From a1acaaf478f6eabffca6b342e671c3659c25cac7 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 16 Jul 2026 17:25:49 +0000 Subject: [PATCH] Remove staging STAC patch from HRRR virtual notebook The noaa-hrrr-forecast-48-hour-virtual product is now in the prod STAC catalog used by dynamical_catalog by default, so the staging catalog monkey patch is no longer needed. Co-Authored-By: Claude Opus 4.8 Claude-Session: https://claude.ai/code/session_01BgUQkHQw6z4KXc7EeGmyBs --- noaa-hrrr-forecast-48-hour-virtual.ipynb | 214 ++++++++++++----------- 1 file changed, 114 insertions(+), 100 deletions(-) diff --git a/noaa-hrrr-forecast-48-hour-virtual.ipynb b/noaa-hrrr-forecast-48-hour-virtual.ipynb index 8680d8c..01e55fb 100644 --- a/noaa-hrrr-forecast-48-hour-virtual.ipynb +++ b/noaa-hrrr-forecast-48-hour-virtual.ipynb @@ -18,10 +18,10 @@ "id": "42d5e1e8", "metadata": { "execution": { - "iopub.execute_input": "2026-07-16T14:47:14.506728Z", - "iopub.status.busy": "2026-07-16T14:47:14.506593Z", - "iopub.status.idle": "2026-07-16T14:47:14.509786Z", - "shell.execute_reply": "2026-07-16T14:47:14.509119Z" + "iopub.execute_input": "2026-07-16T17:24:16.615599Z", + "iopub.status.busy": "2026-07-16T17:24:16.615373Z", + "iopub.status.idle": "2026-07-16T17:24:16.619695Z", + "shell.execute_reply": "2026-07-16T17:24:16.618370Z" } }, "outputs": [], @@ -33,14 +33,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "e3efe4ad", "metadata": { "execution": { - "iopub.execute_input": "2026-07-16T14:47:14.512482Z", - "iopub.status.busy": "2026-07-16T14:47:14.512307Z", - "iopub.status.idle": "2026-07-16T14:47:18.379987Z", - "shell.execute_reply": "2026-07-16T14:47:18.379279Z" + "iopub.execute_input": "2026-07-16T17:24:16.621641Z", + "iopub.status.busy": "2026-07-16T17:24:16.621435Z", + "iopub.status.idle": "2026-07-16T17:24:25.635415Z", + "shell.execute_reply": "2026-07-16T17:24:25.634231Z" } }, "outputs": [ @@ -606,19 +606,19 @@ " longitude (y, x) float32 8MB ...\n", " spatial_ref int64 8B ...\n", "Data variables: (12/142)\n", - " aerosol_optical_thickness_atmosphere (init_time, lead_time, y, x) float64 9TB ...\n", - " best_4_layer_lifted_index_180_0mb (init_time, lead_time, y, x) float64 9TB ...\n", " baseflow_groundwater_runoff_surface (init_time, lead_time, y, x) float64 9TB ...\n", - " brightness_temperature_channel_123 (init_time, lead_time, y, x) float64 9TB ...\n", - " brightness_temperature_channel_124 (init_time, lead_time, y, x) float64 9TB ...\n", + " aerosol_optical_thickness_atmosphere (init_time, lead_time, y, x) float64 9TB ...\n", + " brightness_temperature_channel_113 (init_time, lead_time, y, x) float64 9TB ...\n", " brightness_temperature_channel_114 (init_time, lead_time, y, x) float64 9TB ...\n", + " brightness_temperature_channel_123 (init_time, lead_time, y, x) float64 9TB ...\n", + " categorical_freezing_rain_surface (init_time, lead_time, y, x) float64 9TB ...\n", " ... ...\n", - " vertically_integrated_liquid_atmosphere (init_time, lead_time, y, x) float64 9TB ...\n", - " visible_diffuse_downward_solar_flux_surface (init_time, lead_time, y, x) float64 9TB ...\n", - " vertical_v_component_shear_0_6000m (init_time, lead_time, y, x) float64 9TB ...\n", - " wind_gust_surface (init_time, lead_time, y, x) float64 9TB ...\n", " wind_v_10m (init_time, lead_time, y, x) float64 9TB ...\n", + " wind_gust_surface (init_time, lead_time, y, x) float64 9TB ...\n", + " wind_u_80m (init_time, lead_time, y, x) float64 9TB ...\n", + " visible_beam_downward_solar_flux_surface (init_time, lead_time, y, x) float64 9TB ...\n", " wind_v_80m (init_time, lead_time, y, x) float64 9TB ...\n", + " vertically_integrated_liquid_atmosphere (init_time, lead_time, y, x) float64 9TB ...\n", "Attributes:\n", " dataset_id: noaa-hrrr-forecast-48-hour-virtual\n", " dataset_version: 0.5.0\n", @@ -631,10 +631,10 @@ " time_domain: Forecasts initialized 2018-07-13 12:00:00 UTC to Pr...\n", " time_resolution: Forecasts initialized every 6 hours\n", " forecast_domain: Forecast lead time 0-48 hours ahead\n", - " forecast_resolution: Hourly
    • baseflow_groundwater_runoff_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Baseflow-groundwater runoff
      short_name :
      bgrun
      standard_name :
      subsurface_runoff_amount
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • aerosol_optical_thickness_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Aerosol optical thickness
      short_name :
      aotk
      standard_name :
      atmosphere_optical_thickness_due_to_ambient_aerosol_particles
      units :
      1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • brightness_temperature_channel_113
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Simulated brightness temperature (channel 113)
      short_name :
      sbt113
      standard_name :
      toa_brightness_temperature
      units :
      K
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • brightness_temperature_channel_114
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Simulated brightness temperature (channel 114)
      short_name :
      sbt114
      standard_name :
      toa_brightness_temperature
      units :
      K
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • brightness_temperature_channel_123
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Simulated brightness temperature (channel 123)
      short_name :
      sbt123
      standard_name :
      toa_brightness_temperature
      units :
      K
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • categorical_freezing_rain_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Categorical freezing rain
      short_name :
      cfrzr
      units :
      1
      comment :
      0=no; 1=yes
      step_type :
      instant
      flag_values :
      [0, 1]
      flag_meanings :
      no yes
      [1092310687209 values with dtype=float64]
    • categorical_ice_pellets_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Categorical ice pellets
      short_name :
      cicep
      units :
      1
      comment :
      0=no; 1=yes
      step_type :
      instant
      flag_values :
      [0, 1]
      flag_meanings :
      no yes
      [1092310687209 values with dtype=float64]
    • brightness_temperature_channel_124
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Simulated brightness temperature (channel 124)
      short_name :
      sbt124
      standard_name :
      toa_brightness_temperature
      units :
      K
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • categorical_rain_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Categorical rain
      short_name :
      crain
      units :
      1
      comment :
      0=no; 1=yes
      step_type :
      instant
      flag_values :
      [0, 1]
      flag_meanings :
      no yes
      [1092310687209 values with dtype=float64]
    • best_4_layer_lifted_index_180_0mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Best (4-layer) lifted index
      short_name :
      4lftx
      standard_name :
      temperature_difference_between_ambient_air_and_air_lifted_adiabatically
      units :
      K
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • column_integrated_mass_density_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Column-integrated mass density
      short_name :
      colmd
      units :
      kg m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • categorical_snow_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Categorical snow
      short_name :
      csnow
      units :
      1
      comment :
      0=no; 1=yes
      step_type :
      instant
      flag_values :
      [0, 1]
      flag_meanings :
      no yes
      [1092310687209 values with dtype=float64]
    • convective_available_potential_energy_180_0mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective available potential energy
      short_name :
      cape
      standard_name :
      atmosphere_convective_available_potential_energy
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • convective_available_potential_energy_90_0mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective available potential energy
      short_name :
      cape
      standard_name :
      atmosphere_convective_available_potential_energy
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • composite_reflectivity
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Maximum/Composite radar reflectivity
      short_name :
      refc
      standard_name :
      equivalent_reflectivity_factor
      units :
      dBZ
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • convective_available_potential_energy_255_0mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective available potential energy
      short_name :
      cape
      standard_name :
      atmosphere_convective_available_potential_energy
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • convective_available_potential_energy_0_3000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective available potential energy
      short_name :
      cape
      standard_name :
      atmosphere_convective_available_potential_energy
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • convective_inhibition_255_0mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective inhibition
      short_name :
      cin
      standard_name :
      atmosphere_convective_inhibition
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • convective_inhibition_180_0mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective inhibition
      short_name :
      cin
      standard_name :
      atmosphere_convective_inhibition
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • convective_available_potential_energy_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective available potential energy
      short_name :
      cape
      standard_name :
      atmosphere_convective_available_potential_energy
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • convective_inhibition_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective inhibition
      short_name :
      cin
      standard_name :
      atmosphere_convective_inhibition
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • convective_inhibition_90_0mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Convective inhibition
      short_name :
      cin
      standard_name :
      atmosphere_convective_inhibition
      units :
      J kg-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • derived_radar_reflectivity_1000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Derived radar reflectivity
      short_name :
      refd
      standard_name :
      equivalent_reflectivity_factor
      units :
      dBZ
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • derived_radar_reflectivity_4000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Derived radar reflectivity
      short_name :
      refd
      standard_name :
      equivalent_reflectivity_factor
      units :
      dBZ
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • derived_radar_reflectivity_263k
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Derived radar reflectivity
      short_name :
      refd
      standard_name :
      equivalent_reflectivity_factor
      units :
      dBZ
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • dew_point_temperature_2m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      2 metre dewpoint temperature
      short_name :
      2d
      standard_name :
      dew_point_temperature
      units :
      degree_Celsius
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • downward_long_wave_radiation_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Surface downward long-wave radiation flux
      short_name :
      sdlwrf
      standard_name :
      surface_downwelling_longwave_flux_in_air
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • downward_short_wave_radiation_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Surface downward short-wave radiation flux
      short_name :
      sdswrf
      standard_name :
      surface_downwelling_shortwave_flux_in_air
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • echo_top
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Echo top
      short_name :
      retop
      units :
      m
      comment :
      -999 encodes no echo; CF-aware readers mask it to NaN.
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • effective_layer_helicity_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Effective layer helicity
      short_name :
      efhl
      units :
      m2 s-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • frozen_precipitation_run_total_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Frozen precipitation
      short_name :
      frozr
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • geopotential_height_253k
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • critical_angle_0_500m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Critical angle
      short_name :
      cangle
      units :
      degree
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • freezing_rain_run_total_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Freezing Rain
      short_name :
      frzr
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • geopotential_height_263k
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • geopotential_height_adiabatic_condensation_level
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • enhanced_stretching_potential_0_3000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Enhanced stretching potential
      short_name :
      esp
      units :
      1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • frozen_precipitation_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Frozen precipitation
      short_name :
      frozr
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • geopotential_height_highest_tropospheric_freezing_level
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • geopotential_height_0c_isotherm
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • geopotential_height_cloud_top
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height_at_cloud_top
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • geopotential_height_cloud_ceiling
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • geopotential_height_equilibrium_level
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • friction_velocity_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Friction velocity
      short_name :
      zust
      standard_name :
      magnitude_of_surface_friction_velocity_in_air
      units :
      m s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • latent_heat_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Latent heat flux
      short_name :
      lhf
      standard_name :
      surface_upward_latent_heat_flux
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • ground_heat_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Ground heat flux
      short_name :
      gflux
      standard_name :
      downward_heat_flux_in_soil
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • geopotential_height_cloud_base
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • high_cloud_cover
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      High cloud cover
      short_name :
      hcc
      standard_name :
      cloud_area_fraction_in_atmosphere_layer
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • ice_cover_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Ice cover (1=ice, 0=no ice)
      short_name :
      icec
      standard_name :
      sea_ice_area_fraction
      units :
      1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • geopotential_height_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • geopotential_height_level_of_free_convection
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Geopotential height
      short_name :
      gh
      standard_name :
      geopotential_height
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • land_sea_mask_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Land-sea mask
      short_name :
      lsm
      standard_name :
      land_binary_mask
      units :
      1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • lightning_threat_2m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Maximum lightning threat 2 (vertically integrated ice)
      short_name :
      ltngsd
      units :
      1
      comment :
      GSD maximum lightning threat 2 derived from vertically integrated ice, in flashes km-2 (5 min)-1. Encoded in HRRR GRIB as LTNGSD (lightning strike density) at the pseudo-level 2 m above ground. Before the 2022-06-28T12Z cycle this GRIB slot carried lightning potential index (J kg-1), a different quantity that is not included.
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • hourly_maximum_radar_reflectivity_1000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Hourly maximum of simulated reflectivity
      short_name :
      maxref
      standard_name :
      equivalent_reflectivity_factor
      units :
      dBZ
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • lightning_threat_1m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Maximum lightning threat 1 (graupel flux)
      short_name :
      ltngsd
      units :
      1
      comment :
      GSD maximum lightning threat 1 derived from upward graupel flux, in flashes km-2 (5 min)-1. Encoded in HRRR GRIB as LTNGSD (lightning strike density) at the pseudo-level 1 m above ground. Before the 2022-06-28T12Z cycle this GRIB slot carried lightning potential index (J kg-1), a different quantity that is not included.
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • maximum_derived_radar_reflectivity_263k
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Derived radar reflectivity
      short_name :
      refd
      standard_name :
      equivalent_reflectivity_factor
      units :
      dBZ
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • leaf_area_index_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Leaf Area Index
      short_name :
      lai
      standard_name :
      leaf_area_index
      units :
      1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • low_cloud_cover
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Low cloud cover
      short_name :
      lcc
      standard_name :
      cloud_area_fraction_in_atmosphere_layer
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • mass_density_8m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Mass density
      short_name :
      mdens
      units :
      kg m-3
      comment :
      Near-surface smoke concentration. Source values at init_times before 2021-12-21T18Z are in ug m-3; NOAA corrected the encoding to kg m-3 from that cycle onward, so multiply earlier values by 1e-9 to compare.
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • maximum_column_integrated_graupel_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Total column vertically-integrated graupel (snow pellets)
      short_name :
      tcolg
      standard_name :
      atmosphere_mass_content_of_graupel
      units :
      kg m-2
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_downward_vertical_velocity_100_1000mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Maximum downward vertical velocity
      short_name :
      maxdvv
      units :
      m s-1
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_hail_diameter_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Hail
      short_name :
      hail
      units :
      m
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • lightning_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Lightning
      short_name :
      ltng
      units :
      1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • maximum_hail_diameter_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Hail
      short_name :
      hail
      units :
      m
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_hail_diameter_0p1sigma
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Hail
      short_name :
      hail
      units :
      m
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_relative_vorticity_1000_0m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vorticity (relative)
      short_name :
      vo
      standard_name :
      atmosphere_upward_relative_vorticity
      units :
      s-1
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_relative_vorticity_2000_0m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vorticity (relative)
      short_name :
      vo
      standard_name :
      atmosphere_upward_relative_vorticity
      units :
      s-1
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_upward_vertical_velocity_100_1000mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Maximum upward vertical velocity
      short_name :
      maxuvv
      standard_name :
      upward_air_velocity
      units :
      m s-1
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • layer_thickness_261k_256k
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Layer Thickness
      short_name :
      layth
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • maximum_wind_u_component_10m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Maximum 10 metre wind speed u component
      short_name :
      maxuw
      units :
      m s-1
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_updraft_helicity_3000_0m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Updraft Helicity
      short_name :
      uphl
      units :
      m2 s-2
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • medium_cloud_cover
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Medium cloud cover
      short_name :
      mcc
      standard_name :
      cloud_area_fraction_in_atmosphere_layer
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • maximum_updraft_helicity_2000_0m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Updraft Helicity
      short_name :
      uphl
      units :
      m2 s-2
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_vegetation_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Maximum vegetation fraction
      short_name :
      vegmax
      standard_name :
      vegetation_area_fraction
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • maximum_wind_speed_10m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      10 metre wind speed
      short_name :
      10si
      standard_name :
      wind_speed
      units :
      m s-1
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • maximum_wind_v_component_10m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Maximum 10 metre wind speed v component
      short_name :
      maxvw
      units :
      m s-1
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • minimum_updraft_helicity_2000_0m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Minimum updraft helicity
      short_name :
      mnuphl
      units :
      m2 s-2
      step_type :
      min
      [1092310687209 values with dtype=float64]
    • moisture_availability_0m_underground
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Moisture availability
      short_name :
      mstav
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • minimum_vegetation_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Minimum vegetation fraction
      short_name :
      vegmin
      standard_name :
      vegetation_area_fraction
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • percent_frozen_precipitation_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Percent frozen precipitation
      short_name :
      cpofp
      units :
      percent
      comment :
      -50 encodes no/undefined frozen precipitation; CF-aware readers mask it to NaN.
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • minimum_updraft_helicity_5000_2000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Minimum updraft helicity
      short_name :
      mnuphl
      units :
      m2 s-2
      step_type :
      min
      [1092310687209 values with dtype=float64]
    • plant_canopy_surface_water_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Plant canopy surface water
      short_name :
      cnwat
      standard_name :
      canopy_water_amount
      units :
      kg m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • planetary_boundary_layer_height_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Boundary layer height
      short_name :
      blh
      standard_name :
      atmosphere_boundary_layer_thickness
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • minimum_updraft_helicity_3000_0m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Minimum updraft helicity
      short_name :
      mnuphl
      units :
      m2 s-2
      step_type :
      min
      [1092310687209 values with dtype=float64]
    • precipitation_rate_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Precipitation rate
      short_name :
      prate
      standard_name :
      precipitation_flux
      units :
      kg m-2 s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • pressure_0c_isotherm
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Pressure
      short_name :
      pres
      standard_name :
      air_pressure
      units :
      Pa
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • maximum_updraft_helicity_5000_2000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Updraft Helicity
      short_name :
      uphl
      units :
      m2 s-2
      step_type :
      max
      [1092310687209 values with dtype=float64]
    • precipitable_water_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Precipitable water
      short_name :
      pwat
      standard_name :
      atmosphere_mass_content_of_water_vapor
      units :
      kg m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • potential_temperature_2m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Potential temperature
      short_name :
      pt
      standard_name :
      air_potential_temperature
      units :
      K
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • pressure_cloud_base
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Pressure
      short_name :
      pres
      standard_name :
      air_pressure_at_cloud_base
      units :
      Pa
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • pressure_highest_tropospheric_freezing_level
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Pressure
      short_name :
      pres
      standard_name :
      air_pressure
      units :
      Pa
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • pressure_reduced_to_mean_sea_level
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Pressure reduced to MSL
      short_name :
      prmsl
      standard_name :
      air_pressure_at_mean_sea_level
      units :
      Pa
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • relative_humidity_0c_isotherm
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Relative humidity
      short_name :
      r
      standard_name :
      relative_humidity
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • relative_humidity_highest_tropospheric_freezing_level
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Relative humidity
      short_name :
      r
      standard_name :
      relative_humidity
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • pressure_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Surface pressure
      short_name :
      sp
      standard_name :
      surface_air_pressure
      units :
      Pa
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • pressure_of_lifted_parcel_level_255_0mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Pressure of level from which parcel was lifted
      short_name :
      plpl
      standard_name :
      original_air_pressure_of_lifted_parcel
      units :
      Pa
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • relative_humidity_2m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      2 metre relative humidity
      short_name :
      2r
      standard_name :
      relative_humidity
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • pressure_cloud_top
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Pressure
      short_name :
      pres
      standard_name :
      air_pressure_at_cloud_top
      units :
      Pa
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • snow_thickness_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Snow depth
      short_name :
      sde
      standard_name :
      surface_snow_thickness
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • sensible_heat_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Sensible heat flux
      short_name :
      shf
      standard_name :
      surface_upward_sensible_heat_flux
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • storm_relative_helicity_1000_0m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Storm relative helicity
      short_name :
      hlcy
      units :
      m2 s-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • relative_humidity_with_respect_to_precipitable_water_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Relative humidity with respect to precipitable water
      short_name :
      rhpw
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • snow_area_fraction_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Snow cover
      short_name :
      snowc
      standard_name :
      surface_snow_area_fraction
      units :
      1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • storm_relative_helicity_3000_0m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Storm relative helicity
      short_name :
      hlcy
      units :
      m2 s-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • temperature_2m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      2 metre temperature
      short_name :
      2t
      standard_name :
      air_temperature
      units :
      degree_Celsius
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • temperature_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Temperature
      short_name :
      t
      standard_name :
      air_temperature
      units :
      degree_Celsius
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • surface_lifted_index_500_1000mb
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Surface lifted index
      short_name :
      lftx
      standard_name :
      temperature_difference_between_ambient_air_and_air_lifted_adiabatically_from_the_surface
      units :
      K
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • specific_humidity_2m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Specific humidity
      short_name :
      q
      standard_name :
      specific_humidity
      units :
      1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • snowfall_water_equivalent_run_total_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Snowfall water equivalent
      short_name :
      sf
      standard_name :
      snowfall_amount
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • snowfall_water_equivalent_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Snowfall water equivalent
      short_name :
      sf
      standard_name :
      snowfall_amount
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • storm_surface_runoff_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Storm surface runoff
      short_name :
      ssrun
      standard_name :
      surface_runoff_amount
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • surface_roughness_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Forecast surface roughness
      short_name :
      fsr
      standard_name :
      surface_roughness_length
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • snow_water_equivalent_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Snow depth water equivalent
      short_name :
      sd
      standard_name :
      lwe_thickness_of_surface_snow_amount
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • total_cloud_cover_boundary_layer
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Total cloud cover
      short_name :
      tcc
      standard_name :
      cloud_area_fraction_in_atmosphere_layer
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • total_cloud_cover_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Total cloud cover
      short_name :
      tcc
      standard_name :
      cloud_area_fraction
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • total_column_cloud_ice_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Total column-integrated cloud ice
      short_name :
      tcoli
      standard_name :
      atmosphere_mass_content_of_cloud_ice
      units :
      kg m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • total_precipitation_run_total_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Total precipitation
      short_name :
      tp
      standard_name :
      precipitation_amount
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • upward_long_wave_radiation_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Surface upward long-wave radiation flux
      short_name :
      sulwrf
      standard_name :
      surface_upwelling_longwave_flux_in_air
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • total_snowfall_run_total_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Total snowfall
      short_name :
      asnow
      standard_name :
      thickness_of_snowfall_amount
      units :
      m
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • total_precipitation_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Total precipitation
      short_name :
      tp
      standard_name :
      precipitation_amount
      units :
      kg m-2
      step_type :
      accum
      [1092310687209 values with dtype=float64]
    • u_component_storm_motion_0_6000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      U-component storm motion
      short_name :
      ustm
      units :
      m s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • total_column_cloud_water_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Total column-integrated cloud water
      short_name :
      tcolw
      standard_name :
      atmosphere_mass_content_of_cloud_liquid_water
      units :
      kg m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • upward_long_wave_radiation_flux_top_of_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Upward long-wave radiation flux
      short_name :
      ulwrf
      standard_name :
      toa_outgoing_longwave_flux
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • vegetation_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vegetation fraction
      short_name :
      veg
      standard_name :
      vegetation_area_fraction
      units :
      percent
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • vertical_u_component_shear_0_1000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vertical u-component shear
      short_name :
      vucsh
      units :
      s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • vegetation_type_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vegetation Type
      short_name :
      vgtyp
      units :
      1
      comment :
      MODIS-IGBP 20-category land-use classification (WRF MODIFIED_IGBP_MODIS_NOAH), as used by the HRRR land surface model.
      step_type :
      instant
      flag_values :
      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
      flag_meanings :
      evergreen_needleleaf_forest evergreen_broadleaf_forest deciduous_needleleaf_forest deciduous_broadleaf_forest mixed_forest closed_shrublands open_shrublands woody_savannas savannas grasslands permanent_wetlands croplands urban_and_built_up cropland_natural_vegetation_mosaic snow_and_ice barren_or_sparsely_vegetated water wooded_tundra mixed_tundra barren_tundra
      [1092310687209 values with dtype=float64]
    • upward_short_wave_radiation_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Surface upward short-wave radiation flux
      short_name :
      suswrf
      standard_name :
      surface_upwelling_shortwave_flux_in_air
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • vertical_u_component_shear_0_6000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vertical u-component shear
      short_name :
      vucsh
      units :
      s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • vertical_v_component_shear_0_6000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vertical v-component shear
      short_name :
      vvcsh
      units :
      s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • upward_short_wave_radiation_flux_top_of_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Upward short-wave radiation flux
      short_name :
      uswrf
      standard_name :
      toa_outgoing_shortwave_flux
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • v_component_storm_motion_0_6000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      V-component storm motion
      short_name :
      vstm
      units :
      m s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • vertical_v_component_shear_0_1000m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vertical v-component shear
      short_name :
      vvcsh
      units :
      s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • vertical_velocity_geometric_0p5_0p8_sigma
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vertical velocity (geometric)
      short_name :
      dzdt
      standard_name :
      upward_air_velocity
      units :
      m s-1
      step_type :
      avg
      [1092310687209 values with dtype=float64]
    • wind_u_10m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      10 metre U wind component
      short_name :
      10u
      standard_name :
      eastward_wind
      units :
      m s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • visible_diffuse_downward_solar_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Visible Diffuse Downward Solar Flux
      short_name :
      vddsf
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • visibility_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Visibility
      short_name :
      vis
      standard_name :
      visibility_in_air
      units :
      m
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • wind_v_10m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      10 metre V wind component
      short_name :
      10v
      standard_name :
      northward_wind
      units :
      m s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • wind_gust_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Wind speed (gust)
      short_name :
      gust
      standard_name :
      wind_speed_of_gust
      units :
      m s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • wind_u_80m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      80 metre U wind component
      short_name :
      80u
      standard_name :
      eastward_wind
      units :
      m s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • visible_beam_downward_solar_flux_surface
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Visible Beam Downward Solar Flux
      short_name :
      vbdsf
      units :
      W m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • wind_v_80m
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      80 metre V wind component
      short_name :
      80v
      standard_name :
      northward_wind
      units :
      m s-1
      step_type :
      instant
      [1092310687209 values with dtype=float64]
    • vertically_integrated_liquid_atmosphere
      (init_time, lead_time, y, x)
      float64
      ...
      long_name :
      Vertically-integrated liquid
      short_name :
      veril
      units :
      kg m-2
      step_type :
      instant
      [1092310687209 values with dtype=float64]
  • dataset_id :
    noaa-hrrr-forecast-48-hour-virtual
    dataset_version :
    0.5.0
    name :
    NOAA HRRR forecast, 48 hour, virtual
    description :
    Weather forecasts from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.
    attribution :
    NOAA NWS NCEP HRRR data processed by dynamical.org from NOAA Open Data Dissemination archives.
    license :
    CC-BY-4.0
    spatial_domain :
    Continental United States
    spatial_resolution :
    3 km
    time_domain :
    Forecasts initialized 2018-07-13 12:00:00 UTC to Present
    time_resolution :
    Forecasts initialized every 6 hours
    forecast_domain :
    Forecast lead time 0-48 hours ahead
    forecast_resolution :
    Hourly
    • " ], "text/plain": [ " Size: 1PB\n", @@ -666,19 +666,19 @@ " longitude (y, x) float32 8MB ...\n", " spatial_ref int64 8B ...\n", "Data variables: (12/142)\n", - " aerosol_optical_thickness_atmosphere (init_time, lead_time, y, x) float64 9TB ...\n", - " best_4_layer_lifted_index_180_0mb (init_time, lead_time, y, x) float64 9TB ...\n", " baseflow_groundwater_runoff_surface (init_time, lead_time, y, x) float64 9TB ...\n", - " brightness_temperature_channel_123 (init_time, lead_time, y, x) float64 9TB ...\n", - " brightness_temperature_channel_124 (init_time, lead_time, y, x) float64 9TB ...\n", + " aerosol_optical_thickness_atmosphere (init_time, lead_time, y, x) float64 9TB ...\n", + " brightness_temperature_channel_113 (init_time, lead_time, y, x) float64 9TB ...\n", " brightness_temperature_channel_114 (init_time, lead_time, y, x) float64 9TB ...\n", + " brightness_temperature_channel_123 (init_time, lead_time, y, x) float64 9TB ...\n", + " categorical_freezing_rain_surface (init_time, lead_time, y, x) float64 9TB ...\n", " ... ...\n", - " vertically_integrated_liquid_atmosphere (init_time, lead_time, y, x) float64 9TB ...\n", - " visible_diffuse_downward_solar_flux_surface (init_time, lead_time, y, x) float64 9TB ...\n", - " vertical_v_component_shear_0_6000m (init_time, lead_time, y, x) float64 9TB ...\n", - " wind_gust_surface (init_time, lead_time, y, x) float64 9TB ...\n", " wind_v_10m (init_time, lead_time, y, x) float64 9TB ...\n", + " wind_gust_surface (init_time, lead_time, y, x) float64 9TB ...\n", + " wind_u_80m (init_time, lead_time, y, x) float64 9TB ...\n", + " visible_beam_downward_solar_flux_surface (init_time, lead_time, y, x) float64 9TB ...\n", " wind_v_80m (init_time, lead_time, y, x) float64 9TB ...\n", + " vertically_integrated_liquid_atmosphere (init_time, lead_time, y, x) float64 9TB ...\n", "Attributes:\n", " dataset_id: noaa-hrrr-forecast-48-hour-virtual\n", " dataset_version: 0.5.0\n", @@ -702,8 +702,6 @@ "source": [ "import dynamical_catalog\n", "\n", - "dynamical_catalog._stac.STAC_CATALOG_URL = \"https://stac-staging.dynamical.org/catalog.json\" # staging catalog; will be promoted to prod soon\n", - "\n", "ds = dynamical_catalog.open(\"noaa-hrrr-forecast-48-hour-virtual\", chunks=None)\n", "ds" ] @@ -734,10 +732,10 @@ "id": "2dde2a45", "metadata": { "execution": { - "iopub.execute_input": "2026-07-16T14:47:18.382354Z", - "iopub.status.busy": "2026-07-16T14:47:18.382076Z", - "iopub.status.idle": "2026-07-16T14:47:22.934632Z", - "shell.execute_reply": "2026-07-16T14:47:22.933904Z" + "iopub.execute_input": "2026-07-16T17:24:25.640209Z", + "iopub.status.busy": "2026-07-16T17:24:25.639677Z", + "iopub.status.idle": "2026-07-16T17:24:33.818744Z", + "shell.execute_reply": "2026-07-16T17:24:33.817645Z" } }, "outputs": [ @@ -751,6 +749,14 @@ "metadata": {}, "output_type": "execute_result" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/user/notebooks/.venv/lib/python3.12/site-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_1_states_provinces_lakes.zip\n", + " warnings.warn(f'Downloading: {url}', DownloadWarning)\n" + ] + }, { "data": { "image/png": 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qmzB3X+di/nvnEzipwEngTdo8CzMVxeWPuuzbm5JVrzHdl5IKqHmSfKIon1/n4JMVLrvGhCxmBzUDK/ZaaGBQ47cFC0Mpe590CTOr+a8udHhUbGEmGcSXIaPSWMr/qPAXbJtRFOoW8RYTI7ZrEeO/sAdtCRpDFlfdYyyl4LoGJ/1FHgguQ5YWSDxzj1S7SuLmiXIdgqKHE5p76rU9vLaD13CwnTLSyVzy0sIBrr/yt/nGfT9PD68Mjh07xhNa85PW89t0A0KwRwh+YMcODvSs5ReNHim/BqDiRvffB099EgC3srX7d52G6JV5Wvf+IcHMg8/5/KVjtzHwfb8DwMUTf8zjC5dT8GvMMkYrMFlUvtfk0KkbKAFCQ75uXrTFpRy5ZYGW0BgMu65YMyBDxlanhNNZK3PSUpF4AfaYKSsayM3ylsKd7EnOMVJPWE1ans1b1EWelbTUjeGCiZHOhyPMtCf482qb3eWjAByMN4KS+M0i7TBPpNzuZ5o6R4JlMqSBPt3irg0Ojk6Ztcs8rUys+VQ8zrlwgiDNMe2M0MyZuHdRdXi6cR750hzDuenuccdaKVFxHqtvmtjfgtfO4uS+JMp1zMIjq9H++NO/iAvEXoCy466bVjlri4rVWP3qduUEjAwc4Y3uw+Qjw5BLOcFIUxO7sFhNeY9jsptvOhPyl1sH+Kk7G9gdRZIzL8XNHcXKhCa0YbhhLJfQ0SgBjlJYCtZvbHDkzmFCR/PQcJ49Ky0KdcH5ZwyLC6VBKQaxUDYEeXOTnhLrOJeMcrC1g3ZcwLNNuOOXq/+LxNJ0iprUNudsvqOfyNE08im5SHNul7n+eddnRZfYXTjAE3pvVgENulMytdoyzf5de4GLVGCFGllrQ8MshAg7oBKswnA3cfHbrWro4dvH92/bxm4hGHweK3kV10rBR1LFvn37uOCCC17G0b320SPl1wCSpknOWbWML9/wHrzBvaTBYvfv9Yf+ABXVeWTujud8Pr/ujTBp4o+zn/2XlCtNbr3xl3iocwFHm6bcyvJr+CtDRsRCS4pLhnzycxq51CCerBD7cdf6BSD2cev9lBeGcQIn22TIaHnXI1R9Q5Bb/OPsSc6xoZ4gNUwXDZlM2yUWdZlaWmYhGiTNMrgnvHMMFw5w9+ybuPfsm7nPfqO5fiVxm1W8toeMfcLEjPGUGu3GlW9KnwRgyc4RC4tUCM7oIY7FJl55ormFRm0C2SlTL89zdmUbAEKk9BdNQtHJ2Yv4sGuSug6MfomqbOA6xrqLfHMPtFSkdozy2ohMxEMDsbYQiYO2Y5S1RsZCWcjYR4wfJs4S2rBjcAIuLuzjusV5HuivAnDb2QZ9CxaNquLpfps3HzXH/7tted73YIOVYY2dCMqzhjhTV5BrCs4/6tIop9n9VLQ9jUwhtuC8NOWLbYudDwv6toeMHhCI08uojlkICEsgBnPkZY5On6ReMsepCJPPUHZWGPJmWYmNZ+UYY/j9Z2h4AX3ttYXA0YpDIdXMuHkawtyfdckyE9Y8TW1+V6FJUHTbJaKBKbxzZoG5mhiXZKVkMkih3UEtmbh/ungancaouNXNp3ipccnwTVi5oe5i+Hsd+/fv55DW/PQ3sZJXURaCi4TgBy+6kCNK/4P79vBs9Ej5NYC4eQagm0nt9u3ELk8+oySqQdI8+00zU53h81AlY1UmTpvUTvjLB/5fgsEpfnzL/wDgU/f8Z5zUwmvnEBpyy+ZFKxdqqMEy7b6E5JlWcuIiwzz5Wh9902XswOyf+JpT5x/F82vkLENklzsHGKknpBKm8pJzjnGlT4t+mipPS+VpJcWum3aHe4JxscCxymamFnYiVkYAyDWqVOYGyDU9hLKIUuOSPhxuYsBeYcRa6FrhqRCkSHyt8MWzNdul10QpC8fpMFSYAuBNxbvxRcQfzb0HqzGAnbnvT/QdpN9eZjg3zbkL7yRNVwt7J1FOAF4LK0uWSrU0RGxFxve8CmWhlUKrFFUf7v5NOCHF/AJbrHPcOTDEDx41WcxuJAnzmuqC5M1N8DLSexttHtyTcDJXZCDpcHPTLGKEgvnRFCXoJmilEoqBpOkrBhqSez9aZbteQs6HDBTyqLyFNegj/OwYsaK52UdLKO1vMWybJK2bCme4xD8LFsy5Pnd7Rl3ur2tvYaF8P28oPc1o5o6OpWTWLrNkF6npIlcnRi1sQz0hLc8wa/Vxcekx7jl2uXkugzxpfYj2hqexnQ5xaM5pL05gxxvwmj5OM4eOzHxFsweQrtlndYH6UsOpbMEujL8s53ot4Acv2MuFQlD5B6zkVVwlBf8z1dx7771cddVVL8PoXh/okfJrAKtk/EwIy+mWREm3RBrVnvezl47dRv9Nv0rimS9RmpXqBP2zCL/JJx/4NQD80EOmFl7HRWiQjYzIpCTsk3RKbWIv7NYSyzCP36jSP91H7uwa6XW2WCS5NgUZc37OSFtujReILZjN20zZFZYw455RAyymfbTSPJZIcaQ5ToJFWXfYkTvCfG4cvWwsy6Ezo5RPS+KKhRU73VKkU83NhPlzDOSWmXeMNdYQOUq6g6tTxvQSGxzjbWjn8kxpi3aUZ3PlIH/19F8B8MT2CKng026dZ87kqaXzWC7OUnZW+Mcjn+TB0MSPnwaEcrDsECGeETfLrF/x92uRhQIt0YmDdA2JuW6bTcVj3Lp4hg+WbuXm4hcAY3HuPOSyb3fEpQ86HD7fWLPLec3hXJU5UcGXsywOm8XAdH9KaINUdBclgQ3rlgTj8w7VOcHfLFpsic6hppqw0cxbOuihM6WuuCA4viniVEXy9v1QOmvOuQOXlf4UP5BMDXV4st84n+vH38Aj2zr4xZCGN53dN8lpPUKgPc6Xx9k9ZxZZ+cACYp7sb3J/cDFey4RGmlue4P2Tv80lrVm+VtjMvmiHuRf5C5ixUqTaQrWdR5aNGpuwHNLOMsJyke7Lpywo3QKXDN/0vF6o7yXcf//9nNDwz61vTcgABSG4TAjec/XVnFQK8QKIvIde9nUPPXxPYH28wEmnp7LUw7ePH77qSi4TguKLINc3SME88OUvf/mlG9jrDD1L+TWIcGEfdmUSMve1sNzntaYBynt/AlEZRK+KZaUSLRUiX8PzGsQ5Y7WVFwcQSuIEtqlTDo3VqvM+ia9IM3Wq1YSuxA1xQg+/ZtGZlFix+aJObT+CNXCaXaWneIN4ujuOM7kcZ+QgNV3o1hMvpn1MRSM0E2M5rbq7F9I+TlodQu1R8pdZHjLu+4V2kcqJEey2Jl/ro56NPSjazAnFOXuZyDUu0wSJrxIcrZiI6+CY8owVp8RZvZ6RgSP8M/evul7mti3Y548haylpYQWRaWWLlRHqgFOOeDDcy3IWU/W9JkpLtLaeJZwivDaWHeI6bfKOSVByMw9ApFzCJIdnm/rcEW+WAWuZ/oaFVw452WfmcMsidIpw0T6X2pimnjPm79mCS0t4vCE4zTmvwKPrjKXspwKpYSqfp6SMW3ekndDyFeWGhffYMm7sE9k2aTOrmbYFccHUKgOcGok5VCqwLmiRjvpY8+Y45VjhN106ZVjOwddbV5h7teExbi99kQvDc6TZO/qU20egPSbkHJ6OcTMFOCVhIS84EG9l7qlbGb7k0wD8+9yfsnUlYPtJl8q641gDWb5CBZ6WMVOATLdRSTcB4C1eypc/cwOXb3jPs5TuXkrcf+T3uGzy9u95K/mOO+5gVsM7X6CVvApfCK6Ugh+75RamXqS1vHHjRk6dOvWc7T/zMz/Dhz/8YYIg4AMf+AB/+qd/ShiG3HLLLfzu7/4uIyMjL2qMrzb0SPk1iIfOfoZrLvwgTt9mAIR8rsY1GGnN6tW/gigUVjU+EEoSFhqUC/M4MmIhZ9yRuaaJoYrVN+xqCzZLduOUqyUrYEQvGoOzPP19T2DZIUlk3Ma222G4MMWEPUtbmP1nnD5qusBCWgWgpozrsanyLEcDxKmJd5edFXMMkXAyNXG868v3kJQN6X3BuZWp5nWMP5pn+MRAVxmrLlLayuKYFfG4Y2LEg2KF9XqJapSQj6GdEb4tUopuncuLjzLejthnekbQsm0WdZkBd4FacZDEb3av1fMa5Kw2lki7yWhVb4lUW7TjIhFm7iw7pJxbpGA3yFttCtKcc4MzRYJFoF0C5TForV3n+08d4AMTb+IXO19i45z5OrqRwErg5NaY2KZLeo5WFHTIhSdtiuNNKlkcXyqYKxmBlFJsbnQphErLItcUJPNtThc2MJ7WsCoe3DvHiR8d4MlRwdHMep5Ml3nrwYihJxQstGnvN0mE+fMHcDxJs98yTSmyx+Ka6j3cuniGrSdcFoYyjfOJGmesBj4xT7KR+yfNAq4o29zfupjp5Y0k5UX+g/1/ALj58wKUROU0aj3sSsy963ebbOk/zYHiMfb3v4EN6Q0AVKaNe/vlTrr6Xs/w1lrzozffzBVS4H8bLuhLhOBBNJ/5zGd45zvf+YI/99BDD5Gma4mlTz75JDfffDPvete7APjFX/xFPvvZz/Lnf/7nVCoVfvZnf5bbb7+de+6550WP8dWEHim/RhEu7MPKGatNq+dPenEqmxF+Ee253aYSsReRlucZ9adoJGWwjRWXqzkoqTOL+hlfPCmQSuC1PSzHJigYshLrD7C+cBpPhixGg9SEebn7TouSXWchrbJqJ9siYUWVmE2GsElIssduIRqkFVQRQpH36t1TtlSBiqwzac1yaXqsu7040uZP10/gBDsZPGbRN91v5iLXJpAp9dYQDzsm5ntp/gngDPkYnBTSLFIzai2Q5A9yqThEw5HEWay9JR0CXEJlrPPV+HbVWaZiNSjINrW0xIi3pgAWKhdLpARZHfmYP8Vm9zRgaq1XE8yaOkdLFUi0RcWqdz0FI2IRJeCX2l/iqXKRqz9tEr0a5xc4tT5GKlBiLXPV0hqXBCU1Fz3l0imavzWLiqkhhxSJym6dnQoqKxb5uYT2fMDx3Bj/ccuDEChEzsaNBVJrbpkzdcsbTjuU7lkhOt2geaqBP5DVnbsS5VkEvqZu2+xxTJ7ApDXH01Wf4WrCStG8OAPhUxAdFnWZvzj3biqHLgaMTno8cYhdA/u4auwx9hw115987SzBbJvC9irjG4ZJpbHi97aXSOUSRwYO8onROg9tNtnZ5YNDXDRwzTf1CvXw0uCv//qvqQOXfpsxYUcIrpaCn/7BH+QHkgTLsr71h4ChoWdn2P/Gb/wGW7Zs4frrr6dWq/HRj36UT3ziE9x4440AfOxjH2PXrl3cf//9XHHFFd/WWF8N6JHyaxQPnf1Mt6vON8tEtfwBhOuBv0bKqRNTqZ5lwF7plrcA2G2NlqCcZ3/xtCWwYtM4oFVpEq0zVLurfJBhe4FQezSSMnq1d/IzSqZWS2BaaYG5eBBPhqRY1DN3dZDmSFMHy4opOysMO6aEyhMhtkjZxAzVOOkSzSZ7hr7xJ5mLfaTaTL5mLPHy4gDKjom8NvMdkxR2zFnhBms/Tgo134iDAPg64hb7QSJsZtw81dT4r2NhkWgbT4YU7AZD2VhG7AVG5SJNcvgiZNKa617bqWQCT0YMWIZMd9vH8YhY0mUC4a7Jfma86omQQbnCboxL7k0nO3x9vc9bD0Z4SRNdNuGIExsiAsd0a3o+HB9NuHjBxc0s5dnJlGqiecNcxOaHzT5q/yIyZ6OaMQ+EEwyV2vhBgL54kNSVPLpOcd4c9C+bV0B+UcGgjzvok+sktM6ZUqh8ap6LwFOMtxVbyiZhLsRh2qrwjU01rGzBU0oj9uhzTNkVirmlricjKq5w0eCDvMv5GrtWWiS2mZdwvsPyWeNNGDk8yBlTnUe5aVFakVgq5qrx/dzVbxZCoub1CPllhlKKn3rHO7haCtzvIFHrQiF4AM33OQ6fXFl51t88z8Pznt/bt4ooivj4xz/O+9//foQQPPLII8RxzE033dTdZ+fOnaxfv5777rvvNU3KvUSvHnp4nWNBFmikDs2ktwbv4cUhjmMWgJ3fYea0JQTbhWDhssuoVCrP+vn1X//1b/n5v/qrv2JlZYX3ve99AMzMzOC6LtVq9Vn7jYyMMDMz8x2N9ZVG71v6GsZqsss3S3qRbgG8HCpvE2RdHWobDrHDN7G7IM1RmMri0qkGRCZOpYknjdJXazAmyAekTkJt7DTDJWMp9VvL2CIh1B6ptrplQUpZtNM8c6xl+q7EfaTaomg3iJTXjcvGqYsQKbYdMOQsdC1OW6RURYOCDomlIMiECs7oIeLUJS3Pc+6Sma60p7c0atSgZEqaxafrSQk3s9QWXYf5rP/jFqYZits87Y7SwqeESSJLkdgiwcvc1v7qvyIyAhoabJmyQc919/ftiACXC7VpSOEnCXdbu6iKJom2WFGl7hwUZAtfRFyXHKQ/NN6EO9fn2LPSYvCQYOjekM55Zv/YiljI1Lr8VHWvPxWCHY0mk0sWnSJMjRp376YphwtnwZ0NCe83L6TmKRNm8Kou775ymdJMnv935mouPbrM6PvOcN6cZHTaXhUjIyoJvNgBBd5b13Pu1/YBUDlRR+8s0/E0bUewqE0+wLF4PbZI8UTIYmo8Lj9qf4F3PKDoFBscOf/LfFllVkxpiR3uCTa0W4wtWatKrJQuGSZqnENIgWyn1Dzzh+ZkwoacZLmgOa7HutKtjcuGefOPPcmX/s+e7rz23NkvDxyhsb/DiiYpNJdcdtlzMrG/lZUM8NGPfpRbb72V8fHXf814j5Rfo7hk+CaEZR7mb5YZKiwXYVmknqCVaTZ7RZPAc7C1g9r8VkaWqwCkvkQ5EHsKLQVTO437NrUTpJIEhSbu4Gmqzhpx2mTqUSLtuq9bQYVUW9StiCA2LuM49lhfPdLNsl4VCUm1hYry5AvzFGSbYpYYlWjzWLaER+A6TAvz0v9K4xoa9THypTn2lPd3Sby2ocyZcJxIeSy1R7rHlloTWxBIGysb6+Z209TyuuARE0hzrlA42DplwFomlB4WSXadCS18muQYEDUcZY4zlLQpWBEFFXeTq1Zcmw/uO8jBTRF3VsaxM1d+goVNynoxx+RKymzBzJWlNeWORLRj1HCOpWHDkLEFlVBRCqGWgyDzgvupIh9j5DMj2PPlTLh6uQUFBzXb7pJxfTFicGMBu+jQ2VPmohtq7AoafOrPN3Hw/9vJj/yTwywMpbjxWrKYV7QpLmqsSFPuN650eyhHqyA4URWs2Gsvz0cX30ASFPGLC1xWNdKuZ61+nNY8IBgRy5SWzf2OcstURJNYCmpFRc3P2mjeXKWyt4qzkrCyycHSZiH0C/HPoEoWceLSOLKJm7f9CQCX7fy/uevdF3D5X/wHHvxBM5YeIb+2YFkW5fKLqzE/deoUd9xxB5/5zFrC3ejoKFEUsbKy8ixreXZ2ltHR0e/WcF8R9Ej5NQrplkk6c8/7t0vHbgOguP12sCwSX68laImUxWiQpdokMvaJPfMibPd5dEohQbHF1kcVjf4ZkBKRWliJQzh0hhF/oZtRbAtDCE2VpxFVSIKst3Pq0I7yoCWyY758Kr/CXGcMS6RImRJmJURKS4Qd41sdfBl2yXhFlQiEiy1TmuS4p2EUoKZmzwcrZjx/lh3OCXZwFgBHpNSKec7oIe6zTXJRoi0CaZFmpmAFM+7rvib521sEeUJCbGKRxTexsUkZtJYJtEeg3e5YFnSfWTAIuvuXYsV4M0BqCLNv0cRKympEKE9IRZg5LxDw3sOLRK7GDxxGbUNKx0cDqjWHtF+wtEFSK5o59RIotyUdTwOaHdltHjtnc25dwvqDgvAzx0hH1/TGZZTCeX34R1fM9bQSOkshTsUlcWH4BIDiV/oe5g+be/idP9jNpf/2CLlM0tLVim2LMZuaDjIB8f+dD0CnLpieSGhbNr5KWBBVADZWDnNs+Xp0YZmWMuP4fOca+m4yAii/e+6n0OcdAOD80mEWdYUncsM8kYM5bRZZt244yJmdOVqiwH1qN/csX23Gsu+NVK/4BG+p3sm5vmH+3clHzZy2JXs2fZUf3fwT/FD8vwF45KGf5dhV6+jhpYUU8CKroZ57jG/zcx/72McYHh7mtttu62675JJLcByHr3zlK92M7kOHDnH69GmuvPLK72ygrzB6pPwaxTcr07hk+CacyhYAhHRBSmIvJSqY0idHWyw1RxGtKk6r1G0wsTKyQqdUR8sUbSm01UC5NjJ1Sf0WpfI0JbvRdesGyqOmypztrKPRHEY2TSa0jF2kknitUreEamlzizjxwQ5AQScyFlSaOmgliZRLSxW639qWyhMKD19EnEtG2OibjOZoyGWxMUEjKRHg4itDYuNBwLAV0O+0sHOGZJrkKIem+YWvEqoZKd93nWLLsqA10KIlXRxt9reFoiA6DFDnHINUhZmvBVWlpsrYIiHF6pZ5te02lUDhxYLVTK62p8kva5z1MJ7U2JwaS37eyTEzkHCiYtOQLr42c761BgPHU4JBm2ZhLUEuHwo6nqbpafIRbPlaVjO+UGNbn0/89BJOxUVWzMJB5GyinWWclYTC1ca9Vzg/Yvrzp0maMW6bbvMKdcUA31+c5tBhlwf+3Q5O/NI6VL/NDvc429iPUNAcEnxolwlr2KQs6Crny+PMU6GoTI31Ju8UR+2YKMrzVN24k4PF9Xz6r34RMbfEyHVFlq76EmCy1L9Sux6A5ak9Xf30T1bmSJcdvLPbWff0Bsay0rtzb/1L3lb4OtuSGcasZYamzELIXUqY3WWex8Mdo1m+85L/za/oozzxy7/FFz7UE0d5qWALvgvu6xf/GaUUH/vYx/ixH/sxbHuNriqVCj/5kz/J+9//fvr7+ymXy/zcz/0cV1555Ws6yQt6pPy6wqVjt5Ff96ZuowqtIrAswnyEKqwAhghZGae4MIKV2MSeiTWH+SZJ34yRghQaabdRrotSMcJrU3JrlK06SRYPnksGmWpP0q6P4i6NdqUTrcQm18hTWshRGzFEiDTu7VQ5IOlqRSsl0di04yJz8SDDWdvBRFsUZBtfhFSsOrutEwBsKZ/mPudiZjtjHIo2s80z8e2SE+GnmlIasU6a+Pq06CcVAifVDIUxS5554Y/WJDMVha8TUJh/AUUbSypsnRKINaHNQLoE2uu66itqVedZUPOhgqYYZIQnNLMbFF4i8FPF6UwK8rYTDfzAYc/j8LfXh8QiK89aslGeYHE4xQ8Fg/NmAs5MxsSWxkvggvsknSxOnDRj7KJD0owpXT4Cg5k+95UOY2cE2hKIdC1luzjs408USEyPEQAiH0JfcftNR7jXGqP8P0N+4GcO8sRImVJbEubhxLqIGWXEU7585B/xUzs/xHXzi8TWInHmSn+gMsRjI0+xcuwq8gsmbXr7w0X0009CqY8w7xC2jEV8OLqAyqGL2fBZxcD5a2Uuy2N1KrMlCqdjUAFHbzYhk9H8OQoEWNli519cdUH3ufjC9NvRfqsbAtntHmUHZ/n0v/oa8C56eH3hjjvu4PTp0/zET/zEc/72W7/1W0gpeec73/ks8ZDXOnqk3MNzoIVApt+kHqeH1w123DIDSzZ/8ut7EWXFvBcx6MdYG9vILQnqvF5xRg8G1nfBff3tfPzNb34zWj9/lynf9/nwhz/Mhz/84e9sYK8y9Ej5dQRv6ALs8mT3d+lXiTb2URs+g50z4hxJY5DK3BjlxQqJnXZjzUl5Ea+wTBx7xo0sQrAsEArPr9HnLGOLlKXEWD9nW+sJ5zdRmZ3EbxaQmeyU13YpzlnIpTYL65/RRzjxUEqCQ7elY9sq0qhN0AkqLNkd8pnqVlG22WGfBOCh8AJsL+seZZ3motyTPAa00zx1TCwzljUsrQikRZLFfMu0iKUgleAnMJIai7jjCUZrktk8z0rSKsgUXJAoUmF1Y8qr41lFQRkrvxSnhJYgttZeGNW6jZ1AYoFVVt2x7J/Q3Po5c/5bH3Q4uGN1XmxmN2mUhFxbcnST2Z4PBdWsA5ScadOeNufXSqOVprClgtpY5MweM+eVmkXigqjauLFxrwugeOEgbCjR8WFmLOmOM7Y0TRdufNjhzeNTqNEpzqU5Hncc5hdyHF6oED/l4P2fkLef/7/4CfsUI//9HP7Fw7R3meS94Px5Lis+yufLOygdMa5uggjhF0BaDJ/sw2tfBkCu6ZH/6/uIO3VygzcjZs39z93fITz4F8ROAefa78eKjadgMRjmCX8LNXuaY+kkZ3//vwHgPH6crVds4tjlT9CITDb9o+FuEs8iVU6vacRLiO8KKff6Ubwg9Ej5dYJLx24jN34NWkXdlo7ONW/j9PYF2mPHEYl5WRfmJumb7sdv2rT6Q5RtSEb4TSwZk0rHWMqhC74PVkzeaeGJkEB7zHbGABM/HDy9hf6zz86k9JYV4tQ5RKHQ7Y9L5KOVhbZilJWwIYsRB57H/vYgutkHxZku+W2xTzOsayyJEpZI2Nc2Mcskb1GVDYadBSwSQsx1WlqzYrvEwiKvjTs+T4jUuts9yV/jJObLikC4VNMIJ3MIVAJNPmmx5FmUZYuTylxnUXQoijZF0aFMC0eZDxilME0uEqw2hAp8RWJpOp7GUZr+1AhwNGybkxenjJ2x8O6cR+00hNI/Z9zFoa9Jbc36aSc7jumXbCeg+zzyY89I6HIl1kSB1JFMHlrdqtB3TWOPFtDZvrrPQwDKk6z0p4SZeIwXC5SEwJKsDGuqHzElYev6fYbfPMq5vTXS8TKPhudDorjuI0/w8d/axHv372O4k5AvbwBgR7/NdVv38wXv+4k88ww98eMPs/c31qEbK7j3NxiystdLmqCuvpq5nXUGf/NPWXni9wDIjV6B5Q+gLAf70AHWDV4KwBlu4AtD29F2TP/J7Uw+anTL44u3kX98monSTqaz1ppHk8s5MHSGwrmtlLb/ID28NJBCv2KJXt9r6JHy6wTSLSPdAqqzjL/rLQDMXphQHzZt9ZxZ8zKtzg1QnLPQUhPmIqLiCgBe1k4wTTy0tnBrZRLRR1Kdw5ERofZYjvto1A1ZVc9tYvhYBXuhg867iCBjvekZ4mMP4172ZlbFvex2GeWYuPZQYarbRjHQHkdzy7Q7JTbmTrDbOQrAmF4ip2Na+HgyIs0s6APtXezIHWGvc4hFXeFkasZScAI2pAsEwqaUqZuVkwRHaWIJ3jM88X11i2MbNHXpY9kKJ7N882gmFy0q+ZTFwRYbLTNvK7rIiFimTzcJhdMtC6paHQaaEjcWBK45QcfTKGHKsFq2xMqEovNpwtkB8//y2wYZXcoIcinB7lioIUF5VmPPGXWxxo4cQV5jRwIRpOTOM0l0WAIiBanGWgjBMa+56IFZOlMt5OkmVi5T6HrTJBQdWqM2tWKcJaRB01dsPuXixQmJrUmyBhXOaAFvOYV1cH5jhXLhs+DAW96s+OjvT3DXtqu4PX0CUTf7VxdcLuprMVA+053bvZ+8CqwpoyKXJiCNta+jEPHgIwzfMYd281R2/WMAwoWn0GnEXY/8awDesPl9APRbHoeP/B43XPMR/Cu2oMdM8pp7cJr0zCGKf7DIzuqEmatHP4aVGyC/w2Lh6Y8D/4weengto0fKrxMIy0NFLYTlEF9tLMvFiUNoK0Y2+ylnyTiVaR9Z65AM5ugUW5A1qLdkTKocdKeEUDaFlRJSD7BSXCFWLvWkxFIwiLtoXpCjxwZxjs1BmiKCPLphspWTk/sgNS/uXNPIbKZOH2GuTTp0ki3+cXyMm3ZB9ZEqB686zYi9wFDWybhPtQmEw4Ku0k7XrESgW34zIpZpZu7rc3qQQVmnmgZdd7SlTaIUGKt2FVJB3coERqRP3lorQ2rlFLEFA0mHtmPGkmAxmS6Z65CSOWk8A1U3ZFgYwZVVxJaxzBuuoG3ZyCwW1h8o/ATcWNAsKjY8aM6ZVhyCiiB2NbKT0r7TiLrEfxkw+J5tzJxvU87biNYzLiCDnmoRz5h7t/z0CoOXDRHOtEk75tjJEwtEb5tgpT8ltjRzmU72jimbwmfOsbXfZ+Et/fjvMpn6shbT7rPouBG7Trpc/bgJa7S35/mnzoN8ML2RH94zi66auesUYaEAtfogw2rNBhKOiw7a6DiEwHgKdNBERW3SxjTh7D7uO/jbz7qWVQGQB47/IWAaqbzpbZ/D23srulEneepeM8ZCP1g2af0c0eyTAJz5q5/h3O4LsiP1apZfKnxXsq+/O0N53aNHyq8TqKhO0pqmfO3PcXqLidkleRNHLs6P0zdtyMSZbkCqiPM+YaGBnXVDyjtNap1B3Ho/VuqQr3tIitRHfdpxgTh1CZqDjJ01Fop3bJn0zGFkaQDiCDVjMqTDqcdwR/ZAnJCvZbXLlGlVlhkuTDFpzxJgXuyPNi4iivIMlM7hiwg7K0+SWiOFoijahMolUpl1msW1Z9VAt2QJwCeilIZUozUfdVZdg6XBSddczG5k3iwFHWI9I4HkdEWwvqZxUnP+1bHsVmfY0ApoO4IBEXLaM1nm+90JGiPz7FoJyIfmmLFlCDmwBCmy293JT2LKHUnkaDbs1+jMD2gtR+SVw1JWyVPLhD8e3Ndm3cHHufgXdhFuL+FkeuSymQAROBbhU0vM7jclV+OXD2H1+9jNmNxW4xoXroV9uE3+mCC5xsXJEvcKTYm9voSuRSS25olLzZxNzrosVWOe7PeZ+KdPsJjNS3FDkfxlI0ysNPgdvYvbLzaW8Xwl4uH8OMlirjuH2pEIxwHHgzhEx8by12mMjjuozvKz1OcuHbuNh6c/i7R8Lt/wHuKmOXZlz49jlYdJzx2m+dRfcP+R3+t+5qrdv8S9Bz7EM3HjLZ/GvfgmvvDrFXp4aWAJsL5DVu3FlF8YeqTcw3OghUDq9Fvv2MP3DN54+0k+/ZGd/M5/Ow8hwCkkBIOSjZNHiPQVIHuvktczvhviIT1SfmHofZNeJ0g6c1i5IfT2zXRKWWNwO8auD1BerJKbNhaRXlpCVKoEpYgk18bKFK9i5ZLEOSq1PmQq8RoCJV3sTp4wLBGJFHt5lL5zxmWcHnkQFdQRfhHikHjRtFg00p4OBAFeVu4b5S2S8iKD7gJF0eFsalzpq9rXOatNUbS7MVglBANxyBXWYZr5PIdD0+R+VU0s0C5NkWNUGntuTCxRjU3f5NU62lUrddWFvX2/edSfujBmIohp2ZJAWlhdi1pzrE8ybZcoq4C+rB7ZVyn7S1U2hTUqocZ3jSu5IALq0me6GJH314LWxvoWODrtqn/FFnRcRUFJZC1GVUxCV+dLZ0g7CaP9HvKKUZLIHOdEHQ6tKJL/8hR7rhtE/ZhxMbuORDoSkWrc0TzywIqZc1eioxR3fQk9abKjmW5DPQJLMHnU55FLzSJrajTm7A/kGFgp8qntfd1kuZuZ4sm+HJfPBHhVl+OPmZt33u4+Gjty3DtW4sF/vZsfdz6LimDlXJ4HTu1i9P5p+s/9W2Y23Uro3AKOQ7p7E/KhfSauDOi4g47a6DTGG9zbnauHpz8LGJnYyyZvJz9xHQBf/8ZPAnDTb2v0/j951nP+961kgK9+8YX36O2hh1c7eqT8KsS3I7L/2OLdXH/lbxP2SeKcieWhLNwgT2nBR8yaxCXVWIbBQSIvQVkRTpaNFSY+OswbvWIlcGJNEkrsyCcKihD59E+PmzgykDo+VmkQ3V5BRx3c9ZcAIMc3oPM+2rEJM29io68JTkBRtrFIsTPhB8eKiDK/sk1KWRl3Zz5N6A8UI6nivP6TNB2zEAi1R6ItmiqPLVIGpCGOzeEyI01NLpLdEqXVMGdsaTacdlhcZ7YHDgy2IC5pYqnXXMypxlEpjqrTeIZy0LRdoi7yLDoBjgp4+xOrHoQaj26PCCxJnEkVlSKT5DXeVCzkNUHWe7HmCS55ePWYMXLJxNRnDqzQbCoGhx3G9wxQHjEZxbYIaCj4+knNUzNzXP6IiWmf9892Em4v4c5HyKJLPiP3mfvnKQ15lC8eQmfJX6LgQM5CexZxQVDurCZ6mXKo6VKKp2NCYY7xv/ou5rb4cTYedfAuH2HDz20385XAvXtifr/2bpZOX8ofbDULDXsy5XBxN653MZMHimx68jeI3NMslH8Ea6ENm7ahD+03lxzUEZaDVRwmqZ193uf3obOf4ZLhm5617Y6fF7zxL9/EDdd/1Mzjkx8DwOvfjVPeCNBNEuvhpcV3oySqF1N+YeiR8qsQ367Ivj2wlUY1QmeKWSiJ28nhLsSo+XNrO/ousdcEO0ZlOtRp6uGuDFGa9UCBFSRYjsbt5EhWhvBaJQZPlogPfs2ca90ehJ+D6gB6ZJCpi41l2awuU1yRDJ3I0a4Ykg3zbUSWUBVql6Z6dvJWJ80T4OJoQ2J+amK7TgrjyQqTlumnuy/aSTvN40nTb3n1W+4oTSGQ+OHa114qCDzNfCWlXdQcHTdzMtgUtD2Nl2oiqXhGmTFeAo5StCyNynxtoXAY1HUG4pAb7pDIwGR3NzZ7JjnMJrM1YaAp2XjQYmqLYnxBcF9WvptPNHddGWeWvMWV95lPjF/Uz/yBFYYuGSTaUqQSmGzySx6ts39GsRIJFkPBNw5nyVv/4yC7b18Hu/vRqWL2jJG8PDOXsjNRFJsxacncz3MX+OQ7kv5z0ClqctncfHW0yhE9wal4gsvFk91rP18c56JTkqAIM7cWeWLcnPOwO8Rvz/w0g/e/iS3LOU5kpUhxvkF+aYT+mUGsxOP09v/IxNH/Rn/07zly0b+h/0QJspiycHLYGy9ALZqF4Y23fBqA4Ox95DYY6/grn/u+59QYv2Hz+yhf+D7SulE0cytbya27Fmd0N3LAzNVb/l2DeKxE9L9/HZ1G3P34v6eH7z56dcovH3qk/DqC1TdO5CVdbWGhLLx2DrGwTNoySUHWwCQqn8lrypQ0NY+A6lQoLwxjL3RMyU0QY1kppeUSbuBRWijgHJsjLZjyHFEqo/srJBWHxfUtFnY9AoCfX2ZxfhPFpb2EeUNgsd82jTCSKifFOHOJyWyKU5ckylETg8wUBonFkexKUpZyAktrfJUybJux15MSy9EABbvJsL2AT1bOozTFlo3fXkvoil1oFhKUgBNjMZXAvBHanqadsagSghXbJJ0N6wBpaZQwLusl2yQwJUiWRYHbD0bIRoe03+zvNYzox4rtsTkw5GgnkoV1mlohJS7D+bOGCN3YYqGc4qTGUj98fibtecEkffUNrESCgeMpuGb/8y4u4+1vYNuCUtniiSNmHs8uJCSfOs2F7xGInM2BM0k2j5Ar2qgopVMx19nxFImtiTYJ/ECi5Nrq4/r0KWLrIB8L38oF7kEARlnmwU2aOdvnIOs4FJmQwUxjlNG734wVGbLfsH+rOaeXYsUSty2xAgVIDl/1LxhM/hPn3/kfmEt+jNg3iX7SLyBKZaKL1+P8bQOdZecXdrwNa9sFqMEyt/zMqS5pf/nPjHaxU95E2lokqRvrWlge0q8iq8MwZjoBacCuxdhveC/J6QNcufPnAZ6T4d1DD68V9Ej5dQSRL5M6z8hAjn2KSznU4onui1BUB+j0aaKcsWxVZKxWd2XICIFMnUArhQg6CN2hNFuihIc92yI+eD/2RlN+ovsrxH0OS5MtlsemGKyaOHbOanNOb6Vd6RDmDFmlToyK8swE4zSScleNqb2wESssEJfnCbXXrS5qOBYH7TH6dYOyCmhhrLNIeYSJT8FuUpRt+nSWOR6D3xZ4bUgyIS4tNfWcMtb2gs3Y/cZqS8ZynNqZslJQ7D1mMT9ott85VmIgbZMisNDMSjPGAIdNao7y6QRaMem4GcsDl8W0bUEgHJquOYYbCwbOCfpnXKI8ZOsdFgcScpGgEEhKAqJMsWRgxaZvGpyVBBErKJvBly4YZGfZxRvNk9RCxi4whDq7f5nHD4XwyVNsv6xKVpLMBZscKlvLWFeOkjkksBTsH5Hsnktp5BX3j5hYc0W1aUmHchpxtrOOsx3TYWm4+ikesHbxtyfeg7s0Sq5hrr+4XObcjsNs3HceIgUrq3f2ahYiVog0Ia6YgdQGlzh33uVsa1TY/NhHmda3EKkBCFqIOCa3f4bUcojnnjbH2H4juq9Ee1iQc9ZjLfQ/63m2cn0IJ4fIStiS5tluuZ1YXtMnR0p0HCHdPE55PT1892HJXvb1y4Wem7+H54GAXvZ1D98mliYuYoFbGSncAfQ01F8PWM2+/k5+emTzwtCzlF8nuGT4JoZ+/M+7XZ8AvFaJ/AKolWmkuyq/WKEx2CLOWjkSmu3FpUFyJ5vEZ55ED4fQrKPbS6a+NQxJzxwmmj2Ac4FpwZcUbcJSSm1onnjkFCrrHrUYDOO0SoT5Tldm0w59dLtKDag7IToyruEf3/FbfOzEP6e/7xRXOvuoBsYK2ueP8b8X3sM7B/6GHfIsB2KTfbzYGUGIlLzVpioaDCcmoa2vJVc1ULrWabOoKAaCbY8K0numYMK4UpPN5nqlhlwTRhPjyx4YNhbkGTlIS/ur3RixRcolcwHynDlBbcRsX/Is9k7BifVrr5piXeIeWEHVQrxtVWpbTX116GhG5xxmhmMSS1NqZ/2kpebcZo3UkslDxvIEkIM++X4PPeDj1WPIxEMm+32OnTpFJ9KkQcr1bzKWZeG8fvQF/chjDQqxub4JXL4wXuF8vchcUbGtZbwK/zT+BX6j+DtEQnJJ6VGq0jwHH6//AHP19bhLo9iR39WhBlj31E5ElhEnnsGxIlYQJ6yMRdncJxQOXsbwQZ92e4ySXWLAvZe56UlsQPgF0voM4YLps+yuvxzZ7ODXiuZYweqza2rBhVtAFvqxMS7zh+/7eW645iPoxhLpjMn2l4U+0vocyeJRVNRCxeZ6Lpu8/Zu2N+3hxaMXU3750CPl1wly49cSr++j2f8UZHrWfrOInF4iCerYw4bYkn6PZt8cygkgcbHbRlSkUM+jV+ZpH78DVbXQ4QwqzCNWiqT1OYLTd+MO7EKXzEs/qGhalYCoVCOXW6Zgm5dhnLp0iiu4nXy3n7ITeridPLFXQSqLyDcE98kHfg2Gz3BB8Ql2hfNdwY99yTY6c1vYV9rJJnem2zs3CosMlM8w7CywR59irGVIv9iysTuaoCJYGMoSyhzNeV9PSZ5cxB7K0b60CsDyoKLlK5ZzYIWaylHjYt+6zmbfgMMlwVn+yL6uS1ZviR9n58OCzv5FdKopXGTcujfst/jCBfCmqTr5jiHZxNV0nlqiPd2mz5IUS4Y0d04Jjlwc03E1NR/arhl3PoK+hoWS0BiRVJ/IxDYGfJQvEbGGqkv9AuN6zi9X2PS1aY6eirB8C2/U3AvhSuSxBsf/9Bi5ovlKj713G95leTquYrgu+LNx49bdEh7lE1zPD3IX368fYPu8GcvFg8f4v1u/jB35uJ0cdmyO4wQ2TmghUpDq771VpUDECfmaGZ+yNMMHfXjsMVRrmWmuZOPwZ1lwxohOPwxA+8w3uiIh+cWTCMvGWhlF+D7p8dXEsxvM/V54GqswhI7M87KanZ1rznbJ1+3bSu3pj/PY4t1cOnYb3pAJr1i5tRaRPfTwWkKPlF8nyG2+nvn1LdLS4tq2Zg41fxTSGFExyVVBJSVxA1ASmbp4bWNB+g0XvTTDfQd/m/f+4D8hbZ4gXFpCWC7R8lEsfwCrPIrKm0cmKkQExRYqv0LVW6KckXKkPBp2jBN6FOqZVZpKlKVQUpOveQQlY1ktjpvyqlFrgXyomPVN7HAx6UP7LZ5aupDKYJ252kYAhIzZnDvBDc4jDAcBxSx5K9eWaKlJ3LVSqO37bah3kFeMEPTb1PqNiTfXZ0i74Vic2p6y7aRZCey5T/LkbZpPOW/AJ2RQrpjjLGjkfETuwkHopIRZg6fUhv4oZq6k2TNr5sRrQ/wTmxl4qo2qut2u7loKYC0csFpLDaajlBsLOnmFutDMV74OQRHsCJyAbvLa/W+IuXRPFc+vG0JOzdh1J+HAx49z4EzCrgmzc/0mh4G0xb6BHBvbHd6+aGL+6YDF19pXsLUTc9GXFSoj8dKegA9s/DC/Gf0SxScvw3rGIEUKh990JxuH95H7o59/zrNXfNRkxxd9n/TIQ8SLx1CdZaRbZEkMMjb4MKcWJ4jrp3nw1Cef9dlrLvwguc3Xg+Oz8uBqctYNgKlJfnbnpx/ufm5VJ/vrBz7Eqt71w9Of5ardvwSAdEvPGWcP3z6kND/fCXqG8gtDj5RfAK7Y9tPkJq+hdfxzz3mpvFogx7fSGFhCOCG6Y15I+ZqHbiwg3DyiYKyZ1FHIxMEKC9ihj525Ke1AkMwdBS7l8mv28IU/e4DOVIiwPOzihBEFcXPo7IuppCZxjEUepDnCrGlEkOaQYZ7yQon8snm8REYeyhFYoaY2bJitPXwGr7hIUXRo25KzlmkLWQ9KjA89RSOqcPfMzeisHeHk8CEucA/Rn7YoRhovNoOxEnPsRjll48Gsjna6gx7K0Ri3qVfTLlk7mRt200rKmT4YzqzQysN13vV1C254nEPWOO87ZUjs1KBmy2Qe6/FFRMVlYdyQ3qHxhFIEG2bt7pzEPhRmEnTBRjtrr6Cj56f4kcRONdXWmsW56hlQ0mRyn8laXQ7VLJTUuLEgcnS31CuWkNvRZxpUpBrVNPvrVLNcTzlcF1ywyRzbjyQfCv4RP1T8PBvp8P78DwHwO3N/zjWlE/Q3LJpfPEluvXlW1qkhrivNc9f4V9h3ZgeD54zAi1SCI2/5Mj81+VEKBPzuZbcCsOWhbUhj2BPtNOVJ2gLnTBHpV4mXj9GZupfWSdjzpkFccRJdmHjOc2vl+pADEyAtKpf8c4BnEfHztWK8/srfRljec7YDpJ2F593+9/GGze/jgeN/yGWTt2fjGEJF9Vft9/uVRs99/fKhR8ovAIVtt2Fvvhh7eNsrPZRvjpEhwsIxtJLILE7sNjCqW04OnFWCFHidPFbiYMcOImMroaAz/TDww0gpQYWoqI5dnFxT6ZI2MjZM4gS2+XynxIo7RCsybvCw1Uf/9CSlcwK5lGXIKoX2XKy8R1yxSbIMcZmrUXDrBNplxs3zVGrKcC7KHaAo2syoAZ5w9mBlYiOX5p9gTC9RUDGWpktWQoGyjZXsLGambJSSFnOUziacmdRUmmbfSlMSeBo7kTzZ77A0kHWJ2lLCaqXc8jTUdy/xyQ2m8YZNSuP6GfZu68MPJI+uN2PxU0Ffy2hp16rZtkASbbRo5wWJRbchRblpXNRSgZ0IEjsTMvE0Hdc0wWh4dGump/rMIiIfGat6bNm8za561IURRTTikeQEuWkzdtmI8RzB9rKiXDX3eeSQ4IoNjzKlB9lXTHiv/gIAhx2zaOlfkEjXIjhnYs2FXX30NxzeW/kqlSvrPP1nvwXAue2nuGH0S5RpMaUHifPGI1IfCegLPVSpxOnzTQ18asdsX7oQcfcclt+Pzjp2/fK//AX+82/8V448EXXJEOCm7/8y3g/+LPNbDLsPnDT3f3Bg8h/sjeyO7cUqP5fgge6x/yFcNnk7hY1vBqC47R3d7cH0Q9/ys9+rkPTEQ14u9Oaph+dAStnzNfXwXcN5551H2EkZ3bDySg+lhx5e9ehZyi8A1tBmGB9B9vfxprd9DoDw7EOvGvWgN2x+H8UP/CHajiF1cFvGJWnVIuI0NpZy1iXICS38ZqFbzyzTzAUcKCx/gIsGruGn/uN7kZaDsDzzIz2ka2LPqxnCTmhlXaAmacY+cZZcVlgeZPRIH+LU2dUEZuM6tyTakSyub7AyZsQgVJQn8HLMqAESaXXjuBeok/gqoSVPsbd8lBRj3eUJsbQikBbFUJFrm7HbkUkiXyqlDK03bs384RT7RAM1mmdgxaJezKzZyPRArhVSCsp8BmCkYBFUbPZvipiI6ySuOecZPcRhN6UwPEsqFIVkTYRDasn0cIISZlvkaIotiVSia5kDNMopdmJc0WAsZDC9jU9U7G7d8KplrYQgEhKVE1TjmHVJpi6Wh2Szj18z7u6zF2TiJFGOrbuL9E8FVNYbd7x1psVPze/n68P9tPG4tG5yDQaakjN9ipkxybY3TZIeNMIsqmjT8hW33eswd+1BvrzTZDf/m4t+ln/8UBst4cSGWSrn/RoAH/H/BeXZawlLKXGWuJfk2ixuHyU+/1pGP7sbf+sNALz5Rx4H56cZL/8Ve27q4wd+6u/osIPZf345XPl/CJMcnajE0vguALanO6jOvf/5HnUAvvyZG77p314IpFsGy4RtrPJod/t9X+sJjnwz9OqUXz70SPkFQI5O0Bm2sUMHZ/ulANQf/18v+Xn/IRfeM+H27aDdl6BFCpFPccXEZkWtYRoBODmIDWm6DSg4PqmjSJwUL5O3ErUO7tgFlN0Cd371KaRlGwUlt4SwXLzt1yNKVVSmrRzlE1InRShJcWmwW0JTXiiZ9pCWBU5WVuPYBBM+ia9MqVTOuED94gJ5p8mRYDM1Z4HdzlEACipmsJNi6YSNVod53zymK5ZPW3iUkiaFjiTXXJuDxDVqXPOjhmQnGjn0V8+hD63gbl5H0zeE50fGzRy4ikqUcrxsxjg4miIVzHsOhTRlQ2TIagPLKCGIpSAVgnxojlMKQQlNbOlu4pYXa5oFZQi4ktLxzALGUpAazREWS4qzWXLVlN3HEW3csANOjX7MBW2IllnXjql5gnyi2bfOHGeynlAIJOsbFlKtJbWtlBN2XDtO8XQDN4sR41pUmpLSYEBd5tl1zCTRlb++SPUt/fynXbu48R0HuPkRQ0qdoubIgGLnvfO8pzjE9MX/DwC3nmwQf+gQtm+x57x+Rn7A9Hx+YssXWLKuxYolA1k7Tzd0KE3b1CYF6c71KGeDebaUWfSddd6A1DNMHvkvLI+22P6mJ3i7dQ/H5QgH443cn9VbTe8eYPzEWuOK7zai2lHsoglP3PHXN79k53k9QYpu3uK3jR4pvzD0SPlb4MZbPo395ncQlhJSx8Luy5SOnhGLeqnwQggZwBncTqMUomWaqXgZa0nXTmAPb0MOjKEDE7ezmgm+5ZB4Etey8Zcyy29pGas8jMyVsUptPCYpuRsJ554gt+ftRCcexrv6B2hnvX9blTaJE6OlIsy1IWurK5UkN1HBPZl00zW1Y+MtK6IJ3ZX3BMg7LTbnTuCJkFB77It2AOB5EW9pnWKgIWn5ilgaq9632jiqxWBb07fs4s+ahUZStgl8Tdtdy1SOSgJRi2ida2En6/GStTeCVODGklKYdo/9pQ0lUiTbOyvk42fPr9QwsWCb5KvI6R4jcjWhk3RJ2Y0ldspzksoODmE6UqE5Y/fxaGqaPdSiMrZIqaUltrsnOKYnAei3HsHyI865Rab9PipkHasK89ipxq4nCGUznOmWrwwKwov68CwBfcZToPM2habFuqBFlLNoF83EFGsR1WmY2TbIL678Mr91yX8GTOONi2sr1B6dJ3/VEO+dMv2x1z0pmFqKSBNFEqQMXGIegLdvv4cP7j3J9q9O4DbMzbdPzqNbTazh9axMhl2ZVScwc9auNGn2L3P7P3uMg//fAv5/jdn8Uy38ylkC2+Vw3mRxLw/OMd4//oIXpS8W3662fA89vBzokfI3wWrJRfWt/4k4LxGpMlmzmQ/nmW6vVxpWdQItFUJZRqhjtVNSfRFr3VbSyYFuhrDVTlGOkUuUbd11RwvLQqUJsjoCdgGr5GPndqLTmOjY3TjDO0mGizQGjTUXeyGxF5LaMXGhgbJMgpWVOKyMOVTpw1lYEzJBCrSlTDlW1jAj1RaJtijIlPQZ3aMAxpYs/MCQ8sTKKqFqk8W8SrDKXGdcECyXE84/6lJ9sG7+FiniosPgbRt4ZH3EYD3Lyk5ERsqCXCSoZCnQ50d1pIZiIMiFz9aKznfMOZUUrJQNiQ8tOBQDgVR21x0ttclWlgqa+ZRjA2ac55wyLeFRp8DZdJgTobEgG0mJCe8c290TnIrHu60pH7C3MyMHSRObCXuGGiZ0MOQ1uOHhiGDExgo1MxNmviaPSuY2KIrFKsV5cz9TVxC7msCSLIsid2829dg7fnaA2WLCUtpHOL+JnzvzfwC48vz/zcV8lc9/cg+HrHF+8XETYtCOYvKf7qL+Nydw+zzigrmmobhDunE/oj2AXcu0PdOUdPcmOqU2zb4Vwrx5VtwgT+IGRANTVKpn2ZHU2fH+Azz0ifV85jf2MPFD85xTgxTOBjhBhC76iDCivPvHXhAxX3PhB1Fxg87M/T3CfYlgSYH1HZrKQuhvvVMPPVLu4fkgEc+Ubuqhh5cAV737NMf3VXn8S2N4VsiQNYeSgs3H/wvLzjsJv/UheniZ8F1xX393hvK6R4+UvwnKF77P/GdoAKEgVzPWCUFWGxp3XrnB/T0Iv4hQEqEtYy0HVne7GumjNhkRecaaqczm0ZZGSBDPUGjSQQdh2YhSBR054HmI/CB2tA3iAFkdQZxbITdirLYwb64/zrVMLDvzG7fWHaLTN0BtuJ/ichWA6nSRsJQQec/2C8epy3w8SKg9KlYDT5jXcIqFG0kiV5NKkNqMs9iSuJHADQSl/a1uV6X50ZRCR1K9e4XOU6b3cO7qMayNRRY3WlRbaTfJqtiU5JqCyNVYyrSHXEU+FOQDiR/IbtlSYmsCT9PoS2h4a67pwSVN5MOdWyR1aQLGF9TqXTd329OsvoZcEs7oIRZ0ldPxBFNt46aOE59Bd4GmztFSeVpZS8u5ZJBU2yTaoiLrXCGfAsDRKV95k+bK/ebeVWqZ+3pMM3RGYAUpjbE10Y/5/pjAsrgzvJjfb5xnrufUhUTFFfyB01BaIskS9J5o7uWvq2f42/rNjPtTzA4b9/X8kGCmIrhk/U4UMDtmJmzGzRO3q+x7337esdlY2w+0Lmbm5FUIZeFnDS0AwkKDtLBCqTzNxaXH+AOMVOtb5IM8ec0o266Z5j+c/tese+hqANT0KYaXfoe+rQ5q417euPeHaC4l31Q2M7f5epLlMwjL5aKBa4CXxkV96dht3TKvl8Kt/mqG/C4ken2npP69gh4pfxNY27JuSFJitVMjKdiOYckkACXLJ1/B0T0bwrKQqUAkDlbi4NcyneJSlaBPsjK03O0K5bfXIVKBFUvsUECYEWUcIkr9JjkrlgihzXGHJiGOwHHRjTpkrlSvnUNlRKzt2EhPAV6uTmyHtPMrBJnM5NyOBpUzW42724khEzeJvZbp/KQimiKlYBst6wIBga8IXEXTg9WmBnYicCOBlcCTt7nsvofu9sEpSTrfwR0y8c3GjhwnNkTEltHAnq4Yku24ijHp0M4pQsfIXq5Bk0rFcimlnXWbmirYxEKS4lCXPnPCkE1j2xlGWortK+Ckq4sNM++rxO1nGe+W1pzVxm29FA3Q6ZhEPMsOSbTFV2ZuMb9nLZ7yXp0w8dlb2ccGaxpXGSIspCljDWiVNVLRXTj0z0hAs7DJ/AvQzilOVwR+qjgdrO+eUw9Mce3k57jn4D9GSNWtU2+duJS/XL4FmVocvfgO/mjM6E0fTdZx19QtfOCK/8qwqnNGGvHvL7SvZfDwXi67+d91Z2/QXWC6OsvEA9ciU0mnaGrXW5U6rcIKAE+1dzI7ZxYIJ8bWc2X+Uf77yvs47+9uxH7auMyDM6c4dKJE6aLvx7cW2XTp5zlx4nKu3Pnzz9uSUQ5M8LXvMCP7heDh6c++5OfooYceKT8Prt77byh9378GQIRRd4komm3S2ZMA1A9/Cniu5OArBakkIrVwOzmsmrHmRalEqz+kXV3q9lhOnBQntUkd0wNXxIYItOMh/ExkRAuEBBwH4TjgrzFX5aDZ35soYg1aRLk2sVQouWZy+l4TvzCPP2h69boyZGZhnDjXJi2sdBO9Vv9miYR2mqcmzEs8sBxqhZTAgYYjTVMMoJVTuLFAZHHbp680xygEGrujsEbz6GFDymcmY1JpLOHlgqaWlThdeyccuiih6WfZ2l6WhCRdtqYtQLOcg4fzJjt3UZexSfFExKwa4FhkNKQ/G7+RH+r/PJuSRXYurF3PqkDI3w5s4FhqLOLL5EFOtDcw2xmj5Na4YcwIeVRlg2/Ur0aFZqGzGjCo1YcZGd3PBudct2c0GHKfnHLIL2uEAud05q1pxaj1RYJNZtECcHYg5WFvAwfiLSzM7+AXtv4mYHTFv3b03UhAhj5W1pBj9d+ljYf54MBH+J36PwYgfPxWth0f4b9dPIoaOIs1Z+LhG/ZvY/nCgwTao6bMfZsNRyge30PfUQux3KBSNJZ/PDjBykIfsb8bv5bngocyURn/fJ6Kf4QNjTq68TjhifvN9Wda1jfe9KfoLZejky9THmyj1QVcOnbbc8jxi3/wKhb1eZ3gu+G+7uGFoUfKz4PcpjeiZ2cAEKUKIlVGlWppgXjaiOa/qhJKLHMbpZK4gQcZ0ar+Is2+RVKvhYwNsTrB6r7CqHNlhCf8HPg+Wko0ki5FWBY4NtqxQUpELStnOgdS5emUyqR2bFzYgCVj8k6TnNXu6mEXZJuT/bNYnRLCa2M7hkwquQXKdoOCbBNoj0CbzOGaLnKqIrE0BJZEajMWLxYsVhIqtmX+XzLbS21QtsAqu4jprE+0LBBb5jOxpbvNKxpjLutPW9x3fkwp0ixaJlP9DEP0+8eYXBIcrlrdHs5faV7DTxc/xafDG1mIBplrGqIdKExzWK1nTNQIs5rvwZqFnQj+065d3BI+yawzAMDvLL6PxuImsCNkX8oN1mPdW3evvMz8R1nmB8CO2OCfBmBW97EnNs/ixcfNvXMP1omO1qjPmmvVqaYwU2Z4cIKpdWYsXytu4N7wIk61N1IZOMGnGm8FYGppG051hnR+o7nvmaVsxQ5RrgNK8quPfITtdxtL2fRMThk9tpnWwjDrv2EWPfGYRNsx+5p7SbMOYY2pPWw9MoJ67AHkwBjq5CFzjMYc8rHfY//0Z7nlZ8+y9Le/DEB+3fW46y9FVIeQQ+P4Iz9o7vOFb+Pm27+O/qEfhukOUw+9gXWjd9GZG+02nOjh5YUleu7rlws9Un4ehGcfJlfoW9sQuBC0SeZO0Dr5pWzjP3tFxvZMrMbPht/9JyiZaVrHFqsuzGBQ0qosg9dGhIXu57pdf4LUkC0g8vlnHFkiRAqOY/7u2CT9HjLWWFldM6nCXUooDBbplOrd1o3R6b0EXsD6jd8gybYl2gI7Is01cJwOvmPc1K6MsESKJ8JuPBmgqfOccvsoqYC8irpxXzsRNEowXVIMt9YyORMLUk/gWKLbpGFkwSbXFNyzNyafwCUPmOsMK3BoW8RYQ9B26bqj7+1cxA0c5fEJwbLMd8uz/knxL/jjztvIW22mFnZi+1n2uXLZ39nJhsIM477pelQMNIGX8o72E9xbWMffzbwNgMrj19EeO4ccOUbBbrKlU++OfdBdYDE3RtIYXJt+oVhK+vBEyG7n2Nq8FBR9kUV0tMbJe+ZQ2bqpULQIGzGVZszIezcCMDa5zFQwTr01hKiNEI8cBuDysa+zwznB2cERvnb03biBqSKQSpJbrtIePsOWh7YhYjPpoh1BqqhMF7BjC6FMmMJuKwZPbqZZ68dvGkt/9/4K4sBBlEpITu7rjjuceoyHpz/LdZd/iPaZrzHwMyY2vP+dBxCVWVTYwVuYoDJvOjuVFvrJL4zj1FKisRzOG3+IxfpmxjofwdooOXnyJBs3bqSHlw9SCuR3nH3dY+UXgp7MZg/PwbMs5R56eBWgVb6UJ798mpP7Gvz+7//+Kz2cHnp4yfCiLOUb9/4z+q8y8nfR6Ydpvv99nDtvP3s/cR5f/A+Fb/Hp1w7i+kn82FgEMj9BeuYQKqgTnL3/JWuc/u0IJUjLX/0PytKoLJN2tflEs69Dkq+DTLuKW27bwmlnzerjtLuvKvmkeQuRanTTgzToWsmq5BLnNU5bIPPZOS1JUrJxAvBbxW7MWigLK3FYjgaoTxvZRF1YASvGya+Q9+rdBhORcrFJSLEpyBa+MLFwm5QliqRSIlGE2XUpqfETOFrMk4oO+cQsHCJHURuUePMWIlPNL9QFkW9qh/vagvuvNG7dJV9SimHdkmC6BE/ExiIOlcfH/auopWXONico2caa/U/z70e1+vnQll/i/ylt7859nLrUqXJKjbLTmzYbqwkNR3KHs5e/fPRXWP+UcQHXB5ukhRUq/jI7ckcYXM6OYcFweYGjVoyK/W4IgMThVH0rS/4AYd7jLR3TsWrsjEujX6NTRb2hyOfMtZZHfFqLIXEtonDOzNWVG2P+uLhCw+0jBtYVTgKwwzFZ1TdYj/E17+24HeOOdgMPK7bY+ODlCAUrm8x89T2hEGGEc6xF9RiQZvd5pcbosWy80ng+CBfQcQjS5unfyyOXTFx+5zfezi0/dYQ7H/wlbvng/8XUz38CgN8p/D6FJOWgP8gnhm/j7IqJDc+1K9jNKtW5UYZOVBAnjIpYefeP8dWv/Qgf+MAHUEoZjfYeXhb0FL1ePrwoUs6tu4GVe/4LAKVf+SQb3v5vucqe4W/e9m+5avfvAqYH6msdaWceObIRAD0yiGys0H74S9zzxK+9ZOcsbHxLtxfsC51Du2jim6JQICi2SLwAO7Eg6wTUKTUg08P2Mj1sr6ZNFnkWd+6SsidJPG0EUmzbGMpZECnJC6J8jBU7JP0m7msFqtuSMXFi8jXj7o+9EDt2iJ++DlEx5UlaWTi5OiV/GUdG3fFbIiXBpqmyxzB7x1ZlAxtFiINCsuoxDx1NLhLkVUTDtkmFuYbdKxZD36hTv2eauGW2DbiS2g1FNi8IowqWhWuVEGyZtZjuTzmUG+DgiiHlgt1gozXNAVXAlSGH5i4xH4h9sCN+6fR/Apni5zOtaC2RMmU2GeQRx3Q3WsmV+Pzimxn56vexc9om9bIWjX0SrBjf6lDMBELAkPIF9hEekG8gDX1kFt9VUpEurme54lEoPsG6OTM//tmAdtnH21RhYl2NwqC5F4UtFYpA+0Sd+G6zQNhRXsfYdTMcW7gep5Mn1eYYNinrmOeLyRWQOpSWzXPhBCb5zwktDt6wjz1/fd6znjUddCBoP2sbqyV0jSxxa9NGjt0ww7+88n38XNykMW5S2H+p9Gu85+c+ym+yzAd+ZZogWzjd+jWL1HNwLp3nisKjPJXlIITKI9EWzS0lnt6xmbExE3cfLn0f113+Ia6/Jc/U1BSTk5P08PLgu1ES1SPlF4YXRcr2wAY6v/gxABpv/jAX2OdYUSXcTh5huS/JAF8JqKiObqwAoPedRZYHCGYefMnOd/Xef0N+x1uxl01JyAu1mnPjVwCQjlUJc6dwOgX8hkuahYdTOzHJQ4lDcdlkyIowRTTbXTLWGYFry2Q0yxi0ZZnsa89BO5IonxJ5MbZn4WTMKWORfUYwemwsy+aGlZEVZGrRqiyjcsbaFHaM77TwrQ5WRqSrqCclClabQK09PxutaUb1MlH2eAbZ26DjmvKmrbWYoxWHQmYpK2mBb2P5FsGKIf3mfTOMldfT2OpzdFPEtXea6zy5R1No2pxdL/hE663EqTnvf5V/yP9Or+DRlYupeMuGjP8ebLeDzuLkSlmEwJP189mnLjbzfXYX65/aSvGshjBAFA1peh0XYp9IuaykJWaL5u000tRc3poiGfwkvzu9k9ySiakqK8VZHqDdybNj8iTVhextKAVuAGfeXmZ8+x7k0ysAJPMdpGtRunIUBsy4YzALgPICst7PmaZZOBx1ZsCGpsojOyWqp5zu/Y+KppnGeV/eC2mWwR8nRjc9TdFpCpkHCQDHRy/OITaYY1/1b97DDl3huukGO+5WqLw5xkdufJhxscCucJ49ZyzyWbMO+6llrPVFhpuCkfwygWuS24qizbhYIMTlXv8CHs1OV52+Hm/yUnK5e1lZWXlFSPmaCz8I8KppRtPD6w8vipQP/kaTm7f9KwCKosOBaBv7597A3s95fOEltCJfbjwydwdvfOMfAyALA6CSl1QsILfpjciRjQjfJMz4c5e/oM85ExcC0Okzrutco4IdiG7zA2WliMTBaZcoLmfi1EEbpEQXc8Z9rdYSppTU2DEox0ah6YxYxF5KmI9InZSoECMz8rSBdl/C9NbTbHp8s7GwWTuv8tbkNLV6/iW2JdakNQuy3bUix/QS1TQgFhKpNVbWPSmVxlrOh4Lxdky4amArSMdzFK6bIFc141vcarGYV0DK7iccGmNmfMOzMDOW8D+Td3By+lLsgrF8z1ZdFtM+JgunOTpz6Vom9CokpKlDzm1kY7EIwiIqymMvm2SpkZMbyC9I0jzMXtgizBkLsrhcxl8aYTHXYMmbJc78gJtOOeTGEq7iDPb5/47/OffjAOT2X0OU61BcGsRGUe831597MqT4YAf7wirK1cjMUyFz2UT0eai8Gff9b4gJtIfnNYhKNXKHjbV53xaHuepTXJd7mNaj/xo5V+teol8povJ21wNibqYyVnIcQhygn0HK0i+gWjWmrzILrUej8/hx8SWkFkRDLu68IeVN3im2R/MMdhTtHPTNye54k7INmM8H2ty7UblIWXeoC9jinubcwBEApnbuYv39g9i2TRA8Y3HwMuJ7lYy/K4le36WxvN7xokj5+pE7mJTmS7wv2sETU9ew948u5wu/mXtJBvdKQgXL3f/roP4P7PmdQ5aHEKVS93envP5bfubSsdsYeOfvARiyTBy8dg6hoFPJao+lQsY++VofXvbuXa1LNieyDDGDyayWApEqYk+CiKgPtkmdBJlKlKWIvKTb6jF1JGd2H0Y5AUpqjtz4NXN8qZBL48j+s+g060D1DE3rZyJSHrbVpt9aZtReYECYQZbTgKEgYSVT7FqVoQ5t81NB09cWdNyMrJoCEaeEG/N8/XKzEAgsxVgrpdIWNAesbqMKK4GnhgUHzlyNDAuszsYvns4WlVpy1bovcN+Rd3bHqaUEaYGzliGed0wWdrvVT3lhGIDCkkdUhFN7TxAOnusuSsLCOsoLwzjhHo56TRoDWchAGpWyiq8YzNfZWDLksxi/kSjXwY5tjqUTrJSNB6VwYZnC3YvoTxzHuXAQBs33Ti4bgmpu9vnKRWauDzjrqKoGjoz5J7s/yM7EuLW/IvdyLhlhRzrFn77tU+Tn3mXuUZBAqpC1wHhR1DMS/dKU+e/bwPLVn2fLPzEeF53GqCyLfGXYlGxN2LOkSnD/mMub5L8k2WD2vZVPEUiL0FYcGoanRsysFy62KUYxp4oONV3gemkytquJuZ77xXmcjidoRCZDXjgxcnSCXbt28fWvf51LLzUd2y4Zvgnpll+yfI8eejHllxO9TIkengshEbonHt/DqxO33XYbtVqNO+743pK6fCUh5Xf+0yPlF4YXZSmn2uZAbDJKD9Qu4PzPXMsX//3rk9eD2Ue7/3/g+B++pOfSUQCWRGTKWVZ5/Ft+xi5OIgom411JjdfJk2v6aAmxn4mHZFnXuUYe2cgSrNptKBXXzp31KhapxgpBOYJIgB1HBMUWSiqc0MOObbRUdIrGion8kNFjm5na+RSHb7wbf3ozAMVdX2PzyJ0cbW9lpWVipJaVkGqLSLm4kq7YhCVS+u1lNtpTDItlCtpYogUV09cWNBxwlO4mBjUci/7QtEpMpUn6gqyNYtXm1OaYRcfEcatJRCzBTgWprcllnabmxlM+wfXIjrHium7qLHt89/g93H/4h7qiGqqwQqXvNFvyR3n03I00msYqDrwWea8OXgs7zuLzFpw6/yhi/QHW5c8RKTOWWaAT5OmfGaSurmL+jcYrcm7cJMK1HTgrBjh84k0AjLc9CrVRgnzE08E25spG6Wowr+m8pZ/qH3eIj9a6bmtrogBVj49eXmFOVwE4mw4zFY/SiUq8bf4Me79iLN+3D+0nqDzJ314kadQmOHOx8Qi5gUdlrkhuOkG0n+EaXs22ftMf8/7SX/LhP/wRM8Yf9nGueivxXX/NwDkT2/38ujfzgH8x0T0/zLoP/Efmfu8XAbi7cjVUYUvxHB5RV5jljuRqjjV3Q0Oiwzylc+YZSp2Y/O47GPenOP3A+9j4pAkP2I0EvXKaS4bezp/c9ft8+tOf5qabTP7FlTtfPep6PfTwneBFkfL+5h5qS+cDsO2uK1n8zZvhA6/P1epLTcTPRDxzAGtic7f/sCwNf8tkL7sw3k3WsmMLv1nADixSR5NkahtSWcjIwmu7iKaJheL7qEo+a6UoSPIZscUmozr1JKkALRLTJxkjLGHHNspShLmsEYUX4AYe+aURqnMDJDd9HICc1WYuHqQR9OFk7l6lLFLlECY5lPWMWK2MsEVKRbTo0y3SzHFTilNiC1qWjSsVK1np19amKb2JLePS7m+Y/SMfQl8zXdHMW4bwBuIFSqEhZb8tuq0rP7+hxF0n34oQqWngkclLithHhj5Pt25Dxi7aysjICam3hnh01pRD2W1DyqkX0EwcpEzpFM24OsUWauIw7xj6WxJtcyw2YYhGoUJQquGcHGXDA/2cucEsVh4fauHolIbl8Wh0HuVZQ25OYCOVIPYiZqcv5Y6tRuTbWXecckfQ/ukx1v3JAksPzgEwuK5IWnEYTWvcq8z3cynt46mFi/G8BrUcxPsWzJS7FqXd/dweePzRZXcyseFTAHx18U0kj1/PeK2KVUu6ZEwcI/oHuL54H9uCJX6k79MAHPrceq6Rv8uvPvIRdv6uSdAa+buLCJ74O0799dcY/pkP0ekz7ujc09fy+fEJLp/8Mluc093bP9sZI3d6F0FpBSt22XSPUUDj6FFwrqf1wB9z+PFRsv4VmNfVZvi9u4miiEaj0T1W0jzLP4Sr9/4bgJe0guL1DCl64iEvF15cSdSd72DjfvMSW/ijt33PdUp5qRAvH8NbWQQ/S5tWCdIt/4OfsSuTXTUuK5b4bRepBImvSJ21uLEVO7htK2smAXrLOuKSJHU0sZd2ey8XFpy1pC9tYyUCr5MnKDSJvbBb5xx7xopK3JDID+mfGWR+chovNfHN8/IHeaq9E3hmLNkQcawcUm1165Tj1CXw1rKufZ31WRaCJ/t8pmUfZd2mnKlIhTYMtqDtQj4S5NtZVnZesVxOOZPLMZ6sADDW0FRaFv0LFk4An7vCWIq/W38vxD4idZCxix153fNruRZHFakZ82TfIebaEyThJF6r1LWgdZDHih2T1JYReKeyxPeP/A3XpE+zaBVYyRYIp6yIjhMQ5mOKp2f5+Dljbf5NYZ6ys4IlUk4t72TryaFn3eO+6QrVuTfw0dz7ADg5+kVuLD3OaNTm8V8Z5Na7zQKBKTM/F87GPDpmanofWrkcsTxGULZ5dGiIa3eZZ2L5707St7HM0e2Sf5X8FYNZ85LjpfUcmdjEwLkq+bMpup3VHqcpolTEJyKwJBvTeQB+7rFZnADOP/+9/Iu3GsnZ6c0nmTz4MwzfmcLnP8d5d5qGJLV3DdLeeCDT8o742/rNAJS/8QNIJThz2RFUp0Rrwsx54Wye2pd/jfuP/B7fDJ1OB+sZC7zVePL1V5qGFd7W6+g89XnuesRo2PfI+DuDkF2b4Ts4yHdlKK97vChSLvzPT/GFe37W/PKbr++OKZeO3QaAlRt6ya1md2AXydwJpGtIOZp+4lsmrVilsa6lLFKBnZFI7CckjiE3oSRO6Jna5LxxdUclSVBMCPMRsR/jtQ0pydg2OsfKAizs8gzly/6c5rE3AnSJZ1WgRFspqZ0wPzlNtO5pZFZadDTYRJDmSFMbmbmEpUwRIqXfX8CVa8lStkiZcGbxiEiRtKUZy1KuSAufdWqBgTjEyRYLXmIs36G6IHQ0UZbopaSmntOs63S6kpyVlkV1yRDy/RdF/JG6FYCF2fNw6/3duu00m6vUjo2Gt5V2hVAApg/fhJU4lBplKgtr7QiDW/8P4UM/gJXYdEpmwaNyDa7WT7FrKaHtrmBVjMZ1q5Dn6RGLlaURBqTF9h8+DoB/7Xs49P3LDEyNsiGwu8l4yjGWvVCCxfUtJu67EYBvbNzKwZ1fYqt/Al+EXLruYXOthRyNqiKV8Ib0KACfPLOHieMbmdpmkWyQxBNm0WS5ktY9U+wa38LxPYq/mTALAS+MUE5As69D3vfQ8zPda9WnjnFvcBHr/Pnutnv31Lj4uM2RUq6rp15cHqDZ32L8IZ/k1rcis65Pjb4GAyVTPvml+o0MfPaHzf53nWLm+4YgLJBfGu0e+8S7Pdbt/j1u+bUY/eQ+vvSJS/n7mJqaIv8saVgDd9KUp4ntu8hv3cGNt3yar37xnc/Zr4cXB/ldIOWeofzC8KJI+XOf/9GXahyvOqwK30un9Lydab5buGzydgpbbiOa3oeKjTvuhazqrfJwNx3SigXagtQxGdKxZ4hPS0WumUPO1dAlQ8qxp+iUQsJ8B5lazyplIjWCIFbqktaGqD/0LkRlGS0NAQMZaYMdWqROTDB2nLzXxLFMzLqVlAgTH5V4CNe4v4tunQF3gXFnlqpcczn6ImRELFPWHSwUbQwpt/Ap0yIWFpbW+FmJjqWh42oqTYuVQoIqZwsFoXFS49IeynoMl+rm37+9QvNXXM+DR98OQH5pFCf0EEqipWKVfoWS2JGHVBad6jxe3Vh5VuJQqJXpP1fCaSiSvHkzzQSD2F5I4obEBXNNXnmOWFj8wdD5vP/UASZyJkN7t3uUWr5Ee2YQ1Az+te8x4z57gm13nY84egoxNITO1NKEI5+V6lo9Yb6mhaVtLCwM86gXsuGqP+CjWycAuCA+i6M0DdvhXmm8FFsf3kGUT0lLi2yP5nFOm2eiEWQLpXYKiG58t53mQUk6pTaqrx9OZRn8rRWwbKYe/BH+7HJY754z8+LtZ3FHxK/VfppylsNQqBXx2g4Hbz3O+vP+jqdmDUFuvO9qTp27gC/7W9n7J5eT3Ge+S613v43SAsB5lBYKFJ8yq5LC9CBP3raf0sIIG2tbuvHi+w7+NpdN3s5DZz9Dq9XCdde8LNdf+dt8476f75YVRiMeqaPJnd1LDz28lvD6zNLq4TuC7mVf9/Aqx549e5ifn//WO/bwXcFqnfJ38tMzlF8Yel2ingeXjt1Gbtx0YBKW8y3ju98JhOURLx8jmHnwRcXohb+mNW6HgsTTpHlFmO8Q+8ZCFcqisOSh50/AVpM1nzqK2ItI7QQ38Lp1x6uQsUZqB7djMXxyiHM7QpQ0cdPYb+MExmWopaI9fAY/v4xjRYSJsbiSxCdNHYRUXfWrEW+W7e4JFtIqVdFgTJisY0/HuCrF0cZ6K2fiIZ6Iqao2Q3GHSqjxVhVBU6M61synjC/YeIEZe62SMltNGWhK3Nh89VvFlM9s7ufRdDtfO/Lurnt01Uo28yO72s927BB7IfH536Cy7414bbPdb7vklxzsZkpcsagNZ+0ST++mk8mIqvwKAK4VcFiMY5NiJwIrazlZEAFHlvaw+5QDcUT06Np9do6W0Y0lsCxEUMjurYf2XHAshg8aTXIAp6EYfzTP1MVgk7BJmUSvyUbKbEHyq81/QvKw8Qg03/5Z0sRj7/ADbFnSxOvNfQubCcUNJVCmn3RNG8syVG43BNEehBP/yszj9p89g7AcNn9mCwuLP8dd48at/bUNB0hqw+y88zK808ZToJeWEPkC0bVnuDL3KO1Bc87Ko7vZu28TX/jNPm5+7Os8/Xtm+4abf4HchusZ8svouEN7wXSy8nfcxMTWXZzZ8wS6uJe4ttYpaxX+M3p8A3zjvmdnX9ttjSqC6B+kh+8cPff1y4ceKf89XL7hPfgja4pacf00KnrpxENUVH/RhHz5hvfQ95Zf7Qp/WIFES0nipES5DkmWjOV0Cng1jY5DROYO1ZYmdWKj9qUkVpwR1KqKk9KYxCxlGkA0i4aQvRChLBLXuEFTO8YuLJN3WgRJjjA0MVod5UBZyFwNS5p4bb+1jC0S7j7ybu7KNfAyFa3/1vcbAPyP9Af4Ff0Z/GckqFWjhL4OFIM1onUjgZ2IrhBI7K5Z8wNNiZ38/+ydd7xdZZX+v/vd7fRze6/phRTSICRAAiFBQFQUFUdUdBTLjKJiGdRBkAELOooNdfiNjqOijAUFKaETagIBQnqvN7ef3nb7/fGec+69JEBCQhLwPvncT+7dZ5993l3Ou9611rOepRAPyWtyR0MjG502Huw5h2hXe9kQi2LuXbM0VEsy00H2l7Z8PtxH3opmqfjjktimJ11wHfKVgnh9mkxEGiDbn8FVCyiuSk2VzBHXm91stDtZpD+DrXkE7GIunALEmvBUBS/WTaFb9uRO715OoOsMfC2noqoaWMWytZxflrsJgdMYwTblcfSuNFZjmPjMR9jwzIc5rfXvALRt0hlohlRsMZGcTAFM+ekidi5Osye8j7U1GpWt8hiNl04AVSHbaJDyFTjD3gDAbcmLpZoJkAsXmHy/DPsmPjWJip066d9+C+O+fmpTMnxddfEP6VkYQL1zOf3P/BSQZKulH3wRkarm1u53Yw/I8HrDf/yY5/rmMe//zqIiXMO+1pPkeW5YwrLrLdizD2taO498Qm4+Y96NVL4wnt0nQWKsOiJ19GpcC2dQEt307k58mQDOjo3Aqa/4nlGM4kTCqFF+Ccya6WiRFuyEJKlY8a2vWz4ZeE3H1qOdoKpl1SUl70BA4AoXVziUrJZq6yipPMMD0a7qyvyppaNZKlrR2xyh4KSoeMIlFy5gZkzcIkNbOCqFYpmUHUjg06Txt2wfXq5Y+2zroFmyNtnVR4zb86URRgZDle/7WuZy/sN/M1/0/sT1XMzVeVmeo3oQzYJpSSN8U/uk8jGiSooqUnxwVZKitDIBQyUX8NhTb/FItSQubXTaeHjwDKpXLUI4I5fomq2i57Shcwdsn4ue02QXrbiDkhsipBXqTWKNSdLRxJB3rOdAOHgix7TgWgD6nUpUbMJujlBKMKWo8ex0duOr34Srz8DeM1T/vrp/BXNbLkILNiDCdTJ/Cyi+EIqvA6utknzIHZH3T9cUFcvCMdK+4v13VRJhh4KVJLq+vXhRHCq7THaOb+D3wcUwRiquTa7QqI6pJEIWL1YEecSTJVSOo6MgG4rkAzpO0RPtGrMDV7RT2T4f0TSOtR+VC9TodRsw7/gDTHxr2VAumH4Vmb2PMGneCvaPb0PPywXPvCnXs9pdQOCH/4u8ZSeVzyfxP5/HrJ+BkZwNn5gpr/fgZsyNa6jfPI2epX9mSU56yvf5vsxLYds2mjY0jWW3PwCA8EcQVU3ktjzIqFE+chyvfsp79+7lS1/6EnfddReZTIZx48bx3//932U1N8/zuPrqq/nFL35BLBZjwYIF/PSnP2X8+PFHNNbjiVGj/BJ4TgHFCKCo0qA4hfirvOPYQ/VVSdlHp2hIR9q+cu2tkfWjZHJ4QsPTSx2I7HKbPl/SwIxLg6tkClITWxc4igBvyEgLR8HImeQDbpnUhGbhOAYZwMoHy+IbGA4UfNiWH8NMlY9hexqKkUXX82VWNkgDDHCt84ehz3Nl2VPc7/G/gVnlZhW2p7GfGipEkl/N2UmVI8t2dM/BVRQGRQUvOGMBeHBgEYHVZxHtHsnQFa6C4kihD1d4FCPsaDmBngE1Y0PeKjPbCzU6g41pUpUxcuEYrlnslKQVpIymq/Js8mQAfGqWGeENCM9FKyhEtksTtKBb57tnfJdv1rydQKQZxZBh6tNn30Bo/Dtwc4NY+9ci3vsROca7l6Mkq1BzVcTHJhlolrW97pkxJjY+SesfP0fNu6/BKd6K7VMc/qtpAgxQbgxC3sIxBKaZZF+hnjt8MvqzuW4XkboMXV4V96UX0JuVYX3PMlGQaQlXuBQC8h5V9DSg5zSUYAX3/GhYA4jbYNm/dJB75k+ce6U8/5EExUqWfkce46+LzqG9ah1utsAjT1854n4UBtbxxIabRmx7cvPPOG3qlTQ8PpnBxpls75DbZ9yf5Pnzh+Roa2pqeP7555k9e3Z52/AOa6fPvqFcEjWKI4NQjn34enBwkAULFrB48WLuuusuamtr2bx5M5WVleV9vv3tb3PTTTfxq1/9is7OTr72ta+xbNky1q1bd0CK442CUaP8EuT7nkcL1Zf/LvcsPhFR6qU2bAWr2jpkpSEKD4bx0r0ohomrDu3jT/nwx3WMvqEWjopl40YD2AEFIyO1os2MjqO75IouaS6Ykh4igK1jZSM4Rgah5cs1vp6ty9xkohalbmhB85e9F6MIC9dVy52ZVMXBEgo5VZDRhvx5R1FIC507nPnErQjdeXk/YrkaLMvEZ6bYFNrKBEP2Bq4QSWJemNWZqezYPR+A8U/MQXEoe5mlkmmlaLM8VbLWlaJDrDgeigt2WMOqV8mFi2zyyCC5UIp8MInrT5b1rBU9j6blUdVC+XxKiDgFUhGFaL+8VtrOJOe7VfzrGffSeu0T+FrkGH2z3woBP14sjhfvQ1m5Xh57yjzcmjD5qEL/1JW0V60DoMno5vHd56Isup/3Gk+X21GubDC4re/tjB93N6nN0kPYdukqzL5mWoO7GG9uo0WV+WeTAuNzA7SqMR5XTyagy4VNXg/jFc9tOJqeDeBt3MC9v545YnuJBQ0HKmmd+7ke7v5eHfZ9f5H3c9L7yQgX91qXU8Z8aESJ4culbR5feyNnn/dXxnReyN5i+Vrv9KdoXlvL3qmyMiISidDf33/Q9wOjBvloQijlFNhrxmG+/Vvf+hatra3893//d3lbZ2dn+XfP8/j+97/PV7/6Vd72trcB8D//8z/U19fzl7/8hfe+971HNt7jhFGjPIoD4Al1hKc8ilEca9i2jSoyGMnN1O3YgZFJoGzbgRmDHz7wCJlMht27dzN37tzjPdRRHAYcxyGRGMnRMU0T0zQP2Pevf/0ry5Yt4+KLL+bhhx+mubmZT37yk3z0ox8FYPv27ezfv58lS5aU3xONRjnllFN44oknRo3ymwUr9/yJU8Z8qMy4Vv21zGu/hKd3/u44j2wIdnr/ECkI2Qu3BM3SMTPSUw71muX9SkQuPaeh51WMPgulb6gTltNWSyEE2Yo8vqSL5c+w7dSnqNg5Ac3SsXWLQiiGyA+Fg11/ElH0kG276LXnA7hmuuxRAvhEXnrJtolrm3hFzUtVcfha5nK+EJYrYbdYobdXVHF35nSSdpiBTD1utwxJe5qFpzjkvCa2bJvN1pI+tWYRGqih+fkI04vn6emeFOEoSVtbxTC95YJQUDOl/eQx7ICCZboUggXSkQz5gAxTW74MtpmTXrKeQzOkzKiiuHieoD20Tdb4AgE1wxRvNwHbxZfT8OJFb3vdAKGIweKzltN95mfpXVrqm+wRGvDjHwwhBipxX1wpxzRzElYAts5dz/yGB/m35L0AxBVo79zLGfYGznxOxTJkmuLpKUGi5iCb+mdQHZCuvzBTFEIxFgeepN3roUQsmDGYoiqpkgw4zKt5geeLdc0ZK0hOVBYFZ4Y8f08XeJkYL8XLEa6WfvBFBt42jn9xPsKOr/8zvlSKifc+j0juRw0LnA8s4ZJL/g1VtZkypZJsNkuhUMBxnAOONWe6wb71z1MTqcJVg2QilejZCh5Oalx1WQt//vOf37Ahyjcajhb7euXKlUSj0RHbr776ar7+9a8fsP+2bdv46U9/yuc+9zmuuuoqVq5cyac//WkMw+CDH/wg+/fLSoD6+voR76uvry+/9kbEqFE+CJ7a9ktm18nVl7/pdFR/5QFht+OJwsB6vFwGxRnpzQpXoFo6wYQ0EnpfXu6nqigZaST9cRNheSjxlGxc3ygNRLylgCukbnbNVj9myseUe04jF3VI1KTIRKUBV4b1GVbSFdjF9oylBg+e4mBGd6ELi7Ahw9fb8+38U9NvyWFw2+aPYRePUVAtHE/lmoF/RVcL6EIaMddTydl+MulqfF1jMIo5cs3SZRlTziQ0GMBMFkuF4jZKPA35ftCLWtZCoOga+AwZ3rdKWs62DPsLgRfQKRT5afmwRcG0yAfy5EIpCsWyMlcvyDxy0SBrRXJbla+PU4LPUiNimMhrayOosHLs8IUZE01Rbcjx5WMFgoN5LnKf5FNfWYdRFFUp7J+AZbbg6GHCjp/CeVLkWcsrxBqTzJ58K+9RHqRjnzynrjqb92fX0rnbwMzAhk75uaud8TSbe+lXG+nrkGVFH23+JbfwIc6PbWP6i0Z5UWJ0u1i1OjsmeSy0NqEZ8rrsM5rIFdXaAgkX/2AvmhXD3z+AMNdz++39pNNpMpkM6XSaXC5HLpcjn89jWRZesa691Q9NjzjYjwveov8XwufRq3eixgfBnIgVDdFzxjTygTCbjQr+dMYYKisrD1DnWnKTx32fVphdt4SaC34oN1aPR4nHcPZv4KPnfpHW6TV0dq5k2rRpjOL1xdEies2dO5fly5eP2H4wLxnAdV3mzJnD9ddLrsLJJ5/Miy++yM0338wHP/jBIxrLiYxRo/wyKOW6Fp15C3r1WISv8oB95rVfgufkj3kf11Vdd3LORQ8h8rLkBJ8BSHUuzdIw0kXDlIrjJvvwVA01Lo2vrgppmHI5vKoKcjXScKQjSfS8gWapuJqG4rqoORer3sYVUn5TWENeiWrp6NkAbkbm+/xJufrN+zPkfGnaG58kqkom0gvxGWzV+6hX+1BsvSxjWSgEyvrYWU8t/+55Kl4mSrCnBX8qhFrswmTk5bkZGYE+aKHEi2Ewx5Hs8Zcs5RXLLjPPR/SRtsCLBnF8gkJAbs8FClhmAcvM42jDcqvD9LBV1SrnYA2Rp0JJMs7tIujK/XXXpTbrUqUm2FKrMKZQVM/SBUp9gLDlcGrdCuKOvGZrk3VkImEsM0+ixmT22dcAcN++CzDMFO/UHuLsFxUCxdNsz+nsa7JwNI98QGFVVDZwWJJ/lodS09jd3Y26Q8XMZXD2wHvTv2V1Tysv9GgUBl1yrko2LygoKjmfTg6FQlGXfK63EsdbjeKoCFfHw8RTDDxbwxFZ+vtVAoEADQ0NRCIRwuEw0Wi0/FNiQJ97ZR8vXtrDuAl3ltW/1t99BeZvPkbFGR9AqZ9KLizvU8VjXTRfIp/hZf+epneWnPRr79yLSMeZ++3rR+Sczz7vr6i1YxAV9YQnX0LCsVmzZg2ZTOagkpujOHpQjkJOWVFAVVUikUPTfWhsbGTKlCkjtk2ePJk//lE2RWlokCTF7u5uGhsby/t0d3czc+bMIxrr8cSoUS5iTuP56NGxBzBBnVQPRtts9GAFi868hfiLMtRqRMfhb5p/PIYKgJtLoOaKpKuAD2VY+VG5hCaXw+rZgPBVIMJyAldcV3rIjgO6VmbZOrrUwQ73+chUubi7XWLtFq7wZJeowsjVrAxz+lDTITzhlqU99fFPMja4g6DIkPPkewqFAP12BUGRRlg+SmbOEw6e4nJZx8/YaHeysl+Wrji9LUR7GgkPhjHSevl89IyClrRRBuNQOneQ5WG6Lo2yOswwOy5KJouXSeOVwviBEF5NlSR0Bbxym0tHt0fUbw9d6GIdt2pjqDmCmlxoRLQkQSVHhZUnaMszMm1ZLx3ICvwZgZuSxjo6o5p0S4BdOY1ct0Y+ZqBmbVp7N6KnN6K7KaroJvTrJOQVPpy9hXxBZUO2ja0JQcFTsR0FR4G8pqC7Hp4C1v8KPEBVXDwNTjceo0+pwTY0lF6FNi9HOAVBNYfPihGigLWjh85pAXzLKlk7J8easFys/SW7mG3xSXj7JtC4raMs7Sm6+ilseoAPf/jQ8nPWusep2XU2G4ML2FhMYdSoLk/v/B0nVy+kYs/lhKYW9dS7dwCS0X3PtUHOWiYnW2Xq6QAEx54/4tj3//1CFky/isDE81CDctyTJsE999zDO97xjkMa3yjeOFiwYAEbN24csW3Tpk20t8uyv87OThoaGrj//vvLRjiRSPDUU0/xiU984lgP96hh1CgXEexYhmIEmT/p0yMMc3bfCvwTz0E0dWCGK6kQMt9mDW5D+CrxrDQnVy9kdf+KYzper5DByxXzm7kA4MfWHGzdxhXFfGkyRnbfk/jqZ6Hlil1/0ia4Dph+cFyEW2xq4Qpc1SMXsnGEi6tZdHfsIjRQg2ZpBOMR7KLhKqHcc1i45Ir1u++sWs55+TUkhMYdiizD2VQM18adCKqllzswje28n7xr8vfkWfQk2lB75JetpquJiq4QqqWgZVyUeFGhrGhg3XQSrBzoRa3oYBilmJP0SrnJTBKnfw92TJYT6Q1TAVBr6nCDGq6axvPS+JIpVDuHmUrgkUJ4WYQjfzSrgHByqF4Wzc3i8zIYdgFhexh2gf1OlP5CGGyB6yjlqjDhAraH50gmtGII3DsgtVynUkkQ0VM4uoolknh+qA33UuuPUe3PolW65MNQaeYZn3Novn0fISdPiBy+t3fwh7NUdM8hpvpJKFJ1bJzVh/A8HtIn82JcnucnjXsZ26PSdtsgSnsYquSiKdEfJzy3mvWT8+wP+FjndgCQd02EcLDMHAXTJlcjn3N/MohaOawU6lVw/98vZOmlz1E7e365Xabiwuln/CeFrR/jwYpLh+098rilxhGLF/8axQgg9OCI79ZpU68slzydOv5yAE4++SM88MADhzy+Ubw2HA9Fr89+9rOcdtppXH/99bz73e/m6aef5uc//zk///nPi8dTuOKKK7juuusYP358uSSqqamJt7/97Uc22OOIUaM8igMg2deHoX3tugjbRtg2bhzSaYOUq0lP1fKoHBggoKcx3RwNWzeguBaqbVHRPYhrqTgFjap0HDW5HeE4BJImRkZBtW2EbaFYBRTPQnEsFNcCw0LRbVBAQTbRwPFguEyK5kGtg1dd7IusviC3D2oQlwporqriqiqeouJoKo4mcHQVR1NwdA1b07FNQS7gxw0YZMMmvmAW1yeoCMeZENrNZAao0mwMn8v4PdKQBfs99BdjFIryk9riJrbPgG83z2ZF35nlMH0uU4npT3BW9f20in7G5mQOvtcwqbbyjNknqBOJcr52FAdi1qxZ3Hbbbdx4442cc845zJgx43gP6U0JRTn2JVFz587lz3/+M//2b//GtddeS2dnJ9///vf5p3/6p/I+X/ziF0mn03zsYx8jFouxcOFC7r777jc0AXDUKL8EWrBxxN+ruu5kyduWI3wTIeDH6BxqI+e5BexU9zH3kgE8x8LLyGSj4vMjrKE8TSnc6yb7KcS34G86dUgrWzckI9uVeVjFUeh84dtMeLoXPCka7ykuqpVn8f/8FMVTUFCkvVOGmT0FwJP/hAeqiysUPB2e9MbiqeBT8yBgMuvRtQK6atGWeQ5P0fCEitXlw9MUPEx8iopaUHGFSTYkKPgCBPeoeAUP13Lw0HHTaexMEieXwcMnc56eiqdV4qLh5VIU+mS4K7vvUZ7puY9Tx19OYMxS9A4pcEFbK3ZUJx9ysX0OdrHXo2VaFHx5HM3GMnNYReUyx0yDmUHzpagI9NDkkzKOmuaCKqhKFKhJKVBQiOyxcU0Vdc0AiWd7y5OY9uIgG8+pRMNhXGQDi31PA1J1rEPtopY4queyzi91mlVcmnI56ncKdjzZR1W9TAPU9Od552M+QCEXyrF6orz/dUmFhh6d9sa1zKyWkp+7CXLWHb3En+0llLXJnCfzb/mFYSwXnqtVeV50sKbY+3qwUI3j6HiKg6PbWKa8LkZNAK19EovOvIWHHv7Iqz6X51y8AvuchWybtZmxK2WkQF+zE1PkcTyVc77vsfyKV56dH3xQetPz2i9BNaJlr1hRTZZ9cif3/KR9RK/lG264gRUrVvDXv/6VX/3qV7zrXe/itNNOe9WxjuLExwUXXMAFF1zwsq8risK1117LtddeewxH9friH94oz225CIBA6yJwLFwryey6JSMIJk5iP5rjytxtrZzctPR4CrueJtf99PEYNm5uEDchu+QoviAiY2PkdRzdQcvJSc/u245QfSh6EAKSXKQEg3ixgiRHOS6qpSBcm11T3kNf62Rc1SUbSqFZMkcd6Y8S7pZGwQp45cna8tnkA3lSFYPk6naj+aWBeGv9XznVXUdM9fGHgmSw+5Q8cSfMvqcvxTJzZf1sOxQDrYCSC+KP1UqJUKDtxXb2TtrH2F/ncQf2YfVIQ+tmB3ELaRTVAPJ4TpGtba3FTu09oGzt1PGXo0c6UHS/XIxAWQVNKnspiGI5mZk10PM6tu5gmSa5IkM8p1l4WhxVtXA9FccbYp93e5XktX0Ir2hkXFCe6KYwkGNwT4Z9XTLUP/+MJiyhsNR5nnpfPx2OvG9VIonh2TzjTSDl+VmiSAnOnKLjKmBsTbFlj0V6uzzOO+cnscb4UH6+mejsOuqaig0fUiqO5tG212DMdnkNQxuS7PvrDjRDEK31l0VTEmGHndUuoGKj0pOVi9BMuhosH6LIpveKUmtWANSqKEbHKYfUwnT5bbKRy7JvduIE5DG2v8fkVH0/WxKTsAMKM/8mUyn+s25BmbdrhArXcJTu57x22erS3zSf1IqfASNbm2qaxqJFi1i0aBELJl5CPPZLZs6cOUr+OoqQ4etjL7P5j4jDNsqlL8jxYB2/HhjeAcrJDuJk+9GjY0fs47kFyOchGiz3vBW1LSj7176uutivBCuxqyzXqBh+1EwDRiYE+NBScmJO969HC7WgRhpQ1KIxKeVcXQclk8XXZ6LYCr5YgFAoQKwugSdc1KJRDg6YaH1Z0DUURyvng6HoXQaTKHoeOym9vDuUC9hU0Um+YJIvymOaokDskQ+jCxdXdXCL7GY1G8Yx0yiOjpH1U9FdAUC8PkOkrxKsnVg9G3FSUo2qZIQ9p4Dn5rHiUtHrYDXkcxrPRxgRrMQO9JoJQy+4LmrGAVR5Lhl5PqqloLjgCXD0APmwrJWKZ/0kbZ18xX4UxaGLJgASdphKPcZM3zbG54skMsvBAwoDeVwXWluL5Licw6I1Kn+ZqRMlQ07Ir12dk+Kn9tvYk23FdVU6qrsAmOLsIa0JMFSSefAVv6WiPoBqg29KFbH5USoT0tK6Aqp2uoiMg/W4rM989p79+HwKnfOq2XtOmOblxV7FKYuxNX765+qokzdzjyZ5CflYA4qjYuQCmJkhUp+je7gBDbW6jvCkQxdjuOfLQ1PLsmuiPDTxbIJr5yMs6GvbKe/jjjOZ8JkZLF78a4zJZ3DPT9oPeqzhxlkLNb/i4uCxjb/jG9/4Bnv27GHChAkH3WcUrwEClNFGv8cEh2WUF3YspWLcuwBwcgPHheB0tGGnZOMJO9SEW0jhZHsxqiaxcKYsT7HT+wiMWTrEcihqIis+H2q48bjVL2f3P1k2UoowUFsnoGfCqJaOGJATsB5pQ+hhFF+4TIBSQIauAS+TRukDy40S7X4KJ9UORIgB4UFplIw+C3r75fk6YUTRWKuWRjYsQLh46SgVe6X8nXAmsCMwS4puNG4BILjlZMILbsV+5H24ebPcqclVHbx0GMUV+JMB8kU5z8xpf6PtN+/Bs/LY8T3lc/bcPE62Hzu151UXQ8NfP2PejWjJiXJ8hoGS0RFCoOmy9AuAXB4sSzK5TRNfcfEV7KshWR8mWVlHqrKfuCqv3UAwxm7VYmrHZmYHpAymG1BRGwKE/Br+thB2UTwkt3GQ0PihxV9DQYbGN5vV9GVqsNcuomrmX8r1zsLzMFwPVIXFpwRRiwInqAqxGpctHwszaZfHznrJHI9kFeInw5T/y7Dt0Z7y59S3+tGrTHw5BYoRgTW/2U5Tu4/qyETmhVUiNTEA0nkf/mSk3KhEKTbxEK4iCVumid44mVPGfAjgsJ55d93zjPvzWzE2dAFdmO+RDTyM5xejLV+BOn0J2WkNzEz8l9w+7X6e3vk7ll2+jfRpHaz4oDz/knGe23IRJ1dLj/xg849lHSgXOoojg1CHokqvFaNG/dAweplGQb95Ef7cBoLZ4xOKH8UojhZisRjpdHrUSx7FGxaH5SkHmheXQ4Ei3YuZfXkx+DcKSrnjOY2yJtIpxDHFDMwWqanrU3VEpHZka0MAVUUEq/DVzzou3vJwD2HB9Ksw0gtQM9UIXUiPD9DrJkHPBlk+lCk2olc1vEJehrMdu1y/2+W7jObUzST3nQJE8A8U12v79uL27pHtBHM5RFCGzPXKMFq9ip4O40uHqOqS4ipaTqBalfR2xikp3CZbNxNZ/kFMV0HP61hmUQGr2D9Zzxu4qodlyrGozy1BzTi4hVyZsHUkeOTpKzljnsxbapWdCF+k3AUMtRQb1hDBKHSOYf1H7yW4Vtagtz6n448Z2JrMT5bqsZ1sgFxFLyvScwk1yBDwh6bspLIxQsXaLFp7GLFNXgEva2MFBbrn0GENsM2U1+pR62Ryz1yIEC4F12SvK2tv94paLs0/S9dck0bG40blWAfaNAqGQ6jYRGNbSHrzTVqOuKEyxadS2yy3JfoLhD89lYJfQSsAFTIkrWmQTdooaZudNZLgVYInXEKn/4rLArezwpUqWRv+/C18A0UxmnAV/tbTD/v657evQOxfy90Pf4RzP9NFJiYFQ8bfp+OFqyEcIl6fwXn8YgAKP7qExYt/Tb7necLtN9C2X0Y9xn2wwAP3vPMV02Y/+tGPWLp0KcAJJ4/7RsbREQ8ZzSkfCg7LKAdnvRu12CdQBKtQhFEO85aQ73v+DZlrHh7uXDD9KtRKmTtUojUohsyNeqqC55OhV+EEENWNaOlxGIX0iGMdCiHmaCLf+zzBXAbFcnFNgVcl1bWU3hqUwSBOYj+eU+xupOrSEPlCIDQo1i/bOQ3bH8a/fyOKPR1lQMpqOt07cAb3yLrRTBVKoAIAobdjZgJEe2sJ9wXxxYd/4Tyi3WHcohqWf1crvqSBakkt6kKgSKIKFMgHsuRCaXypIP6UrLvVcxq4HnbP5iM2yCUMbxk4p/F8jKrJ+Frmo9VJ/oCoqseLRlj/0XsRwiHaJ6/hQGeBVGWCePN2tHAfFQEZHp4Q2MJEbTv/1L2F1g3FNp+ayqaOAmM1H+FeMHqK22dUYRvg82w26vUsz0mDv2HjhUQtnWTzLtqMPjYVZAogYYfJhQzOCK+hotMjWlQGs4RFNO9RG1fZU2+RFvJ6bQ4YJBQ/8VmD+E+SvYoDLmQCskY4MOjhbJD3c9yZ9ahLWxhoFDwdrMHNyXvhCYe8P8OexFh2BOrptuUCwR8zUPJFNTTdQA1LYtiC6Ve9pFXjgTh99g2A7NZ06u8tzpx/E/krPsGMm4tksoF9eI7kNkT6aslE5Wqjr2Uf1e+6lFDmfWC5VAW6AVj/8zGv+HlPP/00Wzbu4K+/WMuynctGDfJRhCIUlCMMXx9uSdQ/Kg7LKDtTx0BQTlb6njgiXI3eMg1FN3HTJ17f4dcKt5BC0Ytkl9pqPF0rtzh09SKzud5EVLWjtTShvlhXznEB6KHWYzreVV13cs7FK6SspKrjBopqTOEK1EgDTmI/Vs/68v6KaqBWtCGCVdKLRiqEeY0OXjIGuX04/ZJ0ZHW9KEllwkQL1UvvGyDXQLA7iC8eRotbkCs2yNA10FXUgDrCyOp56XlbuouRGXrsktUD2EYO1dIxis0ujIyK0tdLetvfgeFiE0fvei1aeDNaw0REg/TavGiIQo3OBXV3cPv6T5INyesSq9+P3biFpsguKvVBppibATgvv4bWfhi/VkNLFBc8u1LM/msedUIFJAoUpsrvSn+zx/7qAj1qBb9LvJXuHikdGCxKk3qRXjYOTkMpUqTbI1vYVmgj5MswQ9tGf/FyPc8YTjM24LMKWCoYnjTW3SJKvRtnZ2sBrZgLXlOrc/GdNr03r8PoCOGfJj3i/NIG7pll8yvlLJ7YcyaivyjgoVu4wsXOhdjhNLEz0wFAawqUTK4cfRF+mRs366a/qmEe3jrxyffozLniHmpWvo/Cc3KhpVa2oJx6GtaDd+OvPBvfgFz8alYrYz/xSbb+9CfoXRn6tsvyprec9FNm3Dl3RE/l4fjb3/7GV6/+MuPGjXvZMY3itWHUUz52OCyjnKq1SXVIT6HerMXPkGxjSVHJTu876oM81nALiSFilKljhyXzWOTdsrC/bYITAGHJsKcRlROBk+0dweg+VrB6NuJLTkINDTFnFZ8PEazETffLzlKAFd+OFmrGAHCH9KCdxD682jxOch92IY+TlEY5u+9J7NQeFNXEsCaXjbKXyyF64gjLkspiJVa3qqKEI9gtEVKVMnwb6Y+iOEOPWklxzFVdbCOH7c+QicSp7JJGyrc3R2Hto6+bpzO7bgkV8z6HqKjGC8mFgBPS2PGOP2JYbYi8rxxaL4RiBMwUqmKTdgJoFEuOCh7+vIoVVFAelN+Jnqd7qegMow7kMNrCJKWzyf5qmy1Rnd/nljKQqccckGV1wXiEgi+P0TUGy58u96rekqlAMbL0h2vwRYa6ge20mukTleiVj6F6HnrRKHfavezSqhBehil/LjbSeGcAsT1FMmbR+2Q/44peTqDgcrqvkp0zn2HV/g+hFWS4O1OzD/QcaqKWB3dcROOLM+VxdAXVcWX6xh3q5KQYAVRf1WFFhfToWO6+sYZlnyxGCj5cYNxTAq1xPErfAGRSAEQYy7N/+R7pSX20PLKF9jUylXS3fhmhU+9k7OMLqPvwdwBGqu9ls4wZ88re9ChGcaLjsIxy/Kw/kUlIfeK6HbV4uia9s3Qap1/KGQ4v6n9Do+hBeqqCbXp4KpiuQAzISc8gQLLRRsvpCNOPokpjqKgmimoOawJ/bJDZcR9G22yUgH+E/rPn2DjpHuyUXCyVDN0pYz6EXx3KD1ZENqNrGdL79+La3RQGpUc4PFc+t+Wichgcq4C7P4aXS+HlkuV9FMOPoBXVqihvs7WRbfkKQXmMvL+A7c/gRXrJqQVSVdKKBR7YXpZcfD2ghVoQRkAyrYtcgbUXrGBqeDP77/s0bYMB8oGiZ2j5yKSrcT2BLixyfnmf9wQNojkLLStws/L8gtUm+cE8lWePxdMFoQFpCHvGKaxQJ7M/2YS+bSahQXltVEsFH6i2jmMZ5eYXrmvhORqxTB3/mxjqhmP64lxc8xdO2Qr7qxz2heTipj3u8s6nerEf7CK+T6YjTop0UJheQeznDlt7HIyVkv/Rrirkl1UQdTN4LRs4u+4ueU1wyHkGK1OzyK5ZilMUVbFND81noGQyUjO9IPPnTroXJzeAUzj0CFnJgMbuvhqASS2/QMkksbo2o81dDDVVACibduCr6QBCFM45hfCaGAAtvqmk93Siqg7BT/4AgGUf3cw9vxh/yGMYxWvDUWFfjzrKh4RR9vUoAJu6lhTduwK49j+8nswo3qDw+/10d3cf72G8KaGoyhH/cKQynf8gOKwZ+F2Vd/A/RU/Zv6cAA4N4mST2/s2kN5dCWOcd7TEecyiqiefI8KVwPBzdw/Y56BkdNSU9ETVfIKhXYpseRjiMUSlX69lsL4pqoEePbRjt6Z2/Y9HCmzF1EyVcAYBnFXCTPeS6nz2AHf7Utl9y2tQri+pYkE25qMoghcEMbiFx0NCxW0jgpqXHpcTCeLkkTmI/bm4QrbIDAFHRCELF32UTCctwtCtkpAFg64Iny8er2TKVYMN6hHA4rel2nmqQYcpE5pKjdVkOCmFEcAsZRDqFUuzlKrIR1vXOoc4VKI6CZskBR3oaSQA5R0eP7iXlyTx5Whi4igwtGyfJfK3RGYGgztb5KvujLnVJ+Qy1xiBWG8ZyDOpm/Jn88n+W10X1yAVTCFdFuAK31J2q2D/azgdBOAi/9EZbgruY4e7A1jw69um0FXevXVWg67eb6d6XJxKR44725aDJR3W1RnW1RjAiv+p6Q5BYxCatBLFtk+cysoFFrd5HtTbIyaHneXRiBJ6WDGbb54KpF6MKwyIejkW+9/nXpFNQehbPmHcjwgiR71tHpGM61BaZ4J1thDaniS90GWzoRR8r71HH/QX8HRWkq/LEWiQprIJxLL30Oe799UwqKyvZtm3biDZ+oxjFGw2Hx74mh7CKcoV79mLtWI3Vt4nM7vtHlBY5hfgbWlREGOGR+VbdJRcooEYFekBOyl73fnQhUKtC4LrlUrFc9zOSSOWrKqufHSsWaHz9/1KhB9EbJ8sNqoaT2Pey5VpWfFs5/60bGnYuQTji8uPfffug+z/Tcx/zJ30aADMXk8dI7MKonozWUOyIVFENAT8MxKlcK8unrDop85iszxPeO4ba3TJMHatLkMpW8b3Ga/iTu5Ca/5OkLudH18J7rj7i63EwnFy9EF/trAO2T3xkBoWAS6oqy86ld5UNpKraiH0T0AYaSJsptuel6tTEwC4GAilsv4JWK42G0xYgUQ/V/QrPN7rsDMqv1+RYjrOU57g/v4z9XjMhnwyNW2YeT7gUjDzCFUPfLeHiuSqKp+KaacIB2YHrgsBD1GbzNG7XKAQVQk/HAClOsm1bDtf1aGqTOeLs3EpiNS4db29HUQXZubIMS+m2qO3TmRaK8bmWm/h5//sBaDS6OUt5jm2inu7IBrrdcwG5oHICKpppQlodVkqm4zn5I7oXwxnxZy37I7pV1Cd3HayuzYSmLCVZ1V8O99trnsTXOxZfZxvpxiJxMAC+zvEsedtyQtm1RKNRFixYcETjGsWBOB4NKf5RcVhGeY9Xh5Er6claWH2byO5bwTM995XrfFV/7RF/WU8IiKFL4xgO+YDMpfnrpNKVlqvE8xmIeAYsC+GTxk162QUU1cColKSoY6V8trp/BXNbLiKslsrWqnELqZfdf+WePzG7TupT73jGT1VrkEUXzuHHP/4xV155Ja2tQyzyUs2nUSkJbdl9T6KoJnqkA61uvDTGgBcNo1i2ZOvu3AGA3huG+lpCwiTU20A+XGoCkccwMuQUnfWZSdQUG2mYl36NZZdv456fHf1og1B95XNAN/DC8nnOhx36m/u56sx3MSWWJVe8/WvCFfylchFrtr4FvWscW4sufz7wJK4CXc02Rp0c97omG+GBJRRqcy4gF3aDfhC4ePF6vFQF9Yt+Ks9TyfPCwBycwUZEJowoLgQ8W0dBx9MshD/OrPBqABrdQXyOh+JCcK8FNcXWlVsUKiOCtmlRAu+Q12x3k8NN7ZO4sXMdsU4do9h+2lrRRdX+KHP7I4RP6mZd1WMAPDx4BtHKBA8++wkqehromyp1uKu2nERgsFJWIKhDEqtAmUdxNPDAPe9kwfSrAAjN/2fU6jYiK/bij48pkys9x+Ken41h4cxrCM59nxxDOIqXTqEYfhL7q9mxY0f5mMea1/FmRjkEfSTHGE0qHxIOyyhvK7SUNZHRFVR/JVqohbktF5W/oHZqz1GrLT1ekEZVXhpPVbA1B0ezyQeypOqlNxOxqnB8QpYDxeLD6oBN3EJyWOME8DeceszGvnLPn8r1obqql8fwcjjYvbrllltYtWrVCKNc8vYffuLT5W3z2i9BDdUhKurwQsGhA+TyeJkU9n5JFnOzcQwWo6v1ZBs1YnWSlV3wZ5hR8Sy65zA5sIFHlzwKQMX2yaz//OsT/i95+3rdZNxxzdgBOVEkauKoJ9/NW19wqNjglvNfbXOSjG28nQ8G59P03HR2BiSprZY4qwP1rAvY1NvyfDZrTczN7sHnuEzYoxOLSKO8owr+X/6tEOnjuxM+xcl7pZHJmi53ND5Ld0MlTUof21xZnvXnLR9GS1XgBGOEfXGmarLzk+E4WALWT7cYv9mgr0Ea8dqWDiYua2XfRCikpOHcU1VgeeJM/udtOzhjd47WX0j2fXJ3CkVVMCIG1a0moRpJXMzmoizfegmNu1qxfBa+sGSUt5z5E6ytX5GlbmKkUT7aKJVXLZh+FaqvikeevpLTZ99Adp9c0Prq57F4cT8rnhsZRZnb8nGMqkk8/Oz1/Nu/yTKskhzoKI4OjoZRHvWUDw2HZZS78k3lJveKz48I1qL6q3ELCazUbuDgWrRvNLiFxNAfQsHRHdyi5nGuqM/sDwdxdA/Q0fY4OIlSydFWnum5j1PGfAgtVBQgUY1jqhNup2U5k14zAeGrPOz3d3R0sG7dulfdr2LRV3B6t43woBTLxsvlcPv34hU1nhUjgKKqOAGNRE2GVFUfAJHxK1g9MJePVN3DOH03K4uGoHrPAhYtvJmHVnz8sMd+KBBGCBGsRInnGBwrGc+ZSALXCrKpo8Cs/+un52nZyak1NgaWBAGdYJ9KZdOLADxqzWB3oZldsfH8uE4K6Ix1uqnKeozdZZAOueytkMY3YMPGnlP5VsdXeMftDs4z8jy1yVXYS/fxVE2KJ50p3LX5AwD4YrVY/gyKL8Xk8DoiZMpjdxSFjCGPu6tGPpMF3SOvezxXa9Klyjz+E9YMYn113KK/jbcN/h+5vZILkUtY+FIWYmucyrENxDplzW/Lk2eSrEzS37JXHnNQ1i+/uH8CNW1JGuJ+KaJTUkBzLFR/7dG8LWUMr30eXuv8chjuDTuOg+u6eE5+5Pd4FKN4g2CUfT2KAzBt2jT27Xvj15uP4h8PPp+Pvr6+4z2MNx9UgXKkP6Ps60PCYXnKA5kG/CWGqOviFTJY8e1vSjk7LydzsS99jLxiLallughXwdVB0Q0K/VIxqxQOfmrbL8tEL9Vfi+avOzYDZ6SnrxgB5k/69AiRhVdDTU3NIXXaUVRV5t6FKvPIAPk8XrwPJ90/FNIHrK2r0aJnkp+WxSqGgPv6x+EL91CbdWk0BsrH7Rq/l/GL/umAvtZHA/PaL8HfNB8lGAVTp7dD3je1dgfCU/FZ0oOtLRS1zh2PrioHbVsdrq4wMCClMFduOAPLn8EN96O70nP1ORY1CRUjB9kAhIq6Hw9VNNHKC5y1zSL/wB7iW+X5+3ckGTN+Er+vqeeu7ZcQ7ZIksrw/g2OmMY0MppIng0wNFZQMvYYgYOWI1bhM2iVTSQ9MgufUDnY4TTy0WxK0lFg9XkU3Xc++m8HW39P4EUlErB+w8XSFgXZBLGqzfKesBzc6dlGo3U041IPl6hCXDOaaXR1UdIVwfaDqOoou89ieU0ALNb2uedvXEi0Jh8Ps37//TTknHU8ogtHw9THCYRllr7cdrZRTFgIn3f3mIHW9BG4hgVsMvWqWi2qpaJaO4gop+IBsaadaCooD6DqKOJD0MrzV3LEMpY0wyqqOFjy8EpG9e/fi9/tfdb/4o9+XbS3zWbzSxnwWr5BD+CLYaRkCzvdsoTCwnijAmfPwSqwj4L+C3ySQBs1z0IU04llfBjfaRHjCuw5r3AdDSf60tCjSQi2owTow/XiqQtXeNgD6K7vwUpW8ON5HeKFF6GQZvt3VYHF94C2MWzURPW4z9lHJ7O1vGaAQjuML97DfkGSxWivD2kaPsYZFXvfIFL8qNiqXhv5GKCHwHA+3aMS1oI5vv00H3UR3j0MpLnhdzQIjhyoseuwankbqWU/XN9LoDdLnt2jwu2SKt0hFEFXSaIrNwoblADxtzkfbNBczE+DX7S3MaZDppYk9klG9odZhldEBMl2Nf+zTnBJ6gQatj51WM6sdyUVIVcaI9oSkspeuofiGuANOtv91fa7NqUtZOPMakv/xOQAUV1Bx46089PBHygRFs3Ymj6+9sfye2tpatm3bxvTp01+3cf0j4mgRvbxX3+0fHodllJvXj2NwkmQhp6Y1Ehw4Hy3SwmlTrxzxxXijY+WeP7F48a/lH5k8Rt7AypuoloaRlzOtllMQlieZoa77ioSqY80ALX3eWUtuBVVHUXXmtlwESIP9at7nfffdR2dn56t+jqKa4Fh46Xi5sQWACEax0wMjmN8lfW4jZ1JTuxGAvoExmDbkdNhMM45XWvAIFMulMLjlsM775OqFqEZ0aGwM6ZBrIUmiUn3VoOrgOig5m7qtcv9o9xK2nvIMaWHwQpPFFl2qS/0u+Va6tsxl2nM7oaYGMyWNUlVXJbFxLzAl8iJbkYue72fP4a2R5exv7GK/UkmtJ+uL/1/fJdwUvhHV1vAvagZk3jbTlcHUFV6kAzPjL0t7lhp5OK5Of6GGpC2Z/T1WDePNbdhC5YGWJJd3r5H7odDiDLBTaWC7Jb1tyzJRhYurOtz7v7/nhff8OwDTWzfy9t6dqJ7gwcypLG67HYAOdR9T3d0MKEFsTeMFdUje09FddFQ8IcAnFyCKEXxd1fvmNJ5Pzbv/C9VfScOq4jT15e/Rfv5ezv3qu8vP8Omzb2DhzGvQK8egqDpK6kXaJ+W48MILEcUe6KPdokbxRsJhGeVwtyA5VhqlHTM34Bsbpf359xK8v+GY1+S+nji5eiHhsW+Xf3R3428ZQ8HU0WwVMykvmZZxwfVQcjZeLouTO/HaWLq5GAJZSqKFpOfnOXnmtV+Ck+09qHG+66672LBhA9dcc80Brw1Hqd7XtdI4saH8s1o7Bs/KI4xAOXxdWrCJ2lb0vE4ptnJNx7W4aRg0FFZmZ5DNSlJadV8dSvzlS7kOhtl1S9BDreW66yHZ02KTg6DUmxb+SrxCBrd3N8IqoA9Ko6yFgoQv2MrthTOw0VjXNROAMXeex/QN/XjWIEomixaXxzOFji8wSLU2yH1p6T33dZ3E/2ZraAjtIusE6E/IBYHfP0hMM9jWmWP5vAZOulAai7q0xy+ra/jzs1+kJe4nUSPD2qqtYxd85LMRLMtElIy0bbLFmoOSCyIsH9UzpKF9wZrIRcojTBY7edSSTH/RLdnrRs5kx4x19K/4DAATF11BdVxDcxxucW7FUqXvktc94j5QfS7bRR6vuEDS88Xr6Higayi6PH81WPu6khcj0z+Ms3sj5rgzJPMbeHHLBWxKRlHPTdC8ZgcAU096K+nVv0Wr6USEq0k/u5F0coBvf/vbfPnLXwbeHHPS8cZRYV+PMpgOCYdllOv2/ojqB01cVScfySACOfJeDRUTG4iKKbiuyv33349hGOUf0zQxDAOfz1f+u7SttJI90bC6f0W5/y66gdFn4Q+YuMJDy8sHU0kVQ7COC7kMbiH5Mkc7fJS82iP1sO1UNxqU66ZBGinPySOMCHNbLiozaEtez6pVq7jwwgvx+XyveGzViEqGa3YQHAvhq5AvlERXVA3PKRzwvuCASbY44RueQ1IX7DIi9MbqMPfK+uHGNSHsDQ+Q733uFcdQCk2rRrSoZx0pn6fQw8POWUcN1svfi6FmN5sAx4ZEUaFM1ej88mxSp7wH2+cyZZO8v0o+hpcs9kXOpPHq5cJBH7QoFALsKjSzd2cxRF61h+bwdjrNXTy4620YxcYTmWqdXwQvZGN8AZo9iNczFIVoenAsY2IG+bBdrlPWCiZOJoJtZlD62tGycswVsUrMjIllWvSctJK/Js8BoMbo47fuEtYNnERhv8wd+zMh9LyJaqk0bh7PeRdJDe337dsOqIzdpGNuTWO1yWM/dqpF3FARnkfOM7G6ZUvLqngILScQeRcsu9yoBV7fbmiKqiMq6kksGU90nYzO1ewYg6u6CEdQu0veT9GQ5LEXrpdqduEzMWom0huHXG4DTz75JKeeeuzKEd/MKJG1jugYo+HrQ8JhGeVExalkW/KoVgErvA+/5uHGBMLJI/Q8iuKyYcMGbNvGsixs28ZxnPL/ruuO+P+lUBQFzzu02yaEeMUfVVVRFAVVVV/29YNtO//886mqqjqcy/KmQnNz8yjzehRveJxzzjmsWLFi1CiP4qBwHIfHHnuM6dOnU1FRcbyHMwKHZZTzygwGmqUH1dexjYa2J4nlq0jd+w5CW2QY61OfWjTiPSVChh4de9RyULZtl38KhUJ5AWBZVvnHcZzygqC0KBi+rbQwKP24rkssFuOb3/wm1157bVkC8LSpVxJefAW+aAtWwJNhPEDJFSAvA7FuLn1U1Y1Ufy2B9sWcPvuGQ6rTfDnkup/GsCajqEbZa3QLKTwnj+fkMWtnoFdKj6jEC1i/fj1f/epXX/aYJe9UD7UWhVLSMkxdkl8sotRNaDjsXWsxx7aWc8c7RQ2O0c962rAcg9b1Mh/qPHwXA09c/4qh0ZOrF5bJW8KIFH9CZcKdohoIIyjPW9UR/mKuWZfsKMXwowQqJIMcyvW3vqe2jBDJ8GBI89lxUHbKGnAlGMS/fTobkzVUNT8PwGUVf+Cs/m7qujR21D3MZ2qlxrXxt4/gZc5Av+AOwr5B4khPORCrIrLPwCqK5KmWHIOZCZCu2UegawyOZhFIyLGHBmQ+e//YbQgtz0CuBoDks2/D0S0ElKsj9LyJkTOxfBaZOffy6953A/BO/ZuM3+AitsbJb49j+qU3bwmVLXoNz9qTeLjr7DIBTrPltVAyBZRUGjcZK1+b16tOGSC26iZq3nUL/riGF5BzTuMjKbxoCDs67FnLyYjGQys+zpzG8zFrZwAwYcLZ3HnnobWUHMWrQxFHQzzkxKFfq6rK0qVLWb9+/RvbKBce+TMVnR8FIB9oZH9FG+HAALH6GJFqSaR5aZ7JqJIN3f0tp3HWklvJ7nm8/NrhlOmMGLSmoWmvTzejv/3tb1x11VW8//3vZ9asWTy+9kbOnH8T/orLoDGMUmTO4rq4+3eBquGmB4/qGDwnD6qOFmk5ouOs6rqTOY3no0fHlo2yk+3FSu1mdf8KFky/qhx21iNt5feZ5ssvMIRalHYctgjxnIIMYwNeOiavSS5xQG/t2NPfo3bCHJIZGYnYGWnEUVWa6ePsqgfZgGRb33/HebxSY5MSoas0huEGuRy+NoIoRhBFGFK8ZJgxRmgohg/FMIcMsKrKntCl/0ee9NA+pXNOp+lc1czapd0siMgmG6cmupm83sC3OU1dW4Bvz/8vAD59nh/3mfMY9+e3osdttGlywdIzdgPpmhqEq6DlVIKWvLbbTnsWkY2QadwGnsDRhsrT4vVdBFpeIKCn6O6bKDeaOXxpKf8qnGFkOVeQD6Wp83cx2SfV1cY8YyD2J0k914sW0kd07unxKnlo0/toWzMNxZHb84EC2YoC4MMfT+JlYoBUaXs9Ky+e6bmPBdOvIuT8M9TLUPXd35Kpg8WLf40xdTEAXibJ7LpLeabnvhF9nV3XJZ9/81WGHDeoivw5Epxg2cqTTjqJbdu2HRKp9VjisCzbnXe+l3e8928AtJ19Ad2ZhfSM3YAZTpA7ReYDK3d9dsR7SpO9VjcOpaIWvW0GufXLj8bYXxe89a1vZdasWfzwhz/k0Ucf5ROf+AQPP/Fpzrl4BZo4GQJFY2RZWF3rcXODKKqBk+09amMoxLegdT1TZC9fekTHWtV1J6eM+VDZgJUMcgll1S3VYOXKlQghXjGfXDpOydsuwUrIftrqYD2KEcAe3H5AZOSZnvs493M9uFnp+a2ILeDtVXdSq8Tpdyrpb44BMLvu4rKe+vCJtoSDLQxK5yCMYPn34QZZMYpGWfeBqqHoRYM8XM9ZfYmBHg5VHSkzaRVwTAXCA9QoctyVGQUt62FtGkQbzDGrWuqBv23G3dydfBfCks0dStAyEXaduRx970S0gonlk/eiYvtkYu2bwJOzmB2Ree9E4xZaI1vpNHexJj0VNVk9YohGbuh6qJaKkdfpHr8b89EP8t4zZf7ZP6jSdUaQutZxiIzDQJG4ud9U+d3+9zJxxSxZ5leEYzhkQzlc4aGOr8MoXhe778D7e7RRUvZa+r5VADRuT6PoefY9OPSdOGXMh8rkvuEQQhAKhUbzykcJb0bt6+uuu44rr7ySb3zjG8yePZtgMDji9UjkwOfqWOCw3c37/35h+fcz5v2Mhrf+KzsXxknWyNV/7dg5I/YX/mEyj0JFqW3AH74YgCVvW07/imtOOGnO5uZmrr/+en7zm9/wpS99iebmZuKrnqGy6j9QqoqNF5Jx7MQeMrvvR/XXHlWG59G+HsNrSUvHnl23hGDHueXtTnaQH9/8f+x4Yi1869WPWTLKnpsfUaOd73keRTXI9z4PXHngG3Wd6G65gEv648S8MD6lQLu2l+dCknEdKjLFD2aQAVwnh2ZEyosC+X9o5E7FUjBF1RFGoCx6UZaJfCmKBldRVUlmKhngUuj6pbrPRRayYaaoUuT1tVUPV5MaCW7KwlckkM+zt3GfreKpChsWPV8mblWPexS/mmFHsgbL8uFLVgCyUYevrwlPuOSr9oMqPeXTax9khraZZ6xJ9HWdhFb0ihn7DLxwJnpeRxSbeghHoFoCLdpDza65NBZ5iLsmefxTzfs4d8IjtLt9vKB0AHDHwLm03/sWjO7iQsuVjO+I5UPUC7KhHIl6qMzLcLdhnXHE6ZVDxb2/lXPKhIf3E551O+f+W5y7b5ALu6e2/bKcUnkpPvaxj/G9732POXPmvG6RtVG8cXHeeTIad+GFF45YMHieh6IoB+U9HQsc0ZNayrue+xmH/NQiq7W/C2gf2qlYFlOeDIXAq5HhSzV6GnXNv+HM+TeNaHRwIkAIwaWXXsqll17KrbfeyukXdbFy+bfQGmWfWW3J21Ge11FU87AM8pzG8/HVzwMgu+/RclnS0g++iBuT+UrPyo5Y/AD09PQQCoUIBAIcLl7q1YL0Wk+beiXCKIY9jSBN9Um+t+J/XvVYI49bGMF0frV6dXf3Vpo2yEl2fXuInVYzISPDA8mFGFnpzb5cq8kSVvevGJFTljrHKRRh4jnS81MIvvwBSh3AXGekbrcqPWdluKdcNNKl1yhVDOg6tk+mMno8ufBs6RpEHyjAnDrcgEp/Q7GUSVFwhYerg8j7EO0vAOBXM/RkG+lofYK5gee5f7m8doNTVqLE69GyAQIVe8vjsz2VRneQtempKK6KWuy7rG48FeGouMLFzMgFkpFRURywsxG2zNnIilppxH4Q+xAVd7yb3yypI9U9gWk/qACgbf9WFN/TUNsydG0ALRkkkq9EafaTDxRI1cntkUwT/uRbjqmme0VvJbu7p9Fx7beB/yhvX92/grOW3MoD9713xP7Nzc1MnjyZ7373u3z+859H07RjOt43E44G+/pEU/R68MEHj/cQDorR5eMh4L3vfS/jx48nNvhzBpM7iCXaXv1NQCaT4e6778ayLObPn09DQwMVjX4Kwsa1NYygxi9+8Qt8Ph8m9WSlj4Xf7OW2225j+/bt9PX14bouhmGQz+dpbm7miiuuOOrlZJqRJ5fL/UMzz0fx5sOHP/xhfvWrX/GFL3yB5uZmWqfV8eSTT9LZ2Ultbe0JW5Z5wkEcjZzyiWWVzzzzzOM9hIPiqBjlu3/QyJw/SRakuvJmYCiHk++T3Ya06rFotU2gCvkDoJtQX4c57oyXzR+eKJg9ezbXfuPf+cKnv0Jn03oWNeT437d/nOR3/3LQ/WOxGFd+4atkCo0sO6uFX/ziF2SzWRomVOMLD+K6Oooynr/f/iJ6oJKa8AuoDcV8nYjwf7flufILFzFt2rQROd4f/vCH/PSnP+VTn/rUIY/dKcRH/H32BX/n/jvO4/G1N3Lq+MsBaJrSyL7t3a96LNeR91mKkkhPeXg98qupJzmxfbi/uRmA+pZL2Ve9BcdTieVqaOyuOORzKnnLMPQQD1dVE0YIT1goSPEUHFk/rcCIXtnDc8ceJT1vAfoQw7c8lQiB5yt+hq6xd+Ie8ulKdlTKbmCe2IoT0SmEFdIRj20NMkr0mDoV4SrE6zJ4qoNty/tpijwBPUXCquD2Z75MRZF9Xbd6AZ5widf2ku0ZS6RR6nOHRAbDc9DFyPpvzdLLNc6uGCopVFw46a8L2Xzadn52x18AaNkeYPv7/o+WP78H/9YEnhqTp1bRiJdL4XbvQNF9Ze13dB8i00gk10C2xY9lyuvlhDS0+haiJ132svfoSHDq+Mtxsr0javWrnkzS39TKk/l3M7b+NgAqpvyMZ3ruY3D1j1i0MIZvnqzxv/t7dZwy5kM8te2XXHbZZdi2zYoVK6ipqWHFihXccccd5IrMbV3XmTJlChMmTGDGjBmvWqP/jwhFffNqX2cyGXbt2kWhMPJ7dbykWl+zUT51/OWEZ16KCFfjxrpZ9adF8oWLfjlivxIBKrv9QcLjTyY5KUA+ICcrPacR3msgquoJTxoZejpRMHyx0NzczG//+EtSqRQffNdHaGl8mPd858MHNUQPPfQQc+dM4/LLL5f9eyNtpHc8Qe1H/oi7ZzMacZzweLzqBFg5EtE27I6hEhOx8nnmzp15wHg+9alP8dWvfpXbb7+dt73tbYd0DsPDdbPrllC9bCjEbMW3Ujc2QkWlx/YXX73NY+lYJ1cvRBDBLSQkwUqXLQDNmld+kBMv/LI80c7/rc3ORCsxrQ6xdwLRZ2Nyp/fVHNZ5nVy98IAH2ckaqKpeJrKVYfhlr2z1JTliGFYepZZVpDxRXEQW//f04oJSKHSuamH7HHgmLMPxT41/iEUpDfOuLnyGYMI7i01IJkI2lIP5f2K2f3v587oK9WSsELnuCYST0bLMJoCrOlJEJBekwSdZ7HvtBn6nnM3S8MP8WswgPPFhAKxnz0c4omyYy+fjyBK+CQ+3s/Y82XLSf9bDZAemoOUVvHCgfJ4iHMGLDeLGenASPbjpoqiKEQArh5JOEuirxquX98bTZWheDR+ervqh4snNP2NO4/kjtiXu+iZjWr7Dnml+4kU2mvPivwAH52EMT4NomsaiRYsO+lk9PT3cf//9PPzww/zud7/j85///Ihe4qN4c6K3t5fLLruMu+6666Cvv+FyyqEp78I7fQHbZ3STrinQvFbWatZvHk/tnXvpv+MKVnXdOcJYnX3B39n1vrEYbTKnlok10/zCLKqtGrTK1rLncyLlfFZ13Vn2LAHO/C8PCJLfWsHu9Qk8ZTVf+9H7sG27TCYpFAqsWLGCf/kXOWF4Th6jcQbCX0nm/l+w+benAX5m/CKMUlWJJwSZzgC9bXIiNDN+Gva2H2w4CCH493//d6655hr6+vq47LLLDisEVyo1gZnlv7/0pS/x2B/XYLmH3slqdf+KoiJYDVqwoayY5bkHqngNx8o9f2LZv+wBINLSQO0Lp6C4gvq1JvE7ZOMBbjy8UrlyjpmR8ppKVnq7QtXL3rHi6HiFolqXfBFK/6uq9JCFkMYYpNHSJUnL0wWuPowQIqDQ+QL2gExnTE0puD/bxOa1CVqnR/Fl5DXxUUDP6fRuPY1V9Q1UhfaXj5Hp6yDcVy/1rvWh0qeS/nXd+IfK2te9jp9G3z6CSpaO1ifoNCXj/Xnr7eVmFl5RNtPRPYSlgOVhRVVO+tMkedzwdKYNZIA0SpHMVYZuICLVctFSOsdcAntwNwzuhl1DimhqRTMO4CS7Du0mvQa8NHL2+NobmdtyEa2fuQ1zkuQfxBNVdG7bwfZTO17z59TV1XHJJVIm+LHHHuNXv/rVK9bq/0NieITzteIEC19fccUVxGIxnnrqKRYtWsSf//xnuru7ue666/jud7973Mb1mo2yYvgpBCBZv5tw7VYqTGlQCq0reXH8XE5q/MMB77n/jvNoXvs8y6okuWlTqJOtfU1Ub2oBVSs3EzjRkN54O3Maf8yqrjtJXSVLS3wN89CCTWx9Ic22Gdv44he/SDQaJRqNsmbNZs4+ewEdHR2AZD8rhh+9eixOup+Tvlg0XB0WXiiAXWXS09FDvNhGUKSqqF8z6WXH4/P5+MY3vsHPfvYzPv/5z1NRUYFhGHR0dHDxxRe/KtN0eBP5t8y/iOrWMPmcDzt76A0gTq5eiB5qLRtkESyy0nMJTh1/+UHLZUrhxHt+JMlEZ5/3V+rXj8HLxEis+UO5bv21tANc3b+C2XVLhhllc6huWQ8eIG7ySvB0rexBvtQgey+Zl6ZXraL3Mamx3BKEresTNE8KE3hLO8+1y/v8fwMXUpsycfUCbjZKrGhw3YEWwn1FuchiXTEMtQcVjsrA2mXo42UddGdoKzszHfzVqmQgV8P+NZIMGBrmIbtqkVymu2hCxdUVjAG7zKZW+1JSGjafB8cZIZuJVXwudR/CLxcCrmvjZeM46W6sxC7slFxQDd2fY1tuVDH/cwz+4GJqvvhHADSrAcu0OOfiFSy/7eAs7MPBggUL+NOf/kRPTw91dceu3eqJjjdjSdQDDzzA7bffzpw5cxBC0N7ezjnnnEMkEuGGG27g/PPPf/WDvA4YJXodIeyCxmc+8xlc12Xjxo309vZy110+3ve+972un6tpGp/61KewbZsdO3aQzWZ5/PHH+eIXv8jYsWMPOedcNybK1qe7gdE82ihGAfD2t7+d73//+1x33XXHlQhWKBTo6emht7eXgYEBBgcHicfjJBIJ0uk02WyWROLYtYR9syGdTpcXXpWVlfT29jJhwgSmTZvGs88+e9zG9ZqNspseQM+AmagmG+3B1GSdcr3ZjdX8PNvPruf02TeV6xiX/cse7vlRC40bJ5Iqhp7a9L2sregFWlB0H9UXfB+QYgGFXatI77h3RI3t0W54f6jID6wtl9+UxrB48a8R/ii5PdKLEUIwefJkJk+ezBlnnDHi/UblJBRfSApWqJpshgAo/T0ooSC2aRBv3ElV5U4ABjULT59ySGPTNI1x42Td77Rp0wD4/ve/z+c//3kURUHXdSorK/nnf/7ngzKrFaHgWNKLOpy0gVB9qP5a6SX7o4hiWBN/hIC69KDEvZeWOo0s+xrycqrf+3MW/xySNTkqf/8cy39/aN6Y6+QQw2qXPbf0u1XmmHiONVSzfMBJDZuA1VI4WAFxoIfsqWCZLuP0bl7skGFksdul8z9m8+QpFjsCfn5vnS3P54/vwhUe9VvHkg/kcYphamWYhysctZwTtouSmQDCVUnvmwrAC5E6hJaHAAjFJVMjc82R/uphxxkifBUCLkZGIHSBUqpks+wyuc1znKE6bKuAl0uDY+NZufIz6hUyeK5saKIFG8sldKeM+RB2as/r8p18pUiJ9IYXsvSDMkceVU9CzRl44uj5F6effjrPPvsst95661FZXJckfA9mXFOpFOl0mlwudwDRqCTkEwgECAaDRKNRamtrGT9+PDU1NdTW1qKqKj/60Y+OeIyviqPBvj6xHGUmTpzIxo0b6ejoYMaMGfzsZz+jo6ODm2++mcbG14crcSh4zU+y1bcJf8Yh2lvLgGbRF5BGK1lViaHmKPgzmC1DQiLuvi2c+6UgNbrGk2fKSXZyeB34k2yf30fhrCqMoqhCpL+Vqq0nY7xwKs7gPpRi6HHhzGtY8dzVr/lkXysOZqw8K43aOBnTmX3Aa6eM+RB6pLM81sLgBsRZ/45/Vw41WYsodx1KoFg28foMvupdNBYJPRkriGsOTdgl/XAt1ELl278NjoPbu5fBR2QY+qUT2BVXXAHIySCVSvHEE09w/fXX881vfvOA0Pb7P/g+Hhv3GH+56fBU1hTVRI+0SU1poSGKMqu0tCB6+6nwRThzvgxHH2oNellX+2f3s3eyFNmI/etrJ9wMFzUptZFEaChWTnaxEhqKOix867qyTtl1D+hmo7hQ5GeXt+l5wcb/92Ou/Oe3ALCvQuXhTpUv93wFuydC67OyHt2fUcmHbVzVQ7O0svEVjgxZl8LVpdDzyM8VaHm5iLDSFZw09i52ZjrQ1QJKUZ3EVV0E4AoFtzhxqo5aFhKRHzZsVWFZ0iBbBbCKCxcrL9nX2QSelcXNxeSxs4NS39zNj2DZCyOCFmphdt2So26YDyV1kXj8hwBEJ/8YMnmUGQd+D48Eb3nLW/jtb387Ylsul6O/v/8Aw5pMJkmn02QymbJxHd5Yp7Q49vv9BAIBQqEQkUiE1tZWKioqqK6upra2loqKisP2zI+Vp3xU6pRPsJzyZz7zGbq6JCfi6quv5txzz+U3v/kNhmHwy1/+8riN6zUb5UeevpJzP9dDtKcWxW3BKTJHU7Fa0sEkjXuayO/5PSBzsMtL7Gxg1l+kh7RyThVYPrITnmFezePYxUYFq3rnE6ubTntgJvr2FrxMGgDTGSLCHG9kdj+MGmlCq24fwb6eXbcEYUSwEttHtGA8P38NqV//O0bKh0hKprKyywHLonvyWqZHNlCtSv3o7crYohGQKE1651z0EF46hVJbi1I/g6rwKwt1CCGIRCIsW7aM7du389vf/pYPfOADI/ZZsmQJa9as4aIrlpHL5Q65HEQYEUnuEhpqZRO0SbKTVWNiJH1ok+agzpJG9tyvJnHWPTPiGXgpzv3SIKEOmcPprh3E9Sco+BO4sdee1ysZEs8toBRFRVD1comUotrgDFPucpyhUiirWEIl9GGmWClrn5cIXwKF3Z5kzV9Q9y6a7b0EfTFSPZ0UzCKbukJ6rsJRcBEHSACXjLFd9KAtMz9kqIVT9qy9YIyEHabW7CHvGiSoLe+v500UV+AWxydUr1gepeCpyggnxXMcyGelVzw8p1z0NtVgNWpE5rrtvu04ufVY8e1Fpv2Q3rjn5I9qI5bDgRXfCoD3wnN4Vo7s5vvhqqO3YP/b3/7G/v37ufLKIVU6IQSmaeL3+wkGgwSDQcLhMO3t7WXjWl1dTTQaffMpiB0N7esTyybz/ve/v/z77Nmz2blzJxs2bKCtrY2amkOrAHk9cERPTn71Pfij70LLBSnaU6r2RFAtBf3ZzdzzMhJ8ga9/B4Bp776KdLPKtrMeoFodpEFIsli6OsDaQoB9eZMmatB3lSZO+6DHOx54eufvOGXMhwhNefeIMiDXyfHMnpGew5zG8wlNuIief5fnF+2ThLao2oGrC/RQP/VaLzlXTnC5fAgRzwH+EcdxCxmEbuDpGp6porRLL/Kc9zz5qiHe97///Xz7298uTzJCCL7yla8QjUb57Gc/y5///GeuuuoqPvvZzx5SOYgWai6HrZVwZTnc6+geXi6H19lEzwSZ0vAEVDcv4twr+0jc9c2Dqn65+/fiG3M6AJaZg4IPX6yWCcsbYZbcZ/HiX/PgMN3jl2J1/4pyGc2QBKcFjoUnigs6xzrAWwZQHAdPOCiOI73KolHEcqQ3qwtZ81ycmBTHwxMKG856nP0P/Ke8po7KxkCGTMda6na148sUO3OpbtFrFYCLy0hCF0iD7Gjy+baNPJ5wcIWLU7ezvE9TdDvjfduoEEn6nEq61I7yexVXFEPXRblQRxnpKevF75CuSSlRGFIqA5TWdryAD5EryHvpyLEZAx2IbfJ5zex9ZETU6HiqYxX+RxK9vA0Rll+hMDz9cTTQ1dXFtddeO0r2+gdCIBBg1qxZx3sYR2aUH3zwUs65eAVG+xBT2IsPknvxLh54hZDl8BD0qeMvZ0r8Zv52XoDWhtUAqIqD5kuRiQ6SqawgKlN2ZHc+Alx4kCMeHzy17ZcsnHkNimqUjcFBQ91OHhyLxk01rD/3YRINFQDkAuPQLBUhHHKuSY8tV2dusgb2dQFjRhwnv2clanUbQlXBiZSbY6it41gw/aoRrOqXIhQKce2115b//spXvkJPTw/RqJxw3/GOd9DW1sZNN93Ed77znZc9TimUHhp7ISJYhRKQ51KexFPgdTax6fQdNE69A4BWYy+Pr/sg2mwfE7rff7DDcu+vTmLZhyX7vHpfA/lYJZVdEdwXHqM04b6SQS5huAzokPRmocy+Vhx9yFu2ckPlP6XcquuC66IUPWXpd2ooQhkRvHaL9crhyl2IGslaH4i3MOmes3A3NMnFSbE8SXEUHMMZYZgBHAGObmPrFpaZJ10zrKuWcEHP4TOTVPn6AJgaWE+L6KFKSeBTC6hFTezhxr1kiFVLoFoKiisXReX6atNAyelg+lF0o1wSZjWGiTfmEK6GWlDxpeR1MQImmnUSPkDo4fJzvqrrzuNmkBcv/jVrSs/Cua+872vF+eefzw033MBpp53GxRdf/Pp8yBsJR8VTPv6u8uc+97lD3vd73/ve6ziSl8ebLMYyikNFJpNh7NixI7atXLmybKRHMYp/ZCxevJgFCxbwjW98g1QqxWWXvT7KZW8YHBWZzaMzlCPB6tWrR/z97LPPYts2EyfKNqibNm1CVVVmzz66HIXDwREb5dy2RzHSA7hZKeUovZnxh/z+oXrW01j2YakoteaavRhmikIgST5QwCsSo16t2cHxQHr3ciIT3o1RNfll93mm5z7mtV9CZd9l4OgYddKz6gWMZBTH0dldaKY/J0NlFXs7ST/1Y5Ze+jbu/fXM8nFWPHc1Z8y7Eb1mAmrtGERDkVylqvjaDi98N3HiRK655hq+8IUvEApJRu373/9+vvnNb3LvvfeydOnSEfvPaTwfRTXL/bG1yg5EVRNKMCRzsSmpnOU+dT/rbpS5zp2DMoJyZcPveNfUR7lbmc3GZd9/2THlt8pe29VrOsAMovT1k9n5JIcTmizJgJbyn6W8suLIULInrKEQ9vD0gGNLBjJFb7iYW1ZcF891wZXh65KHDLBvUg+6WiBjyeYXFVumY5kuXjmfK1HymF3hYZlDvIiCL0+ypge3eg/V4b10mFLmtEodpEaNkfL8rM1MpsMnQ0Utooe6YptIgMag3L6zog3bCJMNq2gFGT3xp0L4Uz70nPyKazk5bhMd3KA8R9ctn2c+5JKojuHoFqqlE0hI3kMlQYxMPWomgSE01JB8RudP+vRr7od+pDiUiMnRgGEYXH311Xz/+9/nqquu4sMf/nC50uEfDkI5cqLWCeApD29C8b3vfY9wOMyvfvUrKiul7RkcHOSyyy7j9NNPP15DPApGuXsl2X0rjopu9T3/Txq2pj27mRhdy0bA2jUWd39JmvDgKlfHE6v7V7Bg+lVoQdnS7uW0n91CAm/LZnx9TTghmVv2Ir0UXIGbrmSnMwklLsk1U5+rIja4gRW/PpC4UurMddrUKwlOeTsAoqL+sMf98Y9/nDvuuINvfetbfOMb3wBkiLvUFatklOe2XCSZ1tGxCCOEXim9a61hvGxjqQq8WBx6pUrVtq9Ooe28rxOZ+SF6fymNRkz1UUDj2eQMquMHyluW8NDDHwFg0Zm3IHwV2IPby+d7qCiFVOc0nl/W5nYL6fLrilNAgGT0O3ZZE7vMxC4Z5uGKXkV4B/EUBvrGEdwvn8vaXdUHN8aqi605OLqDrdtl4lben8FsXktnaCt1Wh8hIRc2U8QOAl4eFPAFC4QUub1KSaDikkfDpxQY79sGwOSJm7lr17tQsmHUmFwQ2bpFwdSwNYd8IE94MFQck4ah+1CFQEllyqxsPS+wzByFcByRHyL7GXkNPelHVNUXyXFFlTRfxcuKxLyZoGkaV155Jc8++yw///nPqays5GMf+xjV1dWv/uZRnND47ne/y7333ls2yCDrla+77jqWLl3K5z//+eMyriM2yoervHQo8Ap+2vW9ZEIB9lTMJLfzieIri476Zx0NPPbC9Zy15FYAou2nsujMW8jsuK9snOc0no8eHUth6wqaN36MrVXSI1L8SVx/El9fE4FElIoeqaJkBwR895cs+F+HwJ8ePShr+fG1N3LaVGmw/O1nlCMVh4MLLriABx98cIREaHt7O7t27Gbh1MuxC2BUTULoYYQRRPgq0epkFERpasEL+CBXgEwSu0cunPT8LNxCgtS6P5FZfRUA10+N0hrcQU+8g0hgZFnRwVAyzqVrV9JFd9Ldh2yknUK8zAyWDGyZa1Z9VXhuAc+xJHGumFNWKBY8GS9hEzuuzO8KF3SB4hTZzUDzujqaNtTh6HKbp1L2kh3DKbOv84EsjiaNsSscbEOOxfGlafH1UKf10aJ2MxGplhV0CoRtG9XzUM31JBXp0Qe8PA6CqJcBAT5Fligt7z8bHB3F0VEtaTTNjB/NUskH8mQicdxi6Zc/GcA0DQKqKb/8xdy5mvMIxSoZ9GdwfGnyxbKtdN4kGK7EDIfYfEkN438sr5ebS6AFmw7pXrwZMGvWLGbNmsW9997Lddddx4c+9CFmzJhxvId17KAqRy6zeQJ4ysORSCTo7e09YHtvby/JZPI4jEjihMopn/MeKcTR8bb59LfeTZ3ex5aKXvJ9Lxznkb06yprArRNQm8ZiNM/gjHky3G7WzkARJnaqG/O2u5lgybrWLfNfgGg3+cgAtXsaCRZlhJPNNnsnbsL1J2n1zX/ZzyyF8xfOvAbXSgKHH9abNGkS1113HXPnzmXixIl0dHSQSylE6itIDATQQvWIYC2KqiOCVagN0it0K4O4uoKWzODm0ng5mWLIhuPU18/FtZJ0Pia9iRerJrG5KoTRNQZ/t8PhPHbRky9HHz8XAC+T4vTZN5QFaV4JJT1s9SW9pBVhyg5SxUYVJYqUcGxJ+nJUSfqySn3A1XIIW7HcIcJU0Wt2dK9slB3dxfLZ5AN5LFP+AOSDSTzNwlMcPM0CrVjvq1qkbRkmrlYSVNgy9F6Vt9EdkH1b0sT0ofrgpmyBpC5QtTh3W7IMzfNURDZMcKC2HHb2J+XiwtYdPOFS8JeY8C6O7ke4AfyOgZopsq9dWTlhmXlSVX1l7e18IEO6KsDG857ga83f4S8/lyFre3A7dnoYMe0fBEuXLmXq1KnceOONfOc733nzlT69HN6EJVHveMc7uOyyy/jud7/LvHlSU+Cpp57iC1/4AhdddNFxG9cJ9USVynrmtlzE43O+RGvTKjByVCz6ynEe2avD6tsEgFaYhRIOo1Q1YHbKXKibHsDLJXBzMZJrfk1lXScAwfH15Kp3IWp20TUmSKhXkg2E6+D6k5iRHrrG7ODsC2RNZqkpxktxJIIqH/3oR3n00UdZu3Ytq1atYse2XQSiGr5wksp6m4HsBGxd1iOLyFCXoHxUQbUU3Jow7rpBnHQPAI3v+DramPOwBrfBFpk7r9nVgd3VQv3WSsikWXTmr4CRHvHLQQSrICAXPIqq4uT6D/nchoeyhzeqcLIGwimguAVEsfbdhZFec6l+13FQfD4Ux5VNKopG2dUVXF16x06xY5Lls8mGsuRCKQq+DK5ZzG/7E6BaKMJFiKGcshAuyVwl6UAAE4ugLT8zYIHPAtNSaHQ9An5plMfs1zAKOlubLZ6KNtGETBn0+WroUdsws/5yGZaeV8mFC3jCRbV19HzJSFsoPkE+oKPlTMolVK5kzresqaV3rJ9EtbzOjmaRjsgFzE8S76Pm1CJ34sU7sVN7jmtZ1PFCc3Mzruvyla98hW9961vHezhvWnz961/nmmuuGbFt4sSJbNiwAZBiLp///Oe59dZbyefzLFu2jJ/85CfU1x9aOu/mm2/myiuv5H3vex9WcRGuaRof+chHXrEC5fXGCWWUR3F8cPrpp5eJDSdXL8RXO4tAbRuRyjjN9SvYHXsbLpHjPMpRjOLEwbvf/W6eeuqp4z2MY4ejwr4+/PdPnTqV++4b0n0YHpn47Gc/y5133sltt91GNBrlX/7lX7jooot47LHHDunYgUCAn/zkJ3znO99h61bp+IwdO5ZgMHjY4zyaOCGNsjAiTPzwavZ/42JETTfpc+Tq/Kwlt/LAfTK/uPTS50Ywk483SrnOcy56CFHbhpdJ4Cal96jofkRlC6JQjZkdLOdfQ4PtZD1B2Oyjv3Y3Ay2ye5I/aYKr4roqrplDrW475ufjavXEkvUEqnYSCe0kYZyNUlVNoVLmLAsBG8dwiO6Rnpl/gdQIVlYGyXWtxIpvxX1K5mUam64GR7Dhs38hn65kRvzgHv/BYHW9iNo0VLpl1h1+Hm94fllRZd2ysEKIQhj8kuRRqh4Wjo2n+1B8khiluA6e66CEI5Kt/JKJpZQ/BrBMWW9sGzkcX1qGqqHsJataHkVxEULu77oqupbDVPLk0SkUj626Hqal4CsIfAUwi9rk0QFBb73DzpBBLXH2KyPJRrZuldXBLNPBFR6KK1AtnXRUqsWZmRCiqGLm6B5q0fMXlofigppxaFjjR58giYvpaEoqjGWi9KUqMWuk91/ZMofC4GacgcPnMrwZ0NbWxj333HO8h3HscJzC15qm0dDQcMD2eDzOLbfcwm9/+1vOOussAP77v/+byZMn8+STT3LqqYfevSwYDDJ9+iv3gT+WOCGN8vDGBUu/U83uWXJCMT57Pmf9FIwNXXi5FKeM+RAA0dOvxNq1+piVSrwSYk/fRKB1EXZ6f1nM47SpV+LvPAvhj6BXdsqQLBDtCdCVrsTyxVC1PMnqAQAcPYLIB7AyFfiSUXBjR3WMr6RXvLp/BaeOv7zcFzlnVRMIDZAMR/CiQbJRSQzqbeuWTRScairGn4wXlblMo3kGRvMMrK71ZPfIsGb8/77Crts+SnNgL7sHmvGqKl51jOd+pou7f9DIQys+XibRiWA1dmLPYZ9vKb8MUlxEGBHcQgLVPySl5znFULZrQyGDkpMLCsXwS3a7ZYFjoBQNpKIrKEJh+6y1qLU7AAisPwXhCoQtSVeeKIXAdVCzuK5AHaZvLYRDhTlASGRodAdJqnKBY6k5hKeg2QrChao+uRAy0h6GpRBwbDThkPPk/hkrhCccHM0ekvY0bTzhkg2lscxcuVSqBFe4OLqLU5ILtaTIiOtTEckC1Ruk4TbbKknUpFB3j6Wiu4JIUciH2jYC7YvJD6w97PvxZkBzczPJZJLu7u5DDpe+oXGUSqIcxzlAr9s0TUzz4HKtmzdvpqmpCZ/Px/z587nhhhtoa2vjmWeewbIslixZUt530qRJtLW18cQTTxyyUV61ahV/+MMf2LVr1wENQf70p6NPYj4UnJBGeTju/YLKkpukR7B13k76OrahzQ0Q6ZtG46n/T+70zLMnhEGGg7PRH197I2fMuxHhr8Qa3Erp8TO3T6Zu4zR6xupg5PCC0hA4moWW96ElO2jY1kR+y1+L75h5xOMbnl99OdipPbhZybL26RlyyjjwmVhRjVi9VJdK1+xDOAbZcJyusYLaXdKzrAzPxjEVhDuHaFI2jrdX3EtFVwt71PlEu1tQkqUSJf8Bn13C3T+QXVoWL/41WqWU/Uyu+d1rLsEZnvcs6ZMD5SYLwgqhOgU8K42iD4WvRLBaqpYVCiiWiZeXnqUKKD4VT7XQNek9Jur3EO0aFtUw5HbFyGIYGVRhoasFrGLNdIU5wILQ01QrcQzHYfKAHIu/IBCu9Iy1AujpYn/ljENhjCBsuaQNk35bevmOp6K4Kq7qkA8MEbpKXrtbzCuX4KqyPMsxHLy8NL6eqhSlQ8EL6CgpSVILb7DROiKkq/J4qocTKNY9V1SiVrYQbD3nNd2PNzqEELzrXe/iW9/6Fm95y1s455x/zOtwuDiYQNHVV1/N17/+9QP2PeWUU/jlL3/JxIkT6erq4pprruH000/nxRdfZP/+/RiGQUVFxYj31NfXs3///kMay6233soHPvABli1bVtZm2LRpE93d3bzjHe94rad4xDjhjTJA+iefAWCMehMbF67Dqt/JQOMW+iZIQxCeM56l33XhmWe597dzOOdiOQEfjabnRwvDS3lKHj5A/Z5J1M6dw64Z3aSrJD3fCibxD9bQtLkJ/6MbuGeYZOmC6VehCPM1k7sOpZ58VdednDr+cgAM4SfmdOAJge3zSFVKApASjOPzD1Ln78IUeXa2S0nQhLAIG3G6t51eLs9p7DyHWPM6FE8tbnMO+rnDsXDmNQQvvBIRrC7XYYdnHp2F1zM993Fy9UJEIVE2ztJ7TiGMEEIPl+txFVXHy6VkqVSRiQ3gOS7CNWjYNIls0RCHM35SlTEKwSRuaADdLz2CqL8Pv5ohoiVRFYeMU+yGpiZoU7qJeFkCjk13SHoi89ZrRJ9N4g3mUQwVZ0Aad6ZX48sr+GzY5dWzvyDDetlcFMVTEY5altzMBVM4xfC5GNYi0hMujmZj6yqqpZZJaoo5TJbTBcUsGvFcAf+6XrSxUjRE5Iv3TtcQ4Wq0UH05CvGPRvhasGAB06ZN45prrmHlypVcfPHFjB9/6MJJbyR4qnLQOv3DggJz585l+fKRHelezkt+y1veUv59+vTpnHLKKbS3t/OHP/wBv//lF/SHiuuvv57//M//5FOf+hThcJgf/OAHdHZ2cvnll78xWzceSwxXDjr3q63sXFxHbMxawtG9AFjhHtY0tlA/4SSWfdMu94KF49fu8WBY+r5ViPbxVL7nBwC4OzeX8+JLbqpn6zw5+RaCSfS8iX9XjoF7vwJI73t23RJq3nULtDWz7Jos7ovPAK+8+Dhj3o2HLcABQ0prH/7wJ7HSAuG65MKFMqPY9MWp83cx2beZFrWHPYaMa05Rt/PdLVfx3xM/xC9U6T08Ub+YhmdOw8jr7Jm0mebeUgj35Tux+Ccuo/8X7yZ68uWgF7+0QjtqrQJfakBOrl6I5q9DUU3ZAcsvozOKaqAYgWK5lA1OscOXZYHrUrM1SLpejm+gcZB8IIUbGsAX6qPWL1fsreY+oiJBSGTwUWAcspQor+hUOWkq7AKnr9FYP056ygWfh1ftI3bvbvSgRm5Aeq01HRGiAwai2CMhWZAeh5eJImwd1dbKpU+2mcMTDsIyUNyRgi2u6pS9Zcsnw92OLlAtgZFRGUYQx9M1lHwe7YVtKD7/sC5aRYMfrC33Gv9HRCQS4Vvf+hYrV67klltuwefz8YEPfIAxY8a8+pvfSFCORvgaVFUlEnltpNGKigomTJjAli1bOOeccygUCsRisRHecnd390Fz0AfD1q1bOf98qeVuGAbpdBpFUfjsZz/LWWeddQDz+1jhBFAjHcWJDNdWiejrjvcwRjGKExaapjF//ny++c1vct555/HjH/+Yu+6663gP66ii5Ckf0c8RGvVUKsXWrVtpbGxk9uzZ6LrO/fffX35948aN7Nq1i/nzX17bYTgqKyvLIiHNzc28+OKLAMRiMTKZzBGN9UjwunvKw7vKHA3cfV0YCLPs3+ew8QKpjEXHC1TVbSRZtYvu8R3UTz0TgGXfyL/8gY4B5rXLnOrTO3/H6bNvQHziy+ydtA+tGNZt2jCHsy/4O/ffcR5i5fPUNkmWeazWhz8ZAMfFVz+3fDx/0+koVVVk6lUKAY9Q5QIAzrl4xct6y3b60PIrL4fNKwtMXvAMmfjp6LkxZQlGxzEouCYtag+1xImoMk+8z6vhhjFXIfLwxfS9AHTX38f/vXU++7/3XSYMTCH74g3Fo5/0sp+7/Pencur4FvL7hgTkrb5NR8VLPhiGe84lic4SFCOIovtRHBvFKm4PRKQUpxsoh4AdzcYOxdD9Cap8fbSa0iNu1vbTrPQRII/pWYzPyEjOvDUGrgCtoJEPwIQdMtds5MDbGCPYGiK5LYGvqtjS89ke3Kmyxj3mhsssbrQCSjF0XfDJyaTE/Hb1AsIVZW/ZFQ6qK4r75in45PkIR0XPG7jCJGBpiNywiyME6AZurB8vKTkF6L5yK1U9+ibzCo8A8+bN46STTuLrX/86q1ev5oorriBQrLMfxeHhyiuv5K1vfSvt7e3s27ePq6++GlVVueSSS4hGo3zkIx/hc5/7HFVVVUQiEf71X/+V+fPnHzLJ64wzzmD58uVMmzaNiy++mM985jM88MADLF++nLPPPvt1PruXx+tulI+WMX4p7rk2yLx2KSoS/Mrv2HDGSpqan6WzYytbq2UoNV47m86aq1n20c2kV/4WODKhjcNFSWbz7Av+jhZpYaAxRaZjraS5Aq44ic6uM5nTeD7CiFDV/nMAfGPrURxAL+Abt7gsp6kFGyCXB0IUTIt0jVx5Roe1znwpjrSJh5VJkE42U8UfKQx8iYoeGRoaMHMMqAWeMaYww9hIqkjaqlBSbFPqKfj6aFPkKjSjarSIHh5YupE9M6bAJ15dkau0oMlne0nvljmoY5WzXNV15wi2tiJMFGEgHAvhDgu96QaK46E4JXUvqdalqgXCWoKokMa3WolT4yUIulI+c3yX/NoJGwIbkpBzMAsOXqGY3x0fJbVugFRPjmCNSWim1LN2O8NkfOAqMF3dwkP54sThqtjVe4lH+hF5SVJz/QkUIwuJGlxhoA4LYXvCJe/PjGj5qLgCPe+TYiNWEDVTZGW7w/YxTDxdLsrcZA9uNo5nDemKj0IiEAhw/fXX88Mf/pAHHniACy644HgP6cghOOZx1T179nDJJZfQ399PbW0tCxcu5Mknn6S2Vn4f/vM//xMhBO985ztHiIccKn70ox+Ry8nV51e+8hV0Xefxxx/nne98J1/96ldfl3M6FLxuRnlO4/lULfkPnEHpLRxMjWrZRzdzzy9eOzFieOOHJW+LsfXflhGecRtTIjIMsXaMyp68j/b8OPwxOYGde2Ufd9/48rnM1wP333Eei868BQDDGAqLZMNx7PoGKs/4GomVN1NYKT3LQOZ0vMoouC6iuhFfiwzHWH2bcLv34GuswtHNcuef1xOek2fH8zmmLNyL3r2faLRU/tFBIhtgdX2UzYGJZa9NVRxqzW6m+zaww5T5zfvSC+hKdlC9p5Wl33sRp1c2Urj/7y/fG/tgTT2OJYYvAErEPM0tlL1D4dgouomSiuJLyTyznjehUIwkeBp2US1LK4p5Cs8jbLmEEkUGc9bDSxTA8Ug+34eVlscO7k3RtSnJH5+zee88By0oS+UCqoJoq2DCToPfTRsqw6mp3UhIS5J1AgxkitsdDZ+ZoqBaOIONGMVGFa5wyAeTuPpwVxiE5SsbaVnbLL07X85AyQ2PGsjzU3Q/SiGDmyuUGez/iOpeLwdN0xg3bhx79hx+Cd+JiKNC9DrM6erWW299xdd9Ph8//vGP+fGPf3zYQ7FtmzvuuINly5bJoQnBl7/85cM+zuuB180or+q6k2Wf3ImYLcUeln0jj7dpPfHHvl+uQy4Z5KMhBHLf7edwznueZMOXLqRu/EMAhM1BBsatZo89l/auqQDE/vRluPE4dLZxLMysgWUNMQ2NgoltevimzCBqXEFhu9T+tjatQmubKgUrdAO1QrZotPo2Udj1DL6qekL5GrDkZOju28ErkaaOBG4hgZM1sax2/On15DOSlRiMB4A6MgUfeaOdQkQajpr6dUwxt5ByA6yz5P3tSnbgDrSguAJn3kmsO08yuBt3PsCMH04/5oukw0UhvgVFNVGKdcRlxEDoJkZAGqqKaCWWmSdjZujVckRU6Sl3iySa4oCaoAGLgk9qZedaoS4VIf/AHgb3ZMjnpFHcuz2LbXukHYVbnrD4CFKgo60hgGVE2dRisS4/nrBP1u+3+XZRrQ2Sc00GDBle3pKYhK4WcFwdWzhlJryiCvJ6Ds+flBGb4iJC1jlbOLpFPpAlWVPylAMYlFpYOmX9B6WQQ7GyKFYQLSifCdOZenQv/Bscixcv5mtf+9qIhi+jODGgaRof//jHWb9+/fEeygF4XZ+U1IqfEar/GgDZNh9u23SCp/2SZdcX6Z3bdxB/6Maj1v5t+e9PZfHiXzP4ufcD0Dd+Hb7qXYRP+T1rJsm8V8e0n4xQBjsWWHTmLSh6kFCviRevx/MNhfwc3cOu8aM54ylN+XbXBtze3QinSYZIi+pSwgiS2/c0ijDQajpx09IQJtf+Dnj9PEvPyYNn4xYc9EF57wLo2LqJrftxhYMSkIZjWnAt89jIJtHEXV3vkgewfGh5H/6kyf7xgyxu/RsAU7Vt/OiTnwWOX+/SQ8Hq/hXMrltywHY3G0fkEmjFPHPUGYNqNdHtCuKOzuZiyDjhj7Bf30+D1oca2EquU0ZLZm7XyDbqaIZKNuNSKAy1fQyFBLOqLZ7tF9zyhLzm156nEYs6/CE8nXzWJKjJ9EBUTVIhksRgRLVZOleBY5tomUi5LCrZsY5xtc/TnW0kGW8u55oVV0W1dZRivtnyyc/MRi2EZaIRRUnpeGn57IqKOhTDh9YwFOlKPnULcxrPf91SVm80hEIhJkyYwB//+Efe8573HO/hHBneJP2Uh2PevHk899xztLefWC2BR5dvozgk6HqWLGNffcdRjGIUZXR0dLBz587jPYwjhqccOXv6ROsS9clPfpLPfe5z7N69m9mzZx+geX28pDdfV6P82AvXs3CmrPUKveUKklND9HbGQRJIMSe1UjHt5qOqYz1c2WvZNSex7q0RIlM201onc11PLFKZtvVtAMyf9OkRNdCH2hbwcGE0z6CwaxXmrjj+WC3ZNhlizNXtZjARpXpvBaIqgJqXKzY1l5JecLEzk6JKb0YN1vHkMCGRIbx8bvZoQHrKLl4ygYinANAJQb1kHBfCcUJ+6bU3qH1UWVla9V7UhMxjtq6dQLhL4OoKO09/BtuT59PtVaIYWc656KGD9ow+kfBMz30j1NA8p4CiGojcIE5air7osW7CqRn44s30xSPEayUpbkvNXnb4E/iNJFtDbcz2yxKzs7ftQOzL4Nb6iVbpFHLSzfUFNIyQxmxfjr7VecZVydnMm1HFc/WCP++/iJbIViLaUM/XlBug265lT1aqnzmOjuPoeLaOa+awden51r9wCgPmydQv/CXp/ZNRikpfijNEBFNcgXCGEoBWwENxDFRVoOjFKSOXB90Aq4Abkxrvqq+aQOtiFs68Bju976hFwN7IsCwLvVTb/QaGJ8r81Nd+jBPMKL/3vTJa+ulPD82piqLgeR5KURL0eOB195RLbOe5LRdR/e6bUafVMtgoc22J6hj5gJ86dSZnn/fXVyT+vBb03/wuJgf+you1cxhbvQYA00ziBWSgeLhBBsjsvp9Zf/kMdaucYunV0YEyfSaiZwtkMhhZP2rRgOmhAoMtQYKJqWg5ExEthqmrmnDTA7jJHhRVQ/HJFZwIVr/sZ7xeKKlfea6LiK/FCxTZx6aBnjMl4xjIFuT1SnkBDNej0s1wypTfAPCE/k4m/ORpHlrxcT7p3ka3I3PIWwttaFoeUdF6zM/rtWBV153MbZF9Vj0nL/PMOQOhy3N3s4OIng0Y+xfS0N1JTb1MmWQq28hEsww07mO1bwLnTJHdhdwnu0nvSpHqybFtW47ODmnwq2ZUoU+uohL4aFBDSchrPNCsck3qck6vfZDt+TaCQobBNcWm36kk4wTIWfJZUdViSZTi4Fq+IXUvRyHSF8b+y78SqY+VxUZcMXICUq1iCZVa1Mj2qQhLBWQOmpCfXXNjtD7biPugJFYajTPQpy3ArQmj5B2WfVjm6/ru+NfXrZTtRMfAwAChUOh4D2MUB8H27dsP+z033XTTq+/0Elx22WWEw4duT45Z+LqkCX3OxStomi5rb1OdIbKhvCQ7NU8+6p+5qutO5k/6NFPMH7BrhqyJ9dkaYk8PMFJGbfHPofpL9yJWpfEGBjhrmWRC5/Y8QXb/k0fEKlV6BtCaT4JwCMijq5KgVWt2E4vWkqxswx8z0IvhISUYQtH92P2ynZhaKyd3EaxkwfSryo0ujhVW96/g7Jlvo3P2CvqzGml7LKKqmmBfAF9NCCPrJ1n0uNZEJnGW73lq8wUWBaTi2KrMh3hoxcc5dfzlKNf8mD2LZXed6mAXYtdU4o9+C3hjeFWl57hUxqaoJm5BeqzCCiMKYezn/4jZezJa8ZmOVtUQiYao2zyBdRffzT5PLq7UqEnXpi5SKZfxE/xUTpONSrylzext9cj4Xeq6NVRbLiKfG1dAWA6aYhPRkmWjDKApDgFV6msDUmNbGDiuTl44iGElUcJVUC1B9Z4qBhslF6C0uJJesoqryvy2yAsc3cUyFdScUs5NKzmbpg01iD1diEmyLnTt+weZddJ36bFq2Dk4ifZHZPeeGvWnR/UeHA5Om3olRuU43IKM8FiJXVjxrccs771p06YRntgbFUfDUz7R8FpyyVdccQUtLS2oqvrqOwO7d+/mggsuODGNcgnLb1tY9jYqFnyRcOs4cFM4/XuZP+mHwIEe7JFg6FjDpNcOUhe+af4aTprwVzxg3cBMqledK9+14iy824+strnwwgMY08/CqQkhHJucLWt6HUMj6IuTquynqqty6A1CoBh+rMQuqcccljKGii+EUX30Fy+Hgvufu53TJ/8TE057FCe+n0KmGZEP0bC5EiswpIndn6tjT7CSsNpbfm/n1L9wcvXtmFVTCfWoqLvlOXTXRJj03BgefAOGOUu1zKoRLetnQ7HblBHC6tuEV5BGU010ICLVqNFKPFfn2bxkKa+/dCOV9+u0zAihfHAs2zqkYXyhDrq1KHk0TqruIa3JCaApDZ+M/JaV7iTqtV58ilzc2Z5KtTrIVH0L+3JNI8ZZsALgqihFY+roDvmAhWapqJYgmJClT+mIHKt4Seja1h00S5XNKHxDxaqqo6L1Zdl9zXLeXSEbpnzI6aI2X2C3P8gjvmncO1W2Ij1pXSvz2i855mVuZ8y7kfApH0EJV+DlZETA7N2F1b+V02ffgJPrx8nKVJKd2vO6ePOFQuGQZR9PZHgqR1wSdcQ56RMEq1atoq7u0KRlD8cYlzBK9BrFISOfcenqnkp97Ua6jvdgRjGKURwzHBVP+U1gk6+++urDSkdcddVVVFVVHdZnHJZRXjLvK3gp2QTiYC0KDxUHe+/J1Rehh2RucTjh6qxlf+SBe975mj/r1VDqhlT1w29TPXmQWdoGzmh8hufPfQSAFTPPYXLbTa8qjFAqmal59y+hqR6lu4/s07cBIHyVIASFkGwGkM3JRgJ5Xw8BPUUyFCMfsAiWOhMVj2mn9pHvfR4tIj0OtboD4Y8eN5GGp3f+jvmT/oWGGgs31o9SVYFmaWhJhdCgDMkW2lQeLcwiba7jBWsiAKm7/pXV/V9gXvslqHv6GfekZPqpmTas+26Fjx278rSjiZfegzmN56P6a1F91Qh/JYpPetCKqoEoersrF7KxQz7nn5qscufX/8LG8QUeqK7k5/2ylC+1aQKeP0lN1TauiPwPp3bLMqSUD5qtBEHteZ5ThqQtbUUl5QZ4Jj8FQ0jvOW2HyOQj2LkQajZcFgbJhtLkAwLV0jEzJpotx6XnDf4/e+cdJlV5vv/PadNnZ3uBZVmW3qsoRZBmQWPBFiO2aGJ+JmKJNRpLvmqM0ViiicYYW2KPLTZARawoIF3pHbaX2Z1+5pzz++Odmd2lLrDAinNf11y7c+aUd2bOvM/7tPu2ZDMRvpZS20F4y5IhodtNtvQTy7GJo+/mNH0hxy5TcdWKczcVyDRlKLhygmzylaeoPq0GP4pTFP1NecjC/+DPgPYhiDlu7OPi/W6c1WpeOW78U7jGXohZlIMlSykpSlmz0bLsSnEkUgnOXI4qnnZA81oaRz5uv33foqY337zvhcP7ZJQdhUMxG0SRztFlF6dIQNoDLSe4cSPv54Sff0+8Yk27GeRRfUReZ8fQuP1G8aOed6JEp63H4yiK0lvZTH9N5HP9+RmsGNuJvhPv3e25J53yXir01W3eRqLFc5Dqi+iduKb50lNYkQiyKWHJJlZMhK+DcS+aHAMtQlwzsJJqSIoCsooRrmZB+bspRik3J4KiYc8+fCQNX3z3CDfccAMbFy9H82YiKQpmXSV5BYIkZm1vH3WuLF4MnoG/UeTtBy02YBrogS3oK79BqRZSh0ZDOfWLHgV+mEZ5Rywofzel1az6ipvlHx1uJLcHy+Uke52Gu64XAOs4leG9O+FWAzR8OZUzR/0OgEj2bJZG+nCx/V3O/UJHa0rQstpk1veNszkrQk/HNuokERqrtLKoNbKoiBViJCrbo3EHcd0JURdWgsULRJW1EtdQY+JeM3QxBUgt5B3VuIISE+eRTQlDM0X4GpOSZYLI5ld951P2m3U4u2cgeUTOO6NfFrZ8cb4enkqyi78FIDboErxNvxTnC8XxHSOkWA9kwX38BYsBcBx3Puhx7L3GM+Xsz1G6iM/WdsrFhLNkDM1Ci8rYUuQpCpLDg+LOQ9bckPiO1FgISbEdljD7DwFHYvX1/uL555/n5JNP3q0HHAwGeeCBB7jtttv26/z7ZJSVzM7IcbESNyK17SajtyM+/eY6TrgtiP+Ubkz4B1j/ERSVn8y9dL/Pubs8deSeS8Q/lz1D8Zin+epff+Xr4e9R6hSVeY1xr5DAyyxodVzLVbXRVM6UaZ+I8z1o0jtnCVscfsp7CaPUObMEy1+DGuqMomtIEVEhWx8QuSYp7CXqChPOEsbaHfIgZ4j2EiC1+BnVZwb23H4pWcHDAVmW0TQNLTALY4sYx4dvTeGEewVFpFRfxBZbiGgwC7VevL+Vp25nwgRRLHdMz8uRbcKYxOq+P+JoGU0jghGuJu7fKiZ9RIEeMS/gxtJkZFPMTplVhTTFR9HkbgJvA0cbawHQTJMBjo1kxiM41kehUfzm5M5uOm3XWJcTp1vUT6ND5INrjCy6atvwKY1sSLTV+aNZWAn2OEvRsRLV2JKaJAuR0e1RTKV11bWqa8QNKWWUFV1IOpqyRUX38lQVd3aTwrplTQwZkI1cIMYRylepzTcwZegSjjAhQ3y38cvm8cbk6S1a39omGLAnyMUi2hIt8WLKFvZ8H3JIx9LEuA2XyIPLpiSkKKMJrz0SxgzWYxk6kq2FUISioTiyMcK17RaJKi8vx2az7X3HHwAs6cgr9AKhCPXaa6+xbt06rr/+erKzs/n2228pKCigc+fOuzzmoosuoqysjDfffJMBA3YW1QkEAtx5552HxijLvnyIix+xFgtgxhr3csQBoK6e+kKL7X2qyS8SntTk02bz4VtT2vUySYPXcoV8dNnHRH4rtodLgvTd4CKy/K9AM3tRSxWhT+ZeyvF/Tnwu9vfwKU002PwE7GIfyeHFbKxGCcVxBpyEGhNVtoFMHEEPiq4RyvBT0T2hm+vIx6cX44id2KqX+quVjzCqzwwUR85BWxC1BTNmzOBP4T+xee07bF64FpiCVC4Ku3I3l9IQdeBr8hF1iuKhuC2K/RjR7nak967KSkKwQW/CCIr+XWo1VERaQlZk1ERbUfbWDNx+F1sGfAeySY96sbBZlaWxwOrNXWsWg0MhXiTSHYZdoiHbIKLI1CouKi1RHBi3FOKWikOK0dexBoBu9k1s9nRmde1grJAv1Y9MC4+4pUEWSlJy6v+WUCMSYZ9B55WdWD1JpHVWF5gc9e+RbMk0KM8S59GVGO+4+3DFpjUcPV9jbINoQ6zqq1LW50+8/ZP/cfwDJvI2UVxlZflYedJmdFcTeRt6UrhATEfhdx9rswa4bhctW1pIQQ2EsKqFKppiGKjeTCSvV2hCB0QKwIyEMBoriDduTUUyACxDx4jUovvXtYtBNk2TF198kYEDBx7wudI4OFi6dCmTJ0/G5/OxceNGfvGLX5Cdnc3rr7/O5s2bee6553Z7bFlZGaNHj+aZZ55h2rRp7TqufTLKViSImiWqO2VnBoq74KDlN6OL38E3+UJCudupLhUCBnkTdqY6bC+0VHRqHZZPsLzs0NawY0tFQ7d46n+/4SViOIVAAYAewdLDEIlhDznwVYv8mrPJjrdcJu6SWT+8nmC+IK+P26KokVI8eldctSemFIsW1X6eMsz2vCFMOVdwZc9++cA9j31BUVERd911F48//jj9h2YQCAQwtqwCIH/NeLw13bGFZDYM3QggQqXRwyujeSiR5MlOCjWYkQbMYAayogrDbAhjajeclPesxdQiuLI3U+kWHrQuKTQYIprQ0MtOxlZxbymNJmqhii7JhCQ7BQjua58WYJuZT6bUhE8SxsdNhO7OrayKHQOmjGQ102nuaHTFdhlbxI4W1bCHtJTYiaJLyLqFp0rBkiF31RAA7vvf13w3bTb5i8YQ8SSuOeINehtryL/vQ76eVUVTRLRV9Sm1MfKDgSy98BrGy0tokkRE6Gh/NXmNMptyTP7bZxj/K7oWgP5NFzNu5P27NcxHFU8ja9IfxDUd3ZH0ONb2rYSWv7vTMRMnv4Sa3yOlbmU11RCr/f6gthUGAgHuuusuOnXqxNlnn33QrnMoYcmt1nP7dw6pY9V6XXvttVx88cXcd999raqkp06dys9+9rPdHidJEv/+97958sknOeecc7jlllu48847221cR2BAIo1DAY/Hw3XXXUffvn15+umnD/dw0kijQyAUCnHHHXcwYcIEZsyYccQIUSRzygf06EgWGZg/fz6XX375Tts7d+5MRcXudegtSyw2b7nlFt58800efvhhzjjjDILB9pEx3ac7JrZ1MUqnIQDImQVomgNX7bh2GciO+OTzX3HitVUYWj/CiZV43CUddML78IaPOXb4Ehw9RD63LV7o6P7XEb9brNz1cAZb1FJCwRzKEr3HRrAWxZ0DioxkSNii4mO3hRTk+gCa7sQWdhHNFZ5VzNOAv6AJV5UHJasTjrxhra4Xqf6Wgv/3Pg0vC69gaM51hy03e+qpp/KHP/yB4CrB7+txZ+Lp2p14vofC9aJq3FsuE5z7J6D9KUx/CJBkG5ahY0UCougrkMgzaipdlhfir83EmrKYdQ4Rpq6SfPjjGVTmxindqBGbuQUAM2aS7yplrGxSn1FDQY24j5wBiQX9N7Hd4eB1U/wev6qYgLUH18aSTSzZJKmWrMQTGs+mhBqR0RK8JHLUQDIsLE1G0i2KVgiPYt1Fr2FPJBmTaQp/dT8u6/YaFcsbWFhuYU8UcB9T6EBvlDjL9hVjVqhkzxPhawwL3BpZQ1wE+yxmQS+Rjgn3uQJH9YmM7n8d3tGXt5J3HZ4/mcwRMzAbRBW4+dlyAmveSkS6du7hTwrPJLkRRB3Icbv9XA4Ujz/+OEcffXRKEvBIgdkOnnKHcpMBu91OY+POKdjVq1enNJv3hlNOOYV58+Zx+umnc/TRR/P222/vxKG9r9gno/ze+xcyddyDADjLxoPmQHUfvMb4D/6Sz+TTZqN2F5W9VtOag87E8+WK+/f5GFnzkrVNFI1EXYOIeALk12fiXSkmK6tkEGb/HviL4sS1GGaiPSXuMDFyPVgyOANuQk2icMqSDUzZFEVBDjdaRkmr6y2q/Zwp0z5JFXy5u0zhmJ6XiwKjcNUhNdCxWAxJklKf29FlF5Ppug3Z58LpT1T2bth6UDjFfyiwWugwW7EoJIgspICG4lBx19lZ21jCfZFfARAJZXFS8RtEVQtXVZymGqF9vGZpE/0ViaKpJRQqMuEcMcvV5hsoFmxU8ogk2L9+X/wn/m/rjVimCbKQXQRAFUJSkmGKAsZErjlppAG0iIoaTYS7dRP0OJIug6akCCC0ZePp+lUWpktFNgSRglGbQ35JgNxSNzVf6QzMFOfzV0Wx6TKTvlUIZlgEv6kExCLDMyQXV6OL7IjJCM9SAD47+xlqRw+m/6w/U9k9xKSEXK621U/OSQ8hF3ZOqVXFl81sU7X0oWh1isVibN26lauvvvqgXyuNA0fSoXjllVcAEZbevHkzN954I2eeufuuAGkHtas+ffrwzTffcN5553HUUUfx0EMPHdC49jm2kuSyPqbn5Tg6jUzlzQ4WWhd2tY1F5VDj88W3M/GE/wJQXDIIyevDitRh1IrVvDx0BKvHriWWsx05lIm7RlRyy2YmUpYN2ZTQohreGvH+LNnEHrKLCRHQ8vsBIj8WrVrKF0vvYfbrxzG6/zsAOErG4spITIyNVSkRkGjNkoM+GX322Wf06NEj9fzr9c+I1pRyDzaXyB3qfYuZMOH5VmIhRzIkxY4kN+tmJz1lIGWcATAMJN1E0RVK5k6hoaABAGvg5xTKtdjj4scfTwhVLN9u4FlQR888J0ppBi5DXKOyCEKqxCqjhCl2wauNBUcVfMr8ynFYhoYlJ9wcE1DBUnVMU0ZK0msaCoaqpxaMIBaZTkNF8QvDDECC1anr19mg6ygbashdKIys5Mpg5clF/OQiiVt9G6lbJSg8O53RjW26hK3JwlAlHCXC27ZiBlKWA8mEOodML1lEBMZ4vqOxr4MHCy4lWNGXmhKxcLAHvWRV9CB7gw0pJMZn8w8/rEWPLTFv3jw6d+6MLB95WcEjsSXqgQce4KyzziI/P59wOMz48eOpqKhg1KhR3H333bs9Lhm+bomMjAzeeecdbrnlFi6++OIDGtd+JzzmrXmCo8suRrZltCr2GlF0MpqvO5YRPeIrbVtix37LUX1m4CoThWmmVyWWUYcvcyshZyPBRBxIi9oFD3Gi/URN9FIquorL70AKRbFiUZQc4SnLmTlofcam9KCTnMtKRj5yp1IslxMtUILcotVjZNfzMMLVmIbwthSbD0mxt5ux7tWrFwsWLGi1LbL+M2wJlSsAaZn24zLICSUp2eZJbNOQNCfIKpJmR9KS4WsNDBNZt7CFFBrKVgDgkAwi2CjdqCGvq0dzi89xQCcFVZVo/L4eT8xATbTpJXk+Lop+Qa0tcQ9hsT7cjX55C1hRPio1PiverAi1Y6uUpRiCPKRlhbbsxKXYUZsU0I3UQhFA0uPgcNB4iuibV/94Dy/MeoraKdfws6E6IFpKvsgQY+qRpZPdpMBUEV1zVRtYIQNLBptpkW0KfupBtVF0JcIVOc/xpPJT6mrEoi8iG1CRl/LeASTNjqNw5H5+W+2LrVu3UlRUtPcdf4AwpfYp9OpI8Pl8zJ49my+++IIlS5YQCAQYNmwYkyfvuaD4oosuwul07rRdkiTuuecehgwZwuOPP77f49ono/yTf4Dta2F86+bescvVqebrjqPoKGR3DuNHPcLcXUoNHvnQ/etAFvzZlgyy7kA3bChKDN0uwtpRVwhVV9FkDVM2U/J6cU3H7XcgRWKYTTUpL0vOyccqzkcrPosTfrEGd28hQSl5s7GyfcR9GlKuC9U1HACn5kDe4CZWvybVwqV6OqE4shk75E7C2z87YA+jb9++GIbB119/zdFHHw3AZwtvZmjO2FQuvD25zDsyhuaMTbHStYKiIdtcSA4POFzCGAMoiRYk3UI2Zcq+EFX260d/STxXwVURxwzEyBgjJvoudTEioTiNVREcuQ5UU6zYdVWitA6iGshWUh0KOjm20xj3kpu9npAuFgjhcBZm0JcamtRCqAJElCau6UiOhPBEYiZ2aCqaX0KKJDxmPQ6GkPP0vrxEbPIU0OUjmDtqFIFMF5t0YZTXfnMhnVcVo9sNRp13GRe5xeKjd8gGboXKYoPOjeByibB+ToOKbEoMdFYzPuML3qgSzHBdvutD5nw/xqaVSIlqaqNhO7p/39V+DgaWLl3Kr371q8M9jIMC4Snv7CHuE6QDPL6d8dxzz3HuuecyZswYxowZk9oei8V46aWXuPDCC3d53N4KW8855xzOOeec/dI7qEQAAMWzSURBVB7XkRdnSeOQ4+qrr+all15i8eLFh3soaaRx2JCTk8OWLVsO9zDSaCMuueQS/H7/Ttubmpq45JJL9np8bW1t6v8tW7Zw2223cf311/PZZ58d0Lj2yVPeMnA7PcNCdtG2rF+r15Lcz5qvuyhsASSbO7W9I+R8DiVkW0ZzGDlq4fBnE3LlI6l6Kg5kqDpxLZ7Srk0W2uj2KGGPC4/LgRlpJLp9EQAudxZ0ziVQBLbsntgqEwVgehzDoxLKimMpFq6Evq/N6IsdEUY1wqKnVXFmoWR0EoxGSvuwDeXn53P99dfz5z//mZycHLp06cKi2s85bvxT7XL+HwqSxCGSYhOfbZJmU9FAcyDZ7EgOJzgSTFt2G5ZDxdQkTNnCTHgiuasH8WnuKAz390jhOMYGUSGaN64I/7fVqA4Fe2c38Uxxfl3R0eKtY4P3KadjWAqqZGCTY5iq8EIVl0GTKSMHcrCkZvIQKa6lwtotYckmUVfi92yoaIYYoxSNid5zPQZKonLbkUlg5iM0Tb2cd/KOonB1HwB6zKpm6fWfQtQFL/6TIT8Vko6DltZilnpY2gl61UChX/wuKnLiZIRkXDoUuGqRXGLi9FSqGOuX0/BNc+RFUuwdhhbT5/PtcpI/EtAe1dcdLXxtWdZORVsg0hA+n28XRwgsW7aMn/zkJ2zZsoWePXvy0ksvceKJJxIMBpFlmQcffJDXXnuN008/fb/GtU9GWTIUSNDYJfNlICpuk3kdMxYgWrkENVCFqTelcpk/RiTDzkpTDG9tNoaqY6h6KmenxDUhlWfKLQpsBAzNIJ7tQht4bCqnbEUCyFEDQwN/fhR7gnPYEbBjaBa6I54SDwBQc70ooa5gxpEbE8U4ioacEErA6MbIrucBBy4O0KlTJyZOnMj//vc/rrjiCmDXtKhjBv0OObFoOJIrsiVFQ5ITix5ZRbI5wO4Eux3LLrZbLg3DIaO7LGKuOJGE8dPtURrWHsvSoU8wbIkDfZuoNA6vbcBT6kUtdGEMzGZ9XxFKXp1pI1PX0UxoStJMGgqNcS/BuJdI3CkkHIF4xANRF1rQm6ryT8IWEfvILYyzqQiRCsNmEHcoKImqbDkkIxkGGC0K1xLIvux2SkdfiVH7CQAz35nKpMe6oi5dj9VL5i3jWACU//cpc6WBbNMLOLHTV7gskWLJiYfRPBbVmpttZn6qwkgJxNEbKzqkaEQoFGL16tV7zUf+UNFe5CEdAUOHDkWSJCRJYtKkSa16yQ3DYMOGDZx44om7Pf6GG25g4MCB/Oc//+H555/nlFNO4eSTT+bJJ58E4Morr+Tee+89NEa566I82CZUopKi4SC8Qi2rOwBWLEi0ZgXh7Z9jGdEjjtu4rVA9nVNGWfI3kVWei6p3EsITLSZCRVdQdYW4RooBTNE1tKhG1GPiP9pJ1CV4ft1+F95yFVsIIq4YgezEZO1VUXUlNcFaCYF6wyEju1zI3pxU0RUkVIsA2cjBnjuo3d6zzWbDNM097qNldUd2ZLbbNX8IEIVediRFEXnkhOE0NQndZRHyRQl7wkRdyVqDAJZicIv7dN4a8R72YrEAtgXjWPUR6JbBuv5xVuQnDKRl0aBpyJZFTkzccyX2bdTKWfiVELVSbsooJw1c3B7B0dTsDSTvSUUXC8WWUZu4pmNoySJEcY/aQjYIaVgtDLOt/3jUuj74v3iIWc8OYNzIDxJnn0r0yTv5aPHtHH/BYja8ewsA/+e7Ct0eJZRdwcbCbtT5RV977/yFuOUQjZEM1tf3IWNt4h7dXkmkfAEdTcBkyZIlPPPMM0ycOJGysrK9H5DGYUXSWC5evJgTTjihlRSjzWajtLR0jy1R8+fP5+OPP2bQoEEMHjyYf/zjH1xxxRWpqvsrr7ySY47Zf5bFfTLKb14pM7G38IKS/cKj+1+Hu/dPUHLED8rSo0iKjUBAUEa2pIj8seDosotxdZ2E7E5wXIeC2LY6yfY7MO12DEeCTtHeopLVkFJCBbIho0YUIt4YNcXbiWaI3EWdYSMjtzN5m3OwRVWC9oRn5QmmpPhsURVFb17SWpoKmj1liFHU5qpomxPZdmCN7i2xaNEiJk6cuMd9guvexVFwVLtdsyPBNCLIRnSXbYKWYSABliynxOItRcLQDHS7TtQVIuIVoc+Css9wKSFm8DY1nWQyXMIQejZJmIUO1g42WVyg4Uj0HmfqcVw6+O0SWuKWmsRiVmhd2Ch3wrBUojZRLdoUcyKZzd+5PdRcqW9oeqrgMOktG4niQ0ONY8pmKsTuxYHN8CI1NSJ5RXW0VZSHpMewZfVm7JA7iVR+kzjzdalWylnPD6HrAsHPnbO1M1GXRuH6YRjaEDq/syKxz/UcN/4pHPm9GVBQiuXfDkDou/f3i0fgYCIWi/HMM89w3XXX7VbA4EhAy/TK/qKjeMpJ+cXS0lLOPfdcHA7HPh1fV1dHYaHoIPB4PLjdbrKyslKvZ2Vl0dTUtN/j2+eWqB3JO7yjL6dxQjd0h1gt+8od2F0ZQnXFnYfsFf2zk055j/CGjzvcj+pgQFLsyO4cZHfCEzEMqKlBqlNQ3W4Uj5gINZeWmqANu4qRTA2YIscY18SkKxvNud+IO0BjrhNHyIaakN1LCgvIpowtqKFFm6t6ASEFmYAVi4hQavJ5O/aZNzQ00K9fvz3u0zL0OLr/dUfk/WAZUcxYENkZSzzXwYxj6THRRpTIy8q6hWSIHnVVb87pxkwbpY7NDF+nYI9IRFxif0emxqLhOvUOCc0y8cXE954dAl9QoWusedbb3t2kwfKiSnHschR7IqccVKOYjiBmRg1h2UgZZXvIjqnYMLR4yjsGUX0dt0VF6kXTU0balE0yDRe2aB7WdrEAl/xezIZqHF1FC9auFnwTJjxP75KhADSdYKPL1Hv5pTWTWs3OynPEwv60WA/qb7ieoo/CWLFYahGpZXVjaM5YfH2no+aIyFx064LDkgYJhULMnDmTzz77jDFjxhzRBhmOzJzyRRddtN/H7piL3lVuen9xZBCzpnHYEYvFiER+vPUDaRz52LJlC2+88Uaqwrq0tJRbb711t7q6RxJMqR08ZQ5/S1R2djarV68mNzeXrKysPRrTurq63b528cUXY7eLCFYkEuFXv/pVil4zeoDiO+1ilEO+MP48Id2n2/PI17uhH9uVBl8EZ5MYeMZGi53brY9MzFvzBBMmPI+lC0UtSVEhEWq0TKOZAla3Ietx0HVkrxvVlSgA0mTiLglVV3A2ZeAIelqdX0+ErZNV207dhWzK2EMatpCMEk1UyOomkmliAVYi72dFmgSrlKxihhsxIru/8fYFH330Ed27d2+Vn9kbjjQveVHt54woOhkz1ojlzMGKiZy/pYcF77VmB80GWiKnjx2HXwFsQEbqPA3rRnP24H8Ss5k0ei2WFQgXpamPnbKQTlbEwifFyQqKO8kTUnCGZMIuk4wGsW+14gUTgqabej0r1acsJ/iuLd2OrWAdwYJ1AGhfTxGc7FEVLaIRdYmJxdB0VF1Dt0cTjF/iPpZMGTWu4MGLDeHhGquWEF7zEfZOQ5E0ZzOLWQInXL6egR9/wuyPTwLgpImXcXpwBSO/tRF/biXugYITu3bSRlYe+z6vT+3Pt5H+bGwS3Ndl//0pBYNEesSKid+ATQ8f6NfWJmzcuJGHHnqIk046icsuuwyXy7X3g9LocHjwwQdTilAPPvjgfnm4O3rY06dP32mf3fU4twUHbJSD81+goNetOEsEaYKrTkP2h6kcUUewyyqkYCYAnRz9yNnav5Vu8ZGM4Pr3kqKPqLndkBweMSnbnSKcTYIRKRrFrK1CqlOQswUJuuV1o+LAW+PAXWdH0ZM5SAj7dIK+ELo9hjMgljn2kIaiy6gRCSVqISdoGdENMExxPV14sWakEcnQkRQNM1jbbsQL4XCYjIyMve438YT/7sR+diTBiPmRFDtGuDZFtSlapDSQVSHfmEwnmCaKbsOla9hCDrYNmQ9AwYphfDu0jPpuFWygkK2GYO46kW+wx8ETkXBGZTKahAF2NUrYmkyy/TqBUnFNnxkSoWspildtpLyhe/MgJROiLhRvFUqiLaopp468LYWoEQUbGs6AOI+hmcQ1UaVtaAYxhzDWsqEQs8cJ+xQkU0xymt4fpxGnaZn4fe/I6Fc/pTPjje8IHCdoYN/4+g6uzT0P24JaPvqghqzPxAJxREVnRuc5GTp6DQsHrOCzfDG3fPrzFaxcch7d55ehrRNa1Wb44LcgxWIxHn30US699NIfrT7ykRK+bmlQ95cO82Cr4h2wUU4WcIwdchcA4UgtH698hPH/7MTq/K1YNmEMgr4AOQX5uEqPzJaBHfHNphdTPdquLpPQcnuh5nRFcnlTjE6WpiJFo1ihBiKb5mEvHgGAXNgNJZSBAoJ4X0/kfQsKMDQXEZeKLJs4moRn7aoBKdqiNSVJQZjwwjGNlNdixUJYMVHlawQr0QPtQ3agKArx+M7tMTviSDbIILzlZHFjkm4TRO5eSeSWpZgwbFLEi+RyI0XtaCEbRUsFE5stqvHhW0+gO3RqS9YxtufLAGQYMRQTNEMio0nGK4JTaItqMf0xKM1ANsU1B9eGWZALUcuOYakoanNITXX4sdwKhqml6IP0ssVEaieSEXKhRqRUPYIcBUkHUEC2YWliqWlqEoZD1D4kOZGtLC9Kl964gsdTv+DB1PUmnfIeH70zlS0DljJX6YcHcf8VbOpMtimncuyBhP5yrDqMVujGtS7MCNOJ1l/co1GfRmWfAtbKBj0Q3rNWsbMyVHshFovx5Zdf8u677zJixIgfrUGGBM3mARrVjmCUW+Lbb79F07TU9/rWW2/x9NNP069fP+644w5stvbhcdhXtFtOOWmck5h7mcTxfx5BZT/xA3QGHGDEmns3fwRoSZgyuv91OADNmy0qokGEMWUZM9zIp99cx4iikwHIGPRzlMzOoEcwI42pPmXJ0QVTtrBFVWxBDfe2hEe8vRJMQ3Aqu1yQbEvSdaxIGCsSbBVKtPQglhETxUjKvlUe7g5FRUV8//337XKuHzqShjlJbWoZUSwzYRQNHTmWUImKBJCCHiS3F8nhJP+rBP2mrIMiY2Z5qSnT8ZsiAhFUNeqcJrJlkhdXsa0TFZ7++VWobhV3tgM1nOg1tiQcxNgS7URtJJ94WJxD0qI4bU24tHoUySDXJkLGNbFc6o75AOadiK/ShZIYrmRYEIkhBYJYwQA01aXGrmoO5Mx8JG8iQuKwg6ah5nTF1blZ0tWKNCbeloNXN1wsPHUg1xfCUMEyLKZc0IU1s4WAixExIMuOv68TT6VB51oRWYhnKHS2b6OqYBMVPXMBKK7pf8B0vlVVVaxcuZINGzZQXl5OXV0dpmkiSRL5+fn88pe/pGfPnns/0REMqx2qrzuadOPll1/OTTfdxMCBA1m/fj3nnnsu06ZN49VXXyUUCu1S7WnatGltPv/rr+9fP/1BLfSadb3C2CF/AcA1+Ezi1et/NMIEOyIe2IYZrMaKhJATRtMCME3MoGh5Sla2p1qqnD6UzE7IBSJnZ9oVFF3CXWfHtjWItUG0lhj120UvrMOLnJEj8pYAekwY5EhzT7mkaFimJkhEjBiar0VY8wAwbNgwXnrpJVavXk2vXr3a5Zw/ZLRsARyePxkz1ogZC6CE61GCon1CdmQhO33ITRkiveFIJDw0O1YkRLS0P0rYyyq/EHz4Om89Jc5Kim31dN0GVqOIoESbdBSHAll2Agk5x80+iY3xTmzzl2H5C7CFvKnxBApNZNnAoYRxSML65tpqsMlRNg76GpYejUcTqRFnvYIclSHRg5msTTAjjRi16zDWzklV8DvLxiM5PFhGvJXcaFLTeOC/izG3GzBIeCbS6vWsuTfG0BUuZI9G35uFRKu0NQiGhRIHS5WJK+L34pYiVOvCGCerwC2PD1txa73xvSEej/PXv/6VrVtF1bjdbicvL4/i4mJOOOEEevfunc4Z/wiwevVqhgwZAsCrr77K+PHjeeGFF/jiiy/46U9/ukuj3JLpy7Is3njjDXw+HyNGiCjnwoULaWho2CfjvSPS1ddptAtUVSU7O5tY7OBKeaaRxoHANE1uvfVWBgwYwFVXXXVEyiweDBwpOeWWsCwrRXb04YcfcsoppwDQpUsXampqdnlMy3zyjTfeyDnnnMPjjz+OoiSZ9AyuuOKKNtXX7A4H3Si3DmsPONiX67AwYyKEZ0UCEEnkFBFVpJYebLXv1+ufEeHu4lGgOVLhaMkfxhG1ITWFMNYuI7ppnjinoSPb3MLzaqpqptEEzFhIVP8m8sgAsuaGBA2k0k7V1yBycPF4nH/+858UFBTwk5/8pN3O/UNGMo0xouhkjHA1clB8P7LNi6TYUBzZyM5m8gHZkYnWXay8vTX52LaKQqdXu3dmSs//sF4Kc4K5HClXeLM5w/OQB+VQPlDlqzJxr7xsHcdXNeOgrhhb2I0aE7lmVdcw5a74PT6iWVupUsR94VZC2OUovoxy/HnVGJoYj6G58eBABiTTRNYTIfmICJ1bRox4UJB7+Bc+gerpjOouxIy1vqcBPvhTFn0/Mii4XdARrn+tM9OMW1g4/M/4thlUdU9Uk3fNQomDoUJjgYQtUei4Il5GdbgQqTEPR0BEFSTd3KdGmw8++ID8/PxdVsymsXuYkoV5gCpPHaElqiVGjBjBXXfdxeTJk5k7dy5///vfAdiwYQMFBQV7Pf5f//oXn3/+ecogg6itufbaaxk9ejR//vOf92tc+2SUx/c8C+LiR6zYfGi+7pixRr5e/8x+XfzHhAXl7zK6/3UoGZ2Q6xIhEJcXouFd0k5+ueJ+jh3+RxFubmpetVmGjhlpJFa5nGjNktR2xZmXmuST3NKSoiEptlYEIZJiQ3HnC31fQHGIHsuWmtj7i5/+9Kc8++yz9OnTh48//phOnToxfPjwAzrnkYQdiXeSBWEgfk/OTuK5lt8bSjqzfthqhg34D5dIswDoWWtSFbFY7/IQdplsPVHkoAP2HDLCEmCQGRMTn1/JIB7MwtWUiRa1pyQYVV0ju7wTQZ+HIFCRuH62owaXEiLPXsXW7gspyVgOwPrPfw1k4sGBoschyY/tzkLRwxiBqlTu3Iw18uWKPXdWeH9+JdbvXwCg95VfsPw+g5PKfsatfZ5LFRI5DB27YZHfJLHNB/OdIn2zqqkn0epuZG0rIXtrwij7/RgJXve24NNPP+Waa65p8/5pHLl46KGHOP/883nzzTe55ZZb6NFDMNO99tprjB49eq/Hx+NxVq5cSe/evVttX7ly5V7phveEfTLKlhFL5eplWwZaRslOvYhp7B5frrifY3pejis2AQAlq1iwFSkaY4fcuVOxXGDju6iV83ci4D+m5+VIin2n7UNzxu5UuCXUqjJSlcCKM0dsN3QsM9ZufcoA/fr1409/+hMA1157LYMHD263cx+JSC6Cjiqehj13MI6yhFBDl15U9I9S3Hsmf6r5gMIKYXwzPmmgn1Nl5FAbXww26FElDG3xdgVDtWj0mvgS7XDnZ85kbWYvjLpCXI0ZKAn2N1VXkE0JLZqDM+ChplScozZbIWavJ1NroLt3DRvDgm/92Am38encO4BsvIYbORHlQY8hRwKoGcXEg6JAK+Zfu8f3O7r/dXyz6UVO+L+EEQd6fTSOjUM68SvXKCyH8K5dGRUM8S2mIL+GBtPLd41CbUqRDJSIm9zNmdi2CU/dWP89wTX/A/Ze1R8IBDAMo01eUBqtYcjicSA40Ort9sagQYNYtmzZTtv//Oc/t/J+d4dLLrmESy+9lHXr1jFypBBk+vrrr7n33nvbJP24O+yTUf50/dtM6CES2FnH/wH/5FJMxeSE24LUP3sZcOBqQ0c6WvZuHl12MfbcASjuAmy5vZkw4fnUa5KikTnocuKNW1NV2QvK32V4/mQ83U9FceczZtDvAPhi6T3AnvnFk16Z5umCGQsg2zwi9BjYttdj9wemadLY2PijYDs6EAzNGYu7yxTsnYYiF5QCYJTkUdt5NWe4lhANWTgSdXpN31ajB+NkB3Rye2aQW50gj6m3UGuj+HLsyIki4S72MJOy5/C/xkKcTRnYQ8nwtYKiy0iGIKdR1gha1MqeYGYqmJaCQwlT7BRtSMvCfThvwhU89+WDRF05ZGtiUaeWa8iA5s5EdorIj+LI2eN7dXabyMjXomy9ZC4Ag/48kM2nvc5NuY/TWW8kOyK8i/e8PcmWGoliwyOF2K4JI1pi24arT4jVGbX0/ExQeTrWq22O1M2fP/+Ip8M8WDClIy+nnMTChQtTnSP9+vVj2LC2FQ7ef//9FBYW8sADD1BeLhamRUVFXH/99fz2t7/d7/Hsc0456XGZ/Xuw6Zg5aPYgTVmjKPr6rP0exI8VyclkZNfzcJVOFmFLEKpOmh30KGqwJ7YCUX173Pin8PY9DzWzBNmdhWRre4VoS6M7ouhkJMWOZUR3Cqm2F6ZMmcJ9991HJBJh2rRpjBs3bu8H/QiheboIjevMTkiZwriFck1Mu+jvr/RI9Ez8SvVgnHjUxAzHKaxViTlEqDrUBbxeB2rYomCrMNTlWQb95I18mrWZQEM+ziZxr6gRG4ouoegSWlQInwC4Fwxjc7+11OdvwuOsI2KI9Ea2rZbPIiO4evQv+ON392BowurnKj40RYa6OpQEN7UdGD/qEcLbv9ppcX7CbUHU0kFs77mOUzq9CcD//l8WC+vvp+Ark8q/rSB/tDC+I8dsJOKTWF0WQzPgVPtKAPyyTLVjMR9328SHdpFG68Ekjr9gMbOeH7LXz3rVqlU/+tam/YXVDjnljoaqqirOPfdc5s6dS2ZmJiD4+ydMmMBLL71EXl7eHo+XZZkbbriBG264gcZGUTN0IAVeqfMe8BnSSGMXOPnkk7n33nuZMGECq1evPtzDSSMNamtrKS0tPdzDSKOD4MorryQQCLBixQrq6uqoq6tj+fLlNDY2MmPGvvW9Z2RktItBhn30lE+c8EjKszr+ARPCXnRTwdnkQq858Il3VB/xQWRMvgHJ4cDYspbZL++/LuUPBd9sepFjel6Ompno7dTsSA4nlmYTtIwJVSfZnY0VCyM5vEgON0qm4NY+puflO1Ea7gkHyzveFbZs2bJTIUQazVCceciOLCSHO0UqY2gmctTBa1vP44T85ahh4aFkHS08SbmLB2dIpjpP9Aw7ohLVhWCLScTVhA6yAl4rTG/3Kr7JKSLkF9XULr8D2RSMXXLIQomImKKpyZR924uqbjk0dF2NPUPQWBqJcPZbHMcN/W7lz9YfEyMvI0f20pIKSKEM2ZuPmlXKscP/SHi7iM4sKH8XKisxG6qwVCffBER4MLvwewo+Ndn+yHJe/jLCGYlUivPLSgp/0Ze+cRuSCeEEnbrfZ+DICzLMtZoNOSIXuG5kHt0ZzIlXleOf+Ue+WvnIbj/rcDicTqfsJ47EnPIHH3zAhx9+SN++zaxw/fr147HHHuP444/f5THDhg3jo48+aiXVuCeMHTuWl19+eZ/SJvtklD+Y07x6sOZ9yQBlDJYMyrK1zGwH+TTf1FsB2JKoKi1eMJwRRScfUiNyuDBvzRMcN/4pQFS2oruFgLweSRE2JMXkMePitYRGsj23/+EYcptQV1dHcXHx4R5Gh0WKF1tRUwQdii6L8HVTNnqhRGOiLil0TjZrCg2GrbahxiC7XoSeM7eYGA4Ztclg+ShhlGUTFMvCpzRidzYScYvEdNyRhS0kI0dNJN1MMXfJmowqS3RamUve5hw2DRRFW40Fm9Cd9YR0D9/ZNnNpf1G/8FxQGOccvGhJgQ2/EyviRXb7kB0ZKIk2r2QdRGDd22Re+ipVCb7tsT1fxrLLyDK4VIvyKnF/jxydj7+7jW97xehepeAKic/FlC2ywtBHrmGoU1SHb8rpwaaBLrrSA5/jTiZOfgloJixpCVmWD1jB58eKI5Fm0zRNtATlcUtomrbb6unFixezZMmSNi/uFi9evM/33H73Kc9+dWyLZweWpznhN1uZ+WgxgcGCraeuZCGyYaPTd4OOaIM8dsiduEdfDHqMmU/2RK8Xij2SLCZqUw9ixYKtWppkmwfZnYcCSFrCg3a0XrUdVSyK8dzdTwZDJ7Rl7mErwGtoaEh7ynuAGWvaqYNB0WXcNQU0FYhiq42dxOubMlQchow9JJgqtYSXq1REkDNtRAo0yhL6It/1iiFjEjTdSJJBzC2qlYO+EE5/oiUvktB3BkH5qsioURW1SaJPpai+ruzficqe36H6qvig6iROzH8fgJFDn2D+wv+HZMpkJ6RXNECSZSxAcmeiZIhIjqNwZIIGcwbHX7Sczgn/OvgPFxXdIeOeIVz4+BqiDeI+VwtdbOgS4yNnTyq6bKFrUOTXfREJXQG7YTHWEIU5gTwXM6tLqS9qRAtlYTME3/ykqW/z0Xuntvpci4qKWLZsWTqEvR9oD0+5oxnliRMnctVVV/Hiiy/SqZO4V7dt28Y111zDpEmTdnvcpEmTsKy25df3R4WqQzB6zXy0mOPGP0XTDecAIBs2nPW5KJurgfzDO7iDiB1boKLVou/YMmJYRoxY3Xet+LNBeB32vCHY8wejZglSCdnpa6W+5e0jPke18wDQIxjh+oP9VnaJiooKHA5HmjVpD9D963AUDMWKRVMGUtHt2MNOMou/Ya2Wy7fZwug1WB4urV+KoYIaA/tqYWhj39cT3BIg6/guRHuJHvWcJplMVxQk0GSdiF20G4U9AXS7F02WxPVComBKAsGbHomBImMlvrOC+ZBRM4INg1ejZ1bxv03niv1VHYpXEwm7CGULI+uNOlAiMTAMrFgktdiQNW+qD37WswNS3QTROS8wLXATd518LMNn9MSR4LepybCoccVZFe9GhZxDcSKUPsKxEZsJFTYXm2SxgI/E7SCbaBGNuN1Czc0EQC0dzHHjn+KTuZemPuuTTjqJRx55hMGDB1NS0kwDmsaPE48++iinnnoqpaWldOki5tItW7YwYMAA/v3vf+/ymA0b9l1Vb18jhR3CKKdxZCISiRAKhbjvvvs46qijOPbYY1HV9C2XxuFBaWkpp59+Oo899hhZWVnMmDEjzXHdRrRL+Lp9htJu6NKlC99++y0ffvghK1eKCv++ffsyefLulQy7du160MfVYWZIx/CfCCUpoGBNT3LX2Kl/81L4yzOHd2AHESf8Yg36sJ4YmoVzZW0qVD80ZyyqMx/TiOx0TNJzHt3/ulRLlJLZmYwhP+e48U+heIvQuiVYtDQbVkMEU286NG9oB5SWlnL//ffz9ddf89Zbb+H3+zn99NMPy1g6KhZWfcjo/tehNnRHzRFRITXkQotohA0Xf6+5iB5eUUTZWa1ANST82SZF35nUzxLh7WBdjLwBmRCMY6sWIeDMbA0KRO5WU2KgJAQcZBNDMwFZqIiFmsVK0GOiwDAWQ2ohW+daGaNXqDfb++RgqAlBCsUg3G0pWtROxCWuafOpOJsc4FewIk3E64VXEatflWrJmzT1bTJuE/e5Of7P1MZyecl2NEqPeRQ2ilm/1mOx1FnACdY8CvUA3kT/skuHGqdMpezj25jorw6aLqS4hi2qoUZbWA1ZQcvpzuj+1/HlivtTm8eNG8e4ceN4/fXXufXWW7nttttS7TBp7B6mBMYRZpRBhJenTJnClClTDvdQUjgoRrllKHVHjB/1CJJNhOOavn8xZWSsYADfygQ9ZJ2f0Ff/OeLpOyVfFtXd/AQy68nLLGLK2WLiWlT7OcPzJ2Pz9djtZ2nGmkSBECBn5CB16YHqSFSqJ8KgVkM9ZrCeeGD7oXlDu4CqqvTs2RNd1znuuOMO2zg6MsIV89AySpAziwCQinLw1DtZv/FojMwKKm2i0mu4bQUrclWG6QZyQ4yGCrFoi0ZMbGsbcfljeJyi+CtmU2lQ7ShGHMNSSIoeK7omyEOiBlYkgtkoFMrQI0juzJR2txUV8pLI4nyaYVDSlInpEM+VmgA0DSHWx0fYJ+63mCuOUmDHHs1Gqnam2OKSv+OjiqeRfeqDbO4laGNzLZUy5waOk5Yw11VGg1OUWedIfvItP5lGmAHlEhlN4j6Pq7CpB/gtN1ujopq1OtgJS9WJuGJ4DBWiify8KRYkkrJrqdhp06aRnZ3Nfffdxz333LPP31kahxb33nsvN998M1dddVVKvSkSifDb3/6Wl156iWg0ygknnMDf/va3fWJs++ijj3jwwQdT5CF9+/bl6quv3qO3fLDR7kb5xKvKyfzNvznxqnIa3rtjp1YdJaMzilsw/9jzhqS2N859CNkmfpSCoeq69h5ah4NVV42nrie6PYpsyEgOT+q1hVUfijYpT6dUq5juX4cR86M687HnDk7xV0suD1a2D9OlIhkWkl9MqFaokcimLw/74ua+++7j/PPPT3sku8Gi2s8Z2fW8VMGezWbHGSlBGahhBjOpNESh3EJ7JV1s1ahGE0aBg2++F1Wd3/slpnY36BQxcQ8URqnRY7Bc6sG6SBnRuANiIgplDzuxB2SkpiZMfw1Gg2hDwtCRIo3I7mwkzYGlC4MvuTLBjCf0ujWkBK+6pamgx1C/XExGfyE04+8G/vwQPrzYI33Qype3ep+WEQVZSXnb+VoNU9Uv0VGYHRwjFg/AZPcXuK0oDtMgplk0JlQnbbrE6I3weXdnSge6KlCMFNeIusJAi1C0YWDGQhjh2t1+7scddxxz585l8eLFKQm/NHaNw6kSNX/+fJ544gkGDRrUavs111zDu+++y6uvvorP5+M3v/kN06ZN44svvmjTef/2t79x1VVXcdZZZ3HVVVcBMG/ePKZOncqDDz7Ir3/96/0b8AGi3YzycWMfB8B2xi/ZNtBPTm4h3u07aycHVr6G5hOVnS3DSnvqLzxSEVryJj7vxWQU5CL5AwS/ez/xyhAAjHA10Myipvm6o5GowE5EG8QLGpY9wdVqWkhR4e0YjdU7FZMlMbLreZixxoNe3Z5sLUjqjaaxa3yz6cVUO48V9GNk24l5GlCibvJXC+7ntRWlPDGggO4F/6B4lYIj8euNWxKLt5sUFJhIGcIz3JwF7zdOpKayH5gy7hpRXZq9NQO1JoxVU0W8fCXxRqEpnFIaC/tTtJkAshEHzYEVi6C4PEh64r6TZSSHUxR1rRDG1+0dQrB7iJoSP9lyDo6m1r2eC8rfZdIp75G35QQANkZKWOXZTAQbFaHOROpFQcx7hXa2upczVltGdZGJwxRGvEs4gj0O5zQt5c+O5r5PX2URWtSGpbSe9SVFS4ll7A4nnngi7733Xtoo7wUGBx6+NiUhbZhkv0rCbrdjt9t3eUwgEOD888/nySef5K677kpt9/v9PPXUU7zwwgtMnDgRELKKffv2Zd68eRxzzN75Le655x4efPBBfvOb36S2zZgxgzFjxnDPPff88I0yyVBqTRPuRheSQSvPL4k0N3YzPmvV250FtO71TgpOtOStVj1iMjJjQcxI4uY2TSxFQjIsZH8Iq0FUW8uZRZx4VTlmXSX1c/6QOufQnLE4C48BDj4PcCgUShd3tQEn/GIN6+8RpPY5fWczxvN3tjVMJL7iOOSEi+IIeOjlXMM98bOZyOv07yYMcGUoRicP5PX2UT1a/OZ02cCjNuFcOgTZkPFUJgQptjVgbFyFvm0xsiMT51DRPidlZmE1NRJd8SFG43YkLWl8VSRDTxg4A3SjedCyDIqS6p/X1lXhycqmvqiOhqIABb1KAZgw4XnmzLmASae8x0fvTGXS1LcBqCs6k38XHYWsO+g+vy9aovp647BiZucOZmHh92RoDWSooibidPcnRCWNKBo5hrjHnbYmvPUebCERJUpBsyE5vNiy9tyON3z4cF599dV9+q5+jGiPQi8QXq/P52u17fbbb+eOO+7Y5f6//vWvOfnkk5k8eXIro7xw4UJ0XW8VZu7Tpw8lJSV89dVXbTLKDQ0NnHjiiTttP/7447nxxhv3evxFF13EpZde2u4UwulelTQOKlwuF/F4nIceeihV4ZhGGh0F6QXjocVRRx2F3+9v9bj55l0TT7300kt8++23/PGPf9zptYqKCmw2204psYKCAioqKnbaf1c49dRTeeONN3ba/tZbb3HKKafs9Xi/38/kyZPp2bMn99xzD9u2bWvTdfeGdrsj/cufBkDxFpGtjwRdJ1a+kmQoNo39R0sxieH5kxPayRmoQVHQYAUDSEYucZeMFlAhIUQv5+QTGpiHbs8ju8d/ABgz6Hcsqv2cY3pejmzzHvSxy7LMPffcw8svv8yDDz7Iww8/jMPh2PuBPzKER/dg5NCbALir6hPy16icX7aU0wu7YQ+JXOlbfU+k+4sxzEwbeqZKj3PLAOgeM4htCyCd1pV1nUVOOaho9FTX8+HRn9H7/clYHwjvtHHTp6juQpyDT4NupcRyRYRLt5s46zOx6+OIfv8Rlpko+NLDIqcMoniqJdORaYLR7DkbW1aT4RkKZBPyhWkqEvv6hp/EqD4zUimqeP1GAL6fdCpHl11H5kV/ByLMvF3USIzq8ye6lk1GLZtI4+hClg5bBMDa3F441DDZtlqipogSNDUWURhSUSMSSiie6vW2AMnmQM1Is8m1B0xJwtgPIozW5wBFUdrEEb1lyxauuuoqZs+efdDmi379+nH33XfzySefMGqUUB2bN28eX3zxBb/97W955JHmlOquuLDffPNNqquref7553n22We5/fbbmTx5MpdeeimnnXbaLtnC2oJ2M8otDceoPjOQFFurnHEa7YNktfqIopMxswSTmhVqRIoaxHIhVmrHTanYjqiIbcivx7CJkFF23ZUMzxchH8W5ZxWU9oLNZqNLly707t07bZB3wOj+1+G94F6qj2rgGFVUyff4xkLa1sCgUCYXjPk7Hy4XxZJlr/r55sm1dO3mxPWHQdgLhBGL59kJnJSDZEoU1Sd1YHUG+dZSmf81S4/zMnid0GqObl+Ea9T5hIYUEvJFCWSJMLAWtaNFfdi9vubQdQJWLCR0v6HZ6GlJ6armnK0ZrIPl8/EF+uHqnk/UI4xyvCSHjGOv5Oiyi/l6/TPEg8KTGfZmCP+//49IfZycR2Zxws9Fm9NXKx9hyrnzIBrGt6COMlNwZW8caiMedtHYeTWSJM6dub4/akRC88chFIUWes+wM7FOGvuHQ92nvHDhQqqqqlrJKBqGwaeffsqjjz7KzJkzicViNDQ0tPKWKysrKSwsbNM1nnrqKbKysvjuu+/47rvvUtszMzN56qmnUs8lSdqtQEVeXh7XXnst1157Ld9++y1PP/00F1xwAR6Ph+nTp3PFFVfsszLZQYnd/BiLtg41FpS/y7HDE2EdI44UiqDoHoLZUeSSRKWtH2whMeGGPaKaNt69EN+gy3bJDXwwUVJSwty5cw/pNTs6xo28H/tV92N9soAZN95MnZRoCQzFiayqx5Fho/eYrXw25Z9i+4s2VEXCNC0aM00aRoqVeN52Cdff1uPsl41jRCYAQYdJL2c1221rWKqNFTSagOv8W6nqGaWi1yJkvXmB5GjyEfG4sXncqLndMBorWw/WjAtDZyQ8ZQ1wOZGMTKykYZZVJG8u4a9ewrZlGJ7eg8WhPheUlOAbczXH9Lwc7zAhAO9dYJAfsmM1VTBz5pmpS514VTmrrxcdGn8ZchpDt4npPLtWwRaDrzNiPO0YA8DssgzyN4zDFghjNfhTrVxW0I+lR7BioT0uPmOxWJspE3/MMNrBU7Ykqc2WedKkSSxbtqzVtksuuYQ+ffpw44030qVLFzRN46OPPuLMM8W9s2rVKjZv3pzyeveG/WHn2h3Ky8uZPXs2s2fPRlEUpk6dyrJly+jXrx/33Xcf11xzTZvPlU6o/IBhRES7hxUJQFMAR5UDpcVEKxkmdr9EtpFBzCXCjLoLnP3H77Oy1IGiuLgYv99/yK73Q4B20XVYr7yElVXMn+Y/wY1HXQ5AtIsT9XsbKBJeI8op7k8AqB7tYUjhMKSoSaBBJuEsory7lUhAJ14RxLVZeM+dHHbiSoxNmZ2xV3dJebaV/UI0FFRgyYk+XkN41pZsEnXFiBe40UJ9kCpFwZjZWC1C2JCgAhXhcUmRsTQVyeFA8gnKS0VWMRvKsU/7NaH/3IWtXlR2a71GIWVnI3XqinfIBSilovgq1tmLVmNDX/oxrfjzXS4GDPwHP1M/5PT3Tbb9RUzOWxridBuWyagLexI6+isA1BKDWUflMWBdZ8zta5uLHxEevl6/ATPWutq3Jf773//SrVu3ffna0jgE8Hq9DBgwoNU2t9tNTk5Oavull17KtddeS3Z2NhkZGVx55ZWMGjWqTUVe7QFd13n77bd5+umnmTVrFoMGDeLqq6/mZz/7WSpE/8Ybb/Dzn/88bZR/LAhXzAPAltUDFBU5FEDbaksRPmAaSLKC0+HA6RAtB5YsY+kxtIzSQzrWpUuXtlnu7McCWbfQIw1s/c1x9H4mSvlIoTwTzgjhHVNIdVeZiKyyRBcG6+V+IUZ2qmXEpzLeVeHUeerW+Ak16ChOFTWYyP9iZ2uGzLHaIpaFXZg+sViLOWqRTIXMbd3I35BDXbEwWHFNx1QswR/tdSNFEoZWszd7wmYcK5LoX1YUUX2tqc3sX95MZMD8/GMUZxbhrV8CYASrsHcbjZRdiJzdCatJXFMN+UCPIyd4C5ItYQNmz+a1redxn/Yucp3O+wvFNW0yhMJ1DD/ToHutcLnOz5zH3M7jiHbrh/XlOqKVgj8+yXmg+zfsNnS9atUqFixYsMtCojRaw+iAjF4PPvggsixz5plntiIPafN4LIvXXnuNOXPmUFVVtZMy1Ouvv77H44uKijBNk/POO49vvvlml211EyZM2Gd+hrRRTuOQ4Ntvv6WoqOhwDyONNADBBvXEE09wxRVXYLPtmvUrjWaYkoR5wOHrAxvDJ5980uq5w+Hgscce47HHHtuv81199dU88cQTTJgwgYKCgn1WdHrwwQc5++yz91gnk5mZuc9h8rRR/gEjWVx3dNnFxAOVyDY3lqGnpB4VZxayI0vIQEYaAAhvn5dg+Dpw/et9wSmnnML996cL/1pi9tUSxz5fj6vRS3zzt0SsRN9xvo4S16jO1lkudWfp578FYK3fg+u0kxnmjCJXhYlvFB7n+u+DFBRoGOE4VobIM2/tpGNIEtutHEzFSLJsUrguF8jFvroGtEYKQiLMFkuyZgVEMVdLr0bS7Am9Z6V5o2EgJT2LRJWpZBjg9qIoCpLNiZIh+uD1mtWEV83GXjwCJb80RYFpfvoRANGtC4AprLlvKADlH0/G5m1ESRRODywUg68NmBR3sSOHDAxZTKAVdgc5ZhVVpfV0KuzfShVqT3jggQc49thj6dGjR5v2T+PIw/PPP8/rr7/O1KlT9+v4OXPmcPrpp+9klIPBIFdeeSX/+te/9uu8aaN8BMCMNRIPbkfWvSiOnJS4PECs9nvMWADFIUKEGUMuTpE5HEqUl5fvRBqQhiCQmXzabIxTp/LfWiGveJH8ZxqyDaq8Fg2WFy0qUg+mYrFJyufFSeVMf8vBtn+vEdtNC6dXxTu2iNlThbF6zT6CQrmWdxum4KnPRI4KAyrPW8SsF0YwYcL72Hocg5rIESsJyUPMHYKMiirIQWRFEHK09CoNs3VblKIgKU7QbMgOF7JX3HNKZmfiVWuI165DUjTkTsIQKp17YVZsSAmm9HszYYUVGf3Luaz+n0S3Lk6OfiAhsGKCXBXGtMuUrRVT1zuF2cRMO4amI+e0LRLz/vvvYxgGZ5xxRpv2T6OdWqLoWILKPp+PsrKy/T7+2Wef5d5778Xrbd1aGg6Hee6559JG+ccMSbFjy+qJltsrxV0MgKJiiwSI12zA1kMUP2w/M5/G3M4M+V+Qgjc2M/NffQ/JGIcOHcq7777L008/zSWXXHJIrvlDQWTTl3z+yyl0XtELgBW9VSKySrmcxepwN+qLBCnBw/0v5b/xMcxYuYWKJ75j3gqR6z2mv52sIbm8d5GLP70g6gyqS2qxh5xk1Tlx1YBcLriiDSPOhAnPo9evR63tgpzwfiXTxPJ5haGNtKCmlBXxUBJecou+ZPSk+EPCY04yfNntSKYDXKK1SsnMQXb70DcvIV6/BVuO8KAlXxZypx64oFUfM8AxPWfxiHkG5tg3iUni2v1r4nTZ6sWU4fvuIhq0ySykLlRAYa0P9L0XEpqmyezZs7ntttv2um8azTCQMA7QqHa0Gvc77riDO++8k3/96184nc42H9fY2IhlWViWRVNTUytP2TAM3nvvPfLz8/d7XD8oozyqzwycxaOFZKGsYgZF9XFgzRspSsofI77Z9CLjRt6P7M1HzilC0lrmyAqQMwuQCkTvXk3pSnKLllPbOR9Vn8TIruelznFU8bSD9jnKsszvfve7NtHX/diQ5DGX4iIMfE3l79G0KLGoh+nFz/KH7q8AcO6W+7m621/IeqeGbTGTwd3E/jaHgpLnpGcNZJ0vuM5d1/8/LD1I49jObBvYRJd6MekoncuQFPGzN4K1kFjAyaYBsRiS1wua1jz9KgqWHhPG2DCwzKRxVkSxV7LgKwnDAFnGsttAad4uuZxoikp82/eYDaLdSvH6kNxu5LwSXGWTGVF0MiDa/eateYITr6vh/10xhkGdPwXgwrwPqPVECKkSG+yZAKyOdsOoLSFzk0a8fA2tqrh3ge+//56srKy0OMo+QvQpH6hR7lie8jnnnMOLL75Ifn4+paWlO5F9fPvtt7s8LjMzE0mSkCSJXr167fS6JEnceeed+z2uH4RRTlZlOq59hOoSP7Ihk7fOg/T2ywA/aoOcRNO6N1EzilF69ifUPVF5ajdw1auoTXFMTUyQkqmQqTWACxrya+na/7zUOey5gw/qGE3TRFGUve/4I8On31zHlHPn0W+k4L6WVmpsPzOfUcffTL5UzwxTtEp1yl3JLxfXUP1pOcGAgdsjPktvkROryIVNb570rIJcto4I0Ji7DlvEheVJyEICksONmtUFI1jbrBKlR5AzC1LejJT4nqyWnrFppPLByIow0Ebz9ykpisgvm6bwvJMEI5oi2qdy81FiUcyGcnHuhlqkzBzQbCiZnXCXCqGK8f+0qOxezd33nkafGoM1CaP/QPRcBrpWokpx5ofEvbqxZgCdV/VG3lZDYMPHwJ7zg36/H7fbvcd90vhx4KKLLmLhwoVMnz59nwq95syZg2VZTJw4kf/+979kZ2enXrPZbHTt2pVOnTrt97h+EEY5jSMDNpuNeDxOLBZLV7ymcVigKMpOrS9p7B0GMsYBSiV0NE/53XffZebMmYwdO3afjhs/fjwgyEdKSkr2uWp7b+iwRnlk1/Nwl01lzpwLUE84G4DVQ9YTza5ACWaSt67vIWel6shI8lnb+o+nqlT0sPrLhKSea1tP8raI8LUW8rI9VIwed+CO2pEdzTy00ZolB32chYWFfPvtt4eswb8jY3j+ZHJ/9RZoKo3zH0c/T+jFfvvbngz0P81p+kI22zLwfydoUf856GycjTbstw7CZ4KtQfQkh/JVVpTqvJ5fzDerRQi4V76KY9Qr1NX0IR51Efd2AUCNquDyIkWCSJFGjKCQBzUjDcjBWpTMzsiZ+VjsYtFkxFt7zjR71ACWoopiMM0GRnMI3EKEsi2HDTmvUJDdAGZTLZJhINnsoDlQs0oB2N6tlmCf+bwrjaCbNg9vQpmqMlzEutqBIJloFYLwo9fiHji/qyay8H9tovWtra3F49lZvS6NPaO9uK87Erp06dImHu6WWLp0KQMGDECWZfx+/06sYy2xo/5zW3FYjfLQnLHkTX0UyeEmtnZeq4rgbza9yNghibh8ov3BEfSgxLuQt6UQ6ZO5MOO4wzDqjomRXc9DtmUQ37gcLSrCoC53LfnOcsqdjVTKQwCwB700NRaCqWAPOUV1bQKHIg3gcDgIhUIH/To/BGT/bjZSTYBAiYajYBhySBjCE+4MM/CWFWxTPDwX+gkbjikFYE4QvhkWY7vTxjdyT2Y3ihX7Wb53ebH+DKLVDlzZmwHIvehtuspByIXyLUdhJtNldg3J7YaoDxqrsGKi4jserEDWvGDooEeQ3JmACHUDzQY5IU5hxQShhwWpHDUgctQ2O5IvK0XJKUVjWLIsWqgUJVUlbVZvwajdjOzMENzaCX7t7PIsAlk9qM3JYmmWm6WSeP9NlUX0e288kmGhbhb1JJH5/+KDuZcCbWuFWr58OWPGjGnTvmk0w0TGPMI85QceeIAbbriBxx9/nNLS0jYdM2TIECoqKsjPz2fIkCFIkrRLmlZJkjB2WMC2FYfcKLekd/QNuASpmyjMUGu77LTv54tF0QqLlwLQ1d8TKaoRW/hfZqe95FaQFDuKM4d4w2ZyN08BYGWvQhrVMIah4QkK70DVNZrMZmrFQ41t27Yxffr0Q37djoTR/a8DwPGb+6kYa1LT6wts/U+ij9AaofOtNxCx7DwfPoXqRWfS2SdW49nbPsQeslNw8p8Imi5qGroC8OXfX8I/bSFn9PgXXWUh9lBpZbE1XkBtuABL1Vk7YhUA3Zb0wml6kCIRZGcGUqLIzKipJVa3EiNSi90YnJoYrEhAtEQlWqMsQ2/1XpScYnAIFSsMAzQbZGdhelvQvUYENzu6LhjBEpOV5MqEcCN61Sok2ZZSppLuv4t+5/yOxeYFLDEvJn+zqNYe9HEFjR8nq7RzE2dvmzEGUdNQXl7O0Ucf3eZj0jhyMX36dEKhEN27d8flcu1U6FVXV7fTMRs2bCAvLy/1/8HAITfKLfmWJdmGFAhiRcLEypcCx+3ymFnPD2nxzA2kDfKO+Hr9MwzPn4wk2/GsE8o/RfnDqS/sQl5FPvlfi8m0brgDElW+WlQjXpO8sfYtr7K/MAyjVWHEjxHeC+4FwPz4c3Ke+4p/B16hvJPE9T3/HwCbGo/BNBVi60bS66tCJD3RXiFL1Pz6KSpihWzfNgw1Qfyx9Jyv+FXZw/QwKilH9II3mF5UyaC/bwl17s1squ8DgG7vjs2jokQykJtyUMLCa1WcObh7nozsyMCMhdCrhPa1klmCbHNhxcJYelgoRiWg5vcQofCW5AkOO6bPSdzVzM0t2WUURUbyB8A0sCLiHFYkgKRoSLKNWO33uC8SIvbhx35O/RMXsul3L3L8AyY13YVXTknnAxK7efnll+nevXtaQ3k/0C6CFO00lvbCQw89tM/HdO3aNfV/QUHBQVG9O6x355w5F3Ds8D8SD5anlaXaAQurPmRozlgcK0cDkBfsS14oTOOXN/FB4vM94d44DXkiKpG9wcasQ0wiYpom8Xj8Rzkxju5/Hc4r7kddJxZCy/9kcZtjFgM/kOiTqfLLCaKb4KEvnyR/YxHOZ99Cj32RSuscO/yP1E48l8zKAtxZDXT/VBjr0NXvkW/5UTApMkSvrlteSVxSaFRdrFM7U24rAaAxtwlHIAvFZUfKzEFJ8Fp7Sq+AvBzQVJQ6P8p2cW69/HusSCMoGhh6ylO2zBiS24fk9aaqrJMV15YiYSlgaC2nYRXFcEEwCHrCyOoRLENHsrlQHNk0PHI+APJ9z/H1uRq95laQ2TWLjAcFd3VTzfdw076zwsXjcV566SWWLFlyQK0qP2YciYVeF1100QEdn5+fzxlnnMH06dOZNGkSsnxgn08SP76ZMY3DiszMTBYuXJgOIaZxUNHQ0MA777zDmjVraGxspKysjLvvvjtd9Z9GKxiGwZtvvsn3338PQP/+/Tn11FPb1Lr57LPP8sILL3Daaafh8/k499xzmT59OiNGjDigMR12o/zZwkPLwXykY1Ht5wzPF9W65ueRBD92cxTC+n4lxb4BKFELa94XHKqwNcCKFSvw+/0MHz78kF2zIyHjlFtYOuFbBm0WlcOjuzzDSZuakHQTyQSXJbzWni+r6Otm8VELjWFo/q2MHXIn+Rf8nu9//i4Af7S/jleP4Y431whkKjEMCaq1KOvojEMTBV2GpqPbTTRNRnI5kbyCklXyejE9dkxNQjG8SCGRN5Mbq9CrhAB8UnkpBbsTy+VIkYRYuigCk3RTSEImHAdLQZxXU5AcDqwk41wifG1FGnGcey3yG/8AIHD16XDuu2RfdjvRuhV8kuB4bwvi8Tj//Oc/Wb16NUOHDuWSSy6hpKSk3byYHyvMdvCUOxrN5tq1a5k6dSrbtm2jd28hJ/rHP/6RLl268O6779K9e/c9Hn/GGWdwxhln0NTUxGuvvcaLL77IMcccQ1lZGdOnT99v1rjDbpTTaH8srPpwp23jRwnD7Bh6Glp5ELZuZuarh84gg1hZXnXVVT/K0PXw/Mlk3TobNXsbdUf1B+BEbRH52xUML2zvbvLXJhG+XfxwEV0XBDj+ZwuY9ULrVffk02Zju/x2vj96Fffm3QfAWZ8aKDGZVYMtzMS8GVVN/A5oUA0cUgwzUdynJHYwNQlJU6Egka+OxJBrmpBcDqRASLB4JSApNvTGzRAkxaEuKZrIDzvUVHeEpMeR9DjoKpLZ7GmYsoWlJCZkRUFKFoaRi2TGkRxeqnqFqfxPL2yBMF1qJnP2+Q9y4TWDmfVW2z/j8vJyHnjgAfr378999933o7zPDhbikkxcOjDiH0vqWAujGTNm0L17d+bNm5eqc6mtrWX69OnMmDGDd999t03n8Xq9XHLJJVxyySV89913nH/++dx5551HplEe1WcGss3DF0vvOdxD+UFj/KhHcJx2BQD+bha2kIqzxrWXo9ofpml2CHrD0f2vQ/evP6RMcJqvO3LUxKEFUWJiciuL1RL2qIQ9CiuK4nhe+jkAE59/ibwrTie86jWg2ShPmPA88lkXYGgWPxnyF47dKPK72qIGrJhJLzmXxgJh/AzVojo7TrXdoNLMpalarPqLmlwouoSsW0INypX0WmNY/gakkA3LMLCCIjdt6aLnXXHkYERqidWLKm7FmQt6DGQJUxPXVAD0uHigpXLKlmJhoiPL9djYiqpuR7X8KI4GFAIojkaKZ72G9XEMQ5WJZDgwenene/cS+o/Yxj//+U8uu+yyvX7Gjz32GGeddVa6B/4goD1yyh3NU547d24rgwyQk5PDvffeu09tc5FIhLfffpsXXniBDz74gIKCAq6//vr9HleHNsr2goNL+/hjgd64GcUnvuqaztvQonZKthYe8nEMGzaMV155pU0T7MFEW0gm2hvxwFYkwyJmONASVZ+OMyyCHoMml0mFzU3cIVqFPv/wp5zw8++p3S7CtklO6JwrXmfp1LkAXCitxtsojHu8OkysOozLJpNdLjiujTw7pqxCHiz2DyF7s1Bm8tY40UIgRQ1hPCMJj9g0kXyZwkjrcaQEnaYSCWAGq5FtoGYUYwQTvNWajOpfgr22ElmpR9Xr0CJ+lKgfuSmCVBXDkhKFXhJggSXbsXBiSg4MxYuBh5iRT4xsornHsGmcCLFHnSFcjT5OOD6LKVOmcN111+31892+fTu6rqcNchptht1up6mpaaftgUCgTbUHM2fO5IUXXuDNN99EVVXOOussZs2axbhx4w5oXB3aKKdxZGHatGncdttt3HHHHUybNm2/GW/SOAgw46hmPWq4ES1ahWKWo1iNKL5aZG8TshIXdIKWyFtbloQpl2MGCog7MonbMgm5OmNYDuLOQiKFXQjmi0WDYTNQYgq2kILdbyHXC+NLUwDLCANGijhkR8iyTHFxMffffz8nn3wyffvuWtXM5XIRjUZZunRp+r46CDCQiR+wp9yxcMopp/DLX/6Sp556ipEJ3vmvv/6aX/3qV5x66ql7Pf6MM87glFNO4bnnnmPq1Kk79TnvLzq0UW76/sXEf20nCEhjZ3y54n5OvFH0LnuKM8XGw8D/63A4uO+++1iyZAnPPfccZ5xxxo+GXcldegJsrWBj/umUProegGrbW8wvy2OTWchHtRPovrU5ZyckNUVOa0G5+HvC/0VBd2DPLCeGipQg2zAiJg2NUL0sDF0chE2VcMDDNl3li+2dKFkbJW/TTFTdjxZpRI43IRshJEyh6AQgSxiqAzPuwlQyMJwedLWMULAP0comog1BcBYI9i3AioWRM4uQ3V0wCpqpCuW6EJIeRzY05IQ2c1Q1iKsGYEM2VWy68OYlPQ6RMFYkCN8vo7RetG1Z+cWwbgPDp5/NwqoPmTFjBh988AFPP/00o0aN2qUOcmZmJtdddx0PPPAADzzwQLqwq51hoGBwgDnlAzTq7Y1HHnmEiy66iFGjRqUMajwe59RTT+Xhhx/e6/GVlZU7aSm3Bzq0Ud5VwVIae8fQHFHAJSuO1GfY+M7dABRyC5bLgbVqBS3zlYcSgwcPxjRN3nvvvR+NUZ771QxGFJ3MiIn/xZYj8vk3Ln6MzpmfEt6aQfelOczo/R6NjY08/ngjgUCAUCjE+rXbkCRR1dzFkUHxwwpxm863SnfWxoVRlvVeyDkGKhZqA6iSibJNxh822WIWU1QThJBKXMohEi4gFtYw3F0xu/Uhkism2qbcMCFfAE99BlpExREQU4OtPAxGObK8HUlzIHkT+Tc9KhSeTJO4S0qMAySvA6kugOQP40hQdBqahqmYif8trKRiWVJFyohj6RGMLasBsNaFMYO1ODsdC4CqqpxyyimMGTOG22+/ndra2l2mQIqLi3E6ndTX15OTk9Nu310aEEchfoBGuaPllDMzM3nrrbdYu3ZtqiWqb9++9OjRY7fHNDY2pviyLcuisbFxt/vuK692Eh3aKKex7xjd/zoyBwmpP71+fWp7Mo86sut52HMHEdryEXD4Fj02m41QKMTGjRsBod4jy3LqIUkSiqKkdEuT21ruo6pq6v+DBdM0U49IJEIwGCQYDBIKhQgGg4TDYUKhEJFIhHA4TDQabfWIxWJs2hpEUiyGnNGX+NzfIasJ8gxJIia5wfIi+7dQ7sjH6/XStWtXMjMzycnJYcZ/s1l+YgXIMp3WdGdmj4n4NugsHmeR3SQmyZwqhfXdYoRsoCfmzZXuDP4bOIf4pz+jaFaA2PdzgTh6/Rqc/U5C6tSdcIFCIEsUcgWyGgl7/cQ1Hbc/A0tJ6CzrTlSKkDUb6DEkd6ItSs4QtJkeN7ZK0coVK7Aja0J4QvI3oSZapdw40V0WhiYMc7IwTBZfvKDwjMQxphwn7oXZnyC7c9CMMkb3vy5172ZlZfGHP/yB++67jwceeIDLLrsMn8/X6vuKx+MHxXtJ48hCY2MjHo8HWZbp0aNHyhCbptnK8O6IrKwsysvLyc/PT+kq7wjLsn5Y3NdpHFzo/vV4J1wNgNb1Z5xwbxzWbSDw9dOAEPoQOLz94WvXrqWxsZFnn30Wy7JScnqWZaUeyecttyeR3P9gy/AlFwUg8puapqUeNpsNu92eejidTjIzM3E6nbhcLtxuN263m6v+EmDTqZnkbSyh5rxnuTrjOQAyjQg3NFxLtxfOwnz7ZV6NCCrMhk//hBlrRFLshP71FLnd54nBdJ+D89aNbF1azxCzP7NOFu/dlWvwgO0kli/4BYouwnD2sJO8DT60FVtpnPMQul8s0Lx9zkHy5WJ57MRccXRHM5e1qcWIugJoUXvz+zckXDhR5TzBX52EIov2pmgslQrRmjTR+iTLxHrmY1spdJNVQPY5iLsUTLn5O7TsNiSHEyIhLEVFevc9sV0R70F2+tAySjiqeBogBFMyMzP5wx/+wDvvvMNDDz3E7bff3ur7siwrHbo+CIgjtUNOuWN4ym+88QY33ngjixcvxuVq3YUSDoc56qijuP/++/nJT36y07Eff/xxqlp7zpw5B2V8aaN8BOHEm/3M3/o6g98VFYVV/T4H3UGXpUPwNe69cOFQIhgMMnr0aM4777zDPZSDjtAQaCj6js5vb2HUL+fTOSoKneyGxS9z/81HC/vyYaLiGsDT8zRCm+aQOfUOqjPrKdVEPUCJbRuqz47NIdzh30Z+A0DDtsH0/nQw3XQLdVsDAOb2DURWfcjH31wH3M+YQb8DQHYnwrqmhWRIKLqYAjTZji3oJW6LIhsKMYfwfiVTRjYlHNhRHCpSJJ56XxJAKIzVJEJ4clMAqygPKRpDqzIxi0UPtFwXQLZrKLICDimlWCW7NKSoE2JCSpKwOI8ZaRR90IBkc6N6ilt9nslw9scff0xFRQWFhc2dBFlZWSxfvpwhQ4bsz1eVxm5gtktOuWMY5b///e/ccMMNOxlkALfbzY033sijjz66S6Oc1FIG6NatG126dNnJW7Ysiy1btuz3+NJLyjQOC3r27MmiRYv48ssv06LzaewzVFXlwgsv5E9/+hNLly5Nbc/KymLt2rWHcWRpdHQsX76c4447brevjxs3bo86yUl069aN6urqnbbX1dXRrVu3/R5f2lM+ghD57EUmTs6k6inhofTIW0J1NJ/GnG5kRHbuxzucOOqoowiHw3z66ae8/fbb3HvvvYd7SAcNtoXr6S73Jbr0CQKmi5jcvLJWMGj4iZAv1bcJOst441bmrXmC4y9YTP7mvqxxHy92LpvFvN/m0GtrIfVxicb1gj+8y5oyca5la/ngyZ6JM2cBQ1LXkWQRkk55oKEYTr8z9bojZMMestOYW49uj2IPJ/qdtThhTxSwY9MUFLtYx8tRExRZ+D4RkZc2q7chhQJYdqdQjrK3aBExLWTdwtSEWAWA4ZCRPE6kSLSZehMwIw2JMdvA0JGUXfeMjhgxguzsbJ5++mn+9a9/oSgKbrebn/3sZ7v/MtLYL8Qthbh1gIVeVsfwAevr64nH47t9Xdd16uvr93qeZO54RwQCgQNSj0ob5SMIn3z+K064YhOYGwEIGS6CkUxKtuUQXPW/xF5Ca/nosovRMroRqfwGaG67OZQYN24c48aN47rrrsM0zSM2F6iv+wZ7JMSsz3/FsaEH+d4lwq0RNOaEjyZrtXjfH+/AdR3bMI+mrGK29RckOs+YV/E3bTwj+32HHxcnmaJtY9W3j6HM/oTG716nJc95S5h6YlGmOZAUBUIRtHITNSAmD9OuACr2kI36otY6soZmEHWJ3LOa+I4UTUHWZRTZiaQnimIiIcyGSpBV5Iyc5mClw95MxblDUMTSZCSHvQX1JlixIGYs2PzciLE7lJWVMWzYML755htuvvnm/a54TWPPOJKqr0tLS1mwYAF9+vTZ5esLFixoJdG4I6699lpA1Jv8/ve/bxUGNwyDr7/++oDSJ2mjfITBaqqnYLlodaop6kTB5i64Z3/PrBbSmMf0vBzvwPNTXtPhhizL+P1+srKyDvdQDgo+/lDof59wW5C1lw7mUVsvAMyaEvrOGcDM3+16svrk818Bgm4WwLhrJt5KO3865WMk2USpFhKcWy6T4LLj2J0eOZAq9MIUHoIVDEJ1BZIipgDVl4XlcpIRsgPZNBQ0AOD2e9DtOqZsojvimHKCQ1uXUXQJS1FREdXOkq4jGQZWJCDanEIJw+pq9shl3SLpMEmmEK8QJ1SQbWJykxQbpl6BZcSwjBhGeOcQYUt8/vnn3H333QdF2zYNAUEecmTklKdNm8Ytt9zClClTKCgoaPVaRUUFt956K9OnT9/t8YsWLQKEp7xs2bJW7F82m43Bgwe3iYVud0gb5SMMsc0LyXu6AYDsYC16zWv461cBz6T2cRaPRXZnYTRWEa1ZcljG2RInnHACDzzwAHfdddfhHspBwwk//x6zqZbBjw/HzBVGbNkZX1DepwERat49klrj40bez3cvjEYO5OCtKqJoTUJMYtjer5/k+R438n7sRhwz0kiscgWyTfQS20pGohR2xSIDV72bkE+Eu6OuKIquYioWpmmBTbR5WIqFpchYikxyGlF1H7JhYDaAZRipKVgKhYXmsiYLBamUgIUJUR0ME2QFySHarWRHJnK4Hj28ETPWuEejvHHjRpxOZ9ogp9Fm3HTTTbz11lv07NmT6dOnpxSiVq5cyX/+8x+6dOnCTTfdtNvjk1XXl1xyCQ8//HC7R2fSRrkDYnj+5P0mTglv+QwrEfqzFQ9Dcefs5BHr9esxI/WEt391SEUZdodJkyYxZ84cNm/eTElJySG55vD8yci2jEP2/gVDl/B6PQPPAWDAPAmj8WM4/sw9HZoig8n+w+d0O+06vOOvxOqei7Q62Yde1uZxfPrNdYzufx1aRglaVjeUnFIAJJsDKxKG7Exk3cKZCGv7c/3YQ3bimoEpmzTXhpqiehtSnq/l0sCXgQxYTQ2Q6NO0IhERMlc8Ilyt7xDDNk0wDdGvDEiODBRnFvFgOWascY+/hQULFuyR7CGN9kH7hK87RnrK6/XyxRdfcPPNN/Pyyy+n8seZmZlMnz6du+++u0297g899NAuc9N1dXWoqrrfxrpjfEpp/OjhcrloaGg43MNI4weGLVu2UFbW9kVJGvuHuKUe8KOjGOVQKITP5+Nvf/sbNTU1VFZWUlFRQW1tLX/729/anEb76U9/yksvvbTT9ldeeYWf/vSn+z2+tKfcATB2yJ3Ycnunnnt7ncXYIXcS3v7ZPnvMGUf9CkaJqlxTk9C2+mHrglb7fL44Sbgw44DG3Z4YPXo0r7/++gGLCSQVlex5g4lWL9ltAduhoHAd2VX0YHv7n4fRuB0tv08qFL0vWFT7efOTX7dUuNp3Y3TijfWYt/2ROl+IuKYTc4rKaUfAQ6fvMpGbRFGVLZSg2YzYkU0RbjYVi6SsgGwqyKaEokup4i1LkZBcdjA9SIqCFQyIF0wDIhEkWQavq5lm07BgF8V9kqIhaW5kmwdJse/0ekvU1dWlPeVDgPbIKXeUQq/c3FwmTpzIqaeeymmnnbZTXrmt+Prrr/nLX/6y0/bjjjuOW265Zb/HlzbKHQDu0RcTPLoLIZ9gTPLUO/GsbMC+bOhuj5k4+aVUAVFLxCcdQ1WpCMe4G11kbzb59Jv9Lzo4VDjuuOP44IMPqKys3O8fCTRXkR9VPO2wVJS3hLe/MMrKhJNRZImqXuEDPufQnLFkjbhmp0rtvR1TcJmovl8/NUpTyVJkWwhZNpESFjUUdaPb+5G3JQ97k4QWEsd66l0pekxLNsEQE7NsyCi6jGQkjGsCliaDx4kEqSnYisVEjjkUAk1FokWLkyKnDLMVa2YMkxQNxZG9V6McjUbJzc1t82eRRhorV67krbfe4pVXXmHGjBkMHjyYU089lVNPPZWBAwe2+TzRaHSX4Wtd1wmH9/+33jHiCT9STJr6NgBWsImIN0Zt8TZqi7exrfdWAv18KDm7z6/uyiAf/7MFBLOjmIqBqRi46+xEv3nnoI2/vVFYWMgXX3xxQOcY3V/kTGVbx2mNMVwKUZ9EzmY340YemJazrDioX/DgPh1TcNn/2HiizsYTdcK9FlKQs4aSzDUUeTfic9bgc9bgcNXT1Hk9tZ1rqSsOEvWYRD0makRGMiRkY9dezq5aVy1FErrMLie4RM+ypIgdpWhM6DgntZwNM2GYm09kGaL9SlJse/0eTdNEVdO+xcFGsk/5QB6m1TE85ZKSEq688ko+/PBDKisrufrqq1m2bBnHHnssZWVlXH311Xz88cd75a4eOXIk//jHP3ba/vjjjzN8+PD9Hl/6bj6M+Og9QX1Z/9Ft5PR+mbjWCQDdHkPRd38Dn3hrE3qRF9UvJq/ozOf4ZO6lWHqEzE0aWasFSb8+97/UL30CuODgvpF2wPbt21mzZg09evTg+++/p2/fvhxVPA1bdh+i1aJCvC2eb7L1pyMUsPkXPQFATue+qEacwILn+WLpPQd0zn0Nu085dx715w6hsed8ALr7VpOv1aAQx0Ble0xEJZqiWSAbRJ0hJFNGTShbaBExRcimjIGRCGGDqZiYsoWCJPiuAbCQDECWUh6zOFhG0jXQE8Y20qLvOCr+lxQFyZaooG6Kpwzz3qBpGk1NTTsJU6TRvmiPQq+OJt0I4PP5OO+88zjvvPPQdZ05c+bwv//9j0suuYSmpib++te/cv755+/y2LvuuovJkyezZMkSJk2aBMBHH33E/PnzmTVr1n6PKW2UOwCSBmTyaZ8AoHTuhdW0ieCSV2jJyiT2mU3jBeOo7bSFrIo8ALKW9Adg9qtjE3slbv7fnQm0Pcx5OFFYWEj//v3ZuHEjn376KePGjcOW3QdHyVjs+SLPPLLreS0ENXaNjmCMAcYM+t0uFhEHZpD3B0pZX8p7rqE4cw0AfR1ryJQEkUgEW4qlqZwS5HCzVxrXhJeg6gqSkST+kFMes2zIWIqFoVmp10HCMpO5YolkUFty2LAUGUmRhWecpFXVdVHxnfCSJa05VG3pooPAO/A8hudPBna9IHG73WzdujVtlNM4YGiaxvHHH8/xxx/PX//6VxYtWrRH5q8xY8bw1Vdfcd999/HKK6/gdDoZNGgQTz31FD179tztcXtD2iin0SEgyzJXXHEFIGjqfv/73yPvJZ+YRhpdu3Zl+fLl9O/f/3AP5YiGkaigPhB0lEKvlohEIixdupSqqqpWHPySJO1SkGJHDBkyhBdeeKFdx5Q2yh0IH741pcWzruzoJSfhbLDhU3PI3CqMVnjVTOCYgz28QwaPx8Mpp5yCLL9HQ8O7RGI5VNSOwFGw/3maQ40DDVMfKEb1mYGj6CjU06ajFayjj1N4yp3lKtxEMFCotZo9Y0kykKMOlHjrnnZTMVEMUWmdDGmDkHRMesiGJnxiRZcw7BKyDhGfhWyKiI2z0gJNxZJl0OMir4woACMSEkVgioKVzOHJKmYsgGXohNbO2mOxV9++ffnoo48O8NNKY2+IW/KBc193sPD1Bx98wIUXXkhNTc1Or7VVD3ndunU8/fTTrF+/noceeoj8/Hzef/99SkpK9nuhmDbKHQjJdp495U79C/9Ohh4mS1YJbxatMofbABwMTJo0iUmTJmGaJr+47P8RXPPuXukW02iGZ+A5IKv4i2N08WygWK4CIJsAqmUQlOxEsBE0m3l7lbiGbCjIRvPka8oWsmIhGzKyQcoQK7qQdJSMFsVeujDQ64dv4A8jLmatXATAK3P+StevsxFpFTUVvpZsNqwQoEcwI/EUBShmHDMWxDKjWEZsj8VeAwYM4OWXX26XzyyN3cNAxThAc9HRcspXXnklZ599Nrfddtt+dXzMnTuXk046iTFjxvDpp59y1113kZ+fz5IlS3jqqad47bXX9mtcaaPcgdCWQqbWOdOprV4bmjO2dU/rEYDKykqyczKYt+bPh3soPyjI3hziFWvY1ns1423byJaEVrFqGUQljUbc1BhZ1OuCKCEWc+GIa8imnPJwW0K0P0mpnmVFbzbIZiJ7vO6oNZQu7oHUbRHHlEdZ3EkUenVZkgd6TBhjowWblyyDyyuYv/QGzKSectifMsiWEdujp+xwONLSn2nsFyorK7n22mv3uwXzpptu4q677uLaa69txQA2ceJEHn300f0eV9oo/4CRFK539T0VyeEha8Q1h3lE7Q+v14thGKxatSrFUZvG7nF02cUAeAeej9plALbCReQqzTJ0QclOnZXB2ngX1kXKqIuIHl8z6kGL2tGidmRj1x5NkiwEQNZFQZdlSqCJbWXf9sSULbIdNTye25dXF90MQF/DAtNE0uOtC71SHrMddAckjHKyyMuMNWEZUcxY4x7fs8vlYuvWrRQXF+/rx5VGG2G0i3Rjx8opn3XWWXzyySd07959v45ftmzZLvPJ+fn5uwyJtxVpo/wDxMTJL2EbNBHH5SJsbRkWSnkjVKw6zCNrf3g8Hn7zm9/w2GOP8ec///mIlXdsDwzNGYtvwCUANC7+F03/+iuFrm0A1CXyxw2Wl3V6CZtCpQQjmcQTFddqQz6OgBvZkFp5yiJELR6KLqFEhFecZPGSdVPkihFes6XA9vIhvKwfQ+f1pQBEfRL4HDg3BkRbVMIYW7GYYPsCUNSdONotI4oRrsaI+ff4vjt37sySJUvSRvkgIm7JKAecU+5YRvnRRx/l7LPP5rPPPmPgwIFoWuv7b8aMPTMeZmZmUl5eTrdu3VptX7RoEZ07d97vcaWNchodHmVlZRQUFPDyyy9z3nnnHe7hpNHB0K9fP+bPn8/JJ598uIdyxCKOinKA5qKjFXq9+OKLzJo1C4fDwSeffIIkNS8aJEnaq1H+6U9/yo033sirr76KJEmYpskXX3zBddddx4UXXrjf40ob5R8QjiqexvytryO7cwgNzKO6RIRI3H4PuZt1/Iv+zg+lL3lfcfXVV/P73/+ebt26ccwxR06leXtgaM5YVGc+Nl8PopWCaMXZaRRbuq6mQI5SY2SxzhDscBvD3air6o2tMRvFVHCGRaFXRm0mWkRFNiVM2cJSrFbXSBZ1JT3kpNKTZFjIiZC2iYThMun5xUiGXHwZV3tWAPBi10787+WXcdhtSJGo8JBBeMl6DIw4yCqSTeSgJc2NJDfnkfdWJzF69GjeeOMNNm7cSGlp6X5+imn82HDLLbdw5513ctNNN+1XBO6ee+7h17/+NV26dMEwDPr164dhGPzsZz/j1ltv3e9xpY3yDwjJIq/Q2pk4Q1Pw1oviAm+lHX3lx4dEZOFAMaLo5P3ipLbZbNjtdjIzM9t/UD9QJEk17Nn9kW1eFEdOSh9ZnT4DzK+piHSiQc+iLiSKWeJ1nfFWdyKzOgtvpR0l1Nz2YbgUTI2EURbbTDkRrk4Y5CTPdUv5RTmaCEcrCmpEZv3J7/G76DI6PShC51f/VOHpzlVkbC5ECigpyk0LoLgYNm0U59QEo5fs9CE12vbKe52EqqpcfvnlPPzww/zf//0fHo9nXz7GNNoA01IwDjB8bVkdy1OOxWKce+65+50Ss9lsPPnkk/z+979n+fLlBAIBhg4dekDEIZDmvv5B4ssV9yN/9AlZ760n6731WG+8xEfvTN37gYcZY4fciebbv6IKgGAwSJ8+fdpxRD9cHFU8DXveEOx5Q9AySrHl9kXL6oaaVYqaVYrusvCWd6Vp6yCqGps51D0Fqwl0X0pl6WZ0l0U0SyaaJWPaZWTdQvMb2AKghSS0kIQtJKNFZRRdQtYtJN0UBtkwBXe1HkeKiIcSMVl93FdYVd342lGCmu1AzRaGNuL1NwtXKAooCpIjQauZ0FFGUUFRkRN6yrLNi6TYGZozNqUpvTv06dOHIUOG8NZbb7X7Z51GO3Ff72NO+e9//zuDBg0iIyODjIwMRo0axfvvv596PRKJ8Otf/5qcnBw8Hg9nnnkmlZWVbT7/RRdd1C7tdCUlJUydOpVzzjnngA0ypD3lHyxmv35ci2d9D9cw9gnNkpH7B8uyME3zR1/sNbLreWi+MrQMYWxlRxayOwfZmYHkEF6iUmeRL+ezrmALnXwbKLRVpI6vsBWy3VCpLsnCHhIhY2eTHVtIQQsJ46wkDGiS11oyhEFGb/asJT0OpizEJwDJH6bz0mFsG/4lr/lP5p+/EEVn8rZe5GwtZMVJqxnwQlHzG1EU0RYlC+/ZSvYpKyqyIxNZE0ZZVhxt+lx69OjBN998s68fZxodFMXFxdx777307NkTy7J49tlnOe2001i0aBH9+/fnmmuu4d133+XVV1/F5/Pxm9/8hmnTprVZ1MYwDO677z5mzpzJoEGDdir02pUs47XXXtvm8e/q+LYgbZTT+MFg6NChXH/99aiqyumnn86oUaMO95DS6EAIBALY7Wlq1oMBw1IwDpBmc1/D1zvSXN599938/e9/Z968eRQXF/PUU0/xwgsvMHHiRACefvpp+vbty7x589pUd7Js2TKGDhXyuMuXL2/1Wsuir5ZYtGhRm8a+u+Pbgn36lG+55Za93vSyLCPLMoqiIMsyqqqiKErqkXye/KtpWup58qFp2m7/Tz5v+f+Oz202W+raaRx8mKZJPB4nHo+j63qrv/F4nFgsltrW8nXDMFrtl9zXMIzU8+Q+yW2FhYUEg0GeeeYZ1qxZc0BVjj9EHFU8Dc1XhuouRHHnAyA5MpDd2UgOD5Jb1BmYisS6MV8xsuQDemsbUgo/EctOHJU6Vy6Nhdtw1os+Zd3uwt3oQjbtInecLOQyWxR8mVZzj7Esi/8VWShCAVIEep16K0/Vz8VXr7CiSBz7n7xj+aT8RDI8lUBRs35yMAhNiR5kRQU9mji3imRzIdvcyLaMvfYpJ1FbW0tGRseR7DyS0C59ygjqysbG1t+n3W7fq10xDINXX32VYDDIqFGjWLhwIbquM3ny5NQ+ffr0oaSkhK+++qpNRnnOnDn7/B4efvhh+vfvj6Ic2GexJ+yT1br77rv3etPH43EikQjRaJRoNEokEkHXdWKxWOqRfN5ygk5OvMFgMDUZJ/+apolhGKlH8rlpmliWhWEYqdBmclvy7+6wq5WMZVm7XeG05bU9XW/Ha+7rSmrHc+/tWocaycWYJEls31xBSVkxkiShKErqb3Kx1nLBltzWcpGmqioul6vVYstms7X6X5ZlXnnlFZ588kl+8YtfHO63f0gwPH8ymq+7KOhyZiE5xG9Rdmcju33g8iK5RaHXiuMXMaXbK/RXNgAQQYTmIpaY/DRZJ2IPEvY1ey9a1IbNriHrMkpS+cncw32WMK7LzvwSgO6fjeHv2z7Hcfn3REIG4+4fJvYb/Bmfa+NpLO9L+TiVojktOIUNo7lXWW4xHckqss2Tyiu3BRMnTuTee+9l6tSpZGdnt+mYNNoGAwW5HchD5s+fv5Oi1+23384dd9yxy2OWLVvGqFGjiEQieDwe3njjDfr168fixYux2Ww7FX4WFBRQUVGxy3O1B4YOHUpFRQV5eXmUlZUxf/58cnJy2vUa7e5KqqqKx+NJV0DugOSCoeXztmLHHGryeUfNrR47/I88+5+bd9o+ouhkjJi/3ahABwwYwH333cff/vY3fvWrX3XYz+NAkSxy0jxdUJyiwlp2ZCK7heGRM3KQXB7wetg2VtxXo3r+l2HKauyWTlxSUkY5jkLUtIlKWrk14b4lm1iKhSWDqSXlGqXmAi15h4WkImN5nPQoXABA0xmrUGvg+blhBudB6RbR+lTUHc4q+C/L//Mv5Jqm3b7PZFV2kkhE0typvHLyc9jTvVNYWMipp57KPffcw80339zuk2UaB46jjjqK2bNnt9q2Jy+5d+/eLF68GL/fz2uvvcZFF13E3Llz22UsEyZM2KNz9PHHH++0LTMzk/Xr15OXl8fGjRsPCsVrOr57iJD0JH8M+GzhzgYZ2sbtvS+QZZkbbriB+++/n2uuuYbf/va3lJSU7P3AHxg0TxcAVE9nFEc2siMLxZ2DnCGMjuT1Yfm8GFkOlNFPAXCcthCfGQLAL7mIWjYAGgwvTfEMYroLYg7UaKINKSFCYcpWQvUpaZRBkpP9yTKSmZgyEpSZwSIZhyzCzuu2jifis/BqFprSbMx1RWJ+eDDuHQ2y2cJLbomkUba5kJTm1qi2FHxNnDiReDzOk08+yU033bTX/dNoGwxLQT7QnDIiKrYvKQabzUaPHj0AGD58OPPnz+fhhx/m3HPPJRaL0dDQ0MpbrqyspLCwsE3nHjJkSKvnuq6zePFili9fzkUXXbTLY84880zGjx9PUVERkiQxYsSI3Yay169f36Zx7Ii0UU7jB42kYf7000959NFHOeaYYzjxxBNxuZrVj+bMmcOcOXOwLItf//rXbf7RdgQkc8gAqrsQNaMYJaMQOaczkk+ISVjZPiL5KmtO+IjLMz4AIM9owhuPo8sSQclOgyVyzXVGFsG4h3jMiaw7UGPC0ClxFcmUsRQrJcUIJLiuJcBCMmj2lhMc1p5vK1lzcqLNLaOG+YU2elSOIL8J5tnFeb725bF27dEM3oHzWpwn3izZ2BKJNqlkXhnYpzD2rFmz2rRvGm2DMMrt0Kd8gEybpmkSjUYZPnw4mqbx0UcfceaZgjBp1apVbN68uc0FoA8++OAut99xxx0EAoFdvvaPf/yDadOmsXbtWmbMmMEvfvGLVmIU7YG0UU7jiMC4ceNwOBwsWbKE3//+99x+++1kZGTw9NNPs3btWi699FIWLlzIW2+9xeWXX364h5vGQUSywDMQCKTTaO2E9iAP2dc+5ZtvvpmTTjqJkpISmpqaeOGFF/jkk0+YOXMmPp+PSy+9lGuvvZbs7GwyMjK48sorGTVq1AEz/k2fPp2RI0dy//337/L1E088EYCFCxdy1VVXHZlGeX9ZntJIoyVGjhzJyJEj+fjjj7n99ttRVZX8/HzuvPNOVFVlzpw5xOPxwz3MNqNlYReA4i5o9pKzc7GyxGQQKJbYMvkdLi98lr7x7QDkxHRkC3RZwq1EyZRE6NinNFGvBmhSo8i6DSkhPiF0lMWkaSkJ9SfAStJq7ujMKjIYBpZh0OOW4QBs+qmHG/yPYroakFQdubwTAP3f78Vgf1BQFZmmKO6C1PHokVantow4GHERwlZsyDZhWNvqKQP079+fl156icsuu6zNx6TRsVBVVcWFF15IeXk5Pp+PQYMGMXPmTKZMmQIIT1eWZc4880yi0SgnnHACf/vb3w74ul999RUOx95TJU8//TQAa9euZd26dYwbNw6n07nHouC2oEMY5bRBbjvGj3oEJaMzZrCaptVCRPuHQK95KDFx4kSGDRuGYRitin3OO+887r//fn73u99x1VVX7beO6qGCpNgFfaZThKlldw6SNxfJl4Xl8xAoFj/8zf3WckHBi4yIbiE3LKyoQ4esJoU1hQZuU6eLUi1OqkLcrhB0eWiyd8KICUNnKgamYmEqZkI7ucU4TPGwFAkSxrrllCNlivF1fame4OiBrJ/4Md3mTsCzQSyApECLXHLSEEMipyz2sYwWiyWzxf+KluLBTrJ7taVQ8IILLuDmm2/m/fff56STTtrr/mnsGaalYB5o+HofPeWnnnpqj687HA4ee+wxHnvssf0az7Rp01o9tyyL8vJyFixYwO9///u9Hl9XV8fZZ5/NnDlzkCSJNWvWUFZWxqWXXkpWVhYPPPDAfo2rQxjlNPaMUX2EWom7909wHncRKDLmlk1olW1rZP8xYlcc2S6Xi9tuu4333nuPZ599lhtuuOHQD2wfINsyRPWxJtqcZGcGkjcTy+XE8KhUdxGUghMG/51j9HUUN5q4I8LzVQ0Jd0DBpRsEVZMsSRR9xZU6GtRK6uxZNGZUo8dFVbZsKsiGgqq3nniTxtlKFnslc8qyDIpC7cRctIiYRjxVmbjnb2Pgl2WgbAU5ca7MRAuMHm+dT07AMuK78JZ1cX3ZhqSIIrV9YfdSVZXbb7+de++9l7Vr1/LrX//6R1NoeTBgWDLSEcZ9vWNrlizL9O7dmz/84Q8cf/zxez3+6quvRtM0Nm/eTN++zayK5557Ltdee23aKB+JGJozFneXKbh7C2Yb5ejjCHRWcNZLSFtg3ponDvMIf5jQNK1VIVhHhWzLENXHNjFWyeFBcjiwXHZquoUYMUbkvKbHvqSoCXIahMoTgGyCt9pCLgF33ESxhPcZk0JkSk1kq/VUumpoiopzJ2g7UHUVLaKmBCkQtjFBt2kh6YkiMNMEWcYe0lh/zNcADPpLZ4iEmo9LFGtZ25tED7XDkeptbgUzjmXoWHq41WbL0LHMWOq5pNj3KYTt8Xj4wx/+wOOPP86tt97KTTfdlCYXSSOFZPh5fzFr1ixmzpy5k453z5492bRp036fN22UOyiG5ozFWXgMjq6jUQaPBqCxVEI2LZSaIMHl/wMGHN5B/kAhSRKRSGTvOx5GjBn0O4BEXlV4s5JmB4cdS5NRjvs3ZyI4foVBVsiukLFVCyNmaTLRXBVXTMKULCDhoWqgSgZuOUSmvZagU3gLhmwQadG37Pr/7Z13nJ1Vnf/f5zzllpk7PTPpyaTTQk1oIdKRZkHURbGBooCLDVFUEFgVuy6rIO4Crv5WVBRQcRcMqCASWoBQIiWFhGSSTKbPrU87vz/Oc+/MkJDCDJkhOe/X63nN3HOf+8yZm8zzud/eG49RDC0sX1Rc2BWkpPeAKmof72T+Q/FAd99DhSHCshAtE2k/XO+76Y7ViHw/ChCuO+ga1tCGITHK08KuQh9ii1m/F7smynqbkosuuog///nPXHXVVXziE59gzpw5u3QNA0SRRRQN11IeZur1G0g2m92q5nhHH+Byudw2P9x3dXUNq93r2PInGAxvIA888ADr1q3j2GOPZd26daO9HcNu5OSTT+bCCy/kJz/5Cffcc89ob+dNRzn7ejjHrsaU32jWrFnD6aefTlVVFbW1tdTX11NfX09dXR319fU7fP0xxxzDz3/+88pjIQRRFPHtb3+b44477nXvy1jKY4jDJpwOgNuwD+lJi0k0z8eefTCFqfrTmOUrql4uUfzb/7xmgw7Dtnnqqae48847SafTFAoFpk+fPtpb2i4q9GLL0EU4qcp62FSNkjA9uY6GknZJ12Ytqvskief78Ff2AiBcSbIhiZxRgxNC3NALSykS+DRZ3QQJi86E7p9dRNvSRTnUWpBRGpDYpa1vqLUrCrzyVofJf4hdzPG0p+c+pYgaH6G5Jv7gc8cRKL8E/SFUZQZizVv9zv6Q71XkoUJ9jASzZ8/mmmuu4dprr2XlypVceOGFJs68k0RKIoYZEx5rlvK5556LUoqbb76ZlpaWXc6Y/va3v80JJ5zA448/jud5XHbZZTz33HN0dXXt9KSqbWFEeQxRe7Cun7Un7VPp0uQ3pbDjgF/q+U76/u+bPPTctuvnDK/NypUrOfDAA1+zU89Y46HnvssRsz8ed7QaGClndWR5+YQCichlS0KvT0qEVG0K6PrzKxT7tLDVz6rBOngcJUexuUqSDgbEthQrtC1CMq4WcT9IotwCUWRRzMhKqZTlW6TDZNxEZChKSqbchx7BCChcaJ3KlFnX83L7gdT/SpcjybqNKN8j6tyoW2mm47pOKx7Z+CoXdkWQvRyRl0NFpXi9xHCprq7m3/7t37jhhhu44ooruPzyy00t817K8uXLWbZsGXPnzn1dr99///158cUX+dGPfkQmkyGbzXLWWWdx8cUXM2HChB1f4DUwojyGsMbpzk3hAbMo1ipkJEh2hPD0MwDc/YuD4DUK2g2vzT//+U8efPDBMV2zetR+l271Yat637MJOldVHqswRPgBgeOzoTSJu6oWApAct5Tx421UqGjfpK3K6maPVC5g9qo06ck+Gwclmm6KGlnrT6IUuUN+HkrqftgyInC1ABYyeezAwvJdRCSwysasJStJYGVRFsDys5Yhe2aT3jgDd5luM6haWoj++SSEAVEOZJwAhlPOqrYZPPZisCBHfj+Rp7srRV7fTk+M2h5SSi6++GLuuecerrzySi655JIx7zkZbZSSqGFmX4+1aOmCBQt45ZVXXpco+77PW9/6Vn7yk5/w5S9/eUT3ZUR5DHBo84ksa78Xle8BdEKN5QuS7QHBQ0u4767TRneDb2LWrVvHjTfeyKc//ekxfeP1uv651Zo980CUX2D9xTqhr6f1nyQ60tRuGUd773R6k9o9/Ct5DAurH0L4ERMm65Kh1JRqsASBC91Vis1JLYC+0CP4uv06ckGGnKeTWYIggfIT4CUhkqg46Su0A7xEgJO0sXxrYEyjr8c2IuWQjOpTZ/4/Vv78OlIPryHq0o1M2DIQv1fF/nLKGbJOu84rva6toUPmVegReVnCgq6xDgtbRrQm/5RTTmHKlClcd911fPOb38R13R2/aC8lUhZiD0v0+q//+i8+8YlPsGHDBvbff38cZ+j/v/nz57/max3H4emnn35D9mVEeQxQvtH0LP0+AHWAK216n7jFlD0Nk+uvv57zzjtvTAsyDG2gc3DjIhoOv4zuC05EXL2Rz1S/F4DZ+T4emjGFu375G1QhQ1egk1GezR/A+ub7Oeji/SolS8KP8OsceutCupKSLZZ2Gb+ixlGMEnhRgpxXg+fpfAXlpRDFKqxiFbaXwIrrl63ARskIPxnopiLl5iGORPhoYS4HrH2fts9+iZS/Ts9Gjt3SyisMjRfnuvQ13OQ2s68Ho8JSxTp+I5rk7Lvvvuyzzz78/ve/593vfveIX39PQVvKe1ZMecuWLaxatYqPfOQjlbXyCF4h9Ozn7XHuuedy00038c1vfnNE92VE2bDHUp7Vvb1PvAbDCSecwE9/+lPe9a53mcSvvYjzzjuPgw8+mFtvvfV1JXoFQcDNN9/Mvffey6GHHkpVPMu8zPe///3XtS8jymOIx9bf/qqV4TVW39uxbXtYPWhHi+R//A37oU00/7WX4K3dnPOYrqnur7N5fnY/2YYc+929kGLtAv2C03/HC7VJxs8OeHyaXpJKUpSw2a5jo2pgU6jbjfZGNfSGGYphCs9Lo4o6yUmWqpClJG4xjVtI4RZ1naXlW8hIIkNJJFVlzrJ0JCLh6Npj4raYPjp5K12n499u3H0rCoj6+obUHANEfQ6yatulJyr0UFEJFZZGJMFre8yYMYOZM2dy+eWX8/GPf5wZM2a8oT/vzUgUDST/vV7GWknU2rVr+cMf/lAZDbmrPPvssxxyyCEAvPjii0Oee9P3vjYYRprbbruNpUuXjnm3dZlFB12N37cGALd+Lh2v3EfPnbfwFmcDvQ06CtteH/Dz/JnUbs5g9XpkW7UY1iW6+E7uQzRO/inJ2ONWtCTjiz75dJEu6THZagfgpdIMtpSa6cu2oEppZEl/undyGWwvge072L6DFbfbTORdLF8iI4EIh7bdVJZg3eJ+Chmdwb3PbU2601c+r8XZiRsohAHCSRF5eZSfq/zOkZ/DBkQyM8S9LaSO7eqYch9R+MY3evnYxz7G008/zY9//GNmz57NRz/60cq0KQMoZQ0/0WuMua+PP/54li9f/rpF+a9//esI70hj/tcZ9kieeOIJLrvssjfN7OSqBe/DW/kwAH/96wdYfMsX4YCfMtPawE2zdMesXlWNf8cFuL6gMNnl3Weeo88vLuRSbicRKqZ0aWvmlYaQLUkbR4VUMSBqU9wNbCk14yaylCKJiucpi0jilBKxINvIQVaRjAR2USB9hYgG8qRfXriFxEH/R7Gkrd3+A84k80TnwMCJSpZ1EpmqQfkFomI3YaFb/0zLRUgXi7gM6tV1yiNUn7yzzJ8/n29961uVvujnnntuxRIy7HmceeaZfOYzn+GZZ57hgAMO2CrR621ve9uo7MuI8h7CMYdeiwo9Hnzqq6O9lTHBIYccwi9+8Qs+//nPj/ZWdoqe+76OfY2einPyh55l5dfb+JfqpeRUkpXBFAAe3HQS6eZONs3sJUzkOFdpC/WcxD1QAieETbXaqm7Igy9DqkRIUnWyxh6YllVlZ3GkFrxSnFHr+0mcUpJEPoVbspHhgChHUqEsgSiBLJYnPCkKc5axT2oNj/Xp/WWe6h4ylpHBk5+kjXBSCLcKldukL+H3x25qD+kMxOPK/a7Lgyh2J7Ztc/755/PKK69w/fXXc++99/LJT37yTdEr/Q1FCe0eGRZjy1L+xCc+AcA111yz1XM7k+j1RmFEeQ/g0OYTycw5myC7ebS3MmZ417vexb//+79zxRVXcPXVV4/pBJ6F087h0bW3Vjq65X76G+Y0/h+bwiZeKMym/4m3AyDrushPf47GzAYOrl7OId06K7l1vYtbdMjVKMoNubY0BKR9xdqqJEkVkENbxL1hhozdRz5ME7kWYaitg8BP4hfShE4KGbusB1OOJ8vY6FaOJOht5tHCScx4SPdmx2+P5yN74Jd0Fy/Q4hzF85GlWxnFGHlZAk+XTdnVLVAuiXpV7HlnRjWONFOmTOHaa6/lzjvv5Mtf/jKnnXZaZY7v3oiKJGq4MeUx5r5+da/rscLYvVMZDMNASslnPvMZMpkMS5YsGe3tGN6kvOMd7+DKK69k6dKlXHXVVeTz+dHe0ugQWcM/xpgoj1WMpbwH8EbUb+4pfOQjH+Eb3/gGBx98MM3NzaO9nW3y6NpbOeUbPvYP7wSgbeYzdPXO44X8EUx6agGlqa8AEDW0MaF2DUdXP8pFG59h7l3abA03dWGNS5HckCU8WcefrcDh5bkBdYFHKKBT6ZZetghJSI9Q2XjSw3XiOcuJHH4qT5CrJnDCiqVs+RIRUhkJWWkekvc54E+HIjv6odSpnwtDKBVQXgn8IqqYjdfjeHE0yJ0do8JSJcFNuoNc2Ls5nrw96uvrufLKK/njH//IVVddxde+9jXTaORNynXXXccFF1xAMpnkuuuu2+65l1xyyW7a1VCMKBv2aMaNG8dZZ53FN7/5TebOncv5558/JrNq/fv/QMfigRK41rvOwPIFm2d241X3AJBM9jI5sYH39z7NnKXQ+X+6U5Z0JJYrSU/PkNikXcZ2zubYvM3fD/IJhaBF6OSqF9QM8mGaUuRiiRDHKsdvA5TtE9oB0VZDKQRyyFxlEI6FyG6jVCkMKoIcFbV7XXn5ShLX4OxroFLy5HU/j109Kd6LO6KDKEaKM888k2KxyPe+9z0uv3wvGwij5PBjymPAUv7BD37A+9//fpLJJD/4wQ9e8zwhhBFlg+GN4phjjuHII4/kxhtv5Ac/+MGYTf6atlyLUq6pmTVn3EXrXWcQWSHYWtASdpFGq5vGfon/UBtrX9JW7tSZKbIdJZwaF/lSD6BTasJTWmjKK1bUJWlQ/QDMdVdzX+9bKj/TD+P+0yIksjwiKySyFJHUWdYWxKVQSs9UDgd1qZZyoNVmGcuuZF2XhTgq9hB52UpG9eABE4M7dpXrkaVbg7ASu6VGeVd597vfzZe//GUefvhhjjhiL+ojoGIX9LAY/WjpmjVrtvn9WGL03yWDYTdg2zYXXnghbW1tfOMb32DZsmWjvSVA1ycDFNcvxe4PsPsDqjfbtN51BgAyHHojHC+1q7jQluPJ9SFPrg/51X1ZSsUIr7uEtymPtylP2OtRtSkg5QmSUUCfSNMn0jyWP5C8X0XOqyHn1eAHSfwgbvJh+wRuCfUqS1lE6HIoP6ocRErXJAMqU4XKxK7neMgETnKgn7XloEKfsNhJWOwkyLYRZNsIC1uIvD6Wtd/LsvZ7KwMnIq+PsLBFH17vG/G2D4uLLrqI3/3ud6O9jd1LJPe4mHKx+Nr17xs3btyNOxmKEWXDXoOUkm9961uk02leeOGF0d6O4U3KpEmTSKVSW3VxMry5OOSQQ3jqqae2Wv/d7343qq15jfvasFfhui7jx4/H9/0dn7wbKNeVC8uFNTpGXDhmGtUvFvEnVGH7dqXr1rT0yywMVtOZiZhYn8CLDdpNJUlfX8jEGherWpcVhVkf21c4oSAZhayKJlV+phQRhVIGFQ768w8dCByUDAmcgYQsMahUU/gRhPEPjQa+F/1xnNiyUFGISFZVungNRoUeQXb9aw6YeDMlLE6bNo0VK1YwZ86c0d7K7iGS+hgOY8xSPvbYYzniiCO4+uqr+cIXvkAul+Piiy/mN7/5DV//+tdHbV9GlA17HfPnz+c3v/nNaG9jCInm+firHgWgqphHTJyCVVIkCi5uv86c7vCa+HPVgZxctZxD3tnKPnfr1pluV8SqLXDIAY2Q1O5ux7Wgx6OuN01drU8QzzxOyBJ1iS48P00QT4YCEMqCSCIDB9vfxm0hUuAHA6IMiCjSa2U3dhgONA+ByihGId3K92V39ZudZDK5XffnnoaIhj+6UYwxUb7++us5/fTT+ehHP8pdd93Fxo0bqa6u5tFHH2X//fcftX0ZUTbsdey7775ks1k2b95MS0vLaG8HgML6h0i0HAigS4nyeWQ+TarHZc6Z/wPAUYknecLblz7bJdcYcuxV2sX2ls15hGsRzK3Fy+gbX65Gsb7FZ11NxEqnhSCOTdsixBIBluUTWD5WLMrlcY1OKYFTciolUbrn9UByl/BjK9qKraYwRHlxlnS5HCoKUGFQSfQScUxZWC7CSrxB7+DuZebMmab+fQ/g1FNP5ayzzuKGG27Atm3++Mc/jqogg4kpG/ZSzjjjDL71rW9tM6a0uzlswun4vauICt366G9H9ffQOS/kxUWPc7H4AxeLP3B4cR22CLkrPJLfHJxk4xH6YFYtUWuG3gnw7DyPZ+d5bBzn09Bv0WmnWKdaSAqPpPDIhWlCZRPFyTe2l8D2EqT7aqnpqKe2o5Z0bwI3L3HzEukzkHU9yEquWM2D+wWHgS6F6ttClOsiynUS5ToHyqFCb48R5SOOOALP83jggQdGeyu7BaGsYR/Db9M5sqxatYojjzySu+66i3vuuYfLLruMt73tbVx22WWjGt4ylrJhr+TYY49l5syZfP/73+eggw4a1b08vvFPnHTW38i9cBcAMlVP/2mn0PiXLcx/+8+ZuUVbqs812bR7TbQ9dTYPTnkBZl8LwBnk8VzFiy1BZUqUL+GfDRbL1Bx6ogyBGrCUs5FLECQgdLACLaqJfILq7lRl8IT0y7FjpQW55COiCG+qdqV3Tu1nwl9L4DiIeI6s8j09prHYR1TsQXk61hx5OZ15HWdb7ykcdthhrFixgsWLF4/2Vt54IjECMeWR2cpIcdBBB3H66adzzz33UFdXx0knncRpp53GBz/4QZYsWcKTTz45KvsaWx9dDIbdyJQpU3AcZ+9tnWgYFsuXL+foo48e7W3sFoSyKnHl132MMbm5/vrr+dWvfkVdXV1l7aijjuLJJ58c1elgxlI27NXMnTuXr33ta5x++umjeoNdcvuxLJis2/6lZ51Cz7huaresZ6a1gWfGaWv234vvRd5xMd4Jd9OYbufe4DAAkrOWMr5UZFMiiS+0RdwhalgezCaILHqjGsLYUu7x6ygFKVASK1eH7ZctZRcnL7Dyoc6y9ge1xAyjSlJXoVavV3enwc/qJK+4eYhIphDpOkSuC+XlKiMag1wbQXY9j2/80xv8Lu5e5syZwwMPPMABBxww2lsxvA4+8IEPbHM9k8lw00037ebdDGBE2bBXc/755/Pcc89x2223sWTJEs466yxs28Z13dc9/Pz18tj62wE4ct4lTPzyKp69+WBWd5wDL+rOUfnxL3Pt+05gYimPEynWWtpt/D/+KXSHdYQ5m8mJDQCsLrTiSg9XliiEA2MHS0GKbKEB+htI5DKke6sBSGZtLcj9RUTJG5JFjWXpx75P7SNxxnGmemAilNSCL1wXkUxj1U3UzUJiUVZhaY8TZICzzz6byy67bLS3sVsQoYUIh9nRKxpb2ddlVqxYwbp16/C8gbauQgjOPPPMUdmPEWXDXs9+++3Hfvvtxz/+8Q9+//vfI4Sgo6OD2tpaFi9ezAknnLBb9xMWtuC+8wJm/0Ow8sR+Js//AwB/b/t/1D8kCVyH1a0eXQndgvJQ9zmeE7NYW5zK071xBreyECIkYWsRLbfT9Pw0Kl+Lm8+Q7quhulvXEju9IbIri+rvj2PDsSg7bkV0K2sAPb36se9V5iYrXydxiWQVVk0LbjnBKypx2ITT9zhhtm0bIcam0Iw0MpLIYcaUx1pJ1OrVq3nnO9/JM888gxACpXTQu/xvauYpGwyjzNFHH11xYefzef7+97/z+9//freI8sJp55CefiKga3mffe+9HPDTo7l8ynd5++oeACY85kHOR41LMaHaIYynRDzbqD/h+5GL52mrOAoSCBkR2EmEiHRiF6C8FLKUJtVfS3VXFckufSOSHX2org5UMYfyBupvhZtEOIlKP+uyAOMMZFGr+Oalcu0Dr0lWY9WMB6B85p4ozLW1tfztb3/j2GOP3eq5KIq45ZZbeP7554miiAULFvCe97xnTM/2fi1EJPe4OuVPfepTtLa2ct9999Ha2sqjjz5KZ2cnn/vc5/jud787avsyomwwbIN0Os3kyZOprq7eLT+v7ujP0X/GQQBU5c/Dan+Rc646ncVri0z5i05EU90lxBS9H89VtFfpm1xJuWz0WujqnobVNw4AO7SQkSRI6dfKYKB0KdlfS01nLVUdFrIjHgbR1UHUs5mo0DdkxKJwUohkBuHG/bGlvmXITApVLAxY0TGq2A9+EZykPgDhpnEb90GF3h4nzB//+Mf51re+xaJFi7aaPvbLX/6Sjo4OrrjiCmzb5qabbuLzn/88Qghc12X27Nm8+93v3m3/xwxDWbp0KX/5y19oampCSomUkkWLFnHttddyySWXjFr2tRFlg+E1mDlz5qi5sAxvDlpaWpg1axa//vWvef/7319Zb2tr44knnuCb3/xmZfbyv/7rv1ae37x5M0uWLOGKK65g+vTpfOADH6ChoWG3739n0ZbyntVmMwxDMpkMAE1NTbS1tTF37lymTZs2qr3xjSgbDK+B67q7RZRP/sBThH3tvLKfHnAwZda9hF37csYKj7rb2mlftgWAqvEpnE153AObcD2bprx2Pe/nvEJP+mk2paYit0zRey+ksQIbeusJ7QCnpJ3Itm9T3Z2marPQLuvNbQCEnetRfoGo0IuKBhJepFNVKWSxZuxDqVXXKSeebwe/BJaNiFt4KtDXyHUikjVIV7vShZNCeHncep04d3DjIgCe7HzwjXg7dzvnnXceX/rSl5g7dy6HHXYYnufxox/9iPe+970VQX41LS0tnHvuubzvfe/jrrvu4utf/zozZ87kggsuGJPzvqWykMN1X4+xkqj999+f5cuX09rayuGHH863v/1tXNflpz/9KTNmzBi1fY29f32DYYywu2J/6tADcda0EFbp+a4L0sv5aPoOGn7VyYZ/bKZtg07oqt5UYvbx4/EnpZAROPHnhUa/xAGJ1WxufJiH4uEVbJ5Oqr+GVH+adG8SN69/FzsfITv6BxK64tiw1TgZ5ZeQuR7CXCfKi13moY/yCwBsXCTpmL4cgOT+Dcy8oaSzssvu7igAaRP5OSj2EDlxX203rbt6WQ7CcnFrd29W+xtNMpnkC1/4At/97ne54447yOfzLFiwgMMPP3yHr5VS8ra3vY0zzjiDm266iSuvvJKvfe1rb8q485uNr3zlK+RyusHNNddcwxlnnMExxxxDY2Mjv/71r0dtX0aUDYZR5Mh5l1D79qvx50ygadzdADSKXg7bkkVUO2za6NEbW8R1tYLICyk0CupfDnhojr5xb3Kq6RIZWqwOJja8BEBbZGEFM0jkU8hIYGe1gssuXVsskklIJgc2EkXgeYhkFaKqDpXr0cteHqIAa9IcsvU9RCkdgz5hnxtZ8+8fRFiDbiFxvFlIV5dDeVn9uOgiLBcVDljgoC3mPcVabmlp4Tvf+Q7r16+nurp6SEOKnUFKycc+9jG+853vcM8993Dqqae+MRt9veyB7utTTjml8v2sWbN4/vnn6erqor6+flSz6o0oGwyjSM1bPk340nJefO8ETs08BsBve0/n8y/9F6UX2iiUIuqr9c0wU+cgUzaRhAeOi8hb2jXaLarZrOrpiTLUO7o2OFe3nl6lb6Ruycbt1+eKpIsoepWGHxWiCCGlrkl2XJ1xDZDrBb+IyvXT2DYbK242clfxo6hv97D/ZelB1xiUIGa5A3XKURdCJlBRiSDbRljYMrJv4hhi8uTJw75GKpXa8Um7GRlayGHWKYsxWqc8mLEQ1zeibDCMEkftdyk1b7uCwiO/gAmH8UCvLsc6q+5PJDd6bHq+ly1ZxbEHaTewm3GQrkUqC8+n6+gTWhCLOHSE9fSGmcq1691O/BqXXKDF2Cnq8qTqvANFD3y/MnJxCJYFoVVxa4tktY4V93fQ8PdG5FFNABQyvRw39+e0FU+vjGVUoT9EmMtEXpbIayPy+vaY0Y1vBPl8nra2NhYtWjTaW9kKGYk9pk75vPPO26nzbr755jd4J9vGiLLBYDCMAW688UYWLlw4JhO99iR+9rOfMW3aNA4++OBKw5CxhPnXNxhGiYee0w0Kjt7vKwTe/UyufwaA0/pfQuQC3JTF5HpJzbw6AOzpNQCENjSGOX6UHejdWwxSWCIk7eg4riVCknaBfCJHobaLvqa4nWZvLU5WonpzqFxcUww6i3pwk5By60zLAjeJigKCl5+lZqpupJKrHc/yu/+Nrq+2MecrunxEhT7Ky6P8HJGXI/L7Ad0MJSxsQYUlonCgMYlhgHvuuYfu7m4+9alPjfZWtslIlEQJxoalfOGFF3LrrbeyZs0aPvKRj3DuueeOCbd1GSPKBsMocfwNsHlmNz0Hr+PaKdfwod9qQaXPA9ei4dw5rDgF/s/VN8PWHkXCF3RWe2y2GyutMxvvORsnU6Jz0nr668pdtQoIEYKfBBlRqNbX7m9JUutXY4URKtdP0L4SgDDXjkzWI6sakW4aUVWn9yJtkDYibgSiHn4IgAkchcj7jHdm4nu6yYIKfVTkaXe130/k9cfrpcrvvKckdo00K1euZPHixWM261pGw48pj5Xe1z/+8Y/5/ve/z+23387NN9/M5Zdfzumnn87555/PySefPOqtU40oGwy7keOO+wVuq57u9M/Lssyd9jemyzwfuKtA7x2rASj1+0hLUD21mmPW1xHN1LHiVQcL/jBpHCvCVp7oO5D+3kkATOm2yWyUNK6bS75+JgCFTIl8TT+RFaJkVLmhllIe+SabNDVYTMGOLWVvxQtkV/0Bu3oybv1cnPpWAKya8YhktbagQ7tiWatHHyUq9hP0rINwYCC8jh/niLxsRYxVWEKFJRNL3g4NDQ1s3rx5tLfxmuxpva8TiQTnnHMO55xzDmvXruVnP/sZF110EUEQ8Nxzz41qlzUjygbDbuLgxkU0v/0/KR41D4DMuF8zP/k8i8J/wopuir1a3LZsKlHX4JDo9XB6S1h5ndDV2JlgYe1mctVJgiqLlyxdS7ziJJdpTx5Col9Q1aHFN91dRSNVRA546ZBIDsTORCgIEwKZSSPq4ractZNZ9tSAaB4x++MAJCcuxK6bqlttWjYq0reMsGeDdlV72lUNxNZxtpLQFRS01W6s4x0zb9487r777tHexl6JlLIykGIsdPAbm74Sg2GMIKUcMtJtOKTGHwHpDB1Te+iY2kPCLrAhGM8hG0PkxCpa3jGdlndMZ95pE5l44iQyx03GmlgFuQByAbUbI/Z92eG47FqmWxtJyBIJWUKk+tkwezUb5rWxZWaWLTOzFGtDIgfsvCLdIalutypHsldg5yOdhR2GEIYI6bJg8lmVvfq9q/TRvYoo16nLosIAa+JMrIkzB2Vce0OOyOsjyK7nsfW382Tng0aQd5Lx48eTz+dHexuviQzlsI9dtZSvvfZaFixYQCaTobm5mXe84x1btb8sFotcfPHFNDY2Ul1dzbve9a6d8jiUSiVuvfVWTjrpJObMmcMzzzzDj370I9atWzfqvciNKBsM28GyLAqFwmhvw7CHI6UcE1baaxKJSrLX6z52UZTvv/9+Lr74Yh5++GGWLFmC7/ucfPLJlS5cAJ/5zGf44x//yG233cb9999PW1sbZ5111nauChdddBETJkzgm9/8JmeccQavvPIKt912G6eddtqYiOkb97XBsB1s26ZQKFBbWzvsa1nJRu750WRaH34ZgLSy6L/u36g75a/gSqI67abefHw1r4wLqCkIUiXJhFfiTOgIQFFbgLmpNjoSOkmrp7aezX65t/XANKiUkyQhbeySQPrxrFg/QuY9RL6I6ukm2rKucn6i6UAWTjuHyOtDujrTW4UekZ9DeHlE6BO8/OyQ30mFfiXLGvQsaBM73nXuvfde5s2bN9rbGFO82p3/s5/9jObmZpYtW8bixYvp7e3lpptu4pe//CXHH388ALfccgv77LMPDz/8MEccccQ2r/uTn/yEqVOnMmPGDO6//37uv//+bZ53++23j+wvtJMYUTYYtoNt2yPmVkxM1glezFwGwIRkG2JNGyISlCYmeXmWjilf03w0nWE9MzPrOIJ/Yk3Wgnp4m0ckIZSQCT2a3B5Alz8Jp4SyQvyETq6yfAcv4SNCgbIsnLyI19GCHM9OLo9XlFWNuJaDdKsIi12VlpjSyUC5//WgxiCVTOtBWdZhYQuh1zsi79XeRBAELFu2jCuvvHK0t/KayFAgw+ElagmlJzP19fUNWU8kEiQSidd41QC9vfr/Vrl8admyZfi+z4knnlg5Z968eUydOpWlS5e+pih/8IMfHPUM6+1hRNlg2A6WZVEqlXZ84g44/pTfUVy/lCN/eRyHvPsJALIXfZi7T3g7nV9dSc4SzD1WZ1PfuHApGycFPNMiWW5P5ZHSfAB+P76PY5zlpFWJEElW6XaMriyhIolTSJPM6niYU3Kxg61LWIQf6TgyQDggsrKqAVI1erpT/0aUN+AixHK0MMdDJfRrfZSXq8SRQdcjmxjyrnPnnXfS2tq6y/2ydydSjUz29WOPPbaV1+mrX/0qV1111XZfG0URn/70pzn66KPZf//9Adi0aROu6271vrW0tLBp06bXvNbPfvaz17P93YYRZYNhOziOQ7E4/IYX7oKTyT7/W9Lv/AGB0mJ5+H9+hdWLnuN/HvM5eiIc2dwFQHVTkrrqBAcHEXXjVhOk9Pn/b/M5/L1wGulMO80pnegF0OfXQamKdF8tjRvqABCDwpMyGnBfA7qVJqD8IlFhwGqRbhqZqkFYDmHfwE1tcBvNsn0RlRuEeP0VUTZu610nCAL+8Y9/cPXVV4/2VraLbrM5TOtSCRYsWMCSJUuGLO+MlXzxxRfz7LPP8uCDe/6HPiPKBsN2GAlRPvHtS5BV9az/x0f4l+Qv+X3n6QDctvxeuusc5tV57DM3jV07MHu3pkPgFi360yHTUzqbNJ3oY8qvzkD2FgmaUrTN6wCgv6mddC5DVV+aZIfuZy178ygpIeminFdZOHEyi/IKBJ06Lh3kNpHe521E/e0gbWRqqDWjQn/AWgYIfcJiJ2FhC49v/NOw3p+9mSeffJLm5mZqampGeyu7Bcuydvl3/eQnP8ldd93FAw88MGTgx/jx4/E8j56eniHW8ubNmxk/fvxIbXm3M/qpZgbDGMZxnBFxXxsM2+Lhhx/mkEMOGe1t7JARKYnaRUtbKcUnP/lJ7rjjDv7yl7/Q2to65PlDDz0Ux3G47777KmsvvPAC69at48gjjxyR33s0MJaywbAdhiPKxxx6LQDpw95L7qjpLGi+iieK+/HIph/oa28q0vyOVk4dt1G/IIxdzF6EVVLItCDlSaoinXTlSA/Z78H69cjlG5j4J+02tsfPhskNiMhDdWm3c7DhRYTlIDJNyMZmVLWeNIUl9XQoaSHcFDJZD0DVnOPpevssMh1Jwv/6LnaNtkiEm9atNgd17YI4K3uQ69rw+li7di3nn3/+aG9jh4hQIEYg0WtXuPjii/nlL3/J73//ezKZTCVOXFtbSyqVora2lvPPP5/PfvazNDQ0UFNTw7/+679y5JFHvmaS15sBI8oGw3YYjvu60KbjX3+/8XKmbPwThdIkLqr+FVU9+uYWNriI0CHVWkPuxR6ssvvaCwkTgmxNRHsmok/qDOlGt4OVJ7Qz9dnZuOsnYmXjTlpb2oiWP0RU7MPb/Gz8s/9eifEec+i1JGccA4BsmIgCiHTQedNXTgUg051hw/5P0fLC/jz16KUctd+l+vevmYpM1qPiDwZlcQ4L3aZ15jDJZrMIIUin0zs+eZQRIxBT3lVRvuGGGwA49thjh6zfcsstfPjDHwbgBz/4AVJK3vWud1EqlTjllFO4/vrrh7XP0caIssGwHVzXfd0dvUq3/BKAyctXcOC8x7mycBcLbypRerYTADG+CjmxCubUUTOzFuLYr7IEoQt91SGb0zYloROtmu0OVs94ipXJPC1N06hfp1tkWlIiklXIXC8yqeN1du1kDp/xYR5Z/TP+vuzySreu1MSjsKpasJtaUV6Bnklr9Hr/fshSmlK6AFRVJlgdOe8SrGQjwtIfGMqlUkGujbCw5XW9LwbNAw88wPTp00d7G2OWnRmrmEwm+fGPf8yPf/zj3bCj3YMRZYNhO7weUV4w+SxSE4+i45dHATCx5RlsETJtk8PzP32ati26FKl1isvkI8YRvm0KgQuFan0TKiYjujMekze6tFR7hOkeAJqsHiZl1rB2XIqOwEFGuoSqRjZib9TCLeO6YyeZQTpVHLvoJ5Q6VlTczP2r7uTJzgdZvPC7CMuledVHAGh+IMu4Z+ZwzxVDM2GLW57AqZ6CsPR6echE5PWZBK9h8sADD/DRj350tLexU4ho+O5rxtBAirGMEWWDYTskEokx3ZPY8ObkscceI5lMMmvWrNHeyk4xEiVRu+q+3lsxomwwbIdEIrHLlnLtoReiQp+mcbp5flexiS+k/puGDfDUpoCegr47zZTgdRUp1cD6CR5hXAtRsqHfFazcN6LdqmMNuryjJ8yQkCVSqW4K1T30jNOxSDfXTLpUjYwiiPsCC8sCpiNyXSSdKuyqCQAEuY0c3LgIt3YWQXY98oO/ASD64v3I9e3AhCG/y5OdD3Jw4yKkpS1wE0MeGX73u99xwQUXjPY2DGMQI8oGw3Z4PaJ87+9PYtIzzyDzWnyjSFITBAQph1O+fRB0x9ncoSKcmSEIoCsNva5u6pGTDjnpEiLpEhmKSsdz24Mmevx6knaBYu1m8r4Wyt6WFJAh5dQhe/Wa6LJ0vWMUoPxCJZtaulUIy8XvXb0NgZ3AtjBdukaWLVt0LH7GjBmjvJOdR4RDG9K8rmsYS3mnMKJsMGyHRCJBEAQ7PnEQi29RiEMsZn9A94TuvaOXHsdhxTyP8c0u+ZT+s+usjuhKKqZ3+1gKZJzYUpQ260UTL4cT6A1rKh3AcmGafq+WSGmTOnJ0Vnh/g04cC50qUgndetNxbISlhVl5BaJ4b8LPVZK2BlNOCjO88TzyyCNvugSvkeroZdgxRpQNhu2QTCbxfX/HJw4i0QvrD9yXk87YB4BNfXfzuXXXUzX+n7x/1m9ZFWqrtVoUmCNfoX9cL+NKHlHcJN/DpqhcilGC3jDDpuJEAHr7JkBkQehA4CBjsQ4SRfI1fUQyInS1SzvluCQSTYjqKqx0BhnXL4dOCpmsw6mZyuKF3yXI6XUjyLsP3/dJJpOjvY1dQkQjYClHOz7HYETZYNguyWRylyzltxx5HfcvvYS3XtqB7NJuyn3+aiOrfDbeUGCC6ubAaD0Ay53JJJTPWquJLekStZFOKKtSJabILdh2SIjNFtGiLx46JNunIKPyKMeBhnwytIiskFIqnu4USsDGcdJYSRdZp9tmyvxUlOeB76HyfahiFtADM0obl/GPp7/x+t8sw05RX1/Pyy+/PNrbMIxRTJtNg2E7pFKpXbaUDYbtsXDhQjZu3Dja29glRDT8AxNT3imMpWwwbIddtZTvX3oJR867hOSEBfgffj8AQXIWtWsEXesdvjGunuMbda/e1YWp7JtYyVpvIge6L7BR6Dmxc1QbaVWiWfQQ2Osg7pD5nLLo9ZJUdUwkkU/jlHRsWIYCGUlkJLA8bUVb/kAjkihtI6WONZNwwQ8QfoCoqkb16xm1wkkiLIdFB11NcfOjpgb5DSSdTqOUoru7m/r6+tHezk4hQoUIh6eqYieagRiMKBsM2yWVShGGWwfTFh10NekD3s6W//3kkOzko/a7lLCwhd5PvYvuo+8AoNg9mXm9h7P/b+fw9Ic76Y10161mu4NV/lQ6vCZsEVIltfu6we6jKcpSp/IkhE/SibO/q+GZyKLf9in1NVLd3QhAVW81TtHG8iWWr+PSlq/HNYpI30yVpddFJKF8OA7EiWF4JWRVA079DFRU4tBmPTjelEC9MSxevJgbb7yRL37xi6O9lZ2iYu0OB6PJO4URZYNhO7yWKFctOh/V3zNEkI9d9BPcpn1wPvMdXp68Ei/bBEDT6nm8su8GJn/gl3ws9TgJtDs8QNKpamm0unnFm0QQzzlut+qpEiWqVImU8hkntDU7015HsdplReRQSmbpSesuXV5qPPWbxiEjB1XOkPX1TVQWQ/AH7d8PEFEEYQRhGNczA24Comrs+ikASCcDwBGzP87DL904Qu+mocyZZ57JCy+8wNVXX83nPvc5qqurR3tLhjGCEWWDYTu4rjtElBcdpIfRp2afQM/S7wO3V56TVeNwjns73XUeiVwGP62FLXvovUyrXs385PNMU+1k4laVeemSlD5YkLPTtNh6PnIRl3ZRS63Ik1A+YZz6USUKTHI2E9TYrM1PJ2/H17F9QtunoW086bhOWVmCyBEIXyB8EPl4qEakxbjyfRnHRYQBVNVhWzbC0m07rermSpZ2uR+2YWS49NJL+dOf/sSVV17Jl7/8ZRobG0d7S6/JyLivR2gzezhGlA2G7SDl0FzIB5/66qBHiwCdcQ2QWvwBlr9nKZlXZvOht1zE29p1lrXjg90hSJYk+VREZ7UWw65kiOda5ESSHquPZbkDAbBEwJREG1UyR53sx1ZaRAMsbEJa7C2QhjapS6V6lKQUWfQGDjLSQypklAAEIiGR2BDp+LPIFyFOXFPb8AAgbYSTRFbFApEDIV2E5Rqr+Q3g9NNPp7Gxka9//etceumlTJw4cbS3tG2UgmiYqmpiyjuFyb42GAyGUeSII47g/e9/P//xH/9BFJli3r0dYykbDMMkub+eSbzyzDxVbTM44Kj/4Av3bMRarRO3so9uRroW6cUTUWmbhtnaNbxiuk+V7ZGwfGxCMraOEff49bxcnEpPqQFLhEytehmAWqufhNAu66QokbL09XsAEjmKmR5yhbijV9FGhBbSEihHIpz4T11KbSH7r2odGoWoou6brfwiKhwoA1ORR+TlKpOmDCPPoYceyiOPPMLll1/O2WefzYIFC0Z7S0MZAfe1SfTaOYwoGwzD4NhFP8H/xMcAULKN5L5/5af//BtWtwfxTWz5P7o5YGEtFAKiBhfP1evVJZhCET/dxQYxrpJ93S9q2JKbSBAkUF6KFQVdKlVfvYkap4eELBEqm56SdjEHBZ3NHTlFilW6GUghk8LNpwc2Kl/V4tCJW23m+ytLKgxQxX5U6KOKWoDDXDtBbhN+7xoeXXvrSL1thm1w0UUX8dJLL3HzzTfz1FNP8bGPfWy0tzRANBLu65HZyp6OEWWDYRg4E/bnxUOWAyCUxf/lfoxbtClOTuLP0klXBzxcS+hFdN29jvrDW2h+OM6+/lAzszss6qfn6Wl+hQ5ZB4ArSySdHLnQRgEqr7tx9fS20OUUwSmCnyTRo+PHVqKIsuM4sdTuTz/hESST2CVryH6VYyNcF+V5iEwGMjoZDd9HhCFEAVFfH1GxB4Cw0I3fu8ZYybuJ2bNn8/Wvf51vfOMb3HzzzZx33nmjvSVNGGfsD4fhivpegokpGwzb4C1HXldJ4Noecp+DaV41l+ZVc4mqO5n0is3qVo8lC4NKOXDVzFo61xfIdXk88z9rCLM+YdZn/BpJ4MLGDHSRoaQSlFQCAMfykLHAli8kAge3r4Hk5mlUtU/G9hLYXgKnkMYupHFyGdxCGreQRkaSSCpU+S98sKVTTl7zfZRjoxwbkglEMo3yCkN+PxWVUGGJ0OsdibfVsBNIKfniF7/IunXr+N73vsfq1atHe0uG3YixlA2GbXD/0kt27sR0gpbHdJx331P/l24/or7f4p8NFqleLYLW+DT1LQkSdS5VDT5hQXcIS63qR9TXcNAamxX79lOMBbm9MIFCsRYV2hBZyHhEowgtZOBgBQ4ytJBx72vbdwgcHxlaJOKYciqbHDrVp+y+tiRYFsKyUL3dUIxLpVwX4prlKLeFILsZgCDbRuT1mfGNuxnbtrnyyiu5/fbbufnmmxFCcOmll1JbWzs6G4qioSV0rweTfb1TGFE2GAyGMYiUkrPPPpuzzz6bJUuWcNVVV7F48WIAJk6cyIEHHrj7NhONgPvaiPJOYUTZYHidLJx2DpnLbsXp6AFgTWkqf5y5nDu9Y7msdCcqnlusCgH1h7egCgGJQoA9Tluz0fg0jgc9DRENUZZpzgZ9HTFTW8mlKqSfRPr6OraXwAocbN9BRBIZDsSLLV9ndOvpUBoRCkJHIbc1T8OywHFR8UhHZdmITD1W42SiXCd+28MAhIUtptXmGOCkk05i/vz53H777SQSCV566SVuvfVWDjjggN3y80WkdCe44VxDKcDMVN4RRpQNhh0gpcTzPFzXHbL+6NpbOflDz6Jc7Xa2RciN/e8lVBYHbHBIvKyzqYsLG8nXQHWnQgYg2rXLuNA88Oc3pVDATulmHmknSz/NIEPUoCG2MrKwfWdAgGP39WAhHoyyFKVkSOhYpOMBFcKXUC6P8jxwtGs83LIa6RWRVbXYzbNwOloBM2d5LNHS0sKFF15Yeex5Ht/61rdGcUeGNwIjygbDDrAsi0KhsJUoAwg3wTOf0WVF9Q9+mNDx+eX800nmXXr31yVJmbYAtweCKovsOEGuVYt4PhWQTUbkHShZggOETuh5iAVQ0qOhhLKI4oEUkZeo/FwZDRViy7eGrJUHVERSuwzDZBx/LkmdgQ2IQXFkpE3YswH8IjhJ3Jb9ATi4cZGJJ49RXNflU5/6FFdeeeUb/8NGIvvaWMo7hRFlg2EH2LZNoVDYKslm8cLvsuXaD6OS2gX8npM/xpW3rKTjh6sphIrA0zexoi2pnZkhObceObeWctFDMRGRTUBbyqXO92kX+vqhshDpXqz2aViBg5LaWlYyInB87bqO5FYWsowETlH/SdvFeJSjbw/JwlaORBTjm2s6hZC6rMoGov5Owr7NEPqV5iHpSYtH7o00vHkxiV67DSPKBsMOsG2bYjlLOeatl/eSXvRBpi66kX9JPAnAWe3raLv1Ja7/awlbKE6Yqs9tnZ5Euha4EhEpojgTOrAUvoRumeahxFR6Ql0z3F+qR3RPIJGvpmFTE7ka7Qb3E6Vt7k/EFnIkVcUyBhAhKEuPcSyP3VOORCRdVNHTbuxE3BPbcZDxAA1v/RMEuU2V6yycdo5pHGIw7CaMKBsMO8CyrK1E2WDYq4jCgelir/saxlLeGYwoGww7wHEcCoVXNdXo70d1bWJV/xxOSiwFoL7PopgPqbEVfYEgldAWseNqS7bwVAcpoO1gnX3dXq1YlarhFcaxojSbVf2zAfC8NDUdLdRvbCCZtUn1aGu2lAkopj1Kab0XUalTtio1ycrSN74gGQESuyT02L2ypSxBJSwEro4RWvoaKpMGx0YCjlfAX7lOXye7AWElTGx5b2ck3Nemz+ZOYUTZYNgBjuNQKg11Hd/zo8mc9O6XKW6ew9P1swA4I93O3A/Pofm5Z5mbVMx/6yQA3KkZ1OQq7Oe76T2shgnt+ub0YoPieabwRHE/Xu6fTalnAgCJnnHUdNaS6rWx8xHC1zfDRK9FOl1FoTZBKT0wUML2LSxfYvmDy6EG9ioiBmLKliC0BBYg8oOSdxwLki6irhbLn0kydxgAudUdhIUtWO4oNa0wjA3CaAQsZTMBa2cwomww7ADHcbbpvramzGH/Hxf4+fU/BOBPjYfTcliGSW2H8ZGVN9Ew9XEA/qX2jxxaXE/tifUUbY+8rRWyza5nrT+J1b3zCLdMp2HjZACq+tJUb0lgZ0NEfxGR1dObBOAmEtgN1bj1qdga1ohQIEKwS9pitvJhZaqPsgQ4sUVsQeQASGzfQmRjD0BJ98VWUiIyNdiTdPZ1qthD7uU/ExTaR+z9NBgMr40RZYNhBwy2lBdMPgsA63u/xj1gHIm7H+HE63RG13NnfJem1pW4gUdU1UPXOm1t/lfuOP5zxlN8adx/8IvCGUyQOonKDkJeKU3Ez9cx6aXZ1K3XJU+yFCF8H4oeIptD9eu+0yrXiypmEZkmktNaUbXaDR4mZSy0g5ACigGi5CEAVa3PVdJCSEHkQJi2sOLGJCKbRxRLukQqihBJXc7lNM8jWeimuPmxN+CdNbxZUGGoR34O5xom+3qnMKJsMOwAx3HwPG/HJxoMeypRqI/hYER5pzCibDDsANd1KZVKnPyhZ+n+1X8AoOQmpn75b8hjzsL/w68BmDD1bXQd1UkYOrhd45n+lI41JztC/Cenc/3kk0j1J3hsxnoAik1tyEKGCatm0rDKQfbqWcj4gY7f+T6qWEDltKUc5boJ+zYRbXwat7gQa/w0AERtDao2ReQMlD4RqUoSF8VSZTKUSKYr50SOQKZjE9t3tZvcj3tyxvOWRVUdTn0rYbGTBZPP4rH1t4/4+2t4EzAS2dfKxJR3BiPKBsMOuP3/3Udi/Hzyx59FaOus5OlPT2fCkuXMtX/PTzecD8B+v0ryxDveAcCRfw0Qv7gZgPZnbyE9aTHjZp2Mte+hVLdrMY2c6YhQYbdnId9ZuempMLZKfI+or5Mo16XXi31ExW7CQjfFlX/F6dStMK36yVgTZyIa6iotNJUjQQpUwkX4ASTjPtyxTktfoSxB5MQx6HQCSh7K88Af8AqIZBVW/WRcP4cKvYr73oizwfDGYETZYHgVh004ncc3/qny+EMXvoOb/9mGjAQta3VG9VsveDsff6KDYlrxUOsTAAR/VZyQPI22eR1MvuN5Op69BQA71UxiwqHIxkmo6hRetb5uz4R+vGSJ2pZaap+3oW3jVnuRNY3IKp35rPwSVnEyYd9m/I4XKax/SJ+0HpIdC3Am7INoGK9fl8mgki5IiapOE9Rqi7gsyiLUwlxGWQLSSUSxiPIBX8fQVRginCRW1TicmqmV8xdOOwc/+4opk9pbiCLjvt5NGFE2GAZx0rsf5PGNf+Ktn9ICqYoF2FeRVCVeOmYpN0z7LADHP25R1RbQcTA8s2UBAHN6ViMlTHy+iSW3LeKUj/8cgMIRrXQ15bF9i0h6lNJa8Epp3amrUF2EeUnquuJkrLKl7Li6P3XZ9Qzg+8hxU7CbZxJ2aqu91PYk+bX3kSx2Y/dpl7lV04yoG4dIpsBxkJm41jmubLIsgfSHCjNSQjoNvocqxjfgSM9+Fk4Kq6ql0n5TWC7SrdnqA4xhDyUM9DEcTEnUTmFE2WAYROGl+zh20bOs+eEJAMhpT3P2M7ez8qnp3F37KQ74YR8AKlT0nDaOs2s/wuxTngdg89feB3TQ+Lkfs7Dli2z4pC5lUnX3gYhQfgJKaaSvJzPZpSQissjW9yCiBqKpLfpnbuyK59eG2tLN6OEUJOL4b6QQxSacBn2+1TgVf/0z5Fb/L07cHtNKNmDXTMaqiS3n9OEAOE6KIKEInfJwgNh97aNj0JZEOC7E2dcq34eKb8bCcpBuVfz7ewjLRVgJFk47J14rERTajfVsMAwDI8oGww4QCZC++ZRv2IsZgZIo477eOYwoGwwxhzafyLL2e1l8iyI185cALK79B6e1d7IkUuz3hI23QWdIlzqKtJ1XR/v6Q2h5+3wA3GKB+m//hr6rPk/HEX9mamYNAFVWnlKUoBS5FMI0PflmADwvBZEenZhv2ES2fiIA41dNoGrVoLaesYUcZOxKPbJVtHVyFiAz1bi1TdjNs/HWPQpA34u/QVpJ7OrJ2NUTScdzk+19D4Sm1Lbrmsvj+SxryEhHomCI2xpAulWo0EU6GaSrg+SRl8VKjTMtOfdEVFgJZbz+a5gPtjuDEWXDXs0p5/0TgHtu3octT3+KxbcoXjr6Uaa7nQAkRYk1rQHOkxbPHRLwwgk6c/olezw3d5zDAb/bj3uuKP8ZZXjrA2fxwvxlHFL/BNOcNgBsEVBUCbJRmpJKUEzqWHBXUE97QbfW9JJpcrYWvg22z0RrCumOePpTnCHtpxWhE8XtNAXK0j/XstKIhIuVyZBs1NdzJ8yntP5xHnj0UmCg6Um9eyU2cwnrkyhLYMVjHEUpfO2Yn7QRloOyHISj3dcSLcLaha0VXjoZVOihwpIR5j2NkYgpG1HeKYwoG/ZaypYxwKRnnkE2SV4543aSwNqufQFY2z2Pp6xnmCbW8dbO62l+dB6gh0EUp69GZPMsnPZhAKpmnIZ3/vvI1G5gmtPGtLhzl0VIKCyQUMQhp3RC1ya7kVBZhMqmaBfojwuIi0Ab0OJMwi5ahI52G/rJIE4WU0RS4sbzlMO0hXQkImkj0toittJVpOpaOOGM/yW/8u5KCdPR879EFe/BmjUXVZ2o9NXeithVKSwLLBvcFCK2lstIy4FXrQnLwalpRYUlDptwOoBJBDMYdgEjyoa9kuNP+R01+32IUz6+GgDr6peZVrOSfJhGLPkQXPwuQAtKm9yfL2R/TLJxHb0NG/QFXp7P9OX7svm/3kLVlJMAcOcczep5q5ma2kiT7KFG6exqlwBHhVhKEQpBUWjLMml59Lo1FFWCfJhGxlMkepSkCGyJLKq764isAeGMig4ykqiyexk9L1k6AhFayIQWaosMwrKwk2mqUzUc+5abKud7ax7GBeTkVl02VSaMBhLMKhe3wUlCGCDdNFHZhemmwdNzf8Sr3lvpglPbit+r3fem6cibHxUGlYS/130Nk329UxhRNuyV/OUeLbqH3KmFMwhW0rH0A0z7a4LNN5xI8+f/BsBbjrwOpyrFlCn9zPnSfKJOLcpLbp8BC2HBF5srcVbv0BnY02/j4NSzTFBdpJS2IpPKx1ERyTAiFJC3tOiNs3qZZG+mqFx6ZU1lb37SpS90KFX1A4NHNMZu4nBgGhRA6AgsXyIjgWWVJdLFsiQCkHI6iQYdr3b7DyTsaSPY+DyWV6x0BSOZrJReAQMxZQasZeWjY8wwYCGHPirUzUYGMrJ1rFlYico1jDv7TU40EjFlk+i1MxhRNhh2QBjaCGFuKIa9mMjElHcXRpQNezVNLyXiryeAFATP3DfUovviJURRxBe+sIKbvzMbmA3AWz/bTjShkcTXf8vGmbrRSG7S/Zxa/wCHRy+SCT0ioa1WqRTJeIwiUiBjiyGhfOpEP1mhY8xZS9cGJ60CebtE4BTxkhZWMDRVOpFPEcmIKLaKJRIVKYgGHMnKErrVZjqJcOzK3GSRqUE0jEf1dhD1bSHctFZfo3ECIl214zesbC2FPiryUKFH5OUGfm7o66Sw0KtYypHXt+PrGgyv4oEHHuA73/kOy5YtY+PGjdxxxx28I25jC3rq1Fe/+lX+8z//k56eHo4++mhuuOEGZs+ePXqbHgGMKBv2av78eWvowudO2uocGbt1j5j9cR5+6UYAVG2G/ot+jC1CUn4dAMdnHuMoVjCjv0gigFL81+VbICP92EdgsW2rOyl0py9XlrAsD1+GKCtExe7r0PZxSglEJLEDa5vXiMPSlVnKSImScsA17dgIx0G4LsJJEPVs1r9Pzxb9umQqft2g60e2ji1b9oD72nIQoYsiV3FfR37sbo/d+SosbXOPhjcfKvQrZXGv/yK7ZinncjkOPPBAzjvvPM4666ytnv/2t7/Nddddx3//93/T2trKFVdcwSmnnMKKFStIJpPD2+soYkTZYGDnYp41R1zIye97XD849BC8KMFHqn/D76zjATgjWMYh6ySBbWEHAtfTlms+HdFbFWIpCIWi345FFoktQsoaXVTasiyEaTwv7qglQ0BbylbgIEMLGQosb6goi1Bg+QNToqSvBoQZ9FAKqHTtwnEQmTrKUeQo1wu5ft3NiziODK/x8WHblMuhKj8ztpQHx5YNb050otfwRFntYu/sU089lVNPPXXb11KKH/7wh3zlK1/h7W9/OwA///nPaWlp4c477+Rf/uVfhrXX0UTu+BSDYc9ne4J8xOyPo5Si/9T9eenf1/LSv6/lhM+9k7aOeVzddmlc1mQxs0sx8ZmQKU8rmtoktZuhdjPUdVk09trUFARpXyGVqriwa8iRxKOoEvQFGfqCDJ19U4iChDavARFZiMjC9h2ckotbcrB8WTlkFAtyPGRC+kqPbozU1rXH+YIeDSm1MJPOQDqDSFbrJiF+OWlrUAcnaWkLWQ58hi/XJr8aFZa2OvQl3ryWi2HkCMOQvr6+IUeptOselTVr1rBp0yZOPPHEylptbS2HH344S5cuHckt73aMpWww7IDaYy+lq/P7tE/fyJH1+g8+R5Krp1/Djdn3cCm63GeffyjU893IahenNUOpaeDPS0YQScg7gmIsbl0iQ04lySrdVMSLtEWpAgf8JLKUxi4lKzHlcha2CAUyEhVXNX78OBpwWw92X+suXbLyWOVyiNi9V7aISaZRxbweRgFD3deWVcnALouxigKE5VRc1cBWQjzYQjbW8pucKBh+9jWKxx57jNra2iGrX/3qV7nqqqt26UqbNukeAC0tLUPWW1paKs+9WTGibDAYDIbtMhLua6KQBQsWsGTJkiHLiYT5wDYYI8oGw3Y4fMaHqfvofyLWuDS2NdMZ1gPwDh4iL13OyfyRU27RLt/eP6/DSlokJ1Vhu5Kkr5OmgmkunXUh7dWKTYkUK6Vuhbk+bGZz0ERJJUiIEk1uBwAbnRKqlEZEQ+PGSkaEToCftLB8ieMPjT6JMHZZg/5abgYyGMtCuC6qWBy6HoXaGi6XvZQtZcsaaCZiDbpdyK1vHcJKoMLSVtnW5XVTq/wmJvK36t62y6gIy7KoqanZ8bk7YPx4Pf1s8+bNTJgwobK+efNmDjrooGFffzQxomwwbAe3fi7+/X+AKYr2qRuwggwANSpgTXIcx/a0sfm2VQDk+nwcV9Jc62I3JhHrdalQONMlm1BsSbi0yxpWBlMAWFOaSnthAn6QJJ3oI2npIRTVqS7ynZNRtk8UDmriEUlkKPET+uaoYleyXdRxZWW9qrfWYEEOt5H56nvga1fz4AlAQ67yquScivs69MFydBb2IBd2mXL7UtBJdLD9uL3BsCu0trYyfvx47rvvvooI9/X18cgjj3DhhReO7uaGiRFlg2E7hMVO0rMPh+AhvOoeDk49B8AjciqTVSeHPObQG7e2rKpxSDW4WNUOypF0nVAHgBVA2gNPWPRRVSl98qIEfpAkDG16+ybQGw4kT0nbR5aSRI5HEMeSLd/BSw7UKZfSWpwjaeOULEQY1yYTW81SQhQhhohzqI8o3EpwyzHDV7fOrAj2TrgwtxU7NmL85mdksq93rSQqm82ycuXKyuM1a9bw1FNP0dDQwNSpU/n0pz/N1772NWbPnl0piZo4ceKQWuY3I0aUDYbtEGQ3ELz8NOFMRbqrhc1hEwBZlWIWm/Eygsb3ztInZ32K8zKI/pBirUVgx0lXEfyzwWVSscDzKYtspMudQmXh2EWUShNBZYyj8JIQSZQVIn2X0InF1wqRkSR0AsAuJ2ejLEUkFcoSlYlSYOnSikiiwmigJApil7QF0kJ5cearX6z0NhaWjYoG3RrideUXB27MUaDdmdu4UUdhcas1w5sbFXmoyBvmVXatJOrxxx/nuOOOqzz+7Gc/C8CHPvQhfvazn3HZZZeRy+W44IIL6OnpYdGiRdx9991v6hplMKJsMGyXR9feCsD5F9zLjKWNPDTlBABmjVvOPQkYP/8fHBXom4CSSZYe5PFv4j0cknyO+eplABwV0mVV8XSqgef8WbT7WthDZREpiRARwgpQUeyWlg4idBCx69qKe16Htl/pfz2YyIoIYzF2KlWOArCQxXJZ06viz5aFiiyEG2d8A4LiwOCBwS0V4xaLkZdH+fGc57iZRLlxSBnTMGQP5TU+gO0Su2gpH3vssajt9MsWQnDNNddwzTXXDG9fYwxTp2ww7AQKiYwKo70Ng8Gwh2MsZYNhOxw+48Okp51A6xwLK9tH7er9AVgJbKlppqmuB/+wFQCEQnBZ4V+ZdNO5/OHwjfx21hMALGh8mGd6DiDtZAmVRSnQWdmhsghDlzC0UaENYXkKlIv0XWxPW7EyGrCYy7XKSkZEUlSeV5ZCRYrQKVsWg+qYBxOGEEU6ThyGAxZx9CrLuHK+jiUqv6C/enqqloq8yoSocnvNco2yiSHveahoa6/Irl9k19zXeytGlA2GV3HYhNNxG/YBIHPA+7H3XUiUXcUrCzcgojkAJDfOoL9vHLf2TGbt5D8D0O434S8/mZcPa6fQ+jRHND4MQJXMUfSryBVrdaeugs7gFpGFkiEispChg13SbnCnmMYpJbB9B8u3sf2hpVEyHjxRHuGoW2wOdPbSazqWTaR045BBnkfleTrz+lWJXioM4tjyUDel8gsoLx+Lcy4+d2AYReRlAT14wriv90xU6KPk8ER5V9ts7q0YUTYYBnHYhNNJjDuQVKtOMLEOPIrlH3mcxp/3ctSM/+aQfXU26H/0ncu/1P6Rg0ob2GBXA/D7aDErp7xEorqTExr/Rmega5pXFVoJQ1sLcuggfS2+416egVuykaFulynCsuUriORALK0swuVWmmobsygqgyii8uOhva8r+P5A5nUYoOKSKELdsWmrwQOD1pQ/WIBzqKhE5GUJC1vitT6T5GUwDBMjygZDzKHNJ+LUziQx4VDsfRcCsOGogDOn/ZqaRD9z8puYXtSu2u+5N/AYk/mpOIXjhB5SUWv1M3GcdmWvKU2jxtJNNPq9oW0Fy2nTSkbIUItxJBWUhfhV/azFoPwYESpEiUFZ1lsPnwAgitfCSB9+nETmaytZeaUhM3KVXxwoeRrkvi67rMtjGsuCXRbkwdaxcV3vuWhLefdOidpbMaJsMOwE0onwSyYv0rB3okJv+O5rE1PeKYwoGwwx0q0h0bQf9snvpFCrLdHO/ZdwWvgET1jTaeiwmN6l17PJiOnuFubJJYSBXjtZPs7CzAraVR09KsPj+fkARCoW89BBFmpIdeuSKBFJIivC8fWf4WD3tQi1S9oqDbKCo4FhE7JIpVGIsgTCj4acU2mxGUaIklexlCvJXVGA8ooDDUO8wtDYcbkmNU7mKsePVaSt4sjLVlpqlttqDu7iZdizUMqr/Nu//osYUd4ZjCgb9irecuR13L/0km0+Z6XG4U6Yj+zox3F0f95zJ/2Clr6QulBhZ21q+nVAN12QWFGR3hRsSsZZ00SUcMiS4pniPHpKjQAUs01QSuNm60j115LI6+xr27cqyVo6pqz3ISOB9GO3tB8NCO6relmLuPe1KE+AGvS88IOBx74/0JUr0iMZlVcEv0hUzqb2CwO1x3FmNZTF1yfy+ytCDAwRZCPGBsPIYUTZsFdx/9JLOHr+l/jH09/Y6rlky8HIuhZUJs2a9/0WgEtLLzBli83ySGFvkdR2aGs2VweJtCBMK9w4VuYrRRGHFaXZbCk109+nG+WLQgarWIXtJRGRxIqzqXXceGiCF7C1IJe7cYVxy8zBoxgHoaQcaKkZRlAqQXkucjTQKhNfW8iRl0cVtZW7Leu4sh6W48mlIaKswpJJ7NpbCH2UMNnXuwMjyoa9isMmnE7DcVdzypU5SLqIrl4AvKfupXTRR2k75bccVnsTHxSPADBnvaS2S5IOIvz+ACenRS8tJXatIFNSlCy99rKT4Wl/Lt1+PaUgCXLbN6HIil3M5YFMcVJXOY9GhK8S5HCQ9RtF2gW9jZwbMXiiU3xeRZD9WGT90kDHrmign/HgUqfIyw2I8qsSugaLcuj1msSuvQQVeigxTLkwiV47hRFlw17BW7+is6brj/w8YtY+dB5k0X7U3exT/zQAvV4TG3v+RrOT5T3qQeZv0K9r6LRIbwpwNvbjpxNYvVrEUt2KupoEhYQiEcd8G6IsjVY37VYTPSJClLOsbZ/I8YjiZiBKbvvmVI4dV7Kp48zpSt9q3x8QZYZOdqrwamtk0DCJytdo6+ECA9bwgKsaICxsqQiycVPvvWhR3kYt3q5cw8SUdwqTTmow7ASuCCgp8+diMBjeWIylbNjjOe64X2C981wAZP1ieieHbJz9PG9pup/TYzd1n5vkmaoZPJI7hIwf0vqItlrD59oJCwFu2IrnC9RabXGLaof61TZg4zRoK7RkFalO5LFEbJWWC4xtDxU4KBkNsZLLSV7A1i0xIwV+sFXmdMUdvTNW8iCGWNWDh01UnveGNAQpZ1Sb2LEBdBhDhcP8UBpt/f/OsDVGlA17NMe+5SaSR5yFb+kkqp6JHpunr6Nq4nO0+S28nBoHQE4l6QjrmJlczfhOQfb/Xgbg0b91MX2Sg5rajFdbj7dBu3WdcSmCeTUUkwNNO9KB4iB3NU+wH2Hk6H7WMUqGBIkigZcggc6+jqwIyx+40al4j/hqwG3t+7otJui4sF+O6b6GAA++8Ul7q/XyXNxXu68jL0fk91diyeW5yMJK6O+NKO/VqNBDDdOxatzXO4cRZcMey6HNJzLunF/gT62nd4IWlUJ1AW/cK+xf/QIHui9wV+5YANZ27Utz7cucW3MnkQSn1gXgL+sFC4oeeSdHzo7ob9OWcm3KJl8vyKYD8onYqhZgDR41Fw+SILJARigZomRE4GiBTBRcRPgagyMqZU4DSVpDBHlQNy69FmxbkKNAfz+4Hrmc3DU44xp94y23zKz8Cl6fSegyGHYjRpQNeyzVM9+Gqq0hSCgK1XrsYrE6S6a6nVqrn2XefmzobwXgxIl3cUnvP0htEqRKksSB2oKusjbxWIfkEBEQCgsroYXWPqyZQjqio1qxtioeJKFCphSKYEFjajObfG1tRrkGRKBrmaNtZGTLSCAihrbKlALl2DqjOhqU2DW46Ue5ReY2LN8ywnK2fq4s3oNm5Gr3ZGkrUQaMIBtiS1ns+MTtX2RkNrOHY0TZsEdxcOMiQAuJTNYjsjlkVFV53kvmcZXFC4XZ9Hu1nDhOT3j6UtujzPy/IlQ5vHKkC7Er+UOLk3S0+2QbBc9nEtSfrEV87aEWXZmA1dUp1shmAHpVNckqD7sU0u/VEnlp/UMjiVAW0neRkYWMxy+Wh07o7l0Dgqw7dMUPBo9X9Isov0hU6BuaQT3I8i0jLP0hoHLV+HGFWMjLWdb69SbD2rBtVFhiG+NNdg1Tp7xTGFE2GHaCROQTmGIFw16Kirxhi7KJKe8cuyTK3/ve96iursa2bRzH2epr+XBdF9u2cV238rh8OI5DIpHYat22baQ0Nz2DJggCPM/D9/0hX0ulEp7nbfO5IAhoPfJQhFS8992XM+Gw2fQ1/4xEZ0hddx4ZRjjZFIgSMlQk7Kdp8nJEkeBGbz/CfAAK5INyIKY760CYAaElka5izSIda+7KhKysdXlKzOCJwn4AdHpNWCLEEiFShggnjgH7SUTJwfYSOKVEJeva9q0B1/Vg93WkwA8RJQ9VLFTGK6piVrfFjIJKj2pgSFvMVw+iF5arreRteLeVlyPyctt8ncFgGB12SZSPPvpobNve6kbo+z7FYpFsNovv+/i+TxiGlSMIgiFfoyja5jESSCkRQiCEqHw/+Ovgc6SU2zxn8NdXX3Pw64Ah69s6yucM/vpaa9tCDUoc2tb3g7+Wj/J7OXitvD74646eG7y2rfVtfT9S/46D3+fyYVlW5att20O+PnT/CqJQYaVbUErSeehk5k29h1l2Ft+1SDgB0o343/A4+opTUFTT2rqUT3Q+ieuGTH24j19c8zxVNiw+JM34U6YAEM6oxq+2yL+i+N6KWbzgaPHqjSQrc9Ws8evoLOiYcle+GiEiHLtIGAmiko5jk00is0nsfIhd8LEL8f+rbARFgSpERKWw0rmLQJdCqXwe1dtJ1N8BQFTqj93OAcrPDRFlNag1ZuU9tNyt39eyW/tVIh6FJUI/S19f34j8+xl2D7vr36st+yKS4TUPyQa9I7SbPRuhBt/pX4NisUhrayubNm3aHXsyGAwGw04yfvx41qxZQzKZHPFrK6W44YYbWLly5Yhc72Mf+xj77LPPiFxrT2WnRBm0MHuecXEZDAbDWMJ13TdEkA2jw06LssFgMBgMhjcWk1llMBgMBsMYwYiywWAwGAxjBCPKBoPBYDCMEYw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@@ -810,17 +816,17 @@ "id": "76462421", "metadata": { "execution": { - "iopub.execute_input": "2026-07-16T14:47:22.936591Z", - "iopub.status.busy": "2026-07-16T14:47:22.936241Z", - "iopub.status.idle": "2026-07-16T14:47:29.903333Z", - "shell.execute_reply": "2026-07-16T14:47:29.902572Z" + "iopub.execute_input": "2026-07-16T17:24:33.821620Z", + "iopub.status.busy": "2026-07-16T17:24:33.821242Z", + "iopub.status.idle": "2026-07-16T17:24:44.243913Z", + "shell.execute_reply": "2026-07-16T17:24:44.242733Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -867,10 +873,10 @@ "id": "3fac2a62", "metadata": { "execution": { - "iopub.execute_input": "2026-07-16T14:47:29.905088Z", - "iopub.status.busy": "2026-07-16T14:47:29.904923Z", - "iopub.status.idle": "2026-07-16T14:47:33.985852Z", - "shell.execute_reply": "2026-07-16T14:47:33.985283Z" + "iopub.execute_input": "2026-07-16T17:24:44.246054Z", + "iopub.status.busy": "2026-07-16T17:24:44.245806Z", + 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