A solar-powered multi-functional portable charging device (SPMFPCD) with internet-of-things (IoT)-based real-time monitoring—An innovative scheme towards energy access and management

International audienceBattery energy storage system (BESS) Internet of thing (IoT) Real-time monitoring Disaster management technology Economic viability Emergency medical charging solutions Mul-ti-functional charging device Public spaces charging infrastructure Renewable energy integration (REI) Solar-powered portable charging In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet-of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution

Modélisation du thermoformage de structures en composites tubulaires, et de ses conséquences sur ses propriétés en service

International audienceModélisation du thermoformage de structures en composites tubulaires, et de ses conséquences sur ses propriétés en servic

Green's functions for the static curvature and deflection of two-phase peridynamic elastic Euler-Bernoulli beams with exponential kernels

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Virtual forming based on model calibration from heterogeneous tests

International audienceAbstract. This study focuses on calibrating a mechanical model for a DP600 steel using both quasi-homogeneous and heterogeneous tests. Several sample geometries subject to a uniaxial load were selected and tested experimentally, and a finite element model updating method was used to identify the material parameters of an anisotropic plasticity model, leading to several material parameter sets. Then, the virtual forming of cylindrical cup is considered, using these different parameter sets. The strain and stress states of the mechanical tests used for the model calibration are compared with those of the forming process, to analyse their relevance. The main goal is to check the whole chain from the indicator-based design of heterogeneous tests up to a numerical case study of the forming of cylindrical cups

Des nombres de Cantor et de Dedekind aux nombres de Conway - Partie 0: Nombres ordinaux, nombres rationnels et nombres entiers

Cet exposé d'histoire des mathématiques, en deux parties, est une motivation et une présentation du Chapitre 0 de la partie 0 (pages 2 à 14) du livre de John Conway "On Numbers and Games" (1976, 2ème édition 2000).Nous partirons de la construction des nombres ordinaux de Georg Cantor (1895) donnée par Paul R. Halmos (1965) -- à la suite de John Von Neumann (1923) -- et de la construction des nombres réels par Edmund Landau (1927) -- à la suite de Richard Dedekink (1887) -- et présenterons leur généralisation par John Conway (1976)

Volatolomics for Anticipated Diagnosis of Cancers with Chemoresistive Vapour Sensors: A Review

International audienceThe anticipated diagnosis of cancers and other fatal diseases from the simple analysis of the volatiles emitted by the body (volatolome) is getting closer and closer from becoming reality. The promises of vapour sensor arrays are to provide a rapid, reliable, non-invasive and ready-to-use method for clinical applications by making an olfactive fingerprint characteristic of people’s health state, to increase their chance of early recovery. However, the different steps of this complex and ambitious process are still paved with difficulties needing innovative answers. The purpose of this review is to provide a statement of the blocs composing the diagnostic chain to identify the improvements still needed. Nanocomposite chemo-resistive transducers have unique prospects to enhance both the selectivity and sensitivity to volatile biomarkers. The variety of their formulations offers multiple possibilities to chemical functionalization and conductive architectures that should provide solutions to discriminations and stability issues. A focus will be made on the protocols for the collection of organic volatile compounds (VOC) from the body, the choice of vapour sensors assembled into an array (e-nose), in particular, chemo-resistive vapour sensors, their principle, fabrication and characteristics, and the way to extract pertinent features and analyse them with suitable algorithms that are able to find and produce a health diagnosis

Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift

International audienceEstimating the test performance of a model, possibly under distribution shift, without having access to the ground-truth labels is a challenging, yet very important problem for the safe deployment of machine learning algorithms in the wild. Existing works mostly rely on information from either the outputs or the extracted features of neural networks to estimate a score that correlates with the ground-truth test accuracy. In this paper, we investigate -- both empirically and theoretically -- how the information provided by the gradients can be predictive of the ground-truth test accuracy even under distribution shifts. More specifically, we use the norm of classification-layer gradients, backpropagated from the cross-entropy loss after only one gradient step over test data. Our intuition is that these gradients should be of higher magnitude when the model generalizes poorly. We provide the theoretical insights behind our approach and the key ingredients that ensure its empirical success. Extensive experiments conducted with various architectures on diverse distribution shifts demonstrate that our method significantly outperforms current state-of-the-art approaches. The code is available at https://github.com/Renchunzi-Xie/GdScor

Comparative Structural and Biophysical Investigation of Lycosa erythrognatha Toxin I (LyeTx I) and Its Analog LyeTx I-b

International audienceBackground/Objectives: This study investigates the structural and biophysical properties of the wild-type antimicrobial peptide LyeTx I, isolated from the venom of the spider Lycosa erythrognatha, and its analog LyeTx I-b, designed to enhance antibacterial activity, selectivity, and membrane interactions by the acetylation and increased amphipathicty. Methods: To understand the mechanisms behind these enhanced properties, comparative analyses of the structural, topological, biophysical, and thermodynamic aspects of the interactions between each peptide and phospholipid bilayers were evaluated. Both peptides were isotopically labeled with 2 H 3 -Ala and 15 N-Leu to facilitate structural studies via NMR spectroscopy. Results: Circular dichroism and solid-state NMR analyses revealed that, while both peptides adopt α-helical conformations in membrane mimetic environments, LyeTx I-b exhibits a more amphipathic and extended helical structure, which correlates with its enhanced membrane interaction. The thermodynamic properties of the peptide-membrane interactions were quantitatively evaluated in the presence of phospholipid bilayers using ITC and DSC, highlighting a greater propensity of LyeTx I-b to disrupt lipid vesicles. Calcein release studies reveal that both peptides cause vesicle disruption, although DLS measurements indicate distinct effects on phospholipid vesicle organization. While LyeTx I-b permeabilizes anionic membrane retaining the vesicle integrity, LyeTx I promotes significant vesicle agglutination. Furthermore, DSC and calcein release assays indicate that LyeTx I-b exhibits significantly lower cytotoxicity toward eukaryotic membranes compared to LyeTx I, suggesting greater selectivity for bacterial membranes. Conclusions: Our findings provide insights into the structural and functional modifications that enhance the antimicrobial and therapeutic potential of LyeTx I-b, offering valuable guidance for the design of novel peptides targeting resistant bacterial infections and cancer

Leveraging Digital Twins and AI for Enhanced Gearbox Condition Monitoring in Wind Turbines: A Review

International audienceWind power plays a significant role in sustainable energy production, but the reliability of wind turbines depends heavily on the integrity of their gearboxes. Gearbox failures can lead to significant downtime and operational disruption. In this context, this paper provides an overview of the evolution of gearbox monitoring techniques, culminating in the emergence of digital twin (DT) technology. We explore the application of DT technology to gearbox condition monitoring, focusing on two critical components: bearings and gears. This includes a comprehensive review of methodologies involving model-based approaches and data-driven techniques using signal processing (SP) and artificial intelligence (AI) algorithms. We address the challenges of “learning with minimal knowledge” and propose a framework for the effective application of DT technology. Finally, we discuss future research directions and potential contributions to advancing the field of gearbox condition monitoring through the continued development and implementation of DT-based solutions

Discrete HH-theorem for a finite volume discretization of a nonlinear kinetic system: application to hypocoercivity

In this article, we study the long-time behavior of a finite-volume discretization for a nonlinear kinetic reaction model involving two interacting species. Building upon the seminal work of [Favre, Pirner, Schmeiser, ARMA, 2023], we extend the discrete exponential convergence to equilibrium result established in [Bessemoulin-Chatard, Laidin, Rey, IMAJNA, 2025], which was obtained in a perturbative framework using weighted $L^2$ estimates. The analysis applies to a broader class of exponentially decaying initial data, without requiring proximity to equilibrium, by exploiting the properties of the Boltzmann entropy. The proof relies on the propagation of the initial $L^\infty$ bounds, derived from monotonicity properties of the scheme, allowing controlled linearizations within the nonlinear entropy estimates. Moreover, we show that the time-discrete dissipation inherent to the numerical scheme plays a crucial stabilizing role, providing control over the nonlinear terms