HAL Portal Cnam (French National Conservatory for Arts and Crafts)
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A Blockchain-Enabled Multi-Layered Zero-Trust Security Framework for O-RAN
No full textInternational audienc
A gut microbiome-kidney-heart axis predictive of future cardiovascular diseases.
No full textAbstract Cardiometabolic diseases (CMD) are on the rise globally with one billion people expected to suffer from obesity and 643 million from type 2 diabetes by 2030, of which one-third will likely develop chronic kidney disease and two-thirds will die from cardiovascular disease (CVD). However, the mechanistic and molecular drivers of the transition from health to disease remain elusive. Here, in 275 metabolically healthy individuals recruited to the MetaCardis study, we identify a gut microbiome-kidney-heart axis that is predictive of future cardiovascular events. This axis, as evidenced by the associations between gut microbial metabolism of phenylalanine and tyrosine with variations in both kidney functon(as measured by estimated glomerular filtration rate) and circulating pro-atrial natriuretic peptide concentration, shows a depletion pattern in metabolically unhealthy participants of the MetaCardis study (n = 1,602) indicating a loss of health-sustaining microbiome features with CMD progression. We then validate that microbial compounds from the phenylalanine and tyrosine pathways and their host co-metabolites act as mediators of the gut microbiome-kidney associations. Moreover, Mendelian Randomization analysis adds genetic evidence to suggest that the microbial mediator metabolites regulate host kidney function and vice versa. Finally, we demonstrate that plasma metabolites derived from the microbial metabolism of phenylalanine and tyrosine associate with incident CVD in the Canadian Longitudinal Study on Aging (n = 8,669). Collectively, our results depict the presence of a gut microbiome-kidney-heart axis in metabolically healthy individuals. Major aberrations of the gut microbiome as part of this axis throughout life may increase risk of CVD
Model-based thermal drift compensation for high-precision hexapod robot actuators
No full textInternational audienceThermal expansion is a significant source of positioning error in high-precision hexapod robots (Gough-Stewart platforms). Any variation in the temperature of the hexapod's parts induces expansion, which alters their kinematic model and reduces the robot's accuracy and repeatability. These variations may arise from internal heat sources (such as motors, encoders, and electronics) or from environmental changes. In this study, a method is proposed to anticipate and therefore correct the thermal drift of one of the hexapod precision electro-mechanical actuators. This method is based on determining a model that links the expansion state of the actuator at any given moment to the temperature of some well-chosen points on its surface. This model was initially developed theoretically. Its coefficients were then adjusted experimentally on a specific test-bench, based on a rigorous measurement campaign of actuator expansion using a high-precision interferometric measurement system. Experimental validation demonstrates a reduction of thermally induced expansion by more than 80%. This paves the way for thermal drift correction across the entire robot or similar robotics parts
Impact of Shift-Angle Topologies on the Performance of PCB-Embedded Solenoid Inductors
No full textInternational audienceThe integration of magnetic components within printed circuit boards (PCBs) offers an innovative pathway to enhance electromagnetic performance while reducing size and improving manufacturability in power electronics. This study investigates the impact of shift-angle topologies in PCB-embedded solenoid inductors, focusing on minimizing electromagnetic losses to enhance high-frequency performance for applications such as compact DC/DC converters and power modules. Four angular configurations were analyzed, assessing how different trace alignments influence flux distribution, eddy current generation, and inductance behavior. To isolate the effects of angle shift, the study employed Finite Element Method (FEM) simulations under direct current (DC) excitation, eliminating the influence of skin effect, proximity effect, core loss, and parasitic capacitance. Results showed that modifying the shift angle significantly altered flux density-especially the perpendicular (Z) component responsible for eddy current formation. Among the DC-tested topologies, Configurations with two counter wise angles in the coils top-layer and bottom-layer traces demonstrated the lowest flux loss and minimal eddy current dissipation. Experimental validation confirmed the simulation findings, with electromagnetic losses within a 6% margin. Tests under alternating current (AC) conditions using an impedance analyzer verified the benefits of reverse angle configurations but revealed that the angle position affects the resonance frequency
SimSDP, a Rapid Prototyping tool for Radio Astronomy: From NenuFAR Experiments to SKAO-Scale Simulation
No full textInternational audienceThe Square Kilometre Array Observatory (SKAO) will generate unprecedented volumes of data, requiring its Science Data Processor (SDP) to operate at terabyte rates under strict energy and performance constraints. Anticipating the computational cost of imaging algorithms is therefore critical for both astronomers and system designers. Yet, testing new algorithmic strategies directly on large HPC platforms is often impractical, due to cost, limited availability, and long development cycles.To address this, we present SimSDP, a rapid prototyping tool designed to support the development and optimization of radio-astronomy imaging pipelines for the future SKAO SDP. The tool enables early exploration of algorithmic choices and their impact on computational performance and energy consumption without requiring access to large-scale systems. In this work, we illustrate its use through three case studies: (1) modeling different strategies for spectral parallelism (joint vs. distributed deconvolution), (2) simulating pipeline execution on large-scale multi-core, multi-node systems with realistic observation sizes, and (3) prototyping pipelines with real-world data from the NenuFAR instrument. These experiments highlight how SimSDP can help astronomers and instrument designers anticipate the computational cost of imaging algorithms, compare different processing strategies, and better align algorithmic development with the capabilities of future HPC infrastructures. The tool is integrated into the PREESM framework and available as open-source, with future work aiming at extending models to GPU-based systems
Advanced Anomaly Detection for Maritime IoT Systems: Integrating Semi-Markov Processes for Robust Cybersecurity
No full textInternational audienceThe rapid advancement of digital technologies in maritime operations has led to an urgent demand for specialized cybersecurity strategies, particularly to counter Distributed Denial of Service (DDoS) attacks targeting critical communication protocols like NMEA 2000. This paper presents a distinctive approach utilizing stochastic semi-Markov processes specifically designed to enhance the cybersecurity defenses of maritime IoT systems. By modeling and analyzing the unique state transitions within the maritime network, our proposed model detects unusual activity and issues immediate alerts to operators regarding potential DDoS threats, thereby maintaining stable and secure maritime communications. Extensive simulations illustrate the model's high precision, effective detection rates, and very low error margins, positioning it as a reliable and specialized solution for safeguarding critical maritime infrastructure. This work makes a substantial contribution to maritime cybersecurity by delivering proactive and adaptive defenses against increasingly sophisticated cyber threats.</div
Prévalence et tendances des prescriptions d’antibiotiques dans les hôpitaux de Conakry, Guinée : une enquête multicentrique transversale
No full textInternational audienceBackground Inappropriate use of antibiotics is a major driver of antimicrobial resistance (AMR), particularly in low- and middle-income countries. Understanding antibiotic use and patterns in hospital settings is essential for promoting rational use and optimizing antimicrobial stewardship (AMS). This study aims to assess the extent of antibiotic prescribing in secondary and tertiary hospitals in Conakry, Guinea, and to evaluate the appropriateness of these prescriptions on the basis of WHO recommendations via the Access, Watch, Reserve (AWaRe) classification of antibiotics. Methods A multicentre cross-sectional survey was conducted from June to October 2024 to assess patient antibiotic use levels across six hospital wards in Conakry, capital of Guinea. The prevalence of antibiotic prescriptions, with 95% confidence intervals (CI), was compared across patient, prescriber and ward variables. Antibiotic use was categorized by Anatomic Therapeutic Chemical and AWaRe classifications. Associations between categorical variables were assessed using the Chi-square or Fisher's exact test. Univariate and multivariate logistic regression were used to analyse factors associated with antibiotic prescription. Results Of 1482 patients surveyed, the overall prevalence of antibiotic prescriptions was 35.0% (95% CI: 32.6–37.5), with significant differences between inpatients (83.4%, 95% CI: 78.1–87.6) and outpatients (25.1%, 95% CI: 22.7–27.6). The total number of antibiotics prescribed was 669, and the most commonly prescribed antibiotics were beta-lactams/beta-lactamase inhibitors (24.2%), followed by third-generation cephalosporins (21.7%), imidazoles (18.5%), and penicillins (13.6%). Almost all antibiotic courses (99.4%) were started empirically, without microbiological testing to guide choice. Regarding the AWaRe classification of all prescribed antibiotics, Access antibiotics accounted for 64.3% (430/669), and 33.3% (223/669) were from the Watch group. Conclusions The results of this study, conducted in six hospital departments, provide an overview of antibiotic prescriptions in Conakry hospitals, with a high prevalence of antibiotic prescription, particularly among inpatients and almost all courses were initiated empirically without microbiological guidance. These findings underscore the urgent need for AMS programs and interventions.ContexteL’utilisation inappropriée d’antibiotiques est un facteur majeur de la résistance aux antimicrobiens (RAM), en particulier dans les pays à revenu faible et intermédiaire. Comprendre l’utilisation et les habitudes des antibiotiques en milieu hospitalier est essentiel pour promouvoir une utilisation rationnelle et optimiser la gestion des antimicrobiens (AMS). Cette étude vise à évaluer l’étendue de la prescription d’antibiotiques dans les hôpitaux secondaires et tertiaires de Conakry, en Guinée, et à évaluer la pertinence de ces prescriptions sur la base des recommandations de l’OMS via la classification Access, Watch, Reserve (AWaRe) des antibiotiques.MéthodeUne enquête multicentrique transversale a été menée de juin à octobre 2024 pour évaluer les niveaux d’utilisation d’antibiotiques chez les patients dans six services hospitaliers de Conakry, capitale de la Guinée. La prévalence des prescriptions d’antibiotiques, avec des intervalles de confiance (IC) de 95 %, a été comparée entre les variables patient, prescripteur et service. L’utilisation d’antibiotiques était classée par classification anatomique, thérapeutique chimique et AWaRe. Les associations entre variables catégorielles ont été évaluées à l’aide du test du chi-carré ou du test exact de Fisher. La régression logistique univariée et multivariée a été utilisée pour analyser les facteurs associés à la prescription d’antibiotiques.RésultatsParmi les 1482 patients interrogés, la prévalence globale des prescriptions d’antibiotiques était de 35,0 % (IC 95 % : 32,6–37,5), avec des différences significatives entre les patients hospitalisés (83,4 %, IC 95 % : 78,1–87,6) et les patients ambulatoires (25,1 %, IC 95 % : 22,7–27,6). Le nombre total d’antibiotiques prescrits était de 669, et les antibiotiques les plus couramment prescrits étaient les inhibiteurs bêta-lactames/bêta-lactamase (24,2 %), suivis des céphalosporines de troisième génération (21,7 %), des imidazoles (18,5 %) et des pénicillines (13,6 %). Presque toutes les cures d’antibiotiques (99,4 %) ont été commencées empiriquement, sans tests microbiologiques pour guider le choix. Concernant la classification AWaRe de tous les antibiotiques prescrits, les antibiotiques d’accès représentaient 64,3 % (430/669), et 33,3 % (223/669) provenaient du groupe Watch.ConclusionsLes résultats de cette étude, menée dans six services hospitaliers, offrent un aperçu des prescriptions d’antibiotiques dans les hôpitaux de Conakry, avec une forte prévalence de prescriptions d’antibiotiques, en particulier chez les patients hospitalisés, et presque tous les traitements ont été initiés empiriquement sans guidance microbiologique. Ces résultats soulignent l’urgence de programmes et d’interventions AMS
Neural network determination of crystallite size and microstrain from X-ray powder diffraction data
No full textThe present work sets out the development of a convolutional neural network (CNN) dedicated to the determination of crystallite size and microstrain in polycrystalline materials, from powder X-ray diffraction (XRD) data. The CNN was trained using synthetic data generated with a dedicated code based on the GSAS-II software. We employ the example of monoclinic ZrO2 to critically examine the theoretical performances of the CNN. The accuracy of the predictions was systematically investigated within a crystallite size range of 5 -1000 nm and a microstrain range of 0.05-2%. In this context, the role of the resolution of the diffractometer and the XRD peak profile shape is discussed in details. For instance, on a low-resolution laboratory diffractometer it possible to determine the crystallite size within a 5-100 nm with 99.7% accuracy in the absence of microstrain, and 97.3% accuracy when microstrains are present. Higher accuracies are obtained with a highresolution diffractometer and over an extended range of size and microstrain. The potential of the CNN is demonstrated by the analysis of the crystallite size and microstrains in MgAl O formed by a ₂ ₄ solid-state reaction between MgO and Al O using ₂ ₃ in situ XRD at 1200 °C at the BM01 beamline at the ESRF. The CNN can determine the crystallite size and microstrain in 0.004 s per diagram, with values in remarkable agreement with those obtained by a conventional Rietveld refinement. We further demonstrate that the same training dataset can be used for other regression problems, for instance phase fraction quantification, with no additional computational cost and only minor modifications to the network architecture. This work paves the way for real-time data analysis at synchrotron facilities
Sex-Specific Leisure-Time Physical Activity and Sedentary Behavior Patterns in French Adults
No full textInternational audienceBackground : Both physical activity (PA) and sedentary behavior impact health, and defining combined patterns will help design targeted interventions and policies. There are marked differences between sexes in PA level. This work aimed to define sex-specific behavioral patterns combining leisure-time PA and sedentary behavior and to assess their relations with sociodemographic factors and obesity in a population-based national survey. Methods : Data were collected in 2014–2016 from a representative sample of French adults (Esteban cross-sectional study) using the Recent Physical Activity Questionnaire. In 1491 women and 1157 men, behavioral clusters were identified using multiple correspondence analysis and hierarchical classification on most frequently performed leisure-time PA and screen time. Results : Three clusters were identified in each sex. In women, cluster 1 (61.7%) included physically inactive individuals with high screen time. It was associated with lower education level (odds ratio [OR] = 1.6) and higher likelihood of obesity (OR = 2.4). Clusters 2 (22.2%) and 3 (16.1%) included women performing multiple PA of high or low duration, respectively. In men, cluster 1 (39.9%) included individuals with low PA level and high screen time. It was associated with younger age (OR = 3,9), obesity (OR = 2.5), single life (OR = 0.3), and urban residence (OR = 0.5). Cluster 2 (43.1%) included men performing mainly walking, cycling, DIY, and gardening and cluster 3 (17.0%) men with multiple PAs, both with low screen time. Conclusions : In both sexes, the pattern including inactivity or low PA and high sedentary behavior was associated with obesity. Other patterns differed according to sex. The findings can guide targeted interventions to promote healthy behaviors, considering sex differences
ViLU: Apprentissage d'incertitude langage et visuel pour la prédiction d'erreur
No full textInternational audienceReliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new Vision-Language Uncertainty quantification framework that contextualizes uncertainty estimates by leveraging all task-relevant textual representations. \ours constructs an uncertainty-aware multi-modal representation by integrating the visual embedding, the predicted textual embedding, and an image-conditioned textual representation via cross-attention. Unlike traditional UQ methods based on loss prediction, \ours trains an uncertainty predictor as a binary classifier to distinguish correct from incorrect predictions using a weighted binary cross-entropy loss, making it loss-agnostic. In particular, our proposed approach is well-suited for post-hoc settings, where only vision and text embeddings are available without direct access to the model itself. Extensive experiments on diverse datasets show the significant gains of our method compared to state-of-the-art failure prediction methods. We apply our method to standard classification datasets, such as ImageNet-1k, as well as large-scale image-caption datasets like CC12M and LAION-400M. Ablation studies highlight the critical role of our architecture and training in achieving effective uncertainty quantification.La quantification fiable de l'incertitude (UQ) et la prédiction des défaillances restent des défis à relever pour les modèles de vision-langage (VLM). Nous présentons ViLU, un nouveau cadre de quantification de l'incertitude visuelle-linguistique qui contextualise les estimations d'incertitude en exploitant toutes les représentations textuelles pertinentes pour la tâche. \ours construit une représentation multimodale sensible à l'incertitude en intégrant l'intégration visuelle, l'intégration textuelle prédite et une représentation textuelle conditionnée par l'image via une attention croisée. Contrairement aux méthodes UQ traditionnelles basées sur la prédiction des pertes, \ours forme un prédicteur d'incertitude en tant que classificateur binaire afin de distinguer les prédictions correctes des prédictions incorrectes à l'aide d'une perte d'entropie croisée binaire pondérée, ce qui le rend indépendant des pertes. Notre approche est particulièrement adaptée aux configurations post-hoc, où seuls les intégrations visuelles et textuelles sont disponibles sans accès direct au modèle lui-même. Des expériences approfondies sur divers ensembles de données montrent les gains significatifs de notre méthode par rapport aux méthodes de prédiction des défaillances de pointe. Nous appliquons notre méthode à des ensembles de données de classification standard, tels que ImageNet-1k, ainsi qu'à des ensembles de données d'images et de légendes à grande échelle, tels que CC12M et LAION-400M. Des études d'ablation soulignent le rôle essentiel de notre architecture et de notre formation dans la réalisation d'une quantification efficace de l'incertitude