Portail des publications scientifiques IMT Mines Alès
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Promoting empathy in decision-making by turning agent-based models into stories using large-language models
No full textInternational audienceThis study explores how empathy can be integrated into decision-making within three Agent-Based Models (ABMs) of disasters and migrations. We design, implement, and evaluate methods to translate the experiences of simulated agents into empathetic narratives through Large Language Models (LLMs), using GPT-4 as a guiding example. We compare two approaches: a direct method that prompts to create empathetic stories (thus leaving it to GPT’s interpretation of empathy), and an indirect method using style transfer by adopting the voice of well-known empathetic figures. Using a Design of Experiments framework, we evaluate the impact of factors including text length and temperature on readability, sentence accuracy, and character authenticity. Results show the indirect method yields better quality narratives. Human readers generally agree that our generated stories show genuine emotions, although full empathy is limited by the extreme scenarios simulated. Our work is provided open-source to support researchers in transforming their ABMs into narratives
Physical activity and personality change in people with chronic respiratory diseases: Evidence from two longitudinal samples
No full textInternational audienceLower physical activity in people with chronic respiratory diseases is related to detrimental health outcomes. However, no study has investigated the moderating role of physical activity on personality change in this population. This study aimed to fill this gap by examining the association between physical activity and personality change in people with chronic respiratory diseases. Participants (N = 2253; age range: 24-97) were drawn from two US longitudinal samples with multiple self-report measures of personality and physical activity collected over a follow-up period ranging from 12 to 18 years. Controlling for demographic factors, a randomeffects meta-analysis showed that overall higher physical activity during follow-up was related to smaller declines in extraversion and conscientiousness. There was no association with changes in neuroticism, openness to experience and agreeableness. These findings highlight that physical activity in individuals with chronic respiratory diseases predicts individual differences in personality trait change, and may contribute to more favorable trajectories, which could promote better health outcomes
A “fine-cuts” approach disentangling psychopathic, autistic and alexithymic traits in their associations with affective, cognitive and motor empathy
No full textInternational audienceAtypical empathy is seen in relation to psychopathy and autistic traits; however, studies typically conflate affective and cognitive facets of empathy. Moreover, motor empathy has been suggested as another facet of empathy, advocating for further delineation of empathy dimensions. In addition, alexithymia may affect responding to emotional, cognitive or motor states in others. The current study investigated how psychopathic, autistic and alexithymic traits are associated with those empathy facets. Nonclinical participants (N = 212) completed online self-report measures of affective, cognitive and motor empathy, primary and secondary psychopathy, autistic and alexithymic traits. A subsample (N = 157) also completed a behavioral measure of motor empathy (i.e., behavioral synchrony) using a virtual agent. Whilst all traits were associated with reduced cognitive empathy and behavioral synchrony; path analyses supported a mediation model of cognitive empathy difficulties through alexithymia only for primary psychopathy. Secondary psychopathy and alexithymia were associated with increased motor empathy, specifically tendencies to mimic negative emotions. In contrast, primary psychopathy was associated with reduced affective empathy and inhibition of positive emotion imitation, despite reporting self-other overlap experiences induced by behavioral synchrony. Overall, these findings highlight the need for a “fine-cuts” approach; delineating the role of empathy subfacets in atypical empathy
Experimental Investigation of the Influence of Opening Dynamics on the Blast Overpressure Anisotropy in BLEVEs
No full textInternational audienceThe Boiling Liquid Expanding Vapor Explosion (BLEVE) is one of the most dangerous industrial accidents,occurring when a pressure vessel containing a pressure liquefied gas like LPG or heated water suffers acatastrophic structural failure. Once the vessel opens, the vapor phase leaves the vessel pushing thesurrounding atmosphere out of the way, leading to compression waves that then pile up to form a shockoverpressure. The vessel progressively opens causing a rapid depressurization and violent boiling. A BLEVEresults in many other hazards including projectiles, flammable or toxic release and ground loading. To date, many experiments have been carried out to predict the BLEVE hazards. Most of these studies have been performed with limited data on far-field overpressure and mainly focusing on the blast data measured on perpendicular horizontal directions. These studies showed that the blast shape is contingent upon vessel’s geometry; in cylindrical vessels the blast is not directionally uniform. This is also true for spherical vessels. Given that most industrial pressure vessels are cylindrical, this paper investigates blast overpressure resulting from top-initiated failures in cylindrical vessels, utilizing small-scale BLEVE experiments. The results indicated that the Top blast gauges recorded the highest overpressure, followed by the 45-degree angle and end gauges
Détection Automatique des Traînées Astronomiques avec YOLO – Une Approche Exploratoire pour la Connaissance du DomaineSpatial
No full textNational audienceL’identification des traînées transitoires est cruciale pour la Connaissance du Domaine Spatial, mais les méthodes classiques sont coûteuses et inadaptées au temps réel. Nous explorons YOLOv8m pour cette tâche, en l’entraînant sur le StreaksYoloDataset (2 388 images annotées, télescope Stellina). L’approche prend en compte les variations de bruit, d’éclairement et de champ de vision. Les résultats montrent un bon équilibre entre rapidité et précision (mAP@50-95 : 0,90), bien que des améliorations soient nécessaires pour limiter les faux positifs et détecter les traînées faibles
Flammability of flax plant fractions: a whole-plant study from micro- to bench- scale
No full textInternational audienceThe flammability of a large set of 7 flax plant fractions, namely roots, shives, scutched fibres, tows, fines, seeds and capsules, has been studied using pyrolysis-combustion flow calorimetry and cone calorimetry in order to characterize the most performant fractions in terms of fire reaction. In addition, scutched fibres from twelve flax varieties with different growth locations and retting conditions have been considered to assess the variability of flammability properties and understand the scattering of results observed in the literature. The fines, produced in small amounts and rich in minerals, are the least flammable fraction, with a low heat release (6.1 kJ/g after anaerobic pyrolysis at microscale) and a high content of mineral-enriched residue (45 wt %). On the contrary, seeds release a large amount of energy (19.6 kJ/g) during combustion due to their oil-rich composition (> 30 wt %). In addition, variables such as flax variety, growth location and retting have a significant influence on the flammability properties of scutched fibres although the respective contributions of these factors cannot be easily discriminated
Development of a single biofilm extraction method for non-target analysis and bioassays to monitor wastewater micropollutants
No full textInternational audiencePassive samplers (PS) are strong tools to monitor micropollutants due to their ability to accumulate andconcentrate the pollutants present in water. Among existing PS, those using biofilms as a receiving phasehave gained interest for environmental monitoring, notably in waste water, for which conventional PS arelimited by biofouling. Extraction and (bio)analysis of contaminants adsorbed in biofilms still needoptimisation in monitoring context. Non-target analysis has been increasingly used during the last decadeto detect a large range of water micropollutants, including emerging contaminants, which positions it as agreat tool for environmental monitoring. However, this method does not account for the biologicalactivities of the compounds, and the impact of mixture effects on their activity. Hence, as acomplementary approach, in vitro bioassays provide a global bioactivity profile of the water sampleswhile considering all the bio-active micropollutants and their potential mixture effect. The combination ofPS with bioassays and chemical analysis has already shown its effectiveness in characterizing water.This work aims to develop an approach based on the coupling of an innovative biofilm -based PS withnon-target screening and in vitro bioassays to characterize wastewater. This presentation will mainly focuson the development a single biofilm extraction method for both non -target analysis and bioassays,allowing us to have a robust correlation between the compounds analysed and the activity measured.Several solvents, extraction methods, and clean-up strategies were implemented and compared for biofilmextraction. The extracts were then subjected to chemical analysis and in vitro bioassays. For the chemicalanalysis performed on a liquid chromatography coupled to a high-resolution mass spectrometry, theextraction efficiency was evaluated based on characteristics such as standard recoveries, number ofcommon and specific compounds detected with suspect screening, number of common and specificunknown features detected, and range of molecular weight or polarity. For bioassays, the evaluations wereassessed on the response of four nuclear receptors (estrogenic, androgenic, pregnane X, and arylhydrocarbon receptors).Based on the outcome of the results obtained for these tests, a single extraction protocol offering the bestefficiency compromise for both chemical analysis and in vitro bioassays will be presente
Towards more reliable prostate cancer detection: Incorporating clinical data and uncertainty in MRI deep learning
No full textInternational audienceProstate cancer (PCa) is one of the most common cancers among men, and artificial intelligence (AI) is emerging as a promising tool to enhance its diagnosis. This work proposes a classification approach for PCa cases using deep learning techniques. We conducted a comparison between unimodal models based either on biparametric magnetic resonance imaging (bpMRI) or clinical data (such as prostate-specific antigen levels, prostate volume, and age). We also introduced a bimodal model that simultaneously integrates imaging and clinical data to address the limitations of unimodal approaches. Furthermore, we propose a framework that not only detects the presence of PCa but also evaluates the uncertainty associated with the predictions. This approach makes it possible to identify highly confident predictions and distinguish them from those characterized by uncertainty, thereby enhancing the reliability and applicability of automated medical decisions in clinical practice. The results show that the bimodal model significantly improves performance, with an area under the curve (AUC) reaching , a sensitivity of , while maintaining high specificity. Uncertainty analysis revealed that the bimodal model produces more confident predictions, with an uncertainty accuracy of 0.85, surpassing the imaging-only model (which is 0.71). This increase in reliability is crucial in a clinical context, where precise and dependable diagnostic decisions are essential for patient care. The integration of clinical data with imaging data in a bimodal model not only improves diagnostic performance but also strengthens the reliability of predictions, making this approach particularly suitable for clinical use
Development of long-scale interphases in biocomposites: a step towards bioinspired structures
No full textNational audienceSince the 90s, bio-based polymers and composites have undergone significant development. For example, the automotive industry is seeking to develop ever lighter structures and increase the bio-based fraction of its composites, which could be achieved by using plant fibres as reinforcements. Beyond the interesting intrinsic properties of plant fibres, the thermo-mechanical properties of biocomposite materials are highly dependent on the fibre / matrix interface. However, most polymer matrices are apolar and hydrophobic, which implies weak interactions, and therefore low interfacial adhesion with plant fibres, and is not easy to overcome. Inspired by natural biological systems that feature hierarchical nanostructured architectures and achieve high strength and toughness [1], the goal of this work is to develop long-scale interphases in polymer biocomposites. Two strategies are explored: (i) a polymer engineering approach based on radiation-induced cross-linked interphases in LDPE-based biocomposites reinforced by aliphatic phosphonic acid modified flax fibres [2], and (ii) a nanotechnology approach based on nanostructured interphases in PP and epoxy-based biocomposites reinforced by Cellulose NanoCrystals (CNC) modified flax fibres [3, 4]. A multi-scale analysis of the resulting interphases is carried out, from the nano & micrometric scale with the characterization of fibre surface topography, work of adhesion and interfacial shear strength (IFSS) between flax fibres and matrices, to the macroscopic scale with the mechanical properties of biocomposites