HAL-INSA Toulouse
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Exploring the skin Microbiome of free-ranging Dugong dugon in new Caledonia
No full textInternational audienceThe skin microbiome plays a vital role in the health of marine mammals, serving as a protective barrier and interacting with the host’s immune system. There is limited knowledge about the skin microbiome of dugongs (Dugong dugon), a vulnerable species with declining groups due to anthropogenic threats. This study provides the first comprehensive analysis of the skin microbiome of free-ranging dugongs in New Caledonia using 16S rRNA gene sequencing. The results show that the dominant bacterial phyla on dugong skin are Pseudomonadota, Bacteroidota, and Campylobacterota. Within Bacteroidota, the genus Tenacibaculum - which includes known opportunistic pathogens - was notably the most relatively abundant. Among Pseudomonadota, Psychrobacter was the most dominant genus; although it may contribute to maintaining skin homeostasis, its overrepresentation has been associated with compromised health in other marine mammals. Additionally, the genera Arcobacter and Campylobacter, both belonging to Campylobacterota, include zoonotic species and may warrant future monitoring in dugong populations. Distinct variations were noted between sex, with females predominantly hosting Psychrobacter, while males had higher abundances of Kinneretia and Dasania. Our results align with emerging evidence that marine mammal skin microbiota are shaped by host-specific traits, environmental conditions, and geographic context. These findings provide a baseline for future research on the skin microbiome of dugongs and highlight potential indicators of health and disease in this species
Une curieuse mais épatante matrice nilpotente
No full textInternational audienceNous caractérisons les n racines e 1 , e 2 , ..., e n du polynômen! (celui qui apparaît dans le développement de l'exponentielle) par la nilpotence d'une matrice symétrique n × n construite ad hoc.</div
Réseaux de Bragg en nitrure de silicium et amplificateurs optiques en réflexion GaAs pour la réalisation de lasers hybrides à cavité étendue autour de 965nm
No full textInternational audienceNous présentons une nouvelle plateforme de laser hybride émettant autour de 965nm. Cette cavité est composée d’un réflecteur de Bragg en nitrure de silicium et un amplificateur optique à semi-conducteurs en réflexion en GaAs
User equilibria in heterogeneous discriminatory processor sharing queues
No full textInternational audienceWe consider a strategic routing game for a two-class discriminatory processor-sharing queue with an additional cost for joining the premium class. We show that, depending on the specific parameters of the system, various equilibria can coexist, including equilibria where the queueing system is not ergodic for the equilibrium traffic split. We also investigate how the server can select the priority of the classes and the fees charged to the customers to maximise its revenue. We then investigate learning strategies that converge to particular equilibria. Finally, we study how the elasticity of the traffic demand affects the equilibrium solutions
Hybrid extended cavity laser made of silicon nitride Bragg gratings and GaAs optical amplifiers for frequency comb generation around 965 nm
No full textWe present the characterisation of a silicon nitride chip that includes a Bragg reflector, which, butt-coupled to a Reflective Semiconductors Optical Amplifier, will form an extended cavity laser emitting around 965nm for generating a frequency comb
Design of a 2D metallic photonic crystal for spectral control in thermophotovoltaic devices
No full textInternational audienceThermophotovoltaic (TPV) devices convert thermal radiation from a high-temperature emitter into electricity using a photovoltaic (PV) cell. To maximize power output and efficiency, optical and thermal management is crucial [1]. One approach involves using a selective emitter engineered to emit photons primarily with energies above the PV cell’s bandgap. While effective, such emitters often face thermal stability issues at high operating temperatures. An alternative strategy employs a blackbody emitter and spectrally selective optical filter placed above the PV cell to reflect unwanted photons back to the emitter. This filter must exhibit high transmittance for in-band photons (energy > bandgap) and high reflectance for out-of-band photons near the bandgap (energy < bandgap), while maintaining negligible absorption across the spectrum.Although multilayer structures have been widely used to achieve such spectral selectivity, they typically require many layers and exhibit sensitivity to the angle of incidence, limiting their broadband and omnidirectional performance. As a promising alternative, two-dimensional (2D) photonic crystals (PhCs) consisting of a periodic array of cylindrical holes on a host matrix offer tunable radiative properties. Figure 1 illustrates the TPV design incorporating a 2D metallic PhC inspired by a recent simulation work [2].In this work, we design and simulate a metallic 2D PhC using FDTD to achieve the desired spectral selectivity. The influence of key geometric parameters, such as the radius of the cylindrical holes, their periodicity and thickness, on the PhC’s optical response is investigated. Additionally, we analyze the angular stability of the design.[1] B. Roux et al., Journal of Photonics for Energy, 14(4):042403–1, 2024.[2] S. Zhang et al., Optics Express, 31(6):9186-9195, 2023
MultiNMRFit: a software to fit 1D and pseudo-2D NMR spectra
No full textInternational audienceMotivation Nuclear Magnetic Resonance (NMR) is widely used for quantitative analysis of metabolic systems. Accurate extraction of NMR signal parameters—such as chemical shift, intensity, coupling constants, and linewidth—is essential for obtaining information on the structure, concentration, and isotopic composition of metabolites. Results We present MultiNMRFit, an open-source software designed for high-throughput analysis of 1D NMR spectra, whether acquired individually or as pseudo-2D experiments. MultiNMRFit extracts signal parameters (e.g. intensity, area, chemical shift, and coupling constants) by fitting the experimental spectra using built-in or user-defined signal models that account for multiplicity, providing high flexibility along with robust and reproducible results. The software is accessible both as a Python library and via a graphical user interface, enabling intuitive use by end-users without computational expertise. We demonstrate the robustness and flexibility of MultiNMRFit on 1H, 13C, and 31P NMR datasets collected in metabolomics and isotope labeling studies. Availability and implementation MultiNMRFit is implemented in Python 3 and was tested on Unix, Windows, and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/NMRTeamTBI/MultiNMRFit/ and https://multinmrfit.readthedocs.io, respectively
Dynamic time series segmentation for health monitoring of hybrid systems
No full textInternational audienceMonitoring and diagnosing complex, real-world, industrial hybrid systems require accurate and up-to-date models that can adapt to evolving system behaviors. Such systems, characterized by both continuous and discrete dynamics, are best represented by hybrid models. In this article, we present the segmentation step of HyMED (Hybrid Model Enrichment for Diagnosis), a model-based health monitoring and diagnosis method that monitors hybrid systems and automatically updates the system model if necessary. HyMED uses noisy multivariate time series data to dynamically update models, addressing unanticipated degradations and faults. A key feature of HyMED is its online and passive segmentation step (ODS), which enables robust detection of system mode changes in complex, nonlinear time series. Unlike traditional segmentation methods, ODS dynamically determines its segmentation hyperparameters through an automatic parameter selection process. ODS guarantees adaptability without the need for manual adjustment. The effectiveness of HyMED's segmentation method is demonstrated through a case study on an engine timing system, where its performances are compared to the offline method depicted in the Ruptures library
When majority rules, minority loses: bias amplification of gradient descent
No full textDespite growing empirical evidence of bias amplification in machine learning, its theoretical foundations remain poorly understood. We develop a formal framework for majority-minority learning tasks, showing how standard training can favor majority groups and produce stereotypical predictors that neglect minority-specific features. Assuming population and variance imbalance, our analysis reveals three key findings: (i) the close proximity between ``full-data'' and stereotypical predictors, (ii) the dominance of a region where training the entire model tends to merely learn the majority traits, and (iii) a lower bound on the additional training required. Our results are illustrated through experiments in deep learning for tabular and image classification tasks
Evaluating In-Plane Reliability of Unreinforced Masonry Walls through a Mortar Joint Compressive Strength Random Field Model
No full textInternational audienceThis study explores the reliability of brick masonry walls under in-plane loading by examining the spatial variability of mortar joint compressive strength. A comprehensive framework was developed integrating Finite Element Modeling (CAST3M) and Python scripting to analyze random spatial variations in mortar joint properties. An Artificial Neural Network (ANN) surrogate model was created to efficiently predict wall performance, mitigating the high computational costs associatedwith finite element analysis. The research investigated wall response by using mortar joint compressive strength random fields as input and maximum displacement force as the failure criterion. Different coefficient variations (5%, 15%, 25%, and 35%) of mortar joint compressive strength were analyzed to assess structural reliability. The ANN model enabled prediction of global maximum displacement force probability distribution, facilitating comprehensive reliability assessment. Results demonstrated that spatial variability of mortar joints substantially impact masonry wall performance, particularly in terms of ultimate in-plane displacement strength. The study revealed how mortar joint quality and compressive strength variations directly influence the overall structural reliability of masonry walls. By employing advanced computational techniques, the research provides insights into the complex mechanical behavior of unreinforced masonry structures under in-plane loading conditions.Cette étude explore la fiabilité des murs en maçonnerie de briques sous une charge en plan en examinant la variabilité spatiale de la résistance à la compression des joints de mortier. Un cadre complet a été développé, intégrant la modélisation par éléments finis (CAST3M) et des scripts Python pour analyser les variations spatiales aléatoires des propriétés des joints de mortier. Un modèle de substitution par réseau neuronal artificiel (ANN) a été créé pour prédire efficacement la performancedes murs, en atténuant les coûts de calcul élevés associés à l'analyse par éléments finis. La recherche a étudié la réponse des murs en utilisant des champs aléatoires de résistance à la compression des joints de mortier comme données d'entrée et la force de déplacement maximale comme critère de défaillance. Différentes variations de coefficient (5 %, 15 %, 25 % et 35 %) de la résistance à la compression des joints de mortier ont été analysées pour évaluer la fiabilité de la structure. Le modèle ANN a permis de prédire la distribution de probabilité de la force de déplacement maximale globale, facilitant ainsi l'évaluation globale de la fiabilité. Les résultats ont démontré que la variabilité spatiale des joints de mortier a un impact substantiel sur la performance des murs de maçonnerie, en particulier en termes de résistance ultime au déplacement dans le plan. L'étude a révélé comment la qualité des joints de mortier et les variations de la résistance à la compression influencent directementla fiabilité structurelle globale des murs de maçonnerie. En utilisant des techniques de calcul avancées, la recherche donne un aperçu du comportement mécanique complexe des structures de maçonnerie non renforcées dans des conditions de charge en plan