Portail des publications scientifiques IMT Mines Alès
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Effect of Bubbling on Ignition of PMMA Slab: Change in Thermo-Physical and Thermo-Radiative Properties
No full textInternational audienceIn semi-transparent polymers, ignition is not only dependent on conductive thermal transfer into the material but also on in-depth absorption of the radiation. The aim of this work was to investigate the influence of bubbling on the thermo-physical and thermo-radiative properties of PMMA and how it may affect its ignition. PMMA plates of varying thickness were exposed to the heat flux of two radiative sources with different emission spectra. Exposure was stopped after different periods of time to study bubbling kinetics and bubble size distribution by optical microscopy. Front and back surface temperatures of samples were recorded during heat exposure. The results indicate that the bubble size distribution is closely related to the temperature gradient within the sample. Steep thermal gradients lead to small-sized bubbles underneath the exposed surface, while weak thermal gradients generate a wider size distribution with in-depth bubbling. All thermo-physical quantities k, ρ and Cp were shown to decrease with increasing bubbling degree. Likewise, it was highlighted that bubbling modifies the thermo-radiative properties of PMMA, especially in the near-infrared range. Transmittance decreases while absorbance increases with a bubbling degree. The increase in the absorption coefficient was attributed to multiple scattering by bubbles that expand the pathway of radiation into the materials. It was concluded that changes in both the thermo-physical and thermo-radiative properties with bubbling were likely to account for the delay in ignition observed when using the near-infrared heating source
Prodromal dementia with Lewy bodies in REM sleep behavior disorder: A multicenter study
No full textInternational audienceINTRODUCTION Isolated/idiopathic rapid eye movement sleep behavior disorder (iRBD) is a powerful early predictor of dementia with Lewy bodies (DLB) and Parkinson's disease (PD). This provides an opportunity to directly observe the evolution of prodromal DLB and to identify which cognitive variables are the strongest predictors of evolving dementia. METHODS IRBD participants ( n = 754) from 10 centers of the International RBD Study Group underwent annual neuropsychological assessment. Competing risk regression analysis determined optimal predictors of dementia. Linear mixed‐effect models determined the annual progression of neuropsychological testing. RESULTS Reduced attention and executive function, particularly performance on the Trail Making Test Part B, were the strongest identifiers of early DLB. In phenoconverters, the onset of cognitive decline began up to 10 years prior to phenoconversion. Changes in verbal memory best differentiated between DLB and PD subtypes. DISCUSSION In iRBD, attention and executive dysfunction strongly predict dementia and begin declining several years prior to phenoconversion. Highlights Cognitive decline in iRBD begins up to 10 years prior to phenoconversion. Attention and executive dysfunction are the strongest predictors of dementia in iRBD. Decline in episodic memory best distinguished dementia‐first from parkinsonism‐first phenoconversion
Gestion de la Continuité et de l’Intégrité des Processus Métiers à l’aide de Corrections Basées sur des Patterns
No full textThis thesis presents an approach to managing deviations in Business Process Model and Notation (BPMN) workflows. The research addresses the critical need for effective deviation management by integrating a comprehensive framework that includes pattern-based deviation correction and an enriched State Token mechanism. The approach is tested through a case study in the apiculture domain, demonstrating the practical applicability and effectiveness of the proposed method. Key contributions include the development of a library of patterns, the characterization of BPMN elements, and a mechanism to help decision-making in addressing deviations. The results show that the approach can efficiently correct deviations, ensuring workflow continuity and integrity.Cette thèse présente une approche pour la gestion des déviations dans les flux de travail utilisant le Business Process Model and Notation (BPMN). La recherche répond au besoin de gestion efficace des déviations en intégrant un cadre complet comprenant la correction des déviations basée sur des modèles et un mécanisme enrichi de State Token. L’approche est testée par une étude de cas dans le domaine de l’apiculture, démontrant l’applicabilité pratique et l’efficacité de la méthode proposée. Les contributions clés incluent le développement d’une bibliothèque de modèles, la caractérisation des éléments BPMN et un mécanisme pour aider à la prise de décision dans la gestion des déviations. Les résultats montrent que l’approche peut corriger efficacement les déviations, assurant la continuité et l’intégrité du flux de travail
PRIAM
No full textPRIAM is an innovative solution specifically designed to help organizations ensure their applications comply with the stringent requirements of the General Data Protection Regulation (GDPR). As privacy and data protection become increasingly critical, businesses need a reliable framework to address the complexities of GDPR compliance, and PRIAM provides just that.\n\nIn this project, we focus primarily on consent management, which is a key aspect of GDPR compliance. Consent management ensures that organizations properly handle and record user consent for the processing of personal data. This feature is crucial for complying with GDPR's requirements on transparency and user control over their data
Optimising Intrusion Detection in Cyber-Physical Systems
No full textInternational audienceThe application of digital twin or model in industries are recently used as a means for enhancing operational efficiency of Cyber-Physical Systems (CPSs). However, this comes with challenges bordering mostly around detection of cyber-attacks using replication as a means. This study considers a synthetic digital object dataset and its physical object equivalent for intrusion detection using Principal Component Analysis (PCA) and autoencoder for dimensionality reduction. Application of random forest and XGBoost machine learning on both objects to compare the performance on the two objects. To fill a methodological gap, the study implemented the developed model on the digital object dataset and its physical object equivalent to assess intrusion detention capability. The study established that the application of the developed random forest model on the use case digital object dataset varies slightly from its physical equivalent as assessed using standard performance metrics, though revealing very good performance for both objects, which validates the digital object and its applicability in the real world
Automatic Risk Assessment of Pedestrian Crossings
No full textWith pedestrian crossings implicated in a significant proportion of vehicle-pedestrian accidents and the French government's initiatives to improve pedestrian safety, there is a pressing need for efficient, large-scale evaluation of pedestrian crossings. This study proposes the deployment of advanced deep learning neural networks to automate the assessment of pedestrian crossings and roundabouts, leveraging aerial and street-level imagery sourced from Google Maps and Google Street View. Utilizing ConvNextV2, ResNet50, and ResNext50 models, we conducted a comprehensive analysis of pedestrian crossings across various urban and rural settings in France, focusing on nine identified risk factors.Our methodology incorporates Mask R-CNN for precise segmentation and detection of zebra crossings and roundabouts, overcoming traditional data annotation challenges and extending coverage to underrepresented areas. The analysis reveals that the ConvNextV2 model, in particular, demonstrates superior performance across most tasks, despite challenges such as data imbalance and the complex nature of variables like visibility and parking proximity.The findings highlight the potential of convolutional neural networks in improving pedestrian safety by enabling scalable and objective evaluations of crossings. The study underscores the necessity for continued dataset augmentation and methodological advancements to tackle identified challenges. Our research contributes to the broader field of road safety by demonstrating the feasibility and effectiveness of automated, image-based pedestrian crossing audits, paving the way for more informed and effective safety interventions
Electrically conductive polymer composites
No full textInternational audienceWith the rise of the electrification of our uses, particularly in the field of transport, more and moredevices must be lightweight, mechanically resistant, integrated into the structure, and haveproperties such as specific electrical conductivity ranging from dissipative materials (to evacuateelectrostatic charges) to materials for electromagnetic shielding.While dispersing modified graphene nanoplatelets into polymer blends it is possible to reachinteresting electrical conductivity [1,2]. Figure 1 shows that a modified GOxH-r-PMMA graphene (agraphene oxidized then reduced then grafted with PMMA copolymer) dispersed in a co-continuousblend based on PS and PMMA can reach an electrical conductivity of more than 10-3 S/cm.Recently, different biochars were characterized to measure their intrinsic electrical conductivities.A biochar obtained by pyrolysis of chestnut at 700°C and activated by CO2, such as a biocharobtained from the pyrolysis of birch at 450°C and activated by CO2 exhibit an electricalconductivity in the same range as that of a KNG 180 graphene (i.e. between 1 and 3 S/cm). Hencebiochar particles seem to be good candidates to be dispersed in polymeric media to reachdissipative materials or materials for electromagnetic shielding
Should aerial drones replace flux chambers and wind tunnels to sample odorous atmospheres emitted by area sources?
No full textInternational audienceSampling is a key step in environmental analysis and typically for odorousatmospheres to carry out olfactometric or chemical analyses from sample bags.However some protocols like VDI-3880 define how to sample area sources, it wasdemonstrated by the last version of European standard (EN-13725:2022) that aconsensus is impossible to fix a sampling method for such emission sources. Studiesshowed that the device (Flux chamber CF or wind tunnel WT) is highly influencing thesampling and also the expected result. It means than the real emission is hard topredict with these methods and the resulting value is probably underestimated oroverestimated. The result of sampling with CF or WT is always linked to conditions andgives relative values comparable only with the same device used in the sameconditions for the same type of source. The uncertainty of CF and WT is too important;moreover these methods present lots of drawbacks: difficulty to place devices on asurface (without leaks on a solid surface or with acceptable floatability on a liquidsource); difficulty for operators to place and move the device in different locations;necessity to have odourless flux air for inlet in the device, limitation in surface that iseffectively sampled… So, without a clear method for sampling, without consensusabout values obtained using CF and WT, the question of a paradigm shift using aerialdrones is clearly posed to improve assessment of real emission rate from large areasources and their impact after dispersion.This paper deals with all advantages and drawbacks of approaches and illustrates thatthe shift is probably inevitable. Firstly, operators can stay out of the emission sourcethat is a real improvement for safety conditions, without risk of falls or drowning andwith less exposition to odorous pollutants. Secondly, the possibility in terms of samplenumbers in increased with the easiness to change the sampling point and, it also givesa stronger flexibility to average emission from different points. During last years,several examples were given combining aerial drones and chemical sensortechnologies (e-noses). It illustrates the great advantage of drone to be equipped withdifferent types of sensors/detectors. With a thermal camera, a drone can map a surfaceand help distinguishing active or non-active parts on e.g. composting piles or biofilters.An e-nose with a sampling line gives data about level of detected compounds over thesurface. The drone also allows both vertical and horizontal profiles and so a three-dimensional characterization of the source when previous sampling methods only ledto partial two-dimensional data. All types of sensors can be included in the samplingdrone to improve the efficiency of emission assessment. To replace CF or WT, thedrone must be able to collect air for analysis. A small canister (mini-can) with vacuuminside is enough to collect approximatively 1L of air for chemical analysis but forolfactometric analysis a vacuum box with a pump is needed to fill sampling bags of fewliters. Some studies indicate that downdraft from wind drone rotors can be limited if thedrone is at least 8 m from the source so future improvements concerns sampling lines,their warranty to be at controlled distance from the source and also the position of thevacuum box between the sampling inlet and the drone. Technical aspects andimprovements are discussed in this paper
