diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 81734e6..595cc41 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -43,8 +43,10 @@ jobs:
       fail-fast: false
       matrix:
         python-version:
-        - 3.8
-        - 3.9
+        - "3.8"
+        - "3.9"
+        - "3.10"
+        - "3.11"
          
     name: Check Python ${{ matrix.python-version }}
     steps:
diff --git a/.gitignore b/.gitignore
index c80611f..9807513 100644
--- a/.gitignore
+++ b/.gitignore
@@ -135,3 +135,4 @@ dmypy.json
 bridges.md
 inf_lines.md
 tags
+.virtual_documents/
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 7836dab..d298049 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -13,17 +13,22 @@
 import os
 import sys
 
-sys.path.insert(0, os.path.abspath("../../"))
+sys.path.insert(0, os.path.abspath("../../src/"))
+from pycba import __version__ as ver
 
 
 # -- Project information -----------------------------------------------------
 
 project = "PyCBA"
-copyright = "2022, Colin Caprani"
+copyright = "2023, Colin Caprani"
 author = "Colin Caprani"
 
 # The full version, including alpha/beta/rc tags
-release = "0.1"
+# The short Major.Minor.Build version
+_v = ver.split(".")
+_build = "".join([c for c in _v[2] if c.isdigit()])
+version = _v[0] + "." + _v[1] + "." + _build
+release = ver
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/gen/pycba.analysis.BeamAnalysis.rst b/docs/source/gen/pycba.analysis.BeamAnalysis.rst
index 179d89a..b93cb47 100644
--- a/docs/source/gen/pycba.analysis.BeamAnalysis.rst
+++ b/docs/source/gen/pycba.analysis.BeamAnalysis.rst
@@ -16,6 +16,10 @@ pycba.analysis.BeamAnalysis
    .. autosummary::
       :nosignatures:
    
+      ~BeamAnalysis.add_ml
+      ~BeamAnalysis.add_pl
+      ~BeamAnalysis.add_pudl
+      ~BeamAnalysis.add_udl
       ~BeamAnalysis.analyze
       ~BeamAnalysis.plot_results
       ~BeamAnalysis.set_loads
diff --git a/docs/source/gen/pycba.analysis.rst b/docs/source/gen/pycba.analysis.rst
index 7ba27a5..8436fa2 100644
--- a/docs/source/gen/pycba.analysis.rst
+++ b/docs/source/gen/pycba.analysis.rst
@@ -1,4 +1,4 @@
-pycba.analysis
+pycba.analysis
 ==============
 
 .. automodule:: pycba.analysis
diff --git a/docs/source/gen/pycba.beam.Beam.rst b/docs/source/gen/pycba.beam.Beam.rst
index 6389fcb..5d2417b 100644
--- a/docs/source/gen/pycba.beam.Beam.rst
+++ b/docs/source/gen/pycba.beam.Beam.rst
@@ -16,6 +16,7 @@ pycba.beam.Beam
    .. autosummary::
       :nosignatures:
    
+      ~Beam.add_load
       ~Beam.add_span
       ~Beam.get_cnl
       ~Beam.get_local_span_coords
diff --git a/docs/source/gen/pycba.beam.rst b/docs/source/gen/pycba.beam.rst
index 6c8bb2a..2de2936 100644
--- a/docs/source/gen/pycba.beam.rst
+++ b/docs/source/gen/pycba.beam.rst
@@ -1,4 +1,4 @@
-pycba.beam
+pycba.beam
 ==========
 
 .. automodule:: pycba.beam
diff --git a/docs/source/gen/pycba.bridge.rst b/docs/source/gen/pycba.bridge.rst
index 00ecb65..a3855b4 100644
--- a/docs/source/gen/pycba.bridge.rst
+++ b/docs/source/gen/pycba.bridge.rst
@@ -1,4 +1,4 @@
-pycba.bridge
+pycba.bridge
 ============
 
 .. automodule:: pycba.bridge
@@ -21,8 +21,6 @@ pycba.bridge
       :nosignatures:
    
       BridgeAnalysis
-      Vehicle
-      VehicleLibrary
    
    
 
diff --git a/docs/source/gen/pycba.inf_lines.rst b/docs/source/gen/pycba.inf_lines.rst
index b187000..83d2c3e 100644
--- a/docs/source/gen/pycba.inf_lines.rst
+++ b/docs/source/gen/pycba.inf_lines.rst
@@ -1,4 +1,4 @@
-pycba.inf\_lines
+pycba.inf\_lines
 ================
 
 .. automodule:: pycba.inf_lines
diff --git a/docs/source/gen/pycba.load.add_LM.rst b/docs/source/gen/pycba.load.add_LM.rst
new file mode 100644
index 0000000..90377a4
--- /dev/null
+++ b/docs/source/gen/pycba.load.add_LM.rst
@@ -0,0 +1,6 @@
+pycba.load.add\_LM
+==================
+
+.. currentmodule:: pycba.load
+
+.. autofunction:: add_LM
\ No newline at end of file
diff --git a/docs/source/gen/pycba.load.rst b/docs/source/gen/pycba.load.rst
index 05db473..b2c43ad 100644
--- a/docs/source/gen/pycba.load.rst
+++ b/docs/source/gen/pycba.load.rst
@@ -1,4 +1,4 @@
-pycba.load
+pycba.load
 ==========
 
 .. automodule:: pycba.load
@@ -15,6 +15,7 @@ pycba.load
       :toctree:
       :nosignatures:
    
+      add_LM
       parse_LM
    
    
diff --git a/docs/source/gen/pycba.results.rst b/docs/source/gen/pycba.results.rst
index 946a666..d54439c 100644
--- a/docs/source/gen/pycba.results.rst
+++ b/docs/source/gen/pycba.results.rst
@@ -1,4 +1,4 @@
-pycba.results
+pycba.results
 =============
 
 .. automodule:: pycba.results
diff --git a/docs/source/gen/pycba.utils.rst b/docs/source/gen/pycba.utils.rst
index 1da30dc..29edd07 100644
--- a/docs/source/gen/pycba.utils.rst
+++ b/docs/source/gen/pycba.utils.rst
@@ -1,4 +1,4 @@
-pycba.utils
+pycba.utils
 ===========
 
 .. automodule:: pycba.utils
diff --git a/docs/source/gen/pycba.bridge.Vehicle.rst b/docs/source/gen/pycba.vehicle.Vehicle.rst
similarity index 70%
rename from docs/source/gen/pycba.bridge.Vehicle.rst
rename to docs/source/gen/pycba.vehicle.Vehicle.rst
index c94d17a..ed5fb8a 100644
--- a/docs/source/gen/pycba.bridge.Vehicle.rst
+++ b/docs/source/gen/pycba.vehicle.Vehicle.rst
@@ -1,7 +1,7 @@
-pycba.bridge.Vehicle
-====================
+pycba.vehicle.Vehicle
+=====================
 
-.. currentmodule:: pycba.bridge
+.. currentmodule:: pycba.vehicle
 
 .. autoclass:: Vehicle
    :members:
@@ -16,7 +16,6 @@
    .. autosummary::
       :nosignatures:
    
-      ~Vehicle.from_convoy
       ~Vehicle.reverse
    
    
diff --git a/docs/source/gen/pycba.bridge.VehicleLibrary.rst b/docs/source/gen/pycba.vehicle.VehicleLibrary.rst
similarity index 84%
rename from docs/source/gen/pycba.bridge.VehicleLibrary.rst
rename to docs/source/gen/pycba.vehicle.VehicleLibrary.rst
index e5988ea..b062e4e 100644
--- a/docs/source/gen/pycba.bridge.VehicleLibrary.rst
+++ b/docs/source/gen/pycba.vehicle.VehicleLibrary.rst
@@ -1,7 +1,7 @@
-pycba.bridge.VehicleLibrary
-===========================
+pycba.vehicle.VehicleLibrary
+============================
 
-.. currentmodule:: pycba.bridge
+.. currentmodule:: pycba.vehicle
 
 .. autoclass:: VehicleLibrary
    :members:
@@ -19,6 +19,7 @@ pycba.bridge.VehicleLibrary
       ~VehicleLibrary.get_abag_bdouble
       ~VehicleLibrary.get_abag_semitrailer
       ~VehicleLibrary.get_example_permit
+      ~VehicleLibrary.get_la_rail
       ~VehicleLibrary.get_m1600
       ~VehicleLibrary.get_s1600
       ~VehicleLibrary.get_validation_truck
diff --git a/docs/source/gen/pycba.vehicle.make_train.rst b/docs/source/gen/pycba.vehicle.make_train.rst
new file mode 100644
index 0000000..a722b6c
--- /dev/null
+++ b/docs/source/gen/pycba.vehicle.make_train.rst
@@ -0,0 +1,6 @@
+pycba.vehicle.make\_train
+=========================
+
+.. currentmodule:: pycba.vehicle
+
+.. autofunction:: make_train
\ No newline at end of file
diff --git a/docs/source/gen/pycba.vehicle.rst b/docs/source/gen/pycba.vehicle.rst
new file mode 100644
index 0000000..7a3fd27
--- /dev/null
+++ b/docs/source/gen/pycba.vehicle.rst
@@ -0,0 +1,41 @@
+pycba.vehicle
+=============
+
+.. automodule:: pycba.vehicle
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+      :nosignatures:
+   
+      make_train
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+      :nosignatures:
+   
+      Vehicle
+      VehicleLibrary
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/notebooks/bridge.ipynb b/docs/source/notebooks/bridge.ipynb
index 8e4058c..21a67b1 100644
--- a/docs/source/notebooks/bridge.ipynb
+++ b/docs/source/notebooks/bridge.ipynb
@@ -166,7 +166,7 @@
     {
      "data": {
       "text/plain": [
-       "<pycba.results.BeamResults at 0x7f1f0364ff70>"
+       "<pycba.results.BeamResults at 0x7fdac318a430>"
       ]
      },
      "execution_count": 7,
@@ -462,7 +462,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAEoCAYAAABiqYaBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACcJElEQVR4nOydd3gU1deA35PQe5XepYggSldAwYKAit2fiIrlExUrYgMb2HvvDcVeEVREEYkdaRYQUBBBaQLSi7Sc748zawImYZPsZnaT8z7PfXbmTjuz2dw5c+4poqo4juM4juM4jhMbUsIWwHEcx3Ecx3EKE65gO47jOI7jOE4McQXbcRzHcRzHcWKIK9iO4ziO4ziOE0NcwXYcx3Ecx3GcGOIKtuM4juM4juPEEFewHWc3RCRNRP4vbDkcx3GcgkdEPhKRAWHL4SQ3rmAXYUSkq4h8IyLrRGS1iHwtIh2CbWeJyFcJIONCEdkiIhtFZLmIvCAi5Qrw+jH9HkRkuIi8HKvzOY5TtMlpHE8kRKS7iCzewz4viMi2YLxfLSITRKRFnOX6z5isqr1V9cV4Xtcp/LiCXUQRkQrAB8AjQBWgDjAC2JqLc6TGR7r/cIyqlgP2Bw4AhhbQdR2n0CEiKSJygIgcJSKHisheYcvk5I1YjOMFgYgUy8XudwfjfR1gCfBcfKRynPjiCnbRpRmAqr6mqjtVdYuqfqKqP4nIPsCTwIGBJWEt/GtdeEJExonIJqCHiNQWkXdEZKWI/C4il0YuICIdRWSaiKwXkb9E5P6gv5SIvCwif4vIWhGZKiI19iSwqi4HPsYU7cg1OgfWm7Ui8qOIdM+07SwRWSAiGwLZ+gf9u1gsRKShiOjuD4EcvoejROT74L7+FJHhWZxrgIj8ISKrROS6YFsvYBjwv+B8P+Ykp+PEEhFpIiJPA/OBO4F+wCDgUxGZLCJni4g/E5KLbMdx2PNYF7jD3SEiU4LxbIyIVNlt34EislRElonIlZnOVVJEHgy2LQ2WSwbbuovIYhG5RkSWA68BHwG1g7Fvo4jUzunGVHUL8Ca7jvd7et58GzwLlonIoyJSItP2fQOL+OrgeTQshzH5XzdBsRfS60VkkYisEJFRIlJxt+/oP+N9Jpn+8wx0igY+mBZdfgV2isiLItJbRCpHNqjqHOAC4FtVLaeqlTIddxpwG1Ae+AZ4H/gRszYcBlwuIkcG+z4EPKSqFYAm2GAJMACoCNQDqgbX2rIngUWkLtAbUxAQkTrAh8CtmPXmSuAdEakuImWBh4HeqloeOAj4IepvJ+fvYRNwJlAJOAq4UESO2+3wrkBz7Du5UUT2UdXxwO3AG8H52sRCTseJkluBl4Emqnqkqp6uqiep6n5AX+x/8oxQJXRyS7bjeC44EzgHqAXswMajzPQAmgI9gWtE5PCg/zqgM6YAtwE6AtdnOq4mNi43CK7RG1gajH3lVHVpTkIFY2M/Msb7FHJ+3uwEBgPVgAOD7YOCY8sDnwLjgdrA3sDErMbkLEQ5K2g9gMZAOeDR3fb5z3gf9Gf3DHSKAK5gF1FUdT02KCjwDLBSRMbKni3JY1T1a1VNB1oD1VX1ZlXdpqoLgnOdGuy7HdhbRKqp6kZVnZypvyqwd2B1mR7Ikx3vicgG4E9gBXBT0H86ME5Vx6lquqpOAKYBfYLt6UArESmtqstU9efovp2cUdU0VZ0ZXPMnzDpzyG67jQisST9iD4SsBu4IcZHTcTKjqv1U9QtV1Sy2rVDVB93vNLnIxziemZdUdZaqbgJuAE6RXd3/RqjqJlWdCYzElF6A/sDNwW9nJeaakvkFLR24SVW3BtboaLkymC3cENxb5JwdyOF5EzxHJqvqDlVdCDxFxrh8NLBcVe9T1X9UdYOqfhelPP2B+1V1gapuxFwUT5VdZzyzG++zewY6RQBXsIswqjpHVc9S1bpAK+zN/sE9HPZnpuUG2JTf2kjDptsig/u52BTmXDE3kKOD/pcwV4/Xg6nFu0WkeA7XPC6w7nYHWmAWisj1T97t+l2BWsHD4n+YBXqZiHwoMQqWEZFOIjIpmKZcF1yj2m67Lc+0vBmzevyHeMrpOFkhIqki0ldELhWRKyItbLmcvJHHcTwzmcf0RUBxdh3Pdt8ece2oHaxntQ1gpar+kws5ItwbzBY2xGY2mwf9OT5vRKSZiHwgFgy/HrNMR+6jHvBbHmSBrO+zGBnPOch+vM/uGegUAVzBdgBQ1bnAC9gADWYRyXLXTMt/Ar+raqVMrbyq9gnOOU9V+wF7AXcBb4tIWVXdrqojVLUl5hJxNDaFuCcZPw9kvDfT9V/a7fplVfXOYP+PVfUIbOpzLmbtAHPxKJPp1DVzumwWfa8CY4F6qloR89OWPcmf3flykNNx4sH72JR3VczVK9KcJCeLcTyasa5epuX6mNV1VQ7bI64dSzGlN6tt8N+xLrtnSpao6h/AZcBDIlKaPTxvgCew8bNp4JIxjIxx+U/MvSPLS+1BlKzucwfwVxT3kOUzcE/HOYUDV7CLKCLSQkSGBH7NiEg9bOovMoX1F1A3c5BIFkwBNgSBLKUDy1gryUj1d7qIVA/cSdYGx6SLSA8RaR1MQ67HBvT0KEV/EDhCRNpg/qTHiMiRwbVLiQXX1BWRGiJybDCYbQU2ZrrGD8DBIlI/CFbJKStJVt9DeWC1qv4jIh0xv/Ro+QtoGPgTsgc5HSce1FXVE1T1puBFd4SqjghbKCf3RDGO/8Cex7rTRaSliJQBbgbeVtWdmbbfICJlRGRf4GzgjaD/NeD6IOalGnAjNiZnx19A1UCOqAjc/pYCA9nD8wYbl9cDG4NZwAszneoDoJaIXC4WnFleRDplkuvfMTkLXgMGi0gjsRSxEZ/tHXuSP7tnYLT37yQ3rmAXXTYAnYDvxDKCTAZmAUOC7Z8BPwPLRWRVVicIBuGjsSCX3zGrx7NYsBRAL+BnEdmIBXucGvji1QTexgbDOcDnmNvIHgl8/UYBN6rqn8CxmKViJWaluAr7XacAV2CD82rMF+/C4BwTsIfET8B0bPDNjqy+h0HAzYFf+I3kLnDlreDzbxGZkZOcjhMnPhKRnmEL4cSEHMfxKMe6lzCr93KgFHDpbts/xwINJ2LuG58E/bdiMS8/ATOBGUFflgTW9deABYGLR45ZRDJxD3A15paR0/PmSszYsQGbBYy8CKCqG4AjgGOC+5yHBS3Cf8fk3Xke+46+CK77D3BJlLJn9wx0igCSRbyL4ziOU0gRkeMxS2MKNnskgAbT6k4RQkTSgJdV9dkstjXEFMri0VhrHcfZldwkf3ccx3GSn/uxNGYzs8oo4jiO4+QfdxFxHMcpWvwJzHLl2nEcJ364i4jjOE4RQkRewDIqfESmktqq6lXmHMdxYoS7iDiO4xQtfg9aiaA5juM4McYt2I7jOI7jOI4TQ6K2YAd5ev/ZLT+m4ziOkwSIyEhyKCClqucWpDyO4ziFmWwV7CDp+qlAf6AD5qtXMsgF/CHwlKrOLxApHcdxnPySVQ7kesBgILWAZXEcxynUZOsiIiKfA58CY7CI8/SgvwqWoP00YLSq5lS5yXEcx0kwRKQxVqDpYOAB4DlV3RauVI7jOIWHnBTs4qq6PceDo9gnUalWrZo2bNgwV8ds2rSJsmXLxkegEPD7SWz8fhKfvNzT9OnTV6lq9TiJlCNBCenrgQOwCnkvJ1IRkcI2LrtsuSdR5YLElS1R5YLCL1uO47mq7rEBlYH9gLaRFs1xidzatWunuWXSpEm5PiaR8ftJbPx+Ep+83BMwTUMY87CS0AuAi4DqQJXMLQyZdm+FbVx22XJPosqlmriyJapcqoVftpzG8z0GOYrILcBZwG9kBMgocGje9H3HcRwnBDpgY/eVwJCgT4JPxXJjO0WNxYuhTx8YPhxOOCFsaRyn0BBNFpFTgCbq/nmO4zhJi6o2zG6biEh225xCzscfw8yZcOqpMGYM9O4dtkSOUyiIplT6LKBSnOVwnKSgVSsQgR0J47XqOLlDRG7ebT0F8GD1osrkyVCpkg1uJ5wAaWlhS+Q4hYJoFOw7gO9F5GMRGRtp8RbMcRKNv/6Cn3+25XvuCVcWx8kH9URkKICIlARGA/PCFckJjW+/hQMPhE8+gcaN4eijTel2HCdfRKNgvwjcBdwJ3JepOU6R4vbbM5aHD4dZs0ITxXHywzlA60DJfh+YpKrDwxXJCYV162D2bOjcGapVg08/hZo1oVcv+P77sKVznKQmGgV7s6o+rKqTVPXzSIu7ZI6TQMyYAY8/nrFeoQKcfba7ijjJg4i0FZG2WIq+h4D/YZbrL4J+p6gxdSqomoINUKsWTJxoA1zPnjBnTrjyOU4SE42C/aWI3CEiB0YGaB+MnaLE1q0wYADstVdG32OPwbRpcO+94cnlOLkk8wzkncAaoGWw7r/kosi331pQSadOGX0NGpiSnZoKhx0Gv/0WnnyOk8REk0XkgOCzc6Y+T9PnFBmuv97cQT78EI46yvpOPhnefBNuugn69oWWLcOV0XGi4Drg2yB3q5MIqNogUqkSnH76rm/x8WLjRmjXjkbt28OaNbDPPlCx4q77NG1q7iKHHGJK9pdfQr168ZfNcQoRe7Rgq2qPLFpMlGsReV5EVojIrEx9VURkgojMCz4rB/0iIg+LyHwR+SmzFV1EBgT7zxORAbGQzXHAAurvuw/OP99SxUYQMSt2+fLuKuIkDWcA00XkdRE5S0Rqhi1QkWfBArjlFhgyBOrUgWOPhffeg/T0+F1zyhT49VcavPoqfPSRBThmRatWFvi4Zo0p2UuXxk8mxymEZKtgi8iZObQzYnT9F4Beu/VdC0xU1abAxGAdoDfQNGgDgScCOasANwGdgI7ATRGl3HHyw7p15hqy996mZO9OjRrw6KP2vLr//oKXz3Fyg6peqKptgeFYdd4XRORbEbldRA4WkdRwJSyCfPmlfb79NlxxhQ0mxx9vb/OrVsXnmt99B8CSvn1t/eCDs9+3XTsYN86U6x49YNmy+MjkOIWQnCzYHbJptwAjY3FxVf0CWL1b97FY5hKCz+My9Y8KqlNOBiqJSC3gSGCCqq5W1TXABP6rtDtO1CxbBi+8YNmqliyBl1+GsmWz3vd//7Pn4Q03wPPP24yv4yQyqjpXVR9Q1V6Yq99XwMnAd+FKVgT58kuoUsUGkbvugj//hCeesKmztm3/VYZjyuTJ0Lw58y6/HH780VxTcqJLFxg/3gbD7t1dyXacKMlWwVbVSyINuBQbfLsDk4F4BjnWUNXIf/ByoEawXAf4M9N+i4O+7PodJyq2bbPn2bXXwv77Q+3a5vbx22/mBtKxY/bHisAzz0DXrnDuudCkCQwaBGPHwoYNBXUHjpM3VHWLqo4Lxvr2YctT5PjiC+jWDVKCR3GxYnDBBfD11xZk2K2bTZPF6s1d1ZT2Tp1s8Npvv4xr50TXrq5kO04uyTHIUUSKAWcBV2KK9Umq+ksByAWAqqqIxMwmKCIDMfcSatSoQVouK1Zt3Lgx18ckMkX5fpYvL8mUKVWYOrUKM2ZUZvPmYqSmptO69ToGDlxNhw6radJkEyK7FzbrDvCf6wwbBm3b1uSbb6oxcmRlnngilWLF0mnVah0dOqyhY8fVNGmykdwUpC7Kf59kIZnuSUQ2YAHqAJFfomLPgRKqGk3QuxMrli+H+fNNod6ddu0sN+iZZ8Ill5jC/cwzUK5c/q65aJFVzOrcec/77k5Eye7Vy5TstDRL6+c4TpZkO6CKyEXAZZgfdC9VXVhAMv0lIrVUdVngArIi6F8CZA5jrhv0LSGi9WT0p2V1YlV9GngaoH379tq9e/esdsuWtLQ0cntMIlOU7ueff+Dzz+35MH48zJ1r/Q0awBlnQO/e0KNHChUqVMbcU5vkeK2srnPYYfa5bZs9Dz/+OIXx4yvzzDOVeeaZxtSoAUceae2II6B69bzfTzJS2O4HkuueVLV85nURKQdcBJyPVXN04sn27fZZvLh9Rvyvu3XLev/KlWHMGLj7brjuOvjhB3jnnfylLIq4nHTqBOvX5/54V7IdJ2pymht6BKgAdAXGBpk7fhKRmSLyUxxlGgtEMoEMAMZk6j8zyCbSGVgXuJJ8DPQUkcpBcGPPoM8pwqjCr7/Cww9bvFCVKvZMeOIJU6ofeMBqKPz+Ozz5pAXvV6gQm2uXKGHxQHfeac/EpUvNp/vQQy3VX//+FiDZoYP5bn/9tWchcQoOEakkIsOBn4DyQAdVHRKuVEWAPn3sH/+SS8z3+YsvLLjjgAOyPyYlxXzXPv0UVq+2QePVV3N/7eHD4bnnzP+6dGlo3TrPt7GLu8ghh5jfuOM4/yGnKcFG8b64iLyGWZ+richiLBvIncCbInIusAg4Jdh9HNAHmA9sBs4GUNXVInILMDXY72ZV3T1w0ikCbNmSyvvv29j/0UemPAM0awbnnWcK9iGHQJkyBStXrVqWjWTAANi502Z+x4+Hjz+28uu33mppaA8/PMPCXb9+wcroFH5EpBowBKvg+DxwgKquC1eqIsLWrTaFVq8ePP20+VUXK2ZW4IhFOyd69LDS5aeeam/oX39tqYtKltzzsWvXwogRtly6NLRvH901c6JrVxvA+vQxC/zEiRaA4jjOv2SrYKvqIgAR6a2qH2XeJiIXAE/m9+Kq2i+bTYdlsa9i05lZned57IHhFCFUrQBMxO3jiy+6sGOHGYUOOwyuusqU1caNw5Y0g9RUM0JFrNdr1tiz6eOP7R7eecf222cf2HffJmzdalm0SpcOV26nULAIWIllgdoMnCuZggJU1ZNNxosffzQXkbvvNmX5lVfgjTfgnHOiP0ft2jZYDBtmJWSnTLFz7GmAmxrYnnr2tLzWXbrk/T4y06ULfPaZnbdbN7Oye8Utx/mXaIJabhCRrar6GYCIXA30IAYKtuPklrVrbRyPKNVLllh/69Zw4omLGTiwPl26RGfYSQQqV4aTTrKmam4rEev2mDF1ePttKFXKLO+9etkLQ4sW5CpY0nEC7iEjyLF8Tjs6MSai5HboYP5ql1xiLbcULw733AMHHWSpjg44wIIfTzkl+2OmTLHPN96AefPs7T1WtGtnlvkjjrBBasIES8XkOE5UCnZf4AMRuQrLL90Cy0ntOHEnPd1mRj/6yBTPyZPNzaJiRRvTI0pn3bqQlraA7t2T17dCxAxALVtazYnx478CDv5X4R482ParX9/uuVcvs9TvXuXYcbJCVYeDFefa3Y1OROLuElikmTrVyqDHqtz48cebct2vnyXjnzjRAkuy8n/77jt7K69UyRT8WNOqlfmTH3aYWec/+ihvWUocp5CxRwVbVVeJSF/gU2A6lqrPy2k4cWPlSpvJjCiWK1daf7t2Fu/Tu7cFwRcr5EnFSpVKp3t3U6QBFi607+Pjj80Y9cwz5nJy4IEZLxpt20aX1tYp0rwfuP6tBxCRfYC3gFbhilWImTLFEurHcuqpYUNTbG+4wYrUfP01vPnmrm4akbzXkUEkXjRtallRDj/c2tixFtXtOEWYnEqlbxCR9SKyHgssbIZV+4r0OU5M2LEDvvkGbrzRnkE1alhxsfHjzb3vpZcsdeu0aRYQ2KVL4Veus6JhQzj/fHj3Xaui/MUX9sKxZQtcf70Zp2rUsBioyHfmOFlwO6ZklxORdsDbwB7K+Tl5ZsMGywsaD+tx8eKWrmj8eFixwgIYn3suozDNokXW36lT7K+9Ow0a2KDUsKFZQd56K/7XdJwEJqcgR/fRc+LG0qUZgX0TJliwX0qKzSyOGGEGl3bt3BqbHcWLW1xRt2720rFihX2P48eb9T+SyeuAAzLcSQ480FIIOkUbVf1QRIoDn2C+2Mer6q8hi1V4mT7dFN6cSsLmlyOPzCh7/n//Zy4jTz6Z4X9dEAo2WMqkL7+EY44x15W//oKLLy6YaztOgpFToZmGORWXEQs/r6Oqi+MhmFO4iBRfiQQn/hRkUq9d29wJe/WymcXKlcOVM1nZay+zXPfvb37rP/yQ8QJz771m5CpXztwkIwp3I/e6LVKIyCNkBDkCVAR+Ay4WEVT10j0cXw8YBdQIzvO0qj4kIlWAN4CGwELgFFVdEzwjHsLSq24GzlLVGbG9qwTms89oOGpURoL99nGuRF+rlr1d33mnTQdGfK9LlbKS6AVF5cr2tn/qqRbIuWyZWQE8MtspYuQ00X6PiKRghV6mY+mdSgF7Y1lEDsPyVruC7WTJwoUZCvXEibBxo1leu3Y1l8FevSz7h4+7sSUlxXyx27aFoUOtYNtnn2Uo3GOC0k1Nm2b4bnfvbukNnULNtN3Wp+fy+B3AEFWdISLlgekiMgE4C5ioqneKyLXAtcA1QG+gadA6AU8En0WDESNo+MUXtty4MVSrFv9rpqZa1cfu3e1te9w4yziS37zXuaV0acs5OmiQJftftszyfxdF3z6nyJKTi8jJItIS6A+cA9TCrBBzsKIvt6nqPwUipZMUbNliLniRjB+//GL9DRrYzGWvXhb3Ut6djwqUChXguOOsRSpcRpTtZ5+FRx4x15Fu3TIU7lat/MWnsKGqL+bz+GXAsmB5g4jMAepgWaW6B7u9CKRhCvaxwKggKH5yUEGyVnCexGLAAAtsuPBC8x9OTc3f+YL0Ryt69GCvnj2hefPYyBktXbrYNNZNN1ki/TAoVgyeesos6zffbH5sb7zhb/JOkSHH10lVnQ1cV0CyOElGRFmLWKnT0uCff2xGsnt3e1b16mWVFF1ZSwxE7FnfvDlceqn9vb78MkPhvuoqa7VrZ7iSHH64pe51khsReR94Ghivqtt329YYs0QvDAp37elcDYEDgO+AGpmU5uWYCwmY8p25jvbioC+xFOx//rHCL6pm8W3QwKKJzz3XfK/ywrx5sGEDq9u3Z69rr42tvNFSqRI89FA4144gYkE1NWvCRRdZruyxY22AcZxCjs/XOLliwwaYNClDqY6UI2/e3J5JkXLkXnkwOShVyvKJH3GE+WovXpyRCnD0aBg50lxOOnbMsG536JB/A58TCucBVwAPishqMtz+GmGZoh5V1TF7OomIlAPeAS5X1fW7VYNUEclVGlcRGQgMBKhRowZpaWm5OZyNGzfm+pjMlJ0/nw47dzJn6FB2lixJnTFjqDxsGOk33sjKgw9m6bHHsi6Xvmx7TZhAS+CvevVYng/Z4kl+v7dcsc8+VL31Vlrecgs72rRh5u23s7Fp0/DlyiWJKluiygVFXDZVLZKtXbt2mlsmTZqU62MSmWjuJz1d9aefVO+6S7VHD9XixVVBtWxZ1b59VR9/XHXBgvjLGg0F8fcxM1fcL6Oq4f/etm9X/eYb1RtvVO3USVXE7r1yZdVTTlF9/nnVJUuiP1/Y9xMP8nJPwDQNefzDAhIPBPYHyuTiuOLAx8AVmfp+AWoFy7WAX4Llp4B+We2XXQtlXH7lFfthz5yZ0Tdnjupll6lWrGjbWrVSfewx1XXrojvn4MGqpUpp2qef5k+2OBLK/+MPP6jWratapozqmDFZ7pLI40SiypaocqkWftlyGs89CZrzH9assRSm555rFRL32w+uucZcFAcPtoC51astWO7CCz0bRWGlWDFL7TdihFXQXLkSXn8djj3WfO3POQfq1LHfx1VXWSDr1q1hS+1Eg6ouVNVvVfUHVd0czTFBVpDngDmqen+mTWOBAcHyACwwPtJ/phidgXWaiP7Xs2bZj71Zs4y+Fi3gwQdhyRILVChRwlwc6tSxQS+SBik7pk+HNm1Qn+rZlTZtLHXgvvtaUMi992bk7HacQsYeFWwRmRhNn5O8pKfvWsSlWjU45RQraNKli9UtWLzYnil33WXVcD2fctGjalVLbTtypOUx//FH+z1Ur26unhFf7aOPtsDJefP82VnI6AKcARwqIj8ErQ9wJ3CEiMwDDg/WwYLhF2DuJ88Ag0KQec/8/LMp11kNamXLmqVh2jRLe3fSSfDCC6Yodu1qCed3f6sMAhxp165AxE86atWygJ0TT7Q384EDLY+r4xQycsqDXQooA1QTkcpAxAGtAhao4iQxK1fChAk1ePZZ87ddtcr627eHYcPM37YolCN38oaIWa732w+uvtpSMKalZZS3//BD269RI/st9eoFxYq5NS+ZUdWvyHgO7M5hWeyvwEVxFSoWzJq15xzVIhaI0LGjWV1feMEKufTvD5dfbkr4+edbFcMgwNEV7BwoU8Yyitx4I9x2m73kvPWWzRA4TiEhJ/XpfOByoDaWLzUysK4HHo2vWE6s2bHDDDCR4EQrLrYP1atnKEBHHJH3oHmnaFOunFmujz7a1n/7LSMzyahR8MQTUKxYF7p2zchO0qaNZ5dxQmbTJliwwNL0RUvVqjBkiPnLffqp/bjvvtumc3r3tpzXYAr2mjXxkbswkJJi06Zt2sDZZ9v39eabYUvlODEjWxcRVX1IVRsBV6pqY1VtFLQ2quoKdhKwZAk8/zycfLK5fXTtajn/S5a0tKRPPjmd5cvhpZfMEOPKtRMrmjSxGhNjx5q//mefwcknL2bNGit+c8ABNlM8YIDNsq9cGbbERRsR+ShsGUJhzhz7bNUq98empEDPnpZuZ+FCK/AyYwY8+qil52nZMqaiFlpOPtn8sitWhEMPpe7bb7tvmVMo2KMDgKo+IiIHYVHnxTL1j4qjXE4e2LYNvvoqw0o9c6b1165t7m69e1up7Eg58rS0DaR4mKsTZ0qUML99kQV0716fZcusovP48eZKMmqUWbLbtcuYTXH3pNgjIm2z24RlEyl6zJpln3lRsDNTrx7ccou5PHzwgeWxLOjqiclMy5YwdSoMGMDejz0Gf/9twaVelMZJYvb4CBORl4AmwA/AzqBbAVewE4Dff9+1HPmmTTaud+tms5a9enlVPiexiFiuBwyAnTvNXSniTnL77TZrXLGiBU0eeaS1+vXDlrpQMBX4nKz9qCsVrCghMWmSDZSHHGJTej//bFN6TZrE5vzFi8Pxx8fmXEWNChXgnXdYcP75NH7+ebMQvfGGZRxxnCQkGhtRe6BlELDihMyWLfD556aMfPSRVVIEi60580xTqHv08HLkTnKQmpoRO3bDDeayOnFihsL9zju23z77ZBS6OfhgL2SUR+YA56vqvN03iMifWexf+Lj5ZovGve02U6xLlrQfl6fTSwxSUvijf38an3IKnH66BZ8+8IAFkLqVyEkyolGwZwE1SbTytkUEDcqRf/SRKRyff75rOfJBg8z1o2lTH3+c5KdyZcuEdtJJ9tufMydjhubxx+1ZW6qUGSAjCneLFv7bj5LhZB93c0kByhEes2dDv35wxhn2JjdpkuUkdRKLI46wvLADBlje8U8+MZeRKlXClsxxoiYaBbsaMFtEpgD/JvxU1b5xk6qIs2GDBYVFFIuFC62/eXO44AJTLNyK5xR2RMw1s2VLuOIK2LzZXjAj1u3Bg22/+vUzMpMcdpi5lzj/RVXfzmHbewUoSjj8/TesWGHO/r17W3MSlxo1YNw4K/hz7bWWbeSVV+zh5zhJQDQK9vB4C1HUUTV3s4jbx9dfw/btlvrssMOsiuKRR3rFRKdoU6bMrnrRwoWmbH/8sVWYfOYZm+k/8MAMhbttWzyQ1zEiGUM8u0fykJJib9eHHGIzDz16WBqiG24w9x7HSWCiySLyuYg0AJqq6qciUgZwh7V8smYNTJiQYaVeFjjg7LefWeZ694aDDvKKiY6THQ0bmmvm+efbC+nkyRmFbm64wVq1apZJ7cgj7bNmzbCldkJj9mz7dAU7+WjXzlIgXnqp+c+PGWMlZfdUIMhxQiSaLCLnAQOBKlg2kTrAk2RRucvJnvR0y5YQUagnT7a+SpXswR/xJ61dO2xJHSf5iGTO6dbNnr8rVmS8wH7yieXaBsu/HbFuH3igv8AWKWbPtrRv9eqFLYmTF8qVs8IOJ51k5dU7d7ZS6zfdZIEZjpNgROMichHQEfgOQFXniYiXJImCFSvs4f7RR/a5apX5lbZvbzUJevWy7Ame79dxYstee1nxpP797UX2hx8yrNv33gt33pnhghVRuIuSC1aRrG0we7ZlDHGfoeSmTx9LrzhkiP0jjxljinfnzmFL5ji7EI1qt1VVt0kQpi8ixbA82M5u7NiRMU0dKUcO/FuOvHdvC46uXj1cOR2nKJGSYr7YbdvCsGGwfv2uQcRjxth+TZtmzCR17154a1wU2doGs2fbG5WT/FSsaFlFTj4ZzjsPunSBiy+GESNsWthxEoBoFOzPRWQYUFpEjgAGAe/HV6zcIyK9gIcw//BnVfXOgrju4sUZWQ0mTIB16zICrW691R7YBxzgRhPHSRQqVIDjjrMWSYMZ+R9+9ll45BFzHenWLUPhLmTFmopebYN162DJEve/LmwceaRV4xw61P5xX3/dKqydcYY/dJ3QieYXeC2wEpgJnA+MA66Pp1C5RURSgceA3kBLoJ+IxGUk3brV0qdedRW0bm3ufP/3f/Dtt+Ya9tZb5gry5ZfmBtKunf+fO06iImLpLy+91DKCrV5t7lyXXALLl9v/+X77Qd26cM458Oabtk+SE6ltUHSYO9c+XcEufFSoAI89BtOmQePGcNZZVqVzxoywJXOKONFkEUkHnglaotIRmK+qCwBE5HXgWGB2TM6uylEp/fiW01jDRqAcsA34EhgPfMSSJT/z3HPw3HMxuaITZ44H8uZya0a/IXkwZ64GXgZ25Om6TjjUAY5k6dIjGTnyCEaOrIx5VUyhB+OYy30s1c0hy5hrCk9tg6lT6XzKKVbqvG7djFavXsZyrVqeQaQo0Lat5bgdNQquvtqCnS64wKp3VqsWtnROESSaLCJHA7cADYL9BVBVrRBn2XJDHSBzqd/FQKfddxKRgVhGFGrUqEFaWlpUJ0/fqXzEqygpmKF8PDAJ2JQ/qZ0CpwzwGpBXTeJVlrGcWtyXx+NrAgXiu+TEiCXA80FLxd7lj6Q0/8ckbqEd30c9jiQQw8MWIGaUKcPatm2puWOHFRMYN84qEmVGxLJMlCpluR2dwktKilmwjzvOsos8+qgVp7n6arj88sIbWOEkJNH4YD8InADMTHafPVV9GngaoH379tq9e/coj4O1519N8Wcfo/SSBVDjojhKWXCkpaUR7XeQDER1P9dfb3ncHnjA5vxzaYm2dOUbgPW5F/DMM7ljwgTu+PXXqPIxFsm/TzKQns7O1vuzibJMefCOpLunoLZBDaBD0DVFVVeEKVOe2Xdf5l57LTUjfwNV87devPi/bZ99LEDGKfxUqgQPPWRJ8ocNs3H/scdg+HA4+2zL6+k4cSYaBftPYFaCK9dLgMzJTesGfTFBBCoMPheeusfKxV2fUC7oTrT8+Sfcd59VBLv88oK//r332hT1iBHw1FMFf30nNnz4IamzZ1LhlVcoloTPaRE5BbgHSMNmJB8RkatyKqWeNIiYclWpkkWmOkWbli3hvffgq6+sJPL558Mdd5jSPWCAJ8J34ko04XdXA+NEZKiIXBFp8RYsl0wFmopIIxEpAZwKjI3pFZo3Z3X79vDkk1Y2zkk+brnFLFy33x7O9Zs0sYjYkSNN2XeSD1V7QDdsCKecErY0eeU6oIOqDlDVMzG/lxtClslx4kfXrqZkf/CB5ckdOBCaNTNDxz//hC2dU0iJRsG+DdgMlALKZ2oJg6ruAC4GPgbmAG+q6s+xvs6S44+3VE+RxLlO8rBsGbz4ok0PhumHefXVpqTdfXd4Mjh556uvLGXQVVclc4WolN1cQv4mumeB4yQvInDUUfDdd+arX7OmBUHWr2+ziitXhi2hU8iIZlCtraonqOpNqjoi0uIuWS5R1XGq2kxVm6jqbfG4xt+dOply9uij8Ti9E08efthmHoYMCVeOBg1savLpp2HRonBlcXLPnXeaBezss8OWJD+MF5GPReQsETkL+BBLvxpzRKSXiPwiIvNF5Np4XMNxcoWIVX379luYNMnKKQ8fbor2wIHw/fdhS+gUEqJRsMeJSM+4S5IMpKbCoEHw+ecZZRqdxGfpUnspOukk2HvvsKWx6HYRuPHGsCVxcsNPP5nl6/LLoXTpsKXJM6p6FRbsvV/QnlbVa2J9nYKsT+A4uUbESrZ+8IGlcTzjDHj5ZUv316mTufLtnpHGcXJBNHOcFwJXisg2IOJ8nGhp+gqOgQMtC8Udd8DbyR8TVCS49lrYts3+ZolAvXpW2eTee82ivt9+YUvkRMNdd0H58vaSneSo6jvAO3G+THzrEwAidbCkm2/E6pRxwGXLPWHIlUppBtAJ6DYFak2ZQoVzxrKCycxjOZEsD4n6jXV//PGwRciS2r/+CnPmhC0GANt3prBmc0lWbyrJms0lmTdvFUtH7WDLtlS27kjluKuaUrtdrZhdL5pCMwnlbx06FStambfbbrMfzT77hC2RkxPTp8NLL5mS3aRJ2NJkcO21lpFm6FD48MOwpXH2xIIFVoZ5yBDLUJGEiMhXqtpVRDYAmbNCxau2QVzrExhNgSfyIaLjZLAFS62Ttlt/C+ZwAu9yIu9wAN+T+zJjBUCCvvg3i/H5dpDKWiqxhsqspgprqLxL270v8/omyuV47uJV36bphtgVJYoqSkdE+gIHB6tpqvpBzCRIRi67DO6/3yyio0aFLY2TE7fcYgrRtQnm/lmliqWKuvpqSEuzqUoncbnvPgtqDCO9Y4xQ1a7BZ0IZTfJanwBg69Y0Pvjga7p06RIn6fLH11+7bLklUeRShYUL4ZsvdvDB2Cbc+e0wbk+/jkb1d3D0Edvo1WMrhxy4LSFq1yTKd5YVOcmmCqvXCIuXpbJ4aQpLlqfy9xphzdoUVq9NYc06W458rl4rbNiYs2dzmdJK5UrpVK6YTuVKSuNK6VSuGOnbSOWK6VSprFSumM4ff/zAIV1bU7qUUqoUVGnUl+JlY5e6MZpKjndiBQleCbouE5Euqjo0ZlIkG9WqWT7Nhx+24IjGjcOWyMmKH3+0jC8jRtjMQ6Jx8cX2G7rmGpg8OddFb5wC4q+/4PnnLTg1igJBiY6IvATcD9TGjHazgPtU9YwYXyqu9QnA0hhXqbKdGjViedbY4bLlnkSSq2ZN6Ny5GFdcDatWwV13zWX27BY8+2oxHnmuDCVKQLdu0KOHfXbsaAVDC5rtVaqQMF9aQHq6JWaZtbI+qybXyLL20+LFWWdJLFnSbFCVK1ur2xBaV961r3IW65UrQ8mSglXd3XNRqbQ0oUXX2FmsdycaC3YfYH9VTQcQkReB74Giq2ADXHmlVYa6+27Lje0kFqpmbaxUyfydE5HSpeHmm62i5Lvvwoknhi2RkxW33WY+/FdeGbYk+UJEmgDXAKcAewErsfSrzYC9ReQz4MXIWB8D/q1PgCnWpwKnxejcjlOgVKsGRx21nHvuacE//8CXX8L48fDJJxm150qUgA4drLVrZ61Zs8JVQDRSLHX5cst+G/lcutSyGEcU5yVLIiVD2v97bPHiUKeOtXbt4NhjoW7djFanjn3PSRxDvgvRJnKtBKwOlhPQFBgCtWtbqq6RIy0bRCGwbBUqXn7ZXC+efDKxfWbPPNPcD4YNg759vYRvojFzJjz+uOXLbRZrb8IC513MaTkVODDoE2Ab8Aw2tp8BvBiLi6nqDhGJ1CdIBZ6PR30CxyloSpWCI46wBvD335Yi/8sv4euv7bETscyWKQPNm1u4VosW0LSpxbnXqwe1aoU35KtakpQ1a6ytXWufq1dn9GVuq1ebMr18edZW51KlMhTlrl0zltesmUnv3q2pW9cynKYUoYz70SjYdwDfi8gkbDA+GEgwh9aQuPpqePZZU5Duuy9saZwImzeb20XHjnDeeWFLkzOpqebL37cvPPecKXJO4nDlleZedPPNYUuSb1S1DYCI3FFQLn6qOo445dh2nEShalWzxh57rK3v2GE5EGbMgB9+gLlzTfF+9dVdjxMxN5R69WCvvcwWVLGitchyqVKmhBcvbmEgkc8dO8xCvG0b/PhjDX7/3Za3bzcFeP36/7Z163ZVpnfsyP6eROz6md0vunSxl4KaNe0z83LFill7Oaal/U27drH5npONaLKIvCYiaZgfNsA1qro8rlIlC40bQ79+9ro6dKjNbTjh8/DDNmf15pvJ8bp89NH2yj98OJx2GlQomhkwE47vvrP537vvtido4WGaiJyKuYkUw9xE9lbVc8IVy3EKB8WKQevW1gYMyOjfvBl++21XV4o//7S2ZAn8/LMpwWvXmg9z9Pw3m5mIZRWtUCGjVa5sakulSra8+2fmVrFicjw+E5lsFWwRabtb1+Lgs7aI1FbVGfETK4kYOtTcER56yDJWOOGycqXlK44orcmAiGWl6dzZsp0kaD7TIsctt5hifeGFYUsSa0YCnwIzgchjvHd44jhO0aBMmQzFOydUYdMmU7S3bjWrdMRiHVkuVsx8vosXh++//46uXTtRooT1lSwJZcu6ghw2OVmwp2HR5auC9czGfwUOjZdQSUXLlnDCCfDIIxnTyU54XHKJmQnuuitsSXJHhw6W/vGBB6B/f5uLc8Ljww+t3X47lMs5d2oSUlxVT8jcISInhSWM4zi7ImLDTrRDz8qVW2jYMK4iOXkgJwX7CuAkLI3T68BoVd1YIFIlG9ddZ1kg7rwzcaoFFkGqfvstvPGGWR5bJmFF5ltugbfesuwn330XtjRFl7VrrWJrq1ZwxRVhSxMP5onIO8DVwfpFwPQQ5fmX6dOnrxKRRbk8rBoZhqBEw2XLPYkqFySubIkqFxR+2RpktyFbBVtVHwQeFJHGWHqlicHAd7uq/pBPgQoXbdtmZIM4++zCkG0g+di6lb0ffdRCta+5Jmxp8kbZspYSbsAAeO01y1nkFDx33GE+/GPG2Fxr4eN2YBRwPDYbuQPYCpwVokwAqGr13B4jItNUtf2e9yx4XLbck6hyQeLKlqhyQdGWbY8eOqq6ABgDfAJ0JPaVLwsHd91l4b5XX73nfZ3Y88ADlF66FB58MLlT3Z1+OrRvD4MHU2L16j3v78SWZcvM3eu00+zvUDi5A0tOm6qqqapaMg5l0h3HcYo02SrYItJYRIaJyHfACOBHYB9VfbPApEsmataEq64yq9e0aWFLU7T4+WcYPpyV3bpBz55hS5M/UlLgxRdhwwaa3323Rbs4Bcf111sU0fDhYUsST5Zj+a4/FJHPIi1soRzHcQoTOVmw52MVv8YD3wL1gQtF5AoRKZSOifnmssss68B117liVFBs22ZW3woV+HXw4LCliQ0tW8Jdd1H1u+/MJ9spGN57z0qiDxkCe+8dtjTxpD7QF7NizwbKYq4iycrTYQuQAy5b7klUuSBxZUtUuaAIy5aTgn0zMBpL41QOKL9bc3anQgWzgH3yiQU9OvFn+HDL5P/MM2yvXDlsaWLHRRexYe+9Lchuo8cWx53Vqy2wsW3bQlFUZg8I8Co2tq/ECsG0CFWifKCqCfsAd9lyT6LKBYkrW6LKBUVbtpyCHIfH88KFlosvhlGjLF3cYYcldpnuZOebb8z3/eyzrYRWWlrYEsWO1FTmXXYZbS+9FC66CF54IesyWU5suOEGq3c8YYIlki3c/KGqI0TkICwd61LgzJBlchzHKVR4GvJYU6wYPPMM/PWXFaFx4sPGjXDGGVC/vgU2FkLWt2oFN95oL2wvvBC2OIWX776zaqyDBkGbNmFLEzdEZKSIPA9sEJGXgE3As8AEYEGowjmO4xQy9lgq3ckD7dpZLuP77zf/YC8aEnuGDIHff4fPPy/cpcVvuMHu8bLL4IgjoG7dsCUqXKxfbxlD6tYtCpVYP8ii7wtgMFnVWnYcx3HyjFuw48WIEWZdHTjQAvGc2PHhh/D001Y5s1u3sKWJL6mp8OyzVht34EBIT9/zMU70XHQRLFwIr75a6N25VPUdVX0HK5F+BfAMMAh4KVh2HMdxYsQeLdjZZAxZB0z3gjM5UK4cPPEEHHUU3H23BT86+WfZMjj3XGjduihYHI0mTeDee00ZvPnmwp5CruB4+WVrw4cXmVkmEWkBfI65h2wAWqrqDhGZhaVjDZVq1appw1zWfN60aRNly5aNj0D5xGXLPYkqFySubIkqFxR+2aZPn74quwJZ0biItA/a+8H60cBPwAUi8paq3p0v6QozffrA//5niuDJJ0Pz5mFLlNzs2AH9+tm0/sSJhbXKXtZceCFMnWozIx062Iubk3d++82+065dLa1mEUBE3gLaAf8AnYCJQAWx4NmESNPXsGFDpuWyjkBaWhrdu3ePj0D5xGXLPYkqFySubIkqFxR+2YIK51kSjYJdF2irqhuDk90EfAgcDEwHXMHOiQcfhI8/hvPPh0mTPBNEfhg+3PyRX3gB9t03bGkKFhGbEZkxA846C378EWrXDluq5GT7dvO7LlYMXnnFPosGHTBFujo2dtcIPsvgqVedZGbnTliyxOJyfv8dFi2yrEBr1lhbv972SU/PcLMrXTqjVahgxeJq1bLPBg2gRYtC7zbmxJdonix7AVszrW8HaqjqFhHZms0xToSaNeGee+C882DkSDjnnLAlSk7GjYPbbjP3kAEDwpYmHEqVgtdftxLexx9vL2xlyoQtVfLx+OMwZYp9l/Xrhy1NgaGqDcGq9GIFFqoBJYB5QP/wJHOcXLBjh9U+mDoVvv/elmfOhH/+2XW/ihWhcmVrFSvajGdKijVV2//vv2HLFli3DpYvt5fvzNSsCfvsY9mFDjyQkqp2rBvKnCiIRsF+BfhORMYE68cAr4pIWawKWK4RkZOB4VjkekdVnZZp21DgXGAncKmqfhz09wIeAlKBZ1X1zqC/EfA6UBWzxpyhqokVVXjOOfDSSxaUd9RRUKNG2BIlFzNnwqmnwv77wyOPhC1NuOyzj1ldTzgB+veHt9+2QEgnOlasgJtugp494ZRTwpYmLM5S1cODMTwF2IL5ZRcNR3QnuVCFn36yHPVpafDll2aRBlOeDzjA3L1atIBGjaBhQ3txzq0LoaoVnFq2zKzgc+bA3Ln2+dRT8OCDHAhW/Kt7d+jVy8YRf5472bBHBVtVbxGR8cBBQdcFmRTivFo9ZgEnAE9l7hSRlsCpwL5AbeBTEWkWbH4MOAJYDEwVkbGqOhu4C3hAVV8XkScx5fyJPMoVH1JS7B+0TRtzFRk92t+Ao2X5cnspKV8e3n/fpvOKOscdBw88YKkgr7zSlp09s327pc3cvNlct4rY/6CIVAAuAk4WkTrA/wGXAbcBf4Ypm+Pswo4dpki/9x6MGWMuH2BxTP36mYJ74IGmSMfq/1gEqla11qoVHHNMxrbt2+Gnn5g3ahRNV66ETz+1zENg1V+PO87irZo1y/LUTtEkWufDGcCSyP4iUl9V/8jrRVV1TnCe3TcdC7yuqluB30VkPtAx2DZfVRcEx70OHCsic4BDgdOCfV7ELOOJpWCDvV3fcYflb372WXMZcXJm82bo29em8b780nNAZ+ayy8zK8uCDZrW59NKwJUp8Bg82K9jzz9tMQNHjJWAN8CAwDPgNqAQ8rqpXhSeW4wT8+CNNHn/cZiz/+sus0EccYVm4+vQJL+6keHFo144lGzbQtHt38+P+4QcYP97cF2+80dr++5vsZ5zhMTLOnvNgi8glwF9Yta8PsADHrAoWxII67GpJWRz0ZddfFVirqjt2609MLr8cDj/cPn/9NWxpEpv0dBukpk2D114zK4GzK/fdZ77Yl19uVh4ne774Ah57zJTss88OW5qwaAk8DEzFZgrrAW8Cr4mI/4M54bBhg/1vtmkD++9PndGj4aCDzP3t779t5vL//i+xFNaUFHsmDRsGX30Ff/5pheVKloRrrzXL+nHHWc2GnTvDltYJiWgs2JcBzVX179ycWEQ+BWpmsek6VQ1FGxCRgcBAgBo1apCWlpar4zdu3JjrY3anxPnn02HKFP455hi+f+QR0kuUyNf58kMs7icu7NxJ8/vvp9a4ccwfNIjFFSqY790eSNj7ySPR3E/KwIHsP2cOZf/3P3544AE2JLBlNqy/T+qmTbS78EKkVi2m9uxJegxlSLLfXE3gvkzr/wDNgz7FZgMdp2D49VdTrEeONCW7bVt49FG+rVuXLsceG7Z0uaNuXXt5HzwY5s+H556zmbIxY6BePZthPO88C7Z0ig6qmmMDJgHF9rRfXhqQBrTPtD4UGJpp/WPgwKB9vPt+gACrIvLtvl9OrV27dppbJk2alOtjsuS99ywW+dRTVXfujM0580DM7ieW7Nhh3wuoXn+9anp61Icm5P3kg6jv56+/VBs1Uq1USfWbb+IqU34I5e+zebPqwQerFiumOnFizE+fl3sCpmkcxtM9NSxwfH3QNgA7Mi2vj/IcC7FKkD9E7gOogs1wzgs+Kwf9glnM52O1E9ru6fyhjstxwGXLgmnTVI8/3sb44sVV+/dXnTz537G+0HxnW7eqvv226iGH2L2WL686ZIjqH3+EK1cBU9hly2k8j6ZU+gIgTUSGisgVkRa9Cp8rxgKnikjJIDtIU2AKNqXZVEQaiUgJbHpzbHBzk4CTguMHAIk/V37ssXDnnZYm7K67wpYmcdi+3d7yI9/LLbcUuUC0PLHXXpayr1o181f89NOwJUocLr7Y/PdHjYJDi7yB9mlM2R0HXALUVdUKqlpeVSvk4jw9VHV/VW0frF8LTFTVpljxmmuD/t7YGN4UmzlMvNgYp+D46ivLvNG+vY1X118Pf/xh1VQ7dSp8Y32JEnDiiTb7OnWqBes/+KBV5h00CBYvDltCJ85Eo2D/gVklSmDFCCItz4jI8SKyGLM4fygiHwOo6s+YT+BsYDxwkaruVPOxvhizaM8B3gz2BbgGuCIIiKwKPJcf2QqMq6+2YIjrr4/K/aHQs3ixVdUbOdLSqF19ddgSJRcNGpgi2bixDeTukw0vvmjTtNddZ5kHijiqeqGqtsUCwSsDL4jItyJyu4gcLCJ5zfd4LBZgTvB5XKb+UYGhZzJQSURq5f0OnKTk+++hd2/o1s0KZd1xh2UFueUWyzNdFGjf3mKJ5s+3Wg7PPgt7723B6suWhS2dEyeiSdM3ItYXVdXRwOhstt2GpY3avX8cZnnZvX8BGZlGkgcRePppG3yOOcYCORK0nGjc+f13OOwwWLXKAltOPDFsiZKTmjXtZa1PH/sOR460QNGiyGuv2YOsRw97YXP+RVXnAnOBB0SkNNADOBm4H2if07GYr/YnIqLAU6r6NFZ4LKIlLMcqREL2wemuURQFfvsNbrjB/hcrV4a774aLLiraxbEaNrSKvNdcY4XTHnvMlO2rr7aUq2XLhi2hE0OyVbBF5EFVvVxE3scG1V1Q1b5xlawoUL48fPaZTev36QNff21J84sSr71mRQJSUuy7aL+n57uTI1WqWCq6446DM8+EtWvNTaKwTb/mxNixlu/64INtueiUQs81qroFM1z8x3iRDV1VdYmI7AVMEJG5u51PA+U7ahIh+DxeFEXZUjdvpsFLL1H37bfR1FQW9+/Pn6eeyo5y5ayCakhyxYKYyta/P6W7d6fRM8+w1/DhbH30UX4/91yW9+xpz8Ow5IoxRVq27JyzgXbB5yFZteyOS5aWUME0y5ap1q2rWq9eXAIgsiP04IMPPlAVUe3SRXX+/HyfLvT7iTH5up8tW1T79rXgmv79Vdevj5lceaVA/j4ffaRaqpRqx46qGzbE/XJJFuS4gV2DHCPrm4EdeTjfcOBK4BegVtBXC/glWH4K6Jdp/3/3y64l1LgcA4qUbDt3qr7wgmrNmjbunH226tKl4csVQ+Im21dfqXboYN9b+/aqU6cmhlwxoLDLltN4nu1rkqpODz4/z6rFSL93wKb2x4yBdeugY0fzUyvs/PCDlfref3+zuDZpErZEhYtSpeDdd+Hmm22WoF07mDUrbKniywsvwNFHW7W3Dz6AcuXCliih0CCYMWjlMWX4Nsyt46E9HS8iZUWkfGQZ6IlV5R2LBZjDroHmY4EzxegMrNMMVxKnMDFzpsXQnHWWxYN8953FP9Ryl/uo6NIFJk+Gl16yeKSOHc2dZu3asCVz8kG2CraIzBSRn7JrBSlkkaBtW/jmG0tUf+ihUU2lJS3ffAOHHAIVKpgS6OXP40NqqvlATpoEGzdakaPffgtbqtijagFTZ59tPtdffAHVq4ctVcIiIpVEZDiWOq880EFVh0RxaA3gKxH5Ecvu9KGqjgfuBI4QkXnA4cE6mNvJAixzyTPAoJjeiBM+W7ZYEHHbtjBvnr3kfvONKYhO7khJMde2uXPhkkvgySfNWPDaazbGOUlHTo4+RwPHYNk8xgP9g/YR0fvrOblh331NOaha1RSFl14KW6LYs3ChpSncay/zOW/YMGyJCj8HHwwTJ8KOHfbyVpiqiO7YAeefb2WKzzjDKqdVyE3GuaKDiFQTkTuAGVgO7ANU9XqNsoiYqi5Q1TZB21ctIB1V/VtVD1PVpqp6uKquDvpVVS9S1Saq2lpVp8Xt5pyCJy0N9tsPbr89QzEcMCDX/sPOblSsCA89ZFWMGzSA006zoPW//gpbMieX5OQiskhVFwFHqOrVqjozaNdgU4NOPKhf3/KFtm9vQWrnnw///BO2VLFh4UIL5ty+3RShevXClqjosM8+8MknsHmzTeXOnh22RPln0yYrFf/MMzB0qKXlC7EyahKwCOiHpdLbDJxbALUNnMLGli1w+eVmBFK1vPsjR5phyIkdBxxgswF33QXjxpkB7vXX3ZqdRETzqiki0iXTykFRHufklVq1zOJ4zTWWyu+gg5J/an/WLJs2XLbM/M2bNQtboqJH27b28paaCj17Wk7WZGX6dPMrHzcOHn/crGhFKVNK3rgHGBksl8+iOU7OTJ1q48hDD5kbw08/WYpVJz4UK2Yp/GbMsDilfv3gpJPcmp0kRKMonws8LiILRWQR8DhwTnzFcihWzKo9vv++5Ylu1w5GZ5k6PPH59VdT6IoVs0COQw4JW6KiS/PmZsnesgU6dICPPgpbotyxc6cVqujc2fzKP/nE0jw6e0RVh6vVNXhEVUdkbsCosOVzEpjt2y2f/IEH2v/dhAnw8MNFO6d1QdKypblU3nmnBXDvu6/VjHASmj0q2Ko6XVXbAG2A/dRK5BaBNBcJwtFH29tr06ZwwglWlGbu3D0flyi8+665u2zbZoNy8+ZhS+S0bm2WqAYNoG9fePPNsCWKjunTbVp62DBzDXHrWV55X0T+dVQXkX2A90OUx0lk5s41xfrmm80feOZMC5h2CpZixWxW+/vvoVEjOPlkizvxTCMJyx4VbBEpKSKnARcBl4nIjSJyY/xFc/6lUSOb2r/zTguCbNXKUvisXBm2ZNmjCo88YtNZLVvaS8K++4YtlROhcWP7LXXuDKeealXEEtHXX9UU6cMPtxe1H3+EUaPgjTesqI6TF27HlOxyItIOeBs4PWSZnERk1CibPV240Cymo0ZBpUphS1W0adnSfLNvuskyjOy3H5WKQmrfJCQaF5ExwLFY1PmmTM0pSEqWtLfX+fMt8PGppywDx3nnmWUvkfjrL7O2X3qpWUgnTbLgTSexqFABPv4YLrgA7rvPfOR/SpAMnCtXwq23Qu3a0KaNvaDdfz/88YdZbdzfOs+o6ofAA8AnwAvA8ar6Q5gyOQnG5s1wzjmWFSTyYnviiWFL5UQoXhyGDzdFu3Rp9h8yBK64IjGNJEWYaGoI11XVXnGXxImO6tXhsccswOS+++DVV+HZZ02B7doVeve2FHidOlm6n4JE1SyLF19sfnp3323/9KmpBSuHEz1lyliQ4NFH2wO1QwdTbMP6u/3xh1nUv/3W1o86yjLPnHyy57bOJyLyCJA5BUFF4DfgYhFBVS8NRzInoZg92/7f5syB6683S2mxaFQFp8Dp2BG+/54l/ftT54EHzGDy8suWgcQJnWgs2N+ISOu4S+LkjhYtLD3Z0qXwxBM21T9hgln3jjzSrNv33Qfp6QUjz4oVNij362fRzt9/D1dd5cp1stCnj2V6Oeooi1o/9NCC9fX/5x/7HXfsCD//bFlBZsywgJ5Bg1y5jg3TgOmZ2t3AO5nWnaLOCy+YxXrVKgsgvuUWV64TnTJlmHfZZaZcr11rY+jtt1tAuBMq0SjYXYHpIvJLUMVxpldyTCAqVrQp/jfesBR4P/5oA+NBB5lfba9esGhR/K6/aZNN3e+7r2U8ufNOi3beZ5/4XdOJD9WqwTvv2EP2xx/N1//SS+HvqOqQ5A1VS0nZpo0p0g0bmvV66FC3wsQYVX0xpxa2fE6IrF9vxWLOPtuMNT/84IGMyUbPnhaAeuKJVl2ze3fznXdCIxoFuzfQFCsucwwZFR6dRCM11SprHXGEWf6eesqU3ZYtzV9r3brYXWvrVlPEmjWDIUPsujNmmJ+4WzySFxHzu5w3z/z7H3sM6taF//0vtv7ZixaZdaxpU3uQb90K48ebct2yZeyu4/yLiLwvIseISPEstjUWkZtFxFOwFjUmT4b997ciJjffbDOhtWqFLZWTF6pUsb/jyy/beN2mDbzySthSFVmiSdO3CKgHHBosb47mOCdkRGDgQPOn69MHRoywbCQ332yuAHmtBrVokVkX69Uza0etWpbhZOJEzxJSmKhe3Vw2fvwRzj3XZkX239+sJE8/bS5BuUGVMgsXwnPPWWq9hg2tvHn9+laBcfZsc23y4MV4ch7QDZgrIlNFZJyIfCYivwNPAdNV9flwRXQKjJ07zZWga1d7HnzxBdxwg7v1FQb697exu3Vrm5k47TRP5xcCezQ1ishNQHugOVYFrDjwMtAlp+OcBKFBA3jrLcs0csMNFrBy003Qrh3199/frNpdu9qb7+7KTXq6/VP+/juMHQtffgmff27b+va1VIGHHgop/r5VaGnVCh591KzNDzxgaaHOP9+Ku3TrZvlxW7UyhTk93TKTbNwIf/5pAYuRz6lT6bhsmZ2zUSN74TvzTDvOKRBUdTlwNXC1iDQEagFbgF9VdXOYsjkFzJIltLnySnMFOfVUePLJgg+Kd+JLw4aQlmZum8OH22z2Sy/BwQeHLFjRIZq5/OOBA4AZAKq6VES8rG6yESkr/eefpiw/8QSNn3vOLIpgVosKFaB8efssXdqC3DZssO0pKTbdNHSoWcY97V7RonJlm/0YMcL8/N5+235H994LO3Zkf1zFijbb0a0bcxs0oMWAAeaf7y9loaKqC4GFIYvhFDSq5to3ZAgVtmyBkSPNJcxnjgonxYpZJpgjjjCrdo8epmwPG+YzFQVANAr2NlVVEVEAESkbZ5mceFKvnlmeL7qILz/6iG7ly5sP3urVpkxv2GABLxs32j9k8+bmLnDkkRYE5xRtRMzffr/9TOHets38tf/80wbsdeugbFl7AatXz17WApanpdHC3YgcJxx+/dVmn9LSoGtXpg0cSKczzghbKqcg6NTJMntdeKG55k2YYLMWHu8SV6JRsN8UkaeASiJyHnAO8Ex8xXIKgp2lS5t7SNeuYYviJCslSpjvvSvOjpOYbN5sM0233w6lSlkMxbnnsuWLL8KWzClIypc3F5EjjoDBg21GesgQcx0t63bTeBBNkOO9WCnddzA/7BtV9ZF4C+Y4juM4Th5JT7fS5s2bW9zNcceZ299557mLVlElkiXql18s+PGuu8yKPWZM2JIVSqL6L1PVCap6FXAn8Gl8RXIcx3FijYhUEJE7ROQlETltt22PR3F8PRGZJCKzReRnEbks6B8uIktE5Ieg9cl0zFARmR/UUTgy9nflZMmkSVYwZsAAqFnTgtNff92WHad6dfO//+ILs2wfd5wlLliwIGzJChXZKtgi0llE0kTkXRE5QERmAbOAv0TES6c7juMkFyMBwWYjTxWRd0SkZLCtcxTH7wCGqGrLYP+LRCTixPmAqu4ftHEAwbZTgX2BXsDjIuKRVfFk7lw45hjL7vT335YD+bvvPHOEkzXduplv9j33wGefWYXowYPjW1ysCJGTD/ajwDCgIvAZ0FtVJ4tIC+A1YHwByBc3pk+fvkpEclvisBqwKh7yhITfT2Lj95P45OWeGsRDkChooqonBsvvich1wGci0jeag1V1GbAsWN4gInOAOjkccizwuqpuBX4XkflAR+DbPN+BkzWrVlmGnyeegDJl4I474LLLLBuU4+RE8eJW9blfP3Mlevhhs24PHWqVfP03lGdyUrCLqeonACJys6pOBlDVuVIIUvqoavXcHiMi01S1fTzkCQO/n8TG7yfxSbJ7KikiKaqaDqCqt4nIEuALoFxuThTk0T4A+A6riXCxiJwJTMOs3Gsw5XtypsMWk7NC7uSWrVsz8tRv2GBZQoYPh732ClsyJ9moUweefdYs2Ndeay3y2zrjDE/rlwdEs6noJyIzVLXt7stZrRcVkuxhukf8fhIbv5/EJ5nuSUTuBj5R1U936+8FPKKqTaM8Tzngc+A2VX1XRGpgVnwFbgFqqeo5IvIoMFlVXw6Oew74SFXf3u18A4GBADVq1Gj3+uuv5+q+Nm7cSLlyuXo/KDDiJpsq1b74giZPP03ppUv5u1Mnfjv/fDY3ahS+bPkkUeWCxJUtHnJV+uEHGj/5JBV++YUNTZsy75JLWN+6dULIFitiIVuPHj2mZ/sMUNUsG7ATWA9swHzv1mda357dcYW5AdPClsHvx+8nWVthu5/Cek97uN/iwMfAFdlsbwjMCpaHAkMzbfsYODCn87dr105zy6RJk3J9TEERF9nmzFHt0UMVVPfdV3X8+DydJlG/t0SVSzVxZYubXDt3qr7yimqdOvZ7699fdfHixJAtBsRCtpyeAdkGOapqqqpWUNXyqlosWI6sF8+Tqp/8PB22ADHG7yex8ftJfArjPWWJmG/gc8AcVb0/U3+tTLsdjwXDA4zFgilLikgjoCkwpaDkLXRs3gzXXWdFnr7/Hh5/3EqdH+nJWZw4kZICp51mwbPXXWcVfJs3t5zq//wTtnQJjyfDzAWqWqgepn4/iY3fT+JTGO8pB7oAZwCH7paS724RmSkiPwE9gMEAqvoz8CYwGwuKv0hVd4Yke3LzwQdWzOn22y0Ybe5cq8pXLJpacY6TT8qVg1tvhdmzoWdPU7b33dfyZ2vWbsZOdJUcHcdxnCKOqn6FpfnbnXE5HHMbcFvchCrs/PGHZQN57z0rCJKWBoccErZUTlGlcWN491349FP7XR53nCncjz4KTaMK4ShSuAXbcRyniCEiB4nIaSJyZqSFLZOTiW3brMrePvvAJ5/AnXeaW4gr104icPjh5p700EOWZ711a0sT6W4ju+AKtuM4ThFCRF4C7gW6Ah2ClhSZUIoEn38OBxxgadKOOMKm5a+5BkqUCFsyx8mgeHHLkz13LpxwgqWH3G8/s247gCvYjuM4RY32QBdVHaSqlwTt0rCFKvKsWGGlzbt3t4DGsWPNNaRBWHWJHCcKataEV1+1mRZVeyns3x+WLw9bstApsj7Y1apV04YNG+bqmE2bNlG2bNn4CBQCfj+Jjd9P4pOXe5o+ffoqzUOhqxgyC6hJUJXRCZn0dHj6aauct2kTDBtmQWRlyoQtmeNEzxFHwMyZ5s50xx3w4Yf22axZ2JKFRpFVsBs2bMi0adNydUxaWhrdu3ePj0Ah4PeT2Pj9JD55uScRWRQfaaKmGjBbRKYAWyOdqhpVyXQnhsyYYdlApkyBHj3gscfM79pxkpFSpcxVpF8/GDQIBg2ibYsW8NJL0L7oeaEVWQXbcRyniDI8bAGKPCtXWlDYE09A9erw8suWb1iyStLiOElG8+bmi/3qq5S65BLo2BHOPx9uuw2qVAlbugLDfbAdJzekp9s0ruMkKar6OTAXKB+0OUGfE282bDDFunFjU64vvNCCxPr3d+XaKVyIQP/+TBk1Ci65xNygmjeHp56C7dvDlq5AcAXbcXLD9ddb0v1ffglbEsfJEyJyClZR8WTgFOA7ETkpXKkKORs2wAMPQJMmNoV+5JHw88+WP7hSpbClc5y4saNcOUvnN2OGKdgXXGBuUK++agarQowr2I6TG6ZPt89DDoGjjoIJE8KVx3Fyz3VAB1UdoKpnAh2BG0KWqVBSeskSuPJKqFcPrrjC8gV/952VnG7RImzxHKfgaNMGvvwS3n8fypa1WZv994fXXiu0Fm1XsB0nN0RSZu27L/z0E/TtC6+8YtHTjpMcpKjqikzrf+PPgtixY4el1zvySDqdfjo8+KBZrL/7DiZONH9UxymKiMDRR1vRpNdeg61bLfagYUMrxb5ixR5PkUwk7KAqIqki8r2IfBCsNxKR70Rkvoi8ISIlgv6Swfr8YHvDUAV3Cj+1atmDcsYMqFsXTj/dEuw//3zYkjlONIwXkY9F5CwROQv4kBzKnTtRsH07fPwx/N//WV7g44+Hn3/m97POsnLnb7zhirXjREhJgVNPhTlz4IMPbGbnhhtspue44yzod/36sKXMNwmrYAOXAXMyrd8FPKCqewNrgHOD/nOBNUH/A8F+jhN/qlc3JfvLL6FnTxg4EDp1styfjpOgqOpVwNPAfkF7WlWvCVeqJGTHDnvRHjjQXrp79YI337Sx4L33YOFCFg0YALVrhy2p4yQmKSnmajl+vAX7DhoE06bBGWfY87VvX0vxt3Zt2JLmiYRUsEWkLnAU8GywLsChwNvBLi8CxwXLxwbrBNsPC/Z3nPhTvjx07Wo+lWedZX3Dhlkg05dfws6dYUrnOFmiqu+o6hVBGx22PEnDzp1Wyvyii6BOHTj8cAvW6tkTRo+2Ke5XX4Vjj4VingXXcaKmeXMLBP7jD/jmG/sf++EHOPNM2Gsvcy154QVYsyZsSaMmUUeAB4GrsRRSAFWBtaq6I1hfDNQJlusAfwKo6g4RWRfsv2r3k4rIQGAgQI0aNUhLS8uVUBs3bsz1MYmM30/uabZ0KVW3bePbrK5z+umwcyf7jhhB9REjYMQIlh5zDL8OHpynFFz+90l8kumeROQrVe0qIhsAzbwJUFWtEJJoiU16OkyebG4eb70Fy5ZB6dL2wD/lFOjTx6suOk6sSEmBAw+0du+9VoTp7bftf+/DD6F4cXuxPfFEe5GtVi1sibMl4RRsETkaWKGq00WkeyzPrapPY1OjtG/fXnNbga2wVaLz+8kDr74KJUrkfJ3u3c115JVXqP3QQ9RescKqWD3wgA0OUeJ/n8Qnme5JVbsGn+X3tG+RRxWmTs1Qqv/8E0qWNGX6lFNMuS5XLmwpHadwk5ICnTtbu+ce+5+MKNv/939WvKZ7dzjpJPPdrlkzbIl3IeEUbKAL0FdE+gClgArAQ0AlESkWWLHrAkuC/ZcA9YDFIlIMqIhFxccWn+p3oiU1FTp0gHbtoEQJ+PprK4G8bZtNe7VqZfs4TgiIyEuqesae+oocqvZi/Oab1hYutBfiXr3g9tvNH7SCG/kdJxRELFC4Y0e46y7LRPLOO6ZwX3ih+W9362aW7RNOsAQEIZNwPtiqOlRV66pqQ+BU4DNV7Q9MAiLFEAYAY4LlscE6wfbPVDXz9GcshII6dWjhwWtObkhJgbvvNgX76qvhmWcs7+cxx/gLmxMm+2ZeCQwT7eJxIRHpJSK/BFmero3HNfJFerr5ew4ZYtUV27eH+++3QhgjR5pP9dix5v7lyrXjJAYi0LatlV6fO9fS5N54I6xeDZddZtlIIi4mv/8empgJp2DnwDXAFSIyH/Oxfi7ofw6oGvRfAcR+EFeFv/6i5iefWJnbjz6yPseJljvugM8+s+DHjz4yq9i113rZdafAEJGhgf/1fiKyPmgbgL/IMFjE8nqpwGNAb6Al0E9EWsb6OlGzdau5enzyCdx8s7l7VK8OXbpYRcV994XnnoPly2HcOAta9iqLjpPYiNis8PDhpmjPnWuK99atcNVV9uLcrp09g6dMsf4CIl8uIiKSArQBagNbgFm7FTDIF6qaBqQFywuwimO77/MPVvK3YBg+3D4ff9ymJRwnGlJSoEcPa+npljN74kT47TdLuJ+amqdASMeJFlW9A7hDRO5Q1aEFcMmOwPxg7EZEXseyPs2O2RV27KDkypVWYXX5cvjrL2uR5cx9mbMPiJhCfcIJ9j959NFuoXacwkDz5pbJa9gwWLAgw41k2DDbXry41a1o3556IjY21K9vVu9atWLqvpknBVtEmmAW5cOBecBKzF+6mYhsBp4CXlTVQlVofuGZZ9Lw1lvhggtsGuKdd+Dkk83R3nGiJcgwwr332hv222/DoYdaCVnPRuDEnykiUlFV1wGISCWgu6q+F+Pr/JvhKWAx0CmWF6hf/FEasz+wASgLNAYasxPYlkXbjlmCNijsnAXMIkgGOyOWYu1GWhzPnV/SwhYgG9LCFiAH0sIWIEsOOSRsCbJm7dr9Q5wIagxcBaWvgs5brXjNhg3w23r4YSPs3AFPAmxlf97mwU/2hSOOiNnV82rBvhV4Ajh/d39nEdkLOA04g4z81IUCTUmxt5yXXzaleuZMU7YrVrQ8qFWqhC2ik0wMGQI1asCPP5rf55FHwkEHwSWXJESAhlNouSlz7mtVXSsiNwHvhSFMftKn/g3s4L9KdKGy7DhOFKxN0GIsO3fuTBzZihc3Pa1KFUBJ37ad4uk7Sdm2nTWVUvh6wyq2xzDtap4UbFXtl8O2FVge68JL5coWZf7PP3DwwdCvn7kAPP64W7Od6BGxilVnnAEtWsDQoZZvd+xY+OADUrZtC1tCp3CSVexNPDJKRTI8Rcic/elf8pM+dZN2T+hUiS5b7klUuSBxZTO5KoUtRpYk6ncGu8tWP+bnz6uLyME5bVfVL/ImTpJRqpSV+HzvPfOjvfhiWLTIymUfe2zY0jnJxP/9n7XPP7fZkL33pnOlSpbhYJ99wpbOKVxME5H7sQBEgIuA6XG4zlSgqYg0whTrU7HZTcdxnEJPXq0WV2XRp8B+mMWi6CT5rVIFzjnHci8eeqhFqoIp3KeeGq5sTvJxyCGmVH/3HVx/vbmN9O5t5WK7dAlbOqdwcAlwA/BGsD4BU7JjSlBZ92LgY+yZ8Lyq/pzTMdOnT18lIotyealqZFG5N0Fw2XJPosoFiStbosoFhV+2BtltyKuLyDGZ10WkC3A9sBwbvIseFSvCtGnmNtKzp037X3mlpWK7+OKwpXOSiXbtoF07fkpNpf1DD9nL2iuv2GzJfvt5tgMnX6jqJuKRzjTra40DxuVi/+q5vYaITFPV9rk9riBw2XJPosoFiStbosoFRVu2/KbpOwyzhChwu6pOiIlUyYoIlC4N774Ld95pVshLL7W8i+3bm7+2p2JzomRj8+YwezYsXWpuR926WZDGm29aWVjHyQMiUh24Gis4UyrSr6qHhiaU4zhOISNPhWZE5CgR+Qa4ErheVXsUeeU6M9Wrw333wYQJVjL7yiuhe3d46KGwJXOSkdq1zW3kySetEmT//pZp5N13w5bMSU5eAeYCjYARwELMX9pxHMeJEXmt5Pg+FhG+A7haRMZmbrETL8kpXRq++gpmzbKCBldcAR07WhUxrwTp5IZ69SxDzfvvQ8uWVsb5pJNMyd6+PWzpnOSiqqo+B2xX1c9V9Rwgma3XT4ctQA64bLknUeWCxJUtUeWCIixbXl1EesRUisJM8eJWMWzUKBg8GH76CW66yZSiI4+EAw+MaeUgp5BTowZMnQpbtlgFuhNPtN/P8OEWFOk4eybyRrZMRI4ClgJJm8Q/SPOXkLhsuSdR5YLElS1R5YKiLVtegxw/BxCRdqq6S3onETk6FoIVOsqWhaeftjLZ//sf3HqrtXPOgWefdd9sJ3eULg0ffggvvGCzJDfcAJ9+ain97r3Xfm+OkzW3ikhFYAjwCFABGByuSI7jOIWLvLqIRHhGRFpFVkSkHxb06GRHSgq8/roVFLnySnj+ecsacc45loHEcaKlalWrBvn22/Zb2rjRXuKOOcYU7z/+CFtCJwFR1Q9UdZ2qzgriZ9qpqrv2OY7jxJD8Vu86CXhbRE4DugFnAj3zLVVhJzXVskJ07AhlysCXX5pP7Zo1pmgfcoinYnOiJzUV7rnHll96Cc4+GyZNMgX85ZeheXNo1ChcGZ2EQUSaAc8AtYEjgfLAMap6a6iCOY7jFCLyZcFW1QVYda53gROBnqq6LhaCFQlEYMQI+OwzuP9+qwjZty907Qpr14YtnZOMnHEG/PWX+WlXqGBFaho3Nkv32rWwc2fYEjohISIVRWQYMANoCNQC3gQ+AK4SEY+tcRzHiRF5TdM3U0R+EpGfgLexAJlGwHdBX74QkXoiMklEZovIzyJyWdBfRUQmiMi84LNy0C8i8rCIzA/kaptfGQqcwYNh3jwrKjJ3LjRtain+fs6x8Jnj/JeqVS3v+rRp8M47ln3k/vuhcmVo0cJ+X05R5G3gT+BXVW0AzFPV9qpaDysSdqyInBuqhI7jOIWEvLqIxDuQcQcwRFVniEh5YLqITADOAiaq6p0ici1WjewaoDfQNGidgCeCz+Ri772tVapklfsmTLCqkIMHm1W7c+ewJXSSiSpVLD3kCSdAnz7wyy8WALnffvYbGzzYKo16gG2RQFWPABCR00SkCVYgDBE5CVigqpeHKJ7jOE6hIq8K9t+qujGnHUSk3J72yQ5VXQYsC5Y3iMgcoA5wLNA92O1FIA1TsI8FRqmqApNFpJKI1ArOk3z06mVt5kw44gi46ipL9/fOO5aarVy5sCV0ko2+fe3z5JPhsccsN/uwYfDqq+ZC8vjjUKdOuDI6BcVFwKvAviLyN7ACeDBUiRzHcQoZefXBHiMi94nIwSLybz4wEWksIueKyMdAr1gIKCINgQOA74AamZTm5UCNYLkONvUZYXHQl9y0bg1//gnLlkGrVqYkVahgftuOkxcaNrSAyHHj4O67oW5diwHo3BkGDIC33gpbQif+XI8ZV94GxmFja79QJXIcxylk5DUP9mEi0gc4H+gS+ELvAH4BPgQGqOry/AonIuWAd4DLVXW9ZJrKVlUVkVyVQxSRgcBAgBo1apCWlhbdgenpdAe2bdsW/TExptjw4ez12WdU+uEH9ho+nDXvvcfmBg347YILSC9VKk/n3LhxY2j3Ew8K4n6aLV1K1W3b+LYAvre430+HDtChA+VOPJHm99xDyfffp8SoUSzp25fNDRqwvFcvdpYpE7PLFbbfGyTXPYlIBcx6fRzwP+DTYH0IkO/YGcdxHCcTqpqQDSgOfAxckanvF6BWsFwL+CVYfgrol9V+2bV27dpp1OzcqQq64Kyzoj8mXuzYoXrFFart2qmKqHbvrvrss6p//JHrU02aNCn28oVIgdzPeeep1qoV/+toCH+frVtVBwxQBWvt2ql+8IHqvHkxOX1h+72p5u2egGkazpg6BngB+AoYj7nYfQ7sH4Y83rx581aYW34LzcQFMVP1c8AcVb0/06axwIBgeQD2wIj0nxlkE+kMrNNk9b/eE6mpcN99liHixRetit///Z/l1J4+HTZsCFtCJ1kpUcIK1GzZAu+/D3PmwNFHw777WnGkFSvCltDJH41V9SzgOuBAoCuWAeqlWGR/chzHcTJISAUb6AKcARwqIj8ErQ9wJ3CEiMwDDg/WwfwIFwDzsQIKg0KQueA54wxYtQq++85sju3bW+aIZ58NWzInmSlVyhTr+fPh66+t0mi/flCjBvzvf7B1a9gSOnlje/D5HDa+zsUyQh0NHBOWUI7jOIWR/FZyjAuq+hWQXe6ww7LYXzFfwqJHxYpmvZ4yBT78EEaPhvPOgwcftMC1Rx81hclxckutWtY+/dTKsc+caWn+JkyA+vVh1ChL+eckC21EZD1QBngFKA3MxMZaBbx8rOM4TozIt4ItIl2Bpqo6UkSqA+VU9ff8i+bkivr14cILrdT6DTfY9P5zz1k6tg4drNhIq1ZhS+kkI2XKwJln2vJBB8FHH9nL3MEHwyGHWErJCy8MV0Znj6hqKoCIPA5UAt4HfDrCcRwnDuRLwRaRm4D2QHNgJBaY+DLm4uGEQcmSln4N4OWXLdfxc8/BSy/BzTdbIZvevb24iJM3jj/e2qJF9tI2ezaMHWtuSm3awFlnWcVIJ5EpjSnWPTP1KfBuOOI4juMUPvJrwT4ey1E9A0BVlwaVF51E4PTTrS1aZFbGyy6z/osvhtNOI3XLlnDlc5KXBg1g/HhIT4fLLzdXJFULkrz3XnuRa9QobCmdLFDVs8OWwXEcp7CTXwV7m2pGPurMRWecBKJBA/jpJ/jrL3jgAbj/fnj0UdrXqmVT/U2aWAEbx8ktKSnw8MOW2WbSJCvL3rOnZSR54w046iirQuqEjojcmMNmVdVbCkwYx3GcQk5+s4i8KSJPAZVE5DyscMEz+RfLiTnFi1vVvvvus4DI1183C3bbtlCpEtxyi1kgHScvFC9uivXcuVYZcv/9zZWkRAnLdvPPP2FL6MCmLBrAucA1YQnlOI5TGMmXBVtV7xWRI4D1mB/2jao6ISaSOfEjqOA3XYQDV682y+ONN8Lw4ZYV4s03oWnTsKV0kpG6da19+ik8/zzMmwePPQZjx9KxYkXrO/zwsKUskqjqfZHlwJXvMuBs4HXgvuyOcxzHcXJPfoMcGwFfRpRqESktIg1VdWEshHPiy9a99oJTTrFgtT59zPr4zDOWbaRKFeu/8UZzA3Cc3FC+fIbPf58+8MEH6LhxcOSRULu2ZSN54gn7nTkFhohUAa4A+gMvAm1VdU24UmVQrVo1bdiwYa6O2bRpE2XLJqZ3osuWexJVLkhc2RJVLij8sk2fPn2VqlbPalt+fbDfAg7KtL4z6OuQz/M6BYkIDAgKZA4cCI88YpbHESPgySfNIvnEE2b5dpzc0qcP9OnDjI8+ottXX8Eff5h/9rhxGS9y551ncQAlS4YtbaFFRO4BTgCeBlqr6saQRfoPDRs2ZNq0abk6Ji0tje7du8dHoHzisuWeRJULEle2RJULCr9sIrIou235NU0WU9VtkZVguUQ+z+mESaNGFgQ5dqyVYu/dG1auhC5doGFDy7O9fn3YUjpJyM7SpeG22yxl5DffmG/2PvvAddfBXntZpchHH4XPP4d168IWtzAyBKgNXA8sFZH1QdsQFKBxHKcwo2rj77uekbMgyK8Fe6WI9FXVsQAiciywKv9iOaEjYsVFzjwTVq82xWjxYlO633oLqlaFoUPN4u05tZ3c0r69NYCJEy2f9ujRcMkl1lexIpx0kuXUPuss2Hff0EQtLKiq+3o5TlFl0yZ7Xr/6qrnpHX98wT27N260ayWoq0i8yO+AewEwTET+EJE/sEj0gfkXy0koqlSx7CNvvAFffGGWx3r14IILbEp/332tf+3asCV1kpHDDjPFeuJEmDzZXEd69IAxYywFYKtWVk3ygAPgqafgnXdgTcK4DScdInJuFn13hiGL4zj5ZNEi6r/ySs6ZmmbPho4d4fXXoVs3WLoUfi+Agts7d1J79GhzMz311PhfL8HIswVbRFKBC1W1s4iUA0hEnz4nxnTpYi093azZv/xiivchh9j2ww+3wMiaNT0TiZM7RKBTJ1vu3ds+V62CkSNhxQpTvC+4wPrLl7ffYY0aMGgQNG5sL4IekBsNJ4rIP6r6CoCIPIZVd3QcJ5mYOBH+9z8a//23BY6fnUUNqRdegIsusjHzk09szGzdGr780sbNeDF9OlxwAc2mTbOZyE8+gc2bzVhSRMizgq2qO0Wka7DsinVRIyUl45952DB7M1661CzdBx9s/aefbtbJtm0t/Z/j5JZq1eCqq2z5rrss+HbVKgu6/eUX+PZbe9EDe1icdFLBBUqWKUNqmzYFc63YciIwVkTSgV7AWlU9J2SZHMeJFlV71l5zDbRowZbixSk9atSuCvamTaZYv/iizQi++qoZvtLTTeH96quM5AaxZM0auOEGG6P32ovZ119PywMPtKJjX38NRxwR+2smKPn1wf5eRMZimUMiRQtQVfegL0pUqGC+XWAZIWbMsCC2u++Gl182y+TJJ0OtWuZPu//+YUrrJCspKdC8ubUuXaxv3ToL2Fm71lxH7rmnYGQJijJVuv32DGt7ghOk6Ivwf8B7wNfACBGpoqqrQxHMcZzo2bjRsi69/jqceCKMHMmywYNp/NxzsHChJSOYPdueuXPmwE03mcKbmmrHp6TY+Pnll7GVKzKrfc018PffcOGFcOutrPjhB1p26GDFyCZMcAU7F5QC/gYOzdSngCvYRZVatexN9aij4Mor7R/tkUfMjWTdOltu2dJ8sq67Dvbe2zJI+NS+kxcqVsyw2gweXHDX/f57m5nZubPgrpl/pmdaVkCAo4KmQBznix3HyTdz5phS/csvcMcdpsyK8Nfhh5uC/fLL9my96CIoV84U2sMO++95unaFDz6wDGHVs0zhnDu+/96u+e23cOCB8PHHFjMToWxZc2H59NP8XyuJyG8lxywcfsJBRHoBDwGpwLOq6kE7YVO5srWHHrK2Zo1ZtX/5xSzc3brZfq1awbnnWmaS44+3gcFxnFjTVVWX5OcEQezNNGCJqh4dFBt7HaiKKfBnqOo2ESkJjALaYUaY/3kBMsfJB6+9ZpbrMmXMnzmT4ry1Zk3o3h1uvRW2bjWXkFdeMYNXVkSevaNHww8/2P4nn5x7mTK7g1StavEyZ56ZtcHs8MNt31WrzPWvCJAvs6GI1BWR0SKyImjviEjdWAmXCzlSgceA3kBLoJ+ItCxoOZw9ULmyvXW/+6750r74ouXc3rbNrI9nnml5uA8+2FxOpk2zSOdgOt5xnHzxjIhMFpE7RaS7iOTFwHIZMCfT+l3AA6q6N7AGiGQoORdYE/Q/EOznOE5u2brVrMOnnWbuld9/n7VV+oIL7Fl6441muc5OuQZo185iVc4/35Tje+/NnUzp6aZMN29uxw8aZIazs87Kfjb68MPtc9gwu/4TT+TumklIfl1ERgKvApFXn9ODvoJ2sukIzFfVBQAi8jpwLDA7FidPT4fBPMjKFz7itRc853OsEaA60BS4dNUq9vrySzp9+SWln3kGgJ+Bidgc9p74MW5SGnVow3iu5jl5KFOv7mF5T9tzWp6cj2Mjy1uAN4AFOLGhJXU4nAf56YZnmXT99WGLExWq2kdESgHdgeOBe4P0quOB8ar6R07HB8aTo4DbgCtERDD3wNOCXV4EhgNPYOPv8KD/beBRERFVf1t2nKhZtAhOOQWmTIEhQ8xAVbx41vv+738WD1Khwp7PW7KkWawXLLCUu++8E32Gj8zuIAcdZNb0aOKq2rc3l75nnrG4rOefNz/tQkx+Fezqqjoy0/oLInJ5Ps+ZF+oAf2ZaXwx02n0nERlIkKe7Ro0apKWlRXXy9B3pPMxldOFv4ON8C+vsigIrgvZ10FcdOAybdz4LOCMUyf7LYC7kRc7HjHVgrwfEaTlW50sFbgVWA79j+s8K4CvAKybmhW1U52Euoy2fRT2OJAKq+g+BQg0QuHj0xhTgmqraMYfDHwSuBsoH61WxDCQ7gvXF2FgMmcZkVd0hIuuC/b0QmeNEw+jR5jq5c6cpwCecsOdjolGuI7z0kn2OG2cxUlOmmJtJdqxYYdbxZ57ZsztIVhQrZm4rW7eaW8qtt5qLSeXK0cucZORXwf5bRE4HXgvW+2H+dgmJqj4NPA3Qvn17jbYGfXq6fTY962y+GnlznKQreNLS0oj2O0gGCuJ+SgyEWh/A0qXxHxRidT/LlsFzz6WwbFk1PvusGnPndgDMmNCzp8W4XH55/NOWF6bfWyTG8fhbbqV799Zhi5NnVPV34HHgcREpkd1+InI0sEJVp4tI91jKkFfDR4SNGzcm7EuOy5Z7ElUuKBjZUrZupcnjj1Nn7Fg2NGvG7BtvZEuVKpDDdfMjV7GdO+kiwsKXXmJRFttl+3bqjB5Nw1GjSPnnH5YedxwLzz6bHeXKWYG5PbCLbGXLQtmyVKxenQNUmfXII6yKpPUNgXj/PfOrYJ8DPIL52CnwDRBG4OMSoF6m9bpBn+MUeWrVgogXQ3o6zJplyV0efxx+/BH+/NPc4UqWtBm/IUMsBqV9e0/uUpgQkd/Z1YdIMq2rqjbJ4fAuQF8R6YNlj6qABZVXEpFigRU787gbGZMXB77eFcnG+JJXw0eERH5xc9lyT6LKBQUg288/W8XDWbNgyBDK3347nUpk+94bO7lataLRkiU0ynwOVXj/fXsgzJ8PffrAffdRt0ULchNol6VsBx0EQ4fS6q+/craax5l4/z3zpGCLSGdVnayqi4C+MZYpL0wFmgbTnUuAU8nwC3QcJyAlJaPmT48e9rl8OTz3nAV3v/KKZVgE6NAB+va1TIqnnOLKdiGg/W7rKcApwJXA9zkdqKpDgaEAgQX7SlXtLyJvASdhmUQGAGOCQ8YG698G2z9z/2vHyQZVc724/HKruPjRR9CrV8Fdv2tXS/G3c6fly5450xIPTJwI++wTe3lKlLBkBhMn7tq/dKlZhKRwxLrl1YL9ONAWQES+VdUDYydS7gl8/C7GHKRTgedV9edYX2fy5KpcdNGufZl/B7v/JmKxLad9a9QwF60qVXCcPFOzpqUkBxgxwpK3/PabLd9wg/XfeacVSjzlFIulKSTjX5FCVf8GEJEULKzhKuAH4ChVzWtA+DXA6yJyK6akPxf0Pwe8JCLzMef/U/MhuuMUXlautGweo0dbEZZRo2xQLki6dLFpzM8+syxfTz8NlSpZ3Yrzz88+sDI/HH54hnX8q6/s+lOmWMXJfv1if70QyKuCnfnxWioWguQXVR0HjIvHuUXsBW/mzFK8+WbkertfP+vl3GzL7b6bNlnMQeXK9v9xzjm23LGjWxudvFGhAhx6qLVzz4UdO6xg2GOPWYHO0aOttkHNmqZ0R6zgTuIjIsUxt77BWITrcao6P7fnUdU0IC1YXoBlcdp9n3/IyC7lOE5WjBljKWnXrrUaEUOGhPPw7trVPnv2NAv2xRdbBch4Wu8iqQZbtoTt281SXrGiBV3GQsHescMU9w8/tKnYSO7vAiSvCnaKiFTGphgjy/8q3YWt5K6IVRVNS/smoXzDZs2yqf3Vq+G99+Dtt61/332hUyeb2r/wQnsRdZzckpJiM3lnnmlt50546in4+mv47jtTwkVs3HruOfu9OQnN78AOLBvIH8B+IrJfZKOqegVexykI1q2Dyy6zWhD7728VDluHGCxdv75ZS8qUgXvuMWU33rRuDUceaS4xF10Ehxxi06NpaWZNzMs06T//2Hc5ejSMHWt+j2AuL+PHx1T8aMirgl0Rq9oV+QZmZNrmJXcLiFat4IEHbHntWgtYW7TIZnU+/tjSTF5/vSlJxxxjRRKrVbMXR7dwO7klNdXqCQwaZClTn3nGXOaeftrG49694fTT4dhjLWDSSTg+xcbnNkHLjAKuYDtOvPnsMyvIsmSJPaBvuMEe0mEiYnIVJCkp/1V6e/SAt94yH8VoLTbr15vVe/Ro+9y40aZijzrKlJ6PPrJz7thhqQILkDxdTVUbxlgOJ59UqmQvgGDWRjCF++23LWPEyy/bbwygWTNo0gTq1KlPp05QunQoIjtJTJkyZoABuOQSeOghc517/30zyLz/PtSu7S9yiYSqngUgIiVVdWvmbSLikRyOE0/WrYOhQ83XuFkz+OYbm2p2Moh4CKSl5axgr1hh7jWjR1ug5LZtFpR22mmmVB96aMZLi6rl7J4xw/xnCxB//BVi2rSBW26xdGxLlsDs2aYE1alj688+25iKFc2qfdddsHChWSYdJzfUrWuzin/8YS9x8+dbcbBKlazIl5NwvJu5TLqI1AQmhCiP4xRuxowxX+OnnrJMId9/78p1VrRoYYpyVrmpFy60KfuDD7YgoIEDYe5cs/B89ZUpNU89ZdlOMs8IRPJsf/55QdzBLhSsvdwJjfLlbRp/n30y4gcefPAHli7dn59/hmuvtVa1qr1gt2sHjRp5tggnelJT4aST7DkyejS89pq51A0fDp07hy2dk4n3gLdE5CQsV/VYLFWf4zixZNkyUwDfecd8jkePLnAralIhYlbsSZPM8jxrln1no0db9UewPLM33miW6v3227OSUrMmNG9uCvZVV8X7DnbBFewizP77r+Xyy2150iRYsAAefdRSsYH50r72mruQOLmjZUtrp51m2W0uv9xc326/vXKYNQWcAFV9Jqja+B7QEDhfVb8JVSjHKUykp1vk91VXWeDd7bfDlVfGJ91dYaN7dyvd3rixWa1F4MADbZr0+OPNvzW3HHKIpcOK5PkuIPLsIiIiqSIyN5bCOOHRo4elZZs82TKS3HSTBeG2bg233WYpAR0nNzRqBL//boG3LVvCNdfsR/ny8PDDYUtWNBGRKyINS69aH8uD3Tnocxwnv0ybZpaFgQPhgAPgp5/M99qV6+g46ijzY23WDJ580iLpv/7aXlDyolyDKdjr15sf9vz5FvBYAORZwVbVncAvIlI/hvI4IVOypFmuhw+3QLV69SzQuWVLW3ec3FCypGWA+uQTOPPMRXTqZMGRl19uWW7S08OWsEhRPlMrh2UNmZ+pz3GcvLJyJZx3nrmA/P67BdZ99pkpik701KsHixdbKrTzz49N0Z1IBojOnaFpU0u1VgDk10WkMvCziEwB/rVxqmoilE938slRR1n76iu44ALL1d6+PZxwAlx9dYHOtDhJTo0acNZZC+ncuSEnnGBZR8CMCbfe6tlGCgJVHRG2DI5T6NixwwKXbrzRUsQNHmzLFSuGLZkToU4dCzLbuNGm5idMsL9TnMmvgn1DTKRwEpquXW1m5bHHLEvEsGE269W/v23zQjZOtJQqZalK09Ptpe2OO6z16wcvveQvbfFERJ4BHlLVWVlsKwv8D9iqqq8UuHCOk2yompX16qutkMnhh5v/W0EUaXFyzx132OeOHfDKK+aPHWfypWCrasHnPXFCoUQJe+EbPNh+p8OGWcxAq1aWzrO8TzA7uSAlxdJHHnCABYc//bSlMj3oIJtl9d9TXHgMuFFEWgOzgJWYL3ZToALwPODKtePsialT4ZprLDtA48aWJeT44z3tVjLQrZv5dv/4Y9wvlS8FW0Q6A48A+wAlgFRgk6pWiIFsToIydCicfLKNMWecAR06ZFSVrFcvbOmcZKFYMbjwQluuUsVysb/zjlW6HTu2wItuFXpU9QfgFBEpB7QHagFbgDmq+kuYsjlOMlB6yRLLPfrmm1C9uvnyDhwYfiVGJ3q6dbPPL76wqmhxJL+ej48C/YB5QGng/zAriVPI2Xtvm9YfNcrGmfHjzUd7+nQL1nWc3HDHHTZj99RTVtm2dGkLxPffUuxR1Y2qmqaqr6nqe65cO84e+OMPGDSIDgMGwIcfWpqt336Diy925TrZqFfPUlx98UXcL5Xv0CJVnQ+kqupOVR0J9Mq/WE6ycNpp8OWXVpL9p58sCHKffeDPP8OWzEk2RMwY9OqrVpthyhSbdX38cVi+PGzpHMcpcixYYD5re+8Nzz7LsqOPtsjs4cPdjy2ZOfhgU7BV43qZ/CrYm4OCBT+IyN0iMji/5xSRe0Rkroj8JCKjRaRSpm1DRWS+iPwiIkdm6u8V9M0XkWvzc30nb/TqZb60L79sgbrdu5vy/f33YUvmJBv9+sH991tg/uefw0UXmTX7p5/coh0mIlJPRCaJyGwR+VlELgv6q4jIBBGZF3xWDvpFRB4OxuWfRKRtuHfgOFHyyy8wYICl2HvpJXvz/+035l1+eWzSxjnhcvDB8Pff1H7vPUtpddVVprDMmxfTy+RXwT4jOMfFWJq+esCJ+TznBKCVqu4H/AoMBRCRlsCpwL6YlfzxoNhNKuaW0htoCfQL9nUKmNatLbPI6NGWWeSjj+DII83CvWJF2NI5ycb//R+sWWNK9qpV0KaNpfsbNy5syYosO4AhqtoS6AxcFIy11wITVbUpMDFYBxuTmwZtIPBEwYvsOFGiahH7p5xihR/eegsuvdRyWj/6qAcYFSZ69AARmj38sBVleOQRmzJdtSqml8mXgq2qiwABaqnqCFW9InAZyc85P1HVSJmdyUDdYPlY4HVV3aqqv2MFEjoGbb6qLlDVbcDrwb5OSBx6qPliT55sfrUHH2xj06RJYUvmJBvly9vvZ+pUePZZe+4df7zVCxgxwgvV5AYRqSkiT4jIYyJSVUSGi8hMEXlTRGrt6XhVXaaqM4LlDcAcoA423r4Y7PYicFywfCwwSo3JQKVoruM4Bcq2bZa2rVMnmyqbMMFS7y1caFNptfwnW+ho1Ai+/56pzzxjBYK2bDHXnwMPjOll8ptF5BjgXiyDSCMR2R+4OYaFZs4B3giW62AKd4TFQR/An7v1d4rR9Z180Ly55c+eOtVc1o47zvJm9+9vszGOEy3Nmlk7/nhLFTlvnv2mvv3WMthceqlVjHRy5AXgQ6AsMAlLydcHU4ifJBeGCRFpCBwAfAfUUNVlwablQI1guQ7/HZvrAMsy9SEiAzELNzVq1CAtLS3qGwLYuHFjro8pKFy23FNQchVfvZpa48ZR5733KPn332yuV4/Fl1/O8p49SS9dGmbPthaCbLklUeWCBJetZk3SZv2nLEDMyG8irOGYBTkNLA2UiDTa00Ei8imQlSPTdao6JtjnOmxKMmZ5WQvzQJ4XCup+qlWDG28syX33NeP778vw0UelmDx5Pi1arGeffTbELHVoQdzP0qXN2LatKmlp38b1OuC/t+w4+2ybzX311fq8805dJk4sxsiR2znhhCU0aLCJrl3/zr+we2DePMt0988//yTT36iGqj4CICKDVPWuoP8RETk32pMEaf7eAS5X1fWS6R9YVVVEchU5pKpPA08DtG/fXrt3756bw0lLSyO3xxQULlvuiatc27ebj9nzz1s2kJ07zY/x8ssp07MnzVJSyKmweZH8zvJJUZYtvwr2dlVdJ7tqSHscXFX18Jy2i8hZwNHAYar/hnkuwXy8I9QN+sihf/frFtqBPC8U9P2ccorNxBxxBDzySFPAYgvuvjs25y+I+3n1VcvKVBDfm//ecqZHD3jmGfj5Zzj22JI8+2xjAAYNMjelQw+FypVjdrldiFRBLlWqFN27d4jPRWJPZpfAUTlsyxYRKY4p16+o6rtB918iUktVlwUuIJGIi5zGbMcpOObMMaV61CgLCKpRA4YMsbf1Fi3Cls4ppOQ3yPFnETkNSBWRpiLyCPBNfk4oIr2Aq4G+qro506axwKkiUjKwkjcFpgBTgaYi0ijIaHJqsK+TgJQuDRMnwtdfw/nnwz33QJMm5jLyzz9hS+ckI/vuC7/+Chs2mKvI44/DSSdZysi0NFi0KGwJE4YxgfUZVb0+0ikie2MB5TkiZkl5DitMc3+mTWOBAcHyAGBMpv4zg2winYF1mVxJHCe+zJ0Lt95q0dEtW8KDD1qp2LFjLY/sXXe5cu3ElfxasC8BrgO2Aq8BHwO35POcjwIlgQmBZXyyql6gqj+LyJvAbMx15CJV3QkgIhcH104FnlfVn/MpgxNHSpa0ca5zZ6hb11L5vfaaxRr06gWnngp16uz5PI4TISUFypWzjEuXX24K9xlnmJUb7Fk6eLBVhyyq1YxV9cZs+ucDJ0Vxii5Y5qiZIvJD0DcMuBN4M3AzWQScEmwbh/l4zwc2A2fnWXjH2ROq5jP9zjuWASTiW9uli5UZ7tfPLNeOU0DkS8EOLMzXBS0mqOreOWy7Dbgti/5x2GDuJBEpKXB9YEd74gm47DIrk/3YYzBmDDRs6Ln8ndzTqJG1n36yTDavvgrXXGPt4IOtKFL16mFLmXyo6ldY1qisOCyL/RW4KK5CrVhB9bQ0WLcOihc3/63sWlbbU1PjKp4TZ9autSnR8ePh44/NMi1i0fQPPwwnnODWGic08ptFpBlwJdAw87lU9dD8ieUUNS68EM4919L7HXkk7LefuZOMHm3rjpNbata0zDV9+8LRR1vtiPvvh7ZtLcf2oEGuaCc9P//MviNG5P34lBQoU8ac6itUsM9Iy816yZKxuycne9avh+++Mx/DTz/NyAVboQIcfrhZbI4+GmrXDltSx8m3i8hbWHqnZ4Gd+RfHKcqUKGFpKKdOteI0jz0Gxx5rLnTHHQfXXlt0p/edvJOSAmeeact9+9rvaMQIuO8+uPlm89tOyW80ihMOHTsy5fnn6bj//pbPePt2+9y9ZdW/fTts3QqbNpnitm6dtTVrzHE/sr558x7FoESJLJXvFlu2wLvvZq+YV64MVatClSpuTd+dnTstN/HUqVYA5uuvYeZMcwVJSbE35aFDzQLTqZPNUDhOApFfBXuHqnp1LiemNG9u7bjjLND7l19g2DBzqTvgALM+VqoUtpROMtKpkxU8mjvXfluDB8OLL8JRR8GVVxad35WIHMR/Zx53zyyS+JQty+ZGjWxgiBfbt1sEbUThXrduV4U8u/XffqPSihVmZV2/3hTD7BCxH1/Vqv9t1apl3V+1qk3zJTvp6bB0qQVOzJxJ848/tvRSs2ZlRL6XL29BOyecYAE8nTrZi4rjJDB5UrBFpEqw+L6IDAJGY4GOAKjq6hjI5hRxqlUz5UcVrrjCAthefdUCIh94APbe22cCnbzRogV88AG89JJVQb7jDksucOONNmPStGnYEsYPEXkJaAL8QMbMo/Lf1H0OmGW0ShVruWRyJDWlKmzc+F+FfPVq+Ptva6tWZSwvX275J//+247LjtKl/6uAV6oEZcua60vZstm2sr//Dg0aZOxXpkzsp3J27LD7WrHC2l9/2b39/jssWGBt4UKbSQioWrGipQC68ELzFTzgAKsm5RZ+J8nIqwV7OjYgRybsr8q0TYHG+RHKcTIjYgr1ffdZLMsJJ8Ahh9jz4L33LK+24+QWEXMdOfNMc+c8/ng4+eSMVJIxrpqbSLQHWmaqMeDEGxGzwpYvb6mTcsPWrRmKd+aWWSGPtD/+MOV90yZrOfyJs8zeXrp01gp5RAkvWdLOmZ6+a9u61V4ENm2yz0hbuzZrGSpWhMaNTXHu29eWmzSB1q35Zs4cukfS/zhOEpMnBVtV91it0XFiTUoK9OljmZh++cV8aXv2tP6LLjJ/bcfJC4cfbm63v/1mOdkPP9yMe9dfb+uFjFlYJV3PSZ0MlCxpU3W5na5TNReLiLIdaZs3w6ZN/DxlCvs2bJjt9l3a8uX2uW2bDbiZm4jJWK6cWdMbNLDlsmVtfa+9LD1e5s+cfLHmzs3Pt+U4CUNeXUQ6AH+q6vJg/UzgRCwH6nB3EXHiSePG1jp3hqeftpnURx6B997ryL77WnBkY59DcXJJxAvgk0/gtttg2jQYMMDys++/v82aFBKqAbNFZAq7uvb1DU8kJ+aImEU64kayGyvLlIFCVCnWcRKNvLqIPAUcDiAiB2OFBi4B9sdKkUdTtMBx8kXlypbbGKyWwMiRW/juuzIceCD07g2HHWbFRhwnNzRqBM8+azPtBx9shWvAXtwGDQpVtFgxPGwBHMdxCjt5jWhIzWSl/h/wtKq+o6o3ANkWinGceHH++XDnnTP55htTkMaPN9/aQYNgxoycA/gdJysqVIApU2zG+phj4JJLzGX05pvDlix/qOrnwFygfNDmBH2O4zhOjMizgi0iEev3YcBnmbblN/Wf4+SZFi0sK9aSJZbf+IknoF07C4T8+eewpXOSjRIlLGXkq6/ay1qVKnDTTRZwm6yIyCnAFOBkrKz5dyLis46O4zgxJK8K9mvA5yIyBtgCfAkgInsD62Ikm+PkmdRUS+u3dCk8+KBZsdu0sWqR77xjNQwcJ1rKlTM//0mTLJj2lVci/Un5Q7oO6KCqA1T1TKAjcEPIMjmO4xQq8qRgq+ptwBDgBaBrpnRPKZgvtuMkBLVqwWWXWQ2D886D11+Hk06C/v0t/WqkjoHjRENqqr2gTZtmReXatFkbtkh5IUVVV2Ra/5u8G1scx3GcLMjzoKqqk1V1tKpuytT3q6rOiI1ojhM7qlUzd5F16+Cuu+CNN8xXu0ULWLw4bOmcZCI11dyOWrWyRA1JyHgR+VhEzhKRs4APgXEhy+Q4jlOocKuFU6QoVgyuvhq++MKyQqxeDT16wFlnmRuJ4xR2VPUqLNvTfkF7WlWvice1RKSXiPwiIvNF5Np4XMNxHCcRSVgFW0SGiIiKSLVgXUTk4WCg/klE2mbad4CIzAvagPCkdpKFbt0saG30aChVKqMi5MSJFiDpOIWZIOvTFUEbHY9riEgq8BjQG2gJ9BORlvG4luM4TqKRkAq2iNQDegJ/ZOruDTQN2kDgiWDfKsBNQCcsWOcmEalcoAI7Scthh5kv7fTpZt2OVPAbOzZsyRwntojIV8HnBhFZn6ltEJH1cbhkR2C+qi5Q1W3A64DXW3Ucp0iQqCn1HgCuBsZk6jsWGBUEVE4WkUoiUgvoDkyI5OUWkQlALyzTieNERZMm8P33MHUq3H47nHIKtG9vAZGRQiOOk8yoatfgs3wBXbIO8Gem9cWYISRmiLQG7gLeA7YD23Jo2W3fjCW/2r15BHTisjfQAzgYqFLA107McIXevcOWIGtWr25NlYL+E0XJ7rLdeSfst1/szp9wCraIHAssUdUfZdcIoqwG6zo59DtOrqhd21KwdelimUdmz4bBg81lpFMn6OuFpJ1CgIi8pKpn7KmvAOUZiM1KUqNGDdLS0nJxdEms8nuJoBXPtJy5lcyDZNvIWvHeU1ufaXlzHq7r7EoxLFTgIOBATKmuG2xbxq6P/6LL77/HYxIq/+zcmcq6dckh27ff/srq1Rtjdv5QFGwR+RSomcWm64BhmHtIPK6bj4EcNm7cmOtjEhm/n+w57zzLlX3bbS259969AOjWbSVly+5g27YqpKV9G5Pr5IT/fRKfJL2nfTOvBEXD2sXhOkuAepnW6wZ9u6CqT2NBl7Rv3167d+8e9QVUp5GWlkb37h33sJ/9P2/bltG2b4etW2HTJssu9N9WgnXrqgftv9vXr99zhdhixdKpXj2FqlWhalXLZpT5s3p1qFEjo1WrZq5qBYF9b90L5mJRsmMHvPrqFEqX7siMGfDNNzaruGWLba9b1wwgPXpA9+7QrFktbCK7YEjE7wwiclUIW4wsSdTvDLKSrX1Mzx+Kgq2qh2fVLzbf1wiIWK/rAjNEpCPZD9ZLMDeRzP1p2Vw3zwM5JPYPJS/4/eyZQw+FP/+Et9+GIUOqAzbIF8T35n+fxCeZ7klEhmIGjNKZfK4FM9U+HYdLTgWaikgjbJw+FTgtDtfZIyKmuBYrBmXKxOac6emwcWN2yrm1H39cTNmy9Vm1Cv7+22bF/v7bWlbFrkRMyd5rr10V7xo1rK9yZahUyT4jy+XLQ0pCRlNlz9q18Pvv1n77zeJgZs2y72frVntZKl4cDjgAzj8fDjzQWr16OZ/XcRKJhHIRUdWZwF6RdRFZCLRX1VUiMha4WERex/z41qnqMhH5GLg9U2BjT2BoAYvuFFJEoH59uOIKy5s9c6blQHacZENV7wDuEJE7VDXuY6Sq7hCRi4GPgVTgeVX9Od7XLShSUqBCBWvZKX5paQvo3r3+f/pVTQFfsQL++sta5uVImzLFPjfmMGudkgIVK1orW9ZauXIZy5lb5v5Fi/Zi/fr/7luqVEYrWTJn5V3VinVt3mwzAZHP1ath5cqMtmKFtUWLTKlet1u959q1oXVrCzovVmwOp5yyD/vsYzI4TrKSUAr2HhgH9AHmY45tZwOo6moRuQWzlgDcHAl4dJxYcvzx1hwnyZkiIhVVdR2AiFQCuqvqe7G+kKqOI1GjwkJExKzPlSpBs2Z73n/zZlNQ166FNWuy/ly3zpTbSFu9etf1jRt3t5pHlzGxeHEoUcKU6d3b9u17dpNJTTWrfPXqZqzo0gUaNjSDRaRVzpT3Ky3tLw44YJ+oZHOcRCahFWxVbZhpWYGLstnveeD5AhLLcRwnmbkpc+5rVV0rIjdhqTicBKRMGVNK84Oq+Z5HFO5Jk6bQsmXHXZTwTZvMIv3PP+afHlnets1eCnZvxYtnWL7LlMn4rFzZFOrq1W052VxYHCcWJLSC7TiO48ScrNQdfxYUckTM5aNkSahSBerX30z72MZ0OY6TCX+vdBzHKVpME5H7RaRJ0O4HpoctlOM4TmFCdE8OVIUUEVkJLMrlYdWAVXEQJyz8fhIbv5/EJy/31EBVq8dDmGgQkbLADUAkm9ME4FZV3RSWTBEK4bjssuWeRJULEle2RJULCr9s2Y7nRVbBzgsiMk1VC82kmt9PYuP3k/gUxntKNhL5b+Cy5Z5ElQsSV7ZElQuKtmzud+c4jlOEEJHqwNVYwZl/E6Gp6qGhCeU4jlPIcB9sx3GcosUrwFysqNcIYCEZaU4dx3GcGOAKdu6IR7WzMPH7SWz8fhKfZLynqqr6HLBdVT9X1XOAZLZeJ/LfwGXLPYkqFySubIkqFxRh2dwH23EcpwghIpNVtXNQBfdhYCnwtqo2CVk0x3GcQoP7YDuO4xQtbhWRisAQ4BGgAjA4XJEcx3EKF+4iEgUi0ktEfhGR+SJybdjy5AUReV5EVojIrEx9VURkgojMCz4r53SOREJE6onIJBGZLSI/i8hlQX9S3pOIlBKRKSLyY3A/I4L+RiLyXfDbe0NESoQta24QkVQR+V5EPgjWk/Z+RGShiMwUkR9EZFrQl3S/N1X9QFXXqeosVe2hqu1UdWzYcuWFRBqbE3WMTeSxMtHHvUQdvxJ1LBKRSiLytojMFZE5InJggsjVPPiuIm29iFweb9lcwd4DIpIKPAb0BloC/USkZbhS5YkXgF679V0LTFTVpsDEYD1Z2AEMUdWWQGfgouDvkqz3tBU4VFXbAPsDvUSkM3AX8ICq7g2sAc4NT8Q8cRkwJ9N6st9PD1XdP1Nqp6T7vYlIMxGZGFEERWQ/Ebk+bLlySwKOzS+QmGNsIo+ViT7uJfL4lYhj0UPAeFVtAbTBvrvQ5VLVX4Lvan+g3f+3d/bRXpVVHv988yVelUEdug3q1dKo1AEVzNTxampLF2lOlJkzSVNDTioiY2uGsVGnNbOybJWFqzepnFqIjaZoshJfkIZBEORFuEhQCSWGohW+VwJ7/tj7B4efv/vKvfx+57I/a531e85znuecvc89d5/n7Gc/zwO8CtzV67KZWW7tbMCJwOzC/hRgSr3l6qYuzUBrYX8N0BTpJmBNvWXcBd3uBs7sCzoBA4ClwAn4JPh7R/5Oz2Kjb8DwMFqnA/cCKrk+64EDq/JK97wBPwPGAMsKea31kGUX9Wg421wGG9uotrLR7F4j269GtEXA/sA6Ymxfo8hVQ86zgPm7Q7b0YHfMXwFPFfY3RF5fYJiZbYz0M8CwegrTXSQ1A6OARymxTtEduRzYhK+u9ytgs5ltiSJle/ZuxOdb3hb7B1BufQy4X9ISSRMir4zP2wAzW1SVt6VmycamDLa5oZ6PRrSVDWz3bqRx7Vcj2qLDgOeA70dYzTT5qrH1lquajwIzIt2rsmUDOwHA/BOudFPKSBoE/BiYZGYvFo+VTScz22rehTUc9zCOqK9E3UfSWGCTmS2ptyw9yMlmdiweknCppL8pHizR8/a8pLcRskoaB2xsv0qyq9T7+WhUW9mIdq8E9qsRbdHewLHAN81sFPAKVSEXDfA/sC9wLnB79bHekC0b2B3zNHBwYX945PUFnpXUBBC/m+osT5eQtA/+wphuZndGdql1AjCzzcDDeBfkEEmV2X7K9OydBJwraT1wG97N+jXKqw9m9nT8bsLj98ZQzuftUuDbwAhJTwOTgEvqKlH3KINtbojnowy2ssHsXkPbrwa1RRuADWb2aOzfgTe46y1XkbOBpWb2bOz3qmzZwO6YxcARMXp4X7x7oZQj7mtwD3BxpC/GY/NKgSQB3wVWm9lXCodKqZOkgyQNiXR/PEZyNf7CGRfFSqOPmU0xs+Fm1oz/z8wxs4soqT6SBkoaXEnjcXytlPB5M7MnzewM4CDcW3gqcHJ9peoWZbDNdX8+GtlWNqrda2T71ai2yMyeAZ6S9I7Ieh/wRL3lquJCdoSHQG/LVs9g87JswDnAWjw27Op6y9NNHWbg3cCv41+an8Rjyh4CfgE8CAytt5xd0OdkvDtnBbA8tnPKqhNwDLAs9GkFron8w4FFwC/xbq0311vWbujWAtxbZn1C7sdjW1WxA2V63vD5rqcAN+ENGQGX4QOT7q63fN3UqWFsc6Pa2Ea2lWWwe41mvxrZFuEzwTwWf8+ZwF80glwh20Dgd8D+hbxelS1XckySJNkDkHQ3PrXYAty79Jd4I/sKM1teR9GSJEn6HNnATpIk2QOQtNLMjo70Xri39RAz+2N9JUuSJOl7ZAx2kiTJnsHrlYSZbcUHJGXjOkmSpBdID3aSJMkegKSt+NRZ4KEh/fEVzYTPUrVfvWRLkiTpa2QDO0mSJEmSJEl6kAwRSZIkSZIkSZIeJBvYSbeRtFXSckmrJD0u6Z8lvSmOHS/p6+3UbZb0sd0nbeeQtF7Sgb14/hZJ9/bW+TtxfUmaI6nT4QCSxkr6fG/KlSRJkiR9iWxgJ7vCa2Y20szejc+rezZwLYCZPWZmE9up2ww0XAN7D+Ac4HGrWiq5A2YBH5A0oJdkSpJkN1FwjLRKur2r/9eS3irpjkiPlHRO4di5kv617dq7jqSJklZLml6V3yLphdBtuaQHe+BaQyR9poMyH5Rkkjpc4r07DhZJTd2o82VJp3elTtLzZAM76RHMl2ydAFwWXtLthkTSqQWjtyxWoboeOCXyrgyP9jxJS2N7b9RtkTRX0h2Sfi5peqxMhqTRkh4J7/kiSYMl7SXpBkmLJa2Q9Ola8kqaKWlJeN8ntFFmcryEWiVNirzmMO43R937YwWyijwrQqcbJLW2cbv2kzRL0hpJ3yp4/c+StCD0v13SoMi/JvRplfSdgv5zJX1V0mMh02hJd0r6haT/bOPaFxGrVYUuP5d0i6S1cW/PkDQ/zjEm/rYGzAXGtnHOJEnKQ8UxchTwZ+CSrlQ2s9+aWWU1w5H4R3vl2D1mdn2PSVqbzwBnmq+sWM280G2k+Wql29GOJc67wpC4XntcCPxf/PYGk4Gbu1hnKtCrHzpJJ6jHijq59Y0NeLlG3mZgGDuvfvUT4KRIDwL2Lh6P/AFAv0gfATwW6RbgBWA4/kG4AF+ZbF/gSWB0lNsvzjsB+FzkvRlfVeqwGnIOjd/++ApiB8T+euBA4DhgJb760yB8xaxRuOd9CzAyyv8P8HeRbgVOjPT1QGuN67YAf8RX49oLeABfevdA4H+BgVHuX9ixqtnQQv0fAh+I9Fzgi5G+Avgt0BR6b6joVHX9XwODI13R5ei4t0uA7+GzSpwHzCzUuwiYWu9nLrfcctu1rWi38cb1N4Ch+Mp7K4CFwDFx/FR2rP64DBgcdqM1bPBvgOfi+AXAeOCmqNsMzIlzPoTPuQ5wC/B14BHcho9rQ87JcZ1WYFLkfQv/KFgJXFlVvoXCOyXyxuPLYc8BftaOnteF7ZsbMk2M/NuA10K/G2rIOAh4GjgSWFPIPz90VtjktcBb2Pm9ODCuuSju7Xlt3IcniRUjQ5+Z+HtjPb4S6+Sov5Cd3xVLgLfU+3nbk7f0YCe7g/nAVyRNBIaY2ZYaZfYBbpa0El+C9l2FY4vMbIOZbcMNXTPwDmCjmS0GMLMX47xnAR+XtBx4FF8K9Yga15so6XHcKB1co8zJwF1m9oqZvQzcCZwSx9bZjpXvlgDNkobgDdcFkX9rO/djkZk9aT4X8Yy41ntC5/kh+8XAoVH+NEmPxr05HXh34Vz3xO9KYJWZbTSzP+FG+eAa1x5qZi8V9teZ2cq4t6uAh8yt80r8PlfYBLy1HZ2SJCkR4dE9G/9f/w9gmZkdA/wb8IModhVwqZmNxO3fa5X6ZvZn4BrgR+Ye4x9VXWIq8N9xzul4o7pCE273xuLOiGrZjgM+AZyA28Z/lDTKzC7BHQmnmdlXa6hV6RVdLunqyDsWb8Sf2o6eACOA9wNjgGsl7YN7gX8V+n22xvXOA+4zs7XA70JuzOwufCGnS3Hv87Vm9kxV3auBOWY2BjgNuEHSwKr7cBjwh7DpFY4C/hYYDfwX8KqZjcKdTx8vlFsKnFRD5mQ30Z0ukySpiaTDga14Y+ydlXwzu17SLLwrcb6k99eofiXwLPDXuDe1uABG0bhspf3nVsDlZja7HTlbgDNwb/OrkuYC/do5ZzXV8vTvQl2A6rkxDZf7ATPbqZtRUj/cw3S8mT0l6boqWSuybKuSaxu179MWSW+KBjU16hTPV6zfj8LLNUmS0tI/PuIB5gHfxZ0RHwIwszmSDpAPhK44R6YDd5rZhohQ6wwn4g1B8J63LxWOzQwb9ISkYTXqbndwAEiqODiWdXDNeWa2PZRN0njcrv6+cN5aegLMiobsnyRtwntiO+JC4GuRvi32l8T+5bj3faGZzahR9yzgXElXxX4/4BBgdaFME95DUOThcJK8JOkFvIcY/EPpmEK5dIrUmfRgJz2CpIPw7rubwgNaPPa28JJ+EViMewpewrsbK+yPe6S3AX+Ph0+0xxqgSdLouMbg8MjMBv4pvA9IOrLaKxDX+kM0rkfgHpJq5gEflDQg6p8feTUxs824wTshsj7ajuxjJB0WsdcX4PF7C4GTJL095B4o6Uh2NKafj5jscTXP2HnW4OEpXeVI/GWRJEm5qcRgjzSzy8MTXRPzeOpP4U6E+erEQL5OUvyw73SLvZu80nERoGuOHCQNxXsUp0laD3wW+Ih2fIEMxx0VwyrjbKpPAXyo8Lc4xMxWV5V5jTc6f9IpUhKygZ3sCv2jK24V8CBwP94FV82kGKC3Al+u+ad4DNxW+QDFK3Ev7cURtjGCDoxivBQuAKZGnQdwgzINeAJYGoMMv80bDeV9wN6SVuPdkwtrnH8pHiu4CPfuTDOzjrwnn8TDXJbj8XUvtFFuMXAT7qlYh3tqnsPj62bEfVoAjIiG+81443Z21N0VZuFxgF3ltKibJEnfYx4+zqLSw/e8mb3YhnOkSLWjpMgj7HA0XEQ7Doo25Om0g6OL532Dnu2Ub0+/ccAPzexQM2s2s4Nxe35KOHu+h3u0V+Nx0tXMBi4vDFofVaPMWnYO1esK6RSpM7mSY5L0EJIGRbw28qmqmszsijqLtROSmoAfmNmZXagzDLjVzN7Xe5IlSbI7kPSymQ2qyhuKNwgPB14FJpjZCklT8Y/ryhiN8XjYwr1mdlTUm42PofkC7uk+3swuk3Qo8H18APdzwCfM7DeSbon6lan+3iBP5E8G/iF2p5nZjZG/Pq7xfFX5FuCqGiEix5vZZR3oeR0++PPLUa4VGGtm6yXdiode/LQYhy3pYXyQ+X2FvIl4eORGfLzRZPmsWYvxj4RhFRnls0/dCLwXd3auK8peOOdDwKfN7Jc19Nl+L4rHogd3BXB0G2Oekt1ANrCTpIeQdAEwBfeY/xoYH57phkLSR/CBOZ2aCzvCcF4vDOxMkiRJdgOSzgeOM7PPdbHOsWb2770nWdIR2cBOkiRJkiRpUCR9ysymdaH8h/HBnZt7T6qkI7KBnSRJkiRJkiQ9SA5yTJIkSZIkSZIeJBvYSZIkSZIkSdKDZAM7SZIkSZIkSXqQbGAnSZIkSZIkSQ+SDewkSZIkSZIk6UH+HwfO9VfR36YAAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 720x288 with 5 Axes>"
       ]
@@ -496,7 +496,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAEoCAYAAABiqYaBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACblUlEQVR4nOydd3RUZROHn0novRfpVUWqFBVQAaVasAs2rNgVe1es2Ptnwd57AQVBVMBOU+moSO8CSu/M98fcNQGSsEl2c3eTec55z+7evWXuZnPv7LwzvxFVxXEcx3Ecx3Gc2JAStgGO4ziO4ziOk59wB9txHMdxHMdxYog72I7jOI7jOI4TQ9zBdhzHcRzHcZwY4g624ziO4ziO48QQd7Adx3Ecx3EcJ4a4g+04uyEiY0Tk/LDtcBzHcfIeEflCRPqFbYeT3LiDXYARkY4i8qOIrBGR1SLyg4i0Dd47W0S+TwAb54nIJhFZLyLLRORVESmVh8eP6ecgIgNF5M1Y7c9xnIJNVtfxREJEOonIor2s86qIbA2u96tFZJSI7Bdnu/a4JqtqT1V9LZ7HdfI/7mAXUESkDPA58BRQAagB3AlsycY+UuNj3R4co6qlgJZAK+CmPDqu4+Q7RCRFRFqJyFEi0kVEqoRtk5MzYnEdzwtEpFA2Vn8wuN7XABYDL8XHKseJL+5gF1waA6jqO6q6Q1U3qeqXqjpFRPYHngMOCSIJ/8J/0YVnRWS4iGwAOovIPiLykYj8LSJzReSKyAFEpJ2ITBSRtSKyXEQeDZYXE5E3RWSViPwrIhNEpOreDFbVZcBIzNGOHOPgIHrzr4hMFpFO6d47W0TmiMi6wLbTg+W7RCxEpK6I6O43gSw+h6NE5NfgvBaKyMAM9tVPRBaIyEoRuSV4rwdwM3BqsL/JWdnpOLFERBqIyGBgNnA/0Be4BPhKRH4WkXNExO8JyUWm13HY+7UuSIcbJCLjg+vZEBGpsNu6/UVkiYgsFZFr0+2rqIg8Hry3JHheNHivk4gsEpEbRGQZ8A7wBbBPcO1bLyL7ZHViqroJeJ9dr/d7u9/8FNwLlorI0yJSJN37BwQR8dXB/ejmLK7J/6UJiv0gvVVE5ovIChF5XUTK7vYZ7XG9T2fTHvdAp2DgF9OCyx/ADhF5TUR6ikj5yBuqOhO4CPhJVUuparl0250G3AuUBn4EPgMmY9GGI4ABItI9WPcJ4AlVLQM0wC6WAP2AskAtoGJwrE17M1hEagI9MQcBEakBDAPuwaI31wIfiUhlESkJPAn0VNXSQHvgt6g/naw/hw3AWUA54CjgYhE5brfNOwL7Yp/J7SKyv6qOAO4D3gv21yIWdjpOlNwDvAk0UNXuqnqGqp6kqs2BY7H/yTNDtdDJLplex7PBWcC5QHVgO3Y9Sk9noBHQDbhBRI4Mlt8CHIw5wC2AdsCt6barhl2X6wTH6AksCa59pVR1SVZGBdfGvqRd71PI+n6zA7gKqAQcErx/SbBtaeArYASwD9AQ+Dqja3IGppwdjM5AfaAU8PRu6+xxvQ+WZ3YPdAoA7mAXUFR1LXZRUOAF4G8RGSp7jyQPUdUfVHUn0AyorKp3qepWVZ0T7KtPsO42oKGIVFLV9ar6c7rlFYGGQdRlUmBPZnwqIuuAhcAK4I5g+RnAcFUdrqo7VXUUMBHoFby/E2gqIsVVdamqTo/u08kaVR2jqlODY07BojOH77banUE0aTJ2Q8jowh0hLnY6TnpUta+qfquqmsF7K1T1cc87TS5ycR1PzxuqOk1VNwC3AafIrul/d6rqBlWdCryCOb0ApwN3Bd+dv7HUlPQ/0HYCd6jqliAaHS3XBrOF64Jzi+yzLVncb4L7yM+qul1V5wHPk3ZdPhpYpqqPqOpmVV2nquOitOd04FFVnaOq67EUxT6y64xnZtf7zO6BTgHAHewCjKrOVNWzVbUm0BT7Zf/4XjZbmO55HWzK79/IwKbbIhf387ApzFliaSBHB8vfwFI93g2mFh8UkcJZHPO4ILrbCdgPi1BEjn/ybsfvCFQPbhanYhHopSIyTGJULCMiB4nI6GCack1wjEq7rbYs3fONWNRjD+Jpp+NkhIikisixInKFiFwdGWHb5eSMHF7H05P+mj4fKMyu17Pd34+kduwTvM7oPYC/VXVzNuyI8HAwW1gXm9ncN1ie5f1GRBqLyOdixfBrsch05DxqAX/lwBbI+DwLkXafg8yv95ndA50CgDvYDgCqOgt4FbtAg0VEMlw13fOFwFxVLZdulFbVXsE+/1TVvkAV4AHgQxEpqarbVPVOVW2CpUQcjU0h7s3GsYGND6c7/hu7Hb+kqt4frD9SVbtiU5+zsGgHWIpHiXS7rpbVYTNY9jYwFKilqmWxPG3Zm/2Z7S8LOx0nHnyGTXlXxFK9IsNJcjK4jkdzrauV7nltLOq6Mov3I6kdSzCnN6P3YM9rXWb3lAxR1QXAlcATIlKcvdxvgGex62ejICXjZtKuywux9I4MD7UXUzI6z+3A8ijOIcN74N62c/IH7mAXUERkPxG5JshrRkRqYVN/kSms5UDN9EUiGTAeWBcUshQPImNNJU3q7wwRqRykk/wbbLNTRDqLSLNgGnItdkHfGaXpjwNdRaQFlk96jIh0D45dTKy4pqaIVBWR3sHFbAuwPt0xfgMOE5HaQbFKVqokGX0OpYHVqrpZRNpheenRshyoG+QTshc7HSce1FTVE1T1juCH7p2qemfYRjnZJ4rr+G/s/Vp3hog0EZESwF3Ah6q6I937t4lICRE5ADgHeC9Y/g5wa1DzUgm4HbsmZ8ZyoGJgR1QEaX9LgP7s5X6DXZfXAuuDWcCL0+3qc6C6iAwQK84sLSIHpbPrv2tyBrwDXCUi9cQkYiM529v3Zn9m98Boz99JbtzBLrisAw4CxokpgvwMTAOuCd7/BpgOLBORlRntILgIH40VuczFoh4vYsVSAD2A6SKyHiv26BPk4lUDPsQuhjOBsVjayF4Jcv1eB25X1YVAbyxS8TcWpbgO+16nAFdjF+fVWC7excE+RmE3iSnAJOzimxkZfQ6XAHcFeeG3k73ClQ+Cx1Ui8ktWdjpOnPhCRLqFbYQTE7K8jkd5rXsDi3ovA4oBV+z2/lis0PBrLH3jy2D5PVjNyxRgKvBLsCxDguj6O8CcIMUjSxWRdDwEXI+lZWR1v7kWC3asw2YBIz8EUNV1QFfgmOA8/8SKFmHPa/LuvIx9Rt8Gx90MXB6l7ZndA50CgGRQ7+I4juPkU0TkeCzSmILNHgmgwbS6U4AQkTHAm6r6Ygbv1cUcysLRRGsdx9mV7Ii/O47jOMnPo5iM2dSMFEUcx3Gc3OMpIo7jOAWLhcA0d64dx3Hih6eIOI7jFCBE5FVMUeEL0rXUVlXvMuc4jhMjPEXEcRynYDE3GEWC4TiO48QYj2A7juM4juM4TgyJOoId6PRu3k0f03Ecx0kCROQVsmggparn5aU9juM4+ZlMHexAdL0PcDrQFsvVKxpoAQ8DnlfV2XlipeM4jpNbMtJArgVcBaTmsS2O4zj5mkxTRERkLPAVMASrON8ZLK+ACbSfBnyiqll1bnIcx3ESDBGpjzVoOgx4DHhJVbeGa5XjOE7+ISsHu7Cqbsty4yjWSVQqVaqkdevWzdY2GzZsoGTJkvExKAT8fBIbP5/EJyfnNGnSpJWqWjlOJmVJ0EL6VqAV1iHvzURqIpKfrstuV/Zwu6InEW2CgmlXltdzVd3rAMoDzYEDIyOa7RJ5tG7dWrPL6NGjs71NIuPnk9j4+SQ+OTknYKKGcM3DWkLPAS4FKgMV0o8wbNp95KfrstuVPdyu6ElEm1QLpl1ZXc/3WuQoIncDZwN/kVYgo0CXnPn7juM4Tgi0xa7d1wLXBMskeFRMG9sp6MyfD507w1NPwVFHhW2N4yQt0aiInAI0UM/PcxzHSVpUtW5m74mIZPaeU8AYNQrmzoVTToGxY6FNm7AtcpykJJpW6dOAcnG2w3GSglatQAQyKV1wnIRHRO7a7XUK4MXqjjF+PJQtC1WqwNFHm7PtOE62icbBHgT8KiIjRWRoZMTbMMdJNH7+GX77zZ5/+22opjhObqglIjcBiEhR4BPgz3BNcv5jxw4YPRp27gzn+OPGwcEHw/DhsHUr9OoFq1eHY4vjJDHRONivAQ8A9wOPpBuOU2DYuBHOOivt9ZNPhmeL4+SSc4FmgZP9GTBaVQeGa5LzH2++CV26QN++sHlz3h57/XqYNg0OOgj23x8+/RTmzIHjj4ctW/LWFsdJcqJxsDeq6pOqOlpVx0ZG3C1znATihhvgz3Qxvk8/hdneZslJIkTkQBE5EJPoewI4FYtcfxssdxKBn36CQoXg/fehRw/499+8O/akSRY5P+gge33YYfDaazZl169feFF1x0lConGwvxORQSJySOQC7RdjpyDx1Vfw9NMwYEDasuLF4ZprMt3EcRKR9DOQ9wP/AE2C1w+HaJeTnokTzbF96y348Ufo2BEWLozvMTdvtsKScePsddu2ae/16QMPPADvvQc33hhfOxwnHxGNikir4PHgdMtcps8pEPz7L5xzDuy3H9x3Hzz+uC2/7Ta714wYYUEmx0kCbgF+CrRbnURkyxaYMgWuvhpOOw2qVbP0jEMOgS++gGbNYn/Mbdugdm3o1g02bID69aHybn0zrrvO5PseegiqV4erroq9HY6Tz9irg62qnfPCEMdJRK64ApYutVnb4sXTlg8YAC+9BFdeCVOnQpEioZnoONFyJvC0iPwBjABGqOqykG1y0jN5sjm8kQhyly7w3XfQsycceqjlpnXqFNtjTp0Kf/9tEXOw3O/dEbHCkxUrzPmvVAnOPDO2djhOPiPTFBEROSuL4f9ZTr7n44/hjTfgllt2nTEFKFrUotl//AFPPBGKeY6TLVT1YlU9EBiIded9VUR+EpH7ROQwEUkN18ICyJYtu2p+Tphgj+kvOM2b2y/8ffaB7t0tVSOWRNJCzj3XHg85JOP1UlPTCjDPOQeGDYutHY6Tz8gqB7ttJuNu4JVYHFxEXhaRFSIyLd2yCiIySkT+DB7LB8tFRJ4UkdkiMiV9HriI9AvW/1NE+sXCNqdgs3w5XHghHHgg3Hprxuv06mUysXfdZVFux0kGVHWWqj6mqj2wVL/vgZOBceFaVsDYsgXq1LHUjL//tmUTJ5r+dK1au65buzZ8/70VH/bpA48+Gjs7xo+3lJAXX4RvvoHzz8983aJFLYresiWcfDL88EPs7HCcfEamDraqXh4ZwBXYxbcT8DMQqyLHV4HdM1hvBL5W1UbA18FrgJ5Ao2D0B54Fc8iBO4CDgHbAHRGn3HFygqo51+vWWQS7cOHM133sMZOKvf76vLPPcWKFqm5S1eHBtd5b9uUlU6faL/mvvrJf8j//bBHsNm0sJWN3KlSAL7+Ek06yCuurr46Nqsf48dCunR2zc+ddc+EyonRp08iuVcsiDFOn5t4Gx8mHZKkiIiKFROR8YCZwJHCSqp6qqlNicXBV/RbYXcG+N6a9TfB4XLrlr6vxM1BORKoD3YFRqrpaVf8BRrGn0+44UXPffTBkiD02aZL1ug0bwrXX2szpEUdYGuOKFXljp+NkBxFZJyJrg7Eu3euNIrI9bPsKHL/8Yo8ffmhFHIcdBjNm7JmPlp5ixeDdd6045LHH4NRTYdOm6I85bBi1337bmtkArF0LM2eag50dqlSBkSOhRAlLW/Fuj46zB1nlYF8KzABaAz1U9WxV/T0PbKqqqpEJ92VA1eB5DSC9VtGiYFlmyx0nav76C559Fnr3tpSQM8+MvlD+zjvtXjdjBpxxBlStCq1bw803w9ixFuF2nLBR1dKqWiYYpYHqwL3YddYrCfKaSZOgXDk44QRLDene3abPOnbMervUVCsAefhh+OgjK3pcvjy6Y957L/VfeAFOP90uTJMm2TEjutfZoW5dc7I3b4Yjj4TFi7O/D8fJx2SlIvIUsALoCHSQtCkrAVRVm8fZNlRVRSRmklIi0h9LL6Fq1aqMGTMmW9uvX78+29skMgX5fNavT+W338ozYUJ5Jk6swJIlNi1ardomTjppJWeeOYexYzP66nUC2OM4LVta9PrPP0szYUJ5JkyowAMPlGXQIKF48e20avUvbduupm3b1dSoEV13toL890kWkvGcRKQcMAA4C3gbaKuqq8K0qUDyyy+WGiIC5cvbtNn06dFJ8YlYmkiDBuYsH3QQfP45NG2a+Tbbt8Ovv7KxVi1KvPeeRa/bBFlBWUXNs6JpU5MP7NrVpvDGjrUIg+M4WTrY9fLMil1ZLiLVVXVpkAISmXBfDKSv/KgZLFtMxOtJWz4mox2r6mBgMECbNm20UzbljsaMGUN2t0lkCtL57NhhQaIvv7Tx00+2rFQpSzu8+WarNWrYsDgitdj1q7YnmR2nSxfL3wa7f33zDYwYUYiRIyvxxBOVALsndu9uo3NnS2nM7vkkI/ntfCC5zklEKgHXYB0cXwZaqeqacK0qoGzdanrXV16ZtiwlJfs618cdZzJ+xxwD7dvDBx/YhSUjpk+HzZuZd9ZZNKlb1y5UX3xheW4VKuT0TMy5HzbMGgJ07QqjR0PFijnfn+PkEzJ1sFV1PoCI9FTVL9K/JyIXAc/FyaahQD+s01g/YEi65ZeJyLtYQeOawAkfCdyXrrCxG3BTnGxzkoiFC82ZHjnS6oj++ccCP61bW+vzbt1MkSpeGtZlytj977jjbBb2zz/NlpEj4dVX4ZlnrICyffs0h7tlS7vPOk4cmA/8jalAbQTOSzcziarGUJrCyZLp083JPjAGegEHHmhSe0cfDUcdBU89BRdfvOd6EycCsG6//SyXrVw5a2Zz6KG5t+HQQ2HoUDt+t27w9de2f8cpwETTyfE2Edmiqt8AiMj1QGdi4GCLyDtY9LmSiCzC1EDuB94XkfOwG8IpwerDgV7AbOzmcA6Aqq4WkbuBQECUu1R198JJpwCwaVMKw4enOdWzZtnyffYxJ7dbN0sVrFQp720TgcaNbVx+uSl0/fBDmsN98802qlSxIFD37lCyZBbyJY6TfR7CuvACZDJv4uQJkQLH1q1js7+aNU3Gr29fuOQSE+h/+GHL144wcSKULcumffax1yedZMfPTfQ6PUccAZ98YoUsPXvahTiz6TnHKQBE42AfC3wuItdh6hz7YYoeuUZVM2gZBcARGayrwKWZ7OdlbMrTKUDs3GmzrCNH2rX8u+86sm2bFdoffjhccIE5qk2aZKx6FSZFi1o6SZcu8MADsGxZ2g+DkSMjTdU60LJlWnS7QwfvGOnkHFUdCCZtunsQQkTCSglMXF57DZ57zmSCTjghNheRxx+HJUvsH75MGcsXixWlSplG9bXX2nFmz4a3305zcidONIc6/RRZvRj/2Xv2tEY4J59sEfXhw6Fkydgew3GShL1ORqvqSszJ/h+wDybV57oITigsW2ba1GeeCdWrQ6tWcOON1qfhhBMW8eWXlgoyYoTJxB5wQOI51xlRrRqcdZY51suX273wvPPmUKYMPPKIOeIVKliq5dNP273TcXLIZyJSJvJCRPYHPgvRnsTko49Mm/qkk6wIcOTIXbsuZhdVGDQIHnrILmKtWsU+Hyw11SSNnnnG8qsPOQTmzLEps8mTc17MmB2OP94uZN9/b9241q2L/zEdJwHJNIItIutIm04EKALUB04SEVXVMhlv6TixY/Nmu05HihMnT7bllSunpVJ07WrO9pgxc+jUqXa4BseAlBQLNJ1xxgI6darP2rVWNxSJbn/+ua1Xv/6uxZJl/D/SiY77MCf7KGBf4HXg9HBNSkBmzIATT7RftXfcYUV8hx1mAvkdOmR/f0uXmkj+eefB779bOke8uPhiy0c7+WRzqm++GbZtS1MNiTennmo/KM44wz634cOhbNm8ObbjJAhZFTl68pST56ha34NIusTYsdZHoXBhk4cdNMgcyhYtCk4xYJkyltbYO0jMmj07zdl+/XXT7y5UaNdiyXgEx5z8gaoOE5HCwJdYLvbxqvrH3rYTk9d5HetNoMBgVX0i6Kb7HlAXmAecoqr/iFVQPoHVzmwEzlbVX+JwSrFn40aL/J51FvTrZ+3JX3wR7rnHLkS9etnzVq2i32ck7/rss/eudR0LjjjCujQee6yljYA52PPmxf/YYJ9Z4cL22K2bTSuW9ybLTsEhq0YzdbPaUIyaMbfIKXCsWmVpe+edB7VrW1rHVVdZc7ALLrCI7erVJnl3443uPDZsCJdeakX7q1dbdPvaa20m9pZb7B5arZrJ477+uqXVOI6IPCUiT4rIk0AXoCwwF1NnejKKXWwHrlHVJsDBwKUi0gS4EfhaVRsBXwevAXoCjYLRH3g2picUT37/3X7tR1q5Fi1q/3R//WVFEz/9ZOodp55q60bDL79YvlqLFvGze3caNjRbe/eG5s2hTp28OzbYDMBHH8Fvv5nDv8rl1p2CQ1ZFjg+JSAomkzcJk3cqBjTEVESOwFQ/FsXbSCd/sW2bpTZGihMnTrR7WblypvLRrZuNvL4XJCNFilgjt06dLLq/fDmMGpX22b79tq3XosWuxZJFi4ZptRMSE3d7PSk7GwcddpcGz9eJyEysa25v0noRvIb1IbghWP56UKD+s4iUi/Q4yPEZ5BXTp9tjxMGOUKIEXH899O8Pjz5q48MPLSp9++1ZX7R++cXSNvJaWaNsWSt+VA2nIOXYY+34xx9vuWxffWVySY6Tz8kqReTkIDpxOnAu1lZ3IzATk8y7V1Wja0nnFHhmz07Lo/7mG4u2pqZaj4KBA82hbtt2V1UpJ/tUrWppj2ecYSorkyebsz1ihPkCDz5oRf2dOqU53I0aJUchqJM7VPW1WO0rmOFsBYwDqqZzmpdhKSRgzvfCdJstCpYlvoM9Y4blXTVqlPH75crBXXfBZZfB/fdbUeGbb8JFF1m+c0bdDH/5JW9SQzIjzH/ynj1tKvLYY+3iM3Ik1Mq6mZfjJDtZyvSp6gzgljyyxclHrFljjnTEqZ4zx5bXrWu9DSKFed6LIH6kpFg6TURpZd26XYslhw2z9erWTXO2u3TxWqT8ioh8hnWyHaGq23Z7rz5wNjAvkD3Naj+lgI+AAaq6drdmNSoi2ZLaEJH+WAoJVatWzXbr+Xi0q2/67bcUr1GDCT/8sPeVjz2WogcfTJ3XX6f600+zc/BgFp14IluOPvo/uwr/+y8dFi7kr7JlWRhjW7NLPD6vqChUiLL33UezW29le5s2THnwQTami/iHZtdeSES7EtEmcLv2QFUL5GjdurVml9GjR2d7m0Qmluezfbvqzz+r3nmnaocOqqmpqqBaqpTqsceqPv206p9/qu7cGbND7kFe/H1snjXuh1HV+J/P7Nmq//uf/X1KlbLzSk1V7dhR9e67VcePV92xI3bHy2//P6o5OydgooZwzQOqAQ8Cf2GNuYYD32B52KOA3lHsozAwErg63bLfgerB8+rA78Hz54G+Ga2X2UiY63LDhqonnZT97f74Q7VvX1XQbSVLqt51l+qaNaojR9o/2Ndfx97WbBL6/+Gvv6pWrapaoYLdNAJCtysTEtGuRLRJtWDaldX1vACXijm5ZcECK6w/5RSTzTv4YEv32LLFIqZjx1oR3pAhVh/UsKGnIiQSDRpY07chQ6z2aMwYSy/duBFuuw3atbNUyb59rbX70sSf2HeyQFWXqer1qtoAOBm4G7gaOEBVu6rqkKy2D1RBXgJm6q5t1YcC/YLn/bC6ncjys4KC+IOBNZoM+debNtmU2wEHZH/bRo2s8GHyZP5t2dLysuvVM8URyJ7qSH6lZUtrY1u+vE2ZffFF2BY5TlyIppOj4wCwYYM5zZECukgr8ho1rBV59+5WKB5GK3IndxQpYt0vDz/cZH5XrNi1WPLdd2295s3T0kk6dvRiyWRFVedhknrZoQNwJjBVRH4Llt0M3A+8LyLnAfOBU4L3hmMSfbOx+p1zcmV0vFi0yFQ22rc3Wb7Cha2AYfcCx+zQvDnT7rmHTqVLm4b2sGH2i9Zl6owGDczJ7tnT8rJffdVuJI6Tj9irgy0iX6vqEXtb5uQ/IkVyEU3qH36ArVuheHFzxPr3t+LERGxF7uSOKlVM5u/003dtST9ypHVhfughE1RIXyzZuLF/D/Izqvo9kNlfeI/7QTB9emlcjYoFY8ZYAeKUKdYmtWJFW54bBztC69ZW3DdpkjnuThpVq9pnf9xxcMYZ1LrwQrux+EXEySdk1cmxGFACqCQi5Um7sJbBKsGdfMiyZWmFiaNGWSQTLHJ5xRVpkctixcK108k7UlJsVrdlS7jhBli/3u6LEYd7+HBbr06dNGf7iCO8WNJJEmbONPmiJUtMTu6112DlSvvFGCtat47dvvITZcrYBaRfPxo8/zxs326KLEWKhG2Z4+SarCLYFwIDgH0wvdSIg70WeDq+Zjl5RfpW5CNHWhAHLKc6okcdaUXuOAClSsHRR9sAawgUcbbfeQcGDzZ/5eCD0xxu9y+chGXmTCsQqVzZOltdcEHYFhUsihWDd95hXtGi1H3pJWvm8+GHaTMJjpOkZKWD/QTwhIhcrqpP5aFNThzRoBX5yJHw7rvNmDp111bk999vTnVBakXu5I569Uz+96KLdm0iNHKkpZ/efjtUqAAtWjRh7lxzuPfZJ2yrnfSIyBeq2jNsO0Jh5kzYf/+wrSjYpKQw79xzqdutm7X0PfhgS63Zd9+wLXOcHLPXHGxVfUpE2gN106+vqq/H0S4nhqxaZc2zIgVrixfb8tq1i3HBBebwHH64NSBxnNxQuDAceqiNe+6Bv/9OK5b8/POyjB5t6zVtmhbdPvRQTznKC0TkwMzeAlrmoSmJw7Zt1gXr+OPDtsQB65BVv77lZR98sEWyj/ByLyc5iabI8Q2gAfAbsCNYrIA72AnKtm3w009paR+TJlnkunz5tFbkXbvC3LkT6NSpU9jmOvmYypWtsdBpp8Ho0T9RoUKn/6LbTz0FjzySVjQbcbj328/rnOLEBGAsGRcqlstbUxKE2bMt79cj2IlD+/YwfrzloHXvDg8/DFde6RcFJ+mIRqavDdAkqAh3EhBVS1uLRKi/+cYK0SJ5sHfeaU51mza7tiKfOzc8m52Ch4ilHrVoYXrbGzbsWix51VW2Xu3a9n3t3t1+EHq3z5gxE7hQVf/c/Q0RWZjB+vmfmTPt0R3sxKJuXfjxR5NNvOoqe/7SS1C6dNiWOU7URONgT8M6gCV+g4ACRPpW5CNHpjnL9erZLFu3bt722klsSpaEo46yATBvXpqz/f771sQoJQUOOgh69DCHe/cfiU62GAiZNhe7PA/tSBwiDvZ++4Vrh7MnZcrAJ5+YJuhNN1kF/kcf5awBkOOEQDQOdiVghoiMB7ZEFqrqsXGzytmDHTtgwoQ0Cb2ff7ZlpUqZI33tteZUN2wYtqWOkzPq1oULL7SxbRuMG5fmcA8caAWTFSpYVLt7d/u+16wZttXJg6p+mMV7n+ahKYnDzJlQq5ZdSJ3EQ8Smu9q1gz597HHwYBPod5wEJxoHe2C8jXAyZsGCtLSPr76Cf/+1602bNtaKvHt3SwHx/gVOfiOiatOxI9x9t8kSRwp1IxFusGBW+mLJ4sXDtdtJMIYMgREj7KLZrp01j0k/BeIKIslBp07WDOjUU22K9ocf0go4HCdBiUZFZKyI1AEaqepXIlIC8EnaOLB+vbUij6R9/P67La9RA044wSJ2Rx7p8qBOwaNSJQtg9eljNQfTpqU5208/DY8+akok6Ysl99/f66IKPLfdBlOnpr0uUcJE2du1szFrFpx/fnj2OdGzzz6WF3nTTeZcf/stvP22dUFznAQkGhWRC4D+QAVMTaQG8BwZtMZ1skekFXkkSv399zY1HlFVuOgic6rdUXCcNESgWTMb114LGzfaD9OIw3311bZezZppzvaRR5qKjlOAWL3anOu777bI5/jxaePpp2FLkPHYtGm4djrRU7iwqYp07Qpnnw1t28IDD1ibYW/c4CQY0aSIXAq0A8YBqOqfIlIlrlblY5YuTdMFHjXKdILBlBUGDDCH2luRO070lCgBPXvaAJg/P20W6MMPTXwgJcUClhGHu127gl0sWSB6G3z3nT126gSNGtmI5O5u3WrTIL//Dr17h2aik0O6d7eix/PPN5WRYcOsKrpOnbAtc5z/iMbB3qKqWyUIoYpIIUwH24mCzZvtOh8pToy0Iq9SZVcpMm9F7jixoU6dtI7X27fvWix5110mW1muXFqxZPfuVudWUCgwvQ3GjrVIRdu2e75XpAgceKANJzmpXBk+/RReeAGuucZmIh56CPr392i2kxBE42CPFZGbgeIi0hW4BPgsvmYlL6owY0ZaBG3sWHOyixRJa0Xevbuljfk1wHHiS6FC0KGDjbvu2rWraSTCDZaG1b27yQEedli+r50qGL0Nxo61KvCiRcO2xIkXIuZQd+9u0eyLL7YK6BdegAYNwrbOKeBE4+LdCPwNTAUuBIYDt8bTqJwgIj1E5HcRmS0iN+blsVeuhHffhXPPtUhY06aWBzp/vkmODRtm6YBffw033AAtW7pz7ThhULGipeO+/DIsWmQpug8/bPnazz5rDnaFCna/fvRRmD7dfjTnMyK9DfIva9bAb79ZMYuT/6lTx6JagwfDxIl2E7777rQ8e8cJgWhURHYCLwQjIRGRVOB/QFdgETBBRIaq6ox4HG/rVtOhjhQn7t6KvHt3q8GoXTseR3ccJxaI2H24aVObYd640YQJItHta66xUaPGrsWSFSqEbXmuyT+9DebModHjj1unvxo10sbUqVZF7g52wUHE8sJ69bK87NtvhzffhGeegSNck8HJe6JRETkauBuoE6wvgKpqmTjblh3aAbNVdQ6AiLwL9AZi42CrsuDLWQz9sByPPbZrK/JDDrGczu7dTf2pIBdOJQ2zZ5s3lSMCSahIMn12KFUK6tfP4XGdeFOihEWwe/Sw1wsXpjnbH39sUe+UFEvp7d5N6VVjMjROyvD2wLANiBkLF1Llm29M73p3Che2FBGnYFGjhqWJjBgBl11mv4pPOAEefNDTRpw8JZoc7MeBE4CpCZyzVwNYmO71IuCgWO1cdypNe9RgHWWAv4CRwJfs2DGa779fy/ff249lJ/HpAXyRqz0E/wItWuRo64HAnbk6vhMOqUBbdu7szrhx3Rk37hDuoiVH0olO2jls47JF0NugKhCp/huvqivCtCnHHH44PwwdSqd27WDJEli8OG3UrZvvk+mdLOjRIy0H7IEH4LPPTM7v1lutytlx4kw0DvZCYFoCO9dRIyL9MU1vqlatypgxY6LaThUu4Cnas56z+ZL1cbTRiS+3AfOAq3O4fTfqsZF6nJCDbY/HHOzVwFM5PL4TFjuAn4NxJ69QlzUczg38EfV1JFEQkVOAh4Ax2IzkUyJyXVat1BOeEiWgYUMbjhOheHFrNnT++fb46KPwyitWDHXppVCyZNgWOvmYaBzs64HhIjKWXfP1Ho2bVdlnMZBeaKtmsGwXVHUwMBigTZs22qlTp6gP0Hl8SWjXjhMfeSStk0WSM2bMGLLzGSQ6ez2fsWNNE/fpp/n40kvzyqw0du6Enj15cuJEnpw7F8pknWVV4P4+ycKkSdZ6e9CFtDj47GQ8p1uAtpGotYhUBr4CktfBdpysqF7ddLIvuwxuvtkc7EcesecXXuiNJ5y4EI2Wxb3ARqAYUDrdSCQmAI1EpJ6IFAH6AENjeoS2bfm3RQt4/HFrt+gkF6qWx1O1qsm9hEFKCtx3n0nKPP54ODY4uWfQIChb1iTBkpOU3VJCVhHdvcBxkpuWLWH4cPjhB6tuHjDA6mIefNCUZxwnhkRzUd1HVU9Q1TtU9c7IiLtl2UBVtwOXYcnRM4H3VXV6rI+z4NRTrfLpvfdivWsn3nzxhUlE3H57uHmZrVvD8cdb9GT16vDscHLGzJlW8XjZZeZkJycjRGSkiJwtImcDwzD51ZgTpnyq42RK+/amm/vNN3DAARbRrl3bHhfvMfntODkiGgd7uIh0i7sluURVh6tqY1VtoKr3xuMYqw86CJo0sW5RyZ+SXnBQhVtusQryCy4I2xqTnVm3zopvnOTigQfsB9qAAWFbkmNU9TosVa55MAar6g2xPk46+dSeQBOgr4g0ifVxHCfHdO4Mo0aZdnaPHnZNrlMHTjrJnG+/zzu5IJoc7IuBa0VkKxDJjUg0mb68ISUFrr3WUgxGjbJe507iM2KENZ149VWT7gqbZs2gTx948km48kpLW3ESn3nzTFf38suhUqWwrckVqvoR8FGcDxNf+VRAZF+sZPn3WO0yxrhd2SNMu7pQmi7sD+z3ERT76E/W8DuVmMAfnExRVrKF/3SkQqdT//5hm7AHjZcsgXfeCdsMALbsKMTqLSVYvbkkqxdv5tPSM1i7tTgbtxfZY2zdmcr2nalc+1gNGveInZRuNI1mEi3fOlxOO82ioQ895A52sjBokLXYPO20sC1J48474YMP7PGZZ8K2xomGhx6yH9nXXBO2JTlCRL5X1Y4iso5d/YR49TaISj41p+pORkUg+frjOInJOmB8MIoDqRRjPReTwg4O5Ed68SndGUI9/grXUGDLh4lXk1xBlS0iMd/vNi3Eai3Pqp0VWKkVWaUVWbmzoj3/b1kFVu2syEqtwKqdFdhAqb3utzBbKS6bKcJWCrOdQ778niXFFsTM7mgi2IjIscBhwcsxqvp5zCxINooWtajjjTfCr79Cq1ZhW+Rkxddfw3ffwWOPJUb0OkKjRla9/txzps26335hW+RkxaJF8NJL0K+f9VVPQlS1Y/CYUEGT3Kg7qf6YsOo0blf2SES7VOGFFyayeHEbPv30UO6acih38QhNmsBxx0Hv3iYolJLHJcKJ+FlBdHZt2wYrV8KqVTayeh4Z//6b+f5Kl4bKlW1SsVolaFoJKlZMGxUqwMKFv9GlS0vKljVFzxIlLNOvcOEiQJF0ezspBp9CGtF0crwfa0jwVrDoShHpoKo3xdSSZOLCC00N4p574KN4z7I6OWb7dsuVrVsXLroobGv25Pbb4fXX7cfap5+GbY2TFTffbHfbm28O25JcIyJvAI8C+wCbgGnAI6p6ZowPFZV8quMkKiLQuPF6+ve3yca5c2HoULtcP/CAuQH77APHHmvOdufOFoMrqOzYISxYYFoQixbZY2REXi9fnnlqe4kSuzrHdeua4xxxoHcfFStG93mPGfNvKLHQaCLYvYCWqroTQEReA34FCq6DXa6cOW533WW5vS1bhmuPkzEvvwzTptmPoETUOa1SxarWb73VouyHHhq2RU5GTJwIb7xhP4Tq1QvbmhwjIg2AG4BTgCrA35j8amOgoYh8A7wWudbHgP/kUzHHug+QQHlajpM96tWzCewrr7Qo6/Dh5my//rpNRhYtahHtDh1MqKRtW5PgjkPWRJ6gChs2wIoV0Y2//z6MnbtdPUqXtgzNWrWgeXN7rFLFnONKu0Wb81vj1ahSRIByWAM6gKTVpoopV10FTzxhTvbHH4dtjbM7W7fCvffCIYeYLF6ictVV8Oyz9jhuHKSmhm2Rszu3325X/5uSPqbwMdAI6/t+SLBMgK3AC9i1/UzgtVgcTFW3i0hEPjUVeDke8qmOEwYVK8KZZ9rYtMmyEUePhh9/tIzEBx+09SpVshhcixaw775pDUdr1Mj71BKwW+PKldE7zZs2ZbyfMmXMUa5SxQS6DjkENm2az6GH1qVWLcukq1UrmdVMc080DvYg4FcRGY1djA8DXM+0XDlzigYO9Ch2IvLmm7BggYUVEjl8UKKEFc+ddpo52pddFrZFTnrGjzcN9UGD9tp5M9FR1RYAIjIor1L8VHU4cdLYdpxEoXhxOPpoGwCbN1vD119+gcmTzUV4+mnYsiVtmyJFLL0kMqpUsWhvmTL2GHmeUenQ5MkV+ftvc5a3bEl73LQJ1q7NfKxcCf/8k/E5FCmS5jBXqQL777/r6/SjcuWMJ4XHjJlHp051c/tx5huiURF5R0TGYHnYADeo6rK4WpUsXHml/VQdONBzaBOJ9estYa5NG9M2TXT69IFXXrH83hNOsKutEz6RnOsKFeDSS8O2JpZMFJE+WJpIISxNpKGqhtTi1HHyF8WKWZpIhw5py3bssDzk2bNtzJkDS5bYmDYN/v7bnODoGkU3y/SdQoXMMU8/qla1uvpKlTJ3msuUSexYVDKSqYMtIgfutmhR8LiPiOyjqr/Ez6wkoVw5k+y6/XYYOxYOPzxsixywtJ0FC+Dtt5PjiiFiUn1Nm9qsiHcKTQw++MDmfZ94wsJJ+YdXgK+AqUAkY7JneOY4Tv4nNdV62NSpA0cckfl6W7ZYH7K1a+1x+/Y915k0aSLt27ehSBHL+448Fitm0fRkuO0VBLKKYE/EqstXBq/T/8kU6BIvo5KKa64x+a7LLrP5oESSgiuI/PknPPoonHferuGDRKdhQ9NXv/12OOec5Ii852fWrbMfO61awSWXhG1NrCmsqiekXyAisdWnchwnRxQtaiOrXlbr1q2nadO8s8nJGVk52FdjooCbgHeBT1R1fZ5YlUyUKGFpIiecAC++CBdfHLZFBZs77rCr0733hm1J9rn+enjrLXPopnstWKgMHAhLl1oBc6Foa8GThj9F5CPg+uD1pcCkEO35j0mTJq0UkfnZ3KwSaYGgRMLtyh5uV/Qkok1QMO2qk9kbmd45VPVx4HERqY/JK30dXPjuU9XfYm1hUnPccRYtveceOPvs/Kc1kySUnD0b3n3XpO+Ssf140aJW6Nili32XunYN26KCyZQplhZywQVw0B6NB/MD9wGvA8djs5HbgS3A2SHaBICqVs7uNiIyUVXbxMOe3OB2ZQ+3K3oS0SZwu3ZnryIxqjoHGAJ8CbTDNFOd9IiYQ7Rkibe9DosdO9j3kUesIO2668K2Jud07gxnnQUPPUSJefPCtqbgsXOnzSCUL29dJPIng4A2QKqqpqpq0Ti0SXccxynQZOpgi0h9EblZRMYBdwKTgf1V9f08sy6Z6NQJeva0qeX52Z3hdHLNE09QZtYsePJJc7KTmYceglKlaPzoo1Z67uQdr78OP/xgIrYVK4ZtTbxYhuldDxORbyIjbKMcx3HyE1lFsGdjHb9GAD8BtYGLReRqEbk6L4xLOp591h4vvDDzXqBO7FmxAgYOZNVBB0HfvmFbk3uqVIHHHqPc1Kk2M+LkDatX2+xHhw7Qr1/Y1sST2sCxWBR7BlASSxVJVgaHbUAmuF3Zw+2KnkS0CdyuXcjKwb4L+ASTcSoFlN5tOLtTpw7cfz+MHGmRMCdvuOMO2LSJ2Zdckn/0ic46i2Xdupme99ixYVtTMLj5ZuvC8Mwz4bRYyzsEeBu7tv+NNYLZL1SLcoGqJuRN3e3KHm5X9CSiTeB27U5WRY4D89CO/MPFF1uh3VVXQffuUK1a2Bblb374AQYPhksvZVPt2mFbEztE+GPAAKr99ZdJDk6ZYoo1Tnz48kt4/nm4+mpo3jxsa+LNAlW9U0TaY3KsS4CzQrbJcRwnX5GvwzShkJJicn0bN3rb63izfr0VBNapk5yyfHthZ/Hi9l366y/TW3fiw+rVpv7TpEm+TskRkVdE5GVgnYi8AWwAXgRGAXNCNc5xHCefke8EXhOCffe1YsebboKPPoITTwzbovzJtdfC3LmWQpG/Ou2l0amT5QU/9BA0a5Yfm56Ezx13wPLlMGxYfpfY/DyDZd8CVwH757EtjuM4+Rp3sOPFtddaq+VLLzXptWRXtkg0vvjCpvSvuw4OPTRsa+LLoEEwYwZceaXpMrduHbZF+Yfp0604+cILrWtjPkZVPwIQkcZYu/T9seYLbwDbQjTNcRwn37FXBzsTxZA1wCRvOJMFhQpZC/W2bW16/5VXwrYo/7BqleUlN20Kd90VtjXxJzXVimZbtDCVlIkToYzLFueabdvg3HPtsywI3yNARPYDxmLpIeuAJqq6XUSmYXKsoVKpUiWtW7dutrbZsGEDJUuWjI9BucDtyh5uV/Qkok1QMO2aNGnSyswaZEUTwW4TjM+C10cDU4CLROQDVX0wNmbmQ1q2tK6C994LffpY0aOTey69FFauhOHDoVixsK3JGypUgDffhCOOgBNOsHMvUiRsq5Kbu+6C8ePhvfegUqWwrYk7IvIB0BrYDBwEfA2UEVPeSQiZvrp16zJx4sRsbTNmzBg6deoUH4NygduVPdyu6ElEm6Bg2hV0OM+QaBzsmsCBqro+2NkdwDDgMGAS4A52Vtx6q+Vh9+8PU6d65DG3vPuuOUT33ms/YAoShx9usyJnnw0XXWTP84ssYV4zfbql3px1FpxyStjW5BVtMUe6Mnbtrho8lsClV51kZuVKmDkTFiyApUutq/KyZVYIv2mTjS1boHBhC8oULQolS0LVqqb0VbWqFcvvuy/Urp3fZTqdPCIaB7sKsCXd621AVVXdJCJbMtnGiVCsGLz8MnTsCJdfDq+9FrZFycuCBVbkd/DBcP31YVsTDv36marI3XdbzvDll4dtUfKhap9bmTLwyCNhW5NnqGpdsC69WOOFSkAR4E/g9PAsc5woUYWFC+Gnn2z8+qs51n//vet6JUqY41ymjBUuFy9uz7dtg82bzfGeNw+++ca079NTvDg0bmzX14MPplRqqt2/C3nJmpM9ovnGvAWME5EhwetjgLdFpCTWBSzbiMjJwECsyKadqk5M995NwHnADuAKVR0ZLO8BPAGkAi+q6v3B8nrAu0BFLBpzpqpuzYldceOQQ+C226xpSM+eli7iZI9Nmyw1YscOy0cuyBe7gQPht9+s6LFCBTjdfaNs8cgjMHq0FTcWgNSQDDhbVY8MruEpwCYsL7tDuGY5TgasWgWjRllh+9dfw+LFtrx4cZvF7N0b9t/fRr16sM8+pioV7ezeli2mIjRvHsyaBb//bk77sGHw6qu0ARgwwGYQu3eHHj2gUSOfPXT2yl69FFW9W0RGAO2DRRelc4hzemefBpwAPJ9+oYg0AfoABwD7AF8FFe8A/wO6AouACSIyVFVnAA8Aj6nquyLyHOacP5tDu+LHrbfaReKii8zhrlMnbIuSB1VLsZk0CYYOtYtbQSYlxVJljj7aUhyKFIGTTw7bquTgm2+sLuKkk0w5pAAhImWAS4GTRaQGcD5wJXAvsDBM2xxnFxYvhvffNyWuceNg504LJnTtatHkQw6xhlCFC+f+WEWLWlpI7dpw2GFpy1Vh7lxmvPwyTf75x5pRDR9u79WrZ9eQU04xVSd3tp0MiDYM+AuwOLK+iNRW1QU5Paiqzgz2s/tbvYF3VXULMFdEZgPtgvdmq+qcYLt3gd4iMhPoApwWrPMaFhlPPAe7UCErUmvZ0iLYY8bYP7azd+6/3z67u++GY44J25rEoEQJ+7HRowecdpo52b17h21VYrNmjeWvN2pkqj4F76b4BvAP8DhwM/AXUA54RlWvC88sx4HUjRutK+9bb8F335mD27KlBad69jRFrtTUvDNIBOrXZ8WRR9IkUiA3Zw6MHAmffQaPPWb9CerXN3Wnc8+1544TsNdMfhG5HFiOdfv6HCtwzKhhQSyowa6RlEXBssyWVwT+VdXtuy1PTOrVs3zsn3+2luqaEIX7ic3HH8PNN5sTecstYVuTWJQqZRGV1q0tgv3FF2FblLhE8q4XL4ZXX7XPruDRBHgSmIDNFNYC3gfeEZEDwzTMKcD88gtceCHtTzzRZpVWrLA0uFmzLMf6zjut7iYvnevMqF/f7t3Dh1tayUsvQcOGVjDdoIFF2N9/H7YmVpaqEw7RRLCvBPZV1VXZ2bGIfAVUy+CtW1R1SAbL446I9Af6A1StWpUxY8Zka/v169dne5s9qFiRumeeSd1XXuHPUqVYfMIJudtfLojJ+cSRMtOm0eK669iw//781q8fO8eOzXL9RD+f7BLt+RS69VZaXH01JXv3ZsqgQfyboI1owvz71PzgAxq+8QbzzjqLeZs32wxSDEiy71w1IH1V52Zg32CZYrOBjhN/duywGbgHH7SAU/HirOjUieoDB0K7dskxu1ShgkWtzz0XFi2y4NlLL8Gpp0L16nDFFfaDoXz5sC11wkJVsxzAaKDQ3tbLyQDGAG3Svb4JuCnd65HAIcEYuft6gGCdyAoFy3dZL6vRunVrzS6jR4/O9jYZsmOHau/eqqmpqkOHxmafOSBm5xMPJk1SLVtWtVEj1WXLotokoc8nB2TrfFauVG3WTLV4cdUhQ+JmU24I7e8zfLhqSorqCSfY/14Myck5ARM1DtfTvQ2scHxtMNYB29M9XxuGTbuPUK/LMcbtyoDNm1UHD1Zt3FgVVOvXV33iCdV//skfn9f27arDhql27WrnV7Kk6hVXqC5YEJ5NeUhBtCur63k0Yo9zgDEicpOIXB0ZufLqM2co0EdEigbqII2A8diUZiMRqSciRbDpzaHByY0GTgq27weEEh3PFikp8MYbJgN0wgmWBuGkMWOGVWuXLQtffWUapU7WVKxoFfZNm8Lxx8Nzz4VtUWIwc6bVPDRvbuozBVvfdjAwGxgOXA7UVNUyqlpaVV2g34kf27bBCy9YOkX//qby8d57pthxxRVQrlzYFsaG1FTo1csKIn/7DU48EZ55xs77ssvSFFCcAkE0d5sFWP51EawZQWTkGBE5XkQWYRHnYSIyEkBVp2M5gTOAEcClqrpDLcf6MiyiPRN4P1gX4Abg6qAgsiLwUm5syzNKlzbnsW1bK5D45puwLUoM/voLjjzSikK//toqu53oqFzZ5Od69LA8wbvvDtuicFm1yopiixeHIUOssUQBRlUvVtUDsULw8sCrIvKTiNwnIoeJSAIkuTr5ih07rEB9//3Nsa5Rw5zPCRNMgSM/y622aGF9L/76C845B55/3vK0r7xyT91uJ18SjUzfnbE+qKp+AnySyXv3YrJRuy8fjkVedl8+hzSlkeSibFnT2jz0UDjuOMsLPbAA1xotXGitwLduhbFj7Ve/kz1KljRn8rzz4Pbbzbm89tqwrcp7du40ffCFC+3/yn+o/YeqzgJmAY+JSHGgM3Ay8CiY7G9miMg8LKVkB7BdVduISAXgPaAuMA84RVX/EZOJegLoBWzE9Ld/icc5OQnIt9+afvSvv5qz+dlncNRRyZFfHUtq17YZxRtusA7E//ufFVrfcotF74sVC9tCJ05kGsEWkceDx89EZOjuI88szO+UL2+yP+XLmxTRjBz17kl+li61yPU//9jnccABYVuUvBQqZAU3p5wC111nP+IKGk89Zd+jxx83zVwnQ1R1k6oOV9XLVTVL5zodnVW1Zbr1bwS+VtVGwNfBa4CeWJpfI6y4PPHkU53YM2eOaUQffri1MH/rLVMKOfrogudcp6dePXjxRZg61fS2b7jBIvsffOCKYvmUrFJE3ggeH8aqzHcfTqyITJulpECHDtYCtiAxc6Y5QYsXp8nOObkjNdWiJC1awJlnWq5jQeGjjyxqf8wx1tjJAUBE1onI2mCsS/d6o4hs3/seMqU31oOA4PG4dMtfD2qBfgbKiUj1XBzHSWS2bIG77oImTUwy9O67TWrvtNMKeu3Druy/v0Xzv/rKZrFPOQW6dYM//gjbMifGZJoioqqTgsestdGc2LDvvuZYd+tmkeyxY805yu+MGmUazsWK2Tm7cx07ihc3Z7N9e0u9+fbb/N8I4csvTSarXTsrJC7IEbPdUNVdamdEpBTW2fFCMknZy2g3wJciosDzqjoYqKqqS4P3lwGRquTM+hcsTbcsMeRT40BBsqvcr7/S+LHHKLFwISs6d2b2xReztXJlGD8+VLtiQdzsSk2FRx5hn88+o/6LL5LStCkL+vRhwemns3MvTegK3GeVS0KzKzN5EWAqMCWzkdl2yTISVg5q/nzVmjVVy5VTff/9uB4qdEmdZ581qcJmzVTnzcv17kI/nxgTs/OZMkW1QgXVOnXs+xUScf/7zJtn59m0qeratfE9VkAyyfRFBta9cSCmEHUPUDEb29YIHqsAk4HDsGZf6df5J3j8HOiYbvnXpJNlzWgk7HU5BxQIu1avVu3XL01yb8SIHO+qQHxembF0qerpp9vn2KiR6vffh29TDiiIdmV1Pc9q3uZo4BhMzWMEcHowviCDYkMnRtSubZHGffe1qaOHHw7botizYwdcdZUpXXTvDt9/D3XqhG1V/qVZM5sp+Pdf6NIFliwJ26LY88cf0LkzbN9uspelcyV0lC8RkUoiMgj4BdPAbqWqt2o2moip6uLgcQUW9W4HLI+kfgSPK4LVF2PdIiPUDJY5+YGRI+3a8uab1m132jS7njvZp1o1+xy/+sokDQ891NLcNm0K2zInF2TqYKvqfFWdD3RV1etVdWowbgC65Z2JBZB69eC772yq+7rrrBJ7w4awrYoNixZZGszjj9t5DR0KZVyCN+4ceKDdEJcvt3SR5cvDtih2zJplN6T16+2HRKNGYVuUqMwH+mJ50huB87LT20BESopI6chz7D4wDetf0C9YLX0vgqHAWWIcDKzRtFQSJ1lZt85qG3r0sBzin382dYzixcO2LPk54giYMsU6QD7yiPXKGDcubKucHBJN5YGISId0L9pHuZ2TGwoXtl+0l10GTzxhDtKsWWFblXN27oTBg60RyrhxpnLx2GOWh+bkDQcdZEWkCxaYYsvKlWFblHsmT7ZzAftR2i45FTvziIeAV4LnpTMYe6Mq8L2ITMYagA1T1RHA/UBXEfkTODJ4DTbTOQdrbvMCcEmMzsMJi2+/tdqgwYMt+DNpErSJVnzGiYrSpeHZZ62eZONGq6G58UYrInWSimhU3s8DXhaRslhr8n+Ac+NqlWMUKmRyYyeeaOkibdvCrbda5HcvRRAJxezZpsv87bc2jT94sGtch8Whh6bp0XbrZg2OkrWL2ttvw/nnm8TlV19ZWpWTKao6EEBEKqjq6vTvBZ1z97b9HGCPyusgxeSIDJYrVkTpJDvbttm956GHrFD6u+9M8cqJH127WtrNNdfAAw9YcOTtty1I5SQFe41Eq+okVW2BXVibq+mferOAvKRTJ4sUdOpkv2SbNIFPPkl87UxV617VooVNe730knVndOc6XLp0se/P9OnJmS6yebNNUZ9+uqnOTJpkuaBOtHwmIv/lZYnI/sBnIdrjJDLz55tu84MPWjfGyZPduc4rypSxFvPDhtl1uk0bm9HeuTNsy5wo2KuDLSJFReQ0LBJxpYjcLiK3x980Zxdq1bLI48iRJml3wgnmHE2eHLZlGfPvv9C7tzlCHTrYL/Fzz3XZtEShRw9zsmfNMg3yZNHJnjTJ7H3+ebj+eovAV6sWtlXJxn2Yk11KRFoDHwJnhGyTk4gMGWJ5wDNmwHvvWUfCkiXDtqrg0auXNajp2hUGDKD5DTfkz2L1fEY0udRDsIYB24EN6YYTBt26mVP99NP22KqVOdvffps4Ee3ffzcnaMQIK2YcMcKa6TiJRa9e1kZ8/XrL8xs9OmyLMmf1alOdadvWun4OHWrTpoULh21Z0qGqw4DHgC+BV4HjVfW3MG1yEowtWywV8bjjLCXkl18sTdEJjypV7Lr33HOUnToVmje3IImTsETjYNdU1VNV9UFVfSQy4m6ZkzmFCsGll1pu8403WoOWww+36fLXXguvGELV0kBat4a//zZFhyuv9C5eiUzbttbgqEoVKxa8557Emn7cudO+U40bW+7+FVfYD7hjjgnbsqRDRJ4SkSdF5EmgC1AWmAtcFixzHJO87NjRUhEGDIAffoAGDcK2ygGbAb7wQiYOHgx161pw7bzzLEjiJBzReD4/iognOCYi5cvDfffBwoXmfGzZAmefbZrSd96Zt7m18+ebBur555uSw+TJ5vQ7iU+DBjBhAvTtC7fdZrMkM2eGa9POnVbU0769faf23x9+/dVmRMqWDde25GUiMCndeBD4KN1rpyCjaikgrVrBX3/Bp5+a0lMyFdQXEDbVrg0//mj646+8Ai1bupxfAhKNg90RmCQiv4vIFBGZKiJT4m2Ykw1KlIALLrA85y+/tAjywIHWtOacc+C33+J37H//hTvusMrmH3+E//3PFB08JSS5KFXKWosPHmzOdrNm5mxv3Zp3NqhaMeyDD5oiyFFH2Y/H11+3FKjmzfPOlnyIqr6W1QjbPidEli2zWaGLL7bo9bRpVkPjJC5Fipj++Nix1mCrQwe46y577iQE0TjYPYFGWFOBY0jr8OgkGiJWBDFsmBWvnX8+vP++RSQ6dzaJn9Wr976faFizxv6Z69a1x27d7KJ8ySWeEpKsiNgPtdmz4YwzLF2kVi246SZYuza2x9qwwaaen3vOupWeeSZUr26KMzfcYCkr77wDc+fae14cm2tE5DMROUZE9khcF5H6InKXiLgEa0Hj00/tB/XXX8OTT8IXX8A++4RtlRMthx5qM8Z9+1qw67DDbAbCCZ1oZPrmY+1uuwTPN0aznRMy++5r0eRFiywiOGeOyZpVqwannQaffEJqTrpDLltmv5rr1bN/5k6dbOr+o4/M2XaSn8qV4dVXbTakQwcrJmzQwL4/Q4fmOEe72LJldgM/5BCTn+rY0SJm111n6jhduth058KF5nz36WNRGidWXAAcCswSkQkiMlxEvhGRucDzwCRVfTlcE508Y8UKSyk8/nj7If3LL3D55R4gSUbKlrUZyHfeMcWXFi1MaSlRhA8KKHttNCMidwBtgH2xLmCFgTcBF8JMBsqXNwfm6qtt6v+dd2zK/Z136JCaar9+e/SAnj0tzWP3i+vKlVYEt2CBFVBOmGDLjz7a0lBat87zU3LyiK5dbYwfb1Hmr76yWZCyZe0H3EEHWdR5330tV3rnTqhQwaLTc+ZY9Dny+OOPHDxtmu23RQu45RbTdG3Vypzt0qX9xh5nVHUZcD1wvYjUBaoDm4A/VHVjmLY5eYds2waPPmp1Ohs3Wh7vHXf4j9n8QJ8+FhQ591yTyP30U3jxRU/ZDIloOjkeD7QCfgFQ1SUiEk1bXSeRSE2Fgw+28fDD8OOPLHr+eWpPn25KJDfeaJJnNWpAxYomhQZWKLljhz1v0gQGDbLcWG/sUXBo185SjbZvh48/tpy/6dPtwr1p0963r1wZmjVj9iWX0HDAAGjUKO4mO1mjqvOAeSGb4eQ1I0bQtn9/myXq0cOKhr0Dav6iVi2bEXz2WQuuNW1qs9l9+3qqXR4TjYO9VVVVRBRARFxlPtkpXBgOP5w5qtTu1AkWL7Z0gD/+sAvvypVWUJaSYhHKHj0sJ69uXf8HLcgUKmRauBE9XFVzsCdMsOLEwoVh1SprRFG/vqUR1atnBZTAojFjaOjOtePkPbNn2yzmZ59ZEOWzzyxQ4tfz/ElKikn5dusG/fpZet/HH1vNS6VKYVtXYIjGwX5fRJ4HyonIBcC5wAvxNcvJU2rUMLURx8kOIqZgc/jhLsnoOInIunVWM/PYY5YC8sADTGjZksO7dQvbMicvaNQIvvvOZq1vvx2+/x5eftmajDlxJ5oix4exVrofYXnYt6vqU/E2zHEcx3GcHLBzp9Xa7LuvFSn37WszlNdfj3qudcEiNdWUmSZMMHWmo46y4vKciBw42SKqqiJVHaWq1wH3A1/F1yTHcRwn1ohIGREZJCJviMhpu733TFh2OTFmwgQrdOvXz/Jxf/7ZVIGqVw/bMidMmje378Z115nCSMuWVrjuxI1MHWwROVhExojIxyLSSkSmAdOA5SLSI+9MdBzHcWLAK4Bgs5F9ROQjEYm06Tt4bxuLSC0RGS0iM0RkuohcGSwfKCKLReS3YPRKt81NIjI7aFTWPR4n5QSsWgX9+5u6z9y55lT/9JO9dhywrpwPPgijR1sNTdeuVlOzaFHYluVLssrBfhq4GSgLfAP0VNWfRWQ/4B1gRB7YFzcmTZq0UkTmZ3OzSsDKeNgTEn4+iY2fT+KTk3OqEw9DoqCBqp4YPP9URG4BvhGRY6Pcfjtwjar+EihJTRKRUcF7jwXphP8hIk2APsABwD7AVyLSWFV35P5UnP/YuRNeesmUoNasgauuMtm9MmXCtsxJVA4/3BrDPfQQ3HcfDB9unXuvusrlGmNIVg52IVX9EkBE7lLVnwFUdZbkg8pjVa2c3W1EZKKqtomHPWHg55PY+PkkPkl2TkVFJEVVdwKo6r0ishj4Fii1t41VdSmwNHi+TkRmAlkJ7PYG3lXVLcBcEZkNtAN+yuV5OBF++cW6544bZz0NnnnGZNkcZ28UK2ZO9RlnmGN9443W6OvJJ019xMk1WeVgp2/XtrvYrbcHchzHSS4+A7qkX6CqrwLXAFuzs6OgUU0rYFyw6DIRmSIiL4tI+WBZDWBhus0WkbVD7kTLv//CZZdB27aWDvL666ZP7861k13q1bOGNMOGWa+D7t3Nwf7117AtS3pEM2mlKSI7gA1Yzl5xrEU6wetiqlo4TyxMIJIsWrVX/HwSGz+fxCc/ntPeEJFSwFjgXlX9WESqYmkyCtwNVFfVc0XkaeBnVX0z2O4l4AtV/XC3/fUH+gNUrVq19bvvvpste9avX0+pUnsNwOc5cbFLlapffUWDZ5+l8Jo1LO7dm3nnnsv2bBynQH1eMSAR7YqXTbJ1KzWGDKHOm29SeO1alh9xBHPPO4/NURbIJuJnBfG1q3PnzpMyvQeoqo8oB9A/bBv8fPx8knXkt/PJr+e0l/MtDIwErs7k/brAtOD5TcBN6d4bCRyS1f5bt26t2WX06NHZ3iYviLld8+er9uihCqoHHaQ6aVJi2BUj3K7oibtN//yjeuONqsWKqRYurHrllap//x2+XTkknnYBEzWT61lUMn2OoaqDw7Yhlvj5JDZ+PolPfjynzBArvnkJmKmqj6Zbnj68dTymNgUwFFMrKSoi9YBGwPi8sjffsHOntbo+4ABrGvLkk/DDD3DggWFb5uRXypWDQYOsA+hZZ8FTT1l33nvvhY0b97q5Y7iD7TiO40RDB+BMoMtuknwPishUEZkCdAauAlDV6cD7wAxMdepSdQWR7PH776b4cNllcMghpvxw+eXWPMRx4k2NGvDiizB1KnTuDLfeas2L3n3XZP6cLImmVbrjOI6TjxCR9lg6x3/3AFV9PattVPV7rAZnd4Znsc29wL05s7IAs20bPPIIDBwIxYubukO/fpAPFLycJKRJExgyBL79FgYMsM6gTz8NTzwBrVuHbV3C4hFsx3GcAoSIvAE8DHQE2gajQBVqJjS//mrNYW66CY4+GmbOhLPPdufaCZ/DDrNukC+8AH/8YSo2550Hy5aFbVlC4hFsx3GcgkUboElQoOMkChs3wj33WKe9SpXgww/hxBP3vp3j5CWpqXD++XDyyXD33VYT8MEHcNttSIsWYVuXUBRYB7tSpUpat27dbG2zYcMGSpYsGR+DQsDPJ7Hx80l8cnJOkyZNWqk5aHQVQ6YB1Qiaxjghs3MnvP22RawXLYJzzoGHH4YKFcK2zHEyp2xZ+5727w/XXAPXX0/bSM52jx5hW5cQFFgHu27dukycODFb24wZM4ZOnTrFx6AQ8PNJbPx8Ep+cnJOIzI+PNVFTCZghIuOBLZGFqhpty3QnVvz4o3XRGz/eclnffts6MjpOstC4MXz2GYwYYc52z55w/PHw2GNQp07Y1oVKgXWwHcdxCigDwzagQKNqxWL33ANffQX77AOvvgpnngkpXhblJCk9ejDhxRc5/JdfLHVk//3h+uutKLJcubCtCwX/b3ac7LBzJ2zYELYVjpNjVHUsMAsoHYyZwTInnqjCF19YhLpTJ5M+e/BBk+Lr18+dayfp0SJF4MYbrTD3qKPgzjuhbl1Tw/nnn7DNy3P8P9pxssMtt0CpUjA/7Fl+x8kZInIK1vDlZOAUYJyInBSuVfmYTZss9aNNG+jVCxYuNImzuXPhuuvseuI4+Ynata3w8ZdfTD874mjfdhusXh22dXmGO9iOkx3GjbPHXr1s+uv338O1x3Gyzy1AW1Xtp6pnAe2A20K2KX+hSpnp0+HCC6FaNTj9dFi3Dl56Cf78Ey691PStHSc/06oVfPIJ/PYbdO1qaVF161rayMyZIRsXfxLWwRaRVBH5VUQ+D17XE5FxIjJbRN4TkSLB8qLB69nB+3VDNdzJ3zRsaI+rV1sRR9eusGJFuDY5TvZIUdX0X9pVJPC9IKn4+29rENOkCQdedhm8+SYcdxx88w3MmgXnngtFioRtpePkLS1amOzklCmm7f7MM9a8pnNn6wqZT9MuE/mieiWQ/ifOA8BjqtoQ+Ac4L1h+HvBPsPyxYD3HiR/Vq8PSpfDzz3ZDrVsXDjgA/vorbMscJxpGiMhIETlbRM4GhpFFN0ZnL+zcCaNGwSmnWGvpa6+F8uWZde211oDjtdfMkfAca6eg06yZpUstWgT332+pln37mu77ccdZse+qVWFbGTMS8j9eRGoCRwEvBq8F6AJ8GKzyGnBc8Lx38Jrg/SOC9R0nvrRuDcOHm+j+smUmTTR8OKxcGbZljpMpqnodMBhoHozBqnpDuFYlIYsX25R3gwbQrRt8/bWlfkydCj/+yLKjjoLSpcO20nESjypV4IYbYPZsm9254ALL1z7nHKhaFbp0gaeesnqFJCZRZfoeB67HKtwBKgL/qur24PUioEbwvAawEEBVt4vImmB993Kc+NO5s42jjzb9z6OOsgKPSZPsV7njJCCq+hHwUdh2JB3bt9uP6BdesMedO80ZGDTIInDFioVtoeMkDykpaffQJ54wJ/uTT2xccYWN1q0teHX88Sb9l0Tx04RzsEXkaGCFqk4SkU4x3nd/oD9A1apVGTNmTLa2X79+fba3SWT8fLJP4yVLqLh1Kz/tfpwiRSj61luUnj2bJnfdxYb27Vl7wAEsOPVUtlSrlqNj+d8n8UmmcxKR71W1o4isA9K3SRdAVbVMSKYlPn/9BS+/DK+8Yulh1apZBO688yyC7ThO7hAxZ7p1a5sZ+uMPc7Q//RRuvdVG48bmaB93HLRrl/BpVwnnYAMdgGNFpBdQDCgDPAGUE5FCQRS7JrA4WH8xUAtYJCKFgLJY0c4eqOpgbGqUNm3aaHY7sOW3TnR+Pjng7behSJGsj1O/PqVvuYXSI0ZQ47ff4PPPLUc7m/jfJ/FJpnNS1Y7Bo+ctRMOmTfDxx+ZYf/ON3cx79bKUsKOOgkKJePt0nHxC48b2I/aGG2DJEhgyxBzuRx6BBx6wBk29e5vD3akTFC4ctsV7kHDuv6repKo1VbUu0Af4RlVPB0YDEa3WfsCQ4PnQ4DXB+9+oavrojOPkLaedZhq3339v4vpNm0Lz5paj6TghIyJvRLOsQLJliznTl1xixcxnnGH/y3fdZQVZn31mN3V3rh0n79hnH7j4YvjyS1PteuMNOPhgKyDu1s1yus880xzwjRvDtvY/Es7BzoIbgKtFZDaWY/1SsPwloGKw/Grgxrgcffx4UvOplIwTJ1q3hmnTrFhjwwY48khTGpg1K2zLnILNLtMpwcxf63gcSER6iMjvgYxqfK7NuWHNGvjpJ/jf/+DYY6FiRTjiCEsFOeYYGD3aCrFuuw1q1gzbWsdxype3H74ffWSCAkOG2I/e4cPhhBOs9umEE0wi899/QzU1oX+Gq+oYYEzwfA7WEGH3dTZjHcnix86dcNBBHAqwbZtHL5zoqVkTLrvMppUffNCKoT74wH51v/223dAdJw8QkZuAm4HiIrI2shjYSpA6F+PjpQL/A7pihekTRGSoqs6I9bH2yoYN1thi2jSYPj3tMb1KQb161rK8Rw8ruvIOi46T2BQvbj+Mjz3WCpC//dbSuiKFkoUKQZcu1GjcGEqWND3uPNShz5WnKCIpQAtgH2ATMG23Bgb5jx49LLn+jjugaNGwrXGShWLF4PbboX9/eP11e96yJRxyiOnmttvjt6PjxBRVHQQMEpFBqnpTHhyyHTA7CI4gIu9isqqxc7C3b6fI33+b+sDy5XuOFStgzhxL84hkDhYtamoEhx9utRFNm9qoUyepFAocx0lH4EzTpQs8+SRMmGDO9scf0+jLL+Hpp+0+3Lq1NbmpXdtGrVoW9S5f3iQCY5jLnSMHW0QaYCkbRwJ/An9jBYmNRWQj8DzwmqrujJWhCcNff1ku7Zo1Nq3oONmhWjVrsX7ooTBwIIwZA198YUUb++5r09OOE1/Gi0hZVV0DICLlgE6q+mmMj/OfhGrAIuCgWB6gduGnqU9LYC1QHKgL1GUHsA0LzW8GNgRjI7BpC/BbMP5jXjBizZg47DMWjAnbgEwYE7YBmTAmbAP24PDDw7ZgT/79tyXlyoVtBVj280E2ajzA2iIrKIPA2jUwdS2M32TZCP+xipZ8zeNfNYvpPTinEex7gGeBC3cvKBSRKsBpwJmkNYDJF8w9+2zqvfIKXHcdPPywtfjs29dybD3y4WSHQw6BkSOtOvqII6xBBcCdd8JVV/n0tBNP7lDVTyIvVPVfEbkD+DQMY3Ijn7oK2I450ltJc6rzX2THcXbl35DzizNix44diWlXSir/pqZCxUo2ANm5E9m2jZStW5EdO/inQgo/rlnJ1hjKrubIwVbVvlm8twJrFJN/ue8+y50dP96i2JUqWaOR1q3d0Xayxz77wOTJlgt6112WenTHHdC9OykDBoRtnZM/yai4PR6FJREJ1Qjp5VX/IzfyqRu0U8JKJbpd2cPtih6zqVzYZuxBIn5WEK1ddWN+3JymiByW1fuq+m3OzEkSCheGG2+04sfjj7eo4513ws03w733hm2dk2wUKWLNKl5+2aLZM2bAgw9y4B9/QNu2JrrfqFHYVjr5h4ki8ihWgAhwKTApDseZADQSkXqYY90Hm910HMfJ9+Q0anFdBssUaI5FLFJzbFEykZICH34IP/9srXPvuw8WLzan6JJLPJrtZI/UVDjrLHverBk777kHRowwGbGHHoJmzaw4w3Fyx+XAbcB7wetRmJMdU1R1u4hcBozE7gkvq+r0rLaZNGnSShGZn81DVQJW5tDMeOJ2ZQ+3K3oS0SYomHbVyeyNnKaIHJP+tYh0AG4FlmEX74JD4cJWsNa2rTUV+fxzEz9fuNCUIkqUCNtCJxk5/XR+qVGDTmXLWlV0nz4W6R42zPS0HSeHqOoG4tUvYM9jDQeGZ2P9ytk9hohMVNU22d0u3rhd2cPtip5EtAncrt3JVaMZETlCRMZgRY+PqurBqvpZTCxLNooVsy5ff/8NF1xgqhClS1vx2tatYVvnJCutWpnM2KRJ1jq2Z0/rMPfUU2Fb5iQpIlJZRB4SkeEi8k1khG2X4zhOfiJHDraIHCUiPwLXAreqamdVHRVb05IUEXj2WXO2L7gAnnnGNI5few127AjbOicZKV8eDjzQ2sQOGGCO9hVXwNVXw6uvWi2A40TPW8AsoB5wJ6ZPNyFMgxzHcfIbOY1gf4ZVhG8HrheRoelH7MxLUlJTTVXkuefg/fctgn322ZZG8tNPYVvnJCvVq1su9pdfwlFHwWOPwTnnWKMax4meiqr6ErBNVceq6rlAl7CNygUx70IZI9yu7OF2RU8i2gRu1y7k1MHujOlcPww8ksFwIpx8srXkfe896yrWvj2cdJI5SY6TE4oWtRmSTZsskv3YYzZzcsIJu4nnO06GRL4kS4PZyFZAhTANyg2BzF/C4XZlD7crehLRJnC7didHDnYQ9RgLrI88T7esdGxNzAeIwCmnwMyZJu83dix0724NRdK38HWcaBGxvP9HH7WZkosvhk8+sR9vjzwCCSj27yQM94hIWeAaLM3vReCqcE1yHMfJX+SqyBF4QUSaRl6ISF9M/snJiNKlYdAg69535ZXw+ONQv75pH69bF7Z1TjKSmgoXXmi5/nffbSo2115r0WwvrnUyQFU/V9U1qjotqJ9praqe2uc4jhNDcutgnwS8LiL7icgFwCVAt9yblc8pXNic659/Nof7229hv/2gQwdb5jg54dZbzal+/XUYPRrKlrWW7P/8E7ZlTgIhIo1FZKyI/Cki9UWkhYjcGrZdjuM4+YlctcdV1Tki0gf4FFgAdFPVTbEwrEBw0EE2mjeHF1+EiRNN4/iss0ye7fzzvVmNkz1SU+HMM00z+8cfTdHm+OPtO9WrF1SrFraFTkgEaSGXAjcDq4CKwPtAVaCMiPygqqNDNNFxHCffkFOZvqkiMkVEpgAfYgUy9YBxwbJcISK1RGS0iMwQkekicmWwvIKIjAoiL6NEpHywXETkSRGZHdh1YG5tyFN69YKPP4Zx48yxfucd6N/fZP7eftsjkE72OfVUeOIJ6zD63Xdw3nlw8MEwb17Yljnh8SGwEPhDVesAf6pqG1WthTUJ6y0i54VqoeM4Tj4hpykiRwPHpBsHYakhkde5ZTtwjao2AQ4GLhWRJlj3sa9VtRHwNWndyHoCjYLRH3g2BjbkPdWrmzO0ejVccw289BKcfjp07Ai//w4bNoRtoZNs9OtnzY/GjIG1a6FRI1OyGTMmbMucPEZVu6rqG8ByEWkAKICInATMUdUBgXyf4ziOk0ty6mCvUtX5mQ0AESmVU6NUdamq/hI8XwfMBGoAvYHXgtVeA44LnvcGXlfjZ6CciFTP6fFDRwQefhgWLTI5tvnzLUe7ShXLrXWc7FChAhx+uKWMXH89LFsGnTub871sWdjWOXnPpVizmQNEZBVwN5bm5ziO48QI0RxIxInI18BvwBBgkqpuCJbXxzSyTwFeUNUPc22gSF3gW6ApsEBVywXLBfhHVcuJyOfA/ar6fTr7blDVibvtqz8W4aZq1aqt33333eiM2LmTTkccwe+nncbSCy7I7SllmxLz5lFuyhRqfPIJRZcv599WrVjZoQPLevXK1X7Xr19PqVI5/h2UcOTF+TR++GEq/vwzP32Y66/2XonX+aRs2UKdN96g1nvvITt28G/Llsy66Sa2VK4c82OlJ7993yBn59S5c+dJqtomTibtFRF5GWgO/A7sBHYAdVW1U1g2OY7j5DtUNUcD6IVFQeYBa7CimR+BW4BqOd3vbscoBUwCTghe/7vb+/8Ej58DHdMt/xpok9W+W7durVGzY4cq6Jyzz45+m3iweLFqz56qDRqogurAgaojR6pu25aj3Y0ePTq29oVMnpzPBReoVq8e/+NoHpzP77+r3nGHaunSqjVqqJ55puoHH8TtcPnt+6aas3MCJmoMro/ZHUAZ4CZgNdAVEOAyYC4wJAybfPjw4SO/jhyriKjqcGB4TrffGyJSGPgIeEtVPw4WLxeR6qq6NEgBWREsXwzUSrd5zWBZ/mKffWD4cOvWd+KJMHCgLe/f35qNuOKIkx0aN7bv0PHHm2LNyJHwxhtw6aWw//6WQpLPIs4FnDeAf4AZWJOZWzAn+3hV/S1EuxzHcfIdudXBjgtB+sdLwExVfTTdW0OBfsHzfliKSmT5WYGayMHAGlVdmmcG5zWFC8Onn8LUqXD11TB4MLRpY938vFW2k11atIAJE2DhQpP4+9//4LLLLE97xAjrNurkB+qr6tmYY30I0BFTgHojFupPjuM4ThoJ6WADHYAzgS4i8lswegH3A11F5E/gyOA1WCR9DjAbeAFreJO/SUmBpk3hoYes/Xrp0hbFvuQSGD8etm8P20In2ShSxJrUbNhgP+CmT4eePS2a/eGHLheZ/ER+fb+EXV9nYYpQEVUox3EcJ0bkqtFMvFArVsws3+GIDNZXrDK+4JGSYt0gAW64AR580JrWdO1qbbOLFAnXPif5KFECeveGP/+EOXPguuvg5JPtvTPOMPlI/14lIy1EZC1QAqufKQ5Mxa61iuVoO47jODEg1w62iHQEGqnqKyJSGSilqj6nHAb33w/HHgs//GDO9qGH2rj1VihXLmzrnGSjRg0bX38N775rKUlPPAFffgl16sCrr0KTJmFb6USJqqYCiMgzQDngM2BLmDY5juPkV3LlYIvIHUAbYF/gFaAw8CaW4uHkNSLQoYONkiUtkv3EExbJfuklW+442aVkSesECdakZvhwy80+9FA44ghLIznnnHBtdLJDccyx7pZumQIfZ7y64ziOk11yG8E+HmgFRJrCLBGR0rm2ysk9l15q49tvbXq/Y0c45BC46SY4xtMtnRxyyik2/vrLlEfGjYMPPrDH5s3htNN8tiTBUVX/NeQ4jhNncutgb1VVFZFIy92SMbDJiSWHHWZ5tK+8Ao8+aikk118PffuSssVnh50c0qCBdRXdvt0Ka59/3pY//jhcdZVJAB6xR7mEEyIicnsWb6uq3p1nxjiO4+Rzcqsi8r6IPI+1Jr8A+ApT8XASiZIlTXbtjz/gwgutELJVK9pccIHl1a5fH7aFTrJSqJDJRK5bB2PG2OMll8CRR9p37ZVXYHH+k6RPUjZkMADOA24IyyjHcZz8SK4cbFV9GPgQawizL3C7qj4VC8OcOFCoEDz7LIwdC6+8QuE1a2xav2xZuOceUA3bQidZKVUKDj8c5s0zhzqiz37uuSYneffdVBs2DDZtCtvSAouqPhIZwGAsF/sc4F2gfqjGOY7j5DNyW+RYD/hOVUcFr4uLSF1VnRcL45w4IGJpI4cdxsRixThk1SpzuG+7zUbTpvD++6Z97DjZpWhR6zj6yCOmz75woUW0b7+d/QA++cSKbVu3tuWFElIpNN8iIhWAq4HTgdeAA1U1YQTOK1WqpHXr1s3WNhs2bKBkycTLTnS7sofbFT2JaBMUTLsmTZq0UlUrZ/Rebu9uHwDt073eESxrm8v9OnnAlmrVoE8fc3SOPRZ+/92ijs2aWVrJ+eeb9F/hwmGb6iQjlSvb+OknWLeOyS+8QIv33oNvvrGGNg8/DFWrwoknQo8eUKWKOedOXBCRh4ATsOh1M1VNuNywunXrMnHixGxtM2bMGDp16hQfg3KB25U93K7oSUSboGDaJSLzM3svtznYhVR1a+RF8Nw7UCQbItZA5O67YdIkizz27GlFkWXLQv36MHJk2FY6yYoIlCnDP61bW5fRBQvgo4+gTRuLeN90E7RqBTVrwnHHwRVXWOfIHTvCtjy/cQ2wD3ArsERE1gZjXdCAxnGcRGfpUuq88UbO0u1U4bvvYNu2va/r5JrcOth/i8ixkRci0htYmct9OmFSs6blY7/7LgwZAhddZJ39evSw7n3dusHcueAKJE5OEYETToCPP4bvv4fJk+359dfDhAlWGHnyyZbXXbWqOeAffmgFuU6OUdUUVS2uqqVVtUy6UVpVvYuj4yQ6GzfCMcdQ7+WX7ZqYXW66yVJEP3bJ+7wgtw72RcDNIrJARBZglej9c2+WkxAce6xFscePt+n8Sy+FH3+0iHaJElbI9vvv8O+/YVvqJDPNm8Pxx1s60uLF9n364ANLXerQAR54wBzu5s3tde/e5oT//bcXTeYAETkvg2X3h2GL4zhRsnMn9OsHv/zC9uLF4bPPdn3/55+zdpzvv9+upQC//RY3M500cpyDLSKpwMWqerCIlAJIxJw+JwaUKAHXXGPPL7sMPv0Upk+Hxx6zUbIk3Hmntc1u397SShwnp6Smwkkn2QBYsgRWrbKOpJ9+CtOmwdCh9l7x4tZlsn59qFABqlWzCHleUKKE3fSSjxNFZLOqvgUgIv/DFEUcx0lUbr7ZotYPP8yK0aPZZ8QI2LrVZpbHj7e+A9u327WyVKldt33mGYten3aazRhOmxZ/ezdtslSUMgV3cizHDraq7hCRjsFzd6wLCg0apDnbF19s2tqvvw7XXmvLqla15VWrwplnmvPtOLlhn31sNGtmNwlVGDXKZk8mTjTpyZDytcs9/DB06RLKsXPBicBQEdkJ9AD+VdVzQ7bJcZzMeOEFiz5feCFcfTWrNm9mn2HDrFNznTpw1FEmRrBxo10bjz8+bds337TZ52OOgVdftSj4Tz/F197Jk+Hoo63h2Ndfx/dYCUxuVUR+FZGhmHJIpGkBquoJPgWBtm1tnHaa5ccuWwa33goDB9r7991ncmwHHmjpJO5sO7FAxGoBunWz1889ZzUBf/9tIy/44w845xxSkyhFJZDoi3A+8CnwA3CniFRQ1dWhGOY4TuaMHGlBqx494OmnQcQKxosVg5dfhnHjbL0ff7QZ5M8/T3Owhw6Fs8+Gzp1NfrdwYZPifecdawpWunTs7R0+HE491RrYrVxpUfUCKsea27MuBqwC0odwFMhzB1tEegBPAKnAi6rqOYV5hYjlxzZvDl27mrMzYYI52n/8YdP6991n01ZnnWUFbtWr27S+4+SW4sVtlCsHjRrl3TGTj0npnisgwFHBULzZjOMkFlOmWP3JAQfAe+/956juLFbMUkLeecdS1UaPthTNnj1h2DBLXRszBk45xYJcQ4aYQw7mYAPMmAEHHRQ7W1Wp+cEHFvBo0cKc7BtvtJnGAw6I3XGSiFw52Kp6TqwMyQ1BPvj/gK7AImCCiAxV1RnhWlYAEbF/5EMPTZsa+uEHk2VbvBgef9wKJ0VMkq1uXejUyaav8ip31nEKJh1V1fvWO04ysGSJpX6ULm1O8+65zKedBl9+aY53u3a27OijTQHs6actZ7tRI/jii10j1REHe+rU2DnY27bBZZfRcPBgi56/8Yapjd14oxVUuoOdfUSkJvAU0CFY9B1wpaouyq1h2aQdMFtV5wR2vQv0BmLiYKvCj7RH11egXix2WNDo0MEGwOzZMGeO/bp+8UX753/sMSuMLFfOprNatLB1Iw53lI8Vpk5NU5XI5raIRLXOpkWr+HVrG/SHnHwQ2WPq1DIx6fFTtCi0bFlgZ+niwvqNKUymPTs3JlUk+4UgTWQMMAL4XlW3h2uS4zh7sH69Ocv//GO61TVr7rnOaaeZ0lf6gsaePa1I/MorbYb4yy+t+Ds9deta1DtS6KhqQa/vv7dAWEo2xeVWr7Yo+zffMP+000yjOyUF9t3Xbj6//Qann569feYTcnvLfQV4Gzg5eH1GsKxrLvebXWoAC9O9XgTEbO5DFTryA4d/eDtjPcoaU1KBPsAha9ZQb80aet15Z4731TxmVmXObTzEI1wLHfPgYBwYw30tB/4C5gDPBK/nYpkBTnZpTEv+4FfaDurN+PuuCducqFDVXiJSDOgEHA88HMirjgBGqOqCMO1zHAeLBvfpY4WCn31mTbgyY3e1kAoVLE1zyhT46itLxdydlBSLKE+bZs7NjTfCgw/aewsXWtFktPzxh/0QmD8fXnuNubVrUyfioBcubIXpv/4a/f7yGbl1sCur6ivpXr8qIgNyuc+4ISL9CXS6q1atypgxY6LabucOBTozOX6mFVh2AG8FA6AmUB5LDiV4TP88s8e8em8mZUhhNTs5NYqzSxQqAscClYPHM4Ll04D3gRXAG8DGUKxLRiJTdL9D1NeRREBVNxM41AAiUg/oCTwtItVUtV2Y9jlOgUYV+ve3lJBnn4VevbK/jw8+MCe6RInM12na1I5xySWWM3344TB2rOVlR+tgf/ONSammplo6aMeONjOdnpYt4ZNP7LwKYHAytw72KhE5A3gneN0XK3rMaxYDtdK9rhks2wVVHQwMBmjTpo1G25s+InV73Nnn8Mord+XO0gRizJgxRPsZJAN5cT79+1uR9pIlo+J6HIjP+fz7rxWlr1oFTz3VlFmzLB+vcuXnaNAADjnEGiqWL2+ze7EkP33ffv3VxHGuu/seOnVqFrY5OUZV52JTGs+ISJGw7XGcAs1NN5mU3h13WBflnLB7VDsjmja1Zl3PPWfHvOYaqFTJ+lv07Ln37QcPNum/ffe1KHu9TJJnW7a0VNDFizNOc8nn5LaT47nAKcAyYClwEhBG4eMEoJGI1AtuEn2AoSHY4TgJTblyVtx9ySUWrNi61WpQDz/calOfeMJmFUuWtHVGjbK0eSf5EZG5IjIn3Uj/+i9V3Rq2jY5TYHnsMdO6vugic7DjyaGHWuT5/vtN4atiRWvSNWMvZWs7dsCAAabH3bWrSQNm5lxDWnrLpEnw2msF7maSowi2iBysqj+r6nxszjlUVHW7iFwGjMTSel9W1emxPs7y5cX4/vtY7zXnVK2ad6pkTv5DxNLk2re3AVZYPmKEXQcHD7ZZypQUi9w3amQ1NQ0bhmu3k2Pa7PY6BQuQXAsU3ERJxwmbt96yXhEnnfSf1nVcadvWdLDTy402aWIR7MxYswb69jVhgiuvhIcf3nvlfLNmdi5nnQVr11rzuddfj805JAE5TRF5hqACS0R+UtVDYmdSzlDV4cDweO2/RAn44ovqfPFFvI6QM/r0sdmeI46Agw8O2xon2WnWzAbYzOHChXbtf/55S6O75RaTVq1e3Zp3VqoUrr1O9KjqKgARSQHOBK4DfgOOcklTxwmJESPSmsG8+aZFlvOC3bX8DzjA0kYyypeeNQt69zYFsGefjT59pXRp2+/8+RbpnlywKtly6mCn//SLxcKQRCYlBX75BT7/fDItIhJyCcA339is0rvvWgPFLl2gcmW46qrY6sc7BZO6dW0ceqjJly9fbvnZo0ZZw8RXXrE85EMPheuuIyaSgk78EJHCWFrfVcD3wHGqWrDmbB0nkRg3Dk480aJkn34a+8KX7HDAASYPuHAh1K6dtvyzz0xmr1gxK2Y87LDs7Xf4cLs5PPkkPPSQNaIL8zzzkJw62CkiUh6bYow8/8/pzo8td/fdF5Yu/YdEqtE68ki4+27YsMFSt0aMMHWe994zR7tJE3u/dm0bBbCI14kRxYpZcfl779nrKVMswr14sUW1X3wR9tnHlh11VLi2OpkyF9gOPA4sAJqLyH/qlqqa5x14HafA8uuv1v68enVLu9i9kUxe06SJPU6fbg7Dzp1w771w++0WSfn0U6hVK8tdZEhkm5YtrW36jBlZSw/mI3LqYJfF2u5GXLZf0r3nLXfzkNRU+7+8914b69bZD8WFC+3/IfJjs317y6OtVg26dXNn28kdzZubyhOYKtTrr6dJou63HzRuDM88AzVqhGunswtfYdfnFsFIjwLuYDtOXjB9uhUJliljUeFq1cK2KK3b4owZJrl39tnw8cdwxhlWkLN7Skl2icz+T57sDnZWqGrdGNvhxIjSpS2iCBbVHj7cOq4+8ID9v4DlardqBRUrVuHww93ZdnLHySfb2LzZCtJnzLDZlAMOsODFmWdaCokTLqp6NoCIFFXVLenfCzo8Oo4Tb/74w4qmihQx5zo7jV3iSYUK5uiPHGn5fzNnwiOPWM5pLJyEhg2tmO2333K/ryQhtzJ9TgJTtqwV/V5zDcybZ//XL7xg+bNvvw333NOEBg2gXTt44w3TSN7ujZOdHFKsGNx1F3z4IUyYYDUx5cvDDTfYrMn48RX+05R3QuVjEfkvuCIi1YC9CruLSC0RGS0iM0RkuohcGSyvICKjROTP4LF8sFxE5EkRmS0iU0Qklq1JHSf5mDvXnOudO825TjRJpiZNrMhm6VJztK++OnYRuNRUq6AvQIWO7mAXEEqUMJm18883CbbVq+G662bRqpXVHJx1ljlDtWrB//5n0//r1oVttZOs7L+/yZ6OHWspS1OmwA03NKdRI2vU44TKp8AHIpIqInWBL4GbothuO3CNqjYBDgYuFZEmwI3A16raCPg6eA3WIbJRMPoDz8byJBwnqVi0yJQINmywNub77x+2RXty8smmZjJhghV5xZqWLS2CrWqv58yxCvpt22J/rATAHewCSkoK9Oq1jI8+slqLDz+02aC6deGyyyyXtnlzS70aPjzt/8FxsoMIXH651QTcdtsMSpUyLe0TToDbbrNGN07eoqovYPnYnwKfARep6pdRbLdUVX8Jnq8DZgI1gN7Aa8FqrwHHBc97A6+r8TNQTkSqx/BUHCc5WLLEIterV8OXX9rNNRG56CKTJ6sfpzK6li1tqnz6dKuI339/S0EZMSL2x9q0ybS7QyTHDnYQ/ZgVS2OccEhJMaWgq6+2rn5TpliUUcQaNh11lDlFzz4L06aFba2TjBQpAl26rODnn+GCC+w7ds89dj1fsgRPHckDROTqyMDkVWtjOtgHB8uys6+6QCtgHFBVVZcGby0DqgbPawAL0222KFjmOAWHBQtMbWDJEotWtdm931MBIlLo2LatdZE88UR7PXVq7I6xbJlFb2rVgtatY7ffHJBTFRFUdYeI/C4itVV1QSyNcsIjJSWt2UjXribDNnSo6R9//rk5SrffbqljvXtb3q3jREvx4ta0Bux7dPfdVk/TubPde/z7FFdK7/b640yWZ4mIlAI+Agao6lpJl6Opqioi2ZrvEpH+WAoJVatWZcyYMdnZnPXr12d7m7zA7coe+dGuYkuX0uLqqym8bh1THnyQtdu2QQzOMVk/q5RNmzikTBk21qzJ7EsvZV2TJhw0ZgzrvvqKGZF2wjmkxNy51PrgA6p+9RWyfTubq1en+F9/8cMnn7C+cOFQPq8cO9gB5YHpIjIe2BBZqKqht093ck+RItZ86cor4bzzYMUKizjeequ9f+ihJsXWsKE7Rk72ufNOk1f99VcrjjzySAvu3HQTVK269+2d7KGqd+Z2H0Gzmo+At9LpZi8XkeqqujRIAVkRLF8MpBfOrRks292uwcBggDZt2minbDYbGDNmDNndJi9wu7JHvrPrjz9MQmnrVhg7lgNjGE1N6s9qxQrKFilC68gP87ZtKf7nn1TJyfmoWrHoI49Ymknx4jZFOmAAxRcsgCOPpEOZMoxJTQ3l88qtg31bTKxwEp5SpWyMHGn5tGPHWsFks2bW1Obdd61+w3GiRQSOO85GlSrw4IMwfrylKb3/vjWuKSANv/IEEXkBeEJV90j0EpGSwKnAFlV9K5PtBXgJmKmqj6Z7ayjQD7g/eBySbvllIvIucBCwJl0qiePkX2bMsJzrHTtg9OjEzbkOg90v6pGmCps3Rx+p27IF3nnHWllPmWIRmUjOYcWKtk65cvY4ZUpoutu5KnJU1bEZjVgZ5yQeItbk6cwzLW3qjTegUiWLPp51Fvz1V9gWOsnIpZfC/Pnw0Ufwyy9WY9O4saUoOTHjf8DtIjJTRD4QkWdE5GUR+Q74EUsV+TCL7TsAZwJdROS3YPTCHOuuIvIncGTwGmA4MAeYDbwAXBKf03KcBGLyZP5rMDF2rDvXe6N5c/shMnPm3tddscKmPmvXhnPOse1eesluHrfckuZcg0X+qlaNbX53NslVBFtEDgaeAvYHigCpwAZVDbnnp5MXNG5s47jjYOBAk/d7+237ETlokDW9cZzscMwx8N13ljZy001WB3DkkVZsG2k05uQMVf0NOCXIoW4DVAc2YRHp36PY/nvSuvfuzhEZrK/ApTk22HGSje++M0WA0qVNjSPRdK4TkcgPkKwizVOmmJzfW29Zys1RR8GAATZLkJVOd7Nmtm1I5Fam72mgL/AnUBw4H4uSOAWIUqXg4YdN0vLCCy0vu0kTy6v955+wrXOSjfbtLaL9wQewcaNJRXbtarMj+VQuNU9R1fWqOkZV31HVT6Nxrh3H2QuffmoXqqpVzdF25zo6IkVcu0ead+40ZYUjjjD1kffes7zUWbNs+ZFH7r0JTvPmJgm4Y0f87M+CXOtgq+psIFVVd6jqK0CP3OxPRB4SkVlB569PRKRcuvduCrqC/S4i3dMt7xEsmy0iN2a4YyfuVK9uUezvv7fI9sCBcNBBllO7eXPY1jnJRvfu1oF0wgRYv96uw1WrwrhxYVvmOI6TjuefN8m5Vq3shpco7c+TgdRUm56MRJrXrzdHYr/9bErzjz9M0m/hQlu+777R77tZM9i8meIh5Rrm1sHeKCJFgN9E5EERuSoG+xwFNFXV5sAfBB3Ggo5hfYADMCf+mUCLOxWLmvcEmgB9g3WdkGjf3gp7v/vONOU7doSaNc1Rcpzs0qyZfZcefNC6jR59tEW4hw8P2zLHcQo0qhZJuugi6NnTOjSmzwN2oqN5c8sLvOEG06++7DK72L/zjk2N33ADVKiQs/0CpebOjbHB0ZFbZ/jMYB+XYTJ9tYATc7NDVf1SVbcHL3/GpJ3AuoK9q6pbVHUuVjjTLhizVXWOqm4F3g3WdUKmQwcrpn7/fShTxmZ0une3NCrHyQ4tWsB115kSU/XqVlx7zDHw1FMW0fZGNY7j5Cnbt5tjfeedVnD3ySdQsmTYViUnzZvDypUmt9etG/z4o13Y+/SBwoVzvt/994eUFEr+9Ze1qB8yxJz3776Lne1ZkKsiR1WdLyLFgeqx0FjNgHOB94LnNTCHO0L6rmC7dws7KA62ODmgUiU4+WRo185aZs+YYQokmzZZpLuJzzU42aBRI5tJ3LDB6lyuuMKWn3++zdKm5DrpLf8iItWAO4CdwO3A5VhAZCZwpUvoOU6UrF0LffvaNNrNN5tE3N7ygZ3MOfts+/yOP94UQmJF8eLQuDE1hgyxSN+WLbZ8xQpr5BFncqsicgzwMKYgUk9EWgJ37a3RjIh8BVTL4K1bVHVIsM4twHYgZvHO/NoxLKfk9flcfTVs3pzCVVe15IILTGjmvPPmcMYZsWkEmhfns2RJY7ZurciYMT/F9Tjg37e9ccstwoknluGHHyry4ou1ef/9bdSrt4HbbptBxYpbY3acjPjzTxPi2Lx5czL9jV4FhgElgdHYtbUXcBzwHD7z5zh756+/bPrszz/h2Wctiu3kjnLlrKNdPDjhBLa98QaFTzjB/m6PPmqFj3lAbhvNDMRSNMaAyUCJSL29baSqR2b1voicDRwNHBFIPUHWXcH22i0sOG6+7BiWU8I6n8MOs0LIl1+Gl16qz+zZ9Wnf3oIAuYlA5sX5vP22dbjMi8/Nv297p2tXm/Hr0gUmTSrMW2+VY8CA9rRvD+eeaylJ8aBsWXssVqwYnTq1jc9BYk9VVX0KQEQuUdUHguVPich5IdrlOMnB6NFw0kn2/MsvoXPncO1x9s699zK+a9e0e88339jfbsuWuHcyy+2E6jZVXbPbMs1wzSgRkR7A9cCxqrox3VtDgT4iUjRw4hsB44EJQCMRqRcUXPYJ1nUSlBIlLM3q9dehf3+b7h80yArXvv46bRbHcaJBxL5Hzz9v18569SzFrmdPuPVWWLUqbAsThvTX+9ezeM9xnN157jm7cVWrZi1n3blOTpo2tfz5P/6I+6Fye1GdLiKnAaki0khEnsI6guWGp7GOYqOCTmHPAajqdOB9YAYwArg0kAbcjhVZjsRyCd8P1nUSnCJFzCkaP95yaZ97zgohe/Z0WT8nZ7RrB99+C7Nnw2mnwb33WlH6hRfCkiVhWxc6Q4ImM6jqrZGFItIQU2xyHGc3ZPt2mya7+GKbEvvpJ2jQIGyznJwS6ViWB2kiuXWwL8dk87YA7wBrgQG52aGqNlTVWqraMhgXpXvvXlVtoKr7quoX6ZYPV9XGwXv35ub4Tt4jYk2afvsNnnzSZuHq17duswsX7m1rx9mTEiXgzTetd8EZZ9hsyYEHwosvWiv2goiq3q6q6zNYPltVTwrDJsdJaBYvpuWAAaa/fO21pkJRxhtVJzX77mva29Omxf1QuXKwVXWjqt6iqm1VtU3w3GOPTrYRMSm2yy+3POfOnc3hPvJIeOABk8h0nOzStKl1gpw40boXX3ABtG4N774btmWO4yQ0X30FrVpRcs4c6yL40EPmmDnJTdGi1gkv0R1sEWksIoNF5EsR+SYyYmWcUzDp29e0socNMzWdG2+0ZjVffun5tE7OOOAAmxGcNcvUmc46y5quPf542JY5jpNQ7NgBd91l+dZVqvDLc8/BKaeEbZUTSyI3hDiT2xSRD4BfgVuB69INx8k1HTvC33/DggXWCbJ7d6hc2QIJjpNdihSx2cFPPzUHu1AhuOoqy/3//fewrXMcJ3QWLDBJojvugNNPh3Hj2BhLXWYnMWja1OQWN27c+7q5ILcyfdtV9dmYWOI4GVCokBWpff89fPYZfP45XH+9zdi1bGmd/IoXD9tKJ5moUMFysbdssXvpxRfb8ocesjTLgoCItAfqku4eoKq7K4s4TsHh/fetGnr7dnjtNeuI5s1j8icHHGBt7mfNsuKcOJGjCLaIVBCRCsBnInKJiFSPLAuWO05MqVzZdI3ff99aZpcvbzraRx1ly9atC9tCJ9koWhRGjbJUpOOPtx9up5wCjz1m1978ioi8gTUI6wi0DUabUI1ynLD45x/o1w9OPdWmuH77zaa43LnOvzRtao/9+lmb9jgVeeU0gj0J07uOfAPTp4UoUD83RjlOZhQqBA8+aM9fe830j0ePtpqFG28sQT7qy+LkASVKQK9e0KmTBazGjYMPPrBOyKefbmpc+fA+2wZokq6Jl+MUTD77zKLWK1bAbbfZKFw4bKuceNOwIXToAJs2mazUiBFWlBNjchTBVtV6qlo/eNx9uHPt5An9+sG//8IXX9jj+ee35bTTYPLksC1zko0SJeCjj2DuXCuyHTgQGjWyQFY+dEOnAdXCNsJxQmP1avvnPvZYqFTJflnfdZc71wWFQoUs73TSJMtBjVPBY05TRNqKSLV0r88SkSEi8qSniDh5SfHi0KOHOdUnn7yQzz6z3OzLL4etW8O2zkk2UlJsZmTIEBgwwLS0u3WDa66xYEc+oRIwQ0RGisjQyAjbKMeJOzt3wiuvWCrIO+/A7bebhmfr1mFb5oRFHBVFcqoi8jywFUBEDgPux1rvrgEGx8Y0x4meatXgoovmsGCBOUZPP22Bia5dYeXKsK1zkonChS2w9eijJhE5f77lZZ91FsybZypeSc5A4DjgPuCRdMNx8i9Tp1r3snPPtZzCSZPgzjtNXsgpuBxwAMycGZcLe04d7FRVXR08PxUYrKofqeptQMPYmOY42ad8eXOGhg0zh+i77yzH9oUXTIHJcaJFBAYNgj/+MIWRDz+EevUsdS/O6k5xRVXHArOA0sGYGSxznPzHypU2pdmqlTlSL79sN4bmzcO2zEkEDjjAJKX++ivmu86xgy0ikQLJI4D0zWVyK/3nOLmmVy+LYr/3nqWP9O8P7dvD0qVhW+YkI1dfbXUwgwbB+PFw3nlhW5RzROQUYDxwMnAKME5EvFW6k7/YvNl+GTdsCM8+azeB33+Hc86xXDDHAXOwIS5pIjn9lr0DjBWRIcAm4DsAEWmIpYk4TkLQu7cViH/3nRVCtmxpDWzyoEuqk48QsUZHN94I//sfrFkDdepArVpJmZh9C9BWVfup6llAO+C2kG1ynNiwbZtFqffbz7Q3O3aEKVPgmWegYsWwrXMSjSZN7DFRHGxVvRe4BngV6JhO7ikFuDw2pjlObChb1q6xQ4bAYYfBn3/C0UdbGomnjTjZ5eKLbSZk3jyoUycpc0VSVHVFuteryH1XX8cJl+3b4fXXYf/9bYqpUiUTuv/88zQnynF2p1Qpi5bEwcHOcTqHqv6cwbI/cmeO48SPI46wMWmSOdpHHw2lS5taj6fjOQWIESIyEpuJBKujGR6PA4lID+AJIBV4UVXvj8dxnALMxo0m/fPoozB7tuVaDx1qF/h8KGLvxIE4KYl41MIpcLRubdfhr7+GMmXM6e7cGb78MmzLHCf+qOp1mNpT82AMVtUbYn0cEUkF/gf0BJoAfUXEQ4lObFiyxATr69SBSy6BChXgk08sgnLMMe5cO9FzwAGWn799e0x3m7AFiSJyDdbOt7KqrhQRwSIhvYCNwNmq+kuwbj/g1mDTe1T1tTBsdpKH6tVtDB8Ot9xiCk7HH29qI82a2XCc/IqqfgR8FOfDtANmq+ocABF5F+gNzIjVAerL/lTnFgrzIoXZ/N8oxJZdXhdJ97woGyjGWoqnG0XYRDzcsTfisM9YkKx2KfAHHVhOZwpRnfU0YCGPMgP4ezxw/DrgzRAsy3vOOCNsC/Zk+fL9eOmlsK3Yk73a9deFsLUZN49cwP5Hxa5XYkI62CJSC+gGpM+Q7Qk0CsZBwLPAQUFjmzuw9r8KTBKRoar6T95a7SQjzZtbt9zly+GQQ6w9Nlgq35lnhmub48QSEfleVTuKyDrsWvnfW4CqapkYH7IGsDDd60XYtXt3u/oD/QGqVq3KmDFjoj5AccqxkEPYSjG2UpQtFGMzxdlJarYMTWEHpVhLadZQieVUZDmVWbbL8yospTJLqcJSirE5W/t3csci6vAZffmEfsxhPwCKs5JtrGU7+a/VajR8803iFVjv3Fma6dOTzy7ZWpkiqYcy/uvxLC8Zu8KshHSwgceA64Eh6Zb1Bl4PCip/FpFyIlId6ASMiuhyi8gooAdp+YWOs1eqVjU5v2nT4NZbrRfBQw/BySfba59tdJIdVe0YPJYO25b0qOpgggZlbdq00U6dOkW97XT9iTFjxrD7Ntu3m0pbZGzZYo+bNsGGDbB2bdpYtw7Wrk1l7dry/PNPeVasqMvy5TB9Gfz9tzX/250yZdJmwSKjWrVdX8+e/T3HHNMx4a4dGX1eicDudi1ebIXpb70FP/5oyzp0gNvOh5NOglKlKmFNSfPWrkTAbGoQthl7kIifFURjV3GgDFA3psdNOAdbRHoDi1V1sux6ZcooGlIji+UZ7TvHkRKA9evXZ3ubRMbPJ2OuuiqVUqXqM3duSW6/vRy//rqI/fdfy+GHr2TJkoZs3VqRMWN+yr3Be8H/PolPMp6TiLyhqmfubVkMWAzUSve6ZrAs7hQqZOIApUrlbj87dpiTvXy5KcekH8uW2eO4cfa4aY8AWUeKFk1zvHd/jDyvVQuqVPEf8Tt3wq+/mujH0KHWwRwsPfa++6BvX6hbN1QTHSdbhOJgi8hXQLUM3roFuBlLD4k5uYmUQOL+Osspfj6Zc/TRdsE/5xx4/fWafPKJ5WlXrmyddfPic/O/T+KTpOd0QPoXQdOw1nE4zgSgkYjUwxzrPsBpcThO3EhNNSe4WjVo0SLz9VQtGh5xupcuhe++m02pUg3/c8b//BO+/RZWr95z+6JFoXZtq9fL6LFWrfzX0Xv7dpgxw3oUfPMNfPVVB9autR8aBx1kTvWxx5rCXkH/8eEkJ6E42Kp6ZEbLRaQZUA+IRK9rAr+ISDsyj4YsxtJE0i8fE3OjnQJHSoqpPz34ILz7LgwYYMtr1gzVLMfJESJyExbAKC4iayOLga0EgYdYoqrbReQyYCQm0/eyqsZeCysBEDG9/bJlYd99bVn16ovo1KnhHutu2WIR8WXLTAhj0SKYP980+efPhy++2LPjrIg5+Zk54FWrWg+VwoXz4GRzwPr1MGuWOdSTJ8OECSb2sTGQka9dG9q3X0nfvtXp1s3O1XGSnYRKEVHVqUCVyGsRmQe0CVREhgKXBZXoBwFrVHVpoOd6n4iUDzbrBtyUx6Y7+ZiqVeHKK+0mMHWqyfw5TrKhqoOAQSIySFXz5BqpqsOJk8Z2shKJVteunfk6W7bs6XhHHn/91XKTt2zZc7syZczRrlTJVOsiaTIlS6Y9X7q0Jn/8kbYs8lismI2iRW2kf15oN09h5860vPbNm61L7sqVNv7+GxYuNFvnz4e5c+11+vM/8EA4/3xo186Ky+vVg7Fjf6dTp+ox+YwdJxFIKAd7LwzHJPpmYzJ95wCo6moRuRubjgS4K1Lw6Dix5PjjbThOkjNeRMqq6hoAESkHdFLVT0O1yvmPokWhQQMbGbFzpzmyEcd7xQpYtcrGypX2uHq1ObYbNlgEef36iFO+Z1R9b6Smmk2pqeZQb9uW9fopKVCjhkXYDzvMmis2aWKjQYM9HXbHyY8k9NdcVeume67ApZms9zLwch6Z5TiOk8zcoaqfRF6o6r8icgfwaXgmOdkhJcVm1qpWtShwtGzfDiNGfEerVof+53hHHtMrrkSi05Hnkdc7dqRFutNHuMuVs6h55cr2WK2aO9GO4/8CjuM4BYuMOvj6vaAAYOoqO6iRoc6W4zixxFulO47jFCwmisijItIgGI8Ck8I2ynEcJz8hlnlR8BCRv4H52dysErAyDuaEhZ9PYuPnk/jk5JzqqGrleBgTDSJSErgNiKg5jQLuUdUNYdkUIZ9dl92u7OF2RU8i2gQF065Mr+cF1sHOCSIyUVXbhG1HrPDzSWz8fBKf/HhOyUai/g3cruzhdkVPItoEbtfueN6d4zhOAUJEKgPXYw1nikWWq2qX0IxyHMfJZ3gOtuM4TsHiLWAW1tTrTmAeaTKnjuM4TgxwBzt7xLzbWcj4+SQ2fj6JTzKeU0VVfQnYpqpjVfVcIJmj14n6N3C7sofbFT2JaBO4XbvgOdiO4zgFCBH5WVUPDrrgPgksAT5U1UzamjiO4zjZxXOwHcdxChb3iEhZ4BrgKaAMcFW4JjmO4+QvPEUkCkSkh4j8LiKzReTGsO3JCSLysoisEJFp6ZZVEJFRIvJn8Fg+TBuzg4jUEpHRIjJDRKaLyJXB8qQ8JxEpJiLjRWRycD53Bsvrici44Lv3nogUCdvW7CAiqSLyq4h8HrxO2vMRkXkiMlVEfhORicGypPu+qernqrpGVaepamdVba2qQ8O2KyckyrU5Ua+viXidTPRrXSJesxL12iMi5UTkQxGZJSIzReSQsO0SkX2Dzyky1orIgDDscgd7L4hIKvA/oCfQBOgrIk3CtSpHvAr02G3ZjcDXqtoI+Dp4nSxsB65R1SbAwcClwd8lWc9pC9BFVVsALYEeInIw8ADwmKo2BP4BzgvPxBxxJTAz3etkP5/OqtoyneRT0n3fRKSxiHwdcQZFpLmI3Bq2Xdklwa7Nr5KY19dEvE4m+rUuUa9ZiXjteQIYoar7AS2wzy1Uu1T19+Bzagm0BjYCn4Ril6r6yGIAhwAj072+CbgpbLtyeC51gWnpXv8OVA+eVwd+D9vGXJzbEKBrfjgnoATwC3AQJo5fKFi+y3fx/+2de7QXVRXHP9/UAgEl1IhCvWoalRqo0EPNq6ktXaRZlJmV9CJLRSRbZZZaq1aWrVJx9ZLKaiGWZmSyEh9IEYIgD+EiSSWUGIpW+K4Edn/s/YPh5+8+uff+5gf7s9as38yZc2b2njt3z5l99pld9gUYihuy44BbATW4PquBPavKGu5+A34PjAIWF8pa6iHLNupRKtvcCPa1bHaybLaurDarjLYH2B1YRczlK4tcVbKcCMypl1zpwW6fVwMPF7bXRNn2wGAzWxvrjwKD6ylMV5HUBIwA7qWBdYqhySXAOjy73l+B9Wa2Iao02r13Jf695U2xvQeNrY8Bt0taKGlclDXi/barmc2vKttQs2a5KbttLtW9USY7WWJbdyXltFlltD37AY8DP4mQmsnyLLH1lqvI+4Gpsd7rcmUHOwHA/LWu4T4pI6k/8Ctggpk9VdzXaDqZ2UbzYa2huIdxWH0l6jqSRgPrzGxhvWXpRo4ys8PwkIRzJL2tuLOB7rcnJB1AyCppDLC27SbJtlDve6NsdrKMtq7kNquMtmdn4DDge2Y2AniWqrCLet73ESt/CnBj9b7ekis72O3zCLB3YXtolG0PPCZpCED8rquzPJ1C0i74Q2OKmd0cxQ2tE4CZrQfuxocjB0qqfO2nke69I4FTJK0GbsCHXK+icfXBzB6J33V4TN8oGvN+Owf4ATBM0iPABODsukrUNcpum0txb5TZTpbM1pXWZpXU9qwB1pjZvbF9E97hrrdcFU4CFpnZY7Hd63JlB7t9FgAHxkzil+JDDg05474GtwBnxfpZeHxeQyBJwI+AFWb27cKuhtRJ0l6SBsZ6XzxOcgX+8BkT1RpGHzO7yMyGmlkT/j8z08zOpEH1kdRP0oDKOh7b10ID3m9m9pCZHQ/shXsOjwGOqq9UXaLstrnu90YZ7WRZbV1ZbVZZbY+ZPQo8LOm1UfR24IF6y1XgDLaEh0A95KpX8HkjLcDJwEo8TuziesvTRR2m4sPAL+Bvnh/D48vuAv4M3AkMqrecndDnKHyIZymwJJaTG1Un4FBgcejTAlwS5fsD84G/4ENdL6u3rF3QrRm4tZH1Cbnvj2V5xQ400v2Gf+/6IuAavFMj4Fx8otJv6i1fF3UqhW0uq30to51sBFtXJptVZtuDfwXmvvhbTgNeXhK5+gH/BHYvlPW6XJnJMUmSZAdA0m/wz4zNxb1Nr8A72eeb2ZI6ipYkSbLdkR3sJEmSHQBJy8zskFjfCfe47mNm/6mvZEmSJNsfGYOdJEmyY/BCZcXMNuITlLJznSRJ0gOkBztJkmQHQNJG/FNa4KEhffEsZ8K/XLVbvWRLkiTZ3sgOdpIkSZIkSZJ0IxkikiRJkiRJkiTdSHawky4jaaOkJZKWS7pf0mckvST2HSHp6jbaNkn6QO9J2zEkrZa0Zw8ev1nSrT11/A6cX5JmSupwOICk0ZK+0pNyJUmSJMn2RHawk23heTMbbmZvwL+rexJwKYCZ3Wdm49to2wSUroO9A3AycL9VpUtuh+nAOyXt2kMyJUnSSxQcIy2Sbuzs/7WkV0m6KdaHSzq5sO8USZ9vvfW2I2m8pBWSplSVN0t6MnRbIunObjjXQEmfbqfOuySZpHbTvXfFwSJpSBfafEvScZ1pk3Q/2cFOugXzFK7jgHPDS7rZkEg6pmD0FkdWqsuBo6PsgvBoz5a0KJa3RttmSbMk3STpT5KmRHYyJI2UdE94z+dLGiBpJ0lXSFogaamkT9aSV9I0SQvD+z6ulToT4yHUImlClDWFcb822t4e2cgq8iwNna6Q1NLK5dpN0nRJD0r6fsHrf6KkuaH/jZL6R/kloU+LpB8W9J8l6TuS7guZRkq6WdKfJX21lXOfSWSwCl3+JOk6SSvj2h4vaU4cY1T8bQ2YBYxu5ZhJkjQOFcfIwcD/gLM709jM/mFmlcyGw/GX9sq+W8zs8m6TtDafBk4wz7JYzezQbbh5ttLNaEu6884wMM7XFmcAf4zfnmAicG0n20wCevRFJ+kAvZ1hJ5ftZwGeqVG2HhjM1pmwfgscGev9gZ2L+6N8V6BPrB8I3BfrzcCTwFD8hXAunp3spcBDwMiot1scdxzwxSh7GZ5lar8acg6K3754NrE9Yns1sCdwOLAMzwjVH8+gNQL3vG8Ahkf9XwIfjPUW4C2xfjnQUuO8zcB/8OxcOwF34Gl49wT+APSLep9jS4azQYX2PwfeGeuzgG/E+vnAP4Ahofeaik5V5/8bMCDWK7ocEtd2IfBj/KsSpwLTCu3OBCbV+57LJZdctm0p2m28c/1dYBCeiW8pMA84NPYfw5YMkIuBAWE3WsIG/x14PPafDowFrom2TcDMOOZd+DfXAa4DrgbuwW34mFbknBjnaQEmRNn38ZeCZcAFVfWbKTxTomwsniJ7JvD7NvS8LGzfrJBpfJTfADwf+l1RQ8b+wCPAQcCDhfLTQmeFTV4JvJKtn4v94pzz49qe2sp1eIjIHhn6TMOfG6vxTKwTo/08tn5WLAReWe/7bUde0oOd9AZzgG9LGg8MNLMNNersAlwraRmejvb1hX3zzWyNmW3CDV0T8FpgrZktADCzp+K4JwIflrQEuBdPj3pgjfONl3Q/bpT2rlHnKODXZvasmT0D3AwcHftW2ZbMdwuBJkkD8Y7r3Ci/vo3rMd/MHjL/FvHUONebQ+c5IftZwL5R/1hJ98a1OQ54Q+FYt8TvMmC5ma01s//iRnnvGuceZGZPF7ZXmdmyuLbLgbvMrfMy/DpXWAe8qg2dkiRpIMKjexL+v/5lYLGZHQp8AfhZVLsQOMfMhuP27/lKezP7H3AJ8Atzj/Evqk4xCfhpHHMK3qmuMAS3e6NxZ0S1bIcDHwHehNvGT0gaYWZn446EY83sOzXUqoyKLpF0cZQdhnfij2lDT4BhwDuAUcClknbBvcB/Df0+W+N8pwK3mdlK4J8hN2b2azyR0zm49/lSM3u0qu3FwEwzGwUcC1whqV/VddgP+HfY9AoHA+8GRgJfA54zsxG48+nDhXqLgCNryJz0El0ZMkmSmkjaH9iId8ZeVyk3s8slTceHEudIekeN5hcAjwFvxL2pxQQYReOykbbvWwHnmdmMNuRsBo7Hvc3PSZoF9GnjmNVUy9O3E20Bqr+Nabjcd5jZVsOMkvrgHqYjzOxhSZdVyVqRZVOVXJuofZ02SHpJdKip0aZ4vGL7PhQerkmSNCx94yUeYDbwI9wZ8R4AM5spaQ/5ROiKc2QKcLOZrYkItY7wFrwjCD7y9s3Cvmlhgx6QNLhG280ODgBJFQfH4nbOOdvMNoeySRqL29V/FY5bS0+A6dGR/a+kdfhIbHucAVwV6zfE9sLYPg/3vs8zs6k12p4InCLpwtjuA+wDrCjUGYKPEBS5O5wkT0t6Eh8hBn9ROrRQL50idSY92Em3IGkvfPjumvCAFvcdEF7SbwALcE/B0/hwY4XdcY/0JuBDePhEWzwIDJE0Ms4xIDwyM4BPhfcBSQdVewXiXP+OzvUw3ENSzWzgXZJ2jfanRVlNzGw9bvDeFEXvb0P2UZL2i9jr0/H4vXnAkZJeE3L3k3QQWzrTT0RM9piaR+w4D+LhKZ3lIPxhkSRJY1OJwR5uZueFJ7om5vHUH8edCHPUgYl8HaT4Yt/hHnsXebb9KkDnHDlIGoSPKE6WtBr4LPA+bXkDGYo7KgZX5tlUHwJ4T+FvsY+Zraiq8zwvdv6kU6RByA52si30jaG45cCdwO34EFw1E2KC3lI8XfPv8Bi4jfIJihfgXtqzImxjGO0YxXgonA5MijZ34AZlMvAAsCgmGf6AFxvK24CdJa3Ahyfn1Tj+IjxWcD7u3ZlsZu15Tz6Gh7kswePrnmyl3gLgGtxTsQr31DyOx9dNjes0FxgWHfdr8c7tjGi7LUzH4wA7y7HRNkmS7Y/Z+DyLygjfE2b2VCvOkSLVjpIi97DF0XAmbTgoWpGnww6OTh73RXq2Ub8t/cYAPzezfc2sycz2xu350eHs+THu0V6Bx0lXMwM4rzBpfUSNOivZOlSvM6RTpM5kJsck6SYk9Y94beSfqhpiZufXWaytkDQE+JmZndCJNoOB683s7T0nWZIkvYGkZ8ysf1XZILxDuD/wHDDOzJZKmoS/XFfmaIzFwxZuNbODo90MfA7N13FP9xFmdq6kfYGf4BO4Hwc+YmZ/l3RdtK986u9F8kT5ROCjsTnZzK6M8tVxjieq6jcDF9YIETnCzM5tR8/L8Mmf34p6LcBoM1st6Xo89OJ3xThsSXfjk8xvK5SNx8Mj1+LzjSbKv5q1AH9JGFyRUf71qSuBt+LOzlVF2QvHvAv4pJn9pYY+m69FcV+M4C4FDmllzlPSC2QHO0m6CUmnAxfhHvO/AWPDM10qJL0Pn5jToW9hRxjOC4WJnUmSJEkvIOk04HAz+2In2xxmZl/qOcmS9sgOdpIkSZIkSUmR9HEzm9yJ+u/FJ3eu7zmpkvbIDnaSJEmSJEmSdCM5yTFJkiRJkiRJupHsYCdJkiRJkiRJN5Id7CRJkiRJkiTpRrKDnSRJkiRJkiTdSHawkyRJkiRJkqQb+T9dzEZ98KP7RgAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x288 with 5 Axes>"
       ]
@@ -529,7 +529,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x288 with 4 Axes>"
       ]
@@ -560,7 +560,7 @@
    "source": [
     "## Example 5 - Access Assessment for Vehicle Spacings\n",
     "\n",
-    "Using the `Vehicle.from_convoy` method we create a `Vehicle` object representing a sequence of vehicles at different spacings. \n",
+    "Using the `make_train` method we create a `Vehicle` object representing a sequence of vehicles at different spacings. \n",
     "Here, we will explore different prime mover and platform trailer combinations commonly adopted in \"superload\" transport convoys.\n",
     "\n",
     "First set up the bridge as per Example 3:"
@@ -605,7 +605,7 @@
    "id": "44f481f4-9082-4e23-af69-4d8ac4837f0b",
    "metadata": {},
    "source": [
-    "Three spacing combinations are explored for the convoy comprising of two front prime movers followed by two platform trailers and then two back prime movers."
+    "Three spacing combinations are explored for a convoy comprising of two front prime movers followed by two platform trailers and then two back prime movers."
    ]
   },
   {
@@ -637,7 +637,7 @@
    "source": [
     "envs = []\n",
     "for s in inter_spaces:\n",
-    "    vehicle = cba.Vehicle.from_convoy([prime_mover]*2 + [platform_trailer]*2 + [prime_mover]*2,s)\n",
+    "    vehicle = cba.make_train([prime_mover]*2 + [platform_trailer]*2 + [prime_mover]*2,spacings=s)\n",
     "    bridge_analysis = cba.BridgeAnalysis(bridge, vehicle)\n",
     "    envs.append(bridge_analysis.run_vehicle(0.1))"
    ]
@@ -802,81 +802,81 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_b0131\">\n",
+       "<table id=\"T_fef79\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th id=\"T_b0131_level0_col0\" class=\"col_heading level0 col0\" >Span (m)</th>\n",
-       "      <th id=\"T_b0131_level0_col1\" class=\"col_heading level0 col1\" >Moment (kNm)</th>\n",
-       "      <th id=\"T_b0131_level0_col2\" class=\"col_heading level0 col2\" >Shear (kN)</th>\n",
-       "      <th id=\"T_b0131_level0_col3\" class=\"col_heading level0 col3\" >Reaction (kN)</th>\n",
+       "      <th id=\"T_fef79_level0_col0\" class=\"col_heading level0 col0\" >Span (m)</th>\n",
+       "      <th id=\"T_fef79_level0_col1\" class=\"col_heading level0 col1\" >Moment (kNm)</th>\n",
+       "      <th id=\"T_fef79_level0_col2\" class=\"col_heading level0 col2\" >Shear (kN)</th>\n",
+       "      <th id=\"T_fef79_level0_col3\" class=\"col_heading level0 col3\" >Reaction (kN)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row0_col0\" class=\"data row0 col0\" >10</td>\n",
-       "      <td id=\"T_b0131_row0_col1\" class=\"data row0 col1\" >2400</td>\n",
-       "      <td id=\"T_b0131_row0_col2\" class=\"data row0 col2\" >1040</td>\n",
-       "      <td id=\"T_b0131_row0_col3\" class=\"data row0 col3\" >1050</td>\n",
+       "      <td id=\"T_fef79_row0_col0\" class=\"data row0 col0\" >10</td>\n",
+       "      <td id=\"T_fef79_row0_col1\" class=\"data row0 col1\" >2400</td>\n",
+       "      <td id=\"T_fef79_row0_col2\" class=\"data row0 col2\" >1040</td>\n",
+       "      <td id=\"T_fef79_row0_col3\" class=\"data row0 col3\" >1050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row1_col0\" class=\"data row1 col0\" >20</td>\n",
-       "      <td id=\"T_b0131_row1_col1\" class=\"data row1 col1\" >6300</td>\n",
-       "      <td id=\"T_b0131_row1_col2\" class=\"data row1 col2\" >1515</td>\n",
-       "      <td id=\"T_b0131_row1_col3\" class=\"data row1 col3\" >1530</td>\n",
+       "      <td id=\"T_fef79_row1_col0\" class=\"data row1 col0\" >20</td>\n",
+       "      <td id=\"T_fef79_row1_col1\" class=\"data row1 col1\" >6300</td>\n",
+       "      <td id=\"T_fef79_row1_col2\" class=\"data row1 col2\" >1515</td>\n",
+       "      <td id=\"T_fef79_row1_col3\" class=\"data row1 col3\" >1530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row2_col0\" class=\"data row2 col0\" >30</td>\n",
-       "      <td id=\"T_b0131_row2_col1\" class=\"data row2 col1\" >12515</td>\n",
-       "      <td id=\"T_b0131_row2_col2\" class=\"data row2 col2\" >2005</td>\n",
-       "      <td id=\"T_b0131_row2_col3\" class=\"data row2 col3\" >2015</td>\n",
+       "      <td id=\"T_fef79_row2_col0\" class=\"data row2 col0\" >30</td>\n",
+       "      <td id=\"T_fef79_row2_col1\" class=\"data row2 col1\" >12515</td>\n",
+       "      <td id=\"T_fef79_row2_col2\" class=\"data row2 col2\" >2005</td>\n",
+       "      <td id=\"T_fef79_row2_col3\" class=\"data row2 col3\" >2015</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row3_col0\" class=\"data row3 col0\" >40</td>\n",
-       "      <td id=\"T_b0131_row3_col1\" class=\"data row3 col1\" >21555</td>\n",
-       "      <td id=\"T_b0131_row3_col2\" class=\"data row3 col2\" >2505</td>\n",
-       "      <td id=\"T_b0131_row3_col3\" class=\"data row3 col3\" >2515</td>\n",
+       "      <td id=\"T_fef79_row3_col0\" class=\"data row3 col0\" >40</td>\n",
+       "      <td id=\"T_fef79_row3_col1\" class=\"data row3 col1\" >21555</td>\n",
+       "      <td id=\"T_fef79_row3_col2\" class=\"data row3 col2\" >2505</td>\n",
+       "      <td id=\"T_fef79_row3_col3\" class=\"data row3 col3\" >2515</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row4_col0\" class=\"data row4 col0\" >50</td>\n",
-       "      <td id=\"T_b0131_row4_col1\" class=\"data row4 col1\" >32560</td>\n",
-       "      <td id=\"T_b0131_row4_col2\" class=\"data row4 col2\" >3015</td>\n",
-       "      <td id=\"T_b0131_row4_col3\" class=\"data row4 col3\" >3025</td>\n",
+       "      <td id=\"T_fef79_row4_col0\" class=\"data row4 col0\" >50</td>\n",
+       "      <td id=\"T_fef79_row4_col1\" class=\"data row4 col1\" >32560</td>\n",
+       "      <td id=\"T_fef79_row4_col2\" class=\"data row4 col2\" >3015</td>\n",
+       "      <td id=\"T_fef79_row4_col3\" class=\"data row4 col3\" >3025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row5_col0\" class=\"data row5 col0\" >60</td>\n",
-       "      <td id=\"T_b0131_row5_col1\" class=\"data row5 col1\" >46405</td>\n",
-       "      <td id=\"T_b0131_row5_col2\" class=\"data row5 col2\" >3525</td>\n",
-       "      <td id=\"T_b0131_row5_col3\" class=\"data row5 col3\" >3535</td>\n",
+       "      <td id=\"T_fef79_row5_col0\" class=\"data row5 col0\" >60</td>\n",
+       "      <td id=\"T_fef79_row5_col1\" class=\"data row5 col1\" >46405</td>\n",
+       "      <td id=\"T_fef79_row5_col2\" class=\"data row5 col2\" >3525</td>\n",
+       "      <td id=\"T_fef79_row5_col3\" class=\"data row5 col3\" >3535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row6_col0\" class=\"data row6 col0\" >70</td>\n",
-       "      <td id=\"T_b0131_row6_col1\" class=\"data row6 col1\" >62385</td>\n",
-       "      <td id=\"T_b0131_row6_col2\" class=\"data row6 col2\" >4025</td>\n",
-       "      <td id=\"T_b0131_row6_col3\" class=\"data row6 col3\" >4035</td>\n",
+       "      <td id=\"T_fef79_row6_col0\" class=\"data row6 col0\" >70</td>\n",
+       "      <td id=\"T_fef79_row6_col1\" class=\"data row6 col1\" >62385</td>\n",
+       "      <td id=\"T_fef79_row6_col2\" class=\"data row6 col2\" >4025</td>\n",
+       "      <td id=\"T_fef79_row6_col3\" class=\"data row6 col3\" >4035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row7_col0\" class=\"data row7 col0\" >80</td>\n",
-       "      <td id=\"T_b0131_row7_col1\" class=\"data row7 col1\" >81045</td>\n",
-       "      <td id=\"T_b0131_row7_col2\" class=\"data row7 col2\" >4525</td>\n",
-       "      <td id=\"T_b0131_row7_col3\" class=\"data row7 col3\" >4535</td>\n",
+       "      <td id=\"T_fef79_row7_col0\" class=\"data row7 col0\" >80</td>\n",
+       "      <td id=\"T_fef79_row7_col1\" class=\"data row7 col1\" >81045</td>\n",
+       "      <td id=\"T_fef79_row7_col2\" class=\"data row7 col2\" >4525</td>\n",
+       "      <td id=\"T_fef79_row7_col3\" class=\"data row7 col3\" >4535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row8_col0\" class=\"data row8 col0\" >90</td>\n",
-       "      <td id=\"T_b0131_row8_col1\" class=\"data row8 col1\" >102740</td>\n",
-       "      <td id=\"T_b0131_row8_col2\" class=\"data row8 col2\" >5020</td>\n",
-       "      <td id=\"T_b0131_row8_col3\" class=\"data row8 col3\" >5030</td>\n",
+       "      <td id=\"T_fef79_row8_col0\" class=\"data row8 col0\" >90</td>\n",
+       "      <td id=\"T_fef79_row8_col1\" class=\"data row8 col1\" >102740</td>\n",
+       "      <td id=\"T_fef79_row8_col2\" class=\"data row8 col2\" >5020</td>\n",
+       "      <td id=\"T_fef79_row8_col3\" class=\"data row8 col3\" >5030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td id=\"T_b0131_row9_col0\" class=\"data row9 col0\" >100</td>\n",
-       "      <td id=\"T_b0131_row9_col1\" class=\"data row9 col1\" >126300</td>\n",
-       "      <td id=\"T_b0131_row9_col2\" class=\"data row9 col2\" >5515</td>\n",
-       "      <td id=\"T_b0131_row9_col3\" class=\"data row9 col3\" >5525</td>\n",
+       "      <td id=\"T_fef79_row9_col0\" class=\"data row9 col0\" >100</td>\n",
+       "      <td id=\"T_fef79_row9_col1\" class=\"data row9 col1\" >126300</td>\n",
+       "      <td id=\"T_fef79_row9_col2\" class=\"data row9 col2\" >5515</td>\n",
+       "      <td id=\"T_fef79_row9_col3\" class=\"data row9 col3\" >5525</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f1f020a1130>"
+       "<pandas.io.formats.style.Styler at 0x7fdac1d786a0>"
       ]
      },
      "execution_count": 30,
@@ -887,6 +887,525 @@
    "source": [
     "df.style.hide()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1ec6df3-8467-4222-ab30-e6a6cde9449a",
+   "metadata": {},
+   "source": [
+    "## Example 7 - AS5100.2 Appendix C\n",
+    "\n",
+    "First we will write a function that accepts a vehicle, loops over a specified span range extracting the critical load effects, and returns a pandas dataframe of the results. We can then use this function for both road and rail.\n",
+    "\n",
+    "The function accepts the low and high spans of the range to consider. While the code examines from 1 to 100 m spans, this takes a while to compute, so for the present purposes, facilitate considering a smaller range.\n",
+    "\n",
+    "The function also accepts a UDL, necessary for the road bridges. This UDL is applied to the `BeamAnalysis` object and retained in the `BridgeAnalysis` object and applied throughout the vehicle traverse.\n",
+    "\n",
+    "We will record both shear and reactions, which can vary slightly due to the step spacing of the load as it traverses the beam."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "a81de659-9fc8-4d5f-ae3b-005732ed8a06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def critical_effects(vehicle,low_span=1,high_span=100,udl=0):\n",
+    "    spans = np.arange(low_span,high_span+1,1)\n",
+    "    M = []\n",
+    "    V = []\n",
+    "    R = []\n",
+    "    for L in spans:    \n",
+    "        # Simply-supported with arbitrary EI\n",
+    "        bridge = cba.BeamAnalysis([L], 30e6, [-1, 0, -1, 0])\n",
+    "        bridge.npts = 500  # Use more points along the beam\n",
+    "        bridge.add_udl(i_span=1,w=udl)  # Add any UDL\n",
+    "        bridge_analysis = cba.BridgeAnalysis(bridge, vehicle)        \n",
+    "        env = bridge_analysis.run_vehicle(0.1)  # small step\n",
+    "    \n",
+    "        # Extract critical values\n",
+    "        cvals = bridge_analysis.critical_values(env)\n",
+    "        m = cvals[\"Mmax\"][\"val\"]\n",
+    "        v = max(cvals[\"Vmax\"][\"val\"], abs(cvals[\"Vmin\"][\"val\"]))\n",
+    "        r = max(cvals[\"Rmax0\"][\"val\"], cvals[\"Rmax1\"][\"val\"])\n",
+    "        \n",
+    "        # now round to nearest 5 as code does\n",
+    "        M.append(round(m / 5) * 5)\n",
+    "        V.append(round(v / 5) * 5)\n",
+    "        R.append(round(r / 5) * 5)\n",
+    "\n",
+    "    results = list(zip(spans, M, V, R))\n",
+    "    columns = [\"Bridge_length\", \"m\", \"v\", \"r\"]\n",
+    "    df = pd.DataFrame(results,columns=columns,index=spans)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d93e381-adee-44ad-a691-38e95b06bc55",
+   "metadata": {},
+   "source": [
+    "Let's examine the range 40 to 50 m here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "e2abd774-1abc-43a8-932f-ff7f320d6608",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "low_span = 40\n",
+    "high_span = 50"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22c63b7a-7f8a-466e-b23f-b288cf67441d",
+   "metadata": {},
+   "source": [
+    "First, we examine the road loading in Table C1 of the code, considering the moving and stationary load models seperately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "6f2d082e-e85c-44c0-a693-39972a720762",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Bridge_length</th>\n",
+       "      <th>m</th>\n",
+       "      <th>v</th>\n",
+       "      <th>r</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>40</td>\n",
+       "      <td>10070</td>\n",
+       "      <td>1120</td>\n",
+       "      <td>1120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>41</td>\n",
+       "      <td>10490</td>\n",
+       "      <td>1130</td>\n",
+       "      <td>1135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>42</td>\n",
+       "      <td>10910</td>\n",
+       "      <td>1145</td>\n",
+       "      <td>1150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>43</td>\n",
+       "      <td>11330</td>\n",
+       "      <td>1160</td>\n",
+       "      <td>1160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>44</td>\n",
+       "      <td>11755</td>\n",
+       "      <td>1170</td>\n",
+       "      <td>1175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>45</td>\n",
+       "      <td>12180</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>1185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>46</td>\n",
+       "      <td>12605</td>\n",
+       "      <td>1195</td>\n",
+       "      <td>1195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>47</td>\n",
+       "      <td>13030</td>\n",
+       "      <td>1205</td>\n",
+       "      <td>1210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>48</td>\n",
+       "      <td>13465</td>\n",
+       "      <td>1215</td>\n",
+       "      <td>1220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>49</td>\n",
+       "      <td>13895</td>\n",
+       "      <td>1225</td>\n",
+       "      <td>1230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>50</td>\n",
+       "      <td>14325</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>1240</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Bridge_length      m     v     r\n",
+       "40             40  10070  1120  1120\n",
+       "41             41  10490  1130  1135\n",
+       "42             42  10910  1145  1150\n",
+       "43             43  11330  1160  1160\n",
+       "44             44  11755  1170  1175\n",
+       "45             45  12180  1180  1185\n",
+       "46             46  12605  1195  1195\n",
+       "47             47  13030  1205  1210\n",
+       "48             48  13465  1215  1220\n",
+       "49             49  13895  1225  1230\n",
+       "50             50  14325  1235  1240"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m1600_vehicle = cba.VehicleLibrary.get_m1600(6.25)\n",
+    "df_m1600 = critical_effects(m1600_vehicle,low_span=low_span,high_span=high_span,udl=6)\n",
+    "df_m1600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f3688943-7ded-4b16-91a6-22f8fd155e8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Bridge_length</th>\n",
+       "      <th>m</th>\n",
+       "      <th>v</th>\n",
+       "      <th>r</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>40</td>\n",
+       "      <td>10695</td>\n",
+       "      <td>1145</td>\n",
+       "      <td>1150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>41</td>\n",
+       "      <td>11180</td>\n",
+       "      <td>1165</td>\n",
+       "      <td>1165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>42</td>\n",
+       "      <td>11665</td>\n",
+       "      <td>1185</td>\n",
+       "      <td>1185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>43</td>\n",
+       "      <td>12160</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>44</td>\n",
+       "      <td>12660</td>\n",
+       "      <td>1220</td>\n",
+       "      <td>1220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>45</td>\n",
+       "      <td>13165</td>\n",
+       "      <td>1240</td>\n",
+       "      <td>1240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>46</td>\n",
+       "      <td>13675</td>\n",
+       "      <td>1255</td>\n",
+       "      <td>1260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>47</td>\n",
+       "      <td>14195</td>\n",
+       "      <td>1275</td>\n",
+       "      <td>1275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>48</td>\n",
+       "      <td>14720</td>\n",
+       "      <td>1290</td>\n",
+       "      <td>1290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>49</td>\n",
+       "      <td>15245</td>\n",
+       "      <td>1305</td>\n",
+       "      <td>1310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>50</td>\n",
+       "      <td>15785</td>\n",
+       "      <td>1325</td>\n",
+       "      <td>1325</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Bridge_length      m     v     r\n",
+       "40             40  10695  1145  1150\n",
+       "41             41  11180  1165  1165\n",
+       "42             42  11665  1185  1185\n",
+       "43             43  12160  1200  1205\n",
+       "44             44  12660  1220  1220\n",
+       "45             45  13165  1240  1240\n",
+       "46             46  13675  1255  1260\n",
+       "47             47  14195  1275  1275\n",
+       "48             48  14720  1290  1290\n",
+       "49             49  15245  1305  1310\n",
+       "50             50  15785  1325  1325"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1600_vehicle = cba.VehicleLibrary.get_s1600(6.25)\n",
+    "df_s1600 = critical_effects(s1600_vehicle,low_span=low_span,high_span=high_span,udl=24)\n",
+    "df_s1600"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44738ef4-5697-49a5-8355-17775e0c90ea",
+   "metadata": {},
+   "source": [
+    "Next, let's examine the 300LA results shown in Table C2 of the code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6d7921f1-90bf-4448-8ee8-6fb88d13e903",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Bridge_length</th>\n",
+       "      <th>m</th>\n",
+       "      <th>v</th>\n",
+       "      <th>r</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>40</td>\n",
+       "      <td>21555</td>\n",
+       "      <td>2505</td>\n",
+       "      <td>2515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>41</td>\n",
+       "      <td>22545</td>\n",
+       "      <td>2555</td>\n",
+       "      <td>2565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>42</td>\n",
+       "      <td>23535</td>\n",
+       "      <td>2610</td>\n",
+       "      <td>2620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>43</td>\n",
+       "      <td>24520</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>2675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>44</td>\n",
+       "      <td>25510</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>2735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>45</td>\n",
+       "      <td>26525</td>\n",
+       "      <td>2775</td>\n",
+       "      <td>2785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>46</td>\n",
+       "      <td>27590</td>\n",
+       "      <td>2825</td>\n",
+       "      <td>2840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>47</td>\n",
+       "      <td>28790</td>\n",
+       "      <td>2875</td>\n",
+       "      <td>2890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>48</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>2925</td>\n",
+       "      <td>2935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>49</td>\n",
+       "      <td>31270</td>\n",
+       "      <td>2970</td>\n",
+       "      <td>2980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>50</td>\n",
+       "      <td>32560</td>\n",
+       "      <td>3015</td>\n",
+       "      <td>3025</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Bridge_length      m     v     r\n",
+       "40             40  21555  2505  2515\n",
+       "41             41  22545  2555  2565\n",
+       "42             42  23535  2610  2620\n",
+       "43             43  24520  2665  2675\n",
+       "44             44  25510  2720  2735\n",
+       "45             45  26525  2775  2785\n",
+       "46             46  27590  2825  2840\n",
+       "47             47  28790  2875  2890\n",
+       "48             48  30000  2925  2935\n",
+       "49             49  31270  2970  2980\n",
+       "50             50  32560  3015  3025"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rail_vehicle = cba.VehicleLibrary.get_la_rail(axle_weight=300)\n",
+    "df_rail = critical_effects(rail_vehicle,low_span=low_span,high_span=high_span)\n",
+    "df_rail"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2ef209f-5dd4-4dd8-b835-fe1be20ec6ef",
+   "metadata": {},
+   "source": [
+    "From the results, it can be seen that the values in the code differ somewhat. Presumably this is because the values in the code were calculated using a coarser increment in the vehicle position for the traverse."
+   ]
   }
  ],
  "metadata": {
diff --git a/pyproject.toml b/pyproject.toml
index e10a657..f16ea52 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,13 +1,67 @@
+[build-system]
+requires = ["setuptools >= 64.0", 
+            "oldest-supported-numpy"]
+build-backend = "setuptools.build_meta"
+
 [project]
+name = "PyCBA"
+dynamic = ["version"]
+description = "Python Continuous Beam Analysis"
+license = {text = "Apache 2.0"}
+keywords = ["beam","bridge","vehicle"]
+authors = [{name = "Colin Caprani", email = "colin.caprani@monash.edu"}]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Topic :: Scientific/Engineering",
+    "Environment :: Console",
+    "Intended Audience :: Science/Research",
+    "Programming Language :: Python :: 3 :: Only",
+    "License :: OSI Approved :: Apache Software License",
+    "Natural Language :: English",
+    "Operating System :: POSIX :: Linux",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: Microsoft :: Windows",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+]
 requires-python = ">=3.8"
+dependencies = [
+    "matplotlib",
+    "numpy",
+    "scipy",
+]
 
-[build-system]
-requires = ["wheel >= 0.31.0", 
-            "setuptools >= 48", 
-            "twine>= 1.11.0",
-            "oldest-supported-numpy", 
-            ]
-build-backend = "setuptools.build_meta"
+[project.readme]
+file = "README.md"
+content-type = "text/markdown"
+
+[project.urls]
+Homepage = "https://ccaprani.github.io/pycba/"
+Documentation = "https://ccaprani.github.io/pycba/"
+Source = "https://github.com/ccaprani/pycba/"
+Tracker = "https://github.com/ccaprani/pycba/issues/"
+
+[project.optional-dependencies]
+test = ["pytest >= 6.2.2"]
+
+[tool.setuptools]
+platforms = ["any"]
+include-package-data = true
+license-files = ["LICENSE"]
+
+# [tool.setuptools.packages]
+# find = {namespaces = false}
+
+[tool.setuptools.dynamic]
+version = {attr = "pycba.__version__"}
+
+[tool.distutils.bdist_wheel]
+universal = 1
+
+[tool.aliases]
+test = "pytest"
 
 [tool.pytest.ini_options]
 minversion = "6.0"
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 1b4cd82..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,52 +0,0 @@
-[metadata]
-name = PyCBA
-version = attr: pycba.__version__
-description = Python Continuous Beam Analysis
-url = https://ccaprani.github.io/pycba/
-long_description = file: README.md
-long_description_content_type = text/markdown
-license = Apache 2.0
-license_file = LICENSE
-keyword = beam, bridge, vehicle
-author = Colin Caprani
-author_email = colin.caprani@monash.edu
-classifiers =
-    Development Status :: 3 - Alpha
-    Topic :: Scientific/Engineering
-    Environment :: Console
-    Intended Audience :: Science/Research
-    Programming Language :: Python :: 3 :: Only
-    License :: OSI Approved :: Apache Software License
-    Natural Language :: English
-    Operating System :: POSIX :: Linux
-    Operating System :: MacOS :: MacOS X
-    Operating System :: Microsoft :: Windows
-    Programming Language :: Python :: 3.8
-    Programming Language :: Python :: 3.9
-project_urls = 
-    Documentation = https://ccaprani.github.io/pycba/
-    Source =  https://github.com/ccaprani/pycba/
-    Tracker = https://github.com/ccaprani/pycba/issues/
-
-[options]
-packages = find:
-platforms = any
-include_package_data = True
-python_requires = >=3.8
-setup_requires =
-    setuptools
-install_requires = 
-    matplotlib
-    numpy
-    scipy
-
-[bdist_wheel]
-universal = 1
-
-[aliases]
-test=pytest
-
-[options.extras_require]
-test =
-    pytest >= 6.2.2
-
diff --git a/pycba/__init__.py b/src/pycba/__init__.py
similarity index 86%
rename from pycba/__init__.py
rename to src/pycba/__init__.py
index 8c6f99c..be6ec41 100644
--- a/pycba/__init__.py
+++ b/src/pycba/__init__.py
@@ -2,7 +2,7 @@
 PyCBA - Continuous Beam Analysis in Python
 """
 
-__version__ = "0.2.0"
+__version__ = "0.3"
 
 from .analysis import *
 from .beam import *
diff --git a/pycba/analysis.py b/src/pycba/analysis.py
similarity index 100%
rename from pycba/analysis.py
rename to src/pycba/analysis.py
diff --git a/pycba/beam.py b/src/pycba/beam.py
similarity index 100%
rename from pycba/beam.py
rename to src/pycba/beam.py
diff --git a/pycba/bridge.py b/src/pycba/bridge.py
similarity index 96%
rename from pycba/bridge.py
rename to src/pycba/bridge.py
index 0b576c5..51f5f55 100644
--- a/pycba/bridge.py
+++ b/src/pycba/bridge.py
@@ -8,6 +8,7 @@
 from .analysis import BeamAnalysis
 from .results import Envelopes, BeamResults
 from .vehicle import Vehicle
+from .load import add_LM
 
 
 class BridgeAnalysis:
@@ -15,6 +16,9 @@ class BridgeAnalysis:
     Performs a bridge crossing analysis for a defined vehicle. The vehicle is moved
     from the zero global x-coordinate of the beam until it has left the beam at the
     far end.
+
+    Any loads already defined on the `BeamAnalysis` object are retained and
+    superimposed in each vehicle position analysis.
     """
 
     def __init__(
@@ -24,6 +28,9 @@ def __init__(
         Can instantiate with nothing and later add or define the objects, or
         with instantiate with pre-defined bridge and vehicle objects.
 
+        Any loads already defined on the `BeamAnalysis` object are retained in
+        each vehicle position analysis.
+
         Parameters
         ----------
         ba : Optional[BeamAnalysis], optional
@@ -40,6 +47,11 @@ def __init__(
         self.vResults = []
         self.pos = []
 
+        self.static_LM = []
+
+        if self.ba:
+            self.static_LM = self.ba.beam.loads
+
     def add_bridge(
         self,
         L: np.ndarray,
@@ -58,9 +70,6 @@ def add_bridge(
             A vector of member flexural rigidities.
         R : np.ndarray
             A vector describing the support conditions at each member end.
-        LM : Optional[list[list[Union[int, float]]]]
-            The load matrix: a list of loads on the beam; each load with several
-            parameters.
         eletype : Optional[np.ndarray]
             A vector of the member types. Defaults to a fixed-fixed element.
 
@@ -70,12 +79,16 @@ def add_bridge(
             A :class:`pycba.analysis.BeamAnalysis` object.
         """
         self.ba = BeamAnalysis(L=L, EI=EI, R=R, eletype=eletype)
+        self.static_LM = self.ba.beam.loads
         return self.ba
 
     def set_bridge(self, ba: BeamAnalysis):
         """
         Set the bridge for the bridge analysis.
 
+        Any loads already defined on the `BeamAnalysis` object are retained in
+        each vehicle position analysis.
+
         Parameters
         ----------
         ba : BeamAnalysis
@@ -87,6 +100,7 @@ def set_bridge(self, ba: BeamAnalysis):
 
         """
         self.ba = ba
+        self.static_LM = self.ba.beam.loads
 
     def add_vehicle(self, axle_spacings: np.ndarray, axle_weights: np.ndarray):
         """
@@ -188,6 +202,9 @@ def _single_analysis(self, pos: float) -> int:
             if ispan != -1:
                 LM.append([ispan + 1, 2, load, pos_in_span, 0])
 
+        # Now add any pre-existing loads on the beam
+        LM = add_LM(self.static_LM, LM)
+
         self.ba.set_loads(LM)
         return self.ba.analyze()
 
diff --git a/pycba/inf_lines.py b/src/pycba/inf_lines.py
similarity index 100%
rename from pycba/inf_lines.py
rename to src/pycba/inf_lines.py
diff --git a/pycba/load.py b/src/pycba/load.py
similarity index 100%
rename from pycba/load.py
rename to src/pycba/load.py
diff --git a/pycba/results.py b/src/pycba/results.py
similarity index 100%
rename from pycba/results.py
rename to src/pycba/results.py
diff --git a/pycba/utils.py b/src/pycba/utils.py
similarity index 100%
rename from pycba/utils.py
rename to src/pycba/utils.py
diff --git a/pycba/vehicle.py b/src/pycba/vehicle.py
similarity index 83%
rename from pycba/vehicle.py
rename to src/pycba/vehicle.py
index 86516dc..3898f07 100644
--- a/pycba/vehicle.py
+++ b/src/pycba/vehicle.py
@@ -75,59 +75,56 @@ def reverse(self, in_place=True):
         else:
             return Vehicle(np.copy(self.axs[::-1]), np.copy(self.axw[::-1]))
 
-    @classmethod
-    def from_convoy(cls, vehicles: List[Vehicle], vehicle_spacings: np.ndarray):
-        """
-        Alternative constructor for :class:`pycba.bridge.Vehicle` object
-        as multiple :class:`pycba.bridge.Vehicle` objects
-        behind one another (eg. superload, queued vehicles, train)
-
-        Parameters
-        ----------
-
-        vehicles : List[Vehicles]
-            A list of :class:`pycba.bridge.Vehicle` objects,
-            length one greater than the length of the
-            vehicle spacings vector.
-        vehicle_spacings : np.ndarray
-            A vector of spacings between vehicles of length one
-            fewer than the length of the
-            list of vehicles.
-
-        Raises
-        ------
-        ValueError
-            If the lengths of the list of vehicles and
-            vector of spacings are inconsistent.
-        ValueError
-            If all list entries are not
-            :class:`pycba.bridge.Vehicle` objects
 
-        Returns
-        -------
-        :class:`pycba.bridge.Vehicle` object
+def make_train(vehicles: List[Vehicle], spacings: np.ndarray):
+    """
+    Makes a train of vehicles from a sequence from multiple
+    :class:`pycba.bridge.Vehicle` objects behind one another (e.g. superload
+    queued vehicles, train).
+
+    Parameters
+    ----------
+
+    vehicles : List[Vehicles]
+        A list of :class:`pycba.bridge.Vehicle` objects, length one greater
+        than the length of the vehicle spacings vector.
+    spacings : np.ndarray
+        A vector of spacings between vehicles, either of length one, for equal
+        spacings between all vehicles, or of of length one fewer than the
+        length of the list of vehicles.
+
+    Raises
+    ------
+    ValueError
+        If the lengths of the list of vehicles and
+        vector of spacings are inconsistent.
+    ValueError
+        If all list entries are not
+        :class:`pycba.bridge.Vehicle` objects
+
+    Returns
+    -------
+    :class:`pycba.bridge.Vehicle` object
 
-        """
+    """
 
-        if len(vehicles) - 1 != len(vehicle_spacings):
-            raise ValueError("Inconsistent vehicle and spacing counts")
+    if len(vehicles) - 1 != len(spacings):
+        raise ValueError("Inconsistent vehicle and spacing counts")
 
-        if not all(isinstance(v, Vehicle) for v in vehicles):
-            raise ValueError("List must contain only Vehicle objects")
+    if not all(isinstance(v, Vehicle) for v in vehicles):
+        raise ValueError("List must contain only Vehicle objects")
 
-        # pre-allocate axle weights and spacings
-        new_vehicle_axles = np.array([])
-        new_vehicle_spaces = np.array([])
+    new_vehicle_axles = np.array([])
+    new_vehicle_spaces = np.array([])
 
-        # loop through each vehicle
-        for veh in vehicles:
-            new_vehicle_axles = np.append(new_vehicle_axles, veh.axw)
-            new_vehicle_spaces = np.append(new_vehicle_spaces, np.insert(veh.axs, 0, 0))
+    # loop through each vehicle
+    for veh in vehicles:
+        new_vehicle_axles = np.append(new_vehicle_axles, veh.axw)
+        new_vehicle_spaces = np.append(new_vehicle_spaces, np.insert(veh.axs, 0, 0))
 
-        # replace 0 spacing (first axle), with vehicle spacings
-        new_vehicle_spaces[new_vehicle_spaces == 0] = np.insert(vehicle_spacings, 0, 0)
+    new_vehicle_spaces[new_vehicle_spaces == 0] = np.insert(spacings, 0, 0)
 
-        return cls(new_vehicle_spaces[1:], new_vehicle_axles)
+    return Vehicle(new_vehicle_spaces[1:], new_vehicle_axles)
 
 
 class VehicleLibrary:
diff --git a/tests/test_bridge.py b/tests/test_bridge.py
index ecb1113..1a785e7 100644
--- a/tests/test_bridge.py
+++ b/tests/test_bridge.py
@@ -55,9 +55,9 @@ def test_la():
     # assert v == pytest.approx([665,1050,1515,2005,2505,3015,5525])
 
 
-def test_from_convoy():
+def test_make_train():
     """
-    Convoy of prime movers and platform
+    Train of prime movers and platform
     """
     L = [25, 25]
     EI = 30 * 1e11 * np.ones(len(L)) * 1e-6
@@ -86,7 +86,7 @@ def test_from_convoy():
 
     envs = []
     for s in inter_spaces:
-        vehicle = cba.Vehicle.from_convoy(
+        vehicle = cba.make_train(
             [prime_mover] * 2 + [platform_trailer] * 2 + [prime_mover] * 2, s
         )
         bridge_analysis = cba.BridgeAnalysis(bridge, vehicle)