diff --git a/docs/sphinx/source/non-tutorials/cite.rst b/docs/sphinx/source/non-tutorials/cite.rst
deleted file mode 100644
index 6ef31aaa..00000000
--- a/docs/sphinx/source/non-tutorials/cite.rst
+++ /dev/null
@@ -1,8 +0,0 @@
-If you find these tutorials useful for your research or simulations, you can
-cite: A Set of Tutorials for the LAMMPS Simulation Package by Simon Gravelle,
-Jacob R. Gissinger, and Axel Kohlmeyer (2025) :cite:`gravelle2025tutorials`. You
-can access the full paper on |gravelle2025tutorials_arXiv|.
-
-.. |gravelle2025tutorials_arXiv| raw:: html
-
-    <a href="https://doi.org/10.48550/arXiv.2503.14020" target="_blank">arXiv</a>
diff --git a/docs/sphinx/source/non-tutorials/contact.rst b/docs/sphinx/source/non-tutorials/contact.rst
index aa6df9cd..c7cb342c 100644
--- a/docs/sphinx/source/non-tutorials/contact.rst
+++ b/docs/sphinx/source/non-tutorials/contact.rst
@@ -1,27 +1,23 @@
+.. include:: ../shared/links.rst
 .. _contact-label:
 
 Contact
 *******
 
-Since its launch in 2019, this website has evolved considerably--and will
-likely continue to do so.
+.. include:: ../shared/cite.rst
 
-Follow the LAMMPS tutorials initiative
-======================================
+About LAMMPS tutorials
+======================
 
-You can follow the progress of LAMMPStutorials via:
+Since its launch in 2019, this website has evolved
+considerably -- and will likely continue to do so.
+A citable pdf version is available on |gravelle2025tutorials_arXiv|.
+
+You can follow the progress of LAMMPS tutorials via:
 
 - the |github_lammps_tutorials| account of LAMMPS tutorials,
 - the |mastodon_lammps_tutorials| account of LAMMPS tutorials.
 
-.. |github_lammps_tutorials| raw:: html
-
-   <a href="https://github.com/lammpstutorials" target="_blank">GitHub</a>
-
-.. |mastodon_lammps_tutorials| raw:: html
-
-   <a href="https://mastodon.social/@simongravelle" target="_blank">Mastodon</a>
-
 Contact the founder
 ===================
 
@@ -29,19 +25,8 @@ You can reach the founder of LAMMPS tutorials, |simongravelle_page|, for suggest
 to report issues with the website, or to ask general questions about LAMMPS
 or molecular simulations.
 
-.. |simongravelle_page| raw:: html
-
-   <a href="https://simongravelle.github.io/" target="_blank">Simon Gravelle</a>
-
 Struggling with your simulation project?
 ========================================
 
 Register on |patreon| and receive personalised help for your LAMMPS research project,
-or simply support the founder of LAMMPStutorials.
-
-.. |patreon| raw:: html
-
-    <a href="https://www.patreon.com/molecularsimulations" target="_blank">patreon</a>
-
-
-
+or simply support the founder of LAMMPS tutorials.
diff --git a/docs/sphinx/source/non-tutorials/running-lammps.rst b/docs/sphinx/source/non-tutorials/running-lammps.rst
index 4d4bafd5..cdab9092 100644
--- a/docs/sphinx/source/non-tutorials/running-lammps.rst
+++ b/docs/sphinx/source/non-tutorials/running-lammps.rst
@@ -58,7 +58,7 @@ LAMMPS--GUI.  Among other options:
   windows is updated every 10 milliseconds.  Set this to 100 milliseconds or more
   if LAMMPS--GUI consumes too many resources during a run.  The ``Charts update interval``
   controls the time interval between redrawing the plots in the ``Charts`` window, in milliseconds.
--The ``Accelerators`` tab enables you to select an accelerator package
+- The ``Accelerators`` tab enables you to select an accelerator package
   for LAMMPS to use.  Only settings supported by the LAMMPS library and local hardware
   are available.  The ``Number of threads`` field allows you to set the maximum
   number of threads for accelerator packages that utilize threading.
diff --git a/docs/sphinx/source/non-tutorials/solutions.rst b/docs/sphinx/source/non-tutorials/solutions.rst
index c4b34aa6..ef0f6f43 100644
--- a/docs/sphinx/source/non-tutorials/solutions.rst
+++ b/docs/sphinx/source/non-tutorials/solutions.rst
@@ -9,12 +9,10 @@ Lennard Jones fluid
 Fix a broken input
 ------------------
 
-.. container:: justify
-
-    A possibility to make the simulation start without error
-    is to reduce the initial *timestep* value as well as
-    the imposed *temperature*. You can download the
-    working |input_broken_solution| I wrote. These are the main commands:
+A possibility to make the simulation start without error
+is to reduce the initial *timestep* value as well as
+the imposed *temperature*. You can download the
+working |input_broken_solution| I wrote. These are the main commands:
 
 .. |input_broken_solution| raw:: html
 
@@ -25,24 +23,18 @@ Fix a broken input
     fix mylgv all langevin 0.001 0.001 0.001 1530917
     timestep 0.0001
 
-.. container:: justify
-
-    Note that to make sure that the temperature of the particles
-    quickly reaches a reasonable value, the *damping* parameter
-    of the *fix Langevin* was also reduced to 0.001 (in time units) instead
-    of the 0.1 used in the rest of the tutorial.
-
-.. container:: justify
-
-    After the first *run* finishes, the energy of the system 
-    should be significantly reduced. Therefore, a second consecutive *run*
-    with the original *timestep* and *Langevin* parameters
-    can start without triggering the *Lost atoms* error. 
+Note that to make sure that the temperature of the particles
+quickly reaches a reasonable value, the *damping* parameter
+of the *fix Langevin* was also reduced to 0.001 (in time units) instead
+of the 0.1 used in the rest of the tutorial.
 
-.. container:: justify
+After the first *run* finishes, the energy of the system 
+should be significantly reduced. Therefore, a second consecutive *run*
+with the original *timestep* and *Langevin* parameters
+can start without triggering the *Lost atoms* error. 
 
-    In some cases, more than two consecutive *runs* with progressively
-    increasing timestep is necessary:
+In some cases, more than two consecutive *runs* with progressively
+increasing timestep is necessary:
 
 ..  code-block:: lammps
 
@@ -56,12 +48,10 @@ Fix a broken input
     timestep 0.01
     run 10000
 
-.. container:: justify
-
-    An alternative solution was proposed by Joni Suopanki from the University
-    of Oulu in Finland. His solution consists of making the LJ potential
-    softer by using small values for :math:`\sigma_{11}`, as least during the
-    very first steps of the simulation:  
+An alternative solution was proposed by Joni Suopanki from the University
+of Oulu in Finland. His solution consists of making the LJ potential
+softer by using small values for :math:`\sigma_{11}`, as least during the
+very first steps of the simulation:  
 
 ..  code-block:: lammps
 
@@ -75,10 +65,8 @@ Fix a broken input
 Create a demixed dense phase
 ----------------------------
 
-.. container:: justify
-
-    You can download the |input_demixed_solution| I wrote. Note that 
-    I use large numbers of particles: 8000 for each type. 
+You can download the |input_demixed_solution| I wrote. Note that 
+I use large numbers of particles: 8000 for each type. 
 
 .. |input_demixed_solution| raw:: html
 
@@ -95,69 +83,55 @@ Create a demixed dense phase
     pair_coeff 2 2 5.0 1.0
     pair_coeff 1 2 0.05 1.0
 
-.. container:: justify
+Here, both particle types have the same :math:`\sigma` value of 1.0
+so that both particles have the same diameter. There is a large energy
+parameter :math:`\epsilon_{11} = \epsilon_{22} = 5.0` for self-interaction (i.e. interaction 
+between particles of the same type), and a low 
+energy parameter :math:`\epsilon_{12} = 0.05` for interaction between particles
+of different types.
 
-    Here, both particle types have the same :math:`\sigma` value of 1.0
-    so that both particles have the same diameter. There is a large energy
-    parameter :math:`\epsilon_{11} = \epsilon_{22} = 5.0` for self-interaction (i.e. interaction 
-    between particles of the same type), and a low 
-    energy parameter :math:`\epsilon_{12} = 0.05` for interaction between particles
-    of different types.
-
-.. container:: justify
-
-    Finally, to easily adjust the system density and create a liquid-looking
-    phase, the pressure was imposed by replacing *fix nve* by *fix nph*:
+Finally, to easily adjust the system density and create a liquid-looking
+phase, the pressure was imposed by replacing *fix nve* by *fix nph*:
 
 ..  code-block:: lammps
 
     fix mynph all nph iso 1.0 1.0 1.0
 
-.. container:: justify
-
-    With *fix nph* and a pressure of 1, LAMMPS adjusts the box dimensions until the 
-    pressure is close to 1. Here, reaching a pressure of 1 requires reducing
-    the initial box dimensions.
+With *fix nph* and a pressure of 1, LAMMPS adjusts the box dimensions until the 
+pressure is close to 1. Here, reaching a pressure of 1 requires reducing
+the initial box dimensions.
 
 From atoms to molecules
 -----------------------
 
-.. container:: justify
-
-    You can download an example of |input_dumbbell_solution| for simulating
-    dumbell molecules. 
+You can download an example of |input_dumbbell_solution| for simulating
+dumbell molecules. 
     
 .. |input_dumbbell_solution| raw:: html
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial1/exercises/dumbbell/input.lammps" target="_blank">input</a>
 
-.. container:: justify
-
-    The first important change to make to the inputs from the
-    tutorial is the *atom_style*: an *atom_style* that allows for the atoms
-    to be connected by bonds is needed.
-    It is also necessary to specify the *bond_style*,
-    i.e. the type of potential (here harmonic) that will keep the atoms
-    together:
+The first important change to make to the inputs from the
+tutorial is the *atom_style*: an *atom_style* that allows for the atoms
+to be connected by bonds is needed.
+It is also necessary to specify the *bond_style*,
+i.e. the type of potential (here harmonic) that will keep the atoms
+together:
 
 ..  code-block:: lammps
 
     atom_style molecular
     bond_style harmonic
 
-.. container:: justify
-
-    When creating the box, it is necessary to make
-    memory space for the bond:
+When creating the box, it is necessary to make
+memory space for the bond:
 
 ..  code-block:: lammps
 
     create_box 2 simulation_box bond/types 1 extra/bond/per/atom 1
 
-.. container:: justify
-
-    Then, one just needs to import the *molecule template*, and use the template
-    when creating the atoms as follows:
+Then, one just needs to import the *molecule template*, and use the template
+when creating the atoms as follows:
 
 ..  code-block:: lammps
 
@@ -165,11 +139,9 @@ From atoms to molecules
     create_atoms 1 random 500 341341 simulation_box
     create_atoms 0 random 5 678865 simulation_box mol dumbell 8754
 
-.. container:: justify
-
-    You can download the molecule template by clicking |mol_dumbbell_solution|.
-    Finally, some parameters for the bond, namely its rigidity (5) and equilibrium
-    length (2.5) need to be specified:
+You can download the molecule template by clicking |mol_dumbbell_solution|.
+Finally, some parameters for the bond, namely its rigidity (5) and equilibrium
+length (2.5) need to be specified:
 
 ..  code-block:: lammps
 
@@ -179,11 +151,9 @@ From atoms to molecules
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial1/exercises/dumbbell/dumbell.mol" target="_blank">here</a>
 
-.. container:: justify
-
-    For the polymer, the angular potential must be defined to give its
-    rigidity to the polymer. You can download the |input_polymer_solution| and
-    |mol_polymer_solution|.
+For the polymer, the angular potential must be defined to give its
+rigidity to the polymer. You can download the |input_polymer_solution| and
+|mol_polymer_solution|.
     
 .. |input_polymer_solution| raw:: html
 
@@ -199,10 +169,8 @@ Pulling on a carbon nanotube
 Plot the strain-stress curves
 -----------------------------
 
-.. container:: justify
-
-    You can download the |input_stress_strain_solution1|
-    and |input_stress_strain_solution2| I wrote.
+You can download the |input_stress_strain_solution1|
+and |input_stress_strain_solution2| I wrote.
 
 .. |input_stress_strain_solution1| raw:: html
 
@@ -212,33 +180,25 @@ Plot the strain-stress curves
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial2/exercises/stress-strain/unbreakable-bonds/input.lammps" target="_blank">input for the unbreakable CNT</a>
 
-.. container:: justify
-
-    The stress is calculated as the total force
-    induced on the CNT by the pulling divided by the 
-    surface area of the CNT. 
+The stress is calculated as the total force
+induced on the CNT by the pulling divided by the 
+surface area of the CNT. 
 
-.. container:: justify
-    
-    On a side note, the surface area 
-    of a CNT is not a well-defined quantity. Here, I choose to 
-    define the area as the perimeter of the CNT multiplied by the 
-    effective width of the carbon atoms.
-
-.. container:: justify
+On a side note, the surface area 
+of a CNT is not a well-defined quantity. Here, I choose to 
+define the area as the perimeter of the CNT multiplied by the 
+effective width of the carbon atoms.
 
-    Be careful with units, as the force is either in kcal/mol/Å
-    when the unit is *real*, i.e. for the unbreakable CNT,
-    or in eV/Å when the unit is *metal*, i.e. for the breakable CNT.
+Be careful with units, as the force is either in kcal/mol/Å
+when the unit is *real*, i.e. for the unbreakable CNT,
+or in eV/Å when the unit is *metal*, i.e. for the breakable CNT.
 
 Solve the flying ice cube artifact
 ----------------------------------
 
-.. container:: justify
-
-    The issue occurs because the atoms have a large momentum in the 
-    :math:`x` direction, as can be seen by looking at the net velocity 
-    of the atoms in the *cnt_molecular.data* file.
+The issue occurs because the atoms have a large momentum in the 
+:math:`x` direction, as can be seen by looking at the net velocity 
+of the atoms in the *cnt_molecular.data* file.
 
 ..  code-block:: lammps
 
@@ -249,39 +209,29 @@ Solve the flying ice cube artifact
     25 0.007861090484107148 9.95045322688365e-06 -0.00014277147407215768
     (...)
 
-.. container:: justify
-
-    The Berendsen thermostat is trying to adjust the temperature of the
-    system by rescaling the velocity of the atoms, but fails due to the
-    large momentum of the system that makes it look like the system is
-    warm, since in MD temperature is measured from the kinetic energy.
-
-.. container:: justify
+The Berendsen thermostat is trying to adjust the temperature of the
+system by rescaling the velocity of the atoms, but fails due to the
+large momentum of the system that makes it look like the system is
+warm, since in MD temperature is measured from the kinetic energy.
 
-    This leads to the system appearing frozen. 
-    
-.. container:: justify
+This leads to the system appearing frozen. 
 
-    The solution is to cancel
-    the net momentum of the atoms, for instance by using *fix momentum*,
-    re-setting the velocity with the *velocity create* command,
-    or use a different thermostat.
+The solution is to cancel
+the net momentum of the atoms, for instance by using *fix momentum*,
+re-setting the velocity with the *velocity create* command,
+or use a different thermostat.
 
 Insert gas in the carbon nanotube
 ---------------------------------
 
-.. container:: justify
-
-    You can download the |input_gas_cnt| I wrote.
+You can download the |input_gas_cnt| I wrote.
 
 .. |input_gas_cnt| raw:: html
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial2/exercises/gas/input.lammps" target="_blank">input</a>
 
-.. container:: justify
-
-    The key is to modify the *.data* file
-    to make space for the second atom type 2.
+The key is to modify the *.data* file
+to make space for the second atom type 2.
 
 ..  code-block:: lammps
 
@@ -296,9 +246,7 @@ Insert gas in the carbon nanotube
     1 12.010700 # CA
     2 39.948 # Ar
 
-.. container:: justify
-
-    The *parm.lammps* must contain the second pair coeff:
+The *parm.lammps* must contain the second pair coeff:
 
 ..  code-block:: lammps
 
@@ -306,22 +254,18 @@ Insert gas in the carbon nanotube
     pair_coeff 2 2 0.232 3.3952 
     bond_coeff 1 469 1.4
 
-.. container:: justify
-
-    Combine the *region* and
-    *create_atoms* commands to
-    create the atoms of type 2 within the CNT:
+Combine the *region* and
+*create_atoms* commands to
+create the atoms of type 2 within the CNT:
 
 ..  code-block:: lammps
 
     region inside_CNT cylinder z 0 0 2.5 ${zmin} ${zmax}
     create_atoms 2 random 40 323485 inside_CNT overlap 1.8 maxtry 50
 
-.. container:: justify
-
-    It is good practice to thermalize the CNT separately from the 
-    gas to avoid having a large temperature difference between the two
-    type of atoms. 
+It is good practice to thermalize the CNT separately from the 
+gas to avoid having a large temperature difference between the two
+type of atoms. 
 
 ..  code-block:: lammps
 
@@ -332,10 +276,8 @@ Insert gas in the carbon nanotube
     fix myber2 all temp/berendsen ${T} ${T} 100
     fix_modify myber2 temp tgas
 
-.. container:: justify
-
-    Here I also choose to keep the CNT near its original
-    position, 
+Here I also choose to keep the CNT near its original
+position, 
 
 ..  code-block:: lammps
 
@@ -344,32 +286,24 @@ Insert gas in the carbon nanotube
 Make a membrane of CNTs
 -----------------------
 
-.. container:: justify
-
-    You can download the |input_membrane_solution1| I wrote.
+You can download the |input_membrane_solution1| I wrote.
 
 .. |input_membrane_solution1| raw:: html
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial2/exercises/membrane/input.lammps" target="_blank">input</a>
 
-.. container:: justify
-
-    The CNT can be replicated using the *replicate* command.
-    It is recommended to adjust the box size before replicating,
-    as done here using the *change_box* command.
+The CNT can be replicated using the *replicate* command.
+It is recommended to adjust the box size before replicating,
+as done here using the *change_box* command.
 
-.. container:: justify
-
-    To allow for the deformation of the box along the 
-    *xy* plane, the box has to be changed to triclinic first:
+To allow for the deformation of the box along the 
+*xy* plane, the box has to be changed to triclinic first:
 
 ..  code-block:: lammps
 
     change_box all triclinic
 
-.. container:: justify
-
-    Deformation can be imposed to the system using:
+Deformation can be imposed to the system using:
 
 ..  code-block:: lammps
 
@@ -381,21 +315,17 @@ Polymer in water
 Extract radial distribution function
 ------------------------------------
 
-.. container:: justify
-
-    You can download the |input_PEG_RDF| file I wrote. 
+You can download the |input_PEG_RDF| file I wrote. 
 
 .. |input_PEG_RDF| raw:: html
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial3/exercises/radial-distribution-function/input.lammps" target="_blank">input</a>
 
-.. container:: justify
-
-    I use the *compute rdf* command of LAMMPS
-    to extract the RDF between atoms of type 8 (oxygen from water)
-    and one of the oxygen types from the PEG (1).
-    The 10 first pico seconds are disregarded. Then, once the force
-    is applied to the PEG, a second *fix ave/time* is used.
+I use the *compute rdf* command of LAMMPS
+to extract the RDF between atoms of type 8 (oxygen from water)
+and one of the oxygen types from the PEG (1).
+The 10 first pico seconds are disregarded. Then, once the force
+is applied to the PEG, a second *fix ave/time* is used.
 
 ..  code-block:: lammps
         
@@ -406,11 +336,9 @@ Extract radial distribution function
 Add salt to the mixture
 -----------------------
 
-.. container:: justify
-
-    You can download the |input_PEG_salt|,
-    |data_PEG_salt|,
-    and |parm_PEG_salt| files I wrote. 
+You can download the |input_PEG_salt|,
+|data_PEG_salt|,
+and |parm_PEG_salt| files I wrote. 
 
 .. |input_PEG_salt| raw:: html
 
@@ -424,10 +352,8 @@ Add salt to the mixture
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial3/exercises/salt/PARM-with-salt.lammps" target="_blank">parm</a>
     
-.. container:: justify
-    
-    It is important to 
-    make space for the two salt atoms by modifying the data file as follows:
+It is important to 
+make space for the two salt atoms by modifying the data file as follows:
 
 ..  code-block:: lammps
 
@@ -435,11 +361,9 @@ Add salt to the mixture
     11 atom types
     (...)
 
-.. container:: justify
-
-    Additional *mass* and *pair_coeff* lines 
-    must also be added to the parm file (be careful to use the 
-    appropriate units):
+Additional *mass* and *pair_coeff* lines 
+must also be added to the parm file (be careful to use the 
+appropriate units):
 
 ..  code-block:: lammps
 
@@ -451,33 +375,25 @@ Add salt to the mixture
     pair_coeff 11 11 0.1500 4.045
     (...)
 
-.. container:: justify
-
-    Finally, here I choose to add the ions using two separate
-    *create_atoms* commands with a very small *overlap*
-    values, followed by an energy minimization. 
+Finally, here I choose to add the ions using two separate
+*create_atoms* commands with a very small *overlap*
+values, followed by an energy minimization. 
 
-.. container:: justify
-
-    Note also the presence of the *set* commands to
-    give a net charge to the ions.
+Note also the presence of the *set* commands to
+give a net charge to the ions.
 
 Evaluate the deformation of the PEG
 -----------------------------------
 
-.. container:: justify
-
-    You can download the |input_PEG_dihedral| file I wrote. 
+You can download the |input_PEG_dihedral| file I wrote. 
 
 .. |input_PEG_dihedral| raw:: html
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial3/exercises/structurePEG/input.lammps" target="_blank">input</a>
 
-.. container:: justify
-
-    The key is to combine the *compute dihedral/local*,
-    which computes the angles of the dihedrals and returns
-    them in a vector, with the *ave/histo* functionalities of LAMMPS:
+The key is to combine the *compute dihedral/local*,
+which computes the angles of the dihedrals and returns
+them in a vector, with the *ave/histo* functionalities of LAMMPS:
 
 ..  code-block:: lammps
 
@@ -485,11 +401,9 @@ Evaluate the deformation of the PEG
     fix myavehisto all ave/histo 10 2000 30000 0 180 500 c_mydihe &
         file initial.histo mode vector
 
-.. container:: justify
-
-    Here I choose to unfix *myavehisto* at the end of the first run,
-    and to re-start it with a different file name during the second phase
-    of the simulation.
+Here I choose to unfix *myavehisto* at the end of the first run,
+and to re-start it with a different file name during the second phase
+of the simulation.
 
 Nanosheared electrolyte
 =======================
@@ -497,16 +411,12 @@ Nanosheared electrolyte
 Induce a Poiseuille flow
 ------------------------
 
-.. container:: justify
+Here, the *input* script written during the last part *Imposed shearing* of the
+tutorial is adapted so that, instead of a shearing induced by the relative motion of the walls,
+the fluid motion is generated by an additional force applied to both water molecules and ions.
 
-    Here, the *input* script written during the last part *Imposed shearing* of the
-    tutorial is adapted so that, instead of a shearing induced by the relative motion of the walls,
-    the fluid motion is generated by an additional force applied to both water molecules and ions.
-    
-.. container:: justify
-
-    To do so, here are the most important commands used to properly
-    thermalize the system:
+To do so, here are the most important commands used to properly
+thermalize the system:
 
 ..  code-block:: lammps
         
@@ -518,44 +428,36 @@ Induce a Poiseuille flow
     fix myber2 wall temp/berendsen 300 300 100
     fix_modify myber2 temp twall
 
-.. container:: justify
-
-    Here, since walls wont move, they can be thermalized in all
-    3 directions and there is
-    no need for recentering. Instead, one can keep the walls 
-    in place by adding springs to every atom:
+Here, since walls wont move, they can be thermalized in all
+3 directions and there is
+no need for recentering. Instead, one can keep the walls 
+in place by adding springs to every atom:
 
 ..  code-block:: lammps
 
     fix myspring wall spring/self 10.0 xyz
 
-.. container:: justify
-
-    Finally, let us apply a force to the fluid group along the :math:`x`
-    direction:
+Finally, let us apply a force to the fluid group along the :math:`x`
+direction:
 
 ..  code-block:: lammps
 
     fix myadf fluid addforce 3e-2 0.0 0.0
 
-.. container:: justify
-
-    The choice of a force equal to :math:`f = 0.03\,\text{kcal/mol/Å}`
-    is discussed below.
-
-.. container:: justify
+The choice of a force equal to :math:`f = 0.03\,\text{kcal/mol/Å}`
+is discussed below.
 
-    One can have a look at the velocity profiles. The fluid shows the characteristic
-    parabolic shape of Poiseuille flow in the case of a non-slip solid surface.
-    To obtain smooth-looking data, I ran the simulation for a total duration of :math:`1\,\text{ns}`. 
-    To lower the duration of the computation, don't hesitate to
-    use a shorter duration like :math:`100\,\text{ps}`.
+One can have a look at the velocity profiles. The fluid shows the characteristic
+parabolic shape of Poiseuille flow in the case of a non-slip solid surface.
+To obtain smooth-looking data, I ran the simulation for a total duration of :math:`1\,\text{ns}`. 
+To lower the duration of the computation, don't hesitate to
+use a shorter duration like :math:`100\,\text{ps}`.
 
-.. figure:: ../tutorials/figures/level2/nanosheared-electrolyte/shearing-poiseuille-light.png
+.. figure:: solutions/shearing-poiseuille-light.png
     :alt: Velocity of the fluid forming a Poiseuille flow
     :class: only-light
 
-.. figure:: ../tutorials/figures/level2/nanosheared-electrolyte/shearing-poiseuille-dark.png
+.. figure:: solutions/shearing-poiseuille-dark.png
     :alt: Velocity of the fluid forming a Poiseuille flow
     :class: only-dark
 
@@ -564,66 +466,54 @@ Induce a Poiseuille flow
     Figure: Velocity profiles of the water molecules along the *z* axis (disks).
     The line is the Poiseuille equation.
     
-.. container:: justify
-
-    The fitting of the velocity profile was made using the following Poiseuille equation,
+The fitting of the velocity profile was made using the following Poiseuille equation,
 
 .. math::
 
     v = - \alpha \dfrac{f \rho}{\eta} \left( \dfrac{z^2}{2} - \dfrac{h^2}{8} \right),
 
-.. container:: justify
-
-    where :math:`\textbf{f}` is the applied force,
-    :math:`\rho` is the fluid density,
-    :math:`\eta` is the fluid viscosity, and
-    :math:`h = 1.2\,\text{nm}` is the pore size. The expression
-    for :math:`v` can be derived
-    from the Stokes equation :math:`\eta \nabla \textbf{v} = - \textbf{f} \rho`.
-    A small correction :math:`\alpha = 0.78` was necessary,
-    since using bulk density and bulk viscosity is obviously
-    not correct in such nanoconfined pores. More subtle corrections could be applied
-    by correcting both density and viscosity based on independent measurements, but this is 
-    beyond the scope of the present exercise.
+where :math:`\textbf{f}` is the applied force,
+:math:`\rho` is the fluid density,
+:math:`\eta` is the fluid viscosity, and
+:math:`h = 1.2\,\text{nm}` is the pore size. The expression
+for :math:`v` can be derived
+from the Stokes equation :math:`\eta \nabla \textbf{v} = - \textbf{f} \rho`.
+A small correction :math:`\alpha = 0.78` was necessary,
+since using bulk density and bulk viscosity is obviously
+not correct in such nanoconfined pores. More subtle corrections could be applied
+by correcting both density and viscosity based on independent measurements, but this is 
+beyond the scope of the present exercise.
 
-.. container:: justify
+**Choosing the right force**
 
-    **Choosing the right force**
-
-.. container:: justify
+The first and most important technical difficulty of any
+out-of-equilibrium simulation is to choose the value of the force :math:`f`.
+If the forcing is too large, the system may not be in a linear response regime,
+meaning that the results are forcing-dependent (and likely quite meaningless). If
+the forcing is too small, the motion of the system will be difficult to measure
+due to the low signal-to-noise ratio.
 
-    The first and most important technical difficulty of any
-    out-of-equilibrium simulation is to choose the value of the force :math:`f`.
-    If the forcing is too large, the system may not be in a linear response regime,
-    meaning that the results are forcing-dependent (and likely quite meaningless). If
-    the forcing is too small, the motion of the system will be difficult to measure
-    due to the low signal-to-noise ratio.
-
-.. container:: justify
-
-    In the present case, one can perform a calibration by running several simulations 
-    with different force values :math:`f`, and then by plotting the velocity of
-    the center of mass :math:`v_\text{cm}` of the fluid as a function of the force.
-    Here, I present the results I have obtained by performing the simulations with 
-    different values of the forcing. :math:`v_\text{cm}` can be extracted by adding the following command
-    to the *input*:
+In the present case, one can perform a calibration by running several simulations 
+with different force values :math:`f`, and then by plotting the velocity of
+the center of mass :math:`v_\text{cm}` of the fluid as a function of the force.
+Here, I present the results I have obtained by performing the simulations with 
+different values of the forcing. :math:`v_\text{cm}` can be extracted by adding the following command
+to the *input*:
 
 ..  code-block:: lammps
 
     variable vcm_fluid equal vcm(fluid,x)
     fix myat1 all ave/time 10 100 1000 v_vcm_fluid file vcm_fluid.dat
 
-.. container:: justify
-
-    The results show that as long as the force is lower
-    than about :math:`0.04\,\text{kcal/mol/Å}`, there is reasonable linearity
-    between force and fluid velocity.
+The results show that as long as the force is lower
+than about :math:`0.04\,\text{kcal/mol/Å}`, there is reasonable linearity
+between force and fluid velocity.
 
-.. figure:: ../tutorials/figures/level2/nanosheared-electrolyte/calibration-force-light.png
+.. figure:: solutions/calibration-force-light.png
     :alt: Velocity of the fluid under imposed force (POISEUILLE FLOW)
     :class: only-light
 
-.. figure:: ../tutorials/figures/level2/nanosheared-electrolyte/calibration-force-dark.png
+.. figure:: solutions/calibration-force-dark.png
     :alt: Velocity of the fluid under imposed force (POISEUILLE FLOW)
     :class: only-dark
 
@@ -638,12 +528,10 @@ Water adsorption in silica
 Mixture adsorption
 ------------------
 
-.. container:: justify
-
-    You can download the |input_mixture| for the combine water and CO2
-    adsorption.
-    One of the first steps is to create both types of molecules
-    before starting the GCMC:
+You can download the |input_mixture| for the combine water and CO2
+adsorption.
+One of the first steps is to create both types of molecules
+before starting the GCMC:
 
 ..  code-block:: lammps
 
@@ -654,10 +542,8 @@ Mixture adsorption
     create_atoms 0 random 5 373823 NULL &
         mol co2mol 989812 overlap 2.0 maxtry 50
 
-.. container:: justify
-
-    One must be careful to properly write the parameters of the system,
-    with all the proper cross coefficients:
+One must be careful to properly write the parameters of the system,
+with all the proper cross coefficients:
 
 ..  code-block:: lammps
 
@@ -677,9 +563,7 @@ Mixture adsorption
     pair_coeff 5 5 lj/cut/tip4p/long 0.0179 2.625854
     pair_coeff 6 6 lj/cut/tip4p/long 0.0106 2.8114421 
 
-.. container:: justify
-
-    Here, I choose to thermalize all species separately:
+Here, I choose to thermalize all species separately:
 
 ..  code-block:: lammps
 
@@ -697,10 +581,8 @@ Mixture adsorption
     fix mynvt3 SiO nvt temp 300 300 0.1
     fix_modify mynvt3 temp ctSiO
 
-.. container:: justify
-
-    Finally, adsorption is made with two separate *fix gcmc* commands
-    placed in a loop: 
+Finally, adsorption is made with two separate *fix gcmc* commands
+placed in a loop: 
 
 ..  code-block:: lammps
 
@@ -722,12 +604,10 @@ Mixture adsorption
     next a
     jump SELF loop
 
-.. container:: justify
-
-    Here I choose to apply the first *fix gcmc* for the :math:`\text{H}_2\text{O}` for 500 steps,
-    then unfix it before starting the second *fix gcmc* for the :math:`\text{CO}_2` for 500 steps as well.
-    Then, thanks to the *jump*, these two fixes are applied successively 30 times each, allowing for the 
-    progressive adsorption of both species.
+Here I choose to apply the first *fix gcmc* for the :math:`\text{H}_2\text{O}` for 500 steps,
+then unfix it before starting the second *fix gcmc* for the :math:`\text{CO}_2` for 500 steps as well.
+Then, thanks to the *jump*, these two fixes are applied successively 30 times each, allowing for the 
+progressive adsorption of both species.
 
 .. |input_mixture| raw:: html
 
@@ -736,34 +616,27 @@ Mixture adsorption
 Adsorb water in ZIF-8 nanopores
 -------------------------------
 
-.. container:: justify
-
-    You can download the |input_zif| for the water adsorption in Zif-8,
-    which you have to place in the same folder as the *zif-8.data*,
-    *parm.lammps*,
-    and *water.mol* files.
+You can download the |input_zif| for the water adsorption in Zif-8,
+which you have to place in the same folder as the *zif-8.data*,
+*parm.lammps*, and *water.mol* files.
 
 .. |input_zif| raw:: html
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial6/Exercises/Zif-8/input.lammps" target="_blank">input</a>
 
-.. container:: justify
+Apart from the parameters and topology, the *input* is
+quite similar to the one developed in the case of the crack
+silica.
 
-    Apart from the parameters and topology, the *input* is
-    quite similar to the one developed in the case of the crack
-    silica.
+You should observe an increase in the number of molecules with time.
+Run a much longer simulation if you want to saturate the porous material
+with water.
 
-.. container:: justify
-
-    You should observe an increase in the number of molecules with time.
-    Run a much longer simulation if you want to saturate the porous material
-    with water.
-
-.. figure:: ../tutorials/figures/level3/water-adsorption-in-silica/number_evolution_zif-light.png
+.. figure:: solutions/number_evolution_zif-light.png
     :alt: Water molecule in Zif material with GCMC in LAMMPS
     :class: only-light
 
-.. figure:: ../tutorials/figures/level3/water-adsorption-in-silica/number_evolution_zif-dark.png
+.. figure:: solutions/number_evolution_zif-dark.png
     :alt: Water molecule in Zif material with GCMC in LAMMPS
     :class: only-dark
 
@@ -777,22 +650,16 @@ Free energy calculation
 The binary fluid that won't mix
 -------------------------------
 
-..  container:: justify
-
-    You can download the |input_binary_wont_mix| here.
+You can download the |input_binary_wont_mix| here.
 
 .. |input_binary_wont_mix| raw:: html
 
     <a href=".../../../../../.dependencies/lammpstutorials-inputs/tutorial7/Exercises/BinaryFluid/input.lammps" target="_blank">input</a>
 
-..  container:: justify
-
-    The solution chosen here was to create two groups (*t1* and *t2*)
-    and apply the two potentials *U1* and *U2* to each group, respectively. 
-
-..  container:: justify
+The solution chosen here was to create two groups (*t1* and *t2*)
+and apply the two potentials *U1* and *U2* to each group, respectively. 
 
-    To to so, two separate *fix addforce* are used:
+To to so, two separate *fix addforce* are used:
 
 ..  code-block:: lammps
     
@@ -812,50 +679,38 @@ The binary fluid that won't mix
     fix myadf2 t2 addforce v_F2 0.0 0.0 energy v_U2
     fix_modify myadf2 energy yes
 
-..  container:: justify
-
-    60 particles of each type are created, with both types having
-    the same properties:
+60 particles of each type are created, with both types having
+the same properties:
 
 ..  code-block:: lammps
 
     mass * 39.95
     pair_coeff * * ${epsilon} ${sigma}
 
-..  container:: justify
-
-    Feel free to insert some size or mass asymmetry in the mixture, and test how/if
-    it impacts the final potential.
+Feel free to insert some size or mass asymmetry in the mixture, and test how/if
+it impacts the final potential.
 
 Particles under convection
 --------------------------
 
-..  container:: justify
-
-    Add a forcing to all the particles using:
+Add a forcing to all the particles using:
 
 ..  code-block:: lammps
 
     fix myconv all addforce 2e-6 0 0
 
-..  container:: justify
-
-    It is crucial to choose a forcing that is not *too large*, or the simulation may crash. 
-    A forcing that is *too weak* won't have any effect on the PMF.  
+It is crucial to choose a forcing that is not *too large*, or the simulation may crash. 
+A forcing that is *too weak* won't have any effect on the PMF.  
 
-..  container:: justify
-
-    One can see from the result that the measured potential
-    is tilted, which is a consequence of the additional force that makes it easier for 
-    the particles to cross the potential in one of the directions. The barrier is also 
-    reduced compared to the case in the absence of additional forcing. 
+One can see from the result that the measured potential
+is tilted, which is a consequence of the additional force that makes it easier for 
+the particles to cross the potential in one of the directions. The barrier is also 
+reduced compared to the case in the absence of additional forcing. 
 
 Surface adsorption of a molecule
 --------------------------------
 
-..  container:: justify
-
-    You can download the |input_adsorption_ethanol| here.
+You can download the |input_adsorption_ethanol| here.
 
 .. |input_adsorption_ethanol| raw:: html
 
@@ -1059,9 +914,7 @@ Reactive silicon dioxide
 Hydrate the structure
 ---------------------
 
-.. container:: justify
-
-    Create a molecule template named *H2O.mol*: 
+Create a molecule template named *H2O.mol*: 
 
 .. code-block:: lammps
 
@@ -1085,29 +938,23 @@ Hydrate the structure
     2        0.5564
     3        0.5564
 
-.. container:: justify
-
-    Then, download the proposed input |input_reax_water|.
+Then, download the proposed input |input_reax_water|.
 
 .. |input_reax_water| raw:: html
 
     <a href="../../../../../.dependencies/lammpstutorials-inputs/tutorial5/Exercices/Hydrate/input.lammps" target="_blank">here</a>
 
-.. container:: justify
-
-    As seen in the *input.lammps* file, the molecules are added to the system
-    using the *create_atoms* command:
+As seen in the *input.lammps* file, the molecules are added to the system
+using the *create_atoms* command:
 
 .. code-block:: lammps
 
     molecule h2omol H2O.mol
     create_atoms 0 random 10 805672 NULL overlap 2.6 maxtry 50 mol h2omol 45585
 
-.. container:: justify
-
-    Some water molecules react with the silica structure during the
-    simulation, leading to the formation of :math:`-OH` group at the solid
-    surface:
+Some water molecules react with the silica structure during the
+simulation, leading to the formation of :math:`-OH` group at the solid
+surface:
 
 .. code-block:: lammps
 
@@ -1120,11 +967,9 @@ Hydrate the structure
 A slightly acidic bulk solution
 -------------------------------
 
-.. container:: justify
-
-    Download the input |input_reax_water_2| as
-    well as the |reaxCHOFe_ff_ex|
-    file. In addition, create a molecule template named *H2O.mol*: 
+Download the input |input_reax_water_2| as
+well as the |reaxCHOFe_ff_ex|
+file. In addition, create a molecule template named *H2O.mol*: 
 
 .. |input_reax_water_2| raw:: html
 
@@ -1156,25 +1001,19 @@ A slightly acidic bulk solution
     2        0.5564
     3        0.5564
 
-.. container:: justify
-
-    Within *input.lammps*, water molecules are created first:
+Within *input.lammps*, water molecules are created first:
 
 .. code-block:: lammps
 
     molecule h2omol H2O.mol
     create_atoms 0 box mol h2omol 45585
 
-.. container:: justify
-
-    Then, a few hydrogen atoms (:math:`H^+`) are added randomly to the system
-    to make the solution slightly acidic:
+Then, a few hydrogen atoms (:math:`H^+`) are added randomly to the system
+to make the solution slightly acidic:
 
 .. code-block:: lammps
 
     create_atoms 2 random 1 305672 NULL overlap 0.5 maxtry 200
 
-.. container:: justify
-
-    As the simulation progresses, some :math:`H_3O^+` ions will form thanks to
-    the reactive force field.
\ No newline at end of file
+As the simulation progresses, some :math:`H_3O^+` ions will form thanks to
+the reactive force field.
\ No newline at end of file
diff --git a/docs/sphinx/source/non-tutorials/solutions/calibration-force-dark.png b/docs/sphinx/source/non-tutorials/solutions/calibration-force-dark.png
new file mode 100644
index 00000000..01ebf02b
Binary files /dev/null and b/docs/sphinx/source/non-tutorials/solutions/calibration-force-dark.png differ
diff --git a/docs/sphinx/source/non-tutorials/solutions/calibration-force-light.png b/docs/sphinx/source/non-tutorials/solutions/calibration-force-light.png
new file mode 100644
index 00000000..33d2cc17
Binary files /dev/null and b/docs/sphinx/source/non-tutorials/solutions/calibration-force-light.png differ
diff --git a/docs/sphinx/source/non-tutorials/solutions/number_evolution-dm.png b/docs/sphinx/source/non-tutorials/solutions/number_evolution-dm.png
new file mode 100644
index 00000000..fcc94033
Binary files /dev/null and b/docs/sphinx/source/non-tutorials/solutions/number_evolution-dm.png differ
diff --git a/docs/sphinx/source/non-tutorials/solutions/number_evolution-pyplot.ipynb b/docs/sphinx/source/non-tutorials/solutions/number_evolution-pyplot.ipynb
new file mode 100644
index 00000000..92c4907f
--- /dev/null
+++ b/docs/sphinx/source/non-tutorials/solutions/number_evolution-pyplot.ipynb
@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e485e34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import sys, os, git, lammps_logfile\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "64adad12",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "level: level3 & tutorial name: water-adsorption-in-silica\n",
+      "data path:  /home/simon/Git/LAMMPS/tutorials/docs/lammpstutorials-inputs/level3/water-adsorption-in-silica/AddingWater/\n"
+     ]
+    }
+   ],
+   "source": [
+    "current_path = os.getcwd()\n",
+    "git_repo = git.Repo(current_path, search_parent_directories=True)\n",
+    "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n",
+    "path_in_folder = current_path[len(git_path)+1:]\n",
+    "level = path_in_folder.split(\"/\")[-2]\n",
+    "tutorial_name = path_in_folder.split(\"/\")[-1]\n",
+    "print(\"level:\" , level, \"& tutorial name:\", tutorial_name)\n",
+    "sys.path.append(git_path + \"/docs/sphinx/source/tutorials/figures/pyplot-perso\")\n",
+    "from plttools import PltTools\n",
+    "path_figures = current_path[len(git_path):] + '/'\n",
+    "data_path = git_path + \"/docs/lammpstutorials-inputs/\" + level + \"/\" + tutorial_name + \"/AddingWater/\"\n",
+    "print(\"data path: \", data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "eb58057b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbermolecule = np.loadtxt(data_path + \"numbermolecule.dat\")\n",
+    "time, numbermolecule = numbermolecule.T[0], numbermolecule.T[1]\n",
+    "time /= 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c5860d2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"number_evolution\"\n",
+    "for dark_mode in [False, True]:\n",
+    "    myplt = PltTools()\n",
+    "    myplt.prepare_figure(fig_size = (18,6), dark_mode = dark_mode,\n",
+    "                        transparency = True, use_serif=True, n_line=1)\n",
+    "    myplt.add_panel()\n",
+    "    myplt.add_plot(x = time, y = numbermolecule, linewidth_data = 3,\n",
+    "                   marker = \"o\", data_color = 0, markersize = 12)\n",
+    "    x = np.arange(20, 48)\n",
+    "    myplt.add_plot(x = x*0+5, y = x, marker= '--', data_color=\"autogray\", linewidth=3.5)\n",
+    "    myplt.complete_panel(ylabel = r'$\\mathrm{number ~ of ~ molecules}$',\n",
+    "                         xlabel = r'$t ~ \\mathrm{(ps)}$',\n",
+    "                         xpad = 10, legend=False, handlelength_legend=1)\n",
+    "    #myplt.set_boundaries(x_ticks=np.arange(-2., 2.2, 0.5), x_boundaries=(-2.3, 2.3),\n",
+    "    #               y_ticks=np.arange(0, 0.033, 0.01))\n",
+    "    #myplt.add_subplotlabels()\n",
+    "    myplt.save_figure(filename = filename, saving_path = './')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d32b33ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"number-article\"\n",
+    "myplt = PltTools()\n",
+    "myplt.prepare_figure(fig_size = (12,6), dark_mode = False,\n",
+    "                    transparency = False, use_serif=False, n_line=1)\n",
+    "myplt.add_panel()\n",
+    "myplt.add_plot(x = time, y = numbermolecule, linewidth_data = 3,\n",
+    "                marker = \"o\", data_color = 0, markersize = 12)\n",
+    "x = np.arange(20, 48)\n",
+    "myplt.add_plot(x = x*0+5, y = x, marker= '--', data_color=\"autogray\", linewidth=3.5)\n",
+    "myplt.complete_panel(ylabel = r'$N_\\mathrm{H2O}$',\n",
+    "                     xlabel = r'$t ~ \\mathrm{(ps)}$',\n",
+    "                     xpad = 10, legend=False, handlelength_legend=1)\n",
+    "#myplt.set_boundaries(x_ticks=np.arange(-2., 2.2, 0.5), x_boundaries=(-2.3, 2.3),\n",
+    "#               y_ticks=np.arange(0, 0.033, 0.01))\n",
+    "#myplt.add_subplotlabels()\n",
+    "myplt.save_figure(filename = filename, saving_path = './')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.6 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/sphinx/source/non-tutorials/solutions/number_evolution.png b/docs/sphinx/source/non-tutorials/solutions/number_evolution.png
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new file mode 100644
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diff --git a/docs/sphinx/source/non-tutorials/solutions/number_evolution_zif-light.png b/docs/sphinx/source/non-tutorials/solutions/number_evolution_zif-light.png
new file mode 100644
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diff --git a/docs/sphinx/source/non-tutorials/solutions/shearing-poiseuille-dark.png b/docs/sphinx/source/non-tutorials/solutions/shearing-poiseuille-dark.png
new file mode 100644
index 00000000..300368d4
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diff --git a/docs/sphinx/source/non-tutorials/solutions/shearing-poiseuille-light.png b/docs/sphinx/source/non-tutorials/solutions/shearing-poiseuille-light.png
new file mode 100644
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diff --git a/docs/sphinx/source/non-tutorials/solutions/shearing-pyplot.ipynb b/docs/sphinx/source/non-tutorials/solutions/shearing-pyplot.ipynb
new file mode 100644
index 00000000..018c6a8a
--- /dev/null
+++ b/docs/sphinx/source/non-tutorials/solutions/shearing-pyplot.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e485e34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import sys, os, git, lammps_logfile\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ebd8e79e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "level: level2 & tutorial name: nanosheared-electrolyte\n",
+      "data path:  /home/simon/Git/LAMMPS/tutorials/docs/lammpstutorials-inputs/level2/nanosheared-electrolyte/shearing/\n"
+     ]
+    }
+   ],
+   "source": [
+    "current_path = os.getcwd()\n",
+    "git_repo = git.Repo(current_path, search_parent_directories=True)\n",
+    "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n",
+    "path_in_folder = current_path[len(git_path)+1:]\n",
+    "level = path_in_folder.split(\"/\")[-2]\n",
+    "tutorial_name = path_in_folder.split(\"/\")[-1]\n",
+    "print(\"level:\" , level, \"& tutorial name:\", tutorial_name)\n",
+    "sys.path.append(git_path + \"/docs/sphinx/source/tutorials/figures/pyplot-perso\")\n",
+    "from plttools import PltTools\n",
+    "path_figures = current_path[len(git_path):] + '/'\n",
+    "data_path = git_path + \"/docs/lammpstutorials-inputs/\" + level + \"/\" + tutorial_name + \"/shearing/\"\n",
+    "print(\"data path: \", data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "822a8e31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, z, _, density_water, velocity_water = np.loadtxt(data_path + \"water.profile_1A.dat\", skiprows=4).T\n",
+    "_, z, _, density_solid, velocity_solid = np.loadtxt(data_path + \"wall.profile_1A.dat\", skiprows=4).T\n",
+    "_, z, _, density_ions, velocity_ions = np.loadtxt(data_path + \"ions.profile_1A.dat\", skiprows=4).T\n",
+    "z /= 10 # nm\n",
+    "velocity_water *= 1e5 # m/s\n",
+    "velocity_solid *= 1e5 # m/s\n",
+    "velocity_ions *= 1e5 # m/s\n",
+    "\n",
+    "hs = 1.8e-9 # m\n",
+    "zs = np.arange(-hs/2, hs/2, hs/1000) # m\n",
+    "vs = zs * 20*2/hs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4101e26d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_wall = np.mean(velocity_solid[(density_solid>0) & (z>0)])\n",
+    "z0 = np.linspace(np.min(z[(density_solid>0) & (z>0)]), np.max(z[(density_solid>0) & (z>0)]), 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1800x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"shearing\"\n",
+    "for dark_mode in [False, True]:\n",
+    "    myplt = PltTools()\n",
+    "    myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n",
+    "                        transparency = True, use_serif=True, n_line=1)\n",
+    "    myplt.add_panel()\n",
+    "    myplt.add_plot(x = z[density_water>0.02], y = velocity_water[density_water>0.02], linewidth_data = 3,\n",
+    "                   marker = \"o\", data_color = 0, markersize = 12, data_label=r'$\\mathrm{water}$')\n",
+    "    myplt.add_plot(x = z0, y = z0*0+v_wall, linewidth_data = 3,\n",
+    "                   marker = \"s\", data_color = \"autogray\", markersize = 12, data_label=r'$\\mathrm{wall}$')\n",
+    "    myplt.add_plot(x = -z0, y = z0*0-v_wall, linewidth_data = 3,\n",
+    "                   marker = \"s\", data_color = \"autogray\", markersize = 12)\n",
+    "    myplt.add_plot(x = zs*1e9, y = vs, linewidth_data = 3,\n",
+    "                   marker = \"-\", data_color = 1, markersize = 12, data_label=r'$\\mathrm{linear~fit}$')\n",
+    "    myplt.complete_panel(ylabel = r'$v_x ~ \\mathrm{(m/s)}$', xlabel = r'$z ~ \\mathrm{(nm)}$',\n",
+    "                         xpad = 10, legend=True, handlelength_legend=1)\n",
+    "    myplt.set_boundaries(x_ticks=np.arange(-1.2, 1.3, 0.3), x_boundaries=(-1.3, 1.3),\n",
+    "                   y_ticks=np.arange(-30, 40, 10), y_boundaries=(-32.5, 32.5))\n",
+    "    # myplt.add_subplotlabels()\n",
+    "    myplt.save_figure(filename = filename, saving_path = './')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "38ef017e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, z, _, density_water, velocity_water = np.loadtxt(data_path + \"water.profile_0.1A.dat\", skiprows=4).T\n",
+    "_, z, _, density_solid, velocity_solid = np.loadtxt(data_path + \"wall.profile_0.1A.dat\", skiprows=4).T\n",
+    "_, z, _, density_ions, velocity_ions = np.loadtxt(data_path + \"ions.profile_0.1A.dat\", skiprows=4).T\n",
+    "z /= 10 # nm\n",
+    "velocity_water *= 1e5 # m/s\n",
+    "velocity_solid *= 1e5 # m/s\n",
+    "velocity_ions *= 1e5 # m/s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d725371c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"density\"\n",
+    "for dark_mode in [False, True]:\n",
+    "    myplt = PltTools()\n",
+    "    myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n",
+    "                        transparency = True, use_serif=True, n_line=1)\n",
+    "    myplt.add_panel()\n",
+    "    myplt.add_plot(x = z[density_water>0.02], y = density_water[density_water>0.02], linewidth_data = 3,\n",
+    "                   marker = \"-\", data_color = 0, markersize = 12, data_label=r'$\\mathrm{water}$')\n",
+    "    myplt.complete_panel(ylabel = r'$\\rho ~ (\\mathrm{g/mol/\\AA}^3$)', xlabel = r'$z ~ \\mathrm{(nm)}$',\n",
+    "                         xpad = 10, legend=False, handlelength_legend=1)\n",
+    "    myplt.set_boundaries(x_ticks=np.arange(-1.2, 1.3, 0.3), x_boundaries=(-1.3, 1.3),\n",
+    "                y_ticks=np.arange(0, 3.1, 1), y_boundaries=(-0.15, 3.15))\n",
+    "    # myplt.add_subplotlabels()\n",
+    "    myplt.save_figure(filename = filename, saving_path = './')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4a156d70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"velocity-article\"\n",
+    "myplt = PltTools()\n",
+    "myplt.prepare_figure(fig_size = (12,5), dark_mode = False,\n",
+    "                    transparency = False, use_serif=False, n_line=1)\n",
+    "myplt.add_panel()\n",
+    "myplt.add_plot(x = z[density_water>0.02], y = velocity_water[density_water>0.02], linewidth_data = 3,\n",
+    "                marker = \"o\", data_color = 0, markersize = 12, data_label=r'$\\mathrm{water}$')\n",
+    "myplt.add_plot(x = z0, y = z0*0+v_wall, linewidth_data = 3,\n",
+    "                marker = \"s\", data_color = \"autogray\", markersize = 12, data_label=r'$\\mathrm{wall}$')\n",
+    "myplt.add_plot(x = -z0, y = z0*0-v_wall, linewidth_data = 3,\n",
+    "                marker = \"s\", data_color = \"autogray\", markersize = 12)\n",
+    "myplt.add_plot(x = zs*1e9, y = vs, linewidth_data = 3,\n",
+    "                marker = \"-\", data_color = 1, markersize = 12, data_label=r'$\\mathrm{linear~fit}$')\n",
+    "myplt.complete_panel(ylabel = r'$v_x ~ \\mathrm{(m/s)}$', xlabel = r'$z ~ \\mathrm{(nm)}$',\n",
+    "                        xpad = 10, legend=True, handlelength_legend=1)\n",
+    "myplt.set_boundaries(x_ticks=np.arange(-1.2, 1.3, 0.3), x_boundaries=(-1.3, 1.3),\n",
+    "                y_ticks=np.arange(-30, 40, 10), y_boundaries=(-32.5, 32.5))\n",
+    "# myplt.add_subplotlabels()\n",
+    "myplt.save_figure(filename = filename, saving_path = './')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3583c9fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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e7dmzR6dPn9YjjzySt4SUJDU1NenIkSM6duyYampqFI/HtWvXLj3zzDN5iwEAAAAAgEwc/OhMRgkpSbo2dV2vjp7JUUSAPXKelDpw4ICefvppxeNxNTY26tSpU2ppacn1yy6otrZWx48f14EDB+TxeBQIBNTc3FzQmAAAAAAAmC0yeVlDl+bvMbWQwTFTkckrNkcE2CenSanXXntNL7zwguLxuPbu3avDhw/ntTJqMX6/X2+99ZZuv/12DQ4OFjxZBgAAAABAqmD0gsXjP7cpEsB+OZ2+t2vXLhmGoWPHjmU8RS9ffD6fTp48qe3bt6u/v19vvfWW7rvvvkKHBThOotnix7FJxs4CAAAAefJxbNLS8Z9MWDseyKWcJaVisZh8Pp+OHz+uykrnN1Y7cuSI2tvbNT4+XuhQAEcZNSd08KMzc5YMM3YWAAAAyK2rU1OWjr/ylbXjgVzK6fa9w4cPuyIhldDT06PNmzcXOgzAMQYujunJgaFF97APXTL15MCQBi6O5SkyAAAAoDQsLy+3dPyKpdaOB3IpZ0kpj8fj2C17C3FjzEAujJoT+un7H+raVHpTPq5NXdfzH3yoUXMix5EBAAAApeMuT4Wl4++stHY8kEs57SkFwL2sjJ3d11CXo6gAAACA4rJY71a/d43eiH6W9fn93ltsjBawF0kpF/rhD3+om2666YY/b2trU3t7ewEiQrGxY+ysr2KFzVEBAAAAxSOT3q11q42srs/rqwyuy5EzPT09Onjw4A1//uWXX6Z9jpwmpSYmJhQKhTQ+Pi6v10u/JpuMjc3dt2dykqkKsIcdY2cfrf6WTdEAAAAAxWXg4pie/2DxVhmJ3q3/11136LexibRba0jSsvIlenjdWquhAvOanJzUhQvW3jtmnJQaHh7W0NCQJKmxsVG33377nM/buXOn+vr6ZvyZx+PR448/rp07d2YRKhKqqqrmrJSqqGCvMOzB2FkAAAAgNxK9W9NtlXFt6rr+n4/P6v+66w79Px+fTSsxtax8iZ7buIHp2MipiooKrVmz5oY///LLL+ctppkto6TUgw8+qFAoNOPP6urq1NPTo29+85szntff35/82DAMmaYp0zTV1dWlwcFBvfLKK5m8NFL86le/UkNDQ6HDQBFj7CwAAACQG9n2bj35j/+sfQ11enX0jAbH5t/KV19l6OF1a0lIIefa29vnbCE0MDCgBx54IK1zpD19L5Foisfj2rx5s2pqahSPxzU4OKgtW7bo008/lSTt3r1b/f39am5u1smTJ/Xpp5/q9OnTGhkZ0bPPPqt4PK5gMKgDBw6k+9IA8oyxswAAAID9rPZuXVZern0Ndfrl5m/rB95bVbPaoz+pXKma1R79wHurfrn5O9rXUEdCCq6RVqVUKBRSf3+/6urqdOTIEXk8HklSLBbTk08+qbfeekvbt2/XgQMH1Nvbq7179+rBBx+ccQ6Px6OOjg41Nzfr3nvvVVdXl1paWlRZyTcL4DR3eSp0ejyW9fGMnQUAAABuZFfvVl/FSnq4oiikVSkVCARUVlam119/PZmQkqYTTQcPHtTmzZt19uxZbdu2TXV1dTckpFL5fD498sgjknTDVkAAzuD33rgvOLPjGTsLAAAAzEbvVmCmtJJSw8PDqq2tnbeqqaenR9J05VRjY+Oi53vooYcUj8dn9J0C4By+ipWqW21kdSxjZwEAAIC50bsVmCmtpFQkEpHX65337w3DUG1traTpSqjFrFq1SpI0Pj6ezssDKIC29Wu1rDzttnOSGDsLAAAALITercBMab3jNAxD0Wh0wefU1dVJ+jrhBMDd1hmVem7jhrQTU4ydBQAAABZ2l8da71V6t6LYpPVus7a2VuFweMHn1NXVKR6Pp/Wig4ODkqSNGzem9XwAhdFwc5X2NdSpvmrhrXz1VYb2NdSp4eaqPEUGAAAAuA+9W4GZ0pq+19raqhMnTujAgQPauXPnnM+pr69XR0dHchvfQoLBoMrKytTc3JxZtADybp1RqX0NdYpMXlYwekGfTEzqyldTWrG0XHdWVsjvvYUeUgAAAEAaEr1bhy6ZGR9L71YUo7SSUn6/X16vV11dXfL7/frmN795w3NqampUU1Oz6LlisZh6e3vV1NSk22+/PfOIARSE1bGziaTWx7FJXZ2a0vLyct3lqZDfu0a+ipU2RgoAAAA4V9v6tXpyYEjXpq6nfQy9W1Gs0u5i3NPTo3g8ri1btuj06dNZv2B7e7vKysr07LPPZn0OAO4xak7oiXeH9KMT7+mN6Gc6PR7T7ycu6/R4TG9EP9OPTrynJ94d0qg5UehQAQAAgJyjdyvwtbSTUrW1tXrttdc0Pj6u//gf/6POnTuX8Yvt3r1boVBInZ2daVVVAXC3gYtjenJgaNHy5KFLpp4cGNLAxbE8RQYAAAAUDr1bgWlpbd9LaGpq0sjIiCTJ4/Fk9EKhUEjRaFR79uxRS0tLRscCcJ9Rc0I/ff9DfXE9vbLka1PX9fwHH2pfQx13gQAAAFD06N0KZJiUkjJPRiU0NjaqsbExq2MBuM/Bj86knZBKuDZ1Xa+OntG+hrocRQUAAAA4i9XerYCbpb19DwDSFZm8nNVEEUkaHDMVmbxic0QAAAAAAKcp+qTUiRMntHPnzkKHAZSUYPSCxeM/tykSAAAAAIBTZbx9zw2Gh4cVCAR09OhRxWKxQocDlJyPY5OWjv9kwtrxAAAAAADnK5qk1MTEhHp7e3Xo0CFFo1FJUjwelySVlZUVMjSg5FydmrJ0/JWvrB0PAAAAAHA+1yel3nrrLQUCAYVCIUlzJ6Kam5sLEhtQqpaXl1s6fsVSa8cDAAAAAJzPlUmpEydOKBgMqre3N/ln8Xh8RiLK4/GopaVFra2t8nq9hQgTKFl3eSp0ejz7rbN3VlbYGA0AAAAAwIlck5Q6d+6cAoGAAoFAsk9UoioqwePxyO/366GHHlJNTU0hwgQgye9dozein1k4/hYbowEAAAAAOJGjk1ITExM6evSoAoGAwuGwpBsTUdL09rz777+fbXqAQ/gqVqputaGhS2bGx9ZXGfJVrMhBVAAAAAAAJ3FkUurEiRMKBALq6+tL/tns7XnSdN+oY8eOURUFOFDb+rV6cmBI16aup33MsvIlenjd2hxGBQAAAABwipwkpU6cOKH6+npVVlamfUy62/NaWlpkmmayn5TP57MvcAC2WWdU6rmNG/T8Bx+mlZhaVr5Ez23coHVG+j83AAAAAADuZWtSKhaL6Z577kkmlQ4ePKitW7fO+/z5tueVlZUlE1KGYcjv98vv96uxsVGS9Morr9gZNoAcabi5Svsa6vTq6BkNjs2/la++ytDD69aSkAIAAACAEmJrUmrr1q0yza/feD755JPavHnzDRVTb731lgKBgEKhUPLPUqui4vF42n2iMqnGApB/64xK7WuoU2TysoLRC/pkYlJXvprSiqXlurOyQn7vLfSQAgAAAIASZFtSanh4WJFIRIZh6LHHHtP/+B//Q6dPn9bLL7+sZ555RsPDwwoEAjp69Oi82/MaGxvl9/vV0tJiV1gAHMJXsVKPVn+r0GEAAAAAABzCtqTUm2++qbKyMr3++uuqqanRI488om9+85sKh8O677775p2eV1tbq/vvv18tLS3yeDx2hQMAAAAAAAAHsy0pFYlEJGnGJLza2loNDg7O2NInTTcnb25uVmtrq7xer10hAAAAAAAAwCVsS0oltuTNpaysLPnY6/Vqz5492rx5s10vDQAAAAAAAJdZYteJamtrJUmffvpp8s+i0ai8Xq88Ho/i8bji8bii0ah27Nihu+++O9lrCgAAAAAAAKXFtqRUfX294vG42tvbdeLECT399NOKxWKqr6/X6dOn9fOf/1y1tbXJ5JRpmgoEAtq6davuvvtu/exnP9O5c+fsCgcAAAAAAAAOZtv2vebmZnm9Xg0NDWnHjh3JP+/s7JQktba2qrW1VdFoVIcOHdJbb72V7EMVi8XU3d2t7u5ueb1ePfTQQ2ppaVFlZaVd4QEAAAAAUJQik5cVjF7Qx7FJXZ2a0vLyct3lqZDfu0a+ipWFDg+Yl22VUpLU09OTrISKx+N69tlnb0gseb1edXZ26p133tHx48f14IMPzjgmGo2qq6tL1dXVevDBB3Xs2DE7QwQAAAAAoCiMmhN64t0h/ejEe3oj+plOj8f0+4nLOj0e0xvRz/SjE+/piXeHNGpOFDpUYE62JqVqa2t18uRJdXZ26vDhw3rkkUcWfH5NTY327t2rTz/9VAcOHFBjY+OMBFUoFFJbW5tuv/12PfPMM3rnnXfsDBcAAAAAAFcauDimJweGNHTJXPB5Q5dMPTkwpIGLY3mKDEifbdv3Erxe76LJqLn4/X75/X7FYjEFAgH19vYmt/dJUiAQUCAQkMfjkc/nszNkAAAAAADywo6tdqPmhH76/of64vr1tJ5/beq6nv/gQ+1rqNM6gzY5cA5bK6Xs4PF41NHRMWN7n2EYMxqkh8Ph5PMPHz6siQlKEQEAAAAAzmXnVruDH51JOyGVcG3qul4dPZNt+EBOOC4plSqxve/06dM6fPiw7rvvPklSPB5PPmfXrl2O6T+VSJj19/crHA7LNBcuowQAAAAAFD87t9pFJi8vep75DI6ZikxeyepYIBds376XK42NjWpsbFQsFtObb76p3t7eGRVToVBIoVBI0vSkv5aWFtXU1OQ8rkgkov379ysYDM6ZhPL5fGpublZrayvbDgEAAACgxNi91S4YvWApnmD0cz1a/S1L5wDs4uhKqbl4PB61trbq2LFjOnnypB555JEZ2/vi8bgCgYC2bt2qe++9VwcOHMjZ9r6uri5t2rRJvb2981ZFRSIRdXd3a9OmTdq1a1dO4gAAAAAAOJPdW+0+jk1aiueTCWvHA3ZyXVIqldfrVWdn55zb++LxuKLRqLq6unT33Xfb/tpdXV3q7u6WJBmGoY6ODvX09Ojw4cM6fPiw9uzZo+bm5hnH9Pb2asuWLbbHAgAAAABwnlxstbs6NWUppitfWTsesJNrtu8tJrG9T5KCwaB6e3uT2/nsFggEkgmp5uZmvfTSSzIM44bntba2yjRNdXV1qbe3V5IUDofV1tamgwcP5iQ2AAAAAIAz5GKr3fLyckvnXLHU2vGAnVxdKTUfv9+vw4cPa2RkRM8++6y8Xq9t5zZNU7t375Y0nZA6ePDgnAmpBMMwtHfvXvX09CT/rK+vL5nUAgAAAAAUp1xstbvLU2HpnHdWWjsesFNRJqUSPB6POjo69M4779h2zpdfflnSdLIpk2onv9+vjo6O5MeBQMC2mAAAAAAAzpOLrXZ+7xpL5/R7b7F0PGCnok5K5UKiwumxxx7L+NjUYyKRyIzpgQAAAACA4pKLrXa+ipWqWz3/bp2F1FcZ8lWssBQTYCeSUhmIRCLJx6lVT+kyDGNG8/M333zTlrgAAAAAAM6Tq612bevXall5Zm/nl5Uv0cPr1lqKB7AbSakM+Hw+dXZ2JhuqZ3uOBNPMbgoDAAAAAMD5crXVbp1Rqec2bkg7MbWsfIme27hB64xKS/EAdstpUmrnzp165plnNDw8nMuXyauOjg7t2bMn6+NXr15tYzQAAAAAAKfK5Va7hpurtK+hTvVVC5+/vsrQvoY6NdxclVUcQC4tzdWJY7GYgsGgpOmm3j6fT62trWppaVFlpbuzs6nVTpm6dOlS8vFCU/sAAAAAAO7Xtn6tnhwY0rWp62kfk+5Wu3VGpfY11CkyeVnB6AV9MjGpK19NacXSct1ZWSG/9xZ6SMHRcpaU8ng8GhkZUSAQ0Jtvvqnh4WF1dXWpq6tLTU1Nam1t1datW3P18o6V2tzcyjZAAAAAAIDzJbbaPf/Bh2klprLZauerWKlHq79lJUygIHK6fc/j8aijo0PHjx/XyZMntXPnTlVWVurtt99WW1ubbr/99qLb3rcQ0zQVCoUkTVdJNTU1FTgiAAAAAECusdUOmFvOKqVm83q96uzsVGdnp/r7+xUIBPTWW2/p0KFDRbe9bz5dXV3Jxy0tLVmfZ2RkJKPn33bbbbrtttuyfj0AAAAAgDVstYNbnT9/XufPn0/7+ZnkLMri8Xg8m6DsEIvF9OabbyoQCGh4eFhlZWWSVJTb+/r7+7Vjxw5J0z2pTp48mfaxAwMDeuCBB7J+7Z/85Cd64oknsj4epSnxy/Lj2KSuTk1peXm57vJUyO9dI1/FykKHBwAAAADIg7/+67/W3/zN32R9/K9//Ws1NDTM+Xd5q5Sai8fjUWtrq1pbWxWNRnXo0CH19vbq7bffVn9/vyQlq6dqamoKGaolpmlq586dyY97enoKGA2wsFFzQgc/OqOhS+YNf3d6PKY3op+pbrWhtvVrGSkLAAAAAMhaTntKZSKxvW9kZESvvfaatm7dqng8rkOHDmnr1q2699579bOf/Uznzp0rdKgZ27Ztm0xz+g3+4cOHVVtbW+CIgLkNXBzTkwNDcyakUg1dMvXkwJAGLo5l/VqRycvaP/I7/fg3g2p/5z39+DeD2j/yO0UmL2d9TgAAAACAexR0+146AoFAcnufJJWVlamurk6tra3J7XBOtmXLluTEvcOHD2fV3Hz29r2uri5VV1enfTw9pZCOUXNCT7w7pC+uZzaqdl9DXUYVUwtVYiVQiQUAAAAAzpBNT6nOzs7kxwtt33N8Uiohsb3vrbfeUiQSSfaf8vv9am1t1b333lvgCGcyTVPbtm1TOByWYRh6/fXXs66Qmp2UWmhBgWw98e7iFVJzSUwIScfAxbGMR+EyeQQAAAAA3COTHIZjtu8tJrG975133tHhw4e1Y8cOxeNxHT16VNu3b9fdd9/tmO19kUhEW7duVTgcVm1trU6dOsWWPThaZPJyVgkpSRocMxWZvLLo80bNCf30/fQSUpJ0beq6nv/gQ42aE1nFBQAAAABwNtckpVI1NjZq7969+vTTT3XgwAHde++9Mk1T+/fv16ZNm9Tc3KzDhw9rYiL/b2bD4bC2bt2qSCSi5uZmHT9+XIZh5D0OIBPB6AWLx3++6HMOfnQmo62B0nRi6tXRM9mGBQAAAABwMFcmpVL5/X4dOXJEIyMjevbZZ3X77bdrcHBQu3btUnV1tR555BG98847eYklGAxqy5YtMk1THR0dOnjwYF5eF7Dq49ikpeM/mVj4+HxUYgEAAAAA3MX1SakEj8ejjo4OnTx5UsePH8/79r7u7m61t7dLknp6emY09QKc7urUlKXjr3y18PH5qMQCAAAAALhL0SSlUtXU1OR1e19XV5e6urokTU/Y8/v9tpwXyJfl5eWWjl+xdOHjc12JBQAAAABwn6JMSqXK9fa+Xbt2qbu7W4Zh6OTJk2pqarIxeiA/7vJUWDr+zsqFj891JRYAAAAAwH2KPimVMNf2Po/Hc8P2vky0tbWpt7dXPp9Pp06dks/ny1H0QG75vWssHn/Lgn+f60osAAAAAID7lExSKlVie9/p06dnbO/r7u7WW2+9ldY5tm/frr6+PtXW1urYsWNM2IOr+SpWqm51dv+G66sM+SpWLPicXFdiAQAAAADcpySTUqlSt/e99tpruu+++xZ8vmma2rJli0KhkBobG3X8+HESUigKbevXall5Zj8SlpUv0cPr1i76vFxXYgEAAAAA3GdpoQNwCo/Ho8bGxkWf197ernA4LEnyer3atWtX1q95xx13qKOjI+vjATutMyr13MYNev6DD3Vt6vqiz19WvkTPbdygdUblos9NVGINXTIzjiudSiwAAAAAgPuQlMpQKBRKPu7t7bV0rsbGRpJScJSGm6u0r6FOr46e0eDY/Amk+ipDD69bm1ZCKqFt/Vo9OTCUVsIrId1KLAAAAACA+5CUAjDDOqNS+xrqFJm8rGD0gj6ZmNSVr6a0Ymm57qyskN97S1aVS7msxAIAAAAAuA9JqQydP3++0CEAeeGrWKlHq79l6zlzWYkFAAAAAHAXklIA8ipXlVgAAAAAAHfJS1Lq3nvv1fj4eD5eak5lZWUaHh4u2OsDuFEuKrEAAAAAAO6Rl6TU+Pi4YrFYPl7qBvF4XGVlZQV5bQAAAAAAAMwtL0mpeDyueDyej5cCAAAAAACAC+QlKTUyMpKPlwEAAAAAAIBLLCl0AAAAAAAAACg9TN8DAAAAACBHElOnP45N6urUlJaXl+suT4X83jXyVawsdHhAQZGUAgAAAADAZqPmhA5+dEZDl8wb/u70eExvRD9T3WpDbevXap1RWYAIgcJz5fa9iYkJnTt3TsPDwzp37lyhwwEAAAAAIGng4pieHBiaMyGVauiSqScHhjRwcSxPkQHO4ppKqRMnTigQCCgUCikWi93w942NjXrooYe0devWAkQHAAAAAMB0hdRP3/9QX1y/ntbzr01d1/MffKh9DXVUTKHkOL5Sanh4WPfee6927Nihvr4+xWIxxePxG/4LhUJqa2tTTU2Njh07VuiwAQAAAAAl6OBHZ9JOSCVcm7quV0fP5CgiwLkcnZT62c9+pq1btyoajc5IQPl8PtXW1srn88kwDElK/t34+Lja2tr0zDPPFDh6AAAAAEApiUxeXnTL3nwGx0xFJq/YHBHgbI7dvvf000+rt7dX8XhchmGopaVFra2t8nq9Nzw3FotpcHBQR48e1WuvvaZ4PK5AIKBoNKre3t4CRA8AAAAAKDXB6AWLx3+uR6u/ZVM0gPM5Mil14MABBQIBSdO9onp6euTxeOZ9vsfjUWNjoxobG9XZ2an29nadOHFC/f39OnDggHbu3Jmv0AGgKDC6GAAAIHMfxyYtHf/JhLXjAbdxXFIqFovphRdeUFlZmZqbm3XgwIGMjjcMQ0eOHFFbW5veeustdXV1qbGxUXfffXeOIgaA4sHoYgAAgOxdnZqydPyVr6wdD7iN43pKdXV1SZqufnrppZeyPs/BgweT/aZefPFFW2IDgGIVmbysv/r703r81AdpjS5+/NQH+qu/P63I5OU8RQgAAOB8y8vLLR2/Yqm14wG3cVylVDAYVFlZmVpbW1VZae0u/KOPPqoXX3xR/f39mpiYsHw+ACg2C1VGLSQu6dTFMZ26OEblFAAADsDWe2e4y1Oh0+OxrI+/s7LCxmgA53NUpVQ0GpVpTr8x+v73v2/5fH6/P/l4cHDQ8vkAoJgMXBzTkwNDWU+ISRi6ZOrJgSENXByzKTIAAJCuUXNCT7w7pB+deE9vRD/T6fGYfj9xObnt/kcn3tMT7w5p1JwodKglwe9dY/H4W2yKBHAHRyWlIpFI8rHP57N8vtRJfdFo1PL5AKBYjJoT+un7H+ra1HVbzndt6rqe/+BDLngBAMgTtt47k69ipepWG1kdW19lyFexwuaIAGdzVFJq9erVycdstQOA3Dn40Rl9cd2ehFTCtanrenX0jK3nBAAAM6VWRp26OKZ4mscltt5TOZV7bevXall5Zm+1l5Uv0cPr1uYoIsC5HJWUSq1sGh4etny+1HOsWrXK8vkAoBhEJi9b3rI3n8ExU5HJKzk5NwAApY6t9+6wzqjUcxs3pJ2YWla+RM9t3EB/TpQkRyWlPB5P8vGJEycsny+1j1RjY6Pl8wFAMQhGL+T4/J/n9PwAAJQitt67S8PNVdrXUKf6qoW38tVXGdrXUKeGm6vyFBngLI5KSknSgw8+qHg8rkOHDlk+14svvqiysjLV1tayHRAA/tXHscmcnv+TidyeHwCAUsTWe/dZZ1RqX0Odfrn52/qB91bVrPboTypXqma1Rz/w3qpfbv6O9jXUUSGFkra00AHM9thjj+m1115TNBrVM888o5/97GdZnefpp5+WaZoqKyvTY489ZnOUAOBe5pf/ktPzfxKb1Kg5wQUWAAA2ycfWexps546vYqUerf5WocMAHMlxlVJerzdZLRUIBHTgwIGMjp+YmNDTTz+tQCCQrJK67777chQtADtFJi9r/8jv9OPfDKr9nff0498Mav/I75gSY5NEY9RPr1zN6etcm7pOnwoAAGzE1nsAxcpxSSlJ2rt3r2pqahSPx9XV1aXNmzfrnXfeWfS41157Td/97nfV29sraTrB9frrr+c6XAAWpU6ReSP6mU6Px/T7ics6PR7TG9HPmBJjA7sao6aLPhUAANiHrfcAipXjtu8l/Pf//t/1H//jf9Tw8LAikYi2b98uSaqtrZXX601O0xsfH1c0GlU4HJYkxePTQ1F9Pp+OHTtGLynA4QYujun5DxZv2pmYEvPcxg00gsxQojGq3X0oFpPoU7GvoS6vrwsAQLG5OjWV0/Nf+Sq35weA+Tg2KeXxeHT8+HF1dXXplVdeSf55OBxOJqBSJZJRktTc3KyXXnppxjQ/AM6TabIkUX1DQ8jM5KIxarroUwEAgHXLy8tzev4VS3N7fgCYjyO376Xq7OzUyMiIHnnkERmGoXg8Pud/0nQy6vjx4+rp6SEhBbhANskSpsRkJpeNUdNFnwoAALI3ak7owtVrOX2Nz69cY8s9gIJwbKVUKo/Ho87OTnV2dioajSoSiSgajUqSVq1aJZ/Pp5qamgJHCSATVpIlVN+kL9eNUdNBnwoAALKTbpsDq/75iy9pk1BkIpOXFYxe0MexSV2dmtLy8nLd5amQ37tGvoqVhQ4PSHJFUiqV1+uV1+stdBgALLKaLAlGP2e0bhrsaIy6RJKVS2H6VAAAkLl894SkTUJxGDUndPCjM3Pe/E0MEapbbaht/VrWGY7g+O17AIqT1WQJ1TfpsdoY9ZsrlmvDKmvboelTAQBA5grRE5I2Ce6W7rTlxAChgYtjeYoMmB9JKQAFYTVZQvVNeqw2Rl31jT/QXZ4KS+egTwUAAJkpZE/IRJsEuEuisi7drZ6Jyjiu0VBojt++d+7cOUUiEcViMUvnue+++2yKCIAdrCZLqL5Jz12eCp0ez/7n552V070H3oh+lvU56FMBAEBmrLY5sLr1njYJ7mNlgNC+hrocRQUszrFJqddee0379+9PNjS3oqyszJbzALCPHckSLM5qQsnvvUW+ihWqW21YumNLnwoAANJntc3BsvJyXbFQlU6bBHdhgBDczJHb955++mnt3r1b0WhU8Xjclv8AOIvfu8bi8bfYFElx81WsVN1qI6tj66uM5AVK2/q1WlZu7VcGfSoAAEiP1TYHX1l8/0ObBHexY4AQUCiOq5Tq7e1VIBCY8Wc+n09er1cej7VmuwCcI5EsyeauTmqyBItrW79WTw4MZTROeln5Ej28bm3y43VGpZ7buMHyWGruxgEAsDirbQ6WlpXpSwvH0ybBXRggBDdzXFKqu7tb0vSWu5qaGvX09Mjr9RY4Kmf54Q9/qJtuuumGP29ra1N7e3sBIgKyY0eyBItbVr5Ef1q1Sr+5OKZ07psuK1+i5zZuuGGbXcPNVdrXUKf//N6I/umL7C916VMBAMD8Rs0JXbh6zdI51ixfpt9PXs76eNokuAsDhFAoPT09Onjw4A1//uWX6b9XcFxSKhKJqKysTB6PR8eOHSt0OI40Njb36M7JSTLccJdMq2/mS5ZgbqPmhA5+dCajarT6KkMPr1s779d4nVGpf7N8maWkFHfjAACY28DFMctVyZL0F/+bT3/13kjWx9MmwV0YIIRCmZyc1IUL1raPOiopNTw8nHz8+OOPFzASZ6uqqpqzUqqigjsacJ9E9c2ro2c0ODZ/8mSxZAlmyuSitkzSd2+u0g/XrU1rWx134wAAsN+oOaGfvv9hxhPUZquvMnTPH/8hbRJKCAOEUCgVFRVas+bGXsFffvnlvMU0szkqKZXaM6qmpqaAkTjbr371KzU0NBQ6DMA264xK7WuoU2TysoLRC/pkYlJXvprSiqXlurOyIjkBDunJ9KI2Lun9sXFdSzPZxN04AADsd/CjM5YTUqltDmiTUDrsmLYMZKO9vX3OFkIDAwN64IEH0jqHo5JSqXw+X6FDAJBnvoqV9BqyQTYXtYnJePsa6hZ9LnfjAACwV2TyclZVTalmtzmgTULpYIAQ3MzafG+beb1eGUZ2o8sBANYuahOT8Rbj995YopsJ7sYBADBTMGqtJ8sffeMm7WuoU8PNVTP+PNEmob5q4fdYy8qX6NYVy/V3Fy8pYqFBOgqnbf1aLSvP7O09lXFwAkclpSTpwQcflDSzvxQAID1WL2qD0c8XfU7iblw21hkV3I0DAGCWj2PWhoCsWbFswSEl+xrq9MvN39YPvLfqTypX3pC8uDZ1Xb+fuKw3op/pRyfe0xPvDmnUnLAUUymLTF7W/pHf6ce/GVT7O+/px78Z1P6R3+U04ZeojEs3MUVlHJzCcUmpxx9/XLfffru6uroKHQoAuI7Vi9p0J+NlczdOkkbNSS50AQCYJR9DRHwVK/Vvb16tz65cXXQ739AlU08ODGngYnqNijFt1JzQE+8O6Ucn3tMb0c90ejym309c1unxWF4SfulWxtVXGXNW1gGF4LiklMfj0eHDh3Xp0iU98sgjhQ4HAFwlX5PxMr0bl4oLXQAAZsrHEJHEIJR0G59fm7qu5z/4kBtJaRq4OKYnB4YWbaOQ6+ug2ZVxNas9+pPKlapZ7dEPvLfql5u/o30NdVRIwTEcl5SSppucv/XWW7p06ZLuvvtuHT58WBMT/DAEgMXkczJeunfj5sKFLgAAX7vLY20ISDpDRKwMQsHCnJjwSwwQ+i//rl49935b/+Xf1evR6m/RRgGO4+jpe3v37lV7e7t27dqlXbt2yev1JqfyeTyetM9VVlamV155JVehAoBj5HsyXuJu3KMn39dvM9w6mMnEPwAAipnfu0ZvRD+zcPzCQ0TsGIRCMmN+uZ58DBQzRyalJiYm1N7erlAoJEmKx+OSpGg0qmg0mtU5SUoBKAW5vqidS2TycsYJqQQudAEA+HqISDaJo/oqY9Hfo3YMQnm0+luWzlGsSPgB1jgyKfXd735XsVhM8XhcZWVlyT9PJKcylXoOAHOLTF5WMHpBH8cmdXVqSsvLy3WXp0J+7xr5KlYWOjykKdcXtXPhQhcAAGsik5e1+qY/UJmkTN7xLCtfoofXrV30efkahFKKuA4CrHFcUuqVV16RaZrJRJLH41FLS4t8Pp9WrVpV2OCAIjRqTujgR2fmTGIkJoXUrTbUtn4tDRFdom39Wj05MJR2XwMp/YvauXChCwBAdha6DlvMsvIlem7jhrSuz/I1CKUUcR0EWOO4pNTLL7+cTEh1dnYygQ/IoYGLY3r+g8WbMiamhDy3cQOjY10gMRkvnbWVMruonQsXugAAZC7d67C51FcZenhd+jcM8zkIpdRwHQRY46jpe7FYTKY5fZegsbGRhBSQQ06cEgL7pDsZr77K0L6GOkvJRi50AQDITKbXYZJUJunfr/kj/XLzd7SvoS6jm0n5mO5XqrgOAqxxVKVUahNzv99fwEiA4seUkOKXmIyX6Bf2ycSkrnw1pRVLy3VnZYX83ltsaayZ74l/AAC4XTbXYXFJ41/+S1a/uwsxCKVUcB0EWOOopJTH40k+rqvjTS+QK0wJKS2+ipU5baBp9UJ3ZDymUXOCnmUAgJJQiOuwQgxCKRUk/ABrHLV9L7WRuWEsvOUEQPbsmBICJCQudLP129iknhwY0sDFMRujAgDAmQp1Hda2fq2WlWf29s/KIJRSYeU6iIQf4LCklMfjUU1NjSQle0sBsB9TQopbZPKy9o/8Tj/+zaDa33lPP/7NoPaP/E6Rycs5e81sLnRT0bMMAFAqCnUdlhiEku7va6uDUEoJCT8ge45KSklSa2ur4vG4jh49WuhQgKLFlJDiNGpO6Il3h/SjE+/pjehnOj0e0+8nLuv0eExvRD/Tj068pyfeHcpJ4ifTC925JHqWAQBQzAp5HZbPQSilhIQfkD1H9ZSSppNSwWBQgUBADz30kL75zW8WOiSg6LhlSkiiQffHsUldnZrS8vJy3eWpkN+7Rr6KlXmJwS3SHSs9dMnUkwNDem7jBtsvNBMXur8Y+USjZnZ3celZBgAodoW+DsvXIJRSk7gOenX0jAbH5t/1U19l6OF1a0lIAf/KcUkpSTpy5Ii2b9+u733vezp48KDuvffeQocEFBWnTwkZNSd08KMzczbjTFT91K021LaeX+jS12Ol053ik9gql+k46XSsMyq1wfBknZSSpntl5LIxOwAAheSU67BcD0IpRST8gMw5MiklTSemXnnlFbW1tam+vl5+v1+NjY26/fbbCx0a4HpOnhLihIoft8lmrHRiq9y+BvsnndKzDACA+Tn5Ogz2IOEHpM9xSam5qqJCoZBCoVDW5ywrK1M0GrUSFlBUnDoW2EkVP25RiLHSi6FnGQAA83PqdRgAFILjGp1HIhFFo9Hkf7HY16Wt8Xg86/8AzOTEKSFWKn5KVaHGSi+k0L0yAABwuu9716i8rCyjY5jWBqAYOa5SShJJJCAPElNC0tkqJ+V+SogTK37cwIlb5ZzSKwMAAKdZqG/mQvI1rY0hMwDyzXFJqU8//bTQIQAlw0lTQuyo+CnFvftO3CpntVfGyHhMo+ZEyW7JBAAUp3T7Zs6Wj+swhswAKBTHJaUA5JdTpoQ4seLHDZy4Vc5KrwxJ+m1skib2AICikmnfTEkqL5OeqVuvf3/LzTmMjCEzAAqLpBQASYWfEuLEih83cOpWubb1a/XkwFDGd4MTaGIPACgm2fTNnIpLR899ntOkFENmABSa4xqdAyhNTqz4cQO/d43F43MzVjrRsyzTZvqpSr2JPQCgONjRNzNXGDIDoNBISgFwhLs81ip2SrU5dmKrXDZyPVY60bNsnZH92uT6YhwAgFxz4qRcydnJMgClg6QUAEdwasWPG7StX5txRVK+xkqvMyq1wfBYOkeuLsYBAMgHp/bNdGqyDEBpyWlSaufOnXrmmWc0PDycy5cBUAScXPHjdJlulcvXWOkEp16MAwCQD07tm8nvZwBOkLOkVCwWUzAY1KFDh7R161Zt3rxZBw4c0MTERK5eEoDLObnix+kSW+XqqxZO7NVXGdrXUJfXqTlOvRgHACAfnNo3k9/PAJwgZ9P3PB6PRkZGFAgE9Oabb2p4eFhdXV3q6upSU1OTWltbtXXr1ly9PAAXSlT8pDOWWMp/xY/TrTMqta+hTpHJywpGL+iTiUld+WpKK5aW687KCvm9txSkosypF+MAAOSDUyfl8vvZPolrr49jk7o6NaXl5eW6y1Mhv3eNfBUrCx0e4Gg5S0pJ04mpjo4OdXR0KBqN6tChQ+rt7dXbb7+t/v5+SVJra6taWlpUU1OTy1AAuESi4ufV0TMaHJu/+WZ9laGH160lITUHX8VKPVr9rUKHkeTUi3EAAPLB712jN6KfWTg+N30z+f1s3ag5oYMfnZmzYfzp8ZjeiH6mutWG2tZzzQrMJ2+Nzr1erzo7OzUyMqLXXntNW7duVTweZ3sfgBskKn5+ufnb+oH3VtWs9uhPKleqZrVHP/Deql9u/s6/TnXjl7sbWG1i/+0/WmVPIAAA5NmoOaG/Pf27rI/PZd9MhsxYM3BxTE8ODC06wXDokqknB4Y0cHEsT5EB7pLTSqn5NDU1qampSbFYTG+++aYCgQDb+wDcwGkVP8hOool9tmOn/+q9Ee4yAgBcZ+DiWNotCeaS676ZVn4/l/qQmVFzQj99/0N9cT29tb02dV3Pf/AhN1WBOeStUmouHo9Hra2tOn78uE6ePKmdO3eqsrJSb7/9ttra2nT77bczvQ8AikA2TexTcZcRgNtEJi9r/8jv9OPfDKr9nff0498Mav/I7xSZvFzo0JAHiaSFlYRUPvpmMmQmOwc/OpN2Qirh2tR1vTp6JkcRAe5VFo/H44UOYrb+/n4FAgG99dZbkqSysjJ5vV75/X61trbq9ttvL3CE+TUwMKAHHngg+fGvf/1rNTQ0FDAiAMic1TvG0vSFMHcZc4+GrUD2Fuoxk0D1Z/F74t3Ft3XNJ999MzP5/ZxIluVziq/TRCYv60cn3sv6+F9u/k5JV5mhNGSSw3BkUipVIBBIbu+TphNUdXV1am1t1Y4dOwoc3Y2CwaD8fr+t5yQpBaBYjJoTizaxX0x9laF9DXU2RoUE3kwXHxKM+cWbe0juTFqk8/uZITPT9o/8zlLj+h94b6U9BYpeUSWlEhLT+9566y1FIhGVlZVJUrJ66t577y1ofKZpqr29XaFQSOfPn7f13CSlACzEjW86T/3jP+mv3vsw6+O5y2i/TN5Ml0n67s1V+uG6Oxz7b6zUkWDMv1FzQk+8O5TRlp7yMunpuvX6D7fcLOnGn+dlkuJxackS6Xpci/58d+Pvg2Lk5qRF4t/QJxOTuvLVlFYsLdedlRXye2/h9+6/+vFvBi1NLaxZ7dF/+Xf1NkYEOE9RJqVShUIhHT16VK+99pqk6eqpRH+qfG/vi0QiCgQC6u7uTv4ZSSkA+eDmN51uvmAvRv/r83/Uz4d+q6ksLgmWlS/RrSuWq261kXzjyxvjwrJarcP6ZefRk+/rt7HJrI69aUmZlpSVZbS9eVn5Ev3RN76hm5Ys0ZfxKf3TtS8XPN6pvw+KEUmL4tb+znv6/UT2veH+pHKleu79to0RAc6TSQ6jINP3rGpsbFRjY6P27t2rYDCoQCCgEydOaP/+/eru7k5u7/P7/aqstO8XbyQSUSgUUiwW09mzZ3XixAlFIhHbzg8A6Ur3TWeiQbjTtoh8nOUbt4RPJqwdX8wySSikk9hczLWp6/r9xGX9fuKy3oh+Nl3ZMcfzTo/H9Eb0szmTWLBPNhOh/vLvT+u2Fct1XXGNf/EvujI1dcPzEutHYuNrie+1oTFTn165oi+vZ3+fd/rYzI6/NnVdn165mvbzs/19QJIyc1fn+B7KxJWvrB2P3FpeXm7p+BVLrR0PFBtXJqVS+f1++f1+xWKxZP+pwcFBDQ0NadeuXbZu7+vr61NXV9cNf+7z+UhOAcibYhhDzAW7/RZKMM2VULCj8fxcFntbPTuJlUhS3fGv20L+4eoXvPG1IJuJUHEp7eTG0CVTj5/6oKS3b9qRzC2Ua1PX9VfvnZ6xZXC21GTbZ1evzvkzgiTzwkhaFLe7PBWWKuHurKywMRrA/VyflErweDzq6OhQR0eHhoeH9d/+23/Ta6+9pqNHjyoYDNq2vc8wDHm9Xq1atUpNTU1qbm6Wz+fTbbfdZuNnAwDzszKG2CkNwrlgt1cmlXOPnfpAf/iNP9DYF/+SYV1GbqQmqWajOmdxqVUs5pf/klHlTLbikk5dHNOpi2Mltza5Subm01Rc6hr8SH89/Nvk9r8lS6aT/fNVys1ndpK51P49zGXUnNCFq9csnYOkhbP5vWsstSDwe2+xMRrA/VzZUyoTqdv7pJnT++zc3pealKKnFJAbbCFw50SfudBTKn2L/bu30g/KTZhUNpOTqnVKZW2yaWReqtYs/4Yq/mBpWs3Zi4ldSUun/K7G/J54dyirn79MEEapKPqeUpnI5/Y+ALmR6bakYhaMXrB4/OeOSOZwl3Fx6fy7n69/UzFy4jbUfJhrGtvlf/lK/3DtC8esfWpfqpvKlxRtEuJvT39CQipNF65+IV39IvlxKWz3y3Rr/XzqqwwSUi7Qtn6tnhwYyng4wcPr1uYwKsCdij4plTDX9r6+vr4btvc988wzhQ4VQAq3N/S2W7E0CPdVrFTdaiPrKo+/Pf1J0SYhI5OX9avRs/rNxbFFkw5OSUrky7Wp63pqYEi3rFh+Q+Kj2CopnVQJlY7ZfamK6YbBqDmhvz39SdaT9fC1Yt7ul83W+tlIWrjHOqNSz23ckPGk02L4tw7Yrei37y0mdXtfWVmZenp6dN9992V8HrbvAfbLZqvEsvIlRV1JUUxjiEfNiYzvMqYqti1DbktCOMliFWNuqs7IJCnpFm79Xi3GtXCiMsn1jfOtbq2XnPt9UmwJf7uNmhN6dfSMBsfm/91dX2Xo4XXFkXwF0sX2vQykbu8bHBxUY2NjoUMC8K+KoaG33YqpQXimdxlnK6btXMXQPLmQMp3455TqjNQ3e5e+/DLjJtNukc7ENydIZ+oc7JfaON9NCeRUVrfW/9E3btJ//nZ1wX8mpaJ1QnrWGZXa11CX/PnxycSkrnw1pRVLy3VnZYX83lvYjgksouSTUgkej4eEFOAgkcnLWVeMDI6ZikxeKcqLgGIbQ9xwc5X2NdQtepdxPm5PQkYmL+vQx1H1/8M/UYmRR0OXTD1+6oO8V2eUctIjMfHtaPTznLyJzbSao1QSgm7j1ATyYqxurV+zYpmjPkdaJ2TOV7HSET07ATciKQXAkYqlobfdirFB+DqjUo9XfyvrrQ9uTEKyVa/w8lmdwXp/za43sekk+Oaq5mAt3MUtSY+rFhOaV75yTkI004btxVS1DKAwSEq50MjISEbPv+2222b0vALcoFgaetvNSoNwJ0/0KaUkJFv1nCdX1Rn0JJpbNtv5rFSZDV0y9dipD7T6pqUa//Ir1sJl3JD0KKat9bROADCX8+fPZ9Q7O5OcBUkpF+rs7Mzo+T/5yU/0xBNP5CgaIDeK6a6j3YpxDHGpJCHtGhmO3Eps7/OuXKE//cNVaU/4K+XteZlKbOf76+HfLlilZmdl06Uvv7J8DhTGtanr+s/vjejn/7bGcX2mRs0JXbh6zdI5nLK1ntYJAOZz5MgR/c3f/E1Ozk1SCoAjFdNdR7sV4xjiUklC/u3pT0hIuURcUuTyFUUuX9Eb0c/mnfCX2CK2cmm5Lrvk36GTLFSlRlUhUv3TF1/qRyfec1SfKbv+jTpla30pVS0DcA5HJaWGh4cVCARydv66ujo1Njbq9ttvz9lrALBHsTX0tlu6DcLdMobYahLywtVrGjUnHPt5jpoT+tvTn+i3FivCUDiLbfkiIWWPRJXanZUr9buJyyIdhdmc0mfKrspXJ22tL5WqZTtkOlwBwPwclZSKRCIKBAIqKyvLyfl7e3slST6fT52dndq6dWtOXifXurq6VF1dnfbz6ScFNyrGht52K6YxxFaTkFe+mnLEm5S5uL3ao77KkP/2W3T6UkxDl0x9doVtacituKSPJy4XOgxbZfp9tHJpuVbd9AdavrRc169L/xK/rovXvuB77185oc9UNr2XZnPa1vpSqVq2YqEtxXMNV3ASEmmwYvv27WpsbEz7+SMjI2m3HXJUUiohHv/6fmRZWdmMj+eTSGSl89yzZ8+qra1NTU1NyUSVm1RXV6uhoaHQYQA5VawNvXOhGMYQW01CSs54kzLb//r8H/Xzod9qKo3fTU5SJqlpzR/poTt9ye+l1IbUyd5Jaby5XiJR7YKS840lS/TNlctVu9qYcYNgru+jTG4ozD5mSZl0/bpUvqRMU/G4ViwtV+XSpfr7fx7Xl0W+Vfja1HX9YuQTvXzPn+b9ta30Xkpw4tZ6WicsLN2bTE6p5ktwcyINzpHL4WmOSkrV1taqp6dHkhSNRtXV1aV4PK6WlhbV1dVp1apVkqTx8XFJUiwW06VLlxQOhxUKhSRJhmGotbVVXq93xrljsZjOnj2rEydOKBKJKB6Pq7+/X4888oheeeWVvH2OANJXjA29MTcrSchUTpkA5Oax84k3SgtdSM9OhC725jqTJBZy66YlZfrOH67WxFdfzZvUSKzdtampRbcI40brjIq0EiXZ3FBI95hRcyIna+e0JPOoOalHT76v/3T3nXl9M22199IffeMm/edvVzsuAUDrhPllul3TKTfK3JpIQ2lxVFLK6/XK6/UqFAqpq6tLfr9fe/fulcfjSev4SCSi3bt3q7u7W52dndq5c+eczwsEAnr66acVj8cVDAbV1NSkHTt22PmpALBBMTb0xvyySULOpdATgNy8XS/bHmSLvVFOJ4n16eWrGv/yX7KOHYvLZn33NdS5tuKvEJaVL9Hj1XcWOowbtndnkxBObCFc/Y2bbkgy/2r0rH5zcWzRXmv58NvYZF7fTI+aE/p/z/+DpXOsWbHMkdcqtE6YXzbbNQt9o8ytiTSUnrJ4Ovvd8mh4eFhbt25Vc3OzDhw4kNU5gsGgdu7cqe9///vzVkGZpqlNmzbJNE35fD698847VsKeUcp2/vx5S+eabWBgQA888EDy41//+tds30NJSeeOr1saemNhdiV0fuC9tSBbGkfNCT3x7pBjJuyl9rFZaLuPE3qQjZoTtiQlIa1Z/g1V/sEf2Lq+bk625ks6VYaFls72v3T/veSqGitb5WXS03XrZ2yRtJtd3wd/UrlSPfd+26ao7PXEu0NZt04odJVyrkQmL+tHJ97L+vhfbv5OQX6/spYopExyGI6qlJKk9vZ2SUq7KdZc/H6/HnnkER04cGDeKijDMLRnzx7t3LlT0WhUw8PDqqmpyfo1AeROMTX0xsISUwV3/V3YUsPU//ezf9D/cdsf5z1J+benPyloQmpZ+RLdtmLhPjZOlWllJG6Uy+R8uhM/S5VbbozY2YNwvt/NiURXvpuzT8WlrsGP9NfDv9WtK5arbrVhawNnu6btSc7uvUTrhBtZ3a4ZjH6e9xtlVvqeFbriHKXHUUmp4eFhRSIRlZWV6fbbb7d0rj//8z/XK6+8oq6urnm35vn9/uTjaDRKUgpwuGJo6I3FrTMqtWb5Mv3ewvStK19N6fFTH+i7N1fph+vuyPlUmVFzQn97+hP91uI47cXMVflUTMlZEh/pyUUlVDrmS0J8eX1K5y9fc8RWrnwqk/71Z8xa13/vWbHY7+Z8b/e7NnVdv5+4rN9PXLa1gbMd0/YSnNx7idYJN/rY4u/2TyZye20wFzcm0lC6HJWUikQiyccTExOqrLT+w800zQXPZRiGYrGYotGo5dcCgHwohZG+VicASdNj5U9dHNOpi2NaVr4kJ3fO8/Fma7FJeMXGjuoLpzVjtotTqnHmSkKU2vY+p6yFG/gqVuqn37m7YNv9hi6Zlm9S2DFtL5XTey+le4OgVL4Prk5lX7ktyVLld7bcmEhD6XJUUirV4OCgNm/enPXxqQmuhZimqbKysrSbqQNAoZTSSF+rE4Bms/vOeb6m67mhR02upFN9sdjEPyc1Y87UfE2mnarYq9zm2xqL9NnRfD1bqTcpsvn5b7XqJFV9leGKfz+0Tvia1Rtlhdiu6cZEGkqXo5JStbW1ycevvPKKpaRUIBDI6PmrVq3K+rUAFEYpVAwllNpIX6sTgBYz39dp9r+pMknxuLRkiXQ9Pn1h6vmDpfr7fx7XlznuHbXOqNDj1fkdc+4m6Uz8K2R1RqaKIemReBPr9ml9bksIus1c0zh3/92w/vmLL/Py+un+nkwmz8ZMnZnMfjt5Kjf2XqJ1gvUbZYXYrunGRBpKl6OSUl6vVz6fT5FIRP39/XrnnXd07733ZnyeaDSqUCiksrIyeb3eebfupW7Z83q9WccNIL9KqWJIKs2Rvr6KlapbbeS0Euna1HX95d+f1m0rluu64hr/4l90xeKdRbusMyr08j1/WugwikIhqzMWU6w9if7DLX+sFUuXumo7X7GuhRv4Klbq//52dV6nb16buq6/eu908gZW6k2ta1PXc1IJWwq9l4qV1Rtlhdiu6cZEGkrXkkIHMNszzzwjSYrH49q+fbveeeedjI6PxWIzGps/9NBD8z43FAolH9PkHHCHgYtjenJg8RG3iTuhAxfH8hRZ7mTTXPXa1HW9OnomRxHlR9v6tVpWnttfU3FJn165qs+uXHNMQmpZ+RI9Xn1nocMoOom7/T33fltH/+xe/XLzt/UD763yVSxXWQHiqa8y9It7Nuqn37m7KJMgie189VVGzl9rWfkSfatypX7gvVWd9eszfs1iXws3SDTXzvXP/FRTcSW3dSduaP3oxHt67NQHtiekVi4t176GOldXMJeyxI2ybBRqu6bfu8bi8c7ue4bi4qhKKWl6Il5NTY1Onz6dTEx1dHQkk1ULGR4eVnt7e3KCn8fjUUtLy7zP7+/vlzRz2+BigsFg1s9JnfYHIHOlWDFUyiN9M50AVAy4k54/qVtS8rHFrxi252Uq11Vq5WXSM3Xr9e9nNf7/D7fcPG+j/PIlZXmfWoj0JBKZvxj5RKNmcTVZ/rNb/03R/FwvpdYJqdrWr824mq+Q2zWtVJy7pe8ZikdZPO68Df+maWrr1q06d+6c4vF4chue3+/X97///RuqmoaHhxUIBNTb2ytJyWOOHTs2bwVULBZTdXW1ysrK1NnZqZ07dy4aVyQS0aZNm7L+vEZGRmQYmWfZBwYG9MADDyQ//vWvf62Ghoas4wDc6ol3F6+Qmkt9laF9DXU5iCj39o/8zlLJ+A+8t7q+F8SoOaFdfxcu+qabpTLFyMnsTp6wJWxudjShL+UhAKXg0ZPv67cWp4c5yS83f8f1PwPSGfBRTK0T5pLJlFEn/IwaNSeySqS5+WYunCOTHIbjKqUkyTAMHTt2TNu2bdPw8LCk6f5P3d3d6u7ulsfjSTYmT+0LlcivGYahAwcOLLgl7+jRo8nHzc3NOfgsANipVCuGGOk7XW3xf976b3La+LyQ5qv2QP7N1YB5rqlTd6/2KHjuc0alZ8lqE3q+tsXvP919Z157TOVSMVSdlNqwlfmkO2XUKT+jMq04p1obheLIpJQ0nVg6fvy4AoGAfvazn8k0v/7GN01Tsdh047bZhV61tbXq6elZtHF5Yjpfc3Ozbr/99rRi8vl8On/+fCafBgCbWB3HHIx+7sqKIUb6Tsv1NL5CccKdVMxvoalTc20RY0tYZhYaOf/Hy74hSfrHa1/wtS1BxbJ9243T9mYrxdYJC1no55YTf0a5LZGG0uTYpFRCa2urWltb1dvbq/7+fp04cUKmac5IRhmGoc2bN6u1tVWNjY1pnffYsWO5ChlwPDf2AyjViiFG+k7LxzS+fFtnVOjx6ju5AHQxRqXbg68j5pLum2mnKpaqEyvDVtzaOiEdbvq55bZEGkqP45NSCS0tLcmm5bFYLLltz+v1yuPxFDI0wDUW6geQmDzj1H4ApVoxxEjfr2XTZNSp1hkVevmePy10GADgaIk30//r83/Uz4d+qynntcKd08ql5drzb2sddy2VqVJtnTAXN97Qnc1NiTSUFtckpVJ5PJ4F+0UBuJHb+wGUasWQ1W1rxTTSt5i2czxefWehwwAA1/gPt/yxVixd6pqf/8Uyba9UWyekcvMNXcAtlhQ6AAC5l+gHkO6FXKIfwKg5kePI0neXx1rFj1srhhLb1rJRDM1VZ0ts56ivyu5rUmjFsp0DAPLNTT//i+WGUKm2TkgYuDimJwcWn/ycuKE7cHEsT5EBxYWkFFACrPQDcAq/d43F4917gdi2fq2WlWf247oYmqvOJ7Gd45ebv617bq5SWaEDSlN9laF9DXWOqkAEADdJ/fn/A++t+pPKlRn/fsy1YrohVKqtE6TiuKELuEXOforHYjG99dZbuTp9Tpw7d851MQOLsaMfgBOUcsVQYttauhfepVKNkxgr/4t7Njr2znmZpHturtIvN3+naCcRAUC+JXrj9Nz7bR39s3sdc5Oi2G4IlWrrBKk4bugCbpHTWwvt7e2umXIXjUa1ZcsWjY+PFzoUwFZ29ANwilKuGEp320IpVuM49c55fZWhX9yzUT/9zt2uTooCgNOl3qRYZxRmu34x3hAq1dYJxXJDF3CLnDU693g8qqysVFtbm/bu3asdO3bk6qUsGx4e1rZt2xSLxVRXV7yjS1GaiqkfQKaNrssk/WnVKkckKKyYPfHlW5UrFY9L5UukqbgY6fuvZk+VKdS0pvIy6Zm69fr3t9yc19cFgFK3zqjUy/f8qR49+b5+a/H6JxP1VYYeXld8ja5LddgKDd6B/Mrp9L1jx47p3nvv1a5duzQ0NKSf/exnuXy5rLz22mvavXu3JGnPnj1M9UPRKbZ+AImKoVdHz2hwbOG7WHFJpy6O6dTFMVdORllo4kuCGz+vfCnEtKbEnfJSqlQDAKf5T3ffqScHhnL2s39Z+RLdtmK5alcbRX1DKNE6IZuqITe3TiimG7qAG+S0fMDn8+nYsWOqrKxUIBBQTU2N3nnnnVy+ZNrOnTunBx98ULt371Y8HldnZ6cefPDBQocF2K4Y+wFk0+jabZNRmPhij2ynNa1cWq7bVizTnZ6V+pOKlbp95fJFK+5KceskADhRpr0YF1MmyVexQj/w3qpfbv6Ojv7ZvTpw77f1aPW3XJt4SVcptk4othu6gNPltFJKkmpra3Xq1Clt27ZNw8PD2r59u/x+v5599lndfvvtuX75G5w7d06BQEDd3d2SpHg8roMHD+q+++7LeyxAPtzlqdDp8VjWxzu5H8C1qet675/Hle7mrMRkFKc3nE5MfEm3waZbPq9CSSQxE9sgP5mY1JWvprSkTLp+XSpfUqapeDytbZCzz8HWSQBwpnQrq5dIWui3bbFuzUtXpq0TiqG3VjHe0AWcLOdJKUkyDEPHjx9XW1ub3nrrLQWDQQWDQTU1Namjo0P33ntvzmM4ceKEuru7FQqFJE0nowzD0Ouvv86WPRS1Yu4HYGUyyr4G5/aPK9bPq9Bm95wq1DkAAPkx302J2TcUuOGwsHQTfMWSwCvmG7qAE+UlKZVw8OBBBQIB/exnP5Npmurv71d/f7+8Xq/8fr8aGxu1efNm215veHhYJ06c0KFDhxSNRiVNJ6MkqbW1VT//+c9tey3AqYq1H4Adk1Gc+LkV6+cFAEChLHZDgRsOi0s3wVcMivmGLuBEeU1KSdPJoNbWVnV3d2v//v0yTVPRaFTd3d3JLXW1tbWqq6tTXV2dvF6vfD6fVq1apcrKubPu586dk2maGhoaUn9/v8LhcDIJlZBIRtXW1uqll16iOgolpW392owbfjq9H0CxTkYp1s8LAAC4Xykk8Ir1hi7gVHlPSiV0dHSoo6NjRnIqIRwOKxwOq7e3N+vzx2eNAG9padFDDz1EMgolqRj7ARTrZJRi/bwAAEDxSVROfRyb1NWpKS0vL9ddngr5vWvkq1hZ6PCyVow3dAGnKlhSKiGRnBoeHtabb76pUCikcDhs+byGYWjz5s1qamrS97//fXk8HhuiBdyr2PoBFOtklGL9vAAAQPEYNSd08KMzc1YTnR6P6Y3oZ6pbbahtvfOvKeeyrHyJ/rRqlX5zcSytgTpuuKELOFXBk1IJNTU1ySqmWCymUCikaDSqs2fPKhqNKhqNKhKJzHmsz+eT1+uV1+tVXV2d6uvrqYgC5lBM/QCKdTJKsX5eAACgOAxcHEur+n7okqknB4b03MYNari5Kk/RWbNQsm0+brmhCziVY5JSqTwej5qbmwsdBlC0iqEfQLFORinWzwsAALjfqDmhn77/YdpTgq9NXdfzH3yofQ11jk/apJtsk6QySd+9uUo/XLfWNTd0AadaUugAACAbfu8ai8c7czJKsX5eAADA/Q5+dCbthFTCtanrenX0TI4iskci2ZZuD6m4pPfHxnXNYtsFACSlALhUYjJKNpw8GaVYPy8AAOBukcnLWU2kk6TBMVORySs2R2SfYk22AW5AUgqAa7WtX6tl5Zn9GCsvk/y3O7ua6PveNSovK8voGCa+AACAXApGL1g8/nObIrFXMSfbADcgKQXAtdYZlXpu44aMElNTcalr8CM98e6QRs2JHEaXuVFzQk+8O6SuwVFNxdOZ9TKNiS8AACDXPo5NWjr+kwlrx+dKsSbbALcgKQXA1RpurtK+hjrVV2W25S0xEWbg4liOIsvMwMUxPTkwlPGduvoqQ/sa6lwz1QYAALjTVYv9k6585cz+S8WabAPcgqQUANdbZ1RqX0OdOuvXZbTtLTERptAVU5k215SmtyH+Zf16V0yzAQAA7re8vNzS8SuWWjs+V4o12Qa4BUkpAEXjaPRCRtveJGc0qcymueZUXDp6jnJxAACQH3d5Kiwdf2elteNzpViTbYBbkJQCUBTc2qTSrXEDAIDS4veusXi8MwfN/Jvl37B0vFOTbYBbkJQCUBTc2qTSrXEDAIDS4qtYqbrVmfXwTFhnVMhXscLmiKxJDJj5/z6/aOk8Tk22AW5BUgpAUXBrk0q3xg0AAEpP2/q1GU09Thg1Jx01+TjbATOz1VcZjku2AW5DUgpAUXBrk0q3xg0AAErPOqNSz23ckFViyimTj7MZMDOXZeVL9PC6tTZFBZQuklIAioLVJpUXrl4ryN07mmsCAAA3abi5Svsa6lRflflWPidMPs5mwMxsy8qX6LmNG5iADNiApBSAomB1IsyVr6YKcveuWCfZAACA4rXOqNS+hjr9b1lcxxRy8rGVATMJ9VWG9jXUqeHmKpuiAkrb0kIHAAB28HvX6I3oZ5bOkbh7t6+hLi93vkbNCY2Mxyydg+aaAACgECKTl/XbLHtjJiYI57sfk9UBM/+/W27W0/XrbYqmsCKTlxWMXtDHsUldnZrS8vJy3eWpkN+7Rr6KlYUODyWEpBSAopCYCGP17lfi7t2+hjqbIpvbwMUxPf+BtX4GNNcEAACFYscE4Uerv2VTNOmxOmDmH659YVMkhTNqTujgR2fmvGY+PR7TG9HPVLfaUNv6tWxPRF6wfQ9A0ch2Isxsibt3uWJHg02aawIAgEJy2wThUXNCv7P4mm4fMJPu1EGnNKVHaSApBaBoWJkIM9trv4vaENHcrDbYpLkmAAAoNDdNEE4kY6xO3HPzgJlMb4o6oSk9SgNJKQBFJTERxupFw//3+UU98e6Q7b+IT/3jP1naYrjOqKC5JgAAKDi3TD62o0I9wc0DZrK5KVrIpvQoHY7uKXXu3DkFAgGFQiFFIhHFYtMNgT0ej3w+n+rq6uT3+7V58+YCRwrASdYZlVqzfJl+P3HZ0nkSpcvPbdxgOQm00P79TGwwPFRIAQCAgrvLU6HTFga2JCYf23GdtRCrFeqp3DpgxsrUwUI1pUfpcGxS6umnn1Zvb2/y43g8nnwci8UUDocVDofV29srj8ej+++/X88++6wqK4v/zdoPf/hD3XTTTTf8eVtbm9rb2wsQEeA8Vu/eJVidyBeZvKxfjZ7Vby6OKb740xeV7/4LAAAAc3HD5GMryZjZ3Dxgxo1N6eEOPT09Onjw4A1//uWXX6Z9DkcmpR588EGFQqEZiSifzydJGh8fl2nO/MESi8UUCAT05ptv6i//8i+1Y8eOvMabb2Njczecm5zkzSqQYPXuXapsJvLZVRk1m9sbbAIAgOLghsnHVpMxCW4fMOO2pvRwj8nJSV24YO37zHFJqQMHDqi/v19lZWXy+Xx69tln1dzcfMPzotGoQqGQgsGgQqGQJMk0Te3atUvBYHBGlVWxqaqqmrNSqqLCvXucAbvZcfcuVSalywMXx/T8B/b0LpjNzQ02AQBAcWlbv9aWBuK52iJmNRkjFceAGTc1pYe7VFRUaM2aNTf8+ZdffjlvMc1sZfHUciQH+OY3vylJMgxDv/nNb9LejhcMBrV//36Fw2GVlZWprq5OR44cKYrtfAMDA3rggQeSH//6179WQ0NDASMC3OGJdxcfeZuJP/zGTfq/v1294EXJ//r8H/Xzod9qKkc/Wn/gvZXyaQAA4Bh23Yyz+xpn1JywnDBbVr4kZ1sL8+nHvxm0tIOgZrVH/+Xf1dsYEYpdJjkMR1VKJSqeysrK9NJLL2WUUPL7/fL7/QoEAnr66ac1NDSkrVu36sSJE7kKF4DD2XX3LuGfv/hSj536QGuWf0MVf7BU1+NSmaR4XPoyPqV/uvZlTqqjUrm1wSYAAChOicnHu/4ubKmi5v/97B/0f9z2x7YkgOxKlN3pqXB9Qkqy3tbCzVMH4XxLCh1AqsR0PUlqbGzM6hytra1655135PF4FIlE9Mgjj9gVHuBakcnL2j/yO/34N4Nqf+c9/fg3g9o/8jtFJq1Np3O6dUalntu4QcvK7f1Rd+HqF/okdlm/n7is301c1u8nL+vTy9dynpByc4NNAABQvBKTj61ITOMbuJjelp/5/K/P/1F/9d6ILddlxZKM8Xtv3F6V2fHcFEXuOCoplWhmLsnStjufz6dXXnlF8XhcwWBQ77zzjh3hAa4zak7oiXeH9KMT7+mN6Gc6PR7T7ycu6/R4TG9EP9OPTrynJ94d0qg5UehQcyZx966+yih0KJa4vcEmAAAobnZMPk5M48vm2jRx3ds1OGpbG4ViScYkmtJng5uiyDVHJaVqamqSj61uu2tqatJ9992neDyu7u5uq6EBrjNwcUxPDizeU2nokmnLXSknW2dUal9Dnf73W24udChZKYYGmwAAoLjd5bGnquja1HX9YuSTjI5J97o3E8WWjGlbvzbj3QPcFEU+OCopJUn33XefJNkyPe/P//zPJUn9/f2WzwW4yag5oZ++n/4+eit3pdzkwW/dXugQMlZfZWhfQ50abq4qdCgAAADzsrpFLNWoOalHT76f1rWpndv1EooxGZNpWwtuiiJfHJeU+su//Mvktrtjx45ZOldzc3Py8fDwsNXQANc4+NEZfXE9s1/M16au69XRMzmKyBmslC4Xwk+/XV0UE18AAEDxs/s667exST1+6gP9KPT3c/ZCzcV2Pam4kzHptrXgpijyyVHT9yTJ6/XqkUce0SuvvKK2tjYdPHhQW7duzepcqY3Tx8fHbYoQcLbI5OWsS5cHx0xFJq8UVanybHZP5MuV+ipD9/zxHxY6DAAAgLTZfZ0VlxS5fEWRy1f0RvQzLStfoj/6xjf01fXr+odrX8i+VNS0ZeVLiv6GYKKtRWTysoLRC/pkYlJXvprSiqXlurOyQn7vLUX9XgDO47hKKUnq7OxUTU2N4vG42tra9LOf/Syr8xw9ejT5OLWJOlDMgtELFo//3KZInClXE/nsVIwl4wAAoPjl+jrr2tR1fXrlqi7kICElSXd6Koo6IZXKV7FSj1Z/S//l39Wr595v67/8u3o9Wv0tElLIO8e+K/vv//2/a/PmzclG5XfffbcOHz6siYn0et6cO3dOL774oiTJ4/Ho9tvd10sGyMbHsUlLx38yYe14N0iULv/RN24qdCg3KOaScQAAUPwS11krllqfxpdvd1ba06wdQPocm5TyeDw6cuSIHnnkEcXjcZmmqV27dqm6uloPPvigDhw4oBMnTtyQpJqYmNCBAwe0adMmmaapsrIydXZ2FuizAPLv6tSUpeOvfGXteLdYZ1Tq5/+2ZvEn5hH79wEAQDFYZ1Tq/7z13xQ6jIz5vbcUOgSg5Diup9RsnZ2damxs1NNPP61oNCpJCoVCCoVCM57n8Xhm9JCKx+MqKytTc3OzHnzwwbzGDBTS8nJrd6XceFcrW4mGnHaOD85UmaTv3lylH65bS7k0AAAoGn7vGr0R/azQYaStvsrgWgwoAMdWSqVqamrSyZMndeDAAdXW1ioej9/wn2maMz6WphNV999/f9pb/oBicJfHWtlxqZUtt61fW7D+UvVVhn5xz0b99Dt3cxEEAACKipumHtPPEygcx1dKpfL7/fL7/RoeHtabb76pcDh8Q8VUWVlZ8nEsFlN7e7uk6QRVY2OjNm7cqJqaGm3evDmvsQP5YvWuVKmVLScacj7/wYd5m8hXXiY9U7de//6Wm/PyegAAAIXghqnH9PMECstVSamEmpoa1dR83QtmeHhYg4OD6u/v1/DwsCKRyA3HmKapvr4+9fX1Jf/M6/WqsbFRfr+fJBWKhpUtaaVatpxoyPnq6BkNjuV2K1/iwoe+UQAAoNgV4uZfJuqrDD28bi0JKaCAyuKJvW5FJBaLaXBwUKFQSOFwWENDQzLNG99oplZVHTx4UFu3bs1nmGkbGBjQAw88kPz417/+tRoaGgoYEZxu1JzI+K7UsvIl2tdQV/K/lCOTl/Wr0bP6zcUx20cNc+EDAABK0ag5oV+MfKJR0xlTnqlaB3IrkxyGKyulFpPYqtfY2Jj8s2g0qnA4PCNZlZqP++CDDxyblAIyleldKcqWv+arWKmffudujZoTliunlpUv0W0rlqt2tSG/95aSrEIDAABYZ1Tq5Xv+VI+efF+/jRU2MUXVOuAsRZmUmovX65XX61Vzc3Pyz6LRqEKhkGKxmFpaWgoYHWC/dLekUb0zt3VGpfY11CkyeVnB6AV9MjGpK19NaUmZdP26VL6kTFPx+A0fr1harjsrK0hCAQAAzPKf7r6zoD2muO4FnKdkklJz8Xq9JKNQ1OZLrJA4SZ+vYqUerf5WocMAAABwvUL1mGK7HuBcJZ2UAkoFiRUAAAA4QT4HzEhs10tX4ib2x7FJXZ2a0vLyct3lqZDfu0a+ipWFDg9FjKQUAAAAACBvZlfzvz92SdHJqwyZKYBRc0IHPzoz5+Tu0+MxvRH9THWrDbWt5+uI3CApBQAAAADIu9RqfjuGzEhSmaTv3lylH65bS5uKRQxcHEtrK+XQJVNPDgxRcYacICkFAAAAACio2dVTQ5dMfXblaka9p6iMSt+oOaGfvv+hvrie3tf32tR1Pf/Bh9rXUMfXF7YiKQUAAAAAcITZvVAXm4TMAJ/sHPzoTNoJqYRrU9f16ugZ7Wuoy1FUKEUkpQAAAAAAjsTAHvtFJi/P2UMqHYNjpiKTV0gAwjYkpYAixPQMAAAAAHMJRi9YPP5zEoWwDUkpoIgwPQMAAADAQj6OTVo6/pMJa8cDqZYUOgAA9hi4OKYnB4YWLcVNTM8YuDiWp8gAAAAAOMXVqSlLx1/5ytrxQCqSUkARSEzPSHc6SWJ6xqg5kePIAAAAADjJ8vJyS8evWGrteCAVSSmgCFiZngEAAACgdNzlqbB0/J2V1o4HUpGUAlzOjukZAAAAAEqD37vG4vG32BQJQFIKcD07pmcAAAAAKA2+ipWqW21kdWx9lSFfxQqbI0IpIykFuBzTMwAAAABkom39Wi0rzywdsKx8iR5etzZHEaFUkZQCXI7pGQAAAAAysc6o1HMbN6SdmFpWvkTPbdygdUZljiNDqSEpBbgc0zMAAAAAZKrh5irta6hTfdXCW/lWLC3XH37jJr32u3PaP/I7RSYv5ylClIKlhQ4AgDV3eSp0ejyW9fFMzwAAAABK0zqjUvsa6hSZvKxg9II+mZjU2BdfavzLf0nuqLjy1ZSufDWl81eu6fR4TG9EP1PdakNt69dSOQXLqJSyUTgcVn9/v/r7+xWJRAodDkoE0zMAAAAAWOGrWKlHq7+lHX9yu8a++HLRFh9Dl0w9OTCkgYtjeYoQxYpKKYsikYi6urrU19d3w98ZhqGWlhZ1dnYWIDKUglFzQgc/OpP18UzPAAAAACBNv7f46fsf6ovr19N6/rWp63r+gw+1r6GOiilkjUopCwKBgDZt2jRnQkqSTNNUd3e3Nm3aROUUbDdwcUxPDgxp6JKZ1fFMzwAAAACQcPCjM2knpBKuTV3Xq6PZ3yQHSEplqbu7W7t375Y0XRHV0dGhw4cP6/z58zp//ryOHz+ulpYWSdPVVJs2bVIwGCxkyCgiibsY16Yy+6WRwPQMAAAAAAmRyctZ3+weHDMVmbxic0QoFSSlstDf36+uri5Jks/n07Fjx9TZ2ammpqbkc2pra7V3714dP35chjE9zaC9vZ2KKdjib09/kvFdjIT6KkP7GurUcHOVzVEBAAAAcKNg9IKl43f/XVij5oRN0aCUkJTKwtNPPy1pukLq2LFj8vl88z63trZWBw4cSH6cqK4CsjFqTujRk+/rt7HJrM/xePWdVEgBAAAASPrYwvsLSfrnL76k8TmyQqPzDAWDwWS107PPPpusglpIU1OTmpub1dfXp1AopEgksmAiC5gtMnlZvxo9q99cHFPc4rmC0c/1aPW3bIkLAAAAgPtdnVp42l46rk1d11/+/WndtmK5bipfouXl5brLUyG/d418FSttiBLFiEqpDL388suSpqukWltb0z7u8ccfTz4OBAK2x4XiNGpO6Il3h/SjE+/plA0JKUn6ZMLaXRAAAAAAxWV5ebkt54lL+vTKVf1+4rJOj8f0RvQz/ejEe3ri3SG292FOJKUyYJqmwuGwJGnz5s0ZHVtbW5t8PN+0PiCV1el687nylfW7IAAAAACKx12eipyef+iSyfY+zImkVAZCoVDy8caNGzM+vrGxUdL0ND7TtDfRgOJidbreQlYstecuCAAAAIDi4PeuyflrXJu6ruc/+JCKKcxAUioDg4ODycc1NTUZH+/1euc8FzDbwY/OZD1dbzF3Vub2LggAAAAAd/FVrFTd6sX7JVt1beq6Xh09k/PXgXuQlMpAosG5JK1evTrj4++4447k4+HhYTtCQhGKTF62fcteKr/3lpydGwAAAIA7ta1fq2XluU8RDI6ZikxeyfnrwB1ISmUgFotZOj61UurSpUtWw0GRCkYv5Ozc9VWGfBUrcnZ+AAAAAO60zqjUcxs35CUxFYx+nvPXgDuQlMrA+Ph48rHH47F0LnpKYT4fx3IzHW9Z+RI9vG5tTs4NAAAAwP0abq7SvoY6/dE3bsrp6zARHAkkpTKQWim1atWqjI9PTWSlJriAVFen7J+Ot6x8iZ7buEHrjErbzw0AAACgeKwzKvXzf5t5D+VMMBEcCUsLHYCb2JlIsrIVcGRkJKPn33bbbbrtttuyfj3k1/Jye6fjrTMq9Hj1nSSkAAAAAKQl0fg8V71umQjuLufPn9f58+fTfn4mOQuSUlkyDGuTCaxs/+vs7Mzo+T/5yU/0xBNPZP16yK+7PBU6PW6tf1nCOqNCL9/zp7acCwAAAEDpaFu/Vk8ODOnalP1TwZkI7i5HjhzR3/zN3+Tk3GzfAxzG711jy3mWlS/R49V32nIuAAAAAKUll43PmQiOBJJSWbLaqDybnlQoDYlSWSvoIQUAAADAqkTj8/oqa+9PUjERHKnYvpeBVatWWUpGpfaRsrL9r6urS9XV1Wk/Px/9pM6fP68jR44kP96+fTt9rCywUipbX2Xo4XVrc5qQYr1LC+tdWljv0sFalxbWu7Sw3qUl1+u9zqjUvoY6/a/P/1E/H/qtpuLxrM/FRHDrCvH9vX37djU2Nqb9/JGRkbTbDpGUysDs6XmZJpZSG6WvXr066ziqq6vV0NCQ9fG5cP78+Rl7TBsbG/nFZ0GiVPb5Dz5MKzFVJum7N1fph+vW5uWuA+tdWljv0sJ6lw7WurSw3qWF9S4t+Vrv/3DLH2vF0qVpv0eZjd0c9ijE93cuh6exfS8DXq83+TgSiWR8fOoxVhqdozSkWypbX2XoF/ds1E+/czdlsAAAAAByJtvtfPVVhvY11Knh5qocRQa3olIqAxs3blRfX5+kmVvx0pWalEpNcAHzSZTKRiYvKxi9oE8mJnXlqymtWFquOysr5PfeQiIKAAAAQN7M9x5lSZl0/bpUvqRMU/E471mQFpJSGaipqUk+7u/vl9/vz+j4aDSafFxfX29bXCh+voqVerT6W4UOAwAAAAAk8R4F9mD7XgZSE0lDQ0MZHx8OhyVJPp/PUqNzAAAAAAAAt6NSKgOGYaixsVGhUEjhcFimaaadXAoEAsnHzc3NuQoRBdDT06PJyUlVVFSovb290OEgx1jv0sJ6lw7WurSw3qWF9S4trHdpYb3dj6RUhlpbWxUKhSRJXV1d2rt3b1rHvfjiizPOgeJx8OBBXbhwQWvWrOEHYQlgvUsL6106WOvSwnqXFta7tLDepYX1dj+272XI7/cnq6N6e3vTmsIXCARkmqak6Sopn8+X0xgBAAAAAACcjqRUFg4cOJB8vHXr1gUTU8FgULt375Y0vf3vpZdeynl8AAAAAAAATkdSKgtNTU1qaWmRJJmmqU2bNqmrqyvZZ8o0TfX396utrW1GCeGBAwdocA4AAAAAACB6SmVt7969uuOOO9TV1SVJ6u7uVnd397zPP3z4sJqamvIVHgAAAAAAgKORlLKgo6NDjY2NOnTokILBYLJvVILP51Nzc7Mee+wxSxVSly9fnvHxyMhI1ufKldkxOTHGXPnyyy+T/x8YGChwNPnBerPepYL1Lp31Zq1LZ60l1nuuj4sZ6816FzvWm/V2mtkxzc5ppCqLx+PxXAdUKsLhsCTp0qVLqq+vt22r3n/9r/9VnZ2dtpwLAAAAAAAgX7q6uvQXf/EXc/4dlVI2qq2tLXQIAAAAAAAArkCjcwAAAAAAAOQdlVIu8Gd/9mczPvb5fFq5cmWBogEAAAAAAJjb5cuXFYlEkh/PzmmkoqcUAAAAAAAA8o7tewAAAAAAAMg7klIAAAAAAADIO5JSAAAAAAAAyDuSUgAAAAAAAMg7klIAAAAAAADIO5JSAAAAAAAAyDuSUgAAAAAAAMg7klIAAAAAAADIO5JSAAAAAAAAyDuSUgAAAAAAAMi7pYUOAAAAAIsLh8O6dOmSJMnn88nn8xU4omlOjcvNnPo1jUQiikQikpwVl9s5db1TJdZ+9erVkiSv1yvDMAoclTs5db3D4bAikYg8Ho+j4kLxIykFAADgUJFIRF1dXerr67vh7wzDUEtLizo7O4mrCDj1a9rf369AIDBnXJLU2Niojo4ONTU15Tkyd3PqeqcKBAIKBAIKh8Nz/r3P51Nzc3PB43QDp663aZp66qmn5o3rscceU0dHR97jKjbBYFB+v7/QYcg0TUWjUV26dEmrV692THK5LB6PxwsdBAAAAGYKBALavXv3os/z+Xw6fPhw3u5qOzUuN3Pi1zQcDuupp56aNyExW21trXp6eljvNDhxvVOFw2Ft27ZNpmmm9fzz58/nOCJ3c+p6B4NBtbe3L/o8vrezZ5qm2tvbFQqFCvZ9EolEtH//fgWDwTm/pxPJ5dbW1oKtMUkpAGmh1Li0OHW9U7GVwD5OXe9S3irU3d2trq4uSV/fRW9sbExWo4TDYR06dEi9vb3JY3p6enJ+J9apcbmZE7+ms9+s1tbW6v7771djY2PyZ20kElEoFFJ3d3fy+9QwDB07dqykvlcz5cT1ni8+aboSzu/3q7GxUatWrUqufWL9+/r6dPLkybzE5kZOXe9du3YlX9Pn86m1tVXNzc3y+XzJaprUuAzD0KlTp7jOSlMkElEgEFB3d3fyzwqRlOrq6poRw2JaWlq0d+/eHEY0N5JSsAUlicWJUuPS4tT1TsVWAvs4db3ZKjT9NdixY4ekxe+cz65oOHnyZM4SAk6Ny82c+DVNTUgZhqG9e/cueo3X1taW/J71+XwkKebhxPVOlVrRU1tbq5deekm1tbU5fc1i5tT1Tl3nxZIQ4XBYW7ZskTT9b+L48eM5icnNEgnaWCyms2fP6sSJE8lEfap8J6VSE1KJ67r6+np5PB5JUjQaVX9//w3XW4VYZ5JSsISSxOJFqXFpcep6J7CVwF5OXG+2Cn1t06ZNikQiad+ZTn3j09jYqCNHjpRUXG7mtK9p6htQn8+nY8eOpX1zr7q6Ovkz+vDhw0WdOM6W09Y7Ver1Fd+v9nDieqd+j6f7GqlxdXZ2cuN3ltnVhQk+n29Gciqf16ap13nNzc166aWX5v33Z5qmurq6ZlTrNTc36+DBg3mJVZIUB7Jw9uzZ+AsvvBC/9dZbk/8VwuwYFvvvqaeeKkicbrN///7k12zDhg3xF154If72228n/35oaCj+1FNPzfjaHj16NOdxpb7mPffcE9+/f3/87Nmz8Xg8Hh8fH78hrg0bNsTHx8dzHpfbOXW954rv1ltvjW/bti1+6NCh+NmzZ5Pre/bs2fjbb78df+GFF+L33HNP3mJzIyeu99GjR2e83ve+9734/v3740NDQzPW+NChQ/F77rlnRvyJnwHFIvVrcejQobSPe/jhh5PH5eJr4tS43MyJX9NDhw5l/fsz9ZqM660bOXG9E8bHx+MbNmxIXl/BOqeu97Zt27I6f+K4DRs22B6T2+3fvz++YcOG+Pe+9734tm3bZrw/KcR75fHx8eRrPvzww2kfN/tabP/+/TmMciaSUlhU4o3A/v3740899dSMNwSFTEqlXvwk3lgdPXo0/vbbb8fffvvt+KFDh2b8YE99s4P5vf322zMSPwv9whoaGkpexOT6DUfiQjmdi92hoSHWO01OXe+E1HX/3ve+Fx8aGsr5axYzJ6536kXQhg0b0kqApf5sL7Y3UN/73veyuvBP/bn3wgsvlExcbubUr+n3vve9GYnqdKUmvDN5I1QqnLre8fjMm378nrWHE9f77NmzM27wZSL1d3U+b0y6XSHeKyfeI2eTQEx9f53P66sl+avJglv19fVp9+7dybK+1KazhZLaOK65uVmnTp1SZ2en/H6/mpqa1NTUpNbWVh08eFAjIyNqaWlJHhsOh9XW1lao0B3v6aeflpRes9La2lodOHAg+XE624GyEQ6Hk+dubGxctAFfbW2tDh8+nDw2kwZ/pcaJ650QDAZnrPvx48fpbWGR09Y7HA4nt4v4fD6dOnUqrf6EBw8eTJahRyIR9ff32x5bIZimmdy+uHnz5oyOTf3emK8fV7HF5WZO/pq+/vrrWW29SwxLwI2cvN6maSa37dTW1vJ71gZOXe9AIJB8nOn3eGNjY/Lxm2++aVtMsF/ifc9jjz2W8bGpx0QikbRbKlhFUgppMQxDtbW1amxsVGdnp06ePFmwJpamac7YI5v65mQuiSadPT09yT/r6+sjUTGHYDCYTDo+++yzafWRaGpqUnNzsyQpFArN2djPqtR92nv27EnrmKampuQv0Jdfftn2mIqBU9dbmv4+37Vrl6TpZAW9Laxz4noPDg5K+jpJlslgitSbDcFg0Na4CiUUCiUfb9y4MePjEz/zIpFI2v3X0uHUuNzMyV/TbAfEpL6Bvv/+++0Kpyg4eb1Tr5GyeROLGzl1vVN/hyeaXafLMIzkz4YTJ07YFhPslbrG2fT+Mgwjed0n5S8BSVIKi+ro6NDIyIiOHz+uI0eOqKOjo6BVUolfnoZhZNSAze/3z/jmTL1bgGmpX9vW1ta0j3v88ceTj+3+uiYmWkjTv6Qz+beX+BxM0yyaN612cuJ6J3R1dSUvxFITysieE9e7tbU1WZGV6Rvh1atXJx+Pj4/bGlehJJJ0klRTU5Px8V6vd85zWeXUuNys2L6mqUlvaWZVBZy93qnNjZ0wSbsYOHW9o9Fo8vGqVasyPr6urk7S9HU1NxicyefzqbOz09LP4NT3WvlaZ5JScB03liS6AaXGpcWp6y2xlSAXnLzebBX6Wuqb+tSkW7ruuOOO5OPh4WE7QpLk3LjcrJi+ppFIJFnZKk3fzMy22qpYOXW9Uytx5vpd29/fr127dmnLli2qrq5WdXW1tmzZwm6DRTh1vWOxWPJxppVSs49xQjIcc+vo6Eh7Z8lcsvk3axVJKbiKW0sS3YBS49Li1PWW2EqQC05eb7YKfS31DUM2Uu+u25m0c2pcblYsX9NwOKytW7fOSGx0dnYWLB6ncup6p/5uSFTBSNPrumXLFu3YsUO9vb0Kh8PJ6phwOKyuri5t2bKFapl5OHW9s0lEzSe16grOY2VXU+q/uXzdYCApBVdxa0miG1BqXFqcut4SWwlywcnrnY1i3SqUug3R6psHO3/eOTUuN3Pz1zQSiSgQCGj79u0zEhOJgRS4kVPXe2hoKPk4cb0UCAS0ZcsWhcNhNTc3a8+ePers7FRHR8eMn7XhcFjbtm2zLZZi4tT1Tr2OziZxlnpNbjXxBudK3UmUr+urpXl5FcBGHR0dM6qdMlWIkkQ3sLvUOJvtOHOxu9TYrrjczqnrnc5WgmAwqKGhoeTFkdfr1f33359V9WSpcOp6Z6OYtwql/rzLJgmf+vPOzj5bTo3Lzdz0NY1EIjOqoeaSSFpgbk5d79k3/gKBgHbv3p0cJDSXSCSiHTt2JNtgdHV1UR03i1PXu7a2Nlkdl02lU+oxZ8+etSssOIhpmsl/I4Zh5O16j0opuJLbShLdgFLj0uLU9WYrQW44db0zVexbhXKVSLLKqXG5mdu+pgv9bG1paSmaasVccep6p8YVjUa1e/dudXZ2LjhIyOfzzZiW2t3dnbNJvG7l1PVO/T794IMPMjo2GAzO+DnADYbilDrxPHXKca5RKYWSU4iSRDeg1Li0OHW959tKsHv3bklSc3OzmpqaFIvFdOnSJYXD4WQiK7GVgO0jN3LqeqcjMYEzGAzOSFo2NjbqyJEjeY0ln6zeNLEzoZ/KqXG5mdO/pqtWrbqhCso0TUWjUQ0NDam3t1e9vb1qbm7WSy+9xA2/RThpvVOvi15++WU1NjamVfFmGIb27t2r9vZ2SdLu3buL+uexFU5a79Sql76+PpmmmXZ8L7/8sgzDSF4DZFMBBmfr7+9PttBItMzJF5JSKCmFKkl0A0qNS4tT15utBLnh1PWeC1uFAGcxDGPBn6mBQEAvvvii+vr6dOLECR04cIDrKxcyTTOjiV1+vz+ZpEi9YQBn6+joSE5PfOqppxasikvo6upSNBpVS0tL8liSz8XFNE3t3Lkz+XFPT09eX5/teygphSpJdANKjUuLU9ebrQS54dT1ng9bhaxXpOXqLrZT43Izt39NW1tbkz+DTdPUjh071N/fX9CYnMxJ6516w6G2tjbj9hip2+yDwaBtcRUTJ623NH1TJ3G91NfXN6NH41x27dql7u5uvf766zP+nB69xWXbtm3Jf6uHDx+es69rLpGUQskoZEmi2zi91DhdiVLjhEJfuDuVk9bb6laChMR2P9zISes9l8RWodT/EokowzDU29urLVu2qK2treh6iFn9GZX6/WPnXWynxuVmxfY19fl8OnDgQPLj1DvucO56p8aVTbI/9Y0rfTu/5tT1TkhNMPX29qq6ulpdXV0KBoPJgTJdXV2qrq5Wb29vMkmResOPrdjFIzFtU5pOSBWi0pXteygJhS5JhDWUGpcmthKUplLeKjR7m2SmP7NSK+LsvIvt1LjcrBi/pk1NTWpsbFQoFJJpmgoEAmptbS10WI7g1PVOjSubIUKpsdAi4WtOXe+E2tpaHT58WDt27JA0fb2VuFae/byXXnopmXxMTZaVQsVysTNNU9u2bVM4HJZhGHr99dfzXiGVQKUUSkKhSxLdhlLj0uKk9WYrQe45ab2zUcxbhVKnHGazBTVXd7GdGpebFevX1O/3Jx8HAoECRuIsTl1vK9OsMT+nrneqpqYmnTx5Ui0tLTOSZoZhqLm5WYcPH9bx48dnvGdKHUbDvx13S/TvDIfDqq2t1alTpwr6/phKKRQ9J5QkzmfTpk157X3T09Mz44Ix1apVqyy9Wc1HqfGWLVskTZcaB4NBtbS0qL6+Xh6PR7FYTIODg+rt7ZVpmsnk4y9+8YvkOQp94c56L86OrQRWGuPbifXOncRWocRd3p07d2pkZKTAUVm3ceNG9fX1Scqud1fqv7fUN0XFGpebFevXtL6+Pvk4ddpxqXPqeqcmFphQbB+nrvdsPp9Pe/fundH+YKGYEtcRVEm5W2JatWmaCw4SyieSUg7jpDcxbuekksT51NTU5PX1FkrKUGqce6z34oppKwHrnVvFuFUo9d9Mf39/xr+fUxOxqcmBYo3LzfialhanrnfquTIdJiNJly5dSj6+4447bIioODh1va1IbYtQrO8dS0EwGFR7e7uk6fYoTumxTFLKYZz0JsbNUkfE19bW6vXXX3fE3f3ZnJCZTvB6vcm7mpFIJOOEQD5Ljffv3z9jsp5hGNq8ebNaW1tvqIRzUqkx6724Qq+RnVjv3PP7/ckL5WJISqW++Uj92ZWuxBr7fD5bf+c5NS43K9avaerPCifFVWhOXe/a2trkVujh4eGMj09d73y/h3Eyp663FYntuIZhuP53banq7u5OTqJ3WmEKSSmHcdKbGLdyYkmiG1BqXFqcut5sJcgNp663VcW2VcgwjGT1Vzgclmmaab8hSe3f09zcXBJxuZlTv6bbt2/XkSNHsj4+tb+bk35WFJpT11uaTu739vYqEolkfNMiNZHlpPYYhebk9c5Gf39/8nfsY489VuBokI2urq7kDhOntbORaHSOIhMMBrVlyxaZpqmOjg4SUhmYXWqcKUqN3cWp681Wgtxw6nrjRql3oBN3NNPx4osvznkOuzg1Ljdz2tc0HA4rFAqpra0tq+NN05wxYII3rzM5bb0THnrooeTj/fv3p31cMBhM3rBwSvLESZy63tl4+umnJU0n21paWgocDTKVGABlGIZOnjzpuISURFIKRaS7uzu5R7anp8cxe2TdglLj0uLU9U5sJZDEVgIbOXW9rSrGrUJ+vz/5uSSqFxYTCASSVaHNzc052Qbr1LjczGlfU6/XK8Mw1NfXl7zBl4n29vYZ2+q5GTST09Y7oba2NplUSjcuaWbyhGvuGzl1vTO1ffv2ZOx79+4tmt+1paKtrU29vb3y+Xw6deqUI/5NzYWkFIpCV1dX8i7E4cOHuRDKQqLUWFKy1DhdlBq7j5PXO/H9m9hKkAm2EszNqeu9fft2S8cX61ahAwcOJB9v3bp1we+DYDCo3bt3S5pe55deeimt12hra0v+N9fQiELFVWqctNaJoTDS9M+Je+65Z8b3/0K6urpmVCcnzoOZnLTeqVKTSovFZZqmtmzZknzOnj17HPtGt9Ccut67du1atGraNE1t3749+X3d0dHB+yuX2b59u/r6+lRbW6tjx445O6EYByy49dZbk/8VylNPPRW/9dZb4xs2bIifPXu2YHEUg6NHjybX86mnnkr7uA0bNiSPc8oa3HPPPcl/F+Pj44UOx5Gcut5DQ0NZxZX6+Tz88MO2x+V2TlvvxDpnu1bj4+MzYjt69KhtsTlB4ndb4r8XXnghPjQ0FB8fH4+Pj4/H33777fjDDz884zlvv/122udPPS6TNch1XKXIaWs9NDQ043sr8TPj6NGjyZ8BZ8+ejQ8NDcUPHTqU/H2b+O/QoUNZfy1KgdPWO+Htt9+eM66Es2fPxvfv3z/j38YLL7yQ0edeipy23ocOHUo+f9u2bfGjR48mr5MT8bzwwgs3fP8jO3a9V3744YeT/+3fv3/B546Pj8e/973vJdfYDcri8Xi80IkxuNdtt92WfHz+/Pm8v35bW5v6+vrk8/mcnwF2ierq6mQVxcmTJxe9+xUIBJJ3dpzSWD71zo7Tpks4jVPXO/G9nW5ckrRp06bkXch0jyk1Tlpv0zR1zz33yDTNrKakpn6fG4ahkZER22JzitRJOYvJtHFp6u/vTNc2l3GVKqettWma6urqUm9vb9qv4/P51NPTo9ra2rSPKVVOW++E1HHxi+ns7FRHR0fa5y5lTlrv1N/r6dizZw8tMNKQ2k8vVer3U09Pz5zPSed9SibrnHp9ZLUH2B133JGX73OSUrCkkEmpxDdcNm9mML/+/n7t2LFD0vQbvWPHjs37xjX14sUwDJ06dSqtdUhtorpx48a0ftjt2rVLfr9/wV/Upmmqvb19RqkxfQ4W5tT1jkQi2rRpU1pxmaapbdu2JbdrcgE1P6etdzgc1pYtW5Kv8eyzz6a1dqlTZCTp+PHjRftGOBwO69ChQwoGgzdsu/T5fGpubtZjjz2W8e9AK29ccxlXKXPiWkciEe3fv3/OmBJqa2t1//33k6DIkBPXW1o8IdnY2MiWvSw4ab0X+75O9IR79NFHWec0pF6zZmNkZGTRdc9knVOfa1VjY6OliazpIikFS+xKSmXyJib1DWi+vlFKza5du2ZcjHR0dOj+++9P9mwZHBxUIBBIVrJImd3ZsXJXp7GxUa2trWpsbJRhGDJNU4ODgwqFQjPepLa0tGjv3r1pxVPqnLbeCakJlNS4EsmHSCSivr4+vfzyy8mLKhKRi3PaeofDYW3btm3GhXFLS4uamppUW1srn8+nSCSiWCymwcFBdXd3z+jLUUpJyETi9dKlS6qvr3dMwsepcbmZE7+mid+30WhUq1atksfjcUxsbufE9Zamfw8npq96vV5HxeZmTlrvcDisSCSi8fFxrVq1Sj6fr2hv8uQKSSnrSEohLZQklh5KjUuLk9Y7FVsJcsNp681WIQAAgNJEUgqLIvtbuig1Li1OWu9UbCXIDSeuN1uFAAAASgtJKSyKpBQkSo1LjZPWOxVbCXLDievNViEAAIDiR1IKAAAAAAAAebek0AEAAAAAAACg9JCUAgAAAAAAQN6RlAIAAAAAAEDekZQCAAAAAABA3pGUAgAAAAAAQN6RlAIAAAAAAEDekZQCAAAAAABA3pGUAgAAAAAAQN6RlAIAAHA50zRlmmahwygovgYAALgPSSkAAACXikQiqq6uVnV1tUKhUKHDKajx8XFt3bpVt912mwKBQKHDAQAAaSiLx+PxQgcBAACAzITDYW3ZskWS1NPTI7/fX+CICs80Td1zzz0yTVOdnZ3q6OgodEgAAGABJKUAAABcJjX5smfPHrW2thY6JMeIRCLatGmTJPG1AQDA4di+BwAA4DLbtm2TaZpqaWkh6TKLz+fTnj17JEm7d+9Wf39/gSMCAADzoVIKAADARdra2tTX1yfDMDQyMlLocBxr+/btyT5bJ0+elM/nK3BEAABgNiqlAAAAXCIQCKivr0+SdODAgQJH42w9PT0yDEPSdMUUAABwHpJSAAAALvHiiy9Kkpqbm9XU1FTgaJzNMAw9++yzkqRQKKRgMFjgiAAAwGxs3wMAAHCB7u5udXV1SWI7Wiaqq6tlmibbHQEAcCAqpQAAAFzg5ZdfliQ1NjaSkMrAY489Jml6YmEgEChwNAAAIBVJKQAAAIfr7u6WaZqSpI6OjgJH4y4tLS3Jx93d3QWMBAAAzEZSCgAAwOESVVI+n49eUhkyDCOZmIpEIgqHwwWOCAAAJJCUAgAAcLBwOJyskmpubi5wNO7k9/uTj3/xi18UMBIAAJBqaaEDAAAAKDTTNDU4OGj5PPX19TIMw4aIvvbmm28mHzc2Ntp6bmn6c49Go5Ikr9dra/ymaWp8fFyxWCyjc0ciEUlSLBZTbW2t5ThSq8tOnDhh+XwAAMAeJKUAAEDJO3r0qHbv3m35PCdPnrQ9KRUKhZKPs9m6d9ttt0ma3vp38uTJ5J8Hg0G9/PLLN2xnS2x36+zszPrcgUBAgUBgznM/9thjc/bFMk1TL7/8snp7e5OVYQk+n08dHR1qbW1dNKb51NbWJqvOwuGwLckuAABgDdv3AABAyRsaGrJ8jlxMxUskUCTZmkTZvn272tvb5+yvZJqmuru7tWnTphuSQ+mee/fu3fOeu6urS9XV1TPOHYlEtHXr1hkN3VNFIhHt3r1bu3btyjiehPvvvz/5ODXRBwAACoekFAAAKHl79+7V+fPnM/pvz549yeMbGxt15MgR2+NKTZ7U1dXZcs7t27crFAqps7NTJ0+e1MjIiI4fP649e/bM2B4YiUT01FNPZXzuoaEhdXR06Pjx4zp58uSc5zZNU+3t7cnX2bp1qySpp6dHJ0+e1MmTJ3X48GF1dnbOqDzr7e1VIBDI6vOuqalJPu7v78/qHAAAwF5l8Xg8XuggAAAA3CQQCCS3+9XW1ur48eM5eZ3u7m51dXVJkvbs2ZPV9rXULXatra0KBAI6fPjwvFVdXV1d6u7uTn48MjIy75bE2ed++eWXdezYsXnPnfr5SNPbHXfv3i2Px6ODBw/O+zls2bIlWXk1e6tguiKRiDZt2iRpehvhyMhIxucAAAD2olIKAAAgA/lKSEnS2bNnk4+9Xq+lc42Pj+vll19WT0/PgtsMZ1cnpbPVLXHuAwcOLHjujo6OGedub2/X0NDQggkpabqCKiESiSQboWciNa5stiUCAAD7kZQCAABIUz4TUpKSU/EkWe5XZZqm6urq0upN5ff7k4/TmUqYOHc6jdhbWlqSj8PhsJ599tlFj/H5fDM+/2ySUpJmJMSyPQcAALAPSSkAAIA0zE5Ivf766zl/zdSk1KpVqyyfL93tf3fccUfycbpVRamJrIXMTq59//vfT+u41EqxWCyW1jELnYOkFAAAhUdSCgAAYBFzJaTm67Nkp9TEiR2vl+4EP4/Hk/G5UxuZLyQ1uebz+dL+vLKJaTY7ElsAAMA+JKUAAAAWUKiEVC5Y3QJo97mt9skCAADuRlIKAABgHsFgsGgSUgAAAE5DUgoAAGAOwWBQ7e3tkkhI5YodW/IAAIB7kZQCAACYhYQUAABA7pGUAgAASEFCqnilNjfPZX8tAACQHpJSAAAA/8ppCanUxEnqJD5kZ3x8PPmYJusAABQeSSkAAAA5LyElzUycpFb5IDvRaDT5uNBrCwAASEoBAAA4MiElzUxKDQ4OFjCS4mCapiS27gEA4BQkpQAAQElzakJKkurq6pKP+/v7CxiJ+6Vuf2TrHgAAzkBSCgAAlCwnJ6Qkqb6+Pvl4eHi4gJG4XygUSj6ura0tYCQAACCBpBQAAChJTk9ISdNxJWKi0bk1qZVm999/fwEjAQAACUsLHQAAAEC+9ff3JxNSkvTYY49l1bOpvr4+54mszZs3q6+vT5IUDoep8snSiRMnJE03OOdrCACAM5CUAgAAJSUcDmvHjh0z/iw1QZWJkydP5jwp1dTUlExKhUIhEipZiEQiySbnmzdvLnA0AAAgge17AACgpLz55pu2nMfn8+Vlitv3v//95ONAIJDz1ytGiaSexNY9AACcpCwej8cLHQQAAEA+JapmFuKk/lK7du1Sb2+vJOnw4cNqamoqcETusmnTJkUiERmGoZGRkUKHAwAA/hWVUgAAoOQYhrHof07y0EMPJR93d3cXMBL36e/vTzaJf+yxxwocDQAASEVSCgAAwOFqa2uTvaRCoRCT+DKQSOIZhqGOjo4CRwMAAFKRlAIAAHCBl156KfmY3lLpCYfDCoVCkqiSAgDAiegpBQAA4BKpvaVOnjyZl0brbrZlyxaFw2F6SQEA4FBUSgEAALjE3r17k/2udu/eXeBonC0YDCocDkuSXn/99QJHAwAA5kJSCgAAwEUSCZZQKMQ2vnlEIhHt2rVLkrRnz55kPy4AAOAsbN8DAABwmWAwqPb2dknS8ePHSbrMsmnTJkUiEbW0tGjv3r2FDgcAAMyDSikAAACX8fv96uzslCRt27aNaXwptm/frkgkotraWhJSAAA4HEkpAAAAF+ro6NCePXtkmqa2bt1KYkpSW1ubQqGQGhsbdfz48UKHAwAAFsH2PQAAABcLh8MaHBzUqlWr5Pf7Cx1OwUQiEYVCoZL/OgAA4CYkpQAAAAAAAJB3bN8DAAAAAABA3pGUAgAAAAAAQN6RlAIAAAAAAEDekZQCAAAAAABA3pGUAgAAAAAAQN6RlAIAAAAAAEDekZQCAAAAAABA3pGUAgAAAAAAQN6RlAIAAAAAAEDe/f8BiBJJ7roZcdsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"density-article\"\n",
+    "myplt = PltTools()\n",
+    "myplt.prepare_figure(fig_size = (12,5), dark_mode = False,\n",
+    "                    transparency = False, use_serif=False, n_line=1)\n",
+    "myplt.add_panel()\n",
+    "myplt.add_plot(x = z[density_water>0.02], y = density_water[density_water>0.02], linewidth_data = 3,\n",
+    "                marker = \"o\", data_color = 0, markersize = 12, data_label=r'$\\mathrm{water}$')\n",
+    "myplt.complete_panel(ylabel = r'$\\rho ~ (\\mathrm{g/mol/\\AA}^3$)', xlabel = r'$z ~ \\mathrm{(nm)}$',\n",
+    "                        xpad = 10, legend=False, handlelength_legend=1)\n",
+    "myplt.set_boundaries(x_ticks=np.arange(-1.2, 1.3, 0.3), x_boundaries=(-1.3, 1.3),\n",
+    "            y_ticks=np.arange(0, 3.1, 1), y_boundaries=(-0.15, 3.15))\n",
+    "# myplt.add_subplotlabels()\n",
+    "myplt.save_figure(filename = filename, saving_path = './')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/sphinx/source/shared/cite.rst b/docs/sphinx/source/shared/cite.rst
index 22683080..caaa6ba5 100644
--- a/docs/sphinx/source/shared/cite.rst
+++ b/docs/sphinx/source/shared/cite.rst
@@ -1,3 +1,5 @@
+.. include:: ../../source/shared/links.rst
+
 .. admonition:: Cite
     :class: non-title-info
 
@@ -5,7 +7,3 @@
     cite *A Set of Tutorials for the LAMMPS Simulation Package* by Simon Gravelle,
     Jacob R. Gissinger, and Axel Kohlmeyer (2025) :cite:`gravelle2025tutorials`. You
     can access the full paper on |gravelle2025tutorials_arXiv|.
-
-.. |gravelle2025tutorials_arXiv| raw:: html
-
-    <a href="https://doi.org/10.48550/arXiv.2503.14020" target="_blank">arXiv</a>
diff --git a/docs/sphinx/source/shared/links.rst b/docs/sphinx/source/shared/links.rst
new file mode 100644
index 00000000..b5a8fae4
--- /dev/null
+++ b/docs/sphinx/source/shared/links.rst
@@ -0,0 +1,20 @@
+
+.. |gravelle2025tutorials_arXiv| raw:: html
+
+    <a href="https://doi.org/10.48550/arXiv.2503.14020" target="_blank">arXiv</a>
+
+.. |github_lammps_tutorials| raw:: html
+
+   <a href="https://github.com/lammpstutorials" target="_blank">GitHub</a>
+
+.. |mastodon_lammps_tutorials| raw:: html
+
+   <a href="https://mastodon.social/@simongravelle" target="_blank">Mastodon</a>
+
+.. |simongravelle_page| raw:: html
+
+   <a href="https://simongravelle.github.io/" target="_blank">Simon Gravelle</a>
+
+.. |patreon| raw:: html
+
+    <a href="https://www.patreon.com/molecularsimulations" target="_blank">patreon</a>
diff --git a/docs/sphinx/source/shared/needhelp.rst b/docs/sphinx/source/shared/needhelp.rst
new file mode 100644
index 00000000..75cfb335
--- /dev/null
+++ b/docs/sphinx/source/shared/needhelp.rst
@@ -0,0 +1,9 @@
+.. admonition:: Struggling with your molecular simulations project?
+   :class: patreon
+
+   Get guidance for your LAMMPS simulations and receive
+   personalized |advice| for your project.
+
+.. |advice| raw:: html
+
+   <a href="https://www.patreon.com/molecularsimulations" target="_blank">advice</a>
diff --git a/docs/sphinx/source/tutorial1/tutorial.rst b/docs/sphinx/source/tutorial1/tutorial.rst
index e9f84380..9cada2d6 100644
--- a/docs/sphinx/source/tutorial1/tutorial.rst
+++ b/docs/sphinx/source/tutorial1/tutorial.rst
@@ -142,6 +142,8 @@ distance of 0.3 units between the randomly placed atoms.  This
 constraint helps avoid close contacts between atoms, which can lead  
 to excessively large forces and simulation instability.
 
+.. include:: ../shared/needhelp.rst
+
 Settings
 --------
 
diff --git a/docs/sphinx/source/tutorial2/tutorial.rst b/docs/sphinx/source/tutorial2/tutorial.rst
index 6eb1e8d1..516b33a9 100644
--- a/docs/sphinx/source/tutorial2/tutorial.rst
+++ b/docs/sphinx/source/tutorial2/tutorial.rst
@@ -59,6 +59,8 @@ cutoff) and its cutoff is set to 14 Å, which means that only the
 atoms closer than this distance interact through the Lennard-Jones
 potential.
 
+.. include:: ../shared/needhelp.rst
+
 The ``bond_style``, ``angle_style``, ``dihedral_style``, and ``improper_style``
 commands specify the different potentials used to constrain the relative
 positions of the atoms.  The ``special_bonds`` command sets the weighting factors
diff --git a/docs/sphinx/source/tutorial3/tutorial.rst b/docs/sphinx/source/tutorial3/tutorial.rst
index 7c965fc4..68b889fc 100644
--- a/docs/sphinx/source/tutorial3/tutorial.rst
+++ b/docs/sphinx/source/tutorial3/tutorial.rst
@@ -41,6 +41,8 @@ interactions :cite:`ewald1921berechnung`.  Finally, the
 :ref:`carbon-nanotube-label`, sets the LJ and Coulomb
 weighting factors for the interaction between neighboring atoms.
 
+.. include:: ../shared/needhelp.rst
+
 Let us create a 3D simulation box of dimensions :math:`6 \times 3 \times 3 \; \text{nm}^3`,
 and make space for 8 atom types (2 for the water, 6 for the polymer), 7 bond types
 (1 for the water, 6 for the polymer), 8 angle types (1 for the water, 7 for the polymer),
diff --git a/docs/sphinx/source/tutorial4/tutorial.rst b/docs/sphinx/source/tutorial4/tutorial.rst
index dc133cec..190d94eb 100644
--- a/docs/sphinx/source/tutorial4/tutorial.rst
+++ b/docs/sphinx/source/tutorial4/tutorial.rst
@@ -36,6 +36,8 @@ atom, bond, and angle styles, as well as interaction
 potential.  Here, ``lj/cut/tip4p/long`` imposes a Lennard-Jones potential with
 a cut-off at :math:`12\,\text{Å}` and a long-range Coulomb potential.
 
+.. include:: ../shared/needhelp.rst
+
 So far, the commands are relatively similar to those in the previous tutorial,
 :ref:`all-atoms-label`, with two major differences: the use
 of ``lj/cut/tip4p/long`` instead of ``lj/cut/coul/long``, and ``pppm/tip4p``
diff --git a/docs/sphinx/source/tutorial5/tutorial.rst b/docs/sphinx/source/tutorial5/tutorial.rst
index c26ffe35..5c95b023 100644
--- a/docs/sphinx/source/tutorial5/tutorial.rst
+++ b/docs/sphinx/source/tutorial5/tutorial.rst
@@ -27,6 +27,8 @@ So far, the input is very similar to what was seen in the previous tutorials.
 Some basic parameters are defined (``units`` and ``atom_style``),
 and a **.data** file is imported by the ``read_data`` command.
 
+.. include:: ../shared/needhelp.rst
+
 The initial topology given by |silica_data_5|
 is a small amorphous silica structure.  This structure was created using a force field called
 Vashishta :cite:`vashishta1990interaction`.  If you open the **silica.data**
diff --git a/docs/sphinx/source/tutorial6/tutorial.rst b/docs/sphinx/source/tutorial6/tutorial.rst
index cb14d4c5..66a00fd9 100644
--- a/docs/sphinx/source/tutorial6/tutorial.rst
+++ b/docs/sphinx/source/tutorial6/tutorial.rst
@@ -20,6 +20,8 @@ The main difference from some of the previous tutorials is the use of the ``Vash
 pair style.  The Vashishta potential implicitly models atomic bonds through
 energy terms dependent on interatomic distances and angles :cite:`vashishta1990interaction`.
 
+.. include:: ../shared/needhelp.rst
+
 Let us create a box for two atom types, ``Si``
 of mass 28.0855 g/mol and ``O`` of mass 15.9994 g/mol.
 Add the following lines to **generate.lmp**:
diff --git a/docs/sphinx/source/tutorial7/tutorial.rst b/docs/sphinx/source/tutorial7/tutorial.rst
index 6dafb98e..ebf74d4d 100644
--- a/docs/sphinx/source/tutorial7/tutorial.rst
+++ b/docs/sphinx/source/tutorial7/tutorial.rst
@@ -27,6 +27,8 @@ that consists of a particles in a box in the presence of a
 position-dependent repulsive force that makes the center of the box a less
 favorable area to explore.
 
+.. include:: ../shared/needhelp.rst
+
 Basic LAMMPS parameters
 -----------------------