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Analytical solution for fuel cell electric vehicle energy management including a long-term predefined velocity profile
No full textInternational audienceThis paper proposes a new Energy Management Strategy (EMS) for Fuel Cell Electric Vehicles (FCEVs) that optimizes energy efficiency and fuel cell durability, while remaining implementable in real-time. The approach uses an analytical solution of the minimization problem for fuel consumption, which provides the constant fuel-cell power required to bring the state of charge (SoC) from its initial value to its desired final value. This global optimization, done for the initial route prediction, is included in a new control framework with a SoC regulator. This controller tracks the desired SoC evolution when the actual vehicle route is perturbed, while respecting constraints that preserve fuel cell longevity by 40 % compared to equivalent SoC controllers in the literature. The performance of the proposed EMS is assessed using Matlab/Simulink simulations for several case studies based on standard and perturbed driving cycles, showing up to a 10 % reduction in hydrogen consumption and up to a 50 % improvement in fuel cell lifespan compared to other energy management strategies
Point Cloud Pretraining Dataset Effects on MaskPoint Classification Performance
No full textInternational audienc
Crunch Time at Trouhard: Evaluating the Smart Money Move for Biscuit Packaging Expansion
No full textThe Case Centre, case study 125-0062-1, teaching note 125-0062-8, teaching note supplement 125-0062-8BThe Case Centre, case study 125-0062-1, teaching note 125-0062-8, teaching note supplement 125-0062-8BThis case study delivers a practical learning experience in capital budgeting and investment evaluation, designed for students in corporate finance and general management programs. Set within the food packaging industry, it challenges students to apply fundamental financial analysis techniques to real-world equipment acquisition decisions. Students assume the role of financial analysts at Trouhard, a specialized food packaging company focused on biscuit wrapping solutions. Their primary responsibility involves evaluating three different financing alternatives for acquiring new packaging equipment using standard capital budgeting methods. The case emphasizes practical application of NPV analysis, cash flow evaluation, and comparative financial analysis. Through this engaging scenario, students strengthen their understanding of investment decision-making fundamentals and develop proficiency in Excel-based financial calculations. They gain hands-on experience applying time value of money concepts to evaluate financing alternatives in an industrial context. This case is suitable for undergraduate and graduate students learning core capital budgeting principles, as well as early-career finance professionals.2025-06-2
Président du jury de thèse en science politique d'Adama Al Fousseni Kafando, « Gouvernance multiniveaux et intégration régionale : Le Conseil des Collectivités Territoriales est-il un organe efficace d’intégration par « le bas » dans l’Union Économique et Monétaire Ouest Africaine (UEMOA) ? », soutenue le 20 mars 2025 à Paris-Est Créteil, dirigée par Sergiu Mişcoiu.
No full textPrésident du jury de thèse en science politique d'Adama Al Fousseni Kafando, « Gouvernance multiniveaux et intégration régionale : Le Conseil des Collectivités Territoriales est-il un organe efficace d’intégration par « le bas » dans l’Union Économique et Monétaire Ouest Africaine (UEMOA) ? », soutenue le 20 mars 2025 à Paris-Est Créteil, dirigée par Sergiu Mişcoiu
ESG Analysis. Methodological approach for the calculation of a composite indicator.
No full textInternational audienc
AI-Driven Detection of Developmental Dysplasia of the Hip in Pediatric Patients
No full textInternational audienceDevelopmental Dysplasia of the Hip (DDH) is a condition affecting newborns and infants, where early diagnosis is crucial to prevent long-term complications. Manual interpretation of radiographs, the current standard for diagnosing DDH, is often time-consuming and prone to human error. This study presents a Convolutional Neural Network (CNN)-based approach for the automatic detection of DDH using pediatric pelvic radiographs. The CNN model achieved an accuracy of 95.5%, with a precision and recall of 96% for DDH cases, outperforming human radiologists in diagnostic accuracy. The use of Gradient-Weighted Class Activation Mapping (GRAD-CAM) provided insight into the model's decision-making process, confirming that the CNN focused on relevant anatomical regions. This study demonstrates the potential of AI to improve the accuracy and efficiency of DDH diagnosis, supporting radiologists in clinical practice
Le “délit de solidarité” en France : histoire, résistances et mutations
No full textInternational audienceCet article retrace l’évolution du « délit de solidarité » en mettant en lumière les interactions complexes entre désobéissance civile et normativité de la règle de droit. Des premières résistances discrètes à la judiciarisation de la cause des aidants, il explore comment les autorités ont tenté de contenir ces élans solidaires par des ajustements législatifs et des stratégies répressives. En réponse, les acteurs de la solidarité ont choisi de transposer leur combat dans l’arène judiciaire, confrontant la norme pénale aux principes constitutionnels. Ce passage des mobilisations citoyennes à la judiciarisation interroge l’efficacité du droit en tant qu’outil de transformation normative face aux résistances institutionnelles
Droit de l'enfant à l'instruction : prérogatives parentales et limites
No full textInternational audienc
Uncoordinated RIS-Aided Uplink IoT Cell-Free Massive MIMO-NOMA Systems
No full textInternational audienceCell-free massive MIMO (CF-mMIMO) has emerged as a promising architecture for beyond 5G networks, offering enhanced coverage and spectral efficiency through distributed antenna arrays. This paper proposes a novel framework integrating reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) in CF-mMIMO for uplink grant-free internet of things (IoT) scenarios. In such scenarios, devices transmit without prior coordination with a central unit, which increases the risk of multi-user interference. NOMA is thus introduced to address this challenge through successive interference cancellation (SIC). IoT devices autonomously select subbands using the multi-armed bandit (MAB) framework. Based on the devices subband choices, RIS angles are configured so as to satisfy the quality-of-service (QoS) requirements of all devices while leveraging NOMA SIC ordering. The resulting optimization problem becomes infeasible in many practical scenarios. To address these cases, we introduce: (1) a classification approach to proactively identify certain infeasible constraints, and (2) an iterative constraint relaxation method to improve the likelihood of finding feasible solutions. Additionally, a dynamic SIC reordering strategy is proposed to further enhance system performance. An enhanced upper confidence bound (UCB) algorithm is also introduced where, upon convergence, devices maintain their subband choices to minimize unnecessary explorations and optimizations. Simulation results demonstrate that our framework achieves higher success rates while reducing the required optimizations compared to baseline approaches
