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Adjusted trajectory of medication exposure taking into account the periodicity of dispensations and the number of dispensed packs and comparative analysis on EFEMERIS database
No full textInternational audienceWe presented an adjustment method for the calculation of medication exposure trajectories based on the number of dispensed packs and the type of dispensations (occasional or regular). A comparative study based on the EFEMERIS data was carried out using three different scenarios of trajectory calculation depending on whether or not the number of packs and the periodicity of medication dispensations were taken into account. The impact of the scenario was highlighted using global indicators on the number of Define-Daily Dose on all women exposed; the study of changes in individual trajectories from one scenario to another was carried out; we also compared the results of a clustering into four groups. If 65% of the trajectories remained unchanged, we could observe on the rest significant changes in number of DDD and/or on individual exposure profile. We observed 4% of trajectories that were attributed to a different cluster, and the clustering was of better quality with the adjustment method. Depending on the study context, an impact on cluster distribution could be observed for some maternal characteristics and neonatal outcomes. This was the case for a higher occurrence of neonatal pathology for neonates from mothers belonging to the cluster with high doses of psychotropics, thus reinforcing the conclusions of previous studies of a link between high exposure to psychotropic medications and presence of pathology for the newborn.</div
ACPAC bilan 2024
No full textNational audienceBilan des activités 2024 de l'action concertée packaging du PEPR électronique, présenté lors des journées scientifiques annuelles de ce PEP
Self-healing performance of thermally damaged ultra-high performance concrete: Rehydration and recovery mechanism
No full textInternational audienceConcrete suffers significant performance degradation when exposed to high temperatures. This study explored the beneficial role of waste glass powder (WGP) in mitigating thermal damage and ultra-high performance concrete (UHPC) after elevated temperature exposure. The mechanism was elucidated through the chemical and microstructure changes, the composition of hydrates after exposure to elevated temperatures, and the subsequent re-curing. The presence of WGP significantly enhanced the residual mechanical properties of UHPC due to more wollastonite generation. The WGP also facilitated the recovery of mechanical properties and surface morphology during the post-fire self-healing process. The microstructural results confirmed that the WGP promoted the formation of the wollastonite phase in the thermal-damaged UHPC by reacting with the dehydrated products. Thermodynamic simulations indicated that the incorporation of WGP in UHPC resulted in an increase of liquid phase and its early appearance at high temperatures led to the transformation of γ-C2S into more stable wollastonite phases. Meanwhile, the activation of unreacted WGP by limewater further generated secondary hydration products to reduce matrix porosity. These hydrates mainly consisted of C-(N)-S-H gels with a low calcium-to-silicon ratio (Ca/Si) and high sodium-to-silicon ratio (Na/Si) ratio, which could effectively fill the micropores and microcracks in UHPC. As a result, the densified microstructure induced by these regenerated C-(N)-S-H gels largely contributed to the recovery of the thermally damaged UHPC. The outcome of this study provides a decarbonization solution to address damages of UHPC exposed to fire conditions
Introducing 28-nm technology in Microwind
No full textThis paper describes the key performance parameters of the CMOS 28-nm technology and its implementation in Microwind. We focus on the supply voltage, the high-k metal gate, ION/OFF tradeoff, process variants, device options and general rules such as the contacted gate pitch, metal pitch and principles for device implementation. The design of basic gates such as inverter, RAM, DRAM, ring oscillator is illustrated along with their characteristics.This paper describes the key performance parameters of the CMOS 28-nm technology and its implementation in Microwind. We focus on the supply voltage, the high-k metal gate, ION/OFF tradeoff, process variants, device options and general rules such as the contacted gate pitch, metal pitch and principles for device implementation. The design of basic gates such as inverter, RAM, DRAM, ring oscillator is illustrated along with their characteristics
Reflection to improve ESD protection simulation based on voltage-dependent dynamic model
No full textDynamic model of ESD protection devices presents many advantages to simulate the response of these components when they are submitted to very high-level transient impulsions such as EMP residue. However, a major issue with these models is that they are defined for a specific operating point and in many cases the operating point of components can change during EMP event. In this article, a way to improve dynamic simulation by building voltage dependent RLC equivalent models will be explored to overcome this limitation and simulate complex circuit such as a snapback TVS in parallel to a capacitor
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
No full textInternational audienceCausal machine learning holds promise for estimating individual treatment effects from complex data. For successful real-world applications of machine learning methods, it is of paramount importance to obtain reliable insights into which variables drive heterogeneity in the response to treatment. We propose PermuCATE, an algorithm based on the Conditional Permutation Importance (CPI) method, for statistically rigorous global variable importance assessment in the estimation of the Conditional Average Treatment Effect (CATE). Theoretical analysis of the finite sample regime and empirical studies show that PermuCATE has lower variance than the Leave-One-Covariate-Out (LOCO) reference method and provides a reliable measure of variable importance. This property increases statistical power, which is crucial for causal inference in the limited-data regime common to biomedical applications. We empirically demonstrate the benefits of PermuCATE in simulated and real-world health datasets, including settings with up to hundreds of correlated variables
Thermochronology of the Maures-Tanneron crystalline basement: insights for SW Europe Triassic to Miocene tectonic history
No full textInternational audienceThis paper presents a thermochronological study of the Western European basement in the Maures-Tanneron massif (MTM), using zircon and apatite fission-track data, in addition to apatite (U-Th-Sm)/He analyses. The combination of these methods with inverse thermal modelling allows us to trace the thermal history of this massif from the Late Triassic to the present day. The study identifies several thermal events that are linked to two major tectonic phases at 120-40 Ma and 40-15 Ma. These new results prompt us to re-evaluate the thermal evolution and exhumation of Western European basement of the Provence region. We distinguish four episodes. (i) A period characterized by constant temperature contemporaneous with Triassic magmatic activity and Tethys rifting (ii) a period of sedimentary burial heating of the MTM associated with the Cretaceous Pyrenean rift evolution (iii) subsequent N-S Pyrenean inversion at 75 Ma, causing cooling and exhumation of the MTM, (iv) opening of the West European rift system and the Liguro-Provençal basin, which resulted in heating from 35 to 15 Ma and post 15 Ma cooling of the MTM. This study also provides insights into the paleogeography of the MTM and demonstrates its evolution at the cross-roads between the Pyrenean and Alpine orogens
Accélérer la recherche sur les nanomatériaux énergétiques : modèles de substitution basés sur les données pour les simulations coûteuses
No full textThis thesis explores the development and application of machine learning techniques, particularly surrogate modeling and adaptive sampling, to address the computational challenges associated with the simulation and optimization of nanothermite combustion. Traditional physical simulations of nanothermites are computationally intensive, limiting their exploitation for structural design through optimization. To overcome these limitations, this research focuses on building efficient surrogate models and implementing adaptive data generation strategies that significantly reduce computational costs while maintaining predictive accuracy. The first phase of this work involves benchmarking various machine learning algorithms to identify the most effective surrogate modeling approach for nanothermite combustion. Multilayer Perceptrons (MLPs) emerged as the optimal choice, achieving superior accuracy and computational efficiency compared to other models, including Gaussian Process Regression (GPR). These surrogates demonstrated the capability to approximate the nonlinear dynamics of combustion processes with remarkable precision, paving the way for their integration into adaptive sampling frameworks. The second phase introduces an Interest Region Bayesian Sampling (IRBS) methodology, designed to inject physical knowledge into the sampling process to generate training data efficiently by focusing on regions of interest within the design space. The research emphasizes two critical advancements: enabling parallelized evaluations of candidate designs to accelerate data collection and enhancing the sampling process through a novel figure of merit (FOM) formulation. This FOM leverages Kernel Density Estimation (KDE) for exploration, replacing the computationally expensive GPR uncertainty metric. The KDE-based approach not only improves sampling efficiency but also enhances the adaptability of the framework to diverse data distributions. Furthermore, the integration of MLPs as surrogates within the IRBS framework validated the methodology's scalability to higher dimensional design spaces. The results underscore the potential of adaptive sampling and surrogate modeling to revolutionize the design and optimization of complex materials. By reducing computational overhead and enabling rapid exploration of the design space, this work addresses a key bottleneck in materials science, particularly for energetic nanomaterials like thermites. Beyond its immediate application, the proposed framework lays the groundwork for extending machine learning techniques to multi-physics simulations and the optimization of other high-dimensional, computationally demanding systems.Cette thèse explore le développement et l'application de techniques d'apprentissage automatique, en particulier la modélisation par substitut (surrogate modeling) et l'échantillonnage adaptatif, pour relever les défis computationnels associés à la simulation et à l'optimisation de la combustion des nanothermites. Les simulations physiques traditionnelles des nanothermites sont extrêmement coûteuses en termes de calcul, ce qui limite leur exploitation pour la conception via l'optimisation. Pour surmonter ces limitations, cette recherche se concentre sur la construction de modèles substitutifs efficaces et la mise en œuvre de stratégies adaptatives de génération de données, permettant de réduire considérablement les coûts de calcul tout en maintenant une précision prédictive élevée. La première phase de ce travail consiste en une évaluation comparative de divers algorithmes d'apprentissage automatique afin d'identifier l'approche de modélisation par substitut la plus efficace pour la combustion des nanothermites. Les Perceptrons Multi-Couches (MLP) se sont révélés être le choix optimal, offrant une précision et une efficacité calculatoire supérieures par rapport à d'autres modèles, y compris la Régression par Processus Gaussiens (GPR). Ces modèles de substitution ont démontré leur capacité à approximer les dynamiques non linéaires des processus de combustion avec une précision remarquable, ouvrant la voie à leur intégration dans des cadres d'échantillonnage adaptatif. La deuxième phase introduit une méthodologie d'échantillonnage bayésien axée sur les régions d'intérêt (Interest Region Bayesian Sampling, IRBS), conçue pour injecter des connaissances physiques dans le processus d'échantillonnage afin de générer efficacement des données d'entraînement en se concentrant sur des régions spécifiques de l'espace de conception. Cette recherche met en avant deux avancées majeures : l'implémentation d'évaluations parallélisées des configurations optimales pour accélérer la collecte des données, et l'amélioration du processus d'échantillonnage à travers une nouvelle formulation d'une fonction de mérite (FOM). Cette fonction utilise une estimation de densité par noyau (Kernel Density Estimation, KDE) pour l'exploration, remplaçant la métrique coûteuse d'incertitude basée sur le GPR. L'approche basée sur le KDE améliore non seulement l'efficacité de l'échantillonnage, mais renforce également l'adaptabilité du cadre à des distributions de données diverses. De plus, l'intégration des MLP comme modèles substitutifs dans le cadre d'IRBS a validé la possibilité d'implémenter cette méthodologie aux espaces de conception de plus haute dimension. Les résultats mettent en lumière le potentiel de l'échantillonnage adaptatif et de la modélisation par substitut pour révolutionner la conception et l'optimisation de matériaux complexes. En réduisant les coûts de calcul et en permettant une exploration rapide de l'espace de conception, ce travail répond à une limitation majeure dans la science des matériaux, en particulier pour les nanomatériaux énergétiques tels que les nanothermites. Au-delà de ses applications immédiates, le cadre proposé établit une base pour l'extension des techniques d'apprentissage automatique à des simulations multi-physiques et à l'optimisation d'autres systèmes de haute dimension et exigeants en puissance de calcul
Diode PIN verticale sur diamant (100) autosupporté à base de couches i et n dopées phosphore
No full textNational audienceCet article présente la réalisation et la caractérisation de diodes bipolaires n + -i-p + en diamant sur substrat autosupporté (100) caractérisées électriquement jusqu'à 600 °C sous vide à l'aide d'un équipement unique en France. Les résultats obtenus mettent en évidence la faisabilité et les performances prometteuses des diodes PIN en diamant pour des applications en électronique de puissance
A Survey on TDoA-based localization schemes for Long Range Wide Area Networks
No full textInternational audienceThe Global Navigation Satellite System (GNSS) is the most widely used technology for localization, offering realtime, high-accuracy positioning with global coverage. However, its limitations, such as high energy consumption and signal obstruction in certain environments, make it unsuitable for some emerging applications. One such challenge is the localization of very low-power devices, which require alternative positioning solutions. As a result, research efforts are increasingly focused on improving localization precision and addressing challenges related to energy efficiency, coverage limitations, and signal reliability. This paper provides an overview of existing Internet of Things (IoT) solutions, with a particular emphasis on LoRaWAN (Long Range Wide Area Networks) as the deployed technology and Time Difference of Arrival (TDoA) as the primary localization technique