From f717435232a10c38b07e5acae7ed30bbf5341768 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 30 Jan 2023 02:06:06 +0000 Subject: [PATCH] Update validation code, test retry of tide model file access --- coastlines/raster.py | 10 +- coastlines/validation.py | 38 +- notebooks/DEACoastlines_generation_CLI.ipynb | 51 +- .../DEACoastlines_generation_raster.ipynb | 47 +- notebooks/DEACoastlines_utils.ipynb | 232 +- notebooks/DEACoastlines_validation.ipynb | 12197 ++++++++++++++-- 6 files changed, 11094 insertions(+), 1481 deletions(-) diff --git a/coastlines/raster.py b/coastlines/raster.py index 943a0eb..11c4d5f 100644 --- a/coastlines/raster.py +++ b/coastlines/raster.py @@ -565,8 +565,14 @@ def generate_rasters( # Add this new data as a new variable in our satellite dataset to allow # each satellite pixel to be analysed and filtered/masked based on the # tide height at the exact moment of satellite image acquisition. - ds["tide_m"], tides_lowres = pixel_tides(ds, resample=True) - log.info(f"Study area {study_area}: Finished modelling tide heights") + try: + ds["tide_m"], tides_lowres = pixel_tides(ds, resample=True, directory='blah') + log.info(f"Study area {study_area}: Finished modelling tide heights") + + except FileNotFoundError: + + log.exception(f"Study area {study_area}: Unable to access tide modelling files") + sys.exit(2) # Based on the entire time-series of tide heights, compute the max # and min satellite-observed tide height for each pixel, then diff --git a/coastlines/validation.py b/coastlines/validation.py index cbfe7a3..6fe580d 100644 --- a/coastlines/validation.py +++ b/coastlines/validation.py @@ -1800,20 +1800,10 @@ def nearest_features(input_gdf, comp_gdf, cols='erode_v'): return out_gdf - # Optionally load either Smartline advanced or basic data - if advanced: - - # Join advanced Smartline data - smartline = gpd.read_file('../input_data/Smartline.gdb', - bbox=bbox.buffer(100)).to_crs('EPSG:3577').rename( - {'INTERTD1_V': 'smartline'}, axis=1) - - else: - - # Join basic Smartline data - smartline = gpd.read_file('../input_data/Smartline_basic/smartline_basic.shp', + # Load Smartline advanced data + smartline = gpd.read_file('https://dea-public-data.s3.ap-southeast-2.amazonaws.com/derivative/dea_coastlines/supplementary/Smartline.gpkg', bbox=bbox.buffer(100)).to_crs('EPSG:3577').rename( - {'erode_v': 'smartline'}, axis=1) + {'INTERTD1_V': 'smartline'}, axis=1) # Identify unique profiles (no need to repeat Smartline extraction for each time) inds = [i[1][0] for i in val_gdf.groupby('id').groups.items()] @@ -1857,9 +1847,23 @@ def deacl_validation(val_path, maxx, miny = val_df.max().loc[[f'{datum}_x', f'{datum}_y']] bbox = gpd.GeoSeries(box(minx, miny, maxx, maxy), crs='EPSG:3577') - # Import corresponding waterline contours - deacl_gdf = (gpd.read_file(deacl_path, - bbox=bbox.buffer(100)) +# # Import corresponding waterline contours +# deacl_gdf = (gpd.read_file(deacl_path, +# bbox=bbox.buffer(100)) +# .to_crs('EPSG:3577') +# .dissolve('year') +# .reset_index()) + +# deacl_gdf = (gpd.read_file(deacl_path, +# bbox=bbox.buffer(100), +# layer='coastlines_v0.0.2_shorelines_annual') +# .to_crs('EPSG:3577') +# .dissolve('year') +# .reset_index()) + + deacl_gdf = (gpd.read_file(deacl_path, + bbox=bbox.buffer(100), + layer='shorelines_annual') .to_crs('EPSG:3577') .dissolve('year') .reset_index()) @@ -1958,4 +1962,4 @@ def deacl_validation(val_path, 'error_m']] # Export data - results_df.to_csv(out_name, index=False) + results_df.to_csv(f"data/validation/processed/{out_name}", index=False) diff --git a/notebooks/DEACoastlines_generation_CLI.ipynb b/notebooks/DEACoastlines_generation_CLI.ipynb index a74adc8..256eb0d 100644 --- a/notebooks/DEACoastlines_generation_CLI.ipynb +++ b/notebooks/DEACoastlines_generation_CLI.ipynb @@ -88,17 +88,17 @@ "metadata": {}, "outputs": [], "source": [ - "study_area = 424\n", - "raster_version = 'testing'\n", - "vector_version = 'testing'\n", - "continental_version = 'testing'\n", - "config_path = 'configs/dea_coastlines_config.yaml'\n", + "# study_area = 424\n", + "# raster_version = 'testing'\n", + "# vector_version = 'testing'\n", + "# continental_version = 'testing'\n", + "# config_path = 'configs/dea_coastlines_config.yaml'\n", "\n", - "# study_area = 9\n", - "# raster_version = 'development'\n", - "# vector_version = 'development'\n", - "# continental_version = 'development'\n", - "# config_path = 'configs/dea_coastlines_config_development.yaml'" + "study_area = 9\n", + "raster_version = 'development'\n", + "vector_version = 'development'\n", + "continental_version = 'development'\n", + "config_path = 'configs/dea_coastlines_config_development.yaml'" ] }, { @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "47397eb8-ddec-46c6-9f7c-431a50bb7eae", "metadata": {}, "outputs": [ @@ -198,17 +198,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "2023-01-25 02:09:06 INFO Study area 424: Loaded study area grid\n", - "2023-01-25 02:09:09 INFO Study area 424: Loaded virtual product\n", + "/env/lib/python3.8/site-packages/distributed/node.py:180: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 38341 instead\n", + " warnings.warn(\n", + "\n", + "2023-01-30 02:03:44 INFO Study area 9: Loaded study area grid\n", + "2023-01-30 02:04:00 INFO Study area 9: Loaded virtual product\n", "Creating reduced resolution tide modelling array\n", "Modelling tides using FES2014 tide model\n", - "Reprojecting tides into original array\n", - "100%|████████████████████████████████████████| 135/135 [00:01<00:00, 114.61it/s]\n", - "2023-01-25 02:09:36 INFO Study area 424: Finished modelling tide heights\n", - "2023-01-25 02:09:36 INFO Study area 424: Calculating low and high tide cutoffs for each pixel\n", - "2023-01-25 02:09:36 INFO Study area 424: Started exporting raster data\n", - "2023-01-25 02:10:44 INFO Study area 424: Completed exporting raster data\n" + "2023-01-30 02:04:00 ERROR Study area 9: Unable to access tide modelling files\n", + "Traceback (most recent call last):\n", + " File \"/home/jovyan/Robbi/dea-coastlines/coastlines/raster.py\", line 569, in generate_rasters\n", + " ds[\"tide_m\"], tides_lowres = pixel_tides(ds, resample=True, directory='blah')\n", + " File \"/env/lib/python3.8/site-packages/dea_tools/coastal.py\", line 592, in pixel_tides\n", + " tide_df = model_tides(\n", + " File \"/env/lib/python3.8/site-packages/dea_tools/coastal.py\", line 336, in model_tides\n", + " model = pyTMD.model(directory, format=\"netcdf\", compressed=False).elevation(model)\n", + " File \"/env/lib/python3.8/site-packages/pyTMD/model.py\", line 842, in elevation\n", + " self.model_file = self.pathfinder(model_files)\n", + " File \"/env/lib/python3.8/site-packages/pyTMD/model.py\", line 1425, in pathfinder\n", + " raise FileNotFoundError(output_file)\n", + "FileNotFoundError: ['blah/fes2014/ocean_tide/2n2.nc', 'blah/fes2014/ocean_tide/eps2.nc', 'blah/fes2014/ocean_tide/j1.nc', 'blah/fes2014/ocean_tide/k1.nc', 'blah/fes2014/ocean_tide/k2.nc', 'blah/fes2014/ocean_tide/l2.nc', 'blah/fes2014/ocean_tide/la2.nc', 'blah/fes2014/ocean_tide/m2.nc', 'blah/fes2014/ocean_tide/m3.nc', 'blah/fes2014/ocean_tide/m4.nc', 'blah/fes2014/ocean_tide/m6.nc', 'blah/fes2014/ocean_tide/m8.nc', 'blah/fes2014/ocean_tide/mf.nc', 'blah/fes2014/ocean_tide/mks2.nc', 'blah/fes2014/ocean_tide/mm.nc', 'blah/fes2014/ocean_tide/mn4.nc', 'blah/fes2014/ocean_tide/ms4.nc', 'blah/fes2014/ocean_tide/msf.nc', 'blah/fes2014/ocean_tide/msqm.nc', 'blah/fes2014/ocean_tide/mtm.nc', 'blah/fes2014/ocean_tide/mu2.nc', 'blah/fes2014/ocean_tide/n2.nc', 'blah/fes2014/ocean_tide/n4.nc', 'blah/fes2014/ocean_tide/nu2.nc', 'blah/fes2014/ocean_tide/o1.nc', 'blah/fes2014/ocean_tide/p1.nc', 'blah/fes2014/ocean_tide/q1.nc', 'blah/fes2014/ocean_tide/r2.nc', 'blah/fes2014/ocean_tide/s1.nc', 'blah/fes2014/ocean_tide/s2.nc', 'blah/fes2014/ocean_tide/s4.nc', 'blah/fes2014/ocean_tide/sa.nc', 'blah/fes2014/ocean_tide/ssa.nc', 'blah/fes2014/ocean_tide/t2.nc']\n" ] } ], diff --git a/notebooks/DEACoastlines_generation_raster.ipynb b/notebooks/DEACoastlines_generation_raster.ipynb index ace4f0a..ce478b3 100644 --- a/notebooks/DEACoastlines_generation_raster.ipynb +++ b/notebooks/DEACoastlines_generation_raster.ipynb @@ -85,7 +85,7 @@ "
\n", "
\n", "

Client

\n", - "

Client-24b8234f-92de-11ed-8bb6-7e1ae5576782

\n", + "

Client-0fa872ba-a040-11ed-80a1-c207350f5a95

\n", " \n", "\n", " \n", @@ -114,7 +114,7 @@ " \n", "
\n", "

LocalCluster

\n", - "

b4fe92ad

\n", + "

ca8bb4ba

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -151,11 +151,11 @@ "
\n", "
\n", "

Scheduler

\n", - "

Scheduler-0bc0dbc0-2854-4706-8208-cc85376de529

\n", + "

Scheduler-6317961d-6539-4a3c-850e-53fadc91c377

\n", "
\n", @@ -126,10 +126,10 @@ "
\n", - " Total threads: 15\n", + " Total threads: 7\n", " \n", - " Total memory: 117.21 GiB\n", + " Total memory: 59.21 GiB\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -174,7 +174,7 @@ " Started: Just now\n", " \n", " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:41271\n", + " Comm: tcp://127.0.0.1:44387\n", " \n", " Workers: 1\n", @@ -166,7 +166,7 @@ " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status\n", " \n", - " Total threads: 15\n", + " Total threads: 7\n", "
\n", - " Total memory: 117.21 GiB\n", + " Total memory: 59.21 GiB\n", "
\n", @@ -197,29 +197,29 @@ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -246,7 +246,7 @@ "" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -311,9 +311,14 @@ "raster_version = 'development'\n", "start_year = 1988\n", "end_year = 2021\n", - "# config_path = 'configs/dea_coastlines_config.yaml'\n", "config_path = 'configs/dea_coastlines_config_development.yaml'\n", "\n", + "# study_area = 8\n", + "# raster_version = 'testing'\n", + "# start_year = 1988\n", + "# end_year = 2021\n", + "# config_path = 'configs/dea_coastlines_config.yaml'\n", + "\n", "# Load analysis params from config file\n", "config = coastlines.utils.load_config(\n", " config_path=config_path)" @@ -771,10 +776,10 @@ " mndwi (time, y, x) float32 dask.array<chunksize=(1, 461, 392), meta=np.ndarray>\n", "Attributes:\n", " crs: epsg:32655\n", - " grid_mapping: spatial_ref
\n", - " Comm: tcp://127.0.0.1:45749\n", + " Comm: tcp://127.0.0.1:43045\n", " \n", - " Total threads: 15\n", + " Total threads: 7\n", "
\n", - " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/40977/status\n", + " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/41143/status\n", " \n", - " Memory: 117.21 GiB\n", + " Memory: 59.21 GiB\n", "
\n", - " Nanny: tcp://127.0.0.1:38899\n", + " Nanny: tcp://127.0.0.1:44175\n", "
\n", - " Local directory: /home/jovyan/Robbi/dea-coastlines/dask-worker-space/worker-s_msvmdw\n", + " Local directory: /home/jovyan/Robbi/dea-coastlines/dask-worker-space/worker-u8bhfcwu\n", "
\n", + " dtype='datetime64[ns]')
  • y
    (y)
    float64
    -4.242e+06 ... -4.256e+06
    units :
    metre
    resolution :
    -30.0
    crs :
    epsg:32655
    array([-4242150., -4242180., -4242210., ..., -4255890., -4255920., -4255950.])
  • x
    (x)
    float64
    3.371e+05 3.371e+05 ... 3.488e+05
    units :
    metre
    resolution :
    30.0
    crs :
    epsg:32655
    array([337110., 337140., 337170., ..., 348780., 348810., 348840.])
  • spatial_ref
    ()
    int32
    32655
    spatial_ref :
    PROJCS["WGS 84 / UTM zone 55N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",147],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32655"]]
    grid_mapping_name :
    transverse_mercator
    array(32655, dtype=int32)
    • mndwi
      (time, y, x)
      float32
      dask.array<chunksize=(1, 461, 392), meta=np.ndarray>
      crs :
      epsg:32655
      units :
      1
      nodata :
      nan
    • \n", " \n", "
    \n", " \n", @@ -890,7 +895,7 @@ "\n", " \n", " \n", - "
  • crs :
    epsg:32655
    grid_mapping :
    spatial_ref
    • " + "
  • crs :
    epsg:32655
    grid_mapping :
    spatial_ref
    • " ], "text/plain": [ "\n", @@ -943,7 +948,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -959,7 +964,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1110/1110 [00:04<00:00, 237.55it/s]\n" + "100%|██████████| 1764/1764 [00:06<00:00, 257.56it/s]\n" ] } ], diff --git a/notebooks/DEACoastlines_utils.ipynb b/notebooks/DEACoastlines_utils.ipynb index 27aa13c..0dbdfcc 100644 --- a/notebooks/DEACoastlines_utils.ipynb +++ b/notebooks/DEACoastlines_utils.ipynb @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -187,7 +187,7 @@ "\n", "\n", "# Load Argo job status\n", - "with open(\"run_status.yaml\") as f:\n", + "with open(\"coastlines_v0.0.3.yaml\") as f:\n", " data = yaml.load(f, Loader=SafeLoader)\n", "\n", "# Get run params\n", @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -274,173 +274,44 @@ " \n", " \n", " \n", - " 0\n", - " 1\n", + " 752\n", + " 753\n", " None\n", - " 2022-10-18 05:32:06+00:00\n", - " 19.390741\n", - " 1287601.0\n", + " 2023-01-25 03:53:51+00:00\n", + " 27.979167\n", + " 1903751.0\n", " succeeded\n", " \n", - " \n", - " 1\n", - " 2\n", - " error code 1\n", - " 2022-10-18 05:29:32+00:00\n", - " 16.520370\n", - " 1096982.0\n", - " failed\n", - " \n", - " \n", - " 2\n", - " 3\n", - " None\n", - " 2022-10-18 10:08:20+00:00\n", - " 40.611111\n", - " 2696951.0\n", - " succeeded\n", - " \n", - " \n", - " 3\n", - " 4\n", - " error code 1\n", - " 2022-10-18 09:34:10+00:00\n", - " 0.148148\n", - " 9838.0\n", - " failed\n", - " \n", - " \n", - " 4\n", - " 5\n", - " None\n", - " 2022-10-18 09:14:43+00:00\n", - " 102.518519\n", - " 6808172.0\n", - " succeeded\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 1291\n", - " 1292\n", - " None\n", - " 2022-10-18 11:19:09+00:00\n", - " 40.057407\n", - " 2660058.0\n", - " succeeded\n", - " \n", - " \n", - " 1292\n", - " 1293\n", - " None\n", - " 2022-10-18 11:09:00+00:00\n", - " 63.111111\n", - " 4191158.0\n", - " succeeded\n", - " \n", - " \n", - " 1293\n", - " 1294\n", - " None\n", - " 2022-10-18 07:23:20+00:00\n", - " 88.946296\n", - " 5906730.0\n", - " succeeded\n", - " \n", - " \n", - " 1294\n", - " 1295\n", - " None\n", - " 2022-10-18 08:35:26+00:00\n", - " 73.814815\n", - " 4901982.0\n", - " succeeded\n", - " \n", - " \n", - " 1295\n", - " 1296\n", - " error code 1\n", - " 2022-10-18 09:09:09+00:00\n", - " 94.409259\n", - " 6269521.0\n", - " failed\n", - " \n", " \n", "\n", - "

    1296 rows × 6 columns

    \n", "" ], "text/plain": [ - " id error finishedAt duration memory \\\n", - "0 1 None 2022-10-18 05:32:06+00:00 19.390741 1287601.0 \n", - "1 2 error code 1 2022-10-18 05:29:32+00:00 16.520370 1096982.0 \n", - "2 3 None 2022-10-18 10:08:20+00:00 40.611111 2696951.0 \n", - "3 4 error code 1 2022-10-18 09:34:10+00:00 0.148148 9838.0 \n", - "4 5 None 2022-10-18 09:14:43+00:00 102.518519 6808172.0 \n", - "... ... ... ... ... ... \n", - "1291 1292 None 2022-10-18 11:19:09+00:00 40.057407 2660058.0 \n", - "1292 1293 None 2022-10-18 11:09:00+00:00 63.111111 4191158.0 \n", - "1293 1294 None 2022-10-18 07:23:20+00:00 88.946296 5906730.0 \n", - "1294 1295 None 2022-10-18 08:35:26+00:00 73.814815 4901982.0 \n", - "1295 1296 error code 1 2022-10-18 09:09:09+00:00 94.409259 6269521.0 \n", - "\n", - " status \n", - "0 succeeded \n", - "1 failed \n", - "2 succeeded \n", - "3 failed \n", - "4 succeeded \n", - "... ... \n", - "1291 succeeded \n", - "1292 succeeded \n", - "1293 succeeded \n", - "1294 succeeded \n", - "1295 failed \n", - "\n", - "[1296 rows x 6 columns]" + " id error finishedAt duration memory status\n", + "752 753 None 2023-01-25 03:53:51+00:00 27.979167 1903751.0 succeeded" ] }, - "execution_count": 219, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tile_status_df" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "metadata": {}, - "outputs": [], - "source": [ - "# Export to geoJSON\n", - "# tiles_gdf = gpd.read_file(run_params[\"tiles-uri\"])\n", - "tiles_gdf = gpd.read_file('https://data.dea.ga.gov.au/derivative/dea_coastlines/supplementary/albers_grids/ga_summary_grid_c3_32km_coastal_clipped.geojson')\n", - "tiles_gdf = tiles_gdf.merge(tile_status_df, on=\"id\")\n", - "tiles_gdf.to_file(f\"coastlines_tile_status_{run_params['result-version']}.geojson\")" + "tile_status_df.loc[tile_status_df.id == 753]" ] }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "79.85277777777777" + "20.575" ] }, - "execution_count": 222, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -451,20 +322,20 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "succeeded 1019\n", - "failed 251\n", - "succeeded after retry 21\n", - "failed after retry 5\n", + "succeeded 642\n", + "failed 118\n", + "succeeded after retry 3\n", + "failed after retry 1\n", "Name: status, dtype: int64" ] }, - "execution_count": 223, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -475,20 +346,20 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "succeeded 78.626543\n", - "failed 19.367284\n", - "succeeded after retry 1.620370\n", - "failed after retry 0.385802\n", + "succeeded 84.031414\n", + "failed 15.445026\n", + "succeeded after retry 0.392670\n", + "failed after retry 0.130890\n", "Name: status, dtype: float64" ] }, - "execution_count": 224, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -499,17 +370,54 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": 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picmTJ7NmzZpDqm9/dt+x/OSTTzo9biqEEKLrbDbbYU2mdfHFF7c/XrmbVqttz+8WCoWor68nEAgwbty4Qz4ffPnllwDcd999Hcrvv/9+oG0Ey96GDRvWfu4DiImJIScn56Dnwbq6OoAO59C92Ww2rrjiivbXOTk5hIeHM3To0A5PLu3+98H2dzixdtfs2bOZPHkyERERHdoy06ZNIxgM8t133wFt59jW1tb2tE0Hsvt92jvV1YHccccd7f9WFIU77rgDn8/XnuYiOzub8ePH89Zbb7WvV19fz5w5c7j66qv3mb5it6+++gqA2267rUP5gdqDXWlz7f7u3XXXXR3K77nnnoNuC23tzOjo6H3GsXc70+FwcPrpp3f4bHJzc7HZbD3azhRCiK6SNsK+Hcq19uFcv65atYrq6mpuu+02TCZTe/n06dMZMmRI+/GVl5ezceNGrr322g79L1OmTGHkyJH7rLsnPqNLL70Uh8PR/np3G+hnP/tZhzzF48ePx+fz7TPdxEAmHb3imLP7ccWWlpZOy/75z38yb968Dnl/9qbT6UhOTj7oPnbt2kVCQkKnzuScnJwuxVhQUMCIESMOuE5+fj6bN2/u0DEdExNDdnY2ANXV1V3aV1dkZGQcdh2pqakdXu8+ER0sz9tjjz1GY2Mj2dnZjBw5kl/96lfdmm31888/Z8KECZhMJiIjI9sfu2lqaur+QRzA5ZdfzkknncRNN91EXFwcV1xxBe+99550+gohRDe1tLQcNLXAgezvnPXaa68xatSo9nzvMTExfPHFF4d8Pti1axcajYasrKwO5fHx8YSHh7Nr164O5T89D0LbubCr+U7V/TzGn5yc3Kmz0eFwdJhnYHcZHPy82xOxdlV+fj5fffVVp7bMtGnTgD1tmdtuu43s7GzOPvtskpOTueGGG9o7UX9q9/t0oA7Y3TQaDYMGDepQtrsdtXfe4muvvZbFixe3f6azZ8/G7/dzzTXXHLD+3d+Rn34nf/qd2VtX2ly76/1pmqnutDNzcnIOOClPfn4+TU1NxMbGdvp8WlpaerSdKYQQXSVthH07lGvtw7l+3R3/vs47Q4YMaV+++7/7Ou/t71zYE5/RT9+P3W2gw2kbDSSSo1cccxwOBwkJCWzatKnTst13evZu3O/NaDSi0fSP+yOhUIiRI0fyzDPP7HP5T3/EDse+ZjVVFGWfF53BYHCfdexvVu/9XbjudvLJJ1NQUMAnn3zC119/zX/+8x+effZZXn75ZW666aYDbvv9999z/vnnc/LJJ/Piiy+SkJCAXq9n1qxZvP322wfcdrf9XSj+9DjNZjPfffcdCxYs4IsvvuCrr77i3XffZerUqXz99ddH1azmQgjRW0pLS2lqaurQ+O/u+WZf56w333yT6667jgsuuIBf/epXxMbGotVqefLJJztMPnUoutKhCId+HoyKigL2fxGyv3oPdX9d3bar58cDCYVCnH766fz617/e5/Ldna6xsbGsW7eOuXPnMmfOHObMmcOsWbO49tpree211zpss/t9io6O7nIcB3PFFVdw77338tZbb/G73/2ON998k3HjxnW5Y7U7emIm+Z4QCoWIjY3tMJJ5bz8dbSWEEL1N2gg9u31/vX7tic+oN9pGA4l09Ipj0vTp0/nPf/7DihUrOOGEE3q8/rS0NObPn09LS0uHUb15eXld2j4zM3OfHdE/XWf9+vWcdtppXT6B9KSIiIh9Pgry07uTPSEyMpLrr7+e66+/npaWFk4++WQeeeSR9o7e/R3/Bx98gMlkYu7cuRiNxvbyWbNmdVp3f3VERETQ2NjYqXxfx6nRaDjttNM47bTTeOaZZ3jiiSd48MEHWbBgQfvoJCGEEPu3e1LNM888s72sJ84377//PoMGDeLDDz/s8Hv/8MMPd1ivO+fTtLQ0QqEQ+fn5DB06tL28qqqKxsZG0tLSulzXgaSmpmI2myksLOyR+nrK7tFCjY2N7Y9/wr4/l/29r5mZmbS0tHTpHGkwGDjvvPM477zzCIVC3Hbbbfzzn//kD3/4Q4eL/sLCQqKjo7vUERkKhdi5c2d7hzLA9u3bATrMBB4ZGcn06dN56623uPrqq1m8eHH7RHEHsvs7UlhYyODBg9vLd+zYcdBtu1Lv7pG5u3Wnnbl8+XL8fv9+J+nJzMzkm2++4aSTTuo3nc9CiGObtBF63sGuX/d3zLvjz8vLa08duVteXl778t3/3dd5rzvnwq5+RqJN/xiaKMQR9utf/xqLxcINN9xAVVVVp+WHe0fnnHPOIRAI8NJLL7WXBYNB/v73v3dp+4svvpj169d3mo1z79guu+wyysrK+Pe//91pHbfbTWtr6yFG3zWZmZls27aNmpqa9rL169e3z9LZU3bnJtzNZrORlZWF1+ttL7NarQCdOmS1Wi2KonS4o1tUVLTP2bitVus+O3QzMzNpamrqkC6ioqKi02dTX1/fadvds3/uHasQQoh9+/bbb3n88cfJyMjg6quvbi/vifPN7hEce5/fly9fztKlSzusZ7FYgM7nk30555xzADp1+O1+0mb69Oldju9A9Ho948aNY9WqVT1SX0/ZnTZgdx5dgNbW1k4jbGH/59jLLruMpUuXMnfu3E7LGhsbCQQCQOe2gEajaZ/p/Kfn2NWrVzNx4sQuH8c//vGP9n+rqso//vEP9Ho9p512Wof1rrnmGrZs2cKvfvUrtFpth7zI+7O7M+LFF1/sUN7V9uD+nH322QD87W9/61Delc5naGtn1tbWdjj23fZuZwaDQR5//PFO6wQCgS79PyKEED1F2gg9ryvXr/u7zh43bhyxsbG8/PLLHc7Dc+bMYevWre3Hl5iYyIgRI3j99dc7pM5ctGgRGzdu7HKsXf2MRBsZ0SuOSYMHD+btt9/myiuvJCcnh6uvvprRo0ejqiqFhYW8/fbbaDSaLuXj3ZfzzjuPk046id/+9rcUFRUxbNgwPvzwwy7n+PnVr37F+++/z6WXXsoNN9xAbm4u9fX1fPrpp7z88suMHj2aa665hvfee49bb72VBQsWcNJJJxEMBtm2bRvvvfcec+fOZdy4cYcUf1fccMMNPPPMM5x55pnceOONVFdX8/LLLzN8+PD2Ce96wrBhwzjllFPIzc0lMjKSVatW8f7773eYPCU3Nxdom5TkzDPPbL8Amz59Os888wxnnXUWV111FdXV1bzwwgtkZWV1yvObm5vLN998wzPPPENiYiIZGRmMHz+eK664gt/85jdceOGF3HXXXbhcLl566SWys7M7JH5/7LHH+O6775g+fTppaWlUV1fz4osvkpyczKRJk3rs/RBCiKPBnDlz2LZtG4FAgKqqKr799lvmzZtHWloan376aYeJPXrifHPuuefy4YcfcuGFFzJ9+nQKCwt5+eWXGTZsWIcLD7PZzLBhw3j33XfJzs4mMjKSESNG7DNv/ujRo5k5cyb/+te/aGxsZMqUKaxYsYLXXnuNCy64gFNPPfXw36gfzZgxgwcffBCn04ndbu+xeg/HGWecQWpqKjfeeGN75+d///tfYmJiKC4u7rBubm4uL730En/84x/JysoiNjaWqVOn8qtf/YpPP/2Uc889l+uuu47c3FxaW1vZuHEj77//PkVFRURHR3PTTTdRX1/P1KlTSU5OZteuXfz9739nzJgxHUZKVVdXs2HDBm6//fYuHYPJZOKrr75i5syZjB8/njlz5vDFF1/wu9/9rtOI4OnTpxMVFcXs2bM5++yziY2NPWj9ubm5XHzxxTz33HPU1dUxYcIEFi1a1D5q+FCfyBozZgxXXnklL774Ik1NTZx44onMnz+/y6Ojrr32Wl5//XXuu+8+VqxYweTJk2ltbeWbb77htttuY8aMGUyZMoVbbrmFJ598knXr1nHGGWeg1+vJz89n9uzZPP/881xyySWHFL8QQhyItBGOjK5cv2ZmZhIeHs7LL79MWFgYVquV8ePHk5GRwVNPPcX111/PlClTuPLKK6mqquL5558nPT2de++9t30/TzzxBDNmzOCkk07i+uuvp6GhgX/84x+MGDFin/Mm7UtXPyPxI1WIY9iOHTvUX/ziF2pWVpZqMplUs9msDhkyRL311lvVdevWdVh35syZqtVq3Wc9M2fOVNPS0jqU1dXVqddcc41qt9tVh8OhXnPNNeratWtVQJ01a9ZBY6urq1PvuOMONSkpSTUYDGpycrI6c+ZMtba2tn0dn8+nPvXUU+rw4cNVo9GoRkREqLm5ueqjjz6qNjU1ta+Xlpamzpw5s/31ggULVEBdsGDBAWN4+OGHVUCtqanZ5/I333xTHTRokGowGNQxY8aoc+fO7fReFBYWqoD6l7/8pdP2gPrwww8fMIY//vGP6gknnKCGh4e3fz5/+tOfVJ/P175OIBBQ77zzTjUmJkZVFEXd+6ftlVdeUQcPHqwajUZ1yJAh6qxZs9qPa2/btm1TTz75ZNVsNqtAh/fr66+/VkeMGKEaDAY1JydHffPNNzvVMX/+fHXGjBlqYmKiajAY1MTERPXKK69Ut2/ffsDjE0KIY8msWbNUoP3PYDCo8fHx6umnn64+//zzqtPp3Od2h3u+CYVC6hNPPKGmpaWpRqNRHTt2rPr555/v8/y9ZMkSNTc3VzUYDB3OU/s6d/j9fvXRRx9VMzIyVL1er6akpKgPPPCA6vF4OqyXlpamTp8+vVNcU6ZMUadMmXLQ962qqkrV6XTqG2+80Wn74cOHd1p/f/sD1Ntvv7399e7Po7Cw8JBiXb16tTp+/HjVYDCoqamp6jPPPLPPOisrK9Xp06erYWFhKtChnubmZvWBBx5Qs7KyVIPBoEZHR6snnnii+te//rX9XP/++++rZ5xxhhobG9u+r1tuuUWtqKjoEM9LL72kWiyW/X6P9ra7XVdQUKCeccYZqsViUePi4tSHH35YDQaD+9zmtttuUwH17bffPmj9u7W2tqq33367GhkZqdpsNvWCCy5Q8/LyVED985//3L7egdpc+/ruud1u9a677lKjoqJUq9WqnnfeeWpJSUmX2laqqqoul0t98MEH27+78fHx6iWXXKIWFBR0WO9f//qXmpubq5rNZjUsLEwdOXKk+utf/1otLy9vX+en343d/y92pb0rhBC7SRuho660EbpzrX2o16+ffPKJOmzYMFWn03X6bX/33XfVsWPHqkajUY2MjFSvvvpqtbS0tFMs77zzjjpkyBDVaDSqI0aMUD/99FP14osvVocMGdKlY+nqZ7S/Onb3f8yePbtD+e7v3MqVKzvtcyBTVPUoyzoshBBCCCHEUebGG29k+/btfP/9930dSr81duxYTjnlFJ599tleqf/ee+/llVdeobKysv0R3kOxbt06xo4dy5tvvtnhEWQhhBDiWDFmzBhiYmKYN29eX4dy1JEcvUIIIYQQQvRzDz/8MCtXruzxXPhHi6+++or8/HweeOCBXqnf4/Hw5ptvcvHFF3erk9ftdncqe+6559BoNJx88sk9GaIQQgjR7/j9/vac+7stXLiQ9evXc8opp/RNUEc5GdErhBBCCCGEEPtQXV3NN998w/vvv8/HH3/MmjVr2ier6YpHH32U1atXc+qpp6LT6ZgzZw5z5szh5ptv5p///GfvBS6EEEL0A0VFRUybNo2f/exnJCYmsm3bNl5++WUcDgebNm0iKiqqr0M86shkbEIIIYQQQgixD1u2bOHqq68mNjaWv/3tb93q5AU48cQTmTdvHo8//jgtLS2kpqbyyCOP8OCDD/ZOwEIIIUQ/EhERQW5uLv/5z3+oqanBarUyffp0/vznP0snby+REb1CCCGEEEIIIYQQQggxwEmOXiGEEEIIIYQQQgghhBjgpKNXCCGEEEIIIYQQQgghBrgBn6M3FApRXl5OWFgYiqL0dThCCCHEYVNVlebmZhITE9Fo5J5sb5D2gxBCiKPJkWo7eDwefD5fj9VnMBgwmUw9Vp/oTNo8Qgx83fmNH/AdveXl5aSkpPR1GEIIIUSPKykpITk5ua/DOCpJ+0EIIcTRqDfbDh6Ph4yMJCor63uszvj4eAoLC6WztxdJm0eIo0dXfuMHfEdvWFgY0Hawdru9j6MRQgghDp/T6SQlJaX9HCd6nrQfhBBCHE2ORNvB5/NRWVlP0a73sNsth12f0+kiPe0yfD6fdPT2ImnzCDHwdec3fsB39O5+9MBut8uPlhBCiKOKPF7Xe6T9IIQQ4mh0JNoOdpsJu818+BWFQodfhzgoafMIcfToym/8gO/oFUIIIYQQQgghxBESCvVMJ6109AohRI+TGV6EEEIIIYQQQgghhBBigJMRvUIIIYQQQgghhOgaGdErhBD9lozoFUIIIYQQQgghhBBCiAFORvQKIYQQQgghhBCia1S17a8n6hFCCNGjpKNXCCGEEEIIIYQQXRNSeyh1g3T0CjEQpf/2iw6vi/48vY8iEfsiqRuEEEIIIYQQQgghhBBigJMRvUIIIYQQQgghhOgamYxNCCH6LenoFUIIIYQQQgghRNdIR68QQvRb0tErhBBCCCGEEEIIIcQAI/lyxU9JR68QQgghhBBCCCG6Rkb0CiFEvyUdvUIIIYQQQgghhOgatYc6elXp6BVCiJ6m6esAhBBCCCGEEEIIIYQQQhweGdErhBBCCCGEEEKILlHUEEoPjMbtiTqEEEJ0JB29QgghhBBCCCGE6BrJ0SuEEP2WpG4QQogeUllWjqqqh7Tt+q9/4POHXzjk7YUQQggx8Ph8Pqqrqw95+yeffJJPPvmkByMSQvSVP//5zyiKwj333NNe5vF4uP3224mKisJms3HxxRdTVVXVd0EKIfo96egVQojDtKu0kI9/mMUrw6/irZ89iKultcNyVVX5dulXBIPB/dbhaWml8rH32fj9it4OVwghhBB9TFVV3nlnNpMnTyMzczgvvfSvTuvUVdfz2dvfHLCenTuL+P3vH+qtMIXYt5Dac38CgJUrV/LPf/6TUaNGdSi/9957+eyzz5g9ezaLFi2ivLyciy66qI+iFEIMBNLRK4QQh2HzjvVsdH5N+CANFqsV//+Ws/Avr+1Zvn09Xy/6nCZrPnX1dfutJ+24YeT8+zZShmQeibCFEEII0Yfuv+9Brr3mbioranG53Nx11y9ZtWp1+/JP35zLf/40m6UfbDpgPVdeeTkPPfT73g5XCNGLWlpauPrqq/n3v/9NREREe3lTUxOvvPIKzzzzDFOnTiU3N5dZs2axZMkSli1b1ocRCyH6M8nRK4QQh6HBVYk1yoirxU1lnIo73cHWkpVEbJxPIORj07YNhEUb0TZoqNZXEhsTu8964tNTiL8p5QhHL4QQQoi+sHLVOgDcbjdZsdGkRETx7Qdv4dDWQsDD/FcWYA5Y0UYceFzO1KlTj0C0QvyE5OjtUbfffjvTp09n2rRp/PGPf2wvX716NX6/n2nTprWXDRkyhNTUVJYuXcqECRP2WZ/X68Xr9ba/djqdvRe8EKLfkY5eIYTohoKS7RRWb2RU2mRio2PJjB9BQ2MikY5oXM9pCBncLP96E57IEhRFwVZlJGlIOFXrQkQPjW6vp6qmgsX5nxNjSmfycaf34REJIYQQordtXz0fT2sTo05ue+T6qaceRq/XYVBD6B95gYXGAENibWTaSgGwB6wEvSoTZpzQoZ7573/HZ88s4Bf/uoqcEYOP+HEIAUhHbw965513WLNmDStXruy0rLKyEoPBQHh4eIfyuLg4Kisr91vnk08+yaOPPtrToQohBgjp6BVCiG4oqF6PPr6V2roqYqNjSYhNIiE2CQBrqRVzgpHMsYl43D7MFiOhYJCGnSoXTrsao9EIwLI1S/h2xcdEJjio8zaj0YEv6GJw/FiSE1L78vCEEEII0cMCgQC6qmWYtbb2shNPHA+0jbQrUEOcFTBS6PLt2cgSYMKVJ3HejecBbTl9X3jsJTZ8upo4m4m8rz6GzVG0en0cd+2tR/JwhBA9pKSkhLvvvpt58+ZhMpl6rN4HHniA++67r/210+kkJUWeHBTiWNFrOXr/9Kc/ceKJJ2KxWDrdgdqtuLiY6dOnY7FYiI2N5Ve/+hWBQKC3QhJCiMMWIoiiKARC/k7LGqpb2fD1VpxbW9FUxFO/QcegyFwunHRdeyfvJ4ve5Nt1H3DC2TkMOT4ZU7iCP7ocJa6R2kaZQVcIaT8IIY5KoSCEfJ2KrVYrW0Iu1pr9VGFlxY4wftig4ZpnruX8284HIBgMcunwuwmuXc//HeflNzlOUqu3kbF+IaYtnUcBCtHrVBXUUA/8HduTsa1evZrq6mqOO+44dDodOp2ORYsW8be//Q2dTkdcXBw+n4/GxsYO21VVVREfH7/feo1GI3a7vcOfEOLY0Wsjen0+H5deeikTJ07klVde6bQ8GAwyffp04uPjWbJkCRUVFVx77bXo9XqeeOKJ3gpLCCEO2Q8rFrA9P5/wWhuaiIYOy1RVpeSdTUS/vomoGybjm9xE5KgQRWs3o1mrUunLw12hx5IQJGNoAq3NHhyRNlwuL64WN67aEMNz0vroyIToP6T9IIQ42sx5+W8o5VX4AyqDz+64rLq0mIi7TmHKqdmsXxkk+fsPiMTPUrcPb/V6tK1lzFljIlhrwZ0RQlEU3IEAAUzUuLy4Mof2zUGJY5ukbugRp512Ghs3buxQdv311zNkyBB+85vfkJKSgl6vZ/78+Vx88cUA5OXlUVxczMSJE/siZCHEANBrHb27c8K8+uqr+1z+9ddfs2XLFr755hvi4uIYM2YMjz/+OL/5zW945JFHMBgMvRWaEEIcEoNVYdSpqdRsDjJx7Mkdlq3euoSoMBsaRaHpre8JXRFGGDG0BhrwRBcSpTOzo7qapJQYgsEItqwqQkHB6olnpPUc4tLj2VW+g1WF84mxpDJu2El9dJRC9C1pPwghjjbptY2klDtZf865Hcpbm500rV9CUko0JpOB/A1fcoIuBGixeksYYY8Gu57fvb4dbSichmY/mxucbPdryLrsWszHn0BuZCSz//4pu9aVccG9Z5E1IqNvDlII0W1hYWGMGDGiQ5nVaiUqKqq9/MYbb+S+++4jMjISu93OnXfeycSJE/c7EZsQQvRa6oaDWbp0KSNHjiQuLq697Mwzz8TpdLJ58+a+CksIIfZLq+hRVZW46ET0en17+cZta6k1bsFLM/Wqh6pY2LmxlB1rKqgsrqN8m5OyrU6a6pqoLGiiptiJQatn+AkZpA2NIyUpFYPBQFH9ZsyJPgqb1rbX3Vhbx7x/vMmrp97K+i8X9cVhC9GvSPtBCDHQKCYTzUDm6DEdyvM/eZOsLfPxu11sWF9IaWUL32FhaWQihZ4AK+t0rHWauPwiF7//VR3ZgxrwVCQzrDEDc9BIZFQUAPP/uZSieTXMm7WnnbA9bwe/vPdxLr/oDhoaOj6FJMRhC6k99ycO6Nlnn+Xcc8/l4osv5uSTTyY+Pp4PP/ywr8MSQvRjfTYZW2VlZYeLNKD99YFmkPR6vXi93vbXTqezdwIUQoifSInLpKgixNgRe+6gq6rKki1zsUebcY4yM3T9ldgjrVQVtJAw2E5A4yUuy4rBZCAYCJCQFd62YUhDKBgiQp/YXpdeYwRc6DDtqfuTr1n68L8whdkwLlzG6HOmHMEjFqL/kfaDEGKgsU2dRuC0aWTk5LSXzf/8O956fhceVyTqu6uhzI5Xo+XWLWcSHm5j+ffbyR0RA4Buk50RZj81kVqsWgMN4XqOy20b7acoChaHGZ8TwmLbJnsLhUI8/sizLFuyHqvNxKaNW5h8sjwpJHqQpG7oNQsXLuzw2mQy8cILL/DCCy/0TUBCiAGnWyN6f/vb36IoygH/tm3b1luxAvDkk0/icDja/2T2SCHEkRIbFc8JIyZ3GM2rKAqRiVYSssKJig8nPDoMjUaD+mO7NT41iuYGNwDBULB9u1AAyvLrGZ41tr0szp6BpjqGMSmnUFlTzv++e5711YvJqNeRWOzF4pJRD2JgkvaDEOJYln7CCWQcf0KHMjWg0LjTgKfSTJglEr3GiCnkoLa2BYDgXiMdQ7q2dkeMDZoCbvxD44mMj21fPvHKMUy4fRgzfnEW//3nbMZmXs727UWUV1eQv7MQZ3PLEThKIYQQorP0337R4U/0vm6N6L3//vu57rrrDrjOoEGDulRXfHw8K1as6FBWVVXVvmx/HnjgAe677772106nUy7WhBB9Squ05QQNqSFCoRBVJXWUFTZgshgwaMIo2LoDV71KZUktRrMRRRMi7+FFjLhiOu6xLkwmE4FAAF2LhpPHnAPAkg3fkDg4gsbKcIKo6FDw1skIRDEwSftBCCE6sthNe14oKoFQgFZNPRvXlwImqssaWb51F1pfkCXLnPzgjKTVaWSnt5J7rz4Lr9eL0WikqamBqVdOICqqreN37mfLcTsVLJF7LvPq6iR1g+hhag+N6FVlRK8QQvS0bnX0xsTEEBMT0yM7njhxIn/605+orq4mNratYTJv3jzsdjvDhg3b73ZGoxGj0dgjMQghRE9orHTh9XipLqvDHmnGGmFmzOgMTs9tmx3XF3qZyBQzra5mErPtAGyLs1H77Je8+ev/4T4xlRirg2BFI4OXzwLAHWhGC8QPjWWZqRVzlJ0Zl0/tq0MU4rBI+0EIIToKC9Nyy4W1WBQ/X+5UUcMisXsjiDVNJCf7JDSNS0j/6l8AbFHiCZYZMGGgMqKZr659g8DIEGfcPRmLwUlIk9De0VtaXA2AzxvAaNQyYsRgpkw5sc+OUxydlFAIpQc6enuiDiGEEB31Wo7e4uJi6uvrKS4uJhgMsm7dOgCysrKw2WycccYZDBs2jGuuuYann36ayspKfv/733P77bfLhZgQYkCJT4rBkhik2RVBVKIdo9mAr3JPmobdI379AV97WZw1DH1BCXVaL3VbC/AbbISirO3L62saCbY6UZUgDlsYo645n+MumHbkDkqIPiLtByHEsSA+LZkIfz3hRgMbHZG0+sNAAy6nBwDFYiUUUtFoFPT6ALtbFUlqNHhUyitKqStaT1MQQoZWsoedAkBqWBQJMVpqvWU4lCE8+eSDpKWl9s1BCiGEEOKI67WO3oceeojXXnut/fXYsW15KBcsWMApp5yCVqvl888/5xe/+AUTJ07EarUyc+ZMHnvssd4KSQgheoVWoweCxKdG0dLoRqvXUFJSyibLevwhLyX5lTTWm/A0B8hfVYHNYaGGVmx2DVFOPWHmSBwFTpqcAVRVRVEU4uPjCca0pVEvibKSMmVMnx6jEEeKtB+EEMeCiMhI8hQN4YBeabsR7At52VVUQH5+LHUt1Xy33YBFb6fO7GPco7GYbBqKt+1kTKadguIoJgUa0Gk1FCgR7fXaNBHU1PjQhplJTtdx/Pix+w5AiMOhqm1/PVGPEEIcpp/m/i368/Q+raev9VpH76uvvsqrr756wHXS0tL48ssveysEIYToVVsLNlDTUkrB9iLCmywoGpXykioS3FG0uBqpCVuLoihEJISRNMROVLoZd7OXmCQH9YmRWJpLQNHhN7T9FGsaPTQ2NBARGUlKdA6rCotAozLk0SsZc9bJfXuwQhwh0n4QQhztXv3Hh1SWNBGsiiBKF069uZ6bXjBgNRmoMmoYlFlHUrLKqnw75qCVyFMVJk6KA0DjbiE9wYhGDVJfWUeswwx+d3vdU68cywfPfU9aSiy/+vuNGAyGvjpMcTQL9VCOXkndIIQQPa7XOnqFEOJoV+usJBhbjT3KRGJOW+5dNCoJg8Jx1rlQFKWt7MeZs212C86atouxkK1ttK5HDVCqtGAfZ8MyKIq5P3zMieNOIT1pEOlJv2jbfuIRPSwhhBBC9KIfvtrMlh9quXikHWOzAVO6iZGZ4QDU1bW1HYxGA4pZAy3gqve0bxuibblOp+GrtVXE2SIodbrYVvYe0++8lCtvO48rbzvviB+TEEIIIfoH6egV4hjU3OJk085VJESlk57UtZnuRWfjh5/Ch/PewK8J7ClU2y7A3G4XBZtK8LT6aahoxeeLR6vVsPOlVdQ3GyiurCQh3ojqDJF1/XHkXJkNQHlBPUXVeaQmyucihBCif6nI30rTjs2kTDoDa5i9r8MZsB58biYP3PQCqrbtpq+rztOeuqm+rpGFX2+gtdZPlb4BTTyUFNfxp3u30doYIJIyGhUVizuIRRNLuNtIoaaFiu83snXiEIYdP6qPj04cE2RErxBC9FvS0SvEMaa6popvN79PfLaNvMraPu/oXb9tFcFgkLHDTtgzAnaAMBgMUOSmZHseBoMeRaNQWVqDqoRwu70MSkzEERlG0fpa0kdHA9BQFMC6oQqjzYOj2YhG0eBr3jNxm6JqaA009tERCSGEEPu2bdFczN99QJbWT4HBRM5p5/ZZLM1Nzaz6agVJg5PIPm5In8VxqDKyUhmf7UVb3Uh0XDg4XSyt1xEeaWHn98WEfaugU7RkJUSRUK+h1a2ydasXjaIl6bgw0v1u0Okp1+vBDarHg1bRUJtfAdLRK4QQoov2zkk7UPPRis6ko1eIfmjpuoUMTh1GdGRst7etqqnC6/HQ1FpPZXUZVocZjUahsdGJIzyMiopKmlqdeDd40QXMcFwvHEAXrd2ynFrLJnxeP/GVySQmJPVdMIco9G0xDotK0lBHW4EKiZl2bJF6fG5/2zrqntEKWocRgLBmFZcSxIaOxtJaNn6TBzV+nASJSffDuCN+KEIIIQawurJySpevZcSMs9Bqtd3aVlVV8vPysJisVOSX4K7ZSXyEEZfbS16xEbPBTGneOuxNVkAhJd1FTu8cRpd88Ng71M2pYOvYMLLfGngdvV6vlxmxtewKRZBV7aYlpEWfaCcjI45tyRX4FCcAqq5txG+CYmGD4sGIFZdXBS2EQiGq/E6cdgV/uJ6aknrCisv78rDEsSSktqcmO+x6hBBC9Cjp6BWiHyrzbKGmoIATgmcRH9O583PzjvU0umoYnpZLuKNtpuXq2kpW7VxAo78Cr9tLxrBESgtryMiKAaC+zokpyk5TTSkpw6Ow2My4y/RH9Lh+qtZTgj5ch0aj4HK39mksh8oyKB7vxtL21+4GP65mL2HhVnaWlFNT2Uh5fh2BQAC9Tke1PUDUyGgMviCVrU1EerU0zCsi+pWdaBSF8NenYU4x4/f70ev79vMRQggxcFRtL8Dx309ZvCWfE397Bzpdx2a+3+9n/XtfojEZGHvRWe1P0RQWrMfdnMe6Ofk0vdOMRTGRNb2OzHAXhXVOVrybhUVrQUlzoVaHA5B0RsSRPrwOin8oxIqJoH9gPvZtMBhotUUS0rQ90aNTNORvayA9PRaNGep8TbTaXAQ8ftRIO3qDgXFnq+QMi2DFikZ+Pd9Es9PHTY8M4qIZWfh8fl45fwW6poH5fogBSFI3CCFEvyUdvUL0Qyoq9iQdS/LmcFHMTQDsKivE6Wyi2LmZBm8FyVkxfLuhmIsm3wDAmsJFWJJ9hJpMuFvaRvKo6p675MrufyoadhdrMR6xY/qpqpoKdmwrRFPiRVUVYrLdB9+oHzrnj7dT9Oc/U7K5EY1GITk2i0HGHNxNrWyuqSR7UjIENGSPSwTAbXNg3pgPgBpnJKLGS2uiFs2PH1DA6SMi3szOXTvIyRraZ8clhBBigAm15XhNXbaVNa+9ywk3Xg3AioWraK5rZumbXzDD6SOEyrIWFxNnXkwoFMLjXEtOVgyb9VosiqlDlRpFAdrOT7vbDoFQAGuk7UgeWQfb12yiKFiN2WLF1DowbxIrikLitb9m7gPPsNhnp9Ht5VfHnc/Oghqis4YRe9UOxkfp+XZdGMk7AuALEpw2iOFDYqipq2fh6y5AhxowoChK28RtVi3Owpq+PjQhhBAD1N5pHH5K0joMLNLRK0Q/UlNXRV7ZOoq3VdLaEE5DtZNl4d+ioLB20yr0NhWNVkN5cRXe5hDOWg8rI79HCWjJm7sevc+Ly+PBP9hKc42PsqKqts5eFarLanG7Y6itdLJ9kZfIKDu3Xn9hnxyn1+tlWcGX2OOMJA2NpWZXM8NzRvRJLIfLYDRy9tWXUWZaRsAfJC2UzeD0tgdamwN1+I3lBAL+9klWlLA9o3S1ZgPgxdPioloLrkEWgmt20lrYQOzVY/voiIQQQgw0O75fQt73Cwg/zooaVCndmofrlS9QFIVPZn2OSWMlpKujOMOIqkLL5vUYP7SAQc9b874hOiacmq21jMpuewpobZGTaquV+pYAUWNrcVj8OH31WBLDsA0dwglnTeyT4yzYsJW8u16jqsGKp87IyScM3IlLoxKTSRg5mYXfbMAxNIz4+AQSEhLJzh5N3q4VaEI1oN0z2jHgbxuBbbOF2tsUdTWtrFmzA2eDm02aSqy+4P52J0TPCqk9NKJXUjcIIURPk45eIfqJYDDI4m1fEp6hISEzgsTMKHwrvXgiSwAIizG254E12BSSMqPZubaK1oidOBta0X9XgfmbYlTVT9qKi4lJCgdg0Ji2PL9mq4nEHAe6ghBr3rdTZtSzelg+p5yWy9oty5n/2RZOPCmXEyf1/iQe3675FEeajubNKqFQiARTTrfzCfYn0RExbK9y4/cFSMzck2rDZohka+lGSgvq8XqDhDvsVGldOEZGo4RUanwtBFMs+Fxe0hdfRGxSJFv+so7Af7aQ9djAyzkohBDiyCvPy8f90psYrV5OuTgZgDf+F6J0/goA0g1mov0WPFk2zhrpBeCb9U5ML31Bjd/NSys+xe12c+O0E7n6pLYnfd7emIhvmx6vz8ANt7iIswdYUmpigq6FXco2Gmur0RnMzPvXu4Q1tzL2lsuJSozv1eN01jey/rF3cIR0aDR+TOFw2S9O69V99jZLWNsI6oh4e4cJaX3WaLbm57O11keNPhy90UTRW9tZYWmipLSJX17VQJxJw466coZVRWMx6vlH0EB8mKOvDkUca1R1zzD/w61HCCFEj5KOXiH6iXXbVmBP+0mhstc/FaVz+Y//tdiMtJo1aA0KePfavkPjqW1lvU6LioqiGvli7lx09kZc0YUUF3rIW7eE0WMysdqsPXRUHQWDQVavX0mZcxueFT5qi1tobUrgxguu6ZX9HQmhUIgF89azy1OJJcxEIC3QviwzOYfHL/kGg3YMY//QQnxqJJ4V1Vg3FgGgDI/Cnl+HWdVCSGn7jO1aLI1+CjdsZdj4PpwpTwghxIBQ/v5nJLr9lIZp2stCqkr7qx+bAsreG6lt7YrWMAPJiQnkFxR0qHP3OL22xA3qno2ANLWF1554hmRHFg0fLyPBoWOL8j6TH7mjZw9s915VFb/fz9cvvEXDrlLKdQFMNh+TLzqdoSOzemWfR0LZzlJKNm1kxBgXerOmw7JAVALp3ihymkPErjSAG0q8VpqLglhCdgZlVjHIbKBFMWExtj0pFGbTUVokqRuEEEKIY5109ArRT9TWV1NRXA8KlO2qxNsSoqqsHp1OD6pKZWkdLlckwWCQ6oo6XA0+KnbVEvAH8LqC3PnuC3xy//9Rvmg9hZsqaKx0UVlSSyDQ1vFYU9GIqzWSxjon2ogiYlNSUUwNrNm6FGuUAX10A2EJ4RTsLGLUqOHdir20Yhd5BVs59cQz0Gg6Xqw0tzhZtOFzist2EpVqo2BzCaNPGozRpCfPW8n4oVNZtOEz0qNHkJk6uMfezyNlxbINfPK/Us66LwKz0UJExJ4JarRaLXq9Bb3WRMjXlkdQte15f1RN22W3SdHhdfrYvnEXxc5aHJk2KncUS0evEEKIgyooq6fEYCavrIHiz72gqNS5akgZYiUYBLfWhdemxRlqYYmzrelf5K7GZTehH5rN2nfWcMopp2GwWVkaaHu6ptFaizoiHK/Pz8drfMTYjRQ5W2jMDicszEJZQTkeg5dWR4h8hxtdacGBQtyv7z+dS9rQZFIHd253lG/ZzJxnn2LSr6ZjMGhwxJZx1g16zHozkSUaxp81mNWvP0futfcc8nvXl3548zvqV5dytt6ALzulwzKrxUK4xYhG2wwYANAoIYKAQaPHq2krC3k81DW7Wb+rBY+rmSbXET4IceySydiE6Lf2zrXb33Pr/jQvcE/FO5Deg94gHb1C9BMaq4/0zGgA9FZIyowmGAzuSb1gMZOQY8fnC+CINRGfFoVWq2PQmFi01fGYTCYuf+FBPB4Pn2/+F9EJDnRaHRk/bm/Ls5GY46Byl5GpY2dQY91A+XYNGaPb8vGZrWas0Ro2bF/CuuqvsNlsxFuyOHHUgR+LdLlaWV7yOd5AgA+WFRBjTOeU3LYfU5/Px4L1n2DPCBHhtxOX4qCqtA6LzdQ2elWFnZ7lRCbYaGysAQZeR2/elio0igY1oMFuje2wzGw2Ex6pp7qyki1rynA2tFAVdKH/3XEY46w0frsDxe0g6PKilDUx9OR03DnN6F8uYfD43k+hIYQQYuDT5zmJ8xnJ10aw+P22VABXXm5hmsOJqqoUjssgOzmCH9YoTApru/lraNYxqLEJ1zUXYrVaWblyGfnLviarZhEAYcdZGTMojOr6Zv55ZxMerNgmZTPogpFkZyvUrZ5PbL6XMo+OsZFu6o3w6e9/T2pFGcTFEX35lSSPHn3AuNfO+Z51f3yXzD9lkbdjLpasaaQMbjv3Ve0soPnlv5PS2kx6ejQ6nZbKMCtWTzNef5AoxU/ED28QdAfa89UONOXrSnG7/HjDNESN65iuSbFF0eL2kl9RSVV4NKZYK966GuyZURg0CtsyUmB0CvlLt7Px7xrC1Aj09hIc0QPvfRADlKr2TH5dSd0ghBA9Tjp6hegndjdzQqEQiuYAK4ZC7c9felxenLvgxJw9Iz/1ej3BJgPV/kbwGGkq8+NI0rN7I62iZ9SwXL5YublDtRrAEWnDlVBDWJgJR6SJpvLKg8bd2NSI3qLgdSnEpNuo3rWzfdmabUsISw8CCqr6Y+oIo5aGWifuVg/1dXUEtC4qinXkxEQddF/90ZqNS4nJNlFXDJNO73xRO+7CVuJyrDirBpGY7UDVBhg0PBmDUY8rvwHLJxUAGCIjMZmNmJOshLwh8r9cSspdA3eSGSGEEEeG+mNni7pXcgbtj0+MBFUV5cfyvbsAq31+9Lknclx2TnuZJTqR/LwAAa2ZRp2R6qYAGqUteQNAWHgYkRGDqatb116ZDg0BIKu+EWerk0FKECrKKcvfDgfp6HWW12IOarEaNCREasiv2AS7O3rfeYNBnlZqVfB6feh0ZnzBANVOFzXNXkorffhrNNS4IdXrxWQyHfob2AfWLVuJy7UTR4qLppE5TD5lfIflisHOd/91YQtauOyZ4Rj0Oj57rxLbwiCgEpaTzogRcbg9LWz8Xz1ajQajqmHF0s1UVlYRHx/XNwcmhBBCiD4nHb2iXwkEAiyfPYegViH3rClY7WF9HdIRo/EbcVV48ftDuOtUnPoQzXUu8leXo6oqteWNNDvD8Xi9NDc2U1vWTKw5lfMnzuxQj1ar5bJTbyEYDKLT6aitr+Hd+f+ioaYepzMCixqNTqdjZPJkvi75iKYSPygK5btqaW5updnpQg02EBntoaXewzLjt6ioNNQ0c87UC9i4dS0ldflYrGYizYms2LIAs0NPZWktXpcPT4PChqjVlDfvIH/7DqKTwkBRqSytBa0fl9NDU10LUXEOYpNiyD4+AWe9i6zIYX30zh+61RtWcObt8VhsZsz1qaQnd+6YjU5wYHPocVY2AZCQHk1zvYuoBAeqbc8EdKHWAA21TlpaPTizbRyf0buT2gghxNGkvrqCXZvXYXdEk3nc8X0dzhGjqirB7BicigFtk5N4q41QMEQ51SzVmvGE/FQvL6RiVyQFu2oJ2g24QiEyr7yBYeMnd6grKWsEwYwnUBSFYRoNm1cuIO/L9/BmgdGkIfmUFOLiktm6bRcuq4aGFAtuH3zb0kq4aqSkoYV6h52QqqBdsZGAT8XjdmMclMqoU0/luzdfJU7nJ4RCiSaJBW8vx2u2Mu+VXYQ0CjHnJbD1uQ+oXFvK5uImlNYU6lsbuHVDCRERNtatd7N2XiSGkI76GA0NlUY0OYYBOZlrYPtyfnt6gCY3BG66vNOIZH+LmxSNFWtQQ22jm8SYMDSGPSMfA562x92H5CSyVFNOg8+Jz+tkxOh0IiMjEKLXSeoGIY4pP02xcKDlx2KqhP5GOnpFvzL//15jy29noei1NP+6iOl/7J2JPfqjaePP71S23PwdZcH1mK1GwuxhJGTbaXG6KNoWIuSCUWPH7bMuRVHQ6XSUlpdQ2VBCQngqEdF2EnPsBCvDARiUMphbU37dvs1c3TsY472UF9agN+qISYxg57pqPFElAFSX1TJ3+QdU1pZhSwRLnIPvfljLiEltM8gpGgWtx0pknJWtDd8TlxKBtcpAQlbbDNBqQENiph2jTYui0WCPsFFrcKEoCl5XgBqlnPSkgTOCtbmlmR82zGH01HQA/CHfPtcza8IJ+JsoL66ivrEeg9ZAXVUjKekq9XgIDo1E9fjx5JUzOCeT0ZNyiPvkckYMGXsEj0YIIQa2yiX/o3X7LlLLaikx/IaUEcdG+htFUbjsnw8BcNZe5Zu/+Q9WXyWRYXoq311NWrAWNahFKa7EPHg0iRnZ+6xvd6dp3vKVqPUBjIU6bvrLBJISI9hV3pZeauiQkxj6t5MAcLlcvDjhEfRY8UU34slrO+cnGOuI3TmXOo+HCluITaVl1C1eyMT4ts7K/y2LxVcQhisQItHopPKSk9g8dwu+HzQoioI+MgxPhQ5dwEZaejQJCZFsTqygRWkGLWh1bZ1DTQ1N1NfUE5c4cEawblvwA97tWyAC7CY9RbXVxMYndFgnbVg2Sx06rHVaFiwtItZqp6iqifQLMzFFmthV34CpMAytVsEYUccYn42RiVmc/d6f++ioxDGnjzp6v/vuO/7yl7+wevVqKioq+Oijj7jgggval6uqysMPP8y///1vGhsbOemkk3jppZcYPHhPerj6+nruvPNOPvvsMzQaDRdffDHPP/88Npvt8I9HCCH6AenoFf3GhkXLaNhZRpM+SLxfR+vOg6cNONqNyjqeof7RVDdUsdw7t61QVYlLiiYmKZz8kjUMGTRyn9uWVhTz4YLXGHVqKjaLH09hW3lTUyN+vx+9Xt9h/ShTMq6qZk5KO5n88o3UlhXR6twzq4fBYMCY0kKUQ4+72QuA2bLXo5KqgiamBb09gLfxx8dI987d9eM/rWEmmupc+Dw+GmrrKdjkxd3qxR3cSEJUKskJqYfxjh05KzYvor6+hnWftKBtUYmOaIZRZ3Za76ThZ1NVXYE6LBx9UgsA5TvMJGaFU5dnxb41DwCbJYyohHAA/MF9dxoLIYToyNXaSmnBZrT+FjQhsOo01FSWwTHS0bs/ibkXYTZbWLdkLg5lLQCKBiYmRYOrjJ1LvyV8xpX73HbNBx9R9/HXDHX6CXk9bU8V1bpQCHRa12g0knbJCKLSYzhjwhC+/dMHtGyoIvrHzhujTkNsKEjS3HlUWfeMWtVr9O3/bVWDJH24jHqDGb8S3R4rgE5jxOP2t5Xp2tJbeUI+6t2tBIJ+gtUhFn21iDMuOJPwSEfPvHm9rODNT6hW6/B5NGgNRsIb6jutY7FaGf78vYQA5/3Pktjgpz5Gy9k3t01at2pjLaNGtXUOF4VHYa+Hlmb3kTwMIfpEa2sro0eP5oYbbuCiiy7qtPzpp5/mb3/7G6+99hoZGRn84Q9/4Mwzz2TLli3tKV6uvvpqKioqmDdvHn6/n+uvv56bb76Zt99+u1ux7Ny5k0GDBs4gFSHEsUM6ekWfUlWVH9bOA22QJZ/Owet24pmQiNmUwNi7Lu7r8Pqc2WzGbDZjtztYs3kJW1fsornJhc/np7q0AZ8TNtnXoWg0tDT50GLEH/CzrWgVtb4imhta2bQsAIpCQ5mThiY7wUCIouJCBmd2HM0zbtik9n+nJWfg8XhY3vIdm/7xFUoIfFFmIhzR4NeQ98Jyakw2qnVujCYjClBRUkNCaiy+Ji+VpXUEXTpqqxqw7jCBAhUl1Sj6ECohKoqr2+ZeUDQkDorFaNJTkddMcU3+gOjo9fl8lNTnYfZqsN+9GkVRMFwXBjd1XtdqsTIoPYuyph0EaevoVdS2EVOaSD0BNYRO0eCvcxMKhSjOryA34cB5DYUQ4ljXWFtNxYJPKCqrxtRQQ6Gzlfi4cPIyRjJ44sl9HV6fi4iIBGDC1Bm8/+3XlAfdFPq9BAIGCKm4iorQrF5OSIWAGsRoNFJdVcWOhT9g27qdyhC0BjV4CBL8ei0JCVG0eHaSld3xSSKtVsulD13b/vqG136Jy+Xih5f+wabKXYQw0dyiEoqPp6yyltk7tahoCctwMfJ0OxqdgbpAEmHJ8bQuK6bJGwRVpbG1AWNiONpAiE0/bMJbXk1xcRk7fRqUgIZonZ5kkx2domXh00s4/uQTBkRHb1HeRnLOMWAvSuBETSsAO7X7HtEYn5oMQF6YFRqaCNa6CAaDaLVagqE9ne6KVY+nupUagzwCL46gUA9NxtbNOs4++2zOPvvsfS5TVZXnnnuO3//+98yYMQOA119/nbi4OD7++GOuuOIKtm7dyldffcXKlSsZN67t9+zvf/8755xzDn/9619JTEzscixZWVlMmTKFG2+8kUsuuWTA5QoXQhy9pKNX9KltOzbhjSpBp9eRcsEgEjMj0NbEM3l055GRx6KW1hZq62tw2B2oRg9Dx6RRX90IKETGOti5topax3oAPn2hmbpiBz5fKxc87Ob4lBx2rqtm0JhYAMq2NpI0NJyAP4AmdKDZ3tqYTCZycyey5db/oN9ew/B//YIpuZcBkPfAfPQWCLOoJA8Lb9tAoyFpiB0AxeAnIdNOS3ML8T+mbgj6ICFz90WYSnxqJM5qN2aLsX2fNa3F+xxt3N+s276ctFFRNNfXEUJFVVUcx2UecBuvO8C2FQXoFAMNVc14XX50JhulOVYiMVK/q5rwogaCPoVhg/c9SlsIIUSbyoWfkV28nPIiF0PLLQwKBgndeB0ZI4b3dWj9QlVZCUazhfKCrYyeeTKDMuMwLi1k4sQMANb++RPSPniBRo8PT5KNZIeJipoAV2fo0GQmsGKLnxGuVlz+APX5pWRU1FBg71rueIvFwuDTpxC142NcQQXdlLuIjo3H/dkidizYSOv6Gsb+zMbJJ4YDsLUxgpysWHaVttL8edvoXXtCBK4dWkDH4PQIhg6KYmNCA1U/PvBiNfvRa9raMkajkVVfrCbj7vSefAt7hbdkBdmJ4VSXeSEI5QE9jsSMA25TjZfiQD2t2hBzvt1OXEIMu3aWY3a1oiVIIKOO+GFBLPFDjtBRCNHznE5nh9dGoxGj0biftfetsLCQyspKpk2b1l7mcDgYP348S5cu5YorrmDp0qWEh4e3d/ICTJs2DY1Gw/Lly7nwwgu7vL81a9Ywa9Ys7rvvPu644w4uv/xybrzxRk444YRuxS2E2ENy/fYM6egVfcpmDcPbFETn0AFtIxF0inwtd9tYsIJGSwHuvAAhbQAwEwqpaLRtFzeKZk+HraruuSOu+bF879QJZduaSInMIRhsJmpwVJf2b7OHcfE3z1GeV8Do0/aM+L1/wat4PR6+fOddWhu9WMON8OP+3S4freVaXDFeQnvn3epww16hurAZr89PXVUDTfUtlG6tJyLBxtxFn3HutM6PYvUnzf5atIqCKcaE8tszGXncaE645KwDbmM12hiam4FWq6Fsq5OkoXZCoRCuGi/2Bje+TDMJg6JwloQG5MQyQghxJIXMVlRVRfkxBYAHlXCHvY+j6j+als/G5q7C19hM+EWnAx3bCbtPyqGf5MfUaDpOCoYa4ruEEYQSIwlauj5Bbsao8RSbrRhMFqJj2zqIJ5w3hQnnTaGyuJwty78gGAyh1e5pxxjCtISfYqNxYQN7z00WUtteaLQqoBCI8eIJBKnxuvEZvTg1LXz39udMvXoKUdHRXY7xSAsGg2hbKwmZdexqdhE+YQbhqYOITT/wo99xMdHElrS2fd+HJZMzJBFvawtjktu++76CSDI0zeTHpRyJwxCijRpq++uJeoCUlI7f34cffphHHnmkW1VVVral/YuL65izOy4urn1ZZWUlsbGxHZbrdDoiIyPb1+mqMWPG8Pzzz/N///d/fPrpp7z66qtMmjSJ7OxsbrjhBq655hpiYmK6VacQQvQE6VETfSolMZ365rGUFOfhbVRo2hXipJxRuFyteL0+wsPDO81EfCwoKS+ivqmO7fl5tGiqUEMq9ZXN1NWH4WxoJsKQRPLoQeBuwlna1kCyOUJkDbKTv62ZmrJGTP4o6quc2HYZCfiDnH/WRQzJHNHtWGJTEolN6fwYk9Fk4sLrZrJ15wbKKwsxBkGtiiDRnMDZlx/Psg0LqfD6WfLJNjKGJVBZVoPW2HZhqW+K5eTxp7G8ZTEBpYz4lGi0fgupIyOxNcR22ld/U1leidLixmIx0zhYS9Txgw76PTWbrHh3X9D+2KjVaDRoHGZoaMVT3oTP4yPNcVxvhy+EEANe9pmXsM3jwtOSTykKgZR4clJSqKutx2wxYbFY+jrEPrFj0ypCgQAbtxUTqTZT3egk77PVREXb2VlSj806GIMRGnQWCmw6mjVu3Fo7rRHp1JWtZ3tTgKaowVS7NlDgC9JgcTDz/t9gMBi6HUvq4H23OeJTE4lLuYmtK79Cq7qobXahKbNy0nlXokw3sfSDpeRVryfpOguDMuN48Q8rMHqq8epbueHVq0nJSmbzN28x0lqFzWQn/wcfdiXU7dF/R5qqqhR4QviCZkIGI4GgFrM98qDbWSLsKIqCoigEfG3tqCB7OtiCWg1FfhNJk87ptdiF6KSHUzeUlJRgt++5Wdff/3/em06n46KLLmL69Om8+OKLPPDAA/zyl7/kd7/7HZdddhlPPfUUCQkJ+93+pZde4qWXXqKoqAiA4cOH89BDD7WnqPB4PNx///288847eL1ezjzzTF588cVOHdpCCLGbdPSKPjc653hGc3yHsvmrP6HFWIo9fxCnnrDvPExHK5/Px6qSuUSkmFAtrQwf3fZIX/l2J4nZdmqKW5hx3M8xGAycMGpy+3bn7/WUkKqqKIrCeSf2frxDB41iKJ0nvZkw6hQmjDqFtVuWU2vZRNCvEp8ZDkB4YzZxMfFkpg+mJqwtb20o1ELLTj2TJozv/aAPU3xiPNq4tkfMFLWe7Z5FeHd4GJ61/9y6mSk5fPX9JqrcRTRWteLz+9FqtNQn6tCYowg1txLVPIrRo3OP1GEIIcSApdPpGHHxDYz4STr/h897lsbSFp5e+isSk7uea/FoULh1LQlFn2Ax6WnxehgR9FPQ6kc3ZycRRiPhx49m7Ni2dsPwYZM7bT/04j3tB67ttLhHKYrCsB/bdzk/WXb+HecD57N581cMGWrif55tBMr06KKtHH9y2zmyOS2ZGH9b+6HGr2I+6xJsYV0fcdwXdDod6cnRDE5zUJlXRebHb1P09efoH3oCm33/+YXjz57ED9vz0NfX4lu6lZaGZip2VrJsVSuGQJB6jKRc/Qts9vAjdzBC9DC73d6ho/dQxMe3PT1QVVXVoWO1qqqKMWPGtK9TXV3dYbtAIEB9fX379t21atUq/vvf//LOO+9gtVr55S9/yY033khpaSmPPvooM2bMYMWKFfvdPjk5mT//+c8MHjwYVVV57bXXmDFjBmvXrmX48OHce++9fPHFF8yePRuHw8Edd9zBRRddxOLFiw8pXnFs2TsVAvSPdAg/jUn0POnoFf2SFh2OSBsWxdbXoRxxFZXlWKJ3P8rYeZSoUWM96Mia/jQKeszQE/jk+2001rQQURpFKKQyMqet81qjaAkEAjQ3uvA2qpx79lX9Kvb90WsMhGh75LWmtIG6mgZazPoDdvSG2exMmziDBdvfxWZxkT6q7fFSp8aKdWs5RjVITJjcmRdCiMNhCTPTqvERERXR16EccYH6UiymH3Pc/zjQLqiC4cfTqqkLjxD3p3NwTMxItm1biikVLAk2UkbvdY7U6PD4/FQ3tBI4fgrZ087tu0C7QdG2td88qp+NTU00eL0E1q1h9Mmn7neb1NHDOeGmc4n6/k12ZcQwarCdBFM8+g+WEWsxUOBwkPiTCXaF6HWhUNtfT9TTQzIyMoiPj2f+/PntHbtOp5Ply5fzi1/8AoCJEyfS2NjI6tWryc1tu3H07bffEgqFGD++e4NNnnnmGWbNmkVeXh7nnHMOr7/+Ouecc057Cr2MjAxeffVV0tPTD1jPeeed1+H1n/70J1566SWWLVtGcnIyr7zyCm+//TZTp04FYNasWQwdOpRly5YxYcKEbsUshDg2SEev6JcmDJ/Kqm3fExuT1NehHHFFJQVsKytGq9dSvb2WxuJK0EBQ0aM1BjH5+2/+uX1RFIULTp7ZoWzusg9oUarJ21BA2pB4TDYDQzOO61cXmAfSUN1K+c4S7NFmLDYzmWPjCVQdfCRRRHgkJ2VcwP/yXwbaPkclrO2i3KhoaSirInVQei9GLoQQR7dbn7+Sbz9YckzOfr5xTR5b6qtQUCnZUUuJJkRpk4scjZ7woAZzTNfy8/cXsbFJxMZewlPvXgJAc2MTC37+G3QNLaxxBzBWmXGFwa2fX9rHkXbdmi3VuFwWWggy4ZaTCA8zk9+Fy7GsE6eytq4G1V8GQHJSFNtVhVgAV0vvBi3EvvRw6oauamlpYceOHe2vCwsLWbduHZGRkaSmpnLPPffwxz/+kcGDB5ORkcEf/vAHEhMTueCCCwAYOnQoZ511Fj//+c95+eWX8fv93HHHHVxxxRUkJnbvKZCXXnqJG264geuuu26/qRliY2N55ZVXulxnMBhk9uzZtLa2MnHiRFavXo3f7+8wwdyQIUNITU1l6dKl0tErhNgn6egV/ZLJZGbSmDP6Oowjyu/3s2TDN/ywcQETpg9Fq9WwdWEjpgfWEFBDjJr9G06YcOZPJlMZmPzaFqKSLMQ1hROZYCfgCzI0eWRfh9VlWenZ6P1NWMINVGx3otFo0Hbx57TJ2YTH7WXH6gq0Oj21tiDhI2LAH6CpvPrgFQghhNivQUMzGPT7jL4O44iqLd5FxScfUragBNeWtlGvgcQoPJV63HYvkxc9dEg5dvub+poaEoobMGq1FBnMmAkjmBmBxWrt69C6bMjQYYxIaSXSrsXnbCYQCKGzmru0bcjvZVdZJS6XC72isK7ERL4ugnqfjyG9HLcQ/cWqVas49dQ9I+Dvu+8+AGbOnMmrr77Kr3/9a1pbW7n55ptpbGxk0qRJfPXVVx1u/r311lvccccdnHbaaWg0Gi6++GL+9re/dTuW/Pz8g65jMBiYOXPmQdfbuHEjEydOxOPxYLPZ+Oijjxg2bBjr1q3DYDAQHh7eYf29J5jbF6/Xi9frbX/tdDoPGoMQ4ughHb1C9BMrNn9HKL4KW5QejUahqa6V8JRB+M4IMuTSKYy/5Cygfz1Weag0ihYIYrWb8Hn8GM16nM2NxMb0/4nYANy+FiwRbRfNPq+f2opGokNdS7uQnTmE8wJXURO2FgCPw45p0xYANC5/7wQshBDiqFU2699klu1ihTYGF2AabUEXq8cyzMSJN0wZUJMaHYg5LIzGUACjVgs/Tkbmd3sPvFE/YzEG0Gg0JMZHsm5HBQ3NPhzjutZ+yL3oWuyLZpPl3wpAeTCCuEoFBRWv13vUfM5igAipPZS6oXsDWE455ZQDDnpRFIXHHnuMxx57bL/rREZG8vbbb3drv/sya9YsbDYbl17a8amC2bNn43K5utTBu1tOTg7r1q2jqamJ999/n5kzZ7Jo0aJDju3JJ5/k0UcfPeTtheiP+iLfcH/McdwV0tErRD+xZuNytGF+iguq0Wi0xNiSufKXt8Ev+zqynrEpbwO1LSU0+asoLiwlymXH7QpQU1ZFoFXD1CvS+zrELlm8bj5rNi8jOiWMsh21mLUOLpl0E9ZujCgKszgodfswmg2E7Nr2cm+93G0XQgjRdbvyC1i8Kp/NWpWC1hp8dhfTL7yUU684ra9D6zHrl85H8dfiaizDmeEiZAkSLPcQHt1KbUZWX4fXZb+68a9kjQnQfGIiGzaUkJY6kTFTzkKn6/rlmKI3w4/3hDXmtg5ve1BLdVkFKZL6SRxJfZS6oT958skn+ec//9mpPDY2lptvvrlbHb0Gg4GsrLbfs9zcXFauXMnzzz/P5Zdfjs/no7GxscOo3qqqqgNOHvfAAw+0j3aGthG9KSkpXY5HCDGwSUevEP1ESnYM4SkGAgE/qRFDOXvSxQffaABQVZVl6xewZvtShk9KxoGWKFcEiVl2QqEQpQU15GRPHDCPlSoKaK0BErIcpDqGMXHk1PZJF7oqIT6Rj96rwB5hoVp1EzEyCk0Qlr03h20vfcakf/+S0adO7KUjEEIIcbSoqnIyf2nbRFyjL2rmpruvYtDwE/o4qp5RW13He099xIixDUw+PhxXmIFSQ4h0Y4gah5kwn5kxv+h6R0pfa6zxYDQ4yBgUSWLiacTFJXS7/dCsmvlueTVKUMO6RjeqMR6v38y4h/7EVpuZM17u/uPnQohDU1xcTEZG51RBaWlpFBcXH1bdoVAIr9dLbm4uer2e+fPnc/HFbdeGeXl5FBcXM3Hi/q8VjEajjPIX4hgmHb1C9BPqj48harQaNFYfbq97wHR+HsicJe9hTHVhtOz5udn9yJVGo6FmY4Br75jcV+F1W6Q1Ab/PTygUosa3k1bXCYTZ7N2qQ6/XM2hwKmHJCs1bqwjb2DapRHBkNJadjYSCgd4IXQghxFEmFNzz6HRKvIOm6hLUYccP+DRPVWXVPH/tf3AXBBg62gKAyaTHR9tx1bhbGPbEA0QndW/ypL408vgMmpsqcDhsbC7ZQEJC9yccjknIIHF7PRadjmVKAmXb2yZ0jU1UMQV74DF6IbosBGpPfOcG7vc2NjaWDRs2kJ6e3qF8/fr1REV1ffLLBx54gLPPPpvU1FSam5t5++23WbhwIXPnzsXhcHDjjTdy3333ERkZid1u584772TixIkyEZvot36a7qC/G2jxdoV09ArRx0KhENt2bGXzmh04SkwUbq9AVVUqi17l55ff3dfhHRa/309R+Q4ifHbqKxspz7OAApWlNWj0UFnYyO03/bqvw+yWwRk5PPV4ELOpAluElSXrv+XMky7odj0GrQnwEgpr6/hWFIVQMAiAp1ZSOAghhDiw5qZGSvK3ELCWAQpFJSqpW+pZGjRw4rTz+jq8w7Jt81pOvi0enU5hy5yN1HzdQMivYjx9MGQmEFYOycNy+jrMbpkwLYMtG3eyeXMlwZCf0tJdJCendauOmKQkCkIqFsCoDbaX+zR6LK0uAoFAt1JBCHHIJHUDV155JXfddRdhYWGcfPLJACxatIi7776bK664osv1VFdXc+2111JRUYHD4WDUqFHMnTuX008/HYBnn322fdI4r9fLmWeeyYsvvtgrxySEODpIS0CIPuTxuPl61QdY0/ygwMgTs9BotQw9Po36ooE1wci+6HQ6opJtxKXZaXY2k5jTNvJVDaqEhVsZN+X0DrPgDgTzv16OtzYDe2wr8anh6Gq7Nlv2TzXXedlVWoFfp6f5sbGEp0Thq6vFMGoiO32FTPix81cIIYT4qcr87dT/+29QWM156SnEuc34PS0M/rqZGrUKpvV1hIcnMSOaQZltjx03zC8lvMAFQEJ2AooxgqkzBl5HtsVYy6DMKIYPj0dVVQp3tna7DqPRyEaNlZ0eDS53LYNH6jEoCgvLNJh9MVT95wvOuXVGL0QvhPipxx9/nKKiIk477bT2GyyhUIhrr72WJ554osv1vPLKKwdcbjKZeOGFF3jhhRcOK14hxLGje4mhhOgmr9eLz+fr6zD6rTV5S7APCqLVahg0vO0RPo1Gg0ajIaDxHHBW2YFAURSUHx+zVPd6NKuhuokIXyaxkQlU11a1l/f341VVlTmfbEGvNxP0qQQDQay68EOqa3jGONJHRTN6aiZx05JIOyOJqKxokoZHEzfCzLqtK3o2eCGEGEBaWloI9cSM7kep2vdeJ93VhEGrJ87ddsNRq4BW0UDD0fBUyJ5LFEW356bnijWlxKSfQl1tZYe1+3v7oaQ4n5Q4L8EfUzMVFzeQmNi90by7peROIbncSqLWRk4dZNSqRJgjsTVZ2fq/dTQ1NPVk6ELs2+4RvT3xN0AZDAbeffddtm3bxltvvcWHH35IQUEB//3vf4+K9HtCiIFLRvSKXqOqKrNOvw33miImvfcgx58zta9D6mDj1rWE26NISUrtUN7c4mTV9kVoVR2Txp7Z7YkyuqO4tJjaHUWohPC7VbR68DkVasoasQRjjooRnfWlrfhd0FDVTPk2Jygqbo+Hwqa1FK5cg7PeTXSigx1bCknLTubkwReRcogXP71t0+aNZExsYajVjb/KRmTUcMYMO7RJb9zeFvQWHYqioAbavmO+UNtNEZ1eR0ntdsaoJxwV3wEhhOiOdWs28bOLfkd4tIkfVr3X1+F04G51sfa75Rw/bRJ6vb7Dsi0rt7Dig1UMmZLDhLPH92ocm2qrKPV62ekP4NC5UDQKZqueslY/trHDenXfR4K7qZk1n61Eg8r22hp8FgdqSGVomIFd2z9jy5qdpCwOEAj42eBTMTuSuP2t3/Z12PtVvOYHjHUFtNY62ZkxEYslFavVekh1hRrbRjeHvF4gDACN0nZTRFujsnzuUs644qweiVsIcXDZ2dlkZ2f3dRjiGHI05pQ9knrq/ftpPUV/nt4j9fYE6egVvaa0qBj1+x2EKzqMYYfWmO0tyzd+x9baxVCsJ2yXDUPIznmTrgLgh41zMKd5CIRC/P3Vp7j83JnUNFQxMmdsj8cRlWAjPn4Q7hYPmbpTyEofjDJewefzHTV3gmPSwolKttDY3EDikLbUDQF/kOShkQBU5DcRmxFGY7ODqGQrtY3V/baj16s0M+r0CADsjdmMHXboF/IllUVUFZSj1xuoqawn5NXQXNHCmrM+hXo3DRYV//RyLn72Nz0Se3OLkw0FKwjUhphy2hk9UqcQQvSGpYvX0eoMkZBk6+tQOvn0t/+mcWcJFf9bhKbeRdrPz+S4Gaehqiqv3/Umxloz6xatRvX5CIsLx2q3kjFsUI/HcdwlY8hKiyCp0MWISTcSCoXQaDT4fL6jYqZ1i9HEyAg/AMsM4VTsartkycpKYOyoOJoqa4lrqECjKBQZDbSUdz8NwpEU42kkS21kh85PVta4w7qJu7N4K2FxrTS7GmmMsmNQwOStYUSuAb3XzRv/qMMRHcP4abk9Evu2tXls/WEbCcclMOGkQ7u5LY5CoVDbX0/UM0AFg0FeffVV5s+fT3V1daenUL799ts+ikwIcayTjl7RK1RV5b0nX8SteIlWwR7f9ZlHe0Nrawv+QIBwRziqqrKjcBvFZTXExEXg9XhwN1SzOXojZpOFmvpqIiN0mK1GorMNLCn9EK3fxEh6vqN3N41Wg1FnaB89fDRcpO2mqG3HtHfjR93rMS1VbbvYsdnNlObVctrpY45ofN3hC3ra/61VDq8jPiomHMewtnQdpnwDCYMdmGNSKb53FQ6/Fs8gM9X/mU/e5WdgjnKQOvjwOgpW5H2LNqGJdX/8Af/GSqbdc+1h1SeEEL1h585CPnr/MwIhH3ZH398krq2pxu4Ix2Aw4PG4cfp24g13ElTduA0Btm9Ygy0tCo3ZTGN1PVHEYdToWfLI12gCkHnNyF7p6IW286jBYERRFLRaLXAUtR+UPU9TaXV72gyhH+cfs4db8IWCmLQ6FDVE4rj+eYO4nbsZAFVvOuwndcZl6slq9rKpxo9a0IJNZ0ATaWZ40AM6HZHmMD7+v/mkD0tBr9cTGRNxyPtSVZU37nkHtVTDF+b/47U3/sGYsaMOK35xlJDJ2Lj77rt59dVXmT59OiNGjJCn8IQQ/YZ09Ipesf6bxSjzt+FQjBTHKsQkJvRpPEu3fk2ztpKRkVMpqy0kZqSGOp+FIeNTUBSFXRvrqLGvwe8L4Am04G42U76zFq/LT1RcJOU7CvlUmYXGb2L6iVf0yIl8246tbN2cDztdaENGpl6Y3ANH2v/Ul7hwt3hwVrso29qEolGoqqhDb9KhKFBZUgPaIAGfSrplXKdHYfuTliYX1VWNtDhbuejkw3ss0qi14v9xwjU11PZ9ioxxUGg3QF0Qb4ubuqCHWfc/QvSgZI4bOx5dpI2TrpnRfkHfHarSdnVsjDaz/ZG3GTZjCokZ/fzCWAhxzHn490+zY4Mbo96AwdT3I73Kl82iTGsmZ+rNbH3tT9x0EqxoiOCEuLbf7RXfFxD77Ea2NjRxeboFvdFN/iAz464cTDAEs19ez4pzi5l42WimX3v6Ycfjam2heOsmln2/jV35DsKSRxx2nf2RLTyGOe8VE2U0YHA1cfwJkWgJUvRtKcbCCBprGqjTBQjXazCHhZh025l9HfIBlWhNNFkTaTTqyDncyhzR0FzF4AgLKzRBbIDf44cfB8CH1Fa2b6rnzul/4IyzhzIiPYmYcUPIPG7kIe3O1+JHjxGNquPhh//Cx5+8Lh1aQgDvvPMO7733Huecc05fhyKEEB1IR6/ocaqqsuEfH6D4gthUHbEhAxaLpdv1FBTnYzKYSIpPOeyYFJ1CRKyVzWU/0OpqJTEhHI1G295Q3T1hmN6gwxZuJSbJgd/vJy41EkekDXeTD3uyhmDQw8pNP3DCyMmHHMuOXVtZV74QR4KB6KQwEockoVZF9usOzsMRkxKGI0VPg7MOc7gWU5gWnyeCxGwHAGpIITHTTigUItHdvzu7o6KiMITXEwzayS/bRGxM3CHXNXbIBF5+fyFmm4HaqkbUYAgUDaFHj8MwOAYlbxdjTx+GNczM5qfWsOuXrxFSVfzNbk678+pu78/dFKS2soYagwd7sp1FL77DlX/pmbQQQgjRE3buLGLT+h0oig01qCUmJumQ6tm2bimp2aOwWA5/RLBZryEzQcP7Lz/MBfZWQAuavTq5gkEURcGrQLLJgkmrw5MQzbAhsQB86K2mel0rXxYvZty00cQlxh5yLFs+ehPjum9JVH1kr9KRqPHT+puj84ads6aO0/ytKAEX1SYDWRUN6BUNzbGRjAoLUKtq2eBViQvqMMY5SM44/LZib0rJSSMrsoXaRh+V5buIP4wUVcrYk3hvyTZ8qh1lmA9TloOW8laKbBZ0IR+hhlb03ni05VrMG0vhh1IK3v4B25u/IS45sXv7UhQCCSpOUysGZ5AdOzayePESJk066ZDjF0cJGdGLwWAgKyurr8MQQohOem+WKXHM+uGjOeQvXUdhax0u1Y8h3HZId/531q5jRdnnNDTWH1Y8Xy5+l+152yjd0oizoYXi7WXsXFdNTYmTvJWlbF1ZTMH6QtZ/s5XCzaW0NLcSCoVodXo71aXVavCrnn3spetqnRVEp1nQG3SgtHWMW3SOw6qzP1OUttGngxJzmJp1NdqaeFT2Tt3Q9l+/N9DvR4jYjHY8bh9arQav2nxYdel0OoYPHkPmcXEkpMSQNCycpKF2kocnkjQ8mpzcVIKBtlG4qqPtnpxGUfCW1x3S/k494RwiohxEJNpxbKmj8eVvyV+54bCOQQghetL//eVF3J5WAqqTUCiEPaz7N4lVVUVb9i07lxzeJG67Nm9nwYMvsnlXPauL3PiCXtYQzmK3kfwWN981wHdVXr4rreC72nq2OVvw/5iiyPfjuT0YDBL0t53k/HUqtTWH9vu9m7Z0O2n6EO5AAF0Iao0akoYenRMAaQz69pZC5gXTiXjxj1QPSmhvM4RbTPg0bS8CgUDfBNkNQcwARIcbaG6oOqy6csafyI7aweQtjWXM8fGcOMZCVqqJIWYfwxxakqMtaH9se/k0bf+1h/TUlVYc0v4uu+8iaosV9Do9W7dt5Te/+e2AeM9FL9udo7cn/gao+++/n+effx5VHbid1UKIo5OM6BU9zldUS3xNELtRj8+kwWs/tM67gOrHEq6joamOiPDIbm1bVFrAguVf4kgyEjJ6CI+zkZDdNmpUYwmSNCiaYMDP4HE/jhZ6twLD/1bgJkjwjZNZN6+YrLgRDLEOx9XYQpotlm3L1mGPtNFSW01roJEYcwpjh07oVlwut4utedtwaasIhVS8zhAx1mRyjzuxW/UMBOVVpRRUbGTXjjIimsKIMqbg8rRQ4dxJdWk9JosJRVGpLKlBowuBAtsr3qPKOZGTj+ufk4XlZI5g7ZdLCRqb0Hi9HG7a5sTwQezyVhIM7p2/uO3/l7iUKLasKASqKAs6SbgxG2OSg60l22k8/z78fj/2qSM57abLsEeEH3RfNquNKUMu5h9f/RGrqmJqDbDqmXcY/D/JtSeE6B9Ki6vRm7QUNG/AZrPgDg3rdh3Nzc3YTBpCIdchxbDhs2/YMPsbFKeXmCofuX9OJznOgcfnZfyotqc4jLU6jpuYAcBd/3qCspIG4u0aolKNlIxycNrU89hZY0Cj1ZF9bh2D7jbR3Oyids4stq6KJvr0y4hJTe9WXOVFJazMq6TQ20qDqxWtxcKomy8lPDr6kI6zP8tftJDCZUsptznQ67XEZWRTsn0VulwLRfVetMYYFKOK97pwQoPiKZhfwB8nPsDP3riF9Kz0vg5/n1JHTOXvv/0LoWYdI6bHM3j4odel0WjInpzF+u3b8PrbxuwMTgqnekMpyXYtBo2LVgKERwaoNCnEnJWKOdZCQ/636D1rqaz3YjYNI/fsU7p0g33i1Fx++Rr8/vdtN4eXLFnOW2+9zcyZkutfHNt++OEHFixYwJw5cxg+fHinpzM//PDDPopMCHGsk45e0eOihqZRYQqQ7DFgVLQQcWgXIclhQ/C43QwaMbjL21TXVlFUlk+JZyMaq5/IVDtgpDLfCbTlWDUYf8xvqtlrQLsviEZRMKPD6dZwzUW3EhO112P5wyBxRxL5dSsgGgzxLoqr1pPRNIRwR3iX49uYvwrF6mb46LaJWUy1qUwYdWqXtx9IVhZ+jSNVS1Sjg8RsO4YaB1W1ZaSPjsIf8JEw2A5A0AcJWbtHNKsouv59Zz8uPg4lvgFXWffz5P7U4IwhfPm/d2ltakWr0aDVaaksrUbRqqhKEJ1WR3ZuCqoKIyYMQlEUNv95Dd5Z6wEoLqni7X/NI/O285h2zzUHvWCLcERyww33MOvLX2FzBqgvLT3sYxBCiJ4yOCedXaU7URQNra0eEhLju12HwWCgPPwEwiK6NzdA/uKV7NqSR8y7S7DYzCQ1aNmlugmztE28qdlrcjBFu+ffPq8fRdFgN1uxTRvJvY/e1uG3OHPwCDZtXoTGmMfgllIiQzVs+/o9Ym76dbfiW/bcJ1hKw4hptKHzuRj72Z+IiurbiW57Q9mOHRhf+S+O5iZyzkshpCj4HXaCjX6yxkagKzAwKqNtpLfOYyBncCyF22rxOf001zf1cfT7Z7FYqd+qJ1ik4jvp8B+ojMl2EDeyhrnzfCxaGEtrs5bRWQrNZi16bS1PHR9GmN7AjtMzOenqMQBsWrCJrEgv1mATK34/m8qPlpBz03QGjx9z0P1NnJrLpddeQE1LLT5vgLxtOw77GMQAp6p7Hss73HoGqPDwcC688MK+DkMIITqRjl7R48acPYWyF+6i4eMVWFNiOOHeKw+pnpHZx3Vr/fXbVvH1ko9IzIxBQUNNRT16gx41FKK8pBq/N0hVeQ0Gkx5nlY/6qmYaSiMIhVRizjqeqg11BMobybAP69jJ+6NhWaNITRjEJ0tmAaCzqHg8HuhG1oVGfyX8eAHYtE3P8RNO6NYxDhSrtyzBFBMCtKBCQ00L4+OGYDKZ+XbLajytXpx1LuxRFtgrjQOqgklj66uwu6TJX004evwh/2HXpdFoGJYzjKKyfFJHto1aV0Nqe8e3EtKi0WiwR5tpdbqwOawQvlcHs1ZDcEc53/31v1S21HLOTVcTlXDgvMFJKalMve9atlzxV7yDBm7jWghx9HnyqQcxP6olJTWeKVNO4vbbb+52HSaTiSG5p3Vrm/f/8Apla9eSompptpnxZTQQnBBJa2WQLU16NM4AeXn1NGzxoOhDuGJthEXa8Pl0XHzZabw+60vMkVYm39/5hpuiKIwccQo/fOfFr/kBAL27e6l/fD4f9SsLiYxWacBL+O8vOyo7eQHK33uPbI2CU6vD4w9RZYxmdGIKrY3lVNYUUdPkwe3RYzbp21N7anTgswVIyeq/+Yq3rNmCd6cPnUaPp7lzarDumjR9MunL3uK9/ER2rG67nLtogo1hDqiNNGCuaSvzN+wZ2R78cT6KhBgHPnMZwY3FfPTU35jx5J1kZ4856M3iW2/9OStXrOONN96nucV92McgBjjJ0cusWbP6OgQhhNgn6egVvWL6DVfADVcc0X1Wt+7CEW0laXAEAKpfJXFI26hRrVEhPtNBs6ee7NEp6PQ6EsuTOHvcj5NaTQD/zJl4PB7CwsL2uw+b1UZa+Ch2rF9FU0MzoZQFOMoiGD/8VIxG40FjdDY2U11aR3NrA831HkIeHeNGTyDMGkZ4eMThvwn9QF1DLRu2rQazD41GQ1VZLQn+GAq8eUw+YSrnHncDwdFB3B4XBeXbSLK1Ym8MR4MWS5if4Zlj+voQ9ktVVYp3ltPidBBy98zPZ5QpmTz/pvbXwWBbXt6WOi/+Rh35S6rxKl5KtxeSlBxPQ6SC7fVp6Bx6vGU1JOZOYEicnS0XzOWT7wu45ovnDjqx38TLzqG1tAZfq1yoCSH6D71ez+N//MMR3afH7aZ+6S6iHVZSatvyjpqn2hgeHcKDnvEnZQJQvbQR29y2p4Oibx5OTvZ5AIx8YgYPPHgvWq0Wk8m03/1MOvlM5ixbQXhFGeUVlQT/8xL6zGwGn3rwTummhkb8eih2V+MIqiz++39wZMRhDQsjMjr2gPsdSEp35VGibcY9MglPKMCO2kb0hlpGA4OGjaepKYdpk8IoKcwj0FKNV/WzsyqC5OFRZPzDQXhkeF8fwn5V5Rcw6sQmLAYVs+Pw5noAMFsseDKGYirak/fZE9IDfkxRUWzLTaV29VqatuZj+FCP3hdgV6sHwgah1Wsx3JHBhJMGE5dfTnJKDZs2f8fIEVMOut/HHn+QiMhwTp589KUcE+JQBAIBFi5cSEFBAVdddRVhYWGUl5djt9ux2fr34BUhxNFLOnrFUcMddKLs/WjlXv9WaUsHEAyATq/7cXnHR+/1ev1BO8gAJh0/hfqlRXhCLRiTWwkZfbz39X+ZccrV2MPsB9z20tNu5Pv1cwjGVKOqKmXbytjs+YqWnSqXTL6ly8faXzU3O1mVtwjMXjJGxwBt77fJaGTs8LbRy7svSK1WK9FRMX0W66FQFIWEjChiUm14yg/esd8VucNPZO3mVbhLDJiNNmIsYdgaYsmITCRt5CAWr/sGf2wZFQWNJGSG0+xqInVCAoqi4NMpxCS13SBQIswwbyuLXvwf0+4+cN48RVE4/f7reiR+IYQYyAq378RUq6J17BlVpiEI6NAY9nSgqr492yjGjs1nq9XapX3lXnYNrX9+FK9GQ9qaJbSs+IGPt+7i/FuvQ6PZ/+P8MXGx/GLh/7H1sXvIdNezpLqRuPnPoygKhRknM/Scq7t2sP1YbW0NLTXLyDgukdGDbAQCAbbOX4cmI7d9HcePqbLSs4YB3c/f3JeSkhycYGmbXLgkrvuTDO5L/HlXEpb/X0ZenIo9xoor1UdBRgLDhx1HWHgEGx5/kJrifE6qLwEgFJPO6FFtKU1CBjMxMW05kKurm4iNNVFdXU5sbOJBjiORv/71Tz0SvxjgZEQvu3bt4qyzzqK4uBiv18vpp59OWFgYTz31FF6vl5dffrmvQxRCHKN6raP3T3/6E1988QXr1q3DYDDQ2NjYaZ19PSL0v//9jyuuOLIjQcXRobHUR319IxU7LKgqVJbWoGggFFTZ/sJKGmIjqfQ3YTHaCIVCaD1myD14vfsyMfss/v3+M+SMaftfKKhr5Zvtb2ANxGEgjNrADnSKmUnZ5xITHdth2whLHIW1JYRFG9HptZjMRrYW5/O30Vcw8aEbOP7i/jkR2cE0NNTz9bbXiUwII1S3V55dVSXFMRSb9ei4q61X2m4GeIMtPVKfoij8/PK797t83NDJzFn9Fora9nsZHmujpclFWLiVUDCA3x+gaFs5pYZmwrNsVP9vDuOvu4Awx4FvOgjRX0n7QRxJ2SOGsOnnIZSSWvRjzfgD8MEXIZKiLVR5WnHWrUQxali/uQyjyUQo6OP4wKE9eh+bnk7+1dfhffs1UMCm07D2g3zWvP8EIy4YQpyvGmXxOjQn5zLp3s43f11JmfjyavCHVCwGPRqNwsZ5i2j9bjOD7ryDyKQDd9L1V9s2LibKmM/gNBsbGtpGTet0OiqdXqacemkfR9czFJO5/d9qQ90B1uy6mLR0bn7+sf0uj77sZ2x77LcEQyG0Gg1B1540DqGQSmNDM5s27WLH0mISLZH4g0Wcf++dPRKbOAaoIQj1wLwaav+em+NA7r77bsaNG8f69es7pNO58MIL+fnPf96HkYmjQfpvv+jwuujP0/sokoHvp+/lsaDXOnp9Ph+XXnopEydO5JVXXtnverNmzeKss85qfx0eHt5bIYmjVKOzkTU7vsMSriPWHLNnYq+QQkKOHbfLBwUtBL8sx3BRBplj42iuczMu9vxD3mdMVBxnTbyUxfPnER5vprK0HpUomtQiqspqiU6IIBhs4rum+WSmZeH1eLCaw1AUhZXrVlHr34Et3ExTjRuv2oLH04pmwy7WvvThgO3oDYVUqooaaK7zUllci1ajBRRcjT6Shqf3dXg9pr68lRanm5YmN+oJapdmrD4cRqORaaMvY/mGhaxfsIaIaBvbd5aTkBZLZVktlkgDyZlx+JMSMc/dAgWtbPrqByZefk6vxiVEb5H2gzhSygq30Fq5meHZ8Xg9zYzUeahv9WCtiEWt1BGMNqKubUJVFPxqOE0FRuJOdnDa1TMOeZ+DJ0+htrycTxbOJdViICqhnDBjFGWLvyYQEyJikJXaHeuJnj8fdDq8Rh3mSDuKomHlxs3UNTaxvaGFqNJ6LAYtGreCo3gnxQsXEXn1oc2J0NcamxqoaWkExUlJcTUufxCNRoPTlnLUpKXQ2WysSovEYjPhCRyZSeMSc4Zy/O/+yOrl86gt2oqztJi1/3Gh93ooctiI1GUxMScW598LSKirotIUwn+Hv0tPtwkh4Pvvv2fJkiUYDIYO5enp6ZSVlfVRVEII0YsdvY8++igAr7766gHXCw8PJz6++7MqCwFQU1vN1h2bCSXWEB2vw71p79QNbTlVndv0PLzuIxrq6rGGWVmfv5IURzwJcYc38uW4UePwhJx4Y4pRVbV9Mi2jxUhith2v20tjbSWN4T62ryxj8LhEFEVBH+1m3NBhBANBSrbVERUXRnVBHYpej3cAJ1Ox2WwkZcUQlRRGSA2RPjoaAE1NNEnxKX0cXc9JTk3GkODC4zZTXLqLtJT0Xt+nzWrjtInnkpY4mDz3InQWDYmZDurqa4lLDW975Dd8z5fHub2k12MSordI+0H0NlVVqSgppqpgLaPjm9A49GxYGwQdVDi9GEJ6nHoX5zx+LUNGDwVgdHkdm77bwoRzjz/sG3wTL7+SsLI8smt2Yh9uY4StlfzqJmLOHEeE3cJ38wqJf+11APIuHf//7N13mFxV+cDx753eZ2dmZ3vvJb0ntEACBJCuwI8iICoqoIANRMCCIiKICoJYABVUEAER6T2k97bJlmzvZXqfuff3x+DGSEghu9mU83mefdiduffc9yyb3XPfe857qJ9diyRJxDJTVOdnE95hZOmzpaSQMZ2pYLimEE/04/cXONxlONxU1CWRJIn1RiPT69PjB3fZvmvGHikKKyqhwkypy0BzKHXIrltaP5nS+slsffFPDKx8lWmxdPmIiD2X4oL0uFVl1cFwnMwwtDU0Ujml/pDFJxzBROkGZFke3Vvjv3V1de11zxdBEITxNuFppeuuu47Pf/7zlJWV8aUvfYmrr756rwPoWCxGLLZryZzf7z8UYQqHGVmWefnd50m6+mlu7iJPdqGkJPp7h9Ea1KBI9HYMosgK3qifl596lrMuTy//mzv5xLELRJWe0fm/P7KyLBPwhdHq0nWAJZU0+nMtkV52rNFqMJr0WGwm9DodGped+dddNHaxHWJ6vR6LlAVEkP5rzCYrh+6G5lAwaxwkCGMw6vH7PUDJIbt2RXE1w5v7GAisRlEUsgoz8A4HiYXjtAYHySjWozMZMQYPbEd3QTgSifGD8En0NTfS+u/nqWxaR5/TismtQ1ZraG4cImmz0uCDyfdUkooprHvvZYrKi8jMzqKk0kxJZdGYxaFKJQCQSP/BDKSgQKshnkgiybt+jiWNatfPtaKgUkloNBoM6nSdeIddSyBuonrykbs5VnHZFDqbd1BUYGe3lI9y5CaA/pdWqyVhcAMBiIf3efxYqzjtM2zctIHh6AAug4ZkIEQ8nmBzQxfNiRE8Jh3otEyPfbKyJIJwLDrttNN44IEHePTRR4H0/V0wGOTOO+/kzDPFyjrh8HQsljE4Fk1oovcHP/gBp5xyCiaTiddee42vfOUrBINBvvrVr37sOXfffffobB/h2BUIBIi7enBkWrHaLeSWpOsiKXEVOR+WblAkmdwKO7oeOHXKJ19mudc4fAH6e4fp6RgkmZIBhbA3jkbSYJcrkAwJIp0xPP1BtqxsRZFlhrsDeENWtHoNgcEI/pEwNXnzqH3n8xRWl49LnIdKsaOOHZ2rCHritG4YQJJURP2DlLp3UpxfNtHhjYnizGpeWbsOo1GPLuPQLL/8b3Mnn8Tk8tm88NZfwKamramPiqn5FNblYH20F4jhusJxyOMShENJjB+ET8q3YyvT2zeDTkuWw0p1ZjqJGtPrqEnFGNKrOf6M9N+rwpYCMrOz9tbcJ9bujTMc0dMd8OLPtNIZkDD5dAyFVWirZ9OdGSLSsJ2drV0EXw2hAB1t/fhsOtqGo4xEB3GcVIiztpaymadi+K8asEcao9FIKJVLc/sI61b0s+7NIZJxyKloo6i07iPLoo9UfnU2qzZ1MBKMUXmIr63XG7j0tvvo72znzWd+xbDHh3skTs20Snryw7jbA0RTCSwZor6/sJ9kxmhG78E3MVHuu+8+Tj/9dOrq6ohGo1x66aU0NTWRmZnJX/7yl4kOTxCEY9gBJXpvueUW7rnnnr0e09DQQE1NzX61d/vtt49+Pn36dEKhEPfee+9eb9RuvfVWbr755tGv/X4/hYVHz7JwYd+2NW2ivaON1sEeBu0GIsEUsiyjUqmQ+K/ZXB9OK5WTyrjdJGgsUFTuQqVWU1CXAYDVW8XMuvm7HZdpySfk2AlAd4OPnEoLyUSKTnkQR46ZHHPBEZ/kBagpn0RN+SQ22tbisW8BoHOLhw29b6LV6MjLLpjgCA9ez3A7FTOzAdB61BMSg8lkwpqlwVKQAYDNYUabbSCpyGgkFbGhQ5+AFoS9EeMHYaJFozGWvbqKvm2NdAQiuJJJYtkOIF2PVJ1KgkaD2vxfm2Ypqo9p7eDlDw9TJifIyDZSq0vgsmqpPvUrH5mVbv/HL6kIpsvxbKvKodymZiTl46RAkOFgOzNPufaITvL+x6RppwLw5lN/ZvWfWgC4/gGZLW/9kSmLr0KjmfBFiAfNPNxLtRKgQ44Tj8cnJIGtqFTMzVZYPyxRUZ4ey6ht6e+tQa0l0DcMlRWHPC7hCCRKN1BQUMDGjRv561//yqZNmwgGg1xzzTVcdtllGI1H/u9lQRCOXAc0avr617/OVVddtddjyso++ay9uXPn8sMf/pBYLIZer9/jMXq9/mPfE45+Hu8wO/xL6fL0Mv2EKuLRBFuXd9DXOgKoee+ZEdQpFRqtwqT5BlQ2N5akaVxiicfj+GKDONGN3ph5+0MUuz86+0f5r+WHg10eFGQKah2YrWZcOXZG+nuAmeMS50QozCmmtXkdGfk6FEXGnqdjbd/LBMLzqS6dNNHhHZQEkdHPU3JiwuJQUAAJFBXekQCBeJz+fJlsq51Ip6jRKxxexPhBmGiv/e1tXvzOSk78lpOFv/4KgUCYD95voqEii3AwzkDUgJLlItbrp6VFA6RQlPGZ3dixfi3OD/9+SLIMSARs7j2XHpF3lT/6104fN013YdRpydDpsQeTdG5poO6E+R897whVfVwxK5/ajiqlRVYkZrqDbH7lQSpP+SIm0/iM5w4VtX8YAKdOYmRwgJz8Q//wO5lMolWDRY4RCkXo6R6mdbCdULaC0apB6W4H5h7yuAThSKXRaLj88ssnOgxBEITdHFCi1+1243a7xysWNmzYgMPhEDdiwsdat3MpjlwzfV1aJElCb9ThynSQV54u3aCX1HgH7SiKzJzJs5k6tW7cYtnZ2ozP4yPkV9P2eiPel/UkkKn53MKPHJuIJxho94MsMXfSyWgMEu0dmxnp96FRq5lRMG/c4pwITkcmx5Wdw87uHfgGu+jYPAxIvN/8Bt3eFipzplKYWzLRYR4wRVFoaGhAa0+iUqmwoofJExNLaCBFMBKit32AIrObmQtr2ShvwbEjQCjejqIoB71hkCCMFTF+ECba679ZAUgYzBpUKhV2u4Wi4hyqa3Jp3dlD0ZQ8Mh0WjBkOqiqPH9dY2latAo0RWVboTcXx6zPwyHsekg+H4hBNEDW5uOgbP2TLshcZjm+hXY6TKM1jwezp4xrrobbo3BNwujPYvHQH21e/T2CrjnAoht/7GxyuTKoXX4JWq53oMA/YYFsb29o9DKs1BGMKFT4fTECiNye/kJfXBclOJPjgp39ldraZycYECz6sJdEc7jvkMQlHJkVWUMZgNu5YtDFR/vjHP+71/c9+9rOHKBJBEITdjds6qI6ODkZGRujo6CCVSrFhwwYAKioqsFgsvPjii/T39zNv3jwMBgOvv/46P/7xj/nGN74xXiEJhzFFUfjN939E0VllTC8+idys/D0ep5OMDHcO4BsIsWlFC1aHkWR81/L5ZCI9WEim4jic41tnrKqyhg0Db5JTbiPwYBLjvxoBCJ46CP+TXz5uxiJg0W6vzeYEFEVBURRUqvFbHjpR3K5s3K5s8txFrG1/A3uhht4dAVTZXlZtf5tV2yT0Vgljyk1cChCRvFhwc9rcCyY69I+1Zcd6FGOI4il5+NtUnDnn0xMSx4hnGGNhArvbjpyAzLx0XWqNywR9MWgbxjMygtPl2ms7q/72b1b/+E9c8K97yS088stqCEcHMX4QDkRXey/vPfoQ9ZVZTP7s9R/799RVZsWjCjM01MvSt6MUlGST+nC2bH+flwK3IX2gNP4leVw1teQu+wCNSkX39AJqCuw0Du5549J5V357t69LK6uZcSWjJauORtMXTGb6gslsfcFM4qkXKQhG0dTtpCTWzz9+cx+TNEnkRILo9OMxuCKoVEl02krKyiboyet+6HvpX+QkFKZGQ+w4+VOU1dVPSBzNb/ybxXIIlUai2WHDaVIjJwMoaj2SJKH2De9XOzfc8DUGBwf561+fGueIhcOWoozNholH8KaLX/va13b7OpFIEA6H0el0mEwmkegVBGHCjFui94477uCJJ54Y/Xr69PSMg7fffpuFCxei1Wp56KGHuOmmm1AUhYqKCu6//36+8IUvjFdIwmFsx5qNBDY2YvpiIdu61nxsoveE6acBsM66HJ99B+FglKY1PfTu1NLVPEDxfDVZedDbMsg7Nz+Ae1olZ3z32nGJubm1EXexGQAluesGTX0AN16SJB31sy4Lc0vIdn2WDza+TjLhAcBakmK4K4irwMGmZcuZsqACIwZiffEJjnbv+sOtyMgoikKurXTCagYqioJKnR4Yp1JJouEordt76bbGcVRa0eq0dGxtwnni3hO9rc9/gGnzAP3NHSLRKxw2xPhBOBD//tP75IZ81HZ00rJ6GZVz9zwb93t//joAjY/9jOJVG/G8l2BHRMHwmoW31ncTVFWi6NT0SXFGXvXhXDiLqoXjVBKhowONSoUsy6M7CxzI2AE4apO8/63+3DMYnFpP4yN/xJXqR5IkMjyNVEjp3ZtW9VqYumA2ADtbIntrakIlEgmU9euQTTq6FRWVp54xccEoMqoPx52yWkufL8hgIsnjrRIGQxbB/ijV+2hClmWefPIp8vLyxj9eQTiMeTyej7zW1NTEl7/8Zb75zW9OQESCIAhp45alePzxx3n88cc/9v0lS5awZMmS8bq8cITJyM6EbBOKoqBR9r0kL6kkkSQJs9WIOycTo1XNCdPOoLlvI3mTLPj6ukk8u46dG1thnBK9JYVltKzdiEoPypR8VCkzciyByWEfl+sdyXQ6HSfPPotfP7WV7q1eUEn0dg4hJ9T4hyL07PAiqSTant9O6+XPU/h/Cznr9i9NdNgfsW3bNkLhIMG2YhbMmj1hcbicmXS/FsKWE6evaxCdXaK8vgAlswvjilYAksF93/SWnncc615aQ6RvZFziDIfDtKzbgr9zgDmfPv2IXG4rHHpi/CAciIxsCyavTDClYLRn7PuERBSNWoXbrKdUL5HSW5j1qf/D8uwa7INRog41jmWNeEx6GKdEr33qVDr6eklEorT71KjtFkZSMmILrI9ylxSh//Z1PH3zLTQbUrSNJHFMz0Clgu4PtrB+fRtqWeaudb009wT43WM/Zfbsw2vPg60rljGoiuORJWwnLMZiG98VZ3tjrarnzYGt5BW7aOv3kjG7hPOybNx5/DtYIxZSOfI+21CpVFx44fk899wL4zazvKenh02btqLRaFi8+OQxb18YA2Iztj2qrKzkJz/5CZdffjnbt2+f6HCEMVJyy0u7fd32k7MO63aPFv/7/TnQ949lR/4WtsJRIaeogBM+ezkmv4maybs26/L5vMTjcdz/s8FZQ8NW/OpunNlWUkkNVocFb/cgF51yLU2t2+lIhYhN6aT6s6ePW8w6nY4z5l8MwOkzLh636xxNPrXw06zvex17jgGVViG3wkYwaiOz2ITOoGPYaEC/fZDkkH+iQ/0Iv99HwaQMUgkbS6ZdPOFJy/MXXcHynmfJcFvJKXICoGTs2sF7f2aKz7n4TLKnVGKymMckplAwCIDZYmHVC6/zxu0Pk73ZiwQoKZnjLz9nTK4jCILwHxd+7nTef1UhUFRAcU16/KAoCj0dXbiy3RgMhtFjg8EA63a2MxTzkqVTobaacRHGXFeObdGp9Ld3knz63/Sao5SetmDcYi6YMpWCKVMBOLoq7I4Pm8PB5FPOIvK7tzFrJaZb06t/QiYD9aH0eCEeSdDa2svw0Edn2E00Y8TDySVaWjLLqb7kigmNJb+8Ek/gOGqLfSS2G8jLS48fDHYdRID9XGX26KO/4dJL/2/M4hocHMTlcqFSqbjpa3fy92efZmDAi8Nhpat724StoBL2QiR6P5ZGo6Gnp2eiwxAE4Rgm/moKh415C3Zfbrlyy7t0xTcy0OVlVvkpzK4/nhHvCJtblxOV/NTNKiPkD9Oysw+NCZSkj57eLuqrp1BfPQU+f/0E9UT4OEV5ZZgNF7J2x/vIcvpmzGTTE4sm0Bl0oE/PCol7QxMZ5h719Hdjd1mIhmMMjwyTk50zofFku7PRttqIJwdIJpO0N/bRkfJhLzOiMxgY7tq/DVWKayvHJJ6G91bzxme+R68hzhV//Qmh3mH0inp0iWi4c2BMriMIgvDf1Go1C8/cfQbM8zf+jO4tLeQbbMx/6AayiwvobNuOZ6CB2vIMqq021rcNgc1K0mqhf92rnPz57+HKzaZu3qwJ6omwN3OvOIedUytYe9fPRzcbVdg1+1SnTtdW9vsDExXixwt/GFPs8CgvkV88lf6BF5GTMsFAiE3rd5Ky9uC2W9Cb48TjcXQ63V7bkCSJk08em5m2d9/1Sx6470/MnFvOcy88weDACFarnYEBLyMjfnbsaKS+fvw2VxaET+qf//znbl8rikJvby8PPvggxx133ARFJQiCIBK9wmFq+fp3GdY0IispckqcDAQ6ePzvD2EvB0e2BdOgHrVahc1hIafQTW65HU9fiIwM50SHLuyDy5nJafPP54XXn6ZxdSsqCdp6BsnMThLIlnC8dhZJWcvqzSuYPXneRIc7amvzOoYTPajVWgZK+yc80QswMuQl5IvQuWOA3GIXkfIczH/pAyLoEod2hsSOv72JZTCKThvl3wtvwmA2EsrQEHEncU120RPYeUjjEQTh2OMb9rDqiRcxr+rDnGkk0wNrn3sTbU6QOXNsFJQaadikoNdqkCWJ2WUutBo1RLP23bgw4cqm1HHZfXfy2CO/pXV9An1+BMvVxyGpVVyUt4bvXlhCKtFMx84misrG5iHmWNjw7gd0xRX8eu8+698eCmFfkH9+ezuKa5CsaSrmWnVoqvVM8nvoj8WJfLiZ1KHyzNOvIMsqhgcDVJctIh5L4coGp9OC0+Fm1aq1ItF7OBIzejnvvPN2+1qSJNxuN6eccgr33Xfffrdz9913849//IPt27djNBpZsGAB99xzD9XVu35jRKNRvv71r/PXv/6VWCzG6aefzq9//Wuys7PHqjuCIBxFRKJXOGzE43H+/t5vMduMtDV2kJnnomNnP9m5LsLBFnwjfhIGF/0dXvraBwnE7Lhy7KTk9MYQeaopZOxPfT7hsHDuqRfx0son0edGGezykVtix+f1k1+X3kBs+TuvgZJias3sQ3rD8XFy83JxudOb7kn+fdewOxTMTh1Tq8rwDoUxWYyk7LuWXI5sOrSJ1awFdfibuqgtyGT7yvWoIxJhk5fv/WYKOq2afyyL8vyXfoz7uHqOu+LcQxqbIAhHt9Ytq0ls+hebByIUTK0jUVmL1NBKxGDF72kkNhhgnc+BooOOHR1Ec80MhRJoNWrafFCwWJRfOlLkFBZw1pe/zG2nPEh5kYsp80oASLa0M03jA/p54p77OekrX6FkyqTDYsPbEimXvM4euuyHxwZ6voERbB0Sst1MiSP9mqJJj2/cOi29GzdgP2nhIYtn4cmzGej3IBNHg4lwKIqkjhEPJekLxXn/3fdYs2Yln//8NaObcwqHAZHoRZbH5n7g3Xff5brrrmP27Nkkk0m+853vcNppp7Ft2zbM5nR5tZtuuomXXnqJZ555BrvdzvXXX88FF1zABx98MCYxCIeHvdWcFfVohQMhEr3CYSMSCWN0K9jdarLiGeSW2RjxDFI2PT3Tpm3jECVTMwGwWixklVsI+iN0N/ag0kiUmBITGb7wCcypWExD51qMARNKvxljMox/MIbNrcfusuJ37ODxN94mW13F1NrZlBSVHlD7HT1teHxDTKmZedA3ewa1hfiHnyfl+F6PPVRqMufSFFhOKqbQ3TZAp3cY96dysZQ6ieUc2n8PCy47hwWXpWvwPjzrCgwNPUR0CUKxJDqtmkRvH4FHN9L/uzfIKM6l/sQ5hzQ+QRCOXgn/EJX2FH1RFbNOLgFAZ5WZVZXe9GrF3cuoeqcz/VD4/DKKXAZ2vtfGuse306eCMy/ImLjghQOWW5DDF399HpvXbaSxUYtKFccv2QjEhrHqNZTFJMK33svT2hSVt15HSUUlzkz3frevKAoN7y7DluWmoK7qoONV5bqhuQc5fHiUbqidPYWtF6xhYMVa4kkVm/sCNMRkorkZaB0W1CNdhzSe+3/xfQBWLFvNxefcipySyC8zpstzKCnWrdvIxs3reffd99m8ecNhkbwXhLH0yiuv7Pb1448/TlZWFmvXruXEE0/E5/Px+9//nqeeeopTTjkFgMcee4za2lpWrFjBvHmHzwpIQRAODyLRKxwWXnrvGboH23GXG0gmk6BKPyHVqHf9iKrVu2ZCSIBWp8GRaSVSlIklQ0+eo+hQhy0cJLcrG7frzF0vTIfhkUG6+zvpia9joNNLZp6Nkd4OGqIevA3TmVa7/wnC1W2vklGgZekGDydMP/WgYm3p2s5ISydqtYYim+ug2horteVTeO+5fxMOh6iYnsuCC6fBhRLOLDsjnbEJi6vw5BkMruvBHdPy+qoBjAkjzU0+zJNcqFMKrRsaRKJXEISDNtTXyzs//T7uCiuVxXok/a6N13ZLBiWSAAzHY9gMKkwGHVk2K5VaH0lnpkgcHYHmLZ7FvMW76ilXV51MZ9M2+kMBwuseJzsjxgIUPDuexdvnRH3Sl7A79u9vd+Pq9Ug//hM9Rg2qe24kr6bioGLdvnMbAxUmRlQycxKJCd/MVZIkKk6rIZLXwc7JhVSfnkFsQxcLSk0ANAdsExLXvAWzKa3IpmXHAEF/GKNBIiszF50ksWjOJSQTKaLRKEajcULiE3anKArKGMzGVZQjd0bvzTffvN/H3n///ft9rM/nA8DpTJckXLt2LYlEgsWLF48eU1NTQ1FREcuXL99jojcWixGL7boX8PsPv42uBUEYPyLRK0yoeDzOOxtexCd1EAh7sQTc9LT0oEJCjqjx9ofo3OJBlmV6OwaJRCOklBT9HSP4o3YcWTYUScZg0bGm8V1KCsonukvCQXI53bicbiQVtEbXYHPriQz7MFoMtPdspCxQjc1q36+2NGoNao2azuHt9PdPJpaIEQj4yM3Jx7mfN3z/kZuXizsTvL0Rjqsdmw1IDtbOtmbKprsZarNgc1gA6GkZhixIpCKjG9Z8Es2rN/Hvux4h2+bCWpRN7WdOoXTa/tXIKzx7PptffAfdUISRJ6K4O71YlBgqXQhtdgYnX33hJ4pJEAThP/pbm+h75QniiX4yc3PZmlCzfX0/HW+8g8qkJmEbQN7pQFZLbBsawaPS0hryUdoxwog/RDiUTv5mjQyz5R/PMukC8XvpSFdYmf4bFdq0jsxty7BqJAIaLaUZsG3Vi9hPv2r/GtKo0EgqnDGFDf/4N8YvX0F/1wAQp7ym5oATtXNuOo2KChuNO+ITnuT9D5NN5uRTalHFE5hMehKp5K43Uwc38/jR3zzOc8//m7qaarJz3Hz5K1djtVr369yTTplGPLgRj68PV2IySp8ac7HCUJPC6RfViSSvcFhZv34969evJ5FIjNbTbWxsRK1WM2PGjNHjDmQsLssyN954I8cddxyTJk0CoK+vD51OR0ZGxm7HZmdn09e3582X7777br7//e8fYI8Orf8uRdD2k7M+9r09vX+kEuUXhENFJHqFCbWuYTna/AB5UiYSanLLM/B5R6iYXIRWpyESiVI4KV1ATKPVkF+bTvDZ7VYyi8zs3N6DklSQFDVtb7bw2J3L0VhNnPjzGyiuPrgZGMLEmlw9g7yRQtp7drKh51VGfINIkop/df6dC8+8HL1ev882Whu6yYrbSGkTPLfqN5TW5dG0rZvCaA7TwqdRnF+23/FoVVqSgEFxYDaZD6JnYyccDaK1aVBkhaE+D31tI3j6w8QCMpFwlL+/+XuKsquYO/nEj22jf6iP9r4m6kqnYzFbRl9fdsdvibb3Et6+g6AiE46E9zvRO/XEebScfSLDP/sXoWw7ECVD0uNx6sk6oZ6uhmby6ypQq1QYTaaD/TYIgnAMGn7/eSarRhhxO6grSo8NmjUxNNsSQALnuTrmKB5IQhITZXE1UYuD0667iFAoSvPzK+maCoqsor1/LfrX+gjIZmYsuWpC+yUcvGmf/TKd246nq2kbbQ1v0tc/Qu9II6bSmZRWTd7n+YFwhCe9GgwJI/1/38LTD/0ELXpOO8dDqMzClBt/cIAJ2/TtlkqV8ck6NC6S5OVlsn1jKyOeDjY1tKE0GdHEJQY8IeKrm3GddQk5ZR8/gWLte+uJBCIcf9aC0dc6Ojq55ds/obgkj6XvbkSWZWbMnMTixafsV1TfuPXLrF3+bfq6gxh1SUioCQUjKIqe2SfWsmrZOmomVWC1WsVM/IkmavRy9tlnY7VaeeKJJ3A40verHo+Hq6++mhNOOIGvf/3rB9zmddddx5YtW1i6dOlBxXbrrbfuNuPY7/dTWFh4UG0KgnDkODx2BRCOSQNDfSxb+w7NWzoJ+sMopMs1pBLpsgwAapV6t3MGO/wk4klQKegMOlCgfHIBeeVO7GodfLCTwLYOnDliB+2jgcvpZsakuSyZdyElVflMmluG5Azxypq/Eo3ufcbJ+m0rcRfaySt3UFiZhd1hQ2/UAQr2bCM7+tbudxzDI0PsbG5l++pOgsOHR31egEFPP30tfno6+1GpVNTNKaW8tpjSaW5q5uWTNATolxsY9gx9bBsfNL5I2LmTpvato68lk0m6hwcYTobxl9vxuvV4O/c8Y+DjnP/Tm3Heei4eOUJ7FgxUWWnNhvovnc0L/3cH91acy9OX3/6J+y4IwrFr06a1rH//Axr7fMjaXXMW5PiuxI/mwxJQsiwjxxO06kBjN6JSqbBaTeRmOakttFNXbCXXbaHCHkUnpj8cNQrrJjP13IvJLZzJCZE45YEA5pE3aVjz2j7P9bz7b3QeI8agGbPKjEEyoZbUxJMSk4PdtLz/xn7H0bGzia2rt/HBv9YQPzxK9ALQ09XN1q297OwPkldZzOIzZlO+rotpjX1kDPmo7NnB0L/+8rHnN2zYwRNX/4Onb/vXbkvv1y7fzAlVC1GnUmRmy2j0I3R3d+53XFarhb+++FOmzCpEZQ5gyAoja0PMWGJjYKSXz3/ha+Tl1vL73//xoPovjIH/JHrH4uMIdd9993H33XePJnkBHA4Hd911F/fdd98Bt3f99dfzr3/9i7fffpuCgoLR13NycojH43i93t2O7+/vJycnZ49t6fV6bDbbbh+CIBw7RKJXmDDr2t4ht9xBYUU2noEAAz0j9DR7CAxF6NnupbvBz2Cfh4bl3Wx+swvZb+D0+iuJdZjpaR1koNuLLm7DFChEP1SIwezCes1Clvzjh1jt4o/Z0aSmsp5s6okE4qBI2EvhtfXP4PV79nh8NBplZ3Ad//0rLuAN07Klk9bGbjYva6WhYRtdPR37df33tj+PV+qhelYBxfkHtiHceJo//UQMOhOZ2U6cWXZUKhVKKj1gVqlUSIoKm9vA8q2vk0jseXM2t64Eu7eWkvzK0dcikQimrUPoByJYm704hxJow8k9nv9xJEli0c1XMPX8RRQPgKPJz9nXf5amp9/G3RrCYTATf24d7/7puU/+DRAE4ZgTCoVo2PRvcreV0fqKi4Z3g6x9aC2rf7eZeGYY53fysd6YQ5vDyhuKm3/0SIQ/tZD6B39Mt11PS0s/bW2DJDQumuUqdiQr8CYNbI+VULbgMxPdPWGMTb/oc2x2VuLSqgCZQnUzm5e/+LHHNy97n5l9zcjaFACJWJxg0kdI30tD7yDPtcFb/9y/mXadLY3Y3/w5Rd4+ygZ7KCmrHoMejY3yylnkZFkoyM8hI8NCZU0hwx8+J0mG0hlpd1cjze++vsfzDUY9Z35/If/3s3N3ez0VlEg0OTBrrLS1teH3+/D5Dqw2qMVi5v7ffJ3iqkIGB6NIkpmvfutqnnziJXy+GLFYgnvv/RVtbe0H3G9BGEt+v5/BwcGPvD44OEggENjvdhRF4frrr+e5557jrbfeorR093uNmTNnotVqefPNN0df27FjBx0dHcyfP/+Td0AQhKOWmLsgHHI9/V2saX0TTUYMKaJgMOqRlRQVU/Ox2EyEfBHyajIAkFMyjjwDrkQNsRYfSjzJp068hDOPv4hkMol2lnZ06db8Kfu3LEw4Ms2ZdAKrNsOWna8S9AdQqdW88t7zFBcX0d3TTWaWA4PKzuxJx7GjZRvxSIKezgEkXQqQSUSTOLIcVE0pJrfEic1hoXukjYK8vW/it7FhDTpnilRfAl9PgvmTZ+31+EPJaDQxu/g0nmt7DL/HQGfTIJ6eILFYNhq1hrbHNzDcJRPv87Mq9wW+985TqFS7P987YdZHN6l75TsPYgrLJJHpX5BDRW0NJ3z3qgOOLyPTxbQLT2VLVCbnuEmUzZ/K377xGCZANmrpMcTo+P6jDDR1UDylmjmfXvIJvxOCIBwLWlrWE4nuxKYzolcF0Ks05DszqJaCEE8g1RUy4/hcALb2uNDpTDiDWZgtRjLcmXz5gYcASCQSlJfpRtutEffJRy2tVsuka77Jykd+wsbN/WRnZ9IzvBajOxtZSTK4sw23ksBQNpOiqkkMedoJnzaDkeWthFUawmof997joj9iZN0WK6tfshKRQoR+FMJs/vgyTslkkuF1b1Jg1CFHIeCup8C2f/sLHApFxTVs2dxPItFBa2sv7e3DdGtkAjYdQW2CZZkOnBlGtr/+FPGwh7ozLtrt/NLqEkqrS3Z7bXhohBd/vwIAVUrPueeezZw5s7n22i8ecHxlZSV85vLFzJrXw/kXLWLp0hU0NfaQlBOo1DA8NMDVV32B8865hFMWz2PylP0rLSWMIVG6gfPPP5+rr76a++67jzlz0psMr1y5km9+85tccMEF+93Oddddx1NPPcULL7yA1Wodrbtrt9sxGo3Y7XauueYabr75ZpxOJzabjRtuuIH58+fvcSM24dA6WusJC0c2kegVxt27a1+mp7+L7Kxs5tcv5tlX/ozBCan+BOFgDJ1VwtsfpLA8vfREo9r1Y6lSSVicBvyRRnb84DV2PvoKn3vt16hUKnQ63cddUjhKzZl8AlqNjm55AxaHgd5mLzFXFyNd7djsMWTdEI88s5SCkmxyK+2k4gp55Rnpk1NqMlVlKJYuwoEIUipOTW7lXq8HsKVvKXEidDT3Ul84B4PBsM9zDqWC3CKKHfVEQ53UzCiiU++hZGomAEGfBuMHbZgAJZpkxd9fYcFFZ+6zzd72ThKT3WiAys99inOuufQTx1c2cxJlMycRCYf51TXfRl9oxx9NMJwMUxTVodsZp+cv7+K56wX4m8Kcz5zxia8lCMLRI+Dx0PzbR/Cq4uRNnYG5op6XHnmW/HIHbU3dlKiSmOISWs2uh1dq/a5yT2oVVFZm8NqrK1g0ZSrbGpZSX3cCgBg/HGN0Oh0nfPUO1q59kUmTTSxd2kBpWRiAkc3dVErDBNZt5rWVbmrLbRS4MrCqMlD8BtQ6K1qNhmjFQpKbXweslJ1cuNckL0DLhpUUDG1g9UiIxrCOhWcs2OvxE6Gm9jhee72F4hIbJ52Uw+YV25ksB4kn1XQ7VVRkaRge1mBqWIp/3mJsDufeG5SgN9KBplqHRmXk+ef/fFDxXXL52QA0Nbbw3N9eY3J1LfGQTNPwEP39A6xaHaRpQ4o//e5fvLn8cexiNd+hpSjpj7Fo5wj1yCOP8I1vfINLL710dOWcRqPhmmuu4d57793vdh5++GEAFi5cuNvrjz32GFdddRUAP//5z1GpVFx44YXEYjFOP/10fv3rX49JPwRBOPqIRK8wSlGUMd/YQJZl+hKNpHSgyvPw7Pu/w1VsIq/KjizL5ITmUJhXxHuDb9Hf1oqkSAwNetHvMKDICj3t/aRSKVKyzOB0IyOePS/VF44d02vn4ujKpLFnA/7hbnLK7dgzLYRDUTIMNjRGBXtR+lebrMij5/W2D1JUM4UF1WeypuF9anJmkOPO/djrKIrCS0v/yvCAh/JpudgWZHLijMNzxulZi8/nleXPIqlCJP975+yMXZvFSEY9r93yINaSHCbPmbGHVnbJLy/G++J2YpkGps2fvV8xtG9vpnnlBpQ+P3q3nRM+l97Bvq2hiWUP/Q3vK+uJxYK4u9PxJeud6KQP6x0bNGiRGGnYv1IagiAcXsZj/NC1agUVTdvY6dCQ/VYTLzydRFpezrAqiKUyh/Pe+gaeoWFW/eYhmrp9yBotLU+vIvqWi6RORW+1Hd9wmIG2IZb/ZQUxo2s00Sscm2bM+BRNTevp640wOOjH7baR/PDH1qrX4NAoFLjS4weNXk0CkEN6Xt8xwmmnz8XvTWLOT3D6NYv2eh2PZ4hl615hUV0FtRY95kQZBRW149y7A6fRaFgw/zx6+94nM1MipTdAJIhOoyGupB+aWHRgDnv55wPf55Lb70Oj+fhbR6fTQVybomlHL3NP3L/NbhVF4b333mPr1m309w3wqbPPZPbs2SiKwlNP/p333l7Na68sp7ywnL7mdCItI9PFwMAQ4XCI3DyJvh4fO1tamT5j6sF/UwThAJhMJn79619z77330tLSAkB5efk+HwT9L2U/kt0Gg4GHHnqIhx566BPFKgjCsUUkegUAnr3xHjr+8g4nP3Ub0xYdP2btqlQqDHEnCWUEgIJJdnqbfQCEg1GcDhdWq5XCgkKGbR4kSSIZVcirTj+Vl1TS6Od6y3SsaveYxSYcuUoKyikpKCc2Oca25o2Y9DECQQ9SSo0uEaBnhx9JpdDXNYjWKKEgo9Fr6Je2oR/WcN7Jl+3zGuu3rcJYEiUz5YKUijllp6FWq/d53kRQq9WcseDT/Ow3tzCycxAJCY1Gw6Apjr3GAZEEw0qE3LYIW+57msl/23ui9/z7vsnWC08mqzif7ML8/Yrh9St/hD8awrF5GNUpNZzwuQtZ9fTLrP7c/UTNapyDCRLGCElFj0ZSEYtECGplKMtEVerGeeIMas4/aSy+HYIgHCJdXd189oov4w/4WbPmnTFtO6t+Ei0vGtEkQ5h0ajKicaJS+uGVzqJHrVaTmZ1FUW4WpX07QUmQUump6h2hPxym7LPnkJuTQby9h6mrt9F35v+NaXzCkUeSJKqqZlBVNYPh4QFamjsx5k6n0deHSq1icP0W1m3pRJVIMe+4GDUXmEilYgTmz6GvfxlTjp9BzqeL93md3q5VVFVmku3Q0jKkoe7kw3cZb0aGk472bN59cwXtvV58YR0oGjwxHwlFg9ZoYVNS4tOTLDSveZuaeR8t9/QfkiTx3Ku/ZMumBmbPnb5f11+7di0LFy6iuKiUzs5ubHYbs2fP5sbr7+C1195lsD9dLzghx4H0GMxkNONwZFBUVEhtRRW1ddVMmz7loL8XwoFR5PTHWLRzpOvt7aW3t5cTTzwRo9E4Lg8/BUEQDoRI9Ap0t3Uw8KtXsMsSasPYL2c854TLefHNv9GxpQ9kmaAvhslgIeCL4yp3AVBXOYVXVzSiLwzt9odRUqWfcCaTSVDJZNv2PcAWjh16vZ7p9XN2e21y8TBLm1/Alq8mGo6TW57eCVeJa3AVWOge3ESFdxKODMeemhw1FO1A51ChpGSMwVwK6w7vnz2VSoX7xQihze2UfPVE1GoVgaUOzNs/3KxksgsTcbyrmgh4fVgzPr5WoEqlombudNY9/wZhb4DSyTX7vL7OaSHV4QUg84RJAGx56DkIxenMVBMrNWIxZNAWD5OlNhFPhLms++84M1373cdYLMampSsJtw4Q3tlLOB7lwp99c7/PFwRhbP397y+wbNl6pk4d+02mXAWFWO77Fe/+6k6CcpigS412qp1YIIK9aNcS7axFS2hra6HEMwiJ9IqBblLMyzABoEooDCZSlCxcPOYxCkculysLlytrt9fUtg8wPfsoTpWClJtJvUsGtDQ47FTXZNDUuI2cnL2PBVKpFBrVCAUFTrY3tOKedulhn/DJziogaySBZHJR0egFUvTVOpmaKQEGGox69DoN8nDbPttyODIoKSvkuef+xRlnLCYjI2Ovx+fk5KDXGzBbTFgsZk488Xh2bG/ir0+9gUIAu82G25VNJOJBV+glGPJSXV7P8lVvodfr97uPg4ODLFu2kr6eEZqbO5g+o45LL9v/GqrCHogavQwPD3PRRRfx9ttvI0kSTU1NlJWVcc011+BwOLjvvvsmOkRBEI5RItF7DAsFgrx0031s7W3D5NahmHU4Cz9+KfsnJUkSWrtCUb4DRVHIDs6irnrSbsdoNBrOPO5i1m5eQbt/JT07/KOlGxLRFMODHiIrelFNtzJnklh6KXw8p8NFqWUGA6n1pFKp0ddDwTCtO4L0to5wWuW+bw6G+kaQIhFUEQsLjvv4GSyHkzMf/jbPfPVufEMBnNl2FMeu2pXxRJIBJUrAP8TSp1/ijC/uve7u81+7l8DDb9B2yTxK//LjfV571i1X8Mx3f47hikks+tZVAAxLMTRmNUUWJ45tHiCKpsCErcsLNjVGs2m/+hUOhfjrtT8g8K91qHwxkkY1GVEVwUILyr3fOOxvogXhaLNixWp+8uNfkoxomFK2gJK8/X9gcyCikTCTMqLkmjS4sgqpvOqmjxyTVVaO9oZv0rV1Mx2/fZKoyUxjME7nPTvQmCRGhrrxqbWoWxopqxez/oSPVzbnOLY1bsW5delo2VBZlhno8xAY8TA0JFGzj+eesizzzq0rsYckMr92OlOLKsY/8IOUnVtAR3AJw8nH+U+0iagMpMcQ4aTCqm19tPR1UbH4s+j2kmANhUKcdeb/0dTUwaOP3sMVn71kr9cuKCjgBz+4k5dffoVrr/0is2fPZtOmzWhN/QwMDJBvOpdwpx7F0s+WztWAxPQZU/c7yfveex/wjW/cxubNDZSVltLflR4Xxj4bF4le4aDddNNNaLVaOjo6qK3dVZ7l4osv5uabbxaJXkEQJoxI9B7D3vv5nwj94T1smRpCcgKrRyG3YP+WaR+ouBLGiIZIKIrdmrHHYyRJYtaU+YwkO9HlpJdqSTqZvHIHwaXDmB9qRntNIVwzLiEKR5HK0hre/+dLdDYFWPZnI/GojL2yhyVX12DXZGMy7TvBaMiUcBY6kAZch90GbB8nu7iA42+4hNfWvkR2QRIvCZK1DqRoksGIn2K1BveImtjGfdfC9SzfhlaSCPcOj74WDoXY/Mr7hDoG6fMMcekPbqJ14zY2vfI+8y45izvf/8tubZx91w0s/coDBP5TixdQWw1EFT++yVls/WANKgU8nX3YS3OpmTUVs8VC0+ZtlNZWodVqaVq5gfdufpCgz4/NL4OkxZtpgq4w6i4f/d095IzT7639JZboCccSRVG49ZYf0981RHQkA6vdgNuZNy7XGuxup8yY/relaD4+sePIycXiyiTw4J8oTMboimsIvpEgBmTNMrKkJM7OkcFxiVE4ugSsmWwKaWnXm9FOqUalhu5fPM8Slxbr/PP2eX7j6o1UDxtQSRIOx5FTbqyocjJvW21sX2BBl21jzYY+Xn9HQ8ibotg+yIlJDbNVepqWrqJ+0cdPuFi3biNNTekxRm9v/+jrO3e2sXrlRpoa2ygpz+Syyy7l8cefAFTcdNONfOtbu1bnTJkymW9+62buvOOHWOw6fBFI+oyo1Rq0Wg16vY6mxma2N7TgDwSYPKWa+vo6IpEoPV09VNWkN9r93W+f4t67f0vvUBPJpMyIZwRFsSFJEh3tPePzjTxAR/T4Qczo5bXXXuPVV1+loKBgt9crKytpb2+foKgEQRBEoveYEwoEaXhvFZNOmc+2ZWvp1/hxefXUf/lsKj99MiqVat+NfAJ9HUMEfAb0KQd5p+89KZOUY+wqICET8kXxtUaxVjipuey0cYlPOLoYDAaqyyfh92yiJ+pADWRlxgm0qzlz/r5ncHT1dqAxp4uGJZXEOEc7tqYtPoG25TvIKNZickv03LoeW1JDIE+NWdaABKlkcp/tFH76BFKLQvhMCv3dPdidDh48/wbcb6QHrk2TzXRfcR6vnvotolYNQ/9eyzXvPrpbGzXHzyL8o8/xzDfvJVykR282MqKEcddkYtnp441rf0rWzhApFHx2FctR05uvpWRbmHcWVTPjlstYc9lP0Q+GkSdnklRkus0JQgYNcr0TtaSic3vLhCZ6n735Xjr+/h4XvPkAxZXlExaHIIy3pqadDA4MMXlKLY07duCwZSIpWm6961KmzxyfmbKRWIi1zjJ8Iz5Kppy+12M9w8PYUkq6jOeHSeFYJsT1MpuNOdTMmDsuMQpHlxlnns3299+gMM9NbV36AcZOZwbb6+Zy3DkX7/P8kS1tqD5M3CWDsXGNdaydfNnl2DMasViM7BxcS/+LIUCLLc+JMRhBVhT2lZKcNm0yl19xHvn5uWjU6XuKNas2c9MNP6ClcRBZlpl1fAZ+v49bb72dYDBJb28v3/nOt3dr52tfu56mpibWvNeKKV+H1gDq0HQkScc7b23glRcuQ6XSkpWrpXlnK/n5bsrzq+nZJvGV287AGx7i5/c+BUi4XE46OnogqaKkIJtMWw6qyNiXqjsQkUiERYtOw2q18OqrL09oLJ+UqNGbnsG+p8kjIyMjB1RaRDi6lNzy0kSHIAgi0XssiUYi/PWcm1G928KrZ5cxZeZURt7diqTSoCp1MeWEOftu5BNQFIWMfAPZxRlkeGv3+eR6cPkIw/9YjWxQESZB5ZJ53Prtn8G393qaIOxmZuWJvP3KitHZEvGITMwwwq+fuofS3DpOW3gmLkfmbufE43He2/AKDU2bcBfZCHmipDwx2L89RQ4b2cZyYnTgznXS4TTCQIKEL8SwAj6nGntkeJ9tnHHbtbt9/crdvyPZsmtGnFHRsPR3z6IfihDJcjLS2bvHdmacfQqYtKw8/Q4Mcgyp3kXG1vT1/fUuJCmMBgmNw4qlPUCOL4JXJ+FbuYllX9rGpKwsotlG+qN+FIeGfA8EjRlkbB4iNimbqSfOO4jv1MFTvGEyusL0bWwSiV7hqLV61Xqu/L/vIBPiBz++keLiIgLBMFrXACeePI+8vLEv+wRgMOmoW1BC804PFVNm7fVYm93Oy5U26mxq4i1erFU+pn3xHOaetnBcYhOOTlqtFttFVzDQ/AGQHsOatSqiy1/msZ3bKZ98PMede85HNmft7+ik/XdPMpDoovISLYocI2E+shK9RUXlbN26nZpaMGXsGqv7oyl2hEfosyjUBUb22obVauW3v/3Fbq/d9/0/ofpwIzWVSoXH4+ONN97E7/diMDjo7OzeY1u/+tUDfPtb3+eRXz8NQGlZLm2t/ciyjMuhIxJS8PnCAPT0DJFhchNPmfnTH58hpcSpr61EktXI0QQ5mfNRYjoMyQQjjWpOunZiB3Z6vZ62tnZAQZblcZtoI4yvE044gT/+8Y/88Ic/BNKrU2VZ5qc//Sknn3zyBEcnCMKxTCR6jyHv/eopVO+2EFWSJLZ2c9rffsVAyEteZhanXn/VuF03GAyiM6UHMCl53zMJr7zhazy9/ns0Pf4qmYoOTebQuMUmHL0ybA5u/8b3WL1qKwa9hs5hHbo8DyVVuXRv8/BB27Pk9U6iqmASXb3tDIa6aOraTPnMbKSuBDnldkK+KJPLjrxZ5OFwhM6NQ2g0GnzFBtRWI9oRmbAxQemIhujyfZdu+F+B5m6Gg15S+SZ0FhOKUYst303BE19Fn++iZvbUjz3XaLOiS0kgQTwaHX3dl4gy7EhicmXg1ySRJrlRQlH80QAlPWpsoTgG1RBmWY0/T43DKyMjkcjQ4/jKqcz66kXodBM7K6f07ONY8fT7DG8XS/SEo9ePf/AI3pEI2QV6lq9Yx+8f+wW/fvD3nL5k8bgleQFQ0mMGtTr9IG5v/94NRiMX3nk3A7/9LoUZZsqHIoQSR9aKDOHwUDxnPuayMlrbBgANuiULmTY9k4UGHcuueJLVbyyj+KZrSWj1tDZ0sfK51zjT10VRUiE2xcrkbA0NUSdTTlg00V05YD29w/gDwwRTPqrr4mgSSTzJfqaXgFWro7O384DbbGvpAVOY7DwjJpOR7EwjZ555CpdffhmFhYVMn77npKskSdjtuzaO1el23bbqDaDVJdEaINtVj9ngwOsbIqUL0tOuoqgsg96mJJCkqtLNYFxBrUtRPsXFzHmT+dwN5x9wP8aSSqXi3HPP5pFHfsO2bduYNGnSvk863ChjVLpBOXJLN/z0pz9l0aJFrFmzhng8zre+9S22bt3KyMgIH3zwwUSHJwjCMUwkeo8hrVt2EJqcyVDET+GUKvQGA9c8cPu4X7d/sJfQgExwwIsu2cDMSfP3eU7tlUtY98yr5ATV6N32fR4vCHui1+s5/oQZAJiboTm2LP20XZGxZRvo929l43sfUDrNjcqqwhTSpWecSwp9TX7qc4+jKL9kYjvxCUyumkG4uxurW4dHZ8HS0osdFSNuC1I0QbzPt8+kyf9a9JPrmHXr5fy76hpMJInVZXHGV6/cr3NLJlXz0mQXuUX56IxJ/Lp+5GCUWCJO+YgayRNEP9mFbXN6xrBU7SAU8JIVNODJ1GEeiKMMhQlqtZTc9hk+d/uXD5uadjPPX0xhYz1mm3WiQxGEcdM32ElxpRWVOkVZWRG1tdX86qGfjvt1Rxqb8b65lZRGjd/zDjNO2vuDN4vNTmfJVKTWDaSUBBq7bdxjFI5OmZnZZGZmAxAKdzE64VKvpaTfw/av38n9K50YZQeOggEyy9LJqnA4xpZUATmnfPqw+Tt1IEqK68nLHybTqWHTY6uwqY2kbBas2vRDWsXnP+A2//7mvbzwj9e4647HgAiLTj2bz39+/zbcKC0tZOr0KkpKCgj4AqhkI35/GFndS/PO7QDMqrqQnp44KSRkZIwmDcFQCDACkEjFcOSZ+c6vPsvxp8w+4PjHy69//SBf//pNlJeL1UBHqkmTJtHY2MiDDz6I1WolGAxywQUXcN1115GbO44PQQVBEPZBJHqPIbM/fTqrnv4xWQVWvvz3Bw7ZdU1GM44iA2argVD3/i1NmnTiXKbcfBFmb4I5X913TTRB2Jfaiik4+jPZ3rWe4EgPvq4EapUePRZSAxYkSYcxpkY/nMvcigomV848YjZh+19ZmTkoO7UAqJy7+hDSyfhscdR2My/87kk+85Wr97tNpzsTR6aLZJYJBmJEe4ZJpVIfWb66J0ajkW9segaAof4B/pp/ETZZTSBPPXojnEx+uBN2gY3iq0/FkZfN9nufRl/mJKuygtKyHOy1RUw/acF+x3yoZI3njEZBOAzMnTudP/z+ea6+5jxuuOGLh+y6GToDFZH0A6DmsG+/zslbeC693RE8UxzMOm58SlIJx5Ypkxezc2cD8XgXw0YNBoMZizGXCo2GnJwC1FonA1NcqExGyitLKZ8544hM8gLo9U5gmJKyHFYYFWxxCHljbM8IMeK0MBL3kNHdSX5+4X63mZ+fS07uro3penv2f2PESy/7NJde9mkAfvPwk3z/O78FIKdk10Mcg1mNdzDF7OPqOOei+Wxa38K61ZuoPWEypeU5ZOVkcNKieeQX5uz3dQ8FSZKoqKiY6DA+OfnDj7Fo5wiUSCRYsmQJjzzyCLfddttEh3NM+d8auG0/OeuwblcQJoJI9B5DZpyzCOf6Isz2QzvzLMudTagjitlqQKvev8L0kiRx6fdvGufIhGNNTnYeOdl5LJx59P/h7tjZg81rYtASJ6PcjhyMgFai2KdF5U/Q/KO/8b7eygnXfHq/25QkiciCHCSXHkOxnY6OVkpLd79JWfqnF1h7y28pvfp0zrnrho+04cpyo+TZoStIpN/LDosGm9tJVCsz6bYLmPa5s8ktLQLgpMvPO6jvgSAIY+O+n/+ASy49lxkzph3S66pMu8YrKoN5v86xu9yc8tWP/u4RhE9KkiTKy+uAOmof2jWrfO9Vo49MI/3ddL21HHU8jip/BLM+RKUviPGOc1lcW8R77zUQT6xhcFCD273/Dzmzc1yU57nINDvR+Pa8VP9LX/g269Y28ON7vsHiU0/8yPv5+buSxbFIHLc7E4fDgTUzxpWf/wxXfuECtFotl15x4P0WDpwiKyhjULphLNqYCFqtlk2bNk10GIIgCHskEr3HmJLaykN+zYGhPsIjKTr9XkLD/azUvUtFfv1HNsISBGFsBIJ+CqqcZOQYCWQ5MLWkNzpRT85EJaU3hzGaTLS/tOKAEr0AU29bhDE3iaIo+PewhFNnNWHrjdDzk3+w7Ph65p+e3oziP7ObJEkiUeXCo5JxDspIKhWWkIryr5zLqV+/6iB6LQjCeFGr1cybd+hnx3qHBliXMCErMp7hDlLbVlJTN/eQxyEIxwp9JMSUQA8ASoaZmmAELBa6lfTqHa0GcnIstO5sO6BEb/2kGsr8Fei9JjTqPSf2jEYjbS2D3PfT31FeUUxJSdFuM6MLCrLJLdITCISIJ5IMDg5TUlLCt2+7gZNOOukgei0In8zll1/O73//e37yk59MdCiCIAi7EYleYdz5A14K6uxotBq6t/mIONto7pZwOT76tF4QhIPXN9CHNTNdf1dx7Po1H4hHGLLE0DtsBDRRks3tPP/Dhzjv9uv2u+1y51R29m5BIxupnfrRzUOyakppv/x4ckqy2frqUtZ+/udU33gBp31jV5mI4ql19O78AOuCWowLqjjx6gvJKdn/ZaCCIBwbrKokleb0w6nt5UbMhlaCwXosFssERyYIRydNyDP6uaxJJ1llWaZ5Wze927uJDYR5qSeAO8dNT082eXll+9VuRoads759HM0rO5h5Vv0ej5k1exISEu7sDO76wf0s/2ATT//j10yaVAtA/eQ6LFYTkkpNdW0d3/nu17jssksnfFPWY9YxXroBIJlM8oc//IE33niDmTNnYjbvvvLk/vvvn6DIhLHwv6UcBGFf/vtnZqJLf4hErzDuqivq2fr+clwVgOrDJ/NH8A6rgnC4y8vO5+V/duLIzMDrgIznl6Ayq+DJLZQ8FkQVjOGbnIl98xADW5/lSYvCSZecTftgIzWF0/c6276qdBJVpR+/O3RJTQUlf/w+ABveXc66B97Bt7ltt2MuvO8bcN83xqSvgiAcvewzFtK/4wPsagWdQYVarSKVSk10WIJw1BoOSexs0CIlNfTnaTFePh+tUY3uwfeY6o0hSRJ9ty6mdnI+mzd9wEi3B7U/SGzEx7TPnLPXti+6/hy4/uPfv+yKT3PZFelVRt+742c83/c+DdsaRxO9Wq2Wd5c+N2Z9FQ6S8uHHWLRzhNm5cyclJSVs2bKFGTPSmz43NjbudsyRWqdbEISjg0j0CuNKlmWefeXPRLVhotvCBEfiBDrs1JZXTXRognDUMpvN5JZm4i6yEE76cFdloDfqGK5woZI6AEjJ6SkUWkWis7+ZNd2voDImeePlLi6+dP83adub4snVrD61ihnXH1h5CEEQhJHBXtb/7kEyEzo2RMI4R1QEQ0amF9snOjRBOGpZHHk4miUghVKbzZRZ6Zr5niwHkq8fgEQ0/bClti6Xh6/+NUs8EjtSATLnTqKgaP9m+O5LbV0Fn7l4MWefc/qYtCcIY6myspLe3l7efvttAC6++GJ++ctfkp2dPcGRCYIgpIlErzAuUqkU6xqWM+jvJq73UlibAYDVW8XMuvkTG5wgHAP0aiMAxZXZeAaD5BQ5CWqj9Jsi6JxWvHEf0iQXalQEX9tJ619bkAaCBEttPP1yK0UXHk9meSEVk2s/cQwOp5MvvPbIWHVJEIRjgHdkkIGmtUgjOygY7KNSr6YlJlO/4FIxQ0oQxpnWYUNWFFSSRKw/XYdflmW6o2F2DgcJaWxI7zcTR4VGq6L47GpS9fnUSQla1/4NX289SXUm5VWTsdg++ebPF19yHhdfct4Y9UoYD8fyZmzK/6xMffnllwmFQhMUjSAIwkeJRK8wLpZtfJNkdg8mp4R3h4Qsy3hak8ycvue6XIIgjC1PTxifJ4VKraKvexg5DsZCC+awBms4CXlqHE3DAMRrM8jo8AIq0BgIPfkBDU9+QG+1hdolx3Pq967FYrdNaH8EQTg2dH/wFHXOKMNyEJWcwp+E5PGLRJJXEA6B7NIi/qCR0GksdK0YIPKZV0iORFkXT9K+vQZJkrj4//KZNbsAAJUKaurzANgx4KEmy8Oqdet54tv/ZNoXF3HcxadOZHeE8SRq9I7638SvcGQ4mBq8on7vsetI+X8vEr3CmOvoaWXNlg/ICTmQFBVJrwbtQAHnH7cIlUo10eEJwjFh/uSTaYovxWwzoNIo5FbYySnLYL1jHXhSJH0hUooOtaQiGU1vdhRWkrSFhnEX6tGZDQSlGB1vrOZlg8Rn7hY1dQVBGF9b33iW393VgMvgJKaPcP5nlyCVVTJtwfETHZogHBMyHA6KT5xH31PNSLIGezSJTmXAqVXo+PBhy0hvZPT4D6tA0d7awzvvt7N+hR/fEJjrRqDzVdp25FJS/fF1/QXhSCRJ0kcePoqHkYIgHE5EolcYU8FQkA3t75FVkEluuY3ASJTZJadQXlQ50aEJwjGlqqyeHcvWgU1GSaUHn+FglN4iicS0TKzFVtCFKCnPJLqzF8+LCtJImCyVmdzOIBBDPTkT2+YhLJeKmpiCIIyv7sbNGNtWYA5b0fkNaKtMzLh8bOqFC4Kw/467YhF/ebGRTK8TrxQkCw0BXz8qW5gct4mBHV7WrHah1apo+ucq+PUIjnicwb5yWhrT440rvm6m0BInZjBOcG+E8aLI6Y+xaOdIoygKV111FXq9HoBoNMqXvvQlzGbzbsf94x//mIjwBEEQRKJXGFvvrHwNn64fFC0AsYBM+SSR5BWEiTCv6nSe/+Ax/N4gklZGY1CY+rMlFNY6Ach8dxV1uWocdgPLHhnEKGkIhHfN1EkmkgC0/vKfvBCMcPzXL0Or0WK1WsXMBUEQxtS6F/6Mxd+JNlYKaiiZMTabOgmCcGByCnKY9pVqVA3L2bY2iFmWqU5InJWvwapR0aAozJqZD0BwpYOq7hHQ6zDpZfwfthFOaHCYVDS+/gih2Rdhy8onw+kaTYwJR4FjuHTDlVdeudvXl19++QRFIgiCsGci0SuMKYfTjqKz0LSuh3AwQrlz+kSHJAjHrP6hHipmZdO5RUtueQYA3X7f6PsJlQFIUVfm5C1HH5InQWfASyrficVmZTARRF/vgmiSrf5t0PM3kvEkQ01RaqumMKPqeCxmy8R0Dti6bA3db63DVJTFcVecK5LPgnAE8za4Wb6mi7mFXtozAiz4rJjNKwgTJc8QpsIZIWCxUzAskW/KolsTxypDostHMplCo1GTMukA6A5HGEl2o8t3YzPr2LBimITDjCqUINjwILNtMt2yxNKeXKaefRpTz1k0YX1TFIWnnnyGjo4eTll0HHPnzp6wWIQj02OPPTbRIQiCIOyVSPQKY0ZRFPrDO3FmmckpcpFtLmHRzDMmOixBOGZFU2EkSSKVSgHp0g3NDa34A050Wj2GiAWLugqMdsKFnRD3Ue83IscTOLp9+B1JrCMJJElC8biwO9NL0kJDKeTsflZtf4tTZp4zIX1r37KDv3/pLoq2BJAVBX97H2fe/qUJiUUQhIPTtG4b8hYPLpud3KSO8usuorC0cKLDEoRjVyj9UFgyqACFwWSYJnOQeG4mVmcG773bRXFpJWF7Pm1fzsPpNDLv+Vb0f/WDH6IuG5UDIQC6zRYcpigOYNtQlOh9L7DDnUH1/JkT0rVHf/1nfve7P7N9Rxu/+uXjPPfCb0Wy9xM4lks3CIIgHO5EolcYMxu2rKW1bSeBhA1ff5TTF4vNUwRhItWUTOHJfy1l2DuE0apDpVfIK86hcmYOANKAi8ppnwKgOPsVkpv9IOnxOPTQF8PiSRJSgUXREGoepK9rmKEuH57eEIqsIMtD/ObuH5LdpSXuCVL5f6cw/exTDknf3vjqz0n1+VAUCZUk0fvaGrj9kFxaEIQx9t6zb9AdG0CjB/+sMmafPH+iQxKEY1vtXN7Z4iM62YtusYGpJgfZSgFTCwwANFnrqaqaQ0ZGJWr5AzIyLGgz2kdPD0eTo59Hogpr/T6Gokbe9g0R19jp/dZvyJ5kJKUkCAQC3HvvPeTl5Y17t7Y3NPGzH/+FzPx0ibmAP8Kbb34gEr2CIAjCUUUkeoUxMxLrYcZJ1Qz0jFBXcQoup3uiQxKEY5rFbOHUBefzzOu/J6vUhkqlwtvXM/p+TN5Vj9eY62JQSdKfLRHMSJHIsKPVuOiL+HFLJjzdI0hI1M8tZfuqToqnuADY+sZ6PA9vBqAj03bIEr1tvV1onGY6M8CWVOPr7EJRFFG+QRCOMLFojJH1QTTRPChVM+PbV4p/x4IwwarnHE9S9mNoWkFttgqA7uFd70vJMABudxaN2yKEIzF2DvURM0Qxma14pQS9n61HY1LT8M8m8lcXY1TrGNb6WbXZC0DD8Gq2NWwB4PLLLz0kid61qzeRk+UgFQ8xrX46IX+M9sb+cb/uUUlhbOrrKmPQhnDUK7nlpYkOQRCOKCLRK4wJRVEYjLWSI2UgpdRMnz4xy7EEQdhdWWEl8+oW09+1DUeOmaH+YeTVKXQ6PYHBKGrpWWKpMN0nS2R/6Twq82xs/+JSzK93AKAqsWJv82FWJOR4OpEqS0lSyRRqjRrZlf4zElYS9HbuoHDbCqKpIB3tHdSWTqW2cjI6nW7M+1Vkc6NZ3YEnR09GXwQ9KbrbOigoLR7zawmCMH5WvbqKVEsCtaQmMz8HZ5Z4SCwIh4P6eWfycnMz4Wg/yZRMa0eAYLcKVUJFgJdRNq5CCXjYke9kxrxyPnvtqTz19Ou4gnqCVjVTZ2SiUqno3DyMcV0MAItqV/sGXbrGv1qSeOf1dxkeCNHR0YcSTnHOp09l8sy6Me9TbV0F/jY9eleC3pEgAJ7h8Jhf51igKOmPsWhHEARBGFsi0SsctEDQz4qN79HR1MdAzyBFmTVoNOJHSxAOFyfNW8zA0GQCAT/lpy5kR/JNjCY921d3os4OYgLsgxZyip0AqDINo+dKxvTyRo2kYrDdw/CADymlYu1bzeQX5OBzgPPVM7FkqFF3h/Bn7AAg1u1lJGMzr6zaxjnHXzHmfTLmOEjQgdpugr4YkkmLJO4WBOGI0rpxO80fLCfm6sUfTLH4AlHXXxAOJ2dc/lXam7ejN1uZJL9P8TvpWXWbnFbKh4YACOdWkZubHj9Idh14wD6ip38wQG62HcmUoC05hJSp4DaEuP5KN/lODR3bVfwovwZXXOHeNZ386bEVABxXPIsPfvM41/7iNM69YvGY9qewKB+dSSE4ogBJQINapdrXaYIwrt577z3uvfde1q5dS29vL8899xznnXfe6PuKonDnnXfy29/+Fq/Xy3HHHcfDDz9MZWXlxAUtCMJhTWTjhIMSCPp5Z8vzdA43M+PkCoIDMgurzprosARB+B9ZmdlkZWaTSqVYsyyBsVxPSkmMzsxNJBJEw1FaG3rpVgexV9vQaLUMp8JQYSflj2BIqKhdWIokSfS0eMgrt2PMLARFhTPbRn+nZ7R8QjwRR5Ik9LlRXvvgBU6atQS9Xj9m/Zl725WsNf4VQ5GTou9UkTWpjPyykjFrXxCE8dWydgtbbv8Ljgwf55VFaJ4xjZmLRJ1MQTjcFFfUABApKiEgK1hVEol4avT9ZChGX88wO9btJFwexVXmpiQ/jxUtfio0dqQiJyef3ESR3cz7HhUL56THAq8HbJQNA2pw6uKj7Ul6BUnR8NSP38GebWbhaWNXs9udlcm1t5xJy/YeTC6FaTPrmTV78pi1fywRm7GNnVAoxNSpU/nc5z7HBRdc8JH3f/rTn/LLX/6SJ554gtLSUm6//XZOP/10tm3bhsFg2EOLgiAc60Si9xjU2dvG2u0fcPaJl6BWqz9xO+FwmFc3PEVmmRG5V0U0mKQu83gy7I4xjFYQhLGkVqtZNOlidnY3UGQ3sH1lE84sOz1dA1izDFRMLkReH8T053QtX6Xeia15BABVUrurdmYq/bvDmZVB304vAV+IoaZ+VnljZOe66e8cxGw2gkahp20VMZWfBTVn4HJkjkk/KudMpfKvU8ekLUEQ9k/L8jeI+EaYtOSig2pn+/traPreM1gjElG3ijZnJnWfv2aMohQEYTyUzp5Hp8nMSEsTze+vYfUHEpEQ2FYt56zMKPP1WqyfPoEZp1QDsHlzD5Mm51FXn0PjBxsBsCYihKJxzAYdKk0c0NEdC6DXeDn/lBwq8m0MdvoxmXOQg3F+852/0Ly5k6tvvPCg7ln+2+dv+MyYtHPMkxmbGr0i0csZZ5zBGWfseUWLoig88MADfPe73+Xcc88F4I9//CPZ2dk8//zzXHLJJYcyVEEQjhAi0XsM2tK1jITR84kHTNtbttDp3U5HWzcac4rgxhCBwSj51VOpKq0f42gFQRhrLkcmLscJALy1+l+E5GHy8nNQqSS0Og0p565ljInYrlk2/Y39BBwJdDoD/W+04+1RSPWF6Pd4cbWFKQyB8e2zyKvKRG1NYXFosDkseIa9WItlPmh9jqK+aUyrFbP2BOFI4/f7MKz6O3LBJ3vAEo/H2f6vV0ht2cbqNi9Rgxr0SZREkjk3fxOzzTbGEQuCMNYK6ydD/WSsk+fy5qNv0LOpG5XZQa4hvVNb0rur3m0qtaucUoM/Rq9XQlLZuf2vQZAy6exLcssXLdQ6c7mgy8aJOemZvn/5QGKoSQtoyXCoeeaH69j0fivfeOByikryD2l/BWGitba20tfXx+LFu8qY2O125s6dy/Lly0WiVxCEPRq3RG9bWxs//OEPeeutt+jr6yMvL4/LL7+c2267bbeNeTZt2sR1113H6tWrcbvd3HDDDXzrW98ar7AEYLgnQHffINuszdTVV+zXOZFImM3Na9m6cx3WQsjMzSDS4WHSlFJ8vTHmVp1GbblY+iQIR5pTZn8KgI7Odt7Y9hTD/V5G5BiRK0twVeUQj/nQzShBZVOj6Rymfl66dEPyH10Yn90OgFzvwhaOgARKKN1ufombvlYvNgdIGoVVb2wlIyODra3P0PHHZeisJqwFWRz32XMnquvCYUiMHQ5fer2BP2+XsA/3kXnCII793DRtoKOdoRXvsu3NpZwwnESSJFYnM5HatejnZHH+jy4jM1dswCYIR5L80nw+e/eVADz2vR/Q293J9lCKzjc3E2wPIXkTdPf0EUGDyhvCZjYxU05PAf1NhxNvl0RSdmEzgNtmpFHykEhq0GrUGPUhIP3gJxQaRHFI9LbH+fZX7mL6whrCoQhnfeo0Zs+eMXHfAEGUbjhE+vr6AMjOzt7t9ezs7NH39iQWixGLxUa/9vv94xOgIAiHpXFL9G7fvh1ZlvnNb35DRUUFW7Zs4Qtf+AKhUIif/exnQPoXzmmnncbixYt55JFH2Lx5M5/73OfIyMjgi1/84niFdswKBP1sbFlBddlkMvQFmEzGPR4XjUbxeEeIxiL4wx4kRcWzb/yJeWfUEJKHKHKXAaDEFWLdZk6o+BROh+tQdkUQhDFWWFBE/AMNFfOyqJxayOBMH7mlLlLr23DX2tEZdPh8odHSDUrmrqRbSgVRJUlfkQblpW14XrOg6o+xngwUtZqg18lZ30xSWJVJuxk6b34Ok6KmqcgqEr3CbsTY4fC0Zc121ryxhUnnfpZIfzdGi2WPxw32DSCpJQZbO0kEwrTv2Ixt5avMdJrpk8xIkoSiKATVcWq/ehynXXPWmC3HFgRhYiy44Hy2/7CDSTGJaDRFWVu69FPcpaYiEgM0bNdrIZKe7Wu3qfACGpWGUFKio9+LPxDniQ1h3KZsdnZFmHNeD3ZNAnZEeee1MlJ+8EkDfP/OXwIwODQiEr0TTFHSH2PRzv5KpVJ873vf489//vPow+CrrrqK7373u7vGp2LjMgDuvvtuvv/97090GPut5JaXJjoEQTiqjFuid8mSJSxZsmT067KyMnbs2MHDDz88erP25JNPEo/H+cMf/oBOp6O+vp4NGzZw//33i5u1cbC9bSNJdy8+X5RNG2K8+Xw/BUU28qpTVFUXoVZp2NK2CnOmGp0dPL1BCqrcdLb0MeWEEvwjEUhocQRqSKRifHrhYnKzxRIqQTgaSJLEeYsuY0XbS2Tk60Zr8JbW5jHSHyCn2MXwyAjr349iNpkZdiuYfnUi2mwTsf4BCmYtoCrXxo7bVmN8cgsApk+fijfqQK8FJe4BoLA6l/VuA6aBBFK3l7adrZhNJtw52R8bm3DsEGOHw9MLv36HbS/1YinexinTgmxYuRZtVSkhl0Je/TS2LV+Gf0WcZJOXXs0gi9VWVJJEKjtCkWKioUchUJTB0FkLkRX48uKFGE2mie6WIAhjoHrKZOQrP4PvsX+QCPpRJD2SJBGLxAAJWZZpHhgmqFahNlrINw9TNz8HoyKzY1UnJ2bBqUYtz/RmYfNrqU46yJkxTJlNT48zzluKjEpSQWhXWanO9h76+/sxmUxYrdaJ67xwSN1zzz08/PDDPPHEE9TX17NmzRquvvpq7HY7X/3qV4Gjb+OynJwcAPr7+8nNzR19vb+/n2nTpn3sebfeeis333zz6Nd+v5/CwsJxi1MQhMPLIa3R6/P5cDqdo18vX76cE088cbflmKeffjr33HMPHo8Hh+Ojm3qJZQif3NDwIIPdI6RSKYb9A/ijVpYt7WRxpRm/I0IqmUJtSeIosBAKRNCZ0oke70CQzFwn8SE1s2tnMKNu7Ha/FQTh8JHjzuMk7QWsaHyd5k09hH1xDCYD/T0e5KREaXUuarWW7EIHCTlG0Ywi1GoVodUJsgrTv68V967f57pYGCSIJyJsX9dONOpCp9XjrTSjyZBI+cM8Mf/zZHpkau77PItuuGyiui4cxsZi7ABi/HAwHHIPs2ZG8MdkdjZ1k5AkWL+Zk0614e5ewY62GPqGCvRoyciwoIqmZ1bpdGoSsg5NfS0zzjqDwqlTJrgngiCMh9pPnU6r24XqN3/kX71DVDjcdA57cDrtqCJRbJKBqUkjBMBrs2PtlAA15klOskxBADQWNfjBpNETkdL1enNNCl5bK86MTFLJJDPqZ5JKqGht7KGsZB45OZm88OITTJpUN4G9P0bJUvpjLNrZT8uWLePcc8/lrLPOAqCkpIS//OUvrFq1Cjg6Ny4rLS0lJyeHN998czSx6/f7WblyJV/+8pc/9jy9Xo9erz9EUQqCcLg5ZIne5uZmfvWrX43OyIF0zZnS0tLdjvtP/Zm+vr493qwdacsQDicmu56iyvTNss6gJ7/GRjyWYNuKDnp3alCS0NMxiByXaG3spHZSDZEeLfm2KirUs6icVzvBPRAEYbw5MlycMecSlsy+mHUNy+kLtRANxsktcyBJEj0t6Zm5xVVZjPT5cOc7SCgxQv4wbTv66SSAqzYDtUaD3tTOqZ93obMqREbyKZ6SLvESNtsxftAOgLrcjmnQR+PNv8dVV8K0RcdNWN+Fw89YjR1AjB8ORlkkSVYkRSQZZ304D5VsZmuyk4GXXWitWiI5PhZ8zUrEn6J7iw+v5EZtMaIvsZF5/plU5eZMdBcEQRhnpXNnUTp3FtFIhPWPPIVv9XqMiTj5aBlKhuA/E3KTESA9szIUhi5NiJakmlY8dGdkoigaVv9bZnhEj3/YhTVLQ2+7QlK2EjD3koiqUTQqFEWir2+EK6+4nrXr35qwfh+rxrpG7/8+fN1TonLBggU8+uijNDY2UlVVxcaNG1m6dCn3338/cORuXBYMBmlubh79urW1lQ0bNuB0OikqKuLGG2/krrvuorKycnSWcl5eHuedd97EBf0x/rcEQ9tPzvrY9w7FNQVhokz0z+UBJ3pvueUW7rnnnr0e09DQQE1NzejX3d3dLFmyhM985jN84QtfOPAo/4tYhvDJJZTI6P9w35AfS6cJk9qB0xYhu8TCUJ8HV1YGJa56zrzsChz2Pd8sC4Jw9JMkiZl1C4AFxGIx/r3uT+hdSbqaBwh6I+g0Ogb7R0gE1ZgMJtoa+qiZWUxqOIr5523pZdt52RTUZgDQOjS4q+3sXfXBPeoEI5kKRoeN9x/+K/FhP3MuOuMQ91YYbxM9dgAxfvikFEUh6Q0AWloTfjS5xSTMZspKpiFvDKDrS2GdosJZ5ETlnMTJ185EozmkC8YEQTiMGIxG5t90DfOBtnUb8fzoQaLeEMutSWxuK76AB32xCa2cojcaYtZ5kznZYabrzz68r6ezwTqHi+BADBVajGaJICk0Kh0ZDjODvVHkmBGTRY3NaiWRiHHvjb/hkq99isJSUVLuSPW/f4/vvPNOvve97+322i233ILf76empga1Wk0qleJHP/oRl12WXhH2STcum2hr1qzh5JNPHv36P2OVK6+8kscff5xvfetbhEIhvvjFL+L1ejn++ON55ZVXjshSFIIgHBoHPBL/+te/zlVXXbXXY8rKykY/7+np4eSTTx59AvffcnJy6O/v3+21/3z9n3o0/+tIX4bQ1NZAh2c7udYy6iqmjnn7Xr+X59/8M448Ewk5Qn+Hl9ySTPQqI20tndiGLPg9AfJM5Zw79/+A9E3chobVaPAw7bh52G32MY9LEIQjl16v56Ta84lGY+imLUOTHQJA16gjr8oG2Ohp8aDWqCmdV0iLcSX2qERoSz+pZIpYNMGODU0E1/Wh7o/T1dxDdrWDlD+MpEDRIEhDAfz6ftbe+DDF86aQXSRu1o4mEz12gCN//LDpb39H7hug8MJzcRWM/b+PjoYNvHvv09iTBuIDPiJqD8XubIY0MrrcMIMqA8NeFf/3i6+QVZiuE+gb8bLplVWobCpqT1w0uhmOIAgCQMmMqXT99BbOcDrp/fHXKNcnWNcfR9sSwqjWEtVIZDvTmzsabUm8pEvy6KX0Dl0xJYAvEsJQbESrVzEcGcKYE8Xj8eIwO+nr8dIvB1gXbiYy8hy3P3Gd+D10iCiKhKIc/Pf6P210dnZis9lGX9/T3+unn36aJ598kqeeemq0Pv+NN95IXl4eV1555UHHMlEWLlyIspdd6SRJ4gc/+AE/+MEPDmFUgiAcyQ440et2u3G73ft1bHd3NyeffDIzZ87kscceQ6VS7fb+/Pnzue2220gkEmi1WgBef/11qqurP3bp5ZFuU++7uIqN9Pa3UseeE72KorBm6zIa2tZhcRkIeSOYjTYyHVlE/QnmTluI3bp7MjYUDrG64R189KC1prAXqgEL8aiMs9AAKDgjVnIr7CS6Cjl1zgWj50qSxPS6OePYa0EQjnTOjHTZBV+4iqH+PnyadpTUrjV70WCSrWta0Sg6EmdV4sytxJ1jpWFlF1nFNvLyHRiuWolGUmHI12DrTgKQqLKO3pQlAVtfiJc+/yOueOkXo38XhCOfGDscnJbVa3H87V/o1WoGJtV9bKLXN+LlhZ8/gbk3QdIbhllWpp0/BUmlx9MdZu7C0z5yTl9bE96Nb5Dlb8bQk8IRTOKNR8m2qcjuDdCdHGJxqYaQEqXy2zeMJnkB7M4MTrj0o20KgiD8R0F5OQBdJ5zFNs8wNYmlrOyQKZRB2xNle1s/bW1RenvCOBbNwp6fga+jFe/AIHGPi6RFy+amTgByyjQ0NHYBkJ2TRR9eVCoVOrvEjlcGeOoXz3PZjedPWF+FT85ms+2W6N2Tb37zm9xyyy2jJRgmT55Me3s7d999N1deeeUn3rhMEAThaDNua+u6u7tZuHAhxcXF/OxnP2NwcNey3f/8Er700kv5/ve/zzXXXMO3v/1ttmzZwi9+8Qt+/vOfj1dYEyocDtPTMcjIkBZ1NIin1IMjY/eb0kQiwWsrnyVq7secK+HM1dHT20n+ZCuyapC2tm4SG6OcedxFo8mRoeEB/vbyH7BnG0kmkgQ8EfQWLWgSKChEIzGC3iiDHX6K7PXMm7JgIrovCMJRoLZsCslkHY8++1MCwSAqlQpFLRPwBJh0fDFqjRpT+fHMmXQiAM+t+g2OAgM2p5Ft1lU4gpAaCpJQtGglNQPDw4RcGkwZVkbiftT1LtjZw7M3/ZRLHrxtgnsrHGpi7LBnA01NhGxawqEI5uFBymKxj8x26m5uZ9l3/0zIN0LmSHo5p7TYTkWlGkjy3INvkCGbqD7l+NFzNr+zirce+iP5LjNokgxpZSRTko7YCKdLGQQSMQImibbayZhmz6Vi7rxD2W1BEI4ik865mFX/fpV3+xvp0/qQjQYMYQvv/myAGslFEXoWvXwRNrudrLfWsPlv/0QLJKO+XY0o6Qd/siwTi4bJyXGQYc+gXWpFX5rBQ79rIafezaJTj99zEMKYGesavfsjHA5/5OGvWq1GltONfNKNy4Rjx3jVCRaEfTnUNXvHLdH7+uuv09zcTHNzMwUFBbu995+lCXa7nddee43rrruOmTNnkpmZyR133MEXv/jF8QprQiko5BRn4C6y0dPoZcXWNznjuE/vdszSNW/TNdxCywdtHH/2NAC0av3oHzWtVoehKMS6LSupLKllQ+MKPJpWMnJN5NemZ/maPWXMrj+BSCRCe2crJp2ZzFo3ppmmQ9pfQRCOTpt2rKH2+DxaNwySW52efZGKyqg1agCiydDosXqVmVAgSMvyNoZOdGGsK6SoyEbrgytxBiS0nhA5ERXG4RDhTBn70DAAoTUth6w/vuERNv/7fepOnY8zJ+uQXVf4KDF22DOD3UpNMs7aaJDsv/yT9qJiqubtvhLnjQf/iq+7kxFVjFw5C51Kg8m96+++PSox8Ou/UzBrKkPd/Wz50+sk3mzGqtJg6tAwIqm56u2fYDAYGOzvx9PbT36Wm+m5OR+5sRYEQfgkfMvbsDcZ0ORLlHmTgBafw4Lkk3AoBrq2t1A3dwYlNQUkiJFKxUiFBjihykWBzU5XoJdweTE+bwC91oq3L0XYk6CmxsDOxvRmsRtW7zhkid5NG7fSurOTT51zKmq1+pBc83ChKGOU6P34igUfcfbZZ/OjH/2IoqIi6uvrWb9+Pffffz+f+9zngPQq1SNp4zJBEITxMm6J3quuumqf9fgApkyZwvvvvz9eYRxW3tv0Iu6iD5ekqBUMht1n4yQSCcJJL9WzC0jJMrFQkr5hL/7hEN0NPuSUTE/HIPFEDO9IEyu2vEH9SYXYMRL2JACIBGPkGNKzhI1GIzVVdYe0j4IgHP1sFif9oRiJZGz0tWAgyNZVPjSSDjncT0pJEJWDrL/nDUyvdmANKNQ9cyYFC9KJ1OBDm7D1+rArRrxOLUZPCstgnJBGizmlxrezh77uHqwZdsxm87j254Wrf0DqX5vZMvtZrl3xhKjvN4HE2OGjfEND6J7/JwBxgxFzSgP/U9ZksLMHpydOmWIjeHUOJbOyiIZkdrYPsnZtN6lUisaRAZwBhY0XfxlNwEp5KoPuuB+n2g5qUHLNGI3pjRKzcnLI2ku9Y0EQhE9Cm5m+D0pIux4eDclBvKooFocR/8vPo9rwDinPIIsmD1CeMBNNZBGXzWT4jSxP+HihdRuSJGG2RODDmr7R+K7xyM6WLjzDHgwmw+jvtPHQ0d7FRed8A78vxpabmrjtzhvG7VpC2q9+9Stuv/12vvKVrzAwMEBeXh7XXnstd9xxx+gxYuMyQRCEcUz0Ch/V3tmG0atjZNhDMBQgozaPUCg0msTY0rgeU1kYkDDqjbjzMwCIR+TR2boqtYr8WjuyLLN1WQc9LSOoFDXxES1Kv4NSaxk15ZMmqIdHhlD3ID2vryXhD6O1mcg7dSbm/P2rHSkIAlQUV/HOs/9ieMhPx9YRNHoVw8M+5i+pQ61Rs3N9P1K2ByPgcFkxBiWQJBID4dE21LlW2OHDT5y+KjPq8lwyqjPx6KI4phRgSYb5fd2lmOMSxtMmM/W6C5h6/BySySTWfdRwO1DxIT9qILi5g+6uLgr+Z+dnQZhI7ds3Ep7kYJMnQkdjP9FAgOlAKBjEbElvYrTp9y+R0RIAwJmfQVVVOkkrGTRMnpyHLMsEQjqKFR3bvcP0JRV6bQlCdj1VZ80lHo1x1meXTFQXBUE4RhQvmc2/X18N2SqKbpiN1qqm8PEVzOuOIUkxdgYGKA21AtBod+McUYHewA61DBGolh3o9Gpi0SS+4UHqc8upznKhl2NMOqEQSzzF+h2dfHvOT0ENUy+r5OQLjqN2UhWBQBCbzTpmffF6vQQCMSRJYtPG7YTDYUymY2f15FhvxrY/rFYrDzzwAA888MDHHiM2LhMEQRCJ3kPG5/ORXWXB4bYSWOFlcslUZlScwIN//jF10yvQa8x0tPagNqVIJGJ4BoPkVdsJDseJBhIM9fixKbkYFPAPRbBlGslwWtBp9WglPcfNW0xhXvFEd/OwNrRmBxvvforOF5ejJFOjr0saNYVnz2fqrZeSOat6zK+rKArXXnstf//73/F4PKxfv36vGwK0tbVRWlo6etw777zDySefjMfjISMj4xPHUVJSwo033siNN974idsQhP84ccbpbBx4A0VSyCrIwO/3kEqlyzfEEtHR42S3lqiSpL9QQ2JVC6GGYaTeKL5pRpy3nUWxU0PWCKMPs3qbfORWpj8PumzY20LE/7mRD97ZyjqXnQBxzn3hJwTb+9m5Zgu6wQiWijymXnQq7vzcPca6Lwt+8iUeu/1ecndG+P3xX+CkW65i4ZcvOfhvkiCMAaMuxeSpVsCKz6vFccpsEgmF31/+HYrdRRiy7PT1bMScnyToS5DlHyESyaa9I4jX46OlRY/fbyKe7yLa5sWrV5Ml2UjYjRSXF7Hwhs9MdBcFQThGlFZXULpwBp7+FqYuKAJguNCF1JMu+ZRIAR9O9lUZJfoTAUby1HSEZUaMesDGNzJmUqGyYAqnkPSQJxlpSAxTNJzEoNbSoyj44umZvMuf38Rf7t9IyXQrNpee6+/8DN2N/fQ37yQjJJM1v5Jpi+eh0+kOuC9Tpk7i67d+htefX0njhjbq6uby6qvPUl1dNRbfqsOfLKHIY7ACaizaEIT/8t+1UMe7DqogHK5EovcQ6eprJyMzPfMmEYHja85iY+sKsgodWAtUQATDsERBfSYASp+TMt1ksiZno52hJZlMji456Rvsob2vkRJLEcfPXSiWGe+H9ueW8u6ld5GKJT7ynpJM0fHcUrr/vZKT/vJdis8b27per7zyCo8//jjvvPMOZWVlZGZm7vX4wsJCent793mcIEykqtJ6Woe3MRLpBiCn2MXKVzeTlZuNfzhM46oeLHYLIzkK1cvPpyrPxrbHtmL+/oZ0AzdOIq/WBUDn4Mhou3Iy/d9kMkl/RopwoQ6DJYORVJgsJNSeJP9YdCO5QwpeJYZKr8UWV/Hm9x+hoLiEnOMmUXPFadTPn7Xffak7cQ7HT53NyPuvoq5z0fKn1/cr0SvLMo0bt5BfWow1w77f1xOEAyHFgqOfD9nVnHnVhbx+xe04FROGzSPACDXTwkwyJpCdCm058xjx5VM3qYRoNIpWq0Wj0cBvz6Bx6WoKdzRTvXAB+eXi4bAgCIfeous/zQNXfn30675UlP5AmJQugxFPEt1JpehMGhL1CgtOP56srAx+d+1yUivTY/jcsiyKvArooM8ShxBU6hx0qxMUyFoc0Th9mjZcbifJRIB507PRJhS6W9q4+7QnUUtq3BVBFkQNdL28k9/99EkCeicnnzWVUz+1gNKyov3uy83f/DJvPrWD1JDCsLyRp59+jttv//Y+z/N6vezcuZNp06aJGuiCIAjCmBOJ3kPEExiip9NPMpnENxjgkb/9FHeJHb20a4mPSq0i6IlijRYzvXI+dtuuxIFGs+t/VY47jxx33iGN/0g2tGbHxyZ5/1sqluDd/7uLM9//xZjO7G1paSE3N5cFCxbs1/FqtXp0d3lBOJzV5M7k2Zcb8A+HceSZKKsuIb/aSTKZzVCnn5xSG0l1Nma7DrVGjZylRlEUJEkivCO96dpQr4etb27F974VqS9O384BhkckEp1eXNkW3J0+II62zoVtW/ocX7kdhnxkSHo8biN0RyjxaWltbWdoYJBtb6/A/dajZOXu/7+jZDACQERKorVp93E0RCMRnjjjq4RaejAajVy89BGcWeLhjDD2Vr7ewQd9UfTWFL2tHdx/+qXMijpRLFZAhSzL6OIROrOykE89i9q580bHDP9bn7Lq+NlUHT97AnohCIKQpjcYqD3tBP74xAfMml1KzKRH6a5GkiR0U4LMnpwBQHhQIjs7ve+IIVNLiPQ4Pq5WIcsJ2hQfXXKKgNWOSqtnaU+CVNRAcCSHTJsKbZ8ZS0qLJprELFuwyAb8yICaweEwmA1IkoQlBiu2NTHUNcD697fw8DN3HVB/wqE4OpWJ3OxsjPtRB3bZ0tV86QvfZPP2pVx11Wd57LE/HND1DheKcmAbqe2tHUEQBGFsiUTvIVJRXMNIrBtbqYxKpaJwkoOAN0Tjmh70TVoSsRShoSQ1ObOYMXP+RId7VNl491P7TPL+RyqWYNNP/sIpf//emFz7qquu4oknngDSNaOKi4t55JFHuOuuu9iyZQtqtZr58+fzi1/8gvLycuCjpRv2ZOnSpdx6662sWbOGzMxMzj//fO6+++7Res8DAwNcc801vPHGG+Tk5HDXXQc2aBWE/VGcX8bNn7+D1Vvfpzu8jaQUB9IPppRUevfpgrJs2rb20h4dIGnW0FKpx220MbC9jzULnkHbF8LgUmPuTY/0zeVmrC3pJZzDObtmucjqXSsXBojgy1FhyrDiIYa61knKE8KqVcjrjEN/iO7NTQeU6J10zafwfmoei6dWUVheus/jl/3hOQZWbMNfbCZHZ2LFky9y5k1X7/f1BGF/Vc2bzfpH3iW5Ms6Skw3UauN0RoYJTrbjcrtobBzErNSSd9GlZJdXTHS4giAI+3TONZeQTCbZsuVVqqYXsF5pxiwZGW4PIcvpe6VUMEBnRz9Nq9vweVrIm2nAbpLpz9ZhvWAmSwodvPOj9TiWpeuTm3Quhts06NCjsyRQfGBQG1Hbo+ABl+Kgy9RGntONXq/hDSWADhttg1GqlEo0gzqS7Qe+SvKmH11AhtPKlJl371eJtYd+8RcC3gTFRcWsW7ee/v5+srOzD/i6E20iavQKgiAI+0ckeseRxzuCXmfAZDLR0deCPmEnlRxCpU4nQKwZZnKLsrFmaclLzWD6qbNFGYYxFuoepPOfyw7onI5/LiPUPTgmG7T9J4H76KOPsnr1atRqNe+99x4333wzU6ZMIRgMcscdd3D++eezYcOG/Vq+1dLSwpIlS7jrrrv4wx/+wODgINdffz3XX389jz32GJBOMPf09PD222+j1Wr56le/ysDAwEH3RxD2ZHb9CSQ3yCxtfY2gP4BWq2e4z4OSBNQyQW+c+vlFqDVq1t++CXvzEHolScQsY4ur8A5FiCla9JKasDeAokikUGgb6SdSaMZoMTMY96OqdpDyhjGpNOT3xaHPh2ZyJrbNQwCo6l1Aetavt6XrgPpQfdzMAzq+bdVmMuNazHo7ji0jDL+xAW46oCYE4WMN9PTiys5CrVbT2diOrd6Np7UdYzKCWq/FH0+ycG4RA0E9i+ZfTHZuwUSHLAiCcEA0Gg3Tpp3F6tXP0Ve0jpJKC4W5RXztBx0QttHX386d1Q1M0+lJKBFOzk2vvFlhzKa2Ph8AdeauW1mDNgVoiMsRPIl+7LkujCY9DlMfM8p1mKJh7J48rENmiMN6osQ9WjJThXiMUYjqGOkO4ff7sR3Apq/nfGbxAfV7W8NWQmEwG1Rs2rSFp59+hhtuuP6A2hAEQRCEvRGJ3nG0oXk5w1ILqqSe7tZBLDYjqm0Sna29eHxm7G4jKpUOlc/FjHlzJjrco1LP62tRUvIBnaMkU/S8sY7KK08/6Ovb7XasVutu5RguvPDC3Y75wx/+gNvtZtu2bUyaNGmfbd59991cdtllo5uqVVZW8stf/pKTTjqJhx9+mI6ODl5++WVWrVrF7NnpJbq///3vqa2tPej+CMLHmV4zlyQRUln9AOh0GvJq0uVnlJQatSb9gEuTY4XOCAZJQ9RlJhryYXNl0G5M4jbZ0cejdJrjWAIy1kCM3GAMtZQgmKPC1ucBIGZRj143kUiOfj4Q8+PNlrDPKGfO5WePa38zXZkMo8YTTt94igkpwljacud9GId8BG1GVBVgNhnpnTtCZ5aL7sFhAhotU/VaklSJJK8gCEe0urrTsN2vpqIinVx99c+vkOhTg5xPrLYTSZIwBuOEEirMWg3Jrn4URWFgwItXCpMs8WO1qijxDnPGEhO2WJj3wrn0bbHDMBTNtjEpHgCVlvUWLaSfDWOyaYl5QCNpiBiHUSw+zrvqpANK8n4SRqMFSQphtzmAVpQjtHaBMkabsY3Jhm6CIAjCbkSidxxF8ZGZm050xKK20Z3ktSbIq3DR0zxMZqqaOXWiVMN4SfjDh/S8/dHU1MQdd9zBypUrGRoaQpbTieiOjo79SvRu3LiRTZs28eSTT46+pigKsizT2tpKY2MjGo2GmTN3zVCsqanZr+VkgvBJGQwGjFoLQdKJ3v9OwAZ9Qf6fvfsOj6La/zj+nu2bTTab3nsHQu+9KaCoiL2Cgnr1cr2IlesV60+uBcUKKldARcWC5YoiIFKl9xLSSO9107f//oisRlqAJAvkvJ5nH9mZMzOfGSGZPXvmew7tKEMlaage443uzkTUgW74VqsZNepqQqIi+O/Au5HvzMULqApQ41Vlwc2hpMZdwqseFOX1NKJCi5yi0lKaAtxx99ZTYqlDneiNzdiAwmolrNhO39l3o/Nwb9fz7XP/ZL77dQ8max26O4cy5F9T2/V4QudRYzSiLygnQKFmd3kpV94Yh0IuY0OwN8PumoDdbmfN6nSy7f1I6NHT1XEFQRDOi06nQ+KP2rae/hrKix0oZGqyrFYKHCY0/h78VNFIpLcBeUMTO7fa6dp9DEOviMG/4CUUMhmYbHRzWEGlQtfUADTX961sAFRgtdspqslHpvdG467F3mAkINYbW42FHDnY1RL3PHpbu5/vjJm3858X3icoyI1rJk3g/vv/1u7HbA+iRq8gCMKFS3T0tgGr1crOwxups1RSVV5LaEgYGrmOfXsPkKQJRW/Q4ZA1d3pUllajdldQV9VEv/AriItMdHH6S5tS73bmRm24XWtcddVVRERE8MEHHxAcHIzdbqdbt26YzeZWbV9XV8d9993Hgw8+eMK68PBw0tLS2jqyILSKzK4ibWcBKpWawpwS1GoVyO3U1tTRa1QMcoUcjUZDeLIPAFKZD6HRkQDowv1p2pkLgNxTi7W4kTJvB0Y/OTa5Bzq5D/n1RnwbZISUKlBbHHiW1NCgMqE2mVBLcozeNkBGfUH5aXNmH0olc/V2oi/rT1Tyuf0MDomL4oG9y87cUBBOo7KylLLyVPJyjhGoVKF29+FYVhEWmRkvuxxFgA6FvLmkj+L3CVrT02sZOfJ23Nx0rowuCILQZopLaikpqUKpVNFnmJGE8XLclSZK0lQMVTqQJAf7JE/ia5qfoimTtOj1etRxCWRbHESowWA3UWtV4HA4KDKW0OCvw91NQ0pFPZ9r/TFXW5C52QgwGsAItiADmnwloMTTR0N2VQ3V1Ua8vAwnzehwOPjxh7UUFpZx480T8fQ8t5G/t952Lbfedu25XShBOE/dnv4ZmdqN7P9c6eoogiC0I9HR2wbW7vwOdXgtSknC1mjE7COjzmwlItEPa5ON/Zsy8NDrKUgxUpRbRnyXWNzt/sR1FZ287S34sj5ICjkOq63V20gKOcFje7dLnoqKClJTU/nggw8YNmwY0Dyx2tno3bs3R44cITb25JPuJCYmYrVa2b17t7N0Q2pqKtXV1eeVXRDOJDQwgjzJgKe3jtoGI4HxHkiSRE1VrbN0g9nyx8SIjdY6559twXoqu/kgd0DDeF/8PhjCgGAP0u/ZjG797/V2Yz0xlBgBDdW+bpDfQLBJSY23EnWVDSoaKQg3EHSS+rwVJfmU7FzGgV3ZVM07hqbeSs7QzURtfL9dr4kgnEptbQ0VlduIjdVTcKySrgYHOIoolpro8+VU8vMqaVy5m/1Ha7BIEhVyC2lp1Xi4dxWdvIIgXFL8/YKJjfNunqvkWC4DqASUNJbrkepqALCqFGBqHjhjLS8DQK1W85vJn03lShqbwgjrkk+PIA1T0PHfRUrcjWoa7EqUChk6mTtGe3PZB0mSMNU3okGN3W7HUl+Du17L0SMZDBrSt0W2FV9/zxvzP8Bdp2f31hwkSYbRWMsjj93XodfoQiMmYxMEQbhwiY7ec+RwODiUug+5TElJ7TEiJH/qjPUofn/yKCulgMj4INRaFcW5VcT0DKCmso44ZSJXD7jdteE7EV2IH2FXDSL3m9Z3poZfPbhNJmI7GS8vL3x8fHj//fcJCgoiNzeXJ5544qz28fjjjzNw4EBmzJjB9OnT0el0HDlyhDVr1vD222+TkJDA+PHjue+++1iwYAEKhYKZM2ei1Wrb5ZwE4Th/vwDiqodwtHgTITE+VJXW4h2gp66pjn2b03BT6yjMKUer0SIpJOqrmuD3CiMhcVFY3loHgOaaCIJjmkf9yoL+GF0vaZQAlEpNlKlkWBL1qJRKKk11SFolPqU2Br35KP2uOXFilNL935PoXkStbx3VdSbsSKRnpGOz2ZDL5Se0F4T2YjQaWb9+M3JZA2MvNwCgdEhA8/Orcp07arWKmNhAqgw6kuoLKWoCr96XEZs4wXXBBUEQ2klMTF927VpFv34eWNRKMDUvL6itoaa+EZW3nkJLPeouXigVdhpr8v7YWBaLKaUCGRA2wptIr99/lhokqAR//KjT1KNvklNfV0+Jdw7RQQZ8GsvZ3Sin0ehA0RTM6t0vnzApttls5pFHnqO4qIJuSQlIkgy73cr+fQc76MoIgiAIwtkTHb3naPW2FciDqynOr8AmWSnMrCbnaAGhkcFkV5RTnl2Ll5cBSdGAuc4OhX4Mi7kK798fWRY6To/Zt1Lw43ZsJssZ28o1Kro/cUu7ZZHJZHz++ec8+OCDdOvWjYSEBN58801GjhzZ6n10796dDRs28OSTTzJs2DAcDgcxMTHcdNNNzjaLFy9m+vTpjBgxgoCAAF544QWeeuqpdjgjQWgpNCCKX/f/D4PBg+KCEpqibASG+AISIdG+aLxk6H0VeBh0lObanR2t7pEBzn3Up5VjtVrJ2JHN0coighM90SiVlJnrsAeo0ZXbUem8MBysaN6gmy+eheWAnNpjRSfNdXBbGvvyqmgqgfwYDV5GB4GljZQVlxAYEtz+F0YQgLKyciZdM4V9e49yR7+u6N/3xealo0BbjyJMj1Uto0TvTk5OGeCgsNaMV9+bCOs1WHxZJwjCJUupVCKTNOzYkUt2dR16vRdKuQ1FSBNjfA1IksRvJTb6GJpH9GYo/3hSzyPYQAPN9wO1jUqM9bVsyKinkAoSIsJwV0qklDdRZZGjt0cwMqSIOGsdZqWCTZV2pDodJoeZgrxCQsNDWuQqKirCXa5nUNdEmswNBIepqCyvob7WSmdnt0vY22AitbbYh+A6kU+sPK/17c3VxxcEVxEdvecgryAHu08FaqUaY1kDXftHAVBbWU9Y1+bC/34+flzR9w4aGhrQ9tYik8lcGblT8+2bwIjP/s2GW144bWevXK1kxKdP4ts3oU2PP3PmTGbOnOl8P3bsWI4cOdKizZ9n3I2MjGzxfuTIkSfMyNuvXz9Wr159ymMGBgbyww8/tFh2xx13nEt8QTgrHh4e+IcY8IvwQFLICP59Fu2ijGoAQqMDKM6qwsOgw+DvxuGjB5HJoURdhfnB4cSGRqAy17BjwHJ8is2EDwghbEQShV9tQhnmjldqNSCj3PrHhywbzRMaVjrMlK3fjF0tZ/Tfbm7xc1eW40394uYROH5xBvQV1cgm9hadvEKH+uTjL9i39ygA0QYDEQ4VVFlQGtT0LGvuqMgdMYCQ4KtxOBxE/eOGE0aYCYIgXIoMBl+iYxQo5U3ENFag0+ooLamC33/HW82N8PukbY6acgpzc6jLy6HWkUnsP3ojk8tZv3MPr75jQ9bkQ/87PPErb8JWUY/B3YC8XAUyaFRqwGpCJVNg8FFSVWPHpKzmo0Vf0qNfN6686o+ngkJDQwmSdac+U8IsL6fAlo2EjAkTh7ngCl1YxGRsgiAIFy7R0XsO8otzKC2rw2SuoOhYOSGxvhi8PVCp1M42arkOSZLQ6UQdvQtBxKShXLHpDQ785zNyv/+tRc1eSSEn/OrBdH/iljbv5BWEzkaSJDyUfkATDlvz3XudsZ70Q7nUVtWjUqkpL6kGmxLkNrKqVhLfJwRdFxgWMZku8ckA7O/Vk9x1u+g+ZSIR3eJxvOugrqaWj4fcS21KPjm1FTgSvFGpVJQ0GpEFa1CW2bCml5Lz/SJ+rq5jwr/udeZKuO0yflu6HpVdArUCS5cArnnrERdcIaEzM3ja+Mc/xnPkSC5WeyNmhwqVTIGSP34nOQzeqNXq0+xFEATh0hMSEk9Bwc8k94gm68dCEkLUqNTw9e5CAg3eFJTW85tCg4dawYHMI4w/9AThSgUWrSdd770KgJF3XsYXXX+iqd7Mrf+8Eq1Wi8PhYNcvu/jfP7+j2lrJuuIaNqt1lFnkNCqq8AkyIBl9+eWnXXy0ZA1BQQH07tt8LyKXyxl9XXf+t+AgSos3Bp9ixo4byPR7b3XlpRIEQRCE0xIdvedAprYT0b25BINKqcLaZKMos4rKkipUR5QYq2oZ1kPU0bvQ+PZNYPRXz1BfUEbh2j1YahpQ6t0IHtu73WryCkJnpLV5kZ9ylILsEmRKBwotRMWGEdat+YkHZYqS4Pjmkb6FaXLniMUmS71zHz0mDKfHhOHO95Ik4eGp56Zf32bpLY8TuOkoenMVaklOhcGCvkqBTJJTXV2HTJI49vFa+FNHb/LoQZS+djf1x4rxC/emz7VjCYgIbdX5OBwO8jKzKNqbSu6+FGwZZRjT8xjwwj30vGLEeV8vofMYMjCcO29unkhz99ebcShtGK0K8sxKHF4GKtOLGNFvoItTCoIgdDydTsf2b/IoKc4nP8gLm1sAMRNj0O5eQaKxiW5yGWUKM7F6Ge6hbpgKzHgAjhqjcx9KpZLb/nF1i/1KkkS/sf0wvdFIygur2GdXsGpH8wRvfbr6oqrVgQx0SjX1tWa++mKVs6MX4P4nbsBmseOhd8MtsD933nVjq5+0sFgs7N69l4MHtzFKFgABAABJREFUU9izZwfZ2TkYjUbWrVuDRqM5/4vmQmIyNkEQhAuX6Og9BybbH50RSpUS32BPABwWOcGJenwLA0mO7+2qeMIZ6EL8iJsyztUxBOGSlZzQl+qcTHyaPAmKNQCQXVHuXG/704j68qJKKisrUCs1JIfGnnHfPn6+3P7hc2yau4SctbtQZ9YQWAU1bmBvMFNusWGN0qMynFguZ8yD5zYRZtrBI/zS8+9okVOd7IvhYDkqoHDzAdHRK5wVh6MJUGG329Ep7MQFeVBZ04BH9y6Eh/uRnjYYnYeHq2MKgiC4RLBPJNGHdyPvl0BiUnO93Dy9Dupq0SgUNNL8tEOEjwdfH8rH10tOrU5Ht1bse+jY4RgUblR++hOylGrsVhlVdXW42d2poJjK2mKCo5Q0Nla32E7v6cG/Xp1+Tufzystv8fxzbyJJEgZvG0VFRYCDnTt3MmzYxV3+QXT0CoIgXLhER+9Z+mX7/ziadQj3Yg0mUxOF2aW4+UXj5asHuR273Y63RtR8FASh8/Ly9MLcYMNsaQKgpqqOtCPHsFjMqJRqCvNKUagVSHIbSNB1QCQAjYXVrdq/f3gI1y14kgM7d7Nl3dfoow2kPb0e/0w7PcoVVMslwqLO3GncWlGJccj7R8KOPCxmMwBWhx2bXHw4EVqnrqaWrx5/B4e1jMwYbwobKwhxqybaV8XBwnoGT/CmoKCa4OBero4qCILgMpLeAICluh673U5GRgGHJBv10SGoAn0pLCpA0rqjkKxE9pHR31dOlclBQfohQuLO3N3bbWRfuo3si3f429h3p5NTUcCysp1UVNQQqPKkpLiWB2dNbbPzGTFyMG/MX0RtbSNeXl7Nk7u5iy/zBEEQhPYlOnrPQkZOGmbvInzMOjz81FQW2YnrEY7DCvlp5ZTmVWEsrWfaNbe5OqogCIJLHXvnAPVHS1A+p8DgryMowoeYXoHIZDLqG+ucpRtqqxqcj0DW/z5rdmsl9+1NdtNR7J7VBAT5oTuaDxIoDG749I1rs3NRqVTc9MM8dnzwDaqSEoL/HoVfzzi6DunbZscQLm3bF/9Ixa4MBmn1pOXmo5Y1May7mZwf0yiut7G/9Htqw6IYfZenq6MKgiC4jMlqYZevN3lZuXik+eHvbyDhqsH0HxABwO7v7CR7NU+svMdoBcx4qSUqctOgFR29x11960QOpb1LXKWO9+obkCQZZaXVKFQKBg3u12bnM2TIQFb++AkrVvyAQgkPPvh3xo27nMjIyDY7hqvYHRL2NhiN2xb7EARBEFoSHb1nIaVgB9pQOQ7JhqLWC3tTPVo/Gd6BeirL8kgaFI68qnlyIEEQhM5KkiTG3n4b2ybMwc1Dg2+wAaVWTmVxDb7BBtQ6GY31TWh1GkpKSjH/1oRW405lsZGuQZlEhsa02F9ZSQmeXl4n/GyVJImrh91GTn4Wxwx7MTlM1PirsfnI6XPnxDY9J4OvD5fPPrdHN4XOrSS3kLKVh9B6apGZJdQjuqA5chiV3Ea0m5wyuRvdqivIHta2f2cFQRAuNt1HjGVveSZBlcdITGx+QtJqLXWutynkgAW73c6hnHKymxQ0NHlg9NtL3JjJLfblcDgoKSkmICDwhJq6AdGR+C99id2ff4f/v1MpKa/C3yeQgUOTiY6OatNz6tuvN337XXol/Rx2CYe9DUo3tME+hI4V+cRKV0cQBOEMTixiKJxU+rFUqk3F7Pklg5qyJvw0EeSnVmIzSTTUNWJpsmO3Ooj07eLqqIIgCC6n8fJAgxxHnYPGBhOmGgepX6Zy6LldmHbAgfU5FGZWEZ0UTECENzG9A/CL8WDD0W/YtHe1cz8bFi7n5V438V7cjRzbc+ikx4oIjWLEQ9MxeyiIKHXQJTQag69PR52qIJxW1obvSBxSgn6IRP61Plzxt9sp0VrIarJhsdlRyhWkab2IHTrS1VEFQRBcTqfX46iuxGazUVpaTX5+Ob9tOcaaNUc5kNfIVnkAqYHR2DzjyPw5jKINBtRoOZLyDfn5GUBzJ+/jt83ju08/Yc+eFdhsthOOI0kSfW+ZxHXXTSJQ3htZVQQTLruyo09XEARBENqcGNHbSpX1RYQl+FFnbCA41hsvTx9m3fckGo2GwuJCDCHH8NP4EReZ6OqogiAILtelfy/KFt2P0s2bcE0coQPD+Pjpf2BbewTP6QFMvOse9pauxjPAi8LMKgDC4wIozqqmUpZJRWUZPt5+7P9xA27uOiokK7u/WUt075M/mukX4IdXkxxJgqaiyo48VUE4La+mcmL1Dkwx0XhEhKDRuTHlzbdRKBTkZ6ShyckivFdflEqlq6MKgiC4nE+3YZiUHqSlehES0psxYwYhV+zEYHAnM8oTk6mO8IgGjoYVU+awIEkSlVn1JCR4kZaaCsTS1NSEpeQITRkyrEENHNMlEpfY9aTHCwuJQOIwSFBSVNWxJ3sRcziaX22xH0EQBKFtiRG9rRQVlEhNeSMalRse3hpSinag1WqRJImQoBAG9x0mOnkvEg5TNY6i7TjyNzT/11Tt6kiCcMmRJImR025gyIgxhIWGI0kSqjAf6nzVxFw7nPCQKAaGXo05352yHCOZe0soOGKk6FgFDUYLRzIOAjB02nX410joa+0UfLCad5JuJPdoxgnHC4+Loeu796O7dRCDX7ino09XEE7JFt4Vh8OBUq8lPkZHVsZutFotSqWSqKSu9Bw/Ea+AQFfHFARBuCD4BIWSPHIi3br1xsvLCz8/fzIzyzl0qJygoBiSk4dRWhKO1sudhsRa/G92w6uHki+nrmTjkz8BoNVqmXJ7b2IU9XRRN2Lf+TF7vpp/0uPdevdV3HTfYCZP7cetd13RgWd6cbMjOev0ntcLUbqhtd555x0iIyPRaDQMGDCAHTt2uDqSIAgXKDGit5X8fQNJqh3JFvMaClKM1BkbKI8pw9fHz9XRhFZy1OTiyF0LFYfAYf9jhSTD4dMNKXwskj687Y/rcHDffffx1VdfUVVVxd69e+nZs+dpt8nOziYqKsrZdv369YwaNYqqqioMBsM5Z4mMjGTmzJnMnDnznPdRXFzMHXfcwW+//YZSqaS6uvqc9yV0Lte++y/sb9vRarUABPgFcbnfdcjlSuRBzaNoLE02PJQ+9E0eCEDva8ZQsPkAx37Ygj61CkpNZP22j/DE2BP2P2z69TD9+o47IUFohbjRV3FM701x+QGsB8uori4msetgV8cSBEG4KEiSRFLideh0OmSy5jFK0dFJREcnER71FYmJvjQ2NvHlHavoceVo53Y975pB7p6tWO124n3VHCvNo6mpCY1G02L/Hh7uzJl7f4eekyCcreXLlzNr1iwWLlzIgAEDmD9/PuPGjSM1NRV/f/82PdZfa/Bm/0eUNBGEtna6Wtdt8W9OjOg9C3FRiQSHBRKS5EnCwCDyS7JdHUloJUfZARx734TyAy07eaH5fXnzekfZgTY/9qpVq1iyZAk//PADRUVFdOt25lmBw8LCWt22o73++usUFRWxb98+0tLSWL9+PZIkXXAdvtnZ2UiSxL59+1wdRfidWq12dvL+mUbp5vyzwy6RENQLrfaPZU1aGSVSIzUJXlQFqClNyeqQvILQFpRKJXEDRxAd7UufPiGEh+tcHUkQBOGi4uHh4ezk/TO5XA2ARqNG1T2IpGtGOtc5HA5U/Xqx6qcStn5dRvnKEnKOHOmoyJc8h0Nqs5dwZq+99hr33HMPd911F126dGHhwoW4ubnx4YcfujqaIAgXIDGi9yw1WepQ/d4/7hBFhS4KjppcHEc+Aof1DA2tze16PdimI3szMzMJCgpi8ODWj+CSy+UEBl6Yj/JmZmbSp08f4uLiADjSRjfNDocDm82GQnHmH0sWi6XN6lmazWZUKlWb7Es4N/YmGVl7S1EolZSVlbEr6xfKjEUM7jEGk8lE8fur8fbVoD/ye+284lrXBgaW3vYEOrWWXvdPJqZfsqvjCBe44uJ8/PzcAZAkce8gCILQFjL2lrN/RSqmUit2ezbHVszHf8T1hCR0Jz19L6Nv6cWu7TnEV9SBTElZablL8+7Zs4fZs/9Nnz49eeyxx87rKT2h8zCbzezevZvZs2c7l8lkMsaOHcvWrVtPuo3JZMJkMjnfG41GAOymBgBqampOebzjbY77a9u/rhcEoW2d6t/n8eWt6YcUHb1nweFwUFBQRF2DO9UVddwweoKrIwmt4Mhde+ZOXmdjK47ctUjd7m6TY0+dOpWlS5cCzY+eRUREkJ2dzapVq3jhhRc4dOgQcrmcQYMG8cYbbxATEwOcWLrhZDZv3szs2bPZtWsXvr6+XHvttcydOxedrnm0WGlpKdOmTWPt2rUEBgbywgsvnDHvzp07+de//sXevXuxWCz07NmT119/nd69ewPNpR9ycnIA+Oijj5gyZYrz/Ly8vACYMmUKS5YswW6389JLL/H+++9TXFxMfHw8Tz31FNdf3/xo/fFyFD/++CP//ve/OXjwIKtXr2bkyJEtMh2/Fp9//jnvvvsu27dvZ+HChUydOpVFixYxb948srKyiIyM5MEHH+SBBx4AICoqCoBevXoBMGLECNavX8/UqVOprq6mX79+vPPOO6jVau666y6++OILDh061OLYPXv25KqrruL5558/47UTzp2fVyDmIF/kchnGumoC4z2oqk0nrzCOIP9gJJ2KpvpGZ/tj6ekcPrqfrok9XBe6rJayNVv5YMs2Hv9tGV4+3q7LIlzwjmxPY9tHB9D5K/BOciehi6sTCYIgXPyUJXqaljfflwZc6UlP9zrStn8HCd1RKFSYzVZs3jqoqMNqt5O5dxeG+GiCok8s/9QR/Pz8WLv2F9asWUtGxjG++OJzl+RoC47fa+y2xX6E0ysvL8dmsxEQENBieUBAAEePHj3pNnPnzuXZZ589YXnBgqkAeM5v/fHPpq0gCOfvTP/mamtr8fT0PG0b0dF7FoxGI4Fxerx8PdDkqfHzadt6OELbc5iqofzQGdu1UHEIh6kaSW047+Mf77x9//332blzJ3K5HID6+npmzZpF9+7dqaurY86cOVx77bXs27fvpI+m/VVmZibjx4/nhRde4MMPP6SsrIwZM2YwY8YMFi9eDDR3MhcWFvLrr7+iVCp58MEHKS0tPe1+a2trmTJlCm+99RYOh4N58+ZxxRVXkJ6ejoeHBzt37uTOO+9Er9fzxhtvoNVqufrqq7nuuutITU1Fr9c7H82fO3cun3zyCQsXLiQuLo6NGzdy++234+fnx4gRI5zHfOKJJ3j11VeJjo52dhafzBNPPMG8efPo1asXGo2GZcuWMWfOHN5++2169erF3r17ueeee9DpdEyZMoUdO3bQv39/1q5dS9euXVuM2v3ll1/Q6/WsWbMGAE9PT5599ll27txJv379ANi7dy8HDhxgxYoVZ/z/IZyfqPAYDh5Yg1+IAb8wT6or6nDTqaioKiMsOJw+rz/AN0+8SuiXY1F5qZHyismRtqPKUhMX1bGTYB5cvYm0z38hp6oUDw8lcWkm8o6k4zVsQIfmEC4upkozjQccNGLBL9jX1XEEQRAuCe7BBuefK0ot0BUaa5qf/omO7sbqD7dR6LDi8dAo1HolnusPUrViAf4Pvey8J+8o8+a9xtat24iICCM/v5C0tLQOPX5ba6uyC6Kjt33Mnj2bWbNmOd/b7XYqKyvx8fFBksQ1by81NTWEhYWRl5eHXq93dZxLWme81g6Hg9raWoKDg8/YVnT0ngW9Xo+lobm+qyRmCL04VKYC9jM2a8Fhh6o0COx/3of39PTEw8PjhFIM1113XYt2H374IX5+fhw5cqRVdXnnzp3Lbbfd5pxULS4ujjfffJMRI0awYMECcnNz+emnn9ixY4ez4/K///0vSUlJp93v6NGjW7x///33MRgMbNiwgYkTJ+Ln5+ess3r8fLy9m0cy+vv7Ox9BM5lMvPjii6xdu5ZBgwYBEB0dzebNm3nvvfdadPQ+99xzXHbZZWc855kzZzJ58mTn+6effpp58+Y5l0VFRXHkyBHee+89pkyZgp9f80SJPj4+J5TB0Ol0LFq0qEXn77hx41i8eLHzei1evJgRI0YQHR19xmzC+Undtpe8o2U0VTmQyeXkFxYTGutDXdM+enTpTdonq4lJaUITrCEwwhs3fxUNtY0cMW8jNjKhQ29Y7TYHpZ9vQRWuw1DnwNQ3lMQBvTrs+MLFSWv4o+a0XNGxnQuCIAiXosaGBkqKjqIdXIvBV8bRgnIemBdMfZWN63Q/M2B4ArrVqXiHKUnu11ySrXxfFnE1xaT+vIIuV9zQoXlzc3P5+usVJCUlYbGYGT/+8g49vnDx8vX1RS6XU1JS0mJ5SUnJKUv9qdVq1Gp1i2WiVEjH0ev1nabz0dU627U+00je40RH71moqCynuqQeS70De724dBcFW9O5bWc9x+1aKT09nTlz5rB9+3bKy8ux25s7o3Nzc1vV0bt//34OHDjAsmXLnMscDgd2u52srCzS0tJQKBT06dPHuT4xMfGMv+BLSkr497//zfr16yktLcVms9HQ0EBubu5ZnV9GRgYNDQ0ndOCazWZnKYXj+vbt26p9/rldfX09mZmZTJs2jXvuuce53Gq1tuqHX3Jy8gl1ee+55x7uvvtuXnvtNWQyGZ9++imvv/56q7IJ5+77J9+g5MVv8f18PGHdmkd0S3KJgAg9FrOVeU88ieeXu1BJCmzVduxhdozldRzZVIabMhBl9XquuGJUh+XtMWE4+h1vk7ZjL546PUljB4oaz8IZKRx5XHG/EWOtHHmE+KJYEAThfDQ2NJDyzJMkF+TiPsSPQL0GP43Enp8k1JI73736M8rMA8RUNlHQWI/NZsNms3E4twBLhQXTpjWEDxuHu0fHdQ68/vprXH31VRw7doyQkBAmTLi4SwDaOeuhNKfcj3B6KpWKPn368MsvvzBp0iSgeYTuL7/8wowZM1wbThCEC5LorTwLB3O2Ed/v92HSpad+xFy4gMg157ad4hy3a6WrrrqKiIgIPvjgA4KDg7Hb7XTr1g2z2dyq7evq6rjvvvt48MEHT1gXHh5+zo+DTZkyhYqKCt544w0iIiJQq9UMGjSo1bn+nA9g5cqVhISEtFj312+Xj9cUPpM/tzu+/w8++IABA1o+Mt+aR/FOdsyrrroKtVrNN998g0qlwmKxOOsJC+2n4Jvf0EgyrCV/1OB1WB2YGk1kHCok89BhgpP06JUaGnflI+kkfAL0mIp1FBcYqDqWxcCBvfD2NnRY5qhuCUR1S+iw4wkXP9/qIySGOAArGQkhZ2wvCIIgnFrugf3EVZUh6dzIscgJBLqHeqLwstNYVYvWy8KOgkNob+pJgI+aNa9/RayXinGedpRGO4FuZg59vYSeU0+8j24vMpmMMWPGMGbMmA47ZnsSpRs61qxZs5gyZQp9+/alf//+zJ8/n/r6eu666y5XRxME4QIkOnpbqaKyArOjAQ3QUGsiXBfm6khCa3gngCRrLsfQWpIMvOLbLVJFRQWpqal88MEHDBs2DGieWO1s9O7dmyNHjhAbe/LJJBITE7FarezevdtZiiA1NZXq6urT7nfLli28++67XHHFFQDk5eVRXn76GYqPj2a02WzOZV26dEGtVpObm9uiTENbCQgIIDg4mGPHjnHbbbe1OtfpKBQKpkyZwuLFi1GpVNx8883OesNC+wm7aTi5y9bTWFxH3qEq5Ao5BbnFKNz9SegZRn5AFr6r89DY6tDdGktQjDeSJOFmMFJVAFWlSn763w5umyIegRQuPFarlcrKCrSWGkBNtklDQAfXlRYEQbjURPbsxW7fQDwsZjJyG6nOB3mTlXEzfRg0MobAAAMffLKV4Xc1P0WWsiSbOIOEJGk5lG8iEDuBx3ZTmn0M/0hRoku48N10002UlZUxZ84ciouL6dmzJ6tWrTphgjbBtdRqNU8//fQJA5uEtieu9emJjt5WyM7PZHfJj3z/WgVKhTs3TO1Kt9GiJuPFQFIbcPh0g/IDrd/Ip1ubTMR2Kl5eXvj4+PD+++8TFBREbm4uTzzxxFnt4/HHH2fgwIHMmDGD6dOno9PpOHLkCGvWrOHtt98mISGB8ePHc99997FgwQIUCgUzZ848Y8dlXFwcH3/8MX379qWmpoZHH330jNtEREQgSRI//PADV1xxBVqtFg8PDx555BEeeugh7HY7Q4cOxWg0smXLFvR6PVOmTDmr8z2ZZ599lgcffBBPT0/Gjx+PyWRi165dVFVVMWvWLPz9/dFqtaxatYrQ0FA0Gs0ZyzpMnz7dWcd4y5Yt551ROLOJT/8dnv47ew5tp0S9H61OTV1dLQHhzU9NhIT6oLHlA9CQVkFVShJ2dT1oyonqb8E7SEHWzp38WJrHkGmT8fQWT1sIF46jqVvIOrqFyEYb31eaGTzlQfQG8XdUEAThfKg1GgbPfRWA+nmvknRwP5IkIfP3Jiiw+Wds//5dnO0rbXI2k4BfxWFSNTpqooJQ+Xmx99WFDJx0NcljhogJqs6S3QH2NhiNa3e0QZhO4vjE28KFS61W88wzz7g6RqcgrvXpyVwd4GKQUXwATz83bA0GAnz9GTf64q6p1NlI4WNBauV3GjJlc/t2JJPJ+Pzzz9m9ezfdunXjoYce4pVXXjmrfXTv3p0NGzaQlpbGsGHD6NWrF3PmzGkxA+PixYsJDg5mxIgRTJ48mXvvvRd/f//T7ve///0vVVVV9O7dmzvuuIMHH3zwjNuEhITw7LPP8sQTTxAQEOC8AXn++ed56qmnmDt3LklJSYwfP56VK1cSFRV1Vud6KtOnT2fRokUsXryY5ORkRowYwZIlS5z7VygUvPnmm7z33nsEBwdzzTXXnHGfcXFxDB48mMTExBNKQgjtq3e3AcQohmI55k1DYx0NtQ0c3plFkbIB0xO9OHh/CMe0Zq4Yeh3XDb2XO265ngHX6+k+zoBnmIqSxz9lxe1zXH0aguDU2NiIRlOFXq6kq7eSuH4j8QuJcHUsQRCES0rff86k4KZbOZzQBXN5Lfn5Zaz/eR9HN+5m7cKfeXnGuxxr0jN88lQSp71M3NVXM/CWwfQem0SAWkftsyvY/vF3rj6Ni87x0g1t8RIEQRDaluRwOC7q79Fqamrw9PTEaDS2y2x7DoeDVxc/hbHWSObqLvz98YEMHS5G815sHGUHcBz5CBzWUzeSFEhd7kTy695xwYQLisPhIC4ujgceeIBZs2a5Ok6ntW7zzxyt2EZCrzBqquqwW8EnyBNjZT2DA6/Hz7f5y4c1279BEWokbUcWjskbsHYPZMb+z12cvm209+82of2v8Z49W6is2kt9ah5dJQvh0/4jJu4TBEFoJ6W5uexe/TW9yMTfQ81vuU0M1jd/zE1TBpF491NA85dwObk/ExdnYNXLW/H6oYzGq7sx5rG7XRm/TXTEvcPxY/w2/G+4K87/kek6q4nBGxeK+x1BEIQ2JEb0nsHhtH30GhNFt17xPPLsEIYM6+nqSMI5kPy6I/V6EHy7N9fgbbFSBr7N60Unb+dVVlbG22+/TXFxsZjYwMVGDx1HUGAQcoUcLz9PTA3NNbY9vXUUluZRUFSAw+FgWI8J2Aq96BE7ksK5yXjNGcu32z8g7Viqi89AEEDr1sSoUd0I6TcYzeX3i05eQRCEduQfHk5s/2H4e6iRJAmr+Y9JXmV1FVitVkqLi9Bqteg9upOZKccW7kXmNTJ0XWHbitddmP7i01y6oW1egiAIQtsSNXrPoNFSh8JDgaebN336ik7Ai5mkD0fqdjcOUzVUpYG1CRQa8Ipv15q8wsXB398fX19f3n//fby8RA1NV+vmN4KUjF04NCYq8upwmGXIFHCo+EeievqRVRzL0F5jGduvuSRHZEAiOwtW4hGgora6wsXpBQEkLIAcvSGQ0Jg4V8cRBEG45EUkJpNqbsJ8YDUOdxlrdXp8wgMpNRqp++hxAr20FPebTnBoNBBNdNQgco7sJKB6A+VYMJlMYmKfVmqrsguidIMgCELbEyN6zyAzJ43UnfkUHivHZrO5Oo7QBiS1ASmwP1Lo8Ob/ik5egeayDWVlZdx6662ujiIAcdEJXD38Nq7pfzfdkpLxjdTiE+6Gl48nOg8t5Y4MamprnO1zStKoqa3kwJeH+O2D79m49BusVis7f/mJnNS9LjwTobPa9fU2Vn+2k+LCSi7yKlmCIAgXBZVKTVLfofS4+zlG/fNlIvonEdktkLETeqHRqAnyVFN1cI2zvUKhwFxygF1HSvl1m5Fff/mK/PxMqqsr+WjpZ2Rn57jwbARBuBA988wzSJLU4pWYmOhc39TUxN///nd8fHxwd3fnuuuuo6SkxIWJLx4bN27kqquuIjg4GEmS+Pbbb1usdzgczJkzh6CgILRaLWPHjiU9Pb1Fm8rKSm677Tb0ej0Gg4Fp06ZRV1fXgWdxYRAdvWcQGOJPQr9Q5Hor67atdHUcQRCETqdLaH9i5SPR18RRU11HxqE8SvLK2X9kp7ONskZN4eQ1eDy0h9o1qZjrGlg6bgYFK1fgdexDslN2nuYIgtD2vPc2oXo3G5k8i/z8XFfHEQRB6HQ06mTMpl4cOtiA0SyxOaWYlKOHWwzeWbXZxqv/kvPduxoqK4qpqiqjpnYzC975mBuu/ZsY6HMKdqQ2ewnCxaZr164UFRU5X5s3b3aue+ihh/jf//7Hl19+yYYNGygsLGTy5MkuTHvxqK+vp0ePHrzzzjsnXf/yyy/z5ptvsnDhQrZv345Op2PcuHE0NTU529x2220cPnyYNWvW8MMPP7Bx40buvffejjqFC4Yo3XAGMmTYAbWbAg+9h6vjCIIgdDoBfkEE+EEMcZSXVSKPLEPrpqahoMrZpveQgfhuXkBJajb9A7wp2XEE2a/plGl9KO1np7q0gMikfi48C6GzkeQy8r0sjIsLRqUU9w+CIAgdLSIiHoCgoBB+PZLC0CQNkiSRvv83EnsPA2DmM/czcORuTE0WYruEUde4n5AQPSGh3uzfcQSr1YpcLnflaQiCcIFRKBQEBgaesNxoNPLf//6XTz/9lNGjRwOwePFikpKS2LZtGwMHDuzoqBeVCRMmMGHChJOuczgczJ8/n3//+99cc01z6b6PPvqIgIAAvv32W26++WZSUlJYtWoVO3fupG/fvgC89dZbXHHFFbz66qsEBwd32Lm4mhjRewZVFTUc+i2LtEPHyKk82uLbAkEQBKFjJcUmI5c3/+qqpRCTyeRcF54YS79rxtJ1YG+sIVqUX4yneHwQB5J6YvM2uCix0FllSVZy1TbyssrIzdvj6jiCIAidWlh0IgqFHLlchqz8qHO5JEkMGtWXkRMGERoRSkFBFQcPFjE53sCKATE0NjaeZq+dl8PRdi9BuNikp6cTHBxMdHQ0t912G7m5zU9u7d69G4vFwtixY51tExMTCQ8PZ+vWra6Ke0nIysqiuLi4xbX19PRkwIABzmu7detWDAaDs5MXYOzYschkMrZv397hmV2p3Tp6s7OzmTZtGlFRUWi1WmJiYnj66acxm80t2vy1vokkSWzbtq29YrWaw+EgJf0QsWFdMVss9L+8Kw51E6u3rXB1NEEQhE4rMiyKA1sySd2ZT02ZiS9Xf8j63SvJK2pZRy8gKYKQQX70G98FS5ON4qo8LBaLi1ILrXWx3zsA1BprOLhtH1FD++PV5E/mjDR2PPgjhzaI8iGCIAiuYpK5sSmlhM05jRzIySMlZTVHj26jvr5l7cbQsBCSk4MITAjGTa2h+LffXJT4wmZ3SG32EoSLyYABA1iyZAmrVq1iwYIFZGVlMWzYMGpraykuLkalUmEwGFpsExAQQHFxsWsCXyKOX7+AgIAWy/98bYuLi/H392+xXqFQ4O3t3emuf7uVbjh69Ch2u5333nuP2NhYDh06xD333EN9fT2vvvpqi7Zr166la9euzvc+Pj7tFavV9qXspNL9EFUFJkK0CchlJgLDvanK63yFnAVBEC4Ucrmc2MBklMF16AxqijKMEFTOvvJVWK1jiAqLBaBLXA++27MD3zAdR47lodEa2bJrPSMHXebiMxBO52K/dwBY88qX1P2Qg+bqcHQRBrRltWgboWhHOt1GiPIhgiAIrqAPjKIxpjs9I2DrgTyiomUolU0cOvQzXbtcg0LR/LHYYffE4TCCr4r06hoKFn1K4hVXuDi9IAgXij+XFujevTsDBgwgIiKCL774Aq1W68JkgvCHduvoHT9+POPHj3e+j46OJjU1lQULFpzwYc3Hx+ekNU5cqbSyiLKCWqwWC10iepF+ZC+GOBsahaerownnyWSrp9JUhM1uQS5T4q0OQi3XuTqWIAitNGnsrazauRwMTdh/H6Tr4aMhI++Qs6NXqVRSWViLxWxG5+ZGQv8QPKv1LkwttMbFfu8AkJdfjDXYTtOBdP6+6HGW3vQ0oUYP3IK9XB1NEASh0wqLiMbHL5DcPf+lT2IQ2dllxMUFEROjIzMjhYTEZACsZfVsf+MrVBXV1Db6ESy5uTj5hcnRRhOpOcRkbMJFzmAwEB8fT0ZGBpdddhlms5nq6uoWo3pLSkouyHvWi8nx61dSUkJQUJBzeUlJCT179nS2KS0tbbGd1WqlsrKy013/Dq3RazQa8fb2PmH51Vdfjb+/P0OHDuX777/vyEinpNRBaFcDXuFuRIXHEBgYQr2xiYy0DFZsWILdbnd1ROEs1ZjLOVT5K9tKvia1egsZNTtIrd7CtpKvOVT5KzXmcldHvChMnTqVSZMmndc+1q9fjyRJVFdXt0kmofMJVsVx4P92kv3zUQpTjRRmVFBTX9mijW+AD0ExXmi1GgByK47iEMXgLjoX070DgL3AgluekgBDEAZfb4ZNUpJ0ayXGQ9+Q8r/PXB1PEASh03JzcyOvUs+BPUXsf3cdu/65nPS/fYIpPdvZxuDjS2B1PUlyFXKDDq8mB3uWr3Rd6AuUqNErCM3q6urIzMwkKCiIPn36oFQq+eWXX5zrU1NTyc3NZdCgQS5MefGLiooiMDCwxbWtqalh+/btzms7aNAgqqur2b17t7PNunXrsNvtDBgwoMMzu1K7jej9q4yMDN56660WI3Lc3d2ZN28eQ4YMQSaT8fXXXzNp0iS+/fZbrr766pPux2QytZh8p6ampl3ymuz1aAFLgx2D3oC/exjoKrDWybBqarBYLKjV6nY5ttD2yhpzOFK1EQcndtA7cFDelEtFUz5dvEbgpw13QUJBEM5GfHwSmzdWoOmmxRCixs3dk5r8lv++Yw29ya/dSV5uEfVNdchRkZmVTmx0vItSC2erre4doGPuH4zVRqwljYAbbj7NT4q4RXRDl7mZ7goTxuLsNj+mIAiC0HqRMV1xP3CYGjvEGeuQy+UUVFc514d2SWLzgD64b91FlrWCJn8t0p719L7pShemFgThQvHII49w1VVXERERQWFhIU8//TRyuZxbbrkFT09Ppk2bxqxZs/D29kav1/OPf/yDQYMGMXDgQFdHv+DV1dWRkZHhfJ+VlcW+ffvw9vYmPDycmTNn8sILLxAXF0dUVBRPPfUUwcHBzkFoSUlJjB8/nnvuuYeFCxdisViYMWMGN998M8HBwS46K9c4647eJ554gpdeeum0bVJSUkhMTHS+LygoYPz48dxwww3cc889zuW+vr7MmjXL+b5fv34UFhbyyiuvnPLD2ty5c3n22WfPNvZZy88twK1aTUO1BWV/JZEhMaSlbgG5HZ8gPau2f8FVQ29DJuvQQdHCOagxl5+yk/fPHNg5UrWBXvIJ6FW+HZROEIRzodFquWPze2QdSaW8pgi7WwVpGenscduOm9qD/KJc8usPo3KXo3aTE9czhMZaCwbDiSNDhfbn6nsH6Jj7h/y8Ywx8JYma4kbskjsAklcQaRVmhutUqErSSPvlB+LHTGzXHIIgCMLJRXTtS7GnH0kDLGS/9TZhZWXs37GXas8wYvt3YcvaHwnv70HF5ePovjWFYeZy0rSi9M5ftdVEamIyNuFik5+fzy233EJFRQV+fn4MHTqUbdu24efnB8Drr7+OTCbjuuuuw2QyMW7cON59910Xp7447Nq1i1GjRjnfH7/fnzJlCkuWLOGxxx6jvr6ee++9l+rqaoYOHcqqVavQaDTObZYtW8aMGTMYM2aM8//Dm2++2eHn4mpn3Uv58MMPk5KSctpXdHS0s31hYSGjRo1i8ODBvP/++2fc/4ABA1r04v/V7NmzMRqNzldeXt7ZnsIZNTQ04BWmJiTJk4jI5tGdHh56untfTnVFLSXH6imtKuSXTava/NhC28utO3jGTt7jHNjJrTvYpsf/6quvSE5ORqvV4uPjw9ixY6mvr2fkyJHMnDmzRdtJkyYxdepU53uTycTjjz9OWFgYarWa2NhY/vvf/zrXHz58mIkTJ6LX6/Hw8GDYsGFkZmY61y9atIikpCQ0Gg2JiYkn/JLJy8vjxhtvxGAw4O3tzTXXXEN2drZzvc1mY9asWRgMBnx8fHjsscdOePTdbrczd+5coqKi0Gq19OjRg6+++qpFmx9//JH4+Hi0Wi2jRo1qcQxBOFcennq6D+oHViXFWdVEJPqRXr+VbNlmjB7paL3kBMd40X1IHOUFNaiafPD1Fl/iuIKr7x2gY+4fVBorgy+LZPwdSSQMjAMgZtBl2IdcyZFxo8kbPpRNW3+jqampzY8tCIIgtE5gaARh0bFkWb3ZVqinOtVE1tO/8s34eVTsyKbnwEgSEoNx6HU4HA6kWDGR5l85kNrsJQgXk88//5zCwkJMJhP5+fl8/vnnxMTEONdrNBreeecdKisrqa+vZ8WKFZ2uPuy5GjlyJA6H44TXkiVLAJAkieeee47i4mKamppYu3Yt8fEtn9T09vbm008/pba2FqPRyIcffoi7u7sLzsa1znpEr5+fn/PbijMpKChg1KhR9OnTh8WLF7dq9Ou+fftaFFf+K7Va3e4lE7Lzj+Ed6AGATmlwLo8MiyKsKAqPKBuHd1bQpKxo1xzC+TPZ6ilvOrsP8xVNeZhs9W0yQVtRURG33HILL7/8Mtdeey21tbVs2rSp1XVC77zzTrZu3cqbb75Jjx49yMrKory8uZZwQUEBw4cPZ+TIkaxbtw69Xs+WLVuwWq1A87dZc+bM4e2336ZXr17s3buXe+65B51Ox5QpU7BYLIwbN45BgwaxadMmFAoFL7zwAuPHj+fAgQOoVCrmzZvHkiVL+PDDD0lKSmLevHl88803jB492plx7ty5fPLJJyxcuJC4uDg2btzI7bffjp+fHyNGjCAvL4/Jkyfz97//nXvvvZddu3bx8MMPn/e1FYTj3HXuBHoZkMlkFGUY0WjVaLRqCtOrASjJq6A8u55eQy5zbdBOzNX3DtAx9w8OmjtwGxtNaLURACgUCnr1G0Vt3Q7Ce+rZ/3Mlv32zmdG3jG3XLIIgCMLpGbTueDm0FNVV4MAHD5mWvP0VWK02wMGx8goUjR4MGDDS1VEFQRAEodXarUZvQUEBI0eOJCIigldffZWysjLnuuPfaCxduhSVSkWvXr0AWLFiBR9++CGLFi1qr1itUmEsobighqLsSm4aPwqbzUZqxhF8vQJICulHev0GlJIKm1stldUVeBt8XJpXOLVKUxFwdlX+HTioMhUR6BZ73scvKirCarUyefJkIiKaP/QnJye3atu0tDS++OIL1qxZw9ixzR0Cfx7x9s477+Dp6cnnn3+OUqkEaPGN1tNPP828efOYPHky0FzA/MiRI7z33ntMmTKF5cuXY7fbWbRoEZLU/G364sWLMRgMrF+/nssvv5z58+cze/Zs5z4WLlzIzz//7DyGyWTixRdfZO3atc4i6NHR0WzevJn33nuPESNGsGDBAmJiYpg3bx4ACQkJHDx48IyPcQtCa/XtOoRvN2VQUJpKcXY1TfXRKBVKSgrKQObA4ONBpEcyCdHdXB1VOIOL+d4BoCT1CLUZDRyrdHD9rZdTXVpOZWkZfhGhlJV7Ag4ay6xk/JoqOnoFQRBcLPGea9mX+jbVxXUUdy0nuV8CvYODWP3xL8QHe3BzvxAK3cc477OFP9gdza+22I8gCILQttqto3fNmjVkZGSQkZFBaGhoi3V/Hs34/PPPk5OTg0KhIDExkeXLl3P99de3V6xWkdQ2gkMMuGs8CQ0K59edK3EElbL2lxxG9bma7D1V1DXUo1Zp2FD6M9defqtL8wqnZrNbzmk76zlu91c9evRgzJgxJCcnM27cOC6//HKuv/56vLzOXOtr3759yOVyRowYccr1w4YNO+nNZ319PZmZmUybNq1FbUur1YqnpycA+/fvJyMjAw8PjxbbNjU1kZmZidFopKioqMUMlQqFgr59+zr/DWdkZNDQ0MBll7UcKWk2m52dMCkpKSfMcilmHRXakkwm45qhd7Jh6WOExATi3aN55KjNZiM4pvnfmrJMc7pdCBeIi/neASBILxHto8HNNwqbzcbuf80nILuSD90aGPXU3/n+b8tRNFQgs2rIPHSYmG5dXR1ZEASh0wqJi8L0+nTCG/Zgt0l0S26erOfIz3XEh7lRWtWEV6S/i1NemESNXkEQhAtXu3X0Tp06tUWt0ZOZMmUKU6ZMaa8I56zJWoMCMNNIeUUpdeoCPGRqfPy8ySjfS4+EvmRVHCKki4Hqgnpq62rwcNe7OrZwEnLZuX0DrzjH7U44vlzOmjVr+O2331i9ejVvvfUWTz75JNu3b0cmk51QwsFi+aODWavVnnbfp1tfV1cHwAcffHBCJ6tcLne26dOnD8uWLTth+9Y+Yn38OCtXriQkJKTFuvZ+RFoQ/qy2toYkr3p2N/zxmL/JbMbhcHDsUCE3DL7WhemE1rqY7x0cDgeYa5Akd2S2Bg5+u5qQnGokmZx4q5b6Pel4u/sSqSwhSVVHyqbVIDp6BUEQXMpqraFLlxD27StwLjOjxGy2klmhZrBvgAvTCYIgCMLZa7eO3otZXkEemlo5FpONtZt/pE5eRU56A5JdQUCcjoqMKiy25jqohhAVBzN3MrjHGBenFk7GWx2EhITjLMo3SEh4qU9f6/FsSJLEkCFDGDJkCHPmzCEiIoJvvvkGPz8/ioqKnO1sNhuHDh1yzjSZnJyM3W5nw4YNztINf9a9e3eWLl2KxWI5YVRvQEAAwcHBHDt2jNtuu+2kuXr37s3y5cvx9/dHrz/5FxVBQUFs376d4cOHA80jgnfv3k3v3r0B6NKlC2q1mtzc3FOOPE5KSuL7779vsWzbtm0nbSsI58pg8KLIdyzlu36ldG8parWa8uIK3NKV6CQf9B7iyzihfRUU5mH08mNfg5yyynKaShpwJLuRcyCLpDp3rL/sJSNUy1i7CVATeHAbxQUFBP7lSzJBEASh4wQHd+Foymay9mbTuCUNhbGezMwS7PUOlNdPcnW8C1ZbTaQmJmMTBEFoe6Kj9y8cDgfufkr8wpsfZ/9u4Rau+dsQHA4HR7bnUJpjBHkdZXmVyJBhsVjQ2RzEBpfg7ye+8b3QqOU6fDRhlDfltnobH01Ym0zEBrB9+3Z++eUXLr/8cvz9/dm+fTtlZWUkJSWh0+mYNWsWK1euJCYmhtdee43q6mrntpGRkUyZMoW7777bORlbTk4OpaWl3HjjjcyYMYO33nqLm2++mdmzZ+Pp6cm2bdvo378/CQkJPPvsszz44IN4enoyfvx4TCYTu3btoqqqilmzZnHbbbfxyiuvcM011/Dcc88RGhpKTk4OK1as4LHHHiM0NJR//vOf/Oc//yEuLo7ExMQTMnp4ePDII4/w0EMPYbfbGTp0KEajkS1btqDX65kyZQp/+9vfmDdvHo8++ijTp09n9+7dzpkzBaEtJQ27nsi+V7Ly0CJ8Q/T4RKoxljfi5XHmUimCcL4aGyrp2au5FvtPP+3Bo6uB4ff35NdVu4hpqqa2SYY6q4LixIHkVdSQl15A76ISPAwGdLq2+Z0jCIIgnB13dw+6dp2ArADCvlmGJEnU4iBarqTUXfxsFgRBEC4+oqP3L0pKi9Homx/9Lc4tJ6ZnEPlpZbgZlARFeuMdqKfOWI9S549fiIGmhiaqy6vYVvo1EeX96ZHU18VnIPxVuHsyFU35OLCfsa0MOeHurZssrTX0ej0bN25k/vz51NTUEBERwbx585gwYQIWi4X9+/dz5513olAoeOihh5yjeY9bsGAB//rXv3jggQeoqKggPDycf/3rXwD4+Piwbt06Hn30UUaMGIFcLqdnz54MGTIEgOnTp+Pm5sYrr7zCo48+ik6nIzk5mZkzZwLg5ubGxo0befzxx5k8eTK1tbWEhIQwZswY5wjfhx9+mKKiIqZMmYJMJuPuu+/m2muvxWg0OjM+//zz+Pn5MXfuXI4dO4bBYKB3797OnOHh4Xz99dc89NBDvPXWW/Tv358XX3yRu+++u82usyAcp9Vq8ZACgQY0bhoqmhpJLzqCvGEVY4aNd3U84RJmszc4/+zp6UVRYQl2eyRqh5zIgOYvj41KFX0vTwDA9KkMz/+8zW6VjEFLF4jJfgRBEFwoZuQY0r75nCjsyBrqqLfr2LJ0Gb69euIX1HZP+l0qxGRsgiAIFy7J8dcioReZmpoaPD09MRqNp3z8/GzU1dXxwbcvExjmS3ZaASERgZgaTWSm5dClfzihMYGk7MomsU8kcrmMI7uyiO8RhkKpwK0ymv7dhrXBWQltrawxlyNVG07b2Ssho4vXCPy04R2YTBCEtpZbkMXPu79A56GhKK+cxAEhaOr9GdN7squjtVpb/24TTtTW13jLxtXoHKkgU5GyIY0gNx12bzUZWYVM6ueFn5cH24xq+g+LA2DTgu102VlAocVEwmcfnLEuuyAIgtC+flu2CFX5UeRyG0e+L6K/XI955i10HTfqzBtfADri3uH4Mb7r9xA6xfnPx1FvNXHNztfF/Y4gCEIbEiN6/8Ld3Z0hPS+jSptKQ60P4d28AZDk4OWnpziritpyEyXpddisVspya5HsBajcZPQMSnBxeuFU/LTh9JJPILfuIBVNeS1q9kpI+GjCCHdPRq/ydWFKQRDaQlhwJF45enwj3bCYbGh1ajwtYjSO0L4GD7uMA1srSY6W0fiDlS4ZOQAcKA/m3a8syL2rCblSQqHRYrXbyMwupLqhFqVaRTcxmlcQBMHlgnv2xu/AUdxUKqr8fak0S3Qd2MfVsQRBEAThrIiO3pPo320Y321Pa/EYpUqhwsOgw8Ogw1znIDix+RtHq9VGVE8/GuubkJpEMfkLmV7lSzfvUZhs9VSZirDaLShkSrzUQW1Wk1cQBNeTJIlQTTcaHZmYTE0UpdcwfFg/V8cSLnGSJKHz647Vuh+5sdG53NQo4eHQQQV07eZNn0RPACxfuRGndnBYCQqFuB0TBEFwtciuvTmQup1kRzZWnQxrt3jcPcUo05MRpRsEQRAuXOKTxSnEePVm+cq1pP1WjVpvwWQvwqJoJCoxuEUHsFLZfAklmYRB7+OquMJZUMt1BLrFujqGIAjtqKaxmvL0WixWC5d3u0V0pAkdIjQsmvSUUtIq7FRo3DFb6smtzMLHGoxersdN98cnWllNA5IkofcXT5IIgiBcKIr25lNXUIXZ4MnoR/7m6jgXLLtDwu44/0FObbEPQRAEoSXxyfcUusX3YoUlhcwUGwCj7lXiH66mOKuKksIqkBzYrDbys0uoa6jD6jDTpY+4nIIgCBcCtZtEcICeAJs75dVl+Pn5uzqS0AloNBqCw5KprlHibZRxuMHCVIMX9YY6UuVlVElB7M41UVpcTk2YlorSeuSWJlfHFgRBEH7nW1ZLRIOFXDeb+JJYEARBuCiJ316nERbpRWZKOQ6HA6WbDTd3LW7uWkBGcIwBAJWbnMAYAyWpDYSHRboyriAIgvA7s70JJSBJUGYsJImuro4kdBJevj5YQnwg34jZQ43WokSLkqJABUOGxwOwfn0t415snhxw3z6zK+MKgiAIf2KrqwPAUlRMXV0d7u7uLk50YXL8/mqL/QiCIAhtS+bqABey8Vf3pc8QD7yCKygpqCBtfw5lRRVodHIAmhqaQGmnJs/GyORrXZxWEARBOE5t11OUbqQ4q4p6k9HVcYROJvjOKzEOjqfcXctmcw05jTW4x/xR3kmpVGO12jhwoImePca4MKkgCILwZzWRYaSp5SjkUJhy1NVxLlgOJGf5hvN5ORClGwRBENqaGNF7GkFBfjz46CQOpOyh0vMgpkYzu349SlRcOIVVRupLHcTHJdIrYTB6D09XxxUEQRCOU9oIimv+udxUZHVxGKGziR85CEYOImPOp6TvyySLRkILSzi85GcKzaBJ6Edujhe9evZGksSHXEEQhAuFT00tEVYrKFUUFxe7Oo4gCIIgnDUxorcV/H0DqS0xo9aq8A/wwytIg4fah7tvnMGIvhNEJ68gCMIFJtAznMY6E3a7nYz0NFfHETqpoC4BWCULGrR0ifHCV2YhpvsIRo6aTHx8H9HJKwiCcKHpkgBAnclM3qEjLg5z4bK34UsQBEFoW2JEbysE+gUTVdWXI7kbUCiUKFQKvPVhyOVyV0cTzkFlRQOH9xXR2GhGq1XRtWcQ3j5uro4lCEIbCvAJZcuWGtTuMrxDdTQ0NODmJv6dCx1rzM1jyD2cR8ryVDzdrNglOd4xya6OJQiCIJyCxdObw6jQWO141VlcHeeC5XBIOBzn/2VlW+xDEARBaEmM6G2lbvG9uKbX/citGspz6zhasBtjTbWrYwlnISujgjf/s55H7l3Bord+Y9miXSx66zceuXcFb/5nPVkZFa6OeNGYOnUqkyZNOq99rF+/HkmSqK6uPq/9fPvtt8TGxiKXy5k5c+Z57Uu4dGjUGvS+aoJjfAiM9CY7L8PVkYRO6q7npzJ722NUqKBS507Kpm9cHUkQBEE4BXd3d+Ka7ITJVVBa7uo4giAIgnDWxIjes6BWq/EN9MQtxAbA0ZwDDEge7uJUQmvs2pbLwnmbsFhOfEDIZnOwe1seB3YXcP/Dw+gzMNwFCYVzdd9993HXXXfx4IMP4uHhwdSpU6murubbb791dbQWnnnmGb799lv27dvn6iidglqtRuFww2q1knmwgDyTiSpTCQ0WI3H+vYkMi3Z1RKETcWCnR4QnAd5uFFZUUltbi4eHh6tjCYIgCH+hDA3G5rBTZzFzOL8M2c5f8PBUUlNTS7++V7o63gWjrcouiNINgiAIbU+M6D1LftoobLbmX0kqucbFaYTWyMqoOGUn759ZLHYWzNskRvZeROrq6igtLWXcuHEEBwe3aceJ2Wxu03atZbGIxwTbSm2pmbLcGqKSglDozVh8C2lUl6PTurs6mtDJeHv7UKkIBqDBKsfdXfwdFARBuBCFd0lkf6AZWYyVm3t4oFEXExtrx0P82BYEQRAuEqKj9yz17TqYQ+sKyNhdTEr2HnYd3uLqSMIZ/O+rg2fs5D3OYrHzw1eH2vT4X331FcnJyWi1Wnx8fBg7diz19fUAjBw58oRyA5MmTWLq1KnO9yaTiccff5ywsDDUajWxsbH897//da4/fPgwEydORK/X4+HhwbBhw8jMzHSuX7RoEUlJSWg0GhITE3n33XdbHC8vL48bb7wRg8GAt7c311xzDdnZ2c71NpuNWbNmYTAY8PHx4bHHHsPhcLTYh91uZ+7cuURFRaHVaunRowdfffVVizY//vgj8fHxaLVaRo0a1eIYp/Laa6+RnJyMTqcjLCyMBx54gLq6OqC59MPxjt3Ro0cjSRIjR45k6dKlfPfdd0iShCRJrF+/vlXnebwcxf/93/8RHBxMQkLCSTM988wz9OzZk0WLFhEVFYVG0/yFT3V1NdOnT8fPzw+9Xs/o0aPZv38/AEuWLOHZZ59l//79zlxLliwBQJIkFixYwNVXX41Op+OFF14gNjaWV199tcVx9+3bhyRJZGSIEgStFREWRVC0NyqNCvPvHeiOehV+vv4uTiZ0Sr69+OHdI2z/No8dry8m94CY5EcQBOFCo1ariQr2J8JNhVwmw1LX/IW+JNO7ONmFxe5ou5cgCILQtkRH71mSJInRA6+krrGWgGQ1RY6DFJcWujqWcAqVFQ3s3ZF/Vtvs3ZlHZUVDmxy/qKiIW265hbvvvpuUlBTWr1/P5MmTT+goPZ0777yTzz77jDfffJOUlBTee+8952iwgoIChg8fjlqtZt26dezevZu7774bq9UKwLJly5gzZw7/93//R0pKCi+++CJPPfUUS5cuBZpHj44bNw4PDw82bdrEli1bcHd3Z/z48c6RqvPmzWPJkiV8+OGHbN68mcrKSr75pmWNyblz5/LRRx+xcOFCDh8+zEMPPcTtt9/Ohg0bgOZO1smTJ3PVVVexb98+pk+fzhNPPHHGc5fJZLz55pscPnyYpUuXsm7dOh577DEABg8eTGpqKgBff/01RUVFfP/999x4442MHz+eoqIiioqKGDx4cKvOE+CXX34hNTWVNWvW8MMPP5wyV0ZGBl9//TUrVqxwlmK44YYbKC0t5aeffmL37t307t2bMWPGUFlZyU033cTDDz9M165dnbluuukm5/6eeeYZrr32Wg4ePMi0adO4++67Wbx4cYtjLl68mOHDhxMbG3vG6yY0c1PqMZutHN2TRVZqDtkHKqiqrGLN1u9cHU3ohLr06k1wz94YTEr0q/Zy7L+iVq8gCMKFSPINpKK+iV8K6jnw8z52/biXiv1byM9o28EgFzMHUpu9BEEQhLYlavSeg+SEXqz97Qfkchke3lqO5u8h0D/Y1bGEkzi8rwj7WX5VbLM5OLK/iKGjY877+EVFRVitViZPnkxERAQAycmtn3E9LS2NL774gjVr1jB27FgAoqP/qC36zjvv4Onpyeeff45SqQQgPj7euf7pp59m3rx5TJ48GYCoqCiOHDnCe++9x5QpU1i+fDl2u51FixYhSc03WosXL8ZgMLB+/Xouv/xy5s+fz+zZs537WLhwIT///LPzGCaTiRdffJG1a9cyaNAgZ8bNmzfz3nvvMWLECBYsWEBMTAzz5s0DICEhgYMHD/LSSy+d9vz/PNo5MjKSF154gb/97W+8++67qFQq/P2bR2Z6e3sTGBgIgFarxWQyOd8DfPLJJ2c8TwCdTseiRYtQqVSnzWU2m/noo4/w8/MDYPPmzezYsYPS0lLUajUAr776Kt9++y1fffUV9957L+7u7igUiha5jrv11lu56667nO+nTp3KnDlz2LFjB/3798disfDpp5+eMMpXOD0VOkpzqojuGoqbXouXrxYPgxslrRhNLgjtIfy6q8nbfhQA/ZF8MnbtJ7ZvDxenEgRBEP4sq8Yd+QEvAtFAsJr+fnZASXr+Xojt5up4giAIgnBaoqP3HMWHJpN7KBu7zY6jqZqRvV2dSDiZxsZzq5/a2NA2dVJ79OjBmDFjSE5OZty4cVx++eVcf/31eHl5tWr7ffv2IZfLGTFixCnXDxs2zNnJ+2f19fVkZmYybdo07rnnHudyq9WKp6cnAPv37ycjI+OE2rZNTU1kZmZiNBopKipiwIABznUKhYK+ffs6RyVnZGTQ0NDAZZdd1mIfZrOZXr16AZCSktJiH4CzU/h01q5dy9y5czl69Cg1NTVYrVaamppoaGjAzc3tjNsfd6bzPC45OfmMnbwAERERzk7e4/uvq6vDx8enRbvGxsYW+z+Vvn37tngfHBzMlVdeyYcffkj//v353//+h8lk4oYbbjjjvoQ/JMV35WjdRlRqJeGxgRRmVuFhcEMl07k6mtBJ+YWHYRsTT7mHRENhDbWpKaKjVxAE4QITnJQAq9IAMB4zAgEA2BWiUO9xbVV2QZRuEARBaHuio/ccjR99Fd/sXIhvuDu2Ek9XxxFOQas9c6fdSbdzO7Hj9FzI5XLWrFnDb7/9xurVq3nrrbd48skn2b59O1FRUchkshPKOPx5Mi6tVnv6nKdZf7yW7QcffHBCJ6tcLne26dOnD8uWLTth+z93ZJ7O8eOsXLmSkJCQFuuOj249F9nZ2UycOJH777+f//u//8Pb25vNmzczbdo0zGbzWXX0tvY8dbrWdQD+tV1dXR1BQUHOesB/ZjAYznp/ANOnT+eOO+7g9ddfZ/Hixdx0001ndc5C85cSUr2Oo3uykdmVVJfX4zDL0DuCXB1N6MSSRwwjOqYKSZI4linuHwRBEC40vl2i2K0yU9lUi8koY8GCXBxNKq7+z2RXR7tgtFXZhbPdR0FBAY8//jg//fQTDQ0NxMbGsnjxYuegCYfDwdNPP80HH3xAdXU1Q4YMYcGCBcTFxZ13VkEQhIuF6Og9R3K5HJ3MC7CgkYvRYReqrj2DkMslbLbWf10sl0t06dF2HUGSJDFkyBCGDBnCnDlziIiI4JtvvmHWrFn4+flRVFTkbGuz2Th06BCjRo0CmkeY2u12NmzY4Czd8Gfdu3dn6dKlWCyWE0b1BgQEEBwczLFjx7jttttOmq13794sX74cf39/9PqTTzIRFBTE9u3bGT58ONA8Ivh4DVqALl26oFaryc3NPeXI46SkJL7//vsWy7Zt23bStsft3r0bu93OvHnzkMmay4l/8cUXp90GQKVSYbPZzvo8z0fv3r0pLi5GoVAQGRnZ6lync8UVV6DT6ViwYAGrVq1i48aNbZS2c4kwdEEfYUOlUZF3uJqAWB3h1u6ujiV0YiEhceTl/URgoCdKpceZNxAEQRA6VEhMNKnDu5CwPgXJIbEuW42bv4GwyChXR+vUqqqqGDJkCKNGjeKnn37Cz8+P9PT0Fk9Kvvzyy7z55pssXbqUqKgonnrqKcaNG8eRI0ecEygLgiBc6sRkbOfIbreTk5ND7sFK0tOaH10XLjzePm707Bd6Vtv06heGt0/bjJzcvn07L774Irt27SI3N5cVK1ZQVlZGUlISAKNHj2blypWsXLmSo0ePcv/991NdXe3cPjIykilTpnD33Xfz7bffkpWVxfr1650dnjNmzKCmpoabb76ZXbt2kZ6ezscff+ycpOzZZ59l7ty5vPnmm6SlpXHw4EEWL17Ma6+9BsBtt92Gr68v11xzDZs2bXLu/8EHHyQ/v3kSu3/+85/85z//4dtvv+Xo0aM88MADLTJ6eHjwyCOP8NBDD7F06VIyMzPZs2cPb731lnPSt7/97W+kp6fz6KOPkpqayqeffsqSJUtOe+1iY2OxWCy89dZbHDt2jI8//piFCxee8ZpHRkZy4MABUlNTKS8vx2KxtOo8z8fYsWMZNGgQkyZNYvXq1WRnZ/Pbb7/x5JNPsmvXLmeurKws9u3bR3l5OSaT6bT7lMvlTJ06ldmzZxMXF9eqUhfCifr06E99afOfLRYz1WUNhAWHuzaU0Kll7NxC6be7+O6VHyk7WnTmDQRBEIQOFzZ+OGZ78xf0Cg8FbkEG1wa6wBwv3dAWr9Z66aWXCAsLY/HixfTv35+oqCguv/xyYmKa51VxOBzMnz+ff//731xzzTV0796djz76iMLCQr799tv2uRCC8BfPPPOMc04Y4exkZ2cjSdIZ+wmEMxMdveeosbERvxh3gpP0NKrKaGwSHb0XqquuT0apbN1fdaVKzsTr226SBb1ez8aNG7niiiuIj4/n3//+N/PmzWPChAkA3H333UyZMoU777yTESNGEB0d7RzNe9yCBQu4/vrreeCBB0hMTOSee+6hvr4eAB8fH9atW0ddXR0jRoygT58+fPDBB87RvdOnT2fRokUsXryY5ORkRowYwZIlS4iKah6R4ObmxsaNGwkPD2fy5MkkJSUxbdo0mpqanCNfH374Ye644w6mTJnCoEGD8PDw4Nprr22R8fnnn+epp55i7ty5JCUlMX78eFauXOk8Tnh4OF9//TXffvstPXr0YOHChbz44ounvXY9evTgtdde46WXXqJbt24sW7aMuXPnnvGa33PPPSQkJNC3b1/8/PzYsmVLq87zfEiSxI8//sjw4cO56667iI+P5+abbyYnJ4eAgOa6btdddx3jx49n1KhR+Pn58dlnn51xv8fLVPx5ojbh7MhkMuT1HqTtKqCypAZfR8wZS6IIQnvSmJroVVqINtNI9je7XB1HEARBOInYXsns9lewX2vD5mkidmIfV0e6oLR1R29NTU2L18kGRHz//ff07duXG264AX9/f3r16sUHH3zgXJ+VlUVxcXGLpyA9PT0ZMGAAW7dubfdrIrSdJUuWIEmS86XRaAgODmbcuHG8+eab1NbWujRfQ0MDzzzzzEnL9gln9umnnzJ//nxXx7ikSY6/Fgi9yNTU1ODp6YnRaGyXR7JP5387llJUmUN4XCD+Td3p3XVghx5faL3d23JZMG8TFov9lG2UShn3PzyMPgPFaD/hwrBp0ybGjBlDXl6es8NYOHt7Dm+jxiuVxgYTXTXjCA+NcHWkM3Ll77bOwlXXuDg3G8Vb/+LXUl9qs92485cXUChEJS1BEIQLzaZn3kG/MZ2KeF9GL/y3q+OcUUf8Xjt+jEXdH8NNfu5zcRzXYDMx/cDLJyx/+umneeaZZ1osO156YdasWdxwww3s3LmTf/7znyxcuJApU6bw22+/MWTIEAoLCwkK+qMM34033ogkSSxfvvy88wodY8mSJdx1110899xzREVFYbFYKC4uZv369axZs4bw8HC+//57und3TTm28vJy/Pz8Tvr31Gq1YrVaRamQ05g4cSKHDh0iOzu7xXKHw4HJZEKpVDrnFBLOjfhkcR7C9UnkphVTaDeSXrqB5Pg+J9RJFS4MfQaG8+Tc8fzw1SH27sxrUbNXLpfo1S+Midd3IyrWx4UpBaGZyWSirKyMZ555hhtuuEF08p6nuPAurMnYhXeABxXVxYRz4Xf0CpeuwPBI9vUYRcHKo5jkJr5+7zNu+vsdro4lCIIg/IV71yjYmI45rxS73e6cM0Jo+8nY8vLyWnROn2xCZ7vdTt++fZ1PBfbq1YtDhw45O3qFS8+ECROcE+0BzJ49m3Xr1jFx4kSuvvpqUlJS2uRJPavVit1uR6U6t4nc/0yhUHT6L/AdDgdNTU1n/f/m+Oht4fyJ31atVFiSz697v2f3kd+cy3ok9qdvzwFE9/Sn22UBLFu5gIt8gPQlLSrWh388MYJX35/MPQ8O5vbp/bjnwcG8+v5k/vHECNHJK1wwPvvsMyIiIqiurubll08c5SCcHQ8PPXV5kHuwgl1HNvHd9g9Jyzrq6lhCJ3EsbQ+pB1ZRXJjlXNbz9nvw1EaiLzNQ8l4aKxZ87sKEgiAIwsmED+3D0W4SilF6fv3uLbat/uDMG3USjjYq23D8o7Ner2/xOllHb1BQEF26dGmxLCkpidzcXAACAwMBKCkpadGmpKTEuU64+I0ePZqnnnqKnJwcPvnkE+fykSNHMnLkyBPaT506tcVk2cfrwL766qvMnz+fmJgY1Go1R44cwWw2M2fOHPr06YOnpyc6nY5hw4bx66+/ttjez88PaJ4P53h5ieMje09Wo9dqtfL88887jxUZGcm//vWvE0qUREZGMnHiRDZv3kz//v3RaDRER0fz0Ucftera1NfX8/DDDxMWFoZarSYhIYFXX321RR9Vt27dTigVCc1fpISEhHD99de3WDZ//ny6du2KRqMhICCA++67j6qqqpPm/vnnn+nbty9arZb33nvvpBlHjhzJypUrycnJcV674/9/Tlajd+rUqbi7u5Obm8vEiRNxd3cnJCSEd955B4CDBw8yevRodDodERERfPrppyccs7q6mpkzZzqvS2xsLC+99BJ2+6mf9r7YiY7eVvpp09ekZO9jV/ZaDh3d51ze0FCPw+Fo/sestvDb/nWuCym0irePG0NHx3DZxESGjo5ps4nXBKGtTJ06FZvNxu7duwkJCXF1nEvChCE3YPB3R+0hxzNMTn2D0dWRhE6gqrKCQ5tXkJ++i5R932E2m53rGuqaa60rHQpy/7edvNSsU+1GEARBcAGfoAC63j6RUSN98NODl/b0E+kK7WvIkCHOCaePS0tLIyKi+UmtqKgoAgMD+eWXX5zra2pq2L59u5jU+BJzxx3NT0KtXr36nPexePFi3nrrLe69917mzZuHt7c3NTU1LFq0iJEjR/LSSy/xzDPPUFZWxrhx49i3bx8Afn5+LFiwAIBrr72Wjz/+mI8//pjJkyef8ljTp09nzpw59O7dm9dff50RI0Ywd+5cbr755hPaZmRkcP3113PZZZcxb948vLy8mDp1KocPHz7t+TgcDq6++mpef/11xo8fz2uvvUZCQgKPPvoos2bNcra76aab2LhxI8XFxS2237x5M4WFhS0y3XfffTz66KMMGTKEN954g7vuuotly5Yxbtw4LBZLi+1TU1O55ZZbuOyyy3jjjTfo2bPnSXM++eST9OzZE19fX+e1O1O9XpvNxoQJEwgLC+Pll18mMjKSGTNmsGTJEsaPH0/fvn156aWX8PDw4M477yQr64976oaGBkaMGMEnn3zCnXfeyZtvvsmQIUOYPXt2i+tyqencY8rPQkCoD+7hBuprG1m/738UVGdyWf9rGd5nPL/s/I56qYzy0io8fI6SXRBFZEi0qyMLgiAIv/PzCcCeIcNkbv6QJpNE3Seh/dmsFvrrHfi7W1i9ex+Hts7A87IbiBl+GTfOv5tvnlxGbXYV5ooKtr/8Bf7v/BO1eGRNEAThgqH28ENqkLBYQK0S9w7H2X9/tcV+Wuuhhx5i8ODBvPjii9x4443s2LGD999/n/fffx9ofux75syZvPDCC8TFxREVFcVTTz1FcHAwkyZNaoO0woUiNDQUT09PMjMzz3kf+fn5ZGRkOEfnQnOHYnZ2dosSDvfccw+JiYm89dZb/Pe//0Wn03H99ddz//330717d26//fbTHmf//v0sXbqU6dOnOycPfOCBB/D39+fVV1/l119/bTHCNjU1lY0bNzJs2DCgucZ0WFgYixcv5tVXXz3lcb7//nvWrVvHCy+8wJNPPgnA3//+d2644QbeeOMNZsyYQUxMDDfddBNz5szhq6++YsaMGc7tly9fjru7O1deeSXQ3PG7aNEili1bxq233upsN2rUKMaPH8+XX37ZYnlGRgarVq1i3Lhxp70el112GSEhIVRVVZ3x2h3X1NTE7bffzuzZswG49dZbCQ4O5u677+azzz7jpptucu47MTGRpUuXOkdYv/baa2RmZrJ3717i4uKA5g7s4OBgXnnlFecI6EuNGNHbSgPjLqei2IhWp8Y72B1laA1H0g4gl8u5fOBkrh1wHzGx0VjsZjbs+98lPQxcEAThYuNwOMjPKqaiuJqDW7LxNQSdeSNBOE++/oGU+vRFkiS0Si3JchOm9d8C4Bfsz72LH+KOz+7nprEWugQf4eA3H7s2sCAIgtBC8bECPvygmmVvZfLJe2WujtOp9evXj2+++YbPPvuMbt268fzzzzN//nxuu+02Z5vHHnuMf/zjH9x7773069ePuro6Vq1aJep+XoLc3d2pra095+2vu+66Fp28AHK53NnJa7fbqaysxGq10rdvX/bs2XNOx/nxxx8BThg9+vDDDwOwcuXKFsu7dOni7OSF5hHECQkJHDt27IzHkcvlPPjggyccx+Fw8NNPPwEQHx9Pz549W0xOaLPZ+Oqrr7jqqqucdXW//PJLPD09ueyyyygvL3e++vTpg7u7e4tyFtA8ov5MnbznY/r06c4/GwwGEhIS0Ol03Hjjjc7lCQkJGAyGFtfqyy+/ZNiwYXh5ebU4j7Fjx2Kz2di4cWO7ZXYlMaK3lXx9/HEc0HHgWCZRiYHIZDIKqjPpRk9nG7vcTGC4N7YQO4fT9pGc2Nt1gQVBEIQW/CP0xAcFUXysguyyo4QEi7IYQvvzj+3B0Y27MDU0fwHsa6knd/c2wvsMBKC6IJdoTy2SJJGWdW4fIgRBEIT2IbO6U7tJhuShwFgAKfvSSOoZ7+pYLudwSDgcbTAZ21nuY+LEiUycOPGU6yVJ4rnnnuO5554732jCBa6urg5/f/9z3j4qKuqky5cuXcq8efM4evRoi/IEp2p/Jjk5OchkMmJjY1ssDwwMxGAwkJOT02J5eHj4Cfvw8vI6oS7uyY4THByMh4dHi+VJSUnO9cfddNNN/Otf/6KgoICQkBDWr19PaWmpc2QsQHp6Okaj8ZTXuLS0tMX7c70+raHRaE7olPf09CQ0NPSEesienp4trlV6ejoHDhw4Yfvj/noelwrR0XsWrh81ja27NpFj3EVDpZGirGPEh/QgKuz3f7S1GuqM9ej0buKxYEEQhAuIJEm4ybxQqewoZGoqTfmujiR0EoFh0QTc+gIHnniTT7bnoNArcNT/j2siYvH09SWqe29+XW5lqJeETOnu6riCIAjCn8T2iWOzdg1UNdFYb+HghhTR0YtrSjcIwnH5+fkYjcYWnaeSJLWYdOw4m8120n0cH7n6Z5988glTp05l0qRJPProo/j7+yOXy5k7d+55lYk4nq815PKT9yOd7NzO1U033cTs2bP58ssvmTlzJl988QWenp6MHz/e2cZut+Pv78+yZctOuo+/dpye7Hq2lVNdk9ZcK7vdzmWXXcZjjz120rbx8Zfmz3PR0XuWZBoHgcHeABirajhStw57roOY8Djc9Frq6oyk7U4l3Feia0IPF6cVBEEQjjMZHRjrqrHWKugZP9LVcYRORJIkNA0SukYtNII2zEjW/Bfo+cJ8JElCVwCHc2xkeVcj376R2AHDXR1ZEARBAAJCA8n1MdMkN9B3ajSX3zHS1ZEEodP7+OPmUld/LhXg5eV10vIGfx0xezpfffUV0dHRrFixokXH7NNPP92iXWs7bQEiIiKw2+2kp6c7R9cClJSUUF1d7ZxM8HxFRESwdu1aamtrW4zqPXr0qHP9cVFRUfTv35/ly5czY8YMVqxYwaRJk1Cr1c42MTExrF27liFDhrR5J+7ZXL/zFRMTQ11dHWPHju2wY14IRI3es1Rcmoe5qXnWbLVKi9ZDSW198+ztCpkSvxADGncN6gALZRUlrowqCIIg/K6pqRGFp5WQJAMBYT6EBbfNTZUgtFZ+Vo6zfr9ObkVZX+sccaDWaolWKAj1cafhyHZR518QBOECsX/HEYoPSVRlyAkICMbg7enqSBcEu6PtXoJwNtatW8fzzz9PVFRUi/rMMTExHD16lLKyP2pp79+/ny1btrR638dHiP55ROj27dvZunVri3Zubm4AVFdXn3GfV1xxBQDz589vsfy1114DcE5+dr6uuOIKbDYbb7/9dovlr7/+OpIkMWHChBbLb7rpJrZt28aHH35IeXl5i7IN0DwJnM1m4/nnnz/hWFartVXnfio6nQ6j0XjO25+NG2+8ka1bt/Lzzz+fsK66uhqr1dohOTqaGNF7lrzcAygsOYjDIsNaL6Mp251uA3sC0CtmGJuzv0Kt0OARoOS3zB8YJrsGby9f14YWBEHo5A6k78IzrPnmrc5c7dowQqfUY3o03jIjJpOM/Fw3POISkCQJhUKBZtI1mD75DKUSkipT2bfsHXrdPqNDRzwIgiAIJ/pmwXocTc0fmQsyxGRsxzl+f7XFfgThVH766SeOHj2K1WqlpKSEdevWsWbNGiIiIvj+++9bTLJ3991389prrzFu3DimTZtGaWkpCxcupGvXrtTU1LTqeBMnTmTFihVce+21XHnllWRlZbFw4UK6dOlCXV2ds51Wq6VLly4sX76c+Ph4vL296datG926dTthnz169GDKlCm8//77VFdXM2LECHbs2MHSpUuZNGkSo0aNOv8LBVx11VWMGjWKJ598kuzsbHr06MHq1av57rvvmDlzJjExMS3a33jjjTzyyCM88sgjeHt7nzDidcSIEdx3333MnTuXffv2cfnll6NUKklPT+fLL7/kjTfe4Prrrz+nrH369GH58uXMmjWLfv364e7uzlVXXXXO5346jz76KN9//z0TJ05k6tSp9OnTh/r6eg4ePMhXX31FdnY2vr6XXn+d6Og9S4N6jWD19go0UQ1EeUUwMHm0c52vtx+DbJNZnf8NRelGHDb4cdNX3H7131yYWBAEoXMzm82kZaZiK6hBrpSjrDO4OpLQCSUMvBrjkR8I8a0neNgUAgKDnOu6XDmB7OhIyjPWcsjPA2ttEzs3r6H/sMtdmFgQBKFzKy8tIKRbI0nDgrHLmkjsmuzqSILQqcyZMwcAlUqFt7c3ycnJzJ8/n7vuuuukk4599NFHzJkzh1mzZtGlSxc+/vhjPv30U9avX9+q402dOpXi4mLee+89fv75Z7p06cInn3zCl19+ecI+Fi1axD/+8Q8eeughzGYzTz/99Ek7eo+3jY6OZsmSJXzzzTcEBgYye/bsE0pCnA+ZTMb333/PnDlzWL58OYsXLyYyMpJXXnmFhx9++IT2oaGhDB48mC1btjB9+nSUSuUJbRYuXEifPn147733+Ne//oVCoSAyMpLbb7+dIUOGnHPWBx54gH379rF48WJef/11IiIi2q2j183NjQ0bNvDiiy/y5Zdf8tFHH6HX64mPj+fZZ5/F0/PSfEpDcrRlVWcXqKmpwdPTE6PRiF6v75BjOhwOdh7aTKBPCOHB0Ses35+yg0LFfrQ6FfYSA6N7XdMhuQRBEIQTHUrdx9GGDUh2CTe9mm5uEwgLOXFG2wuJK363dTauuMY1xioy9m2g1/BrTjpad9++H0nu3vw44LFMA3FxPTsklyAIgnCilG3fUF6ZzeDxvUhPryMxYaKrI51WR/xeO36M+V2eQCvXnHmDM2i0NTHzyH/E/Y4gCEIbEjV6z4EkSfRPHnbSTl6AHkn9sVc1/+LTKHQdGU0QBEH4i+zqg/gGGLA0OGgqVxAUEOzqSEInpff0oveISacsyRAW1o/y8hpMJjNq9aU5wkAQBOFiUFFegrcjD1ttFTabDYtFfKb7M0cbvgRBEIS2JUo3tJOmKqiuq8FUVY0cNQOSh7k6kiAIQqd0+PBhfMq8sNRJTBg6EYVC/OoTLkw+Pn78+NZWjJV1uLsNo25oHV0GiUeFBUEQOlpW6iEytxRTUgCl9gwm3zDN1ZEEQRAEoVXEp9120iWqJ7mKbVSq6ii2HaSmtgd6D/E4iiAIQkdL7BqPZ5gcS7EbkaExZ95AEFwoOjqRlGPbKfw6BWNuhejoFQRBcAGtLpjV8+XIJQUJQ+LFl8R/YXc0v9piP4IgCELbEqUb2klCbFf2bkjHK0iH3s+N/MJsV0cSBEHodBwOB1aHGYAGa62L0wjCmcl8Qmlsaq7Ta8ypcnEaQRCEzqmmohYZcgBKj1W6OM2Fx96GL0EQBKFttWtH79VXX014eDgajYagoCDuuOMOCgsLW7Q5cOAAw4YNQ6PREBYWxssvv9yekTqM3W7HWx1M+bFGDm3MJS46ydWRBEEQOp2s3Cyy0rNJ311E3rFCbDabqyMJZ9CZ7x0A9AHh5DW6URhgwRKicnUcQRCETunw9kN4hpeQ3K8CeVWKq+MIgiAIQqu1a0fvqFGj+OKLL0hNTeXrr78mMzOT66+/3rm+pqaGyy+/nIiICHbv3s0rr7zCM888w/vvv9+esTqEQqGgR/dehHY1EBYdiFKpdHUkQRCETkchk9F1YBRxfYIICPdCLpe7OpJwBp353gEgMCoOlcMf8zEVfsFhro4jCILQKQ2M0fBszzruDqyie4SHq+NccByOtnsJgiAIbatdiw099NBDzj9HRETwxBNPMGnSJCwWC0qlkmXLlmE2m/nwww9RqVR07dqVffv28dprr3Hvvfe2Z7QOYbLWA2CVzM5zFgRBEDpOWGgE27db0epApVJRUVWOj5evq2MJp9HZ7x0AqgtqAKgrr3NxEkEQhM5JERTs/LPJZHJhEkE4f3a7ncLCQjw8PJAkydVxBEE4Bw6Hg9raWoKDg5HJTj9mt8OqyldWVrJs2TIGDx7s7PDcunUrw4cPR6X649HEcePG8dJLL1FVVYWXl1dHxWsXBUX52CsaMVbVUR9fj8FgcHUkQRCETqWmxkhhWiU1FfWolEp+q13HVaNudHUsoZU6472D2Wym57UWJLOaqnpRF1IQBKGjORwOGpVaVivANzoEubeZzLTDxMR3dXW0C4YDCTvn32HoaIN9CGdWWFhIWJh4SkgQLgV5eXmEhoaetk27d/Q+/vjjvP322zQ0NDBw4EB++OEH57ri4mKioqJatA8ICHCuO9mHNZPJ1OJb1ZqamnZKfv50Pkr0oWoUOaDX610dRxAEoVNJyThISu16fII8CO3qhanRjI8pxNWxhFZo63sHuHjuHwpys5g4MgCFQk56pXhcWBAEoaOtfWUJ1hUHiHm+N93ifcgvbcTdx9/VsS4obVV2QZRu6BgeHs33E3l5eaJfQhAuUjU1NYSFhTn/PZ/OWXf0PvHEE7z00kunbZOSkkJiYiIAjz76KNOmTSMnJ4dnn32WO++8kx9++OGcHxmYO3cuzz777Dlt25EcDgdNtjr06PEOcic9K5WEGDEhmyAIQkepa6rBO0CPsagMU6MZXUUsffsPcXWsTsnV9w5w8dw/WJuqUCh/ryVtqnZpFkEQhM7IUdmAm1xFYy2k5jbgl3gN3j5+ro4lCOfs+P2TXq8XHb2CcJFrzeehs+7offjhh5k6depp20RHRzv/7Ovri6+vL/Hx8SQlJREWFsa2bdsYNGgQgYGBlJSUtNj2+PvAwMCT7nv27NnMmjXL+f54r/aFpry8jJryRiwNDqxmC/7hja6OJAiC0KlUNOZRnVdOZmo2fr79MHhe3I/0X8xcfe8AF8/9Q0VqGsaUg9jUSqqUauJHujqRIAhC51FTbaRhVxb59grkey0kDu9KjKePq2NdcOy/v9piP4IgCELbOuuOXj8/P/z8zu0bTbu9+Uf58UcnBw0axJNPPtliorI1a9aQkJBwykcv1Wo1arX6nI7fkZRKJb4ROgy+7lSW1BATEe/qSIIgCJ1GQ0M9BaU5+IbrGHRFN3rpxxIcJMo2uIqr7x3g4rl/8HJzJ6qpGpog0z/W1XEEQRA6lezdW4gfXUW4Tk6hTk/8ZdNcHemCZHc0v9piP4IgCELbOv1Ubedh+/btvP322+zbt4+cnBzWrVvHLbfcQkxMDIMGDQLg1ltvRaVSMW3aNA4fPszy5ct54403Woy4uVgZDF7Y6ps/gDrMStzd3V2cSBAEofNwOMAQ5IanjwfYZFhsZldHElqhs987AGhDY6izNXduSwYxikwQBKEjaRWQ6KXCTaXEbBPjTQVBEISLT7t19Lq5ubFixQrGjBlDQkIC06ZNo3v37mzYsME5osbT05PVq1eTlZVFnz59ePjhh5kzZw733ntve8XqUKpGAwUpRgrSy9i6bx0Wi8XVkQRBEDoFnU6H3KKhKKccS5EbEaFRZ95IcDlx7wDhyT1YVRXGT7l+pBYWk3Vgm6sjCYIgdBqSVwAWq41tuRW4D77G1XEuWI42fAmCcHGy2uxYbXaaLDaKjU2sPlyMsfGPPi/Hn2ZbdIiZFzvUWZduaK3k5GTWrVt3xnbdu3dn06ZN7RXDpUIDI6jQ1WK32WnwySElw5PuSX1cHUsQBOGSZjabmfPoR+jjC4kb6Ely0ABXRxJaSdw7NAvwD0f76zHcBjZg2/c9lqQ+zjIVgiAIQvvIyjpEo5TN+iozflFxRCaLz22nIko3CELnVGRs5NejZfyWWc4PB4pO2qZfpBeDY3z5eFsOlfVmdCo59WYb/SK9mH9zL0IM2g5O3fm0W0evAD2T+rNi215kcjlyuYzi+iy6I24YBEEQ2tPhQxkUZMnxidejVCjxMfi6OpIgnBX/cX0pW5uKn8JGtLuC9D2bSRgwytWxBEEQLmlmSxVduvrAVl/UBjGBqyAIAoDZaufjbTl4qBU887/DNJhtp22/M7uKndlVzvf1v7ffmV3FkP+sY2isL7cPjGB8t1NPoiycH9HR244kSSLSM5mt2b9iNpmoMWbh7ghgcL9hro4mCIJwyTKbzEiSnJIsE56RdRyTZdDb0M/VsQSh1RL69WD3oJ+p3FtJmoeZItsKQrv1R6fTuTqaIAjCJUuSmoeXNii1pBw6RsTVTag1GhenujA5HM2vttiPIAgXhg1pZRwqMHL/iBhkMgmAX1JKmLZ0V4t2nlolXYL0bD1WAcDQWF8W3tEHm83B27+mU2RsQqdSUFFvYm1KKVd2D2LlgSIC9RqKa5rYnFHO5oxyUp4bj1Yl7/Dz7AxER28769NlCMeq9+EX6Q748dsPv4iOXkEQhHYUERVMn+t/xFhXjm9IJGWmY4Do6BUuHiqVil5XjqHsyW+RSRJWi5Htn37H6HtudXU0QRCES1ZFWgkNy3/lWEYlI00KMtb+QteJV7o61gXJ/vurLfYjCIJrNZitLNuWy//9mALAnpwqzDY7+/OqqWmyntD+9Zt6MDox4KT7evLKLidd/s6tUGxsYuDcX5zLhr38K7v+PbYNzkD4q3abjE34Q7S+D6bfi1L7Res4mnHIxYkEQRAuXYGB/kTHhzBwQlfM9XYxWkS4KCWPHEDTqAgAJA8ltl8PiIksBEEQ2lFEcj/Cm4z4uytQy2Q4JMnVkQRBENrN0eIahvxnHcnPrHZ28gL8crSUTenlJ+3kDdCrGRR9bmXxAj01rHxwKMGezU9KlNeZGPnKr+RXNZzbCQinJDp6O0CfbgOxFOlI31VCUX4Ju49udnUkQRCES1ZdfR12RRMqlQJjaT0Kuyj4L1x8ZDIZPe+eQH1Xb0zeEsXF+Wz/5kdXxxIEQbhk1eRk4q5SoLTWkmUxow0JcXWkC9bxydja4iUIgmv8erSMgupGbHYHIQYtPcIMdA3W0yVI72yjUcr4/N6BHHzmctJemMCmx0afV7mFrsGe/DZ7DJE+bgBkVzQw9KVfmb827bzPR/iDKN3QQSYOvYWDR/dS7rGfwsxK6uvr0OncXR1LEAThkrPjwGYaTGbqGxoI8Ixg7ICrXR1JEM5JaGwUoR88RvqWr4jgGAcqcl0dSRAE4ZJVkrUHU2wE/omhWKKvJKZnD1dHEgRBaDdVDWYAruoRzJs390TqwKcYPrt3INOW7OJIUQ0A89emM3NsfIcd/1InRvR2IJOtHkmSCIn1YWfKRlfHEQRBuCQptQ6C4vUEx3ij9VS5Oo4gnD9TLUqFnEg3I4XZ6a5OIwiCcEkK8pLoHqEjIUSPRmZ2dZwLmqMNX4IgdJxGs41pS3byzd583t94DICuwfoO7eQFCPLU8uM/h7H7TzV6fz1a2qEZLmViRG8HKq8oJ/1oJgZ/d9yV4uZBEAShPcjlSmy//9lmER8hhItfRnY+RZYKkiIM1BUegcg4V0cSBEG45EgKNdCA2WzFrhXjoU6nrcouiNINgtBxympN9Pu/tUBzHd7jJnQLdFUkfNzV+HuoKa018fwPRxiV6O+yLJcS0dHbgUb0uxzTwXJk7mbSd+WwyvE14wdd5+pYggvU1BjJyj2GzWFBkjlotNVSXl6Gh5cOO1bKi6sJDApAJsmprW7E39cfo6mc/Lxc/EO8ASjOKycw3A+N5EGXsL4E+AW5+KwEwfXKKkrZf3APnsEakNvwQ3SICRe/rkOuwf3oF9QbG8lM+RW5my8xPYe4OpbQwRwOBwU5+eQfy0ONHFN1HYVpmcR6q7DX1lNUUUZwTz+0Hp5ISOzJkOFp8CFrWw7hUbnEB+lAksg32QkJ9MTmGUV0n9Go1WpXn5oguFxOzmEO78unuqSSotIKLv/wYVdHEgRBaDOb0st44JM9Jywfk+hPhI/OBYn+sOD2Ply34DeOldezOb2coXHnNtmb8AfR0duBtFo3AjXx1HlkEhDsh8OngozsVGIjE1wdTWhHeUVZZBQfxGxvID0lk8BYAzKNnfq6BgzeHngYmn+w1hur0Ad4IQfsNXU4/OXYgKpyI25eNcgBWZkVbXDz7JfWoka0QRagks/WvUtyTD96xw3Fy9PbVacqCC53OGcHan8rhmAVdrNEnCHJ1ZEE4f/Zu+84ucp68eOfc6b32d5732x6JwkkJBBCr4qiUuyKV0VFuT9RwXsvlmsFFQsiUi5KVzoJkEB677vZ3vvu7PR6zu+PwQ2RAIHs7mx2nzeveb1mzsw853uGzcxzvud5vs9py6+YxZFjb1KVMoDHHyN67DWiNYvRakU3birb+8yrDO1pJDzg5nD/EOF2Fe2IBLlRiobiydkeex9nJcX7Bb2+YcoKMzD5438X9/7RhMkfXw9iwRc9lMbidfA8sp0yQxQ1MMA9t26iaN5KLvrkeciyGMEoTF/+QB3JThPV7UHk4mJxAeR9qEionP5U77FoQxCEd+cLRbnxLzvZ0TwEQGWmDaNOwwU1mVgNWj62KD/BEcL8giQunpXFswe6+cR929n3vfNwmkX5vdMhenQTbNHMFbhaokgaCZPVQNPAgUSHJIyDSCRCY3MDnV0d7Du6m0G1iYhlGMkUITXfSnK6nbA/glZ3fMVKFeX9243GGG6NIPdkEnQrRMLxkzutVkaTPcwbTY+z58jWcTsuQZjMFEVhMNxBYWUWrt4A1nAOhbkliQ5LEMZEytwL2VHfR7IJyqxBGve+keiQhHEwNDBIc30j3R1dHHziNdzP74NdHWi8KlaPEaNsQH5bHb23349KKlr5+GNVPT4netgb4qi+gNrUhbjD8eI2kiSh+rS88P+2csdVv6C5tmXcj08QJqOWllry8ixEkgyoqor54isnvF7lmUblePmG07mJyg2CMD72tA2z5ucbmfH9l0aTvPMLknj6y8t4+svL+MI5JXxiSQEaeXJ81312RfHo/Wv/sI1wVEFR1BP6MsKpE0NBJpgkSayd+zEefOnXyI0q7oEeYnNjaDSa93+zMOnFYjF2HdnMkaY95M62EfXHaB/uIzPFQcATQo3JZHgXoqoxbBmVaMM65JiEqioYLX6MLiMqClqzD7PLDIBs92JxmQGVeYUzmV09F0mSWDH3fA4f24fP68Yhhek6Fh+ps8ezkQNHd3PNuhuxmBM7DUMQJtL//e0JjjX6CLojZOYaWf3x+YkOSRDGTEZuIQcyL+L3P3ods1MiZc5+KhauSnRYwhgZ7B1g5yOvcuzZnSQP6YiqMbKW9lBxVpCQAkpGBfk/uAxFUQj5Qxh1WhRFIdfrxWXRQUwh1T1Ca2b8d19CZdU3/JgMFiRJomR+IYVl8ZOowoCfYwe3IqtRcufvZ8EqFYiy+dGfE1pxHhXnXiySXMK0oaoqmx99Bqs/xkjLMPsvvIhF8xcmOixBEIQP7Zl9nXz10X0nbPvp1bO4en7upP19n53n5D/OLeXXrzZQ2+Oh/LsvAHBOeRoP3LQowdGdeUSiNwH0ej0pOWbS852kFynUNR6hunxmosMSTtP2gxvp9NXhzNWht2rQ6bVodRrkmI50tQKbNZlFS1MoyCsak/1JkkRNxVwA8tNK2dXxIrYsLd4RDQFvhFcOPsL8vPOobTuAV+ohqkTw9UfJys6kMHkGFcU1YxKHIEwGtU0H2bKhC+9AvIB/fpaDjFRRt1qYWjQhHVaPBTzg0voTHY4wBnxeH+v/9zEGX23A5JXRmFR0crx77nGkE1q5HDk5g2UlZThTUj5Q26VLT77dZDJTsWg1ALLZRpJ7O0k2PYrbQMGuJ9nXfBTn2Zfhef4ZaGlgJBzlte5CSueUsuYza0jPEQulCFPHU4/fR/IzXVg1BpKRsKYVoNPpEh3WpCcWYxOEyenlwz0nJHnXVKWzsiJ9Uid5/+WW8yvY1zHCpmP9o9s2Huvnjn8e5jvrKjFoxeDIUyUSvROoofUYPp+HoSEXtsz4aE1JklCV95+yL0wuqqrS3NZER28LFqsJGQ1b976O0SHR1a0QcsfQmlQy9WXcdPnH0evHt8ZMVkYOy/SXsa12PVFrfGSvM0/P7oHnCESiZJU4AQPR0AiG7CBN3i24Dg6weObKcY1LECaC2zNC3fBWbCky3gGQNEEqayoTHZYgjJntL7yJ3mqiva4NRVGQZVlMZTtDhcNhDm/fisUKkqyhuamVtn27UO3gMsbw+lRMBQ7m33wxs88d/xEsJTPPor3BzlDrRgD0Wg0zXXU887PbuEATHx3c5w/iPeyl4XADjesbue7e6yieUfxezQrCGaGxcT/nrcvlhT8ewuqHoSw9M2aIRVxPhcrYlF0Qv2SCcPpUVWXQF+bN+gG+9rd9ABSnWvj7F5aSaj2z6o1/47zyExK9APdvbqE6y841C/ISFNWZRyR6J1DTwH70WX4OHW4mVXYwHA3gGfIzd+VliQ5NOAWqqrLhjRdRLD7ckT4sqRqGI16ynfFVITOLksgotjPU4ae0agGzKyd2ikFyUirrlnyUrbs3sb9tE1F//Ipdd1s/KMfvq6hIskR9xxt0+xpINRRwuG4fmcVOFFWhubaDwsISrjj7+kl/1U8QALYdeJ2O/i40tigVs9K59Ool1MwSJ2rC1KAoCpu+/0/sARMpX7Wz7vEyQj7oagkmOjThFPk8HvY+/iSWxkY0La0MZJmZc101qNAx3MLNt2QTCEVoC1hIWfxx0jKyJzS+vNIavBn57Nr1Rx7aEh8tUzsU48VQJqASigSZleUhxSyBCvd+/Q9kZuVQuDCfvRt2YfYZURQVZ/IIiy9bwYLrLp3Q+AXhw2pq2kVLaxDvTCuh5CJWf+UjWO22RIclTCGbNm3ipz/9Kbt376a7u5unnnqKyy+/fPR5VVX5/ve/zx//+EdcLhfLli3jd7/7HWVlx/uxQ0NDfOUrX+Gf//wnsixz1VVX8atf/Qqr1ZqAIxImkxcOdvOlR/bw79f+M+1GnvuPFZj0Z94I2Nl5Tj6xJJ+HtrWdsP1bjx8gEInxqaWFiQnsDCMSvRMoP6mSI21b0Ml6sgqTAehv0+J0JiU4sulleHgIWdZgtVpPqTZyS0cjjX0HGAn30dnbycxlJaRiwTviQ6eP/xNyDwXw9SmYHcUsm7tk3EfwvhtJkphXs5guaT+pWQ7CoQiDPQY0egknORTXzKMv0ogtD6JBFWe+jihdaLtUHLnxmDs7NdgLYzy37WGWVV9IkiM5IcciCKfKFeqjcn4BWVlhrlz80USHIwhjSpZlKj46j8ZnDzOzJJmq6hwArM5YgiObXlRVpa+3D5vdhslkOqULoUeefRHf9n1I9S2ELDAjFACg33B8Wrg/LHMskoshfzaVFbPHLf73Y7XZKSycSe8TmwDQoMMfkNEZtFzylRWk6hVcD+7APKJQq8JAo4uBN13oCmViHfG/RZNVIvjwa2zx+Fj6+WvFxWJhUvP7/WRl66msLKYpbwblZSsTHdIZRZRuODU+n4/Zs2dz0003ceWVV77j+Z/85Cf8+te/5oEHHqCoqIjbb7+dtWvXcuTIEYxGIwDXXXcd3d3dvPLKK0QiEW688UY+97nP8cgjj0z04QiTSMewny8+vOeEbVaDFqdZx7cvqDwjk7z/8t2LqrmwJosFhcn8eXMzP3qhFoDvPXOYwhQLZ5enJTjCyU8keidQZfFMKotn8psHf0zQH8RoNmKUxWJZE2VwqJ+n1j+CrVBFZ9AQCcbobx0hMz8VjaxlqMtLek4KGknDYLeH9OxkNLKWpoZWzE4NKhKRgESkx4yKgm8ogIQZvSGXRZnVZNZMjnqgOp2O2IieAXUEp1LIzR/9zAnPd/YUsKPjOU44/zqhk6Wi1WmxFsTYePgZLlr4CVGrTJjUkp0paDQetJYYA4MDpKakJjokQRhTl37jo0T+I8JLL96DouQjyzJIxkSHNW0c27qX//vl49S9HgOdQlLhIF9ZqYJOT7s7CINObDYbkl7H850STls6OoMOh7SbIruEWqTBFZRoT6uCaJSwIUK9LwPVnMXqGz47ejKfaCnF2dTZo6jAtd/9HDUrT1yQaquko+fuV0F628Xst/UlVMAWVIk8uoVtei1Lb7pmQuIWhA/DYDCg0xnfKqMnap5/UOpb/41FO1PZunXrWLdu3UmfU1WVX/7yl3z3u9/lssviM3z/+te/kpGRwdNPP821117L0aNHefHFF9m5cycLFiwA4O677+bCCy/kf//3f8nOntgZIMLkcNGv3+Bwl3v08Y+vmsmqinTS7ZOjP3G6jDoNZ5XGz+c+s7xoNNEL8OSeDpHoPQUi0ZsA5WUVDA22MNwdQPWIRO9EOHhsD83e3fg1A5TklIyOMomFFVLz49NeQsEQthwJUHC5oxiyQkAIfQ/kzkgiEo5CTOa8eZP7xEWj0fCRVZ8nFAqd9OQxJzOPlcaP8MRLD9J8oBejRU9/zyCmRj2gMtQzQlfjEKCiqgp3//UuViw+l/nVZ8WTCwkQDAToammnqLJMjBAS3sEz7KWztRG9xkR9qJbUlOWJDkkQxpxOpyO9pZPGe+qI6kyEZpwH5YmOamqLxWJsuvsRYk/tAZ0eg2yGGCSbfJTofUCQbvcwef06dIPxUhp9TXZc3nji4tobJRYlhRkOxbCXrmDWlTcAMCtBx/N+yhbWUPTif6Gq6kkv8C69bh1Ns4vZ8+17yDBFkBSJHSMeIpIFVQX/8BDpBj2oKg1/fxy7xoV93iLyZiZupHJ/dy+SLJOaIU4KhROpqkpTUw8dHb243TEqKhIdkTDdNDc309PTw5o1a0a3ORwOFi9ezNatW7n22mvZunUrTqdzNMkLsGbNGmRZZvv27VxxxRUnbTsUChEKhUYfu93uk75OOLN4ghHufrXhhCTvHz+1gPOqMxIY1fjSamS+d3E1dz57BICn93WxoiyNq+bnJjiyyU0kehMgxZaNbBtCo9Wg6RcrF4+n9s423ti1nsFQGxq9RHfLABk5KVhIwW5MJVuTg35AwhcdJuRyMdIeAWTCXolAlxa9xoRdp8EwkEWWJYs1a6sSfUinRJKk9xwhlORM5jMf/SpPbXyApBLwuoJkl8RLiAz2DZFd8rZyDdIAHmc9z77ZyiUrPj7hidahvgEeXfVlIke6uPDonyitKBfJXgGId2LX73qKjqEGqpcUodHISEORRIclCOPGkl1Cha8WVY3RmJKZ6HCmrGg0SsuhPfzzt08h17mQYxF6YxoUu8SCS6pIzSunaUEWamcjrtAhTAYrij+AbLVgUxUy0tKwZ9jwZCbRWFGMs2I2s7PPjBMSrfa9Tw2Kayq45a938Pcrf4LTq8dh1tHbEr8InFKSRH4wCkDYbqNw+yZGtm6i7qrrqDjvgnGP/d/tfmELe37wHJRY+MR9X8dkMk14DMLk1HCoift/+Qe+8t9LSU2143b76O3tJiNjcszOOxOI0g2nr6enB4CMjBOTdBkZGaPP9fT0kJ5+Yr5Aq9WSnJw8+pqTueuuu7jjjjvGOGIh0a65dyu1PZ4Ttk3lJO+/3LS8iEVFyVx895sAfOOx/czOc1KaLupUvxuR6E2AN7avp2SpE6vDgkEr/jjHmqqqbN79GoPRFnTJEUJ6LzMXFgGQnprJFQs/c/LavPMmONAE6+ptR7L7ATNI4B7yY3UaTyjmHo1GCQbCuF0+wqYhtux+nWULVk1onMdajuD43SKQVF6p/T+GI6tZNHPFhMYgTB4+v48DDdvpH+xDMkWwFSmkRNLQaGQi4Sj6qJLoEAVhXLS3NNFycCfl+Rb6/REyC0sTHdKU4/d5OfyPh7B3HKRQEyLakUW0NwtVVTnnxgqu/tY1/9Z/WEnlR05sY8mERpwYO594Haf3rfINkkRMF0QTOd5/UBSFSDSGOxwmFFVo+8vvKV65esLLQLmPvs6Sxc0c8Ydp+p8vkP3V/yUpVYzsna56e9o4sGM7PYfg8PP1DPQHMRh0SJJEKKSCGk10iIIwZm677TZuueWW0cdut5u8vLwERiScLm8oekKS97zqDH517ZzEBTTByjKsZDuMdI3EZ1B9/sFdrL/lHDEA7F2IRO8E6+7pomiRk4A3zEhfiBJrcaJDmlJ2H9lCp/sYHt8IOVVOQING8qGqKu42hRWVl57SAmzTwa4jm/Frg/i9QaJBlZn28/H7fCipWdTt3oUtOb7YjG/EjzPFgs5gIBae+NGSJquFnNx4jR4JmV5vC6q6XHypT1NvHHwOY14A77CL7HwnACF/mM4jLmblnc3MOXMTG6AgjJPAQAMr182kZTBAU0+QtTZ7okOaMoLBAE2v/A1950GsPf2UJhkBmUhYJSpFKPl4OVd982rRf/iX4A5qvhDvD5iimVSsuBz3iI9jr6yn/Y1aNLKEy6QQ+thc9FqZDJePUCg04YnerFQnxQNa+pGosGg4unMTSeuumtAYhMnB6/Xg69uIEmhn+70hNJIGk+LkwP42jMZ0CgsXkZF5Zoy6nyxUGJPqutN4QC+ZmfGZOb29vWRlHR9N3tvby5w5c0Zf09fXd8L7otEoQ0NDo+8/GYPBgMFgGPughYR55chbI8DtBrbdtnranQsbtBpe/eZKfv7KMf6wqYnGfh9f+9s+fnWtOPc7GZHonWBDrn5sSWbsyRZ6W10smrss0SFNCcFgkMdffgDF4EfWyPgGI4R7jMTUKGpIj3mwmOULlopFxd7G6XBiz4zXbjJqIhTllwAwo2IWuu0yal53/IWKTEqWAwDD0MR3GBRiKIqCd8RPV1s/Jsswz7z0dy6/4KMTHouQWK4RFwcO7yNtKIWB7mFQZCSNAlEN1669WSRhhKktFsBs0lOaq0exFSQ6mimjsbGWjqPrsWhi9DiSkINmtHYzSiSEo8bM2T+8gJlLJ2tV3cTITbdTZYlfRMecT0VNGQALzprNK7f+hOy9XfiMOgqynADotTKRyMRfKFYjYcLRKIMjXvYSoLb2YezlcygoKZnwWITE2rbhFYLNrUT6Y+SXd1FktaMPhAgdLOacm69MdHhnJFG64fQVFRWRmZnJhg0bRhO7breb7du388UvfhGApUuX4nK52L17N/Pnzwfg1VdfRVEUFi9enKjQhQRo7PMBsKoifdolef/FqNPw7QsqeeFQN+1DAZ7Z1yUSve9CJHonWFZGHs/982HK5+UjIU/bf6Snq6O7lR373sSRaSYc8xNQRpCTAuSWxEd+WoaLWTjjren98xMY6CS2eMYqDjfsQwJmVp44sryqZBav19ejNUXp7x4kqgYJB6PMzZ74lSrCgTCHj7aQVZBMUpKDghlpmFzJ7/9GYUoZ6B/iydf+yqIL4n+DkiSRXWFHURTUrhSR5BWmPFcAdrR0MKsoPWELY57pVFWlfvMWevubyChPQVUCNDQ0ccHK+Kp27R1DZF358dFRUGJtppNLX3k9De31qBod5TMXjW6XJImMZXPp3d3EkCdAc1sfwXCMIa/KvLkTe6E4EonwzwMB+l7OpSvSy8hgBpKkctZtot893dTW7qL7F6+TEzYDUJFupWBEwa3VkLtkTmKDE6Y8r9dLQ0PD6OPm5mb27dtHcnIy+fn5fO1rX+O//uu/KCsro6ioiNtvv53s7Gwuv/xyAKqqqrjgggv47Gc/y7333kskEuHmm2/m2muvJTs7O0FHJSRCQ58XgJK06V36UyNLPHjTYlb+7+sAFH7nOX517RxWVqTjMIlBff8iEr0TLDkpmeLSImKhCK7ugEj0fkCBQID1O55GTnfTG+olOaMAExAaUjBodKiqiqdVYmbVmbFoWiIZDAbmzTj5leD01ExS6gsYoQmDwUBSqp3hrgC5GYUTGqOqqjQFdjJzaTwRHfFJaHVafCGxcux088ffvowhKz7sQ1EUPMM+RtodpBtKWLzg7ARHJwjjLzm3lIwFBtq6XPT36ChLdEBnmNbDR+h+4K8Ut3cxcMlsSvKTASN9vfHkj88XwhXKolBMdX1fqZk5pGbmnPS52Zefz1b/HlJq69A9fgiTJNGbXTThU4jfeOYN/M9HsEk2zKobD1okVaKlvpOSClE2bboYHh7EaOpEazPAIASJ4rJosBZnUnTdWornz0x0iGcsVeWEdT1Op52pbNeuXaxadXx9k3/Vzb3++uv5y1/+wq233orP5+Nzn/scLpeL5cuX8+KLL56wqPbDDz/MzTffzOrVq5Flmauuuopf//rXE34sQuLsbh3ixcPx0g3zCpISHE3iFaZaWFWRxmt1/QB89dF9XDkvh59/ZE5iA5tERKI3AWyGZAxZQazS1F8hcaxtPbgBv7EXZVBFjWhxjlQRUyNEvS40WjANWli+cJko0TAGygpmcGCoG1uSgtVhweqwUN95kPTUiVvpXZIkNBoN3hEfw4Me2moH6O/rQ1UkDDEH5yxdM2GxCInz/D82c2D7CM7STkrmWDEpqXzxmk++7+rwgjCV5OaW0NV9lNLSTGTJkuhwzji7f/Ig+jYXAzobwUNhmudnoKpR9CaVxm4rOkMKcxaIpM9YsOVVk9TdRspQfHGreX3ttNXXU1RZOWEx6I0GFDVGIBYkGHYT0LqxWSw8+F8PsficOVhttgmLRUicjo5tpCRHGbSHMDvSyb/ubC5cJy4OjwXlrdtYtDOVrVy5Ml7m5l1IksSdd97JnXfe+a6vSU5O5pFHHhmP8IRJKBCO8dC2VtqH/Zj0Gj6zvJhvPnYAgCyHkXlvrVEy3f3XFTP5yL1b6XQFANjf7kpsQJOMOEtOAM9AEI80RJosxuN8ULJeJSM9CUVRsKoZzKla9P5vEj6U/Owi6jpS6W5pxu0eIRKOove4MUUyicVUIoqfvIIs8rLHvlakz+flme33o7NIdDb3kVuWhiPJSl6RjrwZyUiShNobGPP9CpNTv7qPy74bRo3mcunCz4iZEMK0ZDQaOXKok5QUM5CS6HDOOBZ/jAxMeFUVx7IllFYsSHRIU1bpwvPZ/vwzGIdGCCoqYVTU3XsJuv2gKPi9Qcrnz8SW5BjzfTcc2YEytIu2A3WsmzGISYFYVKFlJJlMTXy6a/OeI8w8R9S2nOpcgwO4fv83zLEQ5y5fTPUXb0l0SIIgCO/p77vaufXxAyds+/3GptH7//yKWJD8X3KcJjZ/51zah/ys+MlrtA76iSkqGll8PiASvQmRlOLArXHjd4UTHcoZpb6pjqbGJpyAzpfCRcuuTXRIU97qBZcx4h7GUaqi0cq88AsvW57cB0D5in5mSA6y0q8f85GVtU2HyK6MryjvHfYz2OGlcnEunl4FSZKIRqLYtGN/gihMPrX1h8iaqUdvtBLwh2jvbCM/VyxEJUw/iqJQoAZpeHELWcu+kOhwzhgBn4+OhkZa+joxxuxYbr6UmRetev83Ch+a0Wjk0q/8kK6f384sJYhGltj4wAZgCwBHDWZcaztZffPYL6oa9rRTmWelPcOCxRmjd3CQGUYzjX4JFBgxqZSX5I/5foXJp+O5J1moU0Gnp3GgJ9HhTDliMTZBGFsPbmvl9qcPjT7OdhjpGgmOPr50djapVlFe6t9lO03oNTLhmMILh7q5eJaoXQ0gVvOYYIFAgBG1i6Q0O1lpeYkO54zR1tnMsdDryJYoigJObY5YjGYCyLLM+YuvItRlwDXgQaP3oxICIBZVceRp2HJgw5jvtz/UAsRr9Pa3enFYUzi2v5X6w03s3HAU02Axi2pWjPl+hcnHanYQiymEg2HaDg2QnCRGMgrTU8PBbVTnGrHb7GRln7w+qvBO9U88he6/fsSSy01ELlaYd8XaRIc0LaRlZ5Ny/VdoNjnpDUXps2mPT1+WJFyP7qH1aOOY7tPn82GlO/5AqzK4aCbhYIC9/mEapD6OZgeo+smNpOVmjel+hclJa4mX5+j1B+iziMEBY049Xqf3dG6IRK8wzUVjCr945dhokvfs8jSe/cpytty2mvtvXMjtF1fzrbUV/OwjsxMc6eSkkSXykk0A3PzI3gRHM3mIEb3jrKH1KF6fl1mV85FlmWc3PEFvqBd/yEJe7rJEh3fGaOmvxZJuxG0Mo40YWbbwnESHNG3YbQ4uPusTjIyMcMX/2Nm8aR9PP76ZgcFOju0KEnS3c/a8sT1xbutoxRrQo6KgiRpJMeSQXKzBpHWQUWImjUwxbWUK83g9HGzcQTDmI6R4qTvWiTPDQk3eYqyW6b3SrDB91G58AXtuMdklFcRiMfbveZ1erRuLI118/30A0T176QgHmJ2ahiF9kbhIPIFyZ84he8bd+Hw+PmY0su+Rf9D03Et0jIyQZLCy47EXKPjezWO2v84j+wk+uxsvCu6uAQo+fwVSfwtze9sonJuJ1mKjcLZYrHcq6+5uwePtRIkFOKL2ErvxAlyeIDXlFyY6NEEQhJP6+64OfrWhHoCPL87nvy+vGe3nrapIZ1VFIqM7M9x+cTU33L8TgJ6RIJkO4/u8Y+oTid5x1jJ4BDndzT831+LQZmIu9jPDUUTjwU7yc8T041Nx5Nghdu7disEhMdTjZt6MpeJELQEcjvhoiIqabJZhY9ifRfHsdIb7PYyMuHA4nGO2r+RMG6n5FvyeEGddchkZqVm8eOivSKjojXoa2/dTXixO1qYqnVZHV+QIqdl2TEC6Nw2nOZVl81cnOjRBmBCqqiJvfRqjTuFA9Vp8Di1XfGopsZjCrp39iQ7vjLH+yf/D5e2jVQ1A6yAZc8TvxkSTZRnbWwufpc/Lx6nLwbneTV57hEGXb0z3JcWiVFskQINaPYMZixZx1O+Bv/0ZRZUoMQU4tuNVKhaL35KpyusdoKQkAmjx+W3kFCSj6U4iLU2M4h5rYjE2QRgbf93aAkCq1cD3L6kWF/M/hHPK00bvf/QPW9n4LVGiS2TLxlko5kOWZezFMdrcB7E4DUiSRIojA51Ol+jwJj3XyDANnm3MX1tCzZJiSmbkE1b9tHY2vf+bhTH35t5X2Nz6FElljPbMrA4TRxr3j+l+NJIGAL0ng6L8Up56/m80N7RybFc9Bx47xOHXdo/p/oTJxWg0oo0dvxLrGfZRmFydwIgEYWL1dLaToQnj1MtUH3sZi8mLJElotRqSkkXC4lS0HtvHvMJhrvz2SuZdPpsqWcZdfxiveyTRoU1LOx7+CbbDj1FqCY6efSg9Q/S1tI/dTnTx2oXBSBTj6qtxu1zsePLvbPH62NMxxBu9Ybpbasduf8KkI0lmFCXeQZ01q4hXN9SSl1eT4KimprEo2zBavkEQpqE+d5C/bm2htseDXiOz/pazMWg1iQ7rjCRJEtcvjQ+ibB30U9fjSXBEiSdG9I6zjvYurB4LSiyGdzjMSEeUYCjAgoIzu0acoigoikIg4KdvoJeBkR4UTRgZDSF/lPkzl2Iymtixdys6o0xhVhkpyakfeD+dPe3Ys44nxGUNJBdL7Op4gbSkGzGbzWN5WMJJDLkG6enrQlUVjtYfIWYYobddprt1kGg0AhIM9dWj0+pZMGvpB2pbVVW27ttIy/BhwqEQjmQrCjHam7rp6dJx3uyl7D60DVtlkCxnBXW/Poj2P3cx49efHqejFSaD9s42upsH8XpMyBoZqyaV6lJRl0qYPgbbGunV2olFJPyhCJ6OEfRmMz6vSnX1mT0FORwOo9Pp6OvrYai/F7WtBU0wACYzXq2ZeWvW4HG5aNyxBbPFTMmSFR9qwc+ItwunLZ74k1UZp8nAnNZN1D7Vz4zrbxnrwxJOoqO+Hr9rBDUWo6WhiZBTA0gcc3vo0xqRRyI0/vRuLvrPL5KRU/SB2vaMuNl+z6M0HG3CGrQRC0VojXVz9Y/P5WjtIFfOXsie5/7ERy7NxaAvZL8xh7QcJ3rd/PE5WGFSCAR93HvrBiwhK77eAPO/vBqLKPkkCMIkoigqdz57hL9saRndtqoyDadZn7igpoCvn1fOA1tbAfjPpw7yxBfPSnBEiSUSveNoZMRFRqkNZ6qVgaYAM2efxZyqRYkO60NraK2jvn8XAK0dzch6SM1yYEsyM+D2kVUan9rf2TdC35GjqGENI4NeSudnsLn1IJmtNSiaEKqkEI6EaO1qwpluRSfr6ensJzM3HZ1qxuvyk5zmQCPpaG5sJqQbRlFiuD0evB4/Ho8He7KFrp4OSovLE/mRTAsN7Ufwp8RHUBudKnk18atlOtlEar4Ne5qBxiMK7ezGu8vFygXrTqndnr4udjSux5ITJTXJyGBXiKS8+El5T4uOs6rWgibG/pY3KZ4Xn46hZluo/uvXWPbJy8bhSIXJornnKJYkA7nVTgDMQ8WJDUgQJpgh7GWGQ2EwGKNB52DN2s+i15+5JwC1u19BE+hCUWPUNbRQWZpCqsNA5+4WzjrYgfzWNMXdHbD7L49g0OmIZMgUan0c2LIe8wVXIAf6UGUtrW0NFNQUohLF4w4S3nOQ9PQ0grYc3FqF9JxMVFXDxg2HeCPgw2BU6e3sQdF5SdVCINKR4E9j+uh/9HHyjxwDoOqCLGpy4jM1errTsL3hxoQWd5KOaMM/6AiuIbdkxim1e/jlN+n4w/Mk94fQmiMY++MDAowaCIY1nLPqoxw4sAGjvx2jIz4goKvNTUbuCtLTxUKGU1V8sb9OlMMSak+IqCbI3CVn7nnXZCdKNwjCBxeMxJhz58sEI/G/fJtBy+LiZL55vijEe7qcZj3FaRaa+n3YjSLNKT6BcdTR04ojxQJAecoiZlXNS3BE7y0SifDG/hdp6qjDkWlELxtx9QTIystAJxlobmxFtkSIxaK0N3WRkZfCYJ8Lg1mHKsVG25EkidRsOwA+dxAAe4aBLW88z5wVZQCEg2H0oTD2XAmIYPCCKSsMhPEOu3CmhVGAUNswJfMyAag/2Eb14iI0skR3yzD6HMNEfjzTWiQcRafX4vf6GW61kWrO4cIla9DIOrY2/wOzxYgsS/isbdS31FJWWPm+bQ4O9WMvUAENGiDoC+Nt05BjK+Ojay7EbnPwyMv30N8ziH/HCLKq4/wrPk5pgaixOJUFg0GGYm1EIhEAopEodmNygqMShAnmHQJgSDWx5Ct3JTiY99ff0sord9/NgiV2JBTQW6jd0EteRjayzcLOxhaSnQZkk0K3dxCLNoKS7UQNqaNJXm84jCWqJU8GQhFa0SJJEjXudnZte5Kl89Pj+9KEKC2LJ+92b+9krs2HSQnDSCeHSmZSXBQBIty/O0LThvic4OXXZnH2xWZ8wQi7ekTXd6KEleMpnM4BL5rUAmRnLpfesYajG3fSdeeTKGEN2UlaGlpeJ5hTjNFoet923UdbSe4PAaBEY/hNMdIunsF5Sy6iZtZ8enraCQYaaW3spbdDQpeczaqP3IjZbBm3YxUSr7X1GIWFRkyZBuiJYcpzYDK9/9+TIAjCeAtFYxzqHOGOfx4ZTfL+6to5XDo7W9TkHUO/+fg81v3qDV6r66d9yE9e8vSd/S16u+NowNVLd/cIagwsKcFEh/OeWjoaOdD+JrZ8FXNAJi0/vnCG3x9AnxEAAmh6ohTMipdfCEfDpOU6CHhDtNb2UGCajXMkl6gaZmTgDTiqoqgqPe0DqKpCLBZjuMfLoe3NoMYIBIOUzTy+GJ30tmrRknz8y06Wj9epCXoj9DV6MdsNVGUsIT9XLGY3EQ7V7yYojyBJEq6uMJ+/8sSyCYZjyfh8EZqbenAm2+nxv/iuiV5VVXl11z+JagLUH20gOcdGwBdkuCPIwlkrOGdJvHB6e3czm3Y+gz1XSyBkpXxeFrE+O6UFVXi8Htq72qgoqUSjEXWMpprnXn2C/kgv3Q0DBB5qxO0OcM4zn0p0WIIwobpMEt6aBfR3upjs81YOP/McsWdeIUnrpizFAWjoHeonr7mXzE4XAKo/lWh//MQmdxHk7WthcIeCVzbS9vEvgt9Hc+0xDtS3YYpq0GhiHNvTTX6SBbMmQtTfQzDmw2yUcRmSUBQFWZbxDPsxGY6Xd9K8rVcb9By/AO0N+6gfNKPKZmasvmYiPpZpLxKJUFe3n0GTEUmWGNIWccFFnx99fubqpRx++GV8vgD/eLwXVYXmrqc5/5MfO2l7w4N99B5dj4YY/dp6cm+w0DnoQ/WlccGnP096brx29YaH17Pp3k2Eu0OsWhymRo0yeONZmM0WuptbUZQYOSVilshUtGvXy6RnGDlmauWgoYXK1PcfdCB8eKqqvjWK+vTbEYSpJqaoNA/4KEgx871nDvF/O06sR//5s4u5bI6YYTLWqrLs6DQSkZjKj16s5Z6PzZ22iXSR6B1HskkhO88JgDQ8eX/E6pvreGHb39CawH1shIKKLCLhKCPdYTz9IewWmYA6wsiwj3BnPmnmfBbkl1FdPgudTk8oFMJsNo8m3do8h0jOi0/PkySJnKp4SQeNrKFoThqxmMLOVw8T8IQJjAwjqRLdrf2gyKgKdLf1xTsPikJPez+KqhCLRRns8pBbVc4VSz+RsM9qOsrJy8WQk0Q4FCG59J2jadcuvZJYLMbju+4mIy+Jvlb3u7Z1tOEgano/Bp0WXU8Mg0WmKuMc5l60+ITXHeveR3KRDnAw0h0hGolRnDyT/Yf20uTfjiPbwNE3t3LZshs+VO1GYXJSVRWjQ6ayKI+MIget332SsutWiYUrhWknI9dBWZmDpmQTkUhk0v4beO2JR3C/tgkCXkaSNESiMTzBKJsbg+SUF9IejqBt72JA9VH02cUkl2YhGaFo0XxKZBmvx4PD6QTAq01GunuQIPF1AOoCSRx1x0di3Pq1ACvlMCFfjA0eifrXd6Mi07C9k907LWgNECUI5gPsyulEY5YJebtJKbYQDEfQa1QieSuYueicxH1Y04wsyyz++FzKs810DkWwzD2x76bRaLjugTvZ/vgGen7yGhIQUXzv2l5fwxYqkuP9i4FkLRaziSVrP01a9okX/d/43SZ0/Vp0Gi1avZH2JAvz1qzmzb88guOJ15FUld6Pns+8T4qE/1TidruYMzePoiInGzce5OXfb+Lz3/lyosOa0hQ1fhuLdgThTKcoKo/saONYr4dITHlHYvftfnvdPC6cKRbWHS+fXl7MvRsbee5AN2eXpfLRhfmJDikhRIZkHAWjHv51ajbk70loLO9l2N/DrHPiHeXuxkGW5VxFJBqmeEnp6BWQSCSCMlvBYHhnuYR/rxsYVcLAW4net43O1cjxYbsajYzD6cBoNGJLMaDEFNClkJlvf+uFKtll8eQwkkzeDCcAGVmZXLZQJHkn2llV53OoaRdJOjuzZyw46Ws0Gs1okS17upG6psNUFL+z1l44EkRriX/t2K1JLMq+lLycd375plnzqe3aRCgYprW+CyVYwNorKvjWHTdTMjuDwV4dkiyz59BOFs35YAvACZNXS1szmkwPYCAp1c7Bs7K4/n++lOiwBGFCxUerxqelFxWlUX/sAFVVk3MBqZw8iXO+vRyAgy3QmbUQq93BVZdmjr4mEAgw22BAluV3vP9fSV6AgMs/et8T8xL2Hu+kWnTx0bkGnYZ0ZwoGnZaibDsHt3oYaZaJAt3RQUqNSYQPBQAotCdh88f7LHkl1SLJO8E0Gg32WR+hvrcee2U5SSlpJ32dpDv+dzF8uINYLHbS2TqyEhq9r1qzKTz/kyftk2YvzqHx6QaCqp9DfQMsWXUWAz29bH38Ccp0NmSdBu8rG0Sid4rp7DpIebkTgMKCZL785S9w5ZVXJDYoQRCmjR8+d4T7N7e852uunJvD/1w5E6NOzEgdT19dXcajO9tw+SN8+4mDJJn1nD8j8/3fOMWIRO84am/vwOTRoMYkRvp72GJcj0FjhpiOvMx80tMyEh0iAKHo8ZMrG1nk572zJMKpjiaKxWIc+e/t9Cl6ZKse91k2IuEslFiMrpZ+vEEPsWgUz3CAwowK9IMO+kY6MJPFYMcgkkbF5/YT8BsxmQ3IkkR/h5tsXTV5lulbYyWRbFY7S2ed+56vae9upb12gIArhiRJyJbGExK9A0P97G3aSGNjE450EzFFwRrJOGmSF2BW+Xx2PrOR9DIbmXlplBZVoKoqKXlmCmoyMJnjJ3c6l/ihnEoy07N49OFjJKclE/GqfPwP3yMpPTXRYQnChBoY7Ke/343PGyWmqAxsfxNNeyuSxUHE6qCwrBqzeXL8HqqxAKAnFIqgsVVSVPrOxUROuUamHCR3VidmrZFjAwNcWp2EN+zF449xqLUfn1ePJhrGW+JAX7iYukEXpqwwaRfpaHm+gUhylGAgjE7VIEsysXAU5iVTuKiM8uWitnsiZOWXkJVf8p6vaWysxXi+BlkrYVIj9PV0k5WTO/p8y8FtRLqPcGDjYbrdCgPhEPO+9R8nTfICnP+F89i9cxvZkhF3chr2snL6Dh5mTkoqs3xRUKAlGjvpe4Uz14gryOuv16EqMskphfzkJ7cmOqQpT33rNhbtCMKZbHfr8AlJ3ktnZ+Mw6Vg3M5Omfh9ZDiOrqyZH3mc6MOk1fGttBf/vqUMAPLitVSR6hbFVnj+TaEYnACoq4ZROwkDz3j66ZCv6xnRWzbtkwlbSPnxsPy2ug6hSjO1P+EhNycZmM+CSDmHLjhGNhVECp/cnodFo+MbDv+XFn/8Fz51P4/z8xWRXpQCgNxhILjBiHMhFk2dgydwVJxx7MBgkFoshVUn85R+/wppsxD8cptIxi2VzV59WXML4OtC4lezSFLJL4otm6QePL3gSCoXYfnQDlqIQSkeArIoMeut8XLb2o+/a3v7aHWSV2TA79Wi0I2gkHZv2vEjxrEwCvtBoolchOr4HJkyYjbteoE89Rn5xLhabiTXV12G1WBMdliBMuPS0DAb6M6iotOH3BXD840WKA60A7KuZRS+HiBlmUFq5cELiCQaDNB3dgibcyfa9rby6tZ/UlCSSU5PIz3DRM2wlFgphdNhOaz/LLjqPnppyGu/5E+XePpYa/GAExaowmGJE50wmVHM2Gem5VC6Il/upWhJ/r/t2Nxarhc3PvcTw4y/hDyv0x+ws/+J5zFwy+3Q/AmGcDPb203ekjv/4WrwSdSgcoS8SPv58XzfRYxsodcToDCmUdnjRrVlMYem7115t/NuLFGtsaGWZfiWI0WGn69GniEbetmhwKDRa51k4823b9igDg+2ce+5smhpVamrWJjqkaUGUbhAE+M4TB3h05/EyDU996Szm5ieNPj6rRAxYSYTrFhdg0mm45e/7eaN+gNdq+1hVmZ7osCaUSPSOowVVy9mw5yk0GV5kDQT8YdSYilavx5ZkRnV6eHHH37l0+fiXIzh8bD+d2h1YcvUoikJTbYg2PICHc24yUjw/XjYh0H7yERIfhM1h54rbv8TjwRDKW3lcVVWRJPB2wtoVF5y0c200Gkfv5+XlYS2MYh4uYnGNmG45me09sh1djhc6jpfpUNXjJ1Q7j76BuTAISChvLbjgyNdxoG4Xs98lUTHo6cOtjNDZHqCxtp0sSwWHG3diSzMx1OfGaDSSkuZECnjxR9xU5y0gJVn8kJ7JgnhIy3HSecRFJBoVSV5hWktOnsWxY3sZ6GhhkUVHbyBGulFGb9ZSkGPD6z1G/RGFsurF79/YaWo8+DJVWV5Aw8uvDvHUExsAUNUIR+vvIjMzGUVRaKi3vHdDpyCzoADd177IP7//Q/DEp+p3+n0kocNftoCaC6866fvsjngfpmbRPLqeeRp9wM26P/4Q59tKQwiTz6afPYFu6Hh/Qa/TEg4dX7y4d8ujVDniz6tvdRs1W/fi+5QXi+3kvxG+ujZ8gRFGUGgc8GM+dhR/aJgOfZCwChFAkRTU+/6IKTmZmVdePW0XapkKQqEQaelaZs+ZTUvLEFrtyUuECIIgjKWOYT8bj/WfkOS9+2NzT0jyCol14cwsbvn7fgBu/MtODt2xFqth+qQ/p8+RJoDRaOSisz7GoWP78EX2kxIsxmKw0T68nsbD7RRUZKEzje9o3kDAT1NrPS3dTRhK4r3k1toe1Gjy6P99vVllpCNGpqmMuVUzx2S/Wq2W5V/8KLuaXyfSa8TvCVGZtpQZ5bNOaQTFBYs+ypH6A+TnFo5JPMLYOly/j2NNtTR31aLXG7Ak6+nrHCDiU5BlidDIIKFoEF3MxNHmA1gHdajASI+Hrjo3kgy+8OF3TfRGwlFMDhNJqQ5CviiBqIc55xZjMOnpbBiAQQdpFSoDzQFiab1sOvoMFy/61KRdsEh4b6qq4okOEPNqaG3uoaKsLNEhCUJCZWYWkJycRU/TU7TkG7DOWcHho3s41tCE3aYhLzeVXn9kXGPobW3H7RrGM9yHkmFElmUGho/XSc3PT8LptHLkcACjMZuKirHpP6TkZFN+4Vq6G1tQfH48aUk4Vy5jxoxZ7/ve5MxMpP/5IT11dTgcjjGJRxg74XCYxgNvsOkfO2k55iHc68cT9nP3H/qIxUCJSVSv3EDY3U4s7OPYllYGQioo0K3x47gwA52s0nJ4OzOWnHym14Z+I/3NWWgkHau+k4w1U8Oq336MjY9uJXVvJ9HUDNLqDuDbtIF0vY79fj9zPnn9BH8Swlhpba2luCSFjvZ+jh5op2rW9Fx0JxHEiF5huqrv9XDZbzbjDx+/ULnxWyspSDn9C97C2DHqNLzy9bM57xebAKj5/ku8+e1V5CZNjvJn421cE72XXnop+/bto6+vj6SkJNasWcOPf/xjsrOzAWhpaaGoqOgd79u6dStLliwZz9AmVE35HGrK54w+riiq4fGXHqCneZjhri6eVx9GjhhZu+TKMR1VsPfoNlq8e3Fmm2g73IXTYCIWVhlplLn02lK87jDukQAGn541Z12MxTy2X065hfnkFn7qQ71Xp9Mxu3pyLj4jQN3QdoY1A1Qtz8JkiY/ENlm1ZJY4Aeg8OoKaNkjtgW40Fi3ZVfHRtm6/lbQiMzq9lnAowr4ju5hT/c4F3rJS82iL9GEw6UFS8QfcpBrj04IlZGZUzKGnrxWVeGkUZ6GG13c/x3lLLh//gxfG3MjICMMDLmKSmYoZRVx11k2JDklIINF3iNPr9ay68HiJm5zKGlJaOvnV535DdKCZovwwtXm7yTxnBguvXjNm+41Go2z/3z9gfmMfNkXCfX0hx7qTae30UFY9m9u+W8nQwDCRqJ+e7ixmzhybBO/bnXXl5R/6vUkZGSRliFp4k1FL/SEqpIO85vHTvVEHOHCbAuz6+79eobL2Uj8jrdtZUJmGKyhR1umNPzXTztyi+OCIw42vwbskekvnFjK0rxGNpEGNSKhqvBSEqpMgFKLqK9+g7nu3ovYPopNlkt58jZaKSgoXjf/oeGHs9eyoo+Vbr2PxRMi7+hyqKs9KdEjTRrxG7+lnaUWeVzhTNPV7+Z/na1l/tBcAnUZiSXEK372oWiR5J6myDBs/vXoW33r8AADLf/waLT+6KMFRTYxxTfSuWrWK//zP/yQrK4vOzk6++c1vcvXVV7Nly5YTXrd+/XpmzDi+cFNKSsp4hpVwWq2Way/6NK9sfwYpvRVzdpRYdIQ/Pv5zKkorKc2aRU5m3mnvp9V9iKS8+BULm91McoYVuT+V6z53mZimJnxoDa216K1gjOniI2/f+l1T1bf9TakqGo1M2dwc6nd3j242Wo2EgmF0ei16gw4Cyjvaj0aj6DDRcKADnRm6WvvQ9rpxN2ghBEqKGfN5NlaXX8ZvH/kxSiyGJMlE/CPjfejCODncvJvSudlIkkRvi+utUi/iO2q6En2Hd5dbmMN//t+t/OQTvyC1O4C+p5f2gz309WyjZO5cCuatwmQ6vZEKjfsPkbnxALKkAQmycpNx2K1UpS6lsOT9R9UKwrsJDjSjWlQ0muOpHeXfFkZTVIklNfFFU1TN8d+BWPR4f8Eov7PvoKoq4XAYUyZ4pH7Qxti1owOMYbqOdtBypJ8F6VmYzGZSbvgC//efv2arzkFMkcnbfEQkes9UuxspCGhAq2GwdyjR0QiCMAWMBCLcu7GRZSWpLCtNYWvjIH/b1c4z+7pGX5PjNPG3zy+ZNqNDz2TXLMgbTfQC1PV4qMg8vXUlzgTjmuj9+te/Pnq/oKCA73znO1x++eVEIpETplinpKSQmTn9VsJbs+hSnnjzjwBotBrMSVrUjAH2DjxPY1chZ887vpiAoihIkoSiKLR3tdI2WIeKQrIpm5kVcwE4cuwQoXCQoXAnOllP/ZEWnH0WwrEQI11hqnIWMX+JuNItnJ5hzwBSwIQ2pDJQH0CXp0dGBm+QWJ8VVAnPUC+dR1yAxEDvCOajZiRJIuKBfd96GSXLhsZs4Pybi0fbfXHLE4xIHSCrdDYMULkwF4NRT1ZhCls/9gLWuvg05bL7bqasOL6y++xZc1DTBwAYaR/faczC+DEbbEQkiaA/iOQziSTvNCf6Du8tKcXJ1bdfTPMtjwJgCUrk2HxUBHbT9Pw+DLMuJbfs+Cjbfy06FQ6Hadv5BrGuZtAbsM1ZQXZBIZFIhP1v7IThEcItvbT2dqM1SKjhED6PC0ujm7OL15KTXZigIxamimjIQ3PMgi4pRM4KLWajmeGARFpSFlqdFo1WonbDbtxb+0BRONI+Qp+chKrCwOF+DMMhDioRtEYTxR89vpjahq9+iVS3C70So70riyJDGlpJS2dTA721HoZjOjxaLcv2fA+AvJoaosEChuriSebMRaIm/JnKkJYEdNEd8GKb8c6ZHsL4EaUbhKnqrueP8ujOdn73eiN6rUw4euLFxYtmZvHfV9TgNI9vCU5h7Dz+haVcfe9WANb+Ml7K4e+fX8qiouREhjWuJqxG79DQEA8//DBnnXXWO+poXnrppQSDQcrLy7n11lu59NJL37WdUChEKHS8Rpzb7R63mMebJEnMLVjJwFA34Zgfd38nzfsjhMNhmkYGqClcQFJSMo0t9ezpfhFJklAlCHiDFFTHVw18c/N+2t2HiShhujq7qVqSiwbobB2gYlE29qT4cEtTfxHzq0WSVzh9C2uWA8vf8zV6+QViaX0AeH0ecqritRLDQSMjR4dZdtvnWHrlBSck9CwWM8Y0JwDDPV7MVhMAtiQL1i4/EP/ekPSa0fdoJB3Rt+7HlHeO8BEmv8P1+9hT9waSMYo5SY9WZxMjeoVRY9V3gKnVf5h/1kLU/+cj1DfCcEc/bfoeXMMyis+LZ9sWknNKMJvN7Hv0IbQbnkM1WTg4OMDlMxwYtPHv0Nvv3kVBbikjbYPkJwUoH4pgBBRHjLwhC2ChVzZz/kduxmQyJfR4halh3vk3AFB67ru/Zu9X9lPQHL+A26dNIa0jnsyNpZrQ+ULc+Id70el0J/xGZBgMFOo0gAa72UhAjn9XWCUr+lj8vqw/cX0Io8OIHx8AinriqGLhzPDgTx7j0OZGHB4LMbeJhR7Rb5hIqhq/jUU7gjAZRGIKP3v52AkLrL09yZts0fPHT81nfsHUTQ5OVQsKk7nn43O5+ZG9o9s+8vutNPz3OrSa918/6kw07oneb3/729xzzz34/X6WLFnCs88+O/qc1WrlZz/7GcuWLUOWZZ544gkuv/xynn766Xc9Ybvrrru44447xjvsCVOSX04J5QAMBTtx5MU7pB2HXbzR9TdCtVF6ml2kZjtRFRWfN8Dw4DAhf4RwLIzXHUCbEsBs1ONyHT8RG+x2k5mfQjQSxTsUJj81OyHHJ0xPsqwhBoRDESLR4yNt9UY9slHHrm/8nm2f+wULf/p5VtwUX0VdlrSEolG87gBdHX2gjRHyh9FqtXhn2ekz2VBsWspzj4+88btD9PYOI2tkRga9sHSij1Q4XS3DhyhamExvi4vM/GSGet14PB7sdnuiQxMSaKz7DjD1+g8LLlkJQEPDEQoKe0ZHNz5+8VOs/8OtqE4z4TIPxSWpRKIqYb+JN9qNQIC+kQFCbTq8nfE6pXp7dLRdrVaHqqpE1BjRshSR5BUmlPTWBR1fLArS8QxQLKZSEI6w6wtfRIrEKP/ZT0hJjw96QKcjGI0yHAoz5O1lWKNDq1dRJQ99FgOyVkZ1QCQcQauNn/oEbRH8eREkSaanv3/Cj1M4PcPDLjb9djfalBg5IRtI0LmtBT6d6MgEQTgThaIx1v3qDZr64xcAV1em84WVJYQiCvMKnEhISFJ8gS/hzHTxrGwunpXNw9tb+X9PHQKg6nsv8vBnljAv34ksScjy1LlgKKnqB7uO9p3vfIcf//jH7/mao0ePUllZCcDAwABDQ0O0trZyxx134HA4ePbZZ991tNanPvUpmpubeeONN076/MlG5OTl5TEyMnLGJwYeXf97zFYDsajKsGuAorlpSJJEy/5+CmenEYvGqNvXSvGMHAxGPZIk0XygB5NDhxKV6WkcICM/HTWm0HS0k7OXnktuSglpqRk47GL1aWHi1DUfos/dgUlno672GEHdACarESToOtJBVn46klZioM9Lemo2muQg7fV9ZJU40Rs01B/qYN7ZFej0WiRJoqfRNbrQm6Y/gxWzLwBg+6FNBJKbAQh06lm38GOJOmThQ9iw4x9EUnoxmvR014+QXmSlvz7Atau/nOjQEs7tduNwOKbEbxskvu8AU7f/0NZWjz9wmHBIh8/fR9st28jTxI9Hty7EvOQIMUXltSN6Mo7G0Eky+3XDrDlXz0AEQqqBjZuiZFqTCShhRowqyz91GSnFueQWF75jJLUgjKcDjz2BxutFX13F9pefo7TEhqSROLKvmaxGDSgQi0bZjYYCWxbhTg96WzelEQWTAt1ZMitMYXQamWd7FVoPxUs9qarKeX+5lDmL4+XOfnXTb+nZGB85fM6dS7nguvMSdszCB+N2ufnJp37H8N4gI5o+5piTUVEpvWkmV3zzo+/fwBQ2EX2Hf+3jmtRvo5MNp91eRAnx2MCPz/jf4sluqvUrx9pju9pH67jKEmy6dZWovzuFPbStle8+fegd2xcUJPGDS2dQlWVHMwmTvh/k3/EHHtH7jW98gxtuuOE9X1NcfLzuZmpqKqmpqZSXl1NVVUVeXh7btm1j6dKTD71bvHgxr7zyyru2bTAYMBhO/0dlMrp2zedH7/f399PR10REDVGWlo9+SItF5yCSlEp+rIjgUJC+QDORaBSn0YIz3QpRDRmlVtwtEt/5wpfQ60XdGCExKopqqKAGgLkVS3hi5z2k58e/jPZvc7NgXjUAvp0RPGofZfk5eIYCZBXGF1Pq6x5GkiS8Q0EM1vjXlM8dJOySmZtbNrofnawj8NZ9q945MQcnjInmjkYO1O4mPTsZSRMg6taQGz6L5ctFjb2pKNF9B5i6/Yf8/DIg/r0YjUbJfmAFg0daifmCaNMkGoxaNDYn1pQmrJ+fhetII+qW15AlF0l6GY0SIM9jx+SPkv/ZVZz1qfcugSEI42nWNVeN3lfNUUqNRwEY6DLh2jPILMUKaNgc8mHuCGBGizXTRr43fhFnSAM6TZSOKGjV+LagJUz6wiyqZlePtq1/W23F9MLUCTgyYaw88bsHqVoUJeViEw5nIZ3eDBaecw75xfmJDk0QhDOMqqrcv7mFO589AsB/rC7jC+cUY9ZPWIVTIQGuW5zPnDwnX3p4D21D/tHtu1qHufjuN1lZkcZfblyUwAhP3wf+C05LSyMtLe1D7Ux5q4bm20fU/Lt9+/aRlZX1odqfSt7tc66uePuK1wvxer1EoxH2NmxG8QWwuUo5d/lyUd9SmDQ0Gg1GNQmITx5wOpKQezOIEKAsPYt+jgEQi8Xwe4N4RnwEvSF8DUYWz7qA/uEeomoLleZZFFceT/JuO7CRI8cOoHMoyJJEtiU9EYcnfEi1XTvIzE8lq9yOzxXGaM6korQy0WEJ40T0HSaGVquloKyUgrLSdzxXOuetDuvMahZ89BIG+vuQAz52/P3PWFZmU37tSopnVU1wxILw7pLSC+hr3Ud6kgG9UYNl8Qw8zmTUSJTZOg080gJAVJFwhYMMR4J0haw0X3YVOcvPxv7U08xcl8WiS5eQlJoEgHtoiJZnnqWvrwF3jpaQGqRsZtl7RCFMJiNDLrqePcCXf1WNw2qidSBKespMCkoKEh3atCNq9ApTweqfbaRpIF6uwaLX8OnlRSLJOw1IkkRNjoON31pJKKrw/MFuntrbiTcUZW+bi9fr+nlwawufXFqY6FA/tHH7K96+fTs7d+5k+fLlJCUl0djYyO23305JScnoiJwHHngAvV7P3LnxaVRPPvkkf/7zn/nTn/40XmFNOVZrvF7pqgUXJzgSQXh3M/OW0TfUhoSG5fPzWDH3+Eosf36yjkh0kN7ufvyeADmWapaVnYWKwuDQAKFogBmlcynIjo/26xvoYWfjejQpQQKaIYpnFBKLKbQdquW3j+2htLicLGsJNeVzxQWPSaqu6TBSihfFHe/dy14bqxdfkuCohMlA9B0mTmpa/OLYxd/8YYIjEYSTS8vIob5/Lh5fEHtZCtUra8ipil/EqHF7uPf5W0kNGQhVyxxMKyG9cDY1jiR0RjM9dUfJSLFRunYVRqMRgPrXNuJ57Cnyh0aQPam46gxIDi3/cen3cGrSqFpWyHmfOIuicpE0nKy2/PF5iv1pdPYHsZoMhGwzqaiam+iwpiXlrdtYtPNh/ehHP+K2227jq1/9Kr/85S8BCAaDfOMb3+DRRx8lFAqxdu1afvvb35KRkTEG0QpTyf5212iSF+D+GxfhMIlyVdOJJEkYdRqunJfLlfNyAVj2o1fpdAW4/ZnD/PjFOt64dRVJljNvpvy4JXrNZjNPPvkk3//+9/H5fGRlZXHBBRfw3e9+94Spkz/84Q9pbW1Fq9VSWVnJ3/72N66++urxCksQhAQoyi2hKLfkpM9dueYT7KrdhMscpbxoBmHtMD36fXhcPrIzU5Akia7B0Giit6HrMJa8GKBDiUVRVRWNRqZgdjLdjRL6LD/doT0M7e7lnAXrJvAohVP1+tZXkOwBAq4IGc48FpYuT3RIwiQh+g6CILxdWc3Jy7VY7TYuv+vLHH5yE0O6ERbPqUYT6KJj50aqWvsACERjDM5ZSE5BIQDeF18hf2gEAJ0UTy+pIwbceIiOhHjjUB1vPniYW5+4jpp5YnT7ZOP1uDm85w0yZ+o4Vq9DSZpJzWLRf5iudu7cye9//3tmzZp1wvavf/3rPPfcczz22GM4HA5uvvlmrrzySjZv3pygSIXJ6s+bm0fvH73zAkx6sdCaAL//5HwuvvtNALyhKGf/9DU2fOMc0m3GBEf2wYxbonfmzJm8+uqr7/ma66+/nuuvv368QhAEYZI7VL+XpsF9RPReono3HksT3hEf3r4g3Y2DDPe70cga9BE3Bo2ZmBqlvrEWukJIkoR/JErT3n5MZgOSJNHdPoikaEBS6evYxUC0lUL7bOZXn/xEUZh4G7b/k4LFVozmJFztYdYsuCLRIQmTiOg7CIJwKtbf/Sj9rxxC3xsg/RobtsARhof91LX0E/UH0RgMoNcTe+VFvHkFqJEwz3b04nPHCKugajq55ros9FqVhq5hIv1JyApIikL93T8lnJVO9k2fIbP0nWVQhMTY//s7uOVsFQjTmJtP2ZILEx3StKaqKh9wTfd3beeD8nq9XHfddfzxj3/kv/7rv0a3j4yMcN999/HII49w7rnx2YP3338/VVVVbNu2jSVLlpx2vMLUMRKIAFCcZhFJXmFUTY6Dfd87j6/9bR+v1/XjCUZZ9N8baPnRRYkO7QMRBUgEQZhwA4N9vHbkCTpb+0gvsENEYXjATU4snaRUO5l5aThsDrJLkwHoPDqC2xmv5RvReSmdlQmAKRW0Oh1JqTZGeoOURGZgMuuIOQYJ+qKk5JvoDxzitR1DrFp0Zn05T1Vms5mQOT4tKqwEUBQFWZYTHJUgCIJwJjj8jxfwPvUC3iQ3sxfKoCjs2D1Iwz8MGNDgyDRx3o/XjL5+//+8Qu6u+Ei+I10Kb+6Lj/b92LnpXJLjQVFUDOYU1LVLaH/xCPJhL5lFMmW9XfT+7Me0fvpzFCxYmJBjFU6UardBJD7NOuZ1JTYYAUWN38aiHYivJv9277WA6pe//GUuuugi1qxZc0Kid/fu3UQiEdasOf4dUFlZSX5+Plu3bp20id7CwkJaW1vfsf1LX/oSv/nNb1i5ciUbN2484bnPf/7z3HvvvRMV4pTT7wnxel0/AN88vyLB0QiTjdOs5y83LmLNzzfS0OcFwBOMYDOeOaU9RKJXEIQJ19BxmLQSM16PheySeDJ3eGgEZ4oVjTZ+RVXi+JXVt1/tj8Wio/eT0uwceqONvDlzmV1cRdLceFtHGg7Q7H4WAL1BS/PIAap755ORkTnuxya8t6AnxqFjjRi0Rpz6LJHkFQRBEE6Zb9se8lwBtCVWKh3x0VitSXqSe+N9BtUfOOH1qlYLagwAo/Z43f6RQISDthmYZy5l1eJzkGWZ6Mcu4KXfPEn4YA8AacEAh5/7J87SMhxO5wQcnfBuVFWlxafQ2e1GkXRkzxN1eaeavLy8Ex5///vf5wc/+ME7Xvfoo4+yZ88edu7c+Y7nenp60Ov1OP/t32tGRgY9PT1jGe6Y2rlzJ7FYbPTxoUOHOO+887jmmmtGt332s5/lzjvvHH1sNpsnNMapZuF/rwfArNewrDQ1wdEIk9UTXziL2Xe+DMCQLywSvYIgCO/Fr4wgAyrxBK6iKGj0Mm0NXSgx8HtCDHd5GR5yIksyvuEIDocdWdIiR02Euo1oZA2ypKMqO4MFM5ad0H516SyMGjPPvPwQ6SU2RobdbGh6kPyuuSyfu+YkEQkT4cUtT3CoeSczl5QQi6oUa8UoKUEQBOHUxVo6AFDV+EVCRVHwhvy0+2N4pCjHvIOs33AYnaRBUmBI1mBISkPS6SlQg1yQnY/JbCTJHKXg8ptISj9+AVir1XLRVz/Cyw9L/OXPG7H6NWg0/WQ/8UMC536BzKKyhByzAK999fNo1DDLbVpqNWbKF4ravImmoKJw+kN6/9VGe3s7drt9dPvJRvO2t7fz1a9+lVdeeWV0kcWpIC0t7YTHP/rRjygpKeGcc84Z3WY2m8nMFANWxsIP/nF49P5nVhSLBdiEd+Uw68hyGOkeCfJabR83LCtKdEinTCR6BUGYcPqIkyO7jzDYO4LRpkFFRavRosYksgtT0Rv19DZ6yS6Pd/jCvSbOn/uR+JsXnNo+iotKuSn162w8+iRJc6x4hv24zU3UNh6ksmTmOB2Z8F50Jpk5K8rob3PjMKRRubAm0SEJgiAIZ5DXUws48Mogzh4/f/c7UKIq5uRh1pl05MgmbEGV2ftacRjiK2S3XvpRKtZeDMAPTnEf5193DXkFpRz+7mPEkCmwSTS++SC+tG9hsdrG58CEd6UoCulE6Q1HAA36xavRasUpbKKpwBiU6B1NFdvt9hMSvSeze/du+vr6mDdv3ui2WCzGpk2buOeee3jppZcIh8O4XK4TRvX29vaeMUnScDjMQw89xC233IIkHZ+F8PDDD/PQQw+RmZnJJZdcwu233y5G9X4I4ajCX7e2AJBuM/Af54o67MJ7m5efxHMHu/nN641cf1bhCf8uJzMxZ1YQhAm3fNFKZuetwJFsJbskhZySVIqqsjGajZgsRkKBMCjHXx9Tou/e2Htw2BwsLbmQwJBCS1039QfbeHrDI3i9njE6EuGD8EfceN0B6g+04+oNvP8bBEEQBOFtvvXzbzJjZTE2UyqWgA1bxI5RZ8WpM2LUaDFq9Hhib8s+RcIfaj9Vy+dS9M0L6B9ys6NlgNbOTl5+5DdjdBTCB9Hd3kZ60E93Xz/rAzH8FpFsn65Wr17NwYMH2bdv3+htwYIFXHfddaP3dTodGzZsGH1PXV0dbW1tLF16ZizM/PTTT+NyubjhhhtGt3384x/noYce4rXXXuO2227jwQcf5BOf+MR7thMKhXC73Sfcpoun93Zy9k9eY9F/r2dH89AJz3W6AqN1obf/52q0GpEOE97bXVfFB4j1e0J847H9+EIfLi8x0cTlUEEQEmLR7OXsO7wL71AQs1NPJByl+2CI3v3N9Hf3ojMZ0RlL0Wq0mFXrh95PWkoG5+s/QSBwHznlTiRJYm/9FlbMXTuGRyOcip6OPpLy9ZRUFOLMValvqaWssDLRYQmCIAhnCK1Wy/Xfvozff+tPRDuiSKrEkNfL0JfXsuXJfxAxRnHJFsIaHWj1GJzJH3pf8y8+GyVNoSa3B4NBh2skSHdXK1nZBWN4RML78Qd9uK9fxblZdtwRiXDYjaqqZ8yoqqlqrEs3nAqbzUZNzYmzwSwWCykpKaPbP/3pT3PLLbeQnJyM3W7nK1/5CkuXLp20C7H9u/vuu49169aRnZ09uu1zn/vc6P2ZM2eSlZXF6tWraWxspKSk5KTt3HXXXdxxxx3jHu9ks61pkK/9bd/o4x8+e4R/3Lxs9Pvif1+qA6A6yy6+Q4RTYjfqWFeTyQuHenhyTydpNgO3ratKdFjvSyR6BUFICEmS+Px1X6enr5sRtwuzycLCq808kHcNpUGJjFsv46Kzvjwm+7LZbGRY85Ck+KqZYfxj0q5w6rw+L2n5DpzZBtoGBzFZ9YwMD73/GwVBEAThbfIKs/mvx77H0f1HkTUySalJ9O16gy9VqOg0Otqu/iKl8xeNyb5mzFxI17EnKMrV4XQYaRoYAJHonVAaTYyKigxMJgP9DcOYLTLRaBSdTtTVTCRVZQzSvGNT/uHtfvGLXyDLMldddRWhUIi1a9fy29/+dmx3Mk5aW1tZv349Tz755Hu+bvHixQA0NDS8a6L3tttu45Zbbhl97Ha737Hg3VQTU1Su/cO2E7Yd7Byh6LbnkSVGR/ICrK5Kn+DohDPZb6+bx5ce3sMLh3qo7T4zZgaLRK8gCAmVmZ5FZnoWAJsffIakkAwSxAIfbrrlu5lVuIxHN/wGrUGDmWQQCzZPqNqW/TizDXhHfDTWtpHlLGL2PLEYmyAIgvDhVM0+PqJmoLUe3VtTcJVwaMz2YTZb8IQz2PjqNqSogjlbD5Xzx6x94f1FQh2YTAZaGrs4sKuLZasuFUleYdTrr79+wmOj0chvfvMbfvObM6/Uyv333096ejoXXXTRe75u3759AGRlZb3rawwGw0kXtJvKnt7bOXr/f6+ZzWt1fTx3oBs4MckLcNMZtKiWkHiSJHHDWYW8cKiHo91nxqwSkegVBGHSyF8wg91FdmIuP2kpljFtOyMtk+qyOeiyvPg6RT2midY30I1rcAidSaJ6XinnLb4i0SEJgiAIU4SmuJKG+oMoOj1264cv93QyFTUr6Fr/NIXaEI1JYhHRidb4wGu4uvpICkWYdc4yiotnJzokgcSUbpjKFEXh/vvv5/rrrz9hscHGxkYeeeQRLrzwQlJSUjhw4ABf//rXOfvss5k1a1YCI55cwlGFX6w/BsB31lVy9fxcytKto4neFIueQV98ENFPr55FkkWfsFiFM9OsXCeyBH2eEIe73NTkOBId0nsSiV5BECaNvKpSvtLw5LhdIcuyltAb3UNUiY1L+8K7S0/NxJzkA2Ck/cwoYi8IgiCcGSrWXY6y9lJkeewv5JrMZiKlc6FlG4SDY96+8N7yHCnkdPaDXk+XZWwHAQjCZLF+/Xra2tq46aabTtiu1+tZv349v/zlL/H5fOTl5XHVVVfx3e9+N0GRTk5/2dJMx3CAdJuB65cWAjA7z8kzX15Gqs1AjtOU2ACFM55Jrxm9/4tXjnHfDZN7ZqpI9AqCMKmM5zSI6rJZvPLIk6DIsHjcdiOchEljw6OqhAIhrFJOosMRBEEQppjxSPL+SySjhL+98AYmuZfycduLcDJyRjocqaUjGiV9+bJEhyO8RVHHaETvWBfpPUOdf/75qCf5LPLy8ti4cWMCIjpzRGMKd7/aAMBX15SdkJCbnedMUFTCVPTXmxbzifu2s6G2jy2NA5xVkprokN6VmL8sCMK0MTg8QFFlLrnlKYkOZVrZe3Qbdf07ObS5me6WYUoyZyQ6JEEQBEE4ZS3dUZ59roim1uREhzKt7N/6NPXmAbq+fR59n1lJZllZokMS3qKO4X+CcDp2NA/hCUaxGbVcuzA/0eEIU9jyslRmvlWy4bMP7EpwNO9NjOgVBGFaONZ8mKPuN3Bmm+jrDBAOh9HrRX2midDhriO5REeIZCx6G4X5YgEEQRAE4czwhzv/j1d/cxCNpCPgHruF3oT31t3VTn6Si8E+hcoZ2WgN4rRVEITj2gb93PXCUTbU9gFwbmU6GnlyL5AlnPk+tbSAbz1+AF84hi8UxTJJf5vEiF5BEKaF1q4W2hq6OLKjhZ6mQXp7exId0rQRUeInxmpURiOJlbIFQRCEM0f9kVrMBS5SZrpQTaLvMFG8nhEsZh2luXZ6e12I8UmTiwooY3AT43mFD+uWv+/jhUM9hKMKNTl2vntRdaJDEqaBK+YeL0H4wNaWxAXyPsQvpiAI00JWSg5SbgFanRZVVfF43YkOaVpoaW3C7fISDIbobO0lmBFgaHiQ5CRRPkMQBEGY/D795ZlUmDsAaBgUY2QmSn9/MyO6GFqDk7ZdzSQlhaisWJrosIS3KIxRjV6R6hU+hMZ+L7tahwH4nytmcuW8HIw6zfu8SxBOn1Yjs6YqnfVH+/jNqw1cv7RwUo7qFb0VQRCmhdLCSlx9fiC+4FtUiSQ4ounB4XDizDCTXWEnNc1B8ewMjrUfSHRYgiAIgnBKJEva8QexcOICmWbSMrKpnpXJzFnZ5OZlUlZuZnBwINFhCYKQYJ2uADfevxOAc8rT+PjifJHkFSbUrRdUAuALx7j1icl5Xjv5Us+CIAjjwGg04u2B/q42tJIGixolNTmN7IzcRIc2pSU5k3GoucAgwUgQVVXp87QnOixBEARBOCXJ2SW8/PdnIajSOeAjmvMmpVWL0elEKaLxVFY2n0OHn6CqKoVIWMHpNNPYcIyUlMm7yvl0oqpjs5CaqooRvcJ7U1WVV470svFYP7NyHdzzWgPtQwHyk8389xU1iQ5PmIbKM2z86to5fPXRfTx/sJuWAR+FqZZEh3UCkegVBGHaWDXvYg66XsaaZKSrfoTdnS/RPzyL2ZULEx3alBQKhdi861WGgn2E3T60Gi2mnjLmzxSdMkEQBOHMkJaZi8W0CM1j2+hRguTd3Ebt3i4yCpaSnpGX6PCmpM7Wbpp3b0J2HeXQ1iAjQQ3d6YspKytIdGjCW0TpBmG81fa4ue+NZna2DNEyGJ+V+fD2+HOFKWYe+ewSsp2mBEYoTGeXzcnhyT2dbDzWz5N7Orjl/IpEh3QCkegVBGHaKMgpoqu/DE+wERQJW6aW7sA+PHtdLJ97XqLDm3I27HmKtv5GKhflAnZU1YbFbcZudyQ6NEEQBEE4ZUtuvIKXD7WQsT3AkMvHjOJkuvs20h09i6yc4kSHN6W4hlz89Ko/sPZiLxeUxstsNagGiorKEhyZIAjjKRSN8eDWVup7vWw81k+POzj6nEErE4oqSBJcMz+Xb62tJM1mSGC0ggBrqjPYeKyfve2uRIfyDiLRKwjCtLJ0zrm8st1Nf+d+QoEQkiTh6ttMdmoRxXmliQ5vSglLXhQ5QjQSRavTIkkSQcWb6LAEQRAE4QPRarUsu/3TbLr9XrZtOEJOuhNZUekNtbD2ulswmcyJDnHKaG/tJNSr0DsAvNUtM0fceL0erFZbQmMTjhMjeoWx9sv19fzu9cYTtmlkif84t4xPLS3AbtLhCUZwmvUJilAQTpSfHP/t3y8SvYIgCIl33uLLsRrteOxN6A06ju7wcbD3VVKdadhtYrTp6dq+byuvbXsOR7oBvdZE+24/5aWVGLRmctIKEx2eIAiCIHxgjtRkLvndf7Lvgf9httoHEuw0J9Ow/Slmrrwu0eGd8RRF4Y8/+xO1O49ScEky/gwTDUnVYE6CMgcpGnHaOpmob6V6x6IdYXrrdQe55e/72NwwCMAFMzI5f0YGmQ4jCwuT0Wnk0deKJK8wmZSkxevyuoNRmgd8FE2iOr3iF1MQhGlp6exz2bpPg8tcR1SJYs/Ss6tuE+cuuCTRoU1qiqLQ1dPFsLsfl3sQjQlCUT9dXV3Y0gyEFB9NtW0svqQaSZIAsLsqmFe9JMGRC4IgCMLpK7/6Pzj4zz8xM9qCEolSYO6nrfEw+SUzEh3apBYMBunubmeorxNL/wjqyDDq8DC92VpycqxIsQCV8yQ+843LAGhq6qO46Co0Gk2CIxcEYTx9+4kDo0neykwbP71mFjajWOxSmPxy3lYj+ondHXxz7eSp0ysSvYIgTFtL55zDpj0BnLoo9bt6MBqGeWPPelbMW5Po0EYpisL+o7tp723EkmQgooToaOkiMz2Li1d8bFz2OTQ8SEt3PWElQE9vF9ZkA+FYgKOH68ktT8aeasJkN9LV4yI714kEhHt82HNNgA10WbgGPNiTLHg7oTA/fVziFARBEISJZrZYKb3ksxz85x/pH+qn/kAfocjDaM/7BNml1YkOb5TH42b3ppcI7vejeEJEXH5cvl5mXbWauZeNTz+ns7MZv38QRQ3Rt2E7yYEAkmeExkwbNWsrycpOoqv5KEXPbMXwVgI3VDyf0hwDYKDn6BAAQ0M+/D4rkUhEJHonKVG6QRgLqqqOTnv/46cWcF51RmIDEoQPQJIkPrmkgAe3tbJvkpVvEIleQRCmtbPnXUB0VpQndt9DRl4SwXAr9z31SzJyk9FIWnQxK2sWXzrhccViMTbueR5PrA9rjoy3z4s9w44GoDuMLtvHG3teZsW888d0v/uO7uBA6xbyZyUBMNzWjzMjDSUUITXTTtStA52dgFsh7JYIdhmw6BykGk3oB5PxhV0kSw6CwzGSdXmcu2Th6MheQRAEQZgKzBYLs679GuYtL1PS8BzoYeDVe3nhPpl8iwmsdgxzFlG6cvWEx9bX3Yqrcz+GaCcVaRFeeaqZZNmMBjDmxuj7/Yu0FGZTOHvsktLhcJgNT/6BmuXZFJckA+B+qIXikWEGowqytYhI2EpbaxRHej67rUfJKq1A47ATjBloGEhDjbiQ5FQaGgzYbUXMnl0wZvEJgjA57e8YYdgfQZbgrJKURIcjCB/YlfNyeHBbK7U97kSHcgKR6BUEYdrTarUogXj9J51ei9EuYc1RgQgDbR2oqorH4yGmxEhyJk1ITLIs0x9pIqPQCYAaOz7iIRINYzDp6Bs+Rn//XFJTU087mRqLxXh117PEUvrQaONtKYpC0BtB7k0n35bPeedXn7if+ae1S0EQBEE4o8kWJ95wDKteQ6pRItMzRLFfhX5oNVuIrVjJ8LALi8WCyWSckJhGhnooTRkG4ovERK0S+OPPRVVw+uDIgy+QV1MxJqNlhwZ66Nn1BGuKgtS7g5ANHk+AwYxMWuafTeqCJVyUk3vCe2p+cdlJ25o8k16F9yNG9Apj4dcb6gFYV5OFxSBSU8KZpzLTjlaWGPCGaer3UpxmTXRIgEj0CoIgALBqxlUc69yPRtYRdI3QXT+CJKs0HG3l0egvMVi1SIqG5FA5FaXV1Hfup6urh5zcHBz6NGoq5ozZyNVwOExjSwNttf2MDPrRaXT0dQ0CKsgyXleQ5kO9GExaXm18iP4NPkqKSlhSsZYkZ/KH2udL2x7HWOBnqNtDe3MfJquJLGMZn7vqU2LapCAIgiCcRPHsRTRFQvT0t4LewP31W/AO6ggFFTTbjqH76X+i+CFphomv/+6ThPv6CB7cz0CagczyUhzJhWRm5Y1ZPK6hQZqONtDQcQzCGvCDskyHdWY2BpsWd3cfpnnFFBrg1VfvofcXDRRcsowVX7z2Q+1vaLCfkb2PUpYU5UjzAM06ULFht1VywZfXjdlxCZOP8tZ/Y9GOMD0N+8K8WtsHwJdXlSY4GkH4cEx6DZVZNg51umnq94lEryAIwmSSmZZDZloOAHmppfQPdyMhE8qQ0ZtVktPtABzduhO3tRFbupGRtl4yUmV6I+00bd3PiqqLSU5KPe1Y2jpbOBZ+nZzCNLIrnQCoqkJOdfx+WqGRoT4vmfnxpG7Up8GUF2ZP4yZWz7/8A+3L5R5my5EXaWg9Rqpiw+zUU11Vw6VLPnHaxyEIgiAIU13xghXACgCuCOYQCcfwDPt45bHXUTuMSJKE61CQfd/9fyzSaUjTaPBdsZDSXBdDw1s4sMfBrHkXjEksTa/9k9x9LzGwMQmbNj6COPlbFSxYER9RG9mpMGN2vK9TVp7FP3/cgPrwNo7MKKb67EUfaF9tbXW0tmxDaWwl7DCQbdZiyJpLZfXEl6sQBOHMoqoqn35gJwB6jUxxmiXBEQnCh5dpN3Go082Lh3tYM0nqTItEryAIwr/Jzy4iP7sIgHkzlrC/dhdHmjfh8Xjp6XKhaiJomrR0tvah1+uQZRmNLPPUaw+RXZBGVAnT1dpPTm42cwrOJjcr/5T3/eqOZ/HqO3GkW/D2joxuj0TDo/f1Rj1qJD562Dvio7GuhRG3g7A/xrzis0lKOrVRvV29HexsfQlHvpYUXxJZxXY8LRLnzr38lOMVBEEQBCFu1UVnjd6/7FNr+O1t97P9wSNgD9KjhjjgTEK2muhubEOxaJGA+qOHaH6wiYg3SMQTJKT2Ubp0LvM/ew0mi/mU9jvU00XPy49QPFiPNcNGowFssfhzYdfx/kMsEgLipZna2/o4oriwyxY0D77wgRK99fV7cDi7WHJWHvWddWQ79bRZ5jFjkUjyTheqpKJKpz8aVxWlG6alPW3D7GlzAXD7JdUYdWL2oHDm+sSSfNYf7eXpvZ384NIZWCdBGZLERyAIgjDJza5cwLZnXqGgJhOjbCO/Jp5IDUUD5NckI8vx+r7d9SOYsqKAjL5fxpofY+/AC7T2lDKvcikm0/ufsA2r7chylMM7O/H1R7FZ7WhlHTGvjraDQ2h1GiSNRHfrAEigNUhk5KRRsTCboW4vBsOp1wDc3vgSSYXxn4FwKIy/xcgFC69Er9d/8A9JEARBEIRRWq2WVR+fxWVX+clIsdHQOMyMQhsAWxt8LJoRX3go6hmm4/429LI2vmBavgbbP/by5v5Gcj99MVXL3z8BO9TRTOnwMZpdHvbEZHpTbFgyMpFkidp9w2x7dT8Bt0JndzfDvz2KOQTWkIKkycfVYyBzVuYpH1drayNp6b3YbPHpqT1+0JWvYsb8cz74hyScsdQxqtErEr3T07amIQBWV6bzySVi4UXhzLayIh2HScdIIEJtt5sFhR+ulOJYEoleQRCEU3Du7Mup799NwD2Iqqr0d4yQ5Syivy5McokWd28YvyuKt10mSpBgKD5qxmDT0tS+m4HXhikqKsZsslBaeOJyI27PCK8ffJqYJkh7Yzf51alk5mSQWzyDxTNXAuCt8vLSsT+TkukAQImqZBXH7w92egAIu1U21z5HecZ8CnKKT9iHqqr09vXSN9hNjDBhJUhXax8+n4lwOEKwX8uF6z46ZnWGBUEQBGG6mzl3MUf3eGkdaCMUVQgEI3QMhIhpM2np9JKWpEVvS6I/a5iswmx8dT2Y5PjvsLmxn40//D3qnVpi3iCZlUWk5WSd0P7BjbvY/7tXaB5q4/L/XkB6mo3YMFRlLCYrO94PCD3wCi9/fysAeiUFs7GHfIygBYNOxasqDA4O8qfb/sRn7vrMO44hEonQ3tKCv3uIyLCX4Y5ejh05iE21MOTzUH3t2ZSJJK8gCKdof7uLn75UB0Bp+uSoZyoIp2tGtp0tjYPU9nhEolcQBOFMUVZYRVlhFa7yYTq721g2twq9Xo+iKDS1NFAwrwidTjf6+pdef54jW3aTlG3BmWLFo3Qw5PDR6Q2x75Ud5GcVUpY7g027XsYj95BbmQQYCfjT0PuTWVJxHqkp6aPtWa1WtF4nA21e9GYZJRafLheLxhgadHFoSxi93og/EKCrtY+qsln4gx7qW46Slm8jrATQmWWC3ihZb/34ONMsJOcbcQZKWXrRuRP6eQqCIAjCdFA1L17OoKu9mYFolPI5ZZQDPp+X4aEBFq4uZOFbFQ9CwSAP/+DX7O1vxRzR4gt6GfrG/yFJEu1ODdYbasiuKMOZlsOrf/07wX8OYvLL5EatxGIqfcOZVM1cgVZ7/BSvdH4xW6p2E/KGiXWoRI16CEIgFsGnDqBkazGGtPhb23jiTig5exYNz+zEsFhD+bxUdDqVAy/Wk/73YQCMgCypYI1w8U8+Q1FN2QR/osJkoKAgicXYhA9hc+PA6P1LZmcnMBJBGDul6Va2NA7yypFerlucn/DBUyLRKwiC8AE4HUk4HUmjj2VZprS4/B2vW7vyQrr7ZrG3+Q1iigvUeO0pk9VAV7gbf7LCqw0HcJQb8bUd7+T6+2Ncffk1Jy2fcNnZnwJgcHiQFhrRjWgx6s1UX7iOrd2Pk5wZXzCu8+gIvRzBkKIh1hnAmZsKGADodrlG2/OMeCkNzWbxbDESRxAEQRDGU3Ze0QmPLRYrFsuJo9kMRiM3/ehWDry8mYb7X8FZF8CrDWHTGHG4YmjdLRT6e+ndH8YYGkHyxy8w67V6WlstXPbRVe/Yb1lNCT984TsA1B9uwNXTAyYD6RmpfKTlABW1LwBholGF+x49guulRuwhHcZFOZSWxheYrctoQ1XV0RNXtzPK2h99ioLqkrH+mIQzRLxww1jU6BWJ3ulEUVRePNQDwJdWllCT40hwRIIwNq6al8tft7ay8Vg//zzQzaUJvoghEr2CIAjjJCs9l6z0j9HX30tjuI7OpqPo9Bo8gyFa9g+g0WgIumJEPUbMScVo0fOxi2vet0ZuSlIKKUkpJ2zT1ifjaglhz5dQYgq25Ldq9WqiRMJRdHotAz3D1B9qxzXkxmlOZmHVucyvXjJehy8IgiAIwocw6/xlzDzvLOr3H6Zj2yH6X9yHzmrC5wqhDFiQtVaUghjpX5yF3m7Gmulk5or579tu2YxSmFE6+ribEPVth9D6hinSetE6dNgH4sljb7cPgGg0iuxQ2WEfwJ7mpCCnjI/euI68suKT7kMQBOHdbDzWz4GO+GLTa2ecem1wQZjsZuc5+diifP5vRxv/89xRzq1MT+iibCLRKwiCMM7S0zJIT8sAzgagNbuJQ571WBzxUbamwSIWzVhxWvu49OzriMViPPH6n3EPBmg/HB9tbDE6OLi5mcz8ZIx2HcVleeTOSCLQbhJJXkEQBEGYpCRJonxODeVzauAL1wJweP9rVBR44qUcOgZJL1qN2fz+C72+m6ySSrJK/h8t+3fSsOsFuo0jdCUbiMVA3d9J6ZFMtFpYuKiSvP/NIb8gmd6eTPJKRJJ3ulMkBUkSpRuED+b1uj4APrIgl9l5zsQGIwhj7P9dVMXWxgFaBv3c82oD31lXmbBY5ITtWRAEYZoqyC2mSL+EWK8dTV861UVzxqRdjUbDR1Z/lrNmn4stXYczy0BedTJZeWlkl6SQnGYn9lZtX5XImOxTEARBEISJUVlzNsfaHDS0awlLVZhMpjFpt3D2Qso//T2ySmbi6g4TbdER7paors6mvDwbh8NKJCphNOqJRAJjsk/hzKaM4X/C9NExHP/+mJuf9D6vFIQzj9Wg5eZz43XrH93ZRvuQP2GxiESvIAhCAlSXzGL13CtYMWcdNqt9TNuuLJrJysLr8LlCAKhK/Kve5wnQ3dqP1JeKr1/hN/93F/sP7R7TfQuCIAiCMD40Gg3Vs86homYd5dWLx3yxl8u+eSGX3rkOAG9zjOFhLwBdnf001PVz9IjMkdde5bFffndM9ysIwvQw6AsDkGx57zJ1gnCmWl6ail4r4/JHWPGT19h0rD8hcYjSDYIgCFOMyWTGZDKTZ65BGgT/4H66GofQGiQwRMlNKqPX/zIz5mUTcQUTHa4gCIIgCJNAelY6urP19HyuD3uqlZ1/fZECu4YkvcpwrQ9DwRwu03bS5o8QCoUwGAyJDllIEAUFaQxG44oRvdPLoC8+CCXVKhK9wtSU6TDy62vn8oWH4oOpPvXnHfz06llcsyBvQuMQiV5BEIQpaumccwGIKGFiab0ABPxBNtU9QVZJfCXtUDRxU0oEQRAEQZhcklKcfOzb1wBw7NHdlFniU60L7GGkNx5CcujItmjoammkqKI6kaEKgnCGGfL+a0SvuEgkTF0X1GTy4KcXcd+bzbxe189tTx7EatCybmbWhMUgSjcIgiBMcdlJRch96XQcG0BRFYb7vQQHINBkpaezl1f3PcMLu/+Ph1+5h0BA1N4TBEEQBAHU/Pls82XQP+KjtXsIVWdgTzSZpvzltB18g4ZXH6D+hd+w5/n7Ex2qMMFUlDG7TXc/+MEPkCTphFtl5fFFnILBIF/+8pdJSUnBarVy1VVX0dvbm8CIP5z7NzfjC8cASLOJRK8wta0oS+MPn1zAmqp0oorKlx7Zw31vNk/Y/sWIXkEQhARTVZWNu19gONaBUbZTk7uYvKyiMWu/JL8CNSrTOnKI9KxkZqadw4KaZQBsP/Q6geRWTEBmuoXWjmYqy8QIHUEQBEGY7FxD/XRtfwpt2EXUnk/x0sswGsdmgTaAirPW8cAbgzzwp3bQ5nDjoR+j0WgA0L74O0p03aCDBlEGatpRiCERG5N2BJgxYwbr168ffazVHk/TfP3rX+e5557jsccew+FwcPPNN3PllVeyefPmRIT6od3xzyOj960GkYYSpj69Vub3n1zAD/5xmAe3tfLDZ4/QMeznuxdVo5HHtsb+vxP/wgRBEBLM6/UypGkkOdsOhGjurR3TRC9AcWEJ+3qsWGwmwoPHR+0aZAv/ehSLxgiEvcRiMdraOuno7sLsVAjHAsiqhsWzVo5pTIIgCIIgfHh9Dfuosg4BoKrNNDbVUlY9d0z3MXNZDbvvrUMNQ3NDC6UVJfEnDHZgBICgzw1AIBCgp6eDzoYefL0hvP0+ShYUMWfFrDGNSRCmGq1WS2Zm5ju2j4yMcN999/HII49w7rnxkmz3338/VVVVbNu2jSVLlkx0qB+a06zD5Y/wpZUliQ5FECaMRpa487IZ5CaZuOuFWu7f3EKvO8g9H5uHPI7J3gkp3RAKhZgzZw6SJLFv374Tnjtw4AArVqzAaDSSl5fHT37yk4kISRAEYdKw2Wz0NnjoPuamu8FFd1/nmO9DlmXOKrkEbX8WGY7c0e1ZaXlo+7OwD1eSH1vKvs39XH/1b/nuV17m74+8isdZTyilg+5Aw5jHJAjvRfQdBEEQ3lvfIPz2bj+/+pGXn33fQ2ftwJjvY97yOXz0dxdz9tfm40xyjm7XpZdTZ1hIvXUZmSs/x5atj9DX/xzZOW3seGwzG7+/ld33HKB2c92YxyQknoo6RqUb1EQfyqRQX19PdnY2xcXFXHfddbS1tQGwe/duIpEIa9asGX1tZWUl+fn5bN26NVHhfiiyFE9qXTI7O8GRCMLEkiSJz59Twt0fm4teI/P8wR421feP6z4nZETvrbfeSnZ2Nvv37z9hu9vt5vzzz2fNmjXce++9HDx4kJtuugmn08nnPve5iQhNEARhUsjOT8eRpwMg0D0+X83ZGblkZ+SesC0jLZOMtOMjCHrbQrxGF5LWQHdrmEg4ik6vRWtRaWg9SmlB1bjEJgj/TvQdBEEQ3ps9OQ31iIxO0qADFM/4TINfdsFiuODEbYUzFp7wuLM3iexsA7IsY0nTMUAEgObtbXhGPNgctnGJTUgMRVKQpNOvr6uIGr0sXryYv/zlL1RUVNDd3c0dd9zBihUrOHToED09Pej1epxO5wnvycjIoKen513bDIVChEKh0cdut3u8wj8l4ajCkC++EFuG3ZjQWAQhUS6Znc2+dhf3vdnMPa82sKQ4BaNOMy77GvdE7wsvvMDLL7/ME088wQsvvHDCcw8//DDhcJg///nP6PV6ZsyYwb59+/j5z38uTtYEQZhW9BoTEAUgHE3cgmjnnDuPp/+2l/aWIWRzhO0vHiEnPwutVsv64X9Q37+HcCDGRUs/fkL9MEEYS6LvIAiC8P5ySvLwE8CCGQDfgCdhsVgtBUSjndTVtTBEF87VdlLyrDgzzWz67f9QapZQS+dSedE1CYtRECajdevWjd6fNWsWixcvpqCggL///e+YTB+u5vZdd93FHXfcMVYhnjZfKDp6324U5w/C9HX90kIe2tbKrtZhrvvTdh64adG41Kwe19INvb29fPazn+XBBx/EbDa/4/mtW7dy9tlno9frR7etXbuWuro6hoeHT9pmKBTC7XafcBMEQTjTufuD8dIN9SN0tncnNJbq89x84n81fPGeMkqq8ymak0ZeTRIGmwZTdhRLfoSm1saExihMXePRdwDRfxAEYepJTkmm+FtZzL+niKX3lWKdk7jRkU5nGjt3NlBYmMmi1UXc8ruzueH/zefyT1eRLfsp8XTB0Z0Ji08YWwqxMbsJJ3I6nZSXl9PQ0EBmZibhcBiXy3XCa3p7e09a0/dfbrvtNkZGRkZv7e3t4xz1ewvHjn83aTUTUj1UECal/BQzD9y0CLtRy+7WYX73+viURxy3f2WqqnLDDTfwhS98gQULFpz0NT09PWRkZJyw7V+P320qwl133YXD4Ri95eXljW3ggiAICZCZlUlWuZ2sMgeZpc6EJaF8Ph/JhXqcKTYkSUKNHf+ZCEfiU8C8HTKlRWUJiU+Y2sar7wCi/yAIwtQjSRIzlxaycEU+sxfkkJvvSFgsg4OtLF1agc1mprIym+7u4xfeIhYLqqoSKp2TsPiEsTYW9XkVEKUb3sHr9dLY2EhWVhbz589Hp9OxYcOG0efr6upoa2tj6dKl79qGwWDAbrefcEukR3ckNtEsCJPJkuIUfnL1bADu39zCgDf0Pu/44D5wovc73/kOkiS95622tpa7774bj8fDbbfdNqYBT7arU4IgCGNBLx+vV6WTDfiDvoTEodFoUIkRCoSo299Kw5FW6nd307J/gJFBL037+vCNBJBlcTVeOHWJ7juA6D8IgjBVHZ/dEAhEEhaFLMcXfu3rHeLYgRZe33CQrVub2bmjjWZJ5VBODopFl7D4BGGy+uY3v8nGjRtpaWlhy5YtXHHFFWg0Gj72sY/hcDj49Kc/zS233MJrr73G7t27ufHGG1m6dClLlixJdOin7Ol9Y7/QtCCcydbOyGBWrgN/OMbvXh/7mbIfuBjEN77xDW644Yb3fE1xcTGvvvoqW7duxWAwnPDcggULuO6663jggQfIzMykt7f3hOf/9fjdpiIYDIZ3tCkIgnCmy0wqoHtQRSsbKErJITM9CwCf38f/b+++46Oq8jaAP3daJm3Se4VQQgkQqoB0XpqCIooiKi6IYlkXXcpawC6yiIhSbCiuFcsKNkCQ3hEILXTSII20SZs+5/0jm8FIKIGpyfP1Mx8zM3fOfe6ZhJz85txzf970DYIj/eErD4IFRrSO6Yzw0IirtHh91Go1ys5aYUmoRrPWUVDBF806hgIAotsEoiS3HN4+MpzJPoGk+NYOyUCNj6vHDgDHD0TUOEkIx5kzZkDI0axZZ9vjpw6ewumVXyMuJhQyfw0sCjlSxtzjsBwR4S3w87/+gc7eVvT2UeBo337o0CMRAGAsvoBOygocr8xBhbYU/gFBDstBzmEVFtjj5OCadpq2c+fOYdy4cSguLkZYWBhuvvlm7Nq1C2FhYQCABQsWQCaTYcyYMTAYDBg6dCiWLFni4tQNYzDxfSb6M0mSMG1Iazzw8R58tisLD/VphqiA61uTuz4NLvSGhYXZ/tG5knfeeQevvvqq7X5ubi6GDh2KFStWoEePHgCAnj174rnnnoPJZIJSWfMJ77p169C6dWsEBXEAQERNR7O4FmgW18J2f/WO72BWVKKiyIAqSykiImUwomaWb+6FbIcVegEguWU7mMLPQ5IkGI0XTyVRqRQQZjkCwr1xtHAzNL6BCAtxXA5qPDh2ICJyjFatLi5zU64tx+uj30BQRAAuZBehR1wBmhWcBABkyFWAAwu9moBAJMXFI1qfCwAwV1y8sKxVJYfFbEWybzmObPoCKbc94bAc5BwXl1648Xaauq+//vqKz6vVaixevBiLFy92UiL7M5j5PhP9VZ+WoejeLBh7Mkrw7obTeH10it3adtglD+Pj4+vc9/PzAwAkJSUhNjYWAHDvvffipZdewqRJkzBz5kwcOXIECxcuxIIFCxwVi4jIIyjUAuoIoFpUI+/ABRTnmWDVqWA2KBAetQvaylL06zbEIfu+KWUAft+7CkXWszh9IgsWixne3j6Qy+W4cL4YwipBkllx8PheDO59q0MyUNPEsQMR0fXz9fOF9owW+iN6VKoqkO5dhQqFDBaZNwr1ZlQvfgvB3XqhRXfHnPIdOfohpH/3PoznT+LIroOoLi6FQm+ErKwKU36qgDAGoLj0GP4zoNzla4YSkfMYWegluoQkSZg+tDU+35WFyX2a27VthxV6r0VAQAB+++03PP744+jSpQtCQ0Mxe/ZsPPzww66MRUTkcq3Cu6C6SocOLSPRLbQCs/6xHgpFzWnnEfeWodJQgjOZpxEaHIYAjX0vvqJQKDC05xj8uPY79BqqhlFvQURczUxJCRIikvxQkSPQuc3lLwJB5CgcOxAR1U8ul2Ps4jvhF+iH2Oax2Pn5BmQu2QcAUFqtUIbuQUmzVjhz5BASkttCobDvn4KhsQlQ/W0G9v2yDC3ys3HTiRO25xYXhqK6VGDwxB4s8jYCAhYIOyzdIMBT+psCvZnvM1F9uiUGo1tisN3bdVqhNzExEUKISx7v0KEDtm7d6qwYREQeISH24qd6gQGBkCl1gKgp9FaVWaHuqcMZ00bsO6rHkDb3ITDQPqesl5QWYfv+DYiOjINvkAp6lQp6rd72vE6nh3S+JW7rOQKSJNlln0SXw7EDEVHDdOnTxfa1d5if7WuFTAazygdJP38BL7kMBxKS0W3aLLvt99y508g5vh9BofEI8pZQIhkhhLCNFUJijJj+/mR07d3BbvskIs+glMtgsrDYS+QsLp3RS0REV2a1WpGbfx6KgHz4+VXAP1iJktJi5B4DZHIJkgz4cu1SJCW1QNuYHoiLSmhQ+xWV5dh85Eecyz6HFgltUWEpxPmyHHi3qIBeZsC5s4W4kFWJSq0OcpkcGlkEBt50i4OOloiIiOxBp9MhX3sBpbEl8A9Twz9UjVOhIbC2iYfczwsZ2QXw3fU5oAhCUsrgBl+s8ty5U9CWHURmdgFCgpPQPMkMWVUOYizHUC0z4rS2Gt+dLke4fwiqLQo8/MxodGKRt9GwwgrYYX1dK9fobRLu6hKLT3dmQSW/8VngRHR1LPQSEbmxLX+sgz4kC0MejERUq5pTHU/sNSGufaBtG0kOeEXrkFa0BoczgjCi19irtltRUYH5H70MlUqJyMRAlGm1yC07DblCAbWXN078cQ4hMRo0S46Bv1c1YtrW7O/CuXJUVlba1k4lIiIi92I2m7HwocXI2JOBN79sCT9fNQBgX5kXUm9qBgDQbtChVawMMlkFzhz8ApJfO7Ro2+OK7QohsHPbRhz/YwvkOgvCfXyReyYfVmUBiivNyK04D68QC/zNJgz3UuAPfRiaV9QUdnJ+/wPoP9CxB05OIyDsdDG2S8/aocbrkX72XYeUiOrHQi8RkRsrspxBqLcvrBY99DojpPwwlOafQFlxBSwGCSHRfhCWmtMifYNUOJt1FsdOpsNo0cHfV4Pm8S1tben1euw/vh0V5mJUSgVo1TkG0S3/t+SDTEJ8Ssj/tgxA7plSRMbXPFdWfAGRFg3kchlCY/yRdf4s2rXmrBwiIiJ3tG/zPpTtrEKgCEVusR6KKiWM6jgUFWUgN7cE3t5eaNkqCoXFpYgMC0BCpArbDh6BoUIFY1klElLbIDg81NZe9tkcbPlmD07uzkBmejr+ERXwvyUZqmAMVSCuqAIAoPWWkBwpAUoVdBYLqnxVQJkZACDLLXRFVxCRG6gy1izb4KNi+YnIGfiTRkTkxqxWK4oLynD6aCayz3jhX5MfwpBet6GsrAxKlRKnMo/iRO4OHMzPQFisBiUlxchRbIfKT4XzlXqs//ZHJLVIgsFchf37D6D3re3hJUnwggZ5J8pt+9Ebqm1fV5UZ4GeOgqXAC94yf0wcdRfe+/bfCAj1hUKugCyowBVdQURERNfAbLJCCCu01hJs3W5ERHwbjJw8CklGI4xGI6qqyqHXn8dPi3fCL8sE/2oZtH6AKDsJSZKQofwByiEytG7VHNYLOfjvRiNydv3vwq/mYBRHVCNUXjNL2Pyn5fq9AkJwJjUZMqMeyuSOiLBYcfA//4F3kA+qFdUwGo1QqVQu6BGyNyEsELjxazUIwXVbmwKdrdArd3ESoqaBhV4iIjemL1DAL0kgplkkwpr5obikGKEhoQgKqpltm9quB1Lb9UB1dTW2HV4NRVsZSi9UISJOBR8/NbwDdJBHaOEDoIU1EtqSSgSG+MNqtUJbUgF1thoySYLS5A/9eW/4KP3QMiARrQa0tWUoLy9HQkoIgiNqlo6QF/I0OyIiInfVbWAXpKV8hlbFMlQe9YNPmQEAoFKpoFKp4Ofnh4iIaPzzg27ISj+N/fO/RXlOru3CaYEmFUIkLVoUHQYkIEyjQs7/2lbLfZDhUw6E+kJSKWAOkKO6YzjkwRoMGD0QwZHhthwnt/yCzgNqxg5VehPyz2cjvlkLp/YFOQbX6KWGqDLWzOxnoZfIOVjoJSJyYymtU6ENOo6izAoEBPshM/ckQkNCL9nOx8cHQ3qMQUFhPg6d3oPy7BJYJSPM1RKM+T5QydSI845GtVaPQEUU1AofdB4UjcDAwJoGrrAsn7e3N8zGi8Vdg6i081ESERGRvahUKrTr0gnq30+iXAFYMwtRqS2HX4Dmkm0T2rZA/Ef/wu51W6A/eh7FO45DoVTCJFPCOzgcktoPSf1liB8SD02oL4IjA5HYIuHaLt6mUNq+9FUrkVeYA7DQS9TkVHPpBiKn4k8aEZEbi49KwpbMP3AuOxcGsw4G7Vl0Tel12e0jwiPxf+Gj7JpBqVSi4HQlygqqoFSqAEMZkGrXXRAREZEd+baNQ8XaIzhTWooQgxylb3+CsS/8o95tJUnCTUP6AUMAPHXp8y0vfeiaqMKbYeU3pfCS+cNiVMLPmIOWV77eG3kIATst3QAu3dAUVOj/N6PXizN6iZyBhV4iIjcWEhyK8m1WRCQEI6FlJAAJ2eczER+T6NQcLZIT4R1lAgCUZBqdum8iIiJqmI63DcLarz5EgiwEsQVBKM0ohxDCtjyDM4SER0D5hwaBkhpCCFiSuT5vYyGEFcIOyy4IwaUbGjuLVSCjqOZswIRgHxenIWoaZK4OQEREV9a+RRc0S45FeZEOfgE+OJS9zekZ1DI/29eBqgin75+IiIiunUqlQniXrvCSambSaU5rsfvrX52awV+jgSXUFwBgEBZEdEt26v6JyPWyiqugN1mhVsqQEOLr6jhETQILvUREbq5L215QlIbg/NkiHNl1FpnnTuPA4T1OzaCvsOD43hycTStERtZZp+6biIiIGq7935/AeasOx+TFOBtrxNFDu2AymZyaodjfgtO+WhRFmJC9N82p+ybHsdrxP2rcThfWzOZtEe4Hucx5ZxQQNWVcuoGIyAP063QrqkxlCGpe8892RuYhpKK70/YfGRoN7xblAABTHj+NJyIicne+Gg1uefN1KL3SEBkZDCEEjh/fhvbtBjgtQ8d2CQjfcRgwm6BtHuO0/ZJjCWGnNXoF1+ht7HSmmvdYo1ZeZUsishfO6CUi8gBeXl7wVgRACAEAMCuqnDorx987CCZjzemfMokXUiAiIvIECYnNUFRkAFBz0TWV0rnr7MvDgi/eUbHQQ9TUmCw1f7so5Sw9ETkLZ/QSEXmIAakj8fX691BpKoXMqsCPpd8iKjwCvVIHOXzfwQGhWPFdOkIjgyE3egOdHb5LIiIiukGSJCEkuDu2rP0VltJqFOVWQtaxCqqwGMR36uHQfQshcEGYcdJQAYW3DwJOZqD94P4O3Sc5h4Cwz8XYIOyQhtyZyVLzfcJCL5HzsNBLROQhvLy8MLL3fdh2/Edo4mXIO1OICk0Vjp+JQHJSe4fs81TmMRwv2AOjshzNWschumUQqrI5o5eIiMhTxMYmQZ/ZDbr/fAqdpRzNvQpx8og3rB26QSZzTPHl2LrvIZ3eherj2WhtioTMLEEfHOqQfRGR+6ot9KoUXJ+XyFn4sQoRkQcJDgxBpHermmUUrDJ4eatwvGQnikouOGR/R/K3wT8OCInUAJaaAq/VvxIFF/Idsj8iIiKyvxY33wRt2+ZQag3QGc1oIatG2n/edsi+Mo8fRmLmBrT2NqBXkgaFQg8AqNx5xCH7I+cTwmq3GzVuRjNn9BI5G2f0EhF5mN1HN0Hha0FpQSX0VUbI5XL8XvQj7hr6N7vOzCkvL4fw0gNQwWq14lxWHopLLkAmKdDGuwIRYZF22xcRERE5TvbpM8javAMqtQ9+zNUhPjYC1spcHNn5O9r3tO8SUIb8DKiVNR8O+6mVOO5VhtIQGYRvtV33Q65ksdOiC7wYW2PHNXqJnI+FXiIiD9OiZTN4x5iQc6QUce2DAAAGnRE557KQEN/MbvtJz9yPrKNF0JdbAJkVvn5qtO0Zjws55Ui0436IiIjIsUKiIpEoD0S0VY3K9gFoH6MGAJwqOADAvoXePeu3QHdbVyi85PDylqNHs0J0TQ7GyXy1XfdDRO6Pa/QSOR9/2oiIPIy3PAAAYLFenAXh5a1CWWWJ3fYhhEBhZRYi40IQ3TII0UkhkKtkkCQJEJLD1vQjIiIi+/Px8YE1LgIAYDJd/B0uGSrsup+i3Fz4HD+LFm0j0KZ9DJonRUJSqAAAAlyjs7Hg0g10rWxr9Mr580/kLJzRS0TkYUK8o7Fx524UZpXCZDJBoVBCIZfD4ldot31sT/sdfvFWVJy4+JhFmFB8Vo92EX1Y6CUiIvIgkiTB0CIGO89mQX9UD501BHKZHBU6I2Krq+Ht42OX/WQuWYL+Pr7IzipG6+RoAIC2yojj5xWIbHmzXfZBrldToL3xwh0LvY2fkTN6iZyOhV4iIg/TtlUHpJdshQwSWnSuWSe3+LgF7Vt1tNs+Tmcfh6LYgrzsC5DkEiS5Ff7+/ujV6lZEhcfYbT9ERETkHAFt28L75yzoA+XoFSXDuTIDypv3hFKlskv7Op0OuRojLENTkDZvF07qvGHR6mGJDcTgD5+0yz6IyLOYzP9bo1fBQq+rWSwWmEwmV8egK1AqlZDL5TfcDgu9REQeRi6XQyXzQUi0CeWlVQgI9kNwUBgiQqOvqz0hBA6k70Fe1UmYhQkXzpdB7aVGbFt/6PV6RLfS2LbNu5DDQi8REZEHCkmMQoZkRVWpBYAaEX4KGCOaQ6G4vj8Jq6oqkZG5DwpFOY4dOYM2iXGI6ZaE1DZByN+kRUSuAYASeZllEELULP9EjYIVVkj2mNELzuht7Cr0NYVFzuh1HSEE8vPzUVZW5uoodA0CAwMRGRl5Q78zWeglIvJAnRMGYuPh/yLzaBZCI4JhqsrDgNSR1/UL4cDRPSjVHIVvkByAHFU6JaJb+QIAjGa9bbvyLIFuHVPsdQhERETkRM07tEXmhJuQve5XbDmmhkySwVKxHUkpXa+rvbNnt6NtOy8A/rBaghEq06JVTM1FYmVBSgAGmK0WhIzpySJvI8OlG+hqhBCYuiINq9JyAQD+Xiw9uUptkTc8PBw+Pj7899hNCSFQXV2NwsKa5RijoqKuuy3+tBEReaCE6OaYEPVP/Lj3YwTEKmAxW/DT1i8xqu/4Brflqw5AKYBKbRUyT+Sj9Hw1qivDoFAoYDXKkL4jB7pyMybdORW+Pr72PxgiIiJyigGT7kJ8j9aI/+ltyGQSis4fwelNq9Gi//AGt+Wl9oXVakR2dgHO55Zhf2YBEhKioPDyRnmCFRGp4cgpBO555G4HHAkRubPThZVYlZYLuUzCXV1iMbZbnKsjNUkWi8VW5A0JCXF1HLoKb29vAEBhYSHCw8OvexkHFnqJiDyUJElQyX0AGCFXyKFXFV/zqZEGgwFHTx9AeYUWF8yZOJ+Tg5gWYUhOTcB5tRYJHS4OBHLPlCAmzh+BmkDHHQwRERE5nCRJiGyWhHydCdG+KoTKLSjPOgbg2gq9RUUFKC3ORllZIXJyjsNsjkZwsA8GDEjBkSNhSEmpmYGk11eiW6wXAqtiOXusERLC4lbtkPs5fF4LAOgcH4g3xnRwcZqmq3ZNXh87XXCTHK/2vTKZTCz0EhE1RUatFVnnsqGQq2A2WfD9uk8RFByMML9YpLROvewfV3+kb4Mp4hwQBIRBDZMlFGExgTVtmox1tpWscsh9rTiXl43YqHhHHxIRERE5kJ+fH9ZVeOFwoRUWoUbJ2dMwG5bCpNEgrEs3RLdsddnXFpzfgeRWPkC8AtoyOVq1uriOoNl88TR8v0A1qnUGGLS5Dj8ecj4BAdhhfd2adqgxOnSuptCb8r+/L8i1+IGb57DHe8UVsYmIPNjwm+9Cx4S+iEgIQHKPGBi9SyGPLsUF34P4aduXl32dj5df3QdEzaeF5aWVOHn0DI7vzcLZtEJkHylBRYERwaI5OD4gIiJqHHo9/Qqssb2hPBgIc5YZcXt3oPnva2B54yWcWLv6sq9Tqi4u4ZQQG4Di4nJYrVacOXMee7afwFfv78OHc/Zh85dlWH3AAvjFQQgW8+jGzZkzB926dYO/vz/Cw8Nx++2348SJE3W20ev1ePzxxxESEgI/Pz+MGTMGBQUFLkp8ba7luPr37w9JkurcpkyZ4qLE1+ZChQEAEB/s7eIkRE0PZ/QSEXkwlUqFnp364cBRNfKrDkGy1hRs5XIZzKrKy77u0JFDOHFYC4vBC4YqICCxEJLCAqVaQrfBbaBQKBEcrgEAmPJ90CtlsFOOh4iIiBwvIj4aI579G34xfADD2nwYYyxQyeUIlMlQkZVZ72vyc3Lxw8e7kFdeCW2pEaUletz7cHO0bx+L0DB/yKsC8Ps7F8ceKb16IOWm/s45IHIqe11ErSHtbN68GY8//ji6desGs9mMZ599FkOGDEF6ejp8fWs+gHjqqafwyy+/4Ntvv0VAQACeeOIJ3HHHHdi+fbtd8jrCtRwXAEyePBkvv/yy7b6nnIrPmaRkT/3790enTp3w9ttvO33fmzZtwoABA1BaWorAwECn778hWOglImoEUtv1wPcbj6EoNx8mvQUyuQwFuYX4tvQTxMckITIwHlaLBbEx8TUzAYIqkZHmBZU8EAAQlmRCVPMgWCxWVBz3RrH+HMy6ckiQwVBe4dqDIyIiIruTy+VIGTcIlZkXsPBcNpTKIFQbZfCXnQB2/gS5Tyg0YfGwmK2IjY/DuZPZyNyRj9+PF9naiIrqidbJ0cjMLIMq0BeKyDIoVHJUlehRkHPBhUdHjc2aNWvq3F++fDnCw8Oxb98+9O3bF1qtFsuWLcOXX36JgQMHAgA++eQTtGnTBrt27cJNN93kithXdbXjquXj44PIyEhnx7tuRZU1M3q9Vde3xiiRK9VXUO7Vqxfy8vIQEBDgumDXiIVeIqJGYsyAB7Ep7ScgvAQAEN7cByUFZdCFZCC9+jjOp5cgsEADhc4PcW2D4OtfDlN1zWtL82vW5dXm6zCs9922WQJCCFgsvFAGERFRY9SsXSs0+3w2HrnpJVRm1MyubBkuoY1vNoBslGdux8dvXIAmIAywCqiqzXVeX1KkAwAY9N7426NT8LdHax43m83XfREZcn+umNH7V1ptzRqwwcHBAIB9+/bBZDJh8OCLZ6ElJycjPj4eO3fudNtC71/99bhqffHFF/j8888RGRmJkSNHYtasWZed1WswGGAwGGz3y8vLHRe4HkazFWk5ZQBqLsZG5C5MJhOUSuV1vValUnnMhy1co5eIqBGJDWqByuKagZ1KrYLVVPPPvLePF/RGPUpLi3Gu9CxO7ctFcItcdPHMKBMAAC6qSURBVBhWjB53lSKytRH+pS3ROWpYnUGjJElQKPiZIBERUWPW5dYWtq/Pnamwraur8fMGfAQy004i72w2fHysGNtfgZl3B+PffwuBIT0TZ88EICame532FAoFT9luxASsdrsBNYXIP9/+XKSsj9VqxdSpU9G7d2+0b98eAJCfnw+VSnXJKdURERHIz893SD/YW33HBQD33nsvPv/8c2zcuBHPPPMMPvvsM9x3332XbWfOnDkICAiw3eLi4pwR3+bweS0MZiuCfJRICvO7+guI6lFVVYUHHngAfn5+iIqKwvz58+s8L0kSVq5cWeexwMBALF++HACQmZkJSZKwYsUK9OvXD2q1Gl988QWKi4sxbtw4xMTEwMfHBykpKfjqq69sbTz44IPYvHkzFi5caFsTOzMzE5s2bYIkSSgrK7Nt+/3336Ndu3bw8vJCYmLiJRkTExPx+uuvY+LEifD390d8fDw++OADu/ZTffjXOxFRI9IioQ0y95yCpcAMlcwb2vx8SEILIbOiuKgM3Tu1hbePFwDA19cPmggviEoVSqp0iA5JRFRktIuPgIiIiJztzkdHIP90KQKC/WFWReLRqWdgqlKhqsyIxAgdenmHQG6VwWr2Q/O2efDSeMFkFSiVC7Rsmerq+OTh/lqIfOGFF/Diiy9edvvHH38cR44cwbZt2xyczLkud1wPP/yw7euUlBRERUVh0KBBOHPmDJKSki5p55lnnsHTTz9tu19eXu7UYu/ezJqzC7smBvMDHzcjhIDO5JqzNb2V8gZ9P0yfPh2bN2/GqlWrEB4ejmeffRb79+9Hp06dGrTff/3rX5g/fz5SU1OhVquh1+vRpUsXzJw5ExqNBr/88gvuv/9+JCUloXv37li4cCFOnjyJ9u3b29bFDgsLQ2ZmZp129+3bh7Fjx+LFF1/E3XffjR07duCxxx5DSEgIHnzwQdt28+fPxyuvvIJnn30W3333HR599FH069cPrVu3btBxNAQLvUREjczg7qNsXxutesgjak7X0lUaUJxVBZ9gA4LC/WE2mVF9QYGuSb2QGNOCp1gSERE1UcEhQZi9/AkANYWA+9o9B2OhAoACsjZKlJWaoNYDvjIvFBvl8A2IRvI9U9Dal7P1miJ7L92Qk5MDjUZje9zLy+uyr3niiSfw888/Y8uWLYiNjbU9HhkZCaPRiLKysjqzegsKCjzidOvLHVd9evToAQA4ffp0vYVeLy+vK/aho/3xv0Jv98Tgq2xJzqYzWdB29lqX7Dv95aHwUV1bCbKyshLLli3D559/jkGDBgEAPv3006v+bNRn6tSpuOOOO+o8Nm3aNNvXf//737F27Vp888036N69OwICAqBSqa66LvZbb72FQYMGYdasWQCAVq1aIT09HfPmzatT6B0xYgQee+wxAMDMmTOxYMECbNy40aGFXi7dQETUiAWro1FeokN1hQGtIlMxfuBUdA4Zie0/H8LZ/WdQ/OsprH57GYu8REREBKDmdNi2/eJgFRZ4hVoxftYEjN/wBix3JSFLXYQzJXoYS3JwcM33HD80UUJY7XYDAI1GU+dWX5FSCIEnnngCP/zwAzZs2IBmzZrVeb5Lly5QKpX4/fffbY+dOHEC2dnZ6Nmzp2M75AZc7bjqk5aWBgCIiopycLqGs1oF9maWAgC6NWOhl67PmTNnYDQabR9qADXrVl9PcbRr16517lssFrzyyitISUlBcHAw/Pz8sHbtWmRnZzeo3WPHjqF37951HuvduzdOnTpV5xo3HTp0sH0tSRIiIyNRWFjY4ONoCM7oJSJqxFLb9EBMUSKMRiNiW9ecshUZHok2HVvi3N4zUD63DyH/vMXFKYmIiMidPLtkCo49ehIx8ZEICKy5wnjXHp1g3HQApTERaBVmRVWbFBenpKbk8ccfx5dffolVq1bB39/ftu5uQEAAvL29ERAQgEmTJuHpp59GcHAwNBoN/v73v6Nnz55ufSG2qx3XmTNn8OWXX2LEiBEICQnBoUOH8NRTT6Fv3751Ckju4lRhJbQ6E7yVcrSL1lz9BeRU3ko50l8e6rJ925MkSbb15GuZTKZLtvP19a1zf968eVi4cCHefvttpKSkwNfXF1OnToXRaLRrvlp/vfibJEmwWu1zVsTlsNBLRNTIhYdG1LmvVqvRt+UYHDTsRshvt6HT4JtdlIyIiIjckSRJaNuh7sypVn174qyPN8rPnoKlVzckxCa6Jhy5AXsVKa69naVLlwIA+vfvX+fxTz75xHaa9IIFCyCTyTBmzBgYDAYMHToUS5YssVNWx7jacalUKqxfvx5vv/02qqqqEBcXhzFjxuD55593Qdoryy3TYfmODABAanwglHKeQO5uJEm65uUTXCkpKQlKpRK7d+9GfHw8AKC0tBQnT55Ev379ANSsm5uXl2d7zalTp1BdXX3Vtrdv347bbrvNdkFDq9WKkydPom3btrZtVCpVnVm59WnTpg22b99+SdutWrVy+dku7v8OExGR3YWFhGNwv5GujkFEREQepHnXTmjetZOrY5CL2XuN3mvbVlx1G7VajcWLF2Px4sU3EsuprnZccXFx2Lx5s5PSXL8fD+bime8PocpYUxzr2TzExYnIk/n5+WHSpEmYPn06QkJCEB4ejueeew4y2cUPDwYOHIhFixahZ8+esFgsmDlz5iWzZ+vTsmVLfPfdd9ixYweCgoLw1ltvoaCgoE6hNzExEbt370ZmZib8/PwQHHzpMiT//Oc/0a1bN7zyyiu4++67sXPnTixatMgtPlziRyxERERERERERNQgQgjMWnkET351AFVGC1JiAjBjWGtMvPnqaw0TXcm8efPQp08fjBw5EoMHD8bNN9+MLl262J6fP38+4uLi0KdPH9x7772YNm0afHx8rtru888/j86dO2Po0KHo378/IiMjcfvtt9fZZtq0aZDL5Wjbti3CwsLqXb+3c+fO+Oabb/D111+jffv2mD17Nl5++eU6F2JzFUlcy0djbqy8vBwBAQHQarV1rtRJRETkqfi7zfHYx0RE1Jg44/da7T7k8mBI0o3PGRPCCoulhL+LHcyR3xtbTl7AAx/vgSQBTwxogX8MagkFl2xwG3q9HhkZGWjWrBnUarWr49A1uNx71pCfYy7dQERERERERETXpGau2I0v3+Dhc84IwJFcLQDg1g7R+OeQ1lfZmoicgR+1EBERERERERHRNTNZrFh7tAAAkBzp7+I0RFTLKYVeg8GATp06QZIkpKWl2R7PzMyEJEmX3Hbt2uWMWEREROSmOHYgIiJyVxY73shTVBvNMJprZnILIfD6r8dwMKcM/l4KjOoY7eJ0RFTLKUs3zJgxA9HR0Th48GC9z69fvx7t2rWz3Q8J4RUaiYiImjKOHYiIiIhcS2+yYH9WKVYfyceKP3IAAB1jA3CyoBJanQkAMH9sR8QFX/0iWETkHA4v9K5evRq//fYbvv/+e6xevbrebUJCQhAZGenoKEREROQBOHYgIiJyX0JYAUh2aIdr9LqzxRtP490Np6A31V2PeW9mKQBAJZfhn0NaYUg7jseI3IlDC70FBQWYPHkyVq5cCR+fy3/CM2rUKOj1erRq1QozZszAqFGjHBmLiIiI3BTHDkRERO7OPoVegIVed2I0W1GmM8JgsiKjqArz1p4AACjlEkZ2iEavFqHIKq5CXJAP2sVo0DLcHyoFL/vkCazWG794IjmHPd4rhxV6hRB48MEHMWXKFHTt2hWZmZmXbOPn54f58+ejd+/ekMlk+P7773H77bdj5cqVl/2DzWAwwGAw2O6Xl5c76hCIiIjIiRw1dgA4fiAiIiK6krScMox9f2edx4J8lNg/6/8gSfYo7JOzqVQqyGQy5ObmIiwsDCqViu+lmxJCwGg04sKFC5DJZFCpVNfdVoMLvf/6178wd+7cK25z7Ngx/Pbbb6ioqMAzzzxz2e1CQ0Px9NNP2+5369YNubm5mDdv3mX/WJszZw5eeumlhsYmIiIiF3H12AHg+IGIiMhu7LR0A7h0g1uRSYAk1SzJEOCtRKCPEo8PaMHCoAeTyWRo1qwZ8vLykJub6+o4dA18fHwQHx8Pmez6Z8tLooEL41y4cAHFxcVX3KZ58+YYO3Ysfvrppzr/KFgsFsjlcowfPx6ffvppva9dvHgxXn31VeTl5dX7fH0zcuLi4qDVaqHRaBpyKERERG6pvLwcAQEBjeZ3m6vHDgDHD0RE1Lg5Y+xQuw/Ayy7Fv5pShIG/ix2ssY0rqeGEEDCbzbBYLK6OQlcgl8uhUCjq/fe1IT/HDZ7RGxYWhrCwsKtu98477+DVV1+13c/NzcXQoUOxYsUK9OjR47KvS0tLQ1RU1GWf9/LygpeXV8NCExERkcu4euwAcPxARERERE2TJElQKpVQKpWujkJO4LA1euPj4+vc9/PzAwAkJSUhNjYWAPDpp59CpVIhNTUVAPDf//4XH3/8MT766CNHxSIiIiI3xbEDERGRJ+DF2IiI3JXDCr3X6pVXXkFWVhYUCgWSk5OxYsUK3Hnnna6ORURERG6KYwciIiJXEnaq0bLQS0Rkbw1eo9fdcL0ZIiJqbPi7zfHYx0RE1Jg4d41eBSQ7zOgVEADM/F3sYBzzEHk+h67R625q69Tl5eUuTkJERGQftb/TPPyzWLfG8QMRETUmzh07iP8VackTcMxD5Pka8m+8xxd6KyoqAABxcXEuTkJERGRfFRUV/5s5Q/bG8QMRETVGjhw7qFQqREZGIj8/325tRkZGQqVS2a09uhTHPESNR3Fx8VX/jff4pRusVityc3Ph7+8PSbLHgvAE1HxaEBcXh5ycHJ7e4STsc+difzsf+/zaCSFQUVGB6OhoyGQyV8dplDh+cAz+nDsX+9v52OfOxf6+ds4aO+j1ehiNRru1p1KpoFar7dYeXcqZYx53+5llHuZpLHm0Wi3i4+NRWlqKwMDAK27r8TN6ZTKZ7UrcZH8ajcbl39BNDfvcudjfzsc+vzacyetYHD84Fn/OnYv97Xzsc+dif18bZ4wd1Go1C7MexhVjHnf7mWWeK2OeK3OnPNfyQR6nCRERERERERERERF5OBZ6iYiIiIiIiIiIiDwcC71ULy8vL7zwwgvw8vJydZQmg33uXOxv52OfEzV+/Dl3Lva387HPnYv9TeRZ3O1nlnmujHmuzJ3yNCSLx1+MjYiIiIiIiIiIiKip44xeIiIiIiIiIiIiIg/HQi8RERERERERERGRh2Ohl4iIiIiIiIiIiMjDsdBLRERERERERERE5OFY6G1CtmzZgpEjRyI6OhqSJGHlypW250wmE2bOnImUlBT4+voiOjoaDzzwAHJzc+u0UVJSgvHjx0Oj0SAwMBCTJk1CZWWlk4/Ec1ypzwHgxRdfRHJyMnx9fREUFITBgwdj9+7ddbZhn1+7q/X3n02ZMgWSJOHtt9+u8zj7u2Gu1ucPPvggJEmqcxs2bFidbdjnRO6N4wfn4/jBuTh+cD6OH4jIXqxWKwBACOHiJO7HnfrEnbK4I3v2Dwu9TUhVVRU6duyIxYsXX/JcdXU19u/fj1mzZmH//v3473//ixMnTmDUqFF1ths/fjyOHj2KdevW4eeff8aWLVvw8MMPO+sQPM6V+hwAWrVqhUWLFuHw4cPYtm0bEhMTMWTIEFy4cMG2Dfv82l2tv2v98MMP2LVrF6Kjoy95jv3dMNfS58OGDUNeXp7t9tVXX9V5nn1O5N44fnA+jh+ci+MH5+P4gcjz6PV6vPPOO9iwYYOrowAADAYDHnroIUyZMgUAIEmSS/OYTCacOnUKOp0OgOsLmxaLBXq93qUZapnNZmi1WlfHuITFYnF1BAA1OYxGo/0aFNQkARA//PDDFbfZs2ePACCysrKEEEKkp6cLAGLv3r22bVavXi0kSRLnz593ZNxG4Vr6XKvVCgBi/fr1Qgj2+Y24XH+fO3dOxMTEiCNHjoiEhASxYMEC23Ps7xtTX59PmDBB3HbbbZd9DfucyLNw/OB8HD84F8cPzsfxA5H7W7x4sfD39xeDBg0S//3vf4VOp3Npnjlz5ghvb28hSZIYOHCg0Gq1Ls0zf/580bx5c9GxY0fRuXNnsXPnTiGEEBaLxWV5unXrJm655Rbx1ltviZycHCGEEFar1elZ5s2bJ9q2bSt69uwppk+fLk6fPu2yLEIIsXTpUnHvvffa7rsqR60FCxaI/v37ixEjRog333xT5Obm3nCbnNFLl6XVaiFJEgIDAwEAO3fuRGBgILp27WrbZvDgwZDJZJecLkgNZzQa8cEHHyAgIAAdO3YEwD63N6vVivvvvx/Tp09Hu3btLnme/e0YmzZtQnh4OFq3bo1HH30UxcXFtufY50SND8cPzsXxg+Nx/OAaHD8QuYe1a9fiww8/xNKlS7F+/XqMHj0aarXaJTNWf/nlF8TFxeG9997DmjVr8MYbb6CwsBC+vr4uyWMwGDBp0iQsXboUCxYswIsvvoioqCjbLGOZzLklt6qqKowbNw5Lly7Fk08+ifbt2+OHH37A6NGjYTabnT7r+fnnn8e7776L5557DgMGDMDmzZtx6623oqSkxOlZzp49i7vvvhvTp0/Hb7/9ho8++gjAxaU/nO3YsWPo168fFi9ejHvuuQdhYWH48ssvMXv27Btum4Veqpder8fMmTMxbtw4aDQaAEB+fj7Cw8PrbKdQKBAcHIz8/HxXxGwUfv75Z/j5+UGtVmPBggVYt24dQkNDAbDP7W3u3LlQKBR48skn632e/W1/w4YNw3/+8x/8/vvvmDt3LjZv3ozhw4fbTpNhnxM1Lhw/OA/HD87D8YPzcfxA5Hq1BbCVK1ciJSUF48ePR05ODt577z1s2LABmZmZAJy3RMH27dvxwgsvYPLkyThz5gz69u2L7t27IyMjA6dPn3bJ0g2nT5/Gzp078cEHH2DUqFG4/fbbMWLECERHR8NkMgFw7hIOhw8fxpEjR7Bq1Srcd999eOONNzB37lykpaXhlVdecdpSDlarFRUVFdiwYQMee+wx3HvvvXjttdfw/fffo7q6GtOmTUN5eblTstT6448/UFFRgQ8//BBjxozBe++9h9LSUsjlcqcXe/V6PVasWIHw8HBs374djzzyCJYvX46xY8ciMzPzhn+PKeyUkxoRk8mEsWPHQgiBpUuXujpOozdgwACkpaWhqKgIH374IcaOHYvdu3dfMnilG7Nv3z4sXLgQ+/fvd/n6TU3JPffcY/s6JSUFHTp0QFJSEjZt2oRBgwa5MBkR2RvHD87F8YNzcPzgGhw/ELmeTCaD2WzGjh078Pzzz+OHH37AxIkT0aFDB2RkZEChUODXX39FcnKyU/KkpqZiy5Yt8PHxsT1mtVoRGhqKs2fPonXr1k7JAdQUb2t/Jxw/fhwBAQG253799VckJCRg+/bt6Nevn1N+d9TmOXfuHEpKSmxnVQGAUqmETCbDO++8gxEjRqBHjx4OzyGTySCTyZCWloaXXnoJQM06vbGxsXjvvfdw66234s4778SIESMclqU2D1CzfvPo0aMRFhaGAQMGIDg4GPv378e///1vzJkzx2m/3//8fRMdHY2+ffsiPDwcZrMZCoUCEREROHr0aJ3vp+vBGb1UR+0faVlZWVi3bp1tNg4AREZGorCwsM72ZrMZJSUliIyMdHbURsPX1xctWrTATTfdhGXLlkGhUGDZsmUA2Of2tHXrVhQWFiI+Ph4KhQIKhQJZWVn45z//icTERADsb2do3rw5QkNDcfr0aQDsc6LGguMH5+P4wTk4fnAPHD8QOd5XX32FOXPmYOPGjbbZlgqFAm3atMH777+Pb7/9FsuWLcOaNWuwZcsWJCQk4OGHH0ZZWZlT8vj4+MDHxwdCCFsB7+abb0ZRUZEtgyNnZv45T0VFBQAgODgYw4cPx6BBg/Dwww8jKCgIGRkZyMnJwX333YfbbrsNpaWlDs/z59mxcXFxWLVqle3+mjVr8MgjjyA2NhaLFi0CYP9+qr1AX20Rs7ag2bt3b3z22WcAar6XrFYrhg8fjoEDB+Ldd991SJa/5qnNpFQqMWDAAABAr169MHToUKxatQqHDx+GJEkOvTDbn/NYrVao1WpMnDgRAwcOBADI5XIAQGlpKTp06ACVSnVD+2Ohl2xq/0g7deoU1q9fj5CQkDrP9+zZE2VlZdi3b5/tsQ0bNsBqtTr0U6Gmxmq1wmAwAGCf29P999+PQ4cOIS0tzXaLjo7G9OnTsXbtWgDsb2c4d+4ciouLERUVBYB9TtQYcPzgHjh+cAyOH9wDxw9EjpOTk4PevXtj+vTpWL16NcaPH4+77rrLdpp/586dcezYMezduxc333wz1Go1EhMTsWzZMmzbtg3Hjx93Sh6dTgfgYvFOCGH7md+4cSMAx6yJW1+eO++8EwaDAVFRUfjuu++wcuVKZGRkYNKkSTh8+DBWrlyJHTt24Oeff8bWrVudkkcIgdtuuw1dunTBc889h9GjRyMhIQGLFi3C+PHj8cgjj2Dbtm0oLy+3Wz8dPXoUffv2xeDBg/H+++8DgG0tYLVajT59+uD48ePYuXOn7TkAmDlzJtavX4/z58/b9T2rL89fC7hWqxV+fn4YOXIkYmJi8MYbbwC4WGy1p/ry1Ba2FYpLF1jYt28fOnbsCLlcfmNLftzw5dzIY1RUVIgDBw6IAwcOCADirbfeEgcOHBBZWVnCaDSKUaNGidjYWJGWliby8vJsN4PBYGtj2LBhIjU1VezevVts27ZNtGzZUowbN86FR+XertTnlZWV4plnnhE7d+4UmZmZ4o8//hB/+9vfhJeXlzhy5IitDfb5tbtSf9fnr1fNFoL93VBX6vOKigoxbdo0sXPnTpGRkSHWr18vOnfuLFq2bCn0er2tDfY5kXvj+MH5OH5wLo4fnI/jByL38cEHH4jOnTsLrVYrqqqqxL59+0RAQICYOHGiqKysFIcPHxadOnUSUVFRtteYTCah1WpF8+bNxdKlS52SZ/LkySIvL08IIYTVarVtP3z4cDFhwgRhNpvrPO7oPA899JA4f/68EEKIkydPiujoaHH8+HEhRE3/CCFEy5YtxbRp05ySp/b9qqysFCtXrhRPP/20ePfdd4VOpxNCCDF//nzRvXt3YTQa7ZLj6NGj4rbbbhN33HGHuO+++0RCQoKt7drj37Fjhxg4cKB44IEH6rx2+/btIi4uTmzevNkuWa6Wx2KxXLK91WoV77zzjmjdurX46aefhBBC7N6925bdEXlqx8b15TEYDCIqKkqsWrXK9ti5c+eua98s9DYhGzduFAAuuU2YMEFkZGTU+xwAsXHjRlsbxcXFYty4ccLPz09oNBrxt7/9TVRUVLjuoNzclfpcp9OJ0aNHi+joaKFSqURUVJQYNWqU2LNnT5022OfX7kr9XZ/6/lBjfzfMlfq8urpaDBkyRISFhQmlUikSEhLE5MmTRX5+fp022OdE7o3jB+fj+MG5OH5wPo4fiNyD1WoVo0aNEuPHj7fdF0KIVatWieDgYLF8+XIhhBDvvfee8PLyEs8//7zttVu3bhUdOnQQp0+fdkqekJAQ8cknn9iKZLUFudmzZ4vmzZvbLcP15ElPTxft27cXv/zyi+21O3fuFO3atRPbtm1zSp7g4GDxySefCLPZXOc5IWr6asKECWLs2LH1FhmvR3l5uZg1a5Y4cOCA2LNnj0hOThZPPfWUbX+13nrrLdGuXTvx5ptv2h775ptvRHJysiguLrZLlqvlqe2TWrV9c+rUKTF+/HjRvXt3cccddwhJksTu3budnkcIITZs2CBiYmJEdXW1KCoqEhMnThQajUZkZGQ0eN8s9BIRERERERERNSG1Bb8HH3xQ9OnTRwhRtwA1cuRI0bdvX1FcXCy0Wq2YN2+ekMvlYuDAgeKRRx4R4eHh4oknnhA6nc4uM2mvJc+AAQNETk5Ondd9+eWXIiYmRhw9evSGMzQ0T//+/UVRUZHIysoSd911l4iPjxevvfaaeP7550VERISYNGmSqKysdFqev/ZPXl6eyM/PF6+99ppITEwUv/32m12y1L7f1dXVQoia2ahvvvmm0Gg04tSpU7bHhBCiqKhIzJ07V0iSJG699Vbx2GOPicDAQDFjxgxhMpns8r1zLXnqK66WlpaKoUOHCkmSxPDhw8WxY8duOMv15lmyZIkYMWKEbbvevXvbZog3FNfoJSIiIiIiIiJqQmrXRh05ciTS09OxZ88eyOVy23rvc+bMwdatW3HixAloNBpMmzYN3377Lfr27QutVouPP/4Y7777LtRqte2CV47Os2nTJpw7dw7AxbVXJUlCYmIiQkNDbzhDQ/Ns3rwZJ0+eRHx8PF544QUMGTIE27Ztw+bNm7Fs2TJ89NFH8PX1dVqeP/eP2WzGxo0bbRdtXbJkCf7v//7PLllq329vb29YrVaoVCqMGjUKnTp1wlNPPQUAtguKhYSEYMaMGfj666+RkpKC3NxcfPbZZ5g7dy4UCoVdvneuJc9f1+A9deoU+vXrh6NHj2LNmjX49ddfkZycfMNZrjfPjz/+iNWrV2PJkiVYvnw5tm3bhtatW1/f/oW4kRV+iYiIiIiIiIjI3eh0OuTm5iImJgZqtRpWq/WSi18dO3YMTz/9NLy8vLBy5UoANUVUuVyOTp064dZbb8Wrr77qNnlGjhyJV155BUKIGy4S2iPPLbfcgtdee822fXl5OTQajcvy/Pn9unDhArZv347bb7/dIVn+zGq1YsWKFZgyZQpWrFiBYcOGwWQywWKxQK1WN3j/jshjNptthVer1YoffvgBY8aMcWme2pLs66+/jujoaEyePPm68vwZZ/QSERERERERETUir732Gtq1a4c777wTN998Mw4fPgyZTGabCVurTZs2uPPOO7F//3589NFHAGpmGxYWFkKn0yE+Pt6t8sTFxdXZ/nrnLtorT0JCAoCaQh6A6y7y2vv9slqtCAsLu64i77VmqSWEgEwmw4ABAzBy5Eg8++yzOHfuHCZMmIDPPvvssq9zdp4HHngAn376KUwmE2Qy2XUXee2Z5+OPP4ZSqcTs2bPtUuQFAIVdWiEiIiIiIiIiIpeqqqrCQw89hEOHDmHJkiWoqqrCkiVLMGXKFGzfvr3OKeO1s2JHjRqFs2fP4rHHHoPJZELv3r2xefNmWCwWdOvWzS3z1M7mbeisXkfludJsTk/J05Asf1b7HkRGRuKuu+7C6NGjER8fj6SkJLzwwguXfZ2r8iiVSrfK8+dt7IGFXiIiIiIiIiKiRuD48eNIS0vD8uXL0aNHDwDA2bNnsXv3btvp5bX/ry0uhYWF4bXXXoNOp8PSpUsxf/586HQ6LFq0CKmpqczTRPI0JMtfWSwWrF+/HlOmTEF8fDwWLVqEW2+99bqzMM/149INREREREREREQe6s/LF5jNZpw4cQIhISG2x3777TfExcVh69atAC6d7Vn7+rfeegu7du3CV199hfPnz2P06NHM08jz3GiWWhaLBT/88AMmT56MzMzM6y5iMs+NY6GXiIiIiIiIiMjD7Ny5E0Dd075jY2PRv39/3HzzzZg0aRI0Gg0uXLiAkydP4u6778Y999yD8vLyOu3Uvt5qtcLHx+e6l2tgHs/JY68stTlUKhUWLVqEl19+ucFZmMfOBBEREREREREReYS9e/eKzp07C0mSxK+//iqEEMJkMtme12q1YtOmTaJPnz5i1qxZQgghzGazOHr0qJAkSWzYsEEIIYTVamWeJpbHnbIwj2NwRi8RERERERERkQfYvn07pk6diri4OAwfPhxvv/02AEChUNhOM9doNAgJCcGZM2cwefJk22uTk5MRHR2Nbdu2AbDPBaCYx3PyuFMW5nEcFnqJiIiIiIiIiDxAUlISUlNT8e9//xv33nsvcnNzsWTJEgB11xPV6XQIDg5GRkYGAEAul2PTpk0IDAy06/qgzOM5edwpC/M4jsLVAYiIiIiIiIiI6MqEEIiMjMS///1veHt7IygoCNu3b8dHH32EsWPHIjQ0FGazGQqFAhqNBi1btsQDDzyAxx9/HMXFxfj4449x9913o02bNszTxPK4UxbmcSxJ/LksTUREREREREREbs1qtUImk2Ht2rV44YUX0KdPH8ybNw9CCNtp40eOHMG8efNw4cIF6HQ6PP/88xg0aBDzNPE87pSFeeyPhV4iIiIiIiIiIjdTW3CqZbFYIJfLbaeRS5IEnU6HuXPnYsWKFfjqq6/QqVMnGI1GqFQq2+vKy8uh0WiYpwnlcacszONcXKOXiIiIiIiIiMjF5s6di3/84x947733oNfrbYUok8kEoGYtUKCmCCVJEiwWC7y9vTFy5EjEx8dj3rx5yMrKwr333outW7fa2r3eQhTzeE4ed8rCPK7FQi8RERERERERkYucOnUKHTp0wGeffYaCggI899xz+L//+z/s2rULAKBUKgEAK1asQEJCAn788UcAF4tTXbp0weDBg/HNN9+gRYsWSE9PR2JiIvM0gTzulIV53IQgIiIiIiIiIiKXePfdd0X37t2FXq8XQghRUFAgUlNTxZ133imOHTsmzGazGDx4sAgPDxcLFiwQRqPR9lqj0Si+//57ERISIlq1aiXWrFnDPE0ojztlYR73wBm9REREREREREQuYLFYcOjQIYSHh0OhUAAAwsPDMWvWLJw/fx5ffPEFZDIZxowZg7S0NEydOtU2CxEAdDodlixZgn/84x84ceIEhg4dyjxNJI87ZWEe96FwdQAiIiIiIiIioqZAq9UiICDAdl8ul8NkMkGn00EIYbso1OjRo7Fp0yZs2bIFBw8exJQpUy5py2q1QqPRYM2aNbZCFvM03jzulIV53Bdn9BIREREREREROdD69evRrl07LFq0CHq9HgBgNpsBAJMmTcLGjRuRlpYGuVxue3zs2LHIzc3FsWPH6m2z9oJS11OIYh7PyeNOWZjH/bHQS0RERERERETkAGazGR999BGefPJJmM1mvP/++zhx4gSAi0Wkrl27on///pgxYwaAixeC6t27N+RyOdLT0wEAQgjmaUJ53CkL83gOFnqJiIiIiIiIiBzAYDDAYDDgvvvuw759+2CxWLBkyRJUVFTYtlGr1Xj55Zexfft2LFq0yFZ00mq18PHxQUhICABAkiTmaUJ53CkL83gQp176jYiIiIiIiIioCcnOzhYVFRVCCCG+++47oVAoxLp16y7Z7o033hChoaFi0qRJYtOmTWLatGkiISFBHDlyhHmaaB53ysI8nkESohHNTyYiIiIiIiIickNWqxUymQy9evWCn58fPvvsM0RERNTZZvHixfj2229RWloKIQTef/999OzZk3maeB53ysI87o2FXiIiIiIiIiIiB7NYLJDL5Thy5Ag6dOiATz75BPfffz9kMhlMJhOUSqXt1PLMzEw0a9aMeZjH7bIwj3tjoZeIiIiIiIiIyAlqC1KTJk3Ctm3bsGbNGuj1eqxfvx4TJ06Er68v8zCP22dhHvfFi7ERERERERERETlB7UWf3nvvPeTm5mLMmDFo164ddu3axTzM4zFZmMd9KVwdgIiIiIiIiIioKZDJZKisrMRPP/0Eo9EIAFi9ejWGDh3KPMzjMVmYx32x0EtERERERERE5CQbNmzA/fffj9dffx0zZsxwdRzm8aA87pSFedwT1+glIiIiIiIiInISrVYLHx8fKJVKV0cBwDxX40553CkLwDzuiIVeIiIiIiIiIiIiIg/Hi7EREREREREREREReTgWeomIiIiIiIiIiIg8HAu9RERERERERERERB6OhV4iIiIiIiIiIiIiD8dCLxEREREREREREZGHY6GXiIiIiIiIiIiIyMOx0EtERERERERERETk4VjoJSIiIiIiIiIiug5CCDz88MMIDg6GJEkIDAzE1KlTr/n1mZmZkCQJaWlpdt22oRITE/H222/bvV1yLhZ6iYiIiIiIiIiIrsOaNWuwfPly/Pzzz8jLy8PJkyfxyiuvXPPr4+LikJeXh/bt2zsw5fWbM2cO5HI55s2bd8lz/fv3b1BRmxyPhV4iIiIiIiIiIqLrcObMGURFRaFXr16IjIxEeHg4/P39r/n1crkckZGRUCgUDkx5/T7++GPMmDEDH3/8sauj0DVgoZeIiIiIiIiIiKiBHnzwQfz9739HdnY2JElCYmLiJbNcExMT8frrr2PixInw9/dHfHw8PvjgA9vzf12OobS0FOPHj0dYWBi8vb3RsmVLfPLJJ3X2e/bsWQwYMAA+Pj7o2LEjdu7cWef5bdu2oU+fPvD29kZcXByefPJJVFVV2Z4vLCzEyJEj4e3tjWbNmuGLL76o9/g2b94MnU6Hl19+GeXl5dixY0edY9+8eTMWLlwISZIgSRIyMzOvsyfJXljoJSIiIiIiIiIiaqCFCxfi5ZdfRmxsLPLy8rB37956t5s/fz66du2KAwcO4LHHHsOjjz6KEydO1LvtrFmzkJ6ejtWrV+PYsWNYunQpQkND62zz3HPPYdq0aUhLS0OrVq0wbtw4mM1mADUzjIcNG4YxY8bg0KFDWLFiBbZt24YnnnjC9voHH3wQOTk52LhxI7777jssWbIEhYWFl2RZtmwZxo0bB6VSiXHjxmHZsmV1jr1nz56YPHky8vLykJeXh7i4uAb3IdmXe84LJyIiIiIiIiIicmMBAQHw9/e3Lb9wOSNGjMBjjz0GAJg5cyYWLFiAjRs3onXr1pdsm52djdTUVHTt2hVAzYzgv5o2bRpuueUWAMBLL72Edu3a4fTp00hOTsacOXMwfvx426zili1b4p133kG/fv2wdOlSZGdnY/Xq1dizZw+6desGoKag26ZNmzr7KC8vx3fffWebLXzfffehT58+WLhwIfz8/BAQEACVSgUfH58rHjs5F2f0EhEREREREREROUiHDh1sX0uShMjIyHpn0ALAo48+iq+//hqdOnXCjBkz6iyXUF97UVFRAGBr7+DBg1i+fDn8/Pxst6FDh8JqtSIjIwPHjh2DQqFAly5dbG0kJycjMDCwzj6++uorJCUloWPHjgCATp06ISEhAStWrLi+TiCnYKGXiIiIiIiIiIjIQZRKZZ37kiTBarXWu+3w4cORlZWFp556Crm5uRg0aBCmTZt22fYkSQIAW3uVlZV45JFHkJaWZrsdPHgQp06dQlJS0jVnXrZsGY4ePQqFQmG7paen86Jsbo5LNxAREREREREREbmJsLAwTJgwARMmTECfPn0wffp0vPnmm9f02s6dOyM9PR0tWrSo9/nk5GSYzWbs27fPtnTDiRMnUFZWZtvm8OHD+OOPP7Bp0yYEBwfbHi8pKUH//v1x/PhxJCcnQ6VSwWKxXP+Bkt2x0EtEREREREREROQGZs+ejS5duqBdu3YwGAz4+eefL1k/90pmzpyJm266CU888QQeeugh+Pr6Ij09HevWrcOiRYvQunVrDBs2DI888giWLl0KhUKBqVOnwtvb29bGsmXL0L17d/Tt2/eS9rt164Zly5Zh3rx5SExMxO7du5GZmQk/Pz8EBwdDJuPiAa7E3iciIiIiIiIiInIDKpUKzzzzDDp06IC+fftCLpfj66+/vubXd+jQAZs3b8bJkyfRp08fpKamYvbs2YiOjrZt88knnyA6Ohr9+vXDHXfcgYcffhjh4eEAAKPRiM8//xxjxoypt/0xY8bgP//5D0wmE6ZNmwa5XI62bdsiLCwM2dnZN3bwdMMkIYRwdQgiIiIiIiIiIiIiun6c0UtERERERERERETk4VjoJSIiIiIiIiIiIvJwLPQSEREREREREREReTgWeomIiIiIiIiIiIg8HAu9RERERERERERERB6OhV4iIiIiIiIiIiIiD8dCLxEREREREREREZGHY6GXiIiIiIiIiIiIyMOx0EtERERERERERETk4VjoJSIiIiIiIiIiIvJwLPQSEREREREREREReTgWeomIiIiIiIiIiIg83P8DKKqJfXfl3yIAAAAASUVORK5CYII=\n", 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    " + "0.01 3.624931\n", + "0.90 30.226389\n", + "Name: duration, dtype: float64" ] }, + "execution_count": 32, "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tile_status_df.duration.quantile([0.01, 0.9])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Export to geoJSON\n", + "# tiles_gdf = gpd.read_file(run_params[\"tiles-uri\"])\n", + "tiles_gdf = gpd.read_file('https://data.dea.ga.gov.au/derivative/dea_coastlines/supplementary/albers_grids/ga_summary_grid_c3_48km_coastal_clipped.geojson')\n", + "tiles_gdf = tiles_gdf.merge(tile_status_df, on=\"id\")\n", + "tiles_gdf.to_file(f\"coastlines_tile_status_{run_params['result-version']}.geojson\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -529,7 +437,7 @@ "tiles_gdf.plot(column='duration', ax=axd['B'], cmap='magma', vmin=qmin, vmax=qmax, legend=True, edgecolor='black', linewidth=0.1)\n", "axd['C'].set_title('Duration histogram')\n", "qmin, qmax = tile_status_df.duration.quantile([0.001, 0.999])\n", - "tile_status_df.duration.plot.hist(bins=range(int(qmin), int(qmax), int((qmax - qmin) / 50)), xlim=(qmin, qmax), ax=axd['C'])\n", + "tile_status_df.duration.plot.hist(bins=range(int(qmin), int(qmax), int((qmax - qmin) / 40)), xlim=(qmin, qmax), ax=axd['C'])\n", "axd['D'].set_title('Duration over time')\n", "tile_status_df.set_index('finishedAt').sort_index().rolling('30min', min_periods=10).median().plot(y='duration', ax=axd['D'])\n", "plt.gcf().savefig(f\"coastlines_tile_status_{run_params['result-version']}.png\", dpi=250)" @@ -537,7 +445,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "***\n", "\n", diff --git a/notebooks/DEACoastlines_validation.ipynb b/notebooks/DEACoastlines_validation.ipynb index 2f34a17..fd0a30a 100644 --- a/notebooks/DEACoastlines_validation.ipynb +++ b/notebooks/DEACoastlines_validation.ipynb @@ -1,1088 +1,11021 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DEA Coastlines validation\n", - "\n", - "To do:\n", - "* [X] Change output CRS to Australian Albers\n", - "* [X] Discard validation sides with multiple intersects?\n", - "* [X] Split analysis code into:\n", - " * Aggregate multiple profiles and export into single file\n", - " * Analyse and plot single file\n", - "* [X] Add extraction of environmental data for each profile line" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load modules/functions\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext line_profiler\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import os\n", - "import sys\n", - "import glob\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from shapely.geometry import box\n", - "import multiprocessing as mp\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.metrics import mean_absolute_error\n", - "from sklearn.metrics import r2_score\n", - "import statsmodels.api as sm\n", - "lowess = sm.nonparametric.lowess\n", - "\n", - "sys.path.append('/g/data/r78/DEACoastlines/')\n", - "import deacoastlines_validation as deacl_val\n", - "import deacoastlines_statistics as deacl_stats\n", - "\n", - "\n", - "rename_dict = {\n", - " 'Beachrock undiff': 'rocky',\n", - " 'Beachrock undiff dominant': 'rocky',\n", - " 'Boulder or shingle-grade beach undiff': 'rocky',\n", - " 'Boulder groyne or breakwater undiff': 'rocky',\n", - " 'Flat boulder deposit (rock) undiff': 'rocky',\n", - " 'Hard bedrock shore': 'rocky',\n", - " 'Hard bedrock shore inferred': 'rocky',\n", - " 'Hard rock cliff (>5m)': 'rocky',\n", - " 'Hard rocky shore platform': 'rocky',\n", - " 'Rocky shore platform (undiff)': 'rocky',\n", - " 'Sloping boulder deposit (rock) undiff': 'rocky',\n", - " 'Sloping hard rock shore': 'rocky',\n", - " 'Sloping soft `bedrock¿ shore': 'rocky',\n", - " 'Sloping soft \\u2018bedrock\\u2019 shore': 'rocky',\n", - " 'Soft `bedrock¿ shore inferred': 'rocky',\n", - " 'Soft `bedrock¿ shore platform': 'rocky',\n", - " 'Beach (sediment type undiff)': 'sandy',\n", - " 'Fine-medium sand beach': 'sandy',\n", - " 'Fine-medium sandy tidal flats': 'sandy',\n", - " 'Mixed sand and shell beach': 'sandy',\n", - " 'Mixed sandy shore undiff': 'sandy',\n", - " 'Perched sandy beach (undiff)': 'sandy',\n", - " 'Sandy beach undiff': 'sandy',\n", - " 'Sandy beach with cobbles/pebbles (rock)': 'sandy',\n", - " 'Sandy shore undiff': 'sandy',\n", - " 'Sandy tidal flats': 'sandy',\n", - " 'Sandy tidal flats with coarse stony debris': 'sandy',\n", - " 'Sandy tidal flats, no bedrock protruding': 'sandy',\n", - " 'Sloping coffee rock deposit': 'rocky',\n", - " 'Muddy tidal flats': 'muddy',\n", - " 'Tidal flats (sediment undiff)': 'muddy',\n", - " 'Artificial shoreline undiff': 'rocky',\n", - " 'Artificial boulder structures undiff': 'rocky',\n", - " 'Boulder revetment': 'rocky',\n", - " 'Boulder seawall': 'rocky',\n", - " 'Concrete sea wall': 'rocky',\n", - " 'Piles (Jetty)': 'rocky',\n", - " 'Coarse sand beach': 'sandy'\n", - "}\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# to_vector(output_stats, fname='test6.shp', x='0_x', y='0_y', crs='EPSG:3577')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pre-processing\n", - "The following cells standardise varied validation coastal monitoring datasets into a consistent format. \n", - "This allows the subsequent validation analysis to be applied to all validation data without requiring custom code for each.\n", - "\n", - "The standardised output format includes the following columns:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* `id`: A unique string constructed from the beach name, section name and profile name\n", - "* `date`: A string providing the date of the coastal monitoring survey in format `YYYY-MM-DD`\n", - "* `beach`: A string giving the name of the beach or site\n", - "* `section`: A string giving section of a beach or site name if available\n", - "* `profile`: A string giving the name of the individual coastal monitoring profile\n", - "* `name`: A string giving a unique name for each overall dataset, e.g. `cgc` for City of Gold Coast\n", - "* `source`: A standardised string giving the coastal monitoring method used to obtain the data (valid options include `emery/levelling`, `gps`, `aerial photogrammetry`, `total station`, `terrestrial laser scanning`, `satellite`, `lidar`)\n", - "* `foredune_dist`: An optional floating point number giving the distance along the profile to the top of the foredune. This is used to exclude spurious coastline detections behind the shoreline, e.g. coastal lagoons). This only applies to datasets that contain elevation measurements.\n", - "* `slope`: An optional float giving the slope of the intertidal zone at the time of the coastal monitoring survey, calculated by running a slope regression on coastline measurements obtained within a buffer distance of the selected tide datum). This only applies to datasets that contain elevation measurements.\n", - "* `start_x`, `start_y`: Australian Albers (EPSG:3577) x and y coordinates defining the starting point of the coastal monitoring survey\n", - "* `end_x`, `end_y`: Australian Albers (EPSG:3577) x and y coordinates defining the end point of the coastal monitoring survey\n", - "* `0_dist`: The along-profile chainage (e.g. distance in metres along the profile from the start coordinates) of the selected tide datum shoreline. This is used to compare against the distance to the intersection with satellite-derived shorelines.\n", - "* `0_x`, `0_y`: Australian Albers (EPSG:3577) x and y coordinates defining the location of the selected tide datum shoreline." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sunshine Coast" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sites = ['8.Pumicestone - Bribie', '1.Coolum-Sunshine', '5.Dicky Beach', \n", - " '7.Kings Beach', '3.Mooloolaba', '2.Mudjimba-Yaroomba', '6.Shelly Beach',\n", - " '4.South Mooloolaba']\n", - "\n", - "for site in sites:\n", - " deacl_val.preprocess_sunshinecoast(site, datum=0, overwrite=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Moruya" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deacl_val.preprocess_moruya(fname_out='output_data/moruya.csv', datum=0, overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Victoria/Deakin\n", - "* [X] Renovated" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deacl_val.preprocess_vicdeakin(fname='input_data/vicdeakin/z_data_10cm_VIC.csv',\n", - " datum=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### WRL Narrabeen \n", - "* [X] Renovated" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deacl_val.preprocess_narrabeen(fname='input_data/wrl/Narrabeen_Profiles_2019.csv',\n", - " datum=0,\n", - " overwrite=True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NSW Beach Profile Database\n", - "* [X] Renovated" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with mp.Pool(mp.cpu_count()) as pool:\n", - " for fname in glob.glob('input_data/nswbpd/*.csv'):\n", - " pool.apply_async(deacl_val.preprocess_nswbpd, \n", - " [fname, 0, False])\n", - "\n", - " pool.close()\n", - " pool.join()\n", - " \n", - "# fname = '/g/data/r78/DEACoastlines/validation/input_data/nswbpd/photogrammetry_Xsections_Lennox Head.csv'\n", - "# profiles_df, intercept_df = deacl_val.preprocess_nswbpd(fname, 0, True)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### City of Gold Coast\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sites = ['BILINGA', 'BROADBEACH', 'BURLEIGH HEADS', 'COOLANGATTA', 'CURRUMBIN',\n", - " 'DURANABH', 'FINGAL', 'GREENMOUNT HILL', 'KINGSCLIFF', 'KIRRA',\n", - " 'MAIN BEACH', 'MERMAID BEACH', 'MIAMI', 'Main Beach Cross Sections',\n", - " 'NARROWNECK', 'NO*TlH KIRRA', 'PALM BEACH', 'POINT DANGER', \n", - " 'RAINBOW BAY', 'SEAWAY CENTRE LINE', 'SNAPPER ROCKS', \n", - " 'SOUTH STRADBROKE', 'SURFERS PARADISE', 'THE SPIT', 'TUGUN', \n", - " 'TWEED RIVER ENTRANCE']\n", - "# sites=['MAIN BEACH']\n", - "\n", - "with mp.Pool(mp.cpu_count()) as pool:\n", - " for site in sites:\n", - " pool.apply_async(deacl_val.preprocess_cgc, \n", - " [site, 0, False])\n", - "\n", - " pool.close()\n", - " pool.join()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TASMARC\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# List of sites to iterate over\n", - "sites = [i.split('/')[2] for i in glob.glob('input_data/tasmarc/*/')]\n", - "# sites = sites[2:]\n", - "\n", - "with mp.Pool(mp.cpu_count()) as pool:\n", - " for site in sites:\n", - " pool.apply_async(deacl_val.preprocess_tasmarc, \n", - " [site, 0, False])\n", - "\n", - " pool.close()\n", - " pool.join()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### WA DoT\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "regions_gdf = gpd.read_file('input_data/WA_tertiaryCC.shp').to_crs('EPSG:3577').iloc[::-1]\n", - "regions_gdf.index = (regions_gdf.LABEL\n", - " .str.replace(' - ', '_')\n", - " .str.replace('-', '')\n", - " .str.replace(' ', '')\n", - " .str.replace('/', '')\n", - " .str.replace(',', '')\n", - " .str.replace('_', '-')\n", - " .str.lower())\n", - "regions_gdf.head(1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deacl_val.coastal_transects_parallel(\n", - " regions_gdf,\n", - " interval=200,\n", - " transect_length=500,\n", - " simplify_length=200,\n", - " transect_buffer=50,\n", - " overwrite=False,\n", - " output_path='input_data/coastal_transects_wadot.geojson')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with mp.Pool(mp.cpu_count()-1) as pool:\n", - " for i, _ in regions_gdf.iterrows():\n", - " pool.apply_async(deacl_val.preprocess_wadot, \n", - " [regions_gdf.loc[[i]], False])\n", - "\n", - " pool.close()\n", - " pool.join()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DaSilva 2021" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deacl_val.preprocess_dasilva2021()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### WA DoT - Stirling" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deacl_val.preprocess_stirling(fname_out='output_data/stirling_stirling.csv',\n", - " datum=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SA Department of Environment and Water" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sites = ['200048',\n", - " '320010',\n", - " '320011',\n", - " '330005',\n", - " '330014',\n", - " '425001',\n", - " '425002',\n", - " '440004',\n", - " '525019',\n", - " '525022',\n", - " '525023',\n", - " '530009',\n", - " '545001',\n", - " '555007',\n", - " '555012',\n", - " '815013']\n", - "\n", - "fname = f'input_data/sadew/{sites[15]}.CSV'\n", - "print(fname)\n", - "profile_df = deacl_val.preprocess_sadew(fname, datum=0, overwrite=True)\n", - "profile_df.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from pathlib import Path\n", - "# for fname in fname_list:\n", - "# preprocess_sadew(fname, datum=0, overwrite=False)\n", - "\n", - "fname_list = glob.glob('input_data/sadew/*.CSV') \n", - "\n", - "with mp.Pool(mp.cpu_count()-1) as pool:\n", - " for fname in fname_list:\n", - " pool.apply_async(deacl_val.preprocess_sadew, \n", - " [fname, 0, True])\n", - "\n", - " pool.close()\n", - " pool.join()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fellowes et al. 20221" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import glob\n", - "import re\n", - "import os.path\n", - "import numpy as np\n", - "import pandas as pd\n", - "import geopandas as gpd\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as colors\n", - "from pathlib import Path\n", - "from io import StringIO\n", - "from pyproj import Transformer\n", - "from itertools import takewhile\n", - "from scipy import stats\n", - "import multiprocessing as mp\n", - "from sklearn.metrics import r2_score\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.metrics import mean_absolute_error\n", - "from shapely.geometry import box, Point, LineString\n", - "pd.read_excel('input_data/fellowes2021/Fellowes_et_al_2021_SUPP_Estuarine_Beach_Shorelines_V2.xlsx', sheet_name=0, header=1).head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "coords = pd.read_excel('input_data/fellowes2021/Fellowes_et_al_2021_SUPP_Estuarine_Beach_Shorelines_V2.xlsx',\n", - " sheet_name='Profile Locations',\n", - " header=1,\n", - " names=['estuary', 'beach', 'profile', 'start_y', 'start_x', 'end_y', 'end_x' ]).drop('estuary', axis=1)\n", - "coords['beach'] = coords.beach.str.replace(\" \", \"\").str.replace(\"(\", \"\").str.replace(\")\", \"\").str.replace('Fishermans','Frenchmans').str.lower()\n", - "coords['section'] = 'all'\n", - "coords['name'] = 'fellowes2021'\n", - "coords['source'] = 'aerial photogrammetry'\n", - "coords['slope'] = np.nan\n", - "coords['id'] = (coords.beach + '_' + coords.section + '_' + coords.profile)\n", - "\n", - "# Reproject coords to Albers and create geodataframe\n", - "trans = Transformer.from_crs('EPSG:4326', 'EPSG:3577', always_xy=True)\n", - "coords['start_x'], coords['start_y'] = trans.transform(coords.start_x.values,\n", - " coords.start_y.values)\n", - "coords['end_x'], coords['end_y'] = trans.transform(coords.end_x.values,\n", - " coords.end_y.values)\n", - "coords" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fellowes_data = pd.read_excel('input_data/fellowes2021/Fellowes_et_al_2021_SUPP_Estuarine_Beach_Shorelines_V2.xlsx', None)\n", - "beach_list = list(fellowes_data.keys())[1:]\n", - "\n", - "for name in beach_list:\n", - "\n", - " beach = name.split(' - ')[1].replace(\" \", \"\").replace(\"(\", \"\").replace(\")\", \"\").lower()\n", - " fname_out = f'output_data/fellowes2021_{beach}.csv'\n", - " print(f'Processing {beach:<80}', end='\\r')\n", - "\n", - " # Load data and convert to long format\n", - " wide_df = fellowes_data[name]\n", - " profiles_df = pd.melt(wide_df, \n", - " id_vars='Date', \n", - " var_name='profile', \n", - " value_name='0_dist').rename({'Date': 'date'}, axis=1)\n", - " profiles_df['date'] = pd.to_datetime(profiles_df.date, yearfirst=True)\n", - " profiles_df['id'] = (f'{beach}_all_' + profiles_df.profile)\n", - "\n", - " # Remove negative distances\n", - " profiles_df = profiles_df.loc[profiles_df['0_dist'] >= 0]\n", - "\n", - " # Restrict to post 1987\n", - " profiles_df = profiles_df[(profiles_df.date.dt.year > 1987)]\n", - "\n", - " # Merge profile coordinate data into transect data\n", - " profiles_df = profiles_df.merge(coords, on=['id', 'profile'])\n", - "\n", - " # Add coordinates at supplied distance along transects\n", - " profiles_df[['0_x', '0_y']] = profiles_df.apply(\n", - " lambda x: pd.Series(deacl_val.dist_along_transect(x['0_dist'], \n", - " x.start_x, \n", - " x.start_y,\n", - " x.end_x,\n", - " x.end_y)), axis=1)\n", - "\n", - " # Keep required columns\n", - " shoreline_df = profiles_df[['id', 'date', 'beach', \n", - " 'section', 'profile', 'name',\n", - " 'source', 'slope', 'start_x', \n", - " 'start_y', 'end_x', 'end_y', \n", - " '0_dist', '0_x', '0_y']]\n", - "\n", - " shoreline_df.to_csv(fname_out, index=False)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conduct DEA Coastlines validation\n", - "This section compares annual shorelines and rates of change derived from DEA Coastlines with the standardised validation datasets generated in the previous pre-processing step. \n", - "\n", - "### Run comparison and load outputs\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import random \n", - "val_paths = glob.glob('output_data/v1.1.0*.csv')\n", - "random.shuffle(val_paths)\n", - "deacl_path = '/g/data/r78/DEACoastlines/releases/DEACoastlines_v1.0.0/Shapefile/DEACoastlines_annualcoastlines_v1.0.0.shp'\n", - "deacl_path = '/g/data/r78/DEACoastlines/releases/DEACoastlines_v1.1.0/Shapefile/DEACoastlines_annualshorelines_v1.1.0.shp'\n", - "\n", - "prefix='v1.1.0'\n", - "\n", - "# # Parallelised\n", - "# with mp.Pool(6) as pool:\n", - "# for val_path in val_paths:\n", - " \n", - "# # Run analysis and close resulting figure\n", - "# pool.apply_async(deacl_val.deacl_validation, \n", - "# [val_path, deacl_path, 0, prefix, False])\n", - "\n", - "# pool.close()\n", - "# pool.join()\n", - " \n", - "# Non-parallel (for testing)\n", - "for val_path in val_paths:\n", - " try:\n", - " deacl_val.deacl_validation(val_path, deacl_path, 0, prefix, True)\n", - " except:\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load all results into a single file\n", - "print('Combining data')\n", - "stats_list = glob.glob(f'{prefix}_*.csv')\n", - "stats_df = pd.concat([pd.read_csv(csv) for csv in stats_list])\n", - "\n", - "# Rename smartline categories to smaller subset\n", - "stats_df['smartline'] = stats_df.smartline.replace(rename_dict)\n", - "\n", - "# Export to file\n", - "# stats_df.to_csv('deacl_results.csv', index=False)\n", - "\n", - "# Run stats\n", - "deacl_val.deacl_val_stats(stats_df.val_dist,\n", - " stats_df.deacl_dist,\n", - " n=stats_df.n,\n", - " remove_bias=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Export evaluation vector\n", - "output_name = prefix\n", - "export_eval(stats_df.set_index('id'), output_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Annual shoreline validation results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read in results\n", - "stats_df = pd.read_csv('deacl_results.csv')\n", - "stats_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Greater than Landsat frequency" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Stats by substrate, no bias correction\n", - "by_smartline = stats_df.query(\"n >= 22\").groupby('smartline').apply(\n", - " lambda x: deacl_val.deacl_val_stats(x.val_dist, x.deacl_dist, x.n))\n", - "by_smartline_nobias = stats_df.query(\"n >= 22\").groupby('smartline').apply(\n", - " lambda x: deacl_val.deacl_val_stats(x.val_dist, x.deacl_dist, x.n, True))\n", - "\n", - "out = deacl_val.rse_tableformat(by_smartline, by_smartline_nobias, 'smartline')\n", - "out" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Less than Landsat frequency" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Stats by substrate, no bias correction\n", - "by_smartline = stats_df.query(\"n < 22\").groupby('smartline').apply(\n", - " lambda x: deacl_val.deacl_val_stats(x.val_dist, x.deacl_dist, x.n))\n", - "by_smartline_nobias = stats_df.query(\"n < 22\").groupby('smartline').apply(\n", - " lambda x: deacl_val.deacl_val_stats(x.val_dist, x.deacl_dist, x.n, True))\n", - "\n", - "out = deacl_val.rse_tableformat(by_smartline, by_smartline_nobias, 'smartline')\n", - "out" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Rates of change validation results\n", - "\n", - "#### All data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Filter to sites with long temporal record (at least 10 years of data)\n", - "long_temporal = stats_df.groupby(['id']).filter(lambda x: len(x) >= 10).set_index('id')\n", - "\n", - "# Compute rates of change for both validation and DEAC data\n", - "deacl_rates = long_temporal.groupby(['id']).apply(lambda x: deacl_stats.change_regress(\n", - " y_vals=x.deacl_dist, x_vals=x.year, x_labels=x.year))\n", - "val_rates = long_temporal.groupby(['id']).apply(lambda x: deacl_stats.change_regress(\n", - " y_vals=x.val_dist, x_vals=x.year, x_labels=x.year))\n", - "\n", - "# Combine rates of change\n", - "slope_df = pd.merge(val_rates, \n", - " deacl_rates, \n", - " left_index=True, \n", - " right_index=True, \n", - " suffixes=('_val', '_deacl'))\n", - "\n", - "deacl_val.deacl_val_stats(val_dist=slope_df.slope_val, deacl_dist=slope_df.slope_deacl)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Significant results only" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "slope_df_sig = slope_df.loc[(slope_df.pvalue_deacl <= 0.01) | (slope_df.pvalue_val <= 0.01)]\n", - "deacl_val.deacl_val_stats(val_dist=slope_df_sig.slope_val, deacl_dist=slope_df_sig.slope_deacl)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Validation dataset stats\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Export validation sites as shapefile and convex hull" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# stats_gdf = gpd.GeoDataFrame(data=stats_df,\n", - "# geometry=gpd.points_from_xy(\n", - "# x=stats_df.lon,\n", - "# y=stats_df.lat,\n", - "# crs='EPSG:4326')).to_crs('EPSG:3577')\n", - "\n", - "# stats_gdf.to_file('../bishoptaylor_2020/Validation_extent/validation_points.shp')\n", - "\n", - "# # Load and reverse buffer Australian boundary\n", - "# aus_inside = (gpd.read_file('/g/data/r78/rt1527/shapefiles/australia/australia/cstauscd_r.shp')\n", - "# .query(\"FEAT_CODE=='mainland'\")\n", - "# .to_crs('EPSG:3577')\n", - "# .assign(dissolve=1)\n", - "# .dissolve('dissolve')\n", - "# .simplify(10000)\n", - "# .buffer(-100000)\n", - "# .buffer(50000))\n", - "\n", - "# # Compute convex hulls for each validation dataset\n", - "# convex_hulls = stats_gdf.dissolve('name').convex_hull.buffer(50000)\n", - "\n", - "# # Clip convex hulls by Australia coastline\n", - "# gpd.overlay(gpd.GeoDataFrame(geometry=convex_hulls), \n", - "# gpd.GeoDataFrame(geometry=aus_inside), \n", - "# how='difference').buffer(100000).to_file('../bishoptaylor_2020/Validation_extent/validation_extent.shp')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Number of validation sites by source:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stats_df['n'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stats_df.groupby(\"name\")['n'].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stats_df.groupby(\"name\")['year'].agg([np.min,np.max])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rename_dict = dict(zip(stats_df.groupby([\"source\"])['year'].count().sort_values().index[0:4], ['Other']*4))\n", - "rename_dict = {**rename_dict, **{'aerial photogrammetry': 'Aerial photogrammetry',\n", - " 'drone photogrammetry': 'Drone photogrammetry',\n", - " 'hydrographic survey': 'Hydrographic survey',\n", - " 'lidar': 'LiDAR'}}\n", - "\n", - "counts_per_year = (stats_df\n", - " .pipe(lambda x: x.assign(source_updated = x.source.replace(rename_dict)))\n", - " .groupby([\"year\", \"source_updated\"])['n']\n", - " .sum()\n", - " .unstack()) \n", - "counts_per_year = counts_per_year / counts_per_year.sum().sum()\n", - "counts_per_year" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import matplotlib.ticker as mtick\n", - "\n", - "fig, ax = plt.subplots(1,1, figsize=(11, 8))\n", - "counts_per_year.plot(ax=ax, \n", - " kind='bar', \n", - " stacked=True, \n", - " width=1.0, \n", - " edgecolor='#484746', \n", - " linewidth=1.0, \n", - " color=['#e5c494', '#ff7f0e', '#7295c1', '#8dbe8d', 'lightgrey']\n", - " ) \n", - "ax.yaxis.set_ticks_position('right')\n", - "ax.spines['left'].set_visible(False)\n", - "ax.spines['top'].set_visible(False)\n", - "ax.xaxis.label.set_visible(False)\n", - "plt.yticks([0, 0.05, 0.10, 0.15, 0.20], ['0%', '5%', '10%', '15%', '20%']);\n", - "plt.legend(loc=\"upper left\", ncol=5, bbox_to_anchor=(0, -0.08), fancybox=False, shadow=False, frameon=False)\n", - "\n", - "# Export to file\n", - "fig.savefig(fname='../bishoptaylor_2020/Validation_extent/validation_temporal.png', \n", - " dpi=300, pad_inches=0, bbox_inches=\"tight\")" + "# !unzip ../data/validation/validation_data.zip" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jovyan/Robbi/dea-coastlines\n" + ] + } + ], "source": [ - "stats_df.groupby(\"name\")[\"source\"].unique()" + "cd .." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ - "out['n'] / out['n'].sum()" + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import pandas as pd\n", + "\n", + "# Load DEA Coastlines code\n", + "import coastlines.validation\n", + "\n", + "\n", + "rename_dict = {\n", + " 'Beachrock undiff': 'rocky',\n", + " 'Beachrock undiff dominant': 'rocky',\n", + " 'Boulder or shingle-grade beach undiff': 'rocky',\n", + " 'Boulder groyne or breakwater undiff': 'rocky',\n", + " 'Flat boulder deposit (rock) undiff': 'rocky',\n", + " 'Hard bedrock shore': 'rocky',\n", + " 'Hard bedrock shore inferred': 'rocky',\n", + " 'Hard rock cliff (>5m)': 'rocky',\n", + " 'Hard rocky shore platform': 'rocky',\n", + " 'Rocky shore platform (undiff)': 'rocky',\n", + " 'Sloping boulder deposit (rock) undiff': 'rocky',\n", + " 'Sloping hard rock shore': 'rocky',\n", + " 'Sloping soft `bedrock¿ shore': 'rocky',\n", + " 'Sloping soft \\u2018bedrock\\u2019 shore': 'rocky',\n", + " 'Soft `bedrock¿ shore inferred': 'rocky',\n", + " 'Soft `bedrock¿ shore platform': 'rocky',\n", + " 'Beach (sediment type undiff)': 'sandy',\n", + " 'Fine-medium sand beach': 'sandy',\n", + " 'Fine-medium sandy tidal flats': 'sandy',\n", + " 'Mixed sand and shell beach': 'sandy',\n", + " 'Mixed sandy shore undiff': 'sandy',\n", + " 'Perched sandy beach (undiff)': 'sandy',\n", + " 'Sandy beach undiff': 'sandy',\n", + " 'Sandy beach with cobbles/pebbles (rock)': 'sandy',\n", + " 'Sandy shore undiff': 'sandy',\n", + " 'Sandy tidal flats': 'sandy',\n", + " 'Sandy tidal flats with coarse stony debris': 'sandy',\n", + " 'Sandy tidal flats, no bedrock protruding': 'sandy',\n", + " 'Sloping coffee rock deposit': 'rocky',\n", + " 'Muddy tidal flats': 'muddy',\n", + " 'Tidal flats (sediment undiff)': 'muddy',\n", + " 'Artificial shoreline undiff': 'rocky',\n", + " 'Artificial boulder structures undiff': 'rocky',\n", + " 'Boulder revetment': 'rocky',\n", + " 'Boulder seawall': 'rocky',\n", + " 'Concrete sea wall': 'rocky',\n", + " 'Piles (Jetty)': 'rocky',\n", + " 'Coarse sand beach': 'sandy'\n", + "}\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "stats_df.groupby('name')['smartline'].unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Results by slope" + "import glob\n", + "\n", + "prefix = 'v0.0.2'\n", + "\n", + "val_paths = glob.glob('data/validation/interim/*.csv')\n", + "# deacl_path = 'https://dea-public-data-dev.s3-ap-southeast-2.amazonaws.com/derivative/dea_coastlines/v0.0.2/coastlines_v0.0.2.shp.zip'\n", + "deacl_path = 'data/releases/coastlines_v0.0.2.gpkg'\n", + " \n", + "# Non-parallel (for testing)\n", + "for val_path in val_paths:\n", + "# try:\n", + " coastlines.validation.deacl_validation(val_path, deacl_path, 0, prefix, True)\n", + "# except:\n", + "# print\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data/validation/interim/sadew_315002.csv data/validation/interim/wadot_benisland-hammerhead.csv data/validation/interim/sadew_710024.csv " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/674 [00:00', \n", - " 'facecolor': 'black', \n", - " 'mutation_scale': 15}, \n", - " ha='center')\n", - "ax1.annotate('Seaward\\nbias', \n", - " xytext=(0.1817, -7.5), \n", - " xy=(0.1817, -14), \n", - " arrowprops={'arrowstyle': '-|>', \n", - " 'facecolor': 'black', \n", - " 'mutation_scale': 15}, \n", - " ha='center')\n", - "\n", - "# plt.savefig(fname=f'../bishoptaylor_2020/SlopeObs/FigureX_Effectofslopeandobs.png', \n", - "# bbox_inches='tight',\n", - "# transparent=True,\n", - "# pad_inches=0.05, dpi=300)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Effect of validation source" + " # Apply func in parallel\n", + " groups = val_paths\n", + " to_iterate = (groups, *(repeat(i, len(groups)) for i in args))\n", + " tqdm(executor.map(coastlines.validation.deacl_validation, *to_iterate), total=len(groups))\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Combining data\n" + ] + }, + { + "data": { + "text/plain": [ + "n 62808.000\n", + "mae 11.720\n", + "rmse 16.750\n", + "stdev 16.750\n", + "corr 0.991\n", + "bias 8.940\n", + "dtype: float64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "by_source = stats_df.groupby('source').apply(\n", - " lambda x: deacl_val.deacl_val_stats(x.val_dist, x.deacl_dist, x.n))\n", - "by_source_nobias = stats_df.groupby('source').apply(\n", - " lambda x: deacl_val.deacl_val_stats(x.val_dist, x.deacl_dist, x.n, True))\n", + "# Load all results into a single file\n", + "print('Combining data')\n", + "stats_list = glob.glob(f'data/validation/processed/{prefix}_*.csv')\n", + "stats_df = pd.concat([pd.read_csv(csv) for csv in stats_list])\n", "\n", - "deacl_val.rse_tableformat(by_source, by_source_nobias)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Effect of yearly validation observations" + "# Rename smartline categories to smaller subset\n", + "stats_df['smartline'] = stats_df.smartline.replace(rename_dict)\n", + "\n", + "# Export to file\n", + "# stats_df.to_csv('deacl_results.csv', index=False)\n", + "\n", + "# Run stats\n", + "coastlines.validation.deacl_val_stats(stats_df.val_dist,\n", + " stats_df.deacl_dist,\n", + " n=stats_df.n,\n", + " remove_bias=True)" ] }, { "cell_type": "code", - 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    nBias (m)MAE (m)RMSE (m)SD (m)Correlation
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    sandy2451-7.910.0 (5.9)11.5 (8.4)8.40.961
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    nBias (m)MAE (m)RMSE (m)SD (m)Correlation
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    sandy551971.011.4 (11.4)16.1 (16.1)16.10.991
    rocky2827-9.614.1 (10.5)18.9 (16.3)16.30.997
    muddy1539-9.416.8 (16.5)26.7 (25.0)25.00.961
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    " + ], + "text/plain": [ + " n Bias (m) MAE (m) RMSE (m) SD (m) Correlation\n", + "smartline \n", + "sandy 55197 1.0 11.4 (11.4) 16.1 (16.1) 16.1 0.991\n", + "rocky 2827 -9.6 14.1 (10.5) 18.9 (16.3) 16.3 0.997\n", + "muddy 1539 -9.4 16.8 (16.5) 26.7 (25.0) 25.0 0.961" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "stats_df[stats_df.n > 1].n.sum()" + "# Stats by substrate, no bias correction\n", + "by_smartline = stats_df.query(\"n < 22\").groupby('smartline').apply(\n", + " lambda x: coastlines.validation.deacl_val_stats(x.val_dist, x.deacl_dist, x.n))\n", + "by_smartline_nobias = stats_df.query(\"n < 22\").groupby('smartline').apply(\n", + " lambda x: coastlines.validation.deacl_val_stats(x.val_dist, x.deacl_dist, x.n, True))\n", + "\n", + "out = coastlines.validation.rse_tableformat(by_smartline, by_smartline_nobias, 'smartline')\n", + "out" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "1 - (stats_df[stats_df.n == 1].n.sum() / stats_df.n.sum())" + "# !pip install git+https://github.com/GeoscienceAustralia/dea-intertidal.git" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "stats_df[stats_df.n >= 22].n.sum()" + "from intertidal import validation" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stats_df[stats_df.n >= 22].n.sum() / stats_df.n.sum()" - ] - }, - { - "cell_type": "markdown", + "execution_count": 37, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Correlation 0.991\n", + "RMSE 14.166\n", + "MAE 10.194\n", + "R-squared 0.979\n", + "Bias -6.290\n", + "Regression slope 0.973\n", + "dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Heatmap and xy scatter plots" + "validation.eval_metrics(x=stats_df.val_dist, y=stats_df.deacl_dist)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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    nBias (m)MAE (m)RMSE (m)SD (m)Correlation
    source
    drone photogrammetry2981.03.6 (3.5)4.6 (4.5)4.50.681
    aerial photogrammetry11725.88.9 (7.4)12.4 (11.0)11.00.993
    hydrographic survey2342.611.1 (11.1)14.9 (14.7)14.70.966
    lidar27115.020.2 (13.5)22.9 (17.3)17.40.981
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    " + ], + "text/plain": [ + " n Bias (m) MAE (m) RMSE (m) SD (m) \\\n", + "source \n", + "drone photogrammetry 298 1.0 3.6 (3.5) 4.6 (4.5) 4.5 \n", + "aerial photogrammetry 1172 5.8 8.9 (7.4) 12.4 (11.0) 11.0 \n", + "hydrographic survey 234 2.6 11.1 (11.1) 14.9 (14.7) 14.7 \n", + "lidar 271 15.0 20.2 (13.5) 22.9 (17.3) 17.4 \n", + "\n", + " Correlation \n", + "source \n", + "drone photogrammetry 0.681 \n", + "aerial photogrammetry 0.993 \n", + "hydrographic survey 0.966 \n", + "lidar 0.981 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# # Export evaluation vector\n", - "output_name = 'test'\n", - "# deacl_val.export_eval(stats_df, output_name)" + "by_source = stats_df.groupby('source').apply(\n", + " lambda x: coastlines.validation.deacl_val_stats(x.val_dist, x.deacl_dist, x.n))\n", + "by_source_nobias = stats_df.groupby('source').apply(\n", + " lambda x: coastlines.validation.deacl_val_stats(x.val_dist, x.deacl_dist, x.n, True))\n", + "\n", + "coastlines.validation.rse_tableformat(by_source, by_source_nobias)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", "# Print stats and plot scatterplot\n", "stats_subset = stats_df \n", "\n", @@ -1090,7 +11023,7 @@ "def val_plot(df, \n", " title='Validation',\n", " scatter=True, \n", - " density=True,\n", + " density=False,\n", " time=True, \n", " time_stat='mean',\n", " time_legend_pos=[0.8, 0.035],\n", @@ -1105,7 +11038,7 @@ " df['deacl_dist'] += offset\n", "\n", " # Compute stats \n", - " n, mae, rmse, stdev, corr, bias = deacl_val.deacl_val_stats(\n", + " n, mae, rmse, stdev, corr, bias = coastlines.validation.deacl_val_stats(\n", " val_dist=df.val_dist, \n", " deacl_dist=df.deacl_dist) \n", " offset_str = 'landward offset' if bias > 0 else 'ocean-ward offset'\n", @@ -1260,9 +11193,9 @@ "\n", "# Run analysis\n", "g = val_plot(df=stats_subset, # stats_subset,\n", - " title='nswbpd_eurobodallabeachessouth',\n", + " title='',\n", " scatter=True, \n", - " density=True,\n", + " density=False,\n", " time=False,\n", " time_stat='median',\n", " time_legend_pos=[0.67, 0.11],\n", @@ -1270,262 +11203,6 @@ " extent=(0, 300))" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extracting data along profiles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import required packages\n", - "import os\n", - "import numpy as np\n", - "import xarray as xr\n", - "import pandas as pd\n", - "import geopandas as gpd \n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as colors\n", - "from scipy import stats\n", - "from otps import TimePoint\n", - "from otps import predict_tide\n", - "from datacube.utils.geometry import CRS\n", - "from shapely.geometry import box, shape\n", - "\n", - "# Widgets and WMS\n", - "from odc.ui import ui_poll, select_on_a_map\n", - "from ipyleaflet import (Map, WMSLayer, WidgetControl, FullScreenControl, \n", - " DrawControl, basemaps, basemap_to_tiles, TileLayer)\n", - "from ipywidgets.widgets import Layout, Button, HTML\n", - "from IPython.display import display\n", - "from types import SimpleNamespace\n", - "\n", - "\n", - "\n", - "def extract_geometry(profile,\n", - " start,\n", - " transect_mode='distance'): \n", - " \n", - " try:\n", - "\n", - " # Convert geometry to a GeoSeries\n", - " profile = gpd.GeoSeries(profile, \n", - " crs='EPSG:4326')\n", - " start = gpd.GeoSeries(start, \n", - " crs='EPSG:4326').to_crs('EPSG:3577')\n", - "\n", - " # Load data from WFS\n", - " xmin, ymin, xmax, ymax = profile.total_bounds\n", - " deacl_wfs = f'https://geoserver.dea.ga.gov.au/geoserver/wfs?' \\\n", - " f'service=WFS&version=1.1.0&request=GetFeature' \\\n", - " f'&typeName=dea:coastlines&maxFeatures=1000' \\\n", - " f'&bbox={ymin},{xmin},{ymax},{xmax},' \\\n", - " f'urn:ogc:def:crs:EPSG:4326'\n", - " deacl = gpd.read_file(deacl_wfs)\n", - " deacl.crs = 'EPSG:3577'\n", - "\n", - " # Raise exception if no coastlines are returned\n", - " if len(deacl.index) == 0:\n", - " raise ValueError('No annual coastlines were returned for the '\n", - " 'supplied transect. Please select another area.')\n", - "\n", - " # Dissolve by year to remove duplicates, then sort by date\n", - " deacl = deacl.dissolve(by='year', as_index=False)\n", - " deacl['year'] = deacl.year.astype(int)\n", - " deacl = deacl.sort_values('year')\n", - "\n", - " # Extract intersections and determine type\n", - " profile = profile.to_crs('EPSG:3577')\n", - " intersects = deacl.apply(\n", - " lambda x: profile.intersection(x.geometry), axis=1)\n", - " intersects = gpd.GeoSeries(intersects[0]) \n", - "\n", - " # Select geometry depending on mode\n", - " intersects_type = (intersects.type == 'Point' if \n", - " transect_mode == 'distance' else \n", - " intersects.type == 'MultiPoint')\n", - "\n", - " # Remove annual data according to intersections\n", - " deacl_filtered = deacl.loc[intersects_type]\n", - " drop_years = ', '.join(deacl.year\n", - " .loc[~intersects_type]\n", - " .astype(str)\n", - " .values.tolist())\n", - "\n", - " # In 'distance' mode, analyse years with one intersection only\n", - " if transect_mode == 'distance': \n", - "\n", - " if drop_years:\n", - " print(f'Dropping years due to multiple intersections: {drop_years}\\n') \n", - "\n", - " # Add start and end coordinate\n", - " deacl_filtered['start'] = start.iloc[0]\n", - " deacl_filtered['end'] = intersects.loc[intersects_type]\n", - "\n", - " # If any data was returned:\n", - " if len(deacl_filtered.index) > 0: \n", - "\n", - " # Compute distance\n", - " deacl_filtered['dist'] = deacl_filtered.apply(\n", - " lambda x: x.start.distance(x.end), axis=1)\n", - "\n", - " # Extract values\n", - " transect_df = pd.DataFrame(deacl_filtered[['year', 'dist']])\n", - " transect_df['dist'] = transect_df.dist.round(2)\n", - "\n", - " # Plot data\n", - "# fig, ax = plt.subplots(1, 1, figsize=(5, 8))\n", - "# transect_df.plot(x='dist', y='year', ax=ax, label='DEA Coastlines')\n", - "# ax.set_xlabel(f'{transect_mode.title()} (metres)')\n", - "\n", - " return transect_df.set_index('year')\n", - " \n", - " except:\n", - " pass\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "profile_list = []\n", - "\n", - "for val_path in val_paths:\n", - " \n", - " val_df = pd.read_csv(val_path).groupby('id').first()\n", - "\n", - " # Convert validation start and end locations to linestrings\n", - " from shapely.geometry import box, Point, LineString\n", - " val_geometry = val_df.apply(\n", - " lambda x: LineString([(x.end_x, x.end_y), (x.start_x, x.start_y)]), axis=1)\n", - "\n", - " # Convert geometries to GeoDataFrame\n", - " val_gdf = gpd.GeoDataFrame(data=val_df,\n", - " geometry=val_geometry,\n", - " crs='EPSG:3577').to_crs('EPSG:4326')\n", - "\n", - " # Get start coord\n", - " val_gdf['start_point'] = val_gdf.apply(\n", - " lambda x: Point(x.geometry.coords[1]), axis=1)\n", - "\n", - " for i in val_gdf.index:\n", - " print(i)\n", - " profile_df = extract_geometry(val_gdf.loc[i].geometry, val_gdf.loc[i].start_point)\n", - " if profile_df is not None:\n", - " profile_list.append(profile_df.rename({'dist': i}, axis=1))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.concat(profile_list, axis=1).to_csv('fellowes_et_al_2021_profiles_deacoastlines.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read in 2020 timeseries data\n", - "narrabeen_2020 = pd.read_csv('input_data/wrl/Narrabeen_Timeseries_2020.csv', \n", - " header=None, \n", - " names=['beach', 'profile', 'date', '0_dist', 'type'],\n", - " parse_dates=['date']).query(\"19880101 < date < 20201231 & type == 'WIDTH'\")\n", - "\n", - "# Standardise to validation format\n", - "narrabeen_2020['profile'] = narrabeen_2020['profile'].str.lower()\n", - "narrabeen_2020['beach'] = 'narrabeen'\n", - "narrabeen_2020['section'] = 'all'\n", - "narrabeen_2020['source'] = 'gps'\n", - "narrabeen_2020['name'] = 'wrl'\n", - "narrabeen_2020['id'] = narrabeen_2020['beach'] + '_' + narrabeen_2020['section'] + '_' + narrabeen_2020['profile']\n", - "\n", - "# Load Narrabeen profile stard/stops\n", - "narrabeen_profiles = pd.read_csv('output_data/wrl_narrabeen.csv') \n", - "narrabeen_profiles = narrabeen_profiles[['profile', 'start_x', 'start_y', 'end_x', 'end_y']].drop_duplicates()\n", - "\n", - "narrabeen_2020 = pd.merge(left=narrabeen_2020, \n", - " right=narrabeen_profiles,\n", - " on='profile')\n", - "narrabeen_2020['0_x'] = (narrabeen_2020.start_x + narrabeen_2020.end_x) / 2.0\n", - "narrabeen_2020['0_y'] = (narrabeen_2020.start_y + narrabeen_2020.end_y) / 2.0\n", - "narrabeen_2020.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "narrabeen_2020.drop('type', axis=1).to_csv('output_data/2020test_narrabeen.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "narrabeen_2020_plot = pd.read_csv('output_data/2020test_narrabeen.csv', parse_dates=['date'])\n", - "narrabeen_2020_plot['year'] = narrabeen_2020_plot.date.dt.year + (narrabeen_2020_plot.date.dt.dayofyear -1)/365" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n", - "profile = 'pf8'\n", - "narrabeen_2020_plot.query(f\"profile == '{profile}'\")[['year', '0_dist']].rename({'0_dist': 'Narrabeen-Collaroy Beach Survey Program'}, axis=1).plot(x='year', \n", - " y='Narrabeen-Collaroy Beach Survey Program', \n", - " alpha=0.8,\n", - " ax=ax)\n", - "stats_subset = stats_df.query(f\"profile == '{profile}'\")\n", - "stats_subset['year'] = stats_subset['year'] + 0.5\n", - "stats_subset.rename({'deacl_dist': 'DEA Coastlines'}, axis=1).plot(x='year', y='DEA Coastlines', ax=ax, linewidth=3)\n", - "plt.suptitle(f'Narrabeen-Collaroy Beach Survey Program profile {profile.upper()}')\n", - "ax.set_ylabel(\"Beach width (m)\")\n", - "\n", - "\n", - "plt.savefig(f\"2020eval_{profile}.png\", dpi=200)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***\n", - "\n", - "## Additional information" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**License:** The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). \n", - "Digital Earth Australia data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n", - "\n", - "**Contact:** For assistance with any of the Python code or Jupyter Notebooks in this repository, please post a [Github issue](https://github.com/GeoscienceAustralia/DEACoastLines/issues/new). For questions or more information about this product, sign up to the [Open Data Cube Slack](https://join.slack.com/t/opendatacube/shared_invite/zt-d6hu7l35-CGDhSxiSmTwacKNuXWFUkg) and post on the [`#dea-coastlines`](https://app.slack.com/client/T0L4V0TFT/C018X6J9HLY/details/) channel.\n", - "\n", - "**Last modified:** November 2021" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1536,7 +11213,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1550,7 +11227,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.10" + "version": "3.8.10" }, "widgets": { "application/vnd.jupyter.widget-state+json": {