Publications
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Culture organisationnelle et mobilité durable : une approche méthodologique en contexte universitaire
Face au changement climatique, les établissements d’enseignement supérieur jouent un rôle clé dans la promotion de comportements durables. Cette étude explore l’impact de la culture organisationnelle sur les choix de transport des étudiants. Pour ce faire, une enquête a été menée auprès de 294 étudiants en mastère spécialisé au CESI, entre février et mai 2024. […]
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Multi-objective optimization of artificial neural networks using Fast NSGA-II for electricity demand forecasting
Accurate short-term electricity demand forecasting is a critical requirement for modern power systems, as forecast errors directly affect generation scheduling, market prices, and operational costs, particularly under dynamic pricing environments and increasing demand volatility. This study proposes an integrated forecasting framework combining Artificial Neural Networks (ANNs) with Multi-Objective Optimization (MOO) to jointly improve predictive accuracy […]
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Bi-Objective Electric Vehicle Charging Scheduling Under Stochastic Charging Durations
This paper addresses the electric vehicle charging scheduling problem under stochastic charging durations, where uncertainty arises from variations in actual charging times that are typically assumed deterministic in existing literature. We formulate a bi-objective optimization problem minimizing the expected values of peak load and total tardiness. We explicitly enforce non-overlapping charging sessions within the objective […]
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ResGNN: a residual GNN approach for leveraging general user preferences in session-based recommender systems
In recent years, session-based recommender systems (SBRSs) have emerged as pioneers for intelligent recommendation environments by capturing short-term user preferences without requiring direct access to user history. However, the challenge remains in effectively considering both short-term and long-term user preferences. Graph neural networks (GNNs) have shown promise in this field by leveraging the structural information […]
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Advanced PV-enabled heat generation system with precise thermal power regulation
This study presents an advanced PV-enabled heat generation system with precise thermal power regulation for resistive heating applications. Conventional solar thermal systems often rely on direct PV–resistor coupling, which leads to poor energy utilization, or MPPT-based operation, which maximizes electrical extraction but provides limited control over chamber temperature. To address these limitations, the proposed system […]
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LOOPER: A framework for synthetic dataset generation with configurable sensors and multi-view XR environments
Collecting multi-view datasets is essential for training and evaluating AI models in domains such as robotics and autonomous systems. However, generating such datasets remains challenging due to sensor synchronization issues and the labeling process, which is often timeconsuming, error-prone, and dependent on manual intervention. To address these limitations, a novel framework, LOOPER (Light Object and […]
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Toward Seamless Human-Robot Collaboration: VR-Enhanced Teleoperation with Transparent Shared Control
Shared control is widely used in robotic telemanipulation to combine human input with autonomous assistance. However, assis- tive behaviors are often embedded within the control loop and remain difficult for operators to interpret, leading to potential misalignment be- tween user intent and system response. To resolve this misalignment, we propose an immersive teleoperation system based […]
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Strength, repairability, and debonding of vitrimer structural adhesive joints
Introduction: Debondable and repairable epoxy-based vitrimer adhesives offer a sustainable solution to conventional epoxy adhesives for structural applications. This study evaluated the performance of a vitrimer adhesive in structural single-lap-bonded joints and investigated its repairability and debonding characteristics in comparison with those of a conventional epoxy adhesive. Materials and methods: Metal substrates, aluminum (Al6061) and […]
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Synergistic data-resource participant selection for efficient Federated Edge Learning in IoT ecosystems
The Internet of Things (IoT), as a concept, is becoming increasingly integral to our daily lives, enabling smart environments through sensing, communication, and computation. However, real-world edge devices exhibit pronounced heterogeneity and inherent limitations in both computational resources and data distributions, posing significant challenges for deploying robust, efficient, and adaptive edge intelligence. We propose FedCDRP, […]
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Decision-support analytics for material selection for production tooling: A systematic review and multi-objective optimisation of biocomposites
Biocomposites are increasingly promoted as sustainable substitutes for conventional composites, yet material selection remains uncertain because existing studies rely on heterogeneous datasets, inconsistent sustainability indicators, and divergent decision models. This paper addresses this gap by combining a systematic literature review with an auditable decision-support workflow for sustainable biocomposite selection, retaining 58 primary studies. The synthesis […]
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DRIFT: Distributed RPL Injection Flooding Threat in IoT Networks
The Routing Protocol for Low-Power and Lossy Networks (RPL) is widely adopted in IoT environments due to its efficiency in handling constrained devices and networks. However, RPL’s vulnerability to specific attacks presents significant security challenges. This paper introduces DRIFT, a novel distributed DIS attack targeting RPL-based IoT networks. DRIFT attack involves multiple malicious nodes flooding […]
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Fed-DCSRW: a privacy-preserving, dynamic client selection framework for heterogeneous federated learning via roulette wheel mechanism
We introduce Fed-DCSRW, a federated-learning framework that combines three pillars: (i) a decentralized, data-parallel client-clustering stage that computes centroids in parallel to scale and compute efficiently across heterogeneous clients; (ii) a centroid-based, noise-tolerant Roulette-Wheel client-selection strategy; and (iii) end-to-end differential privacy on both loss reports and gradient updates. The parallel clustering phase partitions the distance […]