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Evaluating and Influencing Strategy in Real-Time: Example of a Collaborative Strategy Game
No full textInternational audienc
Identification of liquid–vapor phase transition using the co-occurrence matrix and Haralick features
No full textInternational audienceIdentification of a phase transition — specifically, the precise timing of the critical temperature crossing — is challenging due to the thermal inertia of the enclosure holding the fluid, the fluid’s finite thermal conductivity, convective flows, and sedimentation. The human eye is particularly well adapted to detecting such phase transitions in images produced by transmitted light through critical fluids. In this study, we used Haralick image features that most closely correspond to human visual perception to identify gas–liquid phase transitions. The images were recorded during the phase transition of sulfur hexafluoride (SF) in microgravity—a particularly challenging scenario.The first goal of this study was to demonstrate that Haralick features can identify a phase transition from recorded images of supercritical fluids in microgravity. While highly sensitive to minute changes in image detail, Haralick features are not inherently normalized; thus, conclusions based on their values for a given image bit depth cannot be reliably compared to those derived from images captured with different cameras or quantization schemes.The second goal of this study was to identify empirical scaling laws for Haralick features that enable the prediction of their behavior as image bit depth varies. Such flexibility would allow direct comparison of results obtained from phase transition datasets recorded using different quantization schemes and imaging systems
Target trial emulation to replicate randomised clinical trials using registry data in multiple sclerosis
No full textInternational audienceBackground: Target trial emulation (TTE) offers a formal framework for causal inference using observational data, but its validity must be evaluated in each research domain by replicating randomised clinical trials (RCTs). We aimed to replicate eight RCTs evaluating the efficacy of disease-modifying therapies (DMTs) in multiple sclerosis (MS) using French registry data.Methods: This multicentre, retrospective, observational study was conducted using data extracted in December 2023 from the Observatoire Français de la Sclérose en Plaques (OFSEP) database. For each emulated trial, patients were included when they initiated one of the DMT evaluated in the corresponding RCT and met its inclusion criteria. Clinical outcomes were the annualised relapse rate and 3-month confirmed Expanded Disability Status Scale progression. Radiological outcomes were new/enlarged T2-lesions and new gadolinium-enhanced T1-lesions on a brain MRI. A targeted maximum likelihood estimator was used to estimate the treatment effect adjusted for confounding factors between groups and corrected for censoring and missing outcome assessment.Results: 14 111 patients were included in eight emulated trials: ASSESS (fingolimod vs glatiramer acetate), BEYOND (interferon beta vs glatiramer acetate), CONFIRM (dimethyl fumarate (DMF) vs glatiramer acetate), OPERA (ocrelizumab vs interferon beta), REGARD (interferon beta vs glatiramer acetate), RIFUND-MS (rituximab vs DMF), TENERE (teriflunomide vs interferon beta) and TRANSFORMS (fingolimod vs interferon beta). Treatment effects estimated in emulated trials were concordant with RCT findings in seven of eight trials for relapse rate, and in all six trials assessing disability progression. Radiological outcomes were more challenging to replicate; concordance was achieved in three of five trials for new T2-lesions, and one of four trials for new gadolinium-enhanced T1-lesions.Conclusion: The combined use of a TTE methodology and high-quality registry data is a valid tool to evaluate treatment effectiveness in MS
A compressible pore-scale numerical simulation of hydrogen flow into brine: Application to underground hydrogen storage
No full textInternational audienceThis study investigates the dynamics of hydrogen-brine flow in idealized pore geometries, with the aim of improving underground hydrogen storage in saline aquifers. Direct numerical simulations (DNS) using Open-FOAM are carried out to assess the influence of key parameters including injection flow rate, pore geometry, dynamic contact angle, and fluid compressibility on immiscible displacement at the pore scale. Results demonstrate marked differences between compressible and incompressible models in terms of brine sweep efficiency, interface displacement patterns, and pressure drop for a given flow rate. Incompressible simulations fail to capture critical phenomena such as hydrogen bubble formation and associated pressure fluctuations. Variations in flow rate and geometric constriction significantly impact residual brine saturation and inlet pressure dynamics; lower rates reduce pressure buildup and leakage risk while increasing storage efficiency. Incorporating a dynamic contact angle reduces the capillary resistance, accelerates the flow, and mitigates pressure peaks compared to static angle models. Overall, this work demonstrates the applicability of OpenFOAM to multiphase compressible hydrogen-brine flows at the pore scale, providing application-specific validation and novel insights to guide the design and optimization of underground hydrogen storage systems.</div
Apprendre en formation ouverte et à distance : pratiques et stratégies info-documentaires au sein des environnements personnels d’apprentissage
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Identification of transdiagnostic phenomena among patients, the general population, relatives, and mental health professionals using topic modeling techniques
No full textInternational audienceIntroduction Recent research has highlighted the limitations of the categorical approach to mental disorders and has increasingly supported the development of a transdiagnostic perspective. This emerging approach focuses on common distal factors (circumstantial, biological, and social) and psychological processes that contribute to psychological suffering across a range of disorders, as well as on the resulting psychological symptoms. The present study aims to identify transdiagnostic distal factors, psychological processes, and symptoms by analyzing narratives through topic modeling—an unsupervised machine learning technique, specifically within Natural Language Processing (NLP). Topic modeling enables the automatic extraction of latent themes from unstructured text, making it possible to identify psychological patterns grounded in patients’ lived experiences. Methods We recruited four groups of participants: Patients diagnosed with a psychiatric disorder ( N = 445), Individuals from the general population ( N = 570), Relatives of patients with psychiatric disorders ( N = 354), and Mental health professionals ( N = 131). Participants answered open-ended questions exploring the causes of psychological suffering, their wishes for change, and their previous experiences with psychotherapy. Results We identified 258 topics, which were organized into 12 overarching themes. The most prominent topics concerned Emotional and Psychological Difficulties , Family and Social Relationships , and Therapeutic Processes . Each theme showed a comparable prevalence across the different diagnostic categories, supporting the transdiagnostic nature of these phenomena. Conclusion Topic modeling can be used effectively to identify transdiagnostic distal factors, psychological processes, and symptoms from diverse narratives. This approach tends to provide a novel means of supporting the relevance and validity of the transdiagnostic perspective
Recovering intrinsic conduction velocity and action potential duration from electroanatomic mapping data using curvature
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The role of biomolecules produced by invasive macrophytes in lake ecosystem processes
No full textEgeria densa and Lagarosiphon major are the main invasive hydrophytes in French Atlantic Lakes. These exotic species can modify trophic levels by producing large quantities of biomass in lakes; previous investigations revealed the occurrence of endogenous metabolites in these plants known to be able to affect epiphytic communities, phytoplankton, or other plants, through allelopathic activity. Nevertheless, the actual excretion and environmental occurrence of these metabolites remain poorly documented. In order to gain knowledge of the concrete occurrence and potential ecological impact of E. densa and L. major exometabolome, untargetedmetabolomics using high-resolution mass spectrometer and chemometrics approaches is relevant through its ability to depict, as a first step, the exometabolome chemical landscape
Gaussian entropic optimal transport: Schrödinger bridges and the Sinkhorn algorithm
No full textEntropic optimal transport problems are regularized versions of optimal transport problems. These models play an increasingly important role in machine learning and generative modelling. For finite spaces, these problems are commonly solved using Sinkhorn algorithm (a.k.a. iterative proportional fitting procedure). However, in more general settings the Sinkhorn iterations are based on nonlinear conditional/conjugate transformations and exact finite-dimensional solutions cannot be computed. This article presents a finite-dimensional recursive formulation of the iterative proportional fitting procedure for general Gaussian multivariate models. As expected, this recursive formulation is closely related to the celebrated Kalman filter and related Riccati matrix difference equations, and it yields algorithms that can be implemented in practical settings without further approximations. We extend this filtering methodology to develop a refined and self-contained convergence analysis of Gaussian Sinkhorn algorithms, including closed form expressions of entropic transport maps and Schrödinger bridges
