Introduction and aims: Gastro-duodenal ulcers associated with Helicobacter pyloriinfection remain a significant health concern in the semi-arid regions of Algeria, where traditional herbal medicine continues to play a central role in primary healthcare. This study aimed to explore plant-based therapeutic alternatives for managing H. pylori-related gastric disorders in the central Algerian steppe. M&M: An ethnopharmacological survey was conducted in 22 municipalities of the central Algerian steppe. One hundred phytotherapy practitioners were interviewed using questionnaires. Data included socio-demographic characteristics, plant species, plant parts, preparation methods, and perceived therapeutic effectiveness. Results: The socio-demographic profile showed a strong male predominance (79 %), with most practitioners aged 40-60 years (48 %), possessing secondary or university education (55 %), and 10-20 years of professional experience (40 %). A total of 61 medicinal plant species were recorded. The most family importance values were observed for Asphodelaceae (0.68), Amaryllidaceae (0.66), and Cupressaceae (0.63). Leaves and aerial parts were the most frequently used plant organs, primarilyprepared as infusion (51 %) or decoction (27 %). The results also highlight a strong correlation between perceived effectiveness and citation frequency. Conclusions: Traditional knowledge from the central Algerian steppe highlights the potential of certain plants for treating gastro-duodenal ulcers linked to H. pylori but requires scientific validation to ensure efficacy and safety.
The evolution of decentralized power grids has increased the complexity of power-quality monitoring, particularly harmonic fingerprinting and voltage sag diagnosis. Artificial intelligence improves disturbance detection and classification, yet black-box models limit transparency, engineering validation, and operator trust. This review synthesizes 108 selected studies on explainable artificial intelligence (XAI) for power-system diagnostics, focusing on SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), attention-based interpretability, visual analytics, and physics-informed learning. The review integrates harmonic fingerprinting with voltage sag diagnosis through their shared requirements for source attribution, temporal interpretation, physical consistency, and operator-oriented explanation. Four major deployment gaps are identified: data quality, computational latency, physical grounding, and trustworthiness. Future priorities include real-time embedded XAI, physics-informed neural networks, federated learning, standardized trustworthiness metrics, and adaptive model lifecycle management. The findings indicate that reliable autonomous diagnosis requires explainability to be integrated with predictive performance, electrical-system physics, computational efficiency, and field validation. This integration provides a stronger foundation for transparent, resilient, and trustworthy diagnostic systems in decentralized power grids.
Driven by the recent paradigm shift in Algerian higher education towards English as a Medium of Instruction (EMI), this study investigates the motivational orientations of first-year medical students towards learning medical English. Drawing on self-determination theory as its theoretical framework, the research examines the interplay between amotivation, extrinsic motivation, and intrinsic motivation within an English for Medical Purposes (EMP) context. Data were collected through a quantitative research design using a self-administered questionnaire adapted from Noels et al.’s (1999) language learning orientations scale and administered to 91 students at the University of Oum El Bouaghi. The results, analysed via descriptive statistics, indicate that participants exhibit remarkably low levels of amotivation, suggesting a clear recognition of the purpose and utility of the course. Furthermore, findings reveal a robust, balanced motivational profile characterised by a high degree of intrinsic motivation, particularly stimulation, alongside self-endorsed extrinsic motives, most notably identified regulation. These results suggest that while external academic requirements influence engagement, the primary drivers for language acquisition are personal interest and the internalised relevance of English to the students’ future professional identities. The study concludes that fostering autonomy-supportive learning environments is essential to sustaining this autonomous drive and ensuring meaningful engagement within evolving EMI medical faculties.
The emergence of 2D materials as viable candidates for hydrogen storage is driven by their inherently high surface areas and tunable adsorption properties.
