRi9OezvG0t4+HvHEx++wNDYAzQN8e5kJHjTkOhNQ2bSMnVxQ+OleyKKcUyIEFHYtA+cxtr8V5vdDCIiIkeTEZudg/VqSdxBXxc8rgSV6FAJD08a0hMK1b+9rgSO5iQiR2NCxIGF8NoO96h7ohAZ8t5eH1jy0HdZis/rTghXs8gC2GeQEcYFRfKzhBYuGPSja+gcOgbr0DvcDI8rDllJZSjLuhhJ3gzTkx6MDTLCuCArYELEYaTDkYNYoolkGbyOuqV9GgWC/rEXopjCPoOMMC4oUp8ltLAkyLnew2jrPwkdOjITi5GfUonKnMstN9WFsUFGGBdkBdbqLSniNLeGZVflqXuiEDluKlnvWVKV7+a+46wdEoPYZ5ARxgVF6rOE5qe5rxovnX1QrfiyqegmbCu5Q9X8SE8stFwyRDA2yAjjgqyAI0QcRkZMphTIfFGwmjONk3hIztQWHRdSmK2x57A6IKPYwj6DjDAuJvNX10btZ3mqKizVnolcHg1Jm0oRqGlA0G9OZMzn92Pn9gz7+nC89c9IjsvCRSV3wuXyOOI4wyqsFl92FytxQfZmj16UiCxrcLQbh5sfx7qC621zYEZERGQnA6OdONdzGL3DTajKvRqpCXlmN4mIKCbw7IWIFq1/pB1HW/6IDYWvRaI33ezmEBERxQxd19HWX4v67n1qRZjitPWozLnC9AKpRESxhAkRhwkGdDTu7lT3RCHBINBcE1D389Uz1IQTbX/DpqKbEe9JjmTzyETsM8gI44KMMC6WlvwY8fdjyNeNQV+Pum/qPYrMxBJsLr5FrRrjtOMMin2MC7ICJkScRge6T/Wb3QqyGh3oaZ7/p1HnYD1OdbygDtK4zG6MY59BRhgXZIRxsWBBPYDTHS+qz1UZaZkUl4FEbwZykldiWcZWeN2JsTEiZIHHGeQQjAuyANb0dRhZEaD8hkKuDECTSHXv5dvmV+W7tb8WZzr3MBniEOwzyAjjgowwLha+XP3+c48gKS4TF5W+HusLr1crxRSmrUFGYhHiPEmxkQxZ4HEGOQfjgqyAI0QcRj5X49O8rOZM0+Miae4q3/0jHWjo3o8tRbewgKpDsM8gI4wLMsK4WNjIkENNv0NpxhbkpqxErJvvcQY5C+OCrIBnNEQ0b3Vde7A67xomQ4iIiBZpYLQLx1r+hGWZWx2RDCEisjKe1RDRvAu+yRK7Sd5Ms5tCRERkO73DLTjduRsyCWZN/quQHMfPUyIiszEh4jBS+b3u6VZWgKdJpLp3w2H/rFW+T3W+gOzkskXNZ/ZX1y6tgWQa9hlkhHERGZ6qCku9zoJpwNkzGlwV5XAxNMaN+AfUEvVx7iS1bK4UTnWa+RxnkPMwLsgKmBBxGh0YaB42uxVkNTow2DXz0WtT7zH4AkOoyr06qs0iC2CfQUYYF7SIzxInGg0M4WDjb7E675VITciDYzE2yAjjgiyANX0dxuXRUHVHqbonCnG5gcpLvereaKqMFFJdlfuKmKl2T/PHPoOMMC5ooZ8lTtM+cBovNfwShxp/h1V5Vzs7GcLYoBkwLsgKOELEgdw8gCUDM30YdQ+dQ2ZiMVwaP62cin0GGWFckBGe2IyRWiFbi2+Hm0XIxzE2yAjjgszGESJENKuW/hPIT60yuxlERES2wmQIEZH1MSFCRLMa9Q8iwZtmdjOIiIhsI8mbgZ6hJrObQUREc2BCxGGCfh0nH29S90QhwQBweq9P3U8V505UBVXJmdhnkBHGBS30s8RpKnOvRHXbX1VRVWJskDHGBVmBIxMiX/3qV5Geno5PfOIT49taWlrwjne8A5WVlSgsLMQVV1yBRx99dNLzdu/ejcsvvxwbNmzAj3/840nfk9fLy8tDfX39pO133XUX3vWud8FKfIN+s5tAFuQfMd7udSeqUSLkXOwzyAjjghbyWeI0cjFBVpbZf+4RDIx2md0cS2BskBHGBZnNcQmRvXv34oc//CHWr18/afu//Mu/oKamBr/4xS+wa9cu3HTTTXjLW96CAwcOjD/mPe95Dz760Y/iBz/4gUqqnD17dtJryAocX/ziF2FlsiLAaq4MQAuo8p3gTUXfSKsZzSILYJ9BRhgXZIQrRkyWlpCPDYU34HjLU2joPqBWbXMqxgYZYVyQFTiq2lN/fz/e/va34xvf+Aa+8pWvTBv9IUmObdu2qa8l8fHtb38b+/fvx6ZNm9S2wcFB9e/c3FxkZGSo15tIXlue8773vQ9r166F03iqKsxuAkVAYdpadYUrPaEQ6YmF077vr66FE4Ur3p36+yOyAjt+boWrz7DjvttRojcdW0tuV6vOHGh8FGvzX404T5LZzSKKqX7Or/si1haKfY4aIfKRj3wE1113Ha6++upp39u+fTt+85vfoLOzE8FgEA899BBGRkbUFJmQj33sY+pxpaWluPjii7F69epJr3HJJZfgNa95DT772c9GZX+IokGW291Q+FpUtz2NIV+P2c0hIiKyFU1zYWX2JViZvRP7Gx9FW/9Js5tEREROGyEiCQ6Z/vLXv/7V8Ps/+tGP8Na3vhUrVqyAx+NBUlISHnjgAZSXl48/5p/+6Z9w++23Y3R0FJmZmYavc++99+LSSy9V027knigWeN0JWF9wPQ43PY4txbfB4443u0lERES2m0KzreQOnGj7m0qKrM5/pbroQERE5nFEQkRqfUgB1UceeQQJCQmGj/nCF76Anp4eVUg1Ozsbv//971WC5IknnsC6devGH5ecnKxuM5FRI//4j/+oRon88Y9/XFA7ZS62Sxubj60H5aZDc2nQJozjkW3yPc2t4fxDlWBAB/SZt0+c533ikbPjKwNMnf8963ZN5vpd2C5TYfXAhe2h+X9qu7RRw+S2h7a7xr53YZ/Gvjd1ezAo35x5+9T5hqEK1QvaLm03aOPE7fK7CO2raotrwu9g/H2y1z5N3B5qx8kXx4YbStuM3qfk+AxU5FyOo21/wKai16krXmNPwLxjT22PQOxN3T7z+xS+v6eJv/ulvE+h/bXCPk37vU/oM9R+eDTbvU+xGHtW2Cd5nerfXIgLu+7TtM8tu/Tl4Yi9CO1Tza6xFSPC9fkUC8cRE7d73F6sK7oWjb1HcKb7BZRnX2b7fZrv+1T7/FhsxNI+xeL7FO19Ch1/Kos8NnLpGsA637RIjkiISB2QtrY2XHnllePbAoEAnnvuOfz3f/83XnrpJXX/wgsvYM2aNer7spKMjPL4/ve/j69//esL+nn33HOPqkXyu9/9bkHPq7ylGPHesSvvXaf60bS7EwUXZSJzZcr4Y9oO96hb6RW5SCm4kNxp3N2J7lP9WHldAeLTvOPb655uxUDzMCpvKYH7fOfi8rpQ8+g5tUKAFMWb6PhDDfAmeVB+w4VaEQE58H2oAcn5CSi7Km98+0ivTy27mLEiBUXbs6CljP3cgS4dZw/7kbXMhZxlF3rBnuYgmmsCyC93I73gQo/cXh9AR10QxWs9SM680NnJY+U5ZVs8iE+6sL3hsB+DXTrKd0wuwiTLdkmlainONPXgzBMPrNjmndQZy/akDA2l6y/8GYwM6jiz14/0fBcKKsdeXN9Yiv7mYdQ/3YqctenIXZ8+/vjQ+2S3fTJ6n+TnymvM9j7pdaUoWqehsAjwusZ+9rnhlHnHnpCYCXfshcz1PoXz78mT4Q3L+yTxZZV9mul9kj4j6Ava8n2Kxdizyj4VXZKN1KJEW+9T6HPLTp9P4Yq9SO3Tmf0+6IHwfj6F2Ol9mmufKrEZvcMtGGofQPfp5JjYp7nep+7mAFpqYmufYvF9ivY+yXP8o0s7NhrxjeAvD036MUTzpvX09MR8yeu+vj40NDRM2vbud78bq1atwgc+8AFV9Vumt0hh1aqqqvHH3HrrrapeiBRhnYssu/vTn/4UN954o/r6U5/6lBohIlNwsrKycN9998343N7eXvVzrvHcAo/mjehVRblfdWsJjv+qQV1VC+cVOM+qcltkoheaXfefODnnFTjvmgpb7dPE7dIOtxcov8SLky/4EPDP/j419OxFcnw2cpKXq+2jx2ote/V3UtvD/PcUivelvk8SX1bZp2m/d8maJ7hUn3Hi4bGRZXZ7n2Ix9qywT7JNEhYTRxzacZ+mfW7Z4PPJf6I2LLE39XMrHPsk/5aTIjmxmSqWrmiHa58GR7twumMP1uS9Omb2adL2CW2cGBtqv2Jgn2LxfYr2Pqm4OH/8KUmRxR4bSVHVv/gfUed7aWlpCKfQedq1lR+0zJRxf2AET9V8LSL760SOGCGSmpo6bdUXmfYiiQrZ7vP5sHLlSpUc+bd/+zdVH0SmzEi9kQcffHBRP/NDH/oQfvzjH6Ourg633XbbvJ6jTja0yfmp0IHLVNIJGGWyZto+cfj7xAdM2j7T4yc8b7btoQ5ucmdl8HDpaLH07VN/3qK263Nvn7jPoQNMu+/TpM2h7efvQ/E2UxtdiMfI6BCCoQvA+gJiD5GJvWmbZ3yfwvf3ZPS7XMz7NK39Ju4TZtoe+huf8Bi7vE+xGHtW2KfQiffUuLDbPk373LJLXx6O2LPJ51MsHEfMtE/xrgz0D3eqkxsP4mNin+bTRu4T92nS9ontXeSxUdDBS1pHUnd3Nz7zmc+oWQ9DQ0NqBoScK2/evHlez5cZGDLb4syZM6okhZwTf/KTn5y1/IQZHLXKzEy8Xq8quipv1Otf/3pcdtll+MUvfoHvfve7ePWrX72o15Rky/vf/34MDw+Hvb1EZvEHR9HYcxgZiUVmN4WIiMjWNE1DZe4V2N/4CLoGG9SIZSIiKwgGg7jzzjvVOfI73vEOfP7zn1clKGQ2xMmTc6+UJYmUj370o6ocxZe+9CXcdNNN+N73voc3velNsBpHjBAxIiNAJpLVZGRVmcWSgqxTffjDH1Y3q5E510TzzeCH9Aw1obrtr6jMuRKJ3gvz1Cn2sc8gI4wLWsxnCU2WkViMdQWvQUP3ftS2P6eW580+PyU11jA2yAjjwpoeeeQRvPjii/jf//1f3HLLLePlJLZu3YovfvGLuP/++2d8bnNzM7797W/jDW94g0qChFRUVKgkiSxacv3118MqHJsQcSq1MsBDk+upOJm/uhZ2a4+nqiLsPzdUoGum9jTqdehAE9ZjC+IahuGHtX5vVmW1+FoM9hlk17iIRF8ZS31GpH7WsaOw/Htqtb5ZqsetRDH8ej5qBnejCXtQhlVI1JJtu99T2zzTcQY5z9Q4jGSfQYv36KOPIi8vT43sCMnJyVFJESkpMTIygvh445oqUpfT7/fj9ttvn7RdvpaEyK9//WtLJUQ4ZcZpNCBZqv9PrklHTqcBSVJJ3CAuZAhvE85gPXYgTrNGMSmKIvYZZIRxQUYYF0vi0TxYo21FAUpRjf2o00/EzjSaWY4zyMHYZ1jWwYMHsWnTJrgmVugFVB2RwcFB1NbOnGAdHZUKuUBCwoUV50RiYuL4CrBWwoSIw0iFZlmacGKlZiLp62SZsyl9niJlED3wqrnO5DzsM8gI44KMMC7CI0PLwSZcigACOIdTiPXjDHIu9hlLX0lVVsEJ3WTURri0tLQgPz9/2vaCggJ139TUNONzZWqMkCk3E+3atWvO55qBU2aIaFYB+M1uAhERkaPIRYgV+mrsx7Mo8PUhwZtqdpOIHM1fcwrQZIKb+WSZYTF1FdVPfOITuOeeewwLpIZGbcwlPj5e9T+yqozRlJjQttkWDpFVaC666CJ8/etfR2FhIa644gqcOHFCrcIqi5nIa1sJEyJENCMZqluDg2ouMxEREUV5FRp9E463/hmbim7mSE0imuTo0aNITb2QLJ2ppsdzzz2nVoeZjz179mDVqlVqeovRiJPQtqnTYab6yU9+gre+9a24++671ddut1v9W9oy23QbMzAh4jAyFXWk16fuiSbFxaA+LS6O42WkIUsN3yVnYp9BRhgXZIRxEX4pWhpS4nPQ0leNgrTViLXjDHI29hlLI8mQtLS0OR8nCY7vfOc783rN/PPTZOReps0YrSAjZOTHbIqKivDkk0+qJXrldWRFV3nNqqoq9W8rYULEYfSAjpOPW2veFplPDwJn9k6fGjOMQazRtpnSJrIG9hlkhHFBRhgXkbEyeyf2NjyIzKRSxHvGVp6JleMMcjb2GdEhiYg3vvGNC3rOhg0b8Pzzz6vpNhMLq7700ktISkoarxMyF0l+hBIgx48fVwmVu+66C1bC0kZOowEZK1NYzZkm04D0ApdBXGixU+GeFod9BhlhXJARxkVEuDQ31uS/Cgcbf4tR/yBi6ziDHI19hmXdfPPNaG1txW9/+9vxbR0dHXjkkUfwmte8ZtL0nFOnTqnbbCSx8pnPfEYlU972trfBSjhCxGGkinPR9iz01g8g6OeJLo2RxG9BpRt9bUEEAxe2pyAN/ehBKjLMbB6ZiH0GGWFckBHGReTItJnV+ddif+Mj2FB4IxK9cw+Tt8NxBjkb+wzruuWWW3Dfffepuh/V1dXIysrC/fffrxIb//qv/zoteSIOHTo0vu3jH/+4KrwqI038fj9+9atfYe/evfjud7+L0tJSWAkTIjQvnqr5DYuKxf3yV9fG5O954n65PBr0jaXwn2gY/0Aa1ocwgD644Y5am+wkmnFBRNb+TGJ/EB1O/T2H9jtB1xHEIPpOHoJXm74cppXfK6PjDHJmf0n24Ha7VRLj05/+tEpiSHJj69atqhZJZWXlnM/fuHGjSqjIa8iUG3mujDa58sorYTVMiBDRNN16O07jGJZhFZI0LvVHRERkJpm+Wo19KMYKZFk8GUJEsSEzMxPf+ta31G02E0eGhEjNkoXWLTELEyIOI+Ug+puHWc2ZZo2LOpzAOmxHnGa8fBc5B/sMMsK4ICOMi8g5iSNIQioKtTLYEWODjDAuyAqYEHFgNef6p1vNbgZZOC4G9F7EIZ7JkCgJ6kH4MAI3POqmadaqLMY+g4wwLsgI4yIy6vQT0KBhmTb3MHWrYmyQEcYFWQFXmXEYzQXkrk9X90RGcXEWp1AKzjeNhjq9GgfwHGpxBMexD/vxLA7pL2JUHx5/jE8fhV83b6lC9hlkhHFBRhgX4deon4FU9FqJtbAzxgYZYVyQFTD8HEZzaec7HmtdhSbrxMUg+pEMe1WvtysNbuShBOu0i7Be244t2hXIRgE6MXa1pFU/h4N4HofwAhp0c4oJss8gI4wLMsK4CK9WvVF9HlRhs+VGDy4UY4OMMC7ICjhlhogm8SIOIxhGAhLNbkrMCOh+BOBXV/nkNqju+zGCIeSjZNJj5ffeiy7172Y0YBMuVVNpjmEvOvQWZLOYHhFRzOvXe9CI09iInbZPhhARWRkTIkQ0SQFK0YJ6lKHK7KbYnkx9OYqX4FIpDS8SkIwkJCMbeUjESpV8mnqgm4YsVTzPo3sRgA8ezau2V+mbcQC7oOtB9WoiHdlwcZwpEVHMkemrldjIPp6IKMKYEHEYPQh0nepX90RGcZGFfNSjlgmRJfLrPhzGbqzCZqRo85+C5NE8WKNvU6NENuCS8e1uzYN1+sVq1IgOKcQ6ik60oBzrEUnsM8gI44KMMC7Ct8TuAHqRiGTECsYGGWFckBUwIRLjPFXTi2O29QDuyixT2hMrv8Op/NW1YXkds+hBHU27O9W/5WqUS+cVqaWSpYvLsGpByZAQeU6KQR2XeC1RvaY4p59WB8zRjA0z/m7ImhYbF+HC2LEms+MiVpIhUmS7EMtjanQIY8M8Vu4vGRdkBbHT09K8yGdrQaWb1ZxpEilmVbg9S92P6EMIImB2k2x/QNuDDjXaJhLa9Sb1+jKcOpqxQRTCuKBoxMWwPoiT+hE8pT+kloR3glocUgnxIq0MsYR9BhlhXJAV8LTYYaRcQXqBS90ThUiCLHNlCjpdLTiCPajCFrObZNtEiBQ+lVofxVgZsUJ4fehWq9FMfX1ZkeAF/Sns15/DPv0ZtazvqD4SlthgEpUmYlxQJONCphzW6odQjf3IQh6ykY9EpMAJnyHSv2twqSXX5WsjQ/oA2vRGdOvtCNpkrgH7DDLCuCAr4JQZIlKC0NGAWmzCZXBrbrObYyty0HoOp9GKs8hEHtZgq5reEinLUKmW4k3R05B8fkqOtOEsarEd16hh1nKQ3IFmNfTarXtQgfWI1xIi1iYioqWSJMBxvKxW5SpFBSq0DerEX5IhsTR9ZCaS5N6iX4FzOKWSQX6MAros0e5CPBLV70VWJ5PaIqnIUMmTUziKlfpaZGg5ZjefiMiWmBAhIkWDBrf8x2TIgnWhTR2YbsEVUVkeUQqsrtDX4DD2IF3Pgq7KrAbQidbxg2U5echFkbr16d04jBdRqJehSFse8fYRES2GrMglBaMn9qV1qMZWvGLS4/y6XyULZKUu6Q+NSJJYljfvRofqH2UFrzQtE1Yn+12CcnULkQS39O1j65VNXp1MRtMcxV41GjBPKzap1URE9sWEiMPIyMr2+gCrOdO0olbth3vgD7B2yGIkIElduYtGMiQkXcvGZv1SBBFUySz5rwIbEIf4aY+VE4zQlUU5SVhIOyU2mg93YigwqEaayAF5NPeTrEniou1wj7onCldcSEK+WF+Bg3heLTWeoMlC5WloxGkk6aloQ6MqJi1JAS/i4cMIArp8bumIQ4Ja2lxIImQUw2pkSQZyVL8oy5nLayZp9pt6IwnumVackaXZ1+vb1ajBeD0R6Zo1i+azzyAjjAuyAiZEHEamo3bUMRtCk0mCTD6Q5GBKlnXNQ7EjhieHMyEiyYZom++0nBSkq5MFSWbIaBJJnsxHr96FU4GjcB1yqRMKP3yQ67IyhDvEBdf4yYm6Wnv+Y8Xd3a/uJQGTn7oK8Z7YWT6SLvQZROGOi3ytFMl6mhrVtkW/UtW0akKdWoq8CMtVfzY1KSv9zKjqhQdU75aIFYibMkUwQU/CGVRjLbYtql0ySqMb7Wo04AD6MKr6/Int0FVCRgqiShvlJv2hJK0jPfJSPq9lWXapX7VBvwRx2vTEuNnYZ5ARxgVZARMiDiPnuMVrPTh31M9RIjROc2sovSIXwWc2osF/Ei/j71ijbx2vT0GzG0QfAgjgiL4H+SgxLHg68cBdDuzlwH0Yg+omh/EyR1wOpKWAYBJSw3pAK6+1Sb9UnVTswzPI10tRiGXThpqHhpj3oFMNM5err+s9F2PlFSVoeKYNemD6FZygHlAjUOQmJyQyNF2SLm7P2FVYX2AYBxofxarcq8aTImo+PBMkMdFnzBQX5EzhiosULR0pesbYKA8tGcVYMfvP1TTEI0HdZntN6a/69R717/mSEShncFz1i5nIQTqyUIBSlWSe2M9L/yn9ufSgMo2yASdVHyp96TW4FZEmI0XK9XU4jWOowmZYDfsMMsK4ICtgQsRh5LM7OVNT9+x2KETiIaUgAR7NgzJtFQr0Zerq3Fr9InUwSrOTg2spZipLFktxVSlOW6pXTEqMSLHAszipDpQl7SEl8eTAWhIoUixPqoDIlcd2NGEItdiAS8LaRmmHXF2V97YZ9Wo1ITnQH2vdhYN6GZYt7SrDKvVvt+ZSsTFTn+HS3Kr9cpvIk1Ix/u/MxBLUd+1VJyNCrpiO+Puh6/1wwYNM5KoEjRzQk736DH6WUKTiwguvGpUWTpXYgMPYjTK9Sq1cM9f0PynoWo8alKISK7F21sfL96TPlJvUbhJn9OMoRPTqNklh1dP6cVgR+wwywrggK2BCxEI8lSvhcVtvmKPT+atr4bQ2y2oka/Rtqtr/Jv0yTp9ZwBQWOWiW4nYy510OpF26e2zEBNwqIbEcqw0Pqnv0LtTgkPq3JEkiRd5LaYfcoiUpLgOr819p+D1/cBRt/SdxpOcwEr3pWJa5DSnx2VFrm93/1olimfSd4e6jZbScFGqVBPVKfQ3SptTcCC19K6NCZLTeZrXy2uIOl3NQqFaLSdSTkaFFp1+b75RIIiIaw4QIERmSwnNF+nJViE6uqjmBjGCQ/6QuxmIPgENTVCTxIVch5YB+akJJ5qJP3daBVqzAarTiHPIimBCxGo8rDoVpa9Std7gFZ7r2YNTfj+L0jchNqWAyjsiBBvWx1WGWY01kppZgvRrRJzVFavUj8KjDYU0VnxYyfVGWKk/SUpc8elBqekhy5Yx+DMVYqZIkkSxMHe4kEhFRrGNCxGGCQaC5JqDuiUKCAR2NuzvV/dTidlJYs1Y/hJVYF1Mnp+16EzrQgmFZeQXB8doWMpJD6mDIdBJZ4DARSSjCikWtTCAHvfKf/A5lGo3U2JCv5YDVq8ehEhvVaBwhc9N70IG12kWwQ2xEQlpCPtYXvEbVHTnXcxAvNfwSOcnLUZK+CXGepIj/fLJmXJA1habASb8ZOsEPR1z06B2oxWGsw8URLUYqo0Wk1oaMCJHPAJW8hjvsyQpJkK/CJrU8bh1OqCmLq/TN431/OHXqrTOuRmM29hlkhHFBVsCEiNPoQE8zsyE0hQ50nxpbFWQqWcq1BQ2owUFLFmpbiIDuVwVNZelGqWOxDJVIQKKqg2FE6n5IkdFj2Ist+hULTgjJAbDU6pBVaFZgzaSkiiRJpE7LRn0nvFqcGpkSWqHFLrERKV53ApZnbUdZ5sVoHziFI81/UNMJl2VsRXpiYVTbQtaJC4o+mf4nfaAUgR48fy+FQoUmyYPzNYHGByXI/cmZX28s6exSzx27H0tCh+6lZkg/erEJl0atptBY4jqyq8AI+d1JkVhJjA+gZ9YCsIshIw+loKr87iyJfQYZYVyQBVjw6JsiSc7nyrZ4ULePq8zQ5CrfK68rwKknm6dV+ZaDxWy9AC04q07wJaEgN6mkLwfGMopig7bwAqD1eo0aiZGBHJUIGLvJf56xQ+YwjkaRK45ncUqthJKObFXwLlPLnfN5kqjwIgvF+kqVEKrQNyzoiuUJHFBJF6OflaZlolLfiEN4QRVgbcQZlTSxU2xE/GdrGnJTytVtYLQLDV0vo6b9GRSmrUVh6mq4XOZ8hI1dUZ4+FcpJzIwLMjasD6rkhFoGW4tb0mvJSiwncFAtt52EFHWTIqSJSFGvP9MoitniIvR3I/2+tNPoXvr+lUiPub8tSVbI58EabFvUaMO5yO/2JA6jEGWWLU7NPoOMMC7ICpgQcRg5holP4iozZBAXad5pcSEH2J1oVSufyNQSGSmRikykIVMtgygjR+TK3kLV6SfUVTJZXUTGa4xV7vCPTVVR/xpFsp6GSm3jkg8S5SBUDsGl2OliV8wp0ErVa0nyQu7lyl4y0lQyaGohO/lZI+q3NaAO7mdLvEhSZLW+RY1YWY4q9bVdYiPakuMyVWHWQNCHxt6j2Hv2ITVapDRjCxK90VkeulfvxBmcUBEqdWZkWpUUwJUCtZGsCWD2tAhZSlTiOQUZ48P8rRIXJP20rEt1SPU9shSsJH79+qj6niSYL/RTF2I0tLqUpKAlySHTLCTpIaPZhBR4likrC53WMVtchKYQyt+O00iRbUlWRCIZImQ5dalNUqRFr1j2QrHPICOMC7ICJkSIaBJJgjShTiUq5CBZkhZV2DLtwHhIH1CjRrbginm/tiQTZEivDIuW+hlygJyDAsPHHtB3qSkuiyluKj9HhnlLwTxJ3pRo5ViqQm2ZWhpWXluSOTJCRpI3Ez/Cx+agu9SJo5xYyNXVuUjRvjJULbl9TuF2eVGasQkl6RvRPXQWJ9qeVu9JacZmZCUti0hiQl5fllPuRAtWY6uqCRDaLiOHpBBuJFcGMuuKtkoAQlf9gMSyjGIK93LQtDQy0k6S1uVYh1QtY9r3/bofgfNL107sqca/D7/qKwfQqxKzkvyS97wIZRGpceFUfehRSe9IkUTTMq0yYq9PRBTLmBAhIkUGLMsQ6UEMqNEfRsvDyknSX/Ab9e8s5Knhv/MZ2iy1OORkUk6ocqXOvrZ2zufI6x/GbrVc4XzIQbzMdpdRJnJ4KAkJuXKfoeUgnOR3IiNEwj3/mxb+PmQmlarbsK8PDd37capjF/JTq1CUti6sS5iHCuJKMmDi34T8u1hfoZIlsZYQkRMsGWkgS47KEPxOXQoQD875PEkSyYm1JFXlb1JeJwXp6u9Z/j9TvR5auC69DYPoUzUjZkoEejQZITL7oZ6sqEKRJYl5mWoaqYKnveiOyOsSETkBEyIOI6vLNBz2c5UZGifD/uv8tah/+ihy/IWzJhAk+XGNfiva0Xy+0Ooh5OlF49NH5KRxrPBev7oPXZn0IA4ZyMZ67Jj33PZSrQI5euGClhCUJMVSlsul6aTye93TrZauAJ/gTUVl7hVqekdL3wkcaPwtEuMyVBHWlPjsJb12vz5WhncjdhqedPrgs2Yx3CWSfS3X16kpZ2txEc7hjFopY7a4kGRINfbBi3g13UISKZJE7UOXGsUgK2zIn7PU8clCrpp+F2u1IqLpHE6hEpssNV3LDv2FGaT+ihTY1vUg8lEa1vesQ29WI3qsjrFBRhgXZAWxdxRnY/6aU8D5YlieqorI/BAdGOxipxPir66F09t8HC+jQ2/B9qZrkKDNvbSpXOHNQ7G6ycgPuRosN5kGEyrAJ0VL5X6pxd0WW/ODwkgHBpqHJ22KWP+0RBKbhWlr1K13uAVnOndjNDCA4rSNyElZoabbLHT6mJzgr8f2GU9g5MqvFDOs1Q+rpJ8kT2Q6lYxQyjx/0i/JQrlKL38jUo9HkoVLrY8TDVL/pl6vVSuNyKixI9iNAn0ZslGAOD1+UlxIX1CN/WokyMQ6BpLwkASI3IQkrXrQqZKqp3Ecmq6pwspjv6sMS53cW5lMJ5T/LDetxaC/IKjP1i365WjASVXvw617VN9QgpXjU/AWS6aGWnZlmYkYG2SEcUEWwISIw7jcQPkOL06+6ENQZhaQ463TLkavpxPFt6Sh8xE/dP/8nyujPWRaCsUul0dD5S0lqHnkLIJ++yRT0xLysb7wevgCw9h/7lE09OxX0zdkKk1qfJ66yegRKfEoxShldINf71cjkoYxoOqCDGFQTQuL1xJn/Dlywr9Vv1Kd5MtICEkGylLVMr2kC+3qKr6cuMpoKUmMSGKgD93q59nh5F9HQK0qkqXlIU3PRBuaxgp4eoN45S1X4c+PPA2/z6fGcZVhlXrcXEkrSX7ILXRi340OtOIsTuKISrTI8t5Tk6mSlJFkUgea1e8zVMRY+h/5ndvhdxkuEju1OGzJvteu/UU0SOzL34jcZOSUJE+lTk+Zvgo52uKXE1eFcy26ssxEjA0ywrggK2BCxKFJESIxog+rIqdSCHSNZ5lMEDC7SWRBbo99Tza97gQsy9yCYX8fyjK3qQRJ30greodb0dZfq5IhkhSRE2odvepEW1bqkEK3810RQp4ro0PkFiK1AsbqBZQZFro8jn1YpW9a0DLOZkynC/1uhJx0SWFhubmgIduTi424RC2autiEhExxk+kEchOdequqHSTT5SSJJNPwpOCnnPTloEgVtQ1Nu5Pf49nzV9xlCWujoqKxlgiRZJr02fL7kkSQFdm5v4gWSaTKe5ih56AGB9Clt6vCuIuZQqYWM7ZJgpWxQUYYF2Q2JkSIIkAOTtSKIxafHy9FSOVkQ+b7pyELTRhYUM0OIjvISV6BvWd/hdzklUiKy1Sr0chtKn93dKbQLdMq0KY34gCewwb9knnX1Ym2HrSPT3WZTThPxGSESbyeqIqFyqQCqUciiSWjxJEUUJabJG3ms6KT1cj0IRkV049e9XVo1IvsTyrS1Ygi6aNlEW9Z6ln6ZtkuJ84pWrrJradwkLiWJF+z3oAD2IU1+tZ5TV2dKBeFqoixFUcMERHZARMiRGG8mtqNdnSjTQ0Bl4NZyS3I/Hg5pPf0+jHi78eQvxfDvl4Egj41hFZO0HJTypGZOFZoTYbS6npgwfUO5tKrd6mri1LbQ05yJFkjc/albT6MnD8UJ4o98re0sfBGHGz6HdYX3ICkOPNHEuRqRfDqcWrqwxpsHU+kCqtc6ZV+TBIN0b76nKylIhmpsz7mpH5E9WelKMcKrLHM72wh9WmOYq9q/9R6MjKNSOrQSM0Zqcki9WjsMCWCFq9AK0WanqFqeqXqmWpazXzf8yzkq1FDTIgQES0OEyIOI3VDTu9l/ZBI6FJVB86gDJVYiXXqAF3mvcuBrVzl8wdHkRyfjezkFUj0pqmTtEDQj0FfF5p7j6sCkHI65A8MY3C0C1dXvjes7ZMh+nLwLUkbOXhK1FPU+JBM5CDFn4GTjzdx/iZNIzERC7GR4E3DhsLX4lDT71GefZlartfsEVyyotMp/SgGzq9kIz2ILH+9Ud+55EKL4VCMlaoGioxkWa/vmHSCZnZcyLQmMYJhNbHGTiNEpIh1HarVyACjaVkyjWg+I3OsyOy4sLMkLRWb9MvQgRZVW0Sm05SiUi2dPJNevVOt9laFLbA6xgYZYVyQFTAh4kD+EbNbEJvkAFaWlZy4UoKc1MgVPuHJmL4yh9vlQWp8LlJzxwoMCjlhq8q7Juztq8SG8Wr0y7FaJWlkXn5ouL5vcAHVVMlRYiU2Er3p2Fj4OpzrOYjTnS8izp2EovR1akqNWcqxXq08IavVbMEVKoF6GC+qBITZSRFZwWQl1qq6HpJErcRGy8RFqVaOAr1UFVqVlYBkhJ5cKZfVcKxcl6Ver1EFZOW9jtUlwmOlvzCDHDvkoADZer6K7YN4HvF6AgLqP5k2FUQOClGAUrW6m6zWJEuCW3Xa3VSMDTLCuCCzWbvAAUWkoGrlpV4WVo0AOSBZjip14rBYXYNn4XbFISOxKCJLaMpV1VEMq4MumaccOoiSKt+r7yhV90QTxVpsJHhTUZ5zGS4qvROrcq9E52A99p17GIOj3aa0J13Lwmpti1qqVv4upTDoKmxSSZFR3RpLEUpdD5meYrW4kP6rUCvDBu0SbMAliEOcGs0iI26sSiYsSvJcTnQliRNrrBAXsUD6AplWt1W7AhVYr6bVbdEuV4k0mY4ro0Kkgo6dkiGMDTLCuCArYEKEKIykMKnMu18smUYjBfUioUmvV1eXFlqwjShWyTSaVbmvUDcZmSV1HaxACmZKUuSQhZIiLlg7iy6jQgq0ZapAtJwsNupnxmuyWIkkl1Zgrfq3JKeJ5iLLfoeSHpIokeTpOu1iVZzZbrVziIisKDbHa8YAf3V4VjvwVE2fpkEL+/3M572QQqgtaEAjzqgTmcW+TqKuw4927Ot6AAVYpgqyznX1R64yylBa1/n/Ji6TKd+TlSJa0ai+lpMFstbfTbj+1qPJjm2ejUxMSdMT4Yd1hu1KUqRK36KWoJWaImYX1bTL0p5y8rhJvxRncBxHsAer9S2m/O6kflQz6iHVYaQ4aohafQwuLMMqJGqyLDMRERGZiQkRoiWs2iJFEGV5SDmRykURtuBytXLMYsnJxhpsw4g+rBIszWiAXx9FJnJRhir1/SF9QM0t7kSrGvGhjVUCUf8eW4YyqFa3Ua8HDenIUvPqZai21U9miMwydqJqrb+PFC0Ny/XVaiWa1SYWTZSTexkhYpf+Q9opK8906+1q1Y712LHk1+zRO1WfKzVeJFoykafqOBglWxr1OjSjDqWoQCHKbDOlgYiIyImYEHEYWV2mZhdXmQmHepxACSqQitVhL44nxQyXoVL9W67Kys/ah2eg6ZpaglGKqq3H9rD9XKnuffyhBlb5JsfGhiQSIzVdbbH8uowXa0IK0kxth6w0U4Qy28WFJIFl/NxiSd/bqVLTJ5GIZOShWK3UJckzGYEi742M5Jv4+FocUsmjzSo5bq14igY7xAWZg7FBRhgXZAVMiDiQJx4YtcZUeVuSg94ONGMAfWr0RaSvmsrry+iQZfqqiP4sb5IHI70XhnYTOSU25G96EP3wwjpX8mVEgiyVvRJrVALULH16tyqoKitT2S0uZNUvSSAvJh5k9J8kgmTa4lpcNG3FnwQ9aVK8yMi9Ezig3qtizbxVi6zA6nFB5mFskBHGBZnNeZcvHE5Wl1mxjavMLJYMwX4Zf0c/elW192gOIY/kz5Lq3uU3FLLKNzkuNuTk9xSOIg9FptfpmEhGhcioBDnpNmuqihSZlZP81dg6rQ12iAuZNjOMQdTqh9Gqn1O3Nr0R7XoT2vVmdOjN6NRb1LLCXXobWvVGnNSPqD5eliWXJcpXamsNlz+W6YiyjPkBfRf26c+o1cXKsc7xyRA7xAWZg7FBRhgXZAUcIUI0T3LwfBan1EGylU6ciGhxpBhyNfarxEMxVsJKZDpcmV6JDrSoUQqzJXRktRI58ZcpP6nIWHICZWyqSKuaFmI0OsIuZMqKFKTtQYcaASRTXc6Xhz3/n7jwldRiykGBSqTMNd2lRCtHCcpV4WpVzNom9VWIiIhoMiZEiOZhUO9HHWpUMkSWd5zrJGsAvUhECjxhri1CROHh131qFRKpAZGvlcCKpA/pQ/X411Js2YcRJCNN1a9oQK2qVxGHBCQiSa1mIiM61uoXIUlLWfDPk5ET53AGOgJqCfGN2Gn7gqCSqJCE0mxJpaWY6/OAiIiIrI1naw7EgqoLV4ODKEOlWmFAVn3xwQc/Jt6P/VtWepFbElLVYwv1MrXKgB2uHgZY0IocEhtBPYCDeF5NcUjXsmFVMjIjQ89R0zLE2HpScSrhKgVDN+GyaSfkp/XjasRIEhaWEGnUz6AHnViLbfNOgsRaXFB4MC5oJowNMsK4ILMxIeLQVWZoYaQwXzua1cmIB3Hwwqu2pah/Xdg2ccldGSkiV3DlxGudfrGlp9lIde/qhxrMbgZZUCzGhkx+y0eppZMhIWXaKhTopWo6zHymrqhE7JQVYeZ8jt6riojKCLj5Jm9jMS5o6RgXNBPGBhlhXJAVMCES4/zVtZM3aEByfgIGWoZl6rTiqaowpW12Ib+fdahY2O/5/Pz1MqxCvJ6oijauwqZ5/ay5GP2sJTOIi1hltXg37T13cGx0oEkVCrWLeC1xXo8b0HvVFJqEeT5eSA2ME9iPddi+sJFsMRgXFAaMC5oJY4OMMC7IArjKjMO43BrKrspT9xT55Srr9GqcxUmUzpFQMRvjgpwSG7KiiKwQYvfaGEZk1ZPlqFpQMkTqqKjErZbg6Lig8GBc0EwYG2SEcUFWwBEiRGEmU2WOYe/5FQsKVTJk4lQaIoo+WTlFVonqQTvWYBtisUisKriqpc7r8cP6kOqnlqES2Vp+xNtHREREZEVMiBCFyYg+hHrUoBddKEUl8rQis5tERBOW15W1WBY8NcQmpOBqaCHZuRIn53BaLatbiQ1I0dKj0j4iIiIiK2JCxGF0HRjp9al7Ch9ZrlKSIeVYj5VYZ7ulGBkXFKuxISNDjuNlZCMf+VopYjHZU48T6EI7slEwa32RBpzEMAZQgDJsxmVLSgzZPS4oMhgXNBPGBhlhXJAVMCHiMHpAx8nHm8xuRkyREy5JhmzG5aqQqh0xLihWY+M0jiEFGTGZDBEncABpyMQm7ByfmhfQZTHwEYxgCN1oV8kSWRWrBCuRqmWE5efaPS4oMhgXNBPGBhlhXJAVMCHiNBqQsSIF3af7Wc05TAZGO5GMNNsmQxTGBcVgbDTqdfBhFCu1tYhFg3q/KtostYpk2dzQ+yPTZ+IQr27pyFZT+MI+as3GcUERxLigmTA2yAjjgiyACRGHkSrORduz0Fs/oNb+pqWLhXoEjAuKtdiQ1WQkWbAVVyBWJWkpuEa/FRpcUe+H7BoXFFmMC5oJY4OMMC7ICmx8SZvIGhI9aRhCv9nNIKLz+vVetQRtOdbiAHahW29HrJJpMrGQlCUiIiIyA0eIkKP5q2uXVMywGQ1owhksK9oJT1oFosFTVRHR/bKz+fxuKPZr+tTgANbhYsRrCUjRM3AIz+MiXG1204iIiIjIYpgQcRip4tzfPMxqzkswMRGSjxJsweWIS6uCnTEuKFZiowPNqm6GJENEP7qRhXyzmxVz7BYXFB2MC5oJY4OMMC7ICpgQcWA15/qnW81uhm2vPMviuhMTIaFVHeyOcUGxEBuhFZ82Yuf4tkzkoQ416NE7ka5lmdq+WGKnuKDoYVzQTBgbZIRxQVbAGiIOIwuh5K5PV/c0f8P6EA7iefgwohIhJVp5zCRDBOOCYiE2hjCAJKTCo3nHt8nqTxtxCWpxSC1HS86LC4oexgXNhLFBRhgXZAUMP4fRXNr5jodF+OarXW/CUexBBdajTFsVU4mQEMYFxUJsyBK7stTsVJIgkaVpO8GrUE6MC4oexgXNhLFBRhgXZAVMiBDNsXRnI85gMy5HspZmdnOIaI6EiNcgIRLQA2hHEzKRa0q7iIiIiMiamBAhmkGX3oY6VKvVKmTYPRFZmxdxGMaAYaHVbBRMmkpDRERERMSzPIfRg0DXqX51T8Z8+iiO6S+rlWTWYwfcWuzXHmZcUCzERhoy0Y9ejOjDk7bLqjMD6MMBfRda9XOq+Co5Jy4oehgXNBPGBhlhXNjHe9/7XqSnp+POO++c93Oqq6tx2223oaioCGVlZXjHO96B9vZ2WE3sn+nRJHpQR9PuTrObYUlyktSCsziHUyjHOmRoOXAKxgXFQmxomoZKfQOOYS826Zeqr4UswbsOF6lkp6xC04U2rNI3jX+fYjsuKHoYFzQTxgYZYVzYw8svv4yf/exnSEhImPdzzp07h+uvvx5paWn4zGc+g/7+fnzzm9/E0aNH8Ze//AVxcXGwCiZEHEaKFhVclInml7pUJ+R0nqoKdT/k68Hxlj8jNSEfF2e9GS6Xs/40GBcLN7gic87HJJ3umvMx/upaWJndYiNFS0emnoNWnEU+Sid9z6vFqWTnOf20Spqs0bcxKeKQuKDoYFzQTBgbZIRxYY8Lxh//+Mfxhje8AX//+9/n/bz//M//xODgIP72t7+htHTseGzbtm245ZZb8NOf/hRvfetbYRWcMuMwUgojc2UKl7cCMDDaibrOl7D/3KM41vIUKnNfgYqcyxyXDBGMC4ql2ChBBZpQP+P3i7UVanneVpyLartiiR3jgiKPcUEzYWyQEcaF9f3iF7/AsWPH1CiPhfjtb3+L6667bjwZIq6++mpUVFTg4YcfhpUw/MixjjT/AUlxmVhXcB22ltyOlPhss5tERGHgnsfS2MVYgeZZkiZERERETtbX14d7770XH/rQh5Cfnz/v5zU2NqKtrQ1btmyZ9j0ZJXLw4EFYifMuhVuYHz4gwqPFXLqGEd8I/LoPwVBhwcAInDb0q3OoHr7AMDITS9Q2f4z9DuT9XXJc2FEU30e/b3jux8yjPQt9r6LNjrExdH6lmdl+txo0BODHqD4MF+ZOoJD944Iij3FBM2FsUCTjQp1DxcB52kL3VxIWE8XHx6tbuHz5y19WdUPuvvvuBT2vublZ3RcUFEz7niRWurq6MDIyEta2LgUTIhYgRWUkOJ5teTzyP8wP/OWhKdtq4FhP1XzN7CZYg1Fc2FE0Y9kpfzc2jo2n8eicj/k7fheVtsQcG8cFRRDjgmbC2KAIx4WcS0WiUGdUz9MWIDk5GWvXrp207ROf+ATuueeeaY8NBoMYHR2d1+vGx8er+mq1tbX47ne/i/vvv3/BiYvh4bGLhkbvR6gw69DQEBMiNDkwZOjQfAOViIiIiIiIMH7yvZBVUOx+niYj3qcWhp8pwfDcc8/hxhtvnNfr7tmzB6tWrVKFVHfs2IGbb755wW0LvQ9Gv7NQsiQxMRFWwYSIRUjgROKPmIiIiIiIiJx5niYJju985zvzemx+fr5aGeapp57CAw88gLq6uvHv+f1+NbJDtmVmZqoldY2EpsqEps5M1NLSop5rldEhggkRIiIiIiIiohgkSY43vvGN83782bNn1f2b3vQmw4KpGzduxL//+7/j3e9+t+Hzi4qKkJOTg3379k373t69e7FhwwZYCRMiRERERERERIQrr7wSP/3pT6dtf//736+W0f3IRz4yqX7JqVOn1P3KlSvHt9100034+c9/rpIrJSVji1g8/fTTqjbJTIkUs2g9PT0WqZdLRERERERERFazYcMGrFmzBg8++OC07eLQoUPj2yQRcsUVVyA9PR3vete70N/fj2984xsoLi7GX//6V0tNmXGZ3QAiIiIiIiIiig0lJSV4/PHHsWLFCnz2s5/Ff/3Xf+HVr341HnnkEUslQwRHiBARERERERGR43CESIyQAjdvf/vbsXz5clU4Z+fOnXj55ZcnVfS9/fbbUVVVpeZ9yXrUIa997WvVcKaptw984AMm7Q0thAxTM3r/PvzhDxs+XuYETn1sXl7epMcwXmJfX1+fWq9+/fr1qs941atepQpdhTAGYocst/f6179evZfyPv3ud7+btnTfF77wBVWFXmJB5v2ePHly1teUYmpT3/+LLrpo0mNqamrU1SAZXvsf//Ef8+q3vvrVr4ZxzykSMePz+fCZz3xGHWcUFhaqx/zLv/wLmpqaJr3G7t27cfnll6v3+sc//vGk7xm993J76KGHoraPtPg+Q/7+5e9d3v9ly5apPuOll16a9TXZZzg7ZmTKxNT37rbbbpv0GPYZZBYWVY0BXV1duO6669Q8rV//+tfIzs5WB7MZGRnjj/m3f/s3bNmyRQ1Z+tznPqce9w//8A/j33/zm9+MT37yk5Ne10rrQ9PMZB5eIBAY//ro0aO45ZZb1G0mskzWxIOXqeuYM15i33vf+14cO3YM3/ve99TyaDIfVGLmxRdfVNXBGQOxY3BwUCW+pFq8UcX4r3/96yoO7rvvPpSVlankyK233qoOTmdbZlBOWh599NHxrz2eyYcUkki78847sW3bNnzwgx9URdp27Ngx/n2JH4mjiVJSUpa4txTpmJHvHThwAB/96EfViUt3dzc+/vGP4w1veINaqjHkPe95j3qPpX+RhMk111wzXlhPyBKQ11577aTXlhMcsn6fUVFRoRIWchFueHgY3/72t1WfIStKyMoSM2Gf4dyYEfL3PnHp17i4uEnfZ59BZmFCJAbIwawUqJnYyciH1ERywHL11Vdj3bp16nvy9URJSUnqyiDZz9SDj6997Wtqvp5k2WciCZDZ3m/GS2yTNeR/+9vfqurfl112mdp2zz334IknnsD999+PT3/604yBGCKjf+RmREaHSCJETkRk5I/47ne/i8rKSnWF74477pjxdeVkZq5+RJJqcpAsV5J7enqmncgwhuwXM3ICMvGkVsjJsZy8NDQ0qBUIQidImzZtQm5urrpAIwX1pr4O33/7vf9iYnJcfPGLX8RPfvITHD58GFddddWMz2Of4dyYEVI3Yrb3j30GmYVTZmKAnMTIB8g//dM/oby8XJ0I/+hHP5r0GMm0f+xjH1OdjFzZ+cd//EfT2kuRMzo6il/+8pcqOz911MdE8iEjBxyyZJbEgowUmIjxEtv8fr8aVTS1qJWM8HjhhRfUvxkDznDmzBk1PWriSUxoKPuePXtmfa6MRJTh0Rs3bsT//b//V50MTyRX+m6++WZ1tc/lcuGVr3xlxPaDzNXb26s+cyZerZX+Y/v27SpBcvHFF2P16tWmtpEid9whx5zy3odWmpgJ+wxne/bZZ9V5SmgEUGdn56Tvs88gs7CoagwI1X+4++671ZB3qR0itQFkpMBdd9016SSoo6NjWnZVrgrKMPmpQ9dk5IkMXST7+M1vfqMOMo4cOaKurhiRYfByUCJX/uUg9pvf/CZ27dqlToRlpFEI4yW2yZUceQ9/8IMfqD5E5uG+853vVGvIh2qJMAZij5y0SB2hG2+8UX0t76PM2a+urlYnISEyLF1OcKcm10P+9Kc/qcSqjCRpbm7Gl7/8ZVVD4vnnn0dqaur440ZGRlS9mqkj2eTESZ7n9XonbZc4vPTSS8O81xTOmJlKpkxIDEkNGulPJhoYGFAnzJmZmdNeU6Zjud3uSdslHkMjTMja7/8f/vAHvO1tb1NX9aXvkMfIie5M2Gc4O2bkfZKRpTIt8/Tp0/j85z+P5ORkPPXUU5P6AfYZZAZOmYkBUuxQRojce++96msZbiZX/P/nf/5nUkJktqGKchIztQjn1EKbZH0yZFVOdGdKhgjJvsstRObnSib+hz/8IT71qU+Nb2e8xDapGSHzdeUKjBxgSL8h0yP2798//hjGAM1k4tBoGW0mI0rkhOXhhx9WoxVDZBTSTMvrve9975v0GSWkfg3ZhxRYfctb3qKmXhkVt5QTHrkZkWkWU6dXzPbZRdYideueeeYZdZVfEqcSB3/5y1/UqEIj7DOcbeL0S7kgJ7fNmzerGJrYD7DPIDMwIRIDJDMvQxAnkis1UiNgvqTIpgxjI/uqr6/H008/jQceeGBBz5OrLTJ89dSpU/N+DuPF/mQkiKwPL1dj5Gqc9CNyQDu1/tBMGAOxIZTEam1tnTRCpK2tbc7h7xPJfG+Jh4X0I1IAnDFk/2SITHt47LHHVJ+wEJJs5ftvX3LSKu+f3OSiilyYk5VBZlrhbir2Gc4mte7k/ZT3f7a6MxOxz6BIYQ2RGCBX+GtraydtkykRHELmLDI8Ua7MyIpDCyG1JGRlmoknQ+Ssg1p572W1Krm6d8MNN5jdJIqi0FLtE1cHkal0sgqVnOTMlwyFl2HQ7EeclQyRYw0psJqVlWV2k8gCo5VlqsN8sc9wtnPnzqnRRXz/yQo4QiQGvPvd71bzd7/yla+oZc+khogMX/yv//qveb+GzAGVwnoTSX2AqXP4yLoHIpIQkcKXU5exk6XLZEihLJ8qZN6uDFWVEQJSwf0b3/iGusI3ccjqXBgv9ifzdkPLJ8oVms985jNqbvdMy+VNxRiwDznxmHgVtq6uDgcPHlTvlSTO3/Wud6lVQuTKW2jZXTlInTj/+3Wve526veMd7xgvfnj99der58u8fhnKLFOvZluVxqhdU2NICvsudKQBRTdmJDbk80KKLUsRb0mqh95H+f7U2kIzkc+fqe+/rCIy03B5ssb7L8kvOd6U5LkkU6XOlNSOkXogUscuhH2Gs8wWM3L70pe+pArmyqhESYTJMYcchy6kcC77DIoUFlWNEVLc6nOf+5y6WiMHtFJgVa7ezIcUSJTKz1NJJyVFOsn6/vznP+O2225TxTDlBHfq+7ts2TK1tGZoeVUZ3iwfKjJkVeZwSu0QqSExH4yX2CDvlfQZjY2N6mDlpptuUsvtTlwlYiaMAXuROdpGBTFlLr70C1L/QU5OJJEuB5yXXHKJqgcxsS+R6TPyeOk/xFvf+lZVjFmu8EnxQ3mOxI8c4M6HvJ5M85tKXleK85J1Y0aKtss0SyOyVLPUlpjLTP2M1EL70Ic+tIgWU7TefynY/8///M/qeEOSIZIg2bp1q1q6e2JRVfYZzjJbzMjnidxLgkQ+Y+Qi3dVXX62OPedbe4x9BkUSEyJERERERERE5DisIUJEREREREREjsOECBERERERERE5DhMiREREZFsyt1zq2phFitJKG6SIIBEREdkLEyJEREQOJyfzclIvxZljAZMURERENB9cdpeIiIhsa8+ePWrpTSIiIqKFYkKEiIiIbGvVqlVmN4GIiIhsilNmiIiIaN7q6+tx9913Y/Xq1cjJycGaNWvU1w0NDdMeK7U9ZOqKz+fDv//7v2PDhg3Izc3F1q1b8f3vf9/w9Ts6OvC+970P5eXlKCgowFVXXYXHHnsMP/3pT9Vryf1sNUTkZ/zsZz9T/964caP6/sTHhKYHybSahdQkOXbsGO68804UFxejtLQUd9xxB44ePTrr7+r3v/89Xve612HZsmXIy8vDJZdcgm984xsIBAKzPo+IiIiigyNEiIiIaF5qa2tx3XXXob29Hddff71Kikii4IEHHsAf/vAHPPnkk6ioqJj2vLe97W14+eWXce2118LtduPhhx/GRz7yEXi9XrzlLW8Zf1x/fz9uuOEGHD9+HDt27MCll16KxsZG9fxXvvKV82qjJDokIXLo0KHxWiJCkhKLJYkP2W9pnyQ4JFmzd+9etW39+vWGz/nsZz+Lr33taygqKlLPkXbs2rULn/70p/HSSy/hxz/+8aLbQ0REROHBhAgRERHNywc+8AGVDPn617+Ot771rePbZbSHJDg++MEPqtEcU0lS4/nnn0daWpr6WhIVMlriW9/61qSEiLyuJENk23/913+Nb7/rrrtw8803z6uN7373u1UyJJQQKSsrW+JeAx/96EfR29ur9lNGiYR87nOfw1e/+tVpj//LX/6ikiGSxPnJT36C5ORktV3XdXzoQx/C//zP/+DRRx+d9z4RERFRZHDKDBEREc1JpsQ888wzalTIxCSG+Od//mdVy+Pvf/87zp49O+25995773gyRFRWVqqESE1NDfr6+sa3//KXv0RcXBw++clPTnq+TJu55pprYNZ+P/vss2okyMRkiPjwhz88PgJlotB0IEnqhJIhQtM0NXJE7h966KEotJ6IiIhmwxEiRERENCcZcSEuu+wydUI/kcvlUtNbTpw4oR5XUlIy6fubN2+e9noylUT09PQgNTVVjcCQ+iSScJF6G1NJAkVGXkTb4cOHx3/+VCkpKapOiSSKpq58I4kQmUpkRFbFkWQQERERmYsJESIiIppTaCSHUbJCSAHUiY+baOLokBCPZ+wQJFRgNPQ8KbpqZKbtkSaJmtl+vtH2rq4u+P1+fOlLX5rxdQcGBsLYSiIiIloMJkSIiIhoTjKKQ7S2thp+v6WlZdLjFvv6bW1tht+faftCyWgWIQmLqWS0ykzJnIW0S/ZFRtGcPn06DC0mIiKiSGENESIiIpqTLGcrZKUUKQ46kXwt2yc+bqEk8SArwZw6dcowyfDiiy8uOOlhtLxtqOZHU1PTtO8dPHhw2rbQKjIvvPDCtO/JqjNGz7nooovQ2dmJkydPzrvNREREFH1MiBAREdGcSktLccUVV6hldmXllIl++MMforq6GldeeeW0+iELIUVLR0dH8cUvfnHSdqnR8ec//3ner5OZmanuz507Z5h4kaKusurNxISFTNmRVWOM9lvqpkgtkQcffHDS9/7zP//TcFTJO9/5TnV/9913q8SI0Wga+X0RERGRuThlhoiIiJSjR4+qpWqNSBJBlpK97rrr8L73vQ9/+MMfUFVVpZbJffzxx5GTk6O+v9RlfX/729+qZWkl8bJz5061ZO/DDz+M66+/Hk888cT46I/ZvOIVr8A3v/lNvP/978dNN92kCpxKYuMNb3iD+v573vMe9b1XvepVuOWWWxAMBvHUU09hy5Ythq/3la98Re33v/zLv+B3v/sdysvLsXfvXuzbt08Vkw2Njgm59tpr8bGPfQz/7//9P1VQVr6Wny/JEZlGI4//1Kc+pX5/REREZB4mRIiIiGh8GsnPfvYzw+9dfvnl+NCHPoSnn34aX/7yl1UC4cknn1SJkDe96U34+Mc/rqa8LIXU3pCkhyxNK0kWSTjIqjP3338/zpw5o743nxolkuj4/Oc/j//93//Ft771Lfh8PtX+UEJElg2Wbffddx9+/OMfq4Kwd911Fz760Y+q/Zlq7dq1al9l+WAZqSKr3ciqM7JNEi9TEyJClg6WZMn3vvc9/O1vf1MjSbKyslBWVoZ77rln2hK+REREFH1aT0/P5InARERERBbz9re/XU1Z2b17N0dWEBERUViwhggRERFZRnNz87Rtzz77LH7961+raTtMhhAREVG4cMoMERERWcYdd9yBxMREtVpNUlKSKj4q03PcbreqyUFEREQULpwyQ0RERJbxne98R02NkeKjsqytLJMr9TqkfoksZ0tEREQULkyIEBEREREREZHjsIYIERERERERETkOEyJERERERERE5DhMiBARERERERGR4zAhQkRERERERESOw4QIERERERERETkOEyJERERERERE5DhMiBARERERERGR4zAhQkRERERERESOw4QIEREREREREcFp/j+bnXSvCUVzeAAAAABJRU5ErkJggg==", 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