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Integrated Electrochemical Conversion of Plastic Waste and CO <sub>2</sub> to Formate Using Non-Noble-Metal Catalysts: <i>In Situ</i> Raman Study.
No full textInternational audiencePoly(ethylene terephthalate) (PET), a common single-use plastic, significantly contributes to CO2 emissions when discarded or incinerated. In this study, we have employed an innovative approach by combining electrochemical PET hydrolysate oxidation and CO2 reduction reaction (CO2RR) to simultaneously produce formate in a single electrochemical cell. Utilizing simple electrochemical methods, a porous 3D carbon felt (CF) electrode was anodically oxidized to produce activated carbon felt (aCF). The latter was used as a support for the electrochemical deposition of bismuth oxide carbonate (Bi2O2CO3) and nickel cobalt phosphate (NiCoPOx) for CO2RR and anodic PET hydrolysate oxidation, respectively. In situ Raman analysis indicated that MOOH (M = Ni, Co) intermediates acted as active sites for PET hydrolysate oxidation, with the ability to regenerate into lower-valence nickel species post-reaction. Both electrodes exhibited Faradaic efficiencies (FEs) exceeding 90% in their respective half-cell reactions. When implemented in a two-electrolyzer setup, a combined FE of up to 158% for both reactions was recorded at a remarkably low cell voltage of 1.8 V. This research highlights the use of non-noble metals to transform PET plastic waste and CO2 into valuable fuels
Loi du 9 juillet 2025 créant l'homicide routier et visant à lutter contre la violence routière : texte symbolique ou durcissement de la réponse pénale ?
No full textInternational audienc
XAI for wireless communications
No full textInternational audienceThe native artificial intelligence (AI) concept is envisioned to be integrated into 6G future communications. Due to the black box nature of the majority of AI models, the decision-making strategy used by these models is critical, risky, and challenging. This issue can be tackled by developing explainable AI (XAI) schemes that aim to explain the logic behind the black box model behavior, and thus, ensure its efficient and safe deployment. In this context, this chapter highlights the main challenges of the recent AI-based solutions for wireless communications, in particular, physical (PHY) layer applications. In addition to that, the latest research efforts toward designing XAI schemes for PHY layer applications are discussed. As a case study, this chapter presents an XAI-based scheme for channel estimation in wireless communications, where the presented scheme shows that employing XAI can offer numerous advantages, including (1) understanding the black box model behavior, (2) reducing the overall computational complexity of the employed AI model, and (3) improving the performance of the desired application. Finally, a list of future research directions is provided
Une profession dans les marges des catégories statistiques : les guides-conférencières
No full textInternational audienceLe métier de guide[1] (touristique, conférencière, interprète, etc.) est au cœur d’un paradoxe : bien qu’il s’agisse d’une profession réglementée, nécessitant la détention d’une carte professionnelle, il est impossible de connaître le nombre exact de personnes qui l’exercent. Cette difficulté se conjugue avec la complexité d’une description précise de leur situation statutaire. En effet, chaque guide paraît relever d’une configuration d’emploi singulière.À partir du cas des guides et en articulant analyses qualitatives et quantitatives, ce numéro de Connaissance de l’emploi montre les contraintes autant que les possibilités liées au cumul (de statuts, d’activités, d’employeurs/clients), la manière dont le cumul se pratique au quotidien et au fil de la carrière, ainsi que les difficultés d’appréhension statistiques qu’elles entraînent. [1] La féminisation de la profession jusqu’à plus de 80 % - selon les estimations – justifie d’employer le féminin générique
Etude et conception de codebooks pour les systèmes de communications millimétriques à moyenne et grande mobilité: applications aux trains à grande vitesse
No full textThe revolution in railway communication systems is underway, driven by the need forfaster, more reliable communication networks that can support the high-speed demandsof modern trains. At the heart of this transformation are millimeter-wave (mmWave)communication systems, which promise unprecedented data rates, ultra-low latency, andenhanced connectivity for dynamic environments like high-speed railways. This thesisexplores the pivotal role of mmWave technologies, MIMO architectures, and advancedbeamforming techniques in addressing the unique challenges of high-speed train communications, while also shaping the future of next-generation wireless networks. The workbegins by tracing the evolution of wireless communication, from 1G to 5G and beyond(B5G), and highlights how mmWave systems will play a central role in the transition toB5G/6Gnetworks. Special attention is given to the integration of these systems in the Future Railway Mobile Communication System (FRMCS), which seeks to enable seamless,high-performance connectivity for trains, passengers, and infrastructure. Focusing onbeamforming techniques, the research introduces predefined codebook-based approaches tailored to the dynamic conditions of railway environments. Through detailed simulations, the study examines how beam selection strategies impact system performance, showcasing how these methods can improve communication reliability and efficiency for high-speed trains. Building upon this, the thesis explores the integration of machine learning (ML) to enable smart beam prediction. Using ML algorithms like neural networks and support vector machines, a novel, adaptive beamforming solution is proposed, offering enhanced beam accuracy and improved spectral efficiency in real-time dynamic environments. Further advancing the technology, the thesis introduces a vision-aided 3D codebook design for beam tracking in Train-to-Infrastructure (T2I) communication systems. By leveraging location and visual data, this innovative method improves beam alignment, ensuring robust communication even over long distances between base stations and fast-moving trains. Simulation results validate the performance gains of this approach, particularly in line-of-sight (LoS) communication scenarios. The integration of artificial intelligence (AI) into hybrid beamforming techniques is also explored, offering an AI-driven approach to RF beam prediction and baseband precoding. This cutting edge technology promises to enhance communication efficiency, scalability, and reliability, addressing the growing needs of high-speed, complex communication networks. In conclusion, this thesis demonstrates how mmWave communication systems, coupled with advanced beamforming and AI techniques, are poised to revolutionize railway communication. The findings provide a roadmap for enhancing connectivity and performance in high-speed transportation systems, while laying the groundwork for future breakthroughs in B5G/6G wireless networksLa révolution des systèmes de communication ferroviaire est en cours, alimentée par le besoin de réseaux de communication plus rapides et plus fiables capables de répondre aux exigences de haute vitesse des trains modernes. Au cœur de cette transformation se trouvent les systèmes de communication à ondes millimétriques (mmWave), qui promettent des débits de données inédits, une latence ultra-faible et une connectivité améliorée pour des environnements dynamiques comme les chemins de fer à grande vitesse. Cette thèse explore le rôle clé des technologies mmWave, des architectures MIMO et des techniques avancées de formation de faisceau pour relever les défis uniques des communications dans les trains à grande vitesse, tout en façonnant l’avenir des réseaux sans fil de prochaine génération. Le travail commence par retracer l'évolution des communications sans fil, de la 1G à la 5G et au-delà, en mettant en évidence la manière dont les systèmes mmWave joueront un rôle central dans la transition vers les réseaux B5G/6G. Une attention particulière est portée à l’intégration de ces systèmes dans le Future Railway Mobile Communication System (FRMCS), qui vise à permettre une connectivité transparente et haute performance pour les trains, les passagers et les infrastructures
Contrôle opéré par la Cour de cassation sur les motifs de rejet de la demande d’audition d’un mineur formée par une partie à la procédure (commentaire de l’arrêt Cass., 1re civ., 12 juin 2025, n° 23-13.900)
No full textInternational audienceEn matière familiale, lorsque l’une des parties formule une demande d’audition d’un mineur, le juge ne peut rejeter cette demande que pour l’un des quatre motifs limitativement énumérés par l’art. 338-4 C. pr. civ. (l’enfant n’est pas concerné par la procédure ; l’enfant n’est pas capable de discernement ; son audition n’est pas nécessaire à la solution du litige ; son audition est contraire à son intérêt). La Cour de cassation vérifie que le refus d’auditionner l’enfant est effectivement fondé sur l’un de ces motifs
[Invited] Bio-inspired hierarchical phononic crystals and metamaterials: Bioinspired metamaterials
No full textInternational audienceMarco Miniaci is a permanent researcher at the French National Scientific Research Centre (CNRS) appointed at the Institute of Electronics, Microelectronics and Nanotechnology (IEMN) of Villeneuve D’Ascq, in France. His research interests cover theoretical, numerical, and experimental aspects of wave propagation and structural mechanics of phononic crystals and elastic metamaterials. Marco Miniaci currently is the principal investigator of the ERC StG « POSEIDON » (dealing with bioinspired metamaterials, topological protection and underwater acoustics), coordinator of the MAGNIFIC HORIZON-CL4-2022-RESILIENCE-01-10 (dealing with materials for a next generation of (nano-)opto-eòectro-mechanical systems), and participate into the MetAcMed HORIZON-MSCA-2022-DN-01 (dealing with acoustic and mechanical metamaterials for biomedical and energy harvesting applications)
L’intérêt supérieur de l’enfant dans les séparations parentales
No full textInternational audienceLes décisions concernant un enfant après la séparation de ses parents sont prises en principe par eux, lorsqu’ils parviennent à s’entendre sur les conséquences de leur séparation pour leur enfant, ou, à défaut, par le juge aux affaires familiales, quand il est saisi par les parents ou par l’un d’eux aux fins de trancher le différend qui les oppose au sujet de la résidence de leur enfant et des droits de visite et d’hébergement. Dans les deux cas, quelles que soient les personnes ou l’autorité décisionnaire, l’intérêt supérieur de l’enfant doit être une considération primordiale des décisions le concernant. Ce principe a été posé depuis longtemps par les traités internationaux, puis a été repris au sein des deux ordres juridiques européens que constituent l’Union européenne et le Conseil de l’Europe, puis par le législateur français (I). Si le principe de primauté de l’intérêt supérieur de l’enfant, notamment en contexte de séparation parentale, est aujourd’hui affirmé de façon très claire et indiscutable par les textes, il est loisible de s’interroger sur sa mise en œuvre et, partant, de se poser la question de son efficacité (II)
Advancing wheat crop analysis: A survey of deep learning approaches using hyperspectral imaging
No full textInternational audienceAs one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSIbased wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following GitHubRepository.</div
Computer vision in warehouse management automation: A survey on implemented methods with prototyping hardware
No full textInternational audienceIn the context of the fourth industrial revolution, warehouse management systems are increasingly integrating advanced computer vision technologies, such as Autonomous Mobile Robots (AMRs), drones, Unmanned Aerial Vehicles (UAVs), robotic arms, and smart glasses. Despite their growing adoption, a critical gap remains in understanding the specific hardware configurations and computer vision methodologies used in prototyping for warehouse automation. This survey offers a comprehensive analysis of recent prototyping efforts by exploring the relationship between computer vision, vision sensor technologies, and Artificial Intelligence (AI) hardware. Through rigorous manual screening of 1,570 papers, we identify 50 key studies that explicitly detail the implementation of computer vision techniques in warehouse automation tasks since 2013. Unlike existing reviews, our work uniquely highlights the gap between computer vision methods and their deployment on edge AI hardware for warehouse automation. We analyze the adopted techniques in image processing, object detection, classification, segmentation, and navigation, alongside the latest advancements in AI accelerators and robotic platforms. Our findings stress the necessity of transitioning to next-generation AI hardware, implementing standardized benchmarking methods, leveraging photorealistic synthetic datasets, and deploying vision-based AI models on advanced hardware platforms. This surveying method ensures robustness and easy reproducibility for future use, serving as a critical reference for researchers and industry practitioners seeking to stay continuously updated on warehouse automation prototypes with computer vision