OLIVAW: ACIMOV's GitHub robot assisting agile collaborative ontology development

International audienceAgile and collaborative approaches to ontologies design are crucial because they contribute to making them userdriven, up-to-date, and able to evolve alongside the systems they support, hence proper continuous validation tooling is required to ensure ontologies match developers' requirements all along their development. We propose OLIVAW (Ontology Long-lived Integration Via ACIMOV Workflow), a tool supporting the ACIMOV methodology on GitHub. It relies on W3C Standards to assist the development of modular ontologies through GitHub Composite Actions, pre-commit hooks, or a command line interface. OLIVAW was tested on several ontology projects to ensure its usefulness, genericity and reusability. A template repository is available for a quick start. OLIVAW i

New Algorithm for Weak Changes Detection with Application to Financial Data

International audienceDetecting weak changes in time series is crucial in many fields, particularly in finance and economics, where subtle structural shifts may precede major market events. Traditional methods often fail to identify these small yet impactful changes. This work proposes a novel statistical procedure capable of detecting such weak changes, even when masked by noise, and precisely estimating their occurrence times. Piece-wise stationary CHARN models:The proposed model is a piece-wise stationary CHARN model that accounts for k potential change points in a time series of n observations. The model formulation includes stationary and ergodic processes, with a change detection framework based on likelihood ratio tests. The mathematical formulation ensures flexibility and robustness even when model parameters are unknown.Power of the Test: The asymptotic power of the proposed test is derived under general conditions and includes an explicit formulation for practical implementation. This allows the detection method to be both theoretically sound and computationally feasible for real-world data.Automatic Detection Algorithm: An automatic algorithm is developed for sequentially detecting weak changes. It involves comparing intervals within the time series and applying statistical tests for identifying change points. The method is efficient, adaptive, and suitable for real-time applications.Application to Financial Data: The algorithm was applied to financial time series such as the FTSE 100 and S&amp;P 500 indices. Detected change points align with significant historical events including the 2008 financial crisis, the Russian financial crisis, and U.S. monetary policy shifts. These case studies demonstrate the practical relevance of the algorithm. Highlights of the Algorithm:The algorithm is efficient, robust to noise, and provides interpretable outputs including estimated change points and the associated test powers. These features make it highly valuable for monitoring and analysis of large-scale or streaming time series data.</div

Line profile analysis of energy-scanned Laue microdiffraction peaks using the modified Williamson–Hall and modified Warren–Averbach methods

International audienceA combination of the modified Warren–Averbach (mWA) and modified Williamson–Hall (mWH) methods was applied to characterize the local dislocation structure at the micrometre scale of a laser-shock-peened Ni specimen. Peak profiles obtained by energy scanning of Laue microdiffraction peaks were analyzed in terms of dislocation density, stored energy and interaction between dislocations. The applied methods, exploiting the asymptotic form of the Fourier transform of the peak (mWA method) and the long-range screening described by the full width at half-maximum (mWH), are complementary and offer for the first time the possibility of checking the adequacy of an assumed dislocation model. The combined method is applicable to a dilute dislocation structure, when the mWH plot should be linear. The results for the dislocation density are in reasonable agreement with previous literature data obtained by transmission electron microscopy

Economie circulaire et intelligence artificielle : foisonnement d’innovations ?

Blog Alternatives ÉconomiquesDepuis les travaux fondateurs de Joseph Schumpeter sur l’innovation, considérée comme étant une activité linéaire portée par un entrepreneur isolé, le concept d’innovation a considérablement évolué pour intégrer la dimension systémique des mécanismes à l’œuvre (Adatto et al., 2023). L’innovation systémique, bien qu’elle n’ait pas fait l’objet d’un consensus en ce qui concerne sa définition dans la littérature, se conçoit comme un concept qui englobe les multiples perspectives de l’innovation au sein d’un système complexe

Machine learning-based pulse wave analysis for classification of circle of Willis topology: An in silico study with 30,618 virtual subjects

International audienceBackground and Objective: The topology of the circle of Willis (CoW) is crucial in cerebral circulation and significantly impacts patient management. Incomplete CoW structures increase stroke risk and post-stroke damage. Current detection methods using computed tomography and magnetic resonance scans are often invasive, time-consuming, and costly. This study investigated the use of machine learning (ML) to classify CoW topology through arterial blood flow velocity pulse waves (PWs), which can be noninvasively measured with Doppler ultrasound. Methods: A database of in silico PWs from 30,618 virtual subjects, aged 25 to 75 years, with complete and incomplete CoW topologies was created and validated against in vivo data. Seven ML architectures were trained and tested using 45 combinations of carotid, vertebral and brachial artery PWs, with varying levels of artificial noise to mimic real-world measurement errors. SHapley Additive exPlanations (SHAP) were used to interpret the predictions made by the artificial neural network (ANN) models. Results: A convolutional neural network achieved the highest accuracy (98%) for CoW topology classification using a combination of one vertebral and one common carotid velocity PW without noise. Under a 20% noise-tosignal ratio, a multi-layer perceptron model had the highest prediction rate (79%). All ML models performed best for topologies lacking posterior communication arteries. Mean and peak systolic velocities were identified as key features influencing ANN predictions. Conclusions: ML-based PW analysis shows significant potential for efficient, noninvasive CoW topology detection via Doppler ultrasound. The dataset, post-processing tools, and ML code, are freely available to support further research.</div

Économie circulaire

À travers plus d'une centaine d'entrées rédigées par près de 150 chercheuses et chercheurs, ce dictionnaire témoigne de la richesse des travaux consacrés à l'écologie politique et de leur pertinence pour décrypter les transformations contemporaines de nos sociétés.De « Agriculture » à « Zone de sacrifice », il expose la pluralité des concepts, idées et résultats développés par la science politique et les disciplines connexes pour penser les relations entre les humains et leur environnement, montrer leurs évolutions et leurs conséquences politiques. Dévoilant la vivacité des débats scientifiques bien souvent en lien avec les enjeux sociaux et politiques, il contribue à élargir l'espace de la réflexion sur l'écologie alors que nous entrons dans une période d'incertitudes radicales sur les effets des crises environnementales

Microwave sintering of tin oxide and zinc oxide mixtures – Formation of Zn₂SnO₄ spinel phase, densification and evolution of microstructure

International audienceIn this work SnO2-ZnO based ceramics were prepared and sintered using both microwave (MW) and conventional (CV) heating. Five different batches of samples were prepared – pure SnO2, ZnO and different weight ratios of these oxides (S80-Z20, S50-Z50, S20-Z80). Microwave sintering was conducted in a multimode cavity at 2.45 GHz in temperature range 900–1400 °C, with different heating rates 50–100 °C/min and dwell times 0–15 min. The changes of linear shrinkage, bulk density, open porosity and phase composition are discussed with respect to the heating rate, dwell time, sintering temperature and type of heating (MW vs. CV). During the MW heating results show changes of the dielectric loss with higher temperature in all prepared samples. MW sintering of composites significantly improved densification compared to CV sintering, due to favorable MW/material interactions. The formation of the Zn₂SnO₄ spinel phase was also observed, with its content varying depending on the initial composition

La Responsabilité Numérique des Entreprises (RNE) fonde-t-elle l’émergence de nouvelles tensions de rôle pour les managers ?

National audienc

Sobriété et responsabilité de l’IA

National audienc

Microwave sintering of SnO₂/ZnO ceramics: Effects of SiC susceptor

International audienceThis study investigates the effect of a SiC susceptor on the microwave (MW) heating of pure SnO2, pure ZnO and their composites in different weight ratios (S80-Z20, S50-Z50, S20-Z80). All samples were sintered with and without a SiC susceptor in a multimode MW cavity, and additional MW heating experiments were conducted in a single-mode cavity to observe the MW/material interactions of the pure binary oxides. The role of the SiC susceptor—commonly used to enhance MW absorption—was the key focus. Power curves are discussed in detail since they show the changes in material properties at certain temperatures. Our results show that SnO2 and ZnO ceramics can be heated even without a susceptor, with pure SnO2 initiating heating faster than ZnO and composites exhibiting intermediate behavior. The microstructure, linear shrinkage, bulk density, porosity, and phase composition were characterized for all prepared samples under both sintering conditions