diff --git a/scripts/transformer_paper_plots.py b/scripts/transformer_paper_plots.py
index 9921e7d..0f42ba7 100644
--- a/scripts/transformer_paper_plots.py
+++ b/scripts/transformer_paper_plots.py
@@ -467,8 +467,8 @@ def plot_model_load_times_CPU_GPU():
     #sns.barplot(data=gpu_load_times, x="l_cnn", y="cnn_load_time", label="", color="blue", errorbar="sd", capsize=.2)
     #sns.barplot(data=gpu_load_times, x="l_dnn", y="dnn_load_time", label="", color="black", errorbar="sd", capsize=.2)
 
-    sns.barplot(data=cpu_load_times, x="l_tran", y="tran_load_time", label="", linestyle="-", color="green")
-    sns.barplot(data=cpu_load_times, x="l_rnn", y="rnn_load_time", label="", linestyle="-", color="red")
+    sns.barplot(data=cpu_load_times, x="l_tran", y="tran_load_time", label="", linestyle="-", color="green", edgecolor = 'black', linewidth = 2)
+    sns.barplot(data=cpu_load_times, x="l_rnn", y="rnn_load_time", label="", linestyle="-", color="red", edgecolor = 'black', linewidth = 2)
     #sns.barplot(data=cpu_load_times, x="l_cnn", y="cnn_load_time", label="", linestyle="-", color="blue")
     #sns.barplot(data=cpu_load_times, x="l_dnn", y="dnn_load_time", label="", linestyle="-", color="black")
 
@@ -702,6 +702,100 @@ def create_test_precision_plot():
     plt.savefig("../plots/rnn_cnn_dnn_runs_te_prec_defense.png", dpi=dpi, bbox_inches='tight')
     #plt.show()
 
+def make_grouped_stacked_beyond_training():
+    font = {'family': 'serif', 'size': 18}
+    fig_size = (12, 12)
+    fig = plt.figure(figsize=fig_size)
+    plt.rc('font', **font)
+    dpi=300
+    data = pd.DataFrame({
+        'Group': ['Deep learning', 'Deep learning', 'Single-cell', \
+                  'Single-cell', 'Variant calling', 'Variant calling', 'Proteomics', 'Proteomics'],
+        #'Group': ['Keras_train_and_eval', 'Keras_train_and_eval', 'Anndata_import', \
+        #          'Anndata_import', 'Snpeff_sars_cov_2', 'Snpeff_sars_cov_2', 'Proteomics*', 'Proteomics*'],
+        'Model types': ['Transformer', 'RNN', 'Transformer', 'RNN', 'Transformer', 'RNN', 'Transformer', 'RNN'],
+        'Value': [3, 1, 10, 5, 5, 3, 12, 0]
+    })
+
+    # single cell: (anndata_import) [10, 5]
+    # deep learning: "keras_train_and_eval" [3, 1]
+    # variant calling: "snpeff_sars_cov_2" [5, 3]
+    # proteomics: massspectrometryimagingfiltering, cardinalpreprocessing, cardinalsegmentations [12, 0]
+    # Pivot the data to have categories as columns
+    pivot_data = data.pivot(index='Group', columns='Model types', values='Value')
+    palette = {'Transformer': "green", 'RNN': "red"}
+    # Create the stacked bar plot 
+    pivot_data.plot(kind='barh', color=palette, edgecolor = 'black', linewidth = 2) #, stacked=True
+
+    plt.grid(True)
+    plt.xlabel("Number of tools")
+    plt.ylabel("Types of analyses")
+    plt.title("Generalisation")
+    #plt.yticks([0, 2, 4, 6, 8, 10, 12, 14])
+    #plt.ylim((0, 40))
+    #plt.xticks(rotation=45)
+    #plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
+    plt.tight_layout()
+    plt.savefig("../plots/transformer_rnn_stacked_beyond_workflows.pdf", dpi=dpi, bbox_inches='tight')
+    plt.savefig("../plots/transformer_rnn_stacked_beyond_workflows.png", dpi=dpi, bbox_inches='tight')
+    plt.show()
+
+
+def make_age_prediction_scatter_plot():
+    #font = {'family': 'serif', 'size': 18}
+    #fig_size = (12, 12)
+    #fig = plt.figure(figsize=fig_size)
+    #plt.rc('font', **font)
+    fig = plt.figure()
+    dpi=300
+
+    true = pd.read_csv("../plots/test_rows_labels.tabular", sep="\t")["Age"]
+
+    predicted = pd.read_csv("../plots/predicted_age.tabular", sep="\t")["predicted"]
+
+    df_age = pd.DataFrame(zip(true, predicted), columns=["true", "predicted"])
+
+    sns.scatterplot(data=df_age, x="true", y="predicted", color="red")
+
+    # Get the range of values for the x and y axis
+    min_val = min(df_age["true"].min(), df_age["predicted"].min())
+    max_val = max(df_age["true"].max(), df_age["predicted"].max())
+
+    # Plot the y=x line
+    plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')
+
+    plt.grid(True)
+    plt.xlabel("True age")
+    plt.ylabel("Predicted age")
+    plt.title("True vs predicted age (RMSE: 3.76, R2: 0.94)")
+    #plt.yticks([0, 2, 4, 6, 8, 10, 12, 14])
+    #plt.ylim((0, 40))
+    #plt.xticks(rotation=45)
+    #plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
+    plt.tight_layout()
+    plt.savefig("../plots/true_vs_predicted_age.pdf", dpi=dpi, bbox_inches='tight')
+    plt.savefig("../plots/true_vs_predicted_age.png", dpi=dpi, bbox_inches='tight')
+    plt.show()
+
+
+def plot_model_training_times_CPU():
+    print("")
+
+    model_names = ["Transformer", "RNN", "CNN", "DNN"]
+    model_times = [44, 246, 43, 14]
+    df_model_train_time = pd.DataFrame(zip(model_names, model_times), columns=["model_names", "model_times"])
+
+    palette = {'Transformer': "green", 'RNN': "red", "CNN": "blue", "DNN": "black"}
+    sns.barplot(data=df_model_train_time, x="model_names", y="model_times", palette=palette, edgecolor = 'black', linewidth = 2)
+    
+    
+    plt.grid(True)
+    plt.xlabel("Model types")
+    plt.ylabel("Time (seconds)")
+    plt.title("Models vs their training time")
+    plt.savefig("../plots/transformer_rnn_runs_model_train_time_CPU.pdf", dpi=dpi, bbox_inches='tight')
+    plt.savefig("../plots/transformer_rnn_runs_model_train_time_CPU.png", dpi=dpi, bbox_inches='tight')
+    plt.show()
 
 ############ Call methods ###########################
 
@@ -711,6 +805,9 @@ def create_test_precision_plot():
 #plot_model_load_times_CPU_GPU()
 #plot_usage_time_vs_topk()
 #plot_usage_time_vs_seq_len()
-make_bar_beyond_training()
+#make_bar_beyond_training()
+make_grouped_stacked_beyond_training()
+#make_age_prediction_scatter_plot()
+#plot_model_training_times_CPU()
 #create_test_precision_plot()
 #create_ground_truth_beyond_workflows_recommendations_plot()
\ No newline at end of file