All industrial companies are confronted with recurrent problems that have an impact on their overall performance. Despite this, many companies have not developed strategies for managing these problems. This article illustrates the case of an Algerian company where an organisational management strategy for these recurrent problems is proposed. This strategy focuses on the sustainability of a continuous improvement project to reduce these problems. The expected results of this strategy lie in the gradual elimination of recurrent problems and, consequently, an increase in the efficiency of company processes. Hence, there is the possibility of achieving performance in productivity.

Traffic congestion remains a persistent problem that compromises road safety. This phenomenon is often amplified by driver behaviors, particularly those characterized by the herd effect. This study aims to model the emergence and dynamics of the herd effect in traffic jams and to simulate a strategy for mitigating this behavior among drivers. To achieve these objectives, reinforcement learning (RL) was employed within the frameworks of memoryless Markov chains and multi-phase Markov chains. The results demonstrate the effectiveness of Markov chains in accurately modeling the collective behavior of specific drivers. Likewise, the simulations illustrate RL’s capacity to regulate the herd effect and optimize individual decision-making during congestion. The findings suggest that traffic authorities may consider implementing RL-based strategies to mitigate herd behavior, improve traffic flow, and enhance road safety.

Preventing major accidents remains a critical priority in the process industry, given their potentially catastrophic consequences. However, the rare, stochastic, and dynamic nature of such accidents presents significant challenges for conventional risk assessment approaches. This study develops a novel dynamic risk assessment framework that addresses these challenges. The proposed methodology combines Bow-Tie analysis (BT) with Dynamic Bayesian Networks (DBNs) to create a robust decision- support tool for managing major industrial risks. Initially, the BT approach identifies basic events, safety barriers, and potential consequences across multiple equipment operating states. These elements are then systematically integrated into a DBN model, which provides enhanced flexibility in modeling the dynamic failure mechanisms of both basic events and safety barriers through continuous precursor integration. The framework’s effectiveness is demonstrated through a practical application examining a furnace system in a gas treatment unit at Hassi R’mel, Algeria. This case study validates the model’s capability to provide real-time risk assessment updates, offering significant improvements over static risk evaluation methods for industrial safety management.

This study aims to highlight the importance of evaluating explosive atmosphere (ATEX) risks and to
propose a novel, more accurate and comprehensive risk assessment method compared to traditional existing
approaches. To address the limitations of traditional ATEX risk assessment methods, this study introduces a new
approach based on three key parameters: occurrence, detection, and severity. These factors are combined using
a weighted sum (WS) formula to calculate a modified risk priority number (RPN), enhanced by the analytic
hierarchy process.
This hybrid methodology provides a more precise and reliable assessment of ATEX risks. Moreover, the
findings of the proposed WS-RPN method (and its ATEX-specific version, WS-RATEX) demonstrate superior
accuracy and lower duplication rates compared to conventional risk assessment techniques. A sensitivity
analysis confirms the robustness, stability, and reliability of this approach, making it a valuable tool for
quantifying ATEX risks.
It is evident to say that WS-RPN represents a significant advancement in ATEX risk assessments. By
integrating a refined calculation model, and factoring in occurrence, detection, and severity, the latter ensures
a more systematic and reliable evaluation. Thus, with improved risk prioritization, this approach not only enhances
industrial installations’ safety but also serves as a more effective alternative to traditional assessment methods.

The hospital environment is characterised by stressful situations and constant pressures. Resilience is essential for healthcare professionals to cope with these challenges and maintain their well-being and effectiveness. This study aims to assess the level of resilience and its relationship with sociodemographic factors among healthcare professionals working in hospitals in the Algerian province of Batna. To this end, a cross-sectional survey based on an anonymous, self-administered questionnaire (Resilience Scale-25) was conducted in healthcare settings of Batna-Algeria. Sociodemographic characteristics and resilience attributes were analyzed using descriptive and inferential statistics. The results indicate, on the one hand, a low level of resilience among healthcare professionals (mean RS-25 score = 115.63) and, on the other, a significant relationship between resilience and sociodemographic factors (p <0.05). Consequently, the hospitals studied need to adopt appropriate strategies to continuously improve healthcare professionals' resilience.

The current study examined the association between coping strategies and job burnout among health and safety workers of Algerian petroleum refinery, exploring the moderating effect of COVID-19 threat perception. A cross-sectional study was conducted using a self-administrated structured questionnaire between August 2020 and September 2020 among 100 health and safety workers of an Algerian petroleum refinery. Correlations and Moderated linear regressions were used to analyze the data via SPSS V26 and process macro V3.5.3. Results were significant at CI=0.95 and p ≤0.05. The findings indicate that higher problem-focused strategies have been linked to reduced levels of burnout among health and safety workers (B= -4.225, SE= 1.262, p= .001). In contrast, emotion-focused strategies were not related significantly to burnout. COVID-19 threat perception acts as a moderator and mitigates the significant negative relationship between problem-focused
strategies and job burnout (B= -1.1947; SE= .4720; p= .0130). This study contributes to new theoretical and empirical perspectives on the relevance of effective coping strategies to tackle burnout at the organizational and individual levels during the COVID-19 pandemic.

Background: A positive Patient Safety Culture (PSC) is considered as the main barrier to adverse events (AEs) that affect healthcare quality and safety. Thus, the assessment of PSC became a priority for healthcare providers in order to identify problematic areas that need improvement actions.
Method: A cross sectional multi-center study was conducted to evaluate quantitatively PSC in 10 Algerian healthcare establishments (HEs) within the framework of the Algerian Observatory of Safety Culture (ASCO Project). The French version of the HSOPSC was used as a measurement tool where it was administered to participants (N = 1370) using convenience sampling.
Results: A total of 1118 respondents, all professional categories included, participated in this study. The response rate was estimated at 69% of the sample size (N = 1370). After statistical processing, 950 questionnaires were retained. Internal consistency was above 0.7 for all
dimensions. Problematic PSC dimensions were identified, mainly ‘Non-punitive response to error’, ‘Staffing’ and ‘Communication openness’.
Conclusions: This article sheds light on the critical situation of PSC in the Algerian national health system. Quantitative findings were introduced in the framework of the Algerian Safety Culture Observatory project that will serve as a baseline for different stakeholders to guide long-term promotion actions.