diff --git a/mesh-doctor/src/geos/mesh_doctor/actions/convertMD2SG.py b/mesh-doctor/src/geos/mesh_doctor/actions/convertMD2SG.py
new file mode 100644
index 000000000..3063169fe
--- /dev/null
+++ b/mesh-doctor/src/geos/mesh_doctor/actions/convertMD2SG.py
@@ -0,0 +1,413 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright 2023-2024 TotalEnergies.
+# SPDX-FileContributor: GitHub Copilot, Jacques Franc
+from dataclasses import dataclass
+from typing import Optional, Union
+
+from vtkmodules.vtkCommonDataModel import VTK_TRIANGLE, VTK_TRIANGLE_STRIP, VTK_QUAD, VTK_POLYGON, vtkPolyData, vtkKdTree, vtkCell
+from vtkmodules.vtkCommonCore import vtkIdTypeArray, vtkPoints, reference, vtkUnsignedIntArray, vtkDataArray, vtkIdList
+from vtkmodules.vtkCommonDataModel import vtkSelection, vtkSelectionNode, vtkUnstructuredGrid, vtkMultiBlockDataSet
+from vtkmodules.vtkFiltersCore import vtkAppendPolyData, vtkCleanPolyData
+from vtkmodules.vtkFiltersExtraction import vtkExtractSelection
+from vtkmodules.vtkFiltersGeometry import vtkGeometryFilter
+
+from geos.mesh_doctor.parsing.cliParsing import setupLogger
+from geos.mesh.io.vtkIO import readMesh, VtkOutput, writeMesh
+
+
+@dataclass( frozen=True )
+class Options:
+    meshVtkOutput: VtkOutput
+    attrs: tuple[ int, ...] = ()
+    skipCleanCollocated: bool = False
+    skipFilterVolumeCells: bool = False
+
+
+@dataclass( frozen=True )
+class Result:
+    outputMesh: Optional[ vtkUnstructuredGrid ]
+    bounds: tuple[ float, float, float, float, float, float ]
+    numPoints: int
+    numCells: int
+    attrs: tuple[ int, ...]
+    skipCleanCollocated: bool
+    nCleanCollocated: int
+    skipFilterVolumeCells: bool
+    nFilterVolumeCells: int
+    nColors: int
+
+
+TOLERANCE = 1e-6
+
+
+def is_surface_cell_type( t: int ) -> bool:
+    """Checks if the given VTK cell type is a surface cell type (2D)."""
+    surface_types = {
+        VTK_TRIANGLE,
+        VTK_QUAD,
+        VTK_POLYGON,
+        VTK_TRIANGLE_STRIP,
+    }
+    return t in surface_types
+
+
+def __process_block( block: Union[ vtkMultiBlockDataSet, vtkUnstructuredGrid, vtkPolyData ],
+                     append_filter: vtkAppendPolyData, attrs: list[ int ] ) -> None:
+    """Recursively processes a block of the multi-block dataset, extracting surface cells that match the given attributes and adding them to the append filter."""
+    if isinstance( block, vtkMultiBlockDataSet ):
+        for i in range( block.GetNumberOfBlocks() ):
+            child: Union[ vtkMultiBlockDataSet, vtkUnstructuredGrid,
+                          vtkPolyData ] = block.GetBlock( i )  # type: ignore[assignment]
+            if child is not None:
+                __process_block( child, append_filter, attrs )
+        return
+
+    if not hasattr( block, "GetNumberOfCells" ):
+        return
+    if block.GetNumberOfCells() == 0:
+        return
+
+    cell_type = block.GetCellType( 0 )
+    if is_surface_cell_type( cell_type ):
+        cell_attributes = block.GetCellData().GetArray( "attribute" )
+        if len( attrs ) == 0 or ( cell_attributes is not None and cell_attributes.GetTuple1( 0 ) in attrs ):
+            if isinstance( block, vtkPolyData ):
+                append_filter.AddInputData( block )
+            else:
+                gf = vtkGeometryFilter()
+                gf.SetInputData( block )
+                gf.Update()
+                append_filter.AddInputData( gf.GetOutput() )
+
+
+def __extract_first_vol( block: Union[ vtkMultiBlockDataSet, vtkUnstructuredGrid ] ) -> Optional[ vtkUnstructuredGrid ]:
+    """Recursively searches for the first volumetric block in the multi-block dataset."""
+    if isinstance( block, vtkMultiBlockDataSet ):
+        for i in range( block.GetNumberOfBlocks() ):
+            child: Union[ vtkMultiBlockDataSet, vtkUnstructuredGrid ] = block.GetBlock( i )  # type: ignore[assignment]
+            if child is not None:
+                found = __extract_first_vol( child )
+                if found is not None:
+                    return found
+        return None
+
+    if not hasattr( block, "GetNumberOfCells" ):
+        return None
+    if block.GetNumberOfCells() == 0:
+        return None
+
+    is_vol = False
+    for i in range( block.GetNumberOfCells() ):
+        cell_type = block.GetCellType( i )
+        is_vol = not is_surface_cell_type( cell_type )
+        if is_vol:
+            break
+    return block if is_vol else None
+
+
+def _filterVolumeCells( mesh: vtkUnstructuredGrid,
+                        attrs: list[ int ] ) -> tuple[ vtkUnstructuredGrid, vtkUnstructuredGrid, int ]:
+    """Filters out volume cells from the input mesh, keeping only surface cells that match the given attributes. Returns the filtered volume mesh, the extracted surface mesh, and the number of cells removed."""
+    volumeIds = vtkIdTypeArray()
+    surfaceIds = vtkIdTypeArray()
+    nVolume = nSurface = nOther = 0
+
+    for i in range( mesh.GetNumberOfCells() ):
+        dim = mesh.GetCell( i ).GetCellDimension()
+        if dim == 3:
+            volumeIds.InsertNextValue( i )
+            nVolume += 1
+        elif dim == 2:
+            cell_attributes = mesh.GetCellData().GetArray( "attribute" )
+            if cell_attributes is not None and cell_attributes.GetTuple1( 0 ) in attrs:
+                surfaceIds.InsertNextValue( i )
+                nSurface += 1
+        else:
+            nOther += 1
+
+    setupLogger.info( f"  Cell types: {nVolume} volume (3D) | {nSurface} surface (filtered 2D) | {nOther} other" )
+
+    if nSurface == 0 and nOther == 0:
+        setupLogger.info( "No filtering needed (all cells are 3D)" )
+        return mesh, mesh.NewInstance(), nSurface + nOther
+
+    sn = vtkSelectionNode()
+    sn.SetFieldType( vtkSelectionNode.CELL )
+    sn.SetContentType( vtkSelectionNode.INDICES )
+    sn.SetSelectionList( volumeIds )
+    Esn = vtkSelectionNode()
+    Esn.SetFieldType( vtkSelectionNode.CELL )
+    Esn.SetContentType( vtkSelectionNode.INDICES )
+    Esn.SetSelectionList( surfaceIds )
+
+    sel = vtkSelection()
+    sel.AddNode( sn )
+    Esel = vtkSelection()
+    Esel.AddNode( Esn )
+
+    ext = vtkExtractSelection()
+    ext.SetInputData( 0, mesh )
+    ext.SetInputData( 1, sel )
+    ext.Update()
+    Eext = vtkExtractSelection()
+    Eext.SetInputData( 0, mesh )
+    Eext.SetInputData( 1, Esel )
+    Eext.Update()
+
+    setupLogger.info( f"Filtered → {nVolume} cells (removed {nSurface + nOther})" )
+
+    if nVolume > 0:
+        if nSurface > 0:
+            return ext.GetOutput(), Eext.GetOutput(), nSurface + nOther
+        return ext.GetOutput(), mesh.NewInstance(), nSurface + nOther
+
+    return mesh.NewInstance(), mesh.NewInstance(), nSurface + nOther
+
+
+def __clean_collocated( main: vtkUnstructuredGrid ) -> tuple[ vtkUnstructuredGrid, int ]:
+    """Cleans collocated points in the input mesh, returning a new mesh with unique points and updated cell connectivity, as well as the number of points cleaned."""
+    clean_point_set: dict[ tuple[ float, float, float ], int ] = {}
+    reverse_map: dict[ int, int ] = {}
+
+    for pid in range( main.GetNumberOfPoints() ):
+        pt = main.GetPoints().GetPoint( pid )
+        reverse_map[ pid ] = clean_point_set.get( pt, pid )
+        clean_point_set.setdefault( pt, pid )
+
+    old_to_new: dict[ int, int ] = {}
+    clean_points = vtkPoints()
+    for pt, ids in clean_point_set.items():
+        old_to_new[ ids ] = clean_points.InsertNextPoint( pt )
+
+    rewrite_mesh = vtkUnstructuredGrid()
+    rewrite_mesh.SetPoints( clean_points )
+
+    for cell_id in range( main.GetNumberOfCells() ):
+        cell: vtkCell = main.GetCell( cell_id )
+        new_ids: list[ int ] = []
+        for i in range( cell.GetNumberOfPoints() ):
+            pid = cell.GetPointId( i )
+            new_ids.append( old_to_new.get( reverse_map.get( pid, pid ), pid ) )
+        rewrite_mesh.InsertNextCell( cell.GetCellType(), len( new_ids ), new_ids )
+
+    rewrite_mesh.GetCellData().ShallowCopy( main.GetCellData() )
+
+    for array_i in range( main.GetPointData().GetNumberOfArrays() ):
+        arr = main.GetPointData().GetArray( array_i )
+        new_arr = vtkDataArray.CreateDataArray( arr.GetDataType() )
+        new_arr.SetName( arr.GetName() )
+        new_arr.SetNumberOfComponents( arr.GetNumberOfComponents() )
+        new_arr.SetNumberOfTuples( clean_points.GetNumberOfPoints() )
+        for old_id, new_id in old_to_new.items():
+            new_arr.SetTuple( new_id, arr.GetTuple( old_id ) )
+        rewrite_mesh.GetPointData().AddArray( new_arr )
+
+    nCleanCollocated = main.GetNumberOfPoints() - rewrite_mesh.GetNumberOfPoints()
+    return rewrite_mesh, nCleanCollocated
+
+
+def __paintNodes(
+        main: vtkUnstructuredGrid,
+        frac_polys: list[ vtkUnstructuredGrid ] ) -> tuple[ vtkUnstructuredGrid, list[ vtkUnstructuredGrid ] ]:
+    """Paints the nodes of the main mesh that are close to the fracture polygons, returning the modified main mesh and the list of fracture polygons."""
+    kd = vtkKdTree()
+    kd.BuildLocatorFromPoints( main )
+
+    narray = vtkUnsignedIntArray()
+    narray.SetNumberOfComponents( 1 )
+    narray.SetNumberOfTuples( main.GetNumberOfPoints() )
+
+    for i in range( main.GetNumberOfPoints() ):
+        narray.SetTuple1( i, 0 )
+
+    for poly in frac_polys:
+        for i in range( poly.GetNumberOfPoints() ):
+            dist = reference( 0.0 )
+            id_source = kd.FindClosestPoint( poly.GetPoint( i ), dist )  # type: ignore[call-overload]
+            if dist > TOLERANCE:  # type: ignore[operator]
+                setupLogger.warning(
+                    f"[too far point] main point ({id_source}) is too far from frac point ({i}) = ({dist} > {TOLERANCE})"
+                )
+            narray.SetTuple1( id_source, 1 )
+
+    narray.SetName( "faultNodes" )
+    main.GetPointData().AddArray( narray )
+    return main, frac_polys
+
+#TODO refactor with other coloring function to avoid code duplication
+def __coloringNodes(main: vtkUnstructuredGrid) -> tuple[vtkUnstructuredGrid, int]:
+    """Colors the nodes of the main mesh based on their point-connectivity,
+    one array per connected component of faultNodes==1 points.
+    Returns the modified mesh and the number of connected components found."""
+
+    fault_array = main.GetPointData().GetArray("faultNodes")
+    n_pts = main.GetNumberOfPoints()
+
+    # Collect only the fault-node ids up front
+    fault_pids = {
+        pid for pid in range(n_pts)
+        if fault_array.GetTuple1(pid) == 1
+    }
+
+    visited: set[int] = set()
+    color = 0
+
+    for seed in fault_pids:
+        if seed in visited:
+            continue
+
+        # One fresh, zero-filled array per connected component
+        color_array = vtkUnsignedIntArray()
+        color_array.SetNumberOfComponents(1)
+        color_array.SetNumberOfTuples(n_pts)
+        color_array.Fill(0)
+
+        # Iterative DFS — avoids Python recursion-depth limit
+        stack = [seed]
+        while stack:
+            pid = stack.pop()
+            if pid in visited:
+                continue
+            visited.add(pid)
+            color_array.SetTuple1(pid, 1)       # mark this point as belonging to component
+
+            cells = vtkIdList()
+            main.GetPointCells(pid, cells)
+            for ci in range(cells.GetNumberOfIds()):
+                cell = main.GetCell(cells.GetId(ci))
+                for vi in range(cell.GetNumberOfPoints()):
+                    nbr = cell.GetPointId(vi)
+                    if nbr not in visited and nbr in fault_pids:
+                        stack.append(nbr)
+
+        color_array.SetName(f"faultNodes_{color}")
+        main.GetPointData().AddArray(color_array)
+        color += 1
+
+    return main, color
+
+def polydata_to_ugrid( poly: vtkUnstructuredGrid ) -> vtkUnstructuredGrid:
+    """Converts a vtkPolyData to a vtkUnstructuredGrid by copying points, cells, and data."""
+    ugrid = vtkUnstructuredGrid()
+    ugrid.SetPoints( poly.GetPoints() )
+    for cid in range( poly.GetNumberOfCells() ):
+        cell = poly.GetCell( cid )
+        ugrid.InsertNextCell( cell.GetCellType(), cell.GetPointIds() )
+    ugrid.GetPointData().ShallowCopy( poly.GetPointData() )
+    ugrid.GetCellData().ShallowCopy( poly.GetCellData() )
+    return ugrid
+
+
+def meshDoctor_to_surfaceGen( hierachical_mesh: vtkMultiBlockDataSet, attrs: tuple[ int, ...],
+                              skip_clean_collocated: bool ) -> tuple[ vtkUnstructuredGrid, int, int ]:
+    """Converts a mesh-doctor multi-block dataset to a surface mesh compatible with SurfaceGen by extracting surface cells that match the given attributes, optionally cleaning collocated points, and returning the resulting unstructured grid along with the number of points cleaned.
+
+    Args:
+        hierachical_mesh: The input multi-block dataset containing the mesh.
+        attrs: A tuple of attribute values to filter surface cells. If empty, all surface cells are included.
+        skip_clean_collocated: If True, skips the step of cleaning collocated points. If False, collocated points will be cleaned and the number of points cleaned will be returned.
+
+    Returns:
+        A tuple containing the converted surface mesh as a vtkUnstructuredGrid and the number of points cleaned from collocated points (if skip_clean_collocated is False) or 0 (if skip
+    """
+    append_filter = vtkAppendPolyData()
+    __process_block( hierachical_mesh, append_filter, list( attrs ) )
+
+    main = __extract_first_vol( hierachical_mesh )
+    if main is None:
+        raise ValueError( "No volumetric block found in the multi-block mesh." )
+
+    if not skip_clean_collocated:
+        main, nCleanCollocated = __clean_collocated( main )
+
+    append_filter.Update()
+    clean = vtkCleanPolyData()
+    clean.SetInputConnection( append_filter.GetOutputPort() )
+    clean.Update()
+
+    painted_main, _ = __paintNodes( main, [ clean.GetOutput() ] )
+    colored_main, nColors  = __coloringNodes( painted_main )
+    return polydata_to_ugrid( colored_main ), nCleanCollocated, nColors
+
+
+def toSurfaceGen( hierachical_mesh: vtkUnstructuredGrid, attrs: tuple[ int, ...], skip_clean_collocated: bool,
+                  skip_filter_volume_cells: bool ) -> tuple[ vtkUnstructuredGrid, int, int, int ]:
+    """Converts a single unstructured grid mesh to a surface mesh compatible with SurfaceGen by optionally filtering out volume cells, extracting surface cells that match the given attributes, optionally cleaning collocated points, and returning the resulting unstructured grid along with the number of points cleaned and cells filtered.
+
+    Args:
+        hierachical_mesh: The input unstructured grid mesh.
+        attrs: A tuple of attribute values to filter surface cells. If empty, all surface cells are included.
+        skip_clean_collocated: If True, skips the step of cleaning collocated points. If False, collocated points will be cleaned and the number of points cleaned will be returned.
+        skip_filter_volume_cells: If True, skips the step of filtering out volume cells and extracting surface cells. If False, volume cells will be filtered out and surface cells matching the attributes will be extracted, and the number of cells filtered will be returned.
+
+    Returns:
+        A tuple containing the converted surface mesh as a vtkUnstructuredGrid, the number of points cleaned from collocated points (if skip_clean_collocated is False) or 0 (if skip clean_collocated is True), and the number of cells filtered out as volume cells (if skip_filter_volume_cells is False) or 0 (if skip_filter_volume_cells is True).
+    """
+    if skip_filter_volume_cells:
+        main = hierachical_mesh
+        surfaces: list[ vtkUnstructuredGrid ] = []
+    else:
+        main, surfs, nFilteredVolumeCells = _filterVolumeCells( hierachical_mesh, list( attrs ) )
+        surfaces = [ surfs ]
+
+    if not skip_clean_collocated:
+        main, nCleanCollocated = __clean_collocated( main )
+
+    painted_main, _ = __paintNodes( main, surfaces )
+    colored_main, nColors  = __coloringNodes( painted_main )
+    return polydata_to_ugrid( colored_main ), nCleanCollocated, nFilteredVolumeCells, nColors
+
+
+def meshAction( mesh: Union[ vtkMultiBlockDataSet, vtkUnstructuredGrid ], options: Options ) -> Result:
+    """Performs the conversion of the input mesh to a surface mesh compatible with SurfaceGen using the specified options, and returns the result containing the output file path, bounds, number of points and cells, and details about the cleaning and filtering steps.
+
+    Args:
+        mesh: The input mesh to be converted, which can be either a vtkMultiBlockDataSet or a vtkUnstructuredGrid.
+        options: The options for the conversion, including attributes to filter, whether to skip cleaning collocated points, and whether to skip filtering volume cells.
+
+    Returns:
+        A Result object containing the output file path, bounds, number of points and cells in the converted mesh, the attributes used for filtering, whether cleaning collocated points was skipped, whether filtering volume cells was skipped, and the number of points cleaned and cells filtered if those steps were performed.
+
+    """
+    if isinstance( mesh, vtkMultiBlockDataSet ):
+        converted, nCleanCollocated, nColors = meshDoctor_to_surfaceGen( mesh, options.attrs, options.skipCleanCollocated )
+        nFilteredVolumeCells = 0
+    elif isinstance( mesh, vtkUnstructuredGrid ):
+        converted, nCleanCollocated, nFilteredVolumeCells, nColors = toSurfaceGen( mesh, options.attrs,
+                                                                          options.skipCleanCollocated,
+                                                                          options.skipFilterVolumeCells )
+    else:
+        raise TypeError( f"Unsupported mesh type {type( mesh )}." )
+
+    return Result( outputMesh=converted,
+                   bounds=converted.GetBounds(),
+                   numPoints=converted.GetNumberOfPoints(),
+                   numCells=converted.GetNumberOfCells(),
+                   attrs=options.attrs,
+                   skipCleanCollocated=options.skipCleanCollocated,
+                   skipFilterVolumeCells=options.skipFilterVolumeCells,
+                   nCleanCollocated=nCleanCollocated,
+                   nFilterVolumeCells=nFilteredVolumeCells,
+                   nColors=nColors )
+
+
+def action( vtuInputFile: str, options: Options ) -> Result:
+    """Reads a mesh from the input file, converts it to a surface mesh compatible with SurfaceGen using the specified options, and returns the result containing the output file path, bounds, number of points and cells, and details about the cleaning and filtering steps.
+
+    Args:
+        vtuInputFile: The path to the input VTU file containing the mesh to be converted.
+        options: The options for the conversion, including attributes to filter, whether to skip cleaning collocated points, and whether to skip filtering volume cells.
+
+    Returns:
+        A Result object containing the output file path, bounds, number of points and cells in the converted mesh, the attributes used for filtering, whether cleaning collocated points was skipped, whether filtering volume cells was skipped, and the number of points cleaned and cells filtered if those steps were performed.
+    """
+    if vtuInputFile is None:
+        raise ValueError( "An input file must be provided." )
+
+    mesh = readMesh( vtuInputFile )
+    result = meshAction( mesh, options )
+
+    setupLogger.info( f"Writing converted mesh to {options.meshVtkOutput.output}" )
+    writeMesh( result.outputMesh, options.meshVtkOutput )
+
+    return result
diff --git a/mesh-doctor/src/geos/mesh_doctor/parsing/__init__.py b/mesh-doctor/src/geos/mesh_doctor/parsing/__init__.py
index ecdb80d9c..f25f20ece 100644
--- a/mesh-doctor/src/geos/mesh_doctor/parsing/__init__.py
+++ b/mesh-doctor/src/geos/mesh_doctor/parsing/__init__.py
@@ -19,6 +19,7 @@
 ORPHAN_2D = "orphan2d"
 CHECK_INTERNAL_TAGS = "checkInternalTags"
 EULER = "euler"
+CONVERT_MD2SG = "convertMD2SG"
 
 
 @dataclass( frozen=True )
diff --git a/mesh-doctor/src/geos/mesh_doctor/parsing/convertMD2SGParsing.py b/mesh-doctor/src/geos/mesh_doctor/parsing/convertMD2SGParsing.py
new file mode 100644
index 000000000..e31f48fac
--- /dev/null
+++ b/mesh-doctor/src/geos/mesh_doctor/parsing/convertMD2SGParsing.py
@@ -0,0 +1,76 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright 2023-2024 TotalEnergies.
+# SPDX-FileContributor: GitHub Copilot, Jacques Franc
+from __future__ import annotations
+from argparse import _SubParsersAction
+from typing import Any
+
+from geos.mesh_doctor.actions.convertMD2SG import Options, Result
+from geos.mesh_doctor.parsing import CONVERT_MD2SG
+from geos.mesh_doctor.parsing._sharedChecksParsingLogic import getOptionsUsedMessage
+from geos.mesh_doctor.parsing.cliParsing import setupLogger, addVtuInputFileArgument
+from geos.mesh.io.vtkIO import VtkOutput
+
+__ATTRS = "attrs"
+__OUTPUT_FILE = "outputFile"
+__SKIP_CLEAN = "skipCleanCollocated"
+__SKIP_FILTER = "skipFilterVolumeCells"
+
+
+def convert( parsedOptions: dict[ str, Any ] ) -> Options:
+    """Convert parsed command-line options to Options object.
+
+    Args:
+        parsedOptions: Dictionary of parsed command-line options.
+
+    Returns:
+        Options: Converted options object.
+    """
+    return Options( attrs=tuple( parsedOptions.get( __ATTRS, [] ) ),
+                    skipCleanCollocated=parsedOptions.get( __SKIP_CLEAN, False ),
+                    skipFilterVolumeCells=parsedOptions.get( __SKIP_FILTER, False ),
+                    meshVtkOutput=VtkOutput( output=parsedOptions.get( __OUTPUT_FILE ), isDataModeBinary=True ) )
+
+
+def fillSubparser( subparsers: _SubParsersAction[ Any ] ) -> None:
+    """Fill the argument parser for the convertMD2SG action.
+
+    Args:
+        subparsers: Subparsers from the main argument parser.
+    """
+    p = subparsers.add_parser( CONVERT_MD2SG,
+                               help="Convert a mesh-doctor dataset to a SurfaceGen-compatible VTU file." )
+    addVtuInputFileArgument( p )
+    p.add_argument( '-z',
+                    '--' + __ATTRS,
+                    type=int,
+                    nargs='+',
+                    default=[],
+                    help="[int ...]: Attributes to include when filtering surface cells." )
+    p.add_argument( '--' + __OUTPUT_FILE,
+                    type=str,
+                    default="converted.vtu",
+                    help="[string]: Optional output VTU file path." )
+    p.add_argument( '--' + __SKIP_CLEAN, action='store_true', help="Skip the collocated node cleanup step." )
+    p.add_argument( '--' + __SKIP_FILTER,
+                    action='store_true',
+                    help="Skip the surface/volume extraction step when input is a single VTU file." )
+
+
+def displayResults( options: Options, result: Result ) -> None:
+    """Display the results of the convertMD2SG action.
+
+    Args:
+        options: The options used for the conversion.
+        result: The results of the conversion.
+    """
+    setupLogger.results( getOptionsUsedMessage( options ) )
+    setupLogger.results( "Converted mesh saved to: {0}".format( result.outputFile ) )
+    setupLogger.results( f"  Points: {result.numPoints:,}" )
+    setupLogger.results( f"  Cells: {result.numCells:,}" )
+    setupLogger.results( f"  Bounds: {result.bounds}" )
+    setupLogger.results(
+        f"  Skip clean collocated(npoints cleaned): {result.skipCleanCollocated} ({result.nCleanCollocated})" )
+    setupLogger.results(
+        f"  Skip filter volume cells(ncells removed): {result.skipFilterVolumeCells} ({result.nFilterVolumeCells})" )
+    setupLogger.results( f"  Number of connected components for faultNodes: {result.nColors}" )
diff --git a/mesh-doctor/src/geos/mesh_doctor/register.py b/mesh-doctor/src/geos/mesh_doctor/register.py
index 99675376c..41d96a605 100644
--- a/mesh-doctor/src/geos/mesh_doctor/register.py
+++ b/mesh-doctor/src/geos/mesh_doctor/register.py
@@ -59,7 +59,7 @@ def registerParsingActions(
                         parsing.FIX_ELEMENTS_ORDERINGS, parsing.GENERATE_CUBE, parsing.GENERATE_FRACTURES,
                         parsing.GENERATE_GLOBAL_IDS, parsing.MAIN_CHECKS, parsing.NON_CONFORMAL,
                         parsing.SELF_INTERSECTING_ELEMENTS, parsing.SUPPORTED_ELEMENTS, parsing.ORPHAN_2D,
-                        parsing.CHECK_INTERNAL_TAGS, parsing.EULER ):
+                        parsing.CHECK_INTERNAL_TAGS, parsing.EULER, parsing.CONVERT_MD2SG ):
         __HELPERS[ actionName ] = actionName
         __ACTIONS[ actionName ] = actionName
 
diff --git a/mesh-doctor/tests/data/base_tetra_shift.vtm b/mesh-doctor/tests/data/base_tetra_shift.vtm
new file mode 100644
index 000000000..72cfda3fa
--- /dev/null
+++ b/mesh-doctor/tests/data/base_tetra_shift.vtm
@@ -0,0 +1,7 @@
+<?xml version="1.0"?>
+<VTKFile type="vtkMultiBlockDataSet" version="1.0" byte_order="LittleEndian" header_type="UInt32" compressor="vtkZLibDataCompressor">
+  <vtkMultiBlockDataSet>
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+    <DataSet index="1" name="fault" file="base_tetra_shift/domain_fracture_2_ids.vtu"/>
+  </vtkMultiBlockDataSet>
+</VTKFile>
diff --git a/mesh-doctor/tests/data/base_tetra_shift/domain_fracture_2_ids.vtu b/mesh-doctor/tests/data/base_tetra_shift/domain_fracture_2_ids.vtu
new file mode 100644
index 000000000..bd71da8e6
--- /dev/null
+++ b/mesh-doctor/tests/data/base_tetra_shift/domain_fracture_2_ids.vtu
@@ -0,0 +1,55 @@
+<?xml version="1.0"?>
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+      </CellData>
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diff --git a/mesh-doctor/tests/data/base_tetra_shift/domain_ids.vtu b/mesh-doctor/tests/data/base_tetra_shift/domain_ids.vtu
new file mode 100644
index 000000000..958a15a50
--- /dev/null
+++ b/mesh-doctor/tests/data/base_tetra_shift/domain_ids.vtu
@@ -0,0 +1,44 @@
+<?xml version="1.0"?>
+<VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian" header_type="UInt32" compressor="vtkZLibDataCompressor">
+  <UnstructuredGrid>
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p0e+doHT6D/wHP3qTVW/v034T8PDU+AU8An4SvvTwIPgP9K5NwfngG+snH3RSqFXpINe6D5qyXOfDZ7Cv0W/4OfCv0W6k0s/7Q0vmRm6XCj8xO/8XPi3LJFw58X6w8MJ/8NomA7u0njM2SXnkYT/cTRMp9+tGAq/OafkPJrwP43+d3xy9OuZJefvCf9z+JUqPjwI/nNuyXks4X/RvhUfXgQfOq/kPJ7wv4ZfpeLDk+BH55ecfyT8ofCrVnx4E3zpgpLzRML/Zj2q+PAo+NOFJefJhD8cfvWKD6+CT11Ucp5K+CPh16j48Cz41cUl558Jf7Ti8ytqf6NLSs6/Ev730TAdnKz9jS4tOU8n/LHRMB0crf2NGq+CJzV+BOeCI8GP/L5+hbe8DSoe+XCraRWP31Pf1+MPBE+CS8HDXkj40+Hde3MvA34F7/lj5rs/hTrX5R8PD4LbNJ5Dnnjj4UmbV3jjQXAc+M20Cvf75hUOj4H3wGvgM+7DwVXgNO4p8AtgZ7I7due/kvj8YeA58Jvpxc8KD/9xv8A9hfn4NU8c6rNp6Uu/W0vO/Ek/aeKwXOLDk+BHt5WcBfgFJ/12FR+eBD+aVXJeZN+V9NtXfHgTfOndJWfBpJ8r6WdUfHgU/On2krOQc8Wk36Hiw6vgU3eUnIWTfp6k37Hiw7PgV86v4T2LJf38Sb9TxYd3wbfgRs4P4U3k7Vzx4WHwL/jSjHHS71Lx4WXwsV2Lh5fBx/pcHv6k3LtVfPnTB87k3B4exa9p94qvPPKD0/CTg1vsMmmoD7ntjwSX4gewVNpnobQPvxv+Q/x17il94VbkKxd/pOUjDx7HD6DxNnhY411wLv5J8C+4DnwKHgS3+mvyeSi073dIB69bLvotXv5JcKt9QtsfCY4F55rjnxQK5+Hnsc+k/x2fnD2Lh2vtV+HKC9dqvIs/Ej+j9o9y72jzGNDuH505eSiXXxT58LP2l4Kn6Yfykz//Jv5a8B7rBzwKLnR/8aulPO5jwYf2Tn3eV/VLHlwLnibfxrvk1/etHggd7z4VnGy8+1Jwq/Y3cm8d/jc15XNf8I6UY/tQ7xJs4V5T6Lv5waQd555rmO9Rlb96O6p4OF37I/FrOjoUTgkvgh8dEwo/ck+Kv477VNdPGconlz8U+bfaR8AV4VF1n2rTwsHcu7ot/PSSAw9TLnrTA557S+ULZ9wo6dvfhz8P/x33gtxP+m7CvzoaUvedNkq6ddL+jVfBseBb8AN+JP3eDzuf3ws8gD+FdHAY6eEx7H64Q9/nYafDu9jz8Ai4lXqCX6kvcuE65MM9lPP4Kn/jgnCw7yfcO0L8cdwL6vtCj2Se816Vd3C8fyMdPy7pAWsnVf3+PMHfDz8eXgYPujT5XBa6RugKyfCNVb+/SXrvNLU/D7wMPvZWv4f+OeG/Dg9Pgcs0XgNfgavwV4GvnFb5/CXhv1HeUH48v0z4D8PPDA+XkQ98pu9L6V/6Hb355bS/zuJVfuWhb+Nh8C74VuNdxoP+Px7eRH+4BXwH/gGvoffZoe2PBLcQH/4Br4FL4OEb8Bh4EdxnPDwIrrNyaOM99B4Pz6HXeHgNvcbDY+Aq4+EtcJDGU+AS/Hj477R/Dz8dfj78e8bDI+APHZ+cOfefxslP/PYHgofIr/1t4BnwCn4h3uHx/g7/GvgBXABeAT+Ad/CDaX8adjf/EfY5f5HOjx7rFU+PxkvgEnCFxhvkNyu08QDpbg3lRwKfaF48eAO5cIHGC+AK/FjmzbowmjiUR8/GMxpvUV7lky9cYTy8AW6wXWjjCXCB8fACdv+M0MYD2PXj2fvs9vHseXb5ePY6u3s8e5xdPZ69za4ez95mN49nT7OLx7OX2cHtL8Lu7Ps57PGH3eNwrlHvM7gX8sHsG9z78F4Gu5kfyDLpX4u4H5X82LX8Pdi3be+yg/l3sFvZ48tG/mKRzy+EHct+dT/n3vp97wpnx8EZ2HfwhpYnH/ax+zH8EtiBcMMryp+CPwk7VPx7S570D9vHhV4Y6p2Sj5X+KyZ8KfhG9lveA+Ends+UYXn5hbB31Qs7Vj5tX7c/ifC+r8QuPrDClV/9tb9H29P3VfjKKaf6vaLql1x2cvubwC3Y1WtPGMp9ynsLof0+Djv83rLP1TM/ivav4H/hHsd1db+DP4f07Oh1kz9/oRvK/2OUDfxz4dm77M6jQtm//DL4U/B7WC3p2Y3sxfafYF/6veMfHb7vDwm/O/WwW6h7r9tHD354092HrPtK60XPa0Lf6T5N+CdSX0+Gdv9QjvYT0V7ObZ2jO791nm6/zK6zn7Z/tk93nms/71zfub1z9ocS/jvzqXS1n3Uea9/vHsCfEv6r8M6VnaP/LuHeSe7zYufBzn/7vFp9LFg8v4L2Nzg7PLuk7wPI57TKX32Q6xyffOf59GbvzBwbyrOPV1/2++wVuIR3cjdM/1g7/YNfifs5fT+o3209ocKlw4vnvZZ+T5lfivsd3ttxH8h9Iu/vwGG8EwOfgcd4pwUOs3HiTy0c1LtBZ4Q/Za5heZXDfSXlcV/J7x3fO6zeeeEH5d3YF5LfMRm33ms+NjxcCP7B3wkuAueAQ8FD+At9q/Slp3eN7FvsY6zz8EjxtWu3P5znqZRTO3ifR/8Qv/3DtF/ja+TYFzm/sJ+6q9pbeniU9NZt+wH7A+95SUefTu/el/7kPpr6Fk/6byfceHIPT3vze/MeBXnPV/mPrvakR+ff40x7GS9wLP0B3nVcla/fP5J/v1ve7195tw5OvUHw6Xkyfvjxwes2S/r1jYsqh/6qf3Z/1E9nhH8ucrwzblx4T8l4GRun/OLDG+GR5OrXT5a+cETnCvytnWPwb+13pbXnMdU+/b6R9vIOuvuIWybehhOH8V8o+cdWvZpv+/0x+wvvrTkv872AQ0K9c+5d7b9n3jI/eQf8+Kq/CVVO7aH+v1vl3Crl2Cjp4cETKr18Toke+pt3D08KbxzqT9Mif63If7D02DrhGyecf6R+0fh0r2fqW3/ZJ+Ni39BHo9+fol+/V65feH/cuLthwv/Wf2L1W/p2f+7zBfXnnTD1MD3yN4l8ePbEkm8d88679+/5pX4hvHV9znyX8GMqnf5lvb4/vO9QnBAKZ/Yej/o2Lq036v/7Vc5tEr5pwvlrwuvbP9l47vcZ3BNRz3Pe26NHwvtetvz6ewp9fkCee7DKsW3y3yzynQ9NLvnq+V/pb76XoB0ODa+/mS/MI/rfD0uP7RK+ecKdL9nHtX8xuT3PPVPzqXW09xPucbvfrR4eysGZfXTf72UP0Kv9cdUPP23l3D75b5H8+Ss7L+v347q9tZt931zR277OeY/vtfjOie9zOAcSv7+rwo5g95w4TjmNux9XOWekfFsm3Hkhf+x+f8D5Dn9k51DexZpzjhM6M+nZI/3elfO0UXjnXPTcIfptFf2c15DT55vkPpt+rr88533V2MGHhn4t/f7+8HPsvFD2F/uJnzU9d4x+W0c/9q/zo77/r94WqPqbrF/XPqTXU37BfT+cffiz0nOn6Dedn2Lis8f7+yj9ngR72fuPxm1/34N+zpnaf/mM0nvBSUO9vf/x8yrHztF/m8h33qne2j9evfU6Z/8BP+jzMHiC8cheZ+cbt+4Z0HOX6Ldt5Duv1L59P8TBuO/HmDfMZ/zr+dVfWf717Pc+r2PPe7fsLaHOEd8a3jsIyrFr9N8u+cAvlLvP961T5pn+npJxqP8bp8a3c8I+T4Q/eDeNvs45TwsPx1GO3aL/9vy8Iq/xGHiPdplc86t51zlln2eqR++40ce56+nhnbPTc/foNyP6OX83zvr9T+uA+d44fjC8c9I+T1VP3pWjDz/7M8I7p6fnHtFvB/hH5PEH6PsXvuPle1PWnTeE9+6e713pl6eGd47b573q0Tt49OXnf2Z492yUY8/ov6NzmsiDMza+aF8wX+TZF/iulvmo51nzEz/89u9Xz97pp697BmeFh2Mqx17Rfyf+5GNDPft9F/Noz7PeL+Ln3++TqkffzaCPewxnh/dOCT1fEv12jn72G/Yf9J3z/aqan+jpvUb3CPr9U/Xkuxz0cU/inPDu6dDzpdFvl+g3M/LsP9r/45as8xOzX5wvdtek8Pprz7P6tXP/fm9EPboHSV/3NGaGd09IOfaO/rtGf/sN+w/lsf8wT/Y86j1Lfgjtn6AefXeEPu6BnBvePSR67hP9duNPG3nGRePTztX6Hf4ZqXf+vs5h2//ZPNvzsHWNX0X7Z6hn30VRHvddzgvvnpRy7pvy7Z7y2a8YV8pr3HmP8rJQ75p4t7L9dJTDusYPpP1DtIPvstDXOzLnh3dPSzn2i/57RH/7GeOu7z/CqRrH4hfnXrPyeAfFuwzm6Z7HrYv8aNq/Rjv4bozyeOfmgvDukSnn/infnvZBkWdc9vnPfulv+/veQ8r1l4xz97SVx/1t78b1O9fKaV3lt9P3lbST79ooj3d6LgzvnptyHpDy7ZXy2Q8Z130+Ax+x/2aXP55yevfH/ab+3l6/062c1l1+SO2fpJ18d0d5vEd0UXj38JTzZSnfS6Kn/ZRx3f5x/b1E5VU+5XKPq79H2O+MK6d1mR9U3w/TTr4jpDzem744vHuCynlgyvdS9x0iz7hv/71+55ye1mXvFPX7R1dWO9JHe18S3j1Eeh4U/faOfvZbxm37DzqXcj4Bt3gq9d/vsCuHdds7Rv3e0lXVTvTVnpeGdw9SOQ6O/vtED/st47Lfp+934OlpXe7vPvG3U4++w0Qf/cx8754lPQ+JfvvyZ4k846rfxz8y89IRod+M3C+G73fqlcO67X1S5/79vnJ/l4q/Xtez+nfv8bHw7oEq56Ep3378SSLPuOr3/T2Q6LySPXNceOtArxPW7f4uFj/Dbgfl8C77gjln8k6bchwW/fdnT0eecdffH/COO/yNnfh8+r95vtcB63Z/d8t9zt4nKId34f8R3jtxynF49D8g+tvPGXf9fQTffz4pFG4zIfVvHu953rrc3/1yn7T3AcrhXbQnwnunTjmOiP4vi/72a8Zlf7/BuZ3zOrjNE3CRmh+Vw7rb3yXjn9nrvHJ4l22R9B/v6CnHkdH/wOhhv2Zc9vcl4GD/mDwsD/3N4z3PW1f7u2n8Yb0rd5V11/yc3xeN/r6DoBxHRf+Dopf9mHHX37/o7zjQ07rIP+muUOfmvjvW33W7NfJ9L8D7wcrhuwGLRX/fgVCOl0f/g5OP/ZZx19/XsM/bI/aG/eCe4dep+ut67e/K8QPmL+19H+Xw7t7i0d93KpTj6Oh/iPO7yDPu+vsezrWcc8HN/p36nVr11/Xa37WbFfn8u6+17oZ6F/DpWg+U45jofyj/usgz7vr7InC0/0ZfOBa/lnWr/rpe+3tk/Cr5w/V39/hbe6/wOut2qAtIz9R6oZzHpnyHJZ79lnHZ30dZr+qv67W/63d70nsv8Xp2o3k5+T5b6wE9j4t+hyfe/wEsZ84SeJx9nWe4VUXShe/ue+7hooIRA4oJFRVRMaFjGnMaFTM6Y0AlZ5CcBMGAWTCLEkwoJjBHdDAriJhAFMRAzlHi/GC93/PU+s6GP/1Ur+pVVb179+5zOGvfh8o2/aubNrUPyr5H9raFTW3V4qa2i+wtZB8ie7DaRzVuiOxjZB+t9inxD5X9gPzuV5vKN7VrZK+R399qm2ab2qvk97D4DhL+kOx7ZW9n+XaVvaXsQwsx38dSzOdx+f1D7TFqD6tUf4p1Pq34jzOfhVhnufJeK3ttinU2U31Xy+8R8dVLsd77ZG9v9XSTvZXs+ja/Pu/HpljHM+J/gvksxDoKymud7HUp1tFc+V8jv0fFd3CK9QyWvYPl2112NebZ5s/n9bgU63hW/MOYr0Kso0J5rZe9PsU6Wij/xvJ7THyHpFjPENk1LN8esqvLPtzmz+f1+BTrGCX+4cxXIdZRVF4bZG9IsY6Wyv9a+Q0V36Ep1nO/7B0t356yt5Z9hM2fz+sJKdbxnPhHMF+FWEcV5bVR9sYU62il/K+T3+Piq59iPQ+QP/Outr94e8o+0ubP5/XEFOt4XvwjmS/2NbWVyqusQi31yL+18r9e/U/I7bAU63lQ9k7Mu9qbFKeX7KNs/nxe/5liHaPF/yTzxb7GPq689txiU5tRj9o2yr+J7GGCD0+xnodk9xbvLmp3Vrut8n9K9pP0i7eq5q+Bza/P+0kp1vmC4j/FfLKvsc+LP4mf5wl1tlV9TWUPF98RKdb7sOw+4q1pdW6n+o62+fV5PznFOl4U/9PMJ/sa+7jyKlf+PC+oo53ybyZ7hPiOTLGeR2T3Fe+uVsf2yn+U7GfV1hBvNcU/xubX5/0VxTtV/S/LHsV8su+xz4u/IH6eJ9TZXvU1lz1SfEelWC/njBvFu5vVuYPqu0vtgWoPULs3z2+bf78uL8g+S+2ZXE/2E/ZFngPKex/dXzxvqLOD6msh+0nV0yDFejmH9BNvLauzhuo41ub3GZv3EZZndcXdV/nxPCHPjsqvpWzOa5zfyJdzBPvt7pYn+/JxNj/Psk5lPy+/kboe21RRHNmXyP8ttR8q/tvsC7a/jOacac8b6uyk+lrJ5rzG+Y16OWewH+9hdbJvH2/zy7pn3j8qi3V4/jxPyPMG5ddaNucxzmfkyzligOLsaXnurPxek/262gvUniX8BFsnz7EO8bN1/5ric1+M5j5ReybPJfHzvKHOzqqvjWzOa5zfqJdzxkDx7WV17iL+muJ5nfzYZ3m+81xhvbGOZZ9t9/friv+i7CN0n+yg/aqf4nWXzfOGOruovrayOc9xvqNeziE3K4+9rc6aqm+M/M5Te67a09nnlMf2am9U3G6yz0mxzjcU/yV4OLepHSOceeJ5RJ1dVV872Zz3OP9RL+eUW5RnbatzV9W3s3h2UjtA/L3YH+36baP+Orou/0qxzjcV/2U+/8mfc0cf8XeRzfOIOrsJby+b8yDnQ+rlnHKr8trH6tyN56v8TlPLc5J9Yyvtd1uq5bq1l831ps63FP8VPv+Vxzr7Kv+uPMetzu7CO8jmvMj5kXo5x9ymPPe1OmupvlvEx314s+y+5bHu421/Yd/xdf224rPudyiPdd4o/m6yq1udPYR3lM15kfMj9XKOGaQ89rM6d1d9u4iHdTpQ/L15fnAu4lzI/aLryHzsYuP7yD7f7u93lN9Y2WfYfTpWOPfn1jYPPcXfSTbnTc6fzAfnnNuVdx2bhz1U/63i21V8XG/2medz8mTfbZhine8q/quyWcenyp/7dpTd79TZS/FvkH0Kzw21L4mfc8Yd4tnf6tzTno88Fxva85FzxyusC8UdY89VePYWXlPXn+c84/ZT/nvYvkSdvYV3tvnlvtlJOPPBc4vnMc9nnl+3yX83+XM9eY5dxrpT+1+Nf5d9h+ui9k2bj3vUHqS2LvuE5o/1yrrw5wufN7jP+ym/7rIvsOf6exrP8/4mzfMi+S9WO1v9b6ToX9vW81jlez77kdozlP+risf6pp7Rtt5Ps/q4XsSpafcP++MU7fNT1XZW26ZqzJvzDHwvcZ/KZn3dLv6bZHNuI3/qIX+u857y31/j92J+7P5lnzqDdV8W48DPenyefZ9zM/ezrs9brCv2X+G1hLNud7X1y/7DumUe6mSxnvMtX/Ydzqk7yo912F/jezDPfK61c8YpGn+31kld+5xZW59fuP/H2PUaa/tJLbWDFL+/7Nds/XI/nKXxs+Q/m+uutifn4jL55+wPR+sc1UDtQPYhzT/zSN53iH+A5c11us32Fz7vXKx442S/ZffjB+p/Xy337R2sa7Wr5c99znmW8y31ct18X3+PeSjEPKhjX/FzXdj/TtV1Zd87TTbnTdYF+bBeqIfr5vsX5wHOFwPsfMF5j/Mf8Xhuvqg8XlJ7ttqTK2N9rEM+h56rOrj/mZdGahvafs/13Uf51bL1yflmLzsPcp7jfEc9Y2wd8/nRP7+xD7EO7xQ/65S6qHM/u36XWv0fKT77Bt/j8P86nOf4/x7OY+x71MO+SL5c34ZWF/P7qtXD+uO+YJ7HlcV6xsvmOe3P5zmqdy7zYuuH8xb7IPW8anmTL+vybNnv2PzVsfPLIVpng9U+qnkbIpv5IR7rg/XC/eHP5zdsXpgP5ov7kvPW+7aOOZd9Iv7L1f9xWfT/TPa/ZX8qe5xs5nWeWs7znN+XyV6u9m61t9h+wj44rizWw3Vknve35xf5NrL1QJ3cj/Cyj75p15d1yf7zms0r/vva/Q3vRak0/4UWx6/nFWqZf67H+9bfyK7PexbvQuN/w/JnX2N9sL/5fsc65px3kdVBPPiXaD6WqvX9h3ngfMy88ZzgfmUf8v3nUsuH/vNtfyDPcWUxLvdbA/6/iP+X1/3Hfkhc//7xMovHemxo80cejGfe2ddG83lZ54YXZH9EnfBxHfg8pvyYJ58frgv7EdflHJsvzlPcN+S1QPwL1fr5hHob2XxQP/vCRbLZFy6276X4norPvVwP7mfuW9+/fT9jXn29sk4usfXi/cwf46h7kVo/X/L9O593Tld7otbPUNn8DoLnZH3h3L/cr76/vmPzeqmtN+9nft4xXt8nLrDr80HO/r1Y9XIf+/m1bhbzPVB27fIYn+f2AcJ57rC/XViIebBeWIesJ+o+sxjzhYf9ER7638/B+Z7kNM6pao8X/3/kxzzxvGN++ut+raHPnf1k95A9WPUerPY+tfuVxzjD7f8TR8geqnXymNqq4s30+fYk/p9YLefAp2UPZj9Te7DaOuJrwLlD7chijHcyv9+R/Qz/byabz6f8PwafE0+UXxXNU1Ftp01NWSvZ18q/sdoJ6v9T9W0pewu1XTW+nezKFOPcILy17Krm11l4mxT92phfmfKpJnsrtd0Fd7C8iNNFeFsbRx3dhLeX3U5x2qr9Wf2T+f5O9tZqe2n8DbK3kd8NantRp+zr1V6n9hvhszS/1VOss4f4O8reljhqewvvLHt7tdup7Su8K/uO4q7Qel+udkFFHEecPhrfxeomz57CO8n+UvZXapupvVr4F2o/V3sU9yPnb9mHZpHvvynyNjf+a1LpeF8Zjv218YBfq3aD4q9Xu1L5DbH8yHf/8sjbwvgbp9LxvjYce4LxNLZ5+0ztkYpfT/G/4jqoPZp8yyNvS+Onbo83wXDsicYD3kxtudqkdi3zw3VQe4zyq18eeVsZ/3WpdLyJhmN/Yzzg9Lc2/Pq0ef/rzZ5kPOBrVc8y1cN1Xm73W6NCxLkv4W1j/E1StIk3yXDsb40H/APFeZ/vt5VP7Yo4rq2Nb5qiDd+3hmNPNh7wxjYfXj/j2tl41pXzTTYc+zvjAV+jOq/S87Cp4l5djOPa2/jmKdrwfWc49vfGA06967ROWC/MB+M62PgWqTTf94Zj/2A84BsVb5XisY5Wl8dxHW18y1Sa7wfDsX80HnD6OxnO+SPPv5XZPxlPK1tX83WdWVc83xjHuYTxnDuc7yfDsacYj59b/JyTFeI4zj2MZ5zzTTEce6rxgHO+KSge56KKQhzHuYjxnI+cb6rh2D8bDzj9XQ3nfJjn387sacYDzrmqk9oZXJdCHMe5jvGc75xvmuHYvxgPOP3dDWee8/w7mP2r8YDT38Nwzn95/h3Nnm484M01T83UfsfzuBDHca5jPOc755tuOPYM4wGvqjht1HJu5bzNOM7VjOd87XwzDMf+zXjA6e9tOHnk+Xc2e6bxgNPfx3DOz3n+Xcz+3XjA6e9rOOf7PP+uZv9hPOA11FbT9eghu3ohjrvRxndLpfn+MBz7T+MBp7+f4d3T5v27m/2X8YDT399w6szz72H2LOMB5/NdE7V8bl1dEcfdZON7ptJ8swzHnm084PQPMJzPoXn+vcyeYzzgPFfWqZ4WstdXxHEDbXzvVJpvjuHYc40H/ArF4/MYn2MXVsRxN9v4Pqk031zDsecZD/hyxWmk8+I1yufyYhx3i43vm0rzzTMce77xgF+peFep/Vv1L6mI42618Tem0nzzDcdeYDzg9N9meL+0ef9+Zi80HvBrNI+cx+fpe5HxxThukI3vn0rzLTQce5HxgGeK00Qtz6mmxTjudht/UyrNt8hw7MXGA95S8VqpLardUBHH3WHjB6TSfIsNx15iPOA8h/kebAu1qRjH3WnjB6bSfEsMx15qPODE3dLilxfjuLts/M2pNN9Sw7GXGQ94R8XjecdzsFiM4+628bek0nzLDMdebjzg1ypOY7UT+H/fYhx3j42/NZXmW2449grjAedcyDmxXO3aijjuXht/WyrNt8Jw7JXGA07/fYYPSpv3H2T2KuMBpy6eW/48Y9xgG397Ks23ynDs1cYD3lLXsYXaFdrfJhTjuCE2/o5Umm+14dh/Gw+4X8/mVj/j7rfxd6bSfH8bjr3GeMDvUZ33qj1I7T6VcdwDNv6uVJpvjeHYa40HnLj3qa2ndt/KOA7dOOPvTqX51hqOvc54wInL/wPx/z/78bsT5lktOl1+5wsvunDXt3u8dYZjrzcecPIaYv9Pxf9PkRd6W3S4/B4ZXnTdrl/3eOsNx95gPOD/Lx/+X7oy5oWeFp3tATxHxYdu2/XnHm+D4dgbjQf8UM8HnV5lzAu9LDraAwuRF12268s93kbDscuyyANe3/Phd56VMS/0sOhk+R00vOiuXT/u8fAfYnZmPOCHeT78HrUy5oWeFZ0rv9eGF12168M9Hv73m52MB/xwz4d9rDLmhV4VHWs9zlHiRTft+m+Ph/8DZpcbD/gRng/7XGXMC70pOtSDed6LF12067s9Hv4Pml0wHnB04OjDZ/O9TzHmhV4UHSnv5YB3WFnkZ114PPwfMrvCeB6yuOhB0YnyXg3GDS+L47nuzof/w2YXjQec32nyPo4nZB/O71e4DmrRefJeDHhHlEV+rrvHw/8Rs6sYzyMWF70mOk7ea8G4kWVxPNfV+fB/1OxK4wFnPo7ldxA2P+SF3hIdJu+tgPfJssjPdfd4+D9mdlXjeczioodEJ8l7JxiHLpfxXFfnw3+o2VsYz1CLi14RHSPvjWAculvXBzsf/o+bvaXxPG5x0ROiM+S9D4xDF+v6X+fD/wmztzKeJywuej90gLyXgXHoVl2f63z4DzO7mvEMs7jo8dDp8V4FxqErdf2s8+E/3OzqxjPc4qKHQyfHew8Yh+7T9a3Oh/8Is7c2HvBndT+iLzhF7XH8fol9Q/mgc0P3Dy+6Tdenejz8R5q9jfGAn2Lx0NWh62ccukrXjzof/k+ava3xgFfT78VGaV6Yr+GaH/S16K1cbwovukfXf3o8/J8yezvjAX9O+fA7Sn4/eYLyQ/fE7+LQG1Xn90PiRafp+kyPh//TZm9vPE/bvKC3Q6+G/hY/50fv7XpNfr/P78eJi+6S+KxTzwf/Z8zewXjA+X3gMZpPfhfI7/nQsaE3Q4fGeyHg5b0krg/1ePg/a3YN4wFH78V7RnhPFbowxvGeFH8/ivPhP8rsHY0HHN0Z8XjPFN8bMA5dIuNZF86H/3Nm72Q84HwfQTzeE8X3BujknlGLLov3zcCLrtL1oR4P/+fN3tl4wHkvHe9341xch323EPN82fKDF12j6z89Hv6jzd7FeMDJi/ezcW7evxjz8fcckDe86Ppcf+nx8H/B7JrG84LFJR66M35vzDh0d64vdD78XzR7V+MBRyeG3godFu+/YBy6O9f/OR/+L5m9m/GAn8e+rv1mLL+Xr4zj0OW5/s/58H/Z7FrGA873IXxvk/G9YzGOQ1fn+kDnw/8Vs3c3HnD60b25fi/Pf4zZexgPOLpVdGz+/g/GoVtz/Z3z4T/W7D2NBxw9Ms9l18+iVzlX841uhXUBL89J19d5PPxfNXsv4wFHD4eeam/uF9Nb0u/6LnQT6CXQEaBX8Pvp/947UYi85OH6rr1y6kHfhx96UM8fXnRsrr+i/w2bj92M/zWbH+IxHl3bOPG73u1Ni4eOCx0Ieo88fR86qw/F73rUtywe+tRBaleqf5Va3mfAeHRSH4nf9ZhvWzzXw6NDcn0N/e8Yn78PAN3LeI1Hv0T/u8aHXmaY7hO+h6grnXmhyub53jMbXY7rAw/M8ed6oUtBj4Je5cjKOB5djeuX8uK5Xqiu8YBzXfL0foxDB+T6HufD/wOzDzIecFofz7omL+bf9YDwohtyfZXHw3+c2fWMBxx94Idq0RmhL/L3laHrRef+ufjQOcEPH/3jcnDiwUOe4PUM9/Fej4+/0uqD5yOzPR9w1/PwO1N+H3sA96na+zX+Ttmcezh/8J6Ek/kdt2z0IuhHxstGX+F6kI9lo+uon0V8vOH0O7/rVfAHJ+74nPjoK1wP8kmKeR2WRfxjw+l3fq8Hf3DifpwTH32F60E+TTGvw7OIf2I4/c7v9eAPTtxPcuKjz3A9CHoZ8joii/inhtPv/F4P/uDE/TQnPvoM15Og6yEvdD3gnxlOv/N7PfiDE/eznPjoN1xPgm6LvNBtgX9uOP3O7/XgD07cz3Pio+9wvQl6J/JqkEX8C8Ppd36vB39w4n6REx/9h+tR0DuRF7os8C8Np9/5vR78wYn7ZU589CGuV0GXRV7ossC/Mpx+5/d68Acn7lc58dGPuJ4FHSp5/SOL+NeG0+/8Xg/+4MT9Oic++hPXu0xMMa9js4hPMJx+5/d68Acn7oSc+OhTXC+DzpS8jssiPtFw+p3f68EfnLgTc+KjX3E9zaQU8zo+i/g3htPv/F4P/uDE/SYnPvoW19t8m2JeJ2QRn2Q4/c7v9eAPTtxJOfHRx7geZ3KKeZ2YRfxbw+l3fq8Hf3DifpsTH/2M63nQmZDXP7OITzacfuf3evAHJ+7knPjob1zv832KeZ2URfw7w+l3fq8Hf3DifpcTH/2O64V+SDGvk7OIf284/c7v9eAPTtzvc+Kj73G90Y8p5nVKFvEfDKff+b0e/MGJ+0NO/BmyXY/0U4p5nZpF/EfD6Xd+rwd/cOL+mBP/N9muV5qSYl6nZRH/yXD6nd/rwR+cuD/lxJ8p2/VOU1PM6/Qs4lMMp9/5vR78wYk7JSc++iXXS/GeBvI6I4v4VMPpd36vB39w4k7NiY++yfVW01LM68ws4j8bTr/zez34gxP355z46KNcj/VLinmdlUV8muH0O7/Xgz84caflxEdf5XquX1PM6+ws4r8YTr/zez34gxP3l5z46K9cDzY9xbzOySL+q+H0O7/Xgz84cX/NiY9+y/Vi6GTJ619ZxKcbTr/zez34gxN3ek589F2uN/stxbzOzSI+w3D6nd/rwR+cuDNy4qP/cj3azBTzOi+L+G+G0+/8Xg/+4MT9LSc++jDXq/2eYl7nZxGfaTj9zu/14A9O3Jk58dGPuZ7tjxTzaphF/HfD6Xd+rwd/cOL+nhMf/Znr3f5MMa8Lsoj/YTj9zu/14A9O3D9y4qNPc73cXynmdWEW8T8Np9/5vR78wYn7Z0589Guup5uVYl4XZRH/y3D6nd/rwR+cuH/lxEff5nq72SnmdXEW8VmG0+/8Xg/+4MSdlRMf/Zvr8eakmNclWcRnG06/83s9+IMTd3ZOfPRxrtebm2Jel2YRn2M4/c7v9eAPTtw5OfHRz7meb16KeV2WRXyu4fQ7v9eDPzhx5+bER1/ner/5KebVKIv4PMPpd36vB39w4s7LiY/+zvWAC1LM6/Is4vMNp9/5vR78wYk7Pyc++j3XCy5MMa8rsogvMJx+5/d68Acn7oKc+Oj7XG+4KMW8/p1FfKHh9Du/14M/OHEX5sRH/+d6xMUp5vWfLOKLDKff+b0e/MGJuygnPvpA1ysuSTGvK7OILzacfuf3evAHJ+7inPjoB13PuDTFvK7KIr7EcPqd3+vBH5y4S3Lioy90veOyFPO6Oov4UsPpd36vB39w4i7NiY++0PWQy1PM65os4ssMp9/5vR78wYm7LCc++kLXQ65IMa/GWcSXG06/83s9+IMTd3lOfPSFrodcmWJe12YRX2E4/c7v9eAPTtwVOfHRF7oeclWKeV2XRXyl4fQ7v9eDPzhxV+bER1/oesjVKeZ1fRbxVYbT7/xeD/7gxF2VEx99oesh+XvQ5NUki/hqw8ssz9UWb5X5gxN3dU589IWuh+TvOZMXf7c6s/FNrN/5vZ4mhhP375z46AtdD8nfayYv/u50svFNrd/5vZ6mhhN3TU589IWuh+TvMZMXf1e63MY3s37n93qaGU7ctTnx0R+6HpK/t0xe/N3ogo1vbv3O7/U0N5y463Lioz90vSR/T5m8+LvQFTa+hfU7v9fTwnDirs+Jjz7R9ZL8vWTy4u8+F218S+t3fq+npeHE3ZATH32i6yn5e8jkxd91rmLjW1m/83s9rQwn7sac+OgXXU/J3zMmL/5uc6WNb239zv8/77vWCHicdZ1ltFzHsUZ9Tp8rXTFLdsiJKY4pMTMzxDEzM8hsSZbMJMkyxn4xowySUWZmZmZmZk7ivLy1nmr/qL3O6E+t7n2q6uvT0zNzr+4307Oa4f///beeHkfGeIYyPZxd53F38CrGvWN8TlzXK8Z18D1ivGeVeSXOvOt3S88e4vRl7P59YnxunfWWknXtVWVei/eWzlr9Kl0Pp2+t+0X/vjE+r856m5J17V1lXsT7SGdRv1rXw+lbdL/o3y/G59dZb1fJuvapMm/E+0pno35F18Pp2+h+0b9/jC+os94eJevat8q8S7yfdHapX6Pr4fTt0v2i/4AYT66z3p4l69qvyryHeH/p7KF+XboeTt8eul/0HxjjC+ust7tkXftXmfcUHyCdPdWvh66H07en7hf9B8X4ojrr7VWyrlFV5t3iA6WzW/166no4fbt1v+g/OMYX11lv75J1ja4y7yU+SDp7qV+3rofTt5fuF/2HxPiSOuvtU7KuMVXmvcUHS2dv9eul6+H07a37Rf+hMZ5SZ719S9Z1QJV5H/Eh0tlH/Xrrejh9++h+0X9YjKfWWW+/knWNrTLvKz5UOvuqXx9dD6dvX90v+g+P8aV11tu/ZF3jqsz7iQ+Tzn7q11fXw+nbT/eL/iNifFmd9Q4oWdeBVeb9xYdLZ3/166fr4fTtr/tF/xljfHmd9Q4sWddBVeYDxEdI5wD166/r4fQdoPtF/5lifEWd9Q4qWdfBVeYDxWeUzoHqN0DXw+k7UPeL/r+K8ZV11ju4ZF2HVJkPEp9JOgep30BdD6fvIN0v+v86xlfVWe+QknUdWmU+WPxX0jlY/Qbpejh9B+t+0f83MZ5WZ71DS9Z1WJX5EPFfS+cQ9Rus6+H0HaL7Rf/fxvjqOusdVrKuw6vMh4r/RjqHqt8QXQ+n71DdL/r/LsbX1Fnv8JJ1HVFlPkz8t9I5TP2G6no4fYfpftF/5hhfW2e9I0rWdWSV+XDx30nncPUbpuvh9B2u+0X/38f4ujrrnbFkXUdVmY8Qn1k6R6jfcF0Pp+8I3S/63xl8lhjPFXzWknUdXeX1zCj+e+mcUf1G6Ho4dcdH/APPA+J/UJ+ZdH9dD07dCRFn4RyLz6I+cOsZL07didw3zqH4rOoDt54J4tQ9JuJsnCPx2dQHbj0Txak7KeLsnAPx2dUHbj3HiFP32Ihz8DgWn0N94NYzSZy6x0X8I49D8T+qD9x6jhWn7vER5+RxKD6n+sCt5zhx6p4Q8U88DsX/pD5w6zlenLonRuR8zyI+l/rArecE8XlifFc8j8zN46TkvidVmc8qPrd0wK33RHH6MnZ/+v69ynrN56na8633JPF5Y3x3nevMXvL8bKWdo+vkKtczn7dqz/d6/i4+X4zvqXOdOUqen720c3TNV7Vz6z1Z/N46j6nzR43hp4iPjfxxvA/g56FmeuyKuGvEfYLvFuPe/H4wxnvx+7gYH8D7H96n8fNg8MH8fB3jMfw8G+NTQ+9pERfgcRz654/x/ZH3F85ZyfNzlnb+jyrXMWc8p66Ho+v0iAvyOC9Z7wN17oN+5v9U2vmpWrf5/NJzqji6zoi4EI/zkvU+WOc+6Gd+rtLOT9O6zb1fp4mj68yIC3MOStb7UJ37oJ/5uUs7P13rNvd+nS6OrrMiLsJ5LVnvw3Xug37m5ynt/Ayt29z7dYY4us6OuGjEP5es95E690E/8/OWdn6m1m3u/TpTHF3nRFyMc1ay3kfr3Af9zM9X2vlZWre59+sscXSdG3FxzlnJeh+rcx/0M//n0s7P1rrNvV9ni6PrvIhLcM5K1vt4nfugn/m/lHZ+jtZt7v06Rxxd50dcknNWst4n6twH/czPX9r5uVq3uffrXHF0XRBxKc5ZyXqfrHMf9DO/QGnn52nd5t6v88TRNTni0pyzkvU+Vec+6Gd+wdLOz9e6zb1f54uj68KIy3DOStb7dJ37oJ/5hUo7v0DrNvd+XSCOrosiLss5K1nvM3Xug37mFy7tfLLWbe79miyOrosjLsc5K1nvs3Xug37mFynt/EKt29z7daE4ui6JuDznrGS9z9W5D/qZX7S084u0bnPv10Xi6JoScQXOWcl6n69zH/Qzv1hp5xdr3eber4vF0TU14oqcs5L1vlDnPuhnfvHSzi/Rus29X5eIo+vSiCtxzkrW+2Kd+6Cf+SVKO5+idZt7v6aIo+uyiCtzzkrW+1Kd+6Cf+SVLO5+qdZt7v6aKo+vyiKtwzkrW+3Kd+6Cf+aVKO79U6zb3fl0qjq4rIq7KOStZ7yt17oN+5pcu7fwyrdvc+3WZOLqujLga56xkva/WuQ/6mV+mtPPLtW5z79fl4ui6KuLqnLOS9b5W5z7oZ37Z0s6v0LrNvV9XiKNrWsQ1OGcl6329zn3Qz/xypZ1fqXWbe7+uFEfX1RHX5JyVrPeNOvdBP/PLl3Z+ldZt7v26Shxd10Rci3NWst4369wH/cyvUNr5NK3b3Ps1TRxd10b8K+esZL1v1bkP+plfsbTzq7Vuc+/X1eLoui7i2pyzkvW+Xec+6Gd+pdLOr9G6zb1f14ij6/qIf+Oclaz3nTr3QT/zK5d2fq3Wbe79ulYcXTdEXIdzVrLed+vcB/3Mr1La+XVat7n36zpxdN0YcV3OWcl636tzH/Qzv2pp59dr3eber+vF0XVTxPU4ZyXrfb/OfdDP/Gqlnd+gdZt7v24QR9fNEdfnnJWs94M690E/86uXdn6j1m3u/bpRHF23RNyAc1ay3g/r3Af9zK9R2vlNWre59+smcXTdGnFDzlnJej+qcx/0M79maec3a93m3q+bxdF1W8SNOGcl6/24zn3Qz/xapZ3fonWbe79uEUfX7RE35pyVrPeTOvdBP/N/Le38Vq3b3Pt1qzi67oi4CeesZL2f1rkP+plfu7Tz27Ruc+/XbeLoujPippyzkvV+Vuc+6Gf+b6Wd3651m3u/bhdH110RN+Oclaz38zr3QT/z65R2fofWbe79ukMcXXdH3JxzVrLeL+rcB/3Mr1va+Z1at7n3605xdN0TcQvOWcl6v6xzH/Qzv15p53dp3eber7vE0XVvxC05ZyXr/arOfdDP/Pqlnd+tdZt7v+4WR9d9EbfinJWs9+s690E/8xuUdn6P1m3u/bpHHF33R9yac1ay3m/q3Af9zG9Y2vm9Wre59+tecXQ9EHEbzlnJer+tcx/0M79Raef3ad3m3q/7xNH1YMRtOWcl6/2uzn3Qz/zGpZ3fr3Wbe7/uF0fXQxG345yVrPf7OvdBP/OblHb+gNZt7v16QBxdD0fcnnNWst4f6twH/cxvWtr5g1q3uffrQXF0PRJxB85ZyXp/rHMf9DO/WWnnD2nd5t6vh8TR9WjEHTlnJev9qc590M/85qWdP6x1m3u/HhZH12MRd+Kclaz35zr3QT/zW5R2/ojWbe79ekQcXY9H3JlzVrLef9a5D/qZ37K080e1bnPv16Pi6Hoi4i6cs5L1/qvOfdDP/FalnT+mdZt7vx4TR9eTEXflnJWs99917oN+5rcu7fxxrdvc+/W4OLqeirgb56xkvb/UuQ/6md+mtPMntG5z79cT4uh6OuLunLOS9f6nzn3Qz/y2pZ0/qXWbe7+eFEfXMxHxh+5Qst7/5e+apJ/57Uo7f0rrNvd+PSWOrmcj4g/dsWS9+Ft3l37mty/t/Gmt29z79bQ4up6LiD90p5L1ziC96Gd+hw78Ga3b3Pv1jDi6no+IP3TnkvXar4t+5nfswJ/Vus29X8+Ko+uFiPhDdylZr/266Gd+pw78Oa3b3Pv1nDi6XoyIP3TXkvXar4t+5nfuwJ/Xus29X8+Lo+uliPhDdytZr/266Gd+lw78Ba3b3Pv1gji6Xo6IP3T3kvXar4t+5nftwF/Uus29Xy+Ko+uViPhDR5as135d9DO/Wwf+ktZt7v16SRxdr0bEH7pHyXrt10U/87t34C9r3eber5fF0fVaRPyhe5as135d9DM/sgN/Res29369Io6u1yPiD92rZL3266Kf+T068Fe1bnPv16vi6HojIv7QvUvWa78u+pnfswN/Tes29369Jo6uNyPiD92nZL3266Kf+b068Ne1bnPv1+vi6HorIv7QfUvWa78u+pnfuwN/Q+s29369IY6utyPiD92vZL3266Kf+X068De1bnPv15vi6HonIv7Q/UvWa78u+pnftwN/S+s29369JY6udyPiDx1Vsl77ddHP/H4d+Ntat7n3621xdL0XEX/o6JL12q+Lfub378Df0brNvV/viKPr/Yj4Q8eUrNd+XfQzP6oDf1frNvd+vSuOrg8i4g89oGS99uuin/nRHfh7Wre59+s9cXR9GBF/6NiS9dqvi37mx3Tg72vd5t6v98XR9VFE/KHjStZrvy76mT+gA/9A6zb3fn0gjh/UftcDNbZfF/3Mj+3AP9S6zb1fH4qja1wHfdzXI6t27vv9kTi+0wN1Pw6K8ccxPqpq50eq3sfi+E4ZU+fgGH8S46Ordn6U6n0iju+UMXUOifGnMR5ftfOjVe9TcXynjKlzaIw/i/GEqp2PV73PxPGdMqbOYTH+PMYTq3Y+QfU+F8d3ypg6h8f4ixgfU7Xziar3hTi+U8bUOSLGX8Z4UtXOj1G9L8XxnTKmzpEx/irGx1btfJLqfSWO75QxdY6K8dcxPq5q58eq3tfi+E4ZU+foGH8T4+Ordn6c6n0jju+UMXXGx/jbGJ9QtfPjVe9bcfyk9sNOKHl+fAf+XZXrmJ+gft+J4ye1H3ZiyfMTOvDvq1zH/ET1+178hxj/GBH/6DElj+2XhTM/sQP/QXXNT+qgB46unyLiL51U8th+WTjzXs8k1T+5aue+Hz+K3xo+0I0ibhhx7R7TI35W+2mPLXne64Gz7lOqdu778ZM4PlrG1DlOY/jP4pvEej7rmh43jvE6sb47Ynx7RHyef4jred0ZGGNe50bF+D/R74eY3y5+X/xjyfObRv1t+f8+9cP3ir/0BM6/ONfD8X3anwq/O/rcFRGf42xdOd8+4BPVx75X+NzS4fw61vuvGO8c438r3z7fk9THvlb4PKrj/N2j38iIPSP+F31xX3aKuCf/D6X7ZZ/v36XDvlb4vNLpfHR1S98MTc63j/dk9bFvFT6fdDi/T/TDT753jJsY7yZ/On6VL3rl+vbxniId9q3C/yydzue8cM5+0XnDx76X/O17N7m+fbz/Ix32rcL/Ip3O53G8dfTj8b1Nk+e3ioif9b4m17eP9x/SYd8qfH7pdP4O/L80j6uIP/P6HM8Ds0TkvE3oyvXt4z1VOuxbhS8gnc5HVyV9/1S+fbqnqY99qfAFVcf5O+v847f7NB7fTYx/iet35fGo+vbpni4d9qXCF1Id53eHrpER94/+ezQ53z7cM9THvlP4wtLhfD6PYt+I/SLyuRWbxevophEfjPk7e+T69uGeKR32ncIXkU7njwo9oyMOitirybr7S3+PJte3D/cs6bDvFL6odDrfetDJ53HYZ4o/82z1sa8Uvph0OH8onxMS8bDoP1bPl3xuCJ8XwvOqfab4M8+RDvtK4YtLp/N5XdklIn7Nz+P8DWf/gh8Y4wFNrm+f7bnSYV8pfAnpdD7vB/jcFX8ey8s9p8f9I+4XcWSvXN8+2/Okw75S+JLS6fzR2rdBenzBx+g6uH2m+DPPlw77SuFLSafzuW+8r/D7DfZzRMTh2l/7TPFnXiAd9pXCl5ZO5+/D+56I+OVeiHF/PV/sx/u0Jte3z3aydNhXCl9GOp3Pfeur+8f7s3G6b8N0XuwzxZ95oXTYVwpfVjqdz+cO8XlDfN4Rn2/E5xMx7+vtM8WfeZF02FcKX046nW+fKP7Ki1XHvlD48qrj/NGxjlER8ZO92uR8+2AvUR/7PuErSIfzx3DOI+IXe63J+fa5TlEf+zrhK0qH83ldQQd+sNebnG8f61T1sW8TvpJ0OH+sXt/we73R5Hz7VC9VH/sy4StLh/PH8biPiJ/rzSbn24d6mfrYdwlfRTqcfyDPAxHxa73V5Hz7TC9XH/sq4atKh/MP4nk8In6st5ucbx/pFepj3yR8Nelw/sFN1oHf6p0m59sneqX62BcJX106nH9Ik3Xgp3q3yfn2gV6lPvY9wteQDucf2mQd+KXea3K+fZ7T1Me+Rvia0uH8w5qsAz/U+03Ot4/zavWxbxG+lnQ4/3De10bE79T0zvn2aV6jPvYlwv8qHc4/osk68DN19c759mFeqz72HcLXlg7nH9lkHfiVevTO+fZZXqc+9hXC/yYdzj+qyTrwI/XsnfPto7xefewbhK8jHc4/usk68Bt90uR8+yRvUB/7AuHrSofzxzdZB36iT5ucbx/kjepj3x98Pelw/oQm68Av9FmT8+1zvEl97OuDry8dzp/YZB34gT5vcr59jDerj3178A2kw/nHNFkHfp8vmpxvn+It6mNfHnxD6XD+pCbrwM/zZZPz7UO8VX3su4NvJB3OPzR+Tzc4fu48JMZjeuZ8+wxvUx/76uAbS4fz7ZPDX3a76tgXB99EdZxvnxv+sDtUx742+Kaq4/wV4/dUUyJOi32b2iPn28d3p/rYtwbfTDqcbx8a/q27VMe+M/jmquN8+8jwX92tOvaNwbdQHefbB4Z/6h7Vse8LvqXqON8+LvxP96qOfVvwrVTH+fZh4V+6T3Xsu4JvrTrO5/G1ZPf0yONqqe6cb5/Z/epjXxV8G+lwvn1S+IseUB37ouDbqo7z7XPCH/Sg6tjXBN9OdZx/NeeS+xjvK4b2zPn2cT2kPvYtwbeXDufbh4R/52HVse8IvoPqON8+Ivw3j6iOfUPwHVXH+fYB4Z95VHXs+4HvpDrOt48H/8tjqmPfDnxn1XG+fTj4Vx5XHftu4LuojvN5fK0Vcc2IK8X55Pup+B6oFSIuwe+tVRf/yhPSYd8NfFfpdD59+Z6nFSMu2eR8+4yeVB/7auC7SYfz6cv3OK0Ucakm59tH9JT62DcD3106nH9d7Me1EWeO9zcj4vyvEDouicj36Uxpcn37iJ6WDvtm4COl0/l8f9QqEVeOuEyT8+0TekZ97IuB7yEdzr+M3/Pw+yZ+79sj59sH9Kz62PcC31M6nH9h9Jsckb+XnyOep/n7yZvivtwYkfdZ9sHgH3lOOux7ge8lnc5fPvpcHJHvg+LxwvcHrRhjvu9oKn9XoLr4R56XDvte4HtLp/NPjn2aL+K8Eefozvn2+bygPva1wPeRDufbp4K/40XVsS8Fvq/qOJ/vB1st4qoRl2tyvn04L6mPfSfw/aTD+aupH/vP48I+E/wZL6uPfSXw/aXD+TwfrRFxdV5HmpxvH80r6mPfCHyUdDh/DfWbpse7fSL4K15VH/tC4KOlw/mcL74Hay1eR5qcbx/Ma+pj3wd8jHQ438+LfN8Vz5v2eeCPeF197OuAHyAdzrdPA3/DG6pjXwZ8rOo43z4L/Alvqo59FfBxquN8+yTwJ7ylOvZFwA9UHed/QH5E/s58bFfOJw//wtvqY98E/CDpcL59FPgX3lEd+ybgB6uO83m9Wy/iujwfNDmfPPwN76qPfRXwQ6TD+bzu3hxx/YhrNjmfPPwP76mPfRfwQ6XD+Z9H/CIifyd9WFfOJw9/xPvqY18G/DDpcL59GvgjPlAd+zLgh6uO8+3TwB/xoerYlwE/QnWcPzneD/B+bumIi3TnfPLwT3ykPvZtwI+UDucvEX3O5eeh6H9ed84nD3/Fx+pjXwf8KOlwPt8/tUXEzSNu0OR88vBffKI+9n3Aj5YO5/P38bdE5O/df9eV88nDn/Gp+tgXAh8vHc7n+w03i7gpzydNzicPf8Zn6mPfCHyCdDj//wCaz4rEeJx1nXeUVkXSh2Xmvu8MIwoCKpJRRDDnnCPmgDkDiqKSk6KYc86COa85J3TXXXdXXTfnYNhV14QSRAURBfacj9/DOf2cb+afPnWr61dVfftW171vV88ljcv939/XbZa236SdmetfiH9JY8m/tLG8bvmfNSxtX0t7dNqDq1IeuXlpZ0kPfPrDv0x2WP4yXZ+fdrZw4NMf/uXCsfxsXaf/nLRXCOfbNiV/+/rSdvPmpe19obcIvQPX0z4a/v3NJT64C9J+KTvg0x/+201L23fSTkw7su3S9kr1B39u6BVzH8emnZz7Oy50S9qRaSeGP0rXR6R9O/w/hx4Xenza9mmbpO/MtKs0lHYh10HyzdX/jzde/HHy7z/h/wN59cOO8ZIbk/bf4f9d/c9Kuyp2hj8p9OlpO6VdPvwJ2J32g/D/Ffrr3KfDQh8X/uGhj0x7RNo3wn819JDQ84IzuKGUA/dw6fuiVl6n3y8j/+PQAxpLun/oPrVS76xaqXd26AXM77QnEGfS7/iG0i77/1DaH6XtnH7tgv+LhtJu5PFnaOj5kWO8jqpKe7BvqOxDbp7k8ZPr32j8uV/f5foxoU8M/1jhH6n7iX0nJZ6cnLYx7feJA6+l38/Srhe8NTM+x+f6D7n+PfaGv2P4Wwb3gdBbheb+Ha3xwp8D0u/AtDOD+1HsY75wf36h+XVI5A5O+37i2vTQ3Jf5uj+MDy3j92b4P9V8Zj5YP/hHab7g75PMv9izScvS9qHQA8N/LO1zkX9czy3z+fXwfxL6qui/Ou0APV/4xfMF3kHR/0v8Srth5Pun/8LQX4U+jjgQ+uvcp8OyXh0X3MNDM+48z4zTnNBf1kqaceN+Ph1637T7pN2tXtrHczJMz8cnmQ8fp90k/daplf2R9/PFOHqcL6mVfjGejPeg0EcwHmk/ih0/4TlsLP04iTik553nyM/Pp21LPzdNv3VrJd5Cjc9xml+OR7MUf7lP6J1bK/vx/Hp+fda2tHOz4K0XuZNjz/fyn7jT2nozU/OR/uAwPw+NHcSJD2LHy6G5n9ekXTvt6pFfFJr4Nzz2DW4lLp4s+2dEH+OwefqtXyvxfpD8YD33jCvrBP7bX/QSn7/luYu/Q8M/OjTP3ad5jnnuPgs9NPQJaZeA21TqPSx87gfz/fP4zThsEXs3iNwp8XeRxndIcHj+n0rbI/02TRwdJD9my58l6b847anBZxy4jv5TpP+3VenHlum3Ya3EWyz5ZeOclufGzxNxleeJuMO8ZR54Hs5L/3bhky+SR46WXuIb+tE3s23p51bhbxT+acFbovE7QfOf+Ya92LdCVdp5RuTJl2e1Le3YOngbR35E+i9XlfacyPpN3E37Zq7/NDTvCdjB+wR5+x9kxzbRv0n0j0z/NlVpzzD857kjz8r1waHJ87GD94Fx6of871jnQ/9Rdm7L/I/eUcFrqEp7T2L9qEp78eM7nr/Q9bQ1novwO1SlH7zH8D7SEDuHpeV9j7zzT1Xpx3axf7Pgjw5eY1X6wzi2xW7ho3elqrRzSuR5b7oy/a5K2z9t7+QnvIdi5/axb/PYNyZ4VVXaOzw0eTX+/pW4oXjodZ642bEq/Tg7+BND/6Uq7dwh8ltEfmz6c9+wl/vaNnaMSMu4jAzNPDgmNPPl2NCdqtLOc4I/ifW5bWnnjrFvy+Dyfs/8wt5TiU/Rw3r0Vq6/Fpo47Hg9P/idq9LOc3mf5rkLzti0k3N9XOi/yY+dgr9V8PlewPcD/DktdOfIdUp7btad00OvTL+050X+DJ6remnPmbk+PjTfEbBz59i3deyboO8Z2DtC4+Z1kLzgRK3vX8but0KvUpV+nB98xrFDvbT3rFyfEJrvKPixS/RvE/18L+I5x59lz3naheSzPE+h69FzCvc5/FNDr1qVflzA9ySeO56LtFNyfWLof1alH7vG/m2jn+83fAfz9y/WvSp4rHu10F2q0s4L+V7Ec1cv7Tk71yeF5jsQdu4W+7aLfeQD5AfYS35APHAcJW6sVpV2XhR5xqlTvbTnHPKQ0G9XpZ2781009pEPME7YO0b2NCt+YR9x0nG0JfyuVenHxcFnHDvXS3vPJU8J/U5V+rFH7N8h9pNPkF/gD/OQOOk4unzwu1WlnZdEnnFcuV7ac16uTw79blXaOTD27Rj7yDfIP/z9ljjpONou+N2r0s5LI+9xYvw+zvX3Qr9XlXbuGft2in3kE+QX/r5KnHQcXSH4ParSzssizzjdln6389097QZZ//+NvWn3in07xz7yCeYh9jIPGxMvG9KOSvw/KTRx0nGUdalnVfpxefDPD/2fqrRz79i3S+wjX2DeYy/PxRex53O+00Tv/vF/s9Drhb419Pqhe1WlnVcE/4LQ78vOfWLfrrGPfIF5j708F3w34DsC77Ufx97eVWnHlZG/MPQHsmPf6N8tOOQDzGvsYd7zvsv3Ad7LeP/twrpWL/VOCd2nKu28KvgXhf5Qdu4X+3aPHvIB5jX2Mu+xi/c17JsR+1Zj3aqXes8OvXpV2nl18C8O/V/ZuX/s2yN6WO+Zt9jLvB6UeXJQWt67XgzdlXWrXuo9J/QaVWnnNcG/JPRHsvOA2Dcw9rGe83xhL+t7N9aleol7bui+VWnHtZG/lHW7pbTjwOjfM/pZr1m/sYf1uzvrSr3EPS/0mlVpx3WRv4w8oaW046Do3yv6WY9Zn7GH9Zn3YvJl3stnZ/70YF2pl3rPD92vKu28PviXh663lHYOin17Rw/rLesv9rL+8v4xN/bw3vFV6J710h70XhB6x7T3p+U7+gOh16pKP26I/itCN7WUfhwc+/eJXazHrM/4w/rcq17aA+6FvG9WpR03Rv7K0DNkxyHRv2/0s96y/mIP629v4lK9xL0o9ICqtOOmyF8V+nPZcWj07xf9rKesr9jD+tqHuMN7NuMUeu2qtOPmyF/N+iQ7Dov+/aOf9ZD1EXtYH28CN+1G6CP+EXf43ss4hl6nKu28JfjXhJ4pOw+PfQfEPtZD1kfsZX08JHHw4LS8105vLu2+Je3G2EN8JG7VS7suDb1uVfoxNfqvDT1LfhwR+w+M/aynrK/4wzo3P8/hkbF3SPQeFXoT9Of61NDrEl+Je/XSrsvIO+ifdlr0Xxd6tvw4MvYfxO8y6c/6iz+sg/wexfc13hv53WpT2Tst9HrEZ+JivbTrcvIk+qe9NfqvDz1HfhwV+wfFDtZj1mf8YZ3cTPbcGnp94jNxsV7qvYI8l/5pbwv+DaHbt5R2Hh37Do59rMesz9jLOjok82Bw2t9H7xuhN5e9t4XegPhM3KyXdvHdbo/QOyi+E/c3BC/t7bHvxtBz5ecx8e+Q+Md6z/qPv8vW/+j5IfNleOhFobeQP7eH3jA0+3/uTftI+OwD4nsk/mI33yuJZ/h5R+wj7n0lP4+Nf4fGP/IJ8gv8ZZ3nOyDvy7xXN+T+bSl/7iDOsr4Q9+ulXVeHJp7hx53RT9zr2FL6cRz7WWI/+Qb5B/6QB4yKnuXlR2Ps30r23kmcld2Mf5fYMzf07ty3tM9Enn1axD/8vCv2Ec87tZR+Hh//Do9/5CPkJ/hLHjE6etrJzyr+bS1/7gq9Ceud/FyN8Q5N/MOPu6OfON65pfRjcOw/IvaTz5Df4A95yJjoWUF+1GL/NrL37tCbst7Jj66x4+vQxEf8uCf6ieMrt5R+DGG/TOwnHyI/wh/yGL4bryg/6rF/W9l7D3Gb9U5+dIsd34QmPuLHvdFPnP9Wfgxlv0nsJ58iv8If8hy+K7eXH02x/17iUdrt027Oeic/useOeaGJf/hxX/QT5xfIjxPYDxT7ybfIv/CHPIfvyh3kRzPjz7xJy36ve1gP5UeP2D8/NPENP+6PfuL4d/LjRPZjxX7yLfIv/CHP4bvySvKjbeyfwu9sWU/O4rt+6BvkR8/Y/21o4ht+PBD9xOmbictp+0R+YeiF8nNY/Ds2dpCPkZ/hL3kQ36U7ys+W+Hcj63raXtG/IDTxCz8eDD5x+pZ66cfqkf+efK4q/TiJfYCxn3yM/Ax/yJP4bt1Jfiwf+zfS+HlciV/48aPgE4en1ks/1oj9P5BXVKUfJ8f+42M/+Rr5G/6QJ/HdtbP8aBf73+X3qLST0o7O98mNNb4e9+mhD0nLvu59wyf+MQ4PxT7i+DTeC4j77Kshj0o/xmE4+yDTj3yO/I7xIE/i+/PKGocVtN+a/Gv9jH9TxmET3R/ft+2q0s+Ho584z/dS/KzH7sWhF1eln6ewDzT9yOfI7/CXPIrv/KvIzxXj387Mv1xnn+s2rO8af98X4j1+PhL9rAt8P8fPpti9JPSSqvTzVPZZph/5Hvkf/pL/nRmcVeVn+/hHPH847a7cD9Z/jb/vy0Gh9wr9Yui9Q++gde/R2Md6yD7WO9Kyv/XO0MsxHrl+Gvv0cp18kPyQ8SA/5LtyF41DB+5v6AfTPkGcIX/Q/fF9Y38ufj4W/feHfib9nk27H3lv9PduKf0cwT7J0OSL5I/4S/7Id+nV5OdKwT83OJ3yPJ7D75a8X9VLf7gv/J6zU1X6+Xj0c594b6Tegu+EW4ZuqJV+jmSfZK6TT5Jf4i/5Jd+1u8rPjsHnPu2Slvu5De9X9dIf7gvz7aXQxGHi7z7ghWYcnoh9D2qegjcgfjZkfBvjJ+Mwin2YoclHyU8ZD/JTvqt30zh0in/sb+Z9nX3NO4U+M3pWjD2T2TcSepeq9PPJ6CfO3af5zXPMvKlqpZ+j2WcZmnyV/BV/yV/5bt9dfnbW+sI8217z7Zng7Ra8p0M/Iru30P1nXvTl+z/7kdgnGZp8lfwVf8hf+a7fQ36sHPvIa1j/l73Xh3+H5mf/2NMm94fn7EHNQ+Lmmi2lH2PZJxn7yVfJX/GHPIvv/j3lxyqxr2NwVuL30OBPZP5qXrDOETfZj89zunHodvGP+8p9fhQ7Q/drKf0cxz7J6CefJb/FX/IsfjfoJT9XbS7txt77NF/Oib6O8n9S6P2I82lfjv5nQ6/VUvoxPvKnRZ58lvwWf8iT+N2ht/zoEvvPC15nfo8NfQbzl3FRHGf+v9JQ+mH729ZKPyYEf0Suk4+Sn+IPeRC/W/SRH6vF/k7s0+P3bI0v48r3oVVzne9DxDvW7T0V//YS/4XYR91IS630c2L0jwxNPkp+ir/kQfzusbr87Br/2FeNvu66X8xzfk8jjrN+UofxcNqnNR7L10o/JgV/VGjyTZ5L/OG55XePNeRHt+i/gP2kuf/nh2ZdoW4Eu6YHn/vG92We62fD5zszdWL4cXrwR3Of05/nFH94Tvndo6/86B77mc/7tmIfzzPxbYri2wrsA2c/VfhjQpPvsc5gL88dv2usKTt7xL5dyEPTPsl7RugL2S+b8ed+8B2DcWe/fg/dL+Y9z1fX8HnuVpSfk8MfG5p8j7iBv8QTfhfpJz97xn7qireOPdQ5k5e2lx1nhj8uNPkW6x72sB7yu8ZasqMX7zd6fnZNu234F7HfN/Yx3lMUfxjPnrofAzXPn9P8fj/9P+C+pR0ffPIt8i/8Jf/iu3l/+cn+auLJS5F7MS1x5yL2UbAPIP1nhOZ9j/fApyLP+yDftQfIjj7Rz3sTeTdxd+vwL2a/csaL8T47+gdFD++RP8WPqvSHOO74/ZjiS5fgd6iV/VkXng/Nc8H7Ge9lfHdZ9n4WnF78nht8xpN1yOtT++Q3zAfmh+Pfh8H7L+Oi+IOd+I8fjDfrNPOxl+Yzdvfk9/TwL1D8Zp1hvvO7DOM1UPP7cc373eXfsniv/KiXnp/PQ3NOwsWaH/sLh3zludDTcp+mpq1l3BeFZrwG6v4/Llzw0LdH/Pf7IO+JzJOfBO+AXP9x6OdD812f37/5Hb9v7HtR85nnmeeX+/+k5gH3n/MXOEeBcxbOq5V+4WdvzY8D5L/tRz/jxfrBemJ7sJP3cfxgnaK+lDjckvvVlvMTgjuS54f7VpX2Doz8C4ofBzJO4XNuBveR8zS4jzx3H6V1/sm61ZX9SvQL7XzzCY0H6wV5iPOP5zX+fRQvX2FeKk7xPenVhtJv5iPjQj6AXtaJpzWf8OMFx0fdP/xhPQSX+2r8vaXH8514z/17VfG/c+bBU8yvtA81l/r2Fv5Tsp/3G+Id8W0/0TwnPP/7yI+XhO9596rGn+eJuOL45XjDdea367Kna3yZH4emfRl/id+hmU/ML+Yz8Ri7/P7n5w28PTV+2Pmy5je/j7yn30n4fYR4xDh5fJ6S/9wH4scnkf80Le8nkxX/iHe99Xxx37fX+TCcG8N48Vw5PuIn4+X5tL+uv6Lxxe7PWJf1fsU47xR7niDfCk3+xPd2vmeyXjE/mY9+vsh/nk67XcZllaZSjv7+3s/8wM8f6/4Rd7HL8bdH/OzOfkitXzzvPP/4yXvTjPQnjyC/JH/nd2t+r+Z3jU2bS3sYR+IR9g6S/+b3bSz5azSW/jB+PG+r633oYOn5mfDJK5lPu2m++nfD1zS+PLc7pv/jmk/ks2ulJT8kbwT/58E9Ji3xhrpS6sioL6WOjLpi6uOoL6a+uY36Uf9LnSR1l9SHUn85XHrBoT53mOSwk/pS6l+HtYJDPRx1fz7f6DTZtyj9qQulvtd1v9RnLqsfrUo91OdRX0hdGfVv1JdR/8Z+LPaZUZfCvq0W6aF+bqT8wk7qL6kf5XwWzgHy+T8+z+eX4kO/Lhz4nIvy87QbMB8zfpxPw7krPv8I3MHC5zwH63tdfOg3hAOf81R8zg3nIVwl+3z+ErhDhM95INb3hvjQbwrnSI0b5wytH/39ov917kPajbC3VuIOFT5+W9+b4kP/SjjwqTegDvnX2B2ac2Owc+PYt3atxD1B+Jz7YH2/Eh/6LeHA55wuzvvx+STInSh5zn0w3lviQ/9aOPCJi9QLExcX1Eq5YZLnHAvj/Vp86N8IBz77tdnHTdxmPzdyJ0meunvj/UZ86N8KBz71aZxTxX14vl7KnSx5zmEx3m/Fh/6dcOBTp0N9FfU81F0hN1zynMNhvN+JD/174cDn+iniU5ffWv8hov8gHPjUI3LeyYd53l6pl3KnSp66duP9QXzoPwpnqOYx5w8wv4kHyJ0mefoZ74/iQ/9JOPAZN+znnBfqtJAbIXnyD+P9SXzoPwsHPtdHik/e0Vr/YaL/Ihz4nCvAOUJzcn9/VS/lRkme59x4fxEf+q/CgU+dPfv2qSvnvADkRkue/Mt4fxUf+m/Cgc/1MeKTH7bWf7jovwsHPvvByaeoq2XfOHJjJU9+Z7y/iw/9D+HAZ94z/7xeIDdO8uRvxvuH+ND/FA58nweCPehHbrzkyY+N90/xof8lHPhcnyA++XFr/UeIfls48LELefJhzuFCbqLkyZ+N97b40O8IBz7nBnDOBucGNNVLuUmSJ/833jviQ78rHPg8F35P4twl5E6XPOdbGO9d8aHfEw58rp8hPnGktf5jRP9bOD4vdrL4nB/RWv+xov8jHJ9ne6b43MfW+o8T/b5w4L+UefAivxvwvaSplDtL8pzvYLz3xYf+QDg+D3eK+JzP0Fr/CaI/FA58ziXgPALONfykqZQ7W/K85xrvQ/Gh/ysc+Fw/R3zifGv9J4n+SDjwqZP3OTnUWSB3ruQ5P8F4H4kP/bFw4HN+G/GaOD6Heoj08zk51FGAe57wOT/B+j4WH/oT4cDnOwv16LyHUD9KXTt1D9S1U0cA7vnC5/wE6/tEfOhPhQO/q/RRl04dAHIXSJ77brxPxYf+TDjLzleQPurS2YeP3IWS574a7zPxoWcIBz7nrnGuMefCcb5xd9lD3Tr76MG9SPicn2B9M8SH/lw48HtIH3Xr7HNH7mLJM4+M97n40F8IBz7vMZyzwPsO5y30lD3UrbMPHdxLhM88s74vxIeeKRz4nCNKPTnvCUfr/BnsoS6dfeTgXip85qH1zRQfepZwlp2vIH3UlbPPG7nLJM88M94s8aFnCwc+7ynkX7zPLM649ZE91I2zDxvcy4XPPLS+2eJDzxEO/NWlj7pw9kkjd4XkmWfGmyM+9JfCgc973SkaH+qz15A91HWzDxrcK4XPPLO+L8WHnisc+MdlHh+b9jfR+4vQfWUPddnscwb3KuEzD61vrvjQXwkH/prSR101+5CRu1ryzDPjfSU+9NfCgd9P+qh7Zh8xctdInnlmvK/Fh/5GOPDXkj7qltkHjNy1kmceGe8b8aHnCQd+f+mj7ph9ushdJ3nmifHmiQ89XzjwB0gfdcPss0XueskzT4w3X3zob4UDf23po+6XfbLI3SB55onxvhUfeoFw4K8jfdTtsg8WuRslzzwx3gLxob8TDvx1pY+6WvapIneT5JknxvtOfOiFwoG/nvRRF8s+VeRuljzzxHgLxYf+XjjwqWP1OSXsI0XuFskzT4z3vfjQPwgHPnWkPkeEfaTITZU888R4P4gPvUg48Knz9Dkf7GdFbprkmSfGWyQ+9GLhwKcO0+d0cP4QcrdKnnlivMXiQy8RDnzqIH2OBvsQkbtN8swb4y0RH3q5xhLnBumfKjvQj9ztsoN5Yzz63yi6jXBulF7qCX0eEXLUzbo+2Hj0v0l0g3Bukl7q+XzeEHLUrbp+13j0v1l0o3Bull7q7XxeEHLUlbp+1nj0v0V0JZxbpJd6OZ/ngxx1n65vNR79p4quCWeq9FLv5vN2kKPu0vWnxqP/NNF14UyTXurRfF4OctRFuv7TePS/VXSTcG6VXurJfJ4NctQ1uj7TePS/TXSzcG6TXurBfB4NctQlur7SePS/XXRb4dwuvdRr+TwZ5KgbdH2k8eh/h+gW4dwhvdRbUYdFXT1y1P25ftF49L9T9PLCgc85BXelpa6EfXHIUbfn+kPj0f8u0e2EA5/r1M25PrC1/neLXkE48LlOXZvr81rrf4/oFYUDn+vUnbl+rrX+94puLxz4XKduzPVtrfW/T3QH4cDnXA72P67HeZxNpRx1Xa5fMx797xe9knDgU1dEnRH15uzDRI66LteXGY/+D4juKJwHpBd91DVtVy/lqLtyfZnx6P+g6E7CgU8dOvUw1MlQx44cdWCu/zIe/X8kurNw4LN/mnoM9t0/pHhgfOoxXS9D/Qf1Aeilbgz93HfbQ/+HRK8sHPjr5DvONWk5R+Za6p9jJ3UC1BWe0VTici6A69usj/4Pi15FOPDXbS7t4RyY65pLOc4p8PkExqP/I6JXFQ58zhVHH+e0XN9cylGXhTzz0Xj0f1R0F+HAp66ROjbqZyfUSjnqxlz/Zjz6PyZ6NeHAp86POlLXVyLHPHb9mvHo/7jorsKBz/lbnPNAXtlf9Zk8d914XlWPy3XX37Cv3PvR2QeP3a5DpY4cXOxw/U3XVvylvot+1AvbfnCpM3J9DNeh6U+9MvhPaHzQhzx1R65/4frT0kd9Evvw2W9PPTJxAnnqYFy/wvVnpM/1ltSVuL6D688Kj3oU6jKpg+D/HJ9fK+WpQ3F9Bdefkz7X+1B3QB0L9Stcf1541Cu8qHl3kOZfa3gviKYuwvVRa7TSn/vFvHedBPUtyFOX4fqW1vS5XqOvcOBzjtBdOm/obs53CE39DHbtGj7jQt3FVtTls58jeqn7cH2J7aG/60/WFA58WsvzXFAP7v9TTX1zN80DxpG6HPRSd+L6HttD/+mi+wkHPvVt1LVRp0J9CnUp1KkgT3+uT2+FDx442AG/n/iWt72Wd10eOK+Itj3w2bfq/6PIPg72fbnOg9/p2TfoOhx+p+b/llE/wvnEbTJ/2TfA7/PsD2H/imn6sX+E/Tz8H0n+fyT7e7qoP7/Ds9+Dug3qPaj/YJ849RGu52BfP3UZ7Ov3//cdoOvGb+3/bcP/H9Qd4ot4nHWdZZRY1dWGac49QzMhJFi9FChJirsU2iJVqtDS4hbcg1WRQIBgLe7uWi/FIbgTgbjhkJCEQoUkEPjW+tjPj/2se/tnr72fc/d+9z2Z6Qxr3pkdmsX+/3/3RXy0z8fx/sgfj3zHyB+L/IHIVy0fx9VK5veLU3d/8vt0Hs7c+zvmPxH5TtI7qsm6Vi+ZPyBO3f29D+fhzH2gY/6Tke8svQ82WdcaJfNR4tTd3/twHs7cUR3zn4p8F+l9qMm61iyZPyhO3f29D+fhzH2wY/7Tke8qvQ83WddaJfOHxKm7v/fhPJy5D3XMfyby3aT3kSbrWrtk/rA4dff3PpyHM/fhjvnPRr679D7aZF3rlMwfEafu/t6H83DmPtIx/7nI95De1/pmXeuWzHl+HdXd3/twHs7cRzvmj458qPS+3jfrWq9k/po4dff3PpyHM5fc88dEvmeT9b7RN+tav2T+ujh19/c+nIcz93W9L+aPjXyvJut9s2/WtUHJ/A1x6u7vfTgPZ+4bel/MHxf53k3WO6tv1rVhyfxNceru7304D2fum3pfzH8+8n2arHd236xro5L5LHHq7u99OA9n7iy9L+a/EPm+Tdb7TJN1bVwy5/mNVHd/78N5OHNn630xf3zk+zVZ75y+WddXS+Y8v7Hq7u99OA9n7jMd8ydEvr/0zu2bdW1SMp8jTt39vQ/n4cwl9/yJkR/QZL2jm6xr05I5z2+iuvt7H87DmTtX74v5kyI/sMl6xzRZ19dK5qPFqbu/9+E8nLmjO+ZPjvwg6R3bZF1fL5mPEafu/t6H83DmjumYPyXyg6V3XJN1faNkPlacuvt7H87DmTu2Y/7UyA+R3uebrGuzkvk4ceru7304D2fuuI750yI/VHr/3Tfr2rxkzvObqe7+3ofzcOY+3zF/euTDpHd8k3VtUTLn+c1Vd3/vw3k4c8k9f0bkhzVZ74Qm69qyZD5enLr7ex/Ow5k7vmP+zMgPl96JTdb1zZL5BHHq7u99OA9n7oSO+S9GfoT0Tmqyrm+VzCeKU3d/78N5OHMndsx/KfIjpXdyk3V9u2Q+SZy6+3sfzsOZO6lj/suRHyW9U5qs6zsl88ni1N3f+3AeztzJHfNfifyX0ju1ybq+WzKfIk7d/b0P5+HMndIx/9XIfyW905qs63sl86ni1N3f+3AeztypHfNfi/zX0ju9ybq2KplPE6fu/t6H83DmTuuY/3rkv5HeGU3W9f2S+XRx6u7vfTgPZ+70jvlvRP5b6Z3ZZF0/KJnPEKfu/t6H83DmzuiY/2bkv5PeF5us64cl85ni1N3f+3AeztyZHfNnRX609L7UZF0/Kpm/KE7d/b0P5+HMfbFj/uzIj5Hel5us68cl85fEqbu/9+E8nLkvdcx/K/JjpfeVJuv6Scn8ZXHq7u99OA9n7ssd8+dEfpz0vtpkXVuXzF8Rp+7+3ofzcOa+0jF/buTDpbfpzbq2KZnz/Naqu7/34Tycua92zJ8X+fHSW3uzrp+WzBtx6u7vfTgPZy65578d+QlN1tvTm3X9rGRexam7v/fhPJy5Ve+L+f+KfEST9S7em3VtWzLvEafu/t6H83Dm9uh9Mf+dyE9sst5ZTdb185I5z2+ruvt7H87Dmbu43hfz3438pCbrnd1kXb8omc8Sp+7+3ofzcObO6pj/78hPlt63mqxru5L5bHHq7u99OA9n7uyO+f+JfKT0zmmyru1L5m+JU3d/78N5OHPf6pj/38hPkd65Tda1Q8l8jjh19/c+nIczd07H/P9Ffqr0zmuyrh1L5nPFqbu/9+E8nLlzO+a/F/lp0vt2k3XtVDKfJ07d/b0P5+HMndcxf37kp0vvgN6sa+eSOc/vpLr7ex/Ow5n7dsf8BZGfIb3vNFnXLiVznt9Zdff3PpyHM5fc8xdG/vsm6323ybp2LZm/I07d/b0P5+HMfadj/vuR/0F6l+7NunYrmfP8rqq7v/fhPJy573bM/yDyM6V3md6sa/eS+dLi1N3f+3Aezlxyz18U+VlN1rtsb9a1R8l8GXHq7u99OA9n7jJ6X8z/MPKzm6x3ud6sa2jJfFlx6u7vfTgPZ+6yel/M/yjyc5qs970m69qzZM7zQ1V3f+/DeThzl9P7Yv5icf7cJuud32Rde5XM3xOn7v7eh/Nw5r7XMf8Tcf486V3QZF17l8zniy8mnfM17z2dhzN3fsf8PpGfL70Lm6xrn5L5AvFPSOcCzZuv83DmLuiYXyK/QHrfb7KufUvmC8X7SOdCzVug83DmLuyY30R+ofR+0GRd+5XM3xcv0vm+5i3UeThz3++YXyO/SHoXNVnX/iXzD8Qb6fxA897XeThzP+iY3xP5xdL7YZN1HVAyXyRepXOR5n2g83DmLuqYv3jkl0jvR03WdWDJ/EPxHun8UPMW6TycuR92zP9k5JdK72I16zqoZM7zB6ru/t7nQHHmftQxv2/kl0nvCr1Z18Elc54/SHX39z4HiTOX3PN7I7+8yXr71KzrkJI5zx+suvt7n4PFmUvu+f0iv6LJekvNug4tmfcR75XOProf5h8izlxyz18i8iubrLepWdewknkR7yedRfP66DycuUXvi/n9I7+qyXpX7s26DiuZ8/ww1d3f+wwTZ26j98X8JSO/usl6B/VmXYeXzFcW7y+dcO9zmDhzyT1/QOTXNFnv4N6s64iS+SDxJaVzkOatrPNw5g7S+2L+wMiv5fu8yIf0Zl1HlswHiw+QzsGaN0jn4cwdrPfF/KUiv67JevvWrOuokjnPH6m6+3ufI8WZO0Tvi/lLR359k/X21qzrlyXzvuJLSSfc+xwlzlxyz18m8hv4Po+Pw5p1/apk3iu+tHT2al5fnYczt1fvi/nLRn4j3+fxcVizrl+XzPuJLyOd/TSvV+fhzO2n98X85SK/ie/z+DisWddvSuZLiC8rnUtoXj+dhzN3Cb0v5n8q8pv5Po+Pw5p1/bZk3l98Oensr3lL6Dycuf31vpj/6chvabLeATXr+l3JfEnxT0nnkprXX+fhzF1S74v5n4n81ibrHVizrqNL5gPEPy2dAzRvSZ2HM3eA3hfzPxv5bU3Wu1TNuo4pmQ8U/4x0DtS8AToPZ+5AvS/m3xF8k/j89OXgX6hZ17El77OU+GelcynNG6jzcPoeF/FzfB4Q/5zmLK33635w+g6P+Hk+jsU/rzlw6zlOnL7H8974OBT/gubArWe4OH1PiPjFiOv3Zv5FzYFbz/Hi9B0RcfmIG4gvrzlw6zlBnL4nRvxSxA3Fv6Q5cOsZIU7fkyKuEHEj8RU0B249J4rT9+SIK0bcWHxFzYFbz0ni9B0ZcSX+HdbMV9IcuPWcLE7fUyLy8c3H+0jVVxK3npHigyK/k6+j+XdS89xTS+ZfEF9ZOuDWe4o4c8k9n7mnlazXfFBpf956TxUfHPldfJ3Ov+Oa65w3R9fpJfczH1zan/c+p4kPifxuvk7n46Dm+vK1naNrSGnn1nu6+D1NzumzgnL4GeJL9Hwch0Xk5+kOi/zDODc0cvwCe0bOz3/yc5YD+e8wwfvz36Ei5+dteyLn5x/5OcOl+XoYXejh6zy+3+3Jc+mzFF/v9+S+7LUMX89qX35Ob1nm9OQ6Pye3HF/P9eQ6P6f2Kb6eCn5mvO+zIuJv/XK8V/y4+HNX4fNEzfUVazv/Q8l9zMlX1Hk4us6OiL915Zr12m+Mfuor1XZ+pvY2X1V6zhRH1zkR8bcOqlmv/cbop/7l2s7P0t7mvq+zxNF1bkT8rYNr1mu/Mfqpr1zb+dna29z3dbY4us6LiL91SM167TdGP/VBtZ2fo73NfV/niKPr/Ij4W79Ss177jdFPfXBt5+dqb3Pf17ni6LogIv7WVWrWa78x+qkPqe38PO1t7vs6TxxdF0bE37pqzXrtN0Y/9a/Udn6+9jb3fZ0vjq6LIuJvXa1mvfYbo5/6KrWdX6C9zX1fF4ij6+KI+FtXr1mv/cbop75qbecXam9z39eF4ui6JCL+1jVq1mu/Mfqpr1bb+UXa29z3dZE4ui6NiL91zZr12m+Mfuqr13Z+sfY2931dLI6uyyLib12rZr32G6Of+hq1nV+ivc19X5eIo+vyiPhb165Zr/3G6Ke+Zm3nl2pvc9/XpeLouiIi/tZ1atZrvzH6qa9V2/ll2tvc93WZOLqujIi/dd2a9dpvjH7qa9d2frn2Nvd9XS6Orqsi4m9dr2a99hujn/o6tZ1fob3NfV9XiKPr6oj4W9evWa/9xuinvm5t51dqb3Pf15Xi6LomIv7WDWrWa78x+qmvV9v5Vdrb3Pd1lTi6ro2Iv3XDmvXab4x+6uvXdn619jb3fV0tjq7rIuJv3ahmvfYbo5/6BrWdX6O9zX1f14ij6/qI+Fs3rlmv/cbop75hbefXam9z39e14ui6ISL+1q/WrNd+Y/RT36i28+u0t7nv6zpxdN0YEX/rJjXrtd8Y/dQ3ru38eu1t7vu6XhxdN0XE37ppzXrtN0Y/9a/Wdn6D9jb3fd0gjq6bI+Jv/VrNeu03Rj/1TWo7v1F7m/u+bhRH1y0R8bd+vWa99hujn/qmtZ3fpL3NfV83iaPr1oj4W79Rs177jdFP/Wu1nd+svc19XzeLo+u2iPhbN6tZr/3G6Kf+9drOb9He5r6vW8TR9ceI+Fs3r1mv/cbop/6N2s5v1d7mvq9bxdH1p4j4W7eoWa/9xuinvllt57dpb3Pf123i6PpzRPytW9as135j9FPfvLbzP2pvc9/XH8XR9ZeI+Fu/WbNe+43RT32L2s7/pL3NfV9/EkfXXyPib/1WzXrtN0Y/9S1rO/+z9jb3ff1ZHF1/i4i/9ds167XfGP3Uv1nb+V+0t7nv6y/i6Pp7RPyt36lZr/3G6Kf+rdrO/6q9zX1ffxVH1z8i4m/9bs167TdGP/Vv13b+N+1t7vv6mzi6bo+Iv/V7Neu13xj91L9T2/nftbe57+vv4uj6Z0T8rVvVrNd+Y/RT/25t5//Q3ua+r3+Io+uOiPhbv1+zXvuN0U/9e7Wd3669zX1ft4uj686I+Ft/ULNe+43RT32r2s7/qb3NfV//FEfXXRHxt/6wZr32G6Of+vdrO79De5v7vu4QR9fdEfG3/qhmvfYbo5/6D2o7v1N7m/u+7hRH1z0R8bf+uGa99hujn/oPazu/S3ub+77uEkfXvRHxt/6kZr32G6Of+o9qO79be5v7vu4WR9d9EfG3bl2zXvuN0U/9x7Wd36O9zX1f94ij6/6I+Fu3qVmv/cbop/6T2s7v1d7mvq97xdH1QET8rT+tWa/9xuinvnVt5/dpb3Pf133i6BoVEX/rz2rWa78x+qlvU9v5/drb3Pd1vzi6HoyIv3XbmvXab4x+6j+t7fwB7W3u+3pAHF0PRcTf+vOa9dpvjH7qP6vtfJT2Nvd9jRJH18MR8bf+oma99hujn/q2tZ0/qL3NfV8PiqPrkYj4W7erWa/9xuin/vPazh/S3ua+r4fE0fVoRPyt29es135j9FP/RW3nD2tvc9/Xw+Loeiwi/tYdatZrvzH6qW9X2/kj2tvc9/WIOLoej4i/dcea9dpvjH7q29d2/qj2Nvd9PSqOrici4m/dqWa99hujn/oOtZ0/pr3NfV+PiaPryYj4W3euWa/9xuinvmNt549rb3Pf1+Pi6HoqIv7WXWrWa78x+qnvVNv5E9rb3Pf1hDi6no6Iv3XXmvXab4x+6jvXdv6k9jb3fT0pjq5nIuJv3a1mvfYbo5/6LrWdP6W9zX1fT4mj69mI+Ft3r1mv/cbop75rbedPa29z39fT4uh6LiL+1j1q1mu/Mfqp71bb+TPa29z39Yw4ukZHxN86tGa99hujn/rutZ0/q73NfV/PiqNrTET8rXvWrNd+Y/RT36O28+e0t7nv6zlxdI2NiL91r5r12m+MfupDazsfrb3NfV+jxdE1LiL+1r1r1mu/Mfqp71nb+Rjtbe77GiOOrucj4m/dp2a99hujn/peHXys9jb3fY0VR9cLEfG37luzXvuN0U9979rOx2lvc9/XOHF0jY+Iv3W/mvXab4x+6vt08Oe1t7nv63lxdE2IiL91/5r12m+Mfur7dvAXtLe57+sFcXRNjIi/9YCa9dpvjH7q+3Xw8drb3Pc1XhxdkyLibz2wZr32G6Of+v61nU/Q3ua+rwni6JocEX/rQTXrtd8Y/dQPqO18ovY2931NFEfXlIj4Ww+uWa/9xuinfmBt55O0t7nva5I4uqZGxN96SM167TdGP/WDajufrL3NfV+TxdE1LSL+1kNr1mu/MfqpH9zBp2hvc9/XFHF0TY+Iv3VYzXrtN0Y/9UM6+FTtbe77miqOrhkR8bceVrNe+43RT/3QDj5Ne5v7vqaJo2tmRPyth9es135j9FMf1sGna29z39d0cXS9GBF/6xE167XfGP3UD+vgM7S3ue9rhjh+Vvt1j1RuvzH6qR/ewWdqb3Pf10xxdB3RoY/3enRp537fL4rjmz1S7+OoyF+K/JjSzo9Wv5fE8c2S0+eXkb8c+bGlnR+jfi+L45slp8+vIn8l8uNKOz9W/V4RxzdLTp9fR/5q5MNLOz9O/V4VxzdLTp/fRP5a5MeXdj5c/V4TxzdLTp/fRv565CeUdn68+r0ujm+WnD6/i/yNyEeUdn6C+r0hjm+WnD5HR/5m5CeWdj5C/d4UxzdLTp9jIp8V+UmlnZ+ofrPE8c2S0+fYyGdHfnJp5yep32xxfLPk9Dku8rciH1na+cnq95Y4vlhy+gyPfE7kp5R2PlL95ojjd7Vf9/ia68M7+NyS+5ifonlzxedF/nZE/K0n1JzbzwunfnwHn6e+5qd26IGj618R8b+OqDm3nxdO3fuMUP/TSzv3+3hbfNPwea73yY/jVZGvHzl+W/t9T6y57n3g7H1Gaed+H/8Sx+dLTp+TlMPfEb899tkm4tYRt4r9RsY5/Jv4TD+3+McRX6L9k6eobp8sfKQ45+H4Cu1/PFV1+1xPVR/7OuH8fh5+D86W/LxvvIfB0oG/8TTV7WM9TXPs24Tz9ws/EXGxiAtqnm8f6unqY98lfIj6+Hl83vi/58XPiz/ek/XMl94F6m8f6hnSYd8l/Cvq4+fxge8nv/j+Pfl5+0x/rzn2VcJXkQ4/v33M2S7iK/F+7o38tIin8+8l4vKfzP3tM/2DdNhXCV9VOv28fZL4C89UH/si4aupj5/n7/cujPqCiO/qeftAz9Ic+x7hq6uPn18U+X8j5++5/i/yneN97xTxieCjenJ/+0DPlg77HuFrSKefXxRxj5jH34Pk48k+R/yB52iOfY3wNaXDzy+Kz8N7xL83Pi6GRs7fH+XvfPbhPtXfPs9zpcO+Rvha6uPn+fuOi0fsifiRnreP8zzNsW8Rvrb6+Ple9o73wt/7K9yXPr/8h78r3JP728d5vnTYtwhfRzr9PJ/P+L0X/SLyea+f9A4Tt48R/98F0mHfInxd6fTzA7g//T4Qfv8Hv7ejv/bg94HYx4j/70LpsG8Rvp50+vk99P9ffDz+d/H8vH2aF2mOfYnw9aXDz/8fXD3SYnicdd131KVFlTVwQvfz3gaJShBGRRQQBwM4OipgQJIgCpIEyU3OOYuAWQFzALOioCIgYkJxRmfMozPmcYIYAUFyljRrfexfr1V7fd3/nFXPSbvqqTpV9761bx81b4n/9+/oyGUjl54ekYenPYuci1wi+n+Y/4j82NKPyI9Hnp/nHyg9e/pnzx+ft//xybdi5ArwTKM/v09EXlB56NnTP6dwtL/+Ppz24dX/AyMPiLxtwSPyh9MYX9xPRn6wcNCzp//Hwtn+hwTP/Mh5kQ9Gf1yN2/Le4zTGF/fCyA8VDnr29M8tnO3/3Hr+qcgPVxx69vTPqzjtf2T6cUTkfRn/n6V9TPp7bORyxmsa44v76ciPFA569vTPL5ztf9Q04vxb8P18Gv35XRT50cpDz55+48LR/kdPI477k/8X0+jP7+LIj1Ueevb0mxSO9j9mGnE8kPy/nEZ/fp9RRyoPPXv6TQtH+x9rHkQ+mPy/mkZ/fp9VRyoPPXv6FxSO9j9uGnE8lPy/nkZ/fp9TJyoPPXv6FxaO9j9+GnE8nPz/mfapkadEXpv18dtpjC/uJepE4aBnT/+iwtn+J0wjziWWeUT8Rn2fPSLPj/x4nl8wG+OL+3l1pnDQs6d/ceFs/xOnEeeSwfdf0+jP71J1pPLQs6ffrHC0/0nTiGOp5P/vtE/P+3pN5Or2y2mML+5l6kzhoGdP/5LC2f4nTyPOpYPvf6bRn9/l6kzloWdPv3nhaP/Tah5fl/5fM43+/L6gzlQeevb0WxSO9j8j+V4buUbkysnvvbB7bORK0xhf3CvUocJBz55+y8LZ/nCdGblm5KPhM48iZ3l/v5/G+OJ+UZ0qHPTs6bcqnO0P11mRfxf5mOQ/o3AuCL4/TGN8ca9UpwoHPXv6rQtn+8N1duTjIldJ/tcWzmWC74/TGF/cL6lThYOePf1LC2f7w/W6yMdHrpr8ZxbOZYPvT9MYX9wvq2OFg549/TaFs/3hen3kEyJXS/6zCuejgu/P0xhf3K+oY4WDnj39toWz/eF6Q+Ra6mjyn104lwu+a6cxvrhfVecKBz17+pcVzvaH642RT1RHkv91hXP54LtuGuOL+zV1sHDQs6ffrnC2P1xvilxbHUz+1xfOW6O/fhrji3uVOlg46NnTv7xwtj9cb458kjqY/G8onCtm/P4yjfHF/bo6WDjo2dO/onC2P1xviXyyOpj8byycKwXfDdMYX9xvqIOFg549/faFs/3hemvkOupg8r+pcN4R/Y3TGF/cq9XBwkHPnn6Hwtn+cL0tcl11MPnfXDjvjP6v0xhf3G+qg4WDnj39Kwtn+8N1TuR66mDyv6Vw3hX9TdMYX9x/UgcLBz17+h0LZ/vDdW7kU9TB5H9r4bw7+punMb64/6wOFg569vQ7Fc72h+u8yPXVweR/W+FcNevjlmmML+631MHCQc+efufC2f5wvT3yqepg8p9TOFcLvlunMb6431YHCwc9e/pdCmf7w/WOyL9XB6fRn9+/qHOVh549/a6Fo/3lfWfkBupc8l+Yz3GfjFxx7hE5WzDGF/df1bnCQc+e/lWFs/3helfk09S5afTn9x11rPLQs6ffrXC0v7zvjny6OjaN/vy+q05VHnr29LsXjvbvfHCsN43+/L6nDlUeevb0ry4c7f+MyvcedWga/fl9X52pPPTs6fcoHO3/zMr3XnVmGv35/UAdqTz07On3LBztv2Hle586kvYKWQfLR56W9XHcgjG+uD9URwoHPXv6vQpn+29UeN6vjkyjP78fqSOVh549/d6Fo/33ruf/pg5UHHr29PtUnPb/TOrOxZGrZHyXWzD68/uxOlB56NnT71s42n/fev4T67zi0LOn36/itP9+9fzfreOKQ8+efmHFaf9rMl7m5ak1PxeW339Yx5WHnj39/oWj/U+NXC55T0n72LnRn99PrePKQ8+e/oDC0f4H1POfWYcVh549/YEVp/0/knn54chl0q+lFoz+/H5uHVYeevb0BxWO9v9Q1vkHfT+U50vMjf78fmGdVh569vQHF472f37yfiTy06k/H03b98Efi9wg56v5c2N8cX9pHRcOevb0hxTO9j89cgXrIO3j50a8m0W+eN7Yr0Mq7q/UgcJBz57+0MLZ/p8ybr6vjnxe5tNFwfOSwrnxNMYX99fqSOGgZ09/WOFs/4/W+31q3t/ScyMueC5Oe5NpjC/uf6ozhYOePf3hhbP9L0y+F0W+MPK5yf+ZtD8buUXkptMYX9zfqEOFg549/RGFs/1XifxC8l4e+Zlp9Of3X+pU5aFnT39k4Wj/y5Ln0sg18/zRc6M/v/9WpyoPPXv6owpH+x9Vz/9HHao49Ozpj6447f+szEfjelnkxdPoz+9/1ZnKQ8+e/pjC0f7ybRu5jfU6jf78fquOVB569vTHFo72/1zybRW5pXUwjf78rlEnKg89e/rjCkf7H1fPf2edVxx69vTHV5z2P76e/946rTj07OlPqDjtf0I9/4N1WHHo2dOfWHHa//Kaj+qB9X9i+f3ROqw89OzpTyoc7b9tzUd4Np9Gf35/sk4rDz17+pMLR/ure1dEbqcOT6M/vz9bp5WHnj39KYWj/VdNnfO54ew8P7XOo/yutU4rDz17+lMLR/ufWs+vsw4rDj17+tMqTvufVs+vtw4rDj17+tMrTvufXs//Yh1WHHr29K+pOO3/mnp+g3VYcejZ059Rcdr/jHp+o3VWcejZ07+24rT/a+v5X62jikPPnv7MitP+Z9bzm6yTikPPnv6sitP+X8t6vCpyZ+t0Gv353WydVB569vRnF472P7ue32KdVBx69vSvqzjtf109Z3995Osrzq1Lj3rjsEXO41f4u/ts9Od3m3VSeejZ07+h2vxvSNs9wMMi3Rc83H2OyIMij4r+4HruHuTt+Vz6I38vdU6JPMV9POeHeWNe9xOn6M+NPC/S92RPzPi4j3lEpHuq7mf6u4W/D/he93GzMa97pu5JzlXet/t8Ern2bMy7oPIvOY1teI4ovbjviPS92pP0r97PPRnf/xB/Gu3044jyO9Tfb+L/79Nox//e6H+a9l8zT7b3fXji75D2TfNHu91Kv3aef8l7S3uNuTEu/xvVnbnRXpyr543x9vQ9ZOR95nn8b7bO094xdtel/fW0r4pcL/ZPiP426ytyT3XM37/8nS7ytxm/r6T9jXkjbuO3fY0H/Teqf6/297bY7T5vzAsPfHsUPu1b5o/xdprGuDdVfP25Pc93Mc7R7zqNcXcsvDuV/ubKz9588f57fn0j0nt6SuzWqvezc40HvN7Dl62r2K8Z/6srz/rRP3Fu7Ae81+T9fjXtNWqevsl+m+fey071PnYuXN732rU+Vo5cKdI54cS0vSfj9b8LxrhbR14SeWXyfz7tq2s+9vy7M3lelXq0j/eW9tfSNh7rBN/j5sZ+eh/e11+i/3Ok9a4fL52N/dmh8OrftRXHeGwTf58DNk37s2m/IO0vJd7WixmfO4LXOti75r9+abO7YW6097zXj379qeqh/ptP5te5znlzI74bvSf1NO075492+8wb7eC5repb96vHQT7x7qjxEd86M597fXzTe7W/qvfWX8Zhx8h/Nt/SniIPjXSuOCztfYPnzur/brXv9Pwzn/bJ/Pu3tH8c+Z08t76vr/lufr8yOHaYjf39ctp3x/+uyP2Cb/dpfA7/voWf/83J92p1d26Md1f5i/+K4Hh5pDr7xbRvMi6zMa9+3VrvaY/od07bvqpOrBscj4+ffXvvyL+ZZ9Gbx9snnnkFj3PovEjn1QfitzD9vbvG1zipB74vWTV2y8+N/e33YVzMx3+K3CD6J0d/b9r3RO6f/MbJc/gWFr55+hmJR3HIbIx3T/nvUbjMu7+P3ZOCz3vfrt7XFWnPr/HFpznEOJufibevcUr7gNjfW/13blNn1BV1SP207+wa+a/2qbS/nfa3Ip8e/3Xj73OEfvi84Tzs3Osc7dy/ROIfGHvnSv3ZazHzwTyxHuEyT54W+3Wivy9yz+TDI9srbetY/TMvrDd49dPnGed+86DnoXW7MHH2i/S54HtpH5R41qXxsF73i1wY+ZD5nPje179EPjP6p7jnU+Pu84v3cr/9Sz2uc8l+lffByn/wvDGO/uxTuMyjZ8Ruvfj7XOtz7lLqVPR4RPhMeBLPj35Bza8T6vMZXpZ1vaw6lvHH77PO9GffGnf8RONwT9Vt9dM++yXrKm11tc8TeEr6iYexcfTLTGM/Tww+fLe55LEf4yEdbt+Ovfemv/stZry9h/vtw2mbZxsmzvrR93lMf/UPD0o/8Tw2id770M+Tgs97O6L67z0vmf4dWe9ZnKWMS+J5b8ZjYb2XPsfZF/Gk9AMPZNPoH1Xz6uTEN9/UG99H+J5CXTrC9xCzMd9P6/sX809/9lfXE2eX2Kub6jkelbh4Ii9wD2ca++H7I98n4WXhR+HVLKjvZ/QLXv1WT3s/vqXqjfWnjqg/eFj6gUfywuiXn8Z+nFrffx0JZ43Hz8yf+r5If9Slh2u9G/d708bDghOP5EXRrzCNOE/zfZx6XOOMVzYLPt8HWqfwWqf2MfsbXvS3fb6bRpx4JC+eG/PBie8GF94i/hdeznLOS7F3LuzvLx+ufdC+ub/zjzocad75vIEnqR94Jpv53F7jhg9mPM9OnLMi3au+Vr7YO3/pj/MX2ee0B+0/Nb/kNe/w5PQDD+Ulvn+odYXHZr29bjb2w73r63wemzfi0x/nr2XM89kY96i0fzeNOPFQNg8+PDY48d3wEV8/G3G6d319ff/r/ASvebCseT4b4x6dNp4cnHgoWwQfHhuc+G54k2/wPX/kbXn+l7Sdh5yP+vv5R5nnszHuMWnjycGJh7Klv1tOI058N+vqjbMR5+15foP55Htv9aLON9YhPOIemzaeHJx4KFsFHx4bnPhu1vubZiPOO+zv5pPv59WxOp8sb57OxrjHpY0nByceytbBh8cGJ74bXuqbfY8TeWee/9V8ir1zAbzOCyuYp7Mx7vFp48nBiYfy0uDDY4MT3w3f8y2zEeddeX6T+RR75wd4nR9WNE9nY9wT0saTgxMPZZvgw2ODE98N3/OtsxHn3Xl+s/kUe+cDeNXplczT2Rj3xLTx5ODEQ9nW91bTiBPfDd/zbbMR5z15fov5FHv7f//9a2XzdDbGPSltPDk48VBeFnx4bHDiu+F7njMbcd6b57eaT7G3/8Nr/3+0eTob456cNp4cnHgo2wUfHhuc+G74nufORpz35flt5pO/y9X5xP7/GPN0NsY9JW08OTjxVF4efHhscOK74XuunO/5Voo8I34npm0/t7/Da59dxTy1zxvntPHk4MRTeUXy4LHBie+G72k/tj/DYx9d1TycjX6npY0HBweeyvbJj6cGBz4bPqf9duXCY59czTybjX6np43nBgeeyg7Jj4cGB74avuYvYvfLyKN97s/7sd8+uvDaJ1c3z+zDxrHOY3DisbzS332mESe+Gr6m/dT+Co998LHq0mz0O6POU3DgseyY/HhmcOCj4WMal19FHhN5SMbHfrpK4bUPrqEu2WeNY52n4MRz2Sl58MzgxEfDx7Rf2j/hsc+tqe7MRr8z67wEBx7LzsmPZwYHPhq+5XsT5z2RS/peznyI/WqF1z73d+qOfdQ41nkJTjyXXZLHfQQ48dHcW3hW4rw/0n3pD5gvsbd/wmufc88BHnGd152X4MST2TX48MLgxFdzn8J+aH+Exz72+Pp8wM953HkIDjyZVyU/Xljz0/Ap7XdrFB771BPq/M/Pedt5Bw48mt2SHy+s+Wnn1HvwfubF7wHzIfZrFl771Fp1/hfXedt5B048nN2Tx32Y5q+5N/PhyOdFure+ofkQe/sfvPYp92zgEdd523kHTjydVwef+zLNbzuvcMGDB7CR+RJ7+x+89jH3cOAR13nbeQdOPJ49gs99m+a3uZdjveGX4CFYl/ZD+yO89jn3eOAR13nbeQdOPKA9g2+DacT5gcR3X8h+aH+Exz725Drf83Oe/ts04sAD2iv5nzaNOM5P/HfWfrdW4bGPrVPnd37Oy/dPIw48oL2T/+nTiOOCxH9X7XdPLDz2qXXrfM7PefiBacSB57NP8j9jGnF8MPHfXfvZ2oXHPrVenb/5Oe8+OI048Hj2Tf5nTiOODyX+e6yfxMEPw/fBG7PfPanw2qeeUudvcZ3LH5pGnHg8+wXfhtOI88OJ/97az+xv8NiH1ldXZqPfeWk/PI048HQWJv9G6od+J/77ar9ap/DYh56bPB+KvFA/0n6qujIb47497WepV+pF4r+/9qt1C499CI9OP/F89p8b855b6/X2Oq+Ig6dzQNr/oF6pY8n/gbQ3SZyPReIRfNx8iP161Z9zahzOq/V8R9pLzY048XgOTPvZ04jz44l/fu1n9jd47EPvUFdrPd+ZNh4ZHHg+B6X9nGnE8YnEvyBtvOCLIh/j7yo5P9vP1i+89qF3Woe13u+q8wKceEAHp/2P04jzk4mPj7l5vUf3GNx3sp/Z3+C1D71LXa71fnfaeJRw4gkdkrbzBJz4cnij9iv7Fzz2oXery7Xe70l7mhtx4Akdmnafaz6V+M496iHe44uqbtrPNii89imfC+C0zu5Ne849g0g8o8PSxvOEEx/UOcd+Zn+Dxz7lHPS+Oh85b87mRhx4SIen7bwCB76nc4397OmFxz7lc+OvjX/koZn/+Et4RHhKR8QOT7P5m85TF3keiUe0sboc+2cUXvsUXhBeNJ7SUWnjYcKBr4k37F6k+39bmSf21djb3+CxT+k3HpP8Ryb/++v9Wef3p73APYjIHr8XWM+ReKafqP3O/gevfeoLkXgFzTewTnsdPzN6PD848AE/ab5YR7VeH/J5NfYbFV77I17To+KHZ3VM1Rd4rGufF5Z1DyHypHr/1rt+XJL89nm/G2D/g2N+5rd7oJ+K3n3cT9s3E8/+qr/2V/XYfupe2SdqPVuP1ql1j5+GJ2Z8fM/Vde3zya/u2U/tr/CeX/jck7Wf2D+Mj3E5pd7PZlVfL018fHP76bMLzwWVv/mL8qoH8uDDqhvu8RpP9/n8npD98jmFx/6JH4fHhgenznlf8LR+88Lf/OuPes81X5fO/LJfWnfwWpcX1vs1j51b7XfWiXjWiX7h0eHhHefv/9XP1quHn4v8YuJfUrgW4an91X5n/4PXvMcTt67w1E5K233srRaT3zy7qObJ853bYm9d9e9HGBc8QDxCv9+wYo1T640L/WrzR79t7WvBc7l15rwdPPZHeO2Px2ee/C7xfh/5mzy3n9nfxLO/6RceIh7jCe4nVD9bb5zpV58/+pnn1of1az7Zr+xf8Nq/nKfNAzzCk9NW96zTrj/u6+L/4gOrA+qldXdS1U/7mf0NXvubccOzbP6F92+czNMX5v3aP4zXY+NvvsOrjnT9MK/h3yi/R6Ae28/UBf25sHDBuXrNT/uFdSyedazfeKC9Pv3ug/3Qe/R5iV5/n7fMaOc9LV/v03qyn9hf4LXu8a7NIzzQU/SvxuGxNX+vdP4L3q0iL4l8qbyR+CKX1vuBx/vxvuxX6pL+8DOueK49/+WFwzn1xcH31cRTZ76StjqDd+vch4/rPPiFmm/G33u5tPrv/a1c4/cK9dk6mI14tsnzL6ftd0PMR3W+6/sllV//nc9fNo39/2r13/NtanzkV6/lVUesF+uyf9/Afs9vy+n/79/nqh5v9luU3vh3Pbmk+r9l5bmi/C9bzDjYj7wP86vnt/xbVn/1z3ri1/wp+TabjXjkNw6frX5sWvUTHjjNf/NRPxp/f667suaXv+uv6O/5PoelbX7Zr32e8vlp2xrnXn/6eVH1/+KqL1vX+Ou3dStvr9/POfdGrua8EvzqCp5710fx9NP6kc/zyxej32SZUb9x2uqy/qr3fh9Ivd6u8nyt4r+8xuGq6j9++1qR+O+rz43+X4/fLpHqVd/rdb/evV+8D/wJ/A/8FbwIdvgRi/ghwYEv6d7+7r5vi5/77+5/u/8Olzz4C/gX/PTD/Xl5+l63+8PuP+8bHPtE/jjPv+P728gDI92bO8j3dfPHfrj/7f76UoXD/WD3l5u35X6r+7nujeOT4WlcNRv95HG/96Dqd///Te5J45PiITf/uPnaV5deG0+1+e/OBb6f2LDOB+71u8+Pb4ZPJO7uhRPPtfOxf2W1f1tx6PGS8RjxtfEZ+eFtN/+847HfsdrXVJzmU+NFN398cfY7Vft3FYce3/TNkc3/5of3ujj+vHjsd6727ysOPf6G99rvmx8ebvNvOx77Xar9h4qziL+bdWId4Tt9PW38aXxp73Hb2RgXr7f5y52P/a7V/mPF2bXGpXmu5v/P/d3G348iD6q4eL3NP+587F9V7T9VHHp1H19O3ff7EualcezxExfvtvnHnY/9btX+rrpe/ngeeySfuoZvwW+/8sdT7HjfLb329yoOvf7270gYD34Lyx/vpuN9r/Ta36849H7fwfz1uwCXpd2/N2Ce4oOJu3/FxwfqfN8vvfYPKg695weU3jxanP2e1f5hxaHH68Pj9jsWV85GvwPL3/mk4/2w9No/qjh71TqwPy/i9abtnNLnGHxOcQ+q+Pw6349Kr33jgjEOvXnl/TVfvvmqzi34VOIeXDidDzsf+32q/WPnmvL3/JDSO78tzn7fav+k4tCrA+rDD/L8W85Pkc5zeAf45uIeWvGdTzvfT0qvffOCMc4i3m7GGf8YLwgfi99hhcN76njsF1b7lopD77lzYfMXF2e/f7VvrTj0zvnO/9/N82/ORj/n8uYndjz2B1T7topDr65Yl72f4dHubR7Yl9TPxHVub/5h52N/YLVvrzj0zbeEFz5+zvXNL+x47A+q9i/mjXHoPT+69D5/LM7+4GrfuWCMQ++9GR+fR+zb/I4pHHh9HY/9IdW+q+LQ47H17wTgPeJt+b0DvC38ZXHx4pq/1/nYH1rtuysOvbrRn2N9bm6+tPqkbomLF9f8vc7H/rBq31Nx6PEW8SnFUbear48X5Xc7xMWLa/5e52N/eLXvrTiL+H+VD+8Jb5Uf3lvz8zoe+yOqfV/FWcTvq3x4TX6Xhx9eW/PvOh77I6v9t4qziL9X+fCW/O4AP7y15td1PPZHVfv+irOIn1f58JLwo/nhpTV/ruOxP7raD1ScRfy7yod3hPfLD++s+XEdj/0x1X6w4izi11U+vCO8Xn54Zc1/63jsj632QxVnEX+u8uEd4e3ywxtrflvHY39ctR+uOIv4cZUPrwgvlx9eWPPXOh7746vt/8Vt/vtjKh9eEd4tP7yw5qd1PPYnVHvJirOI31b58IbwavnhhTU/reOxP7HaS1WcRfy2yoc3hDfLD++r+Wkdj/1J1V664izit1U+vCC8WH54X80/63jsT672n+eNcRbx1yof3g/eK7+zyt88+T8Rd5FKeJx9nHfUXVXRhwncc/ZNKNI7hN57CQgJRXq3i6KgiIgUQelIL1JDJ50ASUgg9ABp9K4oRaVK7ygtQBKSkMC31uc8f8yzzoF/Zu15Zs/85uxzXnLf9845tTPX//93XP0/+06sj9f63bCniS8VdqHu/+zJsV64m/edrv0n1M353hVn/Z7ywJdWvVNivUg37ztD+/9UN+d7T5z1+8oDX0b1To31ot2870ztP7Fuzve+OOtur5wHvqzqnRbrxbp531nScVLdnI/4k7TuqTzw5VTv9Fgv3s37/tzJ+7lPnI/4k7XupTzw5VXvjFgv0c37zu7k/dwnzkf8KVrPqzzw3qp3ZqyX7OZ953Tyfu4T5yP+VK3nUx74Cqp3Fs9lN+87t5P3c584H/GnaT2/8sBXVL0/81x2877zOnk/94nzEX+61gsoD3wl1Tub57Kb953fyfu5T5yP+DO0ntLJeeArq945PJfdvO8C7ec+cb4p4qwX7JXzwFdRvXN5Lrt5X3/p4D5xPuLP0noh5YGvqnrn8Vx2874LO3k/94nzEf9nrT/v5Dzw1VTvfJ7Lbt53kfZznzjf5+KspyoPfHXVu4Dnspv3Xaz93CfON1Wc9TTlga+hev15Lrt53yXaz33ifNPEWU9XHviaqnchz2U377tU+7lPnG+6OOvFe+U88LVU7yKey27ed5l0cJ84H/Hna72E8sDXVr2LeS67ed/lnbyf+8T5iL9A6yWVB76O6l3Cc9nN+67o5P3cJ85HfH+tl1Ie+LqqdynPZTfvG9DJ+7lPnI/4C7VeWnng66neZTyX3bxvYCfv5z5xPuIv0noZ5YFfHnaDsOvzXHbzvkGdvJ/7xPmIv1jrZZUHfkXYDaVjzW7eN7iT93OfOB/xl2i9nPLAB4TdSDrW6uZ9Qzp5P/eJ8xF/qdbLKw98YNiNpWPtbt43tJP3c584H/GXad1beeCDwm4iHet0875hnbyf+8b5iL9c6x5VzgMfHHZT6Vi3m/dd2cn7uW+cj/grtF6xV84DHxK2j3Ss1837hnfyfu4b5yN+gNYrKQ98gO4Dzp/7r4/0DOU57ea8V3VyfvK5HvEDtV5ZeQZK1yDdH9yfw7gOYTfnOe7mvFd3cn7yuR7xg7SuqpxnkHRxH3H/cP/2CTsk7Aju227Oe00n5yef6xE/WOtaeQZLF/cZ9xf39wvlf/bFsEeFPbRnzjuik/OTz/WIH6J1UZ4h0sV95fuNfSM7eT/xzkf8UK27ygPfJOoMCns1162b943q5P3cd85H/DCt1+iV88Cfj+vNORwZ9pC4/kN1/5TgX3dz3ms7OT/Pg+sRf6XWayoPnOvRN+yWum7sG93J+4fXzfmIH671WsozXHWpdw33cTfvG9PJ+6+qm/MRf5XWaysPnOd1ZNituE+7ed91nbyf6+Z8xF+t9TrKAx/Gz13uxzj/ueL+WCDWYyP+enR3c97rOzk/19H1iL9G63WVB47tp/PifF4KfS+HPTbs4T1z3rGdnJ/r7nrEj9B6PeUZIV2b6ucv58e+Gzp5P+ftfMSP1Hp95Rmp+2ZU2K3DbtbN+27s5P3EOx/xo7TeQHlG6X7oG/Wu49xizc/9o/Xz/7CeOe9NnZz/2rq5HvHXar2h8sDx39zJfHT9zfGjtd5IeeCLVbnvjfl9XMn7bunk/WPq5nzEj9F6Y+WBvxT1Xw57bNjDS96Hzlt5TpXf/c5bctx8ykd/5F087G08Z3X2X6/rtYDyj9H1ox77lwg7jueozv6xqvet2L9k2CXCnhn8xJL3Lxn2dp6TOvtvUL0FS+ZLhb2D+7jO/huVb6GS+bej7zu5T+vsv0n5Fi6ZLxP+8dxndfbfrHyLlG+Ov0XrLWPfhE7m+B2/aMl8Yifvv7X+5ny3at1XeeDvRr33wp4e9oSS903q5P231c35iL9N637KA8d6/2Kcr+7Ds0LfSSXnndzJ+cfVzfWIH6d17yrngd8R9vawK3C/l+zfK+yeYXeKn993dbKf/LcrflwLpx550AnvLe797sf791J/5LlDa+uBnxbX4fSwy/FzLf7/9GjEPcLPy9i/VsQ9Vue4jYOvHXxOrKfFev+Imx7rA2I9V9ivI35G8L/Uuc4mwdcJ/tc6x20afN3g+4Z/Vvhnhv0s+BPB/87Ph+AbBn+8znX6BF8v+N/qHLdZ8PWDz0JH3E8HRtx+sX4q1k/yvMf+jUvWRZ3Ng29Q8j762CL4RsF/GnX2Dss53hPrf8b6H/z7O/b3if1P11ln3+CbBP9XnfNsHXyz4M+G/5mw2/LzvmQ/ebYJvnnJutDRL/imwb8X/jvpK+738bF+Ne7j78f6lVhPiPVKkW/lKvPx4vidn/WdiodTd3xL/ddi/QPpnVhnXatUmU8Qx+/87od4OHUntNR/PdY/lN5Jdda1apX5RHH8zu9+iIdTd2JL/Tdi/SPpnVxnXatVmU8Sx+/87od4OHUntdR/M9Y/lt676qxr9SrzyeL4nd/9EA+n7uSW+m/F+ifSe3edda1RZX6XOH7ndz/Ew6l7V0v9t2O9t/TeU2dda1aZ3y2O3/ndD/Fw6t7dUv/ReP5/Kr331lnXWlXm94jjd373Qzycuve01H8s9P1Meu+rs661q8zvFcfv/O6HeDh1722p/5dY7yO999dZ1zpV5veJ43d+90M8nLr3tdT/a6x/Lr0P1FnXulXm94vjd373Qzycuve31H881r+Q3gfrrGu9KvMHxPE7v/shHk7dB1rq/y3W+0rvQ3XWtX6V+YPi+J3f/RAPp+6DLfU/iPt3P+l9uM66Nqgyf0gcv/O7H+Lh1H2opf4Tsf6l9PLvOHRtWGX+sDh+53c/xMOp+3BL/Sdj/6+kl88D6OJzA/wRcfzO736Ih1P3kZb6H8f+/aWXzxPo4nPLk9q/kfzO736Ih1P30Zb6n4S+X0svn2fQxece+GPi+J3f/RAPp+5jLfWnxPoA6eXzELr43PWJ9m8iv/O7H+Lh1P1LS/1PY/0b6eXzGLr43DZF+zeV3/ndD/Fw6v61pf5nsT5Qevk8hy4+N36q/X3kd373Qzycuo+31H8m7o/fSi+fO9HF587PtH8z+Z3f/RAPp+7fWupPjfVB0svnXXTxuR3+d3H8zu9+iIdT9+8t9afF/t9JL5/L0cXncvgT4vid3/0QD6fuEy31p8f6YOnl8zy6+L3DNO3fQn7ndz/Ew6n7ZEv9L2J9iPTyeR9d/N5huvZvKb/zux/i4dR9qqX+jFgfKr38XgJd/F7iC+3vK7/zux/i4dR9uqX+zFgfJr38PgZd/F5nhvb3k9/53Q/xcOr+o6X+rFj/Xnr5fQ+6+L3RTO3fSn7ndz/Ew6n7z5b6X8b6cOnl91Lo4vdSs7R/a/md3/0QD6fuv1rqz471EdLL79XQxe/VvtT+beR3fvdDPJy6z7TUnxPrP0jvc3XW9Z0q82fF8Tu/+yEeTt1nW+p/Fes/Su/zdda1XZX5c+L4nd/9EA+n7nMt9b+O9ZHS+0KddW1fZf68OH7ndz/Ew6n7fEv9ueLvK0dJ74t11rVDlfkL4vid3/0QD6fuCy31e4S+o6X333XWtWOV+Yvi+J3f/RAPp+6LLfXnjvUx0vtSnXXtVGX+b3H8zu9+iIdT998t9eeJ9bHS+3Kdde1cZf6SOH7ndz/Ew6n7Ukt95vuOk95X6qxrlyrzl8XxO7/7IR5O3Zdb6jP/53nEV+usa9cq81fE8Tu/+yEeTt1XWuozH+h5xdfqrGu3KvNXxfE7v/shHk7dV1vqMz/oecbX66xr9yrz18TxO7/7IR5O3dda6jNf6HnHN+qsa48q89fF8Tu/+yEeTt3XW+ozf+h5yDfrrGvPKvM3xPE7v/shHk7dN1rqM5/oecm36qxrryrzN8XxO7/7IR5O3Tdb6jO/6HnKt+us67tV5m+J43d+90M8nLpvtdRnvtHzlu/UWdf3qszfFsfv/O6HeDh1326pz/yj5zHfrbOu71eZvyOO3/ndD/Fw6r7TUp/5SM9rvldnXT+oMn9XHL/zux/i4dR9t6U+85Oe53y/zrp+WGX+njh+53c/xMOp+15LfeYrPe/5nzrr+lGV+fvi+J3f/RAPp+77LfWZv/Q86H/rrOvHVeb/Ecfv/O6HeDh1/9NSn/lMz4t+UGddP6ky/684fud3P8TDqfvflvrMb3qe9MM669q7yvwDcfzO736Ih1P3g5b6zHd63vSjOuv6aZX5h+L4nd/9EA+n7oct9Zn/9Dzqx3XW9bMq84/E8Tu/+yEeTt2PWuozH+p51U/qrGufKvOPxfE7v/shHk7dj1vqMz/qedYpddb18yrzT8TxO7/7IR5O3U9a6jNf6nnXT+us6xdV5lPE8Tu/+yEeTt0pLfWZP/U87Gd11rVvlfmn4vid3/0QD6fupy31mU/1vOzndda1X5X5Z+L4nd/9EA+n7mct9Zlf9Tzt1Drr+mWV+efi+J3f/RAPp+7nLfWZb/W87bQ66/pVlflUcfzO736Ih1N3akt95l89jzu9zrr2rzKfJo7f+d0P8XDqTmupz3ys53W/qLOuX1eZTxfH7/zuh3g4dae31Gd+1vO8M+qs64Aq8y/E8Tu/+yEeTt0vWuozX+t535l11vWbKvMZ4vid3/0QD6fujJb6zN96HnhWnXUdWGU+U7yHdM5UvRmKh1N3Zkt95m89L/xlnXX9tsp8ljh+53c/B4pTd1ZLfeZvPS88u866Dqoy/1Icv/O7H+Lh1P2ypT7zt54XnlNnXb+rMp8tjt/53Q/xcOrObqnP/K3nhb+qs66Dq8zniFfSOUf1ZiseTt05LfWZz/W88Nd11nVIlflX4rV0fqV6cxQPp+5XLfWZ3/U88VzM6cb60Cpz9h8iv/O7n0PEqft1S33mdz1v3IM57lgfVmU+l3hXOuHu51Bx6rJ2feZ7PW88N3OGEf/7KvMe4vid3/0cJk7dHrpe1Gf+1/PI85Ss6/Aq87nF8Tu/+yEeTt25db2oz3yw55U7zNFGviOqzOcRx+/87od4OHXn0fWiPvPBnmeuStb1hyrzjjh+53c/xMOp29H1oj7zwZ5nrkvW9ccq80ocv/O7H+Lh1K10vajP/LDnmZnzR9eRVea1OH7ndz/Ew6lb63pRn/lizzszh46uo6rMizh+53c/xMOpW3S9qM98seehezLPHPmOrjLviuN3fvdDPJy6XV0v6jOv63noXsxdR75jqsx7iuN3fvdDPJy6PXW9qM9cpOdBmY9EF3PLG2r/MfI7v/shHk7e48Iy7zyvOH7PP1sP8XDyHh/W88vHyb+RuPUQDyfvCWGZn55ffDHVgVvP8dpP3j+FZX56AfHFVQduPSeIk/fEsMxPf0t8CdWBW8+fxMl7UljmqxcUX1J14NZzojh5Tw7L/PVC4kupDtx6ThIn7ylhmc9eWBy/57+t52Rx8p4alvnsRcSXUR249Zyi/eQ9LSzP96Li+D0fbj2nijPf7HnsxUuuy9y3569Pk98/f6yXeDh1Wbs+7xVgfQbPU8m68PfT/tPld373QzyceWjPg6ML/+KlmaOL9yKQz7x31bzf/Zyh/Vy/PcLuHnaHmL9lHtvz4J5PX6I0c3SvUDVz93OmOHPgrMmzlNae34fPw+f3WPN96dmx5vurfE+0y+eZ6J/5EuY4Zkf+qbG/G3GHhuX7YIfFmu+v4f8W/54Jvk/Yn4VlzuC+WDN3zrw53+efyb/n6lxnAf49F/v5/jB9Fj5P6nrwPe2Ors8hkefgsHwP7qlYHxH28LB8D+gZ9vG5Fl183o78v4u4g8LyPcUndF3Rwffcnlbd3/O+oeD/ijXfv+Q6zc+/x9UXOvge4pPsV598j+hZva+bv1P7fdp8PxUd83Gdg/P9Mr7HtUjYefU+av5O6vdFU5e/Q/t92uzj75h+3zN/P+TvdH4fMvuI8/uaqYtOv0/7nHhezg3L/PSycf7Me3uefZmS/UuXZn52lfOYs15a8XB0nReW+WneM4Fez7OjH/8ypZmfo77NV5aec8TRdX5Y5qeXL1mv59nRj3/Z0szPVd/mPq9zxdF1QVjmp3uXrNfz7OjHv1xp5uepb3Of13ni6OoflvnpFUrW63l29ONfvjTz89W3uc/rfHF0XRiW+ekVS9breXb04+9dmvkF6tvc53WBOLouCsv89Eol6/U8O/rxr1CaeX/1be7z6i+OrovDMj+9csl6Pc+OfvwrlmZ+ofo293ldKI6uS8IyP71KyXo9z45+/CuVZn6R+jb3eV0kjq5LwzI/vWrJej3Pjn78K5dmfrH6Nvd5XSyOrsvCMj+9Wsl6Pc+OfvyrlGZ+ifo293ldIo6uy8MyP716yXo9z45+/KuWZn6p+jb3eV0qjq4rwjI/vUbJej3Pjn78q5Vmfpn6Nvd5XSaOrgFhmZ9es2S9nmdHP/7VSzO/XH2b+7wuF0fXwLB+vxR6Pc+OfvxrlGZ+hfo293ldIY6uQWH9fiv0ep4d/fjXLM18gPo293kNEEfX4LB+PxZ6Pc+OfvxrlWY+UH2b+7wGiqNrSFi/nwu9nmdHP/61SzMfpL7NfV6DxNE1NKzf74Vez7OjH/86pZkPVt/mPq/B4ugaFtbvF0Ov59nRj3/d0syHqG9zn9cQcXRdGdbvJ0Ov59nRj3+90syHqm9zn9dQcXQND+v3u6HX8+zox79+aebD1Le5z2uYOLquCuv3t6HX8+zox79BaeZXqm9zn9eV4ui6OqzfT4dez7OjH/+GpZkPV9/mPq/h4ui6JqzfT4dez7OjH/9GpZlfpb7NfV5XiaNrRFi/vw69nmdHP/6NSzO/Wn2b+7yuFkfXyLB+/x96Pc+OfvyblGZ+jfo293ldI46uUWH9fkH0ep4d/fg3Lc18hPo293mNEEfXtWH9/kL0ep4d/fj7lGY+Un2b+7xGiqNrdFi/fxG9nmdHP/7NSjMfpb7NfV6jxNE1Jizz01uUrNfz7H5/5OalmV+rvs19XteKo+u6sMxPb1myXs+zox//t0szH62+zX1eo8XRdX1Y5qf7lqzX8+zox79FaeZj1Le5z2uMOLrGhmV+ul/Jej3Pjn78W5Zmfp36Nvd5XSeOrhvCMj+9Vcl6Pc+Ofvx9SzO/Xn2b+7yuF0fXjWGZn966ZL2eZ0c//n6lmY9V3+Y+r7Hi6LopLPPT25Ss1/Ps6Me/VWnmN6hvc5/XDeLoujks89PblqzX8+zox791aeY3qm9zn9eN4ui6JSzz098pWa/n2dGPf5vSzG9S3+Y+r5vE0XVrWOantytZr+fZ0Y9/29LMb1bf5j6vm8XRdVtY5qe3L1mv59nRj/87pZnfor7NfV63iKNrXFjmp3coWa/n2dGPf7vSzG9V3+Y+r1vF0XV7WOandyxZr+fZ0Y9/+9LMb1Pf5j6v28TRdUdY5qd3Klmv59nRj3+H0szHqW9zn9c4cXTdGZb56Z1L1ut5dvTj37E089vVt7nP63ZxdI0Py/z0LiXr9Tw7+vHvVJr5Herb3Od1hzi6JoRlfnrXkvV6nh39+HcuzfxO9W3u87pTHF0TwzI/vVvJej3Pjn78u5RmPl59m/u8xouja1JY5qd3L1mv59nRj3/X0swnqG9zn9cEcXRNDsv89B4l6/U8O/rx71aa+UT1be7zmiiOrrvCMj+9Z8l6Pc+Ofvy7l2Y+SX2b+7wmiaPr7rDMT+9Vsl7Ps6Mf/x6lmU9W3+Y+r8ni6LonLPPT3y1Zr+fZ0Y9/z9LM/w8LO68peJx1nGW0LdXRRQnJPd2XEIIFJyTE3T14gBD8ubu7wsPd3d3d3R0ez91x3kPj7vaN8WXNH3uO0/fPHlWr9qra3bvr9Lm9+jzRsd7///XM2CPjgVVpHxD7CeFPxX46Y++MBwfvFfuXrTLPQVXph8/4kx0lj/GequdJ4dT1TMY+GbtUZb2/apV5qB//QVV7/Cmt23gv1fOUcOp6NmPfjF2rst5ft8o81I//4Ko9/rTWbdzn62nh1PVcxn4Zu1Vlvb9plXmoH3+Xqj3+jNZt3OfrGeHU9XzG/hm7V2W9v22Veagff9eqPf6s1m3c5+tZ4dT1QsYBXGdVWe/vWmUe6sffrWqPP6d1G/f5ek44dc3MOJDrrCrr/X2rzEP9+LtX7fHntW7jPl/PC6euFzMO4jqrynr/0CrzUD/+HlV7/AWt27jP1wvCqWtWxsFcZ1VZ7x9bZR7qx9+zao/P1LqN+3zNFE5dszMO4Tqrynr/1CrzUD/+XlV7/EWt27jP14vCqWtOxqFcZ1VZ759bZR7qx9+7ao/P0rqN+3zNEk5dczMO4zqrynr/0irzUD/+PlV7fLbWbdzna7Zw6pqXcTjXWVXW+9dWmYf68fet2uNztG7jPl9zhFPX/IwjuM6qst6/tco81I+/X9Uen6t1G/f5miucuhZkHMl1VpX1/r1V5qF+/P2r9vg8rdu4z9c84dS1MOMorrOqrPcfrTIP9eMfULXH52vdxn2+5gunrkUZR3OdVWW9/2yVeagf/8CqPb5A6zbu87VAOHUtzjiG66wq6/1Xq8xD/fgHVe3xhVq3cZ+vhcKpa0nGsVxnVVnvv1tlHurHP7hqjy/Suo37fC0STl1LM47jOqvKev/TKvNQP/4hVXt8sdZt3OdrsXDqWpZxPNdZVdb731aZh/rxD63a40u0buM+X0uEU9fyjBO4zqqy3vVUL/XjH9aAL9W6jft8LRVOXSsyTuQ6q8p6P6B6qR//8AZ8mdZt3OdrmXDqWplxEtdZVda7vuqlfvwjGvDlWrdxn6/lwqlrVcbJXGdVWe8HVS/14x/ZgK/Quo37fK0QTl2rM07hOqvKej+keqkf/6gGfKXWbdzna6Vw6lqTcSrXWVXW26F6qR//6AZ8ldZt3OdrlXDqeinjNK6zqqy3pXqpH/+YBny11m3c52u18Omx66qsb7zsSvVSP/6xDfgardu4z9ca4dQ1rqE+juv0jva4j/dLwjur0oZnQuyXYx/S0R6fLr6XhW9QlTY8E2O/EvvQjvb4IeJ7RfiHq9KGZ1LsV2PP6GiPHyq+V4VvWJU2PJNjvxb7sI72+AzxvSb8I1VpwzMl9uuxD+9ojx8mvteFb1SVNjxTY78R+4iO9vjh4ntD+Eer0oZnWuw3Yx/Z0R4/QnxvCt+4Km14psdeG/uojvb4keJbK3yTqrThOST2uthHd7THjxLfOuGbVqUNz6Gx34p9TEd7/GjxvSV8s6q04ZkR++3Yx3a0x48R39vCN69KG57DYr8T+7iO9vix4ntH+Meq0obn8Njvxj6+oz1+nPjeFf5e7PcznsA+rEp7i6rMA47/8Ab8PfEaP76hHnDq+nnGE9nHVWlvqXrB8Xs9R4r/xI72uI/H+8JPir2V6jmqKv2uF5x1ndTRHvd6fy5866q04TlaNvgvhE+t/zdOyfhGvq+sjn1I7EMzbppxA82bnPH14KtiHxH7yIxbZvxo8OkZp2VcG/yl2DNiH5Zx84wbah7reDP4mthHxT4649YZNwl+KOvM+FbwV2If3irXsUXGjTSPdawL/jL5Mx6Z8efB18Y+jHVmfCf4a8xrlevYKuPGmsc63g7+auxjYx+XcTuOI+eHdWZ8L/gbsTlux2Tchn2geazj3eCv12Xe4zNun/FjdclLndtm3Iz8Oo6/CL5Ox5V1vB/8TfJzHDLyPOsd+Y/JyPO4t+WnDp4nvhV7u1xH22Y8lT4v/ykd7fFjhBMPvn1V5jmNPi8/vMaPFU48+MeV53T6tPzwGj9OOPHgOyjPGfRZ+eE1frxw4sE/oTxn0kflh9f4CcKJB/+k8pxFv5YfXuMnCicefEflOTtxJ8sPr/GThBMP/inlOSdxp8gPr/GThRMP/mnlOZd9Lj+8xk8RTjz4Z5TnPPa5/PAaP1U48eCfVZ7z2efyw2v8NOHEg39OeS5gn8sPr/HThRMP/nnluZB9Lj+8xs8QTjz4F5TnIva5/PAaP1M48eBfVJ6L2efyw2v8LOHEg39JeS5hn8sPr/GzhRMP/mXluZR9Lj+8xs8RTjz4V5TnMva5/PAaP1c48eBfVZ7L2efyw2v8POHEg39Nea5gn8sPr/HzhRMP/nXluZJ9Lj+8xi8QTjz4N5TnKva5/PAav1A48eDfVJ6r2efyw2v8IuHEg39Lea5hn8sPr/GLhRMP/m3luZZ9Lj+8xi8RTjz4d5TnOva5/PAav1Q48eDfVZ7r2efyw2v8MuHEg39PeW5gn8sPr/HLhRMP/n3luZF9Lj+8xq8QTjz4D5TnJva5/PAav1I48eA/VJ6b2efyw2v8KuHEg/9IeW5hn8sPr/GrhRMP/mPluZV9Lj+8xq8RTjz4TspzG/tcfniNXyucePCdled29rn88Bq/Tjjx4Lsozx3sc/nhNX69cOLBd1WeO9nn8sNr/AbhxIPvpjx3sc/lh9f4jcKJB99dee5mn8sPr/GbhBMPvofy3MM+lx9e4zcLJx78J8pzL/tcfniN3yKcePA9lec+9rn88Bq/VTjx4Hspz/3sc/nhNX6bcOLB91aeB9jn8sNr/HbhxIP/VHkeZJ/LD6/xO4QTD76P8jzEPpcfXuN3Cice/GfK8zD7XH54jd8lnHjwfZXnEfa5/PAav1s48eD7Kc+j7HP54TV+j3DiwfdXnsfY5/LDa/xe4cSDH6A8j7PP5YfX+H3CiQc/UHnQ9z8gP7zG7xdOPPhByoM+/0H54TX+gHDiwQ9WHvT1D8kPr/EHhRMP3kV50Mc/LL/fg3hYPNb9g3dVHvTtj8jv9xgeEY91++DdlAd9+qPy+z2ER8Vj3T14d+VBX/6Y/H6P4DHxWDcP3kN50Ic/Lr/fA3hcPNa9g/dUHvTdT8hvHf8T4rFuHbyX8qDPflJ+6/CfFI915+C9lQd99VPyW0f/lHisGwfvozzoo5+W3zr4p8Vj3Td4X+VB3/yM/NaxPyMe67bB+ykP+uRn5bcO/VnxWHcN3l950Bc/J7915M+Jx7pp8AHKgz74efmtA39ePNY9gw9UHvS9L8hvHfcL4rFuGXyQ8qDPnSm/ddgzxWPdMfhg5UFf+6L81lG/KB7rhsGHKA/62FnyWwc9SzzW/YIPVR70rbPlt455tnis2wUfpjzoU+fIbx3yHPFYdws+XHnQl86V3zriueKxbhZ8hPKgD50nv3XA88Rj3Sv4SOVB3zlffut454vHulXwUcqDPnOB/NbhLhCPdafgo5UHfeVC+a2jXSge60bBxygP+shF8lsHu0g81n2Cj1Ue9I2L5beOdbF4rNsEH6c86BOXyG8d6hLxWHcJPl550Cculd860qXisW4SfILyoE9cJj+8xpcKJx58ovKgT1wuP7zGlwknHnyS8qBPXCE/vMaXCycefLLyoE9cKT+8xlcIJx58ivKgT1wlP7zGVwonHnyq8qBPXC0/vMZXCScefJryoE9cIz+8xlcLJx58uvKgT3xJfniNrxFOPPghyoM+8WX54TX+knDiwQ9VHvSJr8gPr/GXhRMPPkN50Ce+Kj+8xl8RTjz4YcqDPvE1+eE1/qpw4sEPVx70ia/LD6/x14QTD36E8qAvfEN+eI2/Lpx48COVB33gm/JbZ/mmeKx7BD9KedD/rZXfOsm14rFuEfxo5UEfuE5+eI2vFU78OvGvE/9bsVvRU/0n9tjY/429XnRWwzKO4z3J2P9K3KDYo3hfrS79AzMuCD4z9nDea8z4gYx/D2+leshPXZ3Miz0h9vp1ybu++P9Rtc83QvhwrX9p8Ll1mZe48axH84ZmXBJ8Dscr48iMK4IvoH7xLws+T/OIWx58PnrGrGOf2AcH/1ns/TLum/HR4PfG7hb7vfB0bZXz4IH/4eB3o9/LPHR122TcrPN/4y/Yvxl7ZP4Bmd8FvWDVvv5bY18bm/cVrov9UKusi/nU2z32+1W53v3rsh7q6676mPee5nN88L+r48fx/mX8B8buye+RiH8/nQ/quycjx3tbHd+BGfmewfeBmfHfKx70iZvr/Byg40G9nE+O70M6/+gHrTfcIvwc1/d1fFnf/tqnjwW/T/ttH51v8sO/v8436/lw6tggI5+/E2Pvq33+SOZzvHbLeFPGu4LfrPNxt44X5wf9oPWGWwdHP2q9KeeH68t6V/jvbpV1ur5fJZ592Ev7D/2e9X7bdZbxzPf+/XVV5unNdaj9zj7z/iKv9YDbd5Z8v1L98DPPer+PZ36fVlknfF0y/7fx/yZj3+Bd69LP/D6aT17rAXfoLPl+o/nwb9Zwnmd0lrzWA34ieL9WuQ7ydQv/DRmvz8j7UlXm/z727zL2z/zudemHv5/4qct6wk92lny/03z4uS/5d8YxvG9el7zWE+4Y/gGtch3k65H53Ff4/ofPa3itJ/wU/TXxf6jKfD0zfz3l4f6F+wJ4rSf8dPgHJf6PVZmvl+4/yMP9B/cL8FpP+JnwD078n7iPi90789dXHu6vuN+A13rCz4Z/SOL/XJX5+mT+B5VnYnDua+C1nvBz4R+a+L9UZb6+mf8h5ZkUnPsueK0n/Hz4hyX+r1WZr1/mdyjP5OCj65LXesIvhJ/7379VZb7+md9SninBx9Qlr/WEXww/97Pc35JvQOZXyjM1+Ni65LWe8EvhH6n7ZfJxv18rz7Tg4+qS13rCL4ef7xP/rMp8fK/oVJ7pwcfXJa/1hF8J/+jE8z3G3182UB7ex5pQl7zWE341/PQr+hf56F8fVh7e85pYl7zWE34t/PQr+hf56F8bKg/vc02qS17rCb8efvoV/Yt89K+PKA/vifE+GrzWE34j/PQr+pe/X26kPLwPxntv8FpP+M3w06/oX/5+9lHl4T0z3meD13rCb4WffkX/Ih/9a2Pl4X0y3puD13rCb4effkX/Ih/9axPl4T013oeD13rC74SffkX/Ih/9a1Pl4X003ruD13rC74affkX/Ih/9azPl4X003qeD13rC74WffkX/Ih/9a3Pl4T033tuD13rC74effkX/Ih/962PKw/t2vI8Hr/WEPwg//Yr+RT761xbKw3t8vPcHr/WEPww//Yr+RT7615bKc0Jw3ueD13rCH/F9KfH0L/LRv7ZSnhOD894gvNYT/jj89Cv6F/noX4MTx3MonhfNin9r1XFS5vM+I3mtN9wp8+ln9Dfqob9tU5d5Tg7O+4rwWm+4c/jpZ/Q38tHftlWeU4LzXiS81hvuEn76Gf2NfPS3oYnjORjPq+bEv53qODXzeW+TvNYj7pr59Dv6H/XQ/7avyzynBee9UXitR9wt/PQ7+h/56H9DEse+YJ2z4/+46jg984+vy7zWK+6e+fRD+iP10B+HJ47ndDxPmxf/DnVZxxmZf0Jd5rWecQ++Fyae/kk99M9P1GWeM4OfWJe81jP+hP8/JJ7+ST7657DEsW9Y59z4P6k6zsr8k+oyr/WOe2Y+/ZT+Sj3015GJ4zkiz/sWxL9jXdZxduafXJd5rYfcK/Ppt/Rf6qH/fqou85wT/JS65LUecu/w02/pv+Sj/45IHPuGdc6P/9Oq49zMP7Uu81ov+dPMpx/Tn6mH/jw6cTzn5Hnkovg/U5d1nJf5p9VlXusp9+H/X4mnP1MP/fmzdZnn/OCn1yWv9ZQ/Cz/9mP5MPvrzqMSxb1jnwvg/pzouyPwz6jKv9Zb7Zj79mv5NPfTvsYnjOSzPS5fE//m6rOPCzD+zLvNaj7lf5tOv6d/UQ//+Ql3muSj4WXXJaz3m/uGnX9O/yUf/HpM49g3rXBz/F1XHxZl/dl3mtV7zgMynX9O/qYf+PT5xPCfmee6y+L9Ul3Vckvnn1GVe6zkPzHz6Nf2beujfX67LPJcGP7cuea3nPIj/7yWe/k0++ve4xLFvWOfS+L+iOi7L/PPqMq/1ngdnPv2a/k099G/+T85zbJ43r4j/q3VZx+WZf35d5rUetEvm06/p39RD//5aXea5IvgFdclrPWjX8NOv6d/ko39PSBz7hnUuj//rquPKzL+wLvNaL9ot8+nX9G/qoX9PThzP2Xkevir+b9RlHVdl/kV1mdd60u6ZT7+mf1MP/fubdZnn6uAX1yWv9aQ9wk+/pn+Tj/49KXHsG9a5Mv5vqY5rMv+SusxrvWnPzKdf07+ph/49NXHoAHhevyb+b9dlHddm/qV1mdd61F6ZT7+mf1MP/fs7dZnnuuCX1SWv9ai9w0+/pn+Tj/49JXHsG9a5Ov7vqo7rM//yusxrvWqfzKdf07+ph/79vbrMc0PwK+qS13rVvuGnH9OfyUd//lDiPsj/SzKfz3XfF6Iv477x+6rzxvBfWZd1We/aL/Pp5/R36qW//6Au89wU/Kq65LXetX/46df0b/LRvzsSx3FAf8Z92w9Vx82Zf3Vd5rUedkDm06/p39RD/251lnWgT+Nz/0d1WcctmX9NXea1Xpbn1vRz+jv10N95bsXzfn5PkLp+XJd1WC9AXutpB2U+/Zz+Tj30d56TkQ/9HPd1O6m+21pl3f6/BfpZ/q9BP6e/Uw/93d9r0cfyvZd+Tf+Gj/5dd5brQJ/HfcWuXBcZ78x8nnf7/wboZ/m/Av2a/k099O8NE8dzePRpfC7wvHrXhvz+3om+lu+l9Gv6N/XQvzsTx3FAP8h94406TvzeI/P8vR79Ld/76ef0d+qhv6+fuA/w/2quw9j0Y/ozfPRndAvUgz6R+5abtE5+T5J57EP2L9cP+4G+aZ0v/ZV+Tn+nXvr7RxLHeUYfyH0Nz5vvyLzbM/Jc2t97fXzo1/Rv6qF/36zjxO9dst/It1PD9UmfoL/ye6v0O/o5/Z166O836frZRPuH+w3rCacFp1/Tv8lH/76lLtfD721yvDmOlY43+5nvZdZB07/p1/Rv6qF/b5Q4zjP6TO47btZx2FT7b3edJ/Qqt+j4k4/jz/mgn9PfqZd598X+Sex7Y98W+4HYe8W+P/Ydsfm+ah33WO0f6ydu0PlhfZtp/1HPHsHviX1rbN/3+/phP9En3R/3rMv136f1499Dx4f89Dt4uU7Zz1w3fM5y/fB5x7xd6vbzdxaPjyfrtk6Z/QXfzprP8d9Fee4QzvFmH3r/7SqeO1U/+5l51lfxvdE6d/oz1731vtN1PVOn69tNdd6l88/3Vevg+d7+YOL31vVwp/bHrdof7J/dVcfdWj/X1e0N19fG9KWM7n+v6bzP0P6Hj33O/iYf/tsacH4/F5zf+6WvsW76Kb83TL/bS3nuFz/HkfXye7Sse28dpwd0fNBv+3dmN9F8zuOBGX8aP7ob9D3ob9D3oFtCH4V+CX0UuiPi0B+hb0IXg/4GfQz6G/LCg74I/RLzqBP9DvogdCXWZ6NfQTdDHehn0OegG4EH/Qj6FHQZ6D/QZ6D/wA8P+hD0J9RFHehr0O+gT0XHbP2y9cgPC8d+RDzg/L6lfw9zq+wP9KboQq3Phrer+NG9Ot8jwrEfFY/15N2Eo+dtit9P9mPiAUdX7N8b3ZL//8f2721u01nydhc/emHne0w49uPiASevf49z285yXg/NR59svseFYz8hHnD8PYWjr22KP1D2k+IBx99LOPrfpviDZD8lHnD8vYWj722KP1j20+IBx99HOPrdpvgusp8RDzj+vsLR9zbFd5X9rHjA8fcTjv62Kb6b7OfEA46/v3D0uU3x3WU/Lx5w/AOE8/nTFN9D9gviAcc/UDifX03xPWXPFA84/kHC+Xxriu8l+0XxgOMfLJzP76b43rJniQcc/xDhfP42xfeRPVs84PiHCuf+oCm+r+w54gHHP0w4n/9N8f1kzxUPOP7hwvl8b4rvL3ueeMDxjxDO/U1T/ADZ88UDjn+kcO5/muIHyl4gHr/PN0o490dN8YNkLxQPOP7Rwrk/a4ofLHuReMDxjxGOPrcpfojsxeIBxz9WOPrcpvihspeIx+8TjhOOPrcpfpjspeLx+47jhaPPbYofLnuZePw+4wTh6HOb4kfIXi4evw85UTj63Kb4kbJXiMfva04Sjj63KX6U7JXiAcc/WTj63Kb40bJXiQcc/xTh6HOb4sfIXi0ecPxThaPPbYofK3uNeMDxTxOOPrcpfpzsl8QDjn+6cPS5TfHjZb8sHnD8hwhHn9sUP0H2K+IB/z9n23zSeJx9m1XUnUUSRZMAf/fFXQZ3d3f3ENydUQYG16DBJcE1QAjuhAhxV9zd3V2jMGvN9H6ovdIfL9+q2tVVp7r/8HTP223t/vffqeV7Svkem2N8TInfFid+R33g5E8TPy431x+n+F31gZM/Xfz43Fx/vOL31AdOvrP4Cbm5/gTF76sPnPwZ4ifm5voTFX+gPnDyZ4qflJvrT1L8ofrAyZ8lfnJurj9Z8UfqAyd/tvgpubn+FMUfqw+c/Dnip+bm+lMVf6I+cPLnip+Wm+tPU/yp+sDJdxE/PTfXn674M/WBkz9PvHNuru+s+HP1gZM/X/yM3Fx/huIv1AdO/gLxM3Nz/ZmKv1QfOPkLxc/KzfVnKf5KfeDkLxI/OzfXn634a/WBk79Y/JzcXH+O4m/UB07+EvFzc3P9uYq/VR84+UvFu+Tm+i6Kv1MfOPnLxM/LzfXnKf5efeDkLxc/PzfXn6/4B/WBk+8qfkFurr9A8Y/qAyffTfzC3Fx/oeKf1AdO/grxi3Jz/UWKf1YfOPkrxS/OzfUXK/5FfeDkrxK/JDfXX6L4V/WBk79a/NLcXH+p4t/UB07+GvHLcnP9ZYp/Vx84+WvFL8/N9Zcrnqg+cPLXiXfNzfVdFU9SHzj568W75eb6boonqw+c/A3iV+Tm+isUT1EfOPkbxa/MzfVXKp6qPnDyN4lflZvrr1I8TX3g5G8Wvzo311+t+A/1gZPvLn5Nbq6/RvGf6gMnf4v4tbm5/lrF7VLsAyd/q+Zcl5vrr1PcXn3g5G9ri/z63Fx/veIO6gMn36Mt8htyc/0NimdQHzj529sivzE319+oeEb1gZPv2Rb5Tbm5/ibFM6kPnPwdbZHfnJvrb1bcpj5w8ne2Rd49N9d3V5zUB07+rrbIb8nN9bcozuoDJ393W+S35ub6WxW31AdO/p62yG/LzfW3KZ5ZfeDk722LvEduru+heBb1gZO/ry3y23Nz/e2KZ1Uf+Kslf3z5Hle+R7b+/52dv7tSPxt/j63Y9/622L9nnv486nsqnk19emoeOh7g71znve+MrVg3U2v6+uk7R/k+yN95jnli6pP6Wy/zOD9n+T7E33mO+bs0L7cin6t8H+bvOMf83erXKuffK/H75du5fE9oxfNzl+8j/B3nmL9H82ZuRT5P+T7K32mO+XvVb5ZW1PVB+Z5Rvie24vl5y7cXf2c55u/TvFlbzfX3K56vfB9ri3y+Sv1srch7t8XzD+Tmfg8onl994OT7tEX+YG6uf1DxAuoD5+vzs7fiub5t8fxDefr9qH9I8YLqA3+kfB8u34X4ey/zt8+xnvOu79cW58AXFPd56/H5HaSPPo8oth74TuX7aPn2L/W9SjywxDuXeAB/RyVeuPRbJEXeS5y8+xM/qno4c3tV5g8qcUfp7Z2jrkVT5I+Jk3d/70M9nLmPVeYPLvEu0tsnR12Lpch7i5N3f+9DPZy5vSvzh5S4k/T2zVHX4inyPuLk3d/7UA9nbp/K/KEl3lV6++Woa4kUeV9x8u7vfaiHM7dvZf6wEu8mvY/nqGvJFHk/cfLu732ohzO3X2X+8BLvLr39c9S1VIr8cXHy7u99qIcz9/HK/BEl3kN6B+Soa+kUeX9x8u7vfaiHM7d/Zf7IEu8pvQNz1LVMinyAOHn39z7Uw5k7oDJ/VIn3kt5BOepaNkU+UJy8+3sf6uHMHViZP7rEe0vv4Bx1LZciHyRO3v29D/Vw5g6qzB9T4n2kd0iOupZPkQ8WJ+/+3od6OHMHV+aPLfG+0js0R10rpMiHiJN3f+9DPZy5Qyrzx5V4P+kdlqOuFVPkQ8XJu7/3oR7O3KGV+eNLvL/0Ds9R10op8mHi5N3f+1APZ+6wyvwJJT5AekfkqGvlFPlwcfLu732ohzN3eGX+EyU+UHpH5qhrlRT5CHHy7u99qIczd0Rl/pMlPkh6R+Woa9UU+Uhx8u7vfaiHM3dkZf5TJT5YekfnqGu1FPkocfLu732ohzN3VGX+0yU+RHrH5Khr9RT5aHHy7u99qIczd3Rl/jMlPlR6x+aoa40U+Rhx8u7vfaiHM3dMZf6zJT5MesflqGvNFPlYcfLu732ohzN3bGX+cyU+XHrH56hrrRT5OHHy7u99qIczd1xl/vMlPkJ6J+Soa+0U+Xhx8u7vfaiHM3d8Zf4LJf6r9D6Ro651UuQTxMm7v/ehHs7cCZX5L5b4b9L7ZI661k2RPyFO3v29D/Vw5j5Rmf9Sif8uvU/lqGu9FPmT4uTd3/tQD2fuk5X5L5f4H9L7dI661k+RPyVO3v29D/Vw5j5Vmf9Kif8pvc/kqGuDFPnT4uTd3/tQD2fu05X5r5b4X9L7bI66NkyRPyNO3v29D/Vw5j5Tmf9aiY+U3udy1LVRivxZcfLu732ohzP32cr810v8b+l9PkddG6fInxMn7/7eh3o4c5+rzH+jxEdJ7ws56tokRf68OHn39z7Uw5n7fGX+myU+WnpfzFHXpinyF8TJu7/3oR7O3Bcq898q8X+k96UcdW2WIn9RnLz7ex/q4cx9sTKf318fI70v56hr8xT5S+Lk3d/7UA9n7kuV+fx+278XfyVHXVukyF8WJ+/+3od6OHNfrszn99/+vfmrOeraMkX+ijh59/c+1MOZ+0plPr8f9+/VX8tR11Yp8lfFybu/96EeztxXK/P5/bl/7/56jrq2TpG/Jk7e/b0P9XDmvlaZz+/X/Xv5N3LUtU2K/HVx8u7vfaiHM/f1ynx+/+7f27+Zo65tU+RviJN3f+9DPZy5b1Tm8/t5/17/rRx1bZcif1OcvPt7H+rhzH2zMp/f3/v3/m/nqGv7FPlb4uTd3/tQD2fuW5X5/H7ffoF3ctS1Q4r8bXHy7u99qIcz9+3KfH7/b7/Buznq2jFF/o44eff3PtTDmftOZT7+AfsV3stR104p8nfFybu/96Eeztx3K/PxH9jv8H6OunZOkb8nTt79vQ/1cOa+V5mPf8F+iQ9y1NUxRf6+OHn39z7Uw5n7fmU+/gf7LT7MUdcuKfIPxMm7v/ehHs7cDyrz8U/Yr/FRjro6pcg/FCfv/t6HejhzP6zMx39hv8fHOeraNUX+kTh59/c+1MOZ+1FlPv4N+0U+yVHXbinyj8XJu7/3oR7O3I8r8/F/2G/yaY66dk+RfyJO3v29D/Vw5n5SmY9/xH6Vz3LUtUeK/FNx8u7vfaiHM/fTynz8J/a7fJ6jrj1T5J+Jk3d/70M9nLmfVebjX7Ff5oscde2VIv9cnLz7ex/q4cz9vDIf/4v9Nl/mqGvvFPkX4uTd3/tQD2fuF5X5+Gfs1/kqR137pMi/FCfv/t6Hejhzv6zMx39jv8/XOeraN0X+lTh59/c+1MOZ+1VlPv4d+4W+yVHXfinyr8XJu7/3oR7O3K8r8/H/2G/0bY669k+RfyNO3v29D/Vw5n5TmY9/yH6l73LUdUCK/Ftx8u7vfaiHM/fbynz8R/Y7fZ+jrgNT5N+Jk3d/70M9nLnfVebjX7Jf6occdR2UIv9enLz7ex/q4cz9vjIf/5P9Vj/mqOvgFPkP4uTd3/tQD2fuD5X5+Kfs1/opR12HpMh/FCfv/t6Hejhzf6zMx39lv9fPOeo6NEX+kzh59/c+1MOZ+1NlPv4t+8V+yVHXYSnyn8XJu7/3oR7O3J8r8/F/2W/2a466Dk+R/yJO3v29D/Vw5v5SmY9/zH6133LUdUSK/Fdx8u7vfaiHM/fXynz8Z/a7/Z6jrr+myH8TJ+/+3od6OHN/q8zHv2a/3MQcdf0tRf67OHn39z7Uw5n7e2U+/jf77SblqOvvKfKJ4u2kc6Lm/a56OHMnVubjn7Nfb3KOuv6RIp8k3l46J2neRNXDmTupMh//nf1+U3LU9c8U+WTxDtI5WfMmqR7O3MmV+fj37BecmqOuf6XIp4jPIJ1TNG+y6uHMnVKZj//PfsNpOeo6MkU+VXxG6ZyqeVNUD2fu1Mp8/IP2K/6Ro65/p8inic8kndM0b6rq4cydVpmP/9B+xz9z1HVUivwP8Tbp/EPzpqkeztw/KvPxL9ov2a4VdR2dIuf8Ucq7v/c5Spy5f1bm43+037J9K+r6T4q8nXiWTrj3OVqcucSej3/Sfs0OrajrmBR5e/GWdLbXvHaqhzO3ve6L+fgR7fecoRV1HZsi7yA+s3R20Lz2qoczt4Pui/n4vuxnw/+FLnyhs+j8scq7v/c5Vpy++E/xc84oPqvmzKj7dT84fU8oX/szj1d+VnHrOV6cvidyb/w7FJ9dc+DWc4I4fU8qX/yhSXwOzYFbz4ni9D25fPGPZvE5NQduPSeJ0/eU8sU/2hKfS3Pg1nOyOH1PLV/8pTOLz605cOs5RZy+p5Uv/tJZxOfRHLj1nCpO39PLF3/prOLzag7cek4Tpy++X/59zyY+n+bAred0cfyd9pvO0Ypz8fXaX9pZef//x3o7izOX2POZe2aKes0XSNM/b71niOMXtd91zlbMz9GaPkfXWSn2M18wTf+89zlTHD+q/bD4a8nP2Zo+R9dCafrces8Sxwc7l/TMrRh+tji+Rvsv5yv83BLjG8XfOm8rxvOoHs45+2nh5OdpTZ/ja7T/cv7Cu6SoexHpX0T9uohzzn5aOHnvA8fXaP/lAoWfl6LuRaXf932eOOfsp51f7+d94Pga7b9csPDzU9S9mPT7vs8X55z9tAvo/bwPHF+j/ZcLFX5BiroXl37f9wXinLOfdkG9n/eB42u0//IvhV+You4lpN/3faE45+ynXUjv533g+Brtv1y48ItS1L2k9Pu+LxLnnP20f9H7eR84vkb7Lxcp/OIUdS8l/b7vi8U5Zz/twno/7wPH12j/5aKFX5Ki7qWl3/d9iTjn7KddRO/nfeD4Gu2/XKzwS1PUvYz0+74vFeec/bSL6v28Dxxfo/2Xixd+WYq6l5V+3/dl4pyzn3YxvZ/3geNrtP9yicIvT1H3ctLv+75cnHP20y6u9/M+cHyN9l8uWXjXFHUvL/2+767inLOfdgm9n/eB42u0/3KpwrulqHsF6fd9dxPnnP20S+r9vA8cX6P9l0sXfkWKuleUft/3FeKcs592Kb2f94Hja7T/cpnCr0xR90rS7/u+Upxz9tMurffzPnB8jfZfLlv4VSnqXln6fd9XiXPOftpl9H7eB46v0f7L5Qq/OkXdq0i/7/tqcc7ZT7us3s/7wPE12n+5fOHXpKh7Ven3fV8jzjn7aZfT+3kfOL5G+y9XKPzaFHWvJv2+72vFOWc/7fJ6P+8Dx9do/+WKhV+Xou7Vpd/3fZ045+ynXUHv533g+Brtv1yp8OtT1L2G9Pu+rxfnnP20K+r9vA8cX6P9lysXfkOKuteUft/3DeKcs592Jb2f94Hja7T/cpXCb0xR91rS7/u+UZxz9tOurPfzPnB8jfZfrlr4TSnqXlv6fd83iXPOftpV9H7eB46v0f7L1Qq/OUXd60i/7/tmcc7ZT7uq3s/7wPE12n+5euHdU9S9rvT7vruLc85+2tX0ft4Hjq/R/ss1Cr8lRd3rSb/v+xZxztlPu7rez/vA8TXaf7lm4bemqHt96fd93yrOOftp19D7eR84vkb7L9cq/LYUdW8g/b7v28Q5Zz/tmno/7wPH12j/5dqF90hR94bS7/vuIc45+2nX0vt5Hzi+Rvsv1yn89hR1byT9vu/bxTlnP+3aej/vA8fXaP/luoX3TFH3xtLv++4pzjn7adfR+3kfOL5G+y/XK/yOFHVvIv2+7zvEOWc/7bp6P+8Dx9do/+X6hd+Zou5Npd/3fac45+ynXU/v533g+Brtv9yg8LtS1L2Z9Pu+7xLnnP206+v9vA8cX6P9lxsWfneKujeXft/33eKcs592A72f94Hja7T/cqPC70lR9xbS7/u+R5xz9tNuqPfzPnB8jfZfblz4vSnq3lL6fd/3inPOftqN9H7eB46v0f7LTQq/L0XdW0m/7/s+cc7ZT7ux3s/7wPE12n+5aeH3p6h7a+n3fd8vzjn7aTfR+3kfOL5G+y83K/yBFHVvI/2+7wfEOWc/7aZ6P+8Dx9do/+XmhT+You5tpd/3/aA45+yn3Uzv533g+Brtv9yi8IdS1L2d9Pu+HxLnnP20m+v9vA8cX6P9l1sW/nCKureXft/3w+Kcs592C72f94Hja7T/cqvCH0lR9w7S7/t+RJxz9tNuqffzPnB8jfZfbl34oynq3lH6fd+PinPOftqt9H7eB46v0f7LbQrvlaLunaTf991LnHP2026t9/M+cHyN9l9uW/hjKereWfp934+Jc85+2m30ft4Hjq/R/svtCu+dou6O0u/77i3OOftpt9X7eR84vkb7L7cvvE+KuneRft93H3HO2U+7nd7P+8DxNdp/uUPhfVPU3Un6fd99xTlnP+32ej/vA8fXaP/ljoX3S1H3rtLv++4nzjn7aXfQ+3kfOL5G+y93KvzxFHXvJv2+78fFOWc/7Y56P+8Dx9do/+XOhfdPUffu0u/77i/OOftpd9L7eR84vkb7LzsWPiBF3XtIv+97gDjn7KfdWe/nfeD4Gu2/3KXwgSnq3lP6fd8DxTlnP21HvZ/3geNrtP+yU+GDUtS9l/T7vgeJc85+2l30ft4Hjq/R/stdCx+cou69pd/3PVicc/bTdtL7eR84vkb7L3crfEiKuveRft/3EHHO2U+7q97P+8DxNdp/uXvhQ1PUva/0+76HinPOftrd9H7eB46v0f7LPQoflqLu/aTf9z1MnHP20+6u9/M+cHyN9l/uWfjwFHXvL/2+7+HinLOfdg+9n/eB42u0/3KvwkekqPsA6fd9jxDnnP20e+r9vA8cX6P9l3sXPjJF3QdKv+97pDjn7KfdS+/nfeD4Gu2/3KfwUSnqPkj6fd+jxDlnP+3eej/vA8fXaP/lvoWPTlH3wdLv+x4tzjn7affR+3kfOL5G+y/3K3xMiroPkX7f9xhxztlPu6/ez/vA8TXaf7l/4WNT1H2o9Pu+x4pzzn7a/fR+3geOr9H+ywMKH5ei7sOk3/c9Tpxz9tPur/fzPnB8jfZfHlj4+BR1Hy79vu/x4pyzn/YAvZ/3geNrtP/yoMInpKj7COn3fU8Q55z9tAfq/bwP/L/3jbCUeJx1mlWUXVUWRSM3qVdYBOKCBXd3d3eNu7uRQCBAcHd3d9duLO6uBHeXNhx6jM6aH3eO6v5Z4+y156r77tsHOK/PL5Va//vfz9GeVau0Y/UqnZ51j2j3aIfq8rq9+vHhfqrUzFNvX12z/2v8X6K94neKP6Oq/Nw99Pw9lDdDPhyf3zx1fx783yrl5+wdv3P8mVXl5+6p5/f7nikf7pdKzTx1fx783yvl5+wTv0v8WVXl5+6l5/f7niUf7tdKzTx1fx78Pyrl5+wbv2v82VXl5+6t5/f7ni0f7rdKzTx1fx78Pyvl5+wXv1v8OVXl5+6j5/f7niMf7vdKzTx1fx78vyrl5+zPPo0/t6r83H31/H7fc+XD/VGpmafuz4NfS885gH2a+ryq8nP30/P7fc+TD/dnpWaeuj8Pfm0950D2adbzq8rP3V/P7/c9Xz7cX5Waeer+PPgLsx5UVX6+XvEXqD5Az+/3vUA+XC3199R78ecx31PPy/Px3LXV30vv33nmWS/inwNZD866jvrxqTvPPOvF7OOsh2RdV/341J1nnvUS9mHWQ7Mu1I9P3XnmWS9lH2U9LOt66sen7jzzrJexD7IennV99eNTd5551suZ46xHZF2lfnzqzjPPegX7JeuRWVfUj0/deeZZv81+zXpU1tXqx6fuPPOsV2Y9OOvRWa+mfnzqzjPP+p2sh2R9Rtarqx+fuvPMs34366FZj8l6DfXjU3eeedbvZT0s67FZr6l+fOrOM8/6/ayHZ31m1mupH5+68/A/yPqsqnLeCOW7Hx+uQXXNPHU/L/6HWY/Tc4xUvvvx4RpW18xT9/Pif5z12dGPoqOqy/VG+nv41P08+B8p3/44PY//fuPq8pqc0VqP0ufBXyc6PvVzo2OYZ/n04zdRjvkmqp8XHascfPrxmyrHfFPVz2dOlYNPP34z5ZhvpvoE5lA5+PTjN1eO+eaqX8AcKAeffvwWyjHfQvULmTfl4NOP31I55luqflH0HOXg04/fSjnmW6l+cXS8cvDpx2+tHPOtVb+EfaAcfPrx2yjHfBvVL2UfKAeffvy2yjHfVvXL2AfKwacff13lmF9X9cvZB8rBpx9/PeWYX0/1K9gHysGnH3995ZhfX/Ur2QfKwacffwPlmN9A9avYB8rBpx9/Q+WY31D1q9kHysGnH7+dcsy3U/0a9oFy8OnH30g55jdS/Vr2gXLw6cffWDnmN1b9OvaBcvDpx99EOeY3Uf169oFy8OnH31Q55jdV/Qb2gXLw6cffTDnmN1P9RvaBcvDpx99cOeY3V/0m9oFy8OnH30I55rdQ/Wb2gXLw6cffUjnmt1T9FvaBcvDpx99KOea3Uv1W9oFy8OnH31o55rdW/Tb2gXLw6cffRjnmt1H9dvaBcvDpx99WOea3Vf0O9oFy8OnH30455rdT/U72gXLw6cffXjnmt1f9LvaBcvDpx99BOeZ3UP1u9oFy8OnH31E55ndU/R72gXLw6cffSTnmd1L9XvaBcvDpx99ZOeZ3Vv0+9oFy8OnH30U55ndR/X72gXLw6cffVTnmd1X9AfaBcvDpx99NOeZ3U/1B9oFy8OnH31055ndX/SH2gXLw6cffQznm91D9YfaBcvDpx99TOeb3VP0R9oFy8OnH30s55vdS/VH2gXLw6cffWznm91b9MfaBcvDpx99HOeb3Uf1x9oFy8OnH31c55vdV/Qn2gXLw6cffTznm91P9SfaBcvDpx99fOeb3V/0p9oFy8OnHP0A55g9Q/Wn2gXLw6cc/UDnmD1T9GfaBcvDpxz9IOeYPUv1Z9oFy8OnHP1g55g9W/Tn2gXLw6cc/RDnmD1H9efaBcvDpxz9UOeYPVf0F9oFy8OnHP0w55g9T/UX2gXLw6cc/XDnmD1f9JfaBcvDpxz9COeaPUP1l9oFy8OnHP1I55o9U/RX2gXLw6cc/Sjnmj1L9VfaBcvDpxz9aOeaPVv1v7APl4NOPf4xyzB+j+t/ZB8rBpx//WOWYP1b119gHysGnH/845Zg/TvXX2QfKwacf/3jlmD9e9TfYB8rBpx//BOWYP0H1N9kHysGnH/9E5Zg/UfW32AfKwacf/yTlmD9J9YnsA+Xg049/snLMn6z6JPaBcvDpxz9FOea5F8S9J+4XTRIPN5l9or+DTz/+qcoxf6rqU9gnysGnH/805Zg/TfWp7BPl4NOPf7pyzJ+u+jT2iXLw6cdvrxzz7VXnft+bysGnH7+Dcsx3UJ37eW8px/cK8Tsqx3xH1ZmzicrxvUD8TsoxP1F1+pnfzsrxfE9Snf7J0S7K4X7bZPX53tuUaFflcP9sivp8L21qtJtyuB82VX2+NzYt2l053N+apj7f65oe7aEc7ldNV5/vXc2I9lQO959mqM/3omZGeymH+00z1ed7T7OivZXD/aVZ6vO9ptnRPsrhftJs9fne0pxoX+Vw/2iO+nwvaW60n3K4XzRXfb53NC/aXzncH5qnPt8rmh8doBzuB81Xn+8NLYgOVA73fxaoz/eCFkYHKYf7PQvV53s/i6KDlcP9nUXq872exdEhyuF+zmL1+d7OkuhQ5XD/Zon6fC9naXSYcrhfs1R9vnezLDpcOdyfWaY+36tZHh2hHO6/LFef78WsiI5UDvdX8Eepj/sfb6sOZ3+FfPrxR+vvcD9kpda+r7JS3Nv/x3cOf++d6BvFKm0fPT36VrRjtEP0tehp0VOjb6qPvInRTsqbFO0sf3K0i/wp0a7yp0a7yZ8W7S5/erSH/BnRnvJnRnvJnxXtLX+Deqv0db2vdqnznnjvG6XO+8bfOHXeH/4mqfP+8DdNnfeHv1nqvD/8zVPn/eEvjg6JDo5ukX7eK9yWqfNe8bdKnfeKv3XqvFf8bVLnveJvm/psvfc9Ul+W9VLmMvV/Z/2vKN8H8838kttH3+fr6uN7nBPtK+4N5cPPjfYTx/fcXvy8aH9xfP8dxM+PDhDHXHQUvyA6UBzz0kn8wuggccxRZ/GL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
+        </DataArray>
+        <DataArray type="UInt8" Name="types" format="binary" RangeMin="5" RangeMax="10">
+          AQAAAACAAAAHJwAAJQAAAA==eJztwaEBAAAMAqC04v8H7weTAUgAAAAAAAAAAAAoHBMecq9+rg==
+        </DataArray>
+      </Cells>
+    </Piece>
+  </UnstructuredGrid>
+</VTKFile>
diff --git a/mesh-doctor/tests/test_convertMD2SG.py b/mesh-doctor/tests/test_convertMD2SG.py
new file mode 100644
index 000000000..2bd1595de
--- /dev/null
+++ b/mesh-doctor/tests/test_convertMD2SG.py
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright 2023-2024 TotalEnergies.
+# SPDX-FileContributor: GitHub Copilot, Jacques Franc
+import argparse
+
+from geos.mesh_doctor.parsing.convertMD2SGParsing import convert, fillSubparser
+from geos.mesh_doctor.actions.convertMD2SG import Result, action
+
+
+def test_convert_md2sg_parser() -> None:
+    """Test the convertMD2SG parser and conversion function."""
+    parser = argparse.ArgumentParser( description='Testing.' )
+    subparsers = parser.add_subparsers()
+    fillSubparser( subparsers )
+
+    args = parser.parse_args( [ 'convertMD2SG', '-i', 'mesh-doctor/tests/data/base_tetra_shift.vtm', '-z', '2' ] )
+
+    options = convert( vars( args ) )
+    assert options.attrs == ( 2, )
+    assert options.skipCleanCollocated is False
+    assert options.skipFilterVolumeCells is False
+    assert options.meshVtkOutput.output == "converted.vtu"
+
+
+def test_convertion() -> None:
+    """Test the convertMD2SG action."""
+    parser = argparse.ArgumentParser( description='Testing.' )
+    subparsers = parser.add_subparsers()
+    fillSubparser( subparsers )
+
+    args = parser.parse_args(
+        [ 'convertMD2SG', '-i', 'mesh-doctor/tests/data/base_tetra_shift.vtm', '-z', '2', '--outputFile', 'my_converted_mesh.vtu' ] )
+
+    options = convert( vars( args ) )
+    actionsResult = action( vars( args )[ 'vtuInputFile' ], options )
+    assert isinstance( actionsResult, Result )
+    assert actionsResult.outputMesh is not None
+    assert actionsResult.nCleanCollocated != 0
+    assert actionsResult.outputMesh.GetNumberOfCells() > 0
+    assert actionsResult.outputMesh.GetPointData().HasArray( "faultNodes" )
+    assert actionsResult.outputMesh.GetPointData().GetArray( "faultNodes" ).GetRange() == ( 0.0, 1.0 )