HAL-INSA Toulouse
Not a member yet
34325 research outputs found
Sort by
Using numerical simulation as a basis of security and environmental sustainability engineering of composite structures subjected to a lightning strike
No full textInternational audienceSustainable engineering of innovative composite structures has become a key area for meeting the global challenges of safety and environmental sustainability for tomorrow's aircraft. Today's aircraft are very well designed to withstand and tolerate lightning damage. Nevertheless, the complexity that is accompanied by a lack of knowledge of the physical mechanisms that occur for all possible combinations of material choice or design of these structures make it very difficult to systematically include sustainability conditions in engineering. The harmful effect of thick layers of paint has recently been demonstrated, against all odds, and without any real understanding of the phenomena involved. To define sustainable design specifications, it is necessary to better understand the relationship between the energy deposited in the very first moments of a lightning strike, and the transformation of this energy into a combination of electrical, thermal and mechanical loads to which materials and their assemblies will react. By isolating the action of these loads, it would be possible to define optimal design strategies to withstand them. The proposed article presents the work we have done to determine the proportion of these charges through the use of numerical simulation models. We illustrate how the use of increasingly complex models and tools is accompanied by the need to identify and characterize the behaviour of materials more and more finely through sophisticated and expensive experimental tests, and the need for simulation tools that must demonstrate both increasing complexity and robustness
A MEMS Electromagnetic Vibration Energy Harvester with Monolithically Integrated NdFeB Micromagnets
No full textInternational audienceThe monolithic integration of high‐performance magnets into microfabricated devices remains a technological challenge despite the great interest for telecommunications, automotive, biomedical or space applications. Here the integration of 50 µm thick sputtered arrays of NdFeB micromagnets into a functional micro‐electro mechanical system (MEMS) in‐plane electromagnetic vibration energy harvester is reported. A combination of analytical modeling and numerical simulations guided the design of the magnet arrays along with ad‐hoc planar coils, to produce a high transduction factor. The resulting energy harvesters deliver a voltage of 2.5 mV, a power of 6 nW under an acceleration of 0.8 g and a normalized power density of 3 × 10 −4 kg s m −3 (20 mW m −3 ) for a device volume of roughly 300 mm 3 , which is comparable to state‐of‐the‐art MEMS electromagnetic vibration harvesters. This study serves as a show‐case for the possibility of integrating high‐performance micromagnets into functional devices using microfabrication processes
On the Demographic History of Chimpanzees and Some Consequences of Integrating Population Structure in Chimpanzees and Other Great Apes
No full textInternational audienceReconstructing the evolutionary history of great apes is of particular importance for our understanding of the demographic history of humans. The reason for this is that modern humans and their hominin ancestors evolved in Africa and thus shared the continent with the ancestors of chimpanzees and gorillas. Common chimpanzees (Pan troglodytes) are our closest relatives with bonobos (Pan paniscus) and most of what we know about their evolutionary history comes from genetic and genomic studies. Most evolutionary studies of common chimpanzees have assumed that the four currently recognised subspecies can be modelled using simple tree models where each subspecies is panmictic and represented by one branch of the evolutionary tree. In addition, one recent genetic study claimed to have detected admixture between bonobos and chimpanzees. However, several studies have identified the existence of significant population structure with evidence of isolation-by-distance (IBD) patterns, both within and between subspecies. This suggests that demographic models integrating population structure may be necessary to improve our understanding of their evolutionary history. Here we propose to use n-island models within each subspecies to infer a demographic history integrating population structure and changes in connectivity (i.e., gene flow). For each subspecies, we use SNIF (Structured non-stationary inference framework), a method developed to infer a piecewise stationary n-island model using PSMC (Pairwise sequentially Markovian coalescent) curves as summary statistics. We then propose a general model integrating the four subspecies as metapopulations within a phylogenetic tree. We find that this model correctly predicts estimates of within subspecies genetic diversity and differentiation, but overestimates genetic differentiation between subspecies as a consequence of the tree structure. We argue that spatial models integrating gene flow between subspecies should improve the prediction of between subspecies differentiation and generate the observed IBD patterns. We also simulated data under a simple spatially structured model for bonobos and chimpanzees (without admixture) and found that it generates potentially spurious signals of admixture between the two species that have been reported and could thus be spurious. This may have implications for our understanding of the evolutionary history of the Homo genus
Bayesian Learning in Mean Field Games
No full textInternational audienceWe consider a mean-field game model where the cost functions depend on a fixed parameter, called \textit{state}, which is unknown to players. Players learn about the state from a a stream of private signals they receive throughout the game. We derive a mean field system satisfied by the equilibrium payoff of the game and prove existence of a solution under standard regularity assumptions. Additionally, we establish the uniqueness of the solution when the cost function satisfies the monotonicity assumption of Lasry and Lions at each state
3D customized silica-based AFM probes fabricated by selective laser etching
No full textInternational audienceAtomic force microscopy (AFM) cantilevers are essential components that function both as force sensors and nanoscale interaction tools that plays a critical role in AFM capabilities, sensitivity and precision. Conventional fabrication techniques for probes, that rely on silicon or silicon nitride bulk micro-machining, generally requires complex fabrication processes associated to low throughput and limited geometric flexibility. Here we explore the development of innovative AFM cantilevers made of silica glass through a novel approach based on selective laser etching, that offers cantilever and tip design flexibility, condense the process into three steps, and reduces the fabrication time and cost while minimizing reliance on complex equipment and clean room facilities. We demonstrate the fabrication and characterization of functional glass cantilevers with thicknesses ranging from 1 to 50 µm and spring constants spanning from 0.02 to 80 N.m-1. The fabricated glass probes show excellent performance in both AFM imaging and force spectroscopy applications. The simple and fast fabrication approach, highlight the potential of selective laser etching to produce innovative versatile silica-based probes for AFM
Robotisation AFM pour le phénotypage mécanique des cellules
No full textInternational audienceL’étude des propriétés mécaniques des populations de cellules notamment de mammifères par AFM présente un enjeu majeur pour l’identification de sous phénotypes cellulaires d’origine mécanique [1,2]. Pour atteindre un nombre de cellules suffisant pour réaliser ce type d’étude, l’utilisation d’un AFM « manuel » ne suffit plus et il est nécessaire d’explorer les modalités d’automatisations de celui-ci [3]. Nous présenterons l’intégration d’algorithme de machine learning au service de la robotisation des me-sures AFM sur des centaines de cellules de mammifère.Références [1]Severac, C.; Proa-Coronado, S.; Formosa-Dague, C.; Martinez-Rivas, A.; Dague, E. Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. Albicans Cells. JoVE (Journal of Visualized Experiments) 2021, No. 170, e61315. https://doi.org/10.3791/61315.[2]Thomas - - Chemin, O.; Séverac, C.; Moumen, A.; Martinez-Rivas, A.; Vieu, C.; Le Lann, M.-V.; Trevisiol, E.; Dague, E. Automated Bio-AFM Generation of Large Mechanome Data Set and Their Analysis by Machine Learning to Classify Cancerous Cell Lines. ACS Appl. Mater. Interfaces 2024, 16 (34), 44504–44517. https://doi.org/10.1021/acsami.4c09218.[3]Thomas- -Chemin, O.; Janel, S.; Boumehdi, Z.; Séverac, C.; Trevisiol, E.; Dague, E.; Duprés, V. Advancing High-Throughput Cellular Atomic Force Microscopy with Automation and Artificial Intelligence. ACS Nano 2025. https://doi.org/10.1021/acsnano.4c07729
Optimal control of intermittent aeration for an activated sludge wastewater treatment plant ⋆
No full textIn an activated sludge wastewater treatment plant (WWTP), the succession of aerobic, anoxic and anaerobic phases are frequently governed by the switching of the aeration. These different phases allow for different biochemical processes to remove the major pollutants. A simplified 3-species model describing nitrogen removal in WWTP is proposed. The process is described by an Ordinary Differential Equations (ODE) system, where the aeration is a source term in oxygen. This model is calibrated with real data in order to obtain realistic values for the parameters, and allows a dynamic estimation of the influent ammonium concentration.. Different aeration control schemes are then proposed and simulated with the objective to minimize the total power consumption i.e. aeration time under a given regulatory threshold for ammonium concentration. A first optimal aeration scenario is designed, by assuming that the incoming load (volume and concentration) is perfectly known. Then a more realistic feedback control approach is proposed and implemented. Both approaches are compared to approximated theoretical aeration time. Another option that is studied is the effect of allowing to reduce the power of aeration by half. In this case the power consumption is substantially reduced.</div
Uncovering the Limitations of Query Performance Prediction: Failures, Insights, and Implications for Selective Query Processing
No full textInternational audienceQuery Performance Prediction (QPP) estimates the effectiveness of retrieval systems for a given query, offering valuable insights for search effectiveness and query processing. Despite extensive research, a critical gap remains in understanding how well QPPs generalize across diverse retrieval paradigms and collections, a question of robustness that has significant implications for their practical utility. This paper provides the first comprehensive cross-paradigm evaluation of QPP robustness and generalization capabilities, examining state-of-the-art QPPs including NQC, WIG, LETOR-based features, and newly explored dense-based predictors MQPPF and BERT-QPP. We systematically assess their performance across diverse sparse (BM25, DFree with and without query expansion), hybrid (SPLADE), and dense (ColBERT, TCT-ColBERT) rankers on four benchmark collections: TREC Robust, GOV2, WT10G, and MS-MARCO. The results reveal fundamental robustness challenges: predictors exhibit significant variability in accuracy, with collection being the dominant factor, followed by ranker type. Some sparse predictors perform adequately on specific collections such as TREC Robust and GOV2, but critically fail to generalize to other collections like WT10G and MS-MARCO. Dense-based predictors, while showing promise in specific scenarios with dense rankers, similarly lack generalization to sparse contexts. We demonstrate that these generalization failures severely limit practical applications: QPP-driven selective query processing achieves only marginal gains (≈4% NDCG improvement), with reliability varying dramatically across settings. Our findings underscore that current QPP methods lack the robustness necessary for real-world deployment and highlight the urgent need for predictors that generalize reliably across diverse collections, align with modern dense retrieval architectures, and provide consistent utility for downstream applications. We publicly release our data and code to facilitate future research on robust QPP methods 1 .</div
Structural and Functional Dissection of GH161 β-Glucan Phosphorylases: Molecular Specificities and Dynamics of Catalysis of Dimeric GH-Q Enzymes
No full textInternational audienceIn the CAZy classification of carbohydrate-active enzymes, the GH-Q clan regroups three distantly related glycoside hydrolase (GH) families: GH94, GH149, and GH161. These families contain glycoside phosphorylases (GPs) that mainly use α–d-glucose-1-phosphate as a glucosyl donor. In the present study, we investigated the structure–function relationships within family GH161, which exhibits β-1,3-glucan phosphorylase activity. Using cryo-electron microscopy (cryo-EM), we determined the dimeric structure of a GH161 enzyme. Comparison between the unliganded form, the acceptor-bound form, and finally a catalytically active complex revealed conformational changes during catalysis that involve coordinated rearrangements within the active site, including loops held by the adjacent protomer. These structural insights, combined with biochemical characterization, deepen our understanding of the molecular determinants driving catalysis in GH-Q enzymes
