diff --git a/README.md b/README.md
index 0a1a894..cd91fe0 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,10 @@ The official documentation can be found here https://memilio.readthedocs.io .
 
 ### Marimo notebook setup
 
+We recommend using the editor VSCode and installing the Python and Marimo extensions. Only this this set up was 
+tested by us. If you encounter any issues with the installation process, please open an issue on our 
+[GitHub](https://github.com/SciCompMod/memilio/issues) page.
+
 First, open a new terminal.
 Go to a directory for the new project, then create a virtual environment (here "venv") and install marimo:
 
diff --git a/exercises/exercise07.py b/exercises/exercise07.py
index d7b1dc6..5a7d0ed 100644
--- a/exercises/exercise07.py
+++ b/exercises/exercise07.py
@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.20.4"
+__generated_with = "0.24.0"
 app = marimo.App(width="medium")
 
 
@@ -128,7 +128,8 @@ def _(AgeGroup, model, np, num_age_groups):
     model.parameters.CriticalPerSevere[AgeGroup(2)] = 0.25 * 2
 
     # Set contact frequency
-    model.parameters.ContactPatterns.cont_freq_mat[0].baseline = np.ones((num_age_groups, num_age_groups)) * 10
+    model.parameters.ContactPatterns.cont_freq_mat[0].baseline = np.ones(
+        (num_age_groups, num_age_groups)) * 10
     return
 
 
@@ -161,8 +162,10 @@ def _(AgeGroup, model, num_age_groups, osecir, total_population_per_region):
     # The population is equally distributed among the age groups
     for group in range(num_age_groups):
         # 1% of the population is initially infected, 0.5% Exposed and 0.5% in the pre- or asymptomatic state
-        model.populations[AgeGroup(group), osecir.InfectionState.Exposed] = 0.005 * total_population_per_region / num_age_groups
-        model.populations[AgeGroup(group), osecir.InfectionState.InfectedNoSymptoms] = 0.005 * total_population_per_region / num_age_groups
+        model.populations[AgeGroup(group), osecir.InfectionState.Exposed] = 0.005 * \
+            total_population_per_region / num_age_groups
+        model.populations[AgeGroup(group), osecir.InfectionState.InfectedNoSymptoms] = 0.005 * \
+            total_population_per_region / num_age_groups
         # The rest of the population is Susceptible
         model.populations.set_difference_from_group_total_AgeGroup(
             (AgeGroup(group), osecir.InfectionState.Susceptible), total_population_per_region / num_age_groups)
@@ -180,7 +183,7 @@ def _(mo):
 @app.cell
 def _(graph, model, t0):
     # Add node with id 0 and copy beforehand initialized model to it
-    graph.add_node(id=0, model=model, t0=t0) 
+    graph.add_node(id=0, model=model, t0=t0)
     return
 
 
@@ -198,7 +201,8 @@ def _(AgeGroup, model, num_age_groups, osecir, total_population_per_region):
     for age in range(num_age_groups):
         # No infected individuals
         model.populations[AgeGroup(age), osecir.InfectionState.Exposed] = 0
-        model.populations[AgeGroup(age), osecir.InfectionState.InfectedNoSymptoms] = 0
+        model.populations[AgeGroup(
+            age), osecir.InfectionState.InfectedNoSymptoms] = 0
         # The total population is Susceptible
         model.populations.set_difference_from_group_total_AgeGroup(
             (AgeGroup(age), osecir.InfectionState.Susceptible), total_population_per_region / num_age_groups)
@@ -251,7 +255,8 @@ def _(mo):
     ### Exercise
 
     Set the mobility coefficients for `Dead` individuals to zero:
-    Hint: For the first age group this can be done via `mobility_coefficients[0 * int(osecir.InfectionState.Dead) + osecir.InfectionState.Dead] = 0`.
+
+    Hint: For the first age group this can be done via `mobility_coefficients[0 * (int(osecir.InfectionState.Dead)+1) + int(osecir.InfectionState.Dead)] = 0`.
     """)
     return
 
@@ -298,21 +303,26 @@ def _(mo):
 
 
 @app.cell
-def _(dt_exchange, graph, osecir, t0):
-    # Create graph simulation and advance until tmax
-    sim = osecir.MobilitySimulation(graph, t0, dt=dt_exchange)
+def _(dt_exchange, graph, osecir, t0, tmax):
+    import os
+    # Silence C++ log output; it can deadlock marimo's output pipe on Windows
+    devnull = os.open(os.devnull, os.O_WRONLY)
+    saved_stdout, saved_stderr = os.dup(1), os.dup(2)
+    os.dup2(devnull, 1)
+    os.dup2(devnull, 2)
+    try:
+        # Create graph simulation and advance until tmax
+        sim = osecir.MobilitySimulation(graph, t0, dt=dt_exchange)
+        sim.advance(tmax)
+    finally:
+        os.dup2(saved_stdout, 1)
+        os.dup2(saved_stderr, 2)
+        os.close(devnull)
+        os.close(saved_stdout)
+        os.close(saved_stderr)
     return (sim,)
 
 
-@app.cell
-def _(mo):
-    mo.md(r"""
-    ### Exercise
-    Please advance the simulation until `tmax`.
-    """)
-    return
-
-
 @app.cell
 def _():
     # Insert code here
@@ -344,8 +354,10 @@ def _(mo):
 
 @app.cell
 def _(osecir, result_region0, result_region1):
-    result_region0_interpolated = osecir.interpolate_simulation_result(result_region0)
-    result_region1_interpolated = osecir.interpolate_simulation_result(result_region1)
+    result_region0_interpolated = osecir.interpolate_simulation_result(
+        result_region0)
+    result_region1_interpolated = osecir.interpolate_simulation_result(
+        result_region1)
     return result_region0_interpolated, result_region1_interpolated
 
 
@@ -368,8 +380,10 @@ def _(osecir, plt, result_region0_interpolated, result_region1_interpolated):
     # Plot the number of non-symptomatically infected for both regions
     fig, ax = plt.subplots()
     time = result_array_0[0, :]
-    InfectedNoSymptoms_0 = result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms), :] + result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms) + int(osecir.InfectionState.Dead) + 1, :] + result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms) + 2 * (int(osecir.InfectionState.Dead) + 1), :]
-    InfectedNoSymptoms_1 = result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms), :] + result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms) + int(osecir.InfectionState.Dead) + 1, :] + result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms) + 2 * (int(osecir.InfectionState.Dead) + 1), :]
+    InfectedNoSymptoms_0 = result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms), :] + result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms) + int(
+        osecir.InfectionState.Dead) + 1, :] + result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms) + 2 * (int(osecir.InfectionState.Dead) + 1), :]
+    InfectedNoSymptoms_1 = result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms), :] + result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms) + int(
+        osecir.InfectionState.Dead) + 1, :] + result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms) + 2 * (int(osecir.InfectionState.Dead) + 1), :]
     ax.plot(time, InfectedNoSymptoms_0, label='Infected No Symptoms Region 1')
     ax.plot(time, InfectedNoSymptoms_1, label='Infected No Symptoms Region 2')
     ax.set_xlabel('Time [days]')
diff --git a/tutorial07.py b/tutorial07.py
index da4dee3..bbdb1fe 100644
--- a/tutorial07.py
+++ b/tutorial07.py
@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.19.11"
+__generated_with = "0.24.0"
 app = marimo.App(width="medium")
 
 
@@ -49,7 +49,7 @@ def _():
 @app.cell
 def _(mo):
     mo.md(r"""
-    We set the simulation start time `t0`, the end time `tmax` and the initial step size `dt` as:
+    We set the simulation start time `t0` and the end time `tmax`.:
     """)
     return
 
@@ -58,7 +58,6 @@ def _(mo):
 def _():
     t0 = 0
     tmax = 100
-    dt = 0.1
     return t0, tmax
 
 
@@ -104,20 +103,20 @@ def _(mo):
 
 @app.cell
 def _(AgeGroup, model, np, num_age_groups):
-    for ag in range(num_age_groups):
+    for i in range(num_age_groups):
         # Set infection state stay times (in days)
-        model.parameters.TimeExposed[AgeGroup(ag)] = 3.2
-        model.parameters.TimeInfectedNoSymptoms[AgeGroup(ag)] = 2.
-        model.parameters.TimeInfectedSymptoms[AgeGroup(ag)] = 6.
-        model.parameters.TimeInfectedSevere[AgeGroup(ag)] = 12.
-        model.parameters.TimeInfectedCritical[AgeGroup(ag)] = 8.
+        model.parameters.TimeExposed[AgeGroup(i)] = 3.2
+        model.parameters.TimeInfectedNoSymptoms[AgeGroup(i)] = 2.
+        model.parameters.TimeInfectedSymptoms[AgeGroup(i)] = 6.
+        model.parameters.TimeInfectedSevere[AgeGroup(i)] = 12.
+        model.parameters.TimeInfectedCritical[AgeGroup(i)] = 8.
 
         # Set infection state transition probabilities
-        model.parameters.RelativeTransmissionNoSymptoms[AgeGroup(ag)] = 0.67
-        model.parameters.TransmissionProbabilityOnContact[AgeGroup(ag)] = 0.1
-        model.parameters.RecoveredPerInfectedNoSymptoms[AgeGroup(ag)] = 0.2
-        model.parameters.RiskOfInfectionFromSymptomatic[AgeGroup(ag)] = 0.25
-        model.parameters.DeathsPerCritical[AgeGroup(ag)] = 0.3
+        model.parameters.RelativeTransmissionNoSymptoms[AgeGroup(i)] = 0.67
+        model.parameters.TransmissionProbabilityOnContact[AgeGroup(i)] = 0.1
+        model.parameters.RecoveredPerInfectedNoSymptoms[AgeGroup(i)] = 0.2
+        model.parameters.RiskOfInfectionFromSymptomatic[AgeGroup(i)] = 0.25
+        model.parameters.DeathsPerCritical[AgeGroup(i)] = 0.3
 
     # The groups have an increasing risk of severe and critical infections
     model.parameters.SeverePerInfectedSymptoms[AgeGroup(0)] = 0.2
@@ -128,7 +127,8 @@ def _(AgeGroup, model, np, num_age_groups):
     model.parameters.CriticalPerSevere[AgeGroup(2)] = 0.25 * 2
 
     # Set contact frequency
-    model.parameters.ContactPatterns.cont_freq_mat[0].baseline = np.ones((num_age_groups, num_age_groups)) * 10
+    model.parameters.ContactPatterns.cont_freq_mat[0].baseline = np.ones(
+        (num_age_groups, num_age_groups)) * 10
     return
 
 
@@ -159,13 +159,15 @@ def _(mo):
 @app.cell
 def _(AgeGroup, model, num_age_groups, osecir, total_population_per_region):
     # The population is equally distributed among the age groups
-    for group in range(num_age_groups):
+    for j in range(num_age_groups):
         # 1% of the population is initially infected, 0.5% Exposed and 0.5% in the pre- or asymptomatic state
-        model.populations[AgeGroup(group), osecir.InfectionState.Exposed] = 0.005 * total_population_per_region / num_age_groups
-        model.populations[AgeGroup(group), osecir.InfectionState.InfectedNoSymptoms] = 0.005 * total_population_per_region / num_age_groups
+        model.populations[AgeGroup(j), osecir.InfectionState.Exposed] = 0.005 * \
+            total_population_per_region / num_age_groups
+        model.populations[AgeGroup(j), osecir.InfectionState.InfectedNoSymptoms] = 0.005 * \
+            total_population_per_region / num_age_groups
         # The rest of the population is Susceptible
         model.populations.set_difference_from_group_total_AgeGroup(
-            (AgeGroup(group), osecir.InfectionState.Susceptible), total_population_per_region / num_age_groups)
+            (AgeGroup(j), osecir.InfectionState.Susceptible), total_population_per_region / num_age_groups)
     return
 
 
@@ -180,7 +182,7 @@ def _(mo):
 @app.cell
 def _(graph, model, t0):
     # Add node with id 0 and copy beforehand initialized model to it
-    graph.add_node(id=0, model=model, t0=t0) 
+    graph.add_node(id=0, model=model, t0=t0)
     return
 
 
@@ -195,13 +197,14 @@ def _(mo):
 @app.cell
 def _(AgeGroup, model, num_age_groups, osecir, total_population_per_region):
     # The population is equally distributed among the age groups
-    for age in range(num_age_groups):
+    for k in range(num_age_groups):
         # No infected individuals
-        model.populations[AgeGroup(age), osecir.InfectionState.Exposed] = 0
-        model.populations[AgeGroup(age), osecir.InfectionState.InfectedNoSymptoms] = 0
+        model.populations[AgeGroup(k), osecir.InfectionState.Exposed] = 0
+        model.populations[AgeGroup(
+            k), osecir.InfectionState.InfectedNoSymptoms] = 0
         # The total population is Susceptible
         model.populations.set_difference_from_group_total_AgeGroup(
-            (AgeGroup(age), osecir.InfectionState.Susceptible), total_population_per_region / num_age_groups)
+            (AgeGroup(k), osecir.InfectionState.Susceptible), total_population_per_region / num_age_groups)
     return
 
 
@@ -216,7 +219,7 @@ def _(mo):
 @app.cell
 def _(graph, model, t0):
     # Add node with id 0 and copy beforehand initialized model to it
-    graph.add_node(id=1, model=model, t0=t0) 
+    graph.add_node(id=1, model=model, t0=t0)
     return
 
 
@@ -231,35 +234,50 @@ def _(mo):
 
 
 @app.cell
-def _(graph, mio, model, np, osecir):
+def _(graph, mio, model, np, num_age_groups, osecir):
     # One coefficient per (age group x compartment)
     mobility_coefficients = 0.1 * np.ones(model.populations.numel())
     # Dead individuals do not commute
-    mobility_coefficients[osecir.InfectionState.Dead] = 0
+    for l in range(num_age_groups):
+        mobility_coefficients[l * (int(osecir.InfectionState.Dead)+1) +
+                              int(osecir.InfectionState.Dead)] = 0
     mobility_params = mio.MobilityParameters(mobility_coefficients)
     # Add two edges to graph
     graph.add_edge(0, 1, mobility_params)
     graph.add_edge(1, 0, mobility_params)
     # Individuals are exchanged every half day
     dt_exchange = 0.5
-    return
+    return (dt_exchange,)
 
 
 @app.cell
 def _(mo):
     mo.md(r"""
     ## Model simulation
-    
+
     We now have finished initializing the metapopulation model. The graph-based simulation is created and advanced until `tmax` via:
     """)
     return
 
 
 @app.cell
-def _(graph, osecir, t0, tmax):
-    # Create graph simulation and advance until tmax
-    sim = osecir.MobilitySimulation(graph, t0, dt=dt_exchange)
-    sim.advance(tmax)
+def _(dt_exchange, graph, osecir, t0, tmax):
+    import os
+    # Silence C++ log output; it can deadlock marimo's output pipe on Windows
+    devnull = os.open(os.devnull, os.O_WRONLY)
+    saved_stdout, saved_stderr = os.dup(1), os.dup(2)
+    os.dup2(devnull, 1)
+    os.dup2(devnull, 2)
+    try:
+        # Create graph simulation and advance until tmax
+        sim = osecir.MobilitySimulation(graph, t0, dt=dt_exchange)
+        sim.advance(tmax)
+    finally:
+        os.dup2(saved_stdout, 1)
+        os.dup2(saved_stderr, 2)
+        os.close(devnull)
+        os.close(saved_stdout)
+        os.close(saved_stderr)
     return (sim,)
 
 
@@ -288,8 +306,10 @@ def _(mo):
 
 @app.cell
 def _(osecir, result_region0, result_region1):
-    result_region0_interpolated = osecir.interpolate_simulation_result(result_region0)
-    result_region1_interpolated = osecir.interpolate_simulation_result(result_region1)
+    result_region0_interpolated = osecir.interpolate_simulation_result(
+        result_region0)
+    result_region1_interpolated = osecir.interpolate_simulation_result(
+        result_region1)
     return result_region0_interpolated, result_region1_interpolated
 
 
@@ -297,7 +317,7 @@ def _(osecir, result_region0, result_region1):
 def _(mo):
     mo.md(r"""
     ## Visualization of model output
-        
+
     Finally, we can compare the trajectories of all infection states for both regions. In the following, we plot the number of `InfectedNoSymptoms` aggregated over all age groups for both regions:
     """)
     return
@@ -312,8 +332,10 @@ def _(osecir, plt, result_region0_interpolated, result_region1_interpolated):
     # Plot the number of non-symptomatically infected for both regions
     fig, ax = plt.subplots()
     time = result_array_0[0, :]
-    InfectedNoSymptoms_0 = result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms), :] + result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms) + int(osecir.InfectionState.Dead) + 1, :] + result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms) + 2 * (int(osecir.InfectionState.Dead) + 1), :]
-    InfectedNoSymptoms_1 = result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms), :] + result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms) + int(osecir.InfectionState.Dead) + 1, :] + result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms) + 2 * (int(osecir.InfectionState.Dead) + 1), :]
+    InfectedNoSymptoms_0 = result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms), :] + result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms) + int(
+        osecir.InfectionState.Dead) + 1, :] + result_array_0[1 + int(osecir.InfectionState.InfectedNoSymptoms) + 2 * (int(osecir.InfectionState.Dead) + 1), :]
+    InfectedNoSymptoms_1 = result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms), :] + result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms) + int(
+        osecir.InfectionState.Dead) + 1, :] + result_array_1[1 + int(osecir.InfectionState.InfectedNoSymptoms) + 2 * (int(osecir.InfectionState.Dead) + 1), :]
     ax.plot(time, InfectedNoSymptoms_0, label='Infected No Symptoms Region 1')
     ax.plot(time, InfectedNoSymptoms_1, label='Infected No Symptoms Region 2')
     ax.set_xlabel('Time [days]')