The principal purpose of this study is to build stochastic neuronal models, for the prediction of heavy metal, contents in the surface waters of the Oued Inaouen catchment area of the TAZA region, according to their Physico-chemical parameters; we have carried out a comparative study: the multiple linear regression (MLR) method and the artificial neural network (ANN) approach. The following statistical indicators were used to evaluate the performance of the stochastic models developed by neural network and MLR: The sum of the quadratic errors (SSE) and the determination coefficient (R²), also through the study of fit graphs. The results show that the predictive modelling using artificial neural networks is very effective. This performance shows a non-linear relation between the studied Physico-chemical characteristics and the heavy metal contents in the surface waters of the Oued Inaouen catchment area.

Self-organizing maps (SOMs) and other artificial intelligence approaches developed by Kohonen can be used to model and solve environmental challenges. To emphasize the classification of Physico-chemical parameters of the Inaouen watershed, we presented a classification strategy based on a self-organizing topological map (SOM) artificial neural network in this study. The use of a self-organizing map to classify samples resulted in the following five categories: Low quantities of Sodium Na (mg/l), Potassium k(mg/l), Magnesium Mg(mg/l), Calcium Ca(mg/l), Sulfates SO4(mg/l), and Total Dissolved Solids TDS (mg/l) distinguish Classes 2 and 3. Bicarbonate HCO3 (mg/l), Total Dissolved Solids TDS (mg/l), Total Alkalinity CaCO3(mg/l), Mg(mg/l), Calcium Ca (mg/l), and electrical conductivity Cond (ms/cm) are slightly greater in Classes 1 and 4. Except for Dissolved Oxygen D.O. (mg/l) and Nitrate NO3(mg/l), Class 5 has exceptionally high values for all metrics. The results suggest that Kohonen's self-organizing topological maps (SOM) classification is an outstanding and fundamental tool for understanding and displaying the spatial distribution of water physicochemical quality.

In the realm of epidemiological modeling, the intricacies of epidemic dynamics are elucidated through the lens of compartmental models, with the SIR (Susceptible-Infectious-Recovered) and its variant, the SIS (Susceptible-Infectious-Susceptible) model, being pivotal. This investigation delves into both deterministic and stochastic frameworks, casting the SIR model as a continuous-time Markov chain (CTMC) in stochastic settings. Such an approach facilitates simulations via Gillespie's algorithm and integration of stochastic differential equations. The latter are formulated through a bivariate Fokker-Planck equation, originating from the continuous limit of the master equation. A focal point of this study is the distribution of extinction time, specifically, the duration until recovery in a population with an initial count of infected individuals. This distribution adheres to a Gumbel distribution, viewed through the prism of a birth and death process. The stochastic analysis reveals several insights: firstly, the SIR model as a CTMC encapsulates random fluctuations in epidemic dynamics. Secondly, stochastic simulation methods, either through Gillespie's algorithm or stochastic differential equations, offer a robust exploration of disease spread variability. Thirdly, the precision of modeling is enhanced by the incorporation of a bivariate Fokker-Planck equation. Fourthly, understanding the Gumbel distribution of extinction time is crucial for gauging recovery periods. Lastly, the non-linear nature of the SIR model, when analyzed stochastically, enriches the comprehension of epidemic dynamics. These findings bear significant implications for epidemic mitigation and recovery strategies, informing healthcare resource planning, vaccine deployment optimization, implementation of social distancing measures, public communication strategies, and swift responses to epidemic resurgences.

This study investigates the impact of nutritional status on academic performance among schoolchildren in Eastern Morocco. Focusing on the prevalence of overweight, obesity, and their associations with academic outcomes, the research underscores the significance of physical well-being in educational achievement. Conducted as a cross-sectional analysis in March 2022, the survey encompassed eight public and two private schools, selected through random sampling. Classes within these schools were also randomly chosen. Utilizing a self-administered, anonymous questionnaire, completed individually by students in the presence of a trained dietician, the study also involved anthropometric measurements and clinical examinations. Additionally, students' grade point averages (GPAs) were obtained from school records. The survey comprised 596 students, with an average age of 14.86 ± 1.98 years, height of 160.47 ± 11.84 cm, and weight of 51.28 ± 11.49 kg. The prevalence of underweight was recorded at 8.7%, overweight at 10.7%, and obesity at 2.7%. Statistical analysis using the Analysis of Variance (ANOVA) test revealed a significant association between obesity and diminished academic performance, indicating the need for attention to obesity among adolescents in this region. The findings suggest that nationallevel prevalence determination of overweight and obesity by health policymakers is crucial for this age group. Identifying risk factors associated with these conditions is imperative for effective prevention and early intervention. In this context, the promotion of physical activity and healthy eating habits is vital for fostering healthy, successful school environments. This research contributes to the understanding of how physical health, particularly nutritional status, influences academic outcomes. It highlights the need for integrated approaches that consider the physical well-being of students as a critical factor in educational success. The study's implications extend beyond academic circles, offering insights for policymakers and educators in developing holistic strategies to enhance both health and educational outcomes.

Conducting extensive, time-consuming analysis campaigns is a typical technique to better understand and manage surface water quality. These usually generate a substantial amount of data that is challenging to comprehend. Principal component analysis may be advantageous for such a project (PCA). From the perspective of such an application, eight physico-chemical parameters are important: Sodium (Na+), Bicarbonate (HCO3-), Magnesium (Mg2+), Total Alkalinity (as CaCO3), Chlorides (Cl-), Potassium (K+), Calcium (Ca2+), Sulfates (SO42-), coming from the analysis of 100 water samples collected between February 2014 and December 2015 on 25 stations distributed on Inaouen catchment areas, were analyzed. The principal component analysis applied to the data showed that the variables could be grouped into two principal components. The interpretation of the results using these tools allowed us to understand that the parameters responsible for water quality are related to component Dim1 (HCO3-, CaCO3, K+, Cl-, Na+ and SO42-) and component Dim2 to the processes associated to (Ca2+ and Mg2+) for the physicochemical parameters, the Dim1 factorial design accounts for 67.80% of the variance; it is expressed towards its positive pole by HCO3-, CaCO3, K+, Cl-, Na+ and SO42-, which present good correlations between them. However, the Dim2 factorial plane represents only 17.60%, defined by the Ca2+ and Mg2+ ions towards its positive pole. The Dim1XDim2 plane's typological structure reveals the individualization of three different groupings based on their hydrochemical quality. A feasible reduction in the number of dimensions without a major loss of information was discovered by the PCA. This tool is a good choice from the standpoint of developing management tools.