diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index be9498f94..5abf63a4b 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -29,6 +29,7 @@ jobs:
         rm -r ../../doc/ford_site/page/python-api
         mv build ../../doc/ford_site/page/python-api
         cd ../../
+        touch doc/ford_site/.nojekyll
 
     - name: Upload Documentation
       uses: actions/upload-artifact@v4
diff --git a/README.md b/README.md
index 17f471350..548c53f41 100644
--- a/README.md
+++ b/README.md
@@ -24,9 +24,9 @@ extensibility of Modern Fortran for scientists to have an easy way to implement
 new thermodynamic models without dealing with lower-level languages but still
 getting decent performance.
 And also this framework provides the possibility of using analytically obtained
-derivatives so both options are easily available.
+derivatives, so both options are easily available.
 
-> This is an experimental work in progress and we recommend the before
+> This is an experimental work in progress, and we recommend the before
 > mentioned libraries if you are intending to use some of this in real work.
 > Big part of the code comes from a refactoring process of older codes so
 > not all parts are easily readable, yet.
@@ -34,6 +34,17 @@ derivatives so both options are easily available.
 We focus mainly on that the addition of a new thermodynamic model as easily as
 possible. Also providing our models too!
 
+## Documentation
+The latest API documentation for the `main` branch can be found:
+   - [Fortran API documentation](https://ipqa-research.github.io/yaeos/)
+   - [Python API documentation](https://ipqa-research.github.io/yaeos/page/python-api/index.html)
+
+The Fortran API documentation can also be generated by processing the source
+files with [FORD](https://github.com/Fortran-FOSS-Programmers/ford). On the
+other hand, the Python API documentation can be generated by processing the
+source files with [Sphinx](https://github.com/sphinx-doc/sphinx).
+
+
 ## Available models
 - CubicEoS
    - SoaveRedlichKwong
diff --git a/c_interface/yaeos_c.f90 b/c_interface/yaeos_c.f90
index 36cca0210..bf557c21f 100644
--- a/c_interface/yaeos_c.f90
+++ b/c_interface/yaeos_c.f90
@@ -29,6 +29,7 @@ module yaeos_c
    ! GeMoels
    public :: nrtl
    public :: unifac_vle
+   public :: uniquac
    public :: ln_gamma
 
    ! Thermoprops
@@ -93,6 +94,28 @@ subroutine unifac_vle(id, nc, ngs, g_ids, g_v)
       call extend_ge_models_list(id)
    end subroutine unifac_vle
 
+   subroutine uniquac(id, qs, rs, aij, bij, cij, dij, eij)
+      use yaeos, only: setup_uniquac
+      integer(c_int), intent(out) :: id
+      real(c_double), intent(in) :: qs(:)
+      !! Molecule's relative areas \(Q_i\)
+      real(c_double), intent(in) :: rs(size(qs))
+      !! Molecule's relative volumes \(R_i\)
+      real(c_double), intent(in) :: aij(size(qs),size(qs))
+      !! Interaction parameters matrix \(a_{ij}\)
+      real(c_double), intent(in) :: bij(size(qs),size(qs))
+      !! Interaction parameters matrix \(b_{ij}\)
+      real(c_double), intent(in) :: cij(size(qs),size(qs))
+      !! Interaction parameters matrix \(c_{ij}\)
+      real(c_double), intent(in) :: dij(size(qs),size(qs))
+      !! Interaction parameters matrix \(d_{ij}\)
+      real(c_double), intent(in) :: eij(size(qs),size(qs))
+      !! Interaction parameters matrix \(e_{ij}\)
+
+      ge_model = setup_uniquac(qs, rs, aij, bij, cij, dij, eij)
+      call extend_ge_models_list(id)
+   end subroutine
+
    subroutine extend_ge_models_list(id)
       !! Find the first available model container and allocate the model
       !! there. Then return the found id.
diff --git a/doc/page/usage/excessmodels/uniquac.md b/doc/page/usage/excessmodels/uniquac.md
new file mode 100644
index 000000000..0d00aea56
--- /dev/null
+++ b/doc/page/usage/excessmodels/uniquac.md
@@ -0,0 +1,243 @@
+---
+title: UNIQUAC
+---
+
+[TOC]
+
+# UNIQUAC
+
+UNIQUAC (**uni**versal **qua**si**c**hemical) Excess Gibbs free energy model.
+
+$$ 
+\frac{G^E}{RT} = \sum_k n_k \ln\frac{\phi_k}{x_k}
++ \frac{z}{2}\sum_k q_k n_k \ln\frac{\theta_k}{\phi_k}
+- \sum_k q_k n_k \ln\left(\sum_l \theta_l \tau_{lk} \right)
+$$
+
+With:
+
+$$x_k = \frac{n_k}{\sum_l n_l}$$
+
+$$ \phi_k = \frac{r_k n_k}{\sum_l r_l n_l} $$
+
+$$ \theta_k = \frac{q_k n_k}{\sum_l q_l n_l} $$
+
+$$ \tau_{lk} = \exp \left[\frac{-\Delta U_{lk}}{R T} \right] $$
+
+$$
+\frac{-\Delta U_{lk}}{R T} = a_{lk}+\frac{b_{lk}}{T}+c_{lk}\ln T + d_{lk}T +
+e_{lk}{T^2}
+$$
+
+## Temperature derivatives
+
+\(\qquad \tau_{lk}:\)
+
+$$
+\frac{d \tau_{lk}}{dT} = \tau_{lk} \left(2 T e_{lk} + d_{lk} + \frac{c_{lk}}{T}
+- \frac{b_{lk}}{T^{2}}\right)
+$$
+
+$$
+\frac{d^2 \tau_{lk}}{dT^2} = \tau_{lk} \left(2 T e_{lk} + d_{lk} + \frac{c_{lk}}{T}
+- \frac{b_{lk}}{T^{2}}\right)^2 + \tau_{lk} \left(2 e_{lk} - \frac{c_{lk}}{T^{2}} +
+\frac{2 b_{lk}}{T^{3}}\right)
+$$
+
+\(\qquad G^E\):
+
+$$
+\frac{\partial G^E}{\partial T} = \frac{G^E}{T} - RT \sum_k q_k n_k \frac{
+\sum_l \theta_l \frac{\partial \tau_{lk}}{\partial T}}{\sum_l \theta_l
+\tau_{lk}}
+$$
+
+$$
+\frac{\partial G^E}{\partial T^2} = -R\left[T \sum_k q_k n_k
+\left(\frac{(\sum_l \theta_l \frac{\partial^2 \tau_{lk}}{\partial T^2})}{\sum_l
+\theta_l \tau_{lk}}
+- \frac{(\sum_l \theta_l \frac{\partial \tau_{lk}}{\partial T})^2}{(\sum_l
+\theta_l \tau_{lk})^2}\right) + 2\left(\sum_k q_k n_k \frac{(\sum_l \theta_l
+\frac{\partial \tau_{lk}}{\partial T} )}{\sum_l \theta_l \tau_{lk}}\right)
+\right]
+$$
+
+## Cross temperature-compositional derivative
+
+$$
+\frac{\partial^2 G^E}{\partial n_i \partial T} = \frac{1}{T} \frac{\partial
+G^E}{\partial n_i} - R T \left(q_i \frac{\sum_l \theta_l \frac{d \tau_{li}}{d
+T}}{\sum_l \theta_l \tau_{li}} + \sum_k q_k n_k \left(\frac{(\sum_l \frac{d
+\theta_l}{d n_i} \frac{d \tau_{lk}}{d T})(\sum_l \theta_l \tau_{lk}) - (\sum_l
+\theta_l \frac{d \tau_{lk}}{d T})(\sum_l \frac{d \theta_l}{d n_i}
+\tau_{lk})}{(\sum_l \theta_l \tau_{lk})^2} \right) \right)
+$$
+
+
+## Compositional derivatives
+
+\(\qquad \phi_k\):
+
+$$
+\frac{d \phi_k}{dn_i} = \begin{cases} - \frac{{n}_{i}
+{r}_{i}^{2}}{\left(\sum_l {n}_{l} {r}_{l}\right)^{2}} +
+\frac{{r}_{i}}{\sum_l {n}_{l} {r}_{l}} & \text{for}\: i = k \\-
+\frac{{n}_{k} {r}_{i} {r}_{k}}{\left(\sum_l {n}_{l} {r}_{l}\right)^{2}}
+& \text{otherwise} \end{cases}
+$$
+
+$$
+\frac{d^2 \phi_k}{dn_i dn_j} = \begin{cases} \frac{2 {n}_{i}
+{r}_{i}^{3}}{\left(\sum_l {n}_{l} {r}_{l}\right)^{3}} - \frac{2
+{r}_{i}^{2}}{\left(\sum_l {n}_{l} {r}_{l}\right)^{2}} & \text{for}\: i
+= k \wedge j = k \\\frac{2 {n}_{i} {r}_{i}^{2} {r}_{j}}{\left(\sum_l
+{n}_{l} {r}_{l}\right)^{3}} - \frac{{r}_{i} {r}_{j}}{\left(\sum_l
+{n}_{l} {r}_{l}\right)^{2}} & \text{for}\: i = k \wedge j \neq k \\\frac{2
+{n}_{j} {r}_{i} {r}_{j}^{2}}{\left(\sum_l {n}_{l} {r}_{l}\right)^{3}} -
+\frac{{r}_{i} {r}_{j}}{\left(\sum_l {n}_{l} {r}_{l}\right)^{2}} &
+\text{for}\: j = k \wedge i \neq k \\\frac{2 {n}_{k} {r}_{i} {r}_{j}
+{r}_{k}}{\left(\sum_l {n}_{l} {r}_{l}\right)^{3}} & \text{otherwise}
+\end{cases}
+$$
+
+\(\qquad \theta_k\):
+
+$$
+\frac{d \theta_k}{dn_i} = \begin{cases} - \frac{{n}_{i}
+{q}_{i}^{2}}{\left(\sum_l {n}_{l} {q}_{l}\right)^{2}} +
+\frac{{q}_{i}}{\sum_l {n}_{l} {q}_{l}} & \text{for}\: i = k \\-
+\frac{{n}_{k} {q}_{i} {q}_{k}}{\left(\sum_l {n}_{l} {q}_{l}\right)^{2}}
+& \text{otherwise} \end{cases}
+$$
+
+$$
+\frac{d^2 \theta_k}{dn_i dn_j} = \begin{cases} \frac{2 {n}_{i}
+{q}_{i}^{3}}{\left(\sum_l {n}_{l} {q}_{l}\right)^{3}} - \frac{2
+{q}_{i}^{2}}{\left(\sum_l {n}_{l} {q}_{l}\right)^{2}} & \text{for}\: i
+= k \wedge j = k \\\frac{2 {n}_{i} {q}_{i}^{2} {q}_{j}}{\left(\sum_l
+{n}_{l} {q}_{l}\right)^{3}} - \frac{{q}_{i} {q}_{j}}{\left(\sum_l
+{n}_{l} {q}_{l}\right)^{2}} & \text{for}\: i = k \wedge j \neq k \\\frac{2
+{n}_{j} {q}_{i} {q}_{j}^{2}}{\left(\sum_l {n}_{l} {q}_{l}\right)^{3}} -
+\frac{{q}_{i} {q}_{j}}{\left(\sum_l {n}_{l} {q}_{l}\right)^{2}} &
+\text{for}\: j = k \wedge i \neq k \\\frac{2 {n}_{k} {q}_{i} {q}_{j}
+{q}_{k}}{\left(\sum_l {n}_{l} {q}_{l}\right)^{3}} & \text{otherwise}
+\end{cases}
+$$
+
+\(\qquad G^E\):
+
+$$
+\frac{\partial \frac{G^E}{RT}}{\partial n_i} = \ln \left(\frac{\phi_i}{x_i}
+\right) + \sum_k n_k \left(\frac{\frac{d \phi_k}{dn_i}}{\phi_k} -
+\frac{\frac{dx_k}{dn_i}}{x_k}\right) +
+\frac{z}{2}{q}_{i}\ln{\left(\frac{\theta_{i}}{\phi_{i}} \right)} + \frac{z}{2}
+\sum_k {n}_{k} {q}_{k} \left(\frac{\frac{d \theta_{k}}{d {n}_{i}}}{\theta_k} -
+\frac{\frac{d \phi_{k}}{d {n}_{i}}}{\phi_k} \right) - {q}_{i}
+\ln{\left(\sum_l \theta_{l} {\tau}_{li} \right)} - \sum_k {n}_{k} {q}_{k}
+\frac{\sum_l \frac{d \theta_{l}}{d {n}_{i}} {\tau}_{lk}}{\sum_l \theta_{l}
+{\tau}_{lk}}
+$$
+
+
+Differentiating each term of the first compositional derivative respect to
+$n_j$
+
+\(\frac{\partial \frac{G^E}{RT}}{\partial n_i \partial n_j} =\)
+
+$$
+\frac{\frac{d \phi_i}{d n_j}}{\phi_i} - \frac{\frac{d x_i}{d n_j}}{x_i} 
+$$
+
+$$
++\frac{\frac{d \phi_j}{dn_i}}{\phi_j} -
+\frac{\frac{dx_j}{dn_i}}{x_j}
++ \sum_k n_k \left(\frac{\frac{d^2\phi_k}{dn_i dn_j} \phi_k -
+  \frac{d\phi_k}{dn_i} \frac{d\phi_k}{dn_j}}{\phi_k^2} \right)
+- \sum_k n_k \left(\frac{\frac{d^2x_k}{dn_i dn_j} x_k -
+  \frac{dx_k}{dn_i} \frac{dx_k}{dn_j}}{x_k^2} \right)
+$$
+
+$$
++ \frac{z}{2} q_i \left( \frac{\frac{d \theta_i}{d n_j}}{\theta_i} - \frac
+{\frac{d \phi_i}{d n_j}}{\phi_i} \right)
+$$
+
+$$
++ \frac{z}{2} q_j \left( \frac{\frac{d \theta_j}{d n_i}}{\theta_j} - \frac
+{\frac{d \phi_j}{d n_i}}{\phi_j} \right)
++ \frac{z}{2} \sum_k n_k q_k \left(\frac{\frac{d^2\theta_k}{dn_i dn_j} \theta_k -
+  \frac{d\theta_k}{dn_i} \frac{d\theta_k}{dn_j}}{\theta_k^2} \right)
+- \frac{z}{2} \sum_k n_k q_k \left(\frac{\frac{d^2\phi_k}{dn_i dn_j} \phi_k -
+  \frac{d\phi_k}{dn_i} \frac{d\phi_k}{dn_j}}{\phi_k^2} \right)
+$$
+
+$$
+- q_i \left( \frac{\sum_l \frac{d \theta_l}{d n_j} \tau_{li}}{\sum_l \theta_l 
+\tau_{li}} \right)
+$$
+
+$$
+- {q}_{j} \frac{\sum_l \frac{d \theta_{l}}{d {n}_{i}} {\tau}_{lj}}{\sum_l
+\theta_{l}{\tau}_{lj}} - \sum_k {n}_{k} {q}_{k} \frac{\left(\sum_l
+\frac{d^2\theta_l}{dn_idn_j} \tau_{lk} \right) \left(\sum_l
+\theta_l \tau_{lk} \right) - \left(\sum_l \frac{d\theta_l}{dn_i}
+\tau_{lk} \right) \left(\sum_l \frac{d\theta_l}{dn_j} \tau_{lk}
+\right)}{(\sum_l \theta_{l} {\tau}_{lk})^2}
+$$
+
+## Examples
+Example from: Gmehling et al. (2012) [2]
+
+An example of having a mixture of Water-Ethanol-Bezene at 298.15 K with 
+constant \(\Delta U\) [K]:
+
+|Water|Ethanol|Benzene|
+|---------|--------|---------|
+| 0       | 526.02 | 309.64  |
+| −318.06 | 0      | −91.532 |
+| 1325.1  | 302.57 | 0       |
+
+
+```fortran
+use yaeos, only: pr, setup_uniquac, UNIQUAC
+
+integer, parameter :: nc = 3
+
+real(pr) :: rs(nc), qs(nc)
+real(pr) :: b(nc, nc)
+real(pr) :: n(nc)
+
+real(pr) :: ln_gammas(nc), T
+
+type(UNIQUAC) :: model
+
+rs = [0.92_pr, 2.1055_pr, 3.1878_pr]
+qs = [1.4_pr, 1.972_pr, 2.4_pr]
+
+T = 298.15_pr
+
+! Calculate bij from DUij. We need -DU/R to get bij
+b(1,:) = [0.0_pr, -526.02_pr, -309.64_pr]
+b(2,:) = [318.06_pr, 0.0_pr, 91.532_pr]
+b(3,:) = [-1325.1_pr, -302.57_pr, 0.0_pr]
+
+model = setup_uniquac(qs, rs, bij=b)
+
+n = [2.0_pr, 2.0_pr, 8.0_pr]
+
+call model%ln_activity_coefficient(n, T, ln_gammas)
+
+print *, exp(ln_gammas) ! [8.856, 0.860, 1.425]
+
+```
+
+
+## References
+1. Maurer, G., & Prausnitz, J. M. (1978). On the derivation and extension of
+   the UNIQUAC equation. Fluid Phase Equilibria, 2(2), 91-99.
+2. Gmehling, Jurgen, Barbel Kolbe, Michael Kleiber, and Jurgen Rarey. Chemical
+   Thermodynamics for Process Simulation. 1st edition. Weinheim: Wiley-VCH,
+   2012.
+3. Caleb Bell and Contributors (2016-2024). Thermo: Chemical properties
+   component of Chemical Engineering Design Library (ChEDL)
+   https://github.com/CalebBell/thermo.
\ No newline at end of file
diff --git a/python/README.md b/python/README.md
index ada50ab4e..37d71bbe4 100644
--- a/python/README.md
+++ b/python/README.md
@@ -1,9 +1,24 @@
 # yaeos Python bindings
-THIS IS A WIP SO THE API WILL DRASTICALLY CHANGE WITH TIME
 
-Set of Python bindings to call `yaeos` functions and models.
+The `yaeos` Python API is on and early stage of development. So the API will 
+change with time.
 
-Editable installation
+## Supported operative systems
+
+- Linux
+
+## Supported Python versions
+
+- Python >= 3.10, < 3.13
+
+## Installation
+To install the last version of `yaeos` Python API, you can use `pip`:
+
+```
+pip install yaeos
+```
+
+### For developers, the editable installation is done by
 
 ```
 cd python
@@ -11,12 +26,31 @@ pip install -r requirements-build.txt
 pip install -e . --no-build-isolation
 ```
 
-If you want to install on your environment instead
+### Building from source
+
+To build `yaeos` from source you can clone the repository and install it with 
+pip. The system dependencies are:
+
+- LAPACK
+- Fortran compiler
+
+To build and install do:
 
 ```shell
+git clone https://github.com/ipqa-research/yaeos.git
+cd yaeos/python
 pip install .
 ```
 
+### Setting a Google Colab to use yaeos
+
+To install `yaeos` on Google Colab you can do the following command to install 
+the last version uploaded to PyPI:
+
+```shell
+%pip install yaeos
+```
+
 To check if the installation worked correctly:
 
 ```python
@@ -33,6 +67,8 @@ model = PengRobinson76(
 model.lnphi_vt(np.array([5.0, 4.0]), 2.0, 303.15)
 ```
 
+You will get:
+
 ```
 array([0.47647471, 0.35338115])
 ```
\ No newline at end of file
diff --git a/python/docs/source/models/excess_gibbs/init.rst b/python/docs/source/models/excess_gibbs/init.rst
index c39b5b1f6..e601bf8f7 100644
--- a/python/docs/source/models/excess_gibbs/init.rst
+++ b/python/docs/source/models/excess_gibbs/init.rst
@@ -9,3 +9,4 @@ Excess Gibbs Models
    :maxdepth: 1
 
    nrtl
+   unifacvle
diff --git a/python/docs/source/models/excess_gibbs/unifacvle.rst b/python/docs/source/models/excess_gibbs/unifacvle.rst
new file mode 100644
index 000000000..0cfb44f36
--- /dev/null
+++ b/python/docs/source/models/excess_gibbs/unifacvle.rst
@@ -0,0 +1,7 @@
+UNIFACVLE
+=========
+
+.. automodule:: yaeos.models.excess_gibbs.unifac
+    :members:
+    :undoc-members:
+    :show-inheritance:
\ No newline at end of file
diff --git a/python/docs/source/tutorial/available_models.ipynb b/python/docs/source/tutorial/available_models.ipynb
deleted file mode 100644
index 05c1e0416..000000000
--- a/python/docs/source/tutorial/available_models.ipynb
+++ /dev/null
@@ -1,328 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Available models\n",
-    "\n",
-    "In `yaeos` there are two families of thermodynamic models available to use\n",
-    "\n",
-    "- `ArModel`s: Models based on the Residual Helmholtz Energy, these are Equation Of State models like PengRobinson EoS\n",
-    "- `GeModel`s: Models based on the Excess Gibbs Energy like NRTL"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Residual Helmholtz Models\n",
-    "With residual Helmholtz models it is possible to calculate\n",
-    "\n",
-    "- $P(n, V, T)$\n",
-    "- $\\ln \\phi(n, P, T)$\n",
-    "- FlashPT calculations\n",
-    "- Saturation points"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### SoaveRedlichKwong EoS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.622487789187332"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Import the model from the library\n",
-    "from yaeos import SoaveRedlichKwong\n",
-    "\n",
-    "# We will use the chemicals library to get the critical properties of methane\n",
-    "import chemicals\n",
-    "\n",
-    "chem = chemicals.CAS_from_any(\"methane\")\n",
-    "\n",
-    "# All properties must be defined as lists, since the library can \n",
-    "# handle multiple components\n",
-    "Tc = [chemicals.critical.Tc(chem)]\n",
-    "Pc = [chemicals.critical.Pc(chem)/1e5]\n",
-    "w =  [chemicals.acentric.omega(chem)]\n",
-    "\n",
-    "model = SoaveRedlichKwong(Tc, Pc, w)\n",
-    "\n",
-    "model.pressure([2.0], 2.5, 290)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### PengRobinson76 EoS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.447831480414184"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Import the model from the library\n",
-    "from yaeos import PengRobinson76\n",
-    "\n",
-    "# We will use the chemicals library to get the critical properties of methane\n",
-    "import chemicals\n",
-    "\n",
-    "chem = chemicals.CAS_from_any(\"methane\")\n",
-    "\n",
-    "# All properties must be defined as lists, since the library can \n",
-    "# handle multiple components\n",
-    "Tc = [chemicals.critical.Tc(chem)]\n",
-    "Pc = [chemicals.critical.Pc(chem)/1e5]\n",
-    "w =  [chemicals.acentric.omega(chem)]\n",
-    "\n",
-    "model = PengRobinson76(Tc, Pc, w)\n",
-    "\n",
-    "model.pressure([2.0], 2.5, 290)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### PengRobinson78"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.44783147380076"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Import the model from the library\n",
-    "from yaeos import PengRobinson78\n",
-    "\n",
-    "# We will use the chemicals library to get the critical properties of methane\n",
-    "import chemicals\n",
-    "\n",
-    "chem = chemicals.CAS_from_any(\"methane\")\n",
-    "\n",
-    "# All properties must be defined as lists, since the library can \n",
-    "# handle multiple components\n",
-    "Tc = [chemicals.critical.Tc(chem)]\n",
-    "Pc = [chemicals.critical.Pc(chem)/1e5]\n",
-    "w =  [chemicals.acentric.omega(chem)]\n",
-    "\n",
-    "model = PengRobinson78(Tc, Pc, w)\n",
-    "\n",
-    "model.pressure([2.0], 2.5, 290)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### RKPR"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.44783147380076"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Import the model from the library\n",
-    "from yaeos import PengRobinson78\n",
-    "from yaeos.constants import R\n",
-    "\n",
-    "# We will use the chemicals library to get the critical properties of methane\n",
-    "import chemicals\n",
-    "\n",
-    "chem = chemicals.CAS_from_any(\"methane\")\n",
-    "\n",
-    "# All properties must be defined as lists, since the library can \n",
-    "# handle multiple components\n",
-    "Tc = [chemicals.critical.Tc(chem)]\n",
-    "Pc = [chemicals.critical.Pc(chem)/1e5]\n",
-    "Vc = [chemicals.critical.Vc(chem)]\n",
-    "w =  [chemicals.acentric.omega(chem)]\n",
-    "\n",
-    "Zc = Pc[0]*Vc[0]/(R*Tc[0])\n",
-    "\n",
-    "model = PengRobinson78(Tc, Pc, w)\n",
-    "\n",
-    "model.pressure([2.0], 2.5, 290)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Excess Gibbs Models\n",
-    "\n",
-    "Excess Gibbs models are ussually well suited for liquid phases at atmospheric\n",
-    "pressure. They are important when modelling liquid-liquid equilibria.\n",
-    "\n",
-    "In `yaeos` there are two models implemented.\n",
-    "\n",
-    "- `NRTL`: Non-Random-Two-Liquid\n",
-    "- `UNIFACVLE`: "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### NRTL\n",
-    "The NRTL model has three parameters, which are asymetric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UNIFAC\n",
-    "\n",
-    "There are multiple UNIFAC models, in `yaeos` there is implemented the original\n",
-    "`UNIFAC VLE` model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{1: 1, 2: 1, 14: 1}, {16: 1}]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We will use the ugropy library to get the UNIFAC groups of ethanol and water\n",
-    "from ugropy import get_groups, unifac, writers\n",
-    "\n",
-    "from yaeos import UNIFACVLE\n",
-    "\n",
-    "def find_groups(molecules):\n",
-    "    groups = []\n",
-    "    for molecule in molecules:\n",
-    "        grp = get_groups(unifac, molecule)\n",
-    "        groups.append(writers.to_thermo(grp.subgroups, unifac))\n",
-    "    return groups\n",
-    "\n",
-    "\n",
-    "molecules = [\"ethanol\", \"water\"]\n",
-    "groups = find_groups(molecules)\n",
-    "\n",
-    "groups"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.18534142, 0.40331396])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = UNIFACVLE(groups)\n",
-    "model.ln_gamma([0.5, 0.5], 298.0)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "thermo",
-   "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": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/python/docs/source/tutorial/cubic_eos.ipynb b/python/docs/source/tutorial/cubic_eos.ipynb
index cf1a5c50c..e7b307241 100644
--- a/python/docs/source/tutorial/cubic_eos.ipynb
+++ b/python/docs/source/tutorial/cubic_eos.ipynb
@@ -38,6 +38,7 @@
       "\n",
       "- excess_gibbs: Excess Gibbs energy models\n",
       "    - NRTL: non-random two-liquid model\n",
+      "    - UNIFACVLE: Original UNIFAC VLE model\n",
       "\n",
       "- residual_helmholtz: Residual Helmholtz energy models\n",
       "    - Cubic EoS:\n",
@@ -71,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -126,13 +127,13 @@
        "Text(0, 0.5, 'Pressure [bar]')"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -180,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,9 +192,9 @@
     "w = np.array([0.0115, 0.0866])  # acentric factors [-]\n",
     "\n",
     "# binary interaction parameters for NRTL model\n",
-    "aij = np.array([[0.0, 0.1], [0.1, 0.0]])\n",
+    "aij = np.array([[0.0, 0.01], [0.01, 0.0]])\n",
     "\n",
-    "bij = np.array([[0.0, 0.01], [0.01, 0.0]])\n",
+    "bij = np.array([[0.0, 0.05], [0.05, 0.0]])\n",
     "\n",
     "cij = np.array([[0.0, 0.02], [0.02, 0.0]])\n",
     "\n",
@@ -223,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -232,13 +233,13 @@
        "Text(0, 0.5, 'Pressure [bar]')"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/python/docs/source/tutorial/instantiate_model.ipynb b/python/docs/source/tutorial/instantiate_model.ipynb
index a3b06a6dc..1a0f2603d 100644
--- a/python/docs/source/tutorial/instantiate_model.ipynb
+++ b/python/docs/source/tutorial/instantiate_model.ipynb
@@ -37,6 +37,8 @@
       "\n",
       "- excess_gibbs: Excess Gibbs energy models\n",
       "    - NRTL: non-random two-liquid model\n",
+      "    - UNIFACVLE: Original UNIFAC VLE model\n",
+      "    - UNIQUAC: UNIversal QUAsiChemical Excess Gibbs free energy model\n",
       "\n",
       "- residual_helmholtz: Residual Helmholtz energy models\n",
       "    - Cubic EoS:\n",
@@ -262,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -431,65 +433,6 @@
    "execution_count": 8,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[0;31mSignature:\u001b[0m\n",
-      "\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvolume\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m    \u001b[0mmoles\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m    \u001b[0mpressure\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m    \u001b[0mtemperature\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m    \u001b[0mroot\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'stable'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mDocstring:\u001b[0m\n",
-      "Calculate volume given pressure and temperature [L].\n",
-      "\n",
-      "Parameters\n",
-      "----------\n",
-      "moles : array_like\n",
-      "    Moles number vector [mol]\n",
-      "pressure : float\n",
-      "    Pressure [bar]\n",
-      "temperature : float\n",
-      "    Temperature [K]\n",
-      "root : str, optional\n",
-      "    Volume root, use: \"liquid\", \"vapor\" or \"stable\", by default\n",
-      "    \"stable\"\n",
-      "\n",
-      "Returns\n",
-      "-------\n",
-      "float\n",
-      "    Volume [L]\n",
-      "\n",
-      "Example\n",
-      "-------\n",
-      ".. code-block:: python\n",
-      "\n",
-      "    import numpy as np\n",
-      "\n",
-      "    from yaeos import PengRobinson76\n",
-      "\n",
-      "\n",
-      "    tc = np.array([320.0, 375.0])   # critical temperatures [K]\n",
-      "    pc = np.array([45.0, 60.0])     # critical pressures [bar]\n",
-      "    w = np.array([0.0123, 0.045])   # acentric factors\n",
-      "\n",
-      "    model = PengRobinson76(tc, pc, w)\n",
-      "\n",
-      "    # Evaluating stable root volume\n",
-      "    # will print: 23.373902973572587\n",
-      "\n",
-      "    print(model.volume(np.array([5.0, 5.6]), 10.0, 300.0))\n",
-      "\n",
-      "    # Liquid root volume (not stable)\n",
-      "    # will print: 0.8156388756398074\n",
-      "\n",
-      "    print(model.volume(np.array([5.0, 5.6]), 10.0, 300.0, \"liquid\"))\n",
-      "\u001b[0;31mFile:\u001b[0m      ~/code/yaeos/python/yaeos/core.py\n",
-      "\u001b[0;31mType:\u001b[0m      method"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -570,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -619,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -658,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -756,14 +699,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ln(phi):  [-0.03026809 -0.03078738]\n"
+      "ln(phi):  [-0.24307099 -0.2526632 ]\n"
      ]
     },
     {
@@ -771,11 +714,11 @@
       "text/plain": [
        "{'dt': None,\n",
        " 'dp': None,\n",
-       " 'dn': array([[-4.89454245e-08,  1.14205991e-07],\n",
-       "        [ 1.14205991e-07, -2.66480645e-07]])}"
+       " 'dn': array([[-2.56426727e-06,  5.98329030e-06],\n",
+       "        [ 5.98329030e-06, -1.39610107e-05]])}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -805,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -837,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -865,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -964,11 +907,6 @@
     "which equals to centijoules $[cJ]$.\n",
     "\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
diff --git a/python/docs/source/tutorial/more_calculations.ipynb b/python/docs/source/tutorial/more_calculations.ipynb
index 05c8aab16..052a51e88 100644
--- a/python/docs/source/tutorial/more_calculations.ipynb
+++ b/python/docs/source/tutorial/more_calculations.ipynb
@@ -24,6 +24,25 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: yaeos in /home/salvador/.virtualenvs/yaeos/lib/python3.10/site-packages (0.1.2)\n",
+      "Requirement already satisfied: numpy in /home/salvador/.virtualenvs/yaeos/lib/python3.10/site-packages (from yaeos) (2.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install yaeos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -31,7 +50,7 @@
        "Text(0, 0.5, 'Pressure [bar]')"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -94,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +128,7 @@
        " 'beta': 0.0}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -168,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -215,7 +234,7 @@
        " 'beta': 0.0}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -241,7 +260,7 @@
        " 'beta': 1.0}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -290,7 +309,7 @@
        "(0.0, 18.0)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -360,23 +379,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " WARN: possible bad root solving:    6.9215890337653613E-310   4.0000000000000000     \n",
-      " WARN: possible bad root solving:    6.9215890337653613E-310   4.0000000000000000     \n",
-      " WARN: possible bad root solving:    6.9215890337653613E-310   4.0000000000000000     \n",
-      " WARN: possible bad root solving:    6.9215890337653613E-310   4.0000000000000000     \n",
-      " WARN: possible bad root solving:    6.9215890337653613E-310   4.0000000000000000     \n",
-      " WARN: possible bad root solving:    6.9215890337653613E-310   4.0000000000000000     \n",
-      " WARN: possible bad root solving:    6.9215890337653613E-310   4.0000000000000000     \n",
-      " WARN: possible bad root solving:    6.9215890337653613E-310   4.0000000000000000     \n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -389,7 +394,7 @@
        " 'beta': 0.3873990240956161}"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -420,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -429,7 +434,7 @@
        "(0.0, 18.0)"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -506,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -532,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -541,7 +546,7 @@
        "dict_keys(['Ts', 'Ps', 'Tcs', 'Pcs'])"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -566,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -575,13 +580,13 @@
        "Text(0, 0.5, 'Pressure [bar]')"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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VqzB58mQEBwdDoVBAJpNhyJAhRdYgPut9lfT3+HEF/f8ePHiAWrVq6crd3NwA/HcvPCo+Pv657g0AT7w/ilNTSZUDkx2qEgrmOnF2di40EuN5tGvXDm5ubvj999/Rtm1b/Pvvv/jggw8KnfvcuXPo3Llzif+T8/LyQmRkZKHyq1ev6rY/quA/3wJCCNy4cQONGjXSxQJo/wMuzXXo1asX1q1bh19//RUzZ8586r716tVDvXr1sHXrVnz11VdFNmcVzDPy0ksv6ZXfvHkToaGhcHZ2xt9///3UBOxx/v7+RY7ga9KkCfbv34/09HS9GoATJ07otpeUEAK3b98uNJcKoE1ajIyMUK9evSe+vnHjxoVG5JRFrcp7772HESNG6J4/XlP3+H0CANeuXYOlpaWuBtLe3r7IiRyLaip70n29adMmjBw5EkuWLNGVKZXKEk0Q+ajn/T329/cHoP3ZBAYG6soDAgJgYmKC06dP681unJeXh4iIiFLPeFxQC/p4rW5+fj5iYmLQu3fvUh2XKh777FCVEBISAltbW3z66adQqVSFtpd02G4BIyMjDBw4ENu3b8eaNWuQn59faITVoEGDEBsbix9++KHQ63NycpCVlfXE4/fo0QMnT55EWFiYriwrKwvff/89vL29C9WI/PLLL3rNRps2bUJ8fDy6d+8OQDtCqE6dOli8eHGRM8M+6zoMHDgQgYGB+OSTT/RiKpCRkaGX7M2aNQsPHjzAuHHjCtUIhIeHY9GiRQgICMCAAQN05QkJCejWrRuMjIywe/fuJzb/PUlwcDAuXrxYqEli4MCBUKvVuhF1gLbZYtWqVWjVqpVeTVV0dLQuoSxQ1LVZsWIF7t27h9DQ0ELbwsPD0bBhw0ITGz7K3t4eXbp00Xs8PiNzaTRo0EDvmI/2zwGAsLAwvT4zMTEx2LZtG7p166arXalTpw7S0tL0aubi4+OLHAVoZWVVZAJjbGxcqMnom2++KXW/oOf9PQ4KCoKZmRlOnz6tV65QKNClSxf8+uuver8/a9asQWZmJl5++WVdWXZ2Nq5evarX36ao82ZkZGDp0qWoUaNGoet/+fJlKJVKToJahbBmh6oEW1tbrFixAq+88gqaNWuGIUOGwMnJCdHR0fjrr7/Qpk0bfPvtt6U69uDBg/HNN99g9uzZCAwM1DXfFHjllVewYcMGjBs3Dvv370ebNm2gVqtx9epVbNiwAbt3735i892MGTOwbt06dO/eHW+//TYcHBzw888/IyoqCn/88UehPk0ODg5o27YtRo8ejcTERCxduhR169bFG2+8AUCbnP3444/o3r07GjZsiNGjR6NmzZqIjY3F/v37YWtri+3btz/xvZqammLz5s3o0qUL2rVrh0GDBqFNmzYwNTXFpUuXsHbtWtjb2+v61wwfPhynTp3CV199hcuXL2P48OGwt7fHmTNn8H//939wdHTEpk2b9IaJh4aG4tatW3jvvfdw5MgRHDlyRLfNxcUFXbt2ferPo0+fPpg/fz4OHjyIbt266cpbtWqFl19+GTNnzkRSUhLq1q2Ln3/+Gbdv38ZPP/2kd4xXX30VBw8e1Pug9vLywuDBgxEYGAhzc3McOXIE69evR5MmTXRDrQuoVCocPHgQb7311lNjlUpAQABCQkLw9ttvQy6X6zpYz507V7fPkCFDMH36dPTr1w9vv/02srOzsWLFCtSrV69Q5+KgoCDs3bsXX3zxBdzd3eHj44NWrVrhpZdewpo1a6BQKNCgQQOEhYVh7969pZ5O4nl/j83NzdGtWzfs3bsX8+bN09v2ySefoHXr1mjfvj3Gjh2Lu3fvYsmSJejWrZteMnvy5El07NgRs2fPxpw5cwBo+xtu3boVvXr1gqenJ+Lj4/F///d/iI6Oxpo1a2BmZqZ3rj179sDS0vKZ9zJVIhKNAiMD8qQZlJ+mtDMo79+/X4SEhAiFQiHMzc1FnTp1xKhRo/SG4Y4cOVJYWVkVem1RQ26F0A7n9vDwEADExx9/XOR58/LyxKJFi0TDhg2FXC4X9vb2IigoSMydO1dv2HJRMyjfvHlTDBw4UNjZ2Qlzc3PRsmVLsWPHjkLvC4BYt26dmDlzpnB2dhYWFhaiZ8+e4s6dO4XiOXv2rOjfv79wdHQUcrlceHl5iUGDBol9+/Y99foVePDggZg1a5YIDAwUlpaWwtzcXAQEBIiZM2eK+Pj4Qvtv3bpVdO3aVdjb2wu5XC7q1q0r3nnnnSJnI8bDodFFPdq3b1+s+Bo1aiTGjBlTqDwnJ0dMmzZNuLq6CrlcLlq0aCF27dpVaL/27dsX+lm//vrrokGDBsLGxkaYmpqKunXriunTp+sN9S+wc+dOAUBcv369WPEW15N+Vwp+/kXNPv04PJzp99dffxW+vr5CLpeLpk2bFvnaf/75RwQEBAgzMzPh5+cnfv311yJ/D65evSratWsnLCwsBADdPfzgwQMxevRoUaNGDWFtbS1CQkLE1atXC93nJX1fxfk9fpLNmzcLmUwmoqOjC207fPiwaN26tTA3NxdOTk5i/PjxhX6+BTHNnj1b7zp17dpVuLq6ClNTU2FnZye6dev2xN+nVq1aiREjRjwzVqo8ZEI8ZRpRIqoQBw4cQMeOHbFx40YMHDhQ6nAkt2bNGowfPx7R0dGws7Or8PP37dsXMpmsyCYfkpZarUaDBg0waNAgzJ8/v8LPHxERgWbNmuHMmTOl6idG0mCfHSKqdIYPHw5PT08sW7asws995coV7NixQ5IPUno2Y2NjzJs3D8uWLSv2iuZlaeHChRg4cCATnSqGNTtElQBrdoiIyg9rdoiIiMigsWaHiIiIDBprdoiIiMigMdkhIiIig2bwkwpqNBrExcXBxsaGi7YRERFVEUIIZGRkwN3d/bkXlTb4ZCcuLq5CF2okIiKishMTE6O38GtpGHyyU7B4YUxMjN7igURERFR5paenw8PDo8hFiEvK4JOdgqYrW1tbJjtERERVTFl0QWEHZSIiIjJoTHaIiIjIoDHZISIiIoNm8H12ikutVkOlUkkdBlVCpqamMDY2ljoMIiIqpWqf7AghkJCQgNTUVKlDoUrMzs4Orq6unKuJiKgKkjTZ8fb2xp07dwqVv/XWW1i2bBmUSiXeeecdrF+/Hrm5uQgJCcHy5cvh4uJSZjEUJDrOzs6wtLTkhxnpEUIgOzsbSUlJAAA3NzeJIyIiopKSNNk5deoU1Gq17vnFixfRtWtXvPzyywCAKVOm4K+//sLGjRuhUCgwYcIE9O/fH0ePHi2T86vVal2i4+joWCbHJMNjYWEBAEhKSoKzszObtIiIqhhJkx0nJye95wsXLkSdOnXQvn17pKWl4aeffsLatWvRqVMnAMCqVatQv359HD9+HC+88MJzn7+gj46lpeVzH4sMW8E9olKpmOwQEVUxlWY0Vl5eHn799Ve89tprkMlkCA8Ph0qlQpcuXXT7+Pv7w9PTE2FhYU88Tm5uLtLT0/Uez8KmK3oW3iNERFVXpUl2tm7ditTUVIwaNQqAti+NmZkZ7Ozs9PZzcXFBQkLCE4+zYMECKBQK3YPrYhEREVVvlSbZ+emnn9C9e3e4u7s/13FmzpyJtLQ03SMmJqaMIqTHzZkzB02aNJE6DCIioqeqFMnOnTt3sHfvXrz++uu6MldXV+Tl5RUaEp6YmAhXV9cnHksul+vWwTLk9bBGjRoFmUxW6BEaGip1aERERJVKpUh2Vq1aBWdnZ/Ts2VNXFhQUBFNTU+zbt09XFhkZiejoaAQHB0sRZqUTGhqK+Ph4vce6deukDouIiKq4vHwNUrPzpA6jzEie7Gg0GqxatQojR46Eicl/g8MUCgXGjBmDqVOnYv/+/QgPD8fo0aMRHBxcJiOxDIFcLoerq6vew97eHoC2Q+2PP/6Ifv36wdLSEr6+vvjzzz8BaK95rVq1sGLFCr3jnT17FkZGRrq5j1JTU/H666/DyckJtra26NSpE86dO/fEeDQaDebNm4datWpBLpejSZMm2LVrl2777du3IZPJsH79erRu3Rrm5uYICAjAwYMH9Y5z8eJFdO/eHdbW1nBxccErr7yC5OTkMrlmRERUWL5ag42nY/Da6lPo8Pl+1J+1C03m7UGvb47gu4M3EZeaI3WIz0XyZGfv3r2Ijo7Ga6+9Vmjbl19+iZdeegkDBgxAu3bt4Orqis2bN5dbLEIIZOflS/IQQpT5+5k7dy4GDRqE8+fPo0ePHhg+fDju378PIyMjDB06FGvXrtXb/7fffkObNm3g5eUFAHj55ZeRlJSEnTt3Ijw8HM2aNUPnzp1x//79Is/31VdfYcmSJVi8eDHOnz+PkJAQ9O7dG9evX9fb791338U777yDs2fPIjg4GL169UJKSgoAbYLVqVMnNG3aFKdPn8auXbuQmJiIQYMGlfn1ISKq7tQaga1nY9H1y0N4d9N5/Hs1CbdTsqHWaD+TLsSmIeeDj7Cxz1j8c6mIwUHz5wNz5lRs0KUgE+XxKVuJpKenQ6FQIC0trVD/HaVSiaioKPj4+MDc3BzZefloMGu3JHFenhcCS7PiT3s0atQo/PrrrzA3N9crf//99/H+++9DJpPhww8/xPz58wEAWVlZsLa2xs6dOxEaGoqIiAg0a9YMt2/fhqenJzQaDTw9PfHhhx9i3LhxOHLkCHr27ImkpCTI5XLd8evWrYv33nsPY8eOxZw5c7B161ZEREQAAGrWrInx48fj/fff1+3fsmVLtGjRAsuWLcPt27fh4+ODhQsXYvr06QCA/Px8+Pj4YOLEiXjvvffw8ccf4/Dhw9i9+7+fw927d+Hh4YHIyEjUq1evxNe2LDx+rxARVXV3H2TjzV/P4EJsGgDA3tIUY9r6IMjLAbWdrGAkk2H3pQTIPv4Yw//6EV++OAJeSxegf7Na2gPMnw/MmgXMmwd89FGZx/e0z++SqvZrY1VlHTt2LNQU5eDgoPu+UaNGuu+trKxga2urW/agSZMmqF+/PtauXYsZM2bg4MGDSEpK0s1efe7cOWRmZhaaWTonJwc3b94sFEt6ejri4uLQpk0bvfI2bdoUavp6tM+ViYkJmjdvjitXrujOu3//flhbWxc6x82bNyVLdoiIDEnYzRSMX3sG97PyYGtugv+1r4ORrb1hLddPC0a84IX8bd9h5zAzTNmwHF9OBs799g0ar/6mXBOdssZk5xEWpsa4PC9EsnOXlJWVFerWrfvE7aampnrPZTIZNBqN7vnw4cN1yc7atWsRGhqqS24yMzPh5uaGAwcOFDru43MflaXMzEz06tULixYtKrSN61IRET0fIQR+PnYb8/+6ArVGoKG7Lb5/tTlq2lk88TUmxkYIWfcttqs1mPLHSuT5/A6oVVUm0QGY7OiRyWQlakqq6oYNG4YPP/wQ4eHh2LRpE1auXKnb1qxZMyQkJMDExATe3t7PPJatrS3c3d1x9OhRtG/fXld+9OhRtGzZUm/f48ePo127dgC0zVjh4eGYMGGC7rx//PEHvL299TqsExHR89FoBD7cdhFrT0QDAPo2cceC/o1gYfbsf7aNjGRo+/NXyNv6E8zUKqhNzWBcRRIdoBJ0UKbSy83NRUJCgt6jJKOWvL290bp1a4wZMwZqtRq9e/fWbevSpQuCg4PRt29f/PPPP7h9+zaOHTuGDz74AKdPny7yeO+++y4WLVqE33//HZGRkZgxYwYiIiIwadIkvf2WLVuGLVu24OrVqxg/fjwePHig66A+fvx43L9/H0OHDsWpU6dw8+ZN7N69G6NHj9ZbNJaIiIpPCIG52y9h7YloGMmAD3vWx5eDmxQr0Slg/8UimKlVyDU2gbEqD2LevHKMuGzxX+cqbNeuXYWadvz8/HD16tViH2P48OF466238Oqrr+pW9wa0tVx///03PvjgA4wePRr37t2Dq6sr2rVrBxcXlyKP9fbbbyMtLQ3vvPMOkpKS0KBBA/z555/w9fXV22/hwoVYuHAhIiIiULduXfz555+oUaMGAOhqh6ZPn45u3bohNzcXXl5eCA0NhZERc3MiopISQmDRrkj8HHYHMhmwZFBj9Gtaq2QHedgZWfnRbAThBby2/1e8M3s2IJNViaYsjsbiCJsKUzAa6+zZs1VumQneK0RUVX3773Us/ucaAODTfoEY1sqzZAd4bNTVh1sv4Nfj0fgu6m+EbFheJUZj8V9lIiIiA/V/R6J0ic6HPeuXPNEBALVaL6F5qZF2Dct36/eGes5c7fZKjs1YREREBmh/ZBLm/3UZADC5iy9ef7F26Q702KSBLbwdYGtugnRlPi6OfxuNPeyeL9AKwJodqjDe3t4QQlS5JiwioqrmTkoWJq07CyGAYa08Mamz77NfVEzGRjK09NFOU3L8VkqZHbc8MdkhIiIyIDl5avxvTTjSlflo6mmH2b0aQCaTlek5XqitncA2jMkOERERVSQhBGZuPo+rCRmoYW2GFcODIDcp+aS1z9LSR5vsnItJLZe1Hcsakx0iIiID8fOx29gaEQdjIxm+HdYMroryGT1az8UGxkYyPMhWITE9t1zOUZaY7BARERmAk1H38fFf2nUG3+9RHy/UdnzGK0rP3NQYdZysAABXE9LL7TxlhckOERFRFZeYrsRbv51BvkagV2N3vNbGu9zP6elgCQCIS1WW+7meF5MdIiKiKkyjEXh73VkkZ+bCz8UGiwYElnmH5KK4KbSz7sel5pT7uZ4Xk51q5sCBA5DJZEhNTX3qft7e3li6dGmZnbdDhw6YPHlymR2vKLdv34ZMJkNERES5noeIqDL5v6NROBF1H5Zmxlj5SlCFLWjtZqftDxSXxmSHyklCQgImTpyI2rVrQy6Xw8PDA7169cK+ffue+rrWrVsjPj4eCoUCALB69WrY2dkV2u/UqVMYO3ZseYRebjw8PBAfH4+AgIBiv2bOnDmc94eIqqxriRn4bHckAOCjlxrAp4ZVhZ27pl3VqdnhDMrPY84cwNi46DVB5s/XTqH92MyTZeH27dto06YN7Ozs8PnnnyMwMBAqlQq7d+/G+PHjn7gQqEqlgpmZGVxdXZ95Dicnp7IOu9wZGxsX670RERkClVqDqRsikJevQQc/Jwxp4VGh5y9oxopPY58dw2ZsrF0cbf58/fKCRdOMy35uAwB46623IJPJcPLkSQwYMAD16tVDw4YNMXXqVBw/fly3n0wmw4oVK9C7d29YWVnhk08+0WvGOnDgAEaPHo20tDTIZDLIZDLMeZicPd6MlZqaiv/9739wcXGBubk5AgICsGPHDgBASkoKhg4dipo1a8LS0hKBgYFYt25did5TQQ3Ld999Bw8PD1haWmLQoEFIS0vT7aPRaDBv3jzUqlULcrkcTZo0wa5du3TbH2/GKniv+/btQ/PmzWFpaYnWrVsjMlL7X9Dq1asxd+5cnDt3Tvf+V69eXaK4iYik8u2/N3AxNh0KC1MsGtCoQvrpPMrt4bD2+FQlNJrKPdcOk53n8dFH2sXRHk14Hlsdtqzdv38fu3btwvjx42FlVbi68vEmqTlz5qBfv364cOECXnvtNb1trVu3xtKlS2Fra4v4+HjEx8dj2rRphY6p0WjQvXt3HD16FL/++isuX76MhQsXwvhhMqdUKhEUFIS//voLFy9exNixY/HKK6/g5MmTJXpvN27cwIYNG7B9+3bs2rULZ8+exVtvvaXb/tVXX2HJkiVYvHgxzp8/j5CQEPTu3RvXr19/6nE/+OADLFmyBKdPn4aJiYnuOgwePBjvvPMOGjZsqHv/gwcPLlHMRERSOBeTim/33wAAfNw3AC625TOfztMUzOGTp9YgNUdV4ecvCTZjPa+ChGbWLODjj4G8vHJLdABtQiCEgL+/f7H2HzZsGEaPHq17fuvWLd33ZmZmUCgUkMlkT23+2bt3L06ePIkrV66gXr16AIDatf9bUK5mzZp6SdLEiROxe/dubNiwAS1btiz2e1Mqlfjll19Qs2ZNAMA333yDnj17YsmSJXB1dcXixYsxffp0DBkyBACwaNEi7N+/H0uXLsWyZcueeNxPPvkE7du3BwDMmDEDPXv2hFKphIWFBaytrWFiYsLmLyKqMpQqNaZuiIBaI/BSIzf0auwuSRymxkaQmxghN1+D7Lx8OFiZSRJHcbBmpyx89BFgZqZNdMzMyi3RAVDiabmbN2/+3OeMiIhArVq1dInO49RqNebPn4/AwEA4ODjA2toau3fvRnR0dInO4+npqUt0ACA4OBgajQaRkZFIT09HXFwc2rRpo/eaNm3a4MqVK089bqNGjXTfu7m5AQCSkpJKFBsRUWXx+e5I3LyXBScbOeb3Kf6AjPJgYaat4c/JU0sax7Mw2SkL8+f/l+jk5RXuw1OGfH19IZPJntgJ+XFFNXWVlIWFxVO3f/755/jqq68wffp07N+/HxEREQgJCUFeXt5zn7ssmJqa6r4vaNPWaDRShUNEVGphN1Pw05EoAMBnAxrBXuLaFEvTh8mOismOYXu0j05ubuE+PGXMwcEBISEhWLZsGbKysgptf9b8OY8zMzODWv30m7RRo0a4e/curl27VuT2o0ePok+fPhgxYgQaN26M2rVrP3Hfp4mOjkZcXJzu+fHjx2FkZAQ/Pz/Y2trC3d0dR48eLXTuBg0alPhcBYrz/omIKgOlSo0Zm88DAIa29EBHf2eJI/qvZiebNTsGrKjOyEV1Wi5jy5Ytg1qtRsuWLfHHH3/g+vXruHLlCr7++msEBweX6Fje3t7IzMzEvn37kJycjOzs7EL7tG/fHu3atcOAAQOwZ88eREVFYefOnbqRUL6+vtizZw+OHTuGK1eu4H//+x8SExNL/L7Mzc0xcuRInDt3DocPH8bbb7+NQYMG6frTvPvuu1i0aBF+//13REZGYsaMGYiIiMCkSZNKfK5H339UVBQiIiKQnJyM3NzKv6AdEVVPy/ffwJ2UbLjYyvF+j/pShwOAzVjVg1pddGfkgoSnnGoMateujTNnzqBjx4545513EBAQgK5du2Lfvn1YsWJFiY7VunVrjBs3DoMHD4aTkxM+++yzIvf7448/0KJFCwwdOhQNGjTAe++9p6sR+fDDD9GsWTOEhISgQ4cOcHV1Rd++fUv8vurWrYv+/fujR48e6NatGxo1aoTly5frtr/99tuYOnUq3nnnHQQGBmLXrl34888/4evrW+JzFRgwYABCQ0PRsWNHODk5lXjIPBFRRbiRlIkVB28CAOb0aggbc9NnvKJiWJpqxzlV9mYsmShpj9cqJj09HQqFAmlpabC1tdXbplQqERUVBR8fH5ibV/ywPfrPnDlzsHXr1kq71APvFSKSihACw344gbBbKejo54T/G9WiwufUeZKR/3cSB6/dw+KXG2NgUK0yPfbTPr9LijU7REREldjWiFiE3UqB3MQI8/oEVJpEB9AOPwe0szlXZkx2iIiIKqm0bBU+3qGdXuPtzr7wcLCUOCJ9moeNQ8aVKAErCpMdqhTmzJlTaZuwiIiksnDXVaRk5aGuszXeeLH2s19QwdQPl4kwMmKyQ0RERCUUfuc+1p3UTs76Sd8AmJlUvo9sXc1O5QtNTyUPr2IYeB9tKgO8R4ioIqnUGnyw5SIA4OWgWmhV21HiiIqmq9lhM1blVTCzblFzyxA9quAeeXQ2ZiKi8rL66G1cTciAnaUpZlaSOXWKUpDsGFfyZqxqvRCosbEx7OzsdOskWVpaVqpe7iQ9IQSys7ORlJQEOzs73UrvRETlJTY1B1/u1c5C/373+pV6gc2q0kG5Wic7AHSz83JhSHoaOzs7roxORBVizp+XkJ2nRgtv+zKfu6asVZUOytU+2ZHJZHBzc4OzszNUKpXU4VAlZGpqyhodIqoQ/1xKwJ7LiTAxkuHjvoGVPolQP+zOyJqdKsLY2JgfaEREJBmlSo252y8DAF5/sTb8XG0kjujZsnPzAQCW8sr9+VmtOygTERFVFj8cuoXY1By4Kczxdue6UodTLJkPkx0beeUevCF5shMbG4sRI0bA0dERFhYWCAwMxOnTp3XbhRCYNWsW3NzcYGFhgS5duuD69esSRkxERFS2EtKUWH5Au9DnjO7+sDSrGg0vmUptsmPFmp0ne/DgAdq0aQNTU1Ps3LkTly9fxpIlS2Bvb6/b57PPPsPXX3+NlStX4sSJE7CyskJISAiUSqWEkRMREZWdz3ZfRY5KjWaedujd2F3qcIpFCIHMPG2yY21euZMzSaNbtGgRPDw8sGrVKl2Zj4+P7nshBJYuXYoPP/wQffr0AQD88ssvcHFxwdatWzFkyJAKj5mIiKgsRcSkYvOZWADA7F4Nq8wUKNl5ahTMt8pmrKf4888/0bx5c7z88stwdnZG06ZN8cMPP+i2R0VFISEhAV26dNGVKRQKtGrVCmFhYUUeMzc3F+np6XoPIiKiykgIgXnbLwEA+jericYedtIGVAIF/XWMjWQwN5W8V8xTSRrdrVu3sGLFCvj6+mL37t1488038fbbb+Pnn38GACQkJAAAXFxc9F7n4uKi2/a4BQsWQKFQ6B4eHh7l+yaIiIhK6c9zcTgTnQoLU2O8F+IvdTglkvGwv4613KTS10ZJmuxoNBo0a9YMn376KZo2bYqxY8fijTfewMqVK0t9zJkzZyItLU33iImJKcOIiYiIykZOnhoLd14FALzVoQ5cFeYSR1QyBTU71vLK3V8HkDjZcXNzQ4MGDfTK6tevj+ho7SqvBTPWJiYm6u2TmJj4xNls5XI5bG1t9R5ERESVzXeHbiI+TYmadhZ4o11tqcMpsQyldiJeJjvP0KZNG0RGRuqVXbt2DV5eXgC0nZVdXV2xb98+3fb09HScOHECwcHBFRorERFRWYlPy8HKg9qh5jN7+MPctHIP3S7K/aw8AIC9VeXunAxIPBprypQpaN26NT799FMMGjQIJ0+exPfff4/vv/8egHYph8mTJ+Pjjz+Gr68vfHx88NFHH8Hd3R19+/aVMnQiIqJSW7TzKpQqDVp426NnoJvU4ZRKcqY22alhLZc4kmeTNNlp0aIFtmzZgpkzZ2LevHnw8fHB0qVLMXz4cN0+7733HrKysjB27Fikpqaibdu22LVrF8zNq1bbJhEREQCciX6ArRFxkMmAWS9VnaHmj0vOzAXAZKdYXnrpJbz00ktP3C6TyTBv3jzMmzevAqMiIiIqexqNwLyH618NbFYLgbUUEkdUeskZ2mTHyabyJzuVe2A8ERGRAdl2LhYRMamwMjPGuyF+UofzXFIe9tlxtDKTOJJnY7JDRERUAbLz8rFop3ZQzlsd68LZtmp3x6hKzVhMdoiIiCrAj4ejkJCuRC17C4xp6/PsF1RyBc1YNdiMRURERPcycvHdw6Hm00Or5lDzRwkhkJxVMBqLzVhERETV3lf7riErT43GtRR4qVHVHGr+qIzcfOTlawCwGYuIiKjau3kvE+tOapcumtmjfpUdav6opHRtE5aN3KRK1FIx2SEiIipHi3ZehVoj0KW+M16o7Sh1OGUiNjUHAFDT3kLiSIqHyQ4REVE5OXX7Pv65nAgjmbavjqGIe5jsuNsx2SEiIqq2hBD49O8rAIDBLTzh62IjcURlJ/bBw5odJjtERETV186LCTgbnQoLU2NM6eIrdThlijU7RERE1Vxevgaf7boKAHijXe0qP4Hg4+7qkp2q8b6Y7BAREZWxtSfu4HZKNmpYyzG2XW2pwylzBTU7tdhBmYiIqPpJV6rw9b83AACTu/jCWi75mttlSq0RSEhTAmAzFhERUbX03cGbuJ+Vh9pOVhjcwkPqcMpcUoYS+RoBEyMZnG3YjEVERFStxKfl4MfDUQCAGaH+MDU2vI/ZgpFYrgpzGBtVjQkSDe+nQEREJJEv/rmG3HwNWnjbo2sDF6nDKRfR97MBVJ3+OgCTHSIiojJxNSEdm87cBWA4y0IU5XZyFgDAp4aVxJEUH5MdIiKiMrBw51UIAfQMdEMzT3upwyk3USnamh0mO0RERNXI8VspOBB5DyZGMkwL8ZM6nHIVlZwJAPB2ZLJDRERULQghsOjhBIJDWnpUqRqPkhJC4HaytmantlPVeZ9MdoiIiJ7DP5cTdctCvN3JsJaFeNy9zFxk5ubDSAZ4OFhKHU6xMdkhIiIqJbVG4PPdkQCA19p6G9yyEI8rqNWpaW8BuYmxxNEUH5MdIiKiUtp85i5uJGVCYWGKse3qSB1OuSvor+NTw1riSEqGyQ4REVEpKFVqfLnnGgBgfMc6UFiYShxR+Yt6WLPj41h1mrAAJjtERESl8uvxO4hLU8LV1hyvBntLHU6F+K9mp+p0TgaY7BAREZVYhlKFZfv/W+zT3LTq9F95HrfuaScU9GayQ0REZNh+OHQLD7JVqO1khYFBtaQOp0Lk5WsQ9XD2ZD9XG4mjKRkmO0RERCVwLyMXPx7RLvb5bjc/mBjgYp9FiUrOQr5GwMbcBK5VbNRZ9fgJERERlZFl+28gO0+NxrUUCA1wlTqcChOZmAEA8HOxqXLrfjHZISIiKqbolGz8duIOAGB6qH+V+9B/HtcStMmOr0vVasICmOwQEREV25d7r0GlFnjRtwZa160hdTgV6r+anao1xw7AZIeIiKhYrsSnY2tELADgvRB/iaOpeNceJjv1qljnZIDJDhERUbF8vjsSQgA9G7khsJZC6nAqVHZePqLvaycU9GMzFhERkeE5GXUf/15NgrGRDNO6+UkdToW7kZQJIYAa1mZwtJZLHU6JMdkhIiJ6CiEEFu26CgAY3MKjys0eXBYiH3ZOrlcFa3UAJjtERERPte9KEsLvPIC5qREmdfaVOhxJMNkhIiIyUGqNwOe7IwEAo9v4wKWKTaZXVi7HpwMA6rsx2SmxOXPmQCaT6T38/f/r4a5UKjF+/Hg4OjrC2toaAwYMQGJiooQRExFRdbL1bCwiEzNga26Cce3qSB2OJIQQuBibBgAIqFk1O2ZLXrPTsGFDxMfH6x5HjhzRbZsyZQq2b9+OjRs34uDBg4iLi0P//v0ljJaIiKqL3Hw1vthzDQDwVse6UFiaShyRNO4+yEG6Mh9mxkbwda6aNTsmkgdgYgJX18LTbaelpeGnn37C2rVr0alTJwDAqlWrUL9+fRw/fhwvvPBCRYdKRETVyNoT0YhNzYGLrRwjg72lDkcyBbU6fq42MDORvI6kVCSP+vr163B3d0ft2rUxfPhwREdHAwDCw8OhUqnQpUsX3b7+/v7w9PREWFiYVOESEVE1kJmbj2//vQEAmNS5HizMjCWOSDoX4wqasGwljqT0JK3ZadWqFVavXg0/Pz/Ex8dj7ty5ePHFF3Hx4kUkJCTAzMwMdnZ2eq9xcXFBQkLCE4+Zm5uL3Nxc3fP09PTyCp+IiAzUj4dvISUrD7VrWGFQ81pShyOpi7Haz9GG7lWzvw4gcbLTvXt33feNGjVCq1at4OXlhQ0bNsDCwqJUx1ywYAHmzp1bViESEVE1k5yZix8O3QIAvNPNDybGkjeCSMYQOicDlaAZ61F2dnaoV68ebty4AVdXV+Tl5SE1NVVvn8TExCL7+BSYOXMm0tLSdI+YmJhyjpqIiAzJsv03kJWnRmBNBXoEPvnzpjpITM9FSlYejI1k8K+Ca2IVqFTJTmZmJm7evAk3NzcEBQXB1NQU+/bt022PjIxEdHQ0goODn3gMuVwOW1tbvQcREVFxxNzPxm/HtX1Hp4f6QyaTSRyRtApqdXydrWFuWnX7LUnajDVt2jT06tULXl5eiIuLw+zZs2FsbIyhQ4dCoVBgzJgxmDp1KhwcHGBra4uJEyciODiYI7GIiKhcfLn3GvLUGrSp64i2vjWkDkdyBZ2Tq3J/HUDiZOfu3bsYOnQoUlJS4OTkhLZt2+L48eNwcnICAHz55ZcwMjLCgAEDkJubi5CQECxfvlzKkImIyEBFJmRgy9lYAMB7If7P2Lt6OH+36o/EAiROdtavX//U7ebm5li2bBmWLVtWQREREVF19fnuSAgB9Ah0RWMPO6nDkZwQAmejHwAAmnraSxzN86lUfXaIiIikcPr2fey9kghjIxne6eYndTiVwp2UbDzIVsHMxAgN3Kp2zQ6THSIiqtaEEPhsl3axz0HNa6GOk7XEEVUOZ2O0tToB7rZVdubkAlU7eiIioue0PzIJJ2/fh9zECG939pU6nEojIjoVANDEo2o3YQFMdoiIqBpTa/6r1RnVxhtuitJNaGuIzsakAgCaetpJGkdZYLJDRETV1raIWFxNyICtuQneal9X6nAqDaVKjctx2mUimOwQERFVUbn5aiz55xoA4M0OdaGwNJU4osrjYmwa8jUCTjZy1LSr+rVdTHaIiKha+u14NGJTc+BiK8eo1t5Sh1OpnH3YX6eph51BzCLNZIeIiKqdDKUK3+6/AQCY3KUeLMyq7lII5aFgJFZVn1+nAJMdIiKqdn44HIX7WXmo7WSFl4NqSR1OpaKdTDAVgGH01wGY7BARUTVzLyMXPx6+BQB4t5sfTIz5Ufiouw9yEJ+mhImRDI1qVe01sQrwJ0xERNXKt/9eR3aeGo097BAa4Cp1OJXOqdv3AQABNRWwNJN0Vakyw2SHiIiqjeiUbKw9GQ0AmB7qZxCdb8vayShtstPKx0HiSMoOkx0iIqo2luyJhEot0K6eE1rXqSF1OJVSQbLTkskOERFR1XIpLg3bIuIAAO+FcLHPoiRlKHErOQsyGdDci8kOERFRlVKwLETvxu4IqGkYHW/L2unb2iHnfi42BjXJIpMdIiIyeGE3U3Dw2j2YGMnwTrd6UodTaRlifx2AyQ4RERk4IQQW7roKABjWyhNejlYSR1R5ndD113GUOJKyxWSHiIgM2u5LCTgXkwpLM2NM7OQrdTiVVlqOClcTtIt/tvAxjJmTCzDZISIig5Wv1uCz3dq+Oq+39YGTjVziiCqv8Dv3IQTgU8MKzjbmUodTppjsEBGRwdoUfhe37mXBwcoMb7SrLXU4ldrxWw+bsLwNq78OwGSHiIgMlFKlxtK91wEA4zvWhY254YwuKg9HbyQDAFrXNaz+OgCTHSIiMlCrj91GQroSNe0sMOIFT6nDqdTuZ+XhUpy2v44hTrZYrEUvpk6dWuIDf/jhh3BwMLyqMCIiqvzSslVYvv8GAGBq13qQmxhLHFHlFnYzBYB2fh1D7NdUrGRn6dKlCA4OhpmZWbEOeuTIEUyYMIHJDhERSWLFwZtIV+bDz8UGfZvWlDqcSu/IwyasNnUNr1YHKGayAwBbtmyBs7Nzsfa1sbEpdUBERETPIyFNiVVHowAA74X6wdiIi30+y1FdsmN4/XWAYvbZWbVqFRSK4k+t/d1338HFxaXUQREREZXWV/uuITdfgxbe9ujkX7x/0quzmPvZiL6fDWMjGVrVrsbJzsiRIyGXy6FWq3Ho0CGkpqY+df9hw4bByoozVBIRUcW6eS8TG07fBQDM6O4PmYy1Os9SUKvTxMMO1vJiN/hUKSUajWVsbIxu3brhwYMH5RUPERFRqS3eHQm1RqBLfRcEGdCq3eXp6MPOyYbaXwcoxdDzgIAA3Lp1qzxiISIiKrWImFTsvJgAI5m2rw49m0YjcKygv04dw2zCAkqR7Hz88ceYNm0aduzYgfj4eKSnp+s9iIiIKpoQAot2ahf77N+sFuq5cKBMcVxNyEBKVh4sTI3R1NOw1sN6VIkb53r06AEA6N27t15bqBACMpkMarW67KIjIiIqhkPXkxF2KwVmJkaY0rWe1OFUGQev3QMAvFDbAWYmhjvPcImTnf3795dHHERERKWi0fxXq/PqC16oaWchcURVx4HIJABABz/DHrVW4mSnffv25REHERFRqWw/H4fL8emwkZtgfMe6UodTZWQoVQi/ox1w1MHPSeJoylepx5hlZ2cjOjoaeXl5euWNGjV67qCIiIiKIy9fgyX/XAMAjG1XG/ZWxZvpn7RDzvM1ArVrWMHL0bCniylxsnPv3j2MHj0aO3fuLHI7++wQEVFF+e3EHUTfz0YNaznGvOgjdThVyv6r2v467Q28VgcoxWisyZMnIzU1FSdOnICFhQV27dqFn3/+Gb6+vvjzzz/LI0YiIqJC0nJU+HrfdQDaxT4tzQxzQrzyIITQdU429P46QClqdv79919s27YNzZs3h5GREby8vNC1a1fY2tpiwYIF6NmzZ3nESUREpGflwZt4kK1CXWdrDGpeS+pwqpSrCRlISFfC3NQIrXwMf/LFEtfsZGVl6RYEtbe3x7172swwMDAQZ86cKdvoiIiIihCXmoP/O6Jd7HNGqD9MjA132HR5OBCp/exuXacGzE2NJY6m/JX47vDz80NkZCQAoHHjxvjuu+8QGxuLlStXws3NrdSBLFy4EDKZDJMnT9aVKZVKjB8/Ho6OjrC2tsaAAQOQmJhY6nMQEZFhWPKPdrHPlj4O6Fzf8Jthytp+3ZBzw++vA5Qi2Zk0aRLi4+MBALNnz8bOnTvh6emJr7/+Gp9++mmpgjh16hS+++67QiO5pkyZgu3bt2Pjxo04ePAg4uLi0L9//1Kdg4iIDMPluHRsPqtd7PP9HvW52GcJpStVOFMw5Lxe9UgUS9xnZ8SIEbrvg4KCcOfOHVy9ehWenp6oUaPki4hlZmZi+PDh+OGHH/Dxxx/rytPS0vDTTz9h7dq16NSpEwBg1apVqF+/Po4fP44XXnihxOciIqKqb8HOKxACeKmRG5p42EkdTpVzMPKedsi5kxU8HS2lDqdCPFcjpxACFhYWaNasWakSHQAYP348evbsiS5duuiVh4eHQ6VS6ZX7+/vD09MTYWFhTzxebm4u1+siIjJQh67dw+HryTA1luG9EH+pw6mS9lzWdgfp2sBF4kgqTqmSnZ9++gkBAQEwNzeHubk5AgIC8OOPP5b4OOvXr8eZM2ewYMGCQtsSEhJgZmYGOzs7vXIXFxckJCQ88ZgLFiyAQqHQPTw8PEocFxERVT5qjcCnf18BALwa7F1taiXKkkqt0fXX6VaNkp0SN2PNmjULX3zxBSZOnIjg4GAAQFhYGKZMmYLo6GjMmzevWMeJiYnBpEmTsGfPHpibm5c0jCeaOXMmpk6dqnuenp7OhIeIyABsORuLqwkZsDE3wQQuC1EqJ27dR4YyHzWszdDEw3BXOX9ciZOdFStW4IcffsDQoUN1Zb1790ajRo0wceLEYic74eHhSEpKQrNmzXRlarUahw4dwrfffovdu3cjLy8PqamperU7iYmJcHV1feJx5XI55HJ5Sd8WERFVYkqVGkv+0Y4EntCxLpeFKKU9l7UtI539XWBsVH06dpc42VGpVGjevHmh8qCgIOTn5xf7OJ07d8aFCxf0ykaPHg1/f39Mnz4dHh4eMDU1xb59+zBgwAAAQGRkJKKjo3U1SkREVD3839EoxKcpUdPOAiNbe0sdTpUkhKiW/XWAUiQ7r7zyClasWIEvvvhCr/z777/H8OHDi30cGxsbBAQE6JVZWVnB0dFRVz5mzBhMnToVDg4OsLW11TWdcSQWEVH1kZKZixX7bwIApoXUqxaT4JWHS3HpiEtTwsLUGG19SzeoqKoqVrLzaB8YmUyGH3/8Ef/8848u6Thx4gSio6Px6quvlmlwX375JYyMjDBgwADk5uYiJCQEy5cvL9NzEBFR5fbNvzeQkZuPhu626NO4ptThVFn/PKzVaVevesya/CiZEEI8a6eOHTsW72AyGf7999/nDqospaenQ6FQIC0tDba2tlKHQ0REJXA7OQtdvjiIfI3Ab6+3Qpu61atGoix1/+owrsSnY/HLjTEwqPKvJVaWn9/FqtnZv3//c52EiIioNBbtuop8jUD7ek5MdJ5DzP1sXIlPh5EM6ORfPWZNfhRXTiMiokrpZNR97LyYACMZMLMHJxB8HrsuakdhtfB2gEM1HMlWrGSnf//+JZqJePjw4UhKSip1UEREVL1pNALzd1wGAAxp6Ql/V3ZDeB5/XdCuadmzUekX7K7KitWMtW3bNty7d69YBxRCYPv27Zg/fz6cnatfVRkRET2/rRGxuBCbBmu5CaZ0qSd1OFVabGoOImJSIZMBoQFPnqfOkBUr2RFCoF493mxERFT+svPy8dku7QSC4zvWhZMNJ4p9Hjsf1uq08HaAs03ZrVhQlZRbB+WaNTk8kIiISu6HQ1FISFeilr0FRrfxljqcKk/XhBVYPZuwgGImO+3bty/vOIiIiJCQpsTKg9oJBGd0969288GUtbjUHJyNrt5NWABHYxERUSWy+J9I5KjUCPKyr9Y1EWVl58NRWM297OFiWz2bsAAmO0REVElcjE3DH2fuAgA+7FkfMln1WaiyvPz9sAmrRzVPHJnsEBGR5ITQDjUXAujTxB1NPe2lDqnKi0/LQfidBwCA7gFMdoiIiCS1+1IiTkTdh9zECO+FcgLBsrDjnLZWp7mXPVwV1bcJCyhlspOfn4+9e/fiu+++Q0ZGBgAgLi4OmZmZZRocEREZvrx8DRbsvAIAeOPF2qhpZyFxRIZh27lYAECfphwdXazRWI+6c+cOQkNDER0djdzcXHTt2hU2NjZYtGgRcnNzsXLlyvKIk4iIDNQvYbdxJyUbTjZyvNmhjtThGIQbSZm4GJsOEyMZO3qjFDU7kyZNQvPmzfHgwQNYWPyXfffr1w/79u0r0+CIiMiw3c/Kw1f7rgMApnWrByt5if8HpyL8GaGt1WlXz6laroX1uBLfVYcPH8axY8dgZqZ/8by9vREbG1tmgRERkeH7au81ZCjzUd/NFgODPKQOxyAIIbA1Ig6AtrM3laJmR6PRQK1WFyq/e/cubGxsyiQoIiIyfNcTM/DriWgAwEc968PYiEPNy0JETCqi72fD0swYXRu4SB1OpVDiZKdbt25YunSp7rlMJkNmZiZmz56NHj16lGVsRERkoIQQmLfjMtQaga4NXNC6bg2pQzIY2x7W6nRr4AJLMzYLAqVoxlq8eDFCQ0PRoEEDKJVKDBs2DNevX0eNGjWwbt268oiRiIgMzJ7LiTh8PRlmJkb4qGcDqcMxGPlqDXacL2jC4iisAiVOdjw8PHDu3Dn8/vvvOHfuHDIzMzFmzBgMHz5cr8MyERFRUZQqNeb/dRkA8MaLPvB0tJQ4IsNx5EYykjPz4GBlhra+rC0rUKJkR6VSwd/fHzt27MDw4cMxfPjw8oqLiIgM1E9HohBzPwcutnK81aGu1OEYlD/OaAcK9WrkBlNjzhtcoERXwtTUFEqlsrxiISIiAxefloNv/70BAHi/R30ONS9DaTkq7L6kXfjz5eYc2faoEqd948ePx6JFi5Cfn18e8RARkQFbuPMqclRqNPeyR+/GHBZdlrafi0Nevgb+rjZo6G4rdTiVSolT6lOnTmHfvn34559/EBgYCCsrK73tmzdvLrPgiIjIcJy+fR/bIuIgkwFzejfkquZlbFO4dsX4gUG1eG0fU+Jkx87ODgMGDCiPWIiIyECpNQKz/7wEABjSwgMBNRUSR2RYbiRlICImFcZGMo7CKkKJk51Vq1aVRxxERGTANpyOwaW4dNiYm2BaNz+pwzE4Gx/W6nT0c4KTjVziaCofdtUmIqJylZajwue7IwEAU7rUg6M1P4zLUr5agy0PR2ENDKolcTSVU4lrdnx8fJ7aFnjr1q3nCoiIiAzL0r3XcD8rD77O1ngl2EvqcAzO4RvJSMrIhb2lKTr5c3mIopQ42Zk8ebLec5VKhbNnz2LXrl149913yyouIiIyANcTM/BL2B0AwKxeDTj3Szn4/WQMAO2MyWYmvL5FKXGyM2nSpCLLly1bhtOnTz93QEREZBiEEJi7Xbv+VbcGLnjR10nqkAxOUroSe68kAgCGtvSUOJrKq8xSwO7du+OPP/4oq8MREVEV98/lRBy5oV3/6kOuf1UuNobfRb5GIMjLHn6uNlKHU2mVWbKzadMmODg4lNXhiIioCsvOy8e87Vz/qjxpNALrT0UDYK3Os5S4Gatp06Z6HZSFEEhISMC9e/ewfPnyMg2OiIiqpm//vYHY1BzUtLPAhI6+UodjkI7cSEbM/RzYmJugZ6Cb1OFUaiVOdvr27av33MjICE5OTujQoQP8/f3LKi4iIqqibiRl4ofD2pG5s3s1gIWZscQRGaZ1J7W1Ov2b1uQ1foYSJzuzZ88ujziIiMgACCEwa9tFqNQCnfyd0bUBh0KXh6QMJfZcftgxuRWbsJ6lxH12zpw5gwsXLuieb9u2DX379sX777+PvLy8Mg2OiIiqlu3n43HsZgrkJkaY04vrX5WXTQ87JjfztIO/Kxf9fJYSJzv/+9//cO3aNQDaCQQHDx4MS0tLbNy4Ee+9916ZB0hERFVDhlKFj3doOyWP71iXnZLLiVoj8NtxdkwuiRInO9euXUOTJk0AABs3bkT79u2xdu1arF69mkPPiYiqsaV7ryMpIxfejpYY26621OEYrH+vJiE2NQf2lqbo1dhd6nCqhBInO0IIaDQaAMDevXvRo0cPAICHhweSk5PLNjoiIqoSrsSnY/Wx2wCAuX0CYG7KDrPl5Zew2wCAwS08eZ2LqcTJTvPmzfHxxx9jzZo1OHjwIHr27AkAiIqKgotLyTqirVixAo0aNYKtrS1sbW0RHByMnTt36rYrlUqMHz8ejo6OsLa2xoABA5CYmFjSkImIqBxpNAIfbb0ItUage4Ar2tfjTMnl5UZSJg5fT4aRDBjOjsnFVuJkZ+nSpThz5gwmTJiADz74AHXr1gWgnVSwdevWJTpWrVq1sHDhQoSHh+P06dPo1KkT+vTpg0uXLgEApkyZgu3bt2Pjxo04ePAg4uLi0L9//5KGTERE5eiPM3dx+s4DWJoZ46OXOFNyeVrzsFanc30XeDiwT1RxyYQQoiwOpFQqYWxsDFNT0+c6joODAz7//HMMHDgQTk5OWLt2LQYOHAgAuHr1KurXr4+wsDC88MILxTpeeno6FAoF0tLSYGvLHutERGUpLVuFTksOICUrDzO7++N/7etIHZLBylCq8MKn+5CVp8avY1qhrW8NqUMqV2X5+V3imp2YmBjcvXtX9/zkyZOYPHkyfvnll+dKdNRqNdavX4+srCwEBwcjPDwcKpUKXbp00e3j7+8PT09PhIWFPfE4ubm5SE9P13sQEVH5+Pyfq0jJyoOvszVea+sjdTgGbcvZWGTlqVHbyQpt6jpKHU6VUuJkZ9iwYdi/fz8AICEhAV27dsXJkyfxwQcfYN68eSUO4MKFC7C2toZcLse4ceOwZcsWNGjQAAkJCTAzM4OdnZ3e/i4uLkhISHji8RYsWACFQqF7eHh4lDgmIiJ6tnMxqfjthHYI9Lw+ATA1LrPlFukxGo3Azw87gI8M9ub8RSVU4jvz4sWLaNmyJQBgw4YNCAgIwLFjx/Dbb79h9erVJQ7Az88PEREROHHiBN58802MHDkSly9fLvFxCsycORNpaWm6R0xMTKmPRURERVNrBD7cehFCAP2a1kRwHdY0lKeD1+7h5r0s2MhN0L9ZTanDqXJKvFyESqWCXC4HoB163rt3bwDaJqb4+PgSB2BmZqbr5BwUFIRTp07hq6++wuDBg5GXl4fU1FS92p3ExES4uro+8XhyuVwXHxERlY/Vx27jQmwabMxNMLMH10Usbz8e0a41NrSVJ2zMn69vbHVU4pqdhg0bYuXKlTh8+DD27NmD0NBQAEBcXBwcHZ8/s9doNMjNzUVQUBBMTU2xb98+3bbIyEhER0cjODj4uc9DRESlc/dBNpb8EwkAeL9HfTjbmEsckWG7HJeOozdSYGwkw8jW3lKHUyWVuGZn0aJF6NevHz7//HOMHDkSjRs3BgD8+eefuuat4po5cya6d+8OT09PZGRkYO3atThw4AB2794NhUKBMWPGYOrUqXBwcICtrS0mTpyI4ODgYo/EIiKisiWEdk6d7Dw1Wno7YHBz9ossbz8diQIA9Ah0Q007C4mjqZpKnOx06NABycnJSE9Ph729va587NixsLQs2Zj/pKQkvPrqq4iPj4dCoUCjRo2we/dudO3aFQDw5ZdfwsjICAMGDEBubi5CQkKwfPnykoZMRERlZMf5eOyPvAczYyN82j8QRkbsKFueEtOV+PNcLABgDEe7lVqJkx1Am9mHh4fj5s2bGDZsGGxsbGBmZlbiZOenn3566nZzc3MsW7YMy5YtK02YRERUhtKyVZi7XTvp6/iOdVHX2VriiAzfL2G3oVILtPC2RxMPO6nDqbJKnOzcuXMHoaGhiI6ORm5uLrp27QobGxssWrQIubm5WLlyZXnESUREEluw8wqSM/NQ19ka4zpwoc/ylp2XrxvaP6Ytr/fzKHEH5UmTJqF58+Z48OABLCz+azvs16+fXmdiIiIyHMdvpWD9Ke1UHgv6B0JuwgUoy9v6kzFIzVbBy9ESXRuUbO1J0lfimp3Dhw/j2LFjMDMz0yv39vZGbGxsmQVGRESVg1KlxvtbLgAAhrXyRAtvB4kjMnx5+Rr8cFg73Px/7erAmH2jnkuJa3Y0Gg3UanWh8rt378LGxqZMgiIiospj+f4buHUvC042ckwP5Zw6FWFbRCzi05RwtpFjQBAnEXxeJU52unXrhqVLl+qey2QyZGZmYvbs2ejRo0dZxkZERBK7lpiBFQdvAgDm9m4IhQUntCtvGo3AyofXfExbHzYZloESN2MtXrwYoaGhaNCgAZRKJYYNG4br16+jRo0aWLduXXnESEREEtBoBGZuvgCVWqBLfRd0D3jy7PVUdvZcSdQuDWFugmGtPKUOxyCUONnx8PDAuXPn8Pvvv+PcuXPIzMzEmDFjMHz4cL0Oy0REVLWtPRmN8DsPYGVmjHl9GnLxyQoghMDyA9panVeDvbg0RBkpUbKjUqng7++PHTt2YPjw4Rg+fHh5xUVERBKKT8vBop1XAQDvhvjBnTP3Voiwmyk4F5MKuYkRRrfhJIJlpUR9dkxNTaFUKssrFiIiqgSEEJjxxwVk5OajiYcdXgn2ljqkamPpvusAgKEtPVHDmotal5USd1AeP348Fi1ahPz8/PKIh4iIJLYx/C4OXrsHMxMjLH65EYc9V5Cwmyk4GXUfZsZGGNe+jtThGJQS99k5deoU9u3bh3/++QeBgYGwsrLS27558+YyC46IiCpWfFoO5u+4DACY2rUe6jpzSpGK8vXDWp3BLTzgquBK8mWpxMmOnZ0dBgwYUB6xEBGRhIQQeH/zBWQo89HYww6vc+HJCnMy6j7CbqXA1FiGNzuwVqeslTjZWbVqVXnEQUREEtsUfle7ormJEZa83AgmxiXu6UClVFCrM6i5BzuDl4Ni38kajQaLFi1CmzZt0KJFC8yYMQM5OTnlGRsREVWQhDQl5j1svprShc1XFSn8zn0cuZHMWp1yVOxk55NPPsH7778Pa2tr1KxZE1999RXGjx9fnrEREVEFEELg/S0Pm69qKfDGi2y+qkiLd18DAAwMqoVa9pYSR2OYip3s/PLLL1i+fDl2796NrVu3Yvv27fjtt9+g0WjKMz4iIipnf5yJxb9Xk2BmbITFLzdm81UFOnojGWG3UmBmbIQJnXylDsdgFfuOjo6O1lv7qkuXLpDJZIiLiyuXwIiIqPwlpCkxd/slAMDkrr7wdWHzVUURQuCz3ZEAtKvJ12RfnXJT7GQnPz8f5ub6Q+FMTU2hUqnKPCgiIip/jzZfNaqlwNgXa0sdUrWy90oSzsWkwsLUGOM71pU6HINW7NFYQgiMGjUKcvl/MzoqlUqMGzdOb64dzrNDRFQ1bGbzlWQ0GoEl/2hrdUa18YaTDWdLLk/FTnZGjhxZqGzEiBFlGgwREVWMR5uvJnXxRT02X1WoHRficTUhAzZyE/yvHWvUyluxkx3Or0NEZBg0GoF3N51D+sPmK37YVqy8fA0WP+yr80a72rCzNJM4IsPHOksiompm9bHbOHw9GeamRvhiUBM2X1WwtSfuIPp+NmpYyzGGs1RXCN7hRETVyLXEDCzcdRUA8EGP+qjrbC1xRNVLulKFr/+9AQCY0tUXVvISL2RApcBkh4iomsjNV2PS+gjk5WvQwc8JI17wkjqkaue7gzdxPysPtZ2sMLi5h9ThVBtMdoiIqokv9lzDlfh0OFiZ4bOBjSCTyaQOqVpJSFPipyNRAIDpof5sPqxAvNJERNXA8Vsp+P7QLQDAgv6BcLYxf8YrqKx9sScSSpUGzb3s0a2Bi9ThVCtMdoiIDFy6UoV3NpyDEMDg5h4IaegqdUjVzsXYNGwMvwsAmNmjPmvVKhiTHSIiAzd72yXEpubAy9ESs3o1kDqcakcIgXk7LkMIoFdjdwR52UsdUrXDZIeIyIBtPxeHLWdjYSQDvhjUhKN/JLDrYgJORt2H3MQIM7r7Sx1OtcRkh4jIQMWn5eCDLRcAABM6+bJGQQJKlRqf/H0FAPC/drW52KdEmOwQERkgjUZg2kbtLMmNaykwsRMXmpTC/x2Nwt0HOXCxlWNchzpSh1NtMdkhIjJAKw/dxNEbKbAwNcaXg5vAlMOcK1xiuhLLHk4gOD3UH5ZmbEKUCu9+IiIDc/r2fSz55xoAYG7vhqjtxFmSpfDJX1eQladGU0879G1SU+pwqjUmO0REBiQ1Ow9vrzsLtUagbxN3vNy8ltQhVUvHbibjz3NxMJIB8/sEwMiIQ82lxGSHiMhACCEwbeN5xKUp4VPDCh/3C+R8LhJQqTWYve0SAGDEC14IqKmQOCJiskNEZCBWHb2NvVcSYWZihG+HNYU1h5lLYvXR27ielAlHKzO809VP6nAITHaIiAzC+bupWLBTO8T5w5710dCdtQlSiE/LwdK92v5S07v7Q2FpKnFEBEic7CxYsAAtWrSAjY0NnJ2d0bdvX0RGRurto1QqMX78eDg6OsLa2hoDBgxAYmKiRBETEVU+6UoVJqw9C5VaILShK17hauaSmb3tErLy1GjmaYeBzdhfqrKQNNk5ePAgxo8fj+PHj2PPnj1QqVTo1q0bsrKydPtMmTIF27dvx8aNG3Hw4EHExcWhf//+EkZNRFR5CCEwc/MFRN/PRi17CyziauaS2X0pAf9cToSJkQwL+jdip+RKRCaEEFIHUeDevXtwdnbGwYMH0a5dO6SlpcHJyQlr167FwIEDAQBXr15F/fr1ERYWhhdeeOGZx0xPT4dCoUBaWhpsbW3L+y0QEVWotSei8f6WCzAxkmHjuGA09eQsyVLIUKrQ9YtDSEhX4q0OdfBeKJeFeF5l+fldqfrspKWlAQAcHBwAAOHh4VCpVOjSpYtuH39/f3h6eiIsLEySGImIKourCemYu1076ue9UD8mOhJa8s81JKQr4eVoibc7+0odDj2m0nTV12g0mDx5Mtq0aYOAgAAAQEJCAszMzGBnZ6e3r4uLCxISEoo8Tm5uLnJzc3XP09PTyy1mIiKppCtVeOvXM8jN16CjnxNeb1tb6pCqrbPRD/Bz2G0AwCd9A2FuaixtQFRIpanZGT9+PC5evIj169c/13EWLFgAhUKhe3h4eJRRhERElYNGIzBtwzncSs6Cu8IcSwY1Yf8QieTmq/HupvMQAujXtCba+taQOiQqQqVIdiZMmIAdO3Zg//79qFXrv97rrq6uyMvLQ2pqqt7+iYmJcHV1LfJYM2fORFpamu4RExNTnqETEVW4FQdv4p/LiTAzNsKKEUFwsDKTOqRq65t9N3AjKRM1rOWY9VIDqcOhJ5A02RFCYMKECdiyZQv+/fdf+Pj46G0PCgqCqakp9u3bpyuLjIxEdHQ0goODizymXC6Hra2t3oOIyFAcvn4PS/7RTtExt09DNPawkzagauxibBpWHLwJAPi4b0PYM+mstCTtszN+/HisXbsW27Ztg42Nja4fjkKhgIWFBRQKBcaMGYOpU6fCwcEBtra2mDhxIoKDg4s1EouIyJDcfZCNt9edhUYAg5t7YGhLT6lDqrby8jWYtvEc1BqBnoFuCA1wkzokegpJk50VK1YAADp06KBXvmrVKowaNQoA8OWXX8LIyAgDBgxAbm4uQkJCsHz58gqOlIhIWkqVGm/+egYPslVoVEuBuX0aSh1StbZs/w1cTciAg5UZfxZVQKWaZ6c8cJ4dIjIE0zedx++nY2BvaYrtE9uilr2l1CFVW+diUtF/xTGoNQLfDG2KXo3dpQ7JIBnsPDtERFTYupPR+P10DIxkwDdDmzHRkVBOnhpTNkRArRHo1didiU4VwWSHiKgSi4hJxext2okDp4X4cWizxBbtuopb97LgYivHfDZfVRlMdoiIKqmkDCXe/DUceWoNQhq64M32daQOqVo7cj0Zq4/dBgB8NrAx7Cw5+qqqYLJDRFQJKVVqjP0lHPFpStRxssLilxtzgU8JJWfmYsqGCADA8FaeaF/PSdqAqESY7BARVTJCCEz/4zwiYlKhsDDFTyNbwMbcVOqwqi2NRuCdDedwLyMXdZ2t8WFPTh5Y1TDZISKqZJYfuIltEXEwMZJhxYhm8K5hJXVI1dqPR27h4LV7kJsYYdmwZrAw49pXVQ2THSKiSmTXxQR8vvu/GZJb12GHZCmdjX6Az3Zpfx6zezWEn6uNxBFRaTDZISKqJC7GpmHK7xEAgFGtvTG8lZe0AVVzaTkqTFx3FvkPZ0ke2pILS1dVTHaIiCqBpAwl3vjlNHJUarzoWwMf9qwvdUjVmhAC72++gLsPclDL3gILBgSyg3gVxmSHiEhij468qu1khW+HNYOJMf88S2ndyRj8dSEeJkYyfDusGWzZQbxK428TEZGEhBCY8djIK4UFP1ildDUhHXO3aydyfC/UD024snyVx2SHiEhCi/+JxNaIOBgbybBieDP4cOSVpNKVKrz12xnk5mvQwc8Jr7etLXVIVAaY7BARSeTX43ewbP9NAMCC/oFoXZcjr6Sk0QhM/T0Ct+5lwU1hjsUvN4aREfvpGAImO0REEthzORGztl0EAEzpUg+DmnOkj9SW7ruOvVeSYGZihO9eCUINa7nUIVEZYbJDRFTBzkQ/wMR1Z6ARwODmHni7c12pQ6r2dl1MwNf7rgMAFvQLRKNadtIGRGWKyQ4RUQWKSs7C6z+fhlKl7RPycb8ADmmW2LXEDLzzcN2r0W28MSColrQBUZljskNEVEGSM3MxatVJ3M/KQ2BNBZYNawZTDjGXVFq2CmN/OY2sPDWCazvi/R6c38gQ8beMiKgCZOflY8zqU7iTkg0PBwv836gWsJKbSB1WtabWCLy9/ixup2Sjpp0Fvh3WlMmngeJPlYionOXla/DWb2dw7m4a7C1N8fPolnCyYedXqX2+OxIHr92Duam2Q7IjOyQbLCY7RETlSK0RmPJ7BA5Eaj9UfxzZHLWdrKUOq9pbfzIaKw9qh/0vGtAIATUVEkdE5YnJDhFROdFoBGZuPo+/LsTD1FiG715pjiAvB6nDqvYORCbhg63aYf8TO9VFnyY1JY6IyhuTHSKiciCEwMd/XcGG03dhJAO+HtIU7es5SR1WtXcxNg3jfzsDtUagf9OamNq1ntQhUQVgskNEVA6+2ncd/3c0CgDw2cDG6B7oJnFEFJuag9dWn0JWnhpt6jpi4YBGHPZfTTDZISIqYz8dicLSvdoJ6ub0aoCBnLdFcmk5Koz6v5NIysiFn4sNVowIgpkJPwKrC/6kiYjK0IZTMZi/4zIA4J2u9TCqjY/EEVFuvhr/W3Ma15My4WIrx6rRLWBrzpXlqxMmO0REZWTzmbuYvvk8AGBsu9qY0InLQEhNoxF4b9N5HL91H9ZyE6wa1RLudhZSh0UVjMkOEVEZ+CP8Lt7ZeA5CAMNbeWJmd3/2B5GYEAIfbbuIbRFxMDGSYfnwZmjgbit1WCQBJjtERM9pU/hdTNukTXRGvOCJ+X243pXUhBD49O8r+O1ENGQy4IvBTdCOo+GqLc5VTkT0HDaejsF7f5xnolPJLN17HT8c1o6GW9S/EXo3dpc4IpISa3aIiEppAxOdSum7gzfx1T7taLjZvRpgUAsPiSMiqTHZISIqhQ2nYzD9YaLzygteTHQqiTVht7Fg51UAwLshfhjN0XAEJjtERCX224k7ukTn1WAvzOvTkIlOJbAp/C4+2nYJADChY12M78jRcKTFPjtERCWw/MANfLYrEgAwMtgLc3oz0akM1p+MxswtFwAAo9t4451uXAaC/sNkh4ioGIQQWLQrUrdS9viOdTCtmx8TnUpg9dEozNmuncjxlRe8MOulBvy5kB4mO0REz6DWaOdrWXsiGgDwfg9/jG1XR+KoCABWHryJhQ/76Lzxog/e71GfiQ4VwmSHiOgpVGoNpm44h+3n4iCTAQv6BWJIS0+pw6r2hBBYuve6btTV253qYkrXekx0qEhMdoiIniAnT43xa8/g36tJMDWW4cvBTfBSI87XIjUhBBbuuorvDt4CoB11xc7I9DRMdoiIipCSmYsxP59GREwqzE2NsHJEEDr4OUsdVrWn1gjM/vMifj2ubVKc9VIDvNaWw8vp6SQden7o0CH06tUL7u7ukMlk2Lp1q952IQRmzZoFNzc3WFhYoEuXLrh+/bo0wRJRtRGVnIX+K44hIiYVdpam+HVMKyY6lYBSpcabv4bj1+PaJSA+6RfARIeKRdJkJysrC40bN8ayZcuK3P7ZZ5/h66+/xsqVK3HixAlYWVkhJCQESqWygiMlouoi/M599F9+FHdSsuHhYIE/3myN5t4OUodV7d3PysOwH47jn8uJMDMxwrJhzTC8lZfUYVEVIWkzVvfu3dG9e/citwkhsHTpUnz44Yfo06cPAOCXX36Bi4sLtm7diiFDhlRkqERUDey8EI/Jv0cgN1+DxrUU+HFkCzjZyKUOq9qLTsnGqFUncSs5CwoLU/zwanO09GECSsVXaWdQjoqKQkJCArp06aIrUygUaNWqFcLCwp74utzcXKSnp+s9iIieRgiBn45E4a21Z5Cbr0GX+s5YN/YFJjqVwIW7aei/4ihuJWehpp0F/ngzmIkOlVilTXYSEhIAAC4uLnrlLi4uum1FWbBgARQKhe7h4cEF4IjoyfLyNXh/ywXM33FZt87Vd680h6UZx29Ibe/lRAz+PgzJmXmo72aLzW+1Rl1nG6nDoiqo0iY7pTVz5kykpaXpHjExMVKHRESVVHJmLob/eBzrTsZAJtNOFjivT0MYG3GuFikJIbBs/w28seY0svPUaFu3Bjb87wW42JpLHRpVUZX2XxdXV1cAQGJiItzc3HTliYmJaNKkyRNfJ5fLIZez6pmInu5SXBrG/hKO2NQc2MhN8PXQpujozxFXUsvOy8e7m87jr/PxAB4u/9CrAUyNDe5/c6pAlfbu8fHxgaurK/bt26crS09Px4kTJxAcHCxhZERU1e28EI+BK8IQm5oDnxpW2DK+DROdSiA2NQcDV4Thr/PxMDGS4dN+gZjfN4CJDj03SWt2MjMzcePGDd3zqKgoREREwMHBAZ6enpg8eTI+/vhj+Pr6wsfHBx999BHc3d3Rt29f6YImoipLrRFYuvcavvlX+3fnRd8a+HZoMygsTSWOjE5G3cebv4YjJSsPjlZmWDEiiB2RqcxImuycPn0aHTt21D2fOnUqAGDkyJFYvXo13nvvPWRlZWHs2LFITU1F27ZtsWvXLpibs92WiEomJTMXk9ZH4MiNZADA6219MKO7P0xYayApIQR+PByFRbuuIl8j0MDNFt+/GoRa9pZSh0YGRCaEEFIHUZ7S09OhUCiQlpYGW1tbqcMhIgmE37mP8b+dRUK6EhamxljQPxB9m9aUOqxqLzU7D9M2nsPeK0kAgF6N3bFoQCBHwhGAsv385h1FRAarYP6chTu1tQZ1nKywckQQfF04fFlqZ6IfYOLas4hNzYGZiRFmvdQAw1t5ctVyKhdMdojIIKVlqzBj83nsvKidl6tXY3cs7B8IKzn/7Enp8QTUy9ESy4Y1Q0BNhdShkQHjbz0RGZyTUfcxef1ZxKUpYWosw4c9G+DVYC/WGkgsKV2J6X+cx/7IewCAnoFuWDggEDbm7CBO5YvJDhEZDJVag6/3Xcey/TegEYCXoyW+GtIUTTzspA6t2vv7Qjw+2HIBD7JVMDMxwkc962PEC0xAqWIw2SEigxCdko1Jv5/F2ehUAMDAoFqY07shrNlsJam0HBXm/HkJW87GAgAautviy8FNUI/9pqgC8a8AEVVpQghsPH0X83ZcRmZuPmzMTfBJv0D0buwudWjV3rEbyZi28Rzi0pQwkgFvdaiLtzv7wsyEw/2pYjHZIaIqKz4tBzP+uICD17R9QJp72WPpkCaco0ViadkqfPr3Ffx+Wrs2obejJZYMaoIgL3uJI6PqiskOEVU5QghsDL+L+TsuI0OZDzMTI0ztWg9vvFibi3hKSAiBvy8kYPafl5CcmQsAGPGCJ2Z2r89RcCQp3n1EVKXEp+Xg/c0XdCN6GnvYYcnLjVDXmX1ApBSfloOPtl7UTRBYx8kKCwc0QgtvLvlA0mOyQ0RVgloj8EvYbSzeHYmsPDXMjI0wpWs9vPGiD5d8kFBevgY/H7uNr/ZdR2ZuPkyNZXizQ12M71gHchNjqcMjAsBkh4iqgIuxaZi5+QIuxKYBAJp52mHRgEacCVlih67dw5ztl3DrXhYA7c9l4YBGHGlFlQ6THSKqtDKUKizdex2rjkZBIwAbcxPM6O6PoS08YcS+OZKJTsnG/L8uY8/lRABADWszvBfqj4HNavHnQpUSkx0iqnQ0GoHNZ2OxaNdV3MvQdnTt1dgdH71UH8425hJHV32l5aiw4sBN/N/RKOTla2BsJMOo1t6Y1MUXtpwFmSoxJjtEVKlExKRi9p+XcC4mFYB22PKc3g3Rwc9Z2sCqMaVKjV+P38G3+28gNVsFAGhT1xFzejVkUyJVCUx2iKhSiE/LweLd1/DHmbsAACszY0zs7IvRbbzZ0VUiGo3A1ohYLPnnGmJTcwAAvs7WmB7qj871nbnUA1UZTHaISFJpOSqsPHgT/3ckCrn5GgDAgGa1MD3UD862bLKSgkYjsPNiAr7adw3XEjMBAC62ckztWg8DmtXi6DeqcpjsEJEkcvPVWBOm3zTS0tsBM3v4o6knZ9qVQlFJjo25Cd7sUAejW/vAwow1bFQ1MdkhogqVl6/BpvC7WLb/BptGKol8tQZ/XYjHsv039JKcMW19MLqNDxQW7HxMVRuTHSKqECq1BpvP3MU3/97A3QfaJIdNI9LKzsvHhlMx+OFwlC7xZJJDhojJDhGVq9x8Nf4Ij8XKgzcRfT8bAOBkI8dbHepgaEtPmJuyaaSi3cvIxZrjd/BL2G1dE6KDlRlGtfbGyNbeTHLI4DDZIaJykaFUYe2JaPx0JApJD+fKqWFthnHt62B4Ky/2/6hgQgiE33mAX8LuYOfFeKjUAgDg6WCJN9rVxstBtZh4ksFiskNEZSohTYmfw27jt+N3kK7MBwC4Kczx+ou1MbSlByzN+GenImXn5WNbRBx+CbuDK/HpuvKmnnZ4vW1thAa4cqV4Mnj8q0NEZeJcTCp+OhKFvy/EI1+jrTWo7WSFce3roG+TmjAzYZ+cinQjKQNrT8RgY3gMMh4mnXITI/Rp4o5Xg70RUFMhcYREFYfJDhGVmlKlxt8X4vHr8Ts4E52qK2/l44DRbXzQtYELaw0qUGp2Hrafi8OmM7G6GagBwMvREiNaeeHl5rVgZ2kmXYBEEmGyQ0QlduteJtadjMbG8Lu6Dq6mxjL0auyO19r4sNagAqnUGhy+fg+bwu9i7+Uk5Km1EzMaG8nQ0c8Zw1/wRHtfJy7QSdUakx0iKpbU7DzsOB+PzWfu6tXi1LSzwLBWnni5eS0u0llBVGoNwm6m4K/z8dh9OUGXcAJAfTdbDAyqhT5N3FHDWi5hlESVB5MdInqivHwNDkQmYfOZWPx79b9aAyMZ0MHPGSNe8ET7es5sqqoA+WoNwm5pE5xdl/QTnBrWZujduCYGBNVEQ3fWqhE9jskOEenRaAQi7qZi69lYbD8XhweP1RoMaFYTvRu7c92qCpCWo8Lh6/fw75Uk7I9M0vtZOFqZITTAFT0buaGVjyMTTqKnYLJDRFCpNThx6z52X0rAP5cTkJieq9vmZCNHv6Y10a9pTdR3s5UwSsMnhMCt5Cz8eyUJ+64m4tTtB1A/HNkGPJLgBLqhpY8DZ50mKiYmO0TVVE6eGgev3cM/lxKw72oS0nL+qzWwMjNGlwYu6N+sFtrUceSHajlKzszFsZspOHo9GUdvJuuW0ijg62yNTv7O6OTvjCAve/4siEqByQ5RNSGEwI2kTBy6nowj1+8h7FYKlCqNbrujlRm6NnBBSENXtK7rCLkJZ9MtD2nZKpy+c1+b4NxIxtWEDL3tZsZGaFXbAZ39ndHJ3wWejpYSRUpkOJjsEBmwlMxcHLmRjMPXk3HkejIS0pV622vZWyCkoStCGroiyMue/T7KmBACdx/k4PSd+zh1+wHCbz9AZGJGof3qu9miTR1HtPGtgZbeDrCS808zUVnibxSRAYlLzcGp2/dx+vYDnLp9v1CtgdzECC19HPCibw286OsEf1cbyGRMcMpKUoYSF2PTcOFuOi7EpuFCbKpe/6cCtWtYoVVtB7SpWwPBtR3hyCHiROWKyQ5RFZWXr8G1xAycjUnF6YcJTmxqTqH96rvZop1vDbT1rYEW3g5c7LEMCCFwLzMXl2LTcf5uGi7EpuFibFqhmjNAO9liQE0FmnvZo7m3A4K87Dn/DVEFY7JDVAWo1NrE5sLdNJx/+MF6NT5DN+9NAWMjGQLcbdHc2wEtvO0R5OUAJxt+sJaWEALJmXm4npSB64mZuJao/Xo9KUNvGHgBmQyo42SNwJoK7aOWAgHuCq7wTiQxJjtElYhGIxCbmoPIhAxEJmYgMiED1xIzcPNeJlRqUWh/hYUpGtVSIMjLHi28HdDEw479PUohKzcfd1KycSclC3fua7/evJeF64lFJzWANrGpXcMKjWrZIeBhctPQ3ZbXn6gS4m8lUQUTQiA1W4WolCxE3ctCVHKW7vvbKVnIzlMX+TpbcxME1lIgsKYdAmsq0KiWArXsLdjnphgyc/MRn5qD+DQl4tNyEJeqRMyDbESnZON2SjaSMwv3qykgkwGeDpbwdbaBr4s16rlYw9fZBnWcrFljQ1RFMNkhKmNqjUBShhKxD3IQm6p9xKXmIPaB9kM2NjUHmbn5T3y9mbER6jhbw8/FGvVcbeDnYgM/VxvUtGNi87jcfDVSMvOQnJmLlMw83MvMRcIjCU1CmhJxaTnIUD75ehewtzSFl6MVvB0t4eloBZ8alkxqiAxElUh2li1bhs8//xwJCQlo3LgxvvnmG7Rs2VLqsKgaUWsE0nNUeJCdh+TMPNzLyEVyZm6RX5MycpGvKdzk9Dg3hTm8Ha3g42SF2jWs4O1oBe8aVvBytIRpNZs4TggBpUqDtBwV0nJUSM3O032flqNCeo4K97PzkJyRh5SsXCQ/THCKk8QUsDE3gbvCAm525nBTWKCmnbn2ejtYwdPREgoL03J8h0QkpUqf7Pz++++YOnUqVq5ciVatWmHp0qUICQlBZGQknJ2dpQ6PKjkhBHLzNcjKzUd2nhrZeWpk5eUjO1eN7Lx83fP0nPyHH6x5j3zgPvywzVYh4yk1MUUxNpLB1dYcNe0tUNPu4cPeAu52/z2vqrUFGo32mubmq5GXr9F9n52nRtbD65qVp0ZWbr7uuhdc80e/ZuUWXPN8pOeoCnW2Li5TYxkcreRwtDaDo7UcrrZyuCks4P4wqXG3M4erwgLW7EtDVG3JhBDP/hdUQq1atUKLFi3w7bffAgA0Gg08PDwwceJEzJgx45mvT09Ph0KhQFpaGmxty25dnwdZecjKy8eTrl5BuYAoouzR/UQRZbrvnvHaJ58D0Ha6fJCtwqM/YlHEfgWlj5YVtZ94yn4F7yVfLaAWAhrNI181Amqh/ZDM1whoxMOyR7/X7QuoNRrka7THylPrf6jm5WuQp3749WGZSq0ptL282chN4GQjRw1rOWrYmGm/Pny42MrhbGMOmUx7rTRC+z7Fw2ukeXgtNEJ7Tf/bR/tViIKy/57/973+V43Qroad//B6qtQaqB9e53y1gFqjgerhtny1QL7u2v73Gl25+r8kJjdfg1yV9lrmqh4+f7itqI7SZcXYSAaFhSkUFqawffhV+zCBveV/19nRWvu9k7UcthYmbN4jMkBl+fldqf/VycvLQ3h4OGbOnKkrMzIyQpcuXRAWFlbka3Jzc5Gb+19nw/T09HKJ7fN/IrH2RHS5HJsqv4zcfGTk5uNWcpbUoUjKSAaYmxrDzMQIlqbGsJSbwMrMGJZmJrCSm8BK/vB7s/+2PVpua/4wmbHUfrUyM2biQkRlrlInO8nJyVCr1XBxcdErd3FxwdWrV4t8zYIFCzB37txyj83USAZz0//6Vcig/QP96N/pgm8f/eOt92dcVrhMb99nbX9sv0dLZTLgXsaTR5hUJ8ZGMliYGsNIVvT1BZ59jR/f/9EtTzqOkUymO6dM9t9zo4fPZXrPn7xNd4yCYxr99xoZtM04xkYymBgbwcRIBhMj7VdjYxlMjWQwNjKCibHs4TbtfsaPfG9ipH29qbEMchNjyE2MIDc1+u97E2PITY1gZvx4uREXpSSiKqFSJzulMXPmTEydOlX3PD09HR4eHmV+nrl9AjC3T0CZH5eIiIjKVqVOdmrUqAFjY2MkJibqlScmJsLV1bXI18jlcsjlnDGWiIiItCp1HbSZmRmCgoKwb98+XZlGo8G+ffsQHBwsYWRERERUVVTqmh0AmDp1KkaOHInmzZujZcuWWLp0KbKysjB69GipQyMiIqIqoNInO4MHD8a9e/cwa9YsJCQkoEmTJti1a1ehTstERERERan08+w8r/KaZ4eIiIjKT1l+flfqPjtEREREz4vJDhERERk0JjtERERk0JjsEBERkUFjskNEREQGjckOERERGTQmO0RERGTQmOwQERGRQWOyQ0RERAat0i8X8bwKJohOT0+XOBIiIiIqroLP7bJY6MHgk52MjAwAgIeHh8SREBERUUllZGRAoVA81zEMfm0sjUaDuLg42NjYQCaTldlx09PT4eHhgZiYGK65VUK8dqXD61Y6vG6lw+tWOrxupVPUdRNCICMjA+7u7jAyer5eNwZfs2NkZIRatWqV2/FtbW15Q5cSr13p8LqVDq9b6fC6lQ6vW+k8ft2et0anADsoExERkUFjskNEREQGjclOKcnlcsyePRtyuVzqUKocXrvS4XUrHV630uF1Kx1et9Ip7+tm8B2UiYiIqHpjzQ4REREZNCY7REREZNCY7BAREZFBY7JDREREBo3JzmMOHTqEXr16wd3dHTKZDFu3btXbLoTArFmz4ObmBgsLC3Tp0gXXr1/X2+f+/fsYPnw4bG1tYWdnhzFjxiAzM7MC30XFe9Z1GzVqFGQymd4jNDRUb5/qeN0WLFiAFi1awMbGBs7Ozujbty8iIyP19lEqlRg/fjwcHR1hbW2NAQMGIDExUW+f6Oho9OzZE5aWlnB2dsa7776L/Pz8inwrFao4161Dhw6F7rlx48bp7VPdrtuKFSvQqFEj3cRtwcHB2Llzp24777WiPeu68V4rnoULF0Imk2Hy5Mm6soq655jsPCYrKwuNGzfGsmXLitz+2Wef4euvv8bKlStx4sQJWFlZISQkBEqlUrfP8OHDcenSJezZswc7duzAoUOHMHbs2Ip6C5J41nUDgNDQUMTHx+se69at09teHa/bwYMHMX78eBw/fhx79uyBSqVCt27dkJWVpdtnypQp2L59OzZu3IiDBw8iLi4O/fv3121Xq9Xo2bMn8vLycOzYMfz8889YvXo1Zs2aJcVbqhDFuW4A8MYbb+jdc5999pluW3W8brVq1cLChQsRHh6O06dPo1OnTujTpw8uXboEgPfakzzrugG8157l1KlT+O6779CoUSO98gq75wQ9EQCxZcsW3XONRiNcXV3F559/ritLTU0VcrlcrFu3TgghxOXLlwUAcerUKd0+O3fuFDKZTMTGxlZY7FJ6/LoJIcTIkSNFnz59nvgaXjetpKQkAUAcPHhQCKG9v0xNTcXGjRt1+1y5ckUAEGFhYUIIIf7++29hZGQkEhISdPusWLFC2Nraitzc3Ip9AxJ5/LoJIUT79u3FpEmTnvgaXjcte3t78eOPP/JeK6GC6yYE77VnycjIEL6+vmLPnj1616oi7znW7JRAVFQUEhIS0KVLF12ZQqFAq1atEBYWBgAICwuDnZ0dmjdvrtunS5cuMDIywokTJyo85srkwIEDcHZ2hp+fH958802kpKTotvG6aaWlpQEAHBwcAADh4eFQqVR695y/vz88PT317rnAwEC4uLjo9gkJCUF6erref56G7PHrVuC3335DjRo1EBAQgJkzZyI7O1u3rbpfN7VajfXr1yMrKwvBwcG814rp8etWgPfak40fPx49e/bUu7eAiv37ZvALgZalhIQEANC76AXPC7YlJCTA2dlZb7uJiQkcHBx0+1RHoaGh6N+/P3x8fHDz5k28//776N69O8LCwmBsbMzrBkCj0WDy5Mlo06YNAgICAGjvJzMzM9jZ2ent+/g9V9Q9WbDN0BV13QBg2LBh8PLygru7O86fP4/p06cjMjISmzdvBlB9r9uFCxcQHBwMpVIJa2trbNmyBQ0aNEBERATvtad40nUDeK89zfr163HmzBmcOnWq0LaK/PvGZIcqxJAhQ3TfBwYGolGjRqhTpw4OHDiAzp07SxhZ5TF+/HhcvHgRR44ckTqUKuVJ1+3R/l6BgYFwc3ND586dcfPmTdSpU6eiw6w0/Pz8EBERgbS0NGzatAkjR47EwYMHpQ6r0nvSdWvQoAHvtSeIiYnBpEmTsGfPHpibm0saC5uxSsDV1RUACvUUT0xM1G1zdXVFUlKS3vb8/Hzcv39ftw8BtWvXRo0aNXDjxg0AvG4TJkzAjh07sH//ftSqVUtX7urqiry8PKSmpurt//g9V9Q9WbDNkD3puhWlVatWAKB3z1XH62ZmZoa6desiKCgICxYsQOPGjfHVV1/xXnuGJ123ovBe0woPD0dSUhKaNWsGExMTmJiY4ODBg/j6669hYmICFxeXCrvnmOyUgI+PD1xdXbFv3z5dWXp6Ok6cOKFruw0ODkZqairCw8N1+/z777/QaDS6XwAC7t69i5SUFLi5uQGovtdNCIEJEyZgy5Yt+Pfff+Hj46O3PSgoCKampnr3XGRkJKKjo/XuuQsXLugli3v27IGtra2umt3QPOu6FSUiIgIA9O656nbdiqLRaJCbm8t7rYQKrltReK9pde7cGRcuXEBERITu0bx5cwwfPlz3fYXdc2XR09qQZGRkiLNnz4qzZ88KAOKLL74QZ8+eFXfu3BFCCLFw4UJhZ2cntm3bJs6fPy/69OkjfHx8RE5Oju4YoaGhomnTpuLEiRPiyJEjwtfXVwwdOlSqt1QhnnbdMjIyxLRp00RYWJiIiooSe/fuFc2aNRO+vr5CqVTqjlEdr9ubb74pFAqFOHDggIiPj9c9srOzdfuMGzdOeHp6in///VecPn1aBAcHi+DgYN32/Px8ERAQILp16yYiIiLErl27hJOTk5g5c6YUb6lCPOu63bhxQ8ybN0+cPn1aREVFiW3btonatWuLdu3a6Y5RHa/bjBkzxMGDB0VUVJQ4f/68mDFjhpDJZOKff/4RQvBee5KnXTfeayXz+Mi1irrnmOw8Zv/+/QJAocfIkSOFENrh5x999JFwcXERcrlcdO7cWURGRuodIyUlRQwdOlRYW1sLW1tbMXr0aJGRkSHBu6k4T7tu2dnZolu3bsLJyUmYmpoKLy8v8cYbb+gNJRSiel63oq4ZALFq1SrdPjk5OeKtt94S9vb2wtLSUvTr10/Ex8frHef27duie/fuwsLCQtSoUUO88847QqVSVfC7qTjPum7R0dGiXbt2wsHBQcjlclG3bl3x7rvvirS0NL3jVLfr9tprrwkvLy9hZmYmnJycROfOnXWJjhC8157kadeN91rJPJ7sVNQ9JxNCiBLXTRERERFVEeyzQ0RERAaNyQ4REREZNCY7REREZNCY7BAREZFBY7JDREREBo3JDhERERk0JjtERERk0JjsEBGVsQMHDkAmk0Emk6Fv374leu2oUaN0r926dWu5xEdU3TDZITJwBR+cT3rMmTNH6hDLnLe3N5YuXSp1GIiMjMTq1at1z0eNGlUo+dm0aRPMzc2xZMkSAMBXX32F+Pj4CoySyPCZSB0AEZWvRz84f//9d8yaNQuRkZG6MmtraynCKjEhBNRqNUxMKu7PVl5eHszMzEr9emdnZ9jZ2T1x+48//ojx48dj5cqVGD16NABAoVBAoVCU+pxEVBhrdogMnKurq+6hUCggk8n0ytavX4/69evD3Nwc/v7+WL58ue61t2/fhkwmw4YNG/Diiy/CwsICLVq0wLVr13Dq1Ck0b94c1tbW6N69O+7du6d7XUENxty5c+Hk5ARbW1uMGzcOeXl5un00Gg0WLFgAHx8fWFhYoHHjxti0aZNue0FT0M6dOxEUFAS5XI4jR47g5s2b6NOnD1xcXGBtbY0WLVpg7969utd16NABd+7cwZQpU3S1VwAwZ84cNGnSRO/aLF26FN7e3oXi/uSTT+Du7g4/Pz8AQExMDAYNGgQ7Ozs4ODigT58+uH379nP9XD777DNMnDgR69ev1yU6RFQ+WLNDVI399ttvmDVrFr799ls0bdoUZ8+exRtvvAErKyuMHDlSt9/s2bOxdOlSeHp64rXXXsOwYcNgY2ODr776CpaWlhg0aBBmzZqFFStW6F6zb98+mJub48CBA7h9+zZGjx4NR0dHfPLJJwCABQsW4Ndff8XKlSvh6+uLQ4cOYcSIEXByckL79u11x5kxYwYWL16M2rVrw97eHjExMejRowc++eQTyOVy/PLLL+jVqxciIyPh6emJzZs3o3Hjxhg7dizeeOONEl+Tffv2wdbWFnv27AEAqFQqhISEIDg4GIcPH4aJiQk+/vhjhIaG4vz586Wq+Zk+fTqWL1+OHTt2oHPnziV+PRGV0HMvYUpEVcaqVauEQqHQPa9Tp45Yu3at3j7z588XwcHBQgghoqKiBADx448/6ravW7dOABD79u3TlS1YsED4+fnpno8cOVI4ODiIrKwsXdmKFSuEtbW1UKvVQqlUCktLS3Hs2DG9c48ZM0YMHTpUCCHE/v37BQCxdevWZ76vhg0bim+++Ub33MvLS3z55Zd6+8yePVs0btxYr+zLL78UXl5eenG7uLiI3NxcXdmaNWuEn5+f0Gg0urLc3FxhYWEhdu/eXWQ8BbE/ePBAr3zkyJHCzMys0PUrCgCxZcuWp+5DRMXDmh2iaiorKws3b97EmDFj9GpA8vPzC/UZadSoke57FxcXAEBgYKBeWVJSkt5rGjduDEtLS93z4OBgZGZmIiYmBpmZmcjOzkbXrl31XpOXl4emTZvqlTVv3lzveWZmJubMmYO//voL8fHxyM/PR05ODqKjo0vy9p8oMDBQr7bm3LlzuHHjBmxsbPT2UyqVuHnzZomP36hRIyQnJ2P27Nlo2bJllekzRVSVMdkhqqYyMzMBAD/88ANatWqlt83Y2Fjvuampqe77gj4wj5dpNJoSn/uvv/5CzZo19bbJ5XK951ZWVnrPp02bhj179mDx4sWoW7cuLCwsMHDgQL3+QEUxMjKCEEKvTKVSFdrv8fNlZmYiKCgIv/32W6F9nZycnnrOotSsWRObNm1Cx44dERoaip07dxZKpIiobDHZIaqmXFxc4O7ujlu3bmH48OFlfvxz584hJycHFhYWAIDjx4/D2toaHh4ecHBwgFwuR3R0tF7/nOI4evQoRo0ahX79+gHQJiOPdxY2MzODWq3WK3NyckJCQgKEELqELSIi4pnna9asGX7//Xc4OzvD1ta2RLE+iZeXFw4ePKhLeHbt2sWEh6gccTQWUTU2d+5cLFiwAF9//TWuXbuGCxcuYNWqVfjiiy+e+9h5eXkYM2YMLl++jL///huzZ8/GhAkTYGRkBBsbG0ybNg1TpkzBzz//jJs3b+LMmTP45ptv8PPPPz/1uL6+vti8eTMiIiJw7tw5DBs2rFCtkre3Nw4dOoTY2FgkJycD0I7SunfvHj777DPcvHkTy5Ytw86dO5/5PoYPH44aNWqgT58+OHz4MKKionDgwAG8/fbbuHv3bqmvj4eHBw4cOICkpCSEhIQgPT291McioqdjskNUjb3++uv48ccfsWrVKgQGBqJ9+/ZYvXo1fHx8nvvYnTt3hq+vL9q1a4fBgwejd+/eehMYzp8/Hx999BEWLFiA+vXrIzQ0FH/99dczz/3FF1/A3t4erVu3Rq9evRASEoJmzZrp7TNv3jzcvn0bderU0TU11a9fH8uXL8eyZcvQuHFjnDx5EtOmTXvm+7C0tMShQ4fg6emJ/v37o379+hgzZgyUSuVz1/TUqlULBw4cQHJyMhMeonIkE483YhMRPadRo0YhNTW12i53cODAAXTs2BEPHjx46qSCTyOTybBly5YSLzdBRIWxZoeIqJzUqlULQ4cOLdFrxo0bxxFaRGWMNTtEVOaqe81OTk4OYmNjAWiX43B1dS32a5OSknTNWW5uboVGhxFRyTHZISIiIoPGZiwiIiIyaEx2iIiIyKAx2SEiIiKDxmSHiIiIDBqTHSIiIjJoTHaIiIjIoDHZISIiIoPGZIeIiIgMGpMdIiIiMmj/D/p7mly2SmGOAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/python/meson.build b/python/meson.build
index 17a7f8d4a..67c6b6782 100644
--- a/python/meson.build
+++ b/python/meson.build
@@ -102,6 +102,7 @@ excess_gibbs_sources = [
   python_lib / 'models' / 'excess_gibbs' / '__init__.py',
   python_lib / 'models' / 'excess_gibbs' / 'nrtl.py',
   python_lib / 'models' / 'excess_gibbs' / 'unifac.py',
+  python_lib / 'models' / 'excess_gibbs' / 'uniquac.py',
 ]
 py.install_sources(excess_gibbs_sources, subdir:python_lib / 'models' / 'excess_gibbs')
 
diff --git a/python/tests/models/gibbs_excess/test_uniquac.py b/python/tests/models/gibbs_excess/test_uniquac.py
new file mode 100644
index 000000000..d8fae95f7
--- /dev/null
+++ b/python/tests/models/gibbs_excess/test_uniquac.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+from yaeos import UNIQUAC
+
+
+def test_ln_gammas():
+    a = np.array(
+        [
+            [0.0, -75.46, -60.15],
+            [120.20, 0.0, 44.22],
+            [120.20, 33.21, 0.0],
+        ]
+    )
+
+    b = np.array(
+        [
+            [0.0, -0.10062, 0.2566],
+            [0.44835, 0.0, -0.01325],
+            [0.44835, 0.124, 0.0],
+        ]
+    )
+
+    c = np.array(
+        [
+            [0.0, -0.0008052, 0.00021],
+            [0.0004704, 0.0, -0.00033],
+            [0.0004704, -0.000247, 0.0],
+        ]
+    )
+
+    d = np.array(
+        [
+            [0.0, -0.001, 0.0002],
+            [-0.001, 0.0, 0.0002],
+            [-0.001, 0.0002, 0.0],
+        ]
+    )
+
+    e = np.array(
+        [
+            [0.0, -0.00001, 0.00001],
+            [-0.00001, 0.0, 0.00001],
+            [-0.00001, 0.00001, 0.0],
+        ]
+    )
+
+    rs = np.array([0.92, 2.1055, 1.5])
+    qs = np.array([1.4, 1.972, 1.4])
+
+    model = UNIQUAC(qs, rs, a, b, c, d, e)
+
+    t = 298.15
+    n = np.array([20.0, 70.0, 10.0])
+
+    ln_gammas = model.ln_gamma(n, t)
+
+    expect = np.array(
+        [-164.62277497059728, -60.906444787104235, -75.52457152449654]
+    )
+
+    assert np.allclose(ln_gammas, expect)
+
+
+def test_give_only_bij():
+    b = np.array(
+        [
+            [0.0, -526.02, -309.64],
+            [318.06, 0.0, 91.532],
+            [-1325.1, -302.57, 0.0],
+        ]
+    )
+
+    rs = np.array([0.92, 2.1055, 3.1878])
+    qs = np.array([1.4, 1.972, 2.4])
+
+    t = 298.15
+
+    model = UNIQUAC(qs, rs, bij=b)
+
+    n = np.array([2.0, 2.0, 8.0])
+
+    gammas = np.exp(model.ln_gamma(n, t))
+
+    excepted = np.array([8.856, 0.860, 1.425])
+
+    assert np.allclose(gammas, excepted, atol=1e-3)
diff --git a/python/yaeos/__init__.py b/python/yaeos/__init__.py
index 872b1d442..c887becc9 100644
--- a/python/yaeos/__init__.py
+++ b/python/yaeos/__init__.py
@@ -5,7 +5,7 @@
 """
 
 from yaeos.lib import yaeos_c
-from yaeos.models.excess_gibbs import NRTL, UNIFACVLE
+from yaeos.models.excess_gibbs import NRTL, UNIFACVLE, UNIQUAC
 from yaeos.models.residual_helmholtz.cubic_eos import (
     MHV,
     PengRobinson76,
@@ -25,5 +25,6 @@
     "QMR",
     "NRTL",
     "UNIFACVLE",
+    "UNIQUAC",
     "MHV",
 ]
diff --git a/python/yaeos/constants.py b/python/yaeos/constants.py
index d9b01c6bb..498461249 100644
--- a/python/yaeos/constants.py
+++ b/python/yaeos/constants.py
@@ -1,5 +1,4 @@
-"""Constants module.
-"""
+"""Constants module."""
 
 # Ideal gas constant in L bar / K mol
 R = 0.08314462618
diff --git a/python/yaeos/core.py b/python/yaeos/core.py
index 9dd113fee..291be495b 100644
--- a/python/yaeos/core.py
+++ b/python/yaeos/core.py
@@ -15,16 +15,16 @@
 class GeModel(ABC):
     """Excess Gibbs (Ge) model abstract class."""
 
-    def ln_gamma(self, n, T):
+    def ln_gamma(self, moles, temperature):
         r"""Calculate activity coefficients.
 
         Calculate :math:`\ln \gamma_i(n,T)` vector.
 
         Parameters
         ----------
-        n : array_like
+        moles : array_like
             Moles number vector [mol]
-        T : float
+        temperature : float
             Temperature [K]
 
         Returns
@@ -47,10 +47,9 @@ def ln_gamma(self, n, T):
 
             nrtl = NRTL(a, b, c)
 
-            print(nrtl.ln_gamma([5.0, 5.6], 300.0)
+            print(nrtl.ln_gamma([5.0, 5.6], 300.0))
         """
-
-        return yaeos_c.ln_gamma(self.id, n, T)
+        return yaeos_c.ln_gamma(self.id, moles, temperature)
 
     def __del__(self) -> None:
         """Delete the model from the available models list (Fortran side)."""
diff --git a/python/yaeos/models/__init__.py b/python/yaeos/models/__init__.py
index ae5a02b3b..12386bf3b 100644
--- a/python/yaeos/models/__init__.py
+++ b/python/yaeos/models/__init__.py
@@ -5,6 +5,7 @@
 - excess_gibbs: Excess Gibbs energy models
     - NRTL: non-random two-liquid model
     - UNIFACVLE: Original UNIFAC VLE model
+    - UNIQUAC: UNIversal QUAsiChemical Excess Gibbs free energy model
 
 - residual_helmholtz: Residual Helmholtz energy models
     - Cubic EoS:
diff --git a/python/yaeos/models/excess_gibbs/__init__.py b/python/yaeos/models/excess_gibbs/__init__.py
index 24bfe5ff8..79e2065de 100644
--- a/python/yaeos/models/excess_gibbs/__init__.py
+++ b/python/yaeos/models/excess_gibbs/__init__.py
@@ -4,10 +4,13 @@
 
 - excess_gibbs: Excess Gibbs energy models
     - NRTL: non-random two-liquid model
+    - UNIFACVLE: Original UNIFAC VLE model
+    - UNIQUAC: UNIversal QUAsiChemical Excess Gibbs free energy model
 """
 
 from .nrtl import NRTL
 from .unifac import UNIFACVLE
+from .uniquac import UNIQUAC
 
 
-__all__ = ["NRTL", "UNIFACVLE"]
+__all__ = ["NRTL", "UNIFACVLE", "UNIQUAC"]
diff --git a/python/yaeos/models/excess_gibbs/unifac.py b/python/yaeos/models/excess_gibbs/unifac.py
index bef001f77..5cb35b672 100644
--- a/python/yaeos/models/excess_gibbs/unifac.py
+++ b/python/yaeos/models/excess_gibbs/unifac.py
@@ -1,5 +1,4 @@
-"""UNIFAC Module.
-"""
+"""UNIFAC Module."""
 
 import numpy as np
 
@@ -14,6 +13,22 @@ class UNIFACVLE(GeModel):
     ----------
     molecules : list of dict
         List of dicts with the groups and their amounts for each molecule.
+
+    Example
+    -------
+    .. code-block:: python
+
+        from yaeos import UNIFACVLE
+
+        # Groups for water and ethanol
+        water = {16: 1}
+        ethanol = {1: 1, 2: 1, 14: 1}
+
+        groups = [water, ethanol]
+
+        model = UNIFAVLE(groups)
+
+        model.ln_gamma([0.5, 0.5], 298.15)
     """
 
     def __init__(self, molecules) -> None:
diff --git a/python/yaeos/models/excess_gibbs/uniquac.py b/python/yaeos/models/excess_gibbs/uniquac.py
new file mode 100644
index 000000000..4565be39f
--- /dev/null
+++ b/python/yaeos/models/excess_gibbs/uniquac.py
@@ -0,0 +1,117 @@
+"""UNIQUAC (UNIversal QUAsiChemical) Excess Gibbs free energy model."""
+
+import numpy as np
+
+from yaeos.core import GeModel
+from yaeos.lib import yaeos_c
+
+
+class UNIQUAC(GeModel):
+    """UNIQUAC (UNIversal QUAsiChemical) Excess Gibbs free energy model.
+
+    Please refer to the `yaeos` user documentation for an in-depth look at the
+    model's information: https://ipqa-research.github.io/yaeos/page/index.html
+
+    Parameters
+    ----------
+    qs : array_like
+        Molecule's relative areas :math:`Q_i`
+    rs : array_like
+        Molecule's relative volumes :math:`R_i`
+    aij : array_like
+        Interaction parameters matrix :math:`a_{ij}` zero matrix if no
+        provided, by default None
+    bij : array_like
+        Interaction parameters matrix :math:`b_{ij}` zero matrix if no
+        provided, by default None
+    cij : array_like
+        Interaction parameters matrix :math:`c_{ij}` zero matrix if no
+        provided, by default None
+    dij : array_like
+        Interaction parameters matrix :math:`d_{ij}` zero matrix if no
+        provided, by default None
+    eij : array_like
+        Interaction parameters matrix :math:`e_{ij}` zero matrix if no
+        provided, by default None
+
+    Attributes
+    ----------
+    qs : array_like
+        Molecule's relative areas :math:`Q_i`
+    rs : array_like
+        Molecule's relative volumes :math:`R_i`
+    aij : array_like
+        Interaction parameters matrix :math:`a_{ij}`
+    bij : array_like
+        Interaction parameters matrix :math:`b_{ij}`
+    cij : array_like
+        Interaction parameters matrix :math:`c_{ij}`
+    dij : array_like
+        Interaction parameters matrix :math:`d_{ij}`
+    eij : array_like
+        Interaction parameters matrix :math:`e_{ij}`
+
+    Example
+    -------
+    .. code-block:: python
+
+        import numpy as np
+
+        from yaeos import UNIQUAC
+
+        b = np.array(
+            [
+                [0.0, -526.02, -309.64],
+                [318.06, 0.0, 91.532],
+                [-1325.1, -302.57, 0.0],
+            ]
+        )
+
+        rs = np.array([0.92, 2.1055, 3.1878])
+        qs = np.array([1.4, 1.972, 2.4])
+
+        t = 298.15
+
+        model = UNIQUAC(qs, rs, bij=b)
+
+        n = np.array([2.0, 2.0, 8.0])
+
+        gammas = np.exp(model.ln_gamma(n, t)) # [8.856, 0.860, 1.425]
+    """
+
+    def __init__(
+        self, qs, rs, aij=None, bij=None, cij=None, dij=None, eij=None
+    ) -> None:
+        self.qs = qs
+        self.rs = rs
+
+        nc = len(qs)
+
+        if aij is not None:
+            self.aij = aij
+        else:
+            self.aij = np.zeros((nc, nc), order="F")
+
+        if bij is not None:
+            self.bij = bij
+        else:
+            self.bij = np.zeros((nc, nc), order="F")
+
+        if cij is not None:
+            self.cij = cij
+        else:
+            self.cij = np.zeros((nc, nc), order="F")
+
+        if dij is not None:
+            self.dij = dij
+        else:
+            self.dij = np.zeros((nc, nc), order="F")
+
+        if eij is not None:
+            self.eij = eij
+        else:
+            self.eij = np.zeros((nc, nc), order="F")
+
+        self.id = yaeos_c.uniquac(
+            qs, rs, self.aij, self.bij, self.cij, self.dij, self.eij
+        )
diff --git a/src/math/math.f90 b/src/math/math.f90
index d9c91a568..d5b1af54a 100644
--- a/src/math/math.f90
+++ b/src/math/math.f90
@@ -86,6 +86,58 @@ function dx_to_dn(x, dx) result(dn)
       dn = dx - sum_xdx
    end function dx_to_dn
 
+   function derivative_dxk_dni(n) result(dxk_dni)
+      !! # derivative_dxk_dni
+      !!
+      !! # Description
+      !! Calculate the mole fraction first derivatives respect to mole numbers
+      real(pr), intent(in) :: n(:)
+      real(pr) :: dxk_dni(size(n), size(n))
+
+      real(pr) :: n_tot
+      integer :: nc, k, i
+
+      n_tot = sum(n)
+      nc = size(n)
+
+      dxk_dni = 0.0_pr
+      do concurrent(k=1:nc, i=1:nc)
+         if (k == i) then
+            dxk_dni(k,i) = (n_tot - n(i)) / n_tot**2
+         else
+            dxk_dni(k,i) = -n(k) / n_tot**2
+         end if
+      end do
+   end function derivative_dxk_dni
+
+   function derivative_d2xk_dnidnj(n) result(d2xk_dnidnj)
+      !! # derivative_d2xk_dnidnj
+      !!
+      !! # Description
+      !! Calculate the mole fraction second derivatives respect to mole numbers
+      real(pr), intent(in) :: n(:)
+      real(pr) :: d2xk_dnidnj(size(n), size(n), size(n))
+
+      real(pr) :: n_tot
+      integer :: nc, k, i, j
+
+      n_tot = sum(n)
+      nc = size(n)
+
+      d2xk_dnidnj = 0.0_pr
+      do concurrent (k=1:nc, i=1:nc, j=1:nc)
+         if (i==k .and. j==k) then
+            d2xk_dnidnj(k,i,j) = -2 * (n_tot - n(i)) / n_tot**3
+         else if (i==k) then
+            d2xk_dnidnj(k,i,j) = (2 * n(i) - n_tot) / n_tot**3
+         else if (j==k) then
+            d2xk_dnidnj(k,i,j) = (2 * n(j) - n_tot) / n_tot**3
+         else
+            d2xk_dnidnj(k,i,j) = 2 * n(k) / n_tot**3
+         end if
+      end do
+   end function derivative_d2xk_dnidnj
+
    subroutine newton_1d(f, x, tol, max_iters)
       procedure(f_1d) :: f
       real(pr), intent(in out) :: x
diff --git a/src/models/excess_gibbs/implementations.f90 b/src/models/excess_gibbs/implementations.f90
index 18ddccf64..a09e95d40 100644
--- a/src/models/excess_gibbs/implementations.f90
+++ b/src/models/excess_gibbs/implementations.f90
@@ -2,6 +2,8 @@ module yaeos__models_ge_implementations
    use yaeos__models_ge_NRTL, only: NRTL
    use yaeos__models_ge_group_contribution_unifac, only: &
       Groups, setup_unifac, UNIFAC, excess_gibbs
+   use yaeos__models_ge_uniquac, only: setup_uniquac, UNIQUAC
    use yaeos__models_ge_group_contribution_psrk, only: setup_psrk
+
    implicit none
 end module yaeos__models_ge_implementations
diff --git a/src/models/excess_gibbs/uniquac.f90 b/src/models/excess_gibbs/uniquac.f90
new file mode 100644
index 000000000..b729a6544
--- /dev/null
+++ b/src/models/excess_gibbs/uniquac.f90
@@ -0,0 +1,596 @@
+module yaeos__models_ge_uniquac
+   !! # UNIQUAC module
+   !! UNIQUAC (**uni**versal **qua**si**c**hemical) Excess Gibbs free energy
+   !! model.
+   !!
+   !! ## References
+   !! 1. Maurer, G., & Prausnitz, J. M. (1978). On the derivation and extension
+   !! of the UNIQUAC equation. Fluid Phase Equilibria, 2(2), 91-99.
+   !! 2. Gmehling, Jurgen, Barbel Kolbe, Michael Kleiber, and Jurgen Rarey.
+   !! Chemical Thermodynamics for Process Simulation. 1st edition. Weinheim:
+   !! Wiley-VCH, 2012.
+   !! 3. Caleb Bell and Contributors (2016-2024). Thermo: Chemical properties
+   !! component of Chemical Engineering Design Library (ChEDL)
+   !! https://github.com/CalebBell/thermo.
+   !!
+   use yaeos__constants, only: pr, R
+   use yaeos__models_ge, only: GeModel
+   use yaeos__math, only: derivative_dxk_dni, derivative_d2xk_dnidnj
+   implicit none
+
+   type, extends(GeModel) :: UNIQUAC
+      !! # UNIQUAC model
+      !!
+      !! # Description
+      !! UNIQUAC (**uni**versal **qua**si**c**hemical) Excess Gibbs free energy
+      !! model.
+      !!
+      !! \[
+      !! \frac{G^E}{RT} = \sum_k n_k \ln\frac{\phi_k}{x_k}
+      !! + \frac{z}{2}\sum_k q_k n_k \ln\frac{\theta_k}{\phi_k}
+      !! - \sum_k q_k n_k \ln\left(\sum_l \theta_l \tau_{kl} \right)
+      !! \]
+      !!
+      !! With:
+      !!
+      !! \[ x_k = \frac{n_k}{\sum_l n_l} \]
+      !!
+      !! \[ \phi_k = \frac{r_k n_k}{\sum_l r_l n_l} \]
+      !!
+      !! \[ \theta_k = \frac{q_k n_k}{\sum_l q_l n_l} \]
+      !!
+      !! \[ \tau_{lk} = \exp \left[\frac{-\Delta U_{lk}}{R T} \right] \]
+      !!
+      !! \[
+      !! \frac{-\Delta U_{lk}}{R T} = a_{lk}+\frac{b_{lk}}{T}+c_{lk}\ln T +
+      !! d_{lk}T + e_{lk}{T^2}
+      !! \]
+      !!
+      !! # Example
+      !!
+      !! ```fortran
+      !! use yaeos, only: pr, setup_uniquac, UNIQUAC
+      !!
+      !! integer, parameter :: nc = 3
+      !!
+      !! real(pr) :: rs(nc), qs(nc)
+      !! real(pr) :: b(nc, nc)
+      !! real(pr) :: n(nc)
+      !!
+      !! real(pr) :: ln_gammas(nc), T
+      !!
+      !! type(UNIQUAC) :: model
+      !!
+      !! rs = [0.92_pr, 2.1055_pr, 3.1878_pr]
+      !! qs = [1.4_pr, 1.972_pr, 2.4_pr]
+      !!
+      !! T = 298.15_pr
+      !!
+      !! ! Calculate bij from DUij. We need -DU/R to get bij
+      !! b(1, :) = [0.0_pr, -526.02_pr, -309.64_pr]
+      !! b(2, :) = [318.06_pr, 0.0_pr, 91.532_pr]
+      !! b(3, :) = [-1325.1_pr, -302.57_pr, 0.0_pr]
+      !!
+      !! model = setup_uniquac(qs, rs, bij=b)
+      !!
+      !! n = [0.8_pr, 0.1_pr, 0.2_pr]
+      !!
+      !! call model%ln_activity_coefficient(n, T, ln_gammas)
+      !!
+      !! print *, exp(ln_gammas) ! [8.856, 0.860, 1.425]
+      !!
+      !! ```
+      !!
+      real(pr) :: z = 10.0_pr
+      !! Model coordination number
+      real(pr), allocatable :: qs(:)
+      !! Molecule's relative areas \(Q_i\)
+      real(pr), allocatable :: rs(:)
+      !! Molecule's relative volumes \(R_i\)
+      real(pr), allocatable :: aij(:,:)
+      !! Interaction parameters matrix \(a_{ij}\)
+      real(pr), allocatable :: bij(:,:)
+      !! Interaction parameters matrix \(b_{ij}\)
+      real(pr), allocatable :: cij(:,:)
+      !! Interaction parameters matrix \(c_{ij}\)
+      real(pr), allocatable :: dij(:,:)
+      !! Interaction parameters matrix \(d_{ij}\)
+      real(pr), allocatable :: eij(:,:)
+      !! Interaction parameters matrix \(e_{ij}\)
+   contains
+      procedure :: excess_gibbs
+      procedure :: taus
+   end type UNIQUAC
+
+contains
+   subroutine excess_gibbs(self, n, T, Ge, GeT, GeT2, Gen, GeTn, Gen2)
+      !! Calculate the excess Gibbs free energy and its derivatives of the
+      !! UNIQUAC model.
+      !!
+      class(UNIQUAC), intent(in) :: self
+      !! UNIQUAC model
+      real(pr), intent(in) :: n(:)
+      !! Moles vector [mol]
+      real(pr), intent(in) :: T
+      !! Temperature [K]
+      real(pr), optional, intent(out) :: Ge
+      !! Excess Gibbs energy
+      real(pr), optional, intent(out) :: GeT
+      !! \(\frac{dG^E}{dT}\)
+      real(pr), optional, intent(out) :: GeT2
+      !! \(\frac{d^2G^E}{dT^2}\)
+      real(pr), optional, intent(out) :: Gen(size(n))
+      !! \(\frac{dG^E}{dn}\)
+      real(pr), optional, intent(out) :: GeTn(size(n))
+      !! \(\frac{d^2G^E}{dTdn}\)
+      real(pr), optional, intent(out) :: Gen2(size(n), size(n))
+      !! \(\frac{d^2G^E}{dn^2}\)
+
+      ! Main terms
+      real(pr) :: thetak(size(n))
+      real(pr) :: dthetak_dni(size(n), size(n))
+      real(pr) :: d2thetak_dnidnj(size(n), size(n), size(n))
+
+      real(pr) :: phik(size(n))
+      real(pr) :: dphik_dn(size(n), size(n))
+      real(pr) :: d2phik_dnidnj(size(n), size(n), size(n))
+
+      real(pr) :: tau(size(n), size(n))
+      real(pr) :: dtau(size(n), size(n))
+      real(pr) :: d2tau(size(n), size(n))
+
+      ! Indexes and lofical for optional arguments
+      integer :: i, j, k
+
+      logical :: dt, dt2, dn, dtn, dn2, dt_or_dtn
+
+      ! Auxiliars
+      integer :: nc
+      real(pr) :: n_tot
+      real(pr) :: xk(size(n))
+      real(pr) :: r_i, q_i, r_j, q_j, r_k, q_k
+      real(pr) :: sum_nq, sum_nr
+      real(pr) :: Ge_comb, Ge_res
+      real(pr) :: Ge_aux
+
+      ! Auxiliars for temperature derivatives
+      real(pr) :: sum_thetal_tau_lk(size(n))
+      real(pr) :: sum_theta_l_dtau_lk(size(n))
+      real(pr) :: sum_theta_l_d2tau_lk(size(n))
+
+      real(pr) :: GeT_aux, GeT2_aux, diff_aux(size(n))
+
+      ! Auxiliars for compositionals derivatives
+      real(pr) :: Gen_comb(size(n)), Gen_res(size(n))
+      real(pr) :: Gen_aux(size(n))
+
+      real(pr) :: dxk_dni(size(n), size(n))
+      real(pr) :: dln_nk_dni(size(n), size(n))
+
+      ! Auxiliars for second compositionals derivatives
+      ! terms of the math expression
+      real(pr) :: trm1, trm2, trm3, trm4, trm5, trm6
+
+      real(pr) :: d2xk_dnidnj(size(n), size(n), size(n))
+      real(pr) :: sum_d2theta_tau_lk(size(n))
+      real(pr) :: Gen2_aux(size(n), size(n))
+
+      ! cross derivative auxiliars
+      real(pr) :: sum_dtheta_l_dtau_lk(size(n), size(n))
+      real(pr) :: sum_dtheta_l_tau_lk(size(n), size(n))
+      real(pr) :: GeTn_aux(size(n))
+
+      ! =======================================================================
+      ! Logical variables
+      ! -----------------------------------------------------------------------
+      dt = present(GeT)
+      dt2 = present(GeT2)
+      dn = present(Gen)
+      dtn = present(GeTn)
+      dn2 = present(Gen2)
+
+      dt_or_dtn = dt .or. dtn
+
+      ! =======================================================================
+      ! Auxiliars
+      ! -----------------------------------------------------------------------
+      nc = size(n)
+
+      n_tot = sum(n)
+
+      xk = n / n_tot
+
+      sum_nq = sum(n * self%qs)
+      sum_nr = sum(n * self%rs)
+      ! =======================================================================
+      ! tau call (temperature dependence term)
+      ! -----------------------------------------------------------------------
+      if (dt_or_dtn .and. .not. dt2) call self%taus(T, tau, dtau)
+      if (.not. dt_or_dtn .and. dt2) call self%taus(T, tau, dtau, d2tau)
+      if (dt_or_dtn .and. dt2) call self%taus(T, tau, dtau, d2tau)
+      if (.not. dt_or_dtn .and. .not. dt2) call self%taus(T, tau)
+
+      ! =======================================================================
+      ! Mole fractions derivatives
+      ! -----------------------------------------------------------------------
+      if (dn .or. dtn .or. dn2) then
+         dxk_dni = derivative_dxk_dni(n)
+      end if
+
+      if (dn2) then
+         d2xk_dnidnj = derivative_d2xk_dnidnj(n)
+      end if
+
+      ! =======================================================================
+      ! theta_k
+      ! -----------------------------------------------------------------------
+      thetak = n * self%qs / sum_nq
+
+      if (dn .or. dn2 .or. dtn) then
+         dthetak_dni = 0
+         do concurrent(k=1:nc, i=1:nc)
+            if (i == k) then
+               dthetak_dni(i,i) = &
+                  (self%qs(i) * sum_nq - n(i) * self%qs(i)**2) / sum_nq**2
+            else
+               dthetak_dni(k,i) = -n(k) * self%qs(i) * self%qs(k) / sum_nq**2
+            end if
+         end do
+      end if
+
+      if (dn2) then
+         d2thetak_dnidnj = 0
+         do concurrent(k=1:nc, i=1:nc, j=1:nc)
+            if (i==k .and. j==k) then
+               q_i = self%qs(i)
+
+               d2thetak_dnidnj(k,i,j) = (&
+                  2.0_pr * (q_i**3 * n(i) - q_i**2 * sum_nq) / sum_nq**3 &
+                  )
+            else if (i==k) then
+               q_i = self%qs(i)
+               q_j = self%qs(j)
+
+               d2thetak_dnidnj(k,i,j) = (&
+                  (2.0_pr * n(i) * q_i**2 * q_j - q_i*q_j*sum_nq) / sum_nq**3 &
+                  )
+            else if (j==k) then
+               q_i = self%qs(i)
+               q_j = self%qs(j)
+
+               d2thetak_dnidnj(k,i,j) = (&
+                  (2.0_pr * n(j) * q_j**2 * q_i - q_i*q_j*sum_nq) / sum_nq**3 &
+                  )
+            else
+               q_i = self%qs(i)
+               q_j = self%qs(j)
+               q_k = self%qs(k)
+
+               d2thetak_dnidnj(k,i,j) = (&
+                  2.0_pr * n(k) * q_k * q_i * q_j / sum_nq**3 &
+                  )
+            end if
+         end do
+      end if
+
+      ! =======================================================================
+      ! phi_k
+      ! -----------------------------------------------------------------------
+      phik = n * self%rs / sum_nr
+
+      if (dn .or. dn2 .or. dtn) then
+         dphik_dn = 0
+         do concurrent(k=1:nc, i=1:nc)
+            if (i == k) then
+               dphik_dn(i,i) = &
+                  (-n(i) * self%rs(i)**2 + self%rs(i) * sum_nr) / sum_nr**2
+            else
+               dphik_dn(k,i) = -n(k) * self%rs(i) * self%rs(k) / sum_nr**2
+            end if
+         end do
+      end if
+
+      if (dn2) then
+         d2phik_dnidnj = 0
+         do concurrent(k=1:nc, i=1:nc, j=1:nc)
+            if (i==k .and. j==k) then
+               r_i = self%rs(i)
+
+               d2phik_dnidnj(k,i,j) = (&
+                  2.0_pr * (r_i**3 * n(i) - r_i**2 * sum_nr) / sum_nr**3 &
+                  )
+            else if (i==k) then
+               r_i = self%rs(i)
+               r_j = self%rs(j)
+
+               d2phik_dnidnj(k,i,j) = (&
+                  (2.0_pr * n(i) * r_i**2 * r_j - r_j*r_i*sum_nr) / sum_nr**3 &
+                  )
+            else if (j==k) then
+               r_i = self%rs(i)
+               r_j = self%rs(j)
+
+               d2phik_dnidnj(k,i,j) = (&
+                  (2.0_pr * n(j) * r_j**2 * r_i - r_j*r_i*sum_nr) / sum_nr**3 &
+                  )
+            else
+               r_i = self%rs(i)
+               r_j = self%rs(j)
+               r_k = self%rs(k)
+
+               d2phik_dnidnj(k,i,j) = (&
+                  2.0_pr * n(k) * r_k * r_i * r_j / sum_nr**3 &
+                  )
+            end if
+         end do
+      end if
+
+      ! ========================================================================
+      ! Ge
+      ! ------------------------------------------------------------------------
+      ! Combinatorial term
+      Ge_comb = ( &
+         sum(n * log(phik / xk)) &
+         + self%z / 2.0_pr * sum(n * self%qs * log(thetak / phik)) &
+         )
+
+      ! Residual term
+      sum_thetal_tau_lk = 0.0_pr
+
+      do k=1,nc
+         sum_thetal_tau_lk(k) = sum(thetak * tau(:,k))
+      end do
+
+      Ge_res = -sum(n * self%qs * log(sum_thetal_tau_lk))
+
+      Ge_aux = R * T * (Ge_comb + Ge_res)
+
+      ! ========================================================================
+      ! Ge Derivatives
+      ! ------------------------------------------------------------------------
+      if (dn .or. dtn .or. dn2) then
+         do concurrent (k=1:nc, i=1:nc)
+            sum_dtheta_l_tau_lk(i,k) = sum(dthetak_dni(:,i) * tau(:,k))
+         end do
+      end if
+
+      ! Compositional derivarives
+      ! dn
+      if (dn .or. dtn) then
+         do i=1,nc
+            ! Combinatorial term
+            Gen_comb(i) = (&
+               log(phik(i) / xk(i)) + sum(n * (dphik_dn(:,i) / phik - dxk_dni(:,i) / xk)) &
+               + self%z / 2.0_pr * self%qs(i) * log(thetak(i) / phik(i)) &
+               + self%z / 2.0_pr * sum(n * self%qs * (dthetak_dni(:,i) / thetak - dphik_dn(:,i) / phik)) &
+               )
+
+            ! Residual term
+            Gen_res(i) = (&
+               -self%qs(i) * log(sum_thetal_tau_lk(i)) &
+               -sum(n * self%qs * sum_dtheta_l_tau_lk(i,:) / sum_thetal_tau_lk) &
+               )
+         end do
+
+         Gen_aux = R * T * (Gen_comb + Gen_res)
+      end if
+
+      ! dn2
+      if (dn2) then
+         do concurrent(i=1:nc, j=1:nc)
+            trm1 = dphik_dn(i,j) / phik(i) - dxk_dni(i,j) / xk(i)
+
+            trm2 = (&
+               dphik_dn(j,i) / phik(j) - dxk_dni(j,i) / xk(j) &
+               + sum(n * (d2phik_dnidnj(:,i,j) * phik - dphik_dn(:,i) * dphik_dn(:,j)) / phik**2) &
+               - sum(n * (d2xk_dnidnj(:,i,j) * xk - dxk_dni(:,i) * dxk_dni(:,j)) / xk**2) &
+               )
+
+            trm3 = self%z / 2 * self%qs(i) * (dthetak_dni(i,j) / thetak(i) - dphik_dn(i,j) / phik(i))
+
+            trm4 = (&
+               self%z / 2 * self%qs(j) * (dthetak_dni(j,i) / thetak(j) - dphik_dn(j,i) / phik(j)) &
+               + self%z / 2 * sum(&
+               self%qs * n * (d2thetak_dnidnj(:,i,j) * thetak - dthetak_dni(:,i) * dthetak_dni(:,j)) / thetak**2 &
+               ) &
+               - self%z / 2 * sum(&
+               self%qs * n * (d2phik_dnidnj(:,i,j) * phik - dphik_dn(:,i) * dphik_dn(:,j)) / phik**2 &
+               ) &
+               )
+
+            trm5 = -self%qs(i) * (sum(dthetak_dni(:,j) * tau(:,i)) / sum_thetal_tau_lk(i))
+
+            do k=1,nc
+               sum_d2theta_tau_lk(k) = sum(d2thetak_dnidnj(:,i,j) * tau(:,k))
+            end do
+
+            trm6 = (&
+               -self%qs(j) * (sum(dthetak_dni(:,i) * tau(:,j)) / sum_thetal_tau_lk(j)) &
+               -sum(self%qs * n * (&
+               sum_d2theta_tau_lk * sum_thetal_tau_lk &
+               - sum_dtheta_l_tau_lk(i,:) * sum_dtheta_l_tau_lk(j,:) &
+               ) / sum_thetal_tau_lk**2) &
+               )
+
+            Gen2_aux(i,j) = R * T * (trm1 + trm2 + trm3 + trm4 + trm5 + trm6)
+         end do
+      end if
+
+      ! Temperature derivatives
+      if (dt .or. dt2 .or. dtn) then
+         sum_theta_l_dtau_lk = 0.0_pr
+
+         do k=1,nc
+            sum_theta_l_dtau_lk(k) = sum(thetak * dtau(:,k))
+         end do
+      end if
+
+      if (dt) then
+         GeT_aux = ( &
+            Ge_aux/T - R*T*sum(n * self%qs * sum_theta_l_dtau_lk / sum_thetal_tau_lk)&
+            )
+      end if
+
+      if (dt2) then
+         sum_theta_l_d2tau_lk = 0.0_pr
+
+         do k=1,nc
+            sum_theta_l_d2tau_lk(k) = sum(thetak * d2tau(:,k))
+         end do
+
+         diff_aux = (&
+            sum_theta_l_d2tau_lk / sum_thetal_tau_lk &
+            - (sum_theta_l_dtau_lk / sum_thetal_tau_lk)**2 &
+            )
+
+         GeT2_aux = -R * ( &
+            T * sum(self%qs * n * diff_aux) &
+            + 2.0_pr*sum(self%qs * n * sum_theta_l_dtau_lk / sum_thetal_tau_lk)&
+            )
+      end if
+
+      ! Cross derivative Tn
+      if (dtn) then
+         do concurrent (k=1:nc, i=1:nc)
+            sum_dtheta_l_dtau_lk(i,k) = sum(dthetak_dni(:,i) * dtau(:,k))
+         end do
+      end if
+
+      if (dtn) then
+         do i=1,nc
+            GeTn_aux(i) = ( &
+               1.0_pr / T  * Gen_aux(i) &
+               -R * T * (&
+               self%qs(i) * sum_theta_l_dtau_lk(i) / sum_thetal_tau_lk(i) &
+               + sum(n * self%qs * (&
+               sum_dtheta_l_dtau_lk(i,:) * sum_thetal_tau_lk &
+               - sum_theta_l_dtau_lk * sum_dtheta_l_tau_lk(i,:)) &
+               / sum_thetal_tau_lk**2) &
+               ) &
+               )
+         end do
+      end if
+      ! =======================================================================
+      ! Excess Gibbs energy returns
+      ! -----------------------------------------------------------------------
+      if (present(Ge)) Ge = Ge_aux
+      if (dt) GeT = GeT_aux
+      if (dt2) GeT2 = GeT2_aux
+      if (dn) Gen = Gen_aux
+      if (dtn) GeTn = GeTn_aux
+      if (dn2) Gen2 = Gen2_aux
+   end subroutine excess_gibbs
+
+   subroutine taus(self, T, tau, tauT, tauT2)
+      !! Calculate the temperature dependence term of the UNIQUAC model.
+      !!
+      class(UNIQUAC), intent(in) :: self
+      !! UNIQUAC model
+      real(pr), intent(in) :: T
+      !! Temperature [K]
+      real(pr), optional, intent(out) :: tau(size(self%qs), size(self%qs))
+      !! UNIQUAC temperature dependence term
+      real(pr), optional, intent(out) :: tauT(size(self%qs), size(self%qs))
+      !! \(\frac{d\tau_{ij}}{dT}\)
+      real(pr), optional, intent(out) :: tauT2(size(self%qs), size(self%qs))
+      !! \(\frac{d^2\tau_{ij}}{dT^2}\)
+
+      ! aux
+      real(pr) :: tau_aux(size(self%qs), size(self%qs))
+      real(pr) :: u(size(self%qs), size(self%qs))
+      real(pr) :: du(size(self%qs), size(self%qs))
+      real(pr) :: d2u(size(self%qs), size(self%qs))
+
+      ! Logical
+      logical :: tt, dt, dt2
+
+      tt = present(tau)
+      dt = present(tauT)
+      dt2 = present(tauT2)
+
+      ! temperature function
+      u = self%aij + self%bij/T + self%cij*log(T) + self%dij*T + self%eij*T**2
+
+      ! tau_ij
+      tau_aux = exp(u)
+
+      ! dT
+      if (dt .or. dt2) then
+         du = -self%bij / T**2 + self%cij / T + self%dij + 2.0_pr * T * self%eij
+      end if
+
+      ! d2T
+      if (dt2) then
+         d2u = 2.0_pr * self%bij / T**3 - self%cij / T**2 + 2.0_pr * self%eij
+      end if
+
+
+      if (tt) tau = tau_aux
+      if (dt) tauT = tau_aux * du
+      if (dt2) tauT2 = tau_aux * du**2 + tau_aux * d2u
+   end subroutine taus
+
+   type(UNIQUAC) function setup_uniquac(qs, rs, aij, bij, cij, dij, eij)
+      !! Instantiate a UNIQUAC model.
+      !!
+      !! Non provided interaction parameters are set to zero matrices.
+      !!
+      real(pr), intent(in) :: qs(:)
+      !! Molecule's relative volumes \(Q_i\)
+      real(pr), intent(in) :: rs(size(qs))
+      !! Molecule's relative areas \(R_i\)
+      real(pr), optional, intent(in) :: aij(size(qs),size(qs))
+      !! Interaction parameters matrix \(a_{ij}\), zero matrix if no provided.
+      real(pr), optional, intent(in) :: bij(size(qs),size(qs))
+      !! Interaction parameters matrix \(b_{ij}\), zero matrix if no provided.
+      real(pr), optional, intent(in) :: cij(size(qs),size(qs))
+      !! Interaction parameters matrix \(c_{ij}\), zero matrix if no provided.
+      real(pr), optional, intent(in) :: dij(size(qs),size(qs))
+      !! Interaction parameters matrix \(d_{ij}\), zero matrix if no provided.
+      real(pr), optional, intent(in) :: eij(size(qs),size(qs))
+      !! Interaction parameters matrix \(e_{ij}\), zero matrix if no provided.
+
+      ! aij
+      if (present(aij)) then
+         setup_uniquac%aij = aij
+      else
+         allocate(setup_uniquac%aij(size(qs),size(qs)))
+         setup_uniquac%aij = 0.0_pr
+      end if
+
+      ! bij
+      if (present(bij)) then
+         setup_uniquac%bij = bij
+      else
+         allocate(setup_uniquac%bij(size(qs),size(qs)))
+         setup_uniquac%bij = 0.0_pr
+      end if
+
+      ! cij
+      if (present(cij)) then
+         setup_uniquac%cij = cij
+      else
+         allocate(setup_uniquac%cij(size(qs),size(qs)))
+         setup_uniquac%cij = 0.0_pr
+      end if
+
+      ! dij
+      if (present(dij)) then
+         setup_uniquac%dij = dij
+      else
+         allocate(setup_uniquac%dij(size(qs),size(qs)))
+         setup_uniquac%dij = 0.0_pr
+      end if
+
+      ! eij
+      if (present(eij)) then
+         setup_uniquac%eij = eij
+      else
+         allocate(setup_uniquac%eij(size(qs),size(qs)))
+         setup_uniquac%eij = 0.0_pr
+      end if
+
+      setup_uniquac%qs = qs
+      setup_uniquac%rs = rs
+   end function setup_uniquac
+end module yaeos__models_ge_uniquac
diff --git a/test/test_implementations/ge_models/test_uniquac.f90 b/test/test_implementations/ge_models/test_uniquac.f90
new file mode 100644
index 000000000..9059e779e
--- /dev/null
+++ b/test/test_implementations/ge_models/test_uniquac.f90
@@ -0,0 +1,432 @@
+module test_uniquac
+   use yaeos, only: pr
+   use testdrive, only: new_unittest, unittest_type, error_type, check
+   use auxiliar_functions, only: allclose, rel_error
+   implicit none
+
+contains
+   subroutine collect_suite(testsuite)
+      !> Collection of tests
+      type(unittest_type), allocatable, intent(out) :: testsuite(:)
+
+      testsuite = [ &
+         new_unittest("Test UNIQUAC consistency mixture", test_uniquac_cons_mix), &
+         new_unittest("Test UNIQUAC consistency pure", test_uniquac_cons_pure), &
+         new_unittest("Test UNIQUAC against Caleb Bell's Thermo lib", test_against_caleb_thermo), &
+         new_unittest("Test UNIQUAC against Gmehling et al. book", test_against_gmehling), &
+         new_unittest("Test UNIQUAC against Gmehling et al. book 2", test_against_gmehling2) &
+         ]
+   end subroutine collect_suite
+
+   subroutine test_uniquac_cons_mix(error)
+      use yaeos, only: pr, R
+      use yaeos, only: Groups, setup_uniquac, UNIQUAC
+      use yaeos__consistency_gemodel, only: ge_consistency
+      use yaeos__consistency_gemodel, only: numeric_ge_derivatives
+
+      type(error_type), allocatable, intent(out) :: error
+
+      type(UNIQUAC) :: model
+
+      integer, parameter :: nc = 3
+
+      real(pr) :: Ge, Gen(nc), GeT, GeT2, GeTn(nc), Gen2(nc, nc)
+      real(pr) :: Gei, Geni(nc), GeTi, GeT2i, GeTni(nc), Gen2i(nc, nc)
+      real(pr) :: Ge_n, Gen_n(nc), GeT_n, GeT2_n, GeTn_n(nc), Gen2_n(nc, nc)
+
+      real(pr) :: n(nc), T, rs(nc), qs(nc)
+      real(pr) :: A(nc,nc), B(nc,nc), C(nc,nc), D(nc,nc), E(nc,nc)
+      real(pr) :: dt, dn
+
+      real(pr) :: eq58, eq59(nc), eq60(nc,nc), eq61(nc)
+
+      integer :: i, j
+
+      T = 350.15_pr
+      n = [5.0_pr, 8.0_pr, 10.0_pr]
+
+      dt = 0.1_pr
+      dn = 0.001_pr
+
+      A(1,:) = [0.0_pr, -75.46_pr, -60.15_pr]
+      A(2,:) = [120.20_pr, 0.0_pr, 44.22_pr]
+      A(3,:) = [120.20_pr, 33.21_pr, 0.0_pr]
+
+      B(1,:) = [0.0_pr, -0.10062_pr, 0.2566_pr]
+      B(2,:) = [0.44835_pr, 0.0_pr, -0.01325_pr]
+      B(3,:) = [0.44835_pr, 0.124_pr, 0.0_pr]
+
+      C(1,:) = [0.0_pr, -0.0008052_pr, 0.00021_pr]
+      C(2,:) = [0.0004704_pr, 0.0_pr, -0.00033_pr]
+      C(3,:) = [0.0004704_pr, -0.000247_pr, 0.0_pr]
+
+      D(1,:) = [0.0_pr, -0.001_pr, 0.0002_pr]
+      D(2,:) = [-0.001_pr, 0.0_pr, 0.0002_pr]
+      D(3,:) = [-0.001_pr, 0.0002_pr, 0.0_pr]
+
+      E(1,:) = [0.0_pr, -0.00001_pr, 0.00001_pr]
+      E(2,:) = [-0.00001_pr, 0.0_pr, 0.00001_pr]
+      E(3,:) = [-0.00001_pr, 0.00001_pr, 0.0_pr]
+
+      rs = [0.92_pr, 2.1055_pr, 1.5_pr]
+      qs = [1.4_pr, 1.972_pr, 1.4_pr]
+
+      model = setup_uniquac(qs, rs, A, B, C, D, E)
+
+      ! ========================================================================
+      ! Call analytic derivatives
+      ! ------------------------------------------------------------------------
+      call model%excess_gibbs(n, T, Ge=Ge, GeT=GeT, GeT2=GeT2, Gen=Gen, Gen2=Gen2, GeTn=GeTn)
+      call model%excess_gibbs(n, T, Ge=Gei)
+      call model%excess_gibbs(n, T, GeT=GeTi)
+      call model%excess_gibbs(n, T, GeT2=GeT2i)
+      call model%excess_gibbs(n, T, Gen=Geni)
+      call model%excess_gibbs(n, T, GeTn=GeTni)
+      call model%excess_gibbs(n, T, Gen2=Gen2i)
+
+      ! ========================================================================
+      ! Test single calls
+      ! ------------------------------------------------------------------------
+      call check(error, abs(Ge - Gei) < 1e-12_pr)
+      call check(error, abs(GeT - GeTi) < 1e-12_pr)
+      call check(error, abs(GeT2 - GeT2i) < 1e-12_pr)
+      call check(error, allclose(Gen, Geni, 1e-12_pr))
+      call check(error, allclose(GeTn, GeTni, 1e-12_pr))
+      call check(error, allclose(Gen2(1,:), Gen2i(1,:), 1e-12_pr))
+      call check(error, allclose(Gen2(2,:), Gen2i(2,:), 1e-12_pr))
+
+      ! ========================================================================
+      ! Test pair calls
+      ! ------------------------------------------------------------------------
+      ! Ge
+      call model%excess_gibbs(n, T, Ge=Gei, GeT=GeTi)
+      call check(error, abs(Ge - Gei) <= 1e-10)
+      call check(error, abs(GeT - GeTi) <= 1e-10)
+
+      call model%excess_gibbs(n, T, Ge=Gei, GeT2=GeT2i)
+      call check(error, abs(Ge - Gei) <= 1e-10)
+      call check(error, abs(GeT2 - GeT2i) <= 1e-10)
+
+      call model%excess_gibbs(n, T, Ge=Gei, Gen=Geni)
+      call check(error, abs(Ge - Gei) <= 1e-10)
+      call check(error, allclose(Gen, Geni, 1e-10_pr))
+
+      call model%excess_gibbs(n, T, Ge=Gei, GeTn=GeTni)
+      call check(error, abs(Ge - Gei) <= 1e-10)
+      call check(error, allclose(GeTn, GeTni, 1e-10_pr))
+
+      call model%excess_gibbs(n, T, Ge=Gei, Gen2=Gen2i)
+      call check(error, abs(Ge - Gei) <= 1e-10)
+      call check(error, allclose(Gen2(1,:), Gen2i(1,:), 1e-10_pr))
+      call check(error, allclose(Gen2(2,:), Gen2i(2,:), 1e-10_pr))
+
+      ! Ge_T
+      call model%excess_gibbs(n, T, GeT=GeTi, GeT2=GeT2i)
+      call check(error, abs(GeT - GeTi) <= 1e-10)
+      call check(error, abs(GeT2 - GeT2i) <= 1e-10)
+
+      call model%excess_gibbs(n, T, GeT=GeTi, Gen=Geni)
+      call check(error, abs(GeT - GeTi) <= 1e-10)
+      call check(error, allclose(Gen, Geni, 1e-10_pr))
+
+      call model%excess_gibbs(n, T, GeT=GeTi, GeTn=GeTni)
+      call check(error, abs(GeT - GeTi) <= 1e-10)
+      call check(error, allclose(GeTn, GeTni, 1e-10_pr))
+
+      call model%excess_gibbs(n, T, GeT=GeTi, Gen2=Gen2i)
+      call check(error, abs(GeT - GeTi) <= 1e-10)
+      call check(error, allclose(Gen2(1,:), Gen2i(1,:), 1e-10_pr))
+      call check(error, allclose(Gen2(2,:), Gen2i(2,:), 1e-10_pr))
+
+      ! Ge_T2
+      call model%excess_gibbs(n, T, GeT2=GeT2i, Gen=Geni)
+      call check(error, abs(GeT2 - GeT2i) <= 1e-10)
+      call check(error, allclose(Gen, Geni, 1e-10_pr))
+
+      call model%excess_gibbs(n, T, GeT2=GeT2i, GeTn=GeTni)
+      call check(error, abs(GeT2 - GeT2i) <= 1e-10)
+      call check(error, allclose(GeTn, GeTni, 1e-10_pr))
+
+      call model%excess_gibbs(n, T, GeT2=GeT2i, Gen2=Gen2i)
+      call check(error, abs(GeT2 - GeT2i) <= 1e-10)
+      call check(error, allclose(Gen2(1,:), Gen2i(1,:), 1e-10_pr))
+      call check(error, allclose(Gen2(2,:), Gen2i(2,:), 1e-10_pr))
+
+      ! Geni
+      call model%excess_gibbs(n, T, Gen=Geni, GeTn=GeTni)
+      call check(error, allclose(Gen, Geni, 1e-10_pr))
+      call check(error, allclose(GeTn, GeTni, 1e-10_pr))
+
+      call model%excess_gibbs(n, T, Gen=Geni, Gen2=Gen2i)
+      call check(error, allclose(Gen, Geni, 1e-10_pr))
+      call check(error, allclose(Gen2(1,:), Gen2i(1,:), 1e-10_pr))
+      call check(error, allclose(Gen2(2,:), Gen2i(2,:), 1e-10_pr))
+
+      ! ========================================================================
+      ! Just one triplet call test
+      ! ------------------------------------------------------------------------
+      call model%excess_gibbs(n, T, Ge=Gei, GeT=GeTi, Gen2=Gen2i)
+      call check(error, abs(Ge - Gei) <= 1e-10)
+      call check(error, abs(GeT - GeTi) <= 1e-10)
+      call check(error, allclose(Gen2(1,:), Gen2i(1,:), 1e-10_pr))
+      call check(error, allclose(Gen2(2,:), Gen2i(2,:), 1e-10_pr))
+
+      ! ========================================================================
+      ! Call numeric derivatives
+      ! ------------------------------------------------------------------------
+      call numeric_ge_derivatives(model, n, T, dn, 0.01_pr, Ge=Ge_n, GeT=GeT_n)
+      call numeric_ge_derivatives(model, n, T, dn, dt, Ge=Ge_n, Gen=Gen_n)
+      call numeric_ge_derivatives(model, n, T, dn, dt, Ge=Ge_n, GeT2=GeT2_n)
+      call numeric_ge_derivatives(model, n, T, dn, dt, Ge=Ge_n, GeTn=GeTn_n)
+      call numeric_ge_derivatives(model, n, T, 0.01_pr, dt, Ge=Ge_n, Gen2=Gen2_n)
+
+      ! Derivatives checks
+      call check(error, abs(Ge - Ge_n) < 1e-10)
+      call check(error, abs(GeT - GeT_n) < 1e-6)
+      call check(error, allclose(Gen, Gen_n, 1e-6_pr))
+      call check(error, abs(GeT2 - GeT2_n) < 1e-6)
+      call check(error, allclose(GeTn, GeTn_n, 1e-6_pr))
+      call check(error, allclose(Gen2(1,:), Gen2_n(1,:), 1e-5_pr))
+      call check(error, allclose(Gen2(2,:), Gen2_n(2,:), 1e-5_pr))
+      call check(error, allclose(Gen2(3,:), Gen2_n(3,:), 1e-5_pr))
+
+      ! ========================================================================
+      ! Consistency tests
+      ! ------------------------------------------------------------------------
+      call ge_consistency(model, n, t, eq58=eq58)
+      call ge_consistency(model, n, t, eq59=eq59)
+      call ge_consistency(model, n, t, eq60=eq60)
+      call ge_consistency(model, n, t, eq61=eq61)
+
+      ! Eq 58
+      call check(error, abs(eq58) < 1e-10_pr)
+
+      ! Eq 59
+      do i=1,size(n)
+         call check(error, abs(eq59(i)) < 1e-10_pr)
+      end do
+
+      ! Eq 60
+      do i=1,size(n)
+         do j=1,size(n)
+            call check(error, abs(eq60(i, j)) < 1e-10_pr)
+         end do
+      end do
+
+      ! Eq 61
+      do i=1,size(n)
+         call check(error, abs(eq61(i)) < 1e-10_pr)
+      end do
+   end subroutine test_uniquac_cons_mix
+
+   subroutine test_uniquac_cons_pure(error)
+      use yaeos, only: pr, UNIQUAC, setup_uniquac
+
+      type(error_type), allocatable, intent(out) :: error
+
+      type(UNIQUAC) :: model
+
+      real(pr) :: Ge, Gen(1), GeT, GeT2, GeTn(1), Gen2(1, 1), ln_gammas(1)
+      real(pr) :: T, n(1), rs(1), qs(1)
+
+      T = 303.15
+      n = [400.0]
+
+      rs = [2.1055_pr]
+      qs = [1.972_pr]
+
+      model  = setup_uniquac(qs, rs)
+
+      ! Evaluate Ge and derivatives
+      call model%excess_gibbs(n, T, Ge, GeT, GeT2, Gen, GeTn, Gen2)
+      call model%ln_activity_coefficient(n, T, ln_gammas)
+
+      ! All must be zero for a pure compounds
+      call check(error, abs(Ge) < 1e-10_pr)
+      call check(error, abs(GeT) < 1e-10_pr)
+      call check(error, abs(GeT2) < 1e-10_pr)
+
+      call check(error, abs(Gen(1)) < 1e-10_pr)
+      call check(error, abs(GeTn(1)) < 1e-10_pr)
+      call check(error, abs(ln_gammas(1)) < 1e-10_pr)
+
+      call check(error, abs(Gen2(1, 1)) < 1e-10_pr)
+   end subroutine test_uniquac_cons_pure
+
+   subroutine test_against_caleb_thermo(error)
+      ! https://github.com/CalebBell/thermo
+      use yaeos, only: pr, R
+      use yaeos, only: Groups, setup_uniquac, UNIQUAC
+
+      type(error_type), allocatable, intent(out) :: error
+
+      type(UNIQUAC) :: model
+
+      integer, parameter :: nc = 3
+
+      real(pr) :: Ge, Gen(nc), GeT, GeT2, GeTn(nc), Gen2(nc, nc)
+      real(pr) :: ln_gammas(nc)
+      real(pr) :: rs(nc), qs(nc)
+      real(pr), dimension(nc,nc) :: A, B, C, D, F, E
+
+      real(pr) :: n(nc), T, n_t
+
+      T = 298.15_pr
+      n = [20.0_pr, 70.0_pr, 10.0_pr]
+      n_t = sum(n)
+
+      A(1,:) = [0.0_pr, -75.46_pr, -60.15_pr]
+      A(2,:) = [120.20_pr, 0.0_pr, 44.22_pr]
+      A(3,:) = [120.20_pr, 33.21_pr, 0.0_pr]
+
+      B(1,:) = [0.0_pr, -0.10062_pr, 0.2566_pr]
+      B(2,:) = [0.44835_pr, 0.0_pr, -0.01325_pr]
+      B(3,:) = [0.44835_pr, 0.124_pr, 0.0_pr]
+
+      C(1,:) = [0.0_pr, -0.0008052_pr, 0.00021_pr]
+      C(2,:) = [0.0004704_pr, 0.0_pr, -0.00033_pr]
+      C(3,:) = [0.0004704_pr, -0.000247_pr, 0.0_pr]
+
+      D(1,:) = [0.0_pr, -0.001_pr, 0.0002_pr]
+      D(2,:) = [-0.001_pr, 0.0_pr, 0.0002_pr]
+      D(3,:) = [-0.001_pr, 0.0002_pr, 0.0_pr]
+
+      E(1,:) = [0.0_pr, -0.00001_pr, 0.00001_pr]
+      E(2,:) = [-0.00001_pr, 0.0_pr, 0.00001_pr]
+      E(3,:) = [-0.00001_pr, 0.00001_pr, 0.0_pr]
+
+      rs = [0.92_pr, 2.1055_pr, 1.5_pr]
+      qs = [1.4_pr, 1.972_pr, 1.4_pr]
+
+      ! setup UNIQUAC model
+      model = setup_uniquac(qs, rs, A, B, C, D, E)
+
+      ! Call all Ge and derivatives
+      call model%excess_gibbs(n, T, Ge, GeT, GeT2, Gen, GeTn, Gen2)
+
+      ! Call GeModel class method
+      call model%ln_activity_coefficient(n, T, ln_gammas)
+
+      ! ========================================================================
+      ! Test against Caleb Bell's implementation
+      ! ------------------------------------------------------------------------
+      ! Ge
+      call check(error, abs(Ge / n_t - (-2060.2989541519596_pr)) <= 1e-7)
+
+      ! Gen
+      call check(error, allclose(&
+         Gen/R/T, [-164.62277497059728_pr, -60.906444787104235_pr, -75.52457152449654_pr], 1e-8_pr))
+
+      ! ! ln_gammas
+      call check(error, allclose(ln_gammas, [-164.62277497059728_pr, -60.906444787104235_pr, -75.52457152449654_pr], 1e-8_pr))
+
+      ! Gen2
+      call check(error, allclose(Gen2(1,:)*100, [1685.266356403747_pr, -484.7600670734814_pr, 22.787756706880828_pr]/n_t, 1e-7_pr))
+      call check(error, allclose(Gen2(2,:)*100, [-484.76006707348006_pr, 7190.325877999089_pr, -49362.76101184667_pr]/n_t, 1e-7_pr))
+      call check(error, allclose(Gen2(3,:)*100, [22.78775670689265_pr, -49362.76101184663_pr, 345493.75156951265_pr]/n_t, 1e-7_pr))
+
+      ! GeT
+      call check(error, abs(GeT * 100 / n_t - (-709.4126206919739_pr)) < 1e-7)
+
+      ! GeT2
+      call check(error, abs(GeT2*100/n_t - (-0.18488719888568747_pr)) < 1e-10)
+
+      ! GeTn
+      call check(error, allclose(GeTn*100, [-1344.5725109529376_pr, -536.5213350370494_pr, -649.331839754517_pr], 1e-8_pr))
+   end subroutine test_against_caleb_thermo
+
+   subroutine test_against_gmehling(error)
+      ! n-butanol - water from Chemical Thermodynamics for Process Simulation
+      ! Gmehling et al.
+      use yaeos, only: pr, R
+      use yaeos, only: setup_uniquac, UNIQUAC
+
+      type(error_type), allocatable, intent(out) :: error
+
+      integer, parameter :: nc = 2
+
+      real(pr) :: rs(nc), qs(nc)
+      real(pr) :: b(nc, nc), b12, b21
+      real(pr) :: x1(9), x2(9)
+      real(pr) :: a1_g(9), a2_g(9)
+
+      real(pr) :: ln_gammas(nc), T
+
+      integer :: i
+
+      type(UNIQUAC) :: model
+
+      rs = [3.4543_pr, 0.92_pr]
+      qs = [3.052_pr, 1.4_pr]
+
+      T = 323.15_pr
+
+      ! Calculate bij from DUij. We need -DU/R to get bij
+      b12 = -129.7_pr * 0.04184_pr / R ! cal to bar L
+      b21 = -489.6_pr * 0.04184_pr / R ! cal to bar L
+
+      b(1,:) = [0.0_pr, b12]
+      b(2,:) = [b21, 0.0_pr]
+
+      model = setup_uniquac(qs, rs, bij=b)
+
+      ! Test against Gmehling et al. book
+      x1 = [0.005_pr, 0.01_pr, 0.015_pr, 0.02_pr, 0.05_pr, 0.1_pr, 0.2_pr, 0.4_pr, 0.6_pr]
+      x2 = 1.0_pr - x1
+
+      a1_g = [0.2824_pr, 0.4972_pr, 0.6598_pr, 0.9801_pr, 1.0495_pr, 0.9605_pr, 0.7253_pr, 0.6141_pr, 0.6802_pr]
+      a2_g = [0.9953_pr, 0.9913_pr, 0.9878_pr, 0.9848_pr, 0.9766_pr, 0.9842_pr, 1.0332_pr, 1.0951_pr, 0.9766_pr]
+
+      do i=1,9
+         call model%ln_activity_coefficient([x1(i), x2(i)], T, ln_gammas)
+
+         call check(error, abs(exp(ln_gammas(1))*x1(i) - a1_g(i)) / a1_g(i) < 0.2)
+         call check(error, abs(exp(ln_gammas(2))*x2(i) - a2_g(i)) / a2_g(i) < 0.2)
+      end do
+   end subroutine test_against_gmehling
+
+   subroutine test_against_gmehling2(error)
+      ! water - ethanol- benzene from Chemical Thermodynamics for Process
+      ! Simulation Gmehling et al.
+      use yaeos, only: pr, R
+      use yaeos, only: setup_uniquac, UNIQUAC
+
+      type(error_type), allocatable, intent(out) :: error
+
+      integer, parameter :: nc = 3
+
+      real(pr) :: rs(nc), qs(nc)
+      real(pr) :: b(nc, nc)
+      real(pr) :: z(nc)
+
+      real(pr) :: ln_gammas(nc), T
+
+      type(UNIQUAC) :: model
+
+      rs = [0.92_pr, 2.1055_pr, 3.1878_pr]
+      qs = [1.4_pr, 1.972_pr, 2.4_pr]
+
+      T = 298.15_pr
+
+      ! Calculate bij from DUij. We need -DU/R to get bij
+      b(1,:) = [0.0_pr, -526.02_pr, -309.64_pr]
+      b(2,:) = [318.06_pr, 0.0_pr, 91.532_pr]
+      b(3,:) = [-1325.1_pr, -302.57_pr, 0.0_pr]
+
+      model = setup_uniquac(qs, rs, bij=b)
+
+      ! Test against Gmehling et al. book
+      z = [0.8_pr, 0.1_pr, 0.2_pr]
+
+      call model%ln_activity_coefficient(z, T, ln_gammas)
+
+      call check(error, allclose(exp(ln_gammas), [1.570_pr, 0.2948_pr, 18.11_pr], 1e-3_pr))
+
+      z = [0.2_pr, 0.2_pr, 0.8_pr]
+
+      call model%ln_activity_coefficient(z, T, ln_gammas)
+
+      call check(error, allclose(exp(ln_gammas), [8.856_pr, 0.860_pr, 1.425_pr], 1e-3_pr))
+
+   end subroutine test_against_gmehling2
+end module test_uniquac
diff --git a/test/test_runner.f90 b/test/test_runner.f90
index 42ddd0e3d..b4278e72e 100644
--- a/test/test_runner.f90
+++ b/test/test_runner.f90
@@ -28,6 +28,7 @@ program tester
     use test_psrk_ge, only: suite_psrk_ge => collect_suite
     use test_unifac_parameters, only: suite_unifac_parameters => collect_suite
     use test_tape_nrtl, only: suite_nrtl => collect_suite
+    use test_uniquac, only: suite_uniquac => collect_suite
 
     ! =========================================================================
     ! Fitting procedures tests
@@ -70,6 +71,7 @@ program tester
         new_testsuite("PSRK", suite_psrk_ge), &
         new_testsuite("UNIFACParameters", suite_unifac_parameters), &
         new_testsuite("NRTL", suite_nrtl), &
+        new_testsuite("UNIQUAC", suite_uniquac), &
         ! =====================================================================
         ! Fitting procedures tests
         ! ---------------------------------------------------------------------