HAL-Université de Bretagne Occidentale
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WAXD investigations on the effect of loading history on strain-induced crystallization for a fully formulated filled natural rubber
No full textInternational audienceModelling crystallization under stretch is a key topic for fatigue design of rubber-like antivibration parts. Nevertheless, most of the academic studies consider unfilled natural rubber while the industrial materials are fully formulated compounds filled with carbon blacks and exhibit a highly dissipative visco-elastic behavior. This behavior is very useful for antivibration systems but complicates the characterization and modelling of the phase change, as the addition of fillers and additives brings in numerous additional dissipation sources and intricates the time effects on the thermomechanical response and on crystallization. In this study, we use well resolved WAXD synchrotron measurements to perform in situ measurements under various mechanical protocols. The objective is to characterize the evolution of the triplet {strain, stress, crystallinity index}, and their derivatives, for various time and mechanical solicitations. First, classic load/unload tension tests over a range of strain rates leading to non-equilibrium cases are achieved, to serve as a reference database on the compound studied. Then, a multi-relaxation cyclic test combining static and monotonic steps is applied in order to describe the crystallization state and kinetics around a relaxed state (sometimes called "equilibrium hysteresis"). The results provide a precious database to identify or challenge the existing thermodynamic models, for conditions seldom met in the literature: fully formulated material and various mechanical loading time histories
Household Waste Reduction: A Segmented Approach Through Social Practice Theory
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Eco-Friendly Extraction of Phlorotannins from Padina pavonica: Identification Related to Purification Methods Towards Innovative Cosmetic Applications
No full textInternational audienceThis study focuses on developing innovative and eco-friendly purification methods for the isolation of bioactive compounds derived from Padina pavonica, a brown abundant macroalga in Djibouti. Three distinct fractions, obtained via liquid-liquid extraction (LLE_FAE), solid-phase extraction (SPE_WE50), and flash chromatography (FC_EtOH20), were selected based on their high phenolic content and antioxidant activities. All fractions were also evaluated for their anti-ageing potential by assessing their ability to inhibit two vital skin-ageing enzymes, tyrosinase and elastase. Structural analysis by 1H-13C HMBC NMR and LC-MS revealed a selectivity of phlorotannins depending on the purification methods. The LLE_FAE fraction exhibited greater structural complexity, including compounds such as phloroglucinol, diphlorethol/difucol, fucophlorethol and bifuhalol, which likely contribute to its enhanced bioactivity compared to the fractions obtained by FC_EtOH20 and SPE_WE50, which were also active and enriched only in phloroglucinol and fucophlorethol. These findings highlight the impact of purification techniques on the selective enrichment of specific bioactive compounds and demonstrated the interest of FC or SPE in producing active phlorotannin-enriched fractions. These two purification methods hold strong potential for innovative cosmeceutical applications. Results are discussed regarding the use of P. pavonica as a promising marine resource in Djibouti to be used for the development of cosmetic industry
Sur la question de l’enseignement privé, il existe clairement des politiques de gauche et de droite
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Fast Operating Room Scattered Radiation Calculation in X-ray Guided Interventions by Using Deep Learning
No full textInternational audienceObjective. Protecting medical personnel from the harmful effects of scattered ionizing radiation during X-ray-guided procedures is a critical concern. Due to the complex and invisible nature of X-rays, monitoring radiation exposure has been challenging. Existing real-time dosimeters have shown low accuracy and practical limitations. Approach. To address these challenges, this study introduces an innovative approach that combines Monte Carlo (MC) simulations and Deep Learning (DL) for realtime estimation of 3D scattered radiation in the operating room. The neural network was trained to map patient morphology and imaging parameters to radiation maps, allowing it to adapt to various clinical scenarios. Main results. The results demonstrate that the system showcases exceptional speed by efficiently computing 3D radiation maps in 11 ms using modern GPU (NVIDIA RTX 2080). Validation experiments confirmed the reliability of the predicted scatter maps, with a mean absolute percentage error (MAPE) of 10.97% relative to MC simulations. When used to compute organ doses via voxelizedsource simulations, the global average organ dose error was 8.2 ± 4.1%. Significance. Therefore, the combination of MC simulations and DL provides a promising solution for enhancing the safety of medical personnel during X-ray-guided procedures
Offline and Online Use of Interval and Set-Based Approaches for Control and State Estimation : A Selection of Methodological Approaches and Their Application
No full textInternational audienceControl and state estimation procedures need to be robust against imprecisely known parameters, uncertainty in initial conditions, and external disturbances. Interval methods and other set-based techniques form the basis for the implementation of powerful approaches that can be used to identify parameters of dynamic system models in the presence of the aforementioned types of uncertainty. Moreover, they are applicable to a verified feasibility and stability analysis of controllers and state estimators. In addition to these approaches which are typically used offline for analysis of system models designed with classical floating point procedures, interval and set-based methods have also been developed in recent years, which allow to directly solve the associated design tasks and to implement reliable techniques that are applicable online, i.e., during system operation. The latter approaches include set-based model predictive control, online parameter adaptation techniques for nonlinear variable-structure and backstepping controllers, interval observers, and fault diagnosis techniques. This paper provides an overview of the methodological background and reviews numerous practical applications for which interval and other set-valued approaches have been employed successfully
Resonance and alienation experiences among skateboarders : a study based on the critical incident method
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L'expérience d'un entraîneur de handball expert lors de matchs clés d'une saison à haut niveau : quelles relations avec les émotions ?
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: observations sous Cass. 1re civ., 17 sept. 2025, no 23-23629
No full textInternational audienceDans un arrêt du 17 septembre 2025, la première chambre civile de la Cour de cassation a rendu un arrêt retenant que le point de départ du délai de prescription doit être fixé à la date du jugement du caractère abusif des clauses litigieuses au contrat de prêt libellé en devises étrangères, à moins que le prêteur prouve que l’emprunteur avait connaissance du caractère abusif de ladite clause préalablement à la décision
Guidelines for cerebrovascular segmentation: Managing imperfect annotations in the context of semi-supervised learning
No full textInternational audienceSegmentation in medical imaging is an essential and often preliminary task in the image processing chain, driving numerous efforts towards the design of robust segmentation algorithms. Supervised learning methods achieve excellent performances when fed with a sufficient amount of labeled data. However, such labels are typically highly time-consuming, error-prone and expensive to produce. Alternatively, semi-supervised learning approaches leverage both labeled and unlabeled data, and are very useful when only a small fraction of the dataset is labeled. They are particularly useful for cerebrovascular segmentation, given that labeling a single volume requires several hours for an expert. In addition to the challenge posed by insufficient annotations, there are concerns regarding annotation consistency. The task of annotating the cerebrovascular tree is inherently ambiguous. Due to the discrete nature of images, the borders and extremities of vessels are often unclear. Consequently, annotations heavily rely on the expert subjectivity and on the underlying clinical objective. These discrepancies significantly increase the complexity of the segmentation task for the model and consequently impair the results. Consequently, it becomes imperative to provide clinicians with precise guidelines to improve the annotation process and construct more uniform datasets. In this article, we investigate the data dependency of deep learning methods within the context of imperfect data and semi-supervised learning, for cerebrovascular segmentation. Specifically, this study compares various state-of-the-art semi-supervised methods based on unsupervised regularization and evaluates their performance in diverse quantity and quality data scenarios. Based on these experiments, we provide guidelines for the annotation and training of cerebrovascular segmentation models