HAL-INSA Toulouse
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Compressible Euler equations with time-dependent damping in the critical regularity setting: global well-posedness and strong relaxation limit
No full textWe investigate the relaxation problem and the diffusion phenomenon for the compressible Euler system with a time-dependent damping coefficient of the form µ (1+t) λ in R d (d ≥ 1). We establish uniform regularity estimates with respect to the relaxation parameter ε and prove the global wellposedness of classical solutions to the Cauchy problem. In addition, we justify the global-in-time strong convergence of the solutions towards those of a general porous medium-type diffusion system, with an explicit rate of convergence, and for ill-prepared initial data. The core of our proof relies on a refined hypocoercivity framework combined with a new time-dependent frequency decomposition, both adapted to handle damping terms with time-dependent coefficients. This enables us to treat the overdamped regime λ ∈ (-∞, 0) and the underdamped regime λ ∈ (0, 1) for any µ > 0, and also the borderline critical case λ = 1 under the improved condition µ > 2ε 2 .</div
Decoding ladybird’s colours: Structural mechanisms of colour production and pigment modulation
No full textInternational audienceThis study investigates the mechanisms underlying colour production in the family Coccinellidae, focusing on two model species: Adalia bipunctata (L.) and Calvia quatuordecimguttata (L.). In this family, colours have traditionally been attributed primarily to pigments such as carotenoids and melanins. We propose an alternative perspective, considering the elytra as an integrated optical medium whose optical properties -and hence colouration -result from both its architectural design and the properties of its constituent materials, including matrix and pigments. In the present work, the elytron microstructure was precisely determined by transmission electron microscopy and the numerical replica was then injected into numerical simulations of the microstructure's interaction with light, showing that the elytron structure is able to select a range of wavelengths and then generate colour. Coupling these results with local pigment analyses and microstructural examination of elytra, we show that while pigments are central to patterning and contribute to colour, the overall colour also results from one or more physical mechanisms that may operate simultaneously. In the light of these results, we suggest that the complex and diverse colouration in the Coccinellidae can only be elucidated by considering the interplay of pigments and the optical properties of the elytron cuticle. From an evolutionary ecology point of view, elytra structure influence on colouration may provide new insights into colour signalling in this insect family
Porting codes to GPUs
No full textInternational audienceIn this presentation I explain the differences between CPUs and GPUs, and why porting codes to GPUs is hard, and sometimes unsuccessful
HIGH-THROUGHPUT MECHANOBIOLOGICAL CELL DISCRIMINATION USING AUTOMATED AFM AND MACHINE LEARNING
No full textInternational audienceMechanobiological measurements offer a promising avenue for distinguishing healthy cells from pathological ones. However, a major limitation of atomic force microscopy (AFM)—a widely used technique for such measurements—is its low throughput and lack of standardization[1]. In this study, we optimized AFM-based mechanical measurements on cell populations and developed a novel technology that integrates cell patterning with AFM automation, significantly increasing measurement efficiency. [2]Our system enables the acquisition of mechanical data from hundreds of cells, with 956 cells analyzed in this study. For each cell, 16 force curves (FCs) were recorded, and seven key mechanical features per FC were extracted, forming a comprehensive mechanome dataset. To classify these measurements, we employed a machine learning-based approach using a fuzzy logic algorithm trained to distinguish between nonmalignant and cancerous cells. The training dataset included up to 120 cells per cell line.As a proof of concept, we first applied our method to prostate cell lines—nonmalignant RWPE-1 and cancerous PC3-GFP—before extending it to skin fibroblast lines—nonmalignant Hs 895.Sk and cancerous Hs 895.T. Despite a high degree of similarity across measurements (ranging from 79% to 100%), our method achieved a classification accuracy of 73% on a validation dataset comprising 194 cells per cell line.These results demonstrate the potential of combining AFM automation with machine learning for high-throughput mechanobiological cell classification. This approach not only enhances measurement efficiency but also provides a standardized framework for analyzing cell mechanics, paving the way for future applications in cancer diagnostics and mechanobiology research.Références : exemple de format ci-dessous[1] Thomas- -Chemin, et al. ACS Nano 19.5 (2025): 5045-5062[2] Thomas - - Chemin, et al., ACS Applied Materials and Interfaces 16.34 (2024): 44505-44517Adresse mail : [email protected]
Latent Space-Driven Quantification of Biofilm Formation using Time Resolved Droplet Microfluidics
No full textBacterial biofilms play a significant role in various fields that impact our daily lives, from detrimental public health hazards to beneficial applications in bioremediation, biodegradation, and wastewater treatment. However, high-resolution tools for studying their dynamic responses to environmental changes and collective cellular behavior remain scarce. To characterize and quantify biofilm development, we present a droplet-based microfluidic platform combined with an image analysis tool for in-situ studies. In this setup, Bacillus subtilis was inoculated in liquid Lysogeny Broth microdroplets, and biofilm formation was examined within emulsions at the water-oil interface. Bacteria were encapsulated in droplets, which were then trapped in compartments, allowing continuous optical access throughout biofilm formation. Droplets, each forming a distinct microenvironment, were generated at high throughput using flow-controlled pressure pumps, ensuring monodispersity. A microfluidic multi-injection valve enabled rapid switching of encapsulation conditions without disrupting droplet generation, allowing side-by-side comparison. Our platform supports fluorescence microscopy imaging and quantitative analysis of droplet content, along with time-lapse bright-field microscopy for dynamic observations. To process high-throughput, complex data, we integrated an automated, unsupervised image analysis tool based on a Variational Autoencoder (VAE). This AI-driven approach efficiently captured biofilm structures in a latent space, enabling detailed pattern recognition and analysis. Our results demonstrate the accurate detection and quantification of biofilms using thresholding and masking applied to latent space representations, enabling the precise measurement of biofilm and aggregate areas
Hybrid Lyapunov-based feedback stabilization of bipedal locomotion based on reference spreading
No full textInternational audienceWe propose a hybrid formulation of the linear inverted pendulum model for bipedal locomotion, where the foot switches are triggered based on the center of mass position, removing the need for pre-defined footstep timings. Using a concept similar to reference spreading, we define nontrivial tracking error coordinates induced by our hybrid model. These coordinates enjoy desirable linear flow dynamics and rather elegant jump dynamics perturbed by a suitable extended class function of the position error. We stabilize this hybrid error dynamics using a saturated feedback controller, selecting its gains by solving a convex optimization problem. We prove local asymptotic stability of the tracking error and provide a certified estimate of the basin of attraction, comparing it with a numerical estimate obtained from the integration of the closed-loop dynamics. Simulations on a full-body model of a real robot show the practical applicability of the proposed framework and its advantages with respect to a standard model predictive control formulation
Micro separator of aerosol particles based on the thermophoretic effect
No full textInternational audienceThe micro-separator of aerosol particles based on thermophoretic effect is proposed. The first step in designing such a micro-device is the development of the mathematical model. The three-dimensional model is proposed and several key parameters are tested such as the length and width of the microchannel, the intensity of the temperature gradient and the velocity of the carrier gas. The next steps will be the fabrication of a prototype and then testing its efficiency
Identification of hybrid systems with dynamics-based modeling through symbolic regression
No full textInternational audienceHybrid systems combine both continuous and discrete behavior. These systems serve as models in many fields, including control systems, robotics, and industrial processes. However, due to their complexity, finding an accurate model is a challenge. This paper presents a holistic approach to learning models of hybrid systems using symbolic regression. Our method leverages symbolic regression to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. An advantage of our algorithm is that it detects transitions between different behavioral modes of a system based on the inherent dynamics. From learned expressions for the dynamical behavior of a system, we form a hybrid system by combining the learned expressions with a decision tree determining the current behavioral mode from data. This hybrid decision tree serves regression, prediction, and further related tasks. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real hybrid system and predict output signals on new input data with high accuracy
TCAD simulations of a barrier structure designed to improve the performance of very-low bandgap InAs/InAsSb thermophotovoltaic cells
No full textInternational audienceVery-low bandgap TPV cells (< 0.4 eV) usually require cryogenic cooling (∼ 70 K) because of their high intrinsic dark current densities. To mitigate this effect, a barrier structure combined with a gallium-free InAs/InAsSb absorber, inspired by infrared photodetectors, is studied through optoelectronic TCAD simulations. This analysis reveals that the power output of the cell could be increased by 24% at 200 K thanks to this design, when compared to an equivalent PIN structure. The quantitative effects of contact doping, parasitic absorption, and carrier diffusion length are discussed in detail. An optimum design is extracted, predicting a power output of 0.3 W/cm2 and pairwise efficiency of 16% at 200 K under the illumination of an 800 °C black-body emitter. The physical mechanisms limiting the performance of the cell at higher temperatures are identified, and a guideline for future improvements is proposed
Etude du comportement à l'impact et à la compression après impact de stratifiés conçus par la méthode Double-Double
No full textInternational audienceEtude du comportement à l'impact et à la compression après impact de stratifiés conçus par la méthode Double-Doubl