Portail des publications scientifiques IMT Mines Alès
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Personality and Risk of Arthritis in Six Longitudinal Samples
No full textInternational audienceAbstract Objectives Personality traits are broadly related to medical conditions, but there is limited research on the association with the risk of arthritis. This multicohort study examines the concurrent and prospective associations between personality traits and arthritis risk. Methods Participants (N > 45,000) were mostly middle-aged and older adults from 6 established longitudinal cohorts. Baseline assessments of personality traits, covariates (age, sex, education, race, ethnicity, depressive symptoms, body mass index, and smoking), and arthritis diagnosis were obtained in each sample. Arthritis incidence was assessed over 8–20 years of follow-up. Results The meta-analyses identified an association between higher neuroticism and an increased risk of concurrent (odds ratio = 1.20, 95% confidence interval [CI] = 1.16–1.24; p < .001, I2 = 40.27) and incident (hazard ratio = 1.11, 95% CI = 1.08–1.14; p < .001, I2 = 0) arthritis and between higher conscientiousness and a decreased risk of concurrent (odds ratio = 0.88, 95% CI = 0.86-0.90; p < .001, I2 = 0) and incident (hazard ratio = 0.95, 95% CI = 0.92–0.98; p = .002, I2 = 41.27) arthritis. Higher extraversion was linked to lower risk of concurrent (odds ratio = 0.92, 95% CI = 0.88–0.96; p < .001, I2 = 76.09) and incident (hazard ratio = 0.97, 95% CI = 0.95–0.99; p = .018, I2 = 0) arthritis, and openness was related to lower risk of concurrent arthritis (odds ratio = 0.96, 95% CI = 0.93–0.99; p = .006, I2 = 35.86). Agreeableness was unrelated to arthritis. These associations were partially accounted for by depressive symptoms, body mass index, and smoking. There was no consistent evidence of moderation by age or sex. Discussion Findings from 6 samples point to low neuroticism and higher conscientiousness as factors that reduce the risk of arthritis
Enhancing Corner Detection: Leveraging 3×3 Structure Tensor Combined with Hourglass Filter
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ISSA Pipeline
No full textThe ISSA pipeline was developed by the ISSA project (https://issa.cirad.fr/) . It orchestrates the automatic indexing of a scientific archive by extracting from the articles full-text thematic descriptors and named entities, and linking them with terminological resources in the Semantic Web format.The repository consists of various tools, scripts and configuration files involved in each step of the pipeline:- retrieve the articles metadata from the archive's API;- download and pre-process the PDF files of the articles;- process the output to extract thematic descriptors and named entities;- translate the output of each processing step into a unified, consistent RDF dataset;- retrieve additional metadata from OpenAlex: topics, Sustainable Devlopment Goals (SDG), authorship with institutions- upload the resulting dataset to a triple store equipped with a SPARQL endpoint
Les troubles de la représentation et de la perception du corps dans le syndrome douloureux régional complexe
No full textInternational audienceBody perception disturbances in Complex Regional Pain Syndrome (CRPS) comprise both alterations in the sensorimotor representation of the limb (changes in the perception of shape, size, and proprioceptive representation) and in the conscious perception of the limb (negative emotions, aversion to appearance or the feeling of disownership). They are more frequent and more significant in CRPS. At present, the pathophysiological hypotheses have not been elucidated. The Bath CRPS Body Perception Disturbance Scale provides a wealth of qualitative and quantitative information. Its originality lies in the drawing of a mental image of the affected limb, which requires cooperation between the patient and the assessor. Therapeutic treatments are still underdeveloped. They involve strategies aimed at modifying, stimulating, and training multisensory perceptions.Les troubles de la perception du corps dans le syndrome douloureux régional complexe (SDRC) sont à la fois des altérations de la représentation sensorimotrice du membre (avec des changements de la perception de la forme, de la taille, de la représentation proprioceptive) et de la perception consciente du membre (avec des émotions négatives, une aversion de l’apparence ou la sensation de non-appartenance). Ils sont plus fréquents et plus importants dans le SDRC. À l’heure actuelle, les hypothèses physiopathologiques ne sont pas élucidées. L’échelle de Bath fournit de nombreuses informations qualitatives et quantitatives sur les troubles de la perception corporelle. Son originalité réside dans le dessin de l’image mentale du membre affecté, qui nécessite une coopération entre le patient et l’évaluateur. Les prises en charge thérapeutiques sont encore peu développées. Elles impliquent des stratégies visant des modifications, stimulation, et entraînement de perceptions multisensorielles
Geological and mining mapping approach by coupling geological and geophysical field data: application to the Central Domain of the Mbere division (Adamawa-Cameroon)
No full textInternational audienceThis study is based on the coupling of geological field data with geophysical studies in the geological mapping of the Central Domain of the Mbere Division (Adamawa-Cameroon). The first step was to calculate the residual grid associated with the geophysical data that will be used for the mapping of the basement. The second step before mapping consisted in calculating the analytical signal associated with the residual grid, used to interpret the geological formations under cover or in intrusion. Subsequently, a geological mapping approach based on the categorisation of geophysical signatures into ranges using the Encom Discover program was adopted. This approach is based on the analysis of the relief of geophysical anomalies. The process of categorisation which is done iteratively, takes into consideration in the same georeferenced space, the resolution of the input data, the variation of the field considered and the structural model previously interpreted. The result of the categorisation is then compared to the spatial distribution of outcrop data to build the geological model. The application of this approach to the Central Domain of the Mbere Division led to the production of a synthetic geological map at a scale of 1/75,000. The superposition of this map to the topographic model allows to observe a concordance between the Cretaceous basins represented by their edge faults and the topographic depression zones. The various mineral indicators (primary and secondary) collected during the fieldwork, combined with the geological model (1:75,000 scale) as well as the Cretaceous deposits and vein intrusion models were used to create the mineral indicators and targets map for this sector. All these models can thus be used to underpin mineral exploration projects in the study area
Analyse prospective de l’impact environnemental de la production mondiale de matières plastiques à moyen et long terme
No full textInternational audienceLa production mondiale de matières plastiques ne cesse d’augmenter depuis les années 50 et a été évaluée à 400 millions de tonnes en 2022 [1]. L’analyse de cycle de vie (ACV) est un outil normé permettant d’évaluer les impacts environnementaux d’un produit ou d’un service tout au long de son cycle de vie. De par sa grande légèreté, le plastique est souvent présenté comme une solution limitant les impacts environnementaux, lorsque des ACVs de produits sont réalisées et qu’une matière plastique est comparée avec des alternatives plus lourdes, comme par exemple dans le domaine de l’emballage [2].Mais qu’en est-il des impacts environnementaux de la production mondiale de plastique ?Très peu d’études se sont intéressées à cette problématique et se focalisent principalement sur l’évaluation de l’impact sur le changement climatique et/ou la consommation énergétique [3,4]. L’objectif de ces travaux est d’évaluer par ACV une sélection d’impacts environnementaux de la production mondiale de matières plastiques en 2022 puis avec des analyses prospectives d’évaluer ceux de la production mondiale en 2030 et 2050.L’analyse de cycle de vie est réalisée selon les normes ISO 14040/14044, du berceau à la porte, soit de l’extraction des matières premières jusqu’à la production des plastiques à l’usine. La modélisation est effectuée avec le logiciel OpenLCA, les données d’inventaire proviennent essentiellement de la littérature et de base de données (ecoinvent, gabi). Pour les analyses prospectives, différents scénarios sont évalués, considérant différentes évolutions possibles pour la production mondiale, différentes origines des matériaux (biosourcé, recyclé) et différentes type de sources d’énergie
QAI-Sport project : characterization of Indoor Air Quality in sports facilities
No full textInternational audienceIndoor air quality (IAQ) in sports facilities is poorly studied compared to that of housing and other public buildings. One objective of the QAI-Sport project consisted in providing an overview of the organic chemicals and microbiological composition in air of 10 sports halls having different activities: dojos, motor skills rooms and weight rooms. Targeted and non-targeted analytical approaches were applied to provide the broadest possible screening of volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs) and microbiological contaminants in indoor air and of SVOCs in settled dusts, with a focus on emerging pollutants. Two sampling campaigns were performed in unoccupied and occupied rooms to assess the impact of sporting activity on IAQ.About 50 VOCs were identified and quantified. Composition and concentrations are globally close to those of other indoor environments, with a predominance of carbonyls, especially hexanal. However, some specific and emerging compounds (like benzothiazole, decamethylcyclopentasiloxane and 1-(2-methoxy-1-methyl ethoxy)-2-propanol)) were highlighted. Acetone and 6-methyl-5-hepten-one, emitted by human body, were identified as occupancy tracers. SVOCs were mainly phthalates (DiBP, DBP in air, DEHP, DiNP in dust), organophosphate flame retardants (TCPP in air, EHDPP, TPP in dust) and PAHs (fluorene, phenanthrene in air, pyrene in dust) with particularly high concentrations in one weight room’s dust, probably issued from a recycled rubber flooring. Microbiological contamination is similar to residential ones and Sars-CoV-2 was not detected.The project will further consist in assessing the human exposure to these pollutants in sports halls, identifying their sources and understanding their indoor partitionin
A contribution for dismantling of nuclear facilities: a functional pattern for dismantling operations and indicators design and management
No full textInternational audienceOptimizing nuclear installation decommissioning and dismantling operations is an ongoing quest. Faced with the complexity of this activity, the Model Based System Engineering promotes relevant principles and modeling techniques. It motivated then the definition of a functional generic pattern model of the waste package production line and the decommissioning of the facility. It proposes a global and generic functional architecture of such system aiming to reduce the level of the pollutant. This pattern is coupled with a process of logistics. Six functions are combined to define this functional pattern. The application of this pattern model to a case of waste recovery in a pit shows the relevance of model-based system engineering approach, reducing the weight of the history in the development of scenarios by optimizing the control means for the nuclear safety and product quality
Sensitivity Analysis of Traffic Sign Recognition to Image Alteration and Training Data Size
No full textInternational audienceAccurately classifying road signs is crucial for autonomous driving due to the high stakes involved in ensuring safety and compliance. As Convolutional Neural Networks (CNNs) have largely replaced traditional Machine Learning models in this domain, the demand for substantial training data has increased. This study aims to compare the performance of classical Machine Learning (ML) models and Deep Learning (DL) models under varying amounts of training data, particularly focusing on altered signs to mimic real-world conditions. We evaluated three classical models: Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA), and one Deep Learning model: Convolutional Neural Network (CNN). Using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which includes approximately 40,000 German traffic signs, we introduced digital alterations to simulate conditions such as environmental wear or vandalism. Additionally, the Histogram of Oriented Gradients (HOG) descriptor was used to assist classical models. Bayesian optimization and k-fold cross-validation were employed for model fine-tuning and performance assessment. Our findings reveal a threshold in training data beyond which accuracy plateaus. Classical models showed a linear performance decrease under increasing alteration, while CNNs, despite being more robust to alterations, did not significantly outperform classical models in overall accuracy. Ultimately, classical Machine Learning models demonstrated performance comparable to CNNs under certain conditions, suggesting that effective road sign classification can be achieved with less computationally intensive approaches
