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Factors Associated With Progression, Resolution and Mortality of Patients With Overt Hepatic Encephalopathy
No full textInternational audienceOvert hepatic encephalopathy (OHE) is a reversible complication of cirrhosis that often results in hospitalization. Factors associated with progression, resolution and mortality are not known, particularly with confounders such as acute-on-chronic liver failure (ACLF). The aim of the study was to evaluate factors associated with progression, resolution, and mortality of patients with OHE. Methods: Data for this study were derived from PREDICT, a prospective cohort study of patients with cirrhosis hospitalized for an acute decompensation or ACLF. Progression to OHE or worsening in severity and resolution from OHE were evaluated at 1 week. Cox regression, interaction analyses, and Kaplan-Meier curves were performed. Results: One thousand two hundred seventy-three patients were included [68% males; 59 (51-67) years; 56% alcohol], 16% admitted with OHE and 16% with ACLF. Older age, metabolic dysfunction-associated steatotic liver disease, previous treatment with lactulose, ACLF, white blood cell counts or albumin levels at admission were associated with OHE (P < 0.05). OHE progressed in 3% patients, which was associated with older age, previous treatment with lactulose and bacterial infections (P < 0.05), with a significantly shorter time-to-death (P < 0.001). Patients who resolved OHE (79%) presented a similar prognosis than those without OHE (P = 0.208). Post hoc analysis of the age-adjusted interaction between OHE and ACLF to predict mortality showed higher differences across ACLF grades compared with OHE. Conclusion: Presence of ACLF and progression of OHE are associated with high short-term mortality rates, while resolution of OHE is associated with significantly better prognosis. Understanding the natural history of OHE will have profound implications on the development of novel approaches
Towards adaptive sustainable scheduling within lithium-ion battery production
No full textInternational audienceThis study studies the increasing complexity of modern manufacturing scheduling, where efficiency, quality, and sustainability must be jointly optimized under flexible machine and operator constraints. Integrating real-time feedback from digital twins into optimization frameworks has emerged as a powerful approach, enabling adaptive and data-informed decision-making. By combining exact methods and metaheuristics, such frameworks can navigate the multi-objective landscape of contemporary production systems effectively. Looking forward, the adoption of surrogate models offers a promising alternative to further enhance performance. By approximating expensive simulations or high-fidelity digital twin responses, surrogate models can significantly reduce computational costs while maintaining solution accuracy
Permanent degradation of p-GaN HEMTs due to repetitive overvoltage stress during hard turn-off switching
No full textInternational audienceThis study investigates the long-term impact of dynamic overvoltage stress on GaN HEMTs using a newly designed test circuit, UIS3, a variant of classic UIS, which isolates key stress factors. Devices were subjected to short-duration repetitive overvoltage stress near and below their dynamic breakdown voltage. Characterization before and after stress reveals permanent degradation in CDS, CDS and IGSS, suggesting deep-trapping or structural damage within the device. A distinct alteration in the CDS curve is observed, may indicate less spreading of the electric-field within the device. RDS,on degradation is also noted, likely due to trapping effects, with partial recovery at room temperature. Higher stress levels accelerate failure. Waveform analysis and post-failure characterization indicate a short-circuit failure mode, likely due to partial dielectric breakdown during overvoltage events. These results provide new insights into GaN HEMT degradation mechanisms under high-voltage stress
Les Monts d'Or lyonnais : écrin paysager, écran bourgeois
No full textInternational audienceOn the outskirts of Lyon, the Monts d'Or is a small massif of golden stone villages offering panoramic views from the Saône valley to the Alps. Nestled in a lush green setting, the Monts d'Or are a haven for the region's wealthy. Indeed, the bourgeoisie asserted its social distinction through the protection of the landscape, notably by the Syndicat Mixte Plaines Monts d'Or (SMPMO). Environmental amenities in this privileged area of western Lyon are as much a source of income for investors as they are a marker of territorial identity for residents.This article examines the way in which landscape enhancement acts as a screen for the strategies deployed by the bourgeoisie to maintain its dominant position. The Monts d'Or is not a social monolith; the distribution of social groups in the area reveals a layering of domination. What's more, the power of the bourgeoisie has its limits in the Monts d'Or. An analysis of the commune of Saint-Germain shows that this power is partly being challenged by the new ecologist municipality, which is seeking to place the value of the landscape at the service of the common good.Aux portes de Lyon, les Monts d’Or sont un petit massif occupé par des villages en pierres dorées qui offrent un panorama de la vallée de la Saône jusqu’aux Alpes. Lovés dans un écrin de verdure, les Monts d’Or font figure d’entre-soi des possédants de la région. En effet, la bourgeoisie affirme sa distinction sociale par l’intermédiaire de la protection du paysage mise en place notamment par le Syndicat Mixte Plaines Monts d’Or (SMPO). Les aménités environnementales dans cet espace privilégié de l’Ouest lyonnais sont autant une rente pour les investisseurs qu’un marqueur d’identité territoriale pour les habitants.L’article questionne la façon dont la valorisation du paysage fait écran aux stratégies déployées par la bourgeoisie pour maintenir sa position dominante. Les Monts d’Or ne sont pas un monolithe social, la répartition des groupes sociaux du territoire révèle un étagement de la domination. De plus, le pouvoir de la bourgeoisie rencontre des limites dans les Monts d’Or. L’analyse de la commune de Saint-Germain montre que ce pouvoir est partiellement remis en cause par la nouvelle municipalité écologiste qui cherche à mettre la valeur paysagère au service du commun
CIP-Net: Continual Interpretable Prototype-based Network
No full textInternational audienceContinual learning constrains models to learn new tasks over time without forgetting what they have already learned. A key challenge in this setting is catastrophic forgetting, where learning new information causes the model to lose its performance on previous tasks. Recently, explainable AI has been proposed as a promising way to better understand and reduce forgetting. In particular, self-explainable models are useful because they generate explanations during prediction, which can help preserve knowledge. However, most existing explainable approaches use post-hoc explanations or require additional memory for each new task, resulting in limited scalability. In this work, we introduce CIP-Net, an exemplar-free self-explainable prototype-based model designed for continual learning. CIP-Net avoids storing past examples and maintains a simple architecture, while still providing useful explanations and strong performance. We demonstrate that CIP-Net achieves state-of-the-art performances compared to previous exemplar-free and self-explainable methods in both task-and class-incremental settings, while bearing significantly lower memory-related overhead. This makes it a practical and interpretable solution for continual learning
Subretinal Photovoltaic Implant to Restore Vision in Geographic Atrophy Due to AMD
No full textInternational audienceBackground Geographic atrophy due to age-related macular degeneration (AMD) is the leading cause of irreversible blindness and affects more than 5 million persons worldwide. No therapies to restore vision in such persons currently exist. The photovoltaic retina implant microarray (PRIMA) system combines a subretinal photovoltaic implant and glasses that project near-infrared light to the implant in order to restore sight to areas of central retinal atrophy.Methods We conducted an open-label, multicenter, prospective, single-group, baseline-controlled clinical study in which the vision of participants with geographic atrophy and a visual acuity of at least 1.2 logMAR (logarithm of the minimum angle of resolution) was assessed with PRIMA glasses and without PRIMA glasses at 6 and 12 months. The primary end points were a clinically meaningful improvement in visual acuity (defined as \\textgreater= 0.2 logMAR) from baseline to month 12 after implantation and the number and severity of serious adverse events related to the procedure or device through month 12.Results A total of 38 participants received a PRIMA implant, of whom 32 were assessed at 12 months. Of the 6 participants who were not assessed, 3 had died, 1 had withdrawn, and 2 were unavailable for testing. Among the 32 participants who completed 12 months of follow-up, the PRIMA system led to a clinically meaningful improvement in visual acuity from baseline in 26 (81%; 95% confidence interval, 64 to 93; P\\textless0.001). Using multiple imputation to account for the 6 participants with missing data, we estimated that 80% (95% CI, 66 to 94; P\\textless0.001) of all participants would have had a clinically meaningful improvement at 12 months. A total of 26 serious adverse events occurred in 19 participants. Twenty-one of these events (81%) occurred within 2 months after surgery, of which 20 (95%) resolved within 2 months after onset. The mean natural peripheral visual acuity after implantation was equivalent to that at baseline.Conclusions In this study involving 38 participants with geographic atrophy due to AMD, the PRIMA system restored central vision and led to a significant improvement in visual acuity from baseline to month 12. (Funded by Science Corporation and the Moorfields National Institute for Health and Care Research Biomedical Research Centre; PRIMAvera ClinicalTrials.gov number, NCT04676854.
Imaging plant–microbe communication under environmental stress with next-generation fluorescent biosensors
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Perceptual evaluation of an auralization model for pulse width modulation noise
No full textInternational audienceElectric motors have become a part of our daily lives, making the question of their noise essential for our acoustic comfort. The sound they emit is often described as tonal or whistling due to the presence of many harmonics, one source of which is a power supply technique called pulse width modulation (PWM). During the design process, auralization models can be used to evaluate the effect of PWM harmonics on sound quality. Engineering-grade models, which are based on simplifying assumptions, are used in the early stages of design when little input data is available. With these models, a direct perceptual comparison between measured and simulated sounds would inevitably reveal significant differences. However, if the model can reliably predict the perceptual sound space, it can be a valuable tool for assessing sound quality. This paper presents a methodology to evaluate the capability of a simplified engineering model to simulate the main attributes of PWM noise. To this end, the authors implemented an auralization model to synthesize the noise emitted by an electric motor from its supply signals. Some sound stimuli were collected from measurements and simulations to conduct a perceptual experiment. The measured and simulated sounds were evaluated separately within two sets of stimuli. The experiment included similarity and pleasantness evaluations. Comparing the results obtained by Individual Difference Scaling (INDSCAL) showed great coherence between the two sound sets, suggesting that the simulated stimuli were evaluated similarly to the measured stimuli. Pleasantness ratings yielded the same result. Therefore, the auralization model appears to reliably reproduce the main sound dimensions underlying the perception of PWM noise
StateFi: Effectively Identifying Wi-Fi Devices through State Transitions
No full textInternational audienceRandomized MAC addresses aim to prevent passive device tracking, yet Wi-Fi management frames still leak structured behavioral patterns. Prior work has relied primarily on syntactic probe-request features such as Information Elements (IEs), sequence numbers (SEQ), or RSSI correlations, which degrade in dense environments and fail under aggressive randomization. We introduce StateFi, a fingerprinting framework that models device behavior as finite-state machines (FSMs), capturing both structural transition patterns and temporal execution logic. These FSMs are embedded into compact feature vectors that support efficient similarity computation and supervised classification. Across five heterogeneous campus environments, StateFi achieves 94-97% accuracy for in-network fingerprinting using full management-frame FSMs. With probe-only FSMs, it re-identifies devices under MAC randomization with up to 97% accuracy across large public datasets comprising more than a million frames. When looking at the discrimination accuracy of the model, StateFi reaches 98%, outperforming the strongest prior signature by up to 17 percentage points. These results demonstrate that FSM-level behavioral dynamics form a powerful and largely unmitigated side channel, stable enough to defeat randomization and expressive enough for robust, scalable device identification
Soil image classification and segmentation: A survey from deep learning, multimodal data and hybrid models
No full textInternational audienceThis survey explores recent advances in the automated analysis of soil images, with a focus on classification and segmentation techniques driven by machine learning and deep learning. The paper provides a comprehensive overview of methods ranging from traditional approaches, such as handcrafted descriptors and statistical classifiers to state-of-the-art neural architectures, including Convolutional Neural Networks, transformers and hybrid multimodal models. While early methods relied heavily on texture and color features, modern deep learning approaches demonstrate enhanced performance through end-to-end learning and the ability to capture complex spatial patterns in soil images. The survey distinguishes between different classification paradigms : object detection, pixel-wise segmentation and general category classification. It emphasizes their relevance for soil analysis tasks such as identifying soil types, mineral compositions or predicting soil properties from RGB or spectral data. Self-supervised and transfer learning techniques are highlighted as promising solutions to the challenge of limited annotated datasets. Beyond classification, the paper discusses the critical role of segmentation in analyzing soil grain morphology, granulometry and spatial distribution. Deep learning-based segmentation methods offer significant improvements over traditional image processing techniques, particularly in heterogeneous or occluded conditions. These techniques enable more accurate particle size estimation and detailed soil texture analysis, information essential for geotechnical, hydrological and environmental applications. Increasingly, hybrid, multimodal and segmentation-aware approaches are being explored, not as an endpoint, but as the next frontier in addressing current analytical limitations to enhance robustness and generalization