ECAM-EPMI

The socio-ecological transition requires not only technological innovation but also new ways of recognizing the social, environmental, and territorial value generated by collective action. Many of these positive externalities remain invisible in conventional assessment frameworks, limiting the legitimacy, financing, and scaling of local sustainability initiatives. This article presents a strategic framework designed to identify and structure positive externalities in collective self-consumption and other transformative projects. The method combines four components: (i) normative identification through the Sustainable Development Goals; (ii) balanced multi-stakeholder participation to surface diverse perspectives; (iii) perceptive mapping using an adapted Kano model; and (iv) strategic articulation. The framework was applied in two contrasting contexts: an energy community centered on shared renewable production, and a women’s empowerment program focused on capability-building and social innovation. These applications do not aim at empirical replication or the validation of results, but at examining how the framework supports collective recognition and strategic structuring in different organizational settings. Across these distinct settings, it led to the formulation of coherent and actionable strategic roadmaps, illustrating how positive externalities can inform governance choices, strengthen institutional legitimacy, and support long-term project consolidation. These results suggest that collective recognition enables externalities to structure strategic action beyond their original sector, demonstrating the potential transferability of the approach. Developed within a research program supported by the French Agency for Ecological Transition (ADEME) and the national urban-transition initiative (PUCA), the framework provides a practical decision architecture for structuring shared value within coordinated strategies.

This article explores controlled fusion as a promising avenue for sustainable energy production. It delves into two leading approaches -Magnetic Confinement Fusion (MCF) and laser-driven Inertial Confinement Fusion (ICF)— highlighting their principles and challenges. These artificial methods are compared with natural stellar fusion to underscore the distinct physical and technical challenges of achieving controlled fusion on Earth. While significant progress has been made, challenges in energy efficiency, plasma stability, and scalability remain, guiding the trajectory of future research.

West African coastlines are increasingly exposed to the combined effects of climate change and human interventions, resulting in accelerated shoreline erosion, sediment imbalance, and heightened risks for coastal cities and infrastructures. This study investigates the recent evolution of the Senegalese–Mauritanian coastline, extending over approximately 300 km between the ports of Tanit and N’Diago, with a particular focus on the impacts of coastal engineering structures. An integrated methodology combining field measurements, multi-temporal satellite imagery (2005– 2023), UAV surveys, and sediment grain-size analysis is applied to identify erosion and accretion patterns and assess sediment transport dynamics. Observations reveal significant shoreline retreat and accretion induced by port jetties and breakwaters, highlighting strong disruptions of longshore sediment transport. To complement these observations, simplified numerical modelling approaches based on one-line shoreline models and finite difference methods are implemented to simulate shoreline evolution under various scenarios. The results demonstrate the relevance of these models for predicting long-term coastal behavior in data-scarce environments. This work provides practical insights for integrated coastal zone management and supports the development of adaptive coastal protection strategies for vulnerable West African coastlines..

Le travail présenté dans cet article propose une évaluation spatio-temporelle du phénomène sec/humide durant la période des pluies dans le nord de l'Algérie, dans le but de fournir des prévisions de sécheresse pour une gestion durable de l’eau. Nous avons utilisé les chaînes de Markov (CM) afin de modéliser le comportement du phénomène sec/humide, qui est catégorisé en neuf classes selon les valeurs de l'indice SPI (Standard Precipitation Index). Les données d’entrée proviennent d’estimations satellitaires des précipitations annuelles sur la période 1985-2021. La méthode a été validée par des prévisions à court terme, qui ont montré une bonne concordance avec les données réelles.

Wheat is a primary carbohydrate-rich food commodity in the world, and the health of the wheat plants influences its production. Farmers can face a challenge due to decreased productivity caused by infected plants. However, wheat farmers have used conventional methods to survey diseases; however, these methods are often ineffective and inefficient, taking a lot of time and labor. And for enhanced wheat production, new strategies must be employed. Deep learning, specifically computer vision-based techniques, has proven significant capability in tasks like image classification, segmentation, and object detection. Deep learning techniques such as You Only Look Once (YOLO) models are state-of-the-art neural network algorithms used for accurate object detection. this study presents a comparative evaluation of two state-of-the-art object detection models, YOLOv5 and YOLOv8, for disease detection. Data augmentation techniques such as image noise, rotation, and flipping were implemented to improve the model’s performance during the training phase. The model’s performance was evaluated using metrics such as precision, recall, F1-score, and mean Average Precision (mAP).. The results show that the YOLOv5 and YOLOV8 models achieved good performance. They were able to detect the healthy and disease in images, These findings demonstrate that while both models are highly effective, but YOLOv8 offers greater robustness and accuracy for real-time detection.

Abstract The dynamics of chaotic memristor-based systems offer promising potential for secure communication. However, existing solutions frequently suffer from drawbacks such as slow synchronization, low key diversity, and poor noise resistance. To overcome these issues, a novel fractional-order chaotic system incorporating a memristor emulator derived from the Shinriki oscillator is proposed. The main contribution lies in the enhanced dynamic complexity and flexibility of the proposed architecture, making it suitable for cryptographic applications. Furthermore, the feasibility of synchronization to ensure secure data transmission is demonstrated through the validation of two strategies: an active control method ensuring asymptotic convergence, and a finite-time control method enabling faster stabilization. The robustness of the scheme is confirmed by simulation results on a color image: χ 2 = 253/237/267 (R/G/B); entropy ≈7.993; correlations between adjacent pixels in all directions are close to zero (e.g., −0.0318 vertically); and high number of pixel change rate and unified average changing intensity (e.g., 33.40% and 99.61%, respectively). Peak signal-to-noise ratio analysis shows that resilience to noise and external disturbances is maintained. It is shown that multiple fractional orders further enrich the chaotic behavior, increasing the systems suitability for secure communication in embedded environments. These findings highlight the relevance of fractional-order chaotic memristive systems for lightweight secure transmission applications.

<p>In the dynamic field of artificial intelligence, genetic algorithms (GAs) offer a powerful approach to solving complex problems by mimicking biological mechanisms such as mutation, crossover, and natural selection. Their efficiency relies primarily on the fitness function, which evaluates the quality of candidate solutions and guides the evolutionary process toward an optimal outcome. A well-designed fitness function not only enhances convergence speed but also reduces the risk of stagnation and improves algorithmic accuracy. This paper explores the fundamental role of fitness functions in optimization, machine learning, multi-objective optimization, and cryptography, highlighting their impact on the performance of GAs. We propose a novel fitness function that incorporates the influence of crossover, mutation, and inversion rates on solution quality. This approach, which diverges from conventional models, demonstrates improved convergence behavior and adaptability across different problem domains. The proposed method enhances GA performance not only in secure data encryption but also in general optimization and learning tasks, making it a valuable contribution for both researchers and practitioners, which can open new avenues for research in the development of more robust evolutionary strategies that can adapt effectively to the specific characteristics and challenges of each problem domain.</p>

This paper evaluates a microgrid's potential for a Lebanese village (1.26MVA), based on a real project. The microgrid primarily sources power from existing synchronous generators and solar power systems. This study explores methods to ensure optimal voltage quality by analyzing the voltage profiles of the microgrid at various buses throughout the year. Using the ETAP power flow analysis module, the steady-state behavior of the voltage profile is assessed across the four seasons. The primary objective is to compare the effectiveness of droop control applied to synchronous generators and the use of shunt capacitor banks in improving the voltage profile at different buses within the microgrid. The findings indicate that droop control is more suitable for large loads, while shunt capacitors are effective for low loads.

In a world grappling with the challenges of climate change and global warming, the adoption of renewable sources of electricity generation emerges as a crucial and imperative solution to address these environmental issues. Among the valuable renewable energy sources, solar photovoltaic (PV) technology is widely recognized as one of the most beneficial and effective renewable energy sources for sustainable electricity generation. In this aim, this paper studies the residential self-consumption using a single-phase standalone PV/battery system. This work presents the modeling and the study of the power flow of the single-phase standalone PV/Battery system implemented on MATLAB/Simulink. Furthermore, this work proposes an efficient control strategy of the PV inverter to maintain a stable output voltage for the PV inverter as well as a steady DC bus voltage. A thoroughly analyzing of the simulation results are presented contributing significantly to a deeper comprehension of the system's overall performance.