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Extracellular DNA filaments associated with surface polysaccharide II give Clostridioides difficile biofilm matrix a network-like structure
No full textInternational audienceClostridioides difficile is an anaerobic, spore-forming, Gram-positive bacterium, and a leading cause of healthcare-associated intestinal infections. Recurrences occur frequently, most of them being relapses. Apart from spores, C. difficile biofilm is hypothesized as a reservoir for relapses. Thus, increased knowledge on in vitro biofilm formation and characteristics is required. We finely characterized the matrix components in 4 C. difficile strains. Confocal microscopy revealed for the first time the presence of eDNA filaments connecting bacteria, with a spider's web-like organization. Biofilm disruption with DNase I suggests that eDNA, even in low abundance, plays a key role in the biofilm scaffold, maintaining biofilm cohesion by connecting bacteria. Observation of strong overlapping staining, particularly in the highest biofilm-producing strain tested between eDNA and polysaccharide II or lipoprotein CD1687, suggests that interactions between these components may enhance biofilm cohesion. Whereas autolysis does not appear to be a major way of matrix component release under our conditions, eDNA was sometimes associated with lipidic round shapes that can evoke vesicle structures. Together, these results suggest that the bacterial aggregation and structuring of the C. difficile biofilm involve several components of the matrix, including eDNA, interacting with each other to build the scaffold of biofilm
Empreinte carbone : tous les barils de pétrole ne se valent pas et cela a son importance pour la transition énergétique
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beta-FPUT chains under time-periodic forcing
No full textInternational audienceRecent works proved a hydrodynamic limit for periodically forced atom chains with harmonic interaction and pinning, together with momentum flip. When energy is the only conserved quantity, one would expect similar results in the anharmonic case, as conjectured for the temperature profile and energy flux. However, outside the harmonic case, explicit computations are generally no longer possible, thus making a rigorous proof of this hydrodynamic limit difficult. Consequently, we numerically investigate the plausibility of this limit for the particular case of a chain with beta-FPUT interactions and harmonic pinning. We present our simulation results suggesting that the conjectured PDE for the limiting temperature profile and Green--Kubo type formula for the limiting energy current conjectured are correct. We then use this Green--Kubo type formula to investigate the relationship between the energy current and period of the forcing. This relationship is investigated in the case of significant rate of momentum flip, small rate of momentum flip and no momentum flip. We compare the relationship observed in the anharmonic case to that of the harmonic case for which explicit formulae are available
Local global watchdogs: Trade, sourcing and the internationalization of social activism
No full textInternational audienceNGO campaigns criticizing firms for infringements along their internationalized value chains are a salient feature of economic globalization. We argue that understanding the international patterns of NGO campaigns requires accounting for the geography of their targets’ economic activities. We propose a model of global sourcing and international trade in which heterogeneous NGOs campaign against heterogeneous firms in response to infringements along their value chains. We find that campaigns are determined by a triadic gravity equation involving the country of the NGO, the country of the firm as well as the sourcing country. Importantly, independent of the location of the NGO, trade costs between the supplier and the firm shape the patterns of NGO campaigns. We use recently available data to estimate our triadic gravity equation at the NGO level and find strong support for this prediction as well as for other predictions specific to our modeling approach
Conception de charpentes en bois vert selon des méthodes historiques : Etude comparée de mesures du séchage du bois vert entre artisan et chercheur
No full textNational audienceCette communication porte sur des structures réalisées en bois vert, ie non séché avant la mise en œuvre, et plus précisément sur le séchage du bois vert et son impact sur la tenue mécanique et vieillissement des charpentes. Ce travail s'intègre en particulier au doctorat d'anthropologie de Joseph Brihiez. La thèse a pour objectif d’examiner et d’analyser la façon dont la controverse de la mise en œuvre du bois vert dans le contexte du chantier des charpentes médiévales de Notre-Dame de Paris s’est posée. Elle vise ce faisant à rendre compte des dynamiques de savoir du bois au sein d'un réseau d'acteurs différents (scientifiques, artisans etc.) concernés et obligés par le bois vert.Actuellement, des artisans mettent en pratique l’utilisation de techniques traditionnelles dans des chantiers actuels et des chercheurs développent des recherches sur le comportement mécanique du bois vert. Cependant des questions restent en suspens pour comprendre la santé structurale de structures en bois vert : cinétique du séchage et mécanismes en jeu, comparaison entre équarrissage ancestral ou sciage, temps "optimal" entre l’équarrissage et la taille des poutres, effet des fissurations et des déformations dues au séchage sur les assemblages, etc… Ces questions en suspens sont en lien avec des pertes de savoir-faire historiques mais aussi avec des connaissances « a priori connues » qui seraient à approfondir. Dans ce contexte, un programme expérimental a été mis en place entre ces différents acteurs pour préciser les cinétiques de séchage du bois vert. Ce travail vise à confronter les questionnements de chaque acteur, mais aussi les techniques de mesures qui peuvent être mises en place de part et d’autre. Dans cette communication, les résultats obtenus dans ce programme seront présentés et discutés, en lien avec les approches et questionnement des différents acteurs
Branch Prediction Analysis of Morris-Pratt and Knuth-Morris-Pratt Algorithms
No full textInternational audienceWe analyze the classical Morris-Pratt and Knuth-Morris-Pratt pattern matching algorithms through the lens of computer architecture, investigating the impact of incorporating a simple branch prediction mechanism into the model of computation. Assuming a fixed pattern and a random text, we derive precise estimates of the number of mispredictions these algorithms produce using local predictors. Our approach is based on automata theory and Markov chains, providing a foundation for the theoretical analysis of other text algorithms and more advanced branch prediction strategies
Statistical modelling of combined sewer overflow
No full textInternational audienceRivers are at the heart of human activity. They provide many ecosystem services: drinking water, agriculture, transport, hydropower, bathing, freshness, etc. They are also hotspots for biodiversity. However, the water quality of these rivers is deteriorated as a result of human activity. The current work focuses on fecal contamination, which is a discriminating criterion for bathing. In urban watersheds, fecal bacteria contamination comes from point sources related to the operation of the drainage network. During rainy weather, the combined sewer network, mixingboth wastewater and stormwater, can become saturated. As a consequence, part of the flow is discharged directly into the river via combined sewer overflows (CSOs). This is the case for the city of Paris. The possible CSO overflow can be modeled by a function linking its discharge toprecipitation. This relationship is currently poorly understood, with little related work, and even less for the Seine river.To build such linking function, we rely on a dataset that includes location and hourly discharged volume of the monitored CSOs in the Seine River within Paris. Urban watersheds have been delineated within the study site. Rainfall height over these watersheds have been obtained from eather radar. We broke down the data timeseries into events. An event begins with the cause, the rain, and ends with the consequence, the overflow. To link rainfall to CSOs a directional graph based on the drainage network map, was created. It represents the wastewater transport from one watershed to another. This highlights which rainfall variables to consider regarding the CSO location. Principal component analysis (PCA) is used to assess for rain characteristics selection. An unsupervised non-linear technique (Isomap) is then used to build linking function structure.The overflow volume in time can be modeled by a triangular shape. This shape is described by the overflow initial time, its total and maximum volume and the time of the maximum. We expect to retrieve these overflow variables by reducing the number of rainfall event characteristics to single indicators using sequentially PCA and Isomap.Modeling and forecasting source discharges would enable better management of bathing and water supply risks, and better evaluation of mitigation infrastructures
FOURIER-BASED ANALYTICAL FRAMEWORK FOR NON-HOMOGENEOUS BALLAST-LESS RAILWAY TRACKS
No full textInternational audienceUnderstanding and predicting the behavior of structures under operational loads has long been a focal point for engineers and researchers. This is crucial to ensuring safety, meeting regulatory standards, and optimizing structural performance. Regarding railway tracks modeling, both numerical and analytical approaches have been extensively utilized, with analytical methods holding particular appeal due to their ability to capture global structural responses while requiring low computational effort.In the context of railway tracks, typically seen as semi-infinite structures, periodicity assumptions are often adopted to reduce the problem to a finite system of equations. Existing analytical models generally consider static loading, treating the applied forces as constant and moving. However, dynamic loads have been shown to produce significantly more critical effects on structural behavior.This work introduces an alternative analytical model capable of capturing the response of ballast-less railway tracks under both static and dynamic periodic loads. The proposed loading function can adopt various forms, provided it remains L-periodic, allowing for the study of a wide range of scenarios. The track structure is modeled as periodic with a defined spatial periodicity L. This L-periodic section is defined as a railway track segment constituted by a finite number of discrete and independent supports, allowing the model to account for non-homogeneous regions and local variations in structural properties. This modelling choice allows to converge into a solution by using Floquet's theorem and representing the problem's governing equations as Fourier series.The proposed analytical framework highlights the amplified impact of dynamic loads on railway tracks. It enables the quantification of overloads induced by dynamic forces, laying the groundwork for developing strategies to mitigate their effects and reduce their severity. This approach not only enhances our understanding of dynamic railway track behavior but also provides insights for designing more resilient and efficient infrastructure.</div
Intelligent Aggregation of Single-Sensor Classifiers for Enhanced Structural Health Monitoring Networks
No full textInternational audienceStructural health monitoring (SHM) systems for large-scale infrastructures often rely on dense sensor networks, which are prone to faults, generate high-volume data, and require computationally efficient algorithms to ensure low-latency inference for real-time monitoring. To enhance overall network accuracy and robustness, aggregating the predictions of individual sensors provides a way to leverage complementary information across the network while mitigating sensor-level errors. In this study, we investigate intelligent aggregation strategies for singlesensor classifiers in SHM networks. We leverage acceleration time series data from the RT345 bridge dataset, collected from a real instrumented structure, to detect and classify structural damages. Individual sensor classifiers produce probabilistic predictions, which are then combined using different aggregation strategies. Soft averaging serves as a baseline, while stacking ensembles employs linear meta-classifiers (Logistic Regression) for interpretable per-sensor weighting and nonlinear metaclassifiers (Random Forest) to capture complex conditional dependencies across sensors, albeit at the cost of interpretability and stability. Experimental results demonstrate that meta-learning strategies significantly improve classification accuracy and robustness. We further evaluate prediction time, scalability, and model size, highlighting trade-offs between linear and nonlinear aggregation for real-time SHM applications. Finally, we extend the study by exploring alternative acceleration time series representations, showing that system efficiency can be improved without compromising damage detection performance
Analysis of the 2024 Hajj heat event and future temperature extremes in Mecca
No full textInternational audienceExtreme heat events in the Middle East have become increasingly frequent and intense due to human-driven climate change. During the Hajj pilgrimage in Mecca, Saudi Arabia, in June 2024, temperatures soared to a record-breaking 51.8 °C, resulting in the tragic deaths of at least 1300 pilgrims and over 2700 non-fatal injuries. Our analysis of future projections, tailored for the region, indicates that in a warmer climate, such hazards may become a regular occurrence. Addressing these challenges through effective climate mitigation and adaptation is essential to building resilience against future extreme heat risks