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III-V growth on patterned graphene covered substrates towards thin film exfoliation and substrate recycling
No full textInternational audienceThis work aims at reducing the cost of III V solar cell by substrate recycling investigating growth on graphene covered substrates It was shown in the literature that the graphene layer allowed the growth of monocrystalline layers with the same orientation as the substrate, while still allowing the exfoliation thanks to the mechanically weak graphene plane. Two possible underlying physical mechanism s could play a role in obtaining a crystallographic alignment of the epi layer: a remote interaction through the graphene, or a nucleation at graphene holes followed by a lateral growth. With our developed process, we have not observed remote interactions. We have therefore investigated the possibility of optimizing growth on patterned graphene. We show that a crucial parameter in obtaining a good quality III V is the graphene opening orientation relative to the substrate. Stripes oriented along <100 > directions on (001) substrates provided the smoothest nanostructures prior coalescence. Surprisingly, afterwards, higher index directions, e.g. 120 4° resulted in the smoothest surfaces, with RMS roughness down to 3.3 nm, and the highest luminescence emission. First exfoliation tests have been carried out, showing a dependance on the peel off direction relative to the stripes orientation, which will be further studied in coming works
What kind of cars do people drive? Including fuel type and emission standards in a car ownership model
No full textInternational audienceCar ownership models are essential for understanding travel behavior and informing transportation policy decisions. However, previous research on car ownership modeling has not addressed the determinants of car ownership related to pollutant emissions such as fuel type or emission standard. This study seeks to fill this gap by comparing the performance of several classification models in predicting the number of cars owned by households, their fuel type and their Euro norm (i.e. car age), while also investigating the significance of explanatory variables. These variables include socioeconomic characteristics, as well as mobility-related variables such as commuting distance, parking availability, and public transportation accessibility to the home and workplace. The methodology is applied to the Paris region. We find that logistic regression performs similarly to supervised learning models, even slightly outperforming them, except for car age estimation in which gradient boosting performs better. Our results show that income and commuting distance jointly have a preponderant explanatory effect on the type of car owned, particularly income for electric cars. This work paves the way for future research evaluating transportation policies related to household car ownership by allowing a deeper understanding of the determinants of the type of car owned by households and by providing the trained classifiers as open data
MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling
No full textRecent years have witnessed a growing interest for time series foundation models, with a strong emphasis on the forecasting task. Yet, the crucial task of out-of-domain imputation of missing values remains largely underexplored. We propose a first step to fill this gap by leveraging implicit neural representations (INRs). INRs model time series as continuous functions and naturally handle various missing data scenarios and sampling rates. While they have shown strong performance within specific distributions, they struggle under distribution shifts. To address this, we introduce MoTM (Mixture of Timeflow Models), a step toward a foundation model for time series imputation. Building on the idea that a new time series is a mixture of previously seen patterns, MoTM combines a basis of INRs, each trained independently on a distinct family of time series, with a ridge regressor that adapts to the observed context at inference. We demonstrate robust in-domain and out-of-domain generalization across diverse imputation scenarios (e.g., block and pointwise missingness, variable sampling rates), paving the way for adaptable foundation imputation models.</div
Evaluating the effects of preprocessing, method selection, and hyperparameter tuning on SAR-based flood mapping and water depth estimation
No full textInternational audienceFlood mapping and water depth estimation from Synthetic Aperture Radar (SAR) imagery are crucial for calibrating and validating hydraulic models. This study uses SAR imagery to evaluate various preprocessing (especially speckle noise reduction), flood mapping, and water depth estimation methods. The impact of the choice of method at different steps and its hyperparameters is studied by considering an ensemble of preprocessed images, flood maps, and water depth fields.The evaluation is conducted for two flood events on the Garonne River (France) in 2019 and 2021, using hydrodynamic simulations and in-situ observations as reference data. Results show that the choice of speckle filter alters flood extent estimations with variations of several square kilometers. Furthermore, the selection and tuning of flood mapping methods also affect performance. While supervised methods outperformed unsupervised ones, tuned unsupervised approaches (such as local thresholding or change detection) can achieve comparable results. The compounded uncertainty from preprocessing and flood mapping steps also introduces high variability in the water depth field estimates.This study highlights the importance of considering the entire processing pipeline, encompassing preprocessing, flood mapping, and water depth estimation methods and their associated hyperparameters. Rather than relying on a single configuration, adopting an ensemble approach and accounting for methodological uncertainty should be privileged. For flood mapping, the method choice has the most influence. For water depth estimation, the most influential processing step was the flood map input resulting from the flood mapping step and the hyperparameters of the methods
Data-driven dimensional analysis applied to single-phase natural circulation
No full textInternational audienc
Modèle de croissance avec externalités pour la transition énergétique via MFG avec variable externe commune
No full textThis article introduces a novel mean-field game model for multi-sector economic growthin which a dynamically evolving externality, influenced by the collective actions of agents,plays a central role. Building on classical growth theories and integrating environmentalconsiderations, the framework incorporates “common noise” to capture shared uncertaintiesamong agents about the externality variable. We establish the existence and uniquenessof the mean-field game equilibrium by reformulating the equilibrium conditions as a For-ward–Backward Stochastic Differential Equation via the stochastic maximum principle,first applying a contraction-mapping argument to guarantee a unique solution, then em-ploying the concept of weak equilibria to prove existence under more general assumptions,and finally invoking a specific monotonicity regime to reaffirm uniqueness. We providea numerical resolution for a specified model using a fixed-point approach combined withneural network approximations.Cet article présente un nouveau modèle de jeu à champ moyen pour la croissance économique multi-sectorielle dans lequel une externalité est dynamiquement influencée par les actions collectives des agents. S'appuyant sur les théories classiques de la croissance et intégrant des considérations environnementales, le modèle intègre un « bruit commun » pour capturer les incertitudes partagées entre les agents concernant la variable d'externalité. Nous démontrons l'existence et le caractère unique d'un équilibre de jeu en champ moyen fort via un argument de contraction, en reformulant les conditions d'équilibre sous la forme d'un système d'équation différentielle stochastique grâce au principe du maximum stochastique. Nous démontrons aussi ce résultat à l'aide d'un théorème d'existence faible ainsi que d'un résultat d'unicité forte, dans un cadre spécifique de monotonie du terme d'interaction.Nous fournissons une résolution numérique pour un modèle spécifié en utilisant une approche de point fixe combinée à des approximations de réseaux neuronaux
Modeling Heat and Mass Transfer for a Floating Photovoltaic Power Plant using the Immersed Boundary Method
No full textInternational audienc
Biomechanical finite element simulation of the pelvic organs under dynamic loading and validation against experimental data from magnetic resonance imaging
No full textInternational audiencePelvic organ prolapse (POP) is a prevalent condition affecting women, particularly those over the age of 50. The etiology and pathophysiology of this condition remain poorly understood within the medical community. In recent years, researchers, particularly medical engineers and biomechanical scientists, have initiated studies on this female pathology. Numerous finite element analyses have been conducted to determine the material properties of tissues involved in POP. Building on the material properties established in prior research, this study presents a patient-specific model derived from patient-specific MRI data. Intra-abdominal pressure (IAP) and boundary conditions were determined from MRI analysis, and the models were validated against MRI simulations encompassing 11 seconds with a 1-second step interval. This study compares the outcomes of our models with MRI results, providing insights into POP biomechanics. A good correlation was observed between MRI data and the finite element method (FEM) models in healthy patients, particularly for the bladder when fluid properties, such as urine, were included. A relative error between 18% and 26% was observed for bladder displacement. Moreover, the models provided acceptable results for the uterus, vagina, and rectum. Visual results supporting these findings are presented in this study
STATION AUTOSALT un nouveau moyen de mesure des débits d’étiage
No full textInternational audienceMonitoring and measuring low water flows is a major challenge for hydrological monitoring.This is because flow conditions are sometimes at the limits of the range of use and uncertainty of traditional measurement methods. Furthermore, fine-scale and/or regulatory monitoring can be difficult to implement due to the operational constraints of measurement networks. This paper discusses the innovative deployment of an automated device for dilution gauging.Le suivi et le jaugeage des débits d’étiage est un enjeu majeur pour le suivi hydrologique des bassins versants.En effet, les conditions d’écoulement sont parfois à la limite des gammes d’utilisation et d'incertitude des moyens de mesures traditionnels, et, de plus le suivi à pas de temps fin et/ou règlementaire peut s’avérer compliqué à mettre en place avec les contraintes opérationnelles des réseaux de mesure. La communication traite du déploiement innovant d'un appareil automatisé pour la réalisation de jaugeages par dilution
Modèle Prédictif de Machine Learning pour la Modélisation des Sources Harmoniques
No full textInternational audienceAvec l'essor des énergies renouvelables et la croissance des convertisseurs électroniques de puissance, les perturbations harmoniques augmentent, affectant ainsi la qualité de l'électricité. Ce papier présente une nouvelle méthodologie de modélisation basée sur l'apprentissage automatique, entraînée sur des bases de données issues de mesures de laboratoire, pour prédire le courant harmonique. Un modèle utilisant les données des chargeurs de véhicules électriques montre des performances prometteuses. L'intégration de ces modèles dans les réseaux basse tension améliore la simulation dans le domaine fréquentiel et renforce l'analyse du réseau