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I Feel Competent, Therefore I Am: Self-Concept and Skill Interact at Different Speeds
No full textDo perceptions about one’s competence shape learning, or are they simply reflections of actual skills? This study revisits this longstanding question by replicating and extending preliminary findings by Marsh et al. (2024) on the temporal dynamics linking students’ academic self-concept (i.e., their perceived academic competence) and their academic skills in mathematics and French (language arts). Using longitudinal data from a large-scale field study (N > 9000 students, 3 measurement points), we tested how academic self-concept and skills relate to each other over time. Consistent with Marsh et al., results revealed a consistent temporal asymmetry: Academic skills predicted concurrent changes in self-concept within the same semester (contemporaneous effects), whereas self-concept predicted changes in academic skills across semesters (lagged effects). These findings were robust to several stress tests, including measurement error, unmeasured confounding, and competing models of change. Together, the results are consistent with a renewed theory of learning behavior, in which perceived competence and skills influence each other at different speeds. This temporal asymmetry helps integrate short-term and long-term cognitive-motivational processes in theories of learning behavior. It also underscores the importance of aligning intervention strategies and model specifications with the timescales of the underlying psychological processes, with implications for both fundamental and intervention research
Vapor–Liquid Equilibrium of the Hydrogen Sulfide (H2S) – Benzene (C6H6) Binary System: Experimental and Modeling Study
No full textInternational audienceThe phase behavior of the hydrogen sulfide (H₂S) - benzene (C₆H₆) binary system is critical for optimizing gas sweetening, aromatic solvent recovery, and high-pressure reservoir in the petroleum industry, while ensuring environmental compliance. This study presents new isothermal vapor-liquid equilibrium (VLE) measurements for the H₂S - C₆H₆ system at 278.21 K, 298.36 K, 323.38 K, and 343.39 K, covering pressures up to 4.5 MPa. The experimental data were obtained using a static-analytic method with two magnetic capillary samplers (ROLSI®), enabling precise sampling and analysis of both liquid and vapor phases via gas chromatography. The measurements have uncertainties of u(T, k=2)= 0.02 K for temperature, u(P, k=2)= 0.0008 MPa for pressure, and u(z) = 0.006 for molar compositions. The VLE data were modeled using the Peng–Robinson equation of state with classical van der Waals mixing rules and an alternative approach combining modified Huron–Vidal mixing rules with the NRTL model for the liquid phase. In addition, the predictive PPR78 and PSRK models were evaluated against the experimental dataset. With optimized binary interaction parameters, all models reproduced the measured data with acceptable deviations, effectively capturing the strongly non-ideal behavior of the H₂S–C₆H₆ system. These results extend the experimental database for H₂S–C₆H₆ mixtures, validate robust EOS-based and predictive modeling frameworks, and provide a reliable foundation for industrial process design, simulation, and optimization
Comportement et caractérisation de la formation de la croûte dans l'autoclave lors de la lixiviation du minerai de latérite de nickel dans des conditions HPAL
No full textInternational audienceScale formation on reactor walls remains a major operational challenge in high-pressure acid leaching (HPAL) of nickel laterites, leading to reduced heat transfer efficiency, increased maintenance, and process downtime. This study investigates the influence of slurry solid content and acid-to-ore (A/O) ratio on autoclave scale formation during laterite leaching. Experiments performed under typical HPAL conditions (265 °C and ∼50 bar) with laterite ore examined how these parameters affect metal extraction, scale quantity, and mineralogical composition. Scale deposits were quantified and analysed to determine their composition and to evaluate the precipitation tendency of potential scale-forming minerals through solution speciation and supersaturation behaviour during HPAL leaching. The results show that increasing slurry solids significantly promotes scale formation, producing denser and more strongly adherent deposits, while higher acid dosage further enhances precipitation of sulphate-bearing phases. Mineralogical analyses indicate that the scales are primarily composed of hematite, hydronium alunite, and magnesium sulphates, whose formation is driven by solution supersaturation during leaching. High solids content also promotes incorporation of valuable metals into the scale matrix, leading to reduced nickel and cobalt recovery. In contrast, operation at moderate solids content and near-stoichiometric acid addition limits scale accumulation while maintaining high metal extraction. These findings highlight the coupling between HPAL operating conditions, solution chemistry, and fouling behaviour, and suggest an operational window for reducing scale formation without the use of chemical additives that may interfere with downstream processing
When pre-training hurts LoRA fine-tuning: a dynamical analysis via single-index models
No full textPre-training on a source task is usually expected to facilitate fine-tuning on similar downstream problems. In this work, we mathematically show that this naive intuition is not always true: excessive pre-training can computationally slow down fine-tuning optimization. We study this phenomenon for low-rank adaptation (LoRA) fine-tuning on single-index models trained under one-pass SGD. Leveraging a summary statistics description of the fine-tuning dynamics, we precisely characterize how the convergence rate depends on the initial fine-tuning alignment and the degree of non-linearity of the target task. The key take away is that even when the pre-training and down- stream tasks are well aligned, strong pre-training can induce a prolonged search phase and hinder convergence. Our theory thus provides a unified picture of how pre-training strength and task difficulty jointly shape the dynamics and limitations of LoRA fine-tuning in a nontrivial tractable model
Dossier "La Société à Mission : Réalités Du Déploiement et Nouvelles Perspectives". Présentation
No full textInternational audienc
Réformer l'écriture céleste: un transfert par occidentalisation de la cithare qin ?
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Community Notes undermoderate polarizing content by design creating risks in electoral processes
No full textCommunity Notes (CNs) of X enables users to collaboratively moderate misleading content. To resolve conflicting moderation, CNs infers a latent ideological dimension and selects notes garnering cross-partisan support. As this system is now deployed worldwide, we evaluate its operation across diverse polarization contexts. We analyze all 1.9 million moderation notes receiving 135 million ratings by March 2025, cross-referencing ideological scaling data on 13 countries. Our results show that the CNs algorithm effectively captures the main polarizing dimensions across countries, surfacing notes that garner cross-partisan support. This also means that, by design, CNs systematically under-moderate polarizing content. We analyze notes relating to four recent elections in the US (2024), the UK (2024), France (2024) and Germany (2025) and demonstrate that they are systematically under-moderated when compared to other notes, posing potential risks to civic discourse and electoral processes
Impact of combined atmospheric and marine heatwaves on the filtration activity of the invasive Asian date mussel, Arcuatula senhousia.
No full textInternational audienceThe Asian date mussel, Arcuatula senhousia, originated from East Asia, is a highly invasive species that severely affects ecosystem functioning and biodiversity in various ecosystems in America and Europe. In recent decades, heatwave events have increased in severity and frequency, causing additional stress for intertidal organisms living in one of the most thermally challenging habitats. Therefore, understanding the impact of stressful environmental conditions on species' behavioural responses is essential for predicting the effects of biological invasions in the context of climate change. This study aimed to evaluate the response of A. senhousia filtration activity under two levels of realistic combined marine and atmospheric short-term heatwaves (strong and extreme), performed during spring and summer conditions. Although numerous intertidal organisms have been shown to suffer greatly from heatwaves, the results indicate that A. senhousia is able to withstand short-term heatwaves. The results showed that, for all intensities and seasons, heatwaves had no significant effect on the clearance rate. Although there was no distinct general trend regarding the influence of heatwaves on the behaviour of valve opening in spring, strong heatwave conditions significantly increased the valve gaping activity (e.g., increasing valve opening and time of active filtration) during the summer experiment without significant difference on the clearance rate. This highlights the importance of considering the season when attempting to understand and predict the impacts of heatwaves. Therefore, this species exhibits a high filtration rate as well as tolerance to heatwaves. However, future investigations should investigate if this resistance have an impact on the species growth and survival at a longer term. In the context of climate change, this species may have advantage over native ones, and its abundances may significantly rise, leading to important ecological consequences in terms of communities structures and habitat modifications
Dual-Continuum Models of Lithium-Ion Batteries are Fast and Accurate Alternatives to the Doyle-Fuller-Newman Approach: II. Model Extensions
No full textInternational audienceIn Part I of this work, we derived and validated a dual-continuum model of a lithium-ion battery using the volume-averaging technique. Such models employ a fully macroscale description of the battery, thus avoiding the strong assumption made in the Doyle-Fuller-Newman (DFN) model that active material particles are isolated spheres. In all cases studied, our dual-continuum model predicted cell voltage more accurately than the DFN model, and required 70–80% less computation time. The physical insight offered by volume averaging gives rise to several interesting extensions of our derivation. Here in Part II, four of these are considered. Firstly, while Part I relied on a quasi-steady-state assumption for the closure problem, we now consider its transient behaviour to improve accuracy under changing battery loads. Secondly, we simulate electrodes with carbon additives and polymer binder, which can be explicitly modelled in the closure problem. Thirdly, we extend the dual-continuum theory to a multi-continuum model, which is particularly interesting for electrodes with a large particle size distribution. Finally, we reduce the dual-continuum theory to two single-continuum formulations, which are comparable with the well-known single-particle model—these maintain much of the accuracy of the dual-continuum approach whilst further reducing computation time
Dual-Continuum Models of Lithium-Ion Batteries are Fast and Accurate Alternatives to the Doyle-Fuller-Newman Approach: I. Derivation and Validation
No full textInternational audienceOne of the main reasons for the Doyle–Fuller–Newman (DFN) model’s success in simulating lithium-ion batteries is also one of its main limitations—its hybrid micro/macro formulation. On one hand, this captures the slow diffusion of lithium within the active material particles. On the other hand, it makes the retention of realistic particle geometries computationally prohibitive, thereby provoking strongly simplifying assumptions on their shape. Dual-continuum models employ a fully macroscale description of the battery, and thus can potentially circumvent these challenges. Despite their widespread use in other fields, they have seen little application in battery modelling. In this work, we derive a dual-continuum model for lithium-ion batteries using the volume-averaging technique. A novel mapping between the volume-averaged and surface-averaged active material concentrations is introduced, based on the microscale source terms that generate concentration fluctuations. Unlike the DFN model, this approach makes no assumptions about particle shape but instead relies on a closure problem solved on the electrode microstructure. The resulting model is validated against a detailed microscale model, the DFN model, a recent dual-continuum formulation, and experimental data. Across all cases considered, our dual-continuum model reproduced cell-voltage data more accurately than the DFN approach while requiring 70–80% less computation time