In this paper, we explore two modeling approaches to understanding the dynamics of infectious diseases in the population: equation-based modeling (EBM) and agent-based modeling (ABM). To achieve this, a comparative study of these approaches was conducted and we highlighted their advantages and disadvantages. Two case studies on the spread of the COVID-19 pandemic were carried out using both approaches. The results obtained show that differential equation-based models are faster but still simplistic, while agent-based models require more machine capabilities but are more realistic and very close to biology. Based on these outputs, it seems that the coupling of both approaches could be an interesting compromise.

This paper deals with modeling and simulation of the novel coronavirus in which the infectious individuals are divided into three subgroups representing three forms of infection. The rigorous analysis of the mathematical model is provided. We provide also a rigorous derivation of the basic reproduction number R0. For R01, we found the co-existing phenomena under some assumptions and parametric values. Elasticity indices for R0 with respect to different parameters are calculated with baseline parameter values estimated. We also prove that a transcritical bifurcation occurs at R0=1. Taking into account the control strategies like screening, treatment and isolation (social distancing measures), we present the optimal control problem of minimizing the cost due to the application of these measures. By reducing the values of some parameters, such as death rates (representing a management effort for all categories of people) and recovered rates (representing the action of reduction in transmission, improved screening, treatment for individuals diagnosed positive to COVID-19 and the implementation of barrier measures limiting contamination for undiagnosed individuals), it appears that after 140-170 days, the peak of the pandemic is reached and shows that by continuing with this strategy, COVID-19 could be eliminated in the population.

Evacuating occupants from a burning building presents significant challenges owing to the complex interplay between environmental factors and evacuee behavior. Numerous models have been proposed in recent years for building evacuation during fire emergencies. However, many of these models fail to adequately account for variations in individuals’ environmental knowledge and health status, both of which can critically affect the evacuation process. To address this gap, this study introduces an agent-based model for building evacuation that differentiates between individuals familiar with the building layout and those who are not, and considers the health status of occupants. The resulting model assigned specific evacuation strategies tailored to each group within the building. Simulations conducted in a hospital setting demonstrate that incorporating factors such as familiarity with a building’s configuration and the health status of evacuees results in more realistic and effective evacuation outcomes. To evaluate the robustness of the proposed model and highlight the significance of our contribution, we conducted multiple simulations in various high-risk settings and presented a benchmarking study comparing our model with several existing models from the literature. The proposed model is sufficiently generalized to be applicable to various types of hospital buildings with minimal modifications.

A logical presentation of the Mean-Median Compromise Method (MMCM) is provided in this paper. The objective is to show that the method is a generalization of majority judgment, where each tie-break step is $L^p$ deepest voting. Therefore, in its tie-breaking procedures, the proposed method returns scores that range from the median to the mean. Among the established characteristics that it satisfies are universality, neutrality, independence of irrelevant alternatives, unanimity, and monotonicity. Additionally covered are robustness, reaching consensus, controlling extremes, responding to single-peakedness, and the impact of outliers. Through computer simulations, it is shown that the MMCM score does not vary by more than 12% even for up to 50% of strategic voters, ensuring the method’s robustness. The 1976 Paris wine taste along with the French presidential poll organized by OpinionWay in 2012 were well-known and highly regarded situations in the area of social choice to which the MMCM was used. The outcomes of MMCM have shown remarkable consistency. On the basis of the democratic standards that are most frequently discussed in the literature, other comparisons were performed. With 19 of the 25 criteria satisfied, the MMCM is in the top ranking. Supporting theorems have shown that MMCM does not necessarily require an absolute majority to pass an opinion for which a minority expresses a strong preference while the majority is only marginally opposed.

In this paper, we propose the use of virtual labs (VLs) as a solution to bridge the gap between theory and practice in physics education. Through an experiment conducted in two towns in the Democratic Republic of the Congo (DRC), we demonstrate that our proposed lab (BRVL) is more effective than global alternatives in correcting misconceptions and ensuring compliance with the current curriculum in the DRC. We combine Conjoint Analysis (from SPSS) to weigh selected criteria—curriculum compliance, knowledge construction, misconception correction, and usability—alongside eight MCDA methods: AHP, CAHP, TOPSIS, ELECTRE I, ELECTRE II, ELECTRE TRI, PROMETHEE I, and PROMETHEE II. Our findings show that, among six VLs, BRVL consistently outperforms global alternatives like Algodoo and Physion in terms of pedagogical alignment, curriculum compliance, and correction of misconceptions for Congolese schools. Methodologically, the respondents are consistent and in agreement, despite individual differences. The sensitivity analysis of the ELECTRE and PROMETHEE methods has shown that changes in parameter values do not alter the conclusion that BRVL is the best among the compared VLs.

This article examines the pedagogical potential of video games and artificial intelligence (AI) in fostering ecological literacy among secondary students in Sub-Saharan Africa. As mobile technologies and digital play become increasingly embedded in youth culture, these tools offer immersive and adaptive pathways to engage with pressing environmental challenges. Grounded in theories of flow and situated learning, the paper proposes a dual approach: using culturally resonant games to cultivate systems thinking and ethical reflection and leveraging AI to personalize ecological simulations and support context-sensitive learning. Design principles emphasize cultural relevance, infrastructure sensitivity, and alignment of ethical artificial intelligence. This article concludes with a research agenda aimed at mapping, testing, and evaluating game-based and AI-enhanced learning environments, inviting interdisciplinary collaboration to transform digital play into a tool for ecological preservation.