Université Hassan II Mohammedia

In the face of growing challenges posed by climate change, enhancing the resilience of the agricultural sector has become a crucial issue to ensure food security and achieve the sustainable development goals. In rainfed areas, agricultural production and the use of natural resources are directly linked to climatic conditions, which explains the multiplicity of adaptation strategies employed by farming households to ensure the resilience of agricultural systems. This research reflects the concerns regarding the strengthening of agricultural resilience in semi-arid regions. The research examines through a Target MOTAD (Minimization of Total Absolute Deviation) model the assessment of different risk level scenarios among farmers in the studied area. The findings reveal that these farmers exhibit a relatively high degree of risk aversion, with an estimated coefficient of ϕ = 1.8. This analysis highlights how farmers balance the risks associated with crop failures and income variability due to climatic uncertainties.The findings underscore the complexity of implementing adaptation strategies that minimize risks while ensuring long-term resilience. Ultimately, the study offers practical insights into how climate resilience can be strengthened through targeted interventions, focusing on both technological adoption and risk management for vulnerable farming communities.

Optimising the control of wind power systems based on Doubly-Fed Induction Generators (DFIG) raises complex technical challenges, intrinsically linked to the non-linear natureof these machines. With this in mind, this study presents a comparison of two distinct control approaches: Proportional-Integral (PI) control, and Backstepping control, designed specifically to address the challenges posed by unstable and variable dynamics.The methodological approach is based on a DFIG model built on the foundations of vector control. This theoretical framework is implemented into a MATLAB/Simulink environment. Backstepping control, in particular, is stabilised by means of a rigorous construction of the Lyapunov function, guaranteeing error convergence and robustness in the face of disturbances. The simulation results highlight the differences in performance. Whilethe classic PI control approach is robust to parametric variations, it results in a slower response time (27.6 ms) and higher static error (0.2%). Its simple structure and efficient implementation make it a reliable choice in industrial environments with limitedresources. In contrast, the Backstepping method significantly reduces overshoot, improves system response time (0.18 ms), and achieves a notable reduction in static error (0.064%), demonstrating its superiority in dynamic and unstable environments. This approach excels in managing the non-linearities inherent in wind energy systems, giving it a clear advantage in unstable or fluctuating environments. In short, this study does not simply juxtapose two methods; it outlines the future of more adaptive, more responsive control. While PI remains a faithful ally in simplicity, Backstepping technology offers a promising approachto the development of smart energy systems.

Recent studies have demonstrated that incorporating both spectral and spatial information significantlyenhances the accuracy of hyperspectral image (HSI) classification. Since HSI data is commonly structuredas 3D cubes, where two dimensions represent spatial information and the third encodes spectral bands,applying 3D spatial filtering becomes an intuitive and effective way to extract spectral–spatial features simultaneously.In this work, we propose a deep 3D Convolutional Neural Network (3D–CNN) architecturefor accurate and efficient HSI classification. Unlike many previous methods, the proposed model processesthe hyperspectral cube directly as a volumetric input, eliminating the need for manual feature extractionor dimensionality reduction. This design allows the network to jointly learn spatial and spectral dependencieswhile maintaining a compact architecture with fewer trainable parameters. Experimental evaluationson two benchmark datasets—Pavia University and Salinas Valley—demonstrate that the proposedmodel achieves 95% overall classification accuracy, compared to 88% obtained with the conventionalK-Nearest Neighbors (KNN) baseline. The 3D–CNN consistently outperforms traditional methods acrossmost land-cover classes, particularly in spectrally similar or complex regions, confirming its effectivenessfor spectral–spatial land cover mapping. These results highlight the practicality and robustness of deepvolumetric learning for real-world hyperspectral image analysis

Exhaust gas cleaning systems are now routinely installed on ships to comply with international sulfur emission limits. While their performance on air emissions is well understood, monitoring of scrubber washwater discharges is still limited in practice. In many cases, operational measurements are available, but their connection to regulatory compliance remains indirect or unclear. In the proposed approach, we focus on how existing shipboard data can be used more effectively for washwater compliance assessment. We propose a data-driven and explainable approach that combines physics-based proxy calculations with standard compliance rules, anomaly detection, and machine-learning tools. High-frequency operational data from several diesel generator units are used to estimate washwater acidity when continuous pH measurements are not available. The proxy calculation relies on engine load, washwater flow rate, and seawater buffering capacity. This makes it possible to follow discharge conditions continuously and compare them with both international and United States regulatory limits. The analysis shows a clear difference between steady and transitional operation. During stable operation at moderate to high engine loads, typically between 70 and 75 percent of rated capacity, washwater chemistry remains stable. In these periods, pH values stay within a narrow range, generally between 5.8 and 6.5. Short departures from compliance are observed mainly during transient phases, such as startup, shutdown, or rapid load changes. These events are brief and usually last only a few minutes. An anomaly detection method based on interquartile ranges is able to identify these excursions without reacting to isolated sensor noise. On top of this analytical layer, we introduce a supervised classification model as an early-warning tool. The model is trained using a set of temporal and operational features derived from the data. Under cross-validation, it achieves a mean area under the receiver operating characteristic curve of 0.89 and a recall of 98 percent. An explainable analysis of the model output shows that predicted violations are primarily associated with changes in buffering capacity, variations in washwater flow, and short-term pH behavior. These results suggest that washwater compliance risks are not random. They are closely linked to operating regimes and to specific system transitions. The proposed approach illustrates how physics-informed and explainable analytics can turn routine shipboard data into practical and auditable compliance information, useful both for ship operators and for regulatory oversight.

Cet article propose une analyse conceptuelle approfondie du rôle stratégique du management responsable appliqué à la fonction Ressources Humaines (RH) dans le renforcement de la performance institutionnelle des universités publiques marocaines. Confrontées à des exigences croissantes de qualité, d’efficience, de transparence et de redevabilité sociale, ces institutions sont appelées à repenser leurs modes de gouvernance et leurs pratiques managériales (Hood, 1991 ; OECD, 2015). Dans ce contexte, les pratiques de gestion responsable des ressources humaines apparaissent comme un levier structurant de transformation organisationnelle, permettant d’articuler performance, éthique et engagement des parties prenantes (Carroll, 1991 ; Greenwood, 2007). S’appuyant sur une revue de littérature pluridisciplinaire portant sur le management responsable, la gestion des ressources humaines, la gouvernance universitaire et la performance institutionnelle, cette recherche développe un cadre conceptuel intégrateur. Quatre leviers fondamentaux de la GRH responsable sont identifiés : l’équité et la justice organisationnelle, la transparence et la redevabilité, la participation et le dialogue social, ainsi que le développement continu des compétences (Ulrich & Brockbank, 2005 ; Paillé, 2017). Ces leviers sont analysés à travers leurs effets attendus sur les dynamiques organisationnelles internes, notamment l’engagement des personnels, la confiance institutionnelle, la qualité du climat social et l’efficience des processus administratifs et académiques (Colquitt et al., 2001 ; Cropanzano & Ambrose, 2015).